Information Processing Problems Research Articles

Inferring general links between energetics and information with unknown environment

Identifying general links between energetics and information in realistic open quantum systems is a long-standing problem in quantum thermodynamics and quantum information processing. However, the generality of existing efforts is often impeded by their specific assumptions about environments. Here we address the problem by developing a trajectory-level thermodynamic inference theory to establish general links using just the knowledge of the system, enabling a single framework applicable to a diverse range of environments. Underpinning the framework is a notion of excess energy introduced for inferring the net energy gain of the system after completing an information processing trajectory. We show that fluctuation behaviors of the excess energy encode general links between energetics and information with a conceptual advantage that they completely avoid assumptions about environments and system-environment coupling forms. Crucially, we obtain a single thermodynamic inequality that integrates upper bounds on heat dissipation and extracted work in terms of system's information content change, providing complementary constraints that greatly expand the context of existing well-adopted results based on the second law of thermodynamics. We also uncover lower bounds on the precision of the fluctuating system's energy and information content changes in terms of their Fano factors and a correlation function between them. By extending relations between energetics and information to higher-order fluctuations, we thus reveal a trade-off that a more precise inference of energy or information content changes requires a looser energetic-information link. We showcase the implications of these general links in a number of quantum information and thermodynamic tasks of application relevance. Our framework provides a toolkit for analyzing the interplay between energetics and information from the trajectory level in generic quantum systems, thereby adding an indispensable structure to the thermodynamics of information. Published by the American Physical Society 2024

Reconfigurable Neuromorphic Computing with 2D Material Heterostructures for Versatile Neural Information Processing.

Reconfigurable neuromorphic computing holds promise for advancing energy-efficient neural network implementation and functional versatility. Previous work has focused on emulating specific neural functions rather than an integrated approach. We propose an all two-dimensional (2D) material-based heterostructure capable of performing multiple neuromorphic operations by reconfiguring output terminals in response to stimuli. Specifically, our device can synergistically emulate the key neural elements of the synapse, neuron, and dendrite, which play important and interrelated roles in information processing. Dendrites, the branches that receive and transmit presynaptic action potentials, possess the ability to nonlinearly integrate and filter incoming signals. The proposed heterostructure allows reconfiguration between different operation modes, demonstrating its potential for diverse computing tasks. As a proof of concept, we show that the device can perform basic Boolean logic functions. This highlights its applicability to complex neural-network-based information processing problems. Our integrated neuromorphic approach may advance the development of versatile, low-power neuromorphic hardware.

Cybersecurity and the Evolution of the information space: a phenomenological analysis of the relationship with the metaverse and the digital world

The subject of the study is cybersecurity, within the framework of the modern changing conditions of the information space, which is filled with such phenomena as the metaverse and the digital world. These systems create new threats and problems in the processing, transmission, preservation and confidentiality of data. Assuming that the intersection of cybersecurity, digital transformation (including the metaverse and the digital world) and phenomenological analysis is an important area of research, the paper examines aspects of building cybersecurity in the country. The merger itself is seen as a centrifugal current that cannot be stopped, but must be regulated, which suggests that the transformation of approaches to cybersecurity plays a crucial role in developing effective strategies to overcome the problems that arise in a rapidly changing digital landscape. The research methods are based on data collection, analysis and synthesis, which revealed key problems. The key method is a phenomenological analysis of the ongoing changes in the information sphere associated with the spread of the phenomena of the metaverse and the digital world, which made it possible to reflect the problems of information processing and insecurity from malicious attacks and propose a new approach to the formation of a cybersecurity system. The scientific novelty of the article lies in an integrated approach to studying the relationship of cybersecurity, the evolution of the information space and such new concepts, like the metaverse and the digital world. The transition to the latter is the next stage in the development of the information space, which can be traced in the gradual expansion of ecosystems and personal information platforms. All this suggests the need to expand the system of providing personal information, as well as data that gets into unsecured information networks. At the same time, domestic cybersecurity systems are being criticized, which indicates the need to find new solutions. One of these options may be a phenomenological approach to the study of data. The study shows how the use of phenomenological research methods allows us to take a fresh look at the capabilities of domestic developers to strengthen cybersecurity measures and adapt them to rapidly changing digital landscapes.

The bearings and distances isolines combinations of the navigational landmarks pair for the vessel fixed position determination

The issues of applying basic navigation parameters (bearing and distance), and their isolines traditionally used in navigation practice in coastal waters are studied in the paper. A new distinctive feature is the use of isolines combinations (three distance arcs, two isoazimuths, a hyperbola and an ellipse) based on measured bearings and distances to the pair of landmarks to obtain the vessel fixed positions with high precision. The traditional navigation methods (the theory of isolines, the generalized method of lines position, the method of least squares) are involved as a mathematical framework. The mathematical argumentation and the isolines characteristics of bearings and distances, their combinations (difference and sum of distances, difference of bearings) are given. The solutions of the surplus equations of the lines position under random error influence by the least squares method and the accuracy assessment of the vessel fix position by the radial error including the reduced variant to compensate the systematic errors are suggested. The principal points of the research are supported by the graphic interpretation, and the given expressions are adjusted to the practical application and navigation systems software development. The computer simulation that shows the radial error of the fixed position by seven position lines is at least two times less than by any pair of position lines has been carried out. The formalization of the proposed methods in the automatic navigation systems or in autonomous ship control systems will enable the navigator on board or at operating the vessel remotely to solve the problems of the navigation information processing in coastal and congested waters on a new level.

Cross-modal Semantically Augmented Network for Image-text Matching

Image-text matching plays an important role in solving the problem of cross-modal information processing. Since there are nonnegligible semantic differences between heterogenous pairwise data, a crucial challenge is how to learn a unified representation. Existing methods mainly rely on the alignment between regional image features and corresponding entity words. However, the regional features in the image are often more concerned with the foreground entity information, and the attribute information of the entities and the relational information are ignored. How to effectively integrate entity-attribute alignment and relationship alignment has not been fully studied. Therefore, we propose a Cross-Modal Semantically Augmented Network for Image-Text Matching (CMSAN), which combines the relationships between entities in the image with the semantics of relational words in the text. CMSAN (1) proposes an adaptive word-type prediction model that classifies the words into four types, i.e., entity word, attribute word, relation word, and unnecessary word. It can align different image features at multiple levels. CMSAN (2) designs a sophisticated relationship alignment module and an entity-attribute alignment module that maximizes the exploitation of the semantic information, which enables the model to have more discriminative power and further improves the matching accuracy.

Task Scheduling of Real-Time Traffic Information Processing Based on Digital Twins

The Intelligent Transportation System under Digital Twins can provide accurate data sources for traffic control. The present work focuses on the real-time information processing and task scheduling problems of the Internet of Vehicles (IoV) system based on Virtual Reality. They are the Quality/Distance Algorithm (QDA), Task Density Algorithm, Distance Balance Algorithm (DBA), and Bionic-DBA (B-DBA). The simulation experiment analysis suggests that the DBA algorithm takes the balance of travel distance into account and effectively improves task quality. The Utility Function in B-DBA and the Biological Heuristic Search Algorithm in Pareto Ant Colony Optimization play a critically important role in enhancing the overall task quality. In addition, a Transmission based on Privacy Protection (TPP) algorithm is designed to protect the attribute-based privacy information in the traffic information transmission system. This algorithm ensures that the real-time traffic information processing system resists various attacks from malicious nodes. It has been verified that when the number of selfish nodes accounts for 30%, the transmission efficiency of the TPP algorithm reaches 0.77. The research content has a practical reference value for providing users with continuous and high-quality IoV network services.

Алгоритмы дискретной фильтрации на основе модифицированной взвешенной ортогонализации Грама –Шмидта для дискретных стохастических систем с мультипликативными и аддитивными шумами

Discrete-time stochastic systems with multiplicative and additive noises describe a wide class of mathematical models of complex systems, for example, industrial-technological, energy, economic, telecommunication, aerospace systems, etc. An important class of algorithms for processing measure ment information in complex systems are Kalman-type discrete-time filtering algorithms. Purpose. Construction of new discrete-time filtering algorithms for discrete-time linear systems with multiplicative and additive noises based on numerically stable modified weighted Gram – Schmidt orthogonalization (MWGS). Methodology. The methods of computational linear algebra were used, namely, the direct procedu re of MWGS-orthogonalization, the theory of Kalman filtering, methods of scientific programming in MATLAB. Findings. New LD-algorithms for discrete-time filtering in covariance and informational form for discrete-time stochastic systems with multiplicative and additive noises are constructed. The algorithms have an extended array form allowing updates for all necessary filter values. The method employs numerically stable modified weighted Gram – Schmidt orthogonalization. Algebraic equiva lence of LD-filters to Kalman-type covariance and informational algorithms for linear discrete time stochastic systems with multiplicative and additive noises is proved. The conducted numerical experiments have shown the effectiveness of the proposed algorithms using the example of solving the problem of parametric estimation of a model of almost rectilinear motion, as well as their savings in computation time compared to previously constructed UD filters. Value. New discrete-time filtering LD-algorithms can be used as a reliable computational alterna tive to the Kalman-type “standard algorithms” since they have the property being numerically stable to machine round-off errors due to the use of the MWGS orthogonalization computational procedure at each iteration of the algorithm. The results can be used to solve problems of measurement information processing in discrete-time systems with multiplicative and additive noise.

A unified model of data uncertainty and data relation uncertainty

Data uncertainty (DU), one of the most interesting topics in information science, inevitably leads to data relation uncertainty (DRU). Moreover, DU greatly affects the results of information processing problems such as sorting and top-k queries with uncertain data. However, studies have not clarified the mechanism, mode, and degree of impact of DU on DRU. Hence, we established a unified mathematical model to reveal the precise relation between DU and DRU. The experimental results showed this model’s high accuracy under various conditions and its relative error and absolute error of less than 0.2%. This model can be used to quantitatively estimate DRU according to a given DU and vice versa. Unexpectedly, the experiments showed that under given conditions, high DU will not necessarily lead to high DRU. This characteristic is highly significant to the design of data sorting and query algorithms with uncertain data.

Real-time on-orbit image quality improvement for a wide-field imaging system with detector arrays.

Wide-field imaging systems are faced with the problem of massive image information processing and transmission. Due to the limitation of data bandwidth and other factors, it is difficult for the current technology to process and transmit massive images in real-time. With the requirement for fast response, the demand for real-time on-orbit image processing is increasing. In practice, nonuniformity correction is an important preprocessing step to improve the quality of surveillance images. This paper presents a new real-time on-orbit nonuniform background correction method, which only uses the local pixels of a single row output in real-time, breaking the dependence of the traditional algorithm on the whole image information. Combined with the FPGA pipeline design, when the local pixels of a single row are read out, the processing is completed, and no cache is required at all, which saves the resource overhead in hardware design. It achieves microsecond-level ultra-low latency. The experimental results show that under the influence of strong stray light and strong dark current, our real-time algorithm has a better image quality improvement effect compared with the traditional algorithm. It will greatly help the on-orbit real-time moving target recognition and tracking.

Investigating False Memory and Illusory Pattern Perception Bias in Schizophrenia Patients with and without Delusions.

Whether the false memory dysfunction in schizophrenia is at the early (encoding, perceptual) or later (retrieval) stages and how delusions affect it remains unclear. In this study, we examined the perception and memory biases in schizophrenia patients with (Sch/D; n = 31) and without delusions (Sch/Nd; n = 32) and compared them with healthy controls (HCs; n = 35). We used the Deese Roediger McDermott (DRM) Paradigm and the Noise Task to measure the false memory and illusory pattern perception (IPP) biases, respectively. We found that the patient groups performed lower in both the recall and recognition phases for the DRM and the Noise tasks and made more errors compared to the HC group. Additionally, the performance of the Sch/D group was remarkably lower than the Sch/Nd and HC groups. Our results indicated that the information-processing problem in schizophrenia exists in both the encoding and retrieval stages. Also found significant relationship between the presence of delusions and the increase in cognitive deficits. The online version contains supplementary material available at 10.1007/s10608-023-10393-6.

Orthogonal Polynomials in the Problems of Digital Information Processing

Orthogonal Polynomials in the Problems of Digital Information Processing

A Primal-Dual Forward-Backward Splitting Algorithm for Distributed Convex Optimization

Motivated by modern large-scale information processing problems in engineering, this paper concentrates on studying distributed constrained convex optimization problems over a connected undirected network. The problem involves a sum of a differentiable convex function with Lipschitz continuous gradient and two non-smooth convex functions with a linear operator. To solve such a problem, we propose a novel distributed primal-dual forward-backward splitting algorithm, called D-PDFBS. Each agent locally computes the Lipschitz continuous gradient and two proximal operators, and exchanges information with its neighbors. D-PDFBS adopts non-identical stepsizes, and we reveal the relationship between selection of stepsizes and parameters of objective functions. The simulation results verify the feasibility of D-PDFBS and the correctness of theoretical findings.

Restructuring moratoriums through an information-processing lens

ABSTRACT Using insights from complex systems theory, it is argued that financially distressed large corporates will seek the protection of a moratorium when the benefits it brings outweigh its signalling and information-processing problems – likely to be in the later stages of distress. Applying this insight, the article offers a somewhat gloomy assessment of the Part A1 moratorium introduced in the UK by the Corporate Insolvency and Governance Act 2020. It is suggested that the UK administration moratorium may be more fit for purpose, but that serious signalling and information processing concerns remain. After touching on possible adaptations of the tools, the article concedes that there may have been a deliberate decision to restrict the usefulness of both of them. The article ends by arguing that if this the case, the decision may not be sustainable in a rapidly changing economic environment, and that recent suggestions for reform should be supported.

APPLICATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN THE ACTIVITIES OF EDUCATIONAL INSTITUTION

The use of information technologies makes the implementation of management information support more effective. The simplest example can be the computerization of a number of functions of administrative work, which positively affects the activity of the institution as a whole. The selection, processing, creation and transfer of information occurs thanks to the use of information technology tools - interdependent software products for a certain type of computer, the technology of which allows to achieve the goal set by the user. Means of information and communication technologies, which are necessary for the system of higher education institutions: technical means (computers, computer complexes, multimedia projectors, touch boards, etc.); software (system, general purpose, application software); means for connecting to the Internet and ensuring the possibility of full-fledged work in it (servers, communication lines, modems, programs for searching various data on the Internet, etc.); information content (content) on the Internet specially created for the education system; methodological support for the use of information and communication technologies in education. Tasks related to the management of an educational institution that can be automated: management of the educational process (formation of educational plans, schedule, monitoring of educational results); financial planning and accounting; accounting of the material and technical base; HR; automation of library activities; document flow; preparation of operational and external reporting; organization of students' intellectual leisure; development of educational web resources (distance learning, massive online courses). However, computer programs do not solve the problems of routine information processing at an adequate level. In the management of the institution of higher education, the latest technologies are used, in particular, cloud technologies. Information support of a higher education institution significantly affects the quality of the educational process, effective management of the organization's resources, establishing relations with other institutions, etc.

Quantum interpolating ensemble: Bi-orthogonal polynomials and average entropies

The density matrix formalism is a fundamental tool in studying various problems in quantum information processing. In the space of density matrices, the most well-known measures are the Hilbert–Schmidt and Bures–Hall ensembles. In this work, the averages of quantum purity and von Neumann entropy for an ensemble that interpolates between these two major ensembles are explicitly calculated for finite-dimensional systems. The proposed interpolating ensemble is a specialization of the [Formula: see text]-deformed Cauchy–Laguerre two-matrix model and new results for this latter ensemble are given in full generality, including the recurrence relations satisfied by their associated bi-orthogonal polynomials when [Formula: see text] assumes positive integer values.

Research and Application of Data Fusion and Data Mining in Early Warning Detection Intelligence Processing System

As a key technology of modern intelligence systems, Intelligence processing is a famous research area, and plays a basic role in the economy of the country especially in the early warning detection intelligence processing system. The basic theory of data fusion and date mining is comprehensively expounded, and real-time requirements of multi-target track data fusion, as well as offline demodulation timely information on characteristics of data mining, is deeply studied. Firstly, this paper discusses roundly the theory of association rules, which is an important research method of early warning intelligence data mining, proposes a new weighted probabilistic data association algorithm and improves joint probabilistic data association algorithm. Secondly, the knowledge of early warning intelligence data warehouse is detailed, and Law of cosines-text clustering algorithm based on distance is highlighted and extended. Finally, through the integration of real-time data integration and off-line data mining, the practical problems in information processing are effectively solved.

GeoBERTSegmenter: Word Segmentation of Chinese Texts in the Geoscience Domain Using the Improved BERT Model

AbstractUnlike English, there is no natural separator‐like gap between words in Chinese, which makes Chinese word segmentation (CWS) a difficult information processing problem. At present, geological texts contain a large number of unregistered geological terms, and the existing rule‐based methods and machine‐learning and deep learning algorithms still cannot be used to solve the problem of word segmentation in geosciences, especially for the large number of unregistered words. In this study, we propose GeoBERTSegmenter, which is a GeoBERT‐based (Geoscience‐Bidirectional Encoder Representation from Transformers) CWS model that is specifically designed with various linguistic irregularities in mind. In this method, a general model is extended to a BERT bidirectional recurrent neural network (BiLSTM) and conditional random field (GeoBERT + BiLSTM + CRF) model with a number of features designed to address the CWS task in geological text. We also train a pretrained language model named GeoBERT on a geological domain that is based on a large amount of Chinese geological text. In open testing, a precision of 94.77%, recall of 96.31% and F1 of 95.44%, are obtained, indicating that the proposed strategy performs much better than alternative methods in our study. In this study, unregistered geological terms can be effectively identified, and the recognition rate of common words is ensured, which lays the foundation for natural language processing in the domain of geoscience through Chinese text word segmentation.

Hoarding mysteries Jack would appreciate

Hoarding disorder (HD) is a multifaceted problem that presents challenges both for understanding its dimensions and for developing effective treatments. We are grateful to have known Dr. Stanley J. Rachman and his incredibly thoughtful approach to clinical psychology and research on anxiety, obsessive-compulsive disorders (OCD) and their treatment. His work has helped set the stage for our own efforts to study this challenging condition. The discussion below reviews a range of mysteries we and others have encountered in working with people who exhibit HD symptoms. Of particular interest to us are questions about biological vulnerabilities like heritability and the high rate of concurrent health problems and whether hoarded objects might serve as safety signals that protect people from traumatic life events. We are curious about the attachment process in HD and whether attachment to objects is related to early parental experiences that affect self-concept. We raise questions about the several information processing problems often seen in people with HD – attention focusing, memory, and associative responses to objects and information. Raising many questions are observations about strong emotional attachments to objects and multiple reasons given for saving them, as well as what sometimes appears to be remarkable aesthetic appreciation and creative interest in objects. Emotions in HD seem to range more widely than in some psychological disorders as both positive and negative reactions appear to reinforce excessive acquisition and difficulty discarding. Clutter blindness may be an effort to avoid confrontation with overwhelming clutter in the home. Finally, we comment on difficulty achieving more positive outcomes following a carefully designed cognitive and behavioral treatment for HD and encourage the next generation of researchers to follow in Jack Rachman's footsteps as they try to unravel these mysteries.

Research on Performance of Public Welfare Crowdfunding Based on Neural Network Intelligent Model

From the appearance of entrepreneurial narratives, it communicates the movement of innovatory suggestion and entrepreneurial madness on revenue deed in technology crowdfunding, outsearch the immediate percussion of innovatory hint on financier act, and the moderating sign of entrepreneurial emotion on the relationship between novelty and resources exploit. Through the mode of text-book analysis, the genuine technology design in JD crowdfunding was interested in bearing empiric trial of the mold. Neural networks have large-scale parallel, distributed storage and processing, self-organization, self-adaptation, and self-learning capabilities and are especially suitable for dealing with inaccurate and ambiguous information processing problems that need to consider many factors and conditions at the same time. The empiric arise shows that forward-looking neologism queue indubitably moves financier completion, and primitive neology suggestion negatively pretends resources accomplishment; pierce People’s entrepreneurial emotion intensify the dogmatic outcome of incremental neology humor on financier work, but has no momentous regulatory performance on the relationship between fundamental novation cue up and revenue exploit. On this base, a nerve plexure pattern is further fabricated for equal analysis. The characteristic efficiency of retrogradation example and nerval meshwork fashion is amended. In addition, based on the sample data of the Dahuotou fair crowdfunding platform, the indicators of the extended resource evaluation are judged based on the video revenue duration of the design scheme, scheme dynamics, extended scoring, non-hidden technology, and employee inclusiveness. There is an interaction manifestation between video and operant allowance data; the penetration sign of the two logical recession molds is emended than that of the multilayer McCulloch–Pitts neuron mold and the divergent base duty design.

Methods for Robust Filtering Based on Numerical Characteristics of Input Processes in Solving Problems of Navigation Information Processing and Motion Control

Methods for Robust Filtering Based on Numerical Characteristics of Input Processes in Solving Problems of Navigation Information Processing and Motion Control