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Because of the unique physical properties and potential applications, the exploration of quantum materials with diverse symmetry-protected topological states has attracted considerable interest in the condensed-matter community in recent years. Most of the topologically nontirvial materials identified thus far have two-dimensional or three-dimensional structural characteristics, while the quasi-one-dimensional (quasi-1D) analogs are rare. Here we report on anisotropic magnetoresistance, Hall effect, and quantum de Haas$-$van Alphen (dHvA) oscillations in TaPtTe$_5$ single crystals, which possess a layered crystal structure with quasi-1D PtTe$_2$ chains. TaPtTe$_5$ manifests an anisotropic magnetoresistance and a nonlinear Hall effect at low temperatures. The analysis of the dHvA oscillations reveals two major oscillation frequencies (63.5 T and 95.2 T). The corresponding light effective masses and the nonzero Berry phases suggest the nontrivial band topology in TaPtTe$_5$, which is further corroborated by the first-principles calculations. Our results suggest that TaPtTe$_5$, in analogy with its sister compounds TaPdTe$_5$ and TaNiTe$_5$, is another quasi-1D material hosting topological Dirac fermions., Comment: There are some errors in the current vertion. After proper modifications, updated manuscript will be resubmitted

The popularity of racket sports (e.g., tennis and table tennis) leads to high demands for data analysis, such as notational analysis, on player performance. While sports videos offer many benefits for such analysis, retrieving accurate information from sports videos could be challenging. In this paper, we propose EventAnchor, a data analysis framework to facilitate interactive annotation of racket sports video with the support of computer vision algorithms. Our approach uses machine learning models in computer vision to help users acquire essential events from videos (e.g., serve, the ball bouncing on the court) and offers users a set of interactive tools for data annotation. An evaluation study on a table tennis annotation system built on this framework shows significant improvement of user performances in simple annotation tasks on objects of interest and complex annotation tasks requiring domain knowledge.

Generally, a superconducting nanowire single-photon detector (SNSPD) is composed of wires with a typical width of ~100 nm. Recent studies have found that superconducting strips with a micrometer-scale width can also detect single photons. Compared with the SNSPD, the superconducting microstrip single-photon detector (SMSPD) has smaller kinetic inductance, higher working current, and lower requirement in fabrication accuracy, providing potential applications in the development of ultra-large active area detectors. However, the study on SMSPD is still in its infancy, and the realization of its high-performance and practical use remains an opening question. This study demonstrates a NbN SMSPD with a saturated system detection efficiency (SDE) of ~92.2% at a dark count rate of ~200 cps, a polarization sensitivity of ~1.03, and a minimum timing jitter of ~48 ps, at the telecom wavelength of 1550 nm when coupled with a single mode fiber and operated at 0.84 K. Furthermore, the detector's SDE is over 70% when operated at a 2.1-K closed-cycle cryocooler., Comment: 10 pages,7 figures, 2 tables

We report a compact, scalable, and high-performance superconducting nanowire single-photon detector (SNSPD) array by using a multichannel optical fiber array-coupled configuration. For single pixels with an active area of 18 um in diameter and illuminated at the telecom wavelength of 1550 nm, we achieved a pixel yield of 13/16 on one chip, an average system detection efficiency of 69% at a dark count rate of 160 cps, a minimum timing jitter of 74 ps, and a maximum count rate of ~40 Mcps. The optical crosstalk coefficient between adjacent channels is better than -60 dB. The performance of the fiber array-coupled detectors is comparable with a standalone detector coupled to a single fiber. Our method is promising for the development of scalable, high-performance, and high-yield SNSPDs., Comment: 8 pages, 12 figures (including Supplementary Material)

In soccer, passing is the most frequent interaction between players and plays a significant role in creating scoring chances. Experts are interested in analyzing players' passing behavior to learn passing tactics, i.e., how players build up an attack with passing. Various approaches have been proposed to facilitate the analysis of passing tactics. However, the dynamic changes of a team's employed tactics over a match have not been comprehensively investigated. To address the problem, we closely collaborate with domain experts and characterize requirements to analyze the dynamic changes of a team's passing tactics. To characterize the passing tactic employed for each attack, we propose a topic-based approach that provides a high-level abstraction of complex passing behaviors. Based on the model, we propose a glyph-based design to reveal the multi-variate information of passing tactics within different phases of attacks, including player identity, spatial context, and formation. We further design and develop PassVizor, a visual analytics system, to support the comprehensive analysis of passing dynamics. With the system, users can detect the changing patterns of passing tactics and examine the detailed passing process for evaluating passing tactics. We invite experts to conduct analysis with PassVizor and demonstrate the usability of the system through an expert interview.

Using causal relations to guide decision making has become an essential analytical task across various domains, from marketing and medicine to education and social science. While powerful statistical models have been developed for inferring causal relations from data, domain practitioners still lack effective visual interface for interpreting the causal relations and applying them in their decision-making process. Through interview studies with domain experts, we characterize their current decision-making workflows, challenges, and needs. Through an iterative design process, we developed a visualization tool that allows analysts to explore, validate, and apply causal relations in real-world decision-making scenarios. The tool provides an uncertainty-aware causal graph visualization for presenting a large set of causal relations inferred from high-dimensional data. On top of the causal graph, it supports a set of intuitive user controls for performing what-if analyses and making action plans. We report on two case studies in marketing and student advising to demonstrate that users can effectively explore causal relations and design action plans for reaching their goals.

Understanding the relation of events plays an important role in different domains, such as identifying the reasons for users' certain actions from application logs as well as explaining sports players' behaviors according to historical records. Co-occurrence has been widely used to characterize the relation of events. However, insights provided by the co-occurrence relation are vague, which leads to difficulties in addressing domain problems. In this paper, we use causation to model the relation of events and present a visualization approach for conducting the causation analysis of event sequences. We integrate automatic causal discovery methods into the approach and propose a model for detecting event causalities. Considering the interpretability, we design a novel visualization named causal flow to integrate the detected causality into timeline-based event sequence visualizations. With this design, users can understand the occurrence of certain events and identify the causal pathways. We further implement an interactive system to help users comprehensively analyze event sequences. Two case studies are provided to evaluate the usability of the approach.

We report the synthesis and systematic studies of a new layered ternary telluride TaPdTe5 with quasi-one-dimensional PdTe2 chains. This compound crystalizes in a layered orthorhombic structure with space group Cmcm. Analysis of its curved field-dependent Hall resistivity, using the two-band model, indicates the hole-dominated transport with a high mobility ${\mu}_h$ = 2.38 $\times$ 10$^3$ cm$^2$ V$^{-1}$ s$^{-1}$ at low temperatures. The in-plane magnetoresistance (MR) displays significant anisotropy with field applied along the crystallographic $b$ axis. The MR with the current applied along the $c$-axis is also measured in high magnetic fields up to 51.7 T. Remarkably, it follows a power-law dependence and reaches (9.5 $\times$ 10$^3$)% at 2.1 K without any signature of saturation. The De Haas-van Alphen oscillations show a small Fermi-surface pocket with a nontrivial Berry phase. The Shubnikov-de Haas (SdH) oscillations are detected at low temperatures and under magnetic fields above 28.5 T. Two effective masses $m^*$ (0.26$m_e$ and 0.41$m_e$) are extracted from the oscillatory SdH data. Our first-principles calculations unveil a topological Dirac cone in its surface states, and, in particular, the topological index indicates that TaPdTe$_5$ is a topologically nontrivial material., Comment: 12 pages,6 figures

Three-dimensional (3D) elastic phononic topological insulator, featuring two-dimensional (2D) surface states, which support the high-efficient and robust elastic wave propagation without backscattering in all spatial dimensions, remains a challenge due to the nature of multiple polarized elastic modes and their complex hybridization in 3D. Here, a 3D elastic phononic topological insulator is designed and observed experimentally by emulating the quantum valley Hall effects. The spatial inversion of adjacent atoms gives rise to a valley topological phase to an insulating regime with complete 3D topological phononic bandgap. The 2D surface states protected by valley topology are unveiled numerically, which are confirmed experimentally to have a great robustness against the straight channel and sharp bends. Further engineering the elastic valley layer with appropriate interlayer coupling, we also demonstrate that layer pseudospin can be created in 3D elastic system which leads to 2D topological layer-dependent surface states and layer-selective transport. Our work will be a key step for the manipulation of elastic wave in 2D topological plane and the applications of 3D elastic topological-insulator-based devices with layer-selective functionality.

Surgical instrument segmentation is extremely important for computer-assisted surgery. Different from common object segmentation, it is more challenging due to the large illumination and scale variation caused by the special surgical scenes. In this paper, we propose a novel bilinear attention network with adaptive receptive field to solve these two challenges. For the illumination variation, the bilinear attention module can capture second-order statistics to encode global contexts and semantic dependencies between local pixels. With them, semantic features in challenging areas can be inferred from their neighbors and the distinction of various semantics can be boosted. For the scale variation, our adaptive receptive field module aggregates multi-scale features and automatically fuses them with different weights. Specifically, it encodes the semantic relationship between channels to emphasize feature maps with appropriate scales, changing the receptive field of subsequent convolutions. The proposed network achieves the best performance 97.47% mean IOU on Cata7 and comes first place on EndoVis 2017 by 10.10% IOU overtaking second-ranking method.