Miracle-dz/UniFAR-FTUs huggingface dataset:
A new distance to the Brick, the dense molecular cloud G0.253+0.016

We analyse the near infrared colour magnitude diagram of a field including the giant molecular cloud G0.253 + 0.016 (a.k.a. The Brick) observed at high spatial resolution, with HAWK-I@VLT. The distribution of red clump stars in a line of sight crossing the cloud, compared with that in a direction just beside it, and not crossing it, allow us to measure the distance of the cloud from the Sun to be 7.20, with a statistical uncertainty of ±0.16 and a systematic error of ±0.20 kpc . This is significantly closer than what is generally assumed, i.e., that the cloud belongs to the near side of the central molecular zone, at 60 pc from the Galactic center. This assumption was based on dynamical models of the central molecular zone, observationally constrained uniquely by the radial velocity of this and other clouds. Determining the true position of the Brick cloud is relevant because this is the densest cloud of the Galaxy not showing any ongoing star formation. This puts the cloud off by 1 order of magnitude from the Kennicutt-Schmidt relation between the density of the dense gas and the star formation rate. Several explanations have been proposed for this absence of star formation, most of them based on the dynamical evolution of this and other clouds, within the Galactic center region. Our result emphasizes the need to include constraints coming from stellar observations in the interpretation of our Galaxy’s central molecular zone.
Comparing early dark energy and extra radiation solutions to the Hubble tension with BBN

A shorter sound horizon scale at the recombination epoch, arising from introducing extra energy components such as extra radiation or early dark energy (EDE), is a simple approach to resolving the so-called Hubble tension. We compare EDE models, an extra radiation model, and a model in which EDE and extra radiation coexist, paying attention to the fit to big bang nucleosynthesis (BBN). We find that the fit to BBN in EDE models is somewhat poorer than that in the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model, because the increased inferred baryon asymmetry leads to a smaller deuterium abundance. We find that an extra radiation--EDE coexistence model gives the largest present Hubble parameter ${H}_{0}$ among the models studied. We also examine the differences between the results obtained with and without consideration of the BBN. The difference in the extra radiation model is $3.22l{N}_{\mathrm{eff}}l3.49(68%)$ for data sets without BBN and $3.16l{N}_{\mathrm{eff}}l3.40(68%)$ for data sets with BBN, which is so large that the $1\ensuremath{\sigma}$ border of the larger side becomes the $2\ensuremath{\sigma}$ border.
Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETs

We address the packet routing problem in highly dynamic mobile ad-hoc networks (MANETs). In the network routing problem each router chooses the next-hop(s) of each packet to deliver the packet to a destination with lower delay, higher reliability, and less overhead in the network. In this paper, we present a novel framework and routing policies, DeepCQ+ routing, using multi-agent deep reinforcement learning (MADRL) which is designed to be robust and scalable for MANETs. Unlike other deep reinforcement learning (DRL)-based routing solutions in the literature, our approach has enabled us to train over a limited range of network parameters and conditions, but achieve realistic routing policies for a much wider range of conditions including a variable number of nodes, different data flows with varying data rates and source/destination pairs, diverse mobility levels, and other dynamic topology of networks. We demonstrate the scalability, robustness, and performance enhancements obtained by DeepCQ+ routing over a recently proposed model-free and non-neural robust and reliable routing technique (i.e. CQ+ routing). DeepCQ+ routing outperforms non-DRL-based CQ+ routing in terms of overhead while maintains same goodput rate. Under a wide range of network sizes and mobility conditions, we have observed the reduction in normalized overhead of 10-15%, indicating that the DeepCQ+ routing policy delivers more packets end-to-end with less overhead used. To the best of our knowledge, this is the first successful application of MADRL for the MANET routing problem that simultaneously achieves scalability and robustness under dynamic conditions while outperforming its non-neural counterpart. More importantly, we provide a framework to design scalable and robust routing policy with any desired network performance metric of interest.
A Comprehensive Survey of 6G Wireless Communications

While fifth-generation (5G) communications are being rolled out worldwide, sixth-generation (6G) communications have attracted much attention from both the industry and the academia. Compared with 5G, 6G will have a wider frequency band, higher transmission rate, spectrum efficiency, greater connection capacity, shorter delay, wider coverage, and stronger anti-interference capability to satisfy various network requirements. In this paper, we present a survey of potential essential technologies in 6G. In particular, we will give an insightful understanding of the paradigms and applications of the future 6G wireless communications by introducing index modulation (IM), artificial intelligence (AI), intelligent reflecting surfaces (IRS), simultaneous wireless information and power transfer (SWIPT), space-air-ground-sea integrated network (SAGSIN), terahertz (THz), visible light communications (VLC), blockchain-enabled wireless network, holographic radio, full-duplex technology (FD), Cell-Free Massive MIMO (CFmMM), and security and privacy problems behind technologies mentioned above.
The Semantic Adjacency Criterion in Time Intervals Mining

We propose a new pruning constraint when mining frequent temporal patterns to be used as classification and prediction features, the Semantic Adjacency Criterion [SAC], which filters out temporal patterns that contain potentially semantically contradictory components, exploiting each medical domain’s knowledge. We have defined three SAC versions and tested them within three medical domains (oncology, hepatitis, diabetes) and a frequent-temporal-pattern discovery framework. Previously, we had shown that using SAC enhances the repeatability of discovering the same temporal patterns in similar proportions in different patient groups within the same clinical domain. Here, we focused on SAC’s computational implications for pattern discovery, and for classification and prediction, using the discovered patterns as features, by four different machine-learning methods: Random Forests, Naïve Bayes, SVM, and Logistic Regression. Using SAC resulted in a significant reduction, across all medical domains and classification methods, of up to 97% in the number of discovered temporal patterns, and in the runtime of the discovery process, of up to 98%. Nevertheless, the highly reduced set of only semantically transparent patterns, when used as features, resulted in classification and prediction models whose performance was at least as good as the models resulting from using the complete temporal-pattern set.
Microstructure and Diffusion Properties of Oil-in-Water Vinyltoluene Microemulsions

Abstract Electrochemical studies on oil-in-water vinyltoluene–sodium dodecyl sulfate (SDS)–water–n-pentanol microemulsions have been carried out to evaluate the microstructure and diffusion properties. Rotating disc voltammetry (RDV), electrical conductivity, and viscosity are measured at 27°C to obtain the diffusion coefficients, droplet radii, surfactant aggregation number, fraction of free ions, number density of the droplets, etc. Ferrocene was used as the electrochemical probe. The droplet radii range from 25 to 75 A. The effects of compositional variations in vinyltoluene and SDS on the microstructure and diffusion properties are also discussed.
Surface state at $BaSnO_3$ evidenced by angle-resolved photoemission spectroscopy and ab initio calculations

Muntaser Naamneh, 2 Abhinav Prakash, Eduardo B. Guedes, W. H. Brito, Ming Shi, Nicholas C. Plumb, Bharat Jalan, and Milan Radović Department of Physics, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel Photon Science Division, Paul Scherrer Institut, CH-5232 Villigen, Switzerland Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave., Minneapolis, MN 55455 Departamento de F́ısica, Universidade Federal de Minas Gerais, C.P. 702, 30123-970 Belo Horizonte, Minas Gerais, Brazil (Dated: January 12, 2021)
Amorphous Order&Non-linear Susceptibilities in Glassy Materials

We review 15 years of theoretical and experimental work on the non-linear response of glassy systems. We argue that an anomalous growth of the peak value of non-linear susceptibilities is a signature of growing “amorphous order” in the system, with spin-glasses as a case in point. Experimental results on supercooled liquids are fully compatible with the RFOT prediction of compact “glassites” of increasing volume as temperature is decreased, or as the system ages. We clarify why such a behaviour is hard to explain within purely kinetic theories of glass formation, despite recent claims to the contrary.
Estimation of Missing Data in Intelligent Transportation System

Missing data is a challenge in many applications, including intelligent transportation systems (ITS). In this paper, we study traffic speed and travel time estimations in ITS, where portions of collected data are missing due to sensor instability and communication errors at collection points. These practical issues can be remediated by missing data analysis, which are mainly categorized as either statistical or machine learning (ML)-based approaches. Statistical methods require the priori probability distribution of the data which is unknown in our application. Therefore, we focus on an ML-based approach, Multi-Directional Recurrent Neural Network (M-RNN). M-RNN utilizes both temporal and spatial characteristics of the data. We evaluate the effectiveness of this approach on a TomTom dataset containing spatio-temporal measurements of average vehicle speed and travel time in the Greater Toronto Area (GTA). We evaluate the method under various conditions, where the results demonstrate that M-RNN outperforms existing solutions, e.g., spline interpolation and matrix completion, by up to 58% decreases in Root Mean Square Error (RMSE).
Cybersecurity of Industrial Cyber-Physical Systems: A Review

Industrial cyber-physical systems (ICPSs) manage critical infrastructures by controlling the processes based on the “physics” data gathered by edge sensor networks. Recent innovations in ubiquitous computing and communication technologies have prompted the rapid integration of highly interconnected systems to ICPSs. Hence, the “security by obscurity” principle provided by air-gapping is no longer followed. As the interconnectivity in ICPSs increases, so does the attack surface. Industrial vulnerability assessment reports have shown that a variety of new vulnerabilities have occurred due to this transition. Although there are existing surveys in this context, very little is mentioned regarding the outputs of these reports. While these reports show that the most exploited vulnerabilities occur due to weak boundary protection, these vulnerabilities also occur due to limited or ill-defined security policies. However, current literature focuses on intrusion detection systems (IDSs), network traffic analysis (NTA) methods, or anomaly detection techniques. Hence, finding a solution for the problems mentioned in these reports is relatively hard. We bridge this gap by defining and reviewing ICPSs from a cybersecurity perspective. In particular, multi-dimensional adaptive attack taxonomy is presented and utilized for evaluating real-life ICPS cyber incidents. Finally, we identify the general shortcomings and highlight the points that cause a gap in existing literature while defining future research directions.
Integrating a joint Bayesian generative model in a discriminative learning framework for speaker verification

— The task for speaker veriﬁcation (SV) is to decide an utterance is spoken by a target or imposter speaker. In most SV studies, a log-likelihood ratio (L LLR) score is estimated based on a generative probability model on speaker features, and compared with a threshold for decision making. However, the generative model usually focuses on feature distributions and does not have the discriminative feature selection ability, which is easy to be distracted by nuisance features. The SV, as a hypothesis test, could be formulated as a binary classiﬁcation task where a neural network (NN) based discriminative learning could be applied. Through discriminative learning, the nuisance features could be removed with the help of label supervision. However, the discriminative learning pays more attention to classiﬁcation boundaries which is prone to overﬁtting to training data and yielding poor generalization on testing data. In this paper, we propose a hybrid learning framework, i.e., integrating a joint Bayesian (JB) generative model into a neural discriminative learning framework for SV. A Siamese NN is built with dense layers to approximate the mapping functions used in the SV pipeline with the JB model, and the L-LLR score estimated based on the JB model is connected to the distance metric in a pair-wised discriminative learning. By initializing the Siamese NN with the parameters learned from the JB model, we further train the model parameters with the pair-wised samples as a binary discrimination task. Moreover, direct evaluation metric in SV, i.e., minimum empirical Bayes risk, is designed and integrated as an objective function in the discriminative learning. We carried out SV experiments on speakers in the wild (SITW) and Voxceleb corpora. Experimental results showed that our proposed model improved the performance with a large margin compared with state-of-the-art
Adaptive modelling of variably saturated seepage problems

In this article, we present a goal-oriented adaptive finite element method for a class of subsurface flow problems in porous media, which exhibit seepage faces. We focus on a representative case of the steady state flows governed by a nonlinear Darcy–Buckingham law with physical constraints on subsurface-atmosphere boundaries. This leads to the formulation of the problem as a variational inequality. The solutions to this problem are investigated using an adaptive finite element method based on a dual-weighted a posteriori error estimate, derived with the aim of reducing error in a specific target quantity. The quantity of interest is chosen as volumetric water flux across the seepage face, and therefore depends on an a priori unknown free boundary. We apply our method to challenging numerical examples as well as specific case studies, from which this research originates, illustrating the major difficulties that arise in practical situations. We summarise extensive numerical results that clearly demonstrate the designed method produces rapid error reduction measured against the number of degrees of freedom.
Electrically nonneutral ground states of stars

To approximately compute the non-relativistic ground state of an electrically non-neutral star, an exactly solvable model was recently introduced, and partly solved, by Krivoruchenko, Nadyozhin, and Yudin. The model generalizes the well-known Lane--Emden equation of a polytropic gas ball of index $n=1$ to a two-fluid setting. Here its complete solution is presented in terms of simple elementary functions; it is also generalized to a more-than-two-fluid setting where it remains exactly solvable. It is shown that, given the number of nuclei, a maximal negatively and a maximal positively charged solution exists, plus a continuous family of solutions which interpolates between these extremes. Numerical comparisons show that this exactly solvable model captures the qualitative behavior of the more physical model it is supposed to approximate. Furthermore, it correctly answers the question: how non-neutral can the star be? The answer is independent of the speed of light $c$ and the Planck quantum $h$. It supports Penrose's weak cosmic censorship hypothesis, in the sense that the bounds on the excess charge are compatible with the bound on the charge of a Reissner--Weyl--Nordstr\"om black hole.
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning

Agents that interact with other agents often do not know a priori what the other agents' strategies are, but have to maximise their own online return while interacting with and learning about others. The optimal adaptive behaviour under uncertainty over the other agents' strategies w.r. t. some prior can in principle be computed using the Interactive Bayesian Reinforcement Learning framework. Unfortunately, doing so is intractable in most settings, and existing approximation methods are restricted to small tasks. To overcome this, we propose to meta-learn approximate belief inference and Bayes-optimal behaviour for a given prior. To model beliefs over other agents, we combine sequential and hierarchical Variational Auto-Encoders, and meta-train this inference model alongside the policy. We show empirically that our approach outperforms existing methods that use a model-free approach, sample from the approximate posterior, maintain memory-free models of others, or do not fully utilise the known structure of the environment.
QAOA-based Fair Sampling on NISQ Devices

: We study the status of fair sampling on Noisy Intermediate Scale Quantum (NISQ) devices, in particular the IBM Q family of backends. Using the recently introduced Grover Mixer-QAOA algorithm for discrete optimization, we generate fair sampling circuits to solve six problems of varying diﬃculty, each with several optimal solutions, which we then run on ten diﬀerent backends available on the IBM Q system. For a given circuit evaluated on a speciﬁc set of qubits, we evaluate: how frequently the qubits return an optimal solution to the problem, the fairness with which the qubits sample from all optimal solutions, and the reported hardware error rate of the qubits. To quantify fairness, we deﬁne a novel metric based on Pearson’s χ 2 test. We ﬁnd that fairness is relatively high for circuits with small and large error rates, but drops for circuits with medium error rates. This indicates that structured errors dominate in this regime, while unstructured errors, which are random and thus inherently fair, dominate in noisier qubits and longer circuits. Our results provide a simple, intuitive means of quantifying fairness in quantum circuits, and show that reducing structured errors is necessary to improve fair sampling on NISQ hardware.
VIDA: A simulation model of domestic VIolence in times of social DistAncing

Violence against women occurs predominantly in the family and domestic context. The COVID-19 pan-demic has led Brazil to recommend and at times, impose social distancing, with the partial closure of economic activities, schools, and restrictions on events and public services. Preliminary evidence shows that intense co-existence increases domestic violence, while social distancing measures may have prevented access to public services and networks, information, and help. We propose an agent-based model (ABM), called VIDA, to for-malize and illustrate a multitude of factors that influence events which could trigger violence. A central part of the model is the construction of a stress indicator, created as a probability trigger of domestic violence occur-ring within the family environment. Having a formal model that replicates observed patterns of violence based on internal familial characteristics enables us to experiment with altering dynamics. We first tested the (a) ab-sence or presence of the deterrence system of domestic violence against women and then (b) the existence of measures to increase social distancing. VIDA presents comparative results for metropolitan regions and neigh-borhoods considered in the experiments. Results suggest that social distancing measures, particularly those encouraging staying at home, may have increased domestic violence against women by about 10%. VIDA sug-gests further that more populated areas have comparatively fewer cases per hundred thousand women than less populous capitals or rural areas of urban concentrations. This paper contributes to the literature by formal-izing, to the best of our knowledge, the first model of domestic violence through agent-based modeling, using empirical detailed socioeconomic, demographic, educational, gender, and race data at the intraurban (census sectors) and household level.
Grid-Parametrize-Split (GriPS) for Improved Scalable Inference in Spatial Big Data Analysis

Rapid advancements in spatial technologies including Geographic Information Systems (GIS) and remote sensing have generated massive amounts of spatially referenced data in a variety of scientific and data-driven industrial applications. These advancements have led to a substantial, and still expanding, literature on the modeling and analysis of spatially oriented big data. In particular, Bayesian inferences for high-dimensional spatial processes are being sought in a variety of remote-sensing applications including, but not limited to, modeling next generation Light Detection and Ranging (LiDAR) systems and other remotely sensed data. Massively scalable spatial processes, in particular Gaussian processes (GPs), are being explored extensively for the increasingly encountered big data settings. Recent developments include GPs constructed from sparse Directed Acyclic Graphs (DAGs) with a limited number of neighbors (parents) to characterize dependence across the spatial domain. The DAG can be used to devise fast algorithms for posterior sampling of the latent process, but these may exhibit pathological behavior in estimating covariance parameters. While these issues are mitigated by considering marginalized samplers that exploit the underlying sparse precision matrix, these algorithms are slower, less flexible, and oblivious of structure in the data. The current article introduces the Grid-Parametrize-Split (GriPS) approach for conducting Bayesian inference in spatially oriented big data settings by a combination of careful model construction and algorithm design to effectuate substantial improvements in MCMC convergence. We demonstrate the effectiveness of our proposed methods through simulation experiments and subsequently undertake the modeling of LiDAR outcomes and production of their predictive maps using G-LiHT and other remotely sensed variables.
CryptoEmu: An Instruction Set Emulator for Computation Over Ciphers

Fully homomorphic encryption (FHE) allows computations over encrypted data. This technique makes privacy-preserving cloud computing a reality. Users can send their encrypted sensitive data to a cloud server, get encrypted results returned and decrypt them, without worrying about data breaches. This project report presents a homomorphic instruction set emulator, CryptoEmu, that enables fully homomorphic computation over encrypted data. The software-based instruction set emulator is built upon an open-source, state-of-the-art homomorphic encryption library that supports gate-level homomorphic evaluation. The instruction set architecture supports multiple instructions that belong to the subset of ARMv8 instruction set architecture. The instruction set emulator utilizes parallel computing techniques to emulate every functional unit for minimum latency. This project report includes details on design considerations, instruction set emulator architecture, and datapath and control unit implementation. We evaluated and demonstrated the instruction set emulator's performance and scalability on a 48-core workstation. CryptoEmu has shown a significant speedup in homomorphic computation performance when compared with HELib, a state-of-the-art homomorphic encryption library.