Computational Representation Research Articles

The GA4GH Variation Representation Specification: A computational framework for variation representation and federated identification

SUMMARYMaximizing the personal, public, research, and clinical value of genomic information will require the reliable exchange of genetic variation data. We report here the Variation Representation Specification (VRS, pronounced “verse”), an extensible framework for the computable representation of variation that complements contemporary human-readable and flat file standards for genomic variation representation. VRS provides semantically precise representations of variation and leverages this design to enable federated identification of biomolecular variation with globally consistent and unique computed identifiers. The VRS framework includes a terminology and information model, machine-readable schema, data sharing conventions, and a reference implementation, each of which is intended to be broadly useful and freely available for community use. VRS was developed by a partnership among national information resource providers, public initiatives, and diagnostic testing laboratories under the auspices of the Global Alliance for Genomics and Health (GA4GH).

الألقاب فی الحضارة الإسلامیة دراسة دلالیة حاسوبیة باستخدام تقنیات الأنطولوجیا وتطبیق المحلل اللغوی

الملخص يقدم هذا البحث معجما محوسبا للألقاب التي تصدر على سبيل التشريف للوظائف في الحضارة الإسلامية بداية من العصر العباسي حتى نهاية العصر المملوکي ، مستخدما في ذلک محرر الانطولوجيا بروتيجي ، ويکشف عن علاقتي الترادف والتضمين ، ويرصد الألفاظ الشائعة في تراکيب الألقاب مستخدما برنامج المحلل اللغوي، موضحا أثر الظروف الاجتماعية والثقافية والسياسة التي أدت إلى ظهور هذا النوع من الألقاب، وتوصل البحث إلى مجموعة نتائج أهمها سهولة تمثيل الدلالة حاسوبيا بواسطة تقنيات الانطولوجيا ، يمکن للمحلل اللغوي أن يعطي نتائج إحصائية دقيقة ، کما أن يمکن أن يرصد المصاحبات اللغوية . Abstract This research presents a computerized glossary of nicknames issued as an honor to jobs in Islamic civilization from the Abbasid period until the end of the Mameluke period, using the Protege ontology editor, revealing the relationships of synonymy and embedding, and monitoring common words in surname structures using the parser program, explaining the impact of The social, cultural and political conditions that led to the emergence of this type of nickname, and the research reached a set of results, the most important of which is the ease of computer representation using ontology techniques, moreover it enables the linguistic analyzer to give accurate statistical results, and it can also monitor the linguistic accompaniments.

ImageNet-trained deep neural networks exhibit illusion-like response to the Scintillating grid.

Deep neural network (DNN) models for computer vision are capable of human-level object recognition. Consequently, similarities between DNN and human vision are of interest. Here, we characterize DNN representations of Scintillating grid visual illusion images in which white disks are perceived to be partially black. Specifically, we use VGG-19 and ResNet-101 DNN models that were trained for image classification and consider the representational dissimilarity ( distance in the penultimate layer) between pairs of images: one with white Scintillating grid disks and the other with disks of decreasing luminance levels. Results showed a nonmonotonic relation, such that decreasing disk luminance led to an increase and subsequently a decrease in representational dissimilarity. That is, the Scintillating grid image with white disks was closer, in terms of the representation, to images with black disks than images with gray disks. In control nonillusion images, such nonmonotonicity was rare. These results suggest that nonmonotonicity in a deep computational representation is a potential test for illusion-like response geometry in DNN models.

Using Facial Recognition Tools for Health Assessment.

The number of applications for facial recognition technology is increasing due to the improvement in image quality, artificial intelligence, and computer processing power that has occurred during the last decades. Algorithms can be used to convert facial anthropometric landmarks into a computer representation, which can be used to help identify nonverbal information about an individual's health status. This article discusses the potential ways a facial recognition tool can perform a health assessment. Because facial attributes may be considered biometric data, clinicians should be informed about the clinical, ethical, and legal issues associated with its use.

Analysis of Confucius’s Humanistic Education Theory Centered on the Analects of Confucius Based on Cognitive Anthropology

Objectives: Confucius’s humanistic education theory centered on the Analects of Confucius based on cognitive anthropology was analyzed in this paper, and the computer technology was used to extract the imagery and thought of the ancient Chinese prose. Methods: First of all, the definition and classification of cognitive anthropology and ancient Chinese prose imagery were described in detail; then based on the Confucius culture centered on the Analects of Confucius, the computer representation model and the classification algorithm of ancient Chinese prose were constructed; Results: in addition, the experiment was carried out to verify the model and algorithm, and the threshold analysis was carried out on the basis of the comparison of tagged word and characteristic words; Conclusion: finally, the optimum range was obtained for each parameter.

Normative Echoes: Use and Manipulation of Player Generated Content by Communities of NPCs

Normative Echoes is an interactive installation that explores ways to combine player-created content with procedural content. Animated autonomous agents inhabit virtual islands on stationary computers; a tablet PC is used as a virtual raft to transfer agents between the islands. The agents on each island communicate with one another and form scenario-based computational representations of their social interactions. These scenarios represent emergent social patterns and norms within the society. Humans can communicate with the animated autonomous agents through the use of a microphone. Utterances spoken by participants are parsed, repeated, and learned by agents, then used by the agents in communications with their social partners. When transferred between islands, agents bring with themselves the utterances and norms they have learned, thus spreading those norms throughout the various communities in the installation. In this way, agents can meaningfully perform procedural operations on player-created content, allowing for a dynamic and engaging experience.

Symbolic Variable Elimination for Discrete and Continuous Graphical Models

Probabilistic reasoning in the real-world often requires inference incontinuous variable graphical models, yet there are few methods for exact, closed-form inference when joint distributions are non-Gaussian. To address this inferential deficit, we introduce SVE -- a symbolic extension of the well-known variable elimination algorithm to perform exact inference in an expressive class of mixed discrete and continuous variable graphical models whose conditional probability functions can be well-approximated as piecewise combinations of polynomials with bounded support. Using this representation, we show that we can compute all of the SVE operations exactly and in closed-form, which crucially includes definite integration w.r.t. multivariate piecewise polynomial functions. To aid in the efficient computation and compact representation of this solution, we use an extended algebraic decision diagram (XADD) data structure that supports all SVE operations. We provide illustrative results for SVE on probabilistic inference queries inspired by robotics localization and tracking applications that mix various continuous distributions; this represents the first time a general closed-form exact solution has been proposed for this expressive class of discrete/continuous graphical models.

The trouble with ‘HIM’: new challenges and old misconceptions in human information modelling

Building performance simulation is not just about buildings’ geometry, construction, and systems. It has become increasingly clear that simulation models need also to more systematically address occupants’ presence and behaviour in buildings. To put it somewhat ostentatiously, it is not just about ‘BIM’ (Building Information Modelling), but also ‘HIM’ (Human Information Modelling). To this end, a balance must be found between representational formalisms and representational content. The former concerns finding the right algorithmic expressions, the latter requires empirical knowledge. Exploring algorithmic formalisms can be exciting, but if lopsided, may fail to recognize the complexity of human perception and behaviour. Therefore, it may be instructive to discuss similar instances of past lop-sided formalism-centric approaches. The present position paper underlines the importance of domain knowledge on occupants’ perceptual and behavioural processes and its inclusion in the computational representation of occupant agents in building information modelling and building performance assessment.

Natural Language Mapping of Electrocardiogram Interpretations to a Standardized Ontology.

Interpretations of the electrocardiogram (ECG) are often prepared using software outside the electronic health record (EHR) and imported via an interface as a narrative note. Thus, natural language processing is required to create a computable representation of the findings. Challenges include misspellings, nonstandard abbreviations, jargon, and equivocation in diagnostic interpretations. Our objective was to develop an algorithm to reliably and efficiently extract such information and map it to the standardized ECG ontology developed jointly by the American Heart Association, the American College of Cardiology Foundation, and the Heart Rhythm Society. The algorithm was to be designed to be easily modifiable for use with EHRs and ECG reporting systems other than the ones studied. An algorithm using natural language processing techniques was developed in structured query language to extract and map quantitative and diagnostic information from ECG narrative reports to the cardiology societies' standardized ECG ontology. The algorithm was developed using a training dataset of 43,861 ECG reports and applied to a test dataset of 46,873 reports. Accuracy, precision, recall, and the F1-measure were all 100% in the test dataset for the extraction of quantitative data (e.g., PR and QTc interval, atrial and ventricular heart rate). Performances for matches in each diagnostic category in the standardized ECG ontology were all above 99% in the test dataset. The processing speed was approximately 20,000 reports per minute. We externally validated the algorithm from another institution that used a different ECG reporting system and found similar performance. The developed algorithm had high performance for creating a computable representation of ECG interpretations. Software and lookup tables are provided that can easily be modified for local customization and for use with other EHR and ECG reporting systems. This algorithm has utility for research and in clinical decision-support where incorporation of ECG findings is desired.

COMPUTATIONAL MODEL OF INTERSECTIONS REPRESENTATION AS APPARATUS OF CONTENTS MULTI-SCALAR, RETROSPECTIVE AND RESILIENT FOR THE RESEARCH OF CONNOTATIONS, SIMULATIONS AND SUSTAINABILITY IN THE LANDSCAPE ASSETS

Abstract. The research project’s objective, based on defining a “Computational Model of Intersections Simulation/Representation” as complex digital cultural infrastructure and computational knowledge, acquires the identity characteristics of the Computational Representation (phenomenological, geometry, visual, resilience, scalability analytical-interpretative knowledge) to promote for landscape and Cultural Heritage (CH): a) of systematize environmental information and protective by intersections of resilience’s categories in the “thematic systems of families and types” of the landscape; b) a model of Smart CH for Industry 4.0 – 2021. The same time favors: a) thematic and retrospective intersections between formal and interpretative layers for the return of information from a spatial and functional; b) innovation by introduction of professional figures of the contemporary digital. The methodology is articulated on following intersection models: Connotations, Composition, Development (combination of complex formal, structural and geometric factors); Morphogenesis (innovative concepts of transformations form), Sustainability. From the point of view the results and conclusion the study allowed to highlight: a) “network of multi-scalar discrete relationships” with sequences of digital models for the relative tessellation of the surfaces; b) “resilience measure” by “building a digital model of morphogenesis”; c) systematize environmental information and protective by intersections of resilience’s categories in the “thematic systems of families and types” of the landscape; d) “operative and phenomenological reading” of the changing complexity of environmental reality with connections between the behavioural and physical structures of the place.

Tissue scale agent-based simulation of premalignant progressions in Barrett’s esophagus

Barrett’s esophagus (BE) is a benign condition of the distal esophagus that initiates a multistage pathway to esophageal adenocarcinoma (EAC). Short of frequent intrusive (and costly) surveillance, effective screening for neoplasia in BE populations is yet to be established since progressors are rare and virtually undetectable without routine biopsies, which often sample only a small portion of the BE tissue. As a result, reliable estimation of the true prevalence of dysplasia in a BE population and evidence-based optimization of screening for at-risk individuals is challenging. Data-driven microsimulations, i.e., model-generated instances of disease history in a predefined virtual population, have found utility in the EAC screening literature as low-overhead alternatives to real-world hypothesis testing of optimal interventions for dysplasia. Despite the successes, computational limitations, paucity of knowledge and data on Barrett’s dysplasia, and the complexities of disease progression as a multiscale multiphysics process have hindered the treatment of disease progression in BE as a spatial process. Agent-based modeling of nucleation and proliferation processes in dysplasia warrants exploration in this context as an approximation that operates at a trade-off between computational tractability and precise representation of the composition and physics of the substrate (tissue). In this study, we describe spatially resolved simulations of premalignant progression toward EAC in a coarse-grained model of Barrett’s tissue that resolves the metaplastic tissue at a length scale of 0.42 mm (~3300 crypts/mm2). The model is calibrated to reproduce historical high-grade dysplasia prevalence when model-generated patients are screened using the Seattle protocol.

Self-supervised multi-body scene flow estimation

In this paper, we address the problem of scene flow estimation from consecutive stereo pairs. In contrast to the state-of-the-art supervised learning based methods, we propose a self-supervised learning based pipeline that removes the requirement of large-scale ground truth annotations. Specifically, we employ a shared encoder StereoFlowNet to simultaneously learn optical flow estimation and disparity estimation, which not only achieves a compact network representation but also exploits the inherent connections between optical flow estimation and disparity estimation. To leverage the scene structure and motion representations, we propose to utilize a piece-wise planar model based disparity computation and multiple rigid body motion representation of the dynamic scene. In this way, the geometric and motion constraints play strong regularizations for the underlying problem. Experimental results on benchmarking dataset show that our proposed method achieves state-of-the-art performance in both optical flow and disparity estimation.

New computational results for a prototype of an excitable system

This present paper uses a well known computational scheme such as the modified (G'/G)-expansion method to the nonlinear predator–prey (NPP) system for forming new computational results that define a prototype of an excitable system. We construct twenty new computational solutions that define hyperbolic, trigonometric, and rational. Two-dimensional, three-dimensional, and contour shapes are depicted to demonstrate the acquired answers' more physical as well as dynamical features. Comparing our acquired responses and that obtained in previously written research articles presents the novelty of our research. The computational scheme's representation demonstrates its helpful and straightforward procedure that produces a kink-type shape, singular kink shape, bright and dark singular lump shape, multiple bright and dark lump shape, and different types of singular kink shapes. Their ability to manipulate many applications of nonlinear partial differential equations (NLPDEs) is also presented.

Stencil Solvers for PDEs on GPUs: An Example From Cosmology

The increasingly diverse ecosystem of high-performance architectures and programming models presents a mounting challenge for programmers seeking to accelerate scientific computing applications. Code generation offers a promising solution, transforming a simple and general representation of computations into lower level, hardware-specialized and -optimized code. We describe the philosophy set forth by the Python package Pystella, a high-performance framework built upon such tools to solve partial differential equations on structured grids. A hierarchy of abstractions provides increasingly expressive interfaces for specifying physical systems and algorithms. We present an example application from cosmology, using finite-difference methods to solve coupled hyperbolic partial differential equations on (multiple) GPUs. Such an approach may enable a broad range of domain scientists to make efficient use of increasingly heterogeneous computational resources while mitigating the drastic effort and expertise nominally required to do so.

Theory of Mind From Observation in Cognitive Models and Humans.

A major challenge for research in artificial intelligence is to develop systems that can infer the goals, beliefs, and intentions of others (i.e., systems that have theory of mind, ToM). In this research, we propose a cognitive ToM framework that uses a well-known theory of decisions from experience to construct a computational representation of ToM. Instance-based learning theory (IBLT) is used to construct a cognitive model that generates ToM from the observation of other agents' behavior. The IBL model of the observer distinguishes itself from previous models of ToM that make unreasonable assumptions about human cognition, are hand-crafted for particular settings, complex, or unable to explain a cognitive development of ToM compared to human's ToM. The IBL model learns from the observation of goal-directed agents' behavior in a gridworld navigation task, and it infers and predicts the behaviors of the agents in new gridworlds across different degrees of decision complexity in similar ways to the way human observers do. We provide evidence for the alignment of the IBL observer's predictions under various levels of decision complexity. We also advance the demonstration of the IBL predictions using a classic test of false beliefs (the Sally-Anne test), which is commonly used to test ToM in humans. We discuss our results and the potential of the IBL observer model to improve human-machine interactions.

Rethinking Educational Assessment from the Perspective of Design Thinking

Because human processes are subtle, complex, and contextualized, computational representations of those processes face highly significant unmet design challenges. Design Thinking (DT) offers a potential new paradigm of creativity and innovation in education capable of effecting meaningful culture change. DT is nonlinear but encompasses elements of empathy, problem definition, ideation, prototyping, and tests that may freely move as needed from and to each other. DT's empathic focus on end users' needs suggests educational measurement's information infrastructures will have to coherently integrate assessment and instruction across multiple levels of complexity in communication. Applying DT reveals the need to attend to previously undeveloped technical issues in communication. Especially important are developmental, horizontal, and vertical forms of coherence, and denotative, metalinguistic, and metacommunicative levels of complexity. New solutions emerge when classrooms are reconceived as meta-design ecosystem niches of creativity and innovation structured from the bottom up by flows of self-organizing information. Recently identified correspondences between educational measurement and metrology support efforts aimed at developing multilevel common languages for the communication of learning outcomes. Prototype reports illustrate how emergent measured constructs can be brought into language in ways that integrate developmental, horizontal, and vertical coherence across levels of complexity. Coherent information infrastructures of these kinds are capable of adapting to new circumstances as populations of persons and items change, doing so without compromising the continuity of comparisons or the uniqueness of locally situated knowledge and practices.

An Overview of Metaphor Computation

Metaphor is very common in natural language, which conveys deep meaning beyond the literal meaning. Metaphor computation is still a challenge in NLP tasks. In this paper, we introduce the conventional metaphor theories, and the computational models and approaches, as well as resource constructions. Meanwhile, we find that metaphor theories fail to explain metaphor from the perspectives of language generation and understanding. Besides, linguistic features are not fully utilized in metaphor computation and metaphor corpus lacks deeper meaning annotation. Then we put forward a novel approach of building metaphor corpus based on Abstract Meaning Representation (AMR), which will supply deep representation for Chinese metaphor computation.

Saliency-Based Image Retrieval as a Refinement to Content-Based Image Retrieval

Searching for an image in a database is important in different applications; hence, many algorithms have been proposed to identify the contents of the image. In some applications, but not all, identifying the content of the image as a whole can offer good results. Searching for an object inside the image is more important in most applications than identifying the image as a whole. Therefore, studies focused on segmenting the image into small sub-images and identified their contents. In view of the concepts of human attention, various literature defined saliency as a computer representation of it, where different algorithms were developed to extract the salient regions. These salient regions, which are the regions that attract human attention, are used to identify the most important regions that contain important objects in the image. In this paper, we introduce a new algorithm that utilises the saliency principles to identify the contents of an image and search for similar objects in the images stored in a database. We also demonstrate that the use of salient objects produces better and more accurate results in the image retrieval process. A new retrieval algorithm is therefore presented here, focused on identifying the objects extracted from the salient regions. To assess the efficiency of the proposed algorithm, a new evaluation method is also proposed which considers the order of the retrieved image in assessing the efficiency of the algorithm.

COMPUTATIONAL CHALLENGES IN SAMPLING AND REPRESENTATION OF UNCERTAIN REACTION KINETICS IN LARGE DIMENSIONS

This work focuses on constructing functional representations of quantities of interest (QoIs) of an uncertain system in high dimension. Attention is focused on the ignition delay time of an iso-octane air mixture, using a detailed chemical mechanism with 3,811 elementary reactions. Uncertainty in all reaction rates is directly accounted for using associated uncertainty factors, assuming independent log-uniform priors. A Latin hypercube sample (LHS) of the ignition delay times was first generated, and the resulting database was then exploited to assess the possibility of constructing polynomial chaos (PC) representations in terms of the canonical random variables parametrizing the uncertain rates. We explored two avenues, namely sparse regression (SR) using LASSO, and a coordinate transform (CT) approach. Preconditioned variants of both approaches were also considered, namely using the logarithm of the ignition delay time as QoI. Both approaches resulted in representations of the ignition delay with similar representation errors. However, the CT approach was able to reproduce better the empirical distribution of the underlying LHS ensemble, and also preserved the positivity of the ignition delay time. When preconditioned representations were considered, however, similar performances were obtained using CT and SR representations. The results also revealed that both the CT and SR representations yield consistent global sensitivity estimates. The results were finally used to test a reduced dimension representation, and to outline potential extensions of the work.

SARS‐CoV‐2 ORF8 Accessory Protein Dimerization Domains and Protein‐Protein Host Interaction

As the world continues to fight the coronavirus pandemic caused by SARS‐CoV‐2, we are gaining valuable insights by comparing this virus to other human coronaviruses such as SARS‐CoV and MERS‐CoV that have also caused outbreaks. Coronaviruses infect many animal species, but disease severity varies between strains. Viral accessory proteins are generally implicated in increased infectivity, pathogenicity, and virulence. The accessory protein open reading frame 8 (ORF8), although not essential for viral replication, appears to play a critical role in disease severity by inhibiting multiple pathways of the immune response. Furthermore, the ORF8 gene is found within a highly variable portion of the genome, increasing the possibility of dangerous mutant forms arising. Structure‐based design of therapeutics holds great promise for effective targeting of such rapidly evolving threats. Thus, we designed physical models and computer representations of ORF8, based on the published structure 7JTL, to better evaluate the structural features responsible for viral pathogenicity. ORF8 exists as a homodimer held together by amino acid residues that interact through hydrophobic interactions, hydrogen bonding, a salt bridge, and a disulfide bond. These interactions occur in a region referred to as the covalent interface. Another interface, referred to as the noncovalent interface, exists between separate homodimers and results in oligomerization. The homodimers in the oligomer are held together by an intermolecular beta sheet that is stabilized by an extensive hydrophobic surface. These two dimerization interfaces are unique to SARS‐CoV‐2 and have been proposed to be involved in the protein's ability to evade and suppress the host immune response. The computer and physical representations allow for better visualization of the ORF8 structure to evaluate the role of the multimeric structure. ORF8 has been found to interact with various host proteins, including Interleukin‐17 Receptor A (IL17RA), a pro‐inflammatory cytokine. To further corroborate the existence of this interaction and to better understand the role oligomerization might play in pathogenicity, we assessed the possible interactions of ORF8 (7JTL) and IL17RA (5N9B) through theoretical modeling using available tools such as HDOCK. A better understanding of these inter‐subunit interactions may improve our understanding of the unique mechanism the virus uses to evade the immune system and may be instrumental in the development of effective therapeutics.