Miracle-dz/UniFAR-FTUs huggingface dataset:
LeanKV: Efficient Garbage Collection for LSM-Based Key-Value Stores on ZNS SSDs through Lifetime-Based SSTable Clustering

Zoned Namespace SSDs (ZNS SSDs) are a new kind of SSDs that can offer stabler writing performance and higher space efficiency than traditional SSDs. In this paper, we propose LeanKV, a novel LSM-tree-based key-value store optimized for ZNS SSDs, which aims to reduce the data migration cost of garbage collection. We note that existing ZNSSSD-aware data management approaches will lead to zone fragmentation in write-heavy workloads, causing extra data migration during garbage collection. To address this problem, we first present a new method to predict the lifetime of an SSTable based on its compaction history. Then, we propose a lifetime-based algorithm to cluster SSTables according to lifetime. The SSTables in the same cluster will be put into the same zone, ensuring the SSTables in each zone have similar lifetimes. With such a mechanism, less valid data is needed to be moved during garbage collection. We implement LeanKV on top of RocksDB and compare LeanKV with RocksDB and ZenFS on a real ZNS SSD (Western Digital ZN540). The results show that LeanKV can effectively reduce the amount of data migration and the number of files migrated during garbage collection under various workloads. Meanwhile, LeanKV achieves higher throughputs than RocksDB and ZenFS.
Experimental Setup of Motion Sickness and Situation Awareness in Automated Vehicle Riding Experience

Automated vehicle users are likely to engage in non-driving tasks while traveling. Most of these activities require their visual attention and prevent them from getting information from outside vehicle. This leads to motion sickness symptom because of conflicts to what they see and what they feel during traveling. In this paper, an experiment is designed to study the effect of enhancing situation awareness by providing haptic feedback to mitigate the motion sickness inside a vehicle while doing a non-driving task. A preliminary study (N=10) shows that the experimental setup has promising results that can provide insightful information between situation awareness and motion sickness for future car designers.
A Bit Level Acceleration of Mixed Precision Neural Network

With the growth of the convolutional neural network (CNN) parameters, the hardware resources become limited when deploying CNN models. Single bit-width quantization may lead to degradation of accuracy, while mixed-precision quantization models maintain higher accuracy. However, current mixed-precision quantization accelerators only consider the algorithm level quantization and fixed-bit-width processing elements (PEs) without fully utilizing the resources. Therefore, we propose a neural network acceleration architecture based on bit-level computational units to improve resource utilization and throughput of mixed-precision quantization accelerators. The 2-bit and 3-bit low-bit computational units are designed to implement the high-bit quantization. We also propose spatio-temporal fusion to satisfy the unique bit widths in each layer with mixed precision quantization. In particular, we use the 2-bit and 3-bit computational units to achieve dynamic layer-level quantization. We also discuss various combinations of different bit widths, which can be dynamically implemented according to the requirements of accuracy, execution time, etc. The proposed acceleration architecture is implemented in Verilog, verified using three network models: ResNet18, VGG7 and LeNet-5, and tested against three accelerators: Eyeriss, Stripes and Bit Fusion. The experimental results show that our accelerators provide more accuracy and the grouping operation reduces the area overhead. It provides a 3.08 to 3.54 times acceleration ratio and 2.4 to 2.57 times reduction in energy consumption on the three networks.
Tensorizing Neural Networks

Deep neural networks currently demonstrate state-of-the-art performance in several domains. At the same time, models of this class are very demanding in terms of computational resources. In particular, a large amount of memory is required by commonly used fully-connected layers, making it hard to use the models on low-end devices and stopping the further increase of the model size. In this paper we convert the dense weight matrices of the fully-connected layers to the Tensor Train [17] format such that the number of parameters is reduced by a huge factor and at the same time the expressive power of the layer is preserved. In particular, for the Very Deep VGG networks [21] we report the compression factor of the dense weight matrix of a fully-connected layer up to 200000 times leading to the compression factor of the whole network up to 7 times.
Exploring the NBTI Aging and PVT effects on RRAM-based FPGA Multiplexers Performance

Among the emerging technologies, Resistive RAM (RRAM) devices are considered a promising candidate for the replacement of Static RAM (SRAM) cells as memory elements in the interconnection fabric of Field Programmable Gate Arrays (FPGAs). Although previous works have investigated their advantages in terms of performance and energy efficiency, these assessments have focused on fresh devices. In this work, the aging degradation on the propagation delay caused by negative bias-temperature instability (NBTI) effects on different-sized multiplexers (MUXs) is studied under various Process-Voltage-Temperature (PVT) conditions. The comparison between the full-CMOS and the RRAM-based implementations reveals that the traditional SRAM-based design is more affected by aging due to the higher number of transistors.
Membrane-permeabilizing activities of Bacillus thuringiensis coleopteran-active toxin CryIIIB2 and CryIIIB2 domain I peptide

Bacillus thuringiensis toxin CryIIIB2 exhibits activity against two agriculturally important pests, the Colorado potato beetle, Leptinotarsa decemlineata, and the Southern corn rootworm, Diabrotica undecimpunctata. CryIIIB2 shows significant structural similarity to Colorado potato beetle-active toxin CryIIIA, whose crystal structure has been determined elsewhere [J. Li, J. Carrol, and D. J. Ellar, Nature (London) 353:815-821, 1991]. A clone limited to the putative 7-alpha-helical bundle domain I peptide of CryIIIB2 was constructed by PCR. The truncated protein was expressed at high levels in Escherichia coli. Domain I peptide was isolated and compared with native CryIIIB2 toxin in promoting ion efflux from synthetic phospholipid vesicles and formation of ion channels in black lipid membranes. The results showed that CryIIIB2 domain I peptide is sufficient for ion channel formation and promotes ion efflux. Both native CryIIIB2 toxin and domain I peptide were inefficient channel-forming proteins that produced noisy ion channels of various conductance states. In ion efflux assays, native toxin promoted greater ion efflux from synthetic vesicles than did the truncated peptide.
Bridgeless Hybrid Converter Fed Switched Reluctance Motor For Household Appliances

A non-isolated hybrid bridgeless (NHB) converter is designed for power quality management in switched reluctance motor (SRM) drive. A low cost SRM motor drive is selected for household appliances and is fed through NHB converter for efficient motor control. This converter is operated in discontinuous current mode (DCM). A mid-point is developed using zeta and canonical switching cell (CSC) converter. NHB converter is developed to compensate for grid voltage variations ranging from 90V to 270V. Split capacitor voltages are regulated for speed control of SRM drive. NHB converter components are selected to perform satisfactorily for 600 W power rating. This study presents an analysis, design, and simulation of a low-cost SRM drive with a front-end converter aimed at household appliances.
Secure and Trusted Copyright Protection for Educational Data on Redactable Blockchains

Due to the explosion of online educational resources, the protection and management of educational multimedia data have become more challenging. Blockchain has emerged as a promising technology for copyright management due to its decentralized and traceable nature. However, it still faces problems such as difficulties in copyright maintenance and delays in the consensus process, especially for educational multimedia data with large amounts of data, high demand for privacy, and many participants. This paper proposes a secure and trustworthy copyright protection method based on a redactable blockchain to address these issues. We use a decentralized chameleon hash function for copyright maintenance to enable trusted blockchain editing. This can promptly modify infringing copyrights to complete efficient copyright maintenance. For the delay during consensus, we design a consensus mechanism called Proof of Behavior (PoB) based on the Bayesian network (BN), which can achieve fast and reliable consensus by predicting user behavior and selecting highly trusted nodes to participate in consensus. Finally, We conduct simulation experiments to validate the performance of our proposed approach. Specifically, our approach effectively reduces storage space by 5%-10% comparable to other blockchain solutions and enhances system scalability and security.
Hot-carrier-degradation measured with charge-pumping in trench devices despite deep contacts

Understanding charge traps near the Si/SiO2 interface of Silicon transistors is important for improving the performance and reliability of these semiconductor devices. The most relevant traps are located in the region where the transistor channel forms and impact the device threshold voltage and on-resistance. Charge pumping (CP) is a very powerful method to characterize interface defects and is frequently used after hot-carrier degradation (HCD) as well as other reliability concerns. However, modern Si trench devices make use of a deep contact which shortens the source and the body in the device. This renders CP at the gate and thus a detailed characterization of HCD on one and the same device impossible. Here, we present a solution by introducing a specially designed structure with a lateral separation of the body and source contacts. We provide details on the experiment parameters and show first results of the HCD characterization for such trench devices.
Binary Malware Detection via Heterogeneous Information Deep Ensemble Learning

Dynamic malware detection refers to detecting mal-ware by inferring the run-time trace of malware, i.e., a sequence of API calls. In this paper, we proposed HeteroNet, a novel dynamic malware detection model. The main idea of HeteroNet is that it integrates multiple deep learning models which use heterogeneous dynamic features of malware samples.Specifically, we implement three heterogeneous deep learning based models to learn various features from three representations, namely API name sequence, API resource graph and API call graph, respectively, each of the representation is built from the run-time trace of malware. Meanwhile, several methods such as attention mechanism and graph neural networks are applied in base models, according to the characteristics of API calls. Finally, an ensemble algorithm is used to integrate the outputs of three base models. We trained and evaluated HeteroNet on a dataset of 28,770 samples. The precision of the model on the testing set reached 98.40%, which is 1.20% higher than the best result of baselines. Moreover, HeteroNet is more robust against concept drift than other baselines.
Power Quality Enhancement of Synchronous Reluctance Generator Based Standalone Distributed System Utilizing Universal Active Filter

This work demonstrates the performance of a synchronous reluctance generator (SyRG) based distributed power generation system (DPGs) using a universal active filter (UAF). The AC source for both linear and nonlinear loads is a SyRG generator. With the incorporation of UAF for SyRG-based standalone DPGs, the power factor at source terminals is improved. UAF's parallel-connected voltage source converter (VSC) injects harmonics currents to ensure balanced and sinusoidal source currents. Series connected VSC of UAF injects voltage to compensate for any voltage drop or distortion in generation. A model is developed in MATLAB/Simulink to analyze the efficacy of PQ enhancement in various situations and obtained results are discussed. Qualities of voltages across sensitive loads and current qualities at generator terminals are simultaneously enhanced and ensure to follow the IEEE standard 519 using UAF.
Randomized Testing Framework for Dissecting NVIDIA GPGPU Thread Block-To-SM Scheduling Mechanisms

NVIDIA General Purpose Graphics Processing Units (GPGPUs) have been extensively utilized in scientific computing, artificial intelligence acceleration, and numerous other fields. The compute unified device architecture (CUDA) programming interface facilitates access to the enormous computing power of NVIDIA GPGPUs. However, the thread block-to-stream multiprocessor (SM) scheduler of NVIDIA GPGPUs has remained a black box, presenting challenges for performance modeling of CUDA programs, especially those featuring concurrent kernel execution, as well as hindering further exploration of the full potential of NVIDIA GPGPUs. To address these issues, we have devised a randomized testing framework targeting thread block-to-SM scheduling. In this framework, concurrent kernel sequences are randomly generated and assessed by a self-implemented scheduling predictor along with an actual NVIDIA GPGPU. Through iteratively scrutinizing the discrepancies between predictions and actual results, we dissect the key thread block-to-SM scheduling mechanisms intrinsic to NVIDIA GPGPUs, revealing their underlying logic and considerations. This process continuously refines the scheduling predictor until it accurately aligns with real-world NVIDIA GPGPU scheduling. Furthermore, we investigate the monitoring and allocation mechanisms for distinct resources in NVIDIA GPGPUs and SMs, elucidating the essential connection between resource management and thread block scheduling. Our comprehensive and precise analysis of Nvidia GPGPU thread block-to-SM scheduling contributes significantly to constructing more accurate simulators for GPGPU performance modeling. Moreover, it facilitates optimizing CUDA programs based on the scheduling mechanisms to exploit more parallelism among thread blocks of concurrent kernels.
Path Loss Prediction for Vehicular-to-Infrastructure Communication Using Machine Learning Techniques

Vehicular communications are becoming increasingly important due to the need for safer, more efficient, and sustainable transportation. They require the development of accurate radio channel models for vehicular environments. This study compares vehicle-to-infrastructure path loss predictions obtained using four machine learning models: artificial neural network, support vector regression, random forest, and gradient tree boosting. The model design employs predictors from the profile environment between the transmitter and receiver positions. A methodology to select the best predictor subset is applied by examining their contribution to performance and interpretability. We propose a generalization test considering unknown streets (scenarios), and the results demonstrate that the gradient tree boosting model significantly improves the path loss prediction compared to the log-distance path loss model.
Efficient Virtual Machine Provisioning Techniques in Cloud Computing: A Systematic Review

Cloud computing has attracted a great deal of attention as a result of the growing service demands of businesses that outsource computationally intensive tasks to the data center. Meanwhile, the infrastructure of a data center is comprised of hardware resources that consume a great deal of energy and release harmful levels of carbon dioxide. Since the number of modern applications and organizations continues to increase, cloud data centers consume huge quantities of electrical energy. To prevent resource waste and improve energy efficiency, virtual machines (VMs) must be dispersed over numerous Physical Machines (PMs) in a cloud data center. The objective of cloud service providers is to reduce operational expenses by lowering energy utilization while still offering clients with competitive services. The aforementioned objectives can be accomplished by reducing the number of active servers, employing live VM migrations, and keeping system performance at the SLA-required levels. While virtual machines play a crucial role in enhancing the performance of data centers, several strategies are applied to boost virtual machine efficiency (e.g., resource and task load balancing). The key virtual machine factors, including makespan, quality of service, energy, data accuracy, and network utilization, are addressed to enhance performance. Changes to numerous VM characteristics boost cloud computing performance quickly. As a result, we are doing this thorough review of the state of art methodologies to examine the numerous technological breakthroughs made in VM's provisioning techniques.
APRG:A Fair Information Granule Model Based on Adaptive Probability Replacement Resampling

Information granule is a classic mathematical paradigm in the field of data mining. Existing research focuses on improving granule quality to optimize models. However, in these studies, they do not consider that the fairness of the granular model will affect the performance of the granule, especially in some severe social issues (Law, Finance, Education, and more), often due to the participation of sensitive features, the application results of the granule (such as classification) cause population bias. Thus, we construct an adaptive probability replacement resampling model (APR) in Pre-Processors to reduce the bias of sensitive features on granular results. Then, in In-Processors, we add fairness optimizations (FO) to improve the fairness of information granules. Finally, we propose a fair information granule model based on adaptive probability replacement resampling called APRG(APR+FO). We select three loan datasets to verify the feasibility of the granular model according to the current sensitive lending issues in the financial field. The experimental results show that compared with the existing granular models, our proposed method dramatically improves the fairness of the constructed granular models, especially the APRG model, which has an average increase of 46.6% on Demographic Parity (DP) and 77.9% on Equalized Odds (EO). Compared with other existing granular models, the granule quality of APRG is increased by 38.88% on average; in terms of classification accuracy, the average decrease is only 2.03%.
Bridgeland stability conditions on threefolds II: An application to Fujita’s conjecture

<p>We apply a conjectured inequality on third Chern classes of stable two-term complexes on threefolds to Fujita’s conjecture. More precisely, the inequality is shown to imply a Reider-type theorem in dimension three which in turn implies that <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="upper K Subscript upper X Baseline plus 6 upper L"> <mml:semantics> <mml:mrow> <mml:msub> <mml:mi>K</mml:mi> <mml:mi>X</mml:mi> </mml:msub> <mml:mo>+</mml:mo> <mml:mn>6</mml:mn> <mml:mi>L</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">K_X + 6L</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is very ample when <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="upper L"> <mml:semantics> <mml:mi>L</mml:mi> <mml:annotation encoding="application/x-tex">L</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is ample, and that <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="5 upper L"> <mml:semantics> <mml:mrow> <mml:mn>5</mml:mn> <mml:mi>L</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">5L</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is very ample when <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="upper K Subscript upper X"> <mml:semantics> <mml:msub> <mml:mi>K</mml:mi> <mml:mi>X</mml:mi> </mml:msub> <mml:annotation encoding="application/x-tex">K_X</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is trivial. </p>
A Typology of Online Consumers of Wine Websites Based on Electronic Service Quality's Dimensions

The online wine market is expanding at an annual growth rate of around 30%. Following the pattern of other consumer goods, the sale of wine on the Internet is experiencing growing success throughout the world. Moreover, the increasingly systematic use of the Internet in consumers' decision-making processes, coupled with the development of e-commerce, has led researchers and practitioners to examine issues concerning service quality in an online context. This paper proposes a typology of online consumers on wine Web sites based on dimensions of electronic service quality. In all, 2,813 customers of 28 wine Web sites filled in an online questionnaire after completing a specific task. Six groups of customers are identified: the "secure seeker", the "opportunist", the "novice", the "customer service seeker", the "browser" and the "rational browser".
Chemical Abundances of OB Stars with High Projected Rotational Velocities

Elemental abundances of carbon, nitrogen, oxygen, magnesium, aluminum, and silicon are presented for a sample of 12 rapidly rotating OB star (v sin i > 60 km s-1) members of the Cep OB2, Cyg OB3, and Cyg OB7 associations. The abundances are derived from spectrum synthesis, using both LTE and non-LTE calculations. As found in almost all previous studies of OB stars, the average abundances are slightly below solar, by about 0.1 to 0.3 dex. In the case of oxygen, even with the recently derived low solar abundances, the OB stars are closer to, but still below, the solar value. Results for the nine Cep OB2 members in this sample can be combined with results published previously for eight Cep OB2 stars with low projected rotational velocities to yield the most complete set of abundances, to date, for this particular association. These abundances provide a clear picture of both the general chemical and individual stellar evolution that has occurred within this association. By placing the Cep OB2 stars studied in an HR-diagram we identify the presence of two distinct age subgroups, with both subgroups having quite uniform chemical abundances. Two stars are found in the older subgroup that show significant N/O overabundances, with both stars being two of the most massive, the most evolved, and the most rapidly rotating of the members studied in Cep OB2. These characteristics of increased N abundances being tied to high mass, rapid rotation, and an evolved phase are those predicted from models of rotating stars that undergo rotationally driven mixing.
Soil Dynamics and Earthquake Engineering

Contours of spatial variations of the duration of strong earthquake ground motion during the 1994 Northridge, California earthquake are smooth over distances as large as tens of kilometers. Visual comparison of those contours with the depth of sediments and with vertical offsets of the basement rocks along the faults in the Los Angeles basin are in excellent qualitative agreement with the trends predicted by the previously published empirical scaling equations of strong-motion duration. It is argued that if the source-to-station distances were measured along the three-dimensional wave paths through the sedimentary wave-guides, rather than along straight lines as is common at present, the accuracy of empirical scaling equations could be improved significantly.