Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity.

Zeroth-order (a.k.a, derivative-free) methods are a class of effective optimization methods for solving complex machine learning problems, where gradients of the objective functions are not available or computationally prohibitive.

Gradient Descent Ascent for Minimax Problems on Riemannian Manifolds.

In the paper, we study a class of useful minimax problems on Riemanian manifolds and propose a class of effective Riemanian gradient-based methods to solve these minimax problems. Specifically, we propose an effective Riemannian gradient descent ascent (RGDA) algorithm for the deterministic minimax optimization. Moreover, we prove that our RGDA has a sample complexity of O(κϵ) for finding an ϵ-stationary solution of the Geodesically-Nonconvex Strongly-Concave (GNSC) minimax problems, where κ denotes the condition number.

State Causality and Adaptive Covariance Decomposition Based Time Series Forecasting.

Time series forecasting is a very vital research topic. The scale of time series in numerous industries has risen considerably in recent years as a result of the advancement of information technology. However, the existing algorithms pay little attention to generating large-scale time series.

Investigation and Analysis of Infection among Inpatients in a Tertiary Hospital in Shanghai.

Objectives: This study is aimed at obtaining information about the prevalence of nosocomial infections (NIs) and the use of antibiotics in hospitalized patients and providing relevant references for further understanding, preventing, and controlling NIs.

Methods: The medical records of adult patients admitted to a hospital in Shanghai from November to December 2021 were analyzed. The patients were divided into the NI group, community-acquired infection (CAI) group, and uninfected or healed group according to their infection status.

Riemannian gradient methods for stochastic composition problems.

In the paper, we study a class of novel stochastic composition optimization problems over Riemannian manifold, which have been raised by multiple emerging machine learning applications such as distributionally robust learning in Riemannian manifold setting. To solve these composition problems, we propose an effective Riemannian compositional gradient (RCG) algorithm, which has a sample complexity of O(ϵ) for finding an ϵ-stationary point. To further reduce sample complexity, we propose an accelerated momentum-based Riemannian compositional gradient (M-RCG) algorithm.

Faster Stochastic Quasi-Newton Methods.

Stochastic optimization methods have become a class of popular optimization tools in machine learning. Especially, stochastic gradient descent (SGD) has been widely used for machine learning problems, such as training neural networks, due to low per-iteration computational complexity. In fact, the Newton or quasi-newton (QN) methods leveraging the second-order information are able to achieve a better solution than the first-order methods.

Hepatocellular carcinoma with worsened hypoglycemia after transarterial chemoembolization: a case report and systematic review.

Non-islet cell tumor hypoglycemia (NICTH) is an extremely uncommon and serious complication of hepatocellular carcinoma (HCC). Here, we reported a case of a 47-year-old male patient with moderate to poorly differentiated HCC complicated by hypoglycemia that worsened after transarterial chemoembolization (TACE). The patient was admitted into The First Affiliated Hospital of Naval Medical University due to fatigue, nausea, dizziness and passage of tea colored urine.

Joint Estimation of Multiple Conditional Gaussian Graphical Models.

In this paper, we propose a joint conditional graphical Lasso to learn multiple conditional Gaussian graphical models, also known as Gaussian conditional random fields, with some similar structures. Our model builds on the maximum likelihood method with the convex sparse group Lasso penalty. Moreover, our model is able to model multiple multivariate linear regressions with unknown noise covariances via a convex formulation.

Fluorescence sensing of ADP over ATP and PPi in 100% aqueous solution.

An anthracene-bridged dinuclear zinc(ii)-dipicolylamine complex was found to show high selectivity for ADP with a significant fluorescence enhancement over ATP, PPi and other common analytes in 100% aqueous solution. This complex can be used for fluorescence detection of ADP in living cells and for monitoring the activity of kinases.

Joint Learning of Multiple Sparse Matrix Gaussian Graphical Models.

We consider joint learning of multiple sparse matrix Gaussian graphical models and propose the joint matrix graphical Lasso to discover the conditional independence structures among rows (columns) in the matrix variable under distinct conditions. The proposed approach borrows strength across the different graphical models and is based on the maximum likelihood with penalized row and column precision matrices, respectively. In particular, our model is more parsimonious and flexible than the joint vector graphical models.

Near-infrared fluorescent probe for detection of thiophenols in water samples and living cells.

The development of probes for rapid, selective, and sensitive detection of the highly toxic thiophenols is of great importance in both environmental and biological science. Despite the appealing advantages of near-infrared (NIR) fluorescent detection, no NIR fluorescent probes have been reported for thiophenols to date. Using the chemical properties of thiophenols that are able to cleave sulfonamide selectively and efficiently under mild conditions, we herein report a dicyanomethylene-benzopyran (DCMB)-based NIR fluorescent probe for thiophenols.

Aldehyde group assisted thiolysis of dinitrophenyl ether: a new promising approach for efficient hydrogen sulfide probes.

We report herein a new approach, which combines fast nucleophilic addition of H2S to an aldehyde group and the subsequent intramolecular thiolysis of dinitrophenyl ether, and can be used to develop efficient and effective H2S probes.

Introducing ligand-based hydrogen bond donors to a receptor: both selectivity and binding affinity for anion recognition in water can be improved.

Introducing hydrogen bond donors to a receptor was found to be an effective approach to improve both its selectivity and binding affinity for pyrophosphate in water. The crystal structure of Zn3-ADP complex showed the improvements come from the combination of H-bonding and metal coordination in a manner similar to many metalloenzymes.