Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinant factors of leprosy-related disability; comparison of acceleration failure time and parametric shared frailty models

Background Leprosy is an illness persisting for a long time or constantly recurring brought about by Mycobacterium leprae. The collusion of the causing agent with Schwann cells leads to incapable of being changed loss of fringe nerve tissue; followed by incapacity and that is not restricted to actual powerlessness yet additionally makes a negative picture, prompting segregation and social disgrace against the altered people also, their families. Methods The analysis of this study comprises 205 samples of patients at All African TB and Leprosy Rehabilitation and Training Centre from January 2015 up to December 2019 G.C who were taking medication for leprosy and who possess all necessary data. Territorial conditions in the region of the patients were utilized as a clustering impact in all frailty models. Acceleration failure time models and parametric shared frailty models with Weibull and log-strategic patterns were utilized to dissect hazard factors related to disability ensued by leprosy. All fitted models were looked at by utilizing AIC. Results From that of 205, 69(33.7%) experienced at least one kind of disability grade during treatment taking. In light of AIC, log-logistic-gamma shared frailty model was the final best fitting model and also there was considerable variation among patients. The final model showed the age of patients, symptom duration, treatment category of patients, and sensory loss were found to be the most significant determinants of leprosy disability. Conclusion In this investigation, there is proof of heterogeneity at the group level and disability was related to the age of patients, symptom duration, treatment category of patient, what’s more, sensory loss subsequently, uncommon consideration ought to be given to these huge indicators, which eventually diminish the event of disability. To lessen the patient-related postponement, the program should lay more noteworthy accentuation on bringing issues to light in the local area by zeroing in on key messages like indications, inability result of the late discovery, accessibility of free treatment what’s more, accessibility of disease care in general wellbeing office.
Strong-field electrophoresis

Abstract We analyse particle electrophoresis in the thin-double-layer limit for asymptotically large applied electric fields. Specifically, we consider fields scaling as ${\delta }^{\ensuremath{-} 1} $, $\delta ~(\ll \hspace *{-2pt}1)$ being the dimensionless Debye thickness. The dominant advection associated with the intense flow mandates a uniform salt concentration in the electro-neutral bulk. The $O({\delta }^{\ensuremath{-} 1} )$ large tangential fields in the diffuse part of the double layer give rise to a novel ‘surface conduction’ mechanism at moderate zeta potentials, where the Dukhin number is vanishingly small. The ensuing $O(1)$ electric current emerging from the double layer modifies the bulk electric field; the comparable $O(1)$ transverse salt flux, on the other hand, is incompatible with the nil diffusive fluxes at the homogeneous bulk. This contradiction is resolved by identifying the emergence of a diffusive boundary layer of $O({\delta }^{1/ 2} )$ thickness, resembling thermal boundary layers at large-Reynolds-number flows. The modified electric field within the bulk gives rise to an irrotational flow, resembling those in moderate-field electrophoresis. At leading order, the particle electrophoretic velocity is provided by Smoluchowski’s formula, describing linear variation with applied field.
The efficacy of alpha‐lipoic acid in the management of burning mouth syndrome: An updated systematic review of randomized controlled clinical trials

Burning mouth syndrome (BMS) causes burning or uncomfortable feelings in the oral cavity without any obvious injuries. This condition's etiopathogenesis is still unknown, consequently, BMS management is very challenging. Alpha‐lipoic acid (ALA) is a naturally occurring potent bioactive compound that has been found to be useful in the management of BMS in many studies. Therefore, we conducted a comprehensive systematic review to investigate the usefulness of ALA in the management of BMS based on randomized controlled trials (RCTs).
Blood pressure—lowering medication prescribing, its adherence to guidelines and relationship with blood pressure control at a family medicine department

In many resource‐constrained countries, control of blood pressure (BP) is low. Antihypertensive drug prescribing practices may influence BP control. However, adherence of prescribing to treatment guidelines may not be optimal in resource‐constrained settings. The aim of this study was to evaluate the pattern of blood pressure‐lowering medication prescribing, and how it adheres to treatment guidelines, and to identify the relationship between medication prescriptions and BP control.
Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers

Multi-Object Tracking (MOT) has achieved aggressive progress and derived many excellent deep learning trackers. Meanwhile, most deep learning models are known to be vulnerable to adversarial examples that are crafted with small perturbations but could mislead the model prediction. In this work, we observe that the robustness on the MOT trackers is rarely studied, and it is challenging to attack the MOT system since its mature association algorithms are designed to be robust against errors during the tracking. To this end, we analyze the vulnerability of popular MOT trackers and propose a novel adversarial attack method called Tracklet-Switch (TraSw) against the complete tracking pipeline of MOT. The proposed TraSw can fool the advanced deep pedestrian trackers (i.e., FairMOT and ByteTrack), causing them fail to track the targets in the subsequent frames by perturbing very few frames. Experiments on the MOT-Challenge datasets (i.e., 2DMOT15, MOT17, and MOT20) show that TraSw can achieve an extraordinarily high success attack rate of over 95% by attacking only four frames on average. To our knowledge, this is the first work on the adversarial attack against the pedestrian MOT trackers. Code is available at https://github.com/JHL-HUST/TraSw .
An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks

Directed networks are conveniently represented as graphs in which ordered edges encode interactions between vertices. Despite their wide availability, there is a shortage of statistical models amenable for inference, specially when contextual information and degree heterogeneity are present. This paper presents an annotated graph model with parameters explicitly accounting for these features. To overcome the curse of dimensionality due to modelling degree heterogeneity, we introduce a sparsity assumption and propose a penalized likelihood approach with $\ell_1$-regularization for parameter estimation. We study the estimation and selection consistency of this approach under a sparse network assumption, and show that inference on the covariate parameter is straightforward, thus bypassing the need for the kind of debiasing commonly employed in $\ell_1$-penalized likelihood estimation. Simulation and data analysis corroborate our theoretical findings.
Eigenvalues of strictly regular Hall-plates

This work is about uniform, plane, singly connected, strictly regular Hall-plates with an arbitrary number of peripheral contacts exposed to a uniform magnetic field of arbitrary strength. The strictly regular symmetry is the highest possible degree of symmetry, and it is found in commercial Hall-plates for magnetic field sensors or circulators. It means that all contacts and contact spacings are equally large, if the Hall-plate is mapped conformally to the unit disk. The indefinite conductance matrices of such Hall-plates are circulant matrices, whose complex eigenvalues can be computed in closed form. It is shown how to express the conductance and resistance matrices of these Hall-plates, how to compute their equivalent resistor circuit, their Hall-output voltages or currents, their signal-to-thermal noise ratio, and their power as functions of the eigenvalues. It is also proven that the noise efficiency of strictly regular Hall-plates with many contacts can be up to 112% better than for conventional Hall-plates with four contacts, and it is explained why their optimal biasing uses patterns of supply voltages or currents, which vary sinusoidally along their boundary.
ERM++: An Improved Baseline for Domain Generalization

Domain Generalization (DG) measures a classifier's ability to generalize to new distributions of data it was not trained on. Recent work has shown that a hyperparameter-tuned Empirical Risk Minimization (ERM) training procedure, that is simply minimizing the empirical risk on the source domains, can outperform most existing DG methods. ERM has achieved such strong results while only tuning hyper-parameters such as learning rate, weight decay, batch size, and dropout. However there are additional hyperparameters which further limit overfitting and catastrophic forgetting. We therefore focus on tuning previously untuned hyper-parameters, including training amount, initialization, and additional regularizers. We call the resulting stronger baseline ERM++. ERM++ improves the performance of DG by over 5% compared to prior ERM baselines on a standard benchmark of 5 datasets with a ResNet-50 and over 15% with a ViT-B/16, and outperforms all SOTA methods on DomainBed with both architectures. We also explore the relationship between DG performance and similarity to pre-training data, and find that similarity to pre-training data distributions is an important driver of performance, but that ERM++ with stronger initializations can deliver strong performance even on dissimilar datasets.Code is released at https://github.com/piotr-teterwak/erm_plusplus.
A Survey on Contextualised Semantic Shift Detection

Semantic Shift Detection (SSD) is the task of identifying, interpreting, and assessing the possible change over time in the meanings of a target word. Traditionally, SSD has been addressed by linguists and social scientists through manual and time-consuming activities. In the recent years, computational approaches based on Natural Language Processing and word embeddings gained increasing attention to automate SSD as much as possible. In particular, over the past three years, significant advancements have been made almost exclusively based on word contextualised embedding models, which can handle the multiple usages/meanings of the words and better capture the related semantic shifts. In this paper, we survey the approaches based on contextualised embeddings for SSD (i.e., CSSDetection) and we propose a classification framework characterised by meaning representation, time-awareness, and learning modality dimensions. The framework is exploited i) to review the measures for shift assessment, ii) to compare the approaches on performance, and iii) to discuss the current issues in terms of scalability, interpretability, and robustness. Open challenges and future research directions about CSSDetection are finally outlined.
FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems

Bird's eye view (BEV) perception is becoming increasingly important in the field of autonomous driving. It uses multi-view camera data to learn a transformer model that directly projects the perception of the road environment onto the BEV perspective. However, training a transformer model often requires a large amount of data, and as camera data for road traffic are often private, they are typically not shared. Federated learning offers a solution that enables clients to collaborate and train models without exchanging data but model parameters. In this article, we introduce FedBEVT, a federated transformer learning approach for BEV perception. In order to address two common data heterogeneity issues in FedBEVT: (i) diverse sensor poses, and (ii) varying sensor numbers in perception systems, we propose two approaches – Federated Learning with Camera-Attentive Personalization (FedCaP) and Adaptive Multi-Camera Masking (AMCM), respectively. To evaluate our method in real-world settings, we create a dataset consisting of four typical federated use cases. Our findings suggest that FedBEVT outperforms the baseline approaches in all four use cases, demonstrating the potential of our approach for improving BEV perception in autonomous driving.
Homogeneity and Cantor manifolds

Some consequences of generalized homogeneity are observed in dimension theory of metrizable spaces. In particular, if X is a connected, locally compact, metric space which is homogeneous with respect to open 0dimensional mappings and if dim X = n > 1 (dim A' = oo), then no subset of dimension < n — 2 (respectively, of a finite dimension) separates X . Thus, homogeneous continua are Cantor manifolds. A space X is said to be homogeneous with respect to a class M of mappings of X into X if for each pair of points x, y of X there exists a surjection / € M such that f(x) = y. If M is the class H(X) of all autohomeomorphisms of X, then X is called homogeneous. Many results concerning generalized homogeneity with respect to Borel classes of mappings have been obtained in recent years (e.g., see [2]). In this paper we observe some effects of generalized homogeneity in dimension theory. Throughout the paper all spaces are assumed to be metrizable and all mappings are continuous. By dim X we understand the covering dimension of a space X . A space X is called countable-dimensional (strongly countable-dimensional) if X can be represented as the union of a sequence Xx, X2, . of subspaces (closed subspaces) such that dim Xt < oo, ¿=1,2,. [5]. A space X is said to be /i-weakly infinite-dimensional (S-weakly infinitedimensional) if for every sequence (Ax, Bx), (A2, B2), . of pairs of disjoint closed subsets of X there exist closed sets Cx, C2, . such that Cn is a partition between An and Bn and fl" i Cn = 0 (HÍLi C„ = ® for some k). If X is not A( S-) weakly infinite-dimensional, then X is called A( S-) strongly infinite-dimensional (see [1, pp. 528-529] or [5, p. 33]). We use the abbreviations A-w.i. d. , A-s.i. d. , S-w.i. d. , S-s.i. d. For compact spaces the notions of A-w.i. d. and S-w.i. d. (A-s.i.d. and S-s.i.d.) spaces coincide and we use the abbreviation w.i. d. (s.i.d.). Received by the editors July 25, 1988 and, in revised form, June 15, 1989. 1980 Mathematics Subject Classification (1985 Revision). Primary 54F45, 54C10.
Equilibrium with Heterogeneous Information Flows

We study a continuous time economy where throughout time, insiders receive private signals regarding the risky assets' terminal payoff. We prove existence of a partial communication equilibrium where, at each private signal time, the public receives a signal of the same form as the associated insider, but of lower quality. This causes a jump in both the public information flow and equilibrium asset price. The resultant markets, while complete between each jump time, are incomplete over each jump. After establishing equilibrium for a finite number of private signal times, we consider the limit as the private signals become more and more frequent. Under appropriate scaling we prove convergence of the public filtration to the natural filtration generated by both the fundamental factor process $X$ and a continuous time process $J$ taking the form $J_t = X_1 + Y_t$ where $X_1$ is the terminal payoff and $Y$ an independent Gaussian process. This coincides with the filtration considered in 'Additional Utility of Insiders with Imperfect Dynamical Information' (Corcuera, et al. Finance&Stochastics 2004). However, while therein the filtration was exogenously assumed to be that of an insider who observes a private signal flow, here it arises endogenously as the public filtration when there are a large number of insiders receiving signals throughout time.
Optimal rates of approximation by shallow ReLUk neural networks and applications to nonparametric regression

We study the approximation capacity of some variation spaces corresponding to shallow ReLU$$^k$$ k neural networks. It is shown that sufficiently smooth functions are contained in these spaces with finite variation norms. For functions with less smoothness, the approximation rates in terms of the variation norm are established. Using these results, we are able to prove the optimal approximation rates in terms of the number of neurons for shallow ReLU$$^k$$ k neural networks. It is also shown how these results can be used to derive approximation bounds for deep neural networks and convolutional neural networks (CNNs). As applications, we study convergence rates for nonparametric regression using three ReLU neural network models: shallow neural network, over-parameterized neural network, and CNN. In particular, we show that shallow neural networks can achieve the minimax optimal rates for learning Hölder functions, which complements recent results for deep neural networks. It is also proven that over-parameterized (deep or shallow) neural networks can achieve nearly optimal rates for nonparametric regression.
Learning and Concentration for High Dimensional Linear Gaussians: an Invariant Subspace Approach

In this work, we study non-asymptotic bounds on correlation between two time realizations of stable linear systems with isotropic Gaussian noise. Consequently, via sampling from a sub-trajectory and using \emph{Talagrands'} inequality, we show that empirical averages of reward concentrate around steady state (dynamical system mixes to when closed loop system is stable under linear feedback policy ) reward , with high-probability. As opposed to common belief of larger the spectral radius stronger the correlation between samples, \emph{large discrepancy between algebraic and geometric multiplicity of system eigenvalues leads to large invariant subspaces related to system-transition matrix}; once the system enters the large invariant subspace it will travel away from origin for a while before coming close to a unit ball centered at origin where an isotropic Gaussian noise can with high probability allow it to escape the current invariant subspace it resides in, leading to \emph{bottlenecks} between different invariant subspaces that span $\mathbb{R}^{n}$, to be precise : system initiated in a large invariant subspace will be stuck there for a long-time: log-linear in dimension of the invariant subspace and inversely to log of inverse of magnitude of the eigenvalue. In the problem of Ordinary Least Squares estimate of system transition matrix via a single trajectory, this phenomenon is even more evident if spectrum of transition matrix associated to large invariant subspace is explosive and small invariant subspaces correspond to stable eigenvalues. Our analysis provide first interpretable and geometric explanation into intricacies of learning and concentration for random dynamical systems on continuous, high dimensional state space; exposing us to surprises in high dimensions
Statistical Models for the Dynamics of Heavy Particles in Turbulence

When very small particles are suspended in a fluid in motion, they tend to follow the flow. How such tracer particles are mixed, transported, and dispersed by turbulent flow has been successfully described by statistical models. Heavy particles, with mass densities larger than that of the carrying fluid, can detach from the flow. This results in preferential sampling, small-scale fractal clustering, and large collision velocities. To describe these effects of particle inertia, one must consider both particle positions and velocities in phase space. In recent years, statistical phase-space models have significantly contributed to our understanding of inertial-particle dynamics in turbulence. These models help to identify the key mechanisms and nondimensional parameters governing the particle dynamics and have made qualitative and, in some cases, quantitative predictions. This article reviews statistical phase-space models for the dynamics of small, yet heavy, spherical particles in turbulence. We evaluate their effectiveness by comparing their predictions with results from numerical simulations and laboratory experiments, and we summarize their successes and failures. Expected final online publication date for the Annual Review of Fluid Mechanics, Volume 56 is January 2024. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material

Diffusion models have become a new SOTA generative modeling method in various fields, for which there are multiple survey works that provide an overall survey. With the number of articles on diffusion models increasing exponentially in the past few years, there is an increasing need for surveys of diffusion models on specific fields. In this work, we are committed to conducting a survey on the graph diffusion models. Even though our focus is to cover the progress of diffusion models in graphs, we first briefly summarize how other generative modeling methods are used for graphs. After that, we introduce the mechanism of diffusion models in various forms, which facilitates the discussion on the graph diffusion models. The applications of graph diffusion models mainly fall into the category of AI-generated content (AIGC) in science, for which we mainly focus on how graph diffusion models are utilized for generating molecules and proteins but also cover other cases, including materials design. Moreover, we discuss the issue of evaluating diffusion models in the graph domain and the existing challenges.
An infinite-times renewal equation

In neuroscience, the time elapsed since the last discharge has been used to predict the probability of the next discharge. Such predictions can be improved taking into account the last two discharge times, and possibly more. Such multi-time processes arise in many other areas and there is no universal limitation on the number of times to be used. This observation leads us to study the infinite-times renewal equation as a simple model to understand the meaning and properties of such partial differential equations depending on an infinite number of variables.We define two notions of solutions, prove existence and uniqueness of solutions, possibly measures. We also prove the long time convergence, with exponential rate, to the steady state in different, strong or weak, topologies depending on assumptions on the coefficients.