Computational Tasks Research Articles

Bayesian optimization acquisition functions for accelerated search of cluster expansion convex hull of multi-component alloys

Atomistic simulations are crucial for predicting material properties and understanding phase stability, essential for materials selection and development. However, the high computational cost of density functional theory calculations challenges the design of materials with complex structures and composition. This study introduces new data acquisition strategies using Bayesian-Gaussian optimization that efficiently integrate the geometry of the convex hull to optimize the yield of batch experiments. We developed uncertainty-based acquisition functions to prioritize the computation tasks of configurations of multi-component alloys, enhancing our ability to identify the ground-state line. Our methods were validated across diverse materials systems including Co-Ni alloys, Zr-O compounds, Ni-Al-Cr ternary alloys, and a planar defect system in intermetallic (Ni1−x, Cox)3Al. Compared to traditional genetic algorithms, our strategies reduce training parameters and user interaction, cutting the number of experiments needed to accurately determine the ground-state line by over 30%. These approaches can be expanded to multi-component systems and integrated with cost functions to further optimize experimental designs.

The relationship between students' computational estimation skills and mathematical self-efficiency

The factors related to estimating skills are twofold: cognitive and affective perspectives. Perception of self-efficacy in mathematics, one of the affective factors, is essential for the individual's capacity, abilities, and self-belief in mathematics for the mathematics course. The present study examined the relationship between students' computational estimation skills and mathematical self-efficacy perceptions. For this purpose, two sets of instruments were used as data collection tools: a computational estimation skills task and a mathematical self-efficacy perceptions scale. A correlational design was used in this study, and a total of 83 eighth-grade students participated in the research. As a result, it was concluded that there was a moderately positive and statistically significant relationship between computational estimation skills and mathematical self-efficacy perceptions. Students experience more stress when making estimations compared to mathematical computations whose results are specific. In addition, students mostly need to correct their estimations in questions related to computations with fractions, and they mostly use the rounding strategy when making estimations. These results are further discussed in relation to the perspective of supporting children’s abilities for estimations at the primary school.

Investigating the use of ChatGPT to solve a GeoGebra based mathematics+computational thinking task in a geometry topic

ChatGPT is a chatbot with potential educational benefits, particularly in enhancing computational thinking (CT) proficiencies such as programming, debugging, and algorithmic thinking for students. Despite its promise, there is limited research on how ChatGPT can specifically support the integration of CT into mathematics education using tools like GeoGebra. The researchers implemented plugged-computational thinking in mathematics (Math+CT) lessons by means of the utilization of GeoGebra, an application that requires students to input commands in order to generate mathematical objects. The present investigation employed an educational design research (EDR) methodology in which the researchers incorporate ChatGPT into our Math+CT lessons to assist students in accomplishing the task. We purposely selected the participants who are mainly postgraduate students and collected data from the participants’ conversation with ChatGPT and recorded their screens while interacting with ChatGPT and our Math+CT task. We analyzed the data through descriptive qualitative method on the participants’ prompts, the final codes and the number of iterations. The researchers examined how ChatGPT could be utilized to assist the participants in writing GeoGebra commands in terms of its benefits and limitations. ChatGPT assisted most participants in completing the task successfully, with only a basic need for proficiency in GeoGebra commands, mathematics, and critical thinking. However, it revealed that participants did not yet utilize an affective prompt to ChatGPT. Furthermore, ChatGPT has the potential to be utilized for differentiated instruction due to the fact that its responses to individual users vary significantly based on the input prompts. Limited understanding of basic GeoGebra commands, and mathematical concepts could hinder the participants from training ChatGPT or prevent them from arguing with ChatGPT. This study enhances the existing literature by illustrating that ChatGPT can facilitate critical CT aspects, including programming and debugging, in a mathematics education context. This suggests that AI tools such as ChatGPT can contribute to the development of independent problem-solving skills, provide tailored support based on the needs of individual students, and enhance personalized learning experiences. Additional research involving students in school is required in order to gain a deeper understanding of the integration of ChatGPT into Math+CT lessons.

Ultrafast Hybrid Computing Systems Enabled by Memristor‐Based Quadratic Programming Circuits

AbstractImplementing algorithms purely on digital computing platforms dramatically halts the performance of conventional computing systems. Revolutionary computing systems with extreme energy efficiency and high accuracy are demanded to handle the growing computing tasks. Here, the research on hybrid analog–digital computing platforms enabled by memristor‐based optimization solvers for achieving ultrafast computations is presented. By utilizing tunable memristors as parameters to solve linear programming (LP) and quadratic programming (QP) problems, a real‐time control algorithm for micro air vehicles (MAVs) and a support vector machine (SVM) algorithm for cancer diagnosis are implemented. These experiments demonstrate over 2000x speed‐up compared to conventional digital platforms, with negligible energy consumption, using a memristor‐based system consisting of six memristors. These findings underscore the vast potential of memristor‐based optimization solvers not only in hybrid analog–digital computing platforms but also as a transformative solution for a wide range of modern computing challenges. This approach promises significant advancements in energy efficiency and ultrafast speed, positioning it as a leading contender for next‐generation computing paradigms.

EfficientNet Convolutional Neural Network with Gram Matrices Modules for Predicting Sadness Emotion

Images are becoming an attractive area of emotional analysis. Recognising emotions in the images of general nature is gaining more and more research attention. Such emotion recognition is more sophisticated and different from conventional computer tasks. Due to human subjectivity, ambiguous judgments, cultural and personal differences, there is no an unambiguous model for such emotion assessment. In this paper, we have chosen sadness as the main emotion, which has significant impact to the richness of human experience and the depth of personal meaning. The main hypothesis of our research is that by extending the capabilities of convolutional neural networks to integrate both deep and shallow layer feature maps, it is possible to improve the detection of sadness emotion in images. We have suggested integration of the different convolutional layers by taking the learned features from the selected layers and applying a pairwise operation to compute the Gram matrices of feature sub-maps. Our findings show that this approach improves the network’s ability to recognize sadness in the context of binary classification, resulting in a higher emotion recognition accuracy. We experimentally evaluated the proposed network for the stated binary classification problem under different parameters and datasets. The results demonstrate that the improved network achieves improved accuracy as compared to the baseline (EfficientNetV2) and the previous state-of-the-art model.

Joint optimization of UAV aided covert edge computing via a deep reinforcement learning framework

In this work, we consider an Unmanned Aerial Vehicle (UAV) aided covert edge computing architecture, where multiple sensors are scattered with a certain distance on the ground. The sensor can implement several computation tasks. In an emergency scenario, the computational capabilities of sensors are often limited, as seen in vehicular networks or Internet of Things (IoT) networks. The UAV can be utilized to undertake parts of the computation tasks, i.e., edge computing. While various studies have advanced the performance of UAV-based edge computing systems, the security of wireless transmission in future 6G networks is becoming increasingly crucial due to its inherent broadcast nature, yet it has not received adequate attention. In this paper, we improve the covert performance in a UAV aided edge computing system. Parts of the computation tasks of multiple ground sensors are offloaded to the UAV, where the sensors offload the computing tasks to the UAV, and Willie around detects the transmissions. The transmit power of sensors, the offloading proportions of sensors and the hovering height of the UAV affect the system covert performance, we propose a deep reinforcement learning framework to jointly optimize them. The proposed algorithm minimizes the system average task processing delay while guaranteeing that the transmissions of sensors are not detected by the Willie under the covertness constraint. Extensive simulations are conducted to verify the effectiveness of the proposed algorithm to decrease the average task processing delay with comparison with other algorithms.

Reinforcement learning-enabled swarm intelligence method for computation task offloading in Internet-of-Things blockchain

Blockchain technology, based on decentralized data storage and distributed consensus design, has became a promising solution to address data security risks and provide privacy protection in the Internet-of-Things (IoT) due to its tamper-proof and non-repudiation features. Although blockchain typically does not require the endorsement of third-party trust organizations, it mostly needs to perform necessary mathematical calculations to prevent malicious attacks, which results in stricter requirements for computation resources on the participating devices. By offloading the computation tasks required to support blockchain consensus to edge service nodes or the cloud, while providing data privacy protection for IoT applications, it can effectively address the limitations of computation and energy resources in IoT devices. However, how to make reasonable offloading decisions for IoT devices remains an open issue. Due to the excellent self-learning ability of Reinforcement Learning (RL), this paper proposes a RL enabled Swarm Intelligence Optimization Algorithm (RLSIOA) that aims to improve the quality of initial solutions and achieve efficient optimization of computation task offloading decisions. The algorithm considers various factors that may affect the revenue obtained by IoT devices executing consensus algorithms (e.g., Proof-of-Work), It optimizes the proportion of sub-tasks to be offloaded and the scale of computing resources to be rented from the edge and cloud to maximize the revenue of devices. Experimental results show that RLSIOA can obtain higher-quality offloading decision-making schemes at lower latency costs compared to representative benchmark algorithms.

Pupillary Responses to Dynamic Negative Versus Positive Facial Expressions of Emotion in Children and Parents: Links to Depression and Anxiety.

Witnessing emotional expressions in others triggers physiological arousal in humans. The current study focused on pupil responses to emotional expressions in a community sample as a physiologicalindex of arousal and attention. We explored the associations between parents' and offspring's responses to dynamic facial expressions of emotion, as well as the links between pupil responses and anxiety/depression. Children (N=90, MAge=10.13, range=7.21-12.94, 47 girls) participated in this lab study with one of their parents (47 mothers). Pupil responses were assessed in a computer task with dynamic happy, angry, fearful, and sad expressions, while participants verbally labeled the emotion displayed on the screen as quickly as possible. Parents and children reported anxiety and depression symptoms in questionnaires. Both parents and children showed stronger pupillary responses to negative versus positive expressions, and children's responses were overall stronger than those of parents. We also found links between the pupil responses of parents and children to negative, especially to angry faces. Child pupil responses were related to their own and their parents' anxiety levels and to their parents' (but not their own) depression. We conclude that child pupils are sensitive to individual differences in parents' pupils and emotional dispositions in community samples.

Application of Big Data and Quantum Computing in the Secure Federated Internet of Things

Federated Internet of Things (IoT) presents both unprecedented opportunities and challenges in security and data management. This study explores the integration of big data analytics and Quantum Computing as potential solutions to address security concerns within the Federated IoT ecosystem. The study examines the implications of leveraging big data analytics to process and analyze the massive volume of data generated by IoT devices. Advanced analytics techniques, including machine learning and anomaly detection algorithms, are employed to enhance the detection and mitigation of security threats such as unauthorized access, data breaches and malicious attacks. Furthermore, the study investigates the role of Quantum Computing infrastructure in providing scalable and reliable resources for securely storing, processing and transmitting IoT data. By offloading computational tasks to quantum-based platforms, the aim is to alleviate the burden on edge devices while ensuring robust security measures are in place to safeguard sensitive information. A comprehensive review of existing literature and case studies identifies key challenges and opportunities in implementing big data and Quantum Computing solutions within the Federated IoT environment. The study also proposes potential frameworks and methodologies for integrating these technologies effectively, considering factors such as data privacy, scalability and interoperability. Overall, this research aims to advance secure IoT systems by leveraging big data analytics and cloud computing. By addressing security concerns proactively and adopting innovative approaches, the goal is to create a more resilient and trustworthy Federated IoT ecosystem, benefiting society at large.

An Implementation of Communication, Computing and Control Tasks for Neuromorphic Robotics on Conventional Low-Power CPU Hardware

An Implementation of Communication, Computing and Control Tasks for Neuromorphic Robotics on Conventional Low-Power CPU Hardware

Integrating Cloud Services with Mobile Applications for Seamless User Experience

In the modern digital era, the integration of cloud services with mobile applications has emerged as a pivotal strategy for delivering a seamless and dynamic user experience. This approach not only enhances the functionality of mobile applications but also ensures that users can access services and data in real-time, regardless of their location. As mobile devices continue to dominate the technology landscape, the demand for responsive, scalable, and user-centric applications has skyrocketed. Cloud services, with their inherent capabilities such as storage, computing power, and advanced analytics, provide the backbone for developing applications that meet these demands. The integration of cloud services with mobile applications offers numerous advantages. It allows for the offloading of heavy computational tasks to cloud servers, thereby preserving the limited processing power and battery life of mobile devices. This capability enables mobile applications to perform complex tasks such as real-time data processing, machine learning inference, and multimedia content rendering without compromising user experience. Additionally, cloud services facilitate the synchronization of data across multiple devices, ensuring that users have consistent access to their data and settings regardless of the device they are using. This is particularly important in scenarios where users switch between devices, such as moving from a smartphone to a tablet or laptop. Moreover, the use of cloud services in mobile applications enhances security and reliability. Cloud providers typically offer robust security features, including data encryption, identity and access management, and continuous monitoring, which are essential for protecting sensitive user data. By leveraging these features, mobile applications can offer a higher level of security than would be feasible with on-device storage and processing. Furthermore, the reliability of cloud services ensures that applications remain available and functional even during peak usage times or when the user is in a location with poor network connectivity. Through techniques such as caching and offline access, cloud-integrated mobile applications can continue to operate smoothly even when real-time access to cloud resources is temporarily unavailable.

A Cascaded Multi-Agent Reinforcement Learning-Based Resource Allocation for Cellular-V2X Vehicular Platooning Networks.

The platooning of cars and trucks is a pertinent approach for autonomous driving due to the effective utilization of roadways. The decreased gas consumption levels are an added merit owing to sustainability. Conventional platooning depended on Dedicated Short-Range Communication (DSRC)-based vehicle-to-vehicle communications. The computations were executed by the platoon members with their constrained capabilities. The advent of 5G has favored Intelligent Transportation Systems (ITS) to adopt Multi-access Edge Computing (MEC) in platooning paradigms by offloading the computational tasks to the edge server. In this research, vital parameters in vehicular platooning systems, viz. latency-sensitive radio resource management schemes, and Age of Information (AoI) are investigated. In addition, the delivery rates of Cooperative Awareness Messages (CAM) that ensure expeditious reception of safety-critical messages at the roadside units (RSU) are also examined. However, for latency-sensitive applications like vehicular networks, it is essential to address multiple and correlated objectives. To solve such objectives effectively and simultaneously, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework necessitates a better and more sophisticated model to enhance its ability. In this paper, a novel Cascaded MADDPG framework, CMADDPG, is proposed to train cascaded target critics, which aims at achieving expected rewards through the collaborative conduct of agents. The estimation bias phenomenon, which hinders a system's overall performance, is vividly circumvented in this cascaded algorithm. Eventually, experimental analysis also demonstrates the potential of the proposed algorithm by evaluating the convergence factor, which stabilizes quickly with minimum distortions, and reliable CAM message dissemination with 99% probability. The average AoI quantity is maintained within the 5-10 ms range, guaranteeing better QoS. This technique has proven its robustness in decentralized resource allocation against channel uncertainties caused by higher mobility in the environment. Most importantly, the performance of the proposed algorithm remains unaffected by increasing platoon size and leading channel uncertainties.

Performance optimization of VPP in fast frequency control ancillary service provision

This paper proposes a computation offloading method for performance optimization in the fast frequency control ancillary service (FFCAS) provision of a virtual power plant (VPP). VPP aggregates massive demand-side resources to provide power systems with the FFCAS. It faces excessive communication and computation tasks. Edge computing addresses these issues through computation offloading. Heavy tasks can be partially offloaded to the edge to relieve the burden of the central server. Existing offloading methods, however, are not specific for the VPP. To fill the gap, first, an age of information (AoI) model is introduced to characterize the data flow of an edge-enabled VPP. Next, an AoI-based FFCAS performance evaluation model is proposed considering the impacts of communication delay, communication failures, and computational delay. Then an FFCAS performance optimization model is formulated. It aims to maximize VPP’s profit through communication offloading and the DSR portfolio determination. Simulation results show that the proposed method can efficiently improve VPP’s profit.

Unsupervised low-light image enhancement by data augmentation and contrastive learning

ABSTRACT Today, with the increasing demand for visual perception and high-level computational vision tasks, the field of low-light enhancement is rapidly developing. However, models trained on existing datasets often fail or suffer significant performance degradation in real-world low-light scenarios. This performance degradation is frequently due to the limitations of current databases, which typically contain small quantities of paired images of a single type. This article proposes an unsupervised model with a unique data augmentation technique that transforms a regular image database into a paired image database. By adjusting image parameters during training to change exposure, a regular image database can be converted into a paired one. The model restores low-exposure images by extracting lighting features through comparative learning. Evaluations of the LOL and DIV2K datasets demonstrate the proposed model's effectiveness, achieving notable results in low-light image enhancement. This method removes dataset restrictions, broadening the model's range of applications.

Multi-GPU RI-HF Energies and Analytic Gradients─Toward High-Throughput Ab Initio Molecular Dynamics.

This article presents an optimized algorithm and implementation for calculating resolution-of-the-identity Hartree-Fock (RI-HF) energies and analytic gradients using multiple graphics processing units (GPUs). The algorithm is especially designed for high throughput ab initio molecular dynamics simulations of small and medium size molecules (10-100 atoms). Key innovations of this work include the exploitation of multi-GPU parallelism and a workload balancing scheme that efficiently distributes computational tasks among GPUs. Our implementation also employs techniques for symmetry utilization, integral screening, and leveraging sparsity to optimize memory usage. Computational results show that the implementation achieves significant performance improvements, including over 3 × speedups in single GPU AIMD throughput compared to previous GPU-accelerated RI-HF and traditional HF methods. Furthermore, utilizing multiple GPUs can provide superlinear speedup when the additional aggregate GPU memory allows for the storage of decompressed three-center integrals.

Computer-controlled electrical stimulation of facial muscles by facial neuromuscular electrical stimulation (fNMES): Hardware and software solutions

BackgroundComputer controlled electrical stimulation of facial muscles is a promising method to study facial feedback effects, though little guidance is available for new adopters. New MethodFacial Neuromuscular Electrical Stimulation (fNMES) offers a spatially and temporally precise means of manipulating facial muscles during experiments, and can be combined with EEG to study the neurological basis of facial feedback effects. Precise delivery of stimulation requires hardware and software solutions to integrate stimulators and a stimulus-presenting computer. We provide open-source hardware schematics and relevant computer code in order to achieve this integration, so as to facilitate the use of fNMES in the laboratory. ResultsHardware schematics are provided for the building of a bespoke control module, which allows researchers to finely control stimulator output whilst participants complete computer tasks. In addition, we published code that new adopters of NMES can use within their experiments to control the module and send event triggers to another computer. These hard- and software solutions were successfully used to investigate the effects of facial muscle activation on felt and perceived emotion. We summarise these findings and discuss the integration of fNMES with EEG and peripheral physiological measures. Comparison with existing methodsOur inexpensive hardware solution allows fNMES parameters to be computer controlled, and thus allows to stimulate facial muscles with high precision. This opens up new possibilities to investigate, for example, facial feedback effects. ConclusionsWe provide tools and guidance to build a control module in order to precisely deliver electrical stimulation to facial muscles using a stimulus computer (while recording EEG or other peripheral physiology).

Efficient computation by molecular competition networks

Most biomolecular systems exhibit computation abilities, which are often achieved through complex networks such as signal transduction networks. Particularly, molecular competition in these networks can introduce crosstalk and serve as a hidden layer for cellular information processing. Despite the increasing evidence of competition contributing to efficient cellular computation, how this occurs and the extent of computational capacity it confers remain elusive. In this study, we introduced a mathematical model for molecular competition networks (MCNs) and employed a machine learning-based optimization method to explore their computational capacity. Our findings revealed that MCNs, when compared to their noncompetitive counterparts, demonstrate superior performance in both discrete decision-making and analog computation tasks. Furthermore, we highlighted the nonnegligible role of weak interactions and limited amounts of resources and examined how biological constraints influence the computational capacity of MCNs. The study suggested the potential of MCNs as efficient computational structures and provided new insights into cellular information processing. Published by the American Physical Society 2024

Multi-Agent DRL-Based Resource Scheduling and Energy Management for Electric Vehicles

With the emergence of vehicular edge computing (VEC) and electric vehicles (EVs), integrating computation and charging tasks presents challenges due to limited resources and dynamic vehicular networks. This research focuses on the joint optimization of computation offloading and charging scheduling in VEC networks. Specifically, we optimize the offloading factor, charging association variable, and charging rates to minimize the system delay and energy consumption by leveraging the multi-attributes of EVs in both information and energy networks. Considering the dynamic environment, we model the problem as a Markov Decision Process, and use the Multi-Agent Reinforcement Learning (MARL) algorithm MADDPG, with its centralized training and distributed execution mechanisms. Simulation results demonstrate that this approach significantly improves utility while reducing energy consumption and latency.

Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.

Protein and nucleic acid binding site prediction is a critical computational task that benefits a wide range of biological processes. Previous studies have shown that feature selection holds particular significance for this prediction task, making the generation of more discriminative features a key area of interest for many researchers. Recent progress has shown the power of protein language models in handling protein sequences, in leveraging the strengths of attention networks, and in successful applications to tasks such as protein structure prediction. This naturally raises the question of the applicability of protein language models in predicting protein and nucleic acid binding sites. Various approaches have explored this potential. This paper first describes the development of protein language models. Then, a systematic review of the latest methods for predicting protein and nucleic acid binding sites is conducted by covering benchmark sets, feature generation methods, performance comparisons, and feature ablation studies. These comparisons demonstrate the importance of protein language models for the prediction task. Finally, the paper discusses the challenges of protein and nucleic acid binding site prediction and proposes possible research directions and future trends. The purpose of this survey is to furnish researchers with actionable suggestions for comprehending the methodologies used in predicting protein-nucleic acid binding sites, fostering the creation of protein-centric language models, and tackling real-world obstacles encountered in this field.

Cognitive abilities predict performance in everyday computer tasks

Fluency with computer applications has assumed a crucial role in work-related and other day-to-day activities. While prior experience is known to predict performance in tasks involving computers, the effects of more stable factors like cognitive abilities remain unclear. Here, we report findings from a controlled study (N=88) covering a wide spectrum of commonplace applications, from spreadsheets to video conferencing. Our main result is that cognitive abilities exert a significant, independent, and broad-based effect on computer users’ performance. In particular, users with high working memory, executive control, and perceptual reasoning ability complete tasks more quickly and with greater success while experiencing lower mental load. Remarkably, these effects are similar to or even larger in magnitude than the effects of prior experience in using computers and in completing tasks similar to those encountered in our study. However, the effects are varying and application-specific. We discuss the role that user interface design bears on decreasing ability-related differences, alongside benefits this could yield for functioning in society.