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1,802.0486 | Lagrange stability of semilinear differential-algebraic equations and
application to nonlinear electrical circuits | We study a semilinear differential-algebraic equation (DAE) with the focus on
the Lagrange stability (instability). The conditions for the existence and
uniqueness of global solutions (a solution exists on an infinite interval) of
the Cauchy problem, as well as conditions of the boundedness of the global
solutions, are obtained. Furthermore, the obtained conditions for the Lagrange
stability of the semilinear DAE guarantee that every its solution is global and
bounded, and, in contrast to theorems on the Lyapunov stability, allow to prove
the existence and uniqueness of global solutions regardless of the presence and
the number of equilibrium points. We also obtain the conditions of the
existence and uniqueness of solutions with a finite escape time (a solution
exists on a finite interval and is unbounded, i.e., is Lagrange unstable) for
the Cauchy problem. We do not use constraints of a global Lipschitz condition
type, that allows to use the work results efficiently in practical
applications. The mathematical model of a radio engineering filter with
nonlinear elements is studied as an application. The numerical analysis of the
model verifies the results of theoretical investigations.
| math.DS math.FA math.SP | we study a semilinear differentialalgebraic equation dae with the focus on the lagrange stability instability the conditions for the existence and uniqueness of global solutions a solution exists on an infinite interval of the cauchy problem as well as conditions of the boundedness of the global solutions are obtained furthermore the obtained conditions for the lagrange stability of the semilinear dae guarantee that every its solution is global and bounded and in contrast to theorems on the lyapunov stability allow to prove the existence and uniqueness of global solutions regardless of the presence and the number of equilibrium points we also obtain the conditions of the existence and uniqueness of solutions with a finite escape time a solution exists on a finite interval and is unbounded ie is lagrange unstable for the cauchy problem we do not use constraints of a global lipschitz condition type that allows to use the work results efficiently in practical applications the mathematical model of a radio engineering filter with nonlinear elements is studied as an application the numerical analysis of the model verifies the results of theoretical investigations | [['we', 'study', 'a', 'semilinear', 'differentialalgebraic', 'equation', 'dae', 'with', 'the', 'focus', 'on', 'the', 'lagrange', 'stability', 'instability', 'the', 'conditions', 'for', 'the', 'existence', 'and', 'uniqueness', 'of', 'global', 'solutions', 'a', 'solution', 'exists', 'on', 'an', 'infinite', 'interval', 'of', 'the', 'cauchy', 'problem', 'as', 'well', 'as', 'conditions', 'of', 'the', 'boundedness', 'of', 'the', 'global', 'solutions', 'are', 'obtained', 'furthermore', 'the', 'obtained', 'conditions', 'for', 'the', 'lagrange', 'stability', 'of', 'the', 'semilinear', 'dae', 'guarantee', 'that', 'every', 'its', 'solution', 'is', 'global', 'and', 'bounded', 'and', 'in', 'contrast', 'to', 'theorems', 'on', 'the', 'lyapunov', 'stability', 'allow', 'to', 'prove', 'the', 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1,802.04861 | An Observer's View on Relativity: Space-Time Splitting and Newtonian
Limit | We motivate and construct a mathematical theory for the separation of space
and time in general relativity. The formalism only requires a single observer
and an optional choice of reference frame at each instant. As the splitting is
done via the observer's past light cone, it is both closer to the experimental
situation and mathematically less restrictive than the splitting via observer
vector fields or spacelike hypersurfaces. Indeed, the theory can in principle
be applied to all spacetimes and adapted to other `metric' theories of gravity.
Instructive examples are developed along with the general theory. In
particular, we obtain an alternative description for accelerated frames of
reference in Minkowski spacetime.
Further, we use the splitting formalism to motivate a new mathematical
approach to the Newtonian limit of the motion of mass points. This employs a
general formula for their observed motion, distinguishing between `actual'
forces (i.e. those detectable via an accelerometer) and pseudo-forces. Via this
formula we show that for inertial frames of reference in Minkowski spacetime
the essential laws of non-gravitational Newtonian mechanics can be derived.
Physically relevant, related, open problems are indicated throughout the
text. These include the proof, that the Newtonian limit gives rise to the
central pseudo-forces known from Newtonian mechanics (`constant gravity',
Euler, Coriolis and centrifugal force) for non-inertial frames of reference in
Minkowski spacetime, as well as the derivation of Newton's law of gravitation
in the Schwarzschild spacetime under said limit.
This is a slightly corrected version of a master's thesis in mathematical
relativity, written at TU Berlin in 2016/2017. Comments by the reviewers have
been taken into account. If there are any remaining errors, they are solely due
to the author.
| math-ph gr-qc math.MP | we motivate and construct a mathematical theory for the separation of space and time in general relativity the formalism only requires a single observer and an optional choice of reference frame at each instant as the splitting is done via the observers past light cone it is both closer to the experimental situation and mathematically less restrictive than the splitting via observer vector fields or spacelike hypersurfaces indeed the theory can in principle be applied to all spacetimes and adapted to other metric theories of gravity instructive examples are developed along with the general theory in particular we obtain an alternative description for accelerated frames of reference in minkowski spacetime further we use the splitting formalism to motivate a new mathematical approach to the newtonian limit of the motion of mass points this employs a general formula for their observed motion distinguishing between actual forces ie those detectable via an accelerometer and pseudoforces via this formula we show that for inertial frames of reference in minkowski spacetime the essential laws of nongravitational newtonian mechanics can be derived physically relevant related open problems are indicated throughout the text these include the proof that the newtonian limit gives rise to the central pseudoforces known from newtonian mechanics constant gravity euler coriolis and centrifugal force for noninertial frames of reference in minkowski spacetime as well as the derivation of newtons law of gravitation in the schwarzschild spacetime under said limit this is a slightly corrected version of a masters thesis in mathematical relativity written at tu berlin in 20162017 comments by the reviewers have been taken into account if there are any remaining errors they are solely due to the author | [['we', 'motivate', 'and', 'construct', 'a', 'mathematical', 'theory', 'for', 'the', 'separation', 'of', 'space', 'and', 'time', 'in', 'general', 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1,802.04862 | Matrix Group Integrals, Surfaces, and Mapping Class Groups I: $U(n)$ | Since the 1970's, physicists and mathematicians who study random matrices in
the GUE or GOE models are aware of intriguing connections between integrals of
such random matrices and enumeration of graphs on surfaces. We establish a new
aspect of this theory: for random matrices sampled from the group
$\mathcal{U}\left(n\right)$ of unitary matrices.
More concretely, we study measures induced by free words on
$\mathcal{U}\left(n\right)$. Let $F_{r}$ be the free group on $r$ generators.
To sample a random element from $\mathcal{U}\left(n\right)$ according to the
measure induced by $w\in F_{r}$, one substitutes the $r$ letters in $w$ by $r$
independent, Haar-random elements from $\mathcal{U}\left(n\right)$. The main
theme of this paper is that every moment of this measure is determined by
families of pairs $\left(\Sigma,f\right)$, where $\Sigma$ is an orientable
surface with boundary, and $f$ is a map from $\Sigma$ to the bouquet of $r$
circles, which sends the boundary components of $\Sigma$ to powers of $w$. A
crucial role is then played by Euler characteristics of subgroups of the
mapping class group of $\Sigma$.
As corollaries, we obtain asymptotic bounds on the moments, we show that the
measure on $\mathcal{U}\left(n\right)$ bears information about the number of
solutions to the equation
$\left[u_{1},v_{1}\right]\cdots\left[u_{g},v_{g}\right]=w$ in the free group,
and deduce that one can ``hear'' the stable commutator length of a word through
its unitary word measures.
| math.GT math-ph math.AT math.GR math.MP | since the 1970s physicists and mathematicians who study random matrices in the gue or goe models are aware of intriguing connections between integrals of such random matrices and enumeration of graphs on surfaces we establish a new aspect of this theory for random matrices sampled from the group mathcaluleftnright of unitary matrices more concretely we study measures induced by free words on mathcaluleftnright let f_r be the free group on r generators to sample a random element from mathcaluleftnright according to the measure induced by win f_r one substitutes the r letters in w by r independent haarrandom elements from mathcaluleftnright the main theme of this paper is that every moment of this measure is determined by families of pairs leftsigmafright where sigma is an orientable surface with boundary and f is a map from sigma to the bouquet of r circles which sends the boundary components of sigma to powers of w a crucial role is then played by euler characteristics of subgroups of the mapping class group of sigma as corollaries we obtain asymptotic bounds on the moments we show that the measure on mathcaluleftnright bears information about the number of solutions to the equation leftu_1v_1rightcdotsleftu_gv_grightw in the free group and deduce that one can hear the stable commutator length of a word through its unitary word measures | [['since', 'the', '1970s', 'physicists', 'and', 'mathematicians', 'who', 'study', 'random', 'matrices', 'in', 'the', 'gue', 'or', 'goe', 'models', 'are', 'aware', 'of', 'intriguing', 'connections', 'between', 'integrals', 'of', 'such', 'random', 'matrices', 'and', 'enumeration', 'of', 'graphs', 'on', 'surfaces', 'we', 'establish', 'a', 'new', 'aspect', 'of', 'this', 'theory', 'for', 'random', 'matrices', 'sampled', 'from', 'the', 'group', 'mathcaluleftnright', 'of', 'unitary', 'matrices', 'more', 'concretely', 'we', 'study', 'measures', 'induced', 'by', 'free', 'words', 'on', 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1,802.04863 | The order of dominance of a monomial ideal | Let S be a polynomial ring in n variables over a field, and let M be a
monomial ideal of S. We introduce a new invariant, called the order of
dominance of S/M, denoted odom(S/M), which has many similarities with the
codimension of S/M. We use this order of dominance to characterize the class of
Scarf ideals that are Cohen-Macaulay, and also to characterize when the Taylor
resolution is minimal. We also show that odom(S/M) has the following
properties: (i) codim(S/M) <= odom(S/M) <= pd(S/M). (ii) pd(S/M)=n if and only
if odom(S/M)=n. (iii) pd(S/M)=1 if and only if odom(S/M)=1. (iv) If
odom(S/M)=n-1 then pd(S/M)=n-1.
| math.AC | let s be a polynomial ring in n variables over a field and let m be a monomial ideal of s we introduce a new invariant called the order of dominance of sm denoted odomsm which has many similarities with the codimension of sm we use this order of dominance to characterize the class of scarf ideals that are cohenmacaulay and also to characterize when the taylor resolution is minimal we also show that odomsm has the following properties i codimsm odomsm pdsm ii pdsmn if and only if odomsmn iii pdsm1 if and only if odomsm1 iv if odomsmn1 then pdsmn1 | [['let', 's', 'be', 'a', 'polynomial', 'ring', 'in', 'n', 'variables', 'over', 'a', 'field', 'and', 'let', 'm', 'be', 'a', 'monomial', 'ideal', 'of', 's', 'we', 'introduce', 'a', 'new', 'invariant', 'called', 'the', 'order', 'of', 'dominance', 'of', 'sm', 'denoted', 'odomsm', 'which', 'has', 'many', 'similarities', 'with', 'the', 'codimension', 'of', 'sm', 'we', 'use', 'this', 'order', 'of', 'dominance', 'to', 'characterize', 'the', 'class', 'of', 'scarf', 'ideals', 'that', 'are', 'cohenmacaulay', 'and', 'also', 'to', 'characterize', 'when', 'the', 'taylor', 'resolution', 'is', 'minimal', 'we', 'also', 'show', 'that', 'odomsm', 'has', 'the', 'following', 'properties', 'i', 'codimsm', 'odomsm', 'pdsm', 'ii', 'pdsmn', 'if', 'and', 'only', 'if', 'odomsmn', 'iii', 'pdsm1', 'if', 'and', 'only', 'if', 'odomsm1', 'iv', 'if', 'odomsmn1', 'then', 'pdsmn1']] | [-0.11319813055006113, 0.11894221226387519, -0.060725418117610695, 0.0061152167717351555, -0.08727955557604103, -0.20439581620566388, -0.034256842007186816, 0.3307813003193587, -0.334306782666356, -0.1876495937925783, 0.09354720066147955, -0.26637816938393294, -0.13111561714879613, 0.11480508181960025, -0.0755767775204112, -0.09775414579904619, 0.018628346961625714, 0.10466911493146673, -0.07247345023689435, -0.30549751646925716, 0.3532270973033093, -0.01406766104690255, 0.15326286060735583, 0.03918474659378184, 0.11082391180791595, -0.015935677465328826, 0.02574837962759936, 0.1027648353384451, -0.1660304017660042, 0.10626899529288107, 0.2309990188780617, 0.15677752440914194, 0.25020642990761616, -0.3461436078626108, -0.1304235215635693, 0.25935801806205766, 0.1552807273333298, -0.02659110510300726, 0.005382659310039053, -0.19227830775359528, 0.2225030726564929, -0.1770776419484235, -0.14132907990851698, -0.1010479356479296, 0.11842753444030445, 0.04206696789751344, -0.33748161194330834, -0.02319732763212511, 0.0910471735393351, 0.09202291201205647, 0.033927401661516185, -0.07601119018100361, -0.07442116089402995, 0.006353885811280479, -0.0025324976502699736, 0.012941198388827926, 0.015463713987512475, -0.115572153355312, -0.1130367126761678, 0.39542718057302717, -0.0986120695857569, -0.1886252883940618, 0.1523301134856933, -0.21433547798524352, -0.11489683369569668, 0.09343930801990977, 0.08739106223096159, 0.13142753637177831, -0.07703370061790338, 0.19745640878496257, -0.11381657467164258, 0.13923444703934676, 0.06312842996533405, 0.03248907427521462, 0.15704510812251332, 0.09908143482715921, 0.06992619469275134, 0.10145527040932645, -0.08018822327474172, 0.01652787839795681, -0.3541058267248755, -0.20387101144173203, -0.15264167830823583, 0.11445409208635225, -0.058668176481931254, -0.14387528514093223, 0.42351099416772103, 0.13100924726298196, 0.19863732898251174, 0.0648842258292171, 0.22497910884347685, 0.06364214704669219, 0.046654420052754116, 0.12330466507379204, 0.15223224069725008, 0.1538653069899041, -0.018040566424036675, -0.17668580999994216, 0.04791141600110271, 0.12316785709922538] |
1,802.04864 | Surveying the quantum group symmetries of integrable open spin chains | Using anisotropic R-matrices associated with affine Lie algebras $\hat g$
(specifically, $A_{2n}^{(2)}, A_{2n-1}^{(2)}, B_n^{(1)}, C_n^{(1)}, D_n^{(1)}$)
and suitable corresponding K-matrices, we construct families of integrable open
quantum spin chains of finite length, whose transfer matrices are invariant
under the quantum group corresponding to removing one node from the Dynkin
diagram of $\hat g$. We show that these transfer matrices also have a duality
symmetry (for the cases $C_n^{(1)}$ and $D_n^{(1)}$) and additional $Z_2$
symmetries that map complex representations to their conjugates (for the cases
$A_{2n-1}^{(2)}, B_n^{(1)}, D_n^{(1)}$). A key simplification is achieved by
working in a certain "unitary" gauge, in which only the unbroken symmetry
generators appear. The proofs of these symmetries rely on some new properties
of the R-matrices. We use these symmetries to explain the degeneracies of the
transfer matrices.
| hep-th math-ph math.MP math.QA | using anisotropic rmatrices associated with affine lie algebras hat g specifically a_2n2 a_2n12 b_n1 c_n1 d_n1 and suitable corresponding kmatrices we construct families of integrable open quantum spin chains of finite length whose transfer matrices are invariant under the quantum group corresponding to removing one node from the dynkin diagram of hat g we show that these transfer matrices also have a duality symmetry for the cases c_n1 and d_n1 and additional z_2 symmetries that map complex representations to their conjugates for the cases a_2n12 b_n1 d_n1 a key simplification is achieved by working in a certain unitary gauge in which only the unbroken symmetry generators appear the proofs of these symmetries rely on some new properties of the rmatrices we use these symmetries to explain the degeneracies of the transfer matrices | [['using', 'anisotropic', 'rmatrices', 'associated', 'with', 'affine', 'lie', 'algebras', 'hat', 'g', 'specifically', 'a_2n2', 'a_2n12', 'b_n1', 'c_n1', 'd_n1', 'and', 'suitable', 'corresponding', 'kmatrices', 'we', 'construct', 'families', 'of', 'integrable', 'open', 'quantum', 'spin', 'chains', 'of', 'finite', 'length', 'whose', 'transfer', 'matrices', 'are', 'invariant', 'under', 'the', 'quantum', 'group', 'corresponding', 'to', 'removing', 'one', 'node', 'from', 'the', 'dynkin', 'diagram', 'of', 'hat', 'g', 'we', 'show', 'that', 'these', 'transfer', 'matrices', 'also', 'have', 'a', 'duality', 'symmetry', 'for', 'the', 'cases', 'c_n1', 'and', 'd_n1', 'and', 'additional', 'z_2', 'symmetries', 'that', 'map', 'complex', 'representations', 'to', 'their', 'conjugates', 'for', 'the', 'cases', 'a_2n12', 'b_n1', 'd_n1', 'a', 'key', 'simplification', 'is', 'achieved', 'by', 'working', 'in', 'a', 'certain', 'unitary', 'gauge', 'in', 'which', 'only', 'the', 'unbroken', 'symmetry', 'generators', 'appear', 'the', 'proofs', 'of', 'these', 'symmetries', 'rely', 'on', 'some', 'new', 'properties', 'of', 'the', 'rmatrices', 'we', 'use', 'these', 'symmetries', 'to', 'explain', 'the', 'degeneracies', 'of', 'the', 'transfer', 'matrices']] | [-0.174882085390202, 0.1488493818729749, -0.055911820297214115, 0.08055260444190494, -0.12015450266754311, -0.21120790692370836, -0.001226693616693162, 0.38395417772346374, -0.3097464567346668, -0.2093581210109264, 0.12635656165057849, -0.26360313829112175, -0.15749644111333924, 0.13570891874560126, -0.07878550974595727, 0.0001649861294550426, 0.03447563889097761, 0.0804873447244366, -0.1742216148448995, -0.25593174504341953, 0.37173519474736444, -0.08002770081458782, 0.24051744832346836, -0.037892537765208435, 0.1310333734886213, 0.014584314187452423, 0.02608893624732666, -0.10435755499513492, -0.16187966978373658, 0.12064182009392728, 0.26648814429945283, 0.031950117688569604, 0.09580442666210177, -0.3644013641204572, -0.12559684996747159, 0.1616234744644978, 0.17418396971311956, 0.0734570012983485, -0.036615125102054495, -0.3194235366853801, 0.08258160007553118, -0.1969409849720471, -0.14419468456374793, -0.0952054818111664, 0.018226726883740135, -0.012118197561269908, -0.24153206033180608, 0.039075296119721534, 0.10169156437686153, 0.11299662939696149, -0.0013935451305852357, -0.16077399194579234, -0.09898058921592592, 0.11438353893093088, -0.010842153324505709, -0.020230837048130168, 0.08394536776221216, -0.12474487776952711, -0.1569242435445741, 0.37135686919756344, 0.04030858965901037, -0.24929848378947514, 0.13640639542736058, -0.11130791008260778, -0.2432930243667215, 0.07963724832657273, 0.06517643546579745, 0.08875538169222912, -0.07741502159677277, 0.19100297111038922, -0.09759248747970119, 0.04663710329138363, 0.07901470856670516, 0.031352375118965, 0.15590495219503558, -0.02043838840979857, 0.056785974450494076, 0.10550942928547914, 0.05152779663551359, -0.09301780018899025, -0.37255703995117184, -0.15569583210515592, -0.12357167825702521, 0.1647591579592589, -0.14641615652770712, -0.15195802436915762, 0.410372155549174, 0.11237843586555259, 0.18224318911035714, 0.0610404899293137, 0.1080499878823475, 0.10106532953002235, 0.13871390851405027, 0.04763296697549804, 0.09879427581119782, 0.24938368040161688, -0.05033447624494632, -0.24547484356761826, -0.08257896658103688, 0.18664266796655615] |
1,802.04865 | Learning Confidence for Out-of-Distribution Detection in Neural Networks | Modern neural networks are very powerful predictive models, but they are
often incapable of recognizing when their predictions may be wrong. Closely
related to this is the task of out-of-distribution detection, where a network
must determine whether or not an input is outside of the set on which it is
expected to safely perform. To jointly address these issues, we propose a
method of learning confidence estimates for neural networks that is simple to
implement and produces intuitively interpretable outputs. We demonstrate that
on the task of out-of-distribution detection, our technique surpasses recently
proposed techniques which construct confidence based on the network's output
distribution, without requiring any additional labels or access to
out-of-distribution examples. Additionally, we address the problem of
calibrating out-of-distribution detectors, where we demonstrate that
misclassified in-distribution examples can be used as a proxy for
out-of-distribution examples.
| stat.ML cs.LG | modern neural networks are very powerful predictive models but they are often incapable of recognizing when their predictions may be wrong closely related to this is the task of outofdistribution detection where a network must determine whether or not an input is outside of the set on which it is expected to safely perform to jointly address these issues we propose a method of learning confidence estimates for neural networks that is simple to implement and produces intuitively interpretable outputs we demonstrate that on the task of outofdistribution detection our technique surpasses recently proposed techniques which construct confidence based on the networks output distribution without requiring any additional labels or access to outofdistribution examples additionally we address the problem of calibrating outofdistribution detectors where we demonstrate that misclassified indistribution examples can be used as a proxy for outofdistribution examples | [['modern', 'neural', 'networks', 'are', 'very', 'powerful', 'predictive', 'models', 'but', 'they', 'are', 'often', 'incapable', 'of', 'recognizing', 'when', 'their', 'predictions', 'may', 'be', 'wrong', 'closely', 'related', 'to', 'this', 'is', 'the', 'task', 'of', 'outofdistribution', 'detection', 'where', 'a', 'network', 'must', 'determine', 'whether', 'or', 'not', 'an', 'input', 'is', 'outside', 'of', 'the', 'set', 'on', 'which', 'it', 'is', 'expected', 'to', 'safely', 'perform', 'to', 'jointly', 'address', 'these', 'issues', 'we', 'propose', 'a', 'method', 'of', 'learning', 'confidence', 'estimates', 'for', 'neural', 'networks', 'that', 'is', 'simple', 'to', 'implement', 'and', 'produces', 'intuitively', 'interpretable', 'outputs', 'we', 'demonstrate', 'that', 'on', 'the', 'task', 'of', 'outofdistribution', 'detection', 'our', 'technique', 'surpasses', 'recently', 'proposed', 'techniques', 'which', 'construct', 'confidence', 'based', 'on', 'the', 'networks', 'output', 'distribution', 'without', 'requiring', 'any', 'additional', 'labels', 'or', 'access', 'to', 'outofdistribution', 'examples', 'additionally', 'we', 'address', 'the', 'problem', 'of', 'calibrating', 'outofdistribution', 'detectors', 'where', 'we', 'demonstrate', 'that', 'misclassified', 'indistribution', 'examples', 'can', 'be', 'used', 'as', 'a', 'proxy', 'for', 'outofdistribution', 'examples']] | [-0.02456858052148504, 0.032103963585305796, -0.05162313360820957, 0.1340631687898095, -0.13617646755154827, -0.2142090476855046, 0.06392358341957811, 0.4436326144370351, -0.24800158122541022, -0.3503957535642728, 0.12275170600228096, -0.27108137547407, -0.19898949819449469, 0.23581714513410124, -0.1544099839926838, 0.07148961374442354, 0.11245578396797394, 0.06792768052102421, -0.027995068003802907, -0.28332140209428003, 0.3078090500173648, 0.05908647698866163, 0.2946126968030342, 0.028445016050027857, 0.11861499916429619, -0.07014295340535774, -0.016234569177835535, 0.014924406950871239, -0.03085886574807374, 0.13242546988151993, 0.3142022809239907, 0.2254525670754931, 0.30786633797713536, -0.4154096903185621, -0.23161244464756772, 0.15666431090761002, 0.1553203864374848, 0.13382474151138457, -0.003108607510459884, -0.30682258604751, 0.14561937490424556, -0.15263368121160717, -0.024227966879420786, -0.17691520696988317, -0.03649289800832383, -0.004495363729409605, -0.3163760480564758, 0.010918982789295612, 0.058330332782042464, 0.0006824930620600851, -0.016470198856211073, -0.0766149394988022, 0.023755472464982767, 0.16420392141942933, -7.420825845689225e-05, 0.02732574872391151, 0.12906421798033466, -0.1706006256159958, -0.14374427125862926, 0.3585401911040594, -0.011762237541457946, -0.25923080981219954, 0.22363515836253708, -0.027392086750499423, -0.16437191224033884, 0.06879184409145185, 0.22078258391442918, 0.13664486402940515, -0.16746253791826746, -0.03220188134582713, -0.04571580218834628, 0.20339567453823246, 0.024814283049819495, -0.010984530220170029, 0.21341326393910665, 0.18741339686822495, 0.04373739671846517, 0.11676151890780026, -0.12678129699817361, -0.018875224225385543, -0.24797450320915781, -0.07361627846605331, -0.20749611337669194, 0.013528714062107506, -0.05968169836826177, -0.16898284473275538, 0.36819811419938964, 0.2576979820310011, 0.23046202437362548, 0.1159596692171055, 0.35283634822360044, 0.08604410912252362, 0.0958874196319995, 0.09271933900765243, 0.20602045898081187, 0.022849595293121264, 0.017810017868822855, -0.1254765985645823, 0.13433633624113722, 0.029181367294526184] |
1,802.04866 | Local Descent For Temporal Logic Falsification of Cyber-Physical Systems
(Extended Technical Report) | One way to analyze Cyber-Physical Systems is by modeling them as hybrid
automata. Since reachability analysis for hybrid nonlinear automata is a very
challenging and computationally expensive problem, in practice, engineers try
to solve the requirements falsification problem. In one method, the
falsification problem is solved by minimizing a robustness metric induced by
the requirements. This optimization problem is usually a non-convex non-smooth
problem that requires heuristic and analytical guidance to be solved. In this
paper, functional gradient descent for hybrid systems is utilized for locally
decreasing the robustness metric. The local descent method is combined with
Simulated Annealing as a global optimization method to search for unsafe
behaviors.
| cs.SY cs.FL math.OC | one way to analyze cyberphysical systems is by modeling them as hybrid automata since reachability analysis for hybrid nonlinear automata is a very challenging and computationally expensive problem in practice engineers try to solve the requirements falsification problem in one method the falsification problem is solved by minimizing a robustness metric induced by the requirements this optimization problem is usually a nonconvex nonsmooth problem that requires heuristic and analytical guidance to be solved in this paper functional gradient descent for hybrid systems is utilized for locally decreasing the robustness metric the local descent method is combined with simulated annealing as a global optimization method to search for unsafe behaviors | [['one', 'way', 'to', 'analyze', 'cyberphysical', 'systems', 'is', 'by', 'modeling', 'them', 'as', 'hybrid', 'automata', 'since', 'reachability', 'analysis', 'for', 'hybrid', 'nonlinear', 'automata', 'is', 'a', 'very', 'challenging', 'and', 'computationally', 'expensive', 'problem', 'in', 'practice', 'engineers', 'try', 'to', 'solve', 'the', 'requirements', 'falsification', 'problem', 'in', 'one', 'method', 'the', 'falsification', 'problem', 'is', 'solved', 'by', 'minimizing', 'a', 'robustness', 'metric', 'induced', 'by', 'the', 'requirements', 'this', 'optimization', 'problem', 'is', 'usually', 'a', 'nonconvex', 'nonsmooth', 'problem', 'that', 'requires', 'heuristic', 'and', 'analytical', 'guidance', 'to', 'be', 'solved', 'in', 'this', 'paper', 'functional', 'gradient', 'descent', 'for', 'hybrid', 'systems', 'is', 'utilized', 'for', 'locally', 'decreasing', 'the', 'robustness', 'metric', 'the', 'local', 'descent', 'method', 'is', 'combined', 'with', 'simulated', 'annealing', 'as', 'a', 'global', 'optimization', 'method', 'to', 'search', 'for', 'unsafe', 'behaviors']] | [-0.10786594523099857, -0.014653458396707652, -0.08763232661855466, 0.141647114768494, -0.1262506220335944, -0.2270793158971115, 0.05488304498310654, 0.38604494068053885, -0.3384928094787062, -0.35249182629749315, 0.14375168060426763, -0.21853163473974538, -0.20013251825483566, 0.1897697199003583, -0.13657175787989426, 0.17616274656403227, 0.11221253424519759, -0.05470630848660655, -0.0732948243075452, -0.24539493133715534, 0.2631567489919723, 0.04723903430847946, 0.28286984617557, 0.005390466460018256, 0.07166901614235013, 0.0026080613285198984, 0.049500880557872835, 0.09073298359546092, -0.029578868671731037, 0.1430715351833304, 0.3585567421390923, 0.22125283564620335, 0.4014607699024021, -0.443575983508191, -0.2321571405878752, 0.13949905659730008, 0.13952701763514805, 0.09949990032975553, -0.052747996389404486, -0.25660582581415364, 0.12122654602991058, -0.1285953546592265, -0.09521187828221453, -0.11935021719732962, -0.03075408623041158, -0.014681183032418063, -0.29780602499355624, 0.0340511611696982, 0.017978877850184472, 0.021604247391223907, -0.05441606198565675, -0.06290461101629045, 0.05441965960359218, 0.04336203786826462, 0.02866086981114034, 0.05230162336668341, 0.12301151181139701, -0.09575198247490468, -0.1459203891695366, 0.40812307526143865, -0.013066919285530618, -0.27452136587924025, 0.16437352581137638, 0.07243261355137744, -0.16317781330077746, 0.12282366849003581, 0.20986861318623254, 0.20917040718838983, -0.22885465996186122, 0.09326165516266958, 0.026960920615104633, 0.15696827752874531, -0.00022386968319534982, -0.045663012035720804, 0.13301383291687305, 0.2439916019729518, 0.19457955172268349, 0.17307169138082226, 0.019187368940905915, -0.12120596380955583, -0.20555730220870277, -0.1115506069922666, -0.19290092618871105, -0.02488599470581586, -0.031236729440183183, -0.15326095770600193, 0.3539114315404531, 0.16683701260886882, 0.12926059122274228, 0.10613726459682808, 0.3884267525474003, 0.14763663603001437, 0.018218311586378808, 0.07336188628073406, 0.1917845725227113, 0.0911031340680367, 0.15103884823962088, -0.27551071566247615, 0.10476578071653637, 0.11931861100564582] |
1,802.04867 | Measurement of the $\Lambda_b$ polarization and angular parameters in
$\Lambda_b\to J/\psi\, \Lambda$ decays from pp collisions at $\sqrt{s}=$ 7
and 8 TeV | An analysis of the decay $\Lambda_b \to J/\psi(\to\mu^+\mu^-)\Lambda(\to p
\pi^-)$ decay is performed to measure the $\Lambda_b$ polarization and three
angular parameters in data from pp collisions at $\sqrt{s} =$ 7 and 8 TeV,
collected by the CMS experiment at the LHC. The $\Lambda_b$ polarization is
measured to be 0.00 $\pm$ 0.06 (stat) $\pm$ 0.06 (syst) and the
parity-violating asymmetry parameter is determined to be 0.14 $\pm$ 0.14 (stat)
$\pm$ 0.10 (syst). The measurements are compared to various theoretical
predictions, including those from perturbative quantum chromodynamics.
| hep-ex | an analysis of the decay lambda_b to jpsitomumulambdato p pi decay is performed to measure the lambda_b polarization and three angular parameters in data from pp collisions at sqrts 7 and 8 tev collected by the cms experiment at the lhc the lambda_b polarization is measured to be 000 pm 006 stat pm 006 syst and the parityviolating asymmetry parameter is determined to be 014 pm 014 stat pm 010 syst the measurements are compared to various theoretical predictions including those from perturbative quantum chromodynamics | [['an', 'analysis', 'of', 'the', 'decay', 'lambda_b', 'to', 'jpsitomumulambdato', 'p', 'pi', 'decay', 'is', 'performed', 'to', 'measure', 'the', 'lambda_b', 'polarization', 'and', 'three', 'angular', 'parameters', 'in', 'data', 'from', 'pp', 'collisions', 'at', 'sqrts', '7', 'and', '8', 'tev', 'collected', 'by', 'the', 'cms', 'experiment', 'at', 'the', 'lhc', 'the', 'lambda_b', 'polarization', 'is', 'measured', 'to', 'be', '000', 'pm', '006', 'stat', 'pm', '006', 'syst', 'and', 'the', 'parityviolating', 'asymmetry', 'parameter', 'is', 'determined', 'to', 'be', '014', 'pm', '014', 'stat', 'pm', '010', 'syst', 'the', 'measurements', 'are', 'compared', 'to', 'various', 'theoretical', 'predictions', 'including', 'those', 'from', 'perturbative', 'quantum', 'chromodynamics']] | [-0.06496530545077153, 0.2244339827851772, -0.13149936774390794, 0.04826315634168817, 0.015256820579192467, -0.1237230009754144, -0.017361140042166448, 0.2920834997175483, -0.21827580848531353, -0.4112889952181528, -0.0326372662661708, -0.49424556780251716, 0.1780036374471993, 0.15816392994574494, 0.03371532537442233, 0.14094770630998982, 0.07597765603904486, -0.06060296918509439, -0.054015345771663954, -0.15963575473038613, 0.09586105211582478, 0.0905796104800954, 0.2788251049572691, 0.05877003719520178, -0.028405146236764267, -0.012369371737198284, -0.06873813658742056, -0.11078022090008571, -0.25276839005805196, 0.03343552567509635, 0.2755951088454042, -0.01463430953056862, 0.06574296303803012, -0.2341477924603082, 0.05418930681688445, 0.14513330228094543, 0.14779643534538559, 0.02034973111447124, 0.06826634633000053, -0.3861715356774983, 0.23865944912124956, -0.1866323442442254, -0.0873394029137368, 0.018379350148496173, 0.08024729342044641, -0.14652795916689293, -0.38880183644747984, 0.24417038184280196, -0.17372188737638117, 0.14887768803496979, -0.016763180430557224, -0.3058751630375073, -0.0938650615294353, -0.05648627760820091, 0.09884227386819908, 0.23708973487373441, 0.15855946130163612, -0.01207485367749108, -0.2021237305820077, 0.4070635526219294, -0.04364306543998066, -0.12099753603321178, 0.021480618976056576, -0.24479847115885822, -0.12984372239138575, 0.19533802670082964, 0.21599264658011852, 0.05462970172742471, -0.23358248579981072, 0.05829357333450822, 0.05526866333647853, 0.2873495170358746, 0.06814879518822722, 0.04937934591795229, 0.19986409263496863, 0.16013533428693855, -0.08919814441885267, -0.04920747858843589, -0.18200668643805243, -0.026117088361865, -0.42494461148799884, -0.00967686031834178, -0.08361748773382888, 0.16960410902503922, -0.13867599086872706, 0.09083095562112119, 0.2857337198774552, 0.09492713536712385, 0.34302104880944606, -0.0014807266416028142, 0.24631142355723395, 0.08224351819288651, 0.0031944531767207776, 0.07724189502741433, 0.35264525482697145, 0.28190744288232444, 0.17048665432126395, -0.25493325378435355, 0.04739395965312031, -0.05138471182103136] |
1,802.04868 | SimplE Embedding for Link Prediction in Knowledge Graphs | Knowledge graphs contain knowledge about the world and provide a structured
representation of this knowledge. Current knowledge graphs contain only a small
subset of what is true in the world. Link prediction approaches aim at
predicting new links for a knowledge graph given the existing links among the
entities. Tensor factorization approaches have proved promising for such link
prediction problems. Proposed in 1927, Canonical Polyadic (CP) decomposition is
among the first tensor factorization approaches. CP generally performs poorly
for link prediction as it learns two independent embedding vectors for each
entity, whereas they are really tied. We present a simple enhancement of CP
(which we call SimplE) to allow the two embeddings of each entity to be learned
dependently. The complexity of SimplE grows linearly with the size of
embeddings. The embeddings learned through SimplE are interpretable, and
certain types of background knowledge can be incorporated into these embeddings
through weight tying. We prove SimplE is fully expressive and derive a bound on
the size of its embeddings for full expressivity. We show empirically that,
despite its simplicity, SimplE outperforms several state-of-the-art tensor
factorization techniques. SimplE's code is available on GitHub at
https://github.com/Mehran-k/SimplE.
| stat.ML cs.LG | knowledge graphs contain knowledge about the world and provide a structured representation of this knowledge current knowledge graphs contain only a small subset of what is true in the world link prediction approaches aim at predicting new links for a knowledge graph given the existing links among the entities tensor factorization approaches have proved promising for such link prediction problems proposed in 1927 canonical polyadic cp decomposition is among the first tensor factorization approaches cp generally performs poorly for link prediction as it learns two independent embedding vectors for each entity whereas they are really tied we present a simple enhancement of cp which we call simple to allow the two embeddings of each entity to be learned dependently the complexity of simple grows linearly with the size of embeddings the embeddings learned through simple are interpretable and certain types of background knowledge can be incorporated into these embeddings through weight tying we prove simple is fully expressive and derive a bound on the size of its embeddings for full expressivity we show empirically that despite its simplicity simple outperforms several stateoftheart tensor factorization techniques simples code is available on github at httpsgithubcommehranksimple | [['knowledge', 'graphs', 'contain', 'knowledge', 'about', 'the', 'world', 'and', 'provide', 'a', 'structured', 'representation', 'of', 'this', 'knowledge', 'current', 'knowledge', 'graphs', 'contain', 'only', 'a', 'small', 'subset', 'of', 'what', 'is', 'true', 'in', 'the', 'world', 'link', 'prediction', 'approaches', 'aim', 'at', 'predicting', 'new', 'links', 'for', 'a', 'knowledge', 'graph', 'given', 'the', 'existing', 'links', 'among', 'the', 'entities', 'tensor', 'factorization', 'approaches', 'have', 'proved', 'promising', 'for', 'such', 'link', 'prediction', 'problems', 'proposed', 'in', '1927', 'canonical', 'polyadic', 'cp', 'decomposition', 'is', 'among', 'the', 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'simplicity', 'simple', 'outperforms', 'several', 'stateoftheart', 'tensor', 'factorization', 'techniques', 'simples', 'code', 'is', 'available', 'on', 'github', 'at', 'httpsgithubcommehranksimple']] | [-0.08751477513093657, 0.0452709219728907, -0.05565357303748897, 0.10558866601210563, -0.18117410660731062, -0.17919960349202788, 0.03232881928003432, 0.42377919401042163, -0.30394970498916035, -0.29343018608293886, 0.08161659082422072, -0.2712136699774419, -0.15840580631872095, 0.1604359860345236, -0.06948508450053244, 0.0008014958675630623, 0.11967069568587856, 0.11393457816802766, -0.08156841371904495, -0.26269887406018216, 0.3246376417555439, -0.002828304032542898, 0.3087195813811074, 0.05410523746104445, 0.1276959663046, -0.008042393206172468, -0.07354378002552646, 0.01937624625134049, -0.08963063896366445, 0.17317207810022714, 0.3213041050379009, 0.24183131005243771, 0.23366312724222857, -0.3884297490512836, -0.1894393750232363, 0.10619366769969929, 0.13272947609251182, 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1,802.04869 | Time-Series Based Thermography on Concrete Block Void Detection | Using thermography as a nondestructive method for subsurface detection of the
concrete structure has been developed for decades. However, the performance of
current practice is limited due to the heavy reliance on the environmental
conditions as well as complex environmental noises. A non-time-series method
suffers from the issue of solar radiation reflected by the target during
heating stage, and issues of potential non-uniform heat distribution. These
limitations are the major constraints of the traditional single thermal image
method. Time series-based methods such as Fourier transform-based pulse phase
thermography, principle component thermography, and high order statistics have
been reported with robust results on surface reflective property difference and
non-uniform heat distribution under the experimental setting. This paper aims
to compare the performance of above methods to that of the conventional static
thermal imaging method. The case used for the comparison is to detect voids in
a hollow concrete block during the heating phase. The result was quantitatively
evaluated by using Signal-to-Noise Ratio. Favorable performance was observed
using time-series methods compared to the single image approach.
| eess.IV | using thermography as a nondestructive method for subsurface detection of the concrete structure has been developed for decades however the performance of current practice is limited due to the heavy reliance on the environmental conditions as well as complex environmental noises a nontimeseries method suffers from the issue of solar radiation reflected by the target during heating stage and issues of potential nonuniform heat distribution these limitations are the major constraints of the traditional single thermal image method time seriesbased methods such as fourier transformbased pulse phase thermography principle component thermography and high order statistics have been reported with robust results on surface reflective property difference and nonuniform heat distribution under the experimental setting this paper aims to compare the performance of above methods to that of the conventional static thermal imaging method the case used for the comparison is to detect voids in a hollow concrete block during the heating phase the result was quantitatively evaluated by using signaltonoise ratio favorable performance was observed using timeseries methods compared to the single image approach | [['using', 'thermography', 'as', 'a', 'nondestructive', 'method', 'for', 'subsurface', 'detection', 'of', 'the', 'concrete', 'structure', 'has', 'been', 'developed', 'for', 'decades', 'however', 'the', 'performance', 'of', 'current', 'practice', 'is', 'limited', 'due', 'to', 'the', 'heavy', 'reliance', 'on', 'the', 'environmental', 'conditions', 'as', 'well', 'as', 'complex', 'environmental', 'noises', 'a', 'nontimeseries', 'method', 'suffers', 'from', 'the', 'issue', 'of', 'solar', 'radiation', 'reflected', 'by', 'the', 'target', 'during', 'heating', 'stage', 'and', 'issues', 'of', 'potential', 'nonuniform', 'heat', 'distribution', 'these', 'limitations', 'are', 'the', 'major', 'constraints', 'of', 'the', 'traditional', 'single', 'thermal', 'image', 'method', 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1,802.0487 | Algorithmic problems in right-angled Artin groups: complexity and
applications | In this paper we consider several classical and novel algorithmic problems
for right-angled Artin groups, some of which are closely related to graph
theoretic problems, and study their computational complexity. We study these
problems with a view towards applications to cryptography.
| math.GR math.AT math.GT | in this paper we consider several classical and novel algorithmic problems for rightangled artin groups some of which are closely related to graph theoretic problems and study their computational complexity we study these problems with a view towards applications to cryptography | [['in', 'this', 'paper', 'we', 'consider', 'several', 'classical', 'and', 'novel', 'algorithmic', 'problems', 'for', 'rightangled', 'artin', 'groups', 'some', 'of', 'which', 'are', 'closely', 'related', 'to', 'graph', 'theoretic', 'problems', 'and', 'study', 'their', 'computational', 'complexity', 'we', 'study', 'these', 'problems', 'with', 'a', 'view', 'towards', 'applications', 'to', 'cryptography']] | [-0.08957912791066053, 0.00839378512123736, -0.09446611078477339, 0.10076337474364243, -0.12608763916281665, -0.15693807902904908, 0.028260708516804366, 0.3944048970119982, -0.4018550024526875, -0.3171810857588198, 0.16536779940219187, -0.26568739384231044, -0.23699029101195132, 0.2593389628227891, -0.2502453788281678, 0.09610294571100939, 0.0326237348021894, 0.016508891815092505, -0.09183154986580698, -0.2829120866933031, 0.37135670273905486, -0.04214303106887311, 0.24574202558071148, 0.0914906251993848, 0.029370535233217043, -0.009230437768032639, -0.0377345363632208, 0.06673311187726695, -0.2177986201671202, 0.2464430798299429, 0.3762833046295294, 0.11698479234899689, 0.3327599725935881, -0.4131391173819216, -0.2107132382736337, 0.16749966800462726, 0.14673042786875512, 0.11874596711827397, -0.10717073314618773, -0.2525398450472006, 0.13394379570921203, -0.11344035747801749, -0.10096805065688563, -0.03279359563730839, -0.03343631522502841, 0.011583431226360361, -0.16868735041764632, -0.008426863450284412, 0.03734779003553274, 0.10072291338043969, 0.007187436346146391, -0.13200318218186136, 0.15831326387777198, 0.12492759035127919, 0.05025601560511149, -0.020924515942702206, 0.08148347190013383, -0.1621825212283378, -0.23573706682953166, 0.45836390355011314, 0.11202583727767555, -0.16313084689673127, 0.22445787562102806, -0.06296513243237646, -0.29532473853493973, 0.028240122650636405, 0.23453621658860002, 0.1194045008046598, -0.09272979513355871, 0.09192094254227946, -0.09102951889721358, 0.08244576854858457, 0.03416693527478634, 0.0598454384542093, 0.11816533795762353, 0.12991733830876467, 0.11316305855516254, 0.23988517557793274, 0.08424697508581164, -0.10124962554290527, -0.27227723861976366, -0.16409412139981258, -0.07728727654803817, 0.010237490622008719, -0.10317210497536197, -0.20905623571924503, 0.40158079905299154, 0.1799739758844669, 0.12832974056463417, 0.13806633022329884, 0.2807405272657733, 0.051064772122516866, -0.049843046842597244, 0.06259508380984388, 0.09469665976868738, 0.1966009161594074, 0.06090416159571671, -0.1577889432267445, -0.025349197789981234, 0.11717411797357405] |
1,802.04871 | Tumbling dynamics of inertial chains in extensional flow | The dynamics of elongated inertial particles in an extensional flow is
studied numerically by performing simulations of freely jointed bead-rod
chains. The coil-stretch transition and the tumbling instability are
characterized as a function of three parameters: The Peclet number, the Stokes
number and the chain length. Numerical results show that in the limit of
infinite chain length, particles are trapped in a coiled or stretched state.
The coil-stretch transition is also shown to depend non-linearly on the Stokes
and Peclet number. Results also reveal that tumbling occurs close to the
coil-stretch transition and that the persistence time is a non-linear function
of Stokes and Peclet numbers.
| physics.flu-dyn | the dynamics of elongated inertial particles in an extensional flow is studied numerically by performing simulations of freely jointed beadrod chains the coilstretch transition and the tumbling instability are characterized as a function of three parameters the peclet number the stokes number and the chain length numerical results show that in the limit of infinite chain length particles are trapped in a coiled or stretched state the coilstretch transition is also shown to depend nonlinearly on the stokes and peclet number results also reveal that tumbling occurs close to the coilstretch transition and that the persistence time is a nonlinear function of stokes and peclet numbers | [['the', 'dynamics', 'of', 'elongated', 'inertial', 'particles', 'in', 'an', 'extensional', 'flow', 'is', 'studied', 'numerically', 'by', 'performing', 'simulations', 'of', 'freely', 'jointed', 'beadrod', 'chains', 'the', 'coilstretch', 'transition', 'and', 'the', 'tumbling', 'instability', 'are', 'characterized', 'as', 'a', 'function', 'of', 'three', 'parameters', 'the', 'peclet', 'number', 'the', 'stokes', 'number', 'and', 'the', 'chain', 'length', 'numerical', 'results', 'show', 'that', 'in', 'the', 'limit', 'of', 'infinite', 'chain', 'length', 'particles', 'are', 'trapped', 'in', 'a', 'coiled', 'or', 'stretched', 'state', 'the', 'coilstretch', 'transition', 'is', 'also', 'shown', 'to', 'depend', 'nonlinearly', 'on', 'the', 'stokes', 'and', 'peclet', 'number', 'results', 'also', 'reveal', 'that', 'tumbling', 'occurs', 'close', 'to', 'the', 'coilstretch', 'transition', 'and', 'that', 'the', 'persistence', 'time', 'is', 'a', 'nonlinear', 'function', 'of', 'stokes', 'and', 'peclet', 'numbers']] | [-0.19112316711384447, 0.3061819047856954, -0.04132600117120005, 0.011349052041103798, 0.0031710289418697356, -0.1298518480866083, -0.034086524741724133, 0.3385073591201078, -0.286369549749153, -0.24774837525827545, 0.09002669719823947, -0.26258250833267255, -0.1257822262500191, 0.2068563815184115, 0.028380661328057093, 0.061786183918870634, 0.06216177012815717, 0.04690067423285828, 0.009338374721950718, -0.17409115523408955, 0.23095538608197655, 0.03554621571674943, 0.3013430645334579, 0.024445589556403104, 0.10634543285483405, -0.040808962671352284, 0.06120921388445866, 0.10742691038619905, -0.22594873105900617, -0.00989118062314533, 0.1892606748578449, 0.013632402005827143, 0.21800363688685354, -0.4170030820937384, -0.17984185888476314, 0.044495324499993806, 0.2006525220349431, 0.11567564989839281, -0.004946232337637671, -0.2507547050670144, 0.034917171113193035, -0.13430674412243424, -0.13853163060155652, -0.05337780529544467, 0.08736594069216932, 0.13635717287570948, -0.273732336158199, 0.11881312735412004, 0.06874811565281734, 0.08195138385608083, -0.031001826060847157, -0.043893661216965744, -0.08691921023918049, 0.09222756207233206, 0.1208838878576422, -0.023657636277909788, 0.17826780763765177, -0.14713758111022235, -0.03851330164110377, 0.3635571972067867, -0.052643191907554863, -0.22749303357586975, 0.21062977514894946, -0.17516027334634038, -0.0569690572896174, 0.22072625683531874, 0.13518861297163226, 0.1353378972836903, -0.03332438535456147, 0.03663481234606089, -0.07225994288677438, 0.2162894076468157, 0.08797169568992796, -0.054346562252335605, 0.1761048307021459, 0.1731143148349864, 0.03648037157531473, 0.19010632835062488, -0.11324698866256291, -0.15826996292231515, -0.28035069260568846, -0.168987726491122, -0.23022761080591453, -0.0006574292667210103, -0.12263249937553025, -0.19926964049088947, 0.3324458210063832, 0.10890571225957371, 0.22689269348269417, 0.09417836016842297, 0.24263550310528703, 0.12679630047752566, -0.006346033744159199, 0.06814882620016025, 0.24188037117322286, 0.1532424748121273, 0.09170702726447157, -0.2821684044719275, 0.043072317128202746, 0.10771313032933644] |
1,802.04872 | The effect of extreme ionisation rates during the initial collapse of a
molecular cloud core | What cosmic ray ionisation rate is required such that a non-ideal
magnetohydrodynamics (MHD) simulation of a collapsing molecular cloud will
follow the same evolutionary path as an ideal MHD simulation or as a purely
hydrodynamics simulation? To investigate this question, we perform
three-dimensional smoothed particle non-ideal magnetohydrodynamics simulations
of the gravitational collapse of rotating, one solar mass, magnetised molecular
cloud cores, that include Ohmic resistivity, ambipolar diffusion, and the Hall
effect. We assume a uniform grain size of $a_\text{g} = 0.1\mu$m, and our free
parameter is the cosmic ray ionisation rate, $\zeta_\text{cr}$. We evolve our
models, where possible, until they have produced a first hydrostatic core.
Models with $\zeta_\text{cr}\gtrsim10^{-13}$ s$^{-1}$ are indistinguishable
from ideal MHD models and the evolution of the model with
$\zeta_\text{cr}=10^{-14}$ s$^{-1}$ matches the evolution of the ideal MHD
model within one per cent when considering maximum density, magnetic energy,
and maximum magnetic field strength as a function of time; these results are
independent of $a_\text{g}$. Models with very low ionisation rates
($\zeta_\text{cr}\lesssim10^{-24}$ s$^{-1}$) are required to approach
hydrodynamical collapse, and even lower ionisation rates may be required for
larger $a_\text{g}$. Thus, it is possible to reproduce ideal MHD and purely
hydrodynamical collapses using non-ideal MHD given an appropriate cosmic ray
ionisation rate. However, realistic cosmic ray ionisation rates approach
neither limit, thus non-ideal MHD cannot be neglected in star formation
simulations.
| astro-ph.SR astro-ph.GA astro-ph.HE | what cosmic ray ionisation rate is required such that a nonideal magnetohydrodynamics mhd simulation of a collapsing molecular cloud will follow the same evolutionary path as an ideal mhd simulation or as a purely hydrodynamics simulation to investigate this question we perform threedimensional smoothed particle nonideal magnetohydrodynamics simulations of the gravitational collapse of rotating one solar mass magnetised molecular cloud cores that include ohmic resistivity ambipolar diffusion and the hall effect we assume a uniform grain size of a_textg 01mum and our free parameter is the cosmic ray ionisation rate zeta_textcr we evolve our models where possible until they have produced a first hydrostatic core models with zeta_textcrgtrsim1013 s1 are indistinguishable from ideal mhd models and the evolution of the model with zeta_textcr1014 s1 matches the evolution of the ideal mhd model within one per cent when considering maximum density magnetic energy and maximum magnetic field strength as a function of time these results are independent of a_textg models with very low ionisation rates zeta_textcrlesssim1024 s1 are required to approach hydrodynamical collapse and even lower ionisation rates may be required for larger a_textg thus it is possible to reproduce ideal mhd and purely hydrodynamical collapses using nonideal mhd given an appropriate cosmic ray ionisation rate however realistic cosmic ray ionisation rates approach neither limit thus nonideal mhd cannot be neglected in star formation simulations | [['what', 'cosmic', 'ray', 'ionisation', 'rate', 'is', 'required', 'such', 'that', 'a', 'nonideal', 'magnetohydrodynamics', 'mhd', 'simulation', 'of', 'a', 'collapsing', 'molecular', 'cloud', 'will', 'follow', 'the', 'same', 'evolutionary', 'path', 'as', 'an', 'ideal', 'mhd', 'simulation', 'or', 'as', 'a', 'purely', 'hydrodynamics', 'simulation', 'to', 'investigate', 'this', 'question', 'we', 'perform', 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1,802.04873 | Random Linear Network Coding for 5G Mobile Video Delivery | An exponential increase in mobile video delivery will continue with the
demand for higher resolution, multi-view and large-scale multicast video
services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a
number of new opportunities for optimizing video delivery across both 5G core
and radio access networks. One of the promising approaches for video quality
adaptation, throughput enhancement and erasure protection is the use of
packet-level random linear network coding (RLNC). In this review paper, we
discuss the integration of RLNC into the 5G NR standard, building upon the
ideas and opportunities identified in 4G LTE. We explicitly identify and
discuss in detail novel 5G NR features that provide support for RLNC-based
video delivery in 5G, thus pointing out to the promising avenues for future
research.
| cs.NI | an exponential increase in mobile video delivery will continue with the demand for higher resolution multiview and largescale multicast video services novel fifth generation 5g 3gpp new radio nr standard will bring a number of new opportunities for optimizing video delivery across both 5g core and radio access networks one of the promising approaches for video quality adaptation throughput enhancement and erasure protection is the use of packetlevel random linear network coding rlnc in this review paper we discuss the integration of rlnc into the 5g nr standard building upon the ideas and opportunities identified in 4g lte we explicitly identify and discuss in detail novel 5g nr features that provide support for rlncbased video delivery in 5g thus pointing out to the promising avenues for future research | [['an', 'exponential', 'increase', 'in', 'mobile', 'video', 'delivery', 'will', 'continue', 'with', 'the', 'demand', 'for', 'higher', 'resolution', 'multiview', 'and', 'largescale', 'multicast', 'video', 'services', 'novel', 'fifth', 'generation', '5g', '3gpp', 'new', 'radio', 'nr', 'standard', 'will', 'bring', 'a', 'number', 'of', 'new', 'opportunities', 'for', 'optimizing', 'video', 'delivery', 'across', 'both', '5g', 'core', 'and', 'radio', 'access', 'networks', 'one', 'of', 'the', 'promising', 'approaches', 'for', 'video', 'quality', 'adaptation', 'throughput', 'enhancement', 'and', 'erasure', 'protection', 'is', 'the', 'use', 'of', 'packetlevel', 'random', 'linear', 'network', 'coding', 'rlnc', 'in', 'this', 'review', 'paper', 'we', 'discuss', 'the', 'integration', 'of', 'rlnc', 'into', 'the', '5g', 'nr', 'standard', 'building', 'upon', 'the', 'ideas', 'and', 'opportunities', 'identified', 'in', '4g', 'lte', 'we', 'explicitly', 'identify', 'and', 'discuss', 'in', 'detail', 'novel', '5g', 'nr', 'features', 'that', 'provide', 'support', 'for', 'rlncbased', 'video', 'delivery', 'in', '5g', 'thus', 'pointing', 'out', 'to', 'the', 'promising', 'avenues', 'for', 'future', 'research']] | [-0.22839366141852224, 0.027257381240929135, 0.05785959612694569, 0.0037424975880639977, -0.06961844731267774, -0.2238500263010792, 0.07357841845532676, 0.39254326300579123, -0.255275863193674, -0.23237011436503963, 0.0805855120115666, -0.23999736304676844, -0.21362636674530222, 0.1802444832137553, -0.12842275980074191, 0.09326745877024223, 0.06236277330117446, -0.03583204228721115, -0.047224341358742095, -0.3103910038335016, 0.2409099807300663, 0.1318944500744692, 0.42462073812203016, 0.09208234313427965, -0.019110772122076014, -0.02350410304552497, -0.12778798368526623, -0.08410805642506602, -0.0997948706909142, 0.18334966918973805, 0.37848758685868233, 0.2651477487088414, 0.3284984942074516, -0.4650624595924455, -0.314885840874922, 0.0044351165342959575, 0.20427543996629538, 0.06043851085632923, -0.13395258151012968, -0.28044119444348325, 0.17882132237491533, -0.301857484764696, -0.08498451738705626, -0.04850342455756618, -0.02886498390489578, 0.07538836332241772, -0.29780920502525987, -0.06320886044159124, -0.03247031713544857, 0.02273653687825572, -0.0468837043044914, -0.07664997034225962, 0.07327334299043287, 0.2029629990720423, 0.03543079669725557, 0.06279330479446799, 0.07219159797705288, -0.15215561923741916, -0.16791471433725746, 0.45097994924617524, -0.03467083733994514, -0.13986806186585454, 0.12427935291816539, -0.02165963696097606, -0.20719504775297537, 0.11317902043811046, 0.29887433096882887, 0.0010159170415136032, -0.19991522368218284, -0.005225760509802058, 0.04625183332609595, 0.14523794388514943, 0.11140150756364164, 0.16797003881583805, 0.2629298795473005, 0.3062765858412604, 0.14192765962070553, 0.09732790484486031, -0.13508613421072369, -0.09734596027738007, -0.21270830678986385, -0.17591320021892898, -0.13566442768933484, 0.023994427618163172, -0.1454116970120367, -0.06938426911801798, 0.4101775160524994, 0.21284989621926798, 0.04488396927990834, 0.1272592073380565, 0.42767810623627156, 0.0029774753220408456, 0.11612076054007048, 0.11541724379929974, 0.1701909063358471, -0.010125631190021522, 0.2537221861821308, -0.11893131005126634, 0.0012429637881723465, 0.018158992203098023] |
1,802.04874 | GILBO: One Metric to Measure Them All | We propose a simple, tractable lower bound on the mutual information
contained in the joint generative density of any latent variable generative
model: the GILBO (Generative Information Lower BOund). It offers a
data-independent measure of the complexity of the learned latent variable
description, giving the log of the effective description length. It is
well-defined for both VAEs and GANs. We compute the GILBO for 800 GANs and VAEs
each trained on four datasets (MNIST, FashionMNIST, CIFAR-10 and CelebA) and
discuss the results.
| stat.ML cs.LG | we propose a simple tractable lower bound on the mutual information contained in the joint generative density of any latent variable generative model the gilbo generative information lower bound it offers a dataindependent measure of the complexity of the learned latent variable description giving the log of the effective description length it is welldefined for both vaes and gans we compute the gilbo for 800 gans and vaes each trained on four datasets mnist fashionmnist cifar10 and celeba and discuss the results | [['we', 'propose', 'a', 'simple', 'tractable', 'lower', 'bound', 'on', 'the', 'mutual', 'information', 'contained', 'in', 'the', 'joint', 'generative', 'density', 'of', 'any', 'latent', 'variable', 'generative', 'model', 'the', 'gilbo', 'generative', 'information', 'lower', 'bound', 'it', 'offers', 'a', 'dataindependent', 'measure', 'of', 'the', 'complexity', 'of', 'the', 'learned', 'latent', 'variable', 'description', 'giving', 'the', 'log', 'of', 'the', 'effective', 'description', 'length', 'it', 'is', 'welldefined', 'for', 'both', 'vaes', 'and', 'gans', 'we', 'compute', 'the', 'gilbo', 'for', '800', 'gans', 'and', 'vaes', 'each', 'trained', 'on', 'four', 'datasets', 'mnist', 'fashionmnist', 'cifar10', 'and', 'celeba', 'and', 'discuss', 'the', 'results']] | [-0.03173724925145507, 0.035445370341767556, -0.10591285527916625, 0.16824914265453117, -0.12237962083308958, -0.18298503871774302, 0.07114656512858346, 0.42021301374770703, -0.22723380665993317, -0.3405742228962481, 0.030510230745130686, -0.2877384641440585, -0.16679459284059703, 0.18088065148622262, -0.1375819842098281, 0.06791776344180107, 0.08962705638259649, 0.09190163101302459, -0.09186913333833217, -0.31627044001361354, 0.2781624842085876, 0.028374446043744683, 0.3825620244635502, 0.013119433383690194, 0.1814131096121855, -0.03333266745903529, -0.019506602801266128, -0.07223994602682068, -0.15846201612148433, 0.22718251659534872, 0.2287799138808623, 0.23355961501365527, 0.2964100768556818, -0.37242782598477786, -0.2265792963313288, 0.0980497509095585, 0.06382192411692814, 0.09856517237076332, 0.0005901648051803932, -0.34782775342464445, 0.044002016738522796, -0.15905004608721357, 0.057103207710315476, -0.1856020081555471, -0.031582410435657945, -0.05256555920932442, -0.30696222042897714, 0.06545605702376633, 0.144682877487503, 0.040396616281941536, -0.06773464421276003, -0.15833610509289428, -0.03543322577606887, 0.11915368052432314, -0.007501083954412025, 0.0720496730908053, 0.09223079736111686, -0.2056650622660527, -0.10071291515487246, 0.2925280023366213, -0.13264683859306386, -0.20989305663388222, 0.16718086717301048, -0.042464557371567936, -0.14204515070887283, 0.07126006747130305, 0.21686683817533775, 0.15207796433242038, -0.13298882574308662, 0.05235355342447292, -0.12097979715181281, 0.1546539995353669, 0.04244092870503664, 0.04660415120888502, 0.1251191294126329, 0.24216778110712767, 0.02502408447326161, 0.1742475482489681, -0.1778367596154567, -0.08516774294548668, -0.24345480280462653, -0.09640872351155849, -0.2536137474642601, 0.011713298805989324, -0.20447883068627562, -0.1579579680255847, 0.40116765052080156, 0.18880885913968087, 0.26024431665427983, 0.17799529280673596, 0.32193508370546625, 0.05878456785576418, 0.05967820329969982, 0.148539671121398, 0.13411884749075398, 0.09230710826377617, 0.010308448795694859, -0.15997653827653266, 0.11670521430205553, 0.04907010429596994] |
1,802.04875 | Designing and discovering a new family of semiconducting quaternary
Heusler compounds based on the 18-electron rule | Intermetallic compounds with sizable band gaps are attractive for their
unusual properties but rare. Here, we present a new family of stable
semiconducting quaternary Heusler compounds, designed and discovered by means
of high-throughput \textit{ab initio} calculations based on the 18-electron
rule. The 99 new semiconductors reported here adopt the ordered quaternary
Heusler structure with the prototype of LiMgSnPd (F$\bar{\mathbf{4}}$3m,
No.\,216) and contain 18 valence electrons per formula unit. They are realized
by filling the void in the half Heusler structure with a small and
electropositive atom, i.e., lithium. These new stable quaternary Heusler
semiconductors possess a range of band gaps from 0.3 to 2.5\,eV, and exhibit
some unusual properties different from conventional semiconductors, such as
strong optical absorption, giant dielectric screening, and high Seebeck
coefficient, which suggest these semiconductors have potential applications as
photovoltaic and thermoelectric materials. While this study opens up avenues
for further exploration of this novel class of semiconducting quaternary
Heuslers, the design strategy used herein is broadly applicable across a
potentially wide array of chemistries to discover new stable materials.
| cond-mat.mtrl-sci | intermetallic compounds with sizable band gaps are attractive for their unusual properties but rare here we present a new family of stable semiconducting quaternary heusler compounds designed and discovered by means of highthroughput textitab initio calculations based on the 18electron rule the 99 new semiconductors reported here adopt the ordered quaternary heusler structure with the prototype of limgsnpd fbarmathbf43m no216 and contain 18 valence electrons per formula unit they are realized by filling the void in the half heusler structure with a small and electropositive atom ie lithium these new stable quaternary heusler semiconductors possess a range of band gaps from 03 to 25ev and exhibit some unusual properties different from conventional semiconductors such as strong optical absorption giant dielectric screening and high seebeck coefficient which suggest these semiconductors have potential applications as photovoltaic and thermoelectric materials while this study opens up avenues for further exploration of this novel class of semiconducting quaternary heuslers the design strategy used herein is broadly applicable across a potentially wide array of chemistries to discover new stable materials | [['intermetallic', 'compounds', 'with', 'sizable', 'band', 'gaps', 'are', 'attractive', 'for', 'their', 'unusual', 'properties', 'but', 'rare', 'here', 'we', 'present', 'a', 'new', 'family', 'of', 'stable', 'semiconducting', 'quaternary', 'heusler', 'compounds', 'designed', 'and', 'discovered', 'by', 'means', 'of', 'highthroughput', 'textitab', 'initio', 'calculations', 'based', 'on', 'the', '18electron', 'rule', 'the', '99', 'new', 'semiconductors', 'reported', 'here', 'adopt', 'the', 'ordered', 'quaternary', 'heusler', 'structure', 'with', 'the', 'prototype', 'of', 'limgsnpd', 'fbarmathbf43m', 'no216', 'and', 'contain', '18', 'valence', 'electrons', 'per', 'formula', 'unit', 'they', 'are', 'realized', 'by', 'filling', 'the', 'void', 'in', 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1,802.04876 | HiGrad: Uncertainty Quantification for Online Learning and Stochastic
Approximation | Stochastic gradient descent (SGD) is an immensely popular approach for online
learning in settings where data arrives in a stream or data sizes are very
large. However, despite an ever-increasing volume of work on SGD, much less is
known about the statistical inferential properties of SGD-based predictions.
Taking a fully inferential viewpoint, this paper introduces a novel procedure
termed HiGrad to conduct statistical inference for online learning, without
incurring additional computational cost compared with SGD. The HiGrad procedure
begins by performing SGD updates for a while and then splits the single thread
into several threads, and this procedure hierarchically operates in this
fashion along each thread. With predictions provided by multiple threads in
place, a $t$-based confidence interval is constructed by decorrelating
predictions using covariance structures given by a Donsker-style extension of
the Ruppert--Polyak averaging scheme, which is a technical contribution of
independent interest. Under certain regularity conditions, the HiGrad
confidence interval is shown to attain asymptotically exact coverage
probability. Finally, the performance of HiGrad is evaluated through extensive
simulation studies and a real data example. An R package \texttt{higrad} has
been developed to implement the method.
| stat.ML cs.DC math.OC stat.ME | stochastic gradient descent sgd is an immensely popular approach for online learning in settings where data arrives in a stream or data sizes are very large however despite an everincreasing volume of work on sgd much less is known about the statistical inferential properties of sgdbased predictions taking a fully inferential viewpoint this paper introduces a novel procedure termed higrad to conduct statistical inference for online learning without incurring additional computational cost compared with sgd the higrad procedure begins by performing sgd updates for a while and then splits the single thread into several threads and this procedure hierarchically operates in this fashion along each thread with predictions provided by multiple threads in place a tbased confidence interval is constructed by decorrelating predictions using covariance structures given by a donskerstyle extension of the ruppertpolyak averaging scheme which is a technical contribution of independent interest under certain regularity conditions the higrad confidence interval is shown to attain asymptotically exact coverage probability finally the performance of higrad is evaluated through extensive simulation studies and a real data example an r package texttthigrad has been developed to implement the method | [['stochastic', 'gradient', 'descent', 'sgd', 'is', 'an', 'immensely', 'popular', 'approach', 'for', 'online', 'learning', 'in', 'settings', 'where', 'data', 'arrives', 'in', 'a', 'stream', 'or', 'data', 'sizes', 'are', 'very', 'large', 'however', 'despite', 'an', 'everincreasing', 'volume', 'of', 'work', 'on', 'sgd', 'much', 'less', 'is', 'known', 'about', 'the', 'statistical', 'inferential', 'properties', 'of', 'sgdbased', 'predictions', 'taking', 'a', 'fully', 'inferential', 'viewpoint', 'this', 'paper', 'introduces', 'a', 'novel', 'procedure', 'termed', 'higrad', 'to', 'conduct', 'statistical', 'inference', 'for', 'online', 'learning', 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1,802.04877 | Learning via social awareness: Improving a deep generative sketching
model with facial feedback | In the quest towards general artificial intelligence (AI), researchers have
explored developing loss functions that act as intrinsic motivators in the
absence of external rewards. This paper argues that such research has
overlooked an important and useful intrinsic motivator: social interaction. We
posit that making an AI agent aware of implicit social feedback from humans can
allow for faster learning of more generalizable and useful representations, and
could potentially impact AI safety. We collect social feedback in the form of
facial expression reactions to samples from Sketch RNN, an LSTM-based
variational autoencoder (VAE) designed to produce sketch drawings. We use a
Latent Constraints GAN (LC-GAN) to learn from the facial feedback of a small
group of viewers, by optimizing the model to produce sketches that it predicts
will lead to more positive facial expressions. We show in multiple independent
evaluations that the model trained with facial feedback produced sketches that
are more highly rated, and induce significantly more positive facial
expressions. Thus, we establish that implicit social feedback can improve the
output of a deep learning model.
| cs.LG cs.CV cs.HC | in the quest towards general artificial intelligence ai researchers have explored developing loss functions that act as intrinsic motivators in the absence of external rewards this paper argues that such research has overlooked an important and useful intrinsic motivator social interaction we posit that making an ai agent aware of implicit social feedback from humans can allow for faster learning of more generalizable and useful representations and could potentially impact ai safety we collect social feedback in the form of facial expression reactions to samples from sketch rnn an lstmbased variational autoencoder vae designed to produce sketch drawings we use a latent constraints gan lcgan to learn from the facial feedback of a small group of viewers by optimizing the model to produce sketches that it predicts will lead to more positive facial expressions we show in multiple independent evaluations that the model trained with facial feedback produced sketches that are more highly rated and induce significantly more positive facial expressions thus we establish that implicit social feedback can improve the output of a deep learning model | [['in', 'the', 'quest', 'towards', 'general', 'artificial', 'intelligence', 'ai', 'researchers', 'have', 'explored', 'developing', 'loss', 'functions', 'that', 'act', 'as', 'intrinsic', 'motivators', 'in', 'the', 'absence', 'of', 'external', 'rewards', 'this', 'paper', 'argues', 'that', 'such', 'research', 'has', 'overlooked', 'an', 'important', 'and', 'useful', 'intrinsic', 'motivator', 'social', 'interaction', 'we', 'posit', 'that', 'making', 'an', 'ai', 'agent', 'aware', 'of', 'implicit', 'social', 'feedback', 'from', 'humans', 'can', 'allow', 'for', 'faster', 'learning', 'of', 'more', 'generalizable', 'and', 'useful', 'representations', 'and', 'could', 'potentially', 'impact', 'ai', 'safety', 'we', 'collect', 'social', 'feedback', 'in', 'the', 'form', 'of', 'facial', 'expression', 'reactions', 'to', 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0.16382938851496542, 0.016969668649835512] |
1,802.04878 | Differentiating the pseudo determinant | A class of derivatives is defined for the pseudo determinant $Det(A)$ of a
Hermitian matrix $A$. This class is shown to be non-empty and to have a unique,
canonical member $\mathbf{\nabla Det}(A)=Det(A)A^+$, where $A^+$ is the
Moore-Penrose pseudo inverse. The classic identity for the gradient of the
determinant is thus reproduced. Examples are provided, including the maximum
likelihood problem for the rank-deficient covariance matrix of the degenerate
multivariate Gaussian distribution.
| stat.OT | a class of derivatives is defined for the pseudo determinant deta of a hermitian matrix a this class is shown to be nonempty and to have a unique canonical member mathbfnabla detadetaa where a is the moorepenrose pseudo inverse the classic identity for the gradient of the determinant is thus reproduced examples are provided including the maximum likelihood problem for the rankdeficient covariance matrix of the degenerate multivariate gaussian distribution | [['a', 'class', 'of', 'derivatives', 'is', 'defined', 'for', 'the', 'pseudo', 'determinant', 'deta', 'of', 'a', 'hermitian', 'matrix', 'a', 'this', 'class', 'is', 'shown', 'to', 'be', 'nonempty', 'and', 'to', 'have', 'a', 'unique', 'canonical', 'member', 'mathbfnabla', 'detadetaa', 'where', 'a', 'is', 'the', 'moorepenrose', 'pseudo', 'inverse', 'the', 'classic', 'identity', 'for', 'the', 'gradient', 'of', 'the', 'determinant', 'is', 'thus', 'reproduced', 'examples', 'are', 'provided', 'including', 'the', 'maximum', 'likelihood', 'problem', 'for', 'the', 'rankdeficient', 'covariance', 'matrix', 'of', 'the', 'degenerate', 'multivariate', 'gaussian', 'distribution']] | [-0.08704376562019352, 0.06154958639239919, -0.04722763433296611, 0.11577218006857658, -0.0895163598065467, -0.13891390105709434, -0.03605534109975333, 0.3405763619187949, -0.3286692157819651, -0.20386926964789198, 0.14450806617463494, -0.285603745719013, -0.17357829549228368, 0.14738200550588468, -0.04322745193106433, 0.09284418554085752, 0.03673799246873544, 0.09424760376872576, -0.15782187546373924, -0.21799326180716624, 0.37229507578455884, 0.029114412503195523, 0.21606726728487705, 0.008247271483845037, 0.1348871262676582, -0.005077011640305104, -0.0007925346169782722, -0.001543309688028218, -0.03702067916848413, 0.10681000862952214, 0.2285024720426325, 0.1528527291569238, 0.28830266491496476, -0.27083275958463765, -0.18963721193427194, 0.19921624846756458, 0.10570255475139002, 0.016400593400433445, -0.0346674924324928, -0.24684990710322408, 0.13194254216500997, -0.15892535138645789, -0.16995379840955138, -0.03044535781837244, 0.025456117682050968, -0.004450335275327814, -0.4091879747592021, 0.08594600881949283, 0.07090170390607006, 0.03913475492753196, 0.02010071913347296, -0.209686695476589, 0.010132900547182215, 0.053421806307860475, 0.008469335275931635, -0.016748394655144733, 0.06215348819513684, -0.07842059753011858, -0.04666986808423763, 0.3509764227260282, -0.054070200109719364, -0.2633391321785208, 0.05315677739976757, -0.09011720709394717, -0.11224610586583182, 0.13101469090991263, 0.1293698734185402, 0.14636222669260873, -0.16051698975480985, 0.14986117456050968, -0.1148627238429111, 0.08915563122085902, 0.017136047034105963, -0.03691922778776591, 0.15591242563897284, 0.09003769623223638, 0.07847066476360719, 0.15920136470610843, -0.03845089994559901, -0.12270067154726796, -0.30006134877170343, -0.22384730325606855, -0.27500160743036994, 0.09673689864575863, -0.10498737751660646, -0.255837132575149, 0.4128717909688535, 0.030523853713943474, 0.2070525264826374, 0.12002691479426796, 0.214997295992098, 0.18017964374166037, 0.07403934652498667, 0.07000862750514963, 0.17853204447074214, 0.25364777943173394, 0.024292131737414478, -0.1898487192997034, 0.0687487943948287, 0.12162897309315378] |
1,802.04879 | Weierstrass Prym eigenforms in genus four | We prove that for each discriminant $D \equiv 0,1 \mod 4, D \not\in\{4,9\}$,
the corresponding Prym eigenform locus discovered by McMullen in the stratum
$\mathcal{H}(6)$ is connected. Thus, the projection of any of those loci in the
moduli space is a single Teichm\"uller curve. Along the way, we obtain a
classification of primitive square-tiled surfaces in the locus
$\mathrm{Prym}(6)$ of Prym forms in $\mathcal{H}(6)$.
| math.GT math.DS | we prove that for each discriminant d equiv 01 mod 4 d notin49 the corresponding prym eigenform locus discovered by mcmullen in the stratum mathcalh6 is connected thus the projection of any of those loci in the moduli space is a single teichmuller curve along the way we obtain a classification of primitive squaretiled surfaces in the locus mathrmprym6 of prym forms in mathcalh6 | [['we', 'prove', 'that', 'for', 'each', 'discriminant', 'd', 'equiv', '01', 'mod', '4', 'd', 'notin49', 'the', 'corresponding', 'prym', 'eigenform', 'locus', 'discovered', 'by', 'mcmullen', 'in', 'the', 'stratum', 'mathcalh6', 'is', 'connected', 'thus', 'the', 'projection', 'of', 'any', 'of', 'those', 'loci', 'in', 'the', 'moduli', 'space', 'is', 'a', 'single', 'teichmuller', 'curve', 'along', 'the', 'way', 'we', 'obtain', 'a', 'classification', 'of', 'primitive', 'squaretiled', 'surfaces', 'in', 'the', 'locus', 'mathrmprym6', 'of', 'prym', 'forms', 'in', 'mathcalh6']] | [-0.26987175246079764, 0.03306163517409004, -0.14665306552002827, 0.03690484558270934, -0.020310604642145336, -0.15842944324637454, 0.036619104988252126, 0.26447846145213894, -0.3398156040503333, -0.17908897930756212, 0.05177918324091782, -0.2974704788532108, -0.1786395978492995, 0.23715278546248253, -0.1855640532138447, 0.0008931733823070924, 0.031002805513950685, 0.1058556554839015, -0.09269852603708083, -0.34668304679604867, 0.42607102897018195, -0.11142619818759461, 0.181062088010367, -0.025989786768332124, 0.07130056526511908, -0.003909904963802546, 0.013709915708750487, -0.09255865424687121, -0.16103364179980417, 0.17482480922092994, 0.32614875473082067, 0.07519001449691132, 0.1479688038448027, -0.28207216821610925, -0.164822943865632, 0.2532361754837135, 0.1732733381912112, -0.019485345700134833, 0.08994341754587368, -0.17401449306247135, 0.07794634765014052, -0.076315799211928, -0.25470020618134487, -0.05759479944438984, 0.11538959581327314, 0.01279179706859092, -0.1536315327975899, 0.01358008555447062, 0.07556314240209758, 0.21471823701479784, -0.09147922263170281, -0.15960082273619872, -0.13521410953253507, 0.03933096551336348, -0.029322645061377748, 0.136341016072159, 0.09896499426686205, -0.12364908284119641, -0.07290209219014893, 0.3369119453476742, -0.07063573690441748, -0.23105740438525876, 0.1039965599548547, -0.2217822503686572, -0.17895555415501196, 0.2040993416061004, 0.09321936191360389, 0.1735827403453489, -0.02553631323001658, 0.18835978872763615, -0.1134070786104227, 0.10628098009619862, 0.13755059769997993, -0.116421123725983, 0.15878114049167683, 0.12338730463137229, 0.06395081758188705, 0.11940037397046883, -0.12233607079057644, 0.05167303491616622, -0.38556874841451644, -0.26895236138952894, -0.10485028754919767, 0.09681789358146489, -0.14718667754592996, -0.12882930475752802, 0.4032411091883356, -0.0016827041283249855, 0.27342643703644476, 0.10516964971708755, 0.2034664553279678, -0.0070003733349343145, 0.08647780173147718, 0.08444342726531127, 0.1507372940890491, 0.14885536819153156, -0.08155931763661405, -0.12375373200047761, 0.004862650732199351, 0.23784937089464317] |
1,802.0488 | Generic Existence of Independent Families | We apply the concept of generic existence to p-point, q, and selective
independent families that complements and emulates the ultrafilter generic
existence results from Canjar and Ketonen.
| math.LO | we apply the concept of generic existence to ppoint q and selective independent families that complements and emulates the ultrafilter generic existence results from canjar and ketonen | [['we', 'apply', 'the', 'concept', 'of', 'generic', 'existence', 'to', 'ppoint', 'q', 'and', 'selective', 'independent', 'families', 'that', 'complements', 'and', 'emulates', 'the', 'ultrafilter', 'generic', 'existence', 'results', 'from', 'canjar', 'and', 'ketonen']] | [-0.14398943475232676, 0.13376374845393002, -0.08956874398371348, 0.01786312174338561, -0.11703258215521391, -0.16243570075871852, 0.07160437637223648, 0.2934738320943255, -0.26674245283580744, -0.155579906476375, 0.08327866250720735, -0.24688759392413956, -0.17535560434827438, 0.2420945048618775, -0.10865705800600924, 0.008596472800351106, 0.04528646454295645, -0.026269806620593254, -0.008333948109513866, -0.18813256446558696, 0.43682087613986087, -0.07150139459050618, 0.28184845628073585, 0.08787136023434308, 0.09888066243953429, 0.11208849797885005, 0.021402656029050168, 0.014005523461561937, -0.23265447396960764, 0.11482852324843407, 0.204126819395102, 0.1848181763665567, 0.20541405104673827, -0.29792352880422884, -0.14657685780324614, 0.18518924842087123, 0.1119564655124962, 0.08146962107947239, -0.055879008024930954, -0.2686614220102246, 0.15269579430325672, -0.15629315809704936, -0.18329461488442925, -0.1636122600757517, 0.048830206792515055, 0.06467680387700406, -0.3253113441169262, 0.006401995161118416, 0.2053624035862203, 0.05477680634850493, -0.12996175082830283, -0.09109726078951588, 0.013563262298703194, 0.07785376204321018, 0.02196925677932226, 0.010346253223430652, 0.07353338969943042, 0.007483733388093801, -0.1566634851221282, 0.26070806204986113, -0.12816961446347144, -0.1950024048654506, 0.287565159253203, -0.09455697166805084, -0.18876847981188732, 0.11338526866613673, 0.08774937646320233, 0.11737968127887982, -0.027794465548000656, 0.13603257251088507, -0.14769140706182673, 0.14684396108182576, 0.13810540896912032, 0.07035286346665369, 0.14388662626823553, 0.09138258593156934, 0.09437119256919967, 0.16972988131993377, 0.011490503558889031, -0.06406543894599263, -0.3505791468689075, -0.08388843122296609, -0.135452684516517, 0.05556217480737429, -0.07013800186653568, -0.15885813648884112, 0.41304748619978243, 0.18663838419776696, 0.18460385666157192, 0.08385244187397453, 0.18983287614985153, -0.007408013818069146, -0.0063349369268577834, 0.060000708975936644, 0.17042298070513284, 0.14458158909558103, -0.028048480610148266, -0.1468899312805241, 0.0028288354627035847, 0.13240675470576838] |
1,802.04881 | Satellite Image Forgery Detection and Localization Using GAN and
One-Class Classifier | Current satellite imaging technology enables shooting high-resolution
pictures of the ground. As any other kind of digital images, overhead pictures
can also be easily forged. However, common image forensic techniques are often
developed for consumer camera images, which strongly differ in their nature
from satellite ones (e.g., compression schemes, post-processing, sensors,
etc.). Therefore, many accurate state-of-the-art forensic algorithms are bound
to fail if blindly applied to overhead image analysis. Development of novel
forensic tools for satellite images is paramount to assess their authenticity
and integrity. In this paper, we propose an algorithm for satellite image
forgery detection and localization. Specifically, we consider the scenario in
which pixels within a region of a satellite image are replaced to add or remove
an object from the scene. Our algorithm works under the assumption that no
forged images are available for training. Using a generative adversarial
network (GAN), we learn a feature representation of pristine satellite images.
A one-class support vector machine (SVM) is trained on these features to
determine their distribution. Finally, image forgeries are detected as
anomalies. The proposed algorithm is validated against different kinds of
satellite images containing forgeries of different size and shape.
| cs.CV | current satellite imaging technology enables shooting highresolution pictures of the ground as any other kind of digital images overhead pictures can also be easily forged however common image forensic techniques are often developed for consumer camera images which strongly differ in their nature from satellite ones eg compression schemes postprocessing sensors etc therefore many accurate stateoftheart forensic algorithms are bound to fail if blindly applied to overhead image analysis development of novel forensic tools for satellite images is paramount to assess their authenticity and integrity in this paper we propose an algorithm for satellite image forgery detection and localization specifically we consider the scenario in which pixels within a region of a satellite image are replaced to add or remove an object from the scene our algorithm works under the assumption that no forged images are available for training using a generative adversarial network gan we learn a feature representation of pristine satellite images a oneclass support vector machine svm is trained on these features to determine their distribution finally image forgeries are detected as anomalies the proposed algorithm is validated against different kinds of satellite images containing forgeries of different size and shape | [['current', 'satellite', 'imaging', 'technology', 'enables', 'shooting', 'highresolution', 'pictures', 'of', 'the', 'ground', 'as', 'any', 'other', 'kind', 'of', 'digital', 'images', 'overhead', 'pictures', 'can', 'also', 'be', 'easily', 'forged', 'however', 'common', 'image', 'forensic', 'techniques', 'are', 'often', 'developed', 'for', 'consumer', 'camera', 'images', 'which', 'strongly', 'differ', 'in', 'their', 'nature', 'from', 'satellite', 'ones', 'eg', 'compression', 'schemes', 'postprocessing', 'sensors', 'etc', 'therefore', 'many', 'accurate', 'stateoftheart', 'forensic', 'algorithms', 'are', 'bound', 'to', 'fail', 'if', 'blindly', 'applied', 'to', 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1,802.04882 | Nanocommunication via FRET with DyLight Dyes using Multiple Donors and
Acceptors | The phenomenon of Forster Resonance Energy Transfer, commonly used to measure
the distances between fluorophore molecules and to study interactions between
fluorescent-tagged proteins in life sciences, can also be applied in
nanocommunication networks to transfer information bits. The mechanism offers a
relatively large throughput and very small delays, but at the same time the
channel bit error rate is too high and the transmission ranges are too limited
for communication purposes. In this paper, multiple donors at the transmitter
side and multiple acceptors at the receiver side are considered to decrease the
bit error rate. As nanoantennas, the DyLight fluorescent dyes, which are very
well suited to long range nanocommunication due to their large Forster
distances and high degrees of labeling, are proposed. The reported results of
the recent laboratory experiments confirm efficient communication on distances
over 10 nm.
| physics.bio-ph q-bio.MN | the phenomenon of forster resonance energy transfer commonly used to measure the distances between fluorophore molecules and to study interactions between fluorescenttagged proteins in life sciences can also be applied in nanocommunication networks to transfer information bits the mechanism offers a relatively large throughput and very small delays but at the same time the channel bit error rate is too high and the transmission ranges are too limited for communication purposes in this paper multiple donors at the transmitter side and multiple acceptors at the receiver side are considered to decrease the bit error rate as nanoantennas the dylight fluorescent dyes which are very well suited to long range nanocommunication due to their large forster distances and high degrees of labeling are proposed the reported results of the recent laboratory experiments confirm efficient communication on distances over 10 nm | [['the', 'phenomenon', 'of', 'forster', 'resonance', 'energy', 'transfer', 'commonly', 'used', 'to', 'measure', 'the', 'distances', 'between', 'fluorophore', 'molecules', 'and', 'to', 'study', 'interactions', 'between', 'fluorescenttagged', 'proteins', 'in', 'life', 'sciences', 'can', 'also', 'be', 'applied', 'in', 'nanocommunication', 'networks', 'to', 'transfer', 'information', 'bits', 'the', 'mechanism', 'offers', 'a', 'relatively', 'large', 'throughput', 'and', 'very', 'small', 'delays', 'but', 'at', 'the', 'same', 'time', 'the', 'channel', 'bit', 'error', 'rate', 'is', 'too', 'high', 'and', 'the', 'transmission', 'ranges', 'are', 'too', 'limited', 'for', 'communication', 'purposes', 'in', 'this', 'paper', 'multiple', 'donors', 'at', 'the', 'transmitter', 'side', 'and', 'multiple', 'acceptors', 'at', 'the', 'receiver', 'side', 'are', 'considered', 'to', 'decrease', 'the', 'bit', 'error', 'rate', 'as', 'nanoantennas', 'the', 'dylight', 'fluorescent', 'dyes', 'which', 'are', 'very', 'well', 'suited', 'to', 'long', 'range', 'nanocommunication', 'due', 'to', 'their', 'large', 'forster', 'distances', 'and', 'high', 'degrees', 'of', 'labeling', 'are', 'proposed', 'the', 'reported', 'results', 'of', 'the', 'recent', 'laboratory', 'experiments', 'confirm', 'efficient', 'communication', 'on', 'distances', 'over', '10', 'nm']] | [-0.1428325650074866, 0.10346507578476394, -0.006813594502529442, 0.10779247171267609, -0.011265443767128635, -0.21068432798989825, 0.0899116680014158, 0.43445857578249525, -0.2808981393973758, -0.36300065719869234, 0.07689305023123666, -0.2729937275191837, -0.08562028470591906, 0.20727731492778245, -0.08589933338799398, 0.035953664404414866, 0.08674467741477772, 0.020461704670349613, 0.01904618579989476, -0.21727843428995922, 0.20705063150427475, 0.1519535416424917, 0.30879237262165027, 0.11467021326623673, 0.0750598081244822, -0.026623597662610403, 0.029747894545337255, -0.0782091808335407, -0.1004970760295426, 0.1300001730258665, 0.3342967432427363, 0.08001645654779825, 0.26669912603105944, -0.4229545958380956, -0.23200732774107996, 0.08419457479060566, 0.16509864557055198, 0.15487142247368793, -0.04666648699517913, -0.24022501315513667, 0.08555251369796639, -0.14144787653927168, -0.020988065047515898, 0.0030765950129143078, 0.028273769079904705, 0.07962600962789118, -0.27276664488289476, 0.0762164495963124, -0.0273664021026683, 0.07124130181750676, -0.018225364218785487, -0.1217276324470439, 0.022086277626196506, 0.1842769709633269, 0.040794867396789745, 0.01401443522590056, 0.15092261325581557, -0.09682016431678589, -0.08187183430486351, 0.3598625727701443, -0.05188356636833046, -0.1580010893431545, 0.2598922600409689, -0.1529679082085066, -0.035211868315636025, 0.17316035631819743, 0.19886721098195612, 0.1364483976427357, -0.1566200097054107, -0.002865135787398194, 0.06280913334730741, 0.21172468422021526, 0.12504559239189067, 0.1439982769181476, 0.1869338301221167, 0.14720809816579966, 0.035577926478844925, 0.08175168762979418, -0.16813184930535074, -0.10671342274745124, -0.20385153535294626, -0.1286444569849511, -0.24686983130518755, 0.026575414331317167, -0.06559298684033242, -0.07124470481581062, 0.30602014223861435, 0.12044327443696722, 0.19347054890295776, 0.07511719067523215, 0.317132739268624, 0.050193147168725895, 0.12782285869186133, 0.035696830481535546, 0.24916474106483652, 0.14287874238551967, 0.14325276676507626, -0.21979613586016217, 0.08336600410857367, -0.05922183297221025] |
1,802.04883 | Efficient Discovery of Variable-length Time Series Motifs with Large
Length Range in Million Scale Time Series | Detecting repeated variable-length patterns, also called variable-length
motifs, has received a great amount of attention in recent years. Current
state-of-the-art algorithm utilizes fixed-length motif discovery algorithm as a
subroutine to enumerate variable-length motifs. As a result, it may take hours
or days to execute when enumeration range is large. In this work, we introduce
an approximate algorithm called HierarchIcal based Motif Enumeration (HIME) to
detect variable-length motifs with a large enumeration range in million-scale
time series. We show in the experiments that the scalability of the proposed
algorithm is significantly better than that of the state-of-the-art algorithm.
Moreover, the motif length range detected by HIME is considerably larger than
previous sequence-matching based approximate variable-length motif discovery
approach. We demonstrate that HIME can efficiently detect meaningful
variable-length motifs in long, real world time series.
| cs.DS cs.IR | detecting repeated variablelength patterns also called variablelength motifs has received a great amount of attention in recent years current stateoftheart algorithm utilizes fixedlength motif discovery algorithm as a subroutine to enumerate variablelength motifs as a result it may take hours or days to execute when enumeration range is large in this work we introduce an approximate algorithm called hierarchical based motif enumeration hime to detect variablelength motifs with a large enumeration range in millionscale time series we show in the experiments that the scalability of the proposed algorithm is significantly better than that of the stateoftheart algorithm moreover the motif length range detected by hime is considerably larger than previous sequencematching based approximate variablelength motif discovery approach we demonstrate that hime can efficiently detect meaningful variablelength motifs in long real world time series | [['detecting', 'repeated', 'variablelength', 'patterns', 'also', 'called', 'variablelength', 'motifs', 'has', 'received', 'a', 'great', 'amount', 'of', 'attention', 'in', 'recent', 'years', 'current', 'stateoftheart', 'algorithm', 'utilizes', 'fixedlength', 'motif', 'discovery', 'algorithm', 'as', 'a', 'subroutine', 'to', 'enumerate', 'variablelength', 'motifs', 'as', 'a', 'result', 'it', 'may', 'take', 'hours', 'or', 'days', 'to', 'execute', 'when', 'enumeration', 'range', 'is', 'large', 'in', 'this', 'work', 'we', 'introduce', 'an', 'approximate', 'algorithm', 'called', 'hierarchical', 'based', 'motif', 'enumeration', 'hime', 'to', 'detect', 'variablelength', 'motifs', 'with', 'a', 'large', 'enumeration', 'range', 'in', 'millionscale', 'time', 'series', 'we', 'show', 'in', 'the', 'experiments', 'that', 'the', 'scalability', 'of', 'the', 'proposed', 'algorithm', 'is', 'significantly', 'better', 'than', 'that', 'of', 'the', 'stateoftheart', 'algorithm', 'moreover', 'the', 'motif', 'length', 'range', 'detected', 'by', 'hime', 'is', 'considerably', 'larger', 'than', 'previous', 'sequencematching', 'based', 'approximate', 'variablelength', 'motif', 'discovery', 'approach', 'we', 'demonstrate', 'that', 'hime', 'can', 'efficiently', 'detect', 'meaningful', 'variablelength', 'motifs', 'in', 'long', 'real', 'world', 'time', 'series']] | [-0.12037114152392457, 0.06260343697160584, -0.0523468019174678, 0.0757120933464231, -0.14283609173183603, -0.1479712427782833, 0.07101919648720693, 0.42820594003213974, -0.2860225501603314, -0.3576781432655521, 0.05556237896995381, -0.23004558390965754, -0.23733142790860756, 0.1860776555924011, -0.07091242735120083, 0.06281339358608413, 0.1238816703557688, 0.06170387442146701, -0.02705702832479842, -0.293539280679315, 0.1587755407514821, 0.09295015592001994, 0.3075553107432517, -0.013246943512441297, 0.09862433531570171, -0.010520125991363722, -0.03570619103922404, -0.007537010924092361, -0.08264660842525659, 0.12357078833309443, 0.307994353639214, 0.20336382781484522, 0.2881127936359411, -0.40849641179735946, -0.2355924933252478, 0.14975949332356117, 0.2051124718404354, 0.16543008405120022, -0.054270937314074966, -0.27359610825384917, 0.16808912558901243, -0.17150695784773706, 0.012161408223673925, -0.1002475377819956, 0.045190596576544624, 0.008370111415449782, -0.25552655783783, 0.0799044259476801, 0.049482424549156225, 0.025005694604793887, 0.017073023150485932, -0.10169855986994908, 0.0829930403029119, 0.08646542894504299, 0.010836266940529634, 0.06530366276558909, 0.08802391743091376, -0.05696970055446981, -0.215262574822943, 0.3148738929819792, -0.03989454395688293, -0.11930026344541825, 0.1379430073705551, -0.060679218335308246, -0.2224672187427829, 0.19149688246687943, 0.22376352926029972, 0.17507959630242304, -0.12538715907693332, 0.007009835259050579, -0.09692941613047194, 0.2428527769905732, 0.12297074696315187, 0.010251861930425678, 0.16777206152411445, 0.22925578464081692, 0.0776716263534123, 0.19531682503752804, -0.11184597450931717, -0.09672670870935335, -0.12783029006051838, -0.08937637639210973, -0.20776819233971655, -0.03784398122382511, -0.10273380899675806, -0.18373391759070687, 0.40641829606733826, 0.21477213583245902, 0.20228534290581046, 0.14784434679335445, 0.2898423355212785, -0.0005294333502678271, 0.1522977409002028, 0.0797251727488032, 0.12512652421916337, 0.027099649970685796, 0.07891551779295997, -0.16444517444308035, 0.13017129996542895, 0.09945976521637767] |
1,802.04884 | Routing in FRET-based Nanonetworks | Nanocommunications, understood as communications between nanoscale devices,
is commonly regarded as a technology essential for cooperation of large groups
of nanomachines and thus crucial for development of the whole area of
nanotechnology. While solutions for point-to-point nanocommunications have been
already proposed, larger networks cannot function properly without routing. In
this article we focus on the nanocommunications via Forster Resonance Energy
Transfer (FRET), which was found to be a technique with a very high signal
propagation speed, and discuss how to route signals through nanonetworks. We
introduce five new routing mechanisms, based on biological properties of
specific molecules. We experimentally validate one of these mechanisms.
Finally, we analyze open issues showing the technical challenges for signal
transmission and routing in FRET-based nanocommunications.
| physics.bio-ph q-bio.MN | nanocommunications understood as communications between nanoscale devices is commonly regarded as a technology essential for cooperation of large groups of nanomachines and thus crucial for development of the whole area of nanotechnology while solutions for pointtopoint nanocommunications have been already proposed larger networks cannot function properly without routing in this article we focus on the nanocommunications via forster resonance energy transfer fret which was found to be a technique with a very high signal propagation speed and discuss how to route signals through nanonetworks we introduce five new routing mechanisms based on biological properties of specific molecules we experimentally validate one of these mechanisms finally we analyze open issues showing the technical challenges for signal transmission and routing in fretbased nanocommunications | [['nanocommunications', 'understood', 'as', 'communications', 'between', 'nanoscale', 'devices', 'is', 'commonly', 'regarded', 'as', 'a', 'technology', 'essential', 'for', 'cooperation', 'of', 'large', 'groups', 'of', 'nanomachines', 'and', 'thus', 'crucial', 'for', 'development', 'of', 'the', 'whole', 'area', 'of', 'nanotechnology', 'while', 'solutions', 'for', 'pointtopoint', 'nanocommunications', 'have', 'been', 'already', 'proposed', 'larger', 'networks', 'can', 'not', 'function', 'properly', 'without', 'routing', 'in', 'this', 'article', 'we', 'focus', 'on', 'the', 'nanocommunications', 'via', 'forster', 'resonance', 'energy', 'transfer', 'fret', 'which', 'was', 'found', 'to', 'be', 'a', 'technique', 'with', 'a', 'very', 'high', 'signal', 'propagation', 'speed', 'and', 'discuss', 'how', 'to', 'route', 'signals', 'through', 'nanonetworks', 'we', 'introduce', 'five', 'new', 'routing', 'mechanisms', 'based', 'on', 'biological', 'properties', 'of', 'specific', 'molecules', 'we', 'experimentally', 'validate', 'one', 'of', 'these', 'mechanisms', 'finally', 'we', 'analyze', 'open', 'issues', 'showing', 'the', 'technical', 'challenges', 'for', 'signal', 'transmission', 'and', 'routing', 'in', 'fretbased', 'nanocommunications']] | [-0.15722327686456933, 0.07641181363304314, -0.03890784181364369, 0.06503386175275215, -0.060560625580857036, -0.18784162828500275, 0.07682668191731953, 0.4243489054993528, -0.2577026777473263, -0.3128931397556892, 0.09978012130481237, -0.21799607976477164, -0.21677091432979606, 0.23749699373681815, -0.07577242074557199, 0.08593125489433526, 0.04563611773172867, -0.016769890566585493, 0.032580223430680934, -0.18029429092545246, 0.28474074155550266, 0.0888002403819987, 0.3312484566580321, 0.15389533369175967, 0.09393703558131075, -0.000691849366212111, 0.005768787146347468, -0.03731708666386052, -0.14380102201945102, 0.14119953596506452, 0.31534325209881375, 0.14801435732180357, 0.28881157666337903, -0.4498674993662805, -0.2825341287735071, 0.09563620792770783, 0.20184993788172476, 0.126084990718318, -0.09469495847492983, -0.24846856093766992, 0.1129183619603759, -0.15491945746728814, -0.07825385039416047, -0.0651712073471214, -0.004982679330392695, 0.07232954940466653, -0.19173216746526495, 0.02371997257587729, -0.01318388924643886, 0.05543750952013203, -0.04019265233304783, -0.0917840533122634, 0.028398686981775233, 0.1651986397833365, 0.002393975722237078, -0.02325373701751232, 0.13689315865068225, -0.1297855824976396, -0.17253030909103204, 0.38637431596451605, -0.026998236396094236, -0.18755007133681756, 0.190216193426034, -0.06876550012312975, -0.1371510915656681, 0.08448005058100355, 0.20738494026352514, 0.09369380630125276, -0.2209660584855153, 0.003401022341930628, 0.0438090051907462, 0.16282838059791563, 0.07173856936364634, 0.09527985564600981, 0.18076939572442752, 0.23862172076936627, 0.08130704497605501, 0.12718415458793522, -0.0859163148656915, -0.08880552979086938, -0.211692672079242, -0.170419298055139, -0.17301601557381696, 0.04869822073800844, -0.026197852020664332, -0.09607044856453345, 0.3741411813611134, 0.15920487610200043, 0.16769004408289206, -0.0015009840498067682, 0.33729184883051233, 0.042315797981439675, 0.10184892169657911, 0.05042504503574894, 0.2516123664977609, 0.10085793877913632, 0.13839961343910545, -0.1859005601813284, 0.08381302491761744, -0.01745574442539975] |
1,802.04885 | Distributionally Robust Mean-Variance Portfolio Selection with
Wasserstein Distances | We revisit Markowitz's mean-variance portfolio selection model by considering
a distributionally robust version, where the region of distributional
uncertainty is around the empirical measure and the discrepancy between
probability measures is dictated by the so-called Wasserstein distance. We
reduce this problem into an empirical variance minimization problem with an
additional regularization term. Moreover, we extend recent inference
methodology in order to select the size of the distributional uncertainty as
well as the associated robust target return rate in a data-driven way.
| stat.ME | we revisit markowitzs meanvariance portfolio selection model by considering a distributionally robust version where the region of distributional uncertainty is around the empirical measure and the discrepancy between probability measures is dictated by the socalled wasserstein distance we reduce this problem into an empirical variance minimization problem with an additional regularization term moreover we extend recent inference methodology in order to select the size of the distributional uncertainty as well as the associated robust target return rate in a datadriven way | [['we', 'revisit', 'markowitzs', 'meanvariance', 'portfolio', 'selection', 'model', 'by', 'considering', 'a', 'distributionally', 'robust', 'version', 'where', 'the', 'region', 'of', 'distributional', 'uncertainty', 'is', 'around', 'the', 'empirical', 'measure', 'and', 'the', 'discrepancy', 'between', 'probability', 'measures', 'is', 'dictated', 'by', 'the', 'socalled', 'wasserstein', 'distance', 'we', 'reduce', 'this', 'problem', 'into', 'an', 'empirical', 'variance', 'minimization', 'problem', 'with', 'an', 'additional', 'regularization', 'term', 'moreover', 'we', 'extend', 'recent', 'inference', 'methodology', 'in', 'order', 'to', 'select', 'the', 'size', 'of', 'the', 'distributional', 'uncertainty', 'as', 'well', 'as', 'the', 'associated', 'robust', 'target', 'return', 'rate', 'in', 'a', 'datadriven', 'way']] | [-0.04651254461934491, -0.003711603092889152, -0.09137032068346018, 0.1669283144155885, -0.08711670100527966, -0.08026156465258495, 0.1195277637373771, 0.38181361703225125, -0.3515593641219132, -0.30277241338734273, 0.11565957838427965, -0.28111543842119935, -0.13097750328849128, 0.14154830287256634, -0.19169994783738661, 0.10808039084651772, 0.026901120639026718, 0.004293486199997089, -0.05084713655351489, -0.20470196015580935, 0.3219815449829409, 0.1130062828047408, 0.2972320098498905, -0.015612783772801911, 0.11811994984573512, 0.033079855167019515, -0.053852434626516, 0.03984501028870359, -0.15000794307087306, 0.16791301832051464, 0.2183877935426103, 0.16963858833467518, 0.39847270288953074, -0.3362823775337066, -0.2151540787430641, 0.15815432985991607, 0.09441612133135398, 0.06804512445757419, -0.0003884983203017417, -0.30289689546025556, 0.024276697996681855, -0.18280787489493378, -0.09622240918516009, -0.07374757628335997, -0.0036618860070536164, -0.012529478345158291, -0.3397087221045369, 0.1401857121238186, 0.05518355611050616, 0.023763123198331876, -0.0740460183724393, -0.134327216355567, 0.038872983723441945, 0.07208757032352833, 0.1407791832562559, 0.06496267988243037, 0.08771467891257302, -0.08194871050777075, -0.1256243884678424, 0.328143446430288, -0.0832092923063348, -0.2556258354244041, 0.08781294310235499, -0.05732881765881622, -0.11053952213123809, 0.048322162504687356, 0.218334652138529, 0.10393755284517452, -0.21391539461910725, 0.08610695388878087, -0.04688295073818737, 0.15654895889262357, 0.0209093385686477, 0.017777359607503002, 0.14967069401187294, 0.22908650455927407, 0.17390558116689878, 0.17409416320331303, -0.11217523120231582, -0.14239228674337084, -0.31657600071695113, -0.09937419668400523, -0.1798227627698424, 0.011531639019211318, -0.15465694048458475, -0.14551910853450312, 0.3196776761776871, 0.19494991973732356, 0.23480107212715126, 0.133705528177045, 0.2864637371658543, 0.16945779018598484, 0.012135393326860611, 0.07666506557897837, 0.2027118172335588, 0.08595720592909205, 0.018380765055800663, -0.22705629800250868, 0.13835546567627907, 0.08455205554671494] |
1,802.04886 | Signal generation and storage in FRET-based nanocommunications | The paper is concerned with Forster Resonance Energy Transfer (FRET)
considered as a mechanism for communication between nanodevices. Two solved
issues are reported in the paper, namely: signal generation and signal storage
in FRET-based nanonetworks. First, luciferase molecules as FRET transmitters
which are able to generate FRET signals themselves, taking energy from chemical
reactions without any external light exposure, are proposed. Second,
channelrhodopsins as FRET receivers, as they can convert FRET signals into
voltage, are suggested. Further, medical in-body systems where both molecule
types might be successfully applied, are discussed. Luciferase-channelrhodopsin
communication is modeled and its performance is numerically validated,
reporting on its throughput, bit error rate, propagation delay and energy
consumption.
| physics.med-ph q-bio.MN | the paper is concerned with forster resonance energy transfer fret considered as a mechanism for communication between nanodevices two solved issues are reported in the paper namely signal generation and signal storage in fretbased nanonetworks first luciferase molecules as fret transmitters which are able to generate fret signals themselves taking energy from chemical reactions without any external light exposure are proposed second channelrhodopsins as fret receivers as they can convert fret signals into voltage are suggested further medical inbody systems where both molecule types might be successfully applied are discussed luciferasechannelrhodopsin communication is modeled and its performance is numerically validated reporting on its throughput bit error rate propagation delay and energy consumption | [['the', 'paper', 'is', 'concerned', 'with', 'forster', 'resonance', 'energy', 'transfer', 'fret', 'considered', 'as', 'a', 'mechanism', 'for', 'communication', 'between', 'nanodevices', 'two', 'solved', 'issues', 'are', 'reported', 'in', 'the', 'paper', 'namely', 'signal', 'generation', 'and', 'signal', 'storage', 'in', 'fretbased', 'nanonetworks', 'first', 'luciferase', 'molecules', 'as', 'fret', 'transmitters', 'which', 'are', 'able', 'to', 'generate', 'fret', 'signals', 'themselves', 'taking', 'energy', 'from', 'chemical', 'reactions', 'without', 'any', 'external', 'light', 'exposure', 'are', 'proposed', 'second', 'channelrhodopsins', 'as', 'fret', 'receivers', 'as', 'they', 'can', 'convert', 'fret', 'signals', 'into', 'voltage', 'are', 'suggested', 'further', 'medical', 'inbody', 'systems', 'where', 'both', 'molecule', 'types', 'might', 'be', 'successfully', 'applied', 'are', 'discussed', 'luciferasechannelrhodopsin', 'communication', 'is', 'modeled', 'and', 'its', 'performance', 'is', 'numerically', 'validated', 'reporting', 'on', 'its', 'throughput', 'bit', 'error', 'rate', 'propagation', 'delay', 'and', 'energy', 'consumption']] | [-0.165502594907659, 0.09661252527321512, 0.01424606943026873, 0.07981302206580704, -0.026342783681209292, -0.23402271603930508, 0.048345661229193884, 0.4390921870515019, -0.2893930738540115, -0.33163933988727695, 0.0618687875061254, -0.28829297424621275, -0.14066878446956743, 0.2324910439332729, -0.07458464943107453, 0.0747511092319598, 0.07100851331084447, 0.03418566737961662, 0.06250343413767614, -0.18524831354299243, 0.21221103515006132, 0.10222888279806923, 0.3168425543846244, 0.10306411645131873, 0.0754785658414098, -0.02594459279406782, 0.03890692246742211, -0.08260088388992658, -0.08813547785079258, 0.09317232378934687, 0.3532149117893061, 0.12905522235375536, 0.23434303712670332, -0.4767779378421806, -0.27955814650668215, 0.11707103278840313, 0.18990047147229044, 0.12658838736342248, -0.11215120423173527, -0.26194875633662884, 0.056589631216919366, -0.15971444507322466, 0.022569243464753166, -0.05320673478716934, -0.05616531296641574, 0.11117555770282592, -0.2879722102520031, 0.07358143339353101, -0.03109018308706246, 0.04677426004950125, -0.09423844481247838, -0.11107968802454772, -0.029256445221468672, 0.1548160311667907, 0.013904128620098974, -0.029301647205946146, 0.2150447056287908, -0.058180358820397675, -0.1502146350433798, 0.3816194461306205, -0.014939700131648564, -0.20581270792513146, 0.16406153637938398, -0.038044938554112624, -0.03819180636138127, 0.14265574005155549, 0.21501323857621565, 0.06262860437939027, -0.24887437007582938, -0.010992645530915307, 0.11298783181674636, 0.21698238915419793, 0.14488207592066746, 0.08671681615859606, 0.1725438044842769, 0.19193570989578426, 0.02500796937257857, 0.123799674589447, -0.13570064593836464, -0.09012114634812884, -0.2035865522784743, -0.13470653022077303, -0.23554200596419406, 0.059462292921133676, 0.01908833601106659, -0.019775571217676542, 0.3471834736756628, 0.10544841594339625, 0.14406257126530683, 0.03508193298297523, 0.3639555439722162, 0.1148391975098298, 0.0612723397317569, 0.04070187268594095, 0.2530598566481458, 0.11618273013044854, 0.14773706936168376, -0.23532624649086353, 0.08793412417441875, -0.044089359426367526] |
1,802.04887 | Probabilistic Warnings in National Security Crises: Pearl Harbor
Revisited | Imagine a situation where a group of adversaries is preparing an attack on
the United States or U.S. interests. An intelligence analyst has observed some
signals, but the situation is rapidly changing. The analyst faces the decision
to alert a principal decision maker that an attack is imminent, or to wait
until more is known about the situation. This warning decision is based on the
analyst's observation and evaluation of signals, independent or correlated, and
on her updating of the prior probabilities of possible scenarios and their
outcomes. The warning decision also depends on the analyst's assessment of the
crisis' dynamics and perception of the preferences of the principal decision
maker, as well as the lead time needed for an appropriate response. This
article presents a model to support this analyst's dynamic warning decision. As
with most problems involving warning, the key is to manage the tradeoffs
between false positives and false negatives given the probabilities and the
consequences of intelligence failures of both types. The model is illustrated
by revisiting the case of the attack on Pearl Harbor in December 1941. It shows
that the radio silence of the Japanese fleet carried considerable information
(Sir Arthur Conan Doyle's "dog in the night" problem), which was misinterpreted
at the time. Even though the probabilities of different attacks were relatively
low, their consequences were such that the Bayesian dynamic reasoning described
here may have provided valuable information to key decision makers.
| cs.AI cs.LG | imagine a situation where a group of adversaries is preparing an attack on the united states or us interests an intelligence analyst has observed some signals but the situation is rapidly changing the analyst faces the decision to alert a principal decision maker that an attack is imminent or to wait until more is known about the situation this warning decision is based on the analysts observation and evaluation of signals independent or correlated and on her updating of the prior probabilities of possible scenarios and their outcomes the warning decision also depends on the analysts assessment of the crisis dynamics and perception of the preferences of the principal decision maker as well as the lead time needed for an appropriate response this article presents a model to support this analysts dynamic warning decision as with most problems involving warning the key is to manage the tradeoffs between false positives and false negatives given the probabilities and the consequences of intelligence failures of both types the model is illustrated by revisiting the case of the attack on pearl harbor in december 1941 it shows that the radio silence of the japanese fleet carried considerable information sir arthur conan doyles dog in the night problem which was misinterpreted at the time even though the probabilities of different attacks were relatively low their consequences were such that the bayesian dynamic reasoning described here may have provided valuable information to key decision makers | [['imagine', 'a', 'situation', 'where', 'a', 'group', 'of', 'adversaries', 'is', 'preparing', 'an', 'attack', 'on', 'the', 'united', 'states', 'or', 'us', 'interests', 'an', 'intelligence', 'analyst', 'has', 'observed', 'some', 'signals', 'but', 'the', 'situation', 'is', 'rapidly', 'changing', 'the', 'analyst', 'faces', 'the', 'decision', 'to', 'alert', 'a', 'principal', 'decision', 'maker', 'that', 'an', 'attack', 'is', 'imminent', 'or', 'to', 'wait', 'until', 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1,802.04888 | The false positive risk: a proposal concerning what to do about p values | It is widely acknowledged that the biomedical literature suffer from a
surfeit of false positive results. Part of the reason for this is the
persistence of the myth that observation of a p value less than 0.05 is
sufficient justification to claim that you've made a discovery.
It is hopeless to expect users to change their reliance on p values unless
they are offered an alternative way of judging the reliability of their
conclusions. If the alternative method is to have a chance of being adopted
widely, it will have to be easy to understand and to calculate. One such
proposal is based on calculation of false positive risk.
It is suggested that p values and confidence intervals should continue to be
given, but that they should be supplemented by a single additional number that
conveys the strength of the evidence better than the p value. This number could
be the minimum false positive risk (that calculated on the assumption of a
prior probability of 0.5, the largest value that can be assumed in the absence
of hard prior data). Alternatively one could specify the prior probability that
it would be necessary to believe in order to achieve a false positive risk of,
say, 0.05.
| stat.AP | it is widely acknowledged that the biomedical literature suffer from a surfeit of false positive results part of the reason for this is the persistence of the myth that observation of a p value less than 005 is sufficient justification to claim that youve made a discovery it is hopeless to expect users to change their reliance on p values unless they are offered an alternative way of judging the reliability of their conclusions if the alternative method is to have a chance of being adopted widely it will have to be easy to understand and to calculate one such proposal is based on calculation of false positive risk it is suggested that p values and confidence intervals should continue to be given but that they should be supplemented by a single additional number that conveys the strength of the evidence better than the p value this number could be the minimum false positive risk that calculated on the assumption of a prior probability of 05 the largest value that can be assumed in the absence of hard prior data alternatively one could specify the prior probability that it would be necessary to believe in order to achieve a false positive risk of say 005 | [['it', 'is', 'widely', 'acknowledged', 'that', 'the', 'biomedical', 'literature', 'suffer', 'from', 'a', 'surfeit', 'of', 'false', 'positive', 'results', 'part', 'of', 'the', 'reason', 'for', 'this', 'is', 'the', 'persistence', 'of', 'the', 'myth', 'that', 'observation', 'of', 'a', 'p', 'value', 'less', 'than', '005', 'is', 'sufficient', 'justification', 'to', 'claim', 'that', 'youve', 'made', 'a', 'discovery', 'it', 'is', 'hopeless', 'to', 'expect', 'users', 'to', 'change', 'their', 'reliance', 'on', 'p', 'values', 'unless', 'they', 'are', 'offered', 'an', 'alternative', 'way', 'of', 'judging', 'the', 'reliability', 'of', 'their', 'conclusions', 'if', 'the', 'alternative', 'method', 'is', 'to', 'have', 'a', 'chance', 'of', 'being', 'adopted', 'widely', 'it', 'will', 'have', 'to', 'be', 'easy', 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1,802.04889 | Understanding Membership Inferences on Well-Generalized Learning Models | Membership Inference Attack (MIA) determines the presence of a record in a
machine learning model's training data by querying the model. Prior work has
shown that the attack is feasible when the model is overfitted to its training
data or when the adversary controls the training algorithm. However, when the
model is not overfitted and the adversary does not control the training
algorithm, the threat is not well understood. In this paper, we report a study
that discovers overfitting to be a sufficient but not a necessary condition for
an MIA to succeed. More specifically, we demonstrate that even a
well-generalized model contains vulnerable instances subject to a new
generalized MIA (GMIA). In GMIA, we use novel techniques for selecting
vulnerable instances and detecting their subtle influences ignored by
overfitting metrics. Specifically, we successfully identify individual records
with high precision in real-world datasets by querying black-box machine
learning models. Further we show that a vulnerable record can even be
indirectly attacked by querying other related records and existing
generalization techniques are found to be less effective in protecting the
vulnerable instances. Our findings sharpen the understanding of the fundamental
cause of the problem: the unique influences the training instance may have on
the model.
| cs.CR cs.LG stat.ML | membership inference attack mia determines the presence of a record in a machine learning models training data by querying the model prior work has shown that the attack is feasible when the model is overfitted to its training data or when the adversary controls the training algorithm however when the model is not overfitted and the adversary does not control the training algorithm the threat is not well understood in this paper we report a study that discovers overfitting to be a sufficient but not a necessary condition for an mia to succeed more specifically we demonstrate that even a wellgeneralized model contains vulnerable instances subject to a new generalized mia gmia in gmia we use novel techniques for selecting vulnerable instances and detecting their subtle influences ignored by overfitting metrics specifically we successfully identify individual records with high precision in realworld datasets by querying blackbox machine learning models further we show that a vulnerable record can even be indirectly attacked by querying other related records and existing generalization techniques are found to be less effective in protecting the vulnerable instances our findings sharpen the understanding of the fundamental cause of the problem the unique influences the training instance may have on the model | [['membership', 'inference', 'attack', 'mia', 'determines', 'the', 'presence', 'of', 'a', 'record', 'in', 'a', 'machine', 'learning', 'models', 'training', 'data', 'by', 'querying', 'the', 'model', 'prior', 'work', 'has', 'shown', 'that', 'the', 'attack', 'is', 'feasible', 'when', 'the', 'model', 'is', 'overfitted', 'to', 'its', 'training', 'data', 'or', 'when', 'the', 'adversary', 'controls', 'the', 'training', 'algorithm', 'however', 'when', 'the', 'model', 'is', 'not', 'overfitted', 'and', 'the', 'adversary', 'does', 'not', 'control', 'the', 'training', 'algorithm', 'the', 'threat', 'is', 'not', 'well', 'understood', 'in', 'this', 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1,802.0489 | Core electrons in the electronic stopping of heavy ions | Electronic stopping power in the \(\mathrm{keV/\AA}\) range is accurately
calculated from first principles. The energy loss to electrons in
self-irradiated nickel, a paradigmatic transition metal, using real-time
time-dependent density functional theory is studied. Different core states are
explicitly included in the simulations to understand their involvement in the
dissipation mechanism. The experimental data are well reproduced in the
projectile velocity range of \(1.0 - 12.0~\mathrm{atomic~units}\). The core
electrons of the projectile are found to open additional dissipation channels
as the projectile velocity increases. Almost all of the energy loss is
accounted for, even for high projectile velocities, when core electrons as deep
as \(2s\) are explicitly treated. In addition to their expected excitation at
high velocities, a flapping dynamical response of the core electrons is
observed at intermediate projectile speeds.
| cond-mat.mtrl-sci physics.comp-ph | electronic stopping power in the mathrmkevaa range is accurately calculated from first principles the energy loss to electrons in selfirradiated nickel a paradigmatic transition metal using realtime timedependent density functional theory is studied different core states are explicitly included in the simulations to understand their involvement in the dissipation mechanism the experimental data are well reproduced in the projectile velocity range of 10 120mathrmatomicunits the core electrons of the projectile are found to open additional dissipation channels as the projectile velocity increases almost all of the energy loss is accounted for even for high projectile velocities when core electrons as deep as 2s are explicitly treated in addition to their expected excitation at high velocities a flapping dynamical response of the core electrons is observed at intermediate projectile speeds | [['electronic', 'stopping', 'power', 'in', 'the', 'mathrmkevaa', 'range', 'is', 'accurately', 'calculated', 'from', 'first', 'principles', 'the', 'energy', 'loss', 'to', 'electrons', 'in', 'selfirradiated', 'nickel', 'a', 'paradigmatic', 'transition', 'metal', 'using', 'realtime', 'timedependent', 'density', 'functional', 'theory', 'is', 'studied', 'different', 'core', 'states', 'are', 'explicitly', 'included', 'in', 'the', 'simulations', 'to', 'understand', 'their', 'involvement', 'in', 'the', 'dissipation', 'mechanism', 'the', 'experimental', 'data', 'are', 'well', 'reproduced', 'in', 'the', 'projectile', 'velocity', 'range', 'of', '10', '120mathrmatomicunits', 'the', 'core', 'electrons', 'of', 'the', 'projectile', 'are', 'found', 'to', 'open', 'additional', 'dissipation', 'channels', 'as', 'the', 'projectile', 'velocity', 'increases', 'almost', 'all', 'of', 'the', 'energy', 'loss', 'is', 'accounted', 'for', 'even', 'for', 'high', 'projectile', 'velocities', 'when', 'core', 'electrons', 'as', 'deep', 'as', '2s', 'are', 'explicitly', 'treated', 'in', 'addition', 'to', 'their', 'expected', 'excitation', 'at', 'high', 'velocities', 'a', 'flapping', 'dynamical', 'response', 'of', 'the', 'core', 'electrons', 'is', 'observed', 'at', 'intermediate', 'projectile', 'speeds']] | [-0.09219882180034293, 0.2223408940985183, -0.030393463566781968, 0.14226272643917776, 0.009384449677703183, -0.09276106053556309, 0.029490449835223653, 0.4069822424304063, -0.2508721958596523, -0.3485378763513772, -0.008753833058473515, -0.3064031759936979, -0.0031423120664095315, 0.16034666271733605, 0.03007093602864761, 0.04635795194464992, 0.052559840448904695, 0.007637987690647756, -0.05201819691051707, -0.13833455905670256, 0.27435594986698875, 0.09313736485448432, 0.25460156183836496, 0.10677916725078143, 0.07906934797972823, -0.025923230074800727, 0.04161227015250429, -0.0005657690591875493, -0.07924902613282087, 0.03978380033935193, 0.25807228899106205, 0.020549932982420593, 0.26194651847279915, -0.4542987355999062, -0.25649554970102223, -0.0029511554771667627, 0.16438070198477017, 0.12125761253029607, -0.04792151755472741, -0.22452715336249804, 0.04167420422936988, -0.2164368116714823, -0.1787418425009828, -0.05037228443373845, 0.050695894663698796, 0.08278746951342099, -0.2335741426795721, 0.12509303340802014, -0.0166499747779664, 0.04327635131922998, -0.12597114722589103, -0.14703526352275545, -0.09637095406101504, 0.11735074089012314, 0.061269280691374474, 0.026086262317271684, 0.21175672064029324, -0.12769172921058114, -0.03246337863686311, 0.4274086575720489, -0.01864460975896039, -0.12463576386235362, 0.1818974273183977, -0.17679671299328484, -0.058130261665402624, 0.21182239786025106, 0.18238871739297283, 0.09966644953100348, -0.14117211574107852, 0.005336645440616477, 0.016219598540896916, 0.14466203502141028, 0.06752883257183033, 0.04467967024263669, 0.17431820498588751, 0.15914678066330926, -0.028048544374155247, 0.09511928281592806, -0.1288752892186616, -0.098408647431574, -0.2870417615970758, -0.10633433243171789, -0.19453638493666411, 0.015526225889380288, -0.04588377878700537, -0.10142767438976695, 0.3479119832077482, 0.09203092770020324, 0.18781353333803613, -0.0013394276006513928, 0.2992840673752481, 0.1476507742429064, 0.05306763094153226, 0.09567320176700908, 0.29115522339001415, 0.14945278686469451, 0.11569387466448733, -0.2722810929050801, 0.08143539902732128, 0.002167532255315757] |
1,802.04891 | Modified Gravity (MOG) and its test on galaxy clusters | The MOdified Gravity (MOG) theory of J. Moffat assumes a massive vector
particle which causes a repulsive contribution to the tensor gravitation. For
the galaxy cluster A1689 new data for the X-ray gas and the strong lensing
properties are presented. Fits to MOG are possible by adjusting the galaxy
density profile. However, this appears to work as an effective dark matter
component, posing a serious problem for MOG. New gas and strong lensing data
for the cluster A1835 support these conclusions and point at a tendency of the
gas-alone to overestimate the lensing effects in MOG theory.
| astro-ph.CO | the modified gravity mog theory of j moffat assumes a massive vector particle which causes a repulsive contribution to the tensor gravitation for the galaxy cluster a1689 new data for the xray gas and the strong lensing properties are presented fits to mog are possible by adjusting the galaxy density profile however this appears to work as an effective dark matter component posing a serious problem for mog new gas and strong lensing data for the cluster a1835 support these conclusions and point at a tendency of the gasalone to overestimate the lensing effects in mog theory | [['the', 'modified', 'gravity', 'mog', 'theory', 'of', 'j', 'moffat', 'assumes', 'a', 'massive', 'vector', 'particle', 'which', 'causes', 'a', 'repulsive', 'contribution', 'to', 'the', 'tensor', 'gravitation', 'for', 'the', 'galaxy', 'cluster', 'a1689', 'new', 'data', 'for', 'the', 'xray', 'gas', 'and', 'the', 'strong', 'lensing', 'properties', 'are', 'presented', 'fits', 'to', 'mog', 'are', 'possible', 'by', 'adjusting', 'the', 'galaxy', 'density', 'profile', 'however', 'this', 'appears', 'to', 'work', 'as', 'an', 'effective', 'dark', 'matter', 'component', 'posing', 'a', 'serious', 'problem', 'for', 'mog', 'new', 'gas', 'and', 'strong', 'lensing', 'data', 'for', 'the', 'cluster', 'a1835', 'support', 'these', 'conclusions', 'and', 'point', 'at', 'a', 'tendency', 'of', 'the', 'gasalone', 'to', 'overestimate', 'the', 'lensing', 'effects', 'in', 'mog', 'theory']] | [-0.06818454840807438, 0.060597536846671574, -0.14523827061930206, 0.15068889594719317, -0.15066600178640024, -0.12111105361448911, -0.01576329889212502, 0.3127968278131448, -0.21309474287651634, -0.34405893434692797, 0.008529915196656171, -0.2954393641266506, -0.08880913793109357, 0.14086714646570422, 0.01269436334647859, 0.03925669276698803, 0.0215157541060762, -0.021483597277741257, -0.022219309301362955, -0.2739217933024823, 0.3469884872223095, 0.11013714822668892, 0.22638176400899587, 0.02135832025669515, 0.0753070255498945, 0.0025423825087879473, -0.07128982399687327, 0.059803547813013815, -0.13927714978490258, 0.06804763985443667, 0.1944007957742239, 0.10478677211600977, 0.26096770973769406, -0.3622926943935454, -0.27567389055426855, 0.06781843544740696, 0.16339133743895218, 0.14453551056188493, -0.11130575860928123, -0.32295778603414266, 0.057052762368887976, -0.2347340578950631, -0.18753223018817758, -0.006276618936226441, 0.03140020435481953, -0.0036544239604457593, -0.2848538955828796, 0.186664640339283, 0.02840567431121599, -0.02693826428730972, -0.09810958283681732, -0.09521965409900683, 0.0022811002660697946, 0.026465725420469727, 0.07874916685492887, 0.08559814388596958, 0.19086400070227683, -0.202289457381994, -0.001284378510414778, 0.4481462310844411, -0.12549520642399634, -0.10469012562922823, 0.18206803095138943, -0.10888014291898192, -0.2040886313025112, 0.05059616920091988, 0.14453218784183264, 0.016802792604721617, -0.16026830014501078, 0.06830402031785827, -0.025798742151285598, 0.1906031637011741, 0.04307648706405113, -0.007436644465087738, 0.3718340070336126, 0.05910866558285003, 0.06398242156137712, 0.05890500919000866, -0.16242523462278768, -0.06830618784260878, -0.25790800398681313, -0.08178043936883721, -0.14787463763786945, -0.005857731768628582, -0.1473654507373491, -0.16247941973172905, 0.3001913634119167, 0.10724276729888516, 0.1443160781836923, 0.06009229756212638, 0.3142786127864383, 0.09285996735767792, 0.07052718663180713, 0.05905932453849042, 0.35093032111763023, 0.22069888659219336, 0.06842070431957836, -0.2139360695388556, -0.010172901296755299, -0.002905902557055621] |
1,802.04892 | Edge fires drive the shape and stability of tropical forests | In tropical regions, fires propagate readily in grasslands but typically
consume only edges of forest patches. Thus forest patches grow due to tree
propagation and shrink by fires in surrounding grasslands. The interplay
between these competing edge effects is unknown, but critical in determining
the shape and stability of individual forest patches, as well the
landscape-level spatial distribution and stability of forests. We analyze
high-resolution remote-sensing data from protected areas of the Brazilian
Cerrado and find that forest shapes obey a robust perimeter-area scaling
relation across climatic zones. We explain this scaling by introducing a
heterogeneous fire propagation model of tropical forest-grassland ecotones.
Deviations from this perimeter-area relation determine the stability of
individual forest patches. At a larger scale, our model predicts that the
relative rates of tree growth due to propagative expansion and long-distance
seed dispersal determine whether collapse of regional-scale tree cover is
continuous or discontinuous as fire frequency changes.
| q-bio.PE | in tropical regions fires propagate readily in grasslands but typically consume only edges of forest patches thus forest patches grow due to tree propagation and shrink by fires in surrounding grasslands the interplay between these competing edge effects is unknown but critical in determining the shape and stability of individual forest patches as well the landscapelevel spatial distribution and stability of forests we analyze highresolution remotesensing data from protected areas of the brazilian cerrado and find that forest shapes obey a robust perimeterarea scaling relation across climatic zones we explain this scaling by introducing a heterogeneous fire propagation model of tropical forestgrassland ecotones deviations from this perimeterarea relation determine the stability of individual forest patches at a larger scale our model predicts that the relative rates of tree growth due to propagative expansion and longdistance seed dispersal determine whether collapse of regionalscale tree cover is continuous or discontinuous as fire frequency changes | [['in', 'tropical', 'regions', 'fires', 'propagate', 'readily', 'in', 'grasslands', 'but', 'typically', 'consume', 'only', 'edges', 'of', 'forest', 'patches', 'thus', 'forest', 'patches', 'grow', 'due', 'to', 'tree', 'propagation', 'and', 'shrink', 'by', 'fires', 'in', 'surrounding', 'grasslands', 'the', 'interplay', 'between', 'these', 'competing', 'edge', 'effects', 'is', 'unknown', 'but', 'critical', 'in', 'determining', 'the', 'shape', 'and', 'stability', 'of', 'individual', 'forest', 'patches', 'as', 'well', 'the', 'landscapelevel', 'spatial', 'distribution', 'and', 'stability', 'of', 'forests', 'we', 'analyze', 'highresolution', 'remotesensing', 'data', 'from', 'protected', 'areas', 'of', 'the', 'brazilian', 'cerrado', 'and', 'find', 'that', 'forest', 'shapes', 'obey', 'a', 'robust', 'perimeterarea', 'scaling', 'relation', 'across', 'climatic', 'zones', 'we', 'explain', 'this', 'scaling', 'by', 'introducing', 'a', 'heterogeneous', 'fire', 'propagation', 'model', 'of', 'tropical', 'forestgrassland', 'ecotones', 'deviations', 'from', 'this', 'perimeterarea', 'relation', 'determine', 'the', 'stability', 'of', 'individual', 'forest', 'patches', 'at', 'a', 'larger', 'scale', 'our', 'model', 'predicts', 'that', 'the', 'relative', 'rates', 'of', 'tree', 'growth', 'due', 'to', 'propagative', 'expansion', 'and', 'longdistance', 'seed', 'dispersal', 'determine', 'whether', 'collapse', 'of', 'regionalscale', 'tree', 'cover', 'is', 'continuous', 'or', 'discontinuous', 'as', 'fire', 'frequency', 'changes']] | [-0.07773845001891558, 0.17660635854495135, -0.04814631147373065, 0.09733638331935918, -0.05006463435554021, -0.10306592914275825, 0.09735739320151608, 0.35080513877580194, -0.3203498494333109, -0.3018737287084396, 0.12304003679545948, -0.30044944844259, -0.16471964232403333, 0.11140333169385337, -0.046038355267719946, 8.500383960750155e-05, 0.025143127480372937, -0.04376238804763636, 0.0535288121444299, -0.20347104393373672, 0.28334884439292995, 0.08129291576836761, 0.3378374372369836, -0.005005563184543437, 0.07406088425203641, -0.03173392420364041, -0.08826972557093345, 0.057906186651765695, -0.1085411799994708, 0.06109661090694489, 0.24078150631740336, 0.13222945512923714, 0.24497363343834877, -0.4519781713197763, -0.27300070185506103, 0.13368290472096084, 0.17116175353294238, 0.10269215988700052, 0.017770120663241157, -0.22413162800414185, 0.055805917303833004, -0.11571538645767833, -0.1417369420854123, -0.01568654734590972, 0.03564629830873093, 0.04460704063190811, -0.24202504266066024, 0.12079422312436273, 0.03279330775443963, 0.09410791294696438, -0.02132533902201701, -0.0596730132167215, -0.11956880407131, 0.17353491223813663, -0.013327736871606493, -0.014515495896842834, 0.16973719902563136, -0.16808644236959014, -0.049923164272882244, 0.34708124319582273, -0.05348257750636155, -0.12098616841595268, 0.21199144594799224, -0.17548419007515484, -0.1129043116784856, 0.13737333622584874, 0.2483284033369273, 0.05270704871509224, -0.10186928298837203, 0.02212382723280678, -0.0109518368703288, 0.1400700828463242, 0.13422571893504545, -0.033772933523397185, 0.2622829670766117, 0.16147683547004252, 0.09597963864940244, 0.12156686915138404, -0.12133232368835928, -0.08694790299144901, -0.23155917511433693, -0.06491718096406879, -0.12963551826573708, -0.007134912379016797, -0.16737224303476586, -0.24692952825461287, 0.40507813378212015, 0.1947770555176445, 0.244390773448489, 0.08159881662022844, 0.2635695096262655, 0.05961392235209083, 0.09143140283413231, 0.093626466979323, 0.2149368004961493, 0.09082265692523907, 0.061499929628209084, -0.19651136521054935, 0.1483846612564112, 0.05292900623218786] |
1,802.04893 | Uncertainty Estimation via Stochastic Batch Normalization | In this work, we investigate Batch Normalization technique and propose its
probabilistic interpretation. We propose a probabilistic model and show that
Batch Normalization maximazes the lower bound of its marginalized
log-likelihood. Then, according to the new probabilistic model, we design an
algorithm which acts consistently during train and test. However, inference
becomes computationally inefficient. To reduce memory and computational cost,
we propose Stochastic Batch Normalization -- an efficient approximation of
proper inference procedure. This method provides us with a scalable uncertainty
estimation technique. We demonstrate the performance of Stochastic Batch
Normalization on popular architectures (including deep convolutional
architectures: VGG-like and ResNets) for MNIST and CIFAR-10 datasets.
| stat.ML cs.LG | in this work we investigate batch normalization technique and propose its probabilistic interpretation we propose a probabilistic model and show that batch normalization maximazes the lower bound of its marginalized loglikelihood then according to the new probabilistic model we design an algorithm which acts consistently during train and test however inference becomes computationally inefficient to reduce memory and computational cost we propose stochastic batch normalization an efficient approximation of proper inference procedure this method provides us with a scalable uncertainty estimation technique we demonstrate the performance of stochastic batch normalization on popular architectures including deep convolutional architectures vgglike and resnets for mnist and cifar10 datasets | [['in', 'this', 'work', 'we', 'investigate', 'batch', 'normalization', 'technique', 'and', 'propose', 'its', 'probabilistic', 'interpretation', 'we', 'propose', 'a', 'probabilistic', 'model', 'and', 'show', 'that', 'batch', 'normalization', 'maximazes', 'the', 'lower', 'bound', 'of', 'its', 'marginalized', 'loglikelihood', 'then', 'according', 'to', 'the', 'new', 'probabilistic', 'model', 'we', 'design', 'an', 'algorithm', 'which', 'acts', 'consistently', 'during', 'train', 'and', 'test', 'however', 'inference', 'becomes', 'computationally', 'inefficient', 'to', 'reduce', 'memory', 'and', 'computational', 'cost', 'we', 'propose', 'stochastic', 'batch', 'normalization', 'an', 'efficient', 'approximation', 'of', 'proper', 'inference', 'procedure', 'this', 'method', 'provides', 'us', 'with', 'a', 'scalable', 'uncertainty', 'estimation', 'technique', 'we', 'demonstrate', 'the', 'performance', 'of', 'stochastic', 'batch', 'normalization', 'on', 'popular', 'architectures', 'including', 'deep', 'convolutional', 'architectures', 'vgglike', 'and', 'resnets', 'for', 'mnist', 'and', 'cifar10', 'datasets']] | [-0.022518177562652945, -0.05060404046777806, -0.10812466104443257, 0.08760326149842093, -0.12913418751514444, -0.22875463461969048, 0.10099822532188577, 0.48441986342032367, -0.2939315671772839, -0.35033171630213755, 0.05947021354110733, -0.1651142515027179, -0.19685610189816755, 0.19790095552497616, -0.1553525841400887, 0.13875612564040168, 0.15282770586558259, -0.01421953053571857, -0.09813521641067033, -0.30766429985949517, 0.2305962465257975, 0.11214978409715023, 0.38957098544163343, -0.022617187565908983, 0.1725228773877741, -0.012545862338111665, -0.043078812898154586, -0.04242276630982535, -0.10849833939608708, 0.1849736215081066, 0.2001364529491044, 0.2356752485771162, 0.3658572630956769, -0.42605417809234214, -0.16783844088562405, 0.1061004773744991, 0.15159513973216796, 0.12841504456292357, -0.012337825284563363, -0.24983065660433987, 0.06915756975873731, -0.21198359618070894, 0.0369432571147067, -0.22435215085323973, -0.050636180121308334, -0.05043926009966526, -0.3346348164083723, 0.08832800632360606, 0.09282573503710759, -0.004686738740509519, -0.017710570888504244, -0.15312863130552265, 0.09082074876194103, 0.06741988302047293, 0.01046039519916611, 0.017005171613373723, 0.14281893255583084, -0.13604810713485888, -0.14685598654726234, 0.2971387666900857, -0.09639131960172492, -0.20825599510974896, 0.14068496542481276, 0.05476869580944857, -0.21344845445575908, 0.05675331528898543, 0.28955430675369614, 0.13919071445259482, -0.1625734607760723, 0.05966149053934854, 0.00953082491357166, 0.18728222707376027, 0.027188349244310163, -0.016228964476165578, 0.09064503646536301, 0.30920273760476935, 0.07027516935844548, 0.2026023437233212, -0.1785176396213221, -0.07198768622653845, -0.21306906316357738, -0.13194689361765854, -0.15091438757148212, -0.0012483554287777783, -0.1678565631728606, -0.19635729068137991, 0.3441407088494788, 0.26389967258840513, 0.22832854651810172, 0.21092826463035846, 0.3887264301570562, 0.08827285514248964, 0.0823416799853126, 0.1509774869460111, 0.1600275819694686, 0.06031238687976908, 0.08376600480047412, -0.19335420523174646, 0.09329397862221903, 0.05184003427096356] |
1,802.04894 | Computer-Aided Knee Joint Magnetic Resonance Image Segmentation - A
Survey | Osteoarthritis (OA) is one of the major health issues among the elderly
population. MRI is the most popular technology to observe and evaluate the
progress of OA course. However, the extreme labor cost of MRI analysis makes
the process inefficient and expensive. Also, due to human error and subjective
nature, the inter- and intra-observer variability is rather high.
Computer-aided knee MRI segmentation is currently an active research field
because it can alleviate doctors and radiologists from the time consuming and
tedious job, and improve the diagnosis performance which has immense potential
for both clinic and scientific research. In the past decades, researchers have
investigated automatic/semi-automatic knee MRI segmentation methods
extensively. However, to the best of our knowledge, there is no comprehensive
survey paper in this field yet. In this survey paper, we classify the existing
methods by their principles and discuss the current research status and point
out the future research trend in-depth.
| cs.CV | osteoarthritis oa is one of the major health issues among the elderly population mri is the most popular technology to observe and evaluate the progress of oa course however the extreme labor cost of mri analysis makes the process inefficient and expensive also due to human error and subjective nature the inter and intraobserver variability is rather high computeraided knee mri segmentation is currently an active research field because it can alleviate doctors and radiologists from the time consuming and tedious job and improve the diagnosis performance which has immense potential for both clinic and scientific research in the past decades researchers have investigated automaticsemiautomatic knee mri segmentation methods extensively however to the best of our knowledge there is no comprehensive survey paper in this field yet in this survey paper we classify the existing methods by their principles and discuss the current research status and point out the future research trend indepth | [['osteoarthritis', 'oa', 'is', 'one', 'of', 'the', 'major', 'health', 'issues', 'among', 'the', 'elderly', 'population', 'mri', 'is', 'the', 'most', 'popular', 'technology', 'to', 'observe', 'and', 'evaluate', 'the', 'progress', 'of', 'oa', 'course', 'however', 'the', 'extreme', 'labor', 'cost', 'of', 'mri', 'analysis', 'makes', 'the', 'process', 'inefficient', 'and', 'expensive', 'also', 'due', 'to', 'human', 'error', 'and', 'subjective', 'nature', 'the', 'inter', 'and', 'intraobserver', 'variability', 'is', 'rather', 'high', 'computeraided', 'knee', 'mri', 'segmentation', 'is', 'currently', 'an', 'active', 'research', 'field', 'because', 'it', 'can', 'alleviate', 'doctors', 'and', 'radiologists', 'from', 'the', 'time', 'consuming', 'and', 'tedious', 'job', 'and', 'improve', 'the', 'diagnosis', 'performance', 'which', 'has', 'immense', 'potential', 'for', 'both', 'clinic', 'and', 'scientific', 'research', 'in', 'the', 'past', 'decades', 'researchers', 'have', 'investigated', 'automaticsemiautomatic', 'knee', 'mri', 'segmentation', 'methods', 'extensively', 'however', 'to', 'the', 'best', 'of', 'our', 'knowledge', 'there', 'is', 'no', 'comprehensive', 'survey', 'paper', 'in', 'this', 'field', 'yet', 'in', 'this', 'survey', 'paper', 'we', 'classify', 'the', 'existing', 'methods', 'by', 'their', 'principles', 'and', 'discuss', 'the', 'current', 'research', 'status', 'and', 'point', 'out', 'the', 'future', 'research', 'trend', 'indepth']] | [-0.030869763598801864, 0.021829227398484363, -0.024914679367181013, 0.08425228433853399, -0.13903778141646303, -0.10445726232750244, 0.05160924089508817, 0.41623226852205236, -0.2143981472664133, -0.36321144369982283, 0.1631338868916684, -0.2829325568531395, -0.17518117452824586, 0.21133910158134409, -0.1546905492861314, 0.05510426692336822, 0.12729023906132697, 0.007159356272926456, 0.0030811964966951087, -0.29343775578155673, 0.25154343122190576, 0.11519970612961945, 0.3720084073781771, 0.10396185016572035, 0.04234207089298012, -0.0449677810155615, -0.11617397259626734, -0.04707051056826267, -0.09221994478579482, 0.13599961299424698, 0.3520941553931487, 0.2130258288571464, 0.42875583972291725, -0.4329306759295593, -0.22020275150670818, 0.1085842807843667, 0.18380191518640218, 0.06262645075070108, -0.05589722114995024, -0.2745900308823605, 0.06952421527813912, -0.1553670033126285, -0.06369723151376668, -0.0859339148201367, 0.03443793664949848, -0.05317870257912498, -0.2113631235521385, 0.08637673663734428, 0.016737485794644607, 0.17183945551330812, -0.07315217415146578, -0.13611478404307395, 0.060034086025552824, 0.23147971862253094, 0.1579868166596785, 0.10692353205645065, 0.15611495597955868, -0.22213610365107392, -0.13830007094657049, 0.36445217762191434, 0.05116838292757932, -0.11239858573198465, 0.22717363692630133, -0.12387292583987705, -0.1458363621342486, 0.10153799899394232, 0.1734154672663671, 0.09932366774515494, -0.22478367399736798, 0.043138170512975194, 0.06956898076092138, 0.16526945004347668, 0.022757286144616574, -0.02981272939130002, 0.20444708900667088, 0.2468183861928992, 0.02276593169862233, 0.0653502165606417, -0.10804070814169552, -0.04728085650621276, -0.1812223319368633, -0.1541703006920503, -0.13609823288003864, 0.011134900897128605, -0.012510543513551391, -0.1592383606138786, 0.40123020960201855, 0.24305875232676044, 0.09239086247812099, -0.01504219766617385, 0.3853005923968005, 0.01941028859628683, 0.11286413132697098, 0.047750630198463206, 0.25180277869865103, 0.00756022609972493, 0.16609915084279092, -0.20926649337419995, 0.1220247709134128, -0.032576539723729515] |
1,802.04895 | Low-mass X-ray binaries ejected from globular clusters | We explore the population of mass-transferring binaries ejected from globular
clusters (GCs) with both black hole (BH) and neutron star (NS) accretors. We
use a set of 137 fully evolved globular cluster models which span a large range
in cluster properties and, overall, match very well the properties of old GCs
observed in the Milky Way. We identify all binaries ejected from our set of
models that eventually undergo mass-transfer. These binaries are ejected from
their host clusters over a wide range of ejection times and include white
dwarf, giant, and main sequence donors. We calculate the orbits of these
ejected systems in the Galactic potential to determine their present-day
positions in the Galaxy and compare to the distribution of observed low-mass
X-ray binaries (XRBs) in the Milky Way. We estimate $\sim 300$
mass-transferring NS binaries and $\sim 180$ mass-transferring BH binaries may
currently be present in the Milky Way that originated from within GCs. Of
these, we estimate, based on mass-transfer rates and duty cycles at the present
time, at most a few would be observable as BH--XRBs and NS--XRBs at the present
day. Based on our results, XRBs that originated from GCs are unlikely to
contribute significantly to the total population of low-mass XRBs in the
Galactic field.
| astro-ph.HE | we explore the population of masstransferring binaries ejected from globular clusters gcs with both black hole bh and neutron star ns accretors we use a set of 137 fully evolved globular cluster models which span a large range in cluster properties and overall match very well the properties of old gcs observed in the milky way we identify all binaries ejected from our set of models that eventually undergo masstransfer these binaries are ejected from their host clusters over a wide range of ejection times and include white dwarf giant and main sequence donors we calculate the orbits of these ejected systems in the galactic potential to determine their presentday positions in the galaxy and compare to the distribution of observed lowmass xray binaries xrbs in the milky way we estimate sim 300 masstransferring ns binaries and sim 180 masstransferring bh binaries may currently be present in the milky way that originated from within gcs of these we estimate based on masstransfer rates and duty cycles at the present time at most a few would be observable as bhxrbs and nsxrbs at the present day based on our results xrbs that originated from gcs are unlikely to contribute significantly to the total population of lowmass xrbs in the galactic field | [['we', 'explore', 'the', 'population', 'of', 'masstransferring', 'binaries', 'ejected', 'from', 'globular', 'clusters', 'gcs', 'with', 'both', 'black', 'hole', 'bh', 'and', 'neutron', 'star', 'ns', 'accretors', 'we', 'use', 'a', 'set', 'of', '137', 'fully', 'evolved', 'globular', 'cluster', 'models', 'which', 'span', 'a', 'large', 'range', 'in', 'cluster', 'properties', 'and', 'overall', 'match', 'very', 'well', 'the', 'properties', 'of', 'old', 'gcs', 'observed', 'in', 'the', 'milky', 'way', 'we', 'identify', 'all', 'binaries', 'ejected', 'from', 'our', 'set', 'of', 'models', 'that', 'eventually', 'undergo', 'masstransfer', 'these', 'binaries', 'are', 'ejected', 'from', 'their', 'host', 'clusters', 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'originated', 'from', 'gcs', 'are', 'unlikely', 'to', 'contribute', 'significantly', 'to', 'the', 'total', 'population', 'of', 'lowmass', 'xrbs', 'in', 'the', 'galactic', 'field']] | [-0.06691265644538881, 0.1285189914916243, -0.0638276197770167, 0.14708328858638803, -0.09749350369730521, -0.004647381200144688, 0.10207453676931826, 0.4118130854818793, -0.15866256795125083, -0.39621920713855485, 0.012075066489411429, -0.3147393987939549, -0.0222127786599144, 0.26731569448352926, -0.0541019364448619, -0.04992265749966637, 0.15040743342279234, -0.0451061981838263, -0.0634319570104015, -0.3067753859268989, 0.31608214062150747, 0.015123686602427846, 0.04844088484754874, -0.11963608789124659, 0.06306733145999412, -0.08102295474548425, -0.0012459898212303718, -0.07128350842478019, -0.16153195067471687, 0.021691925383527717, 0.2667878381568742, 0.15474164929418338, 0.18561276154193496, -0.38019765865007815, -0.1943731866444328, 0.05816802548111549, 0.24529800872939328, 0.06235170599483397, 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1,802.04896 | Non-normal purely log terminal centres in characteristic $p \geq 3$ | In this note we show, building on a recent work of Totaro, that for every
prime number $p \geq 3$ there exists a purely log terminal pair $(Z,S)$ of
dimension $2p+2$ whose plt centre $S$ is not normal.
| math.AG | in this note we show building on a recent work of totaro that for every prime number p geq 3 there exists a purely log terminal pair zs of dimension 2p2 whose plt centre s is not normal | [['in', 'this', 'note', 'we', 'show', 'building', 'on', 'a', 'recent', 'work', 'of', 'totaro', 'that', 'for', 'every', 'prime', 'number', 'p', 'geq', '3', 'there', 'exists', 'a', 'purely', 'log', 'terminal', 'pair', 'zs', 'of', 'dimension', '2p2', 'whose', 'plt', 'centre', 's', 'is', 'not', 'normal']] | [-0.24372689791121765, 0.10709915479252997, -0.052364687288278026, 0.010548201977814498, -0.025254783288753396, -0.21057624793856553, 0.036984558796240505, 0.33029208307791696, -0.23314551348908266, -0.23217375400035004, 0.012674546233794996, -0.29228726147036804, -0.12206259926192854, 0.1957186328271698, -0.08973568369095263, -0.08224952641850043, 0.02984915129031594, 0.10893267515654627, -0.02442571648862213, -0.3157804106411181, 0.35999027286705215, -0.0850971900907002, 0.17690792062785476, 0.04048619585994043, 0.08456921964687736, 0.06690912582225313, 0.015375704406515547, 0.003236727691010425, -0.1988653046805427, 0.10774677806827974, 0.2761772117871595, 0.13907578704560078, 0.2823884587146734, -0.3146709253311488, -0.1433524447983425, 0.25699879721689384, 0.15787978585515366, 0.015718356075108443, -0.05850753932272231, -0.12961735365618215, 0.20377228281607754, -0.18217473614372706, -0.183247248513477, 0.024954734419129397, 0.19672452918204822, -0.041350505804937134, -0.2671337252311212, -0.030389495953721434, 0.16465743064978405, 0.1183262537509252, 0.06594639171434162, -0.16505885729566216, -0.042617733369728454, 0.0008908048315022729, -0.04005140154990122, 0.13534023151978067, 0.017066743409438794, -0.06768743636846346, -0.11494299660338775, 0.3047308106048915, -0.06247834807359859, -0.15899695098204047, 0.18629559502005577, -0.1850217482516248, -0.20244031164819667, 0.11246910181484725, 0.08661047713585983, 0.1870161644918354, 0.0014201850679359939, 0.1993160753918346, -0.16376120979456524, 0.17747829796893425, 0.13698622451997117, -0.032681079765193556, 0.10971212220427237, 0.13674995413442192, 0.10440921568997989, 0.015167953887660252, -0.04006368526576185, 0.08704575651178234, -0.39919183618928256, -0.1968483571208229, -0.19361483023845052, 0.18084534956793313, -0.045294354927088866, -0.10973467604306184, 0.2931364097289349, 0.053476739337814876, 0.2514785640922032, 0.06230946543234352, 0.24820325281899913, 0.07789619847838032, -0.012274198283098246, 0.15243818006772353, 0.10884669670639069, 0.09227048470883777, -0.026428776437808808, -0.12624627201033659, 0.005176054610944304, 0.11639153598317582] |
1,802.04897 | On the centralizer of generic braids | We study the centralizer of a braid from the point of view of Garside theory,
showing that generically a minimal set of generators can be computed very
efficiently, as the ultra summit set of a generic braid has a very particular
structure. We present an algorithm to compute the centralizer of a braid whose
generic-case complexity is quadratic on the length of the input, and which
outputs a minimal set of generators in the generic case.
| math.GR math.GT | we study the centralizer of a braid from the point of view of garside theory showing that generically a minimal set of generators can be computed very efficiently as the ultra summit set of a generic braid has a very particular structure we present an algorithm to compute the centralizer of a braid whose genericcase complexity is quadratic on the length of the input and which outputs a minimal set of generators in the generic case | [['we', 'study', 'the', 'centralizer', 'of', 'a', 'braid', 'from', 'the', 'point', 'of', 'view', 'of', 'garside', 'theory', 'showing', 'that', 'generically', 'a', 'minimal', 'set', 'of', 'generators', 'can', 'be', 'computed', 'very', 'efficiently', 'as', 'the', 'ultra', 'summit', 'set', 'of', 'a', 'generic', 'braid', 'has', 'a', 'very', 'particular', 'structure', 'we', 'present', 'an', 'algorithm', 'to', 'compute', 'the', 'centralizer', 'of', 'a', 'braid', 'whose', 'genericcase', 'complexity', 'is', 'quadratic', 'on', 'the', 'length', 'of', 'the', 'input', 'and', 'which', 'outputs', 'a', 'minimal', 'set', 'of', 'generators', 'in', 'the', 'generic', 'case']] | [-0.15223085536547987, 0.10232403055373365, -0.10606803531807504, 0.04585828775031443, -0.07144893416644711, -0.1061102622556255, 0.040778613082549875, 0.29933790030497076, -0.33144232561340015, -0.25665955870461304, 0.09241097515883953, -0.2497553280922339, -0.17084267086006308, 0.22147454087941074, -0.09603774464248042, 0.018158077323613197, 0.07254806729523759, 0.12997954385967828, -0.1009303148986012, -0.22593985580974013, 0.32702293791685644, 0.05121764676694415, 0.22976943462057725, -0.020243897151790168, 0.14258324574850695, -0.029949225025790695, 0.006534070536298187, 0.04801041184385356, -0.1045304732432621, 0.14889564182560303, 0.25240074936300516, 0.1146083532183088, 0.18001956492968785, -0.3912127426041192, -0.1235288341505159, 0.16083025505864307, 0.11983977687103968, 0.10220476732999821, -0.054080011012142916, -0.22030458515693777, 0.1503797579498496, -0.19440635945647955, -0.12250102758039966, -0.033020190502467905, 0.021630337143218832, -0.01968034364491407, -0.25669959325750824, -0.07922857975293147, 0.0627170375940439, 0.0957671553264127, -0.0016525727329089453, -0.06798772828800506, -0.0281646602915747, 0.11988577266272746, -0.015105257261127821, 0.032935281709988454, 0.1043594750563467, -0.14341525495738575, -0.12339199225320235, 0.4305485703639294, -0.05956338450778276, -0.20837078155263475, 0.16701184673308345, -0.14511541734253497, -0.16245385802865617, 0.1280773547723105, 0.15446870180925257, 0.11742560598558109, -0.09556685721480263, 0.19226862741925288, -0.16649257193172448, 0.13509519353369556, 0.04727488754954385, 0.012549643754027784, 0.1609458780700439, 0.14483579012602077, 0.07289445940037503, 0.18787319983790726, -0.052075437003575066, -0.01888610423530305, -0.377622768969128, -0.19566160400005939, -0.17652147861295625, 0.09452517864778393, -0.11596747699762713, -0.24493797095247397, 0.4511298020860474, 0.09792375556533348, 0.19206024907333286, 0.12190086620179691, 0.22431523838129483, 0.08116793239892822, 0.06959531803027187, 0.09012490493171897, 0.1071962359434876, 0.1181064847884332, -0.06553603276169222, -0.21849463374268166, 0.01977424227975701, 0.17985383649111578] |
1,802.04898 | Direct Observation of Broadband Nonclassical States in a
Room-temperature Light-matter Interface | Nonclassical state is an essential resource for quantum-enhanced
communication, computing and metrology to outperform their classical
counterpart. The nonclassical states that can operate at high bandwidth and
room temperature while being compatible with quantum memory are highly
desirable to enable the scalability of quantum technologies. Here, we present a
direct observation of broadband nonclasscal states in a room-temperature
light-matter interface, where the atoms can also be controlled to store and
interfere with photons. With a single coupling pulse and far off-resonance
configuration, we are able to induce a multi-field interference between light
and atoms to create the desired nonclassical states by spectrally selecting the
two correlated photons out of seven possible emissions. We explicitly confirm
the nonclassicality by observing a cross correlation up to 17 and a violation
of Cauchy-Schwarz inequality with 568 standard deviations. Our results
demonstrate the potential of a state-built-in, broadband and room-temperature
light-matter interface for scalable quantum information networks.
| quant-ph | nonclassical state is an essential resource for quantumenhanced communication computing and metrology to outperform their classical counterpart the nonclassical states that can operate at high bandwidth and room temperature while being compatible with quantum memory are highly desirable to enable the scalability of quantum technologies here we present a direct observation of broadband nonclasscal states in a roomtemperature lightmatter interface where the atoms can also be controlled to store and interfere with photons with a single coupling pulse and far offresonance configuration we are able to induce a multifield interference between light and atoms to create the desired nonclassical states by spectrally selecting the two correlated photons out of seven possible emissions we explicitly confirm the nonclassicality by observing a cross correlation up to 17 and a violation of cauchyschwarz inequality with 568 standard deviations our results demonstrate the potential of a statebuiltin broadband and roomtemperature lightmatter interface for scalable quantum information networks | [['nonclassical', 'state', 'is', 'an', 'essential', 'resource', 'for', 'quantumenhanced', 'communication', 'computing', 'and', 'metrology', 'to', 'outperform', 'their', 'classical', 'counterpart', 'the', 'nonclassical', 'states', 'that', 'can', 'operate', 'at', 'high', 'bandwidth', 'and', 'room', 'temperature', 'while', 'being', 'compatible', 'with', 'quantum', 'memory', 'are', 'highly', 'desirable', 'to', 'enable', 'the', 'scalability', 'of', 'quantum', 'technologies', 'here', 'we', 'present', 'a', 'direct', 'observation', 'of', 'broadband', 'nonclasscal', 'states', 'in', 'a', 'roomtemperature', 'lightmatter', 'interface', 'where', 'the', 'atoms', 'can', 'also', 'be', 'controlled', 'to', 'store', 'and', 'interfere', 'with', 'photons', 'with', 'a', 'single', 'coupling', 'pulse', 'and', 'far', 'offresonance', 'configuration', 'we', 'are', 'able', 'to', 'induce', 'a', 'multifield', 'interference', 'between', 'light', 'and', 'atoms', 'to', 'create', 'the', 'desired', 'nonclassical', 'states', 'by', 'spectrally', 'selecting', 'the', 'two', 'correlated', 'photons', 'out', 'of', 'seven', 'possible', 'emissions', 'we', 'explicitly', 'confirm', 'the', 'nonclassicality', 'by', 'observing', 'a', 'cross', 'correlation', 'up', 'to', '17', 'and', 'a', 'violation', 'of', 'cauchyschwarz', 'inequality', 'with', '568', 'standard', 'deviations', 'our', 'results', 'demonstrate', 'the', 'potential', 'of', 'a', 'statebuiltin', 'broadband', 'and', 'roomtemperature', 'lightmatter', 'interface', 'for', 'scalable', 'quantum', 'information', 'networks']] | [-0.12613649806770272, 0.1950901399208703, -0.06036802178976552, 0.02985133237856138, -0.04772791615841503, -0.23950554377750943, 0.09952314638699226, 0.44004363815812086, -0.2594630611560844, -0.3110568569664726, 0.027008649513791533, -0.30020033312646904, -0.07929317018792761, 0.22862250806258017, -0.020550861384176854, 0.09609379903074132, 0.05334476687997851, -0.03945311805116173, -0.011301886905925287, -0.19293780393346477, 0.2501836497550954, 0.0601485641064147, 0.306742117983838, 0.08541448979595342, 0.09892564469991585, -0.02674865927857529, 0.04749806476595028, -0.02232897926017099, -0.050599431312446014, 0.14524550736220307, 0.28999446764193626, 0.0838768866602393, 0.23861405727398022, -0.4267571711117118, -0.19452032077493184, 0.0982719047746443, 0.1171604727228351, 0.16236270931919433, -0.05601450057687941, -0.3430271035072622, 0.026885242304696844, -0.1434060039572319, -0.10660087709185599, -0.14017633136534532, -0.02538463595440451, -0.005341714516741575, -0.27913317821660877, 0.04252900371913157, 0.0004927920245423617, 0.019061527319795248, 0.021550128501624067, -0.027795119019879015, 0.004644166750660697, 0.10855891075392334, -0.09222828412570355, -0.016721243064215296, 0.15256589495373385, -0.14975237979326977, -0.19140585460730933, 0.37081356829229667, -0.082207280388472, -0.13282010283950246, 0.1894659463544407, -0.13237127174533878, -0.0762456975467133, 0.13751491046331774, 0.14360338625411767, 0.07615600186801016, -0.14819477175343906, 0.01040692273442578, 0.03601919239177214, 0.2190802166283143, 0.08253534660606787, 0.22220553305086416, 0.23061603456365548, 0.12443613312176324, 0.07252283614523561, 0.1605915110751012, -0.10339803619785172, -0.09883899208495936, -0.27864722963052474, -0.19488509785459493, -0.2146993042578896, 0.07882319369331381, -0.058385719941363864, -0.11286566363622909, 0.37492203738984486, 0.17152869163369389, 0.12515073137385585, 0.03588430521491644, 0.30738414718541285, 0.10123552563708177, 0.07549103693296479, 0.06324406813472411, 0.309556772815224, 0.14136069442842478, 0.11288648495752833, -0.2375204465475353, 0.02520398201655276, -0.09648235656790564] |
1,802.04899 | Field-Programmable Deep Neural Network (DNN) Learning and Inference
accelerator: a concept | An accelerator is a specialized integrated circuit designed to perform
specific computations faster than if those were performed by CPU or GPU. A
Field-Programmable DNN learning and inference accelerator (FProg-DNN) using
hybrid systolic and non-systolic techniques, distributed information-control
and deep pipelined structure is proposed and its microarchitecture and
operation presented here. Reconfigurability attends diverse DNN designs and
allows for different number of workers to be assigned to different layers as a
function of the relative difference in computational load among layers. The
computational delay per layer is made roughly the same along pipelined
accelerator structure. VGG-16 and recently proposed Inception Modules are used
for showing the flexibility of the FProg-DNN reconfigurability. Special
structures were also added for a combination of convolution layer, map
coincidence and feedback for state of the art learning with small set of
examples, which is the focus of a companion paper by the author (Franca-Neto,
2018). The accelerator described is able to reconfigure from (1) allocating all
a DNN computations to a single worker in one extreme of sub-optimal performance
to (2) optimally allocating workers per layer according to computational load
in each DNN layer to be realized. Due the pipelined architecture, more than 50x
speedup is achieved relative to GPUs or TPUs. This speed-up is consequence of
hiding the delay in transporting activation outputs from one layer to the next
in a DNN behind the computations in the receiving layer. This FProg-DNN concept
has been simulated and validated at behavioral-functional level.
| cs.LG cs.NE | an accelerator is a specialized integrated circuit designed to perform specific computations faster than if those were performed by cpu or gpu a fieldprogrammable dnn learning and inference accelerator fprogdnn using hybrid systolic and nonsystolic techniques distributed informationcontrol and deep pipelined structure is proposed and its microarchitecture and operation presented here reconfigurability attends diverse dnn designs and allows for different number of workers to be assigned to different layers as a function of the relative difference in computational load among layers the computational delay per layer is made roughly the same along pipelined accelerator structure vgg16 and recently proposed inception modules are used for showing the flexibility of the fprogdnn reconfigurability special structures were also added for a combination of convolution layer map coincidence and feedback for state of the art learning with small set of examples which is the focus of a companion paper by the author francaneto 2018 the accelerator described is able to reconfigure from 1 allocating all a dnn computations to a single worker in one extreme of suboptimal performance to 2 optimally allocating workers per layer according to computational load in each dnn layer to be realized due the pipelined architecture more than 50x speedup is achieved relative to gpus or tpus this speedup is consequence of hiding the delay in transporting activation outputs from one layer to the next in a dnn behind the computations in the receiving layer this fprogdnn concept has been simulated and validated at behavioralfunctional level | [['an', 'accelerator', 'is', 'a', 'specialized', 'integrated', 'circuit', 'designed', 'to', 'perform', 'specific', 'computations', 'faster', 'than', 'if', 'those', 'were', 'performed', 'by', 'cpu', 'or', 'gpu', 'a', 'fieldprogrammable', 'dnn', 'learning', 'and', 'inference', 'accelerator', 'fprogdnn', 'using', 'hybrid', 'systolic', 'and', 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1,802.049 | Analysing and Patching SPEKE in ISO/IEC | Simple Password Exponential Key Exchange (SPEKE) is a well-known Password Authenticated Key Exchange (PAKE) protocol that has been used in Blackberry phones for secure messaging and Entrust's TruePass end-to-end web products. It has also been included into international standards such as ISO/IEC 11770-4 and IEEE P1363.2. In this paper, we analyse the SPEKE protocol as specified in the ISO/IEC and IEEE standards. We identify that the protocol is vulnerable to two new attacks: an impersonation attack that allows an attacker to impersonate a user without knowing the password by launching two parallel sessions with the victim, and a key-malleability attack that allows a man-in-the-middle (MITM) to manipulate the session key without being detected by the end users. Both attacks have been acknowledged by the technical committee of ISO/IEC SC 27, and ISO/IEC 11770-4 revised as a result. We propose a patched SPEKE called P-SPEKE and present a formal analysis in the Applied Pi Calculus using ProVerif to show that the proposed patch prevents both attacks. The proposed patch has been included into the latest revision of ISO/IEC 11770-4 published in 2017. | cs.CR | simple password exponential key exchange speke is a wellknown password authenticated key exchange pake protocol that has been used in blackberry phones for secure messaging and entrusts truepass endtoend web products it has also been included into international standards such as isoiec 117704 and ieee p13632 in this paper we analyse the speke protocol as specified in the isoiec and ieee standards we identify that the protocol is vulnerable to two new attacks an impersonation attack that allows an attacker to impersonate a user without knowing the password by launching two parallel sessions with the victim and a keymalleability attack that allows a maninthemiddle mitm to manipulate the session key without being detected by the end users both attacks have been acknowledged by the technical committee of isoiec sc 27 and isoiec 117704 revised as a result we propose a patched speke called pspeke and present a formal analysis in the applied pi calculus using proverif to show that the proposed patch prevents both attacks the proposed patch has been included into the latest revision of isoiec 117704 published in 2017 | [['simple', 'password', 'exponential', 'key', 'exchange', 'speke', 'is', 'a', 'wellknown', 'password', 'authenticated', 'key', 'exchange', 'pake', 'protocol', 'that', 'has', 'been', 'used', 'in', 'blackberry', 'phones', 'for', 'secure', 'messaging', 'and', 'entrusts', 'truepass', 'endtoend', 'web', 'products', 'it', 'has', 'also', 'been', 'included', 'into', 'international', 'standards', 'such', 'as', 'isoiec', '117704', 'and', 'ieee', 'p13632', 'in', 'this', 'paper', 'we', 'analyse', 'the', 'speke', 'protocol', 'as', 'specified', 'in', 'the', 'isoiec', 'and', 'ieee', 'standards', 'we', 'identify', 'that', 'the', 'protocol', 'is', 'vulnerable', 'to', 'two', 'new', 'attacks', 'an', 'impersonation', 'attack', 'that', 'allows', 'an', 'attacker', 'to', 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1,802.04901 | The shadow of a collapsing dark star | The shadow of a black hole is usually calculated, either analytically or
numerically, on the assumption that the black hole is eternal, i.e., that it
existed for all time. Here we ask the question of how this shadow comes about
in the course of time when a black hole is formed by gravitational collapse. To
that end we consider a star that is spherically symmetric, dark and
non-transparent and we assume that it begins, at some instant of time, to
collapse in free fall like a ball of dust. We analytically calculate the
dependence on time of the angular radius of the shadow, first for a static
observer who is watching the collapse from a certain distance and then for an
observer who is falling towards the centre following the collapsing star.
| gr-qc | the shadow of a black hole is usually calculated either analytically or numerically on the assumption that the black hole is eternal ie that it existed for all time here we ask the question of how this shadow comes about in the course of time when a black hole is formed by gravitational collapse to that end we consider a star that is spherically symmetric dark and nontransparent and we assume that it begins at some instant of time to collapse in free fall like a ball of dust we analytically calculate the dependence on time of the angular radius of the shadow first for a static observer who is watching the collapse from a certain distance and then for an observer who is falling towards the centre following the collapsing star | [['the', 'shadow', 'of', 'a', 'black', 'hole', 'is', 'usually', 'calculated', 'either', 'analytically', 'or', 'numerically', 'on', 'the', 'assumption', 'that', 'the', 'black', 'hole', 'is', 'eternal', 'ie', 'that', 'it', 'existed', 'for', 'all', 'time', 'here', 'we', 'ask', 'the', 'question', 'of', 'how', 'this', 'shadow', 'comes', 'about', 'in', 'the', 'course', 'of', 'time', 'when', 'a', 'black', 'hole', 'is', 'formed', 'by', 'gravitational', 'collapse', 'to', 'that', 'end', 'we', 'consider', 'a', 'star', 'that', 'is', 'spherically', 'symmetric', 'dark', 'and', 'nontransparent', 'and', 'we', 'assume', 'that', 'it', 'begins', 'at', 'some', 'instant', 'of', 'time', 'to', 'collapse', 'in', 'free', 'fall', 'like', 'a', 'ball', 'of', 'dust', 'we', 'analytically', 'calculate', 'the', 'dependence', 'on', 'time', 'of', 'the', 'angular', 'radius', 'of', 'the', 'shadow', 'first', 'for', 'a', 'static', 'observer', 'who', 'is', 'watching', 'the', 'collapse', 'from', 'a', 'certain', 'distance', 'and', 'then', 'for', 'an', 'observer', 'who', 'is', 'falling', 'towards', 'the', 'centre', 'following', 'the', 'collapsing', 'star']] | [-0.10806551887512658, 0.12206420932896876, -0.12813869399851133, 0.1021424796225884, -0.08128106545050148, -0.10579042956868018, 0.03674977894655912, 0.36355411307886243, -0.2080518416919266, -0.2623810646138295, 0.1413846909347216, -0.2868249444485021, -0.09594986284906609, 0.16045560118932786, -0.0475182916962474, -0.015557807838459585, 0.021602698814328476, 0.10514204120827896, -0.07660739166683263, -0.23701512619542578, 0.4033969908063723, 0.10242068678295861, 0.17328531921234433, 0.01972490492643732, 0.09938013685909523, 0.009186845544415215, 0.01898102106343052, 0.055663968521085655, -0.15648115304556445, 0.0239112349720954, 0.16452807425097984, 0.18904016279813016, 0.2865974820398895, -0.43132946360856295, -0.19987479504197836, 0.09674896127685453, 0.1450019816225959, 0.15427123749219446, -0.09748891443472751, -0.2503578063439239, 0.10203769417994683, -0.20121479168477835, -0.1448430047806169, 0.06552880021362481, 0.11440516987138173, -0.022885007434524596, -0.20551080256197538, 0.08587593755289687, 0.08463946738365022, -0.0693256504071707, -0.10722570950659274, 0.006635561098598621, -0.02422802305646297, 0.12880551450727967, 0.09281881753932711, 0.0399865202771528, 0.18436062841845507, -0.10944507759113824, -0.05344101285322033, 0.3936599913744651, -0.05215665419713001, -0.15233326047150927, 0.15614351980867935, -0.22934269955889744, -0.08591741894611693, 0.1119599722054166, 0.14923597017662937, 0.17789922751817908, -0.12850656634724478, 0.07361045019625277, -0.05004200611217653, 0.17000848704959604, 0.1505410272599847, -0.021622833591236762, 0.34831500842560537, 0.11639543375614184, 0.05847338944758204, 0.15329182921259693, -0.09076398143290797, -0.10866257735655051, -0.3029877689991598, -0.1623560353023508, -0.20241394147689623, 0.11998610483538924, -0.09269664319336615, -0.1595862273631307, 0.3360093537943833, 0.12114332226867025, 0.20128398034439393, 0.05719145646347015, 0.27933073388130375, 0.08732791731017642, 0.012840079510087062, 0.13876995557185376, 0.2821219829239646, 0.04289407876962231, 0.11518143425752042, -0.2270823357301362, 0.03655363469751495, 0.055018971337245384] |
1,802.04902 | The Massive Star-Forming Regions Omnibus X-Ray Catalog, Second
Installment | We present the second installment of the Massive Star-forming Regions (MSFRs)
Omnibus X-ray Catalog (MOXC2), a compilation of X-ray point sources detected in
Chandra/ACIS observations of 16 Galactic MSFRs and surrounding fields. MOXC2
includes 13 ACIS mosaics, three containing a pair of unrelated MSFRs at
different distances, with a total catalog of 18,396 point sources. The MSFRs
sampled range over distances of 1.3 kpc to 6 kpc and populations varying from
single massive protostars to the most massive Young Massive Cluster known in
the Galaxy. By carefully detecting and removing X-ray point sources down to the
faintest statistically-significant limit, we facilitate the study of the
remaining unresolved X-ray emission. Through comparison with mid-infrared
images that trace photon-dominated regions and ionization fronts, we see that
the unresolved X-ray emission is due primarily to hot plasmas threading these
MSFRs, the result of feedback from the winds and supernovae of massive stars.
The 16 MSFRs studied in MOXC2 more than double the MOXC1 sample, broadening the
parameter space of ACIS MSFR explorations and expanding Chandra's substantial
contribution to contemporary star formation science.
| astro-ph.SR astro-ph.GA astro-ph.HE | we present the second installment of the massive starforming regions msfrs omnibus xray catalog moxc2 a compilation of xray point sources detected in chandraacis observations of 16 galactic msfrs and surrounding fields moxc2 includes 13 acis mosaics three containing a pair of unrelated msfrs at different distances with a total catalog of 18396 point sources the msfrs sampled range over distances of 13 kpc to 6 kpc and populations varying from single massive protostars to the most massive young massive cluster known in the galaxy by carefully detecting and removing xray point sources down to the faintest statisticallysignificant limit we facilitate the study of the remaining unresolved xray emission through comparison with midinfrared images that trace photondominated regions and ionization fronts we see that the unresolved xray emission is due primarily to hot plasmas threading these msfrs the result of feedback from the winds and supernovae of massive stars the 16 msfrs studied in moxc2 more than double the moxc1 sample broadening the parameter space of acis msfr explorations and expanding chandras substantial contribution to contemporary star formation science | [['we', 'present', 'the', 'second', 'installment', 'of', 'the', 'massive', 'starforming', 'regions', 'msfrs', 'omnibus', 'xray', 'catalog', 'moxc2', 'a', 'compilation', 'of', 'xray', 'point', 'sources', 'detected', 'in', 'chandraacis', 'observations', 'of', '16', 'galactic', 'msfrs', 'and', 'surrounding', 'fields', 'moxc2', 'includes', '13', 'acis', 'mosaics', 'three', 'containing', 'a', 'pair', 'of', 'unrelated', 'msfrs', 'at', 'different', 'distances', 'with', 'a', 'total', 'catalog', 'of', '18396', 'point', 'sources', 'the', 'msfrs', 'sampled', 'range', 'over', 'distances', 'of', '13', 'kpc', 'to', '6', 'kpc', 'and', 'populations', 'varying', 'from', 'single', 'massive', 'protostars', 'to', 'the', 'most', 'massive', 'young', 'massive', 'cluster', 'known', 'in', 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1,802.04903 | Molecular Structure Extraction From Documents Using Deep Learning | Chemical structure extraction from documents remains a hard problem due to
both false positive identification of structures during segmentation and errors
in the predicted structures. Current approaches rely on handcrafted rules and
subroutines that perform reasonably well generally, but still routinely
encounter situations where recognition rates are not yet satisfactory and
systematic improvement is challenging. Complications impacting performance of
current approaches include the diversity in visual styles used by various
software to render structures, the frequent use of ad hoc annotations, and
other challenges related to image quality, including resolution and noise. We
here present end-to-end deep learning solutions for both segmenting molecular
structures from documents and for predicting chemical structures from these
segmented images. This deep learning-based approach does not require any
handcrafted features, is learned directly from data, and is robust against
variations in image quality and style. Using the deep-learning approach
described herein we show that it is possible to perform well on both
segmentation and prediction of low resolution images containing moderately
sized molecules found in journal articles and patents.
| cs.LG physics.chem-ph | chemical structure extraction from documents remains a hard problem due to both false positive identification of structures during segmentation and errors in the predicted structures current approaches rely on handcrafted rules and subroutines that perform reasonably well generally but still routinely encounter situations where recognition rates are not yet satisfactory and systematic improvement is challenging complications impacting performance of current approaches include the diversity in visual styles used by various software to render structures the frequent use of ad hoc annotations and other challenges related to image quality including resolution and noise we here present endtoend deep learning solutions for both segmenting molecular structures from documents and for predicting chemical structures from these segmented images this deep learningbased approach does not require any handcrafted features is learned directly from data and is robust against variations in image quality and style using the deeplearning approach described herein we show that it is possible to perform well on both segmentation and prediction of low resolution images containing moderately sized molecules found in journal articles and patents | [['chemical', 'structure', 'extraction', 'from', 'documents', 'remains', 'a', 'hard', 'problem', 'due', 'to', 'both', 'false', 'positive', 'identification', 'of', 'structures', 'during', 'segmentation', 'and', 'errors', 'in', 'the', 'predicted', 'structures', 'current', 'approaches', 'rely', 'on', 'handcrafted', 'rules', 'and', 'subroutines', 'that', 'perform', 'reasonably', 'well', 'generally', 'but', 'still', 'routinely', 'encounter', 'situations', 'where', 'recognition', 'rates', 'are', 'not', 'yet', 'satisfactory', 'and', 'systematic', 'improvement', 'is', 'challenging', 'complications', 'impacting', 'performance', 'of', 'current', 'approaches', 'include', 'the', 'diversity', 'in', 'visual', 'styles', 'used', 'by', 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1,802.04904 | The Structure of Decoherence-free Subsystems | Decoherence-free subsystems have been successfully developed as a tool to
preserve fragile quantum information against noises. In this letter, we develop
a structure theory for decoherence-free subsystems. Based on it, we present an
effective algorithm to construct a set of maximal decoherence-free subsystems
in the sense that any other such subsystem is a subspace of one of them. As an
application of these techniques in quantum many body systems, we propose a
simple and numerically robust method to determine if two irreducible tensors
are repeated, a key step in deciding if they are equivalent in generating
matrix product states.
| quant-ph | decoherencefree subsystems have been successfully developed as a tool to preserve fragile quantum information against noises in this letter we develop a structure theory for decoherencefree subsystems based on it we present an effective algorithm to construct a set of maximal decoherencefree subsystems in the sense that any other such subsystem is a subspace of one of them as an application of these techniques in quantum many body systems we propose a simple and numerically robust method to determine if two irreducible tensors are repeated a key step in deciding if they are equivalent in generating matrix product states | [['decoherencefree', 'subsystems', 'have', 'been', 'successfully', 'developed', 'as', 'a', 'tool', 'to', 'preserve', 'fragile', 'quantum', 'information', 'against', 'noises', 'in', 'this', 'letter', 'we', 'develop', 'a', 'structure', 'theory', 'for', 'decoherencefree', 'subsystems', 'based', 'on', 'it', 'we', 'present', 'an', 'effective', 'algorithm', 'to', 'construct', 'a', 'set', 'of', 'maximal', 'decoherencefree', 'subsystems', 'in', 'the', 'sense', 'that', 'any', 'other', 'such', 'subsystem', 'is', 'a', 'subspace', 'of', 'one', 'of', 'them', 'as', 'an', 'application', 'of', 'these', 'techniques', 'in', 'quantum', 'many', 'body', 'systems', 'we', 'propose', 'a', 'simple', 'and', 'numerically', 'robust', 'method', 'to', 'determine', 'if', 'two', 'irreducible', 'tensors', 'are', 'repeated', 'a', 'key', 'step', 'in', 'deciding', 'if', 'they', 'are', 'equivalent', 'in', 'generating', 'matrix', 'product', 'states']] | [-0.1256659471128851, 0.11659155807938812, -0.11273374572170503, 0.05718167458256387, 0.0001095188781619072, -0.19303736146163158, -0.00977733406130074, 0.3989450712076793, -0.29333245519059475, -0.22983253367640305, 0.11788025046251903, -0.23410379618048818, -0.20136046002976446, 0.19311888410587502, -0.07912662482321864, 0.10824484729932414, 0.07626114948182319, 0.0544244342794021, -0.05065882107539272, -0.25962324963289907, 0.3455663370862227, -0.0028348255530940463, 0.26175816287521764, -0.007548996459015391, 0.1534762478638158, 0.016351943879124867, 0.01609189338474111, 0.024003922515972095, -0.10156980498655373, 0.13107728483943232, 0.3196071360655355, 0.18098801903360795, 0.3027018397467945, -0.45158480821798247, -0.16935069023659735, 0.15528544937871924, 0.13867550415005722, 0.19831398546441711, -0.04770787317667984, -0.25866587538121627, 0.108747107099102, -0.22360960993859352, -0.11126367614670384, -0.17483514191987312, 0.039073454446601444, -0.08028855872566276, -0.24600470915373246, 0.0030225299782651203, 0.07657944758874223, 0.0475891614991306, -0.019332988733294035, -0.05456712154314072, 0.041305968410928144, 0.15699891498161836, -0.04263017621277032, -0.0033093552972952075, 0.1379801638499655, -0.05035283925209307, -0.15016498784134824, 0.3505920485753303, -0.031182123665613206, -0.27014753599698194, 0.2058285859371112, -0.04732373907028538, -0.17458994084536428, 0.056901824154956926, 0.1601099989028892, 0.11980987151360346, -0.161991734284028, 0.08301474192977008, -0.05725000151656474, 0.17832294082024483, -0.021133887562712634, 0.10676543820517684, 0.18070496978341705, 0.11289668091539895, 0.1367675031581188, 0.16384699177808326, -0.019877327799225564, -0.06976282907499358, -0.28756731742936553, -0.19783205879061963, -0.20714749491801768, 0.03571005436742321, -0.028791938884732915, -0.18784455043461287, 0.40124089460857587, 0.1457011371549934, 0.1840715793789261, -0.006786531742869152, 0.3187284399143825, 0.09779120728769222, 0.05315952416923311, 0.10375144050898727, 0.19781275106372895, 0.16730964150881827, -0.02557451281263822, -0.18482957810817305, 0.050322224748217396, 0.09788704138587821] |
1,802.04905 | Connecting discrete particle mechanics to continuum granular
micromechanics: Anisotropic continuum properties under compaction | A systematic and mechanistic connection between granular materials'
macroscopic and grain level behaviors is developed for monodisperse systems of
spherical elastic particles under die compaction. The Granular Micromechanics
Approach (GMA) with static assumption is used to derive the stiffness tensor of
transversely isotropic materials, from the average behavior of
particle-particle interactions in all different directions at the microscale.
Two particle-scale directional density distribution functions, namely the
directional distribution of a combined mechano-geometrical property and the
directional distribution of a purely geometrical property, are proposed and
parametrized by five independent parameters. Five independent components of the
symmetrized tangent stiffness tensor are also determined from discrete particle
mechanics (PMA) calculations of nine perturbations around points of the loading
path. Finally, optimal values for these five GMA parameters were obtained by
minimizing the error between PMA calculations and GMA closed-form predictions
of stiffness tensor during the compaction process. The results show that GMA
with static assumption is effective at capturing the anisotropic evolution of
microstructure during loading, even without describing contacts independently
but rather accounting for them in an average sense.
| cond-mat.soft physics.comp-ph | a systematic and mechanistic connection between granular materials macroscopic and grain level behaviors is developed for monodisperse systems of spherical elastic particles under die compaction the granular micromechanics approach gma with static assumption is used to derive the stiffness tensor of transversely isotropic materials from the average behavior of particleparticle interactions in all different directions at the microscale two particlescale directional density distribution functions namely the directional distribution of a combined mechanogeometrical property and the directional distribution of a purely geometrical property are proposed and parametrized by five independent parameters five independent components of the symmetrized tangent stiffness tensor are also determined from discrete particle mechanics pma calculations of nine perturbations around points of the loading path finally optimal values for these five gma parameters were obtained by minimizing the error between pma calculations and gma closedform predictions of stiffness tensor during the compaction process the results show that gma with static assumption is effective at capturing the anisotropic evolution of microstructure during loading even without describing contacts independently but rather accounting for them in an average sense | [['a', 'systematic', 'and', 'mechanistic', 'connection', 'between', 'granular', 'materials', 'macroscopic', 'and', 'grain', 'level', 'behaviors', 'is', 'developed', 'for', 'monodisperse', 'systems', 'of', 'spherical', 'elastic', 'particles', 'under', 'die', 'compaction', 'the', 'granular', 'micromechanics', 'approach', 'gma', 'with', 'static', 'assumption', 'is', 'used', 'to', 'derive', 'the', 'stiffness', 'tensor', 'of', 'transversely', 'isotropic', 'materials', 'from', 'the', 'average', 'behavior', 'of', 'particleparticle', 'interactions', 'in', 'all', 'different', 'directions', 'at', 'the', 'microscale', 'two', 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1,802.04906 | Ultrahigh-dimensional Robust and Efficient Sparse Regression using
Non-Concave Penalized Density Power Divergence | We propose a sparse regression method based on the non-concave penalized
density power divergence loss function which is robust against infinitesimal
contamination in very high dimensionality. Present methods of sparse and robust
regression are based on $\ell_1$-penalization, and their theoretical properties
are not well-investigated. In contrast, we use a general class of folded
concave penalties that ensure sparse recovery and consistent estimation of
regression coefficients. We propose an alternating algorithm based on the
Concave-Convex procedure to obtain our estimate, and demonstrate its robustness
properties using influence function analysis. Under some conditions on the
fixed design matrix and penalty function, we prove that this estimator
possesses large-sample oracle properties in an ultrahigh-dimensional regime.
The performance and effectiveness of our proposed method for parameter
estimation and prediction compared to state-of-the-art are demonstrated through
simulation studies.
| stat.ME | we propose a sparse regression method based on the nonconcave penalized density power divergence loss function which is robust against infinitesimal contamination in very high dimensionality present methods of sparse and robust regression are based on ell_1penalization and their theoretical properties are not wellinvestigated in contrast we use a general class of folded concave penalties that ensure sparse recovery and consistent estimation of regression coefficients we propose an alternating algorithm based on the concaveconvex procedure to obtain our estimate and demonstrate its robustness properties using influence function analysis under some conditions on the fixed design matrix and penalty function we prove that this estimator possesses largesample oracle properties in an ultrahighdimensional regime the performance and effectiveness of our proposed method for parameter estimation and prediction compared to stateoftheart are demonstrated through simulation studies | [['we', 'propose', 'a', 'sparse', 'regression', 'method', 'based', 'on', 'the', 'nonconcave', 'penalized', 'density', 'power', 'divergence', 'loss', 'function', 'which', 'is', 'robust', 'against', 'infinitesimal', 'contamination', 'in', 'very', 'high', 'dimensionality', 'present', 'methods', 'of', 'sparse', 'and', 'robust', 'regression', 'are', 'based', 'on', 'ell_1penalization', 'and', 'their', 'theoretical', 'properties', 'are', 'not', 'wellinvestigated', 'in', 'contrast', 'we', 'use', 'a', 'general', 'class', 'of', 'folded', 'concave', 'penalties', 'that', 'ensure', 'sparse', 'recovery', 'and', 'consistent', 'estimation', 'of', 'regression', 'coefficients', 'we', 'propose', 'an', 'alternating', 'algorithm', 'based', 'on', 'the', 'concaveconvex', 'procedure', 'to', 'obtain', 'our', 'estimate', 'and', 'demonstrate', 'its', 'robustness', 'properties', 'using', 'influence', 'function', 'analysis', 'under', 'some', 'conditions', 'on', 'the', 'fixed', 'design', 'matrix', 'and', 'penalty', 'function', 'we', 'prove', 'that', 'this', 'estimator', 'possesses', 'largesample', 'oracle', 'properties', 'in', 'an', 'ultrahighdimensional', 'regime', 'the', 'performance', 'and', 'effectiveness', 'of', 'our', 'proposed', 'method', 'for', 'parameter', 'estimation', 'and', 'prediction', 'compared', 'to', 'stateoftheart', 'are', 'demonstrated', 'through', 'simulation', 'studies']] | [-0.037817560682507384, -0.05982823734836044, -0.11603366020415679, 0.059230836005041136, -0.08910257618566204, -0.16468780378187844, 0.0432296638758643, 0.4565640955995348, -0.2527477995219423, -0.26567411194450424, 0.1603073385759446, -0.24554079451731273, -0.23697415505985805, 0.19193646026411115, -0.14289326475639091, 0.16201942547747894, 0.08309335149544522, -0.013311443752364108, -0.13992079657491968, -0.2752114852008067, 0.2682444874978015, 0.06847350892638858, 0.34977210229052635, 0.01739910100823044, 0.12569591029077992, 0.052823558579290046, -0.028712697498696416, 0.030519094281388742, -0.12499192664874595, 0.13512151517303087, 0.23439534936849504, 0.1432119503011577, 0.3565452050389652, -0.3692199914835225, -0.21432112922709912, 0.10101624890545705, 0.10178872064470236, 0.04609347762158351, -0.08986287752229412, -0.2530954475549603, 0.10345482240830149, -0.14609233331271357, -0.06560826188835658, -0.17533995390561571, -0.09367398450263124, 0.037096536837350154, -0.3846817188966263, 0.12273423653358552, 0.023658944962588243, 0.04876821724823991, -0.06404427636880428, -0.17574845265537584, 0.04401459041072574, 0.03797701644690189, 0.07089284789062252, -0.018744845640026313, 0.12631711693256534, -0.10485270628320488, -0.08929088673269012, 0.30308186899366457, -0.0795606946401102, -0.2708551256210172, 0.18422881859578752, -0.05910984611600861, -0.15598692977450845, 0.1088690161950102, 0.25159869980963323, 0.15160912884678518, -0.1230198147468605, 0.06270630269082039, -0.03469726718661088, 0.1617866772226114, -0.01533957905682238, 0.022316893604251424, 0.09674902438340162, 0.2022649044262007, 0.11701333824601165, 0.17050904032067024, -0.12554501132038248, -0.04537458701936276, -0.2708847456562676, -0.07272983473529548, -0.22845131944500863, -0.056242650596112456, -0.1552315851091626, -0.2120053590436403, 0.39544667475821826, 0.21053896670726904, 0.20194646411337294, 0.14664394500983977, 0.3302419513071838, 0.1286643373524364, 0.009661321260413169, 0.11031668914019838, 0.23280495743173582, 0.13708142346450428, -0.017394000751883687, -0.24793451356778579, 0.1393048668164704, 0.06957797438459457] |
1,802.04907 | Compressive Sensing Using Iterative Hard Thresholding with Low Precision
Data Representation: Theory and Applications | Modern scientific instruments produce vast amounts of data, which can
overwhelm the processing ability of computer systems. Lossy compression of data
is an intriguing solution, but comes with its own drawbacks, such as potential
signal loss, and the need for careful optimization of the compression ratio. In
this work, we focus on a setting where this problem is especially acute:
compressive sensing frameworks for interferometry and medical imaging. We ask
the following question: can the precision of the data representation be lowered
for all inputs, with recovery guarantees and practical performance? Our first
contribution is a theoretical analysis of the normalized Iterative Hard
Thresholding (IHT) algorithm when all input data, meaning both the measurement
matrix and the observation vector are quantized aggressively. We present a
variant of low precision normalized {IHT} that, under mild conditions, can
still provide recovery guarantees. The second contribution is the application
of our quantization framework to radio astronomy and magnetic resonance
imaging. We show that lowering the precision of the data can significantly
accelerate image recovery. We evaluate our approach on telescope data and
samples of brain images using CPU and FPGA implementations achieving up to a 9x
speed-up with negligible loss of recovery quality.
| stat.ML cs.LG | modern scientific instruments produce vast amounts of data which can overwhelm the processing ability of computer systems lossy compression of data is an intriguing solution but comes with its own drawbacks such as potential signal loss and the need for careful optimization of the compression ratio in this work we focus on a setting where this problem is especially acute compressive sensing frameworks for interferometry and medical imaging we ask the following question can the precision of the data representation be lowered for all inputs with recovery guarantees and practical performance our first contribution is a theoretical analysis of the normalized iterative hard thresholding iht algorithm when all input data meaning both the measurement matrix and the observation vector are quantized aggressively we present a variant of low precision normalized iht that under mild conditions can still provide recovery guarantees the second contribution is the application of our quantization framework to radio astronomy and magnetic resonance imaging we show that lowering the precision of the data can significantly accelerate image recovery we evaluate our approach on telescope data and samples of brain images using cpu and fpga implementations achieving up to a 9x speedup with negligible loss of recovery quality | [['modern', 'scientific', 'instruments', 'produce', 'vast', 'amounts', 'of', 'data', 'which', 'can', 'overwhelm', 'the', 'processing', 'ability', 'of', 'computer', 'systems', 'lossy', 'compression', 'of', 'data', 'is', 'an', 'intriguing', 'solution', 'but', 'comes', 'with', 'its', 'own', 'drawbacks', 'such', 'as', 'potential', 'signal', 'loss', 'and', 'the', 'need', 'for', 'careful', 'optimization', 'of', 'the', 'compression', 'ratio', 'in', 'this', 'work', 'we', 'focus', 'on', 'a', 'setting', 'where', 'this', 'problem', 'is', 'especially', 'acute', 'compressive', 'sensing', 'frameworks', 'for', 'interferometry', 'and', 'medical', 'imaging', 'we', 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1,802.04908 | Conditional Density Estimation with Bayesian Normalising Flows | Modeling complex conditional distributions is critical in a variety of
settings. Despite a long tradition of research into conditional density
estimation, current methods employ either simple parametric forms or are
difficult to learn in practice. This paper employs normalising flows as a
flexible likelihood model and presents an efficient method for fitting them to
complex densities. These estimators must trade-off between modeling
distributional complexity, functional complexity and heteroscedasticity without
overfitting. We recognize these trade-offs as modeling decisions and develop a
Bayesian framework for placing priors over these conditional density estimators
using variational Bayesian neural networks. We evaluate this method on several
small benchmark regression datasets, on some of which it obtains state of the
art performance. Finally, we apply the method to two spatial density modeling
tasks with over 1 million datapoints using the New York City yellow taxi
dataset and the Chicago crime dataset.
| stat.ML | modeling complex conditional distributions is critical in a variety of settings despite a long tradition of research into conditional density estimation current methods employ either simple parametric forms or are difficult to learn in practice this paper employs normalising flows as a flexible likelihood model and presents an efficient method for fitting them to complex densities these estimators must tradeoff between modeling distributional complexity functional complexity and heteroscedasticity without overfitting we recognize these tradeoffs as modeling decisions and develop a bayesian framework for placing priors over these conditional density estimators using variational bayesian neural networks we evaluate this method on several small benchmark regression datasets on some of which it obtains state of the art performance finally we apply the method to two spatial density modeling tasks with over 1 million datapoints using the new york city yellow taxi dataset and the chicago crime dataset | [['modeling', 'complex', 'conditional', 'distributions', 'is', 'critical', 'in', 'a', 'variety', 'of', 'settings', 'despite', 'a', 'long', 'tradition', 'of', 'research', 'into', 'conditional', 'density', 'estimation', 'current', 'methods', 'employ', 'either', 'simple', 'parametric', 'forms', 'or', 'are', 'difficult', 'to', 'learn', 'in', 'practice', 'this', 'paper', 'employs', 'normalising', 'flows', 'as', 'a', 'flexible', 'likelihood', 'model', 'and', 'presents', 'an', 'efficient', 'method', 'for', 'fitting', 'them', 'to', 'complex', 'densities', 'these', 'estimators', 'must', 'tradeoff', 'between', 'modeling', 'distributional', 'complexity', 'functional', 'complexity', 'and', 'heteroscedasticity', 'without', 'overfitting', 'we', 'recognize', 'these', 'tradeoffs', 'as', 'modeling', 'decisions', 'and', 'develop', 'a', 'bayesian', 'framework', 'for', 'placing', 'priors', 'over', 'these', 'conditional', 'density', 'estimators', 'using', 'variational', 'bayesian', 'neural', 'networks', 'we', 'evaluate', 'this', 'method', 'on', 'several', 'small', 'benchmark', 'regression', 'datasets', 'on', 'some', 'of', 'which', 'it', 'obtains', 'state', 'of', 'the', 'art', 'performance', 'finally', 'we', 'apply', 'the', 'method', 'to', 'two', 'spatial', 'density', 'modeling', 'tasks', 'with', 'over', '1', 'million', 'datapoints', 'using', 'the', 'new', 'york', 'city', 'yellow', 'taxi', 'dataset', 'and', 'the', 'chicago', 'crime', 'dataset']] | [-0.029651831003740945, -0.024567172720382023, -0.10888581281238846, 0.1197993781401551, -0.11519358189262707, -0.16665994325203115, 0.07439894258959925, 0.4575379005536951, -0.24078179593077573, -0.3638451087095871, 0.08053202572728282, -0.25613015820111695, -0.176162075747511, 0.2140980627949767, -0.14192214422730792, 0.10785453725357315, 0.09783518671636181, -0.019832935781571373, -0.08317525118974776, -0.2902199344565, 0.28566244186929846, 0.004807658602708372, 0.35480320677803506, -0.022767758918604972, 0.13644469013951463, -0.003986251588653901, -0.03187707087410421, -0.00018320184779064408, -0.1226914001323668, 0.20363669431736242, 0.3047857080939515, 0.22674400529197963, 0.38646358364293804, -0.39779025961099, -0.25486314682016986, 0.11290700936625744, 0.12376358856382812, 0.08795567081111146, 0.015622118871723269, -0.30761288866593406, 0.01832507641279492, -0.21068440309629358, -0.01779280283070844, -0.18417036287438382, -0.03122918980902639, 0.009506797103275512, -0.30392382395421635, 0.09868401575689044, -0.004305286244649826, 0.08947342938647188, -0.023086508491943623, -0.15336074010982853, 0.028608713259695676, 0.11589819196258383, 0.06147188666629894, -0.01508342840805136, 0.11502138751334158, -0.14182405910602217, -0.12171828075119391, 0.30069092849293594, -0.054677127772572866, -0.22389506796808464, 0.20753040842226192, -0.03945862174291035, -0.17064481819819274, 0.07257303486995656, 0.2681605670890161, 0.1234646380092178, -0.18271264111751626, 0.019693749361864195, -0.02663783300587715, 0.15969841403180154, 0.03470238326759688, -0.054176853355502, 0.18138332309939995, 0.2615465705828934, 0.054001743860285854, 0.10715727932211253, -0.16506172770108418, -0.12325875778072352, -0.23916416304378674, -0.10548414950186773, -0.17284759152038345, -0.006062759302460171, -0.14837948898905245, -0.19566918882382792, 0.3922823225710027, 0.2364434646747621, 0.18425594155259173, 0.14065769348887663, 0.3492493621105778, 0.05523301914880245, 0.030307163516509122, 0.1105401710414424, 0.10176097970588358, 0.07061681455193923, 0.08002909465195161, -0.1261030174968443, 0.11607197227502435, 0.016063759261782377] |
1,802.04909 | Measuring the Black Hole Mass Spectrum from Redshifts of aLIGO Binary
Merger Events | The binary black hole merger events observed by the Advanced LIGO (aLIGO) and
VIRGO collaboration can shed light on the origins of black holes. Many studies
based on black hole stellar origins have shown a maximum mass for stellar black
holes, which can be measured or constrained from the observed black hole mass
distribution. In this paper, we point out that the redshift distribution of the
observed merger events can provide complementary information for studying the
black hole mass distribution, because the detectability correlates the event
redshift to the black hole masses. Based on the five observed events and using
the Kolmogorov-Smirnov test, we have found that the maximum of the stellar
black hole masses are constrained to be below $76\,M_\odot$ at 90\% confidence
level, for a negative power-law index of 2.3 for the heavier black hole. With
the improved sensitivity of aLIGO, a few dozen merger events may be obtained,
for which we estimate that the maximum mass will be constrained to
$10\,M_\odot$ accuracy.
| astro-ph.HE hep-ph | the binary black hole merger events observed by the advanced ligo aligo and virgo collaboration can shed light on the origins of black holes many studies based on black hole stellar origins have shown a maximum mass for stellar black holes which can be measured or constrained from the observed black hole mass distribution in this paper we point out that the redshift distribution of the observed merger events can provide complementary information for studying the black hole mass distribution because the detectability correlates the event redshift to the black hole masses based on the five observed events and using the kolmogorovsmirnov test we have found that the maximum of the stellar black hole masses are constrained to be below 76m_odot at 90 confidence level for a negative powerlaw index of 23 for the heavier black hole with the improved sensitivity of aligo a few dozen merger events may be obtained for which we estimate that the maximum mass will be constrained to 10m_odot accuracy | [['the', 'binary', 'black', 'hole', 'merger', 'events', 'observed', 'by', 'the', 'advanced', 'ligo', 'aligo', 'and', 'virgo', 'collaboration', 'can', 'shed', 'light', 'on', 'the', 'origins', 'of', 'black', 'holes', 'many', 'studies', 'based', 'on', 'black', 'hole', 'stellar', 'origins', 'have', 'shown', 'a', 'maximum', 'mass', 'for', 'stellar', 'black', 'holes', 'which', 'can', 'be', 'measured', 'or', 'constrained', 'from', 'the', 'observed', 'black', 'hole', 'mass', 'distribution', 'in', 'this', 'paper', 'we', 'point', 'out', 'that', 'the', 'redshift', 'distribution', 'of', 'the', 'observed', 'merger', 'events', 'can', 'provide', 'complementary', 'information', 'for', 'studying', 'the', 'black', 'hole', 'mass', 'distribution', 'because', 'the', 'detectability', 'correlates', 'the', 'event', 'redshift', 'to', 'the', 'black', 'hole', 'masses', 'based', 'on', 'the', 'five', 'observed', 'events', 'and', 'using', 'the', 'kolmogorovsmirnov', 'test', 'we', 'have', 'found', 'that', 'the', 'maximum', 'of', 'the', 'stellar', 'black', 'hole', 'masses', 'are', 'constrained', 'to', 'be', 'below', '76m_odot', 'at', '90', 'confidence', 'level', 'for', 'a', 'negative', 'powerlaw', 'index', 'of', '23', 'for', 'the', 'heavier', 'black', 'hole', 'with', 'the', 'improved', 'sensitivity', 'of', 'aligo', 'a', 'few', 'dozen', 'merger', 'events', 'may', 'be', 'obtained', 'for', 'which', 'we', 'estimate', 'that', 'the', 'maximum', 'mass', 'will', 'be', 'constrained', 'to', '10m_odot', 'accuracy']] | [-0.08698122571549015, 0.13311587151696497, -0.06565437393785431, 0.20781901902902486, -0.08951289595598809, -0.0875612800974944, 0.053810725014844185, 0.34111335297773887, -0.10490287314083926, -0.40173842848801034, 0.11117026741863456, -0.35746999992421125, -0.03613652519792284, 0.2527869785213048, -0.020609838280417934, 0.05030022535214723, 0.07029573474556389, 0.013741613233855234, -0.12598856586289292, -0.26208171349518544, 0.3404838317755337, 0.13946173930676972, 0.16362273736243568, -0.01856973721850209, 0.08935142545705298, -0.03506886958508048, -0.0069503764786598525, 0.03237824017613581, -0.16358369359157932, 0.030799193296399786, 0.21734211789642846, 0.20995463039896384, 0.19529720667249909, -0.34052496214919703, -0.22971463116686563, 0.08155155089604328, 0.16851018088168418, 0.07903556290942261, -0.1472005256552465, -0.2818754589669236, 0.10097345185647832, -0.26019036027932224, -0.14398961691129045, 0.06686414394629892, 0.03585479752921567, 0.006164499388144511, -0.22074556200077938, 0.16272561673483835, 0.0059319089391132495, -0.10377036437687533, -0.09341913327097712, -0.08872100112323718, -0.10601025053992777, 0.06857004973372989, 0.10950023848165917, 0.04712585575055905, 0.21673887001816183, -0.06779040813037171, -0.13167542449934067, 0.32670610502543973, -0.05561810873715752, -0.07649963656326801, 0.1712714946113964, -0.29307598351424974, -0.1530664550231361, 0.11711734703673822, 0.23799579414800265, 0.1557398392512213, -0.19303424788707094, -0.001197208776053541, 0.03950565260914495, 0.23990924883631723, 0.12444784599299566, 0.0698715197632271, 0.4678353655220168, 0.15699533878743263, -0.0010009917587456407, 0.06188197353664536, -0.18890902966902615, -0.014923042265652883, -0.19975071611087314, -0.08732387777304322, -0.16796404774380258, 0.118613749044016, -0.17804301967940198, -0.11814370520307155, 0.3415430055692701, 0.13485637486794474, 0.238087031550183, 0.03862980344593979, 0.21976380968110945, 0.11602090885749132, 0.06316942204550909, 0.08567084815699562, 0.41343313054658654, 0.10397545638403333, 0.060337812804457984, -0.2002282118887595, 0.035115584645920045, 0.02804893841285531] |
1,802.0491 | Asymptotic Prethermalization in Periodically Driven Classical Spin
Chains | We reveal a continuous dynamical heating transition between a prethermal and
an infinite-temperature stage in a clean, chaotic periodically driven classical
spin chain. The transition time is a steep exponential function of the drive
frequency, showing that the exponentially long-lived prethermal plateau,
originally observed in quantum Floquet systems, survives the classical limit.
Even though there is no straightforward generalization of Floquet's theorem to
nonlinear systems, we present strong evidence that the prethermal physics is
well described by the inverse-frequency expansion. We relate the stability and
robustness of the prethermal plateau to drive-induced synchronization not
captured by the expansion. Our results set the pathway to transfer the ideas of
Floquet engineering to classical many-body systems, and are directly relevant
for photonic crystals and cold atom experiments in the superfluid regime.
| cond-mat.stat-mech cond-mat.other cond-mat.quant-gas | we reveal a continuous dynamical heating transition between a prethermal and an infinitetemperature stage in a clean chaotic periodically driven classical spin chain the transition time is a steep exponential function of the drive frequency showing that the exponentially longlived prethermal plateau originally observed in quantum floquet systems survives the classical limit even though there is no straightforward generalization of floquets theorem to nonlinear systems we present strong evidence that the prethermal physics is well described by the inversefrequency expansion we relate the stability and robustness of the prethermal plateau to driveinduced synchronization not captured by the expansion our results set the pathway to transfer the ideas of floquet engineering to classical manybody systems and are directly relevant for photonic crystals and cold atom experiments in the superfluid regime | [['we', 'reveal', 'a', 'continuous', 'dynamical', 'heating', 'transition', 'between', 'a', 'prethermal', 'and', 'an', 'infinitetemperature', 'stage', 'in', 'a', 'clean', 'chaotic', 'periodically', 'driven', 'classical', 'spin', 'chain', 'the', 'transition', 'time', 'is', 'a', 'steep', 'exponential', 'function', 'of', 'the', 'drive', 'frequency', 'showing', 'that', 'the', 'exponentially', 'longlived', 'prethermal', 'plateau', 'originally', 'observed', 'in', 'quantum', 'floquet', 'systems', 'survives', 'the', 'classical', 'limit', 'even', 'though', 'there', 'is', 'no', 'straightforward', 'generalization', 'of', 'floquets', 'theorem', 'to', 'nonlinear', 'systems', 'we', 'present', 'strong', 'evidence', 'that', 'the', 'prethermal', 'physics', 'is', 'well', 'described', 'by', 'the', 'inversefrequency', 'expansion', 'we', 'relate', 'the', 'stability', 'and', 'robustness', 'of', 'the', 'prethermal', 'plateau', 'to', 'driveinduced', 'synchronization', 'not', 'captured', 'by', 'the', 'expansion', 'our', 'results', 'set', 'the', 'pathway', 'to', 'transfer', 'the', 'ideas', 'of', 'floquet', 'engineering', 'to', 'classical', 'manybody', 'systems', 'and', 'are', 'directly', 'relevant', 'for', 'photonic', 'crystals', 'and', 'cold', 'atom', 'experiments', 'in', 'the', 'superfluid', 'regime']] | [-0.16038706867103716, 0.22298154559687172, -0.12021062209436945, 0.09064589605879673, -0.0048466398662259415, -0.16311413229194058, 0.054553828187748905, 0.3249176153407771, -0.29370639676791294, -0.23244376017805912, 0.07581117966522773, -0.2503045833590188, -0.14900687243067479, 0.20229745028150636, 0.009715533226930587, 0.07797621887234986, 0.05290184427023858, -0.030175889497478455, -0.060718445022511044, -0.17443784427676348, 0.27658982480724537, 0.03584311923910972, 0.26786545725438254, 0.03237555566778074, 0.06238393370394212, -0.01667624692858536, 0.06616314483762271, -0.02072613039451052, -0.12442154955723983, 0.013357904927386331, 0.2578143309765719, 0.02629345842969221, 0.25592047568092974, -0.4381885706309893, -0.22227742850722731, 0.10819300455077327, 0.16168111371952146, 0.1887545584188413, -0.06426205571362742, -0.30968112413331056, 0.018708538331719506, -0.17315657441540397, -0.16862524977646942, -0.11616197846967062, 0.01687058456992918, -0.007391928181651431, -0.23878911891663374, 0.1428502581042577, 0.1354776778176712, 0.03813744761293859, -0.04613396908019402, 0.0076516312727516935, -0.017620106597698127, 0.06864508790823147, -0.011643881583992434, 0.03118038137602194, 0.15623869784826108, -0.13220700230305046, -0.1302596052328861, 0.3381340532282064, -0.10607182984547184, -0.08895695055365678, 0.2424002370775439, -0.16779829758443227, -0.10694484696037664, 0.13744599819760914, 0.10088125438530489, 0.06903502647584492, -0.08753914430772691, 0.06509311643593571, 0.0012110516486703888, 0.18608090291954865, 0.024292681396888324, 0.056676831167703855, 0.2406988824580529, 0.18706897507573284, 0.036903161042940245, 0.1712289749150157, -0.026355367830716247, -0.19573059168377127, -0.27498798379169187, -0.1308243385905963, -0.2241361664929391, 0.060979525650812626, -0.05018012728785749, -0.19208166474844654, 0.3909230684146805, 0.12833153637773845, 0.1820303524967081, 0.019856887212557385, 0.2592910208789877, 0.19021352561375274, 0.01575405414887639, 0.06403725863498222, 0.2767987111258472, 0.1517062726802402, 0.14490003536939045, -0.29319005258572683, 0.04332987899538274, 0.04668257719757714] |
1,802.04911 | Large-Scale Sparse Inverse Covariance Estimation via Thresholding and
Max-Det Matrix Completion | The sparse inverse covariance estimation problem is commonly solved using an
$\ell_{1}$-regularized Gaussian maximum likelihood estimator known as
"graphical lasso", but its computational cost becomes prohibitive for large
data sets. A recent line of results showed--under mild assumptions--that the
graphical lasso estimator can be retrieved by soft-thresholding the sample
covariance matrix and solving a maximum determinant matrix completion (MDMC)
problem. This paper proves an extension of this result, and describes a
Newton-CG algorithm to efficiently solve the MDMC problem. Assuming that the
thresholded sample covariance matrix is sparse with a sparse Cholesky
factorization, we prove that the algorithm converges to an $\epsilon$-accurate
solution in $O(n\log(1/\epsilon))$ time and $O(n)$ memory. The algorithm is
highly efficient in practice: we solve the associated MDMC problems with as
many as 200,000 variables to 7-9 digits of accuracy in less than an hour on a
standard laptop computer running MATLAB.
| stat.ML cs.LG math.OC stat.CO | the sparse inverse covariance estimation problem is commonly solved using an ell_1regularized gaussian maximum likelihood estimator known as graphical lasso but its computational cost becomes prohibitive for large data sets a recent line of results showedunder mild assumptionsthat the graphical lasso estimator can be retrieved by softthresholding the sample covariance matrix and solving a maximum determinant matrix completion mdmc problem this paper proves an extension of this result and describes a newtoncg algorithm to efficiently solve the mdmc problem assuming that the thresholded sample covariance matrix is sparse with a sparse cholesky factorization we prove that the algorithm converges to an epsilonaccurate solution in onlog1epsilon time and on memory the algorithm is highly efficient in practice we solve the associated mdmc problems with as many as 200000 variables to 79 digits of accuracy in less than an hour on a standard laptop computer running matlab | [['the', 'sparse', 'inverse', 'covariance', 'estimation', 'problem', 'is', 'commonly', 'solved', 'using', 'an', 'ell_1regularized', 'gaussian', 'maximum', 'likelihood', 'estimator', 'known', 'as', 'graphical', 'lasso', 'but', 'its', 'computational', 'cost', 'becomes', 'prohibitive', 'for', 'large', 'data', 'sets', 'a', 'recent', 'line', 'of', 'results', 'showedunder', 'mild', 'assumptionsthat', 'the', 'graphical', 'lasso', 'estimator', 'can', 'be', 'retrieved', 'by', 'softthresholding', 'the', 'sample', 'covariance', 'matrix', 'and', 'solving', 'a', 'maximum', 'determinant', 'matrix', 'completion', 'mdmc', 'problem', 'this', 'paper', 'proves', 'an', 'extension', 'of', 'this', 'result', 'and', 'describes', 'a', 'newtoncg', 'algorithm', 'to', 'efficiently', 'solve', 'the', 'mdmc', 'problem', 'assuming', 'that', 'the', 'thresholded', 'sample', 'covariance', 'matrix', 'is', 'sparse', 'with', 'a', 'sparse', 'cholesky', 'factorization', 'we', 'prove', 'that', 'the', 'algorithm', 'converges', 'to', 'an', 'epsilonaccurate', 'solution', 'in', 'onlog1epsilon', 'time', 'and', 'on', 'memory', 'the', 'algorithm', 'is', 'highly', 'efficient', 'in', 'practice', 'we', 'solve', 'the', 'associated', 'mdmc', 'problems', 'with', 'as', 'many', 'as', '200000', 'variables', 'to', '79', 'digits', 'of', 'accuracy', 'in', 'less', 'than', 'an', 'hour', 'on', 'a', 'standard', 'laptop', 'computer', 'running', 'matlab']] | [-0.037690525791458006, -0.02612401016172139, -0.07395572835983305, 0.08951785200490402, -0.1214761339596935, -0.177392614621122, 0.014494222124396999, 0.41826647063196226, -0.3252930345235171, -0.32375785167543936, 0.1954161675094946, -0.2532022000889626, -0.1831094113185064, 0.17113235104386207, -0.12138383031067262, 0.1293183663803791, 0.13360668193887582, 0.02332755044376204, -0.1199702872700136, -0.3060672774559239, 0.19751287940189477, 0.08581603996103981, 0.24317297820894088, -0.04185390058527147, 0.12757534523400108, 0.03321859877478409, -0.02164851956336492, 0.014530897739675495, -0.024489606345268585, 0.09820250841101853, 0.29289659393228384, 0.21089908390605522, 0.36855310468179, -0.3712571050797327, -0.14613270298480155, 0.14704417588654906, 0.16652495718001017, 0.08390842158008706, -0.004630149187645516, -0.23678194932717753, 0.10816004815824017, -0.1520781228253683, -0.09003683279261931, -0.06768117259562641, -0.04490177583770006, -0.07942142417830239, -0.3821877131590268, 0.10357884721915153, 0.003299280177228726, 0.029651338452720766, 0.004247759564102374, -0.19511691168263243, 0.11340287427085784, 0.0029857616995337542, 0.05521751603704284, 0.023498186274998254, 0.10795403099325779, -0.10038610697167086, -0.10383847561645342, 0.3566373520289789, -0.049602769530468646, -0.22726032511702368, 0.10866230938211571, -0.03760301705182797, -0.14368405607952314, 0.1568332152258787, 0.2205070798998451, 0.13776938384724455, -0.171022336772306, 0.12111847209166149, -0.11170572586498229, 0.19259181738525002, 0.027112302448522377, -0.05996631473543813, 0.05825715908663465, 0.1773791169142025, 0.1349428591112463, 0.14022044517780438, -0.07233835090891218, -0.05485779271702041, -0.2306552797822946, -0.13473485283214937, -0.3026589024290963, -0.001005865555334758, -0.20615789153211395, -0.21729755586573293, 0.35406237705187366, 0.15448176524588852, 0.18977000813123646, 0.1618063059526372, 0.32146184823729773, 0.15613706443064773, 0.04104026741514282, 0.16260236629017896, 0.11932877348605674, 0.17529222676595607, 0.07438299868750822, -0.2030599008939535, 0.09515535912942141, 0.09692563713280732] |
1,802.04912 | Chemical freezeout parameters within generic nonextensive statistics | The particle production in relativistic heavy-ion collisions seems to be
created in a dynamically disordered system which can be best described by an
extended exponential entropy. In distinguishing between the applicability of
this and Boltzmann-Gibbs (BG) in generating various particle-ratios, generic
(non)extensive statistics (GNS) is introduced to the hadron resonance gas
model. Accordingly, the degree of (non)extensivity is determined by the
possible modifications in the phase space. Both BG extensivity and Tsallis
nonextensivity are included as very special cases defined by specific values of
the equivalence classes $(c, d)$. We found that the particle ratios at energies
ranging between $3.8$ and $2760~$GeV are best reproduced by nonextensive
statistics, where $c$ and $d$ range between $\sim0.9$ and $\sim1$. The present
work aims at illustrating that the proposed approach is well capable to
manifest the statistical nature of the system on interest. We don't aim at
highlighting deeper physical insights. In other words, while the resulting
nonextensivity is neither BG nor Tsallis, the freezeout parameters are found
very compatible with BG and accordingly with the well-known freezeout
phase-diagram, which is in an excellent agreement with recent lattice
calculations. We conclude that the particle production is nonextensive but
should not necessarily be accompanied by a radical change in the intensive or
extensive thermodynamic quantities, such as internal energy and temperature.
Only, the two critical exponents defining the equivalence classes $(c, d)$ are
the physical parameters characterizing the (non)extensivity.
| nucl-th hep-ph hep-th | the particle production in relativistic heavyion collisions seems to be created in a dynamically disordered system which can be best described by an extended exponential entropy in distinguishing between the applicability of this and boltzmanngibbs bg in generating various particleratios generic nonextensive statistics gns is introduced to the hadron resonance gas model accordingly the degree of nonextensivity is determined by the possible modifications in the phase space both bg extensivity and tsallis nonextensivity are included as very special cases defined by specific values of the equivalence classes c d we found that the particle ratios at energies ranging between 38 and 2760gev are best reproduced by nonextensive statistics where c and d range between sim09 and sim1 the present work aims at illustrating that the proposed approach is well capable to manifest the statistical nature of the system on interest we dont aim at highlighting deeper physical insights in other words while the resulting nonextensivity is neither bg nor tsallis the freezeout parameters are found very compatible with bg and accordingly with the wellknown freezeout phasediagram which is in an excellent agreement with recent lattice calculations we conclude that the particle production is nonextensive but should not necessarily be accompanied by a radical change in the intensive or extensive thermodynamic quantities such as internal energy and temperature only the two critical exponents defining the equivalence classes c d are the physical parameters characterizing the nonextensivity | [['the', 'particle', 'production', 'in', 'relativistic', 'heavyion', 'collisions', 'seems', 'to', 'be', 'created', 'in', 'a', 'dynamically', 'disordered', 'system', 'which', 'can', 'be', 'best', 'described', 'by', 'an', 'extended', 'exponential', 'entropy', 'in', 'distinguishing', 'between', 'the', 'applicability', 'of', 'this', 'and', 'boltzmanngibbs', 'bg', 'in', 'generating', 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1,802.04913 | Active crystals on a sphere | Two-dimensional crystals on curved manifolds exhibit nontrivial defect
structures. Here, we consider "active crystals" on a sphere, which are composed
of self-propelled colloidal particles. Our work is based on a new
phase-field-crystal-type model that involves a density and a polarization field
on the sphere. Depending on the strength of the self-propulsion, three
different types of crystals are found: a static crystal, a self-spinning
"vortex-vortex" crystal containing two vortical poles of the local velocity,
and a self-translating "source-sink" crystal with a source pole where
crystallization occurs and a sink pole where the active crystal melts. These
different crystalline states as well as their defects are studied theoretically
here and can in principle be confirmed in experiments.
| cond-mat.soft | twodimensional crystals on curved manifolds exhibit nontrivial defect structures here we consider active crystals on a sphere which are composed of selfpropelled colloidal particles our work is based on a new phasefieldcrystaltype model that involves a density and a polarization field on the sphere depending on the strength of the selfpropulsion three different types of crystals are found a static crystal a selfspinning vortexvortex crystal containing two vortical poles of the local velocity and a selftranslating sourcesink crystal with a source pole where crystallization occurs and a sink pole where the active crystal melts these different crystalline states as well as their defects are studied theoretically here and can in principle be confirmed in experiments | [['twodimensional', 'crystals', 'on', 'curved', 'manifolds', 'exhibit', 'nontrivial', 'defect', 'structures', 'here', 'we', 'consider', 'active', 'crystals', 'on', 'a', 'sphere', 'which', 'are', 'composed', 'of', 'selfpropelled', 'colloidal', 'particles', 'our', 'work', 'is', 'based', 'on', 'a', 'new', 'phasefieldcrystaltype', 'model', 'that', 'involves', 'a', 'density', 'and', 'a', 'polarization', 'field', 'on', 'the', 'sphere', 'depending', 'on', 'the', 'strength', 'of', 'the', 'selfpropulsion', 'three', 'different', 'types', 'of', 'crystals', 'are', 'found', 'a', 'static', 'crystal', 'a', 'selfspinning', 'vortexvortex', 'crystal', 'containing', 'two', 'vortical', 'poles', 'of', 'the', 'local', 'velocity', 'and', 'a', 'selftranslating', 'sourcesink', 'crystal', 'with', 'a', 'source', 'pole', 'where', 'crystallization', 'occurs', 'and', 'a', 'sink', 'pole', 'where', 'the', 'active', 'crystal', 'melts', 'these', 'different', 'crystalline', 'states', 'as', 'well', 'as', 'their', 'defects', 'are', 'studied', 'theoretically', 'here', 'and', 'can', 'in', 'principle', 'be', 'confirmed', 'in', 'experiments']] | [-0.15364204463957434, 0.2350269495019395, -0.08350952368294984, -0.007668730185015879, -0.06518302583584987, -0.12516510121017826, 0.027547086531851898, 0.4106338489147132, -0.20718229131920166, -0.2684346793480871, 0.0588791608509047, -0.27845234392831725, -0.15100956758843703, 0.1533638708795862, 0.05544200868819628, 0.014305175514891744, -0.018836040311215215, 0.026860580861307028, -0.04318752958237095, -0.1956568018455679, 0.31229964384335307, -0.02425841215115629, 0.28888556696964723, 0.05727807212653652, 0.1422173777113162, -0.006176702183949058, 0.06694924098790803, 0.08892755952598363, -0.16826585520360376, 0.06676444460039452, 0.19438275243073963, -0.07722712699078808, 0.1649546053775243, -0.45927678628644925, -0.2352638601799283, 0.04050947119596235, 0.14287824719621425, 0.1386183852099062, -0.09993167591522235, -0.26659266914857815, 0.052142488758953845, -0.10306017946966581, -0.11994631782893025, -0.03995616808370279, -0.02076605594361593, 0.07226041711768821, -0.21341500853049455, 0.08733890005673298, 0.048857387095044384, 0.09208745265935074, -0.08772496375042879, -0.10380368338608828, -0.07983674621209502, 0.05860803866138061, 0.021748264197652276, 0.0061786658163264134, 0.22080192063820728, -0.1178839963905742, -0.1351689069548197, 0.4304105021353615, -0.017263428643019052, -0.2239182512010366, 0.2205459919141344, -0.12699148853579045, -0.11455117176662673, 0.16136328648906528, 0.19177139749578936, 0.16046440281104624, -0.10159658244810998, 0.04829813059411224, -0.09264212719618196, 0.18898025388715037, 0.11620893502779502, 0.009820009046642665, 0.27597542428983407, 0.19281150502961522, 0.02463434420101214, 0.17411224556200436, -0.12850530759897083, -0.09704432411838257, -0.25678857929571614, -0.19005541713385468, -0.2211847701712446, 0.019798987283649153, -0.08451948402267717, -0.21576921955126813, 0.3885418877348696, 0.013777781612844322, 0.18126571364598676, -0.017280037856963054, 0.19029109549234835, 0.008174422063268395, 0.05611684360835505, 0.00786541203821176, 0.26460662217026476, 0.13371287153323033, 0.07024665361499055, -0.22148687896020547, 0.00035387124016619565, 0.03478706006181279] |
1,802.04914 | Web-Scale Responsive Visual Search at Bing | In this paper, we introduce a web-scale general visual search system deployed
in Microsoft Bing. The system accommodates tens of billions of images in the
index, with thousands of features for each image, and can respond in less than
200 ms. In order to overcome the challenges in relevance, latency, and
scalability in such large scale of data, we employ a cascaded learning-to-rank
framework based on various latest deep learning visual features, and deploy in
a distributed heterogeneous computing platform. Quantitative and qualitative
experiments show that our system is able to support various applications on
Bing website and apps.
| cs.CV | in this paper we introduce a webscale general visual search system deployed in microsoft bing the system accommodates tens of billions of images in the index with thousands of features for each image and can respond in less than 200 ms in order to overcome the challenges in relevance latency and scalability in such large scale of data we employ a cascaded learningtorank framework based on various latest deep learning visual features and deploy in a distributed heterogeneous computing platform quantitative and qualitative experiments show that our system is able to support various applications on bing website and apps | [['in', 'this', 'paper', 'we', 'introduce', 'a', 'webscale', 'general', 'visual', 'search', 'system', 'deployed', 'in', 'microsoft', 'bing', 'the', 'system', 'accommodates', 'tens', 'of', 'billions', 'of', 'images', 'in', 'the', 'index', 'with', 'thousands', 'of', 'features', 'for', 'each', 'image', 'and', 'can', 'respond', 'in', 'less', 'than', '200', 'ms', 'in', 'order', 'to', 'overcome', 'the', 'challenges', 'in', 'relevance', 'latency', 'and', 'scalability', 'in', 'such', 'large', 'scale', 'of', 'data', 'we', 'employ', 'a', 'cascaded', 'learningtorank', 'framework', 'based', 'on', 'various', 'latest', 'deep', 'learning', 'visual', 'features', 'and', 'deploy', 'in', 'a', 'distributed', 'heterogeneous', 'computing', 'platform', 'quantitative', 'and', 'qualitative', 'experiments', 'show', 'that', 'our', 'system', 'is', 'able', 'to', 'support', 'various', 'applications', 'on', 'bing', 'website', 'and', 'apps']] | [-0.1047000830823725, 0.0029461076106838506, -0.03489221912818124, 0.05399361119201087, -0.0998239613918945, -0.15105069570497356, 0.05304563726472546, 0.4162402402120407, -0.261176999968787, -0.3803775791229323, 0.10471388261125546, -0.34018281437080317, -0.16973362795331262, 0.2499607164680845, -0.12557437855484566, 0.10062161757789477, 0.11865362414097733, 0.04556329369855424, -0.02292242983704188, -0.2815744171478795, 0.287561359299074, 0.03439809064671275, 0.3203960983364871, 0.07464601896289322, 0.06799620743564594, -0.027867723663215233, -0.05818206488596971, -0.010420187423476066, -0.03445784259163316, 0.18990047861624396, 0.31287928337626386, 0.19922999354700247, 0.33745487096996957, -0.44481816099523896, -0.17890130169572063, 0.05507146308405532, 0.14852374160575277, 0.04748637815043707, -0.09287208680185781, -0.33368499921352573, 0.14806095564342808, -0.22468565879483718, -0.035198716449109144, -0.1367102236289418, 0.0009175028024015553, 0.03220678072874293, -0.24795128600765962, -0.007533942191682831, -0.014051896397664089, 0.09550688120846947, -0.03946270872344912, -0.049924809909002346, 0.07172571889341178, 0.15614302938032631, -0.015889527372583145, 0.007652516617919459, 0.15242851340221336, -0.1693132053990143, -0.15386537744691878, 0.4074850088418132, -0.08981101135585123, -0.1460236917588521, 0.22977413947581116, -0.04595089959206455, -0.19368080854547595, 0.08466199895536358, 0.29782612570044065, 0.12423208497483472, -0.16878727749178205, 0.014340914383111992, -0.01688654206203993, 0.23136173082383896, 0.03981700033593848, 0.03483409774454423, 0.18416982844490745, 0.28643524489657146, 0.04457622449468784, 0.13824744247819645, -0.12094757939698268, -0.07075576626253549, -0.19257035665684427, -0.14470683553754682, -0.16922737302427943, -0.03512744173275852, -0.10995899301626122, -0.11984572098637471, 0.4062325938444848, 0.2976581763683094, 0.22075561899221455, 0.05712515160422584, 0.33355460605687565, -0.01825387690644335, 0.15049521950534497, 0.09739815772539287, 0.15438212221958722, -0.041372905579405, 0.19962606254513515, -0.11399278525413588, 0.019492626039668767, -0.01499635675884407] |
1,802.04915 | On the Feasibility of Decentralized Derivatives Markets | In this paper, we present Velocity, a decentralized market deployed on
Ethereum for trading a custom type of derivative option. To enable the smart
contract to work, we also implement a price fetching tool called PriceGeth. We
present this as a case study, noting challenges in development of the system
that might be of independent interest to whose working on smart contract
implementations. We also apply recent academic results on the security of the
Solidity smart contract language in validating our codes security. Finally, we
discuss more generally the use of smart contracts in modelling financial
derivatives.
| cs.CR cs.CY cs.ET cs.HC | in this paper we present velocity a decentralized market deployed on ethereum for trading a custom type of derivative option to enable the smart contract to work we also implement a price fetching tool called pricegeth we present this as a case study noting challenges in development of the system that might be of independent interest to whose working on smart contract implementations we also apply recent academic results on the security of the solidity smart contract language in validating our codes security finally we discuss more generally the use of smart contracts in modelling financial derivatives | [['in', 'this', 'paper', 'we', 'present', 'velocity', 'a', 'decentralized', 'market', 'deployed', 'on', 'ethereum', 'for', 'trading', 'a', 'custom', 'type', 'of', 'derivative', 'option', 'to', 'enable', 'the', 'smart', 'contract', 'to', 'work', 'we', 'also', 'implement', 'a', 'price', 'fetching', 'tool', 'called', 'pricegeth', 'we', 'present', 'this', 'as', 'a', 'case', 'study', 'noting', 'challenges', 'in', 'development', 'of', 'the', 'system', 'that', 'might', 'be', 'of', 'independent', 'interest', 'to', 'whose', 'working', 'on', 'smart', 'contract', 'implementations', 'we', 'also', 'apply', 'recent', 'academic', 'results', 'on', 'the', 'security', 'of', 'the', 'solidity', 'smart', 'contract', 'language', 'in', 'validating', 'our', 'codes', 'security', 'finally', 'we', 'discuss', 'more', 'generally', 'the', 'use', 'of', 'smart', 'contracts', 'in', 'modelling', 'financial', 'derivatives']] | [-0.13060226480956771, -0.0016157112428724456, -0.08275467045192879, 0.05253078016054739, -0.14422520933051905, -0.15164909694188586, 0.09955049763326922, 0.41496853173399967, -0.2438393964015025, -0.2550828437912666, 0.19313023894210346, -0.2456605558618321, -0.16727113764500245, 0.23435949723534577, -0.18332891278259922, 0.03138024519527486, 0.02085609404578766, -0.0192547725479623, -0.00334581336210249, -0.29506145033034653, 0.2838211400327661, 0.060479087761147333, 0.28917249352283153, 0.0849237021660277, 0.05514824882751176, -0.005291860453629245, -0.04210621214588173, 0.0034628642485283003, -0.13485000062102395, 0.20189725309076798, 0.32919624019026134, 0.18915945098759343, 0.37847752171728644, -0.46737535133919056, -0.14278410685559115, 0.07384031287316854, 0.08232784379894535, 0.06335780953425758, -0.08509371506033858, -0.25547926026774803, 0.07471150562438804, -0.3393521154357586, -0.16383301138800258, -0.11732754516515342, -0.0007042386957133809, 0.03130933256519105, -0.25129663927630946, -0.0664750798271901, 0.02145023625333427, 0.06553076367708854, -0.017187299691916753, -0.06199402890949083, -0.004504507798022435, 0.1101174587092828, 0.06675945821916685, -0.08253608710462383, 0.15727674299948072, -0.07842096573343345, -0.1556591093346166, 0.3891384127006556, -0.035101532655365496, -0.1720999367244076, 0.13611455999004343, -0.032808556037101276, -0.21146525019139517, -0.017751407638816847, 0.2709909728728235, 0.09710221867620324, -0.17325895459604604, 0.04606940293645797, -0.02490910050983075, 0.1876975375901869, 0.0437322946236236, -0.00045239165774546564, 0.20866255589862703, 0.2388114076651012, 0.11423080674527834, 0.14889513404947743, -0.020817782926314976, -0.14491029475660375, -0.3070518798025053, -0.2009550104655015, -0.14273879359825514, 0.012086715908177817, -0.07051107387815136, -0.15909355047430532, 0.3997874426810692, 0.26981179971577757, 0.07283841603202745, 0.09311522762588235, 0.3634008421019341, 0.05098597126683065, 0.06425541195979652, 0.10682413559698034, 0.17050660051366626, -0.021203283792904887, 0.23065377094220216, -0.13019577932209359, 0.1253455496201544, 0.014666004796102547] |
1,802.04916 | Dark-matter-like solutions to Einstein's unified field equations | Einstein originally proposed a nonsymmetric tensor field, with its symmetric
part associated with the spacetime metric and its antisymmetric part associated
with the electromagnetic field, as an approach to a unified field theory. Here
we interpret it more modestly as an alternative to Einstein-Maxwell theory,
approximating the coupling between the electromagnetic field and spacetime
curvature in the macroscopic classical regime. Previously it was shown that the
Lorentz force can be derived from this theory, albeit with deviation on the
scale of a universal length constant $\ell$. Here we assume that $\ell$ is of
galactic scale and show that the modified coupling of the electromagnetic field
with charged particles allows a non-Maxwellian equilibrium of non-neutral
plasma. The resulting electromagnetic field is "dark" in the sense that its
modified Lorentz force on the plasma vanishes, yet through its modified
coupling to the gravitational field it engenders a nonvanishing, effective mass
density. We obtain a solution for which this mass density asymptotes
approximately to that of the pseudo-isothermal model of dark matter. The
resulting gravitational field produces radial acceleration, in the context of a
post-Minkowskian approximation, which is negligible at small radius but yields
a flat rotation curve at large radius. We further exhibit a family of such
solutions which, like the pseudo-isothermal model, has a free parameter to set
the mass scale (in this case related to the charge density) and a free
parameter to set the length scale (in this case an integer multiple of $\ell$).
Moreover, these solutions are members of a larger family with more general
angular and radial dependence. They thus show promise as approximations of
generalized pseudo-isothermal models, which in turn are known to fit a wide
range of mass density profiles for galaxies and clusters.
| gr-qc astro-ph.GA | einstein originally proposed a nonsymmetric tensor field with its symmetric part associated with the spacetime metric and its antisymmetric part associated with the electromagnetic field as an approach to a unified field theory here we interpret it more modestly as an alternative to einsteinmaxwell theory approximating the coupling between the electromagnetic field and spacetime curvature in the macroscopic classical regime previously it was shown that the lorentz force can be derived from this theory albeit with deviation on the scale of a universal length constant ell here we assume that ell is of galactic scale and show that the modified coupling of the electromagnetic field with charged particles allows a nonmaxwellian equilibrium of nonneutral plasma the resulting electromagnetic field is dark in the sense that its modified lorentz force on the plasma vanishes yet through its modified coupling to the gravitational field it engenders a nonvanishing effective mass density we obtain a solution for which this mass density asymptotes approximately to that of the pseudoisothermal model of dark matter the resulting gravitational field produces radial acceleration in the context of a postminkowskian approximation which is negligible at small radius but yields a flat rotation curve at large radius we further exhibit a family of such solutions which like the pseudoisothermal model has a free parameter to set the mass scale in this case related to the charge density and a free parameter to set the length scale in this case an integer multiple of ell moreover these solutions are members of a larger family with more general angular and radial dependence they thus show promise as approximations of generalized pseudoisothermal models which in turn are known to fit a wide range of mass density profiles for galaxies and clusters | [['einstein', 'originally', 'proposed', 'a', 'nonsymmetric', 'tensor', 'field', 'with', 'its', 'symmetric', 'part', 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1,802.04917 | Collective Coordinate Models of Domain Wall Motion in Perpendicularly
Magnetized Systems under the Spin Hall Effect and Longitudinal Fields | Recent studies on heterostructures of ultrathin ferromagnets sandwiched
between a heavy metal layer and an oxide have highlighted the importance of
spin-orbit coupling (SOC) and broken inversion symmetry in domain wall (DW)
motion. Specifically, chiral DWs are stabilized in these systems due to the
Dzyaloshinskii-Moriya interaction (DMI). SOC can also lead to enhanced current
induced DW motion, with the spin Hall effect (SHE) suggested as the dominant
mechanism for this observation. The efficiency of SHE driven DW motion depends
on the internal magnetic structure of the DW, which could be controlled using
externally applied longitudinal in-plane fields. In this work, micromagnetic
simulations and collective coordinate models are used to study current-driven
DW motion under longitudinal in-plane fields in perpendicularly magnetized
samples with strong DMI. Several extended collective coordinate models are
developed to reproduce the micromagnetic results. While these extended models
show improvements over traditional models of this kind, there are still
discrepancies between them and micromagnetic simulations which require further
work.
| cond-mat.mes-hall | recent studies on heterostructures of ultrathin ferromagnets sandwiched between a heavy metal layer and an oxide have highlighted the importance of spinorbit coupling soc and broken inversion symmetry in domain wall dw motion specifically chiral dws are stabilized in these systems due to the dzyaloshinskiimoriya interaction dmi soc can also lead to enhanced current induced dw motion with the spin hall effect she suggested as the dominant mechanism for this observation the efficiency of she driven dw motion depends on the internal magnetic structure of the dw which could be controlled using externally applied longitudinal inplane fields in this work micromagnetic simulations and collective coordinate models are used to study currentdriven dw motion under longitudinal inplane fields in perpendicularly magnetized samples with strong dmi several extended collective coordinate models are developed to reproduce the micromagnetic results while these extended models show improvements over traditional models of this kind there are still discrepancies between them and micromagnetic simulations which require further work | [['recent', 'studies', 'on', 'heterostructures', 'of', 'ultrathin', 'ferromagnets', 'sandwiched', 'between', 'a', 'heavy', 'metal', 'layer', 'and', 'an', 'oxide', 'have', 'highlighted', 'the', 'importance', 'of', 'spinorbit', 'coupling', 'soc', 'and', 'broken', 'inversion', 'symmetry', 'in', 'domain', 'wall', 'dw', 'motion', 'specifically', 'chiral', 'dws', 'are', 'stabilized', 'in', 'these', 'systems', 'due', 'to', 'the', 'dzyaloshinskiimoriya', 'interaction', 'dmi', 'soc', 'can', 'also', 'lead', 'to', 'enhanced', 'current', 'induced', 'dw', 'motion', 'with', 'the', 'spin', 'hall', 'effect', 'she', 'suggested', 'as', 'the', 'dominant', 'mechanism', 'for', 'this', 'observation', 'the', 'efficiency', 'of', 'she', 'driven', 'dw', 'motion', 'depends', 'on', 'the', 'internal', 'magnetic', 'structure', 'of', 'the', 'dw', 'which', 'could', 'be', 'controlled', 'using', 'externally', 'applied', 'longitudinal', 'inplane', 'fields', 'in', 'this', 'work', 'micromagnetic', 'simulations', 'and', 'collective', 'coordinate', 'models', 'are', 'used', 'to', 'study', 'currentdriven', 'dw', 'motion', 'under', 'longitudinal', 'inplane', 'fields', 'in', 'perpendicularly', 'magnetized', 'samples', 'with', 'strong', 'dmi', 'several', 'extended', 'collective', 'coordinate', 'models', 'are', 'developed', 'to', 'reproduce', 'the', 'micromagnetic', 'results', 'while', 'these', 'extended', 'models', 'show', 'improvements', 'over', 'traditional', 'models', 'of', 'this', 'kind', 'there', 'are', 'still', 'discrepancies', 'between', 'them', 'and', 'micromagnetic', 'simulations', 'which', 'require', 'further', 'work']] | [-0.18329008867796395, 0.17274130750760242, -0.028849987764351118, -0.003037125412345812, -0.13578423866076592, -0.12373846301068522, -0.0052338533178665995, 0.47476710023948493, -0.3066956425455879, -0.31266615771752154, 0.01603554957690909, -0.21633601985779238, -0.11988726325655068, 0.2181024014348199, 0.036281149844517524, -0.004735425584631399, 0.019722594595398546, -0.09059690293329566, -0.01821526151254588, -0.198867369177086, 0.24933833116303944, -0.0295072924953116, 0.35701511938830616, 0.06544178229234592, 0.03588207332946661, -0.017276182551636876, 0.07063281964163602, 0.053744141787568235, -0.14528208505940735, 0.08878881353140167, 0.20359571900528564, -0.1513490060247446, 0.17522839514787383, -0.5324001334006001, -0.21026664901154568, 0.009695928445467667, 0.1745417136641115, 0.19627801274811352, -0.08562637214412948, -0.33947366504982024, 0.05571170723548749, -0.1823673081000029, -0.11266462107460877, -0.12454532771024228, 0.021061757021230754, 0.01658177453415723, -0.2679032732733317, 0.09024576544425428, 0.1299665793718666, 0.11211277128006361, -0.08133383987902415, -0.10597122480705241, -0.09340768915410183, 0.05096023925507777, 0.1294485031517211, 0.09113611384318066, 0.1830970227800784, -0.15608536805639403, -0.19045512687186392, 0.3389932827688522, -0.04166834874900047, -0.21761739797172894, 0.1921236640947252, -0.1418156711072285, -0.04341812929867402, 0.08969267582092781, 0.1781037669131382, 0.10070871734436347, -0.12973316971780963, 0.08387814158348147, 0.018000847850897297, 0.14625481387415196, 0.039643559333006416, 0.015806598844793365, 0.27538041643485983, 0.17021265972885796, -0.0015533208161569345, 0.10997721850828655, -0.11012756559142398, -0.11445906625262328, -0.20860397730670546, -0.1247999072743736, -0.1827207741989922, 0.03546548065561685, -0.05883277691785038, -0.13711656103350267, 0.3780938983397408, 0.22660880037903372, 0.11234222126760236, -0.10847452017391876, 0.2503068987453669, 0.08703919670686287, 0.11080269883994175, 0.0336797890970657, 0.2969266285726465, 0.17933782567602716, 0.1461411908981882, -0.32435098532240214, 0.09175424488703865, -0.0213108179665616] |
1,802.04918 | Prophit: Causal inverse classification for multiple continuously valued
treatment policies | Inverse classification uses an induced classifier as a queryable oracle to
guide test instances towards a preferred posterior class label. The result
produced from the process is a set of instance-specific feature perturbations,
or recommendations, that optimally improve the probability of the class label.
In this work, we adopt a causal approach to inverse classification, eliciting
treatment policies (i.e., feature perturbations) for models induced with causal
properties. In so doing, we solve a long-standing problem of eliciting
multiple, continuously valued treatment policies, using an updated framework
and corresponding set of assumptions, which we term the inverse classification
potential outcomes framework (ICPOF), along with a new measure, referred to as
the individual future estimated effects ($i$FEE). We also develop the
approximate propensity score (APS), based on Gaussian processes, to weight
treatments, much like the inverse propensity score weighting used in past
works. We demonstrate the viability of our methods on student performance.
| cs.LG stat.ML | inverse classification uses an induced classifier as a queryable oracle to guide test instances towards a preferred posterior class label the result produced from the process is a set of instancespecific feature perturbations or recommendations that optimally improve the probability of the class label in this work we adopt a causal approach to inverse classification eliciting treatment policies ie feature perturbations for models induced with causal properties in so doing we solve a longstanding problem of eliciting multiple continuously valued treatment policies using an updated framework and corresponding set of assumptions which we term the inverse classification potential outcomes framework icpof along with a new measure referred to as the individual future estimated effects ifee we also develop the approximate propensity score aps based on gaussian processes to weight treatments much like the inverse propensity score weighting used in past works we demonstrate the viability of our methods on student performance | [['inverse', 'classification', 'uses', 'an', 'induced', 'classifier', 'as', 'a', 'queryable', 'oracle', 'to', 'guide', 'test', 'instances', 'towards', 'a', 'preferred', 'posterior', 'class', 'label', 'the', 'result', 'produced', 'from', 'the', 'process', 'is', 'a', 'set', 'of', 'instancespecific', 'feature', 'perturbations', 'or', 'recommendations', 'that', 'optimally', 'improve', 'the', 'probability', 'of', 'the', 'class', 'label', 'in', 'this', 'work', 'we', 'adopt', 'a', 'causal', 'approach', 'to', 'inverse', 'classification', 'eliciting', 'treatment', 'policies', 'ie', 'feature', 'perturbations', 'for', 'models', 'induced', 'with', 'causal', 'properties', 'in', 'so', 'doing', 'we', 'solve', 'a', 'longstanding', 'problem', 'of', 'eliciting', 'multiple', 'continuously', 'valued', 'treatment', 'policies', 'using', 'an', 'updated', 'framework', 'and', 'corresponding', 'set', 'of', 'assumptions', 'which', 'we', 'term', 'the', 'inverse', 'classification', 'potential', 'outcomes', 'framework', 'icpof', 'along', 'with', 'a', 'new', 'measure', 'referred', 'to', 'as', 'the', 'individual', 'future', 'estimated', 'effects', 'ifee', 'we', 'also', 'develop', 'the', 'approximate', 'propensity', 'score', 'aps', 'based', 'on', 'gaussian', 'processes', 'to', 'weight', 'treatments', 'much', 'like', 'the', 'inverse', 'propensity', 'score', 'weighting', 'used', 'in', 'past', 'works', 'we', 'demonstrate', 'the', 'viability', 'of', 'our', 'methods', 'on', 'student', 'performance']] | [-0.01064729332086324, 0.01768229231836068, -0.08904506243927866, 0.09418029141432503, -0.15702852269559597, -0.14069034542025835, 0.12208822219876335, 0.4321927661794994, -0.2849392563998449, -0.3012148298029382, 0.06049264338095641, -0.25763334266391374, -0.1692365032914501, 0.15613956049335856, -0.08776697331931117, 0.10396652559799786, 0.07892341934607033, 0.06828439018170306, -0.05332728403295397, -0.2633814232723925, 0.32974588740054495, 0.10495564471555237, 0.32700495800040913, -0.011978214909791346, 0.11054308586535137, 0.038312793073228574, -0.06595598568127259, 0.03826452664493414, -0.10938083360776588, 0.1398917482876775, 0.2792709684156902, 0.19284122263475154, 0.37658866439889743, -0.3739035995489599, -0.22688796243086556, 0.1419620572014293, 0.0827298526583402, 0.10707849522524407, -0.03388587722307518, -0.31237353274173685, 0.0593702221291621, -0.1746482984444139, -0.07611159856537444, -0.093350893609232, -0.039969132088074746, -0.031735487509637623, -0.3650721881738645, 0.05821772471157015, 0.08076930863371794, 0.0267388540751382, -0.09442296594493842, -0.14480808064116527, 0.048945790517687694, 0.11895167621340128, 0.056430231390525616, 0.04232456579166871, 0.1508768380400037, -0.152180792446617, -0.17825875614125958, 0.32764064251826513, -0.053122630733496594, -0.2574846988493864, 0.15412883488180104, -0.03836600464181162, -0.12581398641931255, 0.06613637266005545, 0.26026421701715285, 0.14758580386319867, -0.1807898018234128, 0.009669233188142487, -0.032948730195459956, 0.14846992376104198, 0.038139516140815594, -0.020027928527807128, 0.18924435523601135, 0.18509281973898312, 0.06275927790459371, 0.13653077845517922, -0.09488797277030879, -0.07009885004165288, -0.2692792563915609, -0.1194494780240543, -0.15605300297216831, 0.02191911717633023, -0.07320841813712746, -0.2022431642692161, 0.39589342432121605, 0.2255826605696406, 0.21246666443102913, 0.09928170241331119, 0.2662093226341593, 0.09289319153297723, 0.05534844070746432, 0.07803945508691788, 0.15351363460864598, 0.054954688646049306, 0.03520148545003578, -0.17776337022980637, 0.13394091859863816, 0.072693961064625] |
1,802.04919 | New Universal Deformation Formulas for deformation quantization | Universal Deformation Formulas (UDFs) for the deformation of associative
algebras play a key role in deformation quantization. Here we present examples
for certain classes of infinitesimals. A basic representable 2-cocycle $F$ of
an associative algebra $\mathcal A$ is one for which there exist commuting
derivations $D,\dots, D_n$ of $\mathcal A$ such that $F = \sum_{ij}a_{ij}D_i
\smile D_j$, where the $a_{ij}$ are central elements of $\mathcal A$. When
$\mathcal A$ is defined over the rationals, there is a natural definition of
the exponential of such a cocycle. With this $\exp \hbar F$ defines a formal
one-parameter family of deformations of $\mathcal A$, where $\hbar$ is a
deformation parameter. The rational quantization of smooth functions on a
smooth manifold using a bivector field as an infinitesimal deformation is a
special case.
| math.QA math.RA | universal deformation formulas udfs for the deformation of associative algebras play a key role in deformation quantization here we present examples for certain classes of infinitesimals a basic representable 2cocycle f of an associative algebra mathcal a is one for which there exist commuting derivations ddots d_n of mathcal a such that f sum_ija_ijd_i smile d_j where the a_ij are central elements of mathcal a when mathcal a is defined over the rationals there is a natural definition of the exponential of such a cocycle with this exp hbar f defines a formal oneparameter family of deformations of mathcal a where hbar is a deformation parameter the rational quantization of smooth functions on a smooth manifold using a bivector field as an infinitesimal deformation is a special case | [['universal', 'deformation', 'formulas', 'udfs', 'for', 'the', 'deformation', 'of', 'associative', 'algebras', 'play', 'a', 'key', 'role', 'in', 'deformation', 'quantization', 'here', 'we', 'present', 'examples', 'for', 'certain', 'classes', 'of', 'infinitesimals', 'a', 'basic', 'representable', '2cocycle', 'f', 'of', 'an', 'associative', 'algebra', 'mathcal', 'a', 'is', 'one', 'for', 'which', 'there', 'exist', 'commuting', 'derivations', 'ddots', 'd_n', 'of', 'mathcal', 'a', 'such', 'that', 'f', 'sum_ija_ijd_i', 'smile', 'd_j', 'where', 'the', 'a_ij', 'are', 'central', 'elements', 'of', 'mathcal', 'a', 'when', 'mathcal', 'a', 'is', 'defined', 'over', 'the', 'rationals', 'there', 'is', 'a', 'natural', 'definition', 'of', 'the', 'exponential', 'of', 'such', 'a', 'cocycle', 'with', 'this', 'exp', 'hbar', 'f', 'defines', 'a', 'formal', 'oneparameter', 'family', 'of', 'deformations', 'of', 'mathcal', 'a', 'where', 'hbar', 'is', 'a', 'deformation', 'parameter', 'the', 'rational', 'quantization', 'of', 'smooth', 'functions', 'on', 'a', 'smooth', 'manifold', 'using', 'a', 'bivector', 'field', 'as', 'an', 'infinitesimal', 'deformation', 'is', 'a', 'special', 'case']] | [-0.2188510461013735, 0.11237790963752745, -0.059429411134704596, 0.020009857481418866, -0.0904602844664079, -0.1550879451174905, -0.05415758888391176, 0.304600938802629, -0.35713109539777743, -0.11996821819357281, 0.05628019951808259, -0.24585166697456376, -0.18860957469409845, 0.1924608451805598, -0.15219410450881124, -0.006972797090319667, 0.0047613529270280185, 0.14874421740244106, -0.12881037567771503, -0.1912485985965358, 0.3966743125044924, -0.03300872825152116, 0.17265576659885096, 0.0035684627103142615, 0.1577190104046998, -0.01909041752686768, 0.05742769171007153, -0.008553168010406606, -0.18209910563471238, 0.09888950419267566, 0.3016747672928453, 0.08321673227620759, 0.28190189264128057, -0.3353192876349753, -0.11318681224095305, 0.19749107210361583, 0.1404978635970239, -0.01880170921561323, -0.033310087218946655, -0.2120997871906592, 0.06631161138052341, -0.22283703235425348, -0.1163886937156492, -0.10005274826044759, 0.17213356486195333, 0.010514314964736305, -0.3460747325044917, 0.010592243729496565, 0.1463565030914372, 0.1506648905479943, -0.039648368639328815, -0.07704123036613263, -0.06012132543590155, 0.040717582477991815, -0.0218683867843631, 0.11637690234342664, 0.13349693149143066, -0.0893934786862393, -0.0858391395565599, 0.3861242795127488, -0.022646249172930406, -0.2754680865011581, 0.03158114153129144, -0.1313185226208875, -0.19728834624585556, 0.08302711901588615, 0.05383525796667269, 0.18602076233534223, -0.030819698088957313, 0.2586028229217888, -0.11913461004215198, 0.09451200794171924, 0.0793457454781774, -0.0014423040717136202, 0.14580050952123905, 0.12131096792817996, 0.03755039365331488, 0.09975637089989083, 0.03683478119104778, -0.06197058060389804, -0.4550146198882831, -0.1850590246736886, -0.12474162530843315, 0.198976301724929, -0.13460366297549914, -0.24226829296667277, 0.387740583144363, 0.023371927029504552, 0.2370913309433798, 0.10050442986516649, 0.1852662735801982, 0.12657763573293407, 0.12180885941699499, 0.016955092431247275, 0.10557794588980976, 0.18989834908256673, -0.03136848886845648, -0.11635733928712906, -0.022237368529354495, 0.1792703196919692] |
1,802.0492 | DVAE++: Discrete Variational Autoencoders with Overlapping
Transformations | Training of discrete latent variable models remains challenging because
passing gradient information through discrete units is difficult. We propose a
new class of smoothing transformations based on a mixture of two overlapping
distributions, and show that the proposed transformation can be used for
training binary latent models with either directed or undirected priors. We
derive a new variational bound to efficiently train with Boltzmann machine
priors. Using this bound, we develop DVAE++, a generative model with a global
discrete prior and a hierarchy of convolutional continuous variables.
Experiments on several benchmarks show that overlapping transformations
outperform other recent continuous relaxations of discrete latent variables
including Gumbel-Softmax (Maddison et al., 2016; Jang et al., 2016), and
discrete variational autoencoders (Rolfe 2016).
| cs.LG stat.ML | training of discrete latent variable models remains challenging because passing gradient information through discrete units is difficult we propose a new class of smoothing transformations based on a mixture of two overlapping distributions and show that the proposed transformation can be used for training binary latent models with either directed or undirected priors we derive a new variational bound to efficiently train with boltzmann machine priors using this bound we develop dvae a generative model with a global discrete prior and a hierarchy of convolutional continuous variables experiments on several benchmarks show that overlapping transformations outperform other recent continuous relaxations of discrete latent variables including gumbelsoftmax maddison et al 2016 jang et al 2016 and discrete variational autoencoders rolfe 2016 | [['training', 'of', 'discrete', 'latent', 'variable', 'models', 'remains', 'challenging', 'because', 'passing', 'gradient', 'information', 'through', 'discrete', 'units', 'is', 'difficult', 'we', 'propose', 'a', 'new', 'class', 'of', 'smoothing', 'transformations', 'based', 'on', 'a', 'mixture', 'of', 'two', 'overlapping', 'distributions', 'and', 'show', 'that', 'the', 'proposed', 'transformation', 'can', 'be', 'used', 'for', 'training', 'binary', 'latent', 'models', 'with', 'either', 'directed', 'or', 'undirected', 'priors', 'we', 'derive', 'a', 'new', 'variational', 'bound', 'to', 'efficiently', 'train', 'with', 'boltzmann', 'machine', 'priors', 'using', 'this', 'bound', 'we', 'develop', 'dvae', 'a', 'generative', 'model', 'with', 'a', 'global', 'discrete', 'prior', 'and', 'a', 'hierarchy', 'of', 'convolutional', 'continuous', 'variables', 'experiments', 'on', 'several', 'benchmarks', 'show', 'that', 'overlapping', 'transformations', 'outperform', 'other', 'recent', 'continuous', 'relaxations', 'of', 'discrete', 'latent', 'variables', 'including', 'gumbelsoftmax', 'maddison', 'et', 'al', '2016', 'jang', 'et', 'al', '2016', 'and', 'discrete', 'variational', 'autoencoders', 'rolfe', '2016']] | [-0.0027708253517997367, 0.07352979176779505, -0.11536692572804559, 0.06398767689164697, -0.1892321757862673, -0.1839112309394639, 0.07923574498345871, 0.43773559280553787, -0.31949095784670856, -0.35752024360689794, 0.07478082912754924, -0.2094361148383312, -0.1977883606166931, 0.13510679944120974, -0.11676300279483073, 0.13839398081093526, 0.10612429442087642, -0.03723625911540594, -0.10390891852429598, -0.3053852265726133, 0.27495545652076725, 0.021465414962848695, 0.3164430675840303, -0.08088581833769293, 0.18272058422356344, 0.012225353969026263, -0.060737703778162726, -0.018342495316780415, -0.0912849936530849, 0.1622986201854313, 0.23275393202682976, 0.17740850024582708, 0.32044426928188247, -0.4103304374466079, -0.32147773985453576, 0.13307557586079635, 0.08120222102475017, 0.10284619429246562, -0.02436012287718095, -0.3389827381132734, -0.0009570569210850141, -0.1685044621704381, 0.03894073395997662, -0.16012503422734117, -0.005436004699059143, 0.04980997164819796, -0.3558117968867682, 0.13336097056587484, 0.10881216300479254, 0.020977493688589383, -0.05513072348818058, -0.13735515246277347, -0.01311670382460188, 0.022557030187356247, -0.024626909593754142, 0.047138328349976916, 0.051378594644899876, -0.09495858450766512, -0.18030315996421611, 0.3024701707440765, -0.0851714367755227, -0.22852969956153832, 0.22716221694850183, 0.01741079770873825, -0.2317598171986076, 0.057332420267728196, 0.2669377596980399, 0.16464154950163068, -0.18226464985705473, 0.08186489644024067, -0.11813416759319165, 0.16554886328267762, 0.041444567233561776, -0.0742782239974359, 0.14237954137267686, 0.17449685163842907, 0.03599353547661933, 0.15077483070282532, -0.13162979453228837, -0.12358694320579819, -0.24064449501075164, -0.08975415430207807, -0.22556796907877721, -0.021981401916812447, -0.07850197719044674, -0.18025945231686308, 0.36546064405154216, 0.15737492917301213, 0.2505528872183078, 0.1054339083576841, 0.22835571397127225, 0.08634850245454356, 0.03190098760039115, 0.16850746525921115, 0.14602346899537683, 0.10902651184935029, 0.04217147665536579, -0.12649304427582175, 0.06441303361657255, 0.0950753119296762] |
1,802.04921 | Stability of circulant graphs | The canonical double cover $\mathrm{D}(\Gamma)$ of a graph $\Gamma$ is the
direct product of $\Gamma$ and $K_2$. If
$\mathrm{Aut}(\mathrm{D}(\Gamma))=\mathrm{Aut}(\Gamma)\times\mathbb{Z}_2$ then
$\Gamma$ is called stable; otherwise $\Gamma$ is called unstable. An unstable
graph is nontrivially unstable if it is connected, non-bipartite and distinct
vertices have different neighborhoods. In this paper we prove that every
circulant graph of odd prime order is stable and there is no arc-transitive
nontrivially unstable circulant graph. The latter answers a question of Wilson
in 2008. We also give infinitely many counterexamples to a conjecture of
Maru\v{s}i\v{c}, Scapellato and Zagaglia Salvi in 1989 by constructing a family
of stable circulant graphs with compatible adjacency matrices.
| math.CO | the canonical double cover mathrmdgamma of a graph gamma is the direct product of gamma and k_2 if mathrmautmathrmdgammamathrmautgammatimesmathbbz_2 then gamma is called stable otherwise gamma is called unstable an unstable graph is nontrivially unstable if it is connected nonbipartite and distinct vertices have different neighborhoods in this paper we prove that every circulant graph of odd prime order is stable and there is no arctransitive nontrivially unstable circulant graph the latter answers a question of wilson in 2008 we also give infinitely many counterexamples to a conjecture of maruvsivc scapellato and zagaglia salvi in 1989 by constructing a family of stable circulant graphs with compatible adjacency matrices | [['the', 'canonical', 'double', 'cover', 'mathrmdgamma', 'of', 'a', 'graph', 'gamma', 'is', 'the', 'direct', 'product', 'of', 'gamma', 'and', 'k_2', 'if', 'mathrmautmathrmdgammamathrmautgammatimesmathbbz_2', 'then', 'gamma', 'is', 'called', 'stable', 'otherwise', 'gamma', 'is', 'called', 'unstable', 'an', 'unstable', 'graph', 'is', 'nontrivially', 'unstable', 'if', 'it', 'is', 'connected', 'nonbipartite', 'and', 'distinct', 'vertices', 'have', 'different', 'neighborhoods', 'in', 'this', 'paper', 'we', 'prove', 'that', 'every', 'circulant', 'graph', 'of', 'odd', 'prime', 'order', 'is', 'stable', 'and', 'there', 'is', 'no', 'arctransitive', 'nontrivially', 'unstable', 'circulant', 'graph', 'the', 'latter', 'answers', 'a', 'question', 'of', 'wilson', 'in', '2008', 'we', 'also', 'give', 'infinitely', 'many', 'counterexamples', 'to', 'a', 'conjecture', 'of', 'maruvsivc', 'scapellato', 'and', 'zagaglia', 'salvi', 'in', '1989', 'by', 'constructing', 'a', 'family', 'of', 'stable', 'circulant', 'graphs', 'with', 'compatible', 'adjacency', 'matrices']] | [-0.2065018946395337, 0.21350196481908976, -0.08018864108050622, 0.0764205216054886, -0.10445302486582433, -0.2059166928735173, 0.012333861419798815, 0.3991199970715544, -0.28552832689534113, -0.2236813963029208, 0.10018507516106537, -0.2948287164643226, -0.18937661067483205, 0.14029570090249904, -0.09443570774735756, -0.013294216980072625, 0.1559465915525278, 0.12379419510659662, 0.010590464869080093, -0.27358331987566487, 0.36066919720270535, -0.020879667272646594, 0.1445179677351369, 0.06591122703699087, 0.06296879840671148, 0.01390248046989001, -0.004098560606825699, 0.04090523094083499, -0.18061490418216794, 0.0827861239990102, 0.2766145414953093, 0.11903028827368563, 0.20477518334406095, -0.32583011815508656, -0.11759312210143408, 0.27564167026972886, 0.13071765315049844, -0.0035098732711331356, 0.010217440130360859, -0.2022759089861246, 0.18684225046125047, -0.1557963229534985, -0.12251412987627668, -0.06444150379415854, 0.13751041636015604, -0.04531434806769045, -0.2916285600965318, -0.02728637854723504, 0.1435111896089703, 0.003222473942249724, 0.05877083562567685, -0.09647185029914078, -0.06457562270818405, 0.06025874948364149, 0.00029890354297765827, 0.037316199694986195, 0.012383430404475556, -0.03564938066020897, -0.15873511715620323, 0.33677573124731175, 0.009473277602878227, -0.15262968878451627, 0.09764593269097284, -0.10927936568566897, -0.22406154767343395, 0.18287963660577147, 0.06509825307875872, 0.15142045834653298, -0.03594372291298746, 0.1959730131908781, -0.16330610085685637, 0.11282786167932005, 0.16189864042748525, -0.05561573392128373, 0.1369114062540884, 0.10189564154908351, 0.17342161095319136, 0.1465436733029421, 0.040689178190908386, -0.007061299343493957, -0.2747067175082211, -0.13486721370260693, -0.2053707322214269, 0.11023167126079642, -0.1551499289137795, -0.2790946089165303, 0.41596254577986824, 0.025381400706517756, 0.1713472793457433, 0.03657989937233404, 0.21067389548367377, 0.05811398210980504, 0.013851791005400777, 0.18797866512345404, 0.13777678854564585, 0.23665161136293658, -0.06802053911412514, -0.11725017568548617, 0.020138695216692478, 0.13859360084814715] |
1,802.04922 | Supersymmetric Sawada-Kotera Equation: B\"{a}cklund-Darboux
Transformations and Applications | In this paper, we construct a Darboux transformation and the related
B\"acklund transformation for the supersymmetric Sawada-Kotera (SSK) equation.
The associated nonlinear superposition formula is also worked out. We
demonstrate that these are natural extensions of the similar results of the
Sawada-Kotera equation and may be applied to produce the solutions of the SSK
equation. Also, we present two semi-discrete systems and show that the
continuum limit of one of them goes to the SKK equation.
| nlin.SI math-ph math.MP | in this paper we construct a darboux transformation and the related backlund transformation for the supersymmetric sawadakotera ssk equation the associated nonlinear superposition formula is also worked out we demonstrate that these are natural extensions of the similar results of the sawadakotera equation and may be applied to produce the solutions of the ssk equation also we present two semidiscrete systems and show that the continuum limit of one of them goes to the skk equation | [['in', 'this', 'paper', 'we', 'construct', 'a', 'darboux', 'transformation', 'and', 'the', 'related', 'backlund', 'transformation', 'for', 'the', 'supersymmetric', 'sawadakotera', 'ssk', 'equation', 'the', 'associated', 'nonlinear', 'superposition', 'formula', 'is', 'also', 'worked', 'out', 'we', 'demonstrate', 'that', 'these', 'are', 'natural', 'extensions', 'of', 'the', 'similar', 'results', 'of', 'the', 'sawadakotera', 'equation', 'and', 'may', 'be', 'applied', 'to', 'produce', 'the', 'solutions', 'of', 'the', 'ssk', 'equation', 'also', 'we', 'present', 'two', 'semidiscrete', 'systems', 'and', 'show', 'that', 'the', 'continuum', 'limit', 'of', 'one', 'of', 'them', 'goes', 'to', 'the', 'skk', 'equation']] | [-0.10257042992222858, 0.04952005440932944, -0.10977623383759667, 0.1063185065990853, -0.0913292029382367, -0.15103486367182709, -0.020680629355698137, 0.31246152314308445, -0.3080670117694688, -0.22146516168293984, 0.11380543526856375, -0.31072895459242555, -0.2155941255439661, 0.19806510811720632, -0.05299740117410884, 0.08086663973517716, 0.050628930363975665, 0.01777880974604111, -0.1266967198737946, -0.23467401634460608, 0.32900922804286603, -0.04794855467288902, 0.23599468040476113, 0.01308310345599526, 0.1591750099875131, -0.0488163007386519, -0.0196136437405489, -0.04205966876256034, -0.13357728023459603, 0.0843298373756146, 0.22958278285927677, 0.08885989970478572, 0.17819729164291762, -0.39818612645429213, -0.19478991851945848, 0.10541178583853732, 0.16525609132613203, 0.14810226055009193, -0.03256848429966914, -0.3007896409468039, 0.0504318096638216, -0.16268445584481875, -0.18878258496375852, -0.11076083687428188, -0.0035729100728579062, 0.07543287037773744, -0.2237257294258789, 0.0956561923585947, 0.1059415339558128, -0.05318984742840066, -0.12465591588376188, -0.0800593581179304, -0.04491382939618473, 0.04132319388200382, 0.04760729474027788, -0.014099874650128186, 0.0036714067376267755, -0.08310376243408475, -0.09898377597135932, 0.4141296892377891, -0.09213793553460978, -0.27037513139657676, 0.15973236566519758, -0.07407619063374832, -0.1732983506885112, 0.09434506602242197, 0.13758742211288527, 0.14242243122211412, -0.17751812320937843, 0.09045214811445285, -0.07724829756434222, 0.13759451062957706, 0.1075265604916862, -0.015631386740623338, 0.1262525077051434, 0.09569112958974745, 0.028001962523711354, 0.1821819262986537, -0.01954562286042155, -0.13763982466863176, -0.36430441789132983, -0.2069373913231845, -0.0938644801192966, 0.05876613951496486, -0.06969949295441773, -0.12469587506922453, 0.3904747680123699, 0.19326500276771472, 0.13570288052831433, 0.06774043724075646, 0.18087140801914134, 0.2433868094690536, 0.037447027175834306, 0.03533439436853912, 0.21931569109131632, 0.17533939003356194, 0.10849414878573857, -0.24450325369712358, -0.09089759134957076, 0.14314320031553507] |
1,802.04923 | Beamforming with Multiple One-Bit Wireless Transceivers | Classical beamforming techniques rely on highly linear transmitters and
receivers to allow phase-coherent combining at the transmitter and receiver.
The transmitter uses beamforming to steer signal power towards the receiver,
and the receiver uses beamforming to gather and coherently combine the signals
from multiple receiver antennas. When the transmitters and receivers are
instead constrained for power and cost reasons to be non-linear one-bit
devices, the potential advantages and performance metrics associated with
beamforming are not as well understood. We define beamforming at the
transmitter as a codebook design problem to maximize the minimum distance
between codewords. We define beamforming at the receiver as the maximum
likelihood detector of the transmitted codeword. We show that beamforming with
one-bit transceivers is a constellation design problem, and that we can come
within a few dB SNR of the capacity attained by linear transceivers.
| cs.IT math.IT | classical beamforming techniques rely on highly linear transmitters and receivers to allow phasecoherent combining at the transmitter and receiver the transmitter uses beamforming to steer signal power towards the receiver and the receiver uses beamforming to gather and coherently combine the signals from multiple receiver antennas when the transmitters and receivers are instead constrained for power and cost reasons to be nonlinear onebit devices the potential advantages and performance metrics associated with beamforming are not as well understood we define beamforming at the transmitter as a codebook design problem to maximize the minimum distance between codewords we define beamforming at the receiver as the maximum likelihood detector of the transmitted codeword we show that beamforming with onebit transceivers is a constellation design problem and that we can come within a few db snr of the capacity attained by linear transceivers | [['classical', 'beamforming', 'techniques', 'rely', 'on', 'highly', 'linear', 'transmitters', 'and', 'receivers', 'to', 'allow', 'phasecoherent', 'combining', 'at', 'the', 'transmitter', 'and', 'receiver', 'the', 'transmitter', 'uses', 'beamforming', 'to', 'steer', 'signal', 'power', 'towards', 'the', 'receiver', 'and', 'the', 'receiver', 'uses', 'beamforming', 'to', 'gather', 'and', 'coherently', 'combine', 'the', 'signals', 'from', 'multiple', 'receiver', 'antennas', 'when', 'the', 'transmitters', 'and', 'receivers', 'are', 'instead', 'constrained', 'for', 'power', 'and', 'cost', 'reasons', 'to', 'be', 'nonlinear', 'onebit', 'devices', 'the', 'potential', 'advantages', 'and', 'performance', 'metrics', 'associated', 'with', 'beamforming', 'are', 'not', 'as', 'well', 'understood', 'we', 'define', 'beamforming', 'at', 'the', 'transmitter', 'as', 'a', 'codebook', 'design', 'problem', 'to', 'maximize', 'the', 'minimum', 'distance', 'between', 'codewords', 'we', 'define', 'beamforming', 'at', 'the', 'receiver', 'as', 'the', 'maximum', 'likelihood', 'detector', 'of', 'the', 'transmitted', 'codeword', 'we', 'show', 'that', 'beamforming', 'with', 'onebit', 'transceivers', 'is', 'a', 'constellation', 'design', 'problem', 'and', 'that', 'we', 'can', 'come', 'within', 'a', 'few', 'db', 'snr', 'of', 'the', 'capacity', 'attained', 'by', 'linear', 'transceivers']] | [-0.25160466498096606, 0.013292354907441352, -0.030820508731994777, -0.029847730191457752, -0.15526129328645766, -0.3510860726935789, 0.11169905333247568, 0.4285713256253595, -0.3109454741967576, -0.24139395829822335, 0.11794081619425145, -0.3028887226479128, -0.1732234989126612, 0.10252957665195156, -0.1261251141329272, 0.06711186029549156, 0.0076613470113703185, 0.03432453069004363, -0.05127449617929025, -0.24123403825464526, 0.25169875483843496, 0.17798572863851275, 0.3307897781287985, -0.0760982678970322, 0.20285008468199522, 0.019767587525503977, 0.030617946023786705, -0.08717454038227775, -0.06118353882141361, 0.02486423643006544, 0.41632947247209295, 0.26331713858193584, 0.22320092916821263, -0.41841475030939496, -0.21957060459202954, 0.09235373357244368, 0.14447891006212948, 0.10126901531725058, -0.03101413828470478, -0.24949045154665198, 0.1443403024376104, -0.1701522690177496, 0.03551617326458134, 0.0729273777970645, -0.17839355833296264, 0.10689113309739956, -0.35890684700238384, -0.007318675376674426, -0.052589609426546044, 0.029321371441307878, -0.02867308345292778, -0.19568827415350826, 0.03193263942542087, 0.1666653882389905, -0.004194973264488259, -0.006864882606480803, 0.08734594324071493, -0.04152776082836291, -0.12294565109353113, 0.3416148393398284, 0.019814598669264733, -0.2547573550670807, 0.10083157042120416, -0.12663570273268435, -0.021308060549199582, 0.1768438482790121, 0.30217354465276003, 0.022142180895545087, -0.16051254191853723, -0.009891031878734274, 0.07084434039425105, 0.22047268470549689, 0.10802462281426414, 0.17668929287631596, 0.18698718473481546, 0.1461210754849682, 0.22116428567761823, 0.1556492185477899, -0.1668093879229023, -0.04281430383811572, -0.2203688006764943, -0.09527895032827344, -0.33104769148464713, 0.009165432178685607, -0.08015253360499627, -0.001917052678098636, 0.3281462970206381, 0.1343673839512381, 0.10265396744278925, 0.17346826686324285, 0.4491389672238646, 0.0964199043228291, 0.06167821599935581, 0.14942049135653568, 0.25584056178257536, 0.17540835090736176, 0.11688062839010464, -0.21082179547769817, 0.0015391680877655744, -0.0537657425605825] |
1,802.04924 | Exploring Hidden Dimensions in Parallelizing Convolutional Neural
Networks | The past few years have witnessed growth in the computational requirements
for training deep convolutional neural networks. Current approaches parallelize
training onto multiple devices by applying a single parallelization strategy
(e.g., data or model parallelism) to all layers in a network. Although easy to
reason about, these approaches result in suboptimal runtime performance in
large-scale distributed training, since different layers in a network may
prefer different parallelization strategies. In this paper, we propose
layer-wise parallelism that allows each layer in a network to use an individual
parallelization strategy. We jointly optimize how each layer is parallelized by
solving a graph search problem. Our evaluation shows that layer-wise
parallelism outperforms state-of-the-art approaches by increasing training
throughput, reducing communication costs, achieving better scalability to
multiple GPUs, while maintaining original network accuracy.
| cs.LG cs.DC cs.NE | the past few years have witnessed growth in the computational requirements for training deep convolutional neural networks current approaches parallelize training onto multiple devices by applying a single parallelization strategy eg data or model parallelism to all layers in a network although easy to reason about these approaches result in suboptimal runtime performance in largescale distributed training since different layers in a network may prefer different parallelization strategies in this paper we propose layerwise parallelism that allows each layer in a network to use an individual parallelization strategy we jointly optimize how each layer is parallelized by solving a graph search problem our evaluation shows that layerwise parallelism outperforms stateoftheart approaches by increasing training throughput reducing communication costs achieving better scalability to multiple gpus while maintaining original network accuracy | [['the', 'past', 'few', 'years', 'have', 'witnessed', 'growth', 'in', 'the', 'computational', 'requirements', 'for', 'training', 'deep', 'convolutional', 'neural', 'networks', 'current', 'approaches', 'parallelize', 'training', 'onto', 'multiple', 'devices', 'by', 'applying', 'a', 'single', 'parallelization', 'strategy', 'eg', 'data', 'or', 'model', 'parallelism', 'to', 'all', 'layers', 'in', 'a', 'network', 'although', 'easy', 'to', 'reason', 'about', 'these', 'approaches', 'result', 'in', 'suboptimal', 'runtime', 'performance', 'in', 'largescale', 'distributed', 'training', 'since', 'different', 'layers', 'in', 'a', 'network', 'may', 'prefer', 'different', 'parallelization', 'strategies', 'in', 'this', 'paper', 'we', 'propose', 'layerwise', 'parallelism', 'that', 'allows', 'each', 'layer', 'in', 'a', 'network', 'to', 'use', 'an', 'individual', 'parallelization', 'strategy', 'we', 'jointly', 'optimize', 'how', 'each', 'layer', 'is', 'parallelized', 'by', 'solving', 'a', 'graph', 'search', 'problem', 'our', 'evaluation', 'shows', 'that', 'layerwise', 'parallelism', 'outperforms', 'stateoftheart', 'approaches', 'by', 'increasing', 'training', 'throughput', 'reducing', 'communication', 'costs', 'achieving', 'better', 'scalability', 'to', 'multiple', 'gpus', 'while', 'maintaining', 'original', 'network', 'accuracy']] | [-0.13152811344164286, -0.02315733425196011, -0.022312051930659733, 0.010693963876990386, -0.12682337481745107, -0.23575243318106892, 0.10418698764243704, 0.5059008783617511, -0.33293007422498494, -0.37833796457438046, 0.03259082539674876, -0.19939403077048276, -0.16034104885324085, 0.15969464634075875, -0.14420713469667665, 0.11251407918022123, 0.20157854163355027, -0.042795683040695136, -0.10193009638812306, -0.3847566824092786, 0.24980714717832964, 0.10158798950629547, 0.39168590476895254, -0.0043920917732482275, 0.1091176661231389, -0.04387123161176032, 0.008487937074017444, -0.011546121690018002, -0.010534048431607995, 0.1720232397648181, 0.3269622759323192, 0.22219774728756428, 0.41672033117723095, -0.510519942536304, -0.22634359098277812, 0.06297474124743206, 0.2113469217441635, 0.10134609395731962, -0.014638765148327564, -0.23662570772273828, 0.1155794026969044, -0.17905893769519504, 0.039580630833511384, -0.14623971492893156, -0.07694948833190308, -0.02424831566122873, -0.27216449871572646, -0.02063849729724055, 0.04340766517755886, 0.0394473962716816, 0.006688838017731725, -0.12415966112527621, 0.019293757969542413, 0.13318019378172277, 0.015676944401988746, 0.06477824759727359, 0.13498046393316715, -0.17746036564534198, -0.19653435368740627, 0.31816237513697887, -0.021593077057727086, -0.21590475263362824, 0.1875244619328413, 0.034758947879913474, -0.17525993501984102, 0.11068737015034281, 0.2647455993880944, 0.07847983497483728, -0.18249663616018008, 0.022799946421887294, 0.024878120591300866, 0.19651781753167624, 0.09602647884063019, 0.015588691864206, 0.13677763673765658, 0.33403371812282995, 0.09164504761186738, 0.1495123425951632, -0.08444529190630645, -0.10749846850630156, -0.12904451150075325, -0.12905624499652796, -0.19699108422925163, -0.04276342859199314, -0.15655124554944824, -0.11912009972616915, 0.3826817594641863, 0.2434654573182568, 0.18761612157303076, 0.16529517969506424, 0.41561620711356173, -0.002754094594432575, 0.19459252245859568, 0.19860778190556538, 0.16169044137174307, -0.011179842778604275, 0.1967977642662852, -0.160063582151233, 0.09930986376811368, 0.019459786131804765] |
1,802.04925 | Bias Correction Estimation for Continuous-Time Asset Return Model with
Jumps | In this paper, local linear estimators are adapted for the unknown
infinitesimal coefficients associated with continuous-time asset return model
with jumps, which can correct the bias automatically due to their simple bias
representation. The integrated diffusion models with jumps, especially infinite
activity jumps are mainly investigated. In addition, under mild conditions, the
weak consistency and asymptotic normality is provided through the conditional
Lindeberg theorem. Furthermore, our method presents advantages in bias
correction through simulation whether jumps belong to the finite activity case
or infinite activity case. Finally, the estimators are illustrated empirically
through the returns for stock index under five-minute high sampling frequency
for real application.
| math.ST stat.TH | in this paper local linear estimators are adapted for the unknown infinitesimal coefficients associated with continuoustime asset return model with jumps which can correct the bias automatically due to their simple bias representation the integrated diffusion models with jumps especially infinite activity jumps are mainly investigated in addition under mild conditions the weak consistency and asymptotic normality is provided through the conditional lindeberg theorem furthermore our method presents advantages in bias correction through simulation whether jumps belong to the finite activity case or infinite activity case finally the estimators are illustrated empirically through the returns for stock index under fiveminute high sampling frequency for real application | [['in', 'this', 'paper', 'local', 'linear', 'estimators', 'are', 'adapted', 'for', 'the', 'unknown', 'infinitesimal', 'coefficients', 'associated', 'with', 'continuoustime', 'asset', 'return', 'model', 'with', 'jumps', 'which', 'can', 'correct', 'the', 'bias', 'automatically', 'due', 'to', 'their', 'simple', 'bias', 'representation', 'the', 'integrated', 'diffusion', 'models', 'with', 'jumps', 'especially', 'infinite', 'activity', 'jumps', 'are', 'mainly', 'investigated', 'in', 'addition', 'under', 'mild', 'conditions', 'the', 'weak', 'consistency', 'and', 'asymptotic', 'normality', 'is', 'provided', 'through', 'the', 'conditional', 'lindeberg', 'theorem', 'furthermore', 'our', 'method', 'presents', 'advantages', 'in', 'bias', 'correction', 'through', 'simulation', 'whether', 'jumps', 'belong', 'to', 'the', 'finite', 'activity', 'case', 'or', 'infinite', 'activity', 'case', 'finally', 'the', 'estimators', 'are', 'illustrated', 'empirically', 'through', 'the', 'returns', 'for', 'stock', 'index', 'under', 'fiveminute', 'high', 'sampling', 'frequency', 'for', 'real', 'application']] | [-0.07174917433481172, 0.08917467290851867, -0.09647668884047922, 0.13279430753384489, -0.08715905292170509, -0.16228972950660325, 0.06136049158576841, 0.43180704640470585, -0.28662906741758565, -0.2595267462206978, 0.18288353285071318, -0.247935747200588, -0.1495658566863364, 0.20160104696559808, -0.11677064263604511, 0.05741612514357944, 0.05526049232619973, 0.024903225227048235, -0.024993174762096046, -0.27924649845720884, 0.2590491884511034, 0.055957376271626856, 0.3286907458765749, -0.022102109544233965, 0.13689541320573487, 0.005983534479900351, -0.05206213308111677, 0.028000986243088572, -0.09938949082081891, 0.06180107402000225, 0.24686602873108948, 0.0322347599925157, 0.3187685121061667, -0.3874584171956159, -0.17082676721982798, 0.14096001554104798, 0.1093759379494619, 0.05592269586060934, -0.06470858243970587, -0.28434342156023773, 0.0972065569982284, -0.18000257271781284, -0.13078941338731828, -0.07480946252614541, -0.0019392046752333078, 0.07279093280645474, -0.3523084461812, 0.15824419917222463, 0.058752932638492225, 0.10904391065793428, -0.042564302111782554, -0.08864067336061161, -0.02480644737765685, 0.11235468525588864, 0.1068902155263613, -0.09321882238914699, 0.14151374613514767, -0.06316213452318718, -0.12371920222330857, 0.26972111996333553, -0.09190187529706888, -0.24600727391925659, 0.1708138082483959, -0.15102458511531916, -0.14638906961333287, 0.11857064556822462, 0.16407865103123323, 0.06285786393448878, -0.16407807169038416, 0.12156895665344256, 0.013052428299385422, 0.10479130225809608, 0.03908524299831182, -0.017571551694517146, 0.15337572564186422, 0.11431021838150215, 0.0859547977177602, 0.15682015761311324, -0.08177837988038829, -0.12665955842701052, -0.3320886124593188, -0.07273809060032638, -0.15908501055618784, 0.016857478542765165, -0.16539176049593393, -0.20927203482210213, 0.38474727061770436, 0.21562714244344466, 0.15477910286852353, 0.126199661052285, 0.28156129211486086, 0.18998009222230153, -0.01836286146382643, 0.07018268919760748, 0.15616712082212544, 0.14988133575732135, 0.0735839663714803, -0.19602178544561677, 0.19497051990134115, 0.051118738356239674] |
1,802.04926 | A three-player coherent state embezzlement game | We introduce a three-player nonlocal game, with a finite number of classical
questions and answers, such that the optimal success probability of $1$ in the
game can only be achieved in the limit of strategies using arbitrarily
high-dimensional entangled states. Precisely, there exists a constant $0 <c\leq
1$ such that to succeed with probability $1-\varepsilon$ in the game it is
necessary to use an entangled state of at least $\Omega(\varepsilon^{-c})$
qubits, and it is sufficient to use a state of at most $O(\varepsilon^{-1})$
qubits.
The game is based on the coherent state exchange game of Leung et al. (CJTCS
2013). In our game, the task of the quantum verifier is delegated to a third
player by a classical referee. Our results complement those of Slofstra
(arXiv:1703.08618) and Dykema et al. (arXiv:1709.05032), who obtained
two-player games with similar (though quantitatively weaker) properties based
on the representation theory of finitely presented groups and $C^*$-algebras
respectively.
| quant-ph | we introduce a threeplayer nonlocal game with a finite number of classical questions and answers such that the optimal success probability of 1 in the game can only be achieved in the limit of strategies using arbitrarily highdimensional entangled states precisely there exists a constant 0 cleq 1 such that to succeed with probability 1varepsilon in the game it is necessary to use an entangled state of at least omegavarepsilonc qubits and it is sufficient to use a state of at most ovarepsilon1 qubits the game is based on the coherent state exchange game of leung et al cjtcs 2013 in our game the task of the quantum verifier is delegated to a third player by a classical referee our results complement those of slofstra arxiv170308618 and dykema et al arxiv170905032 who obtained twoplayer games with similar though quantitatively weaker properties based on the representation theory of finitely presented groups and calgebras respectively | [['we', 'introduce', 'a', 'threeplayer', 'nonlocal', 'game', 'with', 'a', 'finite', 'number', 'of', 'classical', 'questions', 'and', 'answers', 'such', 'that', 'the', 'optimal', 'success', 'probability', 'of', '1', 'in', 'the', 'game', 'can', 'only', 'be', 'achieved', 'in', 'the', 'limit', 'of', 'strategies', 'using', 'arbitrarily', 'highdimensional', 'entangled', 'states', 'precisely', 'there', 'exists', 'a', 'constant', '0', 'cleq', '1', 'such', 'that', 'to', 'succeed', 'with', 'probability', '1varepsilon', 'in', 'the', 'game', 'it', 'is', 'necessary', 'to', 'use', 'an', 'entangled', 'state', 'of', 'at', 'least', 'omegavarepsilonc', 'qubits', 'and', 'it', 'is', 'sufficient', 'to', 'use', 'a', 'state', 'of', 'at', 'most', 'ovarepsilon1', 'qubits', 'the', 'game', 'is', 'based', 'on', 'the', 'coherent', 'state', 'exchange', 'game', 'of', 'leung', 'et', 'al', 'cjtcs', '2013', 'in', 'our', 'game', 'the', 'task', 'of', 'the', 'quantum', 'verifier', 'is', 'delegated', 'to', 'a', 'third', 'player', 'by', 'a', 'classical', 'referee', 'our', 'results', 'complement', 'those', 'of', 'slofstra', 'arxiv170308618', 'and', 'dykema', 'et', 'al', 'arxiv170905032', 'who', 'obtained', 'twoplayer', 'games', 'with', 'similar', 'though', 'quantitatively', 'weaker', 'properties', 'based', 'on', 'the', 'representation', 'theory', 'of', 'finitely', 'presented', 'groups', 'and', 'calgebras', 'respectively']] | [-0.09744766054985424, 0.09940763634561639, -0.10545432794218262, 0.0633347867274036, -0.02615723446632425, -0.2132526827417314, 0.10730084790770586, 0.35535467009991406, -0.23086348082947855, -0.31225377212278543, 0.06987886008418476, -0.2923903062287718, -0.1369301587746789, 0.15333270182833075, -0.15099764425035878, 0.05646643827902153, 0.07425056791398674, 0.06332479051779956, 0.04388336416023473, -0.3375026259183263, 0.3299943301298966, 0.017748987080994993, 0.2124449794991718, 0.020855586379766464, 0.12225513422551254, 0.02617730076269557, 0.04318129098818948, 0.03914968809733788, -0.13995042826407977, 0.08339064359703723, 0.2968293106819813, 0.1600159806245938, 0.35184168881426253, -0.39464695181697607, -0.13596486774428437, 0.11773240888258442, 0.06380564468621742, 0.1274929116014391, -0.02373302018425117, -0.31815701150024933, 0.09690819834048549, -0.18065987015763918, -0.057728447237362465, -0.039286241463851186, 0.052499821080515784, -0.015739816325561453, -0.2988662022414307, 0.017149431745832167, 0.07158654071856291, 0.04660231879601876, 0.02110791686611871, -0.10951283173635602, -0.0015538728260435163, 0.14917685077525675, -0.053267344975223146, 0.050331785570209224, 0.07352752668472627, -0.1317692591257704, -0.2243801760673523, 0.33052061509341, -0.05902649000597497, -0.1603763980201135, 0.1769900824711658, -0.10120416743836055, -0.14279215960996225, 0.0672486029130717, 0.07831832953728736, 0.1567948769343396, -0.06892149762560924, 0.0925252964394167, -0.12014502337512871, 0.20656469842108588, 0.07060690994064013, 0.04290963953360915, 0.09993830747281511, 0.11373372073595722, 0.12191786490691205, 0.10782578629208729, 0.03131249992487331, -0.13377214810267712, -0.2827083660158678, -0.14955006196241205, -0.2152523045785104, 0.08156224450096489, -0.05188080017571337, -0.10993413753341884, 0.346222324706614, 0.1286627861042507, 0.13934779616693654, 0.06958406778052449, 0.24775609347581243, 0.08301486708185014, -0.020725408725750943, 0.121278599180514, 0.21148880359871933, 0.1549653975929444, 0.08713599590584636, -0.18995421993546188, 0.08807953273023789, 0.08081622142965594] |
1,802.04927 | Geometry-Based Data Generation | Many generative models attempt to replicate the density of their input data.
However, this approach is often undesirable, since data density is highly
affected by sampling biases, noise, and artifacts. We propose a method called
SUGAR (Synthesis Using Geometrically Aligned Random-walks) that uses a
diffusion process to learn a manifold geometry from the data. Then, it
generates new points evenly along the manifold by pulling randomly generated
points into its intrinsic structure using a diffusion kernel. SUGAR equalizes
the density along the manifold by selectively generating points in sparse areas
of the manifold. We demonstrate how the approach corrects sampling biases and
artifacts, while also revealing intrinsic patterns (e.g. progression) and
relations in the data. The method is applicable for correcting missing data,
finding hypothetical data points, and learning relationships between data
features.
| cs.LG | many generative models attempt to replicate the density of their input data however this approach is often undesirable since data density is highly affected by sampling biases noise and artifacts we propose a method called sugar synthesis using geometrically aligned randomwalks that uses a diffusion process to learn a manifold geometry from the data then it generates new points evenly along the manifold by pulling randomly generated points into its intrinsic structure using a diffusion kernel sugar equalizes the density along the manifold by selectively generating points in sparse areas of the manifold we demonstrate how the approach corrects sampling biases and artifacts while also revealing intrinsic patterns eg progression and relations in the data the method is applicable for correcting missing data finding hypothetical data points and learning relationships between data features | [['many', 'generative', 'models', 'attempt', 'to', 'replicate', 'the', 'density', 'of', 'their', 'input', 'data', 'however', 'this', 'approach', 'is', 'often', 'undesirable', 'since', 'data', 'density', 'is', 'highly', 'affected', 'by', 'sampling', 'biases', 'noise', 'and', 'artifacts', 'we', 'propose', 'a', 'method', 'called', 'sugar', 'synthesis', 'using', 'geometrically', 'aligned', 'randomwalks', 'that', 'uses', 'a', 'diffusion', 'process', 'to', 'learn', 'a', 'manifold', 'geometry', 'from', 'the', 'data', 'then', 'it', 'generates', 'new', 'points', 'evenly', 'along', 'the', 'manifold', 'by', 'pulling', 'randomly', 'generated', 'points', 'into', 'its', 'intrinsic', 'structure', 'using', 'a', 'diffusion', 'kernel', 'sugar', 'equalizes', 'the', 'density', 'along', 'the', 'manifold', 'by', 'selectively', 'generating', 'points', 'in', 'sparse', 'areas', 'of', 'the', 'manifold', 'we', 'demonstrate', 'how', 'the', 'approach', 'corrects', 'sampling', 'biases', 'and', 'artifacts', 'while', 'also', 'revealing', 'intrinsic', 'patterns', 'eg', 'progression', 'and', 'relations', 'in', 'the', 'data', 'the', 'method', 'is', 'applicable', 'for', 'correcting', 'missing', 'data', 'finding', 'hypothetical', 'data', 'points', 'and', 'learning', 'relationships', 'between', 'data', 'features']] | [-0.06423736393409676, 0.08300893692145671, -0.11724084337536049, 0.1091871860475417, -0.10324900495098498, -0.13464156688450085, 0.07561827454316829, 0.42527807912880317, -0.3312035326572849, -0.32402868831138076, 0.10909739400512238, -0.30408514984988405, -0.19759193877164358, 0.16094373135400333, -0.12337870103012967, 0.03528276988072321, 0.10169960262912109, -0.027699843603116284, -0.033732117351522685, -0.19979917250146487, 0.3535102189621797, 0.04902797101303599, 0.3318839784364979, -0.04586314590983933, 0.15356911662081957, -0.00800299970433116, -0.06466935845564603, 0.010136904349004416, -0.06347012086588709, 0.16183968831231832, 0.26189542407489097, 0.16528043705493883, 0.2691854702053185, -0.42120746567957384, -0.2316659220067182, 0.08877649407525708, 0.1324035018927346, 0.13520212081274985, -0.0658823360121788, -0.29461008376841036, 0.055569171571151765, -0.07112965774127192, -0.0860231323237706, -0.13248058045072889, -0.016110510322006236, 0.008760918207077967, -0.27103205630774574, 0.06614381774075687, 0.057986033982352206, 0.059834031234110206, -0.03646208158487636, -0.0994593428966022, -0.050511316893468224, 0.13289634623613797, 0.03928482758307556, 0.043896293594971075, 0.16829663122605001, -0.10984597112668357, -0.08277172381163371, 0.3473935007516827, -0.029427185976751764, -0.24513933714479208, 0.13264769967995527, -0.0779101993661913, -0.12482112237861506, 0.17699389576323724, 0.1811786348742426, 0.08026211677098129, -0.16623935924494582, 0.05080925068061771, 0.019759629621114442, 0.1613853703694124, 0.03200978646404985, -0.046924553233850044, 0.18678035030379556, 0.1624803501916559, 0.04632967277701342, 0.1194354605447865, -0.14938313324578611, -0.0546272182662068, -0.22751524739135476, -0.09558699277922847, -0.2288982062285444, -0.004499680865065832, -0.10758625250966183, -0.17305936457913568, 0.3888309047648445, 0.19709511143960676, 0.28768367924608457, 0.03529850684019476, 0.32875276838002127, 0.02979063116630217, 0.09745373685592155, 0.10305195789840213, 0.09308850884619624, 0.0840005106310171, 0.0632529668737539, -0.16883505945079924, 0.11382811449650199, 0.01321964982119774] |
1,802.04928 | A Posteriori Error Estimate for Computing $\mathrm{tr}(f(A))$ by Using
the Lanczos Method | An outstanding problem when computing a function of a matrix, $f(A)$, by
using a Krylov method is to accurately estimate errors when convergence is
slow. Apart from the case of the exponential function which has been
extensively studied in the past, there are no well-established solutions to the
problem. Often the quantity of interest in applications is not the matrix
$f(A)$ itself, but rather, matrix-vector products or bilinear forms. When the
computation related to $f(A)$ is a building block of a larger problem (e.g.,
approximately computing its trace), a consequence of the lack of reliable error
estimates is that the accuracy of the computed result is unknown. In this
paper, we consider the problem of computing $\mathrm{tr}(f(A))$ for a symmetric
positive-definite matrix $A$ by using the Lanczos method and make two
contributions: (i) we propose an error estimate for the bilinear form
associated with $f(A)$, and (ii) an error estimate for the trace of $f(A)$. We
demonstrate the practical usefulness of these estimates for large matrices and
in particular, show that the trace error estimate is indicative of the number
of accurate digits. As an application, we compute the log-determinant of a
covariance matrix in Gaussian process analysis and underline the importance of
error tolerance as a stopping criterion, as a means of bounding the number of
Lanczos steps to achieve a desired accuracy.
| math.NA | an outstanding problem when computing a function of a matrix fa by using a krylov method is to accurately estimate errors when convergence is slow apart from the case of the exponential function which has been extensively studied in the past there are no wellestablished solutions to the problem often the quantity of interest in applications is not the matrix fa itself but rather matrixvector products or bilinear forms when the computation related to fa is a building block of a larger problem eg approximately computing its trace a consequence of the lack of reliable error estimates is that the accuracy of the computed result is unknown in this paper we consider the problem of computing mathrmtrfa for a symmetric positivedefinite matrix a by using the lanczos method and make two contributions i we propose an error estimate for the bilinear form associated with fa and ii an error estimate for the trace of fa we demonstrate the practical usefulness of these estimates for large matrices and in particular show that the trace error estimate is indicative of the number of accurate digits as an application we compute the logdeterminant of a covariance matrix in gaussian process analysis and underline the importance of error tolerance as a stopping criterion as a means of bounding the number of lanczos steps to achieve a desired accuracy | [['an', 'outstanding', 'problem', 'when', 'computing', 'a', 'function', 'of', 'a', 'matrix', 'fa', 'by', 'using', 'a', 'krylov', 'method', 'is', 'to', 'accurately', 'estimate', 'errors', 'when', 'convergence', 'is', 'slow', 'apart', 'from', 'the', 'case', 'of', 'the', 'exponential', 'function', 'which', 'has', 'been', 'extensively', 'studied', 'in', 'the', 'past', 'there', 'are', 'no', 'wellestablished', 'solutions', 'to', 'the', 'problem', 'often', 'the', 'quantity', 'of', 'interest', 'in', 'applications', 'is', 'not', 'the', 'matrix', 'fa', 'itself', 'but', 'rather', 'matrixvector', 'products', 'or', 'bilinear', 'forms', 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1,802.04929 | Context-Specific Validation of Data-Driven Models | With an increasing use of data-driven models to control robotic systems, it
has become important to develop a methodology for validating such models before
they can be deployed to design a controller for the actual system.
Specifically, it must be ensured that the controller designed for a learned
model would perform as expected on the actual physical system. We propose a
context-specific validation framework to quantify the quality of a learned
model based on a distance measure between the closed-loop actual system and the
learned model. We then propose an active sampling scheme to compute a
probabilistic upper bound on this distance in a sample-efficient manner. The
proposed framework validates the learned model against only those behaviors of
the system that are relevant for the purpose for which we intend to use this
model, and does not require any a priori knowledge of the system dynamics.
Several simulations illustrate the practicality of the proposed framework for
validating the models of real-world systems, and consequently, for controller
synthesis.
| cs.SY cs.LG cs.RO | with an increasing use of datadriven models to control robotic systems it has become important to develop a methodology for validating such models before they can be deployed to design a controller for the actual system specifically it must be ensured that the controller designed for a learned model would perform as expected on the actual physical system we propose a contextspecific validation framework to quantify the quality of a learned model based on a distance measure between the closedloop actual system and the learned model we then propose an active sampling scheme to compute a probabilistic upper bound on this distance in a sampleefficient manner the proposed framework validates the learned model against only those behaviors of the system that are relevant for the purpose for which we intend to use this model and does not require any a priori knowledge of the system dynamics several simulations illustrate the practicality of the proposed framework for validating the models of realworld systems and consequently for controller synthesis | [['with', 'an', 'increasing', 'use', 'of', 'datadriven', 'models', 'to', 'control', 'robotic', 'systems', 'it', 'has', 'become', 'important', 'to', 'develop', 'a', 'methodology', 'for', 'validating', 'such', 'models', 'before', 'they', 'can', 'be', 'deployed', 'to', 'design', 'a', 'controller', 'for', 'the', 'actual', 'system', 'specifically', 'it', 'must', 'be', 'ensured', 'that', 'the', 'controller', 'designed', 'for', 'a', 'learned', 'model', 'would', 'perform', 'as', 'expected', 'on', 'the', 'actual', 'physical', 'system', 'we', 'propose', 'a', 'contextspecific', 'validation', 'framework', 'to', 'quantify', 'the', 'quality', 'of', 'a', 'learned', 'model', 'based', 'on', 'a', 'distance', 'measure', 'between', 'the', 'closedloop', 'actual', 'system', 'and', 'the', 'learned', 'model', 'we', 'then', 'propose', 'an', 'active', 'sampling', 'scheme', 'to', 'compute', 'a', 'probabilistic', 'upper', 'bound', 'on', 'this', 'distance', 'in', 'a', 'sampleefficient', 'manner', 'the', 'proposed', 'framework', 'validates', 'the', 'learned', 'model', 'against', 'only', 'those', 'behaviors', 'of', 'the', 'system', 'that', 'are', 'relevant', 'for', 'the', 'purpose', 'for', 'which', 'we', 'intend', 'to', 'use', 'this', 'model', 'and', 'does', 'not', 'require', 'any', 'a', 'priori', 'knowledge', 'of', 'the', 'system', 'dynamics', 'several', 'simulations', 'illustrate', 'the', 'practicality', 'of', 'the', 'proposed', 'framework', 'for', 'validating', 'the', 'models', 'of', 'realworld', 'systems', 'and', 'consequently', 'for', 'controller', 'synthesis']] | [-0.07755003011406227, 0.0032141316352024517, -0.1224526791989491, 0.07081643167275288, -0.06619514481437777, -0.177167382085707, 0.07173530508145065, 0.40401237496470443, -0.25211148550120477, -0.3502417900013977, 0.12582752860533242, -0.20924245944793174, -0.19392356419277762, 0.23730095916338442, -0.09345961534724889, 0.11658518881576058, 0.07260182337880046, 0.04935603524965351, -0.04982926493613774, -0.21575139811217875, 0.2970550974446187, 0.0910704639310876, 0.29623558161735625, 0.0008389475381749119, 0.13405490793630379, -0.011272998385878648, 0.016142708413033818, 0.0038241906716170427, -0.10316684496811031, 0.15397498316604163, 0.23198934128344764, 0.21025843872036226, 0.31400332413962345, -0.4235802684536951, -0.2401632419313321, 0.10860646941593129, 0.13532018951212843, 0.12460667999603502, -0.03617705197790136, -0.298561078479547, 0.1161935007991548, -0.2128500924055314, -0.09901670769169302, -0.13871767865298706, -0.03396199409526488, -0.017457857008599593, -0.31914461747893763, -0.03754122887353251, 0.060998413915338806, 0.029725817769513503, -0.08218867259593625, -0.07411827503851319, -0.003951226921562485, 0.18649579932101762, -0.014131201024747225, -0.0004171296970885314, 0.1469308909608754, -0.10094325130939216, -0.1219690095801966, 0.3738445523055549, -0.027925723080361585, -0.24698914004166325, 0.20343933133045936, -0.04399046899533334, -0.11040550759508314, 0.06409629634832462, 0.2693197351049431, 0.11026370154504112, -0.1941950701366731, 0.013829907672521574, -0.04309261018901082, 0.22007128633112608, -0.07348284968398065, -0.014580863336948845, 0.19880675369717493, 0.23804724609066627, 0.0671331416735353, 0.12524605048457885, -0.0632877279850961, -0.1025456648771679, -0.29541377216008696, -0.12691877314627092, -0.17056020911403758, -0.034813286286195275, -0.06266936655883039, -0.14113742275676594, 0.37244732753159937, 0.26338908508208075, 0.18390560839157885, 0.10577005797977598, 0.3513356113393685, 0.08897765509123373, 0.073406099849994, 0.0732224301468514, 0.24696046402182953, 0.039290683054013884, 0.10651814827101562, -0.20252309647015113, 0.12944146416367527, 0.04302014087181828] |
1,802.0493 | Gallai-Ramsey numbers for books | Given a graph $G$ and a positive integer $k$, the \emph{Gallai-Ramsey number}
is defined to be the minimum number of vertices $n$ such that any $k$-edge
coloring of $K_n$ contains either a rainbow (all different colored) triangle or
a monochromatic copy of $G$. In this paper, we obtain general upper and lower
bounds on the Gallai-Ramsey numbers for books $B_{m} = K_{2} +
\overline{K_{m}}$ and prove sharp results for $m \leq 5$.
| math.CO | given a graph g and a positive integer k the emphgallairamsey number is defined to be the minimum number of vertices n such that any kedge coloring of k_n contains either a rainbow all different colored triangle or a monochromatic copy of g in this paper we obtain general upper and lower bounds on the gallairamsey numbers for books b_m k_2 overlinek_m and prove sharp results for m leq 5 | [['given', 'a', 'graph', 'g', 'and', 'a', 'positive', 'integer', 'k', 'the', 'emphgallairamsey', 'number', 'is', 'defined', 'to', 'be', 'the', 'minimum', 'number', 'of', 'vertices', 'n', 'such', 'that', 'any', 'kedge', 'coloring', 'of', 'k_n', 'contains', 'either', 'a', 'rainbow', 'all', 'different', 'colored', 'triangle', 'or', 'a', 'monochromatic', 'copy', 'of', 'g', 'in', 'this', 'paper', 'we', 'obtain', 'general', 'upper', 'and', 'lower', 'bounds', 'on', 'the', 'gallairamsey', 'numbers', 'for', 'books', 'b_m', 'k_2', 'overlinek_m', 'and', 'prove', 'sharp', 'results', 'for', 'm', 'leq', '5']] | [-0.23525729760919037, 0.18984548864545356, -0.01868195001008934, 0.042182229459285736, -0.11069405740936814, -0.1616083957740794, 0.08773051105284442, 0.33571472944543307, -0.21050440522536729, -0.3802017309041559, 0.04968166769569929, -0.37573611072224117, -0.12143397787113444, 0.13149849500210173, -0.08281835751014127, -0.0238982104304908, 0.09604926397214117, 0.1589221502384306, 0.048023240441672395, -0.28398980359096365, 0.24431928075121148, -0.1333209173888832, 0.09698580513181894, 0.11456482154924584, 0.05322223664868785, 0.039528164222998464, 0.02629838171887441, 0.09718830976635218, -0.2555114009686066, 0.07190354815616772, 0.24423520225167705, 0.15480339222425676, 0.23964107723609693, -0.371946447380427, -0.12363934547950824, 0.2452129247341899, 0.10823424241127635, -0.01223899409228909, -0.017292672678814743, -0.17562204703732487, 0.18924129805595113, -0.07813002680684777, -0.06976947972577983, 0.044189449608919844, 0.1398786033463219, -0.008282262065947272, -0.3292666149960048, -0.042435191335507494, 0.12036254853550074, 0.07324985620199595, 0.0867272323820794, -0.2627023562967804, -0.048282966530625374, 0.09158905032192073, -0.07287970475811997, 0.10904685103678671, -0.01200513688482992, -0.11401505145350592, -0.11746000325766162, 0.3436581316687491, -0.09231178620425255, -0.15029801380402152, 0.059933937262689724, -0.1604547797418807, -0.18860876468428667, 0.1300151575140763, 0.10189744083048857, 0.19743102570266827, -0.04483861111077494, 0.1428015660334284, -0.17235289713826732, 0.13920847801626593, 0.19785402843431718, 0.038846842989332275, 0.1162935298314129, 0.04045478479288842, 0.17957140020081314, 0.16976812641318564, 0.009859869901117856, 0.1225907543566132, -0.3551105997268704, -0.14773579057899938, -0.2612641634861601, 0.12897965097394976, -0.20150621932449556, -0.20606407994046752, 0.3380650696547135, 0.05121336329350437, 0.22230392581094865, 0.12694471287608577, 0.24780497982072225, 0.09719383039687207, 0.010710084183222574, 0.16364741845704292, 0.06184741398454576, 0.17893840568275124, -0.0807688266268351, -0.1311544743636488, -0.005469570121984335, 0.15544010654253804] |
1,802.04931 | Energy Spatio-Temporal Pattern Prediction for Electric Vehicle Networks | Information about the spatio-temporal pattern of electricity energy carried
by EVs, instead of EVs themselves, is crucial for EVs to establish more
effective and intelligent interactions with the smart grid. In this paper, we
propose a framework for predicting the amount of the electricity energy stored
by a large number of EVs aggregated within different city-scale regions, based
on spatio-temporal pattern of the electricity energy. The spatial pattern is
modeled via using a neural network based spatial predictor, while the temporal
pattern is captured via using a linear-chain conditional random field (CRF)
based temporal predictor. Two predictors are fed with spatial and temporal
features respectively, which are extracted based on real trajectories data
recorded in Beijing. Furthermore, we combine both predictors to build the
spatio-temporal predictor, by using an optimal combination coefficient which
minimizes the normalized mean square error (NMSE) of the predictions. The
prediction performance is evaluated based on extensive experiments covering
both spatial and temporal predictions, and the improvement achieved by the
combined spatio-temporal predictor. The experiment results show that the NMSE
of the spatio-temporal predictor is maintained below 0.1 for all investigate
regions of Beijing. We further visualize the prediction and discuss the
potential benefits can be brought to smart grid scheduling and EV charging by
utilizing the proposed framework.
| cs.LG eess.SP | information about the spatiotemporal pattern of electricity energy carried by evs instead of evs themselves is crucial for evs to establish more effective and intelligent interactions with the smart grid in this paper we propose a framework for predicting the amount of the electricity energy stored by a large number of evs aggregated within different cityscale regions based on spatiotemporal pattern of the electricity energy the spatial pattern is modeled via using a neural network based spatial predictor while the temporal pattern is captured via using a linearchain conditional random field crf based temporal predictor two predictors are fed with spatial and temporal features respectively which are extracted based on real trajectories data recorded in beijing furthermore we combine both predictors to build the spatiotemporal predictor by using an optimal combination coefficient which minimizes the normalized mean square error nmse of the predictions the prediction performance is evaluated based on extensive experiments covering both spatial and temporal predictions and the improvement achieved by the combined spatiotemporal predictor the experiment results show that the nmse of the spatiotemporal predictor is maintained below 01 for all investigate regions of beijing we further visualize the prediction and discuss the potential benefits can be brought to smart grid scheduling and ev charging by utilizing the proposed framework | [['information', 'about', 'the', 'spatiotemporal', 'pattern', 'of', 'electricity', 'energy', 'carried', 'by', 'evs', 'instead', 'of', 'evs', 'themselves', 'is', 'crucial', 'for', 'evs', 'to', 'establish', 'more', 'effective', 'and', 'intelligent', 'interactions', 'with', 'the', 'smart', 'grid', 'in', 'this', 'paper', 'we', 'propose', 'a', 'framework', 'for', 'predicting', 'the', 'amount', 'of', 'the', 'electricity', 'energy', 'stored', 'by', 'a', 'large', 'number', 'of', 'evs', 'aggregated', 'within', 'different', 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1,802.04932 | Unambiguous Evidence of Filament Splitting-Induced Partial Eruptions | Coronal mass ejections are often considered to result from the full eruption
of a magnetic flux rope (MFR). However, it is recognized that, in some events,
the MFR may release only part of its flux, with the details of the implied
splitting not completely established due to limitations in observations. Here,
we investigate two partial eruption events including a confined and a
successful one. Both partial eruptions are a consequence of the vertical
splitting of a filament-hosting MFR involving internal reconnection. A loss of
equilibrium in the rising part of the magnetic flux is suggested by the
impulsive onset of both events and by the delayed onset of reconnection in the
confined event. The remaining part of the flux might be line-tied to the
photosphere in a bald patch separatrix surface, and we confirm the existence of
extended bald-patch sections for the successful eruption. The internal
reconnection is signified by brightenings in the body of one filament and
between the rising and remaining parts of both filaments. It evolves quickly
into the standard current sheet reconnection in the wake of the eruption. As a
result, regardless of being confined or successful, both eruptions produce hard
X-ray sources and flare loops below the erupting but above the surviving flux,
as well as a pair of flare ribbons enclosing the latter.
| astro-ph.SR | coronal mass ejections are often considered to result from the full eruption of a magnetic flux rope mfr however it is recognized that in some events the mfr may release only part of its flux with the details of the implied splitting not completely established due to limitations in observations here we investigate two partial eruption events including a confined and a successful one both partial eruptions are a consequence of the vertical splitting of a filamenthosting mfr involving internal reconnection a loss of equilibrium in the rising part of the magnetic flux is suggested by the impulsive onset of both events and by the delayed onset of reconnection in the confined event the remaining part of the flux might be linetied to the photosphere in a bald patch separatrix surface and we confirm the existence of extended baldpatch sections for the successful eruption the internal reconnection is signified by brightenings in the body of one filament and between the rising and remaining parts of both filaments it evolves quickly into the standard current sheet reconnection in the wake of the eruption as a result regardless of being confined or successful both eruptions produce hard xray sources and flare loops below the erupting but above the surviving flux as well as a pair of flare ribbons enclosing the latter | [['coronal', 'mass', 'ejections', 'are', 'often', 'considered', 'to', 'result', 'from', 'the', 'full', 'eruption', 'of', 'a', 'magnetic', 'flux', 'rope', 'mfr', 'however', 'it', 'is', 'recognized', 'that', 'in', 'some', 'events', 'the', 'mfr', 'may', 'release', 'only', 'part', 'of', 'its', 'flux', 'with', 'the', 'details', 'of', 'the', 'implied', 'splitting', 'not', 'completely', 'established', 'due', 'to', 'limitations', 'in', 'observations', 'here', 'we', 'investigate', 'two', 'partial', 'eruption', 'events', 'including', 'a', 'confined', 'and', 'a', 'successful', 'one', 'both', 'partial', 'eruptions', 'are', 'a', 'consequence', 'of', 'the', 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1,802.04933 | The Diminished Quantum Uncertainty in Multipartite Entanglement | The uncertainty principle and entanglement are two fundamental, but yet not
well understood, features of quantum theory. The uncertainty relation reflects
the capability limit in acquiring the knowledge of different physical
properties of a particle simultaneously, while on the other side, the quantum
entanglement renders the entangled quanta lose their independence, including
measurements imposed on them. By virtue of the majorization, here we establish
a general correlation relation for quantum uncertainty and multipartite
entanglement. Within this scheme, the optimization problems for entropy and
majorization uncertainty relation are solved. We obtain a diminished
uncertainty relation in the presence of multipartite entanglement, where the
lower bound is connected with the entanglement class. This result is inspiring,
reveals the intrinsic quantitative connection between uncertainty relation and
entanglement, and may have a deep impact on quantum measurement in application.
| quant-ph | the uncertainty principle and entanglement are two fundamental but yet not well understood features of quantum theory the uncertainty relation reflects the capability limit in acquiring the knowledge of different physical properties of a particle simultaneously while on the other side the quantum entanglement renders the entangled quanta lose their independence including measurements imposed on them by virtue of the majorization here we establish a general correlation relation for quantum uncertainty and multipartite entanglement within this scheme the optimization problems for entropy and majorization uncertainty relation are solved we obtain a diminished uncertainty relation in the presence of multipartite entanglement where the lower bound is connected with the entanglement class this result is inspiring reveals the intrinsic quantitative connection between uncertainty relation and entanglement and may have a deep impact on quantum measurement in application | [['the', 'uncertainty', 'principle', 'and', 'entanglement', 'are', 'two', 'fundamental', 'but', 'yet', 'not', 'well', 'understood', 'features', 'of', 'quantum', 'theory', 'the', 'uncertainty', 'relation', 'reflects', 'the', 'capability', 'limit', 'in', 'acquiring', 'the', 'knowledge', 'of', 'different', 'physical', 'properties', 'of', 'a', 'particle', 'simultaneously', 'while', 'on', 'the', 'other', 'side', 'the', 'quantum', 'entanglement', 'renders', 'the', 'entangled', 'quanta', 'lose', 'their', 'independence', 'including', 'measurements', 'imposed', 'on', 'them', 'by', 'virtue', 'of', 'the', 'majorization', 'here', 'we', 'establish', 'a', 'general', 'correlation', 'relation', 'for', 'quantum', 'uncertainty', 'and', 'multipartite', 'entanglement', 'within', 'this', 'scheme', 'the', 'optimization', 'problems', 'for', 'entropy', 'and', 'majorization', 'uncertainty', 'relation', 'are', 'solved', 'we', 'obtain', 'a', 'diminished', 'uncertainty', 'relation', 'in', 'the', 'presence', 'of', 'multipartite', 'entanglement', 'where', 'the', 'lower', 'bound', 'is', 'connected', 'with', 'the', 'entanglement', 'class', 'this', 'result', 'is', 'inspiring', 'reveals', 'the', 'intrinsic', 'quantitative', 'connection', 'between', 'uncertainty', 'relation', 'and', 'entanglement', 'and', 'may', 'have', 'a', 'deep', 'impact', 'on', 'quantum', 'measurement', 'in', 'application']] | [-0.12582237436091182, 0.17021290228046754, -0.10475252542506766, 0.12330147248037436, -0.05077232149671073, -0.16410980461924166, 0.08325594940386644, 0.3338247171016755, -0.27802113656092575, -0.3403732175352397, 0.08146523171604646, -0.289026881257693, -0.10930050978506053, 0.19040001007185006, -0.07358367437048367, 0.10291158087827541, 0.04881599927838478, 0.053611057926900685, -0.08977334086817723, -0.19796798966773269, 0.3422509411453373, 0.018032598441803, 0.33973159283330595, 0.12768642591416007, 0.11160414163368168, 0.014561807560837931, -0.030679639097717072, 0.033142050486747864, -0.12700850913232123, 0.18210851644269294, 0.25390945841316825, 0.14732970011386054, 0.2503263942034984, -0.38218200170883426, -0.22870280732987103, 0.10442544344674658, 0.09712120186735841, 0.1232682976268094, -0.0068402719364880965, -0.29136023006781386, -0.029067265345818466, -0.1577146156863482, -0.07872828880531921, -0.06826421118996762, 4.251790405423553e-05, -0.05568800781793134, -0.22145877950824797, 0.16786827913450975, 0.0981238578558313, 0.06723627817161658, 0.0027278652845847385, -0.018771445436437648, 0.01624971290670887, 0.1487390144797111, -0.011855489853769541, -0.03445807313546538, 0.1168668232375273, -0.13988724300568856, -0.13247474313636, 0.3507802311710461, 0.0017456271771893457, -0.2123846385627985, 0.16505631806880788, -0.1291250519350999, -0.15981570193888964, 0.002966443297487718, 0.12536720470266624, 0.07291258243774926, -0.13188353021525676, 0.06967069683725842, -0.027884947825913078, 0.16660877351683598, 0.04255706603569841, 0.18258261320362282, 0.20910754860896202, 0.1174389810387597, 0.08535277944713555, 0.18658047406632383, -0.038637276035431704, -0.1683152019598142, -0.3117222110085465, -0.18528951588884443, -0.22720087943339928, 0.07780000722150025, -0.13339102222288837, -0.08209767905926263, 0.358074515808834, 0.12788630033136103, 0.15189314520469419, 0.07150455142750785, 0.2797627573625909, 0.1359464808496543, 0.0488565219686953, 0.05356143764047711, 0.32598508825456657, 0.20834566663985174, 0.04139147320486329, -0.26299694335191615, 0.11356815966536049, 0.04402361771574727] |
1,802.04934 | Nodes: A Proposed Solution to Fermi's Paradox | Within the SETI community, a school of thought holds that ET might prefer to
send information physically in so-called "probes," rather than by radio or
optical beacon, in effect, a message in a bottle. In this paper, a related
solution to Fermi's Paradox (also known as the "Great Silence") is proposed
whereby ET civilizations aggregate knowledge into a system of "nodes,"
interspersed throughout the galaxy. Each node explores and serves the local
star systems, detecting through its exploration non-technological life, and
enlisting newly emergent technological civilizations, such as ourselves, into
its central system. Each node would download information to new member
civilizations and upload information from those civilizations, passing it along
to its immediately adjacent nodes, such that new information would pass through
the entire galaxy at near light speed. The most local node would not be
directly detectible by us until it signaled Earth in response to a detection of
its artificial electromagnetic (EM) leakage. This is because Earth would not be
in the narrow beam of the signal pathways between our local node and its
adjacent nodes, nor between the local node and other local civilizations, while
the spillover signal strength of far distant nodes would be too weak to be
detectable. Thus, the Great Silence of the galaxy is simply the result of the
fact that Earth is not currently situated within a communications pathway in an
otherwise well interconnected galaxy.
| physics.pop-ph | within the seti community a school of thought holds that et might prefer to send information physically in socalled probes rather than by radio or optical beacon in effect a message in a bottle in this paper a related solution to fermis paradox also known as the great silence is proposed whereby et civilizations aggregate knowledge into a system of nodes interspersed throughout the galaxy each node explores and serves the local star systems detecting through its exploration nontechnological life and enlisting newly emergent technological civilizations such as ourselves into its central system each node would download information to new member civilizations and upload information from those civilizations passing it along to its immediately adjacent nodes such that new information would pass through the entire galaxy at near light speed the most local node would not be directly detectible by us until it signaled earth in response to a detection of its artificial electromagnetic em leakage this is because earth would not be in the narrow beam of the signal pathways between our local node and its adjacent nodes nor between the local node and other local civilizations while the spillover signal strength of far distant nodes would be too weak to be detectable thus the great silence of the galaxy is simply the result of the fact that earth is not currently situated within a communications pathway in an otherwise well interconnected galaxy | [['within', 'the', 'seti', 'community', 'a', 'school', 'of', 'thought', 'holds', 'that', 'et', 'might', 'prefer', 'to', 'send', 'information', 'physically', 'in', 'socalled', 'probes', 'rather', 'than', 'by', 'radio', 'or', 'optical', 'beacon', 'in', 'effect', 'a', 'message', 'in', 'a', 'bottle', 'in', 'this', 'paper', 'a', 'related', 'solution', 'to', 'fermis', 'paradox', 'also', 'known', 'as', 'the', 'great', 'silence', 'is', 'proposed', 'whereby', 'et', 'civilizations', 'aggregate', 'knowledge', 'into', 'a', 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1,802.04935 | Multiplicity of closed characteristics on $P$-symmetric compact convex
hypersurfaces in $\mathbb{R}^{2n}$ | There is a long standing conjecture that there are at least $n$ closed
characteristics for any compact convex hypersurface $\Sigma$ in
$\mathbb{R}^{2n}$, and the symmetric case, i.e. $\Sigma=-\Sigma$, has already
been proved by C. Liu, Y. Long and C. Zhu in [Math. Ann., 323(2002), pp.
201-215]. In this paper, we extend the result in that paper to the
$P$-symmetric case $\Sigma=P\Sigma$ for a certain class of symplectic matrix
$P$, and prove that there are at least $[\frac{3n}{4}]$ closed characteristics
on $\Sigma$ for any positive integer $n$, where
$[a]:=\sup\{l\in\mathbb{Z},l\leq a\}$. To obtain our result, the key problem is
to estimate (3.13) in which the method is based on the theorem called Common
Index Jump Theorem. By using the Bott-type iteration formulas of Maslov index
and Maslov-type index for a certain kind of iteration symplectic path, we
provide the some new estimations (4.9-4.11), which are not considered in other
papers.
| math.DS | there is a long standing conjecture that there are at least n closed characteristics for any compact convex hypersurface sigma in mathbbr2n and the symmetric case ie sigmasigma has already been proved by c liu y long and c zhu in math ann 3232002 pp 201215 in this paper we extend the result in that paper to the psymmetric case sigmapsigma for a certain class of symplectic matrix p and prove that there are at least frac3n4 closed characteristics on sigma for any positive integer n where asuplinmathbbzlleq a to obtain our result the key problem is to estimate 313 in which the method is based on the theorem called common index jump theorem by using the botttype iteration formulas of maslov index and maslovtype index for a certain kind of iteration symplectic path we provide the some new estimations 49411 which are not considered in other papers | [['there', 'is', 'a', 'long', 'standing', 'conjecture', 'that', 'there', 'are', 'at', 'least', 'n', 'closed', 'characteristics', 'for', 'any', 'compact', 'convex', 'hypersurface', 'sigma', 'in', 'mathbbr2n', 'and', 'the', 'symmetric', 'case', 'ie', 'sigmasigma', 'has', 'already', 'been', 'proved', 'by', 'c', 'liu', 'y', 'long', 'and', 'c', 'zhu', 'in', 'math', 'ann', '3232002', 'pp', '201215', 'in', 'this', 'paper', 'we', 'extend', 'the', 'result', 'in', 'that', 'paper', 'to', 'the', 'psymmetric', 'case', 'sigmapsigma', 'for', 'a', 'certain', 'class', 'of', 'symplectic', 'matrix', 'p', 'and', 'prove', 'that', 'there', 'are', 'at', 'least', 'frac3n4', 'closed', 'characteristics', 'on', 'sigma', 'for', 'any', 'positive', 'integer', 'n', 'where', 'asuplinmathbbzlleq', 'a', 'to', 'obtain', 'our', 'result', 'the', 'key', 'problem', 'is', 'to', 'estimate', '313', 'in', 'which', 'the', 'method', 'is', 'based', 'on', 'the', 'theorem', 'called', 'common', 'index', 'jump', 'theorem', 'by', 'using', 'the', 'botttype', 'iteration', 'formulas', 'of', 'maslov', 'index', 'and', 'maslovtype', 'index', 'for', 'a', 'certain', 'kind', 'of', 'iteration', 'symplectic', 'path', 'we', 'provide', 'the', 'some', 'new', 'estimations', '49411', 'which', 'are', 'not', 'considered', 'in', 'other', 'papers']] | [-0.12846195461018614, 0.07757804766428512, -0.0789522794599999, 0.040095002733131636, -0.05076323816196954, -0.19183509771935117, 0.019644440979775513, 0.3488808207945164, -0.2423293182436167, -0.2537562010455614, 0.10516565209369398, -0.25358097958148945, -0.16358632266621473, 0.21030492489029404, -0.1080446228614318, 0.029002869636161437, 0.043175975995426864, 0.07483903885819838, -0.046236628622249504, -0.2628719775058644, 0.3307637951572911, -0.04465195179169237, 0.1932430960185511, 0.0870900222782413, 0.10186055220965244, 0.016513227570382222, -0.01615506992675364, 0.010381887043813164, -0.15688867266957024, 0.10335425682395408, 0.2994605203037819, 0.1082769338798141, 0.2691168386069402, -0.3477985675735268, -0.18764893344150935, 0.16666185356099422, 0.10432234457300478, 0.036837483675901214, -0.02300388491130434, -0.222296998845938, 0.19678536586834014, -0.11946001048134246, -0.1593495332635939, -0.02527295973342122, 0.10769265458564943, 0.000678256190132717, -0.27418192545719394, 0.026595269088817, 0.13841348481331756, 0.07604330093894994, -0.018393479833628615, -0.15612841847444503, -0.017022301067440758, 0.05903019937752208, 0.03377370013770136, 0.09125419397769548, 0.016064223771075338, -0.0339178307732882, -0.11488045126029199, 0.33431819414483827, -0.08254670325837189, -0.23869912765106896, 0.1390735720281004, -0.1284326576811432, -0.20556830213746238, 0.1102599731095555, 0.12787097574475642, 0.15479785148841393, -0.08737168726052197, 0.15921007201869145, -0.11989861651336853, 0.11144926641907045, 0.14173120350726964, -0.02019582395556606, 0.09717938795007977, 0.0807808622993319, 0.10659353260580502, 0.10008546629712366, -0.0356612492051028, -0.05373339546831246, -0.3431947374073657, -0.19240477436195902, -0.18526107631623745, 0.1046444631697533, -0.08440253617935552, -0.16338249987227396, 0.34894717875605735, 0.05910672556484302, 0.18680135538788434, 0.10270361567053839, 0.21170039344447808, 0.14467757963985337, 0.008158763029455195, 0.1457114716086694, 0.17404983570888594, 0.1628587241842031, 0.050312232711470464, -0.12320133817585474, 0.0399047542383081, 0.15905320235121417] |
1,802.04936 | MemeSequencer: Sparse Matching for Embedding Image Macros | The analysis of the creation, mutation, and propagation of social media
content on the Internet is an essential problem in computational social
science, affecting areas ranging from marketing to political mobilization. A
first step towards understanding the evolution of images online is the analysis
of rapidly modifying and propagating memetic imagery or `memes'. However, a
pitfall in proceeding with such an investigation is the current incapability to
produce a robust semantic space for such imagery, capable of understanding
differences in Image Macros. In this study, we provide a first step in the
systematic study of image evolution on the Internet, by proposing an algorithm
based on sparse representations and deep learning to decouple various types of
content in such images and produce a rich semantic embedding. We demonstrate
the benefits of our approach on a variety of tasks pertaining to memes and
Image Macros, such as image clustering, image retrieval, topic prediction and
virality prediction, surpassing the existing methods on each. In addition to
its utility on quantitative tasks, our method opens up the possibility of
obtaining the first large-scale understanding of the evolution and propagation
of memetic imagery.
| cs.SI cs.CV cs.MM | the analysis of the creation mutation and propagation of social media content on the internet is an essential problem in computational social science affecting areas ranging from marketing to political mobilization a first step towards understanding the evolution of images online is the analysis of rapidly modifying and propagating memetic imagery or memes however a pitfall in proceeding with such an investigation is the current incapability to produce a robust semantic space for such imagery capable of understanding differences in image macros in this study we provide a first step in the systematic study of image evolution on the internet by proposing an algorithm based on sparse representations and deep learning to decouple various types of content in such images and produce a rich semantic embedding we demonstrate the benefits of our approach on a variety of tasks pertaining to memes and image macros such as image clustering image retrieval topic prediction and virality prediction surpassing the existing methods on each in addition to its utility on quantitative tasks our method opens up the possibility of obtaining the first largescale understanding of the evolution and propagation of memetic imagery | [['the', 'analysis', 'of', 'the', 'creation', 'mutation', 'and', 'propagation', 'of', 'social', 'media', 'content', 'on', 'the', 'internet', 'is', 'an', 'essential', 'problem', 'in', 'computational', 'social', 'science', 'affecting', 'areas', 'ranging', 'from', 'marketing', 'to', 'political', 'mobilization', 'a', 'first', 'step', 'towards', 'understanding', 'the', 'evolution', 'of', 'images', 'online', 'is', 'the', 'analysis', 'of', 'rapidly', 'modifying', 'and', 'propagating', 'memetic', 'imagery', 'or', 'memes', 'however', 'a', 'pitfall', 'in', 'proceeding', 'with', 'such', 'an', 'investigation', 'is', 'the', 'current', 'incapability', 'to', 'produce', 'a', 'robust', 'semantic', 'space', 'for', 'such', 'imagery', 'capable', 'of', 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1,802.04937 | Ground-State Wave Function with Interactions between Different Species
in $M$-Component Miscible Bose-Einstein Condensates | We construct a variational ground-state wave function of weakly interacting
M-component Bose-Einstein condensates beyond the mean-field theory by
incorporating the dynamical 3/2-body processes, where one of the two colliding
particles drops into the condensate and vice versa. Our numerical results with
various masses and particle numbers show that the 3/2-body processes between
different particles make finite contributions to lowering the ground-state
energy, implying that many-body correlation effects between different particles
are essential even in the weak-coupling regime of the Bose--Einstein
condensates. We also consider the stability condition for $2$-component
miscible states using the new ground-state wave function. Through this
calculation, we obtain the relation $U^{2}_{AB}/U_{AA}U_{BB}<1+\alpha$, where
$U_{ij}$ is the effective contact potential between particles $i$ and $j$ and
$\alpha$ is the correction, which originates from the $3/2$-body and $2$-body
processes.
| cond-mat.quant-gas | we construct a variational groundstate wave function of weakly interacting mcomponent boseeinstein condensates beyond the meanfield theory by incorporating the dynamical 32body processes where one of the two colliding particles drops into the condensate and vice versa our numerical results with various masses and particle numbers show that the 32body processes between different particles make finite contributions to lowering the groundstate energy implying that manybody correlation effects between different particles are essential even in the weakcoupling regime of the boseeinstein condensates we also consider the stability condition for 2component miscible states using the new groundstate wave function through this calculation we obtain the relation u2_abu_aau_bb1alpha where u_ij is the effective contact potential between particles i and j and alpha is the correction which originates from the 32body and 2body processes | [['we', 'construct', 'a', 'variational', 'groundstate', 'wave', 'function', 'of', 'weakly', 'interacting', 'mcomponent', 'boseeinstein', 'condensates', 'beyond', 'the', 'meanfield', 'theory', 'by', 'incorporating', 'the', 'dynamical', '32body', 'processes', 'where', 'one', 'of', 'the', 'two', 'colliding', 'particles', 'drops', 'into', 'the', 'condensate', 'and', 'vice', 'versa', 'our', 'numerical', 'results', 'with', 'various', 'masses', 'and', 'particle', 'numbers', 'show', 'that', 'the', '32body', 'processes', 'between', 'different', 'particles', 'make', 'finite', 'contributions', 'to', 'lowering', 'the', 'groundstate', 'energy', 'implying', 'that', 'manybody', 'correlation', 'effects', 'between', 'different', 'particles', 'are', 'essential', 'even', 'in', 'the', 'weakcoupling', 'regime', 'of', 'the', 'boseeinstein', 'condensates', 'we', 'also', 'consider', 'the', 'stability', 'condition', 'for', '2component', 'miscible', 'states', 'using', 'the', 'new', 'groundstate', 'wave', 'function', 'through', 'this', 'calculation', 'we', 'obtain', 'the', 'relation', 'u2_abu_aau_bb1alpha', 'where', 'u_ij', 'is', 'the', 'effective', 'contact', 'potential', 'between', 'particles', 'i', 'and', 'j', 'and', 'alpha', 'is', 'the', 'correction', 'which', 'originates', 'from', 'the', '32body', 'and', '2body', 'processes']] | [-0.12374126329989166, 0.25468731414916557, -0.08804955215431577, 0.09520499374042446, 0.012328546328195768, -0.14357930761960502, 0.037167058217108195, 0.2972816159649095, -0.23584721194103705, -0.26090707442210626, -0.009250623550331004, -0.3212921731751557, -0.12127265439173857, 0.11406763983518607, 0.10449282203389462, 0.03186339604877686, 0.029635436378072862, -0.023800802568710127, -0.0811508707034351, -0.17515473503280113, 0.3860244197417195, -0.024827791186583827, 0.22890114765402197, 0.12512176058637772, 0.09280028771211993, 0.03753432557672667, 0.04850373238816049, -0.021815661196560823, -0.18104814674379033, 0.0354049022416873, 0.1874243480229187, 0.029988073839538897, 0.2242728769346032, -0.4358488854396251, -0.19905686998007055, 0.12335220868013395, 0.18681548992641156, 0.14479838535749145, -0.038355557791267834, -0.30302858246533676, -0.0192392491573078, -0.1880776242807854, -0.1562945126635275, -0.08904369419455066, 0.029211390850155852, 0.0756351969659675, -0.27861825156552616, 0.13295948360333681, 0.05778664284698889, 0.010150037409896536, -0.06926327104889607, -0.10915618116945722, -0.03472320699717763, 0.09469403098914916, 0.037896643979781736, 0.003812449590786714, 0.13350196256367272, -0.17167799171581938, -0.060062939611122794, 0.3906298412638175, -0.08613338303782024, -0.20780275220614533, 0.2331465843689534, -0.15329481022479374, -0.07986137762218136, 0.12705048049489656, 0.13405015558733555, 0.09318098675864821, -0.13228547961140671, 0.06610788108353222, -0.0168754374028577, 0.15202252137792377, 0.03838033056204287, 0.03865961868734669, 0.22249987318231038, 0.13578601644280575, 0.008647160640693912, 0.16611150585208811, -0.07031378851726998, -0.18311646871644166, -0.29769770024243253, -0.14489421842605338, -0.17437217449191814, 0.02015696274357588, -0.09294978788340474, -0.13466002083520673, 0.34855035933225537, 0.13478170207145718, 0.18472442612893042, 0.05262207898199414, 0.2660763527577122, 0.15372438220281961, -0.0002872200372318427, 0.050042741646802474, 0.28076795234467633, 0.15359228125831126, 0.05568575070333458, -0.27006018313384333, -0.05423295061472197, 0.09034996986201452] |
1,802.04938 | Higher bottomonium zoo | In this work, we study higher bottomonia up to the $nL=8S$, $6P$, $5D$, $4F$,
$3G$ multiplets using the modified Godfrey-Isgur (GI) model, which takes
account of color screening effects. The calculated mass spectra of bottomonium
states are in reasonable agreement with the present experimental data. Based on
spectroscopy, partial widths of all allowed radiative transitions, annihilation
decays, hadronic transitions, and open-bottom strong decays of each state are
also evaluated by applying our numerical wave functions. Comparing our results
with the former results, we point out difference among various models and
derive new conclusions obtained in this paper. Notably, we find a significant
difference between our model and the GI model when we study $D, F$, and $G$ and
$n\ge 4$ states. Our theoretical results are valuable to search for more
bottomonia in experiments, such as LHCb, and forthcoming Belle II.
| hep-ph | in this work we study higher bottomonia up to the nl8s 6p 5d 4f 3g multiplets using the modified godfreyisgur gi model which takes account of color screening effects the calculated mass spectra of bottomonium states are in reasonable agreement with the present experimental data based on spectroscopy partial widths of all allowed radiative transitions annihilation decays hadronic transitions and openbottom strong decays of each state are also evaluated by applying our numerical wave functions comparing our results with the former results we point out difference among various models and derive new conclusions obtained in this paper notably we find a significant difference between our model and the gi model when we study d f and g and nge 4 states our theoretical results are valuable to search for more bottomonia in experiments such as lhcb and forthcoming belle ii | [['in', 'this', 'work', 'we', 'study', 'higher', 'bottomonia', 'up', 'to', 'the', 'nl8s', '6p', '5d', '4f', '3g', 'multiplets', 'using', 'the', 'modified', 'godfreyisgur', 'gi', 'model', 'which', 'takes', 'account', 'of', 'color', 'screening', 'effects', 'the', 'calculated', 'mass', 'spectra', 'of', 'bottomonium', 'states', 'are', 'in', 'reasonable', 'agreement', 'with', 'the', 'present', 'experimental', 'data', 'based', 'on', 'spectroscopy', 'partial', 'widths', 'of', 'all', 'allowed', 'radiative', 'transitions', 'annihilation', 'decays', 'hadronic', 'transitions', 'and', 'openbottom', 'strong', 'decays', 'of', 'each', 'state', 'are', 'also', 'evaluated', 'by', 'applying', 'our', 'numerical', 'wave', 'functions', 'comparing', 'our', 'results', 'with', 'the', 'former', 'results', 'we', 'point', 'out', 'difference', 'among', 'various', 'models', 'and', 'derive', 'new', 'conclusions', 'obtained', 'in', 'this', 'paper', 'notably', 'we', 'find', 'a', 'significant', 'difference', 'between', 'our', 'model', 'and', 'the', 'gi', 'model', 'when', 'we', 'study', 'd', 'f', 'and', 'g', 'and', 'nge', '4', 'states', 'our', 'theoretical', 'results', 'are', 'valuable', 'to', 'search', 'for', 'more', 'bottomonia', 'in', 'experiments', 'such', 'as', 'lhcb', 'and', 'forthcoming', 'belle', 'ii']] | [-0.05261416007721086, 0.13660560294369356, -0.0577199288824801, 0.1025104291130715, -0.015547212008399413, -0.12582982501363477, 0.06076428732288693, 0.362112112865924, -0.15793256386076, -0.29634101147488723, -0.003937527168836954, -0.3448450374919519, -0.0840153005845148, 0.15742928753506472, 0.06557778055652333, 0.05868765878785911, 0.11168274502169422, -0.03829564032205146, -0.09097435682837804, -0.2217431704651082, 0.3104327968154237, 0.008844921948294417, 0.22611800771417673, 0.12564531899653644, -0.03673530768572936, -0.020789300480174074, -0.04887928778185047, 0.002424666297910132, -0.1661853179864907, 0.09761014788012898, 0.2262182090721286, 0.10844830115888562, 0.17755649204989024, -0.41149460293835016, -0.17172568899130436, 0.09684608957107119, 0.14067252814307435, 0.11945042536985934, -0.06922993452588462, -0.35814645893121366, 0.10667397191734623, -0.16527110148596463, -0.06968419077946879, -0.1091320688481168, 0.020037501567988086, -0.018917272466221463, -0.3253539267042987, 0.07910241504185565, -0.0061741108795051736, 0.044395942624433055, -0.0911282555136508, -0.22230725451339062, -0.05921102200028413, 0.08990625944930772, 0.05394517091059347, 0.05564845823548841, 0.08138929577644834, -0.11310535384904888, -0.1456127534385958, 0.38184274031559556, -0.09633910640899976, -0.12580315088359104, 0.17854385242442325, -0.1851726438340952, -0.14511026142553674, 0.10183711641756214, 0.16307168802340255, 0.12865465107916607, -0.1334047351161683, 0.05425443079928941, -0.03391437281730079, 0.1581008531743235, 0.03745922081929585, 0.07125044878015903, 0.15671063654908657, 0.15168183463166648, -0.04818840440121486, 0.1176611483080326, -0.09992200581577988, -0.07134728516592719, -0.35706573710006345, -0.11756378122715937, -0.11489356721989248, 0.015437033989197883, -0.05418524271247426, -0.06807989241193524, 0.3973947924079387, 0.14818380206233736, 0.23469851609560655, 0.03756172725974361, 0.27061241133500347, 0.1099506504657819, 0.037785238187706516, 0.054112363901498504, 0.27556824313624084, 0.13831887560521366, 0.08477846567230903, -0.27743083500578036, -0.0009217123574704575, 0.01906926901475024] |
1,802.04939 | On Mellin-Barnes integral representations for GKZ hypergeometric
functions | We consider Mellin-Barnes integral representations of GKZ hypergeometric
equations. We construct integration contours in an explicit way and show that
suitable analytic continuations give rise to a basis of solutions.
| math.CA math.AP | we consider mellinbarnes integral representations of gkz hypergeometric equations we construct integration contours in an explicit way and show that suitable analytic continuations give rise to a basis of solutions | [['we', 'consider', 'mellinbarnes', 'integral', 'representations', 'of', 'gkz', 'hypergeometric', 'equations', 'we', 'construct', 'integration', 'contours', 'in', 'an', 'explicit', 'way', 'and', 'show', 'that', 'suitable', 'analytic', 'continuations', 'give', 'rise', 'to', 'a', 'basis', 'of', 'solutions']] | [-0.19134654166797796, -0.06554453321296023, -0.16624804073944688, 0.12876547180737058, -0.1855242748434345, -0.062374136333043374, -0.009633801970630884, 0.35130838118493557, -0.24385061304395397, -0.18871372205515702, 0.08896511127629007, -0.2267104817709575, -0.2577294561391075, 0.2637697325398525, -0.05277588724469145, 0.023246795187393823, 0.0917102553260823, -0.0020695362240076066, -0.19123042364759993, -0.23615559352717053, 0.36252338240544, -0.11719698250914613, 0.19921483049790065, -0.026370531568924587, 0.2047406873355309, 0.0024051672468582788, -0.06117467762281497, -0.13822291288524866, -0.21402725873170614, 0.20668362795064846, 0.3426727579285701, 0.09848932122501235, 0.1838323777463908, -0.4765518970787525, -0.08209462524391711, 0.06784934783354402, 0.2605437273935725, 0.08524495965490739, -0.04080991524582108, -0.2659763164042185, -0.029718180613902707, -0.23197223382691542, -0.26715238724524776, -0.27465337080260116, 0.02384715105096499, 0.07148483451455831, -0.3002600964469214, 0.02070244817684094, 0.01074555873249968, 0.05978944630672534, -0.15393161941319705, -0.08728842427954078, 0.03374270231773456, 0.040262347630535565, -0.0009619794747171303, 0.005075176161093016, 0.020030388267089923, -0.14463210362785806, -0.11391770398865143, 0.30548974523941674, -0.08563595845674475, -0.3479916425421834, 0.0929596789026012, -0.09433218482881785, -0.15039898306131363, 0.13326627276837827, 0.12954167467541994, 0.14105236555139225, -0.1596442616234223, 0.16136684541706928, -0.05670237684001525, 0.08879850532781954, 0.1537367059228321, -0.004630632589881619, 0.1485598093519608, 0.019438067947824798, 0.049666324319938816, 0.2005532938366135, 0.08498259337308506, -0.10999175760274132, -0.4108105224867662, -0.26338397289315857, -0.02125180932295431, 0.1116237757106622, -0.11855884233130685, -0.2872851885234316, 0.3938352340211471, 0.12618408592728278, 0.22000774517655372, 0.16217124282071987, 0.21388638180991013, 0.28079768338551125, 0.031432381831109524, 0.029918535643567643, 0.048110344695548216, 0.11805720441043377, 0.015508832037448883, -0.12342504607513546, -0.09352187678838769, 0.23080694659923515] |
1,802.0494 | Quantization of Conductance in Quasi-Periodic Quantum Wires | We study charge transport in the Peierls-Harper model with a quasi-periodic
cosine potential. We compute the Landauer-type conductance of the wire. Our
numerical results show the following: (i) When the Fermi energy lies in the
absolutely continuous spectrum that is realized in the regime of the weak
coupling, the conductance is quantized to the universal conductance. (ii) For
the regime of localization that is realized for the strong coupling, the
conductance is always vanishing irrespective of the value of the Fermi energy.
Unfortunately, we cannot make a definite conclusion about the case with the
critical coupling. We also compute the conductance of the Thue-Morse model.
Although the potential of the model is not quasi-periodic, the energy spectrum
is known to be a Cantor set with zero Lebesgue measure. Our numerical results
for the Thue-Morse model also show the quantization of the conductance at many
locations of the Fermi energy, except for the trivial localization regime.
Besides, for the rest of the values of the Fermi energy, the conductance shows
a similar behavior to that of the Peierls-Harper model with the critical
coupling.
| cond-mat.mes-hall math-ph math.MP | we study charge transport in the peierlsharper model with a quasiperiodic cosine potential we compute the landauertype conductance of the wire our numerical results show the following i when the fermi energy lies in the absolutely continuous spectrum that is realized in the regime of the weak coupling the conductance is quantized to the universal conductance ii for the regime of localization that is realized for the strong coupling the conductance is always vanishing irrespective of the value of the fermi energy unfortunately we cannot make a definite conclusion about the case with the critical coupling we also compute the conductance of the thuemorse model although the potential of the model is not quasiperiodic the energy spectrum is known to be a cantor set with zero lebesgue measure our numerical results for the thuemorse model also show the quantization of the conductance at many locations of the fermi energy except for the trivial localization regime besides for the rest of the values of the fermi energy the conductance shows a similar behavior to that of the peierlsharper model with the critical coupling | [['we', 'study', 'charge', 'transport', 'in', 'the', 'peierlsharper', 'model', 'with', 'a', 'quasiperiodic', 'cosine', 'potential', 'we', 'compute', 'the', 'landauertype', 'conductance', 'of', 'the', 'wire', 'our', 'numerical', 'results', 'show', 'the', 'following', 'i', 'when', 'the', 'fermi', 'energy', 'lies', 'in', 'the', 'absolutely', 'continuous', 'spectrum', 'that', 'is', 'realized', 'in', 'the', 'regime', 'of', 'the', 'weak', 'coupling', 'the', 'conductance', 'is', 'quantized', 'to', 'the', 'universal', 'conductance', 'ii', 'for', 'the', 'regime', 'of', 'localization', 'that', 'is', 'realized', 'for', 'the', 'strong', 'coupling', 'the', 'conductance', 'is', 'always', 'vanishing', 'irrespective', 'of', 'the', 'value', 'of', 'the', 'fermi', 'energy', 'unfortunately', 'we', 'can', 'not', 'make', 'a', 'definite', 'conclusion', 'about', 'the', 'case', 'with', 'the', 'critical', 'coupling', 'we', 'also', 'compute', 'the', 'conductance', 'of', 'the', 'thuemorse', 'model', 'although', 'the', 'potential', 'of', 'the', 'model', 'is', 'not', 'quasiperiodic', 'the', 'energy', 'spectrum', 'is', 'known', 'to', 'be', 'a', 'cantor', 'set', 'with', 'zero', 'lebesgue', 'measure', 'our', 'numerical', 'results', 'for', 'the', 'thuemorse', 'model', 'also', 'show', 'the', 'quantization', 'of', 'the', 'conductance', 'at', 'many', 'locations', 'of', 'the', 'fermi', 'energy', 'except', 'for', 'the', 'trivial', 'localization', 'regime', 'besides', 'for', 'the', 'rest', 'of', 'the', 'values', 'of', 'the', 'fermi', 'energy', 'the', 'conductance', 'shows', 'a', 'similar', 'behavior', 'to', 'that', 'of', 'the', 'peierlsharper', 'model', 'with', 'the', 'critical', 'coupling']] | [-0.18101491438300807, 0.1283305368556008, -0.07015151852093514, 0.0492703945502042, 0.002108327658507376, -0.14160589815132854, 0.06342517339897172, 0.32199402013536316, -0.2828218641572565, -0.24311755909918917, 0.03489601499727767, -0.3068179339684999, -0.14245675015391895, 0.21743654240390997, -0.01293124021828504, 0.033632979367538715, 0.03390776388892564, 0.09430952180300464, -0.07478962807351072, -0.20658379167868904, 0.33839504712543295, 0.06499639409690715, 0.2866783700934843, 0.11805104312502383, 0.045649069291925394, -0.028929360529912112, 0.07094688338191552, 0.035785713634397115, -0.11335839598644004, 0.06758465597763637, 0.1984739497056982, -0.03657849329570051, 0.2269203487670471, -0.35145784901115446, -0.1994637585713307, 0.10977731491323273, 0.10576269792094416, 0.11255601185337943, -0.017682513016281206, -0.2568489004509235, 0.08011511955809013, -0.1375166132148273, -0.14969987639107674, -0.05707358239599354, 0.009957144800985222, 0.0224051875286113, -0.2461920323568134, 0.10805023534355931, 0.07157085773673999, 0.010778896856148733, -0.10469280464512949, -0.07608742917790104, -0.05171633795937986, 0.11700006107910979, 0.06783466073439987, 0.00331580867985706, 0.10079670480313552, -0.1459812446811252, -0.07964377832027841, 0.3690502523336539, -0.09700272721109798, -0.17911315540067893, 0.15993492800269007, -0.2103765772074173, -0.10157213899054827, 0.13250494143766098, 0.06819763494840725, 0.0616749775343837, -0.09219069696503926, 0.1325014225279305, -0.05723027775684135, 0.16647396544354726, 0.020759468476252003, 0.030718362879893067, 0.21626307839754125, 0.16372466487655654, 0.08091450053445985, 0.1183830006839193, -0.14231714331004666, -0.08151467298337603, -0.34065462524156237, -0.1557210441267128, -0.2323071203408094, 0.07004414564940836, -0.059689215613395304, -0.21613607592160367, 0.46818655605932175, 0.1765353245305628, 0.24309382830020967, 0.08550091746433006, 0.2653252613519766, 0.20615171251160244, 0.056070133591539006, 0.06204891485774385, 0.27643097802280775, 0.10770753900731467, 0.09492907313969583, -0.28189116131349656, 0.027940217056954847, 0.0451981450961028] |
1,802.04941 | Axial Charge Fluctuation and Chiral Magnetic Effect from Stochastic
Hydrodynamics | The amount of axial charge produced in heavy ion collisions is one of the key
quantities in understanding chiral magnetic effect. Current phenomenological
studies assume large axial charge chemical potential $\mu_5$ produced in Glasma
phase and assume the conservation of axial charge throughout the evolution,
which is valid in the long relaxation time limit. Based on the solution of
stochastic hydrodynamics with phenomenological parameters, our study suggests
that the situation of heavy ion collisions may be close to the opposite limit,
in which axial charge fluctuation approaches thermodynamic limit. Using $\mu_5$
set by the thermodynamic limit for chiral magnetic effect and a background from
parity-even $v_1$, we obtain a reasonable description of the centrality
dependence of charged particle correlation measured in experiment.
| nucl-th hep-ph nucl-ex | the amount of axial charge produced in heavy ion collisions is one of the key quantities in understanding chiral magnetic effect current phenomenological studies assume large axial charge chemical potential mu_5 produced in glasma phase and assume the conservation of axial charge throughout the evolution which is valid in the long relaxation time limit based on the solution of stochastic hydrodynamics with phenomenological parameters our study suggests that the situation of heavy ion collisions may be close to the opposite limit in which axial charge fluctuation approaches thermodynamic limit using mu_5 set by the thermodynamic limit for chiral magnetic effect and a background from parityeven v_1 we obtain a reasonable description of the centrality dependence of charged particle correlation measured in experiment | [['the', 'amount', 'of', 'axial', 'charge', 'produced', 'in', 'heavy', 'ion', 'collisions', 'is', 'one', 'of', 'the', 'key', 'quantities', 'in', 'understanding', 'chiral', 'magnetic', 'effect', 'current', 'phenomenological', 'studies', 'assume', 'large', 'axial', 'charge', 'chemical', 'potential', 'mu_5', 'produced', 'in', 'glasma', 'phase', 'and', 'assume', 'the', 'conservation', 'of', 'axial', 'charge', 'throughout', 'the', 'evolution', 'which', 'is', 'valid', 'in', 'the', 'long', 'relaxation', 'time', 'limit', 'based', 'on', 'the', 'solution', 'of', 'stochastic', 'hydrodynamics', 'with', 'phenomenological', 'parameters', 'our', 'study', 'suggests', 'that', 'the', 'situation', 'of', 'heavy', 'ion', 'collisions', 'may', 'be', 'close', 'to', 'the', 'opposite', 'limit', 'in', 'which', 'axial', 'charge', 'fluctuation', 'approaches', 'thermodynamic', 'limit', 'using', 'mu_5', 'set', 'by', 'the', 'thermodynamic', 'limit', 'for', 'chiral', 'magnetic', 'effect', 'and', 'a', 'background', 'from', 'parityeven', 'v_1', 'we', 'obtain', 'a', 'reasonable', 'description', 'of', 'the', 'centrality', 'dependence', 'of', 'charged', 'particle', 'correlation', 'measured', 'in', 'experiment']] | [-0.13416984004586874, 0.2289655537394135, -0.09725847428847777, 0.06646106915250725, 0.014185674816797503, -0.08320398786731187, -0.006060516003702507, 0.29720054408077334, -0.20952577425212768, -0.2805583704049226, -0.027792601210645355, -0.30812915702747395, -0.017928693056121835, 0.09888392931602315, 0.051979558695046627, 0.05782835179802458, 0.037075114092927, 0.04993247406435062, -0.0553838175961931, -0.13165408705259085, 0.2927107706582021, 0.04932230189213621, 0.3088146771273774, 0.163756518952976, 0.05757572379375457, 0.01270129663304838, -0.0055031709861559945, 0.06277158411723546, -0.1322753851927104, 0.0045474436421321365, 0.19489097426317203, -0.013920825948083743, 0.16584397163089426, -0.46923218102606595, -0.2212974807155914, 0.11080388786637636, 0.1616151431786778, 0.170066245562458, -0.10301921627060587, -0.21473991322773892, 0.046242883146480944, -0.1953820731322907, -0.1860374332964802, -0.07405149350973365, 0.0541785248921665, 0.04720617746781619, -0.3227693564296685, 0.1551319591531179, 0.038160764849859245, 0.07483146535843367, -0.08741193597174449, -0.14527605561020432, -0.06645319027994133, 0.06567156333552643, 0.14919658333872307, 0.07666123510513943, 0.2238729094521555, -0.1476528206859242, -0.0828617891410297, 0.3754805532497827, -0.09128561975877182, -0.19983260605301037, 0.12409308409417567, -0.21045737684566956, -0.14261928851502476, 0.11326067574841321, 0.15572658595323685, 0.11334269048004854, -0.18875824981445416, 0.07498893706933366, -0.0010541637321231795, 0.1315881489429811, 0.05709209564126662, 0.06895189882508006, 0.28006942994648315, 0.2089712202814255, -2.6654784155429388e-05, 0.09167021475794351, -0.07775426211148562, -0.1290889469998293, -0.33970139841320085, -0.10048778554745262, -0.18234803750500328, 0.07721364619706558, -0.11600488835356969, -0.13347773885995637, 0.37363248055831333, 0.19723106994098968, 0.21399400338194655, -0.05642401299355399, 0.2935239731441023, 0.10611031209435944, 0.06336757808267215, 0.05294927901237226, 0.2866479240006721, 0.17621032419599225, 0.18098916161087814, -0.31811501421454197, 0.03859681024055806, 0.08894894495759098] |
1,802.04942 | Isolating Sources of Disentanglement in Variational Autoencoders | We decompose the evidence lower bound to show the existence of a term
measuring the total correlation between latent variables. We use this to
motivate our $\beta$-TCVAE (Total Correlation Variational Autoencoder), a
refinement of the state-of-the-art $\beta$-VAE objective for learning
disentangled representations, requiring no additional hyperparameters during
training. We further propose a principled classifier-free measure of
disentanglement called the mutual information gap (MIG). We perform extensive
quantitative and qualitative experiments, in both restricted and non-restricted
settings, and show a strong relation between total correlation and
disentanglement, when the latent variables model is trained using our
framework.
| cs.LG cs.AI stat.ML | we decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables we use this to motivate our betatcvae total correlation variational autoencoder a refinement of the stateoftheart betavae objective for learning disentangled representations requiring no additional hyperparameters during training we further propose a principled classifierfree measure of disentanglement called the mutual information gap mig we perform extensive quantitative and qualitative experiments in both restricted and nonrestricted settings and show a strong relation between total correlation and disentanglement when the latent variables model is trained using our framework | [['we', 'decompose', 'the', 'evidence', 'lower', 'bound', 'to', 'show', 'the', 'existence', 'of', 'a', 'term', 'measuring', 'the', 'total', 'correlation', 'between', 'latent', 'variables', 'we', 'use', 'this', 'to', 'motivate', 'our', 'betatcvae', 'total', 'correlation', 'variational', 'autoencoder', 'a', 'refinement', 'of', 'the', 'stateoftheart', 'betavae', 'objective', 'for', 'learning', 'disentangled', 'representations', 'requiring', 'no', 'additional', 'hyperparameters', 'during', 'training', 'we', 'further', 'propose', 'a', 'principled', 'classifierfree', 'measure', 'of', 'disentanglement', 'called', 'the', 'mutual', 'information', 'gap', 'mig', 'we', 'perform', 'extensive', 'quantitative', 'and', 'qualitative', 'experiments', 'in', 'both', 'restricted', 'and', 'nonrestricted', 'settings', 'and', 'show', 'a', 'strong', 'relation', 'between', 'total', 'correlation', 'and', 'disentanglement', 'when', 'the', 'latent', 'variables', 'model', 'is', 'trained', 'using', 'our', 'framework']] | [-0.0857584437845569, 0.03473671188602518, -0.11750450572489124, 0.1551898715527434, -0.1077084299961203, -0.12309119550413207, 0.09582760575178423, 0.43663993664085865, -0.2720151738900887, -0.35762395882292797, 0.015616945612342342, -0.27512407859934396, -0.17772604673120537, 0.12102414834999332, -0.039421362547498, 0.0632157185556073, 0.13147187569227659, 0.03011219102129536, -0.15047007896569803, -0.23683694467487695, 0.32124736202439585, 0.041503426719358874, 0.3124490983294029, 0.04886694649528516, 0.15179844195586875, 0.03632094144429031, -0.06412910434270376, 0.005445318786721481, -0.1205815440141841, 0.166229085575201, 0.23064790717816275, 0.1963987296006005, 0.33263467283380266, -0.38274419101837437, -0.22632877185548606, 0.1393553708630957, 0.09291041982789083, 0.07235752834791416, -0.017582864326571947, -0.3009857136167978, 0.01657440693754899, -0.17119215278592156, 0.03701188262355955, -0.18307538566816794, -0.025743695544569116, -0.02767463559774976, -0.3427822232001314, 0.11959782538592423, 0.10032938252889405, 0.08208841339341905, -0.08700988943639555, -0.09594315017671569, -0.000391860684919122, 0.14666742584331108, 0.04955959908149548, 0.05269652134861405, 0.06822729059249948, -0.1360951410729046, -0.10236532472282354, 0.26637756001008184, -0.11459279592933232, -0.2188033042769683, 0.19110629732083334, -0.06065574336895033, -0.15383955786298764, 0.05054183121353976, 0.21021810432797985, 0.11861443374011861, -0.15830774149247812, -0.011936150925037893, -0.07171348030433843, 0.23829441765004672, 0.006090110881058009, 0.024732021657799027, 0.1546204795466589, 0.1870771491287374, 0.05281985599645658, 0.21938544604927301, -0.12829287623079788, -0.08864578983109249, -0.307983309010926, -0.1573433802297682, -0.18428855039130307, -0.01661419965767939, -0.12075991260414747, -0.10486810306136153, 0.3625909129206679, 0.21371330865413735, 0.2367452660526492, 0.13593292452002825, 0.29508040712067957, 0.07355682064877136, 0.07288298409456681, 0.09955191263732942, 0.2182616325095296, 0.10790274399087617, 0.03860198894613667, -0.2515748073003794, 0.09963382244306175, 0.02697225143691819] |
1,802.04943 | $\mathcal{CIRFE}$: A Distributed Random Fields Estimator | This paper presents a communication efficient distributed algorithm,
$\mathcal{CIRFE}$ of the \emph{consensus}+\emph{innovations} type, to estimate
a high-dimensional parameter in a multi-agent network, in which each agent is
interested in reconstructing only a few components of the parameter. This
problem arises for example when monitoring the high-dimensional distributed
state of a large-scale infrastructure with a network of limited capability
sensors and where each sensor is tasked with estimating some local components
of the state. At each observation sampling epoch, each agent updates its local
estimate of the parameter components in its interest set by simultaneously
processing the latest locally sensed information~(\emph{innovations}) and the
parameter estimates from agents~(\emph{consensus}) in its communication
neighborhood given by a time-varying possibly sparse graph. Under minimal
conditions on the inter-agent communication network and the sensing models,
almost sure convergence of the estimate sequence at each agent to the
components of the true parameter in its interest set is established.
Furthermore, the paper establishes the performance of $\mathcal{CIRFE}$ in
terms of asymptotic covariance of the estimate sequences and specifically
characterizes the dependencies of the component wise asymptotic covariance in
terms of the number of agents tasked with estimating it. Finally, simulation
experiments demonstrate the efficacy of $\mathcal{CIRFE}$.
| math.OC cs.IT math.IT math.PR math.ST stat.TH | this paper presents a communication efficient distributed algorithm mathcalcirfe of the emphconsensusemphinnovations type to estimate a highdimensional parameter in a multiagent network in which each agent is interested in reconstructing only a few components of the parameter this problem arises for example when monitoring the highdimensional distributed state of a largescale infrastructure with a network of limited capability sensors and where each sensor is tasked with estimating some local components of the state at each observation sampling epoch each agent updates its local estimate of the parameter components in its interest set by simultaneously processing the latest locally sensed informationemphinnovations and the parameter estimates from agentsemphconsensus in its communication neighborhood given by a timevarying possibly sparse graph under minimal conditions on the interagent communication network and the sensing models almost sure convergence of the estimate sequence at each agent to the components of the true parameter in its interest set is established furthermore the paper establishes the performance of mathcalcirfe in terms of asymptotic covariance of the estimate sequences and specifically characterizes the dependencies of the component wise asymptotic covariance in terms of the number of agents tasked with estimating it finally simulation experiments demonstrate the efficacy of mathcalcirfe | [['this', 'paper', 'presents', 'a', 'communication', 'efficient', 'distributed', 'algorithm', 'mathcalcirfe', 'of', 'the', 'emphconsensusemphinnovations', 'type', 'to', 'estimate', 'a', 'highdimensional', 'parameter', 'in', 'a', 'multiagent', 'network', 'in', 'which', 'each', 'agent', 'is', 'interested', 'in', 'reconstructing', 'only', 'a', 'few', 'components', 'of', 'the', 'parameter', 'this', 'problem', 'arises', 'for', 'example', 'when', 'monitoring', 'the', 'highdimensional', 'distributed', 'state', 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1,802.04944 | Edge Attention-based Multi-Relational Graph Convolutional Networks | Graph convolutional network (GCN) is generalization of convolutional neural
network (CNN) to work with arbitrarily structured graphs. A binary adjacency
matrix is commonly used in training a GCN. Recently, the attention mechanism
allows the network to learn a dynamic and adaptive aggregation of the
neighborhood. We propose a new GCN model on the graphs where edges are
characterized in multiple views or precisely in terms of multiple
relationships. For instance, in chemical graph theory, compound structures are
often represented by the hydrogen-depleted molecular graph where nodes
correspond to atoms and edges correspond to chemical bonds. Multiple attributes
can be important to characterize chemical bonds, such as atom pair (the types
of atoms that a bond connects), aromaticity, and whether a bond is in a ring.
The different attributes lead to different graph representations for the same
molecule. There is growing interests in both chemistry and machine learning
fields to directly learn molecular properties of compounds from the molecular
graph, instead of from fingerprints predefined by chemists. The proposed GCN
model, which we call edge attention-based multi-relational GCN (EAGCN), jointly
learns attention weights and node features in graph convolution. For each bond
attribute, a real-valued attention matrix is used to replace the binary
adjacency matrix. By designing a dictionary for the edge attention, and forming
the attention matrix of each molecule by looking up the dictionary, the EAGCN
exploits correspondence between bonds in different molecules. The prediction of
compound properties is based on the aggregated node features, which is
independent of the varying molecule (graph) size. We demonstrate the efficacy
of the EAGCN on multiple chemical datasets: Tox21, HIV, Freesolv, and
Lipophilicity, and interpret the resultant attention weights.
| stat.ML cs.LG | graph convolutional network gcn is generalization of convolutional neural network cnn to work with arbitrarily structured graphs a binary adjacency matrix is commonly used in training a gcn recently the attention mechanism allows the network to learn a dynamic and adaptive aggregation of the neighborhood we propose a new gcn model on the graphs where edges are characterized in multiple views or precisely in terms of multiple relationships for instance in chemical graph theory compound structures are often represented by the hydrogendepleted molecular graph where nodes correspond to atoms and edges correspond to chemical bonds multiple attributes can be important to characterize chemical bonds such as atom pair the types of atoms that a bond connects aromaticity and whether a bond is in a ring the different attributes lead to different graph representations for the same molecule there is growing interests in both chemistry and machine learning fields to directly learn molecular properties of compounds from the molecular graph instead of from fingerprints predefined by chemists the proposed gcn model which we call edge attentionbased multirelational gcn eagcn jointly learns attention weights and node features in graph convolution for each bond attribute a realvalued attention matrix is used to replace the binary adjacency matrix by designing a dictionary for the edge attention and forming the attention matrix of each molecule by looking up the dictionary the eagcn exploits correspondence between bonds in different molecules the prediction of compound properties is based on the aggregated node features which is independent of the varying molecule graph size we demonstrate the efficacy of the eagcn on multiple chemical datasets tox21 hiv freesolv and lipophilicity and interpret the resultant attention weights | [['graph', 'convolutional', 'network', 'gcn', 'is', 'generalization', 'of', 'convolutional', 'neural', 'network', 'cnn', 'to', 'work', 'with', 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1,802.04945 | Improved Monte-Carlo method for solving of integral Fredholm's equations
of a second kind, with confidence regions in the uniform norm | We offer in this article some modification of Monte-Carlo method for solving
of a linear integral Fredholm's equation of a second kind (Fredholm's well
posed problem).
We prove that the rate of convergence of offered method is optimal under
natural conditions still in an uniform norm, and construct an asymptotic as
well as non-asymptotic confidence region, again in the uniform norm.
| math.NA | we offer in this article some modification of montecarlo method for solving of a linear integral fredholms equation of a second kind fredholms well posed problem we prove that the rate of convergence of offered method is optimal under natural conditions still in an uniform norm and construct an asymptotic as well as nonasymptotic confidence region again in the uniform norm | [['we', 'offer', 'in', 'this', 'article', 'some', 'modification', 'of', 'montecarlo', 'method', 'for', 'solving', 'of', 'a', 'linear', 'integral', 'fredholms', 'equation', 'of', 'a', 'second', 'kind', 'fredholms', 'well', 'posed', 'problem', 'we', 'prove', 'that', 'the', 'rate', 'of', 'convergence', 'of', 'offered', 'method', 'is', 'optimal', 'under', 'natural', 'conditions', 'still', 'in', 'an', 'uniform', 'norm', 'and', 'construct', 'an', 'asymptotic', 'as', 'well', 'as', 'nonasymptotic', 'confidence', 'region', 'again', 'in', 'the', 'uniform', 'norm']] | [-0.09670544392810981, -0.002743234288722246, -0.10716029086062441, 0.10288844013311824, -0.061384991006009644, -0.09823144459333576, 0.0590694238752371, 0.3581442847909009, -0.3241311436305281, -0.2459324896869967, 0.15404752657374704, -0.20545849552164314, -0.13969485821645158, 0.2398782700727709, -0.12201850677002603, 0.11011225496586717, 0.04414289368728756, 0.06601297583614217, -0.08788131156432458, -0.2689581883185711, 0.2943299376963042, 0.08550370713604279, 0.24063775599094445, 0.07465947753287944, 0.15552037117666886, -0.010192495938695844, 0.02299761576730697, 0.012848674747176835, -0.17625897495349446, 0.107703745093189, 0.24781418000882277, 0.13539018480061385, 0.34712953767815574, -0.38752695380664265, -0.15619493756809685, 0.1048589341311914, 0.15974266689698227, 0.07971396353706474, -0.0929772123946335, -0.24764440457535083, 0.08196071500042607, -0.15767166832248208, -0.20521240757747752, -0.0880357299503855, -0.04949329077403565, 0.041152723438915656, -0.354875611034451, 0.12687508589359092, 0.11070107977425099, 0.06759484686323854, -0.1151416379820983, -0.10615988576509913, 0.09190075794906645, 0.06379758025382141, 0.058650910377227625, 0.0254354897397952, 0.011082044263946687, -0.11352718967684834, -0.07223365584113559, 0.330812095721695, -0.10021220552964044, -0.24129483024360704, 0.10239942579484376, -0.1051800786135871, -0.14877936527606284, 0.0856526774644363, 0.16274236548753057, 0.1985983167790243, -0.17467082510167947, 0.13233421461897155, -0.09712240983899988, 0.11366522877064884, 0.09130342564255488, 0.05067141212095491, 0.06733330537671925, 0.17661983422080024, 0.18788516915357503, 0.18226534273231126, -0.019811759985311598, -0.09772134520357749, -0.3564617065865485, -0.17454386544108513, -0.19265785764475338, 0.042343168271339084, -0.10445595454003807, -0.19745883718132973, 0.3334882191580827, 0.11643150334673949, 0.18553023070829813, 0.09924971043575005, 0.2556009653222854, 0.17229383722420966, -0.04839935746104991, 0.08816427650449217, 0.1999947481572086, 0.15372891808081357, 0.06352776589757594, -0.1776487818870457, 0.04986762368410337, 0.14899852635247296] |
1,802.04946 | I-V Characteristics of Graphene Nanoribbon/h-BN Heterojunctions and
Resonant Tunneling | We present the first principle calculations of the electrical properties of
graphene sheet/h-BN heterojunction(GS/h-BN) and 11-armchair graphene nanoribbon
heterojunction(11-AGNR/h-BN), which were carried out using the density
functional theory(DFT) method and the non-equilibrium Green's function(NEGF)
technique. Since 11-AGNR belongs to the conductive (3n-1)-family of AGNR, both
are metallic nanomaterials with two transverse arrays of h-BN, which is a
wide-gap semi-conductor. The two h-BN arrays act as double barriers. The
transmission functions(TF) and I-V characteristics of GS/h-BN and 11-AGNR/h-BN
are calculated by DFT and NEGF, and they show that quantum double barrier
tunneling occurs. The TF becomes very spiky in both materials, and it leads to
step-wise I-V characteristics rather than negative resistance, which is the
typical behavior of double barriers in semiconductors.
| cond-mat.mes-hall | we present the first principle calculations of the electrical properties of graphene sheethbn heterojunctiongshbn and 11armchair graphene nanoribbon heterojunction11agnrhbn which were carried out using the density functional theorydft method and the nonequilibrium greens functionnegf technique since 11agnr belongs to the conductive 3n1family of agnr both are metallic nanomaterials with two transverse arrays of hbn which is a widegap semiconductor the two hbn arrays act as double barriers the transmission functionstf and iv characteristics of gshbn and 11agnrhbn are calculated by dft and negf and they show that quantum double barrier tunneling occurs the tf becomes very spiky in both materials and it leads to stepwise iv characteristics rather than negative resistance which is the typical behavior of double barriers in semiconductors | [['we', 'present', 'the', 'first', 'principle', 'calculations', 'of', 'the', 'electrical', 'properties', 'of', 'graphene', 'sheethbn', 'heterojunctiongshbn', 'and', '11armchair', 'graphene', 'nanoribbon', 'heterojunction11agnrhbn', 'which', 'were', 'carried', 'out', 'using', 'the', 'density', 'functional', 'theorydft', 'method', 'and', 'the', 'nonequilibrium', 'greens', 'functionnegf', 'technique', 'since', '11agnr', 'belongs', 'to', 'the', 'conductive', '3n1family', 'of', 'agnr', 'both', 'are', 'metallic', 'nanomaterials', 'with', 'two', 'transverse', 'arrays', 'of', 'hbn', 'which', 'is', 'a', 'widegap', 'semiconductor', 'the', 'two', 'hbn', 'arrays', 'act', 'as', 'double', 'barriers', 'the', 'transmission', 'functionstf', 'and', 'iv', 'characteristics', 'of', 'gshbn', 'and', '11agnrhbn', 'are', 'calculated', 'by', 'dft', 'and', 'negf', 'and', 'they', 'show', 'that', 'quantum', 'double', 'barrier', 'tunneling', 'occurs', 'the', 'tf', 'becomes', 'very', 'spiky', 'in', 'both', 'materials', 'and', 'it', 'leads', 'to', 'stepwise', 'iv', 'characteristics', 'rather', 'than', 'negative', 'resistance', 'which', 'is', 'the', 'typical', 'behavior', 'of', 'double', 'barriers', 'in', 'semiconductors']] | [-0.13615183357719904, 0.1275449345215551, -0.07177327921077072, 0.033260710833191466, 0.006238943714698827, -0.20987775040421267, 0.07281807997276553, 0.45258520728161744, -0.25938004911415746, -0.28393249916802116, -0.0029861227512728793, -0.3172402240101013, -0.1934215736214642, 0.2435459775057294, 0.06757656035122571, 0.05286398925274445, 0.024672651806057575, -0.0808699030831859, -0.0713076005880679, -0.19025156569776233, 0.26085666355651776, 0.027452132944122527, 0.3530644898529391, 0.10461670759780047, 0.013839581519835166, 0.003177154079280995, 0.09189955754675441, 0.06260659978599162, -0.13600458197358856, 0.09430037017676744, 0.25490464728854195, -0.10087150170023108, 0.2337152372189873, -0.5061045903544705, -0.18848205821773223, -0.03845181792496225, 0.11051058887529212, 0.13521976347882636, -0.058133662317518715, -0.2237981315366588, 0.08034482706120019, -0.12579178420390497, -0.08540594636101846, -0.05644810239171928, -0.03697792701590974, 0.06310153099590796, -0.19275344372097705, 0.08400402912400193, 0.029769495314171723, -0.005491499657501983, -0.060830689933117445, -0.14068685649046758, -0.09052795677672366, 0.0638508147073423, 0.014265167480754154, -0.04103616905725888, 0.23931388288467853, -0.10594672833093198, -0.1045128160852704, 0.364769764546607, -0.030481607534294436, -0.12482820443918106, 0.16686421931670928, -0.16223064503215068, -0.03403236163645849, 0.14385371882183193, 0.055726711566954315, 0.1299694325976275, -0.18657888395938318, 0.09688284858769679, 0.03973513228613212, 0.11831654415063157, 0.094700322251532, 0.07108742247444687, 0.22522581344352918, 0.1855875044339546, 0.03722081205866358, 0.13237721696160398, -0.16566981615599347, -0.059726163065312685, -0.22535531993023003, -0.22221748592533372, -0.21469438936863397, 0.08718112810003059, -0.03726654744254496, -0.2999309673066343, 0.4094941770607555, 0.07777256978390452, 0.14415004976966359, -0.010104904199849713, 0.2443399968313741, 0.1437975033679306, 0.10258739729210534, -0.02252290421165526, 0.21678780883483523, 0.19841724765478624, 0.10968390122257374, -0.2525365782486325, 0.0693121488449407, -0.02230547896485675] |
1,802.04947 | Attack RMSE Leaderboard: An Introduction and Case Study | In this manuscript, we briefly introduce several tricks to climb the
leaderboards which use RMSE for evaluation without exploiting any training
data.
| cs.LG | in this manuscript we briefly introduce several tricks to climb the leaderboards which use rmse for evaluation without exploiting any training data | [['in', 'this', 'manuscript', 'we', 'briefly', 'introduce', 'several', 'tricks', 'to', 'climb', 'the', 'leaderboards', 'which', 'use', 'rmse', 'for', 'evaluation', 'without', 'exploiting', 'any', 'training', 'data']] | [-0.023796206636523657, 0.0096827994778075, -0.04351500053466721, 0.07906279574804516, -0.1458032490177588, -0.15265311224555428, 0.08481581852009351, 0.4374694439836524, -0.22072076306424357, -0.3298750807615844, 0.1807658601160669, -0.24134203541854565, -0.21406037876890463, 0.20194088041105054, -0.17309244866059584, 0.02646342690356753, 0.1550489425320517, -0.015070486813783646, -0.15715335986830972, -0.34698908221484587, 0.30444545739076356, 0.061460199088535526, 0.2522517092610625, 0.06493752856146205, 0.12337302438258617, 0.02184391231276095, -0.03701665561476892, -0.01479428739879619, -0.17502880333499474, 0.17535360403020273, 0.27051690068434586, 0.19260095364668153, 0.3476266403767196, -0.42026901194317773, -0.179418261078271, 0.1348906309682537, 0.1194036287594248, 0.18124386863465505, -0.061964261176233944, -0.3305897672067989, 0.07702457591552626, -0.18418447859585285, -0.08203897583933378, -0.20077005807649007, -0.08722405681725252, -0.011002647351812233, -0.24233734353699468, 0.005764595783230933, 0.05686968717385422, 0.13920176469466902, -0.006262246577534825, -0.12142523587681353, 0.12029307962141254, 0.11993386825038628, 0.08050149145790121, 0.050789736274799165, 0.08872914313211698, -0.12101972252342173, -0.156007491517812, 0.3491463183679364, -0.05112428277392279, -0.26349862732670526, 0.10177738642828031, -0.01112043485045433, -0.16163982857357373, 0.05219248737293211, 0.1919990871524946, 0.029603045772422443, -0.22207862363112243, -0.028793149585412306, 0.05739152160557834, 0.1484257739714601, 0.12347927833483978, -0.04820167666978457, 0.0558728181164373, 0.19054591994393955, -0.006156258454377001, 0.18078963833183728, -0.14516780034384943, -0.04339236850765618, -0.3409995360469276, -0.19530643641271375, -0.15731456575237893, -0.00199696904217655, -0.07764380046335811, -0.1473487078381533, 0.3637661297636276, 0.346532030031085, 0.19934944280910052, 0.14979998518141324, 0.34152067645283585, -0.007115038185888393, 0.14021592866629362, 0.13545200279490513, 0.16061580020257019, -0.03545653566040776, 0.172730208882554, -0.15422481249763884, 0.054688593753698195, 0.021123655039859426] |
1,802.04948 | Graph2Seq: Scalable Learning Dynamics for Graphs | Neural networks have been shown to be an effective tool for learning
algorithms over graph-structured data. However, graph representation
techniques---that convert graphs to real-valued vectors for use with neural
networks---are still in their infancy. Recent works have proposed several
approaches (e.g., graph convolutional networks), but these methods have
difficulty scaling and generalizing to graphs with different sizes and shapes.
We present Graph2Seq, a new technique that represents vertices of graphs as
infinite time-series. By not limiting the representation to a fixed dimension,
Graph2Seq scales naturally to graphs of arbitrary sizes and shapes. Graph2Seq
is also reversible, allowing full recovery of the graph structure from the
sequences. By analyzing a formal computational model for graph representation,
we show that an unbounded sequence is necessary for scalability. Our
experimental results with Graph2Seq show strong generalization and new
state-of-the-art performance on a variety of graph combinatorial optimization
problems.
| cs.LG | neural networks have been shown to be an effective tool for learning algorithms over graphstructured data however graph representation techniquesthat convert graphs to realvalued vectors for use with neural networksare still in their infancy recent works have proposed several approaches eg graph convolutional networks but these methods have difficulty scaling and generalizing to graphs with different sizes and shapes we present graph2seq a new technique that represents vertices of graphs as infinite timeseries by not limiting the representation to a fixed dimension graph2seq scales naturally to graphs of arbitrary sizes and shapes graph2seq is also reversible allowing full recovery of the graph structure from the sequences by analyzing a formal computational model for graph representation we show that an unbounded sequence is necessary for scalability our experimental results with graph2seq show strong generalization and new stateoftheart performance on a variety of graph combinatorial optimization problems | [['neural', 'networks', 'have', 'been', 'shown', 'to', 'be', 'an', 'effective', 'tool', 'for', 'learning', 'algorithms', 'over', 'graphstructured', 'data', 'however', 'graph', 'representation', 'techniquesthat', 'convert', 'graphs', 'to', 'realvalued', 'vectors', 'for', 'use', 'with', 'neural', 'networksare', 'still', 'in', 'their', 'infancy', 'recent', 'works', 'have', 'proposed', 'several', 'approaches', 'eg', 'graph', 'convolutional', 'networks', 'but', 'these', 'methods', 'have', 'difficulty', 'scaling', 'and', 'generalizing', 'to', 'graphs', 'with', 'different', 'sizes', 'and', 'shapes', 'we', 'present', 'graph2seq', 'a', 'new', 'technique', 'that', 'represents', 'vertices', 'of', 'graphs', 'as', 'infinite', 'timeseries', 'by', 'not', 'limiting', 'the', 'representation', 'to', 'a', 'fixed', 'dimension', 'graph2seq', 'scales', 'naturally', 'to', 'graphs', 'of', 'arbitrary', 'sizes', 'and', 'shapes', 'graph2seq', 'is', 'also', 'reversible', 'allowing', 'full', 'recovery', 'of', 'the', 'graph', 'structure', 'from', 'the', 'sequences', 'by', 'analyzing', 'a', 'formal', 'computational', 'model', 'for', 'graph', 'representation', 'we', 'show', 'that', 'an', 'unbounded', 'sequence', 'is', 'necessary', 'for', 'scalability', 'our', 'experimental', 'results', 'with', 'graph2seq', 'show', 'strong', 'generalization', 'and', 'new', 'stateoftheart', 'performance', 'on', 'a', 'variety', 'of', 'graph', 'combinatorial', 'optimization', 'problems']] | [-0.051336732424056994, 0.023533570752198134, -0.08112669701463189, 0.058422415857685026, -0.12853751737335375, -0.14699866365488812, -0.007587290933387804, 0.4599195778578648, -0.2880705637343485, -0.32570606413586384, 0.0969280925828257, -0.23893713190958932, -0.20523720608446105, 0.21239772184495012, -0.1234090273107948, 0.11605582779096761, 0.1464528810361336, 0.030063449312001465, -0.04473027670510105, -0.26535738520183727, 0.3082881996584735, 0.012742590329385009, 0.2844463239616618, 0.04882276780774881, 0.11399195412061466, -0.014358030632138252, -0.014449347683694214, 0.06638381900586958, -0.09828503660843628, 0.1584745243500822, 0.29697756355483856, 0.17873870304239722, 0.27135264671985704, -0.42061988898906216, -0.2510550991644087, 0.16115370949505475, 0.16078475231559675, 0.12825482966185645, -0.02274522569415898, -0.28316289738848294, 0.13400822849301944, -0.14819138399726745, -0.023406680943123225, -0.1461153094785224, 0.058695749440712146, 0.027074285014532507, -0.27523073080228644, -0.0284735441235986, 0.08779267189895798, 0.0685377637379008, -0.00440825505406949, -0.14858041225836194, 0.0395049181566092, 0.1409640155435572, -0.014156083098676955, 0.0345582910749162, 0.06251315974855218, -0.14292993678486554, -0.20406487553605232, 0.3338275227904448, -0.017639063385411585, -0.21372569042482767, 0.1925998417010274, -0.05405947873047714, -0.21112591438958872, 0.11562542386096099, 0.1956841825491524, 0.11160312653198068, -0.1250854927922021, 0.08828860541388135, -0.061729332650529926, 0.14456764512920173, 0.062243395099609064, 0.026520824433577343, 0.14055267295896492, 0.21455751262476733, 0.09168665433655782, 0.15525222205283956, -0.020770009178347114, -0.06573674718463986, -0.19164429615283834, -0.062371610411330415, -0.2175920025895125, 0.021462314424269574, -0.18209019414398112, -0.19381774149188805, 0.406468349409386, 0.16414598093111196, 0.22487461533525893, 0.171489172904142, 0.2926157578440576, 0.05063408723036791, 0.11408618629412276, 0.12230344649789662, 0.11490229663342752, 0.1439318234839573, 0.08517349644419576, -0.12603171101594665, 0.06607272440712128, 0.08659447251722731] |
1,802.04949 | ForkBase: An Efficient Storage Engine for Blockchain and Forkable
Applications | Existing data storage systems offer a wide range of functionalities to
accommodate an equally diverse range of applications. However, new classes of
applications have emerged, e.g., blockchain and collaborative analytics,
featuring data versioning, fork semantics, tamper-evidence or any combination
thereof. They present new opportunities for storage systems to efficiently
support such applications by embedding the above requirements into the storage.
In this paper, we present ForkBase, a storage engine specifically designed to
provide efficient support for blockchain and forkable applications. By
integrating the core application properties into the storage, ForkBase not only
delivers high performance but also reduces development effort. Data in ForkBase
is multi-versioned, and each version uniquely identifies the data content and
its history. Two variants of fork semantics are supported in ForkBase to
facilitate any collaboration workflows. A novel index structure is introduced
to efficiently identify and eliminate duplicate content across data objects.
Consequently, ForkBase is not only efficient in performance, but also in space
requirement. We demonstrate the performance of ForkBase using three
applications: a blockchain platform, a wiki engine and a collaborative
analytics application. We conduct extensive experimental evaluation of these
applications against respective state-of-the-art system. The results show that
ForkBase achieves superior performance while significantly lowering the
development cost.
| cs.DB cs.CR cs.DC | existing data storage systems offer a wide range of functionalities to accommodate an equally diverse range of applications however new classes of applications have emerged eg blockchain and collaborative analytics featuring data versioning fork semantics tamperevidence or any combination thereof they present new opportunities for storage systems to efficiently support such applications by embedding the above requirements into the storage in this paper we present forkbase a storage engine specifically designed to provide efficient support for blockchain and forkable applications by integrating the core application properties into the storage forkbase not only delivers high performance but also reduces development effort data in forkbase is multiversioned and each version uniquely identifies the data content and its history two variants of fork semantics are supported in forkbase to facilitate any collaboration workflows a novel index structure is introduced to efficiently identify and eliminate duplicate content across data objects consequently forkbase is not only efficient in performance but also in space requirement we demonstrate the performance of forkbase using three applications a blockchain platform a wiki engine and a collaborative analytics application we conduct extensive experimental evaluation of these applications against respective stateoftheart system the results show that forkbase achieves superior performance while significantly lowering the development cost | [['existing', 'data', 'storage', 'systems', 'offer', 'a', 'wide', 'range', 'of', 'functionalities', 'to', 'accommodate', 'an', 'equally', 'diverse', 'range', 'of', 'applications', 'however', 'new', 'classes', 'of', 'applications', 'have', 'emerged', 'eg', 'blockchain', 'and', 'collaborative', 'analytics', 'featuring', 'data', 'versioning', 'fork', 'semantics', 'tamperevidence', 'or', 'any', 'combination', 'thereof', 'they', 'present', 'new', 'opportunities', 'for', 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1,802.0495 | Whitham Deformations and the Space of Harmonic Tori in $\mathbb{S}^3$ | In this paper we investigate the space of harmonic maps from a 2-torus to
$\mathbb{S}^3$ using the spectral curve correspondence and Whitham
deformations. In an open and dense subset of a parameter space we find that the
space of harmonic maps is smooth and has dimension two. We also show that the
points that correspond to minimal tori (conformal harmonic maps) are either
smooth points of dimension two or singular.
| math.DG | in this paper we investigate the space of harmonic maps from a 2torus to mathbbs3 using the spectral curve correspondence and whitham deformations in an open and dense subset of a parameter space we find that the space of harmonic maps is smooth and has dimension two we also show that the points that correspond to minimal tori conformal harmonic maps are either smooth points of dimension two or singular | [['in', 'this', 'paper', 'we', 'investigate', 'the', 'space', 'of', 'harmonic', 'maps', 'from', 'a', '2torus', 'to', 'mathbbs3', 'using', 'the', 'spectral', 'curve', 'correspondence', 'and', 'whitham', 'deformations', 'in', 'an', 'open', 'and', 'dense', 'subset', 'of', 'a', 'parameter', 'space', 'we', 'find', 'that', 'the', 'space', 'of', 'harmonic', 'maps', 'is', 'smooth', 'and', 'has', 'dimension', 'two', 'we', 'also', 'show', 'that', 'the', 'points', 'that', 'correspond', 'to', 'minimal', 'tori', 'conformal', 'harmonic', 'maps', 'are', 'either', 'smooth', 'points', 'of', 'dimension', 'two', 'or', 'singular']] | [-0.16651852024453026, 0.08560085599497792, -0.0743612514043759, 0.03800219885805356, -0.03789981487872345, -0.08645229781312602, -0.013025525011055703, 0.403696421992832, -0.2736515343455332, -0.14334335642259768, 0.1305155619746074, -0.2918493206080581, -0.21568815757720067, 0.21539790842881693, -0.11608254035402621, 0.04953930743836931, 0.0360639101027378, 0.036425086563186986, -0.12365955702760922, -0.2487794699396805, 0.47059462049177714, -0.07094099979315485, 0.19244508664788945, 0.02495727007543402, 0.14448229453659484, -0.05000756401568651, 0.006528871120618922, 0.030334633760503493, -0.16070248181754973, 0.15927603520852116, 0.22955731880917613, 0.07833235853883837, 0.1752710231180702, -0.3677537708144103, -0.2509026702626475, 0.21067278017289937, 0.12345729416369328, 0.05310032812080213, 0.01839785463775375, -0.24345559839691436, 0.06772203335006322, -0.06998756519917931, -0.16320847339395966, -0.10797882714707936, 0.03117025052862508, 0.021195044315287046, -0.23924326839457666, 0.017574996909908287, 0.10077002310593214, 0.10744499402519847, -0.08845466298184225, 0.009199782447623355, -0.09662315786949226, 0.11351015676877328, 0.012095602013037674, 0.10480219606709268, 0.06922774449922145, -0.09347429606464824, -0.0775560644455254, 0.371282020587075, -0.1051986673979887, -0.2774658940466387, 0.19817717812423194, -0.19190341385214457, -0.14698130214320762, 0.13888691433572342, 0.15582981944483307, 0.10209770088217088, -0.08337616996307458, 0.18645819897647015, -0.0933625081287963, 0.15072122646462438, 0.12031429396010936, -0.03337187597395054, 0.17894668725452254, 0.06827922266508851, 0.14482588545818414, 0.16663401693637883, -0.11190646147754575, -0.08740569536042, -0.3472256593938385, -0.17936059961627637, -0.16320563615964992, 0.07015729526590024, -0.12465204841261895, -0.191840730288199, 0.4199659709752138, 0.057830221002015084, 0.29941133854112456, 0.05458553433418274, 0.22915815338492393, 0.08176819860403027, 0.004349038164530482, 0.10893413568181651, 0.19168113859237304, 0.09667692224362066, -0.011876672248555613, -0.14249924375742143, -0.15775810634451254, 0.1654618145099708] |
1,802.04951 | Optimal Fairness-Aware Time and Power Allocation in Wireless Powered
Communication Networks | In this paper, we consider the sum $\alpha$-fair utility maximization problem
for joint downlink (DL) and uplink (UL) transmissions of a wireless powered
communication network (WPCN) via time and power allocation. In the DL, the
users with energy harvesting receiver architecture decode information and
harvest energy based on simultaneous wireless information and power transfer.
While in the UL, the users utilize the harvested energy for information
transmission, and harvest energy when other users transmit UL information. We
show that the general sum $\alpha$-fair utility maximization problem can be
transformed into an equivalent convex one. Tradeoffs between sum rate and user
fairness can be balanced via adjusting the value of $\alpha$. In particular,
for zero fairness, i.e., $\alpha=0$, the optimal allocated time for both DL and
UL is proportional to the overall available transmission power. Tradeoffs
between sum rate and user fairness are presented through simulations.
| cs.IT math.IT | in this paper we consider the sum alphafair utility maximization problem for joint downlink dl and uplink ul transmissions of a wireless powered communication network wpcn via time and power allocation in the dl the users with energy harvesting receiver architecture decode information and harvest energy based on simultaneous wireless information and power transfer while in the ul the users utilize the harvested energy for information transmission and harvest energy when other users transmit ul information we show that the general sum alphafair utility maximization problem can be transformed into an equivalent convex one tradeoffs between sum rate and user fairness can be balanced via adjusting the value of alpha in particular for zero fairness ie alpha0 the optimal allocated time for both dl and ul is proportional to the overall available transmission power tradeoffs between sum rate and user fairness are presented through simulations | [['in', 'this', 'paper', 'we', 'consider', 'the', 'sum', 'alphafair', 'utility', 'maximization', 'problem', 'for', 'joint', 'downlink', 'dl', 'and', 'uplink', 'ul', 'transmissions', 'of', 'a', 'wireless', 'powered', 'communication', 'network', 'wpcn', 'via', 'time', 'and', 'power', 'allocation', 'in', 'the', 'dl', 'the', 'users', 'with', 'energy', 'harvesting', 'receiver', 'architecture', 'decode', 'information', 'and', 'harvest', 'energy', 'based', 'on', 'simultaneous', 'wireless', 'information', 'and', 'power', 'transfer', 'while', 'in', 'the', 'ul', 'the', 'users', 'utilize', 'the', 'harvested', 'energy', 'for', 'information', 'transmission', 'and', 'harvest', 'energy', 'when', 'other', 'users', 'transmit', 'ul', 'information', 'we', 'show', 'that', 'the', 'general', 'sum', 'alphafair', 'utility', 'maximization', 'problem', 'can', 'be', 'transformed', 'into', 'an', 'equivalent', 'convex', 'one', 'tradeoffs', 'between', 'sum', 'rate', 'and', 'user', 'fairness', 'can', 'be', 'balanced', 'via', 'adjusting', 'the', 'value', 'of', 'alpha', 'in', 'particular', 'for', 'zero', 'fairness', 'ie', 'alpha0', 'the', 'optimal', 'allocated', 'time', 'for', 'both', 'dl', 'and', 'ul', 'is', 'proportional', 'to', 'the', 'overall', 'available', 'transmission', 'power', 'tradeoffs', 'between', 'sum', 'rate', 'and', 'user', 'fairness', 'are', 'presented', 'through', 'simulations']] | [-0.2301246419693504, -0.021549672894240035, 0.040266985805897874, 0.04055497546134324, -0.12183823131410212, -0.27587238531880853, 0.15936463766907952, 0.4111042207171177, -0.3482995118432004, -0.2684577436379061, 0.048682940059245144, -0.302755301342956, -0.13971321743367046, 0.11522465648514957, -0.11397711287075975, 0.031370611765004434, 0.038819354756309744, 0.08159507637650802, 0.009795065752455387, -0.2886282981678458, 0.2861131165690463, 0.1452816104516387, 0.4266697546017581, 0.07805839139197407, 0.08432895194674875, 0.06473257703674508, -0.01669673658265122, -0.04014867447910766, -0.13675708356338498, 0.11177085383217139, 0.37563458259121096, 0.3005874543258085, 0.3144098501202875, -0.4276237528000412, -0.2512274249874312, 0.14324949956659613, 0.18920758801737222, -0.0843287399508348, -0.007793600141905762, -0.18841837058807243, 0.12962814914223192, -0.26709619399063805, 0.1007073031545713, 0.0248361443619019, -0.0889752903632049, 0.10003066539467345, -0.41327997309252107, 0.039568455966896024, -0.047646848205677717, 0.0015116655392219023, -0.12586915529622086, -0.08645244049919962, 0.005579448500969287, 0.20800395021716872, 0.06283303548437383, -0.07723251487738614, 0.09128304716071178, -0.10124485242694359, -0.10186465052908622, 0.3760809654883783, 0.019639352834301776, -0.2528401548749414, 0.06943836810613244, -0.031475207370159956, -0.0207116330973804, 0.14060171913227132, 0.2611546776931861, 0.05764148009555607, -0.2000569902799004, 0.012967760384018565, 0.00465006869414757, 0.16562090725637973, 0.11787992046889047, 0.16163309452099853, 0.17239908944686938, 0.13741579058676445, 0.1818449157193817, 0.12747056806853427, -0.0710295905728407, -0.11067018839048928, -0.1925013483678601, -0.1422224663133765, -0.2590334333415175, 0.0723144999030849, -0.11261578877536772, 0.04794618756157057, 0.3635911695032425, 0.07653818727181903, 0.10282753840859594, 0.1931867456513232, 0.44572840987479895, 0.18963062505012956, 0.005889148361467082, 0.18634341432038568, 0.18302662231284997, 0.06024462553383461, 0.18819041512424833, -0.27253893597436873, 0.033258513120356305, -0.018274656164556227] |
1,802.04952 | Spin-state ice in geometrically frustrated spin-crossover materials | Spin crossover materials contain metal ions that can access two spin-states:
one low-spin (LS), the other high-spin (HS). We propose that frustrated elastic
interactions can give rise to spin-state ices -- phases of matter without
long-range order, characterized by a local constraint or `ice rule'. The
low-energy physics of spin-state ices is described by an emergent
divergence-less gauge field with a gap to topological excitations that are
deconfined quasi-particles with spin fractionalized midway between the spins of
the LS and HS states.
| cond-mat.str-el cond-mat.soft cond-mat.stat-mech physics.chem-ph | spin crossover materials contain metal ions that can access two spinstates one lowspin ls the other highspin hs we propose that frustrated elastic interactions can give rise to spinstate ices phases of matter without longrange order characterized by a local constraint or ice rule the lowenergy physics of spinstate ices is described by an emergent divergenceless gauge field with a gap to topological excitations that are deconfined quasiparticles with spin fractionalized midway between the spins of the ls and hs states | [['spin', 'crossover', 'materials', 'contain', 'metal', 'ions', 'that', 'can', 'access', 'two', 'spinstates', 'one', 'lowspin', 'ls', 'the', 'other', 'highspin', 'hs', 'we', 'propose', 'that', 'frustrated', 'elastic', 'interactions', 'can', 'give', 'rise', 'to', 'spinstate', 'ices', 'phases', 'of', 'matter', 'without', 'longrange', 'order', 'characterized', 'by', 'a', 'local', 'constraint', 'or', 'ice', 'rule', 'the', 'lowenergy', 'physics', 'of', 'spinstate', 'ices', 'is', 'described', 'by', 'an', 'emergent', 'divergenceless', 'gauge', 'field', 'with', 'a', 'gap', 'to', 'topological', 'excitations', 'that', 'are', 'deconfined', 'quasiparticles', 'with', 'spin', 'fractionalized', 'midway', 'between', 'the', 'spins', 'of', 'the', 'ls', 'and', 'hs', 'states']] | [-0.1894305156842794, 0.3648034571240624, -0.04045807525736889, 0.09004489423988823, -0.05133355168602717, -0.21228582075118652, 0.039599408473198615, 0.3486569393196224, -0.24820335773912108, -0.30810137362115914, -0.01911948233221968, -0.333713676676982, -0.05600548765254149, 0.02468481153698155, 0.09714543918308652, -0.06222356562675149, -0.05901827207869954, -0.021934391999686206, -0.13969014134591468, -0.2066412064861966, 0.2817258216082449, -0.024377049452820678, 0.23191867902713978, 0.05917576483140389, 0.07091829194658562, -0.009057006074322594, 0.17740146192595546, -0.030472709383401606, -0.10923492877280992, 0.0816147001539242, 0.3089057240911104, -0.06643352792080906, 0.0818305460098027, -0.5013247192319896, -0.21428034683965422, 0.05940838222518379, 0.13441118368028124, 0.14709096926229973, -0.049518066012863945, -0.3267715904332789, -0.0500961593725937, -0.2055708784552544, -0.12584996328798387, -0.1810573252481351, -0.02997706460415811, -0.025309203296071954, -0.23638062366071722, 0.13940140776577647, 0.11827476854999493, 0.08058079456577054, -0.12416784303768734, -0.13786776131593886, -0.09613564164653697, -0.009870035593961308, 0.05008389433719402, 0.060180582382060865, 0.15982199900059235, -0.1447022048940445, -0.1866587032046583, 0.3755848318292403, -0.01881569502285946, -0.11026256608799744, 0.25899507639423747, -0.13930535075893585, -0.10027062871642871, 0.2286871216248399, 0.061508901455341894, 0.06744403724363189, -0.13831265621393182, 0.07550554850110339, -0.009198685873725257, 0.19960350855426104, -0.030862902561317622, 0.13049288351019775, 0.3671974466630706, 0.13990907129949465, 0.06662212779032595, 0.1560591337749631, -0.10563293108907039, -0.12313643362902013, -0.2212717303155381, -0.18779663371359123, -0.24603025939453532, 0.06954497005790472, -0.03803072718563518, -0.15081039835687404, 0.3606670657632711, 0.06870801796716994, 0.14239427528200058, -0.11312675847453468, 0.16392221750783514, 0.05814775974018338, 0.027416277579089373, 0.026531255913231477, 0.2400790406965915, 0.17765927440659315, 0.09147280567314153, -0.30993018534471406, 0.03723071997893261, 0.10330479538332625] |
1,802.04953 | Degree distributions of bipartite networks and their projections | Bipartite (two-mode) networks are important in the analysis of social and
economic systems as they explicitly show conceptual links between different
types of entities. However, applications of such networks often work with a
projected (one-mode) version of the original bipartite network. The topology of
the projected network, and the dynamics that take place on it, are highly
dependent on the degree distributions of the two different node types from the
original bipartite structure. To date, the interaction between the degree
distributions of bipartite networks and their one-mode projections is well
understood for only a few cases, or for networks that satisfy a restrictive set
of assumptions. Here we show a broader analysis in order to fill the gap left
by previous studies. We use the formalism of generating functions to prove that
the degree distributions of both node types in the original bipartite network
affect the degree distribution in the projected version. To support our
analysis, we simulate several types of synthetic bipartite networks using a
configuration model where node degrees are assigned from specific probability
distributions, ranging from peaked to heavy-tailed distributions. Our findings
show that when projecting a bipartite network onto a particular set of nodes,
the degree distribution for the resulting one-mode network follows the
distribution of the nodes being projected on to, but only so long as the degree
distribution for the opposite set of nodes does not have a heavier tail.
Furthermore, we show that bipartite degree distributions are not the only
feature driving topology formation of projected networks, in contrast to what
is commonly described in the literature.
| physics.soc-ph cond-mat.stat-mech cs.SI | bipartite twomode networks are important in the analysis of social and economic systems as they explicitly show conceptual links between different types of entities however applications of such networks often work with a projected onemode version of the original bipartite network the topology of the projected network and the dynamics that take place on it are highly dependent on the degree distributions of the two different node types from the original bipartite structure to date the interaction between the degree distributions of bipartite networks and their onemode projections is well understood for only a few cases or for networks that satisfy a restrictive set of assumptions here we show a broader analysis in order to fill the gap left by previous studies we use the formalism of generating functions to prove that the degree distributions of both node types in the original bipartite network affect the degree distribution in the projected version to support our analysis we simulate several types of synthetic bipartite networks using a configuration model where node degrees are assigned from specific probability distributions ranging from peaked to heavytailed distributions our findings show that when projecting a bipartite network onto a particular set of nodes the degree distribution for the resulting onemode network follows the distribution of the nodes being projected on to but only so long as the degree distribution for the opposite set of nodes does not have a heavier tail furthermore we show that bipartite degree distributions are not the only feature driving topology formation of projected networks in contrast to what is commonly described in the literature | [['bipartite', 'twomode', 'networks', 'are', 'important', 'in', 'the', 'analysis', 'of', 'social', 'and', 'economic', 'systems', 'as', 'they', 'explicitly', 'show', 'conceptual', 'links', 'between', 'different', 'types', 'of', 'entities', 'however', 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1,802.04954 | A sound and complete definition of linearizability on weak memory models | Linearizability is a widely accepted notion of correctness for concurrent
objects. Recent research has investigated redefining linearizability for
particular hardware weak memory models, in particular for TSO. In this paper,
we provide an overview of this research and show that such redefinitions of
linearizability are not required: under an interpretation of specification
behaviour which abstracts from weak memory effects, the standard definition of
linearizability is sound and complete on all hardware weak memory models. We
prove our result with respect to a definition of object refinement which takes
a weak memory model as a parameter. The main consequence of our findings is
that we can leverage the range of existing techniques and tools for standard
linearizability when verifying concurrent objects running on hardware weak
memory models.
| cs.LO | linearizability is a widely accepted notion of correctness for concurrent objects recent research has investigated redefining linearizability for particular hardware weak memory models in particular for tso in this paper we provide an overview of this research and show that such redefinitions of linearizability are not required under an interpretation of specification behaviour which abstracts from weak memory effects the standard definition of linearizability is sound and complete on all hardware weak memory models we prove our result with respect to a definition of object refinement which takes a weak memory model as a parameter the main consequence of our findings is that we can leverage the range of existing techniques and tools for standard linearizability when verifying concurrent objects running on hardware weak memory models | [['linearizability', 'is', 'a', 'widely', 'accepted', 'notion', 'of', 'correctness', 'for', 'concurrent', 'objects', 'recent', 'research', 'has', 'investigated', 'redefining', 'linearizability', 'for', 'particular', 'hardware', 'weak', 'memory', 'models', 'in', 'particular', 'for', 'tso', 'in', 'this', 'paper', 'we', 'provide', 'an', 'overview', 'of', 'this', 'research', 'and', 'show', 'that', 'such', 'redefinitions', 'of', 'linearizability', 'are', 'not', 'required', 'under', 'an', 'interpretation', 'of', 'specification', 'behaviour', 'which', 'abstracts', 'from', 'weak', 'memory', 'effects', 'the', 'standard', 'definition', 'of', 'linearizability', 'is', 'sound', 'and', 'complete', 'on', 'all', 'hardware', 'weak', 'memory', 'models', 'we', 'prove', 'our', 'result', 'with', 'respect', 'to', 'a', 'definition', 'of', 'object', 'refinement', 'which', 'takes', 'a', 'weak', 'memory', 'model', 'as', 'a', 'parameter', 'the', 'main', 'consequence', 'of', 'our', 'findings', 'is', 'that', 'we', 'can', 'leverage', 'the', 'range', 'of', 'existing', 'techniques', 'and', 'tools', 'for', 'standard', 'linearizability', 'when', 'verifying', 'concurrent', 'objects', 'running', 'on', 'hardware', 'weak', 'memory', 'models']] | [-0.16916880611005047, 0.02033588105636198, -0.07033365412193927, 0.0727587219841394, -0.13012445570590594, -0.1373097660419132, 0.023228093247217613, 0.3676188492940532, -0.2674228358304217, -0.32460012534376426, 0.11200496404420673, -0.19397762796235463, -0.11224623796870074, 0.22818882088084513, -0.12136977947583157, 0.07370805378545016, 0.0948600076316368, 0.027039353527167503, -0.059330168728982766, -0.2598730934939037, 0.31845748675284935, 0.03516962667483659, 0.28773180828503675, 0.05287987818444399, 0.08398615003430418, 0.016863622100255084, -0.028397928964021425, 0.04035168043559506, -0.08872933601205227, 0.10458675281898606, 0.23803594677788387, 0.19560668333470527, 0.29516624851477524, -0.4428480723872781, -0.19773561549594715, 0.0760864007493688, 0.07691607015606548, 0.1226577938271923, -0.030871610791008506, -0.28129724966036895, 0.15850703561648963, -0.18809682132291888, -0.08259459825173493, -0.127128442106325, 0.03581154893433291, 0.007480664958988893, -0.2575464076916909, -0.012850087963872485, 0.18332354952433397, 0.09931821545164678, -0.048725031042796754, -0.055961333152409344, 0.020363202919104387, 0.08521523667681437, 0.04831524631415036, 0.008247564591112592, 0.08607446752296435, -0.14170554055488624, -0.15861297964990612, 0.380601403643451, -0.04074529170738681, -0.17129657623018063, 0.2449318661007084, -0.04359634235383026, -0.2339376843855938, 0.03894801015803029, 0.14469559380548105, 0.11081592867424386, -0.16565546469768927, 0.13144561176286226, -0.03773542788500587, 0.20661603348950544, 0.033444881908565996, 0.08835686531679202, 0.17019959534740164, 0.21288989623084606, 0.05669434604397605, 0.14188963873311877, -0.0002671359735171473, -0.10081585283790316, -0.3481959094485593, -0.17994804046900262, -0.11463930109785574, 0.017874459981930345, -0.07071631510766493, -0.19789063297433868, 0.3597804370691024, 0.2287809726124304, 0.11287458465316348, 0.13741886849138177, 0.3492308581425321, 0.09219263079816416, 0.11082526813778612, 0.07257587964912611, 0.19567607188438405, 0.0892792613807297, 0.1346550122834742, -0.13310254710478828, 0.13205676121241783, 0.06691941882054957] |
1,802.04955 | Multiterminal Secret Key Agreement at Asymptotically Zero Discussion
Rate | In the multiterminal secret key agreement problem, a set of users want to
discuss with each other until they share a common secret key independent of
their discussion. We want to characterize the maximum secret key rate, called
the secrecy capacity, asymptotically when the total discussion rate goes to
zero. In the case of only two users, the capacity is equal to the
G\'acs-K\"orner common information. However, when there are more than two
users, the capacity is unknown. It is plausible that a multivariate extension
of the G\'acs-K\"orner common information is the capacity, however, proving the
converse is challenging. We resolved this for the hypergraphical sources and
finite linear sources, and provide efficiently computable characterizations. We
also give some ideas of extending the techniques to more general source models.
| cs.IT math.IT | in the multiterminal secret key agreement problem a set of users want to discuss with each other until they share a common secret key independent of their discussion we want to characterize the maximum secret key rate called the secrecy capacity asymptotically when the total discussion rate goes to zero in the case of only two users the capacity is equal to the gacskorner common information however when there are more than two users the capacity is unknown it is plausible that a multivariate extension of the gacskorner common information is the capacity however proving the converse is challenging we resolved this for the hypergraphical sources and finite linear sources and provide efficiently computable characterizations we also give some ideas of extending the techniques to more general source models | [['in', 'the', 'multiterminal', 'secret', 'key', 'agreement', 'problem', 'a', 'set', 'of', 'users', 'want', 'to', 'discuss', 'with', 'each', 'other', 'until', 'they', 'share', 'a', 'common', 'secret', 'key', 'independent', 'of', 'their', 'discussion', 'we', 'want', 'to', 'characterize', 'the', 'maximum', 'secret', 'key', 'rate', 'called', 'the', 'secrecy', 'capacity', 'asymptotically', 'when', 'the', 'total', 'discussion', 'rate', 'goes', 'to', 'zero', 'in', 'the', 'case', 'of', 'only', 'two', 'users', 'the', 'capacity', 'is', 'equal', 'to', 'the', 'gacskorner', 'common', 'information', 'however', 'when', 'there', 'are', 'more', 'than', 'two', 'users', 'the', 'capacity', 'is', 'unknown', 'it', 'is', 'plausible', 'that', 'a', 'multivariate', 'extension', 'of', 'the', 'gacskorner', 'common', 'information', 'is', 'the', 'capacity', 'however', 'proving', 'the', 'converse', 'is', 'challenging', 'we', 'resolved', 'this', 'for', 'the', 'hypergraphical', 'sources', 'and', 'finite', 'linear', 'sources', 'and', 'provide', 'efficiently', 'computable', 'characterizations', 'we', 'also', 'give', 'some', 'ideas', 'of', 'extending', 'the', 'techniques', 'to', 'more', 'general', 'source', 'models']] | [-0.15503951896148077, 0.05477026685379272, -0.05473263248396936, 0.12653562102409066, -0.11022091647537872, -0.24817112472644726, 0.12939661333351865, 0.3504254475755747, -0.2956403604484806, -0.22968494746285353, 0.1313941133111109, -0.32299636073352755, -0.09914619700853215, 0.19488392103790098, -0.14455950283806396, 0.05021571699356617, 0.0010931327570836212, 0.1144869708072109, -0.0386193752164281, -0.32654640116086303, 0.3391807908697646, 0.07022961384306352, 0.2784284429350334, 0.0684088878261373, 0.06721111049861178, 0.007542020273070002, -0.05672212843169538, -0.03580385329591673, -0.16854094377162282, 0.13687956117205466, 0.32987237028731237, 0.20264934512190727, 0.3056626518567403, -0.3699910783447152, -0.21385719648981105, 0.1301077614906569, 0.132152209681993, 0.10327297564624816, -0.03634295994239903, -0.19921479298184488, 0.12127542019507653, -0.1476634617988742, -0.07072252432577485, -0.018701442250216655, -0.028115596448959307, 0.008951386850139544, -0.27101793792820716, 0.033097296218385974, 0.07234261763578122, 0.028207925668116227, -0.0060028090726497564, -0.09186873865014217, 0.033099052064886045, 0.20575723237793012, 0.05746621617329011, 0.02121727212057846, 0.07028977523463988, -0.11634586821490711, -0.0883343133591693, 0.35389112267469947, -0.013628076219253509, -0.21197378273345818, 0.17647905452826687, -0.13463246875781884, -0.1266172583081377, 0.11438919429111388, 0.14700619900931222, 0.11073731988408538, -0.1879272713163929, 0.013642779810447968, -0.10356049777492303, 0.1921771248557078, 0.07070119031019105, 0.11257528652099291, 0.19349305819748908, 0.11458007836454483, 0.11170608977346803, 0.1638889721194968, -0.058395044394415015, -0.1311989429681165, -0.2818756926905855, -0.15891098069359166, -0.1944853485987861, 0.04815753929085972, -0.107732892480215, -0.09538525238167495, 0.3472996850230897, 0.1928465818091072, 0.1487730887732938, 0.08109709523011778, 0.34482777166331924, 0.08894987958007358, 0.05288520172173374, 0.16861064076900137, 0.18170937781367458, 0.15435250112317617, 0.06906222199385827, -0.14252290060624567, 0.12951095018701902, -0.006352977935484675] |
1,802.04956 | D2KE: From Distance to Kernel and Embedding | For many machine learning problem settings, particularly with structured
inputs such as sequences or sets of objects, a distance measure between inputs
can be specified more naturally than a feature representation. However, most
standard machine models are designed for inputs with a vector feature
representation. In this work, we consider the estimation of a function
$f:\mathcal{X} \rightarrow \R$ based solely on a dissimilarity measure
$d:\mathcal{X}\times\mathcal{X} \rightarrow \R$ between inputs. In particular,
we propose a general framework to derive a family of \emph{positive definite
kernels} from a given dissimilarity measure, which subsumes the widely-used
\emph{representative-set method} as a special case, and relates to the
well-known \emph{distance substitution kernel} in a limiting case. We show that
functions in the corresponding Reproducing Kernel Hilbert Space (RKHS) are
Lipschitz-continuous w.r.t. the given distance metric. We provide a tractable
algorithm to estimate a function from this RKHS, and show that it enjoys better
generalizability than Nearest-Neighbor estimates. Our approach draws from the
literature of Random Features, but instead of deriving feature maps from an
existing kernel, we construct novel kernels from a random feature map, that we
specify given the distance measure. We conduct classification experiments with
such disparate domains as strings, time series, and sets of vectors, where our
proposed framework compares favorably to existing distance-based learning
methods such as $k$-nearest-neighbors, distance-substitution kernels,
pseudo-Euclidean embedding, and the representative-set method.
| stat.ML cs.LG | for many machine learning problem settings particularly with structured inputs such as sequences or sets of objects a distance measure between inputs can be specified more naturally than a feature representation however most standard machine models are designed for inputs with a vector feature representation in this work we consider the estimation of a function fmathcalx rightarrow r based solely on a dissimilarity measure dmathcalxtimesmathcalx rightarrow r between inputs in particular we propose a general framework to derive a family of emphpositive definite kernels from a given dissimilarity measure which subsumes the widelyused emphrepresentativeset method as a special case and relates to the wellknown emphdistance substitution kernel in a limiting case we show that functions in the corresponding reproducing kernel hilbert space rkhs are lipschitzcontinuous wrt the given distance metric we provide a tractable algorithm to estimate a function from this rkhs and show that it enjoys better generalizability than nearestneighbor estimates our approach draws from the literature of random features but instead of deriving feature maps from an existing kernel we construct novel kernels from a random feature map that we specify given the distance measure we conduct classification experiments with such disparate domains as strings time series and sets of vectors where our proposed framework compares favorably to existing distancebased learning methods such as knearestneighbors distancesubstitution kernels pseudoeuclidean embedding and the representativeset method | [['for', 'many', 'machine', 'learning', 'problem', 'settings', 'particularly', 'with', 'structured', 'inputs', 'such', 'as', 'sequences', 'or', 'sets', 'of', 'objects', 'a', 'distance', 'measure', 'between', 'inputs', 'can', 'be', 'specified', 'more', 'naturally', 'than', 'a', 'feature', 'representation', 'however', 'most', 'standard', 'machine', 'models', 'are', 'designed', 'for', 'inputs', 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1,802.04957 | Axisymmetric magnetic modes of neutron stars having mixed poloidal and
toroidal magnetic fields | We calculate axisymmetric magnetic modes of a neutron star possessing a mixed
poloidal and toroidal magnetic field, where the toroidal field is assumed to be
proportional to a dimensionless parameter $\zeta_0$. Here, we assume an
isentropic structure for the neutron star and consider no effects of rotation.
Ignoring the equilibrium deformation due to the magnetic field, we employ a
polytrope of the index $n=1$ as the background model for our modal analyses.
For the mixed poloidal and toroidal magnetic field with $\zeta_0\not=0$,
axisymmetric spheroidal and toroidal modes are coupled. We compute axisymmetric
spheroidal and toroidal magnetic modes as a function of the parameter $\zeta_0$
from $0$ to $\sim 1$ for the surface field strengths $B_S=10^{14}$G and
$10^{15}$G. We find that the frequency $\omega$ of the magnetic modes decreases
with increasing $\zeta_0$. We also find that the frequency of the spheroidal
magnetic modes is almost exactly proportional to $B_S$ for $\zeta_0\lesssim 1$
but that this proportionality holds only when $\zeta_0\ll 1$ for the toroidal
magnetic modes. The wave patterns of the spheroidal magnetic modes and toroidal
magnetic modes are not strongly affected by the coupling so long as
$\zeta_0\lesssim 1$. We find no unstable modes having $\omega^2<0$.
| astro-ph.HE | we calculate axisymmetric magnetic modes of a neutron star possessing a mixed poloidal and toroidal magnetic field where the toroidal field is assumed to be proportional to a dimensionless parameter zeta_0 here we assume an isentropic structure for the neutron star and consider no effects of rotation ignoring the equilibrium deformation due to the magnetic field we employ a polytrope of the index n1 as the background model for our modal analyses for the mixed poloidal and toroidal magnetic field with zeta_0not0 axisymmetric spheroidal and toroidal modes are coupled we compute axisymmetric spheroidal and toroidal magnetic modes as a function of the parameter zeta_0 from 0 to sim 1 for the surface field strengths b_s1014g and 1015g we find that the frequency omega of the magnetic modes decreases with increasing zeta_0 we also find that the frequency of the spheroidal magnetic modes is almost exactly proportional to b_s for zeta_0lesssim 1 but that this proportionality holds only when zeta_0ll 1 for the toroidal magnetic modes the wave patterns of the spheroidal magnetic modes and toroidal magnetic modes are not strongly affected by the coupling so long as zeta_0lesssim 1 we find no unstable modes having omega20 | [['we', 'calculate', 'axisymmetric', 'magnetic', 'modes', 'of', 'a', 'neutron', 'star', 'possessing', 'a', 'mixed', 'poloidal', 'and', 'toroidal', 'magnetic', 'field', 'where', 'the', 'toroidal', 'field', 'is', 'assumed', 'to', 'be', 'proportional', 'to', 'a', 'dimensionless', 'parameter', 'zeta_0', 'here', 'we', 'assume', 'an', 'isentropic', 'structure', 'for', 'the', 'neutron', 'star', 'and', 'consider', 'no', 'effects', 'of', 'rotation', 'ignoring', 'the', 'equilibrium', 'deformation', 'due', 'to', 'the', 'magnetic', 'field', 'we', 'employ', 'a', 'polytrope', 'of', 'the', 'index', 'n1', 'as', 'the', 'background', 'model', 'for', 'our', 'modal', 'analyses', 'for', 'the', 'mixed', 'poloidal', 'and', 'toroidal', 'magnetic', 'field', 'with', 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1,802.04958 | Fluctuations in Arctic Sea Ice Extent: Comparing Observations and
Climate Models | The fluctuation statistics of the observed sea ice extent during the
satellite era are compared with model output from CMIP5 models using a
multi-fractal time series method. The two robust features of the observations
are that on annual to bi-annual time scales the ice extent exhibits white noise
structure and there is a decadal scale trend associated with the decay of the
ice cover. It is shown that (i) there is a large inter-model variability in the
time scales extracted from the models, (ii) none of the models exhibit the
decadal time scales found in the satellite observations, (iii) {5} of the 21
models examined exhibit the observed white noise structure, and (iv) the
multi-model ensemble mean exhibits neither the observed white noise structure
nor the observed decadal trend. It is proposed that the observed fluctuation
statistics produced by this method serve as an appropriate test bed for
modeling studies.
| physics.ao-ph physics.geo-ph | the fluctuation statistics of the observed sea ice extent during the satellite era are compared with model output from cmip5 models using a multifractal time series method the two robust features of the observations are that on annual to biannual time scales the ice extent exhibits white noise structure and there is a decadal scale trend associated with the decay of the ice cover it is shown that i there is a large intermodel variability in the time scales extracted from the models ii none of the models exhibit the decadal time scales found in the satellite observations iii 5 of the 21 models examined exhibit the observed white noise structure and iv the multimodel ensemble mean exhibits neither the observed white noise structure nor the observed decadal trend it is proposed that the observed fluctuation statistics produced by this method serve as an appropriate test bed for modeling studies | [['the', 'fluctuation', 'statistics', 'of', 'the', 'observed', 'sea', 'ice', 'extent', 'during', 'the', 'satellite', 'era', 'are', 'compared', 'with', 'model', 'output', 'from', 'cmip5', 'models', 'using', 'a', 'multifractal', 'time', 'series', 'method', 'the', 'two', 'robust', 'features', 'of', 'the', 'observations', 'are', 'that', 'on', 'annual', 'to', 'biannual', 'time', 'scales', 'the', 'ice', 'extent', 'exhibits', 'white', 'noise', 'structure', 'and', 'there', 'is', 'a', 'decadal', 'scale', 'trend', 'associated', 'with', 'the', 'decay', 'of', 'the', 'ice', 'cover', 'it', 'is', 'shown', 'that', 'i', 'there', 'is', 'a', 'large', 'intermodel', 'variability', 'in', 'the', 'time', 'scales', 'extracted', 'from', 'the', 'models', 'ii', 'none', 'of', 'the', 'models', 'exhibit', 'the', 'decadal', 'time', 'scales', 'found', 'in', 'the', 'satellite', 'observations', 'iii', '5', 'of', 'the', '21', 'models', 'examined', 'exhibit', 'the', 'observed', 'white', 'noise', 'structure', 'and', 'iv', 'the', 'multimodel', 'ensemble', 'mean', 'exhibits', 'neither', 'the', 'observed', 'white', 'noise', 'structure', 'nor', 'the', 'observed', 'decadal', 'trend', 'it', 'is', 'proposed', 'that', 'the', 'observed', 'fluctuation', 'statistics', 'produced', 'by', 'this', 'method', 'serve', 'as', 'an', 'appropriate', 'test', 'bed', 'for', 'modeling', 'studies']] | [-0.08310024028566355, 0.15828223207742365, -0.12181627098470926, 0.13005221933048838, 0.008749610036611557, -0.0952829946934556, -0.0067545059292266766, 0.35130111439774436, -0.2441812794158856, -0.34412860963493586, 0.1250517040832589, -0.29436298028876384, -0.15857297950113813, 0.19291586678319922, -0.029119124403223395, 0.01631144268826271, 0.06708434962667525, -0.0057114158334055296, -0.008087484369364878, -0.2544150972319767, 0.24500956295679013, 0.11653131978586316, 0.3083346993351976, -0.035813797244724506, 0.1024943135958165, -0.10265592376235873, -0.1219191937148571, 0.02695133596115435, -0.09963235564292215, 0.021639897072066865, 0.20467664287396473, 0.12686164289402466, 0.2287650561503445, -0.3738661355401079, -0.2554981173326572, 0.09044697533516834, 0.07507376232494911, 0.052772314371541146, -0.02016611467814073, -0.26165879582986235, 0.01839514103407661, -0.13793477652594446, -0.10952405447605998, -0.014385122377425431, 0.050150180689524856, 0.026818728543197116, -0.24309166159364395, 0.14975619043534, 0.07297533804744792, 0.09883473489433527, -0.1044137650510917, -0.13105292784360548, -0.04266529594237606, 0.10734677442970375, 0.06743395353628633, 0.0003388383332639933, 0.12176060748596985, -0.10374743228002141, -0.0873385068650047, 0.3795680038301119, -0.1371256635679553, -0.10498008684798454, 0.18578110861436775, -0.20265738456199567, -0.1233736941870302, 0.16000063228110473, 0.14449213785429796, 0.039935408821329475, -0.10962264214448321, 0.03762838140052433, -0.02430664752299587, 0.23492212995576361, 0.021341365436092018, 0.012070178808644414, 0.2631510923181971, 0.2168821539233128, 0.015983948620657128, 0.07768053306887547, -0.22523724794717662, -0.11574249493346239, -0.2507047935699423, -0.04704217332104842, -0.17040070129713664, 0.014215157071109085, -0.06541298617667053, -0.16808380970168704, 0.40521431069200237, 0.1867960415687412, 0.20902836530779798, 0.05113157943977664, 0.2486518167393903, 0.10256516352218266, 0.11379937908456972, 0.09341716873769959, 0.23542445669571557, 0.09545474365819245, 0.1726313974801451, -0.22289840505458414, 0.11743607618225117, -0.03430335957246522] |
1,802.04959 | Pathway towards programmable wave anisotropy in cellular metamaterials | In this work, we provide a proof-of-concept experimental demonstration of the
wave control capabilities of cellular metamaterials endowed with populations of
tunable electromechanical resonators. Each independently tunable resonator
comprises a piezoelectric patch and a resistor-inductor shunt, and its resonant
frequency can be seamlessly re-programmed without interfering with the cellular
structure's default properties. We show that, by strategically placing the
resonators in the lattice domain and by deliberately activating only selected
subsets of them, chosen to conform to the directional features of the beamed
wave response, it is possible to override the inherent wave anisotropy of the
cellular medium. The outcome is the establishment of tunable spatial patterns
of energy distillation resulting in a non-symmetric correction of the
wavefields.
| physics.app-ph cond-mat.mtrl-sci | in this work we provide a proofofconcept experimental demonstration of the wave control capabilities of cellular metamaterials endowed with populations of tunable electromechanical resonators each independently tunable resonator comprises a piezoelectric patch and a resistorinductor shunt and its resonant frequency can be seamlessly reprogrammed without interfering with the cellular structures default properties we show that by strategically placing the resonators in the lattice domain and by deliberately activating only selected subsets of them chosen to conform to the directional features of the beamed wave response it is possible to override the inherent wave anisotropy of the cellular medium the outcome is the establishment of tunable spatial patterns of energy distillation resulting in a nonsymmetric correction of the wavefields | [['in', 'this', 'work', 'we', 'provide', 'a', 'proofofconcept', 'experimental', 'demonstration', 'of', 'the', 'wave', 'control', 'capabilities', 'of', 'cellular', 'metamaterials', 'endowed', 'with', 'populations', 'of', 'tunable', 'electromechanical', 'resonators', 'each', 'independently', 'tunable', 'resonator', 'comprises', 'a', 'piezoelectric', 'patch', 'and', 'a', 'resistorinductor', 'shunt', 'and', 'its', 'resonant', 'frequency', 'can', 'be', 'seamlessly', 'reprogrammed', 'without', 'interfering', 'with', 'the', 'cellular', 'structures', 'default', 'properties', 'we', 'show', 'that', 'by', 'strategically', 'placing', 'the', 'resonators', 'in', 'the', 'lattice', 'domain', 'and', 'by', 'deliberately', 'activating', 'only', 'selected', 'subsets', 'of', 'them', 'chosen', 'to', 'conform', 'to', 'the', 'directional', 'features', 'of', 'the', 'beamed', 'wave', 'response', 'it', 'is', 'possible', 'to', 'override', 'the', 'inherent', 'wave', 'anisotropy', 'of', 'the', 'cellular', 'medium', 'the', 'outcome', 'is', 'the', 'establishment', 'of', 'tunable', 'spatial', 'patterns', 'of', 'energy', 'distillation', 'resulting', 'in', 'a', 'nonsymmetric', 'correction', 'of', 'the', 'wavefields']] | [-0.19349647062615707, 0.14335246890003228, -0.0178513801497463, -0.03404708312024386, -0.1194648841698455, -0.1695157854626767, 0.08166961084840357, 0.4477014330207792, -0.2679839718919725, -0.2623019298124645, 0.024310764013869196, -0.25897182608978486, -0.16055465473705888, 0.16948202737949342, -0.02338671593620386, 0.037340730838636815, 0.013659894458440125, -0.03152915690682279, 0.02158521555322342, -0.1500462074465572, 0.29125547707458943, 0.08742642391106895, 0.3146323408925524, -0.0023384843634552937, 0.12820309961839524, -0.011752759084567172, 0.012383316677366184, 0.010140302001984201, -0.07980402154902953, 0.14326682068710017, 0.27363131693604154, 0.05273755914710749, 0.28596832187703025, -0.4655690988860069, -0.24603945933855498, 0.055364986610972985, 0.12235515984123509, 0.13026987619172686, -0.0168323697309352, -0.3158857171288413, 0.06956748963675947, -0.1578881190214147, -0.1561284026083274, -0.04211881258485154, -0.03797557834599517, 0.09037746559096198, -0.26611918484808034, -0.016247281342004545, 0.050132113869071133, 0.021572162946447346, -0.06438588513445476, -0.03467833879005769, -0.04424859900584715, 0.11545271228234737, -0.023298410585341163, -0.01837965425872045, 0.1871983309233617, -0.1172740746818833, -0.12432943904597281, 0.36987289579079735, -0.022571763151094444, -0.216804095910082, 0.15844937909044263, -0.1330731248116901, -0.01945084957485525, 0.13515802634418264, 0.1834026778307863, 0.03171977544358621, -0.15984035010596848, 0.023005522889558736, -0.012090205465285227, 0.22495003177139622, 0.11561264006150329, 0.10587173517650136, 0.22293859443221337, 0.2071647945429302, 0.04748660440230344, 0.164905929312102, -0.06875590903363112, 0.0015138152993729927, -0.2723738893102377, -0.11120418746533811, -0.21886417307914832, 0.02808181105818874, -0.102971492995509, -0.17958840909294593, 0.43311312708717126, 0.13604050712126634, 0.15003967348040423, 0.006326682825819549, 0.2980971305479861, 0.08358775310090974, 0.12956505816063693, 0.004315860294856322, 0.25425527626887345, 0.12702555235666343, 0.10070290580953066, -0.2611816089687885, 0.05586656680903756, -0.044240188816737414] |
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