Learning a Pose Lexicon for Semantic Action Recognition

This paper presents a novel method for learning a pose lexicon comprising semantic poses defined by textual instructions and their associated visual poses defined by visual features. The proposed method simultaneously takes two input streams, semantic poses and visual pose candidates, and statistically learns a mapping between them to construct the lexicon. With the learned lexicon, action recognition can be cast as the problem of finding the maximum translation probability of a sequence of semantic poses given a stream of visual pose candidates. Experiments evaluating pre-trained and zero-shot action recognition conducted on MSRC-12 gesture and WorkoutSu-10 exercise datasets were used to verify the efficacy of the proposed method.Comment: Accepted by the 2016 IEEE International Conference on Multimedia and Expo (ICME 2016). 6 pages paper and 4 pages supplementary materia

Learning Discriminative Stein Kernel for SPD Matrices and Its Applications

Stein kernel has recently shown promising performance on classifying images represented by symmetric positive definite (SPD) matrices. It evaluates the similarity between two SPD matrices through their eigenvalues. In this paper, we argue that directly using the original eigenvalues may be problematic because: i) Eigenvalue estimation becomes biased when the number of samples is inadequate, which may lead to unreliable kernel evaluation; ii) More importantly, eigenvalues only reflect the property of an individual SPD matrix. They are not necessarily optimal for computing Stein kernel when the goal is to discriminate different sets of SPD matrices. To address the two issues in one shot, we propose a discriminative Stein kernel, in which an extra parameter vector is defined to adjust the eigenvalues of the input SPD matrices. The optimal parameter values are sought by optimizing a proxy of classification performance. To show the generality of the proposed method, three different kernel learning criteria that are commonly used in the literature are employed respectively as a proxy. A comprehensive experimental study is conducted on a variety of image classification tasks to compare our proposed discriminative Stein kernel with the original Stein kernel and other commonly used methods for evaluating the similarity between SPD matrices. The experimental results demonstrate that, the discriminative Stein kernel can attain greater discrimination and better align with classification tasks by altering the eigenvalues. This makes it produce higher classification performance than the original Stein kernel and other commonly used methods.Comment: 13 page

Image feature recognition and gas permeability prediction of Gaomiaozi bentonite based on digital images and machine learning

Gas permeability, which is measured mainly through gas permeability experiments, is a critical technical index in many engineering fields. In this study, permeability is firstly calculated based on information from a digital image and an improved permeability prediction model. The calculated results are experimentally verified. Subsequently, a self-developed image-processing program is used to extract feature parameters from a scanning electron microscopy image. Meanwhile, an extreme learning machine algorithm is used to input the image feature parameters obtained using the image-processing program into the extreme learning machine algorithm for machine learning. Additionally, we compare several typically used machine learning algorithms, which confirmed the reliability and accuracy of our algorithm. The best activation function can be obtained by comparing the predicted permeability using an appropriate number of neuron nodes. Experimental results show that the program can accurately identify the features of the microscopy image. Combining the program with an extreme learning machine neural network algorithmgas permeability results to be obtained with high accuracy. This method yields good predictions of permeability in certain cases and has been adapted to other geomaterials.Cited as: Liu, J., Ma, S., Shen, W., Zhou, J., Hong, Y. Image feature recognition and gas permeability prediction of Gaomiaozi bentonite based on digital images and machine learning. Advances in Geo-Energy Research, 2022, 6(4): 314-323. https://doi.org/10.46690/ager.2022.04.0

Beyond covariance: feature representation with nonlinear kernel matrices

Covariance matrix has recently received increasing attention in computer vision by leveraging Riemannian geometry of symmetric positive-definite (SPD) matrices. Originally proposed as a region descriptor, it has now been used as a generic representation in various recognition tasks. However, covariance matrix has shortcomings such as being prone to be singular, limited capability in modeling complicated feature relationship, and having a fixed form of representation. This paper argues that more appropriate SPD-matrix-based representations shall be explored to achieve better recognition. It proposes an open framework to use the kernel matrix over feature dimensions as a generic representation and discusses its properties and advantages. The proposed framework significantly elevates covariance representation to the unlimited opportunities provided by this new representation. Experimental study shows that this representation consistently outperforms its covariance counterpart on various visual recognition tasks. In particular, it achieves significant improvement on skeleton-based human action recognition, demonstrating the state-of-the-art performance over both the covariance and the existing non-covariance representations

Data-Centric Financial Large Language Models

Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance. LLMs have difficulty reasoning about and integrating all relevant information. We propose a data-centric approach to enable LLMs to better handle financial tasks. Our key insight is that rather than overloading the LLM with everything at once, it is more effective to preprocess and pre-understand the data. We create a financial LLM (FLLM) using multitask prompt-based finetuning to achieve data pre-processing and pre-understanding. However, labeled data is scarce for each task. To overcome manual annotation costs, we employ abductive augmentation reasoning (AAR) to automatically generate training data by modifying the pseudo labels from FLLM's own outputs. Experiments show our data-centric FLLM with AAR substantially outperforms baseline financial LLMs designed for raw text, achieving state-of-the-art on financial analysis and interpretation tasks. We also open source a new benchmark for financial analysis and interpretation. Our methodology provides a promising path to unlock LLMs' potential for complex real-world domains

Epigenetic Regulation of BMP2 by 1,25-dihydroxyvitamin D3 through DNA Methylation and Histone Modification.

Genetic hypercalciuric stone-forming (GHS) rats have increased intestinal Ca absorption, decreased renal tubule Ca reabsorption and low bone mass, all of which are mediated at least in part by elevated tissue levels of the vitamin D receptor (VDR). Both 1,25-dihydroxyvitamin D3(1,25(OH)2D3) and bone morphogenetic protein 2 (BMP2) are critical for normal maintenance of bone metabolism and bone formation, respectively. The complex nature of bone cell regulation suggests a potential interaction of these two important regulators in GHS rats. In the present study, BMP2 expression is suppressed by the VDR-1,25(OH)2D3 complex in Bone Marrow Stromal Cells (BMSCs) from GHS and SD rat and in UMR-106 cell line. We used chromatin immunoprecipitation (ChIP) assays to identify VDR binding to only one of several potential binding sites within the BMP2 promoter regions. This negative region also mediates suppressor reporter gene activity. The molecular mechanisms underlying the down-regulation of BMP2 by 1,25(OH)2D3 were studied in vitro in BMSCs and UMR-106 cells using the DNA methyltransferase inhibitor 5-aza-2′-deoxycytidine (DAC) and the histone deacetylase inhibitor trichostatin A (TSA). Both DAC and TSA activate BMP2 expression in combination with 1,25(OH)2D3. Bisulfite DNA pyrosequencing reveals 1,25(OH)2D3 to completely hypermethylate a single CpG site in the same BMP2 promoter region identified by the ChIP and reporter gene assays. ChIP assays also show that 1,25(OH)2D3 can increase the repressive histone mark H3K9me2 and reduce the acetylation of histone H3 at the same BMP2 promoter region. Taken together, our results indicate that 1,25(OH)2D3 binding to VDR down-regulates BMP2 gene expression in BMSCs and osteoblast-like UMR-106 cells by binding to the BMP2 promoter region. The mechanism of this 1,25(OH)2D3-induced transcriptional repression of BMP2 involves DNA methylation and histone modification. The study provides novel evidence that 1,25(OH)2D3represses bone formation through down-regulating BMP2 expression both in vivo and in vitro

Correlation between obstructive sleep apnea and hypoperfusion in patients with acute cerebral infarction

PurposeTo explore the relationship between obstructive sleep apnea (OSA) and hypoperfusion during ultra-early acute cerebral infarction.Patients and methodsData were retrospectively collected from patients admitted to our hospital with acute cerebral infarction between January 2020 and January 2022, who underwent comprehensive whole-brain computed tomography perfusion imaging and angiography examinations within 6 h of onset. The F-stroke software automatically assessed and obtained relevant data (Tmax). The patients underwent an initial screening for sleep apnea. Based on their Apnea-Hypopnea Index (AHI), patients were categorized into an AHI ≤15 (n = 22) or AHI >15 (n = 25) group. The pairwise difference of the time-to-maximum of the residue function (Tmax) > 6 s volume was compared, and the correlation between AHI, mean pulse oxygen saturation (SpO2), oxygen desaturation index (ODI), percentage of time with oxygen saturation < 90% (T90%), and the Tmax >6 s volume was analyzed.ResultsThe Tmax >6 s volume in the AHI > 15 group was significantly larger than that in the AHI ≤ 15 group [109 (62–157) vs. 59 (21–106) mL, p = 0.013]. Spearman’s correlation analysis revealed Tmax >6 s volume was significantly correlated with AHI, mean SpO2, ODI, and T90% in the AHI > 15 group, however, no significant correlations were observed in the AHI ≤ 15 group. Controlling for the site of occlusion and Multiphase CT angiography (mCTA) score, AHI (β = 0.919, p < 0.001), mean SpO2 (β = −0.460, p = 0.031), ODI (β = 0.467, p = 0.032), and T90% (β =0.478, p = 0.026) remained associated with early hypoperfusion in the AHI > 15 group.ConclusionIn patients with acute cerebral infarction and AHI > 15, AHI, mean SpO2, ODI and T90% were associated with early hypoperfusion. However, no such relationship exists among patients with AHI ≤ 15

FBXW7 and human tumors: mechanisms of drug resistance and potential therapeutic strategies

Drug therapy, including chemotherapy, targeted therapy, immunotherapy, and endocrine therapy, stands as the foremost therapeutic approach for contemporary human malignancies. However, increasing drug resistance during antineoplastic therapy has become a substantial barrier to favorable outcomes in cancer patients. To enhance the effectiveness of different cancer therapies, an in-depth understanding of the unique mechanisms underlying tumor drug resistance and the subsequent surmounting of antitumor drug resistance is required. Recently, F-box and WD Repeat Domain-containing-7 (FBXW7), a recognized tumor suppressor, has been found to be highly associated with tumor therapy resistance. This review provides a comprehensive summary of the underlying mechanisms through which FBXW7 facilitates the development of drug resistance in cancer. Additionally, this review elucidates the role of FBXW7 in therapeutic resistance of various types of human tumors. The strategies and challenges implicated in overcoming tumor therapy resistance by targeting FBXW7 are also discussed

Antifungal activity of nisin against clinical isolates of azole-resistant Candida tropicalis

The rapid emergence of invasive infections caused by azole-resistant Candida tropicalis has become a public health concern, and there is an urgent need for alternative treatment strategies. Studies have demonstrated the antibacterial effects of nisin, a well-known peptide naturally produced by Lactococcus lactis subsp. lactis. However, there is scant information about the antifungal effect of nisin against C. tropicalis. The present study aims to investigate the in vitro antifungal activity of nisin against clinical isolates of azole-resistant C. tropicalis strains, as well as its inhibitory effect on biofilm formation. A total of 35 C. tropicalis strains isolated from patients with invasive fungal infections were divided into the azole-resistant group and the azole-sensitive group, containing 21 and 14 strains, respectively. The relative expression levels of the ERG11 and UPC2 genes in the azole-resistant group were higher than those in the azole-sensitive group (p < 0.0001), while no significant differences were observed in the expression levels of the MDR1 and CDR1 genes. The minimum inhibitory concentration of nisin against C. tropicalis ranged from 2 to 8 μg/mL. Nisin treatment inhibited the growth of azole-resistant C. tropicalis, with over a four-fold reduction in OD600 nm values observed at the 8-h time point, while it promoted the transition of C. tropicalis from the spore phase to the hyphal phase, as observed on cryo-scanning electron microscopy. The results of biofilm quantification using crystal violet staining indicated a significant decrease in OD570 nm values in the nisin-treated group compared to the controls (p < 0.0001). Among the 21 azole-resistant C. tropicalis strains, the biofilm formation was inhibited in 17 strains (17/21, 81%), and more than 85% inhibition of biofilm formation was observed in the representative strains. With regard to the molecular mechanisms, the expression of the BCR1 and UPC2 genes in the azole-resistant strains was down-regulated on nisin treatment (p < 0.05). In conclusion, we demonstrated, for the first time, that nisin has antifungal activity and significant anti-biofilm activity against clinical isolates of azole-resistant C. tropicalis strains. Based on the findings, nisin could be a promising alternative antifungal agent for combating azole-resistant C. tropicalis infections