Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Science Platform Applied to Health in Contribution to the Brazilian Unified Health System

The Data Science Platform Applied to Health (PCDaS) is a research and technological development project that aims to develop and apply novel data analysis methods to public health data. It fills a technological gap between the variety of data sources available in legacy and unstandardized formats and the current needs and possibilities of Data Science applications to consume and explore data for the benefit of the Brazilian Health System. PCDaS provides democratic access to health-related datasets and information by requiring fewer technological abilities from its users while maintaining a continuously updated stack of technologies. As a data ecosystem, our primary goal is to provide secure and remote access to health data, technological tools, and a robust infrastructure provided by our platform to process and analyze a large amount of data that generally demand computational power often unavailable to researchers. The infrastructure consists of multi-region on-premise and cloud servers prepared to deal with the heavy analysis of Big Data from anywhere from multiple users simultaneously. Providing secure and remote access to health databases, whether in their original form or processed, is a daily breakthrough for a public health researcher. Knowing that there is a place where they can access integrated data in a standard format makes the research process much more manageable. To ensure quality, our data engineering and governance teams process these data sources following a gold standard based on cross-tables provided by the Health Ministry (the TabNET system) and decoding the original variables into meaningful names provided by the sources. It is very relevant to emphasize the comprehensive documentation of metadata, attributes, and the ETL (Extract, Transform, Load) process for databases. Every part of these steps is described in detail on the PCDaS website, ensuring the comprehension and reproducibility of the process. These features ensure that PCDaS users can effectively leverage the platform’s resources and capabilities, enabling them to conduct research, perform data analysis, and collaborate within a secure and supportive environment to contribute to the Brazilian Health System.
A real-time hand gesture recognition system for daily information retrieval from the internet

To get daily information from the internet has become most people's living habits today. In order to reduce the steps of receiving the information, such as complex mouse or keyboard steps, we propose a system designed for easily getting daily information without mouse and keyboard actions in this paper. We have implemented an approach providing daily information retrieved from the internet. Once the function has been selected by hand gestures, the system will report action information to users by synthesised speech. The system also utilises face recognition to identify each user, bringing up personalised services to each user. In this paper, we use PCA method to recognise faces as also hand gestures, and then a number of hand gestures and system controls are acquired and stored into this system. Experiment results show good performance in face recognition and hand gesture recognition.
GAMUT: Matrix Multiplication-Like Tasks on GPUs

Data scientists are often eager to develop new operators for data science and machine learning applications, which can be written as matrix multiplication-like nested loops. However, these matrix multiplication-like tasks are not supported in standard libraries and often struggle to achieve high performance. Deep learning compilers can optimize some of these operations, but they require significant time to search for optimal configurations. To address these issues, we introduce operators in relational algebra to extend matrix multiplications from a database point of view and develop a matrix multiplication-like task library for GPU. Our library, Gamut, can recognize and generate optimized code for a variety of tasks, and we propose a tile-based model to incorporate relational algebra operators. Through experiments, we demonstrate that Gamut achieves high performance with low compilation overhead compared to both matrix multiplication libraries and deep learning compilers.
A comprehensive survey on object detection YOLO

As a single-stage object detection framework, the YOLO (You Only Look Once) technique has emerged as a prominent technique for various object detection tasks owing to its impressive balance between speed and precision. This research article presents a comprehensive review of the YOLO family of algorithms. This review covers the evolutionary journey of YOLO from its initial release to the latest versions, encompassing an in-depth analysis of the performance and critical characteristics exhibited by each iteration. Particular emphasis is given to exploring the applications of YOLO in diverse domains, focusing on its role in real-time object detection on embedded systems. Furthermore, the paper delves into the latest advancements in compressing algorithms for optimizing the cumbersome YOLO models and practical implementation examples. The potential of deploying YOLO on resource-constrained devices is further unlocked by addressing the challenge of model size reduction. Finally, this study outlines potential research trends and improvements for the YOLO family of algorithms, including novel architectural designs and innovative training strategies. Overall, the thorough investigation presented in this review is a valuable reference for researchers seeking to explore the YOLO framework and its evolving landscape in object detection.
Post-Moore's Law Fusion: High-Bandwidth Memory, Accelerators, and Native Half-Precision Processing for CPU-Local Analytics

Modern data management systems aim to provide both cutting-edge functionality and hardware efficiency. With the advent of AI-driven data processing and the post-Moore Law era, traditional memory-bound scale-up data management operations face scalability challenges. On the other hand, using accelerators such as GPUs has long been explored to offload complex analytical patterns while trading-off data movement over an interconnect. GPUs typically provide massive parallelism and high-bandwidth memory, while CPUs are near-data processors and coordinators that are often memory-bound. In this work, we provide a first look over an architecture that mixes the best of the CPU and GPU world: high-bandwidth memory (HBM), core-local accelerators for matrix multiplications (AMX), and native half-precision data processing inside 4th Generation Intel Xeon Scalable processors known as Sapphire Rapids. We analyze the system, provide an overview of its hierarchical NUMA architecture, focus on individual components, and explore their interplay and how they impact the traditional DRAM bandwidth wall on typical data access patterns and novel AI-DB interactions of vector data processing.
An Overview of the Gaea Project

The long-term goal of the Gaea project is to develop an extensible, object-oriented data management and analysis system to be used by researchers in the eld of global change [1]. The current goal is to develop a prototype, which can be used by geographers in a user-friendly manner, yet permits integration of heterogeneous and complex data types, and interactive development of sophisticated methods for data analysis, prediction, and display. Focus is on the object manipulation and analysis aspects of the system, and the management of \meta-data," that is, data about the data. The general abilities of the system will include: 1) deriving information about spatio-temporal objects, 2) maintaining information on the evolution of data objects, 3) providing derivation semantics for data objects, 4) the management of user-speci ed experiments, 5) providing a visual environment for browsing, querying, and analysis, and 6) providing user-extensible data and operator types.
Akuter Schlaganfall: Nachweis distaler Gefäßverschlüsse mittels CT-Perfusion

Der Nachweis intrakranieller distaler Gefäßverschlüsse in der CT-Angiografie stellt eine Herausforderung dar, wird jedoch klinisch für Therapieentscheidungen beim akuten Schlaganfall zunehmend relevant. Die vorliegende Arbeit ist der Frage nachgegangen, ob anhand von mittels CT-Perfusion erstellter Time-toMaximum (Tmax)-Karten die diagnostische Treffsicherheit bei dieser Fragestellung verbessert werden kann.
Peer Clustering for the InterPlanetary File System

Distributed Hash Tables (DHT) are once again attracting research interest, 20 years after their inception, as a promising solution for scalable and decentralized content storage. A prominent example is the InterPlanetary File System (IPFS), a distributed peer-to-peer storage system with more than 20K online peers, which uses the Kademlia DHT. We design and implement H-Kademlia, a hierarchical version of Kademlia for IPFS, where peers are distributed into disjoint sets of users, or clusters. Peer clustering can offer resilience to network partition, privacy of in-cluster content lookups, as well as improved caching, content filtering and access control. We assess the performance of IPFS over H-Kademlia via simulations that use real traces from the IPFS network. Our findings show that our design delivers the benefits of peer clustering, without significant performance penalties.
Management in America

The Census Bureau recently conducted a survey of management practices in over 30,000 plants across the US, the first large-scale survey of management in America. Analyzing these data reveals several striking results. First, more structured management practices are tightly linked to better performance: establishments adopting more structured practices for performance monitoring, target setting and incentives enjoy greater productivity and profitability, higher rates of innovation and faster employment growth. Second, there is a substantial dispersion of management practices across the establishments. We find that 18% of establishments have adopted at least 75% of these more structured management practices, while 27% of establishments adopted less than 50% of these. Third, more structured management practices are more likely to be found in establishments that export, who are larger (or are part of bigger firms), and have more educated employees. Establishments in the South and Midwest have more structured practices on average than those in the Northeast and West. Finally, we find adoption of structured management practices has increased between 2005 and 2010 for surviving establishments, particularly for those practices involving data collection and analysis.
Institutional investors and information acquisition: Implications for asset prices and informational efficiency

all U.S. stocks are held by institutional investors, such as banks, pension funds, mutual funds, insurance companies and hedge funds. Consequently, institutional investors are major players in financial markets and have a substantial impact on asset prices and financial-market efficiency. The performance of these institutional investors is usually evaluated relative to a benchmark (stock-market index) – a practice that has the potential to substantially alter the investment strategies of fund managers and, in turn, affect financial-market equilibrium. Understanding such effects is the objective of the work by Adrian Buss, Assistant Professor of Finance at INSEAD, and Matthijs Breugem, Collegio Carlo Alberto. In a study recently published in The Review of Financial Studies, Breugem & Buss (2019) have developed a theoretical framework that seeks to explain how the rise of benchmarked institutional investors affects financial markets. Their model extends previous theories by explicitly accounting for both information and portfolio choice in the presence of institutional investors who are concerned about their performance relative to a benchmark. Notably, their findings suggest that benchmarking can cause considerable distortions in the value of private information and, hence, informational efficiency.
An Efficient Program to Detect DDoS Attacks using Machine Learning Algorithms

This paper investigates the efficacy of machine learning algorithms for the detection of Distributed Denial of Service (DDoS) attacks [4][5]. The study explores different approaches, including Support Vector Machines (SVM), logistic regression, and decision trees, and evaluates their performance using metrics such as accuracy, precision, recall, and F1-score [6]. The results demonstrate the effectiveness of SVM models with polynomial or radial basis function (RBF) kernels, logistic regression models with a polynomial degree of 4, and decision tree models with depths exceeding 10 [7][8]. These algorithm configurations exhibit promising potential in mitigating DDoS attacks and safeguarding network infrastructures [9]. However, limitations such as dataset availability, imbalanced data, and the focus on offline detection warrant further research. Enhancements in these areas can lead to more robust and efficient DDoS detection systems. The findings of this study contribute to the advancement of network security and offer insights for organizations aiming to counter the growing threat of DDoS attacks.
Effects of hypertension on the static properties and chemical composition of the rat aorta’

AUTHORS’ SYNOPSIS The contributions of the relative radius, relative wall thickness, incremental strain, incremental elastic modulus, and medial scleroprotein content to the static elastic properties of the rat aortic wall have been examined in three groups of rats. Controls, rats made hypertensive at four weeks of age, and rats whose blood pressure was lowered after 6 weeks hypertension, were studied. The results show evidence of adaptive changes in the aorta of hypertensive animals, and that irreversible alterations in the mechanical properties of the wall may be induced by a brief period of hypertension. A direct relationship between aortic medial scleroprotein content and the elastic properties of the wall is demonstrated.
Evidence for physical therapy after stroke

Background and purpose The purpose of this study was to determine the effects of augmented exercise therapy on gait, gait-related activities, and (basic and extended) activities of daily living within the first 6 months poststroke. Methods A systematic literature search in electronic databases from 1990 until October 2010 was performed. Randomized controlled trials were included in which the experimental group spent augmented time in lower limb exercise therapy compared with the control group. Outcomes were gait, gait-related activities, and (extended) activities of daily living. Results from individual studies were pooled by calculating the summary effect sizes. Subgroup analyses were applied for a treatment contrast of ≥16 hours, timing poststroke, type of control intervention, and methodological quality. Results Fourteen (N=725) of 4966 identified studies were included. Pooling resulted in small to moderate significant summary effect sizes in favor of augmented exercise therapy for walking ability, walking speed, and extended activities of daily living. No significant effects were found for basic activities of daily living. Subgroup analysis did not show a significant effect modification. Conclusions Dose-response trials in stroke rehabilitation are heterogeneous. The present meta-analysis suggests that increased time spent on exercise of gait and gait-related activities in the first 6 months poststroke results in significant small to moderate effects in terms of walking ability, walking speed, and extended activities of daily living. High-quality dose-response exercise therapy trials are needed with identical treatment goals but incremental levels of intensity.
World knowledge competitiveness index 2002:benchmarking the globes high-performing regions

The World Knowledge Competitiveness Index 2002 is the first composite and relative measure of the knowledge economies of the globe's best performing regions. It represents an integrated and overall benchmark of the knowledge capacity, capability and sustainability of each region and the extent to which this knowledge is translated into economic value and transferred into the wealth of the citizens of each region. This publication has over 50 pages and covers the following sections: The Economics of Knowledge Competitiveness The Rankings - World Knowledge Competitiveness Index Human Capital Components Knowledge Capital Components Regional Economy Outputs Knowledge Sustainability Components Driving Knowledge-Based Growth
No Repetition: Fast and Reliable Sampling with Highly Concentrated Hashing

Stochastic sample-based estimators are among the most fundamental and universally applied tools in statistics. Such estimators are particularly important when processing huge amounts of data, where we need to be able to answer a wide range of statistical queries reliably, yet cannot afford to store the data in its full length. In many applications we need the sampling to be coordinated which is typically attained using hashing. In previous work, a common strategy to obtain reliable sample-based estimators that work within certain error bounds with high probability has been to design one that works with constant probability, and then boost the probability by taking the median over r independent repetitions. Aamand et al. (STOC'20) recently proposed a fast and practical hashing scheme with strong concentration bounds , Tabulation-1Permutation, the first of its kind. In this paper, we demonstrate that using such a hash family for the sampling, we achieve the same high probability bounds without any need for repetitions. Using the same space, this saves a factor r in time, and simplifies the overall algorithms. We validate our approach experimentally on both real and synthetic data. We compare Tabulation-1Permutation with other hash functions such as strongly universal hash functions and various other hash functions such as MurmurHash3 and BLAKE3, both with and without resorting to repetitions. We see that if we want reliability in terms of small error probabilities, then Tabulation-1Permutation is significantly faster.
Meta Perturbed Re-Id Defense

Adversarial attacks have gained significant attention in object re-identification (Re-Id). However, very few works target defense, and they primarily adopt adversarial training. While adversarial training has been shown to be a major line of defense, we observe that vanilla adversarial training alone does not provide a robust defense against adversarial attacks. Towards this, we propose a novel meta perturbed defense algorithm for Re-Id task. Our contributions are, (i) we introduce anisotropic and isotropic perturbations to design a stochastic neural network, and train it with a novel meta-learning strategy with tasks as vanilla, perturbed, and perturbed-adversarial training; (ii) we show the generalizability of our model against various unseen attacks; and (iii) we derive a novel feature covariance alignment (FCA) loss which gives us a high clean performance while providing robustness against different attacks. Extensive experiments on Market-1501, MSMT17 and VeRi-776 reveal SOTA performance. 1
Physics Engine-Based Whole-Hand Haptic Rendering for Sensorimotor Neurorehabilitation

Whole-hand haptic rendering could lead to more naturalistic and intuitive virtual hand-object interactions, which could be especially beneficial for applications such as sensorimotor robotic neurorehabilitation. However, the majority of previously proposed whole-hand haptic rendering algorithms rely on effortful custom implementations or are not suited for the grounded haptic devices often used in neurorehabilitation. Therefore, we suggest a framework for whole-hand haptic rendering based on a readily available physics engine. We employ a bilateral position-position teleoperation framework between a haptic rehabilitation device and a simulated hand avatar with added exercise-specific haptic rendering. Moreover, in consideration of the needs of neurological patients, we introduce an adaptive damping of the haptic device during hand-object interactions for increased stabilization of the patient’s limb. We present the first results of the feasibility of the proposed framework in a haptic rehabilitation exercise. In an ongoing clinical study, the practical application of the presented framework is currently investigated.
Mitigating Partial Discharge in PWM Voltage Systems Using Electrets

Advancing power semiconductor technology offers high efficiency and enables both high gravimetric and volumetric power densities. However, the locally enhanced electric field in the power-dense designs and recurring, steep pulse width modulated (PWM) voltage stress caused by PEC promote partial discharge (PD), a chronic dielectric issue that accelerate dielectric aging and device failure. Unlike conventional electric field grading approaches, the proposed electret-based PD mitigation approach neutralizes electric field to prevent PDs generated at triple points, sharp edges, and cavities. In this study, the electret-based PD mitigation approach is carried out in a PWM driven system. PD experiments were conducted with uncharged polyvinylidene fluoride (PVDF) and charged PVDF (electret) for various duty cycles while the PD magnitudes and PD repetition rates were recorded. The measured PD data are compared to evaluate the PD-mitigating performance of electrets.
Optimal Sampling Methodologies for High-rate Structural Twinning

In high-rate structural health monitoring, it is crucial to quickly and accurately assess the current state of a component under dynamic loads. State information is needed to make informed decisions about timely interventions to prevent damage and extend the structure’s life. In previous studies, a dynamic reproduction of projectiles in ballistic environments (DROPBEAR) testbed was used to evaluate the accuracy of state estimation techniques through dynamic analysis. This paper extends previous research by incorporating the local eigenvalue modification procedure (LEMP) and data fusion techniques to create a more robust state estimate using optimal sampling methodologies. The process of estimating the state involves taking a measured frequency response of the structure, proposing frequency response profiles, and accepting the most similar profile as the new mean for the position estimate distribution. Utilizing LEMP allows for a faster approximation of the proposed model with linear time complexity, making it suitable for 2D or sequential damage cases. The current study focuses on two proposed sampling methodology refinements: distilling the selection of candidate test models from the position distribution and applying a Kalman filter after the distribution update to find the mean. Both refinements were effective in improving the position estimate and the structural state accuracy, as shown by the time response assurance criterion and the signal-to-noise ratio with up to 17% improvement. These two metrics demonstrate the benefits of incorporating data fusion techniques into the high-rate state identification process.