Representation Approach Research Articles

Evaluating restriction enzyme selection for reduced representation sequencing in conservation genomics.

Conservation genomic studies in non-model organisms generally rely on reduced representation sequencing techniques based on restriction enzymes to identify population structure as well as candidate loci for local adaptation. While the expectation is that the reduced representation of the genome is randomly distributed, the proportion of the genome sampled might depend on the GC content of the recognition site of the restriction enzyme used. Here, we evaluated the distribution and functional composition of loci obtained after a reduced representation approach using Genotyping-by-Sequencing (GBS). To do so, we compared experimental data from two endemic fish species (Symphodus ocellatus and Symphodus tinca, EcoT22I enzyme) and two ecosystem engineer sea urchins (Paracentrotus lividus and Arbacia lixula, ApeKI enzyme). In brief, we mapped the sequenced loci to the phylogenetically closest reference genome available (Labrus bergylta in the fish and Strongylocentrotus purpuratus in the sea urchin datasets), classified them as exonic, intronic and intergenic, and studied their function by using Gene Ontology (GO) terms. We also simulated the effect of using both enzymes in the two reference genomes. In both simulated and experimental data, we detected an enrichment towards exonic or intergenic regions depending on the restriction enzyme used and failed to detect differences between total loci and candidate loci for adaptation in the empirical dataset. Most of the functions assigned to the mapped loci were shared between the four species and involved a myriad of general functions. Our results highlight the importance of restriction enzyme selection and the need for high-quality annotated genomes in conservation genomic studies.

Improving Science Process Skills of Students: A Review of Literature

This paper aims to review and assess the strategies available in literature to improve the practices of science process skills (SPS) among students. SPS encompasses the mental and physical activities of students in collecting and organizing information and then using the information to make predictions, explain phenomena, solve problems, understand the scientific endeavor, and learn science. Students were required to develop and use these skills in the teaching and learning process. The commonly identified and described SPS are observation, measuring, classifying, communicating, predicting, inferring, using numbers, using space/time relationship, questioning, identifying and controlling variables, hypothesizing, defining operationally, designing experiments, interpreting data, and modeling. These skills are important for students as tools for exploration and investigation of the natural world, improving academic achievements and attitudes towards science, learning science with understanding, and promoting development of mental and intellectual processes. From the literature reviewed the following strategies were recognized for the development of SPS in students: integration of SPS in curriculum materials, classroom lessons and assessments, use of instructional strategies or methods, and providing explicit instruction or training on SPS. The instructional strategies identified for enhancing SPS were student-centered teaching methods and multiple representation approaches. Student-centered teaching approaches promote the development of SPS in students by engaging them in mind-on and hand-on activities. Multiple representation approaches involve presenting lessons by using a combination of various teaching methods or various media in the same lesson and are found important for SPS development in students. Therefore, it was recommended to use these strategies for developing SPS to attain science education goals.

Cross-domain transferable discriminant dictionary based sparse representation approach for EEG emotion-level recognition

Cross-domain transferable discriminant dictionary based sparse representation approach for EEG emotion-level recognition

Responsefun: Fun with Response Functions in the Algebraic Diagrammatic Construction Framework.

We present the open-source responsefun package, which implements a universally applicable procedure for computing molecular response properties within the algebraic diagrammatic construction (ADC) framework, exploiting the intermediate state representation (ISR) approach. With symbolic mathematics, the user can simply enter textbook sum-over-states (SOS) expressions from time-dependent perturbation theory, which are then automatically translated into the corresponding symbolic ADC/ISR formulations. Using the data structures provided by the hybrid Python/C++ module adcc for calculating excited states with ADC, the specified response property is directly evaluated, and the result is returned to the user. Employing the novel responsefun package, we present the first ADC/ISR calculations of second-order hyperpolarizability tensors and three-photon-absorption matrix elements.

The dyadic regulation approach of coping and illness representations in female cancer patients and their partners.

Adjustment to any illness is a 'dyadic' process whereby patients and their partners mutually determine each other's perceptions, behaviours, and well-being. The present study explored the association between dyadic coping strategies and illness representations in newly diagnosed female cancer patients and their partners. The sample consisted of 92 female cancer patient-partner pairs from 3 oncology hospitals in Greece and Cyprus. The Actor Partner Interdependence Model was applied to test for dyadic regulation effects. The findings revealed that patients' evaluations of dyadic coping were related to their own illness representations and, in some cases, to partners' illness representations of control. However, partner evaluations of dyadic coping were not associated with either patients' or their own illness representations. Relationship satisfaction did not moderate the relationship between dyadic coping and illness representations. The study suggests that patients' perceptions of support provided by themselves and their partners play a significant role in shaping their illness representations. Future research could delve into the underlying reasons for the observed differences in the impact of dyadic coping on illness representations between patients and partners, considering factors such as gender roles and specific gender-related issues.

Total-Activity Conservation Analysis and Design of Boolean Networks.

The law of conservation of mass, represented in Boolean networks (BNs) as total-activity conservation, is one of the typical properties of biological networks. This article analyzes the total-activity conservation of BNs based on the algebraic state-space representation (ASSR) approach. First, the total-activity-conservative matrix is defined and a matrix-based criterion is proposed to verify the total-activity conservation of BNs. Meanwhile, when function perturbation is considered, robust total-activity conservation is investigated. Second, by means of the pseudo-Boolean function generated by total-activity conservation, a constructive design procedure of the Boolean dynamics is given to achieve the total-activity conservation. Third, the total-activity conservation of switched Boolean networks (SBNs) under arbitrary switching signal is studied, which together with network aggregation achieves the total-activity conservation of large-scale BNs.

LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images.

Deep learning based fusion methods have been achieving promising performance in image fusion tasks. This is attributed to the network architecture that plays a very important role in the fusion process. However, in general, it is hard to specify a good fusion architecture, and consequently, the design of fusion networks is still a black art, rather than science. To address this problem, we formulate the fusion task mathematically, and establish a connection between its optimal solution and the network architecture that can implement it. This approach leads to a novel method proposed in the paper of constructing a lightweight fusion network. It avoids the time-consuming empirical network design by a trial-and-test strategy. In particular we adopt a learnable representation approach to the fusion task, in which the construction of the fusion network architecture is guided by the optimisation algorithm producing the learnable model. The low-rank representation (LRR) objective is the foundation of our learnable model. The matrix multiplications, which are at the heart of the solution are transformed into convolutional operations, and the iterative process of optimisation is replaced by a special feed-forward network. Based on this novel network architecture, an end-to-end lightweight fusion network is constructed to fuse infrared and visible light images. Its successful training is facilitated by a detail-to-semantic information loss function proposed to preserve the image details and to enhance the salient features of the source images. Our experiments show that the proposed fusion network exhibits better fusion performance than the state-of-the-art fusion methods on public datasets. Interestingly, our network requires a fewer training parameters than other existing methods.

Matrix representations of the inverse problem in the graph model for conflict resolution with fuzzy preference

The inverse analysis of the graph model for conflict resolution (GMCR) determines all the preference relationships required by each decision-maker (DM) according to a given stability definition to ensure that the given state is stable. In the paper, a matrix representation approach for the inverse graph model with fuzzy preference relations is proposed to obtain all the preference relations for each DM making the stability result based on the fuzzy stability definition, including fuzzy Nash, fuzzy general metarationality, fuzzy symmetric metarationality, and fuzzy sequential stability definition. For the two-DM graph model, the fuzzy preference relation over states, unilateral movements, and fuzzy unilateral improvements are refreshed by using the matrix forms. Furthermore, for the multiple DMs model, the joint movements and joint improvement for a collection including at least two DM are represented by using matrix representation. Finally, the matrix relationship for specifying the required preference relation in the framework of inverse graph model with fuzzy preferences is derived. To illustrate the usefulness of the matrix representation of inverse graph model with fuzzy preference relations, this paper demonstrates it using a real-life conflict of the Elmira groundwater contamination conflict.

Quantum State Assignment Flows.

This paper introduces assignment flows for density matrices as state spaces for representation and analysis of data associated with vertices of an underlying weighted graph. Determining an assignment flow by geometric integration of the defining dynamical system causes an interaction of the non-commuting states across the graph, and the assignment of a pure (rank-one) state to each vertex after convergence. Adopting the Riemannian-Bogoliubov-Kubo-Mori metric from information geometry leads to closed-form local expressions that can be computed efficiently and implemented in a fine-grained parallel manner. Restriction to the submanifold of commuting density matrices recovers the assignment flows for categorical probability distributions, which merely assign labels from a finite set to each data point. As shown for these flows in our prior work, the novel class of quantum state assignment flows can also be characterized as Riemannian gradient flows with respect to a non-local, non-convex potential after proper reparameterization and under mild conditions on the underlying weight function. This weight function generates the parameters of the layers of a neural network corresponding to and generated by each step of the geometric integration scheme. Numerical results indicate and illustrate the potential of the novel approach for data representation and analysis, including the representation of correlations of data across the graph by entanglement and tensorization.

Comprehensive Study on Shape Representation Methods for Shape-Based Object Recognition

This study presents a thorough analysis of shape representation techniques. Furthermore, a taxonomy of strategies for form representation and description is provided. This study aims to review the current achievements comprehensively, highlight the weaknesses and advantages of various existing methods in shape representation methods, addressing current research issues and challenging tasks in this research scope. The contributions of this study lie in providing a comprehensive analysis of shape representation techniques, along with a taxonomy of strategies for form representation and description. The research explores the application of shape border information and inner content in various domains, such as form retrieval, shape classification, and medical image analysis. The novelties include the systematic categorization of methods, addressing current research issues, and offering insights into the benefits and drawbacks of existing shape representation approaches, thus paving the way for potential advancements in the field. In the end, the performance of existing methods is discussed to address the drawbacks and effectiveness of current methods.

Medical image retrieval via nearest neighbor search on pre-trained image features

Nearest neighbor search, also known as NNS, is a technique used to locate the points in a high-dimensional space closest to a given query point. This technique has multiple applications in medicine, such as searching large medical imaging databases, disease classification, and diagnosis. However, when the number of points is significantly large, the brute-force approach for finding the nearest neighbor becomes computationally infeasible. Therefore, various approaches have been developed to make the search faster and more efficient to support the applications. With a focus on medical imaging, this paper proposes DenseLinkSearch (DLS), an effective and efficient algorithm that searches and retrieves the relevant images from heterogeneous sources of medical images. Towards this, given a medical database, the proposed algorithm builds an index that consists of pre-computed links of each point in the database. The search algorithm utilizes the index to efficiently traverse the database in search of the nearest neighbor. We also explore the role of medical image feature representation in content-based medical image retrieval tasks. We propose a Transformer-based feature representation technique that outperformed the existing pre-trained Transformer-based approaches on benchmark medical image retrieval datasets. We extensively tested the proposed NNS approach and compared the performance with state-of-the-art NNS approaches on benchmark datasets and our created medical image datasets. The proposed approach outperformed the existing approaches in terms of retrieving accurate neighbors and retrieval speed. In comparison to the existing approximate NNS approaches, our proposed DLS approach outperformed them in terms of lower average time per query and ≥99% R@10 on 11 out of 13 benchmark datasets. We also found that the proposed medical feature representation approach is better for representing medical images compared to the existing pre-trained image models. The proposed feature extraction strategy obtained an improvement of 9.37%, 7.0%, and 13.33% in terms of P@5, P@10, and P@20, respectively, in comparison to the best-performing pre-trained image model. The source code and datasets of our experiments are available at https://github.com/deepaknlp/DLS.

Latent Regression Bayesian Network for Speech Representation

In this paper, we present a novel approach for speech representation using latent regression Bayesian networks (LRBN) to address the issue of poor performance in low-resource language speech systems. LRBN, a lightweight unsupervised learning model, learns data distribution and high-level features, unlike computationally expensive large models, such as Wav2vec 2.0. To evaluate the effectiveness of LRBN in learning speech representations, we conducted experiments on five different low-resource languages and applied them to two downstream tasks: phoneme classification and speech recognition. Our experimental results demonstrate that LRBN outperforms prevailing speech representation methods in both tasks, highlighting its potential in the realm of speech representation learning for low-resource languages.

NRVC: Neural Representation for Video Compression with Implicit Multiscale Fusion Network.

Recently, end-to-end deep models for video compression have made steady advancements. However, this resulted in a lengthy and complex pipeline containing numerous redundant parameters. The video compression approaches based on implicit neural representation (INR) allow videos to be directly represented as a function approximated by a neural network, resulting in a more lightweight model, whereas the singularity of the feature extraction pipeline limits the network's ability to fit the mapping function for video frames. Hence, we propose a neural representation approach for video compression with an implicit multiscale fusion network (NRVC), utilizing normalized residual networks to improve the effectiveness of INR in fitting the target function. We propose the multiscale representations for video compression (MSRVC) network, which effectively extracts features from the input video sequence to enhance the degree of overfitting in the mapping function. Additionally, we propose the feature extraction channel attention (FECA) block to capture interaction information between different feature extraction channels, further improving the effectiveness of feature extraction. The results show that compared to the NeRV method with similar bits per pixel (BPP), NRVC has a 2.16% increase in the decoded peak signal-to-noise ratio (PSNR). Moreover, NRVC outperforms the conventional HEVC in terms of PSNR.

High Dimensional Model Representation Approach for Prediction and Optimization of the Supercritical Water Gasification System Coupled with Photothermal Energy Storage

Supercritical water gasification (SCWG) coupled with solar energy systems is a new biomass gasification technology developed in recent decades. However, conventional solar-powered biomass gasification technology has intermittent operation issues and involves multi-variable characteristics, strong coupling, and nonlinearity. To solve the above problems, firstly, a solar-driven biomass supercritical water gasification technology combined with a molten salt energy storage system is proposed in this paper. This system effectively overcomes the intermittent problem of solar energy and provides a new method for the carbon-neutral process of hydrogen production. Secondly, the high dimensional model representation (HDMR) approach, as a surrogate model, was used to predict the production and lower heating value of syngas developed in Aspen Plus, which were validated using experimental data obtained from the literature. The ultimate analysis of biomass, temperature, pressure, and biomass-to-water ratio (BWR) were selected as input variables for the model. The non-dominated sorted genetic algorithm II (NSGA II) was considered to maximize the gasification yield of H2 and the LHV of syngas in the SCWG process for five different types of biomass. Firstly, the results showed that HDMR models demonstrated high performance in predicting the mole fraction of H2, CH4, CO, CO2, gasification yield of H2, and lower heating value (LHV) with R2 of 0.995, 0.996, 0.997, 0.996, 0.999, and 0.995, respectively. Secondly, temperature and BWR were found to have significant effects on SCWG compared to pressure. Finally, the multi-objective optimization results for five different types of biomass are discussed in this paper. Therefore, these operating parameters can provide an optimal solution for increasing the economics and characteristics of syngas, thus keeping the process energy efficient.

Incorporating keyword extraction and attention for multi-label text classification

As one of the fundamental tasks in natural language processing, Multi-Label Text Classification (MLTC) is used to mark one or more relevant labels for a given text from a large set of labels. Existing MLTC methods have increasingly focused on improving classification effectiveness by fusing the correlations of labels. Still, the research suffers from difficulties in comprehensively extracting text features and distinguishing similar labels. This paper proposed a multi-label text classification model based on keyword extraction and attention mechanism. The model proposed using keywords to represent labels, adopting both self-attention and interactive attention mechanisms (between labels and text) to extract text features and create text vectors. Finally, fusing text vectors as the classifier’s input. Experiments were conducted on two public datasets and a self-built dataset of illegal advertisements. The experimental results showed that the keyword-based label representation approach proposed in this paper can better obtain label semantics, avoid noise and improve the performance of the multi-label text classification.

Modeling Barcelona sidewalks: A high resolution urban scale assessment of the geometric attributes of the walkable network.

Within the framework of urban pedestrian networks, this paper presents a method of modeling the street network from the perspective of foot traffic, beyond the vehicle-focused street centerline representation approach in transportation research. A scalable method to extract the centerlines of the complete walkable urban area from its polygon representation at a one-meter resolution is discussed, using open-source tools. To evaluate the betweenness centrality in a spatially directed graph, the process is applied to the study of the 'walkable Barcelona', focusing on three key parameters: 1) the street width, 2) the longitudinal slope, and 3) the crosswalks connecting the sidewalk platforms. The results identify the uneven distribution of these parameters within a complex urban fabric, and the high-resolution cartography allows the identification of critical areas within the network, introducing future lines of research and applications of the workflow. This is especially relevant considering the increasing awareness of citizens and the urban agendas worldwide, aimed at improving and widening the sidewalk infrastructure that supports local activity in cities.

An investigation of politicians' responses to urban diversity and disadvantage: The case of the Danish 'parallel societies'.

We examine how Danish politicians articulate views on the 'parallel society agreement' (aka, the 'ghetto-laws'), a controversial legislative intervention aiming to manage urban migration-related diversity. Through nationwide urban redevelopment aimed at facilitating residential 'mixing', the goal of the legislation is to eliminate so-called 'parallel societies'-socio-economically deprived neighbourhoods characterized by high concentrations of ethnic minorities. In-depth interviews with Danish politicians (n = 11) explored how this proposal was supported, contested or rejected in situated discourse. Following social representations theory, we focus on how 'parallel societies' were constructed in relation to differing ideas about 'mainstream society' and value-laden oppositional meaning-categories (i.e. themata). In particular, we highlight processes of socio-ethical reasoning that occurred through thematization of a shared oppositional meaning-category: 'freedom-constraint'. Views on the intervention were articulated around this oppositional meaning-category. Moreover, a connection was observed between the views articulated by individual politicians and sets of congruent ideas and images mobilized to represent 'parallel societies'. We discuss the theoretical value of taking a social representations approach to urban policy debates, and the practical limitations of dominant representations for successfully promoting intercultural dialogue and engagement-the stated goal of this intervention.

A Graph Representation Approach Based on Light Gradient Boosting Machine for Predicting Drug-Disease Associations.

Determining the association between drug and disease is important in drug development. However, existing approaches for drug-disease associations (DDAs) prediction are too homogeneous in terms of feature extraction. Here, a novel graph representation approach based on light gradient boosting machine (GRLGB) is proposed for prediction of DDAs. After the introduction of the protein into a heterogeneous network, nodes features were extracted from two perspectives: network topology and biological knowledge. Finally, the GRLGB classifier was applied to predict potential DDAs. GRLGB achieved satisfactory results on Bdataset and Fdataset through 10-fold cross-validation. To further prove the reliability of the GRLGB, case studies involving anxiety disorders and clozapine were conducted. The results suggest that GRLGB can identify novel DDAs.

A sparse dictionary representation approach for anti-scattering grid artifact removal in X-ray images

The aim of this work is to introduce a new joint estimation approach for anatomical content and artifacts in digital radiography. Herein, we apply this approach to the anti-scattering grid suppression problem. These grids are generally used in low-dose radiography to improve the image contrast by absorbing the scattered radiation. However, they introduce an irregular stripe pattern in the clinical images which can hinder the diagnosis made by the radiologist.The problem is tackled in a sparse dictionary representation framework with an observation and prior models accounting for the physical model of image contents. Thus, the anatomical content is supposed to be sparsely represented by a Fourier or K-SVD dictionary. Furthermore, the artifact is modeled by a frequency dictionary. In addition, the noise is modeled by a locally stationary Gaussian distribution with zero mean and unknown variance. The joint estimation of all the projections is then performed using two methods: Beam-forming (IAA) and Bayesian (SUBLIM). Moreover, patch-wise processing is applied to avoid models’ errors due to spatial variations of the clinical contents and the artifact.IAA and SUBLIM performance is demonstrated for the anti-scattering grid extraction problem using experimental and real clinical images. The proposed methods remove gridline artifacts while maintaining the image details and avoiding ringing artifacts. In addition, they produce better results compared to the conventional filtering approaches, especially when the metallic materials are present in or on the patients. SUBLIM outperforms IAA in those regions thanks to a better clinical content model and physical noise prior.

Drug-CoV: a drug-origin knowledge graph discovering drug repurposing targeting COVID-19

Drug repurposing is a technique for probing new usages of existing medicines, but its traditional methods, such as computational approaches, can be time-consuming and laborious. Recently, knowledge graphs (KGs) have emerged as a powerful approach for graph-based representation in drug repurposing, encoding entities and relations to predict new connections and facilitate drug discovery. As COVID-19 has become a major public health concern, it is critical to establish an appropriate COVID-19 KG for drug repurposing to combat the spread of the virus. However, most publicly available COVID-19 KGs lack support for multi-relations and comprehensive entity types. Moreover, none of them originates from COVID-19-related drugs, making it challenging to identify effective treatments. To tackle these issues, we developed Drug-CoV, a drug-origin and multi-relational COVID-19 KG. We evaluated the quality of Drug-CoV by performing link prediction and comparing the results to another publicly available COVID-19 KG. Our results showed that Drug-CoV outperformed the comparing KG in predicting new links between entities. Overall, Drug-CoV represents a valuable resource for COVID-19 drug repurposing efforts and demonstrates the potential of KGs for facilitating drug discovery.