Publications of Larry M. Manevitz

Publications of Larry Manevitz (Sorted by Forum)

 

To see the Publications of Larry Manevitz in Reverse Chronological Order look here.

Books

  1. All the changes and corrections to Introduction to Model Theory and the Metamathematics of Algebra by A. Robinson for the new editionby North-Holland were made by me (at Robinson’s request)just prior to Robinson’s death. (284 pages.) (I am acknowledged in the preface.)
  2. L. Manevitz (editor)  The Collected Works of Lev Brutman, CRI Publications, 2005. (265 pages)
  3. L. Manevitz (editor and reviewer).  “Logic for Computer Science” by Yoram Hirshfeld of Tel Aviv University.   I am one of the main editors and reviewers of this new Open University Book. (As is customary with the Open University I will be acknowledged in the book.)

B2.  Patents

  1.  U.S. Patent Number 7,225,173  L. Manevitz, Akram Bitar and Dan Givoli, Apparatus and Method for Efficient Adaptation of Finite Element Meshes for Numerical Solutions of Partial Differential Equations; submitted via Carmel – Haifa University Economic Corporation.   Awarded May, 2007. [link, pdf, ps]

 Articles in Refereed Journals

  1. Manevitz, L.  Robinson forcing is not absolute, Israel Journal of Mathematics,Vol. 25, 211-232, 1976. [link, PDF, PS]
  2.  Manevitz, L.  Internal end extensions of Peano arithmetic and a problem of Gaifman,London Mathematical Society Journal, Vol. 13, 80-82, 1976. [link, PDF, PS]
  3.  Manevitz, L. and Stavi, J.  D02-operators and alternating sentences in arithmetic, Journal of Symbolic Logic, Vol. 45, 144-154, March 1980. [link, PDF, PS]
  4.  Manevitz, L. and Miller, A. Lindelof models of the reals:  Solution to a problem of Sikorski, Israel Journal of Mathematics, Vol. 45, 209-218, 1980. [link, PDF, PS]
  5.  Herfort, W. and Manevitz, L.  Topological and Frobenius groups and non-standard analysis, Journal of Algebra, Vol. 92, 16-32, 1985. [link, PDF, PS]
  6.  Manevitz, L.  Applied model theory and meta-mathematics, Israel Journal of Mathematics, Vol. 49, 3-14, 1986.  [link, PDF, PS]
  7.  Manevitz, L.  and Rowen, L.  Freedom via forcing: Uniform construction of relatively free or generic structures, Classification Theory,   Lecture Notes in Mathematics  Vol. 1292 230-246,  1987. [link, PDF, PS]
  8.  Manevitz, L., Givoli, D. and Margi, M.   Heuristic finite element node numbering: an expert system approach, Computing Systems in Engineering Vol.  4,  p. 159-168, 1993. [link, PDF, PS]
  9.  Manevitz, L.  and  Weinberger, S.  Discrete Circle Actions: A Note Using Non-Standard Analysis,  Israeli J. Math., Vol.  94,  147-155, 1996. [link, PDF, PS]
  10.  Meltser, M., Shoham, M. and Manevitz, L.  Approximating Functions by Neural Networks: A Constructive Solution in the Uniform Norm,  Neural Networks, Vol. 9  965-978, 1996. [link, PDF, PS]
  11.  Hummel, R.  and Manevitz, L. A Statistical Approach to the Representation of Uncertainty in Beliefs Using Spread of Opinions,  IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, Vol. 26 No. 3 378-384,1996.[link,PDF,PS]
  12.  Manevitz, L.  and Merzbach, M. Multi-Parameter Stochastic Processes via Non-Standard Analysis,  Israeli Mathematics Conference Proceedings.  Vol. 10, 151 – 167, 1996. [link, PDF, PS]
  13.  Hoffman, M.,  Manevitz, L.  and Wong, E. Fuzzy Independence and Extended Conditional Probability,  Information Sciences,  Vol.  90, 137 – 156, 1996. [link, PDF, PS]
  14.  Hummel, R.  and Manevitz, L. Combination Calculi for Uncertainty Reasoning: Representing Uncertainty Using Distributions, Annals of Mathematics and Artificial Intelligence, Vol.  20, p. 69 – 109, 1997. [link, PDF, PS]
  15.  Manevitz, L.  and Zemach, Y.   Using Sparse Distributed Memory for Multi-Level Cognitive Tasks: Assigning Meaning to Data, Neurocomputing Vol.  14 No.1  15 – 39, 1997. [link, PDF, PS]
  16.  Manevitz, L., Yousef, M. and Givoli, D. Finite Element Mesh Generation Using Self-Organizing Neural Networks, Special Issue on Machine Learning of MicroComputers in Civil Engineering,  Vol.  12  No. 4, 233 – 250, 1997. [link, PDF, PS]
  17.  Manevitz, L. Interweaving Kohonen Maps of Different Dimensions to Handle Measure Zero Constraints on Topological Mappings, Neural Processing Letters, Vol.  5  No. 2, 155-161, 1997. [link, PDF, PS]
  18.  Manevitz, L. and Yousef, M. One Class SVM for Document Classification, Journal of Machine Learning Research, Vol. 2 (Dec),139-154, 2001. [link, PDF, PS]
  19.  Manevitz, L. and Marom, S., Modeling the Process of Rate Selection in Neuronal Activity, Journal Theoretical Biology, Vol.  216 , 337-343, 2002. [link, PDF, PS]
  20.  Manevitz, L. and Givoli, D., Towards Automating the Finite Element Method: A Test-Bed for Soft Computing,  Applied Soft Computing, Vol.  3 , 37-51, 2003. [link, PDF, PS]
  21.  Manevitz, L. and Yousef, M.,A Web Navigation System Based on a Neural Network User-Model Trained with Only Positive Web Documents,  Web Intelligence and Agent Systems , Vol.  2 , No. 2, 137-144, 2004. [link, PDF, PS]
  22.  Manevitz, L., Givoli, D. and Bitar, A., Neural Network Times Series Forecasting of Finite-Element Mesh Adaptation,   Neurocomputing, Vol.  63,  447 – 463, 2005. [link, PDF, PS]
  23.  Manevitz, L. and Yousef, M., One-class Document Classification via Neural Networks,  Neurocomputing,  Vol. 70, 1466 – 1481, 2007. [link, PDF, PS]
  24.  Berkovsky, S. , Eytani, Y. and Manevitz, L.,  Efficient Collaborative Filtering in Content-Addressable Spaces,  IJPRAI (International Journal of Pattern Recognition and Artificial Intelligence) , Vol. 21, No. 2, 265-289  2007. [link, PDF, PS]
  25.  Maimon, M. and Manevitz, L. , A Simulation Tool for Modeling the Influence of Anatomy on Information Flow Using Discrete Integrate and Fire Neurons, Journal of Combinatorial Optimization, doi:10.1007/s10878-007-9103-3, Vol. 15, 287-304, 2008. [link, PDF, PS]
  26. Yousef, M., Ketany, M., Manevitz, L., Showe, L. and Showe, M., Classification and Biomarker Identification Using Gene Network Modules and Support Vector Machines, BMC Bioinformatics, doi:10.1186/1471-2105-10-337, Vol. 10:337. [link, PDF, PS]
  27.  Peleg,O., Manevitz, L., Hazan, H., Eviatar, Z., (2010). Two Hemispheres – Two Networks A Computational Model Explaining Hemispheric Asymmetries While Reading Ambiguous Words. Annals of Mathematics and Artificial Intelligence (AMAI). Volume 59, Number 1, 125-147. http://dx.doi.org/10.1007/s10472-010-9210-1, 2010. [link, PDF, PS]
  28. Omer Boehm, David R. Hardoon and Larry M. Manevitz,Classifying Cognitive States of Brain Activity via One-Class Neural Networks with Feature Selection by Genetic Algorithms, International Journal of Machine Learning and Cybernetics, Volume 2 (3), Pages 125-134, 2011 [ Link | PDF ]
  29. Hazan, H. and Manevitz, L.,  Temporal Pattern Recognition via Temporal Networks of  Temporal Neurons, Expert Systems with Applications, Volume 39, Issue 2, Pages 1597-1606, http://dx.doi.org/10.1016/j.eswa.2011.06.052, February 2012. [link, PDF, PS]
  30. Paolo Avesani, Hananel Hazan, Ester Koilis, Larry M. Manevitz, Diego Sona. Non-parametric temporal modeling of the hemodynamic response function via a liquid state machine. http://dx.doi.org/10.1016/j.neunet.2015.04.009.  Neural Networks. Volume 70, October 2015, Pages 61–73.
  31. Tali Atir-Sharon, Asaf Gilboa, Hananel Hazan, Ester Koilis, and Larry M. Manevitz. Decoding the Formation of New Semantics: MVPA Investigation of Rapid Neocortical Plasticity during Associative Encoding through Fast Mapping, Neural Plasticity, vol. 2015, Article ID 804385, 17 pages, 2015. doi:10.1155/2015/804385
  32. Alex Frid, Hananel Hazan, Ester Koilis, Larry M. Manevitz, Maayan Merhav and
    Gal Star. The Existence of Two Variant Processes in Human Declarative Memory: Evidence Using Machine Learning Classification Techniques in Retrieval Tasks, Transactions on Computational Collective Intelligence, LNCS (accepted, March 28, 2016, to appear)

 

Articles or Chapters in Books Which are Not Conference Proceedings

All articles in this section were refereed.

  1. Manevitz, L. Givoli, D. and Margi, M., An Expert System for the Efficient Numbering of Finite Element Nodes, in Artificial Intelligence and Structural Engineering, B.H.V. Topping, ed., pp. 63-72, Civil-Comp Press, Edinburgh, 1991. [link, PDF, PS]
  2.  Meltser, M., Shoham, M. and Manevitz, L. Neural Networks: Using a Game Theoretic Derivative for Minimizing Maximal Errors and Designing Network Architecture The Bar Hillel Memorial Volume,1996, (editors:  M. Koppel and E. Shamir), pp. 169-177. [link, PDF, PS]
  3.  Manevitz, L., Yousef, M. and Givoli,  D. Automatic Mesh Generation (for Finite Element Method) Using Self-Organizing Neural Networks, The Bar Hillel Memorial Volume, 1996, editor (M. Koppel, E. Shamir), pp. 149-158. [link, PDF, PS]
  4.  Manevitz, L. and Givoli, D.  Soft Computing and the FEM, in  Advances in Soft Computing – Engineering Design and Manufacturing, R. Roy, T. Furuhashi and P.K. Chawdhry (Eds.), Springer-Verlag London Limited, 1999. [link, PDF, PS]
  5.  Manevitz, L.,  A. Bitar and D. Givoli, Finite-Element Mesh Adaptation via Time Series Prediction Using Neural Networks”,  in Soft Computing and Industry, R. Rajkumar et al,. eds., p 769 – 782, Springer-Verlag Berlin, 2002. [link, PDF, PS]
  6.  Shlomo Berkovsky, Yaniv Eytani, Larry Manevitz, Retrieval of Collaborative Filtering Nearest Neighbors in a Content-Addressable Space, in Y.Manolopoulos, J.Filipe, P.Constantopoulos, J.Cordeiro (eds.): Enterprise Information Systems VIII, pp. 159-178, Springer, 2007. [link, PDF, PS]
  7.  Peleg,O., Eviatar, Z., Hazan, H, and  Manevitz, L. , Differences and Interactions between Cerebral Hemispheres When Processing Ambiguous Homographs, in Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint, L. Paletta and E. Rome (Eds.),  in Lecture Notes in Artificial Intelligence,  subseries of Lecture Notes in Computer Science, Vol. 4840, 2008. [link, PDF, PS]
  8.  O. Peleg, Z. Eviatar, H. Hazan , L. Manevitz,  Using Neural Network Models to Model Cerebral Hemispheric Differences in Processing Ambiguous Words,  Neuro-Symbolic Reasoning 3,  A. Garcez, P. Hitzler, and G. Tamburrini (Eds.). Vol. 230 of CEUR Workshop Publications.  ISSN 1613-0073. 2008  (This is an on-line publication ,  http://ftp.informati.rwth-achen.de/Publications/CEUR-WS/Vol-230/), [link, PDF, PS]
  9.  Manevitz, L. Hazan,H. (2010). Stability and Topology in Reservoir Computing Lecture Notes in Computer Science (LNAI). 6438, http://dx.doi.org/10.1007/978-3-642-16773-7 [link, PDF, PS]
  10.  Omer Boehm, David R. Hardoon and Larry M. Manevitz Towards One-Class Pattern  Recognition in Brain Activity  via Neural Networks, in  Lecture Notes in Computer Science, 2010, Volume 6438/2010, 126-137, DOI: 10.1007/978-3-642-16773-7_11 [link, PDF, PS]

F1.  Articles in Refereed Conference Proceedings

  1.  Hummel, R. and Manevitz, L. Combining bodies of dependent evidence, Proc. International Joint Conference on Artificial Intelligence (IJCAI) 1987, 1015 – 1017, 1987. [link, PDF, PS]
  2.  Manevitz, L. Knowledge Automata, Simulation and Modelling, 17, 405-412, 1989. [link, PDF, PS]
  3.   Manevitz, L. Calculating Probabilities over fault-trees with dependent evidence, Reliability and Quality Control, 5, 333-336, 1989. [link, PDF, PS]
  4.  Manevitz, L. Implementing a “sense of time” via entropy in associative memories. Artificial Neural Networks Vol. 2, p. 1211-1215 (ICANN-91), 1991.  [link, PDF, PS]
  5.  Hummel, R. and Manevitz L. Statistical approaches to fusion with uncertainty, Proc. IEEE Conference Systems, Man and Cybernetics,  Vol.  2, 831 – 835, 1991.  Expanded and developed in D11.  [link, PDF, PS]
  6. Manevitz, L. “Name that tune”: Storing Temporal Information in Sparse Distributed Memory, Proc. IAICV  8  99-113. (Israeli Symposium on Artificial Intelligence and Computer Vision), 1991. [link, PDF, PS]
  7. Manevitz, L. and Zemach, Y. Assigning Meaning to Data: Multi-Level Information Processing inKanerva’s SDM, Proc. IAICV 8 (1991), 114-130. (Israeli Symposium on Artificial Intelligence and Computer Vision), 1991.  Expanded and developed in D15. [link, PDF, PS]
  8. Manevitz, L. Temporal Sparse Distributed Memory: Identifying temporal patterns via homeomorphic contractions of memory, Artificial Neural Networks II  Vol. 2, p. 1651-1654. (ICANN-92), 1992. [link, PDF, PS]
  9. Shoham, M., Meltser, M. and Manevitz, L. Constructive Uniform Approximation of Differentiable Vector-Functions by Neural Network Methods, WCNN’94 (World Conference on Neural Networks),   Vol. 2. p. 373 – 378, 1994. Expanded and developed in D10.  [link, PDF, PS]
  10.  Hoffman, M., Manevitz, L. and Wong, E. Fuzzy Independence and Extended Conditional Probability, Joint Conference on Information Sciences, 1994. Expanded and developed in D13. [link, PDF, PS]
  11. Meltser, M., Shoham, M. and Manevitz, L. Neural Networks: Using a Game Theoretic Derivative for Minimizing Maximal Errors and Designing Network Architecture, Proceedings of BISFAI-95, pp. 156 – 165, 1995.  also republished as E2.  [link, PDF, PS]
  12. Manevitz, L.,  Yousef, M.  and Givoli, D.  Automatic Mesh Generation (for Finite Element Method) Using Self-Organizing Neural Networks,  Proceedings of BISFAI -95, pp. 241 – 249, 1995. also republished as E3. [link, PDF, PS]
  13. Manevitz, L.,  Yousef, M. and Givoli, D. Placing Geometry on Topology in Meshes: Applying Self-Organizing Neural Networks to the Finite Element Method, IAICV – 12,  (Israeli Symposium on Artificial Intelligence and Computer Vision), 1996.  Expanded and developed in D16. [link, PDF, PS]
  14. Manevitz, L., Givoli, D., Margi, M. and Yousef, M., AI and NN tools for the FEM,  in Proceedings of the First World Online Symposium on Soft Computing, (WSC1), Nagoya, Japan, 1996. (editor: T. Furuhashi), pp. 192- 197, 1996. [link, PDF, PS]
  15. Manevitz, L. and Givoli, D. Automating the Finite Element Method: A Test-Bed for Soft Computing,  Proceedings of Conference on Mathematics and Artificial Intelligence, 1998, This is an “on-line publication”, http://rutcor.rutgers.edu/~amai/Proceedings.html, 1998. [link, PDF, PS]
  16.  Manevitz, L. and Givoli, D.  Soft Computing and the FEM, World Online Symposium on Soft Computing 3,  (WSC3), Japan, 1998. On the world wide web:  http://www/bioele.nagoya-u.ac.jp/wsc3/ , 1998. This is also republished in Book Chapter E4 [link, PDF, PS]
  17.  Manevitz, L. and Yousef, M.,  Document Classification on Neural Networks using Only Positive Examples, Proceedings of 23rd ACM SIGIR Conference on Research in Information Retrieval (Sigir 2000), Athens, August, 2000, p. 304-307, 2000.  Expanded and developed in D23. [link, PDF, PS]
  18. Manevitz, L.and Yousef, M.,  The Surfer’s Apprentice, Neural Network Models and Intelligent Data Mining, Proceedings of BRAIN-MACHINE, Ankara, December, 2000. [link, PDF, PS]
  19. Manevitz, L., Givoli, D. and Bitar, A.  Temporal Adaptation of  FEM Meshes via Neural Networks, Proceedings of World Symposium of Soft Computing 6 (WSC6),  2001 http://www.viison.fhg.de/wsc6/.   Winner of Best Paper Award . This is also republished as E5.   Expanded and developed in D22. [link, PDF, PS]
  20. Hardoon, D. and Manevitz, L.  One-class Machine Learning Approach for fMRI Analysis, PREP-2005;  http://www.prep.org/, 2005. [link, PDF, PS]
  21. Shlomo Berkovsky, Yaniv Eytani, Larry Manevitz, “Fast Collaborative Filtering in Distributed Environment”, in Biennial Israeli Symposium on the Foundations of Artificial Intelligence (BISFAI), http://cri.haifa.ac.il /bisfai05/schedule.php,  Haifa, Israel, June 2005. [link, PDF, PS]
  22. Hardoon, D. and Manevitz, L., Classifying Cognitive Tasks to MRI Data Using Machine Learning Techniques,   in Biennial Israeli Symposium on the Foundations of Artificial Intelligence (BISFAI), http://cri.haifa.ac.il /bisfai05/schedule.php,  Haifa, Israel, June 2005. [link, PDF, PS]
  23. Hardoon, D. and Manevitz, L.  fMRI Analysis and Compression Neural Networks, International Joint Conference in Artificial Intelligence, IJCAI-2005, 1604 -1606, 2005. [link, PDF, PS]
  24. Shlomo Berkovsky, Yaniv Eytani, Larry Manevitz, “Collaborative Filtering Based on Content-Addressing”, in Proceedings of the International Conference on Enterprise Information Systems (ICEIS), Vol.2., p. 91-98,  Paphos, Cyprus, May 2006. Expanded and developed in D24. [link, PDF, PS]
  25. Shlomo Berkovsky, Ariel Gorfinkel, Tsvi Kuflik, Larry Manevitz, “Case-Based to Content-Based User Model Mediation”,  Proceedings European Conference on Artificial Intelligence (ECAI-06)  Workshop on Ubiquitous User Modeling, p. 1 – 4,   Riva del Garda, Italy, August, 2006 . [link, PDF, PS]
  26. O. Peleg,  Z. Eviatar, L. Manevitz, H. Hazan,  The Disambiguation of Heterophonic and Homophonic Homographs in Hebrew:  a Parallel Distributed Account, BISFAI-05, http://cri.haifa.ac.il /bisfai05/schedule.php, (also presented at ISCOL-05), 2005. [link, PDF, PS]
  27. M. Maimon  and L. Manevitz, A Simulation Tool for Modeling the Influence of Anatomy on Information Flow Using Discrete Integrate and fire Neurons, poster, Neurobiology and the the Modulation of Memory Formation – Brain and Behavior Meeting, 2004. [link, PDF, PS]
  28. D. R. Hardoon and L. Manevitz, Reading the Mind: fMRI Analysis via One-Class Machine Learning Techniques, DIMACS Workshop on Data Mining, Systems Analysis and Optimization in Neuroscience, http://dimacs.rutgers.edu/Workshops/Neuroscience/program.html  2006 [link, PDF, PS]
  29. M. Maimon and L. Manevitz, A Simulation Tool Using Discrete Integrate and Fire Neurons in Very Large Simulated Networks,  DIMACS Workshop on Data Mining, Systems Analysis and Optimization in Neuroscience,  http://dimacs.rutgers.edu/Workshops/Neuroscience/program.html ,  2006.  Expanded and developed in D24, [link, PDF, PS]
  30. Feintuch, U., Manevitz, L., Mednikov, E., Rand, D, Dvorkin, A.,Kizony R., Shahar, M. & Weiss, P. L. (2006). Integrating Artificial Intelligence and Virtual Reality in the diagnostic process – a Feasibility study. In: B. K. Wiederhold, S. Bouchard, & G. Riva (Eds). Annual Review of CyberTherapy and Telemedicine. San Diego, CA. pp 207-208, 2006. [link, PDF, PS]
  31. O. Peleg, Z. Eviatar, H. Hazan , L. Manevitz,  Using Neural Network Models to Model Cerebral Hemispheric Differences in Processing Ambiguous Words,Proc. of  Workshop on Neuro-Symbolic Learning and Reasoning, International Joint Conference of Artificial Intelligence, (IJCAI-07), p. 27-32, 2007.  also republished as E8. [link, PDF, PS]
  32. O. Peleg,  Z. Eviatar, H. Hazan, L. Manevitz, Differences and Interactions between Cerebral Hemispheres When Processing Ambiguous Homographs,  Proc.WAPCV-07, p. 21 – 34,  2007. Also republished as E7. [link, PDF, PS]
  33. Feintuch, U., Manevitz, L., Mednikov, E., Rand, D, Dvorkin, A.,Kizony R., Shahar, M., Erez, N., & Weiss, P. L. (2006) Integrating Artificial Intelligence and Virtual Reality in the diagnostic process – a Feasibility study. Paper presented in the 57th Annual Meeting of The Israeli Association of Physical &Rehabilitation Medicine. Lod, Israel, Nov 30-Dec 1, 2006. [link, PDF, PS]
  34. Z. Eviatar, O. Peleg,  H. Hazan, L. Manevitz, Differences and Interactions between Cerebral Hemispheres When Processing Ambiguous Homographs, accepted, to appear, Proceeding Cognitive Neurosciences Society, New York, 2007.  This entire conference is poster presentations,  This is a shorter version of F32. [link, PDF, PS]
  35.  L. Manevitz, O. Peleg, Z. Eviatar, H. Hazan, Using Neural Network Models to Model Cerebral Hemispheric Differences in Processing Ambiguous Words, accepted, to appear in the Proc. Cognitive Neurosciences Society, New York, 2007.  This entire conference is poster presentations.  This is a shorter version of F31 [link, PDF, PS]
  36. Shlomo Berkovsky, Ariel Gorfinkel, Tsvi Kuflik, Larry Manevitz, Evaluating User Model Effectiveness by Simulation,  Proc. International Workshop on Personalization Enhanced Access to Cultural Heritage, User Modelling, 2007, p. 29-38. [link, PDF, PS]
  37. Larry Manevitz and Hananel Hazan,  History-Dependent Neurons and Identification of Temporal Sequences,  BISFAI-IX, 2007 (abstract only p. 14) [link, PDF, PS]
  38. Shlomo Berkovsky, Ariel Gorfinkel, Tsvi Kuflik, Larry Manevitz, Case-Based to Content-Based User Model Mediation and Its Effectiveness, BISFAI-IX, 2007 (abstract only p. 17).  Note:  This is the same presentation as No. 25 above. [link, PDF, PS]
  39.  Z. Eviatar, O. Peleg,  H. Hazan, L. Manevitz, Differences and Interactions between Cerebral Hemispheres When Processing Ambiguous Homographs, BISFAI-IX, 2007 (abstract only p. 18).  Note:  This is the same presentation as No. 32 above. [link, PDF, PS]
  40. Larry Manevitz,  Computational Modeling of Brain Structure and Cognition, BISFAI-X, 2009  (abstract only  p.7) http://u.cs.biu.ac.il/~kurzbed/bisfai09/booklet09.pdf [link, PDF, PS]
  41.  M. Yousef, M. Ketany, and Larry Manevitz, Recursive Gene Network Elimination for Feature Selection and Classification from Gene Expression Data,  Bar Ilan Symposium on the Foundations of Artificial Intelligence, BISFAI-X. 2009, (abstract only p. 9) http://u.cs.biu.ac.il/~kurzbed/bisfai09/booklet09.pdf [link, PDF, PS]
  42. Larry Manevitz, David Hardoon and Omer Boehm, Cognitive Pattern Recognition from Brain Activity via One-Class Classification, Bar Ilan Symposium on the Foundations of Artificial Intelligence, BISFAI-X, 2009, (abstract only  p. 9) http://u.cs.biu.ac.il/~kurzbed/bisfai09/booklet09.pdf . [link, PDF, PS]
  43. Larry Manevitz and Hananel Hazan,The Liquid State Machine is Not Robust to Problems in Its Components, Bar Ilan Symposium on the Foundations of Artificial Intelligence, BISFAI-X, 2009, (abstract  only p. 7) . http://u.cs.biu.ac.il/~kurzbed/bisfai09/booklet09.pdf [link, PDF, PS]
  44. Rom Timor, Larry Manevitz, Hananel Hazan, Zohar Eviatar and Orna Peleg,The effect of inter-hemispheric connectivity when resolving ambiguities in reading, Computational Cognitive Neuroscience, CCNC-2009, Boston,  accepted, to appear. [link, PDF, PS]
  45. Hananel Hazan and Larry Manevitz,  The Liquid State Machine is Not Robust to Problems in Its Components,  Computational Cognitive Neuroscience,CCNC-2009 Boston, accepted, to appear.  Note:  This is an expansion of  No. 43. above. [link, PDF, PS]
  46. Malik Yousef, Mohamed Ketany, Larry Manevitz, Louise Showe and Michael Showe,  Classification and biomarker identification using gene network modules and support vector machines, The Fifth Benelux Bioinformatics Conference, BBC09,  Dec. 2009. [link, PDF, PS]
  47. Feintuch U., Manevitz L. and Silnitsky N. (2010). Classification And Clustering Of Brain Injuries From Motion Data Of Patients In A Virtual Reality Environment. In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation, pages 246-252. DOI: 10.5220/0003057302460252 [link, PDF, PS]
  48. Gilboa, A., Hazan, H., Koilis, E., Manevitz, L. and Sharon, T., Multiple Declarative Memory Systems: Classification with Machine Learning Techniques, Proc. International Joint Conference on Neural Networks 2011, p.54,  San Jose, Ca. [link, PDF, PS]
  49. Omer Boehm, David R. Hardoon and Larry Manevitz, “ Towards One-Class Pattern Recognition in Brain Activity via Neural Networks” In Proceedings of the 9th Mexican International Conference on Artificial Intelligence (MICAI), 2010 [link, PDF, PS]
  50. Gilboa, A., Hazan, H., Koilis, E., Manevitz, L. and Sharon, T., Modeling and Identifying Human Memory Systems,  Bar Ilan Symposium on the Foundations of Artificial Intelligence 2011, (BISFAI – 2011). [link, PDF, PS]
  51. Manevitz, L. Hazan,H. (2010). Stability and Topology in Reservoir Computing, In Proceedings of the 9th Mexican International Conference on Artificial Intelligence (MICAI), 2010 [link, PDF, PS]
  52.  Eviatar Z., Hazan H., Manevitz L., Peleg O. and Timor R. (2010). Interactions Between Hemispheres When Disambiguating Ambiguous Homograph Words During Silent Reading . In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation, pages 271-278. DOI: 10.5220/0003059802710278. [link, PDF, PS]
  53.  Hazan H. and Manevitz L. (2010). The Liquid State Machine Is Not Robust To Problems In Its Components But Topological Constraints Can Restore Robustness. In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation, pages 258-264. DOI: 10.5220/0003058902580264 [link, PDF, PS]
  54. Avesani, P.; Hazan, H.; Koilis, E.; Manevitz, L.; Sona, D. , Learning BOLD Response in fMRI by Reservoir Computing, Pattern Recognition in NeuroImaging (PRNI), 2011 International Workshop on , vol., no., pp.57-60, 16-18 May 2011
    doi: 10.1109/PRNI.2011.16
    . [link, PDF, PS]
  55.  Eviatar Z., Hazan H., Manevitz L., Peleg O. and Timor R. (2010). Interactions Between Hemispheres When Disambiguating Ambiguous Homograph Words During Silent Reading ,  Bar Ilan Symposium on the Foundations of Artificial Intelligence 2011, (BISFAI – 2011). [link, PDF, PS]
  56. Paolo Avesani, Hananel Hazan, Ester Koilis, Larry Manevitz and Diego Sona, Learning BOLD Response in fMRI by Reservoir Computing,  Bar Ilan Symposium on the Foundations of Artificial Intelligence 2011, (BISFAI-2011). [link, PDF, PS]
  57. Frid, A., Hazan, H. and Manevitz, L., Temporal Pattern Recognition via Temporal Networks of  Temporal Neurons ,  in 2012 IEEE 27th Convention of Electrical Electronics Engineers in Israel (IEEEI), 2012, pp. 1 –4.
  58. Hazan, H. and Manevitz, L.,  The Liquid State Machine is Not Robust to Problems in its Components but Topological Constraints can Restore Robustness, Expert Systems with Applications, Volume 39, Issue 2, Pages 1597-1606, http://dx.doi.org/10.1016/j.eswa.2011.06.052, February 2012. [link, PDF, PS]
  59. Hazan Hananel and Manevitz Larry,(2012) Topological Choices, Sliding Thresholds, and STDP Learning Variants Can Make Reservoir Computing Appropriate For Spatio-Temporal Pattern Recognition. IAAI 2012 Symposium [link, PDF, PS].
  60. Zohar Eviatar, Hananel Hazan, Larry Manevitz,(2012) Orna Peleg and Rom Timor, Disambiguation during silent reading, IAAI 2012 Symposium, [link, PDF, PS]
  61. Frid, A., Hazan, H. and Manevitz, L., Temporal Pattern Recognition via Temporal Networks of  Temporal Neurons ,  in 2012 IEEE 27th Convention of Electrical Electronics Engineers in Israel (IEEEI), 2012, pp. 1 –4. [link, PDF, PS]
  62. Hazan, H., Hilu, D., Manevitz, L., and Sapir, S., Early Diagnosis of Parkinson’s Disease via Machine Learning on Speech Data,  in 2012 IEEE 27th Convention of Electrical Electronics Engineers in Israel (IEEEI), 2012, pp. 1 –4.[link,PDF, PS]
  63. Hazan, H. , Hertz, S. and Manevitz, L. Deep Learning for One-Class Classification of Cognitive Tasks from fMRI Data, 17th International Conference on Cognitive and Neural Systems, Boston, 2013.
  64. Frid, A., Hazan, H. and Manevitz, L. ,  Towards Classifying Human Phonemes without Encodings via Spatiotemporal Liquid State Machines,  17th International Conference on Cognitive and Neural Systems, Boston, 2013.
  65. Alex Frid, Hananel Hazan and Larry Manevitz. Temporal Pattern Recognition via Temporal Networks of Temporal Neurons. BISFAI 2013. Bar-Ilan University.
  66. Gal Sabina Star, Hananel Hazan and Larry Manevitz. Identifying Human Declarative Memory Systems via Machine Learning on Brain Activity Data Obtained during Memory Recall Tasks. BISFAI 2013. Bar-Ilan University.
  67. Stav Hertz, Hananel Hazan and Larry Manevitz. Deep Learning for One-Class Classification of Cognitive Tasks from fMRI data. BISFAI 2013. Bar-Ilan University.
  68. Hananel Hazan, Dan Hilu, Larry M. Manevitz, Shimon Sapir, Poster: Using Machine learning to Identify Patients with Parkinson Disease. BISFAI 2013. Bar-Ilan University.
  69. Alex Frid, Hananel Hazan, and Larry Manevitz, Towards classifying human phonemes without encodings via spatiotemporal liquid state.  International Conference on Cognitive and Neural Systems. Boston University. 2013.
  70. Hananel Hazan, Stav Hertz, and Larry Manevitz, Deep learning for one-class classification of cognitive tasks from fMRI data. International Conference on Cognitive and Neural Systems. Boston University. 2013
  71. Alex Frid, Hananel Hazan, Larry Manevitz, Towards Classifying Human Phonemes without Encodings via Spatiotemporal Liquid State Machines, in Software Science, IEEE International Conference Software Science, Technology and Engineering (SWSTE), 2014, 63-64.
  72. Alex Frid, Hananel Hazan, Dan Hilu, Larry Manevitz, Lorraine O Ramig, Shimon Sapir, Computational Diagnosis of Parkinson’s Disease Directly from Natural Speech Using Machine Learning Techniques, in IEEE International Conference Software Science, Technology and Engineering (SWSTE), 2014, 50-53.
  73. Haim Shalelashvili, Tali Bitan, Alex Frid, Hananel Hazan, Stav Hertz, Yael Weiss and
    Larry Manevitz. Recognizing Deep Grammatical Information during Reading from Event Related fMRI. in 2014 IEEE 28h Convention of Electrical Electronics Engineers in Israel (IEEEI), 2014.
  74. Alex Frid, Hananel Hazan, Ester Koilis, Larry M. Manevitz, Maayan Merhav and Gal Star. Machine Learning Techniques and The Existence of Variant Processes in Humans Declarative Memory. The 7th International Joint Conference on Computational Intelligence. In Press.[Link, PDF]
  75. Alex Frid# and Larry M. Manevitz, Computational differential diagnosis and prognosis for Parkinson diseases from natural speech, Israeli Symposium for Neuroscience (ISFN), 2016. Eilat
  76. Tali Bitan, Alex Frid#, Hananel Hazan#, Larry M. Manevitz, Haim Shalelashvili# , Yael Weiss. Classification from Generation:Recognizing Deep Grammatical Information During Reading from Rapid Event-Related fMRI, International Joint Conference on Neural Networks IJCNN (Part of World Conference on Computational Intelligence WCCI), http://www.wcci2016.org/ Vancouver, 2016 , vol 3, pp 4637-4642. Vancouver
  77. N.E. Nawa, A. Frid, L. M. Manevitz and H. Ando, “Classifying Valence of Autobiographical Memories from Functional Magnetic Resonance Imaging”, Annual Meeting of Society for Neuroscience (SFN, 2016), https://www.sfn.org/annual-meeting/neuroscience-2016 San Diego, California, USA

F2. Articles in non-refereed Conference Proceedings

  1.  Manevitz, L., Infinite model theoretic forcing and absoluteness, J. of Symbolic Logic, Vol. 34, No. 2, p. 394, contributed paper, Association of Symbolic Logic meeting, Atlanta, December, 1973. [link, PDF, PS]
  2. Manevitz, L., Givoli, D., Margi, M. and Yousef, M., AI and NN tools for the FEM, World Online Symposium on Soft Computing, Nagoya, Japan, 1996. On the world wide web:  http://www.bioele.nagoya-u.ac.jp/wsc1/, 1996. [link, PDF, PS]
  3. Manevitz, L. and Givoli, D., Soft-Computing Tools for the Finite Element Method, Proc of ISCM-6, 1998, p. 13-14, 1998, [link, PDF, PS]
  4. Manevitz, L. and Yousef, M., Automated Document Classification using Neural Networks and Postitive Information Only, Abstract Proceedings Bar Ilan Workshop on Knowledge Discovery and Data Mining, Ramat Gan, 1998. [link, PDF, PS]
  5. Manevitz, L. (Abstract) How Does Time Emerge from Structure?  Two Examples from Neurophysiology, in Abstract Proceedings of Workshop on Computational/Mathematical Problems Arising from Neurophysiology, Haifa, January, 2001. [link, PDF, PS]
  6. Manevitz, L., Bitar, A. and Givoli, D. (Abstract)  Finite Element Mesh Adaptation via Time Series Prediction Using Neural Networks,  in Abstract Proceedings Haifa Winter Workshop on Computer Science and Statistics, 2002. [link, PDF, PS]

H. Other Scientific Publications

 Conference Proceedings Editing

  1. Editor of the Abstract Proceedings of the Workshop on Computational/Mathematical Problems (and Solutions?) Arising from Neurophysiology.   This is the abstracts of the talks presented at the above meeting, Haifa, 2001.  Published by Caesarea Rothschild Institute for Interdisciplinary Computer Science, (35 pages).
  2.  Co-editor of the Proceedings of the International Meeting: “Brain and Behavior:  What is learning in the neural system?” This meeting took place at Caesarea Rothschild Institute under the auspices of the HIACS and Brain and Behavior Research Centers. (32 pages).

Technical Reports

  1. Hirshfeld, J.  and Manevitz, L. Profinite and *-finite groups, Bar Ilan Univ. preprint, 1984 (15 pages). [link, PDF, PS]
  2. Manevitz, L. and Morgenstern, L. Consequential closure and the scam: a model for reasoning over time, NYU preprint 1987 (14 pages). [link, PDF, PS]

Additional Comments and Information on My Scientific Activity and Research Plans

 

Main areas of research over my career

Applied Model Theory and Mathematical Logic (applications to other areas of mathematics.)

Artifical Intelligence (including combining uncertain information, pattern recognition and artificial neural networks)

Computational Brain and Psychological Modeling

    The focus is on both the development and application of abstractions of cognitive structures from the computational and mathematical viewpoint.   This includes, on the one hand using techniques of machine learning and neurocomputation for pattern identification (note the work on fMRI identification F22, F23, F28  and text processing D18, D21, D23), cognitive modeling (note the work on reading D26 and memory D15), and brain modeling (note the work on the development of the cortex model D25 and an abstraction of rate selection D19).

    The applied aspect is also of interest. See for example, the work on user modeling via neural networks F36 F8, applications to the Finite Element Method D8 D16 D20 D22,  and applications to patient treatment and diagnosis in virtual reality environments F33..

   My approach in all of this is unified, in the sense that I try to look at these items from  the computational viewpoint.   Nonetheless, the work is very interdisciplinary.

Current projects underway in my laboratory include:  (i) feature selection appropriate for one-class fMRI classification tasks  (ii) development and applications of a general cortical modeling tool that uses discrete integrate and fire neurons  (iii) applications of neural network technology for user modeling in (a) virtual reality environment  (b) museum visitor  (iv) computational modeling of left and right hemisphere interaction in reading cognitive tasks (v) computational modeling of the hippocampus memory system (vi) machine learning applications to gene classification (vii) developing a data driven model free BOLD response curves for MRI analysis.   Much of this work is highly interdisciplinary and collaborative.

In addition, I am looking at some theoretical issues in developing tools for temporal pattern recognition.

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