Miracle-dz/UniFAR-FTUs huggingface dataset:
Spine and Whiplash Injuries Related to Passenger Car Crashes in Malaysia

Spinal injury is one of the most devastating traumas that can be sustained in a motor vehicle crash. In passenger car crashes, the most common injuries related to the spine is whiplash injury or Whiplash Associated Disorder (WAD); however, the injury was relatively unknown in Malaysia. This study explored the possibility of elucidating whiplash injury locally by studying the insurance claims made by passenger car occupants involved in traffic crashes. The objectives of the study were to investigate the prevalence and to identify the injury pattern of the spine and whiplash injury. The data were collected retrospectively from closed files of the Third-Party Bodily Injury (TPBI) insurance claims database for the year 2013-2015. Each occupants’ injuries were extracted from the medical report and were classified according to the Abbreviated Injury Scale (AIS). A total of 500 cases of passenger car crashes were obtained from the insurance database and out of the total occupants involved, 866 injured occupants were included in the analysis. The results demonstrated that spine injuries have very low occurrence in Malaysia relative to other injury types, and whiplash-specific injuries are even rarer among passenger car crash victims in Malaysia. The most common vertebra level to be affected was the cervical spine, especially the C5-C6 vertebra, and this can be seen mostly for the drivers and front passengers. The low occurrence of spine injury is comparable to other countries, but the rare whiplash injury may indicate the ‘whiplash culture’ is not prevalent in Malaysia.
New temperature-assisted ionic liquid-based dispersive liquid–liquid microextraction method for the determination of glyphosate and aminomethylphosphonic acid in water samples

ABSTRACT A new analytical temperature-assisted ionic liquid-based dispersive liquid–liquid microextraction (TA-IL-DLLME) method was developed for glyphosate and aminomethylphosphonic acid determination in water samples. Extracted analytes were derivatized using 9-fluoroenylmethylchloroformate and quantified by liquid chromatography with fluorescence detection. For the TA-IL-DLLME method, two strategies for phase solubilization were evaluated; in approach 1, the ionic liquid and aqueous matrix sample were mixed and then heated, while in approach 2, the aqueous sample was first heated and then the ionic liquid was injected. For both approaches, optimization included parameters that significantly affect extraction efficiency: ionic liquid type and volume, solubilization temperature and time, cooling and centrifugation time. Among the evaluated ionic liquids, 1-decyl-3-methylimidazolium tetrafluoroborate showed the best performance for TA-IL-DLLME and was selected for the two solubilization approaches; with approach 2, slightly better results were obtained. Thus, sample analyses were performed using a procedure based on approach 2. An important matrix effect, attributed to the presence of salts and metals in real water samples was observed. Sample acidification before derivatization allowed this problem to diminish, with recoveries ranging from 75 and 99%, and enrichment factors between 57 and 76 for target analytes. GRAPHICAL ABSTRACT
Image Analysis and High Dimensional Control Chart for Inspection of Sausage Color Homogeneity and Uniformity

Abstract Sausage color usually influences consumers’ selection due to the perceptions of quality. Extensive studies have applied image processing to capture the characteristics of food products according to the high-dimensional nature of the resultant images. However, the color homogeneity (i. e. “within pack” variation) and uniformity (i. e. “between-pack” variation) have rarely been studied. Therefore, this paper proposes a new framework to detect both variations using images. In addition, a new approach has been developed to deal with high-dimension data involving colorimetric characteristics, namely L*, a*, b*, hue (h) and chroma (C*). These high-dimensional data are transformed to represent color homogeneity and uniformity. Hotelling T2 chart is used to detect color abnormalities. Our approach indicates that the out-of-control items can be identified with the control chart signals. Nonetheless, the out-of-control signals alone are inadequate for determination of the possible causes. Then, the proposed analysis framework was subsequently applied to identify possible causes that contributed to the process deviations. Furthermore, prior to the experiments with sausages, the image inspection device was tested for gauge repeatability and reproducibility.
Dynamic Modelling of the Non-Catalytic Process of Ethylene Oxide Hydrolysis

A non-catalytic process of ethylene oxide (EO) hydrolysis producing monoethylene glycol (MEG), diethylene glycol (DEG), and triethylene glycol (TEG) has been considered. The reactor unit consists of three basic elements: a stirred mixing tank, a heat exchanger and a tubular reactor. Mathematical models of these three basic elements have been developed. Material balances on ethylene oxide and water in the stirred mixing tank have been discussed. Energy balances on liquid and vapor phases in the heat exchanger have been presented, while steady-state equations of the tubular reactor have been developed. In order to investigate an optimal control system of this process an adequate dynamic model of ethylene oxide hydrolysis process has been proposed. Multivariable block-flow diagram of the process dynamics has been proposed. Using a standard procedure of linearization, a linear model of the reactor unit in the transform domain has been obtained.
Examination sports injury surveillance system is a feasible and valid approach to measuring handball exposure , injury occurrence , and consequences in elite youth

Current methods of sports injury surveillance are limited by lack of medical validation of selfreported injuries, and/or incomplete information about injury consequences beyond time-loss from sport. The aims of this study were to 1) evaluate the feasibility of the SMS, Phone and medical Examination injury surveillance (SPEx) system 2) to evaluate the proportion of injuries and injury consequences reported by SPEx when compared to outcomes from a modified version of the Oslo Sports Trauma Research Centre (OSTRC) Overuse Injury Questionnaire. We followed 679 elite adolescent handball players over 31 weeks using the SPEx system. During the last 7 weeks, we also implemented a modified OSTRC questionnaire in a subgroup of 271 players via telephone interviews. The weekly response proportions to the primary SPEx questions ranged from 85% to 96% (mean 92%). SMS responses were received from 79% of the participants within 1 day. 95% of reported injuries were classified through the telephone interview within a week, and 67% were diagnosed by medical personnel. Comparisons between reported injuries from SPEx and OSTRC demonstrated fair [κ=39.5% (25.1% to 54.0%)] to substantial [PABAK=66.8% (95% CI 58.0% to 75.6%)] agreement. The average injury severity score difference between SPEx and the OSTRC approach was -0.2 (95% CI -3.69 to 3.29) out of possible 100 with 95% limits of agreement from A cc ep te d A rt ic le This article is protected by copyright. All rights reserved. (-14.81 to 14.41). These results support the feasibility and validity of the SPEx injury surveillance system in elite youth sport. Future studies should evaluate the external validity of SPEx system in different cohorts of athletes.
Continuous Decoding of Daily-Life Hand Movements from Forearm Muscle Activity for Enhanced Myoelectric Control of Hand Prostheses

State-of-the-art motorized hand prostheses are endowed with actuators able to provide independent and proportional control of as many as six degrees of freedom (DOFs). The control signals are derived from residual electromyographic (EMG) activity, recorded concurrently from relevant forearm muscles. Nevertheless, the functional mapping between forearm EMG activity and hand kinematics is only known with limited accuracy. Therefore, no robust method exists for the reliable computation of control signals for the independent and proportional actuation of more than two DOFs. A common approach to deal with this limitation is to preprogram the prostheses for the execution of a restricted number of behaviors (e.g., pinching, grasping, and wrist rotation) that are activated by the detection of specific EMG activation patterns. However, this approach severely limits the range of activities users can perform with the prostheses during their daily living. In this work, we introduce a novel method, based on a long short-term memory (LSTM) network, to map forearm EMG activity onto hand kinematics online. Critically, unlike previous research efforts that tend to focus on simple and highly controlled motor tasks, we tested our method on a dataset of daily living activities (ADLs): the KIN-MUS UJI dataset. To the best of our knowledge, ours is the first reported work on the prediction of hand kinematics that uses this challenging dataset. Remarkably, we show that our network is able to generalize to novel untrained ADLs. Our results suggest that the presented method is suitable for the generation of control signals for the independent and proportional actuation of the multiple DOFs of state-of-the-art hand prostheses.
What Are Bayesian Neural Network Posteriors Really Like?

The posterior over Bayesian neural network (BNN) parameters is extremely high-dimensional and non-convex. For computational reasons, researchers approximate this posterior using inexpensive mini-batch methods such as mean-field variational inference or stochastic-gradient Markov chain Monte Carlo (SGMCMC). To investigate foundational questions in Bayesian deep learning, we instead use full-batch Hamiltonian Monte Carlo (HMC) on modern architectures. We show that (1) BNNs can achieve significant performance gains over standard training and deep ensembles; (2) a single long HMC chain can provide a comparable representation of the posterior to multiple shorter chains; (3) in contrast to recent studies, we find posterior tempering is not needed for near-optimal performance, with little evidence for a"cold posterior"effect, which we show is largely an artifact of data augmentation; (4) BMA performance is robust to the choice of prior scale, and relatively similar for diagonal Gaussian, mixture of Gaussian, and logistic priors; (5) Bayesian neural networks show surprisingly poor generalization under domain shift; (6) while cheaper alternatives such as deep ensembles and SGMCMC methods can provide good generalization, they provide distinct predictive distributions from HMC. Notably, deep ensemble predictive distributions are similarly close to HMC as standard SGLD, and closer than standard variational inference.
Regret Bounds for Gaussian-Process Optimization in Large Domains

The goal of this paper is to characterize Gaussian-Process optimization in the setting where the function domain is large relative to the number of admissible function evaluations, i.e., where it is impossible to find the global optimum. We provide upper bounds on the suboptimality (Bayesian simple regret) of the solution found by optimization strategies that are closely related to the widely used expected improvement (EI) and upper confidence bound (UCB) algorithms. These regret bounds illuminate the relationship between the number of evaluations, the domain size (i.e. cardinality of finite domains / Lipschitz constant of the covariance function in continuous domains), and the optimality of the retrieved function value. In particular, we show that even when the number of evaluations is far too small to find the global optimum, we can find nontrivial function values (e.g. values that achieve a certain ratio with the optimal value).
A neural anisotropic view of underspecification in deep learning

The underspecification of most machine learning pipelines means that we cannot rely solely on validation performance to assess the robustness of deep learning systems to naturally occurring distribution shifts. Instead, making sure that a neural network can generalize across a large number of different situations requires to understand the specific way in which it solves a task. In this work, we propose to study this problem from a geometric perspective with the aim to understand two key characteristics of neural network solutions in underspecified settings: how is the geometry of the learned function related to the data representation? And, are deep networks always biased towards simpler solutions, as conjectured in recent literature? We show that the way neural networks handle the underspecification of these problems is highly dependent on the data representation, affecting both the geometry and the complexity of the learned predictors. Our results highlight that understanding the architectural inductive bias in deep learning is fundamental to address the fairness, robustness, and generalization of these systems.
FX Market Volatility

This paper aims at solving FX market volatility modeling problem and finding the most becoming approach to this task. Validity of two competing approaches, classical econometric generalized conditional heteroscedasticity and mathematical (singular spectrum analysis and dynamical systems stability analysis) are tested on major currency pairs (EUR/USD, USD/JPY, GBP/USD) and unique high-frequency USD/RUB data. The study shows that both mathematical tools, understudied in econometric discourse, have a great potential in scope of discussed problematic, as for all experiments covered in this research, both of them show promising results.
AutoFlow: Learning a Better Training Set for Optical Flow

Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications. To automate the process, we present AutoFlow, a simple and effective method to render training data for optical flow that optimizes the performance of a model on a target dataset. AutoFlow takes a layered approach to render synthetic data, where the motion, shape, and appearance of each layer are controlled by learnable hyperparameters. Experimental results show that AutoFlow achieves state-of-the-art accuracy in pre-training both PWC-Net and RAFT. Our code and data are available at autoflow-google.github. io.
A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information

Over the last decades, the incidence of skin cancer, melanoma and non-melanoma, has increased at a continuous rate. In particular for melanoma, the deadliest type of skin cancer, early detection is important to increase patient prognosis. Recently, deep neural networks (DNNs) have become viable to deal with skin cancer detection. In this work, we present a smartphone-based application to assist on skin cancer detection. This application is based on a Convolutional Neural Network(CNN) trained on clinical images and patients demographics, both collected from smartphones. Also, as skin cancer datasets are imbalanced, we present an approach, based on the mutation operator of Differential Evolution (DE) algorithm, to balance data. In this sense, beyond provides a flexible tool to assist doctors on skin cancer screening phase, the method obtains promising results with a balanced accuracy of 85% and a recall of 96%.
ON MULTIFRACTIONALITY OF SPHERICAL RANDOM FIELDS WITH COSMOLOGICAL APPLICATIONS

Abstract This paper investigates spatial data on the unit sphere. Traditionally, isotropic Gaussian random fields are considered as the underlying mathematical model of the cosmic microwave background (CMB) data. We discuss the generalized multifractional Brownian motion and its pointwise Hölder exponent on the sphere. The multifractional approach is used to investigate the CMB data from the Planck mission. These data consist of CMB radiation measurements at narrow angles of the sky sphere. The results obtained suggest that the estimated Hölder exponents for different CMB regions do change from location to location. Therefore, the CMB temperature intensities are multifractional. The methodology developed is used to suggest two approaches for detecting regions with anomalies in the cleaned CMB maps.
ELF-VC: Efficient Learned Flexible-Rate Video Coding

While learned video codecs have demonstrated great promise, they have yet to achieve sufficient efficiency for practical deployment. In this work, we propose several novel ideas for learned video compression which allow for improved performance for the low-latency mode (I- and P-frames only) along with a considerable increase in computational efficiency. In this setting, for natural videos our approach compares favorably across the entire R-D curve under metrics PSNR, MS-SSIM and VMAF against all mainstream video standards (H.264, H.265, AV1) and all ML codecs. At the same time, our approach runs at least 5x faster and has fewer parameters than all ML codecs which report these figures.Our contributions include a flexible-rate framework allowing a single model to cover a large and dense range of bitrates, at a negligible increase in computation and parameter count; an efficient backbone optimized for ML-based codecs; and a novel in-loop flow prediction scheme which leverages prior information towards more efficient compression.We benchmark our method, which we call ELF-VC (Efficient, Learned and Flexible Video Coding) on popular video test sets UVG and MCL-JCV under metrics PSNR, MS-SSIM and VMAF. For example, on UVG under PSNR, it reduces the BD-rate by 44% against H.264, 26% against H.265, 15% against AV1, and 35% against the current best ML codec. At the same time, on an NVIDIA Titan V GPU our approach encodes/decodes VGA at 49/91 FPS, HD 720 at 19/35 FPS, and HD 1080 at 10/18 FPS.
Thermal diffusivity and its lower bound in orthorhombic SnSe

Valentina Martelli, Fabio Abud, Julio Larrea Jiménez, Elisa Baggio-Saitovich, Li-Dong Zhao, and Kamran Behnia (1) Laboratory for Quantum Matter under Extreme Conditions, Instituto de F́ısica, Universidade de São Paulo, 05508-090, São Paulo, Brazil (2) Instituto de F́ısica, Universidade de São Paulo, 05508-090, São Paulo, Brazil (3) Centro Brasileiro de Pesquisas F́ısica (CBPF) (4) School of Materials Science and Engineering, Beihang University, Beijing 100191, China (5) Laboratoire de Physique et d’Étude des Matériaux (ESPCI Paris CNRS Sorbonne Université), PSL Research University, 75005 Paris, France
Hubble Space Telescope UV and Hα Measurements of the Accretion Excess Emission from the Young Giant Planet PDS 70 b

Recent discoveries of young exoplanets within their natal disks offer exciting opportunities to study ongoing planet formation. In particular, a planet’s mass accretion rate can be constrained by observing the accretion-induced excess emission. So far, planetary accretion is only probed by the Hα line, which is then converted to a total accretion luminosity using correlations derived for stars. However, the majority of the accretion luminosity is expected to emerge from hydrogen continuum emission, and is best measured in the ultraviolet (UV). In this paper, we present HST/WFC3/UVIS F336W (UV) and F656N (Hα) high-contrast imaging observations of PDS 70. Applying a suite of novel observational techniques, we detect the planet PDS 70 b with signal-to-noise ratios of 5.3 and 7.8 in the F336W and F656N bands, respectively. This is the first time that an exoplanet has been directly imaged in the UV. Our observed Hα flux of PDS 70 b is higher by than the most recent published result. However, the light curve retrieved from our observations does not support greater than 30% variability in the planet’s Hα emission in six epochs over a five month timescale. We estimate a mass accretion rate of . Hα accounts for 36% of the total accretion luminosity. Such a high proportion of energy released in line emission suggests efficient production of Hα emission in planetary accretion, and motivates using the Hα band for searches of accreting planets. These results demonstrate HST/WFC3/UVIS’s excellent high-contrast imaging performance and highlight its potential for planet formation studies.
SoCRATES: System-on-Chip Resource Adaptive Scheduling using Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is being increasingly applied to the problem of resource allocation for emerging System-on-Chip (SoC) applications, and has shown remarkable promises. In this paper, we introduce SoCRATES (SoC Resource AdapTivE Scheduler), an extremely efficient DRL-based SoC scheduler which maps a wide range of hierarchical jobs to heterogeneous resources within SoC using the Eclectic Interaction Matching (EIM) technique. It is noted that the majority of SoC resource management approaches have been targeting makespan minimization with fixed number of jobs in the system. In contrast, SoCRATES aims at minimizing average latency in a steady-state condition while assigning tasks in the ready queue to heterogeneous resources (processing elements). We first show that the latency-minimization-driven SoC applications operate high-frequency job workload and distributed/parallel job execution. We then demonstrate SoCRATES successfully addresses the challenge of concurrent observations caused by the task dependency inherent in the latency minimization objective. Extensive tests show that SoCRATES outperforms other existing neural and non-neural schedulers with as high as 38% gain in latency reduction under a variety of job types and incoming rates. The resulting model is also compact in size and has very favorable energy consumption behaviors, making it highly practical for deployment in future SoC systems with built-in neural accelerator.
Fine-grained Generalization Analysis of Vector-valued Learning

Many fundamental machine learning tasks can be formulated as a problem of learning with vector-valued functions, where we learn multiple scalar-valued functions together. Although there is some generalization analysis on different specific algorithms under the empirical risk minimization principle, a unifying analysis of vector-valued learning under a regularization framework is still lacking. In this paper, we initiate the generalization analysis of regularized vector-valued learning algorithms by presenting bounds with a mild dependency on the output dimension and a fast rate on the sample size. Our discussions relax the existing assumptions on the restrictive constraint of hypothesis spaces, smoothness of loss functions and low-noise condition. To understand the interaction between optimization and learning, we further use our results to derive the first generalization bounds for stochastic gradient descent with vector-valued functions. We apply our general results to multi-class classification and multi-label classification, which yield the first bounds with a logarithmic dependency on the output dimension for extreme multi-label classification with the Frobenius regularization. As a byproduct, we derive a Rademacher complexity bound for loss function classes defined in terms of a general strongly convex function.