Computational Capabilities Research Articles

QuanFormer: A Transformer-Based Precise Peak Detection and Quantification Tool in LC-MS-Based Metabolomics.

In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reliability and reproducibility of results. To address these challenges, we developed QuanFormer, a deep learning method based on object detection designed to accurately quantify peak signals. Our algorithm combines the feature extraction capabilities of convolutional neural networks (CNNs) with the global computation capability of Transformer architecture. Data training in QuanFormer by using nearly 20,000 annotated regions-of-interest (ROIs) ensures unique prediction via bipartite matching, achieving 96.5% of the average precision value on the test set. Even without retraining, QuanFormer achieves over 90% accuracy in distinguishing true from false peaks. Performance was further analyzed using visualization techniques applied to the encoder and decoder layers. We also demonstrated that QuanFormer could correct retention time shifts for peak alignment and generally surpass the existing methods, including MZmine 3 and PeakDetective, to obtain a larger number of picked peaks and higher accurate quantification. Finally, we also carried out metabolomic analysis in a clinical cohort of breast cancer patients and utilized QuanFormer to detect and quantify the potential biomarkers. QuanFormer is open-source and available at https://github.com/LinShuhaiLAB/QuanFormer.

Introduction to Memristive Mechanisms and Models.

The increase in computational power demand led by the development of Artificial Intelligence is rapidly becoming unsustainable. New paradigms of computation, which potentially differ from digital computation, together with novel hardware architecture and devices, are anticipated to reduce the exorbitant energy demand for data-processing tasks. Memristive systems with resistive switching behavior are under intense research, given their prominent role in the fabrication of memory devices that promise the desired hardware revolution in our intensive data-driven era. They are suggested to provide the hardware substrate to scale up computational capabilities while improving their energy expenditure and speed. This work provides an orientation map for those interested in the vast topic of memristive systems with application to neuromorphic computing. We address the description of the most notable emerging devices and we illustrate models that capture the complex dynamical behavior of these systems under the dynamical-systems framework developed by Chua. We then review the memristive behavior under the perspective of statistical physics and percolation theory suited to describe fluctuations and disorder which are otherwise precluded in the dynamical-system approach. Percolation theory allows the investigation of these systems at the mesoscopic level, enabling material-independent modeling of non-linear conductance networks. We finally discuss recent and less recent successes in deep learning methods that bridge the field of physics-based and biological- inspired neuromorphic computing.

Large AI Models and Their Applications: Classification, Limitations, and Potential Solutions

ABSTRACTBackgroundIn recent years, Large Models (LMs) have been rapidly developed, including large language models, visual foundation models, and multimodal LMs. They are updated and iterated at a very fast pace. These LMs can accomplish many tasks, e.g., daily work assistant, intelligent customer service, and intelligent factory scheduling. Their development has contributed to various industries in human society.AimsThe architectural flaws of LMs lead to several problems, including illusions and difficulty in locating errors, limiting their performance. Solving these problems properly can facilitate their further development.MethodsThis work first introduces the development of LMs and identifies their current problems, including data and energy consumption, catastrophic forgetting, reasoning ability, localization fault, and ethical problems. Then, potential solutions to these problems are provided, including increase data and computation capability, neural‐symbolic synergy, and data orientation to human pattern.DiscussionThis work discusses developing vertical domain LMs on top of some base LMs. In addition, this work introduces three typical real‐world applications of LMs, including autonomous driving, smart industrial productions, and intelligent medical assistance.ConclusionBy embracing the advantages of LMs and solving their fundamental problems, many industries are expected to achieve promising prospects in the future.

Critical Factors in Parabolic Nozzle Design and Performance Analysis with CFD

Abstract This study presents a comprehensive analysis of contoured nozzle designs using the Method of Characteristics (MOC) and Fluent simulations. The study aims to compare the performance and flowfield predictions of these methods and explore the influence of various design param- eters on nozzle performance. Initial investigations validate the MOC algorithm’s accuracy against commercial software, highlighting its rapid computational capability despite limita- tions in handling real gas effects. Fluent simulations consistently predict slightly lower specific impulse values due to differing exit pressure predictions but align closely with MOC results in inviscid flow conditions. The research explores the role of the initial circular arc in defin- ing the nozzle’s expansion and straightening sections, finding that smaller circular arcs create larger property gradients, necessitating quicker expansions and leading to potential isentropic losses, which were almost imperceptible in the Fluent case, in fact, a more axial flow was deliv- ered. Variations in design parameters, such as the contour’s exit angle and cone nozzle fraction, demonstrate their impact on nozzle performance, with higher exit angles generally improving specific impulse, contrary to expectation. For some pairs of design options, longer nozzles al- low smoother expansions, resulting in lower average exit angles and improved specific impulse. The findings emphasize the contemporary relevance of the MOC for preliminary nozzle studies, highlighting the balance between accuracy and computational cost.

Harnessing ferro-valleytricity in pentalayer rhombohedral graphene for memory and compute

Two-dimensional materials with multiple degrees of freedom, including spin, valleys, and orbitals, open up an exciting avenue for engineering multifunctional devices. Beyond spintronics, these degrees of freedom can lead to novel quantum effects such as valley-dependent Hall effects and orbital magnetism, which could revolutionize next-generation electronics. However, achieving independent control over valley polarization and orbital magnetism has been a challenge due to the need for large electric fields. A recent breakthrough involving pentalayer rhombohedral graphene has demonstrated the ability to individually manipulate anomalous Hall signals and orbital magnetic hysteresis, forming what is known as a valley-magnetic quartet. Here, we leverage the electrically tunable ferro-valleytricity of pentalayer rhombohedral graphene to develop nonvolatile memory and in-memory computation applications. We propose an architecture for a dense, scalable, and selector-less nonvolatile memory array that harnesses the electrically tunable ferro-valleytricity. In our designed array architecture, nondestructive read and write operations are conducted by sensing the valley state through two different pairs of terminals, allowing for independent optimization of read/write peripheral circuits. The power consumption of our PRG-based array is remarkably low, with only ∼6 nW required per write operation and ∼2.3 nW per read operation per cell. This consumption is orders of magnitude lower than that of the majority of state-of-the-art cryogenic memories. Additionally, we engineer in-memory computation by implementing majority logic operations within our proposed nonvolatile memory array without modifying the peripheral circuitry. Our framework presents a promising pathway toward achieving ultra-dense cryogenic memory and in-memory computation capabilities.

Exploring In-Network Computing with Information-Centric Networking: Review and Research Opportunities

The advent of 6G networks and beyond calls for innovative paradigms to address the stringent demands of emerging applications, such as extended reality and autonomous vehicles, as well as technological frameworks like digital twin networks. Traditional cloud computing and edge computing architectures fall short in providing their required flexibility, scalability, and ultra-low latency. Cloud computing centralizes resources in distant data centers, leading to high latency and increased network congestion, while edge computing, though closer to data sources, lacks the agility to dynamically adapt to fluctuating workloads, user mobility, and real-time requirements. In-network computing (INC) offers a transformative solution by integrating computational capabilities directly into the network fabric, enabling dynamic and distributed task execution. This paper explores INC through the lens of information-centric networking (ICN), a revolutionary communication paradigm implementing routing-by-name and in-network caching, and thus emerging as a natural enabler for INC. We review state-of-the-art advancements involving INC and ICN, addressing critical topics such as service naming, executor selection strategies, compute reuse, and security. Furthermore, we discuss key challenges and propose research directions for deploying INC via ICN, thereby outlining a cohesive roadmap for future investigation.

Application of Statistical Models in Air Quality Monitoring

Currently, statistical models are vital instruments for examining and forecasting diverse environmental variables, including air quality analysis. With the development of big data, foundational statistical techniquesincluding regression and time series analysisare adapting to manage larger datasets and complex environmental dynamics. This paper examines the recent applications of statistical models used to assess and forecast air quality in Chinese cities based on existing literature and data research. By focusing on various modeling approaches, such as Poisson regression, grey correlation, and neural networks, it discusses the advantages and drawbacks of each model. Furthermore, it examines the enhancement of classical analytical methodologies inside a big data framework to augment the accuracy of air quality analysis and forecasting. The result shows that although datasets have been more extensive, to increase the accuracy of pollution forecasting, diverse data and more excellent computational capabilities are still needed. Advances in machine learning and optimisation algorithms show promise for overcoming these challenges in the future

All-optical perception based on partially coherent optical neural networks.

In the field of image processing, optical neural networks offer advantages such as high speed, high throughput, and low energy consumption. However, most existing coherent optical neural networks (CONN) rely on coherent light sources to establish transmission models. The use of laser inputs and electro-optic modulation devices at the front end of these neural networks diminishes their computational capability and energy efficiency, thereby limiting their practical applications in object detection tasks. This paper proposes a partially coherent optical neural network (PCONN) transmission model based on mutual intensity modulation. This model does not depend on coherent light source inputs or active electro-optic modulation devices, allowing it to directly compute and infer using natural light after simple filtering, thus achieving full optical perception from light signal acquisition to computation and inference. Simulation results indicate that the model achieves a highest classification accuracy of 96.80% and 86.77% on the MNIST and Fashion-MNIST datasets, respectively. In a binary classification simulation test based on the ISDD segmentation dataset, the model attained an accuracy of 94.69%. It is estimated that this system's computational inference speed for object detection tasks is 100 times faster than that of traditional CONN, with energy efficiency approximately 50 times greater. In summary, our proposed PCONN model addresses the limitations of conventional optical neural networks in coherent light environments and is anticipated to find applications in practical object detection scenarios.

Application of Google Earth Engine to Monitor Greenhouse Gases: A Review

Google Earth Engine (GEE) is a cloud-based platform revolutionizing geospatial analysis by providing access to vast satellite datasets and computational capabilities for monitoring environmental and societal issues. It incorporates machine learning (ML) techniques and algorithms as part of its tools for analyzing and processing large geospatial data. This review explores the diverse applications of GEE in monitoring and mitigating greenhouse gas emissions and uptakes. GEE is a cloud-based platform built on Google’s infrastructure for analyzing and visualizing large-scale geospatial datasets. It offers large datasets for monitoring greenhouse gas (GHG) emissions and understanding their environmental impact. By leveraging GEE’s capabilities, researchers have developed tools and algorithms to analyze remotely sensed data and accurately quantify GHG emissions and uptakes. This review examines progress and trends in GEE applications, focusing on monitoring carbon dioxide (CO2), methane (CH4), and nitrous oxide/nitrogen dioxide (N2O/NO2) emissions. It discusses the integration of GEE with different machine learning methods and the challenges and opportunities in optimizing algorithms and ensuring data interoperability. Furthermore, it highlights GEE’s role in pinpointing emission hotspots, as demonstrated in studies monitoring uptakes. By providing insights into GEE’s capabilities for precise monitoring and mapping of GHGs, this review aims to advance environmental research and decision-making processes in mitigating climate change.

Energy-Efficient Aerial STAR-RIS-Aided Computing Offloading and Content Caching for Wireless Sensor Networks.

Unmanned aerial vehicle (UAV)-based wireless sensor networks (WSNs) hold great promise for supporting ground-based sensors due to the mobility of UAVs and the ease of establishing line-of-sight links. UAV-based WSNs equipped with mobile edge computing (MEC) servers effectively mitigate challenges associated with long-distance transmission and the limited coverage of edge base stations (BSs), emerging as a powerful paradigm for both communication and computing services. Furthermore, incorporating simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) as passive relays significantly enhances the propagation environment and service quality of UAV-based WSNs. However, most existing studies place STAR-RISs in fixed positions, ignoring the flexibility of STAR-RISs. Some other studies equip UAVs with STAR-RISs, and UAVs act as flight carriers, ignoring the computing and caching capabilities of UAVs. To address these limitations, we propose an energy-efficient aerial STAR-RIS-aided computing offloading and content caching framework, where we formulate an energy consumption minimization problem to jointly optimize content caching decisions, computing offloading decisions, UAV hovering positions, and STAR-RIS passive beamforming. Given the non-convex nature of this problem, we decompose it into a content caching decision subproblem, a computing offloading decision subproblem, a hovering position subproblem, and a STAR-RIS resource allocation subproblem. We propose a deep reinforcement learning (DRL)-successive convex approximation (SCA) combined algorithm to iteratively achieve near-optimal solutions with low complexity. The numerical results demonstrate that the proposed framework effectively utilizes resources in UAV-based WSNs and significantly reduces overall system energy consumption.

A Data-Driven Approach to Refine the Partially Stirred Reactor Closure for Turbulent Premixed Flames

AbstractAccurately predicting turbulent combustion processes is fundamental for optimizing efficiency, reducing pollutant emissions, and ensuring operational safety in combustion systems. To this purpose, computational fluid dynamics (CFD) simulations are widely employed. In particular, large eddy simulations (LES) balance prediction accuracy with computational efficiency by resolving only the most energy-containing scales of turbulence and rely on modeling the turbulence-chemistry interactions (TCI) occurring at the smallest scales. Among the existing closures, the partially stirred reactor (PaSR) model incorporates finite-rate chemistry and estimates a cell reacting fraction based on the local Damköhler number to account for the subfilter-scale TCI. Although widely validated in CFD computations, the PaSR model was found limited by the way it computes the cell reacting fraction. To tackle this point, our study proposes a machine learning (ML) enhanced partially stirred reactor model for LES. A fully connected neural network is trained on direct numerical simulation (DNS) data of turbulent premixed jet flames to compute a correction coefficient for the cell reacting fraction. Maintaining the original model shape, this ML-enhanced closure aims at bridging the gap between physics-based models and advanced data-driven techniques. The proposed formulation not only improves the prediction accuracy of quantities of interest such as the heat release rate but also features computational feasibility and generalisation capabilities over a large range of LES grid refinement. This demonstrates the significant potential of ML-aided TCI closures in future applications of combustion engineering.

THE NEUROBEHAVIORAL STATE HYPOTHESIS.

Since the early attempts to understand the brain made by Greek philosophers more than 2000 years ago, one of the main questions in neuroscience has been how the brain perceives all the stimuli in the environment and uses this information to implement a response. Recent hypotheses of the neural code rely on the existence of an ideal observer, whether on specific areas of the cerebral cortex or distributed network composed of cortical and subcortical elements. The Neurobehavioral State hypothesis stipulates that neurons are in a quasi-stable state due to the dynamic interaction of their molecular components. This increases their computational capabilities and electrophysiological behavior further than a binary active/inactive state. Together, neuronal populations across the brain learn to identify and associate internal and external stimuli with actions and emotions. Furthermore, such associations can be stored through the regulation of neuronal components as new quasi-stable states. Using this framework, behavior arises as the result of the dynamic interaction between internal and external stimuli together with previously established quasi-stable states that delineate the behavioral response. Finally, the Neurobehavioral State hypothesis is firmly grounded on present evidence of the complex dynamics within the brain, from the molecular to the network level, and avoids the need for a central observer by proposing the brain configures itself through experience-driven associations.

DLIIoT: A Deep Learning based Intelligent Attack Detection in IoT Networks using Cooja Simulator

Internet of things (IoT) has incredibly transformed the whole domain of communication process. The extensive dependency on these devices leads to various advanced cyber security threats. IoT devices fall easily into the ambit of malicious threats and are susceptible to vast range of attacks due to their limited computation capabilities and memory constraints. Intrusion Detection Systems (IDSs) are dedicated outstanding frameworks to protect these devices from cyber threats. In this study, a comprehensive review of different AI based IDS applied on IoTs is done. It has been observed that machine learning and deep learning has widely influenced the domain of IoT security. The focus of the research carried out is to earmark the techniques that are performing best on a given data set. Features selection, type of attacks, proposed solutions in solving security menaces are taken into consideration. Further, we have presented DLIIoT, a deep learning based intelligent attack detection in IoT networks by generating precise IoT datasets in Cooja Simulator. Four Deep learning algorithms are utilized and analysed under standard performance criteria metrics such as Precision, Recall, Accuracy and F1-score. It was found that deep learning algorithms have remarkable potential in detecting and recognizing malicious data patterns in IoT networks.

FPGA-accelerated simulation technology research to unlocking powerful computational capabilities

FPGA-accelerated simulation technology research to unlocking powerful computational capabilities

CLOUD-BASED AI AND MULTIVARIATE OPTIMIZATION METHODS FOR REAL-TIME SENTIMENT ANALYSIS ON SOCIAL MEDIA

Social media has emerged as a widely used platform for individuals and businesses to share updates, opinions, and emotions. Real-time sentiment analysis of social media data provides valuable insights, enabling organizations to make informed, data-driven decisions. However, analyzing vast amounts of social media data in real-time presents significant challenges, requiring high computational power and advanced analytical capabilities. This is where cloud-based AI and multivariate optimization techniques become essential. Cloud-based AI leverages the scalability and speed of cloud computing to process large volumes of data efficiently in real-time. The multivariate optimization model enhances the analysis by handling complex, diverse datasets and evaluating multiple variables simultaneously. This research focuses on delivering a unified framework that performs real-time sentiment analysis, and the system integrates cloud-based AI with multivariate optimization strategies to automatically collect, process, and analyze social media data in real-time, delivering actionable insights with improved accuracy and efficiency.

Authentication in IOT

With the expansion of the Internet of Things (IoT), there is an increasing demand for reliable approaches to authenticate users to maintain safety from unauthorized access. Special consideration should be given to IoT devices from home appliances to industrial equipment that have limited computation capabilities. In the present work, the various types of authentications in IoT such as passwords, tokens, biometrics, and device-based authentication are surveyed. Further, we also discuss the limitations of these methods and specify some of the new technologies coming up including AI and blockchain which can enhance IoT security. : IoT, User Authentication, Security, Connected Devices

Effect of Depth Band Replacement on Red, Green and Blue Image for Deep Learning Weed Detection.

Automated agricultural robots are becoming more common with the decreased cost of sensor devices and increased computational capabilities of single-board computers. Weeding is one of the mundane and repetitive tasks that robots could be used to perform. The detection of weeds in crops is now common, and commercial solutions are entering the market rapidly. However, less work is carried out on combatting weeds in pastures. Weeds decrease the grazing yield of pastures and spread over time. Mowing the remaining weeds after grazing is not guaranteed to remove entrenched weeds. Periodic but selective cutting of weeds can be a solution to this problem. However, many weeds share similar textures and structures with grazing plants, making their detection difficult using the classic RGB (Red, Green, Blue) approach. Pixel depth estimation is considered a viable source of data for weed detection. However, systems utilizing RGBD (RGB plus Depth) are computationally expensive, making them nonviable for small, lightweight robots. Substituting one of the RGB bands with depth data could be a solution to this problem. In this study, we examined the effect of band substitution on the performance of lightweight YOLOv8 models using precision, recall and mAP50 metrics. Overall, the RDB band combination proved to be the best option for YOLOv8 small and medium detection models, with 0.621 and 0.634 mAP50 (for a mean average precision at 50% intersection over union) scores, respectively. In both instances, the classic RGB approach yielded lower accuracies of 0.574 and 0.613.

Reservoir computing with generalized readout based on generalized synchronization

Reservoir computing is a machine learning framework that exploits nonlinear dynamics, exhibiting significant computational capabilities. One of the defining characteristics of reservoir computing is that only linear output, given by a linear combination of reservoir variables, is trained. Inspired by recent mathematical studies of generalized synchronization, we propose a novel reservoir computing framework with a generalized readout, including a nonlinear combination of reservoir variables. Learning prediction tasks can be formulated as an approximation problem of a target map that provides true prediction values. Analysis of the map suggests an interpretation that the linear readout corresponds to a linearization of the map, and further that the generalized readout corresponds to a higher-order approximation of the map. Numerical study shows that introducing a generalized readout, corresponding to the quadratic and cubic approximation of the map, leads to a significant improvement in accuracy and an unexpected enhancement in robustness in the short- and long-term prediction of Lorenz and Rössler chaos. Towards applications of physical reservoir computing, we particularly focus on how the generalized readout effectively exploits low-dimensional reservoir dynamics.

A Lightweight Small Target Detection Algorithm for UAV Platforms

The targets in the aerial view of UAVs are small, scenes are complex, and background noise is strong. Additionally, the low computational capability of UAVs is challenged when trying to meet the requirements of large neural networks. Therefore, a lightweight object detection algorithm tailored for UAV platforms, called RSG-YOLO, is proposed. The algorithm introduces an attention module constructed with receptive field attention and coordinate attention, which helps reduce background noise interference while improving long-range information dependency. It also introduces and refines a fine-grained downsampling structure to minimize the loss of target information during the downsampling process. A general efficient layer aggregation network enhances the base feature extraction module, improving gradient flow information. Additionally, a detection layer rich in small target information is added, while redundant large object detection layers are removed, achieving a lightweight design while enhancing detection accuracy. Experimental results show that, compared to the baseline algorithm, the improved algorithm increases the P, R, mAP@0.5, and mAP@0.5:0.95 by 6.9%, 7.2%, 8.4%, 5.8%, respectively, on the VisDrone 2019 dataset, and by 5.7%, 9%, 9.3%, 3.6%, respectively, on the TinyPerson dataset, while reducing the number of parameters by 23.3%. This significantly enhances the model’s detection performance and robustness, making it highly suitable for object detection tasks on low-computing-power UAV platforms.

Pixel‐Level Hardware Strategy for Large‐Scale Convolution Calculation in Neuromorphic Devices

AbstractFor convolution neural networks, increasing the performance of hardware computer systems is crucial in the era of big data. Benefiting from the neuromorphic devices, producing the convolutional calculation at the crossbar array circuit has become a promising approach for high‐performance hardware computer systems. However, as computation scales, this hardware system faces the challenge of low resource utilization efficiency and low power efficiency. Here, a novel pixel‐level strategy, leveraging the dynamic change of electron concentration as the process of convolution calculation, is proposed for high‐performance hardware computer systems. Compared with the crossbar array circuit‐based strategy, instead of at least four devices, raised the power efficiency to 413% and decreased the training epochs to 38%. This work presents a novel physics‐based approach that enables highly efficient convolutional calculation, improves power efficiency, speeds up convergency, and paves the way for building complex convolution neural networks with large‐scale convolutional computation capabilities.