Efficient Convolutional Neural Network for FMCW Radar Based Hand Gesture Recognition

FMCW radar could detect object's range, speed and Angleof-Arrival, advantages are robust to bad weather, good range resolution, and good speed resolution. In this paper, we consider the FMCW radar as a novel interacting interface on laptop. We merge sequences of object's range, speed, azimuth information into single input, then feed to a convolution neural network to learn spatial and temporal patterns. Our model achieved 96% accuracy on test set and real-time test.Comment: Poster in Ubicomp 201

Self-Domain Adaptation for Face Anti-Spoofing

Although current face anti-spoofing methods achieve promising results under intra-dataset testing, they suffer from poor generalization to unseen attacks. Most existing works adopt domain adaptation (DA) or domain generalization (DG) techniques to address this problem. However, the target domain is often unknown during training which limits the utilization of DA methods. DG methods can conquer this by learning domain invariant features without seeing any target data. However, they fail in utilizing the information of target data. In this paper, we propose a self-domain adaptation framework to leverage the unlabeled test domain data at inference. Specifically, a domain adaptor is designed to adapt the model for test domain. In order to learn a better adaptor, a meta-learning based adaptor learning algorithm is proposed using the data of multiple source domains at the training step. At test time, the adaptor is updated using only the test domain data according to the proposed unsupervised adaptor loss to further improve the performance. Extensive experiments on four public datasets validate the effectiveness of the proposed method.Comment: Camera Ready, AAAI 202

Study on Evaluation Index System of Optimal Allocation to Coal Resource

Abstract. optimal allocation to coal resource is a major theme that cannot be ignored to healthy development of Chinese coal industry. In this paper, on the basis of an analysis to the main factors that affect optimal allocation to coal resource, an evaluation index system of optimal allocation to coal resource is put forward from the sustainable development, new-type industrialization, industrial safety, recycling economy, resource monitoring. The index system laid the foundation for quantitative evaluation of optimal allocation to coal resource

Redefining Qualitative Analysis in the AI Era: Utilizing ChatGPT for Efficient Thematic Analysis

Thematic analysis is a cornerstone of qualitative research, yet it is often marked by labor-intensive procedures. Recent advances in artificial intelligence (AI), especially with large-scale language models (LLMs) such as ChatGPT, present potential avenues to enhance qualitative data analysis. This research delves into the effectiveness of ChatGPT in refining the thematic analysis process. We conducted semi-structured interviews with 17 participants, inclusive of a 4-participant pilot study, to identify the challenges and reservations concerning the incorporation of ChatGPT in qualitative analysis. In partnership with 13 qualitative analysts, we crafted cueing frameworks to bolster ChatGPT's contribution to thematic analysis. The results indicate that these frameworks not only amplify the quality of thematic analysis but also bridge a significant connection between AI and qualitative research. These insights carry pivotal implications for academics and professionals keen on harnessing AI for qualitative data exploration

Quantification of gas hydrate saturation and morphology based on a generalized effective medium model

Highlights • A modified cementation theory is developed by introducing generalized pressure-dependent normalized contact-cemented radii. • A generalized effective medium model is proposed to merge the effective medium theory and cementation theory. • Modeling and inversion schemes are proposed to quantify hydrate saturation and morphology from laboratory and well-log data. • Hydrates mainly grow as matrix-supporting form (~54%) in sands and as pore-filling form (~59%) in clay-rich marine sediments. Abstract Numerous models have been developed for prediction of gas hydrate saturation based on the microstructural relationship between gas hydrates and sediment grains. However, quantification of hydrate saturation and morphology from elastic properties has been hindered by failing to account for complex hydrate distributions. Here, we develop a generalized effective medium model by applying the modified Hashin-Shtrikman bounds to a newly developed cementation theory. This model is validated by experimental data for synthetic methane and tetrahydrofuran hydrates. Good comparison of model predictions with experimental measurements not only reveals its ability to merge the results of contact cementation theory and effective medium theory, but also indicates its feasibility for characterizing complex morphologies. Moreover, the results of inverting acoustic measurements quantitatively confirm that for synthetic samples in “excess-gas” condition gas hydrates mainly occur as a hybrid-cementing morphology with a low percentage of pore-filling morphology, whereas for pressure-core hydrate-bearing sediments in natural environments they exist as matrix-supporting and pore-filling morphologies with a very low percentage of hybrid-cementing morphology. The hydrate saturations estimated from sonic and density logs in several regions including northern Cascadia margin (Integrated Ocean Drilling Program Expedition 311, Hole U1326D and Hole U1327E), Alaska North Slope (Mount Elbert test well) and Mackenzie Delta (Mallik 5L-38), are comparable to the referenced hydrate saturations derived from core data and resistivity, and/or nuclear magnetic resonance log data, confirming validity and applicability of our model. Furthermore, our results indicate that ~8% hybrid-cementing, ~33% matrix-supporting and ~59% pore-filling hydrates may coexist in the fine-grained and clay-rich marine sediments on the northern Cascadia margin, whereas ~10% hybrid-cementing, ~54% matrix-supporting and ~36% pore-filling hydrates may coexist in the coarse-grained and sand-dominated terrestrial sediments of the Alaska North Slope and Mackenzie Delta

Intelligence of Astronomical Optical Telescope: Present Status and Future Perspectives

Artificial intelligence technology has been widely used in astronomy, and new artificial intelligence technologies and application scenarios are constantly emerging. There have been a large number of papers reviewing the application of artificial intelligence technology in astronomy. However, relevant articles seldom mention telescope intelligence separately, and it is difficult to understand the current development status and research hotspots of telescope intelligence from these papers. This paper combines the development history of artificial intelligence technology and the difficulties of critical technologies of telescopes, comprehensively introduces the development and research hotspots of telescope intelligence, then conducts statistical analysis on various research directions of telescope intelligence and defines the research directions' merits. All kinds of research directions are evaluated, and the research trend of each telescope's intelligence is pointed out. Finally, according to the advantages of artificial intelligence technology and the development trend of telescopes, future research hotspots of telescope intelligence are given.Comment: 19 pages, 6 figure, for questions or comments, please email [email protected]

Roles of circRNA dysregulation in esophageal squamous cell carcinoma tumor microenvironment

Esophageal squamous cell carcinoma (ESCC) is the most prevalent histological esophageal cancer characterized by advanced diagnosis, metastasis, resistance to treatment, and frequent recurrence. In recent years, numerous human disorders such as ESCC, have been linked to abnormal expression of circular RNAs (circRNAs), suggesting that they are fundamental to the intricate system of gene regulation that governs ESCC formation. The tumor microenvironment (TME), referring to the area surrounding the tumor cells, is composed of multiple components, including stromal cells, immune cells, the vascular system, extracellular matrix (ECM), and numerous signaling molecules. In this review, we briefly described the biological purposes and mechanisms of aberrant circRNA expression in the TME of ESCC, including the immune microenvironment, angiogenesis, epithelial-to-mesenchymal transition, hypoxia, metabolism, and radiotherapy resistance. As in-depth research into the processes of circRNAs in the TME of ESCC continues, circRNAs are promising therapeutic targets or delivery systems for cancer therapy and diagnostic and prognostic indicators for ESCC

Elucidating the impact of in vitro cultivation on Nicotiana tabacum metabolism through combined in silico modeling and multiomics analysis

The systematical characterization and understanding of the metabolic behaviors are the basis of the efficient plant metabolic engineering and synthetic biology. Genome-scale metabolic networks (GSMNs) are indispensable tools for the comprehensive characterization of overall metabolic profile. Here we first constructed a GSMN of tobacco, which is one of the most widely used plant chassis, and then combined the tobacco GSMN and multiomics analysis to systematically elucidate the impact of in-vitro cultivation on the tobacco metabolic network. In-vitro cultivation is a widely used technique for plant cultivation, not only in the field of basic research but also for the rapid propagation of valuable horticultural and pharmaceutical plants. However, the systemic effects of in-vitro cultivation on overall plant metabolism could easily be overlooked and are still poorly understood. We found that in-vitro tobacco showed slower growth, less biomass and suppressed photosynthesis than soil-grown tobacco. Many changes of metabolites and metabolic pathways between in-vitro and soil-grown tobacco plants were identified, which notably revealed a significant increase of the amino acids content under in-vitro condition. The in silico investigation showed that in-vitro tobacco downregulated photosynthesis and primary carbon metabolism, while significantly upregulated the GS/GOGAT cycle, as well as producing more energy and less NADH/NADPH to acclimate in-vitro growth demands. Altogether, the combination of experimental and in silico analyses offers an unprecedented view of tobacco metabolism, with valuable insights into the impact of in-vitro cultivation, enabling more efficient utilization of in-vitro techniques for plant propagation and metabolic engineering

Gold nanocages covered with thermally-responsive polymers for controlled release by high-intensity focused ultrasound

This paper describes the use of Au nanocages covered with smart, thermally-responsive polymers for controlled release with high-intensity focused ultrasound (HIFU). HIFU is a highly precise medical procedure that uses focused ultrasound to heat and destroy pathogenic tissue rapidly and locally in a non-invasive or minimally invasive manner. The released dosage could be remotely controlled by manipulating the power of HIFU and/or the duration of exposure. We demonstrated localized release within the focal volume of HIFU by using gelatin phantom samples containing dye-loaded Au nanocages. By placing chicken breast tissues on top of the phantoms, we further demonstrated the feasibility of this system for controlled release at depths up to 30 mm. Because it can penetrate more deeply into soft tissues than near-infrared light, HIFU is a potentially more effective external stimulus for rapid, on-demand drug release

3D Unet-based Kidney and Kidney Tumer Segmentation with Attentive Feature Learning

To study the kidney diseases and kidney tumor from Computed Tomography(CT) imaging data, it is helpful to segment the region of interest through computer aided auto-segmentation tool. In the KiTs 2019 challenge [1], we are provided 3D volumetric CT data to train a model for kidney and kidney tumor segmentation. We introduce an improved deep 3D Unet by enriching the feature representation in CT images using an attention module. We achieve 1.5% improvement in the segmentation accuracy when evaluated on the validation set