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There were two objectives. The first was to determine if a real time graphing activity will influence the interpretation of the subject of kinematic graphs, as measured by the TUGK. The second was to determine if attitude, as measured by…

Physics Education · Physics 2020-08-13 Stanley Sobolewski , Brandon Vought , Kathryn Hagood

The paper gives a symplectic-geometric account of semiclassical Gaussian wave packet dynamics. We employ geometric techniques to "strip away" the symplectic structure behind the time-dependent Schr\"odinger equation and incorporate it into…

Mathematical Physics · Physics 2013-09-20 Tomoki Ohsawa , Melvin Leok

We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of…

Disordered Systems and Neural Networks · Physics 2015-06-16 Danielle S. Bassett , Nicholas F. Wymbs , Mason A. Porter , Peter J. Mucha , Scott T. Grafton

We theoretically study the geometric effect of quantum dynamical evolution in the presence of a nonequilibrium noisy environment. We derive the expression of the time dependent geometric phase in terms of the dynamical evolution and the…

Quantum Physics · Physics 2019-03-12 Xiangji Cai , Ruixuan Meng , Yanhui Zhang , Lifei Wang

With an increasing demand for training powers for deep learning algorithms and the rapid growth of computation resources in data centers, it is desirable to dynamically schedule different distributed deep learning tasks to maximize resource…

Machine Learning · Computer Science 2019-05-03 Haibin Lin , Hang Zhang , Yifei Ma , Tong He , Zhi Zhang , Sheng Zha , Mu Li

According to theorems of Shnirelman and followers, in the semiclassical limit the quantum wavefunctions of classically ergodic systems tend to the microcanonical density on the energy shell. We here develop a semiclassical theory that…

Spatio-temporal feature learning is of central importance for action recognition in videos. Existing deep neural network models either learn spatial and temporal features independently (C2D) or jointly with unconstrained parameters (C3D).…

Computer Vision and Pattern Recognition · Computer Science 2019-03-05 Chao Li , Qiaoyong Zhong , Di Xie , Shiliang Pu

This paper is mainly a semi-tutorial introduction to elementary algebraic topology and its applications to Ising-type models of statistical physics, using graphical models of linear and group codes. It contains new material on systematic…

Information Theory · Computer Science 2018-12-20 G. David Forney

We are at the dawn of the second quantum revolution, where our ability to create and control individual quantum systems is poised to drive transformative advancements in basic science, computation, and everyday life. However, quantum theory…

The educational use of video and multimedia is increasing rapidly in secondary and higher education across all disciplines. Videos for physics education can be found in many universities and other educational institutions websites all over…

Physics Education · Physics 2012-11-20 M. Pilakouta , K. Mitritsakis , E. Fragkedakis , C. P. Varsamis

Projective measurements of a single two-level quantum mechanical system (a qubit) evolving under a time-independent Hamiltonian produce a probability distribution that is periodic in the evolution time. The period of this distribution is an…

Quantum Physics · Physics 2012-06-05 Christopher Ferrie , Christopher E. Granade , D. G. Cory

Phase diagrams of charged semi-hard-core bosons are studied in the mean field approximation. An increase in the parameter of local correlations is shown to lead to the transformation of the phase diagram of the system from the form…

Statistical Mechanics · Physics 2022-01-05 V. V. Konev , Yu. D. Panov

Temporal information has long been considered to be essential for perception. While there is extensive research on the role of image information for perceptual tasks, the role of the temporal dimension remains less well understood: What can…

Computer Vision and Pattern Recognition · Computer Science 2026-02-17 Willem Davison , Xinyue Hao , Laura Sevilla-Lara

The semiclassical description of the dynamics of wave packets in periodic potentials and subject to an applied force relies on the concepts of effective mass and anomalous transport. This picture is valid if the force changes slowly in time…

Mesoscale and Nanoscale Physics · Physics 2014-11-11 Y. Fang , Federico Duque-Gomez , J. E. Sipe

The concepts and methods used for the study of disordered systems have proven useful in the analysis of the evolution equations of quantum chromodynamics in the high-energy regime: Indeed, parton branching in the semi-classical…

High Energy Physics - Phenomenology · Physics 2015-07-06 Stephane Munier

This paper endeavors to learn time-varying graphs by using structured temporal priors that assume underlying relations between arbitrary two graphs in the graph sequence. Different from many existing chain structure based methods in which…

Machine Learning · Computer Science 2022-02-24 Xiang Zhang , Qiao Wang

Students are taught several models of conductivity, both at the introductory and the advanced level. From early macroscopic models of current flow in circuits, through the discussion of microscopic particle descriptions of electrons flowing…

Physics Education · Physics 2009-11-07 Michael C. Wittmann , Richard N. Steinberg , Edward F. Redish

The interplay between interactions and quenched disorder can result in rich dynamical quantum phenomena far from equilibrium, particularly when many-body localization prevents the system from full thermalization. With the aim of tackling…

Disordered Systems and Neural Networks · Physics 2018-02-14 S. J. Thomson , M. Schiró

We propose a simple discrete time semi-supervised graph embedding approach to link prediction in dynamic networks. The learned embedding reflects information from both the temporal and cross-sectional network structures, which is performed…

Machine Learning · Statistics 2016-10-17 Ryohei Hisano

Heterogeneous graph neural networks have become popular in various domains. However, their generalizability and interpretability are limited due to the discrepancy between their inherent inference flows and human reasoning logic or…

Machine Learning · Computer Science 2023-12-12 Tianqianjin Lin , Kaisong Song , Zhuoren Jiang , Yangyang Kang , Weikang Yuan , Xurui Li , Changlong Sun , Cui Huang , Xiaozhong Liu