Representation Method Research Articles

VPRF: Visual Perceptual Radiance Fields for Foveated Image Synthesis.

Neural radiance fields (NeRF) has achieved revolutionary breakthrough in the novel view synthesis task for complex 3D scenes. However, this new paradigm struggles to meet the requirements for real-time rendering and high perceptual quality in virtual reality. In this paper, we propose VPRF, a novel visual perceptual based radiance fields representation method, which for the first time integrates the visual acuity and contrast sensitivity models of human visual system (HVS) into the radiance field rendering framework. Initially, we encode both the appearance and visual sensitivity information of the scene into our radiance field representation. Then, we propose a visual perceptual sampling strategy, allocating computational resources according to the HVS sensitivity of different regions. Finally, we propose a sampling weight-constrained training scheme to ensure the effectiveness of our sampling strategy and improve the representation of the radiance field based on the scene content. Experimental results demonstrate that our method renders more efficiently, with higher PSNR and SSIM in the foveal and salient regions compared to the state-of-the-art FoV-NeRF. The results of the user study confirm that our rendering results exhibit high-fidelity visual perception.

Cyber risk management technology to strengthen the information security of the national economy

Purpose. Developing a technology for managing cyber risks based on their improved classification by the level of impact on the occurrence of an extreme situation. Methodology. To achieve the goal, general scientific and special methods of cognition were used in the study: dialectical and systemic approaches, analysis and synthesis, logical generalization and grouping, structural-logical method, iterative approach, modeling, method of formal representations of uncertainty. Findings. A cyber risk management technology has been developed, consisting of four main stages: analysis of cyber threats (context establishment; security audit; formation of scenario concepts); scenario modeling (threat decomposition; scenario formation; setting criteria; setting probability estimates of concepts (variables); building a network architecture; formation of a private threat model; scenario analysis); risk assessment; object classification. The proposed approach to cybersecurity risk management provides vulnerability detection and risk assessment (risk potential) and simplifies the development of management solutions to prevent events affecting cybersecurity. Originality. The proposed technology differs from the existing ones by focusing on identifying those vulnerabilities and cyber threats that, according to their improved classification by the level of impact on the occurrence of an extreme situation, can lead to serious disruptions in the functioning of critical information infrastructure of the national economy. Practical value. The practical significance of the study lies in the fact that the proposed cyber risk management technology is one of the tools for preventing the realization of risks in cyberspace and the basis for strengthening the information security of economic entities in particular and the national economy as a whole.

Towards AI-Generated Essay Classification Using Numerical Text Representation

The detection of essays written by AI compared to those authored by students is increasingly becoming a significant issue in educational settings. This research examines various numerical text representation techniques to improve the classification of these essays. Utilizing a diverse dataset, we undertook several preprocessing steps, including data cleaning, tokenization, and lemmatization. Our system analyzes different text representation methods such as Bag of Words, TF-IDF, and fastText embeddings in conjunction with multiple classifiers. Our experiments showed that TF-IDF weights paired with logistic regression reached the highest accuracy of 99.82%. Methods like Bag of Words, TF-IDF, and fastText embeddings achieved accuracies exceeding 96.50% across all tested classifiers. Sentence embeddings, including MiniLM and distilBERT, yielded accuracies from 93.78% to 96.63%, indicating room for further refinement. Conversely, pre-trained fastText embeddings showed reduced performance, with a lowest accuracy of 89.88% in logistic regression. Remarkably, the XGBoost classifier delivered the highest minimum accuracy of 96.24%. Specificity and precision were above 99% for most methods, showcasing high capability in differentiating between student-created and AI-generated texts. This study underscores the vital role of choosing dataset-specific text representations to boost classification accuracy.

A knowledge-guided process planning approach with reinforcement learning

With the widespread application of computer-aided technologies such as CAD and CAM in the manufacturing industry, a growing number of process documents and design documents generate multi-source process knowledge and expert experience. However, due to the diverse and complex representation of process knowledge, there is a need for more effective methods to mine a large amount of multi-source information and to exploit the explicit and implicit relationships between the knowledge contained in process knowledge. Effective knowledge reuse in process planning still needs to be improved. This paper proposes a reinforcement learning approach that combines knowledge graphs and process decision-making activities in process planning to exploit the learning potential of process knowledge graphs. Firstly, a reinforcement learning environment for process planning is introduced to model the process planning decision-making phase as a sequential recommendation of process knowledge. Then, this paper designs in detail the state representation method that combines process decision sequences and potential relationships between processes. This paper also creates the composite reward function that combines the process planning environment. In addition, a new algorithm is proposed for learning the proposed model more efficiently. Experimental results show that the network structure has more accurate recommendation results than other methods.

FSH3D: 3D Representation via Fibonacci Spherical Harmonics

AbstractSpherical harmonics are a favorable technique for 3D representation, employing a frequency‐based approach through the spherical harmonic transform (SHT). Typically, SHT is performed using equiangular sampling grids. However, these grids are non‐uniform on spherical surfaces and exhibit local anisotropy, a common limitation in existing spherical harmonic decomposition methods. This paper proposes a 3D representation method using Fibonacci Spherical Harmonics (FSH3D). We introduce a spherical Fibonacci grid (SFG), which is more uniform than equiangular grids for SHT in the frequency domain. Our method employs analytical weights for SHT on SFG, effectively assigning sampling errors to spherical harmonic degrees higher than the recovered band‐limited function. This provides a novel solution for spherical harmonic transformation on non‐equiangular grids. The key advantages of our FSH3D method include: 1) With the same number of sampling points, SFG captures more features without bias compared to equiangular grids; 2) The root mean square error of 32‐degree spherical harmonic coefficients is reduced by approximately 34.6% for SFG compared to equiangular grids; and 3) FSH3D offers more stable frequency domain representations, especially for rotating functions. FSH3D enhances the stability of frequency domain representations under rotational transformations. Its application in 3D shape reconstruction and 3D shape classification results in more accurate and robust representations. Our code is publicly available at https://github.com/Miraclelzk/Fibonacci-Spherical-Harmonics.

Disk B‐spline on 𝕊2: A Skeleton‐based Region Representation on 2‐Sphere

AbstractDue to the widespread applications of 2‐dimensional spherical designs, there has been an increasing requirement of modeling on the 𝕊2 manifold in recent years. Due to the non‐Euclidean nature of the sphere, it has some challenges to find a method to represent 2D regions on 𝕊2 manifold. In this paper, a skeleton‐based representation method of regions on 𝕊2, disk B‐spline(DBSC) on 𝕊2 is proposed. Firstly, we give the definition and basic algorithms of DBSC on 𝕊2. Then we provide the calculation method of DBSC on 𝕊2, which includes calculating the boundary points, internal points and their corresponding derivatives. Based on that, we give some modeling methods of DBSC on 𝕊2, including approximation, deformation. In the end, some stunning application examples of DBSC on 𝕊2 are shown. This work lays a theoretical foundation for further applications of DBSC on 𝕊2.

Differential evolution-based topology optimization using Karhunen-Loève expansion

Topology optimization is a widely adopted structural optimization approach that aims to maximize desired performance based on mathematical and physical principles. Gradient-based methods are common for obtaining optimal configurations; however, they struggle with problems involving numerous local optima and non-differentiable problems, which prevent the derivation of design sensitivity. To address these issues, this study explores the application of the Success History-Based Differential Evolution with Linear Population Size Reduction and Semi-Parameter Adaptation, which is regarded as LSHADE-SPA algorithm, known for its strong search capabilities. Nevertheless, its computational cost makes it impractical for topology optimization, especially as the number of design variables increases. To overcome this challenge, a novel structure representation method utilizing the Karhunen-Loève (KL) expansion is constructed. Applying truncated KL expansion instead of conventional density methods to represent structural configurations reduces the number of design variables. This article introduces a differential evolution-based topology optimization method utilizing truncated KL expansion and validates its effectiveness through applications in various structural and fluid problems. The method is particularly suited to highly nonlinear problems, such as negative Poisson's ratio structures and micro-mixer designs. The findings here indicate that differential evolution with truncated KL expansion can serve as an efficient and robust topology optimization method, proving particularly effective for solving optimization problems that are challenging for conventional gradient-based approaches. Notably, the proposed methodology not only delivers superior results but also alleviates computational burdens, thereby advancing the prospects of topology optimization in various engineering applications.

The principle of the budgetary system unity amid implementation of regional budget independence: A case study of the Chechen Republic

Aim. To identify the ways to improve the observance of the principle of unity in the process of formation of the budgetary system of the Russian Federation (RF) on the basis of the analysis of the revenue part of the regional budget of the Chechen Republic.Objectives. To identify problems related to the observance of the principle of unity in the process of forming the budget system of the Russian Federation; to reveal the problem of the socalled chronic income insufficiency in regional and local budgets; to provide evidence of the effectiveness or inefficiency of the current mechanism of inter-institutional financial relations in Russia on the example of the Chechen Republic.Methodology. In the course of the research, the authors used general scientific methods and a number of special ones, including statistical and economic analysis, economic-mathematical and abstract-logical methods, as well as the method of graphical representation of information.Results. The idea is substantiated that the principle of independence of regional budgets should not contradict the principle of unity of the country’s budget system. It is proposed to develop a budget financing mechanism that takes into account not only the difference in providing the region with financial resources for independent development, but also the opportunity to give the funds allocated from the state budget an investment character.Conclusions. The article defines the directions for improving the mechanism while respecting the principle of unity in the process of forming the budget system of the Russian Federation. The country’s budget system should become one of the main sources of investment in the economy of the country and regions.

MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information

Migraine is considered one of the debilitating primary headache conditions with an estimated worldwide occurrence of approximately 14–15%, contributing highly to factors responsible for global disability. Calcitonin gene-related peptide (CGRP) is a neuropeptide that plays a crucial role in the pathophysiology of migraines and thus, its inhibition can help relieve migraine symptoms. However, conventional process of CGRP drug development has been laborious and time-consuming with incurred costs exceeding one billion dollars. On the other hand, machine learning (ML)-based approaches that are capable of accurately identifying CGRP inhibitors could greatly facilitate in expediting the discovery of novel CGRP drugs. Therefore, this study proposes a novel and high-accuracy meta-model, namely MetaCGRP, that can precisely identify CGRP inhibitors. To the best of our knowledge, MetaCGRP is the first SMILES-based approach that has been developed to identify CGRP inhibitors without the use of 3D structural information. In brief, we initially employed different molecular representation methods coupled with popular ML algorithms to construct a pool of baseline models. Then, all baseline models were optimized and used to generate multi-view features. Finally, we employed the feature selection method to optimize the multi-view features and determine the best feature subset to enable the construction of the meta-model. Both cross-validation and independent tests indicated that MetaCGRP clearly outperforms several conventional ML classifiers, with accuracies of 0.898 and 0.799 on the training and independent test datasets, respectively. In addition, MetaCGRP in conjunction with molecular docking was utilized to identify five potential natural product candidates from Thai herbal pharmacopoeia and analyze their binding affinity and interactions to CGRP. To facilitate community-wide efforts in expediting the discovery of novel CGRP inhibitors, a user-friendly web server for MetaCGRP is freely available at https://pmlabqsar.pythonanywhere.com/MetaCGRP.

Feature Vector Effectiveness Evaluation for Pattern Selection in Computational Lithography

Pattern selection is crucial for optimizing the calibration process of optical proximity correction (OPC) models in computational lithography. However, it remains a challenge to achieve a balance between representative coverage and computational efficiency. This work presents a comprehensive evaluation of the feature vectors’ (FVs’) effectiveness in pattern selection for OPC model calibration, leveraging key performance indicators (KPIs) based on Kullback–Leibler divergence and distance ranking. Through the construction of autoencoder-based FVs and fast Fourier transform (FFT)-based FVs, we compare their efficacy in capturing critical pattern features. Validation experimental results indicate that autoencoder-based FVs, particularly augmented with the lithography domain knowledge, outperform FFT-based counterparts in identifying anomalies and enhancing lithography model performance. These results also underscore the importance of adaptive pattern representation methods in calibrating the OPC model with evolving complexities.

Asynchronous Visual-Inertial Odometry for Event Cameras

Abstract. Event cameras are bio-inspired visual sensors that output changes in pixel-level brightness asynchronously instead of standard intensity frames at a fixed rate. These cameras offer reliable visual information in high-speed motion and high dynamic range (HDR) scenes, addressing the limitations of traditional cameras in such scenarios. Therefore, research of integrating event cameras into established visual algorithms holds significant value. In this study, based on traditional Visual-Inertial Odometry (VIO) frameworks, we proposed an innovative asynchronous monocular event-based inertial odometry method to fully exploit the benefits of event cameras. First, the corner features are extracted separately from the raw event stream and the time surface map, followed by uniform feature selection to accurately describe three-dimensional spatial geometry. Then, feature tracking is achieved by integrating these two event representation methods. In addition, our method obtains stable and high frequency state estimation by fusing event and IMU measurements through graph optimization. We validate the effectiveness of our proposed approach, comparing with several state-of-the-art EVIO systems and VIO systems.

Efficient Storage and Analysis of Genomic Data: A k-mer Frequency Mapping and Image Representation Method.

k-mer frequencies are crucial for understanding DNA sequence patterns and structure, with applications in motif discovery, genome classification, and short read assembly. However, the exponential increase in the dimension of frequency tables with increasing k-mer length poses storage challenges. In this study, we present a novel method for compressing k-mer data without information loss, aiming to optimize storage and analysis processes. We employed Chaos Game Representation (CGR) to map k-mers to coordinates and used these components to generate raster images of k-mers. The CGR maps were partitioned and labeled based on substrings, with each substring mapped to a subframe, creating a fractal-like structure. The entire k-mer frequency set of each genomic sequence was represented as a single image, with each pixel corresponding to a specific k-mer and its occurrence. This approach reduced file size by up to 16-fold compared to plain text and 3-fold compared to binary format. Furthermore, we demonstrated the feasibility of performing alignment-free similarity analyses on images derived from k-mer frequencies of whole genome sequences from 14 plant species. Our results highlight the potential of this method as a fast and efficient tool for accessing, processing, and analyzing large biological sequence datasets, enabling the retrieval of k-mer frequencies and image reconstruction.

Accurate Estimation of Gross Primary Production of Paddy Rice Cropland with UAV Imagery-Driven Leaf Biochemical Model

Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for improving GPP estimation. The key parameter, maximum carboxylation rate at the top of the canopy (Vcmax,025), was quantified using various spatial information representation methods, including mean (μref) and standard deviation (σref) of reflectance, gray-level co-occurrence matrix (GLCM)-based features, local binary pattern histogram (LBPH), and convolutional neural networks (CNNs). Our models were evaluated using a two-year eddy covariance (EC) system and UAV measurements. The result shows that incorporating spatial information can vastly improve the accuracy of Vcmax,025 and GPP estimation. CNN methods achieved the best Vcmax,025 estimation, with an R of 0.94, an RMSE of 19.44 μmol m−2 s−1, and an MdAPE of 11%, and further produced highly accurate GPP estimates, with an R of 0.92, an RMSE of 6.5 μmol m−2 s−1, and an MdAPE of 23%. The μref-GLCM texture features and μref-LBPH joint-driven models also gave promising results. However, σref contributed less to Vcmax,025 estimation. The Shapley value analysis revealed that the contribution of input features varied considerably across different models. The CNN model focused on nir and red-edge bands and paid much attention to the subregion with high spatial heterogeneity. The μref-LBPH joint-driven model mainly prioritized reflectance information. The μref-GLCM-based features joint-driven model emphasized the role of GLCM texture indices. As the first study to leverage the spatial information from high-resolution UAV imagery for GPP estimation, our work underscores the critical role of spatial information and provides new insight into monitoring the carbon cycle.

Wittgenstein and Toulmin’s Model of Argument: The Riddle Explained Away

AbstractThe article undertakes the problem of a Wittgensteinian background of Toulmin’s model of argument. While appreciating the original character of the investigations set out by Toulmin in The Uses of Argument, Wittgenstein’s ideas taken to be forerunners of both Toulmin’s philosophical method and the particular elements of the model of substantial argument are traced backward, to Toulmin’s earlier books: The Philosophy of Science (Toulmin, The philosophy of science. An introduction, Hutchinson University Library, London, 1953) and An Examination of the Place of Reason in Ethics (Toulmin, An examination of the place of reason in ethics, Cambridge University Press, Cambridge, 1950). The technique of pinpointing the constituents of that model in the books preceding The Uses of Argument is superposing the layout of Toulmin’s model on the crucial arguments concerning the earlier books: the scientific one based on Newtonian optics and the moral one concerning keeping promises. Such a procedure allows identifying backing for warrants and argument fields with the methods of representation in The Philosophy of Science and with modes of reasoning in An Examination of the Place of Reason in Ethics. The former is traced to passages 6.3 ff of Wittgenstein’s Tractatus, while the latter—to the concept of word-games (the later Wittgenstein’s language games). The claim regarding Wittgenstein’s background is that in Toulmin’s view of Wittgenstein, some parts of Tractatus concerning representing are in line with Wittgenstein’s later reflections on language games; as well as that the overall method of The Uses of Argument goes along with Wittgenstein’s therapeutic approach to philosophical problems that have to be placed in the context of their ordinary use.

Tabular affiliation extraction based on graph convolutional network

This paper studies the problem of extracting affiliation relationships between cells in a table in the field of table recognition and analysis. The task of extracting affiliation relationships between tables is defined. Combining the similarity between table and graph structure, a graph representation method for cells in a table is given, and a graph convolutional network-based affiliation relationshipextraction model is proposed. The model aggregates features of cells in a table and their neighboring cells through a graph convolutional network, predicts whether there is an affiliation relationship between cells, and realizes relationship extraction. In order to verify the effectiveness of the model, two datasets, Rel-forms for Chinese forms and Rel-SciTSR for English forms, are annotated. Through experiments, the F1 scores on the above two datasets and the joint dataset reached , respectively 98.61%、96.55%、97.05%, verifying the effectiveness of the affiliation relationship extraction model on these two datasets, and analyzing the influence of different factors such as text content, coordinate information, cell attributes, and relative direction between cells on the experimental results of affiliation relationship extraction.

Nondirect-Product Local Diabatic Representation with Smolyak Sparse Grids.

Modeling nonadiabatic conical intersection dynamics is critical for understanding a wide range of photophysical, photochemical, and biological phenomena. Here we develop a nonadiabatic conical intersection wave packet dynamic method in the local diabatic representation using Smolyak sparse grids. Employing sparse grids avoids the direct-product grids in configuration space and alleviates the exponential scaling of computation costs with the molecular size. Numerical demonstrations are first performed for a two-dimensional vibronic model of pyrazine, where the results using sparse grids are in excellent agreement with those using direct-product grids, with sparse grids being much faster. Moreover, we demonstrate that for a four-dimensional pyrazine model, where direct-product grids are computationally infeasible, sparse grids can provide almost exact results. The sparse grid local diabatic representation method is further applied to a realistic model system of phenol photodissociation with much more complex potential energy surfaces; the results using sparse grids still agree very well with the direct-product grids. Finally, by combining with electronic structure calculations, we apply our method to the Shin-Metiu model without quasi-diabatization. The sparse grid and direct-product grid results are in good agreement, with the sparse grid computational cost being half of the full grid.

SeqDance: A Protein Language Model for Representing Protein Dynamic Properties.

Proteins perform their functions by folding amino acid sequences into dynamic structural ensembles. Despite the important role of protein dynamics, their complexity and the absence of efficient representation methods have limited their integration into studies on protein function and mutation fitness, especially in deep learning applications. To address this, we present SeqDance, a protein language model designed to learn representation of protein dynamic properties directly from sequence alone. SeqDance is pre-trained on dynamic biophysical properties derived from over 30,400 molecular dynamics trajectories and 28,600 normal mode analyses. Our results show that SeqDance effectively captures local dynamic interactions, co-movement patterns, and global conformational features, even for proteins lacking homologs in the pre-training set. Additionally, we showed that SeqDance enhances the prediction of protein fitness landscapes, disorder-to-order transition binding regions, and phase-separating proteins. By learning dynamic properties from sequence, SeqDance complements conventional evolution- and static structure-based methods, offering new insights into protein behavior and function.

HR-brand: features of formation and development

Relevance. The development of HR management stems from the need for organizations to attract and retain highly qualified employees in the competitive environment of the modern labor market. HR branding is a strategy based on the formation and support of a positive image of the company in the eyes of employees, as well as the company's commitment to the principles and values that bind employees and the organization. Positive evaluations of the organization's employees and their recommendations can attract new specialists and strengthen the company's reputation as a reliable and attractive employer.The purpose of the article is to study a special category of reputation and perception of the company – the HR brand.Objectives: to study the conditions for building an HR brand, its structure and key elements; to consider the history of the origin of the idea of HR branding; to assess the interest in creating a special reputation in the field of human resource management; to justify why it is impossible to build an EVP (employer's value proposition) without HR branding.Methodology. In the course of scientific research, empirical, theoretical, statistical methods and methods of graphical representation were used.Results: the theoretical aspects of building a company's HR brand were studied; the popularity of this topic in science and business was shown. A set of knowledge about the features of HR branding in the Russian and foreign markets has been formed and the prospects for the development of this area are shown. It shows why it is important to work on an HR brand and how to start forming a positive image of the company, its reputation and prestige.Conclusions. The article emphasizes the relevance and importance of the topic under consideration, taking into account the latest trends, prerequisites and prospects for new companies. The possibilities that open up for an employer when forming an original and popular HR brand are considered.

О представительной природе органов публичной власти

The article discusses the issues of the representative nature of public authorities. Emphasis is placed on the essential features that make it possible to assess the nature of representation. The main attention is paid to representative bodies of power as the most accurate reflection of the nature of representation. When formulating the criteria for distinguishing the features of representation, the formal-logical and comparative-legal methods were used. It is concluded that the nature of representation is most fully revealed in formation and functioning of collegial elected bodies of public power.

Joint intent detection and slot filling with syntactic and semantic features using multichannel CNN-BiLSTM

Understanding spoken language is crucial for conversational agents, with intent detection and slot filling being the primary tasks in natural language understanding (NLU). Enhancing the NLU tasks can lead to an accurate and efficient virtual assistant thereby reducing the need for human intervention and expanding their applicability in other domains. Traditionally, these tasks have been addressed individually, but recent studies have highlighted their interconnection, suggesting better results when solved together. Recent advances in natural language processing have shown that pretrained word embeddings can enhance text representation and improve the generalization capabilities of models. However, the challenge of poor generalization in joint learning models for intent detection and slot filling remains due to limited annotated datasets. Additionally, traditional models face difficulties in capturing both the semantic and syntactic nuances of language, which are vital for accurate intent detection and slot filling. This study proposes a hybridized text representation method using a multichannel convolutional neural network with three embedding channels: non-contextual embeddings for semantic information, part-of-speech (POS) tag embeddings for syntactic features, and contextual embeddings for deeper contextual understanding. Specifically, we utilized word2vec for non-contextual embeddings, one-hot vectors for POS tags, and bidirectional encoder representations from transformers (BERT) for contextual embeddings. These embeddings are processed through a convolutional layer and a shared bidirectional long short-term memory (BiLSTM) network, followed by two softmax functions for intent detection and slot filling. Experiments on the air travel information system (ATIS) and SNIPS datasets demonstrated that our model significantly outperformed the baseline models, achieving an intent accuracy of 97.90% and slot filling F1-score of 98.86% on the ATIS dataset, and an intent accuracy of 98.88% and slot filling F1-score of 97.07% on the SNIPS dataset. These results highlight the effectiveness of our proposed approach in advancing dialogue systems, and paving the way for more accurate and efficient natural language understanding in real-world applications.