Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical empathy in medical students in Madrid: A proposal for empathy level cut-off points for Spain

This study evaluates the degree of empathy among medical students and its influencing factors at three critical moments of their degree studies (beginning of first year and end of third and sixth years) as well as establishes low-, medium-, and high-empathy cut-off points to obtain valid and reliable results that can be extrapolated to the general population. This cross-sectional study of the eight (public and private) medical schools in the province of Madrid, used an electronic questionnaire with the Jefferson Scale of Empathy (JSE), Medical Student Well-Being Index, and other independent characteristics as measuring instruments. Of the 2,264 student participants, 1,679 (74.0%) were women, with a 50.7% participation rate. No significant differences were found in empathy levels by academic year. Regarding range, percentile and cut-off point tables were established to identify students with high, medium, and low empathy levels. Women (p<0.001), volunteer workers (p<0.001), and those preferring general specialties (internal medicine, psychiatry, pediatrics, or family medicine) scored higher on the JSE (p<0.02). Moreover, 41.6% presented high level of psychological distress. Women reported a lower well-being level and a higher risk of psychological distress (p = 0.004). In sum, the empathy of medical students in Madrid did not differ among the three critical moments of their university studies. The established cut-off points could be taken into account when accessing the medical degree and identifying students with low levels of empathy to implement curricular interventions to rectify this perceived deficiency. There was a high percentage of medical students with high levels of psychological distress.
Robust Modeling and Controls for Racing on the Edge

Race cars are routinely driven to the edge of their handling limits in dynamic scenarios well above 200mph. Similar challenges are posed in autonomous racing, where a software stack, instead of a human driver, interacts within a multi-agent environment. For an Autonomous Racing Vehicle (ARV), operating at the edge of handling limits and acting safely in these dynamic environments is still an unsolved problem. In this paper, we present a baseline controls stack for an ARV capable of operating safely up to 140mph. Additionally, limitations in the current approach are discussed to highlight the need for improved dynamics modeling and learning.
Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition

Detection of fights is an important surveillance application in videos. Most existing methods use supervised binary action recognition. Since frame-level annotations are very hard to get for anomaly detection, weakly supervised learning using multiple instance learning is widely used. This paper explores the detection of fights in videos as one special type of anomaly detection and as binary action recognition. We use the UBI-Fight and NTU-CCTV-Fight datasets for most of the study since they have frame-level annotations. We find that the anomaly detection has similar or even better performance than the action recognition. Furthermore, we study to use anomaly detection as a toolbox to generate training datasets for action recognition in an iterative way conditioned on the performance of the anomaly detection. Experiment results should show that we achieve state-of-the-art performance on three fight detection datasets.
NPU-BOLT: A Dataset for Bolt Object Detection in Natural Scene Images

Bolt joints are very common and important in engineering structures. Due to extreme service environment and load factors, bolts often get loose or even disengaged. To real-time or timely detect the loosed or disengaged bolts is an urgent need in practical engineering, which is critical to keep structural safety and service life. In recent years, many bolt loosening detection methods using deep learning and machine learning techniques have been proposed and are attracting more and more attention. However, most of these studies use bolt images captured in laboratory for deep leaning model training. The images are obtained in a well-controlled light, distance, and view angle conditions. Also, the bolted structures are well designed experimental structures with brand new bolts and the bolts are exposed without any shelter nearby. It is noted that in practical engineering, the above well controlled lab conditions are not easy realized and the real bolt images often have blur edges, oblique perspective, partial occlusion and indistinguishable colors etc., which make the trained models obtained in laboratory conditions loss their accuracy or fails. Therefore, the aim of this study is to develop a dataset named NPU-BOLT for bolt object detection in natural scene images and open it to researchers for public use and further development. In the first version of the dataset, it contains 337 samples of bolt joints images mainly in the natural environment, with image data sizes ranging from 400*400 to 6000*4000, totaling approximately 1275 bolt targets. The bolt targets are annotated into four categories named blur bolt, bolt head, bolt nut and bolt side. The dataset is tested with advanced object detection models including yolov5, Faster-RCNN and CenterNet. The effectiveness of the dataset is validated.
All you need is logs: improving code completion by learning from anonymous IDE usage logs

In this work, we propose an approach for collecting completion usage logs from the users in an IDE and using them to train a machine learning based model for ranking completion candidates. We developed a set of features that describe completion candidates and their context, and deployed their anonymized collection in the Early Access Program of IntelliJ-based IDEs. We used the logs to collect a dataset of code completions from users, and employed it to train a ranking CatBoost model. Then, we evaluated it in two settings: on a held-out set of the collected completions and in a separate A/B test on two different groups of users in the IDE. Our evaluation shows that using a simple ranking model trained on the past user behavior logs significantly improved code completion experience. Compared to the default heuristics-based ranking, our model demonstrated a decrease in the number of typing actions necessary to perform the completion in the IDE from 2.073 to 1.832. The approach adheres to privacy requirements and legal constraints, since it does not require collecting personal information, performing all the necessary anonymization on the client's side. Importantly, it can be improved continuously: implementing new features, collecting new data, and evaluating new models - this way, we have been using it in production since the end of 2020.
Evaluating Performance of Machine Learning Models for Diabetic Sensorimotor Polyneuropathy Severity Classification using Biomechanical Signals during Gait

Background: Diabetic sensorimotor polyneuropathy (DSPN) is one of the prevalent forms of neuropathy affected by diabetic patients that involves alterations in biomechanical changes in human gait. In literature, for the last 50 years, researchers are trying to observe the biomechanical changes due to DSPN by studying muscle electromyography (EMG), and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we are proposing to use Machine learning techniques to identify DSPN patients by using EMG, and GRF data. Method: We have collected a dataset from the literature which involves, four patient groups: Absent (n=29), Mild (n=18), Moderate (n=16), and Severe (n=14.) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius medialis (GM) and 3-dimensional GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and a newly proposed feature extraction technique scheme from literature was applied to extract the best features from the signals. The extracted feature list was ranked using Relief feature ranking techniques, and highly correlated features were removed. In this study, we have considered different combinations of muscles, and GRF components to find out the best performing feature list for the identification of DSPN Severity. We have trained different ML models to find out the best-performing model and optimized that model. We trained the optimized ML models for different combinations of muscles and GRF components features, and the performance matrix was evaluated. Results: This study has found ensemble classifier model was performing in identifying DSPN Severity, and we optimized it before training. For EMG analysis, we have found the best accuracy of 92.89% using the Top 14 features for features from GL, VL and TA muscles combined. In the GRF analysis, the model showed 94.78% accuracy by using the Top 15 features for the feature combinations extracted from GRFx, GRFy and GRFz signals. Conclusion: The performance of ML-based DSPN severity classification models, improved significantly, indicating their reliability in DSPN severity classification, for biomechanical data.
ALITA: A Large-scale Incremental Dataset for Long-term Autonomy

For long-term autonomy, most place recognition methods are mainly evaluated on simplified scenarios or simulated datasets, which cannot provide solid evidence to evaluate the readiness for current Simultaneous Localization and Mapping (SLAM). In this paper, we present a long-term place recognition dataset for use in mobile localization under large-scale dynamic environments. This dataset includes a campus-scale track and a city-scale track: 1) the campus-track focuses the long-term property, we record LiDAR device and an omnidirectional camera on 10 trajectories, and each trajectory are repeatly recorded 8 times under variant illumination conditions. 2) the city-track focuses the large-scale property, we mount the LiDAR device on the vehicle and traversing through a 120km trajectories, which contains open streets, residential areas, natural terrains, etc. They includes 200 hours of raw data of all kinds scenarios within urban environments. The ground truth position for both tracks are provided on each trajectory, which is obtained from the Global Position System with an additional General ICP based point cloud refinement. To simplify the evaluation procedure, we also provide the Python-API with a set of place recognition metrics is proposed to quickly load our dataset and evaluate the recognition performance against different methods. This dataset targets at finding methods with high place recognition accuracy and robustness, and providing real robotic system with long-term autonomy. The dataset and the provided tools can be accessed from https://github.com/MetaSLAM/ALITA.
Athlete rating in multi-competitor games with scored outcomes via monotone transformations

Sports organizations often want to estimate athlete strengths. For games with scored outcomes, a common approach is to assume observed game scores follow a normal distribution conditional on athletes' latent abilities, which may change over time. In many games, however, this assumption of conditional normality does not hold. To estimate athletes' time-varying latent abilities using non-normal game score data, we propose a Bayesian dynamic linear model with flexible monotone response transformations. Our model learns nonlinear monotone transformations to address non-normality in athlete scores and can be easily fit using standard regression and optimization routines, which we implement in the dlmt package in R. We demonstrate our method on data from several Olympic sports, including biathlon, diving, rugby, and fencing.
Symptom Identification for Interpretable Detection of Multiple Mental Disorders on Social Media

Mental disease detection (MDD) from social media has suffered from poor generalizability and interpretability, due to lack of symptom modeling. This paper introduces PsySym, the first annotated symptom identification corpus of multiple psychiatric disorders, to facilitate further research progress. PsySym is annotated according to a knowledge graph of the 38 symptom classes related to 7 mental diseases complied from established clinical manuals and scales, and a novel annotation framework for diversity and quality. Experiments show that symptom-assisted MDD enabled by PsySym can outperform strong pure-text baselines. We also exhibit the convincing MDD explanations provided by symptom predictions with case studies, and point to their further potential applications.
Accelerated Minimax Algorithms Flock Together

Several new accelerated methods in minimax optimization and fixed-point iterations have recently been discovered, and, interestingly, they rely on a mechanism distinct from Nesterov's momentum-based acceleration. In this work, we show that these accelerated algorithms exhibit what we call the merging path (MP) property; the trajectories of these algorithms merge quickly. Using this novel MP property, we establish point convergence of existing accelerated minimax algorithms and derive new state-of-the-art algorithms for the strongly-convex-strongly-concave setup and for the prox-grad setup.
Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument

The Dark Energy Spectroscopic Instrument (DESI) embarked on an ambitious 5 yr survey in 2021 May to explore the nature of dark energy with spectroscopic measurements of 40 million galaxies and quasars. DESI will determine precise redshifts and employ the baryon acoustic oscillation method to measure distances from the nearby universe to beyond redshift z > 3.5, and employ redshift space distortions to measure the growth of structure and probe potential modifications to general relativity. We describe the significant instrumentation we developed to conduct the DESI survey. This includes: a wide-field, 3. °2 diameter prime-focus corrector; a focal plane system with 5020 fiber positioners on the 0.812 m diameter, aspheric focal surface; 10 continuous, high-efficiency fiber cable bundles that connect the focal plane to the spectrographs; and 10 identical spectrographs. Each spectrograph employs a pair of dichroics to split the light into three channels that together record the light from 360–980 nm with a spectral resolution that ranges from 2000–5000. We describe the science requirements, their connection to the technical requirements, the management of the project, and interfaces between subsystems. DESI was installed at the 4 m Mayall Telescope at Kitt Peak National Observatory and has achieved all of its performance goals. Some performance highlights include an rms positioner accuracy of better than 0. ″1 and a median signal-to-noise ratio of 7 of the [O ii] doublet at 8 × 10−17 erg s−1 cm−2 in 1000 s for galaxies at z = 1.4–1.6. We conclude with additional highlights from the on-sky validation and commissioning, key successes, and lessons learned.
Robust density estimation with the $\mathbb{L}_{1}$-loss. Applications to the estimation of a density on the line satisfying a shape constraint

We solve the problem of estimating the distribution of presumed i.i. d. observations for the total variation loss. Our approach is based on density models and is versatile enough to cope with many different ones, including some density models for which the Maximum Likelihood Estimator (MLE for short) does not exist. We mainly illustrate the properties of our estimator on models of densities on the line that satisfy a shape constraint. We show that it possesses some similar optimality properties, with regard to some global rates of convergence, as the MLE does when it exists. It also enjoys some adaptation properties with respect to some specific target densities in the model for which our estimator is proven to converge at parametric rate. More important is the fact that our estimator is robust, not only with respect to model misspecification, but also to contamination, the presence of outliers among the dataset and the equidistribution assumption. This means that the estimator performs almost as well as if the data were i.i. d. with density $p$ in a situation where these data are only independent and most of their marginals are close enough in total variation to a distribution with density $p$. We also show that our estimator converges to the average density of the data, when this density belongs to the model, even when none of the marginal densities belongs to it. Our main result on the risk of the estimator takes the form of an exponential deviation inequality which is non-asymptotic and involves explicit numerical constants. We deduce from it several global rates of convergence, including some bounds for the minimax $\mathbb{L}_{1}$-risks over the sets of concave and log-concave densities. These bounds derive from some specific results on the approximation of densities which are monotone, convex, concave and log-concave. Such results may be of independent interest.
A Deep Gradient Correction Method for Iteratively Solving Linear Systems

We present a novel deep learning approach to approximate the solution of large, sparse, symmetric, positive-deﬁnite linear systems of equations. These systems arise from many problems in applied science, e.g., in numerical methods for partial differential equations. Algorithms for approximating the solution to these systems are often the bottleneck in problems that require their solution, particularly for modern applications that require many millions of unknowns. Indeed, numerical linear algebra techniques have been investigated for many decades to alleviate this computational burden. Recently, data-driven techniques have also shown promise for these problems. Motivated by the conjugate gradients algorithm that iteratively selects search directions for minimizing the matrix norm of the approximation error, we design an approach that utilizes a deep neural network to accelerate convergence via data-driven improvement of the search directions. Our method leverages a carefully chosen convolutional network to approximate the action of the inverse of the linear operator up to an arbitrary constant. We train the network using unsupervised learning with a loss function equal to the L 2 difference between an input and the system matrix times the network evaluation, where the unspeciﬁed constant in the approximate inverse is accounted for. We demonstrate the efﬁcacy of our approach on spatially discretized Poisson equations with millions of degrees of freedom arising in computational ﬂuid dynamics applications. Unlike state-of-the-art learning approaches, our algorithm is capable of reducing the linear system
Nonparametric Learning of Kernels in Nonlocal Operators

Nonlocal operators with integral kernels have become a popular tool for designing solution maps between function spaces, due to their efficiency in representing long-range dependence and the attractive feature of being resolution-invariant. In this work, we provide a rigorous identifiability analysis and convergence study for learning kernels in nonlocal operators. It is found that kernel estimation is an ill-posed or even ill-defined inverse problem, leading to divergent estimators in the presence of modeling errors or measurement noises. To resolve this issue, we propose a nonparametric regression algorithm with a novel data-adaptive RKHS Tikhonov regularization method based on the function space of identifiability. The method yields a noisy-robust convergent estimator of the kernel as the data resolution refines, on both synthetic and real-world datasets. In particular, the method successfully learns a homogenized model for stress wave propagation in a heterogeneous solid, revealing the unknown governing laws from real-world data at the microscale. Our regularization method outperforms baseline methods in robustness, generalizability, and accuracy.
CPA-Lasing Associated with the Quasibound States in the Continuum in Asymmetric Non-Hermitian Structures

Non-Hermitian photonic systems with loss and gain attract much attention due to their exceptional abilities in molding the flow of light. Introducing asymmetry to the $\mathcal{PT}$-symmetric system with perfectly balanced loss and gain, we reveal the mechanism of transition from the quasibound state in the continuum (quasi-BIC) to the simultaneous coherent perfect absorption (CPA) and lasing in a layered structure comprising epsilon-near-zero (ENZ) media. Two types of asymmetry (geometric and non-Hermitian) are analyzed with the scattering matrix technique. The effect of the CPA-lasing associated with the quasi-BIC is characterized with the unusual linear dependence of the quality factor on the inverse of asymmetry parameter. Moreover, the counter-intuitive loss-induced-lasing-like behavior is found at the CPA-lasing point under the non-Hermitian asymmetry. The reported features of non-Hermitian structures are perspective for sensing and lasing applications.
Bayesian Clustering of Neural Activity with a Mixture of Dynamic Poisson Factor Analyzers

Modern neural recording techniques allow neuroscientists to observe the spiking activity of many neurons simultaneously. Although previous work has illustrated how activity within and between known populations of neurons can be summarized by low-dimensional latent vectors, in many cases what determines a unique population may be unclear. Neurons diﬀer in their anatomical location, but also, in their cell types and response properties. Moreover, multiple distinct populations may not be well described by a single low-dimensional, linear representation. To tackle these challenges, we develop a clustering method based on a mixture of dynamic Poisson factor analyzers (DPFA) model, with the number of clusters and dimension of latent states treated as unknown parameters. To analyze DPFA model, we propose a Markov chain Monte Carlo (MCMC) algorithm to eﬃciently sample its posterior distribution. Validating our proposed MCMC algorithm with simulations, we ﬁnd that it can accurately recover the true clustering, and is insensitive to the initial cluster assignments. We then apply the proposed mixture of DPFA model to multi-region experimental recordings, where we ﬁnd that the proposed method can identify novel, reliable clusters of neurons based on their activity, and may, thus, be a useful tool for neural data analysis.
Mapping Emulation for Knowledge Distillation

This paper formalizes the source-blind knowledge distillation problem that is essential to federated learning. A new geometric perspective is presented to view such a problem as aligning generated distributions between the teacher and student. With its guidance, a new architecture MEKD is proposed to emulate the inverse mapping through generative adversarial training. Unlike mimicking logits and aligning logit distributions, reconstructing the mapping from classifier-logits has a geometric intuition of decreasing empirical distances, and theoretical guarantees using the universal function approximation and optimal mass transportation theories. A new algorithm is also proposed to train the student model that reaches the teacher's performance source-blindly. On various benchmarks, MEKD outperforms existing source-blind KD methods, explainable with ablation studies and visualized results.