Miracle-dz/UniFAR-FTUs huggingface dataset:
Josephson Currents through Junctions with Normal Metal Barriers

The SNS-contact junction in the thick-barrier limit (~0/2d~1) at low temperature (T ~Tc) is simulated by a step-wise one-dimensional off-diagonal potential, with different mac­ roscopic phases for each of the superconductors, comprising the junction. The one-particle Green's functions are constructed by taking into account the boundary scattering effects at the superconductor-normal metal interfaces completely, and are used to calculate the zero­ voltage barrier supercurrent. The supercurrent is shown to be a sectionally linear periodic function of relative phase between the superconductors, the amplitude of which is inversely proportional to the thickness (2d) of the normal barrier. The associated phase coupling energy is conjectured by making use of the close analogy of the diagrammatical structures of the barrier supercurrents of SNS junction to that of SIS junction.
Adaptive regulator of partially feedback linearizable uncertain nonlinear systems

We consider single-input nonlinear systems with unknown unmodelled time-varying parameters or disturbances which are bounded. The main goal is to identify classes of uncertain systems for which the control exist and to provide design procedures. Assuming that the undisturbed nominal system (f,g) is partially state feedback linearizable, that a strict triangularity condition, a linear parametrization condition, and f/spl isin/G/sub r-1/ hold for the uncertain terms, and that some condition is satisfied in the transformed partially linear system, we design an adaptive regulating dynamic control. First, we identify classes of nonlinear uncertain systems and give a systematic procedure for the design of a robust regulation for the nonlinear systems.
Multi-view Improved Monitored Distillation for Depth Completion

—This paper proposes a new depth completion method based on multi-view improved monitored distillation to generate more accurate depth maps. Based on the state-of-the-art depth completion method named ensemble distillation, we introduce an existing stereo-based model as a teacher model to improve ensemble distillation accuracy and generate a more accurate student model in training by avoiding inherent error modes of completion-based teachers as well as minimizing the reconstruction error for a given image. We also leverage multi-view depth consistency and multi-scale minimum reprojection to provide self-supervised information. These methods use the existing structure constraints to yield supervised signals for student model training without great expense on gathering ground truth information of depth. Our extensive experimental evaluation demonstrates that our proposed method can effectively improve the accuracy of baseline method of monitored distillation.
PhaBOX: a web server for identifying and characterizing phage contigs in metagenomic data

Abstract Motivation There is accumulating evidence showing the important roles of bacteriophages (phages) in regulating the structure and functions of the microbiome. However, lacking an easy-to-use and integrated phage analysis software hampers microbiome-related research from incorporating phages in the analysis. Results In this work, we developed a web server, PhaBOX, which can comprehensively identify and analyze phage contigs in metagenomic data. It supports integrated phage analysis, including phage contig identification from the metagenomic assembly, lifestyle prediction, taxonomic classification, and host prediction. Instead of treating the algorithms as a black box, PhaBOX also supports visualization of the essential features for making predictions. The web server is designed with a user-friendly graphical interface that enables both informatics-trained and nonspecialist users to analyze phages in microbiome data with ease. Availability and implementation The web server of PhaBOX is available via: https://phage.ee. cityu.edu. hk. The source code of PhaBOX is available at: https://github.com/KennthShang/PhaBOX.
Statistical Inference for Matrix-variate Gaussian Graphical Models and False Discovery Rate Control

Matrix-variate Gaussian graphical models (GGM) have been widely used for modelling matrix-variate data. Since the supports of sparse row and column precision matrices encode the conditional independence among rows and columns of the data, it is of great interest to conduct support recovery. A commonly used approach is the penalized log-likelihood method. However, due to the complicated structure of the precision matrices of matrix-variate GGMs, the log-likelihood is non-convex, which brings a great challenge for both computation and theoretical analysis. In this paper, we propose an alternative approach by formulating the support recovery problem into a multiple testing problem. A new test statistic is proposed and based on that, we further develop a method to control false discovery rate (FDR) asymptotically. Our method is computationally attractive since it only involves convex optimization. Theoretically, our method allows very weak conditions, i.e., even when the sample size is a constant and the dimensions go to infinity, the asymptotic normality of the test statistics and FDR control can still be guaranteed. The finite sample performance of the proposed method is illustrated by both simulated and real data analysis.
Mathematical Challenges in Deep Learning

Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012. The size of deep models is increasing ever since, which brings new challenges to this field with applications in cell phones, personal computers, autonomous cars, and wireless base stations. Here we list a set of problems, ranging from training, inference, generalization bound, and optimization with some formalism to communicate these challenges with mathematicians, statisticians, and theoretical computer scientists. This is a subjective view of the research questions in deep learning that benefits the tech industry in long run.
Information-Theoretic GAN Compression with Variational Energy-based Model

We propose an information-theoretic knowledge distillation approach for the compression of generative adversarial networks, which aims to maximize the mutual information between teacher and student networks via a variational optimization based on an energy-based model. Because the direct computation of the mutual information in continuous domains is intractable, our approach alternatively optimizes the student network by maximizing the variational lower bound of the mutual information. To achieve a tight lower bound, we introduce an energy-based model relying on a deep neural network to represent a flexible variational distribution that deals with high-dimensional images and consider spatial dependencies between pixels, effectively. Since the proposed method is a generic optimization algorithm, it can be conveniently incorporated into arbitrary generative adversarial networks and even dense prediction networks, e.g., image enhancement models. We demonstrate that the proposed algorithm achieves outstanding performance in model compression of generative adversarial networks consistently when combined with several existing models.
X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance

Text-driven 3D stylization is a complex and crucial task in the fields of computer vision (CV) and computer graphics (CG), aimed at transforming a bare mesh to fit a tar-get text. Prior methods adopt text-independent multilayer perceptrons (MLPs) to predict the attributes of the target mesh with the supervision of CLIP loss. However, such text-independent architecture lacks textual guidance during predicting attributes, thus leading to unsatisfactory stylization and slow convergence. To address these limitations, we present X-Mesh, an innovative text-driven 3D stylization framework that incorporates a novel Text-guided Dynamic Attention Module (TDAM). The TDAM dynamically integrates the guidance of the target text by utilizing text-relevant spatial and channel-wise attentions during vertex feature extraction, resulting in more accurate attribute prediction and faster convergence speed. Furthermore, existing works lack standard benchmarks and automated metrics for evaluation, often relying on subjective and non-reproducible user studies to assess the quality of stylized 3D assets. To overcome this limitation, we introduce a new standard text-mesh benchmark, namely MIT-30, and two automated metrics, which will enable future research to achieve fair and objective comparisons. Our extensive qualitative and quantitative experiments demonstrate that X-Mesh outperforms previous state-of-the-art methods. Our codes and results are available at our project webpage: https://xmu-xiaoma666.github. io/Projects/X-Mesh/
Modeling Population Movements under Uncertainty at the Border in Humanitarian Crises: A Situational Analysis Tool

Humanitarian agencies must be prepared to mobilize quickly in response to complex emergencies, and their effectiveness depends on their ability to identify, anticipate, and prepare for future needs. These are typically highly uncertain situations in which predictive modeling tools can be useful but challenging to build. To better understand the need for humanitarian support -- including shelter and assistance -- and strengthen contingency planning and protection efforts for displaced populations, we present a situational analysis tool to help anticipate the number of migrants and forcibly displaced persons that will cross a border in a humanitarian crisis. The tool consists of: (i) indicators of potential intent to move drawn from traditional and big data sources; (ii) predictive models for forecasting possible future movements; and (iii) a simulation of border crossings and shelter capacity requirements under different conditions. This tool has been specifically adapted to contingency planning in settings of high uncertainty, with an application to the Brazil-Venezuela border during the COVID-19 pandemic.
A Survey on Malware Detection with Graph Representation Learning

Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep Learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. More recently, the application of such techniques on graph-structured data has achieved state-of-the-art performance in various domains and demonstrates promising results in learning more robust representations from malware. Yet, no literature review focusing on graph-based deep learning for malware detection exists. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures, leading to an efficient detection by downstream classifiers. This paper also reviews adversarial attacks that are utilized to fool graph-based detection methods. Challenges and future research directions are discussed at the end of the paper.
Generalized Rotating‐Wave Approximation for the Quantum Rabi Model with Optomechanical Interaction

The spectrum of energy and eigenstates of an hybrid cavity optomechanical system, where a cavity field mode interacts with a mechanical mode of a vibrating end mirror via radiation pressure and with a two level atom via electric dipole interaction are investigated. In the spirit of approximations developed for the quantum Rabi model beyond rotating‐wave approximation (RWA), the so‐called generalized RWA (GRWA) to diagonalize the tripartite Hamiltonian for arbitrary large couplings is implemented. Notably, the GRWA approach still allows to rewrite the hybrid Hamiltonian in a bipartite form, like a Rabi model with dressed atom‐field states (polaritons) coupled to mechanical modes through reparametrized coupling strength and Rabi frequency. A more accurate energy spectrum for a wide range of values of the atom‐photon and photon–phonon couplings, when compared to the RWA results is found. The fidelity between the numerical eigenstates and its approximated counterparts is also calculated. The degree of polariton‐phonon entanglement of the eigenstates presents a non‐monotonic behavior as the atom‐photon coupling varies, in contrast to the characteristic monotonic increase in the RWA treatment.
Coordinated Multi-Robot Shared Autonomy Based on Scheduling and Demonstrations

Shared autonomy methods, where a human operator and a robot arm work together, have enabled robots to complete a range of complex and highly variable tasks. Existing work primarily focuses on one human sharing autonomy with a single robot. By contrast, in this letter we present an approach for multi-robot shared autonomy that enables one operator to provide real-time corrections across two coordinated robots completing the same task in parallel. Sharing autonomy with multiple robots presents fundamental challenges. The human can only correct one robot at a time, and without coordination, the human may be left idle for long periods of time. Accordingly, we develop an approach that aligns the robot's learned motions to best utilize the human's expertise. Our key idea is to leverage Learning from Demonstration (LfD) and time warping to schedule the motions of the robots based on when they may require assistance. Our method uses variability in operator demonstrations to identify the types of corrections an operator might apply during shared autonomy, leverages flexibility in how quickly the task was performed in demonstrations to aid in scheduling, and iteratively estimates the likelihood of when corrections may be needed to ensure that only one robot is completing an action requiring assistance. Through a preliminary study, we show that our method can decrease the scheduled time spent sanding by iteratively estimating the times when each robot could need assistance and generating an optimized schedule that allows the operator to provide corrections to each robot during these times.
The Cospectral Gap and Turbulent Flux Calculations

Abstract An alternative method to Fourier analysis is discussed for studying the scale dependence of variances and covariances in atmospheric boundary layer time series. Unlike Fourier decomposition, the scale dependence based on multiresolution decomposition depends on the scale of the fluctuations and not the periodicity. An example calculation is presented in detail. Multiresolution decomposition is applied to tower datasets to study the cospectral gap scale, which is the timescale that separates turbulent and mesoscale fluxes of heat, moisture, and momentum between the atmosphere and the surface. It is desirable to partition the flux because turbulent fluxes are related to the local wind shear and temperature stratification through similarity theory, while mesoscale fluxes are not. Use of the gap timescale to calculate the eddy correlation flux removes contamination by mesoscale motions, and therefore improves similarity relationships compared to the usual approach of using a constant averaging timesc.
VIDIMU. Multimodal video and IMU kinematic dataset on daily life activities using affordable devices

Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient tracking solutions for remote daily life activities recognition and kinematic analysis. The dataset includes 13 activities registered using a commodity camera and five inertial sensors. The video recordings were acquired in 54 subjects, of which 16 also had simultaneous recordings of inertial sensors. The novelty of VIDIMU lies in: i) the clinical relevance of the chosen movements, ii) the combined utilization of affordable video and custom sensors, and iii) the implementation of state-of-the-art tools for multimodal data processing of 3D body pose tracking and motion reconstruction in a musculoskeletal model from inertial data. The validation confirms that a minimally disturbing acquisition protocol, performed according to real-life conditions can provide a comprehensive picture of human joint angles during daily life activities.
Massive binary black holes from Population II and III stars

Population III stars, born from the primordial gas in the Universe, lose a negligible fraction of their mass via stellar winds and possibly follow a top-heavy mass function. Hence, they have often been regarded as the ideal progenitors of massive black holes (BHs), even above the pair instability mass gap. Here, we evolve a large set of Population III binary stars (metallicity Z = 10−11) with our population-synthesis code sevn, and compare them with Population II binary stars (Z = 10−4). In our models, the lower edge of the pair-instability mass gap corresponds to a BH mass of ≈86 (≈91) M⊙ for single Population III (II) stars. Overall, we find only mild differences between the properties of binary BHs (BBHs) born from Population III and II stars, especially if we adopt the same initial mass function and initial orbital properties. Most BBH mergers born from Population III and II stars have primary BH mass below the pair-instability gap, and the maximum secondary BH mass is <50 M⊙. Only up to ≈3.3% (≈0.09%) BBH mergers from Population III (II) progenitors have primary mass above the gap. Unlike metal-rich binary stars, the main formation channel of BBH mergers from Population III and II stars involves only stable mass transfer episodes in our fiducial model.
Novel applications of an approximate profile inversion for one-dimensional medium

In one-dimensional inverse scattering, the approximate formulas to reconstruct the permittivity profiles are very interesting because of their closed forms. Nevertheless, as the result of Born approximation, they are invalid under the strong-scattering conditions which restrict their applications. In this paper, a novel approximation for inverting the permittivity profile of a one-dimensional inhomogeneous medium is derived by using an equivalent microwave networking technique. Compared with the published formulas of Ladoucer and Jordan (1985), the novel approximation is not only more general in the form, but suitable for both weak-scattering and strong-scattering conditions. Reconstruction examples show that when the relative permittivity reaches 80, the novel formula is effective, which show the validity of this scheme. >
Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?

There are many measures to report so-called treatment or causal effect: absolute difference, ratio, odds ratio, number needed to treat, and so on. The choice of a measure, eg absolute versus relative, is often debated because it leads to different appreciations of the same phenomenon; but it also implies different heterogeneity of treatment effect. In addition some measures -- but not all -- have appealing properties such as collapsibility, matching the intuition of a population summary. We review common measures and their pros and cons typically brought forward. Doing so, we clarify notions of collapsibility and treatment effect heterogeneity, unifying different existing definitions. Our main contribution is to propose to reverse the thinking: rather than starting from the measure, we start from a non-parametric generative model of the outcome. Depending on the nature of the outcome, some causal measures disentangle treatment modulations from baseline risk. Therefore, our analysis outlines an understanding what heterogeneity and homogeneity of treatment effect mean, not through the lens of the measure, but through the lens of the covariates. Our goal is the generalization of causal measures. We show that different sets of covariates are needed to generalize an effect to a different target population depending on (i) the causal measure of interest, (ii) the nature of the outcome, and (iii) the generalization's method itself (generalizing either conditional outcome or local effects).
Search for lithium-rich giants in 32 open clusters with high-resolution spectroscopy

Lithium-rich giant stars are rare and their existence challenges our understanding of stellar structure and evolution. We profit from the high-quality sample gathered with HARPS and UVES, in order to search for Li-rich giants and to identify the Li enrichment mechanisms responsible. We derive stellar parameters for 247 stars belonging to 32 open clusters, with 0.07 Ga<ages<3.6 Ga. We employed the spectral synthesis technique code FASMA for the abundance analysis of 228 stars from our sample. We also determined ages, distances, and extinction using astrometry and photometry from Gaia and PARSEC isochrones to constrain their evolutionary stage. Our sample covers a wide range of stellar masses from 1 to more than 6 solar masses where the majority of the masses are above 2 solar masses. We have found 14 canonical Li-rich giant stars which have experienced the first dredge-up. This corresponds to 6% of our total sample, which is higher than what is typically found for field stars. Apart from the canonical limit, we use the maximum Li abundance of the progenitor stars as a criterion for Li enrichment. We find Li enhancement also among eight stars which have passed the first dredge up and show strong Li lines based on the fact that stars at the same evolutionary stage in the same cluster have significantly different Li abundances. We confirm that giants with higher Li abundance correspond to a higher fraction of fast-rotating giants, suggesting a connection between Li enhancement and stellar rotation as predicted by stellar models. Our Li-rich giants are found in various evolutionary stages implying that no unique Li production mechanism is responsible for Li enrichment but rather different intrinsic or external mechanisms can be simultaneously at play.
Optimal Spatial Deconvolution and Message Reconstruction from a Large Generative Model of Models

We introduce a univariate signal deconvolution method based on the principles of an approach to Artificial General Intelligence in order to build a general-purpose model of models independent of any arbitrarily assumed prior probability distribution. We investigate how non-random data may encode information about the physical properties, such as dimensions and length scales of the space in which a signal or message may have been originally encoded, embedded, or generated. Our multidimensional space reconstruction method is based on information theory and algorithmic probability, so that it is proven to be agnostic vis-a-vis the arbitrarily chosen encoding-decoding scheme, computable or semi-computable method of approximation to algorithmic complexity, and computational model. The results presented in this paper are useful for applications in coding theory, particularly in zero-knowledge one-way communication channels, such as in deciphering messages from unknown generating sources about which no prior knowledge is available and to which no return message can be sent. We argue that this method has the potential to be of great value in cryptography, signal processing, causal deconvolution, life and technosignature detection.