Miracle-dz/UniFAR-FTUs huggingface dataset:
Harnessing the Crowd for Autotuning High-Performance Computing Applications

This paper presents GPTuneCrowd, a crowd-based autotuning framework for tuning high-performance computing applications. GPTuneCrowd collects performance data from various users using a user-friendly tuner interface. GPTuneCrowd then presents novel autotuning techniques, based on transfer learning and parameter sensitivity analysis, to maximize tuning quality using collected data from the crowd. This paper shows several real-world case studies of GPTuneCrowd. Our evaluation shows that GPTuneCrowd’s transfer learning improves the tuned performance of ScaLAPACK’s PDGEQRF by 1.57x and a plasma fusion code NIMROD by 2.97x, over a non-transfer learning autotuner. We use GPTuneCrowd’s sensitivity analysis to reduce the search space of SuperLU_DIST and Hypre. Tuning on the reduced search space achieves 1.17x and 1.35x better tuned performance of SuperLU_DIST and Hypre, respectively, compared to the original search space.
An AIOT-based Evaluator Design for Sesame Oil

Sesame oil is a commonly used as a seasoning oil. The edible oil quality is usually determined by chemical indexes, including acid value, peroxide value and p-anisidine value. It is difficult for a user to test whether a sesame oil is bad. Hence, this study designs an AIOT-based Sesame Oil Evaluator, which is called ASOE, composed of ten gas sensors and a machine learning inference engine. Ten gas sensors sense ethanol, methane, hydrogen sulphide, and ammonia. Sensed data is analyzed by the Linear Discriminant Analysis (LDA). Evaluation results indicate that the correction rate of identifying one to nine week-old sesame oil by LDA is 86%. ASOE can help a user easily and quickly evaluate the quality of a sesame oil to substitute chemical indexes. It can be a reference model for developing a food flavoring oil detector in the future.
An Approach to EEG based BCI for Motor Imagery using Explainable Transfer Learning

In this contribution we investigate possibilities of using neural network classification methods for EEG based BCI. We focus on image-based feature representation. The time-frequency based representation and its selected variants along with bi-spectrum were used. Set of more experiments including performance comparison with KNN and more feature mappings is provided. The explainability analysis for the neural networks approach is provided along with corresponding findings.
Scaling and manipulation of turbulent structures in the torsatron TJ-K

In this work, the scaling properties of characteristic turbulent structures and the influence of sheared plasma flows on them were investigated. Analyses were carried out on data with high temporal resolution, which were acquired simultaneously from an 8x8 Langmuir probe array. The analyses reveal the spatio-temporal evolution of propagating density structures. For small ion masses, the scaling properties of these characteristic structures turned out to be consistent with the prediction for drift-wave turbulence. The influence of shear flows on turbulence was demonstrated in simulations and in the experiment. In order to study the mechanism of the transport reduction due to shear flows, electric fields were induced. It was shown that shear flows change the correlation time and the cross-phase between poloidal electric field and density fluctuations rather than the correlation length as it is usually assumed.
Drill: Log-based Anomaly Detection for Large-scale Storage Systems Using Source Code Analysis

Large-scale storage systems, a critical part of modern computing systems, are subject to various runtime bugs, failures, and anomalies in production. Identifying their anomalies at runtime is thus critical for users and administrators. Since runtime logs record the important status of the systems, log-based anomaly detection has been studied extensively for timely identifying system malfunctions. However, existing log-based anomaly detection solutions share common limitations in representing log entries accurately and robustly, hence can not effectively handle log entries that were not seen in the historical logs, which is a common real-world scenario due to logs' inherent rarity and the continuous evolution of the systems. To address the issues of existing methods, we propose Drill, a new log pre-processing method to generate high-quality vector representation of runtime logs by leveraging both storage system-specific sentiment-classifying language models and log contexts built from the source code. Through extensive evaluations of two representative distributed storage systems (Apache HDFS and Lustre), we show that Drill can achieve up to 41% improvement when compared with state-of-the-art anomaly detection solutions, showing it is a promising solution for general anomaly detection.
Optimal Target Capture and Station Keeping Control of Mobile Agents without Global Position Information

The target capture problem, i.e., the problem of reaching a target zone, by a mobile robotic agent that cannot sense its own global position requires reactive motion control algorithms based on onboard sensor data. Although the existing solutions to the target capture problem provide robust convergence guarantees, they do not address the mobile agent’s path and motion optimality. We address the agent path and motion optimality in target capture control and its extension to station keeping, i.e., steering the agent to a location that is pre-defined with respect to a set of beacons, in global positioning system (GPS)-denied environments. We formulate optimal control problems aiming to minimize the agent-target distance for target capture, and the difference of desired and actual agent-station distances for station keeping. We design and analyze a linear quadratic optimal control scheme involving a Luenberger observer based state estimator, for each of the target capture and station keeping problems. The proposed schemes outperform the previous approaches in numerical simulations in terms of agent path length and smoothness.
Robustness analysis of a fractional order control system for the hemodynamic variables in anesthetized patients

Computer-based control of anesthesia is a puzzle requiring multiple other states to be suitably monitored and controlled. Among these, hemodynamic stabilization is a must. Very few studies have been reported regarding the automatic control of the combined anesthesia-hemodynamic systems. Hemodynamic states are strongly affected by hypnotic drugs. At the same time, drugs used for hemodynamic control influence the depth of hypnosis. In this paper, a solution to maintain anesthetic and hemodynamic variables within safe operating ranges is presented and consists in a multivariable fractional order controller. The robustness of the proposed solution is analysed by considering a benchmark patient model and data from 24 patients.
Stability analysis for linear systems with asymmetric input backlash and dead-zone through LMI conditions

This paper deals with the stability analysis of the interconnection between a linear system and a nonlinear operator. This operator is the combination between an asymmetric backlash and a dead-zone. It is a memory-based operator which is only piecewise differentiable. To handle such a nonlinearity, which is more complex than the standard ones, we aim to capture the closed-loop trajectories in some suitable regions of the state space. Uniform Ultimate Boundednes (UUB) property is addressed, using the intrinsic behavior of the operator and a Lyapunov-based approach. Sufficient conditions are provided in terms of Linear Matrix Inequalities (LMIs). A numerical illustration is presented to assess our contribution.
A variable terminal set NMPC construction: application to multicopter stabilisation

In this paper we relax the standard NMPC (Nonlinear Model Predictive Control) through a variable terminal set construction for stabilizing a multicopter system. This contribution allows us to increase the feasible domain and/or reduce the required prediction horizon length. Furthermore, to reduce the complexity of the computations we use zonotopic sets which prove instrumental due to their efficient representation. The theoretical results are validated in simulation and experiment for the stabilisation along a trajectory of a nano-multicopter’s translational dynamics.
CRS: Consistent Replica Selection Algorithm for Distributed Key-Value Storage

The communication between data accessibility and consistency becomes a challenge of replica selection for a Key-Value Storage (KVS). Applying a fixed replica selection policy in numerous data centers may decrease the capacity of a data store. Dynamic scaling advances the KVS with dynamical addition or removing data nodes. To expand the effective addition of the data nodes, the consistent hashing method is applied in KVS because of the adjustability of node variants. The benefits of the hashing algorithms are that scalability, direct control, and adaptation to node transfers while it is not entirely restructuring data layout. The three algorithms are proposed in this paper for selecting the consistent replicas according to the order of the data nodes id's hash values, total Round Trip Time (RTT), and Probability of Bounded Staleness (PBS) among the replicas in a datacenter. By selecting the appropriate node, it can reduce the write execution time for regenerating the data. Without applying the random of the existing consistent hashing technique, it is effective in reducing latency cost, memory cost, and replication cost. The experimental results state that the node selection procedure significantly improves the throughput and consistency rate.
ANDROMEDA: Astronomical Data Mediation for Virtual Observatories

This paper ANDROMEDA, an astronomical data mediation system that enables transparent access to astronomical data servers. Transparent access is achieved by a global view that expresses requirements of community of users (e.g., astronomers) and data integration mechanisms adapted to astronomical data characteristics. Instead of providing an ad hoc mediator, ANDROMEDA can be configured for giving access to different data servers according to different user requirements (data types, content, data quality, and provenance). ANDROMEDA can be also adapted when new sources are integrated to the community or new requirements are specified.
Data Distribution Schemes for Dense Linear Algebra Factorizations on Any Number of Nodes

In this paper, we consider the problem of distributing the tiles of a dense matrix onto a set of homogeneous nodes. We consider both the case of non-symmetric (LU) and symmetric (Cholesky) factorizations. The efficiency of the well-known 2D Block-Cyclic (2DBC) distribution degrades significantly if the number of nodes P cannot be written as the product of two close numbers. Similarly, the recently introduced Symmetric Block Cyclic (SBC) distribution is only valid for specific values of P. In both contexts, we propose generalizations of these distributions to adapt them to any number of nodes. We show that this provides improvements to existing schemes (2DBC and SBC) both in theory and in practice, using the flexibility and ease of programming induced by task-based runtime systems like Chameleon and StarPU.
An efficient transient temperature monitoring of fused filament fabrication process with physics-based compressive sensing

Abstract Sensors play an important role in manufacturing processes. Different types of sensors have been used in process monitoring to ensure the quality of products. As a result, the cost of quality control is rising. Processing a large amount of sensor data for real-time process monitoring is also challenging. Recently, a Physics-Based Compressive Sensing (PBCS) approach was proposed to reduce the number of sensors and the amount of data collection associated with manufacturing process monitoring. PBCS significantly improves the compression ratio from traditional compressed sensing by incorporating the knowledge of physical phenomena in specific applications. In this article the PBCS approach is demonstrated with the dynamic process of fused filament fabrication where the constantly changing temperature field needs to be continuously monitored. A transient thermal model for PBCS is formulated. Based on the model, three-dimensional thermal distributions in manufacturing processes can be efficiently monitored by reconstructing distributions from sparse samplings in both spatial and temporal domains. The systematic error from reconstruction can also be predicted and compensated based on a Gaussian process uncertainty quantification approach.
Attachment Process of A Downward Negative Flash with Multiple Return Strokes Struck to a 356-m-Tall Tower in Southern China

A downward negative flash with nine return strokes struck to the 356-m Shenzhen Tower was analyzed. The high-speed camera captured upward connecting leader (UCL) channels in the first and two subsequent strokes of F1612. The observation results show that none of the downward negative leader channels completely propagated to the tower tip before the occurrence of return stroke (RS), so that the UCL was supposed to be initiated from the tower tip and the attachment process occurred in each RS of this flash. The possible altitude range of the attachment point in each subsequent RS was different. It was found that the larger value of downward dart leader speed might cause the higher altitude of the attachment point. These results could help researchers gain a better understanding of the lightning attachment process, especially occurred in the subsequent return stroke.
Analyzing the TVS Influenced by IOSS to Assess the Power Load Buses in Power Systems

This paper proposes a new algorithm for analyzing the initial optimal steady-state (IOSS) with the optimal power flows (OPF) influencing the transient voltage stability (TVS) to assess the power load buses related to short circuit failure occurring in multi-machine power system operations. We propose a new formula as a new criterion to evaluate the TVS’ estimation of large power load buses involved in the severe disturbances occurring in multi-machine power systems. We compare the transient voltage stability estimation values according to the initial state with those according to the optimized state in daily power system operations for assessing and ranking the power load buses in multi-machine power systems. In this article, we simulate the transient voltage stability estimation’s value under the influence of the power network’s nonlinearity caused by the angular frequency changing in time under the actions of the automatic frequency regulation (AFR) and the automatic voltage regulation (AVR). The nonlinear programming method is applied with the Newton iteration technique to optimize the power output levels of the hydroelectric and thermoelectric generators for simulating the IOSS in daily power system operations. The typical numerical examples and results shown prove the advantages of the new proposed algorithm with new criteria evaluating the transient voltage stability.
Evaluating Asynchronous Parallel I/O on HPC Systems

Parallel I/O is an effective method to optimize data movement between memory and storage for many scientific applications. Poor performance of traditional disk-based file systems has led to the design of I/O libraries which take advantage of faster memory layers, such as on-node memory, present in high-performance computing (HPC) systems. By allowing caching and prefetching of data for applications alternating computation and I/O phases, a faster memory layer also provides opportunities for hiding the latency of I/O phases by overlapping them with computation phases, a technique called asynchronous I/O. Since asynchronous parallel I/O in HPC systems is still in the initial stages of development, there hasn't been a systematic study of the factors affecting its performance.In this paper, we perform a systematic study of various factors affecting the performance and efficacy of asynchronous I/O, we develop a performance model to estimate the aggregate I/O bandwidth achievable by iterative applications using synchronous and asynchronous I/O based on past observations, and we evaluate the performance of the recently developed asynchronous I/O feature of a parallel I/O library (HDF5) using benchmarks and real-world science applications. Our study covers parallel file systems on two large-scale HPC systems: Summit and Cori, the former with a GPFS storage and the latter with a Lustre parallel file system.
Reachability and Controllability Characterizations for Linear Switched Systems in Discrete Time: A Geometric Approach

This article presents the reachability and con- trollability characterizations for discrete-time linear switched systems under a fixed and known switching signal. A geometric approach is used, and we are able to provide alternative conditions which are more computationally friendly compared to existing results by utilizing the solution formula at switching times. Furthermore, the proposed conditions make it easier to study the dependency of the reachability and controllability on the switching times and the mode sequences; this is a new result currently not investigated in the literature. Some academic examples are provided to illustrate the novel features found in this study.
Integrated Multi-Model Optimization of Voice Coil Motor for High-Frequency Free-Piston Stirling Generator

This paper proposes a novel integrated multi-model optimization method to enhance the accuracy and practicality of motor structure optimization for free-piston Stirling generators (FPSGs). Previous studies have been limited by oversimplification of the coupling relationship between the Stirling engine and motor, leading to imprecise or impractical results. The proposed method addresses this issue by incorporating the thermodynamic, hydrodynamic, dynamic, and electromagnetic models of the Stirling engine and motor. This comprehensive approach facilitates the appropriate consideration of various motor factors, such as load force and mass, on the stability, stroke, and frequency characteristics of the FPSG system. Furthermore, the proposed method utilizes conformal transformation and Particle Swarm Optimization to enhance computational efficiency. Motor structure optimization is carried out using the proposed approach, achieving a power density of 115.67 W/kg. The efficacy of the devised method is corroborated through finite element analysis and towing test bench experiments.
Signal Processing for Brain–Computer Interfaces: A review and current perspectives

Brain–computer interfaces (BCIs) employ neurophysiological signals derived from the brain to control computers or external devices. By enhancing or replacing human peripheral functioning capacity, BCIs offer supplementary degrees of freedom, significantly improving individuals’ quality of life, particularly offering hope for those with locked-in syndrome (LIS). Moreover, BCI applications have expanded across medical and nonmedical domains, including rehabilitation, clinical diagnosis, cognitive and affective computing, and gaming. Over the past decades, with a wealth of brain signals captured invasively or noninvasively, BCI has made spectacular progress. However, this also poses new challenges for signal processing techniques, such as characterization and classification. In this review, we first introduce signal enhancement and characterization methods to mine inherent patterns of nonstationary and time-varying brain signals. Then, we highlight widely adopted classification methods in BCI and the challenges they face. This article aims to comprehensively overview crucial signal processing techniques in BCI and provide suggestions for future directions.
Robust Prescribed-Time Practical Tracking and Disturbance Attenuation for Flexible-Joint Manipulators with Input Unmodeled Dynamics

A prescribed-time robust nonlinear controller for multi-link robotic manipulators with flexible joints and uncertain input unmodeled dynamics is considered. The controller achieves regulation of the tracking error to within an arbitrarily specified neighborhood of zero (i.e., “practical” tracking) within an arbitrary time interval specified by the control designer. The controller is robust to disturbance torques as well as uncertainties in parameters of the robotic manipulator in addition to uncertain actuator dynamics in the form of input unmodeled dynamics. Time-varying control gains in a vector backstepping approach along with a dynamic scaling based approach to handle input unmodeled dynamics are used to enforce convergence of the tracking error to within the specified neighborhood within the specified time interval. Simulation studies for a two-link manipulator with flexible joints and input unmodeled dynamics are presented to demonstrate the efficacy of the proposed controller design.
Microservice-based in-network security framework for FPGA NICs

Recent advancements in programmable network hardware such as smartNICs (e.g., NPU, DPU, FPGA)1 and programmable switches (e.g., Intel Tofino) have created opportunities to save precious CPU cycles and achieve low latencies by offloading applications to these devices [1] –[3].
Applications of SEEG brain-electrode interface modelling to electrical parameters identification and tissue classification

This paper inspects dynamical modelling for brain-electrode interface in the context of StereoElectroEncephaloGraphic (SEEG) recordings using electrodes directly implanted into brain tissue. Considering a physical-based non-integer-order transfer function modelling approach, it is first emphasized how it can be usable for tissue classification (between grey and white matters) near each SEEG contact. In addition, it is shown how the model parameters can also provide more insights on electrical properties in the areas where measurements are collected. Validating identification and classification results are finally presented for clinical data, the former providing estimates of resistivity and capacitivity-related coefficients, and the latter showing more than 70% of accuracy.
RGB-Guided Resolution Enhancement of IR Images

This paper introduces a novel method for RGB-Guided Resolution Enhancement of infrared (IR) images called Guided IR Resolution Enhancement (GIRRE). In the area of single image super resolution (SISR) there exists a wide variety of algorithms like interpolation methods or neural networks to improve the spatial resolution of images. In contrast to SISR, even more information can be gathered on the recorded scene when using multiple cameras. In our setup, we are dealing with multi image super resolution, especially with stereo super resolution. We consider a color camera and an IR camera. Current IR sensors have a very low resolution compared to color sensors so that recent color sensors take up 100 times more pixels than IR sensors. To this end, GIRRE increases the spatial resolution of the low-resolution IR image. After that, the upscaled image is filtered with the aid of the high-resolution color image. We show that our method achieves an average PSNR gain of 1.2dB and at best up to 1.8 dB compared to state-of-the-art methods, which is visually noticeable.
Suboptimal State Feedback H2 Control of Positive Linear Systems

In this paper, the state control problem is studied for a class of linear continuous-time positive systems with dynamics defined by the system matrix of a purely Metzler structure. By preserving the closed-loop system positivity and asymptotic stability, the auxiliary goal is to ensure combined compactness of the system performance within H2 norm limit and Metzler parametric constraints. The LMI-based numerical method is presented to provide an optimal solution of this diagonal stabilization problems, where sufficient conditions for the existence of a non-negative gain of the control law are given in terms of a feasible set of linear matrix inequalities. The theory and the algorithm behavior are illustrated by a numerical example and detailed discussed to put in evidence the efficiency of the proposed design methodology.