Miracle-dz/UniFAR-FTUs huggingface dataset:
HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines

Modern server hardware is increasingly heterogeneous as hardware accelerators, such as GPUs, are used together with multicore CPUs to meet the computational demands of modern data analytics workloads.Unfortunately, query parallelization techniques used by analytical database engines are designed for homogeneous multicore servers, where query plans are parallelized across CPUs to process data stored in cache coherent shared memory. Thus, these techniques are unable to fully exploit available heterogeneous hardware, where one needs to exploit task-parallelism of CPUs and data-parallelism of GPUs for processing data stored in a deep, noncache-coherent memory hierarchy with widely varying access latencies and bandwidth. In this paper, we introduce HetExchange-a parallel query execution framework that encapsulates the heterogeneous parallelism of modern multi-CPU-multi-GPU servers and enables the parallelization of (pre-)existing sequential relational operators. In contrast to the interpreted nature of traditional Exchange, HetExchange is designed to be used in conjunction with JIT compiled engines in order to allow a tight integration with the proposed operators and generation of efficient code for heterogeneous hardware. We validate the applicability and efficiency of our design by building a prototype that can operate over both CPUs and GPUs, and enables its operators to be parallelismand data-location-agnostic. In doing so, we show that efficiently exploiting CPU-GPU parallelism can provide 2.8x and 6.4x improvement in performance compared to state-of-the-art CPU-based and GPU-based DBMS. PVLDB Reference Format: P. Chrysogelos, M. Karpathiotakis, R. Appuswamy, and A. Ailamaki. HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines. PVLDB, 12(5): 544-556, 2019. DOI: https://doi.org/10.14778/3303753.3303760
Deep Learning Models for Speech Emotion Recognition

Emotions play a vital role in the efficient and natural human computer interaction. Recognizing human emotions from their speech is truly a challenging task when accuracy, robustness and latency are considered. With the recent advancements in deep learning now it is possible to get better accuracy, robustness and low latency for solving complex functions. In our experiment we have developed two deep learning models for emotion recognition from speech. We compare the performance of a feed forward Deep Neural Network (DNN) with the recently developed Recurrent Neural Network (RNN) which is known as Gated Recurrent Unit (GRU) for speech emotion recognition. GRUs are currently not explored for classifying emotions from speech. The DNN model gives an accuracy of 89.96% and the GRU model gives an accuracy of 95.82%. Our experiments show that GRU model performs very well on emotion classification compared to the DNN model.
Design and performance analysis of various feature selection methods for anomaly‐based techniques in intrusion detection system

Intrusion detection system (IDS) is essential for the network; the intruder can steal sensitive information about networks. The IDS must have the ability to take care of large and real‐time data. The predicted rate must be high based on the available attribute. This work deals with a real intrusion detection problem, by its behavior. In this paper, we developed a hybrid model, which can detect intrusion by its action. We used an NSL‐KDD data set, the multiclass problem and binary problems are 25% tested. This model can be used to guess the availability of intrusion, able to determine the scope of intrusions based on the transaction of data in the network; training requires optimal features of a network transaction. The accuracy of the model is better for both binary class for the multiclass in NSL‐KDD data set. The complication of false data alarm rates is the most significant challenge in the IDS system, and it may be the low false rate or high false rate. Proposed work also addresses this problem. The first step that data will be filtered by Vote algorithm, the Information Gain will get associated with a base learner, to choose the necessary features, which directly affects the accuracy of the model. It uses the following classifier: RandomTree, REPTree, RandomForrest AdaBoostM1, Meta Pagging, DesicionStump, J48, LMT, Bagging, and Naive Bayes. On the based on the proposed model, it is observed as low false rate, high accuracy.
Fault Diagnosis of Rotary Machine Bearings Under Inconsistent Working Conditions

This article proposes a fault diagnosis (FD) method that is based on bispectrum analysis and a convolutional neural network (CNN) to identify bearing faults under inconsistent working conditions, such as high shaft speed variations with cracks of multiple scales and compound faults. First, the bispectra of the vibration signals are extracted, which exhibit consistent patterns under inconsistent working conditions. Second, CNN based on a stochastic optimization function (AdaMax) is proposed to extract interclass representations from the bispectra, hence reducing the overhead of feature extraction and selection. The AdaMax optimization is computationally efficient and is well suited to problems with big data sets. The obtained results of this article are compared with the current state-of-the-art bearing FD techniques, which demonstrates the superiority of the proposed model under inconsistent working conditions.
Area-Proportional Visualization for Circular Data

Abstract Data visualization is important for statistical analysis, as it helps convey information efficiently and shed lights on the hidden patterns behind data in a visual context. It is particularly helpful to display circular data in a two-dimensional space to accommodate its nonlinear support space and reveal the underlying circular structure which is otherwise not obvious in one-dimension. In this article, we first formally categorize circular plots into two types, either height- or area-proportional, and then describe a new general methodology that can be used to produce circular plots, particularly in the area-proportional manner, which in our opinion is the more appropriate choice. Formulas are given that are fairly simple yet effective to produce various circular plots, such as smooth density curves, histograms, rose diagrams, dot plots, and plots for multiclass data. Supplemental materials for this article are available online.
Model for Population III microquasars

Context. Current simulations indicate that the first stars were formed predominantly in binary systems. Studying the contribution of the first accreting binary systems to the reionization and heating of the intergalactic medium requires formulating a concrete model for Population III microquasars. Aims. We aim at constructing a complete model for microquasars with a Population III donor star. Methods. We considered that the mass loss of the star is exclusively caused by the spill of matter through the Roche lobe toward the black hole. We calculated the spectral energy distribution of the radiation produced by the accretion disk, the radiation-pressure-driven wind, and the relativistic particles in the jets, within the framework of a lepto-hadronic model. In addition, we estimated the effect on the reionization by the first microquasars. Results. We determine that Population III microquasars are powerful sources of ultraviolet radiation produced by the winds of their super-critical disks, and that they generate a broadband nonthermal emission in jets. Conclusions. Our results indicate that microquasars in the early Universe could have been important for the reionization and heating of the intergalactic medium.
Impact of the New ERA5 Reanalysis in the Computation of Radar Altimeter Wet Path Delays

Satellite altimetry allows the estimation of accurate water surface heights only with accurate determination of all involved terms, namely, the wet tropospheric correction (WTC) or its symmetric value, the wet path delay (WPD). WPD is best determined from onboard microwave radiometer (MWR) measurements; however, the corresponding WPD retrievals become invalid close to land (e.g., coastal and inland waters). Alternative WPD sources are numerical weather models (NWMs), e.g., from the European Centre for Medium-Range Weather Forecasts (ECMWF). NWMs provide the parameters at 6-h intervals; however, ERA5 (the latest ECMWF reanalysis) provides hourly atmospheric parameters at $0.25^{\circ } \times 0.25^{\circ }$ . The best spatial resolution is provided by the ECMWF operational model at $0.125^{\circ } \times 0.125^{\circ }$ . Motivated by this new and improved temporal resolution, the focus of this paper is the global assessment of the impact of different temporal resolutions of ERA5 in the WPD computation. The same assessment is also performed concerning the spatial resolution of ERA5 and operational models. Aiming to identify the best compromise between spatial/temporal resolutions, accuracy, and computational time, WPDs computed using various combinations of these resolutions were inter-compared and compared with MWR-derived WPD. The results show that the global root mean square (RMS) of the differences between MWR and ERA5 is 1.2 cm and the effect of using 1-h intervals instead of 6-h intervals is small, significant only for latitudes 30°–60° S and 30°–60° N. Hourly intervals do not have a significant impact on the WPD from ERA5, being a temporal resolution of 3 h high enough to ensure the same accuracy of 1 h, showing that ERA5 cannot map the WPD short space and time scales.
Semi-supervised Gaussian and t-distribution hybrid mixture model for water leak detection

The last few years have seen a great number of announcements and projections on cities of the future, where technological interconnected metering infrastructure is the main smart-grid unit, promoting higher sustainability due to its more efficient management capability. The water supply network is one of the grids that has been given additional attention due to the problem of waste caused by water leakage, usually requiring rapid detection for fast intervention to prevent high costs. With centralised information coming from the grid, like the measurement of pressure and flow, it is revealed that anomaly detection could be an important tool for quick automatic detection without needing permanent analysis by a human operator. However, there is a need for a more robust approach, especially when noisy data are present. In this paper, we propose the implementation of a new approach based on a hybrid expectation maximization (EM) Gaussian model combined with a t-distribution mixture. This approach is compared to both a pure EM Gaussian mixture model and a t-distribution mixture model that can use labelled data or not. Each EM algorithm was applied to real data acquired from a water supply grid with the aim of automatically detecting water leaks. Using the newly developed approach, the results show that detection is both possible and more accurate for this type of database.
A compact CPW‐fed UWB antenna with quadruple rejected bands

A compact coplanar‐waveguide (CPW)‐fed ultrawideband (UWB) antenna with quadruple rejected bands is proposed. To realize the bandwidth requirement of UWB communication, a gradient folk‐shaped radiating patch and a symmetrical ground plane are employed, which help to cover a frequency band from 2.7 to 10.7 GHz. In order to obtain four rejected bands within the UWB frequency band, four different slots including a cap‐shaped slot, two meandering slots, and a split‐ring are utilized, which contribute to generate rejected bands at C‐band (3.60‐4.32 GHz), wireless local area networks bands (5.17‐5.32 GHz and 5.80‐5.93 GHz), and International Telecommunication Union service band (7.8‐8.2 GHz), respectively. Excellent band‐notched features are obtained by optimizing the positions and lengths of the four slots. Simulated and measured results show that the proposed antenna is especially suitable for UWB communication.
High-Order Spectral Simulation of Graphene Ribbons

The plasmonics of graphene and other two-dimensional materials has attracted enormous amounts of attention in the scientific literature over the past decade. Both the possibility of exciting plasmons in the terahertz to midinfrared regime, and the active tunability of graphene via electrical gating or chemical doping has generated a great deal of excitement among engineers seeking sensing devices which operate in this regime. Consequently there is significant demand for robust and highly accurate computational capabilities which can incorporate such materials. Standard volumetric approaches can answer this demand, but require vast computational resources in exchange. Here we describe an algorithm which addresses this issue in two ways, first, we model the graphene layer with a surface current which is applicable to a wide class of two-dimensional materials. In addition, we reformulate the governing volumetric equations in terms of surface quantities using Dirichlet-Neumann Operators. These surface equations can be numerically simulated in an efficient, stable, and accurate fashion using a novel High-Order Perturbation of Envelopes methodology. We utilize an implementation of this algorithm to study absorbance spectra of TM polarized plane-waves scattered by a periodic grid of graphene ribbons. AMS subject classifications: 78A45, 65N35, 78B22, 35J05, 41A58
Towards Energy Efficiency for IoT: A Cross Layer Design in Cognitive Cellular Networks

With the proliferation of user devices, the userlevel energy efficient (EE) design becomes more critical in the era of Internet-of-Things (IoT). Besides, user generated data traffic requires sufficient radio spectrum to support but current spectrum are not fully utilized. In this paper, we propose a novel cognitive radio (CR) mesh network and augment it with the current cellular networks. Under this architecture, we investigate the user-centric, rather than the conventional infrastructurelevel, EE design by jointly considering user association, power control, OFDM sub-channel allocation, flow routing and link scheduling. The design is cast into a cross-layer optimization problem which however proves to be difficult to tackle. In light of this, we propose a parametric transformation technique to convert the original problem into a more approachable one while further introducing the {\Delta}-confidence level and integer relaxation approach to address the complexity of the problem. Furthermore, we present a two-layer algorithm, consisting of an inner and outer loop optimization, to solve the transformed problem. A benchmark scheme is used to demonstrate the advantage of our design. We then carry out extensive simulations to exhibit the optimality of our proposed algorithms and the network performance under various settings.
A Three-Dimensional Deep Learning Framework for Human Behavior Analysis Using Range-Doppler Time Points

Deep neural networks have shown promise in the radar-based human activity analysis application. Different from existing deep learning models that take either micro-Doppler spectrograms or range profiles as their input, the proposed method can process micromotion signatures in a 3-D way. In this letter, we first transform radar echoes into range-Doppler (RD) time points and then directly process the point sets via a designed 3-D network called the RD PointNet. In fact, our point model is a discrete representation of the motion trajectory. Through this quantitative model, we can use the 3-D network to simultaneously capture human motion profiles and temporal variations. The motion capture simulations and ultrawideband radar measurements show that the proposed framework can achieve superior classification accuracy and noise robustness when compared with image-based methods.
Auto-regressive moving average parameter estimation for 1/f process under colored Gaussian noise background

Current algorithms for estimating auto-regressive moving average parameters of transistor 1/f process are usually under noiseless background. Transistor noises are measured by a non-destructive cross-spectrum measurement technique, with transistor noise first passing through dual-channel ultra-low noise amplifiers, then inputting the weak signals into data acquisition card. The data acquisition card collects the voltage signals and outputs the amplified noise for further analysis. According to our studies, the output transistor 1/f noise can be characterized more accurately as non-Gaussian α-stable distribution rather than Gaussian distribution. We define and consistently estimate the samples normalized cross-correlations of linear SαS processes, and propose a samples normalized cross-correlations-based auto-regressive moving average parameter estimation method effective in noisy environments. Simulation results of auto-regressive moving average parameter estimation exhibit good performance.
Two-Timescale Stochastic Approximation Convergence Rates with Applications to Reinforcement Learning

Two-timescale Stochastic Approximation (SA) algorithms are widely used in Reinforcement Learning (RL). Their iterates have two parts that are updated with distinct stepsizes. In this work we provide a recipe for analyzing two-timescale SA. Using it, we develop the first convergence rate result for them. From this result we extract key insights on stepsize selection. As an application, we obtain convergence rates for two-timescale RL algorithms such as GTD(0), GTD2, and TDC.
Warhead fragment position measurement method by using two light filed cameras

The warhead fragments formed by projectile proximity explosion are easily occluded and coincided at the same time, it is very difficult to identify the spatial position of each warhead fragment, which will affect the analysis and research of the launching performance of weapons. To obtain dynamic parameter of each warhead fragment, this article proposes a new measurement method by using two light field cameras with micro‐lens array imaging, establishes the spatial position calculation model of warhead fragment, researches the spatial position recognition algorithm of warhead fragment, gives the calculation function of the depth value of the warhead fragment based on digital refocusing principle of light field camera. According to the depth information of the warhead fragment in the optical imaging region, sets up the recognition model and provides the processing algorithm of multiple warhead fragments occlusion and coincidence. In accordance with the spatial position calculation function of warhead fragment, utilizes the differential method to analyze the testing error. Through simulation and experimental analysis, the results of simulation and contrastive testing show that the proposed method can effectively recognize and distinguish the occlusive warhead fragments, which makes up for the shortcomings of the traditional binocular vision testing method.
Optical properties of electrically controlled arc-electrode liquid-crystal microlens array for wavefront measurement and adjustment.

An electrically controlled arc-electrode liquid-crystal microlens array (AE-LCMLA), with tuning and swing focus, is proposed, which can be utilized to replace the traditional mechanically controlled microlenses and also cooperate with photosensitive arrays to solve the problems of measuring and further adjusting a strong distortion wavefront. The top patterned electrode of a single LC microlens is composed of three arc-electrodes distributed symmetrically around a central microhole for constructing the key controlling structures of the LC cavity in the AE-LCMLA. All the arc-electrodes are individually controlled, and then the focal spot of each microlens can be moved freely in a three-dimensional fashion including along the optical axial direction and over the focal plane by simply adjusting the driving signal voltage applied over each arc-electrode, independently. The featured performances of the AE-LCMLA in a wavelength range of ∼501-561 nm are the driving signal voltage being relatively low (less than ∼11 Vrms), the focal length tuning range being from ∼2.54 mm to ∼3.50 mm, the maximum focus swing distance being ∼52.92 μm, and the focus swing ratio K being ∼20‰.
LSTM-based dynamic probability continuous hand gesture trajectory recognition

In the field of continuous hand-gesture trajectory recognition, aiming at the problems of existing a lot of noise for handwriting trajectories, and difficult to segment multiple continuous hand gestures accurately, a long short-term memory-based dynamic probability (DP-LSTM) method is proposed. Firstly, obtain the classification result for each sub-period in the whole time period by using LSTM; secondly, cluster the classification results by non-maximum suppression for trajectory algorithm to eliminate interference of invalid subsets; Finally, the end point of the valid trajectory is obtained according to the characteristics of the probability change, thus realising dynamic trajectory segmentation and recognition. In order to evaluate the performance of the DP-LSTM, this method is evaluated by using an Arabic numerals gesture database. The experiments show that the DP-LSTM has a high recognition rate for continuous hand gestures and can recognise its in real time.
Generation and Thermal Properties of a Dual-Transverse-Mode With Dual-Polarization and Dual-Frequency by a Dual-Medium Microchip Laser

A dual-polarization, dual-frequency and dual-transvers-mode laser beam is generated by a dual-medium microchip laser. The dual-transvers-mode includes a linearly polarized LG01 mode and a perpendicular polarized TEM00 mode, which have different optical frequencies. How is the beat frequency of the dual-mode influenced by the thermal factors, including cooling temperature of birefringent crystal and thermal effect of gain medium, is experimentally investigated and theoretically analyzed. These studies offer an extension and better understanding of the dual-medium based dual-frequency laser, which is of important applications in coherent Lidar detection and optical communications.
Roughness effects in laminar channel flow

The effects of roughness on the frictional drag and pressure drop in laminar channel flow are investigated numerically. The inflow is fully developed smooth wall flow, and square rib roughness, aligned normal to the bulk flow direction, is introduced as a step change. The roughness height and spacing are systematically varied, and the flow is examined as it develops over the rough wall and becomes fully developed. The length of the development region depends primarily on the roughness height, although the effects of spacing become more important as the height decreases. In the fully developed rough wall regime, the friction coefficients always increase with roughness when compared to the smooth wall case, but the increase depends crucially on the roughness height and to a lesser extent on the spacing. Using the constricted diameter in the definition of the friction factor collapses the data on the smooth wall value to within 10 % for all roughnesses studied here, with the remaining deviation increasing linearly with roughness spacing. The friction factors scale with the inverse of the Reynolds number, as seen elsewhere. The scaling of the development length and the friction coefficient can be explained by the relative contributions made by the pressure drop on each element and the skin friction acting over the surface area. These observations are examined in terms of the flow patterns in the vicinity of the roughness elements, which leads us to propose a definition for fully rough laminar flow.