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2,453 results for “Architecture”
Figure 1 - VisiRule architecture-Intelligent Flowcharting Developmental Approach to Legal Knowledge Based System
<p>In the development of RBR we used the intelligent flowcharting approach. VisiRule is a tool<br> for creating decision support software purely by drawing flowcharts. The end result is Flex or<br> Prolog code which is automatically generated, compiled and ready to run, but which can also be<br> copied and used in a separate program. Not only can VisiRule be used by people with minimal<br> programming skills. VisiRule also enhances productivity by considerably reducing the time it takes<br> to produce a decision support system. VisiRule is an intelligent flowcharting tool in two senses.<br> Firstly, it is used to create knowledge-based systems and, secondly, it intelligently guides the<br> construction process by constraining what you can and can't do on the basis of the semantic content<br> of the emerging program. VisiRule provides the automatic construction of menu dialogues from<br> questions. These are populated by items inferred from expression boxes throughout the flowchart<br> tree which have a path to the question.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 6. Overview of Brain-Inspired Architecture for Machine Perception
<p>Figure 6 gives an overview about the developed architecture for human-like machine perception which bases on insights about the working mechanisms of the human perceptual system. The central element of the model is the so-called “neuro-symbolic network”, which processes data coming from different sensor sources and additionally considers information coming from<br> “higher-level” sources referred to as memory, knowledge, and focus of attention . Within the neuro-symbolic network, so called “neuro-symbolic information processing” takes place based on information exchange of “neuro-symbols”. The focus in this article will be on the description of the<br> functioning of neuro-symbols and the neuro-symbolic network. Details about the other modules and functional aspects of the model can amongst others be found in.</p>
Figure 1. Agent Architecture-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>The multi agent method creates a non-structured environment using agents, these agents, in<br> order to reach the global optimum, must be capable of performing transactions between each other,<br> achieving several “deals”, which is performed though a common vocabulary, a finite set of<br> information exchange, a finite set of possible actions, penalties, etc. The power of each agent<br> depends on the degree of contribution of its represented process to the final output of the entire<br> system. Each agent communicates with the environment and adapts to its own internal state as well<br> as to the state of the entire system. In this systems, for the realization of the multi agent architecture<br> the JADE 4.1.1 (Java Agent DEvelopment Framework) have been used [7].</p>
Genetic architecture of disease resistance and tolerance in Douglas-fir trees
<p><span>Understanding the genetic architecture of tolerance and resistance to pathogens is important to monitor and maintain resilient tree populations. Here we investigate the genetic basis of tolerance and resistance to needle cast disease in Douglas-fir (<em>Pseudotsuga menziesii</em>) caused by two fungal pathogens: Swiss needle cast (SNC) caused by <em>Nothophaeocryptopus gaeumannii</em>, and Rhabdocline needle cast (RNC) caused by <em>Rhabdocline pseudotsugae</em>). We performed a case-control genome-wide association analysis (GWA) and found these traits to be polygenic and under selection.</span> <span>We showed that stomatal regulation as well as ethylene and jasmonic acid pathways are important for resisting SNC infection and secondary metabolite pathways play a role in tolerating SNC once the plant is infected. We identified a key upstream transcription factor of plant defence, ERF1, as the main candidate for RNC resistance. Our findings contribute to the understanding of the highly polygenic architectures underlying disease resistance and tolerance in Douglas-fir and have important implications for forestry and conservation as the climate changes.</span></p>
Data from: Genome-wide association mapping within a local Arabidopsis thaliana population more fully reveals the genetic architecture for defensive metabolite diversity
<p>A paradoxical finding from genome-wide association studies (GWAS) in plants is that variation in metabolite profiles typically maps to a small number of loci, despite the complexity of underlying biosynthetic pathways. This discrepancy may partially arise from limitations presented by geographically diverse mapping panels. Properties of metabolic pathways that impede GWAS by diluting the additive effect of a causal variant, such as allelic and genic heterogeneity and epistasis, would be expected to increase in severity with the geographic range of the mapping panel. We hypothesized that a population from a single locality would reveal an expanded set of associated loci. We tested this in a French <em>Arabidopsis thaliana</em> population (< 1 km transect) by profiling and conducting GWAS for glucosinolates, a suite of defensive metabolites that have been studied in depth through functional and genetic mapping approaches. For two distinct classes of glucosinolates, we discovered more associations at biosynthetic loci than previous GWAS with continental-scale mapping panels. Candidate genes underlying novel associations were supported by concordance between their observed effects in the TOU-A population and previous functional genetic and biochemical characterization. Local populations complement geographically diverse mapping panels to reveal a more complete genetic architecture for metabolic traits.</p>
Dataset and Replication Package for the View-Based Retriever Approach To Reverse Engineering Software Architecture Models
<div> <div><span>Dataset and replication package for the view-based Retriever approach to reverse engineering software architecture models. Each Dataset project is structured as follows:</span></div> <ul> <li><span>The .ruleengine.yml file contains the configuration for running the Retriever approach.</span> <ul> <li><span>The repository value is the ID of a GitHub repository.</span></li> <li><span>The current_version value is the latest version of the retriever approach used to build the architectural models.</span></li> <li><span>The rules values are the rules used to build the architectural models.</span></li> </ul> </li> <li><span>The model_re folder contains the architectural model of the system automatically generated by the Retriever approach.</span> <ul> <li><span>The pcm folder contains the Palladio Component Model (PCM) of the system.</span></li> <li><span>The uml folder contains the PlantUML model.</span></li> </ul> </li> <li><span>The model_gs folder contains our manual gold standards for the system.</span></li> </ul> <div><span>The easiest way to use our approach is to use the CLI application with the given parameters: ./eclipse -i /path/to/input/directory -o /path/to/output/directory -r supported_rules</span></div> </div>
Raw data for the article entitled ''Evaluation of in-plane architecture in a thermo-electrochemical cell with nanostructured and porous Sb:SnO2 electrodes''
<p>Raw data for the plots in the article entitled ''Evaluation of in-plane architecture in a thermo-electrochemical cell with nanostructured and porous Sb:SnO2 electrodes''.</p>
Data and scripts for: Bayesian Phylogenetic Analysis on multi-core Compute Architectures: Implementation and evaluation of BEAGLE in RevBayes with MPI
<p>Phylogenies are central to many research areas in biology and commonly estimated using likelihood-based methods. Unfortunately, any likelihood-based method, including Bayesian inference, can be restrictively slow for large datasets–with many taxa and/or many sites in the sequence alignment–or complex substitution models. The primary limiting factor when using large datasets and/or complex models in probabilistic phylogenetic analyses is the likelihood calculation, which dominates the total computation time. To address this bottleneck, we incorporated the high-performance phylogenetic library BEAGLE into RevBayes, which enables multi-threading on multi-core CPUs and GPUs, as well as hardware-specific vectorized instructions for faster likelihood calculations. Our new implementation of RevBayes+BEAGLE retains the flexibility and dynamic nature that users expect from vanilla RevBayes. Additionally, we implemented a native parallelization within RevBayes without an external library using the message passing interface (MPI); RevBayes+MPI. We evaluated our new implementation of RevBayes+BEAGLE using multi-threading on CPUs and a powerful NVidia Titan V GPU against our native implementation of RevBayes+MPI. We found good improvements in speedup when multiple cores were used with up to 20-fold speedup when using multiple CPUs and over 90-fold speedup when using multiple GPU cores. The improvement depended on the data type used, DNA or amino acids, and the size of the alignment, but less on the size of the tree. We additionally investigated the cost of rescaling partial likelihoods to avoid numerical underflow and showed that unnecessarily frequent rescaling can increase runtimes 2.5 to 3-fold. Finally, we presented and compared a new approach to store partial likelihoods on branches instead of nodes which can speed up computations but comes at twice the memory requirements.</p> <p>Availability: The software described in the paper is available at https://github.com/revbayes/revbayes with documentation and tutorials found at https://revbayes.github.io.</p>
Fig. 3 in Nest architectures of myrmecophilous stingless bees, Trigona sp. cfr. cilipes and Paratrigona sp., from Peruvian Amazon (Hymenoptera: Apidae, Apinae, Meliponini)
Fig. 3 – Sticky resin deposits between the outer layers of the Dolichoderus quadridenticulatus nest.
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 16. Architecture for Affective Situation Assessment of Perceptual Images (Internal Connections between Emotions are not Depicted for Better Clarity of the Graphic)
<p>Based on the concept of affective neuro-symbols, a model was developed according to<br> which emotions can be represented by affective neuro-symbolic networks (see right half of Figure<br> 16, referred to as architecture of “internal perception” in contrast to the “external perception”<br> architecture of the left half of Figure 16, which has already been presented in Section 4.2).<br> The individual affective neuro-symbols (depicted as circles) represent different emotions<br> (fear, anger, guilt, joy, rage, panic, love, happiness, etc.).</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 17. Autonomous Decision-Making Architecture
<p>An overview of the decision-making architecture is presented in Figure 17. The architecture was<br> guided by two core concepts. The first core concept is that human intelligence bases on a<br> combination of low-level and high-level mechanisms. Low-level mechanisms are mainly<br> predefined. They are not in all situations completely accurate but have the advantage of being fast.<br> High-level mechanisms are not predefined and thus slower but more accurate. The second core<br> concept concerns the use of so-called emotions as mechanism for the evaluation of information</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 14. Implementation of Overall Architecture of the Perception Model in AnyLogic 4.3
<p>To perform complex functions, individual neuro-symbols are then connected to networks.<br> Figure 14 shows a screenshot of the AnyLogic implementation of the overall system at the<br> beginning of the learning phase. The lowest neuro-symbolic levels receive the direct sensor<br> information as input. The higher neuro-symbolic levels are originally not interconnected amongst<br> each other. Instead, they are connected to so-called “learning ports”, which additionally receive<br> control information needed for the supervised learning process. Details about the multi-stage multilevel<br> learning process can be found in.</p>
Figure 11. Cognitive architecture of the process of social signals perception-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>A possible cognitive architecture and formalization of the process of learning via<br> multisensory integration is presented in figure 11. The formal description of the proposed cognitive<br> architecture, capable of interpreting social-communication signals, signs and symbols, is based on<br> multisensory integration at the level of perception, parallel processing at the level of interpretation<br> and decision making followed by verbalization, as well as performing an action (eye contact,<br> gesture, mimicking) at the level of behaviour.</p>
Figure 1. CNN architecture (adopted from Krizhevsky et al. '12)-Measuring Customer Behavior with Deep Convolutional Neural Networks
<p>The architecture of a CNN can be described as following. A small pixel region goes to input neurons and then connects to a first convolution hidden layer (Figure1). There we can see a set of learnable filters, which are activated during the presentation some particular type of feature in pixel region in the input. On this phase, CNN does shift invariance, which is carried by feature map. Subsampling layer goes next. There we have two processes: local averaging and sampling. As a result, we get declining resolution of feature map. To correspond this task CNN needs supervised learning. Before starting the experiment, we gave a set of labeled videos with different emotional experience. The system analyses images and finds similar features. Then the system creates a map, where it arranges videos in accordance with similar features. Thereby, images with similar emotions form certain class. To test the system, we add other videos and correct the system when it refers them improperly. The proposed model consists of four convolutional layers, followed by max-pooling layers, and three fully-connected layers with a final classificatory presented with MLP (with six basic outputs, corresponding to basic emotions for emotion classification and two outputs for motion classification for typical and non-typical behavior). The input data was presented as infrared camera output.</p>
Figure 2. Ecophone architecture (Danubianu, Tobolcea, & Pentiuc, 2009)
<p>The construction of the Ecophone has as starting point idea that a clear and correct reception of its own sounds creates to an individual a real image of his pronunciation, and, by the awareness of progress in therapy, he reinforces his will to correct its defects as soon as possible. The Ecophone is a system which allows achieving the delayed auditory feedback (DAF) (Tobolcea, 2001). It was the first device built to help patients to correct their speech rhythm and fluency. Its architecture is presented in Figure 2. With the help of the microphone, the patient utters the words in a rhythmical manner, with a view to hear them like an echo. With the help of this device, the patient has the possibility to acknowledge his/ her own articulation, while controlling his/ her own speech and voice. During the occurrence of verbal disfluencies or spasms, spontaneously and following a certain break, the patient with speech disorders regains his/her speech, managing to pronounce the words in a fluent and correct way. It can be concluded that, with the help of the Echophone, great performances can be achieved in the speech re-education of the patients with speech disorders, by delaying the auditory feedback. Additionally, the Echophone contributes to increasing confidence and courage in the patient, as (s)he acknowledges the progress (s)he made; this encourages the patient to express him/herself correctly and fluently.</p>
Figure 1. DiagAgentExpertDM architecture (adapted from Ioniță, 2014) -Intelligent System for Diagnosis of a Three-Phase Separator
<p>For the current hybrid system</p> <p>(DiagAgentExpertDM)</p> <p>, data from different sources and in</p> <p>various forms are preprocessed, in order to represe</p> <p>nt them in a unified way to be able to upload</p> <p>them in a learning module. Simultaneously, inconsis</p> <p>tency tests are made to eliminate the</p> <p>measurement errors caused by improper calibration o</p> <p>f transducers etc.</p> <p>In condition of using a diagnosis method based on p</p> <p>rocess history, the next step in fault</p> <p>identification referring to the gas-oil separation</p> <p>process is to scan the historical data. Retrieved d</p> <p>ata</p> <p>will possess a label with a certain priority which</p> <p>will be used in the next retrieval process. The</p> <p>learning module is supplied with multiple preproces</p> <p>sed data samples, in order to extract knowledge</p> <p>from them. For each data samples, a data mining alg</p> <p>orithm will be applied (figure 1).</p> <p> </p>
Figure .5 Architecture of the Terapers system-Modern Tools in Patient-Centred Speech Therapy for Romanian Language
<p>Shown in Figure 5, the architecture of the Terapers system implies the existence of two main connected components: on the one hand, an intelligent system which is installed on the office computer of each speech therapist and, on the other, a mobile system which is used as a virtual friend in the therapy applied to the child (Danubianu et al., 2008). The intelligent system – which represents the fixed component of the system – is installed on each computer from the office of the speech therapist; it is made up of the following parts: • an information management module for children; • an expert system, able to produce inferences based on the data given by the assessment module; • a mouth virtual module which allows the display of all hidden movements that are likely to occur during speech; • a management module of the exercises uses, which allows creating or modifying the exercises, depending on the various therapy stages, as well as their organization into complex issues.</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 1. General architecture of an online (BCI)
<p>One major challenge of our BCI system is to describe the signals EEG by a few relevant values called features i.e. step 3 in Fig (1). The success of the mental imagery classification depends on the choice of features used to characterize the raw EEG signals. These features can then be used in step 4 in order to classify the user’s mental state. Several approaches for feature extraction have been proposed in literature. </p>
Duhumbi housing and architecture
<p>This collection of photo files displays aspects of traditional Duhumbi housing style and architecture between 2012 and 2017.</p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Molecular Architecture of the Major Membrane Ring Component of the Nuclear Pore Complex
<p>This repository contains the modeling files and the analysis related to the article <a href="https://www.ncbi.nlm.nih.gov/pubmed/28162953">"Molecular Architecture of the Major Membrane Ring Component of the Nuclear Pore Complex"</a> by Upla et al. in Structure 2017.</p> <p><strong>For more information</strong> about how to reproduce this modeling, see the <a href="https://salilab.org/pom152/">Sali lab website</a> or the README file.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.