Miracle-dz/UniFAR-FTUs huggingface dataset:
Shoulder Injuries in Boxing. A systematic Review.

Background: High injury rates are to be expected in combat sports. Although case reports and epidemiological studies have documented shoulder injuries in boxers, numbers differ and there is currently no systematic review reporting injury prevalence. Purpose: The aim of this study was to offer an analysis of existing studies documenting shoulder injuries in boxing. Additionally, we wanted to evaluate, if chronic shoulder pathologies seen in former boxers, originate from acute injuries or result from chronic overuse syndromes.Study Design: Systematic ReviewMethods: We performed a systematic database research according to the PRISMA guidelines on PubMed, Scopus and Google Scholar for the keywords “boxing”, “injury” and “shoulder” or their respective synonyms. Any epidemiological cohort- and cross-sectional studies on boxing, that documented shoulder injuries and were published in German or English language up to January 2020, were included. Statistical analysis including individual and overall proportion with 95% Clopper-Pearson confidence intervals was performed to determine shoulder injury rates for amateur and professional cohorts separately.Results: Methodological quality was assessed using the STROBE statement and a modified Downs&Black's checklist. 13 studies were included, 10 of which met the criteria for statistical analyses. The heterogeneity in study design and cohort characteristics did not allow for detailed quantitative analysis. Overall, shoulder injuries occurred almost twice as often in amateur athletes than in professionals (overall proportion [95% CI]: amateur athletes: 9% [6%; 12%], professionals: 4% [2%; 8%]). Conclusion: No study investigating the long-term effects of boxing on shoulder pathologies was identified. Although specific information on injury type is mostly missing, the few studies addressing it report shoulder dislocations, strains, tendonitis, or chronic impingement syndromes. Unlike head trauma, shoulder injuries do not necessarily lead to cessation of fight, therefore at the ringside gross underreporting of shoulder pathologies must be taken into consideration.
Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentials: Defects and segregation

We develop a fast and accurate machine-learned interatomic potential for the Mo–Nb–Ta–V–W quinary system and use it to study segregation and radiation damage in the body-centred cubic refractory high-entropy alloy MoNbTaVW. In the bulk alloy, we observe clear ordering of mainly Mo–Ta and V–W binaries at low temperatures. In damaged crystals, our simulations reveal clear segregation of vanadium, the smallest atom in the alloy, to compressed interstitial-rich regions like radiation-induced dislocation loops. Vanadium also dominates the population of single selfinterstitial atoms. In contrast, due to its larger size and low surface energy, niobium segregates to spacious regions like the inner surfaces of voids. When annealing samples with supersaturated concentrations of defects, we find that in complete contrast to W, interstitial atoms in MoNbTaVW cluster to create only small (∼ 1 nm) experimentally invisible dislocation loops enriched by vanadium. By comparison to W, we explain this by the reduced but three-dimensional migration of interstitials, the immobility of dislocation loops, and the increased mobility of vacancies in the high-entropy alloy, which together promote defect recombination over clustering.
Learning Combinatorial Node Labeling Algorithms

We present a novel neural architecture to solve graph optimization problems where the solution consists of arbitrary node labels, allowing us to solve hard problems like graph coloring. We train our model using reinforcement learning, specifically policy gradi-ents, which gives us both a greedy and a probabilistic policy. Our architecture builds on a graph attention network and uses several inductive biases to improve solution quality. Our learned deterministic heuristics for graph coloring give better solutions than classical degree-based greedy heuristics and only take seconds to apply to graphs with tens of thousands of vertices. Moreover, our probabilistic policies outperform all greedy state-of-the-art coloring baselines and a machine learning baseline. Finally, we show that our approach also generalizes to other problems by evaluating it on minimum vertex cover and outperforming two greedy heuristics.
On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework

Lossy compression algorithms are typically designed to achieve the lowest possible distortion at a given bit rate. However, recent studies show that pursuing high perceptual quality would lead to increase of the lowest achievable distortion (e.g., MSE). This paper provides nontrivial results theoretically revealing that, \textit{1}) the cost of achieving perfect perception quality is exactly a doubling of the lowest achievable MSE distortion, \textit{2}) an optimal encoder for the"classic"rate-distortion problem is also optimal for the perceptual compression problem, \textit{3}) distortion loss is unnecessary for training a perceptual decoder. Further, we propose a novel training framework to achieve the lowest MSE distortion under perfect perception constraint at a given bit rate. This framework uses a GAN with discriminator conditioned on an MSE-optimized encoder, which is superior over the traditional framework using distortion plus adversarial loss. Experiments are provided to verify the theoretical finding and demonstrate the superiority of the proposed training framework.
Learning Curves for SGD on Structured Features

The generalization performance of a machine learning algorithm such as a neural network depends in a non-trivial way on the structure of the data distribution. To analyze the influence of data structure on test loss dynamics, we study an exactly solveable model of stochastic gradient descent (SGD) which predicts test loss when training on features with arbitrary covariance structure. We solve the theory exactly for both Gaussian features and arbitrary features and we show that the simpler Gaussian model accurately predicts test loss of nonlinear random-feature models and deep neural networks trained with SGD on real datasets such as MNIST and CIFAR-10. We show that the optimal batch size at a fixed compute budget is typically small and depends on the feature correlation structure, demonstrating the computational benefits of SGD with small batch sizes. Lastly, we extend our theory to the more usual setting of stochastic gradient descent on a fixed subsampled training set, showing that both training and test error can be accurately predicted in our framework on real data.
Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming

In this paper, we propose a novel deep unsupervised learning-based approach that jointly optimizes antenna selection and hybrid beamforming to improve the hardware and spectral efficiencies of massive multiple-input-multiple-output (MIMO) downlink systems. By employing ResNet to extract features from the channel matrices, two neural networks, i.e., the antenna selection network (ASNet) and the hybrid beamforming network (BFNet), are respectively proposed for dynamic antenna selection and hybrid beamformer design. Furthermore, a deep probabilistic subsampling trick and a specially designed quantization function are respectively developed for ASNet and BFNet to preserve the differentiability while embedding discrete constraints into the network structures. With the aid of a flexibly designed loss function, ASNet and BFNet are jointly trained in a phased unsupervised way, which avoids the prohibitive computational cost of acquiring training labels in supervised learning. Simulation results demonstrate the advantage of the proposed approach over conventional optimization-based algorithms in terms of both the achieved rate and the computational complexity.
Cost-Effective Mapping of Mobile Robot Based on the Fusion of UWB and Short-Range 2-D LiDAR

Environment mapping is an essential prerequisite for mobile robots to perform different tasks such as navigation and mission planning. With the availability of low-cost 2-D LiDARs, there are increasing applications of such 2-D LiDARs in industrial environments. However, environment mapping in an unknown and featureless environment with such low-cost 2-D LiDARs remains a challenge. The challenge mainly originates from the short range of LiDARs and complexities in performing scan matching in these environments. In order to resolve these shortcomings, we propose to fuse the ultrawideband (UWB) with 2-D LiDARs to improve the mapping quality of a mobile robot. The optimization-based approach is utilized for the fusion of UWB ranging information and odometry to first optimize the trajectory. Then, the LiDAR-based loop closures are incorporated to improve the accuracy of the trajectory estimation. Finally, the optimized trajectory is combined with the LiDAR scans to produce the occupancy map of the environment. The performance of the proposed approach is evaluated in an indoor featureless environment with a size of 20 × 20 m. Obtained results show that the mapping error of the proposed scheme is 85.5% less than that of the conventional GMapping algorithm with short-range LiDAR (for example, Hokuyo URG-04LX in our experiment with a maximum range of 5.6 m).
Intrusive deconvolutional neural networks for enhancing PIC/FLIP solutions

Traditional fluid flow predictions require large computational resources. Despite recent progress in parallel and GPU computing, the ability to run fluid flow predictions in real-time is often infeasible. Recently developed machine learning approaches, which are trained on high-fidelity data, perform unsatisfactorily outside the training set and remove the ability of utilising legacy codes after training. We propose a novel methodology that uses a deep learning approach that can be used within a low-fidelity fluid flow solver to significantly increase the accuracy of the low-fidelity simulations. The resulting solver enables accurate while reducing computational times up to 100 times. The deep neural network is trained on a combination of low- and high-fidelity data, and the resulting solver is referred to as a multi-fidelity solver. The proposed methodology is demonstrated by means of enhancing a fluid flow simulator, known as PIC/FLIP, which is a popular fluid flow simulator in the field of computer generated imagery.
Iteration of z 7 → λ + z + tan z : Topologically hyperbolic maps

Iteration of the function fλ(z) = λ+z+tan z, z ∈ C is investigated in this article. It is proved that for every λ, the Fatou set of fλ has a completely invariant Baker domain B; we call it the primary Fatou component. The rest of the results deals with fλ when it is topologically hyperbolic. For all real λ or λ such that λ = πk+ iλ2 for some integer k and 0 < λ2 < 1, the only other Fatou component is shown to be another completely invariant Baker domain. It is proved that if |2+λ2| < 1, then the Fatou set is the union of B and infinitely many invariant attracting domains. Every such domain U has exactly one invariant access to infinity and is unbounded in a special way; {=(z) : z ∈ U} is unbounded whereas {<(z) : z ∈ U} is bounded . If =(λ) > √ 2+sinh−1 1 then it is found that the primary Fatou component is the only Fatou component and the Julia set is disconnected. For every natural number k, the Fatou set of fλ for λ = kπ + i π 2 is shown to contain k wandering domains with distinct grand orbits. These wandering domains are found to be escaping. The Fatou set is the union of B, these wandering domains and their pre-images.
DINs: Deep Interactive Networks for Neurofibroma Segmentation in Neurofibromatosis Type 1 on Whole-Body MRI

Neurofibromatosis type 1 (NF1) is an autosomal dominant tumor predisposition syndrome that involves the central and peripheral nervous systems. Accurate detection and segmentation of neurofibromas are essential for assessing tumor burden and longitudinal tumor size changes. Automatic convolutional neural networks (CNNs) are sensitive and vulnerable as tumors’ variable anatomical location and heterogeneous appearance on MRI. In this study, wepropose deep interactive networks (DINs) to address the above limitations. User interactions guide the model to recognize complicated tumors and quickly adapt to heterogeneous tumors. We introduce a simple but effective Exponential Distance Transform (ExpDT) that converts user interactions into guide maps regarded as the spatial and appearance prior. Comparing with popular Euclidean and geodesic distances, ExpDT is more robust to various image sizes, which reserves the distribution of interactive inputs. Furthermore, to enhance the tumor-related features, we design a deep interactive module to propagate the guides into deeper layers. We train and evaluate DINs on three MRI data sets from NF1 patients. The experiment results yield significant improvements of 44% and 14% in DSC comparing with automated and other interactive methods, respectively. We also experimentally demonstrate the efficiency of DINs in reducing user burden when comparing with conventional interactive methods.
Construction and convergence results for stable webs

We introduce a new metric for collections of aged paths and a robust set of criteria for compactness for a set of collection of aged paths in the topology corresponding to this metric. We show that the distribution of stable webs (1 < α ≤ 2) made up of collections of stable paths is tight in this topology. We then show the weak convergence of appropriately normalized systems of coalescing random walks in the domain of attraction of stable laws for 1 < α ≤ 2 under this metric to the corresponding stable web. We obtain some path results in the brownian case.
Towards robust and domain agnostic reinforcement learning competitions

Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field. Despite this, a majority of challenges suffer from the same fundamental problems: participant solutions to the posed challenge are usually domain-specific, biased to maximally exploit compute resources, and not guaranteed to be reproducible. In this paper, we present a new framework of competition design that promotes the development of algorithms that overcome these barriers. We propose four central mechanisms for achieving this end: submission retraining, domain randomization, desemantization through domain obfuscation, and the limitation of competition compute and environment-sample budget. To demonstrate the efficacy of this design, we proposed, organized, and ran the MineRL 2020 Competition on Sample-Efficient Reinforcement Learning. In this work, we describe the organizational outcomes of the competition and show that the resulting participant submissions are reproducible, non-specific to the competition environment, and sample/resource efficient, despite the difficult competition task.
Cosmic expansion parametrization: Implication for curvature and $\text{H}_{0}$ tension

We propose an analytical parametrization of the comoving distance and Hubble parameter to study the cosmic expansion history beyond the vanilla ΛCDM model. The parametrization is generalized enough to include the contribution of spatial curvature and to capture the higher redshift behaviors. With this parameterization, we study the late time cosmic behavior and put constraints on the cosmological parameters like present values of Hubble parameter (H0), matter energy density parameter (Ωm0), spatial curvature energy density parameter (Ωk0) and baryonic matter energy density parameter (Ωb0) using different combinations data like CMB (Cosmic microwave background), BAO (baryon acoustic oscillation), and SN (Pantheon sample for type Ia supernovae). We also rigorously study the Hubble tension in the framework of late time modification from the standard ΛCDM model. We find that the late time modification of the cosmic expansion can solve the Hubble tension between CMB & SHOES (local distance ladder observation for H0), between CMB+BAO & SHOES and between CMB+SN & SHOES, but the late time modification can not solve the Hubble tension between CMB+BAO+SN and SHOES. That means CMB, BAO, and SN data combined put strong enough constraints on H0 (even with varying Ωk0) and on other background cosmological parameters so that the addition of H0 prior from SHOES (or from similar other local distance observations) can not significantly pull the H0 value towards the corresponding SHOES value.
Data-Driven Adaptive Network Slicing for Multi-Tenant Networks

Network slicing to support multi-tenancy plays a key role in improving the performance of 5G and beyond networks. In this paper, we study dynamically slicing network resources in the backhaul and Radio Access Network (RAN) prior to user demand observations across multiple tenants, where each tenant owns and operates several slices to provide different services to users. In the proposed two time-scale scheme, a subset of network slices is activated via a novel sparse optimization framework in the long time-scale with the goal of maximizing the expected utilities of tenants while in the short time-scale the activated slices are reconfigured according to the time-varying user traffic and channel states. Specifically, using the statistics from users and channels and also considering the expected utility from serving users of a slice and the reconfiguration cost, we formulate a sparse optimization problem to update the configuration of a slice resources such that the maximum isolation of reserved resources is enforced. The formulated optimization problems for long and short time-scales are non-convex and difficult to solve. We use the <inline-formula><tex-math notation="LaTeX">$\ell _q$</tex-math></inline-formula>-norm, <inline-formula><tex-math notation="LaTeX">$0< q< 1$</tex-math></inline-formula>, and group LASSO regularizations to iteratively find convex approximations of the optimization problems. We propose a Frank-Wolfe algorithm to iteratively solve approximated problems in long time-scales. To cope with the dynamical nature of traffic variations, we propose a fast, distributed algorithm to solve the approximated optimization problems in short time-scales. Simulation results demonstrate the maximized tenant utilities from slice activation via our approach relative to the optimal solution. Moreover, we compare the maximized tenant utilities by our slice reconfiguration approach against the existing state-of-the-art method based on <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> regularization.
Multi‐agent modeling of hazard–household–infrastructure nexus for equitable resilience assessment

Infrastructure service disruptions impact households in an affected community disproportionally. To enable integrating social equity considerations in infrastructure resilience assessments, this study created a new computational multi‐agent simulation model, which enables integrated assessment of hazard, infrastructure system, and household elements and their interactions. With a focus on hurricane‐induced power outages, the model consists of three elements: (1) the hazard component simulates exposure of the community to a hurricane with varying intensity levels; (2) the physical infrastructure component simulates the power network and its probabilistic failures and restoration under different hazard scenarios; and (3) the households component captures the dynamic processes related to preparation, information‐seeking, and response actions of households facing hurricane‐induced power outages. We used empirical data from household surveys from three hurricanes (Harvey, Florence, and Michael) in conjunction with theoretical decision‐making models to abstract and simulate the underlying mechanisms affecting the experienced hardship of households when facing power outages. The multi‐agent simulation model was then tested in the context of Harris County, Texas, and verified and validated using empirical results from Hurricane Harvey in 2017. Then, the model was used to examine the effects of different factors—such as forewarning durations, social network types, and restoration and resource allocation strategies—on reducing the societal impacts of service disruptions in an equitable manner. The results show that improving the restoration prioritization strategy to focus on vulnerable populations is an effective approach, especially during high‐intensity events, to enhance equitable resilience. The results show the capability of the proposed computational model for capturing the dynamic and complex interactions in the nexus of households, hazards, and infrastructure systems to better integrate human‐centric aspects in resilience planning and assessment of infrastructure systems in disasters. Hence, the proposed model and its results could provide a new tool for infrastructure managers and operators, as well as for disaster managers, in devising hazard mitigation and response strategies to reduce the societal impacts of power outages in an equitable manner.
Increase and Conquer: Training Graph Neural Networks on Growing Graphs

Graph neural networks (GNNs) use graph convolutions to exploit network in-variances and learn meaningful features from network data. However, on large-scale graphs convolutions incur in high computational cost, leading to scalability limitations. Leveraging the graphon — the limit object of a graph — in this paper we consider the problem of learning a graphon neural network (WNN) — the limit object of a GNN — by training GNNs on graphs sampled Bernoulli from the graphon. Under smoothness conditions, we show that: (i) the expected distance between the learning steps on the GNN and on the WNN decreases asymptotically with the size of the graph, and (ii) when training on a sequence of growing graphs, gradient descent follows the learning direction of the WNN. Inspired by these re-sults, we propose a novel algorithm to learn GNNs on large-scale graphs that, starting from a moderate number of nodes, successively increases the size of the graph during training. This algorithm is benchmarked on both a recommendation system and a decentralized control problem where it is shown to retain comparable performance to its large-scale counterpart at a reduced computational cost.
Set-Estimation based Networked Model Predictive Control for Energy Management of Faulty Microgrids

This paper addresses the issue of power flow control for partially faulty microgrids. In microgrid control systems, faults may occur in both electrical and communication layers. This may have severe effects on the operation of microgrids. In addition, disturbances always coexist with faults in microgrids, which may further deteriorate system performance. To address the faults and disturbances simultaneously, a model predictive control (MPC) method based on set-membership estimation (SME) that transmits information via a communication network is proposed. When electrical devices are nonfunctional or communication failures occur, the corresponding system states will become unavailable. To this end, the SME method is employed to estimate the states with the existence of unknown-but-bounded process and measurement disturbances. The networked MPC method is designed to schedule the power dispatch by using the forecasts of photovoltaic (PV) generation and load demand. With these two methods, the fault-tolerant control can be achieved. Further, a deviation compensation method is proposed to compensate for the forecast errors. The effectiveness of the proposed control strategy is demonstrated through wireless communication tests using Raspberry Pis.