scholarly journals Computational challenges in explaining communication: How deep the rabbit hole goes

2021 ◽  
Author(s):  
Laura van de Braak ◽  
Mark Dingemanse ◽  
Ivan Toni ◽  
Iris van Rooij ◽  
Mark Blokpoel
Keyword(s):  
Computational Models ◽  
State Of The Art ◽  
Chance Level ◽  
Intended Meaning ◽  
Potential Target

When people are unsure of the intended meaning of a word, they often ask for clarification. One way of doing so—often assumed in models of communication—is to point at a potential target: “Do you mean [points at the rabbit]?” However, what if the target is unavailable? Then the only recourse is language itself, which seems equivalent to pulling oneself up from a swamp by one’s hair. We created two computational models of communication, one able to point and one not. The latter incorporates inference to resolve the meaning of non-pointing signals. Simulations show agents in both models reach perceived understanding equally quickly. While this means agents think they are successfully communicating, non-pointing agents understand each other only at chance level. This shows that state- of-the-art computational explanations have difficulty explaining how people solve the puzzle of underdetermination, and that doing so will require a fundamental leap forward.

Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration

2021 ◽  
Vol 376 (1821) ◽  
pp. 20190765 ◽  
Author(s):  
Giovanni Pezzulo ◽  
Joshua LaPalme ◽  
Fallon Durant ◽  
Michael Levin
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
State Of The Art ◽  
Memory Representation ◽  
Theme Issue ◽  
Evolutionary Innovation ◽  
New Interpretation ◽  
The Brain

Nervous systems’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states—in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo . This article is part of the theme issue ‘Basal cognition: multicellularity, neurons and the cognitive lens’.

scholarly journals The Understanding of Deep Learning: A Comprehensive Review

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ranjan Kumar Mishra ◽  
G. Y. Sandesh Reddy ◽  
Himanshu Pathak
Keyword(s):  
Deep Learning ◽  
Deep Neural Network ◽  
Computational Models ◽  
Review Paper ◽  
State Of The Art ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Levels Of Abstraction ◽  
Comprehensive Review ◽  
Computer Based

Deep learning is a computer-based modeling approach, which is made up of many processing layers that are used to understand the representation of data with several levels of abstraction. This review paper presents the state of the art in deep learning to highlight the major challenges and contributions in computer vision. This work mainly gives an overview of the current understanding of deep learning and their approaches in solving traditional artificial intelligence problems. These computational models enhanced its application in object detection, visual object recognition, speech recognition, face recognition, vision for driverless cars, virtual assistants, and many other fields such as genomics and drug discovery. Finally, this paper also showcases the current developments and challenges in training deep neural network.

scholarly journals Comparing Deep-Learning Architectures and Traditional Machine-Learning Approaches for Satire Identification in Spanish Tweets

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2075
Author(s):  
Óscar Apolinario-Arzube ◽  
José Antonio García-Díaz ◽  
José Medina-Moreira ◽  
Harry Luna-Aveiga ◽  
Rafael Valencia-García
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
User Interfaces ◽  
State Of The Art ◽  
Learning Approaches ◽  
Word Embeddings ◽  
Linguistic Features ◽  
Intended Meaning ◽  
Language User ◽  
Learning Architectures

Automatic satire identification can help to identify texts in which the intended meaning differs from the literal meaning, improving tasks such as sentiment analysis, fake news detection or natural-language user interfaces. Typically, satire identification is performed by training a supervised classifier for finding linguistic clues that can determine whether a text is satirical or not. For this, the state-of-the-art relies on neural networks fed with word embeddings that are capable of learning interesting characteristics regarding the way humans communicate. However, as far as our knowledge goes, there are no comprehensive studies that evaluate these techniques in Spanish in the satire identification domain. Consequently, in this work we evaluate several deep-learning architectures with Spanish pre-trained word-embeddings and compare the results with strong baselines based on term-counting features. This evaluation is performed with two datasets that contain satirical and non-satirical tweets written in two Spanish variants: European Spanish and Mexican Spanish. Our experimentation revealed that term-counting features achieved similar results to deep-learning approaches based on word-embeddings, both outperforming previous results based on linguistic features. Our results suggest that term-counting features and traditional machine learning models provide competitive results regarding automatic satire identification, slightly outperforming state-of-the-art models.

scholarly journals Towards computational models of multilingual sentence processing

2019 ◽  
Author(s):  
Stefan L. Frank
Keyword(s):  
Computational Linguistics ◽  
Sentence Processing ◽  
Computational Models ◽  
State Of The Art ◽  
Cognitive Models ◽  
The State ◽  
Cognitive Modelling ◽  
Current Review ◽  
Multiple Languages ◽  
Human Sentence Processing

Although computational models can simulate aspects of human sentence processing, research on this topic has remained almost exclusively limited to the single language case. The current review presents an overview of the state of the art in computational cognitive models of sentence processing, and discusses how recent sentence-processing models can be used to study bi- and multilingualism. Recent results from cognitive modelling and computational linguistics suggest that phenomena specific to bilingualism can emerge from systems that have no dedicated components for handling multiple languages. Hence, accounting for human bi-/multilingualism may not require models that are much more sophisticated than those for the monolingual case.

scholarly journals A Novel Approach for Predicting Disease-lncRNA Associations Based on the Distance Correlation Set and Information of the miRNAs

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Haochen Zhao ◽  
Linai Kuang ◽  
Lei Wang ◽  
Zhanwei Xuan
Keyword(s):  
Computational Models ◽  
State Of The Art ◽  
Experimental Studies ◽  
Research Field ◽  
Computational Method ◽  
Biological Processes ◽  
Distance Correlation ◽  
Novel Approach ◽  
Disease Associations ◽  
Leave One Out

Recently, accumulating laboratorial studies have indicated that plenty of long noncoding RNAs (lncRNAs) play important roles in various biological processes and are associated with many complex human diseases. Therefore, developing powerful computational models to predict correlation between lncRNAs and diseases based on heterogeneous biological datasets will be important. However, there are few approaches to calculating and analyzing lncRNA-disease associations on the basis of information about miRNAs. In this article, a new computational method based on distance correlation set is developed to predict lncRNA-disease associations (DCSLDA). Comparing with existing state-of-the-art methods, we found that the major novelty of DCSLDA lies in the introduction of lncRNA-miRNA-disease network and distance correlation set; thus DCSLDA can be applied to predict potential lncRNA-disease associations without requiring any known disease-lncRNA associations. Simulation results show that DCSLDA can significantly improve previous existing models with reliable AUC of 0.8517 in the leave-one-out cross-validation. Furthermore, while implementing DCSLDA to prioritize candidate lncRNAs for three important cancers, in the first 0.5% of forecast results, 17 predicted associations are verified by other independent studies and biological experimental studies. Hence, it is anticipated that DCSLDA could be a great addition to the biomedical research field.

A literature review on computational models for laminated composite and sandwich panels

2011 ◽  
Vol 1 (1) ◽  
Author(s):  
Ireneusz Kreja
Keyword(s):  
Numerical Model ◽  
Computational Models ◽  
State Of The Art ◽  
Sandwich Panels ◽  
Laminated Composite ◽  
Theoretical Models ◽  
Layered Composite ◽  
Multilayered Plates ◽  
Plates And Shells ◽  
Universal Representation

AbstractThe present paper is devoted to a state-of-the-art review on the computational treatment of laminated composite and sandwich panels. Over two hundred texts have been included in the survey with the focus put on theoretical models for multilayered plates and shells, and FEM implementation of various computational concepts. As a result of the review, one could notice a lack of a single numerical model capable for a universal representation of all layered composite and sandwich panels. Usually, with the increase of the range of rotations considered in the particular model, one can observe the decrease of the degree of complexity of the through-the-thickness representation of deformation profiles.

scholarly journals Computational Models in Electroencephalography

2021 ◽  
Author(s):  
Katharina Glomb ◽  
Joana Cabral ◽  
Anna Cattani ◽  
Alberto Mazzoni ◽  
Ashish Raj ◽  
...  
Keyword(s):  
In Silico ◽  
Computational Models ◽  
State Of The Art ◽  
Brain Activity ◽  
Eeg Signal ◽  
Comprehensive Understanding ◽  
Healthcare Applications ◽  
The One ◽  
And Behavior ◽  
Design Experiments

AbstractComputational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.

scholarly journals Computational Models of Narrative: Review of a Workshop

AI Magazine ◽  
2010 ◽  
Vol 31 (2) ◽  
pp. 97 ◽  
Author(s):  
Mark A. Finlayson ◽  
Whitman Richards ◽  
Patrick Henry Winston
Keyword(s):  
Computational Modeling ◽  
Computational Models ◽  
State Of The Art ◽  
Data Bank ◽  
The State ◽  
Narrative Review ◽  
Current Work ◽  
Standard Data ◽  
Abstract Level ◽  
Different Levels

On October 8-10, 2009 an interdisciplinary group met at the Wylie Center in Beverley, Massachusetts to evaluate the state of the art in the computational modeling of narrative. Three important findings emerged: (1) current work in computational modeling is described by three different levels of representation; (2) there is a paucity of studies at the highest, most abstract level aimed at inferring the meaning or message of the narrative; and (3) there is a need to establish a standard data bank of annotated narratives, analogous to the Penn Treebank.

scholarly journals The challenges of joint attention

2006 ◽  
Vol 7 (2) ◽  
pp. 135-169 ◽  
Author(s):  
Frédéric Kaplan ◽  
Verena V. Hafner
Keyword(s):  
Joint Attention ◽  
Computational Models ◽  
Developmental Psychology ◽  
State Of The Art ◽  
Potential Contribution ◽  
Development Research ◽  
Social Coordination ◽  
Current State ◽  
Development Of Language ◽  
Open Issues

This article discusses the concept of joint attention and the different skills underlying its development. Research in developmental psychology clearly states that the development of skills to understand, manipulate and coordinate attentional behavior plays a pivotal role for imitation, social cognition and the development of language. However, beside the fact that joint attention has recently received an increasing interest in the robotics community, existing models concentrate only on partial and isolated elements of these phenomena. In the line of Tomasello’s research, we argue that joint attention is much more than simultaneous looking because it implies a shared intentional relation to the world. This requires skills for attention detection, attention manipulation, social coordination and, most importantly, intentional understanding. After defining joint attention and its challenges, the current state-of-the-art of robotic and computational models relevant for this issue is discussed in relation to a developmental timeline drawn from results in child studies. From this survey, we identify open issues and challenges that still need to be addressed to understand the development of the various aspects of joint attention and conclude with the potential contribution of robotic models.

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