Miracle-dz/UniFAR-FTUs huggingface dataset:
Postadiabatic Hamiltonian for low-energy excitations in a slowly-time-dependent BCS-BEC crossover

We develop a Hamiltonian that describes the time-dependent formation of a molecular Bose-Einstein condensate (BEC) from a Bardeen-Cooper-Schrieffer (BCS) state of fermionic atoms as a result of slowly sweeping through a Feshbach resonance. In contrast to many other calculations in the field (see e.g. [1-4]), our Hamiltonian includes the leading post-adiabatic effects that arise because the crossover proceeds at a non-zero sweep rate. We apply a path integral approach and a stationary phase approximation for the molecular zero momentum background, which is a good approximation for narrow resonances (see e.g. [5, 6]). We use two-body adiabatic approximations to solve the atomic evolution within this background. The dynamics of the non-zero momentum molecular modes is solved within a dilute gas approximation and by mapping it onto a purely bosonic Hamiltonian. Our main result is a post-adiabatic effective Hamiltonian in terms of the instantaneous bosonic (Anderson-)Bogoliubov modes, which holds throughout the whole resonance, as long as the Feshbach sweep is slow enough to avoid breaking Cooper pairs.
Misspecified Multivariate Regression Models Using the Genetic Algorithm and Information Complexity as the Fitness Function

M odel misspecification is a major challenge faced by all statistical modeling techniques. Real world multivariate data in high dimensions frequently exhibit higher kurtosis and heavier tails, asymmetry, or both. In this paper, we extend Akaike's AIC-type model selection criteria in two ways. We use a more encompassing notion of information complexity (ICOMP) of Bozdogan for multivariate regression to allow certain types of model misspecification to be detected using the newly proposed criterion so as to protect the researchers against model misspecification. We do this by employing the "sandwich"or "robust"covariance matrix ˆ F −1 ˆ Rˆ F −1 , which is computed with the sample kurtosis and skewness. Thus, even if the data modeled do not meet the standard Gaussian assumptions, an appro- priate model can still be found. Theoretical results are then applied to multivariate regression models in subset selection of the best predictors in the presence of model misspecification by using the novel genetic algorithm (GA), with our extended ICOMP as the fitness function. We demonstrate the power of the confluence of these techniques on both simulated and real-world datasets. Our simulations are very challenging, combining multicolinearity, unnecessary variables, and redundant variables with asymmetrical or leptokurtic behavior. We also demonstrate our model selec- tion prowess on the well-known body fat data. Our findings suggest that when data are overly peaked or skewed - both characteristics often seen in real data, ICOMP based on the sandwich covariance matrix should be used to drive model selection. 2010 Mathematics Subject Classifications: 62H86, 62J05, 62N02, 65G20, 62-07, 62F12, 62E20, 81T80, 78M34, 62B10
Plasma Pressure Driven Asymmetric Supernovae and Highly Collimated Gamma-Ray Bursts

During the process of collapse of a massive star, a cavity is generated between the central iron core and an outer stellar envelope. The dynamics of this cavity, filled with plasma and magnetic field of the rapidly rotating proto-magnetar's magnetosphere, is believed to be very relevant in understanding supernovae and gamma-ray bursts. The interactions of the pressurized conducting plasma and the magnetic fields are described by a set of magnetohydrodynamic (MHD) equations with poloidal and toroidal plasma flows not aligned with magnetic fields. A sequence of MHD equilibria in response to the increasing plasma pressure in the cavity, by continuous filling from the rotating magnetosphere, is solved to account for asymmetric supernovae, highly collimated gamma-ray burst jets, and also active galactic nucleus plasma torus. It is shown that the magnetosphere of the central compact star is likely the central engine of supernova and gamma-ray burst by feeding them plasma, magnetic energy, and rotational energy.
Shrinkage estimation in elliptically contoured distribution with restricted parameter space

This paper deals with the problem of estimating the mean vector of an elliptically contoured distribution with unknown scale matrix, where the mean vector is restricted to an unbounded and closed convex set with a smooth or a piecewise smooth boundary. A shrinkage-type estimator is shown to be better than the maximum likelihood estimator subject to the restriction relative to a quadratic loss.
Understanding IoT Security from a Market-Scale Perspective

Consumer IoT products and services are ubiquitous; yet, a proper characterization of consumer IoT security is infeasible without an understanding of what IoT products are on the market, i.e., without a market-scale perspective. This paper seeks to close this gap by developing the IoTSpotter framework, which automatically constructs a market-scale snapshot of mobile-IoT apps, i.e., mobile apps that are used as companions or automation providers to IoT devices. IoTSpotter also extracts artifacts that allow us to examine the security of this snapshot in the IoT context (e.g., devices supported by apps, IoT-specific libraries). Using IoTSpotter, we identify 37,783 mobile-IoT apps from Google Play, the largest set of mobile-IoT apps so far, and uncover 7 key results in the process (ℛ1-ℛ7). We leverage this dataset to perform three key security analyses that lead to 10 impactful security findings (F1-F10) that demonstrate the current state of mobile-IoT apps. Our analysis uncovers severe cryptographic violations in 94.11% (863/917) mobile-IoT apps with >1 million installs each, 65 vulnerable IoT-specific libraries affected by 79 unique CVEs, and used by 40 popular apps, and 7,887 apps that is affected by the Janus vulnerability. Finally, a case study with 18 popular mobile-IoT apps uncovers the critical impact of the vulnerabilities in them on important IoT artifacts and functions, motivating the development of mobile security analysis contextualized to IoT.
Factorisation non-n´egative de tenseurs partiellement observ´es sous contrainte de r´egularit´e

– Contrairement au cas o`u toutes les donn´ees sont observ´ees, le probl`eme de factorisation non-n´egative de tenseur partiellement observ´e n’admet pas n´ecessairement d’optimum global. N´eanmoins, en imposant des contraintes de r´egularit´e sur les facteurs l’existence d’un optimum global peut ˆ etre prouv ´ ee. Nous proposons un algorithme d’approximation de la complexit ´ e maximale garantissant l’existence d’un tel optimum ainsi qu’un algorithme d’optimisation sous cette contrainte. Abstract – Contrary to the case where all entries are observed, the non-negative matrix factorization problem is not guaranteed to have a global optimum when the observed tensor has missing entries. This existence can however be proved in the case where some smoothness constraints are imposed on the factors. We propose an algorithm to estimate the maximal complexity constraint which guarantees the existence of such a global optimum as well as an optimization algorithm to solve the tensor factorization problem under this constraint.
On a regime switching illiquid high volatile prediction model for cryptocurrencies

PurposeThe current paper proposes a prediction model for a cryptocurrency that encompasses three properties observed in the markets for cryptocurrencies—namely high volatility, illiquidity, and regime shifts. As far as the authors’ knowledge extends, this paper is the first attempt to introduce a stochastic differential equation (SDE) for pricing cryptocurrencies while explicitly integrating the mentioned three significant stylized facts.Design/methodology/approachCryptocurrencies are increasingly utilized by investors and financial institutions worldwide as an alternative means of exchange. To the authors’ best knowledge, there is no SDE in the literature that can be used for representing and evaluating the data-generating process for the price of a cryptocurrency.FindingsBy using Ito calculus, the authors provide a solution for the suggested SDE along with mathematical proof. Numerical simulations are performed and compared to the real data, which seems to capture the dynamics of the price path of two main cryptocurrencies in the real markets.Originality/valueThe stochastic differential model that is introduced and solved in this article is expected to be useful for the pricing of cryptocurrencies in situations of high volatility combined with structural changes and illiquidity. These attributes are apparent in the real markets for cryptocurrencies; therefore, accounting explicitly for these underlying characteristics is a necessary condition for accurate evaluation of cryptocurrencies.
Goal-oriented data compression: Application to an Lp-norm type performance metric

– Le nouveau paradigme adopt´e dans cet article consiste, dans un sch´ema de compression, `a adapter l’´etage de transformation lin´eaire et celui de quantification `a l’utilisation finale du signal compress´e. La m´etrique de performance finale retenue est une norme de type Lp qui apparait dans de nombreuses applications et notamment pour le probl`eme fondamental d’ordonnancement de consommation ´electrique flexible (par exemple pour la charge de v ´ ehicules ´ electriques). En appliquant l’approche propos ´ ee ` a des donn ´ ees r ´ eelles issues de la base de donn ´ ees Ausgrid, nous montrons que celle-ci permet d’avoir une perte d’optimalit´e due `a la compression qui est bien moindre qu’un ´etage lin´eaire standard suivi d’un quantificateur uniforme. Abstract – In this paper, a goal-oriented coding scheme comprising both precoding and quantization is considered in smart grids. Instead of searching a trade-off between compression ratio and distortion, our objective is to transmit the most relevant information to the decision-making entity under few bits constraint so that the optimality loss is minimized for a given utility function. Numerical results show that our approach could reduce the optimality loss tremendously compared to conventional compression approach, even when the target bitrate is extremely limited.
Development of High Conductivity Copper Coatings for SRF Cavity Development of High Conductivity Copper Coatings for SRF Cavity

The development of metallic coatings with high purity and high thermal conductivity at cryogenic temperature could be very important for application to the superconducting radiofrequency (SRF) cavity technology. The deposition of such bulk coatings on the outer surface of a niobium cavity could result in higher heat conductance and mechanical stiffness, both of which are crucial for enhancing the cavity performance at a reduced cost. Cold spray technology was used to deposit bulk coatings of pure copper and copper-tungsten alloys on the niobium substrate and the samples of size 2 mm × 2 mm cross section were cut and subjected to annealing at various temperatures ranging from 300 ◦ C to 1000 ◦ C. Thermal conductivity and residual resistivity ratio (RRR) were measured for various samples. A maximum RRR value of ∼ 130 and ∼ 40 was measured for pure Cu samples and CuW samples, respectively. Scanning electron microscopy was used to analyze the grain growth, grain distribution and presence of contaminant particulates.
Scale-conditioned Adaptation for Large Scale Combinatorial Optimization

Deep reinforcement learning (DRL) for combinatorial optimization has drawn attention as an alternative for human-designed solvers. However, training DRL solvers for large-scale tasks remains challenging due to combinatorial optimization problems’ NP-hardness. This paper proposes a novel scale-conditioned adaptation (SCA) scheme that improves the transferability of the pre-trained solvers on larger-scale tasks. The main idea is to design a scale-conditioned policy by plugging a simple deep neural network, denoted as scale-conditioned network (SCN), into the existing DRL model. SCN extracts a hidden vector from a scale value, and then we add it to the representation vector of the pre-trained DRL model. The increment of the representation vector captures the context of scale information and helps the pre-trained model effectively adapt the policy to larger-scale tasks. Our method is verified to improve the zero-shot and few-shot performance of DRL-based solvers in various large-scale combinatorial optimization tasks.
Importance of ﬁ rst-line employees in lean implementation in SMEs: a systematic literature review

Purpose – Existing research evidence shows a fragmented understanding of the roles of ﬁ rst-line employees (FLEs) as essential factors for successful lean implementation in small- and medium-sized enterprises (SMEs), provoking recent calls for additional research on the identi ﬁ cation of enablers and barriers for lean acceptance among workers. Therefore, this paper aims to identify related enablers and barriers to lean implementation among FLEs and determine future research avenues for improving the understandingofleanmethodologyimplementationinSMEs. Design/methodology/approach – Relying on a systematic literature review methodology, the authors aimed to synthesize and evaluate available peer-reviewed papers on the role of FLEs in lean implementation in SMEs. General descriptive and thematic analysis comprehensively depicted the selected research topic and identi ﬁ ed the main themes within collected papers and potential future research questions. Findings – The authors identi ﬁ ed four main themes related to FLEs ’ role in lean implementation: cultural change factors, employee characteristics, management involvement and lean job design. Within each theme, the authors present a comprehensive overview of FLE-related factors and associated enablers and barriers that should be considered for a successful lean implementationin SMEs. Practical implications – The research outcomes are important to practicing managers in SMEs, helping them facilitate leanacceptance andenhancethelikelihoodof successful lean implementation. Originality/value – The insights from this study present building blocks in developing a lean implementationmodel for SMEs that considers theFLEs ’ role more comprehensively.
The Nature of Starburst Activity in M82

We present new evolutionary synthesis models of M82 based mainly on observations consisting of near-infrared integral field spectroscopy and mid-infrared spectroscopy. The models incorporate stellar evolution, spectral synthesis, and photoionization modeling and are optimized forλ = 1-45 μm observations of starburst galaxies. The data allow us to model the starburst regions on scales as small as 25 pc. We investigate the initial mass function (IMF) of the stars and constrain quantitatively the spatial and temporal evolution of starburst activity in M82. We find a typical decay timescale for individual burst sites of a few million years. The data are consistent with the formation of very massive stars (≳50-100 M☉) and require a flattening of the starburst IMF below a few solar masses, assuming a Salpeter slope dN/dm ∝ m-2.35 at higher masses. Our results are well matched by a scenario in which the global starburst activity in M82 occurred in two successive episodes each lasting a few million years, peaking about 107 yr and 5 × 106 yr ago. The first episode took place throughout the central regions of M82 and was particularly intense at the nucleus, while the second episode occurred predominantly in a circumnuclear ring and along the stellar bar. We interpret this sequence as resulting from the gravitational interaction between M82 and its neighbor M81, and subsequent bar-driven evolution. The short burst duration on all spatial scales indicates strong negative feedback effects of starburst activity, both locally and globally. Simple energetics considerations suggest that the collective mechanical energy released by massive stars was able to rapidly inhibit star formation after the onset of each episode.
Autonomous Exploration of Unknown Rough Terrain with Hexapod Walking Robot

The autonomous robotic exploration of an unknown environment with a hexapod walking robot is addressed in this thesis. Since the multi-legged walking robot is capable of traversing rough terrains, the proposed exploration system creates a map of the unknown environment while simultaneously performing the traversability assessment of the explored environment to reach navigational waypoints. The concept of frontiers has been utilized to detect navigational waypoints at the border of unknown space and already explored space represented by an elevation map. The proposed system is targeted to run onboard of a small robot. Therefore, the localization system required to build the elevation map incrementally uses vision-based localization deployed on a small and lightweight camera. The state-of-the-art visual localization ORB-SLAM2 has been chosen as the default localization system, for which we proposed improvements based on sensory fusion with the Global Navigation Satellite System and Inertial Measurement Unit (IMU). Although ORB-SLAM2 provides sufficiently precise localization, we have also studied an alternative embedded vision-based localization system which has an inbuilt processor capable of sensory fusion. The precision and reliability of both localization systems have been experimentally evaluated, and more suitable localization system has been used during the experimental deployments of the proposed exploration system. Based on the presented results, we can conclude that both studied localization systems provide localization of the robot with sufficient precision for deployment in an autonomous exploration scenario, but the novel embedded localization system is more reliable than ORB-SLAM2. The proposed elevation mapping technique enhanced by efficient quadtree based data representation exhibited low computational requirements that supports onboard deployment, and it is capable of covering large areas which have been demonstrated during multiple deployments in the real-world scenario. The developed exploration system enabled the hexapod walking robot to explore indoor and outdoor environments fully autonomously. The selected parts of the developed framework have also been successfully deployed on a tracked robot platform in various scenarios, including mine environment within DARPA Subterranean Challenge.
Estimation et intervalles de cr´edibilit´e pour le taux de reproduction de la Covid19 par ´echantillonnage Monte Carlo Langevin proximal

disponibles, probl`eme difﬁcile. L’objectif de ce travail est de comparer six strat´egies diff´erentes Monte Carlo de type Langevin proximal utilis´ees pour l’estimation par intervalles de cr´edibilit´e du taux de reproduction de la Covid19, et du compte d´ebruit´e de nouvelles infections quotidiennes. La difﬁcult´e r´esulte de la formulation bay´esienne utilis´ee qui, pour produire des estim´ees robustes, fait usage d’une loi a posteriori non r´eguli`ere. L’application `a des donn´ees r´eelles de la Covid19, de plusieurs pays, montre la pertinence des strat´egies propos´ees dites duales . Abstract – Monitoring the time evolution of the intensity of Covid19 pandemic within the pandemic and despite the limited quality of the available Covid19 data is both crucial and challenging. The present work compares six different Langevin proximal Monte Carlo samplers aiming to perform a credibility interval-based estimation of the pandemic Covid19 reproduction number and of the denoised daily new infection counts. The challenge stems from the Bayesian model which, to ensure robust estimates, makes uses of a non differentiable a posteriori distribution. Application to real Covid19 data from several countries shows the relevance of the so-named dual strategies proposed here.
Artiﬁcial Intelligence to Accelerate COVID-19 Identiﬁcation from Chest X-rays

Importance : Clinical signs and symptoms for COVID-19 remain the mainstay of early diagnosis and initial management in the emergency department (ED) and inpatient setting at many hospitals due to delays in obtaining results of PCR testing and limitations in access to rapid antigen testing. The majority of many patients with COVID-19 will present with respiratory symptoms necessitating a chest x-ray (CXR) as a routine part of screening. An AI-based model to predict COVID-19 likelihood from CXR ﬁndings can serve as an important and immediate adjunct to accelerate clinical decision making. Objective : To develop a robust AI-based diagnostic model to identify CXRs with COVID-19 compared with all non-COVID-19 CXRs. Setting : Labeled frontal CXR images (samples of COVID-19 and non-COVID-19) from the M Health Fairview (Minnesota, USA), Valencian Region Medical ImageBank (Spain), MIMIC-CXR, Open-I 2013 Chest X-ray Collection, GitHub COVID-19 Image Data Collection (International). Main Outcome and Measure : Model performance assessed via Area under the Receiver Operating Curve (AUROC) and Area Under the Precision and Recall Curve (AUPRC). Results : Patients with COVID-19 had signiﬁcantly higher COVID-19 Diagnostic Scores than patients without COVID-19 on both real-time electronic health record and external (non-publicly available) validation. The model performed well across all four methods for model validation with AUROCs ranging between 0.7 – 0.96 and high PPV and speci-ﬁcity. The model performed had improved discrimination for patients with “severe” as compared to “moderate” COVID-19 disease. The model had unrealistic performance using publicly available databases, reﬂecting the inherent limitations in many previously developed models relying on publicly available data for training and validation.
Functional Verification of Arithmetic Circuits using Linear Algebra Methods

FUNCTIONAL VERIFICATION OF ARITHMETIC CIRCUITS USING LINEAR ALGEBRA METHODS SEPTEMBER 2011 MOHAMED ABDUL BASITH AMEER ABDUL KADER B.E. , ANNA UNIVERSITY, INDIA M.S.E.C. E. , UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor Maciej Ciesielski This thesis describes an efficient method for speeding up functional verification of arithmetic circuits namely linear network such as wallace trees, counters using linear algebra techniques. The circuit is represented as a network of half adders, full adders and inverters, and modeled as a system of linear equations. The proof of functional correctness of the design is obtained by computing its algebraic signature using standard linear programming (LP) solver and comparing it with the reference signature provided by the designer. Initial experimental results and comparison with Satisfiability Modulo Theorem (SMT) solvers show that the method is efficient, scalable and applicable to complex arithmetic designs, including large multipliers. It is intended to provide a new front end theory/engine to enhance SMT solvers.