Miracle-dz/UniFAR-FTUs huggingface dataset:
A Combined Mode of Control for Unified Power Quality Conditioner Connected to a Low Voltage Distribution System

Abstract This paper presents a novel and easy to implement control scheme for a unified power quality conditioner (UPQC). The firing pulses for the series and shunt active power filters (APFs) of the UPQC are generated based on sinusoidal pulse-width modulation (SPWM) and hysteresis control, respectively. This control is achieved without using any transformation for generating the reference voltages/currents and thus avoiding complex computations for implementation. Proportional-integral control is used for maintaining the DC-link voltage and load voltage. The effectiveness of the proposed control scheme is tested on the UPQC connected to a low voltage distribution system. The voltage sag, swell, unbalance and harmonic mitigation capabilities of the UPQC are verified through computer simulation using PSCAD/EMTDC. The simulation results are compared with the results of applying only either hysteresis or SPWM control to both APFs. The proposed scheme gives appreciable results in mitigation of power quality problems.
Towards technological adaptation of advanced farming through AI, IoT, and Robotics: A Comprehensive overview

The population explosion of the 21st century has adversely affected the natural resources with restricted availability of cultivable land, increased average temperatures due to global warming, and carbon footprint resulting in a drastic increase in floods as well as droughts thus making food security significant anxiety for most countries. The traditional methods were no longer sufficient which paved the way for technological ascents such as a substantial rise in Artificial Intelligence (AI), Internet of Things (IoT), as well as Robotics that provides high productivity, functional efficiency, flexibility, cost-effectiveness in the domain of agriculture. AI, IoT, and Robotics-based devices and methods have produced new paradigms and opportunities in agriculture. AI's existing approaches are soil management, crop diseases identification, weed identification, and management in collaboration with IoT devices. IoT has utilized automatic agricultural operations and real-time monitoring with few personnel employed in real-time. The major existing applications of agricultural robotics are for the function of soil preparation, planting, monitoring, harvesting, and storage. In this paper, researchers have explored a comprehensive overview of recent implementation, scopes, opportunities, challenges, limitations, and future research instructions of AI, IoT, and Robotics based methodology in the agriculture sector.
Neural network QCD analysis of charged hadron fragmentation functions in the presence of SIDIS data

In this paper, we present a QCD analysis to extract the Fragmentation Functions (FFs) of unidentified light charged hadron entitled as SHK22.h from high-energy lepton-lepton annihilation and lepton-hadron scattering data sets. This analysis includes the data from all available single inclusive electron-positron annihilation (SIA) processes and semi-inclusive deep-inelastic scattering (SIDIS) measurements for the unidentified light charged hadron productions. The SIDIS data which has been measured by the COMPASS experiment could allow the flavor dependence of the FFs to be well constrained. We exploit the analytic derivative of the Neural Network (NN) for fitting of FFs at next-to-leading-order (NLO) accuracy in the perturbative QCD (pQCD). The Monte Carlo method is implied for all sources of experimental uncertainties and the Parton distribution functions (PDFs) as well. Very good agreements are achieved between the SHK22.h FFs set and the most recent QCD fits available in literature, namely JAM20 and NNFF1.1h. In addition, we discuss the impact arising from the inclusion of SIDIS data on the extracted light-charged hadron FFs. The global QCD resulting at NLO for charged hadron FFs provides valuable insights for applications in present and future high-energy measurement of charged hadron final state processes.
Submodlib: A Submodular Optimization Library

Submodular functions are a special class of set functions which naturally model the notion of representativeness, diversity, coverage etc. and have been shown to be computationally very efficient. A lot of past work has applied submodular optimization to find optimal subsets in various contexts. Some examples include data summarization for efficient human consumption, finding effective smaller subsets of training data to reduce the model development time (training, hyper parameter tuning), finding effective subsets of unlabeled data to reduce the labeling costs, etc. A recent work has also leveraged submodular functions to propose submodular information measures which have been found to be very useful in solving the problems of guided subset selection and guided summarization. In this work, we present Submodlib which is an open-source, easy-to-use, efficient and scalable Python library for submodular optimization with a C++ optimization engine. Submodlib finds its application in summarization, data subset selection, hyper parameter tuning, efficient training and more. Through a rich API, it offers a great deal of flexibility in the way it can be used. Source of Submodlib is available at https://github.com/decile-team/submodlib.
Universal Classical Optical Computing Inspired by Quantum Information Process

Quantum computing has attracted much attention in recent decades, since it is believed to solve certain problems substantially faster than traditional computing methods. Theoretically, such an advance can be obtained by networks of the quantum operators in universal gate sets, one famous example of which is formed by quantum Control‐not gate and single qubit gates. However, realizing a device that performs practical quantum computing is tricky. This is because it requires a scalable qubit system with long coherence time and good controls, which is harsh for most current platforms. Here, it is demonstrated that the information process based on a relatively stable system—classical optical system, can be considered as an analogy of universal quantum computing. By encoding the information via the polarization state of classical beams, the optical computing elements that correspond to the universal gate set are presented and their combination for a general information process is theoretically illustrated. Taking the analogy of two‐qubit processor as an example, it is experimentally verified that the proposal works well. Considering the potential of optical system for reliable and low‐energy‐consuming computation, the results open a new way toward advanced information processing with high quality and efficiency.
Qualitative Behavior of a Metabolic Pathway with Hybrid Feedback

We study the qualitative behavior of a model to represent local regulation in a metabolic network. The model is based on the end-product control structure introduced in [A. Goelzer, F. Bekkal Brikci, I. Martin-Verstraete et al., BMC Syst Biol 2 (2008), pp. 1--18]. In this class of regulation, the metabolite effector is the end-product of a metabolic pathway. We suppose the input to the pathway to switch between zero and a positive value according to the concentration of the metabolite effector. Considering the switched system as a differential inclusion, we prove that it converges to a globally uniformly asymptotically stable equilibrium point, reaches the sliding mode or oscillates around the sliding mode depending on the positive value of the input. Finally, we show that in any case the solution of the switched system is the limit of solutions of equation sequences with smooth or piecewise linear inputs.
Improving Classification Model Performance on Chest X-Rays through Lung Segmentation

Chest radiography is an effective screening tool for diagnosing pulmonary diseases. In computer-aided diagnosis, extracting the relevant region of interest, i.e., isolating the lung region of each radiography image, can be an essential step towards improved performance in diagnosing pulmonary disorders. Methods: In this work, we propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets. The proposed pipeline is evaluated on Shenzhen Hospital (SH) data set for the segmentation module, and COVIDx data set for both segmentation and classification modules. Novel statistical analysis is conducted in addition to regular evaluation metrics for the segmentation module. Furthermore, the results of the optimized approach are analyzed with gradient-weighted class activation mapping (Grad-CAM) to investigate the rationale behind the classification decisions and to interpret its choices. Results and Conclusion: Different data sets, methods, and scenarios for each module of the proposed pipeline are examined for designing an optimized approach, which has achieved an accuracy of 0.946 in distinguishing abnormal CXR images (i.e., Pneumonia and COVID-19) from normal ones. Numerical and visual validations suggest that applying automated segmentation as a pre-processing step for classification improves the generalization capability and the performance of the classification models.
Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation

Computing the noisy sum of real-valued vectors is an important primitive in differentially private learning and statistics. In private federated learning applications, these vectors are held by client devices, leading to a distributed summation problem. Standard Secure Multiparty Computation (SMC) protocols for this problem are susceptible to poisoning attacks, where a client may have a large influence on the sum, without being detected. In this work, we propose a poisoning-robust private summation protocol in the multiple-server setting, recently studied in PRIO. We present a protocol for vector summation that verifies that the Euclidean norm of each contribution is approximately bounded. We show that by relaxing the security constraint in SMC to a differential privacy like guarantee, one can improve over PRIO in terms of communication requirements as well as the client-side computation. Unlike SMC algorithms that inevitably cast integers to elements of a large finite field, our algorithms work over integers/reals, which may allow for additional efficiencies.
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned

Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more broadly applicable methods, organizers need to enforce the use of general techniques, the use of sample-efficient methods, and the reproducibility of the results. While beneficial for the research community, these restrictions come at a cost -- increased difficulty. If the barrier for entry is too high, many potential participants are demoralized. With this in mind, we hosted the third edition of the MineRL ObtainDiamond competition, MineRL Diamond 2021, with a separate track in which we permitted any solution to promote the participation of newcomers. With this track and more extensive tutorials and support, we saw an increased number of submissions. The participants of this easier track were able to obtain a diamond, and the participants of the harder track progressed the generalizable solutions in the same task.
Semi-Implicit Hybrid Gradient Methods with Application to Adversarial Robustness

Adversarial examples, crafted by adding imperceptible perturbations to natural inputs, can easily fool deep neural networks (DNNs). One of the most successful methods for training adversarially robust DNNs is solving a nonconvex-nonconcave minimax problem with an adversarial training (AT) algorithm. However, among the many AT algorithms, only Dynamic AT (DAT) and You Only Propagate Once (YOPO) guarantee convergence to a stationary point. In this work, we generalize the stochastic primal-dual hybrid gradient algorithm to develop semi-implicit hybrid gradient methods (SI-HGs) for finding stationary points of nonconvex-nonconcave minimax problems. SI-HGs have the convergence rate $O(1/K)$, which improves upon the rate $O(1/K^{1/2})$ of DAT and YOPO. We devise a practical variant of SI-HGs, and show that it outperforms other AT algorithms in terms of convergence speed and robustness.
Hubble Space Telescope Imaging of Luminous Extragalactic Infrared Transients and Variables from the Spitzer Infrared Intensive Transients Survey

The SPitzer InfraRed Intensive Transients Survey (SPIRITS) searched for luminous infrared (IR) transients and variables in nearly 200 nearby galaxies from 2014 to 2019, using the warm Spitzer telescope at 3.6 and 4.5 μm. Among the SPIRITS variables are IR-bright objects that are undetected in ground-based optical surveys. We classify them as (1) transients, (2) periodic variables, and (3) irregular variables. The transients include eSPecially Red Intermediate-luminosity Transient Events (SPRITEs), having maximum luminosities fainter than supernovae, red IR colors, and a wide range of outburst durations (days to years). Here we report deep optical and near-IR imaging with the Hubble Space Telescope (HST) of 21 SPIRITS variables. They were initially considered SPRITE transients, but many eventually proved instead to be periodic or irregular variables as more data were collected. HST images show most of these cool and dusty variables are associated with star-forming regions in late-type galaxies, implying an origin in massive stars. Two SPRITEs lacked optical progenitors in deep preoutburst HST images; however, one was detected during eruption at J and H, indicating a dusty object with an effective temperature of ∼1050 K. One faint SPRITE turned out to be a dusty classical nova. About half the HST targets proved to be periodic variables, with pulsation periods of 670–2160 days; they are likely dusty asymptotic-giant-branch (AGB) stars with masses of ∼5–10 M ⊙. A few of them were warm enough to be detected in deep HST frames, but most are too cool. Out of six irregular variables, two were red supergiants with optical counterparts in HST images; four were too enshrouded for HST detection.
3D ToF LiDAR in Mobile Robotics: A Review

In the past ten years, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has grown rapidly. Based on our accumulation of relevant research, this article systematically reviews and analyzes the use 3D ToF LiDARs in research and industrial applications. The former includes object detection, robot localization, long-term autonomy, LiDAR data processing under adverse weather conditions, and sensor fusion. The latter encompasses service robots, assisted and autonomous driving, and recent applications performed in response to public health crises. We hope that our efforts can effectively provide readers with relevant references and promote the deployment of existing mature technologies in real-world systems.
Behaviour-neutral Smart Charging of Plugin Electric Vehicles: Reinforcement learning approach

Smart charging aims to satisfy EV charging demand without overloading the grid. A common approach is to postpone the charging to oﬀ-peak hours or dynamically control the charging speed depending on available grid capacity using dynamic pricing. However, this requires either accurately predicting the plugin duration to ensure the EV is charged before it is plugged out or in-ﬂuencing the driver behaviour to ensure the that a vehicle remains connected until the EV is charged. In this paper, we introduce a novel approach that uses reinforcement learning for EV charging and requires neither coordination with the grid nor predicting the session duration. The algorithm trains on a driver’s personal history of past charging sessions to identify the optimal charging parameters that minimise the eﬀective charging current while satisfying the target energy demand. We evaluate the performance on a dataset of 3.1 million charging sessions from 22,731 domestic charge stations. Our experiments demonstrate that the approach results in 31% of aggregate peak reduction and signiﬁcant reduction in charging power demand. The results also reveal two interesting features: Firstly, our approach is able to spread the charging power in time without knowing the session duration. Secondly, the proposed approach does not require any coordination with the grid or other charge points and can be implemented on a standalone charge point reducing the complexity.
Robust and Online LiDAR-inertial Initialization

—For most LiDAR-inertial odometry, accurate initial state, including temporal offset and extrinsic transformation between LiDAR and 6-axis IMUs, play a signiﬁcant role and are often considered as prerequisites. However, such information may not be always available in customized LiDAR-inertial systems. In this paper, we propose a full and online LiDAR-inertial system initialization process that calibrates the temporal offset and extrinsic parameter between LiDARs and IMUs, and also the gravity vector and IMU bias by aligning the state estimated from LiDAR measurements with that measured by IMU. We implement the proposed method as an initialization module, which, if enabled, automatically detects the degree of excitation of the collected data and calibrate, on-the-ﬂy, the temporal offset, extrinsic, gravity vector, and IMU bias, which are then used as high-quality initial state values for online LiDAR-inertial odometry systems. Experiments conducted with different types of LiDARs and LiDAR-inertial combinations show the robustness, adaptability and efﬁciency of our initialization method. The implementation of our LiDAR-inertial initialization procedure and test data are open-sourced on Github 1 and also integrated into a state-of-the-art LiDAR-inertial odometry system FAST-LIO2.
Adaptive Cholesky Gaussian Processes

We present a method to approximate Gaussian process regression models for large datasets by considering only a subset of the data. Our approach is novel in that the size of the subset is selected on the fly during exact inference with little computational overhead. From an empirical observation that the log-marginal likelihood often exhibits a linear trend once a sufficient subset of a dataset has been observed, we conclude that many large datasets contain redundant information that only slightly affects the posterior. Based on this, we provide probabilistic bounds on the full model evidence that can identify such subsets. Remarkably, these bounds are largely composed of terms that appear in intermediate steps of the standard Cholesky decomposition, allowing us to modify the algorithm to adaptively stop the decomposition once enough data have been observed.
Accelerating Primal-dual Methods for Regularized Markov Decision Processes

Entropy regularized Markov decision processes have been widely used in reinforcement learning. This paper is concerned with the primal-dual formulation of the entropy regularized problems. Standard first-order methods suffer from slow convergence due to the lack of strict convexity and concavity. To address this issue, we first introduce a new quadratically convexified primal-dual formulation. The natural gradient ascent descent of the new formulation enjoys global convergence guarantee and exponential convergence rate. We also propose a new interpolating metric that further accelerates the convergence significantly. Numerical results are provided to demonstrate the performance of the proposed methods under multiple settings.
Quantum regression in dephasing phenomena

We investigate the validity of quantum regression for a family of quantum Hamiltonians on a multipartite system leading to phase-damping reduced dynamics. After finding necessary and sufficient conditions for the CP-divisibility of the corresponding channel, we evaluate a hierarchy of equations equivalent to the validity of quantum regression under arbitrary interventions; in particular, we find necessary conditions for a nontrivial dephasing to be compatible with quantum regression. In this framework, we study a class of dephasing-type generalized spin–boson models, investigating the existence of qubit-environment coupling functions that ensure the exact validity of quantum regression.
Magnetic properties of equiatomic CrMnFeCoNi

Magnetic, specific heat, and structural properties of the equiatomic Cantor alloy system are reported for temperatures between 5 kelvin and 300 kelvin, and up to fields of 70 kilo-oersted. Magnetization measurements performed on as-cast, annealed, and cold-worked samples reveal a strong processing history dependence and that high-temperature annealing after cold-working does not restore the alloy to a pristine state. Measurements on known precipitates show that the two transitions, detected at 43 kelvin and 85 kelvin, are intrinsic to the Cantor alloy and not the result of an impurity phase. Experimental and ab initio density functional theory (DFT) computational results suggest that these transitions are a weak ferrimagnetic transition and a spin-glass-like transition, respectively, and magnetic and specific heat measurements provide evidence of significant Stoner enhancement and electron-electron interactions within the material.
CaMEL: Mean Teacher Learning for Image Captioning

Describing images in natural language is a fundamental step towards the automatic modeling of connections between the visual and textual modalities. In this paper we present CaMEL, a novel Transformer-based architecture for image captioning. Our proposed approach leverages the interaction of two interconnected language models that learn from each other during the training phase. The interplay between the two language models follows a mean teacher learning paradigm with knowledge distillation. Experimentally, we assess the effectiveness of the proposed solution on the COCO dataset and in conjunction with different visual feature extractors. When comparing with existing proposals, we demonstrate that our model provides state-of-the-art caption quality with a significantly reduced number of parameters. According to the CIDEr metric, we obtain a new state of the art on COCO when training without using external data. The source code and trained models will be made publicly available at: https://github.com/aimagelab/camel.
Order-Optimal Error Bounds for Noisy Kernel-Based Bayesian Quadrature

In this paper, we study the sample complexity of noisy Bayesian quadrature (BQ), in which we seek to approximate an integral based on noisy black-box queries to the underlying function. We consider functions in a Reproducing Kernel Hilbert Space (RKHS) with the Matérn-ν kernel, focusing on combinations of the parameter ν and dimension d such that the RKHS is equivalent to a Sobolev class. In this setting, we provide near-matching upper and lower bounds on the best possible average error. Speciﬁcally, we ﬁnd that when the black-box queries are subject to Gaussian noise having variance σ 2 , any algorithm making at most T queries (even with adaptive sampling) must incur a mean absolute error of Ω( T − νd − 1 + σT − 12 ) , and there exists a non-adaptive algorithm attaining an error of at most O ( T − νd − 1 + σT − 12 ) . Hence, the bounds are order-optimal, and establish that there is no adaptivity gap in terms of scaling laws.