Skeleton-Based Gesture Recognition With Learnable Paths and Signature Features

For the skeleton-based gesture recognition, graph convolutional networks (GCNs) have achieved remarkable performance since the human skeleton is a natural graph. However, the biological structure might not be the crucial one for motion analysis. Also, spatial differential information like joint distance and angle between bones may be overlooked during the graph convolution. In this paper, we focus on obtaining meaningful joint groups and extracting their discriminative features by the path signature (PS) theory. Firstly, to characterize the constraints and dependencies of various joints, we propose three types of paths, i.e., spatial, temporal, and learnable path. Especially, a learnable path generation mechanism can group joints together that are not directly connected or far away, according to their kinematic characteristic. Secondly, to obtain informative and compact features, a deep integration of PS with few parameters are introduced. All the computational process is packed into two modules, i.e., spatial-temporal path signature module (ST-PSM) and learnable path signature module (L-PSM) for the convenience of utilization. They are plug-and-play modules available for any neural network like CNNs and GCNs to enhance the feature extraction ability. Extensive experiments have conducted on three mainstream datasets (ChaLearn 2013, ChaLearn 2016, and AUTSL). We achieved the state-of-the-art results with simpler framework and much smaller model size. By inserting our two modules into the several GCN-based networks, we can observe clear improvements demonstrating the great effectiveness of our proposed method

Anisotropic magnetic properties and tunable conductivity in two-dimensional layered NaCrX2 (X=Te,Se,S) single crystals

Monolayer NaCrX2 (X=Te,Se,S) were theoretically proposed to be two-dimensional intrinsic ferromagnetic semiconductors while their physical properties have not been thoroughly investigated in bulk single crystals. We report the single-crystal growth, structural, magnetic and electronic transport properties of NaCr(Te1-xSex)2 (0 6 x 6 1) and NaCrS2. For NaCr(Te1-xSex)2, the strong perpendicular magnetic anisotropy of NaCrTe2 can be gradually tuned to be a nearly isotropic one by Se-doping. Meanwhile, a systematic change in the conductivity with increasing x is observed, displaying a doping-induced metal-insulator-like transition. Under magnetic field larger than 30 koe, both NaCrTe2 and NaCrSe2 can be polarized to a ferromagnetic state. While for NaCrS2, robust antiferromagnetism is observed up to 70 kOe and two field-induced metamagnetic transitions are identified along H||ab. These intriguing properties together with the potential to be exfoliated down to few-layer thickness make NaCrX2 (X=Te,Se,S) promising for exploring spintronic applications

Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each of the eight teams implemented different modifications of the known algorithms.Comment: 27 pages, 17 figure

Abdomen anatomic characteristics on CT scans as predictive markers for short-term complications following radical resection of colorectal cancer

BackgroundPrediction and management of short-term postoperative complications in patients with colorectal cancer are essential in postoperative rehabilitation. Through CT scan images, we can easily measure some parameters of abdomen anatomic characteristics. This study aimed to assess whether there is a relationship between the abdomen anatomic characteristics and short-term postoperative complications.Materials and methodsWe conducted a retrospective study. Eighty patients in each complication group and non-complication group were recruited with propensity score match. Demographics, perioperative laboratory results and surgical information were collected and compared between groups with univariate analysis. Significant elements were brought into subsequent logistic regression analysis and ROC analysis for further identification.ResultsUnivariate analysis showed that preoperative white blood cells, preoperative neutrophil counts, rectus abdominis thickness (RAT), subcutaneous fat thickness (SFT), and abdomen depth (AD) were significantly different between the complication group and non-complication group. Logistic regression analysis demonstrated that higher RAT (p = 0.002), SFT (p < 0.001) and AD (p < 0.001) independently predicted the incidence of short-term postoperative complications.ConclusionsIn this study on patients undergoing radical resection of colorectal cancer, abdomen anatomic characteristics including higher RAT, SFT and AD are associated with an increased risk of short-term postoperative complications

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

Chemistry and materials science are complex. Recently, there have been great successes in addressing this complexity using data-driven or computational techniques. Yet, the necessity of input structured in very specific forms and the fact that there is an ever-growing number of tools creates usability and accessibility challenges. Coupled with the reality that much data in these disciplines is unstructured, the effectiveness of these tools is limited. Motivated by recent works that indicated that large language models (LLMs) might help address some of these issues, we organized a hackathon event on the applications of LLMs in chemistry, materials science, and beyond. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines

How to Motivate Engineering and Technical Personnel to Innovate? The Impact of Human Resource Management Strength on Employee Innovation Behavior

Based on a survey of 611 engineering and technical personnel in Chinese manufacturing enterprises, this paper uses hierarchical regression analysis and structural equation model to examine the influence path of human resources management strength on employee innovation behavior. Further, the Bootstrap method was used to test the mediating effect. The results show that human resource management strength has a significant positive impact on employee innovation behavior and its two dimensions. Organizational atmosphere and employee psychological conditions play multiple mediating roles between human resource management strength and employee innovation behavior. Compared with employees’ psychological conditions, organizational atmosphere has a more significant mediating effect on innovation ideas generate. There is no significant difference between the mediating effect of organizational climate and employee psychological conditions on the implementation of innovation ideas generate

Recovery of absolute phases for the fringe patterns of three selected wavelengths with improved anti-error capability

In a recent published work, we proposed a technique to recover the absolute phase maps of fringe patterns with two selected fringe wavelengths. To achieve higher anti-error capability, the proposed method requires employing the fringe patterns with longer wavelengths; however, longer wavelength may lead to the degradation of the signal-to-noise ratio (SNR) in the surface measurement. In this paper, we propose a new approach to unwrap the phase maps from their wrapped versions based on the use of fringes with three different wavelengths which is characterized by improved anti-error capability and SNR. Therefore, while the previous method works on the two-phase maps obtained from six-step phase-shifting profilometry (PSP) (thus 12 fringe patterns are needed), the proposed technique performs very well on three-phase maps from three steps PSP, requiring only nine fringe patterns and hence more efficient. Moreover, the advantages of the two-wavelength method in simple implementation and flexibility in the use of fringe patterns are also reserved. Theoretical analysis and experiment results are presented to confirm the effectiveness of the proposed method

Absolute phase map recovery of two fringe patterns with flexible selection of fringe wavelengths

A novel approach is proposed to unwrap the phase maps of two fringe patterns in fringe pattern projection-based profilometry. In contrast to existing techniques, where spatial frequencies (i.e., the number of fringes on a pattern) of the two fringe patterns must be integers and coprime, the proposed method is applicable for any two fringe patterns with different fringe wavelengths (i.e., the number of pixels in a fringe) and thus provides more flexibility in the use of fringe patterns. Moreover, compared to the existing techniques, the proposed method is simpler in its implementation and has better antierror capability. Theoretical analysis and experiment results are presented to confirm the effectiveness of the proposed method