Miracle-dz/UniFAR-FTUs huggingface dataset:
Increased soluble urokinase plasminogen activator levels modulate monocyte function to promote atherosclerosis

People with kidney disease are disproportionately affected by atherosclerosis for unclear reasons. Soluble urokinase plasminogen activator receptor (suPAR) is an immune-derived mediator of kidney disease, levels of which are strongly associated with cardiovascular outcomes. We assessed suPAR’s pathogenic involvement in atherosclerosis using epidemiologic, genetic, and experimental approaches. We found serum suPAR levels to be predictive of coronary artery calcification and cardiovascular events in 5,406 participants without known coronary disease. In a genome-wide association meta-analysis including over 25,000 individuals, we identified a missense variant in the plasminogen activator, urokinase receptor (PLAUR) gene (rs4760), confirmed experimentally to lead to higher suPAR levels. Mendelian randomization analysis in the UK Biobank using rs4760 indicated a causal association between genetically predicted suPAR levels and atherosclerotic phenotypes. In an experimental model of atherosclerosis, proprotein convertase subtilisin/kexin–9 (Pcsk9) transfection in mice overexpressing suPAR (suPARTg) led to substantially increased atherosclerotic plaques with necrotic cores and macrophage infiltration compared with those in WT mice, despite similar cholesterol levels. Prior to induction of atherosclerosis, aortas of suPARTg mice excreted higher levels of CCL2 and had higher monocyte counts compared with WT aortas. Aortic and circulating suPARTg monocytes exhibited a proinflammatory profile and enhanced chemotaxis. These findings characterize suPAR as a pathogenic factor for atherosclerosis acting at least partially through modulation of monocyte function.
A reinforcement learning approach to improve the performance of the Avellaneda-Stoikov market-making algorithm

Market making is a high-frequency trading problem for which solutions based on reinforcement learning (RL) are being explored increasingly. This paper presents an approach to market making using deep reinforcement learning, with the novelty that, rather than to set the bid and ask prices directly, the neural network output is used to tweak the risk aversion parameter and the output of the Avellaneda-Stoikov procedure to obtain bid and ask prices that minimise inventory risk. Two further contributions are, first, that the initial parameters for the Avellaneda-Stoikov equations are optimised with a genetic algorithm, which parameters are also used to create a baseline Avellaneda-Stoikov agent (Gen-AS); and second, that state-defining features forming the RL agent’s neural network input are selected based on their relative importance by means of a random forest. Two variants of the deep RL model (Alpha-AS-1 and Alpha-AS-2) were backtested on real data (L2 tick data from 30 days of bitcoin–dollar pair trading) alongside the Gen-AS model and two other baselines. The performance of the five models was recorded through four indicators (the Sharpe, Sortino and P&L-to-MAP ratios, and the maximum drawdown). Gen-AS outperformed the two other baseline models on all indicators, and in turn the two Alpha-AS models substantially outperformed Gen-AS on Sharpe, Sortino and P&L-to-MAP. Localised excessive risk-taking by the Alpha-AS models, as reflected in a few heavy dropdowns, is a source of concern for which possible solutions are discussed.
Age‐specific sensitivity analysis of stable, stochastic and transient growth for stage‐classified populations

Abstract Sensitivity analysis in ecology and evolution is a valuable guide to rank demographic parameters depending on their relevance to population growth. Here, we propose a method to make the sensitivity analysis of population growth for matrix models solely classified by stage more fine‐grained by considering the effect of age‐specific parameters. The method applies to stable population growth, the stochastic growth rate, and transient growth. The method yields expressions for the sensitivity of stable population growth to age‐specific survival and fecundity from which general properties are derived about the pattern of age‐specific selective forces molding senescence in stage‐classified populations.
Reno-protective effects of GLP-1 receptor agonist and anti-platelets in experimentally induced diabetic kidney disease in male albino rats

Objective(s): The prevalence of chronic kidney disease in diabetics is progressively increasing with an increased risk of fatal complications. Materials and Methods: Sixty male albino rats were used in the study, and type 2 diabetes mellitus were induced. Diabetic rats were divided randomly into 5 groups, the control diabetic group and 4 treated groups were treated with metformin (group3), dulaglutide (group 4), metformin & cilostazol (group 5), and the last group was treated with dulaglutide & cilostazol (group 6). At the end of the experiment, the weight of rats and systolic blood pressure were estimated. After overnight fasting, the serum levels of blood glucose, lipid profile, and kidney function were measured. After scarification, gene expression of eNOS and NFKB in kidney tissue were estimated and kidney tissues were examined for histopathology. Results: Diabetic rats showed a significant increase in body weight, blood pressure, serum blood glucose, lipid profile, and impaired kidney function. Metformin and dulaglutide are associated with a significant decrease in blood pressure, blood glucose level, serum lipid profile, and improved kidney function. These changes are associated with a significant increase in anti-oxidative markers, and decreased inflammatory and fibrotic markers, especially with the addition of cilostazol. Conclusion: Metformin and dulaglutide have been shown to ameliorate kidney damage in diabetics by stimulating the anti-oxidant defense system, normalizing kidney functional parameters, and improving histopathological changes. The addition of cilostazol to metformin or dulaglutide increased some of their anti-oxidants and anti-inflammatory properties.
Electrical properties of ion‐implanted layers in Hg0.79Cd0.21Te

The influence of ion‐irradiation damage on the electrical properties of Hg0.79Cd0. 21Te has been investigated. A large damage‐induced n‐type conductivity with sheet carrier concentration up to approximately 1014 cm−2 and sheet resistance up to 10 Ω/square at 77 K was found for doses of 1×1015 ions/cm2 at 150 keV. The observed n‐type conductivity is produced by various implanted ions regardless of whether they are donors or acceptors. The implanted n‐type layer maintains its properties when subjected to temperatures as high as 120 °C. The damage is slightly annealed for this moderate treatment: the conductivity increases slightly, the sheet electron concentration decreases, and the effective mobility increases. The results also show that the implanted layer can be utilized for surface passivation of n‐type photoconductors and for making Ohmic contacts to n‐type material.
Sufficient statistics and intrinsic accuracy

It is proved that if there exists a sufficient statistic for the estimation of an unknown parameter of a population, the frequency function of the population must be of a certain type. It is shown that some modification of previous theory of the intrinsic accuracy of statistics is necessary when the range of the population sampled is a function of the parameter to be estimated. Finally, the theory is extended to sufficient sets of statistics, i.e. sets of statistics which together contain all the information provided by a sample about an unknown parameter.
Towards Efficient Visual Simplification of Computational Graphs in Deep Neural Networks

A computational graph in a deep neural network (DNN) denotes a specific data flow diagram (DFD) composed of many tensors and operators. Existing toolkits for visualizing computational graphs are not applicable when the structure is highly complicated and large-scale (e.g., BERT [1]). To address this problem, we propose leveraging a suite of visual simplification techniques, including a cycle-removing method, a module-based edge-pruning algorithm, and an isomorphic subgraph stacking strategy. We design and implement an interactive visualization system that is suitable for computational graphs with up to 10 thousand elements. Experimental results and usage scenarios demonstrate that our tool reduces 60% elements on average and hence enhances the performance for recognizing and diagnosing DNN models. Our contributions are integrated into an open-source DNN visualization toolkit, namely, MindInsight [2].
What should clubs monitor to predict future value of football players

Huge amounts of money are invested every year by football clubs on transfers. For both growth and survival, it is crucial for recruiting departments to make smart choices when targeting players. Therefore, it is very important to identify the right parameters to monitor to predict market value. The following paper aims at determining the relevant features that successfully forecast future value for football players. Success is measured against their market value from TransferMarkt. To select prominent features, we use Lasso regressions and Random Forest algorithms. Some obvious variables are selected but we also observe some subtle dependencies between features and future market value. Finally, we rank the Golden Boy nominees using our forecasts and show our methodology can successfully compare football players based on their quality.
BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the flexibility needed to handle the requirements of modern autotuning tasks. Particularly, it deals with permutation, ordered, and continuous parameter types along with both known and unknown parameter constraints. To reason about these parameter types and efficiently deliver high-quality code, BaCO uses Bayesian optimiza tion algorithms specialized towards the autotuning domain. We demonstrate BaCO's effectiveness on three modern compiler systems: TACO, RISE&ELEVATE, and HPVM2FPGA for CPUs, GPUs, and FPGAs respectively. For these domains, BaCO outperforms current state-of-the-art autotuners by delivering on average 1.36x-1.56x faster code with a tiny search budget, and BaCO is able to reach expert-level performance 2.9x-3.9x faster.
Positronium Formation from Hydrogen Negative Ion, H -

Theoretical results for the positronium (Ps) formation from hydrogen negative ion, H-, targets are presented. An exact analytical expression is constructed for the transition matrix for Ps formation in collisions of positrons with H- in the framework of Born approximation using three different target wave functions. The corresponding differential and total cross sections are presented for Ps formation in ground state. Comparison between these cross sections for different wave functions reveals the dependence of the Ps-formation process on the choice of the target wavefunction and the effect of correlation among the target electrons on the process.
Inference for Model Misspecification in Interest Rate Term Structure using Functional Principal Component Analysis

Level, slope, and curvature are three commonly-believed principal components in interest rate term structure and are thus widely used in modeling. This paper characterizes the heterogeneity of how misspecified such models are through time. Presenting the orthonormal basis in the Nelson-Siegel model interpretable as the three factors, we design two nonparametric tests for whether the basis is equivalent to the data-driven functional principal component basis underlying the yield curve dynamics, considering the ordering of eigenfunctions or not, respectively. Eventually, we discover high dispersion between the two bases when rare events occur, suggesting occasional misspecification even if the model is overall expressive.
Adversarial Attacks and Defences for Skin Cancer Classification

There has been a concurrent significant improvement in the medical images used to facilitate diagnosis and the performance of machine learning techniques to perform tasks such as classification, detection, and segmentation in recent years. As a result, a rapid increase in the usage of such systems can be observed in the healthcare industry, for instance in the form of medical image classification systems, where these models have achieved diagnostic parity with human physicians. One such application where this can be observed is in computer vision tasks such as the classification of skin lesions in dermatoscopic images. However, as stakeholders in the healthcare industry, such as insurance companies, continue to invest extensively in machine learning infrastructure, it becomes increasingly important to understand the vulnerabilities in such systems. Due to the highly critical nature of the tasks being carried out by these machine learning models, it is necessary to analyze techniques that could be used to take advantage of these vulnerabilities and methods to defend against them. This paper explores common adversarial attack techniques. The Fast Sign Gradient Method (FGSM) and Projected Descent Gradient (PGD) are used against a Convolutional Neural Network (CNN) trained to classify dermatoscopic images of skin lesions. Following that, it also discusses one of the most popular adversarial defense techniques, adversarial training. The performance of the model that has been trained on adversarial examples is then tested against the previously mentioned attacks, and recommendations to improve neural network’s robustness are thus provided based on the results of the experiment.
In-Sensor & Neuromorphic Computing Are all You Need for Energy Efficient Computer Vision

Due to the high activation sparsity and use of accumulates (AC) instead of expensive multiply-and-accumulates (MAC), neuromorphic spiking neural networks (SNNs) have emerged as a promising low-power alternative to traditional DNNs for several computer vision (CV) applications. However, most existing SNNs require multiple time steps for acceptable inference accuracy, hindering real-time deployment and increasing spiking activity and, consequently, energy consumption. Recent works proposed direct encoding that directly feeds the analog pixel values in the first layer of the SNN in order to significantly reduce the number of time steps. Although the overhead for the first layer MACs with direct encoding is negligible for deep SNNs and the CV processing is efficient using SNNs, the data transfer between the image sensors and the downstream processing costs significant bandwidth and may dominate the total energy. To mitigate this concern, we propose an in-sensor computing hardware-software co-design framework for SNNs targeting image recognition tasks. Our approach reduces the bandwidth between sensing and processing by 12−96× and the resulting total energy by 2.32× compared to traditional CV processing, with a 3.8% reduction in accuracy on ImageNet.
A universal approach to Krylov state and operator complexities

We present a general framework in which both Krylov state and operator complexities can be put on the same footing. In our formalism, the Krylov complexity is defined in terms of the density matrix of the associated state which, for the operator complexity, lives on a doubled Hilbert space obtained through the channel-state map. This unified definition of complexity in terms of the density matrices enables us to extend the notion of Krylov complexity, to subregion or mixed state complexities and also naturally to the Krylov mutual complexity. We show that this framework also encompasses nicely the holographic notions of complexity.
Semi-Supervised Bifold Teacher-Student Learning for Indoor Presence Detection Under Time-Varying CSI

—In recent years, there have been abundant re-searches focused on indoor human presence detection based on laborious supervised learning (SL) and channel state information (CSI). These existing studies adopt spatial information of CSI to improve detection accuracy. However, channel is susceptible to arbitrary environmental changes in practice, such as the object movement, atmospheric factors and machine rebooting, which leads to degraded prediction accuracy. However, the existing SL-based methods require to re-train a new model with time-consuming labeling. Therefore, designing a semi-supervised learning (SSL) based scheme by continuously monitoring model ”life-cycle” becomes compellingly imperative. In this paper, we propose bifold teacher-student (BTS) learning for presence detection system, which combines SSL by utilizing partial labeled and unlabeled dataset. The proposed primal-dual teacher-student network is capable of intelligently learning spatial and temporal features from labeled and unlabeled CSI. Additionally, the enhanced penalized loss function leveraging entropy and distance measure can distinguish the drifted data, i.e., features of new dataset are affected by time-varying effect and are alternated from the original distribution. The experimental results demonstrate that the proposed BTS system can sustain the asymptotic accuracy after retraining the model with unlabeled data. Moreover, label-free BTS outperforms the existing SSL-based models in terms of the highest detection accuracy, while achieving the similar performance of SL-based methods.
Aggregate Markov models in life insurance: estimation via the EM algorithm

In this paper, we consider statistical estimation of time-inhomogeneous aggregate Markov models. Unaggregated models, which corresponds to Markov chains, are commonly used in multi-state life insurance to model the biometric states of an insured. By aggregating microstates to each biometric state, we are able to model dependencies between transitions of the biometric states as well as the distribution of occupancy in these. This allows for non--Markovian modelling in general. Since only paths of the macrostates are observed, we develop an expectation-maximization (EM) algorithm to obtain maximum likelihood estimates of transition intensities on the micro level. Special attention is given to a semi-Markovian case, known as the reset property, which leads to simplified estimation procedures where EM algorithms for inhomogeneous phase-type distributions can be used as building blocks. We provide a numerical example of the latter in combination with piecewise constant transition rates in a three-state disability model with data simulated from a time-inhomogeneous semi-Markov model. Comparisons of our fits with more classic GLM-based fits as well as true and empirical distributions are provided to relate our model with existing models and their tools.
A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability

Data augmentation (DA) is indispensable in modern machine learning and deep neural networks. The basic idea of DA is to construct new training data to improve the model's generalization by adding slightly disturbed versions of existing data or synthesizing new data. In this work, we review a small but essential subset of DA -- Mix-based Data Augmentation (MixDA) that generates novel samples by mixing multiple examples. Unlike conventional DA approaches based on a single-sample operation or requiring domain knowledge, MixDA is more general in creating a broad spectrum of new data and has received increasing attention in the community. We begin with proposing a new taxonomy classifying MixDA into, Mixup-based, Cutmix-based, and hybrid approaches according to a hierarchical view of the data mix. Various MixDA techniques are then comprehensively reviewed in a more fine-grained way. Owing to its generalization, MixDA has penetrated a variety of applications which are also completely reviewed in this work. We also examine why MixDA works from different aspects of improving model performance, generalization, and calibration while explaining the model behavior based on the properties of MixDA. Finally, we recapitulate the critical findings and fundamental challenges of current MixDA studies, and outline the potential directions for future works. Different from previous related works that summarize the DA approaches in a specific domain (e.g., images or natural language processing) or only review a part of MixDA studies, we are the first to provide a systematical survey of MixDA in terms of its taxonomy, methodology, applications, and explainability. This work can serve as a roadmap to MixDA techniques and application reviews while providing promising directions for researchers interested in this exciting area.
3D Highlighter: Localizing Regions on 3D Shapes via Text Descriptions

We present 3D Highlighter, a technique for localizing semantic regions on a mesh using text as input. A key feature of our system is the ability to interpret “out-of-domain” localizations. Our system demonstrates the ability to reason about where to place non-obviously related concepts on an input 3D shape, such as adding clothing to a bare 3D animal model. Our method contextualizes the text description using a neural field and colors the corresponding region of the shape using a probability-weighted blend. Our neural optimization is guided by a pre-trained CLIP encoder, which bypasses the need for any 3D datasets or 3D annotations. Thus, 3D Highlighter is highly flexible, general, and capable of producing localizations on a myriad of input shapes. Our code is publicly available at https://github.com/threedle/3DHighlighter.
SLGTformer: An Attention-Based Approach to Sign Language Recognition

Sign language is the preferred method of communication of deaf or mute people, but similar to any language, it is difﬁcult to learn and represents a signiﬁcant barrier for those who are hard of hear-ing or unable to speak. A person’s entire frontal appearance dictates and conveys speciﬁc meaning. However, this frontal appearance can be quantiﬁed as a temporal sequence of human body pose, leading to Sign Language Recognition through the learning of spatiotemporal dynamics of skeleton keypoints. We propose a novel, attention-based approach to Sign Language Recognition exclusively built upon decoupled graph and temporal self-attention: the Sign Language Graph Time Transformer (SLGTformer). SLGTformer ﬁrst deconstructs spatiotemporal pose sequences separately into spatial graphs and temporal windows. SLGTformer then leverages novel Learnable Graph Relative Positional Encodings (LGRPE) to guide spatial self-attention with the graph neighborhood context of the human skeleton. By modeling the temporal dimension as intra- and inter-window dynamics, we introduce Temporal Twin Self-Attention (TTSA) as the combination of locally-grouped temporal attention (LTA) and global sub-sampled temporal attention (GSTA). We demonstrate the effectiveness of SLGTformer on the World-Level American Sign Language (WLASL) dataset, achieving state-of-the-art performance with an ensemble-free approach on the keypoint modality. The code is available at https://github.com/neilsong/slt
The Inverse Problem for the Euler-Poisson-Darboux Equation and Shifted $k$-Plane Transforms

The inverse problem for the Euler-Poisson-Darboux equation deals with reconstruction of the Cauchy data for this equation from incomplete information about its solution. In the present article, this problem is studied in connection with the injectivity of the shifted k -plane transform, which assigns to functions in L p ( R n ) their mean values over all k-planes at a ﬁxed distance from the given k -planes. Several generalizations, including the Radon transform over strips of ﬁxed width in R 2 and a similar transform over tubes of ﬁxed diameter in R 3 , are considered.