{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":3,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":63,"n_cols":5,"rows_kept":63,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[12,2,12,3],[13,2,13,3],[16,2,16,3],[17,2,17,3],[17,4,17,5],[18,2,18,3],[18,4,18,5],[19,2,19,3],[19,4,19,5],[20,2,20,3],[21,2,21,3],[22,2,22,3],[23,2,23,3],[24,2,24,3],[29,2,29,3],[29,4,29,5],[30,2,30,3],[30,4,30,5],[31,2,31,3],[32,2,32,3],[33,2,33,3],[34,2,34,3],[35,2,35,3],[36,2,36,3],[37,2,37,3],[49,2,49,3],[50,2,50,3],[53,2,53,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910816743503321"]
[null,null,"Autore",null,"Lawther R (Ross), <1960->"]
[null,null,"Titolo",null,"Maximal abelian sets of roots / / R. Lawther"]
[null,null,"Pubbl/distr/stampa",null,"Providence, Rhode Island : , : American Mathematical Society, , 2017"]
[{"$nulls":4},"©2017"]
[null,null,"ISBN",null,"1-4704-4208-6"]
[null,null,"Descrizione fisica",null,"1 online resource (234 pages)"]
[null,null,"Collana",null,"Memoirs of the American Mathematical Society, , 1947-6221 ; ; Volume 250, Number 1192"]
[null,null,"Disciplina",null,"512.482"]
[null,null,"Soggetti",null,"Root systems (Algebra)"]
[{"$nulls":4},"Lie algebras"]
[{"$nulls":4},"Abelian groups"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Introduction -- Root systems of classical type -- The strategy for root systems of exceptional type -- The root system of type G b2 s -- The root system of type F b4 s -- The root system of type E b6 s -- The root system of type E b7 s -- The root system of type E b8 s -- Tables of maximal abelian sets -- Appendix A: Root trees for root systems of exceptional type -- Bibliography."]
[null,"2.","Record Nr.",null,"UNISA996397212803316"]
[null,null,"Autore",null,"Vossius Gerardus Joannes <1577-1649.>"]
[null,null,"Titolo",null,"Ger. Io. Vossii. Elementa rhetorica [[electronic resource] ] : Accessit compendium doctrinæ A. Talæi. De tropis & figuris"]
[null,null,"Pubbl/distr/stampa",null,"Edinburgi, : excudebat Gedeon Lithgo, 1657"]
[null,null,"Descrizione fisica",null,"[36] p"]
[null,null,"Soggetti",null,"Latin language - Rhetoric"]
[null,null,"Lingua di pubblicazione",null,"Latino"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","With ms. notes throughout."]
[{"$nulls":4},"Reproduction of the original in the National Library of Scotland."]
[null,null,null,"Sommario/riassunto","eebo-0097"]
[null,"3.","Record Nr.",null,"UNINA9910483863603321"]
[null,null,"Titolo",null,"Neural Information Processing : 22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015, Proceedings, Part I / / edited by Sabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Liu"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015"]
[null,null,"ISBN",null,"3-319-26532-6"]
[null,null,"Edizione",null,"[1st ed. 2015.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVII, 742 p. 251 illus. in color.)"]
[null,null,"Collana",null,"Theoretical Computer Science and General Issues, , 2512-2029 ; ; 9489"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Pattern recognition systems"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer science"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Automated Pattern Recognition"]
[{"$nulls":4},"Computer Vision"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Theory of Computation"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part I -- Texture Classification with Patch Autocorrelation Features -- 1 Introduction -- 2 Related Work -- 3 Translation and Rotation Invariant Patch Autocorrelation Features -- 3.1 Texture Features -- 4 Texture Classification Experiments -- 4.1 Data Set -- 4.2 Learning Methods -- 4.3 Implementation and Evaluation -- 4.4 Parameter Tuning -- 4.5 Results on Brodatz Data Set -- 5 Conclusion -- References -- Novel Architecture for Cellular Neural Network Suitable for High-Density Integration of Electron Dev ... -- Abstract -- 1 Introduction -- 2 Device Architecture -- 2.1 Neuron -- 2.2 Synapse -- 2.3 Network -- 3 Learning Principle -- 4 Fabrication Process -- 5 Experimental Result -- 6 Conclusion -- References -- Analyzing the Impact of Feature Drifts in "]
[{"$nulls":4},"Streaming Learning -- 1 Introduction -- 2 Problem Statement -- 3 Simulating Feature Drifts -- 4 Analysis -- 4.1 Evaluated Algorithms -- 4.2 Experimental Protocol -- 4.3 Results Obtained -- 5 Conclusion -- References -- Non-linear Metric Learning Using Metric Tensor -- Abstract -- 1 Introduction -- 2 Theoretical Analysis -- 3 Problem Simplification -- 4 Algorithm -- 5 Experiment -- 5.1 Performance in Supervised Metric Learning -- 5.2 Application in Semi-supervised Clustering -- 6 Conclusion -- References -- An Optimized Second Order Stochastic Learning Algorithm for Neural Network Training -- 1 Introduction -- 2 Proposed Algorithm -- 2.1 Overview of Learning Algorithms -- 2.2 Stochastic Diagonal Levenberg-Marquardt Algorithm -- 2.3 Bounded SDLM Algorithm -- 3 Experimental Design -- 4 Results and Discussions -- 5 Conclusion and Future Works -- References -- Max-Pooling Dropout for Regularization of Convolutional Neural Networks -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Max-Pooling Dropout -- 3.1 Max-Pooling Dropout at Training Time."]
[{"$nulls":4},"3.2 Probabilistic Weighted Pooling at Test Time -- 4 Empirical Evaluations -- 4.1 Probabilistic Weighted Pooling vs. (Scaled) Max-Pooling -- 4.2 Max-Pooling Dropout vs. Stochastic Pooling -- 5 Conclusions -- References -- Predicting Box Office Receipts of Movies with Pruned Random Forest -- 1 Introduction -- 2 Methodology -- 2.1 Movie Information Data Collection -- 2.2 Pruned Random Forest -- 2.3 Advice for Screen Schedule -- 3 Results -- 3.1 The Classification Performance of Pruned Random Forest -- 3.2 Comparison with Other Models -- 4 Conclusion -- References -- A Novel 1-graph Based Image Classification Algorithm -- 1 Introduction -- 2 Background -- 2.1 Sparse Representation Based Classification Algorithm -- 2.2 1-Graph -- 3 1-graph Based Image Classification Method -- 3.1 Relationship Between Training Samples and Classes -- 3.2 Classification Process -- 4 Experiment Results -- 4.1 Face Recognition -- 4.2 Handwritten Digit Recognition -- 5 Conclusion and Future Work -- References -- Classification of Keystroke Patterns for User Identification in a Pressure-Based Typing Biometrics S ... -- Abstract -- 1 Introduction -- 2 System Design -- 2.1 Force Sensor -- 2.2 Microprocessor Design with Arduino -- 3 Classification -- 3.1 Particle Swarm Optimization -- 3.2 K-Means -- 4 Experimental Setup and Results -- 5 Conclusions -- References -- Discriminative Orthonormal Dictionary Learning for Fast Low-Rank Representation -- 1 Introduction -- 2 Discriminative Orthonormal Dictionary Learning -- 2.1 Formulation -- 2.2 Optimization -- 3 Fast Low-Rank Representation -- 4 Experiments -- 4.1 Extended Yale B Dataset -- 4.2 AR Dataset -- 4.3 Caltech 101 Dataset -- 5 Conclusions -- References -- Supervised Topic Classification for Modeling a Hierarchical Conference Structure -- 1 Introduction -- 2 Supervised Classification, Flat Case -- 3 Topics Hierarchy."]
[{"$nulls":4},"4 Empirical Results -- 5 Conclusion -- References -- A Framework for Online Inter-subjects Classification in Endogenous Brain-Computer Interfaces -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 Base Classifiers' Weights Initialization -- 2.2 Base Classifiers' Weights Adaptation Using Ensemble Predictions -- 2.3 Base Classifiers' Weights Adaptation Using Ensemble Predictions Reinforced by Interaction Error-Related Potentials -- 3 Experiments -- 3.1 EEG Data Sets -- 3.2 Procedure for Simulating IErrPs -- 3.3 Results -- 4 Conclusion -- References -- A Bayesian Sarsa Learning Algorithm with Bandit-Based Method -- 1 Introduction -- 2 Bayesian Sarsa -- 2.1 Q-values Distribution -- 2.2 Updating Q-Values -- 2.3 Actions Selection -- 3 Experimental Results -- 3.1 Gridworld -- 4 Conclusion -- References -- Incrementally Built Dictionary Learning for Sparse Representation -- "]
[{"$nulls":4},"1 Introduction -- 2 Background on Dictionary Learning -- 3 Incrementally Built Dictionary Learning -- 3.1 Approach Description -- 3.2 Incremental Learning Rule -- 3.3 Sparse Coding-Based Feature Extraction -- 4 Experimentations -- 4.1 Digits Recognition -- 4.2 Face Recognition -- 4.3 The Effects of Incremental Learning -- 5 Conclusion -- References -- Learning to Reconstruct 3D Structure from Object Motion -- 1 Introduction -- 2 Related Work -- 3 DNN Based 3D Reconstruction Method -- 3.1 Reconstruction Unit -- 3.2 Deep Neural Network for 3D Reconstruction -- 3.3 Temporal Integration -- 4 Experiments -- 4.1 Data Generation -- 4.2 Reconstruction on Synthetic Images -- 4.3 Reconstruction on Real Images -- 5 Conclusions -- References -- Convolutional Networks Based Edge Detector Learned via Contrast Sensitivity Function -- 1 Introduction -- 2 The Model Architecture -- 2.1 Convolutional Networks -- 2.2 Multi-channel Structure -- 3 Training Data Generation and Annotation."]
[{"$nulls":4},"3.1 Training Data Generation -- 3.2 Training Data Annotation -- 4 Experiments -- 5 Conclusion -- References -- Learning Algorithms and Frame Signatures for Video Similarity Ranking -- 1 Introduction -- 2 Similar-Video Retrieval -- 2.1 Frame Features -- 2.2 Clustering Algorithms for Exemplar Extraction -- 2.3 Global and Local Alignments -- 3 Video Signature Tools -- 3.1 Frame Signature -- 3.2 Word and Bag of Words -- 4 Experiments on Video Similarity Ranking -- 4.1 Test Video Set and Evaluation Method -- 4.2 Experimental Results -- 5 Discussions -- References -- On Measuring the Complexity of Classification Problems -- 1 Introduction -- 2 Complexity Measures/Indices -- 2.1 Feature/Attribute Overlapping -- 2.2 Separability of Classes -- 2.3 Geometry, Topology and Density -- 3 Conclusion -- References -- The Effect of Stemming and Stop-Word-Removal on Automatic Text Classification in Turkish Language -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 4 Methodology for Dataset -- 5 The Experimental Results -- 6 Conclusion -- References -- Example-Specific Density Based Matching Kernel for Classification of Varying Length Patterns of Speech Using Support Vector Machines -- 1 Introduction -- 2 Dynamic Kernels for Sets of Feature Vectors -- 3 Example-Specific Density Based Matching Kernel for Sets of Feature Vectors -- 4 Experimental Studies on Speech Emotion Recognition and Speaker Identification -- 5 Conclusions -- References -- Possibilistic Information Retrieval Model Based on Relevant Annotations and Expanded Classification -- Abstract -- 1 Introduction -- 2 Related Works -- 3 Filtering Annotation Approach -- 4 Classification of Annotations -- 4.1 Initial Classification -- 4.2 Clusters Extension -- 5 Experimental Evaluation and Analysis of Results -- 5.1 Used Collection of Data -- 5.2 Effects of the Classified and Filtered Annotation."]
[{"$nulls":4},"6 Conclusion and Future Works -- References -- A Transfer Learning Method with Deep Convolutional Neural Network for Diffuse Lung Disease Classification -- 1 Introduction -- 2 Methods -- 2.1 Deep Convolutional Neural Network (DCNN) -- 2.2 Transfer Learning for DCNN -- 2.3 Materials -- 3 Results -- 4 Summary and Discussion -- References -- Evaluation of Machine Learning Algorithms for Automatic Modulation Recognition -- Abstract -- 1 Introduction -- 2 System Model, Signal and Channel Representation -- 3 Feature Extraction -- 3.1 Spectral Features -- 3.2 Statistical Features -- 4 Nonnegative Matrix Factorization (NMF) -- 5 Experimental Results -- 6 Conclusion -- References -- Probabilistic Prediction in Multiclass Classification Derived for Flexible Text-Prompted Speaker Verification -- 1 Introduction -- 2 Probabilistic Prediction for Text-Prompted Speaker Verification -- 2.1 Multistep Speaker and Text Verification Using GEBI "]
[{"$nulls":4},"-- 2.2 Probabilistic Prediction for Speaker and Text Verification -- 2.3 Loss Functions for Evaluating the Performance -- 3 Experiments -- 3.1 Experimental Setting -- 3.2 Experimental Results and Analysis -- 4 Conclusion -- References -- Simple Feature Quantities for Learning of Dynamic Binary Neural Networks -- 1 Introduction -- 2 Dynamic Binary Neural Networks -- 3 Teacher Signal and Feature Quantities -- 4 Greedy Search Based Sparsification Algorithm -- 5 Conclusions -- References -- Transfer Metric Learning for Kinship Verification with Locality-Constrained Sparse Features -- 1 Introduction -- 2 Proposed Approach -- 2.1 Feature Extraction -- 2.2 NRTML -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Experimental Results -- 4 Conclusion -- References -- Unsupervised Land Classification by Self-organizing Map Utilizing the Ensemble Variance Information in Satellite-Borne Polarimetric Synthetic Aperture Radar."]
[{"$nulls":4},"1 Introduction."]
[null,null,null,"Sommario/riassunto","The four volume set LNCS 9489, LNCS 9490, LNCS 9491, and LNCS 9492 constitutes the proceedings of the 22nd International Conference on Neural Information Processing, ICONIP 2015, held in Istanbul, Turkey, in November 2015. The 231 full papers presented were carefully reviewed and selected from 375 submissions. The 4 volumes represent topical sections containing articles on Learning Algorithms and Classification Systems; Artificial Intelligence and Neural Networks: Theory, Design, and Applications; Image and Signal Processing; and Intelligent Social Networks."]