Miracle-dz/UniFAR-FTUs huggingface dataset:
Journal of Quantitative Analysis in Sports Scoring and Shooting Abilities of NBA Players

We propose two new measures for evaluating offensive ability of NBA players, using onedimensional shooting data from three seasons beginning with the 2004-05 season. These measures improve upon currently employed shooting statistics by accounting for the varying shooting patterns of players over different distances from the basket. This variance also provides us with an intuitive metric for clustering players, wherein performance of players is calculated and compared to his cluster center as a baseline. To further improve the accuracy of our measures, we develop our own variation of smoothing and shrinkage, reducing any small sample biases and abnormalities. The first measure, SCAB or, Scoring Ability Above Baseline, measures a player's ability to score as a function of time on court. The second metric, SHTAB or Shooting Ability, calculates a player's propensity to score on a per-shot basis. Our results show that a combination of SCAB and SHTAB can be used to separate out players based on their offensive game. We observe that players who are highly ranked according to our measures are regularly considered as top performers on offense by experts, with the notable exception of LeBron James; the same claim holds for the offensive dregs. We suggest possible explanations for our findings and explore possibilities of future work with regard to player defense.
From Visual Place Recognition to Navigation: Learning Sample-Efficient Control Policies across Diverse Real World Environments

Visual navigation tasks in real world environments often require both self-motion and place recognition feedback. While deep reinforcement learning has shown success in solving these perception and decision-making problems in an end-to-end manner, these algorithms require large amounts of experience to learn navigation policies from high-dimensional inputs, which is generally impractical for real robots due to sample complexity. In this paper, we address these problems with two main contributions. We first leverage place recognition and deep learning techniques combined with goal destination feedback to generate compact, bimodal images representations that can then be used to effectively learn control policies at kilometer scale from a small amount of experience. Second, we present an interactive and realistic framework, called CityLearn, that enables for the first time the training of navigation algorithms across city-sized, real-world environments with extreme environmental changes. CityLearn features over 10 benchmark real-world datasets often used in place recognition research with more than 100 recorded traversals and across 60 cities around the world. We evaluate our approach in two CityLearn environments where our navigation policy is trained using a single traversal. Results show our method can be over 2 orders of magnitude faster than when using raw images and can also generalize across extreme visual changes including day to night and summer to winter transitions.
Equatorial light bending around Kerr-Newman black holes

We study the deflection angle of a light ray as it traverses on the equatorial plane of a charged spinning black hole. We provide detailed analysis of the light ray's trajectory, and derive the closed-form expression of the deflection angle due to the black hole in terms of elliptic integrals. In particular, the geodesic equation of the light ray along the radial direction can be used to define an appropriate ``effective potential". The nonzero charge of the black hole shows stronger repulsive effects to prevent light rays from falling into the black hole as compared with the Kerr case. As a result, the radius of the innermost circular motion of light rays with the critical impact parameter decreases as charge $Q$ of the black hole increases for both direct and retrograde motions. Additionally, the deflection angle decreases when $Q$ increases with the fixed impact parameter. These results will have a direct consequence on constructing the apparent shape of a rotating charged black hole.
Computationally Efficient Tree Variants of Gromov-Wasserstein

We propose two novel variants of Gromov-Wasserstein (GW) between probability measures in different probability spaces based on projecting these measures into the tree metric spaces. Our first proposed discrepancy, named \emph{flow-based tree Gromov-Wasserstein}, hinges upon the tree metric from node to root in each tree to define the structure representation of probability measures on trees. The flow-based tree GW shares similar structures with univariate Wasserstein distance while keeping sufficient spatial information of the original projected probability measures. In order to further explore the structure of tree, we proposed another version of flow-based tree GW, which we refer to as \emph{depth-based tree Gromov-Wasserstein}. That discrepancy considers the alignment of probability measures hierarchically along each depth level of the tree structures. Finally, we demonstrate via extensive simulation studies on large-scale real data sets the relative advantage of the proposed discrepancies.
FGAC-NDN: Fine-Grained Access Control for Named Data Networks

Named data network (NDN) is one of the most promising information-centric networking architectures, where the core concept is to focus on the named data (or contents) themselves. Users in NDN can easily send a request packet to get the desired content regardless of its address. The routers in NDN have cache functionality to make the users instantly retrieve the desired file. Thus, the user can immediately get the desired file from the nearby nodes instead of the remote host. Nevertheless, NDN is a novel proposal and there are still some open issues to be resolved. In view of previous research, it is a challenge to achieve access control on a specific user and support potential receivers simultaneously. In order to solve it, we present a fine-grained access control mechanism tailored for NDN, supporting data confidentiality, potential receivers, and mobility. Compared to previous works, this is the first to support fine-grained access control and potential receivers. Furthermore, the proposed scheme achieves provable security under the DBDH assumption.
Learning Sparse Spatial Codes for Cognitive Mapping Inspired by Entorhinal-Hippocampal Neurocircuit

The entorhinal-hippocampal circuit plays a critical role in higher brain functions, especially spatial cognition. Grid cells in the medial entorhinal cortex (MEC) periodically fire with different grid spacing and orientation, which makes a contribution that place cells in the hippocampus can uniquely encode locations in an environment. But how sparse firing granule cells in the dentate gyrus are formed from grid cells in the MEC remains to be determined. Recently, the fruit fly olfactory circuit provides a variant algorithm (called locality-sensitive hashing) to solve this problem. To investigate how the sparse place firing generates in the dentate gyrus can help animals to break the perception ambiguity during environment exploration, we build a biologically relevant, computational model from grid cells to place cells. The weight from grid cells to dentate gyrus granule cells is learned by competitive Hebbian learning. We resorted to the robot system for demonstrating our cognitive mapping model on the KITTI odometry benchmark dataset. The experimental results show that our model is able to stably, robustly build a coherent semi-metric topological map in the large-scale outdoor environment. The experimental results suggest that the entorhinal-hippocampal circuit as a variant locality-sensitive hashing algorithm is capable of generating sparse encoding for easily distinguishing different locations in the environment. Our experiments also provide theoretical supports that this analogous hashing algorithm may be a general principle of computation in different brain regions and species.
On Scalable Variant of Wasserstein Barycenter

We study a variant of Wasserstein barycenter problem, which we refer to as \emph{tree-sliced Wasserstein barycenter}, by leveraging the structure of tree metrics for the ground metrics in the formulation of Wasserstein distance. Drawing on the tree structure, we propose efficient algorithms for solving the unconstrained and constrained versions of tree-sliced Wasserstein barycenter. The algorithms have fast computational time and efficient memory usage, especially for high dimensional settings while demonstrating favorable results when the tree metrics are appropriately constructed. Experimental results on large-scale synthetic and real datasets from Wasserstein barycenter for documents with word embedding, multilevel clustering, and scalable Bayes problems show the advantages of tree-sliced Wasserstein barycenter over (Sinkhorn) Wasserstein barycenter.
Manifold learning from a teacher's demonstrations

We consider the problem of manifold learning. Extending existing approaches of learning from randomly sampled data points, we consider contexts where data may be chosen by a teacher. We analyze learning from teachers who can provide structured data such as points, comparisons (pairs of points), demonstrations (sequences). We prove results showing that the former two do not yield notable decreases in the amount of data required to infer a manifold. Teaching by demonstration can yield remarkable decreases in the amount of data required, if we allow the goal to be teaching up to topology. We further analyze teaching learners in the context of persistence homology. Teaching topology can greatly reduce the number of datapoints required to infer correct geometry, and allows learning from teachers who themselves do not have full knowledge of the true manifold. We conclude with implications for learning in humans and machines.
Misbehaviour Prediction for Autonomous Driving Systems

Deep Neural Networks (DNNs) are the core component of modern autonomous driving systems. To date, it is still unrealistic that a DNN will generalize correctly to all driving conditions. Current testing techniques consist of offline solutions that identify adversarial or corner cases for improving the training phase. In this paper, we address the problem of estimating the confidence of DNNs in response to unexpected execution contexts with the purpose of predicting potential safety-critical misbehaviours and enabling online healing of DNN-based vehicles. Our approach SelfOracle is based on a novel concept of self-assessment oracle, which monitors the DNN confidence at runtime, to predict unsupported driving scenarios in advance. SelfOracle uses autoencoder- and time series-based anomaly detection to reconstruct the driving scenarios seen by the car, and to determine the confidence boundary between normal and unsupported conditions. In our empirical assessment, we evaluated the effectiveness of different variants of SelfOracle at predicting injected anomalous driving contexts, using DNN models and simulation environment from Udacity. Results show that, overall, SelfOracle can predict 77% misbehaviours, up to six seconds in advance, outperforming the online input validation approach of DeepRoad.
Graph Sampling with Distributed In-Memory Dataflow Systems

Given a large graph, a graph sample determines a subgraph with similar characteristics for certain metrics of the original graph. The samples are much smaller thereby accelerating and simplifying the analysis and visualization of large graphs. We focus on the implementation of distributed graph sampling for Big Data frameworks and in-memory dataflow systems such as Apache Spark or Apache Flink. We evaluate the scalability of the new implementations and analyze to what degree the sampling approaches preserve certain graph metrics compared to the original graph. The latter analysis also uses comparative graph visualizations. The presented methods will be open source and be integrated into Gradoop, a system for distributed graph analytics.
Learning Neutrino Effects in Cosmology with Convolutional Neural Network

Measuring the sum of the three active neutrino masses, M ν , is one of the most important challenges in modern cosmology. Massive neutrinos imprint characteristic signatures on several cosmological observables, in particular, on the large-scale structure of the universe. In order to maximize the information that can be retrieved from galaxy surveys, accurate theoretical predictions in the nonlinear regime are needed. Currently, one way to achieve those predictions is by running cosmological numerical simulations. Unfortunately, producing those simulations requires high computational resources—several hundred to thousand core hours for each neutrino mass case. In this work, we propose a new method, based on a deep-learning network (D3M), to quickly generate simulations with massive neutrinos from standard ΛCDM simulations without neutrinos. We computed multiple relevant statistical measures of deep-learning generated simulations and conclude that our approach is an accurate alternative to the traditional N-body techniques. In particular the power spectrum is within ≃6% down to nonlinear scales k = 0.7 h Mpc−1. Finally, our method allows us to generate massive neutrino simulations 10,000 times faster than the traditional methods.
Subspace Estimation from Unbalanced and Incomplete Data Matrices: $\ell_{2,\infty}$ Statistical Guarantees.

This paper is concerned with estimating the column space of an unknown low-rank matrix $\boldsymbol{A}^{\star}\in\mathbb{R}^{d_{1}\times d_{2}}$, given noisy and partial observations of its entries. There is no shortage of scenarios where the observations --- while being too noisy to support faithful recovery of the entire matrix --- still convey sufficient information to enable reliable estimation of the column space of interest. This is particularly evident and crucial for the highly unbalanced case where the column dimension $d_{2}$ far exceeds the row dimension $d_{1}$, which is the focal point of the current paper. We investigate an efficient spectral method, which operates upon the sample Gram matrix with diagonal deletion. We establish statistical guarantees for this method in terms of both $\ell_{2}$ and $\ell_{2,\infty}$ estimation accuracy, which improve upon prior results if $d_{2}$ is substantially larger than $d_{1}$. To illustrate the effectiveness of our findings, we develop consequences of our general theory for three applications of practical importance: (1) tensor completion from noisy data, (2) covariance estimation with missing data, and (3) community recovery in bipartite graphs. Our theory leads to improved performance guarantees for all three cases.
Probability distribution of astrophysical gravitational-wave background fluctuations

The coalescence of compact binary stars is expected to produce a stochastic background of gravitational waves (GWs) observable with future GW detectors. Such backgrounds are usually characterized by their power spectrum as a function of the frequency. Here, we present a method to calculate the full one-point distribution of strain fluctuations. We focus on time series data, but our approach generalizes to the frequency domain. We illustrate how this probability distribution can be evaluated numerically. In addition, we derive accurate analytical asymptotic expressions for the large strain tail, which demonstrate that it is dominated by the nearest source. As an application, we calculate the distribution of strain fluctuations for the astrophysical GW background produced by binary mergers of compact stars in the Universe, and the distribution of the observed confusion background obtained upon subtracting bright, resolved sources from the signal. We quantify the extent to which they deviate from a Gaussian distribution. Our approach could be useful for the spectral shape reconstruction of stochastic GW backgrounds.
Trajectory Optimization for Coordinated Human-Robot Collaboration

Effective human-robot collaboration requires informed anticipation. The robot must simultaneously anticipate what the human will do and react both instantaneously and fluidly when its predictions are wrong. Even more, the robot must plan its own actions in a way that accounts for the human predictions but also with the knowledge that the human's own behavior will change based on what the robot does. This back-and-forth game of prediction and planning is extremely difficult to model well using standard techniques. In this work, we exploit the duality between behavior prediction and control explored in the Inverse Optimal Control (IOC) literature to design a novel Model Predictive Control (MPC) algorithm that simultaneously plans the robot's behavior and predicts the human's behavior in a joint optimal control model. In the process, we develop a novel technique for bridging finite-horizon motion optimizers to the problem of spatially consistent continuous optimization using explicit sparse reward terms, i.e., negative cost. We demonstrate the framework on a collection of cooperative human-robot handover experiments in both simulation and with a real-world handover scenario.
Robust Dynamic Assortment Optimization in the Presence of Outlier Customers

Robust Dynamic Assortment Optimization Assortment optimization with choice model estimation and learning has been studied extensively in the data-driven revenue management literature. Existing methods and analysis, however, do not take into consideration the fact that some customers arriving at certain time periods might exhibit outlier purchasing behaviors. The work of Chen et al. studies dynamic assortment optimization in the presence of outlier customers modeled by an [Formula: see text]-contamnination model. The impact of outlier customers on the revenue performance of an algorithm is analyzed and discussed.
Learning from Multiple Corrupted Sources, with Application to Learning from Label Proportions

We study binary classification in the setting where the learner is presented with multiple corrupted training samples, with possibly different sample sizes and degrees of corruption, and introduce an approach based on minimizing a weighted combination of corruption-corrected empirical risks. We establish a generalization error bound, and further show that the bound is optimized when the weights are certain interpretable and intuitive functions of the sample sizes and degrees of corruptions. We then apply this setting to the problem of learning with label proportions (LLP), and propose an algorithm that enjoys the most general statistical performance guarantees known for LLP. Experiments demonstrate the utility of our theory.
Rate Optimal Variational Bayesian Inference for Sparse DNN

Sparse deep neural network (DNN) has drawn much attention in recent studies because it possesses a great approximation power and is easier to implement and store in practice comparing to fully connected DNN. In this work, we consider variational Bayesian inference, a computationally efficient alternative to Markov chain Monte Carlo method, on the sparse DNN modeling under spike-and-slab prior. Our theoretical investigation shows that, for any $\alpha$-Holder smooth function, the variational posterior distribution shares the (near-)optimal contraction property, and the variation inference leads to (near-)optimal generalization error, as long as the network architecture is properly tuned according to smoothness parameter $\alpha$. Furthermore, an adaptive variational inference procedure is developed to automatically select optimal network structure even when $\alpha$ is unknown. Our result also applies to the case that the truth is instead a ReLU neural network, and certain contraction bound is obtained.
Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis

Background: Periodontitis is a chronic inflammatory disease leading to tooth loss in severe cases, and early diagnosis is essential for periodontitis prevention. This study aimed to construct a diagnostic model for periodontitis using a random forest algorithm and an artificial neural network (ANN). Methods: Gene expression data of two large cohorts of patients with periodontitis, GSE10334 and GSE16134, were downloaded from the Gene Expression Omnibus database. We screened for differentially expressed genes in the GSE10334 cohort, identified key periodontitis biomarkers using a Random Forest algorithm, and constructed a classification artificial neural network model, using receiver operating characteristic curves to evaluate its diagnostic utility. Furthermore, patients with periodontitis were classified using a consensus clustering algorithm. The immune infiltration landscape was assessed using CIBERSOFT and single-sample Gene Set Enrichment Analysis. Results: A total of 153 differentially expressed genes were identified, of which 42 were downregulated. We utilized 13 key biomarkers to establish a periodontitis diagnostic model. The model had good predictive performance, with an area under the receiver operative characteristic curve (AUC) of 0.945. The independent cohort (GSE16134) was used to further validate the model’s accuracy, showing an area under the receiver operative characteristic curve of 0.900. The proportion of plasma cells was highest in samples from patients with period ontitis, and 13 biomarkers were closely related to immunity. Two molecular subgroups were defined in periodontitis, with one cluster suggesting elevated levels of immune infiltration and immune function. Conclusion: We successfully identified key biomarkers of periodontitis using machine learning and developed a satisfactory diagnostic model. Our model may provide a valuable reference for the prevention and early detection of periodontitis.
Probing bulk viscosity in relativistic flows

We derive an analytical connection between kinetic relaxation rate and bulk viscosity of a relativistic fluid in d spatial dimensions, all the way from the ultra-relativistic down to the near non-relativistic regime. Our derivation is based on both Chapman–Enskog asymptotic expansion and Grad’s method of moments. We validate our theoretical results against a benchmark flow, providing further evidence of the correctness of the Chapman–Enskog approach; we define the range of validity of this approach and provide evidence of mounting departures at increasing Knudsen number. Finally, we present numerical simulations of transport processes in quark-gluon plasmas, with special focus on the effects of bulk viscosity which might prove amenable to future experimental verification. This article is part of the theme issue ‘Fluid dynamics, soft matter and complex systems: recent results and new methods’.
Detection of Phosphorus-bearing Molecules toward a Solar-type Protostar

Phosphorus is a key ingredient in terrestrial biochemistry, but is rarely observed in the molecular interstellar medium and therefore little is known about how it is inherited during the star and planet formation sequence. We present observations of the phosphorus-bearing molecules PO and PN toward the Class I low-mass protostar B1-a using the IRAM 30 m telescope, representing the second detection of phosphorus carriers in a solar-type star-forming region. The P/H abundance contained in PO and PN is ∼10−10−10−9 depending on the assumed source size, accounting for just 0.05%–0.5% of the solar phosphorus abundance and implying significant sequestration of phosphorus in refractory material. Based on a comparison of the PO and PN line profiles with the shock tracers SiO, SO2, and CH3OH, the phosphorus molecule emission seems to originate from shocked gas and is likely associated with a protostellar outflow. We find a PO/PN column density ratio of ∼1–3, which is consistent with the values measured in the shocked outflow of the low-mass protostar L1157, the massive star-forming regions W51 and W3(OH), and the galactic center GMC G+0.693-0.03. This narrow range of PO/PN ratios across sources with a range of environmental conditions is surprising, and likely encodes information on how phosphorus carriers are stored in grain mantles.