Miracle-dz/UniFAR-FTUs huggingface dataset:
Locating arrays with mixed alphabet sizes

Locating arrays (LAs) can be used to detect and identify interaction faults among factors in a component-based system. The optimality and constructions of LAs with a single fault have been investigated extensively under the assumption that all the factors have the same values. However, in real life, different factors in a system have different numbers of possible values. Thus, it is necessary for LAs to satisfy such requirements. We herein establish a general lower bound on the size of mixed-level ( 1 ¯ , t ) -locating arrays. Some methods for constructing LAs including direct and recursive constructions are provided. In particular, constructions that produce optimal LAs satisfying the lower bound are described. Additionally, some series of optimal LAs satisfying the lower bound are presented.
PT-Symmetric Non-Hermitian Supersymmetry

We formulate supersymmetric non-Hermitian quantum field theories with PT symmetry, starting with free chiral boson/fermion models and then including trilinear superpotential interactions. We consider models with both Dirac and Majorana fermions, analyzing them in terms of superfields and at the component level. We also discuss the relation between the equations of motion, the (non-)invariance of the Lagrangian and the (non-)conservation of the supercurrents in the two models. We exhibit a similarity transformation that maps the free-field supersymmetric PT-symmetric Dirac model to a supersymmetric Hermitian theory, but there is in general no corresponding similarity transformation for the Majorana model. In this model we find generically a mass splitting between bosons and fermions, even though its construction is explicitly supersymmetric, offering a novel non-Hermitian mechanism for soft supersymmetry breaking.
Distance mapping applied to four well-known planetary nebulae and a nova shell

Accurate distance estimates of astrophysical objects such as planetary nebulae (PNe), and nova and supernova remnants, among others, allow us to constrain their physical characteristics, such as size, mass, luminosity, and age. An innovative technique based on the expansion parallax method, the so-called distance mapping technique (DMT), provides distance maps of expanding nebulae as well as an estimation of their distances. The DMT combines the tangential velocity vectors obtained from 3D morpho-kinematic models and the observed proper motion vectors to estimate the distance. We applied the DMT to four PNe (NGC 6702, NGC 6543, NGC 6302, and BD+30 3639) and one nova remnant (GK Persei) and derived new distances in good agreement with previous studies. New simple morpho-kinematic shape models were generated for NGC 6543, NGC 6302, and NGC 6702, whereas for BD+30 3639 and GK Persei published models were used. We demonstrate that the DMT is a useful tool to obtain distance values of PNe, in addition to revealing kinematically peculiar regions within the nebulae. Distances are also derived from the trigonometric Gaia parallaxes. The effect of the non-negligible parallax offset in the second Gaia data release is also discussed.
Multi-parameter estimation beyond quantum Fisher information

This review aims at gathering the most relevant quantum multi-parameter estimation methods that go beyond the direct use of the quantum Fisher information concept. We discuss in detail the Holevo Cramér–Rao bound, the quantum local asymptotic normality approach as well as Bayesian methods. Even though the fundamental concepts in the field have been laid out more than forty years ago, a number of important results have appeared much more recently. Moreover, the field drew increased attention recently thanks to advances in practical quantum metrology proposals and implementations that often involve estimation of multiple parameters simultaneously. Since the topics covered in these review are spread in the literature and often served in a very formal mathematical language, one of the main goals of this review is to provide a largely self-contained work that allows the reader to follow most of the derivations and get an intuitive understanding of the interrelations between different concepts using a set of simple yet representative examples involving qubit and Gaussian shift models.
Semantic Discord: Finding Unusual Local Patterns for Time Series

Finding anomalous subsequence in a long time series is a very important but difficult problem. Existing state-of-the-art methods have been focusing on searching for the subsequence that is the most dissimilar to the rest of the subsequences; however, they do not take into account the background patterns that contain the anomalous candidates. As a result, such approaches are likely to miss local anomalies. We introduce a new definition named \textit{semantic discord}, which incorporates the context information from larger subsequences containing the anomaly candidates. We propose an efficient algorithm with a derived lower bound that is up to 3 orders of magnitude faster than the brute force algorithm in real world data. We demonstrate that our method significantly outperforms the state-of-the-art methods in locating anomalies by extensive experiments. We further explain the interpretability of semantic discord.
Path Planning in Dynamic Environments using Generative RNNs and Monte Carlo Tree Search

State of the art methods for robotic path planning in dynamic environments, such as crowds or traffic, rely on hand crafted motion models for agents. These models often do not reflect interactions of agents in real world scenarios. To overcome this limitation, this paper proposes an integrated path planning framework using generative Recurrent Neural Networks within a Monte Carlo Tree Search (MCTS). This approach uses a learnt model of social response to predict crowd dynamics during planning across the action space. This extends our recent work using generative RNNs to learn the relationship between planned robotic actions and the likely response of a crowd. We show that the proposed framework can considerably improve motion prediction accuracy during interactions, allowing more effective path planning. The performance of our method is compared in simulation with existing methods for collision avoidance in a crowd of pedestrians, demonstrating the ability to control future states of nearby individuals. We also conduct preliminary real world tests to validate the effectiveness of our method.
Characterizing hydrostatic mass bias with mock-X

Surveys in the next decade will deliver large samples of galaxy clusters that transform our understanding of their formation. Cluster astrophysics and cosmology studies will become systematics limited with samples of this magnitude. With known properties, hydrodynamical simulations of clusters provide a vital resource for investigating potential systematics. However, this is only realized if we compare simulations to observations in the correct way. Here we introduce the mock-X analysis framework, a multiwavelength tool that generates synthetic images from cosmological simulations and derives halo properties via observational methods. We detail our methods for generating optical, Compton-y and X-ray images. Outlining our synthetic X-ray image analysis method, we demonstrate the capabilities of the framework by exploring hydrostatic mass bias for the IllustrisTNG, BAHAMAS, and MACSIS simulations. Using simulation derived profiles we find an approximately constant bias b ≈ 0.13 with cluster mass, independent of hydrodynamical method, or subgrid physics. However, the hydrostatic bias derived from synthetic observations is mass-dependent, increasing to b = 0.3 for the most massive clusters. This result is driven by a single temperature fit to a spectrum produced by gas with a wide temperature distribution in quasi-pressure equilibrium. The spectroscopic temperature and mass estimate are biased low by cooler gas dominating the emission, due to its quadratic density dependence. The bias and the scatter in estimated mass remain independent of the numerical method and subgrid physics. Our results are consistent with current observations and future surveys will contain sufficient samples of massive clusters to confirm the mass dependence of the hydrostatic bias.
Quantum theory of radiative decay rate and frequency shift of surface plasmon modes

In this paper, we develop a time-domain model to study the coupling between plasmon modes of arbitrarily shaped metal nanoparticles and the electromagnetic radiation, in the framework of the Hopfield model for dielectrics. In particular, we focus on the dynamics of the expectation values of the plasmon creation and annihilation operators in the ground state of the overall system "plasmons+photons". We derive closed form expressions of frequency shift and decay rate of plasmon modes in terms of the spatial distribution of the quasistatic plasmon mode. We validate this method against the Mie Theory for a spherical metal particle of radius comparable to the plasmon wavelength. Then, we investigate the decay rate and frequency shift of plasmons of bow-tie antennas as a function of the gap-size.
Exact accelerated solitons by orbiting charged space debris

We consider the nonlinear wave field induced by the orbiting charged space debris in the plasma environment generated at Low Earth Orbital (LEO) region. The generated nonlinear ion acoustic wave is shown to be governed by the forced Korteweg-de Vries (KdV) equation with the forcing function dependent on the charged space debris. When the forcing debris function depends self consistently on the nonlinear wave field, the forced KdV equation turns to be an integrable system for a specific condition. Exact accelerated solitons (velocity of the soliton changes where as amplitude remains constant) has been derived for the first time in such system and its integrability properties are explored. The amplitude of the solitonic source debris function varies with time and its shape changes over its propagation. Hence its detection by some remote means may pave a new way of measurement in this subject. For a more general condition, the approximate analytic soliton solution of the forced equation is also derived to show its features.
On the Convergence of Stochastic Gradient Descent with Low-Rank Projections for Convex Low-Rank Matrix Problems

We revisit the use of Stochastic Gradient Descent (SGD) for solving convex optimization problems that serve as highly popular convex relaxations for many important low-rank matrix recovery problems such as \textit{matrix completion}, \textit{phase retrieval}, and more. The computational limitation of applying SGD to solving these relaxations in large-scale is the need to compute a potentially high-rank singular value decomposition (SVD) on each iteration in order to enforce the low-rank-promoting constraint. We begin by considering a simple and natural sufficient condition so that these relaxations indeed admit low-rank solutions. This condition is also necessary for a certain notion of low-rank-robustness to hold. Our main result shows that under this condition which involves the eigenvalues of the gradient vector at optimal points, SGD with mini-batches, when initialized with a "warm-start" point, produces iterates that are low-rank with high probability, and hence only a low-rank SVD computation is required on each iteration. This suggests that SGD may indeed be practically applicable to solving large-scale convex relaxations of low-rank matrix recovery problems. Our theoretical results are accompanied with supporting preliminary empirical evidence. As a side benefit, our analysis is quite simple and short.
A NICER View of Spectral and Profile Evolution for Three X-Ray-emitting Millisecond Pulsars

We present two years of Neutron star Interior Composition Explorer (NICER) X-ray observations of three energetic rotation-powered millisecond pulsars (MSPs): PSRs B1937+21, B1821−24, and J0218+4232. We fit Gaussians and Lorentzians to the pulse profiles for different energy sub-bands of the soft X-ray regime to measure the energy dependence of pulse separation and width. We find that the separation between pulse components of PSR J0218+4232 decreases with increasing energy at >3σ confidence. The 95% upper limit on pulse separation evolution for PSRs B1937+21 and B1821−24 is less than 2 milliperiods per keV. Our phase-resolved spectral results provide updated constraints on the non-thermal X-ray emission of these three pulsars. The photon indices of the modeled X-ray emission spectra for each pulse component of PSR B1937+21 are inconsistent with each other at the 90% confidence level, suggesting different emission origins for each pulse. We find that the PSR B1821−24 and PSR J0218+4232 emission spectra are invariant with phase at the 90% confidence level. We describe the implications of our profile and spectral results in the context of equatorial current sheet emission models for these three MSPs with non-thermal, magnetospheric X-ray emission.
Hybrid Tiled Convolutional Neural Networks for Text Sentiment Classification

The tiled convolutional neural network (tiled CNN) has been applied only to computer vision for learning invariances. We adjust its architecture to NLP to improve the extraction of the most salient features for sentiment analysis. Knowing that the major drawback of the tiled CNN in the NLP field is its inflexible filter structure, we propose a novel architecture called hybrid tiled CNN that applies a filter only on the words that appear in the similar contexts and on their neighbor words (a necessary step for preventing the loss of some n-grams). The experiments on the datasets of IMDB movie reviews and SemEval 2017 demonstrate the efficiency of the hybrid tiled CNN that performs better than both CNN and tiled CNN.
Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift

Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security consisting in the detection of hostile action based on the unusual nature of events observed on the Information this http URL our previous work (presented at C\&ESAR 2018 and FIC 2019), we have associated deep neural networks auto-encoders for anomaly detection and graph-based events correlation to address major limitations in UEBA systems. This resulted in reduced false positive and false negative rates, improved alert explainability, while maintaining real-time performances and scalability. However, we did not address the natural evolution of behaviours through time, also known as concept drift. To maintain effective detection capabilities, an anomaly-based detection system must be continually trained, which opens a door to an adversary that can conduct the so-called "frog-boiling" attack by progressively distilling unnoticed attack traces inside the behavioural models until the complete attack is considered normal. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise. We also present preliminary work on adversarial AI conducting deception attack, which, in term, will be used to help assess and improve the defense system. These defensive and offensive AI implement joint, continual and active learning, in a step that is necessary in assessing, validating and certifying AI-based defensive solutions.
Watersheds, extension maps, and the emergence paradigm

In this paper, we investigate the links between the flooding paradigm and the topological watershed. Guided by the analysis of a classical flooding algorithm, we introduce several notions that help us to understand the watershed: minima extension, extension map, and pass values. We investigate the possibility for a flooding to produce a topological watershed, and conclude that this is not feasible. This leads us to reverse the flooding paradigm, and to propose a notion of emergence. An emergence process is a transformation based on a topological criterion, in which points are processed in decreasing altitude order while preserving the number of connected components of lower cross-sections. Our main result states that any emergence watershed is a topological watershed, and more remarkably, that any topological watershed of a given image can be obtained as an emergence watershed of the image.
Leader-cell-driven epithelial sheet fingering

Collective cell migration is crucial in many biological processes such as wound healing, tissue morphogenesis, and tumor progression. The leading front of a collective migrating epithelial cell layer often destabilizes into multicellular finger-like protrusions, each of which is guided by a leader cell at the fingertip. Here, we develop a subcellular-element-based model of this fingering instability, which incorporates leader cells and other related properties of a monolayer of epithelial cells. Our model recovers multiple aspects of the dynamics, especially the traction force patterns and velocity fields, observed in experiments on Madin–Darby canine kidney cells. Our model predicts the necessity of the leader cell and its minimal functions for the formation and maintenance of a stable finger pattern. Meanwhile, our model allows for an analysis of the role of supracellular actin cable on the leading front, predicting that while this observed structure helps maintain the shape of the finger, it is not required in order to form a finger. In addition, we also study the driving instability in the context of continuum active fluid model, which justifies some of our assumptions in the computational approach. In particular, we show that in our model no finger protrusions would emerge in a phenotypically homogenous active fluid and hence the role of the leader cell and its followers are often critical.
Prevalence of tobacco use among young adults in Palestine.

Background Smoking tobacco is a worldwide public health issue. Over the last few decades, smoking patterns have been changing, reflected by increasing rates among young people and females in particular. Aims This study aimed to determine the prevalence and modalities of smoking and to assess the factors, habits and beliefs that might encourage or discourage smoking among young adults in Palestine. Methods A cross-sectional study was conducted in the West Bank in 2014 among Palestinians aged 18-25 years old. Subjects were recruited from six Palestinian universities (n=1997). Participants were asked to complete a questionnaire focusing on sociodemographics, knowledge and beliefs towards tobacco smoking, and the reasons that motivate or hinder smokers to quit. Results The prevalence of tobacco smoking was found to be 47.7%. Males had higher smoking rates, consumption levels, and initiated smoking at younger ages (74.4% started at ≤18 years old). Smoking cigarettes and waterpipe were the most common forms among both sexes. Smokers were also found to consume higher amounts of caffeinated drinks and fast food, showed lower scores towards anti-smoking beliefs, and reported significantly higher prevalence of smoking-related symptoms and diseases, primarily shortness of breath (20.5%) and cough (16.6%). The majority of smokers reported attempting and willingness to quit smoking. Health and financial costs were the strongest factors encouraging quitting while mood changes and lack of self-control were the most reported discouraging factors. Moreover, smoking among family members and peers increased the odds of smoking. Conclusions Increasing rates of smoking among young Palestinians and a growing popularity of waterpipe use should alert stakeholders to the necessity for the implementation of smoking prevention and awareness policies and programmes.
Suppression of Gremlin by RNA interference attenuates glucose-induced fibrogenesis in rat mesangial cells

Background: diabetic nephropathy (DN) is the most common cause of end-stage of renal disease. It is beneficial for us to find effective way to treat the disease. Gremlin is deemed as a key factor in the development of diabetic nephropathy at present. We hypothesized that the pathological changes might be prevented by eliminating Gremlin function in high glucose-induced renal fibrosis. Methods: lentiviral vector targeting Gremlin was applied to inhibit Gremlin expression at a high glucose concentration which simulated diabetic nephropathy in rat mesangial cells. Results: the shRNA vector, designated shGremlin, significantly inhibited Gremlin expression in rat mesangial cells cultured under high glucose conditions. Increase in BMP-7 as well as its downstream genes or proteins including phospholated Smad 1/5/8, type IV collagen and fibronectin was observed. Conclusions: our work provides a valuable method to prevent glomerular/renal fibrosis with RNA interference, and also enable development of new therapies that target Gremlin.
Rational design direct Z-scheme BiOBr/g-C3N4 heterojunction with enhanced visible photocatalytic activity for organic pollutants elimination

A rapid recombination of photo-generated electrons and holes, as well as a narrow visible light adsorption range are two intrinsic defects in graphitic carbon nitride (g-C3N4)-based photocatalysts. Inspired by natural photosynthesis, an artificially synthesized Z-scheme photocatalyst can efficaciously restrain the recombination of photogenerated electron–hole pairs and enhance the photoabsorption ability. Hence, to figure out the above problems, BiOBr/g-C3N4 composite photocatalysts with different mass ratios of BiOBr were successfully synthesized via a facile template-assisted hydrothermal method which enabled the BiOBr microspheres to in situ grow on the surface of g-C3N4 flakes. Furthermore, to explore the origin of the enhanced photocatalytic activity of BiOBr/g-C3N4 composites, the microstructure, photoabsorption ability and electrochemical property of BiOBr/g-C3N4 composites were investigated by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), UV-vis diffuse reflectance spectroscopy (DRS), electrochemical impedance spectroscopy (EIS) and photocurrent (PC) response measurements. As a result, the introduction of BiOBr on g-C3N4 to constitute a direct Z-scheme heterojunction system can effectively broaden the light absorption range and promote the separation of photo-generated electron–hole pairs. Hence, compared with pure g-C3N4 and BiOBr, the resultant BiOBr/g-C3N4 composites exhibit the remarkable activity of photodegradated rhodamine B (RhB) and tetracycline hydrochloride (TC-HCl) under visible light irradiation. Simultaneously, the optimal BiOBr content of the BiOBr/g-C3N4 composites was obtained. The BiOBr/g-C3N4 composites exhibit an excellent photostability and reusability after four recycling runs for degradation RhB. Moreover, the active-group-trapping experiment confirmed that ·OH, ·O2− and h+ were the primary active groups in the degradation process. Based on the above research results, a rational direct Z-scheme heterojunction system is contrastively analyzed and proposed to account for the photocatalytic degradation process of BiOBr/g-C3N4 composites.