Neural data often span multiple indices, such as neuron, experimental condition, trial, and time, resulting in a tensor or multidimensional array. Standard approaches to neural data analysis often rely on matrix factorization techniques, such as principal component analysis or nonnegative matrix factorization. Any inherent tensor structure in the data is lost when flattened into a matrix. Here, we analyze datasets from primary motor cortex from the perspective of tensor analysis, and develop a theory for how tensor structure relates to certain computational properties of the underlying system. Applied to the motor cortex datasets, we reveal that neural activity is best described by condition-independent dynamics as opposed to condition-dependent relations to external movement variables. Motivated by this result, we pursue one further tensor-related analysis, and two further dynamical systems-related analyses. First, we show how tensor decompositions can be used to denoise neural signals. Second, we apply system identification to the cortex- to-muscle transformation to reveal the intermediate spinal dynamics. Third, we fit recurrent neural networks to muscle activations and show that the geometric properties observed in motor cortex are naturally recapitulated in the network model. Taken together, these results emphasize (on the data analysis side) the role of tensor structure in data and (on the theoretical side) the role of motor cortex as a dynamical system.

Lo, Kwun Kit. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 153-161). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Existing Approaches in Program Understanding --- p.2 / Chapter 1.1.1 --- Localized Program Understanding --- p.2 / Chapter 1.1.2 --- Whole System Analysis --- p.3 / Chapter 1.2 --- Example of Function Execution Probability Reduction of the Call Graph --- p.5 / Chapter 1.3 --- Organization of the Dissertation --- p.7 / Chapter 2 --- Preliminary Study --- p.8 / Chapter 2.1 --- Participants --- p.8 / Chapter 2.2 --- Study Design --- p.8 / Chapter 2.3 --- ispell --- p.10 / Chapter 2.3.1 --- Subject I1 (ispell) --- p.10 / Chapter 2.3.2 --- Subject PG1 (ispell) --- p.12 / Chapter 2.3.3 --- Subject PG2 (ispell) --- p.13 / Chapter 2.3.4 --- Subject I2 (ispell) --- p.14 / Chapter 2.3.5 --- ispell Analysis --- p.15 / Chapter 2.4 --- FreeBSD Kernel Malloc --- p.15 / Chapter 2.4.1 --- Subject I1 (FreeBSD) --- p.16 / Chapter 2.4.2 --- Subject PG1 (FreeBSD) --- p.17 / Chapter 2.4.3 --- Subject PG2 (FreeBSD) --- p.18 / Chapter 2.4.4 --- Subject I2 (FreeBSD) --- p.20 / Chapter 2.4.5 --- FreeBSD Analysis --- p.20 / Chapter 2.5 --- Threats to Validity --- p.21 / Chapter 2.6 --- Summary --- p.22 / Chapter 3 --- Approach --- p.24 / Chapter 3.1 --- Building Branch-Preserving Call Graphs --- p.26 / Chapter 3.1.1 --- Branch Reserving Call Graphs --- p.26 / Chapter 3.1.2 --- Branch-Preserving Call Graphs --- p.28 / Chapter 3.1.3 --- Example of BPCG Building Process --- p.31 / Chapter 3.2 --- System Function Removal --- p.34 / Chapter 3.3 --- Function Rating Calculation --- p.35 / Chapter 3.3.1 --- Rating Algorithm Complexity --- p.38 / Chapter 3.4 --- Building the Colored Call Graph --- p.39 / Chapter 3.5 --- Call Graph Reduction --- p.39 / Chapter 3.5.1 --- Remove-high-fan-in-functions Approach (FEPR-fanin) --- p.39 / Chapter 3.5.2 --- Remove-leaf-nodes Approach (FEPR-leaf) --- p.41 / Chapter 4 --- Validation --- p.42 / Chapter 4.1 --- Measures --- p.43 / Chapter 4.1.1 --- Inclusion Accuracy (IA) --- p.43 / Chapter 4.1.2 --- Reduction Efficiency (RE) --- p.44 / Chapter 4.1.3 --- Stability (S) --- p.45 / Chapter 4.2 --- Analysis of FEPR Techniques --- p.45 / Chapter 4.2.1 --- Settings --- p.45 / Chapter 4.2.2 --- Inclusion Accuracy (IA): --- p.47 / Chapter 4.2.3 --- Reduction Efficiency (RE): --- p.47 / Chapter 4.2.4 --- Stability (S) --- p.48 / Chapter 4.3 --- Ying and Tarr´ةs Approach --- p.48 / Chapter 4.3.1 --- Settings --- p.50 / Chapter 4.3.2 --- Inclusion Accuracy (IA) --- p.50 / Chapter 4.3.3 --- Reduction Efficiency (RE) --- p.51 / Chapter 4.3.4 --- Stability (S) --- p.51 / Chapter 4.4 --- Centrality Measure Approach --- p.52 / Chapter 4.4.1 --- Inclusion Accuracy (IA) --- p.53 / Chapter 4.5 --- Top-down Search Approach --- p.56 / Chapter 4.5.1 --- Reduction Efficiency (RE) --- p.57 / Chapter 4.6 --- Synthesized Analysis --- p.58 / Chapter 4.6.1 --- Inclusion Accuracy (IA) --- p.58 / Chapter 4.6.2 --- Reduction Efficiency (RE) --- p.59 / Chapter 4.6.3 --- Stability (S) --- p.59 / Chapter 4.6.4 --- Threats to Validity --- p.59 / Chapter 4.7 --- Summary --- p.60 / Chapter 5 --- Discussion --- p.62 / Chapter 5.1 --- Flexibility of Analysis --- p.62 / Chapter 5.2 --- "Existence of Function Pointers, GOTOs and Early Exits" --- p.62 / Chapter 5.3 --- Precision of Branch-Preserving Call Graphs --- p.63 / Chapter 5.4 --- Function Ranking and Recommender System --- p.64 / Chapter 5.5 --- Extending the Approach Beyond C --- p.66 / Chapter 6 --- Related Work --- p.67 / Chapter 6.1 --- Existing Approaches in Program Understanding --- p.67 / Chapter 6.1.1 --- Localized Program Understanding --- p.67 / Chapter 6.1.2 --- Whole Program Analysis --- p.69 / Chapter 6.2 --- Branch Prediction and Static Profiling --- p.73 / Chapter 7 --- Conclusions --- p.76 / Chapter A --- Call Graphs in Case Studies --- p.78 / Chapter B --- Source Files for BPCG Builder --- p.85 / Bibliography --- p.153

Vita. / Digitized by Kansas Correctional Industries

Thesis. 1977. Ph.D.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / Microfiche copy available in Archives and Barker Engineering Library. / Vita. / Ph.D.

Thesis (Ph. D. in environmental and natural resource science)--Washington State University, December 2007. / Includes bibliographical references.

John P. Costas. / "February 20, 1952." / Bibliography: p. 9. / Army Signal Corps Contract DA36-039 sc-100, Project no. 8-102B-0. Dept. of the Army Project no. 3-99-10-022.

by A. Bensoussan, M.C. Delfour, S.K. Mitter. / Bibliography: leaves [21]-22. / Research supported by Air Force Office of Scientific Research under Grant 72-2273 and by National Science Foundation under Grant GK-41647.

Sherman M. Chan, Norman A. Lehtomaki, Michael Athans. / Bibliography: leaf 6. / "March 20, 1981" / "This research was supported by the U.S. Department of Energy, Division of Electric Energy Systems, under contract DE-AC01-78RA03395."

Thesis (M.Ed.)--York University, 2003. Graduate Programme in Education. / Typescript. Includes bibliographical references (leaves 100-102). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL:http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss&rft%5Fval%5Ffmt=info:ofi/fmt:kev:mtx:dissertation&rft%5Fdat=xri:pqdiss:MQ99292

This dissertation aims to address two optimization problems involving power system reliabilty analysis, namely multi-area power system adequacy planning and transformer maintenance optimization. A new simulation method for power system reliability evaluation is proposed. The proposed method provides reliability indexes and distributions which can be used for risk assessment. Several solution methods for the planning problem are also proposed. The first method employs sensitivity analysis with Monte Carlo simulation. The procedure is simple yet effective and can be used as a guideline to quantify effectiveness of additional capacity. The second method applies scenario analysis with a state-space decomposition approach called global decomposition. The algorithm requires less memory usage and converges with fewer stages of decomposition. A system reliability equation is derived that leads to the development of the third method using dynamic programming. The main contribution of the third method is the approximation of reliability equation. The fourth method is the stochastic programming framework. This method offers modeling flexibility. The implementation of the solution techniques is presented and discussed. Finally, a probabilistic maintenance model of the transformer is proposed where mathematical equations relating maintenance practice and equipment lifetime and cost are derived. The closed-form expressions insightfully explain how the transformer parameters relate to reliability. This mathematical model facilitates an optimum, cost-effective maintenance scheme for the transformer.