The Experts below are selected from a list of 29940 Experts worldwide ranked by ideXlab platform
James J. Gross - One of the best experts on this subject based on the ideXlab platform.
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Emotion regulation: Affective, cognitive, and social consequences
Psychophysiology, 2002Co-Authors: James J. GrossAbstract:One of life's great challenges is successfully regulating emotions. Do some emotion regulation strategies have more to recommend them than others? According to Gross's (1998, Review of General Psychology, 2, 271-299) Process model of emotion regulation, strategies that act early in the emotion-Generative Process should have a different profile of consequences than strategies that act later on. This review focuses on two commonly used strategies for down-regulating emotion. The first, reappraisal, comes early in the emotion-Generative Process. It consists of changing the way a situation is construed so as to decrease its emotional impact. The second, suppression, comes later in the emotion-Generative Process. It consists of inhibiting the outward signs of inner feelings. Experimental and individual-difference studies find reappraisal is often more effective than suppression. Reappraisal decreases emotion experience and behavioral expression, and has no impact on memory. By contrast, suppression decreases behavioral expression, but fails to decrease emotion experience, and actually impairs memory. Suppression also increases physiological responding for suppressors and their social partners. This review concludes with a consideration of five important directions for future research on emotion regulation Processes.
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Emotion regulation in adulthood: Timing is everything
Current Directions in Psychological Science, 2001Co-Authors: James J. GrossAbstract:Emotions seem to come and go as they please. However, we actually hold considerable sway over our emotions: We influence which emotions we have and how we experience and express these emotions. The Process model of emotion regulation described here suggests that how we regulate our emotions matters. Regulatory strategies that act early in the emotion-Generative Process should have quite different outcomes than strategies that act later. This review focuses on two widely used strategies for down-regulating emotion. The first, reappraisal, comes early in the emotion-Generative Process. It consists of changing how we think about a situation in order to decrease its emotional impact. The second, suppression, comes later in the emotion-Generative Process. It involves inhibiting the outward signs of emotion. Theory and research suggest that reappraisal is more effective than suppression. Reappraisal decreases the experience and behavioral expression of emotion, and has no impact on memory. By contrast, suppression decreases behavioral expression, but fails to decrease the experience of emotion, and actually impairs memory. Suppression also increases physiological responding in both the suppressors and their social partners.
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Emotion Regulation in Adulthood: Timing Is Everything
Current Directions in Psychological Science, 2001Co-Authors: James J. GrossAbstract:Emotions seem to come and go as they please. However, we actually hold considerable sway over our emotions: We influence which emotions we have and how we experience and express these emotions. The Process model of emotion regulation described here suggests that how we regulate our emotions matters. Regulatory strategies that act early in the emotion-Generative Process should have quite different outcomes than strategies that act later. This review focuses on two widely used strategies for down-regulating emotion. The first, reappraisal, comes early in the emotion-Generative Process. It consists of changing how we think about a situation in order to decrease its emotional impact. The second, suppression, comes later in the emotion-Generative Process. It involves inhibiting the outward signs of emotion. Theory and research suggest that reappraisal is more effective than suppression. Reappraisal decreases the experience and behavioral expression of emotion, and has no impact on memory. By contrast, suppress...
Jiawei Han - One of the best experts on this subject based on the ideXlab platform.
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minimally supervised categorization of text with metadata
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020Co-Authors: Yu Zhang, Yu Meng, Jiaxin Huang, Xuan Wang, Jiawei HanAbstract:Document categorization, which aims to assign a topic label to each document, plays a fundamental role in a wide variety of applications. Despite the success of existing studies in conventional supervised document classification, they are less concerned with two real problems: (1)the presence of metadata : in many domains, text is accompanied by various additional information such as authors and tags. Such metadata serve as compelling topic indicators and should be leveraged into the categorization framework; (2)label scarcity: labeled training samples are expensive to obtain in some cases, where categorization needs to be performed using only a small set of annotated data. In recognition of these two challenges, we propose MetaCat, a minimally supervised framework to categorize text with metadata. Specifically, we develop a Generative Process describing the relationships between words, documents, labels, and metadata. Guided by the Generative model, we embed text and metadata into the same semantic space to encode heterogeneous signals. Then, based on the same Generative Process, we synthesize training samples to address the bottleneck of label scarcity. We conduct a thorough evaluation on a wide range of datasets. Experimental results prove the effectiveness of MetaCat over many competitive baselines.
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minimally supervised categorization of text with metadata
arXiv: Computation and Language, 2020Co-Authors: Yu Zhang, Yu Meng, Jiaxin Huang, Xuan Wang, Jiawei HanAbstract:Document categorization, which aims to assign a topic label to each document, plays a fundamental role in a wide variety of applications. Despite the success of existing studies in conventional supervised document classification, they are less concerned with two real problems: (1) \textit{the presence of metadata}: in many domains, text is accompanied by various additional information such as authors and tags. Such metadata serve as compelling topic indicators and should be leveraged into the categorization framework; (2) \textit{label scarcity}: labeled training samples are expensive to obtain in some cases, where categorization needs to be performed using only a small set of annotated data. In recognition of these two challenges, we propose \textsc{MetaCat}, a minimally supervised framework to categorize text with metadata. Specifically, we develop a Generative Process describing the relationships between words, documents, labels, and metadata. Guided by the Generative model, we embed text and metadata into the same semantic space to encode heterogeneous signals. Then, based on the same Generative Process, we synthesize training samples to address the bottleneck of label scarcity. We conduct a thorough evaluation on a wide range of datasets. Experimental results prove the effectiveness of \textsc{MetaCat} over many competitive baselines.
Yu Zhang - One of the best experts on this subject based on the ideXlab platform.
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minimally supervised categorization of text with metadata
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020Co-Authors: Yu Zhang, Yu Meng, Jiaxin Huang, Xuan Wang, Jiawei HanAbstract:Document categorization, which aims to assign a topic label to each document, plays a fundamental role in a wide variety of applications. Despite the success of existing studies in conventional supervised document classification, they are less concerned with two real problems: (1)the presence of metadata : in many domains, text is accompanied by various additional information such as authors and tags. Such metadata serve as compelling topic indicators and should be leveraged into the categorization framework; (2)label scarcity: labeled training samples are expensive to obtain in some cases, where categorization needs to be performed using only a small set of annotated data. In recognition of these two challenges, we propose MetaCat, a minimally supervised framework to categorize text with metadata. Specifically, we develop a Generative Process describing the relationships between words, documents, labels, and metadata. Guided by the Generative model, we embed text and metadata into the same semantic space to encode heterogeneous signals. Then, based on the same Generative Process, we synthesize training samples to address the bottleneck of label scarcity. We conduct a thorough evaluation on a wide range of datasets. Experimental results prove the effectiveness of MetaCat over many competitive baselines.
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Minimally Supervised Categorization of Text with Metadata
2020Co-Authors: Yu Zhang, Yu Meng, Huang Jiaxin, Xu, Frank F., Wang Xuan, Han JiaweiAbstract:Document categorization, which aims to assign a topic label to each document, plays a fundamental role in a wide variety of applications. Despite the success of existing studies in conventional supervised document classification, they are less concerned with two real problems: (1) \textit{the presence of metadata}: in many domains, text is accompanied by various additional information such as authors and tags. Such metadata serve as compelling topic indicators and should be leveraged into the categorization framework; (2) \textit{label scarcity}: labeled training samples are expensive to obtain in some cases, where categorization needs to be performed using only a small set of annotated data. In recognition of these two challenges, we propose \textsc{MetaCat}, a minimally supervised framework to categorize text with metadata. Specifically, we develop a Generative Process describing the relationships between words, documents, labels, and metadata. Guided by the Generative model, we embed text and metadata into the same semantic space to encode heterogeneous signals. Then, based on the same Generative Process, we synthesize training samples to address the bottleneck of label scarcity. We conduct a thorough evaluation on a wide range of datasets. Experimental results prove the effectiveness of \textsc{MetaCat} over many competitive baselines.Comment: 10 pages; Accepted to SIGIR 202
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minimally supervised categorization of text with metadata
arXiv: Computation and Language, 2020Co-Authors: Yu Zhang, Yu Meng, Jiaxin Huang, Xuan Wang, Jiawei HanAbstract:Document categorization, which aims to assign a topic label to each document, plays a fundamental role in a wide variety of applications. Despite the success of existing studies in conventional supervised document classification, they are less concerned with two real problems: (1) \textit{the presence of metadata}: in many domains, text is accompanied by various additional information such as authors and tags. Such metadata serve as compelling topic indicators and should be leveraged into the categorization framework; (2) \textit{label scarcity}: labeled training samples are expensive to obtain in some cases, where categorization needs to be performed using only a small set of annotated data. In recognition of these two challenges, we propose \textsc{MetaCat}, a minimally supervised framework to categorize text with metadata. Specifically, we develop a Generative Process describing the relationships between words, documents, labels, and metadata. Guided by the Generative model, we embed text and metadata into the same semantic space to encode heterogeneous signals. Then, based on the same Generative Process, we synthesize training samples to address the bottleneck of label scarcity. We conduct a thorough evaluation on a wide range of datasets. Experimental results prove the effectiveness of \textsc{MetaCat} over many competitive baselines.
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Learning Latent Representations for Speech Generation and Transformation
arXiv: Computation and Language, 2017Co-Authors: Wei-ning Hsu, Yu Zhang, James GlassAbstract:An ability to model a Generative Process and learn a latent representation for speech in an unsupervised fashion will be crucial to Process vast quantities of unlabelled speech data. Recently, deep probabilistic Generative models such as Variational Autoencoders (VAEs) have achieved tremendous success in modeling natural images. In this paper, we apply a convolutional VAE to model the Generative Process of natural speech. We derive latent space arithmetic operations to disentangle learned latent representations. We demonstrate the capability of our model to modify the phonetic content or the speaker identity for speech segments using the derived operations, without the need for parallel supervisory data.
Ajay Divakaran - One of the best experts on this subject based on the ideXlab platform.
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Generative Process tracking for audio analysis
International Conference on Acoustics Speech and Signal Processing, 2006Co-Authors: R Radhakrishnan, Ajay DivakaranAbstract:The problem of Generative Process tracking involves detecting and adapting to changes in the underlying Generative Process that creates a time series of observations. It has been widely used for visual background modelling to adaptively track the Generative Process that generates the pixel intensities. In this paper, we extend this idea to audio background modelling and show its applications in surveillance domain. We adaptively learn the parameters of the Generative audio background Process and detect foreground events. We have tested the effectiveness of the proposed algorithms using synthetic time series data and show its performance on elevator audio surveillance
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ICASSP (5) - Generative Process Tracking for Audio Analysis
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 1Co-Authors: R Radhakrishnan, Ajay DivakaranAbstract:The problem of Generative Process tracking involves detecting and adapting to changes in the underlying Generative Process that creates a time series of observations. It has been widely used for visual background modelling to adaptively track the Generative Process that generates the pixel intensities. In this paper, we extend this idea to audio background modelling and show its applications in surveillance domain. We adaptively learn the parameters of the Generative audio background Process and detect foreground events. We have tested the effectiveness of the proposed algorithms using synthetic time series data and show its performance on elevator audio surveillance
Santosh Kumar - One of the best experts on this subject based on the ideXlab platform.
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a Generative Process planning system for parts produced by rapid prototyping
International Journal of Production Research, 2008Co-Authors: Sarang Pande, Santosh KumarAbstract:This paper presents a Generative Process planning system for parts produced by the rapid prototyping Process (i.e. fused deposition modelling–FDM). The proposed Process planning involves optimal selection of orientating the model with a proper support structure and then provides an intelligent slicing methodology, such as direct or adaptive, to minimise the built up time, keeping the geometry and cusp height errors in control. Pre- and post-slicing Processes have been used to minimise the sliced data error. The Computer Aided Process Planning (CAPP) model has been arranged into five modules: orientation, support structure generation, slicing, path planning and Numerical Control (NC) program generation, and model build up. The CAPP model has been implemented in C language having a unique methodology consisting of 42 simplified steps. The CAPP model has been tested for several examples and shows satisfactory results.
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A Generative Process planning system for cold extrusion
International Journal of Production Research, 2003Co-Authors: Santosh Kumar, Kripa Shanker, G.k. LalAbstract:In this paper, a CAPP system GIFTEP (Generative, Interactive, Feature-based Technology for Extruded Products) based on a Generative approach is proposed for forward cold extrusion of complex-shape solid components to be extruded through single-hole dies. This is an attempt, using the previous efforts of Kumar et al. 1999 and 2002), made by the authors to bridge the gap between CAD and CAM practices related to cold extrusion. A feature recognition methodology, an upper bound model, an optimum die profile, a defect prediction criteria, criteria to choose a die arrangement and a simple adiabatic model to account for the temperature of extrudate proposed in the previous work of Kumar are taken as the foundation modules for the purpose. Based on the component layout, a recognition module provides necessary data to carry out the die design Process. The input parameter selection module first selects the input parameters provided by the user (material, reduction, billet condition, production type, etc.) or the de...