Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation

We introduce an interpretable deep learning approach for direction of arrival (DOA) estimation with a single snapshot. Classical subspace-based methods like MUSIC and ESPRIT use spatial smoothing on uniform linear arrays for single snapshot DOA estimation but face drawbacks in reduced array aperture and inapplicability to sparse arrays. Single-snapshot methods such as compressive sensing and iterative adaptation approach (IAA) encounter challenges with high computational costs and slow convergence, hampering real-time use. Recent deep learning DOA methods offer promising accuracy and speed. However, the practical deployment of deep networks is hindered by their black-box nature. To address this, we propose a deep-MPDR network translating minimum power distortionless response (MPDR)-type beamformer into deep learning, enhancing generalization and efficiency. Comprehensive experiments conducted using both simulated and real-world datasets substantiate its dominance in terms of inference time and accuracy in comparison to conventional methods. Moreover, it excels in terms of efficiency, generalizability, and interpretability when contrasted with other deep learning DOA estimation networks.Comment: 10 pages, 10 figure

Deep Learning based 3D Segmentation: A Survey

3D object segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving, robotics, augmented reality and medical image analysis. It has received significant attention from the computer vision, graphics and machine learning communities. Traditionally, 3D segmentation was performed with hand-crafted features and engineered methods which failed to achieve acceptable accuracy and could not generalize to large-scale data. Driven by their great success in 2D computer vision, deep learning techniques have recently become the tool of choice for 3D segmentation tasks as well. This has led to an influx of a large number of methods in the literature that have been evaluated on different benchmark datasets. This paper provides a comprehensive survey of recent progress in deep learning based 3D segmentation covering over 150 papers. It summarizes the most commonly used pipelines, discusses their highlights and shortcomings, and analyzes the competitive results of these segmentation methods. Based on the analysis, it also provides promising research directions for the future.Comment: Under review of ACM Computing Surveys, 36 pages, 10 tables, 9 figure

First Place Solution to the CVPR'2023 AQTC Challenge: A Function-Interaction Centric Approach with Spatiotemporal Visual-Language Alignment

Affordance-Centric Question-driven Task Completion (AQTC) has been proposed to acquire knowledge from videos to furnish users with comprehensive and systematic instructions. However, existing methods have hitherto neglected the necessity of aligning spatiotemporal visual and linguistic signals, as well as the crucial interactional information between humans and objects. To tackle these limitations, we propose to combine large-scale pre-trained vision-language and video-language models, which serve to contribute stable and reliable multimodal data and facilitate effective spatiotemporal visual-textual alignment. Additionally, a novel hand-object-interaction (HOI) aggregation module is proposed which aids in capturing human-object interaction information, thereby further augmenting the capacity to understand the presented scenario. Our method achieved first place in the CVPR'2023 AQTC Challenge, with a Recall@1 score of 78.7\%. The code is available at https://github.com/tomchen-ctj/CVPR23-LOVEU-AQTC.Comment: Winner of CVPR2023 Long-form Video Understanding and Generation Challenge (Track 3

Giant pressure-enhancement of multiferroicity in CuBr2

Type-II multiferroic materials, in which ferroelectric polarization is induced by inversion non-symmetric magnetic order, promise new and highly efficient multifunctional applications based on the mutual control of magnetic and electric properties. Although this phenomenon has to date been limited to low temperatures, here we report a giant pressure-dependence of the multiferroic critical temperature in CuBr$_2$. At 4.5 GPa, $T_\mathrm{C}$ is enhanced from 73.5 to 162 K, to our knowledge the highest value yet reported for a non-oxide type-II multiferroic. This growth shows no sign of saturating and the dielectric loss remains small under these high pressures. We establish the structure under pressure and demonstrate a 60\% increase in the two-magnon Raman energy scale up to 3.6 GPa. First-principles structural and magnetic energy calculations provide a quantitative explanation in terms of dramatically pressure-enhanced interactions between CuBr$_2$ chains. These large, pressure-tuned magnetic interactions motivate structural control in cuprous halides as a route to applied high-temperature multiferroicity.Comment: 10 pages, 6 figure

Application of multivariate linear methods to the development of a stratified system in Block II of Area A

This study is a numerical simulation study of different stratigraphic development schemes for three encrypted well networks based on historical fitting by establishing a fine geological model for Block II of Area A. The relationship between reservoir physical parameters and recovery rates for different stratigraphic combinations is obtained by applying multiple linearity regression methods to guide the delineation of stratigraphic development sections in multi-layered non-homogeneous reservoirs

Characterization of microbial community in industrial bioleaching heap of copper sulfide ore at Monywa mine, Myanmar

Microbial communities and activities in multi-lift bioleaching heap of copper sulfide ore were investigated at Monywa copper mine, Myanmar. The high-throughput sequencing method revealed a microbial community dominated by archaeal Ferroplasma (about 70%), rather than more commonly reported genera of Acidithiobacillus and Leptospirillum (together about 25% in Monywa heap). Multi-lift stacking operation without heap aeration resulted in low oxygen concentrations in the heaps (leachate oxygen concentration of 0.35 mg L-1 to 0.68 mg L-1). Therefore, low oxygen concentrations, high organic matter concentrations and moderate temperature in the heaps favored the growth of versatile Ferroplasma which can both undergo autotrophic and heterotrophic growth. Total cell number in the irrigation solution and leachate was in the range of 4.86-8.80 x 106 mL(-1), and the detected microbes in ore residues and leaching solutions were mainly those with iron-oxidizing ability. Their iron-oxidizing rate showed highest at the temperature of 35 degrees C, while in heaps may be limited by the low oxygen supply. Monywa bioleaching system with the above mentioned microbial community and activity formed a leaching solution of low redox potential, resulting in appropriate pyrite oxidation during chalcocite dissolution, thus helped maintain relatively stable acid and iron concentration in the cycling leaching solution. This study explained the formation of the microbial community and activity in Monywa heap leaching, linking to its heap physical and chemical conditions, and suggested that the role of Ferroplasma in bioleaching system may have been overlooked previously, especially in oxygen-limited bioleaching heaps. (C) 2016 Elsevier B.V. All rights reserved.</p