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1,802.0576 | The mid-infrared properties and gas content of active galaxies over
large look-back times | Upon an expansion of all of the searches for redshifted HI 21-cm absorption
(0.0021 < z 5.19), we update recent results regarding the detection of 21-cm in
the non-local Universe. Specifically, we confirm that photo-ionisation of the
gas is the mostly likely cause of the low detection rate at high redshift, in
addition to finding that at z < 0.1 there may also be a decrease in the
detection rate, which we suggest is due to the dilution of the absorption
strength by 21-cm emission. By assuming that associated and intervening
absorbers have similar cosmological mass densities, we find evidence that the
spin temperature of the gas evolves with redshift, consistent with heating by
ultra-violet photons. From the near--infrared (3.4, 4.6 and 12 micron) colours,
we see that radio galaxies become more quasar-like in their activity with
increasing redshift. We also find that the non-detection of 21-cm absorption at
high redshift is not likely to be due to the selection of gas-poor ellipticals,
in addition to a strong correlation between the ionising photon rate and the
[3.4] - [4.6] colour, indicating that the UV photons arise from AGN activity.
Like previous studies, we find a correlation between the detection of 21-cm
absorption and the [4.6] - [12] colour, which is a tracer of star-forming
activity. However, this only applies at the lowest redshifts (z < 0.1), the
range considered by the other studies.
| astro-ph.GA | upon an expansion of all of the searches for redshifted hi 21cm absorption 00021 z 519 we update recent results regarding the detection of 21cm in the nonlocal universe specifically we confirm that photoionisation of the gas is the mostly likely cause of the low detection rate at high redshift in addition to finding that at z 01 there may also be a decrease in the detection rate which we suggest is due to the dilution of the absorption strength by 21cm emission by assuming that associated and intervening absorbers have similar cosmological mass densities we find evidence that the spin temperature of the gas evolves with redshift consistent with heating by ultraviolet photons from the nearinfrared 34 46 and 12 micron colours we see that radio galaxies become more quasarlike in their activity with increasing redshift we also find that the nondetection of 21cm absorption at high redshift is not likely to be due to the selection of gaspoor ellipticals in addition to a strong correlation between the ionising photon rate and the 34 46 colour indicating that the uv photons arise from agn activity like previous studies we find a correlation between the detection of 21cm absorption and the 46 12 colour which is a tracer of starforming activity however this only applies at the lowest redshifts z 01 the range considered by the other studies | [['upon', 'an', 'expansion', 'of', 'all', 'of', 'the', 'searches', 'for', 'redshifted', 'hi', '21cm', 'absorption', '00021', 'z', '519', 'we', 'update', 'recent', 'results', 'regarding', 'the', 'detection', 'of', '21cm', 'in', 'the', 'nonlocal', 'universe', 'specifically', 'we', 'confirm', 'that', 'photoionisation', 'of', 'the', 'gas', 'is', 'the', 'mostly', 'likely', 'cause', 'of', 'the', 'low', 'detection', 'rate', 'at', 'high', 'redshift', 'in', 'addition', 'to', 'finding', 'that', 'at', 'z', '01', 'there', 'may', 'also', 'be', 'a', 'decrease', 'in', 'the', 'detection', 'rate', 'which', 'we', 'suggest', 'is', 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1,802.05761 | Prediction of spatial functional random processes: Comparing functional
and spatio-temporal kriging approaches | In this paper, we present and compare functional and spatio-temporal (Sp.T.)
kriging approaches to predict spatial functional random processes (which can
also be viewed as Sp.T. random processes). Comparisons with respect to
computational time and prediction performance via functional cross-validation
is evaluated, mainly through a simulation study but also on two real data sets.
We restrict comparisons to Sp.T. kriging versus ordinary kriging for functional
data (OKFD), since the more flexible functional kriging approaches, pointwise
functional kriging (PWFK) and functional kriging total model, coincide with
OKFD in several situations. We contribute with new knowledge by proving that
OKFD and PWFK coincide under certain conditions. From the simulation study, it
is concluded that the prediction performance for the two kriging approaches in
general is rather equal for stationary Sp.T. processes, with a tendency for
functional kriging to work better for small sample sizes and Sp.T. kriging to
work better for large sample sizes. For non-stationary Sp.T. processes, with a
common deterministic time trend and/or time varying variances and dependence
structure, OKFD performs better than Sp.T. kriging irrespective of sample size.
For all simulated cases, the computational time for OKFD was considerably lower
compared to those for the Sp.T. kriging methods.
| stat.ME | in this paper we present and compare functional and spatiotemporal spt kriging approaches to predict spatial functional random processes which can also be viewed as spt random processes comparisons with respect to computational time and prediction performance via functional crossvalidation is evaluated mainly through a simulation study but also on two real data sets we restrict comparisons to spt kriging versus ordinary kriging for functional data okfd since the more flexible functional kriging approaches pointwise functional kriging pwfk and functional kriging total model coincide with okfd in several situations we contribute with new knowledge by proving that okfd and pwfk coincide under certain conditions from the simulation study it is concluded that the prediction performance for the two kriging approaches in general is rather equal for stationary spt processes with a tendency for functional kriging to work better for small sample sizes and spt kriging to work better for large sample sizes for nonstationary spt processes with a common deterministic time trend andor time varying variances and dependence structure okfd performs better than spt kriging irrespective of sample size for all simulated cases the computational time for okfd was considerably lower compared to those for the spt kriging methods | [['in', 'this', 'paper', 'we', 'present', 'and', 'compare', 'functional', 'and', 'spatiotemporal', 'spt', 'kriging', 'approaches', 'to', 'predict', 'spatial', 'functional', 'random', 'processes', 'which', 'can', 'also', 'be', 'viewed', 'as', 'spt', 'random', 'processes', 'comparisons', 'with', 'respect', 'to', 'computational', 'time', 'and', 'prediction', 'performance', 'via', 'functional', 'crossvalidation', 'is', 'evaluated', 'mainly', 'through', 'a', 'simulation', 'study', 'but', 'also', 'on', 'two', 'real', 'data', 'sets', 'we', 'restrict', 'comparisons', 'to', 'spt', 'kriging', 'versus', 'ordinary', 'kriging', 'for', 'functional', 'data', 'okfd', 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1,802.05762 | Framing Matters: Predicting Framing Changes and Legislation from Topic
News Patterns | News has traditionally been well researched, with studies ranging from
sentiment analysis to event detection and topic tracking. We extend the focus
to two surprisingly under-researched aspects of news: \emph{framing} and
\emph{predictive utility}. We demonstrate that framing influences public
opinion and behavior, and present a simple entropic algorithm to characterize
and detect framing changes. We introduce a dataset of news topics with framing
changes, harvested from manual surveys in previous research. Our approach
achieves an F-measure of $F_1=0.96$ on our data, whereas dynamic topic modeling
returns $F_1=0.1$. We also establish that news has \emph{predictive utility},
by showing that legislation in topics of current interest can be foreshadowed
and predicted from news patterns.
| cs.CY | news has traditionally been well researched with studies ranging from sentiment analysis to event detection and topic tracking we extend the focus to two surprisingly underresearched aspects of news emphframing and emphpredictive utility we demonstrate that framing influences public opinion and behavior and present a simple entropic algorithm to characterize and detect framing changes we introduce a dataset of news topics with framing changes harvested from manual surveys in previous research our approach achieves an fmeasure of f_1096 on our data whereas dynamic topic modeling returns f_101 we also establish that news has emphpredictive utility by showing that legislation in topics of current interest can be foreshadowed and predicted from news patterns | [['news', 'has', 'traditionally', 'been', 'well', 'researched', 'with', 'studies', 'ranging', 'from', 'sentiment', 'analysis', 'to', 'event', 'detection', 'and', 'topic', 'tracking', 'we', 'extend', 'the', 'focus', 'to', 'two', 'surprisingly', 'underresearched', 'aspects', 'of', 'news', 'emphframing', 'and', 'emphpredictive', 'utility', 'we', 'demonstrate', 'that', 'framing', 'influences', 'public', 'opinion', 'and', 'behavior', 'and', 'present', 'a', 'simple', 'entropic', 'algorithm', 'to', 'characterize', 'and', 'detect', 'framing', 'changes', 'we', 'introduce', 'a', 'dataset', 'of', 'news', 'topics', 'with', 'framing', 'changes', 'harvested', 'from', 'manual', 'surveys', 'in', 'previous', 'research', 'our', 'approach', 'achieves', 'an', 'fmeasure', 'of', 'f_1096', 'on', 'our', 'data', 'whereas', 'dynamic', 'topic', 'modeling', 'returns', 'f_101', 'we', 'also', 'establish', 'that', 'news', 'has', 'emphpredictive', 'utility', 'by', 'showing', 'that', 'legislation', 'in', 'topics', 'of', 'current', 'interest', 'can', 'be', 'foreshadowed', 'and', 'predicted', 'from', 'news', 'patterns']] | [-0.05045216614108131, 0.02154703326899785, -0.09595872217430435, 0.06783552792077081, -0.17142265906061874, -0.12031099868408583, 0.07661416897883451, 0.44860778241888233, -0.21615040974281385, -0.334858025480254, 0.10793533664273627, -0.3687039230941632, -0.20640711465833383, 0.22180583860742253, -0.1324867664092998, -0.00048714844347618586, 0.07558073756208114, 0.052805793317838, -0.018798179149097944, -0.28383430035212376, 0.283367628769932, 0.06113957314230843, 0.36270938763275334, 0.10293188435666331, 0.09955394490940524, -0.006739204109714734, -0.1399625254572805, 0.04322478170463935, -0.12942826859069584, 0.139538093943308, 0.3582337142455332, 0.23671712641125767, 0.33283468464030586, -0.344500102401805, -0.22758417521013852, 0.0702444530554637, 0.08952599743364054, 0.09569714890462945, -0.06813958183533315, -0.35453216343639643, 0.057828416244699324, -0.2092080861885408, -0.02789346166937655, -0.12519494682516252, 0.022525595817202274, 0.03337793169893461, -0.21467965962621596, 0.06340269834724888, 0.04330901828607322, 0.12065611053429065, -0.01808775556401922, -0.0994218045358606, 0.023107142409805312, 0.1841631969394282, 0.16564304325216955, 0.01604145257135665, 0.16939767590647034, -0.16416905543322616, -0.220775623387153, 0.37115349410392273, -0.07828084841678175, -0.1468468648125204, 0.15137190092782224, -0.081015712339132, -0.16611585311950483, 0.06818961481555202, 0.22626587049608385, 0.0994779614714581, -0.15462121805774237, -0.011437468981549759, -0.06279789111277925, 0.2188672350224005, 0.06359857214409687, -0.018776504344247078, 0.2088573343214502, 0.22651042471802152, 0.02387633914589335, 0.13462613203344917, -0.09459056576004747, -0.11123440821373135, -0.16918721454166766, -0.08410150925906529, -0.14343021754944407, 0.02645099816796874, -0.028219842769839982, -0.09972067530813772, 0.41870671322723046, 0.22517210767612955, 0.16486518004206394, 0.06175659822274444, 0.26797433976306145, -0.012009264121618155, 0.029848341394318353, 0.07976619249429776, 0.18306548269721892, -0.027595582016564292, 0.17802749715217842, -0.13250791972904646, 0.1250308602364785, -0.005680801816831488] |
1,802.05763 | ASP:A Fast Adversarial Attack Example Generation Framework based on
Adversarial Saliency Prediction | With the excellent accuracy and feasibility, the Neural Networks have been
widely applied into the novel intelligent applications and systems. However,
with the appearance of the Adversarial Attack, the NN based system performance
becomes extremely vulnerable:the image classification results can be
arbitrarily misled by the adversarial examples, which are crafted images with
human unperceivable pixel-level perturbation. As this raised a significant
system security issue, we implemented a series of investigations on the
adversarial attack in this work: We first identify an image's pixel
vulnerability to the adversarial attack based on the adversarial saliency
analysis. By comparing the analyzed saliency map and the adversarial
perturbation distribution, we proposed a new evaluation scheme to
comprehensively assess the adversarial attack precision and efficiency. Then,
with a novel adversarial saliency prediction method, a fast adversarial example
generation framework, namely "ASP", is proposed with significant attack
efficiency improvement and dramatic computation cost reduction. Compared to the
previous methods, experiments show that ASP has at most 12 times speed-up for
adversarial example generation, 2 times lower perturbation rate, and high
attack success rate of 87% on both MNIST and Cifar10. ASP can be also well
utilized to support the data-hungry NN adversarial training. By reducing the
attack success rate as much as 90%, ASP can quickly and effectively enhance the
defense capability of NN based system to the adversarial attacks.
| cs.CV cs.CR cs.LG | with the excellent accuracy and feasibility the neural networks have been widely applied into the novel intelligent applications and systems however with the appearance of the adversarial attack the nn based system performance becomes extremely vulnerablethe image classification results can be arbitrarily misled by the adversarial examples which are crafted images with human unperceivable pixellevel perturbation as this raised a significant system security issue we implemented a series of investigations on the adversarial attack in this work we first identify an images pixel vulnerability to the adversarial attack based on the adversarial saliency analysis by comparing the analyzed saliency map and the adversarial perturbation distribution we proposed a new evaluation scheme to comprehensively assess the adversarial attack precision and efficiency then with a novel adversarial saliency prediction method a fast adversarial example generation framework namely asp is proposed with significant attack efficiency improvement and dramatic computation cost reduction compared to the previous methods experiments show that asp has at most 12 times speedup for adversarial example generation 2 times lower perturbation rate and high attack success rate of 87 on both mnist and cifar10 asp can be also well utilized to support the datahungry nn adversarial training by reducing the attack success rate as much as 90 asp can quickly and effectively enhance the defense capability of nn based system to the adversarial attacks | [['with', 'the', 'excellent', 'accuracy', 'and', 'feasibility', 'the', 'neural', 'networks', 'have', 'been', 'widely', 'applied', 'into', 'the', 'novel', 'intelligent', 'applications', 'and', 'systems', 'however', 'with', 'the', 'appearance', 'of', 'the', 'adversarial', 'attack', 'the', 'nn', 'based', 'system', 'performance', 'becomes', 'extremely', 'vulnerablethe', 'image', 'classification', 'results', 'can', 'be', 'arbitrarily', 'misled', 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1,802.05764 | Characterization of methanol as a magnetic field tracer in star-forming
regions | Magnetic fields play an important role during star formation. Direct magnetic
field strength observations have proven specifically challenging in the
extremely dynamic protostellar phase. Because of their occurrence in the
densest parts of star forming regions, masers, through polarization
observations, are the main source of magnetic field strength and morphology
measurements around protostars. Of all maser species, methanol is one of the
strongest and most abundant tracers of gas around high-mass protostellar disks
and in outflows. However, as experimental determination of the magnetic
characteristics of methanol has remained largely unsuccessful, a robust
magnetic field strength analysis of these regions could hitherto not be
performed. Here we report a quantitative theoretical model of the magnetic
properties of methanol, including the complicated hyperfine structure that
results from its internal rotation. We show that the large range in values of
the Land\'{e} g-factors of the hyperfine components of each maser line lead to
conclusions which differ substantially from the current interpretation based on
a single effective g-factor. These conclusions are more consistent with other
observations and confirm the presence of dynamically important magnetic fields
around protostars. Additionally, our calculations show that (non-linear) Zeeman
effects must be taken into account to further enhance the accuracy of
cosmological electron-to-proton mass ratio determinations using methanol.
| astro-ph.GA astro-ph.SR | magnetic fields play an important role during star formation direct magnetic field strength observations have proven specifically challenging in the extremely dynamic protostellar phase because of their occurrence in the densest parts of star forming regions masers through polarization observations are the main source of magnetic field strength and morphology measurements around protostars of all maser species methanol is one of the strongest and most abundant tracers of gas around highmass protostellar disks and in outflows however as experimental determination of the magnetic characteristics of methanol has remained largely unsuccessful a robust magnetic field strength analysis of these regions could hitherto not be performed here we report a quantitative theoretical model of the magnetic properties of methanol including the complicated hyperfine structure that results from its internal rotation we show that the large range in values of the lande gfactors of the hyperfine components of each maser line lead to conclusions which differ substantially from the current interpretation based on a single effective gfactor these conclusions are more consistent with other observations and confirm the presence of dynamically important magnetic fields around protostars additionally our calculations show that nonlinear zeeman effects must be taken into account to further enhance the accuracy of cosmological electrontoproton mass ratio determinations using methanol | [['magnetic', 'fields', 'play', 'an', 'important', 'role', 'during', 'star', 'formation', 'direct', 'magnetic', 'field', 'strength', 'observations', 'have', 'proven', 'specifically', 'challenging', 'in', 'the', 'extremely', 'dynamic', 'protostellar', 'phase', 'because', 'of', 'their', 'occurrence', 'in', 'the', 'densest', 'parts', 'of', 'star', 'forming', 'regions', 'masers', 'through', 'polarization', 'observations', 'are', 'the', 'main', 'source', 'of', 'magnetic', 'field', 'strength', 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1,802.05765 | On the algorithm to find S-related Lie algebras | In this article we describe the Java library that we have recently
constructed to automatize the S-expansion method, a powerful mathematical
technique allowing to relate different Lie algebras. An important input in this
procedure is the use of abelian semigroups and thus, we start with a brief
review about the classification of non-isomorphic semigroups made in the
literature during the last decades, and explain how the lists of non-isomorphic
semigroups up to order 6 can be used as inputs in many of the methods of our
library. After describing the main features of the classes that compose our
library we present a new method called fillTemplate which tuns out to be very
useful to answer whether two given algebras can be S-related.
| physics.comp-ph hep-th math-ph math.MP math.NA | in this article we describe the java library that we have recently constructed to automatize the sexpansion method a powerful mathematical technique allowing to relate different lie algebras an important input in this procedure is the use of abelian semigroups and thus we start with a brief review about the classification of nonisomorphic semigroups made in the literature during the last decades and explain how the lists of nonisomorphic semigroups up to order 6 can be used as inputs in many of the methods of our library after describing the main features of the classes that compose our library we present a new method called filltemplate which tuns out to be very useful to answer whether two given algebras can be srelated | [['in', 'this', 'article', 'we', 'describe', 'the', 'java', 'library', 'that', 'we', 'have', 'recently', 'constructed', 'to', 'automatize', 'the', 'sexpansion', 'method', 'a', 'powerful', 'mathematical', 'technique', 'allowing', 'to', 'relate', 'different', 'lie', 'algebras', 'an', 'important', 'input', 'in', 'this', 'procedure', 'is', 'the', 'use', 'of', 'abelian', 'semigroups', 'and', 'thus', 'we', 'start', 'with', 'a', 'brief', 'review', 'about', 'the', 'classification', 'of', 'nonisomorphic', 'semigroups', 'made', 'in', 'the', 'literature', 'during', 'the', 'last', 'decades', 'and', 'explain', 'how', 'the', 'lists', 'of', 'nonisomorphic', 'semigroups', 'up', 'to', 'order', '6', 'can', 'be', 'used', 'as', 'inputs', 'in', 'many', 'of', 'the', 'methods', 'of', 'our', 'library', 'after', 'describing', 'the', 'main', 'features', 'of', 'the', 'classes', 'that', 'compose', 'our', 'library', 'we', 'present', 'a', 'new', 'method', 'called', 'filltemplate', 'which', 'tuns', 'out', 'to', 'be', 'very', 'useful', 'to', 'answer', 'whether', 'two', 'given', 'algebras', 'can', 'be', 'srelated']] | [-0.015233604966973265, 0.047069477263842904, -0.10720082772313617, 0.08674997488560621, -0.10920918957951169, -0.13029106027679518, 0.009692266496131197, 0.4029554807658618, -0.29694923222996294, -0.3134184551270058, 0.12096706075438608, -0.22312874594936147, -0.162412870535627, 0.25232046115755413, -0.09827627487635861, 0.012222316100572546, 0.07600522343224535, 0.04164864235790446, -0.07798690940447463, -0.3052832107476812, 0.3456670087703969, 0.045675821567419915, 0.20799599245826053, 0.04487171276317289, 0.08582139154120037, -0.03221771758204947, -0.07898831513011828, 0.0064686793353757824, -0.1312522380376322, 0.15251595369772986, 0.3306822161034991, 0.17002681914406517, 0.26762834942589203, -0.4097264185703049, -0.14890451105311514, 0.13030443322689583, 0.1442713691542546, 0.13369198531048218, -0.03367055385218312, -0.2483316614370172, 0.10318170104001183, -0.21601020619855263, -0.10684340801090002, -0.12989416815495738, 0.0407572339521721, 0.00490915853685389, -0.20328965177080438, -0.0354174292840374, 0.0895192177403563, 0.06126579812723018, -0.011932641128078103, -0.09977258845077207, 0.01901566075781981, 0.16614029766448463, 0.012328635703602231, 0.025874830976439018, 0.07374852666786562, -0.06289735022777071, -0.163541820885924, 0.35809737600696584, -0.02226160914869979, -0.15859147969943782, 0.19128154119534885, -0.07899583752732724, -0.21202587846200913, 0.11995163928562154, 0.1731882926154261, 0.1477847378196505, -0.18096306539761522, 0.06889577837331065, -0.07970324396931877, 0.144056351405258, 0.0602421603378995, -0.025199757469817997, 0.1960354505494858, 0.16005850669462235, -0.008704425354274766, 0.17452727611428903, 0.01664397322262327, -0.027750523291373005, -0.32488276332927246, -0.1603339132387191, -0.1090586117565787, 0.037192388460971414, -0.03464446293243479, -0.16575082562242946, 0.45604583582219976, 0.23762618096079677, 0.17439295889344067, 0.04235798102745321, 0.2444828600118247, 0.05693124680256005, 0.12006532212641711, 0.07123576130834408, 0.16357847464969383, 0.17205353057943285, 0.07038048208535959, -0.12398664753466922, 0.006361641009182979, 0.10934546444332227] |
1,802.05766 | Learning to Count Objects in Natural Images for Visual Question
Answering | Visual Question Answering (VQA) models have struggled with counting objects
in natural images so far. We identify a fundamental problem due to soft
attention in these models as a cause. To circumvent this problem, we propose a
neural network component that allows robust counting from object proposals.
Experiments on a toy task show the effectiveness of this component and we
obtain state-of-the-art accuracy on the number category of the VQA v2 dataset
without negatively affecting other categories, even outperforming ensemble
models with our single model. On a difficult balanced pair metric, the
component gives a substantial improvement in counting over a strong baseline by
6.6%.
| cs.CV cs.CL | visual question answering vqa models have struggled with counting objects in natural images so far we identify a fundamental problem due to soft attention in these models as a cause to circumvent this problem we propose a neural network component that allows robust counting from object proposals experiments on a toy task show the effectiveness of this component and we obtain stateoftheart accuracy on the number category of the vqa v2 dataset without negatively affecting other categories even outperforming ensemble models with our single model on a difficult balanced pair metric the component gives a substantial improvement in counting over a strong baseline by 66 | [['visual', 'question', 'answering', 'vqa', 'models', 'have', 'struggled', 'with', 'counting', 'objects', 'in', 'natural', 'images', 'so', 'far', 'we', 'identify', 'a', 'fundamental', 'problem', 'due', 'to', 'soft', 'attention', 'in', 'these', 'models', 'as', 'a', 'cause', 'to', 'circumvent', 'this', 'problem', 'we', 'propose', 'a', 'neural', 'network', 'component', 'that', 'allows', 'robust', 'counting', 'from', 'object', 'proposals', 'experiments', 'on', 'a', 'toy', 'task', 'show', 'the', 'effectiveness', 'of', 'this', 'component', 'and', 'we', 'obtain', 'stateoftheart', 'accuracy', 'on', 'the', 'number', 'category', 'of', 'the', 'vqa', 'v2', 'dataset', 'without', 'negatively', 'affecting', 'other', 'categories', 'even', 'outperforming', 'ensemble', 'models', 'with', 'our', 'single', 'model', 'on', 'a', 'difficult', 'balanced', 'pair', 'metric', 'the', 'component', 'gives', 'a', 'substantial', 'improvement', 'in', 'counting', 'over', 'a', 'strong', 'baseline', 'by', '66']] | [-0.059237779774481344, -0.01386603828342188, -0.029161460963743075, 0.074946886738984, -0.09329451766264225, -0.16938239913877276, 0.048107854467594906, 0.43467801901556197, -0.22413278607917683, -0.38485086335844937, 0.03597194091901959, -0.2847814081437675, -0.13947922443704946, 0.2184104914050771, -0.1783463184233932, 0.06349727131781124, 0.17112623836312976, 0.0647135510242411, -0.007903141821069376, -0.2784760011138306, 0.32023411737089713, 0.02268845322320149, 0.30586384387598153, 0.07033006615404572, 0.1223595199175179, -0.033549901752156164, -0.027468192918847004, 0.02427917356115012, -0.036272558766497584, 0.1641677165448311, 0.2621509160848897, 0.1470231899902934, 0.30945458910760604, -0.3618016109225296, -0.23606983317683142, 0.14023753051712576, 0.12188629668560766, 0.1188591055638556, -0.03379137799742499, -0.31465380324516445, 0.07864600398156438, -0.19728751516058332, -0.009302318734782083, -0.12777944569520297, 0.005483182516348149, -0.05814072482199186, -0.241703206487, 0.04948556184813026, 0.11309097678444925, 0.0431666610385513, -0.031661185846730534, -0.09504593287018083, 0.06598374187014996, 0.14072550264896735, 0.036536183402252696, 0.08428071636617893, 0.10514295284769365, -0.23909395703868497, -0.16405709136632227, 0.39853715838066167, -0.10285186468667927, -0.24393516586029104, 0.2252633477317258, -0.044680166989564896, -0.16133724628016352, 0.10328532409455095, 0.19347732796200684, 0.1508860352936955, -0.09457830858549901, 0.027127536341348398, -0.11798090590607552, 0.21324122894466632, 0.05544085058028854, -0.00852382222545289, 0.2223447533590453, 0.2387678824808626, 0.03814878793921144, 0.16708181483637807, -0.1167193990710768, -0.05794003312697723, -0.1959790141986949, -0.0655783219551224, -0.1060465035117453, 0.0614908644463867, -0.0629891744414289, -0.15650278419433605, 0.42629139046184716, 0.2549549896802221, 0.2682169410000954, 0.10059279080569035, 0.33174013107837663, 0.024306384780045067, 0.1030354912259749, 0.039941391459156185, 0.1849860329447048, 0.03181177520725344, 0.06816201209024127, -0.16194202536051827, 0.053057274475161516, 0.07470373707779107] |
1,802.05767 | Generators and relations for Lie superalgebras of Cartan type | We give an analog of a Chevalley-Serre presentation for the Lie superalgebras
W(n) and S(n) of Cartan type. These are part of a wider class of Lie
superalgebras, the so-called tensor hierarchy algebras, denoted W(g) and S(g),
where g denotes the Kac-Moody algebra A_r, D_r or E_r. Then W(A_{n-1}) and
S(A_{n-1}) are the Lie superalgebras W(n) and S(n). The algebras W(g) and S(g)
are constructed from the Dynkin diagram of the Borcherds-Kac-Moody
superalgebras B(g) obtained by adding a single grey node (representing an odd
null root) to the Dynkin diagram of g. We redefine the algebras W(A_r) and
S(A_r) in terms of Chevalley generators and defining relations. We prove that
all relations follow from the defining ones at level -2 and higher. The
analogous definitions of the algebras in the D- and E-series are given. In the
latter case the full set of defining relations is conjectured.
| math.RT hep-th | we give an analog of a chevalleyserre presentation for the lie superalgebras wn and sn of cartan type these are part of a wider class of lie superalgebras the socalled tensor hierarchy algebras denoted wg and sg where g denotes the kacmoody algebra a_r d_r or e_r then wa_n1 and sa_n1 are the lie superalgebras wn and sn the algebras wg and sg are constructed from the dynkin diagram of the borcherdskacmoody superalgebras bg obtained by adding a single grey node representing an odd null root to the dynkin diagram of g we redefine the algebras wa_r and sa_r in terms of chevalley generators and defining relations we prove that all relations follow from the defining ones at level 2 and higher the analogous definitions of the algebras in the d and eseries are given in the latter case the full set of defining relations is conjectured | [['we', 'give', 'an', 'analog', 'of', 'a', 'chevalleyserre', 'presentation', 'for', 'the', 'lie', 'superalgebras', 'wn', 'and', 'sn', 'of', 'cartan', 'type', 'these', 'are', 'part', 'of', 'a', 'wider', 'class', 'of', 'lie', 'superalgebras', 'the', 'socalled', 'tensor', 'hierarchy', 'algebras', 'denoted', 'wg', 'and', 'sg', 'where', 'g', 'denotes', 'the', 'kacmoody', 'algebra', 'a_r', 'd_r', 'or', 'e_r', 'then', 'wa_n1', 'and', 'sa_n1', 'are', 'the', 'lie', 'superalgebras', 'wn', 'and', 'sn', 'the', 'algebras', 'wg', 'and', 'sg', 'are', 'constructed', 'from', 'the', 'dynkin', 'diagram', 'of', 'the', 'borcherdskacmoody', 'superalgebras', 'bg', 'obtained', 'by', 'adding', 'a', 'single', 'grey', 'node', 'representing', 'an', 'odd', 'null', 'root', 'to', 'the', 'dynkin', 'diagram', 'of', 'g', 'we', 'redefine', 'the', 'algebras', 'wa_r', 'and', 'sa_r', 'in', 'terms', 'of', 'chevalley', 'generators', 'and', 'defining', 'relations', 'we', 'prove', 'that', 'all', 'relations', 'follow', 'from', 'the', 'defining', 'ones', 'at', 'level', '2', 'and', 'higher', 'the', 'analogous', 'definitions', 'of', 'the', 'algebras', 'in', 'the', 'd', 'and', 'eseries', 'are', 'given', 'in', 'the', 'latter', 'case', 'the', 'full', 'set', 'of', 'defining', 'relations', 'is', 'conjectured']] | [-0.17164253070289995, 0.08977580624851332, -0.02825081260901288, 0.06326197048381685, -0.1514065009145502, -0.15030086015067096, -0.006680753788785309, 0.32521719355895745, -0.3151150735240456, -0.24249881830277806, 0.10435224050930074, -0.2692548245825666, -0.1279750552395002, 0.13796863147942678, -0.11094493261069482, -0.12222865482475212, 0.017366296281470052, 0.1607296129767882, -0.14341980228960144, -0.25586410908438, 0.4055008402041404, -0.0497523311142486, 0.2245748549045876, -0.06503514723244605, 0.08943138028997002, 0.010486417400472342, -0.029466081674851423, -0.0030471312562466092, -0.17579005776058448, 0.10806895527123402, 0.28029673856498133, 0.08623541705475472, 0.1613820707887834, -0.33222531274965045, -0.06323482106268036, 0.17778429640655188, 0.18854285913671479, 0.018482797630527553, 0.013687778880212007, -0.2612700758630687, 0.10682924685616654, -0.24914513040888817, -0.14093976383302229, 0.022090575353949204, 0.0881882928219353, 0.005238550569835707, -0.22153315624446734, 0.05370421871437248, 0.08714277146001023, 0.14662919787141818, -0.09577617810127582, -0.174494612841464, -0.12031850274421063, 0.08988774849385922, -0.11258116308500633, -0.002446186754163949, 0.05773844091215085, -0.12101006767124678, -0.15861357589820335, 0.39178690978107933, 0.039586976789770294, -0.17912906566832928, 0.11580637052278728, -0.19818364293479646, -0.157964531788008, 0.05772377168825755, 0.016575530156532816, 0.1084641224394242, -0.06594295043445436, 0.1660379355154925, -0.056446779601883594, -0.04000609948015805, 0.08769902935357275, 0.006577856762744222, 0.13456074217440073, 0.10529633125963997, -0.00405339190260844, 0.11846722741049866, 0.02267032096547187, 0.014844165108470472, -0.4013997193703309, -0.1786551816497949, -0.1093297956374322, 0.09741924390698789, -0.17934324566499044, -0.1447055371971933, 0.4078960415530712, 0.09718479209204686, 0.16424030436125273, 0.12118377886276613, 0.13245298397720706, 0.151664141095875, 0.15541772909647394, 0.08384701960384912, 0.1469994508793432, 0.29458247221748723, -0.005734763980070327, -0.16197099802224627, -0.0623265243653895, 0.2328621891214915] |
1,802.05768 | The Causal Link between News Framing and Legislation | We demonstrate that framing, a subjective aspect of news, is a causal
precursor to both significant public perception changes, and to federal
legislation. We posit, counter-intuitively, that topic news volume and mean
article similarity increase and decrease together. We show that specific
features of news, such as publishing volume , are predictive of both sustained
public attention, measured by annual Google trend data, and federal
legislation. We observe that public attention changes are driven primarily by
periods of high news volume and mean similarity, which we call \emph{prenatal
periods}. Finally, we demonstrate that framing during prenatal periods may be
characterized by high-utility news \emph{keywords}.
| cs.CY | we demonstrate that framing a subjective aspect of news is a causal precursor to both significant public perception changes and to federal legislation we posit counterintuitively that topic news volume and mean article similarity increase and decrease together we show that specific features of news such as publishing volume are predictive of both sustained public attention measured by annual google trend data and federal legislation we observe that public attention changes are driven primarily by periods of high news volume and mean similarity which we call emphprenatal periods finally we demonstrate that framing during prenatal periods may be characterized by highutility news emphkeywords | [['we', 'demonstrate', 'that', 'framing', 'a', 'subjective', 'aspect', 'of', 'news', 'is', 'a', 'causal', 'precursor', 'to', 'both', 'significant', 'public', 'perception', 'changes', 'and', 'to', 'federal', 'legislation', 'we', 'posit', 'counterintuitively', 'that', 'topic', 'news', 'volume', 'and', 'mean', 'article', 'similarity', 'increase', 'and', 'decrease', 'together', 'we', 'show', 'that', 'specific', 'features', 'of', 'news', 'such', 'as', 'publishing', 'volume', 'are', 'predictive', 'of', 'both', 'sustained', 'public', 'attention', 'measured', 'by', 'annual', 'google', 'trend', 'data', 'and', 'federal', 'legislation', 'we', 'observe', 'that', 'public', 'attention', 'changes', 'are', 'driven', 'primarily', 'by', 'periods', 'of', 'high', 'news', 'volume', 'and', 'mean', 'similarity', 'which', 'we', 'call', 'emphprenatal', 'periods', 'finally', 'we', 'demonstrate', 'that', 'framing', 'during', 'prenatal', 'periods', 'may', 'be', 'characterized', 'by', 'highutility', 'news', 'emphkeywords']] | [-0.10460331501602886, 0.15282925292803434, -0.06529121923647674, 0.12082183328434123, -0.16541304616349758, -0.09227070387563494, 0.11536539723753172, 0.46841694801898287, -0.23444964683218167, -0.3281526545403839, 0.13215345090199238, -0.3613832356396136, -0.21919549659521567, 0.19788569074566706, -0.1684680277052898, -0.036505766784643186, 0.08594118445964143, 0.026808865635775694, -0.008816002086634831, -0.3126411281599857, 0.2761629072598892, 0.0722034173870072, 0.3743544677364649, 0.10262270087127269, 0.0942406098111671, -0.03664387373938364, -0.15289581656262352, 0.051584515231892024, -0.09536764370964777, 0.13120293614381376, 0.3240655731354462, 0.20543859827120114, 0.360225074883034, -0.36960873358172947, -0.19809551666131114, 0.0980359868625972, 0.05695948286885672, 0.06243595566098016, -0.06677374260679883, -0.3466682708407245, 0.062498224788520594, -0.251356204616662, -0.049490476419024244, -0.11813095161036616, 0.0673156981402547, 0.03992918677775577, -0.21086213279441737, 0.11125969312310514, 0.01299556753238534, 0.1513723693972472, -0.048255177943201936, -0.06261098687998065, -0.06764815600201635, 0.1771483535620172, 0.15792171592768836, 0.0225723483369197, 0.19208986374277548, -0.14954116460198702, -0.1430820094900999, 0.3440390115806667, -0.0996837503314802, -0.09376189173507564, 0.12623693892215887, -0.1042794966309488, -0.12267431772618305, 0.08819688547697693, 0.2577043750719859, -0.0035022073194827183, -0.14159604173965087, -0.06884776695117373, -0.04479240582757952, 0.23547222593448836, 0.11314982739231079, -0.013803209715273858, 0.24659779839365198, 0.1389552645447507, 0.019354353552813282, 0.0924190982241451, -0.088177714515293, -0.12890652770932653, -0.1997334839093803, -0.14297593340028705, -0.1230315003869133, 0.01979894531973739, -0.06411316424148909, -0.10453056894003017, 0.3660689169485675, 0.15076977771313257, 0.1563874633134444, 0.038655535807823854, 0.235041588049216, 0.007243169866972557, 0.04959265539643935, 0.08581467596629616, 0.17651759405242334, -0.0822990037671447, 0.19997233936636902, -0.16428602564766412, 0.15097177552179167, -0.04049865759466544] |
1,802.05769 | The polar clasps of a bank vole PrP(168--176) prion protofibril
revisiting | On 2018-01-17 two electron crystallography structures (with PDB entries 6AXZ,
6BTK) on a prion protofibril of bank vole PrP(168-176) (a segment in the PrP
$\beta$2-$\alpha$2 loop) were released into the PDB Bank. The paper published
by [Nat Struct Mol Biol 25(2):131-134 (2018)] reports some polar clasps for
these two crystal structures, and "an intersheet hydrogen bond between Tyr169
and the backbone carbonyl of Asn171 on an opposing strand." - this hydrogen
bond is not between the neighbouring Chain B and Chain A directly. In addition,
by revisiting the polar clasps, we found another two hydrogen bonds
(B.Asn171@H-A.Gln172@OE1, B.Tyr169@OH-A.Gln172@N) between the strand A of one
sheet and the opposing strand B of the mating sheet. For the neighbouring two
single $\beta$-sheets AB, the two new hydrogen bonds are completely different
from the experimental one (an intersheet hydrogen bond between Tyr169 and the
backbone carbonyl of Asn171 on an opposing strand) in [Nat Struct Mol Biol
25(2):131-134 (2018)].
| physics.bio-ph q-bio.BM | on 20180117 two electron crystallography structures with pdb entries 6axz 6btk on a prion protofibril of bank vole prp168176 a segment in the prp beta2alpha2 loop were released into the pdb bank the paper published by nat struct mol biol 252131134 2018 reports some polar clasps for these two crystal structures and an intersheet hydrogen bond between tyr169 and the backbone carbonyl of asn171 on an opposing strand this hydrogen bond is not between the neighbouring chain b and chain a directly in addition by revisiting the polar clasps we found another two hydrogen bonds basn171hagln172oe1 btyr169ohagln172n between the strand a of one sheet and the opposing strand b of the mating sheet for the neighbouring two single betasheets ab the two new hydrogen bonds are completely different from the experimental one an intersheet hydrogen bond between tyr169 and the backbone carbonyl of asn171 on an opposing strand in nat struct mol biol 252131134 2018 | [['on', '20180117', 'two', 'electron', 'crystallography', 'structures', 'with', 'pdb', 'entries', '6axz', '6btk', 'on', 'a', 'prion', 'protofibril', 'of', 'bank', 'vole', 'prp168176', 'a', 'segment', 'in', 'the', 'prp', 'beta2alpha2', 'loop', 'were', 'released', 'into', 'the', 'pdb', 'bank', 'the', 'paper', 'published', 'by', 'nat', 'struct', 'mol', 'biol', '252131134', '2018', 'reports', 'some', 'polar', 'clasps', 'for', 'these', 'two', 'crystal', 'structures', 'and', 'an', 'intersheet', 'hydrogen', 'bond', 'between', 'tyr169', 'and', 'the', 'backbone', 'carbonyl', 'of', 'asn171', 'on', 'an', 'opposing', 'strand', 'this', 'hydrogen', 'bond', 'is', 'not', 'between', 'the', 'neighbouring', 'chain', 'b', 'and', 'chain', 'a', 'directly', 'in', 'addition', 'by', 'revisiting', 'the', 'polar', 'clasps', 'we', 'found', 'another', 'two', 'hydrogen', 'bonds', 'basn171hagln172oe1', 'btyr169ohagln172n', 'between', 'the', 'strand', 'a', 'of', 'one', 'sheet', 'and', 'the', 'opposing', 'strand', 'b', 'of', 'the', 'mating', 'sheet', 'for', 'the', 'neighbouring', 'two', 'single', 'betasheets', 'ab', 'the', 'two', 'new', 'hydrogen', 'bonds', 'are', 'completely', 'different', 'from', 'the', 'experimental', 'one', 'an', 'intersheet', 'hydrogen', 'bond', 'between', 'tyr169', 'and', 'the', 'backbone', 'carbonyl', 'of', 'asn171', 'on', 'an', 'opposing', 'strand', 'in', 'nat', 'struct', 'mol', 'biol', '252131134', '2018']] | [-0.147300761999746, 0.14100011984832442, 0.04115973203606956, -0.03160377838124167, 0.0258335286003708, -0.156154684824082, 0.11774161756514165, 0.41817344512633037, -0.2661737819713816, -0.2525479927606566, 0.0320435708714977, -0.316186099796897, -0.12536047845029494, 0.09180539235526787, 0.01860737359203713, -0.06991080863034012, 0.032003094304457934, -0.04769033079960344, 0.027184367022463973, -0.21281159705233196, 0.21902419019385544, 0.08593010447995612, 0.28662166635597675, 0.07223639837433388, 0.06124230229269556, 0.037532002179765364, -0.012672495841324119, -0.042190969583880106, -0.17039429367533032, 0.18149871248866653, 0.18239026639977812, 0.02282284053278641, 0.22682198101628415, -0.5152394181563401, -0.12654500906880367, 0.007034221198409796, 0.07937212602865003, 0.14678817980883407, 0.010131277829329943, -0.26933798916690366, 0.013272917055538003, -0.19096288456529065, -0.021987888666892975, 0.04795905515144933, 0.08234070012555786, 0.05710207658055776, -0.1874202155068674, 0.07912336860153772, 0.05247503802492644, 0.09530420344926312, -0.061527990483970736, -0.18625063365030195, -0.10900677247038505, 0.12557105327018497, 0.001068604229555898, 0.0634419664209315, 0.16529227220322149, -0.06477944744901705, -0.12845242353366085, 0.37804890638479677, -0.012571533951698475, -0.1018002962391876, 0.2039288840532093, -0.09655818139189776, -0.20028323651448016, 0.153011999952174, 0.05066766344546996, 0.0506844195589738, -0.16342001143370716, 0.000777312617955572, -0.04940313132952007, 0.18000283876670078, 0.19130795671027417, -0.10342666237208416, 0.21506141782016822, 0.09878328838713572, 0.01750219763997732, 0.15324020934533852, -0.15048209875559723, -0.1140852413717395, -0.21231466336306973, -0.22770184843348418, -0.14804600568612855, 0.010332458411629514, -0.05450321473180223, -0.17699362640105135, 0.3665538957067044, 0.02157112997490554, 0.18527217879629052, -0.029860264759405817, 0.18558649015678486, -0.052049319948476384, 0.045829569639697094, 0.015789831762241437, 0.19926710083320717, 0.13476042602125976, 0.08063860920349687, -0.20853487819328276, 0.11444653359136846, 0.07615923112615759] |
1,802.0577 | Hyperbolicity of Links in Thickened Surfaces | Menasco showed that a non-split, prime, alternating link that is not a
2-braid is hyperbolic in $S^3$. We prove a similar result for links in closed
thickened surfaces $S \times I$. We define a link to be fully alternating if it
has an alternating projection from $S\times I$ to $S$ where the interior of
every complementary region is an open disk. We show that a prime, fully
alternating link in $S\times I$ is hyperbolic. Similar to Menasco, we also give
an easy way to determine primeness in $S\times I$. A fully alternating link is
prime in $S\times I$ if and only if it is "obviously prime". Furthermore, we
extend our result to show that a prime link with fully alternating projection
to an essential surface embedded in an orientable, hyperbolic 3-manifold has a
hyperbolic complement.
| math.GT | menasco showed that a nonsplit prime alternating link that is not a 2braid is hyperbolic in s3 we prove a similar result for links in closed thickened surfaces s times i we define a link to be fully alternating if it has an alternating projection from stimes i to s where the interior of every complementary region is an open disk we show that a prime fully alternating link in stimes i is hyperbolic similar to menasco we also give an easy way to determine primeness in stimes i a fully alternating link is prime in stimes i if and only if it is obviously prime furthermore we extend our result to show that a prime link with fully alternating projection to an essential surface embedded in an orientable hyperbolic 3manifold has a hyperbolic complement | [['menasco', 'showed', 'that', 'a', 'nonsplit', 'prime', 'alternating', 'link', 'that', 'is', 'not', 'a', '2braid', 'is', 'hyperbolic', 'in', 's3', 'we', 'prove', 'a', 'similar', 'result', 'for', 'links', 'in', 'closed', 'thickened', 'surfaces', 's', 'times', 'i', 'we', 'define', 'a', 'link', 'to', 'be', 'fully', 'alternating', 'if', 'it', 'has', 'an', 'alternating', 'projection', 'from', 'stimes', 'i', 'to', 's', 'where', 'the', 'interior', 'of', 'every', 'complementary', 'region', 'is', 'an', 'open', 'disk', 'we', 'show', 'that', 'a', 'prime', 'fully', 'alternating', 'link', 'in', 'stimes', 'i', 'is', 'hyperbolic', 'similar', 'to', 'menasco', 'we', 'also', 'give', 'an', 'easy', 'way', 'to', 'determine', 'primeness', 'in', 'stimes', 'i', 'a', 'fully', 'alternating', 'link', 'is', 'prime', 'in', 'stimes', 'i', 'if', 'and', 'only', 'if', 'it', 'is', 'obviously', 'prime', 'furthermore', 'we', 'extend', 'our', 'result', 'to', 'show', 'that', 'a', 'prime', 'link', 'with', 'fully', 'alternating', 'projection', 'to', 'an', 'essential', 'surface', 'embedded', 'in', 'an', 'orientable', 'hyperbolic', '3manifold', 'has', 'a', 'hyperbolic', 'complement']] | [-0.21578562806049983, 0.09997113686781463, -0.10370685387126825, 0.048260560974961626, -0.1223503698180947, -0.2014047261327505, 0.001972645348696797, 0.4210502946404395, -0.31950550995491167, -0.1952979918293379, 0.09265476640538071, -0.2522865347840168, -0.172856021931188, 0.17915720249077788, -0.15276448161155848, -0.09084169073202819, 0.04930086755366237, 0.06806229730999028, -0.04731673691421747, -0.26507810260410664, 0.3079297047280879, -0.06776891261753108, 0.1698635154300266, 0.07375076564235819, 0.0945718179732837, -0.011173916754692242, 0.01925072649286853, 0.054887447623467006, -0.18025518488936573, 0.07214367790654284, 0.2915856698596919, 0.06206683722945551, 0.18258179751293802, -0.4136698830068646, -0.13790934097315014, 0.15994700192025416, 0.19919821583424455, -0.01844999899018418, -0.065974014647374, -0.17927249820420035, 0.1719332577522706, -0.17850608248953467, -0.15321578690092322, -0.02190520127909689, 0.06412387341605845, -0.0012746577254599996, -0.24771207309224538, -0.059693725683286576, 0.1313626684386421, 0.07179908926978155, 0.003911628477551319, -0.06428855536850514, 0.01306406283509676, 0.10115470831817085, -0.017876698701056064, 0.13652713349502948, 0.0019128276237838522, -0.04628666185911883, -0.10735812756336398, 0.32280696390748576, -0.08226328898497202, -0.27068509207693514, 0.16586810537717409, -0.15100960770483923, -0.18931427438294998, 0.1661395838070247, 0.07893820658446876, 0.11525568717883693, -0.06725213594251761, 0.12499490351269574, -0.18562080117839355, 0.15910909333766474, 0.02190908975209351, -0.09903192067587817, 0.16177293472681883, 0.12523402146029253, 0.18156154933213084, 0.13382229422029385, -0.05339390717584778, 0.02932599973347452, -0.3034702227485401, -0.28162717174876617, -0.17334374747825443, 0.1413984124256617, -0.04245302799250499, -0.1671417941649755, 0.36139347620084195, 0.018322641257610586, 0.18198472178730837, 0.08109618441581175, 0.28479475782390823, 0.06735905828213112, 0.056277457169360584, 0.1498029386555707, 0.16385579049518262, 0.16242367216144446, -0.06717376604124352, -0.14202749001000214, 0.0008482201128370232, 0.14138985781206026] |
1,802.05771 | The Type IIA Flux Potential, 4-forms and Freed-Witten anomalies | We compute the full classical 4d scalar potential of type IIA Calabi-Yau
orientifolds in the presence of fluxes and D6-branes. We show that it can be
written as a bilinear form $V = Z^{AB} \rho_A\rho_B$, where the $\rho_A$ are in
one-to-one correspondence with the 4-form fluxes of the 4d effective theory.
The $\rho_A$ only depend on the internal fluxes, the axions and the topological
data of the compactification, and are fully determined by the Freed-Witten
anomalies of branes that appear as 4d string defects. The quadratic form
$Z^{AB}$ only depends on the saxionic partners of these axions. In general, the
$\rho_A$ can be seen as the basic invariants under the discrete shift
symmetries of the 4d effective theory, and therefore the building blocks of any
flux-dependent quantity. All these polynomials may be obtained by derivation
from one of them, associated to a universal 4-form. The standard N=1
supergravity flux superpotential is uniquely determined from this {\it master
polynomial}, and vice versa.
| hep-th | we compute the full classical 4d scalar potential of type iia calabiyau orientifolds in the presence of fluxes and d6branes we show that it can be written as a bilinear form v zab rho_arho_b where the rho_a are in onetoone correspondence with the 4form fluxes of the 4d effective theory the rho_a only depend on the internal fluxes the axions and the topological data of the compactification and are fully determined by the freedwitten anomalies of branes that appear as 4d string defects the quadratic form zab only depends on the saxionic partners of these axions in general the rho_a can be seen as the basic invariants under the discrete shift symmetries of the 4d effective theory and therefore the building blocks of any fluxdependent quantity all these polynomials may be obtained by derivation from one of them associated to a universal 4form the standard n1 supergravity flux superpotential is uniquely determined from this it master polynomial and vice versa | [['we', 'compute', 'the', 'full', 'classical', '4d', 'scalar', 'potential', 'of', 'type', 'iia', 'calabiyau', 'orientifolds', 'in', 'the', 'presence', 'of', 'fluxes', 'and', 'd6branes', 'we', 'show', 'that', 'it', 'can', 'be', 'written', 'as', 'a', 'bilinear', 'form', 'v', 'zab', 'rho_arho_b', 'where', 'the', 'rho_a', 'are', 'in', 'onetoone', 'correspondence', 'with', 'the', '4form', 'fluxes', 'of', 'the', '4d', 'effective', 'theory', 'the', 'rho_a', 'only', 'depend', 'on', 'the', 'internal', 'fluxes', 'the', 'axions', 'and', 'the', 'topological', 'data', 'of', 'the', 'compactification', 'and', 'are', 'fully', 'determined', 'by', 'the', 'freedwitten', 'anomalies', 'of', 'branes', 'that', 'appear', 'as', '4d', 'string', 'defects', 'the', 'quadratic', 'form', 'zab', 'only', 'depends', 'on', 'the', 'saxionic', 'partners', 'of', 'these', 'axions', 'in', 'general', 'the', 'rho_a', 'can', 'be', 'seen', 'as', 'the', 'basic', 'invariants', 'under', 'the', 'discrete', 'shift', 'symmetries', 'of', 'the', '4d', 'effective', 'theory', 'and', 'therefore', 'the', 'building', 'blocks', 'of', 'any', 'fluxdependent', 'quantity', 'all', 'these', 'polynomials', 'may', 'be', 'obtained', 'by', 'derivation', 'from', 'one', 'of', 'them', 'associated', 'to', 'a', 'universal', '4form', 'the', 'standard', 'n1', 'supergravity', 'flux', 'superpotential', 'is', 'uniquely', 'determined', 'from', 'this', 'it', 'master', 'polynomial', 'and', 'vice', 'versa']] | [-0.1424999083655904, 0.16737179818955608, -0.035296136680287575, 0.11207665005367859, -0.08948824391553893, -0.15012185498134895, 0.009999761159484912, 0.2831809600879608, -0.2480889001931503, -0.2901881139669802, 0.10398822157854601, -0.2674303131306884, -0.17263058524977112, 0.13605360368723576, -0.07426333558048655, -0.023443126336396114, -0.02783902249820296, 0.07549948621330396, -0.10227436838823857, -0.2634824089377931, 0.3357324526709674, -0.020281449376177863, 0.2616045605303403, 0.039455599102862296, 0.0802186891654764, -0.042253499098690285, -0.007929029230192397, 0.005423573818081205, -0.08883220180929506, 0.12942949331544176, 0.21341067961997018, 0.09992197668253087, 0.06284797072129429, -0.45078265806367185, -0.19927000811428958, 0.12772575173081843, 0.16932649358392418, 0.10500545785143685, 0.005865808873607209, -0.27630084693877016, 0.06634737991760103, -0.1245776168422176, -0.13508783840228739, -0.08520829860112225, 0.009013613527816421, -0.012253926905844384, -0.24078231547965007, 0.06420497344799468, 0.012162004187468838, 0.03633534117402661, -0.062421666144879547, -0.09866512696911821, -0.1276023190925425, 0.10110530627996854, 0.09081911202959435, 0.05821020180746069, 0.13694699603548777, -0.16292597333104028, -0.09943244177130572, 0.3800930892375046, -0.0838914165815482, -0.23186944998945724, 0.10395384058883449, -0.10758066579476937, -0.1492910310801751, 0.11087681180153205, 0.0586778998430584, 0.1603109413573413, -0.11056410964379239, 0.20419678881101064, -0.029441601371263747, 0.11362180448834251, 0.07968198175618202, 0.050074896891235295, 0.25628681782612567, 0.039317788128629794, 0.034180674093128215, 0.0857093197201326, -0.013418953657646681, -0.0859995325997072, -0.4154621790669882, -0.14752747362725577, -0.15505172978316392, 0.13795147665099786, -0.1429969582719686, -0.1663434142803293, 0.3713783788576769, 0.06217020788887492, 0.1919130990984586, 0.030037429488305026, 0.19079895924085621, 0.14629251571773383, 0.10028163708582427, 0.04126499329968912, 0.24141906030218369, 0.14509528214931958, 0.07015354099607318, -0.2456215369638126, -0.07450624244450885, 0.15459607990382462] |
1,802.05772 | Stable convergence of inner functions | Let $\mathscr J$ be the set of inner functions whose derivative lies in the
Nevanlinna class. In this paper, we discuss a natural topology on $\mathscr J$
where $F_n \to F$ if the critical structures of $F_n$ converge to the critical
structure of $F$. We show that this occurs precisely when the critical
structures of the $F_n$ are uniformly concentrated on Korenblum stars. The
proof uses Liouville's correspondence between holomorphic self-maps of the unit
disk and solutions of the Gauss curvature equation. Building on the works of
Korenblum and Roberts, we show that this topology also governs the behaviour of
invariant subspaces of a weighted Bergman space which are generated by a single
inner function.
| math.CV | let mathscr j be the set of inner functions whose derivative lies in the nevanlinna class in this paper we discuss a natural topology on mathscr j where f_n to f if the critical structures of f_n converge to the critical structure of f we show that this occurs precisely when the critical structures of the f_n are uniformly concentrated on korenblum stars the proof uses liouvilles correspondence between holomorphic selfmaps of the unit disk and solutions of the gauss curvature equation building on the works of korenblum and roberts we show that this topology also governs the behaviour of invariant subspaces of a weighted bergman space which are generated by a single inner function | [['let', 'mathscr', 'j', 'be', 'the', 'set', 'of', 'inner', 'functions', 'whose', 'derivative', 'lies', 'in', 'the', 'nevanlinna', 'class', 'in', 'this', 'paper', 'we', 'discuss', 'a', 'natural', 'topology', 'on', 'mathscr', 'j', 'where', 'f_n', 'to', 'f', 'if', 'the', 'critical', 'structures', 'of', 'f_n', 'converge', 'to', 'the', 'critical', 'structure', 'of', 'f', 'we', 'show', 'that', 'this', 'occurs', 'precisely', 'when', 'the', 'critical', 'structures', 'of', 'the', 'f_n', 'are', 'uniformly', 'concentrated', 'on', 'korenblum', 'stars', 'the', 'proof', 'uses', 'liouvilles', 'correspondence', 'between', 'holomorphic', 'selfmaps', 'of', 'the', 'unit', 'disk', 'and', 'solutions', 'of', 'the', 'gauss', 'curvature', 'equation', 'building', 'on', 'the', 'works', 'of', 'korenblum', 'and', 'roberts', 'we', 'show', 'that', 'this', 'topology', 'also', 'governs', 'the', 'behaviour', 'of', 'invariant', 'subspaces', 'of', 'a', 'weighted', 'bergman', 'space', 'which', 'are', 'generated', 'by', 'a', 'single', 'inner', 'function']] | [-0.1866305182404492, 0.12165677388725074, -0.06532843602396061, 0.03150536204263082, -0.07791311617373772, -0.046411563038987955, 0.015107792103663087, 0.3274929454145224, -0.3168713380137215, -0.19209784279051034, 0.06792117032910819, -0.2765318208212114, -0.17699092749870665, 0.17765882535592106, -0.09098842467140893, 0.021471649428586596, 0.025583862197463926, 0.07857514738469668, -0.10353729213466463, -0.23110503959064574, 0.42122433652048524, -0.03781940416716363, 0.20261575891282005, 0.031596224228649036, 0.09348635937937576, -0.023941513154979634, 0.010854092535927245, -0.03875712508806432, -0.22143742268381175, 0.14564643730611904, 0.2327086416437574, 0.08341189486660448, 0.2865813433658332, -0.36308995465869487, -0.16248535828098007, 0.19078502506017686, 0.13829331710307008, -0.07198187658138087, 0.011994949996511897, -0.25657751928994676, 0.11343201014172771, -0.0746491919717063, -0.1467020405780362, -0.04905704862802573, 0.06351116511403866, 0.06997240601795847, -0.28469933604102826, 0.040632563601399815, 0.1539080795224594, 0.0654919603649709, -0.06493908134086625, -0.08800461982255396, -0.09387737107463182, 0.07484238063435718, 0.006198196307472561, 0.11019242188447843, 0.12783890765403275, -0.07406898252868457, -0.052640529333249385, 0.34732592270264157, -0.09665908093928643, -0.2423489392451618, 0.12549201383295913, -0.23519493588043944, -0.15926965496138387, 0.07101594091109291, 0.12051144187622097, 0.19602509461262305, -0.05407632790903217, 0.21000586499804225, -0.11111293019645888, 0.11253285656642655, 0.11110334955130542, -0.008160371873932688, 0.1538317082044871, 0.07948263514503513, 0.12339528771522253, 0.14103413849319937, -0.016376023194955094, -0.08744034371136324, -0.35620622881080793, -0.17541749494595696, -0.1944752263917547, 0.12774499406754647, -0.0901833948595753, -0.20747528314266517, 0.40450164451790244, 0.07426218745582129, 0.22579251573947462, 0.10955524450755151, 0.19571621148725565, 0.11683374773622389, 0.048115020962027105, 0.1255739505443236, 0.14953565553195128, 0.16385446013882757, 0.014959880964749534, -0.15955207422983064, 0.013673310180235168, 0.18630908433671878] |
1,802.05773 | Experimental investigation of high-dimensional quantum key distribution
protocols with twisted photons | Quantum key distribution is on the verge of real world applications, where
perfectly secure information can be distributed among multiple parties. Several
quantum cryptographic protocols have been theoretically proposed and
independently realized in different experimental conditions. Here, we develop
an experimental platform based on high-dimensional orbital angular momentum
states of single photons that enables implementation of multiple quantum key
distribution protocols with a single experimental apparatus. Our versatile
approach allows us to experimentally survey different classes of quantum key
distribution techniques, such as the 1984 Bennett \& Brassard (BB84),
tomographic protocols including the six-state and the Singapore protocol, and
to investigate, for the first time, a recently introduced differential phase
shift (Chau15) protocol using twisted photons. This enables us to
experimentally compare the performance of these techniques and discuss their
benefits and deficiencies in terms of noise tolerance in different dimensions.
| quant-ph | quantum key distribution is on the verge of real world applications where perfectly secure information can be distributed among multiple parties several quantum cryptographic protocols have been theoretically proposed and independently realized in different experimental conditions here we develop an experimental platform based on highdimensional orbital angular momentum states of single photons that enables implementation of multiple quantum key distribution protocols with a single experimental apparatus our versatile approach allows us to experimentally survey different classes of quantum key distribution techniques such as the 1984 bennett brassard bb84 tomographic protocols including the sixstate and the singapore protocol and to investigate for the first time a recently introduced differential phase shift chau15 protocol using twisted photons this enables us to experimentally compare the performance of these techniques and discuss their benefits and deficiencies in terms of noise tolerance in different dimensions | [['quantum', 'key', 'distribution', 'is', 'on', 'the', 'verge', 'of', 'real', 'world', 'applications', 'where', 'perfectly', 'secure', 'information', 'can', 'be', 'distributed', 'among', 'multiple', 'parties', 'several', 'quantum', 'cryptographic', 'protocols', 'have', 'been', 'theoretically', 'proposed', 'and', 'independently', 'realized', 'in', 'different', 'experimental', 'conditions', 'here', 'we', 'develop', 'an', 'experimental', 'platform', 'based', 'on', 'highdimensional', 'orbital', 'angular', 'momentum', 'states', 'of', 'single', 'photons', 'that', 'enables', 'implementation', 'of', 'multiple', 'quantum', 'key', 'distribution', 'protocols', 'with', 'a', 'single', 'experimental', 'apparatus', 'our', 'versatile', 'approach', 'allows', 'us', 'to', 'experimentally', 'survey', 'different', 'classes', 'of', 'quantum', 'key', 'distribution', 'techniques', 'such', 'as', 'the', '1984', 'bennett', 'brassard', 'bb84', 'tomographic', 'protocols', 'including', 'the', 'sixstate', 'and', 'the', 'singapore', 'protocol', 'and', 'to', 'investigate', 'for', 'the', 'first', 'time', 'a', 'recently', 'introduced', 'differential', 'phase', 'shift', 'chau15', 'protocol', 'using', 'twisted', 'photons', 'this', 'enables', 'us', 'to', 'experimentally', 'compare', 'the', 'performance', 'of', 'these', 'techniques', 'and', 'discuss', 'their', 'benefits', 'and', 'deficiencies', 'in', 'terms', 'of', 'noise', 'tolerance', 'in', 'different', 'dimensions']] | [-0.14165654885250012, 0.10876085999448801, -0.11845465518334615, 0.01351684563726719, -0.01689174044378368, -0.21128127205052546, 0.06997903874975496, 0.38300185320632796, -0.23929985214365712, -0.3163064098817163, 0.032424917628356656, -0.21222501430903712, -0.12798201201616655, 0.2772816572804004, -0.10602605024219623, 0.18959976038230317, 0.029074100917205213, -0.05780992515252105, -0.027247828932013364, -0.28668564849483247, 0.32161350199104555, 0.06650175776698493, 0.3681746553862467, 0.053362055901171906, 0.11388294138014317, 0.03762525177693793, -0.007524925649132846, -0.05604918225269232, -0.12140904829164875, 0.12664723747667658, 0.26601023758329184, 0.1444566573265807, 0.26833073037809557, -0.4226270500903151, -0.19716909074091485, 0.08260424351485979, 0.1356321616497423, 0.15349493884215398, -0.05206095593748614, -0.32266195356579763, 0.05120461978284376, -0.22964686282378222, -0.09470776868817796, -0.1353936202674439, -0.02977552198405777, 0.017320483206172608, -0.2422958656679839, 0.03176218020131013, -0.013722924053269837, 0.05205992200145764, 0.012350495789516053, -0.07281698823706913, 0.046374546628378865, 0.17699163654553038, -0.07237907010962122, -0.03916669634553338, 0.1359675823211936, -0.051972646504041875, -0.23786221382075123, 0.34915630542340553, -0.012147952892285372, -0.16490983477394497, 0.17532781161800293, -0.077245461363678, -0.14054991339466402, 0.041319204589983984, 0.16454336398913125, 0.10846770586753597, -0.14680742456444673, 0.03747229991671962, -0.030593731648488238, 0.18842829201769615, 0.0495490251042481, 0.1455316842034725, 0.16651941636311157, 0.11328619900824768, 0.0318121000775136, 0.14483601934126844, -0.11051611542768244, -0.18772031657198177, -0.2462306464764489, -0.20466568853506553, -0.21857927120955928, 0.029981970745471438, -0.07198366249659947, -0.09604377012167659, 0.4034251196925262, 0.21672553837498917, 0.16228934853570537, -0.023802421637810767, 0.3801395073616212, 0.043009735108353195, 0.04786682347600747, 0.058724721005585574, 0.2196157781556914, 0.13676784597337246, 0.1182601694549833, -0.18557884816379686, 0.10315460510152791, -0.01649384178620364] |
1,802.05774 | Growth tradeoffs produce complex microbial communities on a single
limiting resource | The relationship between the dynamics of a community and its constituent
pairwise interactions is a fundamental problem in ecology. Higher-order
ecological effects beyond pairwise interactions may be key to complex
ecosystems, but mechanisms to produce these effects remain poorly understood.
Here we show that higher-order effects can arise from variation in multiple
microbial growth traits, such as lag times and growth rates, on a single
limiting resource with no other interactions. These effects produce a range of
ecological phenomena: an unlimited number of strains can exhibit multistability
and neutral coexistence, potentially with a single keystone strain; strains
that coexist in pairs do not coexist all together; and the champion of all
pairwise competitions may not dominate in a mixed community. Since variation in
multiple growth traits is ubiquitous in microbial populations due to pleiotropy
and non-genetic variation, our results indicate these higher-order effects may
also be widespread, especially in laboratory ecology and evolution experiments.
| q-bio.PE | the relationship between the dynamics of a community and its constituent pairwise interactions is a fundamental problem in ecology higherorder ecological effects beyond pairwise interactions may be key to complex ecosystems but mechanisms to produce these effects remain poorly understood here we show that higherorder effects can arise from variation in multiple microbial growth traits such as lag times and growth rates on a single limiting resource with no other interactions these effects produce a range of ecological phenomena an unlimited number of strains can exhibit multistability and neutral coexistence potentially with a single keystone strain strains that coexist in pairs do not coexist all together and the champion of all pairwise competitions may not dominate in a mixed community since variation in multiple growth traits is ubiquitous in microbial populations due to pleiotropy and nongenetic variation our results indicate these higherorder effects may also be widespread especially in laboratory ecology and evolution experiments | [['the', 'relationship', 'between', 'the', 'dynamics', 'of', 'a', 'community', 'and', 'its', 'constituent', 'pairwise', 'interactions', 'is', 'a', 'fundamental', 'problem', 'in', 'ecology', 'higherorder', 'ecological', 'effects', 'beyond', 'pairwise', 'interactions', 'may', 'be', 'key', 'to', 'complex', 'ecosystems', 'but', 'mechanisms', 'to', 'produce', 'these', 'effects', 'remain', 'poorly', 'understood', 'here', 'we', 'show', 'that', 'higherorder', 'effects', 'can', 'arise', 'from', 'variation', 'in', 'multiple', 'microbial', 'growth', 'traits', 'such', 'as', 'lag', 'times', 'and', 'growth', 'rates', 'on', 'a', 'single', 'limiting', 'resource', 'with', 'no', 'other', 'interactions', 'these', 'effects', 'produce', 'a', 'range', 'of', 'ecological', 'phenomena', 'an', 'unlimited', 'number', 'of', 'strains', 'can', 'exhibit', 'multistability', 'and', 'neutral', 'coexistence', 'potentially', 'with', 'a', 'single', 'keystone', 'strain', 'strains', 'that', 'coexist', 'in', 'pairs', 'do', 'not', 'coexist', 'all', 'together', 'and', 'the', 'champion', 'of', 'all', 'pairwise', 'competitions', 'may', 'not', 'dominate', 'in', 'a', 'mixed', 'community', 'since', 'variation', 'in', 'multiple', 'growth', 'traits', 'is', 'ubiquitous', 'in', 'microbial', 'populations', 'due', 'to', 'pleiotropy', 'and', 'nongenetic', 'variation', 'our', 'results', 'indicate', 'these', 'higherorder', 'effects', 'may', 'also', 'be', 'widespread', 'especially', 'in', 'laboratory', 'ecology', 'and', 'evolution', 'experiments']] | [-0.13844610443308514, 0.20180579824548883, -0.06050833032803622, 0.12063079099503479, -0.06678411787430236, -0.11206300228580156, 0.020127023183763353, 0.37082766329978484, -0.32654118769523965, -0.3105655070575704, 0.05950605070109588, -0.2699870071986194, -0.22025254501828126, 0.1777747004726061, -0.04518730893077982, -0.04904845038424367, 0.04459310140061591, -0.05497457397883976, 0.04793980626038626, -0.22568763437523665, 0.26736190636618085, 0.0049985333590756185, 0.2816757290917388, 0.08559280529908546, 0.03598832718532097, -0.041742484680945414, -0.004976861520561498, 0.07083701617507772, -0.07728632541221049, 0.08882685823217881, 0.3170398196882822, 0.13538574834668696, 0.3272888467181474, -0.4690171733692095, -0.2801346459559032, 0.17184197787473632, 0.16784380557463058, 0.161924852681189, -0.050660378707107157, -0.2317073976805482, 0.012552991477687929, -0.1595241761488187, -0.09988693053045691, -0.08858819821601516, 0.004263362705151176, 0.05859702223739526, -0.2533132596177279, 0.15063291483890573, 0.024686035672928413, 0.09998153309963716, -0.06381725319948459, -0.10024764089597171, -0.07209363396131349, 0.1963688593914176, 0.07300230819246635, -0.05666642741741637, 0.14184197482604582, -0.14176783164019707, -0.1528938581755796, 0.40998007604465275, -0.045204332134650124, -0.19080063485383214, 0.3047135861427221, -0.16986765296428236, -0.16973105163386018, 0.11598557054802969, 0.22213062731391908, 0.05730515464765395, -0.1536495670307331, 0.009133108783446784, 0.014059894022761614, 0.18588996949520978, 0.05036532822482336, 0.08901180778312581, 0.25107459807405613, 0.16575124443738493, 0.030267881732978313, 0.03427826367921873, -0.02672009466536488, -0.14012155697269776, -0.20121707346414325, -0.08092291182959331, -0.10174991055421967, 0.01709646142300297, -0.09123008721753767, -0.1886651364862677, 0.35013311078118814, 0.13710749209117654, 0.1942167256553597, 0.031886066053086866, 0.22563035048048508, 0.01565632504689229, 0.10404083914389568, -0.0009716368936510248, 0.2276337371123108, 0.07857155568244589, 0.06102089715105566, -0.2298485540993536, 0.19419533856116333, -0.06297437266700646] |
1,802.05775 | Optimal Shelter Location-Allocation during Evacuation with
Uncertainties: A Scenario-Based Approach | Evacuation planning is an important and challenging element in emergency
management due to the high level of uncertainty and numerous players and
agencies involved in the event. To address all the factors with conflicting
objectives, mathematical modeling has gained an extensive application over all
aspects of evacuation planning to help responders and policy makers evaluate
required time for evacuation and estimate numbers and distribution of
casualties under different disaster scenarios. Correspondingly, mathematical
formulation of evacuation optimization problems and solution methods are
important when planning for evacuation. In this paper, the bi-level programming
formulation of shelter location-allocation problem is considered. To account
for stochasticity, a scenario-based approach is taken to address the
uncertainty in the population to be evacuated from a small town in Lombardy
region, Italy. Genetic algorithm is used as the solution method. Four scenarios
are considered to study the optimal number and location of shelters for
evacuation during normal weekdays, at nights, during weekends, and during
vacation times with visiting travelers. The results highlight how different
scenarios need different number and location of shelters for an optimal
evacuation.
| math.OC | evacuation planning is an important and challenging element in emergency management due to the high level of uncertainty and numerous players and agencies involved in the event to address all the factors with conflicting objectives mathematical modeling has gained an extensive application over all aspects of evacuation planning to help responders and policy makers evaluate required time for evacuation and estimate numbers and distribution of casualties under different disaster scenarios correspondingly mathematical formulation of evacuation optimization problems and solution methods are important when planning for evacuation in this paper the bilevel programming formulation of shelter locationallocation problem is considered to account for stochasticity a scenariobased approach is taken to address the uncertainty in the population to be evacuated from a small town in lombardy region italy genetic algorithm is used as the solution method four scenarios are considered to study the optimal number and location of shelters for evacuation during normal weekdays at nights during weekends and during vacation times with visiting travelers the results highlight how different scenarios need different number and location of shelters for an optimal evacuation | [['evacuation', 'planning', 'is', 'an', 'important', 'and', 'challenging', 'element', 'in', 'emergency', 'management', 'due', 'to', 'the', 'high', 'level', 'of', 'uncertainty', 'and', 'numerous', 'players', 'and', 'agencies', 'involved', 'in', 'the', 'event', 'to', 'address', 'all', 'the', 'factors', 'with', 'conflicting', 'objectives', 'mathematical', 'modeling', 'has', 'gained', 'an', 'extensive', 'application', 'over', 'all', 'aspects', 'of', 'evacuation', 'planning', 'to', 'help', 'responders', 'and', 'policy', 'makers', 'evaluate', 'required', 'time', 'for', 'evacuation', 'and', 'estimate', 'numbers', 'and', 'distribution', 'of', 'casualties', 'under', 'different', 'disaster', 'scenarios', 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1,802.05776 | Maximum-A-Posteriori Signal Recovery with Prior Information:
Applications to Compressive Sensing | This paper studies the asymptotic performance of maximum-a-posteriori
estimation in the presence of prior information. The problem arises in several
applications such as recovery of signals with non-uniform sparsity pattern from
underdetermined measurements. With prior information, the maximum-a-posteriori
estimator might have asymmetric penalty. We consider a generic form of this
estimator and study its performance via the replica method. Our analyses
demonstrate an asymmetric form of the decoupling property in the large-system
limit. Employing our results, we further investigate the performance of
weighted zero-norm minimization for recovery of a non-uniform sparse signal.
Our investigations illustrate that for a given distortion, the minimum number
of required measurements can be significantly reduced by choosing weighting
coefficients optimally.
| cs.IT math.IT | this paper studies the asymptotic performance of maximumaposteriori estimation in the presence of prior information the problem arises in several applications such as recovery of signals with nonuniform sparsity pattern from underdetermined measurements with prior information the maximumaposteriori estimator might have asymmetric penalty we consider a generic form of this estimator and study its performance via the replica method our analyses demonstrate an asymmetric form of the decoupling property in the largesystem limit employing our results we further investigate the performance of weighted zeronorm minimization for recovery of a nonuniform sparse signal our investigations illustrate that for a given distortion the minimum number of required measurements can be significantly reduced by choosing weighting coefficients optimally | [['this', 'paper', 'studies', 'the', 'asymptotic', 'performance', 'of', 'maximumaposteriori', 'estimation', 'in', 'the', 'presence', 'of', 'prior', 'information', 'the', 'problem', 'arises', 'in', 'several', 'applications', 'such', 'as', 'recovery', 'of', 'signals', 'with', 'nonuniform', 'sparsity', 'pattern', 'from', 'underdetermined', 'measurements', 'with', 'prior', 'information', 'the', 'maximumaposteriori', 'estimator', 'might', 'have', 'asymmetric', 'penalty', 'we', 'consider', 'a', 'generic', 'form', 'of', 'this', 'estimator', 'and', 'study', 'its', 'performance', 'via', 'the', 'replica', 'method', 'our', 'analyses', 'demonstrate', 'an', 'asymmetric', 'form', 'of', 'the', 'decoupling', 'property', 'in', 'the', 'largesystem', 'limit', 'employing', 'our', 'results', 'we', 'further', 'investigate', 'the', 'performance', 'of', 'weighted', 'zeronorm', 'minimization', 'for', 'recovery', 'of', 'a', 'nonuniform', 'sparse', 'signal', 'our', 'investigations', 'illustrate', 'that', 'for', 'a', 'given', 'distortion', 'the', 'minimum', 'number', 'of', 'required', 'measurements', 'can', 'be', 'significantly', 'reduced', 'by', 'choosing', 'weighting', 'coefficients', 'optimally']] | [-0.11444400392634713, 0.01086086177421511, -0.09772158335570408, 0.024731152797239305, -0.06206471561251775, -0.09821738692243462, 0.0754996897905822, 0.36606508326392784, -0.30155651990081306, -0.3006014913158572, 0.1667652413127539, -0.250514605463199, -0.20019492897731456, 0.15443678751749837, -0.10121403523599325, 0.09402406234243561, 0.09515877746592236, 0.036310414812001195, -0.1485150822762238, -0.25734092241112627, 0.25791677664155543, 0.09374570993747076, 0.3252470379634558, 0.013161142316201458, 0.07025181415855236, 0.07293964698505791, -0.03916017313933243, 0.016906525891112245, -0.12804179863286783, 0.12394484526850409, 0.26618880110428383, 0.17007100502757921, 0.3079655349092639, -0.3826622958092586, -0.2699406388580151, 0.15106097285676262, 0.14748866132096103, 0.10918109748998414, -0.09761848838432975, -0.27356050024213996, 0.10225421878023316, -0.1474201241184188, -0.07322563267839344, -0.07954990702843212, -0.12206434622892867, 0.03796044367406031, -0.37706812335097273, 0.09475329893681667, 0.09341884862388605, 0.02345869993062123, -0.043853084067814053, -0.13810960540507475, 0.054672138712308166, 0.08419139636680484, 0.07002530907098528, -0.02153132625086152, 0.11514306747627889, -0.1207001350455634, -0.09696555875100034, 0.3214512757714028, -0.07740177483419361, -0.2465503597955989, 0.11949830032156214, -0.0942228801265035, -0.13887358878577208, 0.13976700272330123, 0.21450080320886944, 0.10777368557679912, -0.1525149424438891, 0.06627582433461414, -0.07598227243870496, 0.1491271486181928, 0.04980940077929636, 0.06570434681666286, 0.13392738822807113, 0.17462687391139892, 0.11728531258464184, 0.19050877565700236, -0.12733008125796913, -0.051207166531568635, -0.2603456253201053, -0.0891476464741256, -0.2335528143317155, -0.006175305784436995, -0.17848915775577315, -0.13839888333788383, 0.3935839891879131, 0.1826934358722571, 0.20801935020996176, 0.07191575908142588, 0.34106143345327483, 0.13430047913494966, 0.010119179486418549, 0.06271301933523754, 0.22490664653318085, 0.13521206303823577, 0.021133490117347758, -0.2624850501731524, 0.10996331711016272, 0.01844594302263273] |
1,802.05777 | A-priori bounds for a quasilinear problem in critical dimension | We establish uniform a-priori bounds for solutions of the quasilinear problem $-\Delta_Nu=f(u)$ in $\Omega$, with $u=0$ on $\partial\Omega$, where $\Omega\subset\mathbb{R}^N$ is a bounded smooth and convex domain, and $f$ is a positive superlinear and subcritical function in the sense of the Trudinger-Moser inequality. The typical growth of $f$ is thus exponential. Finally, a generalization of the result for nonhomogeneous nonlinearities is given. Using a blow-up approach, this paper completes the results in [Damascelli-Pardo, Nonlinear Anal. Real World Appl. 41 (2018)] and [Lorca-Ruf-Ubilla, J. Differential Equations 246 no. 5 (2009)], enlarging the class of nonlinearities for which the uniform a-priori bound applies. | math.AP | we establish uniform apriori bounds for solutions of the quasilinear problem delta_nufu in omega with u0 on partialomega where omegasubsetmathbbrn is a bounded smooth and convex domain and f is a positive superlinear and subcritical function in the sense of the trudingermoser inequality the typical growth of f is thus exponential finally a generalization of the result for nonhomogeneous nonlinearities is given using a blowup approach this paper completes the results in damascellipardo nonlinear anal real world appl 41 2018 and lorcarufubilla j differential equations 246 no 5 2009 enlarging the class of nonlinearities for which the uniform apriori bound applies | [['we', 'establish', 'uniform', 'apriori', 'bounds', 'for', 'solutions', 'of', 'the', 'quasilinear', 'problem', 'delta_nufu', 'in', 'omega', 'with', 'u0', 'on', 'partialomega', 'where', 'omegasubsetmathbbrn', 'is', 'a', 'bounded', 'smooth', 'and', 'convex', 'domain', 'and', 'f', 'is', 'a', 'positive', 'superlinear', 'and', 'subcritical', 'function', 'in', 'the', 'sense', 'of', 'the', 'trudingermoser', 'inequality', 'the', 'typical', 'growth', 'of', 'f', 'is', 'thus', 'exponential', 'finally', 'a', 'generalization', 'of', 'the', 'result', 'for', 'nonhomogeneous', 'nonlinearities', 'is', 'given', 'using', 'a', 'blowup', 'approach', 'this', 'paper', 'completes', 'the', 'results', 'in', 'damascellipardo', 'nonlinear', 'anal', 'real', 'world', 'appl', '41', '2018', 'and', 'lorcarufubilla', 'j', 'differential', 'equations', '246', 'no', '5', '2009', 'enlarging', 'the', 'class', 'of', 'nonlinearities', 'for', 'which', 'the', 'uniform', 'apriori', 'bound', 'applies']] | [-0.13985427416747018, 0.009778566539705711, -0.033848643706807374, 0.012868784426245838, -0.1207343303189822, -0.13461168074258129, 0.02042964020653685, 0.2532167190303361, -0.2462432748752133, -0.2565965789740867, 0.14427542497703275, -0.29371089480665263, -0.15106774296858633, 0.21791652726052252, -0.10962816053165161, 0.10315353935109262, 0.022605710330286195, 0.0062604395064468285, -0.0589963758093476, -0.2796759246183293, 0.3551393990661493, -0.10039762911453311, 0.2255777316966821, 0.09609715211946442, 0.09783964672325444, -0.026849097334684765, -0.004156229498425537, -0.008632702030697647, -0.23406286662377948, 0.10573238670251009, 0.24602308163029732, 0.062035223901537914, 0.3435367093497545, -0.36342013593078876, -0.18553472306503324, 0.15890046661928753, 0.095373988370126, 0.03078966337251382, -0.02670904911569871, -0.29882037137844125, 0.1122882581715073, -0.06961078240241551, -0.17790605216192043, -0.0051151457508759835, 0.08557916909684332, 0.06663152486458895, -0.4019735764084878, 0.16739880456113998, 0.2001118594542982, 0.06422095687430927, -0.125254337628353, -0.08472249473264555, -0.00594234939816655, 0.014813649240044914, -0.017016826608522356, 0.10017909096169038, 0.019922157274369075, -0.10692474267169909, -0.07924066696966979, 0.3177264432745929, -0.10642372617528927, -0.25982505750215174, 0.16151382841112816, -0.16192542458884418, -0.12108494823190327, 0.10067662339876121, 0.19667518758500108, 0.18194127244618247, -0.11616961864222373, 0.25375622406136245, -0.07934544668580425, 0.1527321019723099, 0.14494827991275458, -0.04336375727589071, 0.0005961930764154817, 0.13082420620687155, 0.20011117245184676, 0.12644862689372457, -0.006260877748362112, -0.07628754302099043, -0.3894050331140051, -0.13389887665315264, -0.20197071056166777, 0.113790528611185, -0.11300211218696525, -0.17505380406628876, 0.33600420173381135, 0.036689115441119184, 0.1720219582167207, 0.11788194221848318, 0.18852525965633746, 0.16830139420451407, -0.040776514272414605, 0.136141440623463, 0.1919126967514618, 0.15217924525020454, 0.15724417192823426, -0.16331135968520896, 0.034488633859484474, 0.09231309747196881] |
1,802.05778 | A comparison of machine learning techniques for taxonomic classification
of teeth from the Family Bovidae | This study explores the performance of modern, accurate machine learning
algorithms on the classification of fossil teeth in the Family Bovidae.
Isolated bovid teeth are typically the most common fossils found in southern
Africa and they often constitute the basis for paleoenvironmental
reconstructions. Taxonomic identification of fossil bovid teeth, however, is
often imprecise and subjective. Using modern teeth with known taxons, machine
learning algorithms can be trained to classify fossils. Previous work by Brophy
et. al. 2014 uses elliptical Fourier analysis of the form (size and shape) of
the outline of the occlusal surface of each tooth as features in a linear
discriminant analysis framework. This manuscript expands on that previous work
by exploring how different machine learning approaches classify the teeth and
testing which technique is best for classification. Five different machine
learning techniques including linear discriminant analysis, neural networks,
nuclear penalized multinomial regression, random forests, and support vector
machines were used to estimate these models. Support vector machines and random
forests perform the best in terms of both log-loss and misclassification rate;
both of these methods are improvements over linear discriminant analysis. With
the identification and application of these superior methods, bovid teeth can
be classified with higher accuracy.
| stat.AP | this study explores the performance of modern accurate machine learning algorithms on the classification of fossil teeth in the family bovidae isolated bovid teeth are typically the most common fossils found in southern africa and they often constitute the basis for paleoenvironmental reconstructions taxonomic identification of fossil bovid teeth however is often imprecise and subjective using modern teeth with known taxons machine learning algorithms can be trained to classify fossils previous work by brophy et al 2014 uses elliptical fourier analysis of the form size and shape of the outline of the occlusal surface of each tooth as features in a linear discriminant analysis framework this manuscript expands on that previous work by exploring how different machine learning approaches classify the teeth and testing which technique is best for classification five different machine learning techniques including linear discriminant analysis neural networks nuclear penalized multinomial regression random forests and support vector machines were used to estimate these models support vector machines and random forests perform the best in terms of both logloss and misclassification rate both of these methods are improvements over linear discriminant analysis with the identification and application of these superior methods bovid teeth can be classified with higher accuracy | [['this', 'study', 'explores', 'the', 'performance', 'of', 'modern', 'accurate', 'machine', 'learning', 'algorithms', 'on', 'the', 'classification', 'of', 'fossil', 'teeth', 'in', 'the', 'family', 'bovidae', 'isolated', 'bovid', 'teeth', 'are', 'typically', 'the', 'most', 'common', 'fossils', 'found', 'in', 'southern', 'africa', 'and', 'they', 'often', 'constitute', 'the', 'basis', 'for', 'paleoenvironmental', 'reconstructions', 'taxonomic', 'identification', 'of', 'fossil', 'bovid', 'teeth', 'however', 'is', 'often', 'imprecise', 'and', 'subjective', 'using', 'modern', 'teeth', 'with', 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1,802.05779 | Quantum Variational Autoencoder | Variational autoencoders (VAEs) are powerful generative models with the
salient ability to perform inference. Here, we introduce a quantum variational
autoencoder (QVAE): a VAE whose latent generative process is implemented as a
quantum Boltzmann machine (QBM). We show that our model can be trained
end-to-end by maximizing a well-defined loss-function: a 'quantum' lower-bound
to a variational approximation of the log-likelihood. We use quantum Monte
Carlo (QMC) simulations to train and evaluate the performance of QVAEs. To
achieve the best performance, we first create a VAE platform with discrete
latent space generated by a restricted Boltzmann machine (RBM). Our model
achieves state-of-the-art performance on the MNIST dataset when compared
against similar approaches that only involve discrete variables in the
generative process. We consider QVAEs with a smaller number of latent units to
be able to perform QMC simulations, which are computationally expensive. We
show that QVAEs can be trained effectively in regimes where quantum effects are
relevant despite training via the quantum bound. Our findings open the way to
the use of quantum computers to train QVAEs to achieve competitive performance
for generative models. Placing a QBM in the latent space of a VAE leverages the
full potential of current and next-generation quantum computers as sampling
devices.
| quant-ph cs.LG stat.ML | variational autoencoders vaes are powerful generative models with the salient ability to perform inference here we introduce a quantum variational autoencoder qvae a vae whose latent generative process is implemented as a quantum boltzmann machine qbm we show that our model can be trained endtoend by maximizing a welldefined lossfunction a quantum lowerbound to a variational approximation of the loglikelihood we use quantum monte carlo qmc simulations to train and evaluate the performance of qvaes to achieve the best performance we first create a vae platform with discrete latent space generated by a restricted boltzmann machine rbm our model achieves stateoftheart performance on the mnist dataset when compared against similar approaches that only involve discrete variables in the generative process we consider qvaes with a smaller number of latent units to be able to perform qmc simulations which are computationally expensive we show that qvaes can be trained effectively in regimes where quantum effects are relevant despite training via the quantum bound our findings open the way to the use of quantum computers to train qvaes to achieve competitive performance for generative models placing a qbm in the latent space of a vae leverages the full potential of current and nextgeneration quantum computers as sampling devices | [['variational', 'autoencoders', 'vaes', 'are', 'powerful', 'generative', 'models', 'with', 'the', 'salient', 'ability', 'to', 'perform', 'inference', 'here', 'we', 'introduce', 'a', 'quantum', 'variational', 'autoencoder', 'qvae', 'a', 'vae', 'whose', 'latent', 'generative', 'process', 'is', 'implemented', 'as', 'a', 'quantum', 'boltzmann', 'machine', 'qbm', 'we', 'show', 'that', 'our', 'model', 'can', 'be', 'trained', 'endtoend', 'by', 'maximizing', 'a', 'welldefined', 'lossfunction', 'a', 'quantum', 'lowerbound', 'to', 'a', 'variational', 'approximation', 'of', 'the', 'loglikelihood', 'we', 'use', 'quantum', 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1,802.0578 | Numerical simulation of BOD5 dynamics in Igap\'o I lake, Londrina,
Paran\'a, Brazil: Experimental measurement and mathematical modeling | The concentration of biochemical oxygen demand, BOD5, was studied in order to
evaluate the water quality of the Igap\'o I Lake, in Londrina, Paran\'a State,
Brazil. The simulation was conducted by means of the discretization in
curvilinear coordinates of the geometry of Igap\'o I Lake, together with finite
difference and finite element methods. The evaluation of the proposed numerical
model for water quality was performed by comparing the experimental values of
BOD5 with the numerical results. The evaluation of the model showed
quantitative results compatible with the actual behavior of Igap\'o I Lake in
relation to the simulated parameter. The qualitative analysis of the numerical
simulations provided a better understanding of the dynamics of the BOD5
concentration at Igap\'o I Lake, showing that such concentrations in the
central regions of the lake have values above those allowed by Brazilian law.
The results can help to guide choices by public officials, as: (i) improve the
identification mechanisms of pollutant emitters on Lake Igap\'o I, (ii)
contribute to the optimal treatment of the recovery of the polluted environment
and (iii) provide a better quality of life for the regulars of the lake as well
as for the residents living on the lakeside.
| q-bio.QM math.NA | the concentration of biochemical oxygen demand bod5 was studied in order to evaluate the water quality of the igapo i lake in londrina parana state brazil the simulation was conducted by means of the discretization in curvilinear coordinates of the geometry of igapo i lake together with finite difference and finite element methods the evaluation of the proposed numerical model for water quality was performed by comparing the experimental values of bod5 with the numerical results the evaluation of the model showed quantitative results compatible with the actual behavior of igapo i lake in relation to the simulated parameter the qualitative analysis of the numerical simulations provided a better understanding of the dynamics of the bod5 concentration at igapo i lake showing that such concentrations in the central regions of the lake have values above those allowed by brazilian law the results can help to guide choices by public officials as i improve the identification mechanisms of pollutant emitters on lake igapo i ii contribute to the optimal treatment of the recovery of the polluted environment and iii provide a better quality of life for the regulars of the lake as well as for the residents living on the lakeside | [['the', 'concentration', 'of', 'biochemical', 'oxygen', 'demand', 'bod5', 'was', 'studied', 'in', 'order', 'to', 'evaluate', 'the', 'water', 'quality', 'of', 'the', 'igapo', 'i', 'lake', 'in', 'londrina', 'parana', 'state', 'brazil', 'the', 'simulation', 'was', 'conducted', 'by', 'means', 'of', 'the', 'discretization', 'in', 'curvilinear', 'coordinates', 'of', 'the', 'geometry', 'of', 'igapo', 'i', 'lake', 'together', 'with', 'finite', 'difference', 'and', 'finite', 'element', 'methods', 'the', 'evaluation', 'of', 'the', 'proposed', 'numerical', 'model', 'for', 'water', 'quality', 'was', 'performed', 'by', 'comparing', 'the', 'experimental', 'values', 'of', 'bod5', 'with', 'the', 'numerical', 'results', 'the', 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1,802.05781 | The formalism of neutrino oscillations: an introduction | The recent wide recognition of the existence of neutrino oscillations
concludes the pioneer stage of these studies and poses the problem of how to
communicate effectively the basic aspects of this branch of science. In fact,
the phenomenon of neutrino oscillations has peculiar features and requires to
master some specific idea and some amount of formalism. The main aim of these
introductory notes is exactly to cover these aspects, in order to allow the
interested students to appreciate the modern developments and possibly to begin
to do research in neutrino oscillations.
| hep-ph | the recent wide recognition of the existence of neutrino oscillations concludes the pioneer stage of these studies and poses the problem of how to communicate effectively the basic aspects of this branch of science in fact the phenomenon of neutrino oscillations has peculiar features and requires to master some specific idea and some amount of formalism the main aim of these introductory notes is exactly to cover these aspects in order to allow the interested students to appreciate the modern developments and possibly to begin to do research in neutrino oscillations | [['the', 'recent', 'wide', 'recognition', 'of', 'the', 'existence', 'of', 'neutrino', 'oscillations', 'concludes', 'the', 'pioneer', 'stage', 'of', 'these', 'studies', 'and', 'poses', 'the', 'problem', 'of', 'how', 'to', 'communicate', 'effectively', 'the', 'basic', 'aspects', 'of', 'this', 'branch', 'of', 'science', 'in', 'fact', 'the', 'phenomenon', 'of', 'neutrino', 'oscillations', 'has', 'peculiar', 'features', 'and', 'requires', 'to', 'master', 'some', 'specific', 'idea', 'and', 'some', 'amount', 'of', 'formalism', 'the', 'main', 'aim', 'of', 'these', 'introductory', 'notes', 'is', 'exactly', 'to', 'cover', 'these', 'aspects', 'in', 'order', 'to', 'allow', 'the', 'interested', 'students', 'to', 'appreciate', 'the', 'modern', 'developments', 'and', 'possibly', 'to', 'begin', 'to', 'do', 'research', 'in', 'neutrino', 'oscillations']] | [-0.09278056387799782, 0.12536643899383618, -0.07192692883148953, 0.08633387571835256, -0.15810438706079027, -0.09535616114326231, 0.03769347948728354, 0.3104255678219168, -0.283019142759488, -0.36973773385142233, 0.08845234473087507, -0.2850140369609817, -0.16912310263449018, 0.19881616863498852, -0.12036214286850376, 0.037008685929762135, 0.0791740896241678, 0.047211282525805164, -0.055159785610797644, -0.30386824210962426, 0.3336988029653554, 0.0846445654550245, 0.24776409644183223, 0.06711737469739311, 0.07233678299650949, -0.04825468163855456, -0.08480908326129173, -0.05764300869086982, -0.125882836569652, 0.14855975664737583, 0.34027384297287727, 0.1776777960482862, 0.30427539824497896, -0.47206435911357403, -0.20641353990432326, 0.1007560900864089, 0.15071849021446573, 0.11548677359158839, -0.02598419581836724, -0.27103206041463457, 0.035383819812233305, -0.11620422050479677, -0.17473648349334905, -0.040282521361055285, 0.009649998283238856, 0.019430254698618428, -0.15296398332997502, 0.027338150745401017, 0.06592995417048479, 0.03710821346676612, -0.005817782819516711, -0.12296991411514663, 0.07371465414557811, 0.19317167290169132, 0.12350263908328898, -0.024619909460083224, 0.07822116256875733, -0.165558742453948, -0.10627083951136568, 0.3907560421624681, -0.0035837194997469313, -0.10564611564632787, 0.2049220356729475, -0.17752597148908855, -0.18030389234587385, 0.08702770073048688, 0.19282380954597841, 0.06929863606078121, -0.16141695064584632, 0.05761348750636718, 0.004021421397727873, 0.11273082243380966, 0.01408073653026924, 0.03339078080850643, 0.27487488099179425, 0.16844174786276378, 0.010270479585525098, 0.04979148403239938, -0.04803616674565761, -0.09647256459344875, -0.3343771786971407, -0.11000497762986444, -0.09464838311772097, 0.047443430993583176, 0.02360314208627832, -0.13325751795443697, 0.4602830028468436, 0.22499705471145118, 0.1595658570282407, -0.03571363773509082, 0.2727943787167033, 0.04805937288636035, 0.0592858095520309, 0.009417931866514814, 0.23875727110150893, 0.14484060246620203, 0.18100046232383657, -0.22640177940663236, 0.03546475718254582, 0.04238991543476644] |
1,802.05782 | The TAP-Plefka variational principle for the spherical SK model | We reinterpret the Thouless-Anderson-Palmer approach to mean field spin glass
models as a variational principle in the spirit of the Gibbs variational
principle and the Bragg-Williams approximation. We prove this TAP-Plefka
variational principle rigorously in the case of the spherical
Sherrington-Kirkpatrick model.
| math.PR math-ph math.MP | we reinterpret the thoulessandersonpalmer approach to mean field spin glass models as a variational principle in the spirit of the gibbs variational principle and the braggwilliams approximation we prove this tapplefka variational principle rigorously in the case of the spherical sherringtonkirkpatrick model | [['we', 'reinterpret', 'the', 'thoulessandersonpalmer', 'approach', 'to', 'mean', 'field', 'spin', 'glass', 'models', 'as', 'a', 'variational', 'principle', 'in', 'the', 'spirit', 'of', 'the', 'gibbs', 'variational', 'principle', 'and', 'the', 'braggwilliams', 'approximation', 'we', 'prove', 'this', 'tapplefka', 'variational', 'principle', 'rigorously', 'in', 'the', 'case', 'of', 'the', 'spherical', 'sherringtonkirkpatrick', 'model']] | [-0.012743244901662919, 0.09167436237742262, -0.16785187333277085, 0.17329114643436652, -0.03453461080789566, -0.0695429443290866, 0.061892594051797215, 0.31178384142496235, -0.29862803527403897, -0.2721992404450004, 0.030106416920472574, -0.18815281381830573, -0.14343468429202713, 0.10284220556183378, -0.04812073845149404, 0.09673845718001448, 0.043820106833264594, 0.046378237689368246, -0.11778964791106196, -0.18826341960670018, 0.26292813285367517, -0.007436326954786371, 0.3454464274663024, 0.054813903458302886, 0.19134409246375647, 0.10658683214427495, 0.14997846342441512, 0.03583144462417539, -0.21277513879737475, 0.15552594508157996, 0.19230286041446806, 0.06547659155685545, 0.2914363942222624, -0.40882069108689706, -0.29001381100587004, 0.127137128456791, 0.06421576635684909, 0.21681745502552607, 0.05783176003591862, -0.2971882687188776, -0.008335144486187435, -0.2190578624424411, -0.23131449693223324, -0.1097505798040912, -0.14494333685114524, 0.018981536850333214, -0.2699532643778295, 0.17636121707264243, 0.15953609701700328, 0.06148172886587861, -0.10927592419803416, -0.125320099032961, 0.057163310796590296, -0.017488916075175127, 0.07469933441124584, 0.02938956651473191, 0.06863847790603958, -0.07061944427763725, -0.14595222665087842, 0.32679387647658587, -0.1112406946854984, -0.240468050103362, 0.13405793895036344, -0.06697136406764025, -0.17838794994735863, 0.006887322973187377, 0.13004709399736872, 0.1948444914163613, -0.1880912462976284, 0.1312788042520945, -0.07142525495643295, 0.06005744331675332, -0.007144883513541483, -0.09100279938883898, 0.11264038185884313, 0.1652173535307733, 0.09453791217515017, 0.18884609412492775, -0.13995476940465046, -0.23778773798811725, -0.33657668896655485, -0.18988565832558202, -0.28987236825249546, 0.05370348699890622, -0.12992187788129342, -0.1760147325637773, 0.33761006518744113, 0.19196128573768387, 0.07058942935815672, 0.15496175767990147, 0.24105135037949899, 0.1422498584128689, 0.0003035493285917654, 0.06979235157188846, 0.2881479780530421, 0.25810484388252586, 0.10529719090952379, -0.18803406900317385, -0.017156666689893095, 0.21574051373797218] |
1,802.05783 | Superresolution Interferometric Imaging with Sparse Modeling Using Total
Squared Variation --- Application to Imaging the Black Hole Shadow | We propose a new superresolution imaging technique for interferometry using
sparse modeling, utilizing two regularization terms: the $\ell_1$-norm and a
new function named Total Squared Variation (TSV) of the brightness
distribution. TSV is an edge-smoothing variant of Total Variation (TV), leading
to reducing the sum of squared gradients. First, we demonstrate that our
technique may achieve super-resolution of $\sim 30$% compared to the
traditional CLEAN beam size using synthetic observations of two point sources.
Second, we present simulated observations of three physically motivated static
models of Sgr A* with the Event Horizon Telescope (EHT) to show the performance
of proposed techniques in greater detail. We find that $\ell_1$+TSV
regularization outperforms $\ell_1$+TV regularization with the popular
isotropic TV term and the Cotton-Schwab CLEAN algorithm, demonstrating that TSV
is well-matched to the expected physical properties of the astronomical images,
which are often nebulous. Remarkably, in both the image and gradient domains,
the optimal beam size minimizing root-mean-squared errors is $\lesssim 10$% of
the traditional CLEAN beam size for $\ell_1$+TSV regularization, and
non-convolved reconstructed images have smaller errors than beam-convolved
reconstructed images. This indicates that the traditional post-processing
technique of Gaussian convolution in interferometric imaging may not be
required for the $\ell_1$+TSV regularization. We also propose a feature
extraction method to detect circular features from the image of a black hole
shadow with the circle Hough transform (CHT) and use it to evaluate the
performance of the image reconstruction. With our imaging technique and the
CHT, the EHT can constrain the radius of the black hole shadow with an accuracy
of $\sim 10-20$% in present simulations for Sgr A*.
| astro-ph.IM astro-ph.HE | we propose a new superresolution imaging technique for interferometry using sparse modeling utilizing two regularization terms the ell_1norm and a new function named total squared variation tsv of the brightness distribution tsv is an edgesmoothing variant of total variation tv leading to reducing the sum of squared gradients first we demonstrate that our technique may achieve superresolution of sim 30 compared to the traditional clean beam size using synthetic observations of two point sources second we present simulated observations of three physically motivated static models of sgr a with the event horizon telescope eht to show the performance of proposed techniques in greater detail we find that ell_1tsv regularization outperforms ell_1tv regularization with the popular isotropic tv term and the cottonschwab clean algorithm demonstrating that tsv is wellmatched to the expected physical properties of the astronomical images which are often nebulous remarkably in both the image and gradient domains the optimal beam size minimizing rootmeansquared errors is lesssim 10 of the traditional clean beam size for ell_1tsv regularization and nonconvolved reconstructed images have smaller errors than beamconvolved reconstructed images this indicates that the traditional postprocessing technique of gaussian convolution in interferometric imaging may not be required for the ell_1tsv regularization we also propose a feature extraction method to detect circular features from the image of a black hole shadow with the circle hough transform cht and use it to evaluate the performance of the image reconstruction with our imaging technique and the cht the eht can constrain the radius of the black hole shadow with an accuracy of sim 1020 in present simulations for sgr a | [['we', 'propose', 'a', 'new', 'superresolution', 'imaging', 'technique', 'for', 'interferometry', 'using', 'sparse', 'modeling', 'utilizing', 'two', 'regularization', 'terms', 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1,802.05784 | Integral and rational mapping classes | Let $X$ and $Y$ be finite complexes. When $Y$ is a nilpotent space, it has a
rationalization $Y \to Y_{(0)}$ which is well-understood. Early on it was found
that the induced map $[X,Y] \to [X,Y_{(0)}]$ on sets of mapping classes is
finite-to-one. The sizes of the preimages need not be bounded; we show,
however, that as the complexity (in a suitable sense) of a rational mapping
class increases, these sizes are at most polynomial. This ``torsion''
information about $[X,Y]$ is in some sense orthogonal to rational homotopy
theory but is nevertheless an invariant of the rational homotopy type of $Y$ in
at least some cases. The notion of complexity is geometric and we also prove a
conjecture of Gromov \cite{GrMS} regarding the number of mapping classes that
have Lipschitz constant at most $L$.
| math.AT math.DG | let x and y be finite complexes when y is a nilpotent space it has a rationalization y to y_0 which is wellunderstood early on it was found that the induced map xy to xy_0 on sets of mapping classes is finitetoone the sizes of the preimages need not be bounded we show however that as the complexity in a suitable sense of a rational mapping class increases these sizes are at most polynomial this torsion information about xy is in some sense orthogonal to rational homotopy theory but is nevertheless an invariant of the rational homotopy type of y in at least some cases the notion of complexity is geometric and we also prove a conjecture of gromov citegrms regarding the number of mapping classes that have lipschitz constant at most l | [['let', 'x', 'and', 'y', 'be', 'finite', 'complexes', 'when', 'y', 'is', 'a', 'nilpotent', 'space', 'it', 'has', 'a', 'rationalization', 'y', 'to', 'y_0', 'which', 'is', 'wellunderstood', 'early', 'on', 'it', 'was', 'found', 'that', 'the', 'induced', 'map', 'xy', 'to', 'xy_0', 'on', 'sets', 'of', 'mapping', 'classes', 'is', 'finitetoone', 'the', 'sizes', 'of', 'the', 'preimages', 'need', 'not', 'be', 'bounded', 'we', 'show', 'however', 'that', 'as', 'the', 'complexity', 'in', 'a', 'suitable', 'sense', 'of', 'a', 'rational', 'mapping', 'class', 'increases', 'these', 'sizes', 'are', 'at', 'most', 'polynomial', 'this', 'torsion', 'information', 'about', 'xy', 'is', 'in', 'some', 'sense', 'orthogonal', 'to', 'rational', 'homotopy', 'theory', 'but', 'is', 'nevertheless', 'an', 'invariant', 'of', 'the', 'rational', 'homotopy', 'type', 'of', 'y', 'in', 'at', 'least', 'some', 'cases', 'the', 'notion', 'of', 'complexity', 'is', 'geometric', 'and', 'we', 'also', 'prove', 'a', 'conjecture', 'of', 'gromov', 'citegrms', 'regarding', 'the', 'number', 'of', 'mapping', 'classes', 'that', 'have', 'lipschitz', 'constant', 'at', 'most', 'l']] | [-0.154447385467205, 0.1079851319589732, -0.08834038011886122, 0.0648728426170841, -0.0850282842791035, -0.15768904608269116, 0.0009603293546838046, 0.3803035112823011, -0.3449357648483186, -0.21739974625778336, 0.09528173464225989, -0.2669389377017541, -0.1273242937230313, 0.19854315891435356, -0.14303498002096215, -0.011464079648242084, 0.011583936022139574, 0.0929557659364668, -0.10235949137376067, -0.31279297200749623, 0.35464471264720987, -0.04998129178265124, 0.20714735458250635, 0.05228917783513694, 0.13139602680200044, -0.03621678815386791, 0.0023280385177838437, 0.05535376667768376, -0.13996752347162425, 0.10373846976662353, 0.3055279528302716, 0.10437825442832105, 0.2681347015136071, -0.29421579050085017, -0.17884012936607344, 0.21106167562672057, 0.13717349216640565, 0.03023012440193103, -0.0015950153158792784, -0.22799067872483994, 0.16404567910382747, -0.08717921692593635, -0.12205221069948251, -0.05023010894798368, 0.09640418837657412, 0.026311320504209184, -0.25648838407466656, -0.0411846533967342, 0.11051902316069899, 0.1053092812813591, -0.006070412700739867, -0.07224361960145641, -0.07873644140364161, 0.10116175413136157, 0.02323278499312187, 0.12217534887017896, 0.07483393550701155, -0.0745198137841351, -0.06359970258755027, 0.3744233221611904, -0.025576316113477104, -0.24271954128248546, 0.17870663467862197, -0.1915836293989693, -0.20492920731056574, 0.148954417080199, 0.06874668616659314, 0.14052358034728024, -0.06620587716254545, 0.20774083950588806, -0.11635882528285489, 0.14457719873473446, 0.09296773348131594, 0.02498155400398692, 0.14846793884582077, 0.10402396964424696, 0.13051402520207075, 0.10314994342163035, -0.00439820114793327, -0.03042767505438728, -0.3336584930030899, -0.17664754958041295, -0.17318439474400446, 0.12429342322944191, -0.10317804539226927, -0.18387148216480517, 0.34181648919728536, 0.08429419820175599, 0.20266099777854696, 0.1161386683744997, 0.21005465982496055, 0.07057373481002842, 0.02078249537428656, 0.0656056841213313, 0.1538683175849586, 0.1573245211554165, -0.012728662905461005, -0.13926954779645984, 0.08757750817240421, 0.16148265708228554] |
1,802.05785 | Energy equality for the Navier-Stokes equations in weak-in-time Onsager
spaces | Onsager's conjecture for the 3D Navier-Stokes equations concerns the validity
of energy equality of weak solutions with regards to their smoothness. In this
note we establish energy equality for weak solutions in a large class of
function spaces. These conditions are weak-in-time with optimal space
regularity and therefore weaker than all previous classical results. Heuristics
using intermittency argument suggests the possible sharpness of our results.
| math.AP | onsagers conjecture for the 3d navierstokes equations concerns the validity of energy equality of weak solutions with regards to their smoothness in this note we establish energy equality for weak solutions in a large class of function spaces these conditions are weakintime with optimal space regularity and therefore weaker than all previous classical results heuristics using intermittency argument suggests the possible sharpness of our results | [['onsagers', 'conjecture', 'for', 'the', '3d', 'navierstokes', 'equations', 'concerns', 'the', 'validity', 'of', 'energy', 'equality', 'of', 'weak', 'solutions', 'with', 'regards', 'to', 'their', 'smoothness', 'in', 'this', 'note', 'we', 'establish', 'energy', 'equality', 'for', 'weak', 'solutions', 'in', 'a', 'large', 'class', 'of', 'function', 'spaces', 'these', 'conditions', 'are', 'weakintime', 'with', 'optimal', 'space', 'regularity', 'and', 'therefore', 'weaker', 'than', 'all', 'previous', 'classical', 'results', 'heuristics', 'using', 'intermittency', 'argument', 'suggests', 'the', 'possible', 'sharpness', 'of', 'our', 'results']] | [-0.14246750757592963, 0.035195795357140014, -0.05770986383140553, 0.16014559557333996, -0.08793444124967209, -0.10999485871434445, 0.025572113030648325, 0.3024159579508705, -0.265481831273064, -0.29920340434182435, 0.10074780137256312, -0.22910929894351284, -0.11673050341960334, 0.22538208638434298, -0.08862076535297092, 0.13756532117258757, 0.07236540208396036, -0.02399239626538474, -0.10435004117698554, -0.2710432466556085, 0.39775017357897013, 0.004334724275395274, 0.28617435925116297, 0.0957794469431974, 0.0630832044626004, -0.026975794156896882, 0.005841799822519533, 0.03717334787461368, -0.23834388460818445, 0.12692791229346767, 0.22284102525372873, 0.08975896475749323, 0.30473717738641426, -0.4165435944451019, -0.208621421657881, 0.11205750750377774, 0.08023701268393779, 0.09184865015413379, -0.015614389951224439, -0.2792037990584504, 0.1426507793730707, -0.08926900229562307, -0.21406609489349648, -0.08731416397495195, -0.013559056154917926, 0.11017805588198826, -0.29900093979085796, 0.15615898251417093, 0.1351178803415678, 0.06324308151670266, -0.1749367956945207, -0.0816185639341711, 0.0067059318207611796, 0.05732374078797875, 0.12319385807131766, -0.009415996079042088, 0.012895836953248363, -0.13786525844989228, -0.08974034419952659, 0.32494986068923026, -0.03174941841029977, -0.2517479486996308, 0.22937868099688785, -0.16931117945932783, -0.17346822664694628, 0.09070111598703079, 0.07846282686659833, 0.14532420020987047, -0.08196611581661273, 0.10944766020020325, -0.08432237195665948, 0.1261470104509499, 0.08492321644007461, 0.07536990002699895, 0.10085378563962877, 0.11629483072465518, 0.12933326071652118, 0.14120761489175493, 0.0062276113021653146, -0.09297838847487583, -0.3612472691675066, -0.17204370553372428, -0.1542045164223964, 0.07513823265617248, -0.161757229834393, -0.13499214896000922, 0.3300373909805785, 0.18774004914575926, 0.12843228918791283, 0.16119752144732047, 0.2426776634529233, 0.13531980462175852, -0.00894908832924557, 0.07261153010767885, 0.28818737863912247, 0.14121504252034356, 0.1439773229212733, -0.14630974133615382, 0.05893229694629554, 0.16247081474284641] |
1,802.05786 | Truth Validation with Evidence | In the modern era, abundant information is easily accessible from various
sources, however only a few of these sources are reliable as they mostly
contain unverified contents. We develop a system to validate the truthfulness
of a given statement together with underlying evidence. The proposed system
provides supporting evidence when the statement is tagged as false. Our work
relies on an inference method on a knowledge graph (KG) to identify the
truthfulness of statements. In order to extract the evidence of falseness, the
proposed algorithm takes into account combined knowledge from KG and
ontologies. The system shows very good results as it provides valid and concise
evidence. The quality of KG plays a role in the performance of the inference
method which explicitly affects the performance of our evidence-extracting
algorithm.
| cs.AI stat.ML | in the modern era abundant information is easily accessible from various sources however only a few of these sources are reliable as they mostly contain unverified contents we develop a system to validate the truthfulness of a given statement together with underlying evidence the proposed system provides supporting evidence when the statement is tagged as false our work relies on an inference method on a knowledge graph kg to identify the truthfulness of statements in order to extract the evidence of falseness the proposed algorithm takes into account combined knowledge from kg and ontologies the system shows very good results as it provides valid and concise evidence the quality of kg plays a role in the performance of the inference method which explicitly affects the performance of our evidenceextracting algorithm | [['in', 'the', 'modern', 'era', 'abundant', 'information', 'is', 'easily', 'accessible', 'from', 'various', 'sources', 'however', 'only', 'a', 'few', 'of', 'these', 'sources', 'are', 'reliable', 'as', 'they', 'mostly', 'contain', 'unverified', 'contents', 'we', 'develop', 'a', 'system', 'to', 'validate', 'the', 'truthfulness', 'of', 'a', 'given', 'statement', 'together', 'with', 'underlying', 'evidence', 'the', 'proposed', 'system', 'provides', 'supporting', 'evidence', 'when', 'the', 'statement', 'is', 'tagged', 'as', 'false', 'our', 'work', 'relies', 'on', 'an', 'inference', 'method', 'on', 'a', 'knowledge', 'graph', 'kg', 'to', 'identify', 'the', 'truthfulness', 'of', 'statements', 'in', 'order', 'to', 'extract', 'the', 'evidence', 'of', 'falseness', 'the', 'proposed', 'algorithm', 'takes', 'into', 'account', 'combined', 'knowledge', 'from', 'kg', 'and', 'ontologies', 'the', 'system', 'shows', 'very', 'good', 'results', 'as', 'it', 'provides', 'valid', 'and', 'concise', 'evidence', 'the', 'quality', 'of', 'kg', 'plays', 'a', 'role', 'in', 'the', 'performance', 'of', 'the', 'inference', 'method', 'which', 'explicitly', 'affects', 'the', 'performance', 'of', 'our', 'evidenceextracting', 'algorithm']] | [-0.09449157160670721, 0.0010379457120041817, -0.10917368334494243, 0.08995640097554167, -0.10869884885695957, -0.09365855780422919, 0.07760747271597154, 0.36482981579421564, -0.19001983380118428, -0.37579883200426895, 0.1008566921845786, -0.27132383488522943, -0.1391322053210853, 0.23231326115801354, -0.05167641225740198, 0.007022885937792386, 0.10430546055093061, 0.0833406849139796, -0.0029962429778592646, -0.26102431510983926, 0.28277206093130647, 0.06826656666230554, 0.30873437261194453, 0.08308492093744088, 0.11543727067615946, -0.005642984875535607, -0.06696903014685525, 0.010001627396243487, -0.09844683725098076, 0.15574633038346475, 0.2660393290972525, 0.22240735303841588, 0.29863208325854107, -0.4150135218519573, -0.17253907347033018, 0.04564841706377938, 0.12655188050482047, 0.12757145461370056, -0.07020558822582397, -0.3058298572883472, 0.11536190253068772, -0.15628322481372675, -0.06478807281038558, -0.11601125936262137, -0.019124912016378903, -0.02951949918299711, -0.2778822410375226, 0.049109322430436, 0.1110369692621536, 0.049986864552046206, -0.06185725125007678, -0.12046487272496863, 0.019879823347531952, 0.17708472994832114, 0.05565311054359804, 0.029181370913238778, 0.12359880642557532, -0.12915986086162487, -0.09276696778301424, 0.39947001582603586, -0.047452702729370765, -0.17570088365541178, 0.190377127509131, -0.08446031529456377, -0.16172897902699981, 0.1645351868418818, 0.1586495241828898, 0.0910866254761945, -0.1384319195857988, 0.0421028214230657, -0.04776554852868466, 0.24042313518508, -0.0021711957077820633, 0.041451083640604056, 0.2244817893843838, 0.21147227189648002, 0.04156562966446197, 0.11862891017416016, -0.04895553419315942, -0.07236580514933827, -0.3056546515972866, -0.17952249286147628, -0.17344424440417178, 0.016862576715503782, -0.08600604579068338, -0.1297791970220517, 0.36096856216791756, 0.21317008023633977, 0.16427395286764082, 0.045807346684475565, 0.3123663917703684, 0.06375098289008606, 0.0724147246363609, 0.08388415027713013, 0.21896787419510905, 0.10522390928032786, 0.1006037577762911, -0.13527593119142128, 0.15114110997199376, 0.023378921912752034] |
1,802.05787 | Tunneling magnetoresistance enhancement by symmetrization in spin-orbit
torque magnetic tunnel junction | Heavy metals with strong spin-orbit coupling (SOC) have been employed to
generate spin current to control the magnetization dynamics by spin-orbit
torque (SOT). Magnetic tunnel junction based on SOT (SOT-MTJ) is a promising
application with efficient writing operation. Unfortunately, SOT-MTJ faces the
low tunneling magnetoresistance (TMR) problem. In this work, we present an ab
initio calculation on the TMR in SOT-MTJ. It is demonstrated that TMR would be
enhanced by SOT-MTJ symmetry structure. The symmetrization induces interfacial
resonant states (IRSs). When IRSs match identical resonances at the opposite
barrier interface, resonant tunneling occurs in SOT-MTJ, which significantly
contributes to the conductance in parallel configuration and improves TMR. We
demonstrate the occurrence of resonant tunneling by transmission spectra,
density of scattering states and differential density of states. We also point
out that the thickness of heavy metal has limited influence on TMR. This work
would benefit the TMR optimization in SOT-MTJ, as well as the SOT spintronics
device.
| cond-mat.mes-hall physics.app-ph quant-ph | heavy metals with strong spinorbit coupling soc have been employed to generate spin current to control the magnetization dynamics by spinorbit torque sot magnetic tunnel junction based on sot sotmtj is a promising application with efficient writing operation unfortunately sotmtj faces the low tunneling magnetoresistance tmr problem in this work we present an ab initio calculation on the tmr in sotmtj it is demonstrated that tmr would be enhanced by sotmtj symmetry structure the symmetrization induces interfacial resonant states irss when irss match identical resonances at the opposite barrier interface resonant tunneling occurs in sotmtj which significantly contributes to the conductance in parallel configuration and improves tmr we demonstrate the occurrence of resonant tunneling by transmission spectra density of scattering states and differential density of states we also point out that the thickness of heavy metal has limited influence on tmr this work would benefit the tmr optimization in sotmtj as well as the sot spintronics device | [['heavy', 'metals', 'with', 'strong', 'spinorbit', 'coupling', 'soc', 'have', 'been', 'employed', 'to', 'generate', 'spin', 'current', 'to', 'control', 'the', 'magnetization', 'dynamics', 'by', 'spinorbit', 'torque', 'sot', 'magnetic', 'tunnel', 'junction', 'based', 'on', 'sot', 'sotmtj', 'is', 'a', 'promising', 'application', 'with', 'efficient', 'writing', 'operation', 'unfortunately', 'sotmtj', 'faces', 'the', 'low', 'tunneling', 'magnetoresistance', 'tmr', 'problem', 'in', 'this', 'work', 'we', 'present', 'an', 'ab', 'initio', 'calculation', 'on', 'the', 'tmr', 'in', 'sotmtj', 'it', 'is', 'demonstrated', 'that', 'tmr', 'would', 'be', 'enhanced', 'by', 'sotmtj', 'symmetry', 'structure', 'the', 'symmetrization', 'induces', 'interfacial', 'resonant', 'states', 'irss', 'when', 'irss', 'match', 'identical', 'resonances', 'at', 'the', 'opposite', 'barrier', 'interface', 'resonant', 'tunneling', 'occurs', 'in', 'sotmtj', 'which', 'significantly', 'contributes', 'to', 'the', 'conductance', 'in', 'parallel', 'configuration', 'and', 'improves', 'tmr', 'we', 'demonstrate', 'the', 'occurrence', 'of', 'resonant', 'tunneling', 'by', 'transmission', 'spectra', 'density', 'of', 'scattering', 'states', 'and', 'differential', 'density', 'of', 'states', 'we', 'also', 'point', 'out', 'that', 'the', 'thickness', 'of', 'heavy', 'metal', 'has', 'limited', 'influence', 'on', 'tmr', 'this', 'work', 'would', 'benefit', 'the', 'tmr', 'optimization', 'in', 'sotmtj', 'as', 'well', 'as', 'the', 'sot', 'spintronics', 'device']] | [-0.23920954603725558, 0.11201004743523033, -0.021820699609222876, 0.011294743182229554, -0.06479754966274379, -0.18793788234032927, 0.07685960010643218, 0.4083954367051079, -0.2675007742912669, -0.34308385606973796, -0.0458335946202812, -0.26920123313179917, -0.17452035734584187, 0.21708380442207595, -0.005644817092859299, 0.0004603445980769054, 0.03718012084318384, -0.08486150304347893, -0.042265091175183796, -0.21417554588706653, 0.2832024445457704, 0.06835527587563368, 0.3402716881672668, 0.1341314461782195, 0.0324939808861655, 0.04134671814193961, 0.16246574688228263, 0.01660048470470556, -0.0937216524647402, 0.035226401403934876, 0.2573500958117093, -0.11025484577028948, 0.15660327275366684, -0.48816813697006295, -0.13300589331133575, -0.04101256720920443, 0.1745920939204894, 0.16414539641952794, -0.08140792981884591, -0.2692389612940324, 0.04360875311444045, -0.15285053726724662, -0.06897597494802088, -0.11267008059094571, 0.013729988988844833, -0.03690722277187772, -0.2650069830583996, 0.04091143901559975, 0.037335392802502886, 0.07786868925497031, -0.01863501831640009, -0.14225154025253994, -0.08011125805372503, 0.027091602651902125, 0.04874688048941932, 0.011526863778804897, 0.21784243930893457, -0.08985524482070023, -0.15743579769768057, 0.26993668565725326, -0.08448269489001792, -0.11581241838325551, 0.12489767276509932, -0.1798892653262492, -0.05811749089355017, 0.15229515587187306, 0.11209949931229174, 0.1365957872936158, -0.14206706234159402, 0.12337581873044615, 0.0268681396175603, 0.13066182317672695, 0.08684422121128174, 0.06339915560641486, 0.249909872951425, 0.20624913325515476, 0.09406036860309541, 0.10723276994158484, -0.1569841494768929, -0.046227990689681496, -0.17621984066824267, -0.15012838060309172, -0.2172618017822599, 0.11579125653359161, -0.019589391392974515, -0.1811814749210029, 0.3765211278531155, 0.20772954157203627, 0.1764126874233602, -0.05875644774938441, 0.3449195527388554, 0.17948428017478793, 0.1314621830542993, 0.00921016371257271, 0.2873287377107865, 0.1618218286731132, 0.15784038383228954, -0.3558744957305752, 0.13427495138992548, -0.050195819686410154] |
1,802.05788 | Schur Ring over Group $\Z_{2}^{n}$, Circulant $S-$Sets Invariant by
Decimation and Hadamard Matrices | In this paper a variety of issues are discussed, Schur ring, $S$-sets,
circulant orbits, decimation operator and Hadamard matrices and their relation
between them is shown. Firstly we define the complete $S$-sets. Next, we study
the structure of Schur ring with circulant basic sets over $\Z_{2}^{n}$ and we
define the free and non-free circulant $S$-sets, the symmetric, non-symmetric
and antisymmetric circulant $S$-sets. We prove that all this $S$-sets are
invariants under decimation. Finally, we prove that if a Hadamard matrix exist
then this is contained in a complete $S$-set. Also, we prove that can't exist
circulant and with one core Hadamard matrices with some particular structure.
These theorems include a result known on symmetric circulant Hadamard matrices
of order $4n$ only when $n$ is an odd number.
| math.CO math.GR | in this paper a variety of issues are discussed schur ring ssets circulant orbits decimation operator and hadamard matrices and their relation between them is shown firstly we define the complete ssets next we study the structure of schur ring with circulant basic sets over z_2n and we define the free and nonfree circulant ssets the symmetric nonsymmetric and antisymmetric circulant ssets we prove that all this ssets are invariants under decimation finally we prove that if a hadamard matrix exist then this is contained in a complete sset also we prove that cant exist circulant and with one core hadamard matrices with some particular structure these theorems include a result known on symmetric circulant hadamard matrices of order 4n only when n is an odd number | [['in', 'this', 'paper', 'a', 'variety', 'of', 'issues', 'are', 'discussed', 'schur', 'ring', 'ssets', 'circulant', 'orbits', 'decimation', 'operator', 'and', 'hadamard', 'matrices', 'and', 'their', 'relation', 'between', 'them', 'is', 'shown', 'firstly', 'we', 'define', 'the', 'complete', 'ssets', 'next', 'we', 'study', 'the', 'structure', 'of', 'schur', 'ring', 'with', 'circulant', 'basic', 'sets', 'over', 'z_2n', 'and', 'we', 'define', 'the', 'free', 'and', 'nonfree', 'circulant', 'ssets', 'the', 'symmetric', 'nonsymmetric', 'and', 'antisymmetric', 'circulant', 'ssets', 'we', 'prove', 'that', 'all', 'this', 'ssets', 'are', 'invariants', 'under', 'decimation', 'finally', 'we', 'prove', 'that', 'if', 'a', 'hadamard', 'matrix', 'exist', 'then', 'this', 'is', 'contained', 'in', 'a', 'complete', 'sset', 'also', 'we', 'prove', 'that', 'cant', 'exist', 'circulant', 'and', 'with', 'one', 'core', 'hadamard', 'matrices', 'with', 'some', 'particular', 'structure', 'these', 'theorems', 'include', 'a', 'result', 'known', 'on', 'symmetric', 'circulant', 'hadamard', 'matrices', 'of', 'order', '4n', 'only', 'when', 'n', 'is', 'an', 'odd', 'number']] | [-0.20179487397347937, 0.1484156735636721, -0.005067109925980408, 0.06250168867115899, -0.05554307662463916, -0.1679586949208238, -0.03931723006241992, 0.41831174242038893, -0.31890028193006364, -0.16140685488623896, 0.1546673590031372, -0.26516420851043593, -0.26007587603645355, 0.12658698483934905, -0.07182409571975058, 0.011672526953639243, 0.09233275126284501, 0.08211575024742926, -0.1750473368887269, -0.3201122875838709, 0.39711037908834734, -0.049317924782457785, 0.15484955790269328, 0.05365048785277782, 0.061908786883577704, 0.027618418851001993, -0.025813227823722786, -0.026507669546472743, -0.15300036635240458, 0.09597627399305952, 0.27037289046396423, 0.13160400894675844, 0.20252392344235434, -0.39829329094724863, -0.07653127024390215, 0.22415637372035943, 0.12637665717107222, 0.05955046428230914, -0.016402248809238764, -0.24522072078264134, 0.17383459895321235, -0.2065683944722799, -0.1342936565175535, -0.09541288255273885, 0.06030562892922853, 0.02151331422652492, -0.2960134525352532, -0.015623777725515653, 0.14498689668033066, 0.015249400927619202, 0.015251809188465434, -0.1663764920177334, 0.022378118958060196, 0.06681951042562613, -0.04520367517678698, -0.0544697104010907, 0.030270980869045876, 0.035423817807867186, -0.14133351969745334, 0.29597231772119603, 0.010664783891631422, -0.24899479383089412, 0.08771547161072023, -0.16650463868753881, -0.16711350679984244, 0.05808759593325997, 0.0786729299529331, 0.14820577278659922, -0.07033611508418612, 0.16993945058688198, -0.16424824578474884, 0.1289522799307583, 0.1441015384973973, 0.04671285594960191, 0.09684079410288278, 0.018380047152126867, 0.13686248022874115, 0.1930815952896016, 0.03488827722974847, -0.0561187274416718, -0.3143504819004085, -0.18374513292787817, -0.20426481838363744, 0.13423197443677684, -0.16083809986319136, -0.23514330470049938, 0.43548627749202756, 0.08703963550203662, 0.14564934873029473, 0.0941173501485064, 0.1972911086439441, 0.05051500677299781, 0.06544295993017575, 0.13993631038259288, 0.09908935574078602, 0.3011570455264447, -0.03794511473704657, -0.14454089986127397, -0.040126714023108793, 0.17160370082623788] |
1,802.05789 | Achieving Direct Electrochemical Oxidation of Carbon below 600oC through
a Novel Direct Carbon Fuel Cell | Direct carbon fuel cells (DCFCs) are highly efficient power generators fueled
by abundant and cheap solid carbons. However, the limited formation of triple
phase boundaries (TPBs) within fuel electrodes inhibits their performance even
at high temperatures due to the limitation of mass transfer. It also results in
low direct-utilization of the fuel. To address the challenges of low carbon
oxidation activity and low carbon utilization simultaneously, a highly
efficient, 3-D solid-state architected anode has been developed to enhance the
performance of DCFCs below 600C. The cells with the 3-D textile anode,
Gd:CeO2-Li/Na2CO3 composite electrolyte, and Sm0.5Sr0.5CoO3 (SSC) cathode have
demonstrated excellent performance with maximum power densities of 143, 196,
and 325 mW cm-2 at 500, 550, and 600C, respectively. At 500C, the cells could
be operated steadily with a rated power density of ~0.13 W cm-2 at a constant
current density of 0.15 A cm-2 with a carbon utilization over 86%. The
significant improvement of the cell performance at such temperatures attributes
to the high synergistic conduction of the composite electrolyte and the
superior 3-D anode structure which offers more paths for carbon catalytic
oxidation. Our results indicate the feasibility of direct electrochemical
oxidation of solid carbon at 500-600C with a high carbon utilization,
representing a promising strategy to develop 3-D architected electrodes for
fuel cells and other electrochemical devices.
| physics.app-ph | direct carbon fuel cells dcfcs are highly efficient power generators fueled by abundant and cheap solid carbons however the limited formation of triple phase boundaries tpbs within fuel electrodes inhibits their performance even at high temperatures due to the limitation of mass transfer it also results in low directutilization of the fuel to address the challenges of low carbon oxidation activity and low carbon utilization simultaneously a highly efficient 3d solidstate architected anode has been developed to enhance the performance of dcfcs below 600c the cells with the 3d textile anode gdceo2lina2co3 composite electrolyte and sm05sr05coo3 ssc cathode have demonstrated excellent performance with maximum power densities of 143 196 and 325 mw cm2 at 500 550 and 600c respectively at 500c the cells could be operated steadily with a rated power density of 013 w cm2 at a constant current density of 015 a cm2 with a carbon utilization over 86 the significant improvement of the cell performance at such temperatures attributes to the high synergistic conduction of the composite electrolyte and the superior 3d anode structure which offers more paths for carbon catalytic oxidation our results indicate the feasibility of direct electrochemical oxidation of solid carbon at 500600c with a high carbon utilization representing a promising strategy to develop 3d architected electrodes for fuel cells and other electrochemical devices | [['direct', 'carbon', 'fuel', 'cells', 'dcfcs', 'are', 'highly', 'efficient', 'power', 'generators', 'fueled', 'by', 'abundant', 'and', 'cheap', 'solid', 'carbons', 'however', 'the', 'limited', 'formation', 'of', 'triple', 'phase', 'boundaries', 'tpbs', 'within', 'fuel', 'electrodes', 'inhibits', 'their', 'performance', 'even', 'at', 'high', 'temperatures', 'due', 'to', 'the', 'limitation', 'of', 'mass', 'transfer', 'it', 'also', 'results', 'in', 'low', 'directutilization', 'of', 'the', 'fuel', 'to', 'address', 'the', 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1,802.0579 | Orbital-angular-momentum-enhanced estimation of sub-Heisenberg-limited
angular displacement with two-mode squeezed vacuum and parity detection | We report on an orbital-angular-momentum-enhanced scheme for angular
displacement estimation based on two-mode squeezed vacuum and parity detection.
The sub-Heisenberg-limited sensitivity for angular displacement estimation is
obtained in an ideal situation. Several realistic factors are also considered,
including photon loss, dark counts, response-time delay, and thermal photon
noise. Our results indicate that the effects of the realistic factors on the
sensitivity can be offset by raising orbital angular momentum quantum number
$\ell$. This reflects that the robustness and the practicability of the system
can be improved via raising $\ell$ without changing mean photon number $N$.
| quant-ph | we report on an orbitalangularmomentumenhanced scheme for angular displacement estimation based on twomode squeezed vacuum and parity detection the subheisenberglimited sensitivity for angular displacement estimation is obtained in an ideal situation several realistic factors are also considered including photon loss dark counts responsetime delay and thermal photon noise our results indicate that the effects of the realistic factors on the sensitivity can be offset by raising orbital angular momentum quantum number ell this reflects that the robustness and the practicability of the system can be improved via raising ell without changing mean photon number n | [['we', 'report', 'on', 'an', 'orbitalangularmomentumenhanced', 'scheme', 'for', 'angular', 'displacement', 'estimation', 'based', 'on', 'twomode', 'squeezed', 'vacuum', 'and', 'parity', 'detection', 'the', 'subheisenberglimited', 'sensitivity', 'for', 'angular', 'displacement', 'estimation', 'is', 'obtained', 'in', 'an', 'ideal', 'situation', 'several', 'realistic', 'factors', 'are', 'also', 'considered', 'including', 'photon', 'loss', 'dark', 'counts', 'responsetime', 'delay', 'and', 'thermal', 'photon', 'noise', 'our', 'results', 'indicate', 'that', 'the', 'effects', 'of', 'the', 'realistic', 'factors', 'on', 'the', 'sensitivity', 'can', 'be', 'offset', 'by', 'raising', 'orbital', 'angular', 'momentum', 'quantum', 'number', 'ell', 'this', 'reflects', 'that', 'the', 'robustness', 'and', 'the', 'practicability', 'of', 'the', 'system', 'can', 'be', 'improved', 'via', 'raising', 'ell', 'without', 'changing', 'mean', 'photon', 'number', 'n']] | [-0.14875767640869625, 0.19123826708966335, -0.05476068154788546, 0.05659881227910118, -0.03114001087403746, -0.10713580829562039, 0.04084937409898366, 0.3762684940330444, -0.21829618723382072, -0.3709429145780622, 0.0493373530503783, -0.2577687652621378, -0.06252078900504257, 0.2584905284106411, -0.06077691221169086, 0.08539204282425744, 0.07751800864934921, 0.019923243793078086, -0.05315309995284644, -0.2339842264447862, 0.30853992860804325, 0.10042568175397533, 0.28666972649353806, 0.07172782893288851, 0.10230974651752942, 0.04622118784645472, -0.04048822607885125, -0.004689197589252745, -0.0865599229196099, 0.05430041846158283, 0.1535506069670201, 0.08016512160419777, 0.22418857102722972, -0.3687987362625458, -0.1896033691322451, 0.12391317000372275, 0.14354769578103416, 0.13284224841333125, -0.03671584647118805, -0.3028136589914881, 0.024515613177490812, -0.17849205489400574, -0.12147845327353446, -0.11831443642155938, -0.006954440366356603, 0.003317090998133344, -0.29412218261640316, 0.11362245280805083, 0.02656958744891228, 0.029868079139099966, -0.002642447774809214, -0.1669715742708274, -0.01365745196529534, 0.0909270927540317, 0.006712107087976189, -0.007623271290613439, 0.18973694271558234, -0.1798199276121894, -0.1284638855866687, 0.3371401099708452, -0.08045635857589302, -0.25696153245786185, 0.08639863556280973, -0.13682631702132284, -0.0766643883450137, 0.14732023826249505, 0.16952595413507274, 0.06284044372038014, -0.07186695590354902, 0.02466526019990304, -0.004891357920382933, 0.2251943144922255, 0.09791654598228233, 0.14288018817102077, 0.21472157732213057, 0.12496131738667847, 0.06762998871585374, 0.13294088796422046, -0.17805128504762485, -0.04985410094221113, -0.31136059568774316, -0.1277128265449597, -0.20948874852770277, 0.0741030718124802, -0.1319034601612109, -0.06045415725619081, 0.3742831904750229, 0.15803927946735613, 0.17521522494824102, 0.036197736483788295, 0.36639838275169173, 0.15651385386609623, 0.006381916633296398, 0.04274702170783634, 0.2813794234346959, 0.10699723595102388, 0.053920854321776096, -0.3315242349886165, 0.05200204083956377, -0.03159629305203756] |
1,802.05791 | A Faster FPTAS for #Knapsack | Given a set $W = \{w_1,\ldots, w_n\}$ of non-negative integer weights and an
integer $C$, the #Knapsack problem asks to count the number of distinct subsets
of $W$ whose total weight is at most $C$. In the more general integer version
of the problem, the subsets are multisets. That is, we are also given a set $
\{u_1,\ldots, u_n\}$ and we are allowed to take up to $u_i$ items of weight
$w_i$.
We present a deterministic FPTAS for #Knapsack running in
$O(n^{2.5}\varepsilon^{-1.5}\log(n \varepsilon^{-1})\log (n \varepsilon))$
time. The previous best deterministic algorithm [FOCS 2011] runs in $O(n^3
\varepsilon^{-1} \log(n\varepsilon^{-1}))$ time (see also [ESA 2014] for a
logarithmic factor improvement). The previous best randomized algorithm [STOC
2003] runs in $O(n^{2.5} \sqrt{\log (n\varepsilon^{-1}) } + \varepsilon^{-2}
n^2 )$ time. Therefore, in the natural setting of constant $\varepsilon$, we
close the gap between the $\tilde O(n^{2.5})$ randomized algorithm and the
$\tilde O(n^3)$ deterministic algorithm.
For the integer version with $U = \max_i \{u_i\}$, we present a deterministic
FPTAS running in $O(n^{2.5}\varepsilon^{-1.5}\log(n\varepsilon^{-1} \log U)\log
(n \varepsilon) \log^2 U)$ time. The previous best deterministic algorithm
[APPROX 2016] runs in $O(n^3\varepsilon^{-1}\log(n \varepsilon^{-1} \log U)
\log^2 U)$ time.
| cs.DS | given a set w w_1ldots w_n of nonnegative integer weights and an integer c the knapsack problem asks to count the number of distinct subsets of w whose total weight is at most c in the more general integer version of the problem the subsets are multisets that is we are also given a set u_1ldots u_n and we are allowed to take up to u_i items of weight w_i we present a deterministic fptas for knapsack running in on25varepsilon15logn varepsilon1log n varepsilon time the previous best deterministic algorithm focs 2011 runs in on3 varepsilon1 lognvarepsilon1 time see also esa 2014 for a logarithmic factor improvement the previous best randomized algorithm stoc 2003 runs in on25 sqrtlog nvarepsilon1 varepsilon2 n2 time therefore in the natural setting of constant varepsilon we close the gap between the tilde on25 randomized algorithm and the tilde on3 deterministic algorithm for the integer version with u max_i u_i we present a deterministic fptas running in on25varepsilon15lognvarepsilon1 log ulog n varepsilon log2 u time the previous best deterministic algorithm approx 2016 runs in on3varepsilon1logn varepsilon1 log u log2 u time | [['given', 'a', 'set', 'w', 'w_1ldots', 'w_n', 'of', 'nonnegative', 'integer', 'weights', 'and', 'an', 'integer', 'c', 'the', 'knapsack', 'problem', 'asks', 'to', 'count', 'the', 'number', 'of', 'distinct', 'subsets', 'of', 'w', 'whose', 'total', 'weight', 'is', 'at', 'most', 'c', 'in', 'the', 'more', 'general', 'integer', 'version', 'of', 'the', 'problem', 'the', 'subsets', 'are', 'multisets', 'that', 'is', 'we', 'are', 'also', 'given', 'a', 'set', 'u_1ldots', 'u_n', 'and', 'we', 'are', 'allowed', 'to', 'take', 'up', 'to', 'u_i', 'items', 'of', 'weight', 'w_i', 'we', 'present', 'a', 'deterministic', 'fptas', 'for', 'knapsack', 'running', 'in', 'on25varepsilon15logn', 'varepsilon1log', 'n', 'varepsilon', 'time', 'the', 'previous', 'best', 'deterministic', 'algorithm', 'focs', '2011', 'runs', 'in', 'on3', 'varepsilon1', 'lognvarepsilon1', 'time', 'see', 'also', 'esa', '2014', 'for', 'a', 'logarithmic', 'factor', 'improvement', 'the', 'previous', 'best', 'randomized', 'algorithm', 'stoc', '2003', 'runs', 'in', 'on25', 'sqrtlog', 'nvarepsilon1', 'varepsilon2', 'n2', 'time', 'therefore', 'in', 'the', 'natural', 'setting', 'of', 'constant', 'varepsilon', 'we', 'close', 'the', 'gap', 'between', 'the', 'tilde', 'on25', 'randomized', 'algorithm', 'and', 'the', 'tilde', 'on3', 'deterministic', 'algorithm', 'for', 'the', 'integer', 'version', 'with', 'u', 'max_i', 'u_i', 'we', 'present', 'a', 'deterministic', 'fptas', 'running', 'in', 'on25varepsilon15lognvarepsilon1', 'log', 'ulog', 'n', 'varepsilon', 'log2', 'u', 'time', 'the', 'previous', 'best', 'deterministic', 'algorithm', 'approx', '2016', 'runs', 'in', 'on3varepsilon1logn', 'varepsilon1', 'log', 'u', 'log2', 'u', 'time']] | [-0.1940275304080929, 0.08641157681758456, 0.006676034852757715, 0.013313621775053568, -0.05989787047414967, -0.22631667199638789, 0.13949910927648274, 0.36826285148520815, -0.3055042389844377, -0.3597012935101651, 0.031899364843697736, -0.3014091860667771, -0.08723586373517729, 0.1423178872569684, -0.058976326409852904, 0.13227105953042614, 0.04044691828770165, 0.015934214181674834, -0.01774460315086952, -0.39881584726357727, 0.18395555917477088, -0.002577345691401553, 0.12112435375423997, -0.023593251781768344, 0.07438831247083236, 0.0043605228211061, -0.011247170178659177, -0.03513499348440072, -0.18398466422583082, -0.008489870264854156, 0.28038832846913864, 0.15724753947363476, 0.3170756554084547, -0.37420409682384703, -0.03043470051913951, 0.2040373948795114, 0.138508462653171, 0.03498105050307918, 0.052019175205734854, -0.20233206087828018, 0.09573086732514517, -0.08063225440289469, -0.04484470897246926, 0.008197623213460103, 0.16884474245454656, -0.03466626867819368, -0.3823790868948308, 0.0421078003013606, 0.06594054930152769, -0.05653624331024004, 0.010055615429326975, -0.24978041579734975, 0.08762732683419344, 0.04047891616643396, -0.04414295857468766, 0.18715441434937163, 0.0014597316785307413, -0.036193074840330255, -0.16084995326256443, 0.3457922520733365, -0.09091067609502777, -0.15736330554982794, 0.05869542242726311, -0.10119325722034057, -0.18684101852410462, 0.11620437114216974, 0.173243545775375, 0.17558929900863743, -0.023787796421478807, 0.2552337943922252, -0.15648079179737487, 0.20789546524619243, 0.13534099773080036, 0.005422929324421592, -0.017985851571926576, 0.12183471522267825, 0.146164292094214, 0.08727659807237012, 0.01945867612133891, -0.0547640665612194, -0.3295455662261569, -0.14916971688993666, -0.22112304114297116, 0.06436245296584714, -0.20019203316123718, -0.1297465553297923, 0.3179767068574896, 0.10303931135757502, 0.23681610727452496, 0.19573142490182283, 0.23812711391639843, 0.11497087025209828, -0.06873419238097464, 0.2397197011617546, 0.0714024854300732, 0.017241989435062985, 0.06666858388657755, -0.2233400994419968, 0.06610994242828894, 0.1814913387839379] |
1,802.05792 | Masked Conditional Neural Networks for Automatic Sound Events
Recognition | Deep neural network architectures designed for application domains other than
sound, especially image recognition, may not optimally harness the
time-frequency representation when adapted to the sound recognition problem. In
this work, we explore the ConditionaL Neural Network (CLNN) and the Masked
ConditionaL Neural Network (MCLNN) for multi-dimensional temporal signal
recognition. The CLNN considers the inter-frame relationship, and the MCLNN
enforces a systematic sparseness over the network's links to enable learning in
frequency bands rather than bins allowing the network to be frequency shift
invariant mimicking a filterbank. The mask also allows considering several
combinations of features concurrently, which is usually handcrafted through
exhaustive manual search. We applied the MCLNN to the environmental sound
recognition problem using the ESC-10 and ESC-50 datasets. MCLNN achieved
competitive performance, using 12% of the parameters and without augmentation,
compared to state-of-the-art Convolutional Neural Networks.
| cs.LG cs.SD eess.AS stat.ML | deep neural network architectures designed for application domains other than sound especially image recognition may not optimally harness the timefrequency representation when adapted to the sound recognition problem in this work we explore the conditional neural network clnn and the masked conditional neural network mclnn for multidimensional temporal signal recognition the clnn considers the interframe relationship and the mclnn enforces a systematic sparseness over the networks links to enable learning in frequency bands rather than bins allowing the network to be frequency shift invariant mimicking a filterbank the mask also allows considering several combinations of features concurrently which is usually handcrafted through exhaustive manual search we applied the mclnn to the environmental sound recognition problem using the esc10 and esc50 datasets mclnn achieved competitive performance using 12 of the parameters and without augmentation compared to stateoftheart convolutional neural networks | [['deep', 'neural', 'network', 'architectures', 'designed', 'for', 'application', 'domains', 'other', 'than', 'sound', 'especially', 'image', 'recognition', 'may', 'not', 'optimally', 'harness', 'the', 'timefrequency', 'representation', 'when', 'adapted', 'to', 'the', 'sound', 'recognition', 'problem', 'in', 'this', 'work', 'we', 'explore', 'the', 'conditional', 'neural', 'network', 'clnn', 'and', 'the', 'masked', 'conditional', 'neural', 'network', 'mclnn', 'for', 'multidimensional', 'temporal', 'signal', 'recognition', 'the', 'clnn', 'considers', 'the', 'interframe', 'relationship', 'and', 'the', 'mclnn', 'enforces', 'a', 'systematic', 'sparseness', 'over', 'the', 'networks', 'links', 'to', 'enable', 'learning', 'in', 'frequency', 'bands', 'rather', 'than', 'bins', 'allowing', 'the', 'network', 'to', 'be', 'frequency', 'shift', 'invariant', 'mimicking', 'a', 'filterbank', 'the', 'mask', 'also', 'allows', 'considering', 'several', 'combinations', 'of', 'features', 'concurrently', 'which', 'is', 'usually', 'handcrafted', 'through', 'exhaustive', 'manual', 'search', 'we', 'applied', 'the', 'mclnn', 'to', 'the', 'environmental', 'sound', 'recognition', 'problem', 'using', 'the', 'esc10', 'and', 'esc50', 'datasets', 'mclnn', 'achieved', 'competitive', 'performance', 'using', '12', 'of', 'the', 'parameters', 'and', 'without', 'augmentation', 'compared', 'to', 'stateoftheart', 'convolutional', 'neural', 'networks']] | [-0.07707394841596109, -0.0015621780118027287, -0.04720920936050389, 0.08112845843131888, -0.13968840975099975, -0.17825931404958473, 0.03540691334365619, 0.4835457893745076, -0.30492761989104017, -0.30170934778644887, 0.07247317533290161, -0.2364164960233322, -0.2158499990033803, 0.19524461453907835, -0.12655227202017583, 0.13760074423693067, 0.11484503500053002, 0.04146685715758061, -0.07686459845628776, -0.24990134108677614, 0.27349869323653175, 0.06794247984456521, 0.40521870613794964, -0.025873134837304947, 0.13247611062775413, -0.006692155848907159, -0.06366777054382582, -0.021951775012953246, -0.019051589554162335, 0.17350641499514685, 0.31400492145150577, 0.17905398230436895, 0.29671798494021345, -0.40103900407126053, -0.2713368697922757, 0.14306393807264808, 0.1647521668689982, 0.11077212224383219, 0.0515828683449746, -0.39513434110571155, 0.07270852550395208, -0.1711908489157506, 0.09294380754774283, -0.13509700192798518, -0.050339030856082365, -0.01999145799162362, -0.3043245094252094, 0.03723006544231853, 0.10165866910942274, 0.10440187567590381, -0.04975271741934992, -0.09998010832350701, 0.023490225423877832, 0.16339539313666263, -0.02052266977345504, 0.07003345461595777, 0.14005748819846245, -0.20723850439007815, -0.11450780420932624, 0.3440428151608371, -0.0812039771496248, -0.23850419150879806, 0.18063983940954903, -0.009440427525437993, -0.13658276299456898, 0.09729632750830258, 0.25116675545414574, 0.07302612083019328, -0.17167611576990716, -0.02143169547055962, 0.012619033898059413, 0.22797127717791005, 0.10784547173304201, 0.03599294350840982, 0.16693905579167412, 0.2782624112961398, 0.04631696521889137, 0.17033734351435087, -0.18906643834608278, -0.03140176028656445, -0.14315301452582474, -0.026070386124439807, -0.17920426047648297, -0.049454305713670495, -0.12211091335311787, -0.11952420446700115, 0.4516099109347883, 0.22368291086473724, 0.18372855244184302, 0.16104418165703713, 0.36431040132431675, 0.040090728517648555, 0.1734029193234583, 0.08153409910515594, 0.17903850655064737, 0.03185445552845921, 0.1647438157438602, -0.17259533709022318, 0.09202335725694212, 0.0545321235520323] |
1,802.05793 | Some Remarks on the Operator T^*T | This note deals with the operator $T^*T$, where $T$ is a densely defined
operator on a complex Hilbert space. We reprove a recent result of Z.
Sebesty\'en and Zs. Tarcsay [13]: If $T^*T$ and $TT^*$ are self-adjoint, then
$T$ is closed. In addition, we describe the Friedrichs extension of $S^2$,
where $S$ is a symmetric operator, recovering results due to Yu. Arlinski\u{i}
and Yu. Kovalev [1], [2].
| math.SP | this note deals with the operator tt where t is a densely defined operator on a complex hilbert space we reprove a recent result of z sebestyen and zs tarcsay 13 if tt and tt are selfadjoint then t is closed in addition we describe the friedrichs extension of s2 where s is a symmetric operator recovering results due to yu arlinskiui and yu kovalev 1 2 | [['this', 'note', 'deals', 'with', 'the', 'operator', 'tt', 'where', 't', 'is', 'a', 'densely', 'defined', 'operator', 'on', 'a', 'complex', 'hilbert', 'space', 'we', 'reprove', 'a', 'recent', 'result', 'of', 'z', 'sebestyen', 'and', 'zs', 'tarcsay', '13', 'if', 'tt', 'and', 'tt', 'are', 'selfadjoint', 'then', 't', 'is', 'closed', 'in', 'addition', 'we', 'describe', 'the', 'friedrichs', 'extension', 'of', 's2', 'where', 's', 'is', 'a', 'symmetric', 'operator', 'recovering', 'results', 'due', 'to', 'yu', 'arlinskiui', 'and', 'yu', 'kovalev', '1', '2']] | [-0.11922040951366608, 0.13091046417740962, -0.019039959051252272, 0.04905730517796026, -0.09447647255818503, -0.1672635328024626, -0.039433249329718256, 0.330598495556758, -0.2868442537406316, -0.16944247416865368, 0.1468636013334617, -0.3363940918244995, -0.14117336220896015, 0.1298258266877383, -0.10147346997132095, -0.00816040555588328, 0.02526914389947286, 0.0746021106480979, -0.11463010882099088, -0.25288492411804886, 0.3620000208226534, -0.02133956213410084, 0.10819822382181883, 0.07384647671133279, 0.08280770479199978, 0.03875926503051932, -0.08357319098252516, -0.02915069557627878, -0.15771408321049352, 0.07717941720038653, 0.2451851176814391, 0.09827356748999311, 0.19755759763603026, -0.30970470551401375, -0.13695552029575292, 0.15995352829161744, 0.12594019643819102, -0.0030267447085442164, 0.0058130879367056945, -0.2732921222009911, 0.12389672378507945, -0.17014149855822325, -0.11723088397142979, -0.05408748770849063, 0.10651985238162943, -0.015121518562619504, -0.3441970461549667, 0.06960157564339729, 0.14643783107973063, 0.05991817447715081, -0.03565450798576841, -0.11505144009939754, -0.03723913250634303, -0.030118826131981153, -0.019871217367024376, 0.17446576472944939, 0.020682482493038362, 0.005403848447335454, -0.10997597746407757, 0.30735850425866934, -0.10834408008332293, -0.21245835889130832, 0.14405561229930475, -0.18797844274399372, -0.14369080417001476, 0.025311964875660262, 0.08694141163992194, 0.19099287390708924, -0.07795268927629177, 0.3074523342961374, -0.09757677330993689, 0.09157911805400196, 0.06497956127949202, -0.02155877599456849, 0.03158703515162835, 0.11024753582448914, 0.09065997395664453, 0.10894236100765949, -0.0160338837474298, -0.02814040548669604, -0.3324408773619395, -0.19157928635294622, -0.17773692143651154, 0.12553135712559407, -0.04329798957265806, -0.11258289762235318, 0.4162290410281947, 0.04683554413943337, 0.2851252217633793, 0.06917412688310903, 0.2250335305212782, 0.11480904718717704, 0.002142625158795944, 0.11825193018437578, 0.0957046719124684, 0.18537507836635297, 0.10856828625815419, -0.1340547396443211, -0.06781276743679952, 0.16977380408117404] |
1,802.05794 | Spectral Properties of Limit-Periodic Operators | We survey results concerning the spectral properties of limit-periodic
operators. The main focus is on discrete one-dimensional Schr\"odinger
operators, but other classes of operators, such as Jacobi and CMV matrices,
continuum Schr\"odinger operators and multi-dimensional Schr\"odinger
operators, are discussed as well.
We explain that each basic spectral type occurs, and it does so for a dense
set of limit-periodic potentials. The spectrum has a strong tendency to be a
Cantor set, but there are also cases where the spectrum has no gaps at all. The
possible regularity properties of the integrated density of states range from
extremely irregular to extremely regular. Additionally, we present background
about periodic Schr\"odinger operators and almost-periodic sequences.
In many cases we outline the proofs of the results we present.
| math.SP math-ph math.DS math.MP | we survey results concerning the spectral properties of limitperiodic operators the main focus is on discrete onedimensional schrodinger operators but other classes of operators such as jacobi and cmv matrices continuum schrodinger operators and multidimensional schrodinger operators are discussed as well we explain that each basic spectral type occurs and it does so for a dense set of limitperiodic potentials the spectrum has a strong tendency to be a cantor set but there are also cases where the spectrum has no gaps at all the possible regularity properties of the integrated density of states range from extremely irregular to extremely regular additionally we present background about periodic schrodinger operators and almostperiodic sequences in many cases we outline the proofs of the results we present | [['we', 'survey', 'results', 'concerning', 'the', 'spectral', 'properties', 'of', 'limitperiodic', 'operators', 'the', 'main', 'focus', 'is', 'on', 'discrete', 'onedimensional', 'schrodinger', 'operators', 'but', 'other', 'classes', 'of', 'operators', 'such', 'as', 'jacobi', 'and', 'cmv', 'matrices', 'continuum', 'schrodinger', 'operators', 'and', 'multidimensional', 'schrodinger', 'operators', 'are', 'discussed', 'as', 'well', 'we', 'explain', 'that', 'each', 'basic', 'spectral', 'type', 'occurs', 'and', 'it', 'does', 'so', 'for', 'a', 'dense', 'set', 'of', 'limitperiodic', 'potentials', 'the', 'spectrum', 'has', 'a', 'strong', 'tendency', 'to', 'be', 'a', 'cantor', 'set', 'but', 'there', 'are', 'also', 'cases', 'where', 'the', 'spectrum', 'has', 'no', 'gaps', 'at', 'all', 'the', 'possible', 'regularity', 'properties', 'of', 'the', 'integrated', 'density', 'of', 'states', 'range', 'from', 'extremely', 'irregular', 'to', 'extremely', 'regular', 'additionally', 'we', 'present', 'background', 'about', 'periodic', 'schrodinger', 'operators', 'and', 'almostperiodic', 'sequences', 'in', 'many', 'cases', 'we', 'outline', 'the', 'proofs', 'of', 'the', 'results', 'we', 'present']] | [-0.11346671508105036, 0.09220836449023936, -0.05246086491106619, 0.12328401737895253, -0.06644113414785674, -0.11663728086428056, 0.0029538520442832623, 0.41617124494645863, -0.24622938927683619, -0.2106860574267264, 0.15814456274458807, -0.33691446682918935, -0.16945579152313933, 0.19321667614032426, -0.04564211443967877, 0.08410037423093472, 0.07834691517224025, 0.036149625200778246, -0.0889862530189754, -0.18871800398506644, 0.3953232836736847, -0.008342441207083363, 0.18384334796311094, 0.05717133329204854, 0.03518963868038789, -0.017335606292040357, -0.03349744135536434, -0.018332139932300057, -0.09960495471991195, 0.10884263566948639, 0.2397887126782969, 0.0918271788358388, 0.2400134866456351, -0.4117672895081341, -0.2159853643857904, 0.17273611642209993, 0.15817438844486228, 0.09475303287012314, -0.05567870486336159, -0.2948436698530831, 0.07117888806087355, -0.11965225381596435, -0.19789032388718858, -0.10215018874207031, 0.06920447635404285, 0.09112213702774757, -0.25914160182301493, 0.07676082527083758, 0.10268727112880667, 0.041710544189798734, -0.10574764769614464, -0.1295268183138462, -0.03072148549648362, 0.10933814213926633, 0.007378297465507903, -0.055885690242810655, 0.057328278807202177, -0.08333925522052713, -0.0973910458551179, 0.3773851381885188, -0.05789918230936652, -0.19147973382214625, 0.21590691689409375, -0.1835301650177327, -0.17126190938401006, 0.0728658918045356, 0.1297751404269929, 0.12079969896454244, -0.1340569265922832, 0.15924913588453143, -0.08440292479879703, 0.15793384001153754, 0.0810393592147457, 0.13269018882701564, 0.14338440385707205, 0.08835823298431933, 0.11885136829063882, 0.11491160998224176, -0.008379162224422719, -0.08703126065900564, -0.32931647392650765, -0.07896372339417858, -0.1692633361550003, 0.07518870086078683, -0.062433139281629045, -0.22695780754269612, 0.449413992550915, 0.1261015401180503, 0.20532818365609273, 0.04422477632385468, 0.18662908890344684, 0.18278264670258185, 0.06882187632952756, 0.06674603661597375, 0.15886126740107073, 0.1634221722338078, 0.11534178904032395, -0.15725258372408607, -0.04624862834856275, 0.1010226424137551] |
1,802.05795 | Duality Gap in Interval Linear Programming | This paper deals with the problem of linear programming with inexact data
represented by real closed intervals. Optimization problems with interval data
arise in practical computations and they are of theoretical interest for more
than forty years. We extend the concept of duality gap (DG), the difference
between the primal and its dual optimal value, into interval linear
programming. We consider two situations: First, DG is zero for every
realization of interval parameters (the so called strongly zero DG) and,
second, DG is zero for at least one realization of interval parameters (the so
called weakly zero DG). We characterize strongly and weakly zero DG and its
special case where the matrix of coefficients is real. We discuss computational
complexity of testing weakly and strongly zero DG for commonly used types of
interval linear programs and their variants with the real matrix of
coefficients. We distinguish the NP-hard cases and the cases that are
efficiently decidable. Based on DG conditions, we extend previous results about
the bounds of the optimal value set given by Rohn. We provide equivalent
statements for the bounds
| math.OC cs.CC | this paper deals with the problem of linear programming with inexact data represented by real closed intervals optimization problems with interval data arise in practical computations and they are of theoretical interest for more than forty years we extend the concept of duality gap dg the difference between the primal and its dual optimal value into interval linear programming we consider two situations first dg is zero for every realization of interval parameters the so called strongly zero dg and second dg is zero for at least one realization of interval parameters the so called weakly zero dg we characterize strongly and weakly zero dg and its special case where the matrix of coefficients is real we discuss computational complexity of testing weakly and strongly zero dg for commonly used types of interval linear programs and their variants with the real matrix of coefficients we distinguish the nphard cases and the cases that are efficiently decidable based on dg conditions we extend previous results about the bounds of the optimal value set given by rohn we provide equivalent statements for the bounds | [['this', 'paper', 'deals', 'with', 'the', 'problem', 'of', 'linear', 'programming', 'with', 'inexact', 'data', 'represented', 'by', 'real', 'closed', 'intervals', 'optimization', 'problems', 'with', 'interval', 'data', 'arise', 'in', 'practical', 'computations', 'and', 'they', 'are', 'of', 'theoretical', 'interest', 'for', 'more', 'than', 'forty', 'years', 'we', 'extend', 'the', 'concept', 'of', 'duality', 'gap', 'dg', 'the', 'difference', 'between', 'the', 'primal', 'and', 'its', 'dual', 'optimal', 'value', 'into', 'interval', 'linear', 'programming', 'we', 'consider', 'two', 'situations', 'first', 'dg', 'is', 'zero', 'for', 'every', 'realization', 'of', 'interval', 'parameters', 'the', 'so', 'called', 'strongly', 'zero', 'dg', 'and', 'second', 'dg', 'is', 'zero', 'for', 'at', 'least', 'one', 'realization', 'of', 'interval', 'parameters', 'the', 'so', 'called', 'weakly', 'zero', 'dg', 'we', 'characterize', 'strongly', 'and', 'weakly', 'zero', 'dg', 'and', 'its', 'special', 'case', 'where', 'the', 'matrix', 'of', 'coefficients', 'is', 'real', 'we', 'discuss', 'computational', 'complexity', 'of', 'testing', 'weakly', 'and', 'strongly', 'zero', 'dg', 'for', 'commonly', 'used', 'types', 'of', 'interval', 'linear', 'programs', 'and', 'their', 'variants', 'with', 'the', 'real', 'matrix', 'of', 'coefficients', 'we', 'distinguish', 'the', 'nphard', 'cases', 'and', 'the', 'cases', 'that', 'are', 'efficiently', 'decidable', 'based', 'on', 'dg', 'conditions', 'we', 'extend', 'previous', 'results', 'about', 'the', 'bounds', 'of', 'the', 'optimal', 'value', 'set', 'given', 'by', 'rohn', 'we', 'provide', 'equivalent', 'statements', 'for', 'the', 'bounds']] | [-0.12550904325677778, 0.06593920568139855, -0.028813105027938445, 0.05980083501544668, -0.10806739740815668, -0.17692966531491378, 0.03879962094540063, 0.3748970913270918, -0.32505458008477484, -0.25709586978664845, 0.1709322172330928, -0.2495798269442825, -0.11779899585731123, 0.21331248127671143, -0.09187080359321784, 0.051118880873008705, 0.08754588472963935, 0.047081693729474, -0.10547177980181884, -0.2749844950273285, 0.33232290318215285, -0.025371827195388275, 0.2174219208600634, 0.0445313873959214, 0.11596098918964125, 0.005092840419140655, -0.030146380456594322, 0.06583972382091756, -0.15639727424010974, 0.13357651320089084, 0.3110190796829596, 0.13023556842837114, 0.2944781062203449, -0.3960959590211252, -0.13468234774728233, 0.13140052995659862, 0.08793379893401064, 0.04162446671478312, 0.001995956581095515, -0.2065681485487353, 0.14836929601509696, -0.13740893716381475, -0.07150220714642533, -0.037845631124922535, 0.03942654192048524, 0.021047358659536136, -0.3036306554292953, 0.05094976009862436, 0.05347389374747054, 0.06150529316657192, -0.022065590548963582, -0.1540587379003537, 0.025008775469089684, 0.0883717871665095, 0.038539059287579, -0.0428223650554543, 0.05876630775158417, -0.07965849728098365, -0.13991976494819008, 0.38144946041015476, -0.06399207058464046, -0.24548235124713935, 0.20931648043531842, -0.12439940585166338, -0.13905511318659922, 0.09362741698226942, 0.13759916631829833, 0.20067518841152868, -0.07571009735085561, 0.14366660440458331, -0.06456459365314343, 0.12993483079065168, 0.03830889210227754, 0.01875077081066403, 0.13511429421199425, 0.1350719434759902, 0.13658809657131865, 0.15367927751248026, -0.027817648160655132, -0.11138113677133243, -0.3281257780381366, -0.14146792302982739, -0.17063556976902944, -0.021909395135570685, -0.10018682841321576, -0.17326647817070218, 0.3971304500086622, 0.14190576190210796, 0.17669146293313703, 0.12914716437008683, 0.2617308068508794, 0.18102310783913228, 0.009485531018811513, 0.10072427404440612, 0.17555336577428893, 0.17332380021676666, 0.06012767346084609, -0.1957011857713831, 0.04233177428793899, 0.10451717124126114] |
1,802.05796 | Local yield stress statistics in model amorphous solids | We develop and extend a method presented in [S. Patinet, D. Vandembroucq, and
M. L. Falk, Phys. Rev. Lett., 117, 045501 (2016)] to compute the local yield
stresses at the atomic scale in model two-dimensional Lennard-Jones glasses
produced via differing quench protocols. This technique allows us to sample the
plastic rearrangements in a non-perturbative manner for different loading
directions on a well-controlled length scale. Plastic activity upon shearing
correlates strongly with the locations of low yield stresses in the quenched
states. This correlation is higher in more structurally relaxed systems. The
distribution of local yield stresses is also shown to strongly depend on the
quench protocol: the more relaxed the glass, the higher the local plastic
thresholds. Analysis of the magnitude of local plastic relaxations reveals that
stress drops follow exponential distributions, justifying the hypothesis of an
average characteristic amplitude often conjectured in mesoscopic or continuum
models. The amplitude of the local plastic rearrangements increases on average
with the yield stress, regardless of the system preparation. The local yield
stress varies with the shear orientation tested and strongly correlates with
the plastic rearrangement locations when the system is sheared correspondingly.
It is thus argued that plastic rearrangements are the consequence of shear
transformation zones encoded in the glass structure that possess weak slip
planes along different orientations. Finally, we justify the length scale
employed in this work and extract the yield threshold statistics as a function
of the size of the probing zones. This method makes it possible to derive
physically grounded models of plasticity for amorphous materials by directly
revealing the relevant details of the shear transformation zones that mediate
this process.
| cond-mat.soft cond-mat.stat-mech | we develop and extend a method presented in s patinet d vandembroucq and m l falk phys rev lett 117 045501 2016 to compute the local yield stresses at the atomic scale in model twodimensional lennardjones glasses produced via differing quench protocols this technique allows us to sample the plastic rearrangements in a nonperturbative manner for different loading directions on a wellcontrolled length scale plastic activity upon shearing correlates strongly with the locations of low yield stresses in the quenched states this correlation is higher in more structurally relaxed systems the distribution of local yield stresses is also shown to strongly depend on the quench protocol the more relaxed the glass the higher the local plastic thresholds analysis of the magnitude of local plastic relaxations reveals that stress drops follow exponential distributions justifying the hypothesis of an average characteristic amplitude often conjectured in mesoscopic or continuum models the amplitude of the local plastic rearrangements increases on average with the yield stress regardless of the system preparation the local yield stress varies with the shear orientation tested and strongly correlates with the plastic rearrangement locations when the system is sheared correspondingly it is thus argued that plastic rearrangements are the consequence of shear transformation zones encoded in the glass structure that possess weak slip planes along different orientations finally we justify the length scale employed in this work and extract the yield threshold statistics as a function of the size of the probing zones this method makes it possible to derive physically grounded models of plasticity for amorphous materials by directly revealing the relevant details of the shear transformation zones that mediate this process | [['we', 'develop', 'and', 'extend', 'a', 'method', 'presented', 'in', 's', 'patinet', 'd', 'vandembroucq', 'and', 'm', 'l', 'falk', 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1,802.05797 | Security and Privacy Approaches in Mixed Reality: A Literature Survey | Mixed reality (MR) technology development is now gaining momentum due to
advances in computer vision, sensor fusion, and realistic display technologies.
With most of the research and development focused on delivering the promise of
MR, there is only barely a few working on the privacy and security implications
of this technology. This survey paper aims to put in to light these risks, and
to look into the latest security and privacy work on MR. Specifically, we list
and review the different protection approaches that have been proposed to
ensure user and data security and privacy in MR. We extend the scope to include
work on related technologies such as augmented reality (AR), virtual reality
(VR), and human-computer interaction (HCI) as crucial components, if not the
origins, of MR, as well as numerous related work from the larger area of mobile
devices, wearables, and Internet-of-Things (IoT). We highlight the lack of
investigation, implementation, and evaluation of data protection approaches in
MR. Further challenges and directions on MR security and privacy are also
discussed.
| cs.CR cs.CY cs.HC | mixed reality mr technology development is now gaining momentum due to advances in computer vision sensor fusion and realistic display technologies with most of the research and development focused on delivering the promise of mr there is only barely a few working on the privacy and security implications of this technology this survey paper aims to put in to light these risks and to look into the latest security and privacy work on mr specifically we list and review the different protection approaches that have been proposed to ensure user and data security and privacy in mr we extend the scope to include work on related technologies such as augmented reality ar virtual reality vr and humancomputer interaction hci as crucial components if not the origins of mr as well as numerous related work from the larger area of mobile devices wearables and internetofthings iot we highlight the lack of investigation implementation and evaluation of data protection approaches in mr further challenges and directions on mr security and privacy are also discussed | [['mixed', 'reality', 'mr', 'technology', 'development', 'is', 'now', 'gaining', 'momentum', 'due', 'to', 'advances', 'in', 'computer', 'vision', 'sensor', 'fusion', 'and', 'realistic', 'display', 'technologies', 'with', 'most', 'of', 'the', 'research', 'and', 'development', 'focused', 'on', 'delivering', 'the', 'promise', 'of', 'mr', 'there', 'is', 'only', 'barely', 'a', 'few', 'working', 'on', 'the', 'privacy', 'and', 'security', 'implications', 'of', 'this', 'technology', 'this', 'survey', 'paper', 'aims', 'to', 'put', 'in', 'to', 'light', 'these', 'risks', 'and', 'to', 'look', 'into', 'the', 'latest', 'security', 'and', 'privacy', 'work', 'on', 'mr', 'specifically', 'we', 'list', 'and', 'review', 'the', 'different', 'protection', 'approaches', 'that', 'have', 'been', 'proposed', 'to', 'ensure', 'user', 'and', 'data', 'security', 'and', 'privacy', 'in', 'mr', 'we', 'extend', 'the', 'scope', 'to', 'include', 'work', 'on', 'related', 'technologies', 'such', 'as', 'augmented', 'reality', 'ar', 'virtual', 'reality', 'vr', 'and', 'humancomputer', 'interaction', 'hci', 'as', 'crucial', 'components', 'if', 'not', 'the', 'origins', 'of', 'mr', 'as', 'well', 'as', 'numerous', 'related', 'work', 'from', 'the', 'larger', 'area', 'of', 'mobile', 'devices', 'wearables', 'and', 'internetofthings', 'iot', 'we', 'highlight', 'the', 'lack', 'of', 'investigation', 'implementation', 'and', 'evaluation', 'of', 'data', 'protection', 'approaches', 'in', 'mr', 'further', 'challenges', 'and', 'directions', 'on', 'mr', 'security', 'and', 'privacy', 'are', 'also', 'discussed']] | [-0.11053039577822092, 0.0021123764792803762, -0.007814625150870618, 0.03785370144354136, -0.11001288457665333, -0.19308153903490843, 0.015316611221520404, 0.3806320418885281, -0.2300343648757282, -0.36042488872365985, 0.12648514382606155, -0.2918894278889977, -0.19132875080956796, 0.25089749979223447, -0.16200768709669044, 0.0808168749898932, 0.05335827365918302, 0.00843648671519098, 0.0018107594453816323, -0.27254077585056685, 0.33510404732078314, 0.04577171177803647, 0.3749948558086248, 0.1473002375332303, 0.028215319157827125, 0.01416029329486137, -0.04095629726125057, 0.022670042627967547, -0.08990261960541322, 0.1790830938248397, 0.31709489500763294, 0.21964806487626765, 0.36683836573908146, -0.45515502113199163, -0.1787384572786788, 0.04082223399932128, 0.16355614657351358, 0.07676631251220094, -0.1457124637598226, -0.3389364938240845, 0.09634550744505209, -0.19932312368913446, -0.07549693287195582, -0.09128447187254625, 0.026053532163428445, 0.00024323712839463421, -0.20219509606351155, -0.04672593683113349, 0.011207894001335114, 0.13564170414560237, -0.061526523755885645, -0.1277107753043691, 0.015990063349099092, 0.16326156508199177, 0.10063420510363527, 0.024014506248085824, 0.1517211249352671, -0.1897440718941737, -0.13091357125762612, 0.3697508595190769, 0.06519521238433934, -0.12772640779879768, 0.222143828450583, -0.09218749807075445, -0.14863252371203067, 0.03176524008551158, 0.24771908117284955, 0.028581605171026706, -0.17528050839711456, 0.057574208101276635, 0.05765594249093082, 0.1342318382487784, 0.025889106568620476, 0.11680339804991301, 0.19741788342172756, 0.2062158761979189, 0.06355326232289032, 0.06148280732896785, -0.09711089433132952, -0.09518051626929623, -0.24105653291308257, -0.19710986292650273, -0.14212141882054247, 0.02746397995014017, 0.02106751446948227, -0.12009328062142527, 0.37082373524128004, 0.2577640514761253, 0.1564884238800597, -0.011492981190142103, 0.39781152162440986, 0.01913657603341393, 0.1357452991136978, 0.05849061192414009, 0.2210984386444135, 0.07675906021126308, 0.24358505323031093, -0.1309467873801369, 0.10681036100614556, -0.052370654849530476] |
1,802.05798 | Detecting Anomalous Faces with 'No Peeking' Autoencoders | Detecting anomalous faces has important applications. For example, a system
might tell when a train driver is incapacitated by a medical event, and assist
in adopting a safe recovery strategy. These applications are demanding, because
they require accurate detection of rare anomalies that may be seen only at
runtime. Such a setting causes supervised methods to perform poorly. We
describe a method for detecting an anomalous face image that meets these
requirements. We construct a feature vector that reliably has large entries for
anomalous images, then use various simple unsupervised methods to score the
image based on the feature. Obvious constructions (autoencoder codes;
autoencoder residuals) are defeated by a 'peeking' behavior in autoencoders.
Our feature construction removes rectangular patches from the image, predicts
the likely content of the patch conditioned on the rest of the image using a
specially trained autoencoder, then compares the result to the image. High
scores suggest that the patch was difficult for an autoencoder to predict, and
so is likely anomalous. We demonstrate that our method can identify real
anomalous face images in pools of typical images, taken from celeb-A, that is
much larger than usual in state-of-the-art experiments. A control experiment
based on our method with another set of normal celebrity images - a 'typical
set', but nonceleb-A are not identified as anomalous; confirms this is not due
to special properties of celeb-A.
| cs.CV | detecting anomalous faces has important applications for example a system might tell when a train driver is incapacitated by a medical event and assist in adopting a safe recovery strategy these applications are demanding because they require accurate detection of rare anomalies that may be seen only at runtime such a setting causes supervised methods to perform poorly we describe a method for detecting an anomalous face image that meets these requirements we construct a feature vector that reliably has large entries for anomalous images then use various simple unsupervised methods to score the image based on the feature obvious constructions autoencoder codes autoencoder residuals are defeated by a peeking behavior in autoencoders our feature construction removes rectangular patches from the image predicts the likely content of the patch conditioned on the rest of the image using a specially trained autoencoder then compares the result to the image high scores suggest that the patch was difficult for an autoencoder to predict and so is likely anomalous we demonstrate that our method can identify real anomalous face images in pools of typical images taken from celeba that is much larger than usual in stateoftheart experiments a control experiment based on our method with another set of normal celebrity images a typical set but nonceleba are not identified as anomalous confirms this is not due to special properties of celeba | [['detecting', 'anomalous', 'faces', 'has', 'important', 'applications', 'for', 'example', 'a', 'system', 'might', 'tell', 'when', 'a', 'train', 'driver', 'is', 'incapacitated', 'by', 'a', 'medical', 'event', 'and', 'assist', 'in', 'adopting', 'a', 'safe', 'recovery', 'strategy', 'these', 'applications', 'are', 'demanding', 'because', 'they', 'require', 'accurate', 'detection', 'of', 'rare', 'anomalies', 'that', 'may', 'be', 'seen', 'only', 'at', 'runtime', 'such', 'a', 'setting', 'causes', 'supervised', 'methods', 'to', 'perform', 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1,802.05799 | Horovod: fast and easy distributed deep learning in TensorFlow | Training modern deep learning models requires large amounts of computation,
often provided by GPUs. Scaling computation from one GPU to many can enable
much faster training and research progress but entails two complications.
First, the training library must support inter-GPU communication. Depending on
the particular methods employed, this communication may entail anywhere from
negligible to significant overhead. Second, the user must modify his or her
training code to take advantage of inter-GPU communication. Depending on the
training library's API, the modification required may be either significant or
minimal.
Existing methods for enabling multi-GPU training under the TensorFlow library
entail non-negligible communication overhead and require users to heavily
modify their model-building code, leading many researchers to avoid the whole
mess and stick with slower single-GPU training. In this paper we introduce
Horovod, an open source library that improves on both obstructions to scaling:
it employs efficient inter-GPU communication via ring reduction and requires
only a few lines of modification to user code, enabling faster, easier
distributed training in TensorFlow. Horovod is available under the Apache 2.0
license at https://github.com/uber/horovod
| cs.LG stat.ML | training modern deep learning models requires large amounts of computation often provided by gpus scaling computation from one gpu to many can enable much faster training and research progress but entails two complications first the training library must support intergpu communication depending on the particular methods employed this communication may entail anywhere from negligible to significant overhead second the user must modify his or her training code to take advantage of intergpu communication depending on the training librarys api the modification required may be either significant or minimal existing methods for enabling multigpu training under the tensorflow library entail nonnegligible communication overhead and require users to heavily modify their modelbuilding code leading many researchers to avoid the whole mess and stick with slower singlegpu training in this paper we introduce horovod an open source library that improves on both obstructions to scaling it employs efficient intergpu communication via ring reduction and requires only a few lines of modification to user code enabling faster easier distributed training in tensorflow horovod is available under the apache 20 license at httpsgithubcomuberhorovod | [['training', 'modern', 'deep', 'learning', 'models', 'requires', 'large', 'amounts', 'of', 'computation', 'often', 'provided', 'by', 'gpus', 'scaling', 'computation', 'from', 'one', 'gpu', 'to', 'many', 'can', 'enable', 'much', 'faster', 'training', 'and', 'research', 'progress', 'but', 'entails', 'two', 'complications', 'first', 'the', 'training', 'library', 'must', 'support', 'intergpu', 'communication', 'depending', 'on', 'the', 'particular', 'methods', 'employed', 'this', 'communication', 'may', 'entail', 'anywhere', 'from', 'negligible', 'to', 'significant', 'overhead', 'second', 'the', 'user', 'must', 'modify', 'his', 'or', 'her', 'training', 'code', 'to', 'take', 'advantage', 'of', 'intergpu', 'communication', 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1,802.058 | Tree-CNN: A Hierarchical Deep Convolutional Neural Network for
Incremental Learning | Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown
remarkable performance in most computer vision tasks. These tasks traditionally
use a fixed dataset, and the model, once trained, is deployed as is. Adding new
information to such a model presents a challenge due to complex training
issues, such as "catastrophic forgetting", and sensitivity to hyper-parameter
tuning. However, in this modern world, data is constantly evolving, and our
deep learning models are required to adapt to these changes. In this paper, we
propose an adaptive hierarchical network structure composed of DCNNs that can
grow and learn as new data becomes available. The network grows in a tree-like
fashion to accommodate new classes of data, while preserving the ability to
distinguish the previously trained classes. The network organizes the
incrementally available data into feature-driven super-classes and improves
upon existing hierarchical CNN models by adding the capability of self-growth.
The proposed hierarchical model, when compared against fine-tuning a deep
network, achieves significant reduction of training effort, while maintaining
competitive accuracy on CIFAR-10 and CIFAR-100.
| cs.CV cs.AI eess.IV stat.ML | over the past decade deep convolutional neural networks dcnns have shown remarkable performance in most computer vision tasks these tasks traditionally use a fixed dataset and the model once trained is deployed as is adding new information to such a model presents a challenge due to complex training issues such as catastrophic forgetting and sensitivity to hyperparameter tuning however in this modern world data is constantly evolving and our deep learning models are required to adapt to these changes in this paper we propose an adaptive hierarchical network structure composed of dcnns that can grow and learn as new data becomes available the network grows in a treelike fashion to accommodate new classes of data while preserving the ability to distinguish the previously trained classes the network organizes the incrementally available data into featuredriven superclasses and improves upon existing hierarchical cnn models by adding the capability of selfgrowth the proposed hierarchical model when compared against finetuning a deep network achieves significant reduction of training effort while maintaining competitive accuracy on cifar10 and cifar100 | [['over', 'the', 'past', 'decade', 'deep', 'convolutional', 'neural', 'networks', 'dcnns', 'have', 'shown', 'remarkable', 'performance', 'in', 'most', 'computer', 'vision', 'tasks', 'these', 'tasks', 'traditionally', 'use', 'a', 'fixed', 'dataset', 'and', 'the', 'model', 'once', 'trained', 'is', 'deployed', 'as', 'is', 'adding', 'new', 'information', 'to', 'such', 'a', 'model', 'presents', 'a', 'challenge', 'due', 'to', 'complex', 'training', 'issues', 'such', 'as', 'catastrophic', 'forgetting', 'and', 'sensitivity', 'to', 'hyperparameter', 'tuning', 'however', 'in', 'this', 'modern', 'world', 'data', 'is', 'constantly', 'evolving', 'and', 'our', 'deep', 'learning', 'models', 'are', 'required', 'to', 'adapt', 'to', 'these', 'changes', 'in', 'this', 'paper', 'we', 'propose', 'an', 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1,802.05801 | Uniform-in-Submodel Bounds for Linear Regression in a Model Free
Framework | For the last two decades, high-dimensional data and methods have proliferated
throughout the literature. Yet, the classical technique of linear regression
has not lost its usefulness in applications. In fact, many high-dimensional
estimation techniques can be seen as variable selection that leads to a smaller
set of variables (a ``sub-model'') where classical linear regression applies.
We analyze linear regression estimators resulting from model-selection by
proving estimation error and linear representation bounds uniformly over sets
of submodels. Based on deterministic inequalities, our results provide ``good''
rates when applied to both independent and dependent data. These results are
useful in meaningfully interpreting the linear regression estimator obtained
after exploring and reducing the variables and also in justifying post
model-selection inference. All results are derived under no model assumptions
and are non-asymptotic in nature.
| math.ST stat.TH | for the last two decades highdimensional data and methods have proliferated throughout the literature yet the classical technique of linear regression has not lost its usefulness in applications in fact many highdimensional estimation techniques can be seen as variable selection that leads to a smaller set of variables a submodel where classical linear regression applies we analyze linear regression estimators resulting from modelselection by proving estimation error and linear representation bounds uniformly over sets of submodels based on deterministic inequalities our results provide good rates when applied to both independent and dependent data these results are useful in meaningfully interpreting the linear regression estimator obtained after exploring and reducing the variables and also in justifying post modelselection inference all results are derived under no model assumptions and are nonasymptotic in nature | [['for', 'the', 'last', 'two', 'decades', 'highdimensional', 'data', 'and', 'methods', 'have', 'proliferated', 'throughout', 'the', 'literature', 'yet', 'the', 'classical', 'technique', 'of', 'linear', 'regression', 'has', 'not', 'lost', 'its', 'usefulness', 'in', 'applications', 'in', 'fact', 'many', 'highdimensional', 'estimation', 'techniques', 'can', 'be', 'seen', 'as', 'variable', 'selection', 'that', 'leads', 'to', 'a', 'smaller', 'set', 'of', 'variables', 'a', 'submodel', 'where', 'classical', 'linear', 'regression', 'applies', 'we', 'analyze', 'linear', 'regression', 'estimators', 'resulting', 'from', 'modelselection', 'by', 'proving', 'estimation', 'error', 'and', 'linear', 'representation', 'bounds', 'uniformly', 'over', 'sets', 'of', 'submodels', 'based', 'on', 'deterministic', 'inequalities', 'our', 'results', 'provide', 'good', 'rates', 'when', 'applied', 'to', 'both', 'independent', 'and', 'dependent', 'data', 'these', 'results', 'are', 'useful', 'in', 'meaningfully', 'interpreting', 'the', 'linear', 'regression', 'estimator', 'obtained', 'after', 'exploring', 'and', 'reducing', 'the', 'variables', 'and', 'also', 'in', 'justifying', 'post', 'modelselection', 'inference', 'all', 'results', 'are', 'derived', 'under', 'no', 'model', 'assumptions', 'and', 'are', 'nonasymptotic', 'in', 'nature']] | [-0.010062670338728274, 0.01996581838288494, -0.10334471524302058, 0.10631823529603088, -0.08547277134338407, -0.20292680985329828, 0.061994936698443394, 0.3906188270802261, -0.281027338332461, -0.27533336140513986, 0.18736037168783343, -0.2292375154651076, -0.15404965450235408, 0.28118775776260396, -0.11717730634383687, 0.13015720931384783, 0.0892838801698091, -0.003504451166772646, -0.09952491964924483, -0.345995057331583, 0.25742581640619017, 0.021542046237772488, 0.29074314770374077, -0.04590938956243917, 0.10412940124533217, 0.07025805209542958, -0.10095918744461227, 0.046215174142946666, -0.12383337178337965, 0.12539523869809757, 0.33421911616304906, 0.17153463125417057, 0.3441716453686129, -0.39237129817830524, -0.24250666425965073, 0.1315952864553751, 0.1429476997475602, 0.11496768690617258, -0.00955805946443628, -0.2563556855196091, 0.05426843306458491, -0.11493864625302243, -0.040621662702596255, -0.15447919445382502, -0.01961662418400968, 0.033323768714713, -0.32203972783696816, 0.12086712007694309, 0.09225405440159241, 0.06989027870523935, -0.051155321108966444, -0.1779902359428069, 0.01105772223650613, 0.09456075642161064, 0.09646735461482081, -0.023323094820646383, 0.0938695184191074, -0.10774217610681103, -0.13724475600028, 0.317566285521247, -0.06257123893254814, -0.25053422724586405, 0.19234872054493496, -0.11428961762180907, -0.17358574154075831, 0.08701695801818189, 0.2129192125507211, 0.08164952959125961, -0.1734263441932631, 0.09742025522611154, -0.05713695764385221, 0.14795902432546815, 0.02749066272011126, 0.03517072619639252, 0.13618542998318225, 0.1567552529012372, 0.05254026632604922, 0.11025963380367616, -0.07958498912621716, -0.11147017131794158, -0.29193261230741235, -0.07483710267204251, -0.1907767305721152, -0.051494902923933356, -0.17522692303853074, -0.14940184732909734, 0.3502476034927687, 0.17131899186065208, 0.2097796809429459, 0.09686852207066107, 0.27632272853480494, 0.14108667127401053, 0.03565861509032263, 0.10720175093942756, 0.2322420493676505, 0.16903060594509633, 0.034081174848949576, -0.12568607303435658, 0.14147084139692703, 0.026058303234913878] |
1,802.05802 | End-to-end Analysis and Design of a Drone Flight Controller | Timing guarantees are crucial to cyber-physical applications that must bound
the end-to-end delay between sensing, processing and actuation. For example, in
a flight controller for a multirotor drone, the data from a gyro or inertial
sensor must be gathered and processed to determine the attitude of the
aircraft. Sensor data fusion is followed by control decisions that adjust the
flight of a drone by altering motor speeds. If the processing pipeline between
sensor input and actuation is not bounded, the drone will lose control and
possibly fail to maintain flight.
Motivated by the implementation of a multithreaded drone flight controller on
the Quest RTOS, we develop a composable pipe model based on the system's task,
scheduling and communication abstractions. This pipe model is used to analyze
two semantics of end-to-end time: reaction time and freshness time. We also
argue that end-to-end timing properties should be factored in at the early
stage of application design. Thus, we provide a mathematical framework to
derive feasible task periods that satisfy both a given set of end-to-end timing
constraints and the schedulability requirement. We demonstrate the
applicability of our design approach by using it to port the Cleanflight flight
controller firmware to Quest on the Intel Aero board. Experiments show that
Cleanflight ported to Quest is able to achieve end-to-end latencies within the
predicted time bounds derived by analysis.
| cs.SY cs.OS | timing guarantees are crucial to cyberphysical applications that must bound the endtoend delay between sensing processing and actuation for example in a flight controller for a multirotor drone the data from a gyro or inertial sensor must be gathered and processed to determine the attitude of the aircraft sensor data fusion is followed by control decisions that adjust the flight of a drone by altering motor speeds if the processing pipeline between sensor input and actuation is not bounded the drone will lose control and possibly fail to maintain flight motivated by the implementation of a multithreaded drone flight controller on the quest rtos we develop a composable pipe model based on the systems task scheduling and communication abstractions this pipe model is used to analyze two semantics of endtoend time reaction time and freshness time we also argue that endtoend timing properties should be factored in at the early stage of application design thus we provide a mathematical framework to derive feasible task periods that satisfy both a given set of endtoend timing constraints and the schedulability requirement we demonstrate the applicability of our design approach by using it to port the cleanflight flight controller firmware to quest on the intel aero board experiments show that cleanflight ported to quest is able to achieve endtoend latencies within the predicted time bounds derived by analysis | [['timing', 'guarantees', 'are', 'crucial', 'to', 'cyberphysical', 'applications', 'that', 'must', 'bound', 'the', 'endtoend', 'delay', 'between', 'sensing', 'processing', 'and', 'actuation', 'for', 'example', 'in', 'a', 'flight', 'controller', 'for', 'a', 'multirotor', 'drone', 'the', 'data', 'from', 'a', 'gyro', 'or', 'inertial', 'sensor', 'must', 'be', 'gathered', 'and', 'processed', 'to', 'determine', 'the', 'attitude', 'of', 'the', 'aircraft', 'sensor', 'data', 'fusion', 'is', 'followed', 'by', 'control', 'decisions', 'that', 'adjust', 'the', 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1,802.05803 | MPC-Inspired Neural Network Policies for Sequential Decision Making | In this paper we investigate the use of MPC-inspired neural network policies
for sequential decision making. We introduce an extension to the DAgger
algorithm for training such policies and show how they have improved training
performance and generalization capabilities. We take advantage of this
extension to show scalable and efficient training of complex planning policy
architectures in continuous state and action spaces. We provide an extensive
comparison of neural network policies by considering feed forward policies,
recurrent policies, and recurrent policies with planning structure inspired by
the Path Integral control framework. Our results suggest that MPC-type
recurrent policies have better robustness to disturbances and modeling error.
| cs.LG | in this paper we investigate the use of mpcinspired neural network policies for sequential decision making we introduce an extension to the dagger algorithm for training such policies and show how they have improved training performance and generalization capabilities we take advantage of this extension to show scalable and efficient training of complex planning policy architectures in continuous state and action spaces we provide an extensive comparison of neural network policies by considering feed forward policies recurrent policies and recurrent policies with planning structure inspired by the path integral control framework our results suggest that mpctype recurrent policies have better robustness to disturbances and modeling error | [['in', 'this', 'paper', 'we', 'investigate', 'the', 'use', 'of', 'mpcinspired', 'neural', 'network', 'policies', 'for', 'sequential', 'decision', 'making', 'we', 'introduce', 'an', 'extension', 'to', 'the', 'dagger', 'algorithm', 'for', 'training', 'such', 'policies', 'and', 'show', 'how', 'they', 'have', 'improved', 'training', 'performance', 'and', 'generalization', 'capabilities', 'we', 'take', 'advantage', 'of', 'this', 'extension', 'to', 'show', 'scalable', 'and', 'efficient', 'training', 'of', 'complex', 'planning', 'policy', 'architectures', 'in', 'continuous', 'state', 'and', 'action', 'spaces', 'we', 'provide', 'an', 'extensive', 'comparison', 'of', 'neural', 'network', 'policies', 'by', 'considering', 'feed', 'forward', 'policies', 'recurrent', 'policies', 'and', 'recurrent', 'policies', 'with', 'planning', 'structure', 'inspired', 'by', 'the', 'path', 'integral', 'control', 'framework', 'our', 'results', 'suggest', 'that', 'mpctype', 'recurrent', 'policies', 'have', 'better', 'robustness', 'to', 'disturbances', 'and', 'modeling', 'error']] | [-0.08575440280121537, -0.015589014355870089, -0.06917467179636543, 0.10433525852456044, -0.13126303592266944, -0.15481447957706854, 0.10773251645486408, 0.5530515783108197, -0.297251870479578, -0.2707533743053388, 0.10311222208726506, -0.17889675599424384, -0.25497281883984285, 0.20077177870552987, -0.1758739524252283, 0.11241473896930423, 0.16242395083491618, -0.015421011665099425, -0.030315705547064472, -0.2812788910008609, 0.3195406820831032, 0.08149768680316181, 0.3234001665125386, -0.02482321446027177, 0.16737670002755925, 0.007299126267473464, -0.006665160413831472, -0.0052406562384791095, -0.08030008826699486, 0.17661964289772397, 0.31614009647343594, 0.23388672333497268, 0.3593730589648028, -0.45860610575451016, -0.2484036385699605, 0.08636716014454858, 0.1175892108764786, 0.06766054333107044, -0.015346267278521107, -0.34165255272259504, 0.09670007795536478, -0.24359180797518304, -0.006382515960677455, -0.20702575588634667, -0.051262292907967306, 0.005953323175512755, -0.3429405791735357, -0.06594230803434933, 0.09642692955542141, 0.049239834748872315, -0.10299361932825726, -0.0790240907540115, 0.034815830239005245, 0.1601257529288817, 0.020708424263060667, 0.04299744315749894, 0.1271570439880284, -0.14870105283961704, -0.2504657912784471, 0.2927915578564772, -0.021279432432493195, -0.23510596078211585, 0.15530077899949482, -0.012460265425033867, -0.1638722402903323, 0.0852138126680914, 0.2928690080835412, 0.09827603883324908, -0.17153239046960012, 0.020388228425872512, -0.02060643830918707, 0.12973983211514467, -0.0013414490587400417, 0.026157605555804018, 0.10899564055055705, 0.3065071157537974, 0.12075394967597766, 0.18044697551298527, -0.05073878668302383, -0.13142741162076932, -0.2149335950290641, -0.12725058741097411, -0.08965317666297778, 0.0023145505245058583, -0.1059422241336366, -0.12547131076168555, 0.35781393366722536, 0.2468657507829798, 0.1713512682920107, 0.21268492645825832, 0.3338307636216856, 0.059515931774512865, 0.09329485843101373, 0.1921467335151437, 0.1764043748441439, 0.014703864719754515, 0.1458147838487863, -0.2052565809135558, 0.10677056441692492, 0.055616691045892924] |
1,802.05804 | Automorphism groups of superextensions of groups | The superextension $\lambda(X)$ of a set $X$ consists of all maximal linked
families on $X$. Any associative binary operation $*: X\times X \to X$ can be
extended to an associative binary operation $*:
\lambda(X)\times\lambda(X)\to\lambda(X)$. In the paper we study isomorphisms of
superextensions of groups and prove that two groups are isomorphic if and only
if their superextensions are isomorphic. Also we describe the automorphism
groups of superextensions of all groups of cardinality $\leq 5$.
| math.GR | the superextension lambdax of a set x consists of all maximal linked families on x any associative binary operation xtimes x to x can be extended to an associative binary operation lambdaxtimeslambdaxtolambdax in the paper we study isomorphisms of superextensions of groups and prove that two groups are isomorphic if and only if their superextensions are isomorphic also we describe the automorphism groups of superextensions of all groups of cardinality leq 5 | [['the', 'superextension', 'lambdax', 'of', 'a', 'set', 'x', 'consists', 'of', 'all', 'maximal', 'linked', 'families', 'on', 'x', 'any', 'associative', 'binary', 'operation', 'xtimes', 'x', 'to', 'x', 'can', 'be', 'extended', 'to', 'an', 'associative', 'binary', 'operation', 'lambdaxtimeslambdaxtolambdax', 'in', 'the', 'paper', 'we', 'study', 'isomorphisms', 'of', 'superextensions', 'of', 'groups', 'and', 'prove', 'that', 'two', 'groups', 'are', 'isomorphic', 'if', 'and', 'only', 'if', 'their', 'superextensions', 'are', 'isomorphic', 'also', 'we', 'describe', 'the', 'automorphism', 'groups', 'of', 'superextensions', 'of', 'all', 'groups', 'of', 'cardinality', 'leq', '5']] | [-0.22422134941837318, 0.1263778845037245, -0.041732765130803616, 0.01293355039760313, -0.0751860798008635, -0.13145326494409795, 0.017893974185580204, 0.4233770271251634, -0.3587128579039389, -0.18408041541845027, 0.1440714584961927, -0.30119173197259363, -0.11418083190104701, 0.2079682384525806, -0.13035076615256322, -0.0963094617489358, 0.031361077557986894, 0.149689118686119, -0.12284795437204901, -0.3430246222833119, 0.3876690069790667, -0.10429284110350508, 0.16761053593593164, -0.030339161799827093, 0.13845563325112764, 0.0013304334592966127, -0.0048209775475339155, -0.01073588311514804, -0.13250000846072535, 0.10176045646969702, 0.29636435577986947, 0.10666693993975264, 0.175284601095408, -0.33641361392719643, -0.09302313274271051, 0.25158841373123436, 0.1277746391338362, -0.08285738775690257, 0.008010411269398747, -0.20393942565080042, 0.18052406959288136, -0.22646041993628924, -0.08902650038567438, -0.0755041342648402, 0.12104200894459033, 0.04935039950757694, -0.22236261633552715, -0.03306257085174545, 0.1282587500262848, 0.06872737843176009, -0.06551740674013404, -0.11210407433427975, -0.1211798272626868, 0.1269241244479699, -0.05843812805718519, 0.03095022951331462, 0.07920386040137267, -0.017648148914458046, -0.13647883928472726, 0.3800197198567256, 0.009367409791849869, -0.1804882980568308, 0.15062279893908467, -0.2030467421068928, -0.235918093879353, 0.12179349785880514, 0.10484359869864625, 0.1687872284813456, -0.07822784318254185, 0.2080002892746026, -0.13999196228531885, 0.1535766668827601, 0.09988080930214008, 0.03023603891061855, 0.13023670253173356, 0.08836190432677386, 0.07254966709133186, 0.1300128307832982, 0.04661556875372542, 0.07083064507902928, -0.36082630011607225, -0.17906668753212343, -0.0643402967392139, 0.15358152227397537, -0.08787904599391279, -0.11995413911227189, 0.35595728079198113, 0.08990248638323285, 0.15486689702883152, 0.07061828825045639, 0.1354956614758662, -0.016233249240953304, 0.06994943237128917, 0.0838410481285881, 0.07807735804940613, 0.2336696817106764, -0.1471537968363534, -0.1178320968904021, -0.015448663361586998, 0.17687797350940151] |
1,802.05805 | OSSOS IX: two objects in Neptune's 9:1 resonance -- implications for
resonance sticking in the scattering population | We discuss the detection in the Outer Solar System Origins Survey (OSSOS) of
two objects in Neptune's distant 9:1 mean motion resonance at semimajor axis
$a\approx~130$~au. Both objects are securely resonant on 10~Myr timescales,
with one securely in the 9:1 resonance's leading asymmetric libration island
and the other in either the symmetric or trailing asymmetric island. These
objects are the largest semimajor axis objects with secure resonant
classifications, and their detection in a carefully characterized survey allows
for the first robust resonance population estimate beyond 100~au. The detection
of these objects implies a 9:1 resonance population of $1.1\times10^4$ objects
with $H_r<8.66$ ($D~\gtrsim~100$~km) on similar orbits (95\% confidence range
of $\sim0.4-3\times10^4$). Integrations over 4~Gyr of an ensemble of clones
spanning these objects' orbit fit uncertainties reveal that they both have
median resonance occupation timescales of $\sim1$~Gyr. These timescales are
consistent with the hypothesis that these objects originate in the scattering
population but became transiently stuck to Neptune's 9:1 resonance within the
last $\sim1$~Gyr of solar system evolution. Based on simulations of a model of
the current scattering population, we estimate the expected resonance sticking
population in the 9:1 resonance to be 1000-4500 objects with $H_r<8.66$; this
is marginally consistent with the OSSOS 9:1 population estimate. We conclude
that resonance sticking is a plausible explanation for the observed 9:1
population, but we also discuss the possibility of a primordial 9:1 population,
which would have interesting implications for the Kuiper belt's dynamical
history.
| astro-ph.EP | we discuss the detection in the outer solar system origins survey ossos of two objects in neptunes distant 91 mean motion resonance at semimajor axis aapprox130au both objects are securely resonant on 10myr timescales with one securely in the 91 resonances leading asymmetric libration island and the other in either the symmetric or trailing asymmetric island these objects are the largest semimajor axis objects with secure resonant classifications and their detection in a carefully characterized survey allows for the first robust resonance population estimate beyond 100au the detection of these objects implies a 91 resonance population of 11times104 objects with h_r866 dgtrsim100km on similar orbits 95 confidence range of sim043times104 integrations over 4gyr of an ensemble of clones spanning these objects orbit fit uncertainties reveal that they both have median resonance occupation timescales of sim1gyr these timescales are consistent with the hypothesis that these objects originate in the scattering population but became transiently stuck to neptunes 91 resonance within the last sim1gyr of solar system evolution based on simulations of a model of the current scattering population we estimate the expected resonance sticking population in the 91 resonance to be 10004500 objects with h_r866 this is marginally consistent with the ossos 91 population estimate we conclude that resonance sticking is a plausible explanation for the observed 91 population but we also discuss the possibility of a primordial 91 population which would have interesting implications for the kuiper belts dynamical history | [['we', 'discuss', 'the', 'detection', 'in', 'the', 'outer', 'solar', 'system', 'origins', 'survey', 'ossos', 'of', 'two', 'objects', 'in', 'neptunes', 'distant', '91', 'mean', 'motion', 'resonance', 'at', 'semimajor', 'axis', 'aapprox130au', 'both', 'objects', 'are', 'securely', 'resonant', 'on', '10myr', 'timescales', 'with', 'one', 'securely', 'in', 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1,802.05806 | Duty-cycle and energetics of remnant radio-loud AGN | Deriving the energetics of remnant and restarted active galactic nuclei
(AGNs) is much more challenging than for active sources due to the complexity
in accurately determining the time since the nucleus switched-off. I resolve
this problem using a new approach that combines spectral ageing and dynamical
models to tightly constrain the energetics and duty-cycles of dying sources.
Fitting the shape of the integrated radio spectrum yields the fraction of the
source age the nucleus is active; this, in addition to the flux density, source
size, axis ratio, and properties of the host environment, provides a constraint
on dynamical models describing the remnant radio source. This technique is used
to derive the intrinsic properties of the well-studied remnant radio source B2
0924+30. This object is found to spend $50^{+14}_{-12}$ Myr in the active phase
and a further $28^{+6}_{-5}$ Myr in the quiescent phase, have a jet kinetic
power of $3.6^{+3.0}_{-1.7}\times 10^{37}$ W, and a lobe magnetic field
strength below equipartition at the $8\sigma$ level. The integrated spectra of
restarted and intermittent radio sources is found to yield a 'steep-shallow'
shape when the previous outburst occurred within 100 Myr. The duty-cycle of B2
0924+30 is hence constrained to be $\delta < 0.15$ by fitting the shortest time
to the previous comparable outburst that does not appreciably modify the
remnant spectrum. The time-averaged feedback energy imparted by AGNs into their
host galaxy environments can in this manner be quantified.
| astro-ph.GA | deriving the energetics of remnant and restarted active galactic nuclei agns is much more challenging than for active sources due to the complexity in accurately determining the time since the nucleus switchedoff i resolve this problem using a new approach that combines spectral ageing and dynamical models to tightly constrain the energetics and dutycycles of dying sources fitting the shape of the integrated radio spectrum yields the fraction of the source age the nucleus is active this in addition to the flux density source size axis ratio and properties of the host environment provides a constraint on dynamical models describing the remnant radio source this technique is used to derive the intrinsic properties of the wellstudied remnant radio source b2 092430 this object is found to spend 5014_12 myr in the active phase and a further 286_5 myr in the quiescent phase have a jet kinetic power of 3630_17times 1037 w and a lobe magnetic field strength below equipartition at the 8sigma level the integrated spectra of restarted and intermittent radio sources is found to yield a steepshallow shape when the previous outburst occurred within 100 myr the dutycycle of b2 092430 is hence constrained to be delta 015 by fitting the shortest time to the previous comparable outburst that does not appreciably modify the remnant spectrum the timeaveraged feedback energy imparted by agns into their host galaxy environments can in this manner be quantified | [['deriving', 'the', 'energetics', 'of', 'remnant', 'and', 'restarted', 'active', 'galactic', 'nuclei', 'agns', 'is', 'much', 'more', 'challenging', 'than', 'for', 'active', 'sources', 'due', 'to', 'the', 'complexity', 'in', 'accurately', 'determining', 'the', 'time', 'since', 'the', 'nucleus', 'switchedoff', 'i', 'resolve', 'this', 'problem', 'using', 'a', 'new', 'approach', 'that', 'combines', 'spectral', 'ageing', 'and', 'dynamical', 'models', 'to', 'tightly', 'constrain', 'the', 'energetics', 'and', 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1,802.05807 | Optimal Actuator Design for Semi-linear Systems | Actuator location and design are important choices in controller design for
distributed parameter systems. Semi-linear partial differential equations model
a wide spectrum of physical systems with distributed parameters. It is shown
that under certain conditions on the nonlinearity and the cost function, an
optimal control input together with an optimal actuator choice exists.
First-order necessary optimality conditions are derived. The results are
applied to optimal actuator and controller design in a nonlinear railway track
model as well as semi-linear wave models.
| math.OC | actuator location and design are important choices in controller design for distributed parameter systems semilinear partial differential equations model a wide spectrum of physical systems with distributed parameters it is shown that under certain conditions on the nonlinearity and the cost function an optimal control input together with an optimal actuator choice exists firstorder necessary optimality conditions are derived the results are applied to optimal actuator and controller design in a nonlinear railway track model as well as semilinear wave models | [['actuator', 'location', 'and', 'design', 'are', 'important', 'choices', 'in', 'controller', 'design', 'for', 'distributed', 'parameter', 'systems', 'semilinear', 'partial', 'differential', 'equations', 'model', 'a', 'wide', 'spectrum', 'of', 'physical', 'systems', 'with', 'distributed', 'parameters', 'it', 'is', 'shown', 'that', 'under', 'certain', 'conditions', 'on', 'the', 'nonlinearity', 'and', 'the', 'cost', 'function', 'an', 'optimal', 'control', 'input', 'together', 'with', 'an', 'optimal', 'actuator', 'choice', 'exists', 'firstorder', 'necessary', 'optimality', 'conditions', 'are', 'derived', 'the', 'results', 'are', 'applied', 'to', 'optimal', 'actuator', 'and', 'controller', 'design', 'in', 'a', 'nonlinear', 'railway', 'track', 'model', 'as', 'well', 'as', 'semilinear', 'wave', 'models']] | [-0.1960857296348147, 0.025129269691037567, -0.022691344198437383, 0.00657211325597018, -0.11446941033994526, -0.24111373387966994, -0.013630232075229287, 0.39497021346548455, -0.30577885545790195, -0.31143472003348077, 0.17578324522926575, -0.2052273493271643, -0.19676432813759204, 0.24549148743397292, -0.11295782409056469, 0.23651525567941092, 0.044033401648594456, 0.010678490002950033, -0.03063199055786417, -0.1918154787502171, 0.30884966450128243, 0.04889701267727363, 0.2815454200393072, -0.03696906332998755, 0.18195944748542928, -0.004725152543274525, 0.07551237892497469, -0.008205159912230793, -0.15794471152643114, 0.00535604677071082, 0.3209266631343825, 0.11768373023189696, 0.3009872790426016, -0.40802888128400583, -0.21520361362921972, 0.05637447718806841, 0.06659950737347022, 0.08170730307882215, -0.04039743251058009, -0.27015105499253594, 0.08823652946631666, -0.14153028557789915, -0.1314301351890152, -0.057521772832483234, 0.012282285908306087, 0.10996630594111335, -0.37354697314677415, 0.002756579166830138, 0.0174875059466878, 0.02234739524220335, -0.16742474170125746, -0.08872364660334071, -0.048673625796872344, 0.12063564716861296, -0.013007606784985573, -0.022777822404743436, 0.16055024070320306, -0.11914552110074847, -0.09804051716662483, 0.3726914973906529, 0.0014804532912410335, -0.3056760663051664, 0.1391324753280913, -0.020383017080157627, -0.07980775696305949, 0.11794793375273362, 0.2519339761685258, 0.11645857728787778, -0.20794271360392924, 0.07343226499685926, -0.008626750841942317, 0.22631487491782065, 0.021039571840155088, 0.0354386622166652, 0.11681624107568721, 0.2009242068010348, 0.2140478004620951, 0.14310416987131316, 0.02877056606999242, -0.15163223684569935, -0.357547795606984, -0.05266806694828434, -0.07733169126749774, -0.016319690064712497, -0.12320866467798398, -0.15723885520020742, 0.3554977754843824, 0.16586603647684334, 0.15162526733345455, 0.04943679411677115, 0.2952112979931113, 0.20063460271311118, 0.030357252472988618, 0.06596941894702153, 0.22655995491386197, 0.12170340533851197, 0.13840964504368145, -0.24243122480096824, 0.11543201202710654, 0.023430057329896056] |
1,802.05808 | Nearly associative deformation quantization | We study several classes of non-associative algebras as possible candidates
for deformation quantization in the direction of a Poisson bracket that does
not satisfy Jacobi identities. We show that in fact alternative deformation
quantization algebras require the Jacobi identities on the Poisson bracket and,
under very general assumptions, are associative. At the same time, flexible
deformation quantization algebras exist for any Poisson bracket.
| math-ph hep-th math.MP | we study several classes of nonassociative algebras as possible candidates for deformation quantization in the direction of a poisson bracket that does not satisfy jacobi identities we show that in fact alternative deformation quantization algebras require the jacobi identities on the poisson bracket and under very general assumptions are associative at the same time flexible deformation quantization algebras exist for any poisson bracket | [['we', 'study', 'several', 'classes', 'of', 'nonassociative', 'algebras', 'as', 'possible', 'candidates', 'for', 'deformation', 'quantization', 'in', 'the', 'direction', 'of', 'a', 'poisson', 'bracket', 'that', 'does', 'not', 'satisfy', 'jacobi', 'identities', 'we', 'show', 'that', 'in', 'fact', 'alternative', 'deformation', 'quantization', 'algebras', 'require', 'the', 'jacobi', 'identities', 'on', 'the', 'poisson', 'bracket', 'and', 'under', 'very', 'general', 'assumptions', 'are', 'associative', 'at', 'the', 'same', 'time', 'flexible', 'deformation', 'quantization', 'algebras', 'exist', 'for', 'any', 'poisson', 'bracket']] | [-0.199640169554937, 0.07528490500731601, -0.08715242395059221, 0.11492021582902424, -0.1871103893019377, -0.1854737136764304, -0.07360158885814368, 0.39022836417314555, -0.3684868450084376, -0.22117365456171453, 0.06226694488912703, -0.1499620663623015, -0.233539240700858, 0.1807698275125216, -0.1661648360775813, -0.033476583437905424, 0.09787813000499256, 0.12127737344671337, -0.20860583376645717, -0.27960272043174694, 0.4157286139892503, 0.07915019587288419, 0.28146121173446614, -0.05430038473365592, 0.14189748016614762, 0.028403271521840776, -0.014844298000550933, -0.04681509966215503, -0.14990379771569723, 0.023019155709161646, 0.24983590400762976, 0.005800265072297956, 0.15102749820502978, -0.4139673092535564, -0.12033118838296523, 0.12036537053802657, 0.15771157222814738, 0.05309531104470056, -0.04756986415789773, -0.19644133737754255, 0.09704523946025542, -0.18358720068834603, -0.1305352140602375, -0.1198996218542258, 0.04816902192708637, 0.05380823827969531, -0.2665300681551416, 0.08317775619290178, 0.12804469429252166, 0.06660379176693303, -0.15441618569283969, -0.1195579865662795, -0.0014502443077545318, 0.02084943163578236, -0.0415576541027616, -0.044102760360738825, 0.14879868034687307, -0.08487074927146, -0.16320938454379166, 0.381535017715087, 0.06127767054371477, -0.34376311855065445, 0.08877931925512496, -0.12995936665111146, -0.3320132718672828, 0.08631450836251062, 0.014696360713550969, 0.09392997082914152, -0.11948631983619952, 0.17452423812522153, -0.06172622809748328, -0.022956609518991575, 0.18390538944818435, 0.019231229722093318, 0.17075583495436206, 0.021421391206483047, 0.06921701312124257, 0.06324110975459454, -0.004641441373331915, -0.1417224691036926, -0.4195959155876485, -0.19436037045947852, -0.10374207332331155, 0.17122820453253568, -0.1656961230603176, -0.1948735818030342, 0.34408634435385466, 0.14633331521754228, 0.15061483436101486, 0.12817856743340456, 0.18450958083664615, 0.13021494283910012, 0.19765394263797337, 0.030286501087839642, 0.14839064272948438, 0.2433375832107332, 0.07633847875579719, -0.07657737025366505, -0.014375281416707568, 0.14117790034247768] |
1,802.05809 | Two-scale series expansions for travelling wave packets in
one-dimensional periodic media | Starting from the wave equation for a medium with material properties that
vary periodically, we study a system of recurrence relations that describe
propagation of wave packets that oscillate on the microscale (i.e. on lengths
of the order of the period of the medium) and vary slowly on the macroscale
(i.e. on lengths that contain a large number of periods). The resulting
equations contain a version of the geometric optics and the overall energy
transport description for periodic media. We illustrate the developed
asymptotic theory using the example of a point pulse propagating through a
periodic arrangement of two materials with highly contrasting elastic moduli.
| math-ph math.MP | starting from the wave equation for a medium with material properties that vary periodically we study a system of recurrence relations that describe propagation of wave packets that oscillate on the microscale ie on lengths of the order of the period of the medium and vary slowly on the macroscale ie on lengths that contain a large number of periods the resulting equations contain a version of the geometric optics and the overall energy transport description for periodic media we illustrate the developed asymptotic theory using the example of a point pulse propagating through a periodic arrangement of two materials with highly contrasting elastic moduli | [['starting', 'from', 'the', 'wave', 'equation', 'for', 'a', 'medium', 'with', 'material', 'properties', 'that', 'vary', 'periodically', 'we', 'study', 'a', 'system', 'of', 'recurrence', 'relations', 'that', 'describe', 'propagation', 'of', 'wave', 'packets', 'that', 'oscillate', 'on', 'the', 'microscale', 'ie', 'on', 'lengths', 'of', 'the', 'order', 'of', 'the', 'period', 'of', 'the', 'medium', 'and', 'vary', 'slowly', 'on', 'the', 'macroscale', 'ie', 'on', 'lengths', 'that', 'contain', 'a', 'large', 'number', 'of', 'periods', 'the', 'resulting', 'equations', 'contain', 'a', 'version', 'of', 'the', 'geometric', 'optics', 'and', 'the', 'overall', 'energy', 'transport', 'description', 'for', 'periodic', 'media', 'we', 'illustrate', 'the', 'developed', 'asymptotic', 'theory', 'using', 'the', 'example', 'of', 'a', 'point', 'pulse', 'propagating', 'through', 'a', 'periodic', 'arrangement', 'of', 'two', 'materials', 'with', 'highly', 'contrasting', 'elastic', 'moduli']] | [-0.2074796596054165, 0.18177492632752373, -0.10511142581041592, 0.007900610645966871, -0.05690338671917007, -0.07097088627162434, 0.031955690849350675, 0.3546887870877981, -0.2903602209518708, -0.2602627803615871, 0.10442957936653069, -0.29393942634979753, -0.1647820116153785, 0.2281349186208986, 0.037066669019293924, 0.03231510247680403, 0.0680176712528226, 0.03543984746072619, -0.05922843248893817, -0.17504918737381342, 0.3280986900086559, -0.0003704915554927928, 0.26142510922093476, -0.019408088704083292, 0.13269444509143277, 0.05823677039838263, -0.003239742521518132, 0.03058411477222329, -0.14326174980877632, 0.11371042558790317, 0.16698934605256432, 0.024458282600556103, 0.2112042924877079, -0.49150068270308633, -0.25195847854373, 0.027937914404485908, 0.11699817677782405, 0.11695396449594271, -0.03311595375250493, -0.23916596407514243, 0.039749849891467466, -0.12703329129255422, -0.2033968659250864, -0.013677806306319932, 0.03594763461677801, 0.1258850404571387, -0.19400459947951493, 0.06596688337934514, 0.028914059066612804, 0.051751412332634486, -0.0900604347288165, -0.05565926660007487, -0.03773316869822641, 0.09682624165767005, 0.05188807519374504, -0.018286092200183443, 0.09825206350623852, -0.13845814478450588, -0.04404486112651371, 0.3995408155290144, -0.06014027632662051, -0.1940991590420405, 0.17692030716155258, -0.12163585073264715, -0.055227056640155966, 0.18428349578753114, 0.24134362278550509, 0.10286808251181528, -0.11502262735295864, 0.030152073785263512, -0.0678506789249479, 0.2054613605355068, 0.1304247164633125, 0.06618606209577549, 0.20020617054154474, 0.1761341869232378, 0.016486883125755757, 0.12415297568021785, -0.06194182283555468, -0.10636149518901393, -0.3165764050380815, -0.1459250696912585, -0.15963250517046879, 0.07535979009449616, -0.12246532468540994, -0.2258855005460126, 0.4396444120045219, 0.14613853021790937, 0.1888736407494261, 0.04140166905265124, 0.22405859173408577, 0.11536024038990339, 0.02433851771902049, 0.06761180509236597, 0.21998550097812855, 0.14030876443943097, 0.11421223042444104, -0.23702449957574054, 0.04264190946927383, 0.06668183012821136] |
1,802.0581 | Implementation of nearly arbitrary spatially-varying polarization
transformations: a non-diffractive and non-interferometric approach using
spatial light modulators | A fast and automated scheme for general polarization transformations holds
great value in adaptive optics, quantum information, and virtually all
applications involving light-matter and light-light interactions. We present an
experiment that uses a liquid crystal on silicon spatial light modulator
(LCOS-SLM) to perform polarization transformations on a light field. We
experimentally demonstrate the point-by-point conversion of uniformly polarized
light fields across the wave front to realize arbitrary, spatially varying
polarization states. Additionally, we demonstrate that a light field with an
arbitrary spatially varying polarization can be transformed to a spatially
invariant (i.e., uniform) polarization.
| physics.optics | a fast and automated scheme for general polarization transformations holds great value in adaptive optics quantum information and virtually all applications involving lightmatter and lightlight interactions we present an experiment that uses a liquid crystal on silicon spatial light modulator lcosslm to perform polarization transformations on a light field we experimentally demonstrate the pointbypoint conversion of uniformly polarized light fields across the wave front to realize arbitrary spatially varying polarization states additionally we demonstrate that a light field with an arbitrary spatially varying polarization can be transformed to a spatially invariant ie uniform polarization | [['a', 'fast', 'and', 'automated', 'scheme', 'for', 'general', 'polarization', 'transformations', 'holds', 'great', 'value', 'in', 'adaptive', 'optics', 'quantum', 'information', 'and', 'virtually', 'all', 'applications', 'involving', 'lightmatter', 'and', 'lightlight', 'interactions', 'we', 'present', 'an', 'experiment', 'that', 'uses', 'a', 'liquid', 'crystal', 'on', 'silicon', 'spatial', 'light', 'modulator', 'lcosslm', 'to', 'perform', 'polarization', 'transformations', 'on', 'a', 'light', 'field', 'we', 'experimentally', 'demonstrate', 'the', 'pointbypoint', 'conversion', 'of', 'uniformly', 'polarized', 'light', 'fields', 'across', 'the', 'wave', 'front', 'to', 'realize', 'arbitrary', 'spatially', 'varying', 'polarization', 'states', 'additionally', 'we', 'demonstrate', 'that', 'a', 'light', 'field', 'with', 'an', 'arbitrary', 'spatially', 'varying', 'polarization', 'can', 'be', 'transformed', 'to', 'a', 'spatially', 'invariant', 'ie', 'uniform', 'polarization']] | [-0.1467095593050603, 0.2140662484800428, -0.10686097230814819, 0.003588905407776756, -0.06638876876984072, -0.18141632079977982, 0.01768497462254218, 0.5058746783323186, -0.2819290145710912, -0.27397707122958875, 0.04232647915631132, -0.2242150450505877, -0.11872214129975726, 0.2123493398990362, -0.003529695907898087, 0.05936607524004054, -0.00205750110989777, -0.08123963570562742, -0.061659368405979804, -0.1962905702161132, 0.27888411350588327, 0.008345366639625882, 0.34959315922191386, 0.03850665236873332, 0.19319456675520508, 0.09816326496882304, -0.007059129515802989, 0.01109610729280018, -0.04278342102160452, 0.09458066405408004, 0.22286982622037652, 0.04510929441374917, 0.15447610308245946, -0.42322967746244966, -0.23600078722141604, 0.09110438557321666, 0.1208947010999245, 0.20208642704872995, -0.12288537112368841, -0.30124473149177206, -0.030213970525731004, -0.07522821153253717, -0.18580852739352693, -0.1154088784680171, -0.004696076337788855, 0.018675567316559834, -0.31785159491963927, -0.003895481993314079, -0.017935521013143482, 0.05036316670837902, -0.030965208206125486, -0.014020246870425199, -0.0012050339531513952, 0.05770597386083776, -0.0778369430893211, 0.07357600221024846, 0.14230310276491187, -0.10408728186952411, -0.11573361192945071, 0.35677054256040563, -0.12851622477123734, -0.20491666941633147, 0.1287918683862494, -0.16889309276255868, -0.06336772708242298, 0.14597893097708303, 0.2355978116186546, 0.15075592574242863, -0.12791077477697244, 0.034883347593413126, -0.02287620391120248, 0.26095925186390195, 0.0956022971740333, 0.12287850523560298, 0.2565463486738423, 0.12957056478086498, 0.047487257072897325, 0.15576618159144756, -0.10060838972448662, -0.05640993435787017, -0.2857899661426262, -0.164583993625016, -0.14265190052514476, 0.06015949223678113, -0.098315979438735, -0.13958431643143457, 0.4190802378768914, 0.13560357788497562, 0.11410432473646216, -0.012705128777944934, 0.31424406569411034, 0.07700262519891464, 0.04937817180849692, 0.06592025683932407, 0.2678824238584287, 0.16812556525391917, 0.1137914675818656, -0.22612386156794845, -0.009507217419443912, -0.0694979939294318] |
1,802.05811 | Distributed Stochastic Optimization via Adaptive SGD | Stochastic convex optimization algorithms are the most popular way to train
machine learning models on large-scale data. Scaling up the training process of
these models is crucial, but the most popular algorithm, Stochastic Gradient
Descent (SGD), is a serial method that is surprisingly hard to parallelize. In
this paper, we propose an efficient distributed stochastic optimization method
by combining adaptivity with variance reduction techniques. Our analysis yields
a linear speedup in the number of machines, constant memory footprint, and only
a logarithmic number of communication rounds. Critically, our approach is a
black-box reduction that parallelizes any serial online learning algorithm,
streamlining prior analysis and allowing us to leverage the significant
progress that has been made in designing adaptive algorithms. In particular, we
achieve optimal convergence rates without any prior knowledge of smoothness
parameters, yielding a more robust algorithm that reduces the need for
hyperparameter tuning. We implement our algorithm in the Spark distributed
framework and exhibit dramatic performance gains on large-scale logistic
regression problems.
| stat.ML cs.LG | stochastic convex optimization algorithms are the most popular way to train machine learning models on largescale data scaling up the training process of these models is crucial but the most popular algorithm stochastic gradient descent sgd is a serial method that is surprisingly hard to parallelize in this paper we propose an efficient distributed stochastic optimization method by combining adaptivity with variance reduction techniques our analysis yields a linear speedup in the number of machines constant memory footprint and only a logarithmic number of communication rounds critically our approach is a blackbox reduction that parallelizes any serial online learning algorithm streamlining prior analysis and allowing us to leverage the significant progress that has been made in designing adaptive algorithms in particular we achieve optimal convergence rates without any prior knowledge of smoothness parameters yielding a more robust algorithm that reduces the need for hyperparameter tuning we implement our algorithm in the spark distributed framework and exhibit dramatic performance gains on largescale logistic regression problems | [['stochastic', 'convex', 'optimization', 'algorithms', 'are', 'the', 'most', 'popular', 'way', 'to', 'train', 'machine', 'learning', 'models', 'on', 'largescale', 'data', 'scaling', 'up', 'the', 'training', 'process', 'of', 'these', 'models', 'is', 'crucial', 'but', 'the', 'most', 'popular', 'algorithm', 'stochastic', 'gradient', 'descent', 'sgd', 'is', 'a', 'serial', 'method', 'that', 'is', 'surprisingly', 'hard', 'to', 'parallelize', 'in', 'this', 'paper', 'we', 'propose', 'an', 'efficient', 'distributed', 'stochastic', 'optimization', 'method', 'by', 'combining', 'adaptivity', 'with', 'variance', 'reduction', 'techniques', 'our', 'analysis', 'yields', 'a', 'linear', 'speedup', 'in', 'the', 'number', 'of', 'machines', 'constant', 'memory', 'footprint', 'and', 'only', 'a', 'logarithmic', 'number', 'of', 'communication', 'rounds', 'critically', 'our', 'approach', 'is', 'a', 'blackbox', 'reduction', 'that', 'parallelizes', 'any', 'serial', 'online', 'learning', 'algorithm', 'streamlining', 'prior', 'analysis', 'and', 'allowing', 'us', 'to', 'leverage', 'the', 'significant', 'progress', 'that', 'has', 'been', 'made', 'in', 'designing', 'adaptive', 'algorithms', 'in', 'particular', 'we', 'achieve', 'optimal', 'convergence', 'rates', 'without', 'any', 'prior', 'knowledge', 'of', 'smoothness', 'parameters', 'yielding', 'a', 'more', 'robust', 'algorithm', 'that', 'reduces', 'the', 'need', 'for', 'hyperparameter', 'tuning', 'we', 'implement', 'our', 'algorithm', 'in', 'the', 'spark', 'distributed', 'framework', 'and', 'exhibit', 'dramatic', 'performance', 'gains', 'on', 'largescale', 'logistic', 'regression', 'problems']] | [-0.08205163445417406, -0.03438312115874241, -0.10280520867033885, 0.05788218200307422, -0.1288730098593344, -0.2159715568243594, 0.06840163593485003, 0.4717579809494498, -0.2928672109733381, -0.34736716839280435, 0.10019350698055904, -0.1933906296425692, -0.19204603179028576, 0.2401455817516984, -0.12224659598404088, 0.1435508240630464, 0.1304520226662933, -0.0297215861813506, -0.07307869506568261, -0.3626407090469445, 0.2231703766198579, 0.09049378563763529, 0.3264055460174682, -0.02321525144805314, 0.12471943016649542, -0.009662015627992975, -0.030456093717054124, 0.011225803558595433, -0.061769266351070766, 0.16804014127587946, 0.28072587070013605, 0.21027211052019196, 0.40984319627466725, -0.41111387859848214, -0.19033569364990222, 0.15630667113199284, 0.16062547661958224, 0.13260690712869713, -0.07146776129433774, -0.19485611302306785, 0.08266808831263561, -0.1427677775812676, -0.012696652974161069, -0.18265555482272558, -0.058892679312720714, 0.006904465904482073, -0.3297367351808977, 0.05316649098233206, 0.08846793550907112, 0.02693701054469296, 0.00991675919120586, -0.12010457470342971, 0.09410600897873093, 0.06963117084772576, 0.043968635656489, 0.06527343818423816, 0.15470291899017444, -0.11653055265125614, -0.20145994264393954, 0.3177022152131696, -0.04759191872449819, -0.20139602918274382, 0.17699161425116472, 0.012826664557839493, -0.17905819237448004, 0.1528028704754145, 0.2813009261295608, 0.13159964461640522, -0.1626328884953315, 0.1062322470487987, -0.012137507372422189, 0.20047195709208254, 0.011717190483312418, -0.026687279397553606, 0.058964009483333496, 0.2615488192035317, 0.1394127412392508, 0.1249040620083489, -0.06304032040373762, -0.1400677448929367, -0.21618760883575305, -0.12211809107514167, -0.17657381013921666, -0.016452689219987382, -0.19253485167667728, -0.1719525502426777, 0.3459168341317464, 0.2185442521259552, 0.17058480102512077, 0.13892956546553206, 0.3929906488941392, 0.07090044323647418, 0.10991432123090618, 0.19180786045177317, 0.18142065901107654, 0.07438933525785305, 0.13417874956957815, -0.21954677050305735, 0.13461237416102936, 0.031215784772229666] |
1,802.05812 | Minimal dissipation model for bipartite quantum systems at finite
temperature | We consider the reduced dynamics in a bipartite quantum system (consisting of
a central system and an intermediate environment) coupled to a heat bath at
finite temperature. To describe this situation, in the simplest possible -- yet
physically meaningful way, we introduce the "depolarizing heat bath" as a new
minimal dissipation model. We conjecture that at sufficiently strong
dissipation, any other dissipation model implemented in the form of a Markovian
quantum master equation will yield the same reduced dynamics of the central
system, as the minimal model.
To support this conjecture, we study a two-level system coupled to an
oscillator mode. For the coupling between the two parts, we consider the
Jaynes-Cummings or a dephasing coupling, while the coupling to the heat bath is
modeled by the quantum optical or the Caldeira-Leggett master equation
(neglecting any direct coupling between central system and heat bath). We then
provide ample numerical evidence, for both, model-independence and accuracy of
the depolarizing heat bath model. Alongside with our study, we investigate
different regimes, where the strong coupling condition leads to coherence
and/or population stabilization.
| quant-ph | we consider the reduced dynamics in a bipartite quantum system consisting of a central system and an intermediate environment coupled to a heat bath at finite temperature to describe this situation in the simplest possible yet physically meaningful way we introduce the depolarizing heat bath as a new minimal dissipation model we conjecture that at sufficiently strong dissipation any other dissipation model implemented in the form of a markovian quantum master equation will yield the same reduced dynamics of the central system as the minimal model to support this conjecture we study a twolevel system coupled to an oscillator mode for the coupling between the two parts we consider the jaynescummings or a dephasing coupling while the coupling to the heat bath is modeled by the quantum optical or the caldeiraleggett master equation neglecting any direct coupling between central system and heat bath we then provide ample numerical evidence for both modelindependence and accuracy of the depolarizing heat bath model alongside with our study we investigate different regimes where the strong coupling condition leads to coherence andor population stabilization | [['we', 'consider', 'the', 'reduced', 'dynamics', 'in', 'a', 'bipartite', 'quantum', 'system', 'consisting', 'of', 'a', 'central', 'system', 'and', 'an', 'intermediate', 'environment', 'coupled', 'to', 'a', 'heat', 'bath', 'at', 'finite', 'temperature', 'to', 'describe', 'this', 'situation', 'in', 'the', 'simplest', 'possible', 'yet', 'physically', 'meaningful', 'way', 'we', 'introduce', 'the', 'depolarizing', 'heat', 'bath', 'as', 'a', 'new', 'minimal', 'dissipation', 'model', 'we', 'conjecture', 'that', 'at', 'sufficiently', 'strong', 'dissipation', 'any', 'other', 'dissipation', 'model', 'implemented', 'in', 'the', 'form', 'of', 'a', 'markovian', 'quantum', 'master', 'equation', 'will', 'yield', 'the', 'same', 'reduced', 'dynamics', 'of', 'the', 'central', 'system', 'as', 'the', 'minimal', 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1,802.05813 | Chain Posets | A chain poset, by definition, consists of chains of ordered elements in a
poset. We study the chain posets associated to two posets: the Boolean algebra
and the poset of isotropic flags. We prove that, in both cases, the chain
posets satisfy the strong Sperner property and are rank-log concave.
| math.CO | a chain poset by definition consists of chains of ordered elements in a poset we study the chain posets associated to two posets the boolean algebra and the poset of isotropic flags we prove that in both cases the chain posets satisfy the strong sperner property and are ranklog concave | [['a', 'chain', 'poset', 'by', 'definition', 'consists', 'of', 'chains', 'of', 'ordered', 'elements', 'in', 'a', 'poset', 'we', 'study', 'the', 'chain', 'posets', 'associated', 'to', 'two', 'posets', 'the', 'boolean', 'algebra', 'and', 'the', 'poset', 'of', 'isotropic', 'flags', 'we', 'prove', 'that', 'in', 'both', 'cases', 'the', 'chain', 'posets', 'satisfy', 'the', 'strong', 'sperner', 'property', 'and', 'are', 'ranklog', 'concave']] | [-0.21549810870189448, 0.13611680470711116, 0.04648612844472637, 0.10594351008078273, -0.1098189754601644, -0.12755906587580637, 0.00941315806489818, 0.42284240497618303, -0.41941312511394524, -0.14408351339361802, 0.11696280759036998, -0.29780345273261166, -0.08761328709672908, 0.08892135896092775, -0.10140597352720036, -0.01639674548819965, 0.0922055141620186, 0.050987876229444326, -0.06311332900552269, -0.2484961519830347, 0.40482164668489473, -0.046918464938596804, 0.26766560701368264, 0.009508232034894884, 0.10700440945635949, 0.06418101428722849, 0.04633250752730029, 0.09366292561363869, -0.22872300944555365, 0.12192155481601248, 0.26917913924826653, 0.0950897161288149, 0.19675179107152685, -0.35612811733569416, -0.0346089883194286, 0.21605701782569595, 0.11643747275467126, 0.04190135270129053, 0.04586378425568798, -0.2382497036810584, 0.08454142156418185, -0.21756757594340917, -0.15265126852318645, -0.031120214962913673, 0.03961893307919405, 0.11533049982022114, -0.2811440447801534, -0.06774260666297406, 0.2383683977686629, 0.14133713261357375, -0.001678116838162651, -0.1332990530669233, -0.11239168369116223, 0.020758544836117297, -0.06530035572240547, -0.04033044320341123, 0.03906876002723465, -0.03624993279500275, -0.21428480017597654, 0.3407106581049002, 0.036701158069226206, -0.20683129899659936, 0.09470130515531922, -0.20366333904961237, -0.2431744159085258, 0.15534582724604679, 0.04810639328266285, 0.07156350132914222, -0.06122237014375171, 0.16674243490335208, -0.18545981713247542, 0.07160182856023312, 0.11202760769662504, 0.0312963202310612, 0.16079454473694976, 0.14405955011215138, 0.09623885060403953, 0.2969523275993308, 0.07060471070545478, -0.0911403089305576, -0.2757326392456889, -0.17763309469636607, -0.13208970100124728, 0.00633134160723005, -0.12675994753360045, -0.24296356410700448, 0.36397299804363625, 0.11994880751999702, 0.15287025497123904, 0.15595766244342132, 0.1508903844966268, 0.01008443792863768, 0.07501883276415114, -0.032536979688673605, 0.049858304272804944, 0.2689168460106439, -0.07460751601171736, -0.14664328638084081, 0.0821934918283808, 0.24589800188431934] |
1,802.05814 | Variational Autoencoders for Collaborative Filtering | We extend variational autoencoders (VAEs) to collaborative filtering for
implicit feedback. This non-linear probabilistic model enables us to go beyond
the limited modeling capacity of linear factor models which still largely
dominate collaborative filtering research.We introduce a generative model with
multinomial likelihood and use Bayesian inference for parameter estimation.
Despite widespread use in language modeling and economics, the multinomial
likelihood receives less attention in the recommender systems literature. We
introduce a different regularization parameter for the learning objective,
which proves to be crucial for achieving competitive performance. Remarkably,
there is an efficient way to tune the parameter using annealing. The resulting
model and learning algorithm has information-theoretic connections to maximum
entropy discrimination and the information bottleneck principle. Empirically,
we show that the proposed approach significantly outperforms several
state-of-the-art baselines, including two recently-proposed neural network
approaches, on several real-world datasets. We also provide extended
experiments comparing the multinomial likelihood with other commonly used
likelihood functions in the latent factor collaborative filtering literature
and show favorable results. Finally, we identify the pros and cons of employing
a principled Bayesian inference approach and characterize settings where it
provides the most significant improvements.
| stat.ML cs.IR cs.LG | we extend variational autoencoders vaes to collaborative filtering for implicit feedback this nonlinear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering researchwe introduce a generative model with multinomial likelihood and use bayesian inference for parameter estimation despite widespread use in language modeling and economics the multinomial likelihood receives less attention in the recommender systems literature we introduce a different regularization parameter for the learning objective which proves to be crucial for achieving competitive performance remarkably there is an efficient way to tune the parameter using annealing the resulting model and learning algorithm has informationtheoretic connections to maximum entropy discrimination and the information bottleneck principle empirically we show that the proposed approach significantly outperforms several stateoftheart baselines including two recentlyproposed neural network approaches on several realworld datasets we also provide extended experiments comparing the multinomial likelihood with other commonly used likelihood functions in the latent factor collaborative filtering literature and show favorable results finally we identify the pros and cons of employing a principled bayesian inference approach and characterize settings where it provides the most significant improvements | [['we', 'extend', 'variational', 'autoencoders', 'vaes', 'to', 'collaborative', 'filtering', 'for', 'implicit', 'feedback', 'this', 'nonlinear', 'probabilistic', 'model', 'enables', 'us', 'to', 'go', 'beyond', 'the', 'limited', 'modeling', 'capacity', 'of', 'linear', 'factor', 'models', 'which', 'still', 'largely', 'dominate', 'collaborative', 'filtering', 'researchwe', 'introduce', 'a', 'generative', 'model', 'with', 'multinomial', 'likelihood', 'and', 'use', 'bayesian', 'inference', 'for', 'parameter', 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1,802.05815 | Robust Eco-Driving Control of Autonomous Vehicles Connected to Traffic
Lights | This paper focuses on the speed planning problem for connected and automated
vehicles (CAVs) communicating to traffic lights. The uncertainty of traffic
signal timing for signalized intersections on the road is considered. The
eco-driving problem is formulated as a data-driven chance constrained robust
optimization problem. Effective red light duration (ERD) is defined as a random
variable, and describes the feasible passing time through the signalized
intersections. In practice, the true probability distribution for ERD is
usually unknown. Consequently, a data-driven approach is adopted to formulate
chance constraints based on empirical sample data. This incorporates robustness
into the eco-driving control problem with respect to uncertain signal timing.
Dynamic programming (DP) is employed to solve the optimization problem.
Simulation results demonstrate that the proposed method can generate optimal
speed reference trajectories with 40% less vehicle fuel consumption, while
maintaining the arrival time at a similar level when compared to a modified
intelligent driver model (IDM). The proposed control approach significantly
improves the controller robustness in the face of uncertain signal timing,
without requiring to know the distribution of the random variable a priori.
| math.OC | this paper focuses on the speed planning problem for connected and automated vehicles cavs communicating to traffic lights the uncertainty of traffic signal timing for signalized intersections on the road is considered the ecodriving problem is formulated as a datadriven chance constrained robust optimization problem effective red light duration erd is defined as a random variable and describes the feasible passing time through the signalized intersections in practice the true probability distribution for erd is usually unknown consequently a datadriven approach is adopted to formulate chance constraints based on empirical sample data this incorporates robustness into the ecodriving control problem with respect to uncertain signal timing dynamic programming dp is employed to solve the optimization problem simulation results demonstrate that the proposed method can generate optimal speed reference trajectories with 40 less vehicle fuel consumption while maintaining the arrival time at a similar level when compared to a modified intelligent driver model idm the proposed control approach significantly improves the controller robustness in the face of uncertain signal timing without requiring to know the distribution of the random variable a priori | [['this', 'paper', 'focuses', 'on', 'the', 'speed', 'planning', 'problem', 'for', 'connected', 'and', 'automated', 'vehicles', 'cavs', 'communicating', 'to', 'traffic', 'lights', 'the', 'uncertainty', 'of', 'traffic', 'signal', 'timing', 'for', 'signalized', 'intersections', 'on', 'the', 'road', 'is', 'considered', 'the', 'ecodriving', 'problem', 'is', 'formulated', 'as', 'a', 'datadriven', 'chance', 'constrained', 'robust', 'optimization', 'problem', 'effective', 'red', 'light', 'duration', 'erd', 'is', 'defined', 'as', 'a', 'random', 'variable', 'and', 'describes', 'the', 'feasible', 'passing', 'time', 'through', 'the', 'signalized', 'intersections', 'in', 'practice', 'the', 'true', 'probability', 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1,802.05816 | ISEC: Iterative over-Segmentation via Edge Clustering | Several image pattern recognition tasks rely on superpixel generation as a
fundamental step. Image analysis based on superpixels facilitates
domain-specific applications, also speeding up the overall processing time of
the task. Recent superpixel methods have been designed to fit boundary
adherence, usually regulating the size and shape of each superpixel in order to
mitigate the occurrence of undersegmentation failures. Superpixel regularity
and compactness sometimes imposes an excessive number of segments in the image,
which ultimately decreases the efficiency of the final segmentation, specially
in video segmentation. We propose here a novel method to generate superpixels,
called iterative over-segmentation via edge clustering (ISEC), which addresses
the over-segmentation problem from a different perspective in contrast to
recent state-of-the-art approaches. ISEC iteratively clusters edges extracted
from the image objects, providing adaptive superpixels in size, shape and
quantity, while preserving suitable adherence to the real object boundaries.
All this is achieved at a very low computational cost. Experiments show that
ISEC stands out from existing methods, meeting a favorable balance between
segmentation stability and accurate representation of motion discontinuities,
which are features specially suitable to video segmentation.
| cs.CV | several image pattern recognition tasks rely on superpixel generation as a fundamental step image analysis based on superpixels facilitates domainspecific applications also speeding up the overall processing time of the task recent superpixel methods have been designed to fit boundary adherence usually regulating the size and shape of each superpixel in order to mitigate the occurrence of undersegmentation failures superpixel regularity and compactness sometimes imposes an excessive number of segments in the image which ultimately decreases the efficiency of the final segmentation specially in video segmentation we propose here a novel method to generate superpixels called iterative oversegmentation via edge clustering isec which addresses the oversegmentation problem from a different perspective in contrast to recent stateoftheart approaches isec iteratively clusters edges extracted from the image objects providing adaptive superpixels in size shape and quantity while preserving suitable adherence to the real object boundaries all this is achieved at a very low computational cost experiments show that isec stands out from existing methods meeting a favorable balance between segmentation stability and accurate representation of motion discontinuities which are features specially suitable to video segmentation | [['several', 'image', 'pattern', 'recognition', 'tasks', 'rely', 'on', 'superpixel', 'generation', 'as', 'a', 'fundamental', 'step', 'image', 'analysis', 'based', 'on', 'superpixels', 'facilitates', 'domainspecific', 'applications', 'also', 'speeding', 'up', 'the', 'overall', 'processing', 'time', 'of', 'the', 'task', 'recent', 'superpixel', 'methods', 'have', 'been', 'designed', 'to', 'fit', 'boundary', 'adherence', 'usually', 'regulating', 'the', 'size', 'and', 'shape', 'of', 'each', 'superpixel', 'in', 'order', 'to', 'mitigate', 'the', 'occurrence', 'of', 'undersegmentation', 'failures', 'superpixel', 'regularity', 'and', 'compactness', 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1,802.05817 | Role of typical elements in Nd$_{2}$Fe$_{14}$$X$ ($X$ = B, C, N, O, F) | The magnetic properties and structural stability of Nd$_{2}$Fe$_{14}X$ ($X$ =
B, C, N, O, F) are theoretically studied by first-principles calculations
focusing on the role of $X$. We find that B reduces the magnetic moment (per
formula unit) and magnetization (per volume) in Nd$_{2}$Fe$_{14}$B. The
crystal-field parameter $A_2^0 \langle r^2 \rangle$ of Nd is not enhanced
either, suggesting that B has minor roles in the uniaxial magnetocrystalline
anisotropy of Nd. These findings are in contrast to the long-held belief that B
works positively for the magnetic properties of Nd$_{2}$Fe$_{14}$B. As $X$
changes from B to C, N, O and F, both the magnetic properties and stability
vary significantly. The formation energies of Nd$_{2}$Fe$_{14}X$ and
$\alpha$-Fe relative to that of Nd$_{2}$Fe$_{17}X$ are negative for $X$ = B and
C, whereas they are positive when $X$ = N, O and F. This indicates that B plays
an important role in stabilizing the Nd$_{2}$Fe$_{14}$B phase.
| cond-mat.mtrl-sci physics.app-ph physics.comp-ph | the magnetic properties and structural stability of nd_2fe_14x x b c n o f are theoretically studied by firstprinciples calculations focusing on the role of x we find that b reduces the magnetic moment per formula unit and magnetization per volume in nd_2fe_14b the crystalfield parameter a_20 langle r2 rangle of nd is not enhanced either suggesting that b has minor roles in the uniaxial magnetocrystalline anisotropy of nd these findings are in contrast to the longheld belief that b works positively for the magnetic properties of nd_2fe_14b as x changes from b to c n o and f both the magnetic properties and stability vary significantly the formation energies of nd_2fe_14x and alphafe relative to that of nd_2fe_17x are negative for x b and c whereas they are positive when x n o and f this indicates that b plays an important role in stabilizing the nd_2fe_14b phase | [['the', 'magnetic', 'properties', 'and', 'structural', 'stability', 'of', 'nd_2fe_14x', 'x', 'b', 'c', 'n', 'o', 'f', 'are', 'theoretically', 'studied', 'by', 'firstprinciples', 'calculations', 'focusing', 'on', 'the', 'role', 'of', 'x', 'we', 'find', 'that', 'b', 'reduces', 'the', 'magnetic', 'moment', 'per', 'formula', 'unit', 'and', 'magnetization', 'per', 'volume', 'in', 'nd_2fe_14b', 'the', 'crystalfield', 'parameter', 'a_20', 'langle', 'r2', 'rangle', 'of', 'nd', 'is', 'not', 'enhanced', 'either', 'suggesting', 'that', 'b', 'has', 'minor', 'roles', 'in', 'the', 'uniaxial', 'magnetocrystalline', 'anisotropy', 'of', 'nd', 'these', 'findings', 'are', 'in', 'contrast', 'to', 'the', 'longheld', 'belief', 'that', 'b', 'works', 'positively', 'for', 'the', 'magnetic', 'properties', 'of', 'nd_2fe_14b', 'as', 'x', 'changes', 'from', 'b', 'to', 'c', 'n', 'o', 'and', 'f', 'both', 'the', 'magnetic', 'properties', 'and', 'stability', 'vary', 'significantly', 'the', 'formation', 'energies', 'of', 'nd_2fe_14x', 'and', 'alphafe', 'relative', 'to', 'that', 'of', 'nd_2fe_17x', 'are', 'negative', 'for', 'x', 'b', 'and', 'c', 'whereas', 'they', 'are', 'positive', 'when', 'x', 'n', 'o', 'and', 'f', 'this', 'indicates', 'that', 'b', 'plays', 'an', 'important', 'role', 'in', 'stabilizing', 'the', 'nd_2fe_14b', 'phase']] | [-0.18050434232421525, 0.18417766652548803, 0.02835502684409794, -0.005344794514550738, 0.009326081034848594, -0.12772621688981578, 0.060376986565406444, 0.4095481153807207, -0.2632070851021076, -0.2998841929772537, -0.02031495331703647, -0.3075026859057276, -0.08783886728852937, 0.14871748830734, 0.025576178871146212, -0.05078742383622041, -0.02820103441537294, 0.051709497822703766, -0.08509346640793199, -0.2606037997334481, 0.2510229706483549, 0.032376461566668256, 0.22675635720872678, 0.06995095950487541, -0.02089245964721968, 0.00289428766979517, 0.06542780379803009, 0.0609234327016628, -0.1574807422471728, 0.08625672057504034, 0.1879346164275114, 0.05733415175369647, 0.22788050220896527, -0.3717897932096193, -0.14980190903688334, 0.0706193451195547, 0.13678425977210681, -0.001962798596551157, -0.05347837481331973, -0.20024346365044787, 0.1321906967101338, -0.07324848161716882, -0.10978680108801449, -0.09504715983487971, 0.12590088081316486, 0.05503651177174131, -0.308279048271911, 0.10053082205469191, 0.13828455817381163, 0.0945960015695114, -0.07741632942295289, -0.2100443459396595, -0.15368039980977263, 0.06001750153608218, 0.06777995577630626, 0.14790358621553734, 0.1465026919143147, -0.09579028865507459, -0.04444764274784861, 0.37558921991547645, -0.08149895132264148, -0.1245704299247224, 0.13045469145226765, -0.22096820592988692, -0.15168999713105596, 0.12482624058292745, 0.12703543192745276, 0.15280008395457614, -0.04568803810392114, 0.18667106222311888, -0.016328878722461387, 0.17253187856930371, 0.03468169248981835, 0.03619388704449704, 0.18459749473609693, 0.06760589576052017, 0.024066345629039896, 0.06385285285746437, -0.1174547042018313, -0.025241401201203363, -0.2816635373890502, -0.19434167857103254, -0.14586864757887408, 0.09162990951423026, -0.10468088291286035, -0.15720067351214487, 0.2851758459072611, 0.10579898616630737, 0.231805060695927, -0.06504200409128241, 0.1867630377225259, 0.05427025341626563, 0.028642371565196025, 0.0614580076844878, 0.21361485016468454, 0.19350529869074282, 0.09430302410029283, -0.29949094374083646, 0.1400008805555432, 0.044315828386796256] |
1,802.05818 | Disentangling Aspect and Opinion Words in Target-based Sentiment
Analysis using Lifelong Learning | Given a target name, which can be a product aspect or entity, identifying its
aspect words and opinion words in a given corpus is a fine-grained task in
target-based sentiment analysis (TSA). This task is challenging, especially
when we have no labeled data and we want to perform it for any given domain. To
address it, we propose a general two-stage approach. Stage one extracts/groups
the target-related words (call t-words) for a given target. This is relatively
easy as we can apply an existing semantics-based learning technique. Stage two
separates the aspect and opinion words from the grouped t-words, which is
challenging because we often do not have enough word-level aspect and opinion
labels. In this work, we formulate this problem in a PU learning setting and
incorporate the idea of lifelong learning to solve it. Experimental results
show the effectiveness of our approach.
| cs.CL cs.AI | given a target name which can be a product aspect or entity identifying its aspect words and opinion words in a given corpus is a finegrained task in targetbased sentiment analysis tsa this task is challenging especially when we have no labeled data and we want to perform it for any given domain to address it we propose a general twostage approach stage one extractsgroups the targetrelated words call twords for a given target this is relatively easy as we can apply an existing semanticsbased learning technique stage two separates the aspect and opinion words from the grouped twords which is challenging because we often do not have enough wordlevel aspect and opinion labels in this work we formulate this problem in a pu learning setting and incorporate the idea of lifelong learning to solve it experimental results show the effectiveness of our approach | [['given', 'a', 'target', 'name', 'which', 'can', 'be', 'a', 'product', 'aspect', 'or', 'entity', 'identifying', 'its', 'aspect', 'words', 'and', 'opinion', 'words', 'in', 'a', 'given', 'corpus', 'is', 'a', 'finegrained', 'task', 'in', 'targetbased', 'sentiment', 'analysis', 'tsa', 'this', 'task', 'is', 'challenging', 'especially', 'when', 'we', 'have', 'no', 'labeled', 'data', 'and', 'we', 'want', 'to', 'perform', 'it', 'for', 'any', 'given', 'domain', 'to', 'address', 'it', 'we', 'propose', 'a', 'general', 'twostage', 'approach', 'stage', 'one', 'extractsgroups', 'the', 'targetrelated', 'words', 'call', 'twords', 'for', 'a', 'given', 'target', 'this', 'is', 'relatively', 'easy', 'as', 'we', 'can', 'apply', 'an', 'existing', 'semanticsbased', 'learning', 'technique', 'stage', 'two', 'separates', 'the', 'aspect', 'and', 'opinion', 'words', 'from', 'the', 'grouped', 'twords', 'which', 'is', 'challenging', 'because', 'we', 'often', 'do', 'not', 'have', 'enough', 'wordlevel', 'aspect', 'and', 'opinion', 'labels', 'in', 'this', 'work', 'we', 'formulate', 'this', 'problem', 'in', 'a', 'pu', 'learning', 'setting', 'and', 'incorporate', 'the', 'idea', 'of', 'lifelong', 'learning', 'to', 'solve', 'it', 'experimental', 'results', 'show', 'the', 'effectiveness', 'of', 'our', 'approach']] | [-0.05646048136491408, 0.024846845536913017, -0.08901232944022044, 0.09700314990568457, -0.17961557806760126, -0.18062060853426762, 0.08413371440941296, 0.4406114082704199, -0.27455209452883467, -0.3071883323879794, 0.0929586636860267, -0.25650379931924083, -0.17227894964244825, 0.14034658216735915, -0.15049553860031756, 0.024839835679439287, 0.14064136989982734, 0.10766619111655663, -0.0023977148341649073, -0.2746823779736639, 0.343758401595525, -0.0027186749354401168, 0.314521579251869, 0.05277723466964267, 0.13158771596891897, -0.011967118832791317, -0.034581588138661065, 0.016931231169968, -0.0652060408049415, 0.1521900880789543, 0.37922258593194874, 0.22568095757804335, 0.36307884007692337, -0.3588067500039618, -0.17411360304128934, 0.1078855277745859, 0.1531177125210669, 0.1615826773427506, -0.0281176738191647, -0.2803548859475273, 0.0994874921376348, -0.20253742551629214, 0.003471075563571013, -0.10466867258792069, 0.0018194864160572806, -0.05963510105465638, -0.28977776528831495, 0.01694755818272102, 0.13420514764919786, 0.03556463469136903, -0.04809792870440989, -0.08097708044847778, 0.0992663025720613, 0.1751108613768873, 0.06884840065415235, 0.06369504062062566, 0.10088707788200085, -0.14124735240025302, -0.11778671327146127, 0.41018137712269387, -0.0029232101977294217, -0.2710906288005306, 0.1924544393352451, -0.04661246129238965, -0.20128522283589165, 0.031068147444133217, 0.20603227038481373, 0.1634130517304842, -0.14476693085948392, 0.014542158187370325, -0.0856841663228916, 0.23593858167436635, 0.04822342924720852, -0.054682791396952396, 0.188925534929314, 0.25495708629270974, 0.032252530387358355, 0.15613850466956564, -0.09460659751813885, -0.05456796737060823, -0.20223380191291265, -0.1485789221899703, -0.1927674493012646, -0.009699565762920628, -0.03965742847044599, -0.15377376712066063, 0.37563507659162615, 0.22379640165505046, 0.23871155678466333, 0.07421811433106079, 0.31151840295345384, 0.06448678420988697, 0.058056706908168866, 0.08226434244148446, 0.14317411282066758, -0.00788073550830496, 0.14505659013405336, -0.1453081556308539, 0.12022235284143305, 0.06352063992179613] |
1,802.05819 | Nuclear Structure of Two-Proton Halo-Nucleus 17Ne | Theoretical investigation of two-proton halo-nucleus 17Ne has revealed that
the valence protons are more likely to be positioned in the d-state than the
s-state. In this study, this finding is clarified by calculation of the binding
energy, it is found that the theoretical values for the d-state are closer to
the experimental values, in contrast with those obtained for the s-state. The
three-body model and MATLAB software are utilised to obtain theoretical values
for the three-body-model 17Ne binding energy and matter radius. 17Ne has halo
properties of a weakly bound valence proton, a binding energy of less than 1
MeV, and a large matter radius. The core deformation parameter has zero and
negative values; thus, the 17Ne core exhibits both spherical and oblate shapes
depending on the binding energy of the three-body system. This suggests 17Ne
has two-proton halo.
| nucl-th | theoretical investigation of twoproton halonucleus 17ne has revealed that the valence protons are more likely to be positioned in the dstate than the sstate in this study this finding is clarified by calculation of the binding energy it is found that the theoretical values for the dstate are closer to the experimental values in contrast with those obtained for the sstate the threebody model and matlab software are utilised to obtain theoretical values for the threebodymodel 17ne binding energy and matter radius 17ne has halo properties of a weakly bound valence proton a binding energy of less than 1 mev and a large matter radius the core deformation parameter has zero and negative values thus the 17ne core exhibits both spherical and oblate shapes depending on the binding energy of the threebody system this suggests 17ne has twoproton halo | [['theoretical', 'investigation', 'of', 'twoproton', 'halonucleus', '17ne', 'has', 'revealed', 'that', 'the', 'valence', 'protons', 'are', 'more', 'likely', 'to', 'be', 'positioned', 'in', 'the', 'dstate', 'than', 'the', 'sstate', 'in', 'this', 'study', 'this', 'finding', 'is', 'clarified', 'by', 'calculation', 'of', 'the', 'binding', 'energy', 'it', 'is', 'found', 'that', 'the', 'theoretical', 'values', 'for', 'the', 'dstate', 'are', 'closer', 'to', 'the', 'experimental', 'values', 'in', 'contrast', 'with', 'those', 'obtained', 'for', 'the', 'sstate', 'the', 'threebody', 'model', 'and', 'matlab', 'software', 'are', 'utilised', 'to', 'obtain', 'theoretical', 'values', 'for', 'the', 'threebodymodel', '17ne', 'binding', 'energy', 'and', 'matter', 'radius', '17ne', 'has', 'halo', 'properties', 'of', 'a', 'weakly', 'bound', 'valence', 'proton', 'a', 'binding', 'energy', 'of', 'less', 'than', '1', 'mev', 'and', 'a', 'large', 'matter', 'radius', 'the', 'core', 'deformation', 'parameter', 'has', 'zero', 'and', 'negative', 'values', 'thus', 'the', '17ne', 'core', 'exhibits', 'both', 'spherical', 'and', 'oblate', 'shapes', 'depending', 'on', 'the', 'binding', 'energy', 'of', 'the', 'threebody', 'system', 'this', 'suggests', '17ne', 'has', 'twoproton', 'halo']] | [-0.07015045962626128, 0.1739817433973543, -0.09256297125596635, 0.11621258892781733, -0.005993941638416468, -0.11866488820749138, 0.022045036764258015, 0.3744112161280465, -0.148223059659783, -0.330990820620073, -0.0660705689984938, -0.31178520314300256, -0.01322202973176528, 0.16158569281764437, 0.08581059947771354, 0.03295173255402092, 0.0901899574779953, 0.07701631475335163, -0.0808687562566509, -0.1420471216687919, 0.3058207830427772, 0.12119817926147342, 0.21421193937859395, 0.15411745150825076, -0.030917743530233195, -0.017864554782268883, 0.06111666939500039, -0.04017448074517459, -0.1831759017148441, 0.13713398148869946, 0.21877153210994535, 0.048999313431635605, 0.22726797004209925, -0.3771685987207903, -0.18315968743799912, 0.09322905135986796, 0.15808776130451122, 0.11891416233733133, -0.056001468303797756, -0.2725681121950548, 0.06393461658816485, -0.20858044939516723, -0.16434208742159345, -0.06346238437822482, 0.08074548333053932, 0.04136406456079524, -0.23971173924523115, 0.12124187477531224, 0.013269144273533004, 0.008926757985222949, -0.14202907019002486, -0.23583766292138228, -0.06913963459894387, 0.055730372351206786, 0.09670253848082583, 0.05027001870653327, 0.17350989388451524, -0.10941840793740304, -0.01918411517964445, 0.4130611223556156, 0.020986756422670803, -0.14656606632672306, 0.15124000082077987, -0.1677356833612451, -0.10371582036375673, 0.17330663902583077, 0.10464096765958425, 0.08631845664090212, -0.11292844187522674, 0.07615283307114644, -0.033322683866917543, 0.22531992549171848, 0.07666892598274361, 0.011356525136715305, 0.20892806960878227, 0.1830698200671451, 0.04086071097829046, 0.10874710006921477, -0.13149224905752624, -0.1295286811306991, -0.24509368038797466, -0.1014753680588698, -0.19542827274324032, 0.02006198283978296, -0.05142089279560138, -0.1444011466903952, 0.37473725333102864, 0.041373214942749835, 0.21807621157367843, 0.025375668047825352, 0.27974795664313934, 0.1227141047970657, 0.07656699259948992, 0.05736359139482906, 0.3375656150322217, 0.1662310326393748, 0.041471959122313856, -0.27470418387670476, 0.07447562003712149, 0.005255893804132938] |
1,802.0582 | Phonemic evidence reveals interwoven evolution of Chinese dialects | Han Chinese experienced substantial population migrations and admixture in
history, yet little is known about the evolutionary process of Chinese
dialects. Here, we used phylogenetic approaches and admixture inference to
explicitly decompose the underlying structure of the diversity of Chinese
dialects, based on the total phoneme inventories of 140 dialect samples from
seven traditional dialect groups: Mandarin, Wu, Xiang, Gan, Hakka, Min and Yue.
We found a north-south gradient of phonemic differences in Chinese dialects
induced from historical population migrations. We also quantified extensive
horizontal language transfers among these dialects, corresponding to the
complicated socio-genetic history in China. We finally identified that the
middle latitude dialects of Xiang, Gan and Hakka were formed by admixture with
other four dialects. Accordingly, the middle-latitude areas in China were a
linguistic melting pot of northern and southern Han populations. Our study
provides a detailed phylogenetic and historical context against family-tree
model in China.
| q-bio.PE | han chinese experienced substantial population migrations and admixture in history yet little is known about the evolutionary process of chinese dialects here we used phylogenetic approaches and admixture inference to explicitly decompose the underlying structure of the diversity of chinese dialects based on the total phoneme inventories of 140 dialect samples from seven traditional dialect groups mandarin wu xiang gan hakka min and yue we found a northsouth gradient of phonemic differences in chinese dialects induced from historical population migrations we also quantified extensive horizontal language transfers among these dialects corresponding to the complicated sociogenetic history in china we finally identified that the middle latitude dialects of xiang gan and hakka were formed by admixture with other four dialects accordingly the middlelatitude areas in china were a linguistic melting pot of northern and southern han populations our study provides a detailed phylogenetic and historical context against familytree model in china | [['han', 'chinese', 'experienced', 'substantial', 'population', 'migrations', 'and', 'admixture', 'in', 'history', 'yet', 'little', 'is', 'known', 'about', 'the', 'evolutionary', 'process', 'of', 'chinese', 'dialects', 'here', 'we', 'used', 'phylogenetic', 'approaches', 'and', 'admixture', 'inference', 'to', 'explicitly', 'decompose', 'the', 'underlying', 'structure', 'of', 'the', 'diversity', 'of', 'chinese', 'dialects', 'based', 'on', 'the', 'total', 'phoneme', 'inventories', 'of', '140', 'dialect', 'samples', 'from', 'seven', 'traditional', 'dialect', 'groups', 'mandarin', 'wu', 'xiang', 'gan', 'hakka', 'min', 'and', 'yue', 'we', 'found', 'a', 'northsouth', 'gradient', 'of', 'phonemic', 'differences', 'in', 'chinese', 'dialects', 'induced', 'from', 'historical', 'population', 'migrations', 'we', 'also', 'quantified', 'extensive', 'horizontal', 'language', 'transfers', 'among', 'these', 'dialects', 'corresponding', 'to', 'the', 'complicated', 'sociogenetic', 'history', 'in', 'china', 'we', 'finally', 'identified', 'that', 'the', 'middle', 'latitude', 'dialects', 'of', 'xiang', 'gan', 'and', 'hakka', 'were', 'formed', 'by', 'admixture', 'with', 'other', 'four', 'dialects', 'accordingly', 'the', 'middlelatitude', 'areas', 'in', 'china', 'were', 'a', 'linguistic', 'melting', 'pot', 'of', 'northern', 'and', 'southern', 'han', 'populations', 'our', 'study', 'provides', 'a', 'detailed', 'phylogenetic', 'and', 'historical', 'context', 'against', 'familytree', 'model', 'in', 'china']] | [-0.03056338563488082, 0.07293924755872903, -0.0685579174003653, 0.20717755966301385, -0.10441871020904653, -0.04668226272142046, 0.10732820572463911, 0.3917043771186512, -0.2469236464239657, -0.35074008596193507, 0.04217953134682157, -0.32593866709415636, -0.16008748660782632, 0.16326959932714138, -0.12993495967015878, -0.09179485979017941, 0.08382585750931032, -0.006526362901228187, 0.0008680889102285856, -0.27069262170698494, 0.2538568502202732, 0.051717767970754495, 0.31813105959636606, -0.04089974662391086, 0.09906625992232576, -0.0763884692553316, -0.09999331021809006, -0.037192328928727404, -0.08282330147835326, 0.14667601477632247, 0.31858158161327854, 0.22284641640088584, 0.2502698571475427, -0.4040638979345764, -0.16400362976845864, 0.03505666241646196, 0.11927396240835525, 0.07575376475929946, -0.0322443588663926, -0.3097063100718166, 0.01995978360494905, -0.22325064183279794, 0.007238305566117984, 0.030503141473698085, 0.07210736344839536, 0.008691881025809641, -0.16750379465639986, 0.11345700818342827, 0.07197070214897394, 0.17341847394350662, -0.06697044206291998, -0.25926147796028637, -0.08672706350964839, 0.17908559410118383, 0.07198675125195447, 0.0335001026181351, 0.09051255411659814, -0.07926817470523592, -0.1393283941684935, 0.3291753308069318, -0.08553872576616513, -0.11456899964992173, 0.2105067291796169, -0.13508051471172333, -0.1777302552212336, 0.06282689556964252, 0.20156369813118283, 0.08078258820133258, -0.1883332471794621, 0.03789202160957869, -0.05906884506510647, 0.20271911919882443, 0.17856743866705324, -0.058553513589200294, 0.2052099607878505, 0.2215618484124082, -0.0539611421066196, 0.1499762701660822, -0.1850174837953041, -0.15225715656471375, -0.14574035460907608, -0.1107061449310755, -0.06644785629540732, -0.005787669574165691, -0.06979778517067133, -0.145402283855867, 0.4223387693357973, 0.10681562562001078, 0.10785399758486613, 0.021704279983492746, 0.2505714130358235, -0.06792349017094439, 0.09612839597066838, 0.07911042256076654, 0.12699080715418398, 0.07087485789965633, 0.1461052181524518, -0.1699558086434143, 0.17206226054687462, 0.03695288909556725] |
1,802.05821 | Learning Latent Features with Pairwise Penalties in Low-Rank Matrix
Completion | Low-rank matrix completion has achieved great success in many real-world data
applications. A matrix factorization model that learns latent features is
usually employed and, to improve prediction performance, the similarities
between latent variables can be exploited by pairwise learning using the graph
regularized matrix factorization (GRMF) method. However, existing GRMF
approaches often use the squared loss to measure the pairwise differences,
which may be overly influenced by dissimilar pairs and lead to inferior
prediction. To fully empower pairwise learning for matrix completion, we
propose a general optimization framework that allows a rich class of
(non-)convex pairwise penalty functions. A new and efficient algorithm is
developed to solve the proposed optimization problem, with a theoretical
convergence guarantee under mild assumptions. In an important situation where
the latent variables form a small number of subgroups, its statistical
guarantee is also fully considered. In particular, we theoretically
characterize the performance of the complexity-regularized maximum likelihood
estimator, as a special case of our framework, which is shown to have smaller
errors when compared to the standard matrix completion framework without
pairwise penalties. We conduct extensive experiments on both synthetic and real
datasets to demonstrate the superior performance of this general framework.
| stat.ML cs.LG | lowrank matrix completion has achieved great success in many realworld data applications a matrix factorization model that learns latent features is usually employed and to improve prediction performance the similarities between latent variables can be exploited by pairwise learning using the graph regularized matrix factorization grmf method however existing grmf approaches often use the squared loss to measure the pairwise differences which may be overly influenced by dissimilar pairs and lead to inferior prediction to fully empower pairwise learning for matrix completion we propose a general optimization framework that allows a rich class of nonconvex pairwise penalty functions a new and efficient algorithm is developed to solve the proposed optimization problem with a theoretical convergence guarantee under mild assumptions in an important situation where the latent variables form a small number of subgroups its statistical guarantee is also fully considered in particular we theoretically characterize the performance of the complexityregularized maximum likelihood estimator as a special case of our framework which is shown to have smaller errors when compared to the standard matrix completion framework without pairwise penalties we conduct extensive experiments on both synthetic and real datasets to demonstrate the superior performance of this general framework | [['lowrank', 'matrix', 'completion', 'has', 'achieved', 'great', 'success', 'in', 'many', 'realworld', 'data', 'applications', 'a', 'matrix', 'factorization', 'model', 'that', 'learns', 'latent', 'features', 'is', 'usually', 'employed', 'and', 'to', 'improve', 'prediction', 'performance', 'the', 'similarities', 'between', 'latent', 'variables', 'can', 'be', 'exploited', 'by', 'pairwise', 'learning', 'using', 'the', 'graph', 'regularized', 'matrix', 'factorization', 'grmf', 'method', 'however', 'existing', 'grmf', 'approaches', 'often', 'use', 'the', 'squared', 'loss', 'to', 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1,802.05822 | Auto-Encoding Total Correlation Explanation | Advances in unsupervised learning enable reconstruction and generation of
samples from complex distributions, but this success is marred by the
inscrutability of the representations learned. We propose an
information-theoretic approach to characterizing disentanglement and dependence
in representation learning using multivariate mutual information, also called
total correlation. The principle of total Cor-relation Ex-planation (CorEx) has
motivated successful unsupervised learning applications across a variety of
domains, but under some restrictive assumptions. Here we relax those
restrictions by introducing a flexible variational lower bound to CorEx.
Surprisingly, we find that this lower bound is equivalent to the one in
variational autoencoders (VAE) under certain conditions. This
information-theoretic view of VAE deepens our understanding of hierarchical VAE
and motivates a new algorithm, AnchorVAE, that makes latent codes more
interpretable through information maximization and enables generation of richer
and more realistic samples.
| cs.LG stat.ML | advances in unsupervised learning enable reconstruction and generation of samples from complex distributions but this success is marred by the inscrutability of the representations learned we propose an informationtheoretic approach to characterizing disentanglement and dependence in representation learning using multivariate mutual information also called total correlation the principle of total correlation explanation corex has motivated successful unsupervised learning applications across a variety of domains but under some restrictive assumptions here we relax those restrictions by introducing a flexible variational lower bound to corex surprisingly we find that this lower bound is equivalent to the one in variational autoencoders vae under certain conditions this informationtheoretic view of vae deepens our understanding of hierarchical vae and motivates a new algorithm anchorvae that makes latent codes more interpretable through information maximization and enables generation of richer and more realistic samples | [['advances', 'in', 'unsupervised', 'learning', 'enable', 'reconstruction', 'and', 'generation', 'of', 'samples', 'from', 'complex', 'distributions', 'but', 'this', 'success', 'is', 'marred', 'by', 'the', 'inscrutability', 'of', 'the', 'representations', 'learned', 'we', 'propose', 'an', 'informationtheoretic', 'approach', 'to', 'characterizing', 'disentanglement', 'and', 'dependence', 'in', 'representation', 'learning', 'using', 'multivariate', 'mutual', 'information', 'also', 'called', 'total', 'correlation', 'the', 'principle', 'of', 'total', 'correlation', 'explanation', 'corex', 'has', 'motivated', 'successful', 'unsupervised', 'learning', 'applications', 'across', 'a', 'variety', 'of', 'domains', 'but', 'under', 'some', 'restrictive', 'assumptions', 'here', 'we', 'relax', 'those', 'restrictions', 'by', 'introducing', 'a', 'flexible', 'variational', 'lower', 'bound', 'to', 'corex', 'surprisingly', 'we', 'find', 'that', 'this', 'lower', 'bound', 'is', 'equivalent', 'to', 'the', 'one', 'in', 'variational', 'autoencoders', 'vae', 'under', 'certain', 'conditions', 'this', 'informationtheoretic', 'view', 'of', 'vae', 'deepens', 'our', 'understanding', 'of', 'hierarchical', 'vae', 'and', 'motivates', 'a', 'new', 'algorithm', 'anchorvae', 'that', 'makes', 'latent', 'codes', 'more', 'interpretable', 'through', 'information', 'maximization', 'and', 'enables', 'generation', 'of', 'richer', 'and', 'more', 'realistic', 'samples']] | [-0.04556900741773899, 0.06175507614054907, -0.13886116225686337, 0.1341541605613505, -0.1576741615637999, -0.17022830507614548, 0.07158713972678891, 0.40802720164259276, -0.2676191417845311, -0.33777447305964653, 0.03006393880566099, -0.21957422922261888, -0.22303280842111067, 0.18629516545269226, -0.13031292168975428, 0.07937390513511168, 0.07807590649980638, 0.02202652713490857, -0.137490322685766, -0.25421186420652603, 0.33477950986405763, 0.07045653306499676, 0.36684796278606413, 0.03012683440120546, 0.14257161710066377, 0.022605562506726493, -0.012535216528232451, -0.03136525944703155, -0.1488049026014913, 0.25493062317923265, 0.27518404072530017, 0.23169562646222336, 0.3341731708996964, -0.40237830584109935, -0.26605493494619925, 0.12149423396352817, 0.1383663222823223, 0.11803277439127366, -0.04211324711386287, -0.3010299793323847, 0.04584597749980512, -0.1579477433529165, 0.005170743671866754, -0.1734024765866774, -0.03432685354793513, -0.05717486751231331, -0.3163000574464806, 0.07727089812785076, 0.11897453020706221, 0.05362793124039416, -0.05220650163720603, -0.10342307597812679, 0.04439689169327418, 0.07643723252608821, 0.042921780004959414, 0.025139595787213358, 0.10542831822315714, -0.17038244932234564, -0.10013287459862315, 0.30607256414713685, -0.0494310544838232, -0.21854359963046455, 0.19451463729557064, -0.0681257818877283, -0.20838386592696662, 0.11431557623048623, 0.2035825411495925, 0.12226190149991048, -0.1943646918223412, 0.0248986227140348, -0.07261087523635339, 0.13837789542524628, 0.04441790221845386, 0.0799333511489547, 0.1442119867273572, 0.22105331003183015, 0.09702818292986463, 0.17332873607520013, -0.08041649859536577, -0.10096092865008999, -0.2268366146142836, -0.12193302199399719, -0.2068055619813364, 0.02873084465455678, -0.12423642702277801, -0.1170157788010935, 0.36454771180740664, 0.21689023455708392, 0.20778705180381182, 0.11668208737284187, 0.2984465344636529, 0.060316775539993414, 0.07961124926201861, 0.09048391517113756, 0.18125556999390635, 0.11230868290488918, 0.05447713321557751, -0.12373127608249584, 0.1472566500875271, 0.016793186410709662] |
1,802.05823 | Single Transits and Eclipses Observed by K2 | Photometric survey data from the Kepler mission have been used to discover
and characterize thousands of transiting exoplanet and eclipsing binary (EB)
systems. These discoveries have enabled empirical studies of occurrence rates
which reveal that exoplanets are ubiquitous and found in a wide variety of
system architectures and physical compositions. Because the detection strategy
of these missions is most sensitive to short orbital periods, the vast majority
of these objects reside within 1 AU of their host star. Although other
detection techniques have successfully identified exoplanets at wider orbits
beyond the snow lines of their respective host stars (e.g., radial velocity,
microlensing, direct imaging), occurrence rates within this population remain
poorly constrained. As such, identifying long period objects (LPOs) from
archival Kepler and K2 data is valuable from both a statistical and theoretical
standpoint, particularly for massive gas giants which are thought to heavily
influence the formation and evolution dynamics of their respective systems.
Here we present a catalog of 164 single transit and eclipse candidates detected
during a comprehensive survey of all currently available K2 data.
| astro-ph.EP astro-ph.SR | photometric survey data from the kepler mission have been used to discover and characterize thousands of transiting exoplanet and eclipsing binary eb systems these discoveries have enabled empirical studies of occurrence rates which reveal that exoplanets are ubiquitous and found in a wide variety of system architectures and physical compositions because the detection strategy of these missions is most sensitive to short orbital periods the vast majority of these objects reside within 1 au of their host star although other detection techniques have successfully identified exoplanets at wider orbits beyond the snow lines of their respective host stars eg radial velocity microlensing direct imaging occurrence rates within this population remain poorly constrained as such identifying long period objects lpos from archival kepler and k2 data is valuable from both a statistical and theoretical standpoint particularly for massive gas giants which are thought to heavily influence the formation and evolution dynamics of their respective systems here we present a catalog of 164 single transit and eclipse candidates detected during a comprehensive survey of all currently available k2 data | [['photometric', 'survey', 'data', 'from', 'the', 'kepler', 'mission', 'have', 'been', 'used', 'to', 'discover', 'and', 'characterize', 'thousands', 'of', 'transiting', 'exoplanet', 'and', 'eclipsing', 'binary', 'eb', 'systems', 'these', 'discoveries', 'have', 'enabled', 'empirical', 'studies', 'of', 'occurrence', 'rates', 'which', 'reveal', 'that', 'exoplanets', 'are', 'ubiquitous', 'and', 'found', 'in', 'a', 'wide', 'variety', 'of', 'system', 'architectures', 'and', 'physical', 'compositions', 'because', 'the', 'detection', 'strategy', 'of', 'these', 'missions', 'is', 'most', 'sensitive', 'to', 'short', 'orbital', 'periods', 'the', 'vast', 'majority', 'of', 'these', 'objects', 'reside', 'within', '1', 'au', 'of', 'their', 'host', 'star', 'although', 'other', 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1,802.05824 | Combinatorial minimal surfaces in pseudomanifolds | We define combinatorial analogues of stable and unstable minimal surfaces in
the setting of weighted pseudomanifolds. We prove that, under mild conditions,
such combinatorial minimal surfaces always exist. We use a technique, adapted
from work of Johnson and Thompson, called thin position. Thin position is
defined using orderings of the cells of a pseudomanifold. In addition to
defining and finding combinatorial minimal surfaces, from thin orderings, we
derive invariants of even-dimensional closed simplicial pseudomanifolds called
width and trunk. We study additivity properties of these invariants under
connected sum and prove theorems analogous to those in knot theory and
3-manifold theory.
| math.GT math.CO | we define combinatorial analogues of stable and unstable minimal surfaces in the setting of weighted pseudomanifolds we prove that under mild conditions such combinatorial minimal surfaces always exist we use a technique adapted from work of johnson and thompson called thin position thin position is defined using orderings of the cells of a pseudomanifold in addition to defining and finding combinatorial minimal surfaces from thin orderings we derive invariants of evendimensional closed simplicial pseudomanifolds called width and trunk we study additivity properties of these invariants under connected sum and prove theorems analogous to those in knot theory and 3manifold theory | [['we', 'define', 'combinatorial', 'analogues', 'of', 'stable', 'and', 'unstable', 'minimal', 'surfaces', 'in', 'the', 'setting', 'of', 'weighted', 'pseudomanifolds', 'we', 'prove', 'that', 'under', 'mild', 'conditions', 'such', 'combinatorial', 'minimal', 'surfaces', 'always', 'exist', 'we', 'use', 'a', 'technique', 'adapted', 'from', 'work', 'of', 'johnson', 'and', 'thompson', 'called', 'thin', 'position', 'thin', 'position', 'is', 'defined', 'using', 'orderings', 'of', 'the', 'cells', 'of', 'a', 'pseudomanifold', 'in', 'addition', 'to', 'defining', 'and', 'finding', 'combinatorial', 'minimal', 'surfaces', 'from', 'thin', 'orderings', 'we', 'derive', 'invariants', 'of', 'evendimensional', 'closed', 'simplicial', 'pseudomanifolds', 'called', 'width', 'and', 'trunk', 'we', 'study', 'additivity', 'properties', 'of', 'these', 'invariants', 'under', 'connected', 'sum', 'and', 'prove', 'theorems', 'analogous', 'to', 'those', 'in', 'knot', 'theory', 'and', '3manifold', 'theory']] | [-0.16428979311487638, 0.10858911384828389, -0.10834996342658997, 0.10976728889741935, -0.09737398233031853, -0.15562888697721064, 0.05205062799854204, 0.3758596959291026, -0.32530752291204407, -0.2511265481635928, 0.07736146042239853, -0.2157443016394973, -0.20541377417277545, 0.17745770929148422, -0.2166879300540313, 0.005750406924635172, 0.03890055460855365, 0.0048934487556107345, -0.08796569641774113, -0.21843917589634657, 0.3887642672192305, -0.07052759365644307, 0.22994053659960628, 0.07298559063463472, 0.10080758186057209, 0.021914239497855304, -0.011289287297986448, 0.10054556289222091, -0.30076525010903427, 0.14423561064060778, 0.25485782985342664, 0.062410171590745446, 0.13085013834759593, -0.4411653485987335, -0.15267594902776183, 0.16546604888513683, 0.07484984141774476, 0.01958720253314823, -0.022459609769575763, -0.21488413004204632, 0.14664585812948644, -0.10277761515695602, -0.19400330184958875, -0.061231137926224616, -0.019121929700486363, 0.0038342784508131446, -0.19010464002785737, -0.004334771127905697, 0.11389881373848766, 0.11313877328298987, -0.080287744065281, -0.09619514246471227, -0.064827647684142, 0.08318967895582319, -0.003101457201410085, -0.013199833619873971, 0.10115104492288082, -0.08921484587714076, -0.1450768674071878, 0.3361427206220105, -0.06937720134854317, -0.2494859452266246, 0.1663433360401541, -0.11411429558182135, -0.17794192395173014, 0.12174815725069493, 0.09934975358657538, 0.22024636972695588, -0.07214109283871949, 0.15081522629712707, -0.13858959180302918, 0.03639886769000441, 0.18069929451681674, 0.04544346421491355, 0.1524901609774679, 0.0915878997463733, 0.11732946704141796, 0.20551848784089088, -0.020849176226183774, -0.08805893703363836, -0.319113531364128, -0.2106799299409613, -0.10048825514502824, 0.12150626593385823, -0.1045512238694937, -0.24700727666262537, 0.3458131648506969, 0.05145979825407267, 0.15181028621387668, 0.12192635306157172, 0.23662604530109094, 0.02692449536640197, 0.029192476887255906, 0.09563692693132907, 0.1223293164675124, 0.23891349926590918, -0.0635874703033187, -0.10016262653691228, 0.03664590554777533, 0.20536681215744465] |
1,802.05825 | A Comparison of Constraint Handling Techniques for Dynamic Constrained
Optimization Problems | Dynamic constrained optimization problems (DCOPs) have gained researchers
attention in recent years because a vast majority of real world problems change
over time. There are studies about the effect of constrained handling
techniques in static optimization problems. However, there lacks any
substantial study in the behavior of the most popular constraint handling
techniques when dealing with DCOPs. In this paper we study the four most
popular used constraint handling techniques and apply a simple Differential
Evolution (DE) algorithm coupled with a change detection mechanism to observe
the behavior of these techniques. These behaviors were analyzed using a common
benchmark to determine which techniques are suitable for the most prevalent
types of DCOPs. For the purpose of analysis, common measures in static
environments were adapted to suit dynamic environments. While an overall
superior technique could not be determined, certain techniques outperformed
others in different aspects like rate of optimization or reliability of
solutions.
| cs.NE math.OC | dynamic constrained optimization problems dcops have gained researchers attention in recent years because a vast majority of real world problems change over time there are studies about the effect of constrained handling techniques in static optimization problems however there lacks any substantial study in the behavior of the most popular constraint handling techniques when dealing with dcops in this paper we study the four most popular used constraint handling techniques and apply a simple differential evolution de algorithm coupled with a change detection mechanism to observe the behavior of these techniques these behaviors were analyzed using a common benchmark to determine which techniques are suitable for the most prevalent types of dcops for the purpose of analysis common measures in static environments were adapted to suit dynamic environments while an overall superior technique could not be determined certain techniques outperformed others in different aspects like rate of optimization or reliability of solutions | [['dynamic', 'constrained', 'optimization', 'problems', 'dcops', 'have', 'gained', 'researchers', 'attention', 'in', 'recent', 'years', 'because', 'a', 'vast', 'majority', 'of', 'real', 'world', 'problems', 'change', 'over', 'time', 'there', 'are', 'studies', 'about', 'the', 'effect', 'of', 'constrained', 'handling', 'techniques', 'in', 'static', 'optimization', 'problems', 'however', 'there', 'lacks', 'any', 'substantial', 'study', 'in', 'the', 'behavior', 'of', 'the', 'most', 'popular', 'constraint', 'handling', 'techniques', 'when', 'dealing', 'with', 'dcops', 'in', 'this', 'paper', 'we', 'study', 'the', 'four', 'most', 'popular', 'used', 'constraint', 'handling', 'techniques', 'and', 'apply', 'a', 'simple', 'differential', 'evolution', 'de', 'algorithm', 'coupled', 'with', 'a', 'change', 'detection', 'mechanism', 'to', 'observe', 'the', 'behavior', 'of', 'these', 'techniques', 'these', 'behaviors', 'were', 'analyzed', 'using', 'a', 'common', 'benchmark', 'to', 'determine', 'which', 'techniques', 'are', 'suitable', 'for', 'the', 'most', 'prevalent', 'types', 'of', 'dcops', 'for', 'the', 'purpose', 'of', 'analysis', 'common', 'measures', 'in', 'static', 'environments', 'were', 'adapted', 'to', 'suit', 'dynamic', 'environments', 'while', 'an', 'overall', 'superior', 'technique', 'could', 'not', 'be', 'determined', 'certain', 'techniques', 'outperformed', 'others', 'in', 'different', 'aspects', 'like', 'rate', 'of', 'optimization', 'or', 'reliability', 'of', 'solutions']] | [-0.09633954485118586, -0.0022274564918533493, -0.0714430492815537, 0.08301021784197808, -0.10128787554812764, -0.18015320580085054, 0.011724563743643413, 0.37943184376069294, -0.25210775098821614, -0.34899161626335146, 0.15230045769529074, -0.24736806992318874, -0.17567032825747064, 0.25047518016837883, -0.10308447619593751, 0.09202304862877722, 0.08770560429011791, 0.01223946582850706, -0.09728233361889388, -0.27302392134381653, 0.2890307929207522, 0.03356946742324477, 0.32981576622267694, 0.026242543885018677, 0.06925534772208745, 0.03478683982240526, -0.0768810416438531, 0.08220648460892814, -0.09485679455132126, 0.13101516712126077, 0.32615000538766925, 0.20116232067199522, 0.33445530862081796, -0.4354118898167814, -0.26340036791455196, 0.1399804046101819, 0.12985441461818195, 0.08419658160557064, -0.06071215606674454, -0.2327610923859634, 0.10778351905939512, -0.13986837143985262, -0.09430854944022078, -0.09541408476606623, -0.006141907650376915, 0.02740395490247082, -0.23009288313455487, 0.03497831090230887, 0.015028067441706202, 0.07315418648067862, -0.06111425838561876, -0.13999270586186627, 0.07001492850433447, 0.13051374089742526, 0.08557444910356456, -0.020062601538053348, 0.12794818329980204, -0.1382188248397525, -0.1809860795267588, 0.40419404916955454, -0.021242750505304064, -0.2020867421867335, 0.24818129636543362, -0.048315450746725365, -0.21635345117752686, 0.12727799413168164, 0.2184943534061619, 0.19025383795011103, -0.19220440746731152, 0.06942856329528456, 0.011517881048157028, 0.1553791400112555, 0.05660864150176119, 0.027223444292566887, 0.1745965732920204, 0.1996903960381659, 0.05242164798332133, 0.10568997232430295, -0.027531764206574542, -0.15433661902237586, -0.16985320249927768, -0.07599943481373454, -0.11094476046702384, -0.023384587269327694, -0.0680803873201512, -0.16647852502003507, 0.358697263511682, 0.18654800193555848, 0.1578738971714445, -0.0014147551398644665, 0.3166178851694751, 0.09778458372947123, 0.10668323225170177, 0.06736890365075517, 0.21450672414012892, 0.06369968548747956, 0.15707580623673398, -0.18124179670581056, 0.11272974097473228, -0.01583397911408132] |
1,802.05826 | Dealing with indistinguishable particles and their entanglement | Here we discuss a particle-based approach to deal with systems of many
identical quantum objects (particles) which never employs labels to mark them.
We show that it avoids both methodological problems and drawbacks in the study
of quantum correlations associated to the standard quantum mechanical treatment
of identical particles. The core of this approach is represented by the
multiparticle probability amplitude whose structure in terms of single-particle
amplitudes we here derive by first principles. To characterise entanglement
among the identical particles, this new method utilises the same notions, such
as partial trace, adopted for nonidentical ones. We highlight the connection
between our approach and second quantization. We also define spin-exchanged
multipartite states (SPES) which contain a generalisation of W states to
identical particles. We prove that their spatial overlap plays a role on the
distributed entanglement within multipartite systems and is responsible for the
appearance of nonlocal quantum correlations.
| quant-ph | here we discuss a particlebased approach to deal with systems of many identical quantum objects particles which never employs labels to mark them we show that it avoids both methodological problems and drawbacks in the study of quantum correlations associated to the standard quantum mechanical treatment of identical particles the core of this approach is represented by the multiparticle probability amplitude whose structure in terms of singleparticle amplitudes we here derive by first principles to characterise entanglement among the identical particles this new method utilises the same notions such as partial trace adopted for nonidentical ones we highlight the connection between our approach and second quantization we also define spinexchanged multipartite states spes which contain a generalisation of w states to identical particles we prove that their spatial overlap plays a role on the distributed entanglement within multipartite systems and is responsible for the appearance of nonlocal quantum correlations | [['here', 'we', 'discuss', 'a', 'particlebased', 'approach', 'to', 'deal', 'with', 'systems', 'of', 'many', 'identical', 'quantum', 'objects', 'particles', 'which', 'never', 'employs', 'labels', 'to', 'mark', 'them', 'we', 'show', 'that', 'it', 'avoids', 'both', 'methodological', 'problems', 'and', 'drawbacks', 'in', 'the', 'study', 'of', 'quantum', 'correlations', 'associated', 'to', 'the', 'standard', 'quantum', 'mechanical', 'treatment', 'of', 'identical', 'particles', 'the', 'core', 'of', 'this', 'approach', 'is', 'represented', 'by', 'the', 'multiparticle', 'probability', 'amplitude', 'whose', 'structure', 'in', 'terms', 'of', 'singleparticle', 'amplitudes', 'we', 'here', 'derive', 'by', 'first', 'principles', 'to', 'characterise', 'entanglement', 'among', 'the', 'identical', 'particles', 'this', 'new', 'method', 'utilises', 'the', 'same', 'notions', 'such', 'as', 'partial', 'trace', 'adopted', 'for', 'nonidentical', 'ones', 'we', 'highlight', 'the', 'connection', 'between', 'our', 'approach', 'and', 'second', 'quantization', 'we', 'also', 'define', 'spinexchanged', 'multipartite', 'states', 'spes', 'which', 'contain', 'a', 'generalisation', 'of', 'w', 'states', 'to', 'identical', 'particles', 'we', 'prove', 'that', 'their', 'spatial', 'overlap', 'plays', 'a', 'role', 'on', 'the', 'distributed', 'entanglement', 'within', 'multipartite', 'systems', 'and', 'is', 'responsible', 'for', 'the', 'appearance', 'of', 'nonlocal', 'quantum', 'correlations']] | [-0.1096943576892561, 0.15931553412165894, -0.09950742516508165, 0.09205283234493707, 0.00249435448062581, -0.1445762028923963, 0.025327728220381197, 0.3512723312549595, -0.24564803648280684, -0.288384952355881, -0.003841148955256653, -0.271464542655677, -0.18104192524726778, 0.1402049724254883, -0.05130611116918452, 0.06778089221562196, 0.06716369718904733, 0.027394453598488425, -0.04529280074859491, -0.20681082640579473, 0.3531453925321772, 0.009632195431009136, 0.266317806219229, 0.04617487981198104, 0.1346435234543704, 0.036329034596329204, -0.044243358663988076, 0.02455376656222585, -0.1038931581932883, 0.13123911596768587, 0.24201754620928015, 0.10399373093113772, 0.26391575159033054, -0.4201003564773379, -0.19072117950260714, 0.12584269842183268, 0.1318574578507577, 0.15234369633646958, -0.006187996023957542, -0.2971789862306134, 0.053558034910442864, -0.17285922447823593, -0.1530174594687147, -0.09302856712098662, -0.0026832927754652258, -0.004688868001795601, -0.2226510205155874, 0.09641602713550988, 0.08471637242984946, 0.0048024164232727444, -0.016968031243949726, -0.06719001483902134, 0.03363278600433684, 0.1370977539399236, -0.024896660061376925, -0.054902346092087495, 0.10587423549637802, -0.11963532676895124, -0.17284816804631437, 0.37731749056429426, -0.015937624351322854, -0.22575404206164987, 0.21280646964756622, -0.10158266083689758, -0.17478892446269054, 0.06679323769797496, 0.1545201115580779, 0.10443655792875467, -0.15571728440645038, 0.043429664841529006, -0.024872745846624713, 0.15515760200828113, 0.0679823163144819, 0.13197836924243617, 0.21178547739258943, 0.09903115571659007, 0.05406247491506243, 0.18492700147593544, -0.05424446949945303, -0.18198286089416896, -0.3216491650437584, -0.202887034775985, -0.2034521641247478, 0.03219391399604344, -0.05642885659934207, -0.1861743868975208, 0.38133026864631353, 0.16498413146473467, 0.18352575127878604, 0.041513608922753985, 0.2505315890255686, 0.09593744214460556, 0.0699219269523825, 0.05308833635865232, 0.23293151150337998, 0.17136595531671964, 0.07111460797337664, -0.24292588680907864, 0.045671100038374345, 0.07170781631154839] |
1,802.05827 | Single-Track Melt-Pool Measurements and Microstructures in Inconel 625 | We use single track laser melting experiments and simulations on Inconel 625
to estimate the dimensions and microstructures of the resulting melt pools. Our
work is based on a design-of-experiments approach which uses multiple laser
power and scan speed combinations. Single track experiments generate melt pools
of certain dimensions. These dimensions reasonably agree with our finite
element calculations. Phase-field simulations predict the size and segregation
of the cellular microstructures that form along the melt pool boundaries for
the solidification conditions that change as a function of melt pool
dimensions.
| physics.app-ph cond-mat.mtrl-sci | we use single track laser melting experiments and simulations on inconel 625 to estimate the dimensions and microstructures of the resulting melt pools our work is based on a designofexperiments approach which uses multiple laser power and scan speed combinations single track experiments generate melt pools of certain dimensions these dimensions reasonably agree with our finite element calculations phasefield simulations predict the size and segregation of the cellular microstructures that form along the melt pool boundaries for the solidification conditions that change as a function of melt pool dimensions | [['we', 'use', 'single', 'track', 'laser', 'melting', 'experiments', 'and', 'simulations', 'on', 'inconel', '625', 'to', 'estimate', 'the', 'dimensions', 'and', 'microstructures', 'of', 'the', 'resulting', 'melt', 'pools', 'our', 'work', 'is', 'based', 'on', 'a', 'designofexperiments', 'approach', 'which', 'uses', 'multiple', 'laser', 'power', 'and', 'scan', 'speed', 'combinations', 'single', 'track', 'experiments', 'generate', 'melt', 'pools', 'of', 'certain', 'dimensions', 'these', 'dimensions', 'reasonably', 'agree', 'with', 'our', 'finite', 'element', 'calculations', 'phasefield', 'simulations', 'predict', 'the', 'size', 'and', 'segregation', 'of', 'the', 'cellular', 'microstructures', 'that', 'form', 'along', 'the', 'melt', 'pool', 'boundaries', 'for', 'the', 'solidification', 'conditions', 'that', 'change', 'as', 'a', 'function', 'of', 'melt', 'pool', 'dimensions']] | [-0.023208615491801702, 0.17969107762275432, -0.0998780385720734, -0.04273352148514029, -0.012800496280595491, -0.13028163886205715, 0.06566463225118985, 0.37083268914879725, -0.24824625320880758, -0.2931365023621104, 0.09824611779715103, -0.29077360999177804, -0.07704761885543121, 0.15346138525108638, 0.01621556280985136, 0.08409841720607471, 0.10991325315924107, -0.041038446594029665, -0.051912297918037933, -0.25403088132258167, 0.2590619466685563, 0.03215853115332059, 0.35206539825197647, 0.03867830087388442, 0.1349711164366454, -0.03967834487345747, 0.02812932742844251, 0.03498202942269431, -0.20408816751048356, 0.07984766451409087, 0.23212503533895043, 0.04249450040515512, 0.19841849427162248, -0.49224022845737636, -0.2941990886941891, 0.01711821856654503, 0.09634865317705342, 0.10811225913692563, -0.06913548591811294, -0.2018539580934554, 0.09983091252797749, -0.14131419771787146, -0.1204568940024315, -0.0995042827509513, -0.05138160489414903, 0.05784333085003626, -0.2995894691081379, 0.059103061556181107, -0.003489293464967473, 0.06134372197324079, -0.1003637735795399, -0.09471621401528617, -0.057066871514755556, 0.12614414742248217, 0.015704550142717464, -0.005454745614604855, 0.2463633018778637, -0.0886036450359494, -0.0885553780879656, 0.3790350330316208, -0.037096677702555265, -0.18285575780001553, 0.25592821701271035, -0.1361274209656668, -0.07982972317735072, 0.19332816880011128, 0.20355482831258664, 0.13519170571287925, -0.09143470344927417, -0.03044877365623093, -0.06557432144075971, 0.25202942304630677, 0.14915452276843347, -0.06368070614354854, 0.21129882066849281, 0.24128082502813247, -0.007602885725315322, 0.1445683887948028, -0.12899448404575445, -0.12557763788341123, -0.27385188286280027, -0.17824127167907797, -0.18528832264498554, -0.02910998450930823, -0.12318317914336645, -0.18739250434604895, 0.37375740448161116, 0.1446502895851154, 0.17540340153077108, 0.0935294793889625, 0.26380572810938413, 0.022260856911254286, 0.09630477669733492, 0.002907838296695528, 0.1224455088588663, 0.07560914450335655, 0.12223935608794405, -0.26440164883388206, 0.09245393640065397, 0.06505526329221373] |
1,802.05828 | Improving Power Grid Resilience Through Predictive Outage Estimation | In this paper, in an attempt to improve power grid resilience, a machine
learning model is proposed to predictively estimate the component states in
response to extreme events. The proposed model is based on a multi-dimensional
Support Vector Machine (SVM) considering the associated resilience index, i.e.,
the infrastructure quality level and the time duration that each component can
withstand the event, as well as predicted path and intensity of the upcoming
extreme event. The outcome of the proposed model is the classified component
state data to two categories of outage and operational, which can be further
used to schedule system resources in a predictive manner with the objective of
maximizing its resilience. The proposed model is validated using \"A-fold
cross-validation and model benchmarking techniques. The performance of the
model is tested through numerical simulations and based on a well-defined and
commonly-used performance measure.
| cs.SY stat.AP | in this paper in an attempt to improve power grid resilience a machine learning model is proposed to predictively estimate the component states in response to extreme events the proposed model is based on a multidimensional support vector machine svm considering the associated resilience index ie the infrastructure quality level and the time duration that each component can withstand the event as well as predicted path and intensity of the upcoming extreme event the outcome of the proposed model is the classified component state data to two categories of outage and operational which can be further used to schedule system resources in a predictive manner with the objective of maximizing its resilience the proposed model is validated using afold crossvalidation and model benchmarking techniques the performance of the model is tested through numerical simulations and based on a welldefined and commonlyused performance measure | [['in', 'this', 'paper', 'in', 'an', 'attempt', 'to', 'improve', 'power', 'grid', 'resilience', 'a', 'machine', 'learning', 'model', 'is', 'proposed', 'to', 'predictively', 'estimate', 'the', 'component', 'states', 'in', 'response', 'to', 'extreme', 'events', 'the', 'proposed', 'model', 'is', 'based', 'on', 'a', 'multidimensional', 'support', 'vector', 'machine', 'svm', 'considering', 'the', 'associated', 'resilience', 'index', 'ie', 'the', 'infrastructure', 'quality', 'level', 'and', 'the', 'time', 'duration', 'that', 'each', 'component', 'can', 'withstand', 'the', 'event', 'as', 'well', 'as', 'predicted', 'path', 'and', 'intensity', 'of', 'the', 'upcoming', 'extreme', 'event', 'the', 'outcome', 'of', 'the', 'proposed', 'model', 'is', 'the', 'classified', 'component', 'state', 'data', 'to', 'two', 'categories', 'of', 'outage', 'and', 'operational', 'which', 'can', 'be', 'further', 'used', 'to', 'schedule', 'system', 'resources', 'in', 'a', 'predictive', 'manner', 'with', 'the', 'objective', 'of', 'maximizing', 'its', 'resilience', 'the', 'proposed', 'model', 'is', 'validated', 'using', 'afold', 'crossvalidation', 'and', 'model', 'benchmarking', 'techniques', 'the', 'performance', 'of', 'the', 'model', 'is', 'tested', 'through', 'numerical', 'simulations', 'and', 'based', 'on', 'a', 'welldefined', 'and', 'commonlyused', 'performance', 'measure']] | [-0.0856723094582033, 0.012287885789422508, -0.10346401058273508, 0.06083740380881223, -0.04746060749814308, -0.13592234906792955, 0.07517693138723983, 0.3923627062844978, -0.24433211635955623, -0.3286437220005712, 0.12788754665921195, -0.26763537221155326, -0.14090351689431463, 0.18609888464561664, -0.10332503122471037, 0.1243184599824558, 0.08266167892110338, 0.07167141578114317, 0.002002990675080095, -0.26603876512405844, 0.2837211992132517, 0.15115586307529413, 0.3712625179889048, 0.016821747835935423, 0.09747030663723782, -0.02409139790893598, -0.011792718233830872, 0.04518731621513799, -0.051063278029456774, 0.11808297292347407, 0.264508694188971, 0.20119594688966713, 0.29249114281511013, -0.3881719588485717, -0.24853536403413312, 0.10517911199795109, 0.08975549173397078, 0.02280945706379626, -0.0031413499111781866, -0.2922326957111732, 0.08741472765449172, -0.23056037412484137, -0.08346501954759038, -0.08798417269619523, -0.034418529739052475, 0.027686230267699754, -0.32100917751126934, 0.03850823369296923, 0.025541651809529047, 0.037224478980290215, -0.07388706924475756, -0.0747926739711826, -0.03717466711204394, 0.13448305619621492, 0.04776838099629894, 0.03968096588853858, 0.14496783976784158, -0.12426280119935129, -0.18142398785878444, 0.38024907014135956, -0.07561903377350511, -0.20313089458652261, 0.18588532796326343, -0.03950284976302318, -0.08009353032479928, 0.09103726115058058, 0.24810642476232958, 0.07156474635818265, -0.14992755884960504, -0.01102701515782441, 0.02217007901037479, 0.1915437183687201, -0.012636821293993525, -0.0065345373103054055, 0.17753958722567686, 0.25562136826879334, 0.03764586456277421, 0.13838719854600282, -0.13726803852273087, -0.0873803167782632, -0.25826196401940765, -0.13370275410207014, -0.19497373251533004, -0.025391541844443748, -0.07485641180443361, -0.13746669585488633, 0.4452892319810852, 0.21332273806296093, 0.18523991172051082, 0.07454095043046859, 0.3615280059199046, 0.1292193621187143, 0.05650306853116695, 0.08837386298741043, 0.22531692159745817, 0.06447950597445835, 0.06467801386582285, -0.2017641824589793, 0.12163995457222869, 0.035583276923870484] |
1,802.05829 | Magnetic Field Analysis of the Bow and Terminal Shock of the SS433 Jet | We report a polarization analysis of the eastern region of W50, observed with
the Australia Telescope Compact Array (ATCA) at 1.4 - 3.0 GHz. In order to
study the physical structures in the region where the SS433 jet and W50
interact, we obtain an intrinsic magnetic field vector map of that region. We
find that the orientation of the intrinsic magnetic field vectors are aligned
along the total intensity structures, and that there are characteristic,
separate structures related to the jet, the bow shock, and the terminal shock.
The Faraday rotation measures (RMs), and the results of Faraday Tomography
suggest that a high intensity, filamentary structure in the north-south
direction of the eastern-edge region can be separated into at least two parts
to the north and south. The results of Faraday Tomography also show that there
are multiple components along the line of sight and/or within the beam area. In
addition, we also analyze the X-ray ring-like structure observed with
XMM-Newton. While the possibility still remains that this X-ray ring is real,
it seems that the structure is not ring-like at radio wavelengths. Finally, we
suggest that the structure is a part of the helical structure that coils the
eastern ear of W50.
| astro-ph.GA | we report a polarization analysis of the eastern region of w50 observed with the australia telescope compact array atca at 14 30 ghz in order to study the physical structures in the region where the ss433 jet and w50 interact we obtain an intrinsic magnetic field vector map of that region we find that the orientation of the intrinsic magnetic field vectors are aligned along the total intensity structures and that there are characteristic separate structures related to the jet the bow shock and the terminal shock the faraday rotation measures rms and the results of faraday tomography suggest that a high intensity filamentary structure in the northsouth direction of the easternedge region can be separated into at least two parts to the north and south the results of faraday tomography also show that there are multiple components along the line of sight andor within the beam area in addition we also analyze the xray ringlike structure observed with xmmnewton while the possibility still remains that this xray ring is real it seems that the structure is not ringlike at radio wavelengths finally we suggest that the structure is a part of the helical structure that coils the eastern ear of w50 | [['we', 'report', 'a', 'polarization', 'analysis', 'of', 'the', 'eastern', 'region', 'of', 'w50', 'observed', 'with', 'the', 'australia', 'telescope', 'compact', 'array', 'atca', 'at', '14', '30', 'ghz', 'in', 'order', 'to', 'study', 'the', 'physical', 'structures', 'in', 'the', 'region', 'where', 'the', 'ss433', 'jet', 'and', 'w50', 'interact', 'we', 'obtain', 'an', 'intrinsic', 'magnetic', 'field', 'vector', 'map', 'of', 'that', 'region', 'we', 'find', 'that', 'the', 'orientation', 'of', 'the', 'intrinsic', 'magnetic', 'field', 'vectors', 'are', 'aligned', 'along', 'the', 'total', 'intensity', 'structures', 'and', 'that', 'there', 'are', 'characteristic', 'separate', 'structures', 'related', 'to', 'the', 'jet', 'the', 'bow', 'shock', 'and', 'the', 'terminal', 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'coils', 'the', 'eastern', 'ear', 'of', 'w50']] | [-0.14702006279310184, 0.10628098509900227, -0.057566941637008345, 0.04757349684241399, -0.09884203725438843, -0.03966470642174033, -0.02339277323335409, 0.4897333040129189, -0.27105146732681723, -0.27669344025094117, 0.10807511755103125, -0.2638919502971648, -0.10496697988852266, 0.18865835943041526, 0.03706558279876148, -0.07902440129760746, 0.032370337665043956, -0.028877688569494, -0.03990145489244504, -0.14670501335347952, 0.30665311374381854, 0.08349958837376352, 0.2737657394913484, 0.040257869269791524, 0.11483975344416292, -0.09988144952664847, -0.019455297729602798, 0.04869284112914581, -0.07049673492488114, 0.0947052750320044, 0.21793836509966436, 0.09200246629087412, 0.17970206331002958, -0.3988538409133826, -0.19120447826455927, 0.022741479312639628, 0.15753106493734528, 0.0385597292762435, -0.010275918647498633, -0.2854102497308779, 0.09506802802235907, -0.127278398824346, -0.19463282166076684, 0.048615503815276115, 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1,802.0583 | Teichm\"uller theory of the universal hyperbolic lamination | We construct an Ahlfors-Bers complex analytic model for the Teichm\"uller
space of the universal hyperbolic lamination (also known as Sullivan's
Teichm\"uller space) and the renormalized Weil-Petersson metric on it as an
extension of the usual one. In this setting, we prove that Sullivan's
Teichm\"uller space is K\"ahler isometric biholomorphic to the space of
continuous functions from the profinite completion of the fundamental group of
a compact Riemann surface of genus greater than or equal to two to the
Teichm\"uller space of this surface; i.e. We find natural K\"ahler coordinates
for the Sullivan's Teichm\"uller space. This is the main result. As a
corollary, we show the expected fact that the Nag-Verjovsky embedding is
transversal to the Sullivan's Teichm\"uller space contained in the universal
one.
| math.CV | we construct an ahlforsbers complex analytic model for the teichmuller space of the universal hyperbolic lamination also known as sullivans teichmuller space and the renormalized weilpetersson metric on it as an extension of the usual one in this setting we prove that sullivans teichmuller space is kahler isometric biholomorphic to the space of continuous functions from the profinite completion of the fundamental group of a compact riemann surface of genus greater than or equal to two to the teichmuller space of this surface ie we find natural kahler coordinates for the sullivans teichmuller space this is the main result as a corollary we show the expected fact that the nagverjovsky embedding is transversal to the sullivans teichmuller space contained in the universal one | [['we', 'construct', 'an', 'ahlforsbers', 'complex', 'analytic', 'model', 'for', 'the', 'teichmuller', 'space', 'of', 'the', 'universal', 'hyperbolic', 'lamination', 'also', 'known', 'as', 'sullivans', 'teichmuller', 'space', 'and', 'the', 'renormalized', 'weilpetersson', 'metric', 'on', 'it', 'as', 'an', 'extension', 'of', 'the', 'usual', 'one', 'in', 'this', 'setting', 'we', 'prove', 'that', 'sullivans', 'teichmuller', 'space', 'is', 'kahler', 'isometric', 'biholomorphic', 'to', 'the', 'space', 'of', 'continuous', 'functions', 'from', 'the', 'profinite', 'completion', 'of', 'the', 'fundamental', 'group', 'of', 'a', 'compact', 'riemann', 'surface', 'of', 'genus', 'greater', 'than', 'or', 'equal', 'to', 'two', 'to', 'the', 'teichmuller', 'space', 'of', 'this', 'surface', 'ie', 'we', 'find', 'natural', 'kahler', 'coordinates', 'for', 'the', 'sullivans', 'teichmuller', 'space', 'this', 'is', 'the', 'main', 'result', 'as', 'a', 'corollary', 'we', 'show', 'the', 'expected', 'fact', 'that', 'the', 'nagverjovsky', 'embedding', 'is', 'transversal', 'to', 'the', 'sullivans', 'teichmuller', 'space', 'contained', 'in', 'the', 'universal', 'one']] | [-0.14079337724347096, 0.09197432564435046, -0.12146295519301394, 0.13478905788897613, -0.12988190840716002, -0.05405951790572679, -0.029769928893074393, 0.32779240161638523, -0.3369037034448053, -0.18176853313248176, 0.0907870772443559, -0.2538589795394877, -0.2119553907579727, 0.236965662089833, -0.20372972227194056, 0.029833721607679227, -0.0005968903233373507, 0.11923467362543842, -0.1347654406334365, -0.264164547008447, 0.4729897848819001, -0.007769776260113863, 0.20627531341322866, 0.038544549705979765, 0.13476216303734262, -0.010905122869174744, 0.0008117787455987247, -0.027721150567111955, -0.18686784235209505, 0.171111913955175, 0.26052757929705206, 0.08829410480465129, 0.1833717310705726, -0.3428657258341669, -0.2383615261279657, 0.21881518003504083, 0.11950681584536052, -0.015080196823023993, 0.02635436149058314, -0.278793338533552, 0.02457038802476447, -0.10044495785822634, -0.19057656252054406, -0.06665563028015685, 0.06336682646336858, -0.07213903358969533, -0.13701451475899973, -0.049928579916729075, 0.09469947941051643, 0.041747946979203185, -0.08815874412777971, -0.043141727148723163, -0.10003113700076938, 0.1099735613946696, 0.05936732634875282, 0.19154275143442706, 0.1061882427053862, -0.02695137893086391, -0.07491020734620388, 0.40015600449939975, -0.12730007488569187, -0.26076327175756947, 0.13695856902870487, -0.18496445987037705, -0.1526029146329851, 0.1298646561602955, 0.12537335941461145, 0.16626622649978418, -0.022909257263250528, 0.21155480075518784, -0.1273113014415044, 0.09594508481105088, 0.08525333539811802, -0.0075918443913220385, 0.10352380914025802, 0.15676696145845975, 0.15890192789346225, 0.13930667086397527, 0.005406188478952915, -0.06392695597128668, -0.4150010994101157, -0.289612229295136, -0.1685852455681373, 0.12554535546653034, -0.16867226981698655, -0.23803708897750886, 0.37791277508281906, 0.04549405368075508, 0.19411056904794008, 0.17321250760988866, 0.26299890029992234, 0.02030992682509842, 0.0634453189521112, 0.1120365116875008, 0.189553409104892, 0.12736388210390434, -0.025137261132572274, -0.11940173809531099, -0.05003196908901522, 0.21972945156949955] |
1,802.05831 | Component Outage Estimation based on Support Vector Machine | Predicting power system component outages in response to an imminent
hurricane plays a major role in preevent planning and post-event recovery of
the power system. An exact prediction of components states, however, is a
challenging task and cannot be easily performed. In this paper, a Support
Vector Machine (SVM) based method is proposed to help estimate the components
states in response to anticipated path and intensity of an imminent hurricane.
Components states are categorized into three classes of damaged, operational,
and uncertain. The damaged components along with the components in uncertain
class are then considered in multiple contingency scenarios of a proposed
Event-driven Security-Constrained Unit Commitment (E-SCUC), which considers the
simultaneous outage of multiple components under an N-m-u reliability
criterion. Experimental results on the IEEE 118-bus test system show the merits
and the effectiveness of the proposed SVM classifier and the E-SCUC model in
improving power system resilience in response to extreme events.
| cs.SY | predicting power system component outages in response to an imminent hurricane plays a major role in preevent planning and postevent recovery of the power system an exact prediction of components states however is a challenging task and cannot be easily performed in this paper a support vector machine svm based method is proposed to help estimate the components states in response to anticipated path and intensity of an imminent hurricane components states are categorized into three classes of damaged operational and uncertain the damaged components along with the components in uncertain class are then considered in multiple contingency scenarios of a proposed eventdriven securityconstrained unit commitment escuc which considers the simultaneous outage of multiple components under an nmu reliability criterion experimental results on the ieee 118bus test system show the merits and the effectiveness of the proposed svm classifier and the escuc model in improving power system resilience in response to extreme events | [['predicting', 'power', 'system', 'component', 'outages', 'in', 'response', 'to', 'an', 'imminent', 'hurricane', 'plays', 'a', 'major', 'role', 'in', 'preevent', 'planning', 'and', 'postevent', 'recovery', 'of', 'the', 'power', 'system', 'an', 'exact', 'prediction', 'of', 'components', 'states', 'however', 'is', 'a', 'challenging', 'task', 'and', 'can', 'not', 'be', 'easily', 'performed', 'in', 'this', 'paper', 'a', 'support', 'vector', 'machine', 'svm', 'based', 'method', 'is', 'proposed', 'to', 'help', 'estimate', 'the', 'components', 'states', 'in', 'response', 'to', 'anticipated', 'path', 'and', 'intensity', 'of', 'an', 'imminent', 'hurricane', 'components', 'states', 'are', 'categorized', 'into', 'three', 'classes', 'of', 'damaged', 'operational', 'and', 'uncertain', 'the', 'damaged', 'components', 'along', 'with', 'the', 'components', 'in', 'uncertain', 'class', 'are', 'then', 'considered', 'in', 'multiple', 'contingency', 'scenarios', 'of', 'a', 'proposed', 'eventdriven', 'securityconstrained', 'unit', 'commitment', 'escuc', 'which', 'considers', 'the', 'simultaneous', 'outage', 'of', 'multiple', 'components', 'under', 'an', 'nmu', 'reliability', 'criterion', 'experimental', 'results', 'on', 'the', 'ieee', '118bus', 'test', 'system', 'show', 'the', 'merits', 'and', 'the', 'effectiveness', 'of', 'the', 'proposed', 'svm', 'classifier', 'and', 'the', 'escuc', 'model', 'in', 'improving', 'power', 'system', 'resilience', 'in', 'response', 'to', 'extreme', 'events']] | [-0.15304260180595233, 0.039945999447234884, -0.038111609435873106, 0.019215112295473534, -0.030034528499910312, -0.14472135519118687, 0.04947989504235968, 0.36267800994621785, -0.2338677141325254, -0.28918817472080455, 0.13989442364943802, -0.27324770307658536, -0.155746567074139, 0.20014726065088134, -0.12340785159827455, 0.12114800104724341, 0.08755077361466217, 0.029696330688526167, 0.020353989715467355, -0.25939201769927567, 0.2687553311223287, 0.11193418013863266, 0.3498375473396951, 0.04496330319632666, 0.0968672659471275, 0.023973214777039464, -0.006644644364745258, 0.004573756504166675, -0.010804938206044761, 0.10867112202571375, 0.30022111843879284, 0.1958704012825987, 0.30427336716705833, -0.43284321479548354, -0.22448185312341115, 0.10122015830251928, 0.09973862353026082, 0.006877118992155087, 0.008049633435898852, -0.2864435271507031, 0.08568587144959326, -0.20931470494925683, -0.10423494741859797, -0.0584837988594939, 0.010376614270315162, 0.018692984859195638, -0.3253932120554198, 0.048638587652117406, 0.0569058637880325, 0.07085297401308228, -0.12234778873971663, -0.10347504911509245, -0.003691550549487338, 0.13910674097592404, 0.04960286736987703, -0.009951239586562702, 0.14395735237447202, -0.1402954303132566, -0.16772316783164187, 0.3751437113693866, -0.024914613168220967, -0.2076763242274817, 0.17067893879567764, -0.03475367689603254, -0.14243966260753377, 0.11375968952932836, 0.2679271744747989, 0.044995647910247114, -0.17140013578127286, -0.03868626441276559, 0.008611786464155701, 0.17757696864244185, 0.0021629310458114274, -0.0037328967882785946, 0.21494083457434354, 0.20740877476974243, 0.05222122199378837, 0.1509253407842296, -0.13072301279661538, -0.09337452296657782, -0.25853941630722194, -0.1372360963838506, -0.15131200784143353, -0.03495680557793285, -0.07051726214769205, -0.13013709278924293, 0.43800279060011044, 0.1946788425300742, 0.17266676854265633, 0.04116008646470666, 0.3543285127900737, 0.11121219785663446, 0.034995445595613045, 0.10023976619451883, 0.21803598427581355, 0.07794880726080584, 0.08703753487880979, -0.2132856135063584, 0.12309992767864299, 0.005341203181734799] |
1,802.05832 | A Reputation-based Stackelberg Game Model to Enhance Secrecy Rate in
Spectrum Leasing to Selfish IoT Devices | The problem of cooperative spectrum leasing to unlicensed Internet of Things
(IoT) devices is studied to account for potential selfish behavior of these
devices. A distributed game theoretic framework for spectrum leasing is
proposed where the licensed users can willingly lease a portion of their
spectrum access to unlicensed IoT devices, and in return the IoT devices
provide cooperative services, firstly to enhance information secrecy of
licensed users via adding intentional jamming to protect them from potential
eavesdroppers, and secondly to enhance the quality of communication through
cooperative relaying. The cooperative behavior of the potentially selfish IoT
devices is monitored using a reputation-based mechanism to enable the primary
users to only interact with the reliable IoT devices. The simulation results
show that using the proposed reputation-based method enhances the secrecy rate
of the primary users by reducing the possibility of attacks from selfish IoT
devices. Hence, this model can offer a practical solution for spectrum leasing
with mobile IoT devices when assuring the required quality of communication and
information secrecy for the spectrum owners.
| cs.IT math.IT | the problem of cooperative spectrum leasing to unlicensed internet of things iot devices is studied to account for potential selfish behavior of these devices a distributed game theoretic framework for spectrum leasing is proposed where the licensed users can willingly lease a portion of their spectrum access to unlicensed iot devices and in return the iot devices provide cooperative services firstly to enhance information secrecy of licensed users via adding intentional jamming to protect them from potential eavesdroppers and secondly to enhance the quality of communication through cooperative relaying the cooperative behavior of the potentially selfish iot devices is monitored using a reputationbased mechanism to enable the primary users to only interact with the reliable iot devices the simulation results show that using the proposed reputationbased method enhances the secrecy rate of the primary users by reducing the possibility of attacks from selfish iot devices hence this model can offer a practical solution for spectrum leasing with mobile iot devices when assuring the required quality of communication and information secrecy for the spectrum owners | [['the', 'problem', 'of', 'cooperative', 'spectrum', 'leasing', 'to', 'unlicensed', 'internet', 'of', 'things', 'iot', 'devices', 'is', 'studied', 'to', 'account', 'for', 'potential', 'selfish', 'behavior', 'of', 'these', 'devices', 'a', 'distributed', 'game', 'theoretic', 'framework', 'for', 'spectrum', 'leasing', 'is', 'proposed', 'where', 'the', 'licensed', 'users', 'can', 'willingly', 'lease', 'a', 'portion', 'of', 'their', 'spectrum', 'access', 'to', 'unlicensed', 'iot', 'devices', 'and', 'in', 'return', 'the', 'iot', 'devices', 'provide', 'cooperative', 'services', 'firstly', 'to', 'enhance', 'information', 'secrecy', 'of', 'licensed', 'users', 'via', 'adding', 'intentional', 'jamming', 'to', 'protect', 'them', 'from', 'potential', 'eavesdroppers', 'and', 'secondly', 'to', 'enhance', 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1,802.05833 | Load Curtailment Estimation in Response to Extreme Events | A machine learning model is proposed in this paper to help estimate potential
nodal load curtailment in response to an extreme event. This is performed
through identifying which grid components will fail as a result of an extreme
event, and consequently, which parts of the power system will encounter a
supply interruption. The proposed model to predict component outages is based
on a Support Vector Machine (SVM) model. This model considers the category and
the path of historical hurricanes, as the selected extreme event in this paper,
and accordingly trains the SVM. Once trained, the model is capable of
classifying the grid components into two categories of outage and operational
in response to imminent hurricanes. The obtained component outages are then
integrated into a load curtailment minimization model to estimate the nodal
load curtailments. The merits and the effectiveness of the proposed models are
demonstrated using the standard IEEE 30-bus system based on various hurricane
path/intensity scenarios.
| cs.SY | a machine learning model is proposed in this paper to help estimate potential nodal load curtailment in response to an extreme event this is performed through identifying which grid components will fail as a result of an extreme event and consequently which parts of the power system will encounter a supply interruption the proposed model to predict component outages is based on a support vector machine svm model this model considers the category and the path of historical hurricanes as the selected extreme event in this paper and accordingly trains the svm once trained the model is capable of classifying the grid components into two categories of outage and operational in response to imminent hurricanes the obtained component outages are then integrated into a load curtailment minimization model to estimate the nodal load curtailments the merits and the effectiveness of the proposed models are demonstrated using the standard ieee 30bus system based on various hurricane pathintensity scenarios | [['a', 'machine', 'learning', 'model', 'is', 'proposed', 'in', 'this', 'paper', 'to', 'help', 'estimate', 'potential', 'nodal', 'load', 'curtailment', 'in', 'response', 'to', 'an', 'extreme', 'event', 'this', 'is', 'performed', 'through', 'identifying', 'which', 'grid', 'components', 'will', 'fail', 'as', 'a', 'result', 'of', 'an', 'extreme', 'event', 'and', 'consequently', 'which', 'parts', 'of', 'the', 'power', 'system', 'will', 'encounter', 'a', 'supply', 'interruption', 'the', 'proposed', 'model', 'to', 'predict', 'component', 'outages', 'is', 'based', 'on', 'a', 'support', 'vector', 'machine', 'svm', 'model', 'this', 'model', 'considers', 'the', 'category', 'and', 'the', 'path', 'of', 'historical', 'hurricanes', 'as', 'the', 'selected', 'extreme', 'event', 'in', 'this', 'paper', 'and', 'accordingly', 'trains', 'the', 'svm', 'once', 'trained', 'the', 'model', 'is', 'capable', 'of', 'classifying', 'the', 'grid', 'components', 'into', 'two', 'categories', 'of', 'outage', 'and', 'operational', 'in', 'response', 'to', 'imminent', 'hurricanes', 'the', 'obtained', 'component', 'outages', 'are', 'then', 'integrated', 'into', 'a', 'load', 'curtailment', 'minimization', 'model', 'to', 'estimate', 'the', 'nodal', 'load', 'curtailments', 'the', 'merits', 'and', 'the', 'effectiveness', 'of', 'the', 'proposed', 'models', 'are', 'demonstrated', 'using', 'the', 'standard', 'ieee', '30bus', 'system', 'based', 'on', 'various', 'hurricane', 'pathintensity', 'scenarios']] | [-0.12087973117107345, 0.02588299983199836, -0.07048565593938674, 0.06510194648928459, -0.06348782511007402, -0.1405752265255057, 0.06367125567799854, 0.35465008725352104, -0.2521981165654236, -0.30889436303968393, 0.14643405176879418, -0.28319967116619793, -0.1498148538329969, 0.18247191749828598, -0.1286158134079268, 0.07896614417493825, 0.08618520527717567, 0.06146389269720643, 0.017568141155906263, -0.258583916800337, 0.3036494888290162, 0.1086013653465817, 0.3501754744278808, 0.01915887597227289, 0.10520058967869714, -0.007564700067403817, -0.011018905423641685, 0.005774068581541219, -0.027847152166519194, 0.14798170216681977, 0.2764507177251348, 0.19304563873297265, 0.3042753140359456, -0.4422486740254587, -0.2537134714845207, 0.11648608578459149, 0.11436858176734419, 0.016158972673245797, -0.008598076090997745, -0.26567247156654633, 0.07860035332400472, -0.2452236571859929, -0.08298473621328031, -0.04128364508430804, -0.007991560422364742, 0.05001447017514898, -0.3101651129282771, 0.002991110149530634, 0.04569214706219012, 0.06247188292684094, -0.09870031411429087, -0.07361806661506454, -0.04400125986881434, 0.1081799453034276, 0.0696373266813856, -0.0073823748892473595, 0.14335526238766408, -0.11555236072369642, -0.13580575226956318, 0.3905726618343784, -0.030989821279241193, -0.1776701619488097, 0.15712267905774135, -0.02842165158701039, -0.09776146463569134, 0.09692257622617387, 0.28047926454774796, 0.045602814420577016, -0.17775629696646525, -0.02469367635954592, 0.0029722119050641215, 0.13860439555869708, 0.0008053853657216795, -0.06662704324530017, 0.2193567854223206, 0.22949685957432994, 0.03595328007725203, 0.14638254617851587, -0.1588569223249872, -0.09183944903254028, -0.2800137888745315, -0.12126343700914613, -0.16020415369421243, -0.026229708671810164, -0.062110183082592314, -0.147090377482856, 0.45165188764372177, 0.22192373105752913, 0.19774475836104924, 0.07279508821516027, 0.37325586647606424, 0.12259080629765746, 0.05947188533872606, 0.09049424324485082, 0.204744360634997, 0.05704519487616996, 0.11695690915033581, -0.18862220999754725, 0.09122707068679795, 0.06111447238513539] |
1,802.05834 | On discrete Wigner transforms | In this work, we derive a discrete analog of the Wigner transform over the
space $(\mathbb{C}^p)^{\otimes N}$ for any prime $p$ and any positive integer
$N$. We show that the Wigner transform over this space can be constructed as
the inverse Fourier transform of the standard Pauli matrices for $p=2$ or more
generally of the Heisenberg-Weyl group elements for $p > 2$. We connect our
work to a previous construction by Wootters of a discrete Wigner transform by
showing that for all $p$, Wootters' construction corresponds to taking the
inverse symplectic Fourier transform instead of the inverse Fourier transform.
Finally, we discuss some implications of these results for the numerical
simulation of many-body quantum spin systems.
| math-ph math.MP | in this work we derive a discrete analog of the wigner transform over the space mathbbcpotimes n for any prime p and any positive integer n we show that the wigner transform over this space can be constructed as the inverse fourier transform of the standard pauli matrices for p2 or more generally of the heisenbergweyl group elements for p 2 we connect our work to a previous construction by wootters of a discrete wigner transform by showing that for all p wootters construction corresponds to taking the inverse symplectic fourier transform instead of the inverse fourier transform finally we discuss some implications of these results for the numerical simulation of manybody quantum spin systems | [['in', 'this', 'work', 'we', 'derive', 'a', 'discrete', 'analog', 'of', 'the', 'wigner', 'transform', 'over', 'the', 'space', 'mathbbcpotimes', 'n', 'for', 'any', 'prime', 'p', 'and', 'any', 'positive', 'integer', 'n', 'we', 'show', 'that', 'the', 'wigner', 'transform', 'over', 'this', 'space', 'can', 'be', 'constructed', 'as', 'the', 'inverse', 'fourier', 'transform', 'of', 'the', 'standard', 'pauli', 'matrices', 'for', 'p2', 'or', 'more', 'generally', 'of', 'the', 'heisenbergweyl', 'group', 'elements', 'for', 'p', '2', 'we', 'connect', 'our', 'work', 'to', 'a', 'previous', 'construction', 'by', 'wootters', 'of', 'a', 'discrete', 'wigner', 'transform', 'by', 'showing', 'that', 'for', 'all', 'p', 'wootters', 'construction', 'corresponds', 'to', 'taking', 'the', 'inverse', 'symplectic', 'fourier', 'transform', 'instead', 'of', 'the', 'inverse', 'fourier', 'transform', 'finally', 'we', 'discuss', 'some', 'implications', 'of', 'these', 'results', 'for', 'the', 'numerical', 'simulation', 'of', 'manybody', 'quantum', 'spin', 'systems']] | [-0.09902016940555257, 0.13445803684411212, -0.14314136221972212, 0.08381463175337239, -0.061081593673423, -0.10084360548327759, 0.020795264679595436, 0.3443674381246398, -0.3056694811859511, -0.21919710347183832, 0.09430014262314919, -0.2362389273435008, -0.19437807072730115, 0.23641960500134973, -0.0318995832012182, 0.09696920681949052, 0.002615425425271193, 0.055573757974790375, -0.1766354943821697, -0.2578125773242822, 0.32411906982592326, 0.005459456883264673, 0.227924273831345, -0.04469711536525242, 0.06844247332283933, 0.09607279630941584, -0.061252177407017404, -0.06133826279638972, -0.08994444445443948, 0.10635717678815126, 0.2505686219716281, 0.10304830527227175, 0.23847067754781037, -0.4049752162475335, -0.2055853953816838, 0.14826436888982067, 0.15271570292513884, 0.06836785740431464, -0.05309325092119316, -0.29364316296159176, 0.10915690233620505, -0.14367647824416818, -0.11982134982878179, -0.11010013904683945, 0.0733973842375634, -0.012890236427713381, -0.2895279617228529, 0.04353282210139329, 0.14120910887651375, 0.03497367098788617, -0.06469726845777284, -0.1518261597242631, 0.06578330729736767, 0.069389890865096, -0.031321767635904905, -0.00286074743397828, 0.06358954945697629, -0.022997265318081828, -0.14130241695072568, 0.36489852153483715, -0.01579108345067014, -0.24073351995831585, 0.09505701049949908, -0.19297042466585704, -0.13944246778019556, 0.11369514338704839, 0.124849454793883, 0.0937346593199069, -0.05998547645612506, 0.16561442357436917, -0.10511021506400746, 0.13157411302815666, 0.09819879486601342, 0.02267670106927031, 0.10758831958172091, 0.054797914154876615, 0.07762079988657333, 0.14810168166469226, -0.05998290647505456, -0.06967116273089982, -0.36017551171805773, -0.2591231503128687, -0.27436379652997284, 0.11295437476630404, -0.0801749209659695, -0.12690766896693068, 0.3799420160633561, 0.08937939684102802, 0.18197044840474663, 0.1126513748343936, 0.2385186180880849, 0.1666396600316818, 0.03217021494352242, 0.026564952339915755, 0.11116257607819423, 0.17568438302055656, 0.04260892720623432, -0.24659647880859362, -0.06505412926382728, 0.13020367967373317] |
1,802.05835 | An Anytime Algorithm for Task and Motion MDPs | Integrated task and motion planning has emerged as a challenging problem in
sequential decision making, where a robot needs to compute high-level strategy
and low-level motion plans for solving complex tasks. While high-level
strategies require decision making over longer time-horizons and scales, their
feasibility depends on low-level constraints based upon the geometries and
continuous dynamics of the environment. The hybrid nature of this problem makes
it difficult to scale; most existing approaches focus on deterministic, fully
observable scenarios. We present a new approach where the high-level decision
problem occurs in a stochastic setting and can be modeled as a Markov decision
process. In contrast to prior efforts, we show that complete MDP policies, or
contingent behaviors, can be computed effectively in an anytime fashion. Our
algorithm continuously improves the quality of the solution and is guaranteed
to be probabilistically complete. We evaluate the performance of our approach
on a challenging, realistic test problem: autonomous aircraft inspection. Our
results show that we can effectively compute consistent task and motion
policies for the most likely execution-time outcomes using only a fraction of
the computation required to develop the complete task and motion policy.
| cs.AI | integrated task and motion planning has emerged as a challenging problem in sequential decision making where a robot needs to compute highlevel strategy and lowlevel motion plans for solving complex tasks while highlevel strategies require decision making over longer timehorizons and scales their feasibility depends on lowlevel constraints based upon the geometries and continuous dynamics of the environment the hybrid nature of this problem makes it difficult to scale most existing approaches focus on deterministic fully observable scenarios we present a new approach where the highlevel decision problem occurs in a stochastic setting and can be modeled as a markov decision process in contrast to prior efforts we show that complete mdp policies or contingent behaviors can be computed effectively in an anytime fashion our algorithm continuously improves the quality of the solution and is guaranteed to be probabilistically complete we evaluate the performance of our approach on a challenging realistic test problem autonomous aircraft inspection our results show that we can effectively compute consistent task and motion policies for the most likely executiontime outcomes using only a fraction of the computation required to develop the complete task and motion policy | [['integrated', 'task', 'and', 'motion', 'planning', 'has', 'emerged', 'as', 'a', 'challenging', 'problem', 'in', 'sequential', 'decision', 'making', 'where', 'a', 'robot', 'needs', 'to', 'compute', 'highlevel', 'strategy', 'and', 'lowlevel', 'motion', 'plans', 'for', 'solving', 'complex', 'tasks', 'while', 'highlevel', 'strategies', 'require', 'decision', 'making', 'over', 'longer', 'timehorizons', 'and', 'scales', 'their', 'feasibility', 'depends', 'on', 'lowlevel', 'constraints', 'based', 'upon', 'the', 'geometries', 'and', 'continuous', 'dynamics', 'of', 'the', 'environment', 'the', 'hybrid', 'nature', 'of', 'this', 'problem', 'makes', 'it', 'difficult', 'to', 'scale', 'most', 'existing', 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1,802.05836 | Impacts of nuclear-physics uncertainties in the s-process determined by
Monte-Carlo variations | The s-process, a production mechanism based on slow-neutron capture during
stellar evolution, is the origin of about half the elements heavier than iron.
Abundance predictions for s-process nucleosynthesis depend strongly on the
relevant neutron-capture and $\beta$-decay rates, as well as on the details of
the stellar model being considered. Here, we have used a Monte-Carlo approach
to evaluate the nuclear uncertainty in s-process nucleosynthesis. We considered
the helium burning of massive stars for the weak s-process and low-mass
asymptotic-giant-branch stars for the main s-process. Our calculations include
a realistic and general prescription for the temperature dependent uncertainty
for the reaction cross sections. We find that the adopted uncertainty for
(${\rm n},\gamma$) rates, tens of per cent on average, effects the production
of s-process nuclei along the line of $\beta$-stability, and that the
uncertainties in $\beta$-decay from excited state contributions, has the
strongest impact on branching points.
| astro-ph.SR nucl-ex nucl-th | the sprocess a production mechanism based on slowneutron capture during stellar evolution is the origin of about half the elements heavier than iron abundance predictions for sprocess nucleosynthesis depend strongly on the relevant neutroncapture and betadecay rates as well as on the details of the stellar model being considered here we have used a montecarlo approach to evaluate the nuclear uncertainty in sprocess nucleosynthesis we considered the helium burning of massive stars for the weak sprocess and lowmass asymptoticgiantbranch stars for the main sprocess our calculations include a realistic and general prescription for the temperature dependent uncertainty for the reaction cross sections we find that the adopted uncertainty for rm ngamma rates tens of per cent on average effects the production of sprocess nuclei along the line of betastability and that the uncertainties in betadecay from excited state contributions has the strongest impact on branching points | [['the', 'sprocess', 'a', 'production', 'mechanism', 'based', 'on', 'slowneutron', 'capture', 'during', 'stellar', 'evolution', 'is', 'the', 'origin', 'of', 'about', 'half', 'the', 'elements', 'heavier', 'than', 'iron', 'abundance', 'predictions', 'for', 'sprocess', 'nucleosynthesis', 'depend', 'strongly', 'on', 'the', 'relevant', 'neutroncapture', 'and', 'betadecay', 'rates', 'as', 'well', 'as', 'on', 'the', 'details', 'of', 'the', 'stellar', 'model', 'being', 'considered', 'here', 'we', 'have', 'used', 'a', 'montecarlo', 'approach', 'to', 'evaluate', 'the', 'nuclear', 'uncertainty', 'in', 'sprocess', 'nucleosynthesis', 'we', 'considered', 'the', 'helium', 'burning', 'of', 'massive', 'stars', 'for', 'the', 'weak', 'sprocess', 'and', 'lowmass', 'asymptoticgiantbranch', 'stars', 'for', 'the', 'main', 'sprocess', 'our', 'calculations', 'include', 'a', 'realistic', 'and', 'general', 'prescription', 'for', 'the', 'temperature', 'dependent', 'uncertainty', 'for', 'the', 'reaction', 'cross', 'sections', 'we', 'find', 'that', 'the', 'adopted', 'uncertainty', 'for', 'rm', 'ngamma', 'rates', 'tens', 'of', 'per', 'cent', 'on', 'average', 'effects', 'the', 'production', 'of', 'sprocess', 'nuclei', 'along', 'the', 'line', 'of', 'betastability', 'and', 'that', 'the', 'uncertainties', 'in', 'betadecay', 'from', 'excited', 'state', 'contributions', 'has', 'the', 'strongest', 'impact', 'on', 'branching', 'points']] | [0.00964999685425962, 0.1708581071053568, -0.015449294822221966, 0.15360317043784674, -0.00021734406090379783, -0.03204857395150163, 0.08636744957168471, 0.35851351881068044, -0.14431960977397043, -0.2946090326133768, -0.010133530962127836, -0.3424820770598846, 0.044039169533739556, 0.22050597078855827, 0.0015279051165211282, 0.0141870590996903, 0.1254285338768506, 0.054816466427650555, -0.061642949616102734, -0.24849558460896146, 0.364404726154745, 0.09720172278400852, 0.19138401948961697, 0.06365492113240778, 0.010679399833874139, -0.08121371663124492, -0.037972139366439936, -0.10199670116289532, -0.14303870554361572, 0.08196002731182651, 0.21573086358504753, 0.13805719668185976, 0.14460531147579625, -0.3899058512450285, -0.19395664508441743, 0.07600574448149754, 0.18034733834434047, 0.10867640050128102, -0.13086765961903338, -0.23697495420163575, 0.054150500280578454, -0.2251688769272184, -0.10490461499494029, 0.017353828160781157, 0.03624971142383165, 0.04398059002770511, -0.27914695305213943, 0.0752351451872478, 0.028996265409609992, 0.053972172829486374, -0.06995675824534735, -0.21334974761423975, -0.08674655888510281, 0.09183481585653817, 0.08738310387348501, -0.03141273922276562, 0.21788347203424838, -0.05977460677365331, -0.031909802682978446, 0.4295675887148955, -0.10865001954755839, -0.06277637335484566, 0.11848477166216208, -0.179114283746657, -0.23598465901219018, 0.14424659168724072, 0.21396269943335489, 0.18517983909907643, -0.1560093880350357, 0.030763557592202742, 0.06778162146108352, 0.19818452599040576, 0.036332735455314284, 0.05091732881256787, 0.21418790398675897, 0.2233053675106382, -0.027438765676366803, -0.0216873724664217, -0.18169327855129305, -0.11351451292404369, -0.3085869918577373, -0.11819895079250971, -0.030430653358949986, 0.08525326005055582, -0.11960317126167491, -0.14570245705903762, 0.32267426518238573, 0.13326791588740092, 0.2016655402497248, -0.015764617408970245, 0.326225771453895, 0.10130760771914525, 0.0812564665214031, 0.061529006136019956, 0.3332006215693569, 0.24615102038680486, 0.0643782568553284, -0.30870403880604275, 0.23142879681457598, 0.04185057567011514] |
1,802.05837 | Sensitivity to neutron captures and beta-decays of the enhanced
s-process in rotating massive stars at low metallicities | The s-process in massive stars, producing nuclei up to $A\approx 90$, has a
different behaviour at low metallicity if stellar rotation is significant. This
enhanced s-process is distinct from the s-process in massive stars around solar
metallicity, and details of the nucleosynthesis are poorly known. We
investigated nuclear physics uncertainties in the enhanced s-process in
metal-poor stars within a Monte-Carlo framework. We applied
temperature-dependent uncertainties of reaction rates, distinguishing
contributions from the ground state and from excited states. We found that the
final abundance of several isotopes shows uncertainties larger than a factor of
2, mostly due to the neutron capture uncertainties. A few nuclei around
branching points are affected by uncertainties in the $\beta$-decay.
| astro-ph.SR nucl-ex nucl-th | the sprocess in massive stars producing nuclei up to aapprox 90 has a different behaviour at low metallicity if stellar rotation is significant this enhanced sprocess is distinct from the sprocess in massive stars around solar metallicity and details of the nucleosynthesis are poorly known we investigated nuclear physics uncertainties in the enhanced sprocess in metalpoor stars within a montecarlo framework we applied temperaturedependent uncertainties of reaction rates distinguishing contributions from the ground state and from excited states we found that the final abundance of several isotopes shows uncertainties larger than a factor of 2 mostly due to the neutron capture uncertainties a few nuclei around branching points are affected by uncertainties in the betadecay | [['the', 'sprocess', 'in', 'massive', 'stars', 'producing', 'nuclei', 'up', 'to', 'aapprox', '90', 'has', 'a', 'different', 'behaviour', 'at', 'low', 'metallicity', 'if', 'stellar', 'rotation', 'is', 'significant', 'this', 'enhanced', 'sprocess', 'is', 'distinct', 'from', 'the', 'sprocess', 'in', 'massive', 'stars', 'around', 'solar', 'metallicity', 'and', 'details', 'of', 'the', 'nucleosynthesis', 'are', 'poorly', 'known', 'we', 'investigated', 'nuclear', 'physics', 'uncertainties', 'in', 'the', 'enhanced', 'sprocess', 'in', 'metalpoor', 'stars', 'within', 'a', 'montecarlo', 'framework', 'we', 'applied', 'temperaturedependent', 'uncertainties', 'of', 'reaction', 'rates', 'distinguishing', 'contributions', 'from', 'the', 'ground', 'state', 'and', 'from', 'excited', 'states', 'we', 'found', 'that', 'the', 'final', 'abundance', 'of', 'several', 'isotopes', 'shows', 'uncertainties', 'larger', 'than', 'a', 'factor', 'of', '2', 'mostly', 'due', 'to', 'the', 'neutron', 'capture', 'uncertainties', 'a', 'few', 'nuclei', 'around', 'branching', 'points', 'are', 'affected', 'by', 'uncertainties', 'in', 'the', 'betadecay']] | [-0.024060136049925147, 0.23041713185932325, -0.026363926273821246, 0.15500428549155754, 0.009946943180757048, -0.025306934723124393, 0.09849448023849855, 0.36732491377903065, -0.168092635727447, -0.3468825212639311, -0.03801650254804965, -0.34441659862742474, 0.03788488178878375, 0.18493367444885814, -0.061866734057421914, -0.004868032703297618, 0.11960459047847467, 0.0015018055984831376, -0.09363309308765051, -0.22535275678939956, 0.32333883251185, 0.0721780690326578, 0.1606302931094947, 0.0036253970602284306, -0.007495674847260766, -0.16941881411425444, -0.03118648467099537, -0.05484098930559728, -0.10286411129010326, 0.050314875260380136, 0.27470806347732635, 0.1111609925556442, 0.1706355729666741, -0.35785605170480583, -0.18117915190675338, 0.07079541844255088, 0.23207365067918664, 0.13251429695798003, -0.1337434341726096, -0.2699932832372091, 0.07887868763797957, -0.20989975124779764, -0.13541265712727024, 0.007047430502817683, 0.03655277989480806, 0.007451806271297834, -0.2531515507594399, 0.1073505934641656, 0.0016842626541367044, 0.1044379278531541, -0.06328327183323183, -0.23105582211814496, -0.06689342197597675, 0.08280843154972663, 0.10252289016245177, 0.015540174979200263, 0.20500677575802673, -0.09540327500511447, 0.004902785514359889, 0.43578401654756266, -0.0745187176782486, -0.029073428054866582, 0.1550772119799386, -0.24750456592553985, -0.22595784473435387, 0.18955033147383643, 0.19300679577534777, 0.1717492441892239, -0.19545851387893376, 0.03753457941322425, 0.07686333604075986, 0.20694685707820337, 0.0493824862125937, 0.05004376589482569, 0.2432438428149275, 0.21111053669744212, -0.02765958538359922, -0.004076958115658035, -0.1844280857410606, -0.10806158160709817, -0.22138935328744677, -0.054129014988227385, -0.04446760549857889, 0.08621014189177557, -0.12593946600226563, -0.10560970299183796, 0.3494594014695157, 0.10677279440443153, 0.197808046043729, -0.0521440586966017, 0.3085731509910977, 0.0721370760432404, 0.09511314671810554, 0.12063383954759363, 0.35625642475408864, 0.24538197081495564, 0.04136143510189393, -0.28996595356046506, 0.1715601455582225, -0.028292951299606458] |
1,802.05838 | Mode Analysis for Energetics of a Moving Charge In Lorentz- and
CPT-Violating Electrodynamics | In isotropic but Lorentz- and CPT-violating electrodynamics, it is known that
a charge in unifom motion does not lose any energy to Cerenkov radiation. This
presents a puzzle, since the radiation appears to be kinematically allowed for
many modes. Studying the Fourier transforms of the most important terms in the
modified magnetic field and Poynting vector, we confirm the vanishing of the
the radiation rate. Moreover, we show that the Fourier transform of the field
changes sign between small and large wave numbers. This enables modes with very
long wavelengths to carry negative energies, which cancel out the positive
energies carried away by modes with shorter wavelengths. This cancelation had
previously been inferred but never explicitly demonstrated.
| hep-th | in isotropic but lorentz and cptviolating electrodynamics it is known that a charge in unifom motion does not lose any energy to cerenkov radiation this presents a puzzle since the radiation appears to be kinematically allowed for many modes studying the fourier transforms of the most important terms in the modified magnetic field and poynting vector we confirm the vanishing of the the radiation rate moreover we show that the fourier transform of the field changes sign between small and large wave numbers this enables modes with very long wavelengths to carry negative energies which cancel out the positive energies carried away by modes with shorter wavelengths this cancelation had previously been inferred but never explicitly demonstrated | [['in', 'isotropic', 'but', 'lorentz', 'and', 'cptviolating', 'electrodynamics', 'it', 'is', 'known', 'that', 'a', 'charge', 'in', 'unifom', 'motion', 'does', 'not', 'lose', 'any', 'energy', 'to', 'cerenkov', 'radiation', 'this', 'presents', 'a', 'puzzle', 'since', 'the', 'radiation', 'appears', 'to', 'be', 'kinematically', 'allowed', 'for', 'many', 'modes', 'studying', 'the', 'fourier', 'transforms', 'of', 'the', 'most', 'important', 'terms', 'in', 'the', 'modified', 'magnetic', 'field', 'and', 'poynting', 'vector', 'we', 'confirm', 'the', 'vanishing', 'of', 'the', 'the', 'radiation', 'rate', 'moreover', 'we', 'show', 'that', 'the', 'fourier', 'transform', 'of', 'the', 'field', 'changes', 'sign', 'between', 'small', 'and', 'large', 'wave', 'numbers', 'this', 'enables', 'modes', 'with', 'very', 'long', 'wavelengths', 'to', 'carry', 'negative', 'energies', 'which', 'cancel', 'out', 'the', 'positive', 'energies', 'carried', 'away', 'by', 'modes', 'with', 'shorter', 'wavelengths', 'this', 'cancelation', 'had', 'previously', 'been', 'inferred', 'but', 'never', 'explicitly', 'demonstrated']] | [-0.15645623805627612, 0.20474497005664583, -0.09150580367362447, 0.0932348298093561, -0.11363110427969489, -0.11249613557171462, -0.0005378607793004606, 0.36938023541508047, -0.23917998470353957, -0.28539761249778856, 0.06625443742437899, -0.24174333413549023, -0.0933485234894886, 0.19413974725416508, 0.008433678444346478, 0.007774780561609579, 0.02794454214444127, 0.02712924326448862, -0.04554817047981738, -0.18209783000678853, 0.2844901922608501, 0.11353267123119841, 0.2666153478323771, 0.07117763060096909, 0.09262814583931246, -0.008653668916567454, -0.053020109936338045, 0.019864783224906642, -0.07898177077644003, 0.021846189752287845, 0.22812042085068493, 0.04643167416235946, 0.23400450263027872, -0.44340379470317015, -0.22682830172687254, 0.14021674111088564, 0.16665733786869857, 0.12215755489988028, -0.053205279986663113, -0.23818144932939223, 0.05009417078891319, -0.11772850232503923, -0.14949453619292713, -0.08479347749944909, 0.04734415037292537, -0.02398168153514893, -0.23488652814332203, 0.07692164246390316, 0.057562508042259465, 0.0038971853370232314, -0.06576111222063352, -0.11769846478348663, -0.02138003395420724, 0.08480531611511934, 0.15507072319702148, 0.042756525407232154, 0.11169855464811854, -0.12213222952439161, -0.07461989389236712, 0.3733810608692724, -0.058822374710620476, -0.18734337256494749, 0.18154414602884508, -0.24318204887195266, -0.08518118054329032, 0.22255832939468934, 0.0954308944698102, 0.11808444883329151, -0.12978950697639635, 0.05559834386553246, -0.0025391439215535026, 0.19139085434565867, 0.1267106159317211, 0.06655962912943857, 0.24329362171380942, 0.03379077779667067, 0.024841985211141632, 0.12595106135343265, -0.11747789923262236, -0.05003481847635503, -0.299015854710135, -0.15334915262968118, -0.18238359849633054, 0.07616000803606986, -0.014653457288157621, -0.15939778478090813, 0.38340524721144026, 0.14188108929224183, 0.17145113889449115, 0.014771378066206094, 0.30375567133185166, 0.1440713946659375, 0.16094624856085485, 0.10269743992930985, 0.3552335899238924, 0.13628181800896558, 0.14399429373764272, -0.2477832309648812, -0.008595227617128142, 0.017301620501053425] |
1,802.05839 | New High Performance GPGPU Code Transformation Framework Applied to
Large Production Weather Prediction Code | We introduce "Hybrid Fortran", a new approach that allows a high performance
GPGPU port for structured grid Fortran codes. This technique only requires
minimal changes for a CPU targeted codebase, which is a significant advancement
in terms of productivity. It has been successfully applied to both dynamical
core and physical processes of ASUCA, a Japanese mesoscale weather prediction
model with more than 150k lines of code. By means of a minimal weather
application that resembles ASUCA's code structure, Hybrid Fortran is compared
to both a performance model as well as today's commonly used method, OpenACC.
As a result, the Hybrid Fortran implementation is shown to deliver the same or
better performance than OpenACC and its performance agrees with the model both
on CPU and GPU. In a full scale production run, using an ASUCA grid with 1581 x
1301 x 58 cells and real world weather data in 2km resolution, 24 NVIDIA Tesla
P100 running the Hybrid Fortran based GPU port are shown to replace more than
50 18-core Intel Xeon Broadwell E5-2695 v4 running the reference implementation
- an achievement comparable to more invasive GPGPU rewrites of other weather
models.
| cs.DC physics.ao-ph | we introduce hybrid fortran a new approach that allows a high performance gpgpu port for structured grid fortran codes this technique only requires minimal changes for a cpu targeted codebase which is a significant advancement in terms of productivity it has been successfully applied to both dynamical core and physical processes of asuca a japanese mesoscale weather prediction model with more than 150k lines of code by means of a minimal weather application that resembles asucas code structure hybrid fortran is compared to both a performance model as well as todays commonly used method openacc as a result the hybrid fortran implementation is shown to deliver the same or better performance than openacc and its performance agrees with the model both on cpu and gpu in a full scale production run using an asuca grid with 1581 x 1301 x 58 cells and real world weather data in 2km resolution 24 nvidia tesla p100 running the hybrid fortran based gpu port are shown to replace more than 50 18core intel xeon broadwell e52695 v4 running the reference implementation an achievement comparable to more invasive gpgpu rewrites of other weather models | [['we', 'introduce', 'hybrid', 'fortran', 'a', 'new', 'approach', 'that', 'allows', 'a', 'high', 'performance', 'gpgpu', 'port', 'for', 'structured', 'grid', 'fortran', 'codes', 'this', 'technique', 'only', 'requires', 'minimal', 'changes', 'for', 'a', 'cpu', 'targeted', 'codebase', 'which', 'is', 'a', 'significant', 'advancement', 'in', 'terms', 'of', 'productivity', 'it', 'has', 'been', 'successfully', 'applied', 'to', 'both', 'dynamical', 'core', 'and', 'physical', 'processes', 'of', 'asuca', 'a', 'japanese', 'mesoscale', 'weather', 'prediction', 'model', 'with', 'more', 'than', '150k', 'lines', 'of', 'code', 'by', 'means', 'of', 'a', 'minimal', 'weather', 'application', 'that', 'resembles', 'asucas', 'code', 'structure', 'hybrid', 'fortran', 'is', 'compared', 'to', 'both', 'a', 'performance', 'model', 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1,802.0584 | Nonlinear spherical perturbations in Quintessence Models of Dark Energy | Observations have confirmed the accelerated expansion of the universe. The
accelerated expansion can be modelled by invoking a cosmological constant or a
dynamical model of dark energy. A key difference between these models is that
the equation of state parameter $w$ for dark energy differs from $-1$ in
dynamical dark energy (DDE) models. Further, the equation of state parameter is
not constant for a general DDE model. Such differences can be probed using the
variation of scale factor with time by measuring distances. Another significant
difference between the cosmological constant and DDE models is that the latter
must cluster. Linear perturbation analysis indicates that perturbations in
quintessence models of dark energy do not grow to have a significant amplitude
at small length scales. In this paper we study the response of quintessence
dark energy to non-linear perturbations in dark matter. We use a fully
relativistic model for spherically symmetric perturbations. In this study we
focus on thawing models. We find that in response to non-linear perturbations
in dark matter, dark energy perturbations grow at a faster rate than expected
in linear perturbation theory. We find that dark energy perturbation remains
localised and does not diffuse out to larger scales. The dominant drivers of
the evolution of dark energy perturbations are the local Hubble flow and a
supression of gradients of the scalar field. We also find that the equation of
state parameter $w$ changes in response to perturbations in dark matter such
that it also becomes a function of position. The variation of $w$ in space is
correlated with density contrast for matter. Variation of $w$ and perturbations
in dark energy are more pronounced in response to large scale perturbations in
matter while the dependence on the amplitude of matter perturbations is much
weaker.
| astro-ph.CO gr-qc | observations have confirmed the accelerated expansion of the universe the accelerated expansion can be modelled by invoking a cosmological constant or a dynamical model of dark energy a key difference between these models is that the equation of state parameter w for dark energy differs from 1 in dynamical dark energy dde models further the equation of state parameter is not constant for a general dde model such differences can be probed using the variation of scale factor with time by measuring distances another significant difference between the cosmological constant and dde models is that the latter must cluster linear perturbation analysis indicates that perturbations in quintessence models of dark energy do not grow to have a significant amplitude at small length scales in this paper we study the response of quintessence dark energy to nonlinear perturbations in dark matter we use a fully relativistic model for spherically symmetric perturbations in this study we focus on thawing models we find that in response to nonlinear perturbations in dark matter dark energy perturbations grow at a faster rate than expected in linear perturbation theory we find that dark energy perturbation remains localised and does not diffuse out to larger scales the dominant drivers of the evolution of dark energy perturbations are the local hubble flow and a supression of gradients of the scalar field we also find that the equation of state parameter w changes in response to perturbations in dark matter such that it also becomes a function of position the variation of w in space is correlated with density contrast for matter variation of w and perturbations in dark energy are more pronounced in response to large scale perturbations in matter while the dependence on the amplitude of matter perturbations is much weaker | [['observations', 'have', 'confirmed', 'the', 'accelerated', 'expansion', 'of', 'the', 'universe', 'the', 'accelerated', 'expansion', 'can', 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1,802.05841 | Rapid Bayesian optimisation for synthesis of short polymer fiber
materials | The discovery of processes for the synthesis of new materials involves many
decisions about process design, operation, and material properties.
Experimentation is crucial but as complexity increases, exploration of
variables can become impractical using traditional combinatorial approaches. We
describe an iterative method which uses machine learning to optimise process
development, incorporating multiple qualitative and quantitative objectives. We
demonstrate the method with a novel fluid processing platform for synthesis of
short polymer fibers, and show how the synthesis process can be efficiently
directed to achieve material and process objectives.
| stat.ML physics.comp-ph | the discovery of processes for the synthesis of new materials involves many decisions about process design operation and material properties experimentation is crucial but as complexity increases exploration of variables can become impractical using traditional combinatorial approaches we describe an iterative method which uses machine learning to optimise process development incorporating multiple qualitative and quantitative objectives we demonstrate the method with a novel fluid processing platform for synthesis of short polymer fibers and show how the synthesis process can be efficiently directed to achieve material and process objectives | [['the', 'discovery', 'of', 'processes', 'for', 'the', 'synthesis', 'of', 'new', 'materials', 'involves', 'many', 'decisions', 'about', 'process', 'design', 'operation', 'and', 'material', 'properties', 'experimentation', 'is', 'crucial', 'but', 'as', 'complexity', 'increases', 'exploration', 'of', 'variables', 'can', 'become', 'impractical', 'using', 'traditional', 'combinatorial', 'approaches', 'we', 'describe', 'an', 'iterative', 'method', 'which', 'uses', 'machine', 'learning', 'to', 'optimise', 'process', 'development', 'incorporating', 'multiple', 'qualitative', 'and', 'quantitative', 'objectives', 'we', 'demonstrate', 'the', 'method', 'with', 'a', 'novel', 'fluid', 'processing', 'platform', 'for', 'synthesis', 'of', 'short', 'polymer', 'fibers', 'and', 'show', 'how', 'the', 'synthesis', 'process', 'can', 'be', 'efficiently', 'directed', 'to', 'achieve', 'material', 'and', 'process', 'objectives']] | [-0.05648612780020234, 0.05881526086903415, -0.10697780368553306, 0.018202420693590037, -0.1475190712477673, -0.1723444953042252, 0.05406753185459159, 0.46108038871633733, -0.3461101495301012, -0.34550647596320644, 0.11738961297404868, -0.1949812520020218, -0.2058380218011073, 0.2423594046532261, -0.09792373967568645, 0.12290863004322587, 0.11480219560326077, -0.07361330301500857, -0.03835328238801594, -0.22012490012936972, 0.2519429558888078, 0.07769054445353421, 0.3364149885548448, 0.017400864749850534, 0.12228825629096139, 0.05395899416709488, -0.01993215116883882, -0.01747565327042883, -0.0973352677008899, 0.16029358707072044, 0.3052750138687605, 0.21888718591071665, 0.3208423519284803, -0.45569018547592516, -0.22803767621834678, 0.0686308803653281, 0.14977025121267865, 0.10835061774097002, -0.07541267672257329, -0.21756808334876868, 0.05051911328866316, -0.13883856473892758, -0.06862290644362061, -0.17369886634829032, -0.03872102709084919, 0.026484262409873983, -0.2999830556542623, -0.043182931761045686, 0.07563234505307098, 0.047237831181105736, -0.03568742288519967, -0.1318169160301543, 0.05587742809439078, 0.16755096932915461, 0.012226124872210097, -0.018515661379999736, 0.20181780708001248, -0.15035372179276732, -0.19533009493765843, 0.37968948495108634, -0.012475928244053979, -0.1535843908257613, 0.2266863055463711, -0.01556999341383661, -0.16954951788548814, 0.15281064110141332, 0.2451908020937646, 0.1418768778729083, -0.19515938368964603, 0.02563439602487382, 0.07368658368729732, 0.21171190999236636, -0.007707523801152341, 0.019191720988601446, 0.200111969524931, 0.3140928044661202, 0.057853468555549625, 0.1798602720095501, -0.019345814224586567, -0.09219696865925057, -0.21838356725493213, -0.2162681715033779, -0.1555264323119032, 0.002920333253727718, -0.07218290243856079, -0.15734612704678017, 0.34632609123681585, 0.19031702473082326, 0.1501949687689458, 0.053334676164509306, 0.315529842173088, 0.060522367943618025, 0.060146386343562466, 0.03997901629720053, 0.18271028270034798, 0.08684356211134317, 0.1348364525368776, -0.18402119789045007, 0.13001057601121085, 0.02934629401583648] |
1,802.05842 | Neural Granger Causality | While most classical approaches to Granger causality detection assume linear
dynamics, many interactions in real-world applications, like neuroscience and
genomics, are inherently nonlinear. In these cases, using linear models may
lead to inconsistent estimation of Granger causal interactions. We propose a
class of nonlinear methods by applying structured multilayer perceptrons (MLPs)
or recurrent neural networks (RNNs) combined with sparsity-inducing penalties
on the weights. By encouraging specific sets of weights to be zero--in
particular, through the use of convex group-lasso penalties--we can extract the
Granger causal structure. To further contrast with traditional approaches, our
framework naturally enables us to efficiently capture long-range dependencies
between series either via our RNNs or through an automatic lag selection in the
MLP. We show that our neural Granger causality methods outperform
state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge
data. This data consists of nonlinear gene expression and regulation time
courses with only a limited number of time points. The successes we show in
this challenging dataset provide a powerful example of how deep learning can be
useful in cases that go beyond prediction on large datasets. We likewise
illustrate our methods in detecting nonlinear interactions in a human motion
capture dataset.
| stat.ML | while most classical approaches to granger causality detection assume linear dynamics many interactions in realworld applications like neuroscience and genomics are inherently nonlinear in these cases using linear models may lead to inconsistent estimation of granger causal interactions we propose a class of nonlinear methods by applying structured multilayer perceptrons mlps or recurrent neural networks rnns combined with sparsityinducing penalties on the weights by encouraging specific sets of weights to be zeroin particular through the use of convex grouplasso penaltieswe can extract the granger causal structure to further contrast with traditional approaches our framework naturally enables us to efficiently capture longrange dependencies between series either via our rnns or through an automatic lag selection in the mlp we show that our neural granger causality methods outperform stateoftheart nonlinear granger causality methods on the dream3 challenge data this data consists of nonlinear gene expression and regulation time courses with only a limited number of time points the successes we show in this challenging dataset provide a powerful example of how deep learning can be useful in cases that go beyond prediction on large datasets we likewise illustrate our methods in detecting nonlinear interactions in a human motion capture dataset | [['while', 'most', 'classical', 'approaches', 'to', 'granger', 'causality', 'detection', 'assume', 'linear', 'dynamics', 'many', 'interactions', 'in', 'realworld', 'applications', 'like', 'neuroscience', 'and', 'genomics', 'are', 'inherently', 'nonlinear', 'in', 'these', 'cases', 'using', 'linear', 'models', 'may', 'lead', 'to', 'inconsistent', 'estimation', 'of', 'granger', 'causal', 'interactions', 'we', 'propose', 'a', 'class', 'of', 'nonlinear', 'methods', 'by', 'applying', 'structured', 'multilayer', 'perceptrons', 'mlps', 'or', 'recurrent', 'neural', 'networks', 'rnns', 'combined', 'with', 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1,802.05843 | Minimal Algorithmic Information Loss Methods for Dimension Reduction,
Feature Selection and Network Sparsification | We present a novel, domain-agnostic, model-independent, unsupervised, and
universally applicable Machine Learning approach for dimensionality reduction
based on the principles of algorithmic complexity. Specifically, but without
loss of generality, we focus on addressing the challenge of reducing certain
dimensionality aspects, such as the number of edges in a network, while
retaining essential features of interest. These features include preserving
crucial network properties like degree distribution, clustering coefficient,
edge betweenness, and degree and eigenvector centralities but can also go
beyond edges to nodes and weights for network pruning and trimming. Our
approach outperforms classical statistical Machine Learning techniques and
state-of-the-art dimensionality reduction algorithms by preserving a greater
number of data features that statistical algorithms would miss, particularly
nonlinear patterns stemming from deterministic recursive processes that may
look statistically random but are not. Moreover, previous approaches heavily
rely on a priori feature selection, which requires constant supervision. Our
findings demonstrate the effectiveness of the algorithms in overcoming some of
these limitations while maintaining a time-efficient computational profile. Our
approach not only matches, but also exceeds, the performance of established and
state-of-the-art dimensionality reduction algorithms. We extend the
applicability of our method to lossy compression tasks involving images and any
multi-dimensional data. This highlights the versatility and broad utility of
the approach in multiple domains.
| cs.DS cs.IT math.IT physics.soc-ph | we present a novel domainagnostic modelindependent unsupervised and universally applicable machine learning approach for dimensionality reduction based on the principles of algorithmic complexity specifically but without loss of generality we focus on addressing the challenge of reducing certain dimensionality aspects such as the number of edges in a network while retaining essential features of interest these features include preserving crucial network properties like degree distribution clustering coefficient edge betweenness and degree and eigenvector centralities but can also go beyond edges to nodes and weights for network pruning and trimming our approach outperforms classical statistical machine learning techniques and stateoftheart dimensionality reduction algorithms by preserving a greater number of data features that statistical algorithms would miss particularly nonlinear patterns stemming from deterministic recursive processes that may look statistically random but are not moreover previous approaches heavily rely on a priori feature selection which requires constant supervision our findings demonstrate the effectiveness of the algorithms in overcoming some of these limitations while maintaining a timeefficient computational profile our approach not only matches but also exceeds the performance of established and stateoftheart dimensionality reduction algorithms we extend the applicability of our method to lossy compression tasks involving images and any multidimensional data this highlights the versatility and broad utility of the approach in multiple domains | [['we', 'present', 'a', 'novel', 'domainagnostic', 'modelindependent', 'unsupervised', 'and', 'universally', 'applicable', 'machine', 'learning', 'approach', 'for', 'dimensionality', 'reduction', 'based', 'on', 'the', 'principles', 'of', 'algorithmic', 'complexity', 'specifically', 'but', 'without', 'loss', 'of', 'generality', 'we', 'focus', 'on', 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1,802.05844 | A Unified View of Causal and Non-causal Feature Selection | In this paper, we aim to develop a unified view of causal and non-causal
feature selection methods. The unified view will fill in the gap in the
research of the relation between the two types of methods. Based on the
Bayesian network framework and information theory, we first show that causal
and non-causal feature selection methods share the same objective. That is to
find the Markov blanket of a class attribute, the theoretically optimal feature
set for classification. We then examine the assumptions made by causal and
non-causal feature selection methods when searching for the optimal feature
set, and unify the assumptions by mapping them to the restrictions on the
structure of the Bayesian network model of the studied problem. We further
analyze in detail how the structural assumptions lead to the different levels
of approximations employed by the methods in their search, which then result in
the approximations in the feature sets found by the methods with respect to the
optimal feature set. With the unified view, we are able to interpret the output
of non-causal methods from a causal perspective and derive the error bounds of
both types of methods. Finally, we present practical understanding of the
relation between causal and non-causal methods using extensive experiments with
synthetic data and various types of real-word data.
| cs.AI cs.LG stat.ML | in this paper we aim to develop a unified view of causal and noncausal feature selection methods the unified view will fill in the gap in the research of the relation between the two types of methods based on the bayesian network framework and information theory we first show that causal and noncausal feature selection methods share the same objective that is to find the markov blanket of a class attribute the theoretically optimal feature set for classification we then examine the assumptions made by causal and noncausal feature selection methods when searching for the optimal feature set and unify the assumptions by mapping them to the restrictions on the structure of the bayesian network model of the studied problem we further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set with the unified view we are able to interpret the output of noncausal methods from a causal perspective and derive the error bounds of both types of methods finally we present practical understanding of the relation between causal and noncausal methods using extensive experiments with synthetic data and various types of realword data | [['in', 'this', 'paper', 'we', 'aim', 'to', 'develop', 'a', 'unified', 'view', 'of', 'causal', 'and', 'noncausal', 'feature', 'selection', 'methods', 'the', 'unified', 'view', 'will', 'fill', 'in', 'the', 'gap', 'in', 'the', 'research', 'of', 'the', 'relation', 'between', 'the', 'two', 'types', 'of', 'methods', 'based', 'on', 'the', 'bayesian', 'network', 'framework', 'and', 'information', 'theory', 'we', 'first', 'show', 'that', 'causal', 'and', 'noncausal', 'feature', 'selection', 'methods', 'share', 'the', 'same', 'objective', 'that', 'is', 'to', 'find', 'the', 'markov', 'blanket', 'of', 'a', 'class', 'attribute', 'the', 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1,802.05845 | Crystallization of Diblock Copolymer from Microphase Separated Melt | Diblock copolymers by virtue of the chemical dissimilarity between the
constituting blocks exhibit microphase separation in the melt state. The phase
separated melt can successfully be exploited to control the morphology of the
final semi crystalline materials by allowing an extended thermal annealing.
Thermal annealing accelerates coalescence of microdomains, yielding a phase
separated melt that would exhibit a distinctly different crystallization
behaviour than microphase separated melt without annealing. In this paper, we
report simulation results on the crystallization behavior of A-B diblock
copolymer, wherein the melting temperature of A-block is higher than B-block,
instigated from microphase separated melt. During crystallization, the
morphological evolution of microphase separated melt is extensively driven by
thermal history. Annealing of microphase separated melt at high temperature
successfully reorients melt morphology, and remains almost unaltered during the
subsequent crystallization (isothermal and non-isothermal), which is attributed
to the hard confinement resulted during microphase separation. Annealing
induces a change in bond orientation of A-block, whereas there is no
appreciable change in bond orientation of B-block keeping crystallinity and
lamellar thickness unaffected. Isothermal crystallization confines
crystallization in phase separated microdomain whereas non-isothermal
crystallization results in morphological perturbation. The crystallization rate
of annealed melt is much faster than the non-annealed melt due to less
entanglement and more relaxed structure, achieved via annealing. At a higher
composition of B-block, A-block produces thicker crystals, which is attributed
to the dilution effect exhibited by B-block. Two-step compared to one-step
isothermal crystallization yields thicker crystals with higher crystallinity of
A-block.
| cond-mat.soft | diblock copolymers by virtue of the chemical dissimilarity between the constituting blocks exhibit microphase separation in the melt state the phase separated melt can successfully be exploited to control the morphology of the final semi crystalline materials by allowing an extended thermal annealing thermal annealing accelerates coalescence of microdomains yielding a phase separated melt that would exhibit a distinctly different crystallization behaviour than microphase separated melt without annealing in this paper we report simulation results on the crystallization behavior of ab diblock copolymer wherein the melting temperature of ablock is higher than bblock instigated from microphase separated melt during crystallization the morphological evolution of microphase separated melt is extensively driven by thermal history annealing of microphase separated melt at high temperature successfully reorients melt morphology and remains almost unaltered during the subsequent crystallization isothermal and nonisothermal which is attributed to the hard confinement resulted during microphase separation annealing induces a change in bond orientation of ablock whereas there is no appreciable change in bond orientation of bblock keeping crystallinity and lamellar thickness unaffected isothermal crystallization confines crystallization in phase separated microdomain whereas nonisothermal crystallization results in morphological perturbation the crystallization rate of annealed melt is much faster than the nonannealed melt due to less entanglement and more relaxed structure achieved via annealing at a higher composition of bblock ablock produces thicker crystals which is attributed to the dilution effect exhibited by bblock twostep compared to onestep isothermal crystallization yields thicker crystals with higher crystallinity of ablock | [['diblock', 'copolymers', 'by', 'virtue', 'of', 'the', 'chemical', 'dissimilarity', 'between', 'the', 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1,802.05846 | Train on Validation: Squeezing the Data Lemon | Model selection on validation data is an essential step in machine learning.
While the mixing of data between training and validation is considered taboo,
practitioners often violate it to increase performance. Here, we offer a
simple, practical method for using the validation set for training, which
allows for a continuous, controlled trade-off between performance and
overfitting of model selection. We define the notion of
on-average-validation-stable algorithms as one in which using small portions of
validation data for training does not overfit the model selection process. We
then prove that stable algorithms are also validation stable. Finally, we
demonstrate our method on the MNIST and CIFAR-10 datasets using stable
algorithms as well as state-of-the-art neural networks. Our results show
significant increase in test performance with a minor trade-off in bias
admitted to the model selection process.
| stat.ML cs.LG | model selection on validation data is an essential step in machine learning while the mixing of data between training and validation is considered taboo practitioners often violate it to increase performance here we offer a simple practical method for using the validation set for training which allows for a continuous controlled tradeoff between performance and overfitting of model selection we define the notion of onaveragevalidationstable algorithms as one in which using small portions of validation data for training does not overfit the model selection process we then prove that stable algorithms are also validation stable finally we demonstrate our method on the mnist and cifar10 datasets using stable algorithms as well as stateoftheart neural networks our results show significant increase in test performance with a minor tradeoff in bias admitted to the model selection process | [['model', 'selection', 'on', 'validation', 'data', 'is', 'an', 'essential', 'step', 'in', 'machine', 'learning', 'while', 'the', 'mixing', 'of', 'data', 'between', 'training', 'and', 'validation', 'is', 'considered', 'taboo', 'practitioners', 'often', 'violate', 'it', 'to', 'increase', 'performance', 'here', 'we', 'offer', 'a', 'simple', 'practical', 'method', 'for', 'using', 'the', 'validation', 'set', 'for', 'training', 'which', 'allows', 'for', 'a', 'continuous', 'controlled', 'tradeoff', 'between', 'performance', 'and', 'overfitting', 'of', 'model', 'selection', 'we', 'define', 'the', 'notion', 'of', 'onaveragevalidationstable', 'algorithms', 'as', 'one', 'in', 'which', 'using', 'small', 'portions', 'of', 'validation', 'data', 'for', 'training', 'does', 'not', 'overfit', 'the', 'model', 'selection', 'process', 'we', 'then', 'prove', 'that', 'stable', 'algorithms', 'are', 'also', 'validation', 'stable', 'finally', 'we', 'demonstrate', 'our', 'method', 'on', 'the', 'mnist', 'and', 'cifar10', 'datasets', 'using', 'stable', 'algorithms', 'as', 'well', 'as', 'stateoftheart', 'neural', 'networks', 'our', 'results', 'show', 'significant', 'increase', 'in', 'test', 'performance', 'with', 'a', 'minor', 'tradeoff', 'in', 'bias', 'admitted', 'to', 'the', 'model', 'selection', 'process']] | [-0.01931509563226753, -0.016801229197555397, -0.06534342207501072, 0.07875336249938965, -0.08950826713690228, -0.18573925031501967, 0.11164687495372856, 0.44947081376145137, -0.24281888670607735, -0.35024861176150723, 0.09661619908023458, -0.23525962920404656, -0.16839188928611631, 0.26045892133947407, -0.1318325175219146, 0.11300449489977267, 0.16685745904027527, 0.008030874202889738, -0.053876833932877365, -0.32843944225321287, 0.2695178106642648, 0.0735849220333482, 0.37048798604562544, 0.0354400351261303, 0.10910312098072175, -0.035412844638826685, 0.004057680018621483, 0.0040225264157599475, -0.08921536508759305, 0.10069288808892397, 0.2688530023963844, 0.20551598832574186, 0.3435753705119019, -0.3561019692569971, -0.17563956460321267, 0.1296120784975199, 0.15007075212666515, 0.11835930899314238, -0.052117098524207386, -0.283894047919494, 0.09599624649767499, -0.1595638941829232, -0.009624810719084161, -0.1943222324141482, -0.02387238956584748, 0.007521116247299864, -0.3540882894859663, 0.0591193915971593, 0.0818300395865174, 0.08211486856105613, -0.04187257301084934, -0.11017663430582399, -0.027746395011599274, 0.14137470051420353, 0.04930315851259963, 0.00709240620177406, 0.11013967748870378, -0.1251982905304254, -0.16840330468588022, 0.36068430156516496, -0.07498341311097716, -0.23778133922075825, 0.1838749743678343, -0.006904346754586797, -0.17054405958919938, 0.07618672386117613, 0.26097429513152853, 0.0955126948607738, -0.13154505502255356, 0.008630861102954359, -0.019881426128680442, 0.16998137806111308, 0.03055090727215049, -0.03364030087936614, 0.11813295482303392, 0.31359964591770695, 0.053461068643451626, 0.15379650038910278, -0.11445259370618681, -0.08563441307103234, -0.3049383289319699, -0.12244544014918493, -0.18970055480612746, -0.03583590410562465, -0.11915988127890811, -0.16987425438823428, 0.3382475435761596, 0.24401127272494025, 0.2147877902676935, 0.11715758486421529, 0.35022326438416684, 0.03240548367379668, 0.09114202494218723, 0.09594202726912587, 0.23035879709250504, 0.025609496306739192, 0.056934234896438445, -0.18314458353535287, 0.10181655951275198, -0.011946810569280564] |
1,802.05847 | Interplay of phase boundary anisotropy and electro-autocatalytic surface
reactions on the lithium intercalation dynamics in Li$_X$FePO$_4$
platelet-like nanoparticles | Experiments on single crystal Li$_X$FePO$_4$ (LFP) nanoparticles indicate
rich nonequilibrium phase behavior, such as suppression of phase separation at
high lithiation rates, striped patterns of coherent phase boundaries,
nucleation by binarysolid surface wetting and intercalation waves. These
observations have been successfully predicted (prior to the experiments) by 1D
depth-averaged phase-field models, which neglect any subsurface phase
separation. In this paper, using an electro-chemo-mechanical phase-field model,
we investigate the coherent non-equilibrium subsurface phase morphologies that
develop in the $ab$- plane of platelet-like single-crystal platelet-like
Li$_X$FePO$_4$ nanoparticles. Finite element simulations are performed for 2D
plane-stress conditions in the $ab$- plane, and validated by 3D simulations,
showing similar results. We show that the anisotropy of the interfacial tension
tensor, coupled with electroautocatalytic surface intercalation reactions,
plays a crucial role in determining the subsurface phase morphology. With
isotropic interfacial tension, subsurface phase separation is observed,
independent of the reaction kinetics, but for strong anisotropy, phase
separation is controlled by surface reactions, as assumed in 1D models.
Moreover, the driven intercalation reaction suppresses phase separation during
lithiation, while enhancing it during delithiation, by electro-autocatalysis,
in quantitative agreement with {\it in operando} imaging experiments in
single-crystalline nanoparticles, given measured reaction rate constants.
| cond-mat.mtrl-sci physics.chem-ph | experiments on single crystal li_xfepo_4 lfp nanoparticles indicate rich nonequilibrium phase behavior such as suppression of phase separation at high lithiation rates striped patterns of coherent phase boundaries nucleation by binarysolid surface wetting and intercalation waves these observations have been successfully predicted prior to the experiments by 1d depthaveraged phasefield models which neglect any subsurface phase separation in this paper using an electrochemomechanical phasefield model we investigate the coherent nonequilibrium subsurface phase morphologies that develop in the ab plane of plateletlike singlecrystal plateletlike li_xfepo_4 nanoparticles finite element simulations are performed for 2d planestress conditions in the ab plane and validated by 3d simulations showing similar results we show that the anisotropy of the interfacial tension tensor coupled with electroautocatalytic surface intercalation reactions plays a crucial role in determining the subsurface phase morphology with isotropic interfacial tension subsurface phase separation is observed independent of the reaction kinetics but for strong anisotropy phase separation is controlled by surface reactions as assumed in 1d models moreover the driven intercalation reaction suppresses phase separation during lithiation while enhancing it during delithiation by electroautocatalysis in quantitative agreement with it in operando imaging experiments in singlecrystalline nanoparticles given measured reaction rate constants | [['experiments', 'on', 'single', 'crystal', 'li_xfepo_4', 'lfp', 'nanoparticles', 'indicate', 'rich', 'nonequilibrium', 'phase', 'behavior', 'such', 'as', 'suppression', 'of', 'phase', 'separation', 'at', 'high', 'lithiation', 'rates', 'striped', 'patterns', 'of', 'coherent', 'phase', 'boundaries', 'nucleation', 'by', 'binarysolid', 'surface', 'wetting', 'and', 'intercalation', 'waves', 'these', 'observations', 'have', 'been', 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1,802.05848 | Homotopy type of Neighborhood Complexes of Kneser graphs, $KG_{2,k}$ | Schrijver identified a family of vertex critical subgraphs of the Kneser
graphs called the stable Kneser graphs $SG_{n,k}$. Bj\"{o}rner and de
Longueville proved that the neighborhood complex of the stable Kneser graph
$SG_{n,k}$ is homotopy equivalent to a $k-$sphere.
In this article, we prove that the homotopy type of the neighborhood complex
of the Kneser graph $KG_{2,k}$ is a wedge of $(k+4)(k+1)+1$ spheres of
dimension $k$. We construct a maximal subgraph $S_{2,k}$ of $KG_{2,k}$, whose
neighborhood complex is homotopy equivalent to the neighborhood complex of
$SG_{2,k}$. Further, we prove that the neighborhood complex of $S_{2,k}$
deformation retracts onto the neighborhood complex of $SG_{2,k}$.
| math.CO | schrijver identified a family of vertex critical subgraphs of the kneser graphs called the stable kneser graphs sg_nk bjorner and de longueville proved that the neighborhood complex of the stable kneser graph sg_nk is homotopy equivalent to a ksphere in this article we prove that the homotopy type of the neighborhood complex of the kneser graph kg_2k is a wedge of k4k11 spheres of dimension k we construct a maximal subgraph s_2k of kg_2k whose neighborhood complex is homotopy equivalent to the neighborhood complex of sg_2k further we prove that the neighborhood complex of s_2k deformation retracts onto the neighborhood complex of sg_2k | [['schrijver', 'identified', 'a', 'family', 'of', 'vertex', 'critical', 'subgraphs', 'of', 'the', 'kneser', 'graphs', 'called', 'the', 'stable', 'kneser', 'graphs', 'sg_nk', 'bjorner', 'and', 'de', 'longueville', 'proved', 'that', 'the', 'neighborhood', 'complex', 'of', 'the', 'stable', 'kneser', 'graph', 'sg_nk', 'is', 'homotopy', 'equivalent', 'to', 'a', 'ksphere', 'in', 'this', 'article', 'we', 'prove', 'that', 'the', 'homotopy', 'type', 'of', 'the', 'neighborhood', 'complex', 'of', 'the', 'kneser', 'graph', 'kg_2k', 'is', 'a', 'wedge', 'of', 'k4k11', 'spheres', 'of', 'dimension', 'k', 'we', 'construct', 'a', 'maximal', 'subgraph', 's_2k', 'of', 'kg_2k', 'whose', 'neighborhood', 'complex', 'is', 'homotopy', 'equivalent', 'to', 'the', 'neighborhood', 'complex', 'of', 'sg_2k', 'further', 'we', 'prove', 'that', 'the', 'neighborhood', 'complex', 'of', 's_2k', 'deformation', 'retracts', 'onto', 'the', 'neighborhood', 'complex', 'of', 'sg_2k']] | [-0.21759853331372142, 0.06359388968907297, -0.04876557228388265, 0.06660985077032819, -0.10490790469106287, -0.07633495581336319, 0.040965388031909244, 0.3411317218048498, -0.31770158593542874, -0.21994351592846215, 0.06650101476465352, -0.2765878996998072, -0.20252280436456205, 0.07797703742515295, -0.1771495164372027, -0.02219623275101185, 0.09903824131004513, 0.14535816429182888, 0.04464252008358017, -0.2465324472944485, 0.3883019236475229, -0.06614650350995362, 0.14773597308460013, 0.040107959350571036, 0.09669183132238686, -0.004796994449570775, 0.03390186511911452, 0.12233290819916874, -0.22377325256231415, 0.15191541124368085, 0.28029124811291695, 0.13840230222151148, 0.22078034345060588, -0.3037941231019795, -0.1691854986920953, 0.2515944920107722, 0.1369160482659936, -0.00816293672658503, 0.036870775991119445, -0.28626388049684465, 0.20944963980466128, -0.09257969904690981, -0.16069588444195687, -0.029422958783106877, 0.12296475671231746, -0.0027594544983003287, -0.22905311255715788, -0.07142372931004502, 0.1497299569658935, 0.0798834624607116, 0.03920863605337217, -0.09651452743448317, -0.10262270835228264, 0.053324234996689486, -0.09352214012062177, 0.054588586082682015, 0.1071756147313863, -0.06781201688339934, -0.12167147348634899, 0.36776345389604104, 0.00985824346076697, -0.13050908809527756, 0.1230703049339354, -0.14071616620756686, -0.22036185694858432, 0.15742452702019363, 0.055851984871551394, 0.22211546937003732, -0.033283725390210746, 0.21792998813965825, -0.14677419018931687, 0.09866528013255448, 0.1736699106544256, -0.03681862212251872, 0.06653744273819029, 0.13526761448010802, 0.21361218486446887, 0.16123241094872356, 0.030140662812627853, -0.05525387935340405, -0.2597172706760466, -0.18658546010032295, -0.18817014381289482, 0.13344097847584635, -0.20844572581569082, -0.2878494758438319, 0.4469042943790555, 0.05195161500945687, 0.17096610000357032, 0.13515161304734646, 0.1482852683425881, -0.00753813655115664, 0.011347603425383568, 0.11481362881604582, 0.1312458307109773, 0.2654831288009882, -0.03356688061729073, -0.11834822830278427, -0.009905446451157332, 0.2512133102165535] |
1,802.05849 | Phonon-induced decoherence of a charge quadrupole qubit | Many quantum dot qubits operate in regimes where the energy splittings
between qubit states are large and phonons can be the dominant source of
decoherence. The recently proposed charge quadrupole qubit, based on one
electron in a triple quantum dot, employs a highly symmetric charge
distribution to suppress the influence of charge noise. To study the effects of
phonons on the charge quadrupole qubit, we consider Larmor and Ramsey pulse
sequences to identify favorable operating parameters. We show that it is
possible to implement typical gates with $>99.99\%$ fidelity in the presence of
phonons and charge noise.
| cond-mat.mes-hall quant-ph | many quantum dot qubits operate in regimes where the energy splittings between qubit states are large and phonons can be the dominant source of decoherence the recently proposed charge quadrupole qubit based on one electron in a triple quantum dot employs a highly symmetric charge distribution to suppress the influence of charge noise to study the effects of phonons on the charge quadrupole qubit we consider larmor and ramsey pulse sequences to identify favorable operating parameters we show that it is possible to implement typical gates with 9999 fidelity in the presence of phonons and charge noise | [['many', 'quantum', 'dot', 'qubits', 'operate', 'in', 'regimes', 'where', 'the', 'energy', 'splittings', 'between', 'qubit', 'states', 'are', 'large', 'and', 'phonons', 'can', 'be', 'the', 'dominant', 'source', 'of', 'decoherence', 'the', 'recently', 'proposed', 'charge', 'quadrupole', 'qubit', 'based', 'on', 'one', 'electron', 'in', 'a', 'triple', 'quantum', 'dot', 'employs', 'a', 'highly', 'symmetric', 'charge', 'distribution', 'to', 'suppress', 'the', 'influence', 'of', 'charge', 'noise', 'to', 'study', 'the', 'effects', 'of', 'phonons', 'on', 'the', 'charge', 'quadrupole', 'qubit', 'we', 'consider', 'larmor', 'and', 'ramsey', 'pulse', 'sequences', 'to', 'identify', 'favorable', 'operating', 'parameters', 'we', 'show', 'that', 'it', 'is', 'possible', 'to', 'implement', 'typical', 'gates', 'with', '9999', 'fidelity', 'in', 'the', 'presence', 'of', 'phonons', 'and', 'charge', 'noise']] | [-0.17350725112386892, 0.24143764549463065, 0.009758748128510936, 0.06069956370029291, 0.0207294960237425, -0.21928753914580362, 0.055275709658726746, 0.3873231546550067, -0.20937219663455928, -0.2672436517692104, -0.016414693334696757, -0.2980521032322659, -0.050322393089840094, 0.21736847791945582, -0.04803735458474491, 0.035445053245603424, 0.038180668627094363, 0.011764023702630062, -0.05589757120349569, -0.18852220344856457, 0.3064126312281437, 0.06035259405160611, 0.3130845780839625, 0.06173408683389425, 0.1054312100384346, 0.006449713287683031, 0.09626345648478295, -0.060910601639332845, -0.09725216489332336, 0.026084274607446504, 0.27327257787482334, -0.013965650096766114, 0.20445716751201712, -0.4571701331428953, -0.1754463365020653, 0.0835656758803471, 0.0907647909510151, 0.20818581230957636, -0.038017515047482145, -0.28579155114700194, 0.03293731321398284, -0.18009065382528244, -0.052783866676972394, -0.10829551680224765, 0.024506458897412438, 0.03135227341176078, -0.294649659684792, 0.0912072049122611, 0.06748447877825382, -0.021031824871897697, 0.013882042571287794, -0.049389310370765854, -0.004204617342767641, 0.10412718526978218, 0.005471662312179573, 0.014105730601250357, 0.2488924516364932, -0.062229382288258174, -0.15035308114032156, 0.31647849747354223, -0.05800714323623586, -0.2127872124199093, 0.13724384586249969, -0.19324926088189648, -0.06455536863344155, 0.09726290101396669, 0.13388460307122813, 0.1013879135500678, -0.13388804814384617, 0.05022961583730198, 0.0770561235678411, 0.2376565145479861, 0.06567457577540088, 0.20062013968498899, 0.22623789508241354, 0.1426061131876231, 0.08974829724132918, 0.16345917003243662, -0.1598797956116728, -0.1066021343372695, -0.24096430089209497, -0.12061787452356718, -0.24199499957954762, 0.11214840574881312, -0.0391121805717835, -0.14284665886427939, 0.44796251117722274, 0.15665523534983916, 0.14517307261331486, -0.0923924350477371, 0.293964847115819, 0.14519307845915408, 0.09074305856393977, 0.05532897672539135, 0.25411489978192625, 0.21200235901621275, 0.008841390596683492, -0.37336041951628846, -0.022539555464784818, -0.0285204794901764] |
1,802.0585 | Study of Magnetized accretion flow with cooling processes | We have studied shock in magnetized accretion flow/funnel flow in case of
neutron star with bremsstrahlung cooling and cyclotron cooling. All accretion
solutions terminate with a shock close to the neutron star surface, but at some
region of the parameter space, it also harbours a second shock away from the
star surface. We have found that cyclotron cooling is necessary for correct
accretion solutions which match the surface boundary conditions.
| astro-ph.HE | we have studied shock in magnetized accretion flowfunnel flow in case of neutron star with bremsstrahlung cooling and cyclotron cooling all accretion solutions terminate with a shock close to the neutron star surface but at some region of the parameter space it also harbours a second shock away from the star surface we have found that cyclotron cooling is necessary for correct accretion solutions which match the surface boundary conditions | [['we', 'have', 'studied', 'shock', 'in', 'magnetized', 'accretion', 'flowfunnel', 'flow', 'in', 'case', 'of', 'neutron', 'star', 'with', 'bremsstrahlung', 'cooling', 'and', 'cyclotron', 'cooling', 'all', 'accretion', 'solutions', 'terminate', 'with', 'a', 'shock', 'close', 'to', 'the', 'neutron', 'star', 'surface', 'but', 'at', 'some', 'region', 'of', 'the', 'parameter', 'space', 'it', 'also', 'harbours', 'a', 'second', 'shock', 'away', 'from', 'the', 'star', 'surface', 'we', 'have', 'found', 'that', 'cyclotron', 'cooling', 'is', 'necessary', 'for', 'correct', 'accretion', 'solutions', 'which', 'match', 'the', 'surface', 'boundary', 'conditions']] | [-0.07419388929881371, 0.16256976080383512, -0.07908455003390386, 0.09839908411031238, -0.12996394350868312, -0.11382774109749691, 0.016484593618256244, 0.38900825601287076, -0.20012799844793652, -0.2857156391592993, 0.1070263255758724, -0.27007178906891227, 0.025907497853040695, 0.2761599685780812, -0.008683954592055195, 0.0010183628486550372, 0.08043654041999168, 0.02152316404533559, -0.08640647063747156, -0.1968299830433629, 0.36150286098321277, 0.062293891565523285, 0.14145414968547615, 0.05124558927491307, 0.03632003485994494, -0.15704544221280495, 0.06413651392727658, -0.04247647075132683, -0.21562772788551546, -0.05576040211768038, 0.23849658046484642, 0.08207876192054887, 0.1750346887046876, -0.4541354675851492, -0.2928265503513208, 0.017493093521266743, 0.17124781075973008, 0.07542651595945969, -0.09812223518510228, -0.18153697297827381, 0.047675613583861916, -0.23228348896879217, -0.15774471176437277, 0.048850356020789215, 0.032931056157078434, 0.03237582623040763, -0.24514230732144654, 0.07004260722222144, 0.10019178811015318, -0.008294074971606766, -0.17836004327240307, 0.005920686235607507, -0.08223325982554883, 0.03518709065257639, 0.11106767442981726, 0.11219356105446923, 0.20901499579971036, -0.1576443035290509, 0.01233584342011507, 0.3875178103537663, -0.04556843959658906, -0.07651108730098476, 0.24249649639882526, -0.31225695489379374, -0.13206700446839997, 0.2747658143035959, 0.121310261636635, 0.15334940383183784, -0.10209985327083564, -0.020877218536148524, -0.044011926303779626, 0.1162331650249552, 0.15181233854694426, -0.033774839440846575, 0.35282875265440217, 0.15766553387747725, 0.06532300215727393, 0.1211813074550119, -0.1863213837976851, -0.03688117817881099, -0.29241661082251347, -0.09092053573956524, -0.11393461055358521, 0.032300070751512394, -0.06873302154923769, -0.16168218945539845, 0.2752007371880978, 0.08434407440000687, 0.15052550230715153, -0.03898006010685872, 0.270152237860189, 0.15005248400342205, 0.03073206850314054, 0.23708659513732014, 0.3222739594145779, 0.19896648145294277, 0.11660754359057308, -0.34586122482922266, 0.06311364492833399, 0.0580558520404325] |
1,802.05851 | On triangle meshes with valence $6$ dominant vertices | We study triangulations $\cal T$ defined on a closed disc $X$ satisfying the
following condition: In the interior of $X$, the valence of all vertices of
$\cal T$ except one of them (the irregular vertex) is $6$. By using a flat
singular Riemannian metric adapted to $\cal T$, we prove a uniqueness theorem
when the valence of the irregular vertex is not a multiple of $6$. Moreover,
for a given integer $k >1$, we exhibit non isomorphic triangulations on $X$
with the same boundary, and with a unique irregular vertex whose valence is
$6k$.
| math.DG | we study triangulations cal t defined on a closed disc x satisfying the following condition in the interior of x the valence of all vertices of cal t except one of them the irregular vertex is 6 by using a flat singular riemannian metric adapted to cal t we prove a uniqueness theorem when the valence of the irregular vertex is not a multiple of 6 moreover for a given integer k 1 we exhibit non isomorphic triangulations on x with the same boundary and with a unique irregular vertex whose valence is 6k | [['we', 'study', 'triangulations', 'cal', 't', 'defined', 'on', 'a', 'closed', 'disc', 'x', 'satisfying', 'the', 'following', 'condition', 'in', 'the', 'interior', 'of', 'x', 'the', 'valence', 'of', 'all', 'vertices', 'of', 'cal', 't', 'except', 'one', 'of', 'them', 'the', 'irregular', 'vertex', 'is', '6', 'by', 'using', 'a', 'flat', 'singular', 'riemannian', 'metric', 'adapted', 'to', 'cal', 't', 'we', 'prove', 'a', 'uniqueness', 'theorem', 'when', 'the', 'valence', 'of', 'the', 'irregular', 'vertex', 'is', 'not', 'a', 'multiple', 'of', '6', 'moreover', 'for', 'a', 'given', 'integer', 'k', '1', 'we', 'exhibit', 'non', 'isomorphic', 'triangulations', 'on', 'x', 'with', 'the', 'same', 'boundary', 'and', 'with', 'a', 'unique', 'irregular', 'vertex', 'whose', 'valence', 'is', '6k']] | [-0.18274070120396765, 0.14625711222377388, -0.06741750967302458, 0.02963184238042425, -0.05820328863634867, -0.1932391051222828, 0.05009389426583979, 0.35278445470364805, -0.24876313646660841, -0.22323214023329435, 0.08146515194675885, -0.3223884903647481, -0.10961866691371704, 0.07540276578746419, -0.08936559771524465, 0.020686785313994326, 0.06494383217192236, 0.13572795949925848, -0.12370644337398574, -0.2666890363585442, 0.3484810329775544, -0.0964084106436782, 0.13530099805335494, 0.06012600674194858, 0.1328232046513957, 0.02027873481386044, 0.01796931435572023, 0.08353427979857364, -0.1716027856814338, 0.06922398837025653, 0.23387070198325402, 0.06162425027318061, 0.21316996500753063, -0.3638578215812115, -0.1460364222665258, 0.14000763428060614, 0.0642888108367457, -0.019837278298082503, 0.030952455520510992, -0.2140210489296612, 0.1807533707085779, -0.08543191343902591, -0.18607911592636733, 0.015290109069146057, 0.0871811395169216, -0.030316620492970847, -0.29474546146539454, 0.006811524780982352, 0.14507387808345734, 0.05679502516509371, -0.026235129495963773, -0.1492249147689089, -0.1298053524843318, 0.07743983386249152, -0.04621126316735481, 0.12901418738096873, 0.03798407481666258, -0.07472799109057227, -0.1131093644368601, 0.35575545198492153, -0.05422481002305892, -0.20002015272373372, 0.15268423143239257, -0.22620031326771417, -0.14112490344237774, 0.15004119387223444, 0.10469788713925617, 0.22233590615933088, -0.09348579599014904, 0.21941081892161513, -0.10034676460658219, 0.07671773802251258, 0.12499906947994803, 0.01344912439743255, 0.1573332453077223, 0.12755722218401175, 0.13750399264477273, 0.148786190560365, -0.03939520278382066, -0.021049544720494365, -0.3667952272640987, -0.11042336327707435, -0.17331816917508247, 0.1787230947930824, -0.16233959293690822, -0.22236687283804443, 0.3724677787308502, 0.005821724268706872, 0.2526822401052143, 0.06468210375416984, 0.18531317230155495, 0.07560412454953853, 0.05161702106805875, 0.15497588163657866, 0.09905892630210493, 0.15992331192481946, -0.0008572449739229806, -0.1977680212312794, -0.010434630356649769, 0.14679710120160847] |
1,802.05852 | Numerical stability of plasma sheath | We are interested in developing a numerical method for capturing stationary
sheaths, that a plasma forms in contact with a metallic wall. This work is
based on a bi-species (ion/electron) Vlasov-Amp{\`e}re model proposed in [3].
The main question addressed in this work is to know if classical numerical
schemes can preserve stationary solutions with boundary conditions, since these
solutions are not a priori conserved at the discrete level. In the context of
high-order semi-Lagrangian method, due to their large stencil, interpolation
near the boundary of the domain requires also a specific treatment.
| math.NA physics.comp-ph | we are interested in developing a numerical method for capturing stationary sheaths that a plasma forms in contact with a metallic wall this work is based on a bispecies ionelectron vlasovampere model proposed in 3 the main question addressed in this work is to know if classical numerical schemes can preserve stationary solutions with boundary conditions since these solutions are not a priori conserved at the discrete level in the context of highorder semilagrangian method due to their large stencil interpolation near the boundary of the domain requires also a specific treatment | [['we', 'are', 'interested', 'in', 'developing', 'a', 'numerical', 'method', 'for', 'capturing', 'stationary', 'sheaths', 'that', 'a', 'plasma', 'forms', 'in', 'contact', 'with', 'a', 'metallic', 'wall', 'this', 'work', 'is', 'based', 'on', 'a', 'bispecies', 'ionelectron', 'vlasovampere', 'model', 'proposed', 'in', '3', 'the', 'main', 'question', 'addressed', 'in', 'this', 'work', 'is', 'to', 'know', 'if', 'classical', 'numerical', 'schemes', 'can', 'preserve', 'stationary', 'solutions', 'with', 'boundary', 'conditions', 'since', 'these', 'solutions', 'are', 'not', 'a', 'priori', 'conserved', 'at', 'the', 'discrete', 'level', 'in', 'the', 'context', 'of', 'highorder', 'semilagrangian', 'method', 'due', 'to', 'their', 'large', 'stencil', 'interpolation', 'near', 'the', 'boundary', 'of', 'the', 'domain', 'requires', 'also', 'a', 'specific', 'treatment']] | [-0.10420420291260458, 0.06737605800627025, -0.07772134679912047, 0.04910776350688148, -0.06621072585131604, -0.1553849898135433, 0.0187397101730272, 0.37194076090396105, -0.236271431771936, -0.26221383762146744, 0.13147789481413233, -0.214466960138672, -0.13698302404852686, 0.18991954289670973, -0.09615971185113457, 0.08774340941014529, 0.07896527207896605, 0.012970265983552724, -0.09195494182167349, -0.21196783970591132, 0.3426195953072018, 0.011655625380616595, 0.27047262839195163, 0.09286267752247443, 0.08632494963623666, -0.07077299759999572, -0.03375073584502788, 0.024338757148483298, -0.1312350214692952, 0.09805214170182308, 0.2590107617101499, 0.03826179724585797, 0.3238715856530509, -0.46181789781529825, -0.24149666732473934, 0.07614376609198299, 0.15756607877479478, 0.13810031903027506, -0.05555464856193534, -0.23829022024190155, 0.12021900562999341, -0.12110795266414573, -0.15621946845203638, -0.07116712139571434, -0.026041132029514392, 0.04079358615646405, -0.29302142932499337, 0.08835409537877549, 0.06527629281601428, 0.04117781653038257, -0.09331887208723597, -0.04006538222861159, -0.006701431216692532, 0.09183477182043615, 0.03799932383021319, 0.012571768414620618, 0.05972123173178061, -0.13398119624560842, -0.06911663839511656, 0.38580311945342755, -0.02427680339091099, -0.3175574776685827, 0.23215622483793605, -0.1651833413301834, -0.1258534906677656, 0.13707338840506234, 0.16819551724500462, 0.18197604826991165, -0.16218998362975462, 0.10117540597547214, -0.04507985139767138, 0.1361712426993648, 0.08272316120564938, -0.03394526052339883, 0.1792113205397522, 0.17043068709240838, 0.08551168753151464, 0.10208969835478526, -0.048410148870271556, -0.12225214147346687, -0.33494206261053516, -0.14145881288613266, -0.1905092443186012, 0.00108837120629627, -0.043240577734720245, -0.20745236159476277, 0.3714036432882914, 0.1653500661904128, 0.13861799781507517, 0.02115088591741009, 0.30749507622977534, 0.13543571693474774, 0.030293412140703628, 0.12700665717596535, 0.19469401438894507, 0.10358408267435791, 0.1344758217366269, -0.20492288085458044, 0.059315611726021046, 0.12033130749446022] |
1,802.05853 | Articulatory information and Multiview Features for Large Vocabulary
Continuous Speech Recognition | This paper explores the use of multi-view features and their discriminative
transforms in a convolutional deep neural network (CNN) architecture for a
continuous large vocabulary speech recognition task. Mel-filterbank energies
and perceptually motivated forced damped oscillator coefficient (DOC) features
are used after feature-space maximum-likelihood linear regression (fMLLR)
transforms, which are combined and fed as a multi-view feature to a single CNN
acoustic model. Use of multi-view feature representation demonstrated
significant reduction in word error rates (WERs) compared to the use of
individual features by themselves. In addition, when articulatory information
was used as an additional input to a fused deep neural network (DNN) and CNN
acoustic model, it was found to demonstrate further reduction in WER for the
Switchboard subset and the CallHome subset (containing partly non-native
accented speech) of the NIST 2000 conversational telephone speech test set,
reducing the error rate by 12% relative to the baseline in both cases. This
work shows that multi-view features in association with articulatory
information can improve speech recognition robustness to spontaneous and
non-native speech.
| cs.CL cs.SD eess.AS | this paper explores the use of multiview features and their discriminative transforms in a convolutional deep neural network cnn architecture for a continuous large vocabulary speech recognition task melfilterbank energies and perceptually motivated forced damped oscillator coefficient doc features are used after featurespace maximumlikelihood linear regression fmllr transforms which are combined and fed as a multiview feature to a single cnn acoustic model use of multiview feature representation demonstrated significant reduction in word error rates wers compared to the use of individual features by themselves in addition when articulatory information was used as an additional input to a fused deep neural network dnn and cnn acoustic model it was found to demonstrate further reduction in wer for the switchboard subset and the callhome subset containing partly nonnative accented speech of the nist 2000 conversational telephone speech test set reducing the error rate by 12 relative to the baseline in both cases this work shows that multiview features in association with articulatory information can improve speech recognition robustness to spontaneous and nonnative speech | [['this', 'paper', 'explores', 'the', 'use', 'of', 'multiview', 'features', 'and', 'their', 'discriminative', 'transforms', 'in', 'a', 'convolutional', 'deep', 'neural', 'network', 'cnn', 'architecture', 'for', 'a', 'continuous', 'large', 'vocabulary', 'speech', 'recognition', 'task', 'melfilterbank', 'energies', 'and', 'perceptually', 'motivated', 'forced', 'damped', 'oscillator', 'coefficient', 'doc', 'features', 'are', 'used', 'after', 'featurespace', 'maximumlikelihood', 'linear', 'regression', 'fmllr', 'transforms', 'which', 'are', 'combined', 'and', 'fed', 'as', 'a', 'multiview', 'feature', 'to', 'a', 'single', 'cnn', 'acoustic', 'model', 'use', 'of', 'multiview', 'feature', 'representation', 'demonstrated', 'significant', 'reduction', 'in', 'word', 'error', 'rates', 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1,802.05854 | The K2 M67 Study: A Curiously Young Star in an Eclipsing Binary in an
Old Open Cluster | We present an analysis of a slightly eccentric ($e=0.05$), partially
eclipsing long-period ($P = 69.73$ d) main sequence binary system (WOCS 12009,
Sanders 1247) in the benchmark old open cluster M67. Using Kepler K2 and
ground-based photometry along with a large set of new and reanalyzed spectra,
we derived highly precise masses ($1.111\pm0.015$ and $0.748\pm0.005 M_\odot$)
and radii ($1.071\pm0.008\pm0.003$ and $0.713\pm0.019\pm0.026 R_\odot$, with
statistical and systematic error estimates) for the stars. The radius of the
secondary star is in agreement with theory. The primary, however, is
approximately $15\%$ smaller than reasonable isochrones for the cluster
predict. Our best explanation is that the primary star was produced from the
merger of two stars, as this can also account for the non-detection of
photospheric lithium and its higher temperature relative to other cluster main
sequence stars at the same $V$ magnitude. To understand the dynamical
characteristics (low measured rotational line broadening of the primary star
and the low eccentricity of the current binary orbit), we believe that the most
probable (but not the only) explanation is the tidal evolution of a close
binary within a primordial triple system (possibly after a period of
Kozai-Lidov oscillations), leading to merger approximately 1Gyr ago. This star
appears to be a future blue straggler that is being revealed as the cluster
ages and the most massive main sequence stars die out.
| astro-ph.SR | we present an analysis of a slightly eccentric e005 partially eclipsing longperiod p 6973 d main sequence binary system wocs 12009 sanders 1247 in the benchmark old open cluster m67 using kepler k2 and groundbased photometry along with a large set of new and reanalyzed spectra we derived highly precise masses 1111pm0015 and 0748pm0005 m_odot and radii 1071pm0008pm0003 and 0713pm0019pm0026 r_odot with statistical and systematic error estimates for the stars the radius of the secondary star is in agreement with theory the primary however is approximately 15 smaller than reasonable isochrones for the cluster predict our best explanation is that the primary star was produced from the merger of two stars as this can also account for the nondetection of photospheric lithium and its higher temperature relative to other cluster main sequence stars at the same v magnitude to understand the dynamical characteristics low measured rotational line broadening of the primary star and the low eccentricity of the current binary orbit we believe that the most probable but not the only explanation is the tidal evolution of a close binary within a primordial triple system possibly after a period of kozailidov oscillations leading to merger approximately 1gyr ago this star appears to be a future blue straggler that is being revealed as the cluster ages and the most massive main sequence stars die out | [['we', 'present', 'an', 'analysis', 'of', 'a', 'slightly', 'eccentric', 'e005', 'partially', 'eclipsing', 'longperiod', 'p', '6973', 'd', 'main', 'sequence', 'binary', 'system', 'wocs', '12009', 'sanders', '1247', 'in', 'the', 'benchmark', 'old', 'open', 'cluster', 'm67', 'using', 'kepler', 'k2', 'and', 'groundbased', 'photometry', 'along', 'with', 'a', 'large', 'set', 'of', 'new', 'and', 'reanalyzed', 'spectra', 'we', 'derived', 'highly', 'precise', 'masses', '1111pm0015', 'and', '0748pm0005', 'm_odot', 'and', 'radii', '1071pm0008pm0003', 'and', '0713pm0019pm0026', 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1,802.05855 | Hadronic decays of the (pseudo-)scalar charmonium states $\eta_c$ and
$\chi_{c0}$ in the extended Linear Sigma Model | We study the phenomenology of the ground-state (pseudo-)scalar charmonia
$\eta_c$ and $\chi_{c0}$ in the framework of a $U(4)_r \times U(4)_l$ symmetric
linear sigma model with (pseudo-)scalar and (axial-) vector mesons. Based on
previous results for the spectrum of charmonia and the spectrum and
(OZI-dominant) strong decays of open charmed mesons, we extend the study of
this model to OZI-suppressed charmonia decays. This includes decays into
'ordinary' mesons but also particularly interesting channels with
scalar-isoscalar resonances $f_0(1370),\, f_0(1500),\, f_0(1710)$ that may
include sizeable contributions from a scalar glueball. We study the variation
of the corresponding decay widths assuming different mixings between glueball
and quark-antiquark states. We also compute the decay width of the pseudoscalar
$\eta_c$ into a pseudoscalar glueball. In general, our results for decay widths
are in reasonable agreement with experimental data where available. Order of
magnitude predictions for as yet unmeasured states and channels are potentially
interesting for BESIII, Belle II, LHCb as well as the future PANDA experiment
at the FAIR facility.
| hep-ph | we study the phenomenology of the groundstate pseudoscalar charmonia eta_c and chi_c0 in the framework of a u4_r times u4_l symmetric linear sigma model with pseudoscalar and axial vector mesons based on previous results for the spectrum of charmonia and the spectrum and ozidominant strong decays of open charmed mesons we extend the study of this model to ozisuppressed charmonia decays this includes decays into ordinary mesons but also particularly interesting channels with scalarisoscalar resonances f_01370 f_01500 f_01710 that may include sizeable contributions from a scalar glueball we study the variation of the corresponding decay widths assuming different mixings between glueball and quarkantiquark states we also compute the decay width of the pseudoscalar eta_c into a pseudoscalar glueball in general our results for decay widths are in reasonable agreement with experimental data where available order of magnitude predictions for as yet unmeasured states and channels are potentially interesting for besiii belle ii lhcb as well as the future panda experiment at the fair facility | [['we', 'study', 'the', 'phenomenology', 'of', 'the', 'groundstate', 'pseudoscalar', 'charmonia', 'eta_c', 'and', 'chi_c0', 'in', 'the', 'framework', 'of', 'a', 'u4_r', 'times', 'u4_l', 'symmetric', 'linear', 'sigma', 'model', 'with', 'pseudoscalar', 'and', 'axial', 'vector', 'mesons', 'based', 'on', 'previous', 'results', 'for', 'the', 'spectrum', 'of', 'charmonia', 'and', 'the', 'spectrum', 'and', 'ozidominant', 'strong', 'decays', 'of', 'open', 'charmed', 'mesons', 'we', 'extend', 'the', 'study', 'of', 'this', 'model', 'to', 'ozisuppressed', 'charmonia', 'decays', 'this', 'includes', 'decays', 'into', 'ordinary', 'mesons', 'but', 'also', 'particularly', 'interesting', 'channels', 'with', 'scalarisoscalar', 'resonances', 'f_01370', 'f_01500', 'f_01710', 'that', 'may', 'include', 'sizeable', 'contributions', 'from', 'a', 'scalar', 'glueball', 'we', 'study', 'the', 'variation', 'of', 'the', 'corresponding', 'decay', 'widths', 'assuming', 'different', 'mixings', 'between', 'glueball', 'and', 'quarkantiquark', 'states', 'we', 'also', 'compute', 'the', 'decay', 'width', 'of', 'the', 'pseudoscalar', 'eta_c', 'into', 'a', 'pseudoscalar', 'glueball', 'in', 'general', 'our', 'results', 'for', 'decay', 'widths', 'are', 'in', 'reasonable', 'agreement', 'with', 'experimental', 'data', 'where', 'available', 'order', 'of', 'magnitude', 'predictions', 'for', 'as', 'yet', 'unmeasured', 'states', 'and', 'channels', 'are', 'potentially', 'interesting', 'for', 'besiii', 'belle', 'ii', 'lhcb', 'as', 'well', 'as', 'the', 'future', 'panda', 'experiment', 'at', 'the', 'fair', 'facility']] | [-0.051926285427393075, 0.25263255642849114, -0.0742619431992175, 0.17359918365453755, -0.06833010634390364, -0.18022068294344423, 0.053432767953093326, 0.3352380590797288, -0.13557542860507965, -0.17880674688111445, -0.056390111485819856, -0.3742252020648022, -0.006628098621149547, 0.11810516998478461, 0.13422312858946076, 0.15974371593229744, 0.12713565092204457, 0.03411824486740842, -0.03280296627852339, -0.18155149600849055, 0.3146486739420741, -0.029935380296281924, 0.1835338816006963, 0.19347307156975857, -0.06866832771665053, -0.008459182827741975, -0.006651576138499034, -0.0940295879022082, -0.14578410666355213, 0.06673254910217621, 0.23634768375933807, 0.11401996015082681, 0.11608390850526076, -0.3152196218327778, -0.10217853952426373, 0.1463000434493396, 0.20379984997519543, 0.11456836091649787, -0.035910035560912705, -0.4100008760351779, 0.13803886332394408, -0.1891883413801424, -0.09543232024056701, -0.11306508971809796, 0.016136831436419814, -0.08504058484828508, -0.3657099711935876, 0.11376083766290543, -0.10558123754482239, 0.009845385367686792, -0.08998889742822318, -0.31028883435079124, 0.003816695165502407, 0.02818767860724886, 0.16390924366892362, 0.07505112945062403, 0.1366133621734817, -0.1723475675126992, -0.18433524390026676, 0.3889621607732119, -0.11579653601766927, -0.16270678176930764, 0.10976450660382397, -0.17712452265195458, -0.12766045096710824, 0.1165813368109123, 0.2264619012897062, 0.03940449618942405, -0.15182079993778977, 0.09031959347747744, -0.08009320677530657, 0.16377815657448613, 0.06956439215417315, 0.17523695667775271, 0.18234893633336646, 0.17744501610286534, -0.06697956296024708, 0.10542582200056441, -0.025528518308419734, -0.09871001692102631, -0.42910718580526186, -0.12438712714711295, -0.04354029708746367, 0.06355946158093013, -0.014326645466875251, -0.07255516019039333, 0.4153382190724653, 0.03421145342304598, 0.300483760628395, 0.03515282625945785, 0.3114164809756004, 0.0570477456004904, 0.04642533944215535, 0.08871407001620023, 0.3141925258607399, 0.24682431459631315, 0.13820330864783922, -0.2929645821994466, -0.023934098994132222, -0.011754083222845888] |
1,802.05856 | Algorithmic Complexity and Reprogrammability of Chemical Structure
Networks | Here we address the challenge of profiling causal properties and tracking the
transformation of chemical compounds from an algorithmic perspective. We
explore the potential of applying a computational interventional calculus based
on the principles of algorithmic probability to chemical structure networks. We
profile the sensitivity of the elements and covalent bonds in a chemical
structure network algorithmically, asking whether reprogrammability affords
information about thermodynamic and chemical processes involved in the
transformation of different compound classes. We arrive at numerical results
suggesting a correspondence between some physical, structural and functional
properties. Our methods are capable of separating chemical classes that reflect
functional and natural differences without considering any information about
atomic and molecular properties. We conclude that these methods, with their
links to chemoinformatics via algorithmic, probability hold promise for future
research.
| q-bio.MN cs.CE cs.IT math.IT | here we address the challenge of profiling causal properties and tracking the transformation of chemical compounds from an algorithmic perspective we explore the potential of applying a computational interventional calculus based on the principles of algorithmic probability to chemical structure networks we profile the sensitivity of the elements and covalent bonds in a chemical structure network algorithmically asking whether reprogrammability affords information about thermodynamic and chemical processes involved in the transformation of different compound classes we arrive at numerical results suggesting a correspondence between some physical structural and functional properties our methods are capable of separating chemical classes that reflect functional and natural differences without considering any information about atomic and molecular properties we conclude that these methods with their links to chemoinformatics via algorithmic probability hold promise for future research | [['here', 'we', 'address', 'the', 'challenge', 'of', 'profiling', 'causal', 'properties', 'and', 'tracking', 'the', 'transformation', 'of', 'chemical', 'compounds', 'from', 'an', 'algorithmic', 'perspective', 'we', 'explore', 'the', 'potential', 'of', 'applying', 'a', 'computational', 'interventional', 'calculus', 'based', 'on', 'the', 'principles', 'of', 'algorithmic', 'probability', 'to', 'chemical', 'structure', 'networks', 'we', 'profile', 'the', 'sensitivity', 'of', 'the', 'elements', 'and', 'covalent', 'bonds', 'in', 'a', 'chemical', 'structure', 'network', 'algorithmically', 'asking', 'whether', 'reprogrammability', 'affords', 'information', 'about', 'thermodynamic', 'and', 'chemical', 'processes', 'involved', 'in', 'the', 'transformation', 'of', 'different', 'compound', 'classes', 'we', 'arrive', 'at', 'numerical', 'results', 'suggesting', 'a', 'correspondence', 'between', 'some', 'physical', 'structural', 'and', 'functional', 'properties', 'our', 'methods', 'are', 'capable', 'of', 'separating', 'chemical', 'classes', 'that', 'reflect', 'functional', 'and', 'natural', 'differences', 'without', 'considering', 'any', 'information', 'about', 'atomic', 'and', 'molecular', 'properties', 'we', 'conclude', 'that', 'these', 'methods', 'with', 'their', 'links', 'to', 'chemoinformatics', 'via', 'algorithmic', 'probability', 'hold', 'promise', 'for', 'future', 'research']] | [-0.07870724172160769, 0.050238213616619704, -0.1081143096835843, 0.06753811589975393, -0.07800829990674521, -0.10914799445557571, 0.1495912443297268, 0.41980064882121926, -0.32660886739871203, -0.3129418783657651, 0.03497591750511459, -0.2696741267270942, -0.20980778767350522, 0.1679072750462148, -0.02800396435631027, 0.0565168485658613, 0.04268136413817699, -0.014123683386546283, -0.11151526758770765, -0.20615382027058712, 0.34343964703565905, 0.044628361851431944, 0.2694065775046879, 0.10174071656198788, 0.07742457659932862, -0.020667870774267967, -0.03919137723367121, 0.03492431345712109, -0.1571159517637566, 0.18518634129827485, 0.2692004097838893, 0.17637049894111642, 0.26947159847273056, -0.47728628699333614, -0.25105533434632626, 0.08877455288693593, 0.06673059490587534, 0.12139369567984161, -0.08916197425440552, -0.257008014922208, 0.08100916968635462, -0.10536583446993543, -0.1228158805618873, -0.12103955591815517, -0.016495336331906977, 0.05134073591556485, -0.22381620027768276, 0.051785360876182435, 0.05463009437666993, 0.12692393247350936, -0.08089723975667801, -0.13672797494205355, -0.016521626989222098, 0.18564144594822335, 0.0075452578474433365, -0.026486667889731984, 0.17440595008248022, -0.1383294963867951, -0.13514292179358028, 0.37942506756832584, 0.01574291518661869, -0.1782438516161824, 0.24028932210102408, -0.08579924263998524, -0.21887684232406035, 0.08012244416727819, 0.13107873094872435, 0.06247708191455321, -0.1944474494244676, 0.06995021990048948, 0.03234762787278599, 0.16613749403840655, 0.07030809373872085, 0.07661615154361139, 0.2147818391339299, 0.19299663482091695, 0.012637564400566443, 0.10014439446190443, -0.03529135351609581, -0.10757163909925077, -0.23643118968475182, -0.18373722425583777, -0.13835665407299086, 0.04140926506512493, -0.1279531159815432, -0.13169176157791423, 0.3905178671809383, 0.18449073426340146, 0.15996951942566698, 0.037313542399868255, 0.24853665828402688, 0.028483920564469776, 0.03804437187144832, 0.029721792377638168, 0.1585074723709307, 0.15492916370578508, 0.091197520993003, -0.21299805614265493, 0.14032697722045634, 0.038156397118169164] |
1,802.05857 | Ising-QCD phenomenology close to the critical point | We employ the recently introduced Ising-QCD partition function (N.~G.
Antoniou {\it et al.}, Phys. Rev. D 97, 034015 (2018)) to explore in detail the
behaviour of the moments of the baryon-number, within the critical region
around the critical endpoint. Our analysis is based on the relation of
finite-size scaling in real space with intermittency in transverse momentum
space. It demonstrates in practice the recent observation (N.~G. Antoniou {\it
et al.}, Phys. Rev. D 97, 034015 (2018)) that combined measurements of the
intermittency index $\phi_2$ and the freeze-out parameters $\mu_b$
(baryochemical potential), $T$ (temperature), provide us with a powerful tool
to detect the critical point. We also show that the finite-size scaling (FSS)
region, as a part of the critical region, is very narrow in both the chemical
potential and the temperature direction, even for light nuclei. Furthermore,
using published experimental results for $(\mu_b,T,\phi_2)$ in A+A collisions
at $\sqrt{s_{NN}}=17.2$ GeV (NA49 experiment, CERN-SPS), we are able to make a
set of predictions for the freeze-out states of Ar + Sc and Xe + La collisions
at the same energy in the NA61/SHINE experiment (CERN-SPS). In particular, we
find that the Ar + Sc system freezes out outside the FSS region but very close
to its boundary, a property which may leave characteristic traces in
intermittency analysis.
| hep-ph | we employ the recently introduced isingqcd partition function ng antoniou it et al phys rev d 97 034015 2018 to explore in detail the behaviour of the moments of the baryonnumber within the critical region around the critical endpoint our analysis is based on the relation of finitesize scaling in real space with intermittency in transverse momentum space it demonstrates in practice the recent observation ng antoniou it et al phys rev d 97 034015 2018 that combined measurements of the intermittency index phi_2 and the freezeout parameters mu_b baryochemical potential t temperature provide us with a powerful tool to detect the critical point we also show that the finitesize scaling fss region as a part of the critical region is very narrow in both the chemical potential and the temperature direction even for light nuclei furthermore using published experimental results for mu_btphi_2 in aa collisions at sqrts_nn172 gev na49 experiment cernsps we are able to make a set of predictions for the freezeout states of ar sc and xe la collisions at the same energy in the na61shine experiment cernsps in particular we find that the ar sc system freezes out outside the fss region but very close to its boundary a property which may leave characteristic traces in intermittency analysis | [['we', 'employ', 'the', 'recently', 'introduced', 'isingqcd', 'partition', 'function', 'ng', 'antoniou', 'it', 'et', 'al', 'phys', 'rev', 'd', '97', '034015', '2018', 'to', 'explore', 'in', 'detail', 'the', 'behaviour', 'of', 'the', 'moments', 'of', 'the', 'baryonnumber', 'within', 'the', 'critical', 'region', 'around', 'the', 'critical', 'endpoint', 'our', 'analysis', 'is', 'based', 'on', 'the', 'relation', 'of', 'finitesize', 'scaling', 'in', 'real', 'space', 'with', 'intermittency', 'in', 'transverse', 'momentum', 'space', 'it', 'demonstrates', 'in', 'practice', 'the', 'recent', 'observation', 'ng', 'antoniou', 'it', 'et', 'al', 'phys', 'rev', 'd', '97', '034015', '2018', 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1,802.05858 | Quantitative modelling of nutrient-limited growth of bacterial colonies
in microfluidic cultivation | Nutrient gradients and limitations play a pivotal role in the life of all
microbes, both in their natural habitat as well as in artificial, microfluidic
systems. Spatial concentration gradients of nutrients in densely packed cell
configurations may locally affect the bacterial growth leading to heterogeneous
micropopulations. A detailed understanding and quantitative modelling of
cellular behaviour under nutrient limitations is thus highly desirable. We use
microfluidic cultivations to investigate growth and microbial behaviour of the
model organism Corynebacterium glutamicum under well-controlled conditions.
With a reaction-diffusion type model, parameters are extracted from
steady-state experiments with a one-dimensional nutrient gradient.
Subsequentially, we employ particle-based simulations with these parameters to
predict the dynamical growth of a colony in two dimensions. Comparing the
results of those simulations with microfluidic experiments yields excellent
agreement. Our modelling approach lays the foundation for a better
understanding of dynamic microbial growth processes, both in nature and in
applied biotechnology.
| physics.bio-ph | nutrient gradients and limitations play a pivotal role in the life of all microbes both in their natural habitat as well as in artificial microfluidic systems spatial concentration gradients of nutrients in densely packed cell configurations may locally affect the bacterial growth leading to heterogeneous micropopulations a detailed understanding and quantitative modelling of cellular behaviour under nutrient limitations is thus highly desirable we use microfluidic cultivations to investigate growth and microbial behaviour of the model organism corynebacterium glutamicum under wellcontrolled conditions with a reactiondiffusion type model parameters are extracted from steadystate experiments with a onedimensional nutrient gradient subsequentially we employ particlebased simulations with these parameters to predict the dynamical growth of a colony in two dimensions comparing the results of those simulations with microfluidic experiments yields excellent agreement our modelling approach lays the foundation for a better understanding of dynamic microbial growth processes both in nature and in applied biotechnology | [['nutrient', 'gradients', 'and', 'limitations', 'play', 'a', 'pivotal', 'role', 'in', 'the', 'life', 'of', 'all', 'microbes', 'both', 'in', 'their', 'natural', 'habitat', 'as', 'well', 'as', 'in', 'artificial', 'microfluidic', 'systems', 'spatial', 'concentration', 'gradients', 'of', 'nutrients', 'in', 'densely', 'packed', 'cell', 'configurations', 'may', 'locally', 'affect', 'the', 'bacterial', 'growth', 'leading', 'to', 'heterogeneous', 'micropopulations', 'a', 'detailed', 'understanding', 'and', 'quantitative', 'modelling', 'of', 'cellular', 'behaviour', 'under', 'nutrient', 'limitations', 'is', 'thus', 'highly', 'desirable', 'we', 'use', 'microfluidic', 'cultivations', 'to', 'investigate', 'growth', 'and', 'microbial', 'behaviour', 'of', 'the', 'model', 'organism', 'corynebacterium', 'glutamicum', 'under', 'wellcontrolled', 'conditions', 'with', 'a', 'reactiondiffusion', 'type', 'model', 'parameters', 'are', 'extracted', 'from', 'steadystate', 'experiments', 'with', 'a', 'onedimensional', 'nutrient', 'gradient', 'subsequentially', 'we', 'employ', 'particlebased', 'simulations', 'with', 'these', 'parameters', 'to', 'predict', 'the', 'dynamical', 'growth', 'of', 'a', 'colony', 'in', 'two', 'dimensions', 'comparing', 'the', 'results', 'of', 'those', 'simulations', 'with', 'microfluidic', 'experiments', 'yields', 'excellent', 'agreement', 'our', 'modelling', 'approach', 'lays', 'the', 'foundation', 'for', 'a', 'better', 'understanding', 'of', 'dynamic', 'microbial', 'growth', 'processes', 'both', 'in', 'nature', 'and', 'in', 'applied', 'biotechnology']] | [-0.06801129689115105, 0.13456299773811195, -0.058847451740375804, 0.03630009265975939, -0.02545054919155152, -0.11943691445517685, 0.02024071355625337, 0.3660421767615832, -0.23520891001798064, -0.2742694127654669, 0.07773983804430913, -0.22904259923395495, -0.2267041692585253, 0.2206194146861316, -0.0667123930003719, 0.06946403471926452, 0.06974297312350831, -0.059167209450130494, 0.050452529040209, -0.2003774041647479, 0.22455335664094095, 0.05727430653052042, 0.3541692400588685, 0.03355479209399438, 0.08204780430407982, -0.050845093912626274, -0.026533625870724303, 0.04444440096091704, -0.18994136072145212, 0.13811683430548932, 0.26681795242498785, 0.10965887119527906, 0.31027458900673277, -0.5286894458277314, -0.3009782053355203, 0.09775826837179945, 0.16181667256145582, 0.12527518325029002, -0.10659647259889359, -0.2149036502084326, 0.061070861883974016, -0.12076016338448346, -0.16869123866969107, -0.08803985531648133, -0.02347113939639706, 0.09733513520753961, -0.27951676075365517, 0.11183484536970671, 0.01453205294326579, 0.11450176840318342, -0.12772808836549582, -0.09359006465947092, -0.0578937621743437, 0.19492107042386478, 0.007520931247065482, -0.04957955538751495, 0.23563542286709152, -0.1582552620972313, -0.08677601137967318, 0.40426875838397336, -0.0662912154474902, -0.23594968980951736, 0.28683685624484445, -0.12914757496496074, -0.12383968241812798, 0.09045265631393948, 0.2323333800367186, 0.07270191945862213, -0.18690607322367445, 0.01441775647854595, -0.007848002110301648, 0.13744137856320965, 0.058367434516002195, 0.013350383362499607, 0.21835828904256274, 0.2848090786665003, 0.03252714650467938, 0.09872115262621552, -0.026737572140866677, -0.16917424624467006, -0.19706326051171394, -0.1468352040450945, -0.0796917867776469, 0.03643417571090793, -0.11761960275932185, -0.16734891431093615, 0.3603487527029176, 0.13553357934157345, 0.17731727299674246, 0.05874898633607132, 0.23534566630146797, -0.008363614400267451, 0.05468214601872101, -0.0085154868093563, 0.15675287156197049, 0.12969349814116354, 0.13854680274575698, -0.25967209099404415, 0.13921597633423322, 0.00011386384799976477] |
1,802.05859 | A Parameterized Strongly Polynomial Algorithm for Block Structured
Integer Programs | The theory of $n$-fold integer programming has been recently emerging as an
important tool in parameterized complexity. The input to an $n$-fold integer
program (IP) consists of parameter $A$, dimension $n$, and numerical data of
binary encoding length $L$. It was known for some time that such programs can
be solved in polynomial time using $O(n^{g(A)}L)$ arithmetic operations where
$g$ is an exponential function of the parameter. In 2013 it was shown that it
can be solved in fixed-parameter tractable (FPT) time using $O(f(A)n^3L)$
arithmetic operations for a single-exponential function $f$. This, and a faster
algorithm for a special case of combinatorial $n$-fold IP, have led to several
very recent breakthroughs in the parameterized complexity of scheduling,
stringology, and computational social choice. In 2015 it was shown that it can
be solved in strongly polynomial time using $O(n^{g(A)})$ arithmetic
operations.
Here we establish a result which subsumes all three of the above results by
showing that $n$-fold IP can be solved in strongly polynomial FPT time using
$O(f(A)n^3)$ arithmetic operations. In fact, our results are much more general,
briefly outlined as follows.
- There is a strongly polynomial algorithm for ILP whenever a so-called
Graver-best oracle is realizable for it.
- Graver-best oracles for the large classes of multi-stage stochastic and
tree-fold ILPs can be realized in FPT time. Together with the previous oracle
algorithm, this newly shows two large classes of ILP to be strongly polynomial;
in contrast, only few classes of ILP were previously known to be strongly
polynomial.
- We show that ILP is FPT parameterized by the largest coefficient
$\|A\|_\infty$ and the primal or dual treedepth of $A$, and that this
parameterization cannot be relaxed, signifying substantial progress in
understanding the parameterized complexity of ILP.
| cs.DS cs.CC cs.DM math.CO math.OC | the theory of nfold integer programming has been recently emerging as an important tool in parameterized complexity the input to an nfold integer program ip consists of parameter a dimension n and numerical data of binary encoding length l it was known for some time that such programs can be solved in polynomial time using ongal arithmetic operations where g is an exponential function of the parameter in 2013 it was shown that it can be solved in fixedparameter tractable fpt time using ofan3l arithmetic operations for a singleexponential function f this and a faster algorithm for a special case of combinatorial nfold ip have led to several very recent breakthroughs in the parameterized complexity of scheduling stringology and computational social choice in 2015 it was shown that it can be solved in strongly polynomial time using onga arithmetic operations here we establish a result which subsumes all three of the above results by showing that nfold ip can be solved in strongly polynomial fpt time using ofan3 arithmetic operations in fact our results are much more general briefly outlined as follows there is a strongly polynomial algorithm for ilp whenever a socalled graverbest oracle is realizable for it graverbest oracles for the large classes of multistage stochastic and treefold ilps can be realized in fpt time together with the previous oracle algorithm this newly shows two large classes of ilp to be strongly polynomial in contrast only few classes of ilp were previously known to be strongly polynomial we show that ilp is fpt parameterized by the largest coefficient a_infty and the primal or dual treedepth of a and that this parameterization cannot be relaxed signifying substantial progress in understanding the parameterized complexity of ilp | [['the', 'theory', 'of', 'nfold', 'integer', 'programming', 'has', 'been', 'recently', 'emerging', 'as', 'an', 'important', 'tool', 'in', 'parameterized', 'complexity', 'the', 'input', 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