Stochastic smoothing accelerated gradient method for nonsmooth convex composite optimization

We propose a novel stochastic smoothing accelerated gradient (SSAG) method for general constrained nonsmooth convex composite optimization, and analyze the convergence rates. The SSAG method allows various smoothing techniques, and can deal with the nonsmooth term that is not easy to compute its proximal term, or that does not own the linear max structure. To the best of our knowledge, it is the first stochastic approximation type method with solid convergence result to solve the convex composite optimization problem whose nonsmooth term is the maximization of numerous nonlinear convex functions. We prove that the SSAG method achieves the best-known complexity bounds in terms of the stochastic first-order oracle ($\mathcal{SFO}$), using either diminishing smoothing parameters or a fixed smoothing parameter. We give two applications of our results to distributionally robust optimization problems. Numerical results on the two applications demonstrate the effectiveness and efficiency of the proposed SSAG method

A distributionally robust index tracking model with the CVaR penalty: tractable reformulation

We propose a distributionally robust index tracking model with the conditional value-at-risk (CVaR) penalty. The model combines the idea of distributionally robust optimization for data uncertainty and the CVaR penalty to avoid large tracking errors. The probability ambiguity is described through a confidence region based on the first-order and second-order moments of the random vector involved. We reformulate the model in the form of a min-max-min optimization into an equivalent nonsmooth minimization problem. We further give an approximate discretization scheme of the possible continuous random vector of the nonsmooth minimization problem, whose objective function involves the maximum of numerous but finite nonsmooth functions. The convergence of the discretization scheme to the equivalent nonsmooth reformulation is shown under mild conditions. A smoothing projected gradient (SPG) method is employed to solve the discretization scheme. Any accumulation point is shown to be a global minimizer of the discretization scheme. Numerical results on the NASDAQ index dataset from January 2008 to July 2023 demonstrate the effectiveness of our proposed model and the efficiency of the SPG method, compared with several state-of-the-art models and corresponding methods for solving them

CRISPR accelerates the cancer drug discovery

Emerging cohorts and basic studies have associated certain genetic modifications in cancer patients, such as gene mutation, amplification, or deletion, with the overall survival prognosis, underscoring patients??? genetic background may directly regulate drug sensitivity/resistance during chemotherapies. Understanding the molecular mechanism underpinning drug sensitivity/resistance and further uncovering the effective drugs have been the major ambition in the cancer drug discovery. The emergence and popularity of CRISPR/Cas9 technology have reformed the entire life science research, providing a precise and simplified genome editing tool with unlimited editing possibilities. Furthermore, it presents a powerful tool in cancer drug discovery, which hopefully facilitates us with a rapid and reliable manner in developing novel therapies and understanding the molecular mechanisms of drug sensitivity/resistance. Herein, we summarized the application of CRISPR/Cas9 in drug screening, with the focus on CRISPR/Cas9 mediated gene knockout, gene knock-in, as well as transcriptional modification. Additionally, this review provides the concerns, cautions, and ethnic considerations that need to be taken when applying CRISPR in the drug discovery.Peer reviewe

Exploring Moral Saints

In “Saints and Heroes,” J. O. Urmson (1958) defines moral saints by reference to their supererogatory actions. He believes that saintly actions are praiseworthy but not obligatory. However, Andrew Flescher (2003) and Tom Dougherty (2017) argue that people have duties to improve themselves morally and to increase how much they sacrifice for others gradually. In this paper, I will propose an Aristotelian-inspired definition of “saint” and discuss the moral duties of saints and ordinary people (i.e., people who are not saints) based on Dougherty’s dynamic view of beneficence. I hold that ordinary people have prima facie duties to become saints, although not everyone has an all-things-considered duty to do so. For the few people with all-things-considered duties to become saints, failing these duties can be morally wrong yet not blameworthy. In many cases, ordinary people like us lack the standing to blame them

Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation

Data-hunger and data-imbalance are two major pitfalls in many deep learning approaches. For example, on highly optimized production lines, defective samples are hardly acquired while non-defective samples come almost for free. The defects however often seem to resemble each other, e.g., scratches on different products may only differ in a few characteristics. In this work, we introduce a framework, Defect Transfer GAN (DT-GAN), which learns to represent defect types independent of and across various background products and yet can apply defect-specific styles to generate realistic defective images. An empirical study on the MVTec AD and two additional datasets showcase DT-GAN outperforms state-of-the-art image synthesis methods w.r.t. sample fidelity and diversity in defect generation. We further demonstrate benefits for a critical downstream task in manufacturing -- defect classification. Results show that the augmented data from DT-GAN provides consistent gains even in the few samples regime and reduces the error rate up to 51% compared to both traditional and advanced data augmentation methods.Comment: Accepted by BMVC 202

LAPTM4B-35 promotes cancer cell migration via stimulating integrin beta1 recycling and focal adhesion dynamics

Metastasis is the main cause of cancer patients' death despite tremendous efforts invested in developing the related molecular mechanisms. During cancer cell migration, cells undergo dynamic regulation of filopodia, focal adhesion, and endosome trafficking. Cdc42 is imperative for maintaining cell morphology and filopodia, regulating cell movement. Integrin beta1 activates on the endosome, the majority of which distributes itself on the plasma membrane, indicating that endocytic trafficking is essential for this activity. In cancers, high expression of lysosome-associated protein transmembrane 4B (LAPTM4B) is associated with poor prognosis. LAPTM4B-35 has been reported as displaying plasma membrane distribution and being associated with cancer cell migration. However, the detailed mechanism of its isoform-specific distribution and whether it relates to cell migration remain unknown. Here, we first report and quantify the filopodia localization of LAPTM4B-35: mechanically, that specific interaction with Cdc42 promoted its localization to the filopodia. Furthermore, our data show that LAPTM4B-35 stabilized filopodia and regulated integrin beta1 recycling via interaction and cotrafficking on the endosome. In our zebrafish xenograft model, LAPTM4B-35 stimulated the formation and dynamics of focal adhesion, further promoting cancer cell dissemination, whereas in skin cancer patients, LAPTM4B level correlated with poor prognosis. In short, this study establishes an insight into the mechanism of LAPTM4B-35 filopodia distribution, as well as into its biological effects and its clinical significance, providing a novel target for cancer therapeutics development.Peer reviewe

Differential expression and correlation of immunoregulation related piRNA in rheumatoid arthritis

BackgroundAlthough PIWI-interacting RNAs (piRNAs) have recently been associated with germline development and many human diseases, their expression pattern and relationship in autoimmune diseases remain indistinct. This study aimed to investigate the presence and correlation of piRNAs in rheumatoid arthritis (RA).MethodsWe first analyzed the expression profile of piRNAs using small RNA sequencing in peripheral leukocytes of three new-onset untreated RA patients and three healthy controls (HCs). We then selected piRNAs related to immunoregulation by bioinformatics analysis and verified them in 42 new-onset RA patients and 81 HCs by RT-qPCR. Furthermore, a receiver operating characteristic curve was generated to quantify the diagnostic performance of these piRNAs. A correlation analysis was conducted to observe the link between piRNA expression and RA clinical characteristics.ResultsA total of 15 upregulated and 9 downregulated piRNAs among 1,565 known piRNAs were identified in peripheral leukocytes of RA patients. Dysregulated piRNAs were enriched in numerous pathways related to immunity. After selection and validation, two immunoregulation piRNAs (piR-hsa-27620 and piR-hsa-27124) were significantly elevated in RA patients and have good abilities to distinguish patients from controls, which have the potential to serve as biomarkers. PIWI and other proteins implicated in the piRNA pathway were also associated with RA

Interval model of a wind turbine power curve

The wind turbine power curve model is critical to a wind turbine’s power prediction and performance analysis. However, abnormal data in the training set decrease the prediction accuracy of trained models. This paper proposes a sample average approach-based method to construct an interval model of a wind turbine, which increases robustness against abnormal data and further improves the model accuracy. We compare our proposed methods with the traditional neural network-based and Bayesian neural network-based models in experimental data-based validations. Our model shows better performance in both accuracy and computational time