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1,802.0466 | Implanting germanium into graphene | Incorporating heteroatoms into the graphene lattice may be used to tailor its
electronic, mechanical and chemical properties. Direct substitutions have thus
far been limited to incidental Si impurities and P, N and B dopants introduced
using low-energy ion implantation. We present here the heaviest impurity to
date, namely $^{74}$Ge$^+$ ions implanted into monolayer graphene. Although
sample contamination remains an issue, atomic resolution scanning transmission
electron microscopy imaging and quantitative image simulations show that Ge can
either directly substitute single atoms, bonding to three carbon neighbors in a
buckled out-of-plane configuration, or occupy an in-plane position in a
divacancy. First principles molecular dynamics provides further atomistic
insight into the implantation process, revealing a strong chemical effect that
enables implantation below the graphene displacement threshold energy. Our
results show that heavy atoms can be implanted into the graphene lattice,
pointing a way towards advanced applications such as single-atom catalysis with
graphene as the template.
| cond-mat.mtrl-sci | incorporating heteroatoms into the graphene lattice may be used to tailor its electronic mechanical and chemical properties direct substitutions have thus far been limited to incidental si impurities and p n and b dopants introduced using lowenergy ion implantation we present here the heaviest impurity to date namely 74ge ions implanted into monolayer graphene although sample contamination remains an issue atomic resolution scanning transmission electron microscopy imaging and quantitative image simulations show that ge can either directly substitute single atoms bonding to three carbon neighbors in a buckled outofplane configuration or occupy an inplane position in a divacancy first principles molecular dynamics provides further atomistic insight into the implantation process revealing a strong chemical effect that enables implantation below the graphene displacement threshold energy our results show that heavy atoms can be implanted into the graphene lattice pointing a way towards advanced applications such as singleatom catalysis with graphene as the template | [['incorporating', 'heteroatoms', 'into', 'the', 'graphene', 'lattice', 'may', 'be', 'used', 'to', 'tailor', 'its', 'electronic', 'mechanical', 'and', 'chemical', 'properties', 'direct', 'substitutions', 'have', 'thus', 'far', 'been', 'limited', 'to', 'incidental', 'si', 'impurities', 'and', 'p', 'n', 'and', 'b', 'dopants', 'introduced', 'using', 'lowenergy', 'ion', 'implantation', 'we', 'present', 'here', 'the', 'heaviest', 'impurity', 'to', 'date', 'namely', '74ge', 'ions', 'implanted', 'into', 'monolayer', 'graphene', 'although', 'sample', 'contamination', 'remains', 'an', 'issue', 'atomic', 'resolution', 'scanning', 'transmission', 'electron', 'microscopy', 'imaging', 'and', 'quantitative', 'image', 'simulations', 'show', 'that', 'ge', 'can', 'either', 'directly', 'substitute', 'single', 'atoms', 'bonding', 'to', 'three', 'carbon', 'neighbors', 'in', 'a', 'buckled', 'outofplane', 'configuration', 'or', 'occupy', 'an', 'inplane', 'position', 'in', 'a', 'divacancy', 'first', 'principles', 'molecular', 'dynamics', 'provides', 'further', 'atomistic', 'insight', 'into', 'the', 'implantation', 'process', 'revealing', 'a', 'strong', 'chemical', 'effect', 'that', 'enables', 'implantation', 'below', 'the', 'graphene', 'displacement', 'threshold', 'energy', 'our', 'results', 'show', 'that', 'heavy', 'atoms', 'can', 'be', 'implanted', 'into', 'the', 'graphene', 'lattice', 'pointing', 'a', 'way', 'towards', 'advanced', 'applications', 'such', 'as', 'singleatom', 'catalysis', 'with', 'graphene', 'as', 'the', 'template']] | [-0.047437815577723086, 0.1746020533321012, -0.02718208872982742, -0.0015387024287926913, -0.007109783792034968, -0.21128010891659774, 0.10994475939131869, 0.47896342408402187, -0.29814811107567757, -0.3054135988074306, -0.04240324920331324, -0.3440813532537217, -0.08657992492111302, 0.12903058965457603, 0.04416983654936015, 0.01864660972396929, 0.05755067346571626, -0.10357818231109138, -0.027867313036327494, -0.2023629731426665, 0.18714838878306755, 0.10047258443113319, 0.301455444219755, 0.13522889790903336, 0.019910796530128113, 0.0302484449700722, 0.12374764312469204, 0.03770221817448098, -0.1411245782563511, 0.0891318766455362, 0.23540999843757354, -0.03474403173803646, 0.24420165468779342, -0.5538769459937708, -0.2364843063221901, 0.006552505527475947, 0.20917747750699114, 0.20168299770815984, -0.14770834007312691, -0.2580036434465958, 0.052965740755960146, -0.0917237664919077, -0.10932031491507874, -0.08732136359321885, -0.014084687965039751, -0.009974608406896311, -0.24529473368678928, 0.021906565551609292, 0.045812928776477316, 0.0939984256646743, -0.10675507000154252, -0.17040919116085493, -0.10396731970219039, 0.0801351880861483, -0.03548046317289271, 0.022456065403898003, 0.29761236932628027, -0.050554797685962466, -0.09062561562505404, 0.40799166613846627, -0.014084322756389156, -0.12018494090482004, 0.1751660600796305, -0.14134869838859518, -0.09853619420410771, 0.15819384830109284, 0.10407479421969344, 0.07762100683191277, -0.18678573993537084, 0.057383864097860886, 0.016247825684810157, 0.21199480433444096, 0.11223821574822068, 0.10016986675021287, 0.23493990895191305, 0.21057686086293725, 0.05296544165493718, 0.11284786359091843, -0.16456970535708884, 0.027035338085720707, -0.1521663984044847, -0.21917502624503532, -0.20070356232922917, 0.13159770990253156, -0.048104733454046686, -0.17113017344289752, 0.35024468628628375, 0.11973735786082934, 0.15600959848195903, -0.12158046452647173, 0.2714364622355022, 0.04901816393938038, 0.08277752939619332, -0.07376201849700392, 0.23389597325340697, 0.15918065323862002, 0.07707396144197494, -0.2528047021708182, 0.07300435330053963, 0.011776919240793703] |
1,802.04661 | Modelling of cavity optomechanical magnetometers | Cavity optomechanical magnetic field sensors, constructed by coupling a
magnetostrictive material to a micro-toroidal optical cavity, act as
ultra-sensitive room temperature magnetometers with tens of micrometre size and
broad bandwidth, combined with a simple operating scheme. Here, we develop a
general recipe for predicting the field sensitivity of these devices. Several
geometries are analysed, with a highest predicted sensitivity of
180~p$\textrm{T}/\sqrt{\textrm{Hz}}$ at 28~$\mu$m resolution limited by thermal
noise in good agreement with previous experimental observations. Furthermore,
by adjusting the composition of the magnetostrictive material and its annealing
process, a sensitivity as good as 20~p$\textrm{T}/\sqrt{\textrm{Hz}}$ may be
possible at the same resolution. This method paves a way for future design of
magnetostrictive material based optomechanical magnetometers, possibly allowing
both scalar and vectorial magnetometers.
| physics.app-ph physics.optics | cavity optomechanical magnetic field sensors constructed by coupling a magnetostrictive material to a microtoroidal optical cavity act as ultrasensitive room temperature magnetometers with tens of micrometre size and broad bandwidth combined with a simple operating scheme here we develop a general recipe for predicting the field sensitivity of these devices several geometries are analysed with a highest predicted sensitivity of 180ptextrmtsqrttextrmhz at 28mum resolution limited by thermal noise in good agreement with previous experimental observations furthermore by adjusting the composition of the magnetostrictive material and its annealing process a sensitivity as good as 20ptextrmtsqrttextrmhz may be possible at the same resolution this method paves a way for future design of magnetostrictive material based optomechanical magnetometers possibly allowing both scalar and vectorial magnetometers | [['cavity', 'optomechanical', 'magnetic', 'field', 'sensors', 'constructed', 'by', 'coupling', 'a', 'magnetostrictive', 'material', 'to', 'a', 'microtoroidal', 'optical', 'cavity', 'act', 'as', 'ultrasensitive', 'room', 'temperature', 'magnetometers', 'with', 'tens', 'of', 'micrometre', 'size', 'and', 'broad', 'bandwidth', 'combined', 'with', 'a', 'simple', 'operating', 'scheme', 'here', 'we', 'develop', 'a', 'general', 'recipe', 'for', 'predicting', 'the', 'field', 'sensitivity', 'of', 'these', 'devices', 'several', 'geometries', 'are', 'analysed', 'with', 'a', 'highest', 'predicted', 'sensitivity', 'of', '180ptextrmtsqrttextrmhz', 'at', '28mum', 'resolution', 'limited', 'by', 'thermal', 'noise', 'in', 'good', 'agreement', 'with', 'previous', 'experimental', 'observations', 'furthermore', 'by', 'adjusting', 'the', 'composition', 'of', 'the', 'magnetostrictive', 'material', 'and', 'its', 'annealing', 'process', 'a', 'sensitivity', 'as', 'good', 'as', '20ptextrmtsqrttextrmhz', 'may', 'be', 'possible', 'at', 'the', 'same', 'resolution', 'this', 'method', 'paves', 'a', 'way', 'for', 'future', 'design', 'of', 'magnetostrictive', 'material', 'based', 'optomechanical', 'magnetometers', 'possibly', 'allowing', 'both', 'scalar', 'and', 'vectorial', 'magnetometers']] | [-0.12777668260382252, 0.17253595809726155, -0.007194114105478555, -0.0658403788129974, -0.07953658032802348, -0.18489018155402748, 0.057569501532179095, 0.46735656347532734, -0.23469803727255398, -0.3630741491890317, 0.08747150704260728, -0.22980607513870513, -0.08154760776948528, 0.27227622463109613, -0.020231938298729036, 0.0815594323178041, 0.028228004959983236, -0.03169232592800585, -0.03526904071170539, -0.15082605057709045, 0.2124141550705056, 0.1329970746966345, 0.2988462433622007, 0.026893472746053727, 0.1277729887593569, -0.04587658169139333, 0.057424682200330646, 0.057441158548874015, -0.11573790173420972, 0.12129893098143908, 0.26571145660768286, -0.010740780396809597, 0.2545524207843828, -0.43833243997157123, -0.23025298707124567, 0.05270233443946022, 0.09879904229021749, 0.13920519240650528, -0.09336640796929348, -0.27093485271080764, 0.0385044550600446, -0.15841612697602445, -0.15283372529237943, -0.10896647705876526, -0.05055814272990184, 0.03375650889498471, -0.2844513318337062, 0.02385984351388922, 0.0018493182323246944, 0.1139474704034016, -0.07764223293450705, -0.10983532234099928, 0.023154217303738373, 0.06139209517473433, -0.06320409800787848, 0.028163827786969506, 0.23762971583162942, -0.13529165568487608, -0.1010539357231523, 0.35125197498362604, -0.11136986371599325, -0.10853741734539073, 0.17503666869822515, -0.14167590658063023, -0.023775203697414708, 0.13305243402680367, 0.1432684559268415, 0.10162506353877046, -0.17953885696875324, 0.02217466143760126, 0.034884374123066664, 0.19951911885565257, 0.09985962252234336, 0.13378061343784425, 0.25883445312746434, 0.24906691913532109, 0.04496236294586243, 0.15924501244053088, -0.11131992740478336, 0.026762415153588785, -0.27208302106645676, -0.13939031629095308, -0.18395850857516297, 0.06440085796600192, -0.13144068141282486, -0.11556969421813969, 0.3723561705605598, 0.1908293544762854, 0.1741894224517736, -0.005174649367695909, 0.3282971631211205, 0.04626431610110458, 0.1237598881184147, -0.03866387991344228, 0.31154429664475325, 0.17748438847689507, 0.1249288902813647, -0.2543951762970263, 0.017077661080270253, -0.07352313641047872] |
1,802.04662 | Integrable Toda system as a quantum approximation to the anisotropy of
Mixmaster | We present a regularisation approach to the study of the quantum dynamics of
the Mixmaster universe which allows to approximate the anisotropy potential
with the explicitly integrable periodic 3-particle Toda system. This approach
is based on a covariant Weyl-Heisenberg integral quantization. Such a procedure
naturally amplifies the dynamical role of the underlying Toda system by
smoothing out the three canyons of the anisotropy potential. Since the
respective eigenfunctions can be explicitly constructed, our finding paves the
way to a novel perturbative approach to the quantum Mixmaster dynamics.
| gr-qc | we present a regularisation approach to the study of the quantum dynamics of the mixmaster universe which allows to approximate the anisotropy potential with the explicitly integrable periodic 3particle toda system this approach is based on a covariant weylheisenberg integral quantization such a procedure naturally amplifies the dynamical role of the underlying toda system by smoothing out the three canyons of the anisotropy potential since the respective eigenfunctions can be explicitly constructed our finding paves the way to a novel perturbative approach to the quantum mixmaster dynamics | [['we', 'present', 'a', 'regularisation', 'approach', 'to', 'the', 'study', 'of', 'the', 'quantum', 'dynamics', 'of', 'the', 'mixmaster', 'universe', 'which', 'allows', 'to', 'approximate', 'the', 'anisotropy', 'potential', 'with', 'the', 'explicitly', 'integrable', 'periodic', '3particle', 'toda', 'system', 'this', 'approach', 'is', 'based', 'on', 'a', 'covariant', 'weylheisenberg', 'integral', 'quantization', 'such', 'a', 'procedure', 'naturally', 'amplifies', 'the', 'dynamical', 'role', 'of', 'the', 'underlying', 'toda', 'system', 'by', 'smoothing', 'out', 'the', 'three', 'canyons', 'of', 'the', 'anisotropy', 'potential', 'since', 'the', 'respective', 'eigenfunctions', 'can', 'be', 'explicitly', 'constructed', 'our', 'finding', 'paves', 'the', 'way', 'to', 'a', 'novel', 'perturbative', 'approach', 'to', 'the', 'quantum', 'mixmaster', 'dynamics']] | [-0.15163537490599113, 0.07804020704037842, -0.16277490830284425, 0.07720081572924023, -0.09439872507134388, -0.10297179145701016, -0.01638120614613096, 0.30124805254551273, -0.3062357422949254, -0.24048210549885515, 0.046628660656212045, -0.2074783798121518, -0.21716376153441766, 0.1657527336722304, -0.006781095954664479, 0.08439303121004982, 0.044422651598251414, 0.019375904382945133, -0.0947415244219632, -0.25310331454564783, 0.3488096125509547, 0.07162962272367172, 0.2757027129610551, 0.022464267604438395, 0.1345225684291244, 0.025854700678509885, 0.002533030958482246, -0.021968811809288703, -0.1269189077911192, 0.1370953116003938, 0.20727759329923268, 0.04187817500647286, 0.2679134156426479, -0.3935278498396363, -0.22864429448228116, 0.06955730486875293, 0.18642855310362988, 0.1886132111299205, -0.009161728119809481, -0.33405347731521073, 0.02204582296932737, -0.15297725541923923, -0.2094500339590013, -0.11954184003366992, -0.04067845997031264, -0.014926928108900615, -0.24412989362688928, 0.09226377952801085, 0.06375734005277259, 0.0063598099105788035, -0.07707860226938287, -0.020694990580815864, -0.016684750638281304, 0.08433822281765697, 0.002516817823373552, 0.045987600503467965, 0.12271595109339761, -0.07807554017174347, -0.12083338475224145, 0.39335820140937966, -0.06681731316373395, -0.2535654943056362, 0.10739837720155201, -0.10640165411556761, -0.14287007759571418, 0.09889062935882516, 0.13479365038032504, 0.09270426345154129, -0.1819658082449573, 0.1341936840574075, -0.004831457821031411, 0.10247079836975398, 0.007174557588737586, 0.023770820627781165, 0.2400701967035902, 0.14617391799887022, 0.04524265806693798, 0.14988582514242493, -0.046065425615350916, -0.19526308533018347, -0.3478642983426308, -0.1371067361370928, -0.16847762695631419, 0.1032914633959018, -0.11539516037051704, -0.19547347409714913, 0.4490847512116206, 0.191250698196691, 0.166354158786179, 0.02977850543530444, 0.25650282264633595, 0.13756122675205143, 0.06656305549195272, 0.033416156416180835, 0.20566235127559082, 0.152240463864358, 0.09570172398977367, -0.3078159513975622, -0.03286615327728548, 0.1496519457077843] |
1,802.04663 | The Third Evolution Equation for Optimal Control Computation | The Variation Evolving Method (VEM) that originates from the continuous-time
dynamics stability theory seeks the optimal solutions with variation evolution
principle. After establishing the first and the second evolution equations
within its frame, the third evolution equation is developed. This equation only
solves the control variables along the variation time to get the optimal
solution, and its definite conditions may be arbitrary since the equation can
eliminate possible infeasibilities. With this equation, the dimension of the
resulting Initial-value Problem (IVP), transformed via the semi-discrete
method, is greatly reduced. Therefore it might relieve the computation burden
in seeking solutions. Illustrative examples are solved and it is shown that the
proposed equation may produce more precise numerical solutions than the second
evolution equation, and its computation time may be shorter for the dense
discretization.
| cs.SY math.OC | the variation evolving method vem that originates from the continuoustime dynamics stability theory seeks the optimal solutions with variation evolution principle after establishing the first and the second evolution equations within its frame the third evolution equation is developed this equation only solves the control variables along the variation time to get the optimal solution and its definite conditions may be arbitrary since the equation can eliminate possible infeasibilities with this equation the dimension of the resulting initialvalue problem ivp transformed via the semidiscrete method is greatly reduced therefore it might relieve the computation burden in seeking solutions illustrative examples are solved and it is shown that the proposed equation may produce more precise numerical solutions than the second evolution equation and its computation time may be shorter for the dense discretization | [['the', 'variation', 'evolving', 'method', 'vem', 'that', 'originates', 'from', 'the', 'continuoustime', 'dynamics', 'stability', 'theory', 'seeks', 'the', 'optimal', 'solutions', 'with', 'variation', 'evolution', 'principle', 'after', 'establishing', 'the', 'first', 'and', 'the', 'second', 'evolution', 'equations', 'within', 'its', 'frame', 'the', 'third', 'evolution', 'equation', 'is', 'developed', 'this', 'equation', 'only', 'solves', 'the', 'control', 'variables', 'along', 'the', 'variation', 'time', 'to', 'get', 'the', 'optimal', 'solution', 'and', 'its', 'definite', 'conditions', 'may', 'be', 'arbitrary', 'since', 'the', 'equation', 'can', 'eliminate', 'possible', 'infeasibilities', 'with', 'this', 'equation', 'the', 'dimension', 'of', 'the', 'resulting', 'initialvalue', 'problem', 'ivp', 'transformed', 'via', 'the', 'semidiscrete', 'method', 'is', 'greatly', 'reduced', 'therefore', 'it', 'might', 'relieve', 'the', 'computation', 'burden', 'in', 'seeking', 'solutions', 'illustrative', 'examples', 'are', 'solved', 'and', 'it', 'is', 'shown', 'that', 'the', 'proposed', 'equation', 'may', 'produce', 'more', 'precise', 'numerical', 'solutions', 'than', 'the', 'second', 'evolution', 'equation', 'and', 'its', 'computation', 'time', 'may', 'be', 'shorter', 'for', 'the', 'dense', 'discretization']] | [-0.12170831176671058, 0.07031093156811866, -0.13123914321565253, 0.06629071206605824, -0.11024429942093168, -0.13322031635184292, -0.003443249937845394, 0.30275371281613567, -0.35457340640255786, -0.30709243242480705, 0.1761092094794466, -0.25287292199562106, -0.14309834995462248, 0.15976299802688035, -0.04393189185374825, 0.07703156521496236, 0.11183571184290375, 0.006379696731032295, -0.10923187369807044, -0.2629893096163869, 0.2781838897128843, 0.04418130990851558, 0.25263640244851227, 0.011639405299457863, 0.14150725461006392, -0.04572350481193456, -0.028322504014905655, 0.004342069623596741, -0.11719732558953128, 0.09481399808922134, 0.2263153780965667, 0.12456124650039287, 0.31952053359286353, -0.43453267785353644, -0.24144507587576905, 0.12122953053084061, 0.17536097555774066, 0.15833282447067287, -0.03364705270613547, -0.27740916947721306, 0.06324227978863443, -0.10762244991421925, -0.2011812694836408, -0.06785718334316643, 0.013265582295416883, 0.00898235124733412, -0.25527179395783495, 0.11580636056323508, 0.01998800546756353, -0.07904497997581282, -0.14862341480946986, -0.05854715734974227, -0.04187115151999575, 0.09154505028643391, 0.05085849586281587, 0.019494655240454118, 0.05796599719729839, -0.0764647366401429, -0.06541783108629963, 0.3974993639223447, -0.05860357301781099, -0.27828654143112624, 0.13851214665125802, -0.09064937578725883, -0.08578131248412485, 0.1651394453859239, 0.1434592442237772, 0.16455716249589442, -0.17695737568393463, 0.09375743970987528, 0.0045379174901454735, 0.18496989630952929, 0.0626994729179635, -0.023612027486899133, 0.10984209718387292, 0.15913805602802755, 0.12058496719339129, 0.11710526507919315, 4.997836059015809e-05, -0.1785315307986986, -0.3057355240726434, -0.13720500287176532, -0.14419879676125041, 0.04165901490275494, -0.11525558668610315, -0.1290699412883524, 0.36798906306891394, 0.16900851471070905, 0.10041988971925368, 0.05535177114617192, 0.2852521554538698, 0.24931847811394342, 0.029100880071299882, 0.10958871509435332, 0.21950310286642474, 0.14776468041501092, 0.14964110438119282, -0.2950858388322751, 0.11544888137458739, 0.13661732699609164] |
1,802.04664 | Recovering Loss to Followup Information Using Denoising Autoencoders | Loss to followup is a significant issue in healthcare and has serious
consequences for a study's validity and cost. Methods available at present for
recovering loss to followup information are restricted by their expressive
capabilities and struggle to model highly non-linear relations and complex
interactions. In this paper we propose a model based on overcomplete denoising
autoencoders to recover loss to followup information. Designed to work with
high volume data, results on various simulated and real life datasets show our
model is appropriate under varying dataset and loss to followup conditions and
outperforms the state-of-the-art methods by a wide margin ($\ge 20\%$ in some
scenarios) while preserving the dataset utility for final analysis.
| cs.LG stat.AP stat.ML | loss to followup is a significant issue in healthcare and has serious consequences for a studys validity and cost methods available at present for recovering loss to followup information are restricted by their expressive capabilities and struggle to model highly nonlinear relations and complex interactions in this paper we propose a model based on overcomplete denoising autoencoders to recover loss to followup information designed to work with high volume data results on various simulated and real life datasets show our model is appropriate under varying dataset and loss to followup conditions and outperforms the stateoftheart methods by a wide margin ge 20 in some scenarios while preserving the dataset utility for final analysis | [['loss', 'to', 'followup', 'is', 'a', 'significant', 'issue', 'in', 'healthcare', 'and', 'has', 'serious', 'consequences', 'for', 'a', 'studys', 'validity', 'and', 'cost', 'methods', 'available', 'at', 'present', 'for', 'recovering', 'loss', 'to', 'followup', 'information', 'are', 'restricted', 'by', 'their', 'expressive', 'capabilities', 'and', 'struggle', 'to', 'model', 'highly', 'nonlinear', 'relations', 'and', 'complex', 'interactions', 'in', 'this', 'paper', 'we', 'propose', 'a', 'model', 'based', 'on', 'overcomplete', 'denoising', 'autoencoders', 'to', 'recover', 'loss', 'to', 'followup', 'information', 'designed', 'to', 'work', 'with', 'high', 'volume', 'data', 'results', 'on', 'various', 'simulated', 'and', 'real', 'life', 'datasets', 'show', 'our', 'model', 'is', 'appropriate', 'under', 'varying', 'dataset', 'and', 'loss', 'to', 'followup', 'conditions', 'and', 'outperforms', 'the', 'stateoftheart', 'methods', 'by', 'a', 'wide', 'margin', 'ge', '20', 'in', 'some', 'scenarios', 'while', 'preserving', 'the', 'dataset', 'utility', 'for', 'final', 'analysis']] | [-0.04618504872459061, -0.022198454662333285, -0.04657927204533357, 0.08431408483333128, -0.13355955115799684, -0.16090754399372809, 0.05776187446920317, 0.4536275039056102, -0.21125053111216532, -0.3727418104664678, 0.13953568492665788, -0.32197851222832646, -0.15176807402888626, 0.21253858365950926, -0.13751858891507165, 0.11719781368047791, 0.16782945740671285, -0.014524860565130294, -0.07741696450012464, -0.3058179984099965, 0.2953638682642589, 0.09123382144566394, 0.30849140600622754, 0.08058836009606835, 0.11878722883550763, 0.011156524857094067, -0.038111651679954235, -0.0039051057321670573, -0.08217667497331853, 0.142713996511678, 0.2832004708790146, 0.21258888319464384, 0.3436568571188677, -0.3905305385391797, -0.2686158816023128, 0.09758318569593594, 0.0968269595559027, 0.07803859107880914, -0.06912124891501678, -0.30568301976880935, 0.09285285428735073, -0.16374369606013056, -0.05929755790373393, -0.16241963172488047, -0.0025916495484060945, -0.022354373387452546, -0.32126615897843, 0.09592723662437525, 0.013117668569186884, 0.07294056876933416, -0.10209682976324276, -0.11015130643139437, 0.019644194522633555, 0.1193947633563549, 0.037662133999968324, 0.027656067332739127, 0.08703812697502594, -0.18401624616523957, -0.10594933863576765, 0.3799978598902843, -0.06627445689822851, -0.21910276441974977, 0.23393891523056456, -0.08344969746665215, -0.1428392569996021, 0.1032364066830317, 0.25814881185646604, 0.11274852470981073, -0.17812525140599603, 0.013604333393988652, 0.007225857870406018, 0.15287745821050705, 0.013119479661922803, 0.030956743411777492, 0.14005987444507223, 0.23644925096789293, 0.017555949822649262, 0.13449627153762922, -0.11205101636675212, -0.045972562454789215, -0.2217392245455917, -0.08251148909412787, -0.17383842983703435, -0.013593318046944265, -0.06625400134066091, -0.09855930200588386, 0.3679661153964039, 0.2382741500829569, 0.18898600512967173, 0.1151825335387767, 0.35374013882591926, 0.01501892505943017, 0.08830644580766361, 0.042770090698371684, 0.19654189209805067, 0.050317874429665045, 0.10737936524468251, -0.1669869721347911, 0.07463978185211267, -0.03715604996160332] |
1,802.04665 | Near Horizon Symmetry and Entropy Formula for Kerr-Newman (A)dS Black
Holes | In this paper we provide the first non-trivial evidence for universality of
the entropy formula $4\pi J_{0}^{+}J_{0}^{-}$ beyond pure Einstein gravity in
4-dimensions. We consider the Einstein-Maxwell theory in the presence of
cosmological constant, then write near horizon metric of the Kerr-Newman (A)dS
black hole in the Gaussian null coordinate system. We consider near horizon
fall-off conditions for metric and $U(1)$ gauge field. We find asymptotic
combined symmetry generator, consists of diffeomorphism and $U(1)$ gauge
transformation, so that it preserves fall-off conditions. Consequently, we find
supertranslation, supperrotation and multiple-charge modes and then we show
that the entropy formula is held for the Kerr-Newman (A)dS black hole.
Supperrotation modes suffer from a problem. By introducing new combined
symmetry generator, we cure that problem.
| gr-qc hep-th | in this paper we provide the first nontrivial evidence for universality of the entropy formula 4pi j_0j_0 beyond pure einstein gravity in 4dimensions we consider the einsteinmaxwell theory in the presence of cosmological constant then write near horizon metric of the kerrnewman ads black hole in the gaussian null coordinate system we consider near horizon falloff conditions for metric and u1 gauge field we find asymptotic combined symmetry generator consists of diffeomorphism and u1 gauge transformation so that it preserves falloff conditions consequently we find supertranslation supperrotation and multiplecharge modes and then we show that the entropy formula is held for the kerrnewman ads black hole supperrotation modes suffer from a problem by introducing new combined symmetry generator we cure that problem | [['in', 'this', 'paper', 'we', 'provide', 'the', 'first', 'nontrivial', 'evidence', 'for', 'universality', 'of', 'the', 'entropy', 'formula', '4pi', 'j_0j_0', 'beyond', 'pure', 'einstein', 'gravity', 'in', '4dimensions', 'we', 'consider', 'the', 'einsteinmaxwell', 'theory', 'in', 'the', 'presence', 'of', 'cosmological', 'constant', 'then', 'write', 'near', 'horizon', 'metric', 'of', 'the', 'kerrnewman', 'ads', 'black', 'hole', 'in', 'the', 'gaussian', 'null', 'coordinate', 'system', 'we', 'consider', 'near', 'horizon', 'falloff', 'conditions', 'for', 'metric', 'and', 'u1', 'gauge', 'field', 'we', 'find', 'asymptotic', 'combined', 'symmetry', 'generator', 'consists', 'of', 'diffeomorphism', 'and', 'u1', 'gauge', 'transformation', 'so', 'that', 'it', 'preserves', 'falloff', 'conditions', 'consequently', 'we', 'find', 'supertranslation', 'supperrotation', 'and', 'multiplecharge', 'modes', 'and', 'then', 'we', 'show', 'that', 'the', 'entropy', 'formula', 'is', 'held', 'for', 'the', 'kerrnewman', 'ads', 'black', 'hole', 'supperrotation', 'modes', 'suffer', 'from', 'a', 'problem', 'by', 'introducing', 'new', 'combined', 'symmetry', 'generator', 'we', 'cure', 'that', 'problem']] | [-0.1751748748131472, 0.1133731721524744, -0.0927836184884899, 0.10657319362361391, -0.10401305436164517, -0.18063124684429094, -0.01698277597589528, 0.3001055411961196, -0.16161439291146748, -0.2381981864822011, 0.09343269473614813, -0.31269985235867503, -0.15398140300159194, 0.1105844402576194, -0.07690912849042847, 0.05361661717624349, -0.007246059287308144, 0.05270962292321629, -0.1416803843384812, -0.19715950240384975, 0.39492899352418526, 0.04630610281053711, 0.29404246318741006, 0.01757549785249749, 0.1603475210574571, 0.034952466460257896, 0.037829667950017735, 0.047974723915592966, -0.18669379935613314, 0.0004672572631075853, 0.18872363382496504, 0.14395679595308894, 0.15558969060412975, -0.4255007714377481, -0.1990040846857704, 0.08751430558803797, 0.1393400401174396, 0.18486975183506973, -0.09249577868119382, -0.27681948000784307, 0.08323451546764299, -0.2196650662295082, -0.20701642598009848, -0.08133109248078921, 0.005223893941020076, -0.13978159147463426, -0.2510441524708377, 0.1160012683142385, 0.0812416759303951, 0.008273228549030648, -0.12685028948828952, 0.001872720107819666, -0.030948789258088385, 0.04317875838635035, 0.2091430220552407, 0.031045309322721818, 0.137133058260328, -0.11220278388804936, -0.09954192053072718, 0.33925610547493984, -0.11091927302699975, -0.23619598680872375, 0.09335195683060261, -0.2049987202113056, -0.15246898223872946, 0.0806812230544556, 0.14094636999383695, 0.17497836703731984, -0.13975615958495363, 0.21548839658303107, -0.01605737831506158, 0.1198121102824181, 0.14479639399953248, 0.05392107680173857, 0.29065479692697777, 0.03189221565306437, 0.0955762463747015, 0.1999264770625707, -0.01809374974383151, -0.10176220154311477, -0.41653379879328384, -0.17335838314239718, -0.1376895188655266, 0.11545228307769329, -0.15585715236375108, -0.17331163087437132, 0.3182453437857017, 0.14281902004066588, 0.12303504434672474, 0.08059070504685285, 0.18242012438591287, 0.11899674969271389, 0.04964846166456314, 0.15427477032087056, 0.2883158583104016, 0.08740820259270611, 0.10110785233878195, -0.2714745350624239, -0.10858453741344083, 0.15059396666044197] |
1,802.04666 | The LHC Higgs Boson Discovery: Updated implications for Finite Unified
Theories and the SUSY breaking scale | Finite Unified Theories (FUTs) are N = 1 supersymmetric Grand Unified
Theories which can be made finite to all orders in perturbation theory, based
on the principle of reduction of couplings. The latter consists in searching
for renormalization group invariant relations among parameters of a
renormalizable theory holding to all orders in perturbation theory. FUTs have
proven very successful so far. In particular, they predicted the top quark mass
one and half years before its experimental discovery, while around five years
before the Higgs boson discovery a particular FUT was predicting the light
Higgs boson in the mass range ~ 121 - 126 GeV, in striking agreement with the
discovery at LHC. Here we review the basic properties of the supersymmetric
theories and in particular finite theories resulting from the application of
the method of reduction of couplings in their dimensionless and dimensionful
sectors. Then we analyse the phenomenologically favoured FUT, based on SU(5).
This particular FUT leads to a finiteness constrained version of the MSSM,
which naturally predicts a relatively heavy spectrum with coloured
supersymmetric particles above 2.7 TeV, consistent with the non-observation of
those particles at the LHC. The electroweak supersymmetric spectrum starts
below 1 TeV and large parts of the allowed spectrum of the lighter might be
accessible at CLIC. The FCC-hh will be able to fully test the predicted
spectrum.
| hep-ph | finite unified theories futs are n 1 supersymmetric grand unified theories which can be made finite to all orders in perturbation theory based on the principle of reduction of couplings the latter consists in searching for renormalization group invariant relations among parameters of a renormalizable theory holding to all orders in perturbation theory futs have proven very successful so far in particular they predicted the top quark mass one and half years before its experimental discovery while around five years before the higgs boson discovery a particular fut was predicting the light higgs boson in the mass range 121 126 gev in striking agreement with the discovery at lhc here we review the basic properties of the supersymmetric theories and in particular finite theories resulting from the application of the method of reduction of couplings in their dimensionless and dimensionful sectors then we analyse the phenomenologically favoured fut based on su5 this particular fut leads to a finiteness constrained version of the mssm which naturally predicts a relatively heavy spectrum with coloured supersymmetric particles above 27 tev consistent with the nonobservation of those particles at the lhc the electroweak supersymmetric spectrum starts below 1 tev and large parts of the allowed spectrum of the lighter might be accessible at clic the fcchh will be able to fully test the predicted spectrum | [['finite', 'unified', 'theories', 'futs', 'are', 'n', '1', 'supersymmetric', 'grand', 'unified', 'theories', 'which', 'can', 'be', 'made', 'finite', 'to', 'all', 'orders', 'in', 'perturbation', 'theory', 'based', 'on', 'the', 'principle', 'of', 'reduction', 'of', 'couplings', 'the', 'latter', 'consists', 'in', 'searching', 'for', 'renormalization', 'group', 'invariant', 'relations', 'among', 'parameters', 'of', 'a', 'renormalizable', 'theory', 'holding', 'to', 'all', 'orders', 'in', 'perturbation', 'theory', 'futs', 'have', 'proven', 'very', 'successful', 'so', 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1,802.04667 | Shadow of rotating black holes on a standard background screen | We present the shape of the black hole shadow on the standard background
screen as it is registered by the distant observer. The screen is an infinite
plane, emitting the quanta uniformly distributed to a hemisphere. The source of
emission is considered to be optically thin and optically thick. It is shown
that the shape of a black hole shadow depends crucially on the angle between
the plane and the view line to the distant observer. The shadow shapes for the
different values of this angle are also presented. Both Schwarzschild and Kerr
metrics are considered.
| gr-qc astro-ph.GA | we present the shape of the black hole shadow on the standard background screen as it is registered by the distant observer the screen is an infinite plane emitting the quanta uniformly distributed to a hemisphere the source of emission is considered to be optically thin and optically thick it is shown that the shape of a black hole shadow depends crucially on the angle between the plane and the view line to the distant observer the shadow shapes for the different values of this angle are also presented both schwarzschild and kerr metrics are considered | [['we', 'present', 'the', 'shape', 'of', 'the', 'black', 'hole', 'shadow', 'on', 'the', 'standard', 'background', 'screen', 'as', 'it', 'is', 'registered', 'by', 'the', 'distant', 'observer', 'the', 'screen', 'is', 'an', 'infinite', 'plane', 'emitting', 'the', 'quanta', 'uniformly', 'distributed', 'to', 'a', 'hemisphere', 'the', 'source', 'of', 'emission', 'is', 'considered', 'to', 'be', 'optically', 'thin', 'and', 'optically', 'thick', 'it', 'is', 'shown', 'that', 'the', 'shape', 'of', 'a', 'black', 'hole', 'shadow', 'depends', 'crucially', 'on', 'the', 'angle', 'between', 'the', 'plane', 'and', 'the', 'view', 'line', 'to', 'the', 'distant', 'observer', 'the', 'shadow', 'shapes', 'for', 'the', 'different', 'values', 'of', 'this', 'angle', 'are', 'also', 'presented', 'both', 'schwarzschild', 'and', 'kerr', 'metrics', 'are', 'considered']] | [-0.12121894910887931, 0.09632893244891723, -0.06371637687940772, 0.07594261862080505, -0.09281855716835707, -0.11184647823877943, -0.01499708568492982, 0.4234285119649333, -0.2105355392365406, -0.2985044213904378, 0.11558560597768519, -0.3276379030236664, -0.05432149527284006, 0.23880845222932598, -0.06192216222795347, 0.0012470566472681337, -0.03708629552662993, 0.014022516397138437, -0.03733659652789356, -0.17734841061367965, 0.34850678688114084, 0.12707874706878405, 0.26657670832355507, 0.0033299792654967555, 0.10545812927011866, 0.02179147795929263, 0.001358800441569959, 0.054406822426244617, -0.08296133691609005, 0.0837748161526785, 0.18954216005901495, 0.11006968724541366, 0.14724251789448317, -0.35482034825448255, -0.2028260875313208, 0.06102054715544606, 0.13432921917168036, 0.10944881857237003, -0.07740124505896044, -0.3095290535323632, 0.048380499036284164, -0.11835085378940373, -0.17435451546043623, 0.08064348843618063, 0.07578150886304987, -0.01082616298905729, -0.17980260787686575, 0.04116679523182635, 0.07642919067075127, -0.03563678200104429, -0.06611207537935115, -0.02443091068804885, -0.058327416384903095, 0.14357599936192855, 0.10604886272146057, 0.04835800456445819, 0.24642784616541272, -0.06520449698958448, -0.05187785841311173, 0.35686893591385643, -0.024257884389953688, -0.22691694784831876, 0.14089571989219016, -0.22951230163986716, -0.013870330731151626, 0.1555462642281782, 0.1471488247407251, 0.2298953013960272, -0.12533661881267713, 0.07128557710348105, -0.07081093122057307, 0.2120304892838855, 0.08916770568854797, 0.03393668327771593, 0.36428598314523697, 0.088936383612842, 0.038815168828781076, 0.1994584173844487, -0.19076012468334133, -0.0709787046071142, -0.31610797957788844, -0.13931885270236913, -0.17719971430536438, 0.04468457713179911, -0.15228115728389943, -0.21171171407422662, 0.3502824791163827, 0.09633173893477458, 0.23126186213630717, -0.045629377224637814, 0.32109412950618815, 0.07755638177453268, 0.03476786953494108, 0.09174039649042243, 0.37948370010174887, 0.07337018121324945, 0.09697694161407829, -0.2045570826982536, 0.039173734852132235, 0.03480682276858715] |
1,802.04668 | Weakly supervised collective feature learning from curated media | The current state-of-the-art in feature learning relies on the supervised
learning of large-scale datasets consisting of target content items and their
respective category labels. However, constructing such large-scale
fully-labeled datasets generally requires painstaking manual effort. One
possible solution to this problem is to employ community contributed text tags
as weak labels, however, the concepts underlying a single text tag strongly
depends on the users. We instead present a new paradigm for learning
discriminative features by making full use of the human curation process on
social networking services (SNSs). During the process of content curation, SNS
users collect content items manually from various sources and group them by
context, all for their own benefit. Due to the nature of this process, we can
assume that (1) content items in the same group share the same semantic concept
and (2) groups sharing the same images might have related semantic concepts.
Through these insights, we can define human curated groups as weak labels from
which our proposed framework can learn discriminative features as a
representation in the space of semantic concepts the users intended when
creating the groups. We show that this feature learning can be formulated as a
problem of link prediction for a bipartite graph whose nodes corresponds to
content items and human curated groups, and propose a novel method for feature
learning based on sparse coding or network fine-tuning.
| cs.CV cs.MM cs.SI | the current stateoftheart in feature learning relies on the supervised learning of largescale datasets consisting of target content items and their respective category labels however constructing such largescale fullylabeled datasets generally requires painstaking manual effort one possible solution to this problem is to employ community contributed text tags as weak labels however the concepts underlying a single text tag strongly depends on the users we instead present a new paradigm for learning discriminative features by making full use of the human curation process on social networking services snss during the process of content curation sns users collect content items manually from various sources and group them by context all for their own benefit due to the nature of this process we can assume that 1 content items in the same group share the same semantic concept and 2 groups sharing the same images might have related semantic concepts through these insights we can define human curated groups as weak labels from which our proposed framework can learn discriminative features as a representation in the space of semantic concepts the users intended when creating the groups we show that this feature learning can be formulated as a problem of link prediction for a bipartite graph whose nodes corresponds to content items and human curated groups and propose a novel method for feature learning based on sparse coding or network finetuning | [['the', 'current', 'stateoftheart', 'in', 'feature', 'learning', 'relies', 'on', 'the', 'supervised', 'learning', 'of', 'largescale', 'datasets', 'consisting', 'of', 'target', 'content', 'items', 'and', 'their', 'respective', 'category', 'labels', 'however', 'constructing', 'such', 'largescale', 'fullylabeled', 'datasets', 'generally', 'requires', 'painstaking', 'manual', 'effort', 'one', 'possible', 'solution', 'to', 'this', 'problem', 'is', 'to', 'employ', 'community', 'contributed', 'text', 'tags', 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1,802.04669 | Optimal Sequential Contests | I study sequential contests where the efforts of earlier players may be
disclosed to later players by nature or by design. The model has a range of
applications, including rent seeking, R&D, oligopoly, public goods provision,
and tragedy of the commons. I show that information about other players'
efforts increases the total effort. Thus, the total effort is maximized with
full transparency and minimized with no transparency. I also show that in
addition to the first-mover advantage, there is an earlier-mover advantage.
Finally, I derive the limits for large contests.
| cs.GT | i study sequential contests where the efforts of earlier players may be disclosed to later players by nature or by design the model has a range of applications including rent seeking rd oligopoly public goods provision and tragedy of the commons i show that information about other players efforts increases the total effort thus the total effort is maximized with full transparency and minimized with no transparency i also show that in addition to the firstmover advantage there is an earliermover advantage finally i derive the limits for large contests | [['i', 'study', 'sequential', 'contests', 'where', 'the', 'efforts', 'of', 'earlier', 'players', 'may', 'be', 'disclosed', 'to', 'later', 'players', 'by', 'nature', 'or', 'by', 'design', 'the', 'model', 'has', 'a', 'range', 'of', 'applications', 'including', 'rent', 'seeking', 'rd', 'oligopoly', 'public', 'goods', 'provision', 'and', 'tragedy', 'of', 'the', 'commons', 'i', 'show', 'that', 'information', 'about', 'other', 'players', 'efforts', 'increases', 'the', 'total', 'effort', 'thus', 'the', 'total', 'effort', 'is', 'maximized', 'with', 'full', 'transparency', 'and', 'minimized', 'with', 'no', 'transparency', 'i', 'also', 'show', 'that', 'in', 'addition', 'to', 'the', 'firstmover', 'advantage', 'there', 'is', 'an', 'earliermover', 'advantage', 'finally', 'i', 'derive', 'the', 'limits', 'for', 'large', 'contests']] | [-0.08764904678872462, 0.052828708649357896, -0.06719991357557559, 0.039638656785376786, -0.09643121366650703, -0.152021624603089, 0.11687578076726934, 0.41053962468933525, -0.22520405713081612, -0.3322848058358002, 0.11079967472299389, -0.306060176771762, -0.12482274534590176, 0.12158048193603545, -0.09865695270814337, -0.03409456109021152, 0.08000762057176718, 0.0012202296311851967, 0.05214302127741361, -0.3266086588009376, 0.303623545271334, 0.04760925822859902, 0.2826316397155771, 0.0759752996114156, 0.06074841464922, 0.017138347469186514, -0.06182924785593713, 0.04302372577275788, -0.15516039222204833, 0.11079317780232496, 0.29095425100976163, 0.22487013324509175, 0.4066562559246347, -0.41863596895604993, -0.1727623788985225, 0.16440009792973653, 0.10206151777470296, 0.06721691767360723, -0.04404759503838219, -0.2847754111764639, 0.04496855283511823, -0.21317753938466227, -0.07986867841165722, -0.04778360371383723, 0.04496860022792656, 0.011933466542533107, -0.2788898671213328, -0.04962618782458398, 0.04047392901514521, 0.056748829384365776, -0.03376117312830737, -0.15537044878661885, -0.025017036355278466, 0.18560184451249126, 0.0914085348204741, 7.58598404256313e-05, 0.10234607381123548, -0.19824366307074434, -0.17486778952003446, 0.4032557433408298, -0.04147503893296601, -0.10857144708671931, 0.15815977222715202, -0.11841478201382782, -0.08786345765519799, 0.09908950198023173, 0.16379638550949566, 0.041964559371091344, -0.15265346079897346, 0.06476015393397237, -0.054761444532385704, 0.18233238415939085, 0.021778139973770868, 0.06182780325428447, 0.16839440826201038, 0.15202488963429428, 0.13356265941048773, 0.09846569992944577, -0.0057771016393652124, -0.14467928616403278, -0.2563950019375829, -0.1531319190261446, -0.13508178469505203, 0.04601352523196112, -0.038188458153169624, -0.040793186817527485, 0.3215846673538385, 0.17024714140917352, 0.1177807258222294, 0.035279949384040374, 0.3217434678854567, 0.062407303240485074, 0.08630474242957288, 0.09808350985430264, 0.22543628539973765, -0.015977115877851677, 0.12125605670212025, -0.2083908040390423, 0.1222344430979718, -0.05103851465660074] |
1,802.0467 | Equilibrium solutions of three player Kuhn poker with $N>3$ cards: A new
numerical method using regularization and arc-length continuation | We study the equilibrium solutions of three player Kuhn poker with $N>3$
cards. We compute these solutions as a function of the initial pot size, $P$,
using a novel method based on regularizing the system of polynomial equations
and inequalities that defines the solutions, and solving the resulting system
of nonlinear, algebraic equations using a combination of Newton's method and
arc-length continuation. We find that the structure of the equilibrium solution
curve is very complex, even for games with a small number of cards. Standard
three player Kuhn poker, which is played with $N=4$ cards, is qualitatively
different from the game with $N>4$ cards because of the simplicity of the
structure of the value betting and bluffing ranges of each player. When $N>5$,
we find that there is a new type of equilibrium bet with midrange cards that
acts as a bluff against one player and a value bet against the other.
| math.OC cs.GT | we study the equilibrium solutions of three player kuhn poker with n3 cards we compute these solutions as a function of the initial pot size p using a novel method based on regularizing the system of polynomial equations and inequalities that defines the solutions and solving the resulting system of nonlinear algebraic equations using a combination of newtons method and arclength continuation we find that the structure of the equilibrium solution curve is very complex even for games with a small number of cards standard three player kuhn poker which is played with n4 cards is qualitatively different from the game with n4 cards because of the simplicity of the structure of the value betting and bluffing ranges of each player when n5 we find that there is a new type of equilibrium bet with midrange cards that acts as a bluff against one player and a value bet against the other | [['we', 'study', 'the', 'equilibrium', 'solutions', 'of', 'three', 'player', 'kuhn', 'poker', 'with', 'n3', 'cards', 'we', 'compute', 'these', 'solutions', 'as', 'a', 'function', 'of', 'the', 'initial', 'pot', 'size', 'p', 'using', 'a', 'novel', 'method', 'based', 'on', 'regularizing', 'the', 'system', 'of', 'polynomial', 'equations', 'and', 'inequalities', 'that', 'defines', 'the', 'solutions', 'and', 'solving', 'the', 'resulting', 'system', 'of', 'nonlinear', 'algebraic', 'equations', 'using', 'a', 'combination', 'of', 'newtons', 'method', 'and', 'arclength', 'continuation', 'we', 'find', 'that', 'the', 'structure', 'of', 'the', 'equilibrium', 'solution', 'curve', 'is', 'very', 'complex', 'even', 'for', 'games', 'with', 'a', 'small', 'number', 'of', 'cards', 'standard', 'three', 'player', 'kuhn', 'poker', 'which', 'is', 'played', 'with', 'n4', 'cards', 'is', 'qualitatively', 'different', 'from', 'the', 'game', 'with', 'n4', 'cards', 'because', 'of', 'the', 'simplicity', 'of', 'the', 'structure', 'of', 'the', 'value', 'betting', 'and', 'bluffing', 'ranges', 'of', 'each', 'player', 'when', 'n5', 'we', 'find', 'that', 'there', 'is', 'a', 'new', 'type', 'of', 'equilibrium', 'bet', 'with', 'midrange', 'cards', 'that', 'acts', 'as', 'a', 'bluff', 'against', 'one', 'player', 'and', 'a', 'value', 'bet', 'against', 'the', 'other']] | [-0.10143468131527218, 0.0506416820479851, -0.1454721661284566, 0.031436879339468614, -0.05222831518805929, -0.23051356603115747, 0.04343121383939624, 0.31342790476169047, -0.24908794951012456, -0.3112131266480949, 0.12563194721128398, -0.3156274945083025, -0.13798931859874805, 0.13619793291620322, -0.06265971795533244, 0.027938897954300046, 0.08624200181925278, 0.03683108012008138, 0.003969572252428502, -0.2829072435790869, 0.33720544885114867, -0.037631596317576045, 0.21357915431297825, -0.0035051666244199672, 0.1559105048960957, 0.005980240646749735, 0.05852174712957716, 0.0833989458746816, -0.12553743559252134, 0.08267542273939685, 0.2079496173629243, 0.1271114840591939, 0.3244840193104832, -0.41777721926030753, -0.09640210548164568, 0.11657948115546453, 0.08670853277037263, 0.11592495920501135, -0.04562111764818454, -0.22712926904971123, 0.0963737190927771, -0.17158425567169233, -0.1300389970450564, -0.04240349631781053, 0.03947062352917304, 0.04478643487029905, -0.2944209046001656, -0.01372851736040933, 0.023438569476926012, 0.05429363049839403, -0.049423485508764554, -0.15975708459690827, -0.03748896956676617, 0.13876758251698398, 0.05288996632321199, 0.003801560744728991, 0.12096635634876102, -0.17263213523639073, -0.14004474657122046, 0.40408065574439733, -0.07509871741977374, -0.20643523597084967, 0.14075231351307593, -0.10552332946405697, -0.12096971356444747, 0.07010624512392831, 0.10819087395104457, 0.15087236369972265, -0.07450688833121508, 0.0702768565369249, -0.11961376603070867, 0.20842233693570292, 0.10115279563653626, -0.044758899796043375, 0.12656989648530725, 0.17150649786191552, 0.11820994787788215, 0.12956618375526313, -0.03384230574317228, -0.17679821087145492, -0.29733561013065474, -0.1560593793615944, -0.14365822874548795, 0.05859988440428615, -0.1423644930468742, -0.1847961048938726, 0.39222624849552584, 0.11935017945238781, 0.14338264073318752, 0.09593720779536709, 0.2721615238605361, 0.13465601974626465, 0.049183551781951745, 0.09543076794790595, 0.1863235809601304, 0.06554011736328616, 0.11046493683917154, -0.2193106122377733, 0.0713860148159591, 0.1291439823659235] |
1,802.04671 | Transient Stability Assessment of Cascade Tripping of Renewable Sources
Using SOS | There has been significant increase in penetration of renewable generation
(RG) sources all over the world. Localized concentration of many such
generators could initiate a cascade tripping sequence that might threaten the
stability of the entire system. Understanding the impact of cascade tripping
process would help the system planner identify trip sequences that must be
blocked in order to increase stability. In this work, we attempt to understand
the consequences of cascade tripping mechanism through a Lyapunov approach. A
conservative definition for the stability region (SR) along with its estimation
for a given cascading sequence using sum of squares (SOS) programming is
proposed. Finally, a simple probabilistic definition of the SR is used to
visualize the risk of instability and understand the impact of blocking trip
sequences. A 3-machine system with significant RG penetration is used to
demonstrate the idea.
| cs.SY math.DS | there has been significant increase in penetration of renewable generation rg sources all over the world localized concentration of many such generators could initiate a cascade tripping sequence that might threaten the stability of the entire system understanding the impact of cascade tripping process would help the system planner identify trip sequences that must be blocked in order to increase stability in this work we attempt to understand the consequences of cascade tripping mechanism through a lyapunov approach a conservative definition for the stability region sr along with its estimation for a given cascading sequence using sum of squares sos programming is proposed finally a simple probabilistic definition of the sr is used to visualize the risk of instability and understand the impact of blocking trip sequences a 3machine system with significant rg penetration is used to demonstrate the idea | [['there', 'has', 'been', 'significant', 'increase', 'in', 'penetration', 'of', 'renewable', 'generation', 'rg', 'sources', 'all', 'over', 'the', 'world', 'localized', 'concentration', 'of', 'many', 'such', 'generators', 'could', 'initiate', 'a', 'cascade', 'tripping', 'sequence', 'that', 'might', 'threaten', 'the', 'stability', 'of', 'the', 'entire', 'system', 'understanding', 'the', 'impact', 'of', 'cascade', 'tripping', 'process', 'would', 'help', 'the', 'system', 'planner', 'identify', 'trip', 'sequences', 'that', 'must', 'be', 'blocked', 'in', 'order', 'to', 'increase', 'stability', 'in', 'this', 'work', 'we', 'attempt', 'to', 'understand', 'the', 'consequences', 'of', 'cascade', 'tripping', 'mechanism', 'through', 'a', 'lyapunov', 'approach', 'a', 'conservative', 'definition', 'for', 'the', 'stability', 'region', 'sr', 'along', 'with', 'its', 'estimation', 'for', 'a', 'given', 'cascading', 'sequence', 'using', 'sum', 'of', 'squares', 'sos', 'programming', 'is', 'proposed', 'finally', 'a', 'simple', 'probabilistic', 'definition', 'of', 'the', 'sr', 'is', 'used', 'to', 'visualize', 'the', 'risk', 'of', 'instability', 'and', 'understand', 'the', 'impact', 'of', 'blocking', 'trip', 'sequences', 'a', '3machine', 'system', 'with', 'significant', 'rg', 'penetration', 'is', 'used', 'to', 'demonstrate', 'the', 'idea']] | [-0.14721688072396708, 0.0837708584432091, -0.10064172133237921, 0.0796083482344719, -0.062202762536305405, -0.08398251809246306, 0.07184232190816796, 0.3422781660088471, -0.30412775665588143, -0.2908573683417801, 0.11412473028675386, -0.2732859477101426, -0.15467283525836786, 0.18329539018949229, -0.09537665742869389, 0.06223554667542755, 0.05927670908526384, 0.030337839182798882, -0.0031121945873435054, -0.22036767959528203, 0.2851397003878706, 0.09387186042565321, 0.30089929994761144, 0.05376088071913857, 0.08148982616008392, -0.020936272910330445, -0.005142711617684524, 0.014733529290450471, -0.09468265565696389, 0.12044028979726136, 0.23880191279874582, 0.16943788606108035, 0.33332031436397563, -0.4044828902331314, -0.245360995077395, 0.13208156411669084, 0.1907160250336996, 0.07908577892064517, -0.03848194530666141, -0.23129158340993206, 0.11888096785239344, -0.21969093575774293, -0.12695861947057502, -0.059100185494337765, -0.012523553264327348, 0.01250880914075034, -0.2851357448214133, -0.008454782482502716, 0.06732593525666744, 0.06074360199937863, -0.04296801196261575, -0.05085307700542866, -0.03973709890685443, 0.15236013972732637, 0.07208062134096897, 0.00923247354304684, 0.13179575873696844, -0.08422652234190277, -0.13239050184243492, 0.357832301201831, -0.0368697971716756, -0.16824536877019064, 0.16202497595555282, -0.11283012850742255, -0.120616694662853, 0.16638635479279662, 0.2180224344267377, 0.07416315337936144, -0.13430518365390265, -0.000804487000069847, 0.007331855408847332, 0.1742379150286849, 0.05016591146122664, 0.036072509489687425, 0.22431405030323992, 0.22795590660035878, 0.07325058328278829, 0.1389702774509455, -0.07031655781902373, -0.11507375712306904, -0.26859724385597344, -0.16434606217912265, -0.11735183930972458, 0.04380073949661372, -0.07464878860118915, -0.17197485863123022, 0.4043768545571116, 0.21759956873554204, 0.15179106541244047, 0.03470851812578205, 0.2897145962276097, 0.12975061502573745, 0.07745079514404227, 0.03929396755660751, 0.20921664624183905, 0.0705991384839373, 0.10681289114374003, -0.24246374407583582, 0.13930456693550305, 0.07160176679026335] |
1,802.04672 | Delta-Ramp Encoder for Amplitude Sampling and its Interpretation as Time
Encoding | The theoretical basis for conventional acquisition of bandlimited signals
typically relies on uniform time sampling and assumes infinite-precision
amplitude values. In this paper, we explore signal representation and recovery
based on uniform amplitude sampling with assumed infinite precision timing
information. The approach is based on the delta-ramp encoder which consists of
applying a one-level level-crossing detector to the result of adding an
appropriate sawtooth-like waveform to the input signal. The output samples are
the time instants of these level crossings, thus representing a time-encoded
version of the input signal. For theoretical purposes, this system can be
equivalently analyzed by reversibly transforming through ramp addition a
nonmonotonic input signal into a monotonic one which is then uniformly sampled
in amplitude. The monotonic function is then represented by the times at which
the signal crosses a predefined and equally-spaced set of amplitude values. We
refer to this technique as amplitude sampling. The time sequence generated can
be interpreted alternatively as nonuniform time sampling of the original source
signal. We derive duality and frequency-domain properties for the functions
involved in the transformation. Iterative algorithms are proposed and
implemented for recovery of the original source signal. As indicated in the
simulations, the proposed iterative amplitude-sampling algorithm achieves a
faster convergence rate than frame-based reconstruction for nonuniform
sampling. The performance can also be improved by appropriate choice of the
parameters while maintaining the same sampling density.
| eess.SP cs.IT math.CV math.IT | the theoretical basis for conventional acquisition of bandlimited signals typically relies on uniform time sampling and assumes infiniteprecision amplitude values in this paper we explore signal representation and recovery based on uniform amplitude sampling with assumed infinite precision timing information the approach is based on the deltaramp encoder which consists of applying a onelevel levelcrossing detector to the result of adding an appropriate sawtoothlike waveform to the input signal the output samples are the time instants of these level crossings thus representing a timeencoded version of the input signal for theoretical purposes this system can be equivalently analyzed by reversibly transforming through ramp addition a nonmonotonic input signal into a monotonic one which is then uniformly sampled in amplitude the monotonic function is then represented by the times at which the signal crosses a predefined and equallyspaced set of amplitude values we refer to this technique as amplitude sampling the time sequence generated can be interpreted alternatively as nonuniform time sampling of the original source signal we derive duality and frequencydomain properties for the functions involved in the transformation iterative algorithms are proposed and implemented for recovery of the original source signal as indicated in the simulations the proposed iterative amplitudesampling algorithm achieves a faster convergence rate than framebased reconstruction for nonuniform sampling the performance can also be improved by appropriate choice of the parameters while maintaining the same sampling density | [['the', 'theoretical', 'basis', 'for', 'conventional', 'acquisition', 'of', 'bandlimited', 'signals', 'typically', 'relies', 'on', 'uniform', 'time', 'sampling', 'and', 'assumes', 'infiniteprecision', 'amplitude', 'values', 'in', 'this', 'paper', 'we', 'explore', 'signal', 'representation', 'and', 'recovery', 'based', 'on', 'uniform', 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1,802.04673 | Magnetic-field Induced Pair Density Wave State in the Cuprate Vortex
Halo | When very high magnetic fields suppress the superconductivity in underdoped
cuprates, an exceptional new electronic phase appears. It supports remarkable
and unexplained quantum oscillations and exhibits an unidentified density wave
(DW) state. Although generally referred to as a "charge" density wave (CDW)
because of the observed charge density modulations, theory indicates that this
could actually be the far more elusive electron-pair density wave state (PDW).
To search for evidence of a field-induced PDW in cuprates, we visualize the
modulations in the density of electronic states $N(\bf{r})$ within the halo
surrounding Bi$_2$Sr$_2$CaCu$_2$O$_8$ vortex cores. This reveals multiple
signatures of a field-induced PDW, including two sets of $N(\bf{r})$
modulations occurring at wavevectors $\bf{Q}_P$ and $2\bf{Q}_P$, both having
predominantly $s$-symmetry form factors, the amplitude of the latter decaying
twice as rapidly as the former, along with induced energy-gap modulations at
$\bf{Q}_P$ . Such a microscopic phenomenology is in detailed agreement with
theory for a field-induced primary PDW that generates secondary CDWs within the
vortex halo. These data indicate that the fundamental state generated by
increasing magnetic fields from the underdoped cuprate superconducting phase is
actually a PDW with approximately eight CuO$_2$ unit-cell periodicity ($\lambda
= 8a_0$) and predominantly $d$-symmetry form factor.
| cond-mat.supr-con | when very high magnetic fields suppress the superconductivity in underdoped cuprates an exceptional new electronic phase appears it supports remarkable and unexplained quantum oscillations and exhibits an unidentified density wave dw state although generally referred to as a charge density wave cdw because of the observed charge density modulations theory indicates that this could actually be the far more elusive electronpair density wave state pdw to search for evidence of a fieldinduced pdw in cuprates we visualize the modulations in the density of electronic states nbfr within the halo surrounding bi_2sr_2cacu_2o_8 vortex cores this reveals multiple signatures of a fieldinduced pdw including two sets of nbfr modulations occurring at wavevectors bfq_p and 2bfq_p both having predominantly ssymmetry form factors the amplitude of the latter decaying twice as rapidly as the former along with induced energygap modulations at bfq_p such a microscopic phenomenology is in detailed agreement with theory for a fieldinduced primary pdw that generates secondary cdws within the vortex halo these data indicate that the fundamental state generated by increasing magnetic fields from the underdoped cuprate superconducting phase is actually a pdw with approximately eight cuo_2 unitcell periodicity lambda 8a_0 and predominantly dsymmetry form factor | [['when', 'very', 'high', 'magnetic', 'fields', 'suppress', 'the', 'superconductivity', 'in', 'underdoped', 'cuprates', 'an', 'exceptional', 'new', 'electronic', 'phase', 'appears', 'it', 'supports', 'remarkable', 'and', 'unexplained', 'quantum', 'oscillations', 'and', 'exhibits', 'an', 'unidentified', 'density', 'wave', 'dw', 'state', 'although', 'generally', 'referred', 'to', 'as', 'a', 'charge', 'density', 'wave', 'cdw', 'because', 'of', 'the', 'observed', 'charge', 'density', 'modulations', 'theory', 'indicates', 'that', 'this', 'could', 'actually', 'be', 'the', 'far', 'more', 'elusive', 'electronpair', 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1,802.04674 | Preliminary Measurements for a Sub-Femtosecond Electron Bunch Length
Diagnostic | With electron beam durations down to femtoseconds and sub-femtoseconds
achievable in current state-of-the-art accelerators, longitudinal bunch length
diagnostics with resolution at the attosecond level are required. In this
paper, we present such a novel measurement device which combines a high power
laser modulator with an RF deflecting cavity in the orthogonal direction. While
the laser applies a strong correlated angular modulation to a beam, the RF
deflector ensures the full resolution of this streaking effect across the bunch
hence recovering the temporal beam profile with sub-femtosecond resolution.
Preliminary measurements to test the key components of this concept were
carried out at the Accelerator Test Facility (ATF) at Brookhaven National
Laboratory recently, the results of which are presented and discussed here.
Moreover, a possible application of the technique for novel accelerator schemes
is examined based on simulations with the particle-tracking code elegant and
our beam profile reconstruction tool.
| physics.acc-ph physics.ins-det | with electron beam durations down to femtoseconds and subfemtoseconds achievable in current stateoftheart accelerators longitudinal bunch length diagnostics with resolution at the attosecond level are required in this paper we present such a novel measurement device which combines a high power laser modulator with an rf deflecting cavity in the orthogonal direction while the laser applies a strong correlated angular modulation to a beam the rf deflector ensures the full resolution of this streaking effect across the bunch hence recovering the temporal beam profile with subfemtosecond resolution preliminary measurements to test the key components of this concept were carried out at the accelerator test facility atf at brookhaven national laboratory recently the results of which are presented and discussed here moreover a possible application of the technique for novel accelerator schemes is examined based on simulations with the particletracking code elegant and our beam profile reconstruction tool | [['with', 'electron', 'beam', 'durations', 'down', 'to', 'femtoseconds', 'and', 'subfemtoseconds', 'achievable', 'in', 'current', 'stateoftheart', 'accelerators', 'longitudinal', 'bunch', 'length', 'diagnostics', 'with', 'resolution', 'at', 'the', 'attosecond', 'level', 'are', 'required', 'in', 'this', 'paper', 'we', 'present', 'such', 'a', 'novel', 'measurement', 'device', 'which', 'combines', 'a', 'high', 'power', 'laser', 'modulator', 'with', 'an', 'rf', 'deflecting', 'cavity', 'in', 'the', 'orthogonal', 'direction', 'while', 'the', 'laser', 'applies', 'a', 'strong', 'correlated', 'angular', 'modulation', 'to', 'a', 'beam', 'the', 'rf', 'deflector', 'ensures', 'the', 'full', 'resolution', 'of', 'this', 'streaking', 'effect', 'across', 'the', 'bunch', 'hence', 'recovering', 'the', 'temporal', 'beam', 'profile', 'with', 'subfemtosecond', 'resolution', 'preliminary', 'measurements', 'to', 'test', 'the', 'key', 'components', 'of', 'this', 'concept', 'were', 'carried', 'out', 'at', 'the', 'accelerator', 'test', 'facility', 'atf', 'at', 'brookhaven', 'national', 'laboratory', 'recently', 'the', 'results', 'of', 'which', 'are', 'presented', 'and', 'discussed', 'here', 'moreover', 'a', 'possible', 'application', 'of', 'the', 'technique', 'for', 'novel', 'accelerator', 'schemes', 'is', 'examined', 'based', 'on', 'simulations', 'with', 'the', 'particletracking', 'code', 'elegant', 'and', 'our', 'beam', 'profile', 'reconstruction', 'tool']] | [-0.08960312326783187, 0.12904452047821482, -0.09117368918090854, 0.014567058273489038, -0.02220549007195163, -0.17685393818131048, 0.0021657765801511193, 0.45306708867827505, -0.22307337708420388, -0.3254402942496212, 0.04308034547872535, -0.23751740339722432, -0.0015753329961165174, 0.2675626277285692, -0.01364357442569549, 0.1228431812296175, 0.07451659070697855, -0.04981336112402073, -0.05203009933777064, -0.15310990309928008, 0.2534195339763282, 0.23934238067866392, 0.36895656083353273, 0.07480548923693227, 0.19541897980674897, 0.019595669512716695, -0.042890991718980344, -0.04115544667161287, -0.1022053631469097, 0.07620078245854031, 0.25232226569970995, 0.10506483662254751, 0.2566941836673435, -0.4336534589656616, -0.20679206138138648, -0.01068361255353036, 0.1220201702074033, 0.11872652485290516, -0.10233607343122944, -0.25336952675899416, 0.06336774081246901, -0.16362189389892842, -0.1648893318528131, -0.012176498943742058, -0.0534240160095952, 0.09200617749752693, -0.284539750528407, 0.0011816326311904274, 0.001857515298107588, 0.10760968342448955, 0.005292989315197178, -0.07942869664769467, 0.07764059743582437, 0.0467229964449198, -0.009411579146279558, 0.07876197927115543, 0.1451101670239427, -0.07917930680320738, -0.10277691098051273, 0.35599497154559173, -0.03258941141921989, -0.13558239138953082, 0.15166125053297475, -0.20963766799652822, -0.07674535061551692, 0.14365549201515745, 0.187712358619399, 0.10671331656918134, -0.12219105577989392, -0.017089383994398178, 0.010842949655927615, 0.2126193244667239, 0.14799025580318875, 0.05672892450103026, 0.20816813228326556, 0.22632387295773584, 0.06944025268140114, 0.13130047819529317, -0.19677171796043594, 0.0032163787890729582, -0.3061632558078288, -0.09107199848361619, -0.1330834588782275, 0.00807366236787782, 0.00036848321178695187, -0.07656697755398816, 0.46471455671237655, 0.16224350616056193, 0.1053747675534695, -0.01693618295683162, 0.4099350297083593, 0.09838384491202505, 0.0815969698660535, 0.020647605565338307, 0.2221302067931129, 0.12608026166698474, 0.14842880257143806, -0.2652076236642132, -0.006966181032436147, -0.01130153396492186] |
1,802.04675 | Attention based Sentence Extraction from Scientific Articles using
Pseudo-Labeled data | In this work, we present a weakly supervised sentence extraction technique
for identifying important sentences in scientific papers that are worthy of
inclusion in the abstract. We propose a new attention based deep learning
architecture that jointly learns to identify important content, as well as the
cue phrases that are indicative of summary worthy sentences. We propose a new
context embedding technique for determining the focus of a given paper using
topic models and use it jointly with an LSTM based sequence encoder to learn
attention weights across the sentence words. We use a collection of articles
publicly available through ACL anthology for our experiments. Our system
achieves a performance that is better, in terms of several ROUGE metrics, as
compared to several state of art extractive techniques. It also generates more
coherent summaries and preserves the overall structure of the document.
| cs.IR cs.AI cs.CL | in this work we present a weakly supervised sentence extraction technique for identifying important sentences in scientific papers that are worthy of inclusion in the abstract we propose a new attention based deep learning architecture that jointly learns to identify important content as well as the cue phrases that are indicative of summary worthy sentences we propose a new context embedding technique for determining the focus of a given paper using topic models and use it jointly with an lstm based sequence encoder to learn attention weights across the sentence words we use a collection of articles publicly available through acl anthology for our experiments our system achieves a performance that is better in terms of several rouge metrics as compared to several state of art extractive techniques it also generates more coherent summaries and preserves the overall structure of the document | [['in', 'this', 'work', 'we', 'present', 'a', 'weakly', 'supervised', 'sentence', 'extraction', 'technique', 'for', 'identifying', 'important', 'sentences', 'in', 'scientific', 'papers', 'that', 'are', 'worthy', 'of', 'inclusion', 'in', 'the', 'abstract', 'we', 'propose', 'a', 'new', 'attention', 'based', 'deep', 'learning', 'architecture', 'that', 'jointly', 'learns', 'to', 'identify', 'important', 'content', 'as', 'well', 'as', 'the', 'cue', 'phrases', 'that', 'are', 'indicative', 'of', 'summary', 'worthy', 'sentences', 'we', 'propose', 'a', 'new', 'context', 'embedding', 'technique', 'for', 'determining', 'the', 'focus', 'of', 'a', 'given', 'paper', 'using', 'topic', 'models', 'and', 'use', 'it', 'jointly', 'with', 'an', 'lstm', 'based', 'sequence', 'encoder', 'to', 'learn', 'attention', 'weights', 'across', 'the', 'sentence', 'words', 'we', 'use', 'a', 'collection', 'of', 'articles', 'publicly', 'available', 'through', 'acl', 'anthology', 'for', 'our', 'experiments', 'our', 'system', 'achieves', 'a', 'performance', 'that', 'is', 'better', 'in', 'terms', 'of', 'several', 'rouge', 'metrics', 'as', 'compared', 'to', 'several', 'state', 'of', 'art', 'extractive', 'techniques', 'it', 'also', 'generates', 'more', 'coherent', 'summaries', 'and', 'preserves', 'the', 'overall', 'structure', 'of', 'the', 'document']] | [-0.02889709518386156, 0.008863449199260442, -0.060842443155911814, 0.09392770524726997, -0.16647960450893767, -0.13583519278277814, 0.05185809686556983, 0.463150021213461, -0.25196741747958457, -0.29772268515408146, 0.033628690305729986, -0.30420778405813265, -0.19460770450729195, 0.20235588726904047, -0.12038497684989125, 0.04672798724651573, 0.1434118388948827, 0.08110599318411325, -0.05291799521139285, -0.30801687050409104, 0.3522231537991361, 0.07983953998121597, 0.332618599810021, 0.004684782409909325, 0.13882251945631677, -0.015897097075882723, -0.08717332325849525, -0.024631412283346896, -0.08124517187460581, 0.22289344628329608, 0.36102495488701375, 0.23506511349073597, 0.34376299458811665, -0.3450614001051011, -0.2161538184417123, 0.0252506818243591, 0.14579555979445347, 0.11854252502860003, -0.05808236733258089, -0.3165992760873387, 0.09244509846706625, -0.19662428985949948, 0.06336203053541167, -0.16565830283716296, -0.005053219129659341, 0.0035585246539123778, -0.24761510579100757, 0.022017293638987533, 0.1319959895860758, 0.04700771292907671, -0.040201560636772445, -0.1275047468715085, 0.03764663206767553, 0.17894756484409452, 0.04826368208651588, 0.0941467501229646, 0.09432544271973237, -0.17500729515163135, -0.14893508211604622, 0.3835367096955298, -0.10602572649753403, -0.2256946185551269, 0.15877969081277474, -0.0018844190578330572, -0.1856600687688243, 0.051317432846113196, 0.21604260854082, 0.13732895047201374, -0.18229658125520973, -0.021820012523881226, -0.08624038053348555, 0.22588878637015766, 0.04480743542490658, 0.04038855998816205, 0.20247522270811957, 0.293669027564916, -0.008003985650286498, 0.18851909762605842, -0.08171068042890549, -0.040900759812129636, -0.25924407292000007, -0.13504625574528226, -0.15462250562063354, -0.04818222521555046, -0.04857893932498434, -0.16675828958967698, 0.44292029675463557, 0.27086530111461193, 0.21102447387873863, 0.09263564580227543, 0.30539300562125227, -0.006277879007311512, 0.0730786956139614, 0.09730262873353253, 0.12268509504854233, 0.013667407611214464, 0.11914866571240848, -0.11911214228709635, 0.10471357238433049, 0.08205197497478417] |
1,802.04676 | Variable Selection and Task Grouping for Multi-Task Learning | We consider multi-task learning, which simultaneously learns related
prediction tasks, to improve generalization performance. We factorize a
coefficient matrix as the product of two matrices based on a low-rank
assumption. These matrices have sparsities to simultaneously perform variable
selection and learn and overlapping group structure among the tasks. The
resulting bi-convex objective function is minimized by alternating optimization
where sub-problems are solved using alternating direction method of multipliers
and accelerated proximal gradient descent. Moreover, we provide the performance
bound of the proposed method. The effectiveness of the proposed method is
validated for both synthetic and real-world datasets.
| stat.ML | we consider multitask learning which simultaneously learns related prediction tasks to improve generalization performance we factorize a coefficient matrix as the product of two matrices based on a lowrank assumption these matrices have sparsities to simultaneously perform variable selection and learn and overlapping group structure among the tasks the resulting biconvex objective function is minimized by alternating optimization where subproblems are solved using alternating direction method of multipliers and accelerated proximal gradient descent moreover we provide the performance bound of the proposed method the effectiveness of the proposed method is validated for both synthetic and realworld datasets | [['we', 'consider', 'multitask', 'learning', 'which', 'simultaneously', 'learns', 'related', 'prediction', 'tasks', 'to', 'improve', 'generalization', 'performance', 'we', 'factorize', 'a', 'coefficient', 'matrix', 'as', 'the', 'product', 'of', 'two', 'matrices', 'based', 'on', 'a', 'lowrank', 'assumption', 'these', 'matrices', 'have', 'sparsities', 'to', 'simultaneously', 'perform', 'variable', 'selection', 'and', 'learn', 'and', 'overlapping', 'group', 'structure', 'among', 'the', 'tasks', 'the', 'resulting', 'biconvex', 'objective', 'function', 'is', 'minimized', 'by', 'alternating', 'optimization', 'where', 'subproblems', 'are', 'solved', 'using', 'alternating', 'direction', 'method', 'of', 'multipliers', 'and', 'accelerated', 'proximal', 'gradient', 'descent', 'moreover', 'we', 'provide', 'the', 'performance', 'bound', 'of', 'the', 'proposed', 'method', 'the', 'effectiveness', 'of', 'the', 'proposed', 'method', 'is', 'validated', 'for', 'both', 'synthetic', 'and', 'realworld', 'datasets']] | [-0.06280541921168872, -0.028560240810602596, -0.06968115281599295, 0.04015107851561889, -0.11786637061573181, -0.18285843271032437, 0.02074600106162816, 0.489151281871132, -0.3303515850806359, -0.32033008478159447, 0.09556941272326044, -0.22190207115748956, -0.2150279391729801, 0.18086307054174314, -0.07012408384189163, 0.09136511882786284, 0.11769410540330578, 0.017499274077654826, -0.13856077106842368, -0.3272939847839862, 0.2782650910433113, 0.021564799898601685, 0.3423854639350446, 0.020984761071266587, 0.1895410215695744, 0.017632370808680263, -0.02402343055876644, 0.016695401247245136, -0.0006145159953955523, 0.17735602735714584, 0.29951750055752385, 0.20290932368928777, 0.32780323069321815, -0.39047727825302514, -0.17735753034135884, 0.12249666932471019, 0.149774202524886, -0.0008822687535208275, -0.0648565892209347, -0.29513912442495527, 0.08533057275458633, -0.13076292820900112, 0.0020923742870848205, -0.167855555616492, -0.11303382431944237, 0.030036155228051788, -0.3662480345054664, 0.08839208698716283, 0.03041053819710139, -0.014127986524080061, -0.06800047871320672, -0.1892918649984082, 0.07019307441798221, 0.12164928742021937, 0.07039426655878227, 0.008185574919292607, 0.16560420066060633, -0.06347801881007005, -0.16156819964922273, 0.33048972253178815, -0.06359381227125174, -0.2848257552256289, 0.14726254373510397, 0.0028085604204421805, -0.14134076952012545, 0.09794512441016964, 0.2647536696155661, 0.18475705754376717, -0.15090634110299678, 0.04552217544603747, -0.09639981316676191, 0.1262021981390942, 0.04228018735506639, -0.06709393170499003, 0.07732347323866465, 0.18010523044216187, 0.1285964442565845, 0.18266142918110212, -0.09570585989834952, -0.08867982814338096, -0.18333521445963494, -0.11062844110876034, -0.23221178232576967, -0.0767078528291133, -0.14556180367896757, -0.12050485024399715, 0.43495349198118927, 0.17729548548981933, 0.2156212306629444, 0.11323647513222333, 0.35195010816006317, 0.08681412646907007, 0.07856109231403194, 0.11184140410485495, 0.1769557240246267, 0.13156787762610414, 0.043761531445383785, -0.27754517512266363, 0.07371414590569347, 0.15347263009345025] |
1,802.04677 | Evolutionary homology on coupled dynamical systems | Time dependence is a universal phenomenon in nature, and a variety of
mathematical models in terms of dynamical systems have been developed to
understand the time-dependent behavior of real-world problems. Originally
constructed to analyze the topological persistence over spatial scales,
persistent homology has rarely been devised for time evolution. We propose the
use of a new filtration function for persistent homology which takes as input
the adjacent oscillator trajectories of a dynamical system. We also regulate
the dynamical system by a weighted graph Laplacian matrix derived from the
network of interest, which embeds the topological connectivity of the network
into the dynamical system. The resulting topological signatures, which we call
evolutionary homology (EH) barcodes, reveal the topology-function relationship
of the network and thus give rise to the quantitative analysis of nodal
properties. The proposed EH is applied to protein residue networks for protein
thermal fluctuation analysis, rendering the most accurate B-factor prediction
of a set of 364 proteins. This work extends the utility of dynamical systems to
the quantitative modeling and analysis of realistic physical systems.
| math.AT math.DS q-bio.QM | time dependence is a universal phenomenon in nature and a variety of mathematical models in terms of dynamical systems have been developed to understand the timedependent behavior of realworld problems originally constructed to analyze the topological persistence over spatial scales persistent homology has rarely been devised for time evolution we propose the use of a new filtration function for persistent homology which takes as input the adjacent oscillator trajectories of a dynamical system we also regulate the dynamical system by a weighted graph laplacian matrix derived from the network of interest which embeds the topological connectivity of the network into the dynamical system the resulting topological signatures which we call evolutionary homology eh barcodes reveal the topologyfunction relationship of the network and thus give rise to the quantitative analysis of nodal properties the proposed eh is applied to protein residue networks for protein thermal fluctuation analysis rendering the most accurate bfactor prediction of a set of 364 proteins this work extends the utility of dynamical systems to the quantitative modeling and analysis of realistic physical systems | [['time', 'dependence', 'is', 'a', 'universal', 'phenomenon', 'in', 'nature', 'and', 'a', 'variety', 'of', 'mathematical', 'models', 'in', 'terms', 'of', 'dynamical', 'systems', 'have', 'been', 'developed', 'to', 'understand', 'the', 'timedependent', 'behavior', 'of', 'realworld', 'problems', 'originally', 'constructed', 'to', 'analyze', 'the', 'topological', 'persistence', 'over', 'spatial', 'scales', 'persistent', 'homology', 'has', 'rarely', 'been', 'devised', 'for', 'time', 'evolution', 'we', 'propose', 'the', 'use', 'of', 'a', 'new', 'filtration', 'function', 'for', 'persistent', 'homology', 'which', 'takes', 'as', 'input', 'the', 'adjacent', 'oscillator', 'trajectories', 'of', 'a', 'dynamical', 'system', 'we', 'also', 'regulate', 'the', 'dynamical', 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1,802.04678 | Development of dark disk model of positron anomaly origin | Dark disk model could be a remedy for dark matter (DM) explanation of
positron anomaly (PA) in cosmic rays (CR). The main difficulty in PA
explanation relates to cosmic gamma-radiation which is inevitably produced in
DM annihilation or decay leading to tension with respective observation data.
Introduction of "active" (producing CR) DM component concentrating in galactic
disk alleviates this tension. Earlier we considered two-lepton modes, with
branching ratios being chosen to fit in the best way all the observation data.
Here we considered, in framework of the same dark disk model, two cases:
two-body final state annihilation and four-body one, and in each case a quark
mode is added to the leptonic ones. It is shown that 4-body mode case is a
little better than 2-body one from viewpoint of quality of observation data
description at the fixed all other parameters (of CR propagation, background,
disk height). The values of DM particle mass around 350 GeV and 500 GeV are
more favourable for 2- and 4-body modes respectively. Higher values would
improve description of data on positrons only but accounting for data on
gamma-radiation prevents it because of unwanted more abundant high-energy gamma
production. Inclusion of the quark modes improves a little fitting data in both
4- and 2-body mode cases, contrary to naive expectations. In fact, quark mode
has a bigger gammas yield than that of most gamma-productive leptonic mode~---
tau, but they are softer due to bigger final state hadron multiplicity.
| astro-ph.HE | dark disk model could be a remedy for dark matter dm explanation of positron anomaly pa in cosmic rays cr the main difficulty in pa explanation relates to cosmic gammaradiation which is inevitably produced in dm annihilation or decay leading to tension with respective observation data introduction of active producing cr dm component concentrating in galactic disk alleviates this tension earlier we considered twolepton modes with branching ratios being chosen to fit in the best way all the observation data here we considered in framework of the same dark disk model two cases twobody final state annihilation and fourbody one and in each case a quark mode is added to the leptonic ones it is shown that 4body mode case is a little better than 2body one from viewpoint of quality of observation data description at the fixed all other parameters of cr propagation background disk height the values of dm particle mass around 350 gev and 500 gev are more favourable for 2 and 4body modes respectively higher values would improve description of data on positrons only but accounting for data on gammaradiation prevents it because of unwanted more abundant highenergy gamma production inclusion of the quark modes improves a little fitting data in both 4 and 2body mode cases contrary to naive expectations in fact quark mode has a bigger gammas yield than that of most gammaproductive leptonic mode tau but they are softer due to bigger final state hadron multiplicity | [['dark', 'disk', 'model', 'could', 'be', 'a', 'remedy', 'for', 'dark', 'matter', 'dm', 'explanation', 'of', 'positron', 'anomaly', 'pa', 'in', 'cosmic', 'rays', 'cr', 'the', 'main', 'difficulty', 'in', 'pa', 'explanation', 'relates', 'to', 'cosmic', 'gammaradiation', 'which', 'is', 'inevitably', 'produced', 'in', 'dm', 'annihilation', 'or', 'decay', 'leading', 'to', 'tension', 'with', 'respective', 'observation', 'data', 'introduction', 'of', 'active', 'producing', 'cr', 'dm', 'component', 'concentrating', 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1,802.04679 | Deformed preprojective algebras of Dynkin type $\mathbb{E}_6$ | We prove that every deformed preprojective algebra of Dynkin type
$\mathbb{E}_6$ is isomorphic to the preprojective algebra of Dynkin type
$\mathbb{E}_6$.
| math.RT | we prove that every deformed preprojective algebra of dynkin type mathbbe_6 is isomorphic to the preprojective algebra of dynkin type mathbbe_6 | [['we', 'prove', 'that', 'every', 'deformed', 'preprojective', 'algebra', 'of', 'dynkin', 'type', 'mathbbe_6', 'is', 'isomorphic', 'to', 'the', 'preprojective', 'algebra', 'of', 'dynkin', 'type', 'mathbbe_6']] | [-0.1533464169815967, -0.00828689018166379, -0.08845904861625872, 0.06775819502562579, -0.2990438179358056, -0.2629908559549796, -0.12685354348076017, 0.3872566223144531, -0.5399428504078012, -0.04487505348056162, 0.10916249755475867, -0.2109084388143138, -0.17683469111982145, 0.04006823965985524, -0.3780128055889356, -0.33209772444771307, 0.17570175387357412, 0.25287713228087677, -0.12977965067042724, -0.33104013573182256, 0.5144418901518771, -0.12257017587360583, 0.21642338876661502, -0.1069217479640716, 0.05426773595574655, 0.09949048512958382, 0.017405900810109943, -0.031162554594246966, -0.21730958024770525, 0.022070040068540134, 0.4175776741222331, 0.04212418574790813, 0.1360898992402087, -0.2962087772366909, 0.07842408022598217, 0.36579358714975807, 0.3177183385154134, -0.027243195346703653, 0.006479002866207769, -0.26327773507096264, 0.10221869018124907, -0.44505448306077405, -0.2858755496869746, 0.022558198996672506, 0.20348543654146947, -0.029979657381772995, -0.19212776167612328, 0.017868870575177043, 0.061513523130040416, 0.15596612508555777, -0.22872125867166018, -0.13353072660730073, -0.25890286835400683, -0.022201844736149435, -0.22452652842835769, 0.06291360199745548, 0.11554684519069269, -0.0482632439387472, -0.3514571175175278, 0.2969285768799876, 0.15791327182791734, -0.21336300043683304, -0.0270856626723942, -0.2295124671470962, -0.2782768148340677, 0.1375717536399239, -0.144213636454783, 0.11506491703422446, 0.05611949472835189, 0.22637604667167915, -0.1597894089982698, -0.07567114824135053, 0.08454077249686968, -0.047739346922179196, -0.0009339758636135804, 0.16191273319878077, -0.047530918774244035, 0.20632586873283512, 0.10693795782955069, 0.03154854496058665, -0.42042656870264755, -0.16610269640621386, 0.01601727630354856, 0.23493129313972436, -0.22097571395141513, -0.1943719436071421, 0.33586800961117996, 0.07850109589727301, -0.0021534052211791277, 0.1817034571676662, -0.005949566434872777, 0.0776129229680488, 0.2281593776455051, 0.020140292588621378, 0.0688224113301227, 0.45596055215910863, -0.09963166174527846, -0.14983628222130632, -0.1163253250017174, 0.5006389457144236] |
1,802.0468 | Training and Inference with Integers in Deep Neural Networks | Researches on deep neural networks with discrete parameters and their
deployment in embedded systems have been active and promising topics. Although
previous works have successfully reduced precision in inference, transferring
both training and inference processes to low-bitwidth integers has not been
demonstrated simultaneously. In this work, we develop a new method termed as
"WAGE" to discretize both training and inference, where weights (W),
activations (A), gradients (G) and errors (E) among layers are shifted and
linearly constrained to low-bitwidth integers. To perform pure discrete
dataflow for fixed-point devices, we further replace batch normalization by a
constant scaling layer and simplify other components that are arduous for
integer implementation. Improved accuracies can be obtained on multiple
datasets, which indicates that WAGE somehow acts as a type of regularization.
Empirically, we demonstrate the potential to deploy training in hardware
systems such as integer-based deep learning accelerators and neuromorphic chips
with comparable accuracy and higher energy efficiency, which is crucial to
future AI applications in variable scenarios with transfer and continual
learning demands.
| cs.LG | researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics although previous works have successfully reduced precision in inference transferring both training and inference processes to lowbitwidth integers has not been demonstrated simultaneously in this work we develop a new method termed as wage to discretize both training and inference where weights w activations a gradients g and errors e among layers are shifted and linearly constrained to lowbitwidth integers to perform pure discrete dataflow for fixedpoint devices we further replace batch normalization by a constant scaling layer and simplify other components that are arduous for integer implementation improved accuracies can be obtained on multiple datasets which indicates that wage somehow acts as a type of regularization empirically we demonstrate the potential to deploy training in hardware systems such as integerbased deep learning accelerators and neuromorphic chips with comparable accuracy and higher energy efficiency which is crucial to future ai applications in variable scenarios with transfer and continual learning demands | [['researches', 'on', 'deep', 'neural', 'networks', 'with', 'discrete', 'parameters', 'and', 'their', 'deployment', 'in', 'embedded', 'systems', 'have', 'been', 'active', 'and', 'promising', 'topics', 'although', 'previous', 'works', 'have', 'successfully', 'reduced', 'precision', 'in', 'inference', 'transferring', 'both', 'training', 'and', 'inference', 'processes', 'to', 'lowbitwidth', 'integers', 'has', 'not', 'been', 'demonstrated', 'simultaneously', 'in', 'this', 'work', 'we', 'develop', 'a', 'new', 'method', 'termed', 'as', 'wage', 'to', 'discretize', 'both', 'training', 'and', 'inference', 'where', 'weights', 'w', 'activations', 'a', 'gradients', 'g', 'and', 'errors', 'e', 'among', 'layers', 'are', 'shifted', 'and', 'linearly', 'constrained', 'to', 'lowbitwidth', 'integers', 'to', 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1,802.04681 | Examining the Tip of the Iceberg: A Data Set for Idiom Translation | Neural Machine Translation (NMT) has been widely used in recent years with
significant improvements for many language pairs. Although state-of-the-art NMT
systems are generating progressively better translations, idiom translation
remains one of the open challenges in this field. Idioms, a category of
multiword expressions, are an interesting language phenomenon where the overall
meaning of the expression cannot be composed from the meanings of its parts. A
first important challenge is the lack of dedicated data sets for learning and
evaluating idiom translation. In this paper we address this problem by creating
the first large-scale data set for idiom translation. Our data set is
automatically extracted from a widely used German-English translation corpus
and includes, for each language direction, a targeted evaluation set where all
sentences contain idioms and a regular training corpus where sentences
including idioms are marked. We release this data set and use it to perform
preliminary NMT experiments as the first step towards better idiom translation.
| cs.CL | neural machine translation nmt has been widely used in recent years with significant improvements for many language pairs although stateoftheart nmt systems are generating progressively better translations idiom translation remains one of the open challenges in this field idioms a category of multiword expressions are an interesting language phenomenon where the overall meaning of the expression cannot be composed from the meanings of its parts a first important challenge is the lack of dedicated data sets for learning and evaluating idiom translation in this paper we address this problem by creating the first largescale data set for idiom translation our data set is automatically extracted from a widely used germanenglish translation corpus and includes for each language direction a targeted evaluation set where all sentences contain idioms and a regular training corpus where sentences including idioms are marked we release this data set and use it to perform preliminary nmt experiments as the first step towards better idiom translation | [['neural', 'machine', 'translation', 'nmt', 'has', 'been', 'widely', 'used', 'in', 'recent', 'years', 'with', 'significant', 'improvements', 'for', 'many', 'language', 'pairs', 'although', 'stateoftheart', 'nmt', 'systems', 'are', 'generating', 'progressively', 'better', 'translations', 'idiom', 'translation', 'remains', 'one', 'of', 'the', 'open', 'challenges', 'in', 'this', 'field', 'idioms', 'a', 'category', 'of', 'multiword', 'expressions', 'are', 'an', 'interesting', 'language', 'phenomenon', 'where', 'the', 'overall', 'meaning', 'of', 'the', 'expression', 'can', 'not', 'be', 'composed', 'from', 'the', 'meanings', 'of', 'its', 'parts', 'a', 'first', 'important', 'challenge', 'is', 'the', 'lack', 'of', 'dedicated', 'data', 'sets', 'for', 'learning', 'and', 'evaluating', 'idiom', 'translation', 'in', 'this', 'paper', 'we', 'address', 'this', 'problem', 'by', 'creating', 'the', 'first', 'largescale', 'data', 'set', 'for', 'idiom', 'translation', 'our', 'data', 'set', 'is', 'automatically', 'extracted', 'from', 'a', 'widely', 'used', 'germanenglish', 'translation', 'corpus', 'and', 'includes', 'for', 'each', 'language', 'direction', 'a', 'targeted', 'evaluation', 'set', 'where', 'all', 'sentences', 'contain', 'idioms', 'and', 'a', 'regular', 'training', 'corpus', 'where', 'sentences', 'including', 'idioms', 'are', 'marked', 'we', 'release', 'this', 'data', 'set', 'and', 'use', 'it', 'to', 'perform', 'preliminary', 'nmt', 'experiments', 'as', 'the', 'first', 'step', 'towards', 'better', 'idiom', 'translation']] | [-0.06189258564118063, 0.02715039760014406, -0.03377435859583784, 0.09691995173780014, -0.15740656504349318, -0.12713446930865757, 0.03341185454119113, 0.40691991078201684, -0.303594529198017, -0.31739974818483463, 0.07454435160107095, -0.32219021232558587, -0.11673881921888096, 0.25387079280917535, -0.10579190758832055, 0.09772293547612207, 0.15763878205689252, 0.05744231662974926, -0.023387531367188786, -0.2811035234721203, 0.32101413624332054, -0.0009580512769389316, 0.34649375867593335, -0.007200941411429085, 0.10727399749521283, -0.061437503648267014, -0.03019486953562591, -0.04797817109138123, -0.037515555985009996, 0.1872284637960547, 0.367089999554446, 0.2518740302868537, 0.32612912617405526, -0.3866238268557936, -0.15548780431272463, 0.07828544265357777, 0.13571574144880288, 0.16491958884362248, -0.060891206783708185, -0.3234986659081187, 0.08761348080952303, -0.1760283559677191, 0.044674548369948754, -0.14806836752177333, 0.06301493345526979, -0.03280471119942376, -0.22693586568711907, -0.005540131971065421, 0.148737772993627, 0.16480734554788795, -0.039282901175465665, -0.11813199610332958, 0.04928611975192325, 0.18023700853555055, 0.06667454335693038, 0.15501538758690003, 0.07472880872082896, -0.13403318966156802, -0.1604086466890294, 0.4302413762314245, -0.045463107508112444, -0.23935482924598545, 0.18019927084969822, -0.0385223827091977, -0.2346785482019186, 0.06035409916657954, 0.21683207902970025, 0.06994570295501035, -0.2107439120998606, 0.04589217438333435, -0.06966179495211691, 0.21527489927466376, 0.09214701613091165, -0.02228951848228462, 0.2264837248600088, 0.2614191870394279, -0.0646638270427502, 0.16954763306639506, -0.059701580950058994, -0.059279545277240685, -0.24798102616914547, -0.12756348552647978, -0.1399382692827203, -0.03221584311531842, -0.027146924461339948, -0.15084280260052765, 0.3917118167737499, 0.2176422597025521, 0.17689870244357736, 0.10284120129508664, 0.3062134623527527, 0.015731870953823092, 0.14474103016545997, 0.09673529679421336, 0.11352143506519496, -0.03584824339254737, 0.1262788238498615, -0.100204730394762, 0.10521785532546346, 0.04151893464731984] |
1,802.04682 | On the geometric Mumford-Tate conjecture for subvarieties of Shimura
varieties | We study the image of $\ell$-adic representations attached to subvarieties of
Shimura varieties $Sh_K(G,X)$ that are not contained in a smaller Shimura
subvariety and have no isotrivial components. We show that, for $\ell$ large
enough (depending on the Shimura datum $(G,X)$ and the subvariety), such image
contains the $\mathbb{Z}_\ell$-points coming from the simply connected cover of
the derived subgroup of $G$. This can be regarded as a geometric version of the
integral $\ell$-adic Mumford-Tate conjecture.
| math.AG math.NT | we study the image of elladic representations attached to subvarieties of shimura varieties sh_kgx that are not contained in a smaller shimura subvariety and have no isotrivial components we show that for ell large enough depending on the shimura datum gx and the subvariety such image contains the mathbbz_ellpoints coming from the simply connected cover of the derived subgroup of g this can be regarded as a geometric version of the integral elladic mumfordtate conjecture | [['we', 'study', 'the', 'image', 'of', 'elladic', 'representations', 'attached', 'to', 'subvarieties', 'of', 'shimura', 'varieties', 'sh_kgx', 'that', 'are', 'not', 'contained', 'in', 'a', 'smaller', 'shimura', 'subvariety', 'and', 'have', 'no', 'isotrivial', 'components', 'we', 'show', 'that', 'for', 'ell', 'large', 'enough', 'depending', 'on', 'the', 'shimura', 'datum', 'gx', 'and', 'the', 'subvariety', 'such', 'image', 'contains', 'the', 'mathbbz_ellpoints', 'coming', 'from', 'the', 'simply', 'connected', 'cover', 'of', 'the', 'derived', 'subgroup', 'of', 'g', 'this', 'can', 'be', 'regarded', 'as', 'a', 'geometric', 'version', 'of', 'the', 'integral', 'elladic', 'mumfordtate', 'conjecture']] | [-0.20884839682648443, -0.0031684129825818724, -0.16630104180961236, 0.0595117864463708, -0.12759025496979282, -0.1324730683118105, -0.04594349675837343, 0.2899700213121633, -0.32868308640302046, -0.20784701039529827, 0.11663579659963869, -0.2282957153184314, -0.09027623675431903, 0.2672444500526643, -0.22060105438688643, -0.053906752217612035, 0.12531177263566587, 0.11581999163003001, -0.04831162793561816, -0.3576732850187037, 0.44048793881028026, -0.13549280871815775, 0.18278672717103403, 0.04403374022772306, 0.11434235908601383, 0.003874022532764771, 0.03820089874023648, -0.08170097045702478, -0.10083703643882373, 0.16413935219027, 0.3794137768141211, 0.0942291562917502, 0.15261873730525297, -0.39059177245179266, -0.15628629144797843, 0.28953078279450334, 0.1316164026151679, 0.0020487894852683967, 0.03785670084770361, -0.26927091141728915, 0.1016259616980814, -0.1680729483088402, -0.17717859386275076, -0.080758388779343, 0.06453240634745931, 0.04033444931870964, -0.22503992279175006, -0.009678467447958784, 0.08688434763262941, 0.19357292882281624, -0.04895064953638062, -0.1475483134439955, -0.08380733193727592, 0.06291227309632894, -0.004669928642576688, 0.11451679513285778, 0.11722153020793036, -0.1561035145491151, -0.04657986405471416, 0.41328462133541294, -0.10406054446652327, -0.20365075173800531, 0.1059969479492419, -0.19803426523807727, -0.20762247375965323, 0.16854247145597145, 0.09798657202659404, 0.12875635619594217, 0.03444833083920283, 0.17028067788995854, -0.18461352864867203, 0.07238523221785229, 0.10712263638102641, -0.029293378561492753, 0.1815894778103453, 0.06315791485384617, 0.03900247674396705, 0.12422924495002331, -0.06292414053802518, 0.06396350993022118, -0.41900574533294327, -0.16121752115527857, -0.15179675897864037, 0.20739778277024862, -0.0770987397987134, -0.17143021063394334, 0.3866114109142186, 0.0625754231204317, 0.21789313139623567, 0.0936202157180348, 0.198876055123361, 0.024552877729261065, 0.11454428192738393, 0.07370125561671918, 0.11195884884832656, 0.2118863754280626, -0.08953851664898126, -0.11606895127206122, 0.0005629199608037733, 0.18060402516339433] |
1,802.04683 | Photoassociation of rovibrational Rydberg molecules | In this work we discuss the rotational structure of Rydberg molecules. We
calculate the complete wave function in a laboratory fixed frame and derive the
transition matrix elements for the pho- toassociation of free ground state
atoms. We discuss the implications for the excitation of different rotational
states as well as the shape of the angular nuclear wave function. We find a
rather com- plex shape and unintuitive coupling strengths, depending on the
angular momenta coupling that are relevant for the states. This work explains
the different steps to calculate the wave functions and the transition matrix
elements in a way, that they can be directly transferred to different molecular
states, atomic species or molecular coupling cases.
| physics.atom-ph physics.chem-ph | in this work we discuss the rotational structure of rydberg molecules we calculate the complete wave function in a laboratory fixed frame and derive the transition matrix elements for the pho toassociation of free ground state atoms we discuss the implications for the excitation of different rotational states as well as the shape of the angular nuclear wave function we find a rather com plex shape and unintuitive coupling strengths depending on the angular momenta coupling that are relevant for the states this work explains the different steps to calculate the wave functions and the transition matrix elements in a way that they can be directly transferred to different molecular states atomic species or molecular coupling cases | [['in', 'this', 'work', 'we', 'discuss', 'the', 'rotational', 'structure', 'of', 'rydberg', 'molecules', 'we', 'calculate', 'the', 'complete', 'wave', 'function', 'in', 'a', 'laboratory', 'fixed', 'frame', 'and', 'derive', 'the', 'transition', 'matrix', 'elements', 'for', 'the', 'pho', 'toassociation', 'of', 'free', 'ground', 'state', 'atoms', 'we', 'discuss', 'the', 'implications', 'for', 'the', 'excitation', 'of', 'different', 'rotational', 'states', 'as', 'well', 'as', 'the', 'shape', 'of', 'the', 'angular', 'nuclear', 'wave', 'function', 'we', 'find', 'a', 'rather', 'com', 'plex', 'shape', 'and', 'unintuitive', 'coupling', 'strengths', 'depending', 'on', 'the', 'angular', 'momenta', 'coupling', 'that', 'are', 'relevant', 'for', 'the', 'states', 'this', 'work', 'explains', 'the', 'different', 'steps', 'to', 'calculate', 'the', 'wave', 'functions', 'and', 'the', 'transition', 'matrix', 'elements', 'in', 'a', 'way', 'that', 'they', 'can', 'be', 'directly', 'transferred', 'to', 'different', 'molecular', 'states', 'atomic', 'species', 'or', 'molecular', 'coupling', 'cases']] | [-0.11964928036844678, 0.19495212865575892, -0.05058929465203305, 0.07001760424363651, -0.008656449104129368, -0.07249899992542662, 0.044180187990824724, 0.38329793321739497, -0.26116227879839277, -0.2749429424293339, 0.013545673261276158, -0.23523599625532998, -0.10606557833018777, 0.11579963649976356, 0.06842619884791303, 0.03823988951325159, 0.0395433909665183, 0.016834663528274615, -0.11308643734985802, -0.11908947788219064, 0.3557187156944439, 0.035552734019363236, 0.24615097053124216, 0.0744493219676299, 0.09757823901700563, 0.001808595949992666, 0.055715268722671116, -0.05504728635011408, -0.14240711666947203, 0.1060816762765171, 0.2428709801511261, 0.08687531295762366, 0.18066204867951957, -0.45500359809475727, -0.19171405414215706, 0.06393432423095058, 0.14545821993820499, 0.20302999922845127, -0.013139214150904646, -0.27166511637062346, -0.027073908637374126, -0.17016656972178867, -0.16567170473843298, -0.10155588662219715, 0.05443636926517276, 0.06852495331810948, -0.2562987206946328, 0.059916351038705684, 0.01317606427494019, 0.04033840922157055, -0.0997647976237831, -0.15553931353196246, -0.042013015664295, 0.13894928556039993, 0.019007095039404672, 0.01945528576094336, 0.18360850161969147, -0.12886237804496917, -0.07200374984551736, 0.4075223977661467, -0.08034339175832554, -0.20254154810842512, 0.2064229510082253, -0.16999518188069865, -0.11845223758678787, 0.09577589018214173, 0.1678326717569846, 0.13240905373020034, -0.09998641799631577, 0.04704568079714904, -0.015898444364273897, 0.16697149712528134, 0.07778163618359972, 0.09352296237946199, 0.20630733797262454, 0.07449234667202008, 0.031264710973683295, 0.13346512796425516, -0.10830864802621112, -0.09906322488978762, -0.2891827387863706, -0.1828853135470879, -0.20612537085504173, 0.03416639018579535, -0.04821675732003722, -0.12273892239418588, 0.44544861191915797, 0.11155441508614378, 0.2500981648837955, 0.0012196589919642128, 0.25828642385072426, 0.1402888135474721, 0.048879236705472756, 0.02398470360765651, 0.28021011022240694, 0.14671658522626063, 0.0675237345758119, -0.2816467225342861, 0.05010550893072424, 0.018147031123778013] |
1,802.04684 | Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions | Learning algorithms that aggregate predictions from an ensemble of diverse
base classifiers consistently outperform individual methods. Many of these
strategies have been developed in a supervised setting, where the accuracy of
each base classifier can be empirically measured and this information is
incorporated in the training process. However, the reliance on labeled data
precludes the application of ensemble methods to many real world problems where
labeled data has not been curated. To this end we developed a new theoretical
framework for binary classification, the Strategy for Unsupervised Multiple
Method Aggregation (SUMMA), to estimate the performances of base classifiers
and an optimal strategy for ensemble learning from unlabeled data.
| stat.ML cs.LG | learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods many of these strategies have been developed in a supervised setting where the accuracy of each base classifier can be empirically measured and this information is incorporated in the training process however the reliance on labeled data precludes the application of ensemble methods to many real world problems where labeled data has not been curated to this end we developed a new theoretical framework for binary classification the strategy for unsupervised multiple method aggregation summa to estimate the performances of base classifiers and an optimal strategy for ensemble learning from unlabeled data | [['learning', 'algorithms', 'that', 'aggregate', 'predictions', 'from', 'an', 'ensemble', 'of', 'diverse', 'base', 'classifiers', 'consistently', 'outperform', 'individual', 'methods', 'many', 'of', 'these', 'strategies', 'have', 'been', 'developed', 'in', 'a', 'supervised', 'setting', 'where', 'the', 'accuracy', 'of', 'each', 'base', 'classifier', 'can', 'be', 'empirically', 'measured', 'and', 'this', 'information', 'is', 'incorporated', 'in', 'the', 'training', 'process', 'however', 'the', 'reliance', 'on', 'labeled', 'data', 'precludes', 'the', 'application', 'of', 'ensemble', 'methods', 'to', 'many', 'real', 'world', 'problems', 'where', 'labeled', 'data', 'has', 'not', 'been', 'curated', 'to', 'this', 'end', 'we', 'developed', 'a', 'new', 'theoretical', 'framework', 'for', 'binary', 'classification', 'the', 'strategy', 'for', 'unsupervised', 'multiple', 'method', 'aggregation', 'summa', 'to', 'estimate', 'the', 'performances', 'of', 'base', 'classifiers', 'and', 'an', 'optimal', 'strategy', 'for', 'ensemble', 'learning', 'from', 'unlabeled', 'data']] | [0.013816927637284001, -0.012929783864280951, -0.1019694111881243, 0.06269813772823235, -0.10402766225376615, -0.1824437193895897, 0.09895651217515546, 0.463843985012284, -0.2671316956963252, -0.3345626175696789, 0.08583249572742109, -0.27424030545754013, -0.15171064625772285, 0.22168591193554607, -0.13191936636792012, 0.11449087880275867, 0.15434079762996622, 0.10380696949842214, -0.03345162641576112, -0.3427106120399441, 0.31137792436681966, 0.05645777499820623, 0.3876236341893673, -0.03711860803714781, 0.12726234142233064, -0.023510069424648665, -0.04160305655332869, 0.037001861504029, -0.03787804363303251, 0.19343530125398603, 0.3651698955886618, 0.25088249678999464, 0.35408368347971525, -0.3696857121152182, -0.23718970539738182, 0.15617823628363786, 0.17119788523349497, 0.15483059364164042, -0.026666012925690867, -0.30748950009324794, 0.09203096889541484, -0.19303195525167716, 0.008454810784853719, -0.15268171895230706, -0.059793689292510624, 0.011110559501469618, -0.34062556124344057, -0.010010291684280944, 0.0593545501771452, 0.09776993851712043, -0.08145486105112704, -0.1564347188730069, 0.050393409764014734, 0.18095256646025787, 0.04453834390922027, 0.011885223680615632, 0.1395639625283096, -0.13304879273238368, -0.19506103643733594, 0.3183662934159791, -0.035214904374529225, -0.20742167017993374, 0.20912535146258218, 0.0006757461836699535, -0.17552101319759256, 0.14197882338797813, 0.27291893513217846, 0.13552120270173032, -0.2084577624099674, 0.03207412089923983, -0.0401777774354236, 0.16309404436119454, 0.03281267256803672, -0.04642429474861948, 0.14975365813168334, 0.26142998632901626, 0.005305681128649869, 0.08857162711555483, -0.11983885157948222, -0.08247781795863476, -0.18588922321591614, -0.08005226008525049, -0.24258904569540862, -0.029126552580203646, -0.11389872577031686, -0.15683134430502024, 0.3142457832582295, 0.22693167786821034, 0.23164974356984236, 0.08379173119276485, 0.3440860796857763, -0.00016657481211479063, 0.10025225606471024, 0.09129123410848142, 0.19273508164419415, 0.028561054703055158, 0.10071765735348756, -0.1494266740456051, 0.08082741182901103, 0.029149161802639288] |
1,802.04685 | The two-dimensional Centralizer Conjecture | A result by C. C.-A. Cheng, J. H. Mckay and S. S.-S. Wang says the following:
Suppose the Jacobian of $A$ and $B$ is invertible in $\mathbb{C}[x,y]$ and the
Jacobian of $A$ and $w$ is zero for $A,B,w \in \mathbb{C}[x,y]$. Then $w \in
\mathbb{C}[A]$. We show that in CMW's result it is possible to replace
$\mathbb{C}$ by any field of characteristic zero, and we conjecture the
following 'two-dimensional Centralizer Conjecture over $D$': Suppose the
Jacobian of $A$ and $B$ is invertible in $D[x,y]$ and the Jacobian of $A$ and
$w$ is zero for $A,B,w \in D[x,y]$, $D$ is an integral domain of characteristic
zero. Then $w \in D[A]$. We show that if the famous two-dimensional Jacobian
Conjecture is true, then the two-dimensional Centralizer Conjecture is true.
| math.AC | a result by c ca cheng j h mckay and s ss wang says the following suppose the jacobian of a and b is invertible in mathbbcxy and the jacobian of a and w is zero for abw in mathbbcxy then w in mathbbca we show that in cmws result it is possible to replace mathbbc by any field of characteristic zero and we conjecture the following twodimensional centralizer conjecture over d suppose the jacobian of a and b is invertible in dxy and the jacobian of a and w is zero for abw in dxy d is an integral domain of characteristic zero then w in da we show that if the famous twodimensional jacobian conjecture is true then the twodimensional centralizer conjecture is true | [['a', 'result', 'by', 'c', 'ca', 'cheng', 'j', 'h', 'mckay', 'and', 's', 'ss', 'wang', 'says', 'the', 'following', 'suppose', 'the', 'jacobian', 'of', 'a', 'and', 'b', 'is', 'invertible', 'in', 'mathbbcxy', 'and', 'the', 'jacobian', 'of', 'a', 'and', 'w', 'is', 'zero', 'for', 'abw', 'in', 'mathbbcxy', 'then', 'w', 'in', 'mathbbca', 'we', 'show', 'that', 'in', 'cmws', 'result', 'it', 'is', 'possible', 'to', 'replace', 'mathbbc', 'by', 'any', 'field', 'of', 'characteristic', 'zero', 'and', 'we', 'conjecture', 'the', 'following', 'twodimensional', 'centralizer', 'conjecture', 'over', 'd', 'suppose', 'the', 'jacobian', 'of', 'a', 'and', 'b', 'is', 'invertible', 'in', 'dxy', 'and', 'the', 'jacobian', 'of', 'a', 'and', 'w', 'is', 'zero', 'for', 'abw', 'in', 'dxy', 'd', 'is', 'an', 'integral', 'domain', 'of', 'characteristic', 'zero', 'then', 'w', 'in', 'da', 'we', 'show', 'that', 'if', 'the', 'famous', 'twodimensional', 'jacobian', 'conjecture', 'is', 'true', 'then', 'the', 'twodimensional', 'centralizer', 'conjecture', 'is', 'true']] | [-0.16470966889478622, 0.09777523270202523, -0.07200931479030895, -0.014585190724271039, -0.03464747748027245, -0.22444321605051676, 0.004806775754938523, 0.3146672932105878, -0.33730730915530804, -0.14263764892276082, 0.052233382727810376, -0.2640563243933554, -0.1534481141272755, 0.1679366613509104, -0.11768826290166803, -0.010462378315712577, 0.035340439653881485, 0.08391849516377238, -0.08445425082916497, -0.31524848145624945, 0.34578467807954266, -0.0811823828973704, 0.19852178898526335, 0.0964647867566248, 0.08832888376699495, 0.036014190788144274, -0.004536225696996091, -0.026123452015102855, -0.14312839868447524, 0.048815623401582894, 0.21264392352526237, 0.11494578030477795, 0.24018750851234746, -0.2809937589077486, -0.1294484588036698, 0.15670980397789253, 0.12457376419906578, 0.011141189240983554, 0.01885237601227201, -0.20472690270888427, 0.1980894128644159, -0.155509563000311, -0.1734433079596668, 0.00402173666756541, 0.17417944763465562, -0.021969967987388372, -0.32841069830788505, 0.04458594518632347, 0.17032956265445268, 0.09042514396447038, -0.031082421751870287, -0.12324131986067172, -0.09256983130046773, 0.022076050421991754, -0.0037843497573501533, 0.13751851475708896, 0.00443561504121929, -0.08957870405108209, -0.06918195955499652, 0.3449132645813127, -0.11336545796976202, -0.18274325974995181, 0.12116177193704986, -0.17278886658124745, -0.10986989606190325, 0.10451693736785461, 0.031206991702746895, 0.11517053061268395, -0.009757757319935731, 0.25628635179000125, -0.1606322012930399, 0.08579832928935213, 0.08584041923625461, -0.0887186155332962, 0.11575797399032921, 0.04249848865179552, 0.07270699781587436, 0.0887438504991772, -0.05775490892489278, 0.048943177315994124, -0.33663179018786027, -0.2697946693127354, -0.23352273043599867, 0.18321410337433455, -0.04102122034814711, -0.1212146001653598, 0.3581885364911859, 0.07879786514991244, 0.2220400680255677, 0.043197509522239365, 0.18292150865235027, 0.08242432280175785, 0.0023704315702031764, 0.1278126608351216, 0.13235406606044206, 0.23419748456956493, 0.014290509519115504, -0.18163262709369143, 0.008631372077774906, 0.1853787296909898] |
1,802.04686 | Remote sensing of geomagnetic fields and atomic collisions in the
mesosphere | Magnetic-field sensing has contributed to the formulation of the
plate-tectonics theory, the discovery and mapping of underground structures on
Earth, and the study of magnetism in other planets. Filling the gap between
space-based and near-Earth observation, we demonstrate a novel method for
remote measurement of the geomagnetic field at an altitude of 85-100 km. The
method consists of optical pumping of atomic sodium in the upper mesosphere
with an intensity-modulated laser beam, and simultaneous ground-based
observation of the resultant magneto-optical resonance when driving the
atomic-sodium spins at the Larmor precession frequency. The experiment was
carried out at the Roque de Los Muchachos Observatory in La Palma (Canary
Islands) where we validated this technique and remotely measured the Larmor
precession frequency of sodium as 260.4(1) kHz, corresponding to a mesospheric
magnetic field of 0.3720(1) G. We demonstrate a magnetometry accuracy level of
0.28 mG/$\sqrt{\text{Hz}}$ in good atmospheric conditions. In addition, these
observations allow us to characterize various atomic-collision processes in the
mesosphere. Remote detection of mesospheric magnetic fields has potential
applications such as mapping of large-scale magnetic structures in the
lithosphere and the study of electric-current fluctuations in the ionosphere.
| physics.ao-ph astro-ph.IM physics.atom-ph physics.ins-det | magneticfield sensing has contributed to the formulation of the platetectonics theory the discovery and mapping of underground structures on earth and the study of magnetism in other planets filling the gap between spacebased and nearearth observation we demonstrate a novel method for remote measurement of the geomagnetic field at an altitude of 85100 km the method consists of optical pumping of atomic sodium in the upper mesosphere with an intensitymodulated laser beam and simultaneous groundbased observation of the resultant magnetooptical resonance when driving the atomicsodium spins at the larmor precession frequency the experiment was carried out at the roque de los muchachos observatory in la palma canary islands where we validated this technique and remotely measured the larmor precession frequency of sodium as 26041 khz corresponding to a mesospheric magnetic field of 037201 g we demonstrate a magnetometry accuracy level of 028 mgsqrttexthz in good atmospheric conditions in addition these observations allow us to characterize various atomiccollision processes in the mesosphere remote detection of mesospheric magnetic fields has potential applications such as mapping of largescale magnetic structures in the lithosphere and the study of electriccurrent fluctuations in the ionosphere | [['magneticfield', 'sensing', 'has', 'contributed', 'to', 'the', 'formulation', 'of', 'the', 'platetectonics', 'theory', 'the', 'discovery', 'and', 'mapping', 'of', 'underground', 'structures', 'on', 'earth', 'and', 'the', 'study', 'of', 'magnetism', 'in', 'other', 'planets', 'filling', 'the', 'gap', 'between', 'spacebased', 'and', 'nearearth', 'observation', 'we', 'demonstrate', 'a', 'novel', 'method', 'for', 'remote', 'measurement', 'of', 'the', 'geomagnetic', 'field', 'at', 'an', 'altitude', 'of', '85100', 'km', 'the', 'method', 'consists', 'of', 'optical', 'pumping', 'of', 'atomic', 'sodium', 'in', 'the', 'upper', 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1,802.04687 | Neural Relational Inference for Interacting Systems | Interacting systems are prevalent in nature, from dynamical systems in
physics to complex societal dynamics. The interplay of components can give rise
to complex behavior, which can often be explained using a simple model of the
system's constituent parts. In this work, we introduce the neural relational
inference (NRI) model: an unsupervised model that learns to infer interactions
while simultaneously learning the dynamics purely from observational data. Our
model takes the form of a variational auto-encoder, in which the latent code
represents the underlying interaction graph and the reconstruction is based on
graph neural networks. In experiments on simulated physical systems, we show
that our NRI model can accurately recover ground-truth interactions in an
unsupervised manner. We further demonstrate that we can find an interpretable
structure and predict complex dynamics in real motion capture and sports
tracking data.
| stat.ML cs.LG | interacting systems are prevalent in nature from dynamical systems in physics to complex societal dynamics the interplay of components can give rise to complex behavior which can often be explained using a simple model of the systems constituent parts in this work we introduce the neural relational inference nri model an unsupervised model that learns to infer interactions while simultaneously learning the dynamics purely from observational data our model takes the form of a variational autoencoder in which the latent code represents the underlying interaction graph and the reconstruction is based on graph neural networks in experiments on simulated physical systems we show that our nri model can accurately recover groundtruth interactions in an unsupervised manner we further demonstrate that we can find an interpretable structure and predict complex dynamics in real motion capture and sports tracking data | [['interacting', 'systems', 'are', 'prevalent', 'in', 'nature', 'from', 'dynamical', 'systems', 'in', 'physics', 'to', 'complex', 'societal', 'dynamics', 'the', 'interplay', 'of', 'components', 'can', 'give', 'rise', 'to', 'complex', 'behavior', 'which', 'can', 'often', 'be', 'explained', 'using', 'a', 'simple', 'model', 'of', 'the', 'systems', 'constituent', 'parts', 'in', 'this', 'work', 'we', 'introduce', 'the', 'neural', 'relational', 'inference', 'nri', 'model', 'an', 'unsupervised', 'model', 'that', 'learns', 'to', 'infer', 'interactions', 'while', 'simultaneously', 'learning', 'the', 'dynamics', 'purely', 'from', 'observational', 'data', 'our', 'model', 'takes', 'the', 'form', 'of', 'a', 'variational', 'autoencoder', 'in', 'which', 'the', 'latent', 'code', 'represents', 'the', 'underlying', 'interaction', 'graph', 'and', 'the', 'reconstruction', 'is', 'based', 'on', 'graph', 'neural', 'networks', 'in', 'experiments', 'on', 'simulated', 'physical', 'systems', 'we', 'show', 'that', 'our', 'nri', 'model', 'can', 'accurately', 'recover', 'groundtruth', 'interactions', 'in', 'an', 'unsupervised', 'manner', 'we', 'further', 'demonstrate', 'that', 'we', 'can', 'find', 'an', 'interpretable', 'structure', 'and', 'predict', 'complex', 'dynamics', 'in', 'real', 'motion', 'capture', 'and', 'sports', 'tracking', 'data']] | [-0.0765979368833528, 0.055109741018922236, -0.12217821708316848, 0.09841498590843833, -0.10924152618584533, -0.13096009780386247, -0.03681408431030054, 0.4150957039192967, -0.3246880971549916, -0.33838807759077655, 0.022582585634022573, -0.2767433909888285, -0.26268826796592254, 0.15784334655061527, -0.0644348452400848, 0.009638005516667297, 0.1013042960582517, 0.05379134898775838, -0.014901323552729318, -0.19858472191845067, 0.3097339338265305, 0.022731260656365666, 0.27948088546838623, 0.010289611109275964, 0.12004259646620494, -0.002535271218748412, 0.01094765805506883, 0.010802839676836047, -0.05044528445058318, 0.15251188094903162, 0.276011694795064, 0.17119092337679173, 0.2425519821750324, -0.4615851704544131, -0.2839133813336114, 0.0896845764979936, 0.16393485317509487, 0.129769930567093, -0.0244119687974318, -0.33489591547328496, 0.02686378724900061, -0.16578692501491826, -0.024290306765275698, -0.1815129999253575, -0.027271007930023083, -0.01366978966288498, -0.29748572918891814, 0.06565657550084364, 0.06657672251901164, 0.06205057479657125, -0.09891085970667664, -0.04475328049675622, -0.006392263929676805, 0.1540183260348504, -0.026940963258021984, 0.019663380769391853, 0.12911822255430877, -0.16526742272274703, -0.15564377959041545, 0.3981298064522823, -0.04389330070799864, -0.22033853674361456, 0.2323221489129102, -0.07364222827785905, -0.1562575224712761, 0.08366377414136693, 0.292894348434672, 0.07535325489479347, -0.18621601985026812, 0.04758874930827743, -0.050006590027740036, 0.20598016172795947, -0.0599450246832482, -0.0454090160175996, 0.21081483491402847, 0.25576568523124943, -0.012842146317490151, 0.13142833665883896, -0.06506013985473555, -0.1398614758045237, -0.220334201477522, -0.08294681367714066, -0.17920074919643608, 0.018941850981850555, -0.10603486907812244, -0.16190026021576015, 0.4237953343519779, 0.2460813259307727, 0.25120765065598855, 0.06346108313258467, 0.315696781690138, 0.05307771923675107, 0.07817403079293993, 0.07546211589478712, 0.2061303102283104, 0.057664239256089364, 0.07878709695490914, -0.17978035815336835, 0.1191461891192349, 0.014187366755652254] |
1,802.04688 | Unrestricted wreath products and sofic groups | We show that the unrestricted wreath product of a sofic group by an amenable
group is sofic. We use this result to present an alternative proof of the known
fact that any group extension with sofic kernel and amenable quotient is again
a sofic group. Our approach exploits the famous Kaloujnine-Krasner theorem and
extends, with an additional argument, to hyperlinear-by-amenable groups.
| math.GR | we show that the unrestricted wreath product of a sofic group by an amenable group is sofic we use this result to present an alternative proof of the known fact that any group extension with sofic kernel and amenable quotient is again a sofic group our approach exploits the famous kaloujninekrasner theorem and extends with an additional argument to hyperlinearbyamenable groups | [['we', 'show', 'that', 'the', 'unrestricted', 'wreath', 'product', 'of', 'a', 'sofic', 'group', 'by', 'an', 'amenable', 'group', 'is', 'sofic', 'we', 'use', 'this', 'result', 'to', 'present', 'an', 'alternative', 'proof', 'of', 'the', 'known', 'fact', 'that', 'any', 'group', 'extension', 'with', 'sofic', 'kernel', 'and', 'amenable', 'quotient', 'is', 'again', 'a', 'sofic', 'group', 'our', 'approach', 'exploits', 'the', 'famous', 'kaloujninekrasner', 'theorem', 'and', 'extends', 'with', 'an', 'additional', 'argument', 'to', 'hyperlinearbyamenable', 'groups']] | [-0.10629917705684143, 0.12460674277493944, -0.1887559520743661, 0.055853028010213905, -0.16402857340240884, -0.13130948384750193, 0.10402638211814781, 0.39423259688636003, -0.33028996846307135, -0.1589738800174604, 0.11243813331791405, -0.24772301938195349, -0.15819222364827232, 0.23273088562033944, -0.19930262961109185, -0.05231470362109653, 0.08352090449909032, 0.1532704512730746, -0.08694168820314235, -0.23999170883227203, 0.3935584828959999, 0.007160123832271261, 0.24893718427520686, 0.07140875597468625, 0.1166067114810191, 0.07615081276113199, -0.08980699423384869, -0.009271792919469713, -0.11948277506216674, 0.11183134947856099, 0.28622597484391626, 0.11230166556345204, 0.2813793624498708, -0.31215451529912525, -0.16190838889550355, 0.16990940898885729, 0.11133974601151579, 0.0751831164362572, -0.11324161480543977, -0.339389153109011, 0.05359643925833753, -0.31877999001387824, -0.14615561017545603, -0.10819153203550032, 0.03146741809954835, -0.07364893597313914, -0.21886030496177028, 0.050608436734873356, 0.1926155848649599, 0.09501111254853717, -0.015676805110237862, -0.05986733641475439, 0.03508235550075138, 0.0880885456799198, 0.034430224361624254, 0.05153572534100484, 0.11448150386212994, 0.0455482424418363, -0.13238765378245862, 0.41110223478053587, -0.10805002104301574, -0.1909356420323, 0.20143978517123703, -0.09951145894099343, -0.2421418503242529, 0.1126455554770211, 0.0026957665459584382, 0.1507482174783945, -0.037164364972988424, 0.16650855876465911, -0.18943927899571295, 0.17425957492600053, -0.00221852470441895, -0.06073417965228022, 0.019381673154184372, 0.09859403358551405, 0.15181274585804697, 0.18657646463962935, 0.1254606856412049, 0.03496088730802728, -0.3151609176548861, -0.21772559556194535, -0.14184636374840798, 0.0747826257641664, -0.12232781941874221, -0.1777600953868448, 0.32243806515204704, 0.13516578009210023, 0.08965708004405439, 0.22386739096777925, 0.24828697507411746, 0.10856392718575326, 0.09654995828267124, 0.10374449044308168, 0.0671827872678385, 0.22941016185223678, -0.12968303960890082, -0.14451618854082743, -0.030950030753925696, 0.24672070248999586] |
1,802.04689 | A bump in the road in elementary topology | We observe a subtle and apparently generally unnoticed difficulty with the
definition of the relative topology on a subset of a topological space, and
with the weak topology defined by a function.
| math.GN | we observe a subtle and apparently generally unnoticed difficulty with the definition of the relative topology on a subset of a topological space and with the weak topology defined by a function | [['we', 'observe', 'a', 'subtle', 'and', 'apparently', 'generally', 'unnoticed', 'difficulty', 'with', 'the', 'definition', 'of', 'the', 'relative', 'topology', 'on', 'a', 'subset', 'of', 'a', 'topological', 'space', 'and', 'with', 'the', 'weak', 'topology', 'defined', 'by', 'a', 'function']] | [-0.183814802498091, 0.10914508931455202, -0.0971086296485737, 0.08659594928030856, -0.07328981067985296, -0.07540912719559856, 0.14968921454419615, 0.33476885221898556, -0.29208633955568075, -0.32993489372893237, 0.06535390506905969, -0.22445883564068936, -0.21700576320290565, 0.1650549852856784, -0.09744099038653076, -0.011378862269339152, 0.03646891692187637, 0.05777864124684129, -0.1432715214905329, -0.17158139910316095, 0.43607220728881657, -0.02224138443125412, 0.2594685552176088, 0.03620764770312235, 0.11504190546111204, 0.010432451890665106, -0.04687011451460421, 0.14344536764747318, -0.13227031711721793, 0.1449598259641789, 0.14748391741886735, 0.07013450108934194, 0.3056626266334206, -0.34489435236901045, -0.19590102118672803, 0.10372832564462442, 0.04422217118553817, 0.04864990874193609, -0.034130113450373756, -0.3522166624898091, 0.09191077534342185, -0.1235456489957869, -0.10808323636592831, -0.06311391666531563, 0.036879313935060054, 0.0024691747967153788, -0.17917643126565963, 0.005378542962716892, 0.09020962641193364, 0.08414867382089142, -0.033793501381296664, -0.0006430367939174175, -0.028483472939115018, 0.0814222891931422, 0.06598434172337875, 0.10950711953046266, 0.06493090135336388, -0.13783780334415496, -0.09069804733735509, 0.37310259544756263, -0.058967536548152566, -0.24344204820226878, 0.25066145780147053, -0.17990117701992858, -0.11370117508340627, 0.11394634703174233, 0.06277917453553528, 0.11040249303914607, -0.05612073343945667, 0.12697688303524046, -0.04253951128339395, 0.15843218920053914, 0.0023694989504292607, 0.10781077668070793, 0.19341578081366606, 0.1247087446245132, 0.13210092709050514, 0.15269255149178207, -0.060975746739131864, -0.07797649980057031, -0.320660273428075, -0.15352352757145127, -0.1775349379167892, 0.11135551950428635, -0.04729364393188007, -0.24740388651844114, 0.42444266767415684, 0.0405269663897343, 0.31292618572479114, 0.008015947823878378, 0.2660617856599856, 0.0978083393711131, 0.07467003492638469, 0.056788568617776036, 0.19830096466466784, 0.10652609101089183, 0.06549270429241005, -0.1829569763649488, 0.11864681781298714, 0.10114140072982991] |
1,802.0469 | The Galactic halo pulsar population | Most population studies of pulsars have hitherto focused on the disc of the
Galaxy, the Galactic centre, globular clusters, and nearby galaxies. It is
expected that pulsars, by virtue of their natal kicks, are also to be found in
the Galactic halo. We investigate the possible population of canonical (i.e.
non-recycled) radio pulsars in the halo, estimating the number of such pulsars,
and the fraction that is detectable via single pulse and periodicity searches.
Additionally, we explore the distributions of flux densities and dispersion
measures of this population. We also consider the effects of different velocity
models and the evolution of inclination angle and magnetic field on our
results. We show that $\sim$33 % of all pulsars beaming towards the Earth are
in the halo but the fraction reduces to $\sim$1.5 % if we let the inclination
angle and the magnetic field evolve as a falling exponential. Moreover, the
fraction that is detectable is significantly limited by the sensitivity of
surveys. This population would be most effectively probed by surveys using
time-domain periodicity search algorithms. The current non-detections of
pulsars in the halo can be explained if we assume that the inclination angle
and magnetic field of pulsars evolve with time. We also highlight a possible
confusion between bright pulses from halo pulsars and Fast Radio Bursts with
low dispersion measures where further follow-up is warranted.
| astro-ph.HE | most population studies of pulsars have hitherto focused on the disc of the galaxy the galactic centre globular clusters and nearby galaxies it is expected that pulsars by virtue of their natal kicks are also to be found in the galactic halo we investigate the possible population of canonical ie nonrecycled radio pulsars in the halo estimating the number of such pulsars and the fraction that is detectable via single pulse and periodicity searches additionally we explore the distributions of flux densities and dispersion measures of this population we also consider the effects of different velocity models and the evolution of inclination angle and magnetic field on our results we show that sim33 of all pulsars beaming towards the earth are in the halo but the fraction reduces to sim15 if we let the inclination angle and the magnetic field evolve as a falling exponential moreover the fraction that is detectable is significantly limited by the sensitivity of surveys this population would be most effectively probed by surveys using timedomain periodicity search algorithms the current nondetections of pulsars in the halo can be explained if we assume that the inclination angle and magnetic field of pulsars evolve with time we also highlight a possible confusion between bright pulses from halo pulsars and fast radio bursts with low dispersion measures where further followup is warranted | [['most', 'population', 'studies', 'of', 'pulsars', 'have', 'hitherto', 'focused', 'on', 'the', 'disc', 'of', 'the', 'galaxy', 'the', 'galactic', 'centre', 'globular', 'clusters', 'and', 'nearby', 'galaxies', 'it', 'is', 'expected', 'that', 'pulsars', 'by', 'virtue', 'of', 'their', 'natal', 'kicks', 'are', 'also', 'to', 'be', 'found', 'in', 'the', 'galactic', 'halo', 'we', 'investigate', 'the', 'possible', 'population', 'of', 'canonical', 'ie', 'nonrecycled', 'radio', 'pulsars', 'in', 'the', 'halo', 'estimating', 'the', 'number', 'of', 'such', 'pulsars', 'and', 'the', 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1,802.04691 | Active Perception and Modeling of Deformable Surfaces using Gaussian
Processes and Position-based Dynamics | Exploring and modeling heterogeneous elastic surfaces requires multiple
interactions with the environment and a complex selection of physical material
parameters. The most common approaches model deformable properties from sets of
offline observations using computationally expensive force-based simulators. In
this work we present an online probabilistic framework for autonomous
estimation of a deformability distribution map of heterogeneous elastic
surfaces from few physical interactions. The method takes advantage of Gaussian
Processes for constructing a model of the environment geometry surrounding a
robot. A fast Position-based Dynamics simulator uses focused environmental
observations in order to model the elastic behavior of portions of the
environment. Gaussian Process Regression maps the local deformability on the
whole environment in order to generate a deformability distribution map. We
show experimental results using a PrimeSense camera, a Kinova Jaco2 robotic arm
and an Optoforce sensor on different deformable surfaces.
| cs.RO | exploring and modeling heterogeneous elastic surfaces requires multiple interactions with the environment and a complex selection of physical material parameters the most common approaches model deformable properties from sets of offline observations using computationally expensive forcebased simulators in this work we present an online probabilistic framework for autonomous estimation of a deformability distribution map of heterogeneous elastic surfaces from few physical interactions the method takes advantage of gaussian processes for constructing a model of the environment geometry surrounding a robot a fast positionbased dynamics simulator uses focused environmental observations in order to model the elastic behavior of portions of the environment gaussian process regression maps the local deformability on the whole environment in order to generate a deformability distribution map we show experimental results using a primesense camera a kinova jaco2 robotic arm and an optoforce sensor on different deformable surfaces | [['exploring', 'and', 'modeling', 'heterogeneous', 'elastic', 'surfaces', 'requires', 'multiple', 'interactions', 'with', 'the', 'environment', 'and', 'a', 'complex', 'selection', 'of', 'physical', 'material', 'parameters', 'the', 'most', 'common', 'approaches', 'model', 'deformable', 'properties', 'from', 'sets', 'of', 'offline', 'observations', 'using', 'computationally', 'expensive', 'forcebased', 'simulators', 'in', 'this', 'work', 'we', 'present', 'an', 'online', 'probabilistic', 'framework', 'for', 'autonomous', 'estimation', 'of', 'a', 'deformability', 'distribution', 'map', 'of', 'heterogeneous', 'elastic', 'surfaces', 'from', 'few', 'physical', 'interactions', 'the', 'method', 'takes', 'advantage', 'of', 'gaussian', 'processes', 'for', 'constructing', 'a', 'model', 'of', 'the', 'environment', 'geometry', 'surrounding', 'a', 'robot', 'a', 'fast', 'positionbased', 'dynamics', 'simulator', 'uses', 'focused', 'environmental', 'observations', 'in', 'order', 'to', 'model', 'the', 'elastic', 'behavior', 'of', 'portions', 'of', 'the', 'environment', 'gaussian', 'process', 'regression', 'maps', 'the', 'local', 'deformability', 'on', 'the', 'whole', 'environment', 'in', 'order', 'to', 'generate', 'a', 'deformability', 'distribution', 'map', 'we', 'show', 'experimental', 'results', 'using', 'a', 'primesense', 'camera', 'a', 'kinova', 'jaco2', 'robotic', 'arm', 'and', 'an', 'optoforce', 'sensor', 'on', 'different', 'deformable', 'surfaces']] | [-0.12683101723974582, 0.0499193510653679, -0.11633131781127304, 0.01626327926182225, -0.09605993530613945, -0.12211144245262687, 0.012534556575185547, 0.4158528915850829, -0.2615462731948149, -0.3154015396919815, 0.063351472642385, -0.2438171887076757, -0.19597462185572648, 0.19654202446216018, -0.06895236401812088, 0.10022422930982017, 0.09022599328408375, -0.032499348004976066, -0.015944224148196108, -0.15918707982092065, 0.27779026499599324, 0.05579295601854299, 0.26647671966001074, -0.029643332328092544, 0.15963383445166157, 0.05902595781549441, 0.0006565462784669923, -0.008001023077848533, -0.11157075327603107, 0.1462279778470926, 0.22422529481919418, 0.10379432368336629, 0.2707026125665041, -0.46531971755391316, -0.2646630961573452, 0.08206884332120101, 0.09417993863317983, 0.08411003023894596, -0.038256895860511786, -0.32704842255734806, -0.02307553328111345, -0.17414398623175475, -0.11148213276454637, -0.06820932804137891, -0.002957534097822, 0.03313138062945164, -0.29540214854034974, 0.02349641278769213, 0.016813071718743296, 0.07316200951434645, -0.10344488048638673, -0.06745065563087556, 0.027840440110336487, 0.15884130306753283, -0.03287942999010371, 0.0013512061076595427, 0.22248506574422544, -0.18899270806565605, -0.07304332939683335, 0.38497103084909157, -0.019109339497864565, -0.2365743729487352, 0.23431760999076862, -0.07278152372925839, -0.13751263718999235, 0.14179004330174827, 0.2715793205641874, 0.12797244087089488, -0.19961345994329832, 0.05647653654087269, -0.004384778713698479, 0.1882223987866008, -0.01547901819836586, -0.03871337663223769, 0.19792614051583388, 0.2677449371982762, 0.02269567693880898, 0.15208604204442008, -0.12768123935287198, -0.11190328436593215, -0.2638528538355005, -0.11232766828744124, -0.1707635905976731, -0.008467578135946013, -0.14081391798473106, -0.1695299494387384, 0.34719166757063347, 0.1774467489358487, 0.2178250382231641, 0.06071853107303804, 0.3642018060232942, -0.015455637492919803, 0.05092270569116247, 0.05681592173558984, 0.19276348421728265, 0.07531178411069914, 0.07518375991903087, -0.16097584366468146, 0.11021716429782595, 0.025689720542744755] |
1,802.04692 | BIRNet: Brain Image Registration Using Dual-Supervised Fully
Convolutional Networks | In this paper, we propose a deep learning approach for image registration by
predicting deformation from image appearance. Since obtaining ground-truth
deformation fields for training can be challenging, we design a fully
convolutional network that is subject to dual-guidance: (1) Coarse guidance
using deformation fields obtained by an existing registration method; and (2)
Fine guidance using image similarity. The latter guidance helps avoid overly
relying on the supervision from the training deformation fields, which could be
inaccurate. For effective training, we further improve the deep convolutional
network with gap filling, hierarchical loss, and multi-source strategies.
Experiments on a variety of datasets show promising registration accuracy and
efficiency compared with state-of-the-art methods.
| cs.CV | in this paper we propose a deep learning approach for image registration by predicting deformation from image appearance since obtaining groundtruth deformation fields for training can be challenging we design a fully convolutional network that is subject to dualguidance 1 coarse guidance using deformation fields obtained by an existing registration method and 2 fine guidance using image similarity the latter guidance helps avoid overly relying on the supervision from the training deformation fields which could be inaccurate for effective training we further improve the deep convolutional network with gap filling hierarchical loss and multisource strategies experiments on a variety of datasets show promising registration accuracy and efficiency compared with stateoftheart methods | [['in', 'this', 'paper', 'we', 'propose', 'a', 'deep', 'learning', 'approach', 'for', 'image', 'registration', 'by', 'predicting', 'deformation', 'from', 'image', 'appearance', 'since', 'obtaining', 'groundtruth', 'deformation', 'fields', 'for', 'training', 'can', 'be', 'challenging', 'we', 'design', 'a', 'fully', 'convolutional', 'network', 'that', 'is', 'subject', 'to', 'dualguidance', '1', 'coarse', 'guidance', 'using', 'deformation', 'fields', 'obtained', 'by', 'an', 'existing', 'registration', 'method', 'and', '2', 'fine', 'guidance', 'using', 'image', 'similarity', 'the', 'latter', 'guidance', 'helps', 'avoid', 'overly', 'relying', 'on', 'the', 'supervision', 'from', 'the', 'training', 'deformation', 'fields', 'which', 'could', 'be', 'inaccurate', 'for', 'effective', 'training', 'we', 'further', 'improve', 'the', 'deep', 'convolutional', 'network', 'with', 'gap', 'filling', 'hierarchical', 'loss', 'and', 'multisource', 'strategies', 'experiments', 'on', 'a', 'variety', 'of', 'datasets', 'show', 'promising', 'registration', 'accuracy', 'and', 'efficiency', 'compared', 'with', 'stateoftheart', 'methods']] | [-0.016518634151328693, -0.043304002083790365, -0.08003998565866988, 0.06322729517864487, -0.11766031956384805, -0.18823755309409038, 0.01570478638062592, 0.5064175089990551, -0.26194044866345145, -0.3744854516434399, 0.09775348771680993, -0.18639758322387934, -0.22129870741204782, 0.1742930695753206, -0.1739311048253016, 0.10361500768498941, 0.1921110725527714, -0.0064119489355520766, -0.10139064902058718, -0.2738515644884584, 0.34167246290440245, 0.07736165703814053, 0.3737033469433134, 0.025914083878425035, 0.12086169818331573, -0.01651722632179206, -0.017684157491153613, 0.01711713277777149, -0.07158478579559331, 0.2210401175543666, 0.3066203189294108, 0.1522762553079784, 0.3008426927707412, -0.42826233325018126, -0.27793987627268174, 0.06409665093841878, 0.1491457966918295, 0.14769255430450853, -0.08265843228225342, -0.37013459080745553, 0.06082360156049783, -0.14412004828707062, 0.043278688072248106, -0.20145275428975848, -0.07143668094226582, -0.0390732970173386, -0.3456740805244243, 0.05998014094895387, 0.04548715552293949, 0.07017548875036565, -0.07630467420782555, -0.10590728784657337, 0.03709643154235726, 0.16696732883680274, 0.014703723045319996, 0.10672515583520925, 0.1459138774736361, -0.2462985734180124, -0.1001413566547192, 0.3611569492654367, -0.0496455504135652, -0.2329993333836848, 0.17834613583135334, 0.013486116472631694, -0.11625460369343107, 0.15659436563199217, 0.23377911387466488, 0.11764572659846056, -0.1334782647476955, 0.008284449390537867, 0.020559576293453573, 0.17885605312714523, 0.07349029906750233, -0.056525654460049486, 0.17412072057327763, 0.3040908539329063, 0.04976303502493961, 0.12744427111888812, -0.19276103036033668, -0.0024861135393042457, -0.2302201361310753, -0.08307228891009634, -0.1942841138733043, 0.013340114687823437, -0.11578245727220994, -0.10361669078384611, 0.36962979916821825, 0.22737048303454438, 0.2270493313500827, 0.09737687751925973, 0.3492828808216886, -0.004651178368790583, 0.15059111440046266, 0.0566363695373928, 0.23034293133426798, -0.0038906116377223623, 0.09830997037616643, -0.16051825159652666, 0.04763378642965108, 0.08070476275276053] |
1,802.04693 | All-electrical manipulation of silicon spin qubits with tunable
spin-valley mixing | We show that the mixing between spin and valley degrees of freedom in a
silicon quantum bit (qubit) can be controlled by a static electric field acting
on the valley splitting $\Delta$. Thanks to spin-orbit coupling, the qubit can
be continuously switched between a spin mode (where the quantum information is
encoded into the spin) and a valley mode (where the the quantum information is
encoded into the valley). In the spin mode, the qubit is more robust with
respect to inelastic relaxation and decoherence, but is hardly addressable
electrically. It can however be brought into the valley mode then back to the
spin mode for electrical manipulation. This opens new perspectives for the
development of robust and scalable, electrically addressable spin qubits on
silicon. We illustrate this with tight-binding simulations on a so-called
"corner dot" in a silicon-on-insulator device where the confinement and valley
splitting can be independently tailored by a front and a back gate.
| cond-mat.mes-hall | we show that the mixing between spin and valley degrees of freedom in a silicon quantum bit qubit can be controlled by a static electric field acting on the valley splitting delta thanks to spinorbit coupling the qubit can be continuously switched between a spin mode where the quantum information is encoded into the spin and a valley mode where the the quantum information is encoded into the valley in the spin mode the qubit is more robust with respect to inelastic relaxation and decoherence but is hardly addressable electrically it can however be brought into the valley mode then back to the spin mode for electrical manipulation this opens new perspectives for the development of robust and scalable electrically addressable spin qubits on silicon we illustrate this with tightbinding simulations on a socalled corner dot in a silicononinsulator device where the confinement and valley splitting can be independently tailored by a front and a back gate | [['we', 'show', 'that', 'the', 'mixing', 'between', 'spin', 'and', 'valley', 'degrees', 'of', 'freedom', 'in', 'a', 'silicon', 'quantum', 'bit', 'qubit', 'can', 'be', 'controlled', 'by', 'a', 'static', 'electric', 'field', 'acting', 'on', 'the', 'valley', 'splitting', 'delta', 'thanks', 'to', 'spinorbit', 'coupling', 'the', 'qubit', 'can', 'be', 'continuously', 'switched', 'between', 'a', 'spin', 'mode', 'where', 'the', 'quantum', 'information', 'is', 'encoded', 'into', 'the', 'spin', 'and', 'a', 'valley', 'mode', 'where', 'the', 'the', 'quantum', 'information', 'is', 'encoded', 'into', 'the', 'valley', 'in', 'the', 'spin', 'mode', 'the', 'qubit', 'is', 'more', 'robust', 'with', 'respect', 'to', 'inelastic', 'relaxation', 'and', 'decoherence', 'but', 'is', 'hardly', 'addressable', 'electrically', 'it', 'can', 'however', 'be', 'brought', 'into', 'the', 'valley', 'mode', 'then', 'back', 'to', 'the', 'spin', 'mode', 'for', 'electrical', 'manipulation', 'this', 'opens', 'new', 'perspectives', 'for', 'the', 'development', 'of', 'robust', 'and', 'scalable', 'electrically', 'addressable', 'spin', 'qubits', 'on', 'silicon', 'we', 'illustrate', 'this', 'with', 'tightbinding', 'simulations', 'on', 'a', 'socalled', 'corner', 'dot', 'in', 'a', 'silicononinsulator', 'device', 'where', 'the', 'confinement', 'and', 'valley', 'splitting', 'can', 'be', 'independently', 'tailored', 'by', 'a', 'front', 'and', 'a', 'back', 'gate']] | [-0.1601561257433277, 0.25992145863488453, -0.042636724549292286, -0.023254549425913935, -0.04492375263529029, -0.2367687007163171, 0.08138072407940865, 0.40143338055498184, -0.302756933062367, -0.27352934322879013, 0.05536781707674763, -0.25051333141886883, -0.09911092382352978, 0.22638835103375612, -0.0007266591593718074, -0.006201078158738032, 0.0031332691099233687, -0.05212125846831377, -0.059039383389613335, -0.16508021429744638, 0.2500715489457747, -0.0047933708596084815, 0.29565095968817356, 0.07386226308158343, 0.1329245096410678, 0.04626720023614349, 0.0952855346688799, -0.016560072137137793, -0.05089107903402194, 0.09412819514300223, 0.2513415593131902, -0.019045267082086415, 0.23932820597402515, -0.48643148457334867, -0.1472143658976646, 0.01363300347098618, 0.15976747288813542, 0.2233301361038047, -0.06820589451500805, -0.32659931148099863, 0.03887090780386689, -0.1581495363787291, -0.07077733361737648, -0.09325066145960313, -0.021631988313399065, -0.0594894289948153, -0.25735513107246083, 0.01236666859851284, 0.05582988291243269, -0.013258957814211679, 0.024184367527509, -0.03865184066935804, -0.08753849064432986, 0.08795556306259138, -0.02733006372834277, 0.06659754981868159, 0.21163750215059823, -0.10269108836117918, -0.17262700959968907, 0.3213601515741105, -0.06746753476483, -0.24524532187326697, 0.11345959123473164, -0.1598151520541187, -0.019857519731862814, 0.06706644018228124, 0.1517939793693412, 0.09811493227572812, -0.16011849205324036, 0.07718785155926398, 0.03823468283673001, 0.2103006268204872, 0.03575534907400988, 0.14260428807635905, 0.30999261055402694, 0.19280559092961774, 0.12369725470628387, 0.17432067346622704, -0.12390725101806983, -0.11197663070693327, -0.21815777208157786, -0.20270791135514807, -0.24781845953403736, 0.13183709007397199, -0.0535248312856305, -0.0758421814608963, 0.4787592855904987, 0.09057779404910127, 0.14726083913438354, -0.05400109679622066, 0.3165610507605182, 0.13582554303560476, 0.13134737483041872, 0.053117486701649466, 0.27125668161471556, 0.19531294747676914, 0.06153569462157454, -0.3307865909419384, 0.018703690587667524, -0.059256992649856455] |
1,802.04694 | A proof of the Bunkbed conjecture on the complete graph for
$p\geqslant1/2$ | The bunkbed of a graph $G$ is the graph $G\times\left\{ 0,1\right\} $. It has
been conjectured that in the independent bond percolation model, the
probability for $\left(u,0\right)$ to be connected with $\left(v,0\right)$ is
greater than the probability for $\left(u,0\right)$ to be connected with
$\left(v,1\right)$, for any vertex $u$, $v$ of $G$. In this article, we prove
this conjecture for the complete graph in the case of the independent bond
percolation of parameter $p\geqslant1/2$.
| math.PR | the bunkbed of a graph g is the graph gtimesleft 01right it has been conjectured that in the independent bond percolation model the probability for leftu0right to be connected with leftv0right is greater than the probability for leftu0right to be connected with leftv1right for any vertex u v of g in this article we prove this conjecture for the complete graph in the case of the independent bond percolation of parameter pgeqslant12 | [['the', 'bunkbed', 'of', 'a', 'graph', 'g', 'is', 'the', 'graph', 'gtimesleft', '01right', 'it', 'has', 'been', 'conjectured', 'that', 'in', 'the', 'independent', 'bond', 'percolation', 'model', 'the', 'probability', 'for', 'leftu0right', 'to', 'be', 'connected', 'with', 'leftv0right', 'is', 'greater', 'than', 'the', 'probability', 'for', 'leftu0right', 'to', 'be', 'connected', 'with', 'leftv1right', 'for', 'any', 'vertex', 'u', 'v', 'of', 'g', 'in', 'this', 'article', 'we', 'prove', 'this', 'conjecture', 'for', 'the', 'complete', 'graph', 'in', 'the', 'case', 'of', 'the', 'independent', 'bond', 'percolation', 'of', 'parameter', 'pgeqslant12']] | [-0.12088139320942848, 0.13353428118588292, -0.06285929580232785, -0.012909413917976268, -0.05464015836717889, -0.1502370594055666, 0.061676792218349874, 0.3843715727767524, -0.23406394735416947, -0.23126018178813598, 0.06277398156474673, -0.30399753550163416, -0.13309153951430583, 0.11156433606554535, -0.06635914668988656, 0.004611856632811182, 0.05613483440624002, 0.15076021844630733, 0.02986295076484299, -0.26926621272843265, 0.32034361174823167, -0.04589618057669962, 0.19362665023243822, 0.13050242771586293, 0.05494323793305632, 0.03684022410101641, 0.03249241744496805, 0.07221323705059202, -0.19642394007689076, 0.09172804980977055, 0.2422435935038854, 0.10065333174198339, 0.24380247404470162, -0.3669346270754057, -0.224583226519719, 0.2265413570601274, 0.10203614830970764, 0.05903368441881064, 0.07081531148116268, -0.24791822588988854, 0.1601220621891758, -0.18193395383765593, -0.1503496607628596, 0.06570052399354823, 0.15926767105017514, 0.010826627312995055, -0.32250481648096707, 0.03763420833274722, 0.07999034800655756, 0.012884872167727308, 0.0567988468991483, -0.11820448232431184, -0.062311815152711725, 0.11450561116148225, -0.022849208019632736, 0.1494228432968478, 0.0017452464260927895, -0.11063415987625964, -0.13929870111100814, 0.37597755812492, -0.02535883975253776, -0.17269710736239657, 0.096715162453406, -0.18370534068740466, -0.21587487516979523, 0.10592135631903897, 0.08878463291672661, 0.11463454234249451, -0.10178594069336266, 0.13122085299726358, -0.09246584325207069, 0.09852577989701838, 0.05915108054657193, -0.04188106898629271, 0.1031947961241445, 0.20302601945449544, 0.1850949496797779, 0.14485178297207527, -0.008959818631410599, -0.0070479266304413184, -0.2851622034384705, -0.15821578496080987, -0.26541297899230437, 0.08479588374714642, -0.17752672095016084, -0.19271151264033773, 0.399711844018277, 0.13642459664055528, 0.1751211983946097, 0.09384281058138345, 0.16951392043162794, 0.15399710176622167, 0.05012908740044462, 0.12224636897992562, 0.16699523877297692, 0.18951542018090978, -0.03168398851309629, -0.1339382201241439, 0.10653608068070539, 0.11636629276087179] |
1,802.04695 | A Concurrent Constraint Programming Interpretation of Access Permissions | A recent trend in object oriented (OO) programming languages is the use of
Access Permissions (APs) as an abstraction for controlling concurrent
executions of programs. The use of AP source code annotations defines a
protocol specifying how object references can access the mutable state of
objects. Although the use of APs simplifies the task of writing concurrent
code, an unsystematic use of them can lead to subtle problems. This paper
presents a declarative interpretation of APs as Linear Concurrent Constraint
Programs (lcc). We represent APs as constraints (i.e., formulas in logic) in an
underlying constraint system whose entailment relation models the
transformation rules of APs. Moreover, we use processes in lcc to model the
dependencies imposed by APs, thus allowing the faithful representation of their
flow in the program. We verify relevant properties about AP programs by taking
advantage of the interpretation of lcc processes as formulas in Girard's
intuitionistic linear logic (ILL). Properties include deadlock detection,
program correctness (whether programs adhere to their AP specifications or
not), and the ability of methods to run concurrently. By relying on a focusing
discipline for ILL, we provide a complexity measure for proofs of the above
mentioned properties. The effectiveness of our verification techniques is
demonstrated by implementing the Alcove tool that includes an animator and a
verifier. The former executes the lcc model, observing the flow of APs and
quickly finding inconsistencies of the APs vis-a-vis the implementation. The
latter is an automatic theorem prover based on ILL. This paper is under
consideration for publication in Theory and Practice of Logic Programming
(TPLP).
| cs.LO | a recent trend in object oriented oo programming languages is the use of access permissions aps as an abstraction for controlling concurrent executions of programs the use of ap source code annotations defines a protocol specifying how object references can access the mutable state of objects although the use of aps simplifies the task of writing concurrent code an unsystematic use of them can lead to subtle problems this paper presents a declarative interpretation of aps as linear concurrent constraint programs lcc we represent aps as constraints ie formulas in logic in an underlying constraint system whose entailment relation models the transformation rules of aps moreover we use processes in lcc to model the dependencies imposed by aps thus allowing the faithful representation of their flow in the program we verify relevant properties about ap programs by taking advantage of the interpretation of lcc processes as formulas in girards intuitionistic linear logic ill properties include deadlock detection program correctness whether programs adhere to their ap specifications or not and the ability of methods to run concurrently by relying on a focusing discipline for ill we provide a complexity measure for proofs of the above mentioned properties the effectiveness of our verification techniques is demonstrated by implementing the alcove tool that includes an animator and a verifier the former executes the lcc model observing the flow of aps and quickly finding inconsistencies of the aps visavis the implementation the latter is an automatic theorem prover based on ill this paper is under consideration for publication in theory and practice of logic programming tplp | [['a', 'recent', 'trend', 'in', 'object', 'oriented', 'oo', 'programming', 'languages', 'is', 'the', 'use', 'of', 'access', 'permissions', 'aps', 'as', 'an', 'abstraction', 'for', 'controlling', 'concurrent', 'executions', 'of', 'programs', 'the', 'use', 'of', 'ap', 'source', 'code', 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1,802.04696 | Elastic Provisioning of Cloud Caches: a Cost-aware TTL Approach | We consider elastic resource provisioning in the cloud, focusing on in-memory
key-value stores used as caches. Our goal is to dynamically scale resources to
the traffic pattern minimizing the overall cost, which includes not only the
storage cost, but also the cost due to misses. In fact, a small variation on
the cache miss ratio may have a significant impact on user perceived
performance in modern web services, which in turn has an impact on the overall
revenues for the content provider that uses those services. We propose and
study a dynamic algorithm for TTL caches, which is able to obtain
close-to-minimal costs. Since high-throughput caches require low complexity
operations, we discuss a practical implementation of such a scheme requiring
constant overhead per request independently from the cache size. We evaluate
our solution with real-world traces collected from Akamai, and show that we are
able to obtain a 17% decrease in the overall cost compared to a baseline static
configuration.
| cs.DC | we consider elastic resource provisioning in the cloud focusing on inmemory keyvalue stores used as caches our goal is to dynamically scale resources to the traffic pattern minimizing the overall cost which includes not only the storage cost but also the cost due to misses in fact a small variation on the cache miss ratio may have a significant impact on user perceived performance in modern web services which in turn has an impact on the overall revenues for the content provider that uses those services we propose and study a dynamic algorithm for ttl caches which is able to obtain closetominimal costs since highthroughput caches require low complexity operations we discuss a practical implementation of such a scheme requiring constant overhead per request independently from the cache size we evaluate our solution with realworld traces collected from akamai and show that we are able to obtain a 17 decrease in the overall cost compared to a baseline static configuration | [['we', 'consider', 'elastic', 'resource', 'provisioning', 'in', 'the', 'cloud', 'focusing', 'on', 'inmemory', 'keyvalue', 'stores', 'used', 'as', 'caches', 'our', 'goal', 'is', 'to', 'dynamically', 'scale', 'resources', 'to', 'the', 'traffic', 'pattern', 'minimizing', 'the', 'overall', 'cost', 'which', 'includes', 'not', 'only', 'the', 'storage', 'cost', 'but', 'also', 'the', 'cost', 'due', 'to', 'misses', 'in', 'fact', 'a', 'small', 'variation', 'on', 'the', 'cache', 'miss', 'ratio', 'may', 'have', 'a', 'significant', 'impact', 'on', 'user', 'perceived', 'performance', 'in', 'modern', 'web', 'services', 'which', 'in', 'turn', 'has', 'an', 'impact', 'on', 'the', 'overall', 'revenues', 'for', 'the', 'content', 'provider', 'that', 'uses', 'those', 'services', 'we', 'propose', 'and', 'study', 'a', 'dynamic', 'algorithm', 'for', 'ttl', 'caches', 'which', 'is', 'able', 'to', 'obtain', 'closetominimal', 'costs', 'since', 'highthroughput', 'caches', 'require', 'low', 'complexity', 'operations', 'we', 'discuss', 'a', 'practical', 'implementation', 'of', 'such', 'a', 'scheme', 'requiring', 'constant', 'overhead', 'per', 'request', 'independently', 'from', 'the', 'cache', 'size', 'we', 'evaluate', 'our', 'solution', 'with', 'realworld', 'traces', 'collected', 'from', 'akamai', 'and', 'show', 'that', 'we', 'are', 'able', 'to', 'obtain', 'a', '17', 'decrease', 'in', 'the', 'overall', 'cost', 'compared', 'to', 'a', 'baseline', 'static', 'configuration']] | [-0.1703918243116998, 0.005116977920091664, -0.041107466748956614, 0.053131606870582736, -0.08624014354592084, -0.1455639593475996, 0.1635328956320296, 0.4107291904283957, -0.25835627787482907, -0.36215229724891157, 0.10559978643827632, -0.28242841139933644, -0.1011517586496969, 0.1494417169567713, -0.16296249189056783, 0.04992142539330811, 0.08248418298702424, 0.03981008436882271, -0.015591592908655322, -0.32013141341820806, 0.25335498127292655, 0.11782243523934165, 0.34608664952874557, 0.10694605871191565, 0.05609667127122429, -0.020688966019137663, -0.052020546970317876, -0.009783470134086814, -0.04968679966977958, 0.1128888237480449, 0.3015835728209302, 0.20818380992449587, 0.2915443124208576, -0.47482163405174727, -0.1519914018341674, 0.07966313765928992, 0.12372044535462141, 0.06938249709849018, -0.06210371967311363, -0.22544094625678873, 0.11764569468239802, -0.23752324865199625, -0.039046284429283824, -0.08460295198590688, -0.005056360317687006, 0.038574010715087724, -0.28745098817074355, -0.026075274061372083, -0.05572092144474762, 0.0068791189652709465, -0.0378662305137248, -0.09721539170867852, 0.005228056307331477, 0.14167074225374363, 0.03923535162111475, 0.020364025677993614, 0.18465087240350977, -0.1226906238409596, -0.11061887909019799, 0.42924109176269865, -0.027681053598534386, -0.18878571547966147, 0.17517188480182452, -0.04186521666768774, -0.15739496773893727, 0.13385344908057284, 0.2659144982660435, 0.05009331842650126, -0.15451965952341873, 0.04761942152505735, -0.011151463552612413, 0.2569864406748294, 0.07009161564773358, 0.10780636807420414, 0.13887043864431106, 0.2281443859339229, 0.13530246732293763, 0.16444579094835798, -0.07761449156381753, -0.11494165035140402, -0.20707295666911504, -0.14857396328493766, -0.1885966144600274, 0.01857707461603235, -0.11093191346193887, -0.12689856349039566, 0.3372205534369241, 0.1784672384954251, 0.186755723674685, 0.12904050345314774, 0.3862485934470622, 0.0637427211611725, 0.16757002660459927, 0.1852855122027298, 0.1400963690998794, -0.06784604727759161, 0.20872317703803941, -0.2077390049180051, 0.13403843549162928, -0.0003183774310369162] |
1,802.04697 | Learning to Search with MCTSnets | Planning problems are among the most important and well-studied problems in
artificial intelligence. They are most typically solved by tree search
algorithms that simulate ahead into the future, evaluate future states, and
back-up those evaluations to the root of a search tree. Among these algorithms,
Monte-Carlo tree search (MCTS) is one of the most general, powerful and widely
used. A typical implementation of MCTS uses cleverly designed rules, optimized
to the particular characteristics of the domain. These rules control where the
simulation traverses, what to evaluate in the states that are reached, and how
to back-up those evaluations. In this paper we instead learn where, what and
how to search. Our architecture, which we call an MCTSnet, incorporates
simulation-based search inside a neural network, by expanding, evaluating and
backing-up a vector embedding. The parameters of the network are trained
end-to-end using gradient-based optimisation. When applied to small searches in
the well known planning problem Sokoban, the learned search algorithm
significantly outperformed MCTS baselines.
| cs.AI cs.LG stat.ML | planning problems are among the most important and wellstudied problems in artificial intelligence they are most typically solved by tree search algorithms that simulate ahead into the future evaluate future states and backup those evaluations to the root of a search tree among these algorithms montecarlo tree search mcts is one of the most general powerful and widely used a typical implementation of mcts uses cleverly designed rules optimized to the particular characteristics of the domain these rules control where the simulation traverses what to evaluate in the states that are reached and how to backup those evaluations in this paper we instead learn where what and how to search our architecture which we call an mctsnet incorporates simulationbased search inside a neural network by expanding evaluating and backingup a vector embedding the parameters of the network are trained endtoend using gradientbased optimisation when applied to small searches in the well known planning problem sokoban the learned search algorithm significantly outperformed mcts baselines | [['planning', 'problems', 'are', 'among', 'the', 'most', 'important', 'and', 'wellstudied', 'problems', 'in', 'artificial', 'intelligence', 'they', 'are', 'most', 'typically', 'solved', 'by', 'tree', 'search', 'algorithms', 'that', 'simulate', 'ahead', 'into', 'the', 'future', 'evaluate', 'future', 'states', 'and', 'backup', 'those', 'evaluations', 'to', 'the', 'root', 'of', 'a', 'search', 'tree', 'among', 'these', 'algorithms', 'montecarlo', 'tree', 'search', 'mcts', 'is', 'one', 'of', 'the', 'most', 'general', 'powerful', 'and', 'widely', 'used', 'a', 'typical', 'implementation', 'of', 'mcts', 'uses', 'cleverly', 'designed', 'rules', 'optimized', 'to', 'the', 'particular', 'characteristics', 'of', 'the', 'domain', 'these', 'rules', 'control', 'where', 'the', 'simulation', 'traverses', 'what', 'to', 'evaluate', 'in', 'the', 'states', 'that', 'are', 'reached', 'and', 'how', 'to', 'backup', 'those', 'evaluations', 'in', 'this', 'paper', 'we', 'instead', 'learn', 'where', 'what', 'and', 'how', 'to', 'search', 'our', 'architecture', 'which', 'we', 'call', 'an', 'mctsnet', 'incorporates', 'simulationbased', 'search', 'inside', 'a', 'neural', 'network', 'by', 'expanding', 'evaluating', 'and', 'backingup', 'a', 'vector', 'embedding', 'the', 'parameters', 'of', 'the', 'network', 'are', 'trained', 'endtoend', 'using', 'gradientbased', 'optimisation', 'when', 'applied', 'to', 'small', 'searches', 'in', 'the', 'well', 'known', 'planning', 'problem', 'sokoban', 'the', 'learned', 'search', 'algorithm', 'significantly', 'outperformed', 'mcts', 'baselines']] | [-0.05285569126081439, 0.06760528307310118, -0.0645896822737634, 0.12000036002454921, -0.13292002132139313, -0.1771589410693749, 0.0756192718970919, 0.4316143628603183, -0.29535652101777427, -0.3496765997781424, 0.1144089652876144, -0.25175999732317855, -0.19536552206115645, 0.20509742565094408, -0.028410077193370145, 0.09180519156236062, 0.12807725023431993, 0.044556732260570026, -0.0315606735035002, -0.27661941385602357, 0.2538041052251383, 0.08857852634424286, 0.27335761100782646, -0.02738465689359846, 0.0822685303727616, 0.010906552716580607, -0.03378714267699545, 0.025930901221975812, -0.08547365699646349, 0.1465372168372229, 0.3235980625024285, 0.24816176734332526, 0.3050734384320908, -0.42824274307892, -0.1806544284423038, 0.1472467722621405, 0.17524304676089458, 0.08972310185512335, -0.030930211097262336, -0.2988029139764283, 0.09375976549348684, -0.12020145786741043, -0.036145747483054304, -0.09612443476195175, -0.04127662881821255, 0.02412068958268414, -0.2672105412380712, -0.06750439878506875, 0.017635565636511776, -0.008645567496347687, -0.025173839776030518, -0.1645071176133807, 0.01456535553201015, 0.12547415221252434, 0.009266314051918084, 0.08136074180766847, 0.15298805318437286, -0.1686910364453454, -0.23448127961098592, 0.3991499284314142, -0.028813483675808778, -0.2080881611519256, 0.18649839918470437, -0.015359776522617162, -0.1678031154399194, 0.09751939755048811, 0.24841340501671252, 0.1666165317339325, -0.20175034907888345, 0.00932013195352224, -0.027017291519582643, 0.13366401709199693, 0.02788188753314085, -0.029208055837296805, 0.15345426199173914, 0.2581044335629768, 0.08110615810970863, 0.14870478054190414, -0.09193341964595482, -0.16579152708178999, -0.23075683321225346, -0.09312654316558369, -0.1693116108617913, -0.05386494012167735, -0.07962737905142846, -0.15226396968017364, 0.3818921394970106, 0.2586441953487185, 0.1651240081026693, 0.08624927783515073, 0.3332952765504951, 0.0581116108867334, 0.13639048912695476, 0.14228710966106642, 0.20466766277001303, -9.914754126336634e-05, 0.09640827068121907, -0.1841955974738559, 0.10840406832545534, 0.06974269645809202] |
1,802.04698 | Subtyping for Hierarchical, Reconfigurable Petri Nets | Hierarchical Petri nets allow a more abstract view and reconfigurable Petri
nets model dynamic structural adaptation. In this contribution we present the
combination of reconfigurable Petri nets and hierarchical Petri nets yielding
hierarchical structure for reconfigurable Petri nets. Hierarchies are
established by substituting transitions by subnets. These subnets are
themselves reconfigurable, so they are supplied with their own set of rules.
Moreover, global rules that can be applied in all of the net, are provided.
| cs.DM cs.LO | hierarchical petri nets allow a more abstract view and reconfigurable petri nets model dynamic structural adaptation in this contribution we present the combination of reconfigurable petri nets and hierarchical petri nets yielding hierarchical structure for reconfigurable petri nets hierarchies are established by substituting transitions by subnets these subnets are themselves reconfigurable so they are supplied with their own set of rules moreover global rules that can be applied in all of the net are provided | [['hierarchical', 'petri', 'nets', 'allow', 'a', 'more', 'abstract', 'view', 'and', 'reconfigurable', 'petri', 'nets', 'model', 'dynamic', 'structural', 'adaptation', 'in', 'this', 'contribution', 'we', 'present', 'the', 'combination', 'of', 'reconfigurable', 'petri', 'nets', 'and', 'hierarchical', 'petri', 'nets', 'yielding', 'hierarchical', 'structure', 'for', 'reconfigurable', 'petri', 'nets', 'hierarchies', 'are', 'established', 'by', 'substituting', 'transitions', 'by', 'subnets', 'these', 'subnets', 'are', 'themselves', 'reconfigurable', 'so', 'they', 'are', 'supplied', 'with', 'their', 'own', 'set', 'of', 'rules', 'moreover', 'global', 'rules', 'that', 'can', 'be', 'applied', 'in', 'all', 'of', 'the', 'net', 'are', 'provided']] | [-0.12719816615106538, 0.1083513358856241, -0.009783065244555474, 0.08121371222504725, -0.14403127177928884, -0.15148257293427983, 0.0634349699659894, 0.4898694465557734, -0.34481609088679155, -0.287618864501516, 0.1108570176952829, -0.22971465232471625, -0.21107470060388248, 0.10277307394581536, -0.10019937384873628, 0.06772106933407486, 0.038992258086800576, -0.06306183124892413, 0.020403445631576082, -0.1939223622623831, 0.270805787332356, -0.021747197937220335, 0.256270276264598, -0.031745049686481557, 0.13746004225065311, -0.024244796310861905, -0.0295241292193532, 0.07123034841070573, -0.014702578265084108, 0.1684459269916018, 0.3131261691777036, 0.2028379162028432, 0.1935263916136076, -0.50650167769442, -0.1712646107872327, 0.1078126415113608, 0.11783292774111033, 0.13468040003130832, 0.0010411832565053677, -0.31000964271525544, 0.15295007854079207, -0.22772112803533673, -0.0367102593369782, -0.2019725693933045, -0.026629249552885693, 0.11945853129650155, -0.21869257853444043, -0.07482904125005006, 0.21722093115250268, 0.11556556039800246, -0.052578343115746974, -0.13085473754035776, -0.14739490662080545, 0.09543398866429925, -0.14457190840349843, -0.038582675407330194, 0.11653194083521763, -0.0859542073464642, -0.24510238823791344, 0.29810492631047963, 0.03685732109782596, -0.2812018858641386, 0.189467840216433, 0.008088811251024406, -0.1754836103444298, 0.11705285803880543, 0.1799663873606672, 0.10934696378652006, -0.26947193522083884, 0.04709241562057286, -0.029827419308324655, 0.140314619752268, 0.09319916406646371, 0.021391846605887017, 0.3396920222354432, 0.26568647363223136, 0.010292278490960599, 0.17337918042360495, 0.017635813472637287, -0.19991721404095492, -0.2602424169331789, -0.07928983861580491, -0.05840809808578342, -0.07541503091342747, -0.07385401582927444, -0.18977159027631085, 0.3125802315026522, 0.134110204000026, 0.2009451608429663, 0.24158981410165628, 0.24646351349695275, 0.10159228107368108, 0.2221600846356402, 0.02974745150655508, 0.16494318344940742, 0.10757916292796532, 0.1819918112705151, -0.0893750921015938, 0.09816644011065363, 0.08601956675450007] |
1,802.04699 | Probing the use of spectroscopy to determine the meteoritic analogues of
meteors | Determining the source regions of meteorites is one of the major goals of
current research in planetary science. Whereas asteroid observations are
currently unable to pinpoint the source regions of most meteorite classes,
observations of meteors with camera networks and the subsequent recovery of the
meteorite may help make progress on this question. The main caveat of such an
approach, however, is that the recovery rate of meteorite falls is low,
implying that the meteoritic analogues of at least 80% of the observed falls
remain unknown.
Aims: Spectroscopic observations of bolides may have the potential to
mitigate this problem by classifying the incoming material.
Methods: To probe the use of spectroscopy to determine the meteoritic
analogues of bolides, we collected emission spectra in the visible range
(320-880nm) of five meteorite types (H,L,LL,CM,eucrite) acquired in atmospheric
entry-like conditions in a plasma wind tunnel at the University of Stuttgart
(Germany). A detailed spectral analysis including line identification and mass
ratio determinations (Mg/Fe,Na/Fe) was subsequently performed on all spectra.
Results: Spectroscopy, via a simple line identification, allows us to
distinguish the main meteorite classes (chondrites, achondrites and irons) but
does not have the potential to distinguish for example an H from a CM
chondrite.
Conclusions: The source location within the main belt of the different
meteorite classes (H, L, LL, CM, etc.) should continue to be investigated via
fireball observation networks. Spectroscopy of incoming bolides only marginally
helps precisely classify the incoming material (iron meteorites only). To reach
a statistically significant sample of recovered meteorites along with accurate
orbits (>100) within a reasonable time frame (10-20 years), the optimal
solution may be the spatial extension of existing fireball observation
networks.
| astro-ph.EP | determining the source regions of meteorites is one of the major goals of current research in planetary science whereas asteroid observations are currently unable to pinpoint the source regions of most meteorite classes observations of meteors with camera networks and the subsequent recovery of the meteorite may help make progress on this question the main caveat of such an approach however is that the recovery rate of meteorite falls is low implying that the meteoritic analogues of at least 80 of the observed falls remain unknown aims spectroscopic observations of bolides may have the potential to mitigate this problem by classifying the incoming material methods to probe the use of spectroscopy to determine the meteoritic analogues of bolides we collected emission spectra in the visible range 320880nm of five meteorite types hlllcmeucrite acquired in atmospheric entrylike conditions in a plasma wind tunnel at the university of stuttgart germany a detailed spectral analysis including line identification and mass ratio determinations mgfenafe was subsequently performed on all spectra results spectroscopy via a simple line identification allows us to distinguish the main meteorite classes chondrites achondrites and irons but does not have the potential to distinguish for example an h from a cm chondrite conclusions the source location within the main belt of the different meteorite classes h l ll cm etc should continue to be investigated via fireball observation networks spectroscopy of incoming bolides only marginally helps precisely classify the incoming material iron meteorites only to reach a statistically significant sample of recovered meteorites along with accurate orbits 100 within a reasonable time frame 1020 years the optimal solution may be the spatial extension of existing fireball observation networks | [['determining', 'the', 'source', 'regions', 'of', 'meteorites', 'is', 'one', 'of', 'the', 'major', 'goals', 'of', 'current', 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1,802.047 | On Double Smoothed Volatility Estimation of Potentially Nonstationary
Jump-Diffusion Model | In this paper, we present the double smoothed nonparametric approach for
infinitesimal conditional volatility of jump-diffusion model based on high
frequency data. Under certain minimal conditions, we obtain the strong
consistency and asymptotic normality for the estimator as the time span $T
\rightarrow \infty$ and the sample interval $\Delta_{n} \rightarrow 0.$ The
procedure and asymptotic behavior can be applied for both null Harris recurrent
and positive Harris recurrent processes.
| math.ST stat.TH | in this paper we present the double smoothed nonparametric approach for infinitesimal conditional volatility of jumpdiffusion model based on high frequency data under certain minimal conditions we obtain the strong consistency and asymptotic normality for the estimator as the time span t rightarrow infty and the sample interval delta_n rightarrow 0 the procedure and asymptotic behavior can be applied for both null harris recurrent and positive harris recurrent processes | [['in', 'this', 'paper', 'we', 'present', 'the', 'double', 'smoothed', 'nonparametric', 'approach', 'for', 'infinitesimal', 'conditional', 'volatility', 'of', 'jumpdiffusion', 'model', 'based', 'on', 'high', 'frequency', 'data', 'under', 'certain', 'minimal', 'conditions', 'we', 'obtain', 'the', 'strong', 'consistency', 'and', 'asymptotic', 'normality', 'for', 'the', 'estimator', 'as', 'the', 'time', 'span', 't', 'rightarrow', 'infty', 'and', 'the', 'sample', 'interval', 'delta_n', 'rightarrow', '0', 'the', 'procedure', 'and', 'asymptotic', 'behavior', 'can', 'be', 'applied', 'for', 'both', 'null', 'harris', 'recurrent', 'and', 'positive', 'harris', 'recurrent', 'processes']] | [-0.07178252954305946, 0.0533752269718958, -0.09086811083598413, 0.14340873765657938, -0.037646294358874795, -0.15994044779327468, 0.08454757352339347, 0.39522964597099286, -0.2752180921750656, -0.18571603931216657, 0.14346882092017357, -0.22830172987828584, -0.1246595502615083, 0.18262207717972173, -0.07736773570270641, 0.1311136671220479, 0.057351068580064224, 0.020112507814622444, -0.056581556419099586, -0.2426441716860332, 0.24495553750328827, 0.05291486223754675, 0.32413312202940386, -0.04103789281899757, 0.12820612874887613, 0.03982938866576423, 0.0007059695064157679, -0.034209139557366354, -0.2016840677411444, -0.00190184502929881, 0.20609253727277552, 0.12015559400796243, 0.30816512922014017, -0.37796675727609463, -0.15764794131551962, 0.1625196883265955, 0.14025153807850313, 0.012515334223491558, 0.04182127210399325, -0.30613170939403167, 0.12069591694929893, -0.14411545301909032, -0.10286332359132559, -0.09850212750767452, 0.00048550634064536166, 0.050089732551461326, -0.4016320215997057, 0.13378809823695084, 0.1629863936939965, 0.09798180823512982, -0.030319900130448135, -0.1141669894501135, -0.00961606967114452, 0.07367021697775825, 0.10587330961404233, -0.00230065372349033, 0.07564982101051272, -0.05459449167550622, -0.08127766875027126, 0.21958247701758923, -0.17003709506378442, -0.21523338272843673, 0.15088687158540648, -0.1965247585127751, -0.18763073051915222, 0.07106102525216082, 0.18788769263504207, 0.15906194082851036, -0.15735478451360774, 0.1760733706937374, -0.03552172561108634, 0.043165707366837974, 0.07058872626689465, -0.019589584769573117, 0.13801046191593228, 0.16940464690813553, 0.08530722172471925, 0.13059606253122236, -0.14367433174081362, -0.04740551414400123, -0.3980097920782324, -0.13834201556670925, -0.1598802367090315, 0.0924902830434882, -0.18954221061279025, -0.21762212411756965, 0.363604199519192, 0.1592514393859597, 0.21825730644058491, 0.20937112705323144, 0.2136985381282326, 0.1604121090368489, -0.060976045619547906, 0.07999309312984132, 0.13241284913585885, 0.1553071053509695, 0.08752641722262985, -0.19791717593477148, 0.1439079148222463, 0.11185020655123652] |
1,802.04701 | The fundamental and rigidity theorems for pseudohermitian submanifolds
in the Heisenberg groups | In this paper, we study some basic geometric properties of pseudohermitian
submanifolds of the Heisenberg groups. In particular, we obtain the uniqueness
and existence theorems, and some rigidity theorems.
| math.DG | in this paper we study some basic geometric properties of pseudohermitian submanifolds of the heisenberg groups in particular we obtain the uniqueness and existence theorems and some rigidity theorems | [['in', 'this', 'paper', 'we', 'study', 'some', 'basic', 'geometric', 'properties', 'of', 'pseudohermitian', 'submanifolds', 'of', 'the', 'heisenberg', 'groups', 'in', 'particular', 'we', 'obtain', 'the', 'uniqueness', 'and', 'existence', 'theorems', 'and', 'some', 'rigidity', 'theorems']] | [-0.19429961832818285, 0.06242144510856476, -0.1128608722817795, 0.12807339084222272, -0.07470385428389599, -0.09184071909764717, -0.0008036915879247004, 0.3383557082892492, -0.2579500887157588, -0.17405805467017765, 0.17164019514517537, -0.24695221244775015, -0.21215147393402353, 0.1738849713884551, -0.19195321793186254, 0.02007614455089487, 0.05401830715609008, 0.04734957898999083, -0.1768443888888277, -0.22050954478568044, 0.42872681988981265, -0.06480374932289124, 0.1674021420807674, 0.15248174493698852, 0.048744031228125095, 0.05879060623781948, 0.03318184312304546, 0.011243896071140751, -0.33608848338240177, 0.1963037239580319, 0.2632335113554165, 0.04591615388876405, 0.22170030428417797, -0.39376238759221704, -0.1552517155249571, 0.22727346649907273, 0.06645476618974373, 0.06842021204446892, -0.06572390983587709, -0.3288271880612291, 0.09512746716239329, -0.0654072006200922, -0.2464365746569017, -0.15769959382455925, -0.061911647625524424, 0.0976552175084964, -0.08114045575774949, 0.10199388852021818, 0.24964100764743213, 0.12990226949854145, -0.16981251512108178, -0.06021289649451601, 0.03444526103678448, 0.1359368735474759, 0.11197609839768245, -0.0996465341043498, 0.06660379870826828, -0.06682584151336603, -0.1543810029738936, 0.34657819337885953, -0.014018263298504311, -0.2316452208906412, 0.16060310730646396, -0.15508433580719705, -0.33318573981523514, -0.01198991128155191, 0.16141437906129608, 0.1337724274730888, -0.17820902981249423, 0.17852361226887925, -0.13727977871894836, 0.0136393706217922, 0.12330795487324739, 0.13266408674675842, 0.032304101334563615, 0.11145496319433482, 0.11925807974204936, 0.18579658949426536, 0.023796044627268767, -0.07311938831518436, -0.4145771822538869, -0.2529750785806441, -0.08564653607278035, 0.17017466086765815, -0.16581396793782036, -0.17108693931669253, 0.43750771918687326, 0.1422894074388876, 0.13485900781534868, 0.13312782439531695, 0.18685055774604453, 0.02368490288740602, -0.09820554944975622, 0.0784772102688921, 0.23542594445010262, 0.3699227060223448, 0.07913615278385837, -0.09754070254235432, -0.0782691294263149, 0.2060820409578496] |
1,802.04702 | Full-dimensional Quantum Dynamics of SiO in Collision with H$_2$ | We report the first full-dimensional potential energy surface (PES) and
quantum mechanical close-coupling calculations for scattering of SiO due to
H$_2$. The full-dimensional interaction potential surface was computed using
the explicitly correlated coupled-cluster (CCSD(T)-F12b) method and fitted
using an invariant polynomial approach. Pure rotational quenching cross
sections from initial states $v_1=0$, $j_1$=1-5 of SiO in collision with H$_2$
are calculated for collision energies between 1.0 and 5000 cm$^{-1}$.
State-to-state rotational rate coefficients are calculated at temperatures
between 5 and 1000 K. The rotational rate coefficients of SiO with para-H$_2$
are compared with previous approximate results which were obtained using SiO-He
PESs or scaled from SiO-He rate coefficients. Rovibrational state-to-state and
total quenching cross sections and rate coefficients for initially excited
SiO($v_1=1, j_1$=0 and 1) in collisions with para-H$_2$($v_2=0,j_2=0$) and
ortho-H$_2$($v_2=0,j_2=1$) were also obtained. The application of the current
collisional rate coefficients to astrophysics is briefly discussed.
| physics.chem-ph astro-ph.GA | we report the first fulldimensional potential energy surface pes and quantum mechanical closecoupling calculations for scattering of sio due to h_2 the fulldimensional interaction potential surface was computed using the explicitly correlated coupledcluster ccsdtf12b method and fitted using an invariant polynomial approach pure rotational quenching cross sections from initial states v_10 j_115 of sio in collision with h_2 are calculated for collision energies between 10 and 5000 cm1 statetostate rotational rate coefficients are calculated at temperatures between 5 and 1000 k the rotational rate coefficients of sio with parah_2 are compared with previous approximate results which were obtained using siohe pess or scaled from siohe rate coefficients rovibrational statetostate and total quenching cross sections and rate coefficients for initially excited siov_11 j_10 and 1 in collisions with parah_2v_20j_20 and orthoh_2v_20j_21 were also obtained the application of the current collisional rate coefficients to astrophysics is briefly discussed | [['we', 'report', 'the', 'first', 'fulldimensional', 'potential', 'energy', 'surface', 'pes', 'and', 'quantum', 'mechanical', 'closecoupling', 'calculations', 'for', 'scattering', 'of', 'sio', 'due', 'to', 'h_2', 'the', 'fulldimensional', 'interaction', 'potential', 'surface', 'was', 'computed', 'using', 'the', 'explicitly', 'correlated', 'coupledcluster', 'ccsdtf12b', 'method', 'and', 'fitted', 'using', 'an', 'invariant', 'polynomial', 'approach', 'pure', 'rotational', 'quenching', 'cross', 'sections', 'from', 'initial', 'states', 'v_10', 'j_115', 'of', 'sio', 'in', 'collision', 'with', 'h_2', 'are', 'calculated', 'for', 'collision', 'energies', 'between', '10', 'and', '5000', 'cm1', 'statetostate', 'rotational', 'rate', 'coefficients', 'are', 'calculated', 'at', 'temperatures', 'between', '5', 'and', '1000', 'k', 'the', 'rotational', 'rate', 'coefficients', 'of', 'sio', 'with', 'parah_2', 'are', 'compared', 'with', 'previous', 'approximate', 'results', 'which', 'were', 'obtained', 'using', 'siohe', 'pess', 'or', 'scaled', 'from', 'siohe', 'rate', 'coefficients', 'rovibrational', 'statetostate', 'and', 'total', 'quenching', 'cross', 'sections', 'and', 'rate', 'coefficients', 'for', 'initially', 'excited', 'siov_11', 'j_10', 'and', '1', 'in', 'collisions', 'with', 'parah_2v_20j_20', 'and', 'orthoh_2v_20j_21', 'were', 'also', 'obtained', 'the', 'application', 'of', 'the', 'current', 'collisional', 'rate', 'coefficients', 'to', 'astrophysics', 'is', 'briefly', 'discussed']] | [-0.051910948631413975, 0.14169638165829304, -0.0023015375724799343, 0.022961889347564025, 0.07080009109977124, -0.11469738959274489, 0.010930125735592927, 0.43028240444527255, -0.23019745481274217, -0.29409850585415104, 0.003796027753863939, -0.3107138938009203, -0.016948050142620966, 0.19808410517134362, 0.10447859713847903, 0.09521490286553291, 0.0876920962163495, -0.00012288702419027686, -0.06662296519192645, -0.22690103055319663, 0.27444660323236486, 0.10407182602664095, 0.1926818639525755, 0.13661515930493315, 0.06959575096734887, -0.039092290192581175, -0.033539010588709506, -0.06245233271632799, -0.20771103587818981, 0.11435616264524266, 0.27231505885259616, 0.005865964040213781, 0.13259464282569267, -0.4245291021150008, -0.14664632477965073, 0.023472546490927716, 0.11311602119771803, 0.1421548327282004, -0.00428205793187451, -0.24171571878099934, 0.041573214659587944, -0.19313486135169436, -0.12867869220325628, -0.09629183362084005, 0.07491953882452422, 0.07349956715680497, -0.2937016837339071, 0.1816583032258736, -0.0647416073349874, 0.12563736852807647, -0.15845156024568613, -0.22010190747410274, -0.11886090338029129, 0.03907377566311428, 0.01441904529761657, 0.05351535722331928, 0.1911733373396581, -0.06711670126943521, -0.0997343854111847, 0.39763385474701957, -0.1188012727288355, -0.12523667799751106, 0.21420664171581055, -0.16178688146642967, -0.0878395379701857, 0.2557422105296642, 0.13915099494373626, 0.14718400980781415, -0.13550749238205256, 0.04889012351565117, 0.07373834846929896, 0.1696319665555447, 0.14909164166319178, 0.028222514196255464, 0.12528071946484579, 0.051590386098049744, -0.03886533886355033, 0.06556830686021248, -0.17017236887309833, -0.11937821336532775, -0.2671356685828123, -0.12303159077377092, -0.16563601944454873, 0.06013160642645181, -0.04760770633416384, -0.02615451536027433, 0.3003867088863961, 0.03970096828482991, 0.24387605318786215, 0.02152602819193208, 0.2718406999952716, 0.177038191080683, 0.01076312832159104, 0.10057847234654985, 0.2668818088529779, 0.1924843637158813, 0.06350083180817126, -0.28673484090635243, 0.05646524628751623, 0.061625629952411914] |
1,802.04703 | Regularising data for practical randomness generation | Non-local correlations that obey the no-signalling principle contain
intrinsic randomness. In particular, for a specific Bell experiment, one can
derive relations between the amount of randomness produced, as quantified by
the min-entropy of the output data, and its associated violation of a Bell
inequality. In practice, due to finite sampling, certifying randomness requires
the development of statistical tools to lower-bound the min-entropy of the data
as a function of the estimated Bell violation. The quality of such bounds
relies on the choice of certificate, i.e., the Bell inequality whose violation
is estimated. In this work, we propose a method for choosing efficiently such a
certificate. It requires sacrificing a part of the output data in order to
estimate the underlying correlations. Regularising this estimate then allows
one to find a Bell inequality that is well suited for certifying practical
randomness from these specific correlations. We then study the effects of
various parameters on the obtained min-entropy bound and explain how to tune
them in a favourable way. Lastly, we carry out several numerical simulations of
a Bell experiment to show the efficiency of our method: we nearly always obtain
higher min-entropy rates than when we use a pre-established Bell inequality,
namely the Clauser-Horne-Shimony-Holt inequality.
| quant-ph | nonlocal correlations that obey the nosignalling principle contain intrinsic randomness in particular for a specific bell experiment one can derive relations between the amount of randomness produced as quantified by the minentropy of the output data and its associated violation of a bell inequality in practice due to finite sampling certifying randomness requires the development of statistical tools to lowerbound the minentropy of the data as a function of the estimated bell violation the quality of such bounds relies on the choice of certificate ie the bell inequality whose violation is estimated in this work we propose a method for choosing efficiently such a certificate it requires sacrificing a part of the output data in order to estimate the underlying correlations regularising this estimate then allows one to find a bell inequality that is well suited for certifying practical randomness from these specific correlations we then study the effects of various parameters on the obtained minentropy bound and explain how to tune them in a favourable way lastly we carry out several numerical simulations of a bell experiment to show the efficiency of our method we nearly always obtain higher minentropy rates than when we use a preestablished bell inequality namely the clauserhorneshimonyholt inequality | [['nonlocal', 'correlations', 'that', 'obey', 'the', 'nosignalling', 'principle', 'contain', 'intrinsic', 'randomness', 'in', 'particular', 'for', 'a', 'specific', 'bell', 'experiment', 'one', 'can', 'derive', 'relations', 'between', 'the', 'amount', 'of', 'randomness', 'produced', 'as', 'quantified', 'by', 'the', 'minentropy', 'of', 'the', 'output', 'data', 'and', 'its', 'associated', 'violation', 'of', 'a', 'bell', 'inequality', 'in', 'practice', 'due', 'to', 'finite', 'sampling', 'certifying', 'randomness', 'requires', 'the', 'development', 'of', 'statistical', 'tools', 'to', 'lowerbound', 'the', 'minentropy', 'of', 'the', 'data', 'as', 'a', 'function', 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1,802.04704 | Proof systems: from nestings to sequents and back | In this work, we explore proof theoretical connections between sequent,
nested and labelled calculi. In particular, we show a general algorithm for
transforming a class of nested systems into sequent calculus systems, passing
through linear nested systems. Moreover, we show a semantical characterisation
of intuitionistic, multi-modal and non-normal modal logics for all these
systems, via a case-by-case translation between labelled nested to labelled
sequent systems.
| cs.LO | in this work we explore proof theoretical connections between sequent nested and labelled calculi in particular we show a general algorithm for transforming a class of nested systems into sequent calculus systems passing through linear nested systems moreover we show a semantical characterisation of intuitionistic multimodal and nonnormal modal logics for all these systems via a casebycase translation between labelled nested to labelled sequent systems | [['in', 'this', 'work', 'we', 'explore', 'proof', 'theoretical', 'connections', 'between', 'sequent', 'nested', 'and', 'labelled', 'calculi', 'in', 'particular', 'we', 'show', 'a', 'general', 'algorithm', 'for', 'transforming', 'a', 'class', 'of', 'nested', 'systems', 'into', 'sequent', 'calculus', 'systems', 'passing', 'through', 'linear', 'nested', 'systems', 'moreover', 'we', 'show', 'a', 'semantical', 'characterisation', 'of', 'intuitionistic', 'multimodal', 'and', 'nonnormal', 'modal', 'logics', 'for', 'all', 'these', 'systems', 'via', 'a', 'casebycase', 'translation', 'between', 'labelled', 'nested', 'to', 'labelled', 'sequent', 'systems']] | [-0.10332326239977892, 0.06214382681422509, -0.06865809808771771, 0.1364804388489574, -0.16353540967146937, -0.1554641013391889, 0.11205996580851765, 0.4101091530460578, -0.329983280119128, -0.1971578912093089, 0.026150719605636998, -0.2504962552052278, -0.1548724810704768, 0.20804734645554654, -0.15128087401390075, 0.07009012230600302, 0.08434478710763729, -0.009257864128225124, -0.08515649215819744, -0.17619331512743464, 0.35434756135711304, -0.11835826698404092, 0.2076010317613299, -0.055061905078988414, 0.1471317590142672, 0.09591962674704309, 0.0019805704744962544, 0.05937525781874473, -0.11672830762198338, 0.20856022387742995, 0.3469232323937691, 0.2177738099430616, 0.32704793454076236, -0.4428246458562521, -0.11349336251329917, 0.10885760807074034, 0.1610144285771709, 0.12433338662466177, 0.01395458670762869, -0.3408892328349444, 0.03074113428592682, -0.2233466321745744, -0.02199796173775282, -0.16587763159320904, 0.016902595249792703, 0.028996905283286023, -0.21103209992154287, -0.07774403929137266, 0.3008931712462352, 0.2112892077662624, -0.01942493816694388, -0.07610622744541615, -0.015012135166818133, -0.029357571283785196, -0.10896615357353137, -0.11181544051147424, 0.00242644501133607, -0.020328422288338727, -0.2294523727435332, 0.2669955348882538, 0.019439452342115915, -0.2666743801620144, 0.22648659135000063, -0.05953897251747549, -0.24884997105870682, 0.05137182354497222, 0.18040728374169424, 0.1648997962474823, -0.1588026974350214, 0.13170717018835534, -0.06081779501759089, 0.2246535785890256, 0.16488359110573164, 0.056060394543545465, 0.23712022204238634, 0.2545151206485641, -0.006800901818160827, 0.19726514811985768, 0.06354196985753682, -0.18592972794117835, -0.30464002810991725, -0.1902459292457654, -0.057353679765947164, -0.06366446974973838, -0.06664751192080215, -0.22994613772114883, 0.2901041956188587, 0.1377520921998299, 0.14612045523065786, 0.2217736723152204, 0.2956434461646355, 0.13085313995297138, 0.10044313072441863, 0.011708800351390472, 0.10000284847875054, 0.187625052797823, 0.06554273755934376, -0.1029461682559206, 0.025788392405956984, 0.15140570983863794] |
1,802.04705 | Hadamard Response: Estimating Distributions Privately, Efficiently, and
with Little Communication | We study the problem of estimating $k$-ary distributions under
$\varepsilon$-local differential privacy. $n$ samples are distributed across
users who send privatized versions of their sample to a central server. All
previously known sample optimal algorithms require linear (in $k$)
communication from each user in the high privacy regime $(\varepsilon=O(1))$,
and run in time that grows as $n\cdot k$, which can be prohibitive for large
domain size $k$.
We propose Hadamard Response (HR}, a local privatization scheme that requires
no shared randomness and is symmetric with respect to the users. Our scheme has
order optimal sample complexity for all $\varepsilon$, a communication of at
most $\log k+2$ bits per user, and nearly linear running time of $\tilde{O}(n +
k)$.
Our encoding and decoding are based on Hadamard matrices, and are simple to
implement. The statistical performance relies on the coding theoretic aspects
of Hadamard matrices, ie, the large Hamming distance between the rows. An
efficient implementation of the algorithm using the Fast Walsh-Hadamard
transform gives the computational gains.
We compare our approach with Randomized Response (RR), RAPPOR, and
subset-selection mechanisms (SS), both theoretically, and experimentally. For
$k=10000$, our algorithm runs about 100x faster than SS, and RAPPOR.
| cs.LG cs.DS cs.IT math.IT | we study the problem of estimating kary distributions under varepsilonlocal differential privacy n samples are distributed across users who send privatized versions of their sample to a central server all previously known sample optimal algorithms require linear in k communication from each user in the high privacy regime varepsilono1 and run in time that grows as ncdot k which can be prohibitive for large domain size k we propose hadamard response hr a local privatization scheme that requires no shared randomness and is symmetric with respect to the users our scheme has order optimal sample complexity for all varepsilon a communication of at most log k2 bits per user and nearly linear running time of tildeon k our encoding and decoding are based on hadamard matrices and are simple to implement the statistical performance relies on the coding theoretic aspects of hadamard matrices ie the large hamming distance between the rows an efficient implementation of the algorithm using the fast walshhadamard transform gives the computational gains we compare our approach with randomized response rr rappor and subsetselection mechanisms ss both theoretically and experimentally for k10000 our algorithm runs about 100x faster than ss and rappor | [['we', 'study', 'the', 'problem', 'of', 'estimating', 'kary', 'distributions', 'under', 'varepsilonlocal', 'differential', 'privacy', 'n', 'samples', 'are', 'distributed', 'across', 'users', 'who', 'send', 'privatized', 'versions', 'of', 'their', 'sample', 'to', 'a', 'central', 'server', 'all', 'previously', 'known', 'sample', 'optimal', 'algorithms', 'require', 'linear', 'in', 'k', 'communication', 'from', 'each', 'user', 'in', 'the', 'high', 'privacy', 'regime', 'varepsilono1', 'and', 'run', 'in', 'time', 'that', 'grows', 'as', 'ncdot', 'k', 'which', 'can', 'be', 'prohibitive', 'for', 'large', 'domain', 'size', 'k', 'we', 'propose', 'hadamard', 'response', 'hr', 'a', 'local', 'privatization', 'scheme', 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1,802.04706 | Automatic thread painting generation | ThreadTone is an NPR representation of an input image by half-toning using
threads on a circle. Current approaches to create ThreadTone paintings greedily
draw the chords on the circle. We introduce the concept of chord space, and
design a new algorithm to improve the quality of the thread painting. We use an
optimization process that estimates the fitness of every chord in the chord
space, and an error-diffusion based sampling process that selects a moderate
number of chords to produce the output painting. We used an image similarity
measure to evaluate the quality of our thread painting and also conducted a
user study. Our approach can produce high quality results on portraits,
sketches as well as cartoon pictures.
| cs.GR | threadtone is an npr representation of an input image by halftoning using threads on a circle current approaches to create threadtone paintings greedily draw the chords on the circle we introduce the concept of chord space and design a new algorithm to improve the quality of the thread painting we use an optimization process that estimates the fitness of every chord in the chord space and an errordiffusion based sampling process that selects a moderate number of chords to produce the output painting we used an image similarity measure to evaluate the quality of our thread painting and also conducted a user study our approach can produce high quality results on portraits sketches as well as cartoon pictures | [['threadtone', 'is', 'an', 'npr', 'representation', 'of', 'an', 'input', 'image', 'by', 'halftoning', 'using', 'threads', 'on', 'a', 'circle', 'current', 'approaches', 'to', 'create', 'threadtone', 'paintings', 'greedily', 'draw', 'the', 'chords', 'on', 'the', 'circle', 'we', 'introduce', 'the', 'concept', 'of', 'chord', 'space', 'and', 'design', 'a', 'new', 'algorithm', 'to', 'improve', 'the', 'quality', 'of', 'the', 'thread', 'painting', 'we', 'use', 'an', 'optimization', 'process', 'that', 'estimates', 'the', 'fitness', 'of', 'every', 'chord', 'in', 'the', 'chord', 'space', 'and', 'an', 'errordiffusion', 'based', 'sampling', 'process', 'that', 'selects', 'a', 'moderate', 'number', 'of', 'chords', 'to', 'produce', 'the', 'output', 'painting', 'we', 'used', 'an', 'image', 'similarity', 'measure', 'to', 'evaluate', 'the', 'quality', 'of', 'our', 'thread', 'painting', 'and', 'also', 'conducted', 'a', 'user', 'study', 'our', 'approach', 'can', 'produce', 'high', 'quality', 'results', 'on', 'portraits', 'sketches', 'as', 'well', 'as', 'cartoon', 'pictures']] | [-0.055457578996277374, 0.040083762450249985, -0.13879175014469936, 0.05723982327633522, -0.12752777520243241, -0.06969018113677916, 0.07047848512782999, 0.4445211881852668, -0.26406592086119496, -0.3366003952434529, 0.07515943494483666, -0.26293239941739516, -0.1928359419107437, 0.20899046364888224, -0.17730623995885253, 0.04059659638239638, 0.10606595047182688, 0.05627383747664483, -0.03550733670025416, -0.285941030026373, 0.30955327992206033, 0.04253233385717739, 0.31509374360675396, -0.01727443182759959, 0.14832730556473783, 0.024964458924596724, -0.0411380668150504, 0.005161300341537951, -0.12896595517560616, 0.14854804466114097, 0.20786064539874052, 0.23213418237460048, 0.2901576565700057, -0.3856036515286415, -0.13136720955346548, 0.03993999522746257, 0.14323686309482742, 0.08132334881786095, -0.07087558792073928, -0.3101092980729173, 0.06954862481873969, -0.13313803003857966, -0.033835773545560305, -0.10599544704385588, -0.028676447582066708, 0.01040268517099321, -0.2576788156819732, -0.08126475719314918, 0.04962852342641386, 0.07497269134644581, -0.013350925317196094, -0.09358313048420393, -0.018749493528562394, 0.21869026750083204, 0.016576205604486978, 0.11774509572019072, 0.13715260534132223, -0.1420535458711801, -0.17402975715534844, 0.3790433116257191, -0.059108259294020096, -0.25819273814558985, 0.16339156573597827, -0.059037781050732444, -0.09959091490701488, 0.1389128306151732, 0.21331545633628315, 0.10442404238223706, -0.08615653841229884, -0.04197552279599578, -0.08851621660363415, 0.21736558981887671, 0.09819464900773829, -0.05674943905728667, 0.18797234144750172, 0.19053678579790437, 0.061289983485703886, 0.19689923326494982, -0.1131441421156405, -0.013824108610456081, -0.2352272691205144, -0.1890481158119181, -0.22658438630726027, -0.044076453388223184, -0.13070376742512252, -0.2373200316791949, 0.4397708653350887, 0.20732414283101325, 0.2674219673256511, 0.0780703661315467, 0.348027663645537, 0.06983379451962916, 0.058489157973140804, 0.03472293630728255, 0.10769933594957642, 0.022674087736674627, 0.09542486633214614, -0.1456566203021161, 0.05080853550006514, 0.13094006414808657] |
1,802.04707 | Universality for bounded degree spanning trees in randomly perturbed
graphs | We solve a problem of Krivelevich, Kwan and Sudakov [SIAM Journal on Discrete
Mathematics 31 (2017), 155-171] concerning the threshold for the containment of
all bounded degree spanning trees in the model of randomly perturbed dense
graphs. More precisely, we show that, if we start with a dense graph $G_\alpha$
on $n$ vertices with $\delta(G_\alpha)\ge \alpha n$ for $\alpha>0$ and we add
to it the binomial random graph $G(n,C/n)$, then with high probability the
graph $G_\alpha\cup G(n,C/n)$ contains copies of all spanning trees with
maximum degree at most $\Delta$ simultaneously, where $C$ depends only on
$\alpha$ and $\Delta$.
| math.CO | we solve a problem of krivelevich kwan and sudakov siam journal on discrete mathematics 31 2017 155171 concerning the threshold for the containment of all bounded degree spanning trees in the model of randomly perturbed dense graphs more precisely we show that if we start with a dense graph g_alpha on n vertices with deltag_alphage alpha n for alpha0 and we add to it the binomial random graph gncn then with high probability the graph g_alphacup gncn contains copies of all spanning trees with maximum degree at most delta simultaneously where c depends only on alpha and delta | [['we', 'solve', 'a', 'problem', 'of', 'krivelevich', 'kwan', 'and', 'sudakov', 'siam', 'journal', 'on', 'discrete', 'mathematics', '31', '2017', '155171', 'concerning', 'the', 'threshold', 'for', 'the', 'containment', 'of', 'all', 'bounded', 'degree', 'spanning', 'trees', 'in', 'the', 'model', 'of', 'randomly', 'perturbed', 'dense', 'graphs', 'more', 'precisely', 'we', 'show', 'that', 'if', 'we', 'start', 'with', 'a', 'dense', 'graph', 'g_alpha', 'on', 'n', 'vertices', 'with', 'deltag_alphage', 'alpha', 'n', 'for', 'alpha0', 'and', 'we', 'add', 'to', 'it', 'the', 'binomial', 'random', 'graph', 'gncn', 'then', 'with', 'high', 'probability', 'the', 'graph', 'g_alphacup', 'gncn', 'contains', 'copies', 'of', 'all', 'spanning', 'trees', 'with', 'maximum', 'degree', 'at', 'most', 'delta', 'simultaneously', 'where', 'c', 'depends', 'only', 'on', 'alpha', 'and', 'delta']] | [-0.10041248013486008, 0.15549213585970728, 0.01506787226290295, -0.014571244872518275, -0.07205189901257032, -0.15496013565479141, 0.09715180934259766, 0.3716689650508526, -0.2599105267748727, -0.32669659947094165, 0.06654135678331123, -0.34891138602244226, -0.14028032808809687, 0.11956046367505271, -0.08160874193632289, 0.0405622602599722, 0.08541586493681136, 0.10819017954758908, 0.045102946335253746, -0.29424022129265054, 0.3067838839696426, -0.02062884851505882, 0.14272410389232007, 0.049845161847770214, 0.11309963015741424, 0.07500336701785655, -0.04277372801259748, 0.06368606672867348, -0.19914840711809167, 0.058481532657568, 0.2091069762251879, 0.16813845464371538, 0.2524505914630074, -0.348395683969322, -0.16249273221841767, 0.19200109226237003, 0.08186741868695735, 0.026388782128618147, 0.04973550618083927, -0.20961625553471477, 0.15775797179115839, -0.09922474247041943, -0.09851527105232602, 0.03831721883463232, 0.1286434855704245, 0.03488306435756385, -0.30967060640258226, 0.05408121345466689, 0.0857810991129985, 0.017580014722127665, 0.0817936366448473, -0.19111125110403487, -0.054977386601603936, 0.08136043918662166, -0.09148082601911339, 0.07244312062884044, 0.00717574748161592, -0.1367001787581677, -0.12910098949643342, 0.3483243406211075, -0.03369747186569791, -0.15458468752177923, 0.1439868741178591, -0.17969228145911506, -0.250940548265843, 0.10023625659217175, 0.16675353051212274, 0.15176573201621835, -0.060790166033333856, 0.1783600630220271, -0.10580255753899875, 0.1648408302596133, 0.19935123032253038, -0.03009236757398436, 0.06912523062997743, 0.12406579344778469, 0.14438060875982045, 0.13298711797075444, -0.010288623954463554, -0.030525414537834493, -0.2743765098483939, -0.06464037195730367, -0.2098484672434432, 0.07501296408498954, -0.2147332114849154, -0.21376293546667224, 0.3882240405600322, 0.0997030457089606, 0.2221847662310067, 0.11871468713016886, 0.18000108949644, 0.09250924386770318, -0.04173870078398307, 0.22624344288028383, 0.09417129924618885, 0.1714421609710706, 0.018989062921977357, -0.13664801284474762, 0.030204039005758732, 0.12548503845142764] |
1,802.04708 | Lengths of Words Accepted by Nondeterministic Finite Automata | We consider two natural problems about nondeterministic finite automata.
First, given such an automaton M of n states, and a length l, does M accept a
word of length l? We show that the classic problem of triangle-free graph
recognition reduces to this problem, and give an O(n^{\omega} (log
n)^{1+{\epsilon}} log l)-time algorithm to solve it, where {\omega} is the
optimal exponent for matrix multiplication. Second, provided L(M) is finite, we
consider the problem of listing the lengths of all words accepted by M.
Although this problem seems like it might be significantly harder, we show that
this problem can be solved in O(n^{\omega}(log n)^{2+{\epsilon}}) time.
Finally, we give a connection between NFA acceptance and the strong
exponential-time hypothesis.
| cs.FL cs.CC | we consider two natural problems about nondeterministic finite automata first given such an automaton m of n states and a length l does m accept a word of length l we show that the classic problem of trianglefree graph recognition reduces to this problem and give an onomega log n1epsilon log ltime algorithm to solve it where omega is the optimal exponent for matrix multiplication second provided lm is finite we consider the problem of listing the lengths of all words accepted by m although this problem seems like it might be significantly harder we show that this problem can be solved in onomegalog n2epsilon time finally we give a connection between nfa acceptance and the strong exponentialtime hypothesis | [['we', 'consider', 'two', 'natural', 'problems', 'about', 'nondeterministic', 'finite', 'automata', 'first', 'given', 'such', 'an', 'automaton', 'm', 'of', 'n', 'states', 'and', 'a', 'length', 'l', 'does', 'm', 'accept', 'a', 'word', 'of', 'length', 'l', 'we', 'show', 'that', 'the', 'classic', 'problem', 'of', 'trianglefree', 'graph', 'recognition', 'reduces', 'to', 'this', 'problem', 'and', 'give', 'an', 'onomega', 'log', 'n1epsilon', 'log', 'ltime', 'algorithm', 'to', 'solve', 'it', 'where', 'omega', 'is', 'the', 'optimal', 'exponent', 'for', 'matrix', 'multiplication', 'second', 'provided', 'lm', 'is', 'finite', 'we', 'consider', 'the', 'problem', 'of', 'listing', 'the', 'lengths', 'of', 'all', 'words', 'accepted', 'by', 'm', 'although', 'this', 'problem', 'seems', 'like', 'it', 'might', 'be', 'significantly', 'harder', 'we', 'show', 'that', 'this', 'problem', 'can', 'be', 'solved', 'in', 'onomegalog', 'n2epsilon', 'time', 'finally', 'we', 'give', 'a', 'connection', 'between', 'nfa', 'acceptance', 'and', 'the', 'strong', 'exponentialtime', 'hypothesis']] | [-0.16383238863742225, 0.1293302472448474, -0.04586584031438598, 0.12052339791722246, -0.09781847136646397, -0.19988961164783844, 0.08840924980015391, 0.3783023678578245, -0.33312301587663656, -0.30355547064055616, 0.06960856898102719, -0.2507495975466873, -0.1344458931304801, 0.17480348014127478, -0.09955493731495853, 0.056139801326406814, 0.0759908835688192, 0.09165878194328557, -0.04494075649457737, -0.3015782465657287, 0.2804791300028212, 0.004951959481844917, 0.1991638619826836, 0.0537710693012127, 0.09493548286935458, 0.0012291409692161868, -0.017327185748065382, 0.04749430194656309, -0.13649987654910228, 0.05083788187356475, 0.2987737562427791, 0.2067786108973062, 0.2818331142616832, -0.39197893765492314, -0.15598394478169772, 0.19135842813799778, 0.16068899589710128, 0.07560928484313509, 0.021420200896632467, -0.20190875388236126, 0.15723718877515605, -0.12064245470568664, -0.06134031403364024, 0.02448785158757789, 0.11424919351553306, -0.051186389425116725, -0.2750569980683076, 0.007846608355991606, 0.1500762949586233, 0.010019840839772653, -0.0045044817802551975, -0.13683854715906593, 0.09849963168032531, 0.08989918069977663, 0.0026809872868351447, 0.04745972558107768, 0.027665694853147633, -0.06306005045413397, -0.15197384976742104, 0.39268402697948307, -0.04783947953136447, -0.21637237229599401, 0.12687496101865783, -0.08248774398070498, -0.15324090965366802, 0.10530526875558062, 0.11274591611268428, 0.15670285905655632, -0.051609705035121016, 0.1512052197025245, -0.15258172858888522, 0.24504249350319052, 0.11031816067158157, -0.03392150716330761, 0.09541981386788921, 0.16099471486627293, 0.14012206193362164, 0.1640092912547959, 0.012668101001594566, -0.02420411997128469, -0.27829360073177606, -0.17098492621961567, -0.17330342454887512, 0.08041603506431302, -0.10367460013130922, -0.17514890276341358, 0.3050588807449318, 0.15097341873945716, 0.21258757151981705, 0.17600939340857613, 0.2369816834345842, 0.1549657501009667, 0.008842301679850739, 0.15486543621613175, 0.08741158959714489, 0.0882224270190375, 0.04532622085072291, -0.25258597600871785, 0.06309042351606947, 0.14296820132523522] |
1,802.04709 | Micromagnetic evaluation of the dissipated heat in cylindrical magnetic
nanowires | Magnetic nanowires (NW) are promising candidates for heat generation under
AC-field application due to their large shape anisotropy. They may be used for
catalysis, hyperthermia or water purification treatments. In the present work
we theoretically evaluate the heat dissipated by a single magnetic nanowire,
originated from the domain wall dynamics under the action of an AC-field. We
compare the Permalloy NWs (which demagnetize via the transverse wall
propagation) with the Co fcc NWs whose reversal mode is via a vortex domain
wall. The average hysteresis loop areas -which are proportional to the Specific
Absorption Rate (SAR)- as a function of the field frequency have a pronounced
maximum in the range 200MHz-1GHz. This maximum frequency is smaller in
Permalloy than in Co and depends on the nanowire length. A simple model related
to the nucleation and propagation time and domain wall velocity (higher for the
vortex than for the transverse domain wall) is proposed to explain the
non-monotonic SAR dependence on the frequency.
| cond-mat.mes-hall | magnetic nanowires nw are promising candidates for heat generation under acfield application due to their large shape anisotropy they may be used for catalysis hyperthermia or water purification treatments in the present work we theoretically evaluate the heat dissipated by a single magnetic nanowire originated from the domain wall dynamics under the action of an acfield we compare the permalloy nws which demagnetize via the transverse wall propagation with the co fcc nws whose reversal mode is via a vortex domain wall the average hysteresis loop areas which are proportional to the specific absorption rate sar as a function of the field frequency have a pronounced maximum in the range 200mhz1ghz this maximum frequency is smaller in permalloy than in co and depends on the nanowire length a simple model related to the nucleation and propagation time and domain wall velocity higher for the vortex than for the transverse domain wall is proposed to explain the nonmonotonic sar dependence on the frequency | [['magnetic', 'nanowires', 'nw', 'are', 'promising', 'candidates', 'for', 'heat', 'generation', 'under', 'acfield', 'application', 'due', 'to', 'their', 'large', 'shape', 'anisotropy', 'they', 'may', 'be', 'used', 'for', 'catalysis', 'hyperthermia', 'or', 'water', 'purification', 'treatments', 'in', 'the', 'present', 'work', 'we', 'theoretically', 'evaluate', 'the', 'heat', 'dissipated', 'by', 'a', 'single', 'magnetic', 'nanowire', 'originated', 'from', 'the', 'domain', 'wall', 'dynamics', 'under', 'the', 'action', 'of', 'an', 'acfield', 'we', 'compare', 'the', 'permalloy', 'nws', 'which', 'demagnetize', 'via', 'the', 'transverse', 'wall', 'propagation', 'with', 'the', 'co', 'fcc', 'nws', 'whose', 'reversal', 'mode', 'is', 'via', 'a', 'vortex', 'domain', 'wall', 'the', 'average', 'hysteresis', 'loop', 'areas', 'which', 'are', 'proportional', 'to', 'the', 'specific', 'absorption', 'rate', 'sar', 'as', 'a', 'function', 'of', 'the', 'field', 'frequency', 'have', 'a', 'pronounced', 'maximum', 'in', 'the', 'range', '200mhz1ghz', 'this', 'maximum', 'frequency', 'is', 'smaller', 'in', 'permalloy', 'than', 'in', 'co', 'and', 'depends', 'on', 'the', 'nanowire', 'length', 'a', 'simple', 'model', 'related', 'to', 'the', 'nucleation', 'and', 'propagation', 'time', 'and', 'domain', 'wall', 'velocity', 'higher', 'for', 'the', 'vortex', 'than', 'for', 'the', 'transverse', 'domain', 'wall', 'is', 'proposed', 'to', 'explain', 'the', 'nonmonotonic', 'sar', 'dependence', 'on', 'the', 'frequency']] | [-0.11741789158503091, 0.16639779053813913, -0.007586282787856108, 0.004351883397440524, -0.10657444784290487, -0.09686969609074988, 0.023199273321794937, 0.43697111241473174, -0.25020252919888925, -0.2763782464609892, 0.08791164753044614, -0.24348357951511507, -0.0739351574473245, 0.23216404063303187, 0.007797582873276302, 0.029894771875802036, -0.006865735440543905, 0.00795998588574599, -0.01297429106299482, -0.1575319153945083, 0.25703921951122166, 0.04241670427774513, 0.38268801531037144, 0.10978443507916404, 0.054959384154713485, -0.033769306949461286, 0.07701921201837451, 0.02479435320544456, -0.16662397682249203, 0.052530559379266144, 0.175829174020183, -0.05217503603787873, 0.19789452276804329, -0.4706279458304555, -0.23355529937091984, 0.052535341873255946, 0.16948557665907077, 0.12210225902341584, -0.03647920985630951, -0.24787177526108597, 0.07551589570447849, -0.1094587733766631, -0.10231165525195517, 0.008001390201768474, 0.03425228818224292, 0.02965877606585529, -0.25905014869542964, 0.12284576777160539, 0.059633662776662884, 0.07998228251864636, -0.07551767114804399, -0.10220234466843908, -0.05096879849638443, 0.048596111477659264, 0.07012906848808739, 0.07385228667408228, 0.23029432487377596, -0.13889092822107799, -0.11494056765360307, 0.36232851030339736, -0.09093235543967364, -0.2034593126187043, 0.15959759576604787, -0.19171959671852762, 0.0021507745082287684, 0.16310350169421908, 0.18032660525616095, 0.1383963406161168, -0.13565122208092362, 0.02165487938689253, 0.019930868061051144, 0.16445188471101663, 0.12476047831847802, 0.02441609806456366, 0.2493136459368611, 0.2099707536110134, 0.04841771309830896, 0.2082990833973188, -0.1908026618611137, -0.060279510695464125, -0.244357424117162, -0.15770388495680293, -0.2171459948383373, 0.019231868315609576, -0.11353557258643722, -0.1750173829553753, 0.40787486308572457, 0.11655487898697062, 0.16849215455340244, -0.0002293951204914299, 0.28996205516448764, 0.14363922213959096, 0.1379847526825062, 0.04835305784348838, 0.22562958373289024, 0.15496009337840175, 0.16829234575100752, -0.3106083838017484, 0.0806517670081454, -0.022918786629398138] |
1,802.0471 | On the metric compactification of infinite-dimensional $\ell_{p}$ spaces | The notion of metric compactification was introduced by Gromov and later
rediscovered by Rieffel; and has been mainly studied on proper geodesic metric
spaces. We present here a generalization of the metric compactification that
can be applied to infinite-dimensional Banach spaces. Thereafter we give a
complete description of the metric compactification of infinite-dimensional
$\ell_{p}$ spaces for all $1\leq p < \infty$. We also give a full
characterization of the metric compactification of infinite-dimensional Hilbert
spaces.
| math.FA | the notion of metric compactification was introduced by gromov and later rediscovered by rieffel and has been mainly studied on proper geodesic metric spaces we present here a generalization of the metric compactification that can be applied to infinitedimensional banach spaces thereafter we give a complete description of the metric compactification of infinitedimensional ell_p spaces for all 1leq p infty we also give a full characterization of the metric compactification of infinitedimensional hilbert spaces | [['the', 'notion', 'of', 'metric', 'compactification', 'was', 'introduced', 'by', 'gromov', 'and', 'later', 'rediscovered', 'by', 'rieffel', 'and', 'has', 'been', 'mainly', 'studied', 'on', 'proper', 'geodesic', 'metric', 'spaces', 'we', 'present', 'here', 'a', 'generalization', 'of', 'the', 'metric', 'compactification', 'that', 'can', 'be', 'applied', 'to', 'infinitedimensional', 'banach', 'spaces', 'thereafter', 'we', 'give', 'a', 'complete', 'description', 'of', 'the', 'metric', 'compactification', 'of', 'infinitedimensional', 'ell_p', 'spaces', 'for', 'all', '1leq', 'p', 'infty', 'we', 'also', 'give', 'a', 'full', 'characterization', 'of', 'the', 'metric', 'compactification', 'of', 'infinitedimensional', 'hilbert', 'spaces']] | [-0.10485854558646679, 0.1285269488423207, -0.13109708819978846, 0.16005813475144473, -0.1492010175716132, -0.08483999201751037, -0.00774624920764787, 0.3755502848307023, -0.2679978274375301, -0.1634763708665363, 0.14989001234062016, -0.21964389871101123, -0.15899999154026848, 0.1872917367397128, -0.22359987752940003, 0.0456109930433937, 0.03855138176476356, 0.06172403645374485, -0.14449016034061946, -0.3343592268993726, 0.46946316850497516, 0.01981652154259988, 0.2086030492697515, 0.055585715340802797, 0.1722038740644584, 0.014892509030933315, -0.047200353998640504, 0.06227207231977199, -0.2480674696921228, 0.12207665928714984, 0.2785096112638712, 0.14107617663181815, 0.26404647765731487, -0.30946971833504533, -0.24636408224752224, 0.19737516064196825, 0.13677921725084652, -0.012589788530021906, 0.02819469937349896, -0.37524060250536817, 0.060091523667545735, -0.1488773678806988, -0.12125513493712689, -0.16473237769929944, 0.09960645709086109, -0.06625202412062602, -0.20757244431061317, -0.05077409448858816, 0.16002493136486895, 0.08008747651163847, -0.14855583542894973, -0.04793036786361119, -0.036323226016090324, 0.013897646187074683, 0.0038164600264281034, 0.11903333145420293, 0.06455928063951433, 0.06538352292232416, -0.12875812872300377, 0.36216667358688004, -0.05354686948152989, -0.2588777618130317, 0.08834155309139877, -0.15713894519501845, -0.124333993544349, 0.05944331844865873, 0.12908996955013355, 0.19386147395581813, -0.06845050621022647, 0.27727613224792674, -0.0918629641932352, 0.017706896128082596, 0.14477727418715083, 0.07908260890886791, 0.02969186344007785, 0.1413682522503911, 0.12219504780463271, 0.16670105124137843, 0.028402498674679647, -0.05351506863607446, -0.3737397122926809, -0.19148356292547808, -0.15383008383749355, 0.1912764412747394, -0.17233832747434782, -0.18121593980420683, 0.32690216256053867, 0.023850675370242144, 0.1927271568895997, 0.08195978044406385, 0.1685330127145642, 0.02512104219027065, -0.003126666767875085, 0.04099605934744751, 0.20264742551119747, 0.2172465514001512, 0.009421071345993393, -0.0743920064446315, -0.06293123515599684, 0.3035916858667357] |
1,802.04711 | Quantum-classical correspondence in the vicinity of periodic orbits | Quantum-classical correspondence in chaotic systems is a long-standing
problem. We describe a method to quantify Bohr's correspondence principle and
calculate the size of quantum numbers for which we can expect to observe
quantum-classical correspondence near periodic orbits of Floquet systems. Our
method shows how the stability of classical periodic orbits affects quantum
dynamics. We demonstrate our method by analyzing quantum-classical
correspondence in the quantum kicked top (QKT), which exhibits both regular and
chaotic behavior. We use our correspondence conditions to identify signatures
of classical bifurcations even in a deep quantum regime. Our method can be used
to explain the breakdown of quantum-classical correspondence in chaotic
systems.
| quant-ph | quantumclassical correspondence in chaotic systems is a longstanding problem we describe a method to quantify bohrs correspondence principle and calculate the size of quantum numbers for which we can expect to observe quantumclassical correspondence near periodic orbits of floquet systems our method shows how the stability of classical periodic orbits affects quantum dynamics we demonstrate our method by analyzing quantumclassical correspondence in the quantum kicked top qkt which exhibits both regular and chaotic behavior we use our correspondence conditions to identify signatures of classical bifurcations even in a deep quantum regime our method can be used to explain the breakdown of quantumclassical correspondence in chaotic systems | [['quantumclassical', 'correspondence', 'in', 'chaotic', 'systems', 'is', 'a', 'longstanding', 'problem', 'we', 'describe', 'a', 'method', 'to', 'quantify', 'bohrs', 'correspondence', 'principle', 'and', 'calculate', 'the', 'size', 'of', 'quantum', 'numbers', 'for', 'which', 'we', 'can', 'expect', 'to', 'observe', 'quantumclassical', 'correspondence', 'near', 'periodic', 'orbits', 'of', 'floquet', 'systems', 'our', 'method', 'shows', 'how', 'the', 'stability', 'of', 'classical', 'periodic', 'orbits', 'affects', 'quantum', 'dynamics', 'we', 'demonstrate', 'our', 'method', 'by', 'analyzing', 'quantumclassical', 'correspondence', 'in', 'the', 'quantum', 'kicked', 'top', 'qkt', 'which', 'exhibits', 'both', 'regular', 'and', 'chaotic', 'behavior', 'we', 'use', 'our', 'correspondence', 'conditions', 'to', 'identify', 'signatures', 'of', 'classical', 'bifurcations', 'even', 'in', 'a', 'deep', 'quantum', 'regime', 'our', 'method', 'can', 'be', 'used', 'to', 'explain', 'the', 'breakdown', 'of', 'quantumclassical', 'correspondence', 'in', 'chaotic', 'systems']] | [-0.1714730457016179, 0.09206077268452577, -0.1881093516822834, 0.12788227217311463, -0.0028778245641832363, -0.18170271866146545, 0.056970656579272506, 0.3107021530070957, -0.3055919425667457, -0.2448478029021677, 0.018439668900649644, -0.22277896415512516, -0.2590790004557315, 0.2533080352663572, -0.10459265254332491, 0.09858985154731374, 0.10735413794546335, 0.012160957977615015, -0.09175099666397316, -0.1901313559029181, 0.2858133528201472, -0.03827973118492288, 0.2678909392781415, 0.038085458079739565, 0.05569852418969122, -0.021256730006740016, 0.08609121141709247, -0.0013839568556198534, -0.13193715603380762, 0.10270182620009126, 0.2658810424882005, 0.08167314322946488, 0.23389235900644706, -0.4205175665699227, -0.20968783754011933, 0.09397282685979076, 0.1514892190703595, 0.19104707421831577, -0.029044498390746566, -0.3292548957847516, 0.0974530198892473, -0.1434123827636523, -0.1952072113163699, -0.12739377076966021, -0.028857852033567877, -0.031050921516615967, -0.21495197685259693, 0.11303463078297253, 0.08671430493968557, 0.07199227703594656, -0.003334789220595135, 0.0679127218131468, 0.01997748348765286, 0.12750136530255232, -0.032043338250280974, -0.03623356835319186, 0.10732383631556383, -0.09606600709299047, -0.1837783175397594, 0.35626453818437065, -0.05746648806797446, -0.18629976629964867, 0.2671599388737583, -0.16388100977407172, -0.11969176744866483, 0.05546784185681422, 0.14180115921387695, 0.1258971929277803, -0.10381937698671981, 0.08925213430072444, -0.04553486851377869, 0.16654957974118725, 0.0831434381950014, 0.04031661692303869, 0.2700470424134214, 0.1418876784836065, 0.030705406453530742, 0.193732984152069, -0.0796677548051724, -0.2560625205185475, -0.2666766751944175, -0.14663020513494904, -0.17253809893187486, 0.05154792158776578, -0.043984611806150015, -0.173441739515665, 0.3937454154574365, 0.2006159177487101, 0.20048531781846904, 0.029358133335404518, 0.22626217831154619, 0.13485100610568276, -0.008652660240120482, 0.0484860547036284, 0.27128402685377534, 0.17659319312320496, 0.10871987474449682, -0.30534892657965, -0.027393939182215003, 0.11831535687582251] |
1,802.04712 | Attention-based Deep Multiple Instance Learning | Multiple instance learning (MIL) is a variation of supervised learning where
a single class label is assigned to a bag of instances. In this paper, we state
the MIL problem as learning the Bernoulli distribution of the bag label where
the bag label probability is fully parameterized by neural networks.
Furthermore, we propose a neural network-based permutation-invariant
aggregation operator that corresponds to the attention mechanism. Notably, an
application of the proposed attention-based operator provides insight into the
contribution of each instance to the bag label. We show empirically that our
approach achieves comparable performance to the best MIL methods on benchmark
MIL datasets and it outperforms other methods on a MNIST-based MIL dataset and
two real-life histopathology datasets without sacrificing interpretability.
| cs.LG stat.ML | multiple instance learning mil is a variation of supervised learning where a single class label is assigned to a bag of instances in this paper we state the mil problem as learning the bernoulli distribution of the bag label where the bag label probability is fully parameterized by neural networks furthermore we propose a neural networkbased permutationinvariant aggregation operator that corresponds to the attention mechanism notably an application of the proposed attentionbased operator provides insight into the contribution of each instance to the bag label we show empirically that our approach achieves comparable performance to the best mil methods on benchmark mil datasets and it outperforms other methods on a mnistbased mil dataset and two reallife histopathology datasets without sacrificing interpretability | [['multiple', 'instance', 'learning', 'mil', 'is', 'a', 'variation', 'of', 'supervised', 'learning', 'where', 'a', 'single', 'class', 'label', 'is', 'assigned', 'to', 'a', 'bag', 'of', 'instances', 'in', 'this', 'paper', 'we', 'state', 'the', 'mil', 'problem', 'as', 'learning', 'the', 'bernoulli', 'distribution', 'of', 'the', 'bag', 'label', 'where', 'the', 'bag', 'label', 'probability', 'is', 'fully', 'parameterized', 'by', 'neural', 'networks', 'furthermore', 'we', 'propose', 'a', 'neural', 'networkbased', 'permutationinvariant', 'aggregation', 'operator', 'that', 'corresponds', 'to', 'the', 'attention', 'mechanism', 'notably', 'an', 'application', 'of', 'the', 'proposed', 'attentionbased', 'operator', 'provides', 'insight', 'into', 'the', 'contribution', 'of', 'each', 'instance', 'to', 'the', 'bag', 'label', 'we', 'show', 'empirically', 'that', 'our', 'approach', 'achieves', 'comparable', 'performance', 'to', 'the', 'best', 'mil', 'methods', 'on', 'benchmark', 'mil', 'datasets', 'and', 'it', 'outperforms', 'other', 'methods', 'on', 'a', 'mnistbased', 'mil', 'dataset', 'and', 'two', 'reallife', 'histopathology', 'datasets', 'without', 'sacrificing', 'interpretability']] | [0.00607894897106881, -0.014704042632774496, -0.046315979214446724, 0.07844426564310393, -0.14995839050393706, -0.19541596813119702, 0.08664808992762119, 0.44632043877293254, -0.2794581303711642, -0.32194346884527236, -0.004951793226136341, -0.29721258001879225, -0.15262295316504546, 0.15360230672334838, -0.1547921945702685, 0.09416916209065225, 0.17496167662484216, 0.149403336369394, -0.0402254727264974, -0.3114952067401999, 0.3598225493992348, -0.005763072878399418, 0.3540959968116165, 0.04938187527524169, 0.16341579841326706, -0.05047592266909162, 0.04510686544158827, 0.0010591144788979498, -0.02184423558043089, 0.1727786297834871, 0.30721186236901715, 0.2178998860291952, 0.3700571006417952, -0.33437391231221353, -0.27409285733337735, 0.10523515912773442, 0.11359531848021776, 0.1050683781757185, -0.016179674114523965, -0.33082353899424727, 0.09436270479528495, -0.19220745286419372, 0.07915318919699793, -0.11681300020692023, -0.034570127229152384, -0.0524838991032159, -0.350898017152405, 0.04535233519571133, 0.11123739857388244, -0.008049351559597845, -0.09697496485011267, -0.18459119331784363, 0.05137145388602041, 0.11086509818180411, 0.04273659704072201, 0.07003958134196145, 0.11051511310025557, -0.20180505293051998, -0.17447613332081924, 0.3698003054711937, -0.09767945440994068, -0.25092461491619383, 0.1633491147355723, 0.009117290008166605, -0.1795868415169972, 0.05980201748253639, 0.21197964214096385, 0.15657337893508683, -0.14173714724207712, 0.02598417218108777, -0.1277113144924818, 0.20853646344713067, 0.04891786788982793, -0.04249814101148489, 0.10457981676094172, 0.32985116305476253, 0.00949215645370774, 0.17891657727718555, -0.17065413606984248, -0.09651002679828338, -0.22371585565197766, -0.09810044609726029, -0.20878084130923857, -0.04129558167428025, -0.14240132077488363, -0.17934323361620677, 0.43039420491069064, 0.2627813422913886, 0.23090326275738063, 0.14407943770258633, 0.31945394268952126, 0.02186112328622998, 0.11485048250703156, 0.08536111327582847, 0.13015654852444475, -0.005298870898521513, 0.0955486459823995, -0.1789371954077039, 0.11935213945655937, 0.09679664964672209] |
1,802.04713 | Exclusive vector meson photoproduction in fixed - target collisions at
the LHC | The exclusive $\rho$, $\omega$ and $J/\Psi$ photoproduction in fixed - target
collisions at the LHC is investigated. We estimate, for the first time, the
rapidity and transverse momentum distributions of the vector meson
photoproduction in $p He$, $p Ar$, $Pb He$ and $Pb Ar$ fixed - target
collisions at the LHC using the STARlight Monte Carlo and present our results
for the total cross sections. Predictions for the kinematical range probed by
the LHCb detector are also presented. Our results indicate that the
experimental analysis of this process in fixed - target collisions at the LHC
is feasible. Such future analysis will probe the QCD dynamics in a kinematical
range complementary to that studied in the collider mode.
| hep-ph hep-ex nucl-ex nucl-th | the exclusive rho omega and jpsi photoproduction in fixed target collisions at the lhc is investigated we estimate for the first time the rapidity and transverse momentum distributions of the vector meson photoproduction in p he p ar pb he and pb ar fixed target collisions at the lhc using the starlight monte carlo and present our results for the total cross sections predictions for the kinematical range probed by the lhcb detector are also presented our results indicate that the experimental analysis of this process in fixed target collisions at the lhc is feasible such future analysis will probe the qcd dynamics in a kinematical range complementary to that studied in the collider mode | [['the', 'exclusive', 'rho', 'omega', 'and', 'jpsi', 'photoproduction', 'in', 'fixed', 'target', 'collisions', 'at', 'the', 'lhc', 'is', 'investigated', 'we', 'estimate', 'for', 'the', 'first', 'time', 'the', 'rapidity', 'and', 'transverse', 'momentum', 'distributions', 'of', 'the', 'vector', 'meson', 'photoproduction', 'in', 'p', 'he', 'p', 'ar', 'pb', 'he', 'and', 'pb', 'ar', 'fixed', 'target', 'collisions', 'at', 'the', 'lhc', 'using', 'the', 'starlight', 'monte', 'carlo', 'and', 'present', 'our', 'results', 'for', 'the', 'total', 'cross', 'sections', 'predictions', 'for', 'the', 'kinematical', 'range', 'probed', 'by', 'the', 'lhcb', 'detector', 'are', 'also', 'presented', 'our', 'results', 'indicate', 'that', 'the', 'experimental', 'analysis', 'of', 'this', 'process', 'in', 'fixed', 'target', 'collisions', 'at', 'the', 'lhc', 'is', 'feasible', 'such', 'future', 'analysis', 'will', 'probe', 'the', 'qcd', 'dynamics', 'in', 'a', 'kinematical', 'range', 'complementary', 'to', 'that', 'studied', 'in', 'the', 'collider', 'mode']] | [-0.030435608329413378, 0.1882159780063059, -0.1544190370477736, 0.10944189929725089, 0.0034740802001855943, -0.09794960580361278, 0.012812733908345841, 0.3774599262229774, -0.20143905280400878, -0.24404517339704476, -0.05339638098122795, -0.35454258619119294, 0.07189758850372922, 0.13904673350347288, 0.09881340986441658, 0.14455281540589487, 0.14001233162277418, 0.0018090460510195598, -0.03134301696377604, -0.2262685222632211, 0.2844615345415862, 0.10785635531394054, 0.2153546798326399, 0.1418879910169736, 0.048044964590150376, 0.09837143454781692, -0.056671777209671946, -0.03718708493422879, -0.15717114359178358, 0.05065878428311249, 0.33881711720326996, 0.10366165923845509, 0.14307032120211616, -0.35569138222414515, -0.12122177238697592, 0.09899739027833161, 0.15118865785796357, 0.06455891772942698, -0.07618408980576888, -0.3059460006006386, 0.14066191634072153, -0.1937056224145319, -0.12074612424020534, -0.0184817249805707, 0.046998600835871436, -0.01826534180537514, -0.32848931871678516, 0.01120085657305975, -0.05934582574940894, 0.08147247724397028, -0.03842043082999146, -0.2194827765064395, -0.06825548578053713, -0.018691858691770747, 0.05061421117659294, 0.08148693375532394, 0.18617523635292182, -0.13558737968206, -0.15342126037601544, 0.32733476939408673, -0.017690941003029762, -0.14622202711584775, 0.1619869267244054, -0.26961071781571144, -0.14467514767513975, 0.1647264565381667, 0.25278256969506163, 0.13255444774809091, -0.16593663520787072, 0.09956818499359424, -0.030960285789130824, 0.14719772389077623, 0.07959872749431626, 0.03484425959379776, 0.13928236692495968, 0.2284089360224164, -0.01856487921398619, 0.047348934901959225, -0.18240805791447992, -0.08293612477896006, -0.4851143317499562, -0.10694401186445485, -0.09868881637711362, 0.0014746540104565414, -0.06146577454182198, 0.0010747030701326286, 0.3103352273611919, 0.12037298323627076, 0.30106821627477587, -0.015521879415230259, 0.3156796558314691, 0.10509835125514022, 0.00815173864855593, 0.0684661750057864, 0.3187689484299525, 0.1419045930829547, 0.20346219386581493, -0.26667260390746855, 0.0550950199527585, 0.008590191784922195] |
1,802.04714 | An X-ray Imaging Survey of Quasar Jets -- The Complete Survey | We present Chandra X-ray imaging of a flux-limited sample of flat spectrum
radio-emitting quasars with jet-like structure. X-rays are detected from 59% of
56 jets. No counterjets were detected. The core spectra are fitted by power law
spectra with photon index $\Gamma_x$ whose distribution is consistent with a
normal distribution with mean 1.61{+0.04}{-0.05} and dispersion
0.15{+0.04}{-0.03}. We show that the distribution of $\alpha_{rx}$, the
spectral index between the X-ray and radio band jet fluxes, fits a Gaussian
with mean 0.974 $\pm$ 0.012 and dispersion 0.077 $\pm$ 0.008. We test the model
in which kpc-scale X-rays result from inverse Compton scattering of cosmic
microwave background photons off the jet's relativistic electrons (the IC-CMB
model). In the IC-CMB model, a quantity Q computed from observed fluxes and the
apparent size of the emission region depends on redshift as $(1+z)^{3+\alpha}$.
We fit $Q \propto (1+z)^{a}$, finding $a = 0.88 \pm 0.90$ and reject at 99.5%
confidence the hypothesis that the average $\alpha_{rx}$ depends on redshift in
the manner expected in the IC-CMB model. This conclusion is mitigated by lack
of detailed knowledge of the emission region geometry, which requires deeper or
higher resolution X-ray observations. Furthermore, if the IC-CMB model is valid
for X-ray emission from kpc-scale jets, then the jets must decelerate on
average: bulk Lorentz factors should drop from about 15 to 2-3 between pc and
kpc scales. Our results compound the problems that the IC-CMB model has in
explaining the X-ray emission of kpc-scale jets.
| astro-ph.HE | we present chandra xray imaging of a fluxlimited sample of flat spectrum radioemitting quasars with jetlike structure xrays are detected from 59 of 56 jets no counterjets were detected the core spectra are fitted by power law spectra with photon index gamma_x whose distribution is consistent with a normal distribution with mean 161004005 and dispersion 015004003 we show that the distribution of alpha_rx the spectral index between the xray and radio band jet fluxes fits a gaussian with mean 0974 pm 0012 and dispersion 0077 pm 0008 we test the model in which kpcscale xrays result from inverse compton scattering of cosmic microwave background photons off the jets relativistic electrons the iccmb model in the iccmb model a quantity q computed from observed fluxes and the apparent size of the emission region depends on redshift as 1z3alpha we fit q propto 1za finding a 088 pm 090 and reject at 995 confidence the hypothesis that the average alpha_rx depends on redshift in the manner expected in the iccmb model this conclusion is mitigated by lack of detailed knowledge of the emission region geometry which requires deeper or higher resolution xray observations furthermore if the iccmb model is valid for xray emission from kpcscale jets then the jets must decelerate on average bulk lorentz factors should drop from about 15 to 23 between pc and kpc scales our results compound the problems that the iccmb model has in explaining the xray emission of kpcscale jets | [['we', 'present', 'chandra', 'xray', 'imaging', 'of', 'a', 'fluxlimited', 'sample', 'of', 'flat', 'spectrum', 'radioemitting', 'quasars', 'with', 'jetlike', 'structure', 'xrays', 'are', 'detected', 'from', '59', 'of', '56', 'jets', 'no', 'counterjets', 'were', 'detected', 'the', 'core', 'spectra', 'are', 'fitted', 'by', 'power', 'law', 'spectra', 'with', 'photon', 'index', 'gamma_x', 'whose', 'distribution', 'is', 'consistent', 'with', 'a', 'normal', 'distribution', 'with', 'mean', '161004005', 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1,802.04715 | Online Variance Reduction for Stochastic Optimization | Modern stochastic optimization methods often rely on uniform sampling which
is agnostic to the underlying characteristics of the data. This might degrade
the convergence by yielding estimates that suffer from a high variance. A
possible remedy is to employ non-uniform importance sampling techniques, which
take the structure of the dataset into account. In this work, we investigate a
recently proposed setting which poses variance reduction as an online
optimization problem with bandit feedback. We devise a novel and efficient
algorithm for this setting that finds a sequence of importance sampling
distributions competitive with the best fixed distribution in hindsight, the
first result of this kind. While we present our method for sampling datapoints,
it naturally extends to selecting coordinates or even blocks of thereof.
Empirical validations underline the benefits of our method in several settings.
| stat.ML cs.LG | modern stochastic optimization methods often rely on uniform sampling which is agnostic to the underlying characteristics of the data this might degrade the convergence by yielding estimates that suffer from a high variance a possible remedy is to employ nonuniform importance sampling techniques which take the structure of the dataset into account in this work we investigate a recently proposed setting which poses variance reduction as an online optimization problem with bandit feedback we devise a novel and efficient algorithm for this setting that finds a sequence of importance sampling distributions competitive with the best fixed distribution in hindsight the first result of this kind while we present our method for sampling datapoints it naturally extends to selecting coordinates or even blocks of thereof empirical validations underline the benefits of our method in several settings | [['modern', 'stochastic', 'optimization', 'methods', 'often', 'rely', 'on', 'uniform', 'sampling', 'which', 'is', 'agnostic', 'to', 'the', 'underlying', 'characteristics', 'of', 'the', 'data', 'this', 'might', 'degrade', 'the', 'convergence', 'by', 'yielding', 'estimates', 'that', 'suffer', 'from', 'a', 'high', 'variance', 'a', 'possible', 'remedy', 'is', 'to', 'employ', 'nonuniform', 'importance', 'sampling', 'techniques', 'which', 'take', 'the', 'structure', 'of', 'the', 'dataset', 'into', 'account', 'in', 'this', 'work', 'we', 'investigate', 'a', 'recently', 'proposed', 'setting', 'which', 'poses', 'variance', 'reduction', 'as', 'an', 'online', 'optimization', 'problem', 'with', 'bandit', 'feedback', 'we', 'devise', 'a', 'novel', 'and', 'efficient', 'algorithm', 'for', 'this', 'setting', 'that', 'finds', 'a', 'sequence', 'of', 'importance', 'sampling', 'distributions', 'competitive', 'with', 'the', 'best', 'fixed', 'distribution', 'in', 'hindsight', 'the', 'first', 'result', 'of', 'this', 'kind', 'while', 'we', 'present', 'our', 'method', 'for', 'sampling', 'datapoints', 'it', 'naturally', 'extends', 'to', 'selecting', 'coordinates', 'or', 'even', 'blocks', 'of', 'thereof', 'empirical', 'validations', 'underline', 'the', 'benefits', 'of', 'our', 'method', 'in', 'several', 'settings']] | [-0.05198104945873773, 0.003273977509255659, -0.1147820764531692, 0.08543775701653902, -0.10275105105333582, -0.11615590913635161, 0.09872508446806696, 0.4023679165600765, -0.2843072825801317, -0.33380977022461594, 0.11627989229314994, -0.20575814983738516, -0.1664321342224462, 0.19796723065104474, -0.1472249111643544, 0.03950416181943621, 0.10284891328860626, -0.031530048806841175, -0.059130638321706404, -0.2826312489428178, 0.2956116971577069, 0.08523067846480344, 0.3158251465022288, -0.025900040384106062, 0.13298613036167808, 0.028956195125701252, -0.043537569135703426, 0.03088528860481111, -0.10601599945619926, 0.16183351716802766, 0.26513307697174177, 0.1830333226567341, 0.36913597419409566, -0.37631334311984205, -0.23425422767522158, 0.13081064689214583, 0.1582736809218423, 0.13787690256118637, -0.10620895977804644, -0.23887639581053347, 0.07274548889706946, -0.1446639443739076, -0.0695933407396768, -0.12315201466144235, -0.06398931286401219, 0.030057899529048075, -0.3327948026967028, 0.05715989728061551, 0.10230436098451416, 0.0018896960411910657, -0.029162845224211063, -0.14813161913512482, 0.08921059967122144, 0.12265652818087902, 0.0698623915306396, 0.025532996937356615, 0.0984730209344653, -0.10378001070132963, -0.1563655378676399, 0.377767222147021, -0.02851575399162593, -0.2208688188706421, 0.17302714863498866, -0.07057991583779868, -0.18130803664594336, 0.1385449948893101, 0.21919223291592466, 0.1381096293942796, -0.15226873638220476, 0.061727053259447634, -0.04443861663479496, 0.1387122716030313, 0.014569388255821885, 0.01690697322289149, 0.11635207087146464, 0.21696012544756135, 0.12716221269737515, 0.14710369806874682, -0.08204645021577124, -0.11447805781650391, -0.2614125628300287, -0.11854543605198463, -0.2066628566632668, 0.002210621001129901, -0.12440533397225577, -0.18235590885634775, 0.3612723147151647, 0.23466515067309418, 0.23097976110875607, 0.09928897122110895, 0.3665752203652152, 0.08950032448413334, 0.049234277189329816, 0.08125577309272355, 0.19030530411077456, 0.05850594312263032, 0.07971496396974005, -0.1868428040338956, 0.10455390709900746, 0.03466423229417867] |
1,802.04716 | Broadband MIMO Couplers Characterization and Comparison | This paper focuses on MIMO Power Line Communication (PLC) to provide
increased performance. In MIMO PLC, appropriate coupling methods are necessary
in order to enable the effective injection of the signal through the broad band
PLC channel so that high data rates can be achieved. In this paper, we want to
analytically characterize strengths and weaknesses of each coupler design
(topology) from an electrical circuit perspective. We dwell on the description
and analysis of the three main and common configurations used for the MIMO
couplers: star (S), triangle ($\Delta$) and T.
| eess.SP cs.IT math.IT | this paper focuses on mimo power line communication plc to provide increased performance in mimo plc appropriate coupling methods are necessary in order to enable the effective injection of the signal through the broad band plc channel so that high data rates can be achieved in this paper we want to analytically characterize strengths and weaknesses of each coupler design topology from an electrical circuit perspective we dwell on the description and analysis of the three main and common configurations used for the mimo couplers star s triangle delta and t | [['this', 'paper', 'focuses', 'on', 'mimo', 'power', 'line', 'communication', 'plc', 'to', 'provide', 'increased', 'performance', 'in', 'mimo', 'plc', 'appropriate', 'coupling', 'methods', 'are', 'necessary', 'in', 'order', 'to', 'enable', 'the', 'effective', 'injection', 'of', 'the', 'signal', 'through', 'the', 'broad', 'band', 'plc', 'channel', 'so', 'that', 'high', 'data', 'rates', 'can', 'be', 'achieved', 'in', 'this', 'paper', 'we', 'want', 'to', 'analytically', 'characterize', 'strengths', 'and', 'weaknesses', 'of', 'each', 'coupler', 'design', 'topology', 'from', 'an', 'electrical', 'circuit', 'perspective', 'we', 'dwell', 'on', 'the', 'description', 'and', 'analysis', 'of', 'the', 'three', 'main', 'and', 'common', 'configurations', 'used', 'for', 'the', 'mimo', 'couplers', 'star', 's', 'triangle', 'delta', 'and', 't']] | [-0.19977014419458972, 0.023150665543693282, -0.008689458059801996, 0.008102318965369365, -0.08466904182601107, -0.21686630206858062, 0.08233746908129203, 0.4426895784480231, -0.24620615910472615, -0.2967646591222057, 0.07301007657808221, -0.2385282184209695, -0.16704215672235567, 0.20749866370377795, -0.0746079606546478, 0.07046582325678932, 0.03446189043941079, -0.0009092132654936555, -0.0420178660565148, -0.20992631035355422, 0.29019076106967506, 0.07540791893152746, 0.3274988498497795, 0.07438310150793948, 0.03020671705055245, 0.011272035140011991, -0.003204870672261977, -0.032941521401261234, -0.15837851930500818, 0.09415328436643704, 0.3272379671458851, 0.17027108180643682, 0.2157631257835489, -0.4183156044109837, -0.18661438361588087, 0.08818041695445612, 0.18412429914594844, 0.045316747548365656, -0.013360106352982777, -0.24677903943306223, 0.09655192664282007, -0.20284300706871264, -0.045288014583862744, -0.006666664781233111, -0.02532240079593036, 0.04601351499485855, -0.30147829687140965, -0.03814429845462635, 0.051311654054078754, 0.08957664535513946, -0.005301835225219582, -0.09053752189604464, 0.018268120296012897, 0.13791090928678207, -0.008674714474941688, -0.02055511833424424, 0.10078075455992923, -0.09495229249728004, -0.09336243384098122, 0.35828771692915606, -0.04637760634835973, -0.18815772987103888, 0.1667684070531862, -0.10833294694516603, -0.10533700171080264, 0.12142005098656147, 0.2540977437581335, 0.042557482628131305, -0.1821571699353856, 0.04768990032927512, 0.06792230445605058, 0.18215390397389267, 0.03968749134698985, 0.11207889444143562, 0.1704383626053171, 0.1939538932497521, 0.08964937283647256, 0.1388083287594062, -0.12190477921535353, -0.06716931955172466, -0.2672997370853529, -0.11582577492591443, -0.16460597456258896, 0.03236441660524029, -0.10507163617876358, -0.057903858285479164, 0.42892919251552, 0.18979745120280883, 0.14447776845830318, 0.056578188868505616, 0.36861897781900654, 0.13728116022852752, 0.06506652378855826, 0.0703327002331287, 0.27032293675078467, 0.1540783867137609, 0.14087716008505816, -0.23939302716158575, 0.02824832281036364, -0.03129158386510316] |
1,802.04717 | Quadratic magnetooptic spectroscopy setup based on photoelastic light
modulation | In most of the cases the magnetooptic Kerr effect (MOKE) techniques rely
solely on the effects linear in magnetization ($\bm{M}$). Nevertheless, a
higher-order term being proportional to $\bm{M}$$^2$ and called quadratic MOKE
(QMOKE) can additionally contribute to experimental data. Handling and
understanding the underlying origin of QMOKE could be the key to utilize this
effect for investigation of antiferromagnetic materials in the future due to
their vanishing first order MOKE contribution. Also, better understanding of
QMOKE and hence better understanding of magnetooptic (MO) effects in general is
very valuable, as the MO effect is very much employed in research of ferro- and
ferrimagnetic materials. Therefore, we present our QMOKE and longitudinal MOKE
spectroscopy setup with a spectral range of 0.8--5.5\,eV. The setup is based on
light modulation through a photoelastic modulator and detection of
second-harmonic intensity by a lock-in amplifier. To measure the Kerr
ellipticity an achromatic compensator is used within the setup, whereas without
it Kerr rotation is measured. The separation of QMOKE spectra directly from the
measured data is based on measurements with multiple magnetization directions.
So far the QMOKE separation algorithm is developed and tested for but not
limited to cubic (001) oriented samples. The QMOKE spectra yielded by our setup
arise from two quadratic MO parameters $G_s$ and $2G_{44}$, being elements of
quadratic MO tensor $\bm{G}$, which describe perturbation of the permittivity
tensor in the second order in $\bm{M}$.
| physics.ins-det physics.optics | in most of the cases the magnetooptic kerr effect moke techniques rely solely on the effects linear in magnetization bmm nevertheless a higherorder term being proportional to bmm2 and called quadratic moke qmoke can additionally contribute to experimental data handling and understanding the underlying origin of qmoke could be the key to utilize this effect for investigation of antiferromagnetic materials in the future due to their vanishing first order moke contribution also better understanding of qmoke and hence better understanding of magnetooptic mo effects in general is very valuable as the mo effect is very much employed in research of ferro and ferrimagnetic materials therefore we present our qmoke and longitudinal moke spectroscopy setup with a spectral range of 0855ev the setup is based on light modulation through a photoelastic modulator and detection of secondharmonic intensity by a lockin amplifier to measure the kerr ellipticity an achromatic compensator is used within the setup whereas without it kerr rotation is measured the separation of qmoke spectra directly from the measured data is based on measurements with multiple magnetization directions so far the qmoke separation algorithm is developed and tested for but not limited to cubic 001 oriented samples the qmoke spectra yielded by our setup arise from two quadratic mo parameters g_s and 2g_44 being elements of quadratic mo tensor bmg which describe perturbation of the permittivity tensor in the second order in bmm | [['in', 'most', 'of', 'the', 'cases', 'the', 'magnetooptic', 'kerr', 'effect', 'moke', 'techniques', 'rely', 'solely', 'on', 'the', 'effects', 'linear', 'in', 'magnetization', 'bmm', 'nevertheless', 'a', 'higherorder', 'term', 'being', 'proportional', 'to', 'bmm2', 'and', 'called', 'quadratic', 'moke', 'qmoke', 'can', 'additionally', 'contribute', 'to', 'experimental', 'data', 'handling', 'and', 'understanding', 'the', 'underlying', 'origin', 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1,802.04718 | The random walk of cars and their collision probabilities with planets | On February 6th, 2018 SpaceX launched a Tesla Roadster on a Mars-crossing
orbit. We perform N-body simulations to determine the fate of the object over
the next 15 Myr. The orbital evolution is initially dominated by close
encounters with the Earth. While a precise orbit can not be predicted beyond
the next several centuries due to these repeated chaotic scatterings, one can
reliably predict the long-term outcomes by statistically analyzing a large
suite of possible trajectories with slightly perturbed initial conditions.
Repeated gravitational scatterings with Earth lead to a random walk. Collisions
with the Earth, Venus and the Sun represent primary sinks for the Roadster's
orbital evolution. Collisions with Mercury and Mars, or ejections from the
Solar System by Jupiter, are highly unlikely. We calculate a dynamical
half-life of the Tesla of approximately 15 Myr, with some 22%, 12% and 12% of
Roadster orbit realizations impacting the Earth, Venus, and the Sun within one
half-life, respectively. Because the eccentricities and inclinations in our
ensemble increase over time due to mean-motion and secular resonances, the
impact rates with the terrestrial planets decrease beyond a few million years,
whereas the impact rate on the Sun remains roughly constant.
| astro-ph.EP | on february 6th 2018 spacex launched a tesla roadster on a marscrossing orbit we perform nbody simulations to determine the fate of the object over the next 15 myr the orbital evolution is initially dominated by close encounters with the earth while a precise orbit can not be predicted beyond the next several centuries due to these repeated chaotic scatterings one can reliably predict the longterm outcomes by statistically analyzing a large suite of possible trajectories with slightly perturbed initial conditions repeated gravitational scatterings with earth lead to a random walk collisions with the earth venus and the sun represent primary sinks for the roadsters orbital evolution collisions with mercury and mars or ejections from the solar system by jupiter are highly unlikely we calculate a dynamical halflife of the tesla of approximately 15 myr with some 22 12 and 12 of roadster orbit realizations impacting the earth venus and the sun within one halflife respectively because the eccentricities and inclinations in our ensemble increase over time due to meanmotion and secular resonances the impact rates with the terrestrial planets decrease beyond a few million years whereas the impact rate on the sun remains roughly constant | [['on', 'february', '6th', '2018', 'spacex', 'launched', 'a', 'tesla', 'roadster', 'on', 'a', 'marscrossing', 'orbit', 'we', 'perform', 'nbody', 'simulations', 'to', 'determine', 'the', 'fate', 'of', 'the', 'object', 'over', 'the', 'next', '15', 'myr', 'the', 'orbital', 'evolution', 'is', 'initially', 'dominated', 'by', 'close', 'encounters', 'with', 'the', 'earth', 'while', 'a', 'precise', 'orbit', 'can', 'not', 'be', 'predicted', 'beyond', 'the', 'next', 'several', 'centuries', 'due', 'to', 'these', 'repeated', 'chaotic', 'scatterings', 'one', 'can', 'reliably', 'predict', 'the', 'longterm', 'outcomes', 'by', 'statistically', 'analyzing', 'a', 'large', 'suite', 'of', 'possible', 'trajectories', 'with', 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1,802.04719 | The Birthday Problem and Zero-Error List Codes | As an attempt to bridge the gap between the probabilistic world of classical
information theory and the combinatorial world of zero-error information
theory, this paper studies the performance of randomly generated codebooks over
discrete memoryless channels under a zero-error list-decoding constraint. This
study allows the application of tools from one area to the other. Furthermore,
it leads to an information-theoretic formulation of the birthday problem, which
is concerned with the probability that in a given population, a fixed number of
people have the same birthday. Due to the lack of a closed-form expression for
this probability when the distribution of birthdays is not uniform, the
resulting expression is not simple to analyze; in the information-theoretic
formulation, however, the asymptotic behavior of this probability can be
characterized exactly for all distributions.
| cs.IT cs.DM math.CO math.IT | as an attempt to bridge the gap between the probabilistic world of classical information theory and the combinatorial world of zeroerror information theory this paper studies the performance of randomly generated codebooks over discrete memoryless channels under a zeroerror listdecoding constraint this study allows the application of tools from one area to the other furthermore it leads to an informationtheoretic formulation of the birthday problem which is concerned with the probability that in a given population a fixed number of people have the same birthday due to the lack of a closedform expression for this probability when the distribution of birthdays is not uniform the resulting expression is not simple to analyze in the informationtheoretic formulation however the asymptotic behavior of this probability can be characterized exactly for all distributions | [['as', 'an', 'attempt', 'to', 'bridge', 'the', 'gap', 'between', 'the', 'probabilistic', 'world', 'of', 'classical', 'information', 'theory', 'and', 'the', 'combinatorial', 'world', 'of', 'zeroerror', 'information', 'theory', 'this', 'paper', 'studies', 'the', 'performance', 'of', 'randomly', 'generated', 'codebooks', 'over', 'discrete', 'memoryless', 'channels', 'under', 'a', 'zeroerror', 'listdecoding', 'constraint', 'this', 'study', 'allows', 'the', 'application', 'of', 'tools', 'from', 'one', 'area', 'to', 'the', 'other', 'furthermore', 'it', 'leads', 'to', 'an', 'informationtheoretic', 'formulation', 'of', 'the', 'birthday', 'problem', 'which', 'is', 'concerned', 'with', 'the', 'probability', 'that', 'in', 'a', 'given', 'population', 'a', 'fixed', 'number', 'of', 'people', 'have', 'the', 'same', 'birthday', 'due', 'to', 'the', 'lack', 'of', 'a', 'closedform', 'expression', 'for', 'this', 'probability', 'when', 'the', 'distribution', 'of', 'birthdays', 'is', 'not', 'uniform', 'the', 'resulting', 'expression', 'is', 'not', 'simple', 'to', 'analyze', 'in', 'the', 'informationtheoretic', 'formulation', 'however', 'the', 'asymptotic', 'behavior', 'of', 'this', 'probability', 'can', 'be', 'characterized', 'exactly', 'for', 'all', 'distributions']] | [-0.11776641613732164, 0.04489339240759504, -0.13207237845143446, 0.08762178409826726, -0.06621634684180698, -0.14866399429738522, 0.10774839571498047, 0.31024070253591235, -0.2923085613606068, -0.3047454435038022, 0.09141010631216116, -0.2542635082142974, -0.17590177054875172, 0.16879652090943775, -0.157825324505281, 0.05894963928999809, 0.03148300930714378, 0.10835916383120303, -0.04435448894683549, -0.25163433747724273, 0.30043152935373096, 0.08022722747678367, 0.3192385689355433, 0.05892304469464132, 0.08935459721475267, 0.02797247303578143, -0.007037584065423848, 0.011194793686557274, -0.13919758252970776, 0.14192308022622735, 0.2930788330053194, 0.1828301942656533, 0.2950510254765574, -0.39568586740642786, -0.1983500879544478, 0.1379818289182507, 0.12426154770386907, 0.1365081959610017, -0.02058796531144673, -0.2614856188042233, 0.09516648351429746, -0.18324544464524548, -0.09842926718366261, 0.024638027497209034, -0.00481120183204229, -0.0021400755781751987, -0.27141938121058046, 0.03952149587253539, 0.07636210409781108, 0.04819209172318761, -0.02399719315267598, -0.08332853358095656, 0.0445978925563395, 0.18212727949027724, 0.08307785159156013, 0.006557822675229265, 0.058150857031488645, -0.13652987110858353, -0.13030015375608436, 0.3495011682299754, -0.028757810624889458, -0.2350813065440609, 0.15641394636522118, -0.12602182879972343, -0.12496245009824633, 0.12740006261469367, 0.16480340958358003, 0.12460710250127774, -0.1959557935153358, 0.09740809781718641, -0.09912857138909972, 0.15520211865122502, 0.06893823561616814, 0.07832516329004788, 0.1778213819542613, 0.11165032068697306, 0.09427308287435712, 0.17941537237179894, -0.06250025227379341, -0.16593425708393064, -0.27959374983150226, -0.15414582793959059, -0.23009439644564947, 0.0765012173879838, -0.08391537923856118, -0.17390937755744046, 0.35469831617978903, 0.1405810546035914, 0.172072308180997, 0.14222830565592562, 0.30000047751057607, 0.12291862663651745, -0.007894447748549282, 0.09499420050459985, 0.1621585551420979, 0.14629754620389296, 0.06912842548649328, -0.18359628320993998, 0.11704722269294927, 0.04681453618865747] |
1,802.0472 | Impacts of gravitational-wave standard siren observation of the Einstein
Telescope on weighing neutrinos in cosmology | We investigate the impacts of the gravitational-wave (GW) standard siren
observation of the Einstein Telescope (ET) on constraining the total neutrino
mass. We simulate 1000 GW events that would be observed by the ET in its
10-year observation by taking the standard $\Lambda$CDM cosmology as a fiducial
model. We combine the simulated GW data with other cosmological observations
including cosmic microwave background (CMB), baryon acoustic oscillations
(BAO), and type Ia supernovae (SN). We consider three mass hierarchy cases for
the neutrino mass, i.e., normal hierarchy (NH), inverted hierarchy (IH), and
degenerate hierarchy (DH). Using Planck+BAO+SN, we obtain $\sum m_\nu<0.175$ eV
for the NH case, $\sum m_\nu<0.200$ eV for the IH case, and $\sum m_\nu<0.136$
eV for the DH case. After considering the GW data, i.e., using
Planck+BAO+SN+GW, the constraint results become $\sum m_\nu<0.151$ eV for the
NH case, $\sum m_\nu<0.185$ eV for the IH case, and $\sum m_\nu<0.122$ eV for
the DH case. We find that the GW data can help reduce the upper limits of $\sum
m_\nu$ by 13.7%, 7.5%, and 10.3% for the NH, IH, and DH cases, respectively. In
addition, we find that the GW data can also help break the degeneracies between
$\sum m_{\nu}$ and other parameters. We show that the GW data of the ET could
greatly improve the constraint accuracies of cosmological parameters.
| astro-ph.CO gr-qc hep-ph hep-th | we investigate the impacts of the gravitationalwave gw standard siren observation of the einstein telescope et on constraining the total neutrino mass we simulate 1000 gw events that would be observed by the et in its 10year observation by taking the standard lambdacdm cosmology as a fiducial model we combine the simulated gw data with other cosmological observations including cosmic microwave background cmb baryon acoustic oscillations bao and type ia supernovae sn we consider three mass hierarchy cases for the neutrino mass ie normal hierarchy nh inverted hierarchy ih and degenerate hierarchy dh using planckbaosn we obtain sum m_nu0175 ev for the nh case sum m_nu0200 ev for the ih case and sum m_nu0136 ev for the dh case after considering the gw data ie using planckbaosngw the constraint results become sum m_nu0151 ev for the nh case sum m_nu0185 ev for the ih case and sum m_nu0122 ev for the dh case we find that the gw data can help reduce the upper limits of sum m_nu by 137 75 and 103 for the nh ih and dh cases respectively in addition we find that the gw data can also help break the degeneracies between sum m_nu and other parameters we show that the gw data of the et could greatly improve the constraint accuracies of cosmological parameters | [['we', 'investigate', 'the', 'impacts', 'of', 'the', 'gravitationalwave', 'gw', 'standard', 'siren', 'observation', 'of', 'the', 'einstein', 'telescope', 'et', 'on', 'constraining', 'the', 'total', 'neutrino', 'mass', 'we', 'simulate', '1000', 'gw', 'events', 'that', 'would', 'be', 'observed', 'by', 'the', 'et', 'in', 'its', '10year', 'observation', 'by', 'taking', 'the', 'standard', 'lambdacdm', 'cosmology', 'as', 'a', 'fiducial', 'model', 'we', 'combine', 'the', 'simulated', 'gw', 'data', 'with', 'other', 'cosmological', 'observations', 'including', 'cosmic', 'microwave', 'background', 'cmb', 'baryon', 'acoustic', 'oscillations', 'bao', 'and', 'type', 'ia', 'supernovae', 'sn', 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1,802.04721 | Predict and Constrain: Modeling Cardinality in Deep Structured
Prediction | Many machine learning problems require the prediction of multi-dimensional
labels. Such structured prediction models can benefit from modeling
dependencies between labels. Recently, several deep learning approaches to
structured prediction have been proposed. Here we focus on capturing
cardinality constraints in such models. Namely, constraining the number of
non-zero labels that the model outputs. Such constraints have proven very
useful in previous structured prediction approaches, but it is a challenge to
introduce them into a deep learning framework. Here we show how to do this via
a novel deep architecture. Our approach outperforms strong baselines, achieving
state-of-the-art results on multi-label classification benchmarks.
| cs.LG | many machine learning problems require the prediction of multidimensional labels such structured prediction models can benefit from modeling dependencies between labels recently several deep learning approaches to structured prediction have been proposed here we focus on capturing cardinality constraints in such models namely constraining the number of nonzero labels that the model outputs such constraints have proven very useful in previous structured prediction approaches but it is a challenge to introduce them into a deep learning framework here we show how to do this via a novel deep architecture our approach outperforms strong baselines achieving stateoftheart results on multilabel classification benchmarks | [['many', 'machine', 'learning', 'problems', 'require', 'the', 'prediction', 'of', 'multidimensional', 'labels', 'such', 'structured', 'prediction', 'models', 'can', 'benefit', 'from', 'modeling', 'dependencies', 'between', 'labels', 'recently', 'several', 'deep', 'learning', 'approaches', 'to', 'structured', 'prediction', 'have', 'been', 'proposed', 'here', 'we', 'focus', 'on', 'capturing', 'cardinality', 'constraints', 'in', 'such', 'models', 'namely', 'constraining', 'the', 'number', 'of', 'nonzero', 'labels', 'that', 'the', 'model', 'outputs', 'such', 'constraints', 'have', 'proven', 'very', 'useful', 'in', 'previous', 'structured', 'prediction', 'approaches', 'but', 'it', 'is', 'a', 'challenge', 'to', 'introduce', 'them', 'into', 'a', 'deep', 'learning', 'framework', 'here', 'we', 'show', 'how', 'to', 'do', 'this', 'via', 'a', 'novel', 'deep', 'architecture', 'our', 'approach', 'outperforms', 'strong', 'baselines', 'achieving', 'stateoftheart', 'results', 'on', 'multilabel', 'classification', 'benchmarks']] | [-0.012355997921013743, -0.01228791436567755, -0.07347508902560071, 0.08572481312134331, -0.14747968233089045, -0.2075016048072957, 0.020889202208894463, 0.4698190172663936, -0.28572300887934055, -0.3592153621358004, 0.044143955663565805, -0.26933356785759477, -0.2062359575993649, 0.2220744262945073, -0.11832424068113438, 0.12081455098151571, 0.17895615834853437, 0.029921799639959147, -0.09884460329995629, -0.2908357012472899, 0.2882339176191662, 0.008155703287869047, 0.3670799454747893, 0.06937115248016054, 0.13226700110342537, -0.04409286772629412, -0.03264044324687618, 0.04310433057570221, -0.0659659711104768, 0.19923663391999089, 0.3416157627123377, 0.23934897935100932, 0.37443178680052264, -0.4178641669753026, -0.3253643629648308, 0.1165256928099264, 0.12375925831660388, 0.13358236627773115, -0.04834118659958465, -0.30830265561321585, 0.06682780721814989, -0.18973369582436314, 0.0732146730383692, -0.20168749582354384, -0.04454108866595543, -0.020066464882307122, -0.30425730562944076, 0.0061230864329724615, 0.10000907755018866, 0.018306113097189675, -0.05597439970092682, -0.16577602355362903, 0.08222535273087865, 0.15388423653485456, 0.059446359745982925, 0.03581395603734815, 0.06744274003419674, -0.23117836892816912, -0.18374634668747386, 0.3439726142009886, -0.08041907331472871, -0.23336158506572247, 0.2550343850922614, 0.011112941263040693, -0.2489786217828272, 0.05311025934533613, 0.2749132287808427, 0.1516651711841621, -0.13342860284823888, 0.04178592071886921, -0.10032393623807348, 0.16647498151932907, -0.016471702172098184, 0.011638146672987997, 0.21851324910611505, 0.31169649662615934, -0.00029353590712117233, 0.08183166810938407, -0.13154057784283782, -0.07765107378501925, -0.17541080848821025, -0.024034130875722017, -0.16413957318567698, -0.022211375720061287, -0.0845125710665442, -0.1556559623873765, 0.37150166306810656, 0.27846729580663365, 0.2279467088917252, 0.13047966506737885, 0.37091970574664007, 0.03303019714076072, 0.14858045549095045, 0.10236610866400718, 0.2236286219495284, 0.036826072454378744, 0.06411740652718904, -0.1386307832014996, 0.11164030185577893, 0.04673548806735342] |
1,802.04722 | Long-lasting injection of solar energetic electrons into the heliosphere | The main sources of solar energetic particle (SEP) events are solar flares
and shocks driven by coronal mass ejections (CMEs). While it is generally
accepted that energetic protons can be accelerated by shocks, whether or not
these shocks can also efficiently accelerate solar energetic electrons is still
debated. In this study we present observations of the extremely widespread SEP
event of 26 Dec 2013. To the knowledge of the authors, this is the widest
longitudinal SEP distribution ever observed together with unusually
long-lasting energetic electron anisotropies at all observer positions. Further
striking features of the event are long-lasting SEP intensity increases, two
distinct SEP components with the second component mainly consisting of
high-energy particles, a complex associated coronal activity including a
pronounced signature of a shock in radio type-II observations, and the
interaction of two CMEs early in the event. The observations require a
prolonged injection scenario not only for protons but also for electrons. We
therefore analyze the data comprehensively to characterize the possible role of
the shock for the electron event. Remote-sensing observations of the complex
solar activity are combined with in-situ measurements of the particle event. We
also apply a Graduated Cylindrical Shell (GCS) model to the coronagraph
observations of the two associated CMEs to analyze their interaction. We find
that the shock alone is likely not responsible for this extremely wide SEP
event. Therefore we propose a scenario of trapped energetic particles inside
the CME-CME interaction region which undergo further acceleration due to the
shock propagating through this region, stochastic acceleration, or ongoing
reconnection processes inside the interaction region. The origin of the second
component of the SEP event is likely caused by a sudden opening of the particle
trap.
| physics.space-ph | the main sources of solar energetic particle sep events are solar flares and shocks driven by coronal mass ejections cmes while it is generally accepted that energetic protons can be accelerated by shocks whether or not these shocks can also efficiently accelerate solar energetic electrons is still debated in this study we present observations of the extremely widespread sep event of 26 dec 2013 to the knowledge of the authors this is the widest longitudinal sep distribution ever observed together with unusually longlasting energetic electron anisotropies at all observer positions further striking features of the event are longlasting sep intensity increases two distinct sep components with the second component mainly consisting of highenergy particles a complex associated coronal activity including a pronounced signature of a shock in radio typeii observations and the interaction of two cmes early in the event the observations require a prolonged injection scenario not only for protons but also for electrons we therefore analyze the data comprehensively to characterize the possible role of the shock for the electron event remotesensing observations of the complex solar activity are combined with insitu measurements of the particle event we also apply a graduated cylindrical shell gcs model to the coronagraph observations of the two associated cmes to analyze their interaction we find that the shock alone is likely not responsible for this extremely wide sep event therefore we propose a scenario of trapped energetic particles inside the cmecme interaction region which undergo further acceleration due to the shock propagating through this region stochastic acceleration or ongoing reconnection processes inside the interaction region the origin of the second component of the sep event is likely caused by a sudden opening of the particle trap | [['the', 'main', 'sources', 'of', 'solar', 'energetic', 'particle', 'sep', 'events', 'are', 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1,802.04723 | Joint Demosaicing and Denoising with Perceptual Optimization on a
Generative Adversarial Network | Image demosaicing - one of the most important early stages in digital camera
pipelines - addressed the problem of reconstructing a full-resolution image
from so-called color-filter-arrays. Despite tremendous progress made in the
pase decade, a fundamental issue that remains to be addressed is how to assure
the visual quality of reconstructed images especially in the presence of noise
corruption. Inspired by recent advances in generative adversarial networks
(GAN), we present a novel deep learning approach toward joint demosaicing and
denoising (JDD) with perceptual optimization in order to ensure the visual
quality of reconstructed images. The key contributions of this work include: 1)
we have developed a GAN-based approach toward image demosacing in which a
discriminator network with both perceptual and adversarial loss functions are
used for quality assurance; 2) we propose to optimize the perceptual quality of
reconstructed images by the proposed GAN in an end-to-end manner. Such
end-to-end optimization of GAN is particularly effective for jointly exploiting
the gain brought by each modular component (e.g., residue learning in the
generative network and perceptual loss in the discriminator network). Our
extensive experimental results have shown convincingly improved performance
over existing state-of-the-art methods in terms of both subjective and
objective quality metrics with a comparable computational cost.
| cs.CV | image demosaicing one of the most important early stages in digital camera pipelines addressed the problem of reconstructing a fullresolution image from socalled colorfilterarrays despite tremendous progress made in the pase decade a fundamental issue that remains to be addressed is how to assure the visual quality of reconstructed images especially in the presence of noise corruption inspired by recent advances in generative adversarial networks gan we present a novel deep learning approach toward joint demosaicing and denoising jdd with perceptual optimization in order to ensure the visual quality of reconstructed images the key contributions of this work include 1 we have developed a ganbased approach toward image demosacing in which a discriminator network with both perceptual and adversarial loss functions are used for quality assurance 2 we propose to optimize the perceptual quality of reconstructed images by the proposed gan in an endtoend manner such endtoend optimization of gan is particularly effective for jointly exploiting the gain brought by each modular component eg residue learning in the generative network and perceptual loss in the discriminator network our extensive experimental results have shown convincingly improved performance over existing stateoftheart methods in terms of both subjective and objective quality metrics with a comparable computational cost | [['image', 'demosaicing', 'one', 'of', 'the', 'most', 'important', 'early', 'stages', 'in', 'digital', 'camera', 'pipelines', 'addressed', 'the', 'problem', 'of', 'reconstructing', 'a', 'fullresolution', 'image', 'from', 'socalled', 'colorfilterarrays', 'despite', 'tremendous', 'progress', 'made', 'in', 'the', 'pase', 'decade', 'a', 'fundamental', 'issue', 'that', 'remains', 'to', 'be', 'addressed', 'is', 'how', 'to', 'assure', 'the', 'visual', 'quality', 'of', 'reconstructed', 'images', 'especially', 'in', 'the', 'presence', 'of', 'noise', 'corruption', 'inspired', 'by', 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1,802.04724 | Achieving the Age-Energy Tradeoff with a Finite-Battery Energy
Harvesting Source | We study the problem of minimizing the time-average expected Age of
Information for status updates sent by an energy-harvesting source with a
finite-capacity battery. In prior literature, optimal policies were observed to
have a threshold structure under Poisson energy arrivals, for the special case
of a unit-capacity battery. In this paper, we generalize this result to any
(integer) battery capacity, and explicitly characterize the threshold
structure. We obtain tools to derive the optimal policy for arbitrary energy
buffer (i.e. battery) size. One of these results is the unexpected equivalence
of the minimum average AoI and the optimal threshold for the highest energy
state.
| cs.IT math.IT | we study the problem of minimizing the timeaverage expected age of information for status updates sent by an energyharvesting source with a finitecapacity battery in prior literature optimal policies were observed to have a threshold structure under poisson energy arrivals for the special case of a unitcapacity battery in this paper we generalize this result to any integer battery capacity and explicitly characterize the threshold structure we obtain tools to derive the optimal policy for arbitrary energy buffer ie battery size one of these results is the unexpected equivalence of the minimum average aoi and the optimal threshold for the highest energy state | [['we', 'study', 'the', 'problem', 'of', 'minimizing', 'the', 'timeaverage', 'expected', 'age', 'of', 'information', 'for', 'status', 'updates', 'sent', 'by', 'an', 'energyharvesting', 'source', 'with', 'a', 'finitecapacity', 'battery', 'in', 'prior', 'literature', 'optimal', 'policies', 'were', 'observed', 'to', 'have', 'a', 'threshold', 'structure', 'under', 'poisson', 'energy', 'arrivals', 'for', 'the', 'special', 'case', 'of', 'a', 'unitcapacity', 'battery', 'in', 'this', 'paper', 'we', 'generalize', 'this', 'result', 'to', 'any', 'integer', 'battery', 'capacity', 'and', 'explicitly', 'characterize', 'the', 'threshold', 'structure', 'we', 'obtain', 'tools', 'to', 'derive', 'the', 'optimal', 'policy', 'for', 'arbitrary', 'energy', 'buffer', 'ie', 'battery', 'size', 'one', 'of', 'these', 'results', 'is', 'the', 'unexpected', 'equivalence', 'of', 'the', 'minimum', 'average', 'aoi', 'and', 'the', 'optimal', 'threshold', 'for', 'the', 'highest', 'energy', 'state']] | [-0.171753913109887, 0.07610875144004664, -0.01641259963163039, 0.06358257117251712, -0.06583225724368709, -0.15387018667353156, 0.1676928514843011, 0.3847897889395471, -0.2924062744375246, -0.33672860696984147, 0.12542455003470254, -0.2593433453172481, -0.07909770893810082, 0.14471081625534565, -0.1250267815719657, 0.08720134126273373, 0.05451380166234177, 0.13603961444233806, -0.040475574398901565, -0.23869372413773993, 0.31305610329518546, 0.15177263466424298, 0.31497904212548605, 0.06088756772212558, 0.10980908062763385, 0.013893912319157237, 0.028296835964672196, -0.00929902939270567, -0.1962007613029236, 0.08360036206431687, 0.29931969508456374, 0.12490722384231473, 0.2690758614900332, -0.413085821672406, -0.23036080044558616, 0.1478356706448695, 0.06253043468363438, 0.0867729517043505, -0.0448197420252469, -0.18818328688734942, 0.14780915843648385, -0.21789138580591066, -0.0882342752313368, 0.041467410344992156, -0.0034646607067399813, 0.06436933817803064, -0.31200883282208364, 0.035597096265121864, 0.03978195468424955, -0.00014964817091822624, -0.1509654785628494, -0.1475024278438424, 0.019524321059506494, 0.18035396581597046, 0.04104856327311723, -0.029735962257865203, 0.12243622138607994, -0.09840737070065463, -0.1301087438387715, 0.29794828977110316, -0.02276607526656132, -0.1846505572015727, 0.08125771694699103, -0.07875446894305568, -0.10969936067817136, 0.13510533066543307, 0.20748008211658706, 0.10076693814356351, -0.19258437856005559, 0.05666161789581433, -0.033991587945052144, 0.14659422164945637, 0.07478773757562186, 0.0582539595808219, 0.13133067139017496, 0.16257138922358455, 0.16213330865421702, 0.18948178567236415, -0.0629902586151023, -0.10349168135667021, -0.2842681043989305, -0.1637456801638586, -0.18564492967251, 0.08291012822148544, -0.09139307496082535, -0.10639578136570245, 0.3633021394387611, 0.13980589552954273, 0.1666010054262373, 0.11351512888563996, 0.3099473507948292, 0.15726987372188675, -0.0031975942700016267, 0.16434453170547642, 0.1853421052140229, 0.08181045356080008, 0.11814943031923285, -0.2405262607583794, 0.13515431057318178, 0.02440515865834014] |
1,802.04725 | Superposition-Assisted Stochastic Optimization for Hawkes Processes | We consider the learning of multi-agent Hawkes processes, a model containing
multiple Hawkes processes with shared endogenous impact functions and different
exogenous intensities. In the framework of stochastic maximum likelihood
estimation, we explore the associated risk bound. Further, we consider the
superposition of Hawkes processes within the model, and demonstrate that under
certain conditions such an operation is beneficial for tightening the risk
bound. Accordingly, we propose a stochastic optimization algorithm assisted
with a diversity-driven superposition strategy, achieving better learning
results with improved convergence properties. The effectiveness of the proposed
method is verified on synthetic data, and its potential to solve the cold-start
problem of sequential recommendation systems is demonstrated on real-world
data.
| stat.ML | we consider the learning of multiagent hawkes processes a model containing multiple hawkes processes with shared endogenous impact functions and different exogenous intensities in the framework of stochastic maximum likelihood estimation we explore the associated risk bound further we consider the superposition of hawkes processes within the model and demonstrate that under certain conditions such an operation is beneficial for tightening the risk bound accordingly we propose a stochastic optimization algorithm assisted with a diversitydriven superposition strategy achieving better learning results with improved convergence properties the effectiveness of the proposed method is verified on synthetic data and its potential to solve the coldstart problem of sequential recommendation systems is demonstrated on realworld data | [['we', 'consider', 'the', 'learning', 'of', 'multiagent', 'hawkes', 'processes', 'a', 'model', 'containing', 'multiple', 'hawkes', 'processes', 'with', 'shared', 'endogenous', 'impact', 'functions', 'and', 'different', 'exogenous', 'intensities', 'in', 'the', 'framework', 'of', 'stochastic', 'maximum', 'likelihood', 'estimation', 'we', 'explore', 'the', 'associated', 'risk', 'bound', 'further', 'we', 'consider', 'the', 'superposition', 'of', 'hawkes', 'processes', 'within', 'the', 'model', 'and', 'demonstrate', 'that', 'under', 'certain', 'conditions', 'such', 'an', 'operation', 'is', 'beneficial', 'for', 'tightening', 'the', 'risk', 'bound', 'accordingly', 'we', 'propose', 'a', 'stochastic', 'optimization', 'algorithm', 'assisted', 'with', 'a', 'diversitydriven', 'superposition', 'strategy', 'achieving', 'better', 'learning', 'results', 'with', 'improved', 'convergence', 'properties', 'the', 'effectiveness', 'of', 'the', 'proposed', 'method', 'is', 'verified', 'on', 'synthetic', 'data', 'and', 'its', 'potential', 'to', 'solve', 'the', 'coldstart', 'problem', 'of', 'sequential', 'recommendation', 'systems', 'is', 'demonstrated', 'on', 'realworld', 'data']] | [-0.09460184231464364, 0.01386090303450283, -0.07133585017842067, 0.09653825850011699, -0.04840658796359826, -0.16703156773330627, 0.08407867535849851, 0.4140889990778096, -0.3045691831647295, -0.29338104721257646, 0.09426069248156556, -0.23946591573808573, -0.1972811994826899, 0.20255486121137453, -0.09161854441178781, 0.12553536093256032, 0.08585637058048978, 0.01567447461204323, -0.009668115833444537, -0.27025114261520516, 0.3150234923458996, 0.07960518372072055, 0.343360624080359, 0.031818789328485565, 0.15404749746780547, 0.037052914595017125, -0.01852704009321411, -0.02909071890192222, -0.08979655694675197, 0.13618940090558665, 0.24407415952434583, 0.23102235529917928, 0.36496802801843237, -0.40508405720058877, -0.24783442337972533, 0.13827363680945132, 0.09412672614886845, 0.06972504519255815, -0.055143811915119266, -0.31410266196899184, 0.03283362208857341, -0.17046889210062152, -0.06750159386743222, -0.09898013639700624, -0.06854079373230317, 0.039153457577010636, -0.3766246952973636, 0.05770863048666346, 0.05728864241535652, 0.026993660322200935, -0.08357003970026343, -0.12251573188675452, 0.026421816891834007, 0.08040517600319158, 0.056921910072174085, -0.062345985689950464, 0.14077293883290026, -0.11445093523109315, -0.21662665560297603, 0.33607224525834345, -0.0869867118710108, -0.2148826886710208, 0.18449700297375696, -0.06471474745220183, -0.1643915571915233, 0.11402868920838279, 0.29186543427506645, 0.1345210770491214, -0.2032474048551073, 0.044026910074454924, -0.036881972854314125, 0.12455811877364079, 0.010532483831460101, -0.0005377322894858971, 0.13557526787157806, 0.2779184319202549, 0.08559905373351237, 0.1837669272837023, -0.09132519458777032, -0.15589319481061095, -0.2339165170261826, -0.08829358421715257, -0.15160755554741595, -0.019608525670570347, -0.15597777184785602, -0.13104837126535387, 0.3564652744721852, 0.21501885300947238, 0.17495461976139154, 0.10436946130357683, 0.3190476374980886, 0.1414601898348305, -0.0007122749873783142, 0.08841890800513524, 0.16530853371028748, 0.07898290395176252, 0.06889856950816724, -0.23984072370190931, 0.14846022181270593, 0.014036446898367949] |
1,802.04726 | A comparison theorem for subharmonic functions | In this article, we prove an extension of the mean value theorem and a
comparison theorem for subharmonic functions. These theorems are used to answer
the question whether we can conclude that two subharmonic functions which agree
almost everywhere on a surface with respect to the surface measure must
coincide everywhere on that surface. We prove that this question has a positive
answer in the case of hypersurfaces, and we also provide a counterexample in
the case of surfaces of higher co-dimension. We also apply these results to
Ahlfors-David sets and we prove other versions of the main results in terms of
measure densities.
| math.CV | in this article we prove an extension of the mean value theorem and a comparison theorem for subharmonic functions these theorems are used to answer the question whether we can conclude that two subharmonic functions which agree almost everywhere on a surface with respect to the surface measure must coincide everywhere on that surface we prove that this question has a positive answer in the case of hypersurfaces and we also provide a counterexample in the case of surfaces of higher codimension we also apply these results to ahlforsdavid sets and we prove other versions of the main results in terms of measure densities | [['in', 'this', 'article', 'we', 'prove', 'an', 'extension', 'of', 'the', 'mean', 'value', 'theorem', 'and', 'a', 'comparison', 'theorem', 'for', 'subharmonic', 'functions', 'these', 'theorems', 'are', 'used', 'to', 'answer', 'the', 'question', 'whether', 'we', 'can', 'conclude', 'that', 'two', 'subharmonic', 'functions', 'which', 'agree', 'almost', 'everywhere', 'on', 'a', 'surface', 'with', 'respect', 'to', 'the', 'surface', 'measure', 'must', 'coincide', 'everywhere', 'on', 'that', 'surface', 'we', 'prove', 'that', 'this', 'question', 'has', 'a', 'positive', 'answer', 'in', 'the', 'case', 'of', 'hypersurfaces', 'and', 'we', 'also', 'provide', 'a', 'counterexample', 'in', 'the', 'case', 'of', 'surfaces', 'of', 'higher', 'codimension', 'we', 'also', 'apply', 'these', 'results', 'to', 'ahlforsdavid', 'sets', 'and', 'we', 'prove', 'other', 'versions', 'of', 'the', 'main', 'results', 'in', 'terms', 'of', 'measure', 'densities']] | [-0.11706713481824128, 0.05326506286501871, -0.10406724264836296, 0.12050172096216837, -0.01757556560019461, -0.10071304112744446, 0.022756275488063693, 0.3789102321204085, -0.22699854121758387, -0.22100904718088435, 0.1106806668640974, -0.29595738336837923, -0.16960590816317841, 0.25980146972534174, -0.1430133029330486, 0.005518764929057887, 0.05127610985297137, 0.061281952673687974, -0.0718279945901416, -0.29181645705942244, 0.40817263915848273, -0.05020029472115521, 0.22578328084917024, 0.1623288541662847, 0.07257755319229685, -0.040006288906554874, -0.004508044512476772, 0.04400180143196709, -0.21586437702794334, 0.13904286201040333, 0.24282592746357506, 0.10779775624709706, 0.2614468911017936, -0.3805132614529262, -0.15954841737850353, 0.16116559090397248, 0.10083944084684705, 0.07381315617889274, -0.019908857428862784, -0.21267380965246746, 0.15091941596675987, -0.0967273879012702, -0.20678587146372032, -0.07813102553168741, -0.004358985192643909, 0.028709500311658934, -0.2410337066080851, 0.05765189810951527, 0.1609079300497587, 0.06147483290316394, -0.12786284341620138, -0.08744642828018047, -0.01957465620048774, 0.09751019094479628, 0.05295118883929298, 0.06506128526234534, 0.05506574353001689, -0.057146367155767694, -0.13665345089527994, 0.30467650284005615, -0.12256172091628496, -0.2623459593607829, 0.20547060496532, -0.1987240441555444, -0.15676048502343923, 0.04134378685221936, 0.1616727209273869, 0.1481999856992983, -0.08162335650614785, 0.09045734227346167, -0.11946706204281117, 0.13995365898769635, 0.13823439887850186, 0.007219819992315024, 0.16548698418102084, 0.06067171543299292, 0.1551591776436768, 0.14791530205548042, -0.007183266163337976, -0.04148673470794725, -0.34768117065183246, -0.20164601254957512, -0.15224843953127185, 0.07695670968958158, -0.0526328863249476, -0.18597827206115022, 0.3883535561438363, 0.15701090816247204, 0.20480275691415256, 0.13099397408278982, 0.23387674919257945, 0.12621456754501337, -0.003776957717491314, 0.07539432195391363, 0.19829064230613697, 0.15677387666745254, 0.03768464431274109, -0.10437807843286115, 0.019605399539264348, 0.11161151319706383] |
1,802.04727 | Extragalactic maser surveys | Since the IAU (maser-)Symposium 287 in Stellenbosch/South Africa (Jan. 2012),
great progress has been achieved in studying extragalactic maser sources.
Sensitivity has reached a level allowing for dedicated maser surveys of
extragalactic objects. These included, during the last years, water vapor
(H2O), methanol (CH3OH), and formaldehyde (H2CO), while surveys related to
hydroxyl (OH), cyanoacetylene (HC3N) and ammonia (NH3) may soon become (again)
relevant. Overall, with the upgraded Very Large Array (VLA), the Atacama Large
Millimeter/submillimeter Array (ALMA), FAST (Five hundred meter Aperture
Synthesis Telescope) and the low frequency arrays APERTIF (APERture Tile in
Focus), ASKAP (Australian Square Kilometer Array Pathfinder) and MeerKAT (Meer
Karoo Array Telescope), extragalactic maser studies are expected to flourish
during the upcoming years. The following article provides a brief sketch of
past achievements, ongoing projects and future perspectives.
| astro-ph.GA | since the iau masersymposium 287 in stellenboschsouth africa jan 2012 great progress has been achieved in studying extragalactic maser sources sensitivity has reached a level allowing for dedicated maser surveys of extragalactic objects these included during the last years water vapor h2o methanol ch3oh and formaldehyde h2co while surveys related to hydroxyl oh cyanoacetylene hc3n and ammonia nh3 may soon become again relevant overall with the upgraded very large array vla the atacama large millimetersubmillimeter array alma fast five hundred meter aperture synthesis telescope and the low frequency arrays apertif aperture tile in focus askap australian square kilometer array pathfinder and meerkat meer karoo array telescope extragalactic maser studies are expected to flourish during the upcoming years the following article provides a brief sketch of past achievements ongoing projects and future perspectives | [['since', 'the', 'iau', 'masersymposium', '287', 'in', 'stellenboschsouth', 'africa', 'jan', '2012', 'great', 'progress', 'has', 'been', 'achieved', 'in', 'studying', 'extragalactic', 'maser', 'sources', 'sensitivity', 'has', 'reached', 'a', 'level', 'allowing', 'for', 'dedicated', 'maser', 'surveys', 'of', 'extragalactic', 'objects', 'these', 'included', 'during', 'the', 'last', 'years', 'water', 'vapor', 'h2o', 'methanol', 'ch3oh', 'and', 'formaldehyde', 'h2co', 'while', 'surveys', 'related', 'to', 'hydroxyl', 'oh', 'cyanoacetylene', 'hc3n', 'and', 'ammonia', 'nh3', 'may', 'soon', 'become', 'again', 'relevant', 'overall', 'with', 'the', 'upgraded', 'very', 'large', 'array', 'vla', 'the', 'atacama', 'large', 'millimetersubmillimeter', 'array', 'alma', 'fast', 'five', 'hundred', 'meter', 'aperture', 'synthesis', 'telescope', 'and', 'the', 'low', 'frequency', 'arrays', 'apertif', 'aperture', 'tile', 'in', 'focus', 'askap', 'australian', 'square', 'kilometer', 'array', 'pathfinder', 'and', 'meerkat', 'meer', 'karoo', 'array', 'telescope', 'extragalactic', 'maser', 'studies', 'are', 'expected', 'to', 'flourish', 'during', 'the', 'upcoming', 'years', 'the', 'following', 'article', 'provides', 'a', 'brief', 'sketch', 'of', 'past', 'achievements', 'ongoing', 'projects', 'and', 'future', 'perspectives']] | [-0.07500640307821763, 0.11464507255900222, 0.07080605143788629, 0.010225414162358412, -0.11652007906232029, -0.11403353239158885, 0.007969529550665846, 0.48638315945863725, -0.09794848550451346, -0.3342883171979338, 0.22180256963026926, -0.2828239447580507, -0.05576125729399232, 0.21065519340336322, 0.009818312454109008, -0.015959129851902478, 0.18666610621775573, -0.27398459591831154, 0.028409436074658654, -0.28741968074956764, 0.06990386161666651, 0.28009915192110035, 0.20942390752024947, 0.03490890154722505, 0.14266486727417663, -0.201356152844472, -0.17986891307653144, -0.06516960563335138, -0.12187253173704868, 0.07685781641540905, 0.4354228690194969, 0.16984724311623722, 0.239399012676082, -0.4218814555412302, -0.16805936795157883, 0.08456154255590473, 0.09003270853919765, 0.0718568678569192, 0.009042954795922225, -0.39812460095406726, -0.06510948298382573, -0.20653079446861877, -0.25885510847259025, 0.10219146857718722, 0.08995819498354998, 0.0783132678108254, -0.13128165878677883, -0.016679720448043483, -0.09488809019542084, 0.18722419892079556, -0.06720209483128901, -0.28431124707713024, 0.012685724904832359, 0.10729629813084522, -0.07191039881167503, 0.138327061497078, 0.16864266062441927, -0.11573499130228392, -0.06205360895882432, 0.36042657064524697, -0.13067556186220966, 0.09609617199211452, 0.19442513631388114, -0.27453740552211037, -0.34893356895862293, 0.20827168161407686, 0.18618755358486222, 0.052035010595304465, -0.1666402381979144, 0.034510954662647024, -0.01987727379712921, 0.2681677481302848, 0.15710413209520854, 0.07618452839266796, 0.4016264003182117, 0.18897005154822882, 0.14432304590725556, 0.17587282171186347, -0.33122991668776824, -0.036628228096434706, -0.18146536527022433, -0.09689198989206209, -0.13587268575882683, 0.11511471335095569, -0.01694722223720209, -0.012415171632668576, 0.31979943865220634, 0.07612042722131054, 0.04287338053780751, -0.012610911724802392, 0.30596814325365884, -0.10275345412262071, 0.134633669444324, -0.02613580568666713, 0.31571897506498947, 0.13079596726473447, 0.23679570894007787, -0.14486616022455004, 0.04347984519285651, -0.015279063508989147] |
1,802.04728 | Numerical estimate of minimal active-sterile neutrino mixing for sterile
neutrinos at GeV scale | Seesaw mechanism constrains from below mixing between active and sterile
neutrinos for fixed sterile neutrino masses. Signal events associated with
sterile neutrino decays inside a detector at fixed target experiment are
suppressed by the mixing angle to the power of four. Therefore sensitivity of
experiments such as SHiP and DUNE should take into account minimal possible
values of the mixing angles. We extend the previous study of this subject
arXiv:1312.2887 to a more general case of non-zero CP-violating phases in the
neutrino sector. Namely, we provide numerical estimate of minimal value of
mixing angles between active neutrinos and two sterile neutrinos with the third
sterile neutrino playing no noticeable role in the mixing. Thus we obtain a
sensitivity needed to fully explore the seesaw type I mechanism for sterile
neutrinos with masses below 2 GeV, and one undetectable sterile neutrino that
is relevant for the fixed-target experiments. Remarkably, we observe a strong
dependence of this result on the lightest active neutrino mass and the neutrino
mass hierarchy, not only on the values of CP-violating phases themselves. All
these effects sum up to push the limit of experimental confirmation of
sterile-active neutrino mixing by several orders of magnitude below the results
of arXiv:1312.2887 from $10^{-10}$ - $10^{-11}$ down to $10^{-12}$ and even to
$10^{-20}$ in parts of parameter space; nonzero CP-violating phases are
responsible for that.
| hep-ph | seesaw mechanism constrains from below mixing between active and sterile neutrinos for fixed sterile neutrino masses signal events associated with sterile neutrino decays inside a detector at fixed target experiment are suppressed by the mixing angle to the power of four therefore sensitivity of experiments such as ship and dune should take into account minimal possible values of the mixing angles we extend the previous study of this subject arxiv13122887 to a more general case of nonzero cpviolating phases in the neutrino sector namely we provide numerical estimate of minimal value of mixing angles between active neutrinos and two sterile neutrinos with the third sterile neutrino playing no noticeable role in the mixing thus we obtain a sensitivity needed to fully explore the seesaw type i mechanism for sterile neutrinos with masses below 2 gev and one undetectable sterile neutrino that is relevant for the fixedtarget experiments remarkably we observe a strong dependence of this result on the lightest active neutrino mass and the neutrino mass hierarchy not only on the values of cpviolating phases themselves all these effects sum up to push the limit of experimental confirmation of sterileactive neutrino mixing by several orders of magnitude below the results of arxiv13122887 from 1010 1011 down to 1012 and even to 1020 in parts of parameter space nonzero cpviolating phases are responsible for that | [['seesaw', 'mechanism', 'constrains', 'from', 'below', 'mixing', 'between', 'active', 'and', 'sterile', 'neutrinos', 'for', 'fixed', 'sterile', 'neutrino', 'masses', 'signal', 'events', 'associated', 'with', 'sterile', 'neutrino', 'decays', 'inside', 'a', 'detector', 'at', 'fixed', 'target', 'experiment', 'are', 'suppressed', 'by', 'the', 'mixing', 'angle', 'to', 'the', 'power', 'of', 'four', 'therefore', 'sensitivity', 'of', 'experiments', 'such', 'as', 'ship', 'and', 'dune', 'should', 'take', 'into', 'account', 'minimal', 'possible', 'values', 'of', 'the', 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1,802.04729 | Lagrangian distributions and Fourier integral operators with quadratic
phase functions and Shubin amplitudes | We study Fourier integral operators with Shubin amplitudes and quadratic
phase functions associated to twisted graph Lagrangians with respect to
symplectic matrices. We factorize such an operator as the composition of a Weyl
pseudodifferential operator and a metaplectic operator and derive a
characterization of its Schwartz kernel in terms of phase space estimates.
Extending the conormal distributions in the Shubin calculus, we define an
adapted notion of Lagrangian tempered distribution. We show that the kernels of
Fourier integral operators are identical to Lagrangian distributions with
respect to twisted graph Lagrangians.
| math.AP | we study fourier integral operators with shubin amplitudes and quadratic phase functions associated to twisted graph lagrangians with respect to symplectic matrices we factorize such an operator as the composition of a weyl pseudodifferential operator and a metaplectic operator and derive a characterization of its schwartz kernel in terms of phase space estimates extending the conormal distributions in the shubin calculus we define an adapted notion of lagrangian tempered distribution we show that the kernels of fourier integral operators are identical to lagrangian distributions with respect to twisted graph lagrangians | [['we', 'study', 'fourier', 'integral', 'operators', 'with', 'shubin', 'amplitudes', 'and', 'quadratic', 'phase', 'functions', 'associated', 'to', 'twisted', 'graph', 'lagrangians', 'with', 'respect', 'to', 'symplectic', 'matrices', 'we', 'factorize', 'such', 'an', 'operator', 'as', 'the', 'composition', 'of', 'a', 'weyl', 'pseudodifferential', 'operator', 'and', 'a', 'metaplectic', 'operator', 'and', 'derive', 'a', 'characterization', 'of', 'its', 'schwartz', 'kernel', 'in', 'terms', 'of', 'phase', 'space', 'estimates', 'extending', 'the', 'conormal', 'distributions', 'in', 'the', 'shubin', 'calculus', 'we', 'define', 'an', 'adapted', 'notion', 'of', 'lagrangian', 'tempered', 'distribution', 'we', 'show', 'that', 'the', 'kernels', 'of', 'fourier', 'integral', 'operators', 'are', 'identical', 'to', 'lagrangian', 'distributions', 'with', 'respect', 'to', 'twisted', 'graph', 'lagrangians']] | [-0.16280623672323094, 0.10956580694021088, -0.14583015733708937, 0.10977239263140492, -0.14936956426439185, -0.05679780280010568, -0.05700950554520306, 0.3865138096527921, -0.31534735897504207, -0.1845925095400566, 0.0678512678662729, -0.27774017764669323, -0.16817611567676066, 0.1289834307739511, -0.13100869455374778, 0.10187072471340394, 0.06553596178483632, 0.055820138480824726, -0.2097044783928949, -0.15274165096084794, 0.4460749482529031, -0.009859100486048393, 0.15511235172808585, -0.002817302942276001, 0.1131085451795823, 0.035213667216400305, -0.08702220696335038, -0.08686697954447785, -0.1260003681531493, 0.14315773907841908, 0.2546753012161288, 0.02724643751151032, 0.17068793867818183, -0.4114687795854277, -0.14597825402807857, 0.1966134422303488, 0.11225458997715679, -0.0678937375648982, 0.047816501392258536, -0.3240871505397889, 0.05055394281452108, -0.18453888475067085, -0.19176367127543523, -0.1507590073471268, -0.0013138569270571073, 0.03653163798929503, -0.30365796521719957, 0.020692247100588347, 0.08061500369674629, 0.06609979952789015, -0.11950740482166616, -0.11512879992628264, -0.044386255034866436, 0.020591637052388655, -0.011102114969657528, 0.05588965150786357, 0.08361807064049774, -0.0837831661829518, -0.12249034967066513, 0.3213027278659865, -0.1280308153325071, -0.3033025910870896, 0.07637589090607233, -0.18578581044760842, -0.1591103700455278, 0.07978190892479486, 0.1260517746417059, 0.1822203824710515, -0.13042104943758911, 0.18663874131743796, -0.0410649616141907, 0.03957111767182748, 0.08946373390758203, 0.08152068828154976, 0.04859272102928824, 0.008014307680746747, 0.15345100393104885, 0.16113700300144654, 0.016785914150144285, -0.1402422011933393, -0.36695330772134993, -0.22082687312116225, -0.15544021897431876, 0.048945822396005194, -0.15158926090177072, -0.265966989689817, 0.44974709924620887, 0.07206888608634472, 0.2178422960711436, 0.12911306206757825, 0.1823699449784019, 0.22100914396837146, 0.09995902670650847, 0.05381534597836435, 0.07735657591062288, 0.2803390703060561, 0.071829020878714, -0.157278188628455, -0.07196050385634105, 0.23167038193593423] |
1,802.0473 | Tensor Comprehensions: Framework-Agnostic High-Performance Machine
Learning Abstractions | Deep learning models with convolutional and recurrent networks are now
ubiquitous and analyze massive amounts of audio, image, video, text and graph
data, with applications in automatic translation, speech-to-text, scene
understanding, ranking user preferences, ad placement, etc. Competing
frameworks for building these networks such as TensorFlow, Chainer, CNTK,
Torch/PyTorch, Caffe1/2, MXNet and Theano, explore different tradeoffs between
usability and expressiveness, research or production orientation and supported
hardware. They operate on a DAG of computational operators, wrapping
high-performance libraries such as CUDNN (for NVIDIA GPUs) or NNPACK (for
various CPUs), and automate memory allocation, synchronization, distribution.
Custom operators are needed where the computation does not fit existing
high-performance library calls, usually at a high engineering cost. This is
frequently required when new operators are invented by researchers: such
operators suffer a severe performance penalty, which limits the pace of
innovation. Furthermore, even if there is an existing runtime call these
frameworks can use, it often doesn't offer optimal performance for a user's
particular network architecture and dataset, missing optimizations between
operators as well as optimizations that can be done knowing the size and shape
of data. Our contributions include (1) a language close to the mathematics of
deep learning called Tensor Comprehensions, (2) a polyhedral Just-In-Time
compiler to convert a mathematical description of a deep learning DAG into a
CUDA kernel with delegated memory management and synchronization, also
providing optimizations such as operator fusion and specialization for specific
sizes, (3) a compilation cache populated by an autotuner. [Abstract cutoff]
| cs.PL cs.LG | deep learning models with convolutional and recurrent networks are now ubiquitous and analyze massive amounts of audio image video text and graph data with applications in automatic translation speechtotext scene understanding ranking user preferences ad placement etc competing frameworks for building these networks such as tensorflow chainer cntk torchpytorch caffe12 mxnet and theano explore different tradeoffs between usability and expressiveness research or production orientation and supported hardware they operate on a dag of computational operators wrapping highperformance libraries such as cudnn for nvidia gpus or nnpack for various cpus and automate memory allocation synchronization distribution custom operators are needed where the computation does not fit existing highperformance library calls usually at a high engineering cost this is frequently required when new operators are invented by researchers such operators suffer a severe performance penalty which limits the pace of innovation furthermore even if there is an existing runtime call these frameworks can use it often doesnt offer optimal performance for a users particular network architecture and dataset missing optimizations between operators as well as optimizations that can be done knowing the size and shape of data our contributions include 1 a language close to the mathematics of deep learning called tensor comprehensions 2 a polyhedral justintime compiler to convert a mathematical description of a deep learning dag into a cuda kernel with delegated memory management and synchronization also providing optimizations such as operator fusion and specialization for specific sizes 3 a compilation cache populated by an autotuner abstract cutoff | [['deep', 'learning', 'models', 'with', 'convolutional', 'and', 'recurrent', 'networks', 'are', 'now', 'ubiquitous', 'and', 'analyze', 'massive', 'amounts', 'of', 'audio', 'image', 'video', 'text', 'and', 'graph', 'data', 'with', 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1,802.04731 | $\eta$ and $\omega$ mesons as new degrees of freedom in the intranuclear
cascade model INCL | The intranuclear cascade model INCL (Li\`ege Intranuclear Cascade) is now
able to simulate spallation reactions induced by projectiles with energies up
to roughly 15 GeV. This was made possible thanks to the implementation of
multipion emission in the NN, $\Delta$N and $\pi$N interactions. The results
obtained with reactions on nuclei induced by nucleons or pions gave confidence
in the model. A next step will be the addition of the strange particles,
$\Lambda$, $\Sigma$ and Kaons, in order to not only refine the high-energy
modeling, but also to extend the capabilities of INCL, as studying hypernucleus
physics. Between those two versions of the code, the possibility to treat the
$\eta$ and $\omega$ mesons in INCL has been performed and this is the topic of
this paper. Production yields of these mesons increase with energy and it is
interesting to test their roles at higher energies. More specifically, studies
of $\eta$ rare decays benefit from accurate simulations of its production.
These are the two reasons for their implementation. Ingredients of the model,
like elementary reaction cross sections, are discussed and comparisons with
experimental data are carried out to test the reliability of those particle
productions.
| nucl-ex nucl-th | the intranuclear cascade model incl liege intranuclear cascade is now able to simulate spallation reactions induced by projectiles with energies up to roughly 15 gev this was made possible thanks to the implementation of multipion emission in the nn deltan and pin interactions the results obtained with reactions on nuclei induced by nucleons or pions gave confidence in the model a next step will be the addition of the strange particles lambda sigma and kaons in order to not only refine the highenergy modeling but also to extend the capabilities of incl as studying hypernucleus physics between those two versions of the code the possibility to treat the eta and omega mesons in incl has been performed and this is the topic of this paper production yields of these mesons increase with energy and it is interesting to test their roles at higher energies more specifically studies of eta rare decays benefit from accurate simulations of its production these are the two reasons for their implementation ingredients of the model like elementary reaction cross sections are discussed and comparisons with experimental data are carried out to test the reliability of those particle productions | [['the', 'intranuclear', 'cascade', 'model', 'incl', 'liege', 'intranuclear', 'cascade', 'is', 'now', 'able', 'to', 'simulate', 'spallation', 'reactions', 'induced', 'by', 'projectiles', 'with', 'energies', 'up', 'to', 'roughly', '15', 'gev', 'this', 'was', 'made', 'possible', 'thanks', 'to', 'the', 'implementation', 'of', 'multipion', 'emission', 'in', 'the', 'nn', 'deltan', 'and', 'pin', 'interactions', 'the', 'results', 'obtained', 'with', 'reactions', 'on', 'nuclei', 'induced', 'by', 'nucleons', 'or', 'pions', 'gave', 'confidence', 'in', 'the', 'model', 'a', 'next', 'step', 'will', 'be', 'the', 'addition', 'of', 'the', 'strange', 'particles', 'lambda', 'sigma', 'and', 'kaons', 'in', 'order', 'to', 'not', 'only', 'refine', 'the', 'highenergy', 'modeling', 'but', 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1,802.04732 | Automated Reporting of GUI Design Violations for Mobile Apps | The inception of a mobile app often takes form of a mock-up of the Graphical
User Interface (GUI), represented as a static image delineating the proper
layout and style of GUI widgets that satisfy requirements. Following this
initial mock-up, the design artifacts are then handed off to developers whose
goal is to accurately implement these GUIs and the desired functionality in
code. Given the sizable abstraction gap between mock-ups and code, developers
often introduce mistakes related to the GUI that can negatively impact an app's
success in highly competitive marketplaces. Moreover, such mistakes are common
in the evolutionary context of rapidly changing apps. This leads to the
time-consuming and laborious task of design teams verifying that each screen of
an app was implemented according to intended design specifications.
This paper introduces a novel, automated approach for verifying whether the
GUI of a mobile app was implemented according to its intended design. Our
approach resolves GUI-related information from both implemented apps and
mock-ups and uses computer vision techniques to identify common errors in the
implementations of mobile GUIs. We implemented this approach for Android in a
tool called GVT and carried out both a controlled empirical evaluation with
open-source apps as well as an industrial evaluation with designers and
developers from Huawei. The results show that GVT solves an important,
difficult, and highly practical problem with remarkable efficiency and accuracy
and is both useful and scalable from the point of view of industrial designers
and developers. The tool is currently used by over one-thousand industrial
designers and developers at Huawei to improve the quality of their mobile apps.
| cs.SE | the inception of a mobile app often takes form of a mockup of the graphical user interface gui represented as a static image delineating the proper layout and style of gui widgets that satisfy requirements following this initial mockup the design artifacts are then handed off to developers whose goal is to accurately implement these guis and the desired functionality in code given the sizable abstraction gap between mockups and code developers often introduce mistakes related to the gui that can negatively impact an apps success in highly competitive marketplaces moreover such mistakes are common in the evolutionary context of rapidly changing apps this leads to the timeconsuming and laborious task of design teams verifying that each screen of an app was implemented according to intended design specifications this paper introduces a novel automated approach for verifying whether the gui of a mobile app was implemented according to its intended design our approach resolves guirelated information from both implemented apps and mockups and uses computer vision techniques to identify common errors in the implementations of mobile guis we implemented this approach for android in a tool called gvt and carried out both a controlled empirical evaluation with opensource apps as well as an industrial evaluation with designers and developers from huawei the results show that gvt solves an important difficult and highly practical problem with remarkable efficiency and accuracy and is both useful and scalable from the point of view of industrial designers and developers the tool is currently used by over onethousand industrial designers and developers at huawei to improve the quality of their mobile apps | [['the', 'inception', 'of', 'a', 'mobile', 'app', 'often', 'takes', 'form', 'of', 'a', 'mockup', 'of', 'the', 'graphical', 'user', 'interface', 'gui', 'represented', 'as', 'a', 'static', 'image', 'delineating', 'the', 'proper', 'layout', 'and', 'style', 'of', 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1,802.04733 | Supersymmetric objects in gauged supergravities | This thesis is focused on supersymmetric objects in gauged supergravities and
their interpretation in string theory. Three particular theories are considered
and analyzed: matter-coupled $d=4, 5$ abelian gauged supergravities with 8
supercharges and minimal $d=7$ gauged supergravity with 16 supercharges.
Respectively in the $d=4$ and $d=5$ cases, the BPS first-order flows of static
extremal black holes and black strings are studied using the Hamilton-Jacobi
formalism. The attractor mechanism is formulated for the case of couplings to
hypermultiplets and two exact BPS black hole solutions are derived in $d=4$
from the first-order equations obtained with the Hamilton-Jacobi approach. One
of these black holes manifests an $\mathrm{AdS}_4$ asymptotics which has not
yet a clear explanation in string theory. Within the minimal $d=7$ theory, a
class of asymptotically $\mathrm{AdS}_7$ BPS solutions involving a non-trivial
profile for a 3-form gauge potential is derived and analyzed in relation to the
embedding in M-theory and massive IIA string theory. The holographic
interpretation of a particular asymptotically $\mathrm{AdS}_7$ solution
characterized by an $\mathrm{AdS}_3$ slicing of the 7-dimensional background is
formulated in terms of a conformal defect $\mathcal{N}=(4,0)$ $\mathrm{SCFT}_2$
within the $\mathcal{N}=(1,0)$ $\mathrm{SCFT}_6$ dual to the $\mathrm{AdS}_7$.
| hep-th | this thesis is focused on supersymmetric objects in gauged supergravities and their interpretation in string theory three particular theories are considered and analyzed mattercoupled d4 5 abelian gauged supergravities with 8 supercharges and minimal d7 gauged supergravity with 16 supercharges respectively in the d4 and d5 cases the bps firstorder flows of static extremal black holes and black strings are studied using the hamiltonjacobi formalism the attractor mechanism is formulated for the case of couplings to hypermultiplets and two exact bps black hole solutions are derived in d4 from the firstorder equations obtained with the hamiltonjacobi approach one of these black holes manifests an mathrmads_4 asymptotics which has not yet a clear explanation in string theory within the minimal d7 theory a class of asymptotically mathrmads_7 bps solutions involving a nontrivial profile for a 3form gauge potential is derived and analyzed in relation to the embedding in mtheory and massive iia string theory the holographic interpretation of a particular asymptotically mathrmads_7 solution characterized by an mathrmads_3 slicing of the 7dimensional background is formulated in terms of a conformal defect mathcaln40 mathrmscft_2 within the mathcaln10 mathrmscft_6 dual to the mathrmads_7 | [['this', 'thesis', 'is', 'focused', 'on', 'supersymmetric', 'objects', 'in', 'gauged', 'supergravities', 'and', 'their', 'interpretation', 'in', 'string', 'theory', 'three', 'particular', 'theories', 'are', 'considered', 'and', 'analyzed', 'mattercoupled', 'd4', '5', 'abelian', 'gauged', 'supergravities', 'with', '8', 'supercharges', 'and', 'minimal', 'd7', 'gauged', 'supergravity', 'with', '16', 'supercharges', 'respectively', 'in', 'the', 'd4', 'and', 'd5', 'cases', 'the', 'bps', 'firstorder', 'flows', 'of', 'static', 'extremal', 'black', 'holes', 'and', 'black', 'strings', 'are', 'studied', 'using', 'the', 'hamiltonjacobi', 'formalism', 'the', 'attractor', 'mechanism', 'is', 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1,802.04734 | Substation Signal Matching with a Bagged Token Classifier | Currently, engineers at substation service providers match customer data with
the corresponding internally used signal names manually. This paper proposes a
machine learning method to automate this process based on substation signal
mapping data from a repository of executed projects. To this end, a bagged
token classifier is proposed, letting words (tokens) in the customer signal
name vote for provider signal names. In our evaluation, the proposed method
exhibits better performance in terms of both accuracy and efficiency over
standard classifiers.
| stat.ML cs.LG | currently engineers at substation service providers match customer data with the corresponding internally used signal names manually this paper proposes a machine learning method to automate this process based on substation signal mapping data from a repository of executed projects to this end a bagged token classifier is proposed letting words tokens in the customer signal name vote for provider signal names in our evaluation the proposed method exhibits better performance in terms of both accuracy and efficiency over standard classifiers | [['currently', 'engineers', 'at', 'substation', 'service', 'providers', 'match', 'customer', 'data', 'with', 'the', 'corresponding', 'internally', 'used', 'signal', 'names', 'manually', 'this', 'paper', 'proposes', 'a', 'machine', 'learning', 'method', 'to', 'automate', 'this', 'process', 'based', 'on', 'substation', 'signal', 'mapping', 'data', 'from', 'a', 'repository', 'of', 'executed', 'projects', 'to', 'this', 'end', 'a', 'bagged', 'token', 'classifier', 'is', 'proposed', 'letting', 'words', 'tokens', 'in', 'the', 'customer', 'signal', 'name', 'vote', 'for', 'provider', 'signal', 'names', 'in', 'our', 'evaluation', 'the', 'proposed', 'method', 'exhibits', 'better', 'performance', 'in', 'terms', 'of', 'both', 'accuracy', 'and', 'efficiency', 'over', 'standard', 'classifiers']] | [-0.042675134300449745, -0.06716146930782009, -0.06638956318298976, 0.028879678291582143, -0.1531968727811343, -0.18476321644230206, 0.18469719759067865, 0.42287327928675544, -0.25449746711478555, -0.3360991327057558, 0.06729226116591168, -0.333587464933962, -0.061779055169519086, 0.1678029212690703, -0.1629356772604364, 0.09589401127506093, 0.13754925979545662, 0.11227923661562396, 0.013961138634907978, -0.3325659912424507, 0.260356612779476, 0.0760635534922282, 0.4217569787791114, -0.013094502880617424, 0.11120028594037357, -0.02218112403549898, -0.0457953160505075, -0.08922258596062477, -0.027620814628554164, 0.13535165100323934, 0.42190082720768307, 0.26298976058922247, 0.3392087629432847, -0.3592735380049289, -0.1158776529662219, 0.11686625511006073, 0.11078893052942959, 0.04106359591960539, -0.030562577442909925, -0.36848449812811096, 0.09924460063332025, -0.2526461180575468, 0.015432157668710492, -0.05308184407386975, -0.04896008436579579, 0.040660918550853284, -0.3173361397377089, -0.012864995214593724, 0.03707197307965454, 0.11557858248735652, -0.005385968494976376, -0.07986997245317957, 0.02417461601384904, 0.17390782448296424, 0.05727653348219098, 0.06283543712493998, 0.1953200510484569, -0.12244697452241118, -0.1898391846463912, 0.38888632016325436, -0.06660838175656987, -0.1853063273255104, 0.1317653527489093, 0.001819457306906029, -0.15893423738371995, 0.09123269830726916, 0.26752835247136747, 0.05306788808723659, -0.27068669595008277, -0.009229095737495816, 0.01135729171657636, 0.22497873530251744, 0.10212879555219394, -0.07389043667066245, 0.19968351695606093, 0.2669465312407996, 0.043659381234995374, 0.12992640359350194, -0.04705101513464786, -0.035580933599928276, -0.20031450602030496, -0.10881860134548244, -0.21832465866787565, -0.05313176871852282, -0.09667699202233322, -0.13504288828483335, 0.42859970640253137, 0.25995761402135276, 0.13709438142631156, 0.12036879525094489, 0.39699688236470576, 0.04404220335260264, 0.1288249478675425, 0.1416581810285326, 0.10718272947188881, -0.0708280695648289, 0.2044644091999802, -0.13764461330828015, 0.1608112940714223, 0.04014487323684641] |
1,802.04735 | Semantic Scene Completion Combining Colour and Depth: preliminary
experiments | Semantic scene completion is the task of producing a complete 3D voxel
representation of volumetric occupancy with semantic labels for a scene from a
single-view observation. We built upon the recent work of Song et al. (CVPR
2017), who proposed SSCnet, a method that performs scene completion and
semantic labelling in a single end-to-end 3D convolutional network. SSCnet uses
only depth maps as input, even though depth maps are usually obtained from
devices that also capture colour information, such as RGBD sensors and stereo
cameras. In this work, we investigate the potential of the RGB colour channels
to improve SSCnet.
| cs.CV | semantic scene completion is the task of producing a complete 3d voxel representation of volumetric occupancy with semantic labels for a scene from a singleview observation we built upon the recent work of song et al cvpr 2017 who proposed sscnet a method that performs scene completion and semantic labelling in a single endtoend 3d convolutional network sscnet uses only depth maps as input even though depth maps are usually obtained from devices that also capture colour information such as rgbd sensors and stereo cameras in this work we investigate the potential of the rgb colour channels to improve sscnet | [['semantic', 'scene', 'completion', 'is', 'the', 'task', 'of', 'producing', 'a', 'complete', '3d', 'voxel', 'representation', 'of', 'volumetric', 'occupancy', 'with', 'semantic', 'labels', 'for', 'a', 'scene', 'from', 'a', 'singleview', 'observation', 'we', 'built', 'upon', 'the', 'recent', 'work', 'of', 'song', 'et', 'al', 'cvpr', '2017', 'who', 'proposed', 'sscnet', 'a', 'method', 'that', 'performs', 'scene', 'completion', 'and', 'semantic', 'labelling', 'in', 'a', 'single', 'endtoend', '3d', 'convolutional', 'network', 'sscnet', 'uses', 'only', 'depth', 'maps', 'as', 'input', 'even', 'though', 'depth', 'maps', 'are', 'usually', 'obtained', 'from', 'devices', 'that', 'also', 'capture', 'colour', 'information', 'such', 'as', 'rgbd', 'sensors', 'and', 'stereo', 'cameras', 'in', 'this', 'work', 'we', 'investigate', 'the', 'potential', 'of', 'the', 'rgb', 'colour', 'channels', 'to', 'improve', 'sscnet']] | [0.007925921257119627, 0.0011878578644245862, -0.059396950677037236, 0.0012521941773593426, -0.10839831140358001, -0.1425355239259079, 0.02413964261417277, 0.4914293795451522, -0.23538632854819297, -0.38470161497592925, 0.029590504204388707, -0.29689472645521164, -0.20679770763963462, 0.13312432219274342, -0.24478502815123648, 0.085282552203862, 0.18297997170477173, 0.060193981542252, -0.0777286919625476, -0.2557114747096784, 0.2753186373633798, 0.035794389536604285, 0.3201924879476428, -0.0013884823955595494, 0.17164489488117396, 0.03406616773456335, -0.0937045208318159, 0.0224310456297826, -0.04267029032416758, 0.16933823105879128, 0.30706454992294313, 0.20203201105468907, 0.17564284451305867, -0.4324042563792318, -0.2709506224095821, 0.03737383929314092, 0.11585072407964617, 0.11171458157710731, -0.02882638887502253, -0.4004669462889433, 0.06242283997591585, -0.15023823417373933, 0.08084150973023498, -0.05302365776151419, 0.035066658109426496, -0.04597835742868483, -0.313102133218199, 0.02493851511244429, 0.08411036421719473, 0.08910488214343787, -0.06941514743448352, -0.058539001774042844, -0.03250836484134197, 0.24577412884216757, -0.08095483062090353, 0.12173472911235876, 0.13751449356321246, -0.24980208218330519, -0.10103000867646188, 0.3563910521101207, -0.03848047188017517, -0.13970055233687162, 0.17451268957694993, -0.04529029040131718, -0.15303504816256464, 0.09760643138550222, 0.19373829788179137, 0.14680265819653868, -0.15257554778829216, 0.033844545166939494, -0.12526364541146903, 0.1807419178809505, 0.0956231729243882, 0.015607474986463785, 0.17471609240397812, 0.25000973855610936, 0.046562807164154944, 0.09084252040833235, -0.21790089052869008, 0.03428315197757911, -0.19874408007599415, -0.11857719278268633, -0.2368888058234006, -0.05141864917241037, -0.08839133787405444, -0.15173797139199452, 0.4044428438600153, 0.2518362953606993, 0.2829474022844806, 0.0982484278967604, 0.40119556441903115, -0.009516673397738486, 0.11408948557451368, 0.0813476652832469, 0.1481453180567769, 0.010760864820331334, 0.17167965280415956, -0.11001555872964673, 0.05534524268354289, 0.16427911965642125] |
1,802.04736 | Amenable uniformly recurrent subgroups and lattice embeddings | We study lattice embeddings for the class of countable groups $\Gamma$
defined by the property that the largest amenable uniformly recurrent subgroup
$A_\Gamma$ is continuous. When $A_\Gamma$ comes from an extremely proximal
action and the envelope of $A_\Gamma$ is co-amenable in $\Gamma$, we obtain
restrictions on the locally compact groups $G$ that contain a copy of $\Gamma$
as a lattice, notably regarding normal subgroups of $G$, product decompositions
of $G$, and more generally dense mappings from $G$ to a product of locally
compact groups.
| math.GR | we study lattice embeddings for the class of countable groups gamma defined by the property that the largest amenable uniformly recurrent subgroup a_gamma is continuous when a_gamma comes from an extremely proximal action and the envelope of a_gamma is coamenable in gamma we obtain restrictions on the locally compact groups g that contain a copy of gamma as a lattice notably regarding normal subgroups of g product decompositions of g and more generally dense mappings from g to a product of locally compact groups | [['we', 'study', 'lattice', 'embeddings', 'for', 'the', 'class', 'of', 'countable', 'groups', 'gamma', 'defined', 'by', 'the', 'property', 'that', 'the', 'largest', 'amenable', 'uniformly', 'recurrent', 'subgroup', 'a_gamma', 'is', 'continuous', 'when', 'a_gamma', 'comes', 'from', 'an', 'extremely', 'proximal', 'action', 'and', 'the', 'envelope', 'of', 'a_gamma', 'is', 'coamenable', 'in', 'gamma', 'we', 'obtain', 'restrictions', 'on', 'the', 'locally', 'compact', 'groups', 'g', 'that', 'contain', 'a', 'copy', 'of', 'gamma', 'as', 'a', 'lattice', 'notably', 'regarding', 'normal', 'subgroups', 'of', 'g', 'product', 'decompositions', 'of', 'g', 'and', 'more', 'generally', 'dense', 'mappings', 'from', 'g', 'to', 'a', 'product', 'of', 'locally', 'compact', 'groups']] | [-0.15038868410573786, 0.16854721362791938, -0.10338879526881058, 0.029402302514340374, -0.11492072402810057, -0.07207544187327758, 0.08585518112938319, 0.4660409169182891, -0.3407628591438489, -0.1802488676094938, 0.1423617296067754, -0.2753106414318262, -0.07429764070548117, 0.2499869392714962, -0.10447499259663302, -0.06866650471316202, 0.10764939954415673, 0.15472391152953996, -0.07539540336888638, -0.1830737775723849, 0.3938762183256802, -0.0711921797122895, 0.22480747679115407, -0.031572312798484096, 0.051956222931732465, -0.01272570432740308, -0.060946901794523, 0.014782305562957412, -0.11588062719151605, 0.13747596757353417, 0.25144481572455596, 0.06509654043752346, 0.2077813982253983, -0.3125887369914424, -0.16135612037032843, 0.21947176729805679, 0.062301219518606864, -0.06024422594124362, -0.06685331298562926, -0.3049075001098182, 0.11169001027675611, -0.19276900474159492, -0.0857437682016531, -0.042209820433830224, 0.12185431631015879, -0.03422349250398665, -0.2619424469635955, 0.01621927816831019, 0.10621500209284325, 0.05228694619256116, 0.0010928900557614508, -0.10696747803705789, -0.07356026731542356, 0.13911972796839628, -0.008557986163179435, 0.09203904400679416, 0.10931680029517295, -0.08592039153778128, -0.08750956691385779, 0.4693414255355795, -0.0983796528307721, -0.18818346752474704, 0.17314505997845636, -0.20658556729488606, -0.23954161820888875, 0.1444968787975432, 0.1411287511118211, 0.17080697048610696, -0.08071148030579761, 0.2084773010227807, -0.13239897283658916, 0.11641114894328417, 0.04090732199672077, -3.200665205007508e-05, 0.1041759646253749, 0.11799186193439666, 0.16137560122158556, 0.14064031699117982, 0.06704018617026686, 0.09904320617871624, -0.35417769640861524, -0.14969571372715845, -0.1457540276294042, 0.1339612686479952, -0.1230542963489175, -0.21381351235877014, 0.3406038377932938, -0.03921391255044866, 0.1541065750699047, 0.09300353790777513, 0.17076203581832705, 0.026050941945175595, 0.07461836472863242, 0.12498749436677567, 0.07137741419276045, 0.2573268784991732, -0.17870323019035692, -0.16195389434939161, -0.018742411863058805, 0.17000664250891923] |
1,802.04737 | Affine function valued valuations | A classification of SL$(n)$ contravariant, continuous function valued
valuations on convex bodies is established. Such valuations are natural
extensions of SL$(n)$ contravariant $L_p$ Minkowski valuations, the
classification of which characterized $L_p$ projection bodies, which are
fundamental in the $L_p$ Brunn-Minkowski theory, for $p \geq 1$. Hence our
result will help to better understand extensions of the $L_p$ Brunn-Minkowski
theory. In fact, our results characterize general projection functions which
extend $L_p$ projection functions ($p$-th powers of the support functions of
$L_p$ projection bodies) to projection functions in the $L_p$ Brunn-Minkowski
theory for $0< p < 1$ and in the Orlicz Brunn-Minkowski theory.
| math.MG | a classification of sln contravariant continuous function valued valuations on convex bodies is established such valuations are natural extensions of sln contravariant l_p minkowski valuations the classification of which characterized l_p projection bodies which are fundamental in the l_p brunnminkowski theory for p geq 1 hence our result will help to better understand extensions of the l_p brunnminkowski theory in fact our results characterize general projection functions which extend l_p projection functions pth powers of the support functions of l_p projection bodies to projection functions in the l_p brunnminkowski theory for 0 p 1 and in the orlicz brunnminkowski theory | [['a', 'classification', 'of', 'sln', 'contravariant', 'continuous', 'function', 'valued', 'valuations', 'on', 'convex', 'bodies', 'is', 'established', 'such', 'valuations', 'are', 'natural', 'extensions', 'of', 'sln', 'contravariant', 'l_p', 'minkowski', 'valuations', 'the', 'classification', 'of', 'which', 'characterized', 'l_p', 'projection', 'bodies', 'which', 'are', 'fundamental', 'in', 'the', 'l_p', 'brunnminkowski', 'theory', 'for', 'p', 'geq', '1', 'hence', 'our', 'result', 'will', 'help', 'to', 'better', 'understand', 'extensions', 'of', 'the', 'l_p', 'brunnminkowski', 'theory', 'in', 'fact', 'our', 'results', 'characterize', 'general', 'projection', 'functions', 'which', 'extend', 'l_p', 'projection', 'functions', 'pth', 'powers', 'of', 'the', 'support', 'functions', 'of', 'l_p', 'projection', 'bodies', 'to', 'projection', 'functions', 'in', 'the', 'l_p', 'brunnminkowski', 'theory', 'for', '0', 'p', '1', 'and', 'in', 'the', 'orlicz', 'brunnminkowski', 'theory']] | [-0.07832752807065844, 0.07335125986952334, -0.10546074877958744, 0.17712886524037458, -0.03903814136516303, -0.13651716338004916, -0.06604521170957015, 0.3183047359995544, -0.39978888992220163, -0.12187848960980772, 0.062410166556946936, -0.29007546458393335, -0.14381567448377608, 0.17814782551722602, -0.17864313811529428, 0.08807416578754783, -0.06760637698695064, 0.026160390991717578, -0.18023469001986087, -0.29806187143549323, 0.33819878002628684, -0.028334110248833894, 0.22621589398011566, 0.05096435451880097, 0.04336989838629961, 0.0639599834010005, -0.0199454111373052, -0.026132963605923577, -0.19647117868997158, 0.22241230624727903, 0.3459002538770437, 0.16283745480235667, 0.2878606371721253, -0.3749502657167614, -0.2104100278019905, 0.1826930870814249, 0.05384350647334941, -0.09729986525024287, 0.001572242755210027, -0.2698195425886661, 0.06494507204275578, -0.1094989753421396, -0.1583474756591022, -0.10385391913354397, 0.08445717921247706, 0.058166684833122415, -0.33934369165450334, 0.08485714320093393, 0.19259075269103051, 0.06166343097575009, -0.21974711121059953, -0.18593263021088205, 0.031921161310747265, 0.018678705492056906, 0.04502256109146401, 0.19569470825605095, 0.1191647237027064, -0.05198473833268508, -0.19339595620520414, 0.3553646175470203, -0.009210228603333235, -0.31405043162871155, 0.13068083326332272, -0.2690971088130027, -0.1268930115085095, 0.07250307426787912, 0.1456407439429313, 0.15279673580080272, -0.039966359904501585, 0.2133018136298051, -0.12625127802137284, 0.06685047474689781, 0.11361383588053287, 0.08169251258310396, 0.08281827172264457, 0.034779141298495235, 0.15279680182226002, 0.11952555745141581, 0.019390882675070317, -0.10147214822936804, -0.40283489220775665, -0.21901320197153837, -0.1956695212703198, 0.07729405130958185, -0.165034004435729, -0.1280105119025393, 0.2725519897881895, 0.011674625668674707, 0.11371876357123256, 0.19002861246117392, 0.2178664774633944, 0.07241400953615085, 0.06396275012753903, 0.0179535098769702, 0.18131511569023132, 0.21501411733450368, -0.003026410290040076, -0.08918869141954928, -0.024340658718720078, 0.21594693044200539] |
1,802.04738 | Joint 3D Reconstruction of a Static Scene and Moving Objects | We present a technique for simultaneous 3D reconstruction of static regions
and rigidly moving objects in a scene. An RGB-D frame is represented as a
collection of features, which are points and planes. We classify the features
into static and dynamic regions and grow separate maps, static and object maps,
for each of them. To robustly classify the features in each frame, we fuse
multiple RANSAC-based registration results obtained by registering different
groups of the features to different maps, including (1) all the features to the
static map, (2) all the features to each object map, and (3) subsets of the
features, each forming a segment, to each object map. This multi-group
registration approach is designed to overcome the following challenges: scenes
can be dominated by static regions, making object tracking more difficult; and
moving object might have larger pose variation between frames compared to the
static regions. We show qualitative results from indoor scenes with objects in
various shapes. The technique enables on-the-fly object model generation to be
used for robotic manipulation.
| cs.CV | we present a technique for simultaneous 3d reconstruction of static regions and rigidly moving objects in a scene an rgbd frame is represented as a collection of features which are points and planes we classify the features into static and dynamic regions and grow separate maps static and object maps for each of them to robustly classify the features in each frame we fuse multiple ransacbased registration results obtained by registering different groups of the features to different maps including 1 all the features to the static map 2 all the features to each object map and 3 subsets of the features each forming a segment to each object map this multigroup registration approach is designed to overcome the following challenges scenes can be dominated by static regions making object tracking more difficult and moving object might have larger pose variation between frames compared to the static regions we show qualitative results from indoor scenes with objects in various shapes the technique enables onthefly object model generation to be used for robotic manipulation | [['we', 'present', 'a', 'technique', 'for', 'simultaneous', '3d', 'reconstruction', 'of', 'static', 'regions', 'and', 'rigidly', 'moving', 'objects', 'in', 'a', 'scene', 'an', 'rgbd', 'frame', 'is', 'represented', 'as', 'a', 'collection', 'of', 'features', 'which', 'are', 'points', 'and', 'planes', 'we', 'classify', 'the', 'features', 'into', 'static', 'and', 'dynamic', 'regions', 'and', 'grow', 'separate', 'maps', 'static', 'and', 'object', 'maps', 'for', 'each', 'of', 'them', 'to', 'robustly', 'classify', 'the', 'features', 'in', 'each', 'frame', 'we', 'fuse', 'multiple', 'ransacbased', 'registration', 'results', 'obtained', 'by', 'registering', 'different', 'groups', 'of', 'the', 'features', 'to', 'different', 'maps', 'including', '1', 'all', 'the', 'features', 'to', 'the', 'static', 'map', '2', 'all', 'the', 'features', 'to', 'each', 'object', 'map', 'and', '3', 'subsets', 'of', 'the', 'features', 'each', 'forming', 'a', 'segment', 'to', 'each', 'object', 'map', 'this', 'multigroup', 'registration', 'approach', 'is', 'designed', 'to', 'overcome', 'the', 'following', 'challenges', 'scenes', 'can', 'be', 'dominated', 'by', 'static', 'regions', 'making', 'object', 'tracking', 'more', 'difficult', 'and', 'moving', 'object', 'might', 'have', 'larger', 'pose', 'variation', 'between', 'frames', 'compared', 'to', 'the', 'static', 'regions', 'we', 'show', 'qualitative', 'results', 'from', 'indoor', 'scenes', 'with', 'objects', 'in', 'various', 'shapes', 'the', 'technique', 'enables', 'onthefly', 'object', 'model', 'generation', 'to', 'be', 'used', 'for', 'robotic', 'manipulation']] | [-0.07261890956305635, 0.020286933200065763, -0.07121399607754318, 0.03945320206115593, -0.06676610730685337, -0.15607970157325957, -0.01602903122622035, 0.4715117832721141, -0.2799034078007919, -0.34204420977611244, 0.08626317408143118, -0.30288881289562736, -0.12264956825149025, 0.16778972933494157, -0.12708124105221016, 0.04992606684517094, 0.08920082143801292, 0.00984731017789572, -0.06959397032873091, -0.1831662794763513, 0.31618150255260613, 0.0045341124429738935, 0.26567571506582505, -0.05342263702540039, 0.14252381056353364, -0.00446153403598966, -0.07519390206310274, 0.06129851317937578, -0.03896973680198795, 0.16078339727202462, 0.28758810563793546, 0.16448891381975372, 0.19495135222190205, -0.40428511083470603, -0.25261408936503515, 0.09205020072121668, 0.13685502659265664, 0.11987032504372751, -0.016886521665520047, -0.3806546246821351, 0.14338620384008424, -0.11535664822238718, -0.046536344690302205, -0.08572965443155842, 0.03794989660866774, 0.0062430486933602765, -0.26880009255958776, -0.008358925824575772, 0.03071832841358449, 0.03955321829238465, -0.14734221471761796, -0.03337892216264884, 0.011065122178174297, 0.27059164914071043, -0.013548070148328007, 0.050182611311414424, 0.1849848767927568, -0.1953083579199701, -0.09893655386414228, 0.4144565884260773, -0.019761747921510153, -0.24576680621702893, 0.2519616731684763, -0.10558693098291927, -0.12955824977508343, 0.15465189671585325, 0.18451050459592322, 0.1943506485771797, -0.1564061973523887, -0.038602284440768055, -0.032591625668560664, 0.17430432082686811, 0.07415973945558502, -0.011634296236958119, 0.2655566417709036, 0.14995507813042644, 0.06104488729672894, 0.18168856979635775, -0.20518121362755995, -0.03770262628492248, -0.24381587179303654, -0.10175551718768443, -0.11612700563963137, -0.0804469169230561, -0.1165359469092447, -0.13905524286021603, 0.4154823430412788, 0.18983860774170283, 0.2810242014231882, 0.030786509513235895, 0.33959530213190986, 0.015234929666251505, 0.10117852429434054, 0.07736003849213954, 0.16717198744653106, 0.009319677208738691, 0.09211367771912345, -0.1223762360491623, 0.009496339082696361, 0.09423765385620339] |
1,802.04739 | Query learning of derived $\omega$-tree languages in polynomial time | We present the first polynomial time algorithm to learn nontrivial classes of
languages of infinite trees. Specifically, our algorithm uses membership and
equivalence queries to learn classes of $\omega$-tree languages derived from
weak regular $\omega$-word languages in polynomial time. The method is a
general polynomial time reduction of learning a class of derived $\omega$-tree
languages to learning the underlying class of $\omega$-word languages, for any
class of $\omega$-word languages recognized by a deterministic B\"{u}chi
acceptor. Our reduction, combined with the polynomial time learning algorithm
of Maler and Pnueli [1995] for the class of weak regular $\omega$-word
languages yields the main result. We also show that subset queries that return
counterexamples can be implemented in polynomial time using subset queries that
return no counterexamples for deterministic or non-deterministic finite word
acceptors, and deterministic or non-deterministic B\"{u}chi $\omega$-word
acceptors.
A previous claim of an algorithm to learn regular $\omega$-trees due to
Jayasrirani, Begam and Thomas [2008] is unfortunately incorrect, as shown in
Angluin [2016].
| cs.LO | we present the first polynomial time algorithm to learn nontrivial classes of languages of infinite trees specifically our algorithm uses membership and equivalence queries to learn classes of omegatree languages derived from weak regular omegaword languages in polynomial time the method is a general polynomial time reduction of learning a class of derived omegatree languages to learning the underlying class of omegaword languages for any class of omegaword languages recognized by a deterministic buchi acceptor our reduction combined with the polynomial time learning algorithm of maler and pnueli 1995 for the class of weak regular omegaword languages yields the main result we also show that subset queries that return counterexamples can be implemented in polynomial time using subset queries that return no counterexamples for deterministic or nondeterministic finite word acceptors and deterministic or nondeterministic buchi omegaword acceptors a previous claim of an algorithm to learn regular omegatrees due to jayasrirani begam and thomas 2008 is unfortunately incorrect as shown in angluin 2016 | [['we', 'present', 'the', 'first', 'polynomial', 'time', 'algorithm', 'to', 'learn', 'nontrivial', 'classes', 'of', 'languages', 'of', 'infinite', 'trees', 'specifically', 'our', 'algorithm', 'uses', 'membership', 'and', 'equivalence', 'queries', 'to', 'learn', 'classes', 'of', 'omegatree', 'languages', 'derived', 'from', 'weak', 'regular', 'omegaword', 'languages', 'in', 'polynomial', 'time', 'the', 'method', 'is', 'a', 'general', 'polynomial', 'time', 'reduction', 'of', 'learning', 'a', 'class', 'of', 'derived', 'omegatree', 'languages', 'to', 'learning', 'the', 'underlying', 'class', 'of', 'omegaword', 'languages', 'for', 'any', 'class', 'of', 'omegaword', 'languages', 'recognized', 'by', 'a', 'deterministic', 'buchi', 'acceptor', 'our', 'reduction', 'combined', 'with', 'the', 'polynomial', 'time', 'learning', 'algorithm', 'of', 'maler', 'and', 'pnueli', '1995', 'for', 'the', 'class', 'of', 'weak', 'regular', 'omegaword', 'languages', 'yields', 'the', 'main', 'result', 'we', 'also', 'show', 'that', 'subset', 'queries', 'that', 'return', 'counterexamples', 'can', 'be', 'implemented', 'in', 'polynomial', 'time', 'using', 'subset', 'queries', 'that', 'return', 'no', 'counterexamples', 'for', 'deterministic', 'or', 'nondeterministic', 'finite', 'word', 'acceptors', 'and', 'deterministic', 'or', 'nondeterministic', 'buchi', 'omegaword', 'acceptors', 'a', 'previous', 'claim', 'of', 'an', 'algorithm', 'to', 'learn', 'regular', 'omegatrees', 'due', 'to', 'jayasrirani', 'begam', 'and', 'thomas', '2008', 'is', 'unfortunately', 'incorrect', 'as', 'shown', 'in', 'angluin', '2016']] | [-0.05937674051796121, 0.03549119378994529, -0.0485918663642546, 0.14412012633342955, -0.12476220830119675, -0.20872858530971444, 0.11290237151829215, 0.38419290459891664, -0.3329213542474693, -0.2946742263746365, 0.0356898071764544, -0.20695195376542927, -0.11999122275310699, 0.22308836268154975, -0.12297209332213749, 0.07740070443906927, 0.0927269153291053, 0.022423783647297305, -0.05011245746351926, -0.33258246475701, 0.2891460964249895, -0.012640572885416826, 0.21229557412428945, -0.015800431111379513, 0.1218302182299144, -0.006117121758576058, -0.02528525378313387, 0.05414295080854687, -0.07786857239570273, 0.08990657909572879, 0.3788659596105424, 0.23464122302004997, 0.28811499786185973, -0.3767696175299868, -0.10952752256689334, 0.15695823781498813, 0.07469624387913773, 0.13846952548100247, -0.009969993127521741, -0.325863391214015, 0.10867709447217139, -0.16782589530560507, -0.026498808452558906, -0.06947108139166067, 0.10157282174199442, 0.030115630091580598, -0.28344303059032117, -0.016968031032371947, 0.20655742372515834, 0.0853185817434252, -0.03212299058876955, -0.08724674020961658, 0.03579338232668329, 0.08035938587640942, -0.024284380891867265, 0.06801701614138068, 0.047805291999137335, -0.03485401223569849, -0.2776205287605253, 0.3350068349391222, -0.06237095605163493, -0.17064419038640924, 0.22950164656319855, -0.06768984169569574, -0.19017295832627842, 0.12422808477875358, 0.166941308823143, 0.1358297080998132, -0.12911009152502387, 0.17152016627443128, -0.12468449593981422, 0.22930297197161031, 0.1605125309168538, 3.814336967572004e-07, 0.08844536844098681, 0.15132502538647172, 0.07844015921663516, 0.14670313787101355, 0.02951196470309662, -0.05520626522093729, -0.23975965498336532, -0.15335429393792455, -0.17329952816915098, 0.002130308825098261, -0.09690813659329285, -0.23128673668681493, 0.3513588975832055, 0.13054702263855952, 0.08771189344056611, 0.2577731074718169, 0.22633686178965093, 0.09977928139608283, 0.051594935012745514, 0.1528629633550874, 0.07706659531244371, 0.0487943757340147, 0.09362182048329187, -0.17982800711459138, 0.162465868788901, 0.156480745830915] |
1,802.0474 | Approximation schemes for viscosity solutions of fully nonlinear
stochastic partial differential equations | The aim of this paper is to develop a general method for constructing
approximation schemes for viscosity solutions of fully nonlinear pathwise
stochastic partial differential equations, and for proving their convergence.
Our results apply to approximations such as explicit finite difference schemes
and Trotter-Kato type mixing formulas. The irregular time dependence disrupts
the usual methods from the classical viscosity theory for creating schemes that
are both monotone and convergent, an obstacle that cannot be overcome by
incorporating higher order correction terms, as is done for numerical
approximations of stochastic or rough ordinary differential equations. The
novelty here is to regularize those driving paths with non-trivial quadratic
variation in order to guarantee both monotonicity and convergence.
We present qualitative and quantitative results, the former covering a wide
variety of schemes for second-order equations. An error estimate is established
in the Hamilton-Jacobi case, its merit being that it depends on the path only
through the modulus of continuity, and not on the derivatives or total
variation. As a result, it is possible to choose a regularization of the path
so as to obtain efficient rates of convergence. This is demonstrated in the
specific setting of equations with multiplicative white noise in time, in which
case the convergence holds with probability one. We also present an example
using scaled random walks that exhibits convergence in distribution.
| math.AP | the aim of this paper is to develop a general method for constructing approximation schemes for viscosity solutions of fully nonlinear pathwise stochastic partial differential equations and for proving their convergence our results apply to approximations such as explicit finite difference schemes and trotterkato type mixing formulas the irregular time dependence disrupts the usual methods from the classical viscosity theory for creating schemes that are both monotone and convergent an obstacle that cannot be overcome by incorporating higher order correction terms as is done for numerical approximations of stochastic or rough ordinary differential equations the novelty here is to regularize those driving paths with nontrivial quadratic variation in order to guarantee both monotonicity and convergence we present qualitative and quantitative results the former covering a wide variety of schemes for secondorder equations an error estimate is established in the hamiltonjacobi case its merit being that it depends on the path only through the modulus of continuity and not on the derivatives or total variation as a result it is possible to choose a regularization of the path so as to obtain efficient rates of convergence this is demonstrated in the specific setting of equations with multiplicative white noise in time in which case the convergence holds with probability one we also present an example using scaled random walks that exhibits convergence in distribution | [['the', 'aim', 'of', 'this', 'paper', 'is', 'to', 'develop', 'a', 'general', 'method', 'for', 'constructing', 'approximation', 'schemes', 'for', 'viscosity', 'solutions', 'of', 'fully', 'nonlinear', 'pathwise', 'stochastic', 'partial', 'differential', 'equations', 'and', 'for', 'proving', 'their', 'convergence', 'our', 'results', 'apply', 'to', 'approximations', 'such', 'as', 'explicit', 'finite', 'difference', 'schemes', 'and', 'trotterkato', 'type', 'mixing', 'formulas', 'the', 'irregular', 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1,802.04741 | Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approach | We present a novel framework for applying deep neural networks (DNN) to soft
decoding of linear codes at arbitrary block lengths. Unlike other approaches,
our framework allows unconstrained DNN design, enabling the free application of
powerful designs that were developed in other contexts. Our method is robust to
overfitting that inhibits many competing methods, which follows from the
exponentially large number of codewords required for their training. We achieve
this by transforming the channel output before feeding it to the network,
extracting only the syndrome of the hard decisions and the channel output
reliabilities. We prove analytically that this approach does not involve any
intrinsic performance penalty, and guarantees the generalization of performance
obtained during training. Our best results are obtained using a recurrent
neural network (RNN) architecture combined with simple preprocessing by
permutation. We provide simulation results that demonstrate performance that
sometimes approaches that of the ordered statistics decoding (OSD) algorithm.
| cs.IT cs.LG cs.NE math.IT | we present a novel framework for applying deep neural networks dnn to soft decoding of linear codes at arbitrary block lengths unlike other approaches our framework allows unconstrained dnn design enabling the free application of powerful designs that were developed in other contexts our method is robust to overfitting that inhibits many competing methods which follows from the exponentially large number of codewords required for their training we achieve this by transforming the channel output before feeding it to the network extracting only the syndrome of the hard decisions and the channel output reliabilities we prove analytically that this approach does not involve any intrinsic performance penalty and guarantees the generalization of performance obtained during training our best results are obtained using a recurrent neural network rnn architecture combined with simple preprocessing by permutation we provide simulation results that demonstrate performance that sometimes approaches that of the ordered statistics decoding osd algorithm | [['we', 'present', 'a', 'novel', 'framework', 'for', 'applying', 'deep', 'neural', 'networks', 'dnn', 'to', 'soft', 'decoding', 'of', 'linear', 'codes', 'at', 'arbitrary', 'block', 'lengths', 'unlike', 'other', 'approaches', 'our', 'framework', 'allows', 'unconstrained', 'dnn', 'design', 'enabling', 'the', 'free', 'application', 'of', 'powerful', 'designs', 'that', 'were', 'developed', 'in', 'other', 'contexts', 'our', 'method', 'is', 'robust', 'to', 'overfitting', 'that', 'inhibits', 'many', 'competing', 'methods', 'which', 'follows', 'from', 'the', 'exponentially', 'large', 'number', 'of', 'codewords', 'required', 'for', 'their', 'training', 'we', 'achieve', 'this', 'by', 'transforming', 'the', 'channel', 'output', 'before', 'feeding', 'it', 'to', 'the', 'network', 'extracting', 'only', 'the', 'syndrome', 'of', 'the', 'hard', 'decisions', 'and', 'the', 'channel', 'output', 'reliabilities', 'we', 'prove', 'analytically', 'that', 'this', 'approach', 'does', 'not', 'involve', 'any', 'intrinsic', 'performance', 'penalty', 'and', 'guarantees', 'the', 'generalization', 'of', 'performance', 'obtained', 'during', 'training', 'our', 'best', 'results', 'are', 'obtained', 'using', 'a', 'recurrent', 'neural', 'network', 'rnn', 'architecture', 'combined', 'with', 'simple', 'preprocessing', 'by', 'permutation', 'we', 'provide', 'simulation', 'results', 'that', 'demonstrate', 'performance', 'that', 'sometimes', 'approaches', 'that', 'of', 'the', 'ordered', 'statistics', 'decoding', 'osd', 'algorithm']] | [-0.08118082841515149, 0.032255145880442704, -0.07027030353262824, 0.0640983615234482, -0.09260666307435665, -0.23071580549922624, 0.07142887355631071, 0.4532707993088192, -0.2818922991864383, -0.2973557893297096, 0.08488500194691465, -0.20260057934511247, -0.23625456288496743, 0.21660467731692878, -0.11119155707389214, 0.1229733469418103, 0.15167039606450616, 0.018430883155432592, -0.0827861210518198, -0.33292784547359733, 0.2658665470478713, 0.08021989769105475, 0.34282335060599606, -0.023280326341352377, 0.1176712768509261, 0.01193975011272797, -0.01193132484331727, -0.00628139816399198, -0.07023521931602629, 0.15952029757471264, 0.2905326206773201, 0.21121702489510522, 0.30007907823585955, -0.4377442085931666, -0.2429558498084876, 0.0845346851366278, 0.1538081343353138, 0.1470007332751074, -0.01958504400528526, -0.26884211515272527, 0.1392868132067011, -0.18682785794800638, 0.00224519779562558, -0.12781311281612848, -0.08339116038558514, 0.002446955993563231, -0.31770877476695825, 0.025550350749031885, 0.10763369997699843, -0.0019566079927723252, -0.029439167210320624, -0.149257467934053, 0.03737484811541723, 0.1554370732261113, 0.04092966273088008, 0.037453492450846455, 0.11966804548022705, -0.11935874281028334, -0.15174079414052694, 0.2899425126685712, -0.021575559610746017, -0.2061493897988265, 0.18114682211690755, -0.009662835435990832, -0.14332541265053136, 0.15205052383795478, 0.2130242760146135, 0.09348983078043123, -0.1319539157649208, 0.0066404657102937465, -0.024867376815037506, 0.187585495277553, 0.041584189737669044, 0.024231089815178786, 0.13408158181578314, 0.22746686039394454, 0.03719863015813692, 0.18112782408915287, -0.10450169691122076, -0.06782453140629896, -0.250443499376017, -0.07044490341918151, -0.20645134985649125, -0.014411013173517668, -0.11734694024639075, -0.14172432792838663, 0.3854000950661047, 0.21534836280318959, 0.17794981631940524, 0.18147651465551462, 0.3710314056738035, 0.04070641217358092, 0.13020803271884737, 0.136845966195994, 0.19547484893547862, 0.07218279224655003, 0.07969695523895912, -0.2010754154080611, 0.11400249365282147, 0.07412764362954094] |
1,802.04742 | Quantifying Uncertainty in Discrete-Continuous and Skewed Data with
Bayesian Deep Learning | Deep Learning (DL) methods have been transforming computer vision with
innovative adaptations to other domains including climate change. For DL to
pervade Science and Engineering (S&E) applications where risk management is a
core component, well-characterized uncertainty estimates must accompany
predictions. However, S&E observations and model-simulations often follow
heavily skewed distributions and are not well modeled with DL approaches, since
they usually optimize a Gaussian, or Euclidean, likelihood loss. Recent
developments in Bayesian Deep Learning (BDL), which attempts to capture
uncertainties from noisy observations, aleatoric, and from unknown model
parameters, epistemic, provide us a foundation. Here we present a
discrete-continuous BDL model with Gaussian and lognormal likelihoods for
uncertainty quantification (UQ). We demonstrate the approach by developing UQ
estimates on `DeepSD', a super-resolution based DL model for Statistical
Downscaling (SD) in climate applied to precipitation, which follows an
extremely skewed distribution. We find that the discrete-continuous models
outperform a basic Gaussian distribution in terms of predictive accuracy and
uncertainty calibration. Furthermore, we find that the lognormal distribution,
which can handle skewed distributions, produces quality uncertainty estimates
at the extremes. Such results may be important across S&E, as well as other
domains such as finance and economics, where extremes are often of significant
interest. Furthermore, to our knowledge, this is the first UQ model in SD where
both aleatoric and epistemic uncertainties are characterized.
| cs.LG cs.AI stat.ML | deep learning dl methods have been transforming computer vision with innovative adaptations to other domains including climate change for dl to pervade science and engineering se applications where risk management is a core component wellcharacterized uncertainty estimates must accompany predictions however se observations and modelsimulations often follow heavily skewed distributions and are not well modeled with dl approaches since they usually optimize a gaussian or euclidean likelihood loss recent developments in bayesian deep learning bdl which attempts to capture uncertainties from noisy observations aleatoric and from unknown model parameters epistemic provide us a foundation here we present a discretecontinuous bdl model with gaussian and lognormal likelihoods for uncertainty quantification uq we demonstrate the approach by developing uq estimates on deepsd a superresolution based dl model for statistical downscaling sd in climate applied to precipitation which follows an extremely skewed distribution we find that the discretecontinuous models outperform a basic gaussian distribution in terms of predictive accuracy and uncertainty calibration furthermore we find that the lognormal distribution which can handle skewed distributions produces quality uncertainty estimates at the extremes such results may be important across se as well as other domains such as finance and economics where extremes are often of significant interest furthermore to our knowledge this is the first uq model in sd where both aleatoric and epistemic uncertainties are characterized | [['deep', 'learning', 'dl', 'methods', 'have', 'been', 'transforming', 'computer', 'vision', 'with', 'innovative', 'adaptations', 'to', 'other', 'domains', 'including', 'climate', 'change', 'for', 'dl', 'to', 'pervade', 'science', 'and', 'engineering', 'se', 'applications', 'where', 'risk', 'management', 'is', 'a', 'core', 'component', 'wellcharacterized', 'uncertainty', 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1,802.04743 | Direct sampling of the self-energy with Connected Determinant Monte
Carlo | In this note, we present an efficient algorithm to sample directly the
self-energy in the framework of the Connected Determinant technique. The
introduction of the formalism of many-variable formal power series is essential
to the proof, and more generally it is a natural mathematical tool for
diagrammatic expansions.
| cond-mat.str-el | in this note we present an efficient algorithm to sample directly the selfenergy in the framework of the connected determinant technique the introduction of the formalism of manyvariable formal power series is essential to the proof and more generally it is a natural mathematical tool for diagrammatic expansions | [['in', 'this', 'note', 'we', 'present', 'an', 'efficient', 'algorithm', 'to', 'sample', 'directly', 'the', 'selfenergy', 'in', 'the', 'framework', 'of', 'the', 'connected', 'determinant', 'technique', 'the', 'introduction', 'of', 'the', 'formalism', 'of', 'manyvariable', 'formal', 'power', 'series', 'is', 'essential', 'to', 'the', 'proof', 'and', 'more', 'generally', 'it', 'is', 'a', 'natural', 'mathematical', 'tool', 'for', 'diagrammatic', 'expansions']] | [-0.08789934364176588, 0.007564652742985345, -0.18507155341406664, 0.09934200048883213, -0.11565236316528171, -0.07334850490830529, 0.02913329712949538, 0.3538247206597589, -0.2666140731501703, -0.27924546657595783, 0.05082152241084259, -0.22838056564796716, -0.23108215276927999, 0.22541525245954594, -0.04472891274296368, 0.036248755253230534, 0.0304603548720479, 0.03452603658661246, -0.03349839560299491, -0.2158129511323447, 0.2950791683445762, 0.0799491496485037, 0.27192987261029583, 0.08385247288485213, 0.06555401964578778, 0.035706151655176654, -0.07876505228341557, -0.014329309934206927, -0.10265648848144338, 0.19733413575643985, 0.2846391141647473, 0.1413931087202703, 0.28420514428095583, -0.42184971701666046, -0.11814694716061543, 0.06287678214721382, 0.14332464288842553, 0.1383695074279482, -0.004029382666885795, -0.21336300026935837, 0.08238646931325395, -0.22408428327374472, -0.17282713373424485, -0.13790037614914277, 0.01102685509249568, -0.07498948081289807, -0.2645337697661792, 0.03941999172093347, 0.1117615079274401, 0.06134733761427924, 0.01443584063660334, -0.06850885401945561, 0.0932116944726052, 0.085425317985937, 0.006350430165184662, 0.034661705925827846, 0.06148098328655275, -0.0747876232053386, -0.042156955654112004, 0.36017581055057235, -0.04637377713030825, -0.17865391312322268, 0.14784973751132688, -0.09284841763292206, -0.18670164839810846, 0.12523164382825294, 0.1329690157144796, 0.19521733303554356, -0.21454422969448692, 0.10527899604130653, -0.015472266388921222, 0.12570022058207542, -0.012124234262349395, 0.00274583010468632, 0.1514343825595764, 0.1788074955305395, 0.051512511912733316, 0.14783003426661404, 0.01444162220771735, -0.09577346977312118, -0.33606669048701104, -0.22518935928572623, -0.19882319803582504, 0.030822009076170314, -0.08394417144366646, -0.22298100370001825, 0.4180329282147189, 0.18046105261115977, 0.12752022130734986, 0.08567496779141948, 0.3711154408908139, 0.18335718600428663, 0.05665121798301698, 0.015950125409290195, 0.13216578277448812, 0.18703478823105493, 0.09353255402917664, -0.17837246945903948, 0.01042405268526636, 0.1507110634993296] |
1,802.04744 | A Short Survey on Sense-Annotated Corpora | Large sense-annotated datasets are increasingly necessary for training deep
supervised systems in Word Sense Disambiguation. However, gathering
high-quality sense-annotated data for as many instances as possible is a
laborious and expensive task. This has led to the proliferation of automatic
and semi-automatic methods for overcoming the so-called knowledge-acquisition
bottleneck. In this short survey we present an overview of sense-annotated
corpora, annotated either manually- or (semi)automatically, that are currently
available for different languages and featuring distinct lexical resources as
inventory of senses, i.e. WordNet, Wikipedia, BabelNet. Furthermore, we provide
the reader with general statistics of each dataset and an analysis of their
specific features.
| cs.CL | large senseannotated datasets are increasingly necessary for training deep supervised systems in word sense disambiguation however gathering highquality senseannotated data for as many instances as possible is a laborious and expensive task this has led to the proliferation of automatic and semiautomatic methods for overcoming the socalled knowledgeacquisition bottleneck in this short survey we present an overview of senseannotated corpora annotated either manually or semiautomatically that are currently available for different languages and featuring distinct lexical resources as inventory of senses ie wordnet wikipedia babelnet furthermore we provide the reader with general statistics of each dataset and an analysis of their specific features | [['large', 'senseannotated', 'datasets', 'are', 'increasingly', 'necessary', 'for', 'training', 'deep', 'supervised', 'systems', 'in', 'word', 'sense', 'disambiguation', 'however', 'gathering', 'highquality', 'senseannotated', 'data', 'for', 'as', 'many', 'instances', 'as', 'possible', 'is', 'a', 'laborious', 'and', 'expensive', 'task', 'this', 'has', 'led', 'to', 'the', 'proliferation', 'of', 'automatic', 'and', 'semiautomatic', 'methods', 'for', 'overcoming', 'the', 'socalled', 'knowledgeacquisition', 'bottleneck', 'in', 'this', 'short', 'survey', 'we', 'present', 'an', 'overview', 'of', 'senseannotated', 'corpora', 'annotated', 'either', 'manually', 'or', 'semiautomatically', 'that', 'are', 'currently', 'available', 'for', 'different', 'languages', 'and', 'featuring', 'distinct', 'lexical', 'resources', 'as', 'inventory', 'of', 'senses', 'ie', 'wordnet', 'wikipedia', 'babelnet', 'furthermore', 'we', 'provide', 'the', 'reader', 'with', 'general', 'statistics', 'of', 'each', 'dataset', 'and', 'an', 'analysis', 'of', 'their', 'specific', 'features']] | [-0.05967276325157168, 0.02220553374996491, 0.009508348366829986, 0.10311535055014104, -0.19496179828146362, -0.1644389625483503, 0.06561804888769984, 0.44795619024365557, -0.24411134486652764, -0.3660116732010947, 0.1094737722351234, -0.32099819727534173, -0.07688097656001429, 0.22745107415397012, -0.13745735598358272, 0.05759027996557016, 0.1803556349238052, 0.05910100507368471, -0.013970514830754266, -0.26191065139958963, 0.3317697142871718, 0.031026197864169564, 0.33209388018311825, 0.012924671944632543, 0.09281682141476731, -0.07359672974630752, -0.10629642025485415, -0.026479741438593277, -0.0747090712074543, 0.19543398904680348, 0.4086365955060019, 0.2546584533947502, 0.34789249413263273, -0.4092590585898827, -0.14009075547458932, 0.07622150332192142, 0.17682124960108422, 0.14510414584476486, -0.041216180454391765, -0.33937472932259827, 0.08118701973409556, -0.1821007259719658, 0.055365155428173204, -0.17681421198900424, 0.06414759225741613, 0.03329244896874526, -0.2320129204559706, 0.04753937564023277, 0.0704483600497684, 0.17291254886225157, -0.03869431143781791, -0.1323789057456523, 0.048129601475974434, 0.21510074905567236, 0.05188710757704707, 0.03554458496521464, 0.07061501656426117, -0.21029147750917165, -0.1525884788202615, 0.37560555525124073, -0.013921106569172231, -0.17115505045170293, 0.23683235243729808, 0.017202054771284263, -0.2039260929724311, 0.07984444269951542, 0.1841320175513187, 0.11531470027551347, -0.23380232744795434, 0.012913390713305595, -0.03228165455819929, 0.21750039719592998, 0.08159642480770309, 0.025905645688123747, 0.18611075809481098, 0.2716292163046698, 0.00397588333112699, 0.12401820692991582, -0.0759667867141794, -0.012699533418829864, -0.23363754570064152, -0.13386054639675993, -0.1833814102243267, -0.026746758938922238, -0.05454278464700716, -0.23183872964342728, 0.32798653120613275, 0.22478985958336054, 0.16023303264542027, 0.08942203785922817, 0.3341313199158393, -0.02749244126607664, 0.13401496455794676, 0.06497103099736348, 0.09669991794918828, -0.07395724227250207, 0.18019924554791228, -0.10329321754169558, 0.09777671714112454, 0.017854382073981503] |
1,802.04745 | A counterexample to a nonlinear version of the Krein-Rutman theorem by
R. Mahadevan | In this short note we present a simple counterexample to a nonlinear version
of the Krein-Rutman theorem reported in [Nonlinear Anal. 11 (2007), 3084-3090].
Correct versions of this theorem, and related results for superadditive maps
are also presented.
| math.FA | in this short note we present a simple counterexample to a nonlinear version of the kreinrutman theorem reported in nonlinear anal 11 2007 30843090 correct versions of this theorem and related results for superadditive maps are also presented | [['in', 'this', 'short', 'note', 'we', 'present', 'a', 'simple', 'counterexample', 'to', 'a', 'nonlinear', 'version', 'of', 'the', 'kreinrutman', 'theorem', 'reported', 'in', 'nonlinear', 'anal', '11', '2007', '30843090', 'correct', 'versions', 'of', 'this', 'theorem', 'and', 'related', 'results', 'for', 'superadditive', 'maps', 'are', 'also', 'presented']] | [-0.07175820721061649, -0.0029574581421911716, -0.0996608557431279, 0.12259183588085344, -0.05146308827239114, -0.1429594519824998, 0.024064973166615173, 0.3387073890582935, -0.21647611300687533, -0.25553109829087517, 0.12658952876950638, -0.2521072679476158, -0.2547630859488571, 0.26035869353123614, -0.20116278403312773, 0.02108127057451654, 0.09306867362780345, -0.058147384055160185, -0.061634447475945625, -0.2785379997032971, 0.2714078159549752, 0.02173688423794669, 0.19950316970966556, 0.13125368268103213, 0.10015460672612125, 0.055031217044735375, -0.10695195515212175, 0.012926707396636138, -0.2066181980677553, 0.15132437419851083, 0.27663021129430143, 0.051161625227463, 0.270237636676914, -0.3321507354392796, -0.12134441250079386, 0.11824814007441337, 0.010012702341506106, 0.14736632319053988, -0.05475010458581351, -0.2797310556712988, 0.10360342108115957, -0.1487407667461682, -0.1741426957620157, -0.06066751726777167, 0.03498718915255489, 0.022539982723223197, -0.26275891448194916, 0.17759317264662478, 0.24469908314278802, 0.06645828931013474, -0.06674096601184558, -0.08284345958885309, 0.05288424284977687, -0.0038056103764353573, -0.016913872048560832, 0.026394790655153023, -0.0064187599471896084, -0.042604610286149626, -0.1811877561078684, 0.29984027781599276, -0.08687263405000842, -0.19795168639236205, 0.1995135581221532, -0.060479747181808624, -0.24632789596059435, 0.08575154929044279, 0.17714295585362896, 0.13876486484062028, -0.17934788448887096, 0.08312011619469754, -0.15568795452850895, 0.16404036533188177, 0.12163712450529675, 0.002211809963793368, 0.07704405705570369, 0.07832576686868796, 0.042600837787864984, 0.18594893886758065, 0.03154920852063475, -0.05823594674065306, -0.3932192880358245, -0.15791584030297157, -0.14013331674819662, 0.08279716183204909, -0.0253763585473329, -0.13524207261365814, 0.4069371385550177, 0.16796900404340312, 0.12900339731493513, 0.13035580075371103, 0.19749180902097677, 0.1679403166966261, -0.032617185365509345, 0.09900577635680502, 0.24091299799447125, 0.22109561005758271, 0.1811472790164722, -0.056969199833032245, -0.06267174376124465, 0.15555445701387283] |
1,802.04746 | Topological complexity of symplectic manifolds | We prove that the topological complexity of every symplectically atoroidal
manifold is equal to twice its dimension. This is the analogue for topological
complexity of a result of Rudyak and Oprea, who showed that the
Lusternik--Schnirelmann category of a symplectically aspherical manifold equals
its dimension. Symplectically hyperbolic manifolds are symplectically
atoroidal, as are symplectically aspherical manifolds whose fundamental group
does not contain free abelian subgroups of rank two. Thus we obtain many new
calculations of topological complexity, including iterated surface bundles and
symplectically aspherical manifolds with hyperbolic fundamental groups. Our
result also applies in the greater generality of cohomologically symplectic
manifolds.
| math.AT math.SG | we prove that the topological complexity of every symplectically atoroidal manifold is equal to twice its dimension this is the analogue for topological complexity of a result of rudyak and oprea who showed that the lusternikschnirelmann category of a symplectically aspherical manifold equals its dimension symplectically hyperbolic manifolds are symplectically atoroidal as are symplectically aspherical manifolds whose fundamental group does not contain free abelian subgroups of rank two thus we obtain many new calculations of topological complexity including iterated surface bundles and symplectically aspherical manifolds with hyperbolic fundamental groups our result also applies in the greater generality of cohomologically symplectic manifolds | [['we', 'prove', 'that', 'the', 'topological', 'complexity', 'of', 'every', 'symplectically', 'atoroidal', 'manifold', 'is', 'equal', 'to', 'twice', 'its', 'dimension', 'this', 'is', 'the', 'analogue', 'for', 'topological', 'complexity', 'of', 'a', 'result', 'of', 'rudyak', 'and', 'oprea', 'who', 'showed', 'that', 'the', 'lusternikschnirelmann', 'category', 'of', 'a', 'symplectically', 'aspherical', 'manifold', 'equals', 'its', 'dimension', 'symplectically', 'hyperbolic', 'manifolds', 'are', 'symplectically', 'atoroidal', 'as', 'are', 'symplectically', 'aspherical', 'manifolds', 'whose', 'fundamental', 'group', 'does', 'not', 'contain', 'free', 'abelian', 'subgroups', 'of', 'rank', 'two', 'thus', 'we', 'obtain', 'many', 'new', 'calculations', 'of', 'topological', 'complexity', 'including', 'iterated', 'surface', 'bundles', 'and', 'symplectically', 'aspherical', 'manifolds', 'with', 'hyperbolic', 'fundamental', 'groups', 'our', 'result', 'also', 'applies', 'in', 'the', 'greater', 'generality', 'of', 'cohomologically', 'symplectic', 'manifolds']] | [-0.21836172407585205, 0.11238483557567498, -0.11078349615696191, 0.1143442614495035, -0.13513460784394404, -0.20853804693428227, -0.034737500001325985, 0.3604853961566039, -0.2521052616832796, -0.21888354707435512, 0.07841715420075314, -0.2462577866851398, -0.19528236560117784, 0.2274536530067683, -0.2684539319984246, -0.049110877349520896, 0.08144913135225525, 0.098682858771414, -0.09359681271800384, -0.3606156472884358, 0.48433927232676216, -0.09331093983042359, 0.21133074526315426, 0.10794423837767969, 0.10197388596191892, -0.05202245816016699, 0.0330229606294986, 0.03640960313213786, -0.1710493489173483, 0.1347720216968936, 0.2824853853608417, 0.011024793879691474, 0.13748450204384247, -0.35025807621624866, -0.19678299389625403, 0.21659694835174792, 0.152619443861491, -0.01993241071894691, -0.004421685575645896, -0.2894639326443914, 0.09098199659976924, -0.1343106352551241, -0.2352178181235743, -0.0931357404881037, 0.0410661778083108, -0.05324815405707917, -0.09001125560815235, -0.028512454603505922, 0.1970598857347291, 0.07966680367559939, -0.07325988565110556, -0.10221780105355648, -0.13309733664367976, 0.13558187933020885, -0.0005821146366029683, 0.04095615985305371, 0.1266100385202104, -0.013045839492766427, -0.12127876379711572, 0.4048887796349602, -0.023411997492508132, -0.2942280189161844, 0.16749593994515663, -0.14029484255571323, -0.22849638507434047, 0.2376406072814128, 0.0934971304850118, 0.14304565647553588, 0.04453175822457317, 0.2084788160737475, -0.13062833834181328, 0.10000563952334152, 0.08268516231328249, 0.0037095535749403557, 0.07072665296163004, 0.09212312322930608, 0.17641130568733956, 0.10453663303950193, 0.06345837572958357, -0.00629463563194858, -0.2915360150065753, -0.3034467980643679, -0.12733919299806995, 0.24584382549542036, -0.13986068259029058, -0.22609201166317752, 0.3433331072994388, -0.04930309104816158, 0.12427953013799864, 0.1868619583362695, 0.2680754971708918, -0.05206558604305149, 0.05376340335437862, 0.13219369086667454, 0.13729776673037375, 0.2778365977873823, -0.10182872714271934, -0.0973910654590714, -0.0810795141070491, 0.26390035287477076] |
1,802.04747 | Viscosity Solutions of Systems of PDEs with Interconnected Obstacles and
Switching Problem without Monotonicity Condition | We show the existence and uniqueness of a continuous viscosity solution of a
system of partial differential equations (PDEs for short) without assuming the
usual monotonicity conditions on the driver function as in Hamad\`ene and
Morlais's article \cite{hamadene2013viscosity}. Our method strongly relies on
the link between PDEs and reflected backward stochastic differential equations
with interconnected obstacles for which we already know that the solution
exists and is unique for general drivers.
| math.OC | we show the existence and uniqueness of a continuous viscosity solution of a system of partial differential equations pdes for short without assuming the usual monotonicity conditions on the driver function as in hamadene and morlaiss article citehamadene2013viscosity our method strongly relies on the link between pdes and reflected backward stochastic differential equations with interconnected obstacles for which we already know that the solution exists and is unique for general drivers | [['we', 'show', 'the', 'existence', 'and', 'uniqueness', 'of', 'a', 'continuous', 'viscosity', 'solution', 'of', 'a', 'system', 'of', 'partial', 'differential', 'equations', 'pdes', 'for', 'short', 'without', 'assuming', 'the', 'usual', 'monotonicity', 'conditions', 'on', 'the', 'driver', 'function', 'as', 'in', 'hamadene', 'and', 'morlaiss', 'article', 'citehamadene2013viscosity', 'our', 'method', 'strongly', 'relies', 'on', 'the', 'link', 'between', 'pdes', 'and', 'reflected', 'backward', 'stochastic', 'differential', 'equations', 'with', 'interconnected', 'obstacles', 'for', 'which', 'we', 'already', 'know', 'that', 'the', 'solution', 'exists', 'and', 'is', 'unique', 'for', 'general', 'drivers']] | [-0.15931066168823105, 0.005079510034588368, -0.07521418514121594, 0.057057321323014366, -0.11020698565382349, -0.12675732610396284, 0.011680069823236461, 0.29559954803814925, -0.32159286475591903, -0.25700301390843117, 0.15863540868926118, -0.2873870641452031, -0.1783177798370952, 0.21584519309977043, -0.0633937222748131, 0.09109788050578124, 0.07815584217778583, -0.0041463843621043625, -0.06071464478821102, -0.1779147786108534, 0.4098427244934483, -0.07296283521514008, 0.20984322585813378, 0.0626844888067116, 0.2351184943623409, 0.03034687334118222, -0.030328207635793133, 0.02800648388169382, -0.1564927303568212, 0.05990841901064783, 0.22048090556568967, 0.10407910341229476, 0.2895377035374227, -0.436474272603358, -0.1808764919017752, 0.07251548541682785, 0.09657053630486927, 0.09422754819093244, -0.06786105622861373, -0.28413123916834593, 0.09689300248156422, -0.09902966648772143, -0.1902079572496207, -0.036034924909472466, 0.023397495889145394, 0.12371946404607076, -0.30114105871568125, 0.09479469271457713, 0.10539721835242666, 0.03165053218211709, -0.10580144520255103, -0.04849189068055779, -0.05443532663343501, 0.07811110596293988, 0.05696525858323751, -0.02053370394244574, 0.03748943805829554, -0.1314932152736878, -0.09411084435988164, 0.3392788012228582, -0.12355330646834403, -0.2982542432466711, 0.2193546464558745, -0.1311015654625236, -0.1293327707554335, 0.13240338317995917, 0.17269478502440389, 0.17889582554715266, -0.189217082562222, 0.08269987313373797, -0.07095696223710758, 0.17077183129562848, 0.059988566998230373, 0.0035296542666164105, 0.10366324794249258, 0.17342712298926452, 0.19582166517342348, 0.07262512936216334, 0.034678687489982964, -0.14213578082769568, -0.35477955668381805, -0.18527530804883852, -0.09006439938979305, 0.07118777263963567, -0.08112115931715749, -0.1823706985923691, 0.3475384491806229, 0.13547193891017872, 0.13536867742305217, 0.08644115799477836, 0.2600697566927208, 0.21200398739724272, -0.0304526770622402, 0.07411877933106777, 0.21297442926552848, 0.16179668308451664, 0.17694469406336977, -0.22478125706665975, 0.13145079577773594, 0.12357674211101688] |
1,802.04748 | Gamma-ray beams with large orbital angular momentum via nonlinear
Compton scattering with radiation reaction | Gamma-ray beams with large angular momentum are a very valuable tool to study
astrophysical phenomena in a laboratory. We investigate generation of
well-collimated $\gamma$-ray beams with a very large orbital angular momentum
using nonlinear Compton scattering of a strong laser pulse of twisted photons
at ultra-relativistic electrons. Angular momentum conservation among absorbed
laser photons, quantum radiation and electrons are numerically demonstrated in
the quantum radiation dominated regime. We point out that the angular momentum
of the absorbed laser photons is not solely transferred to the emitted
$\gamma$-photons, but due to radiation reaction shared between the
$\gamma$-photons and interacting electrons. The efficiency of the angular
momentum transfer is optimized with respect to the laser and electron beam
parameters. The accompanying process of electron-positron pair production is
furthermore shown to enhance the orbital angular momentum gained by the
$\gamma$-ray beam.
| physics.plasm-ph | gammaray beams with large angular momentum are a very valuable tool to study astrophysical phenomena in a laboratory we investigate generation of wellcollimated gammaray beams with a very large orbital angular momentum using nonlinear compton scattering of a strong laser pulse of twisted photons at ultrarelativistic electrons angular momentum conservation among absorbed laser photons quantum radiation and electrons are numerically demonstrated in the quantum radiation dominated regime we point out that the angular momentum of the absorbed laser photons is not solely transferred to the emitted gammaphotons but due to radiation reaction shared between the gammaphotons and interacting electrons the efficiency of the angular momentum transfer is optimized with respect to the laser and electron beam parameters the accompanying process of electronpositron pair production is furthermore shown to enhance the orbital angular momentum gained by the gammaray beam | [['gammaray', 'beams', 'with', 'large', 'angular', 'momentum', 'are', 'a', 'very', 'valuable', 'tool', 'to', 'study', 'astrophysical', 'phenomena', 'in', 'a', 'laboratory', 'we', 'investigate', 'generation', 'of', 'wellcollimated', 'gammaray', 'beams', 'with', 'a', 'very', 'large', 'orbital', 'angular', 'momentum', 'using', 'nonlinear', 'compton', 'scattering', 'of', 'a', 'strong', 'laser', 'pulse', 'of', 'twisted', 'photons', 'at', 'ultrarelativistic', 'electrons', 'angular', 'momentum', 'conservation', 'among', 'absorbed', 'laser', 'photons', 'quantum', 'radiation', 'and', 'electrons', 'are', 'numerically', 'demonstrated', 'in', 'the', 'quantum', 'radiation', 'dominated', 'regime', 'we', 'point', 'out', 'that', 'the', 'angular', 'momentum', 'of', 'the', 'absorbed', 'laser', 'photons', 'is', 'not', 'solely', 'transferred', 'to', 'the', 'emitted', 'gammaphotons', 'but', 'due', 'to', 'radiation', 'reaction', 'shared', 'between', 'the', 'gammaphotons', 'and', 'interacting', 'electrons', 'the', 'efficiency', 'of', 'the', 'angular', 'momentum', 'transfer', 'is', 'optimized', 'with', 'respect', 'to', 'the', 'laser', 'and', 'electron', 'beam', 'parameters', 'the', 'accompanying', 'process', 'of', 'electronpositron', 'pair', 'production', 'is', 'furthermore', 'shown', 'to', 'enhance', 'the', 'orbital', 'angular', 'momentum', 'gained', 'by', 'the', 'gammaray', 'beam']] | [-0.12626623489262967, 0.2878639720299322, -0.08096649892399581, 0.1202895133303331, -0.05088132465480948, -0.11237635463813617, -0.012637829202213798, 0.43225039768478146, -0.2563892946295116, -0.32772060463321395, -0.10647724901724175, -0.3240683984223996, 0.0862756184288773, 0.25810567282098706, 0.05987683555070797, 0.061210525522440454, 0.09855362742403657, -0.1080951887736286, 0.0020565275301940847, -0.10796884861538537, 0.3183735537855629, 0.21955249551514947, 0.26459203231607337, 0.08203687565401196, 0.19462778340296252, 0.036141879630504525, -0.04301461583278412, -0.08281158052884258, -0.05775897982016761, 0.06365298780788115, 0.227049706259108, 0.006396740688469963, 0.17096767718380695, -0.4145672798993579, -0.20730889091889063, 0.08034687490620906, 0.16680531349181588, 0.089533703130049, -0.10006787333691465, -0.24120427300964575, -0.047050302973071084, -0.19646390217720813, -0.1519310841323349, -0.03783649355308085, 0.016223715722857825, 0.08941778303562677, -0.2539245367617063, 0.03321985017332802, 0.029709840014549918, -0.010044482055768047, -0.033723480395459825, -0.02873096557037122, -0.09836469420933745, -0.002778315680452447, 0.09344748453978631, 0.07470927258512255, 0.19909632941530814, -0.13889508240896722, -0.0870609948668035, 0.39875979289628455, -0.01439290090828486, -0.1655837872834957, 0.1548382764078164, -0.2603330832494396, -0.02776529004905319, 0.2572849855852732, 0.19247556429363086, 0.13630193737470478, -0.14349667831202564, 0.010188214236657823, 0.030061466352798154, 0.17794186298577688, 0.12570461024111812, 0.14522122312674596, 0.3083345784225326, 0.12276071848590737, -0.027424990577434284, 0.15382148064942897, -0.179142194914807, -0.06776513650412738, -0.2533743731353594, -0.1177836988489076, -0.23520163033643496, 0.13096736839213627, -0.01731298527432744, -0.039987378491154545, 0.3740409443769064, 0.0958933301769413, 0.15068001040271012, -0.08350475355341652, 0.36087663455501845, 0.16190663202112351, 0.03475884193945946, 0.09612368601306841, 0.3387187750051743, 0.1694540227037868, 0.1692155897246161, -0.3099200892012458, -0.023444235331608332, -0.019071329897944477] |
1,802.04749 | MDroid+: A Mutation Testing Framework for Android | Mutation testing has shown great promise in assessing the effectiveness of
test suites while exhibiting additional applications to test-case generation,
selection, and prioritization. Traditional mutation testing typically utilizes
a set of simple language specific source code transformations, called
operators, to introduce faults. However, empirical studies have shown that for
mutation testing to be most effective, these simple operators must be augmented
with operators specific to the domain of the software under test. One
challenging software domain for the application of mutation testing is that of
mobile apps. While mobile devices and accompanying apps have become a mainstay
of modern computing, the frameworks and patterns utilized in their development
make testing and verification particularly difficult. As a step toward helping
to measure and ensure the effectiveness of mobile testing practices, we
introduce MDroid+, an automated framework for mutation testing of Android apps.
MDroid+ includes 38 mutation operators from ten empirically derived types of
Android faults and has been applied to generate over 8,000 mutants for more
than 50 apps.
| cs.SE | mutation testing has shown great promise in assessing the effectiveness of test suites while exhibiting additional applications to testcase generation selection and prioritization traditional mutation testing typically utilizes a set of simple language specific source code transformations called operators to introduce faults however empirical studies have shown that for mutation testing to be most effective these simple operators must be augmented with operators specific to the domain of the software under test one challenging software domain for the application of mutation testing is that of mobile apps while mobile devices and accompanying apps have become a mainstay of modern computing the frameworks and patterns utilized in their development make testing and verification particularly difficult as a step toward helping to measure and ensure the effectiveness of mobile testing practices we introduce mdroid an automated framework for mutation testing of android apps mdroid includes 38 mutation operators from ten empirically derived types of android faults and has been applied to generate over 8000 mutants for more than 50 apps | [['mutation', 'testing', 'has', 'shown', 'great', 'promise', 'in', 'assessing', 'the', 'effectiveness', 'of', 'test', 'suites', 'while', 'exhibiting', 'additional', 'applications', 'to', 'testcase', 'generation', 'selection', 'and', 'prioritization', 'traditional', 'mutation', 'testing', 'typically', 'utilizes', 'a', 'set', 'of', 'simple', 'language', 'specific', 'source', 'code', 'transformations', 'called', 'operators', 'to', 'introduce', 'faults', 'however', 'empirical', 'studies', 'have', 'shown', 'that', 'for', 'mutation', 'testing', 'to', 'be', 'most', 'effective', 'these', 'simple', 'operators', 'must', 'be', 'augmented', 'with', 'operators', 'specific', 'to', 'the', 'domain', 'of', 'the', 'software', 'under', 'test', 'one', 'challenging', 'software', 'domain', 'for', 'the', 'application', 'of', 'mutation', 'testing', 'is', 'that', 'of', 'mobile', 'apps', 'while', 'mobile', 'devices', 'and', 'accompanying', 'apps', 'have', 'become', 'a', 'mainstay', 'of', 'modern', 'computing', 'the', 'frameworks', 'and', 'patterns', 'utilized', 'in', 'their', 'development', 'make', 'testing', 'and', 'verification', 'particularly', 'difficult', 'as', 'a', 'step', 'toward', 'helping', 'to', 'measure', 'and', 'ensure', 'the', 'effectiveness', 'of', 'mobile', 'testing', 'practices', 'we', 'introduce', 'mdroid', 'an', 'automated', 'framework', 'for', 'mutation', 'testing', 'of', 'android', 'apps', 'mdroid', 'includes', '38', 'mutation', 'operators', 'from', 'ten', 'empirically', 'derived', 'types', 'of', 'android', 'faults', 'and', 'has', 'been', 'applied', 'to', 'generate', 'over', '8000', 'mutants', 'for', 'more', 'than', '50', 'apps']] | [-0.07478919866774786, -0.0033453781887233377, -0.029784582312762115, 0.09082432732421217, -0.09094101357706157, -0.2316070437622589, 0.0669570763102023, 0.4082234670807208, -0.19513084772500275, -0.3320096831081346, 0.1382039842380133, -0.2446563060899886, -0.13574351922337277, 0.26008823252881746, -0.10959852837757873, 0.08844510810740758, 0.09469933444744952, -0.0010097146843604389, 0.0006202034544133182, -0.2909107946463683, 0.29137051474147785, 0.05486525734886527, 0.3400895759335808, 0.04155145209508538, 0.04662457593613432, 0.0018288760828519507, -0.06938800597951437, 0.0020829637457306185, -0.04843828067991819, 0.14049792149758322, 0.33766314034194994, 0.23367021325837067, 0.3631838356322121, -0.406865142041906, -0.16790245399793743, 0.10447813247931965, 0.15268187753307366, 0.10468528619302171, -0.09571428354415513, -0.30765994780674755, 0.17298721884643392, -0.21501807117342, -0.09091554003498459, -0.12310495693513769, 0.049200336897878776, 0.007898019159342982, -0.29804690509765697, -0.043183970566696234, 0.006219113733442057, 0.14981707536415861, -0.015827601103800198, -0.10263041135511317, 0.011800572582398586, 0.18648117420075105, 0.08307710467211325, -0.0276468158311521, 0.19529006920631564, -0.11568228911069343, -0.17860536358577, 0.37166537071711253, 0.012828648465507459, -0.1607592186801845, 0.2531302712519564, -0.019549640438829858, -0.18006217219157233, 0.07093329824731752, 0.19803508628337158, 0.09957825050168183, -0.24151164588762358, 0.05780016513502536, 0.06498053332602251, 0.16759708719438918, 0.0779617088181632, 0.0013896939289268283, 0.18375789058821587, 0.22216699943722537, 0.06005069238433082, 0.13551137319605222, -0.07744395913184798, -0.07147602057306185, -0.21342966007068753, -0.18851815231028013, -0.11894059090298556, 0.008469684052258907, -0.07819290899542033, -0.20331871179535352, 0.3966974614789554, 0.2256227984521718, 0.08233699328376956, 0.07585961310680778, 0.2741741718325232, 0.04613010184376825, 0.2041234213829739, 0.06320722839917012, 0.13370241987563314, 0.03465031123293253, 0.10702157171285112, -0.1602859066181173, 0.1361449811638089, 0.001113912866761287] |
1,802.0475 | Simulation of the propagation of a cylindrical shear wave : non linear
and dissipative modelling | The simulation of a wave propagation caused by seismic stimulation allows to
study the behaviour of the environment and to evaluate the consequences. The
model involves the wave equation with a hysteresis loop in the stress-strain
relationship. This induces non-linearities and, at the vertices of the loop,
non-differentiable mathematical operators. This paper offers a numerical
process which works out this simulation.
| physics.geo-ph cs.CE nlin.CD | the simulation of a wave propagation caused by seismic stimulation allows to study the behaviour of the environment and to evaluate the consequences the model involves the wave equation with a hysteresis loop in the stressstrain relationship this induces nonlinearities and at the vertices of the loop nondifferentiable mathematical operators this paper offers a numerical process which works out this simulation | [['the', 'simulation', 'of', 'a', 'wave', 'propagation', 'caused', 'by', 'seismic', 'stimulation', 'allows', 'to', 'study', 'the', 'behaviour', 'of', 'the', 'environment', 'and', 'to', 'evaluate', 'the', 'consequences', 'the', 'model', 'involves', 'the', 'wave', 'equation', 'with', 'a', 'hysteresis', 'loop', 'in', 'the', 'stressstrain', 'relationship', 'this', 'induces', 'nonlinearities', 'and', 'at', 'the', 'vertices', 'of', 'the', 'loop', 'nondifferentiable', 'mathematical', 'operators', 'this', 'paper', 'offers', 'a', 'numerical', 'process', 'which', 'works', 'out', 'this', 'simulation']] | [-0.16635831869894363, 0.0952670507430725, -0.10471049590860723, 0.022727609525362392, -0.10383264064315523, -0.06789184905985585, 0.06987290328834206, 0.31590942942277817, -0.2654097907733722, -0.25408290702177855, 0.06444605023668865, -0.27486062006758655, -0.2567169591173774, 0.17687463843965995, -0.017464610379662546, 0.07433319813190181, 0.07014116516611615, 0.002419874331623804, -0.053201439088118856, -0.16623712738319377, 0.34221344085822464, 0.0876356978885463, 0.2665782700918737, 0.05904484729542107, 0.13790649537962754, -0.0016076449976592767, -0.022510812144543303, 0.00881883833313086, -0.10802895631870163, 0.08795049780078965, 0.21144732903139513, 0.06849329277384476, 0.2891605751680546, -0.4711704917374204, -0.2765652419174792, 0.09024070342239297, 0.10693254596630081, 0.09962811046776163, -0.01732895343152226, -0.27708772386683794, 0.015109419891396995, -0.12990453399595667, -0.16814456077781123, -0.021744329757133467, -0.0050933675413004685, 0.007325971525803697, -0.28361509673175267, 0.06785839475447038, 0.04788147948193745, 0.03332994847756918, -0.03262637624398759, -0.011445537041567389, -0.017834166332041143, 0.10977277409315842, 0.06779035148506633, 0.030651824483953295, 0.11396756956781276, -0.1354506806704, -0.11234103556966683, 0.3652193655000358, -0.022567466909035307, -0.1965589772100698, 0.16738069889929574, -0.1422855670761401, -0.07042098562920192, 0.12182835318514558, 0.18561189328549338, 0.08249375153882582, -0.18611735331474757, 0.07172257157584622, 0.02515845498514789, 0.12420254887738189, 0.06450085254905165, -0.04298443579282917, 0.17224699307660588, 0.21239027011467784, 0.01130678504705429, 0.2185726038134489, -0.07402482031096445, -0.12305913902208453, -0.3468614534520712, -0.12343572741221698, -0.08815317887996064, 0.02347489014853815, -0.08520334649050501, -0.1977032605925056, 0.4664545232949198, 0.19668507244682215, 0.16615125399510391, 0.07325961167702726, 0.3172767625541472, 0.1554348997954951, 0.04857736444253413, 0.04081290407625378, 0.23526279785532933, 0.16273507723569502, 0.1564784256100166, -0.29623215472455455, 0.09108161117087622, 0.04525866186948585] |
1,802.04751 | A study on a minimally broken residual TBM-Klein symmetry with its
implications on flavoured leptogenesis and ultra high energy neutrino flux
ratios | We present a systematic study on minimally perturbed neutrino mass matrices
which at the leading order give rise to Tri-BiMaximal (TBM) mixing due to a
residual $\mathbb{Z}_2\times \mathbb{Z}_2^{\mu\tau}$ Klein symmetry in the
neutrino mass term of the low energy effective seesaw Lagrangian. Considering
only the breaking of $\mathbb{Z}_2^{\mu\tau}$ with two relevant breaking
parameters ($\epsilon_{4,6}^\prime$), after a comprehensive numerical analysis,
we show that the phenomenologically viable case in this scenario is a special
case of TM1 mixing. For this class of models, from the phenomenological
perspective, one always needs large breaking (more than $ 45\%$) in one of the
breaking parameters. However, to be consistent the maximal mixing of
$\theta_{23}$, while more than $ 35\%$ breaking is needed in the other, a range
$49.4^\circ-53^\circ$ and $38^\circ-40^\circ$ could be probed allowing breaking
up to $ 25\%$ in the same parameter. Thus though this model cannot distinguish
the octant of $\theta_{23}$, non-maximal mixing is preferred from the viewpoint
of small breaking. The model is also interesting from leptogenesis perspective.
Unlike the standard $N_1$-leptogenesis scenario, here all the RH neutrinos
contribute to lepton asymmetry due to the small mass splitting controlled by
the $\mathbb{Z}_2^{\mu\tau}$ breaking parameters. Inclusion of flavour coupling
effects (In general, which have been partially included in all the leptogenesis
studies in perturbed TBM framework) makes our analysis and results pertaining
to a successful leptogenesis more accurate than any other studies in existing
literature. Finally, in the context of recent discovery of the ultra high
energy (UHE) neutrino events at IceCube, assuming UHE neutrinos originate from
purely astrophysical sources, we obtain prediction on the neutrino flux ratios
at neutrino telescopes.
| hep-ph | we present a systematic study on minimally perturbed neutrino mass matrices which at the leading order give rise to tribimaximal tbm mixing due to a residual mathbbz_2times mathbbz_2mutau klein symmetry in the neutrino mass term of the low energy effective seesaw lagrangian considering only the breaking of mathbbz_2mutau with two relevant breaking parameters epsilon_46prime after a comprehensive numerical analysis we show that the phenomenologically viable case in this scenario is a special case of tm1 mixing for this class of models from the phenomenological perspective one always needs large breaking more than 45 in one of the breaking parameters however to be consistent the maximal mixing of theta_23 while more than 35 breaking is needed in the other a range 494circ53circ and 38circ40circ could be probed allowing breaking up to 25 in the same parameter thus though this model cannot distinguish the octant of theta_23 nonmaximal mixing is preferred from the viewpoint of small breaking the model is also interesting from leptogenesis perspective unlike the standard n_1leptogenesis scenario here all the rh neutrinos contribute to lepton asymmetry due to the small mass splitting controlled by the mathbbz_2mutau breaking parameters inclusion of flavour coupling effects in general which have been partially included in all the leptogenesis studies in perturbed tbm framework makes our analysis and results pertaining to a successful leptogenesis more accurate than any other studies in existing literature finally in the context of recent discovery of the ultra high energy uhe neutrino events at icecube assuming uhe neutrinos originate from purely astrophysical sources we obtain prediction on the neutrino flux ratios at neutrino telescopes | [['we', 'present', 'a', 'systematic', 'study', 'on', 'minimally', 'perturbed', 'neutrino', 'mass', 'matrices', 'which', 'at', 'the', 'leading', 'order', 'give', 'rise', 'to', 'tribimaximal', 'tbm', 'mixing', 'due', 'to', 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1,802.04752 | Subordination principles for the multi-dimensional space-time-fractional
diffusion-wave equation | This paper is devoted to an in deep investigation of the first fundamental
solution to the linear multi-dimensional space-time-fractional diffusion-wave
equation. This equation is obtained from the diffusion equation by replacing
the first order time-deri\-va\-ti\-ve by the Caputo fractional derivative of
order $\beta,\ 0 <\beta \leq 2$ and the Laplace operator by the fractional
Laplacian $(-\Delta)^{\frac\alpha 2}$ with $0<\alpha \leq 2$. First, a
representation of the fundamental solution in form of a Mellin-Barnes integral
is deduced by employing the technique of the Mellin integral transform. This
representation is then used for establishing of several subordination formulas
that connect the fundamental solutions for different values of the fractional
derivatives $\alpha$ and $\beta$. We also discuss some new cases of completely
monotone functions and probability density functions that are expressed in
terms of the Mittag-Leffler function, the Wright function, and the generalized
Wright function.
| math.AP | this paper is devoted to an in deep investigation of the first fundamental solution to the linear multidimensional spacetimefractional diffusionwave equation this equation is obtained from the diffusion equation by replacing the first order timederivative by the caputo fractional derivative of order beta 0 beta leq 2 and the laplace operator by the fractional laplacian deltafracalpha 2 with 0alpha leq 2 first a representation of the fundamental solution in form of a mellinbarnes integral is deduced by employing the technique of the mellin integral transform this representation is then used for establishing of several subordination formulas that connect the fundamental solutions for different values of the fractional derivatives alpha and beta we also discuss some new cases of completely monotone functions and probability density functions that are expressed in terms of the mittagleffler function the wright function and the generalized wright function | [['this', 'paper', 'is', 'devoted', 'to', 'an', 'in', 'deep', 'investigation', 'of', 'the', 'first', 'fundamental', 'solution', 'to', 'the', 'linear', 'multidimensional', 'spacetimefractional', 'diffusionwave', 'equation', 'this', 'equation', 'is', 'obtained', 'from', 'the', 'diffusion', 'equation', 'by', 'replacing', 'the', 'first', 'order', 'timederivative', 'by', 'the', 'caputo', 'fractional', 'derivative', 'of', 'order', 'beta', '0', 'beta', 'leq', '2', 'and', 'the', 'laplace', 'operator', 'by', 'the', 'fractional', 'laplacian', 'deltafracalpha', '2', 'with', '0alpha', 'leq', '2', 'first', 'a', 'representation', 'of', 'the', 'fundamental', 'solution', 'in', 'form', 'of', 'a', 'mellinbarnes', 'integral', 'is', 'deduced', 'by', 'employing', 'the', 'technique', 'of', 'the', 'mellin', 'integral', 'transform', 'this', 'representation', 'is', 'then', 'used', 'for', 'establishing', 'of', 'several', 'subordination', 'formulas', 'that', 'connect', 'the', 'fundamental', 'solutions', 'for', 'different', 'values', 'of', 'the', 'fractional', 'derivatives', 'alpha', 'and', 'beta', 'we', 'also', 'discuss', 'some', 'new', 'cases', 'of', 'completely', 'monotone', 'functions', 'and', 'probability', 'density', 'functions', 'that', 'are', 'expressed', 'in', 'terms', 'of', 'the', 'mittagleffler', 'function', 'the', 'wright', 'function', 'and', 'the', 'generalized', 'wright', 'function']] | [-0.09213538308228765, 0.06449904668770615, -0.07325478796431395, 0.07826796834207406, -0.08207975104783795, -0.07145501852168569, -0.003976006951415911, 0.30118834724915877, -0.3247859797806346, -0.23762239546382002, 0.12485474866033265, -0.2783388991840184, -0.19551879260023788, 0.17968966104755443, -0.016857597944804834, 0.10705240162881507, -0.03923062676331028, 0.032293762426291193, -0.11720009079934764, -0.19733218210084097, 0.37268296666443346, -0.03327259326823488, 0.16426122968883386, 0.013604466650368912, 0.12112506429465221, 0.0057057448629555954, -0.06996049541713936, -0.06468001219651082, -0.21188041673542882, 0.13652996612114032, 0.2024769391332354, 0.07038241156842559, 0.30905550505433765, -0.3850005756398397, -0.18333739721482353, 0.09528577981649765, 0.15219053557874368, -0.03174947401774781, -0.006292213466284531, -0.2949343533521252, 0.06556511449494533, -0.15557377029742514, -0.1803333904261568, -0.08662623412507986, 0.055163857746603234, 0.10283884781279734, -0.30411404390553276, 0.11988276847550879, 0.06796950814314187, -0.018935026062120285, -0.10789620267002777, -0.18868120105138847, 0.04363317967924688, 0.07933629552634167, 0.050765315080726785, 0.05102941866332133, 0.0007383979424568159, -0.16210920507188087, -0.09012858357080923, 0.3286032236580338, -0.10000430588843301, -0.27306923028081653, 0.04939638776704669, -0.21228297694054032, -0.15094717933763085, 0.10501411821360307, 0.08403180909143494, 0.18036764602043798, -0.18667716911461738, 0.149981349864642, -0.010820301137365667, 0.1415094961412251, 0.12016646204449768, -0.008620905114470848, 0.082711209163868, 0.10601237710631851, 0.07381773943364221, 0.14911037738035832, -0.02985931973505233, -0.09102267288669412, -0.37366277671286036, -0.24021218862035312, -0.22311123980741415, 0.06242765270040503, -0.1487873812800639, -0.13594322774027076, 0.39205985641289903, 0.09460146817977406, 0.12890007998794317, 0.10205330243999404, 0.23103058534907178, 0.2947318079349186, 0.016416836252236472, 0.022429847391322255, 0.13063058020717497, 0.17879726255611916, 0.11934104492954378, -0.1980146147601772, 0.03877171850098031, 0.18071775290882214] |
1,802.04753 | Prospects for next generation Cosmic Microwave Background experiments | In this lecture, after a synthetic review of measurements of CMB temperature
anisotropies and of their cosmological implications, the theoretical background
of CMB polarization is summarized and the concepts of the main experiments that
are ongoing or are being planned are briefly described.
| astro-ph.CO | in this lecture after a synthetic review of measurements of cmb temperature anisotropies and of their cosmological implications the theoretical background of cmb polarization is summarized and the concepts of the main experiments that are ongoing or are being planned are briefly described | [['in', 'this', 'lecture', 'after', 'a', 'synthetic', 'review', 'of', 'measurements', 'of', 'cmb', 'temperature', 'anisotropies', 'and', 'of', 'their', 'cosmological', 'implications', 'the', 'theoretical', 'background', 'of', 'cmb', 'polarization', 'is', 'summarized', 'and', 'the', 'concepts', 'of', 'the', 'main', 'experiments', 'that', 'are', 'ongoing', 'or', 'are', 'being', 'planned', 'are', 'briefly', 'described']] | [-0.10892829755970905, 0.22567696540161622, -0.054429326834546966, 0.07021145054767298, -0.07851657691563285, -0.0542792328633368, -0.07603168539529623, 0.3684799920680911, -0.21501241871264093, -0.28287925146693405, 0.16916461035251878, -0.35947107164145903, -0.106423290148594, 0.25444826175631996, 0.023211132323499337, -0.002775495666144199, 0.021712637495596047, -0.033509906424685966, -0.07721271490959754, -0.3242648133878098, 0.31922480671904807, 0.20693644021399493, 0.24978251874360236, 0.09662655482155293, 0.05336371551506048, -0.09588968023376236, -0.22084924945916418, 0.05412760438657431, -0.1209039532880334, 0.08109986433369476, 0.2706261935622193, 0.20663068855051384, 0.1571052324806535, -0.43547081119965675, -0.18661035575665708, 0.06918719505111492, 0.057271933367172644, 0.1554824971095767, -0.09423407744399684, -0.2931556035872809, 0.02835482469391684, -0.10252075035904729, -0.12002625790706208, -0.06947075198137141, -0.00751953084714884, 0.08184245165957268, -0.1784417741894072, 0.10938282268186815, 0.060593007143240334, 0.09866251186792586, -0.07315623621337203, -0.20048686318359402, -0.0073424406609562945, 0.06327884196747788, 0.08740437313979275, 0.038549397606402636, 0.15729783856591514, -0.14519300203510496, -0.1542523880597464, 0.41303510275171246, -0.06753531990702762, -0.06421261502776382, 0.11731250657764979, -0.19953161367583414, -0.1584460535953038, -0.0028272155932215757, 0.17018069118954415, 0.06927369958396222, -0.15850685067951334, 0.10177567408187992, 0.050871077405158865, 0.11275725045273903, 0.01974247751735844, 0.053245536549839864, 0.36852387529472974, 0.19647647542229227, -0.036357823066240136, 0.0928643855461201, -0.08245044842708943, -0.014652305217676385, -0.41465116835870713, -0.05821474394652733, -0.18993353384525277, 0.038829845679534035, -0.050392251114528844, -0.08956780503395685, 0.40168906445073527, 0.21670588829316373, 0.2151062691605888, -0.002386605367064476, 0.3755416875315267, -0.0017991887519222705, -0.04299551697426238, -0.047070424164493765, 0.32522743088084943, 0.1460437172114156, 0.12805018695287926, -0.1804196348868657, 0.032033303092629105, -0.041585319112379886] |
1,802.04754 | Linear non-degeneracy of the 1d blow-up limit in the phase segregation
of Bose-Einstein condensates | We show that the kernel of the linearization of the blow-up problem at the
regular part of the interface that separates segregated BECs is
one-dimensional, generated by translations in the normal direction to the
interface. This useful non-degeneracy property was previously known only in one
and two dimensions.
| math.AP | we show that the kernel of the linearization of the blowup problem at the regular part of the interface that separates segregated becs is onedimensional generated by translations in the normal direction to the interface this useful nondegeneracy property was previously known only in one and two dimensions | [['we', 'show', 'that', 'the', 'kernel', 'of', 'the', 'linearization', 'of', 'the', 'blowup', 'problem', 'at', 'the', 'regular', 'part', 'of', 'the', 'interface', 'that', 'separates', 'segregated', 'becs', 'is', 'onedimensional', 'generated', 'by', 'translations', 'in', 'the', 'normal', 'direction', 'to', 'the', 'interface', 'this', 'useful', 'nondegeneracy', 'property', 'was', 'previously', 'known', 'only', 'in', 'one', 'and', 'two', 'dimensions']] | [-0.15555220883106813, 0.11436917712368692, -0.07597137732470098, 0.05941095241966347, -0.073418072424829, -0.13541600773654258, -0.02331938404919735, 0.3604618114574502, -0.3281576431666811, -0.1894942745954419, 0.11515147765264071, -0.25483647380800295, -0.13453633693279698, 0.1736708211246878, -0.0007139424366566042, 0.024684732120173674, 0.034755412595889844, 0.037447759920420744, -0.06036189762138141, -0.26606389073034126, 0.3996703355417897, -0.05446295591536909, 0.29586830057087354, 0.04006575373932719, 0.11328770873175624, -0.015192543884040788, 0.04151088462094776, 0.014958532080830386, -0.10561999114634091, 0.09579639619672282, 0.21885749850965416, 0.053557207206419356, 0.2500111092037211, -0.39464541342264664, -0.19238843840624517, 0.06852319136426861, 0.15631790613406338, 0.11078087568360691, -0.052259558093889304, -0.3002635685261339, 0.12892843127095452, -0.06972495212297265, -0.18928766737614447, -0.0028050833110076687, 0.004148394757066853, -0.005896252114325762, -0.23092881447519176, 0.09951242682291195, 0.16451845489670328, 0.04309290160502618, -0.08139268630960335, -0.0629925919638481, -0.05468445318789842, 0.1158300107344985, 0.043096457797219045, 0.02480704982493383, 0.05448247325633323, -0.11863405656670996, -0.05166158242354868, 0.3346932694973172, -0.05781839723689094, -0.2253952152483786, 0.2496040795716302, -0.17336523865621226, -0.09828658909342873, 0.11857170419534668, 0.08758715093911935, 0.11709522025194019, -0.13454226553828144, 0.10592245017445141, -0.08441158529603854, 0.15444642176771595, 0.11299873545067385, -0.05270439008018002, 0.13080175508124134, 0.11923794258230676, 0.12111010223937531, 0.1990249631150315, -0.07132250950477707, -0.09093970954806234, -0.31061624535747495, -0.19110615371998088, -0.21500376716721803, -0.02142038873959488, -0.06693271034328063, -0.17292785429162905, 0.4310354105352114, 0.13412416737992316, 0.1728017762846624, 0.016550766934718315, 0.2784969800462325, 0.14503457218719026, 0.06795609676434348, 0.08648317248056021, 0.2502560124606437, 0.07005497953408242, 0.0934987220534822, -0.21323466431931593, 0.06944912038549471, 0.13779348437674344] |
1,802.04755 | Exploring patterns of demand in bike sharing systems via replicated
point process models | Understanding patterns of demand is fundamental for fleet management of bike
sharing systems. In this paper we analyze data from the Divvy system of the
city of Chicago. We show that the demand of bicycles can be modeled as a
multivariate temporal point process, with each dimension corresponding to a
bike station in the network. The availability of daily replications of the
process allows nonparametric estimation of the intensity functions, even for
stations with low daily counts, and straightforward estimation of pairwise
correlations between stations. These correlations are then used for clustering,
revealing different patterns of bike usage.
| stat.AP | understanding patterns of demand is fundamental for fleet management of bike sharing systems in this paper we analyze data from the divvy system of the city of chicago we show that the demand of bicycles can be modeled as a multivariate temporal point process with each dimension corresponding to a bike station in the network the availability of daily replications of the process allows nonparametric estimation of the intensity functions even for stations with low daily counts and straightforward estimation of pairwise correlations between stations these correlations are then used for clustering revealing different patterns of bike usage | [['understanding', 'patterns', 'of', 'demand', 'is', 'fundamental', 'for', 'fleet', 'management', 'of', 'bike', 'sharing', 'systems', 'in', 'this', 'paper', 'we', 'analyze', 'data', 'from', 'the', 'divvy', 'system', 'of', 'the', 'city', 'of', 'chicago', 'we', 'show', 'that', 'the', 'demand', 'of', 'bicycles', 'can', 'be', 'modeled', 'as', 'a', 'multivariate', 'temporal', 'point', 'process', 'with', 'each', 'dimension', 'corresponding', 'to', 'a', 'bike', 'station', 'in', 'the', 'network', 'the', 'availability', 'of', 'daily', 'replications', 'of', 'the', 'process', 'allows', 'nonparametric', 'estimation', 'of', 'the', 'intensity', 'functions', 'even', 'for', 'stations', 'with', 'low', 'daily', 'counts', 'and', 'straightforward', 'estimation', 'of', 'pairwise', 'correlations', 'between', 'stations', 'these', 'correlations', 'are', 'then', 'used', 'for', 'clustering', 'revealing', 'different', 'patterns', 'of', 'bike', 'usage']] | [-0.16270466722048743, 0.08179117828852392, -0.08792336459857286, 0.08341915379705168, -0.029630065696390786, -0.1379537641253207, 0.10471979358843192, 0.38515863678965373, -0.26727615741139144, -0.31594068966037836, 0.09993467525790263, -0.3141978661478413, -0.13729060946282037, 0.18349930962797292, -0.12880938701792477, 0.053276245813509544, 0.0834655457478708, 0.04587527191429639, -0.010381101745843273, -0.21707593012101872, 0.30763720185255883, 0.07544109920404621, 0.3645876717091221, 0.025523949673725774, 0.11088377545640525, 0.04772307254978907, -0.0779783456135042, -0.020472318578521078, -0.03426618448054459, 0.17410185499616043, 0.3486543792040692, 0.20736395256094559, 0.2963541628428034, -0.4446169691371549, -0.21175856982386604, 0.12597010016786991, 0.13406161625495122, 0.04176633241442368, 0.03486465738087739, -0.2848060770325132, 0.04796090690879939, -0.20421196549132312, -0.11630207468208272, -0.05724794880400613, -0.005484446047891661, 0.10742836133528923, -0.30964831829301476, 0.059918340588391746, -0.025268484660683526, 0.1353684656859673, -0.034699357662604374, -0.07477259011963171, -0.0477612424226593, 0.2440024660792221, 0.08090769948339396, -0.07196023882625951, 0.11495429176606775, -0.1198160522209346, -0.11860691199616827, 0.3900743180780297, -0.0009492804109090075, -0.16006546172783853, 0.14916002004228524, -0.15960504349858798, -0.15724046853822224, 0.05743418861967847, 0.2873791197228447, 0.047762493358099276, -0.18971672922027172, -0.025954588957565364, -0.04571973180071902, 0.1607253405270911, 0.060773579586182055, 0.018530473955570885, 0.21317753499162564, 0.24329387633725233, 0.1400155729815825, 0.104270486223521, -0.1632965720575495, -0.09931293986353677, -0.22733876873383818, -0.13205962083254433, -0.19172032300344446, 0.0040005737982843955, -0.15038081899884795, -0.13786295352062, 0.4222560032491678, 0.1888590214207538, 0.17593474307855994, 0.07905424701904436, 0.31139539581598696, 0.09732942979125134, 0.03874879177256497, 0.11697792703016978, 0.06157412418385142, -0.001608185095654935, 0.17931966226759186, -0.1794082918660427, 0.11500519567130844, -0.01650141962544666] |
1,802.04756 | Resonant production of dark photons in positron beam dump experiments | Positrons beam dump experiments have unique features to search for very
narrow resonances coupled superweakly to $e^+ e^-$ pairs. Due to the continue
loss of energy from soft photon bremsstrahlung, in the first few radiation
lengths of the dump a positron beam can continuously scan for resonant
production of new resonances via $e^+$ annihilation off an atomic $e^-$ in the
target. In the case of a dark photon $A'$ kinetically mixed with the photon,
this production mode is of first order in the electromagnetic coupling
$\alpha$, and thus parametrically enhanced with respect to the $O(\alpha^2)$
$e^+e^- \to \gamma A'$ production mode and to the $O(\alpha^3)$ $A'$
bremsstrahlung in $e^--$nucleon scattering so far considered. If the lifetime
is sufficiently long to allow the $A'$ to exit the dump, $A' \to e^+e^-$ decays
could be easily detected and distinguished from backgrounds. We explore the
foreseeable sensitivity of the Frascati PADME experiment in searching with this
technique for the $17\,$MeV dark photon invoked to explain the $^8$Be anomaly
in nuclear transitions.
| hep-ph hep-ex | positrons beam dump experiments have unique features to search for very narrow resonances coupled superweakly to e e pairs due to the continue loss of energy from soft photon bremsstrahlung in the first few radiation lengths of the dump a positron beam can continuously scan for resonant production of new resonances via e annihilation off an atomic e in the target in the case of a dark photon a kinetically mixed with the photon this production mode is of first order in the electromagnetic coupling alpha and thus parametrically enhanced with respect to the oalpha2 ee to gamma a production mode and to the oalpha3 a bremsstrahlung in enucleon scattering so far considered if the lifetime is sufficiently long to allow the a to exit the dump a to ee decays could be easily detected and distinguished from backgrounds we explore the foreseeable sensitivity of the frascati padme experiment in searching with this technique for the 17mev dark photon invoked to explain the 8be anomaly in nuclear transitions | [['positrons', 'beam', 'dump', 'experiments', 'have', 'unique', 'features', 'to', 'search', 'for', 'very', 'narrow', 'resonances', 'coupled', 'superweakly', 'to', 'e', 'e', 'pairs', 'due', 'to', 'the', 'continue', 'loss', 'of', 'energy', 'from', 'soft', 'photon', 'bremsstrahlung', 'in', 'the', 'first', 'few', 'radiation', 'lengths', 'of', 'the', 'dump', 'a', 'positron', 'beam', 'can', 'continuously', 'scan', 'for', 'resonant', 'production', 'of', 'new', 'resonances', 'via', 'e', 'annihilation', 'off', 'an', 'atomic', 'e', 'in', 'the', 'target', 'in', 'the', 'case', 'of', 'a', 'dark', 'photon', 'a', 'kinetically', 'mixed', 'with', 'the', 'photon', 'this', 'production', 'mode', 'is', 'of', 'first', 'order', 'in', 'the', 'electromagnetic', 'coupling', 'alpha', 'and', 'thus', 'parametrically', 'enhanced', 'with', 'respect', 'to', 'the', 'oalpha2', 'ee', 'to', 'gamma', 'a', 'production', 'mode', 'and', 'to', 'the', 'oalpha3', 'a', 'bremsstrahlung', 'in', 'enucleon', 'scattering', 'so', 'far', 'considered', 'if', 'the', 'lifetime', 'is', 'sufficiently', 'long', 'to', 'allow', 'the', 'a', 'to', 'exit', 'the', 'dump', 'a', 'to', 'ee', 'decays', 'could', 'be', 'easily', 'detected', 'and', 'distinguished', 'from', 'backgrounds', 'we', 'explore', 'the', 'foreseeable', 'sensitivity', 'of', 'the', 'frascati', 'padme', 'experiment', 'in', 'searching', 'with', 'this', 'technique', 'for', 'the', '17mev', 'dark', 'photon', 'invoked', 'to', 'explain', 'the', '8be', 'anomaly', 'in', 'nuclear', 'transitions']] | [-0.07418251491547702, 0.22570450513417661, -0.09019994839550481, 0.10635017165579572, -0.05049335809346445, -0.13562388313532678, 0.043597097746319625, 0.3462452862779805, -0.2582520581196302, -0.292736267660184, -0.011803850547959861, -0.329225757432555, 0.04962162527640512, 0.19243319026957137, 0.07004310361884847, 0.05996543695330397, 0.05100917775521312, 0.03730501294900067, 0.02832330151148765, -0.1633263670694373, 0.2600811792030083, 0.13771798071579752, 0.21164941156706857, 0.11098991482501883, 0.044445413442239345, 0.009755932799858336, 0.005619142204174538, -0.08240164912300195, -0.10041663343572019, 0.07479784293462728, 0.2516027414226001, 0.08538915865781332, 0.17080375477587972, -0.39937600979726473, -0.15903822512192997, 0.18808773906990708, 0.16747559643835722, 0.08400281064329435, -0.07038901299572811, -0.30027346872350946, 0.04723002854037561, -0.19584952518311685, -0.11060195895815324, -0.033480949005569346, 0.010884958626088981, -0.013174019547592022, -0.3078811821042599, 0.004394546949338101, -0.028047877096175077, -0.0662639159451289, -0.020886151595487297, -0.0709085583012038, 0.0007286134906514676, 0.0070661417442836804, 0.08830811414856486, 0.03920207558127265, 0.1744252646643066, -0.15997312859314503, -0.15215269622008154, 0.3896290181116842, -0.11468044312009978, -0.1416332455446754, 0.17736346763837524, -0.20260406425780433, -0.10256820293227044, 0.23947671740622578, 0.20540159801857735, 0.11189973373021819, -0.16004855817997365, 0.08520189021235823, 0.0441433217015374, 0.16848736844850917, 0.10566577860042677, 0.046439826561690135, 0.23583860151068178, 0.1858897304418915, 0.02617031887653256, 0.13137007528333228, -0.15279545983255, 0.0021970058124913963, -0.35537430233584194, -0.13969193117882678, -0.11167064717445177, 0.07745085579125646, 0.0446764255931718, -0.08028241098038628, 0.3675529314149283, 0.06955999262842857, 0.24056888582508365, -0.04246262689873933, 0.32529391813186737, 0.10792954812874783, 0.08139928351407369, 0.02601841500608953, 0.3458542004227638, 0.12489471856984281, 0.11641383306457923, -0.2653308785171945, -0.014763210225337279, -0.03718080731041863] |
1,802.04757 | A mountain pass theorem for minimal hypersurfaces with fixed boundary | In this work, we prove the existence of a third embedded minimal hypersurface
spanning a closed submanifold $\gamma$ contained in the boundary of a compact
Riemannian manifold with convex boundary, when it is known a priori the
existence of two strictly stable minimal hypersurfaces that bound $\gamma$. In
order to do so, we develop min-max methods similar to those of De Lellis and
Ramic, references in the paper, adapted to the discrete setting of Almgren and
Pitts.
| math.DG math.AP | in this work we prove the existence of a third embedded minimal hypersurface spanning a closed submanifold gamma contained in the boundary of a compact riemannian manifold with convex boundary when it is known a priori the existence of two strictly stable minimal hypersurfaces that bound gamma in order to do so we develop minmax methods similar to those of de lellis and ramic references in the paper adapted to the discrete setting of almgren and pitts | [['in', 'this', 'work', 'we', 'prove', 'the', 'existence', 'of', 'a', 'third', 'embedded', 'minimal', 'hypersurface', 'spanning', 'a', 'closed', 'submanifold', 'gamma', 'contained', 'in', 'the', 'boundary', 'of', 'a', 'compact', 'riemannian', 'manifold', 'with', 'convex', 'boundary', 'when', 'it', 'is', 'known', 'a', 'priori', 'the', 'existence', 'of', 'two', 'strictly', 'stable', 'minimal', 'hypersurfaces', 'that', 'bound', 'gamma', 'in', 'order', 'to', 'do', 'so', 'we', 'develop', 'minmax', 'methods', 'similar', 'to', 'those', 'of', 'de', 'lellis', 'and', 'ramic', 'references', 'in', 'the', 'paper', 'adapted', 'to', 'the', 'discrete', 'setting', 'of', 'almgren', 'and', 'pitts']] | [-0.1656843403501338, 0.06718428584587711, -0.109886154654975, 0.09085628101112027, -0.12184738519748575, -0.14619019490323568, 0.015378177106245667, 0.3159936847728922, -0.24601002850594292, -0.24897373134368345, 0.1254640051813208, -0.25663422597082036, -0.13844223846803957, 0.14788079800507004, -0.18069085677301414, 0.0663940669159944, 0.06067871870963197, 0.08856548380878705, -0.06958826811751351, -0.2480936309776122, 0.40206655178611217, -0.04716559025262924, 0.1961617827243907, 0.06025837326887995, 0.08614438541821744, -0.014263305564925662, 0.02642102581203768, 0.05017997023012293, -0.22640913176263366, 0.1930468639751014, 0.27661906962135907, 0.09176580182975158, 0.2541220461441155, -0.3864318986031178, -0.2066005734898346, 0.2062095363002491, 0.12556093760864123, 0.030029693091484278, -0.021763066525301456, -0.27461376916126984, 0.12722119955517547, -0.0820479927290427, -0.1921496550356479, -0.028838222981185505, -0.003355848106653675, -0.02881267543048843, -0.23864139004389903, 0.029188702005500857, 0.14052847356192374, 0.04872550194666378, -0.10219988823672266, -0.053662844418891166, -0.04969134866750162, 0.04554429834618196, 0.008478965987083748, 0.08740344082961153, 0.03822358415192483, -0.009971587548007895, -0.09519353126004142, 0.336244552053119, -0.1066990232257491, -0.28336782431793645, 0.17944103867538566, -0.14483855717423322, -0.16634043230135975, 0.12981361464450233, 0.14695811558263622, 0.2075827857663267, -0.12730436086885197, 0.17968058780809915, -0.07357011602174393, 0.08042260397250127, 0.1231261597835998, -0.03255458650987988, 0.07396726852809814, 0.1214569165757367, 0.1772229947719576, 0.12158320971617573, -0.021760011424224724, -0.07390628267373693, -0.37725424933198254, -0.21452873916821677, -0.16760315246095783, 0.10012611957714207, -0.07302692061938088, -0.23548924937648208, 0.34758250040345284, 0.03719877732280446, 0.1726653549182964, 0.10603602690083024, 0.2487210497921823, 0.03377192894817869, -0.0003562074546751223, 0.15427343940092741, 0.19970746234206385, 0.13993327106398187, 0.048542591583866035, -0.11890052311048892, -0.017909705289639533, 0.12701428120367622] |
1,802.04758 | The Halo Occupation Distribution of Obscured Quasars: Revisiting the
Unification Model | We model the projected angular two-point correlation function (2PCF) of
obscured and unobscured quasars selected using the Wide-field Infrared Survey
Explorer (WISE), at a median redshift of $z \sim 1$ using a five-parameter Halo
Occupation Distribution (HOD) parameterization, derived from a cosmological
hydrodynamic simulation by Chatterjee et al. The HOD parameterization was
previously used to model the 2PCF of optically selected quasars and X-ray
bright active galactic nuclei (AGN) at $z \sim 1$. The current work shows that
a single HOD parameterization can be used to model the population of different
kinds of AGN in dark matter halos suggesting the universality of the
relationship between AGN and their host dark matter halos. Our results show
that the median halo mass of central quasar hosts increases from optically
selected ($4.1^{+0.3}_{-0.4} \times 10^{12} \; h^{-1} \; {M_{sun}}$) and
infra-red (IR) bright unobscured populations ($6.3^{+6.2}_{-2.3} \times 10^{12}
\; h^{-1} \; {M_{sun}}$) to obscured quasars ($10.0^{+2.6}_{-3.7} \times
10^{12} \; h^{-1} \; {M_{sun}}$), signifying an increase in the degree of
clustering. The projected satellite fractions also increase from optically
bright to obscured quasars and tend to disfavor a simple `orientation only'
theory of active galactic nuclei unification. Our results also show that future
measurements of the small-scale clustering of obscured quasars can constrain
current theories of galaxy evolution where quasars evolve from an IR- bright
obscured phase to the optically bright unobscured phase.
| astro-ph.GA astro-ph.CO astro-ph.HE | we model the projected angular twopoint correlation function 2pcf of obscured and unobscured quasars selected using the widefield infrared survey explorer wise at a median redshift of z sim 1 using a fiveparameter halo occupation distribution hod parameterization derived from a cosmological hydrodynamic simulation by chatterjee et al the hod parameterization was previously used to model the 2pcf of optically selected quasars and xray bright active galactic nuclei agn at z sim 1 the current work shows that a single hod parameterization can be used to model the population of different kinds of agn in dark matter halos suggesting the universality of the relationship between agn and their host dark matter halos our results show that the median halo mass of central quasar hosts increases from optically selected 4103_04 times 1012 h1 m_sun and infrared ir bright unobscured populations 6362_23 times 1012 h1 m_sun to obscured quasars 10026_37 times 1012 h1 m_sun signifying an increase in the degree of clustering the projected satellite fractions also increase from optically bright to obscured quasars and tend to disfavor a simple orientation only theory of active galactic nuclei unification our results also show that future measurements of the smallscale clustering of obscured quasars can constrain current theories of galaxy evolution where quasars evolve from an ir bright obscured phase to the optically bright unobscured phase | [['we', 'model', 'the', 'projected', 'angular', 'twopoint', 'correlation', 'function', '2pcf', 'of', 'obscured', 'and', 'unobscured', 'quasars', 'selected', 'using', 'the', 'widefield', 'infrared', 'survey', 'explorer', 'wise', 'at', 'a', 'median', 'redshift', 'of', 'z', 'sim', '1', 'using', 'a', 'fiveparameter', 'halo', 'occupation', 'distribution', 'hod', 'parameterization', 'derived', 'from', 'a', 'cosmological', 'hydrodynamic', 'simulation', 'by', 'chatterjee', 'et', 'al', 'the', 'hod', 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1,802.04759 | The P2 Experiment - A future high-precision measurement of the
electroweak mixing angle at low momentum transfer | This article describes the future P2 parity-violating electron scattering
facility at the upcoming MESA accelerator in Mainz. The physics program of the
facility comprises indirect, high precision search for physics beyond the
Standard Model, measurement of the neutron distribution in nuclear physics,
single-spin asymmetries stemming from two-photon exchange and a possible future
extension to the measurement of hadronic parity violation. The first
measurement of the P2 experiment aims for a high precision determination of the
weak mixing angle to a precision of 0.14% at a four-momentum transfer of Q^2 =
4.5 10^{-3} GeV^2. The accuracy is comparable to existing measurements at the Z
pole. It comprises a sensitive test of the standard model up to a mass scale of
50 TeV, extendable to 70 TeV. This requires a measurement of the parity
violating cross section asymmetry -39.94 10^{-9} in the elastic electron-proton
scattering with a total accuracy of 0.56 10^-9 (1.4 %) in 10,000 h of 150
\micro A polarized electron beam impinging on a 60 cm liquid H_2 target
allowing for an extraction of the weak charge of the proton which is directly
connected to the weak mixing angle. Contributions from gamma Z-box graphs
become small at the small beam energy of 155 MeV. The size of the asymmetry is
the smallest asymmetry ever measured in electron scattering with an
unprecedented goal for the accuracy. We report here on the conceptual design of
the P2 spectrometer, its Cherenkov detectors, the integrating read-out
electronics as well as the ultra-thin, fast tracking detectors. There has been
substantial theory work done in preparation of the determination of the weak
mixing angle. The further physics program in particle and nuclear physics is
described as well.
| nucl-ex hep-ex hep-ph physics.ins-det | this article describes the future p2 parityviolating electron scattering facility at the upcoming mesa accelerator in mainz the physics program of the facility comprises indirect high precision search for physics beyond the standard model measurement of the neutron distribution in nuclear physics singlespin asymmetries stemming from twophoton exchange and a possible future extension to the measurement of hadronic parity violation the first measurement of the p2 experiment aims for a high precision determination of the weak mixing angle to a precision of 014 at a fourmomentum transfer of q2 45 103 gev2 the accuracy is comparable to existing measurements at the z pole it comprises a sensitive test of the standard model up to a mass scale of 50 tev extendable to 70 tev this requires a measurement of the parity violating cross section asymmetry 3994 109 in the elastic electronproton scattering with a total accuracy of 056 109 14 in 10000 h of 150 micro a polarized electron beam impinging on a 60 cm liquid h_2 target allowing for an extraction of the weak charge of the proton which is directly connected to the weak mixing angle contributions from gamma zbox graphs become small at the small beam energy of 155 mev the size of the asymmetry is the smallest asymmetry ever measured in electron scattering with an unprecedented goal for the accuracy we report here on the conceptual design of the p2 spectrometer its cherenkov detectors the integrating readout electronics as well as the ultrathin fast tracking detectors there has been substantial theory work done in preparation of the determination of the weak mixing angle the further physics program in particle and nuclear physics is described as well | [['this', 'article', 'describes', 'the', 'future', 'p2', 'parityviolating', 'electron', 'scattering', 'facility', 'at', 'the', 'upcoming', 'mesa', 'accelerator', 'in', 'mainz', 'the', 'physics', 'program', 'of', 'the', 'facility', 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