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1,802.0686 | The momentum budget of clustered supernova feedback in a 3D, magnetised
medium | While the evolution of superbubbles driven by clustered supernovae has been
studied by numerous authors, the resulting radial momentum yield is uncertain
by as much as an order of magnitude depending on the computational methods and
assumed properties of the surrounding interstellar medium (ISM). In this work,
we study the origin of these discrepancies, and seek to determine the correct
momentum budget for a homogeneous ISM. We carry out 3D hydrodynamic (HD) and
magnetohydrodynamic (MHD) simulations of clustered supernova explosions, using
a Lagrangian method and checking for convergence with respect to resolution. We
find that the terminal momentum of a shell driven by clustered supernovae is
dictated primarily by the mixing rate across the contact discontinuity between
the hot and cold phases, and that this energy mixing rate is dominated by
numerical diffusion even at the highest resolution we can complete, 0.03
$M_\odot$. Magnetic fields also reduce the mixing rate, so that MHD simulations
produce higher momentum yields than HD ones at equal resolution. As a result,
we obtain only a lower limit on the momentum yield from clustered supernovae.
Combining this with our previous 1D results, which provide an upper limit
because they allow almost no mixing across the contact discontinuity, we
conclude that the momentum yield per supernova from clustered supernovae in a
homogeneous ISM is bounded between $2\times 10^5$ and $3\times 10^6$ $M_\odot$
km s$^{-1}$. A converged value for the simple homogeneous ISM remains elusive.
| astro-ph.GA | while the evolution of superbubbles driven by clustered supernovae has been studied by numerous authors the resulting radial momentum yield is uncertain by as much as an order of magnitude depending on the computational methods and assumed properties of the surrounding interstellar medium ism in this work we study the origin of these discrepancies and seek to determine the correct momentum budget for a homogeneous ism we carry out 3d hydrodynamic hd and magnetohydrodynamic mhd simulations of clustered supernova explosions using a lagrangian method and checking for convergence with respect to resolution we find that the terminal momentum of a shell driven by clustered supernovae is dictated primarily by the mixing rate across the contact discontinuity between the hot and cold phases and that this energy mixing rate is dominated by numerical diffusion even at the highest resolution we can complete 003 m_odot magnetic fields also reduce the mixing rate so that mhd simulations produce higher momentum yields than hd ones at equal resolution as a result we obtain only a lower limit on the momentum yield from clustered supernovae combining this with our previous 1d results which provide an upper limit because they allow almost no mixing across the contact discontinuity we conclude that the momentum yield per supernova from clustered supernovae in a homogeneous ism is bounded between 2times 105 and 3times 106 m_odot km s1 a converged value for the simple homogeneous ism remains elusive | [['while', 'the', 'evolution', 'of', 'superbubbles', 'driven', 'by', 'clustered', 'supernovae', 'has', 'been', 'studied', 'by', 'numerous', 'authors', 'the', 'resulting', 'radial', 'momentum', 'yield', 'is', 'uncertain', 'by', 'as', 'much', 'as', 'an', 'order', 'of', 'magnitude', 'depending', 'on', 'the', 'computational', 'methods', 'and', 'assumed', 'properties', 'of', 'the', 'surrounding', 'interstellar', 'medium', 'ism', 'in', 'this', 'work', 'we', 'study', 'the', 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1,802.06861 | Interpreting DNN output layer activations: A strategy to cope with
unseen data in speech recognition | Unseen data can degrade performance of deep neural net acoustic models. To
cope with unseen data, adaptation techniques are deployed. For unlabeled unseen
data, one must generate some hypothesis given an existing model, which is used
as the label for model adaptation. However, assessing the goodness of the
hypothesis can be difficult, and an erroneous hypothesis can lead to poorly
trained models. In such cases, a strategy to select data having reliable
hypothesis can ensure better model adaptation. This work proposes a
data-selection strategy for DNN model adaptation, where DNN output layer
activations are used to ascertain the goodness of a generated hypothesis. In a
DNN acoustic model, the output layer activations are used to generate target
class probabilities. Under unseen data conditions, the difference between the
most probable target and the next most probable target is decreased compared to
the same for seen data, indicating that the model may be uncertain while
generating its hypothesis. This work proposes a strategy to assess a model's
performance by analyzing the output layer activations by using a distance
measure between the most likely target and the next most likely target, which
is used for data selection for performing unsupervised adaptation.
| cs.CL cs.SD eess.AS | unseen data can degrade performance of deep neural net acoustic models to cope with unseen data adaptation techniques are deployed for unlabeled unseen data one must generate some hypothesis given an existing model which is used as the label for model adaptation however assessing the goodness of the hypothesis can be difficult and an erroneous hypothesis can lead to poorly trained models in such cases a strategy to select data having reliable hypothesis can ensure better model adaptation this work proposes a dataselection strategy for dnn model adaptation where dnn output layer activations are used to ascertain the goodness of a generated hypothesis in a dnn acoustic model the output layer activations are used to generate target class probabilities under unseen data conditions the difference between the most probable target and the next most probable target is decreased compared to the same for seen data indicating that the model may be uncertain while generating its hypothesis this work proposes a strategy to assess a models performance by analyzing the output layer activations by using a distance measure between the most likely target and the next most likely target which is used for data selection for performing unsupervised adaptation | [['unseen', 'data', 'can', 'degrade', 'performance', 'of', 'deep', 'neural', 'net', 'acoustic', 'models', 'to', 'cope', 'with', 'unseen', 'data', 'adaptation', 'techniques', 'are', 'deployed', 'for', 'unlabeled', 'unseen', 'data', 'one', 'must', 'generate', 'some', 'hypothesis', 'given', 'an', 'existing', 'model', 'which', 'is', 'used', 'as', 'the', 'label', 'for', 'model', 'adaptation', 'however', 'assessing', 'the', 'goodness', 'of', 'the', 'hypothesis', 'can', 'be', 'difficult', 'and', 'an', 'erroneous', 'hypothesis', 'can', 'lead', 'to', 'poorly', 'trained', 'models', 'in', 'such', 'cases', 'a', 'strategy', 'to', 'select', 'data', 'having', 'reliable', 'hypothesis', 'can', 'ensure', 'better', 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1,802.06862 | Joint Task Assignment and Wireless Resource Allocation for Cooperative
Mobile-Edge Computing | This paper studies a multi-user cooperative mobile-edge computing (MEC)
system, in which a local mobile user can offload intensive computation tasks to
multiple nearby edge devices serving as helpers for remote execution. We focus
on the scenario where the local user has a number of independent tasks that can
be executed in parallel but cannot be further partitioned. We consider a time
division multiple access (TDMA) communication protocol, in which the local user
can offload computation tasks to the helpers and download results from them
over pre-scheduled time slots. Under this setup, we minimize the local user's
computation latency by optimizing the task assignment jointly with the time and
power allocations, subject to individual energy constraints at the local user
and the helpers. However, the joint task assignment and wireless resource
allocation problem is a mixed-integer non-linear program (MINLP) that is hard
to solve optimally. To tackle this challenge, we first relax it into a convex
problem, and then propose an efficient suboptimal solution based on the optimal
solution to the relaxed convex problem. Finally, numerical results show that
our proposed joint design significantly reduces the local user's computation
latency, as compared against other benchmark schemes that design the task
assignment separately from the offloading/downloading resource allocations and
local execution.
| eess.SP cs.IT math.IT | this paper studies a multiuser cooperative mobileedge computing mec system in which a local mobile user can offload intensive computation tasks to multiple nearby edge devices serving as helpers for remote execution we focus on the scenario where the local user has a number of independent tasks that can be executed in parallel but cannot be further partitioned we consider a time division multiple access tdma communication protocol in which the local user can offload computation tasks to the helpers and download results from them over prescheduled time slots under this setup we minimize the local users computation latency by optimizing the task assignment jointly with the time and power allocations subject to individual energy constraints at the local user and the helpers however the joint task assignment and wireless resource allocation problem is a mixedinteger nonlinear program minlp that is hard to solve optimally to tackle this challenge we first relax it into a convex problem and then propose an efficient suboptimal solution based on the optimal solution to the relaxed convex problem finally numerical results show that our proposed joint design significantly reduces the local users computation latency as compared against other benchmark schemes that design the task assignment separately from the offloadingdownloading resource allocations and local execution | [['this', 'paper', 'studies', 'a', 'multiuser', 'cooperative', 'mobileedge', 'computing', 'mec', 'system', 'in', 'which', 'a', 'local', 'mobile', 'user', 'can', 'offload', 'intensive', 'computation', 'tasks', 'to', 'multiple', 'nearby', 'edge', 'devices', 'serving', 'as', 'helpers', 'for', 'remote', 'execution', 'we', 'focus', 'on', 'the', 'scenario', 'where', 'the', 'local', 'user', 'has', 'a', 'number', 'of', 'independent', 'tasks', 'that', 'can', 'be', 'executed', 'in', 'parallel', 'but', 'can', 'not', 'be', 'further', 'partitioned', 'we', 'consider', 'a', 'time', 'division', 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1,802.06863 | Predicting Metamorphic Relation for Matrix Calculation Programs | Matrices often represent important information in scientific applications and
are involved in performing complex calculations. But systematically testing
these applications is hard due to the oracle problem. Metamorphic testing is an
effective approach to test such applications because it uses metamorphic
relations to determine whether test cases have passed or failed. Metamorphic
relations are typically identified with the help of a domain expert and is a
labor intensive task. In this work we use a graph kernel based machine learning
approach to predict metamorphic relations for matrix calculation programs.
Previously, this graph kernel based machine learning approach was used to
successfully predict metamorphic relations for programs that perform numerical
calculations. Results of this study show that this approach can be used to
predict metamorphic relations for matrix calculation programs as well.
| cs.SE | matrices often represent important information in scientific applications and are involved in performing complex calculations but systematically testing these applications is hard due to the oracle problem metamorphic testing is an effective approach to test such applications because it uses metamorphic relations to determine whether test cases have passed or failed metamorphic relations are typically identified with the help of a domain expert and is a labor intensive task in this work we use a graph kernel based machine learning approach to predict metamorphic relations for matrix calculation programs previously this graph kernel based machine learning approach was used to successfully predict metamorphic relations for programs that perform numerical calculations results of this study show that this approach can be used to predict metamorphic relations for matrix calculation programs as well | [['matrices', 'often', 'represent', 'important', 'information', 'in', 'scientific', 'applications', 'and', 'are', 'involved', 'in', 'performing', 'complex', 'calculations', 'but', 'systematically', 'testing', 'these', 'applications', 'is', 'hard', 'due', 'to', 'the', 'oracle', 'problem', 'metamorphic', 'testing', 'is', 'an', 'effective', 'approach', 'to', 'test', 'such', 'applications', 'because', 'it', 'uses', 'metamorphic', 'relations', 'to', 'determine', 'whether', 'test', 'cases', 'have', 'passed', 'or', 'failed', 'metamorphic', 'relations', 'are', 'typically', 'identified', 'with', 'the', 'help', 'of', 'a', 'domain', 'expert', 'and', 'is', 'a', 'labor', 'intensive', 'task', 'in', 'this', 'work', 'we', 'use', 'a', 'graph', 'kernel', 'based', 'machine', 'learning', 'approach', 'to', 'predict', 'metamorphic', 'relations', 'for', 'matrix', 'calculation', 'programs', 'previously', 'this', 'graph', 'kernel', 'based', 'machine', 'learning', 'approach', 'was', 'used', 'to', 'successfully', 'predict', 'metamorphic', 'relations', 'for', 'programs', 'that', 'perform', 'numerical', 'calculations', 'results', 'of', 'this', 'study', 'show', 'that', 'this', 'approach', 'can', 'be', 'used', 'to', 'predict', 'metamorphic', 'relations', 'for', 'matrix', 'calculation', 'programs', 'as', 'well']] | [-0.0042818440604272465, 0.008430212225257562, -0.11975490196273868, 0.1514650314063445, -0.15462132145202798, -0.16892639156928613, 0.06031690792088176, 0.4263983208487052, -0.27364148033199875, -0.3943557899679903, 0.13031659145992364, -0.24211420269526598, -0.2290150297837659, 0.22378088812030966, -0.06237315143750028, 0.15473922050579358, 0.10934965410589717, 0.010983499321546263, -0.06484579579026672, -0.25014196875734424, 0.28223625593285523, 0.03216948567086276, 0.31016819902788134, 0.026045841655430904, 0.0410779174346164, -0.01228842800767481, -0.0479889091402861, 0.0028421062299929948, -0.10565775172762264, 0.13678967886878077, 0.35688228684811646, 0.19438197839834537, 0.30181066357736824, -0.4527511086087418, -0.187005823549196, 0.09657515112651896, 0.11185696534456983, 0.10643503306085202, -0.009791459306143224, -0.21756429587071405, 0.09435136633647875, -0.1870827212830195, -0.06667946393509175, -0.1691996085827189, 0.01714862880824873, -0.04141957217735018, -0.28013853374726444, 0.01632235075399557, 0.029183093743676048, 0.07417728479154931, -0.007063534651093811, -0.09791714533761797, 0.07774711229113279, 0.16662343373372868, 0.042755571160223764, 0.01610813525288529, 0.16127161199994783, -0.08860100075630968, -0.16701739288525272, 0.3991773839727158, 0.023490248242649073, -0.20397035419258452, 0.2274003202977185, -0.04131381260231137, -0.20007853410455562, 0.04609982997804653, 0.23653831517853496, 0.1320531841061782, -0.20651343768419883, 0.03679257498979426, -0.02369796328303468, 0.19155683796248535, 0.044364951447875216, -0.06577509779435092, 0.1860602882953773, 0.1830676792701351, -0.04969757572011951, 0.14024691690712143, -0.03635223052030756, -0.035269526537251836, -0.2070771844200932, -0.20090183776355428, -0.21248927136940013, -0.019373310578787942, -0.043584936941602405, -0.18321067517274947, 0.32262223080255603, 0.2121094119564438, 0.14860079043992716, 0.07115268585705803, 0.28811217013405255, 0.08818964510427375, 0.16424313972469504, 0.06867856430271925, 0.2075798218031876, 0.09408774301729642, 0.07628987664143548, -0.17867747342697424, 0.1442321834389035, 0.056639820900710146] |
1,802.06864 | The NWRA Classification Infrastructure: Description and Extension to the
Discriminant Analysis Flare Forecasting System (DAFFS) | A classification infrastructure built upon Discriminant Analysis has been
developed at NorthWest Research Associates for examining the statistical
differences between samples of two known populations. Originating to examine
the physical differences between flare-quiet and flare-imminent solar active
regions, we describe herein some details of the infrastructure including:
parametrization of large datasets, schemes for handling "null" and "bad" data
in multi-parameter analysis, application of non-parametric multi-dimensional
Discriminant Analysis, an extension through Bayes' theorem to probabilistic
classification, and methods invoked for evaluating classifier success. The
classifier infrastructure is applicable to a wide range of scientific questions
in solar physics. We demonstrate its application to the question of
distinguishing flare-imminent from flare-quiet solar active regions, updating
results from the original publications that were based on different data and
much smaller sample sizes. Finally, as a demonstration of "Research to
Operations" efforts in the space-weather forecasting context, we present the
Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time
operationally-running solar flare forecasting tool that was developed from the
research-directed infrastructure.
| astro-ph.SR physics.data-an physics.space-ph | a classification infrastructure built upon discriminant analysis has been developed at northwest research associates for examining the statistical differences between samples of two known populations originating to examine the physical differences between flarequiet and flareimminent solar active regions we describe herein some details of the infrastructure including parametrization of large datasets schemes for handling null and bad data in multiparameter analysis application of nonparametric multidimensional discriminant analysis an extension through bayes theorem to probabilistic classification and methods invoked for evaluating classifier success the classifier infrastructure is applicable to a wide range of scientific questions in solar physics we demonstrate its application to the question of distinguishing flareimminent from flarequiet solar active regions updating results from the original publications that were based on different data and much smaller sample sizes finally as a demonstration of research to operations efforts in the spaceweather forecasting context we present the discriminant analysis flare forecasting system daffs a nearrealtime operationallyrunning solar flare forecasting tool that was developed from the researchdirected infrastructure | [['a', 'classification', 'infrastructure', 'built', 'upon', 'discriminant', 'analysis', 'has', 'been', 'developed', 'at', 'northwest', 'research', 'associates', 'for', 'examining', 'the', 'statistical', 'differences', 'between', 'samples', 'of', 'two', 'known', 'populations', 'originating', 'to', 'examine', 'the', 'physical', 'differences', 'between', 'flarequiet', 'and', 'flareimminent', 'solar', 'active', 'regions', 'we', 'describe', 'herein', 'some', 'details', 'of', 'the', 'infrastructure', 'including', 'parametrization', 'of', 'large', 'datasets', 'schemes', 'for', 'handling', 'null', 'and', 'bad', 'data', 'in', 'multiparameter', 'analysis', 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1,802.06865 | Automated soft tissue lesion detection and segmentation in digital
mammography using a u-net deep learning network | Computer-aided detection or decision support systems aim to improve breast
cancer screening programs by helping radiologists to evaluate digital
mammography (DM) exams. Commonly such methods proceed in two steps: selection
of candidate regions for malignancy, and later classification as either
malignant or not. In this study, we present a candidate detection method based
on deep learning to automatically detect and additionally segment soft tissue
lesions in DM. A database of DM exams (mostly bilateral and two views) was
collected from our institutional archive. In total, 7196 DM exams (28294 DM
images) acquired with systems from three different vendors (General Electric,
Siemens, Hologic) were collected, of which 2883 contained malignant lesions
verified with histopathology. Data was randomly split on an exam level into
training (50\%), validation (10\%) and testing (40\%) of deep neural network
with u-net architecture. The u-net classifies the image but also provides
lesion segmentation. Free receiver operating characteristic (FROC) analysis was
used to evaluate the model, on an image and on an exam level. On an image
level, a maximum sensitivity of 0.94 at 7.93 false positives (FP) per image was
achieved. Similarly, per exam a maximum sensitivity of 0.98 at 7.81 FP per
image was achieved. In conclusion, the method could be used as a candidate
selection model with high accuracy and with the additional information of
lesion segmentation.
| cs.CV | computeraided detection or decision support systems aim to improve breast cancer screening programs by helping radiologists to evaluate digital mammography dm exams commonly such methods proceed in two steps selection of candidate regions for malignancy and later classification as either malignant or not in this study we present a candidate detection method based on deep learning to automatically detect and additionally segment soft tissue lesions in dm a database of dm exams mostly bilateral and two views was collected from our institutional archive in total 7196 dm exams 28294 dm images acquired with systems from three different vendors general electric siemens hologic were collected of which 2883 contained malignant lesions verified with histopathology data was randomly split on an exam level into training 50 validation 10 and testing 40 of deep neural network with unet architecture the unet classifies the image but also provides lesion segmentation free receiver operating characteristic froc analysis was used to evaluate the model on an image and on an exam level on an image level a maximum sensitivity of 094 at 793 false positives fp per image was achieved similarly per exam a maximum sensitivity of 098 at 781 fp per image was achieved in conclusion the method could be used as a candidate selection model with high accuracy and with the additional information of lesion segmentation | [['computeraided', 'detection', 'or', 'decision', 'support', 'systems', 'aim', 'to', 'improve', 'breast', 'cancer', 'screening', 'programs', 'by', 'helping', 'radiologists', 'to', 'evaluate', 'digital', 'mammography', 'dm', 'exams', 'commonly', 'such', 'methods', 'proceed', 'in', 'two', 'steps', 'selection', 'of', 'candidate', 'regions', 'for', 'malignancy', 'and', 'later', 'classification', 'as', 'either', 'malignant', 'or', 'not', 'in', 'this', 'study', 'we', 'present', 'a', 'candidate', 'detection', 'method', 'based', 'on', 'deep', 'learning', 'to', 'automatically', 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1,802.06866 | Expert System for Diagnosis of Chest Diseases Using Neural Networks | This article represents one of the contemporary trends in the application of
the latest methods of information and communication technology for medicine
through an expert system helps the doctor to diagnose some chest diseases which
is important because of the frequent spread of chest diseases nowadays in
addition to the overlap symptoms of these diseases, which is difficult to right
diagnose by doctors with several algorithms: Forward Chaining, Backward
Chaining, Neural Network(Back Propagation). However, this system cannot replace
the doctor function, but it can help the doctor to avoid wrong diagnosis and
treatments. It can also be developed in such a way to help the novice doctors.
| cs.AI | this article represents one of the contemporary trends in the application of the latest methods of information and communication technology for medicine through an expert system helps the doctor to diagnose some chest diseases which is important because of the frequent spread of chest diseases nowadays in addition to the overlap symptoms of these diseases which is difficult to right diagnose by doctors with several algorithms forward chaining backward chaining neural networkback propagation however this system cannot replace the doctor function but it can help the doctor to avoid wrong diagnosis and treatments it can also be developed in such a way to help the novice doctors | [['this', 'article', 'represents', 'one', 'of', 'the', 'contemporary', 'trends', 'in', 'the', 'application', 'of', 'the', 'latest', 'methods', 'of', 'information', 'and', 'communication', 'technology', 'for', 'medicine', 'through', 'an', 'expert', 'system', 'helps', 'the', 'doctor', 'to', 'diagnose', 'some', 'chest', 'diseases', 'which', 'is', 'important', 'because', 'of', 'the', 'frequent', 'spread', 'of', 'chest', 'diseases', 'nowadays', 'in', 'addition', 'to', 'the', 'overlap', 'symptoms', 'of', 'these', 'diseases', 'which', 'is', 'difficult', 'to', 'right', 'diagnose', 'by', 'doctors', 'with', 'several', 'algorithms', 'forward', 'chaining', 'backward', 'chaining', 'neural', 'networkback', 'propagation', 'however', 'this', 'system', 'can', 'not', 'replace', 'the', 'doctor', 'function', 'but', 'it', 'can', 'help', 'the', 'doctor', 'to', 'avoid', 'wrong', 'diagnosis', 'and', 'treatments', 'it', 'can', 'also', 'be', 'developed', 'in', 'such', 'a', 'way', 'to', 'help', 'the', 'novice', 'doctors']] | [-0.04288847907011127, 0.05367788425159845, -0.07811238228726401, 0.13753851145836654, -0.16891388080700814, -0.18862691127790887, 0.07305501196881599, 0.37583736608797147, -0.29349562477877483, -0.2870698342987589, 0.14681922270650932, -0.3082282782540979, -0.19251986326014398, 0.2150665509262096, -0.1750040189096504, 0.07208996682175409, 0.08305964403063337, 0.08310558006306674, 0.040917810269144025, -0.2964964792236825, 0.25315340750232757, 0.08139242185714925, 0.2850505727404189, 0.085742991487278, 0.028341595758031184, 0.011584129349859518, -0.01630994948236463, -0.04091892052859625, -0.06909557435417844, 0.15159010739178858, 0.4030945333510759, 0.23445497783962813, 0.39805601575525007, -0.4621022047701283, -0.23064477699830535, 0.09819371232362552, 0.20306990048347615, 0.11077081468435022, 0.008779934363735614, -0.30244609728351096, 0.06222309486366043, -0.1874856629243521, -0.1240082648495717, -0.06542076292843775, -0.03951853693454204, -0.0020356849847779235, -0.2541740212619966, 0.0358194441849553, -0.011735210911083583, 0.0754617929989559, -0.006708979264998408, -0.059716215537791356, 0.02388000456511835, 0.24208990221181623, 0.11123051220620765, 0.08122947320361648, 0.14088643586402325, -0.19730875796255457, -0.1269416677957035, 0.35344506003763354, 0.055939538349945826, -0.20293670116323176, 0.218294990960526, -0.08701142771294662, -0.10877331454202394, 0.09372002019538223, 0.1926515852112027, 0.08096441195703277, -0.21492472215703576, -0.06029102963582551, 0.06699629823860025, 0.16799662920437475, 0.08444015864363358, -0.03998175646378615, 0.17447622726392872, 0.17413663863265347, 0.00945007037719555, 0.07356054354437287, -0.07463062182068825, -0.03169724974502748, -0.2040114503214571, -0.21014485587627044, -0.08222724276237955, 0.011197364750094103, -0.036161277264467585, -0.18060149997472763, 0.38371950500453256, 0.2758398664828007, 0.11640458134934306, -0.03524635675815848, 0.33461507465516294, 0.04898380317995064, 0.12384262257560252, 0.005153000337358947, 0.17498941859583997, 0.0435191100315304, 0.15995340564323016, -0.19392309516285347, 0.20042962088275737, 0.004133782401750578] |
1,802.06867 | Almost logarithmic-time space optimal leader election in population
protocols | The model of population protocols refers to a large collection of simple
indistinguishable entities, frequently called {\em agents}. The agents
communicate and perform computation through pairwise interactions. We study
fast and space efficient leader election in population of cardinality $n$
governed by a random scheduler, where during each time step the scheduler
uniformly at random selects for interaction exactly one pair of agents.
We propose the first $o(\log^2 n)$-time leader election protocol. Our
solution operates in expected parallel time $O(\log n\log\log n)$ which is
equivalent to $O(n \log n\log\log n)$ pairwise interactions. This is the
fastest currently known leader election algorithm in which each agent utilises
asymptotically optimal number of $O(\log\log n)$ states.
The new protocol incorporates and amalgamates successfully the power of
assorted {\em synthetic coins} with variable rate {\em phase clocks}.
| cs.DC | the model of population protocols refers to a large collection of simple indistinguishable entities frequently called em agents the agents communicate and perform computation through pairwise interactions we study fast and space efficient leader election in population of cardinality n governed by a random scheduler where during each time step the scheduler uniformly at random selects for interaction exactly one pair of agents we propose the first olog2 ntime leader election protocol our solution operates in expected parallel time olog nloglog n which is equivalent to on log nloglog n pairwise interactions this is the fastest currently known leader election algorithm in which each agent utilises asymptotically optimal number of ologlog n states the new protocol incorporates and amalgamates successfully the power of assorted em synthetic coins with variable rate em phase clocks | [['the', 'model', 'of', 'population', 'protocols', 'refers', 'to', 'a', 'large', 'collection', 'of', 'simple', 'indistinguishable', 'entities', 'frequently', 'called', 'em', 'agents', 'the', 'agents', 'communicate', 'and', 'perform', 'computation', 'through', 'pairwise', 'interactions', 'we', 'study', 'fast', 'and', 'space', 'efficient', 'leader', 'election', 'in', 'population', 'of', 'cardinality', 'n', 'governed', 'by', 'a', 'random', 'scheduler', 'where', 'during', 'each', 'time', 'step', 'the', 'scheduler', 'uniformly', 'at', 'random', 'selects', 'for', 'interaction', 'exactly', 'one', 'pair', 'of', 'agents', 'we', 'propose', 'the', 'first', 'olog2', 'ntime', 'leader', 'election', 'protocol', 'our', 'solution', 'operates', 'in', 'expected', 'parallel', 'time', 'olog', 'nloglog', 'n', 'which', 'is', 'equivalent', 'to', 'on', 'log', 'nloglog', 'n', 'pairwise', 'interactions', 'this', 'is', 'the', 'fastest', 'currently', 'known', 'leader', 'election', 'algorithm', 'in', 'which', 'each', 'agent', 'utilises', 'asymptotically', 'optimal', 'number', 'of', 'ologlog', 'n', 'states', 'the', 'new', 'protocol', 'incorporates', 'and', 'amalgamates', 'successfully', 'the', 'power', 'of', 'assorted', 'em', 'synthetic', 'coins', 'with', 'variable', 'rate', 'em', 'phase', 'clocks']] | [-0.21128017662611223, 0.1403198360581007, -0.025769003954912096, 0.0347668065810296, -0.0647822638073782, -0.28419615282796157, 0.16124865070993738, 0.3792526728081468, -0.26199481001165803, -0.3521475832160086, 0.017216125567079542, -0.27182964891671463, -0.1065107177806444, 0.08336976154959459, -0.056772944433185715, 0.07759899695235815, 0.06383661279206242, 0.08148631038970518, 0.14895934272291406, -0.3391158865848766, 0.22130317902332522, 0.05388322200703161, 0.2647810934717186, -0.10209648761744226, 0.1439569840816907, 0.08236953850723523, -0.02552063147978563, -0.023894003687183113, -0.08596912172055782, 0.030499108782986922, 0.28033778174382923, 0.2048000876539688, 0.32728621752974685, -0.44162966437196655, -0.08205587883949056, 0.16826657602945133, 0.1724884463334106, 0.1126486119810716, -0.0036095368502577557, -0.262755986401125, 0.049865739451567255, -0.1558941647697492, -0.07465728095783397, -0.01530487939743395, 0.04860905607938206, 0.04318753692769635, -0.34110116995030776, 0.0013904959502580919, 0.018594121959592615, -0.03898114375805104, 0.0010909570479079296, -0.0698067537592934, 0.06000572866129976, 0.13572218925236984, -0.04052685058406113, 0.06069176325301422, 0.11502308431571644, -0.049297439289818466, -0.19865234817301197, 0.358338270574472, -0.020175287962183916, -0.12976093644178227, 0.12967649984166474, -0.0560087484930803, -0.17667865748972372, 0.11321463904000427, 0.20460818976135856, 0.17910217289301686, -0.12173688348690818, 0.07985104847904224, -0.08789685611522063, 0.22568766393390646, 0.05751524592182578, 0.029292253812396883, 0.0738032220519687, 0.16219006775689304, 0.16304464616525666, 0.11498970658119236, 0.0009172816491244655, -0.1748792376943437, -0.2420904795723246, -0.14131908751043834, -0.2540614856897216, 0.035032238895283604, -0.16451226814120004, -0.14713792676715307, 0.35304239746953425, 0.11891153872475252, 0.19735260568979315, 0.15136084063643857, 0.3374430212976509, 0.0184147909390425, 0.00974401793813795, 0.22157954542960664, 0.1428335497376362, 0.025679123818986398, 0.04849718127284143, -0.21946815168660105, 0.17919426419092974, 0.11986483060019582] |
1,802.06868 | Optical and Gamma-Ray Variability Behaviors of 3C 454.3 from 2006 to
2011 | We present our photometric monitoring of a flat spectrum radio quasar (FSRQ)
3C 454.3 at Yunnan observatories from 2006 to 2011. We find that the optical
color of 3C 454.3 shows obvious redder-when-brighter trend, which reaches a
saturation stage when the source is brighter than 15.15 mag at V band. We
perform a simulation with multiple values of disk luminosity and spectral index
to reproduce the magnitude-color diagram. The results show that the
contamination caused by the disk radiation alone is difficult to produce the
observed color variability. The variability properties during the outburst in
December 2009 are also compared with $\gamma$-ray data derived from Fermi
$\gamma$-ray space telescope. The flux variation of these two bands follow a
linear relation with $F_{\gamma} \propto F_R^{1.14\pm0.07}$, which provides an
observational evidence for external Compton process in 3C 454.3. Meanwhile,
this flux correlation indicates that electron injection is the main mechanism
for variability origin. We also explore the variation of the flux ratio
$F_{\gamma}/F_R$ and the detailed structures in the lightcurves, and discuss
some possible origins for the detailed variability behaviors.
| astro-ph.HE | we present our photometric monitoring of a flat spectrum radio quasar fsrq 3c 4543 at yunnan observatories from 2006 to 2011 we find that the optical color of 3c 4543 shows obvious redderwhenbrighter trend which reaches a saturation stage when the source is brighter than 1515 mag at v band we perform a simulation with multiple values of disk luminosity and spectral index to reproduce the magnitudecolor diagram the results show that the contamination caused by the disk radiation alone is difficult to produce the observed color variability the variability properties during the outburst in december 2009 are also compared with gammaray data derived from fermi gammaray space telescope the flux variation of these two bands follow a linear relation with f_gamma propto f_r114pm007 which provides an observational evidence for external compton process in 3c 4543 meanwhile this flux correlation indicates that electron injection is the main mechanism for variability origin we also explore the variation of the flux ratio f_gammaf_r and the detailed structures in the lightcurves and discuss some possible origins for the detailed variability behaviors | [['we', 'present', 'our', 'photometric', 'monitoring', 'of', 'a', 'flat', 'spectrum', 'radio', 'quasar', 'fsrq', '3c', '4543', 'at', 'yunnan', 'observatories', 'from', '2006', 'to', '2011', 'we', 'find', 'that', 'the', 'optical', 'color', 'of', '3c', '4543', 'shows', 'obvious', 'redderwhenbrighter', 'trend', 'which', 'reaches', 'a', 'saturation', 'stage', 'when', 'the', 'source', 'is', 'brighter', 'than', '1515', 'mag', 'at', 'v', 'band', 'we', 'perform', 'a', 'simulation', 'with', 'multiple', 'values', 'of', 'disk', 'luminosity', 'and', 'spectral', 'index', 'to', 'reproduce', 'the', 'magnitudecolor', 'diagram', 'the', 'results', 'show', 'that', 'the', 'contamination', 'caused', 'by', 'the', 'disk', 'radiation', 'alone', 'is', 'difficult', 'to', 'produce', 'the', 'observed', 'color', 'variability', 'the', 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1,802.06869 | Invertible Autoencoder for domain adaptation | The unsupervised image-to-image translation aims at finding a mapping between
the source ($A$) and target ($B$) image domains, where in many applications
aligned image pairs are not available at training. This is an ill-posed
learning problem since it requires inferring the joint probability distribution
from marginals. Joint learning of coupled mappings $F_{AB}: A \rightarrow B$
and $F_{BA}: B \rightarrow A$ is commonly used by the state-of-the-art methods,
like CycleGAN [Zhu et al., 2017], to learn this translation by introducing
cycle consistency requirement to the learning problem, i.e. $F_{AB}(F_{BA}(B))
\approx B$ and $F_{BA}(F_{AB}(A)) \approx A$. Cycle consistency enforces the
preservation of the mutual information between input and translated images.
However, it does not explicitly enforce $F_{BA}$ to be an inverse operation to
$F_{AB}$. We propose a new deep architecture that we call invertible
autoencoder (InvAuto) to explicitly enforce this relation. This is done by
forcing an encoder to be an inverted version of the decoder, where
corresponding layers perform opposite mappings and share parameters. The
mappings are constrained to be orthonormal. The resulting architecture leads to
the reduction of the number of trainable parameters (up to $2$ times). We
present image translation results on benchmark data sets and demonstrate
state-of-the art performance of our approach. Finally, we test the proposed
domain adaptation method on the task of road video conversion. We demonstrate
that the videos converted with InvAuto have high quality and show that the
NVIDIA neural-network-based end-to-end learning system for autonomous driving,
known as PilotNet, trained on real road videos performs well when tested on the
converted ones.
| eess.IV cs.CV | the unsupervised imagetoimage translation aims at finding a mapping between the source a and target b image domains where in many applications aligned image pairs are not available at training this is an illposed learning problem since it requires inferring the joint probability distribution from marginals joint learning of coupled mappings f_ab a rightarrow b and f_ba b rightarrow a is commonly used by the stateoftheart methods like cyclegan zhu et al 2017 to learn this translation by introducing cycle consistency requirement to the learning problem ie f_abf_bab approx b and f_baf_aba approx a cycle consistency enforces the preservation of the mutual information between input and translated images however it does not explicitly enforce f_ba to be an inverse operation to f_ab we propose a new deep architecture that we call invertible autoencoder invauto to explicitly enforce this relation this is done by forcing an encoder to be an inverted version of the decoder where corresponding layers perform opposite mappings and share parameters the mappings are constrained to be orthonormal the resulting architecture leads to the reduction of the number of trainable parameters up to 2 times we present image translation results on benchmark data sets and demonstrate stateofthe art performance of our approach finally we test the proposed domain adaptation method on the task of road video conversion we demonstrate that the videos converted with invauto have high quality and show that the nvidia neuralnetworkbased endtoend learning system for autonomous driving known as pilotnet trained on real road videos performs well when tested on the converted ones | [['the', 'unsupervised', 'imagetoimage', 'translation', 'aims', 'at', 'finding', 'a', 'mapping', 'between', 'the', 'source', 'a', 'and', 'target', 'b', 'image', 'domains', 'where', 'in', 'many', 'applications', 'aligned', 'image', 'pairs', 'are', 'not', 'available', 'at', 'training', 'this', 'is', 'an', 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1,802.0687 | Formal Analysis of Galois Field Arithmetics - Parallel Verification and
Reverse Engineering | Galois field (GF) arithmetic circuits find numerous applications in
communications, signal processing, and security engineering. Formal
verification techniques of GF circuits are scarce and limited to circuits with
known bit positions of the primary inputs and outputs. They also require
knowledge of the irreducible polynomial $P(x)$, which affects final hardware
implementation. This paper presents a computer algebra technique that performs
verification and reverse engineering of GF($2^m$) multipliers directly from the
gate-level implementation. The approach is based on extracting a unique
irreducible polynomial in a parallel fashion and proceeds in three steps: 1)
determine the bit position of the output bits; 2) determine the bit position of
the input bits; and 3) extract the irreducible polynomial used in the design.
We demonstrate that this method is able to reverse engineer GF($2^m$)
multipliers in \textit{m} threads. Experiments performed on synthesized
\textit{Mastrovito} and \textit{Montgomery} multipliers with different $P(x)$,
including NIST-recommended polynomials, demonstrate high efficiency of the
proposed method.
| cs.SC cs.CR | galois field gf arithmetic circuits find numerous applications in communications signal processing and security engineering formal verification techniques of gf circuits are scarce and limited to circuits with known bit positions of the primary inputs and outputs they also require knowledge of the irreducible polynomial px which affects final hardware implementation this paper presents a computer algebra technique that performs verification and reverse engineering of gf2m multipliers directly from the gatelevel implementation the approach is based on extracting a unique irreducible polynomial in a parallel fashion and proceeds in three steps 1 determine the bit position of the output bits 2 determine the bit position of the input bits and 3 extract the irreducible polynomial used in the design we demonstrate that this method is able to reverse engineer gf2m multipliers in textitm threads experiments performed on synthesized textitmastrovito and textitmontgomery multipliers with different px including nistrecommended polynomials demonstrate high efficiency of the proposed method | [['galois', 'field', 'gf', 'arithmetic', 'circuits', 'find', 'numerous', 'applications', 'in', 'communications', 'signal', 'processing', 'and', 'security', 'engineering', 'formal', 'verification', 'techniques', 'of', 'gf', 'circuits', 'are', 'scarce', 'and', 'limited', 'to', 'circuits', 'with', 'known', 'bit', 'positions', 'of', 'the', 'primary', 'inputs', 'and', 'outputs', 'they', 'also', 'require', 'knowledge', 'of', 'the', 'irreducible', 'polynomial', 'px', 'which', 'affects', 'final', 'hardware', 'implementation', 'this', 'paper', 'presents', 'a', 'computer', 'algebra', 'technique', 'that', 'performs', 'verification', 'and', 'reverse', 'engineering', 'of', 'gf2m', 'multipliers', 'directly', 'from', 'the', 'gatelevel', 'implementation', 'the', 'approach', 'is', 'based', 'on', 'extracting', 'a', 'unique', 'irreducible', 'polynomial', 'in', 'a', 'parallel', 'fashion', 'and', 'proceeds', 'in', 'three', 'steps', '1', 'determine', 'the', 'bit', 'position', 'of', 'the', 'output', 'bits', '2', 'determine', 'the', 'bit', 'position', 'of', 'the', 'input', 'bits', 'and', '3', 'extract', 'the', 'irreducible', 'polynomial', 'used', 'in', 'the', 'design', 'we', 'demonstrate', 'that', 'this', 'method', 'is', 'able', 'to', 'reverse', 'engineer', 'gf2m', 'multipliers', 'in', 'textitm', 'threads', 'experiments', 'performed', 'on', 'synthesized', 'textitmastrovito', 'and', 'textitmontgomery', 'multipliers', 'with', 'different', 'px', 'including', 'nistrecommended', 'polynomials', 'demonstrate', 'high', 'efficiency', 'of', 'the', 'proposed', 'method']] | [-0.1489461769938077, 0.030668699727883858, -0.07082930576181848, 0.008796967435046099, -0.07376593123435189, -0.18930209321766406, 0.031819062210208936, 0.38420574970567895, -0.3192960831067084, -0.31617515235465316, 0.09258846766629415, -0.20456147447571552, -0.1469464125915884, 0.2429874856055616, -0.0787802470486464, 0.08941856516232867, 0.06936425739197723, 0.033080528018456935, -0.09705323189815604, -0.2784760797244656, 0.23789641361585573, 0.01825713527260201, 0.3063271522342371, 0.0038444124251040385, 0.14154514183878505, 0.005584409408344838, -0.049156051092339975, -0.10848771712718237, -0.07070261219527502, 0.14113126453441127, 0.3182951146078075, 0.19110614732275472, 0.2514133147625743, -0.4475538778903061, -0.12224525842497028, 0.091775503468326, 0.17337751176704555, 0.07032775911303409, -0.0612757072529731, -0.23807873556353643, 0.10771176237674233, -0.14675443654414266, -0.04308390340731038, -0.08358589812230907, 0.008261801271337868, 0.04470008660925822, -0.26694133618576943, -0.02886014788026315, 0.06871970053946011, 0.09756478195470807, 0.005209601566955251, -0.1510169130572314, 0.031109813722555134, 0.1185769873432276, -0.0726256684996004, 0.06134934682019153, 0.16602343090160407, -0.10802783292877537, -0.18392273038013005, 0.3438045899628809, 0.008337188276575608, -0.22698337616824765, 0.14374991768119416, -0.09531239682875917, -0.1619007896576812, 0.11233723979124702, 0.17650681563406434, 0.10122395920533515, -0.09246333476345937, 0.09974068528275606, 0.01682093384136495, 0.22821399106300974, 0.07600379196657359, 0.02558097943217849, 0.12540562881928818, 0.1225770438751696, 0.027618716826270286, 0.17578389424594015, -0.05690915913893198, -0.03819409589433283, -0.2902207347490874, -0.17877557329331084, -0.22101765235665402, 0.0028226735395085263, -0.08035570211751819, -0.12652895345174903, 0.4216564993900982, 0.2012375379042504, 0.12641896037262326, 0.06308628336639806, 0.3588596953226155, 0.08801538432262053, 0.08528933583875187, 0.10122368020299626, 0.15892655794328006, 0.14207327425001973, 0.09387279421973385, -0.18761802541082848, 0.07318675966541234, 0.070063224785324] |
1,802.06871 | A Deterministic Protocol for Sequential Asymptotic Learning | In the classic herding model, agents receive private signals about an
underlying binary state of nature, and act sequentially to choose one of two
possible actions, after observing the actions of their predecessors.
We investigate what types of behaviors lead to asymptotic learning, where
agents will eventually converge to the right action in probability. It is known
that for rational agents and bounded signals, there will not be asymptotic
learning.
Does it help if the agents can be cooperative rather than act selfishly? This
is simple to achieve if the agents are allowed to use randomized protocols. In
this paper, we provide the first deterministic protocol under which asymptotic
learning occurs. In addition, our protocol has the advantage of being much
simpler than previous protocols.
| cs.GT cs.DM | in the classic herding model agents receive private signals about an underlying binary state of nature and act sequentially to choose one of two possible actions after observing the actions of their predecessors we investigate what types of behaviors lead to asymptotic learning where agents will eventually converge to the right action in probability it is known that for rational agents and bounded signals there will not be asymptotic learning does it help if the agents can be cooperative rather than act selfishly this is simple to achieve if the agents are allowed to use randomized protocols in this paper we provide the first deterministic protocol under which asymptotic learning occurs in addition our protocol has the advantage of being much simpler than previous protocols | [['in', 'the', 'classic', 'herding', 'model', 'agents', 'receive', 'private', 'signals', 'about', 'an', 'underlying', 'binary', 'state', 'of', 'nature', 'and', 'act', 'sequentially', 'to', 'choose', 'one', 'of', 'two', 'possible', 'actions', 'after', 'observing', 'the', 'actions', 'of', 'their', 'predecessors', 'we', 'investigate', 'what', 'types', 'of', 'behaviors', 'lead', 'to', 'asymptotic', 'learning', 'where', 'agents', 'will', 'eventually', 'converge', 'to', 'the', 'right', 'action', 'in', 'probability', 'it', 'is', 'known', 'that', 'for', 'rational', 'agents', 'and', 'bounded', 'signals', 'there', 'will', 'not', 'be', 'asymptotic', 'learning', 'does', 'it', 'help', 'if', 'the', 'agents', 'can', 'be', 'cooperative', 'rather', 'than', 'act', 'selfishly', 'this', 'is', 'simple', 'to', 'achieve', 'if', 'the', 'agents', 'are', 'allowed', 'to', 'use', 'randomized', 'protocols', 'in', 'this', 'paper', 'we', 'provide', 'the', 'first', 'deterministic', 'protocol', 'under', 'which', 'asymptotic', 'learning', 'occurs', 'in', 'addition', 'our', 'protocol', 'has', 'the', 'advantage', 'of', 'being', 'much', 'simpler', 'than', 'previous', 'protocols']] | [-0.106618237555027, 0.12225049883604515, -0.13090181880816817, 0.05067137647047639, -0.14349928356334568, -0.23104223468899726, 0.11401512632984669, 0.4261753012239933, -0.2844786498248577, -0.2463167127976194, 0.08334768856689334, -0.2540853971838951, -0.1722014402076602, 0.14768384074233473, -0.13904235456883907, -0.015288932807743549, 0.03608457985147834, 0.12355528882727958, 0.01706554858153686, -0.34900300825387237, 0.2917432234212756, 0.05227934705466032, 0.238338553622365, -0.03127540726261213, 0.08626980049535632, 0.012435829041525721, 0.0215532229016535, -0.013567259246483445, -0.10772334508068161, 0.06423321780562401, 0.30865250592306254, 0.19303318528831004, 0.33339205929636956, -0.45686983662843705, -0.15751743738353252, 0.21433793743234128, 0.16095713362935932, 0.12001638953667135, 0.02274912210833281, -0.29195776477921753, 0.11572124960739165, -0.18346195176243782, -0.09277705420088023, -0.0882521598637104, -0.02965171050094068, 0.016267802134156226, -0.3003977249832824, -0.017975328154861928, 0.09564920687116682, -0.0010396328791975975, -0.040256233410909774, -0.07549099589884281, 0.02365400711633265, 0.21744393883645535, 0.05768655024562031, -0.005429693868383765, 0.14252768605947494, -0.12546769697591661, -0.15348891935124992, 0.36498379956185817, 0.012050915802596138, -0.20574739491194485, 0.21887605056725443, -0.10540174679458142, -0.1165480732396245, 0.09573409456014634, 0.18087054495513438, 0.1278180537521839, -0.13539227046817542, 0.022597485839389263, -0.03962243846058845, 0.17749033865146338, 0.05754533265903592, 0.07833287811279296, 0.14625531758833676, 0.13901650887727737, 0.1564852878525853, 0.09238943115435541, 0.020806284725666046, -0.16254586245911196, -0.23420772721432148, -0.1256251759380102, -0.16223479981906713, 0.07992701836326159, -0.06140065917721949, -0.10515492568537593, 0.3570358333289623, 0.20573961965739726, 0.166157292291522, 0.10332342275697738, 0.31384833706915377, 0.08202032706607133, 0.038128844825550914, 0.12138216248340905, 0.24729754838347434, 0.025492676332592966, 0.0754613543599844, -0.17295652049966156, 0.17234657679125667, 0.026650201607844794] |
1,802.06872 | Population Protocols Are Fast | A population protocol describes a set of state change rules for a population
of $n$ indistinguishable finite-state agents (automata), undergoing random
pairwise interactions. Within this very basic framework, it is possible to
resolve a number of fundamental tasks in distributed computing, including:
leader election, aggregate and threshold functions on the population, such as
majority computation, and plurality consensus. For the first time, we show that
solutions to all of these problems can be obtained \emph{quickly} using
finite-state protocols. For any input, the designed finite-state protocols
converge under a fair random scheduler to an output which is correct with high
probability in expected $O(\mathrm{poly} \log n)$ parallel time. In the same
setting, we also show protocols which always reach a valid solution, in
expected parallel time $O(n^\varepsilon)$, where the number of states of the
interacting automata depends only on the choice of $\varepsilon>0$. The stated
time bounds hold for \emph{any} semi-linear predicate computable in the
population protocol framework.
The key ingredient of our result is the decentralized design of a hierarchy
of phase-clocks, which tick at different rates, with the rates of adjacent
clocks separated by a factor of $\Theta(\log n)$. The construction of this
clock hierarchy relies on a new protocol composition technique, combined with
an adapted analysis of a self-organizing process of oscillatory dynamics. This
clock hierarchy is used to provide nested synchronization primitives, which
allow us to view the population in a global manner and design protocols using a
high-level imperative programming language with a (limited) capacity for loops
and branching instructions.
| cs.DC cs.DS | a population protocol describes a set of state change rules for a population of n indistinguishable finitestate agents automata undergoing random pairwise interactions within this very basic framework it is possible to resolve a number of fundamental tasks in distributed computing including leader election aggregate and threshold functions on the population such as majority computation and plurality consensus for the first time we show that solutions to all of these problems can be obtained emphquickly using finitestate protocols for any input the designed finitestate protocols converge under a fair random scheduler to an output which is correct with high probability in expected omathrmpoly log n parallel time in the same setting we also show protocols which always reach a valid solution in expected parallel time onvarepsilon where the number of states of the interacting automata depends only on the choice of varepsilon0 the stated time bounds hold for emphany semilinear predicate computable in the population protocol framework the key ingredient of our result is the decentralized design of a hierarchy of phaseclocks which tick at different rates with the rates of adjacent clocks separated by a factor of thetalog n the construction of this clock hierarchy relies on a new protocol composition technique combined with an adapted analysis of a selforganizing process of oscillatory dynamics this clock hierarchy is used to provide nested synchronization primitives which allow us to view the population in a global manner and design protocols using a highlevel imperative programming language with a limited capacity for loops and branching instructions | [['a', 'population', 'protocol', 'describes', 'a', 'set', 'of', 'state', 'change', 'rules', 'for', 'a', 'population', 'of', 'n', 'indistinguishable', 'finitestate', 'agents', 'automata', 'undergoing', 'random', 'pairwise', 'interactions', 'within', 'this', 'very', 'basic', 'framework', 'it', 'is', 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1,802.06873 | Multi-scale simulations of black hole accretion in barred galaxies:
Self-gravitating disk models | Due to the non-axisymmetric potential of the central bar, barred spiral
galaxies form, in addition to their characteristic arms and bar, a variety of
structures within the thin gas disk, like nuclear rings, inner spirals and
dust-lanes. These structures in the inner kiloparsec are most important to
explain and understand the rate of black hole feeding. The aim of this work is
to investigate the influence of stellar bars in spiral galaxies on the thin
self-gravitating gas disk. We focus on the accretion of gas onto the central
supermassive black hole and its time-dependent evolution. We conduct
multi-scale simulations simultaneously resolving the galactic disk and the
accretion disk around the central black-hole. We vary in all simulations the
initial gas disk mass. As additional parameter we choose either the gas
temperature for isothermal simulations or the cooling timescale in case of
non-isothermal simulations. Accretion is either driven by a gravitationally
unstable or clumpy accretion disk or by energy dissipation in strong shocks.
Most simulations show a strong dependence of the accretion rate at the outer
boundary of the central accretion disk ($r< 300~\mathrm{pc}$) on the gas flow
at kiloparsec scales. The final black hole masses reach up to $\sim 10^9
M_\odot$ after $1.6~\mathrm{Gyr}$. Our models show the expected influence of
the Eddington limit and a decline in growth rate at the corresponding
sub-Eddington limit.
| astro-ph.GA | due to the nonaxisymmetric potential of the central bar barred spiral galaxies form in addition to their characteristic arms and bar a variety of structures within the thin gas disk like nuclear rings inner spirals and dustlanes these structures in the inner kiloparsec are most important to explain and understand the rate of black hole feeding the aim of this work is to investigate the influence of stellar bars in spiral galaxies on the thin selfgravitating gas disk we focus on the accretion of gas onto the central supermassive black hole and its timedependent evolution we conduct multiscale simulations simultaneously resolving the galactic disk and the accretion disk around the central blackhole we vary in all simulations the initial gas disk mass as additional parameter we choose either the gas temperature for isothermal simulations or the cooling timescale in case of nonisothermal simulations accretion is either driven by a gravitationally unstable or clumpy accretion disk or by energy dissipation in strong shocks most simulations show a strong dependence of the accretion rate at the outer boundary of the central accretion disk r 300mathrmpc on the gas flow at kiloparsec scales the final black hole masses reach up to sim 109 m_odot after 16mathrmgyr our models show the expected influence of the eddington limit and a decline in growth rate at the corresponding subeddington limit | [['due', 'to', 'the', 'nonaxisymmetric', 'potential', 'of', 'the', 'central', 'bar', 'barred', 'spiral', 'galaxies', 'form', 'in', 'addition', 'to', 'their', 'characteristic', 'arms', 'and', 'bar', 'a', 'variety', 'of', 'structures', 'within', 'the', 'thin', 'gas', 'disk', 'like', 'nuclear', 'rings', 'inner', 'spirals', 'and', 'dustlanes', 'these', 'structures', 'in', 'the', 'inner', 'kiloparsec', 'are', 'most', 'important', 'to', 'explain', 'and', 'understand', 'the', 'rate', 'of', 'black', 'hole', 'feeding', 'the', 'aim', 'of', 'this', 'work', 'is', 'to', 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1,802.06874 | Landau Damping in a strong magnetic field: Dissociation of Quarkonia | We have investigated the effects of strong magnetic field on the properties
of quarkonia immersed in a thermal medium of quarks and gluons and studied its
quasi-free dissociation due to the Landau-damping. Thermalizing the Schwinger
propagator in the lowest Landau levels for quarks and the Feynman propagator
for gluons in real-time formalism, we have calculated the resummed retarded and
symmetric propagators, which in turn give the real and imaginary components of
dielectric permittivity, respectively. The magnetic field affects the
large-distance interaction more than the short-distance interaction, as a
result, the real part of potential becomes more attractive and the magnitude of
imaginary part too becomes larger, compared to the thermal medium in absence of
strong magnetic field. As a consequence the average size of $J/\psi$'s and
$\psi^\prime$'s are increased but $\chi_c$'s get shrunk. Similarly the magnetic
field affects the binding of $J/\psi$'s and $\chi_c$'s discriminately, i.e. it
decreases the binding of $J/\psi$ and increases for $\chi_c$. However, the
further increase in magnetic field results in the decrease of binding energies.
On contrary the magnetic field increases the width of the resonances, unless
the temperature is sufficiently high. We have finally studied how the presence
of magnetic field affects the dissolution of quarkonia in a thermal medium due
to the Landau damping, where the dissociation temperatures are found to
increase compared to the thermal medium in absence of magnetic field. However,
further increase of magnetic field decreases the dissociation temperatures. For
example, $J/\psi$'s and $\chi_c$'s are dissociated at higher temperatures at 2
$T_c$ and 1.1 $T_c$ at a magnetic field $eB \approx 6~{\rm{and}}~4~m_\pi^2$,
respectively, compared to the values 1.60 $T_c$ and 0.8 $T_c$ in the absence of
magnetic field, respectively.
| hep-ph hep-th nucl-th | we have investigated the effects of strong magnetic field on the properties of quarkonia immersed in a thermal medium of quarks and gluons and studied its quasifree dissociation due to the landaudamping thermalizing the schwinger propagator in the lowest landau levels for quarks and the feynman propagator for gluons in realtime formalism we have calculated the resummed retarded and symmetric propagators which in turn give the real and imaginary components of dielectric permittivity respectively the magnetic field affects the largedistance interaction more than the shortdistance interaction as a result the real part of potential becomes more attractive and the magnitude of imaginary part too becomes larger compared to the thermal medium in absence of strong magnetic field as a consequence the average size of jpsis and psiprimes are increased but chi_cs get shrunk similarly the magnetic field affects the binding of jpsis and chi_cs discriminately ie it decreases the binding of jpsi and increases for chi_c however the further increase in magnetic field results in the decrease of binding energies on contrary the magnetic field increases the width of the resonances unless the temperature is sufficiently high we have finally studied how the presence of magnetic field affects the dissolution of quarkonia in a thermal medium due to the landau damping where the dissociation temperatures are found to increase compared to the thermal medium in absence of magnetic field however further increase of magnetic field decreases the dissociation temperatures for example jpsis and chi_cs are dissociated at higher temperatures at 2 t_c and 11 t_c at a magnetic field eb approx 6rmand4m_pi2 respectively compared to the values 160 t_c and 08 t_c in the absence of magnetic field respectively | [['we', 'have', 'investigated', 'the', 'effects', 'of', 'strong', 'magnetic', 'field', 'on', 'the', 'properties', 'of', 'quarkonia', 'immersed', 'in', 'a', 'thermal', 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1,802.06875 | LSALSA: Accelerated Source Separation via Learned Sparse Coding | We propose an efficient algorithm for the generalized sparse coding (SC)
inference problem. The proposed framework applies to both the single dictionary
setting, where each data point is represented as a sparse combination of the
columns of one dictionary matrix, as well as the multiple dictionary setting as
given in morphological component analysis (MCA), where the goal is to separate
a signal into additive parts such that each part has distinct sparse
representation within a corresponding dictionary. Both the SC task and its
generalization via MCA have been cast as $\ell_1$-regularized least-squares
optimization problems. To accelerate traditional acquisition of sparse codes,
we propose a deep learning architecture that constitutes a trainable
time-unfolded version of the Split Augmented Lagrangian Shrinkage Algorithm
(SALSA), a special case of the Alternating Direction Method of Multipliers
(ADMM). We empirically validate both variants of the algorithm, that we refer
to as LSALSA (learned-SALSA), on image vision tasks and demonstrate that at
inference our networks achieve vast improvements in terms of the running time,
the quality of estimated sparse codes, and visual clarity on both classic SC
and MCA problems. Finally, we present a theoretical framework for analyzing
LSALSA network: we show that the proposed approach exactly implements a
truncated ADMM applied to a new, learned cost function with curvature modified
by one of the learned parameterized matrices. We extend a very recent
Stochastic Alternating Optimization analysis framework to show that a gradient
descent step along this learned loss landscape is equivalent to a modified
gradient descent step along the original loss landscape. In this framework, the
acceleration achieved by LSALSA could potentially be explained by the network's
ability to learn a correction to the gradient direction of steeper descent.
| cs.LG stat.ML | we propose an efficient algorithm for the generalized sparse coding sc inference problem the proposed framework applies to both the single dictionary setting where each data point is represented as a sparse combination of the columns of one dictionary matrix as well as the multiple dictionary setting as given in morphological component analysis mca where the goal is to separate a signal into additive parts such that each part has distinct sparse representation within a corresponding dictionary both the sc task and its generalization via mca have been cast as ell_1regularized leastsquares optimization problems to accelerate traditional acquisition of sparse codes we propose a deep learning architecture that constitutes a trainable timeunfolded version of the split augmented lagrangian shrinkage algorithm salsa a special case of the alternating direction method of multipliers admm we empirically validate both variants of the algorithm that we refer to as lsalsa learnedsalsa on image vision tasks and demonstrate that at inference our networks achieve vast improvements in terms of the running time the quality of estimated sparse codes and visual clarity on both classic sc and mca problems finally we present a theoretical framework for analyzing lsalsa network we show that the proposed approach exactly implements a truncated admm applied to a new learned cost function with curvature modified by one of the learned parameterized matrices we extend a very recent stochastic alternating optimization analysis framework to show that a gradient descent step along this learned loss landscape is equivalent to a modified gradient descent step along the original loss landscape in this framework the acceleration achieved by lsalsa could potentially be explained by the networks ability to learn a correction to the gradient direction of steeper descent | [['we', 'propose', 'an', 'efficient', 'algorithm', 'for', 'the', 'generalized', 'sparse', 'coding', 'sc', 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1,802.06876 | Spiro-Conjugated Molecular Junctions: between Jahn-Teller Distortion and
Destructive Quantum Interference | The quest for molecular structures exhibiting strong quantum interference
effects in the transport setting has long been on the forefront of chemical
research. Here, we establish theoretically that the unusual geometry of
spiro-conjugated systems gives rise to complete destructive interference in the
resonant-transport regime. This results in a current blockade of the type not
present in meta-connected benzene or similar molecular structures. We further
show that these systems can undergo a transport-driven Jahn-Teller distortion
which can lift the aforementioned destructive-interference effects. The overall
transport characteristics is determined by the interplay between the two
phenomena. Spiro-conjugated systems may therefore serve as a novel platform for
investigations of quantum interference and vibronic effects in the charge
transport setting. The potential to control quantum interference in these
systems can also turn them into attractive components in designing functional
molecular circuits.
| cond-mat.mes-hall physics.chem-ph | the quest for molecular structures exhibiting strong quantum interference effects in the transport setting has long been on the forefront of chemical research here we establish theoretically that the unusual geometry of spiroconjugated systems gives rise to complete destructive interference in the resonanttransport regime this results in a current blockade of the type not present in metaconnected benzene or similar molecular structures we further show that these systems can undergo a transportdriven jahnteller distortion which can lift the aforementioned destructiveinterference effects the overall transport characteristics is determined by the interplay between the two phenomena spiroconjugated systems may therefore serve as a novel platform for investigations of quantum interference and vibronic effects in the charge transport setting the potential to control quantum interference in these systems can also turn them into attractive components in designing functional molecular circuits | [['the', 'quest', 'for', 'molecular', 'structures', 'exhibiting', 'strong', 'quantum', 'interference', 'effects', 'in', 'the', 'transport', 'setting', 'has', 'long', 'been', 'on', 'the', 'forefront', 'of', 'chemical', 'research', 'here', 'we', 'establish', 'theoretically', 'that', 'the', 'unusual', 'geometry', 'of', 'spiroconjugated', 'systems', 'gives', 'rise', 'to', 'complete', 'destructive', 'interference', 'in', 'the', 'resonanttransport', 'regime', 'this', 'results', 'in', 'a', 'current', 'blockade', 'of', 'the', 'type', 'not', 'present', 'in', 'metaconnected', 'benzene', 'or', 'similar', 'molecular', 'structures', 'we', 'further', 'show', 'that', 'these', 'systems', 'can', 'undergo', 'a', 'transportdriven', 'jahnteller', 'distortion', 'which', 'can', 'lift', 'the', 'aforementioned', 'destructiveinterference', 'effects', 'the', 'overall', 'transport', 'characteristics', 'is', 'determined', 'by', 'the', 'interplay', 'between', 'the', 'two', 'phenomena', 'spiroconjugated', 'systems', 'may', 'therefore', 'serve', 'as', 'a', 'novel', 'platform', 'for', 'investigations', 'of', 'quantum', 'interference', 'and', 'vibronic', 'effects', 'in', 'the', 'charge', 'transport', 'setting', 'the', 'potential', 'to', 'control', 'quantum', 'interference', 'in', 'these', 'systems', 'can', 'also', 'turn', 'them', 'into', 'attractive', 'components', 'in', 'designing', 'functional', 'molecular', 'circuits']] | [-0.20994150469954492, 0.1243486935660864, -0.10389526280447502, 0.07545130178826212, -0.03157965418402896, -0.14472038371869447, 0.05651358571600268, 0.3638895152799898, -0.30693119092646876, -0.28420350165811903, 0.010623890330669272, -0.2475865377434247, -0.22832344664140802, 0.20062533081473896, -0.016072794149460442, 0.017441512494714873, 0.01628957737491212, -0.0551480480688483, -0.03846504652491686, -0.18340181723540422, 0.283573077183753, 0.057880824337410035, 0.3024336361911882, 0.12725202891636977, 0.030089333184260988, -0.015154736598946549, 0.06075855474883803, 0.041965374860658565, -0.12183184932203431, 0.09430761089410997, 0.2664851314021331, 0.012602621603009262, 0.25534156682040315, -0.47003106610623724, -0.2642231908217637, 0.06750609363767911, 0.1731600494315375, 0.175127768172057, -0.08941210334270375, -0.27492386956388754, 0.033621247469757996, -0.12789009959493397, -0.09203542091276948, -0.10072333383493859, -0.009985591440151135, 0.03041113209275698, -0.22240539256017655, 0.06094833374597577, 0.07961526226647424, 0.04348849646295562, -0.0378248963018202, -0.0999322049213912, 0.01739626896398311, 0.14797285833276075, -0.03659186132470936, -0.02965906117760548, 0.14117899387863211, -0.1347846297924689, -0.166559139054064, 0.41172327197185066, -0.029654831881282116, -0.1699532460900655, 0.2344290549076642, -0.1444821666386402, -0.12092504183987551, 0.12229001353085606, 0.19304807573048907, 0.05343905997032187, -0.18152859768562132, 0.056030079923044526, 0.007963111218431908, 0.12662338515193286, 0.010991172025431737, 0.15278608765043883, 0.2678627512968061, 0.1684139761579872, 0.05810612780662654, 0.14626979742333238, -0.06892973561879802, -0.13207654233062358, -0.2531715664716268, -0.14986322751395742, -0.11991608356016292, 0.08265381260383892, -0.0028683803604067316, -0.13504315626271296, 0.37204661671509687, 0.14342124563391376, 0.16026063053163164, -0.057133281249799904, 0.2859564095904881, 0.1064651419159914, 0.09071398462930863, 0.004388513923693223, 0.27216662630389415, 0.14747605268017983, 0.08983114256283664, -0.29855736672426714, 0.08125912947721328, -0.0050759724990436525] |
1,802.06877 | Pairwise Concurrence in Cyclically Symmetric Quantum States | We provide an initial characterization of pairwise concurrence in quantum
states which are invariant under cyclic permutations of party labeling. We
prove that maximal entanglement can be entirely described by adjacent pairs,
then give explicit descriptions of those states in specific subsets of 4 and 5
qubit states - X states. We also construct a monogamy bound on shared
concurrences in the same subsets in 4 and 5 qubits, finding that above
non-maximal entanglement thresholds, no other entanglements are possible.
| quant-ph | we provide an initial characterization of pairwise concurrence in quantum states which are invariant under cyclic permutations of party labeling we prove that maximal entanglement can be entirely described by adjacent pairs then give explicit descriptions of those states in specific subsets of 4 and 5 qubit states x states we also construct a monogamy bound on shared concurrences in the same subsets in 4 and 5 qubits finding that above nonmaximal entanglement thresholds no other entanglements are possible | [['we', 'provide', 'an', 'initial', 'characterization', 'of', 'pairwise', 'concurrence', 'in', 'quantum', 'states', 'which', 'are', 'invariant', 'under', 'cyclic', 'permutations', 'of', 'party', 'labeling', 'we', 'prove', 'that', 'maximal', 'entanglement', 'can', 'be', 'entirely', 'described', 'by', 'adjacent', 'pairs', 'then', 'give', 'explicit', 'descriptions', 'of', 'those', 'states', 'in', 'specific', 'subsets', 'of', '4', 'and', '5', 'qubit', 'states', 'x', 'states', 'we', 'also', 'construct', 'a', 'monogamy', 'bound', 'on', 'shared', 'concurrences', 'in', 'the', 'same', 'subsets', 'in', '4', 'and', '5', 'qubits', 'finding', 'that', 'above', 'nonmaximal', 'entanglement', 'thresholds', 'no', 'other', 'entanglements', 'are', 'possible']] | [-0.2041435894627077, 0.2544326941570034, -0.039176926783085625, 0.09771547728524649, 0.0626418456388048, -0.23410526812194457, 0.08284098006301571, 0.37506362041340596, -0.22550489546000202, -0.28634300624621634, 0.03318353039743025, -0.3123256125416654, -0.06385159710587203, 0.15118969152268918, -0.0679172978653938, 0.05638545618946605, 0.0800751816905752, 0.0864521658517379, -0.08111825894897426, -0.31515153281720754, 0.3376903935288421, -0.07378304726591546, 0.26400430177196954, 0.07245398036578576, 0.06263493966971394, -0.01867674093669917, 0.041124989266825625, -0.009987097563622873, -0.1473369671901632, 0.08672738758864801, 0.3047939584225039, 0.19913317531618419, 0.16020334460639948, -0.4047114914636823, -0.12673694382810705, 0.17019477098166377, 0.12567487274756348, 0.1449490287797997, 0.001913523456211426, -0.31655506174304066, 0.0525085524938812, -0.1768793685573943, -0.08309475349592446, -0.10852307760262792, 0.06443343537894985, -0.03365211959197363, -0.25044101807937214, 0.0869285542060344, 0.08928820671207166, 0.09469329269272805, -0.00765437998844287, -0.08932112423479086, -0.04459074556332412, 0.12605602228778262, -0.09247587714344263, -0.01325547289621981, 0.0830671079810473, -0.07915178807805988, -0.20754435122649692, 0.25800478623449047, 0.009438094470791424, -0.24275179996001947, 0.17724722643866192, -0.13258052281917462, -0.14535782407214748, 0.051693238391647044, 0.07423981308606983, 0.09944644220809959, -0.12408522702458821, 0.03415486254131413, -0.11053618156834494, 0.17664519442787655, 0.1580822299576447, 0.1680441770063053, 0.15111729207955585, 0.0008700880387067041, 0.15809510888744005, 0.23200961143364893, 0.0057234993127846644, -0.08309117336890107, -0.3621392028311951, -0.17474296608942713, -0.21514193170197024, 0.1237804912785186, -0.11212926774095638, -0.10785293969313932, 0.4147929877891571, 0.06622379942656395, 0.20245171624495165, 0.042727146375794674, 0.18877901828883192, 0.0437207661180085, 0.044334241153695914, 0.12407644700164659, 0.19700757680531544, 0.12468211926060103, -0.10433847993270412, -0.17781823508064204, 0.05885671446972256, 0.08203952200710773] |
1,802.06878 | Dynamical structure factor of the triangular antiferromagnet: the
Schwinger boson theory beyond the mean field approach | We compute the zero temperature dynamical structure factor $S({\bf
q},\omega)$ of the triangular lattice Heisenberg model (TLHM) using a Schwinger
boson approach that includes the Gaussian fluctuations ($1/N$ corrections) of
the saddle point solution. While the ground state of this model exhibits a
well-known 120$^{\circ}$ magnetic ordering, experimental observations have
revealed a strong quantum character of the excitation spectrum. We conjecture
that this phenomenon arises from the proximity of the ground state of the TLHM
to the quantum melting point separating the magnetically ordered and spin
liquid states. Within this scenario, magnons are described as collective modes
(two spinon-bound states) of a spinon condensate (Higgs phase) that
spontaneously breaks the SU(2) symmetry of the TLHM. Crucial to our results is
the proper account of this spontaneous symmetry breaking. The main qualitative
difference relative to semi-classical treatments ($1/S$ expansion) is the
presence of a high-energy spinon continuum extending up to about three times
the single-magnon bandwidth. In addition, the magnitude of the ordered moment
($m=0.224$) agrees very well with numerical results and the low energy part of
the single-magnon dispersion is in very good agreement with series expansions.
Our results indicate that the Schwinger boson approach is an adequate starting
point for describing the excitation spectrum of some magnetically ordered
compounds that are near the quantum melting point separating this Higgs phase
from the {\it deconfined} spin liquid state.
| cond-mat.str-el | we compute the zero temperature dynamical structure factor sbf qomega of the triangular lattice heisenberg model tlhm using a schwinger boson approach that includes the gaussian fluctuations 1n corrections of the saddle point solution while the ground state of this model exhibits a wellknown 120circ magnetic ordering experimental observations have revealed a strong quantum character of the excitation spectrum we conjecture that this phenomenon arises from the proximity of the ground state of the tlhm to the quantum melting point separating the magnetically ordered and spin liquid states within this scenario magnons are described as collective modes two spinonbound states of a spinon condensate higgs phase that spontaneously breaks the su2 symmetry of the tlhm crucial to our results is the proper account of this spontaneous symmetry breaking the main qualitative difference relative to semiclassical treatments 1s expansion is the presence of a highenergy spinon continuum extending up to about three times the singlemagnon bandwidth in addition the magnitude of the ordered moment m0224 agrees very well with numerical results and the low energy part of the singlemagnon dispersion is in very good agreement with series expansions our results indicate that the schwinger boson approach is an adequate starting point for describing the excitation spectrum of some magnetically ordered compounds that are near the quantum melting point separating this higgs phase from the it deconfined spin liquid state | [['we', 'compute', 'the', 'zero', 'temperature', 'dynamical', 'structure', 'factor', 'sbf', 'qomega', 'of', 'the', 'triangular', 'lattice', 'heisenberg', 'model', 'tlhm', 'using', 'a', 'schwinger', 'boson', 'approach', 'that', 'includes', 'the', 'gaussian', 'fluctuations', '1n', 'corrections', 'of', 'the', 'saddle', 'point', 'solution', 'while', 'the', 'ground', 'state', 'of', 'this', 'model', 'exhibits', 'a', 'wellknown', '120circ', 'magnetic', 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1,802.06879 | Coverings and the heat equation on graphs: stochastic incompleteness,
the Feller property and uniform transience | We study regular coverings of graphs and manifolds with a focus on properties
of the heat equation. In particular, we look at stochastic incompleteness, the
Feller property and uniform transience; and investigate the connection between
the validity of these properties on the base space and its covering. For both
graphs and manifolds, we prove the equivalence of stochastic incompleteness of
the base and that of its cover. Along the way we also give some new conditions
for the Feller property to hold on graphs.
| math.FA math.PR math.SP | we study regular coverings of graphs and manifolds with a focus on properties of the heat equation in particular we look at stochastic incompleteness the feller property and uniform transience and investigate the connection between the validity of these properties on the base space and its covering for both graphs and manifolds we prove the equivalence of stochastic incompleteness of the base and that of its cover along the way we also give some new conditions for the feller property to hold on graphs | [['we', 'study', 'regular', 'coverings', 'of', 'graphs', 'and', 'manifolds', 'with', 'a', 'focus', 'on', 'properties', 'of', 'the', 'heat', 'equation', 'in', 'particular', 'we', 'look', 'at', 'stochastic', 'incompleteness', 'the', 'feller', 'property', 'and', 'uniform', 'transience', 'and', 'investigate', 'the', 'connection', 'between', 'the', 'validity', 'of', 'these', 'properties', 'on', 'the', 'base', 'space', 'and', 'its', 'covering', 'for', 'both', 'graphs', 'and', 'manifolds', 'we', 'prove', 'the', 'equivalence', 'of', 'stochastic', 'incompleteness', 'of', 'the', 'base', 'and', 'that', 'of', 'its', 'cover', 'along', 'the', 'way', 'we', 'also', 'give', 'some', 'new', 'conditions', 'for', 'the', 'feller', 'property', 'to', 'hold', 'on', 'graphs']] | [-0.1363891838963831, 0.04249904828057403, -0.07459837703278199, 0.12296653754033503, -0.101481398625765, -0.06999534858568084, 0.07547355229811122, 0.4040401907792936, -0.2861868017131374, -0.222706676025631, 0.15675675155389832, -0.2605959055945277, -0.13176767140006027, 0.20574273523830233, -0.07833590210481946, 0.017439271029572757, 0.07318493895720513, 0.051978564577265865, -0.07082335740449794, -0.24633255660799996, 0.42421425561908455, -0.008272879845684483, 0.24465973859852447, 0.11103441127176795, 0.1358099285723819, 0.010450586689763614, -0.02889000386598387, 0.05097452115920272, -0.21176820666672333, 0.12094399820835817, 0.15680912500690847, 0.11722542382388686, 0.23496680138521783, -0.4061865442476812, -0.17822577013257182, 0.15101865010469087, 0.037084404317457006, 0.03703462410097321, -0.03691379629058896, -0.2810319827071258, 0.10175045281426892, -0.07876975175791553, -0.14424264443195647, -0.059825564839965886, 0.019910849098648344, 0.12221881041824374, -0.20674284364629006, 0.001841725118754853, 0.1892540755714955, 0.07175217125387419, -0.05486911982093477, -0.07722562772133165, -0.03455597997665228, 0.14039774623788184, 0.0050448096202065544, -0.07395151378087965, 0.03721514820963854, -0.12233643807260142, -0.12027088548555705, 0.3612137107355964, -0.06742981018587238, -0.21782468891303455, 0.2042871213142505, -0.1805917895509906, -0.2250790718521568, 0.05964426880347587, 0.18783779759403496, 0.1368565054615915, -0.12229628222883634, 0.15047226342444664, -0.04949365959813198, 0.06216982290858314, 0.09024425746784323, 0.08075175057941426, 0.1282407920863036, 0.15547218348642455, 0.1477298687456087, 0.19438739859365992, -0.036181370519833375, -0.10366437412864928, -0.34030254367029383, -0.19141588362288617, -0.11779490619942191, 0.08828816701480675, -0.16968156287475722, -0.20430506633905074, 0.3930032290462848, 0.15623834246902593, 0.20037674398294517, 0.15230579878247918, 0.18199358361640147, 0.06627250494646086, -0.014269669518052112, 0.10286624226830013, 0.19265315807708594, 0.24753731921581285, 0.09309777002670758, -0.17807103795487256, 0.06358149608353242, 0.12568605041763345] |
1,802.0688 | The distribution of inelastic dark matter in the Sun | If dark matter is composed of new particles, these may become captured after
scattering with nuclei in the Sun, thermalise through additional scattering,
and finally annihilate into neutrinos that can be detected on Earth. If dark
matter scatters inelastically into a slightly heavier ($\mathcal{O} (10-100)$
keV) state it is unclear whether thermalisation occurs. One issue is that
up-scattering from the lower mass state may be kinematically forbidden, at
which point the thermalisation process effectively stops. A larger evaporation
rate is also expected due to down-scattering. In this work, we perform a
numerical simulation of the capture and thermalisation process in order to
study the evolution of the dark matter distribution. We then calculate and
compare the annihilation rate with that of the often assumed Maxwell--Boltzmann
distribution. We also check if equilibrium between capture and annihilation is
reached and find that this assumption definitely breaks down in a part of the
explored parameter space. We also find that evaporation induced by
down-scattering is not effective in reducing the total dark matter abundance.
| hep-ph astro-ph.HE | if dark matter is composed of new particles these may become captured after scattering with nuclei in the sun thermalise through additional scattering and finally annihilate into neutrinos that can be detected on earth if dark matter scatters inelastically into a slightly heavier mathcalo 10100 kev state it is unclear whether thermalisation occurs one issue is that upscattering from the lower mass state may be kinematically forbidden at which point the thermalisation process effectively stops a larger evaporation rate is also expected due to downscattering in this work we perform a numerical simulation of the capture and thermalisation process in order to study the evolution of the dark matter distribution we then calculate and compare the annihilation rate with that of the often assumed maxwellboltzmann distribution we also check if equilibrium between capture and annihilation is reached and find that this assumption definitely breaks down in a part of the explored parameter space we also find that evaporation induced by downscattering is not effective in reducing the total dark matter abundance | [['if', 'dark', 'matter', 'is', 'composed', 'of', 'new', 'particles', 'these', 'may', 'become', 'captured', 'after', 'scattering', 'with', 'nuclei', 'in', 'the', 'sun', 'thermalise', 'through', 'additional', 'scattering', 'and', 'finally', 'annihilate', 'into', 'neutrinos', 'that', 'can', 'be', 'detected', 'on', 'earth', 'if', 'dark', 'matter', 'scatters', 'inelastically', 'into', 'a', 'slightly', 'heavier', 'mathcalo', '10100', 'kev', 'state', 'it', 'is', 'unclear', 'whether', 'thermalisation', 'occurs', 'one', 'issue', 'is', 'that', 'upscattering', 'from', 'the', 'lower', 'mass', 'state', 'may', 'be', 'kinematically', 'forbidden', 'at', 'which', 'point', 'the', 'thermalisation', 'process', 'effectively', 'stops', 'a', 'larger', 'evaporation', 'rate', 'is', 'also', 'expected', 'due', 'to', 'downscattering', 'in', 'this', 'work', 'we', 'perform', 'a', 'numerical', 'simulation', 'of', 'the', 'capture', 'and', 'thermalisation', 'process', 'in', 'order', 'to', 'study', 'the', 'evolution', 'of', 'the', 'dark', 'matter', 'distribution', 'we', 'then', 'calculate', 'and', 'compare', 'the', 'annihilation', 'rate', 'with', 'that', 'of', 'the', 'often', 'assumed', 'maxwellboltzmann', 'distribution', 'we', 'also', 'check', 'if', 'equilibrium', 'between', 'capture', 'and', 'annihilation', 'is', 'reached', 'and', 'find', 'that', 'this', 'assumption', 'definitely', 'breaks', 'down', 'in', 'a', 'part', 'of', 'the', 'explored', 'parameter', 'space', 'we', 'also', 'find', 'that', 'evaporation', 'induced', 'by', 'downscattering', 'is', 'not', 'effective', 'in', 'reducing', 'the', 'total', 'dark', 'matter', 'abundance']] | [-0.06047547342560706, 0.25632754335730007, -0.14353897953816153, 0.15371702036664878, -0.04711766934569664, -0.10563170115261922, 0.034389079624667154, 0.3547262975303402, -0.2696532557026772, -0.33588087076065265, 0.041971954470091874, -0.2845944334856338, -0.01879312337664833, 0.14578226855370538, 0.027775562470452042, -0.0470515081627643, 0.04836742331584295, 0.04500648362714558, -0.04299428006933664, -0.25657676144813496, 0.31610943101433636, 0.07478735349358914, 0.19773319087737398, 0.09002268375632794, 0.04651278438596654, -0.019640516792015547, -0.005471084468415257, -0.04501637703979426, -0.11698501705222154, 0.020697245499587548, 0.18859848477986604, 0.10881522571639436, 0.18101805212161345, -0.4465228370363601, -0.21418305014788408, 0.1953602699407687, 0.23183546124342067, 0.07851094235080616, -0.09155882326736284, -0.2646838258461733, 0.07773727379475137, -0.19709756979664342, -0.13666671015354886, -0.018564566147475564, 0.02799997253790062, -0.0593034882569544, -0.24887276842049186, 0.11060117537765736, 0.03870208328045164, -0.08104210547906787, -0.06932902621236026, -0.06288591527641473, -0.05356957129310348, 0.02474292044252789, 0.10440574571567217, -0.024204144266581063, 0.21438014279364756, -0.15315326241700097, -0.0174261453515191, 0.4193440240137933, -0.09198627727717565, -0.13298177505620043, 0.184024665472016, -0.16693377944406623, -0.11188737146708874, 0.21508593779678145, 0.15409243375073836, 0.10733968956347988, -0.14947566034936766, 0.07146929320845241, -0.03930465589217537, 0.21707806025433615, 0.07854891615533079, 0.015529633235534903, 0.30234400876025036, 0.18297845566351162, 0.060080105656681704, 0.0870067412673621, -0.11070875450696425, -0.08515716552576431, -0.3005141788442905, -0.1389151192283835, -0.14915349432580413, 0.06762705847346265, -0.0012098265622370761, -0.08210568387900093, 0.30339091984359057, 0.1699870435442035, 0.22706790803795496, -0.0024993220849333017, 0.31887605994373386, 0.12237869695438976, 0.03024603972723435, 0.10545508881448087, 0.3073614967322489, 0.11399141677766929, 0.04453654362607682, -0.23908634546310886, 0.06762848719998742, -0.0017215174565647256] |
1,802.06881 | Automated Playtesting with Procedural Personas through MCTS with Evolved
Heuristics | This paper describes a method for generative player modeling and its
application to the automatic testing of game content using archetypal player
models called procedural personas. Theoretically grounded in psychological
decision theory, procedural personas are implemented using a variation of Monte
Carlo Tree Search (MCTS) where the node selection criteria are developed using
evolutionary computation, replacing the standard UCB1 criterion of MCTS. Using
these personas we demonstrate how generative player models can be applied to a
varied corpus of game levels and demonstrate how different play styles can be
enacted in each level. In short, we use artificially intelligent personas to
construct synthetic playtesters. The proposed approach could be used as a tool
for automatic play testing when human feedback is not readily available or when
quick visualization of potential interactions is necessary. Possible
applications include interactive tools during game development or procedural
content generation systems where many evaluations must be conducted within a
short time span.
| cs.AI | this paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas theoretically grounded in psychological decision theory procedural personas are implemented using a variation of monte carlo tree search mcts where the node selection criteria are developed using evolutionary computation replacing the standard ucb1 criterion of mcts using these personas we demonstrate how generative player models can be applied to a varied corpus of game levels and demonstrate how different play styles can be enacted in each level in short we use artificially intelligent personas to construct synthetic playtesters the proposed approach could be used as a tool for automatic play testing when human feedback is not readily available or when quick visualization of potential interactions is necessary possible applications include interactive tools during game development or procedural content generation systems where many evaluations must be conducted within a short time span | [['this', 'paper', 'describes', 'a', 'method', 'for', 'generative', 'player', 'modeling', 'and', 'its', 'application', 'to', 'the', 'automatic', 'testing', 'of', 'game', 'content', 'using', 'archetypal', 'player', 'models', 'called', 'procedural', 'personas', 'theoretically', 'grounded', 'in', 'psychological', 'decision', 'theory', 'procedural', 'personas', 'are', 'implemented', 'using', 'a', 'variation', 'of', 'monte', 'carlo', 'tree', 'search', 'mcts', 'where', 'the', 'node', 'selection', 'criteria', 'are', 'developed', 'using', 'evolutionary', 'computation', 'replacing', 'the', 'standard', 'ucb1', 'criterion', 'of', 'mcts', 'using', 'these', 'personas', 'we', 'demonstrate', 'how', 'generative', 'player', 'models', 'can', 'be', 'applied', 'to', 'a', 'varied', 'corpus', 'of', 'game', 'levels', 'and', 'demonstrate', 'how', 'different', 'play', 'styles', 'can', 'be', 'enacted', 'in', 'each', 'level', 'in', 'short', 'we', 'use', 'artificially', 'intelligent', 'personas', 'to', 'construct', 'synthetic', 'playtesters', 'the', 'proposed', 'approach', 'could', 'be', 'used', 'as', 'a', 'tool', 'for', 'automatic', 'play', 'testing', 'when', 'human', 'feedback', 'is', 'not', 'readily', 'available', 'or', 'when', 'quick', 'visualization', 'of', 'potential', 'interactions', 'is', 'necessary', 'possible', 'applications', 'include', 'interactive', 'tools', 'during', 'game', 'development', 'or', 'procedural', 'content', 'generation', 'systems', 'where', 'many', 'evaluations', 'must', 'be', 'conducted', 'within', 'a', 'short', 'time', 'span']] | [-0.011638352898164438, 0.06112044945789071, -0.13265950600683796, 0.13630632474758805, -0.14141261543875608, -0.21358781586138484, 0.061750312916373305, 0.4550573297728522, -0.2684241331313761, -0.3570858180188598, 0.0956127519684975, -0.22278803919471102, -0.2090738247303913, 0.1804648853960829, -0.11137636887183479, 0.030976285250150025, 0.1079175191556808, 0.015031799729256771, 0.05015821643908753, -0.2827951898649096, 0.26073313143624777, 0.04168348523000112, 0.28490233671618626, 0.005173404889109616, 0.08870908396462394, 0.02332360785191831, -0.04741079393678751, 0.04094076677752011, -0.0944001199998475, 0.1126582329131615, 0.348846789756023, 0.2598326250265997, 0.3755069384023022, -0.44476348616314143, -0.18798213358222243, 0.09782814833287819, 0.14197884500026703, 0.12100649299100041, -0.07866754420459844, -0.31131777903423286, 0.08745537412902102, -0.2171236365432448, -0.022451585803467494, -0.12731705205395627, -0.047602161227797084, 0.011596732850275423, -0.30913541053171056, -0.04314702430453438, 0.010049453212652737, 0.11718994364043912, -0.006240347072702999, -0.11074172130200821, -0.01305880878592292, 0.21095760131310015, -0.005131064268658893, 0.02727113821236297, 0.17312732252004281, -0.1266354920121674, -0.230350651512615, 0.38694432155730635, -0.06930852616325212, -0.20228840239668408, 0.19025930227675977, 0.016988778574905623, -0.15758881809261555, 0.04859932250856685, 0.2244749007961498, 0.13771380678428194, -0.1868310433266556, 0.03614933052374862, 0.018666594301183254, 0.20418071773691246, 0.028396809701986898, -0.03863401775164769, 0.21616453926514786, 0.2602713262701097, 0.0007123993914902735, 0.12407200399917574, -0.029520453786692366, -0.1313030858101019, -0.24716519656179187, -0.14140817075251386, -0.1452119732962157, -0.019945402414286478, -0.07014524811884024, -0.15816337884499285, 0.35772875886267197, 0.24636105646809134, 0.08553707347108194, 0.06079053858933875, 0.32209489412175923, 0.07004318656683421, 0.07215840020419889, 0.05278124812041195, 0.12723591860133007, 0.013317550771320477, 0.12335449755370903, -0.1549696976338358, 0.15445682681643236, 0.06964140331211428] |
1,802.06882 | Theoretical Framework for Estimating Target-Object Shape by Using
Location-Unknown Mobile Distance Sensors | This paper proposes a theoretical framework for estimating a target-object
shape, the location of which is not given, by using mobile distance sensors the
locations of which are also unknown. Typically, mobile sensors are mounted on
vehicles. Each sensor continuously measures the distance from it to the target
object. The proposed framework does not require any positioning function,
anchor-location information, or additional mechanisms to obtain side
information such as angle of arrival of signal. Under the assumption of a
convex polygon target object, each edge length and vertex angle and their
combinations are estimated and finally the shape of the target object is
estimated. To the best of our knowledge, this is the first result in which a
target-object shape was estimated by using the data of distance sensors without
using their locations.
| eess.SP | this paper proposes a theoretical framework for estimating a targetobject shape the location of which is not given by using mobile distance sensors the locations of which are also unknown typically mobile sensors are mounted on vehicles each sensor continuously measures the distance from it to the target object the proposed framework does not require any positioning function anchorlocation information or additional mechanisms to obtain side information such as angle of arrival of signal under the assumption of a convex polygon target object each edge length and vertex angle and their combinations are estimated and finally the shape of the target object is estimated to the best of our knowledge this is the first result in which a targetobject shape was estimated by using the data of distance sensors without using their locations | [['this', 'paper', 'proposes', 'a', 'theoretical', 'framework', 'for', 'estimating', 'a', 'targetobject', 'shape', 'the', 'location', 'of', 'which', 'is', 'not', 'given', 'by', 'using', 'mobile', 'distance', 'sensors', 'the', 'locations', 'of', 'which', 'are', 'also', 'unknown', 'typically', 'mobile', 'sensors', 'are', 'mounted', 'on', 'vehicles', 'each', 'sensor', 'continuously', 'measures', 'the', 'distance', 'from', 'it', 'to', 'the', 'target', 'object', 'the', 'proposed', 'framework', 'does', 'not', 'require', 'any', 'positioning', 'function', 'anchorlocation', 'information', 'or', 'additional', 'mechanisms', 'to', 'obtain', 'side', 'information', 'such', 'as', 'angle', 'of', 'arrival', 'of', 'signal', 'under', 'the', 'assumption', 'of', 'a', 'convex', 'polygon', 'target', 'object', 'each', 'edge', 'length', 'and', 'vertex', 'angle', 'and', 'their', 'combinations', 'are', 'estimated', 'and', 'finally', 'the', 'shape', 'of', 'the', 'target', 'object', 'is', 'estimated', 'to', 'the', 'best', 'of', 'our', 'knowledge', 'this', 'is', 'the', 'first', 'result', 'in', 'which', 'a', 'targetobject', 'shape', 'was', 'estimated', 'by', 'using', 'the', 'data', 'of', 'distance', 'sensors', 'without', 'using', 'their', 'locations']] | [-0.10534019877597915, 0.06368227845594061, -0.08097374519319132, -0.006681635217374247, -0.10600903427411774, -0.15476617777974092, 0.06466293953108658, 0.42804083332532283, -0.2781580953830571, -0.35802847892045975, 0.13060685059771818, -0.2722189556383951, -0.12043085913914679, 0.16087494705006425, -0.12142881883703398, 0.0782268545484509, 0.06151151130413354, 0.11478540042387039, -0.05129499673366257, -0.18389558921053045, 0.30785892348891747, 0.06706838774015055, 0.2716424309041803, 0.04204111039474479, 0.13436843177645863, 0.05277661171346677, -0.034657762802177756, 0.008972610794995544, -0.09566700287431365, 0.15571806663082857, 0.2372752026898194, 0.17780550035549272, 0.23703822226856242, -0.4143761936204762, -0.21137608159474577, 0.09854275613642213, 0.10389055213255977, 0.07647410212666728, -0.029417777260250383, -0.33023463264389924, 0.10723826611964879, -0.12585943052897963, -0.11366954135750844, 0.04170367120990925, 0.024154166010151985, 0.07143921670362804, -0.285447334308022, 0.00016858770003168365, 0.033666847492427085, 0.038994341681245714, -0.07838440653612258, -0.10183660869987568, -0.0029529291399133704, 0.2091061829917387, 0.035119954209111515, 0.05595918201354586, 0.19352638643503076, -0.12922378287608313, -0.08847199775857117, 0.3530548060961971, 0.016379023494223613, -0.24954745647405754, 0.1502103742183835, -0.1214594475035979, -0.06229949913121703, 0.11249782082229627, 0.1857030625299861, 0.16187571166929873, -0.2101994228168306, 0.02526553406487713, -0.030176900496537037, 0.18442623881679593, 0.06109480477806011, 0.031590580767387466, 0.21851212938429054, 0.14459162430880082, 0.12146174234944876, 0.10707786099307916, -0.19715126678806194, -0.0333234336382399, -0.29629685783716425, -0.0974100808415449, -0.25309363685576525, -0.017529172051802037, -0.10872129839106381, -0.13836610416712408, 0.36867562342773785, 0.15896339492985245, 0.25591138794058654, 0.0467386183912767, 0.3558539234694432, 0.06952017753836558, 0.09867218562938047, 0.07840815018933041, 0.21593176068342995, 0.03318081363933302, 0.059969896411833666, -0.15666928539708766, 0.16203597202812406, 0.054833713827203166] |
1,802.06883 | An electronic ratchet is required in nanostructured intermediate band
solar cells | We investigate in this letter the intrinsic properties that have limited the
efficiency of nanostructured intermediate band solar cells. Those devices take
advantage of intra-band transitions, which occur on narrow energy width, and
present low radiative recombination efficiency. We derive the minimum
requirements in terms of those two characteristics to achieve efficiencies in
excess of the Shockley-Queisser limit, and show that compatible nanostructures
are challenging to obtain. Especially, we evidence that currently
experimentally considered materials cannot overcome the best single junction
cells. In order to solve those issues, we consider devices including an
electronic ratchet mechanism. Firstly, such devices are shown to be much less
sensitive on the limitations of the nanostructures characteristics, so that
requirements for high efficiencies can be met. Secondly, we show that quantum
well devices present advantages over their quantum dots counterparts, although
they have attracted much less interest so far.
| physics.app-ph physics.optics | we investigate in this letter the intrinsic properties that have limited the efficiency of nanostructured intermediate band solar cells those devices take advantage of intraband transitions which occur on narrow energy width and present low radiative recombination efficiency we derive the minimum requirements in terms of those two characteristics to achieve efficiencies in excess of the shockleyqueisser limit and show that compatible nanostructures are challenging to obtain especially we evidence that currently experimentally considered materials cannot overcome the best single junction cells in order to solve those issues we consider devices including an electronic ratchet mechanism firstly such devices are shown to be much less sensitive on the limitations of the nanostructures characteristics so that requirements for high efficiencies can be met secondly we show that quantum well devices present advantages over their quantum dots counterparts although they have attracted much less interest so far | [['we', 'investigate', 'in', 'this', 'letter', 'the', 'intrinsic', 'properties', 'that', 'have', 'limited', 'the', 'efficiency', 'of', 'nanostructured', 'intermediate', 'band', 'solar', 'cells', 'those', 'devices', 'take', 'advantage', 'of', 'intraband', 'transitions', 'which', 'occur', 'on', 'narrow', 'energy', 'width', 'and', 'present', 'low', 'radiative', 'recombination', 'efficiency', 'we', 'derive', 'the', 'minimum', 'requirements', 'in', 'terms', 'of', 'those', 'two', 'characteristics', 'to', 'achieve', 'efficiencies', 'in', 'excess', 'of', 'the', 'shockleyqueisser', 'limit', 'and', 'show', 'that', 'compatible', 'nanostructures', 'are', 'challenging', 'to', 'obtain', 'especially', 'we', 'evidence', 'that', 'currently', 'experimentally', 'considered', 'materials', 'can', 'not', 'overcome', 'the', 'best', 'single', 'junction', 'cells', 'in', 'order', 'to', 'solve', 'those', 'issues', 'we', 'consider', 'devices', 'including', 'an', 'electronic', 'ratchet', 'mechanism', 'firstly', 'such', 'devices', 'are', 'shown', 'to', 'be', 'much', 'less', 'sensitive', 'on', 'the', 'limitations', 'of', 'the', 'nanostructures', 'characteristics', 'so', 'that', 'requirements', 'for', 'high', 'efficiencies', 'can', 'be', 'met', 'secondly', 'we', 'show', 'that', 'quantum', 'well', 'devices', 'present', 'advantages', 'over', 'their', 'quantum', 'dots', 'counterparts', 'although', 'they', 'have', 'attracted', 'much', 'less', 'interest', 'so', 'far']] | [-0.1174915843915947, 0.12423697549282424, 0.007992869520508875, 0.0916045714825175, -0.061413165197822535, -0.1464278574344026, 0.0701418365782433, 0.4629628541480673, -0.23716611918923125, -0.3364930047002965, 0.07058554563722381, -0.2705438535019424, -0.14428576593899667, 0.2355229216292883, -0.09577967336860625, 0.04532522453658589, 0.06475159234992445, -0.04698898495583195, -0.034111121445352034, -0.21031631804501943, 0.22597572942781072, 0.048123678809656976, 0.33968188829260737, 0.09254251155772641, 0.06174596965517083, -0.0909440776521391, 0.06528207233130982, 0.0245991666380265, -0.1487296781330134, 0.09434425586528361, 0.27515373297341883, 0.0359786912760608, 0.24334374284499313, -0.5017889189431827, -0.26631820866397954, 0.10037459099461159, 0.19609472750245963, 0.10708731215539682, -0.06780966639690969, -0.19876171511355534, 0.14625087951163263, -0.1700131166534983, -0.0729538297425391, -0.08296027546110626, -0.003606698105800642, 0.032619470145157856, -0.18970243046570517, 0.017945379959674528, 0.040474166415076805, 0.020184561083960818, -0.024500184544531168, -0.1527776883923838, 0.007580541520800493, 0.11025196393754028, 0.004597997850234531, -0.07976959429662164, 0.16491496231253833, -0.15439784396893044, -0.13324494177016288, 0.4094267678383279, -0.0013011654700178091, -0.17763356300396207, 0.20347738527208056, -0.17114618189046032, -0.10204946084190415, 0.1372454549382881, 0.16386218754573062, 0.12512448620472155, -0.15986542835710443, 0.05510390255929526, 0.049483805129381074, 0.14974005728934522, 0.05538274144135058, 0.2052709599063821, 0.21747487455555428, 0.1572168279089965, 0.0488426553975306, 0.12411162681073869, -0.08912722383026186, -0.07667047688609933, -0.20769342067428187, -0.16219191064528662, -0.18604638813739668, 0.0725590447539006, -0.010999488245419941, -0.12555631120391797, 0.3649335338108956, 0.21711138178505104, 0.19836680087809488, 0.009001501158919593, 0.29288642628960415, 0.14603497578494556, 0.12025866232699178, 0.0389886573275985, 0.31981160022854194, 0.08706604218914187, 0.10890909677534046, -0.2165327800791838, 0.07211249001477271, -0.04261325863955466] |
1,802.06884 | Weak-value amplification forWeyl-point separation in momentum space | The existence of Weyl nodes in the momentum space is a hallmark of a Weyl
semimetal (WSM). A WSM can be confirmed by observing its Fermi arcs with
separated Weyl nodes. In this paper, we study the spin- orbit interaction of
light on the surface of WSM in the limit that the thickness is ultra-thin and
the incident surface does not support Fermi arc. Our results show that the
spin-dependent splitting induced by the spin-orbit interaction is related to
the separation of Weyl nodes. By proposing an amplification technique called
weak measurements, the distance of the nodes can be precisely determined. This
system may have application in characterizing other parameters of WSM.
| cond-mat.mes-hall physics.optics | the existence of weyl nodes in the momentum space is a hallmark of a weyl semimetal wsm a wsm can be confirmed by observing its fermi arcs with separated weyl nodes in this paper we study the spin orbit interaction of light on the surface of wsm in the limit that the thickness is ultrathin and the incident surface does not support fermi arc our results show that the spindependent splitting induced by the spinorbit interaction is related to the separation of weyl nodes by proposing an amplification technique called weak measurements the distance of the nodes can be precisely determined this system may have application in characterizing other parameters of wsm | [['the', 'existence', 'of', 'weyl', 'nodes', 'in', 'the', 'momentum', 'space', 'is', 'a', 'hallmark', 'of', 'a', 'weyl', 'semimetal', 'wsm', 'a', 'wsm', 'can', 'be', 'confirmed', 'by', 'observing', 'its', 'fermi', 'arcs', 'with', 'separated', 'weyl', 'nodes', 'in', 'this', 'paper', 'we', 'study', 'the', 'spin', 'orbit', 'interaction', 'of', 'light', 'on', 'the', 'surface', 'of', 'wsm', 'in', 'the', 'limit', 'that', 'the', 'thickness', 'is', 'ultrathin', 'and', 'the', 'incident', 'surface', 'does', 'not', 'support', 'fermi', 'arc', 'our', 'results', 'show', 'that', 'the', 'spindependent', 'splitting', 'induced', 'by', 'the', 'spinorbit', 'interaction', 'is', 'related', 'to', 'the', 'separation', 'of', 'weyl', 'nodes', 'by', 'proposing', 'an', 'amplification', 'technique', 'called', 'weak', 'measurements', 'the', 'distance', 'of', 'the', 'nodes', 'can', 'be', 'precisely', 'determined', 'this', 'system', 'may', 'have', 'application', 'in', 'characterizing', 'other', 'parameters', 'of', 'wsm']] | [-0.2785369759034698, 0.19433171473909688, -0.06652618896415723, -0.013770022731997804, -0.10097068022254721, -0.15531444792369647, 0.09588991261158039, 0.34310582920443267, -0.28061035415157676, -0.2833532540742973, -0.007246958163575202, -0.3004933218055937, -0.16193607956769743, 0.17192064561407147, -0.007163611602404022, -0.010394237887209914, 0.03353011846775189, 0.005186593399100404, -0.11336268880820301, -0.23125204417426307, 0.3858580114153613, 0.021521089049721404, 0.2762960538363716, 0.07143366253668708, 0.04918423005022175, 0.012421731880749576, 0.07739198817372588, 0.07439520251162841, -0.09815044192678865, 0.06672382558463141, 0.2243124365185005, -0.06367069146446218, 0.1860015661306014, -0.3823152815207972, -0.22150266258644738, 0.0570781352996294, 0.15280311834067106, 0.09263046688075909, -0.054639021896166796, -0.36011302694013075, 0.08140941800749195, -0.15490931704074942, -0.17163589670339466, -0.05081715745784875, -0.011494629001910133, -0.005346199433136333, -0.17957313243615708, 0.05464429557157148, 0.06936526249358264, 0.04143495123467541, -0.06657962539819502, -0.04030728782838976, -0.10009934720541683, 0.09227330253842021, 0.05367376149141429, 0.04142028082735903, 0.1011069411511666, -0.09726335438816543, -0.13769324175414763, 0.37461995039068696, -0.06374765257974754, -0.14306786870994465, 0.15626037288374, -0.15691858517155716, -0.06195425888290629, 0.13303835397320135, 0.1439598782869455, 0.0772950520622544, -0.13773222536214494, 0.11473924787995722, -0.06962726238998584, 0.10123857817961834, 0.037069855914783796, 0.0487115335584219, 0.28359850364878575, 0.17518396970782696, 0.10714803864123366, 0.09365326715020014, -0.18055319758423138, 0.025335071350647404, -0.30316698950316223, -0.21915738025148, -0.32428665227156933, 0.035534255537251216, -0.044425601825553586, -0.16679321329242416, 0.4441109020969764, 0.11973351277265465, 0.2176389938686043, -0.051905139174778014, 0.2411734091251024, 0.13063975224836863, 0.11336127211273249, 0.0777532530690743, 0.30410370639791445, 0.12800718523586901, 0.044289900576196875, -0.26249270907387007, 0.10329775908446338, 0.07317679501803857] |
1,802.06885 | The Allen--Uzawa elasticity of substitution for nonhomogeneous
production functions | This note proves that the representation of the Allen elasticity of
substitution obtained by Uzawa for linear homogeneous functions holds true for
nonhomogeneous functions. It is shown that the criticism of the Allen-Uzawa
elasticity of substitution in the works of Blackorby, Primont, Russell is based
on an incorrect example.
| econ.EM math.OC | this note proves that the representation of the allen elasticity of substitution obtained by uzawa for linear homogeneous functions holds true for nonhomogeneous functions it is shown that the criticism of the allenuzawa elasticity of substitution in the works of blackorby primont russell is based on an incorrect example | [['this', 'note', 'proves', 'that', 'the', 'representation', 'of', 'the', 'allen', 'elasticity', 'of', 'substitution', 'obtained', 'by', 'uzawa', 'for', 'linear', 'homogeneous', 'functions', 'holds', 'true', 'for', 'nonhomogeneous', 'functions', 'it', 'is', 'shown', 'that', 'the', 'criticism', 'of', 'the', 'allenuzawa', 'elasticity', 'of', 'substitution', 'in', 'the', 'works', 'of', 'blackorby', 'primont', 'russell', 'is', 'based', 'on', 'an', 'incorrect', 'example']] | [-0.08852858924681482, 0.053190076149526845, -0.07351859007030725, 0.06223790539155269, -0.038539837640912636, -0.09173881303033103, 0.013780591624748447, 0.28969954359142674, -0.2747551613365826, -0.1817225247581044, 0.09417528949384375, -0.26448164058282325, -0.22397727365403072, 0.19583694513320274, -0.110496049746871, 0.01577850858397458, 0.03356261146457299, 0.005075532841779615, -0.0733858970216597, -0.3180266918371553, 0.36037691795955534, 0.09285819996148348, 0.2910911208221122, 0.0480988344643265, 0.10922379056801615, 0.0672941479993903, -0.03810044303131492, 0.06620857826150629, -0.09580965156055754, 0.17169327826021522, 0.24900887161493301, 0.11539825307124335, 0.2827121225064215, -0.375550950674907, -0.21524937384326578, 0.03492508958215299, 0.06059655903474144, 0.10537482482259689, -0.047411768588379186, -0.23673693827636863, 0.11134815949987134, -0.12158258976009877, -0.1648118155097346, -0.04944359859370667, 0.07839623034891227, 0.049523281500391335, -0.30835327060650225, 0.09650548286331088, 0.19759734634957885, 0.08813218821002089, -0.11064970421948997, -0.15583766616233016, -0.01345815910431354, 0.039329958867038724, 0.046081441727912294, 0.041864799487445016, 0.03607845077615069, -0.08364255746077422, -0.09358786255040247, 0.38628167379647493, -0.06417796673739086, -0.2253102094096982, 0.16495516319738943, -0.11253077025100103, -0.15110758830965293, 0.08325673801743466, 0.05306813291921888, 0.1146707666794891, -0.1389810201185553, 0.15203338003535147, -0.10668362218522183, 0.16934097809565213, 0.1135643030258665, -0.10268665187101325, 0.09422395846037113, 0.11336044766737716, 0.06970184093908123, 0.15947839664295316, 0.009152316675602418, -0.04861077006258394, -0.31610939968579815, -0.2050599101121011, -0.2561852180439493, 0.046272200666388257, -0.07767035449733553, -0.2060051982350516, 0.3328478134682645, 0.14416880988131237, 0.12272676669628076, 0.07411817250692326, 0.18483300117330384, 0.1624492937496499, 0.04763061755701252, 0.022399607377693705, 0.2269746450221409, 0.14427842536394525, 0.0945999008583148, -0.18957962838770903, 0.1308610158942073, 0.13652538943468875] |
1,802.06886 | Plasma-activation of tap water using DBD for agronomy applications:
Identification and quantification of long lifetime chemical species and
production/consumption mechanisms | Cold atmospheric plasmas (CAP) are weakly ionized gases that can be generated
in ambient air. They produce energetic species (e.g. electrons, metastables) as
well as reactive oxygen species, reactive nitrogen species, UV radiations and
local electric field. Their interaction with a liquid such as tap water can
hence change its chemical composition. The resulting "plasma-activated liquid"
can meet many applications, including medicine and agriculture. Consequently, a
complete experimental set of analytical techniques dedicated to the
characterization of long lifetime chemical species has been implemented to
characterize tap water treated using CAP process and intended to agronomy
applications. For that purpose, colorimetry and acid titrations are performed,
considering acid-base equilibria, pH and temperature variations induced during
plasma activation. 16 species are quantified and monitored: hydroxide and
hydronium ions, ammonia and ammonium ions, orthophosphates, carbonate ions,
nitrite and nitrate ions and hydrogen peroxide. The related
consumption/production mechanisms are discussed. In parallel, a chemical model
of electrical conductivity based on Kohlrausch's law has been developed to
simulate the electrical conductivity of the plasma-activated tap water (PATW).
Comparing its predictions with experimental measurements leads to a narrow
fitting, hence supporting the self-sufficiency of the experimental set.
Finally, to evaluate the potential of cold atmospheric plasmas for agriculture
applications, tap water has been daily plasma-treated to irrigate lentils
seeds. Then, seedlings lengths have been measured and compared with untreated
tap water, showing an increase as high as 34.0% and 128.4% after 3 days and 6
days of activation respectively. The interaction mechanisms between plasma and
tap water are discussed as well as their positive synergy on agronomic results.
| physics.app-ph physics.chem-ph physics.plasm-ph | cold atmospheric plasmas cap are weakly ionized gases that can be generated in ambient air they produce energetic species eg electrons metastables as well as reactive oxygen species reactive nitrogen species uv radiations and local electric field their interaction with a liquid such as tap water can hence change its chemical composition the resulting plasmaactivated liquid can meet many applications including medicine and agriculture consequently a complete experimental set of analytical techniques dedicated to the characterization of long lifetime chemical species has been implemented to characterize tap water treated using cap process and intended to agronomy applications for that purpose colorimetry and acid titrations are performed considering acidbase equilibria ph and temperature variations induced during plasma activation 16 species are quantified and monitored hydroxide and hydronium ions ammonia and ammonium ions orthophosphates carbonate ions nitrite and nitrate ions and hydrogen peroxide the related consumptionproduction mechanisms are discussed in parallel a chemical model of electrical conductivity based on kohlrauschs law has been developed to simulate the electrical conductivity of the plasmaactivated tap water patw comparing its predictions with experimental measurements leads to a narrow fitting hence supporting the selfsufficiency of the experimental set finally to evaluate the potential of cold atmospheric plasmas for agriculture applications tap water has been daily plasmatreated to irrigate lentils seeds then seedlings lengths have been measured and compared with untreated tap water showing an increase as high as 340 and 1284 after 3 days and 6 days of activation respectively the interaction mechanisms between plasma and tap water are discussed as well as their positive synergy on agronomic results | [['cold', 'atmospheric', 'plasmas', 'cap', 'are', 'weakly', 'ionized', 'gases', 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1,802.06887 | A Multiclass Mean-Field Game for Thwarting Misinformation Spread in the
Internet of Battlefield Things (IoBT) | In this paper, the problem of misinformation propagation is studied for an
Internet of Battlefield Things (IoBT) system in which an attacker seeks to
inject false information in the IoBT nodes in order to compromise the IoBT
operations. In the considered model, each IoBT node seeks to counter the
misinformation attack by finding the optimal probability of accepting a given
information that minimizes its cost at each time instant. The cost is expressed
in terms of the quality of information received as well as the infection cost.
The problem is formulated as a mean-field game with multiclass agents which is
suitable to model a massive heterogeneous IoBT system. For this game, the
mean-field equilibrium is characterized, and an algorithm based on the forward
backward sweep method is proposed to find the mean-field equilibrium. Then, the
finite IoBT case is considered, and the conditions of convergence of the Nash
equilibria in the finite case to the mean-field equilibrium are presented.
Numerical results show that the proposed scheme can achieve a 1.2-fold increase
in the quality of information (QoI) compared to a baseline scheme in which the
IoBT nodes are always transmitting. The results also show that the proposed
scheme can reduce the proportion of infected nodes by 99% compared to the
baseline.
| cs.GT | in this paper the problem of misinformation propagation is studied for an internet of battlefield things iobt system in which an attacker seeks to inject false information in the iobt nodes in order to compromise the iobt operations in the considered model each iobt node seeks to counter the misinformation attack by finding the optimal probability of accepting a given information that minimizes its cost at each time instant the cost is expressed in terms of the quality of information received as well as the infection cost the problem is formulated as a meanfield game with multiclass agents which is suitable to model a massive heterogeneous iobt system for this game the meanfield equilibrium is characterized and an algorithm based on the forward backward sweep method is proposed to find the meanfield equilibrium then the finite iobt case is considered and the conditions of convergence of the nash equilibria in the finite case to the meanfield equilibrium are presented numerical results show that the proposed scheme can achieve a 12fold increase in the quality of information qoi compared to a baseline scheme in which the iobt nodes are always transmitting the results also show that the proposed scheme can reduce the proportion of infected nodes by 99 compared to the baseline | [['in', 'this', 'paper', 'the', 'problem', 'of', 'misinformation', 'propagation', 'is', 'studied', 'for', 'an', 'internet', 'of', 'battlefield', 'things', 'iobt', 'system', 'in', 'which', 'an', 'attacker', 'seeks', 'to', 'inject', 'false', 'information', 'in', 'the', 'iobt', 'nodes', 'in', 'order', 'to', 'compromise', 'the', 'iobt', 'operations', 'in', 'the', 'considered', 'model', 'each', 'iobt', 'node', 'seeks', 'to', 'counter', 'the', 'misinformation', 'attack', 'by', 'finding', 'the', 'optimal', 'probability', 'of', 'accepting', 'a', 'given', 'information', 'that', 'minimizes', 'its', 'cost', 'at', 'each', 'time', 'instant', 'the', 'cost', 'is', 'expressed', 'in', 'terms', 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1,802.06888 | Superrational types | We present a formal analysis of Douglas Hofstadter's concept of
\emph{superrationality}. We start by defining superrationally justifiable
actions, and study them in symmetric games. We then model the beliefs of the
players, in a way that leads them to different choices than the usual
assumption of rationality by restricting the range of conceivable choices.
These beliefs are captured in the formal notion of \emph{type} drawn from
epistemic game theory. The theory of coalgebras is used to frame type spaces
and to account for the existence of some of them. We find conditions that
guarantee superrational outcomes.
| cs.AI | we present a formal analysis of douglas hofstadters concept of emphsuperrationality we start by defining superrationally justifiable actions and study them in symmetric games we then model the beliefs of the players in a way that leads them to different choices than the usual assumption of rationality by restricting the range of conceivable choices these beliefs are captured in the formal notion of emphtype drawn from epistemic game theory the theory of coalgebras is used to frame type spaces and to account for the existence of some of them we find conditions that guarantee superrational outcomes | [['we', 'present', 'a', 'formal', 'analysis', 'of', 'douglas', 'hofstadters', 'concept', 'of', 'emphsuperrationality', 'we', 'start', 'by', 'defining', 'superrationally', 'justifiable', 'actions', 'and', 'study', 'them', 'in', 'symmetric', 'games', 'we', 'then', 'model', 'the', 'beliefs', 'of', 'the', 'players', 'in', 'a', 'way', 'that', 'leads', 'them', 'to', 'different', 'choices', 'than', 'the', 'usual', 'assumption', 'of', 'rationality', 'by', 'restricting', 'the', 'range', 'of', 'conceivable', 'choices', 'these', 'beliefs', 'are', 'captured', 'in', 'the', 'formal', 'notion', 'of', 'emphtype', 'drawn', 'from', 'epistemic', 'game', 'theory', 'the', 'theory', 'of', 'coalgebras', 'is', 'used', 'to', 'frame', 'type', 'spaces', 'and', 'to', 'account', 'for', 'the', 'existence', 'of', 'some', 'of', 'them', 'we', 'find', 'conditions', 'that', 'guarantee', 'superrational', 'outcomes']] | [-0.11062833804997706, 0.06890870428954561, -0.140148605652509, 0.11208180347782991, -0.109939864623831, -0.12173600070258622, 0.09121517552768872, 0.38636598133191624, -0.2737653880450194, -0.2873626220470635, 0.05762337804746924, -0.21035982237788298, -0.161085081587155, 0.11343913548626006, -0.14229151778804358, 0.0019150000936802357, 0.045040767309406114, 0.051127297403190726, -0.06282236620843891, -0.2592715598362428, 0.41778199036195074, -0.007623441837808137, 0.2199479042983023, -0.005981272166614891, 0.09447217805271027, 0.04606136396747603, -0.052382656753623996, 0.059186910573888814, -0.1726403601333982, 0.1390606857590898, 0.28076893800208647, 0.20667342291844468, 0.3443869438062432, -0.42699950246480844, -0.15571599140117365, 0.12770798592077148, 0.04816768437834276, 0.10219124140047658, 0.01043156827850047, -0.3198087896991481, 0.10033319070334396, -0.20208184850171848, -0.1378083015261318, -0.08434863863337624, -0.0027909410474099023, 0.006084528035028607, -0.2566890039090668, -0.01416206877598519, 0.10630892275241755, 0.07593471368634573, -0.09990666486743477, -0.08609837370734381, -0.013755647245273795, 0.1354919792283126, 0.07423432166306353, -0.05257610381100206, 0.11700237608985395, -0.13696622409136786, -0.15583764634005004, 0.3946802767934979, -0.025811180861207397, -0.19227523342656191, 0.16143160052247024, -0.10978311702849404, -0.1305231797791797, 0.04265966368663896, 0.09849746501253497, 0.12759010987456448, -0.12411393305056438, 0.08499606704134094, -0.09309956314222466, 0.12174823670272505, 0.09041438622760677, 0.064723930782288, 0.1814019133146572, 0.10961927956230538, 0.052236061132643175, 0.09413105018076397, 0.049772216489360796, -0.1808798556104653, -0.34464508466302385, -0.11497761779815278, -0.07849506494809463, 0.0528652962630174, -0.09122533356659465, -0.1374940261024461, 0.37881418549886314, 0.20334038204483448, 0.14503506023276558, 0.11020748555580134, 0.2340154825880002, 0.0811195510229276, 0.021311696268297653, 0.00967805439065541, 0.2243280539214636, 0.1311789683857432, 0.07070854210084485, -0.12942942519051334, 0.09935448125946106, 0.08043558701937918] |
1,802.06889 | The double-trace spectrum of N=4 SYM at strong coupling | The spectrum of IIB supergravity on AdS${}_5 \times S^5$ contains a number of
bound states described by long double-trace multiplets in $\mathcal{N}=4$ super
Yang-Mills theory at large 't Hooft coupling. At large $N$ these states are
degenerate and to obtain their anomalous dimensions as expansions in
$\tfrac{1}{N^2}$ one has to solve a mixing problem. We conjecture a formula for
the leading anomalous dimensions of all long double-trace operators which
exhibits a large residual degeneracy whose structure we describe. Our formula
can be related to conformal Casimir operators which arise in the structure of
leading discontinuities of supergravity loop corrections to four-point
correlators of half-BPS operators.
| hep-th | the spectrum of iib supergravity on ads_5 times s5 contains a number of bound states described by long doubletrace multiplets in mathcaln4 super yangmills theory at large t hooft coupling at large n these states are degenerate and to obtain their anomalous dimensions as expansions in tfrac1n2 one has to solve a mixing problem we conjecture a formula for the leading anomalous dimensions of all long doubletrace operators which exhibits a large residual degeneracy whose structure we describe our formula can be related to conformal casimir operators which arise in the structure of leading discontinuities of supergravity loop corrections to fourpoint correlators of halfbps operators | [['the', 'spectrum', 'of', 'iib', 'supergravity', 'on', 'ads_5', 'times', 's5', 'contains', 'a', 'number', 'of', 'bound', 'states', 'described', 'by', 'long', 'doubletrace', 'multiplets', 'in', 'mathcaln4', 'super', 'yangmills', 'theory', 'at', 'large', 't', 'hooft', 'coupling', 'at', 'large', 'n', 'these', 'states', 'are', 'degenerate', 'and', 'to', 'obtain', 'their', 'anomalous', 'dimensions', 'as', 'expansions', 'in', 'tfrac1n2', 'one', 'has', 'to', 'solve', 'a', 'mixing', 'problem', 'we', 'conjecture', 'a', 'formula', 'for', 'the', 'leading', 'anomalous', 'dimensions', 'of', 'all', 'long', 'doubletrace', 'operators', 'which', 'exhibits', 'a', 'large', 'residual', 'degeneracy', 'whose', 'structure', 'we', 'describe', 'our', 'formula', 'can', 'be', 'related', 'to', 'conformal', 'casimir', 'operators', 'which', 'arise', 'in', 'the', 'structure', 'of', 'leading', 'discontinuities', 'of', 'supergravity', 'loop', 'corrections', 'to', 'fourpoint', 'correlators', 'of', 'halfbps', 'operators']] | [-0.1822932601789944, 0.22334082491075746, -0.04109065910765471, 0.10181133688176768, -0.046421310293506116, -0.16110807799626714, -0.04155243613506453, 0.2729077848981923, -0.15659031011343288, -0.2533145463810517, 0.11317575547526268, -0.31836119190064405, -0.15822021942809583, 0.09100522492260027, -0.02615103284971645, 0.053047569119371474, -0.0016027601064146997, 0.04336577483291666, -0.12852452458061564, -0.25556263217004016, 0.33259049816003355, -0.05684287413560714, 0.23015594489585894, 0.098688716838996, 0.10687590985952948, -0.047692355122238114, 0.02770346148799245, -0.010788425319613172, -0.06914882150291445, 0.13639879941407484, 0.26891290054370004, 0.014186905558185222, 0.09843727551812592, -0.46495020055534464, -0.1749042924001025, 0.08172146120789246, 0.19567762947498032, 0.15441165806483836, 0.08517600580839477, -0.2497882268149764, 0.03180350945331156, -0.19236315901462847, -0.2233599745265495, -0.13936180415196128, 0.0452542702595775, -0.13765529856587258, -0.2834744824671921, 0.11501816753969671, -0.010479875786516529, 0.005206264188298239, -0.005999986861402599, -0.0887044885726377, -0.07009859329599959, 0.12049376224436295, 0.1479093414158202, 0.03294140474463347, 0.07035939522589055, -0.19878033998779407, -0.15922744216200393, 0.3002077153561494, -0.10848984433015665, -0.18294790241186723, 0.13239349252007043, -0.20849848740572968, -0.20417847007495135, 0.1209152858131207, 0.14872361035444415, 0.20073826059972186, -0.12007363435315291, 0.24988556651421207, -0.02703715251901975, 0.08309518018415055, 0.15440248490687317, 0.10021880806352083, 0.2165232443770107, 0.08817510973089017, 0.05623303973809995, 0.1755679032413844, -0.006340951056559929, -0.0747169213808285, -0.41516245000709134, -0.08724908494724122, -0.14303630875992196, 0.15386515406246942, -0.1778966886294466, -0.25011099086035615, 0.36505040957126766, 0.07789044062169873, 0.21919361007614777, 0.07912036012007426, 0.14921512204007462, 0.16017530331513485, 0.1955786804951808, 0.06278575984693956, 0.20910817709787247, 0.20034232025724263, 0.07525716640856654, -0.2930009410931514, -0.12884333550643462, 0.22930669919319022] |
1,802.0689 | Range-Only Localization in n-Dimensional Networks With Arbitrary Anchor
Placement | This paper considers node localization in static sensor networks using
range-only measurements. Similar to state- of-the-art algorithms, such as ECHO
and DILOC, we rely on barycentric coordinates of the nodes to transform the
non-convex node localization problem into a linear system of equations. The
main contribution of this paper is a simple closed-form expression for
generalized barycentric coordinates, which extends existing algorithms from two
to n dimensions and allows arbitrary anchor-node configurations. The result
relies on a connection between the Cayley-Menger bi-determinants of subsets of
n+1 neighbor nodes and the signed volume of the simplices defined by these
neighbor nodes. Hence, for noise-free measurements, the proposed method
computes the optimal sensor network embedding as the solution of a linear
system with coefficients obtained from the generalized barycentric node
coordinates. Using simulations, we provide comparisons with DILOC and Matlab's
MDS implementation. We also show that it is possible to improve our algorithm
run time using fewer subsets of neighbor nodes.
| eess.SP | this paper considers node localization in static sensor networks using rangeonly measurements similar to state oftheart algorithms such as echo and diloc we rely on barycentric coordinates of the nodes to transform the nonconvex node localization problem into a linear system of equations the main contribution of this paper is a simple closedform expression for generalized barycentric coordinates which extends existing algorithms from two to n dimensions and allows arbitrary anchornode configurations the result relies on a connection between the cayleymenger bideterminants of subsets of n1 neighbor nodes and the signed volume of the simplices defined by these neighbor nodes hence for noisefree measurements the proposed method computes the optimal sensor network embedding as the solution of a linear system with coefficients obtained from the generalized barycentric node coordinates using simulations we provide comparisons with diloc and matlabs mds implementation we also show that it is possible to improve our algorithm run time using fewer subsets of neighbor nodes | [['this', 'paper', 'considers', 'node', 'localization', 'in', 'static', 'sensor', 'networks', 'using', 'rangeonly', 'measurements', 'similar', 'to', 'state', 'oftheart', 'algorithms', 'such', 'as', 'echo', 'and', 'diloc', 'we', 'rely', 'on', 'barycentric', 'coordinates', 'of', 'the', 'nodes', 'to', 'transform', 'the', 'nonconvex', 'node', 'localization', 'problem', 'into', 'a', 'linear', 'system', 'of', 'equations', 'the', 'main', 'contribution', 'of', 'this', 'paper', 'is', 'a', 'simple', 'closedform', 'expression', 'for', 'generalized', 'barycentric', 'coordinates', 'which', 'extends', 'existing', 'algorithms', 'from', 'two', 'to', 'n', 'dimensions', 'and', 'allows', 'arbitrary', 'anchornode', 'configurations', 'the', 'result', 'relies', 'on', 'a', 'connection', 'between', 'the', 'cayleymenger', 'bideterminants', 'of', 'subsets', 'of', 'n1', 'neighbor', 'nodes', 'and', 'the', 'signed', 'volume', 'of', 'the', 'simplices', 'defined', 'by', 'these', 'neighbor', 'nodes', 'hence', 'for', 'noisefree', 'measurements', 'the', 'proposed', 'method', 'computes', 'the', 'optimal', 'sensor', 'network', 'embedding', 'as', 'the', 'solution', 'of', 'a', 'linear', 'system', 'with', 'coefficients', 'obtained', 'from', 'the', 'generalized', 'barycentric', 'node', 'coordinates', 'using', 'simulations', 'we', 'provide', 'comparisons', 'with', 'diloc', 'and', 'matlabs', 'mds', 'implementation', 'we', 'also', 'show', 'that', 'it', 'is', 'possible', 'to', 'improve', 'our', 'algorithm', 'run', 'time', 'using', 'fewer', 'subsets', 'of', 'neighbor', 'nodes']] | [-0.1562700614663242, -0.008763545225197567, -0.042856086704208034, 0.007124535198606362, -0.07516032519832819, -0.18001497610578243, 0.08910135939110164, 0.365827774723308, -0.27942564356129, -0.2774353982185167, 0.08876157084942642, -0.2912173905496263, -0.19629853357460728, 0.15750564544025478, -0.061439049639444375, 0.08530162868997719, 0.11101351527326234, 0.060860444760853366, -0.07315388708441707, -0.26676353029582556, 0.3114401383643758, 0.049176926764400226, 0.26242877807043774, -0.007872185218914212, 0.16479220003909373, 0.0655364855111553, -0.04604969896034255, 0.057579087337355214, -0.09716966846403172, 0.12750955821011994, 0.23890032050571156, 0.17101703273556842, 0.21404874222529935, -0.42294676148014354, -0.166533263624186, 0.1294262383038862, 0.15389884334305137, 0.11832452810861618, 0.022150573463995554, -0.29077193485071906, 0.09723530333383079, -0.1609763549817608, -0.09301714760345654, -0.07164728973956802, -0.0014514184797836962, 0.0729400429497414, -0.29951884989373245, 0.03227452312642561, 0.04400329420172103, 0.021446609987488277, -0.05555348505915873, -0.12641045735129214, 0.026981673764854764, 0.13517147221652603, -0.014246609532038528, 0.04801941408386713, 0.08870038797465872, -0.03655336898621879, -0.14992107692044937, 0.36289081675342366, -0.00879817993912103, -0.2503413000480048, 0.15997471821883433, -0.07136354956457604, -0.13818796445568435, 0.09066916389445719, 0.19169942138693, 0.14547107803128376, -0.14310505797001946, 0.07111252023115193, -0.047834786005860455, 0.13496138994829565, 0.06550801555402105, 0.02573159227407054, 0.10760342266964668, 0.14323074331424565, 0.1365288772349116, 0.1587442617190713, -0.10160592847645189, -0.0943520244868926, -0.2592395395069984, -0.13244125405562263, -0.25260192841620044, 0.005022313728739944, -0.1733567664407848, -0.15978328918425155, 0.4005911246743761, 0.14490904008875352, 0.21031441205163498, 0.11021542892853223, 0.3326037031822378, 0.0722742309337747, 0.052970030498278295, 0.1301740039363975, 0.16067259401802114, 0.10748626723814803, 0.07274667514470418, -0.18671017933837314, 0.061035556089474904, 0.16477263013572915] |
1,802.06891 | Fourier Policy Gradients | We propose a new way of deriving policy gradient updates for reinforcement
learning. Our technique, based on Fourier analysis, recasts integrals that
arise with expected policy gradients as convolutions and turns them into
multiplications. The obtained analytical solutions allow us to capture the low
variance benefits of EPG in a broad range of settings. For the critic, we treat
trigonometric and radial basis functions, two function families with the
universal approximation property. The choice of policy can be almost arbitrary,
including mixtures or hybrid continuous-discrete probability distributions.
Moreover, we derive a general family of sample-based estimators for stochastic
policy gradients, which unifies existing results on sample-based approximation.
We believe that this technique has the potential to shape the next generation
of policy gradient approaches, powered by analytical results.
| cs.LG cs.AI | we propose a new way of deriving policy gradient updates for reinforcement learning our technique based on fourier analysis recasts integrals that arise with expected policy gradients as convolutions and turns them into multiplications the obtained analytical solutions allow us to capture the low variance benefits of epg in a broad range of settings for the critic we treat trigonometric and radial basis functions two function families with the universal approximation property the choice of policy can be almost arbitrary including mixtures or hybrid continuousdiscrete probability distributions moreover we derive a general family of samplebased estimators for stochastic policy gradients which unifies existing results on samplebased approximation we believe that this technique has the potential to shape the next generation of policy gradient approaches powered by analytical results | [['we', 'propose', 'a', 'new', 'way', 'of', 'deriving', 'policy', 'gradient', 'updates', 'for', 'reinforcement', 'learning', 'our', 'technique', 'based', 'on', 'fourier', 'analysis', 'recasts', 'integrals', 'that', 'arise', 'with', 'expected', 'policy', 'gradients', 'as', 'convolutions', 'and', 'turns', 'them', 'into', 'multiplications', 'the', 'obtained', 'analytical', 'solutions', 'allow', 'us', 'to', 'capture', 'the', 'low', 'variance', 'benefits', 'of', 'epg', 'in', 'a', 'broad', 'range', 'of', 'settings', 'for', 'the', 'critic', 'we', 'treat', 'trigonometric', 'and', 'radial', 'basis', 'functions', 'two', 'function', 'families', 'with', 'the', 'universal', 'approximation', 'property', 'the', 'choice', 'of', 'policy', 'can', 'be', 'almost', 'arbitrary', 'including', 'mixtures', 'or', 'hybrid', 'continuousdiscrete', 'probability', 'distributions', 'moreover', 'we', 'derive', 'a', 'general', 'family', 'of', 'samplebased', 'estimators', 'for', 'stochastic', 'policy', 'gradients', 'which', 'unifies', 'existing', 'results', 'on', 'samplebased', 'approximation', 'we', 'believe', 'that', 'this', 'technique', 'has', 'the', 'potential', 'to', 'shape', 'the', 'next', 'generation', 'of', 'policy', 'gradient', 'approaches', 'powered', 'by', 'analytical', 'results']] | [-0.054689664153556805, 0.01772949469886953, -0.14596224300112226, 0.08405028603579012, -0.11324532901380735, -0.13876621677991352, 0.08589618710811919, 0.4369766724121291, -0.2868986422672606, -0.2754407396350871, 0.07867446146974544, -0.21425951342098415, -0.17454259113765147, 0.2284794984916516, -0.0793169351600227, 0.10655551255695173, 0.05816453307829761, -0.02783073651335144, -0.10753335182380397, -0.23025375364613865, 0.31020257736963686, 0.021658038487657905, 0.29533589653874515, -0.010368431907409104, 0.14915529669815442, 0.015860927022004034, -0.01963370521843899, 0.022608828003285453, -0.13283867369739255, 0.17306253993410792, 0.26444171537150396, 0.18519955340889283, 0.32755673926658346, -0.39449604838955565, -0.22808917033398757, 0.10920308039385418, 0.16550380554667754, 0.10284605439210281, -0.07020187346824969, -0.2414830998495745, 0.044745582665200345, -0.20413865994487423, -0.11823233431277913, -0.17930070629336115, -0.05228246446131379, 0.08690284413023619, -0.36690536667447304, 0.06771554657677825, 0.05779916410028818, -0.008568682609620737, -0.07837184335403435, -0.18097888467946177, 0.07053528537835518, 0.09941106374935771, 0.05244722055795137, 0.017945544867416174, 0.1402220592312915, -0.09343936528966879, -0.13986751974607614, 0.29713029067488606, -0.10112136691077467, -0.23774802827938402, 0.15451128094537125, -0.08199412485555513, -0.15040492286061635, 0.08780046117317397, 0.22020519573743513, 0.1704092676500295, -0.1589935740121291, 0.06544001999395732, -0.01958317490743866, 0.10783948221796891, 0.04209588770754635, 0.016793655935998686, 0.12177455704659224, 0.11973085739009548, 0.12650192343789968, 0.13948900827654143, -0.07194000100776066, -0.1713296896123211, -0.27604780063120415, -0.12312118559202645, -0.1464890967035899, -0.009194888350975816, -0.15050160257464995, -0.19172207192968926, 0.38537729656121655, 0.1529339900789637, 0.17496617171855178, 0.15709208106363803, 0.308229513500919, 0.17208643968842807, 0.0788080585480202, 0.10332432615905418, 0.1913285727496259, 0.10399344277902856, 0.09164270187466173, -0.17051976426591864, 0.1117469104351585, 0.10877803891344229] |
1,802.06892 | Real World Evaluation of Approaches to Research Paper Recommendation | In this work, we have identified the need for choosing baseline approaches
for research-paper recommendation systems. Following a literature survey of all
research paper recommendation approaches described over the last four years, we
framed criteria that makes for a well-rounded set of baselines. These are
implemented on Mr. DLib a literature recommendation platform. User click data
was collected as part of an ongoing experiment in collaboration with our
partner Gesis. We reported the results from our evaluation for the experiments.
We will be able to draw clearer conclusions as time passes. We find that a term
based similarity search performs better than keyword based approaches. These
results are a good starting point in finding performance improvements for
related document searches.
| cs.IR | in this work we have identified the need for choosing baseline approaches for researchpaper recommendation systems following a literature survey of all research paper recommendation approaches described over the last four years we framed criteria that makes for a wellrounded set of baselines these are implemented on mr dlib a literature recommendation platform user click data was collected as part of an ongoing experiment in collaboration with our partner gesis we reported the results from our evaluation for the experiments we will be able to draw clearer conclusions as time passes we find that a term based similarity search performs better than keyword based approaches these results are a good starting point in finding performance improvements for related document searches | [['in', 'this', 'work', 'we', 'have', 'identified', 'the', 'need', 'for', 'choosing', 'baseline', 'approaches', 'for', 'researchpaper', 'recommendation', 'systems', 'following', 'a', 'literature', 'survey', 'of', 'all', 'research', 'paper', 'recommendation', 'approaches', 'described', 'over', 'the', 'last', 'four', 'years', 'we', 'framed', 'criteria', 'that', 'makes', 'for', 'a', 'wellrounded', 'set', 'of', 'baselines', 'these', 'are', 'implemented', 'on', 'mr', 'dlib', 'a', 'literature', 'recommendation', 'platform', 'user', 'click', 'data', 'was', 'collected', 'as', 'part', 'of', 'an', 'ongoing', 'experiment', 'in', 'collaboration', 'with', 'our', 'partner', 'gesis', 'we', 'reported', 'the', 'results', 'from', 'our', 'evaluation', 'for', 'the', 'experiments', 'we', 'will', 'be', 'able', 'to', 'draw', 'clearer', 'conclusions', 'as', 'time', 'passes', 'we', 'find', 'that', 'a', 'term', 'based', 'similarity', 'search', 'performs', 'better', 'than', 'keyword', 'based', 'approaches', 'these', 'results', 'are', 'a', 'good', 'starting', 'point', 'in', 'finding', 'performance', 'improvements', 'for', 'related', 'document', 'searches']] | [-0.07583079339043858, -0.005650647693619249, -0.0989783514291048, 0.056206432492278205, -0.14711404577440892, -0.15099154545071844, 0.07868812428108261, 0.44497302087644736, -0.16436168986450259, -0.33342053783126174, 0.08463201671935773, -0.35435583422270917, -0.16384950651166338, 0.26786051563879787, -0.05858102643396705, 0.061961998557671906, 0.16149090903636534, 0.041476033123520516, -0.10561389625387771, -0.33368104830539475, 0.2851478372545292, 0.08544823741540312, 0.29409765468056626, 0.04907788968412206, 0.03790038865505873, -0.026638674797140993, -0.11049652785392633, 0.02943766818692287, -0.13910179194317607, 0.11606908572672789, 0.3488448325699816, 0.19859629668062553, 0.27968958707060665, -0.3574372223500783, -0.1733978543857423, 0.0816041119202661, 0.14719419375954507, 0.0945386285893619, -0.10521659505999802, -0.31467585541928805, 0.08182428268385895, -0.2032995766882474, -0.0481821072831129, -0.09818274381201869, 0.006395014534549167, 0.007944478289573453, -0.2813075207173824, 0.007305090333829867, 0.03502699851912136, 0.08503835265679906, -0.06169196346309036, -0.1804576683207415, 0.08805511198588648, 0.16651039891488228, 0.06282222685319236, 0.07222305708952868, 0.10910629801219329, -0.11913342233553219, -0.21598396430102487, 0.3673578512854874, -0.06908630379342261, -0.15492829473417563, 0.19345579824245457, -0.03992091772379354, -0.17584003798353176, 0.061965886896359734, 0.19087301812639149, 0.10724459849298, -0.1737174299157535, -0.013362324152391617, -0.09609874773304909, 0.1830737984867786, 0.05006926736677997, -0.00653605078405235, 0.19502023388437617, 0.26052593293134124, 0.03461535350458386, 0.09236832214034317, -0.08460710219612035, -0.09662038143142127, -0.25796114806241044, -0.12353903859232863, -0.1494045846290343, -0.0033806338615249842, -0.017115291196387262, -0.08613300869086137, 0.40165543705224993, 0.24125377817545085, 0.1858556593787701, 0.07123481725963453, 0.2942934076573389, 0.033874447743801286, 0.09224773512175186, 0.042919642344349994, 0.2307424032750229, -0.04320996935518148, 0.16038440170911297, -0.10656259802053683, 0.0603503781700662, 0.03312802087748423] |
1,802.06893 | Learning Word Vectors for 157 Languages | Distributed word representations, or word vectors, have recently been applied
to many tasks in natural language processing, leading to state-of-the-art
performance. A key ingredient to the successful application of these
representations is to train them on very large corpora, and use these
pre-trained models in downstream tasks. In this paper, we describe how we
trained such high quality word representations for 157 languages. We used two
sources of data to train these models: the free online encyclopedia Wikipedia
and data from the common crawl project. We also introduce three new word
analogy datasets to evaluate these word vectors, for French, Hindi and Polish.
Finally, we evaluate our pre-trained word vectors on 10 languages for which
evaluation datasets exists, showing very strong performance compared to
previous models.
| cs.CL cs.LG | distributed word representations or word vectors have recently been applied to many tasks in natural language processing leading to stateoftheart performance a key ingredient to the successful application of these representations is to train them on very large corpora and use these pretrained models in downstream tasks in this paper we describe how we trained such high quality word representations for 157 languages we used two sources of data to train these models the free online encyclopedia wikipedia and data from the common crawl project we also introduce three new word analogy datasets to evaluate these word vectors for french hindi and polish finally we evaluate our pretrained word vectors on 10 languages for which evaluation datasets exists showing very strong performance compared to previous models | [['distributed', 'word', 'representations', 'or', 'word', 'vectors', 'have', 'recently', 'been', 'applied', 'to', 'many', 'tasks', 'in', 'natural', 'language', 'processing', 'leading', 'to', 'stateoftheart', 'performance', 'a', 'key', 'ingredient', 'to', 'the', 'successful', 'application', 'of', 'these', 'representations', 'is', 'to', 'train', 'them', 'on', 'very', 'large', 'corpora', 'and', 'use', 'these', 'pretrained', 'models', 'in', 'downstream', 'tasks', 'in', 'this', 'paper', 'we', 'describe', 'how', 'we', 'trained', 'such', 'high', 'quality', 'word', 'representations', 'for', '157', 'languages', 'we', 'used', 'two', 'sources', 'of', 'data', 'to', 'train', 'these', 'models', 'the', 'free', 'online', 'encyclopedia', 'wikipedia', 'and', 'data', 'from', 'the', 'common', 'crawl', 'project', 'we', 'also', 'introduce', 'three', 'new', 'word', 'analogy', 'datasets', 'to', 'evaluate', 'these', 'word', 'vectors', 'for', 'french', 'hindi', 'and', 'polish', 'finally', 'we', 'evaluate', 'our', 'pretrained', 'word', 'vectors', 'on', '10', 'languages', 'for', 'which', 'evaluation', 'datasets', 'exists', 'showing', 'very', 'strong', 'performance', 'compared', 'to', 'previous', 'models']] | [-0.01957588253675827, 0.019459362706309925, -0.022707658527892024, 0.13071862196675427, -0.1662254549548148, -0.12974527977279476, 0.04168831411516294, 0.4989888778932038, -0.30323424637465485, -0.32395804425080615, 0.057467051849160934, -0.33083133440878654, -0.13526564308244454, 0.2552226321043683, -0.12736330211110827, 0.09212512185885793, 0.18464433092860474, 0.08676691390647893, -0.032701283944622864, -0.3445096792143193, 0.33233623376526383, 0.011225897537928724, 0.38321521326101254, -0.009837485302890104, 0.13520619218673793, -0.10786158333916868, -0.052529610360839536, -0.0717741936474802, -0.07425808331500443, 0.1974528814843368, 0.4172330274921276, 0.2266762675514208, 0.2851426475699843, -0.3976360138298737, -0.16691023698224436, 0.08981989654860208, 0.1330916527658701, 0.12158760952782662, -0.04475821703044136, -0.34325942769646645, 0.10446080912111534, -0.21711223009264186, 0.08736174748212631, -0.17826992608163328, 0.04043764483538412, 0.0003111027218105774, -0.2224113071542455, -0.005134359713771292, 0.08136068504407173, 0.10020515266879802, -0.04832331867637261, -0.14964017837995752, 0.07426400590754513, 0.20612036195650166, 0.08407935268607819, 0.08118571794872718, 0.08127710054303328, -0.15534916224541598, -0.1915147610245243, 0.39145820404565523, -0.12397214865897753, -0.23672082662464133, 0.272940250682748, -0.03448586402616153, -0.20710719499130925, 0.012322895650175355, 0.2891785381214013, 0.05527818857439025, -0.15421729720389796, -0.0008457698743782997, -0.07137067813337558, 0.23691937022302892, 0.12085215433774191, -0.03202956390847999, 0.15897353481601126, 0.22686017664622457, -0.07253439698436683, 0.14825439476777638, -0.08869553840971396, -0.0166809686592647, -0.18423028083311188, -0.1127056806969146, -0.169670378959309, -0.03609205914905206, -0.10831838127353602, -0.15430335585343918, 0.4063590647586222, 0.2792440391097602, 0.15488546303222103, 0.13025896213597632, 0.27957594644791994, -0.01452074294802659, 0.13327739748453338, 0.12214806732825106, 0.10320798620685107, -0.013743506556022026, 0.116403624289743, -0.08421056008609455, 0.03436848620278022, 0.06321404334749021] |
1,802.06894 | Learning Hidden Markov Models from Pairwise Co-occurrences with
Application to Topic Modeling | We present a new algorithm for identifying the transition and emission
probabilities of a hidden Markov model (HMM) from the emitted data.
Expectation-maximization becomes computationally prohibitive for long
observation records, which are often required for identification. The new
algorithm is particularly suitable for cases where the available sample size is
large enough to accurately estimate second-order output probabilities, but not
higher-order ones. We show that if one is only able to obtain a reliable
estimate of the pairwise co-occurrence probabilities of the emissions, it is
still possible to uniquely identify the HMM if the emission probability is
\emph{sufficiently scattered}. We apply our method to hidden topic Markov
modeling, and demonstrate that we can learn topics with higher quality if
documents are modeled as observations of HMMs sharing the same emission (topic)
probability, compared to the simple but widely used bag-of-words model.
| cs.CL cs.LG eess.SP stat.ML | we present a new algorithm for identifying the transition and emission probabilities of a hidden markov model hmm from the emitted data expectationmaximization becomes computationally prohibitive for long observation records which are often required for identification the new algorithm is particularly suitable for cases where the available sample size is large enough to accurately estimate secondorder output probabilities but not higherorder ones we show that if one is only able to obtain a reliable estimate of the pairwise cooccurrence probabilities of the emissions it is still possible to uniquely identify the hmm if the emission probability is emphsufficiently scattered we apply our method to hidden topic markov modeling and demonstrate that we can learn topics with higher quality if documents are modeled as observations of hmms sharing the same emission topic probability compared to the simple but widely used bagofwords model | [['we', 'present', 'a', 'new', 'algorithm', 'for', 'identifying', 'the', 'transition', 'and', 'emission', 'probabilities', 'of', 'a', 'hidden', 'markov', 'model', 'hmm', 'from', 'the', 'emitted', 'data', 'expectationmaximization', 'becomes', 'computationally', 'prohibitive', 'for', 'long', 'observation', 'records', 'which', 'are', 'often', 'required', 'for', 'identification', 'the', 'new', 'algorithm', 'is', 'particularly', 'suitable', 'for', 'cases', 'where', 'the', 'available', 'sample', 'size', 'is', 'large', 'enough', 'to', 'accurately', 'estimate', 'secondorder', 'output', 'probabilities', 'but', 'not', 'higherorder', 'ones', 'we', 'show', 'that', 'if', 'one', 'is', 'only', 'able', 'to', 'obtain', 'a', 'reliable', 'estimate', 'of', 'the', 'pairwise', 'cooccurrence', 'probabilities', 'of', 'the', 'emissions', 'it', 'is', 'still', 'possible', 'to', 'uniquely', 'identify', 'the', 'hmm', 'if', 'the', 'emission', 'probability', 'is', 'emphsufficiently', 'scattered', 'we', 'apply', 'our', 'method', 'to', 'hidden', 'topic', 'markov', 'modeling', 'and', 'demonstrate', 'that', 'we', 'can', 'learn', 'topics', 'with', 'higher', 'quality', 'if', 'documents', 'are', 'modeled', 'as', 'observations', 'of', 'hmms', 'sharing', 'the', 'same', 'emission', 'topic', 'probability', 'compared', 'to', 'the', 'simple', 'but', 'widely', 'used', 'bagofwords', 'model']] | [-0.011182246812295618, 0.0938113691493314, -0.05242373307519559, 0.1562790896325349, -0.14144071135075487, -0.18020289560700667, 0.055027840053002146, 0.45475434612297844, -0.2823927033104742, -0.3085703526702809, 0.09478825912232913, -0.2738694504055307, -0.12807302045923202, 0.16516875631856262, -0.04092150937158165, 0.06158017889755045, 0.08135133155981568, 0.07784235605586928, -0.0056846582760922085, -0.2239486683979102, 0.25204790613105427, 0.0662569018899866, 0.2957490546569387, -0.003917559388536194, 0.1032116618709216, -0.018407353117454346, -0.043087416965884306, -0.013500027927308008, -0.07497774195704189, 0.1355792028447184, 0.31361643591539023, 0.1985303695199694, 0.25657521089842766, -0.367388527846723, -0.23288316022710387, 0.13624260846545247, 0.14655748634526494, 0.14377124585683543, -0.01026265331748741, -0.30807619290393345, 0.08486703356582645, -0.16346814595691278, -0.06503412544278177, -0.10903408425919553, 0.004149925138748495, 0.0008826497021619997, -0.3258337311955261, 0.06169423827462242, 0.04013055856323121, -0.017003164573816967, -0.001861369085385867, -0.08785564969260748, -0.008948012369070599, 0.13854286609901154, 0.057917902051266415, -0.00991713785374841, 0.09305661635024222, -0.106412262517087, -0.09496627916112965, 0.3815791299170319, -0.06471540327084825, -0.18929473194458807, 0.19236004963213355, -0.123279411144264, -0.1829293291381699, 0.18712179685599362, 0.17391870027500475, 0.16014068602775852, -0.18201001033728534, 1.8083440067233345e-05, -0.03394020928056739, 0.23655866402868472, 0.0047530827462567505, 0.026120131495177536, 0.1899712884844882, 0.1724635598758328, 0.023645575233431308, 0.14547396230623655, -0.1487737586438996, -0.08310306442617471, -0.26559952146485977, -0.11083306307476741, -0.23868204851416833, -0.0017479726340223752, -0.05563700475200766, -0.19817065689368057, 0.35804002107193705, 0.23103777463845115, 0.2019158482612672, 0.11394780556793516, 0.3104261006476318, 0.11113351968889858, 0.05086372476335324, 0.12256645401015032, 0.18179084525727635, 0.09058819619397111, 0.030177740373562835, -0.13623886733936766, 0.16230040024437592, 0.02981667132414085] |
1,802.06895 | Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior
Explanations | There is a growing interest within the AI research community to develop
autonomous systems capable of explaining their behavior to users. One aspect of
the explanation generation problem that has yet to receive much attention is
the task of explaining plans to users whose level of expertise differ from that
of the explainer. We propose an approach for addressing this problem by
representing the user's model as an abstraction of the domain model that the
planner uses. We present algorithms for generating minimal explanations in
cases where this abstract human model is not known. We reduce the problem of
generating explanation to a search over the space of abstract models and
investigate possible greedy approximations for minimal explanations. We also
empirically show that our approach can efficiently compute explanations for a
variety of problems.
| cs.AI | there is a growing interest within the ai research community to develop autonomous systems capable of explaining their behavior to users one aspect of the explanation generation problem that has yet to receive much attention is the task of explaining plans to users whose level of expertise differ from that of the explainer we propose an approach for addressing this problem by representing the users model as an abstraction of the domain model that the planner uses we present algorithms for generating minimal explanations in cases where this abstract human model is not known we reduce the problem of generating explanation to a search over the space of abstract models and investigate possible greedy approximations for minimal explanations we also empirically show that our approach can efficiently compute explanations for a variety of problems | [['there', 'is', 'a', 'growing', 'interest', 'within', 'the', 'ai', 'research', 'community', 'to', 'develop', 'autonomous', 'systems', 'capable', 'of', 'explaining', 'their', 'behavior', 'to', 'users', 'one', 'aspect', 'of', 'the', 'explanation', 'generation', 'problem', 'that', 'has', 'yet', 'to', 'receive', 'much', 'attention', 'is', 'the', 'task', 'of', 'explaining', 'plans', 'to', 'users', 'whose', 'level', 'of', 'expertise', 'differ', 'from', 'that', 'of', 'the', 'explainer', 'we', 'propose', 'an', 'approach', 'for', 'addressing', 'this', 'problem', 'by', 'representing', 'the', 'users', 'model', 'as', 'an', 'abstraction', 'of', 'the', 'domain', 'model', 'that', 'the', 'planner', 'uses', 'we', 'present', 'algorithms', 'for', 'generating', 'minimal', 'explanations', 'in', 'cases', 'where', 'this', 'abstract', 'human', 'model', 'is', 'not', 'known', 'we', 'reduce', 'the', 'problem', 'of', 'generating', 'explanation', 'to', 'a', 'search', 'over', 'the', 'space', 'of', 'abstract', 'models', 'and', 'investigate', 'possible', 'greedy', 'approximations', 'for', 'minimal', 'explanations', 'we', 'also', 'empirically', 'show', 'that', 'our', 'approach', 'can', 'efficiently', 'compute', 'explanations', 'for', 'a', 'variety', 'of', 'problems']] | [-0.07642269196030475, 0.03171684610185651, -0.06898691600411018, 0.10774564245149298, -0.1360798957305891, -0.14419905141642345, 0.077963756406923, 0.3934222402538906, -0.2635541555242363, -0.35296266279947847, 0.06492496668552257, -0.24294215715976794, -0.21151321999026712, 0.20734457887792543, -0.10357983638497709, 0.024888618366181184, 0.05519741389608539, 0.0501110376998671, -0.01756791781274421, -0.23584918880751773, 0.30202071450011275, 0.04293295993372353, 0.2504268459099998, 0.04877344902549217, 0.134773819828962, -0.027093714322032992, -0.013833168504843072, 0.007313130884621889, -0.10337316404281831, 0.19619945134818137, 0.3152340509030508, 0.2419004240212267, 0.3381504118998549, -0.4027548743578703, -0.23214646696231203, 0.14850424255944664, 0.15465123248078042, 0.13819034008566403, -0.05039780135978875, -0.2483322233684472, 0.1271589740297632, -0.18546337140863067, -0.09827678393808875, -0.10439110185026622, 0.03611662843848454, -0.04259475285566148, -0.2743307978032963, -0.022685223217547956, 0.07616204722548153, 0.045484512759400395, -0.08424717914347829, -0.08292421662776883, 0.06399039021839123, 0.14919252365257646, 0.08337834266052623, 0.008314541427448127, 0.07659193229081401, -0.1814787885584215, -0.19538843422891822, 0.43263806002353555, 0.003988863236562752, -0.21504142408870827, 0.1811383380658869, -0.08149707713338143, -0.14740079662540176, 0.08727481180051369, 0.21309413698783367, 0.1255383937230417, -0.16329509322184013, 0.06347687726922041, -0.08909884708891831, 0.15974344484466338, 0.028089124588608575, 0.0007222299116538532, 0.22242915665786436, 0.24492750044411687, 0.08357674462647775, 0.12618854292320894, -0.016665371253030067, -0.10176841875428765, -0.2618298412773258, -0.13276457675357364, -0.15186423018449613, -0.023312686488041898, -0.0369667438615362, -0.1643752259925119, 0.4177761343898557, 0.24923805336454022, 0.1891827732602607, 0.10033522266025807, 0.3208032515622787, 0.10300599897142201, 0.07183737036556617, 0.07110290063863078, 0.16338394872614864, 0.013010994616353801, 0.09375727932410204, -0.17572475206141652, 0.12816928729150834, 0.055197202365970546] |
1,802.06896 | Color-dependent interactions in the three coloring model | Since it was first discussed by Baxter in 1970, the three coloring model has
been studied in several contexts, from frustrated magnetism to superconducting
devices and glassiness. In presence of interactions, when the model is no
longer exactly soluble, it was already observed that the phase diagram is
highly non-trivial. Here we discuss the generic case of `color-dependent'
nearest-neighbor interactions between the vertex chiralities. We uncover
different critical regimes merging into one another: c=1/2 free fermions
combining into c=1 free bosons; c=1 free bosons combining into c=2 critical
loop models; as well as three separate c=1/2 critical lines merging at a
supersymmetric c=3/2 critical point. When the three coupling constants are
tuned to equal one another, transfer-matrix calculations highlight a puzzling
regime where the central charge appears to vary continuously from 3/2 to 2.
| cond-mat.stat-mech | since it was first discussed by baxter in 1970 the three coloring model has been studied in several contexts from frustrated magnetism to superconducting devices and glassiness in presence of interactions when the model is no longer exactly soluble it was already observed that the phase diagram is highly nontrivial here we discuss the generic case of colordependent nearestneighbor interactions between the vertex chiralities we uncover different critical regimes merging into one another c12 free fermions combining into c1 free bosons c1 free bosons combining into c2 critical loop models as well as three separate c12 critical lines merging at a supersymmetric c32 critical point when the three coupling constants are tuned to equal one another transfermatrix calculations highlight a puzzling regime where the central charge appears to vary continuously from 32 to 2 | [['since', 'it', 'was', 'first', 'discussed', 'by', 'baxter', 'in', '1970', 'the', 'three', 'coloring', 'model', 'has', 'been', 'studied', 'in', 'several', 'contexts', 'from', 'frustrated', 'magnetism', 'to', 'superconducting', 'devices', 'and', 'glassiness', 'in', 'presence', 'of', 'interactions', 'when', 'the', 'model', 'is', 'no', 'longer', 'exactly', 'soluble', 'it', 'was', 'already', 'observed', 'that', 'the', 'phase', 'diagram', 'is', 'highly', 'nontrivial', 'here', 'we', 'discuss', 'the', 'generic', 'case', 'of', 'colordependent', 'nearestneighbor', 'interactions', 'between', 'the', 'vertex', 'chiralities', 'we', 'uncover', 'different', 'critical', 'regimes', 'merging', 'into', 'one', 'another', 'c12', 'free', 'fermions', 'combining', 'into', 'c1', 'free', 'bosons', 'c1', 'free', 'bosons', 'combining', 'into', 'c2', 'critical', 'loop', 'models', 'as', 'well', 'as', 'three', 'separate', 'c12', 'critical', 'lines', 'merging', 'at', 'a', 'supersymmetric', 'c32', 'critical', 'point', 'when', 'the', 'three', 'coupling', 'constants', 'are', 'tuned', 'to', 'equal', 'one', 'another', 'transfermatrix', 'calculations', 'highlight', 'a', 'puzzling', 'regime', 'where', 'the', 'central', 'charge', 'appears', 'to', 'vary', 'continuously', 'from', '32', 'to', '2']] | [-0.1452038948451961, 0.2332015583541856, -0.019071239902491946, 0.09411579577439923, -0.023616465715580244, -0.2447320591839058, 0.04724161375536403, 0.36340200039905624, -0.22626053383782394, -0.2747228949594854, 0.054433531262529596, -0.33563648932960943, -0.10565939817730505, 0.14577763545180936, 0.04380487263855983, 0.0011710320432573112, -0.01791079958150191, 0.021118860670935307, -0.08372677544847743, -0.216663796246163, 0.29522108461415925, -0.051719454873818904, 0.24756045343090238, 0.07784658019542139, 0.03250612089507727, 0.0065749322112872086, 0.06545955094738182, 0.015936780088483843, -0.14356028804506232, 0.0023463494378948278, 0.24487442419805705, -0.03146081564735331, 0.19942919945052423, -0.3931726377401779, -0.2232590314042546, 0.09338888482524277, 0.20289825330099173, 0.11581832121140254, -0.023964185622145435, -0.2615582586514569, 0.039338183844698345, -0.15867728739976883, -0.1344791172130282, -0.03535835461922549, 0.03776102444268207, -0.04117762318242397, -0.20989097643872973, 0.06270696519728083, 0.034537215294045354, 0.05075780936961415, -0.060588015856437645, -0.13853114671178107, -0.08441762741370154, 0.14123409704188816, 0.058491682323606325, 0.07739515221371913, 0.11327686068018092, -0.1285164737682998, -0.14165409702682563, 0.3786008817060336, -0.01787742724252948, -0.14398368922020519, 0.2209525050833446, -0.16016005120698862, -0.1780130719355961, 0.14454615595681009, 0.06261710626761248, 0.06452191637224282, -0.16614025797275306, 0.10160555465993082, -0.014937840888638105, 0.16335237493374516, 0.058206849979321865, -0.02446636429956572, 0.25008416863561456, 0.15255474975096311, 0.033222914079148604, 0.17780721852244852, -0.04160785197919763, -0.15913365864231419, -0.2873857010142016, -0.11715370304396015, -0.17064034806064038, 0.02993845274505108, -0.07947040968146714, -0.1316845201497981, 0.3853388775014944, 0.14893276374022574, 0.20497984212906614, -0.05502572866441654, 0.2260309932058427, 0.10384313966870419, 0.10124004735792083, 0.05492425066377245, 0.2647869816074259, 0.14435170296711652, 0.060509511439908126, -0.1905566765787775, -0.011132449620584054, 0.1012259073948154] |
1,802.06897 | Machine Learning Methods for Data Association in Multi-Object Tracking | Data association is a key step within the multi-object tracking pipeline that
is notoriously challenging due to its combinatorial nature. A popular and
general way to formulate data association is as the NP-hard multidimensional
assignment problem (MDAP). Over the last few years, data-driven approaches to
assignment have become increasingly prevalent as these techniques have started
to mature. We focus this survey solely on learning algorithms for the
assignment step of multi-object tracking, and we attempt to unify various
methods by highlighting their connections to linear assignment as well as to
the MDAP. First, we review probabilistic and end-to-end optimization approaches
to data association, followed by methods that learn association affinities from
data. We then compare the performance of the methods presented in this survey,
and conclude by discussing future research directions.
| cs.CV | data association is a key step within the multiobject tracking pipeline that is notoriously challenging due to its combinatorial nature a popular and general way to formulate data association is as the nphard multidimensional assignment problem mdap over the last few years datadriven approaches to assignment have become increasingly prevalent as these techniques have started to mature we focus this survey solely on learning algorithms for the assignment step of multiobject tracking and we attempt to unify various methods by highlighting their connections to linear assignment as well as to the mdap first we review probabilistic and endtoend optimization approaches to data association followed by methods that learn association affinities from data we then compare the performance of the methods presented in this survey and conclude by discussing future research directions | [['data', 'association', 'is', 'a', 'key', 'step', 'within', 'the', 'multiobject', 'tracking', 'pipeline', 'that', 'is', 'notoriously', 'challenging', 'due', 'to', 'its', 'combinatorial', 'nature', 'a', 'popular', 'and', 'general', 'way', 'to', 'formulate', 'data', 'association', 'is', 'as', 'the', 'nphard', 'multidimensional', 'assignment', 'problem', 'mdap', 'over', 'the', 'last', 'few', 'years', 'datadriven', 'approaches', 'to', 'assignment', 'have', 'become', 'increasingly', 'prevalent', 'as', 'these', 'techniques', 'have', 'started', 'to', 'mature', 'we', 'focus', 'this', 'survey', 'solely', 'on', 'learning', 'algorithms', 'for', 'the', 'assignment', 'step', 'of', 'multiobject', 'tracking', 'and', 'we', 'attempt', 'to', 'unify', 'various', 'methods', 'by', 'highlighting', 'their', 'connections', 'to', 'linear', 'assignment', 'as', 'well', 'as', 'to', 'the', 'mdap', 'first', 'we', 'review', 'probabilistic', 'and', 'endtoend', 'optimization', 'approaches', 'to', 'data', 'association', 'followed', 'by', 'methods', 'that', 'learn', 'association', 'affinities', 'from', 'data', 'we', 'then', 'compare', 'the', 'performance', 'of', 'the', 'methods', 'presented', 'in', 'this', 'survey', 'and', 'conclude', 'by', 'discussing', 'future', 'research', 'directions']] | [-0.04407074673339551, -0.02658055614420148, -0.08298859105611495, 0.08423375292291302, -0.14809794115740083, -0.16566663196079376, 0.07329150511027682, 0.45719954319471534, -0.3056821757566559, -0.3417867184833212, 0.16675510632087182, -0.2575576387547938, -0.1569189205801822, 0.18226259743231674, -0.09314442368029335, 0.10992242799044853, 0.11933344798776242, 0.006205459493537282, -0.0337166986220075, -0.2779229488259889, 0.31750416052852604, 0.10052401308952146, 0.3058754339788918, 0.019638298193023037, 0.0924972966493851, 0.029279680929847004, -0.11948067695624376, 0.012404703876037915, -0.11346660187869971, 0.18664753031934966, 0.34957413079375904, 0.2191167481265269, 0.36450613476336, -0.4014228121630212, -0.19218962081679136, 0.0867883205890309, 0.19117589871662619, 0.10394121165019135, -0.0572762182388498, -0.26628551173724174, 0.08935957292208535, -0.16068919222484263, -0.046236962130087496, -0.10525549091344569, 0.023731465812397094, 0.007168409753908482, -0.21522427785260848, 0.02544936400571073, -0.01339608320673971, 0.06199272996348928, -0.06491980593481873, -0.1494948824122187, 0.07050322692989293, 0.1539577585464531, 0.09232473039892214, 0.06364803126283972, 0.08834132635893747, -0.16036815554185144, -0.18511584019035737, 0.39591878338882974, -0.02526632204016512, -0.1641918482338331, 0.21719049413998923, -0.019912896176092616, -0.22174630434497786, 0.08566243672028703, 0.2379001065106817, 0.14198094246504728, -0.19707500444032078, 0.020162407133742705, -0.010242878656126848, 0.09627638765333929, 0.010564819452995363, 0.009219011546802065, 0.20036687615666857, 0.22456611440221005, 0.0737840040485815, 0.11510076850306156, -0.12721740421766864, -0.12347682418893705, -0.21860977847764426, -0.10675961130782384, -0.13881662773697587, -0.016407825395667045, 0.007384111747856779, -0.09881483238741355, 0.37037800290701234, 0.21760954511489056, 0.21591492070520454, 0.07823803371482695, 0.3618299441643117, 0.051174815698588165, 0.09240810735721516, 0.03666555040422105, 0.21701263610134625, 0.10748607002364746, 0.13892916445494016, -0.14956810055102054, 0.06413466662940995, 0.018351350968718066] |
1,802.06898 | EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based
Cameras | Event-based cameras have shown great promise in a variety of situations where
frame based cameras suffer, such as high speed motions and high dynamic range
scenes. However, developing algorithms for event measurements requires a new
class of hand crafted algorithms. Deep learning has shown great success in
providing model free solutions to many problems in the vision community, but
existing networks have been developed with frame based images in mind, and
there does not exist the wealth of labeled data for events as there does for
images for supervised training. To these points, we present EV-FlowNet, a novel
self-supervised deep learning pipeline for optical flow estimation for event
based cameras. In particular, we introduce an image based representation of a
given event stream, which is fed into a self-supervised neural network as the
sole input. The corresponding grayscale images captured from the same camera at
the same time as the events are then used as a supervisory signal to provide a
loss function at training time, given the estimated flow from the network. We
show that the resulting network is able to accurately predict optical flow from
events only in a variety of different scenes, with performance competitive to
image based networks. This method not only allows for accurate estimation of
dense optical flow, but also provides a framework for the transfer of other
self-supervised methods to the event-based domain.
| cs.CV cs.RO | eventbased cameras have shown great promise in a variety of situations where frame based cameras suffer such as high speed motions and high dynamic range scenes however developing algorithms for event measurements requires a new class of hand crafted algorithms deep learning has shown great success in providing model free solutions to many problems in the vision community but existing networks have been developed with frame based images in mind and there does not exist the wealth of labeled data for events as there does for images for supervised training to these points we present evflownet a novel selfsupervised deep learning pipeline for optical flow estimation for event based cameras in particular we introduce an image based representation of a given event stream which is fed into a selfsupervised neural network as the sole input the corresponding grayscale images captured from the same camera at the same time as the events are then used as a supervisory signal to provide a loss function at training time given the estimated flow from the network we show that the resulting network is able to accurately predict optical flow from events only in a variety of different scenes with performance competitive to image based networks this method not only allows for accurate estimation of dense optical flow but also provides a framework for the transfer of other selfsupervised methods to the eventbased domain | [['eventbased', 'cameras', 'have', 'shown', 'great', 'promise', 'in', 'a', 'variety', 'of', 'situations', 'where', 'frame', 'based', 'cameras', 'suffer', 'such', 'as', 'high', 'speed', 'motions', 'and', 'high', 'dynamic', 'range', 'scenes', 'however', 'developing', 'algorithms', 'for', 'event', 'measurements', 'requires', 'a', 'new', 'class', 'of', 'hand', 'crafted', 'algorithms', 'deep', 'learning', 'has', 'shown', 'great', 'success', 'in', 'providing', 'model', 'free', 'solutions', 'to', 'many', 'problems', 'in', 'the', 'vision', 'community', 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1,802.06899 | Hill plot focusing on Ce compounds with high
magnetic-ordering-temperatures and consequent study of Ce2AuP3 | Hill plot is a well-known criterion of the f-electron element interatomic
threshold-distance separating the nonmagnetic state from the magnetic one in
actinides or lanthanides. We have reinvestigated the Hill plot of Ce compounds
using a commercial crystallographic database CRYSTMET, focusing on a
relationship between the Ce-Ce distance and the magnetic ordering temperature,
because a Ce compound with no other magnetic elements scarcely has a magnetic
ordering temperature higher than 20 K. The Hill plot of approximately 730
compounds has revealed that a Ce compound, especially for ferromagnet, showing
the high magnetic-ordering-temperature would require a short Ce-Ce distance
with a suppression of valence instability of Ce ion. Through the study, we had
interest in Ce2AuP3 with the Curie temperature of 31 K. The ferromagnetic
nature has been examined by a doping effect, which suggests a possible increase
of magnetic anisotropy energy.
| cond-mat.mtrl-sci | hill plot is a wellknown criterion of the felectron element interatomic thresholddistance separating the nonmagnetic state from the magnetic one in actinides or lanthanides we have reinvestigated the hill plot of ce compounds using a commercial crystallographic database crystmet focusing on a relationship between the cece distance and the magnetic ordering temperature because a ce compound with no other magnetic elements scarcely has a magnetic ordering temperature higher than 20 k the hill plot of approximately 730 compounds has revealed that a ce compound especially for ferromagnet showing the high magneticorderingtemperature would require a short cece distance with a suppression of valence instability of ce ion through the study we had interest in ce2aup3 with the curie temperature of 31 k the ferromagnetic nature has been examined by a doping effect which suggests a possible increase of magnetic anisotropy energy | [['hill', 'plot', 'is', 'a', 'wellknown', 'criterion', 'of', 'the', 'felectron', 'element', 'interatomic', 'thresholddistance', 'separating', 'the', 'nonmagnetic', 'state', 'from', 'the', 'magnetic', 'one', 'in', 'actinides', 'or', 'lanthanides', 'we', 'have', 'reinvestigated', 'the', 'hill', 'plot', 'of', 'ce', 'compounds', 'using', 'a', 'commercial', 'crystallographic', 'database', 'crystmet', 'focusing', 'on', 'a', 'relationship', 'between', 'the', 'cece', 'distance', 'and', 'the', 'magnetic', 'ordering', 'temperature', 'because', 'a', 'ce', 'compound', 'with', 'no', 'other', 'magnetic', 'elements', 'scarcely', 'has', 'a', 'magnetic', 'ordering', 'temperature', 'higher', 'than', '20', 'k', 'the', 'hill', 'plot', 'of', 'approximately', '730', 'compounds', 'has', 'revealed', 'that', 'a', 'ce', 'compound', 'especially', 'for', 'ferromagnet', 'showing', 'the', 'high', 'magneticorderingtemperature', 'would', 'require', 'a', 'short', 'cece', 'distance', 'with', 'a', 'suppression', 'of', 'valence', 'instability', 'of', 'ce', 'ion', 'through', 'the', 'study', 'we', 'had', 'interest', 'in', 'ce2aup3', 'with', 'the', 'curie', 'temperature', 'of', '31', 'k', 'the', 'ferromagnetic', 'nature', 'has', 'been', 'examined', 'by', 'a', 'doping', 'effect', 'which', 'suggests', 'a', 'possible', 'increase', 'of', 'magnetic', 'anisotropy', 'energy']] | [-0.16508218669204164, 0.19059419938061825, -0.06814555570770822, 0.054676921097458996, -0.03792379106401794, -0.1370509173002039, 0.1314923832028666, 0.3497105994850726, -0.22691690082908333, -0.3103098561234005, 0.0009761759583317839, -0.3611059423864764, -0.05930546216618763, 0.16498906049117282, 0.0618663622847046, -0.03935400426031693, -0.018591529042596984, 0.07461566670270472, -0.15501218105911155, -0.2093427984150099, 0.2672867100670427, 0.08147872639443342, 0.2663384387064177, 0.07228618370059206, 0.030020190067261865, -0.010787885621753316, 0.12117847444160897, 0.048433380708207976, -0.12965975304140395, 0.03679667426930631, 0.22811892431962086, -0.0027559177474091377, 0.24925910191172185, -0.3475022883948815, -0.21709868599667542, 0.032793618204510384, 0.12189266057976056, 0.08447160115996238, -0.10956333987654515, -0.21933499602806372, 0.08329567905799623, -0.14404615806929275, -0.09999771361767404, -0.05182911881574375, 0.012055414752788185, -0.005337632831309319, -0.2682281243203975, 0.10390452461947194, 0.0906768077864613, 0.19556958467343494, -0.08613305650172098, -0.2115371713264133, -0.08023212325882495, 0.016334782623316106, 0.08849797277588323, 0.06257320587653392, 0.14396074777537518, -0.028299067320082995, -0.06176981343571028, 0.36842735799248605, -0.0373665697138409, -0.03983370512586964, 0.16869791430037687, -0.19147390709943413, -0.06938660484122332, 0.18385323895627687, 0.09607022524527137, 0.09531437459586681, -0.14064279638285584, 0.09016908873651547, -0.011284993201329866, 0.20937963611047106, 0.08348834279775688, 0.028622500734533842, 0.2537474282867419, 0.2031934324049336, 0.009762181984329158, 0.13572278233753904, -0.16672387856627158, -0.058636530679530764, -0.14840149425007815, -0.17993639400297337, -0.19553369057351067, 0.03188222231042078, -0.09816546080048069, -0.2069808932034956, 0.36175706730607676, 0.11863179740441196, 0.1862332468633266, -0.12013806299193461, 0.2087614993472337, 0.053484470388331616, 0.08143421630580526, 0.05002286632011151, 0.27234763272709267, 0.18401212766869268, 0.1531367496217546, -0.26671739277491513, 0.14177991274525137, 0.025506021161324017] |
1,802.069 | Exploring the high-pressure materials genome | A thorough in situ characterization of materials at extreme conditions is
challenging, and computational tools such as crystal structural search methods
in combination with ab initio calculations are widely used to guide experiments
by predicting the composition, structure, and properties of high-pressure
compounds. However, such techniques are usually computationally expensive and
not suitable for large-scale combinatorial exploration. On the other hand,
data-driven computational approaches using large materials databases are useful
for the analysis of energetics and stability of hundreds of thousands of
compounds, but their utility for materials discovery is largely limited to
idealized conditions of zero temperature and pressure. Here, we present a novel
framework combining the two computational approaches, using a simple linear
approximation to the enthalpy of a compound in conjunction with
ambient-conditions data currently available in high-throughput databases of
calculated materials properties. We demonstrate its utility by explaining the
occurrence of phases in nature that are not ground states at ambient conditions
and estimating the pressures at which such ambient-metastable phases become
thermodynamically accessible, as well as guiding the exploration of
ambient-immiscible binary systems via sophisticated structural search methods
to discover new stable high-pressure phases.
| cond-mat.mtrl-sci | a thorough in situ characterization of materials at extreme conditions is challenging and computational tools such as crystal structural search methods in combination with ab initio calculations are widely used to guide experiments by predicting the composition structure and properties of highpressure compounds however such techniques are usually computationally expensive and not suitable for largescale combinatorial exploration on the other hand datadriven computational approaches using large materials databases are useful for the analysis of energetics and stability of hundreds of thousands of compounds but their utility for materials discovery is largely limited to idealized conditions of zero temperature and pressure here we present a novel framework combining the two computational approaches using a simple linear approximation to the enthalpy of a compound in conjunction with ambientconditions data currently available in highthroughput databases of calculated materials properties we demonstrate its utility by explaining the occurrence of phases in nature that are not ground states at ambient conditions and estimating the pressures at which such ambientmetastable phases become thermodynamically accessible as well as guiding the exploration of ambientimmiscible binary systems via sophisticated structural search methods to discover new stable highpressure phases | [['a', 'thorough', 'in', 'situ', 'characterization', 'of', 'materials', 'at', 'extreme', 'conditions', 'is', 'challenging', 'and', 'computational', 'tools', 'such', 'as', 'crystal', 'structural', 'search', 'methods', 'in', 'combination', 'with', 'ab', 'initio', 'calculations', 'are', 'widely', 'used', 'to', 'guide', 'experiments', 'by', 'predicting', 'the', 'composition', 'structure', 'and', 'properties', 'of', 'highpressure', 'compounds', 'however', 'such', 'techniques', 'are', 'usually', 'computationally', 'expensive', 'and', 'not', 'suitable', 'for', 'largescale', 'combinatorial', 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'conditions', 'and', 'estimating', 'the', 'pressures', 'at', 'which', 'such', 'ambientmetastable', 'phases', 'become', 'thermodynamically', 'accessible', 'as', 'well', 'as', 'guiding', 'the', 'exploration', 'of', 'ambientimmiscible', 'binary', 'systems', 'via', 'sophisticated', 'structural', 'search', 'methods', 'to', 'discover', 'new', 'stable', 'highpressure', 'phases']] | [-0.07826731935229843, 0.11367042695788006, -0.0674452693739866, 0.04926697212779614, -0.08536347245261754, -0.11679572098568002, 0.09469343824136842, 0.4153375542892884, -0.28648168556874126, -0.33744066121477273, 0.1388362522145623, -0.26052664263988845, -0.15387031444728935, 0.22508205926190983, 0.01797952859972914, 0.10440662228195648, 0.06543169475238149, -0.024535700671314713, -0.1127385407434235, -0.20019630212411885, 0.27232323995896524, 0.08481616371371332, 0.3001244737859315, 0.034046429671317095, 0.041964531697768474, -0.0326087131097372, 0.0098538668625938, 0.05670743113288516, -0.15423427737132248, 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1,802.06901 | Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative
Refinement | We propose a conditional non-autoregressive neural sequence model based on
iterative refinement. The proposed model is designed based on the principles of
latent variable models and denoising autoencoders, and is generally applicable
to any sequence generation task. We extensively evaluate the proposed model on
machine translation (En-De and En-Ro) and image caption generation, and observe
that it significantly speeds up decoding while maintaining the generation
quality comparable to the autoregressive counterpart.
| cs.LG cs.CL stat.ML | we propose a conditional nonautoregressive neural sequence model based on iterative refinement the proposed model is designed based on the principles of latent variable models and denoising autoencoders and is generally applicable to any sequence generation task we extensively evaluate the proposed model on machine translation ende and enro and image caption generation and observe that it significantly speeds up decoding while maintaining the generation quality comparable to the autoregressive counterpart | [['we', 'propose', 'a', 'conditional', 'nonautoregressive', 'neural', 'sequence', 'model', 'based', 'on', 'iterative', 'refinement', 'the', 'proposed', 'model', 'is', 'designed', 'based', 'on', 'the', 'principles', 'of', 'latent', 'variable', 'models', 'and', 'denoising', 'autoencoders', 'and', 'is', 'generally', 'applicable', 'to', 'any', 'sequence', 'generation', 'task', 'we', 'extensively', 'evaluate', 'the', 'proposed', 'model', 'on', 'machine', 'translation', 'ende', 'and', 'enro', 'and', 'image', 'caption', 'generation', 'and', 'observe', 'that', 'it', 'significantly', 'speeds', 'up', 'decoding', 'while', 'maintaining', 'the', 'generation', 'quality', 'comparable', 'to', 'the', 'autoregressive', 'counterpart']] | [-0.01308524771260896, 0.0025612685962447096, -0.05479779844837529, 0.09490325441916607, -0.09748740254768304, -0.1879950847942382, 0.04211173196589308, 0.48982046714850835, -0.27282045991825205, -0.3123794520007713, 0.09440272542248879, -0.22529054928038802, -0.19492578992753157, 0.19379113554688437, -0.11095241361430713, 0.14783337851653675, 0.10248021470116718, 0.06132246946051185, -0.045918589467847985, -0.29327131153217384, 0.27403599772868414, 0.07920041297163283, 0.4427191841815199, -0.04447996601063226, 0.1989647189437944, -0.01703572214048888, -0.04232929317014558, -0.08781821570758308, -0.037903691053777167, 0.17834679975307413, 0.18199346985103537, 0.19183570370743316, 0.2795727937016636, -0.38816856474815203, -0.26201348843585165, 0.08470237122715583, 0.09527995524528836, 0.08404475183425737, -0.051952435578069917, -0.2996992827526161, 0.12240633039535688, -0.15924722143515413, 0.09009465829336218, -0.15818626867341143, -0.05647236930006849, -0.006232715265858653, -0.3183106829279235, 0.026698161195963622, 0.1379584426193365, 0.027904928701796703, -0.07470869881965753, -0.10887195297649928, 0.02343236697571618, 0.0835742938930967, 0.013076017795330179, 0.1278134816392724, 0.10091976523815122, -0.1657884442620603, -0.19164474554626004, 0.4008174191628184, -0.11189083081803151, -0.2409350240336997, 0.21272002944855817, -0.02555990969496114, -0.16820282636742506, 0.07047054389757769, 0.2492559358078454, 0.13608081330146107, -0.10939498135287846, -0.023780242850105944, 0.0024111322393374785, 0.23537453777555908, 0.03113346280796187, -0.02862195784359106, 0.16225727791232722, 0.2623018782253244, 0.006670627604138904, 0.15844680001693112, -0.17004161953061286, -0.09035412980509656, -0.20303068735769816, -0.11134647456929088, -0.19785908172572297, -0.054993744513818194, -0.0787653523154274, -0.14184378483997925, 0.42954956373598957, 0.2678059817690934, 0.15461647408083082, 0.16445787171167986, 0.34949250162712164, 0.10695543438861413, 0.12915221875799554, 0.11317261865811555, 0.14682154692709445, 0.06350764547075544, 0.0897085699618661, -0.1787457975060014, 0.13245009027887136, 0.1451036473736167] |
1,802.06902 | Caching-Aided Collaborative D2D Operation for Predictive Data
Dissemination in Industrial IoT | Industrial automation deployments constitute challenging environments where
moving IoT machines may produce high-definition video and other heavy sensor
data during surveying and inspection operations. Transporting massive contents
to the edge network infrastructure and then eventually to the remote human
operator requires reliable and high-rate radio links supported by intelligent
data caching and delivery mechanisms. In this work, we address the challenges
of contents dissemination in characteristic factory automation scenarios by
proposing to engage moving industrial machines as device-to-device (D2D)
caching helpers. With the goal to improve reliability of high-rate
millimeter-wave (mmWave) data connections, we introduce the alternative
contents dissemination modes and then construct a novel mobility-aware
methodology that helps develop predictive mode selection strategies based on
the anticipated radio link conditions. We also conduct a thorough system-level
evaluation of representative data dissemination strategies to confirm the
benefits of predictive solutions that employ D2D-enabled collaborative caching
at the wireless edge to lower contents delivery latency and improve data
acquisition reliability.
| cs.NI | industrial automation deployments constitute challenging environments where moving iot machines may produce highdefinition video and other heavy sensor data during surveying and inspection operations transporting massive contents to the edge network infrastructure and then eventually to the remote human operator requires reliable and highrate radio links supported by intelligent data caching and delivery mechanisms in this work we address the challenges of contents dissemination in characteristic factory automation scenarios by proposing to engage moving industrial machines as devicetodevice d2d caching helpers with the goal to improve reliability of highrate millimeterwave mmwave data connections we introduce the alternative contents dissemination modes and then construct a novel mobilityaware methodology that helps develop predictive mode selection strategies based on the anticipated radio link conditions we also conduct a thorough systemlevel evaluation of representative data dissemination strategies to confirm the benefits of predictive solutions that employ d2denabled collaborative caching at the wireless edge to lower contents delivery latency and improve data acquisition reliability | [['industrial', 'automation', 'deployments', 'constitute', 'challenging', 'environments', 'where', 'moving', 'iot', 'machines', 'may', 'produce', 'highdefinition', 'video', 'and', 'other', 'heavy', 'sensor', 'data', 'during', 'surveying', 'and', 'inspection', 'operations', 'transporting', 'massive', 'contents', 'to', 'the', 'edge', 'network', 'infrastructure', 'and', 'then', 'eventually', 'to', 'the', 'remote', 'human', 'operator', 'requires', 'reliable', 'and', 'highrate', 'radio', 'links', 'supported', 'by', 'intelligent', 'data', 'caching', 'and', 'delivery', 'mechanisms', 'in', 'this', 'work', 'we', 'address', 'the', 'challenges', 'of', 'contents', 'dissemination', 'in', 'characteristic', 'factory', 'automation', 'scenarios', 'by', 'proposing', 'to', 'engage', 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1,802.06903 | Generalization Error Bounds with Probabilistic Guarantee for SGD in
Nonconvex Optimization | The success of deep learning has led to a rising interest in the
generalization property of the stochastic gradient descent (SGD) method, and
stability is one popular approach to study it. Existing works based on
stability have studied nonconvex loss functions, but only considered the
generalization error of the SGD in expectation. In this paper, we establish
various generalization error bounds with probabilistic guarantee for the SGD.
Specifically, for both general nonconvex loss functions and gradient dominant
loss functions, we characterize the on-average stability of the iterates
generated by SGD in terms of the on-average variance of the stochastic
gradients. Such characterization leads to improved bounds for the
generalization error for SGD. We then study the regularized risk minimization
problem with strongly convex regularizers, and obtain improved generalization
error bounds for proximal SGD. With strongly convex regularizers, we further
establish the generalization error bounds for nonconvex loss functions under
proximal SGD with high-probability guarantee, i.e., exponential concentration
in probability.
| stat.ML cs.LG math.OC | the success of deep learning has led to a rising interest in the generalization property of the stochastic gradient descent sgd method and stability is one popular approach to study it existing works based on stability have studied nonconvex loss functions but only considered the generalization error of the sgd in expectation in this paper we establish various generalization error bounds with probabilistic guarantee for the sgd specifically for both general nonconvex loss functions and gradient dominant loss functions we characterize the onaverage stability of the iterates generated by sgd in terms of the onaverage variance of the stochastic gradients such characterization leads to improved bounds for the generalization error for sgd we then study the regularized risk minimization problem with strongly convex regularizers and obtain improved generalization error bounds for proximal sgd with strongly convex regularizers we further establish the generalization error bounds for nonconvex loss functions under proximal sgd with highprobability guarantee ie exponential concentration in probability | [['the', 'success', 'of', 'deep', 'learning', 'has', 'led', 'to', 'a', 'rising', 'interest', 'in', 'the', 'generalization', 'property', 'of', 'the', 'stochastic', 'gradient', 'descent', 'sgd', 'method', 'and', 'stability', 'is', 'one', 'popular', 'approach', 'to', 'study', 'it', 'existing', 'works', 'based', 'on', 'stability', 'have', 'studied', 'nonconvex', 'loss', 'functions', 'but', 'only', 'considered', 'the', 'generalization', 'error', 'of', 'the', 'sgd', 'in', 'expectation', 'in', 'this', 'paper', 'we', 'establish', 'various', 'generalization', 'error', 'bounds', 'with', 'probabilistic', 'guarantee', 'for', 'the', 'sgd', 'specifically', 'for', 'both', 'general', 'nonconvex', 'loss', 'functions', 'and', 'gradient', 'dominant', 'loss', 'functions', 'we', 'characterize', 'the', 'onaverage', 'stability', 'of', 'the', 'iterates', 'generated', 'by', 'sgd', 'in', 'terms', 'of', 'the', 'onaverage', 'variance', 'of', 'the', 'stochastic', 'gradients', 'such', 'characterization', 'leads', 'to', 'improved', 'bounds', 'for', 'the', 'generalization', 'error', 'for', 'sgd', 'we', 'then', 'study', 'the', 'regularized', 'risk', 'minimization', 'problem', 'with', 'strongly', 'convex', 'regularizers', 'and', 'obtain', 'improved', 'generalization', 'error', 'bounds', 'for', 'proximal', 'sgd', 'with', 'strongly', 'convex', 'regularizers', 'we', 'further', 'establish', 'the', 'generalization', 'error', 'bounds', 'for', 'nonconvex', 'loss', 'functions', 'under', 'proximal', 'sgd', 'with', 'highprobability', 'guarantee', 'ie', 'exponential', 'concentration', 'in', 'probability']] | [-0.0519732462005022, -0.03550540921776175, -0.08141137686493637, 0.1668596798056186, -0.036214029264150176, -0.16346410669257525, 0.05598048152440116, 0.4009185587553858, -0.3237967732539635, -0.2728812004082233, 0.12562503786274354, -0.24006581124878903, -0.18982358919983766, 0.21726514623974855, -0.1925260658286594, 0.14818064225603295, 0.10093242812128563, 0.0007180426996084129, -0.15406972183933798, -0.34172543021703855, 0.24591170269088033, 0.05227554018836931, 0.2824899393760558, 0.04995489866237021, 0.1314727697175863, 0.0036909303791914723, 0.0336799466859479, 0.0221720796902869, -0.12814879802688253, 0.18469426431180616, 0.21374772949460544, 0.18649318061312134, 0.4025110046982578, -0.37721023125773145, -0.20102622679217994, 0.19545923179870694, 0.13320447531574164, 0.03923348377242028, -0.09382486645258237, -0.22184586814903426, 0.08302347544791563, -0.12015225752930409, -0.06525326594886072, -0.13889395584112163, -0.10785268013418284, 0.09036364300035925, -0.36177259054526967, 0.0976108956390863, 0.10866914308811508, 0.03242729269184096, -0.06588965903238375, -0.1812700863375545, 0.059461928100864044, 0.01725338580988863, 0.13570626066417768, 0.06899442063291622, 0.12216578522673661, -0.11724238183771102, -0.1166773689691118, 0.26793082581493277, -0.11439533278041478, -0.2717996446134628, 0.13883467461568239, -0.06767209030471086, -0.1470377007923997, 0.10615602887159437, 0.272914395916551, 0.16322458310509627, -0.1558248496573395, 0.07121478381263584, -0.015510019260523079, 0.05454026581426649, 0.04043663556119369, 0.0831453639961949, 0.035614854998816295, 0.17847441166709616, 0.2494961236407336, 0.18676544342236887, -0.056892288159051875, -0.15768248457227302, -0.25976827568462435, -0.12198857669630703, -0.17132770895676794, -0.006192446393458723, -0.15334192687126078, -0.18453974958872363, 0.38093431612605566, 0.11437672095771574, 0.19002286277992544, 0.21038143434005338, 0.3199428519198917, 0.14098570004410152, 0.0599213800065032, 0.14298249653674355, 0.2804193495053054, 0.1690463098717865, 0.08162474078155539, -0.2315389807658188, 0.13759310443083164, 0.16393236547368784] |
1,802.06904 | Eisenstein series arising from Jordan algebras | We describe poles and the corresponding residual automorphic representations
of Eisenstein series attached to maximal parabolic subgroups whose unipotent
radicals admit Jordan algebra structure.
| math.RT | we describe poles and the corresponding residual automorphic representations of eisenstein series attached to maximal parabolic subgroups whose unipotent radicals admit jordan algebra structure | [['we', 'describe', 'poles', 'and', 'the', 'corresponding', 'residual', 'automorphic', 'representations', 'of', 'eisenstein', 'series', 'attached', 'to', 'maximal', 'parabolic', 'subgroups', 'whose', 'unipotent', 'radicals', 'admit', 'jordan', 'algebra', 'structure']] | [-0.24901899136602879, 0.01352033284395778, -0.1582208798499778, 0.01623446187780549, -0.22141718808173513, -0.13445939624216408, -0.07495140157698188, 0.31079131287212175, -0.4348446944107612, -0.07457142261167367, 0.10292224126988003, -0.2653496409766376, -0.14713669296664497, 0.20240118474854776, -0.14323087354811528, -0.0020797342052295185, 0.0739203109308922, 0.20726782943044478, -0.14279644855802567, -0.28553696895445074, 0.41059866299231845, -0.06462850763152043, 0.22268748620990664, -0.09951915871351957, 0.1267517882612689, -0.051275293090535946, -0.0492464907001704, -0.2643713570626763, -0.08533257773766915, 0.24384693172760308, 0.42717227157360566, -0.09427545136228825, 0.1155063178545485, -0.49153936592241126, 0.012369743819969395, 0.30058697308413684, 0.19543684964689115, -0.0886954259282599, 0.05974632257129997, -0.26718198403250426, -0.004776072261544566, -0.2355328677998235, -0.2191111760524412, -0.132357426530992, 0.07895369939312029, -0.0025409231893718243, -0.20033649979207743, 0.06467909753943483, 0.07161551845880847, 0.2529729327264552, -0.15571049348606417, -0.14323867578059435, -0.08394007481789838, 0.09595904670034845, -0.031076445942744613, -0.06671300415958588, 0.14523322703704858, -0.06113195947060982, -0.10861371205343555, 0.3413251796737313, -0.06700628584561248, -0.221654809700946, 0.09094766884421308, -0.28006926000428695, -0.17027885955758393, 0.18011732323793694, 0.11681848543230444, 0.07630362425697967, 0.008124210636500115, 0.24797927875139672, -0.1987167295689384, -0.06806050214314989, 0.24757816742445962, -0.0857855617068708, 0.14255815814249218, -0.06915761173392336, -0.022310265068275232, 0.09748532824839155, 0.1457305994311658, 0.05955955766451856, -0.3808322303617994, -0.14627819717861712, 0.010480420489329845, 0.14531642198562622, -0.0886260751667578, -0.28485806674386066, 0.5338143395880858, -0.00991923431865871, 0.1619960192280511, 0.16501591094614318, 0.04198792475896577, 0.08716897782869637, 0.1859736849340455, 0.13055939932989227, -0.001995739138995608, 0.35310320633774, -0.2125138911360409, -0.2281040878345569, -0.10657252237433568, 0.33767153854326654] |
1,802.06905 | Communication-Optimal Convolutional Neural Nets | Efficiently executing convolutional neural nets (CNNs) is important in many
machine-learning tasks. Since the cost of moving a word of data, either between
levels of a memory hierarchy or between processors over a network, is much
higher than the cost of an arithmetic operation, minimizing data movement is
critical to performance optimization. In this paper, we present both new lower
bounds on data movement needed for CNNs, and optimal sequential algorithms that
attain these lower bounds. In most common cases, our optimal algorithms can
attain significantly more data reuse than matrix multiplication.
| cs.DS cs.CC | efficiently executing convolutional neural nets cnns is important in many machinelearning tasks since the cost of moving a word of data either between levels of a memory hierarchy or between processors over a network is much higher than the cost of an arithmetic operation minimizing data movement is critical to performance optimization in this paper we present both new lower bounds on data movement needed for cnns and optimal sequential algorithms that attain these lower bounds in most common cases our optimal algorithms can attain significantly more data reuse than matrix multiplication | [['efficiently', 'executing', 'convolutional', 'neural', 'nets', 'cnns', 'is', 'important', 'in', 'many', 'machinelearning', 'tasks', 'since', 'the', 'cost', 'of', 'moving', 'a', 'word', 'of', 'data', 'either', 'between', 'levels', 'of', 'a', 'memory', 'hierarchy', 'or', 'between', 'processors', 'over', 'a', 'network', 'is', 'much', 'higher', 'than', 'the', 'cost', 'of', 'an', 'arithmetic', 'operation', 'minimizing', 'data', 'movement', 'is', 'critical', 'to', 'performance', 'optimization', 'in', 'this', 'paper', 'we', 'present', 'both', 'new', 'lower', 'bounds', 'on', 'data', 'movement', 'needed', 'for', 'cnns', 'and', 'optimal', 'sequential', 'algorithms', 'that', 'attain', 'these', 'lower', 'bounds', 'in', 'most', 'common', 'cases', 'our', 'optimal', 'algorithms', 'can', 'attain', 'significantly', 'more', 'data', 'reuse', 'than', 'matrix', 'multiplication']] | [-0.1144863287652539, 0.046431968663218075, -0.04582258639857173, 0.10571754181443754, -0.08681935377661949, -0.20032167687263017, 0.09809521161767644, 0.43731365644413495, -0.27856498375742533, -0.3576387100974503, 0.12072722382372772, -0.25050843949160795, -0.16128937382777425, 0.267431013708752, -0.1497852609027177, 0.12707770323892043, 0.1267861508683342, 0.048428562874703304, -0.10411635451094733, -0.33097177373407327, 0.24942858490090736, 0.0909190385946599, 0.31787281226285774, 0.004626517148647943, 0.07674077011989025, -0.060890363443278424, 0.027225529694038887, -0.060508025787207174, -0.04123858143564418, 0.2213698524460399, 0.3223971093387302, 0.2125924189787601, 0.3464076305213182, -0.47704879074413126, -0.17711951274364052, 0.17362651266992782, 0.19170090860849165, 0.09147094198219154, -0.0021282672097858385, -0.21711136559631838, 0.10660852930184615, -0.13561116393817507, 0.05433417001293729, -0.10182954044759517, -0.009253408516878668, -0.010237730239801433, -0.28376478750420653, 0.01604924988730446, 0.06932316199653661, 0.07549520915009729, -0.024295770839038913, -0.1580419850675832, 0.03503522668388146, 0.14489202386365554, 0.01730134914902484, 0.04372196092077976, 0.12221727897311845, -0.18351453959020664, -0.1692421593622345, 0.3486777807626387, -0.015656343814676486, -0.22694269647700308, 0.1830449577461442, -0.06759020287300581, -0.15722879170156692, 0.13190927065569785, 0.26668291638666036, 0.1094020424997839, -0.1552973734430017, 0.013201306942906029, -0.010894368048352391, 0.19869896415785543, 0.0858397465604155, 0.048903306596912444, 0.10381895438363047, 0.26574835393820767, 0.15743471330105144, 0.1267226975482038, -0.0654293568577091, -0.08622207382501548, -0.191093101206681, -0.12465974246151745, -0.21123341175363117, -0.03414002661188335, -0.16548545079279828, -0.13276082805469228, 0.37884335106481676, 0.20541375581397797, 0.17850980742166386, 0.1836154248626174, 0.37141581786715466, 0.08385978745544613, 0.15985547946563558, 0.1852500093948987, 0.1502645724027863, 0.03193185812510226, 0.10200882721160863, -0.15674857739055448, 0.10229500721249243, 0.046266921274566455] |
1,802.06906 | Coherent merging of counter-propagating exciton-polariton superfluids | We report the formation of a macroscopic coherent state emerging from
colliding polariton fluids. Four lasers with random relative phases, arranged
in a square, pump resonantly a planar microcavity, creating four coherent
polariton fluids propagating toward each other. When the density (interactions)
increases, the four fluids synchronise and the topological excitations (vortex
or soliton) disappear to form a single superfluid.
| cond-mat.quant-gas cond-mat.other quant-ph | we report the formation of a macroscopic coherent state emerging from colliding polariton fluids four lasers with random relative phases arranged in a square pump resonantly a planar microcavity creating four coherent polariton fluids propagating toward each other when the density interactions increases the four fluids synchronise and the topological excitations vortex or soliton disappear to form a single superfluid | [['we', 'report', 'the', 'formation', 'of', 'a', 'macroscopic', 'coherent', 'state', 'emerging', 'from', 'colliding', 'polariton', 'fluids', 'four', 'lasers', 'with', 'random', 'relative', 'phases', 'arranged', 'in', 'a', 'square', 'pump', 'resonantly', 'a', 'planar', 'microcavity', 'creating', 'four', 'coherent', 'polariton', 'fluids', 'propagating', 'toward', 'each', 'other', 'when', 'the', 'density', 'interactions', 'increases', 'the', 'four', 'fluids', 'synchronise', 'and', 'the', 'topological', 'excitations', 'vortex', 'or', 'soliton', 'disappear', 'to', 'form', 'a', 'single', 'superfluid']] | [-0.24779652480501682, 0.3623787701285134, -0.060324628252419646, -0.018727667319277923, 0.007584912283346057, -0.20600327518768607, 0.016557190122936542, 0.3719445104400317, -0.23256774117859702, -0.24001478763918083, -0.0401143238976753, -0.3243170689791441, -0.059676273450410613, 0.13329704089652902, 0.09912960371002555, 0.020876584574580193, -0.017177050894436736, -0.024749817326664925, 0.0005836626204351584, -0.16731401622916262, 0.29857254568487407, -0.09295840128712977, 0.3652663066284731, -0.008407759872109939, 0.1019275935056309, -0.026308981919040283, 0.08265048298829546, -0.018348474707454442, -0.13774756394947568, 0.05750835857276494, 0.2295827591481308, -0.09513941683884089, 0.22847293230394522, -0.5201790650685628, -0.24468881961656735, 0.05447162953205407, 0.22384604398879068, 0.22153086476027967, -0.0674379052206253, -0.32038904844472804, -0.1082160914549604, -0.16213969696934025, -0.1840534867097934, -0.013513331169572968, 0.008586050818363826, 0.06250513795142372, -0.16275169984437526, 0.05540361584474643, 0.038393461161588975, 0.028074953926261515, -0.07595724879453579, -0.027081504572803776, -0.1160522197606042, 0.03504350307484856, -0.06542600709944964, 0.02988050727484127, 0.20618542256609848, -0.2285229276244839, -0.14514440681474905, 0.38301166786501806, -0.09761731322699536, -0.13587804300089676, 0.21932293898425997, -0.16787353432373492, -0.00884612042233736, 0.2195252878901859, 0.1560242271409758, 0.0845278025760005, -0.033048230003381224, -0.09234726253683524, -0.05408679325482808, 0.19832980441084752, 0.1588332891309013, 0.10638231578438233, 0.3515485347559055, 0.22160772969946266, 0.031845360544199744, 0.20941371269485293, -0.10943899139917145, -0.1218023429159075, -0.2537192382849753, -0.12142329167108983, -0.1649183600054433, 0.056035428789133826, -0.06197046193580415, -0.2018910449774315, 0.4071442776786474, 0.04637992326752283, 0.1338058414204473, -0.051388484348232545, 0.2953079458403712, 0.08512046979740262, -0.0038139752422769865, 0.07209245823323726, 0.283844498017182, 0.2019022658932954, 0.11559904876630753, -0.24459230162125703, -0.08835363534744829, 0.023941451128727444] |
1,802.06907 | High-density two-dimensional electron system induced by oxygen vacancies
in ZnO | We realize a two-dimensional electron system (2DES) in ZnO by simply
depositing pure aluminum on its surface in ultra-high vacuum, and characterize
its electronic structure using angle-resolved photoemission spectroscopy. The
aluminum oxidizes into alumina by creating oxygen vacancies that dope the bulk
conduction band of ZnO and confine the electrons near its surface. The electron
density of the 2DES is up to two orders of magnitude higher than those obtained
in ZnO heterostructures. The 2DES shows two $s$-type subbands, that we compare
to the $d$-like 2DESs in titanates, with clear signatures of many-body
interactions that we analyze through a self-consistent extraction of the system
self-energy and a modeling as a coupling of a 2D Fermi liquid with a Debye
distribution of phonons.
| cond-mat.mtrl-sci | we realize a twodimensional electron system 2des in zno by simply depositing pure aluminum on its surface in ultrahigh vacuum and characterize its electronic structure using angleresolved photoemission spectroscopy the aluminum oxidizes into alumina by creating oxygen vacancies that dope the bulk conduction band of zno and confine the electrons near its surface the electron density of the 2des is up to two orders of magnitude higher than those obtained in zno heterostructures the 2des shows two stype subbands that we compare to the dlike 2dess in titanates with clear signatures of manybody interactions that we analyze through a selfconsistent extraction of the system selfenergy and a modeling as a coupling of a 2d fermi liquid with a debye distribution of phonons | [['we', 'realize', 'a', 'twodimensional', 'electron', 'system', '2des', 'in', 'zno', 'by', 'simply', 'depositing', 'pure', 'aluminum', 'on', 'its', 'surface', 'in', 'ultrahigh', 'vacuum', 'and', 'characterize', 'its', 'electronic', 'structure', 'using', 'angleresolved', 'photoemission', 'spectroscopy', 'the', 'aluminum', 'oxidizes', 'into', 'alumina', 'by', 'creating', 'oxygen', 'vacancies', 'that', 'dope', 'the', 'bulk', 'conduction', 'band', 'of', 'zno', 'and', 'confine', 'the', 'electrons', 'near', 'its', 'surface', 'the', 'electron', 'density', 'of', 'the', '2des', 'is', 'up', 'to', 'two', 'orders', 'of', 'magnitude', 'higher', 'than', 'those', 'obtained', 'in', 'zno', 'heterostructures', 'the', '2des', 'shows', 'two', 'stype', 'subbands', 'that', 'we', 'compare', 'to', 'the', 'dlike', '2dess', 'in', 'titanates', 'with', 'clear', 'signatures', 'of', 'manybody', 'interactions', 'that', 'we', 'analyze', 'through', 'a', 'selfconsistent', 'extraction', 'of', 'the', 'system', 'selfenergy', 'and', 'a', 'modeling', 'as', 'a', 'coupling', 'of', 'a', '2d', 'fermi', 'liquid', 'with', 'a', 'debye', 'distribution', 'of', 'phonons']] | [-0.1065650757718984, 0.20315107963521217, -0.0507571196787586, 0.005644003970388201, 0.04056327144966507, -0.15356080453522258, 0.10055220139319779, 0.4148383532940853, -0.22421760019845308, -0.2957742481118404, -0.04274930245792646, -0.3872250671208393, -0.08486107301127288, 0.17316926159269985, 0.06940865137549422, 0.014716508241511545, -0.017062748660196046, -0.14123215843144743, -0.16423603904731265, -0.18464480206698608, 0.28253256314296704, 0.055462540356350724, 0.29118350515958896, 0.11664466799587989, 0.05265736928759295, 0.0021680613491134567, 0.12672178647061047, 0.011556823672077879, -0.1333266038803186, 0.1127710195838428, 0.2578728552774114, -0.155772346911616, 0.21020391714934747, -0.4956021665702345, -0.23727610083602246, -0.07765075407128354, 0.13716749344631785, 0.12795855434703046, -0.1038784276069997, -0.26496969009215227, 0.03703633067388942, -0.15835442108132677, -0.12495700036343492, -0.05717694088358615, -0.05352923449617429, -0.03293018018636952, -0.19112609396927197, 0.07535679130547787, 0.014836195390671492, 0.05012345890758834, -0.1511514028295737, -0.10263022493196987, -0.13279736406536255, 0.0358809069673088, -0.0022222739892446847, 0.013884584648920163, 0.21419020614311954, -0.0923625111835627, -0.09433248241767898, 0.3950512761738701, -0.09246259843074187, -0.09653325667070439, 0.2059584654653903, -0.23992246501605774, -0.014052049840846266, 0.19488775783569598, 0.11250315675489055, 0.10952556558090766, -0.166269670721987, 0.08773805028195951, -0.026225528125384, 0.18013916755682927, 0.0813238367487936, 0.10979553873719433, 0.2560650498690236, 0.22921515989010452, 0.027533202928292456, 0.13242125808887306, -0.1389463231536239, 0.06014653994915549, -0.15500103763961157, -0.2291662302430596, -0.2572962007195246, 0.0853970045849803, -0.0498691063721857, -0.24308665398294566, 0.4414800035568779, 0.11117743848408877, 0.15368659296607384, -0.10525643729345231, 0.27253654664076987, 0.0872121219910666, 0.064589142742124, 0.01294301827170993, 0.24496293109154604, 0.16212777167818218, 0.06663250409784253, -0.3146147902990065, 0.03814971434586056, 0.01609120475887855] |
1,802.06908 | BFKL Spectrum of N=4 SYM: non-Zero Conformal Spin | We developed a general non-perturbative framework for the BFKL spectrum of
planar N=4 SYM, based on the Quantum Spectral Curve (QSC). It allows one to
study the spectrum in the whole generality, extending previously known methods
to arbitrary values of conformal spin $n$. We show how to apply our approach to
reproduce all known perturbative results for the Balitsky-Fadin-Kuraev-Lipatov
(BFKL) Pomeron eigenvalue and get new predictions. In particular, we re-derived
the Faddeev-Korchemsky Baxter equation for the Lipatov spin chain with non-zero
conformal spin reproducing the corresponding BFKL kernel eigenvalue. We also
get new non-perturbative analytic results for the Pomeron eigenvalue in the
vicinity of $|n|=1,\;\Delta=0$ point and we obtained an explicit formula for
the BFKL intercept function for arbitrary conformal spin up to the 3-loop order
in the small coupling expansion and partial result at the 4-loop order. In
addition, we implemented the numerical algorithm of arXiv:1504.06640 as an
auxiliary file to this arXiv submission. From the numerical result we managed
to deduce an analytic formula for the strong coupling expansion of the
intercept function for arbitrary conformal spin.
| hep-th hep-ph math-ph math.MP nucl-th | we developed a general nonperturbative framework for the bfkl spectrum of planar n4 sym based on the quantum spectral curve qsc it allows one to study the spectrum in the whole generality extending previously known methods to arbitrary values of conformal spin n we show how to apply our approach to reproduce all known perturbative results for the balitskyfadinkuraevlipatov bfkl pomeron eigenvalue and get new predictions in particular we rederived the faddeevkorchemsky baxter equation for the lipatov spin chain with nonzero conformal spin reproducing the corresponding bfkl kernel eigenvalue we also get new nonperturbative analytic results for the pomeron eigenvalue in the vicinity of n1delta0 point and we obtained an explicit formula for the bfkl intercept function for arbitrary conformal spin up to the 3loop order in the small coupling expansion and partial result at the 4loop order in addition we implemented the numerical algorithm of arxiv150406640 as an auxiliary file to this arxiv submission from the numerical result we managed to deduce an analytic formula for the strong coupling expansion of the intercept function for arbitrary conformal spin | [['we', 'developed', 'a', 'general', 'nonperturbative', 'framework', 'for', 'the', 'bfkl', 'spectrum', 'of', 'planar', 'n4', 'sym', 'based', 'on', 'the', 'quantum', 'spectral', 'curve', 'qsc', 'it', 'allows', 'one', 'to', 'study', 'the', 'spectrum', 'in', 'the', 'whole', 'generality', 'extending', 'previously', 'known', 'methods', 'to', 'arbitrary', 'values', 'of', 'conformal', 'spin', 'n', 'we', 'show', 'how', 'to', 'apply', 'our', 'approach', 'to', 'reproduce', 'all', 'known', 'perturbative', 'results', 'for', 'the', 'balitskyfadinkuraevlipatov', 'bfkl', 'pomeron', 'eigenvalue', 'and', 'get', 'new', 'predictions', 'in', 'particular', 'we', 'rederived', 'the', 'faddeevkorchemsky', 'baxter', 'equation', 'for', 'the', 'lipatov', 'spin', 'chain', 'with', 'nonzero', 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1,802.06909 | Canonical $\beta$-extensions | We compare the level zero part of the type of a representation of GL(n) over
a non-archimedean local field with the tame part of its Langlands parameter
restricted to inertia. By normalizing this comparison, we construct canonical
$\beta$-extensions of maximal simple characters.
| math.RT math.NT | we compare the level zero part of the type of a representation of gln over a nonarchimedean local field with the tame part of its langlands parameter restricted to inertia by normalizing this comparison we construct canonical betaextensions of maximal simple characters | [['we', 'compare', 'the', 'level', 'zero', 'part', 'of', 'the', 'type', 'of', 'a', 'representation', 'of', 'gln', 'over', 'a', 'nonarchimedean', 'local', 'field', 'with', 'the', 'tame', 'part', 'of', 'its', 'langlands', 'parameter', 'restricted', 'to', 'inertia', 'by', 'normalizing', 'this', 'comparison', 'we', 'construct', 'canonical', 'betaextensions', 'of', 'maximal', 'simple', 'characters']] | [-0.17772175407125837, 0.043434414642217145, -0.1310335258798053, 0.05680641619255766, -0.13772815168790875, -0.06881301248046968, 0.01971397778418447, 0.25582855611684774, -0.33145910643395926, -0.19158655192170823, 0.05594294224422248, -0.18094994972593018, -0.11896165722005424, 0.18446271143676268, -0.10361851146444678, -0.06548791722577464, -0.0020424535919335626, 0.15385430133236305, -0.140074637097617, -0.2738529107134257, 0.3821040917675765, 0.006533435390641292, 0.227875285432674, -0.04225235543258134, 0.14220557503202663, 0.04576357143204881, -0.02742268094083383, -0.011057807060535111, -0.13056262992765932, 0.18919972860853054, 0.2844613972119987, 0.058501520726297586, 0.2612659942270035, -0.3694940272031263, -0.12057685368095658, 0.21353749296672286, 0.10007193071457247, 0.040397561798315676, 0.03788329406996213, -0.2839687586141129, 0.10360122028249039, -0.2349525225304422, -0.17513234998720387, -0.100707179600639, 0.03129035768021519, 0.024755024085087434, -0.21609429588230947, 0.05033154825131143, 0.07305309307273655, 0.2080055030417584, -0.12629209527845628, -0.11708392451206844, 0.006947134333174853, 0.0958460743450338, 0.0253339840897492, 0.05423632273353481, 0.12070988466785777, -0.18628226246662616, -0.06586534032110303, 0.3428038504817301, -0.1320195908746904, -0.20516258438805207, 0.14669552331213795, -0.16457544521073855, -0.12155431147576087, 0.08136642503640837, 0.08323406891265352, 0.1373102996232254, -0.027683626816031478, 0.21210873089287252, -0.11253822679697935, 0.07632615124540669, 0.0306792408449664, -0.05091280800878026, 0.15228453897898794, 0.08113384619355202, 0.05005239254041087, 0.16519516098889567, -0.04242086733436389, -0.04453930473842081, -0.3867294642896879, -0.18164790592466792, -0.1500891720255216, 0.08415033739237558, -0.12529320961662702, -0.19242723907033601, 0.49371124387142207, 0.1087693682978473, 0.20261669591335313, 0.17307299184834674, 0.23187431140935846, 0.12362014684116557, 0.09121885912359826, 0.038677069591358304, 0.16765025376697026, 0.23673921837360554, -0.04425882709134991, -0.2087244616289224, -0.01627458238952039, 0.1740098815588724] |
1,802.0691 | Single or Multiple Frames Content Delivery for Next-Generation Networks? | This paper addresses the four enabling technologies, namely multi-user sparse
code multiple access (SCMA), content caching, energy harvesting, and physical
layer security for proposing an energy and spectral efficient resource
allocation algorithm for the access and backhaul links in heterogeneous
cellular networks. Although each of the above mentioned issues could be a topic
of research, in a real situation, we would face a complicated scenario where
they should be considered jointly, and hence, our target is to consider these
technologies jointly in a unified framework. Moreover, we propose two novel
content delivery scenarios: 1) single frame content delivery (SFCD), and 2)
multiple frames content delivery (MFCD), where the time duration of serving
user requests is divided into several frames. In the first scenario, the
requested content by each user is served over one frame. However, in the second
scenario, the requested content by each user can be delivered over several
frames. We formulate the resource allocation for the proposed scenarios as
optimization problems where our main aim is to maximize the energy efficiency
of access links subject to the transmit power and rate constraints of access
and backhaul links, caching and energy harvesting constraints, and SCMA
codebook allocation limitations. Due to the practical limitations, we assume
that the channel state information values between eavesdroppers and base
stations are uncertain and design the network for the worst case scenario.
Since the corresponding optimization problems are mixed integer non-linear and
nonconvex programming, NP-hard, and intractable, we propose an iterative
algorithm based on the well-known alternate and successive convex approximation
methods.
| cs.IT cs.NI math.IT | this paper addresses the four enabling technologies namely multiuser sparse code multiple access scma content caching energy harvesting and physical layer security for proposing an energy and spectral efficient resource allocation algorithm for the access and backhaul links in heterogeneous cellular networks although each of the above mentioned issues could be a topic of research in a real situation we would face a complicated scenario where they should be considered jointly and hence our target is to consider these technologies jointly in a unified framework moreover we propose two novel content delivery scenarios 1 single frame content delivery sfcd and 2 multiple frames content delivery mfcd where the time duration of serving user requests is divided into several frames in the first scenario the requested content by each user is served over one frame however in the second scenario the requested content by each user can be delivered over several frames we formulate the resource allocation for the proposed scenarios as optimization problems where our main aim is to maximize the energy efficiency of access links subject to the transmit power and rate constraints of access and backhaul links caching and energy harvesting constraints and scma codebook allocation limitations due to the practical limitations we assume that the channel state information values between eavesdroppers and base stations are uncertain and design the network for the worst case scenario since the corresponding optimization problems are mixed integer nonlinear and nonconvex programming nphard and intractable we propose an iterative algorithm based on the wellknown alternate and successive convex approximation methods | [['this', 'paper', 'addresses', 'the', 'four', 'enabling', 'technologies', 'namely', 'multiuser', 'sparse', 'code', 'multiple', 'access', 'scma', 'content', 'caching', 'energy', 'harvesting', 'and', 'physical', 'layer', 'security', 'for', 'proposing', 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1,802.06911 | Strong enhancement of the spin Hall effect by spin fluctuations near the
Curie point of FexPt1-x alloys | Robust spin Hall effects (SHE) have recently been observed in non-magnetic
heavy metal systems with strong spin-orbit interactions. These SHE are either
attributed to an intrinsic band-structure effect or to extrinsic spin-dependent
scattering from impurities, namely side-jump or skew scattering. Here we report
on an extraordinarily strong spin Hall effect, attributable to spin
fluctuations, in ferromagnetic FexPt1-x alloys near their Curie point, tunable
with x. This results in a damping-like spin-orbit torque being exerted on an
adjacent ferromagnetic layer that is strongly temperature dependent in this
transition region, with a peak value that indicates a lower bound 0.34 (+-)
0.02 for the peak spin Hall ratio within the FePt. We also observe a pronounced
peak in the effective spin-mixing conductance of the FM/FePt interface, and
determine the spin diffusion length in these FexPt1-x alloys. These results
establish new opportunities for fundamental studies of spin dynamics and
transport in ferromagnetic systems with strong spin fluctuations, and a new
pathway for efficiently generating strong spin currents for applications.
| cond-mat.mtrl-sci | robust spin hall effects she have recently been observed in nonmagnetic heavy metal systems with strong spinorbit interactions these she are either attributed to an intrinsic bandstructure effect or to extrinsic spindependent scattering from impurities namely sidejump or skew scattering here we report on an extraordinarily strong spin hall effect attributable to spin fluctuations in ferromagnetic fexpt1x alloys near their curie point tunable with x this results in a dampinglike spinorbit torque being exerted on an adjacent ferromagnetic layer that is strongly temperature dependent in this transition region with a peak value that indicates a lower bound 034 002 for the peak spin hall ratio within the fept we also observe a pronounced peak in the effective spinmixing conductance of the fmfept interface and determine the spin diffusion length in these fexpt1x alloys these results establish new opportunities for fundamental studies of spin dynamics and transport in ferromagnetic systems with strong spin fluctuations and a new pathway for efficiently generating strong spin currents for applications | [['robust', 'spin', 'hall', 'effects', 'she', 'have', 'recently', 'been', 'observed', 'in', 'nonmagnetic', 'heavy', 'metal', 'systems', 'with', 'strong', 'spinorbit', 'interactions', 'these', 'she', 'are', 'either', 'attributed', 'to', 'an', 'intrinsic', 'bandstructure', 'effect', 'or', 'to', 'extrinsic', 'spindependent', 'scattering', 'from', 'impurities', 'namely', 'sidejump', 'or', 'skew', 'scattering', 'here', 'we', 'report', 'on', 'an', 'extraordinarily', 'strong', 'spin', 'hall', 'effect', 'attributable', 'to', 'spin', 'fluctuations', 'in', 'ferromagnetic', 'fexpt1x', 'alloys', 'near', 'their', 'curie', 'point', 'tunable', 'with', 'x', 'this', 'results', 'in', 'a', 'dampinglike', 'spinorbit', 'torque', 'being', 'exerted', 'on', 'an', 'adjacent', 'ferromagnetic', 'layer', 'that', 'is', 'strongly', 'temperature', 'dependent', 'in', 'this', 'transition', 'region', 'with', 'a', 'peak', 'value', 'that', 'indicates', 'a', 'lower', 'bound', '034', '002', 'for', 'the', 'peak', 'spin', 'hall', 'ratio', 'within', 'the', 'fept', 'we', 'also', 'observe', 'a', 'pronounced', 'peak', 'in', 'the', 'effective', 'spinmixing', 'conductance', 'of', 'the', 'fmfept', 'interface', 'and', 'determine', 'the', 'spin', 'diffusion', 'length', 'in', 'these', 'fexpt1x', 'alloys', 'these', 'results', 'establish', 'new', 'opportunities', 'for', 'fundamental', 'studies', 'of', 'spin', 'dynamics', 'and', 'transport', 'in', 'ferromagnetic', 'systems', 'with', 'strong', 'spin', 'fluctuations', 'and', 'a', 'new', 'pathway', 'for', 'efficiently', 'generating', 'strong', 'spin', 'currents', 'for', 'applications']] | [-0.1810822934170978, 0.2292072404287202, -0.026646104995175668, 0.040551075281397106, -0.06285393789962486, -0.167749474796662, 0.05012350946802784, 0.4100504925612498, -0.2904744316122414, -0.28424970167516556, -0.02322736962477067, -0.3398655465528093, -0.14198838057065452, 0.20877769097605328, 0.04557901857126889, -0.02575252037840309, -0.05516215037543005, -0.06664732433740556, -0.10220023048150549, -0.1763650577135936, 0.27812300182384564, 0.00041843141877540837, 0.3049598433520001, 0.154332095866356, 0.04851681480966048, 0.03935817975093133, 0.13464248045541657, 0.03504985860652394, -0.14525582874526147, 0.02695016811291377, 0.21473326019505845, -0.19642119646722014, 0.16883587848142156, -0.4469192934647938, -0.1686620280081205, 0.00485376405880361, 0.11700252791073311, 0.17507921381031427, -0.11865751815091727, -0.28340843973543356, 0.025474942309413977, -0.1704074041529869, -0.10474038420153069, -0.08354598619503739, 0.06580528623516453, -0.04907832691089514, -0.2770307933520756, 0.13023562269769667, 0.11595236924821856, 0.09593491866195827, -0.06494615804030342, -0.18139264795837212, -0.06591560201644668, 0.050632852822021915, 0.06163776483829421, 0.05215670147128863, 0.1809118212039732, -0.13496795242988607, -0.15792862552126158, 0.3033340450744202, -0.09759814405733329, -0.12286934391078022, 0.15992516547694435, -0.23412249366074434, -0.07934998661095713, 0.1665253019115577, 0.18320651900276175, 0.09099587955351138, -0.15881522905575918, 0.04272939097229679, 0.012454548528716887, 0.16960486113600415, -0.01156669492793074, 0.11538392650308432, 0.3237126119158886, 0.182269351536776, 0.05876060981263211, 0.11395587645942507, -0.16548589191379393, -0.03212054852643453, -0.17086705230866317, -0.1527123608584027, -0.2175632534946752, 0.12251802424060894, -0.07876568292758074, -0.17597724981663496, 0.3370121705380303, 0.19370080560753353, 0.1654927401378015, -0.04458266394615265, 0.25294702293144333, 0.1448222260029676, 0.09451061696026843, 0.039619703339139736, 0.2792588774471279, 0.21431628796688776, 0.11803936842472557, -0.3316995262090738, 0.14226157746111032, -0.0329583177578716] |
1,802.06912 | Constraining Dark Matter Models with a Light Mediator from PandaX-II
Experiment | We search for nuclear recoil signals of dark matter models with a light
mediator in PandaX-II, a direct detection experiment in China Jinping
underground Laboratory. Using data collected in 2016 and 2017 runs,
corresponding to a total exposure of 54 ton day, we set upper limits on the
zero-momentum dark matter-nucleon cross section. These limits have a strong
dependence on the mediator mass when it is comparable to or below the typical
momentum transfer. We apply our results to constrain self-interacting dark
matter models with a light mediator mixing with standard model particles, and
set strong limits on the model parameter space for the dark matter mass ranging
from $5~{\rm GeV}$ to $10~{\rm TeV}$.
| hep-ph hep-ex | we search for nuclear recoil signals of dark matter models with a light mediator in pandaxii a direct detection experiment in china jinping underground laboratory using data collected in 2016 and 2017 runs corresponding to a total exposure of 54 ton day we set upper limits on the zeromomentum dark matternucleon cross section these limits have a strong dependence on the mediator mass when it is comparable to or below the typical momentum transfer we apply our results to constrain selfinteracting dark matter models with a light mediator mixing with standard model particles and set strong limits on the model parameter space for the dark matter mass ranging from 5rm gev to 10rm tev | [['we', 'search', 'for', 'nuclear', 'recoil', 'signals', 'of', 'dark', 'matter', 'models', 'with', 'a', 'light', 'mediator', 'in', 'pandaxii', 'a', 'direct', 'detection', 'experiment', 'in', 'china', 'jinping', 'underground', 'laboratory', 'using', 'data', 'collected', 'in', '2016', 'and', '2017', 'runs', 'corresponding', 'to', 'a', 'total', 'exposure', 'of', '54', 'ton', 'day', 'we', 'set', 'upper', 'limits', 'on', 'the', 'zeromomentum', 'dark', 'matternucleon', 'cross', 'section', 'these', 'limits', 'have', 'a', 'strong', 'dependence', 'on', 'the', 'mediator', 'mass', 'when', 'it', 'is', 'comparable', 'to', 'or', 'below', 'the', 'typical', 'momentum', 'transfer', 'we', 'apply', 'our', 'results', 'to', 'constrain', 'selfinteracting', 'dark', 'matter', 'models', 'with', 'a', 'light', 'mediator', 'mixing', 'with', 'standard', 'model', 'particles', 'and', 'set', 'strong', 'limits', 'on', 'the', 'model', 'parameter', 'space', 'for', 'the', 'dark', 'matter', 'mass', 'ranging', 'from', '5rm', 'gev', 'to', '10rm', 'tev']] | [-0.04934185383261361, 0.19457426567443467, -0.09145321701081437, 0.13926954435597158, -0.09056509063546464, -0.09470031088169076, 0.06557936388018884, 0.31470247079095426, -0.1687828739002151, -0.4485043344994713, 0.035966192197520286, -0.35574889163437645, 0.054398624142713584, 0.22321293891097108, 0.07589481809797387, 0.03265450288247513, 0.06793600817521413, 0.03842648173674222, -0.03509223881063231, -0.25007851205749865, 0.26080295619587496, 0.083452081750964, 0.22071959285000176, 0.10600615172695957, 0.1301552142499611, 0.007677021264833839, -0.024528634477112638, -0.12578052566248898, -0.15445973020324713, 0.0369732150187095, 0.19993947844283722, 0.0996964637378002, 0.11564857256190296, -0.3974029657414608, -0.17898863609553428, 0.2259188814725923, 0.08425856011576559, 0.049594412113564385, -0.08457219133688659, -0.37691993784290134, 0.03579307921928264, -0.23617499789811278, -0.06018185709795114, 0.006999117142537184, 0.010480789026539577, -0.03338503016959549, -0.28450172260534345, 0.11046533688919194, -0.08846277496042221, -0.032256644981220904, -0.09164812752013013, -0.16790404535111106, -0.001516858285309322, -0.046280287821693425, 0.07146425171461153, 0.012365001200681977, 0.22951053370100757, -0.19397912078063217, -0.09827053432379391, 0.3856432728240626, -0.17840028630585916, -0.10541397997733663, 0.2257820978687194, -0.15050656805754425, -0.12456892034357511, 0.1428758503558735, 0.2702821110244514, 0.07530737382547702, -0.15908443030205313, 0.11319909113961713, -0.08575672804165566, 0.24612375084114702, 0.05991417235743843, 0.00048328331353044823, 0.2969320648096567, 0.26914152061656577, 0.07412783598339413, -0.02845448145863453, -0.17719265800827233, -0.05912962287517363, -0.35496475073057554, -0.11094517450322185, -0.1052034160759496, 0.026840947805331986, -0.043035661636774115, -0.027400613615387363, 0.3116763805125007, 0.1399885900604555, 0.2360011702668935, 0.031087637078343777, 0.3145364290430096, 0.06225259561554259, 0.048331656954823096, 0.03476373110938687, 0.3577716337116599, 0.14710778368048763, 0.11573022786168415, -0.15831225565348736, -0.051638359367324596, -0.031400987543492465] |
1,802.06913 | ElasticPath2Path: Automated morphological classification of neurons by
elastic path matching | In the study of neurons, morphology influences function. The complexity in
the structure of neurons poses a challenge in the identification and analysis
of similar and dissimilar neuronal cells. Existing methodologies carry out
structural and geometrical simplifications, which substantially change the
morphological statistics. Using digitally-reconstructed neurons, we extend the
work of Path2Path as ElasticPath2Path, which seamlessly integrates the
graph-theoretic and differential-geometric frameworks. By decomposing a neuron
into a set of paths, we derive graph metrics, which are path concurrence and
path hierarchy. Next, we model each path as an elastic string to compute the
geodesic distance between the paths of a pair of neurons. Later, we formulate
the problem of finding the distance between two neurons as a path assignment
problem with a cost function combining the graph metrics and the geodesic
deformation of paths. ElasticPath2Path is shown to have superior performance
over the state of the art.
| eess.IV q-bio.NC | in the study of neurons morphology influences function the complexity in the structure of neurons poses a challenge in the identification and analysis of similar and dissimilar neuronal cells existing methodologies carry out structural and geometrical simplifications which substantially change the morphological statistics using digitallyreconstructed neurons we extend the work of path2path as elasticpath2path which seamlessly integrates the graphtheoretic and differentialgeometric frameworks by decomposing a neuron into a set of paths we derive graph metrics which are path concurrence and path hierarchy next we model each path as an elastic string to compute the geodesic distance between the paths of a pair of neurons later we formulate the problem of finding the distance between two neurons as a path assignment problem with a cost function combining the graph metrics and the geodesic deformation of paths elasticpath2path is shown to have superior performance over the state of the art | [['in', 'the', 'study', 'of', 'neurons', 'morphology', 'influences', 'function', 'the', 'complexity', 'in', 'the', 'structure', 'of', 'neurons', 'poses', 'a', 'challenge', 'in', 'the', 'identification', 'and', 'analysis', 'of', 'similar', 'and', 'dissimilar', 'neuronal', 'cells', 'existing', 'methodologies', 'carry', 'out', 'structural', 'and', 'geometrical', 'simplifications', 'which', 'substantially', 'change', 'the', 'morphological', 'statistics', 'using', 'digitallyreconstructed', 'neurons', 'we', 'extend', 'the', 'work', 'of', 'path2path', 'as', 'elasticpath2path', 'which', 'seamlessly', 'integrates', 'the', 'graphtheoretic', 'and', 'differentialgeometric', 'frameworks', 'by', 'decomposing', 'a', 'neuron', 'into', 'a', 'set', 'of', 'paths', 'we', 'derive', 'graph', 'metrics', 'which', 'are', 'path', 'concurrence', 'and', 'path', 'hierarchy', 'next', 'we', 'model', 'each', 'path', 'as', 'an', 'elastic', 'string', 'to', 'compute', 'the', 'geodesic', 'distance', 'between', 'the', 'paths', 'of', 'a', 'pair', 'of', 'neurons', 'later', 'we', 'formulate', 'the', 'problem', 'of', 'finding', 'the', 'distance', 'between', 'two', 'neurons', 'as', 'a', 'path', 'assignment', 'problem', 'with', 'a', 'cost', 'function', 'combining', 'the', 'graph', 'metrics', 'and', 'the', 'geodesic', 'deformation', 'of', 'paths', 'elasticpath2path', 'is', 'shown', 'to', 'have', 'superior', 'performance', 'over', 'the', 'state', 'of', 'the', 'art']] | [-0.11559879359346167, 0.05497197768444392, -0.08902084547379571, 0.03783825649669678, -0.08574202733224309, -0.10242493969336566, 0.0783034029817726, 0.3988014138562398, -0.30742503693747697, -0.3074851989953863, 0.020183285388662223, -0.27442894516732647, -0.23443395372628906, 0.15523269797752923, -0.07889598509063944, 0.06028263295431518, 0.11374610817034005, 0.054461126513261765, -0.06392912101283502, -0.2200646090887959, 0.29966613647588425, 0.01454925491189998, 0.2792269419878721, 0.01886936380308018, 0.1409332037696408, 0.02377773358603008, -0.030975686573785625, 0.058621960647643495, -0.13025296302253586, 0.16409827478896155, 0.24149812733230647, 0.17842920559875589, 0.26349886890158003, -0.4482646008772362, -0.23788248503115028, 0.1060045876656659, 0.13724573439362253, 0.08653586849929222, 0.04503225671295594, -0.2593206599639315, 0.07412085827349478, -0.12873710439695665, -0.045835718570742756, -0.008195765948686231, 0.012068461979247836, 0.047965679762354635, -0.2152631352869826, 0.03546352862354575, 0.05907097889737795, 0.04349199703816945, -0.05371562112729104, -0.08684818941401318, -0.01670026042524518, 0.18090068442203724, 0.027166786542996608, 0.04738167625651436, 0.1315652966829172, -0.14076773960469938, -0.16492123608865464, 0.34379947551901247, 0.0003219447292697926, -0.21614447653215998, 0.1770436421065824, -0.05013648459700764, -0.10732367503806017, 0.09288376036177699, 0.16878023905964154, 0.08819085898964356, -0.19948415025565838, 0.052312800997848775, -0.012482086776193077, 0.14020760197955598, 0.08768146191141568, 0.053560081895233855, 0.1739002090352086, 0.21500268125156355, 0.09884559504280332, 0.1898662985315443, -0.07467974533210509, -0.0924992071878579, -0.27160706104930593, -0.171196564740967, -0.16017907362483028, 0.02126604746525926, -0.1371516161818565, -0.19045925262111674, 0.4634747036680993, 0.11450311480439268, 0.24661656043018512, 0.12330715484616424, 0.260161314967263, 0.07875281161341263, 0.07612227666767366, 0.07081361523402545, 0.18328335034635124, 0.13965721770364325, 0.05247028608816132, -0.22801192214440866, 0.06567645064529239, 0.09165911247226985] |
1,802.06914 | A Binary Offset Effect in CCD Readout and Its Impact on Astronomical
Data | We have discovered an anomalous behavior of CCD readout electronics that
affects their use in many astronomical applications. An offset in the
digitization of the CCD output voltage that depends on the binary encoding of
one pixel is added to pixels that are read out one, two and/or three pixels
later. One result of this effect is the introduction of a differential offset
in the background when comparing regions with and without flux from science
targets. Conventional data reduction methods do not correct for this offset. We
find this effect in 16 of 22 instruments investigated, covering a variety of
telescopes and many different front-end electronics systems. The affected
instruments include LRIS and DEIMOS on the Keck telescopes, WFC3-UVIS and STIS
on HST, MegaCam on CFHT, SNIFS on the UH88 telescope, GMOS on the Gemini
telescopes, HSC on Subaru, and FORS on VLT. The amplitude of the introduced
offset is up to 4.5 ADU per pixel, and it is not directly proportional to the
measured ADU level. We have developed a model that can be used to detect this
"binary offset effect" in data and correct for it. Understanding how data are
affected and applying a correction for the effect is essential for precise
astronomical measurements.
| astro-ph.IM physics.ins-det | we have discovered an anomalous behavior of ccd readout electronics that affects their use in many astronomical applications an offset in the digitization of the ccd output voltage that depends on the binary encoding of one pixel is added to pixels that are read out one two andor three pixels later one result of this effect is the introduction of a differential offset in the background when comparing regions with and without flux from science targets conventional data reduction methods do not correct for this offset we find this effect in 16 of 22 instruments investigated covering a variety of telescopes and many different frontend electronics systems the affected instruments include lris and deimos on the keck telescopes wfc3uvis and stis on hst megacam on cfht snifs on the uh88 telescope gmos on the gemini telescopes hsc on subaru and fors on vlt the amplitude of the introduced offset is up to 45 adu per pixel and it is not directly proportional to the measured adu level we have developed a model that can be used to detect this binary offset effect in data and correct for it understanding how data are affected and applying a correction for the effect is essential for precise astronomical measurements | [['we', 'have', 'discovered', 'an', 'anomalous', 'behavior', 'of', 'ccd', 'readout', 'electronics', 'that', 'affects', 'their', 'use', 'in', 'many', 'astronomical', 'applications', 'an', 'offset', 'in', 'the', 'digitization', 'of', 'the', 'ccd', 'output', 'voltage', 'that', 'depends', 'on', 'the', 'binary', 'encoding', 'of', 'one', 'pixel', 'is', 'added', 'to', 'pixels', 'that', 'are', 'read', 'out', 'one', 'two', 'andor', 'three', 'pixels', 'later', 'one', 'result', 'of', 'this', 'effect', 'is', 'the', 'introduction', 'of', 'a', 'differential', 'offset', 'in', 'the', 'background', 'when', 'comparing', 'regions', 'with', 'and', 'without', 'flux', 'from', 'science', 'targets', 'conventional', 'data', 'reduction', 'methods', 'do', 'not', 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1,802.06915 | Extinction Maps and Dust-to-Gas Ratios in Nearby Galaxies with LEGUS | We present a study of the dust-to-gas ratios in five nearby galaxies NGC 628
(M74), NGC 6503, NGC 7793, UGC 5139 (Holmberg I), and UGC 4305 (Holmberg II).
Using Hubble Space Telescope broad band WFC3/UVIS UV and optical images from
the Treasury program LEGUS (Legacy ExtraGalactic UV Survey) combined with
archival HST/ACS data, we correct thousands of individual stars for extinction
across these five galaxies using an isochrone-matching (reddening-free Q)
method. We generate extinction maps for each galaxy from the individual stellar
extinctions using both adaptive and fixed resolution techniques, and correlate
these maps with neutral HI and CO gas maps from literature, including The HI
Nearby Galaxy Survey (THINGS) and the HERA CO-Line Extragalactic Survey
(HERACLES). We calculate dust-to-gas ratios and investigate variations in the
dust-to-gas ratio with galaxy metallicity. We find a power law relationship
between dust-to-gas ratio and metallicity, consistent with other studies of
dust-to-gas ratio compared to metallicity. We find a change in the relation
when H$_2$ is not included. This implies that underestimation of $N_{H_2}$ in
low-metallicity dwarfs from a too-low CO-to-H$_2$ conversion factor $X_{CO}$
could have produced too low a slope in the derived relationship between
dust-to-gas ratio and metallicity. We also compare our extinctions to those
derived from fitting the spectral energy distribution (SED) using the Bayesian
Extinction and Stellar Tool (BEAST) for NGC 7793 and find systematically lower
extinctions from SED-fitting as compared to isochrone matching.
| astro-ph.GA | we present a study of the dusttogas ratios in five nearby galaxies ngc 628 m74 ngc 6503 ngc 7793 ugc 5139 holmberg i and ugc 4305 holmberg ii using hubble space telescope broad band wfc3uvis uv and optical images from the treasury program legus legacy extragalactic uv survey combined with archival hstacs data we correct thousands of individual stars for extinction across these five galaxies using an isochronematching reddeningfree q method we generate extinction maps for each galaxy from the individual stellar extinctions using both adaptive and fixed resolution techniques and correlate these maps with neutral hi and co gas maps from literature including the hi nearby galaxy survey things and the hera coline extragalactic survey heracles we calculate dusttogas ratios and investigate variations in the dusttogas ratio with galaxy metallicity we find a power law relationship between dusttogas ratio and metallicity consistent with other studies of dusttogas ratio compared to metallicity we find a change in the relation when h_2 is not included this implies that underestimation of n_h_2 in lowmetallicity dwarfs from a toolow cotoh_2 conversion factor x_co could have produced too low a slope in the derived relationship between dusttogas ratio and metallicity we also compare our extinctions to those derived from fitting the spectral energy distribution sed using the bayesian extinction and stellar tool beast for ngc 7793 and find systematically lower extinctions from sedfitting as compared to isochrone matching | [['we', 'present', 'a', 'study', 'of', 'the', 'dusttogas', 'ratios', 'in', 'five', 'nearby', 'galaxies', 'ngc', '628', 'm74', 'ngc', '6503', 'ngc', '7793', 'ugc', '5139', 'holmberg', 'i', 'and', 'ugc', '4305', 'holmberg', 'ii', 'using', 'hubble', 'space', 'telescope', 'broad', 'band', 'wfc3uvis', 'uv', 'and', 'optical', 'images', 'from', 'the', 'treasury', 'program', 'legus', 'legacy', 'extragalactic', 'uv', 'survey', 'combined', 'with', 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1,802.06916 | Simplicial Closure and higher-order link prediction | Networks provide a powerful formalism for modeling complex systems by using a
model of pairwise interactions. But much of the structure within these systems
involves interactions that take place among more than two nodes at once; for
example, communication within a group rather than person-to person,
collaboration among a team rather than a pair of coauthors, or biological
interaction between a set of molecules rather than just two. Such higher-order
interactions are ubiquitous, but their empirical study has received limited
attention, and little is known about possible organizational principles of such
structures. Here we study the temporal evolution of 19 datasets with explicit
accounting for higher-order interactions. We show that there is a rich variety
of structure in our datasets but datasets from the same system types have
consistent patterns of higher-order structure. Furthermore, we find that tie
strength and edge density are competing positive indicators of higher-order
organization, and these trends are consistent across interactions involving
differing numbers of nodes. To systematically further the study of theories for
such higher-order structures, we propose higher-order link prediction as a
benchmark problem to assess models and algorithms that predict higher-order
structure. We find a fundamental differences from traditional pairwise link
prediction, with a greater role for local rather than long-range information in
predicting the appearance of new interactions.
| cs.SI cond-mat.stat-mech math.AT physics.soc-ph stat.ML | networks provide a powerful formalism for modeling complex systems by using a model of pairwise interactions but much of the structure within these systems involves interactions that take place among more than two nodes at once for example communication within a group rather than personto person collaboration among a team rather than a pair of coauthors or biological interaction between a set of molecules rather than just two such higherorder interactions are ubiquitous but their empirical study has received limited attention and little is known about possible organizational principles of such structures here we study the temporal evolution of 19 datasets with explicit accounting for higherorder interactions we show that there is a rich variety of structure in our datasets but datasets from the same system types have consistent patterns of higherorder structure furthermore we find that tie strength and edge density are competing positive indicators of higherorder organization and these trends are consistent across interactions involving differing numbers of nodes to systematically further the study of theories for such higherorder structures we propose higherorder link prediction as a benchmark problem to assess models and algorithms that predict higherorder structure we find a fundamental differences from traditional pairwise link prediction with a greater role for local rather than longrange information in predicting the appearance of new interactions | [['networks', 'provide', 'a', 'powerful', 'formalism', 'for', 'modeling', 'complex', 'systems', 'by', 'using', 'a', 'model', 'of', 'pairwise', 'interactions', 'but', 'much', 'of', 'the', 'structure', 'within', 'these', 'systems', 'involves', 'interactions', 'that', 'take', 'place', 'among', 'more', 'than', 'two', 'nodes', 'at', 'once', 'for', 'example', 'communication', 'within', 'a', 'group', 'rather', 'than', 'personto', 'person', 'collaboration', 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1,802.06917 | Relative Weak Injectivity for Operator Systems | We investigate the notion of relative weak injectivity and its nuclearity
related properties in the category of operator systems. We obtain several
characterizations of the weak expectation property. We show that
(c,max)-nuclearity characterizes Kirchberg and Wasserman's C*-systems. Namioka
and Phelps' test systems, which detects nuclear C*-algebras, is shown to
characterize nuclear C*-systems. We study quasi-nuclearity in the operator
system setting and prove that quasi-nuclearity and nuclearity are equivalent,
in other words, (er,max)-nuclearity and (min,max)-nuclearity are equivalent.
| math.OA | we investigate the notion of relative weak injectivity and its nuclearity related properties in the category of operator systems we obtain several characterizations of the weak expectation property we show that cmaxnuclearity characterizes kirchberg and wassermans csystems namioka and phelps test systems which detects nuclear calgebras is shown to characterize nuclear csystems we study quasinuclearity in the operator system setting and prove that quasinuclearity and nuclearity are equivalent in other words ermaxnuclearity and minmaxnuclearity are equivalent | [['we', 'investigate', 'the', 'notion', 'of', 'relative', 'weak', 'injectivity', 'and', 'its', 'nuclearity', 'related', 'properties', 'in', 'the', 'category', 'of', 'operator', 'systems', 'we', 'obtain', 'several', 'characterizations', 'of', 'the', 'weak', 'expectation', 'property', 'we', 'show', 'that', 'cmaxnuclearity', 'characterizes', 'kirchberg', 'and', 'wassermans', 'csystems', 'namioka', 'and', 'phelps', 'test', 'systems', 'which', 'detects', 'nuclear', 'calgebras', 'is', 'shown', 'to', 'characterize', 'nuclear', 'csystems', 'we', 'study', 'quasinuclearity', 'in', 'the', 'operator', 'system', 'setting', 'and', 'prove', 'that', 'quasinuclearity', 'and', 'nuclearity', 'are', 'equivalent', 'in', 'other', 'words', 'ermaxnuclearity', 'and', 'minmaxnuclearity', 'are', 'equivalent']] | [-0.14848635744669342, 0.06502659765205213, -0.07798146308798876, 0.16442014123479437, -0.03637361321598291, -0.12206320543773472, -0.04678177820917751, 0.41143512757761136, -0.3449152863867182, -0.16618260563617307, 0.133111234077452, -0.2679844668135047, -0.14181469146694456, 0.19785733720636928, -0.1633533380260425, 0.03160219268257996, 0.07302266114524432, 0.10654347132824893, -0.12410551845096052, -0.20473245456814765, 0.42843405689844594, -0.02702266953087279, 0.24684501268036133, 0.10413487499712833, 0.10636548141815833, -0.008248252691035823, -0.042254358077687876, 0.059874533803667876, -0.15706975137353377, 0.12024658182635903, 0.2256033957004547, 0.14636478495584535, 0.24009047251893206, -0.339286941449557, -0.10524428148886987, 0.17678860320842693, 0.0466972343357546, 0.012699656732313868, 0.0016638767073995301, -0.32847998488162244, 0.14855887755152902, -0.19391316726271596, -0.10717614830604622, -0.1651539788980569, 0.022859583209667887, 0.07138115743374718, -0.2414134876370164, 0.03019256197980472, 0.18216707373555566, 0.05458878953275936, -0.14020783585396462, -0.05372282106129985, -0.00759165408089757, 0.08977820080305848, -0.0009095076770920838, -0.051978554767057566, 0.14871097678717757, -0.08593379881432546, -0.1123823142294506, 0.3657414394829954, -0.050787707374963374, -0.16246560899141643, 0.22581533226184547, -0.19430182739826185, -0.17399263956717081, 0.013550308047394667, 0.0829406107642821, 0.1583019594262753, -0.12938277519174984, 0.1608095473984057, -0.12596430335459965, 0.1277570882013866, 0.0522663743501263, 0.13413244966151458, 0.07026486385480633, 0.06404727314199721, 0.12291886874341539, 0.22115010243391486, 0.04883252885857863, -0.06143388802052609, -0.28163579564009394, -0.1936658833575036, -0.0809472630066531, 0.07399388684758118, -0.031117273772750715, -0.15070249921367837, 0.3698775764554739, 0.17681500944974168, 0.14705140466934868, 0.1150697856426372, 0.17019235462482488, 0.07153295398290668, 0.02318948048965207, 0.06215552134173257, 0.24000898466578552, 0.27439153617514034, 0.008914908287780625, -0.19101777078716883, 0.032550748444295355, 0.20594205159161771] |
1,802.06918 | Perturbative unitarity and higher-order Lorentz symmetry breaking | We study perturbative unitarity in the scalar sector of the Myers-Pospelov
model. The model introduces a preferred four-vector $n$ which breaks Lorentz
symmetry and couples to a five-dimension operator. When the preferred
four-vector is chosen in the pure timelike or lightlike direction, the model
becomes a higher time derivative theory, leading to a cubic dispersion
relation. Two of the poles are shown to be perturbatively connected to the
standard ones, while a third pole, which we call the Lee-Wick-like pole, is
associated to a negative metric, in Hilbert space, threatening the preservation
of unitarity. The pure spacelike case is a normal theory in the sense that it
has only two solutions both being small perturbations over the standard ones.
We analyze perturbative unitarity for purely spacelike and timelike cases using
the optical theorem and considering a quartic self-interaction term. By
computing discontinuities in the loop diagram, we arrive at a pinching
condition which determines the propagation of particles and Lee-Wick-like
particles through the cut. We find that the contribution for Lee-Wick-like
particles vanishes for any external momenta, leaving only the contribution of
particles, thus preserving one-loop unitarity in both cases.
| hep-th hep-ph | we study perturbative unitarity in the scalar sector of the myerspospelov model the model introduces a preferred fourvector n which breaks lorentz symmetry and couples to a fivedimension operator when the preferred fourvector is chosen in the pure timelike or lightlike direction the model becomes a higher time derivative theory leading to a cubic dispersion relation two of the poles are shown to be perturbatively connected to the standard ones while a third pole which we call the leewicklike pole is associated to a negative metric in hilbert space threatening the preservation of unitarity the pure spacelike case is a normal theory in the sense that it has only two solutions both being small perturbations over the standard ones we analyze perturbative unitarity for purely spacelike and timelike cases using the optical theorem and considering a quartic selfinteraction term by computing discontinuities in the loop diagram we arrive at a pinching condition which determines the propagation of particles and leewicklike particles through the cut we find that the contribution for leewicklike particles vanishes for any external momenta leaving only the contribution of particles thus preserving oneloop unitarity in both cases | [['we', 'study', 'perturbative', 'unitarity', 'in', 'the', 'scalar', 'sector', 'of', 'the', 'myerspospelov', 'model', 'the', 'model', 'introduces', 'a', 'preferred', 'fourvector', 'n', 'which', 'breaks', 'lorentz', 'symmetry', 'and', 'couples', 'to', 'a', 'fivedimension', 'operator', 'when', 'the', 'preferred', 'fourvector', 'is', 'chosen', 'in', 'the', 'pure', 'timelike', 'or', 'lightlike', 'direction', 'the', 'model', 'becomes', 'a', 'higher', 'time', 'derivative', 'theory', 'leading', 'to', 'a', 'cubic', 'dispersion', 'relation', 'two', 'of', 'the', 'poles', 'are', 'shown', 'to', 'be', 'perturbatively', 'connected', 'to', 'the', 'standard', 'ones', 'while', 'a', 'third', 'pole', 'which', 'we', 'call', 'the', 'leewicklike', 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1,802.06919 | A generalization of Birch's theorem and vertex-balanced steady states
for generalized mass-action systems | Mass-action kinetics and its generalizations appear in mathematical models of
(bio-)chemical reaction networks, population dynamics, and epidemiology. The
dynamical systems arising from directed graphs are generally non-linear and
difficult to analyze. One approach to studying them is to find conditions on
the network which either imply or preclude certain dynamical properties. For
example, a vertex-balanced steady state for a generalized mass-action system is
a state where the net flux through every vertex of the graph is zero. In
particular, such steady states admit a monomial parametrization. The problem of
existence and uniqueness of vertex-balanced steady states can be reformulated
in two different ways, one of which is related to Birch's theorem in
statistics, and the other one to the bijectivity of generalized polynomial
maps, similar to maps appearing in geometric modelling. We present a
generalization of Birch's theorem, by providing a sufficient condition for the
existence and uniqueness of vertex-balanced steady states.
| math.DS | massaction kinetics and its generalizations appear in mathematical models of biochemical reaction networks population dynamics and epidemiology the dynamical systems arising from directed graphs are generally nonlinear and difficult to analyze one approach to studying them is to find conditions on the network which either imply or preclude certain dynamical properties for example a vertexbalanced steady state for a generalized massaction system is a state where the net flux through every vertex of the graph is zero in particular such steady states admit a monomial parametrization the problem of existence and uniqueness of vertexbalanced steady states can be reformulated in two different ways one of which is related to birchs theorem in statistics and the other one to the bijectivity of generalized polynomial maps similar to maps appearing in geometric modelling we present a generalization of birchs theorem by providing a sufficient condition for the existence and uniqueness of vertexbalanced steady states | [['massaction', 'kinetics', 'and', 'its', 'generalizations', 'appear', 'in', 'mathematical', 'models', 'of', 'biochemical', 'reaction', 'networks', 'population', 'dynamics', 'and', 'epidemiology', 'the', 'dynamical', 'systems', 'arising', 'from', 'directed', 'graphs', 'are', 'generally', 'nonlinear', 'and', 'difficult', 'to', 'analyze', 'one', 'approach', 'to', 'studying', 'them', 'is', 'to', 'find', 'conditions', 'on', 'the', 'network', 'which', 'either', 'imply', 'or', 'preclude', 'certain', 'dynamical', 'properties', 'for', 'example', 'a', 'vertexbalanced', 'steady', 'state', 'for', 'a', 'generalized', 'massaction', 'system', 'is', 'a', 'state', 'where', 'the', 'net', 'flux', 'through', 'every', 'vertex', 'of', 'the', 'graph', 'is', 'zero', 'in', 'particular', 'such', 'steady', 'states', 'admit', 'a', 'monomial', 'parametrization', 'the', 'problem', 'of', 'existence', 'and', 'uniqueness', 'of', 'vertexbalanced', 'steady', 'states', 'can', 'be', 'reformulated', 'in', 'two', 'different', 'ways', 'one', 'of', 'which', 'is', 'related', 'to', 'birchs', 'theorem', 'in', 'statistics', 'and', 'the', 'other', 'one', 'to', 'the', 'bijectivity', 'of', 'generalized', 'polynomial', 'maps', 'similar', 'to', 'maps', 'appearing', 'in', 'geometric', 'modelling', 'we', 'present', 'a', 'generalization', 'of', 'birchs', 'theorem', 'by', 'providing', 'a', 'sufficient', 'condition', 'for', 'the', 'existence', 'and', 'uniqueness', 'of', 'vertexbalanced', 'steady', 'states']] | [-0.12183725503028224, 0.0851239966118316, -0.1089603518594231, 0.09207676730492446, -0.04729462449444997, -0.17072166573397177, 0.01869005398552154, 0.28952243245868503, -0.31431141819829417, -0.25371921073191944, 0.12335575794369154, -0.2381644594807815, -0.15557838363211454, 0.17328844904458443, -0.06033114871733185, 0.0601655871566771, 0.08517245051916689, 0.041147202245682774, -0.03329277872705318, -0.19280149493505105, 0.3599380915080761, -0.035445018329865935, 0.24805489825360527, 0.06771361240029555, 0.11918052802119698, -0.02870053932833559, 0.021107644512085244, 0.02743533413587628, -0.1374891206398484, 0.0820476581295799, 0.25551346548646553, 0.13941661780393733, 0.24604707816563046, -0.4271618575123859, -0.2298208143635604, 0.18421939720313898, 0.09200142843244401, 0.14968676488972146, 0.012334242759511414, -0.2593480764251006, 0.06441362882278075, -0.14361868701311514, -0.1711762663571311, -0.089432736981268, 0.01414171399205531, 0.05250812684014244, -0.26730877811879555, 0.1131451861798077, 0.11787261609960727, 0.04494630000724436, -0.08913206552097108, -0.08053203234923881, -0.07845916972483362, 0.12511218968613416, -0.011651211212952867, -0.0077460250688186775, 0.09770845564191022, -0.15861414812848365, -0.1494406307526668, 0.36316930726293084, -0.04757543966652041, -0.236361866700463, 0.22162398005725423, -0.09997036354309928, -0.18761265037985714, 0.10715653108874042, 0.14927700789351211, 0.11523417404264603, -0.15536391720984524, 0.04578440156106617, -0.0682097931398618, 0.09220807446859237, 0.06975056901022694, 0.021525998451244577, 0.1707535897148773, 0.1282617086536974, 0.11411996821774856, 0.16940196916594255, 0.0014410705554933213, -0.1502037376876136, -0.2768014791409338, -0.12452190849121268, -0.1357027828803678, 0.12579846305552086, -0.07531483770339707, -0.1848189248887234, 0.4182828538943891, 0.09309656363697477, 0.17854662118011938, 0.06422656833953959, 0.21884641450780787, 0.12713184126608357, 0.002494456488516574, 0.06312985029408562, 0.19117721856424683, 0.20466610691111878, 0.08811778320293677, -0.1806446359558705, 0.08402138215445264, 0.11362940519374158] |
1,802.0692 | Layer-wise synapse optimization for implementing neural networks on
general neuromorphic architectures | Deep artificial neural networks (ANNs) can represent a wide range of complex
functions. Implementing ANNs in Von Neumann computing systems, though, incurs a
high energy cost due to the bottleneck created between CPU and memory.
Implementation on neuromorphic systems may help to reduce energy demand.
Conventional ANNs must be converted into equivalent Spiking Neural Networks
(SNNs) in order to be deployed on neuromorphic chips. This paper presents a way
to perform this translation. We map the ANN weights to SNN synapses
layer-by-layer by forming a least-square-error approximation problem at each
layer.
An optimal set of synapse weights may then be found for a given choice of ANN
activation function and SNN neuron. Using an appropriate constrained solver, we
can generate SNNs compatible with digital, analog, or hybrid chip
architectures. We present an optimal node pruning method to allow SNN layer
sizes to be set by the designer. To illustrate this process, we convert three
ANNs, including one convolutional network, to SNNs. In all three cases, a
simple linear program solver was used. The experiments show that the resulting
networks maintain agreement with the original ANN and excellent performance on
the evaluation tasks. The networks were also reduced in size with little loss
in task performance.
| cs.NE | deep artificial neural networks anns can represent a wide range of complex functions implementing anns in von neumann computing systems though incurs a high energy cost due to the bottleneck created between cpu and memory implementation on neuromorphic systems may help to reduce energy demand conventional anns must be converted into equivalent spiking neural networks snns in order to be deployed on neuromorphic chips this paper presents a way to perform this translation we map the ann weights to snn synapses layerbylayer by forming a leastsquareerror approximation problem at each layer an optimal set of synapse weights may then be found for a given choice of ann activation function and snn neuron using an appropriate constrained solver we can generate snns compatible with digital analog or hybrid chip architectures we present an optimal node pruning method to allow snn layer sizes to be set by the designer to illustrate this process we convert three anns including one convolutional network to snns in all three cases a simple linear program solver was used the experiments show that the resulting networks maintain agreement with the original ann and excellent performance on the evaluation tasks the networks were also reduced in size with little loss in task performance | [['deep', 'artificial', 'neural', 'networks', 'anns', 'can', 'represent', 'a', 'wide', 'range', 'of', 'complex', 'functions', 'implementing', 'anns', 'in', 'von', 'neumann', 'computing', 'systems', 'though', 'incurs', 'a', 'high', 'energy', 'cost', 'due', 'to', 'the', 'bottleneck', 'created', 'between', 'cpu', 'and', 'memory', 'implementation', 'on', 'neuromorphic', 'systems', 'may', 'help', 'to', 'reduce', 'energy', 'demand', 'conventional', 'anns', 'must', 'be', 'converted', 'into', 'equivalent', 'spiking', 'neural', 'networks', 'snns', 'in', 'order', 'to', 'be', 'deployed', 'on', 'neuromorphic', 'chips', 'this', 'paper', 'presents', 'a', 'way', 'to', 'perform', 'this', 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'agreement', 'with', 'the', 'original', 'ann', 'and', 'excellent', 'performance', 'on', 'the', 'evaluation', 'tasks', 'the', 'networks', 'were', 'also', 'reduced', 'in', 'size', 'with', 'little', 'loss', 'in', 'task', 'performance']] | [-0.08016892930651764, 0.004417534342543904, -0.024252862010787173, 0.0369635050564528, -0.07891492636238293, -0.20817934901612561, 0.05349432561807789, 0.4696474334883799, -0.30219494037602734, -0.33120610918168253, 0.06357753240764595, -0.23740213648302527, -0.22390082356201985, 0.21728590501233844, -0.0948554563654087, 0.13068189036282823, 0.1461794487547084, 0.012259141181981782, -0.0440499813026158, -0.30653825634920107, 0.24336031423370558, 0.1013625666895234, 0.3125669360933144, -0.005889394927043014, 0.13309967133937767, -0.07715437193287582, 0.0498402525432317, -0.023067661509900594, -0.04405902369283622, 0.15659946142298328, 0.3123103767326783, 0.12191600728121291, 0.32179865892124704, -0.5093731249614459, -0.20746707763613725, 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1,802.06921 | Frequency-dependent impedance and surface waves on the boundary of a
stratified dielectric medium | We analyse waves that propagate along the interface between a dielectric
half-space and a half-space filled with a Lorentz material. We show that the
corresponding interface condition leads to a generalisation of the classical
Leontovich condition on the boundary of a dielectric half-space. We study when
this condition supports propagation of (dispersive) surface waves. We derive
the related dispersion relation for waves propagating along the boundary of a
stratified half-space and determine the relationship between the loss
parameter, frequency and wavenumber for which interfacial waves exist.
| math-ph math.MP math.SP | we analyse waves that propagate along the interface between a dielectric halfspace and a halfspace filled with a lorentz material we show that the corresponding interface condition leads to a generalisation of the classical leontovich condition on the boundary of a dielectric halfspace we study when this condition supports propagation of dispersive surface waves we derive the related dispersion relation for waves propagating along the boundary of a stratified halfspace and determine the relationship between the loss parameter frequency and wavenumber for which interfacial waves exist | [['we', 'analyse', 'waves', 'that', 'propagate', 'along', 'the', 'interface', 'between', 'a', 'dielectric', 'halfspace', 'and', 'a', 'halfspace', 'filled', 'with', 'a', 'lorentz', 'material', 'we', 'show', 'that', 'the', 'corresponding', 'interface', 'condition', 'leads', 'to', 'a', 'generalisation', 'of', 'the', 'classical', 'leontovich', 'condition', 'on', 'the', 'boundary', 'of', 'a', 'dielectric', 'halfspace', 'we', 'study', 'when', 'this', 'condition', 'supports', 'propagation', 'of', 'dispersive', 'surface', 'waves', 'we', 'derive', 'the', 'related', 'dispersion', 'relation', 'for', 'waves', 'propagating', 'along', 'the', 'boundary', 'of', 'a', 'stratified', 'halfspace', 'and', 'determine', 'the', 'relationship', 'between', 'the', 'loss', 'parameter', 'frequency', 'and', 'wavenumber', 'for', 'which', 'interfacial', 'waves', 'exist']] | [-0.20676198107905167, 0.17282097326553708, -0.06570818968290507, 0.020802585791179173, -0.15110458589029, -0.10028997791367908, 0.03555910865001943, 0.3723518366297317, -0.2846691164507552, -0.19886595602038987, 0.08138913092295487, -0.26717864527085494, -0.16751616486066648, 0.18643452553349266, 0.031878731657512656, 0.04375932549754547, 0.033691882583594256, -0.01806438804149281, -0.07351395643727723, -0.08661665680525868, 0.36649030731817667, -0.012550295863928662, 0.31540439698065437, 0.10155192333230273, 0.11423956410049699, 0.011874781607455292, 0.03880025585030401, 0.03829227427432184, -0.2145185455699183, 0.09803641770240785, 0.18728212018897974, -0.001790595376257633, 0.23104378805214235, -0.48430674102937066, -0.25637408845169946, -0.007610677416587985, 0.10707174392882735, 0.09658449342024925, -0.034711251464444974, -0.2460684272092442, 0.010631807300067225, -0.07971102236715946, -0.19634856082239124, 0.07799160394406077, -0.010179882338487132, 0.013788421548967965, -0.2552959424508519, 0.1345395229886784, 0.08550388837059916, 0.039209462887726636, -0.14655652483871076, -0.0028456002257244532, -0.07798527846340263, 0.05947687538499067, 0.061835252658225766, 0.030487621633530877, 0.061234932138346305, -0.13879584612563078, 0.023242613528097093, 0.36674042457584727, -0.10251309700461841, -0.27028663900442595, 0.2139027794703928, -0.1614863227786859, 0.03319863280887867, 0.12115737785541908, 0.217960256086879, 0.07512839290659962, -0.09801788256875223, 0.07952526466024845, -0.0874262448416503, 0.17358918472323134, 0.1992619992017226, -0.011542620527189832, 0.19977360729907834, 0.1010995345747878, 0.08781777421245351, 0.21667985886681912, -0.08232627149693651, -0.004115192284590976, -0.37225246923260913, -0.2517670206752671, -0.13947545559427074, -0.00395366516462419, -0.10900964267869334, -0.2704898381259206, 0.3792169460875177, 0.1407017436028772, 0.17428122036332308, 0.06565977701359581, 0.23721477016265136, 0.1693739281794013, -0.00405123907812806, 0.12143108671596058, 0.2831659957246725, 0.20115898586376463, 0.0690235033560934, -0.21939974486608557, 0.025243479368645093, 0.07301883769849705] |
1,802.06922 | QMCPACK : An open source ab initio Quantum Monte Carlo package for the
electronic structure of atoms, molecules, and solids | QMCPACK is an open source quantum Monte Carlo package for ab-initio
electronic structure calculations. It supports calculations of metallic and
insulating solids, molecules, atoms, and some model Hamiltonians. Implemented
real space quantum Monte Carlo algorithms include variational, diffusion, and
reptation Monte Carlo. QMCPACK uses Slater-Jastrow type trial wave functions in
conjunction with a sophisticated optimizer capable of optimizing tens of
thousands of parameters. The orbital space auxiliary field quantum Monte Carlo
method is also implemented, enabling cross validation between different highly
accurate methods. The code is specifically optimized for calculations with
large numbers of electrons on the latest high performance computing
architectures, including multicore central processing unit (CPU) and graphical
processing unit (GPU) systems. We detail the program's capabilities, outline
its structure, and give examples of its use in current research calculations.
The package is available at http://www.qmcpack.org .
| physics.comp-ph physics.chem-ph | qmcpack is an open source quantum monte carlo package for abinitio electronic structure calculations it supports calculations of metallic and insulating solids molecules atoms and some model hamiltonians implemented real space quantum monte carlo algorithms include variational diffusion and reptation monte carlo qmcpack uses slaterjastrow type trial wave functions in conjunction with a sophisticated optimizer capable of optimizing tens of thousands of parameters the orbital space auxiliary field quantum monte carlo method is also implemented enabling cross validation between different highly accurate methods the code is specifically optimized for calculations with large numbers of electrons on the latest high performance computing architectures including multicore central processing unit cpu and graphical processing unit gpu systems we detail the programs capabilities outline its structure and give examples of its use in current research calculations the package is available at httpwwwqmcpackorg | [['qmcpack', 'is', 'an', 'open', 'source', 'quantum', 'monte', 'carlo', 'package', 'for', 'abinitio', 'electronic', 'structure', 'calculations', 'it', 'supports', 'calculations', 'of', 'metallic', 'and', 'insulating', 'solids', 'molecules', 'atoms', 'and', 'some', 'model', 'hamiltonians', 'implemented', 'real', 'space', 'quantum', 'monte', 'carlo', 'algorithms', 'include', 'variational', 'diffusion', 'and', 'reptation', 'monte', 'carlo', 'qmcpack', 'uses', 'slaterjastrow', 'type', 'trial', 'wave', 'functions', 'in', 'conjunction', 'with', 'a', 'sophisticated', 'optimizer', 'capable', 'of', 'optimizing', 'tens', 'of', 'thousands', 'of', 'parameters', 'the', 'orbital', 'space', 'auxiliary', 'field', 'quantum', 'monte', 'carlo', 'method', 'is', 'also', 'implemented', 'enabling', 'cross', 'validation', 'between', 'different', 'highly', 'accurate', 'methods', 'the', 'code', 'is', 'specifically', 'optimized', 'for', 'calculations', 'with', 'large', 'numbers', 'of', 'electrons', 'on', 'the', 'latest', 'high', 'performance', 'computing', 'architectures', 'including', 'multicore', 'central', 'processing', 'unit', 'cpu', 'and', 'graphical', 'processing', 'unit', 'gpu', 'systems', 'we', 'detail', 'the', 'programs', 'capabilities', 'outline', 'its', 'structure', 'and', 'give', 'examples', 'of', 'its', 'use', 'in', 'current', 'research', 'calculations', 'the', 'package', 'is', 'available', 'at', 'httpwwwqmcpackorg']] | [-0.10352615101932527, 0.07877005149032938, -0.04047364851351093, 0.09991181560326368, -0.05644291331889584, -0.18026513031541105, 0.01694842440715182, 0.466175830059678, -0.2591308344968758, -0.34882583859356214, 0.05998557828193408, -0.28691550874470795, -0.09591352993989513, 0.2712047344709264, 0.07253058467197987, 0.13759127777329191, 0.19021107738728832, -0.0758305403886594, -0.14771669645102042, -0.25532320048405116, 0.19879404034628703, 0.1470195538114525, 0.2423761714637334, 0.02696927090984409, 0.09590411485329162, 0.05901575192170095, -0.0017285675836903771, -0.05411806404565114, -0.1539066384791186, 0.15806555415522375, 0.2532970529511897, 0.11095362523948582, 0.28323477623902643, -0.5011716703102537, -0.1850834461906245, 0.004820360251478035, 0.13225588375145067, 0.13232150605886522, -0.07041462576849536, -0.2485917152796131, 0.0010406362542026016, -0.21312759096061226, -0.10150937931178763, -0.17876004468692963, -0.06084495647827395, 0.047734913409408865, -0.24072308200462483, 0.01534930127481658, -0.09832713174817227, 0.1144967366357922, -0.0027589104682152724, -0.15889455910337014, 0.001963028411489714, 0.058369826532247725, -0.06843174730655295, 0.0723116025940454, 0.19012016570875354, -0.059855199638196695, -0.20447549982298927, 0.3547168221718965, -0.0031025250625871395, -0.19564724026288646, 0.17910905095347524, -0.06517296729364642, -0.17162637942280268, 0.16799489735714057, 0.16549607452657752, 0.1242958668952495, -0.15314895285881486, 0.1477900114684556, 0.04860734884862118, 0.17501717674236636, -0.05891361330248361, 0.016409106926489486, 0.16963127529907998, 0.21320758167871812, -0.04064109817446366, 0.15744298433096413, -0.11856373779332932, -0.2081541107698976, -0.2448064431439351, -0.20620600433319544, -0.25151763759879736, -0.015952044932777572, -0.10707190965187997, -0.2193325157583195, 0.35479701356622423, 0.19884043248084776, 0.05167616646988386, 0.03774050158027043, 0.352376571893828, 0.044644985150844946, 0.10492252688311095, 0.12102256302150768, 0.11366483477308809, 0.13668556477922103, 0.08428026304281161, -0.22915501287716855, 0.048018347801654225, 0.04363119573651874] |
1,802.06923 | The sporadic group Co3, Hauptmodul and Belyi map | We calculate the hauptmodul and Belyi map of a genus zero subgroup of the
modular group defined via a canonical homomorphism by the Conway group group
Co3. Our main result is the Belyi map and its field of definition.
| math.NT | we calculate the hauptmodul and belyi map of a genus zero subgroup of the modular group defined via a canonical homomorphism by the conway group group co3 our main result is the belyi map and its field of definition | [['we', 'calculate', 'the', 'hauptmodul', 'and', 'belyi', 'map', 'of', 'a', 'genus', 'zero', 'subgroup', 'of', 'the', 'modular', 'group', 'defined', 'via', 'a', 'canonical', 'homomorphism', 'by', 'the', 'conway', 'group', 'group', 'co3', 'our', 'main', 'result', 'is', 'the', 'belyi', 'map', 'and', 'its', 'field', 'of', 'definition']] | [-0.21063021522683975, 0.06625296830987701, -0.16392603344642198, 0.057665336653232, -0.08752797276545794, -0.11021501127964793, 0.06086508442576115, 0.3017706819451772, -0.39739611534736097, -0.2540473721157282, 0.06387664111426626, -0.24276347656567127, -0.17269786646685156, 0.18033847728600869, -0.13520793287226787, -0.06032096484873014, 0.010656073784981018, 0.11033253181869021, -0.13092663917594996, -0.25220239993471366, 0.4458434113229697, -0.01856382018050704, 0.2136026379497101, 0.01599779887459217, 0.12541457701426667, 0.002046471318373313, -0.04531948213489392, -0.08642368575032705, -0.11240362865516008, 0.11216954982433563, 0.2714438575009505, 0.055775623386486985, 0.1409993233780066, -0.3115878894638557, -0.10466143608284302, 0.20617978415714625, 0.0901981984050228, -0.013752600990044765, -0.03849572769533365, -0.29011034432989663, 0.05601943215580645, -0.20445808624992004, -0.17311235377565026, -0.07658034317099895, 0.06437452776560512, -0.014307309658481525, -0.17790005814570647, -0.010759038014862781, 0.060501205711028516, 0.2167242952885154, -0.014567785529204859, -0.1012043670560114, -0.14621178119276196, 0.13307951075526384, 0.011487316358118104, 0.18616196618248254, 0.17678984069528106, -0.12533234785764646, -0.10066278798219103, 0.3885181845906071, -0.12633217071206906, -0.14512599484087566, 0.07716860689031772, -0.17535321241340193, -0.19259166669769165, 0.12898204449373177, 0.012536508675951224, 0.14114871797844386, -0.024321142536325332, 0.188906927088586, -0.1592999295068857, 0.05417260421344485, 0.04168887801754933, -0.09970784225525, 0.1116967528867416, 0.02930716170857732, 0.07630196700875576, 0.1999060795761836, 0.032881444749923855, 0.01809885910449502, -0.34930920600891113, -0.26612495182034296, -0.14890447915651095, 0.10686085177346683, -0.12261001075519463, -0.1588588576668348, 0.5136509684798045, 0.030496040884500895, 0.15087749792310673, 0.16881110894087797, 0.20547956255718303, 0.11136611977604051, 0.1117747254215945, 0.060586820416248, 0.055182161478277966, 0.30706886002101386, -0.12172474607061116, -0.20425747001830202, -0.07993857148222816, 0.25933921356231737] |
1,802.06924 | Teaching Categories to Human Learners with Visual Explanations | We study the problem of computer-assisted teaching with explanations.
Conventional approaches for machine teaching typically only provide feedback at
the instance level e.g., the category or label of the instance. However, it is
intuitive that clear explanations from a knowledgeable teacher can
significantly improve a student's ability to learn a new concept. To address
these existing limitations, we propose a teaching framework that provides
interpretable explanations as feedback and models how the learner incorporates
this additional information. In the case of images, we show that we can
automatically generate explanations that highlight the parts of the image that
are responsible for the class label. Experiments on human learners illustrate
that, on average, participants achieve better test set performance on
challenging categorization tasks when taught with our interpretable approach
compared to existing methods.
| cs.CV cs.LG stat.ML | we study the problem of computerassisted teaching with explanations conventional approaches for machine teaching typically only provide feedback at the instance level eg the category or label of the instance however it is intuitive that clear explanations from a knowledgeable teacher can significantly improve a students ability to learn a new concept to address these existing limitations we propose a teaching framework that provides interpretable explanations as feedback and models how the learner incorporates this additional information in the case of images we show that we can automatically generate explanations that highlight the parts of the image that are responsible for the class label experiments on human learners illustrate that on average participants achieve better test set performance on challenging categorization tasks when taught with our interpretable approach compared to existing methods | [['we', 'study', 'the', 'problem', 'of', 'computerassisted', 'teaching', 'with', 'explanations', 'conventional', 'approaches', 'for', 'machine', 'teaching', 'typically', 'only', 'provide', 'feedback', 'at', 'the', 'instance', 'level', 'eg', 'the', 'category', 'or', 'label', 'of', 'the', 'instance', 'however', 'it', 'is', 'intuitive', 'that', 'clear', 'explanations', 'from', 'a', 'knowledgeable', 'teacher', 'can', 'significantly', 'improve', 'a', 'students', 'ability', 'to', 'learn', 'a', 'new', 'concept', 'to', 'address', 'these', 'existing', 'limitations', 'we', 'propose', 'a', 'teaching', 'framework', 'that', 'provides', 'interpretable', 'explanations', 'as', 'feedback', 'and', 'models', 'how', 'the', 'learner', 'incorporates', 'this', 'additional', 'information', 'in', 'the', 'case', 'of', 'images', 'we', 'show', 'that', 'we', 'can', 'automatically', 'generate', 'explanations', 'that', 'highlight', 'the', 'parts', 'of', 'the', 'image', 'that', 'are', 'responsible', 'for', 'the', 'class', 'label', 'experiments', 'on', 'human', 'learners', 'illustrate', 'that', 'on', 'average', 'participants', 'achieve', 'better', 'test', 'set', 'performance', 'on', 'challenging', 'categorization', 'tasks', 'when', 'taught', 'with', 'our', 'interpretable', 'approach', 'compared', 'to', 'existing', 'methods']] | [0.030173169508943276, 0.016570202687007346, -0.09156045976983891, 0.1406648162025676, -0.1891811677875618, -0.2145598585731491, 0.07197200560194413, 0.438007989857755, -0.23827407965732025, -0.3715405814194431, 0.03967337756661105, -0.2645507352991086, -0.20458135529505936, 0.22015823101780066, -0.15447508423081177, 0.009274178375066682, 0.13262998240040333, 0.0839728114116235, -0.038176589357934776, -0.2982496165063684, 0.321285378681778, 0.047173819497622775, 0.32160989428644604, 0.038156395608728584, 0.13548518126040246, -0.05231217940507287, -0.022326506684872915, 0.019919869738673282, -0.05438673962337568, 0.17804394493137035, 0.35301610028890346, 0.23985633031451004, 0.3689354879148375, -0.40370023410033545, -0.217405952082834, 0.07056518052845742, 0.12244326700172016, 0.11777399301839371, -0.06085752129156114, -0.3176801803250176, 0.09927570098961676, -0.15523338755310484, -0.0053801744114466464, -0.17369874362272889, -0.04323461398069577, -0.038631738993513645, -0.2908601303810649, 0.007208135248881511, 0.11247216358032981, 0.0806089464176418, -0.06911789202201886, -0.13168198070277207, 0.06665715328629383, 0.17526335534256016, 0.05043084154286506, 0.030090777148981226, 0.1496535617942837, -0.2182625436297244, -0.20093244249106978, 0.3881005770032943, -0.010207468996999192, -0.22170931903758284, 0.2535977000758673, -0.06614758880724284, -0.12481769518644521, 0.06231009072684118, 0.23778266060363615, 0.09758676282856896, -0.13720599160087382, -0.0019289983165711446, -0.0648829428069858, 0.20474657447387776, 0.016103244804650207, -0.0033099201197425523, 0.1970988262293042, 0.21615715001914368, 0.019300597800986107, 0.11523862775224684, -0.05132584037633161, -0.04343149739769146, -0.2622557227274008, -0.13789293665269559, -0.12390244523571296, -0.015520929545869656, -0.062248685435902604, -0.10169404063686449, 0.4036151808545445, 0.3046043383903011, 0.17444743041414768, 0.10151606812282946, 0.35328797668670164, 0.03781367751875995, 0.09664383603288143, 0.08096002880307479, 0.18755117454918835, -0.022436802056759145, 0.08853727905079722, -0.16574777614600447, 0.13091223104151362, 0.03711708157965349] |
1,802.06925 | Inexact Non-Convex Newton-Type Methods | For solving large-scale non-convex problems, we propose inexact variants of
trust region and adaptive cubic regularization methods, which, to increase
efficiency, incorporate various approximations. In particular, in addition to
approximate sub-problem solves, both the Hessian and the gradient are suitably
approximated. Using rather mild conditions on such approximations, we show that
our proposed inexact methods achieve similar optimal worst-case iteration
complexities as the exact counterparts. Our proposed algorithms, and their
respective theoretical analysis, do not require knowledge of any unknowable
problem-related quantities, and hence are easily implementable in practice. In
the context of finite-sum problems, we then explore randomized sub-sampling
methods as ways to construct the gradient and Hessian approximations and
examine the empirical performance of our algorithms on some real datasets.
| math.OC | for solving largescale nonconvex problems we propose inexact variants of trust region and adaptive cubic regularization methods which to increase efficiency incorporate various approximations in particular in addition to approximate subproblem solves both the hessian and the gradient are suitably approximated using rather mild conditions on such approximations we show that our proposed inexact methods achieve similar optimal worstcase iteration complexities as the exact counterparts our proposed algorithms and their respective theoretical analysis do not require knowledge of any unknowable problemrelated quantities and hence are easily implementable in practice in the context of finitesum problems we then explore randomized subsampling methods as ways to construct the gradient and hessian approximations and examine the empirical performance of our algorithms on some real datasets | [['for', 'solving', 'largescale', 'nonconvex', 'problems', 'we', 'propose', 'inexact', 'variants', 'of', 'trust', 'region', 'and', 'adaptive', 'cubic', 'regularization', 'methods', 'which', 'to', 'increase', 'efficiency', 'incorporate', 'various', 'approximations', 'in', 'particular', 'in', 'addition', 'to', 'approximate', 'subproblem', 'solves', 'both', 'the', 'hessian', 'and', 'the', 'gradient', 'are', 'suitably', 'approximated', 'using', 'rather', 'mild', 'conditions', 'on', 'such', 'approximations', 'we', 'show', 'that', 'our', 'proposed', 'inexact', 'methods', 'achieve', 'similar', 'optimal', 'worstcase', 'iteration', 'complexities', 'as', 'the', 'exact', 'counterparts', 'our', 'proposed', 'algorithms', 'and', 'their', 'respective', 'theoretical', 'analysis', 'do', 'not', 'require', 'knowledge', 'of', 'any', 'unknowable', 'problemrelated', 'quantities', 'and', 'hence', 'are', 'easily', 'implementable', 'in', 'practice', 'in', 'the', 'context', 'of', 'finitesum', 'problems', 'we', 'then', 'explore', 'randomized', 'subsampling', 'methods', 'as', 'ways', 'to', 'construct', 'the', 'gradient', 'and', 'hessian', 'approximations', 'and', 'examine', 'the', 'empirical', 'performance', 'of', 'our', 'algorithms', 'on', 'some', 'real', 'datasets']] | [-0.04775076469190973, -0.07988912429014021, -0.0833916753291899, 0.1321736727879683, -0.09904020093958671, -0.148038170823812, 0.06762856357800197, 0.46386195564534793, -0.28978623826949557, -0.32175689386896605, 0.16497053804014586, -0.2231503922337366, -0.1958137878259136, 0.20149447900928125, -0.11408901259073148, 0.11637253236133328, 0.10075496464067192, -0.027350092926332035, -0.1685997036089428, -0.3261530427339156, 0.2765005583228343, 0.032237507372109356, 0.2706918886608698, 0.02984528957812254, 0.1107852708448248, -0.06689566089810173, -0.00968584015557527, 0.05050212457345044, -0.11048919000800686, 0.14925911967942784, 0.3017782043677193, 0.19735171214084735, 0.35222285588787605, -0.4508705885449717, -0.19012393643367603, 0.1336878879214175, 0.18798362543375408, 0.10495621919228353, -0.04979740161822016, -0.2507872711097406, 0.08916477470712597, -0.08996339151751219, -0.061970581731581986, -0.19877894544465977, -0.112856192633602, 0.06615363850165928, -0.32899290751973703, 0.07403800369640581, 0.030191745390646035, 0.01255551589391202, -0.06323521282169814, -0.18320000610383583, 0.07877882121396367, 0.0753353897920879, 0.061952441145689034, -0.024156104161380983, 0.12733438535601146, -0.08614078996903148, -0.15382641010863846, 0.35218370441839963, -0.03018920784739177, -0.2725694822994145, 0.1952696907187789, -0.0260605590556525, -0.15757359375730773, 0.08197348793845408, 0.24120335266872478, 0.19136813070532704, -0.1186355597605113, 0.1209570914862893, -0.015489842442503152, 0.12099879100237595, 0.05310154065089531, 0.002598803712647747, 0.050848586148051315, 0.09282943856332851, 0.13399167195124137, 0.11994532901362694, -0.028282144003016645, -0.15263453699371293, -0.2685379939729132, -0.09513311878073775, -0.18086802934234855, -0.05877267721401093, -0.17179286427539003, -0.19075174715508483, 0.350823897117949, 0.22461749184568805, 0.16185186611688582, 0.1285049690266548, 0.35698767151098604, 0.1164983601842368, 0.037737425621096256, 0.15984531849899813, 0.21321492299773298, 0.07426686342112905, 0.08784752892530408, -0.24986366493904516, 0.10652297075976394, 0.10787890389684804] |
1,802.06926 | Scale Optimization for Full-Image-CNN Vehicle Detection | Many state-of-the-art general object detection methods make use of shared
full-image convolutional features (as in Faster R-CNN). This achieves a
reasonable test-phase computation time while enjoys the discriminative power
provided by large Convolutional Neural Network (CNN) models. Such designs excel
on benchmarks which contain natural images but which have very unnatural
distributions, i.e. they have an unnaturally high-frequency of the target
classes and a bias towards a "friendly" or "dominant" object scale. In this
paper we present further study of the use and adaptation of the Faster R-CNN
object detection method for datasets presenting natural scale distribution and
unbiased real-world object frequency. In particular, we show that better
alignment of the detector scale sensitivity to the extant distribution improves
vehicle detection performance. We do this by modifying both the selection of
Region Proposals, and through using more scale-appropriate full-image
convolution features within the CNN model. By selecting better scales in the
region proposal input and by combining feature maps through careful design of
the convolutional neural network, we improve performance on smaller objects. We
significantly increase detection AP for the KITTI dataset car class from 76.3%
on our baseline Faster R-CNN detector to 83.6% in our improved detector.
| cs.CV cs.AI | many stateoftheart general object detection methods make use of shared fullimage convolutional features as in faster rcnn this achieves a reasonable testphase computation time while enjoys the discriminative power provided by large convolutional neural network cnn models such designs excel on benchmarks which contain natural images but which have very unnatural distributions ie they have an unnaturally highfrequency of the target classes and a bias towards a friendly or dominant object scale in this paper we present further study of the use and adaptation of the faster rcnn object detection method for datasets presenting natural scale distribution and unbiased realworld object frequency in particular we show that better alignment of the detector scale sensitivity to the extant distribution improves vehicle detection performance we do this by modifying both the selection of region proposals and through using more scaleappropriate fullimage convolution features within the cnn model by selecting better scales in the region proposal input and by combining feature maps through careful design of the convolutional neural network we improve performance on smaller objects we significantly increase detection ap for the kitti dataset car class from 763 on our baseline faster rcnn detector to 836 in our improved detector | [['many', 'stateoftheart', 'general', 'object', 'detection', 'methods', 'make', 'use', 'of', 'shared', 'fullimage', 'convolutional', 'features', 'as', 'in', 'faster', 'rcnn', 'this', 'achieves', 'a', 'reasonable', 'testphase', 'computation', 'time', 'while', 'enjoys', 'the', 'discriminative', 'power', 'provided', 'by', 'large', 'convolutional', 'neural', 'network', 'cnn', 'models', 'such', 'designs', 'excel', 'on', 'benchmarks', 'which', 'contain', 'natural', 'images', 'but', 'which', 'have', 'very', 'unnatural', 'distributions', 'ie', 'they', 'have', 'an', 'unnaturally', 'highfrequency', 'of', 'the', 'target', 'classes', 'and', 'a', 'bias', 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1,802.06927 | On Lyapunov exponents and adversarial perturbation | In this paper, we would like to disseminate a serendipitous discovery
involving Lyapunov exponents of a 1-D time series and their use in serving as a
filtering defense tool against a specific kind of deep adversarial
perturbation. To this end, we use the state-of-the-art CleverHans library to
generate adversarial perturbations against a standard Convolutional Neural
Network (CNN) architecture trained on the MNIST as well as the Fashion-MNIST
datasets. We empirically demonstrate how the Lyapunov exponents computed on the
flattened 1-D vector representations of the images served as highly
discriminative features that could be to pre-classify images as adversarial or
legitimate before feeding the image into the CNN for classification. We also
explore the issue of possible false-alarms when the input images are noisy in a
non-adversarial sense.
| cs.CV cs.LG cs.NE | in this paper we would like to disseminate a serendipitous discovery involving lyapunov exponents of a 1d time series and their use in serving as a filtering defense tool against a specific kind of deep adversarial perturbation to this end we use the stateoftheart cleverhans library to generate adversarial perturbations against a standard convolutional neural network cnn architecture trained on the mnist as well as the fashionmnist datasets we empirically demonstrate how the lyapunov exponents computed on the flattened 1d vector representations of the images served as highly discriminative features that could be to preclassify images as adversarial or legitimate before feeding the image into the cnn for classification we also explore the issue of possible falsealarms when the input images are noisy in a nonadversarial sense | [['in', 'this', 'paper', 'we', 'would', 'like', 'to', 'disseminate', 'a', 'serendipitous', 'discovery', 'involving', 'lyapunov', 'exponents', 'of', 'a', '1d', 'time', 'series', 'and', 'their', 'use', 'in', 'serving', 'as', 'a', 'filtering', 'defense', 'tool', 'against', 'a', 'specific', 'kind', 'of', 'deep', 'adversarial', 'perturbation', 'to', 'this', 'end', 'we', 'use', 'the', 'stateoftheart', 'cleverhans', 'library', 'to', 'generate', 'adversarial', 'perturbations', 'against', 'a', 'standard', 'convolutional', 'neural', 'network', 'cnn', 'architecture', 'trained', 'on', 'the', 'mnist', 'as', 'well', 'as', 'the', 'fashionmnist', 'datasets', 'we', 'empirically', 'demonstrate', 'how', 'the', 'lyapunov', 'exponents', 'computed', 'on', 'the', 'flattened', '1d', 'vector', 'representations', 'of', 'the', 'images', 'served', 'as', 'highly', 'discriminative', 'features', 'that', 'could', 'be', 'to', 'preclassify', 'images', 'as', 'adversarial', 'or', 'legitimate', 'before', 'feeding', 'the', 'image', 'into', 'the', 'cnn', 'for', 'classification', 'we', 'also', 'explore', 'the', 'issue', 'of', 'possible', 'falsealarms', 'when', 'the', 'input', 'images', 'are', 'noisy', 'in', 'a', 'nonadversarial', 'sense']] | [-0.05573653919607636, -0.004341709544867512, -0.06910508563082049, 0.11586343437455243, -0.09813674197231573, -0.20991317013197938, 0.027793044276630843, 0.42460706916007057, -0.2848965188077702, -0.28954872471375726, 0.1103832291650694, -0.28751096582978847, -0.23477722840258983, 0.18808284793672422, -0.12296542444259045, 0.10493691141651136, 0.08325765167425321, 0.05158792197792314, -0.038125021840119365, -0.29836615388039645, 0.32265586266844115, 0.06155473353340305, 0.2855557512829271, -0.03586144843982078, 0.08541805705873251, -0.0585078908585188, -0.014595768203606873, -0.024179914923142788, -0.03946288774280642, 0.10454793993244314, 0.3038719555716659, 0.18294889594640906, 0.31970050346700285, -0.4090285563152137, -0.21853027705425823, 0.08777158835712146, 0.1735899010929829, 0.13192708572607917, -0.04446985023866195, -0.3787207879331403, 0.11374834702097202, -0.1755427206008453, -0.023858540416438513, -0.1644079796603228, -0.028554136994089492, -0.011647875079294798, -0.29505241978143554, 0.022461036542626683, 0.05330156699266637, 0.05438466166241432, -0.06426985617947038, -0.06663803681071231, -0.03163055401784918, 0.16856983631201214, 0.025471357995365547, 0.046244867773007924, 0.1581725148102782, -0.19767870125314954, -0.12831765133979195, 0.3476605930944949, -0.11212295063910639, -0.20162495487831592, 0.18013812902779502, -0.00614589812439375, -0.1347914301408968, 0.04807753170525113, 0.27774269009141, 0.11435413067815751, -0.12135383221891426, -0.031418215159389126, -0.06015447546786621, 0.17229657938912893, 0.07028066335744627, 0.018541652493831502, 0.17553078065402658, 0.22552526697487107, 0.0057106078419042385, 0.2051875926325039, -0.18161337951374218, -0.04870253090066542, -0.22864265151791216, -0.08841620711059728, -0.21131822875862222, 0.04467682292369583, -0.07857803986433025, -0.17734040101938772, 0.43889588551727804, 0.22258007812955852, 0.23998401061756405, 0.13471591401493221, 0.3629465466511883, -0.007914172787050621, 0.1296380877065817, 0.09047495465227, 0.19335711267813455, 0.035084463296619454, 0.1296584433435983, -0.11549153945254763, 0.07386078766921139, 0.06355220348185558] |
1,802.06928 | Memcomputing: Leveraging memory and physics to compute efficiently | It is well known that physical phenomena may be of great help in computing
some difficult problems efficiently. A typical example is prime factorization
that may be solved in polynomial time by exploiting quantum entanglement on a
quantum computer. There are, however, other types of (non-quantum) physical
properties that one may leverage to compute efficiently a wide range of hard
problems. In this perspective we discuss how to employ one such property,
memory (time non-locality), in a novel physics-based approach to computation:
Memcomputing. In particular, we focus on digital memcomputing machines (DMMs)
that are scalable. DMMs can be realized with non-linear dynamical systems with
memory. The latter property allows the realization of a new type of Boolean
logic, one that is self-organizing. Self-organizing logic gates are
"terminal-agnostic", namely they do not distinguish between input and output
terminals. When appropriately assembled to represent a given
combinatorial/optimization problem, the corresponding self-organizing circuit
converges to the equilibrium points that express the solutions of the problem
at hand. In doing so, DMMs take advantage of the long-range order that develops
during the transient dynamics. This collective dynamical behavior, reminiscent
of a phase transition, or even the "edge of chaos", is mediated by families of
classical trajectories (instantons) that connect critical points of increasing
stability in the system's phase space. The topological character of the
solution search renders DMMs robust against noise and structural disorder.
Since DMMs are non-quantum systems described by ordinary differential
equations, not only can they be built in hardware with available technology,
they can also be simulated efficiently on modern classical computers. As an
example, we will show the polynomial-time solution of the subset-sum problem
for the worst...
| cs.ET cs.CC cs.NE math.DS | it is well known that physical phenomena may be of great help in computing some difficult problems efficiently a typical example is prime factorization that may be solved in polynomial time by exploiting quantum entanglement on a quantum computer there are however other types of nonquantum physical properties that one may leverage to compute efficiently a wide range of hard problems in this perspective we discuss how to employ one such property memory time nonlocality in a novel physicsbased approach to computation memcomputing in particular we focus on digital memcomputing machines dmms that are scalable dmms can be realized with nonlinear dynamical systems with memory the latter property allows the realization of a new type of boolean logic one that is selforganizing selforganizing logic gates are terminalagnostic namely they do not distinguish between input and output terminals when appropriately assembled to represent a given combinatorialoptimization problem the corresponding selforganizing circuit converges to the equilibrium points that express the solutions of the problem at hand in doing so dmms take advantage of the longrange order that develops during the transient dynamics this collective dynamical behavior reminiscent of a phase transition or even the edge of chaos is mediated by families of classical trajectories instantons that connect critical points of increasing stability in the systems phase space the topological character of the solution search renders dmms robust against noise and structural disorder since dmms are nonquantum systems described by ordinary differential equations not only can they be built in hardware with available technology they can also be simulated efficiently on modern classical computers as an example we will show the polynomialtime solution of the subsetsum problem for the worst | [['it', 'is', 'well', 'known', 'that', 'physical', 'phenomena', 'may', 'be', 'of', 'great', 'help', 'in', 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1,802.06929 | Thyristor Voltage Equalizing Network for Crowbar Application | Many high voltage applications are realized with series connected thyristors.
Voltage imbalance among series connected thyristors during steady state as well
as in transients is one of the major concerns. This voltage imbalance is
mitigated by using static and dynamic balancing network. Dynamic balancing
networks are typically designed based on reverse recovery charge of the
thyristor during turn-off, which suits many applications. But this is not the
case for a crowbar application, where turn-off of the thyristor is not a major
circuit constraint. This paper proposes the design method for dynamic balancing
network considering gate turn-on delay time and the balancing network component
tolerances. The paper derives two models for the dynamic balancing network
based on its charge-discharge cycle. The importance of charge-discharge cycle
in the design of dynamic balancing network during high di/dt operation is
emphasized. Influence of dynamic balancing resistance and crowbar current
limiting inductance on voltage imbalance, charging current and discharging
current is studied using the analytical model. The proposed design method also
offers flexibility to incorporate differences in propagation delays among the
thyristor drivers that are used to trigger individual thyristors. Such delays
cannot be directly incorporated in the conventional balancing network design
method based on reverse recovery. Further, it is also analytically shown that
designing the dynamic balancing network based on reverse recovery charge makes
the balancing network lossy and bulky for crowbar application. Simulation
studies and experimental results on a 12kV , 1kA crowbar consisting of six
series connected thyristors confirms the theoretical analysis and validates the
proposed approach for crowbar applications.
| eess.SP | many high voltage applications are realized with series connected thyristors voltage imbalance among series connected thyristors during steady state as well as in transients is one of the major concerns this voltage imbalance is mitigated by using static and dynamic balancing network dynamic balancing networks are typically designed based on reverse recovery charge of the thyristor during turnoff which suits many applications but this is not the case for a crowbar application where turnoff of the thyristor is not a major circuit constraint this paper proposes the design method for dynamic balancing network considering gate turnon delay time and the balancing network component tolerances the paper derives two models for the dynamic balancing network based on its chargedischarge cycle the importance of chargedischarge cycle in the design of dynamic balancing network during high didt operation is emphasized influence of dynamic balancing resistance and crowbar current limiting inductance on voltage imbalance charging current and discharging current is studied using the analytical model the proposed design method also offers flexibility to incorporate differences in propagation delays among the thyristor drivers that are used to trigger individual thyristors such delays cannot be directly incorporated in the conventional balancing network design method based on reverse recovery further it is also analytically shown that designing the dynamic balancing network based on reverse recovery charge makes the balancing network lossy and bulky for crowbar application simulation studies and experimental results on a 12kv 1ka crowbar consisting of six series connected thyristors confirms the theoretical analysis and validates the proposed approach for crowbar applications | [['many', 'high', 'voltage', 'applications', 'are', 'realized', 'with', 'series', 'connected', 'thyristors', 'voltage', 'imbalance', 'among', 'series', 'connected', 'thyristors', 'during', 'steady', 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1,802.0693 | Small Signal Audiosusceptibility Model for Series Resonant Converter | Models that accurately predict the output voltage ripple magnitude are
essential for applications with stringent performance target for it. Impact of
dc input ripple on the output ripple for a Series Resonant Converter (SRC)
using discrete domain exact discretization modelling method is analysed in this
paper. A novel discrete state space model along with a small signal model for
SRC considering 3 state variables is presented. The audiosusceptibility (AS)
transfer function which relates the input to output ripple is derived from the
small signal model. Analysis of the AS transfer function indicates a resonance
peak and an expression is derived connecting the AS resonance frequency for
input ripple with different SRC component values. Further analysis is done to
show that a set of values for SRC parameter exists, which forms a design
region, for which the normalized gain offered by the SRC for input ripple is
less than unity at any input ripple frequency. A test setup to introduce the
variable frequency ripple at the input of SRC for the experimental evaluation
of AS transfer function is also proposed. Influence of stray parameters on AS
gain, AS resonance frequency and on SRC tank resonance frequency is addressed.
An SRC is designed at a power level of 10kW. The analysis using the derived
model, simulations, and experimental results are found to be closely matching.
| eess.SP | models that accurately predict the output voltage ripple magnitude are essential for applications with stringent performance target for it impact of dc input ripple on the output ripple for a series resonant converter src using discrete domain exact discretization modelling method is analysed in this paper a novel discrete state space model along with a small signal model for src considering 3 state variables is presented the audiosusceptibility as transfer function which relates the input to output ripple is derived from the small signal model analysis of the as transfer function indicates a resonance peak and an expression is derived connecting the as resonance frequency for input ripple with different src component values further analysis is done to show that a set of values for src parameter exists which forms a design region for which the normalized gain offered by the src for input ripple is less than unity at any input ripple frequency a test setup to introduce the variable frequency ripple at the input of src for the experimental evaluation of as transfer function is also proposed influence of stray parameters on as gain as resonance frequency and on src tank resonance frequency is addressed an src is designed at a power level of 10kw the analysis using the derived model simulations and experimental results are found to be closely matching | [['models', 'that', 'accurately', 'predict', 'the', 'output', 'voltage', 'ripple', 'magnitude', 'are', 'essential', 'for', 'applications', 'with', 'stringent', 'performance', 'target', 'for', 'it', 'impact', 'of', 'dc', 'input', 'ripple', 'on', 'the', 'output', 'ripple', 'for', 'a', 'series', 'resonant', 'converter', 'src', 'using', 'discrete', 'domain', 'exact', 'discretization', 'modelling', 'method', 'is', 'analysed', 'in', 'this', 'paper', 'a', 'novel', 'discrete', 'state', 'space', 'model', 'along', 'with', 'a', 'small', 'signal', 'model', 'for', 'src', 'considering', '3', 'state', 'variables', 'is', 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1,802.06931 | Empirical Bayes Matrix Factorization | Matrix factorization methods - including Factor analysis (FA), and Principal
Components Analysis (PCA) - are widely used for inferring and summarizing
structure in multivariate data. Many matrix factorization methods exist,
corresponding to different assumptions on the elements of the underlying matrix
factors. For example, many recent methods use a penalty or prior distribution
to achieve sparse representations ("Sparse FA/PCA"). Here we introduce a
general Empirical Bayes approach to matrix factorization (EBMF), whose key
feature is that it uses the observed data to estimate prior distributions on
matrix elements. We derive a correspondingly-general variational fitting
algorithm, which reduces fitting EBMF to solving a simpler problem - the
so-called "normal means" problem. We implement this general algorithm, but
focus particular attention on the use of sparsity-inducing priors that are
uni-modal at 0. This yields a sparse EBMF approach - essentially a version of
sparse FA/PCA - that automatically adapts the amount of sparsity to the data.
We demonstrate the benefits of our approach through both numerical comparisons
with competing methods and through analysis of data from the GTEx (Genotype
Tissue Expression) project on genetic associations across 44 human tissues. In
numerical comparisons EBMF often provides more accurate inferences than other
methods. In the GTEx data, EBMF identifies interpretable structure that
concords with known relationships among human tissues. Software implementing
our approach is available at https://github.com/stephenslab/flashr
| stat.ME | matrix factorization methods including factor analysis fa and principal components analysis pca are widely used for inferring and summarizing structure in multivariate data many matrix factorization methods exist corresponding to different assumptions on the elements of the underlying matrix factors for example many recent methods use a penalty or prior distribution to achieve sparse representations sparse fapca here we introduce a general empirical bayes approach to matrix factorization ebmf whose key feature is that it uses the observed data to estimate prior distributions on matrix elements we derive a correspondinglygeneral variational fitting algorithm which reduces fitting ebmf to solving a simpler problem the socalled normal means problem we implement this general algorithm but focus particular attention on the use of sparsityinducing priors that are unimodal at 0 this yields a sparse ebmf approach essentially a version of sparse fapca that automatically adapts the amount of sparsity to the data we demonstrate the benefits of our approach through both numerical comparisons with competing methods and through analysis of data from the gtex genotype tissue expression project on genetic associations across 44 human tissues in numerical comparisons ebmf often provides more accurate inferences than other methods in the gtex data ebmf identifies interpretable structure that concords with known relationships among human tissues software implementing our approach is available at httpsgithubcomstephenslabflashr | [['matrix', 'factorization', 'methods', 'including', 'factor', 'analysis', 'fa', 'and', 'principal', 'components', 'analysis', 'pca', 'are', 'widely', 'used', 'for', 'inferring', 'and', 'summarizing', 'structure', 'in', 'multivariate', 'data', 'many', 'matrix', 'factorization', 'methods', 'exist', 'corresponding', 'to', 'different', 'assumptions', 'on', 'the', 'elements', 'of', 'the', 'underlying', 'matrix', 'factors', 'for', 'example', 'many', 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1,802.06932 | Almost uniform and strong convergences in ergodic theorems for symmetric
spaces | Let $(\Omega,\mu)$ be a $\sigma$-finite measure space, and let $X\subset
L^1(\Omega)+L^\infty(\Omega)$ be a fully symmetric space of measurable
functions on $(\Omega,\mu)$. If $\mu(\Omega)=\infty$, necessary and sufficient
conditions are given for almost uniform convergence in $X$ (in Egorov's sense)
of Ces\`aro averages $M_n(T)(f)=\frac1n\sum_{k = 0}^{n-1}T^k(f)$ for all
Dunford-Schwartz operators $T$ in $L^1(\Omega)+ L^\infty(\Omega)$ and any $f\in
X$. Besides, it is proved that the averages $M_n(T)$ converge strongly in $X$
for each Dunford-Schwartz operator $T$ in $L^1(\Omega)+L^\infty(\Omega)$ if and
only if $X$ has order continuous norm and $L^1(\Omega)$ is not contained in
$X$.
| math.FA | let omegamu be a sigmafinite measure space and let xsubset l1omegalinftyomega be a fully symmetric space of measurable functions on omegamu if muomegainfty necessary and sufficient conditions are given for almost uniform convergence in x in egorovs sense of cesaro averages m_ntffrac1nsum_k 0n1tkf for all dunfordschwartz operators t in l1omega linftyomega and any fin x besides it is proved that the averages m_nt converge strongly in x for each dunfordschwartz operator t in l1omegalinftyomega if and only if x has order continuous norm and l1omega is not contained in x | [['let', 'omegamu', 'be', 'a', 'sigmafinite', 'measure', 'space', 'and', 'let', 'xsubset', 'l1omegalinftyomega', 'be', 'a', 'fully', 'symmetric', 'space', 'of', 'measurable', 'functions', 'on', 'omegamu', 'if', 'muomegainfty', 'necessary', 'and', 'sufficient', 'conditions', 'are', 'given', 'for', 'almost', 'uniform', 'convergence', 'in', 'x', 'in', 'egorovs', 'sense', 'of', 'cesaro', 'averages', 'm_ntffrac1nsum_k', '0n1tkf', 'for', 'all', 'dunfordschwartz', 'operators', 't', 'in', 'l1omega', 'linftyomega', 'and', 'any', 'fin', 'x', 'besides', 'it', 'is', 'proved', 'that', 'the', 'averages', 'm_nt', 'converge', 'strongly', 'in', 'x', 'for', 'each', 'dunfordschwartz', 'operator', 't', 'in', 'l1omegalinftyomega', 'if', 'and', 'only', 'if', 'x', 'has', 'order', 'continuous', 'norm', 'and', 'l1omega', 'is', 'not', 'contained', 'in', 'x']] | [-0.14700042218740644, 0.1650321178710966, -0.07986471676515233, 0.032439552849672475, 0.002289198301518443, -0.16376895608444667, -0.01110270962750689, 0.43015700591535405, -0.30406136710153914, -0.05241134842247542, 0.1485963639744354, -0.2946667412788361, -0.05106927856171354, 0.17899618982390939, -0.1360905428253778, 0.019610129548343777, 0.03440502322353851, 0.1391393100874948, -0.10943553055336849, -0.2911145358250059, 0.31545116289936265, -0.11852595108379235, 0.17161867761149488, 0.0616477200843062, 0.1504242484746822, 0.005830515069009244, 0.05503973719963654, 0.01747662545618034, -0.1777209333849478, 0.011925013525807566, 0.27899278062342225, 0.13124031339632883, 0.2978667113204064, -0.307707532924525, -0.16563246012718855, 0.29817434647602253, 0.13836483088396917, -0.1793699911742032, 0.015662115071257896, -0.28280460613983116, 0.1698645032452548, -0.06639711450699759, -0.13097850576942338, -0.14642305718199616, 0.14785424761752458, 0.07087222153813331, -0.4156654900443023, 0.05163220717068547, 0.12781302016442533, 0.042191384659930206, -0.08377955570260341, -0.09690439588144079, -0.11559034210105223, 0.06931623750120058, -0.05968665911270113, 0.19229889594168328, 0.03481460080064576, 0.013510205012200207, -0.02292787624742493, 0.34456236749330815, -0.11999370308255712, -0.3117265183821149, 0.08401721918248925, -0.3273176422620984, -0.14775609768306217, 0.1082112558357332, 0.08431028476220438, 0.192438630087451, -0.11093224162214446, 0.28262222006357673, -0.09448873760157275, 0.1432412167858525, 0.09631354695228839, 0.0640082872879488, 0.06169212558146181, 0.04940005744561211, 0.2276565084533616, 0.03793714862820644, 0.0633538543626709, 0.01691255557896762, -0.3984022671463846, -0.19100007015259016, -0.1913791970984111, 0.17116034366260685, -0.07725005605356398, -0.1645778619159741, 0.2648610179259003, 0.06259004182942297, 0.18009853051525765, 0.08202480961536539, 0.15499711911804204, 0.11802950259177657, 0.001216356247802661, 0.13774242201798606, 0.05881834653174055, 0.18620733146545018, 0.04107799092521784, -0.08704812794217262, 0.09425367737167525, 0.17497759125591522] |
1,802.06933 | AMC and HARQ: How to Increase the Throughput | In this work, we consider transmissions over block fading channels and assume
that adaptive modulation and coding (AMC) and hybrid automatic repeat request
(HARQ) are implemented. Knowing that in high signal-to-noise ratio, the
conventional combination of HARQ with AMC is counterproductive from the
throughput point of view, we adopt the so-called layer-coded HARQ (L-HARQ).
L-HARQ allows consecutive packets to share the channel and preserves a great
degree of separation between AMC and HARQ; this makes the encoding and decoding
very simple and allows us to use the available/optimized codes. Numerical
examples shown in the paper indicate that L-HARQ can provide significant
throughput gains compared to the conventional HARQ. The L-HARQ is also
implemented using turbo codes indicating that the throughput gains also
materialize in practice.
| cs.IT math.IT | in this work we consider transmissions over block fading channels and assume that adaptive modulation and coding amc and hybrid automatic repeat request harq are implemented knowing that in high signaltonoise ratio the conventional combination of harq with amc is counterproductive from the throughput point of view we adopt the socalled layercoded harq lharq lharq allows consecutive packets to share the channel and preserves a great degree of separation between amc and harq this makes the encoding and decoding very simple and allows us to use the availableoptimized codes numerical examples shown in the paper indicate that lharq can provide significant throughput gains compared to the conventional harq the lharq is also implemented using turbo codes indicating that the throughput gains also materialize in practice | [['in', 'this', 'work', 'we', 'consider', 'transmissions', 'over', 'block', 'fading', 'channels', 'and', 'assume', 'that', 'adaptive', 'modulation', 'and', 'coding', 'amc', 'and', 'hybrid', 'automatic', 'repeat', 'request', 'harq', 'are', 'implemented', 'knowing', 'that', 'in', 'high', 'signaltonoise', 'ratio', 'the', 'conventional', 'combination', 'of', 'harq', 'with', 'amc', 'is', 'counterproductive', 'from', 'the', 'throughput', 'point', 'of', 'view', 'we', 'adopt', 'the', 'socalled', 'layercoded', 'harq', 'lharq', 'lharq', 'allows', 'consecutive', 'packets', 'to', 'share', 'the', 'channel', 'and', 'preserves', 'a', 'great', 'degree', 'of', 'separation', 'between', 'amc', 'and', 'harq', 'this', 'makes', 'the', 'encoding', 'and', 'decoding', 'very', 'simple', 'and', 'allows', 'us', 'to', 'use', 'the', 'availableoptimized', 'codes', 'numerical', 'examples', 'shown', 'in', 'the', 'paper', 'indicate', 'that', 'lharq', 'can', 'provide', 'significant', 'throughput', 'gains', 'compared', 'to', 'the', 'conventional', 'harq', 'the', 'lharq', 'is', 'also', 'implemented', 'using', 'turbo', 'codes', 'indicating', 'that', 'the', 'throughput', 'gains', 'also', 'materialize', 'in', 'practice']] | [-0.225916341586489, 0.0009287727383396975, -0.07452893810255862, 0.007302239896186317, -0.030001072423169163, -0.2743113600436931, 0.15085836524253604, 0.4582288333070957, -0.2660540136644935, -0.24515518453729346, 0.08004421333316714, -0.22297271491786488, -0.19460624891401426, 0.13184384990491094, -0.17812540030818644, 0.05413278614026987, 0.10724598370734753, 0.0019305455065389142, -0.10460999073712458, -0.2902456875227967, 0.24725919028335228, 0.1711627343495384, 0.363690469156557, 0.01651150486772744, 0.07167524733198277, 0.05165380487082208, -0.05779308191977623, -0.05708585540968471, -0.09578878888070046, 0.04702372870188418, 0.2998256919265732, 0.19074385287240148, 0.22258453174092904, -0.3862107158449244, -0.27006074385641793, 0.015297600443898966, 0.2130232914880948, 0.0975651574255785, -0.0624441564842875, -0.18377255101695777, 0.1667288356759714, -0.27246885463073417, 0.02719810639500497, -0.021012616304517912, -0.11106690507745598, 0.06325738733952366, -0.36110101882519763, -0.02474106611292128, 0.0182460183504878, 0.038690608168944596, -0.021214804435110975, -0.11332835804489327, 0.03760541844980534, 0.1565712172901485, 0.04429876225209242, -0.007056714951749739, 0.040824337817001635, -0.0251497899009884, -0.1444580560792389, 0.3465635922502696, -0.0344841333278417, -0.18786813017942497, 0.17021549135144626, -0.052532264576634256, -0.09963773505059942, 0.17859562494360456, 0.19364611056587863, 0.048790115882014116, -0.11659933757264077, 0.015965802112794896, 0.035341537981559104, 0.23199689441016966, 0.1511650763005321, 0.1502275031374207, 0.13800252587875214, 0.14953046380655793, 0.053390679146519036, 0.1417330856684868, -0.14780024306969794, -0.11260367667953658, -0.22727856816860234, -0.13787355941954085, -0.1481222555380148, -0.0169195097083665, -0.10292117083286659, -0.081540066480771, 0.3440579563545866, 0.19161589083647945, 0.10082286175310128, 0.1456496978019614, 0.36676232734831365, 0.07040877108712022, 0.12020353758821219, 0.16751443580307854, 0.2056596209654751, 0.06967864377893568, 0.10931022930417089, -0.2369577764870402, 0.06737131944183654, 0.009186950059819633] |
1,802.06934 | Farthest Point Map on a Centrally Symmetric Convex Polyhedron | The farthest point map sends a point in a compact metric space to the set of
points farthest from it. We focus on the case when this metric space is a
convex centrally symmetric polyhedron, so that we can compose the farthest
point map with the antipodal map. The purpose of this work is to study the
properties of this composition. We show that: 1. the map has no generalized
periodic points; 2. its limit point set coincides with its generalized fixed
point set; 3. each of its orbit converges; 4. its limit set is contained in a
finite union of hyperbolas. We will define some of these terminologies in the
article.
| math.MG math.DS | the farthest point map sends a point in a compact metric space to the set of points farthest from it we focus on the case when this metric space is a convex centrally symmetric polyhedron so that we can compose the farthest point map with the antipodal map the purpose of this work is to study the properties of this composition we show that 1 the map has no generalized periodic points 2 its limit point set coincides with its generalized fixed point set 3 each of its orbit converges 4 its limit set is contained in a finite union of hyperbolas we will define some of these terminologies in the article | [['the', 'farthest', 'point', 'map', 'sends', 'a', 'point', 'in', 'a', 'compact', 'metric', 'space', 'to', 'the', 'set', 'of', 'points', 'farthest', 'from', 'it', 'we', 'focus', 'on', 'the', 'case', 'when', 'this', 'metric', 'space', 'is', 'a', 'convex', 'centrally', 'symmetric', 'polyhedron', 'so', 'that', 'we', 'can', 'compose', 'the', 'farthest', 'point', 'map', 'with', 'the', 'antipodal', 'map', 'the', 'purpose', 'of', 'this', 'work', 'is', 'to', 'study', 'the', 'properties', 'of', 'this', 'composition', 'we', 'show', 'that', '1', 'the', 'map', 'has', 'no', 'generalized', 'periodic', 'points', '2', 'its', 'limit', 'point', 'set', 'coincides', 'with', 'its', 'generalized', 'fixed', 'point', 'set', '3', 'each', 'of', 'its', 'orbit', 'converges', '4', 'its', 'limit', 'set', 'is', 'contained', 'in', 'a', 'finite', 'union', 'of', 'hyperbolas', 'we', 'will', 'define', 'some', 'of', 'these', 'terminologies', 'in', 'the', 'article']] | [-0.14576607348031498, 0.04926669264386874, -0.09555740519135725, 0.024473032107510204, -0.06364071909878735, -0.07906939369526558, 0.11041924515614353, 0.3602817724126258, -0.3255392750662785, -0.16815534670604393, 0.1183904106240204, -0.3485459814879245, -0.17389866398713952, 0.1591216589918726, -0.10938506901480391, 0.00024861372367013246, 0.06724383353555043, 0.11151246567273379, -0.11146317543794534, -0.24027807207312435, 0.40429738255001474, -0.010120989704903747, 0.20623905853738375, -0.003099075165144833, 0.11530494873711307, -0.0038959668267385234, 0.002258969429281673, 0.038645596454963585, -0.12983297329278035, 0.112767979045332, 0.20587803282043232, 0.16702622999686614, 0.25038352984535905, -0.3158180435337791, -0.15587457923850576, 0.21806702976131678, 0.09934598614095844, 0.04307622867470075, -0.0011312605399455475, -0.23917758610540268, 0.16128241570965787, -0.1215875661998455, -0.20015876850811765, 0.0014229267835617065, 0.03772945693760578, 0.020272426654368507, -0.20086445636115968, -0.06396083404043955, 0.09505499935975033, 0.04857883708817618, -0.05207898646975601, -0.08401885977114684, -0.05202479685041388, 0.14035864657411626, -0.025959425849058398, 0.14771634773725445, 0.11343606264979046, -0.049140324893025014, -0.06709090468523625, 0.41997157097128884, -0.029908249656728003, -0.23798743098242475, 0.1872946451081329, -0.20640213854702388, -0.1580778826319147, 0.1307562384962304, 0.13134734976587684, 0.1328208637631698, -0.13011940685789472, 0.18105323314583593, -0.12052717577067337, 0.11397465541007737, 0.08574370807868295, -0.013677553594918988, 0.22695700222227191, 0.14922005880050296, 0.17120773221332847, 0.18633602983235115, -0.1239715226838598, -0.06518415428165879, -0.36091529933868777, -0.1523997104626947, -0.21821777270607917, 0.06069580596106659, -0.11489521522474076, -0.16909327823668718, 0.3903698241421288, 0.12748350081009058, 0.2373564843105019, 0.057395832082615277, 0.2365445508476114, 0.10460691988711394, -0.0031032457731531133, 0.11386036481625135, 0.1745260740314864, 0.06067681028798688, 0.028128795517009815, -0.11466339992434119, -0.02586435421835631, 0.11446021117236731] |
1,802.06935 | Non-Local Graph-Based Prediction For Reversible Data Hiding In Images | Reversible data hiding (RDH) is desirable in applications where both the
hidden message and the cover medium need to be recovered without loss. Among
many RDH approaches is prediction-error expansion (PEE), containing two steps:
i) prediction of a target pixel value, and ii) embedding according to the value
of prediction-error. In general, higher prediction performance leads to larger
embedding capacity and/or lower signal distortion. Leveraging on recent
advances in graph signal processing (GSP), we pose pixel prediction as a
graph-signal restoration problem, where the appropriate edge weights of the
underlying graph are computed using a similar patch searched in a semi-local
neighborhood. Specifically, for each candidate patch, we first examine
eigenvalues of its structure tensor to estimate its local smoothness. If
sufficiently smooth, we pose a maximum a posteriori (MAP) problem using either
a quadratic Laplacian regularizer or a graph total variation (GTV) term as
signal prior. While the MAP problem using the first prior has a closed-form
solution, we design an efficient algorithm for the second prior using
alternating direction method of multipliers (ADMM) with nested proximal
gradient descent. Experimental results show that with better quality GSP-based
prediction, at low capacity the visual quality of the embedded image exceeds
state-of-the-art methods noticeably.
| eess.IV cs.CV | reversible data hiding rdh is desirable in applications where both the hidden message and the cover medium need to be recovered without loss among many rdh approaches is predictionerror expansion pee containing two steps i prediction of a target pixel value and ii embedding according to the value of predictionerror in general higher prediction performance leads to larger embedding capacity andor lower signal distortion leveraging on recent advances in graph signal processing gsp we pose pixel prediction as a graphsignal restoration problem where the appropriate edge weights of the underlying graph are computed using a similar patch searched in a semilocal neighborhood specifically for each candidate patch we first examine eigenvalues of its structure tensor to estimate its local smoothness if sufficiently smooth we pose a maximum a posteriori map problem using either a quadratic laplacian regularizer or a graph total variation gtv term as signal prior while the map problem using the first prior has a closedform solution we design an efficient algorithm for the second prior using alternating direction method of multipliers admm with nested proximal gradient descent experimental results show that with better quality gspbased prediction at low capacity the visual quality of the embedded image exceeds stateoftheart methods noticeably | [['reversible', 'data', 'hiding', 'rdh', 'is', 'desirable', 'in', 'applications', 'where', 'both', 'the', 'hidden', 'message', 'and', 'the', 'cover', 'medium', 'need', 'to', 'be', 'recovered', 'without', 'loss', 'among', 'many', 'rdh', 'approaches', 'is', 'predictionerror', 'expansion', 'pee', 'containing', 'two', 'steps', 'i', 'prediction', 'of', 'a', 'target', 'pixel', 'value', 'and', 'ii', 'embedding', 'according', 'to', 'the', 'value', 'of', 'predictionerror', 'in', 'general', 'higher', 'prediction', 'performance', 'leads', 'to', 'larger', 'embedding', 'capacity', 'andor', 'lower', 'signal', 'distortion', 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1,802.06936 | Online Learning with an Unknown Fairness Metric | We consider the problem of online learning in the linear contextual bandits
setting, but in which there are also strong individual fairness constraints
governed by an unknown similarity metric. These constraints demand that we
select similar actions or individuals with approximately equal probability
(arXiv:1104.3913), which may be at odds with optimizing reward, thus modeling
settings where profit and social policy are in tension. We assume we learn
about an unknown Mahalanobis similarity metric from only weak feedback that
identifies fairness violations, but does not quantify their extent. This is
intended to represent the interventions of a regulator who "knows unfairness
when he sees it" but nevertheless cannot enunciate a quantitative fairness
metric over individuals. Our main result is an algorithm in the adversarial
context setting that has a number of fairness violations that depends only
logarithmically on $T$, while obtaining an optimal $O(\sqrt{T})$ regret bound
to the best fair policy.
| cs.LG | we consider the problem of online learning in the linear contextual bandits setting but in which there are also strong individual fairness constraints governed by an unknown similarity metric these constraints demand that we select similar actions or individuals with approximately equal probability arxiv11043913 which may be at odds with optimizing reward thus modeling settings where profit and social policy are in tension we assume we learn about an unknown mahalanobis similarity metric from only weak feedback that identifies fairness violations but does not quantify their extent this is intended to represent the interventions of a regulator who knows unfairness when he sees it but nevertheless cannot enunciate a quantitative fairness metric over individuals our main result is an algorithm in the adversarial context setting that has a number of fairness violations that depends only logarithmically on t while obtaining an optimal osqrtt regret bound to the best fair policy | [['we', 'consider', 'the', 'problem', 'of', 'online', 'learning', 'in', 'the', 'linear', 'contextual', 'bandits', 'setting', 'but', 'in', 'which', 'there', 'are', 'also', 'strong', 'individual', 'fairness', 'constraints', 'governed', 'by', 'an', 'unknown', 'similarity', 'metric', 'these', 'constraints', 'demand', 'that', 'we', 'select', 'similar', 'actions', 'or', 'individuals', 'with', 'approximately', 'equal', 'probability', 'arxiv11043913', 'which', 'may', 'be', 'at', 'odds', 'with', 'optimizing', 'reward', 'thus', 'modeling', 'settings', 'where', 'profit', 'and', 'social', 'policy', 'are', 'in', 'tension', 'we', 'assume', 'we', 'learn', 'about', 'an', 'unknown', 'mahalanobis', 'similarity', 'metric', 'from', 'only', 'weak', 'feedback', 'that', 'identifies', 'fairness', 'violations', 'but', 'does', 'not', 'quantify', 'their', 'extent', 'this', 'is', 'intended', 'to', 'represent', 'the', 'interventions', 'of', 'a', 'regulator', 'who', 'knows', 'unfairness', 'when', 'he', 'sees', 'it', 'but', 'nevertheless', 'can', 'not', 'enunciate', 'a', 'quantitative', 'fairness', 'metric', 'over', 'individuals', 'our', 'main', 'result', 'is', 'an', 'algorithm', 'in', 'the', 'adversarial', 'context', 'setting', 'that', 'has', 'a', 'number', 'of', 'fairness', 'violations', 'that', 'depends', 'only', 'logarithmically', 'on', 't', 'while', 'obtaining', 'an', 'optimal', 'osqrtt', 'regret', 'bound', 'to', 'the', 'best', 'fair', 'policy']] | [-0.10899010697884175, 0.05635844579936626, -0.08805352508711319, 0.11906404625081146, -0.16518994408038756, -0.23980395738966764, 0.1291645925398916, 0.4400363123168548, -0.2707245130712787, -0.3194144252811869, 0.05015914523353179, -0.29216351377467314, -0.15715456129206964, 0.12627996711526066, -0.20127515405416488, 0.024955801046453417, 0.025380531357756506, 0.11810814635517697, 0.005177399646490812, -0.34506826736032964, 0.33335134043358267, 0.09412720916171868, 0.28810079506908853, 0.032781610684081294, 0.11298886587610468, 0.00428267649995784, -0.010558939011146625, 0.07061692380423969, -0.09460441833381386, 0.08271006198444714, 0.32507277972375354, 0.22618051996765037, 0.39674414734976987, -0.3544602877895037, -0.18637027851616342, 0.16453365225034455, 0.1050764180762538, 0.07016809816782674, -0.01045748043882971, -0.2773866544291377, 0.06296374789749583, -0.15922174309535572, -0.012142046407486002, -0.059673769529908896, -0.037316168509423736, -0.0012054312856222774, -0.333637082024167, 0.03553849517814039, 0.06981813710959008, 0.01933443859840433, -0.06275413574806105, -0.09309799326273302, 0.02438074626183758, 0.16137862332262254, 0.12887659465350831, 0.036754724780718485, 0.14210115688232083, -0.15412304664496332, -0.14338983568983774, 0.35739113518347343, -0.025543386007969578, -0.2137845605192706, 0.17161758373491465, -0.10597113218197289, -0.14418460907569777, 0.089341245641311, 0.2118350704262654, 0.10583632065914571, -0.1745947203960774, 0.04693985046935268, -0.1053942243293083, 0.21992866954455773, 0.07953485470924837, 0.053797777869428194, 0.12116126428047816, 0.12758258517831564, 0.18585892570903526, 0.06293642891182874, 0.03511866291519254, -0.12039331542793662, -0.2850941328642269, -0.09356943380747301, -0.16492698811615505, 0.05433737669780385, -0.13052724821172887, -0.10686840281637464, 0.2970731584200015, 0.1558013911677214, 0.1841624782855312, 0.15190435556229204, 0.28525825165833035, 0.0863171961150753, 0.04547404452382276, 0.17178973734689257, 0.2353325288463384, 0.0037725679157301784, 0.06765849966012562, -0.207564442025808, 0.2112478800645719, 0.011382614793255925] |
1,802.06937 | On the structure of the singular set for the kinetic Fokker-Planck
equations in domains with boundaries | In this paper we compute asymptotics of solutions of the kinetic
Fokker-Planck equation with inelastic boundary conditions which indicate that
the solutions are nonunique if $r < r_c$. The nonuniqueness is due to the fact
that different solutions can interact in a different manner with a Dirac mass
which appears at the singular point $(x,v)=(0,0)$. In particular, this
nonuniqueness explains the different behaviours found in the physics literature
for numerical simulations of the stochastic differential equation associated to
the kinetic Fokker-Planck equation. The asymptotics obtained in this paper will
be used in a companion paper [34] to prove rigorously nonuniqueness of
solutions for the kinetic Fokker-Planck equation with inelastic boundary
conditions.
| math.AP | in this paper we compute asymptotics of solutions of the kinetic fokkerplanck equation with inelastic boundary conditions which indicate that the solutions are nonunique if r r_c the nonuniqueness is due to the fact that different solutions can interact in a different manner with a dirac mass which appears at the singular point xv00 in particular this nonuniqueness explains the different behaviours found in the physics literature for numerical simulations of the stochastic differential equation associated to the kinetic fokkerplanck equation the asymptotics obtained in this paper will be used in a companion paper 34 to prove rigorously nonuniqueness of solutions for the kinetic fokkerplanck equation with inelastic boundary conditions | [['in', 'this', 'paper', 'we', 'compute', 'asymptotics', 'of', 'solutions', 'of', 'the', 'kinetic', 'fokkerplanck', 'equation', 'with', 'inelastic', 'boundary', 'conditions', 'which', 'indicate', 'that', 'the', 'solutions', 'are', 'nonunique', 'if', 'r', 'r_c', 'the', 'nonuniqueness', 'is', 'due', 'to', 'the', 'fact', 'that', 'different', 'solutions', 'can', 'interact', 'in', 'a', 'different', 'manner', 'with', 'a', 'dirac', 'mass', 'which', 'appears', 'at', 'the', 'singular', 'point', 'xv00', 'in', 'particular', 'this', 'nonuniqueness', 'explains', 'the', 'different', 'behaviours', 'found', 'in', 'the', 'physics', 'literature', 'for', 'numerical', 'simulations', 'of', 'the', 'stochastic', 'differential', 'equation', 'associated', 'to', 'the', 'kinetic', 'fokkerplanck', 'equation', 'the', 'asymptotics', 'obtained', 'in', 'this', 'paper', 'will', 'be', 'used', 'in', 'a', 'companion', 'paper', '34', 'to', 'prove', 'rigorously', 'nonuniqueness', 'of', 'solutions', 'for', 'the', 'kinetic', 'fokkerplanck', 'equation', 'with', 'inelastic', 'boundary', 'conditions']] | [-0.13266124281553773, 0.07926823068068388, -0.12393121484991744, 0.08733747884793498, -0.09306237625744228, -0.11578910573547997, -0.045942888284366955, 0.2726783439380313, -0.30816752083360327, -0.2516814823851946, 0.0840887100491331, -0.3060460814291936, -0.1451178181493535, 0.14705667456765786, -0.04028530943489403, 0.0651188054028044, 0.10791213292235491, 0.012629245066109601, -0.10113913571427859, -0.1898536033492638, 0.3662324635597125, -0.005826809383723714, 0.2251490507309043, 0.07085931632633603, 0.11376752707799641, -0.08688343979830537, 0.021201281816593944, 0.014316513429239923, -0.21559321604855466, 0.05099986898157438, 0.27380924647964466, 0.021150775208915455, 0.2588506291082146, -0.4178652022837089, -0.21827595938133812, 0.093603474502409, 0.20608519375246284, 0.12223699647685461, -0.0484486103608965, -0.2567729248308551, 0.07161212533951626, -0.1480262614786625, -0.21578795626896238, -0.03459944989526358, 0.015209295974890574, 0.07106110949385398, -0.2832212466832011, 0.1321972119750096, 0.025640586121947667, -0.028045646991546547, -0.14291696406691548, -0.08811686729260404, -0.03572585969445629, 0.024354106828852327, 0.11523498694087729, -0.02653891310830592, 0.028542387166803858, -0.14598983035576657, -0.054831233072103164, 0.3594786729343201, -0.06192417383843332, -0.2724776148044188, 0.20517315902670316, -0.17422359459392658, -0.14250063105409436, 0.15519099496091382, 0.13182347117487445, 0.1500349913172663, -0.23274537080198254, 0.12351616554030585, -0.04359355616947175, 0.09084944823599814, 0.07785442078041785, -0.015059298696850828, 0.13762385145239875, 0.10359375785241638, 0.05480502849581455, 0.11658236110441968, -0.02624968863829304, -0.13994525186931117, -0.354353851100447, -0.12270170181494104, -0.16038112268022714, 0.11288577502747195, -0.10189109686078555, -0.17643964325650296, 0.3479941827458663, 0.21296204929453655, 0.1787185811634184, 0.04152966087359354, 0.2290166703761991, 0.23079712911073222, -0.030346068613436243, 0.09057565446550009, 0.2443524976047354, 0.14655002015172888, 0.18301551394728482, -0.26728593554228136, 0.02428998149623838, 0.11665297656719478] |
1,802.06938 | Localized Magnetic States in 2D Semiconductors | We study the formation of magnetic states in localized impurities embedded
into two-dimensional semiconductors. By considering various energy
configurations, we illustrate the interplay of the gap and the bands in the
system magnetization. Finally, we consider finite-temperature effects to show
how increasing $T$ can lead to formation and destruction of magnetization.
| cond-mat.mes-hall | we study the formation of magnetic states in localized impurities embedded into twodimensional semiconductors by considering various energy configurations we illustrate the interplay of the gap and the bands in the system magnetization finally we consider finitetemperature effects to show how increasing t can lead to formation and destruction of magnetization | [['we', 'study', 'the', 'formation', 'of', 'magnetic', 'states', 'in', 'localized', 'impurities', 'embedded', 'into', 'twodimensional', 'semiconductors', 'by', 'considering', 'various', 'energy', 'configurations', 'we', 'illustrate', 'the', 'interplay', 'of', 'the', 'gap', 'and', 'the', 'bands', 'in', 'the', 'system', 'magnetization', 'finally', 'we', 'consider', 'finitetemperature', 'effects', 'to', 'show', 'how', 'increasing', 't', 'can', 'lead', 'to', 'formation', 'and', 'destruction', 'of', 'magnetization']] | [-0.1834161334178027, 0.19078609156970153, -0.013149512771918786, 0.088169170507029, 0.035422203697118106, -0.05270499841985749, 0.07175846488269813, 0.3927616820323701, -0.28576311213420885, -0.3139553968520725, 0.03258068828076562, -0.29449931071961627, -0.14845233297377242, 0.1340646823667282, 0.02772913456383143, -0.040781535591711014, 0.0020550256777627797, -0.07054554267932533, -0.10615483158286296, -0.21668287488978868, 0.3650122089128868, 0.00691824464821348, 0.2761221875199208, 0.1614675196800746, -0.027299644023764367, 0.010795224768420061, 0.08630930777529583, 0.06526612800856431, -0.1944841092141966, 0.055332556598609785, 0.19439482834993624, -0.03596906999454779, 0.20939599514902368, -0.5228920604522321, -0.23419951844741316, 0.05445994378304949, 0.1712089823787192, 0.15659069364853934, -0.08013331166961614, -0.2824379338280243, 0.05708327964770005, -0.13906185632096787, -0.13582816045256516, -0.11865004853518936, -0.015270608459033217, 0.03309511118933704, -0.24680738455122886, 0.08284174764127124, 0.06773313166865823, 0.02416724127297308, -0.1536783854677981, -0.05584294363792401, -0.10817244268871624, 0.08293566040183399, 0.05716150433655974, -0.018919183575895195, 0.15945047131903908, -0.1308262107126853, -0.133262496222468, 0.35264675830509146, -0.04935683007009656, -0.13200070712642342, 0.20056858425046883, -0.20394812863064454, -0.07208606388931181, 0.09401616224946052, 0.2120650846365036, 0.09634223353921198, -0.098835141109257, 0.08304473792211901, 0.047057337273715756, 0.13064339223737811, 0.013989951644165843, 0.09933958602064819, 0.279497467949256, 0.21164449275124306, 0.02772759205168661, 0.2504679554087274, -0.1383206632020244, -0.11378106913145851, -0.2173534744218284, -0.17167732641831332, -0.17604300715759688, 0.0743684985620134, -0.038256216976294, -0.14408312460371092, 0.43210120696355314, 0.17884610274660528, 0.20270217784369984, -0.06586991730785254, 0.2150326762713638, 0.12350138921436726, 0.02909086325078034, 0.071985005611079, 0.21830912266730093, 0.1550887294442338, 0.0631181543656424, -0.3516238669992662, 0.01991358374738518, -0.006244864753063987] |
1,802.06939 | Estimator of Prediction Error Based on Approximate Message Passing for
Penalized Linear Regression | We propose an estimator of prediction error using an approximate message
passing (AMP) algorithm that can be applied to a broad range of sparse
penalties. Following Stein's lemma, the estimator of the generalized degrees of
freedom, which is a key quantity for the construction of the estimator of the
prediction error, is calculated at the AMP fixed point. The resulting form of
the AMP-based estimator does not depend on the penalty function, and its value
can be further improved by considering the correlation between predictors. The
proposed estimator is asymptotically unbiased when the components of the
predictors and response variables are independently generated according to a
Gaussian distribution. We examine the behaviour of the estimator for real data
under nonconvex sparse penalties, where Akaike's information criterion does not
correspond to an unbiased estimator of the prediction error. The model selected
by the proposed estimator is close to that which minimizes the true prediction
error.
| stat.ML cs.LG | we propose an estimator of prediction error using an approximate message passing amp algorithm that can be applied to a broad range of sparse penalties following steins lemma the estimator of the generalized degrees of freedom which is a key quantity for the construction of the estimator of the prediction error is calculated at the amp fixed point the resulting form of the ampbased estimator does not depend on the penalty function and its value can be further improved by considering the correlation between predictors the proposed estimator is asymptotically unbiased when the components of the predictors and response variables are independently generated according to a gaussian distribution we examine the behaviour of the estimator for real data under nonconvex sparse penalties where akaikes information criterion does not correspond to an unbiased estimator of the prediction error the model selected by the proposed estimator is close to that which minimizes the true prediction error | [['we', 'propose', 'an', 'estimator', 'of', 'prediction', 'error', 'using', 'an', 'approximate', 'message', 'passing', 'amp', 'algorithm', 'that', 'can', 'be', 'applied', 'to', 'a', 'broad', 'range', 'of', 'sparse', 'penalties', 'following', 'steins', 'lemma', 'the', 'estimator', 'of', 'the', 'generalized', 'degrees', 'of', 'freedom', 'which', 'is', 'a', 'key', 'quantity', 'for', 'the', 'construction', 'of', 'the', 'estimator', 'of', 'the', 'prediction', 'error', 'is', 'calculated', 'at', 'the', 'amp', 'fixed', 'point', 'the', 'resulting', 'form', 'of', 'the', 'ampbased', 'estimator', 'does', 'not', 'depend', 'on', 'the', 'penalty', 'function', 'and', 'its', 'value', 'can', 'be', 'further', 'improved', 'by', 'considering', 'the', 'correlation', 'between', 'predictors', 'the', 'proposed', 'estimator', 'is', 'asymptotically', 'unbiased', 'when', 'the', 'components', 'of', 'the', 'predictors', 'and', 'response', 'variables', 'are', 'independently', 'generated', 'according', 'to', 'a', 'gaussian', 'distribution', 'we', 'examine', 'the', 'behaviour', 'of', 'the', 'estimator', 'for', 'real', 'data', 'under', 'nonconvex', 'sparse', 'penalties', 'where', 'akaikes', 'information', 'criterion', 'does', 'not', 'correspond', 'to', 'an', 'unbiased', 'estimator', 'of', 'the', 'prediction', 'error', 'the', 'model', 'selected', 'by', 'the', 'proposed', 'estimator', 'is', 'close', 'to', 'that', 'which', 'minimizes', 'the', 'true', 'prediction', 'error']] | [-0.06800580904637644, 0.006358389890364934, -0.15214556753151603, 0.08026269622231749, -0.04729271922472187, -0.16844858073738295, 0.077298090637468, 0.3711581283986762, -0.2841958083430087, -0.2913461077063308, 0.14131656196573145, -0.25817305194788503, -0.16093455063929032, 0.15582297759980276, -0.13902337358079173, 0.09234396677559496, 0.05149192071380698, 0.05177984491305111, -0.09696915141679498, -0.2955784515368861, 0.25681870350275526, 0.12043005025676488, 0.30896672681004245, -0.0216745160986494, 0.14572478504922332, 0.05930514584972777, -0.002142558590956516, -0.006180487853271188, -0.12017909148392379, 0.12127263833732381, 0.235348041845159, 0.17470087094365486, 0.3471052077985913, -0.303578805377973, -0.17525982112127098, 0.1521261745803784, 0.1305355379182555, 0.07742688750890507, 0.020944994241573738, -0.26667329961095343, 0.09339375846849923, -0.1412073642139432, -0.07372319748528398, -0.07184587923398653, -0.0794445892868491, 0.028667267036999202, -0.4112674020496862, 0.11946953386189295, 0.052704056836785626, 0.017729582809298844, -0.03518679826414551, -0.1502997508308814, -0.009259038045397633, 0.0943060422993897, 0.09181409883358238, 0.022455926919537416, 0.1324951544603599, -0.08939217897615843, -0.07423568911836845, 0.29829441896313197, -0.06515210277745621, -0.26601024397177164, 0.09289531671901705, -0.09505540723368139, -0.08248384509087456, 0.14134007632768678, 0.20952281948971235, 0.08784910717483174, -0.17853102088475362, 0.0573173238579849, -0.05657954913769643, 0.1614318916193503, -0.00264395789965963, 0.0057781655900785095, 0.14458644274295634, 0.12132294607868829, 0.12349899111850628, 0.12301694725295408, -0.1297002536707328, -0.07853194959945493, -0.3330601263525231, -0.11162305881088605, -0.2837723032087571, -0.03355850776420852, -0.18061402669859075, -0.20896510494413315, 0.3861664073441976, 0.17193384554808622, 0.16647120314505096, 0.10630491148686497, 0.2840497045620502, 0.15125340265645223, 0.05175671066677609, 0.1369548247183685, 0.24682668456807733, 0.14518622879555762, -0.03755351277249684, -0.20542835711513635, 0.16288944449904677, 0.05700864716638605] |
1,802.0694 | Using Automatic Generation of Relaxation Constraints to Improve the
Preimage Attack on 39-step MD4 | In this paper we construct preimage attack on the truncated variant of the
MD4 hash function. Specifically, we study the MD4-39 function defined by the
first 39 steps of the MD4 algorithm. We suggest a new attack on MD4-39, which
develops the ideas proposed by H. Dobbertin in 1998. Namely, the special
relaxation constraints are introduced in order to simplify the equations
corresponding to the problem of finding a preimage for an arbitrary MD4-39 hash
value. The equations supplemented with the relaxation constraints are then
reduced to the Boolean Satisfiability Problem (SAT) and solved using the
state-of-the-art SAT solvers. We show that the effectiveness of a set of
relaxation constraints can be evaluated using the black-box function of a
special kind. Thus, we suggest automatic method of relaxation constraints
generation by applying the black-box optimization to this function. The
proposed method made it possible to find new relaxation constraints that
contribute to a SAT-based preimage attack on MD4-39 which significantly
outperforms the competition.
| cs.AI | in this paper we construct preimage attack on the truncated variant of the md4 hash function specifically we study the md439 function defined by the first 39 steps of the md4 algorithm we suggest a new attack on md439 which develops the ideas proposed by h dobbertin in 1998 namely the special relaxation constraints are introduced in order to simplify the equations corresponding to the problem of finding a preimage for an arbitrary md439 hash value the equations supplemented with the relaxation constraints are then reduced to the boolean satisfiability problem sat and solved using the stateoftheart sat solvers we show that the effectiveness of a set of relaxation constraints can be evaluated using the blackbox function of a special kind thus we suggest automatic method of relaxation constraints generation by applying the blackbox optimization to this function the proposed method made it possible to find new relaxation constraints that contribute to a satbased preimage attack on md439 which significantly outperforms the competition | [['in', 'this', 'paper', 'we', 'construct', 'preimage', 'attack', 'on', 'the', 'truncated', 'variant', 'of', 'the', 'md4', 'hash', 'function', 'specifically', 'we', 'study', 'the', 'md439', 'function', 'defined', 'by', 'the', 'first', '39', 'steps', 'of', 'the', 'md4', 'algorithm', 'we', 'suggest', 'a', 'new', 'attack', 'on', 'md439', 'which', 'develops', 'the', 'ideas', 'proposed', 'by', 'h', 'dobbertin', 'in', '1998', 'namely', 'the', 'special', 'relaxation', 'constraints', 'are', 'introduced', 'in', 'order', 'to', 'simplify', 'the', 'equations', 'corresponding', 'to', 'the', 'problem', 'of', 'finding', 'a', 'preimage', 'for', 'an', 'arbitrary', 'md439', 'hash', 'value', 'the', 'equations', 'supplemented', 'with', 'the', 'relaxation', 'constraints', 'are', 'then', 'reduced', 'to', 'the', 'boolean', 'satisfiability', 'problem', 'sat', 'and', 'solved', 'using', 'the', 'stateoftheart', 'sat', 'solvers', 'we', 'show', 'that', 'the', 'effectiveness', 'of', 'a', 'set', 'of', 'relaxation', 'constraints', 'can', 'be', 'evaluated', 'using', 'the', 'blackbox', 'function', 'of', 'a', 'special', 'kind', 'thus', 'we', 'suggest', 'automatic', 'method', 'of', 'relaxation', 'constraints', 'generation', 'by', 'applying', 'the', 'blackbox', 'optimization', 'to', 'this', 'function', 'the', 'proposed', 'method', 'made', 'it', 'possible', 'to', 'find', 'new', 'relaxation', 'constraints', 'that', 'contribute', 'to', 'a', 'satbased', 'preimage', 'attack', 'on', 'md439', 'which', 'significantly', 'outperforms', 'the', 'competition']] | [-0.09459251003122769, 0.00022341573814092992, -0.09358392870396467, 0.05823327879924213, -0.10787303699180484, -0.13289576176085446, 0.06369846387969136, 0.3090275800669069, -0.32132148760959417, -0.31196926275348186, 0.11076925288203053, -0.22868792987228628, -0.16933942798373683, 0.21044466967917294, -0.05538221172976988, 0.1214332848078817, 0.03021380002181863, -0.01814848392276219, -0.1234841588716754, -0.31110260467633516, 0.3295046289785668, 0.00038432827336496556, 0.2562194061722675, 0.05589194974913462, 0.11238677918545308, 0.03403217973190423, -2.1675386626067704e-05, 0.04121336821278134, -0.13240865249505646, 0.11102824196350858, 0.23635861862370847, 0.20450005942864194, 0.2891694714280954, -0.4063955651641898, -0.14920785646182858, 0.08419282155888487, 0.1057433419163836, 0.11153372787539076, -0.03340312655075265, -0.2771992860590479, 0.09079630073071372, -0.16931952718085397, -0.06232679737560405, -0.0791251471750381, -0.006249292142277847, 0.013059113274844067, -0.30119579114545525, 0.01175535600946679, 0.07594253549043752, -0.01902922919659984, -0.05384363117842061, -0.13894684563613743, 0.04546245159309906, 0.030335274082179998, 0.03166890966397442, 0.06536760325029355, 0.11566847960708915, -0.09507689842428624, -0.16233270532848668, 0.34787088397181837, -0.07365240461330153, -0.21284552875540555, 0.12380513771682795, -0.02905607110095079, -0.18424062350867715, 0.10583188232931486, 0.18644452491861574, 0.18100982245366357, -0.1258815740295143, 0.07336310261219134, -0.07488768024690037, 0.17819058320890724, 0.06531652869896655, -0.03574753529820705, 0.1081353291580625, 0.17312616492375915, 0.09380265427010556, 0.21006309567233605, -0.04153839360345498, -0.07960280576225176, -0.2595885296757876, -0.1265609738176997, -0.19419934767286973, 0.0069913907902189555, -0.06690500959019431, -0.13972087289589497, 0.4140485167686193, 0.18109634660607193, 0.17615310921607985, 0.12336921490346042, 0.29485908064539856, 0.16239352016275865, 0.0819810457246241, 0.09623158682263427, 0.2018088943402117, 0.07005849958146049, 0.045326233267258466, -0.2573313126826167, 0.09822088187743276, 0.12761089999365843] |
1,802.06941 | Distilling Knowledge Using Parallel Data for Far-field Speech
Recognition | In order to improve the performance for far-field speech recognition, this
paper proposes to distill knowledge from the close-talking model to the
far-field model using parallel data. The close-talking model is called the
teacher model. The far-field model is called the student model. The student
model is trained to imitate the output distributions of the teacher model. This
constraint can be realized by minimizing the Kullback-Leibler (KL) divergence
between the output distribution of the student model and the teacher model.
Experimental results on AMI corpus show that the best student model achieves up
to 4.7% absolute word error rate (WER) reduction when compared with the
conventionally-trained baseline models.
| cs.CL cs.SD eess.AS | in order to improve the performance for farfield speech recognition this paper proposes to distill knowledge from the closetalking model to the farfield model using parallel data the closetalking model is called the teacher model the farfield model is called the student model the student model is trained to imitate the output distributions of the teacher model this constraint can be realized by minimizing the kullbackleibler kl divergence between the output distribution of the student model and the teacher model experimental results on ami corpus show that the best student model achieves up to 47 absolute word error rate wer reduction when compared with the conventionallytrained baseline models | [['in', 'order', 'to', 'improve', 'the', 'performance', 'for', 'farfield', 'speech', 'recognition', 'this', 'paper', 'proposes', 'to', 'distill', 'knowledge', 'from', 'the', 'closetalking', 'model', 'to', 'the', 'farfield', 'model', 'using', 'parallel', 'data', 'the', 'closetalking', 'model', 'is', 'called', 'the', 'teacher', 'model', 'the', 'farfield', 'model', 'is', 'called', 'the', 'student', 'model', 'the', 'student', 'model', 'is', 'trained', 'to', 'imitate', 'the', 'output', 'distributions', 'of', 'the', 'teacher', 'model', 'this', 'constraint', 'can', 'be', 'realized', 'by', 'minimizing', 'the', 'kullbackleibler', 'kl', 'divergence', 'between', 'the', 'output', 'distribution', 'of', 'the', 'student', 'model', 'and', 'the', 'teacher', 'model', 'experimental', 'results', 'on', 'ami', 'corpus', 'show', 'that', 'the', 'best', 'student', 'model', 'achieves', 'up', 'to', '47', 'absolute', 'word', 'error', 'rate', 'wer', 'reduction', 'when', 'compared', 'with', 'the', 'conventionallytrained', 'baseline', 'models']] | [0.029378832983740857, 0.03265967341923268, -0.07303060301379344, 0.06848414986128876, -0.11276536105661958, -0.18453169887380622, 0.07284403188811249, 0.40457386627932573, -0.29938851451832, -0.3663428914238194, -0.009708658997709297, -0.3178613144103612, -0.13224462035033746, 0.17567252081592577, -0.1283597978806802, 0.08371193317269959, 0.13534848118705728, 0.09256474127629598, -0.017524612103545358, -0.29750533892868836, 0.26784121853116655, 0.13258543378177368, 0.377148033998837, -0.01898462451977011, 0.13829742203288165, -0.08182019381305185, 0.010541151025306398, -0.08930956493958692, -0.04892465267494967, 0.1534873863522556, 0.2548281489603864, 0.1917454179193581, 0.2960516368561189, -0.34651536822632373, -0.2016582960332526, 0.10481530449719813, 0.06824688762252297, 0.10771387629210949, 0.04040055998457404, -0.35162281434739306, 0.08635657684546764, -0.20068720641119459, 0.0074463718370578, -0.0696574178933401, -0.1107777394441383, -0.010704711871656858, -0.3462673185271359, 0.07084574764507397, 0.13494256728058943, 0.10193389860443144, -0.07109154779551045, -0.14789885389575055, 0.033262657062542215, 0.17628218770972628, 0.07560808189696346, 0.12421233000268585, 0.08641794974106216, -0.19626276111056176, -0.13430406475030582, 0.35250107291215493, -0.12127722866036798, -0.23580168230050008, 0.1194880190784129, -0.09395866990350535, -0.013367124793998828, 0.06090064505202191, 0.2675199221349626, 0.03435681116654076, -0.18454289503972737, 0.024456425565055562, -0.06278809890255471, 0.24480102049343497, 0.022363497292466252, -0.08363175212414325, 0.14156026499689706, 0.2460857784964697, -0.023726694964777643, 0.16925042991365402, -0.1411740070085964, -0.09953225201004506, -0.2589506331665842, -0.09269286888851741, -0.2134523818913464, -0.026711906141070562, -0.11119677736105671, -0.10259211913325658, 0.40456026566223563, 0.24428782622312484, 0.15364013936274867, 0.15907474266034421, 0.33383696414878433, 0.07770089774662367, 0.07922015487538459, 0.10169351495645731, 0.23706689827192018, 0.02607248297231011, 0.08892127955996075, -0.21686558550980953, 0.14403620531138417, 0.056400307584728034] |
1,802.06942 | Comparison Based Learning from Weak Oracles | There is increasing interest in learning algorithms that involve interaction
between human and machine. Comparison-based queries are among the most natural
ways to get feedback from humans. A challenge in designing comparison-based
interactive learning algorithms is coping with noisy answers. The most common
fix is to submit a query several times, but this is not applicable in many
situations due to its prohibitive cost and due to the unrealistic assumption of
independent noise in different repetitions of the same query.
In this paper, we introduce a new weak oracle model, where a non-malicious
user responds to a pairwise comparison query only when she is quite sure about
the answer. This model is able to mimic the behavior of a human in noise-prone
regions. We also consider the application of this weak oracle model to the
problem of content search (a variant of the nearest neighbor search problem)
through comparisons. More specifically, we aim at devising efficient algorithms
to locate a target object in a database equipped with a dissimilarity metric
via invocation of the weak comparison oracle. We propose two algorithms termed
WORCS-I and WORCS-II (Weak-Oracle Comparison-based Search), which provably
locate the target object in a number of comparisons close to the entropy of the
target distribution. While WORCS-I provides better theoretical guarantees,
WORCS-II is applicable to more technically challenging scenarios where the
algorithm has limited access to the ranking dissimilarity between objects. A
series of experiments validate the performance of our proposed algorithms.
| cs.LG cs.DS stat.ML | there is increasing interest in learning algorithms that involve interaction between human and machine comparisonbased queries are among the most natural ways to get feedback from humans a challenge in designing comparisonbased interactive learning algorithms is coping with noisy answers the most common fix is to submit a query several times but this is not applicable in many situations due to its prohibitive cost and due to the unrealistic assumption of independent noise in different repetitions of the same query in this paper we introduce a new weak oracle model where a nonmalicious user responds to a pairwise comparison query only when she is quite sure about the answer this model is able to mimic the behavior of a human in noiseprone regions we also consider the application of this weak oracle model to the problem of content search a variant of the nearest neighbor search problem through comparisons more specifically we aim at devising efficient algorithms to locate a target object in a database equipped with a dissimilarity metric via invocation of the weak comparison oracle we propose two algorithms termed worcsi and worcsii weakoracle comparisonbased search which provably locate the target object in a number of comparisons close to the entropy of the target distribution while worcsi provides better theoretical guarantees worcsii is applicable to more technically challenging scenarios where the algorithm has limited access to the ranking dissimilarity between objects a series of experiments validate the performance of our proposed algorithms | [['there', 'is', 'increasing', 'interest', 'in', 'learning', 'algorithms', 'that', 'involve', 'interaction', 'between', 'human', 'and', 'machine', 'comparisonbased', 'queries', 'are', 'among', 'the', 'most', 'natural', 'ways', 'to', 'get', 'feedback', 'from', 'humans', 'a', 'challenge', 'in', 'designing', 'comparisonbased', 'interactive', 'learning', 'algorithms', 'is', 'coping', 'with', 'noisy', 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1,802.06943 | Behavioral Characteristics and CO+CO2 Production Rates of Halley-Type
Comets Observed by NEOWISE | From the entire dataset of comets observed by NEOWISE, we have analyzed 11
different Halley-Type Comets (HTCs) for dust production rates, CO+CO2
production rates, and nucleus sizes. Incorporating HTCs from previous studies
and multiple comet visits we have a total of 21 stacked visits, 13 of which are
active and 8 for which we calculated upper limits of production. We determined
the nucleus sizes of 27P, P/2006 HR30, P/2012 NJ, and C/2016 S1. Furthermore,
we analyzed the relationships between dust production and heliocentric
distance, and gas production and heliocentric distance. We concluded that for
this population of HTCs, ranging in heliocentric distance from 1.21 AU to 2.66
AU, there was no significant correlation between dust production and
heliocentric distance, nor gas production and heliocentric distance.
| astro-ph.EP | from the entire dataset of comets observed by neowise we have analyzed 11 different halleytype comets htcs for dust production rates coco2 production rates and nucleus sizes incorporating htcs from previous studies and multiple comet visits we have a total of 21 stacked visits 13 of which are active and 8 for which we calculated upper limits of production we determined the nucleus sizes of 27p p2006 hr30 p2012 nj and c2016 s1 furthermore we analyzed the relationships between dust production and heliocentric distance and gas production and heliocentric distance we concluded that for this population of htcs ranging in heliocentric distance from 121 au to 266 au there was no significant correlation between dust production and heliocentric distance nor gas production and heliocentric distance | [['from', 'the', 'entire', 'dataset', 'of', 'comets', 'observed', 'by', 'neowise', 'we', 'have', 'analyzed', '11', 'different', 'halleytype', 'comets', 'htcs', 'for', 'dust', 'production', 'rates', 'coco2', 'production', 'rates', 'and', 'nucleus', 'sizes', 'incorporating', 'htcs', 'from', 'previous', 'studies', 'and', 'multiple', 'comet', 'visits', 'we', 'have', 'a', 'total', 'of', '21', 'stacked', 'visits', '13', 'of', 'which', 'are', 'active', 'and', '8', 'for', 'which', 'we', 'calculated', 'upper', 'limits', 'of', 'production', 'we', 'determined', 'the', 'nucleus', 'sizes', 'of', '27p', 'p2006', 'hr30', 'p2012', 'nj', 'and', 'c2016', 's1', 'furthermore', 'we', 'analyzed', 'the', 'relationships', 'between', 'dust', 'production', 'and', 'heliocentric', 'distance', 'and', 'gas', 'production', 'and', 'heliocentric', 'distance', 'we', 'concluded', 'that', 'for', 'this', 'population', 'of', 'htcs', 'ranging', 'in', 'heliocentric', 'distance', 'from', '121', 'au', 'to', '266', 'au', 'there', 'was', 'no', 'significant', 'correlation', 'between', 'dust', 'production', 'and', 'heliocentric', 'distance', 'nor', 'gas', 'production', 'and', 'heliocentric', 'distance']] | [-0.05440063161245968, 0.16120520964895765, -0.06401817225137606, 0.07004448704913468, 0.07102936138248732, -0.08070008056012974, 0.06922976209241297, 0.4178532554219747, -0.18525477852240474, -0.36565823448942075, 0.043263702273120985, -0.36265933731450667, 0.0011495046407705354, 0.15950684033093915, -0.0224401572636599, 0.016416048417018065, 0.05086173523863357, -0.06833126653556622, -0.017600882897949626, -0.23190751040895138, 0.23955356258703697, 0.09270118513414936, 0.13636327680620935, 0.07146508158984943, 0.10118585362684943, -0.03709156209586053, -0.043681414668812535, -0.02773062425905899, -0.2139808908779096, 0.1257575812257616, 0.1554341141975695, 0.14826370250140766, 0.14196656881305839, -0.320341722529021, -0.1573573043814019, 0.09411759903999947, 0.15344950397153415, 0.040504615647800925, -0.04273166587095587, -0.270861743908045, 0.04783189397365335, -0.2381296479218309, -0.14793106059603875, 0.13240927383453854, 0.18862666003406048, 0.03757281737231214, -0.2372222458530638, 0.14214001692861558, -0.0368330278170986, 0.18761549027417898, -0.1490016448923627, -0.217160081104075, -0.1086148411005495, 0.14186073052734438, 0.0898065559825139, 0.06155358115211129, 0.21636772822708852, 0.015190202226456735, -0.0631254226557963, 0.3450548996277634, -0.08596582011499952, -0.0014293083717817471, 0.2817811896694043, -0.21695045610108682, -0.1441223488392068, 0.19062017517224436, 0.24473622341399953, 0.1047639495452806, -0.21585196671038565, -0.0014979635152192186, -0.029089959841521996, 0.2261890709445241, 0.16097214983610977, 0.06734216714844919, 0.23865657214229297, 0.11173656221569306, -0.030292722047306597, 0.07429909252782489, -0.2793999237774481, -0.0830463635840363, -0.21475988144999475, -0.11776914793412172, -0.12095567122131827, 0.06749635580138513, -0.13038687433401075, -0.029423937596748734, 0.24497921441891982, 0.13239518197013006, 0.2537793435246473, 0.05817271576210436, 0.2004641045425688, -0.008924928024381159, 0.11388768282749, 0.16332627684929438, 0.34370491532008013, 0.10137227461804756, 0.086109978732683, -0.15801880785241543, 0.10622383073167575, 0.025273718086311653] |
1,802.06944 | DeepThin: A Self-Compressing Library for Deep Neural Networks | As the industry deploys increasingly large and complex neural networks to
mobile devices, more pressure is put on the memory and compute resources of
those devices. Deep compression, or compression of deep neural network weight
matrices, is a technique to stretch resources for such scenarios. Existing
compression methods cannot effectively compress models smaller than 1-2% of
their original size. We develop a new compression technique, DeepThin, building
on existing research in the area of low rank factorization. We identify and
break artificial constraints imposed by low rank approximations by combining
rank factorization with a reshaping process that adds nonlinearity to the
approximation function. We deploy DeepThin as a plug-gable library integrated
with TensorFlow that enables users to seamlessly compress models at different
granularities. We evaluate DeepThin on two state-of-the-art acoustic models,
TFKaldi and DeepSpeech, comparing it to previous compression work (Pruning,
HashNet, and Rank Factorization), empirical limit study approaches, and
hand-tuned models. For TFKaldi, our DeepThin networks show better word error
rates (WER) than competing methods at practically all tested compression rates,
achieving an average of 60% relative improvement over rank factorization, 57%
over pruning, 23% over hand-tuned same-size networks, and 6% over the
computationally expensive HashedNets. For DeepSpeech, DeepThin-compressed
networks achieve better test loss than all other compression methods, reaching
a 28% better result than rank factorization, 27% better than pruning, 20%
better than hand-tuned same-size networks, and 12% better than HashedNets.
DeepThin also provide inference performance benefits ranging from 2X to 14X
speedups, depending on the compression ratio and platform cache sizes.
| cs.LG stat.ML | as the industry deploys increasingly large and complex neural networks to mobile devices more pressure is put on the memory and compute resources of those devices deep compression or compression of deep neural network weight matrices is a technique to stretch resources for such scenarios existing compression methods cannot effectively compress models smaller than 12 of their original size we develop a new compression technique deepthin building on existing research in the area of low rank factorization we identify and break artificial constraints imposed by low rank approximations by combining rank factorization with a reshaping process that adds nonlinearity to the approximation function we deploy deepthin as a pluggable library integrated with tensorflow that enables users to seamlessly compress models at different granularities we evaluate deepthin on two stateoftheart acoustic models tfkaldi and deepspeech comparing it to previous compression work pruning hashnet and rank factorization empirical limit study approaches and handtuned models for tfkaldi our deepthin networks show better word error rates wer than competing methods at practically all tested compression rates achieving an average of 60 relative improvement over rank factorization 57 over pruning 23 over handtuned samesize networks and 6 over the computationally expensive hashednets for deepspeech deepthincompressed networks achieve better test loss than all other compression methods reaching a 28 better result than rank factorization 27 better than pruning 20 better than handtuned samesize networks and 12 better than hashednets deepthin also provide inference performance benefits ranging from 2x to 14x speedups depending on the compression ratio and platform cache sizes | [['as', 'the', 'industry', 'deploys', 'increasingly', 'large', 'and', 'complex', 'neural', 'networks', 'to', 'mobile', 'devices', 'more', 'pressure', 'is', 'put', 'on', 'the', 'memory', 'and', 'compute', 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1,802.06945 | Electron emission by long and short wavelength lasers: essentials for
the design of plasmonic photocathodes | Theory of electron emission by metallic photocathodes under the exposure of
long wavelength lasers will be studied. Energy of photons in long wavelength
lasers is less than the work function of the photocathode material, and can
only emit electrons via tunneling through the potential barrier. The optical
resonance effect (e.g. plasmonic resonances) will be studied as an improvement
to the performance of photocathodes. This paper is intended to provide
self-sufficient materials to design optical resonant surfaces (e.g.
metasurfaces) for electron emission applications.
| physics.app-ph physics.optics | theory of electron emission by metallic photocathodes under the exposure of long wavelength lasers will be studied energy of photons in long wavelength lasers is less than the work function of the photocathode material and can only emit electrons via tunneling through the potential barrier the optical resonance effect eg plasmonic resonances will be studied as an improvement to the performance of photocathodes this paper is intended to provide selfsufficient materials to design optical resonant surfaces eg metasurfaces for electron emission applications | [['theory', 'of', 'electron', 'emission', 'by', 'metallic', 'photocathodes', 'under', 'the', 'exposure', 'of', 'long', 'wavelength', 'lasers', 'will', 'be', 'studied', 'energy', 'of', 'photons', 'in', 'long', 'wavelength', 'lasers', 'is', 'less', 'than', 'the', 'work', 'function', 'of', 'the', 'photocathode', 'material', 'and', 'can', 'only', 'emit', 'electrons', 'via', 'tunneling', 'through', 'the', 'potential', 'barrier', 'the', 'optical', 'resonance', 'effect', 'eg', 'plasmonic', 'resonances', 'will', 'be', 'studied', 'as', 'an', 'improvement', 'to', 'the', 'performance', 'of', 'photocathodes', 'this', 'paper', 'is', 'intended', 'to', 'provide', 'selfsufficient', 'materials', 'to', 'design', 'optical', 'resonant', 'surfaces', 'eg', 'metasurfaces', 'for', 'electron', 'emission', 'applications']] | [-0.10318297783208148, 0.1856700563762499, -0.046853465454016884, 0.03597573725444197, -0.042342161461597354, -0.1952518390877763, 0.009718685249071114, 0.508198003875228, -0.21818621596889343, -0.3042442701620663, 0.02976021688948272, -0.31019848301188974, -0.06406935676932335, 0.26732573591218123, -0.043942137871210166, 0.052010220875583796, 0.01853191800324655, -0.10593322464854105, -0.010420825372154757, -0.17355226886901642, 0.22960681769792446, 0.15857775935835047, 0.27779689049575385, 0.1429391446737743, 0.06552415392248005, 0.02855927222578718, 0.10259814789836727, -0.08402195692107808, -0.10239183831187647, 0.07701116207299935, 0.3046620699645179, -0.034374688956432226, 0.24625674794746064, -0.4862820719618623, -0.2817971160203764, 0.04400395151091421, 0.18196463131270565, 0.09050596714951098, -0.08724137088965352, -0.25720685095776147, 0.010207477296850212, -0.13867912350063463, -0.13298639046914149, -0.001465742294563026, -0.0118016896474107, 0.0447466390593568, -0.23325798878582513, -0.020152065570738804, 0.005270018795982185, 0.05027148321344767, -0.05700374694495666, -0.0534672987329342, -0.0021147976470429724, 0.02019527584041764, 0.032503243291568826, -0.006205907119892356, 0.24602090019914435, -0.10978645013954785, -0.1224303864419642, 0.38835944360852875, -0.0762974427107554, -0.09187256185928495, 0.1525985503387524, -0.17728458176844003, 0.061515866971870024, 0.24132760625514316, 0.1758608703456092, 0.1264734003726938, -0.1847075199503906, 0.026932272332487628, 0.04211998565077055, 0.1908555039369342, 0.13593887228507368, 0.1936426083017822, 0.24531979793285177, 0.22222534394482288, 0.02849012959703076, 0.13258844669397193, -0.1378752632611772, 0.051253200985673, -0.2060685747348499, -0.18961541758986508, -0.20556410279397558, 0.08588015383518324, 0.010804063345414939, -0.15477065284304867, 0.4074607006524031, 0.11948819357969957, 0.06866547115510557, -0.057049917755648494, 0.3404284054299862, 0.12404998746081596, 0.09482362095789029, -0.02878124356587849, 0.3370060999366659, 0.14135276384810666, 0.11722997100683065, -0.19935370421196083, 0.012310419453126265, -0.0617853000493175] |
1,802.06946 | Coupon Advertising in Online Social Systems: Algorithms and Sampling
Techniques | Online social systems have become important platforms for viral marketing
where the advertising of products is carried out with the communication of
users. After adopting the product, the seed buyers may spread the information
to their friends via online messages e.g. posts and tweets. In another issue,
electronic coupon system is one of the relevant promotion vehicles that help
manufacturers and retailers attract more potential customers. By offering
coupons to seed buyers, there is a chance to convince the influential users who
are, however, at first not very interested in the product. In this paper, we
propose a coupon based online influence model and consider the problem that how
to maximize the profit by selecting appropriate seed buyers. The considered
problem herein is markedly different from other influence related problems as
its objective function is not monotone. We provide an algorithmic analysis and
give several algorithms designed with different sampling techniques. In
particular, we propose the RA-T and RA-S algorithms which are not only provably
effective but also scalable on large datasets. The proposed theoretical results
are evaluated by extensive experiments done on large-scale real-world social
networks. The analysis of this paper also provides an algorithmic framework for
non-monotone submodular maximization problems in social networks.
| cs.SI | online social systems have become important platforms for viral marketing where the advertising of products is carried out with the communication of users after adopting the product the seed buyers may spread the information to their friends via online messages eg posts and tweets in another issue electronic coupon system is one of the relevant promotion vehicles that help manufacturers and retailers attract more potential customers by offering coupons to seed buyers there is a chance to convince the influential users who are however at first not very interested in the product in this paper we propose a coupon based online influence model and consider the problem that how to maximize the profit by selecting appropriate seed buyers the considered problem herein is markedly different from other influence related problems as its objective function is not monotone we provide an algorithmic analysis and give several algorithms designed with different sampling techniques in particular we propose the rat and ras algorithms which are not only provably effective but also scalable on large datasets the proposed theoretical results are evaluated by extensive experiments done on largescale realworld social networks the analysis of this paper also provides an algorithmic framework for nonmonotone submodular maximization problems in social networks | [['online', 'social', 'systems', 'have', 'become', 'important', 'platforms', 'for', 'viral', 'marketing', 'where', 'the', 'advertising', 'of', 'products', 'is', 'carried', 'out', 'with', 'the', 'communication', 'of', 'users', 'after', 'adopting', 'the', 'product', 'the', 'seed', 'buyers', 'may', 'spread', 'the', 'information', 'to', 'their', 'friends', 'via', 'online', 'messages', 'eg', 'posts', 'and', 'tweets', 'in', 'another', 'issue', 'electronic', 'coupon', 'system', 'is', 'one', 'of', 'the', 'relevant', 'promotion', 'vehicles', 'that', 'help', 'manufacturers', 'and', 'retailers', 'attract', 'more', 'potential', 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1,802.06947 | Entropy Guided Spectrum Based Bug Localization Using Statistical
Language Model | Locating bugs is challenging but one of the most important activities in
software development and maintenance phase because there are no certain rules
to identify all types of bugs. Existing automatic bug localization tools use
various heuristics based on test coverage, pre-determined buggy patterns, or
textual similarity with bug report, to rank suspicious program elements.
However, since these techniques rely on information from single source, they
often suffer when the respective source information is inadequate. For
instance, the popular spectrum based bug localization may not work well under
poorly written test suite. In this paper, we propose a new approach, EnSpec,
that guides spectrum based bug localization using code entropy, a metric that
basically represents naturalness of code derived from a statistical language
model. Our intuition is that since buggy code are high entropic, spectrum based
bug localization with code entropy would be more robust in discriminating buggy
lines vs. non-buggy lines. We realize our idea in a prototype, and performed an
extensive evaluation on two popular publicly available benchmarks. Our results
demonstrate that EnSpec outperforms a state-of-the-art spectrum based bug
localization technique.
| cs.SE | locating bugs is challenging but one of the most important activities in software development and maintenance phase because there are no certain rules to identify all types of bugs existing automatic bug localization tools use various heuristics based on test coverage predetermined buggy patterns or textual similarity with bug report to rank suspicious program elements however since these techniques rely on information from single source they often suffer when the respective source information is inadequate for instance the popular spectrum based bug localization may not work well under poorly written test suite in this paper we propose a new approach enspec that guides spectrum based bug localization using code entropy a metric that basically represents naturalness of code derived from a statistical language model our intuition is that since buggy code are high entropic spectrum based bug localization with code entropy would be more robust in discriminating buggy lines vs nonbuggy lines we realize our idea in a prototype and performed an extensive evaluation on two popular publicly available benchmarks our results demonstrate that enspec outperforms a stateoftheart spectrum based bug localization technique | [['locating', 'bugs', 'is', 'challenging', 'but', 'one', 'of', 'the', 'most', 'important', 'activities', 'in', 'software', 'development', 'and', 'maintenance', 'phase', 'because', 'there', 'are', 'no', 'certain', 'rules', 'to', 'identify', 'all', 'types', 'of', 'bugs', 'existing', 'automatic', 'bug', 'localization', 'tools', 'use', 'various', 'heuristics', 'based', 'on', 'test', 'coverage', 'predetermined', 'buggy', 'patterns', 'or', 'textual', 'similarity', 'with', 'bug', 'report', 'to', 'rank', 'suspicious', 'program', 'elements', 'however', 'since', 'these', 'techniques', 'rely', 'on', 'information', 'from', 'single', 'source', 'they', 'often', 'suffer', 'when', 'the', 'respective', 'source', 'information', 'is', 'inadequate', 'for', 'instance', 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1,802.06948 | On the Totik-Widom property for a Quasidisk | Let $K$ be a quasidisk on the complex plane. We construct a sequence of monic
polynomials $p_n=p_n(\cdot,K)$ with zeros on $K$ such that $||p_n||_K \le O(1)
\mathrm{cap}(K)^n$ as $n\to\infty.$
| math.CV | let k be a quasidisk on the complex plane we construct a sequence of monic polynomials p_np_ncdotk with zeros on k such that p_n_k le o1 mathrmcapkn as ntoinfty | [['let', 'k', 'be', 'a', 'quasidisk', 'on', 'the', 'complex', 'plane', 'we', 'construct', 'a', 'sequence', 'of', 'monic', 'polynomials', 'p_np_ncdotk', 'with', 'zeros', 'on', 'k', 'such', 'that', 'p_n_k', 'le', 'o1', 'mathrmcapkn', 'as', 'ntoinfty']] | [-0.24913793898842954, 0.13386568567646598, -0.05782758207405331, -0.033591274741209216, -0.05059172364848631, -0.12863790405983175, 0.035721552459074664, 0.3242098244803923, -0.2795098418438876, -0.19712756028295392, 0.07552434229809377, -0.3282493719900096, -0.13689232803881168, 0.17310770031892592, -0.02295869776841115, -0.029245744368785784, -0.006670258059683774, 0.12315108206261087, -0.061694196727195824, -0.2778443732057457, 0.2583681662325506, -0.07830937965600579, 0.043620177924081134, -0.039289005687115366, 0.11439795895583099, 0.037904674739197446, 0.13813781850384893, -0.09302163149092209, -0.22270028928808946, 0.007794971077668446, 0.3243755339472382, 0.1552360443122409, 0.22646471071574423, -0.28573652839771024, -0.14400492084247093, 0.294131517548252, 0.25795102930041375, -0.12198171002307424, 0.038478612132301485, -0.22559116622088132, 0.1714904160687217, -0.04092579101712478, -0.18550801232319186, -0.08661351145969497, 0.062427785592498605, 0.09354663419502753, -0.35947326570749283, -0.031683735687423636, 0.09051108670731385, 0.15909618135817624, 0.08487031193174145, -0.2554772635231967, -0.04000816452834341, 0.03335941805400782, -0.08127935492882023, 0.16373153429271448, 0.013798683071164069, -0.00643172015056566, -0.05432679666275227, 0.33607457474594143, -0.13082155585289001, -0.2225655136388485, 0.0815743095482941, -0.20389557464255226, -0.19596270558044868, 0.11267859271417062, 0.17199004193147024, 0.22458733342312, 0.051375071069708574, 0.2299420361717542, -0.18420101498702057, 0.18835852343451093, 0.14855832589307316, -0.027292756932891078, 0.18403273848471818, 0.008564207013006564, 0.07759842036628267, 0.14350227924943385, 0.019901766441762447, 0.013960389851557987, -0.31411636537975735, -0.17356807624714243, -0.26126927736267036, 0.20974610787298945, -0.21980962105509308, -0.18161947004221105, 0.32013929497312615, 0.06436861056351552, 0.2814655524023153, 0.17169177149318987, 0.18062792111326148, 0.11036809743813204, 0.0038082839024287684, 0.06558896483922447, -0.04763340398117348, 0.18576780085762343, -0.016319813051571447, -0.13925853785541323, 0.02625515613773907, 0.15963826259529149] |
1,802.06949 | Efficient Embedding of MPI Collectives in MXNET DAGs for scaling Deep
Learning | Availability of high performance computing infrastructures such as clusters
of GPUs and CPUs have fueled the growth of distributed learning systems. Deep
Learning frameworks express neural nets as DAGs and execute these DAGs on
computation resources such as GPUs. In this paper, we propose efficient designs
of embedding MPI collective operations into data parallel DAGs. Incorrect
designs can easily lead to deadlocks or program crashes. In particular, we
demonstrate three designs: Funneled, Concurrent communication and Dependency
chaining of using MPI collectives with DAGs. These designs automatically enable
overlap of computation with communication by allowing for concurrent execution
with the other tasks. We directly implement these designs into the KVStore API
of the MXNET. This allows us to directly leverage the rest of the
infrastructure. Using ImageNet and CIFAR data sets, we show the potential of
our designs. In particular, our designs scale to 256 GPUs with as low as 50
seconds of epoch times for ImageNet 1K datasets.
| cs.DC cs.LG | availability of high performance computing infrastructures such as clusters of gpus and cpus have fueled the growth of distributed learning systems deep learning frameworks express neural nets as dags and execute these dags on computation resources such as gpus in this paper we propose efficient designs of embedding mpi collective operations into data parallel dags incorrect designs can easily lead to deadlocks or program crashes in particular we demonstrate three designs funneled concurrent communication and dependency chaining of using mpi collectives with dags these designs automatically enable overlap of computation with communication by allowing for concurrent execution with the other tasks we directly implement these designs into the kvstore api of the mxnet this allows us to directly leverage the rest of the infrastructure using imagenet and cifar data sets we show the potential of our designs in particular our designs scale to 256 gpus with as low as 50 seconds of epoch times for imagenet 1k datasets | [['availability', 'of', 'high', 'performance', 'computing', 'infrastructures', 'such', 'as', 'clusters', 'of', 'gpus', 'and', 'cpus', 'have', 'fueled', 'the', 'growth', 'of', 'distributed', 'learning', 'systems', 'deep', 'learning', 'frameworks', 'express', 'neural', 'nets', 'as', 'dags', 'and', 'execute', 'these', 'dags', 'on', 'computation', 'resources', 'such', 'as', 'gpus', 'in', 'this', 'paper', 'we', 'propose', 'efficient', 'designs', 'of', 'embedding', 'mpi', 'collective', 'operations', 'into', 'data', 'parallel', 'dags', 'incorrect', 'designs', 'can', 'easily', 'lead', 'to', 'deadlocks', 'or', 'program', 'crashes', 'in', 'particular', 'we', 'demonstrate', 'three', 'designs', 'funneled', 'concurrent', 'communication', 'and', 'dependency', 'chaining', 'of', 'using', 'mpi', 'collectives', 'with', 'dags', 'these', 'designs', 'automatically', 'enable', 'overlap', 'of', 'computation', 'with', 'communication', 'by', 'allowing', 'for', 'concurrent', 'execution', 'with', 'the', 'other', 'tasks', 'we', 'directly', 'implement', 'these', 'designs', 'into', 'the', 'kvstore', 'api', 'of', 'the', 'mxnet', 'this', 'allows', 'us', 'to', 'directly', 'leverage', 'the', 'rest', 'of', 'the', 'infrastructure', 'using', 'imagenet', 'and', 'cifar', 'data', 'sets', 'we', 'show', 'the', 'potential', 'of', 'our', 'designs', 'in', 'particular', 'our', 'designs', 'scale', 'to', '256', 'gpus', 'with', 'as', 'low', 'as', '50', 'seconds', 'of', 'epoch', 'times', 'for', 'imagenet', '1k', 'datasets']] | [-0.1308057493571738, 0.03991390343541932, -0.0415569816265184, 0.0429508042370509, -0.09269340107644534, -0.17703632751307719, 0.07143837931361287, 0.45190117425421716, -0.2827280202560174, -0.4117823325684828, 0.11108395323872115, -0.2290964422668338, -0.12320584524719483, 0.23045029050884117, -0.0973455564287242, 0.1126635672856763, 0.13526980023665033, -0.029834497129770033, -0.05700760174566393, -0.32323042335356494, 0.2343337247303871, 0.06294200685491436, 0.31807393866003886, 0.015156811892179546, 0.06594156206935455, -0.01946789999700086, -0.0015758602704947732, -0.02536179353690641, -0.045741754573633435, 0.1839046180326118, 0.3427455054898598, 0.2584368434829554, 0.29626934923184145, -0.5011026788336835, -0.13429476423367004, 0.07835416859431062, 0.15163814015745833, 0.06920330787077916, 0.0033366376953792706, -0.2789315898356971, 0.07971039609976148, -0.20860395286303418, 0.003911791019331498, -0.15547191580052333, -0.035736958436952654, 0.060876557921210696, -0.28068020056553517, -0.013011129095462287, 0.03647784114248789, 0.08000674911190393, 0.034295143756443026, -0.11579884184740565, 0.027491704080325023, 0.14330993229849562, -0.0047009057442473755, 0.0003085052624202458, 0.18725109698286482, -0.11043838118232645, -0.24653371757750583, 0.36697319643512655, 0.013032911487730446, -0.16662493460236272, 0.23144472483248943, -0.004652340258738607, -0.1962113145110049, 0.06094676029948506, 0.28345089957450226, 0.06944765297660402, -0.14202141455689055, 0.03283388754905344, 0.027394796490289603, 0.1655789226246108, 0.08550507753233241, 0.041863565852236787, 0.17757549718582918, 0.2412159120290285, 0.04702030492732954, 0.1797967090061569, -0.10099891106194704, -0.08133591495893279, -0.2044898410161637, -0.14890697605427425, -0.15102288906409103, -0.0241724395572807, -0.143759787325513, -0.16099143172703256, 0.3334845081505597, 0.23966495013813588, 0.14739529863533796, 0.1590881750942653, 0.3689193085108878, -0.007820094709438835, 0.22639898190595162, 0.19055277176177615, 0.1157243235546882, 0.007877054999457898, 0.14607848934413994, -0.16756745748767618, 0.05093793595761393, -0.021049402749638967] |
1,802.0695 | TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection | Detecting novelty of an entire document is an Artificial Intelligence (AI)
frontier problem that has widespread NLP applications, such as extractive
document summarization, tracking development of news events, predicting impact
of scholarly articles, etc. Important though the problem is, we are unaware of
any benchmark document level data that correctly addresses the evaluation of
automatic novelty detection techniques in a classification framework. To bridge
this gap, we present here a resource for benchmarking the techniques for
document level novelty detection. We create the resource via event-specific
crawling of news documents across several domains in a periodic manner. We
release the annotated corpus with necessary statistics and show its use with a
developed system for the problem in concern.
| cs.CL | detecting novelty of an entire document is an artificial intelligence ai frontier problem that has widespread nlp applications such as extractive document summarization tracking development of news events predicting impact of scholarly articles etc important though the problem is we are unaware of any benchmark document level data that correctly addresses the evaluation of automatic novelty detection techniques in a classification framework to bridge this gap we present here a resource for benchmarking the techniques for document level novelty detection we create the resource via eventspecific crawling of news documents across several domains in a periodic manner we release the annotated corpus with necessary statistics and show its use with a developed system for the problem in concern | [['detecting', 'novelty', 'of', 'an', 'entire', 'document', 'is', 'an', 'artificial', 'intelligence', 'ai', 'frontier', 'problem', 'that', 'has', 'widespread', 'nlp', 'applications', 'such', 'as', 'extractive', 'document', 'summarization', 'tracking', 'development', 'of', 'news', 'events', 'predicting', 'impact', 'of', 'scholarly', 'articles', 'etc', 'important', 'though', 'the', 'problem', 'is', 'we', 'are', 'unaware', 'of', 'any', 'benchmark', 'document', 'level', 'data', 'that', 'correctly', 'addresses', 'the', 'evaluation', 'of', 'automatic', 'novelty', 'detection', 'techniques', 'in', 'a', 'classification', 'framework', 'to', 'bridge', 'this', 'gap', 'we', 'present', 'here', 'a', 'resource', 'for', 'benchmarking', 'the', 'techniques', 'for', 'document', 'level', 'novelty', 'detection', 'we', 'create', 'the', 'resource', 'via', 'eventspecific', 'crawling', 'of', 'news', 'documents', 'across', 'several', 'domains', 'in', 'a', 'periodic', 'manner', 'we', 'release', 'the', 'annotated', 'corpus', 'with', 'necessary', 'statistics', 'and', 'show', 'its', 'use', 'with', 'a', 'developed', 'system', 'for', 'the', 'problem', 'in', 'concern']] | [-0.09192608940904423, 0.007321480972646988, -0.019323113141581416, 0.08829896856529512, -0.13467088770279187, -0.09547775926589334, 0.06159354589082364, 0.41749069381947235, -0.24815577753203905, -0.3468604588376011, 0.08145616556245486, -0.3222092120450432, -0.17031516643414685, 0.2157598736427598, -0.11618103475262553, 0.0730080355222187, 0.15910162632274677, 0.05829668960861947, 0.005017014279551172, -0.2887262947368041, 0.31839411680870766, 0.03448170803929285, 0.3911867741302003, 0.09844274594945739, 0.08583299396066311, 0.0048746959941651105, -0.11495959670485147, -0.05339050065169616, -0.09852767884556177, 0.19059563582167188, 0.4202396990573507, 0.2820927339578363, 0.3865463924673149, -0.36128488612771664, -0.18803401474872508, 0.0736459470275095, 0.15652322456224868, 0.13760815324040793, -0.09388768860786142, -0.3317263673406915, 0.07993997598729902, -0.20376631665296094, -0.03318018336006898, -0.1039084349127518, 0.055552958333233406, -0.012988042018782758, -0.2130540639785576, 0.03696573974754093, 0.08454752775398477, 0.12171063058362422, -0.0447791473144839, -0.08403876942673193, 0.09087656377479277, 0.18332067214078823, 0.06936987463712409, 0.036293084848123616, 0.1481575265968755, -0.19266124713526628, -0.20122306513742116, 0.40577506094034443, -0.06189558252305487, -0.19859968959472252, 0.16402722996959496, 0.01246937926290399, -0.21415409538500263, 0.06061662693697391, 0.23134076804459347, 0.09778217805581073, -0.20656501963513635, 0.014554985040715911, -0.02354988808175391, 0.21966937457416522, 0.056078727344264105, -0.012702683823467311, 0.20906731091100345, 0.29116695261386744, 0.05347802788334883, 0.14758525814040233, -0.08446098715994287, -0.04996065858562114, -0.24091196055445124, -0.1568066630930782, -0.1767187953721416, -0.04666946488985066, -0.014075707016641993, -0.2105796581007919, 0.41046875702627633, 0.25473537094599047, 0.1274717377096225, 0.01760069089048834, 0.3182612421993419, 0.012523693414365525, 0.06476773857706329, 0.06514704950011761, 0.11807836688881329, -0.055754759625168675, 0.19686472616395204, -0.12357067144515341, 0.09206560055204367, 0.03814804932039421] |
1,802.06951 | Adoption of e-government in the Republic of Kazakhstan | This paper identifies factors that influence Kazakhstan's e-Government portal
usage. It determines challenges encountered by citizens while using the portal.
Targeted respondents for the web-based questionnaire survey were the citizens
of Kazakhstan. The technology acceptance model was used as a methodology to
measure attitude towards portal usage. In addition, this paper discusses the
barriers which were experienced by the respondents and can prevent the
successful adoption of e-Government initiative. The results of the analysis
demonstrate that awareness among citizens is high, i.e. the majority of people
have visited it at least once and they perceive the portal to be useful, but
only a limited percentage of citizens' use it on the regular basis. Further,
paper can be used to help IT managers of the portal to improve management of
informational content and maintain a more effective adoption among citizens.
| cs.CY | this paper identifies factors that influence kazakhstans egovernment portal usage it determines challenges encountered by citizens while using the portal targeted respondents for the webbased questionnaire survey were the citizens of kazakhstan the technology acceptance model was used as a methodology to measure attitude towards portal usage in addition this paper discusses the barriers which were experienced by the respondents and can prevent the successful adoption of egovernment initiative the results of the analysis demonstrate that awareness among citizens is high ie the majority of people have visited it at least once and they perceive the portal to be useful but only a limited percentage of citizens use it on the regular basis further paper can be used to help it managers of the portal to improve management of informational content and maintain a more effective adoption among citizens | [['this', 'paper', 'identifies', 'factors', 'that', 'influence', 'kazakhstans', 'egovernment', 'portal', 'usage', 'it', 'determines', 'challenges', 'encountered', 'by', 'citizens', 'while', 'using', 'the', 'portal', 'targeted', 'respondents', 'for', 'the', 'webbased', 'questionnaire', 'survey', 'were', 'the', 'citizens', 'of', 'kazakhstan', 'the', 'technology', 'acceptance', 'model', 'was', 'used', 'as', 'a', 'methodology', 'to', 'measure', 'attitude', 'towards', 'portal', 'usage', 'in', 'addition', 'this', 'paper', 'discusses', 'the', 'barriers', 'which', 'were', 'experienced', 'by', 'the', 'respondents', 'and', 'can', 'prevent', 'the', 'successful', 'adoption', 'of', 'egovernment', 'initiative', 'the', 'results', 'of', 'the', 'analysis', 'demonstrate', 'that', 'awareness', 'among', 'citizens', 'is', 'high', 'ie', 'the', 'majority', 'of', 'people', 'have', 'visited', 'it', 'at', 'least', 'once', 'and', 'they', 'perceive', 'the', 'portal', 'to', 'be', 'useful', 'but', 'only', 'a', 'limited', 'percentage', 'of', 'citizens', 'use', 'it', 'on', 'the', 'regular', 'basis', 'further', 'paper', 'can', 'be', 'used', 'to', 'help', 'it', 'managers', 'of', 'the', 'portal', 'to', 'improve', 'management', 'of', 'informational', 'content', 'and', 'maintain', 'a', 'more', 'effective', 'adoption', 'among', 'citizens']] | [-0.06301679271965058, 0.07212496143546414, -0.05456233275818058, 0.09459540791441753, -0.16726951946612392, -0.13063729174124697, 0.12686872860143447, 0.36608051319701085, -0.2139798647557905, -0.39731611852682586, 0.11541652150259799, -0.30787607197028893, -0.16173784998987895, 0.16857290706779485, -0.11911670273322829, -0.0013929082863572714, 0.06359402871137296, 0.0343630154232454, 0.05852647335184198, -0.3489968424824917, 0.2866879035398175, 0.10593174045662516, 0.3442037991126594, 0.07989687359203464, 0.05813515617215223, 0.013636892924652151, -0.07831691823261992, -0.027199297708094768, -0.06978942017947017, 0.18620610616061892, 0.35133945691547747, 0.2372572487388886, 0.3900954607145294, -0.38811695880756, -0.15118830624387425, 0.09148066989281148, 0.16756939438367396, 0.04168152812939218, -0.04524199881532273, -0.35472063259626535, 0.09420315156002408, -0.27660730238194053, -0.14402311601325113, -0.09622424914825546, -0.02526816988930754, 0.009730602354334289, -0.20517387417603986, -0.01029236967767339, -0.038324782088739746, 0.09397230180236849, -0.03213920582767468, -0.07879983216348657, -0.05103376444658615, 0.2404146248380235, 0.09179707798192385, -0.015009857990873465, 0.1797540444523042, -0.17531640958243414, -0.06320744779203898, 0.4118531586507848, -0.0009124199826054383, -0.13759919545491753, 0.16385628295087162, -0.0961573905567976, -0.13516728849365303, 0.05624751885762384, 0.24003869107126366, 0.04733779828698523, -0.2204321465873416, 0.03666670905448078, 0.02167492660869291, 0.17365326597422792, 0.07924865092280779, -0.01432517028508195, 0.21088305283067882, 0.18432065494952427, 0.07696159120034966, 0.07331849307334726, 0.005455494151396704, -0.07458994843646129, -0.21220918113122816, -0.21627389726674426, -0.11935813339643073, -0.0127254447712482, -0.020616060540951497, -0.10105214372405485, 0.38691532296681963, 0.22632730142145918, 0.08096956193237903, -0.0259592048545449, 0.2909115493108613, 0.036221456107433776, 0.13531441789582724, 0.07051129011012128, 0.21384021739704887, 0.005445446994533573, 0.21619358187561613, -0.13907588738273235, 0.1712450329484283, -0.025463324222389772] |
1,802.06952 | 64-Qubit Quantum Circuit Simulation | Classical simulations of quantum circuits are limited in both space and time
when the qubit count is above 50, the realm where quantum supremacy reigns.
However, recently, for the low depth circuit with more than 50 qubits, there
are several methods of simulation proposed by teams at Google and IBM. Here, we
present a scheme of simulation which can extract a large amount of measurement
outcomes within a short time, achieving a 64-qubit simulation of a universal
random circuit of depth 22 using a 128-node cluster, and 56- and 42-qubit
circuits on a single PC. We also estimate that a 72-qubit circuit of depth 23
can be simulated in about 16 h on a supercomputer identical to that used by the
IBM team. Moreover, the simulation processes are exceedingly separable, hence
parallelizable, involving just a few inter-process communications. Our work
enables simulating more qubits with less hardware burden and provides a new
perspective for classical simulations.
| quant-ph | classical simulations of quantum circuits are limited in both space and time when the qubit count is above 50 the realm where quantum supremacy reigns however recently for the low depth circuit with more than 50 qubits there are several methods of simulation proposed by teams at google and ibm here we present a scheme of simulation which can extract a large amount of measurement outcomes within a short time achieving a 64qubit simulation of a universal random circuit of depth 22 using a 128node cluster and 56 and 42qubit circuits on a single pc we also estimate that a 72qubit circuit of depth 23 can be simulated in about 16 h on a supercomputer identical to that used by the ibm team moreover the simulation processes are exceedingly separable hence parallelizable involving just a few interprocess communications our work enables simulating more qubits with less hardware burden and provides a new perspective for classical simulations | [['classical', 'simulations', 'of', 'quantum', 'circuits', 'are', 'limited', 'in', 'both', 'space', 'and', 'time', 'when', 'the', 'qubit', 'count', 'is', 'above', '50', 'the', 'realm', 'where', 'quantum', 'supremacy', 'reigns', 'however', 'recently', 'for', 'the', 'low', 'depth', 'circuit', 'with', 'more', 'than', '50', 'qubits', 'there', 'are', 'several', 'methods', 'of', 'simulation', 'proposed', 'by', 'teams', 'at', 'google', 'and', 'ibm', 'here', 'we', 'present', 'a', 'scheme', 'of', 'simulation', 'which', 'can', 'extract', 'a', 'large', 'amount', 'of', 'measurement', 'outcomes', 'within', 'a', 'short', 'time', 'achieving', 'a', '64qubit', 'simulation', 'of', 'a', 'universal', 'random', 'circuit', 'of', 'depth', '22', 'using', 'a', '128node', 'cluster', 'and', '56', 'and', '42qubit', 'circuits', 'on', 'a', 'single', 'pc', 'we', 'also', 'estimate', 'that', 'a', '72qubit', 'circuit', 'of', 'depth', '23', 'can', 'be', 'simulated', 'in', 'about', '16', 'h', 'on', 'a', 'supercomputer', 'identical', 'to', 'that', 'used', 'by', 'the', 'ibm', 'team', 'moreover', 'the', 'simulation', 'processes', 'are', 'exceedingly', 'separable', 'hence', 'parallelizable', 'involving', 'just', 'a', 'few', 'interprocess', 'communications', 'our', 'work', 'enables', 'simulating', 'more', 'qubits', 'with', 'less', 'hardware', 'burden', 'and', 'provides', 'a', 'new', 'perspective', 'for', 'classical', 'simulations']] | [-0.10750849697260971, 0.1333445246120008, -0.052904233981328284, 0.022676830661734322, -0.009449367612097902, -0.189917211016444, 0.1005596818148134, 0.38412719313055277, -0.20580304042577105, -0.38139989094710663, 0.10968361142563242, -0.2552398803680645, -0.11668498151342838, 0.3130041569690404, -0.03549116839330006, 0.08429691898268893, 0.10007676224555134, 0.007357631019610716, -0.06845814377945651, -0.2793666543071403, 0.2262284911029335, 0.07657352791437388, 0.23731688490002661, 0.0036036931866730904, 0.10858581946108882, -0.03208959861627878, 0.004588027123214775, 0.02597781097632067, -0.0669166283969389, 0.12715423177455945, 0.2428682521644269, 0.1352743113805589, 0.3071512387403728, -0.45768204795259454, -0.19297881853938298, 0.09442723041253262, 0.14105016634934336, 0.13618966414038655, -0.051810806708473184, -0.2649434010351175, 0.10156240356522367, -0.18771990320620766, -0.04155449388475206, -0.06308222726281536, 0.049707992449647895, -0.0057467299606957425, -0.23355849981185442, 0.036840391247096704, -0.0006881956360302866, 0.08367931431061343, 0.04697999424962817, -0.09145879141349149, 0.048256674776846344, 0.1157888521413138, -0.10655864638944254, 0.03874312239502998, 0.16039166919462763, -0.11534972729369704, -0.16038373732743294, 0.36556185644708183, -0.021414472297692555, -0.15564106317776205, 0.20558079572449633, -0.10362350891993724, -0.10884989896102955, 0.11249422959027518, 0.17134591961994852, 0.08051460051042102, -0.15542806929184544, 0.056625983925761444, -0.028008068969938904, 0.24583236140652412, 0.03173275417678891, 0.03213707785318164, 0.14785625139101236, 0.20831012267061802, 0.04779034431129204, 0.1324010438670245, -0.10168984877619598, -0.1477608052695072, -0.2866577928898072, -0.16332827906865677, -0.20396677030734472, 0.07383402600106292, -0.08941923852618952, -0.11563876890087206, 0.3684419357995179, 0.16712798639759727, 0.1564093746588026, 0.10225829537911953, 0.3239742612809335, 0.07656591747486106, 0.10739062327621995, 0.11171291561147786, 0.1941390804529778, 0.11651055606412947, 0.0856716538148344, -0.1727284123610987, 0.030946212250886385, -0.03549807257552043] |
1,802.06953 | Distributed Symmetry Breaking in Sampling (Optimal Distributed Randomly
Coloring with Fewer Colors) | We examine the problem of almost-uniform sampling proper $q$-colorings of a
graph whose maximum degree is $\Delta$. A famous result, discovered
independently by Jerrum(1995) and Salas and Sokal(1997), is that, assuming $q >
(2+\delta) \Delta$, the Glauber dynamics (a.k.a. single-site dynamics) for this
problem has mixing time $O(n \log n)$, where $n$ is the number of vertices, and
thus provides a nearly linear time sampling algorithm for this problem. A
natural question is the extent to which this algorithm can be parallelized.
Previous work Feng, Sun and Yin [PODC'17] has shown that a $O(\Delta \log n)$
time parallelized algorithm is possible, and that $\Omega(\log n)$ time is
necessary.
We give a distributed sampling algorithm, which we call the Lazy Local
Metropolis Algorithm, that achieves an optimal parallelization of this classic
algorithm. It improves its predecessor, the Local Metropolis algorithm of Feng,
Sun and Yin [PODC'17], by introducing a step of distributed symmetry breaking
that helps the mixing of the distributed sampling algorithm.
For sampling almost-uniform proper $q$-colorings of graphs $G$ on $n$
vertices, we show that the Lazy Local Metropolis algorithm achieves an optimal
$O(\log n)$ mixing time if either of the following conditions is true for an
arbitrary constant $\delta>0$:
$\bullet$ $q\ge(2+\delta)\Delta$, on general graphs with maximum degree
$\Delta$;
$\bullet$ $q \geq (\alpha^* + \delta)\Delta$, where $\alpha^* \approx 1.763$
satisfies $\alpha^* = \mathrm{e}^{1/\alpha^*}$, on graphs with sufficiently
large maximum degree $\Delta\ge \Delta_0(\delta)$ and girth at least $9$.
| cs.DS | we examine the problem of almostuniform sampling proper qcolorings of a graph whose maximum degree is delta a famous result discovered independently by jerrum1995 and salas and sokal1997 is that assuming q 2delta delta the glauber dynamics aka singlesite dynamics for this problem has mixing time on log n where n is the number of vertices and thus provides a nearly linear time sampling algorithm for this problem a natural question is the extent to which this algorithm can be parallelized previous work feng sun and yin podc17 has shown that a odelta log n time parallelized algorithm is possible and that omegalog n time is necessary we give a distributed sampling algorithm which we call the lazy local metropolis algorithm that achieves an optimal parallelization of this classic algorithm it improves its predecessor the local metropolis algorithm of feng sun and yin podc17 by introducing a step of distributed symmetry breaking that helps the mixing of the distributed sampling algorithm for sampling almostuniform proper qcolorings of graphs g on n vertices we show that the lazy local metropolis algorithm achieves an optimal olog n mixing time if either of the following conditions is true for an arbitrary constant delta0 bullet qge2deltadelta on general graphs with maximum degree delta bullet q geq alpha deltadelta where alpha approx 1763 satisfies alpha mathrme1alpha on graphs with sufficiently large maximum degree deltage delta_0delta and girth at least 9 | [['we', 'examine', 'the', 'problem', 'of', 'almostuniform', 'sampling', 'proper', 'qcolorings', 'of', 'a', 'graph', 'whose', 'maximum', 'degree', 'is', 'delta', 'a', 'famous', 'result', 'discovered', 'independently', 'by', 'jerrum1995', 'and', 'salas', 'and', 'sokal1997', 'is', 'that', 'assuming', 'q', '2delta', 'delta', 'the', 'glauber', 'dynamics', 'aka', 'singlesite', 'dynamics', 'for', 'this', 'problem', 'has', 'mixing', 'time', 'on', 'log', 'n', 'where', 'n', 'is', 'the', 'number', 'of', 'vertices', 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1,802.06954 | Stochastic dominance and weak concentration for sums of independent
symmetric random vectors | Kwapien and Woyczynski asked in their monograph (1992) whether their notion
of superstrong domination is inherited when taking sums of independent
symmetric random vectors (one vector dominates another if, essentially, tail
probabilities of any norm of the two vectors compare up to some scaling
constants). We answer this question positively. As a by-product of our methods,
we establish that a certain notion of weak concentration is also preserved by
taking sums of independent symmetric random vectors.
| math.PR math.FA | kwapien and woyczynski asked in their monograph 1992 whether their notion of superstrong domination is inherited when taking sums of independent symmetric random vectors one vector dominates another if essentially tail probabilities of any norm of the two vectors compare up to some scaling constants we answer this question positively as a byproduct of our methods we establish that a certain notion of weak concentration is also preserved by taking sums of independent symmetric random vectors | [['kwapien', 'and', 'woyczynski', 'asked', 'in', 'their', 'monograph', '1992', 'whether', 'their', 'notion', 'of', 'superstrong', 'domination', 'is', 'inherited', 'when', 'taking', 'sums', 'of', 'independent', 'symmetric', 'random', 'vectors', 'one', 'vector', 'dominates', 'another', 'if', 'essentially', 'tail', 'probabilities', 'of', 'any', 'norm', 'of', 'the', 'two', 'vectors', 'compare', 'up', 'to', 'some', 'scaling', 'constants', 'we', 'answer', 'this', 'question', 'positively', 'as', 'a', 'byproduct', 'of', 'our', 'methods', 'we', 'establish', 'that', 'a', 'certain', 'notion', 'of', 'weak', 'concentration', 'is', 'also', 'preserved', 'by', 'taking', 'sums', 'of', 'independent', 'symmetric', 'random', 'vectors']] | [-0.1464605010052522, 0.20097361230601868, -0.06144306001913113, 0.07900441215528796, -0.07709711037576199, -0.1312656556776104, 0.0922727183625102, 0.3381992461284002, -0.3499421783909202, -0.1993629418934385, 0.08589645371306688, -0.2670194806655248, -0.13461215722064176, 0.14669002197682857, -0.07278681057194869, -0.0016194378460446994, 0.010173420533537864, 0.06965705233315626, -0.07104234670288861, -0.35330477602779864, 0.36552740464607875, 0.01753570523733894, 0.24054649683336418, 0.04970065073420604, 0.11233360503489773, 0.05501358236496647, -0.07868850021933516, 0.02416284984598557, -0.13767422464103826, 0.12659241169691085, 0.19999255621029685, 0.1600791084469529, 0.3225730654100577, -0.3488329910238584, -0.1230764572826835, 0.17895680226385594, 0.1109866465628147, 0.04730501506477594, 0.0009873076528310776, -0.269056431632489, 0.1256523172184825, -0.1362397588789463, -0.14561764317875106, -0.05334846417186782, 0.06133443138251702, 0.04444413802276055, -0.2880540101819982, 0.061419884037459266, 0.18099210381507874, 0.034022533955673374, -0.06947476555282871, -0.13965086879208685, 0.036006575174008806, 0.12032668889694226, 0.09147114903713373, 0.019828363414853813, 0.07321168256768336, -0.08411523572790126, -0.14040681569526592, 0.34726531063516936, -0.06773252196299533, -0.2122982357442379, 0.14761035713056722, -0.15738642904286584, -0.1383051077524821, 0.07827345687896013, 0.1338250949109594, 0.13506292163704833, -0.11578291399540225, 0.12486142326068754, -0.15843515005894004, 0.10187790320254862, 0.15831548309574525, 0.04766452599937717, 0.1457717883338531, 0.01780650851937632, 0.07916599845513701, 0.1561521826001505, 0.047009137260417146, -0.06562148803845048, -0.3046365975340207, -0.13144698705213764, -0.1946723964624107, 0.12457648798823356, -0.14129974641507337, -0.2182565311094125, 0.35534126703937846, 0.10593324736381571, 0.23450178240736325, 0.08849889503171046, 0.2054558013131221, 0.11387853541101019, -0.015229127413282792, 0.06511921774595976, 0.1532312037504744, 0.25098543765954673, 0.018720734501257538, -0.09709430920581023, 0.09960588093847036, 0.1453975335073968] |
1,802.06955 | Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net)
for Medical Image Segmentation | Deep learning (DL) based semantic segmentation methods have been providing
state-of-the-art performance in the last few years. More specifically, these
techniques have been successfully applied to medical image classification,
segmentation, and detection tasks. One deep learning technique, U-Net, has
become one of the most popular for these applications. In this paper, we
propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well
as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net
models, which are named RU-Net and R2U-Net respectively. The proposed models
utilize the power of U-Net, Residual Network, as well as RCNN. There are
several advantages of these proposed architectures for segmentation tasks.
First, a residual unit helps when training deep architecture. Second, feature
accumulation with recurrent residual convolutional layers ensures better
feature representation for segmentation tasks. Third, it allows us to design
better U-Net architecture with same number of network parameters with better
performance for medical image segmentation. The proposed models are tested on
three benchmark datasets such as blood vessel segmentation in retina images,
skin cancer segmentation, and lung lesion segmentation. The experimental
results show superior performance on segmentation tasks compared to equivalent
models including U-Net and residual U-Net (ResU-Net).
| cs.CV | deep learning dl based semantic segmentation methods have been providing stateoftheart performance in the last few years more specifically these techniques have been successfully applied to medical image classification segmentation and detection tasks one deep learning technique unet has become one of the most popular for these applications in this paper we propose a recurrent convolutional neural network rcnn based on unet as well as a recurrent residual convolutional neural network rrcnn based on unet models which are named runet and r2unet respectively the proposed models utilize the power of unet residual network as well as rcnn there are several advantages of these proposed architectures for segmentation tasks first a residual unit helps when training deep architecture second feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks third it allows us to design better unet architecture with same number of network parameters with better performance for medical image segmentation the proposed models are tested on three benchmark datasets such as blood vessel segmentation in retina images skin cancer segmentation and lung lesion segmentation the experimental results show superior performance on segmentation tasks compared to equivalent models including unet and residual unet resunet | [['deep', 'learning', 'dl', 'based', 'semantic', 'segmentation', 'methods', 'have', 'been', 'providing', 'stateoftheart', 'performance', 'in', 'the', 'last', 'few', 'years', 'more', 'specifically', 'these', 'techniques', 'have', 'been', 'successfully', 'applied', 'to', 'medical', 'image', 'classification', 'segmentation', 'and', 'detection', 'tasks', 'one', 'deep', 'learning', 'technique', 'unet', 'has', 'become', 'one', 'of', 'the', 'most', 'popular', 'for', 'these', 'applications', 'in', 'this', 'paper', 'we', 'propose', 'a', 'recurrent', 'convolutional', 'neural', 'network', 'rcnn', 'based', 'on', 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1,802.06956 | On the evolution process of two-component dark matter in the Sun | We introduce dark matter (DM) evolution process in the Sun under a
two-component DM (2DM) scenario. Both DM species $\chi$ and $\xi$ with masses
heavier than 1 GeV are considered. In this picture, both species could be
captured by the Sun through DM-nucleus scattering and DM self-scatterings, e.g.
$\chi\chi$ and $\xi\xi$ collisions. In addition, the heterogeneous
self-scattering due to $\chi$ and $\xi$ collision is essentially possible in
any 2DM models. This new introduced scattering naturally weaves the evolution
processes of the two DM species that was assumed to evolve independently.
Moreover, the heterogeneous self-scattering enhances the number of DM being
captured in the Sun mutually. This effect significantly exists in a broad range
of DM mass spectrum. We have studied this phenomena and its implication for the
solar-captured DM annihilation rate. It would be crucial to the DM indirect
detection when the two masses are close. General formalism of the 2DM evolution
in the Sun as well as its kinematics are studied.
| hep-ph | we introduce dark matter dm evolution process in the sun under a twocomponent dm 2dm scenario both dm species chi and xi with masses heavier than 1 gev are considered in this picture both species could be captured by the sun through dmnucleus scattering and dm selfscatterings eg chichi and xixi collisions in addition the heterogeneous selfscattering due to chi and xi collision is essentially possible in any 2dm models this new introduced scattering naturally weaves the evolution processes of the two dm species that was assumed to evolve independently moreover the heterogeneous selfscattering enhances the number of dm being captured in the sun mutually this effect significantly exists in a broad range of dm mass spectrum we have studied this phenomena and its implication for the solarcaptured dm annihilation rate it would be crucial to the dm indirect detection when the two masses are close general formalism of the 2dm evolution in the sun as well as its kinematics are studied | [['we', 'introduce', 'dark', 'matter', 'dm', 'evolution', 'process', 'in', 'the', 'sun', 'under', 'a', 'twocomponent', 'dm', '2dm', 'scenario', 'both', 'dm', 'species', 'chi', 'and', 'xi', 'with', 'masses', 'heavier', 'than', '1', 'gev', 'are', 'considered', 'in', 'this', 'picture', 'both', 'species', 'could', 'be', 'captured', 'by', 'the', 'sun', 'through', 'dmnucleus', 'scattering', 'and', 'dm', 'selfscatterings', 'eg', 'chichi', 'and', 'xixi', 'collisions', 'in', 'addition', 'the', 'heterogeneous', 'selfscattering', 'due', 'to', 'chi', 'and', 'xi', 'collision', 'is', 'essentially', 'possible', 'in', 'any', '2dm', 'models', 'this', 'new', 'introduced', 'scattering', 'naturally', 'weaves', 'the', 'evolution', 'processes', 'of', 'the', 'two', 'dm', 'species', 'that', 'was', 'assumed', 'to', 'evolve', 'independently', 'moreover', 'the', 'heterogeneous', 'selfscattering', 'enhances', 'the', 'number', 'of', 'dm', 'being', 'captured', 'in', 'the', 'sun', 'mutually', 'this', 'effect', 'significantly', 'exists', 'in', 'a', 'broad', 'range', 'of', 'dm', 'mass', 'spectrum', 'we', 'have', 'studied', 'this', 'phenomena', 'and', 'its', 'implication', 'for', 'the', 'solarcaptured', 'dm', 'annihilation', 'rate', 'it', 'would', 'be', 'crucial', 'to', 'the', 'dm', 'indirect', 'detection', 'when', 'the', 'two', 'masses', 'are', 'close', 'general', 'formalism', 'of', 'the', '2dm', 'evolution', 'in', 'the', 'sun', 'as', 'well', 'as', 'its', 'kinematics', 'are', 'studied']] | [-0.12861243818932855, 0.22914093686846196, -0.07183282440188428, 0.14902090605445106, -0.03756475169790087, -0.11897422107781128, -0.03124142415034845, 0.3391024664995949, -0.24452602385213565, -0.3409270503506157, 0.005642506583906155, -0.25002645071225693, -0.04208077253739101, 0.14650111008285088, 0.054334432787626334, -0.01674264936161268, 0.017137847990462824, 0.0457030035416532, 0.002614441531296727, -0.25502067073016943, 0.30726536063684196, 0.046983151317730266, 0.16997778129630856, 0.08323451870602631, 0.022251806959440436, 0.024904010366208804, -0.03345891879618411, -0.06407798897555989, -0.1255519621507413, 0.05926282583140791, 0.19410203528107936, 0.09137007637782404, 0.12253957393330446, -0.3845198073349199, -0.2404109369211408, 0.1872193617283465, 0.20168893685250222, 0.05940433533035033, -0.058844147683902184, -0.3254350150576706, 0.0699217614127751, -0.23051020809050118, -0.11833490114167379, 0.012881020584392437, 0.0471291731817428, -0.023287796953357524, -0.2710747963995441, 0.12072736085884227, 0.03455355081501885, -0.0516417041444075, -0.06228336206816646, -0.14021048487687587, -0.04853747558189984, 0.028562741475042407, 0.09630634941204787, -0.014943735459992974, 0.20421130173826613, -0.17230314454020387, -0.05595370061747637, 0.43428473808565493, -0.10626409773077448, -0.15376056278969005, 0.24977546620547122, -0.1464395228663448, -0.13914024986808263, 0.12936320579437227, 0.14844827971967828, 0.09957554323673942, -0.16166094087126812, 0.12781500324761633, -0.060229772672936416, 0.1489371242202668, 0.05399743979796767, 0.026109768455538623, 0.2889019701491046, 0.17279505623093813, 0.025506716899816758, 0.022707489580899385, -0.11396080630929759, -0.08720893389870765, -0.2682120550817214, -0.14037390258320934, -0.1196248540886163, 0.0059521624352782965, -0.07132672702397223, -0.05397590039144646, 0.3031173183073677, 0.14830401297969606, 0.25951556091324096, 0.001315572478386186, 0.2914010107333581, 0.10323597429904462, 0.04622599125191893, 0.04221174688518094, 0.293908889052568, 0.1686125500315673, 0.10867787972352937, -0.20345999948501078, 0.0667127742669227, -0.02584486920531168] |
1,802.06957 | MIMO Transmit Beampattern Matching Under Waveform Constraints | In this paper, the multiple-input multiple-output (MIMO) transmit beampattern
matching problem is considered. The problem is formulated to approximate a
desired transmit beampattern (i.e., an energy distribution in space and
frequency) and to minimize the cross-correlation of signals reflected back to
the array by considering different practical waveform constraints at the same
time. Due to the nonconvexity of the objective function and the waveform
constraints, the optimization problem is highly nonconvex. An efficient
one-step method is proposed to solve this problem based on the
majorization-minimization (MM) method. The performance of the proposed
algorithms compared to the state-of-art algorithms is shown through numerical
simulations.
| math.OC cs.IT cs.SY math.IT | in this paper the multipleinput multipleoutput mimo transmit beampattern matching problem is considered the problem is formulated to approximate a desired transmit beampattern ie an energy distribution in space and frequency and to minimize the crosscorrelation of signals reflected back to the array by considering different practical waveform constraints at the same time due to the nonconvexity of the objective function and the waveform constraints the optimization problem is highly nonconvex an efficient onestep method is proposed to solve this problem based on the majorizationminimization mm method the performance of the proposed algorithms compared to the stateofart algorithms is shown through numerical simulations | [['in', 'this', 'paper', 'the', 'multipleinput', 'multipleoutput', 'mimo', 'transmit', 'beampattern', 'matching', 'problem', 'is', 'considered', 'the', 'problem', 'is', 'formulated', 'to', 'approximate', 'a', 'desired', 'transmit', 'beampattern', 'ie', 'an', 'energy', 'distribution', 'in', 'space', 'and', 'frequency', 'and', 'to', 'minimize', 'the', 'crosscorrelation', 'of', 'signals', 'reflected', 'back', 'to', 'the', 'array', 'by', 'considering', 'different', 'practical', 'waveform', 'constraints', 'at', 'the', 'same', 'time', 'due', 'to', 'the', 'nonconvexity', 'of', 'the', 'objective', 'function', 'and', 'the', 'waveform', 'constraints', 'the', 'optimization', 'problem', 'is', 'highly', 'nonconvex', 'an', 'efficient', 'onestep', 'method', 'is', 'proposed', 'to', 'solve', 'this', 'problem', 'based', 'on', 'the', 'majorizationminimization', 'mm', 'method', 'the', 'performance', 'of', 'the', 'proposed', 'algorithms', 'compared', 'to', 'the', 'stateofart', 'algorithms', 'is', 'shown', 'through', 'numerical', 'simulations']] | [-0.14780843451289116, -0.045023083010659035, -0.045647348562092745, 0.0303833752370991, -0.09778303217178988, -0.1420216525484383, 0.021277842103973828, 0.38561460036712075, -0.31976539287173633, -0.31341001702454485, 0.1214002960980915, -0.22856308746793605, -0.1883194232992466, 0.17447796246959169, -0.10163502466471558, 0.1528528244912769, 0.04719841875841172, -0.025219260886745545, -0.0995524591739172, -0.25862652369440325, 0.24861258284631862, 0.14638553909704233, 0.358362537890094, 0.01284627948697765, 0.15480173915710518, 0.014976959683886024, -0.0055415658008994405, -0.03612919516654604, -0.04992438941398292, 0.0867836983395216, 0.3191986218890519, 0.21620550784237177, 0.31460699135238684, -0.410075609237679, -0.2072669142604019, 0.13082988889327327, 0.1538123556851292, 0.07719379743036714, -0.03199051649341342, -0.27536286916402936, 0.11459544571161777, -0.13065945335383555, -0.0443744194531108, 0.043699120555651706, -0.08400078546436666, 0.014914163569151198, -0.3817079721778197, 0.03544613612517353, -0.025721066767006244, -0.06296226268326774, -0.0813956586779683, -0.1356600080119464, 0.06289871221914597, 0.08401730589374212, 0.08240652637492901, 0.05472519647926816, 0.07026333862902072, -0.07175318927965263, -0.11975749119975942, 0.3978160639311576, 0.010785217357356354, -0.30025156993346597, 0.1347868787689876, -0.05074920861086654, -0.0681223932527078, 0.2041930677075373, 0.27015040478515395, 0.1604780869477076, -0.18773141925593892, 0.06147463528215614, 0.005235845385824592, 0.19828067251788542, 0.05602290952415431, 0.028866784809862527, 0.14811946928853767, 0.20262026538150113, 0.17205841070746333, 0.18066866867678258, -0.10851101200910086, -0.09390574879944324, -0.18274542333013424, -0.0896062025872683, -0.2944943332701053, -0.04509856579463604, -0.09489345574455937, -0.09459363182783986, 0.41260786690450846, 0.17271661007702965, 0.12575995347923735, 0.1396896879016919, 0.41749743292632613, 0.17710016185906038, 0.020974162584554982, 0.0862792689235682, 0.2459040987124504, 0.14258181589917795, 0.10160090866299726, -0.293630731140616, 0.035719844453868645, 0.022134829222143274] |
1,802.06958 | Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless
Networks | We consider a dynamic multichannel access problem, where multiple correlated
channels follow an unknown joint Markov model. A user at each time slot selects
a channel to transmit data and receives a reward based on the success or
failure of the transmission. The objective is to find a policy that maximizes
the expected long-term reward. The problem is formulated as a partially
observable Markov decision process (POMDP) with unknown system dynamics. To
overcome the challenges of unknown system dynamics as well as prohibitive
computation, we apply the concept of reinforcement learning and implement a
Deep Q-Network (DQN) that can deal with large state space without any prior
knowledge of the system dynamics. We provide an analytical study on the optimal
policy for fixed-pattern channel switching with known system dynamics and show
through simulations that DQN can achieve the same optimal performance without
knowing the system statistics. We compare the performance of DQN with a Myopic
policy and a Whittle Index-based heuristic through both simulations as well as
real-data trace and show that DQN achieves near-optimal performance in more
complex situations. Finally, we propose an adaptive DQN approach with the
capability to adapt its learning in time-varying, dynamic scenarios.
| cs.NI | we consider a dynamic multichannel access problem where multiple correlated channels follow an unknown joint markov model a user at each time slot selects a channel to transmit data and receives a reward based on the success or failure of the transmission the objective is to find a policy that maximizes the expected longterm reward the problem is formulated as a partially observable markov decision process pomdp with unknown system dynamics to overcome the challenges of unknown system dynamics as well as prohibitive computation we apply the concept of reinforcement learning and implement a deep qnetwork dqn that can deal with large state space without any prior knowledge of the system dynamics we provide an analytical study on the optimal policy for fixedpattern channel switching with known system dynamics and show through simulations that dqn can achieve the same optimal performance without knowing the system statistics we compare the performance of dqn with a myopic policy and a whittle indexbased heuristic through both simulations as well as realdata trace and show that dqn achieves nearoptimal performance in more complex situations finally we propose an adaptive dqn approach with the capability to adapt its learning in timevarying dynamic scenarios | [['we', 'consider', 'a', 'dynamic', 'multichannel', 'access', 'problem', 'where', 'multiple', 'correlated', 'channels', 'follow', 'an', 'unknown', 'joint', 'markov', 'model', 'a', 'user', 'at', 'each', 'time', 'slot', 'selects', 'a', 'channel', 'to', 'transmit', 'data', 'and', 'receives', 'a', 'reward', 'based', 'on', 'the', 'success', 'or', 'failure', 'of', 'the', 'transmission', 'the', 'objective', 'is', 'to', 'find', 'a', 'policy', 'that', 'maximizes', 'the', 'expected', 'longterm', 'reward', 'the', 'problem', 'is', 'formulated', 'as', 'a', 'partially', 'observable', 'markov', 'decision', 'process', 'pomdp', 'with', 'unknown', 'system', 'dynamics', 'to', 'overcome', 'the', 'challenges', 'of', 'unknown', 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1,802.06959 | On the automorphism groups of distance-regular graphs and rank-4
primitive coherent configurations | The minimal degree of a permutation group $G$ is the minimum number of points
not fixed by non-identity elements of $G$. Lower bounds on the minimal degree
have strong structural consequences on $G$. In 2014 Babai proved that the
automorphism group of a strongly regular graph with $n$ vertices has minimal
degree $\geq c n$, with known exceptions. Strongly regular graphs correspond to
primitive coherent configurations of rank 3. We extend Babai's result to
primitive coherent configurations of rank 4. We also show that the result
extends to non-geometric distance-regular graphs of bounded diameter. The
proofs combine structural and spectral methods.
| math.CO | the minimal degree of a permutation group g is the minimum number of points not fixed by nonidentity elements of g lower bounds on the minimal degree have strong structural consequences on g in 2014 babai proved that the automorphism group of a strongly regular graph with n vertices has minimal degree geq c n with known exceptions strongly regular graphs correspond to primitive coherent configurations of rank 3 we extend babais result to primitive coherent configurations of rank 4 we also show that the result extends to nongeometric distanceregular graphs of bounded diameter the proofs combine structural and spectral methods | [['the', 'minimal', 'degree', 'of', 'a', 'permutation', 'group', 'g', 'is', 'the', 'minimum', 'number', 'of', 'points', 'not', 'fixed', 'by', 'nonidentity', 'elements', 'of', 'g', 'lower', 'bounds', 'on', 'the', 'minimal', 'degree', 'have', 'strong', 'structural', 'consequences', 'on', 'g', 'in', '2014', 'babai', 'proved', 'that', 'the', 'automorphism', 'group', 'of', 'a', 'strongly', 'regular', 'graph', 'with', 'n', 'vertices', 'has', 'minimal', 'degree', 'geq', 'c', 'n', 'with', 'known', 'exceptions', 'strongly', 'regular', 'graphs', 'correspond', 'to', 'primitive', 'coherent', 'configurations', 'of', 'rank', '3', 'we', 'extend', 'babais', 'result', 'to', 'primitive', 'coherent', 'configurations', 'of', 'rank', '4', 'we', 'also', 'show', 'that', 'the', 'result', 'extends', 'to', 'nongeometric', 'distanceregular', 'graphs', 'of', 'bounded', 'diameter', 'the', 'proofs', 'combine', 'structural', 'and', 'spectral', 'methods']] | [-0.1993531393928057, 0.1471932481808385, -0.03332088435400683, 0.016233744031731223, -0.09208672004060285, -0.161027108485082, 0.05641208097596865, 0.36094203206138814, -0.27083881893702366, -0.31727491857686846, 0.06469893231319429, -0.29007508836244, -0.15405911661795166, 0.14425927150929993, -0.13968996394720704, 0.00044992319907587354, 0.07798624883341317, 0.1283256767575841, -0.04983758151365241, -0.3232424385920006, 0.3223341103772273, -0.02687988714268892, 0.17417797304593063, 0.06849645737484836, 0.06046402251513878, 0.01838915672448307, -0.008421668911924456, 0.04496641288336256, -0.1802698497040633, 0.10822720872238278, 0.2400955924450761, 0.1301516826797074, 0.17504651088608741, -0.3582419301798143, -0.18566096568650722, 0.2152442893504377, 0.07347357913328445, 0.026431978219142643, 0.02306235086698417, -0.1955364830054269, 0.18196351694330426, -0.13137842264037353, -0.17824906218188383, -0.01588791426234316, 0.12049589877127495, 0.005090411217643483, -0.2297638749823638, -0.026316742180203467, 0.14782819826856697, 0.12648599067389374, 0.07203097626714423, -0.1886825962360985, -0.07464871366074917, 0.08365500211429847, -0.054657213604262116, 0.03442574332993809, 0.03967800555003825, -0.07327639846370952, -0.1645324966584397, 0.3348377490840336, -0.038178120739758015, -0.15926475461000705, 0.1824965483997718, -0.1763831440377796, -0.20185314611264385, 0.14912153818266521, 0.13092465269019699, 0.18709431796180231, -0.023045190665317645, 0.19985179110178908, -0.13682580998370258, 0.18358214551785795, 0.13238122798318025, 0.02996980236603482, 0.08134308371619128, 0.07828672525546707, 0.17697168845397485, 0.14234395789923054, 0.04435160838639234, 0.03245256476030491, -0.3125591441841409, -0.0710876046444778, -0.1977784681631861, 0.09121229024480056, -0.20270361512940316, -0.1786215960654421, 0.40146214411234354, 0.06272033795640611, 0.16506578690270976, 0.12056429167785267, 0.1588678175910828, 0.016647802545956457, 0.055446355927523484, 0.15104018525672283, 0.13006124056709728, 0.24639531581127777, -0.09266145675821175, -0.16532897969966034, 0.02578589976027534, 0.18762752846258549] |
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