Miracle-dz/UniFAR-FTUs huggingface dataset:
Game-Theoretic Distributed Empirical Risk Minimization With Strategic Network Design

This article considers a game-theoretic framework for distributed empirical risk minimization (ERM) problems over networks where the information acquisition at a node is modeled as a rational choice of a player. In the proposed game, players decide both the learning parameters and the network structure. The Nash equilibrium (NE) characterizes the tradeoff between the local performance and the global agreement of the learned classifiers. We first introduce an interleaved approach that features a joint learning process that integrates the iterative learning at each node with the network formation. We show that our game is equivalent to a generalized potential game in the setting of undirected networks. We study the convergence of the proposed interleaved algorithm, analyze the network structures determined by our game, and show the improvement of social welfare compared to a standard distributed ERM over fixed networks. To adapt our framework to streaming data, we derive a distributed Kalman filter. A concurrent algorithm based on the online mirror descent algorithm is also introduced to solve for NE in a holistic manner. In the case study, we use data from telemonitoring of Parkinson's disease to corroborate the results.
Predicting Time to Evaluation After Pediatric Concussion: Factors Affecting Specialty Concussion Care

Background: The timing of clinical evaluation after pediatric concussion represents an important and potentially modifiable clinical milestone for diagnosis, selection of appropriate treatment pathways, and recovery prognosis. Patient demographics, socioeconomic status, or medical history may affect the time to the initial evaluation and subsequently influence recovery outcomes. Purpose/Hypothesis: The purpose of this investigation was to evaluate the association of patient characteristics with the time to specialty evaluation after a concussion. It was hypothesized that patients with a history of concussion, a preexisting relationship with our specialty concussion program, or a higher ZIP code–based income estimate would present for care more quickly after a concussion than patients without these characteristics. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Included were patients with a concussion between 6 and 18 years old who were seen for care at a single sports medicine center between January 1 and December 31, 2019. Patient demographic, socioeconomic, injury, and clinical characteristics were collected through a retrospective review of the medical records. The primary outcome was the number of days between the date of the concussion and the patient’s initial specialty evaluation. Results: Overall, 220 patients (mean age, 14.4 ± 2.5 years; 46% female) were seen for care at a mean of 9.7 ± 5.6 days (range, 1-21 days) after concussion. A shorter time to specialty evaluation was associated with a history of concussion (β = –1.72 [95% CI, –3.24 to –0.20]; P = .03) and a prior clinical relationship with the treating clinical department (β = –1.85 [95% CI, –3.52 to –0.19]; P = .03). Referral by a primary care provider was associated with a longer time to evaluation (β = 3.86 [95% CI, 2.39-5.33]; P < .0001). Conclusion: A history of concussion and having a preexisting clinical relationship with the deparment were associated with a shorter time to evaluation after concussion. Referral from a primary care physician was associated with a longer time to evaluation. Issues may exist in the propensity to access care after an injury, resulting in delays for initiating early treatment.
The interaction effect of reward type and mindset on online travel community promotion intention: the mediating roles of identification and community commitment

ABSTRACT This study explores the interaction effect of reward type and mindset on community promotion in the online travel community guided by implicit theories. Three scenario-based experiments were undertaken to examine the interaction effect. Specifically, community members with a fixed mindset are more inclined to promote community when they obtain material rewards, while community members with a growth mindset are more inclined to promote community when they obtain emotional rewards. Identification and community commitment act as mediators between the interaction effect. In addition, this research also discusses the theoretical and practical implications of the findings.
Machine Learning Techniques for Analyzing Survival Data of Breast Cancer Patients in Baghdad

The Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal component algorithm, The second kernel principal component algorithm, and The last is the faster ICA algorithm. Then the important features extracted in the three algorithms for features extraction will be entered into machine learning algorithms: The first K nearest neighbor algorithm, The second survival tree algorithm (or regression tree), and the last random survival forests algorithm. Two criteria for comparing the best models to estimate survival have relied on the MSE and the C-Index. The best model for estimating and predicting survival is the use of the fastest ICA algorithm with the random survival forest algorithm that gave the lowest amount to MSE and the highest value to the C-Index. Accordingly, we recommend doctors and medical professionals in Iraq adopt this model to estimate survival for patients with breast cancer.
Reinforcement Learning Approach to Stochastic Vehicle Routing Problem With Correlated Demands

We present a novel end-to-end framework for solving the Vehicle Routing Problem with stochastic demands (VRPSD) using Reinforcement Learning (RL). Our formulation incorporates the correlation between stochastic demands through other observable stochastic variables, thereby offering an experimental demonstration of the theoretical premise that non-i.i. d. stochastic demands provide opportunities for improved routing solutions. Our approach bridges the gap in the application of RL to VRPSD and consists of a parameterized stochastic policy optimized using a policy gradient algorithm to generate a sequence of actions that form the solution. Our model outperforms previous state-of-the-art metaheuristics and demonstrates robustness to changes in the environment, such as the supply type, vehicle capacity, correlation, and noise levels of demand. Moreover, the model can be easily retrained for different VRPSD scenarios by observing the reward signals and following feasibility constraints, making it highly flexible and scalable. These findings highlight the potential of RL to enhance the transportation efficiency and mitigate its environmental impact in stochastic routing problems. Our implementation is available in https://github.com/Zangir/SVRP.
RAINFALL BASED CROPPING SYSTEM IN UPLAND CONDITIONS OF GANJAM, ORISSA

The daily rainfall data for the 15 years (1974-1988) recorded at Agricultural Meteoro- logical observatory, Berhampur, Ganjam, were analysed for annual, seasonal, monthly and weekly periods and presented in this paper. Based on the rainfall pattern, a suitable cropping system of growing cowpea fodder, pigeonpea or ground nut, sesamum, horsegram, greenmanure as main crops and fingermillet and greengram as inter crops have been suggested with minimum risk to utilise the rainfall efficiently for increasing the income of the farmers.
False positives are common in single-station template matching

Template matching has become a cornerstone technique of observational seismology. By taking known events, and scanning them against a continuous record, new events smaller than the signal-to-noise ratio can be found, substantially improving the magnitude of completeness of earthquake catalogues. Template matching is normally used in an array setting, however as we move into the era of planetary seismology, we are likely to apply template matching for very small arrays or even single stations. Given the high impact of planetary seismology studies on our understanding of the structure and dynamics of non-Earth bodies, it is important to assess the reliability of template matching in the small-n setting. Towards this goal, we estimate a lower bound on the rate of false positives for single-station template matching by examining the behaviour of correlations of totally uncorrelated white noise. We find that, for typical processing regimes and match thresholds, false positives are likely quite common. We must therefore be exceptionally careful when considering the output of template matching in the small-n setting.
Two-Dimensional Floodplain Modeling

Abstract : A two-dimensional horizontal finite element numerical model (RMA-2) was applied to a 15 mile (24 km) river channel-floodplain reach in West Germany. Previous applications of such models have been restricted to much smaller scales. The results indicate that finite element schemes may successfully estimate river stage in large scale floodplain applications. Computed stage hydrographs compared well with observed data using loss coefficients within expected ranges. Two-dimensional flow models have been applied to certain classes of river channel problems. Applications have included detailed analyses of flow patterns near structures such as bridges and floodplains. In all these problems the scale of interest has been small, e.g. reaches of river a few river widths long. Many estuary studies have been done that were of large scale; some of these utilized a hybrid (numerical plus physical) modeling technique. In a review of the application of finite element methods to river channels, Samuels reported that the river channel was resolved separately from the floodplain in only two studies. Missing in previous work is attention to large scale floodplain modeling. The work reported in this paper focuses on the feasibility and accuracy of applying a two-dimensional flow model to a large floodplain. Traditional floodplain studies have used semi-empirical flow routing with steady, one-dimensional computation of water surface elevations to define inundated areas. Keywords: Army Corps of Engines.
The Utility of Red Cell Distribution Width as a Parameter for Calculating Indices of Allostatic Load

Background Allostatic Load is a construct used to quantify the cumulative burden of exposure to stressors that, over the course of an individual’s life, exert a toll on the body’s physiological functions, increasing risks of various chronic ailments and conditions. Studies attempting to quantify allostatic load have used a variety of clinical biomarkers representing primary and secondary mediators. In this study, we demonstrate the value of including red blood cell distribution width (RDW) among the panel of clinical parameters used to calculate allostatic load. Methods We develop a novel formulation of allostatic load using RDW and other standard biomarkers. This index is computed using clinical laboratory data from the NHANES study. The predictive validity of the new index for tertiary outcomes (all-cause mortality and physician-assessed health status) is compared to that of the current formulation using Harrell’s C index, ROC analysis and regression-based goodness-of-fit measures. Results Inclusion of RDW as an allostatic load biomarker yields a significantly improved index. It demonstrates a superior ability to predict mortality, health status and biological age than the standard formulation currently in use. Conclusion RDW has shown strong correlations with mortality and a broad spectrum of diseases. A review of the existing literature on allostatic load reveals its underutilization in this area, despite being a standard component of blood count panels. This study is the first to demonstrate its usefulness as a potential allostatic load biomarker.
The analysis of the Standardized Precipitation Index in the Mediterranean area: large-scale patterns

Problems related to the identification and monitoring of drought are investigated. In particular, drought conditions in Italy are examined using the Standardized Precipitation Index (SPI) on different time scales for August 2000. Historical reconstruction of drought events is also shown for particular locations of Italy for the last fifty years. These preliminary applications show interesting results and demonstrate that the index considered is a tool that should be used operationally as part of a national drought watch system in Italy to monitor drought conditions objectively. An example of a monthly bulletin of drought for Italy is also shown.
Machine Learning Approaches for Attacking Behaviors in Robot Soccer

In this work, Machine Learning approaches were applied to attacking behaviors in RoboCup Small-Size League autonomous robot soccer. Neural networks were used in order to get a binary prediction of an attacking action’s success, while deep reinforcement learning was leveraged to learn low level skills which control the robot’s wheel speeds and kicker. A trained neural network was used to predict whether a shot would be successful, improving the number of goals scored by the attacking behavior by 84 to 186%. The reinforcement learning methodologies used in this work produced behaviors which were efficient in speed, beating manually programmed behaviors in time taken, but can benefit from future refinements to improve accuracy in shooting towards goal.
Robust Domain Correction Latent Subspace Learning for Gas Sensor Drift Compensation

Subspace learning is a popular machine learning method that has been frequently applied for gas sensor calibration; however, there are the following limitations in the latent subspace learning process: 1) the existence of data distribution differences is not considered and 2) ignoring the inherent information of the original space, such as discriminative information and structure information. To overcome these issues, we design a novel domain correction latent subspace learning (DCLSL) algorithm for gas sensor drift compensation by integrating subspace learning and domain adaptation into a unified framework in this study. First, domain correction is applied to alleviate the distribution difference before and after sensor drift by exploiting the mean distribution discrepancy criterion. Second, for better-local representation and classification results, we consider both discriminative information of the source data and structural information of the target data, revealing the intrinsic geometric structure in the latent subspace and forming a compact intraclass and interclass separated discriminative data layout. In addition, to improve the consistency of features and labels, we use a projection term, a reconstruction term, and a regularization term to simultaneously implement joint learning of the label space and the latent subspace and impose a row-sparsity constraint to enhance the model robustness to noise. Inspired by non-negative matrix factorization, we skillfully adopt multiplication update rules and thus solve the proposed optimization problem. Finally, we conduct experiments for different drift compensation tasks on two common gas sensor datasets, and the results are encouraging.
Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis

Background Unwarranted extended length of stay (LOS) increases the risk of hospital-acquired complications, morbidity, and all-cause mortality and needs to be recognized and addressed proactively. Objective This systematic review aimed to identify validated prediction variables and methods used in tools that predict the risk of prolonged LOS in all hospital admissions and specifically General Medicine (GenMed) admissions. Method LOS prediction tools published since 2010 were identified in five major research databases. The main outcomes were model performance metrics, prediction variables, and level of validation. Meta-analysis was completed for validated models. The risk of bias was assessed using the PROBAST checklist. Results Overall, 25 all admission studies and 14 GenMed studies were identified. Statistical and machine learning methods were used almost equally in both groups. Calibration metrics were reported infrequently, with only 2 of 39 studies performing external validation. Meta-analysis of all admissions validation studies revealed a 95% prediction interval for theta of 0.596 to 0.798 for the area under the curve. Important predictor categories were co-morbidity diagnoses and illness severity risk scores, demographics, and admission characteristics. Overall study quality was deemed low due to poor data processing and analysis reporting. Conclusion To the best of our knowledge, this is the first systematic review assessing the quality of risk prediction models for hospital LOS in GenMed and all admissions groups. Notably, both machine learning and statistical modeling demonstrated good predictive performance, but models were infrequently externally validated and had poor overall study quality. Moving forward, a focus on quality methods by the adoption of existing guidelines and external validation is needed before clinical application. Systematic review registration https://www.crd. york.ac. uk/PROSPERO/, identifier: CRD42021272198.
Highlights From the Annual Meeting of the American Epilepsy Society 2022

With more than 6000 attendees between in-person and virtual offerings, the American Epilepsy Society Meeting 2022 in Nashville, felt as busy as in prepandemic times. An ever-growing number of physicians, scientists, and allied health professionals gathered to learn a variety of topics about epilepsy. The program was carefully tailored to meet the needs of professionals with different interests and career stages. This article summarizes the different symposia presented at the meeting. Basic science lectures addressed the primary elements of seizure generation and pathophysiology of epilepsy in different disease states. Scientists congregated to learn about anti-seizure medications, mechanisms of action, and new tools to treat epilepsy including surgery and neurostimulation. Some symposia were also dedicated to discuss epilepsy comorbidities and practical issues regarding epilepsy care. An increasing number of patient advocates discussing their stories were intertwined within scientific activities. Many smaller group sessions targeted more specific topics to encourage member participation, including Special Interest Groups, Investigator, and Skills Workshops. Special lectures included the renown Hoyer and Lombroso, an ILAE/IBE joint session, a spotlight on the impact of Dobbs v. Jackson on reproductive health in epilepsy, and a joint session with the NAEC on coding and reimbursement policies. The hot topics symposium was focused on traumatic brain injury and post-traumatic epilepsy. A balanced collaboration with the industry allowed presentations of the latest pharmaceutical and engineering advances in satellite symposia.
Performances of Polymer-Dispersed Liquid Crystal Films for Smart Glass Applications

Polymer-dispersed liquid crystal (PDLC) film is an active smart film penetrating the market due to its unique functionalities. These functional characteristics include switchable tint capabilities, which shield building residents from the sun’s harmful ultraviolet (UV) rays, improve energy-saving features, and produce higher cost-efficiency. Although PDLC films are promising in several applications, there is still ambiguity on the performance of PDLC films. Particularly, the sizing effects’ (such as film thickness and area) correlation with visible light transmission (VLT), ultraviolet rejection (UVR), infrared rejection (IRR), light intensity, current consumption, and apparent power consumption is not well understood. Therefore, this study investigated the sizing effects of PDLC films, including the thickness effect on VLT, UVR, IRR, light intensity, and area influence on current and apparent power consumptions. The varying applied voltage effect on the light transmittance of the PDLC film was also effectively demonstrated. A 0.1 mm PDLC film was successfully presented as a cost-efficient film with optimal parameters. Consequently, this study paves the way for a clearer understanding of PDLC films (behavior and sizing effects) in implementing economic PDLC films for large-scale adoption in commercial and residential premises.
PreCaCycleGAN: Perceptual Capsule Cyclic Generative Adversarial Network for Industrial Defective Sample Augmentation

Machine vision is essential for intelligent industrial manufacturing driven by Industry 4.0, especially for surface defect detection of industrial products. However, this domain is facing sparse and imbalanced defect data and poor model generalization, affecting industrial efficiency and quality. We propose a perceptual capsule cycle generative adversarial network (PreCaCycleGAN) for industrial defect sample augmentation, generating realistic and diverse defect samples from defect-free real samples. PreCaCycleGAN enhances CycleGAN with a U-Net and DenseNet-based generator to improve defect feature propagation and reuse and adds a perceptual loss function and a capsule network to improve authenticity and semantic information of generated features, enabling richer and more realistic global and detailed features of defect samples. We experiment on ten datasets, splitting each dataset into training and testing sets to evaluate model generalization across datasets. We train three defect detection models (YOLOv5, SSD, and Faster-RCNN) with original data and augmented data from PreCaCycleGAN and other state-of-the-art methods, such as CycleGAN-TSS and Tree-CycleGAN, and validate them on different datasets. Results show that PreCaCycleGAN improves detection accuracy and rate and reduces the false detection rate of detection models compared to other methods on different datasets, demonstrating its robustness and generalization under various defect conditions.