Computer Processing Research Articles

On the relation between Bekenstein–Hawking entropy and Landauer’s principle

Information is intricately woven into the fabric of physical systems, implying a profound connection between the nature of information and the underlying physics governing computational processes. This deep-seated relationship was illuminated by Landauer in 1961, who revealed that erasing a mere bit of information demands an expenditure of energy equivalent to [Formula: see text], underscoring the thermodynamic costs associated with information processing. In this investigation, Landauer’s principle is applied within the framework of Bekenstein–Hawking entropy. The study demonstrates that the introduction of a single bit of information into a black hole is related to a corresponding tangible increase of one unit of the horizon, namely, [Formula: see text], aligning seamlessly with the holographic principle.

Social environment-based opportunity costs dictate when people leave social interactions

There is an ever-increasing understanding of the cognitive mechanisms underlying how we process others’ behaviours during social interactions. However, little is known about how people decide when to leave an interaction. Are these decisions shaped by alternatives in the environment – the opportunity-costs of connecting to other people? Here, participants chose when to leave partners who treated them with varying degrees of fairness, and connect to others, in social environments with different opportunity-costs. Across four studies we find people leave partners more quickly when opportunity-costs are high, both the average fairness of people in the environment and the effort required to connect to another partner. People’s leaving times were accounted for by a fairness-adapted evidence accumulation model, and modulated by depression and loneliness scores. These findings demonstrate the computational processes underlying decisions to leave, and highlight atypical social time allocations as a marker of poor mental health.

Does the reliability of computational models truly improve with hierarchical modeling? Some recommendations and considerations for the assessment of model parameter reliability : Reliability of computational model parameters.

Computational modeling of behavior is increasingly being adopted as a standard methodology in psychology, cognitive neuroscience, and computational psychiatry. This approach involves estimating parameters in a computational (or cognitive) model that represents the computational processes of the underlying behavior. In this approach, the reliability of the parameter estimates is an important issue. The use of hierarchical (Bayesian) approaches, which place a prior on each model parameter of the individual participants, is thought to improve the reliability of the parameters. However, the characteristics of reliability in parameter estimates, especially when individual-level priors are assumed, as in hierarchical models, have not yet been fully discussed. Furthermore, the suitability of different reliability measures for assessing parameter reliability is not thoroughly understood. In this study, we conduct a systematic examination of these issues through theoretical analysis and numerical simulations, focusing specifically on reinforcement learning models. We note that the heterogeneity in the estimation precision of individual parameters, particularly with priors, can skew reliability measures toward individuals with higher precision. We further note that there are two factors that reduce reliability, namely estimation error and intersession variation in the true parameters, and we discuss how to evaluate these factors separately. Based on the considerations of this study, we present several recommendations and cautions for assessing the reliability of the model parameters.

Moments based entanglement criteria and measures

Quantum entanglement plays a key role in quantum computation and quantum information processing. It is of great significance to find efficient and experimentally friend separability criteria to detect entanglement. In this paper, we firstly propose two easily used entanglement criteria based on matrix moments. The first entanglement criterion only uses the first two realignment moments of a density matrix. The second entanglement criterion is based on the moments related to the partially transposed matrix. By detailed examples we illustrate the effectiveness of these criteria in detecting entanglement. Moreover, we provide an experimentally measurable lower bound of concurrence based on these moments. Finally, we present both bipartite and genuine tripartite entanglement measures based on the moments of the reduced states. By detailed examples, we show that our entanglement measures characterize the quantum entanglement in a more fine ways than the existing measures.

Knowledge-integrated autoencoder model

Data encoding is a common and central operation in most data analysis tasks. The performance of other models downstream in the computational process highly depends on the quality of data encoding. One of the most powerful ways to encode data is using the neural network AutoEncoder (AE) architecture. However, the developers of AE cannot easily influence the produced embedding space, as it is usually treated as a black box technique. This means the embedding space is uncontrollable and does not necessarily possess the properties desired for downstream tasks. This paper introduces a novel approach for developing AE models that can integrate external knowledge sources into the learning process, possibly leading to more accurate results. The proposed Knowledge-integrated AutoEncoder (KiAE) model can leverage domain-specific information to make sure the desired distance and neighborhood properties between samples are preservative in the embedding space. The proposed model is evaluated on three large-scale datasets from three scientific fields and is compared to nine existing encoding models. The results demonstrate that the KiAE model effectively captures the underlying structures and relationships between the input data and external knowledge, meaning it generates a more useful representation. This leads to outperforming the rest of the models in terms of reconstruction accuracy.

Advancements in Deep Learning for Minimally Invasive Surgery: A Journey through Surgical System Evolution

The surge in artificial intelligence (AI) applications across diverse fields owes much to advancements in deep learning and computational processing speed. In medicine, AI's reach extends to medical image analysis and genomic data interpretation. More recently, AI's role in analyzing minimally invasive surgery (MIS) videos has gained traction, with a growing body of research focusing on organ and anatomy identification, instrument recognition, procedure recognition, surgical phase delineation, surgery duration prediction, optimal incision line identification, and surgical education. Concurrently, the development of autonomous surgical robots, exemplified by the Smart Tissue Autonomous Robot (STAR) and RAVEN systems, has shown promising strides. Notably, STAR is currently employed in laparoscopic imaging to discern the surgical site from laparoscopic images and is undergoing trials for an automated suturing system, albeit in animal models. This review contemplates the prospect of fully autonomous surgical robots in the future.

Broadband four-shoulder sine antenna of radio monitoring and direction finding equipment with a note system of two orthogonally combined printed resistance transformers

Problem statement. The article presents material on the development of antennas used in radio monitoring and radio direction finding equipment of electronic warfare equipment. The basic requirements for the design and characteristics of antennas of radio monitoring and direction finding equipment are presented. Aspects of the fundamentals of the development of a four-arm broadband antenna powered by two orthogonally combined printed resistance transformers, which has not been used before, for the possibility of controlling polarization, are considered, the results of modeling the developed antenna are presented and recommendations for its use in the antenna array are given. Purpose. To develop a small-sized antenna that allows to increase the efficiency of the operation of radio direction finding and radio monitoring complexes in an ultra-wide frequency band, with a special power supply system of two orthogonally combined printed resistance transformers for the possibility of implementing polarization control. Results. Based on the methods of electrodynamic modeling, computational methods of technical electrodynamics, methods of full–scale experimental measurements of antenna characteristics and methods of computer processing of data of full-scale experimental measurements, an antenna has been developed that allows the control of amplitude, phase and polarization parameters in an ultra-wide frequency band. The developed antenna is based on a flat four-arm sine element with a power supply system of two orthogonally arranged printed resistance transformers. The resistance transformer significantly reduces the level of the reflection coefficient at the input and radiation losses. The use of this development in radio direction finding and radio monitoring complexes will reduce the number of sublattices of the antenna system due to the functioning of its elements in an ultra-wide frequency band. The practical significance lies in reducing the number of letters of radio direction finding antenna systems consisting of the developed antenna elements, minimizing the degree of distortion of ultra-wideband signals and ultrashort pulses of arbitrary (varying) polarization received by antenna devices and systems from the antenna presented in the article. The developed antenna and antenna devices based on it can be used for electronic warfare, as well as improving communication and remote control systems.

Advancements in Deep Learning for Minimally Invasive Surgery: A Journey through Surgical System Evolution

The surge in artificial intelligence (AI) applications across diverse fields owes much to advancements in deep learning and computational processing speed. In medicine, AI's reach extends to medical image analysis and genomic data interpretation. More recently, AI's role in analyzing minimally invasive surgery (MIS) videos has gained traction, with a growing body of research focusing on organ and anatomy identification, instrument recognition, procedure recognition, surgical phase delineation, surgery duration prediction, optimal incision line identification, and surgical education. Concurrently, the development of autonomous surgical robots, exemplified by the Smart Tissue Autonomous Robot (STAR) and RAVEN systems, has shown promising strides. Notably, STAR is currently employed in laparoscopic imaging to discern the surgical site from laparoscopic images and is undergoing trials for an automated suturing system, albeit in animal models. This review contemplates the prospect of fully autonomous surgical robots in the future.

Bearing capacity of shallow foundations: a focus on the depth factors in combination with the respective N-factors

The ongoing refinement of bearing capacity equations remains pivotal in soil mechanics and foundation engineering, reflecting its critical role in ensuring design efficacy and construction safety. This study conducts a thorough evaluation of classical bearing capacity methods—Terzaghi, Meyerhof, Vesic, and Hansen—and methods included in various design standards, such as EN1997:2004, prEN1997:2023, GEO, AASHTO, FHWA, and API. It explores the performance and applicability of these approaches, identifying areas for potential improvement. In response to identified challenges, the paper proposes the integration of a unified depth factor. This new factor is designed to be applicable across all N-terms, providing a more versatile and accurate tool for bearing capacity predictions. Unlike the original depth factors unique to each method, which may not fully address complex soil and footing conditions, the unified depth factor is developed to enhance prediction accuracy for a wide range of conditions, including both flexible and rigid footings under varying flow rules (ψ = 0 and ψ = φ). This depth factor corrects for modeling errors, emphasizing the importance of pairing the correct set of N-factors with their corresponding depth factor. By offering a singular depth factor that aligns with the outcomes of finite element analysis, this paper not only simplifies the computational process but also enhances the accuracy of bearing capacity predictions across a diverse range of soil conditions and footing types. The comparative analysis, based on finite element analysis, validates the proposed method’s effectiveness, showcasing its potential to significantly refine foundation design practices by comparing it with both traditional and newly developed depth factors.

Artificial Intelligence in Urologic Robotic Oncologic Surgery: A Narrative Review.

With the rapid increase in computer processing capacity over the past two decades, machine learning techniques have been applied in many sectors of daily life. Machine learning in therapeutic settings is also gaining popularity. We analysed current studies on machine learning in robotic urologic surgery. We searched PubMed/Medline and Google Scholar up to December 2023. Search terms included "urologic surgery", "artificial intelligence", "machine learning", "neural network", "automation", and "robotic surgery". Automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction has been a major focus. Early artificial intelligence (AI) therapeutic outcomes are promising. Robot-assisted surgery provides precise telemetry data and a cutting-edge viewing console to analyse and improve AI integration in surgery. Machine learning enhances surgical skill feedback, procedure effectiveness, surgical guidance, and postoperative prediction. Tension-sensors on robotic arms and augmented reality can improve surgery. This provides real-time organ motion monitoring, improving precision and accuracy. As datasets develop and electronic health records are used more and more, these technologies will become more effective and useful. AI in robotic surgery is intended to improve surgical training and experience. Both seek precision to improve surgical care. AI in ''master-slave'' robotic surgery offers the detailed, step-by-step examination of autonomous robotic treatments.

A multi-objective model for selecting response strategies of primary and secondary project risks under interval-valued fuzzy uncertainty

Risks are uncertain events that can affect criteria such as project cost, quality, and completion time and ultimately lead to project failure. That is why the project risk management process, which is one of the most fundamental parts of project management, must be done properly. In this paper, a new multi-objective fuzzy model is proposed to respond appropriately to the primary and secondary project risks. The contributions, such as risk interdependency, considering the project with multi-mode activities, resource considerations, as well as attention to interval-valued fuzzy uncertainties are regarded simultaneously for the first time. The purpose of the proposed multi-objective mathematical model is to minimize cost, quality reduction, and project completion time. Then, a new two-stage uncertain solution approach is introduced in this paper. In this solution approach, using an equivalent method in the first step and then using an extended multi-choice goal programming method in the second step, for both lower and upper limits of the membership functions for interval-valued fuzzy numbers, the computational processes are performed. To show the application and effectiveness of the proposed model, a case study adapted from the literature is given. Finally, sensitivity analysis is conducted to analyze the behavior of the developed mixed-integer programming model. The analysis of obtained results indicates that the utilization of the proposed model leads to better and more appropriate solutions.

Embodied AI for dexterity-capable construction Robots: DEXBOT framework

The construction industry is integrating robots into critical tasks at an accelerated pace, with the aim of enhancing efficiency, safety, and productivity. However, construction tasks requiring dexterity remain a challenge due to the need for precise movements, accurate perception, real-time decision-making, and a comprehensive understanding of the environment. To address these challenges, the introduction of embodied artificial intelligence (AI) represents a significant shift in robotic capabilities to enhance their alignment with the broader spectrum of construction settings. Rooted in cognitive science, embodied AI emphasizes the integration of an agent’s physical form into its computational intelligence processes. It resembles how humans develop motor skills by interacting with physical world. This paper introduces DEXBOT, an exploratory framework for designing construction robots capable of high dexterity using embodied AI principles that mimics human strategies in complex tasks. The framework outlines six key perspectives for solving high-dexterity tasks with embodied AI: scene understanding, localization and motion planning, position-based control, force-based control, sequence planning, and correction decision-making. By presenting preliminary test cases for each perspective, the paper emphasizes the role of embodied AI in advancing dexterity level of construction robots. The DEXBOT framework is expected to encourage interdisciplinary collaboration of designing capable construction robots in the future.

A calculation method for optical properties of yolk shell based on deep learning.

The yolk shell is widely used in optoelectronic devices due to its excellent optical properties. Compared to single metal nanostructures, yolk shells have more controllable degrees of freedom, which may make experiments and simulations more complex. Using neural networks can efficiently simplify the computational process of yolk shell. In our work, the relationship between the size and the absorption efficiency of the yolk-shell structure is established using a backpropagation neural network (BPNN), significantly simplifying the calculation process while ensuring accuracy equivalent to discrete dipole scattering (DDSCAT). The absorption efficiency of the yolk shell was comprehensively described through the forward and reverse prediction processes. In forward prediction, the absorption spectrum of yolk shell is obtained through its size parameter. In reverse prediction, the size parameters of yolk shells are predicted through absorption spectra. A comparison with the traditional DDSCAT demonstrated the high precision prediction capability and fast computation of this method, with minimal memory consumption.

Structural and positional ensembled encoding for Graph Transformer

In the Transformer architecture, positional encoding is a vital component because it provides the model with information about the structure and position of data. In Graph Transformer, there have been attempts to introduce different positional encodings and inject additional structural information. Therefore, in terms of integrating positional and structural information, we propose a Structural and Positional Ensembled Graph Transformer (SPEGT). We developed SPEGT by noting the different properties of structural and positional encodings of graphs and the similarity of their computational processes. We have set a unified component that integrates the functionalities: (i) Random Walk Positional Encoding, (ii) Shortest Path Distance between each node, and (iii) Hierarchical Cluster Encoding. We find a problem with a well-known positional encoding and experimentally verify that combining it with other encodings can solve their problem. In addition, SPEGT outperforms previous models on a variety of graph datasets. We also show that SPEGT using unified positional encoding, performs well on structurally indistinguishable graph data through error case analysis.

Fractal dimension of the morphology on differently oriented surfaces of coal preparations

Choosing the surface of a lump section was justified to determine the degree of metamorphism by the index of gray shade intensity. The main method is optical spectroscopy using a video-optical complex MBI-11, NB 200 with subsequent digital computer processing and calculation of the fractal dimension (Scope photo software) of microphoto images of coal matter preparations by lump sections. The values for the index of gray shade intensity of photographic images of surfaces (section, bedding, cut) for lump sections based on samples of coals of various grades were analyzed. It has been established that the average values of the degree of metamorphism indices for different surfaces of the preparation differ, which is confirmed by low correlation coefficients: 0.585 (section-bedding), 0.666 (section-cut), 0.462 (cut-bedding). It has been found that the fractal dimension, which characterizes the complexity of the morphology of the surfaces under study, reflects an increase in the amount and orderliness of hydrocarbons associated with a change in the degree of metamorphism. It was proven that its most natural decrease related to an increase in the degree of metamorphism, is observed on surfaces perpendicular to the bedding (section).

Advancements in laser-based spatiotemporal measurements of flow boiling

Recent advancements in laser and imaging systems, as well as in computational processing capabilities, have made quantitative optical imaging, which is often combined with laser illumination, highly adaptable, robust and reliable. Laser-based diagnostic techniques, such as planar laser-induced fluorescence (PLIF), offer the possibility of simultaneous spatiotemporally resolved measurements of temperature fields in the liquid phase at boiling conditions. In this paper, we examine the applicability of two-colour PLIF (2cPLIF), where the ratio between individual fluorescent emissions from uniformly dispersed dyes is used to take temperature measurements in the liquid phase in the presence of moving vapour-liquid interfaces typical of boiling flow. The implementation of 2cPLIF necessitates uniformity in the concentration of different dyes across the flow field. However, in the case of a multiphase flow such as boiling, thermophoresis can lead to inhomogeneous dye distributions. To overcome this challenge, a single-dye multispectral planar laser-induced fluorescence (SDMS-PLIF) method has been developed, which employs fluorescent emissions in different spectral bands of the same dye (Nile Red). The spectral characteristics of Nile Red were measured using a spectrometer to identify its temperature-sensitive bands over a wide range of dye concentrations, from 0.3 to 30 mg/L. Following this, we demonstrate the measurement capabilities of SDMS-PLIF thermography as applied to a boiling flow in a miniaturised vertical square channel, gaining insight into the thermohydrodynamic interactions between vapour bubbles and a heated wall.

DAM SRAM CORE: An Efficient High-Speed and Low-Power CIM SRAM CORE Design for Feature Extraction Convolutional Layers in Binary Neural Networks.

This article proposes a novel design for an in-memory computing SRAM, the DAM SRAM CORE, which integrates storage and computational functionality within a unified 11T SRAM cell and enables the performance of large-scale parallel Multiply-Accumulate (MAC) operations within the SRAM array. This design not only improves the area efficiency of the individual cells but also realizes a compact layout. A key highlight of this design is its employment of a dynamic aXNOR-based computation mode, which significantly reduces the consumption of both dynamic and static power during the computational process within the array. Additionally, the design innovatively incorporates a self-stabilizing voltage gradient quantization circuit, which enhances the computational accuracy of the overall system. The 64 × 64 bit DAM SRAM CORE in-memory computing core was fabricated using the 55 nm CMOS logic process and validated via simulations. The experimental results show that this core can deliver 5-bit output results with 1-bit input feature data and 1-bit weight data, while maintaining a static power consumption of 0.48 mW/mm2 and a computational power consumption of 11.367 mW/mm2. This showcases its excellent low-power characteristics. Furthermore, the core achieves a data throughput of 109.75 GOPS and exhibits an impressive energy efficiency of 21.95 TOPS/W, which robustly validate the effectiveness and advanced nature of the proposed in-memory computing core design.

Scalable Cloud Architectures for Deploying AI Applications

The integration of Artificial Intelligence (AI) into cloud computing represents a pivotal shift in the technological landscape, offering unprecedented opportunities for innovation across industries. Deploying AI applications at scale on the cloud poses unique challenges, necessitating the development of scalable cloud architectures tailored to meet the computational demands and data processing needs inherent to AI. This article explores the foundational aspects of cloud computing architectures, emphasizing the principles of scalability essential for AI applications. It delves into the core challenges faced when deploying AI on the cloud, such as computational requirements, data management, network constraints, and security concerns. Further, it presents various architectural models that facilitate scalability, including containerization, serverless computing, and cloud-native AI services, drawing from realworld case studies to illustrate effective strategies and best practices. Additionally, the article examines performance optimization techniques, security considerations, and the future directions of cloud-based AI deployments, highlighting the role of emerging technologies such as quantum computing and edge AI. By providing a comprehensive overview of scalable cloud architectures for AI applications, this article aims to guide researchers, practitioners, and organizations in leveraging cloud computing to its full potential, thereby enabling more efficient, secure, and scalable AI solutions.

Digital Subtraction Angiogram: Beginners Perspective

Digital Subtraction Angiography (DSA) is a modern technique which integrates digital data collection and computer processing to produce a medical image. X-ray signals are detected electronically rather than on film and then converted to digital form to be processed by a computer before being displayed. DSA is an integral investigation in the management of patients with neurovascular diseases. It is basically used for diagnosis, but in many instances, it may be therapeutic in the same sitting. Indications for diagnostic DSA include both extracranial and intracranial vascular diseases. A sound understanding of the principles of appropriate periprocedural care and anatomy, catheter technique and basic disease pathology are vital to use DSA as a diagnostic as well as therapeutic purposes. Bang. J Neurosurgery 2023; 12(2): 102-113

Scale-Aware Edge-Preserving Full Waveform Inversion with Diffusion Filter for Crosshole Sensor Arrays.

Full waveform inversion (FWI) is recognized as a leading data-fitting methodology, leveraging the detailed information contained in physical waveform data to construct accurate, high-resolution velocity models essential for crosshole surveys. Despite its effectiveness, FWI is often challenged by its sensitivity to data quality and inherent nonlinearity, which can lead to instability and the inadvertent incorporation of noise and extraneous data into inversion models. To address these challenges, we introduce the scale-aware edge-preserving FWI (SAEP-FWI) technique, which integrates a cutting-edge nonlinear anisotropic hybrid diffusion (NAHD) filter within the gradient computation process. This innovative filter effectively reduces noise while simultaneously enhancing critical small-scale structures and edges, significantly improving the fidelity and convergence of the FWI inversion results. The application of SAEP-FWI across a variety of experimental and authentic crosshole datasets clearly demonstrates its effectiveness in suppressing noise and preserving key scale-aware and edge-delineating features, ultimately leading to clear inversion outcomes. Comparative analyses with other FWI methods highlight the performance of our technique, showcasing its ability to produce images of notably higher quality. This improvement offers a robust solution that enhances the accuracy of subsurface imaging.