Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fast Hybrid Spatial Index with External Memory Support

Despite claiming to support external memory access, very few learned indices implement their indices on secondary storage. It is challenging to extend learned models to secondary storage while preserving high levels of index performance. In this paper, we propose a Fast Hybrid Spatial Index with External Memory Support (FHSIE). Its core idea is to learn a model which can group spatial objects in a top-down manner with few parameters. We use a height-balanced hierarchical structure, which recursively uses simple unsupervised models to group (i.e., cluster) spatial objects. We associate the learned structure with a grid for accurate query processing, and we utilize the relationship among clusters and grid cells to estimate the grid layout and optimize grid performance. Extensive experiments on real and synthetic data sets with more than 200 million points show that FHSIE is highly efficient and precise no matter where it works, i.e., in memory or on a disk.
Educational chatbot on Data Structures and Algorithms for the visually impaired

Visually impaired people find it difficult to study from educational websites, even though they have a lot of knowledge to provide. They are unable to feel confident about using online tools to learn because of their lack of independence. This becomes a problem for almost every student when they have to search through different websites for answers to their doubts. A chatbot is a conversational AI technology that simulates human interaction. It is more comfortable for users to interact with a chatbot if it is voice-enabled. Our proposed solution is a chatbot that is voice-enabled as well as multilingual. It can interact with more than 180 global languages, although, in this paper, we have focused on the Hindi language. It can help visually impaired people as well as normal students, as they can learn by just having a conversation with the voice bot. Our voice bot can teach students many concepts regarding data structures and algorithms. It gives the user a speech-based response received from a generated . mp3 file. For the voice-bot system, which has been trained with more than 70 intents, we have utilized the RASA NLU principle. It has been deployed onto our e-learning web application. The chatbot is compatible with all the languages mentioned in the python translate module. This chatbot recognizes and converts the speech received from the microphone into text and matches with the trained intents information, and accordingly gives the most appropriate answer and converts text-to-speech and presents the text and speaks in the language selected along with multi-intent classifications. We have made a custom pipeline and also applied various policies to it to improve pipeline performance. Nearly all of the queries are correctly answered by the voice bot, which makes communication more effective.
MySemCloud: Semantic-aware Word Cloud Editing

Word clouds are a popular text visualization technique that summarize an input text by displaying its most important words in a compact image. The traditional layout methods do not take proximity effects between words into account; this has been improved in semantic word clouds, where relative word placement is controlled by edges in a word similarity graph. We introduce MySemCloud, a new human-in-the-loop tool to visualize and edit semantic word clouds. MySemCloud lets users perform computer-assisted local moves of words, which improve or at least retain the semantic quality. To achieve this, we construct a word similarity graph on which a system of forces is applied to generate a compact initial layout with good semantic quality. The force system also allows us to maintain these attributes after each user interaction, as well as preserve the user’s mental map. The tool provides algorithmic support for the editing operations to help the user enhance the semantic quality of the visualization, while adjusting it to their personal preference. We show that MySemCloud provides high user satisfaction as well as permits users to create layouts of higher quality than state-of-the-art semantic word cloud generation tools.
Dynamic Gate Capacitance Model for Switching Transient Analysis in P-GaN Gate HEMTs

In this work, an efficient switching transient analytical model is proposed for P-GaN gate HEMTs, in which the dynamic gate capacitance $C_{\mathrm{G}}(V_{\text{DS}},\ V_{\text{GS}})$ characteristics during switching transient has been taken into consideration. Meanwhile, the modeling of external inductance, PCB, driver IC and surface mounting technology (SMT) components in the double-pulse characterization platform are taken into consideration. The proposed dynamic capacitance model is validated by the $C-V$ measurements. Consequently, the switching transient analytical model featuring dynamic gate capacitance characteristics can yield improved accuracy, in comparison with the conventional approach with merely static gate capacitance model.
Sailfish: Exploring Heterogeneous Query Acceleration on Discrete CPU-FPGA Architecture

The hardware of modern server is being increasingly heterogeneous as advanced accelerators, such as FPGAs, are used together with multicore CPUs to meet the computing requirement of analytical query workloads. Unfortunately, the earlier database engines are designed for homogeneous servers, where query execution is only parallelized across CPUs, but ignores the prized FPGA resources. To exploit the available heterogeneous resources, emerging work try to construct the cross-device query pipeline, wherein a part of the operators run on the FPGA-end and the rest run on the CPU-end respectively. However, when running such a pipeline, the CPU-end and FPGA-end operators could obtain mismatched computing resources, which easily cause uneven processing performance between two ends, thereby limiting whole pipeline performance or wasting computing resources. Of note, it is nearly impossible for query optimizers to assign the matched computing resources to make CPU-end and FPGA-end have the similar processing performance, as several factors (e.g., operator selectivity) affect processing performance. To tackle this problem, we propose the heterogeneous motion operator, which can adjust the runtime computing resources (e.g., parallelism) of CPU-end, so as to match FPGA-end processing performance. In addition, we further implement an FPGA accelerator that supports parallel processing of hash join. By integrating our motion operator into PostgreSQL with the above FPGA accelerator, we build a prototype called Sailfish, whose experimental performance exceeds the native acceleration scheme by a huge margin.
Deep Learning Based Kalman Filter for GNSS/INS Integration: Neural Network Architecture and Feature Selection

This paper provides further details of the deep learning (DL) based integration algorithm for global navigation satellite system (GNSS) and inertial navigation system (INS) integration, where a deep neural network (DNN) is inserted into the flow of an error-state extended Kalman filter (ES-EKF) to learn the complex dynamics of the system. The proposed algorithm learns the optimal Kalman gain along with the errors in the inertial measurement units (IMU) and demonstrates superior performance over ES-EKF in terms of estimated navigation solutions and IMU errors. In this work, we analyze different implementations of the neural networks, the network architectures, and the impact of the various features to the performance of the proposed algorithm. We suggest a convolutional neural network (CNN) to extract spatial information and a long shortterm memory neural network (LSTM) to capture temporal dependencies. We justify the use of LSTM as compared to other types of recurrent neural networks (RNN). Optimal sizes for the fully connected layers, number of layers, and hidden state sizes are determined too. We report the observed difficulties in the learning, namely vanishing and exploding gradients and list the techniques we used to cope with these issues. The computational efficiency of the DL-based ES-EKF is compared to regular ES-EKF, the DL-based algorithm is supposed to fit into real-time requirements. Analysis of the impact different features have on the convergence and performance of the algorithms is carried out.
AP-Coach: formative feedback generation for learning introductory programming concepts

In this work, we aim to improve code writing skill in Python-based introductory programming courses for first-year university students. In such courses, students as novice programmers would benefit from personalised and formative feedback to: 1) quickly identify issues in their computational thinking process or coding techniques, and 2) know how to proceed when facing a certain problem. Due to the large number of students, it is impractical for instructors to manually assess all the work of each student to provide tailored feedback. We design and implement Automatic Programming Coach (AP-Coach), a web-based tool for automatically generating formative feedback for exercises on basic programming concepts. AP-Coach combines software engineering techniques (code similarity measures based on abstract syntax trees, and unit testing), and AI techniques (machine translation) in a novel manner to provide relevant feedback. We report promising results for AP-Coach in the following aspects: 1) quantitative evaluation of code similarity computation and machine translation, 2) qualitative evaluation of the perceived quality and usability of auto-generated feedback, and 3) experience of a selected group of computing students using the system.
A SEB Hardened Trench Gate DMOS with HfO2 Gate Dielectric and Decelerating Electric Field Layer in Parasitic NPN Base

The paper proposes a single-event burnout (SEB) hardened trench-gate DMOS with HfO2 gate dielectric and a decelerating electric field layer in the parasitic NPN. The proposed device presents remarkable potential to tolerate SEB. High-$k$ gate dielectric (HfO2) is adopted to obtain a higher channel doping concentration while maintaining the normal $V_{\text{TH}}$, thereby suppressing the secondary breakdown of parasitic NPN. The decelerating electric field layer will reduce the $\beta$ of parasitic NPN. For the hardened trench gate DMOS with breakdown voltage of 572V under irradiation linear energy transfer value of 1pC/µm (96MeV/mg/cm2), numerical results (without considering the self-heating effect) show that the SEB threshold voltage is 554V, while the conventional device's is 132V. The SEB threshold voltage is increased by 32%. When the LET value is smaller than 0.6pC/µm, the SEB threshold voltage is over 570V. The SEB threshold voltage of proposed device almost equals its original breakdown voltage. It is meaningful for SEB hardening design of power devices.
Research on Error Evaluation of Capacitive Voltage Transformer Based on EMD-LSTM

Capacitive voltage transformer (CVT) is a kind of electrical isolation and metering equipment. At present, the most widely used type of gateway voltage transformer in high-voltage and ultra-high voltage power grids is capacitive voltage transformer. The evaluation of its error determines the fairness and quality of the electric energy market. The safety of the power system. In this paper, by analyzing the error characteristics of the gateway voltage transformer, the error formation mechanism is analyzed and determined, and the deep learning algorithm is used to evaluate the error. Before the evaluation, the empirical mode decomposition method is used to decompose the characteristics of the monitoring data of the voltage transformer, and the components containing different time scale information are extracted. Each component is predicted using the long-short-term memory (LSTM) network, and finally the prediction result is obtained. At the same time, to avoid sudden changes caused by fluctuations and noise, multi-step averaging is used to reduce the possibility of misjudgment. The final false alarm rate is reduced to 0.1790%, improving the performance of the prediction.
Built-Up Area Extraction Combing Densely Connected Dual-Attention Network and Multiscale Context

Built-up areas are the main areas for human production and life. The wide availability of high-resolution satellite images provides new opportunities for fine-scale detection and mapping of built-up areas. However, great challenges remain because built-up areas are large-scale composite geographical objects, which contain diverse object classes and complex scenes, and have extremely large feature heterogeneity across space. For the automatic recognition of built-up areas in high spatial resolution satellite images, a block-level built-up area extraction framework combing densely connected dual-attention network and multiscale context is proposed. The proposed method first divides an image into multiscale blocks with a certain proportion of overlap through multisize grids and their multistep offsets. It then uses the constructed lightweight network integrating dense connection and dual attention to realize the feature representation and discrimination of image blocks. Finally, it achieves the refined detection of built-up areas by integrating the prediction results under different divisions through pixel-level multilabel voting. GaoFen-2 satellite images covering Shenzhen city, China, are used to verify the effectiveness of the proposed method. In the five selected test areas, the F1 score of the proposed method ranges from 0.8720 to 0.8983. Results visually preserve the integrity of internal morphology and have a well-defined boundary. The proposed method shows better performance than the state-of-the-art built-up area extraction methods. It can potentially be applied for fine mapping of large-scale urban built-up areas from high-resolution satellite images.
A Disk-Shaped Antenna with Reconfigurable Axial Ratio Beam

A novel technique is proposed to achieve a circularly polarized antenna. The circle patch placed in the center is excited by four ports with equal amplitude and 90° phase difference, and the double-ring parasitic patch with varactor is placed around the circle patch. The ARBW (axial ratio beam width) of the antenna is changed symmetrical or asymmetrical, the maximum radiation direction as the symmetry point, through adjusting the capacitance value of the varactor. The inner-ring of the double-ring parasitic obtains current through the coupling with the circle patch which is a radiation structure, then the current on the inner-ring flows to the outer-ring, and then the far field of circularly polarized antenna is adjusted by controlling the electrical length of the outer-ring, finally, the change of the far field of the circularly polarized antenna will cause the variation of ARB.
Contextual Information Classification of Remotely Sensed Images

This work proposes a multidisciplinary contextual information extraction and decision fusion approach for increasing the classification accuracy. It improves the image classification with integrating the results of various classifiers. The proposed method is implemented in three-steps: 1) contextual feature extraction using four different feature extractors methods: a) Gray Level Cooccurrence Matrix, b) Gabor filters, c) Laplacian Gaussian filters and d) Gaussian Derivatives Functions; 2) classification of contextual features using four different classification rules (ML, Tree, KNN and SVM) by using only 2% of data for training the classifiers; and 3) finally, decision fusion using six decision fusion rules. The experimental results on real remotely sensed images have been presented.
A Fully Differential Multi-bit MDAC Modeling with Multiple Linear Regression Calibration

In this paper, A fully differential multi-bit multiplying digital-to-analog converter (MDAC) model for pipeline analog-to-digital converter (ADC) is presented. The proposed model considers the coupling between capacitors in differential input end, as well as other non-ideal factors such as gain mismatch, input-referred noise, resulting in a more accurate representation of the actual circuit compared to existing models. Moreover, A multiple linear regression calibration is introduced to compensate for the nonidealities in MDAC. The calibration algorithm utilizes Mini-Batch Gradient Descent (MGD) and Heavy Ball Method (HBM) to mitigate the noise amplification and accelerate the convergence rate, respectively. The presented MDAC model and calibration algorithm are validated by Simulink and S-function.
Emotion Classification Comparison in Convolutional Neural Networks and Fuzzy Logics for Service Robotic Applications

Emotions are a powerful indicator that can positively and negatively affect the long-term health of humans. Emotions are affected by many aspects of our daily lives, including decision-making, reasoning, and physical well-being. Additionally, emotions play a critical role in human interaction. Emotion recognition is a developing area in the development of service robotics system algorithms. This paper compares intelligent systems with convolutional neural networks and fuzzy logic for emotion recognition applications. Two system scenarios have been discussed separately to check the detection accuracy of facial expression recognition in image classification and speech signal recognition. The results reveal that convolution neural networks perform emotion recognition activities with higher accuracy than fuzzy logic applications. These experimental results can be used for future research applications to develop intelligent systems regarding emotional identification for service robotics. The developed convolutional neural networks applications have greater than 95% accuracy in accurate emotion detection.
Optoelectronic Feedback Influence on the Spectral Transmission of Collinear Acousto-Optic Diffraction

The presence of light beams divergence affects the characteristics of acousto-optic (AO) diffraction. It leads to a decrease in the AO interaction efficiency and transformation of the AO devices transmission function - its bandwidth increases and the side lobes are smoothed. In this paper we propose to compensate the light divergence effect on the collinear AO diffraction transmission functions shape applying an optoelectronic feedback circuit connecting the AO cell optical output and its piezoelectric transducer. It is shown that the introduction of feedback makes it possible to narrow the passband and increase the spectral contrast of the transmission function. Unfortunately, feedback does not help to increase the maximal possible AO diffraction efficiency as it is determined by the optical beam divergence, since the feedback does not affect the AO device transfer function
Prediction with Time-Series Mixer for the S&P500 Index

As an essential US economic indicator, the S&P500 Index is used to assess the current state of market performance and gauge the economy’s future course. However, stock market index prediction is challenging due to its nonlinearity and inherently volatile character. Recurrent Neural Networks (RNN) and their variants are de facto standards for sequence modeling. Recently, Convolutional Neural Networks (CNN) and attention-based networks, such as dilated casual convolutions and Transformers, have also become popular in time series forecasting. In this paper, we report on the design of a Time-Series Mixer (TS-Mixer) architecture based on MLP-Mixer, an all-MLP architecture for time series forecasting. To the best of our knowledge, this is the first implementation of MLP-Mixer-based architecture for sequence modeling. Modern deep learning models are increasingly built to handle univariate time series data. They generally pay attention to analyzing temporal dependencies while ignoring the relationship among features. The proposed architecture is specifically created for multivariate time series forecasting to capture temporal feature interactions while simultaneously learning feature correlations. To accomplish this, the proposed Time-Feature Mixer contains two types of MLP layers: feature mixer and temporal mixer. The feature mixer is applied independently to each data point to capture the correlation among features. In contrast, the temporal mixer extracts temporal dependency (trend, seasonal, cyclical, or random characteristics) of each feature across the whole input sequence. Compared to prevalent neural networks in sequence modeling, TS-Mixer exhibits competitive performance regarding S&P500 Index prediction.
A Markovian Sampling Policy Applied to Water Quality Monitoring of Streams

This paper considers a sampling procedure for monitoring the outgoing quality of a process. Though the sampling procedure is applicable to other sampling inspection problems, our specific concern here is the application to quality control sampling of dissolved oxygen in streams. The procedure uses a variable sampling ratio, sampling less frequently when the process is in control. The sampling procedure can be considered as a simple Markov process, as will be indicated. The sampling plan is defined in section 2 along with a consideration of some of the properties of such a procedure. In section 3 the application of this procedure to the problem of sampling streams for control of water pollution is discussed. Finally, an illustrative example is given in section 4 along with some comments on the usefulness of such a procedure for largescale sampling projects such as those used by state water control boards.
Convergence analysis of the nontrivial stationary solution of the memristor-based neural networks with reaction-diffusion terms

This paper considers the convergence and existence problem for the nontivial stationary solution of a memristor-based neural network with reaction-diffusion(MNNs). We address the existence of the nontrivial stationary solution by investigating a compact operator whose fixed point is exactly the stationary solution through the Leary-Schauder theorem. Then, under some mild conditions, a lyapounov function is proposed to prove the convergence of the stationary solution. Numerial examples are provided to support the effectiveness of the conclusions.
Application-Oriented Characterization of Thermally Optimized, Asymmetrical Single Chip Packages for 100 V GaN HEMTs

Gallium nitride transistors have a smaller die area compared to silicon-based devices, which can lead to thermal challenges in high current density applications. Therefore, thermally optimized packages with a high heat spreading capability in combination with small parasitic are necessary. This work investigates the thermal performance a $7\ \mathrm{m}\Omega$, 100 V GaN HEMT in a thermally optimized single chip package with integrated RTD and compares it to the commercial counterpart. The thermal optimized package shows a significantly better transient thermal impedance resulting in a static thermal resistance of 3.1 K/W, which is a 20 % reduction in comparison to the COTS package. The integrated RTD trace has a relative reaction time of 590 ms, which is 30-fold slower in comparison to the junction temperature. To show the identical electrical behavior, although the single chip package is larger, it is compared with the commercial off-the-shelf package and a $5\ \mathrm{m}\Omega$, 100 V GaN single chip package in a 300 kHz, 48 V buck converter. Both $7\ \mathrm{m}\Omega$ versions have identical efficiencies of ≈97.5 % up to 50 A output current, slightly outperforming the $5\ \mathrm{m}\Omega$ GaN transistor. With its combination of improved thermal characteristics and low-inductance, the thermally optimized package of the GaN device offers more degrees of freedom in the design of power converter to exploit trade-offs between longer lifetime, higher temperature operation and power density.