Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling iron-powder inductors at high frequencies

A high-frequency model of iron-powder core inductors is studied. The skin and proximity effects that cause the winding parasitic resistance to increase with the operating frequency are considered. The inductor self-resonance due to the parasitic capacitances is also taken into account. The frequency response of the inductor model is compared to that of an experimentally tested iron-powder core inductor. The first self-resonant frequency is determined from the plot of the measured reactance and allows for the calculation of the parasitic capacitance. Equations for the inductor parasitic resistance are derived in a closed form. Expressions giving the AC resistance as a function of the operating frequency are given. These expressions allow for an accurate prediction of the inductor power loss over a wide frequency range. The measured and calculated values of the inductor impedance magnitude end phase, the real and imaginary parts of the inductor impedance, the inductance, and the inductor quality factor are plotted versus frequency and compared. Theoretical results were in good agreement with those experimentally measured. Therefore, it is demonstrated that the discussed equivalent circuit has a frequency response matching that of the real inductor. Moreover, the circuit model is simple, it allows for an immediate understanding of iron-powder core inductor behavior and can be easily used in computer simulations of electronic circuits. <<ETX>>
Development of a Vision Recognition System for Unmanned Aerial Helicopter Automatic Landing System

An embedded vision recognition system is developed for a regular UAH to provide guidance information during hovering and landing. An innovative landing mark has been designed to facilitate the image processing while provide relative heading, height, and coordinate for navigation. The system can provide 30 Hz updating rate to the UAH avionic system. Simulation and real-world tests have shown promising performance and results for future applications.
Characterization and sex-related differences in the multi-location external workload profile of semiprofessional basketball players. A cross-sectional study

ABSTRACT Commonly, the monitoring of external workload has been performed using a single device on player's scapulae. The human body is a complex multi-articular system and quantification in a single location is insufficient, being necessary the assessment in multiple body locations simultaneously. Therefore, this study aimed to characterize the multi-location external workload in men’s and women’s players and to analyze the sex-related differences during the most common movements in basketball. Twenty-six semi-professional basketball players (n = 13 men, n = 13 women) were evaluated in five tests: linear and curvilinear movements, changing of speed, jumping and in-game movements. PlayerLoadRT was evaluated at six anatomical locations simultaneously (scapulae, lumbar region, knees, and ankles) with WIMU PROTM inertial devices attached to the athlete using a full-body skinsuit. Statistical analysis was composed of a t-test of independent measures and Coheńs d effect size. The main results indicated: (1) the type of movement modified the external workload supported by the musculoskeletal structures; (2) sex-related differences were found in the vertical absorption of external workload (p < 0.05); (3) no sex-related differences were shown in the horizontal profile (p > 0.16). The multi-location monitoring will allow the identification of musculoskeletal structures with high vertical absorption of external load depending on sex (men > women: scapulae-lumbar and knee-ankle; women > men: lumbar-knee) and type of movement (scapulae-lumbar: decelerations; lumbar-knee: jumping; knee-ankle: in-game), just like horizontal differences in lower limb (outer > inner leg: curvilinear). Equally movements distribution throughout training sessions, strengthening and recovery programmes of high-workload muscle groups according to player’s characteristics could contributed to performance enhancement and reduce injury risk. HIGHLIGHTS Men’s and women’s basketball players presented different vertical musculoskeletal absorption of external workload. Men presented higher absorption in scapulae – lumbar and knee – ankle segments while women did so in the lumbar – knee segment. The type of movements had a direct effect in the multi-location external workload profile. A combined effect of sex and type of movement was found in the vertical musculoskeletal absorption of the external workload profile. No sex-related differences were found in the horizontal profile regarding the type of movement.
Faster Deterministic Approximation Algorithms for Correlation Clustering and Cluster Deletion

Correlation clustering is a framework for partitioning datasets based on pairwise similarity and dissimilarity scores, and has been used for diverse applications in bioinformatics, social network analysis, and computer vision. Although many approximation algorithms have been designed for this problem, the best theoretical results rely on obtaining lower bounds via expensive linear programming relaxations. In this paper we prove new relationships between correlation clustering problems and edge labeling problems related to the principle of strong triadic closure. We use these connections to develop new approximation algorithms for correlation clustering that have deterministic constant factor approximation guarantees and avoid the canonical linear programming relaxation. Our approach also extends to a variant of correlation clustering called cluster deletion, that strictly prohibits placing negative edges inside clusters. Our re-sults include 4-approximation algorithms for cluster deletion and correlation clustering, based on simpliﬁed linear programs with far fewer constraints than the canonical relaxations. More importantly, we develop faster techniques that are purely combinatorial, based on computing maximal matchings in certain auxiliary graphs and hypergraphs. This leads to a combinatorial 6-approximation for complete unweighted correlation clustering, which is the best deterministic result for any method that does not rely on linear programming. We also present the ﬁrst combinatorial constant factor approximation for cluster deletion.
Towards safe, explainable, and regulated autonomous driving

There has been recent and growing interest in the development and deployment of autonomous vehicles, encouraged by the empirical successes of powerful artificial intelligence techniques (AI), especially in the applications of deep learning and reinforcement learning. However, as demonstrated by recent traffic accidents, autonomous driving technology is not fully reliable for safe deployment. As AI is the main technology behind the intelligent navigation systems of self-driving vehicles, both the stakeholders and transportation regulators require their AI-driven software architecture to be safe, explainable, and regulatory compliant. In this paper, we propose a design framework that integrates autonomous control, explainable AI (XAI), and regulatory compliance to address this issue, and then provide an initial validation of the framework with a critical analysis in a case study. Moreover, we describe relevant XAI approaches that can help achieve the goals of the framework.
Analysis of Exploration vs. Exploitation in Adaptive Information Sampling

Adaptive information sampling approaches enable efficient selection of mobile robot's waypoints through which accurate sensing and mapping of a physical process, such as the radiation or field intensity, can be obtained. This paper analyzes the role of exploration and exploitation in such information-theoretic spatial sampling of the environmental processes. We use Gaussian processes to predict and estimate predictions with confidence bounds, thereby determining each point's informativeness in terms of exploration and exploitation. Specifically, we use a Gaussian process regression model to sample the Wi-Fi signal strength of the environment. For different variants of the informative function, we extensively analyze and evaluate the effectiveness and efficiency of information mapping through two different initial trajectories in both single robot and multi-robot settings. The results provide meaningful insights in choosing appropriate information function based on sampling objectives.
Sound Field Reproduction With Weighted Mode Matching and Infinite-Dimensional Harmonic Analysis: An Experimental Evaluation

Sound field reproduction methods based on numerical optimization, which aim to minimize the error between synthesized and desired sound fields, are useful in many practical scenarios because of their flexibility in the array geometry of loudspeakers. However, the reproduction performance of these methods in a practical environment has not been sufficiently investigated. We evaluate weighted mode matching, which is a sound field reproduction method based on the spherical wavefunction expansion of the sound field, in comparison with conventional pressure matching. We also introduce a method of infinite-dimensional harmonic analysis for estimating the expansion coefficients of the sound field from microphone measurements. Experimental results indicated that weighted mode matching using the expansion coefficients of the transfer functions estimated by the infinite-dimensional harmonic analysis outperforms conventional pressure matching, especially when the number of microphones is small.
Location-Aware Beamforming for MIMO-Enabled UAV Communications: An Unknown Input Observer Approach

Numerous communications and networking challenges prevent deploying unmanned aerial vehicles (UAVs) in extreme environments where the existing wireless technologies are mainly ground-focused; and, as a consequence, the air-to-air channel for UAVs is not fully covered. In this paper, we address the crucial problem of beamforming in the UAV-based communications by utilizing a spatial estimation algorithm, which relies on the theory of observers in the control systems literature. The proposed spatial estimation algorithm relies on using a delay-tolerant observer-based predictor, which can accurately predict the positions of the target UAVs in the presence of uncertainties due to factors such as wind gust. The solution, which uses discrete-time unknown input observers (UIOs), reduces the joint target detection and communication complication notably by operating on the same device and performs reliably in the presence of channel blockage and interference. The effectiveness of the proposed approach is demonstrated using simulation results.
Automatic Detection of Depression from Stratified Samples of Audio Data

Depression is a common mental disorder which has been affecting millions of people around the world and becoming more severe with the arrival of COVID-19. Nevertheless proper diagnosis is not accessible in many regions due to a severe shortage of psychiatrists. This scarcity is worsened in low-income countries which have a psychiatrist to population ratio 210 times lower than that of countries with better economies. This study aimed to explore applications of deep learning in diagnosing depression from voice samples. We collected data from the DAIC-WOZ database which contained 189 vocal recordings from 154 individuals. Voice samples from a patient with a PHQ-8 score equal or higher than 10 were deemed as depressed and those with a PHQ-8 score lower than 10 were considered healthy. We applied mel-spectrogram to extract relevant features from the audio. Three types of encoders were tested i.e. 1D CNN, 1D CNN-LSTM, and 1D CNN-GRU. After tuning hyperparameters systematically, we found that 1D CNN-GRU encoder with a kernel size of 5 and 15 seconds of recording data appeared to have the best performance with F1 score of 0.75, precision of 0.64, and recall of 0.92.
RoboKit-MV: an Educational Initiative

In this paper, we present a robot model and code base for affordable education in the field of humanoid robotics. We give an overview of the software and hardware of a robot that won several competitions with the team RoboKit in 2019-2021, provide analysis of the contemporary market of education in robotics, and highlight the reasoning beyond certain design solutions.
Comparing the Language of QAnon-Related Content on Parler, Gab, and Twitter

Parler, a “free speech” platform popular among conservatives, was taken offline in January 2021 due to the lack of moderation of harmful content. While other popular social media platforms were also used to spread conspiratorial, hateful and threatening content, Parler suffered the most consequences in the aftermath of the 2020 US presidential elections, having been singled out in the news coverage. Through a comparative study, we identify differences in content using #QAnon across three social media platforms, Parler, Twitter, and Gab, focusing on the volume, the amount of anti-social language, and the context of QAnon-related content over a month-long period. While the number of posts is the highest on Parler, this could be attributed to the differences in the use of hashtags on the platforms, which has consequences for other analyses. In our analysis, Parler exhibits the highest levels of anti-social language, while Gab has the highest proportion of #QAnon posts with hate terms. To get at qualitative differences in the posts, we perform analysis of named entities and narratives, focusing on differences in the focus of conversations and the levels of anti-social language of posts mentioning different groups of political figures.
Investigation of anisotropic negative permeability medium cover for patch antenna

A more directional and higher gain patch antenna with an anisotropic negative permeability medium (NPM) cover is proposed. The patch antenna operates at the frequency where the permeability of split-ring resonator (SRR) is negative, and then the sideward radiation can be forbidden. It leads to a significant enhancement of designing the high gain antenna. We investigate numerically and experimentally the performance of the antenna when NPM composed of SRR is placed above the patch antenna. The measured result has a good agreement with the simulation. Compared with the conventional antenna, the result shows that the beam of antenna with the NPM cover becomes more convergent, half-power beamwidth is smaller by almost 30° in the H-plane and 10° in the E-plane and the gain is higher by 4.03 dB. Moreover, NPM cover can have applications in the other types of antenna such as monopoles, dipole antennas, leak-wave antennas and aperture antennas.
Simulation of quantum physics with Tensor Processing Units: brute-force computation of ground states and time evolution

Tensor Processing Units (TPUs) were developed by Google exclusively to support large-scale machine learning tasks. TPUs can, however, also be used to accelerate and scale up other computationally demanding tasks. In this paper we repurpose TPUs for the challenging problem of simulating quantum spin systems. Consider a lattice model made of $N$ spin-$\frac{1}{2}$ quantum spins, or qubits, with a Hamiltonian $H = \sum_i h_i$ that is a sum of local terms $h_i$ and a wavefunction $|\Psi\rangle$ consisting of $2^N$ complex amplitudes. We demonstrate the usage of TPUs for both (i) computing the ground state $|\Psi_{gs}\rangle$ of the Hamiltonian $H$, and (ii) simulating the time evolution $|\Psi(t)\rangle=e^{-itH}|\Psi(0)\rangle$ generated by this Hamiltonian starting from some initial state $|\Psi(0)\rangle$. The bottleneck of the above tasks is computing the product $H |\Psi\rangle$, which can be implemented with remarkable efficiency utilising the native capabilities of TPUs. With a TPU v3 pod, with 2048 cores, we simulate wavefunctions $|\Psi\rangle$ of up to $N=38$ qubits. The dedicated matrix multiplication units (MXUs), the high bandwidth memory (HBM) on each core, and the fast inter-core interconnects (ICIs) together provide performance far beyond the capabilities of general purpose processors.
Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metrics

This article is a companion paper to our earlier work Miroshnikov et al. (2021) on fairness interpretability, which introduces bias explanations. In the current work, we propose a bias mitigation methodology based upon the construction of post-processed models with fairer regressor distributions for Wasserstein-based fairness metrics. By identifying the list of predictors contributing the most to the bias, we reduce the dimensionality of the problem by mitigating the bias originating from those predictors. The post-processing methodology involves reshaping the predictor distributions by balancing the positive and negative bias explanations and allows for the regressor bias to decrease. We design an algorithm that uses Bayesian optimization to construct the bias-performance efficient frontier over the family of post-processed models, from which an optimal model is selected. Our novel methodology performs optimization in low-dimensional spaces and avoids expensive model retraining.
Case-based off-policy policy evaluation using prototype learning

Importance sampling (IS) is often used to perform off-policy policy evaluation but is prone to several issues, especially when the behavior policy is unknown and must be estimated from data. Significant differences between the target and behavior policies can result in uncertain value estimates due to, for example, high variance and non-evaluated actions. If the behavior policy is estimated using black-box models, it can be hard to diagnose potential problems and to determine for which inputs the policies differ in their suggested actions and resulting values. To address this, we propose estimating the behavior policy for IS using prototype learning. We apply this approach in the evaluation of policies for sepsis treatment, demonstrating how the prototypes give a condensed summary of differences between the target and behavior policies while retaining an accuracy comparable to baseline estimators. We also describe estimated values in terms of the prototypes to better understand which parts of the target policies have the most impact on the estimates. Using a simulator, we study the bias resulting from restricting models to use prototypes.
Extracting Deformation-Aware Local Features by Learning to Deform

Despite the advances in extracting local features achieved by handcrafted and learning-based descriptors, they are still limited by the lack of invariance to non-rigid transformations. In this paper, we present a new approach to compute features from still images that are robust to non-rigid deformations to circumvent the problem of matching deformable surfaces and objects. Our deformation-aware local descriptor, named DEAL, leverages a polar sampling and a spatial transformer warping to provide invariance to rotation, scale, and image deformations. We train the model architecture end-to-end by applying isometric non-rigid deformations to objects in a simulated environment as guidance to provide highly discriminative local features. The experiments show that our method outperforms state-of-the-art handcrafted, learning-based image, and RGB-D descriptors in different datasets with both real and realistic synthetic deformable objects in still images. The source code and trained model of the descriptor are publicly available at https://www.verlab. dcc.ufmg. br/descriptors/neurips2021.
An introduction to relative Calabi-Yau structures

These are notes taken by the second author for a series of three lectures by the first author on absolute and relative Calabi-Yau completions and Calabi-Yau structures given at the workshop of the International Conference on Representations of Algebras which was held online in November 2020. Such structures are relevant for (higher) representation theory as well as for the categorification of cluster algebras with coefficients. After a quick reminder on dg categories and their Hochschild and cyclic homologies, we present examples of absolute and relative Calabi-Yau completions (in the sense of Yeung). In many examples, these are related to higher preprojective algebras in the sense of Iyama-Oppermann. We conclude with the definition of relative (left and right) Calabi-Yau structures after Brav-Dyckerhoff.
Available transfer capability enhancement using FACTS devices

From the viewpoint of operational planning, this paper focuses on the evaluation of the impact of FACTS control on available transfer capability (ATC) enhancement. Technical merits of FACTS technology on ATC boosting are analyzed. An optimal power-flow-based ATC enhancement model is formulated to achieve the maximum power transfer of the specified interface with FACTS control. For better studying the capability of FACTS control, a power injection model of FACTS devices, which enables simulating the control of any FACTS devices, is employed. Studies based on the IEEE 118-bus system with all categories of FACTS devices demonstrate the effectiveness of FACTS control on ATC enhancement.
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control

Sparse model identification enables the discovery of nonlinear dynamical systems purely from data; however, this approach is sensitive to noise, especially in the low-data limit. In this work, we leverage the statistical approach of bootstrap aggregating (bagging) to robustify the sparse identification of the nonlinear dynamics (SINDy) algorithm. First, an ensemble of SINDy models is identified from subsets of limited and noisy data. The aggregate model statistics are then used to produce inclusion probabilities of the candidate functions, which enables uncertainty quantification and probabilistic forecasts. We apply this ensemble-SINDy (E-SINDy) algorithm to several synthetic and real-world datasets and demonstrate substantial improvements to the accuracy and robustness of model discovery from extremely noisy and limited data. For example, E-SINDy uncovers partial differential equations models from data with more than twice as much measurement noise as has been previously reported. Similarly, E-SINDy learns the Lotka Volterra dynamics from remarkably limited data of yearly lynx and hare pelts collected from 1900 to 1920. E-SINDy is computationally efficient, with similar scaling as standard SINDy. Finally, we show that ensemble statistics from E-SINDy can be exploited for active learning and improved model predictive control.
SOC Test Planning Using Virtual Test

Recent advances in tester technology have led to automatic test equipment (ATE) that can operate at up to giga- hertz speeds. However, system-on-chip (SOC) scan chains are typically run at lower frequencies, e.g., 10-50 MHz. The use of high-speed ATE channels to drive slower scan chains leads to an underutilization of resources, thereby resulting in an increase in SOC testing time. We present a new test planning technique to reduce the testing time and test cost by matching high-speed ATE channels to slower scan chains using the concept of vir- tual test access architectures. We also present a new test access mechanism (TAM) optimization framework based on Lagrange multipliers and analyze the impact of virtual TAMs on the overall SOC test power consumption for one of the ITC'02 benchmarks. Experimental results for TAM optimization based on Lagrange multipliers and virtual TAMs are presented for three industrial circuits from the set of ITC'02 SOC test benchmarks.