Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Football Body-Orientation as a Matter of Classification

Orientation is a crucial skill for football players that becomes a differential factor in a large set of events, especially the ones involving passes. However, existing orientation estimation methods, which are based on computer-vision techniques, still have a lot of room for improvement. To the best of our knowledge, this article presents the first deep learning model for estimating orientation directly from video footage. By approaching this challenge as a classification problem where classes correspond to orientation bins, and by introducing a cyclic loss function, a well-known convolutional network is refined to provide player orientation data. The model is trained by using ground-truth orientation data obtained from wearable EPTS devices, which are individually compensated with respect to the perceived orientation in the current frame. The obtained results outperform previous methods; in particular, the absolute median error is less than 12 degrees per player. An ablation study is included in order to show the potential generalization to any kind of football video footage.
Differentially Private Densest Subgraph

Given a graph, the densest subgraph problem asks for a set of vertices such that the average degree among these vertices is max-imized. Densest subgraph has numerous applications in learning, e.g., community detection in social networks, link spam detection, correlation mining, bioinformatics, and so on. Although there are eﬃcient algorithms that output either exact or approximate so-lutions to the densest subgraph problem, ex-isting algorithms may violate the privacy of the individuals in the network, e.g., leaking the existence/non-existence of edges. In this paper, we study the densest subgraph problem in the framework of diﬀerential privacy, and we derive upper and lower bounds for this problem. We show that there exists a linear-time (cid:15) -diﬀerentially private algorithm that ﬁnds a 2-approximation of the densest subgraph with an extra poly-logarithmic additive error. Our algorithm not only reports the approximate density of the densest subgraph, but also reports the vertices that form the dense subgraph. Our upper bound almost matches the famous 2-approximation by Charikar both in performance and in approximation ratio, but we additionally achieve diﬀerential privacy. In comparison with Charikar’s algorithm, our algorithm has an extra poly-logarithmic additive error. We partly justify the additive error with a new lower bound, showing that for any diﬀerentially private algorithm that provides a constant-factor approximation, a sub-logarithmic additive error is inherent. We also practically study our diﬀerentially private algorithm on real-world graphs, and we show that in practice the algorithm ﬁnds a solution which is very close to the optimal.
AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning

Human beings, even small children, quickly become adept at figuring out how to use applications on their mobile devices. Learning to use a new app is often achieved via trial-and-error, accelerated by transfer of knowledge from past experiences with like apps. The prospect of building a smarter smartphone - one that can learn how to achieve tasks using mobile apps - is tantalizing. In this paper we explore the use of Reinforcement Learning (RL) with the goal of advancing this aspiration. We introduce an RL-based framework for learning to accomplish tasks in mobile apps. RL agents are provided with states derived from the underlying representation of on-screen elements, and rewards that are based on progress made in the task. Agents can interact with screen elements by tapping or typing. Our experimental results, over a number of mobile apps, show that RL agents can learn to accomplish multi-step tasks, as well as achieve modest generalization across different apps. More generally, we develop a platform which addresses several engineering challenges to enable an effective RL training environment. Our AppBuddy platform is compatible with OpenAI Gym and includes a suite of mobile apps and benchmark tasks that supports a diversity of RL research in the mobile app setting.
VA-GCN: A Vector Attention Graph Convolution Network for learning on Point Clouds

Owing to the development of research on local aggregation operators, dramatic breakthrough has been made in point cloud analysis models. However, existing local aggregation operators in the current literature fail to attach decent importance to the local information of the point cloud, which limits the power of the models. To fit this gap, we propose an efficient Vector Attention Convolution module (VAConv), which utilizes K-Nearest Neighbor (KNN) to extract the neighbor points of each input point, and then uses the elevation and azimuth relationship of the vectors between the center point and its neighbors to construct an attention weight matrix for edge features. Afterwards, the VAConv adopts a dual-channel structure to fuse weighted edge features and global features. To verify the efficiency of the VAConv, we connect the VAConvs with different receptive fields in parallel to obtain a Multi-scale graph convolutional network, VA-GCN. The proposed VA-GCN achieves state-of-the-art performance on standard benchmarks including ModelNet40, S3DIS and ShapeNet. Remarkably, on the ModelNet40 dataset for 3D classification, VA-GCN increased by 2.4% compared to the baseline.
A Review of Matrix SIR Arino Epidemic Models

Many of the models used nowadays in mathematical epidemiology, in particular in COVID-19 research, belong to a certain subclass of compartmental models whose classes may be divided into three “(x,y,z)” groups, which we will call respectively “susceptible/entrance, diseased, and output” (in the classic SIR case, there is only one class of each type). Roughly, the ODE dynamics of these models contains only linear terms, with the exception of products between x and y terms. It has long been noticed that the reproduction number R has a very simple Formula in terms of the matrices which define the model, and an explicit first integral Formula is also available. These results can be traced back at least to Arino, Brauer, van den Driessche, Watmough, and Wu (2007) and to Feng (2007), respectively, and may be viewed as the “basic laws of SIR-type epidemics”. However, many papers continue to reprove them in particular instances. This motivated us to redraw attention to these basic laws and provide a self-contained reference of related formulas for (x,y,z) models. For the case of one susceptible class, we propose to use the name SIR-PH, due to a simple probabilistic interpretation as SIR models where the exponential infection time has been replaced by a PH-type distribution. Note that to each SIR-PH model, one may associate a scalar quantity Y(t) which satisfies “classic SIR relations”,which may be useful to obtain approximate control policies.
ClustML: A measure of cluster pattern complexity in scatterplots learnt from human-labeled groupings

Visual quality measures (VQMs) are designed to support analysts by automatically detecting and quantifying patterns in visualizations. We propose a new VQM for visual grouping patterns in scatterplots, called ClustML, which is trained on previously collected human subject judgments. Our model encodes scatterplots in the parametric space of a Gaussian Mixture Model and uses a classifier trained on human judgment data to estimate the perceptual complexity of grouping patterns. The numbers of initial mixture components and final combined groups quantify visual cluster patterns in scatterplots. It improves on existing VQMs, first, by better estimating human judgments on two-Gaussian cluster patterns and, second, by giving higher accuracy when ranking general cluster patterns in scatterplots. We use it to analyze kinship data for genome-wide association studies, in which experts rely on the visual analysis of large sets of scatterplots. We make the benchmark datasets and the new VQM available for practical use and further improvements.
Instance-optimal Mean Estimation Under Differential Privacy

Mean estimation under differential privacy is a fundamental problem, but worst-case optimal mechanisms do not offer meaningful utility guarantees in practice when the global sensitivity is very large. Instead, various heuristics have been proposed to reduce the error on real-world data that do not resemble the worst-case instance. This paper takes a principled approach, yielding a mechanism that is instance-optimal in a strong sense. In addition to its theoretical optimality, the mechanism is also simple and practical, and adapts to a variety of data characteristics without the need of parameter tuning. It easily extends to the local and shuffle model as well.
PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World

We propose PIGLeT: a model that learns physical commonsense knowledge through interaction, and then uses this knowledge to ground language. We factorize PIGLeT into a physical dynamics model, and a separate language model. Our dynamics model learns not just what objects are but also what they do: glass cups break when thrown, plastic ones don’t. We then use it as the interface to our language model, giving us a unified model of linguistic form and grounded meaning. PIGLeT can read a sentence, simulate neurally what might happen next, and then communicate that result through a literal symbolic representation, or natural language. Experimental results show that our model effectively learns world dynamics, along with how to communicate them. It is able to correctly forecast what happens next given an English sentence over 80% of the time, outperforming a 100x larger, text-to-text approach by over 10%. Likewise, its natural language summaries of physical interactions are also judged by humans as more accurate than LM alternatives. We present comprehensive analysis showing room for future work.
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models

While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only instantiate limited types of perturbations such as paraphrases or word substitutions. We present Polyjuice, a general-purpose counterfactual generator that allows for control over perturbation types and locations, trained by finetuning GPT-2 on multiple datasets of paired sentences. We show that Polyjuice produces diverse sets of realistic counterfactuals, which in turn are useful in various distinct applications: improving training and evaluation on three different tasks (with around 70% less annotation effort than manual generation), augmenting state-of-the-art explanation techniques, and supporting systematic counterfactual error analysis by revealing behaviors easily missed by human experts.
Exploiting Group-Level Behavior Pattern for Session-Based Recommendation

Session-based recommendation (SBR) is a challenging task, which aims to predict users’ future interests based on anonymous behavior sequences. Existing methods leverage powerful representation learning approaches to encode sessions into a low-dimensional space. However, despite such achievements, the existing studies focus on the instance-level session learning, while neglecting the group-level users’ preferences (e.g., the common preferences of group users in repeat consumption). To this end, we propose a novel Repeat-aware Neural Mechanism for Session-based Recommendation (RNMSR). In RNMSR, we propose to learn the user preference from two levels: (i) instance-level, which employs GNNs on a similarity-based item-pairwise session graph to capture the users’ preference in instance-level. (ii) group-level, which converts sessions into group-level behavior patterns to model the group-level users’ preferences. In RNMSR, we combine instance-level and group-level user preference to model the repeat consumption of users, i.e., whether users take repeated consumption and which items are preferred by users. Extensive experiments are conducted on three real-world datasets, i.e., Diginetica, Yoochoose, and Nowplaying, demonstrating that the proposed method consistently achieves state-of-the-art performance in all the tests.
Towards Light-Weight and Real-Time Line Segment Detection

Previous deep learning-based line segment detection (LSD) suffers from the immense model size and high computational cost for line prediction. This constrains them from real-time inference on computationally restricted environments. In this paper, we propose a real-time and light-weight line segment detector for resource-constrained environments named Mobile LSD (M-LSD). We design an extremely efficient LSD architecture by minimizing the backbone network and removing the typical multi-module process for line prediction found in previous methods. To maintain competitive performance with a light-weight network, we present novel training schemes: Segments of Line segment (SoL) augmentation, matching and geometric loss. SoL augmentation splits a line segment into multiple subparts, which are used to provide auxiliary line data during the training process. Moreover, the matching and geometric loss allow a model to capture additional geometric cues. Compared with TP-LSD-Lite, previously the best real-time LSD method, our model (M-LSD-tiny) achieves competitive performance with 2.5% of model size and an increase of 130.5% in inference speed on GPU. Furthermore, our model runs at 56.8 FPS and 48.6 FPS on the latest Android and iPhone mobile devices, respectively. To the best of our knowledge, this is the first real-time deep LSD available on mobile devices.
Preparation of chitosan microspheres for adsorptive removal of xylenol orange

Using biodegradable chitosan as the raw material,hollow chitosan microspheres were prepared by the ultrasonic emulsifying-chemical cross linking method.Mono dispersive chitosan microspheres were obtained under controlled reaction conditions.Series of adsorption equilibrium capacity experiments were conducted to determine the effects of the initial solution pH,adsorption(contact)time,initial xylenol orange concentration,adsorbent dose and size on the adsorptive removal of xylenol orange,which was measured by the spectrophotometric method.A high removal was achieved using 0.3 g/L of the chitosan microspheres of 74 μm in average diameter for 2.0 h of contact at a pH of 5 and the room temperature.The chitosan microsphere adsorption runs performed on four synthetic wastewaters of different xylenol orange concentrations produced highly reproducible results.
The Annals of Applied Probability , To Appear DEFAULT CLUSTERING IN LARGE PORTFOLIOS : TYPICAL EVENTS By

We develop a dynamic point process model of correlated default timing in a portfolio of firms, and analyze typical default profiles in the limit as the size of the pool grows. In our model, a firm defaults at a stochastic intensity that is influenced by an idiosyncratic risk process, a systematic risk process common to all firms, and past defaults. We prove a law of large numbers for the default rate in the pool, which describes the “typical” behavior of defaults.
Endoscopic ultrasound-guided gastroenterostomy using large-diameter (20 mm) lumen apposing metal stent (LLAMS)

Abstract Background and study aims Endoscopic ultrasound-guided gastroenterostomy (EUS-GE) using a 15-mm lumen apposing metal stent (LAMS) has emerged as a viable alternative to surgical gastrojejunostomy for management of gastric outlet obstruction (GOO). However, given the size of the anastomosis created with a 15-mm LAMS, long-term luminal patency and clinical outcomes may be suboptimal. The aim of this study was to evaluate the technical feasibility, efficacy, and safety of EUS-GE with a large-diameter (20 mm) LAMS (LLAMS). Patients and methods A retrospective analysis of a prospectively maintained database of all patients undergoing EUS-GE with LLAMS between December 1, 2018 and September 30, 2020 was performed. All EUS-GEs were performed using a cautery-enhanced LLAMS. Results Thirty-three patients were referred for endoscopic management of GOO. Two patients were excluded due to a lack of an adequate window for EUS-GE. The remaining 31 patients (93.94 %) (mean age: 61.35 ± 16.52 years; 54.84 % males) underwent EUS-GE using LLAMS for malignant (n = 23) and benign (n = 8) GOO. Technical success was achieved in all patients (100 %) with attempted EUS-GE. Complete clinical success (tolerance of regular diet) was achieved in 93.55 % of patients (n = 29). Two patients (6.45 %) had partial clinical success and died of unrelated causes prior to advancing diet beyond full liquids. Overall mean follow-up was 140.84 ± 160.41 days (median 70, range 4–590). All stents remained patent with no evidence of recurrent GOO symptoms. One patient (3.23 %) developed an asymptomatic clean-based jejunal ulcer on 3-month follow-up endoscopy. Conclusions EUS-GE with LLAMS is a technically feasible, effective and safe option for patients with GOO allowing for tolerability of regular diet. Future prospective, ideally randomized studies comparing long-term outcomes of EUS-GE with 20- and 15-mm LAMS are required.
Colon capsule endoscopy is a viable alternative to colonoscopy for the investigation of intermediate- and low-risk patients with gastrointestinal symptoms: results of a pilot study

Abstract Background and study aims Colon capsule endoscopy (CCE) is a recommended viable alternative to colonoscopy for colonic visualisation in a variety of clinical settings with proven efficacy in polyp detection, surveillance, screening and Inflammatory Bowel Disease (IBD) assessment. CCE efficacy in an unselected average risk symptomatic cohort has yet to be established. The aim of this study was to determine the feasibility of CCE imaging assessment in average risk symptomatic patients as an alternative to colonoscopy with and without additional biomarker assessment. Patients and methods This was a prospective, single-center comparison study of colonoscopy, CCE and biomarker assessment. Results Of 77 invited subjects, 66 underwent both a CCE and colonoscopy. A fecal immunochemical test (FIT) and fecal calprotectin (FC) were available in 56 and 59 subjects. In all 64 % (n = 42) had any positive finding with 16 (24 %) found to have significant disease (high-risk adenomas, IBD) on colonoscopy. The CCE completion rate was 76 %, five (8 %) had an inadequate preparation, the CCE polyp detection rate was high at 35 %. The sensitivity, specificity, positive and negative predictive values of CCE for significant disease were 81 %, 98 %, 93 % and 94 % respectively. In addition, three (5 %) significant small bowel diagnoses were made on CCE. FC and FIT were frequently elevated in patients with both colitis (5/7, 71 %) and high-risk adenomas (4/7 57 %). While both had a low positive predictive value for clinically significant disease, FIT 32 % and FC 26 %. Conclusions CCE is a safe and effective alternative to colonoscopy in symptomatic average risk patients with or without the addition of biomarker screening.
Patients follow three distinct outcome trajectories following total knee arthroplasty.

AIMS Although total knee arthroplasty (TKA) is a highly successful procedure, about 20% of patients remain dissatisfied postoperatively. This proportion is derived from dichotomous models of the assessment of surgical success or failure, which may not reflect the spectrum of outcomes. The aim of this study was to explore differing responses to surgery, and assess whether there are distinct groups of patients with differing patterns of outcome. METHODS This was a secondary analysis of a UK multicentre TKA longitudinal cohort study. We used a group-based trajectory modelling analysis of Oxford Knee Score (OKS) in the first year following surgery with longitudinal data involving five different timepoints and multiple predictor variables. Associations between the derived trajectory groups and categorical baseline variables were assessed, and predictors of trajectory group membership were identified using Poisson regression and multinomial logistic regression, as appropriate. The final model was adjusted for sociodemographic factors (age, sex) and baseline OKS. RESULTS Data from 731 patients were available for analysis. Three distinct trajectories of outcome were identified: "poor" 14.0%, "modest" 39.1%, and "good" 46.9%. The predicted probability of membership for patients assigned to each trajectory group was high (0.89 to 0.93). Preoperative mental, physical health, and psychosocial factors determined which trajectory is likely to be followed. Poor responders were characterized by a comparatively small number of factors, preoperative expectations of pain and limitations, coping strategies, and a lower baseline physical health status, while the good responders were characterized by a combination of clinical, psychosocial, mental health, and quality of life factors. CONCLUSION We identified three distinct response trajectories in patients undergoing TKA. Controlling for baseline score, age, and sex, psychosocial factors such as expectations of pain and limited function and poor coping strategies differentiated the trajectory groups, suggesting a role for preoperative psychosocial support in optimizing the clinical outcome. Cite this article: Bone Joint J 2021;103-B(6):1096-1102.
Optical scatter imaging of resected breast tumor structures matches the patterns of micro-computed tomography

In patients undergoing breast-conserving surgery (BCS), the rate of re-excision procedures to remove residual tumor left behind after initial resection can be high. Projection radiography, and recently, volumetric x-ray imaging are used to assess margin adequacy, but x-ray imaging lacks contrast between healthy, abnormal benign, and malignant fibrous tissues important for surgical decision making. The purpose of this study was to compare micro-CT and optical scatter imagery of surgical breast specimens and to demonstrate enhanced contrast-to intra-tumoral morphologies and tumor boundary features revealed by optical scatter imaging. A total of 57 breast tumor slices from 57 patients were imaged ex vivo by spatially co-registered micro-CT and optical scatter scanning. Optical scatter exhibited greater similarity with micro-CT in 89% (51/57) of specimens versus diffuse white light (DWL) luminance using mutual information (mean ± standard deviation of 0.48 ± 0.21 versus 0.24 ± 0.12; p < 0.001) and in 81% (46/57) of specimens using the Sørensen–Dice coefficient (0.48 ± 0.21 versus 0.33 ± 0.18; p < 0.001). The coefficient of variation (CV) quantified the feature content in each image. Optical scatter exhibited the highest CV in every specimen (optical scatter: 0.70 ± 0.17; diffuse luminance: 0.24 ± 01; micro-CT: 0.15 ± 0.03 for micro-CT; p < 0.001). Optical scatter also exhibited the highest contrast ratios across representative tumor boundaries with adjacent healthy/benign fibrous tissues (1.5–3.7 for optical scatter; 1.0–1.1 for diffuse luminance; 1.0–1.1 for micro-CT). The two main findings from this study were: first, optical scatter contrast was in general similar to the radiological view of the tissue relative to DWL imaging; and second, optical scatter revealed additional features associated with fibrous tissue structures of similar radiodensity that may be relevant to diagnosis. The value of micro-CT lies in its rapid three-dimensional scanning of specimen morphology, and combined with optical scatter imaging with sensitivity to fibrous surface tissues, may be an attractive solution for margin assessment during BCS.