Miracle-dz/UniFAR-FTUs huggingface dataset:
Current Practice in Using Voltage Imaging to Record Fast Neuronal Activity: Successful Examples from Invertebrate to Mammalian Studies

The optical imaging of neuronal activity with potentiometric probes has been credited with being able to address key questions in neuroscience via the simultaneous recording of many neurons. This technique, which was pioneered 50 years ago, has allowed researchers to study the dynamics of neural activity, from tiny subthreshold synaptic events in the axon and dendrites at the subcellular level to the fluctuation of field potentials and how they spread across large areas of the brain. Initially, synthetic voltage-sensitive dyes (VSDs) were applied directly to brain tissue via staining, but recent advances in transgenic methods now allow the expression of genetically encoded voltage indicators (GEVIs), specifically in selected neuron types. However, voltage imaging is technically difficult and limited by several methodological constraints that determine its applicability in a given type of experiment. The prevalence of this method is far from being comparable to patch clamp voltage recording or similar routine methods in neuroscience research. There are more than twice as many studies on VSDs as there are on GEVIs. As can be seen from the majority of the papers, most of them are either methodological ones or reviews. However, potentiometric imaging is able to address key questions in neuroscience by recording most or many neurons simultaneously, thus providing unique information that cannot be obtained via other methods. Different types of optical voltage indicators have their advantages and limitations, which we focus on in detail. Here, we summarize the experience of the scientific community in the application of voltage imaging and try to evaluate the contribution of this method to neuroscience research.
Simultaneous regulation and velocity tracking control of a two-wheeled self-balancing robot

In recent years, two-wheeled self-balancing robots have played an important role in personal transportation research. Presenting control algorithms for this category of mobile robots has always faced challenges due to nonlinear dynamics, structural and non-structural uncertainties, the dependence of model parameters on environmental conditions, and the presence of external disturbances. Therefore, the control of these systems has always been the focus of attention. One of the prominent control issues in this field is tracking the time-varying trajectories. In this paper, the motion control of a two-wheeled self-balancing vehicle is investigated. For this purpose, by designing a cascade control algorithm and the appropriate reference trajectories, the system will be controlled to follow the reference velocity profile while maintaining its stability. The main challenge in controlling this system is that two variables must be justified for the longitudinal control, one is the position or speed of the longitudinal movement of the system and the other is the pitch angle of the two-wheeled self-balancing robot. But the system has only one input to adjust these two variables, which complicates the control of the system to some extent. This issue has been investigated in the provided control methods and the adjustment solution has been investigated. The obtained results show the efficiency of the proposed controller in simultaneous trajectory tracking and maintaining stability.
3-D Vehicle Detection Enhancement Using Tracking Feedback in Sparse Point Clouds Environments

In recent years, vehicle detection in intelligent transportation systems using 3D LIDAR point clouds based on deep neural networks has made substantial progress. However, when the point clouds are very sparse, the detection model cannot generate proposals efficiently, resulting in false negative results. Considering that the object tracking technology accurately predicts vehicles based on historical measurements and motion models, and these prediction results can become proposals for object detection. Therefore, this paper proposes a novel object detection paradigm based on tracking feedback to address the false negative problem based on sparse point clouds. According to the distribution of the state vector from the Kalman prediction, multiple proposals are sampled and fed back to the second stage of two-stage detection models. After regression and non-maximum suppression, the false negative results can be effectively reduced. This method enhances the vehicle detection capability of classical neural networks. Comparing the recall metric of multiple detection models at different distances in the public KITTI and nuSences datasets, the proposed method can promote up to 5.31% compared to the previous method, which reflects the effectiveness and versatility of the proposed method.
A Novel Three-dimensional Mobility Model for mmWave UAV Swarms

Unmanned aerial vehicle (UAV) swarms are expected to become more prevalent in the Internet of Things (IoT) applications such as remote sensing, monitoring, entertainment, etc. The onboard sensors on these UAVs often generate massive data and demand higher data rates for real-time communication to the swarm gateway. The next-generation millimeter-wave (mmWave) communication technologies, such as IEEE 802.11ad/ay, 5G, etc., offer promising speeds and can be suitable for intra-swarm communication. However, to take advantage of these faster speeds, the impact of swarm mobility on the performance of mm Wave communication technologies must be investigated using realistic mobility models. In this paper, we present a three-dimensional group mobility model for UAV swarms guided by the swarm gateway considering real-world restrictions such as obtaining collision-free waypoints for the UAVs while residing within the coverage range of the gateway and maintaining a safe distance from peers. The performance analysis shows that the proposed model offers a superior degree of spatial and temporal dependency in the swarm movement than the existing state-of-the-art models.
Mining Insights From Esports Game Reviews With an Aspect-Based Sentiment Analysis Framework

The explosive growth of player-versus-player games and tournaments has catapulted esports games into a rapidly expanding force in the gaming industry. However, novice and armature players’ voices are often inadvertently overlooked because of a lack of effective analytical methods, despite the close collaboration between professional esports teams and operators. To ensure the quality of esports game services and establish a balanced gaming environment, it is essential to consider the opinions of unprofessional players and comprehensively analyze their reviews. This study proposes a new framework for analyzing esports reviews of players. It incorporates two key components: topic modeling and sentiment analysis. Utilizing the Latent Dirichlet Allocation (LDA) algorithm, the framework effectively identifies diverse topics within reviews. These identified topics were subsequently employed in a prevalence analysis to uncover the associations between players’ concerns and various esports games. Moreover, it leverages cutting-edge Bidirectional Encoder Representations from Transformers (BERT) in conjunction with a Transformer (TFM) downstream layer, enabling accurate detection of players’ sentiments toward different topics. We experimented using a dataset containing 1.6 million English reviews collected up to December 2021 for four esports games on Steam: TEKKEN7, Dota2, PUBG, and CS:GO. The experimental results demonstrated that the proposed framework can efficiently identify players’ concerns and reveal interesting keywords underlying their reviews. Consequently, it provides precise insights and valuable customer feedback to esports game operators, enabling them to enhance their services and provide an improved gaming experience for all players.
On the Compactification of Products

Let {Xa, a ∊ A} be a family of completely regular Hausdorff spaces, {βXa } the corresponding family of their Stone-Čech compactifications and ΠaXa the usual topological product. The following theorem was proved by Glicksberg [2] and subsequently by Frolík [1].
Characterization of Data Compression in Datacenters

Data compression has emerged as a promising technique to alleviate the memory, storage, and network cost with some associated compute overheads in warehouse-scale datacenter services. Despite being one of the most important components of the overall datacenter taxes, there has not been a comprehensive characterization of compression usage in datacenter workloads. Such characterization is paramount for both compression software developers and hardware accelerator designers as it can help them make optimal design trade-offs decisions in terms of performance, power, and cost while meeting service-level agreements of target applications. Moreover, it can provide data-driven insights to application developers to find optimal compression configuration choices for their services. In this paper, we first provide a holistic characterization of compression as used by various warehouse-scale datacenter services at a global social media provider, Meta. Next, we deep dive into a few representative use cases of compression in the production environment and characterize compression usage of the services while running live traffic. Finally, we conduct sensitivity studies to understand how different compression configurations are relevant to the overall infrastructure cost, followed by future research directions for compression hardware and software development.
Effects of Platelet-Rich Plasma on the Oxymetholone-Induced Testicular Toxicity

Oxymetholone is one of the anabolic steroids that has widely been used among teenagers and athletes to increase their muscle bulk. It has undesirable effects on male health and fertility. In this study, the therapeutic effects of platelet-rich plasma (PRP) on oxymetholone-induced testicular toxicity were investigated in adult albino rats. During the experiments, 49 adult male albino rats were divided into 4 main groups: Group 0 (donor group) included 10 rats for the donation of PRP, Group I (control group) included 15 rats, Group II included 8 rats that received 10 mg/kg of oxymetholone orally, once daily, for 30 days, and Group III included 16 rats and was subdivided into 2 subgroups (IIIa and IIIb) that received oxymetholone the same as group II and then received PRP once and twice, respectively. Testicular tissues of all examined rats were obtained for processing and histological examination and sperm smears were stained and examined for sperm morphology. Oxymetholone-treated rats revealed wide spaces in between the tubules, vacuolated cytoplasm, and dark pyknotic nuclei of most cells, as well as deposition of homogenous acidophilic material between the tubules. Electron microscopic examination showed vacuolated cytoplasm of most cells, swollen mitochondria, and perinuclear dilatation. Concerning subgroup IIIa (PRP once), there was a partial improvement in the form of decreased vacuolations and regeneration of spermatogenic cells, as well as a reasonable improvement in sperm morphology. Regarding subgroup IIIb (PRP twice), histological sections revealed restoration of the normal testicular structure to a great extent, regeneration of the spermatogenic cells, and most sperms had normal morphology. Thus, it is recommended to use PRP to minimize structural changes in the testis of adult albino rats caused by oxymetholone.
Group Popularity Prediction in EBSN based on an Improved Self-Excited Hawkes Process

Event-based Social Network (EBSN) is a special online social network that not only provides users with a convenient virtual platform to communicate, but also helps them to participate in offline face-to-face social activities on a group basis. This paper studies the group popularity prediction problem in EBSN which is important for online advertising and event recommendation. Firstly, we construct the EBSN temporal network model with a timeline involved and propose a novel definition of group popularity in EBSN. Then on the basis of the above, we extract corresponding key features from four aspects: inherent group characteristics (i.e., founder, created time), historical popularity, the sentiment of users towards events as an internal factor, and the initiative of newly added users as an external factor. Combining these four features, we propose a group popularity prediction algorithm in EBSN based on an improved self-excited Hawkes Process: EBSN-Hawkes Process Algorithm (EHP). Experiments conducted by real EBSN datasets demonstrate that the proposed EHP algorithm has better prediction results than other comparative algorithms. We also conclude that the external dynamic feature plays a more important role than the internal sentiment feature by the ablation study.
Implementation of Electronic Throttle for Control of a Dual Fuel Diesel Engine

Dual fuel diesel engines can provide lower emissions as well as allow a diversified fuel supply. Dual fueling of diesel engines is typically accomplished using a gaseous fuel in the intake manifold and a quantity of pilot diesel directly injected into the cylinder to initiate combustion. However low load dual fuelling typically leads to a higher unburnt hydrocarbon component. Large engines for heavy machinery and marine applications will continue to be required and thus research on their use instigated this research to mitigate the undesirable unburnt hydrocarbon at part load. An electronic throttle body was implemented in the intake of a two-liter common rail diesel engine that had been converted to dual fuel operation. The throttle body was used to reduce the quantity of air admitted into the engine and thus a lower air-to-fuel ratio (i.e. closer to stochiometric). This was achieved with the aim of achieving better combustion in the end gas region and thus lower unburnt hydrocarbons. A low cost MQ8 hydrocarbon sensor was used to measure the unburnt hydrocarbons in the exhaust.
DeepCall: A Fast and Robust Malware Classification System with DGCNN and Function Call Graph

Malware has been researched hot off the press in cyber security for a long time. With the rise of machine learning algorithms, many research works attempt to apply machine learning-based methods in malware classification. However, existing machine learning-based malware classification methods rely on many various features extracted from malware samples, which may make their system lose processing speed and generality between different operational environments. These methods can not cope with massive malware samples. To improve generality and speed of classification system, we proposed a new model to classify malware with function call graphs (FCGs) extracted from their assembly code. According to previous studies, FCG is a generic feature and it is stable against metamorphic malware. Moreover, FCG extraction is not a time-consuming process. We select DGCNN (Deep Graph Convolutional Neural Network) to embed structural information inherent in FCGs for malware classification. It can make the best of the structure information stored in FCGs and make the results more convincing and accurate compared with other methods using traditional features. We use two large datasets from different operational environments containing nearly 20K malware samples to evaluate our proposed model. The experimental results show that it can classify malware represented as FCG with satisfactory accuracy and faster processing speed.
Content Delivery for Base Station Cooperative Caching in Cellular Networks

In a cache-enabled cellular network, a large number of repeated requests by users for hotspot contents in the network will increase the traffic load and the content delivery delay. Therefore, edge caching technology is introduced to cache highly popular content files in advance in cache devices closer to users to reduce the content delivery delay. However, the improvement of system performance by mobile edge caching technology will be limited by the storage capacity of the base station (BS). This paper studied the content delivery algorithm in cellular networks with BS cooperative caching capability which takes minimizing the system cost as the optimization goal. The content delivery strategy of the accessing BS and the content delivery strategy of the cooperative BS are constructed as two 0-1 matrix variables, which are used to indicate what contents the BSs deliver to the users at a certain moment. the coupling variables are solved by Lagrangian relaxation and primitive dual problem decomposition method respectively. According to the simulation results, the proposed content delivery algorithm can reduce the system transmission cost and improve the local cache hit rate efficiently.
Benchmarking Łukasiewicz Logic Solvers with Properties of Neural Networks

We propose new benchmarks for problems related to Łukasiewicz Infinitely-valued Logic and discuss methods for generating them. Such benchmarks comprehend instances that state properties about neural networks. In particular, reachability properties yield satisfiability instances and robustness properties yield logical consequence instances. We also present the results of empirical experiments where the proposed benchmarks were run in solvers based on SMT and MILP technologies. In this way, we are able, on the one hand, to compare the performance of different solvers and, on the other hand, to delve deeper into the investigation of neural network formal verification.
Smart Agent-Based Direct Load Control of Air Conditioner Populations in Demand Side Management

The rapid fluctuations of renewable resources, such as wind and solar, pose challenges to the reliability of the grid and make it difficult to integrate them into existing power systems. To address this uncertainty and variability in generation, direct load control is a promising method for managing the demand side. Thermostatically controlled appliances, such as air conditioners, can play a significant role in this process. However, to implement direct load control, the system operator needs a reliable estimate of the load's magnitude and the degree to which it can be shifted. This paper introduces a smart-agent architecture that utilizes a mathematical model of the aggregated power consumption of the load to forecast its behavior, even in the presence of changes imposed by the controller. To evaluate the system's performance, a numerical simulator was developed. It was observed that the system can adapt to changes in the system, retrain itself, and develop a new mathematical model to improve its prediction of the aggregated power consumption.
Leveraging Knowledge-Aware Method for Charge Prediction

The legal charge prediction aims to automatically predict the judgemental charges for legal cases, which plays a crucial role in legal assistant systems. Most existing methods mainly focus on predicting charges only based on the fact description of a legal case while ignoring the rich information contained in the contexts of charges and legal articles, which leads to unreliable prediction results. Moreover, suffering from data imbalance in real applications, they are not yet capable of predicting low-frequency charges with limited cases. To address these issues, we propose a novel low-frequency charge prediction framework via knowledge-aware method to fuse abundant information about charge labels and legal articles as additional legal knowledge, named KACP. The abundant information provides legal knowledge for low-frequency charges. Specifically, we devise a legal article-aware layer with double-layer attention to fuse semantics information of legal articles and utilize a charge-aware layer to extract the fusion representation of fact description and charge knowledge as the key features. The experimental results show that our proposed model can outperform other state-of-the-art baselines, especially in dealing with low-frequency charges, demonstrating our model’s superiority and competitiveness.
A Karush-Kuhn-Tucker approach to field-weakening for Surface-Mounted Permanent Magnets Synchronous Motors

This article presents a feedforward analytical solution of Field-Weakening (FW) for Surface-Mounted Permanent Magnets Synchronous Motor (SPMSM) using Karush-Kuhn-Tucker (KKT) optimality conditions. The generated current references take into consideration voltage and current constraints, load torque, and speed references. The formulated optimization problem helps ensure that the generated references stays within the feasible domain given by the constraints. Furthermore, the solution given by the KKT is implemented in real-time using a low cost rapid control prototyping microcontroller (ATSAME54P20A) without the use of look-up tables.
ZAlert: A Real Time Prediction Framework For Network Alert

With the rapid development of enterprise digital transformation and the widespread use of cloud-native architecture applications, the complexity of enterprise-level systems is getting higher and higher. This requires the engineers to be able to locate and repair alert incidents accurately and quickly to ensure the stability of complex network equipment. In this paper, we propose a novel alert prediction framework, ZAlert, which can predict the occurrence of future alerts and locate the equipment that may cause alerts based on real-time alarm data. First, ZAlert extracts text and statistical features from alarm data to build a high-performance comprehensive learning model to predict the category of alert that may occur in the future with an average performance of 0.81 on F1-score. Then, we use the knowledge graph based on the CMDB(Configuration Management Database) to search for alarm devices. In this way, engineers can quickly find devices that may cause alarms, and improve the the overall efficiency of engineers’ operation and maintenance.
A Characterization of the Effects of Software Instruction Prefetching on an Aggressive Front-end

Growing application sizes continue to strain the memory system. As more complex applications are developed, and the instruction memory footprint increases, the cache hierarchy cannot contain relevant instructions causing the front end to become idle as it awaits fetched instructions. Hardware instruction prefetchers can alleviate this problem by learning instruction stream behavior and prefetching instructions into the cache before use. Fetch Directed Prefetching (FDP) is a ubiquitous form of hardware instruction prefetching that uses branch predictor to predict future instruction cache references. Modern processors generally implement aggressive, deep FDP to decouple the front-end from the rest of the machine. Instruction accesses, however, provide a small amount of information over long periods due to instruction stream variability and lack of information regarding the context of instruction accesses. Capturing the instruction stream’s context requires significant storage overhead to correlate instruction accesses and ensure timely accesses. Software prefetching techniques overcome this problem by profiling and statically analyzing an application’s behavior. Prior work demonstrates software instruction prefetching’s potential performance benefit but does not evaluate performance in the context of aggressive, decoupled front-ends. While software prefetching provides ~ 20% improvement in conservative front-ends, we find that it does not yield performance benefit when modeling a baseline with an aggressive FDP, in some cases hurting performance. Our analysis finds that using software instruction prefetching negatively impacts an aggressive front-end’s behavior. We investigate this finding and characterize the different states a front-end can be in and how introducing instructions into an application can change the front-end’s behavior resulting in destructive interference with the software prefetcher.