Miracle-dz/UniFAR-FTUs huggingface dataset:
Who talks about what? Comparing the information treatment in traditional media with online discussions

We study the dynamics of interactions between a traditional medium, the New York Times journal, and its followers in Twitter, using a massive dataset. It consists of the metadata of the articles published by the journal during the first year of the COVID-19 pandemic, and the posts published in Twitter by a large set of followers of the @nytimes account along with those published by a set of followers of several other media of different kind. The dynamics of discussions held in Twitter by exclusive followers of a medium show a strong dependence on the medium they follow: the followers of @FoxNews show the highest similarity to each other and a strong differentiation of interests with the general group. Our results also reveal the difference in the attention payed to U.S. presidential elections by the journal and by its followers, and show that the topic related to the ``Black Lives Matter'' movement started in Twitter, and was addressed later by the journal.
Constraining Weil–Petersson volumes by universal random matrix correlations in low-dimensional quantum gravity

Based on the discovery of the duality between Jackiw–Teitelboim quantum gravity and a double-scaled matrix ensemble by Saad, Shenker and Stanford in 2019, we show how consistency between the two theories in the universal random matrix theory (RMT) limit imposes a set of constraints on the volumes of moduli spaces of Riemannian manifolds. These volumes are given in terms of polynomial functions, the Weil–Petersson (WP) volumes, solving a celebrated nonlinear recursion formula that is notoriously difficult to analyse. Since our results imply linear relations between the coefficients of the WP volumes, they therefore provide both a stringent test for their symbolic calculation and a possible way of simplifying their construction. In this way, we propose a long-term program to improve the understanding of mathematically hard aspects concerning moduli spaces of hyperbolic manifolds by using universal RMT results as input.
Shape‐Guided Mixed Metro Map Layout

Metro or transit maps, are schematic representations of transit networks to facilitate effective route‐finding. These maps are often advertised on a web page or pamphlet highlighting routes from source to destination stations. To visually support such route‐finding, designers often distort the layout by embedding symbolic shapes (e.g., circular routes) in order to guide readers' attention (e.g., Moscow map and Japan railway map). However, manually producing such maps is labor‐intensive and the effect of shapes remains unclear. In this paper, we propose an approach to generalize such mixed metro maps that take user‐defined shapes as an input. In this mixed design, lines that are used to approximate the shapes are arranged symbolically, while the remaining lines follow classical layout convention. A three‐step algorithm, including (1) detecting and selecting routes for shape approximation, (2) shape and layout deformation, and (3) aligning lines on a grid, is integrated to guarantee good visual quality. Our contribution lies in the definition of the mixed metro map problem and the formulation of design criteria so that the problem can be resolved systematically using the optimization paradigm. Finally, we evaluate the performance of our approach and perform a user study to test if the embedded shapes are recognizable or reduce the map quality.
"Prompt-Gamma Neutron Activation Analysis (PGNAA)" Metal Spectral Classification using Deep Learning Method

There is a pressing market demand to minimize the test time of Prompt Gamma Neutron Activation Analysis (PGNAA) spectra measurement machine, so that it could function as an instant material analyzer, e.g. to classify waste samples instantaneously and determine the best recycling method based on the detected compositions of the testing sample. This article introduces a new development of the deep learning classification and contrive to reduce the test time for PGNAA machine. We propose both Random Sampling Methods and Class Activation Map (CAM) to generate"downsized"samples and train the CNN model continuously. Random Sampling Methods (RSM) aims to reduce the measuring time within a sample, and Class Activation Map (CAM) is for filtering out the less important energy range of the downsized samples. We shorten the overall PGNAA measuring time down to 2.5 seconds while ensuring the accuracy is around 96.88 % for our dataset with 12 different species of substances. Compared with classifying different species of materials, it requires more test time (sample count rate) for substances having the same elements to archive good accuracy. For example, the classification of copper alloys requires nearly 24 seconds test time to reach 98 % accuracy.
Snap buckling of bistable beams under combined mechanical and magnetic loading

We investigate the mechanics of bistable, hard-magnetic, elastic beams, combining experiments, finite-element modelling (FEM) and a reduced-order theory. The beam is made of a hard magneto-rheological elastomer, comprising two segments with antiparallel magnetization along the centreline, and is set into a bistable curved configuration by imposing an end-to-end shortening. Reversible snapping is possible between these two stable states. First, we experimentally characterize the critical field strength for the onset of snapping, at different levels of end-to-end shortening. Second, we perform three-dimensional FEM simulations using the Riks method to analyse high-order deformation modes during snapping. Third, we develop a reduced-order centreline-based beam theory to rationalize the observed magneto-elastic response. The theory and simulations are validated against experiments, with an excellent quantitative agreement. Finally, we consider the case of combined magnetic loading and poking force, examining how the applied field affects the bistability and quantifying the maximum load-bearing capacity. Our work provides a set of predictive tools for the rational design of one-dimensional, bistable, magneto-elastic structural elements. This article is part of the theme issue ‘Probing and dynamics of shock sensitive shells’.
LinkGlide-S: A Wearable Multi-Contact Tactile Display Aimed at Rendering Object Softness at the Palm with Impedance Control in VR and Telemanipulation

LinkGlide-S is a novel wearable hand-worn tactile display to deliver multi-contact and multi-modal stimuli at the user’s palm. The array of inverted five-bar linkages generates three independent contact points to cover the whole palm area. The independent contact points generate various tactile patterns at the user’s hand, providing multi-contact tactile feedback. An impedance control delivers the stiffness of objects according to different parameters. Three experiments were performed to evaluate the perception of patterns, investigate the realistic perception of object interaction in Virtual Reality, and assess the users’ softness perception by the impedance control. The experimental results revealed a high recognition rate for the generated patterns. These results confirm that the performance of LinkGlide-S is adequate to detect and manipulate virtual objects with different stiffness. This novel haptic device can potentially achieve a highly immersive VR experience and more interactive applications during telemanipulation.
Kinetics of Drug Release from Clay Using Enhanced Sampling Methods

A key step in the development of a new drug, is the design of drug–excipient complexes that lead to optimal drug release kinetics. Computational chemistry and specifically enhanced sampling molecular dynamics methods can play a key role in this context, by minimizing the need for expensive experiments, and reducing cost and time. Here we show that recent advances in enhanced sampling methodologies can be brought to fruition in this area. We demonstrate the potential of these methodologies by simulating the drug release kinetics of the complex praziquantel–montmorillonite in water. Praziquantel finds promising applications in the treatment of schistosomiasis, but its biopharmaceutical profile needs to be improved, and a cheap material such as the montmorillonite clay would be a very convenient excipient. We simulate the drug release both from surface and interlayer space, and find that the diffusion of the praziquantel inside the interlayer space is the process that limits the rate of drug release.
Optimal Power Allocation for Integrated Visible Light Positioning and Communication System With a Single LED-Lamp

In this paper, we investigate an integrated visible light positioning and communication (VLPC) system with a single LED-lamp. First, by leveraging the fact that the VLC channel model is a function of the receiver’s location, we propose a system model that estimates the channel state information (CSI) based on the positioning information without transmitting pilot sequences. Second, we derive the Cramer-Rao lower bound (CRLB) on the positioning error variance and a lower bound on the achievable rate with on-off keying modulation. Third, based on the derived performance metrics, we optimize the power allocation to minimize the CRLB, while satisfying the rate outage probability constraint. To tackle this non-convex optimization problem, we apply the worst-case distribution of the Conditional Value-at-Risk (CVaR) and the block coordinate descent (BCD) methods to obtain the feasible solutions. Finally, the effects of critical system parameters, such as outage probability, rate threshold, total power threshold, are revealed by numerical results.
Experimental Performance Evaluation of Cell-Free Massive MIMO Systems Using COTS RRU With OTA Reciprocity Calibration and Phase Synchronization

Downlink coherent multiuser transmission is an essential technique for cell-free massive multiple-input multiple-output (MIMO) systems, and the availability of channel state information (CSI) at the transmitter is a basic requirement. To avoid CSI feedback in a time-division duplex system, the uplink channel parameters should be calibrated to obtain the downlink CSI due to the radio frequency circuit mismatch of the transceiver. In this paper, a design of a reference signal for over-the-air reciprocity calibration is proposed. The frequency domain generated reference signals can make full use of the flexible frame structure of the fifth-generation (5G) new radio, which can be completely transparent to commercial off-the-shelf (COTS) remote radio units (RRU) and commercial user equipments. To further obtain the calibration of multiple RRUs, an interleaved RRU grouping with a genetic algorithm is proposed, and an averaged Argos calibration algorithm is also presented. We develop a cell-free massive MIMO prototype system with COTS RRUs, demonstrate the statistical characteristics of the calibration error and the effectiveness of the calibration algorithm, and evaluate the impact of the calibration delay on the different cooperative transmission schemes.
CUAHN-VIO: Content-and-Uncertainty-Aware Homography Network for Visual-Inertial Odometry

—Learning-based visual ego-motion estimation is promising yet not ready for navigating agile mobile robots in the real world. In this article, we propose CUAHN-VIO, a robust and efﬁcient monocular visual-inertial odometry (VIO) designed for micro aerial vehicles (MAVs) equipped with a downward-facing camera. The vision frontend is a content-and-uncertainty-aware homography network (CUAHN) that is robust to non-homography image content and failure cases of network prediction. It not only predicts the homography transformation but also estimates its uncertainty. The training is self-supervised, so that it does not require ground truth that is often difﬁcult to obtain. The network has good generalization that enables “plug-and-play” deployment in new environments without ﬁne-tuning. A lightweight extended Kalman ﬁlter (EKF) serves as the VIO backend and utilizes the mean prediction and variance estimation from the network for visual measurement updates. CUAHN-VIO is evaluated on a high-speed public dataset and shows rivaling accuracy to state-of-the-art (SOTA) VIO approaches. Thanks to the robustness to motion blur, low network inference time ( ∼ 23ms), and stable processing latency ( ∼ 26ms), CUAHN-VIO successfully runs onboard an Nvidia Jetson TX2 embedded processor to navigate a fast autonomous MAV.
Unsupervised Representation Learning in Deep Reinforcement Learning: A Review

This review addresses the problem of learning abstract representations of the measurement data in the context of Deep Reinforcement Learning (DRL). While the data are often ambiguous, high-dimensional, and complex to interpret, many dynamical systems can be effectively described by a low-dimensional set of state variables. Discovering these state variables from the data is a crucial aspect for improving the data efficiency, robustness and generalization of DRL methods, tackling the \textit{curse of dimensionality}, and bringing interpretability and insights into black-box DRL. This review provides a comprehensive and complete overview of unsupervised representation learning in DRL by describing the main Deep Learning tools used for learning representations of the world, providing a systematic view of the method and principles, summarizing applications, benchmarks and evaluation strategies, and discussing open challenges and future directions.
Confounder Selection: Objectives and Approaches

Confounder selection is perhaps the most important step in the design of observational studies. A number of criteria, often with different objectives and approaches, have been proposed, and their validity and practical value have been debated in the literature. Here, we provide a unified review of these criteria and the assumptions behind them. We list several objectives that confounder selection methods aim to achieve and discuss the amount of structural knowledge required by different approaches. Finally, we discuss limitations of the existing approaches and implications for practitioners.
Heavy Elements and Electromagnetic Transients from Neutron Star Mergers

Compact binary mergers involving neutron stars can eject a fraction of their mass to space. Being extremely neutron rich, this material undergoes rapid neutron capture nucleosynthesis, and the resulting radioactivity powers fast, short-lived electromagnetic transients known as kilonova or macronova. Such transients are exciting probes of the most extreme physical conditions and their observation signals the enrichment of the Universe with heavy elements. Here we review our current understanding of the mass ejection mechanisms, the properties of the ejecta and the resulting radioactive transients. The first well-observed event in the aftermath of GW170817 delivered a wealth of insights, but much of today's picture of such events is still based on a patchwork of theoretical studies. Apart from summarizing the current understanding, we also point out questions where no consensus has been reached yet, and we sketch possible directions for the future research. In an appendix, we describe a publicly available heating rate library based on the WinNet nuclear reaction network, and we provide a simple fit formula to alleviate the implementation in hydrodynamic simulations.
Microscopic-scale recording of brain neuronal electrical activity using a diamond quantum sensor

An important tool in the investigation of the disease is the study of dissected living tissue from the brain of an animal model. Such investigations allow the physical structure of individual neurons and neural circuits to be probed [1, 2] alongside neuronal electrical activity, disruption of which can shed light on the mechanisms of emergence of disease. Existing techniques for recording activity rely on potentially damaging direct interaction with the sample, either mechanically as point electrical probes or via intense focused laser light combined with highly speciﬁc genetic modiﬁcation and/or potentially toxic ﬂuorescent dyes[3]. In this work, we instead perform passive, microscopic-scale recording of electrical activity using a biocompatible quantum sensor based on colour centres in diamond [4]. We record biomagnetic ﬁeld induced by ionic currents in mouse corpus callosum axons without direct sample interaction, accurately recovering signals corresponding to action potential propagation while demonstrating in situ pharmacology during biomagnetic recording through tetrodotoxin inhibition of voltage gated sodium channels. Our results open a promising new avenue for the microscopic recording of neuronal signals, oﬀering the prospect of high resolution imaging of electrical circuits in the living mammalian brain.
Texture Extraction Methods Based Ensembling Framework for Improved Classification

Texture-based classification solutions have proven their significance in many domains, from industrial inspections to health-related applications. New methods have been developed based on texture feature learning and CNN-based architectures to address computer vision use cases for images with rich texture-based features. In recent years, architectures solving texture-based classification problems and demonstrating state-of-the-art results have emerged. Yet, one limitation of these approaches is that they cannot claim to be suitable for all types of image texture patterns. Each technique has an advantage for a specific texture type only. To address this shortcoming, we propose a framework that combines more than one texture-based techniques together, uniquely, with a CNN backbone to extract the most relevant texture features. This enables the model to be trained in a self-selective manner and produce improved results over current published benchmarks -- with almost same number of model parameters. Our proposed framework works well on most texture types simultaneously and allows flexibility for additional texture-based methods to be accommodated to achieve better results than existing architectures. In this work, firstly, we present an analysis on the relative importance of existing techniques when used alone and in combination with other TE methods on benchmark datasets. Secondly, we show that Global Average Pooling which represents the spatial information -- is of less significance in comparison to the TE method(s) applied in the network while training for texture-based classification tasks. Finally, we present state-of-the-art results for several texture-based benchmark datasets by combining three existing texture-based techniques using our proposed framework.
Achievable Rates and Algorithms for Group Testing with Runlength Constraints

In this paper, we study bounds on the minimum length of ( k, n, d)-superimposed codes introduced by Agarwal et al. [1], in the context of Non-Adaptive Group Testing algorithms with runlength constraints. A ( k, n, d)-superimposed code of length t is a t × n binary matrix such that any two 1’s in each column are separated by a run of at least d 0’s, and such that for any column c and any other k−1 columns, there exists a row where c has 1 and all the remaining k−1 columns have 0. Agarwal et al. proved the existence of such codes with t = Θ( dk log( n/k) + k2 log( n/k)). Here we investigate more in detail the coefficients in front of these two main terms as well as the role of lower order terms. We show that improvements can be obtained over the construction in [1] by using different constructions and by an appropriate exploitation of the Lovász Local Lemma in this context. Our findings also suggest O( nk) randomized Las Vegas algorithms for the construction of such codes. We also extend our results to Two-Stage Group Testing algorithms with runlength constraints.
GaitFi: Robust Device-Free Human Identification via WiFi and Vision Multimodal Learning

As an important biomarker for human identification, human gait can be collected at a distance by passive sensors without subject cooperation, which plays an essential role in crime prevention, security detection, and other human identification applications. Presently, most research works are based on cameras and computer vision techniques to perform gait recognition. However, vision-based methods are not reliable when confronting poor illuminations, leading to degrading performances. In this article, we propose a novel multimodal gait recognition method, namely, GaitFi, which leverages WiFi signals and videos for human identification. In GaitFi, channel state information (CSI) that reflects the multipath propagation of WiFi is collected to capture human gaits, while videos are captured by cameras. To learn robust gait information, we propose a lightweight residual convolution network (LRCN) as the backbone network and further propose the two-stream GaitFi by integrating WiFi and vision features for the gait retrieval task. The GaitFi is trained by the triplet loss and classification loss on different levels of features. Extensive experiments are conducted in the real world, which demonstrates that the GaitFi outperforms state-of-the-art gait recognition methods based on single WiFi or camera, achieving 94.2% for human identification tasks of 12 subjects.
Complex-Frequency Synchronization of Converter-Based Power Systems

In this paper, we study the phase-amplitude coupled dynamics in converter-based power systems from the complex-frequency perspective. A complex-frequency quantity represents the rate of change of the voltage amplitude and the phase angle by its real and imaginary parts, respectively. This emerging notion is of significance as it accommodates the multivariable characteristics of general power systems where active and reactive power are inherently coupled with both the voltage amplitude and phase. We propose the notion of complex-frequency synchronization to study the phase-amplitude coupled stability of a power system with grid-forming virtual oscillator-controlled converters. To do so, we formulate the system into a linear fast system and another linear slow system. This linearity property makes it tractable to analyze fast complex-frequency synchronization and slower voltage stabilization. From the perspective of complex-frequency synchronization, we provide novel insights into the equivalence of virtual oscillator control to complex-power-frequency droop control, the stability analysis methodologies, and the stability criteria. Our study provides a practical solution to address challenging stability issues in converter-dominated power systems.
PINION: Physics-informed neural network for accelerating radiative transfer simulations for cosmic reionization

With the advent of the Square Kilometre Array Observatory (SKAO), scientists will be able to directly observe the Epoch of Reionization by mapping the distribution of neutral hydrogen at different redshifts. While physically motivated results can be simulated with radiative transfer codes, these simulations are computationally expensive and can not readily produce the required scale and resolution simultaneously. Here we introduce the Physics-Informed neural Network for reIONization (PINION), which can accurately and swiftly predict the complete 4-D hydrogen fraction evolution from the smoothed gas and mass density fields from pre-computed N-body simulation. We trained PINION on the C2-Ray simulation outputs and a physics constraint on the reionization chemistry equation is enforced. With only five redshift snapshots, PINION can accurately predict the entire reionization history between z = 6 and 12. We evaluate the accuracy of our predictions by analysing the dimensionless power spectra and morphology statistics estimations against C2-Ray results. We show that while the network’s predictions are in very good agreement with simulation to redshift z > 7, the network’s accuracy suffers for z < 7. We motivate how PINION performance could be improved using additional inputs and potentially generalized to large-scale simulations.