scholarly journals How chimpanzees integrate sensory information to select figs

2016 ◽  
Vol 6 (3) ◽  
pp. 20160001 ◽  
Author(s):  
Nathaniel J. Dominy ◽  
Justin D. Yeakel ◽  
Uttam Bhat ◽  
Lawrence Ramsden ◽  
Richard W. Wrangham ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Colour Vision ◽  
Sensory Information ◽  
Primate Evolution ◽  
Visuomotor Control ◽  
Quality Information ◽  
Specific Information ◽  
Sensory Cues ◽  
Selective Force ◽  
Domain Specific

Figs are keystone resources that sustain chimpanzees when preferred fruits are scarce. Many figs retain a green(ish) colour throughout development, a pattern that causes chimpanzees to evaluate edibility on the basis of achromatic accessory cues. Such behaviour is conspicuous because it entails a succession of discrete sensory assessments, including the deliberate palpation of individual figs, a task that requires advanced visuomotor control. These actions are strongly suggestive of domain-specific information processing and decision-making, and they call attention to a potential selective force on the origin of advanced manual prehension and digital dexterity during primate evolution. To explore this concept, we report on the foraging behaviours of chimpanzees and the spectral, chemical and mechanical properties of figs, with cutting tests revealing ease of fracture in the mouth. By integrating the ability of different sensory cues to predict fructose content in a Bayesian updating framework, we quantified the amount of information gained when a chimpanzee successively observes, palpates and bites the green figs of Ficus sansibarica . We found that the cue eliciting ingestion was not colour or size, but fig mechanics (including toughness estimates from wedge tests), which relays higher-quality information on fructose concentrations than colour vision. This result explains why chimpanzees evaluate green figs by palpation and dental incision, actions that could explain the adaptive origins of advanced manual prehension.

Knowledge Enhanced LSTM for Coreference Resolution on Biomedical Texts

2021 ◽  
Author(s):  
Yufei Li ◽  
Xiaoyong Ma ◽  
Xiangyu Zhou ◽  
Pengzhen Cheng ◽  
Kai He ◽  
...  
Keyword(s):  
Information Integration ◽  
Short Term Memory ◽  
Superior Performance ◽  
Supplementary Information ◽  
Specific Information ◽  
Coreference Resolution ◽  
Fine Grained ◽  
Domain Specific ◽  
Memory Network ◽  
Biomedical Texts

Abstract Motivation Bio-entity Coreference Resolution focuses on identifying the coreferential links in biomedical texts, which is crucial to complete bio-events’ attributes and interconnect events into bio-networks. Previously, as one of the most powerful tools, deep neural network-based general domain systems are applied to the biomedical domain with domain-specific information integration. However, such methods may raise much noise due to its insufficiency of combining context and complex domain-specific information. Results In this paper, we explore how to leverage the external knowledge base in a fine-grained way to better resolve coreference by introducing a knowledge-enhanced Long Short Term Memory network (LSTM), which is more flexible to encode the knowledge information inside the LSTM. Moreover, we further propose a knowledge attention module to extract informative knowledge effectively based on contexts. The experimental results on the BioNLP and CRAFT datasets achieve state-of-the-art performance, with a gain of 7.5 F1 on BioNLP and 10.6 F1 on CRAFT. Additional experiments also demonstrate superior performance on the cross-sentence coreferences. Supplementary information Supplementary data are available at Bioinformatics online.

PROTEIN STRUCTURE ALIGNMENT AND FAST SIMILARITY SEARCH USING LOCAL SHAPE SIGNATURES

2004 ◽  
Vol 02 (01) ◽  
pp. 215-239 ◽  
Author(s):  
TOLGA CAN ◽  
YUAN-FANG WANG
Keyword(s):  
Protein Structure ◽  
Protein Structures ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Specific Information ◽  
Alignment Algorithm ◽  
Screening Process ◽  
Domain Specific ◽  
Local Sequence ◽  
Shape Signatures

We present a new method for conducting protein structure similarity searches, which improves on the efficiency of some existing techniques. Our method is grounded in the theory of differential geometry on 3D space curve matching. We generate shape signatures for proteins that are invariant, localized, robust, compact, and biologically meaningful. The invariancy of the shape signatures allows us to improve similarity searching efficiency by adopting a hierarchical coarse-to-fine strategy. We index the shape signatures using an efficient hashing-based technique. With the help of this technique we screen out unlikely candidates and perform detailed pairwise alignments only for a small number of candidates that survive the screening process. Contrary to other hashing based techniques, our technique employs domain specific information (not just geometric information) in constructing the hash key, and hence, is more tuned to the domain of biology. Furthermore, the invariancy, localization, and compactness of the shape signatures allow us to utilize a well-known local sequence alignment algorithm for aligning two protein structures. One measure of the efficacy of the proposed technique is that we were able to perform structure alignment queries 36 times faster (on the average) than a well-known method while keeping the quality of the query results at an approximately similar level.

scholarly journals Detour learning ability and the effect of novel sensory cues on learning in Australian bull ants, Myrmecia midas

2021 ◽  
Author(s):  
Muzahid Islam ◽  
Sudhakar Deeti ◽  
Zakia Mahmudah ◽  
J. Frances Kamhi ◽  
Ken Cheng
Keyword(s):  
Animal Cognition ◽  
Sensory Information ◽  
Learning Ability ◽  
Visual Environment ◽  
Sensory Cues ◽  
Complex Environment ◽  
Physical Barriers ◽  
Tactile Cues ◽  
Detour Learning ◽  
Do So

ABSTRACTMany animals navigate in a structurally complex environment which requires them to detour around physical barriers that they encounter. While many studies in animal cognition suggest that they are able to adeptly avoid obstacles, it is unclear whether a new route is learned to navigate around these barriers and, if so, what sensory information may be used to do so. We investigated detour learning ability in the Australian bull ant, Myrmecia midas, which primarily uses visual landmarks to navigate. We first placed a barrier on the ants’ natural path of their foraging tree. Initially, 46% of foragers were unsuccessful in detouring the obstacle. In subsequent trips, the ants became more successful and established a new route. We observed up to eight successful foraging trips detouring around the barrier. When we subsequently changed the position of the barrier, made a new gap in the middle of the obstacle, or removed the barrier altogether, ants mostly maintained their learned motor routine, detouring with a similar path as before, suggesting that foragers were not relying on barrier cues and therefore learned a new route around the obstacle. In additional trials, when foragers encountered new olfactory or tactile cues, or the visual environment was blocked, their navigation was profoundly disrupted. These results suggest that changing sensory information, even in modalities that foragers do not usually need for navigation, drastically affects the foragers’ ability to successful navigate.Subject CategoryNeuroscience and Cognition

scholarly journals DISSECT: DISentangle SharablE ConTent for Multimodal Integration and Crosswise-mapping

2020 ◽  
Author(s):  
Geoffrey Schau ◽  
Erik Burlingame ◽  
Young Hwan Chang
Keyword(s):  
Deep Learning ◽  
Complete Information ◽  
Specific Information ◽  
Multimodal Integration ◽  
Specific Data ◽  
Domain Specific ◽  
Cross Domain ◽  
Input Feature ◽  
Novel Approach ◽  
Latent Representations

AbstractDeep learning systems have emerged as powerful mechanisms for learning domain translation models. However, in many cases, complete information in one domain is assumed to be necessary for sufficient cross-domain prediction. In this work, we motivate a formal justification for domain-specific information separation in a simple linear case and illustrate that a self-supervised approach enables domain translation between data domains while filtering out domain-specific data features. We introduce a novel approach to identify domainspecific information from sets of unpaired measurements in complementary data domains by considering a deep learning cross-domain autoencoder architecture designed to learn shared latent representations of data while enabling domain translation. We introduce an orthogonal gate block designed to enforce orthogonality of input feature sets by explicitly removing non-sharable information specific to each domain and illustrate separability of domain-specific information on a toy dataset.

scholarly journals Measuring Models

2009 ◽  
pp. 147-169 ◽  
Author(s):  
Martin Monperrus ◽  
Jean-Marc Jézéquel ◽  
Joël Champeau ◽  
Brigitte Hoeltzener
Keyword(s):  
Quality Assurance ◽  
Software Development ◽  
Point Of View ◽  
Specific Information ◽  
Model Driven Engineering ◽  
Text Documents ◽  
Model Driven ◽  
Domain Specific ◽  
Wide Range

Model-Driven Engineering (MDE) is an approach to software development that uses models as primary artifacts, from which code, documentation and tests are derived. One way of assessing quality assurance in a given domain is to define domain metrics. We show that some of these metrics are supported by models. As text documents, models can be considered from a syntactic point of view i.e., thought of as graphs. We can readily apply graph-based metrics to them, such as the number of nodes, the number of edges or the fan-in/fan-out distributions. However, these metrics cannot leverage the semantic structuring enforced by each specific metamodel to give domain specific information. Contrary to graph-based metrics, more specific metrics do exist for given domains (such as LOC for programs), but they lack genericity. Our contribution is to propose one metric, called s, that is generic over metamodels and allows the easy specification of an open-ended wide range of model metrics.

scholarly journals The Repository Chemotion: Infrastructure for Sustainable Research in Chemistry

2020 ◽  
Author(s):  
Pierre Tremouilhac ◽  
Chia-Lin Lin ◽  
Pei-Chi Huang ◽  
Yu-Chieh Huang ◽  
An Nguyen ◽  
...  
Keyword(s):  
Analytical Data ◽  
Research Data ◽  
Digital Object Identifier ◽  
Specific Information ◽  
Data Infrastructure ◽  
Domain Specific ◽  
Chemistry Research ◽  
Peer Reviewing ◽  
Institute Of Technology ◽  
New Community

<p>We describe the development of a repository for chemistry research data (called Chemotion) that provides solutions for current challenges to store research data in a feasible manner, allowing the conservation of domain specific information in a machine readable format. A main advantage of the repository Chemotion is the comprehensive functionality, which offers options to collect, prepare and reuse data using discipline specific methods and data processing tools. For selected analytical data, automated procedures are implemented to facilitate the curation of the data. Chemotion provides functions to facilitate the publishing process of data and the citation of the deposited data. It supports automated Digital Object Identifier (DOI) generation, the comparison of the submissions with PubChem instances, and workflows for peer reviewing of the submissions including embargo settings. The described developments were used to establish a research data infrastructure that is hosted at the Karlsruhe Institute of Technology (KIT), including the necessary storage and support to build a new community-driven repository as a comprehensive alternative to commercial databases. </p>

scholarly journals How Does the Fusion of Sensory Information From Audition and Vision Impact Collective Behavior?

2021 ◽  
Vol 7 ◽  
Author(s):  
Subhradeep Roy ◽  
Jeremy Lemus
Keyword(s):  
Simulation Study ◽  
Collective Behavior ◽  
Sensory Information ◽  
Composite Model ◽  
Group Level ◽  
Sensory Cues ◽  
Auditory Cues ◽  
Vicsek Model ◽  
Visual Sensing ◽  
Sense Modality

The present study investigates how combined information from audition and vision impacts group-level behavior. We consider a modification to the original Vicsek model that allows individuals to use auditory and visual sensing modalities to gather information from neighbors in order to update their heading directions. Moreover, in this model, the information from visual and auditory cues can be weighed differently. In a simulation study, we examine the sensitivity of the emergent group-level behavior to the weights that are assigned to each sense modality in this weighted composite model. Our findings suggest combining sensory cues may play an important role in the collective behavior and results from the composite model indicate that the group-level features from pure audition predominate.

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