Combining Modality Theory and Context Models

2006 ◽  
pp. 141-151 ◽  
Author(s):  
Andreas Ratzka
Keyword(s):  
Context Models

TRANSLATION OF SOCIAL AND POLITICAL TERMINOLOGY OF CHINESE LANGUAGE AS A PROCESS OF NORMATIVE PRACTICE IN CONTEXT MODELS

2020 ◽  
Vol 6 (3) ◽  
pp. 172-182
Author(s):  
Saodat Nosirova ◽  
Keyword(s):  
Comparative Analysis ◽  
Law Enforcement ◽  
Chinese Language ◽  
Integrated Approach ◽  
Point Of View ◽  
Modern Chinese ◽  
Normative Practice ◽  
Context Models

The article is devoted to a comparative analysis of the socio -political terminology of the modern Chinese language.The purpose of the article is to search for an integrated approach to the study of the cognitive side of social and political terms of the Chinese language from the point of view of law enforcement in the process of translating official materials from Chinese into Uzbek and / or Russian and vice versa

scholarly journals Memory for events and their spatial context: models and experiments

2002 ◽  
pp. 249-268
Author(s):  
Neil Burgess ◽  
Suzanna Becker ◽  
John A. King ◽  
John O'Keefe
Keyword(s):  
Spatial Context ◽  
Context Models

scholarly journals A retrieved context model of the emotional modulation of memory

2017 ◽  
Author(s):  
Deborah Talmi ◽  
Lynn J. Lohnas ◽  
Nathaniel D. Daw
Keyword(s):  
Emotional Memory ◽  
Temporal Context ◽  
Composition Effect ◽  
Context Model ◽  
Emotional Modulation ◽  
Emotional Information ◽  
Retrieval Processes ◽  
The Rich ◽  
Context Models ◽  
Evolutionary Fitness

AbstractEmotion enhances episodic memory, an effect thought to be an adaptation to prioritise the memories that best serve evolutionary fitness. But viewing this effect largely in terms of prioritising what to encode or consolidate neglects broader rational considerations about what sorts of associations should be formed at encoding, and which should be retrieved later. Although neurobiological investigations have provided many mechanistic clues about how emotional arousal modulates item memory, these effects have not been wholly integrated with the cognitive and computational neuroscience of memory more generally.Here we apply the Context Maintenance and Retrieval Model (CMR, Polyn, Norman & Kahana, 2009) to this problem by extending it to describe the way people may represent and process emotional information. A number of ways to operationalise the effect of emotion were tested. The winning emotional CMR (eCMR) model reconceptualises emotional memory effects as arising from the modulation of a process by which memories become bound to ever-changing temporal and emotional contexts. eCMR provides a good qualitative fit for the emotional list-composition effect and the emotional oddball effect, illuminating how these effects are jointly determined by the interplay of encoding and retrieval processes. eCMR explains the increased advantage of emotional memories in delayed memory tests through the limited ability of retrieval to reinstate the temporal context of encoding.By leveraging the rich tradition of temporal context models, eCMR helps integrate existing effects of emotion and provides a powerful tool to test mechanisms by which emotion affects memory in a broad range of paradigms.

Lossless Compression of Binary Holograms with Context Models and Adaptive Segmentation

2021 ◽  
Author(s):  
Raees Kizhakkumkara Muhamad ◽  
Tobias Birnbaum ◽  
David Blinder ◽  
Colas Schretter ◽  
Peter Schelkens
Keyword(s):  
Lossless Compression ◽  
Adaptive Segmentation ◽  
Context Models

Context-Based Scene Understanding

2018 ◽  
pp. 754-773
Author(s):  
Esfandiar Zolghadr ◽  
Borko Furht
Keyword(s):  
Object Detection ◽  
Latent Dirichlet Allocation ◽  
Model Performance ◽  
High Order ◽  
Multiple Sources ◽  
Context Model ◽  
Unified Framework ◽  
Context Models ◽  
Scene Content ◽  
Model Context

Context plays an important role in performance of object detection. There are two popular considerations in building context models for computer vision applications; type of context (semantic, spatial, scale) and scope of the relations (pairwise, high-order). In this paper, a new unified framework is presented that combines multiple sources of context in high-order relations to encode semantical coherence and consistency of the scenes. This framework introduces a new descriptor called context relevance score to model context-based distribution of the response variables and apply it to two distributions. First model incorporates context descriptor along with annotation response into a supervised Latent Dirichlet Allocation (LDA) built on multi-variate Bernoulli distribution called Context-Based LDA (CBLDA). The second model is based on multi-variate Wallenius' non-central Hyper-geometric distribution and is called Wallenius LDA (WLDA). WLDA incorporates context knowledge as bias parameter. Scene context is modeled as a graph and effectively used in object detection framework to maximize semantical consistency of the scene. The graph can also be used in recognition of out-of-context objects. Annotation metadata of Sun397 dataset is used to construct the context model. Performance of the proposed approaches was evaluated on ImageNet dataset. Comparison between proposed approaches and state-of-art multi-class object annotation algorithm shows superiority of presented approach in labeling of scene content.

Sign in / Sign up

Export Citation Format

Share Document