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8th Linguistic Annotation Workshop, LAW 2014, in conjunction with COLING 2014

作者： Wacholder, Nina Muresan, Smaranda Ghosh, Debanjan Aakhus, Mark School of Communication and Information Rutgers University United States Center for Computational Learning Systems Columbia University

ISBN: (纸本)9781941643297

To computationally model discourse phenomena such as argumentation we need corpora with reliable annotation of the phenomena under study. Annotating complex discourse phenomena poses two challenges: fuzziness of unit boundaries and the need for multiple annotators. We show that current metrics for inter-annotator agreement (IAA) such as P/R/F1 and Krippendorff's α provide inconsistent results for the same text. In addition, IAA metrics do not tell us what parts of a text are easier or harder for human judges to annotate and so do not provide sufficiently specific information for evaluating systems that automatically identify discourse units. We propose a hierarchical clustering approach that aggregates overlapping text segments of text identified by multiple annotators;the more annotators who identify a text segment, the easier we assume that the text segment is to annotate. The clusters make it possible to quantify the extent of agreement judges show about text segments;this information can be used to assess the output of systems that automatically identify discourse units. © LAW 2014 - 8th Linguistic Annotation Workshop, in conjunction with COLING 2014 - Proceedings of the Workshop. All rights reserved.

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Exact Analysis of the Subthreshold Variability for Conductance-Based Neuronal Models with Synchronous Synaptic Inputs

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Physical Review X 2024年第1期14卷 011021-011021页

作者： Logan A. Becker Baowang Li Nicholas J. Priebe Eyal Seidemann Thibaud Taillefumier Center for Theoretical and Computational Neuroscience The University of Texas at Austin Austin Texas 78712 USA Department of Neuroscience The University of Texas at Austin Austin Texas 78712 USA Center for Perceptual Systems The University of Texas at Austin Austin Texas 78712 USA Center for Learning and Memory The University of Texas at Austin Austin Texas 78712 USA Department of Psychology The University of Texas at Austin Austin Texas 78712 USA Department of Mathematics The University of Texas at Austin Austin Texas 78712 USA

The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state, neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically, we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects postspiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime yields realistic subthreshold variability (voltage variance ≃4–9 mV2) only when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that, without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis

关键词： Spiking neurons Brain First-principles calculations Markovian processes Monte Carlo methods Stochastic analysis

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The benefits of a model of annotation 7th

The benefits of a model of annotation

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7th Linguistic Annotation and Interoperability with Discourse Workshop, LAW 2013 and ID 2013

作者： Passonneau, Rebecca J. Carpenter, Bob Center for Computational Learning Systems Columbia University United States Department of Statistics Columbia University United States

ISBN: (纸本)9781937284589

This paper presents a case study of a difficult and important categorical annotation task (word sense) to demonstrate a probabilistic annotation model applied to crowdsourced data. It is argued that standard (chance-adjusted) agreement levels are neither necessary nor sufficient to ensure high quality gold standard labels. Compared to conventional agreement measures, application of an annotation model to instances with crowdsourced labels yields higher quality labels at lower cost. © 2013 Association for computational Linguistics

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Neural timescales from a computational perspective

arXiv

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arXiv 2024年

作者： Zeraati, Roxana Levina, Anna Macke, Jakob H. Gao, Richard Self-organization and optimality in neuronal networks University of Tübingen Tübingen AI Center Germany Max Planck Institute for Biological Cybernetics Germany Bernstein Center for Computational Neuroscience Tübingen Germany Machine Learning in Science University of Tübingen Tübingen AI Center Germany Max Planck Institute for Intelligent Systems Tübingen Germany

Timescales of neural activity are diverse across and within brain areas, and experimental observations suggest that neural timescales reflect information in dynamic environments. However, these observations do not specify how neural timescales are shaped, nor whether particular timescales are necessary for neural computations and brain function. Here, we take a complementary perspective and synthesize three directions where computational methods can distill the broad set of empirical observations into quantitative and testable theories: We review (i) how data analysis methods allow us to capture different timescales of neural dynamics across different recording modalities, (ii) how computational models provide a mechanistic explanation for the emergence of diverse timescales, and (iii) how task-optimized models in machine learning uncover the functional relevance of neural timescales. This integrative computational approach, combined with empirical findings, would provide a more holistic understanding of how neural timescales capture the relationship between brain structure, dynamics, and behavior. © 2024, CC BY.

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Automatic correction and extension of morphological annotations 7th

Automatic correction and extension of morphological annotati...

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7th Linguistic Annotation and Interoperability with Discourse Workshop, LAW 2013 and ID 2013

作者： Eskander, Ramy Habash, Nizar Bies, Ann Kulick, Seth Maamouri, Mohamed Center for Computational Learning Systems Columbia University United States Linguistic Data Consortium University of Pennsylvania United States

ISBN: (纸本)9781937284589

For languages with complex morphologies, limited resources and tools, and/or lack of standard grammars, developing annotated resources can be a challenging task. Annotated resources developed under time/money constraints for such languages tend to tradeoff depth of representation with degree of noise. We present two methods for automatic correction and extension of morphological annotations, and demonstrate their success on three divergent Egyptian Arabic corpora. © 2013 Association for computational Linguistics

关键词： Interoperability

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Reshaping the Cortical Connectivity Gradient by Long-Term Cognitive Training During Development

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Neuroscience Bulletin 2024年第1期40卷 50-64页

作者： Tianyong Xu Yunying Wu Yi Zhang Xi-Nian Zuo Feiyan Chen Changsong Zhou Bio-X Laboratory School of PhysicsZhejiang UniversityHangzhou 310027China Qiushi Academy for Advanced Studies Zhejiang UniversityHangzhou 310027China State Key Laboratory of Modern Optical Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China State Key Laboratory of Cognitive Neuroscience and Learning Bejing Normal UniversityBeijing 100875China Developmental Population Neuroscience Research Center IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijing 100875China Zhejiang Province Key Laboratory of Quantum Technology and Devices Zhejiang UniversityHangzhou 310027China Department of Physics Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems(Hong Kong)Institute of Computational and Theoretical StudiesHong Kong Baptist UniversityHong Kong 999077China

The organization of the brain follows a topologi-cal hierarchy that changes dynamically during ***,it remains unknown whether and how cognitive training administered over multiple years during develop-ment can modify this hierarchical *** measuring the brain and behavior of school children who had carried out abacus-based mental calculation(AMC)training for five years(starting from 7 years to 12 years old)in pre-training and post-training,we revealed the reshaping effect of long-term AMC intervention during development on the brain hierarchical *** observed the development-induced emergence of the default network,AMC training-promoted shifting,and regional changes in cortical ***,the training-induced gradient changes were located in visual and somatomotor areas in association with the visuospatial/motor-imagery *** found that gradient-based features can predict the math ability within *** findings provide novel insights into the dynamic nature of network recruitment impacted by long-term cognitive training during development.

关键词： Connectivity gradient Cognitive training Development Neural plasticity Abacus-based mental calculation

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Work hard, play hard: Email classification on the avocado and enron corpora 11

Work hard, play hard: Email classification on the avocado an...

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11th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2017, in conjunction with the 55th Annual Meeting of the Association for computational Linguistics, ACL 2017

作者： Alkhereyf, Sakhar Rambow, Owen Department of Computer Science Columbia University New York United States Center for Computational Learning Systems Columbia University New York United States

ISBN: (纸本)9781945626609

In this paper, we present an empirical study of email classification into two main categories "Business" and "Personal". We train on the Enron email corpus, and test on the Enron and Avocado email corpora. We show that information from the email exchange networks improves the performance of classification. We represent the email exchange networks as social networks with graph structures. For this classification task, we extract social networks features from the graphs in addition to lexical features from email content and we compare the performance of SVM and Extra-Trees classifiers using these features. Combining graph features with lexical features improves the performance on both classifiers. We also provide manually annotated sets of the Avocado and Enron email corpora as a supplementary contribution. © 2017 Association for computational Linguistics

关键词： Classification (of information)

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Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs

arXiv

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arXiv 2023年

作者： Becker, Logan A. Li, Baowang Priebe, Nicholas J. Seidemann, Eyal Taillefumier, Thibaud Center for Theoretical and Computational Neuroscience The University of Texas Austin United States Department of Neuroscience The University of Texas Austin United States Center for Perceptual Systems The University of Texas Austin United States Center for Learning and Memory The University of Texas Austin United States Department of Psychology The University of Texas Austin United States Department of Mathematics The University of Texas Austin United States

The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives;we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects post-spiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime only yields realistic subthreshold variability (voltage variance 4−9mV2) when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for m

关键词： Neurons

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On discrete symmetries of robotics systems: A group-theoretic and data-driven analysis

arXiv

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arXiv 2023年

作者： Ordonez-Apraez, Daniel Martin, Mario Agudo, Antonio Moreno-Noguer, Francesc Istituto Italiano di Tecnologia IIT Dynamic Legged Systems IIT-DLS Computational Statistics and Machine Learning IIT-CSML Italy Institut de Robòtica i Informàtica Industrial CSIC-UPC Spain Universitat Politècnica de Catalunya UPC Spain Barcelona Supercomputing Center BSC Spain

We present a comprehensive study on discrete morphological symmetries of dynamical systems, which are commonly observed in biological and artificial locomoting systems, such as legged, swimming, and flying animals/robots/virtual characters. These symmetries arise from the presence of one or more planes/axis of symmetry in the system’s morphology, resulting in harmonious duplication and distribution of body parts. Significantly, we characterize how morphological symmetries extend to symmetries in the system’s dynamics, optimal control policies, and in all proprioceptive and exteroceptive measurements related to the system’s dynamics evolution. In the context of data-driven methods, symmetry represents an inductive bias that justifies the use of data augmentation or symmetric function approximators. To tackle this, we present a theoretical and practical framework for identifying the system’s morphological symmetry group G and characterizing the symmetries in proprioceptive and exteroceptive data measurements. We then exploit these symmetries using data augmentation and G-equivariant neural networks. Our experiments on both synthetic and real-world applications provide empirical evidence of the advantageous outcomes resulting from the exploitation of these symmetries, including improved sample efficiency, enhanced generalization, and reduction of trainable *** Codes 37J15, © 2023, CC BY.

关键词： Dynamical systems

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Correction annotation for non-native Arabic texts: Guidelines and corpus 9

Correction annotation for non-native Arabic texts: Guideline...

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9th Linguistic Annotation Workshop, LAW 2015, held in conjuncion with NAACL 2015

作者： Zaghouani, Wajdi Habash, Nizar Bouamor, Houda Rozovskaya, Alla Mohit, Behrang Heider, Abeer Oflazer, Kemal Carnegie Mellon University Qatar New York University Abu Dhabi United Arab Emirates Center for Computational Learning Systems Columbia University United States *** Qatar University Qatar

ISBN: (纸本)9781941643471

We present our correction annotation guidelines to create a manually corrected nonnative (L2) Arabic corpus. We develop our approach by extending an L1 large-scale Arabic corpus and its manual corrections, to include manually corrected non-native Arabic learner essays. Our overarching goal is to use the annotated corpus to develop components for automatic detection and correction of language errors that can be used to help Standard Arabic learners (native and non-native) improve the quality of the Arabic text they produce. The created corpus of L2 text manual corrections is the largest to date. We evaluate our guidelines using inter-annotator agreement and show a high degree of consistency. © 2015 Association for computational Linguistics

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