Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms

Many machine learning tasks can be formulated as a stochastic compositional optimization (SCO) problem such as reinforcement learning, AUC maximization, and meta-learning, where the objective function involves a nested composition associated with an expectation. While a significant amount of studies has been devoted to studying the convergence behavior of SCO algorithms, there is little work on understanding their generalization, i.e., how these learning algorithms built from training examples would behave on future test examples. In this paper, we provide the stability and generalization analysis of stochastic compositional gradient descent algorithms through the lens of algorithmic stability in the framework of statistical learning theory. Firstly, we introduce a stability concept called compositional uniform stability and establish its quantitative relation with generalization for SCO problems. Then, we establish the compositional uniform stability results for two popular stochastic compositional gradient descent algorithms, namely SCGD and SCSC. Finally, we derive dimension-independent excess risk bounds for SCGD and SCSC by trade-offing their stability results and optimization errors. To the best of our knowledge, these are the first-ever-known results on stability and generalization analysis of stochastic compositional gradient descent algorithms.
Pion quasiparticles in isospin medium from holography

The properties of the pion quasiparticle in hot and dense isospin medium, including the temperature and isospin chemical potential dependence of their screening mass, pole mass and thermal width, as well as their relationships with the pion superfluid phase transition, are investigated in the framework of two-flavor ($N_{f}=2$) soft-wall AdS/QCD models. We extract the screening mass of the pion from the pole of the spatial two-point Retarded correlation function. We find that the screening masses of both neutral and charged pions increase monotonously with the increasing of temperature. However, the isospin chemical potential $\mu_{I}$ would depress the screening masses of the charged pions, $m_{\pi^{\pm},\rm{scr}}$. With the increasing of $\mu_{I}$, $m_{\pi^{\pm},\rm{scr}}$ monotonically decrease to zero on the boundary between the normal phase and the pion superfluid phase, while the screening mass of the neutral pion, $m_{\pi^0,\rm{scr}}$, remains almost unchanged. The pole mass $m_{\rm{pole}}$ and thermal width $\Gamma$ of the pion are extracted from the pole of temporal two-point Retarded correlation function, i.e., the corresponding quasi-normal frequencies, $\omega=m_{\rm{pole}}-i\Gamma/2$. The results show that the pole masses of the three modes ($\pi^0, \pi^+, \pi^-$) are splitting at finite $\mu_{I}$. The thermal widths of the three modes monotonically increase with temperature. Furthermore, the pole mass of $\pi^+$ decreases almost linearly with the increasing of $\mu_{I}$ and reaches zero at $\mu_{I}=\mu_{I}^c$, It means that $\pi^+$ becomes a massless Goldstone boson of the pion superfluid phase transition.
Instabilities in flexible channel flow with large external pressure

We examine the stability of laminar high-Reynolds-number flow through an asymmetric flexible-walled channel driven by a fixed upstream flux and subject to a (large) uniform external pressure. We construct a long-wavelength, spatially one-dimensional model using a flow profile assumption, modelling the flexible wall as a thin tensioned membrane subject to a large axial pre-stress. We numerically construct the non-uniform static shape of the flexible wall and consider its stability using both a global eigensolver and numerical simulation of the nonlinear governing equations. The system admits multiple static solutions, including a highly collapsed steady state where the membrane has a single constriction which increases with increasing external pressure. We demonstrate that the non-uniform static state is unstable to two distinct (infinite) families of normal modes which we characterise in the limit of large external pressure. In particular, there is a family of low-frequency oscillatory modes which each persist to low membrane tensions, where the most unstable mode has an oscillating membrane profile which is outwardly bulged at the centre of the domain with a narrow constriction at the downstream end. In addition, there is a family of high-frequency oscillatory modes which are each unstable beyond a critical value of the tension within a two-branch neutral curve. Unstable modes along the lower branch of the neutral curve are sustained by a leading-order balance between unsteady inertia and the restoring force of membrane tension along the channel. In addition, we elucidate the mechanism of energy transfer to sustain the self-excited oscillations: oscillations decrease the mean maximal constriction of the channel over a period, which reduces the overall dissipation of the mean flow and releases energy to sustain the instability. Fully nonlinear simulations indicate that as the Reynolds number increases these unstable normal modes can grow supercritically into sustained large-amplitude ‘slamming’ oscillations, where the membrane is periodically drawn very close to the opposite rigid wall before recovering.
On Adaptive Portfolio Management with Dynamic Black-Litterman Approach

This paper introduces a unified framework for adaptive portfolio management, integrating dynamic Black-Litterman (BL) optimization with the general factor model, Elastic Net regression, and mean-variance portfolio optimization, which allows us to generate investors' views and mitigate potential estimation errors systematically. Specifically, we propose an innovative dynamic sliding window algorithm to respond to the constantly changing market conditions. This algorithm allows for the flexible adjustment of the window size based on market volatility, generating robust estimates for factor modeling, time-varying BL estimations, and optimal portfolio weights. Through extensive empirical studies using the top 100 capitalized assets in the S&P 500 index, accounting for turnover transaction costs, we demonstrate that this combined approach leads to computational advantages and promising trading performances.
Freezing of Gait Prediction From Accelerometer Data Using a Simple 1D-Convolutional Neural Network - 8th Place Solution for Kaggle's Parkinson's Freezing of Gait Prediction Competition

Freezing of Gait (FOG) is a common motor symptom in patients with Parkinson's disease (PD). During episodes of FOG, patients suddenly lose their ability to stride as intended. Patient-worn accelerometers can capture information on the patient's movement during these episodes and machine learning algorithms can potentially classify this data. The combination therefore holds the potential to detect FOG in real-time. In this work I present a simple 1-D convolutional neural network that was trained to detect FOG events in accelerometer data. Model performance was assessed by measuring the success of the model to discriminate normal movement from FOG episodes and resulted in a mean average precision of 0.356 on the private leaderboard on Kaggle. Ultimately, the model ranked 8th out of 1379 teams in the Parkinson's Freezing of Gait Prediction competition. The results underscore the potential of Deep Learning-based solutions in advancing the field of FOG detection, contributing to improved interventions and management strategies for PD patients.
Large AI Model-Based Semantic Communications

Semantic communication (SC) is an emerging intelligent paradigm, offering solutions for various future applications like metaverse, mixed-reality, and the Internet of everything. However, in current SC systems, the construction of the knowledge base (KB) faces several issues, including limited knowledge representation, frequent knowledge updates, and insecure knowledge sharing. Fortunately, the development of the large AI model provides new solutions to overcome above issues. Here, we propose a large AI model-based SC framework (LAM-SC) specifically designed for image data, where we first design the segment anything model (SAM)-based KB (SKB) that can split the original image into different semantic segments by universal semantic knowledge. Then, we present an attention-based semantic integration (ASI) to weigh the semantic segments generated by SKB without human participation and integrate them as the semantic-aware image. Additionally, we propose an adaptive semantic compression (ASC) encoding to remove redundant information in semantic features, thereby reducing communication overhead. Finally, through simulations, we demonstrate the effectiveness of the LAM-SC framework and the significance of the large AI model-based KB development in future SC paradigms.
A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision

Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications.
On the Geometry and Refined Rate of Primal-Dual Hybrid Gradient for Linear Programming

We study the convergence behaviors of primal-dual hybrid gradient (PDHG) for solving linear programming (LP). PDHG is the base algorithm of a new general-purpose first-order method LP solver, PDLP, which aims to scale up LP by taking advantage of modern computing architectures. Despite its numerical success, the theoretical understanding of PDHG for LP is still very limited; the previous complexity result relies on the global Hoffman constant of the KKT system, which is known to be very loose and uninformative. In this work, we aim to develop a fundamental understanding of the convergence behaviors of PDHG for LP and to develop a refined complexity rate that does not rely on the global Hoffman constant. We show that there are two major stages of PDHG for LP: in Stage I, PDHG identifies active variables and the length of the first stage is driven by a certain quantity which measures how close the non-degeneracy part of the LP instance is to degeneracy; in Stage II, PDHG effectively solves a homogeneous linear inequality system, and the complexity of the second stage is driven by a well-behaved local sharpness constant of the system. This finding is closely related to the concept of partial smoothness in non-smooth optimization, and it is the first complexity result of finite time identification without the non-degeneracy assumption. An interesting implication of our results is that degeneracy itself does not slow down the convergence of PDHG for LP, but near-degeneracy does.
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.
Efficient Ground Vehicle Path Following in Game AI

This short paper presents an efficient path following solution for ground vehicles tailored to game AI. Our focus is on adapting established techniques to design simple solutions with parameters that are easily tunable for an efficient benchmark path follower. Our solution pays particular attention to computing a target speed which uses quadratic Bézier curves to estimate the path curvature. The performance of the proposed path follower is evaluated through a variety of test scenarios in a first-person shooter game, demonstrating its effectiveness and robustness in handling different types of paths and vehicles. We achieved a 70% decrease in the total number of stuck events compared to an existing path following solution.
Recent advances in metallographic polishing for SRF application

This paper is an overview of the metallographic polishing R&D program covering Niobium and Copper substrates treatment for thin film coating as an alternative fabrication pathway for 1.3 GHz elliptical cavities. The presented research is the result of a collaborative effort between IJCLab, CEA/Irfu, HZB, and KEK in order to develop innovative surface processing and cavity fabrication protocols capable of meeting stringent requirements for SRF surfaces, including the reduction of safety risks and ecological footprint, enhancing reliability, improving the surface roughness, and potentially allowing cost reduction. The research findings will be disclosed.
QI2 - an Interactive Tool for Data Quality Assurance

The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well known MNIST data set based an handwritten digits.
A Network Resource Allocation Recommendation Method with An Improved Similarity Measure

Recommender systems have been acknowledged as efficacious tools for managing information overload. Nevertheless, conventional algorithms adopted in such systems primarily emphasize precise recommendations and, consequently, overlook other vital aspects like the coverage, diversity, and novelty of items. This approach results in less exposure for long-tail items. In this paper, to personalize the recommendations and allocate recommendation resources more purposively, a method named PIM+RA is proposed. This method utilizes a bipartite network that incorporates self-connecting edges and weights. Furthermore, an improved Pearson correlation coefficient is employed for better redistribution. The evaluation of PIM+RA demonstrates a significant enhancement not only in accuracy but also in coverage, diversity, and novelty of the recommendation. It leads to a better balance in recommendation frequency by providing effective exposure to long-tail items, while allowing customized parameters to adjust the recommendation list bias.
Facial Landmark Detection Evaluation on MOBIO Database

MOBIO is a bi-modal database that was captured almost exclusively on mobile phones. It aims to improve research into deploying biometric techniques to mobile devices. Research has been shown that face and speaker recognition can be performed in a mobile environment. Facial landmark localization aims at finding the coordinates of a set of pre-defined key points for 2D face images. A facial landmark usually has specific semantic meaning, e.g. nose tip or eye centre, which provides rich geometric information for other face analysis tasks such as face recognition, emotion estimation and 3D face reconstruction. Pretty much facial landmark detection methods adopt still face databases, such as 300W, AFW, AFLW, or COFW, for evaluation, but seldomly use mobile data. Our work is first to perform facial landmark detection evaluation on the mobile still data, i.e., face images from MOBIO database. About 20,600 face images have been extracted from this audio-visual database and manually labeled with 22 landmarks as the groundtruth. Several state-of-the-art facial landmark detection methods are adopted to evaluate their performance on these data. The result shows that the data from MOBIO database is pretty challenging. This database can be a new challenging one for facial landmark detection evaluation.
On the Neutrino and Gamma-Ray Emission from NGC 1068

IceCube has recently reported the detection of $\sim 1-10 \,{\rm TeV}$ neutrinos from the nearby active galaxy, NGC 1068. The lack of TeV-scale emission from this source suggests that these neutrinos are generated in the dense corona that surrounds NGC 1068's supermassive black hole. In this paper, we present a physical model for this source, including the processes of pair production, pion production, synchrotron, and inverse Compton scattering. We have also performed a new analysis of Fermi-LAT data from the direction of NGC 1068, finding that the gamma-ray emission from this source is very soft but bright at energies below $\sim 1 \, {\rm GeV}$. Our model can predict a gamma-ray spectrum that is consistent with Fermi-LAT observations, but only if the magnetic field within the corona of this active galactic nucleus (AGN) is quite high, namely $B\gtrsim 6 \, {\rm kG}$. To explain the observed neutrino emission, this source must accelerate protons with a total power that is comparable to its intrinsic X-ray luminosity. In this context, we consider two additional nearby active galaxies, NGC 4151 and NGC 3079, which have been identified as promising targets for IceCube.
Generalised Covariances and Correlations

The covariance of two random variables measures the average joint deviations from their respective means. We generalise this well-known measure by replacing the means with other statistical functionals such as quantiles, expectiles, or thresholds. Deviations from these functionals are defined via generalised errors, often induced by identification or moment functions. As a normalised measure of dependence, a generalised correlation is constructed. Replacing the common Cauchy-Schwarz normalisation by a novel Fr\'echet-Hoeffding normalisation, we obtain attainability of the entire interval $[-1, 1]$ for any given marginals. We uncover favourable properties of these new dependence measures. The families of quantile and threshold correlations give rise to function-valued distributional correlations, exhibiting the entire dependence structure. They lead to tail correlations, which should arguably supersede the coefficients of tail dependence. Finally, we construct summary covariances (correlations), which arise as (normalised) weighted averages of distributional covariances. We retrieve Pearson covariance and Spearman correlation as special cases. The applicability and usefulness of our new dependence measures is illustrated on demographic data from the Panel Study of Income Dynamics.
Persistent disruption of interspecific competition after ultra-low esfenvalerate exposure

Field and mesocosm studies repeatedly show that higher tier process reduce the predictive accuracy of toxicity evaluation and consequently their value for pesticide risk assessment. Therefore, understanding the influence of ecological complexity on toxicant effects is crucial to improve realism of aquatic risk assessment. Here we investigate the influence of repeated exposure to ecologically realistic concentrations of esfenvalerate on the similarly sensitive species Daphnia magna and Culex pipiens in a food limited and highly competitive environment. We show that significant perturbations in population development are only present close to the EC50. In contrast, interspecific competition between species is already reduced at concentrations 3-4 orders of magnitude below the acute EC50. We conclude that extremely low, environmentally relevant concentrations can disrupt species interactions. This toxicant mediated alteration of competitive balances in ecological communities may be the underlying mechanism for shifts in species distribution at ultra-low pesticide concentrations. A realistic risk assessment should therefore consider these processes in order to predict potential pesticide effects on the structure of communities.
A model local interpretation routine for deep learning based radio galaxy classification

Radio galaxy morphological classification is one of the critical steps when producing source catalogues for large-scale radio continuum surveys. While many recent studies attempted to classify source radio morphology from survey image data using deep learning algorithms (i.e., Convolutional Neural Networks), they concentrated on model robustness most time. It is unclear whether a model similarly makes predictions as radio astronomers did. In this work, we used Local Interpretable Model-agnostic Explanation (LIME), an state-of-the-art eXplainable Artificial Intelligence (XAI) technique to explain model prediction behaviour and thus examine the hypothesis in a proof-of-concept manner. In what follows, we describe how LIME generally works and early results about how it helped explain predictions of a radio galaxy classification model using this technique.11Hongming Tang and Shiyu Yue have equally contributed to this work.
Accreting black holes skewing and bending the optical emission from massive wolf-rayet companions—a case study of IC10 X-1

We present a statistical analysis of the He ii 4686 emission line in the spectra of the black hole and Wolf-Rayet (WR) star of the high-mass X-ray binary IC10 X-1. This line is visibly skewed, and the third moment (skewness) varies with the binary’s orbital phase. We describe a new method of extracting such weak/faint features lying barely above a noisy continuum. Using the moments of these features, we have been able to decompose these skewed lines into two symmetric Gaussian profiles as a function of the orbital phase. The astrophysical implications of this decomposition are significant due to the complex nature of wind-accretion stream interactions in such binary systems. Previous studies have already shown a 0.25 phase lag in the radial velocity curve of the star and the X-ray eclipse, which indicates that the He ii emitters might be in the stellar wind, hence not tracing the star’s orbital motion. Results from this work further suggest the existence of two separate emitting regions, one in the stellar wind in the shadow of the WR star, and another in the accretion stream that impacts the black hole’s outer accretion disk; and the observed skewed He ii lines can be reproduced by superposition of the two corresponding time-dependent Gaussian emission profiles.