IEEE Transactions on Pattern Analysis and Machine Intelligence

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

In this review, a general algorithm for constructing coherent states of dynamical groups for a given quantum physical system is presented. The result is that, for a given dynamical group, the coherent states are isomorphic to a coset space of group geometrical space. Thus the topological and algebraic structure of the coherent states as well as the associated dynamical system can be extensively discussed. In addition, a quantum-mechanical phase-space representation is constructed via the coherent-state theory. Several useful methods for employing the coherent states to study the physical phenomena of quantum-dynamic systems, such as the path integral, variational principle, classical limit, and thermodynamic limit of quantum mechanics, are described.

Coherent states: Theory and some Applications

Simple models of quantum chaos: Spectrum and eigenfunctions

information in analogical transfer, Psychol. Bull. 115, 381 (1994). [134] S. Y. Lin and C. Singh, Using isomorphic problems to learn introductory physics, Phys. Rev. ST Phys. Educ. Res. 7, 020104 (2011). [135] D. Hammer, A. Elby, R. Scherr, and E. F. Redish, in Transfer of Learning from a Modern Multidisciplinary Perspective, edited by J. Mestre (Information Age Publishing, Greenwich, CT, 2005), pp. 89–119. [136] J. Tuminaro, A cognitive framework for analyzing and describing introductory students use and understanding of mathematics in physics, Ph.D. thesis, University of Maryland, 2004 (unpublished). [137] J. S. Brown, A. Collins, and P. Duguid, Situated cognition and the culture of learning, Educ. Res. 18, 32 (1989). [138] A. Mason and C. Singh, Revisiting categorization, in Proceedings of the 2009 National Association for Research in Science Teaching (NARST) Annual Meeting (Garden Grove, CA, 2009) Symposium S2.2, pp. 3–21. [139] F. Mateycik, D. Jonassen, and N. S. Rebello, Using Similarity Rating Tasks to Assess Case Reuse in Problem Solving, Proceedings of the Physics Education Research Conference (Ann Arbor, MI, 2010).[AIP Conference Proceedings 1179, 201, (2009)]. [140] S. F. Wolf, D. P. Dougherty, and G. Kortemeyer, Empirical approach to interpreting card-sorting data, Phys. Rev. ST Phys. Educ. Res. 8, 010124 (2012). [141] A. Carmichael, A. Larson, E. Gire, L. Loschky, and N. S. Rebello, How does visual attention differ between experts and novices on physics problems?, AIP Conf. Proc. 1289, 93 (2010). [142] D. Rosengrant, A. Van Heuvelen, and E. Etkina, Do students use and understand free-body diagrams?, Phys. Rev. ST Phys. Educ. Res. 5, 010108 (2009). [143] A. D. Smith, J. P. Mestre, and B. H. Ross, Eye gaze patterns as students study worked out examples in mechanics, Phys. Rev. ST Phys. Educ. Res. 6, 020118 (2010). [144] T. de Jong and M. G. M. Ferguson-Hessler, Cognitive structures of good and poor novice problem solvers in physics, J. Educ. Psychol. 78, 279 (1986). [145] J. L. Docktor, J. P. Mestre, and B. H. Ross, Impact of a short intervention on novices’ categorization criteria, Phys. Rev. ST Phys. Educ. Res. 8, 020102 (2012). [146] S. F. Wolf, D. P. Dougherty, and G. Kortemeyer, Rigging the deck: Selecting good problems for expert-novice card-sorting experiments, Phys. Rev. ST Phys. Educ. Res. 8, 020116 (2012). [147] M. Finegold and R. Mass, Differences in the process of solving physics problems between good problem solvers and poor problem solvers, Res. Sci. Technol. Educ. 3, 59 (1985). [148] L. N. Walsh, R. G. Howard, and B. Bowe, Phenomenographic study of students’ problem solving approaches in SYNTHESIS OF DISCIPLINE-BASED ... PHYS. REV. ST PHYS. EDUC. RES 10, 020119 (2014)

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Synthesis of Discipline-Based Education Research in Physics.

Computational Physics is designed to provide direct experience in the computer modeling of physical systems. Its scope includes the essential numerical techniques needed to "do physics" on a computer. Each of these is developed heuristically in the text, with the aid of simple mathematical illustrations. However, the real value of the book is in the eight Examples and Projects, where the reader is guided in applying these techniques to substantial problems in classical, quantum, or statistical mechanics. These problems have been chosen to enrich the standard physics curriculum at the advanced undergraduate or beginning graduate level. The book will also be useful to physicists, engineers, and chemists interested in computer modeling and numerical techniques. Although the user-friendly and fully documented programs are written in FORTRAN, a casual familiarity with any other high-level language, such as BASIC, PASCAL, or C, is sufficient. The codes in BASIC and FORTRAN are available on the web at http://www.computationalphysics.info. They are available in zip format, which can be expanded on UNIX, Window, and Mac systems with the proper software. The codes are suitable for use (with minor changes) on any machine with a FORTRAN-77 compatible compiler or BASIC compiler. The FORTRAN graphics codes are available as well. However, as they were originally written to run on the VAX, major modifications must be made to make them run on other machines.

Computational Physics: Fortran Version

* Basic Mathematical Operations * Ordinary Differential Equations * Boundary Value and Eigenvalue Problems * Special Functions and Gaussian Quadrature * Matrix Operations * Elliptic Partial Differential Equations * Parabolic Partial Differential Equations * Monte Carlo Methods

Computational Physics--Fortran Version.

To test the connection between chaotic classical motion and quantum spectral and overlap statistics, we examine a schematic three-orbital shell model. This system is novel in that the quantum phase space is compact and the momentum dependence of the classical Hamiltonian is nonstandard. We find good agreement with the expected behavior of the spectral statistics and reasonable agreement for the overlap distributions. Also, there is evidence that the eigenvector statistics are more sensitive to the details of the classical dynamics than are the eigenvalues.

/pdf/quantum-chaos-in-a-schematic-shell-model-4cq31bwny2.pdf

Quantum chaos in a schematic shell model

An introductory physics course that meets the needs of biology students must recognize that the two disciplines see the world differently.

Reinventing physics for life-sciences majors

We present evidence that although students’ mathematical skills in introductory calculus-based physics classes may not be readily applied in physics contexts, these students have strong mathematical resources on which to build effective instruction. Our evidence is based on clinical interviews of problem solving in electrostatics, which are analyzed using the framework of Sherin’s symbolic forms. We find that students use notions of “dependence” and “parts-of-a-whole” to successfully guide their work, even in novel situations. We also present evidence that students’ naive conceptions of the limit may prevent them from viewing integrals as sums.

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Dawn C. Meredith

Papers

Computational Physics: Fortran Version

Computational Physics--Fortran Version.

Quantum chaos in a schematic shell model

Reinventing physics for life-sciences majors

How Students use Mathematical Resources in an Electrostatics Context