Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated lattice perturbation theory for improved quark and gluon actions

Many fundamental parameters of the standard model are difficult to extract from measurements because they require an understanding of the strong interaction at low energies. Lattice gauge theories provide a first principles method to compute such effects. Unfortunately with current computational resources, "naive" lattice gauge theories would not give the precision results that the experiments require. Fortunately, there are several field theoretical methods that we can use to improve accuracy at minimal computational cost. One part of this program involves improving the lattice act ions, to better approximate the continuum theory. This improvement can be done order by order in perturbation theory. For several reasons, perturbative calculations in lattice gauge theory are very complicated. To deal with this, we have developed a set of tools to automate the most difficult parts of the calculations. This thesis introduces the general procedure of perturbatively improving lattice gauge theories, and outlines why perturbative calculations are needed. The calculations are done for the most complex actions currently used by simulations, the Symanzik improved gluon action for the gluons, Fermilab fermions for the heavy quarks, and AsqTad fermions for the light quarks. We report the results of a number of calculations, we have computed various lattice operators (mean link, static quark potential) to second order, and a number of important heavy quark quantities (including matching the clover operator for the Fermilab action) to first order. The latter calculations have the potential to greatly improve lattice QCD determinations of mious heavy quark quantities.
Deep Deconvolution Applied to Distributed Acoustic Sensing for Traffic Analysis

Distributed Acoustic Sensing (DAS) is a nascent technology that facilitates the measurement of vibrations along fibre-optic telecommunication cables, which has numerous novel applications in many domains of science and engineering. In the present study, we use DAS to analyse traffic along a fibre-optic cable deployed along a major road in Nice, France. We recently proposed to use a MUSIC beamforming algorithm for the objective of estimating the speed of individual vehicles. To greatly improve the accuracy and precision of the beamforming results, we here propose a Deep Deconvolution algorithm applied to the data prior to beamforming. The accuracy of the speed estimation is in the range of 0.14-0.25 km h−1, which is at least one order of magnitude better than conventional methods. DAS therefore has great potential in urban traffic analysis applications.
Classification Algorithms for Analyzing Parkinson's Disease Patient

Parkinson's disease is a neurodegenerative disorder characterized by motor and non-motor symptoms; it is difficult to detect and treat promptly. We aimed to classify Parkinson's disease patients versus healthy control subjects. We used for experimentation a real dataset that contains Gait characteristics extracted using inertial motion sensors from a smartphone (iPhone 5S). Sixty people participated in this experiment, 53 of whom were people with Parkinson's disease. We implemented feature selection methods to reduce dimensionality. Furthermore, we implemented four classification algorithms and evaluated them based on their accuracy, sensitivity, and specificity. The Support Vector Machine algorithm obtained an overall accuracy of 97.5%, a sensitivity of 95.3%, and a specificity of 99.8%.
MAC-Layer Traffic Shaping Defense Against WiFi Device Fingerprinting Attacks

WiFi networks are vulnerable to statistical traffic analysis attacks, even when a WiFi network is securely encrypted and the attacker is unable to join the network. Many defenses proposed in the literature are inefficient to deal with profiling attacks against WiFi-based Internet-of-Things (IoT) devices, because they burden the Internet traffic with high bandwidth overhead and pose deliberate delay on packet transmission. In this paper, we propose a new MAC-layer packet injection technique where injected dummy packets only exist within the WiFi link between IoT devices and their connected WiFi access point. This traffic shaping defense is effective against data-link device profiling attacks without adding any Internet-side overhead or time delay in legitimate traffic. We evaluated our approach on four WiFi-based IoT devices against a recent privacy attack, and showed the reduction of attack classification accuracy from the original 100% to 54%, close to random guessing.
Layer-specific analysis of cortical fear engram in mice

Associative learning is a fundamental mechanism for acquiring new experience by the brain. Though molecular, structural and synaptic aspects of this process have been thoroughly studied, less is known about how it happens at the level of cortical circuitries. According to the theory of predictive coding, associative learning leads to the formation of cortical representations or engrams that affect the supragranular (L2/3) and infragranular (L5/6) cortical layers in different ways. We utilized the cued (auditory) fear conditioning paradigm and layer-specific c-Fos neuroimaging of associative (prelimbic and cingulate) and auditory cortical areas after memory retrieval in mice to elucidate this issue. Retrieval of associative memory about auditory conditioned stimulus (CS) resulted in the preferential activation of cingulate and infralimbic cortices in conditioned mice compared to unpaired, CS-only controls and home cage mice. This activation was specific in all layers of cingulate cortex as well as in infragranular layers of prelimbic cortex. In auditory cortex in all mice, presented with auditory stimuli during retrieval (conditioned, unpaired and sound control groups) there was strong a c-fos activation. Such activation was found in all layers. Finally, we used correlation analysis and showed the c-fos expression in prelimbic and primary auditory but not cingulate cortex was highly-correlated across layers. The present findings indicate that associative memory cortical engram is unequal across layers and preferentially includes infragranular neurons.
Preservation Of Interaural Level Difference Cue In A Deep Learning-Based Speech Separation System For Bilateral And Bimodal Cochlear Implants Users

Due to the success of deep neural networks (DNNs) in speech separation, the DNN-based speech separation method has become a potentially feasible front end of cochlear implants (CIs) to reduce noises. However, most DNN-based methods neglect the demand of accurately preserved spatial cues, which are necessary for bilateral and bimodal CI users to localize sounds and benefit from spatial release from masking. In our previous study, a speech separation framework with spatial cues preservation has been designed, by restoring the distorted relative transfer function of the pre-separated speech. In this work, the framework was extended for evaluation in simulated CI hearing scenarios with bilateral and bimodal CI setups. Experiment results showed that the framework significantly reduced the interaural level difference (ILD) errors of the speech separated by the existing DNN-based method, indicating that the proposed DNN-based speech separation framework can effectively preserve the ILD cue in the CI application scenarios.
Superiority of the Neural Network Dynamic Regression Models for Ontario Electricity Demand Forecasting

Electricity demand forecasting is an essential step in balancing the electricity grid. A balanced electricity grid helps make efficient decisions in production planning and scheduling. The study of uncertainty in electricity demand requires more advanced hybrid forecasting approaches, which combine statistical methods and machine learning techniques. In electricity demand forecasting, the features such as temperature and day-type are suggested as the most influential features in the literature. The novelty of this paper is to use Canadian electricity demand data and explore the effect of temperature and day-type on electricity demand using hybrid models. The hybrid neural network dynamic regression (NNDR) model is used to obtain the point forecasts/predictions for Ontario electricity demand data. The experimental results show the superiority of the proposed NNDR model over the commonly used dynamic regression models with the Seasonal Autoregressive Integrated Moving Average (DRSARIMA) errors model and the Prophet model. Moreover, long-term point forecasts and innovations are used to obtain two classes of prediction intervals (PIs) using data-driven probabilistic innovation distribution and bootstrapping for NNDR, DRSARIMA and Prophet models.
Thermal behaviour of magnetron sputtered tungsten and tungsten-boride thin films

Magnetron sputtered tungsten and tungsten diboride nanofilms are proposed to be used in microelectronic devices. Methods of deposition for nanofilms on oxidized silicon followed by etching is among the techniques to be applied for production. However, physical-chemical interactions between various films need to be taken into account. Therefore, a complete surface and in-depth characterization of synthesized films is necessary. Tungsten and tungsten diboride nanofilms are deposited by magnetron sputtering technique, followed by plasma chemical and chemical etching. Characterization of the structures is separated in several main steps including surface morphology, analysis of element composition as well as characterization of thermal properties, including estimation of presence of thermally active emission centers and thermally induced chemical conversions.
Position Control of Traction Upper Limb Rehabilitation Robot Based on Fuzzy Sliding Mode Method

Given the shortcomings of traditional upper limb rehabilitation robots, such as large size and high price. A traction upper limb rehabilitation robot with omnidirectional wheels as a mobile platform is designed. In order to improve the control performance of the robot tracking training reference trajectory, reduce the chattering, and enhance the robustness, a variable reaching law parameter controller based on a fuzzy sliding mode control(FSMC) is designed in this paper. Reaching law parameters can be adjusted by fuzzy control(FC) and sliding mode reachability conditions which can reduce chattering and convergence time. First, the overall structure of the robot is presented. Then a kinematic model and an error kinematic model will be established. Next, sliding mode control(SMC) based on the error kinematic model is introduced, and the parameters of the power exponential reaching law(PERL) are adjusted by the fuzzy control strategy. Finally, the simulations and experiment demonstrate that the control method can effectively reduce chattering to enhance dynamic performance and stability.
Single-Snapshot DOA Estimation via Weighted Hankel-Structured Matrix Completion

In this paper, we consider the problem of estimating direction of arrivals (DOA) using a single snapshot of sparse linear array (SLA); the employed SLA is a sampled version of a uniform linear array (ULA). For the estimation task, we propose a two-step algorithm: (i) we first interpolate for the missing samples of the SLA to form a complete ULA by converting the samples into Hankel matrix and solving a weighted low-rank minimization. (ii) Next, we estimate the DOAs using a subspace method, like Prony. In step (i), the matrix completion problem is approached by adding left and right weight matrices to the Hankel matrix obtained by lifting the antenna observations. Simulation results show that the proposed method has superior accuracy in DOA estimation compared to the other methods proposed in the literature, such as atomic-norm minimization and off-the-grid approaches.
Detection of Lane and Speed Breaker Warning System for Autonomous Vehicles using Machine Learning Algorithm

Vehicle’s camera-based lane and speed breaker detection and the drastic updation in modern technology needs the transportation to be automated and self-driven. It depends on the outcome of detection algorithms. But, the computation and pre-processing of these algorithms directly affects the prediction of objects for the vehicle to drive autonomously. Thus, to make an efficient detection algorithm, machine learning and computer vision algorithms are utilized in our work for the tasks of the lane and speed breaker detection for an autonomous vehicle. The lane detection is performed by using OpenCV for gradient and color thresholds; and Fully Convolutional Network (FCN). The FCN uses the pixels of the lane image to find the region of interest (the lane) irrespective of the environmental conditions. The speed breaker system is detected by using YOLOv5 custom object detection with the help of diverse augmented datasets. For real time object detection, YOLO algorithm uses neural networks. Speed and accuracy is the important feature of this algorithm. Experiments are carried out using collection of dataset for lane (1795) and speed breaker (1870) detection, and the accuracy is increased quantitatively (Normal, Shadowed and Multiple speed breaker are 78%, 65% and 75% respectively).
A K/Ka-Band Diplex Circularly Polarized Horn Antenna with High Isolation

In this paper, a K/Ka-band diplex circularly polarized horn antenna with high isolation is proposed for satellite communication systems. The proposed antenna is composed of a metal waveguide diplexer and a septum circular polarizer. The metal waveguide diplexer will provide dual channels with high isolation characteristics, while the septum circular polarizer is capable of transmitting and receiving two orthogonal circularly polarized signals simultaneously. The simulated results show that the proposed antenna achieves a reflection coefficient of less than −10 dB, an isolation of less than −68 dB, and an axial ratio lower than 3dBat the frequency band of 18.5-21.5 GHz and 27-32 GHz, respectively. The good performances make the proposed antenna a good candidate for the satellite communication system.
THE STRUCTURE OF SPIRAL SHOCKS EXCITED BY PLANETARY-MASS COMPANIONS

Direct imaging observations have revealed spiral structures in protoplanetary disks. Previous studies have suggested that planet-induced spiral arms cannot explain some of these spiral patterns, due to the large pitch angle and high contrast of the spiral arms in observations. We have carried out three-dimensional (3D) hydrodynamical simulations to study spiral wakes/shocks excited by young planets. We find that, in contrast with linear theory, the pitch angle of spiral arms does depend on the planet mass, which can be explained by the nonlinear density wave theory. A secondary (or even a tertiary) spiral arm, especially for inner arms, is also excited by a massive planet. With a more massive planet in the disk, the excited spiral arms have larger pitch angle and the separation between the primary and secondary arms in the azimuthal direction is also larger. We also find that although the arms in the outer disk do not exhibit much vertical motion, the inner arms have significant vertical motion, which boosts the density perturbation at the disk atmosphere. Combining hydrodynamical models with Monte-Carlo radiative transfer calculations, we find that the inner spiral arms are considerably more prominent in synthetic near-IR images using full 3D hydrodynamical models than images based on two-dimensional models assuming vertical hydrostatic equilibrium, indicating the need to model observations with full 3D hydrodynamics. Overall, companion-induced spiral arms not only pinpoint the companion’s position but also provide three independent ways (pitch angle, separation between two arms, and contrast of arms) to constrain the companion’s mass.
A Linear MMSE Filter Using Delayed Remote Microphone Signals for Speech Enhancement in Hearing Aid Applications

Existing methods that use remote microphones with hearing aid (HA) noise reduction systems, assume the wireless transmission to be instantaneous. In practice, however, there exists a time difference of arrival (TDOA) between the wirelessly transmitted target signals and the acoustic signal arriving from the target source to the HA device, which degrades their noise reduction performance. As speech is correlated between consecutive time-frames in the short-time Fourier transform (STFT) domain, we propose a linear minimum mean-square error (MMSE) estimator to estimate the desired signal, by combining multiple HA microphone signals with multiple consecutive time-frames of the remote microphone signal. We derive closed form expressions for the resulting filter weights and interpret them in terms of existing multi-channel and multi-frame methods. The simulation results validate the interpretation and show that using a multi-frame method along with a multi-channel method is an advantage, in the presence of unknown, positive TDOA between the microphone signals.
MEMS Double Sided Touch Mode Capacitive Pressure Sensor using Silicon Carbide –Aluminium Nitride: Fast analysis

Today, pressure sensors occupy the largest market segment in microsystem devices. This is because of their wide range of applications in both commercial and industrial applications. The two major players in MEMS pressure sensors are capacitive and piezoresistive types. Capacitive pressure sensors operating in touch mode have a higher linearity and operating range and are therefore more suitable. Sensor performance can be further enhanced by etching two back to back traditional touch mode capacitive sensors in one substrate. This paper aims to produce a complete mathematical analysis of the double-sided structure of capacitive pressure sensors using Silicon Carbide-Aluminum Nitride as the primary sensing element. The mathematical analysis bypasses the need for using cumbersome simulation software and gives a fast-analytical technique that can be applied to similar simple designs for modelling. The final MATLAB simulations show a significant improvement in both the sensitivity and capacitance generated on using SiC-AlN over Si-SiO2 for the given design.
Combined Transmission and Distribution Test System for Small-Signal Stability Analysis: Initial Results

In modern power systems is recommended to study the overall system with detailed understanding of the large-scale generation, the wider transmission network, the distributed renewable energy sources and the flexible loads. Therefore, detailed simulation models are required. In this paper, a combined transmission and distribution network synthetic model is developed to investigate the dynamic performance of the overall power system and analyse the interactions between its constituent parts. Conventional and modern power system configurations are considered. The small-signal stability of the power system is investigated by means of eigenvalue analysis and by applying the Matrix Pencil algorithm to identify the dominant modes contained in the dynamic responses.
Characterizing Fetal Heart Rate From A State Space Perspective

Nonlinear features have been widely adopted in var-ious biomedical applications including fetal heart rate analysis, and they have often demonstrated superior discriminatory ability as they can reveal information hidden in time series. However, classical nonlinear features have limited discriminatory ability in fetal heart rate analysis. In this paper, we cast nonlinear features into a state space reconstruction framework and show their intrinsic connection with Takens' theorem. From this perspective, we propose a novel state space reconstruction-based feature that is able to better capture the system variability which is of great importance in fetal heart rate analysis. Our experimental results on an open access intrapartum Cardiotocography database show that the proposed feature achieves better diagnostic performance in pH-based fetal heart rate analysis compared to both classical and state-of-the-art nonlinear features.
Rule Placement and Switch Migration-based Scheme for Controller Load Balancing in SDN

In software-defined networks, due to the limited flow table capacity, unreasonable rule placement will cause the flow table overflow problem. These flows without flow rules installed need to be processed by controller, which increases and even unbalances controller load. Based on the average packet end-to-end delay, we propose a rule placement and switch migration-based scheme for controller load balancing. In the routing and rule placement phase, the Cost-Aware Routing (CAR) algorithm takes into account the flow table occupancy while utilizing the installed rules to alleviate flow table overflow and preliminarily balance the controller load. In the switch migration phase, the Benefit-Cost Switch Migration (BCSM) algorithm obtains the migration option with the maximum total benefit. Numerical results show that the CAR algorithm reduces and balances controller load to achieve lower delay than Random and FlowStat. And the BCSM algorithm balances the controller load and reduces packet delay than SMCS and ESMLB.
Knowledge Enriched Schema Matching Framework for Heterogeneous Data Integration

Schema matching is a process of creating the correspondences and mappings from the various schemas, which is a critical phase of migrating and integrating heterogeneous databases from multiple sources. However, the semantic heterogeneity in various schemas brings some obstacles while establishing the correspondences between source schema and target schema, hence human interventions and domain knowledge are required to tackle some complex mapping tasks for heterogeneous data integration. To reduce human intervention and improve the ability to handle complex matching tasks, we present a knowledge-enriched schema matching framework. In this framework, the schema matching task is treated as a classification problem, thereby, a schema matching network is designed as a classifier to give the mapping result. In particular, the external knowledge bases are injected into the schema matching network to capture the background knowledge and provide the common knowledge to handle the semantic heterogeneity of complex mapping tasks. Additionally, the main components of the presented framework and their roles are analyzed, and the feasibility of our framework and the future work are highlighted.
PWM-Based Optimization-Free Active Voltage-Balancing Control of 7-Level Active Neutral-Point-Clamped Flying-Capacitor Multicell Inverters

This paper introduces a new pulse-width-modulation (PWM) -based control technique for 7-level active neutral-point-clamped (A-NPC) flying-capacitor multicell (FCM) inverters. It regulates inverter's output voltage, current, and balances FCs at reference values. It is realized on basis of active control approach which is achieved using instantaneous measurements taken from inverter, and then being interpreted into the logic quantities. Contrary to space-vector modulation (SVM) and model predictive control (MPC) methods, the proposed solution is deemed a direct approach that excludes cost/objective functions and time/software-consuming optimization processes to generate the switching states. It utilizes the measured values of the FC voltages and load current to derive the logic variables and equations that directly determine the inverter's switching states according to the reference voltage-levels. As its advantage, any commonly used modulators such as phase-shifted-carrier (PSC) or phase-disposition (PD) strategy could be readily embedded into control unit to generate inverter's reference voltage-levels. The experimental results are provided to validate the suggested active balancing technique, its effectiveness and fast execution-time.