CAST: Cross-Attention in Space and Time for Video Action Recognition

Recognizing human actions in videos requires spatial and temporal understanding. Most existing action recognition models lack a balanced spatio-temporal understanding of videos. In this work, we propose a novel two-stream architecture, called Cross-Attention in Space and Time (CAST), that achieves a balanced spatio-temporal understanding of videos using only RGB input. Our proposed bottleneck cross-attention mechanism enables the spatial and temporal expert models to exchange information and make synergistic predictions, leading to improved performance. We validate the proposed method with extensive experiments on public benchmarks with different characteristics: EPIC-KITCHENS-100, Something-Something-V2, and Kinetics-400. Our method consistently shows favorable performance across these datasets, while the performance of existing methods fluctuates depending on the dataset characteristics.Comment: This is an accepted NeurIPS 2023. Project webpage is available at https://jong980812.github.io/CAST.github.io/ Code is available at https://github.com/KHU-VLL/CAS

Safe and Efficient Trajectory Optimization for Autonomous Vehicles using B-spline with Incremental Path Flattening

B-spline-based trajectory optimization is widely used for robot navigation due to its computational efficiency and convex-hull property (ensures dynamic feasibility), especially as quadrotors, which have circular body shapes (enable efficient movement) and freedom to move each axis (enables convex-hull property utilization). However, using the B-spline curve for trajectory optimization is challenging for autonomous vehicles (AVs) because of their vehicle kinodynamics (rectangular body shapes and constraints to move each axis). In this study, we propose a novel trajectory optimization approach for AVs to circumvent this difficulty using an incremental path flattening (IPF), a disc type swept volume (SV) estimation method, and kinodynamic feasibility constraints. IPF is a new method that can find a collision-free path for AVs by flattening path and reducing SV using iteratively increasing curvature penalty around vehicle collision points. Additionally, we develop a disc type SV estimation method to reduce SV over-approximation and enable AVs to pass through a narrow corridor efficiently. Furthermore, a clamped B-spline curvature constraint, which simplifies a B-spline curvature constraint, is added to dynamical feasibility constraints (e.g., velocity and acceleration) for obtaining the kinodynamic feasibility constraints. Our experimental results demonstrate that our method outperforms state-of-the-art baselines in various simulated environments. We also conducted a real-world experiment using an AV, and our results validate the simulated tracking performance of the proposed approach.Comment: 14 pages, 21 figures, 4 tables, 3 algorithm

A Study on Job Satisfaction Factors in Retention and Turnover Groups using Dominance Analysis and LDA Topic Modeling with Employee Reviews on Glassdoor.com

HR analytics is an important area for the application of big data analysis techniques, and the organizational insight that it provides enables effective management of employees. In this paper, we analyze employee review data posted on a representative third-party employee review website. We identify the relative importance of factors affecting job satisfaction and then extract topic differences after classifying employees according to retention and turnover. First, LDA Topic Modeling by adopting n-grams is performed on unstructured text data to analyze employee review data. Second, a dominance analysis is conducted to examine the relative importance of job factors. We found that the “Culture and Values” and “Senior Management” factors have the highest influence on both retention and turnover. Our model follows a novel approach in applying the analysis of reviews and text mining to the HR domain and will be of practical relevance for enhancing employee retention

Yeast lunapark regulates the formation of trans-Sey1p complexes for homotypic ER membrane fusion

Summary: The endoplasmic reticulum (ER) consists of the nuclear envelope and a connected peripheral network of tubules and interspersed sheets. The structure of ER tubules is generated and maintained by various proteins, including reticulons, DP1/Yop1p, atlastins, and lunapark. Reticulons and DP1/Yop1p stabilize the high membrane curvature of ER tubules, and atlastins mediate homotypic membrane fusion between ER tubules; however, the exact role of lunapark remains poorly characterized. Here, using isolated yeast ER microsomes and reconstituted proteoliposomes, we directly examined the function of the yeast lunapark Lnp1p for yeast atlastin Sey1p-mediated ER fusion and found that Lnp1p inhibits Sey1p-driven membrane fusion. Furthermore, by using a newly developed assay for monitoring trans-Sey1p complex assembly, a prerequisite for ER fusion, we found that assembly of trans-Sey1p complexes was increased by the deletion of LNP1 and decreased by the overexpression of Lnp1p, indicating that Lnp1p inhibits Sey1p-mediated fusion by interfering with assembly of trans-Sey1p complexes

Predictions of frame compliance and apex morphology in sharp nanoindentations

If all components in a nanoindentation system are well calibrated and a reference material has unique hardness, H and reduced modulus, E r independent of the indentation depths, the load, L and the penetration depth, h in the indentation loading curve of the reference material can be correlated by L=Kh 2. Here the constant K is expressed by H, E r and indenter geometry constants. By using H and E r of a fused silica and the Berkovich geometry, an analytical expression for the indentation loading curve could be derived. To compare with this analytical loading curve, experimental indentation data were measured with two commercial nanoindenters. The experimental loading curves shifted leftward or rightward from the analytical loading curve and this depth deviation was attributed to improper calibration of the nanoindenters. Quantitative calibrations of frame compliance and indenter bluntness were tried for the raw nanoindentation data and this resulted in consistent nanoindentation data regardless of the used nanoindentersclose0

Utilizing Negative Markers for Identifying Mycobacteria Species based on Mass Spectrometry with Machine Learning Methods

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) is a useful tool for rapid identification of microorganisms based on the protein mass profile represented in a mass spectrum of the microorganism. Typically, markers that are specific for particular microorganisms are extracted from the mass information obtained by MALDI-TOF MS, and a machine learning technique is applied to the markers. Identification of mycobacteria is of high clinical importance in that different pathogens must be treated with different antibiotics, but is still challenging because spectral patterns of different mycobacteria appear similar. In this paper, we propose a novel approach to use both positive and negative markers in order to enhance discrimination between the spectral patterns of different mycobacteria. We apply the proposed method to classify species in the Mycobacterium abscessus and Mycobacterium fortuitum groups. Experimental results demonstrate that, when combined with various classifier techniques, our method significantly improves the accuracy of mycobacteria identification.N