Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepSportLab: a Unified Framework for Ball Detection, Player Instance Segmentation and Pose Estimation in Team Sports Scenes

This paper presents a unified framework to (i) locate the ball, (ii) predict the pose, and (iii) segment the instance mask of players in team sports scenes. Those problems are of high interest in automated sports analytics, production, and broadcast. A common practice is to individually solve each problem by exploiting universal state-of-the-art models, \eg, Panoptic-DeepLab for player segmentation. In addition to the increased complexity resulting from the multiplication of single-task models, the use of the off-the-shelf models also impedes the performance due to the complexity and specificity of the team sports scenes, such as strong occlusion and motion blur. To circumvent those limitations, our paper proposes to train a single model that simultaneously predicts the ball and the player mask and pose by combining the part intensity fields and the spatial embeddings principles. Part intensity fields provide the ball and player location, as well as player joints location. Spatial embeddings are then exploited to associate player instance pixels to their respective player center, but also to group player joints into skeletons. We demonstrate the effectiveness of the proposed model on the DeepSport basketball dataset, achieving comparable performance to the SoA models addressing each individual task separately.
Minimal order H(div)-conforming velocity-vorticity approximations for incompressible fluids

We introduce a novel minimal order hybrid Discontinuous Galerkin (HDG) and a novel mass conserving mixed stress (MCS) method for the approximation of incompressible flows. For this we employ theH(div)-conforming linear Brezzi-Douglas-Marini space and the lowest order Raviart-Thomas space for the approximation of the velocity and the vorticity, respectively. Our methods are based on the physically correct diffusive flux −νε(u) and provide exactly divergence-free discrete velocity solutions, optimal (pressure robust) error estimates and a minimal number of coupling degrees of freedom. For the stability analysis we introduce a new Korn-like inequality for vectorvalued element-wise H1 and normal continuous functions. Numerical examples conclude the work where the theoretical findings are validated and the novel methods are compared in terms of condition numbers with respect to discrete stability parameters.
Wafer-Scale Characterization of a Superconductor Integrated Circuit Process, Using a Cryogenic Wafer Prober

Using a fully automated cryogenic wafer prober, we measured superconductor fabrication process control monitors and simple integrated circuits on 200 mm wafers at 4.2 K, including Nb/Al-AlOx/Nb Josephson junctions, SQUID-based circuits for measuring inductors, test structures for measuring critical current of superconducting wires and vias, resistors, etc., to demonstrate the feasibility of using the system for characterizing niobium superconducting devices and integrated circuits on a wafer scale. The data on the wafer-scale distributions of the junction tunnel resistance, energy gap, inductance of Nb layers, critical currents of interlayer vias are presented. The wafers were fabricated in the SFQ5ee process, the fully planarized process with eight niobium layers and a layer of kinetic inductors, developed for superconductor electronics at MIT Lincoln Laboratory. The cryogenic wafer prober was developed at HPD/ FormFactor, Inc.
Adv-4-Adv: Thwarting Changing Adversarial Perturbations via Adversarial Domain Adaptation

Whereas adversarial training can be useful against specific adversarial perturbations, they have also proven ineffective in generalizing towards attacks deviating from those used for training. However, we observe that this ineffectiveness is intrinsically connected to domain adaptability, another crucial issue in deep learning for which adversarial domain adaptation appears to be a promising solution. Consequently, we proposed Adv-4-Adv as a novel adversarial training method that aims to retain robustness against unseen adversarial perturbations. Essentially, Adv-4-Adv treats attacks incurring different perturbations as distinct domains, and by leveraging the power of adversarial domain adaptation, it aims to remove the domain/attack-specific features. This forces a trained model to learn a robust domain-invariant representation, which in turn enhances its generalization ability. Extensive evaluations on Fashion-MNIST, SVHN, CIFAR-10, and CIFAR-100 demonstrate that a model trained by Adv-4-Adv based on samples crafted by simple attacks (e.g., FGSM) can be generalized to more advanced attacks (e.g., PGD), and the performance exceeds state-of-the-art proposals on these datasets.
LGBTQ Privacy Concerns on Social Media

We conducted semi-structured interviews with members of the LGBTQ community about their privacy practices and concerns on social networking sites. Participants used different social media sites for different needs and adapted to not being completely out on each site. We would value the opportunity to discuss the unique privacy and security needs of this population with workshop participants and learn more about the privacy needs of other marginalized user groups from researchers who have worked in those communities.
Descriptive vs. inferential community detection: pitfalls, myths and half-truths

Community detection is one of the most important methodological fields of network science, and one which has attracted a significant amount of attention over the past decades. This area deals with the automated division of a network into fundamental building blocks, with the objective of providing a summary of its large-scale structure. Despite its importance and widespread adoption, there is a noticeable gap between what is arguably the state-of-the-art and the methods which are actually used in practice in a variety of fields. The Elements attempts to address this discrepancy by dividing existing methods according to whether they have a 'descriptive' or an 'inferential' goal. While descriptive methods find patterns in networks based on context-dependent notions of community structure, inferential methods articulate a precise generative model, and attempt to fit it to data. In this way, they are able to provide insights into formation mechanisms and separate structure from noise. This title is also available as open access on Cambridge Core.
Unleashing the Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification

Existing person re-identification (ReID) methods typically directly load the pre-trained ImageNet weights for initialization. However, as a fine-grained classification task, ReID is more challenging and exists a large domain gap between ImageNet classification. Inspired by the great success of self-supervised representation learning with contrastive objectives, in this paper, we design an Unsupervised Pre-training framework for ReID based on the contrastive learning (CL) pipeline, dubbed UP-ReID. During the pre-training, we attempt to address two critical issues for learning fine-grained ReID features: (1) the augmentations in CL pipeline may distort the discriminative clues in person images. (2) the fine-grained local features of person images are not fully-explored. Therefore, we introduce an intra-identity (I$^2$-)regularization in the UP-ReID, which is instantiated as two constraints coming from global image aspect and local patch aspect: a global consistency is enforced between augmented and original person images to increase robustness to augmentation, while an intrinsic contrastive constraint among local patches of each image is employed to fully explore the local discriminative clues. Extensive experiments on multiple popular Re-ID datasets, including PersonX, Market1501, CUHK03, and MSMT17, demonstrate that our UP-ReID pre-trained model can significantly benefit the downstream ReID fine-tuning and achieve state-of-the-art performance. Codes and models will be released to https://github.com/Frost-Yang-99/UP-ReID.
Research the sweet spot on a baseball bat based on optimization algorithms

With the popularity of baseball, numerous studies have been conducted to find methods to improve performance during a baseball match. However, it's still unclear that how the batter should hit a ball so that the batted ball speed is largest. In other words, where is the sweet spot that maximum energy is transferred to the ball when it's hit? In our paper, we take three factors into consideration to determine position of sweet spot. We build three models to confirm corresponding factors: (1) relationship between batted ball speed and distance from hit point to pivot point when the pivot point is fixed, (2) loss of energy caused by vibration of bat, (3) loss of energy caused by rotation of bat. At last, by considering the above three models to get the final energy of ball. We find tha t the COP (Centre of Percussion) point is just the sweet spot. What's more, by considering variation of mass, centre of mass and moment of inertia of bat after corking a bat, we get the new position of COP. Our conclusion is that when a batter does the same work to the two types of bat, corking a bat doesn't change batted ball speed dramatically. Therefore, "corking" doesn't enhance performance during baseball match even though it may lead to better control of the bat. At last, by statistical processing to results of baseball match in the last more than 30 years, we find that the aluminium bat shows a marked increase in hit-ball speeds. So different materials have different behaviours during a match. In addition to that, we also give reasons why Major League Baseball prohibits metal bats according to our model.
Label-Free Model Evaluation with Semi-Structured Dataset Representations

Label-free model evaluation, or AutoEval, estimates model accuracy on unlabeled test sets, and is critical for understanding model behaviors in various unseen environments. In the absence of image labels, based on dataset representations, we estimate model performance for AutoEval with regression. On the one hand, image feature is a straightforward choice for such representations, but it hampers regression learning due to being unstructured (\ie no specific meanings for component at certain location) and of large-scale. On the other hand, previous methods adopt simple structured representations (like average confidence or average feature), but insufficient to capture the data characteristics given their limited dimensions. In this work, we take the best of both worlds and propose a new semi-structured dataset representation that is manageable for regression learning while containing rich information for AutoEval. Based on image features, we integrate distribution shapes, clusters, and representative samples for a semi-structured dataset representation. Besides the structured overall description with distribution shapes, the unstructured description with clusters and representative samples include additional fine-grained information facilitating the AutoEval task. On three existing datasets and 25 newly introduced ones, we experimentally show that the proposed representation achieves competitive results. Code and dataset are available at https://github.com/sxzrt/Semi-Structured-Dataset-Representations.
Towards Futuristic Autonomous Experimentation-A Surprise-Reacting Sequential Experiment Policy

An autonomous experimentation platform in manufacturing is supposedly capable of conducting a sequential search for ﬁnding suitable manufacturing conditions for advanced materials by itself or even for discovering new materials with minimal human intervention. The core of the intelligent control of such platforms is the policy directing sequential experiments, namely, to decide where to conduct the next experiment based on what has been done thus far. Such policy inevitably trades oﬀ exploitation versus exploration and the current practice uses either pure exploration oriented space-ﬁlling designs or some variants of the expected improvement criterion under the Bayesian optimization framework. We discuss whether it is beneﬁcial to trade oﬀ exploitation versus exploration by measuring the element and degree of surprise associated with the immediate past observation. We devise a surprise-reacting policy using two existing surprise metrics, known as the Shannon surprise and Bayesian surprise. Our analysis shows that the surprise-reacting policy appears to be better suited for quickly characterizing the overall landscape of a response surface or a design place under resource constraints. We argue that such capability is much needed for futuristic autonomous experimentation platforms. We do not claim that we have a fully autonomous experimentation platform, but believe that our current eﬀort sheds new lights or provides a diﬀerent view angle as researchers are racing to elevate the autonomy of various primitive autonomous experimentation systems.
Martingale product estimators for sensitivity analysis in computational statistical physics

We introduce a new class of estimators for the linear response of steady states of stochastic dynamics. We generalize the likelihood ratio approach and formulate the linear response as a product of two martingales, hence the name ‘martingale product estimators’. We present a systematic derivation of the martingale product estimator, and show how to construct such estimator so that its bias is consistent with the weak order of the numerical scheme that approximates the underlying stochastic differential equation. Motivated by the estimation of transport properties in molecular systems, we present a rigorous numerical analysis of the bias and variance for these new estimators in the case of Langevin dynamics. We prove that the variance is uniformly bounded in time and derive a specific form of the estimator for second-order splitting schemes for Langevin dynamics. For comparison, we also study the bias and variance of a Green–Kubo (GK) estimator, motivated, in part, by its variance growing linearly in time. We compare on illustrative numerical tests the new estimators with results obtained by the GK method.
Pseudoscalar sterile neutrino self-interactions in light of Planck, SPT and ACT data

We reassess the viability of a cosmological model including a fourth additional sterile neutrino species that self-interacts through a new pseudoscalar degree of freedom. We perform a series of extensive analyses fitting various combinations of cosmic microwave background (CMB) data from Planck, the Atacama Cosmology Telescope (ACT) and the South Pole Telescope (SPT), both alone and in combination with Baryon Acoustic Oscillation (BAO) and Supernova Ia (SnIa) observations. We show that the scenario under study, although capable to resolve the Hubble tension without worsening the so-called S_8 tension about the growth of cosmic structures, is severely constrained by high-multipole polarization data from both Planck and SPT. Intriguingly, when trading Planck TE-EE data for those from ACT, we find a ≳ 3σ preference for a non-zero sterile neutrino mass, ms = 3.6+1.1 0.6 eV (68% C.L.), compatible with the range suggested by longstanding short-baseline (SBL) anomalies in neutrino oscillation experiments. The pseudoscalar model provides indeed a better fit to ACT data compared to ΛCDM (Δχ2 ≃ -5, ΔAIC = -1.3), although in a combined analysis with Planck the ΛCDM model is still favoured, as the preference for a non-zero sterile neutrino mass is mostly driven by ACT favouring a higher value for the primordial spectral index ns with respect to Planck. We show that the mild tension between Planck and ACT is due to the different pattern in the TE and EE power spectra on multipoles between 350 ≲ ℓ ≲ 1000. We also check the impact of marginalizing over the gravitational lensing information in Planck data, showing that the model does not solve the CMB lensing anomaly. Future work including higher precision data from current and upcoming CMB ground-based experiments will be crucial to test these results.
Rigidity of Ext and Tor via flat–cotorsion theory

Abstract Let $\mathfrak{p}$ be a prime ideal in a commutative noetherian ring R and denote by $k(\mathfrak{p})$ the residue field of the local ring $R_\mathfrak{p}$. We prove that if an R-module M satisfies $\operatorname{Ext}_R^{n}(k(\mathfrak{p}),M)=0$ for some $n\geqslant\dim R$, then $\operatorname{Ext}_R^i(k(\mathfrak{p}),M)=0$ holds for all $i \geqslant n$. This improves a result of Christensen, Iyengar and Marley by lowering the bound on n. We also improve existing results on Tor-rigidity. This progress is driven by the existence of minimal semi-flat-cotorsion replacements in the derived category as recently proved by Nakamura and Thompson.
Near-optimal distributed degree+1 coloring

We present a new approach to randomized distributed graph coloring that is simpler and more efficient than previous ones. In particular, it allows us to tackle the (deg+1)-list-coloring (D1LC) problem, where each node v of degree dv is assigned a palette of dv+1 colors, and the objective is to find a proper coloring using these palettes. While for (Δ+1)-coloring (where Δ is the maximum degree), there is a fast randomized distributed O(log3logn)-round algorithm due to Chang, Li, and Pettie, no o(logn)-round algorithms are known for the D1LC problem. We give a randomized distributed algorithm for D1LC that is optimal under plausible assumptions about the deterministic complexity of the problem. Using the recent deterministic algorithm of Ghaffari and Kuhn, our algorithm runs in O(log3 logn) time, matching the best bound known for (Δ+1)-coloring. A key contribution is a subroutine to generate slack for D1LC, which almost immediately leads to a palette sparsification theorem for D1LC when placed into the framework of works by Assadi, Chen, and Khanna and Alon and Assadi. That gives fast algorithms for D1LC in three different models: an O(1)-round algorithm in the MPC model with Õ(n) memory per machine; a single-pass semi-streaming algorithm in dynamic streams; and an Õ(n√n)-time query algorithm.
A Comprehensive Survey on the Convergence of Vehicular Social Networks and Fog Computing

In recent years, the number of IoT devices has been growing fast which leads to a challenging task for managing, storing, analyzing, and making decisions about raw data from different IoT devices, especially for delay-sensitive applications. In a vehicular network (VANET) environment, the dynamic nature of vehicles makes the current open research issues even more challenging due to the frequent topology changes that can lead to disconnections between vehicles. To this end, a number of research works have been proposed in the context of cloud and fog computing over the 5G infrastructure. On the other hand, there are a variety of research proposals that aim to extend the connection time between vehicles. Vehicular Social Networks (VSNs) have been defined to decrease the burden of connection time between the vehicles. This survey paper first provides the necessary background information and definitions about fog, cloud and related paradigms such as 5G and SDN. Then, it introduces the reader to Vehicular Social Networks, the different metrics and the main differences between VSNs and Online Social Networks. Finally, this survey investigates the related works in the context of VANETs that have demonstrated different architectures to address the different issues in fog computing. Moreover, it provides a categorization of the different approaches and discusses the required metrics in the context of fog and cloud and compares them to Vehicular social networks. A comparison of the relevant related works is discussed along with new research challenges and trends in the domain of VSNs and fog computing.
Translation-equivariant Image Quantizer for Bi-directional Image-Text Generation

Recently, vector-quantized image modeling has demonstrated impressive performance on generation tasks such as text-to-image generation. However, we discover that the current image quantizers do not satisfy translation equiv-ariance in the quantized space due to aliasing, degrading performance in the downstream text-to-image generation and image-to-text generation, even in simple experimental setups. Instead of focusing on anti-aliasing, we take a direct approach to encourage translation equivariance in the quantized space. In particular, we explore a desirable property of image quantizers, called ‘Translation Equivariance in the Quantized Space’ and propose a simple but effective way to achieve translation equivariance by regularizing orthogonality in the codebook embedding vectors. Using this method, we improve accuracy by +22% in text-to-image generation and +26% in image-to-text generation, outper-forming the VQGAN.
Attribute Artifacts Removal for Geometry-Based Point Cloud Compression

Geometry-based point cloud compression (G-PCC) can achieve remarkable compression efficiency for point clouds. However, it still leads to serious attribute compression artifacts, especially under low bitrate scenarios. In this paper, we propose a Multi-Scale Graph Attention Network (MS-GAT) to remove the artifacts of point cloud attributes compressed by G-PCC. We first construct a graph based on point cloud geometry coordinates and then use the Chebyshev graph convolutions to extract features of point cloud attributes. Considering that one point may be correlated with points both near and far away from it, we propose a multi-scale scheme to capture the short- and long-range correlations between the current point and its neighboring and distant points. To address the problem that various points may have different degrees of artifacts caused by adaptive quantization, we introduce the quantization step per point as an extra input to the proposed network. We also incorporate a weighted graph attentional layer into the network to pay special attention to the points with more attribute artifacts. To the best of our knowledge, this is the first attribute artifacts removal method for G-PCC. We validate the effectiveness of our method over various point clouds. Objective comparison results show that our proposed method achieves an average of 9.74% BD-rate reduction compared with Predlift and 10.13% BD-rate reduction compared with RAHT. Subjective comparison results present that visual artifacts such as color shifting, blurring, and quantization noise are reduced.
Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph

We target the task of cross-lingual Machine Reading Comprehension (MRC) in the direct zero-shot setting, by incorporating syntactic features from Universal Dependencies (UD), and the key features we use are the syntactic relations within each sentence. While previous work has demonstrated effective syntax-guided MRC models, we propose to adopt the inter-sentence syntactic relations, in addition to the rudimentary intra-sentence relations, to further utilize the syntactic dependencies in the multi-sentence input of the MRC task. In our approach, we build the Inter-Sentence Dependency Graph (ISDG) connecting dependency trees to form global syntactic relations across sentences. We then propose the ISDG encoder that encodes the global dependency graph, addressing the inter-sentence relations via both one-hop and multi-hop dependency paths explicitly. Experiments on three multilingual MRC datasets (XQuAD, MLQA, TyDiQA-GoldP) show that our encoder that is only trained on English is able to improve the zero-shot performance on all 14 test sets covering 8 languages, with up to 3.8 F1 / 5.2 EM improvement on-average, and 5.2 F1 / 11.2 EM on certain languages. Further analysis shows the improvement can be attributed to the attention on the cross-linguistically consistent syntactic path. Our code is available at https://github.com/lxucs/multilingual-mrc-isdg.