NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization

We study a stochastic and distributed algorithm for nonconvex problems whose objective consists of a sum of $N$ nonconvex $L_i/N$-smooth functions, plus a nonsmooth regularizer. The proposed NonconvEx primal-dual SpliTTing (NESTT) algorithm splits the problem into $N$ subproblems, and utilizes an augmented Lagrangian based primal-dual scheme to solve it in a distributed and stochastic manner. With a special non-uniform sampling, a version of NESTT achieves $\epsilon$-stationary solution using $\mathcal{O}((\sum_{i=1}^N\sqrt{L_i/N})^2/\epsilon)$ gradient evaluations, which can be up to $\mathcal{O}(N)$ times better than the (proximal) gradient descent methods. It also achieves Q-linear convergence rate for nonconvex $\ell_1$ penalized quadratic problems with polyhedral constraints. Further, we reveal a fundamental connection between primal-dual based methods and a few primal only methods such as IAG/SAG/SAGA.Comment: 35 pages, 2 figure

Study of Valve Motion in Reciprocating Refrigerator Compressors based on the 3-D Fluid–structure Interaction Model

 Abstract: In this paper, a 3-D fluid-structure interaction model was established to investigate the working process of the small reciprocating refrigeration compressors. According to the numerical calculation, the working process of the small reciprocating refrigeration compressor, the motion of valve and the impact velocity and the contact stress of discharge valve and suction valve were given. Experiments on a small reciprocating refrigeration compressor for testing the p-v graph were carried out .Experimental results agree well with the numeric model. The result provides a guidance to research and design the small reciprocating refrigeration compressors

An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization

The cyclic block coordinate descent-type (CBCD-type) methods have shown remarkable computational performance for solving strongly convex minimization problems. Typical applications include many popular statistical machine learning methods such as elastic-net regression, ridge penalized logistic regression, and sparse additive regression. Existing optimization literature has shown that the CBCD-type methods attain iteration complexity of O(p · log(1/ )), where is a pre-specified accuracy of the objective value, and p is the number of blocks. However, such iteration complexity explicitly depends on p, and therefore is at least p times worse than those of gradient descent methods. To bridge this theoretical gap, we propose an improved convergence analysis for the CBCD-type methods. In particular, we first show that for a family of quadratic minimization problems, the iteration complexity of the CBCD-type methods matches that of the GD methods in term of dependency on p (up to a log 2 p factor). Thus our complexity bounds are sharper than the existing bounds by at least a factor of p/ log 2 p. We also provide a lower bound to confirm that our improved complexity bounds are tight (up to a log 2 p factor) if the largest and smallest eigenvalues of the Hessian matrix do not scale with p. Finally, we generalize our analysis to other strongly convex minimization problems beyond quadratic ones

Assessing the safety of bedaquiline: insight from adverse event reporting system analysis

BackgroundThe development and marketing of Bedaquiline (BDQ) represent significant advancements in treating tuberculosis, particularly multidrug-resistant forms. However, comprehensive research into BDQ’s real-world safety remains limited.PurposeWe obtained BDQ related adverse event (AE) information from the US Food and Drug Administration’s Adverse Event Reporting System (FAERS) to assess its safety and inform drug usage.MethodsThe AE data for BDQ from 2012 Q4 to 2023 Q3 was collected and standardized. Disproportionality analysis, including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagation Neural Network (BCPNN) was used to quantify signals of BDQ-related AEs. Logistic regression was used to analyze the individual data of hepatotoxicity and drug-induced liver injury, and multiple linear regression models were established. Additionally, network pharmacology was employed to identify the potential biological mechanisms of BDQ-induced liver injury.ResultsWe identified 2017 case reports directly related to BDQ. Our analysis identified 341 Preferred Terms (PTs) characterizing these AEs across 27 System Organ Classes (SOC). An important discovery was the identification of AEs associated with ear and labyrinth disorders, which had not been documented in the drug’s official leaflet before. Subgroup analysis revealed a negative correlation between BDQ-related liver injury and females (OR: 0.4, 95%CI: 0.3–0.6). In addition, via network pharmacology approach, a total of 76 potential targets for BDQ related liver injury were predicted, and 11 core target genes were selected based on the characterization of protein-protein interactions. The pathway linked to BDQ-induced liver injury was identified, and it was determined that the PI3K-Akt signaling pathway contained the highest number of associated genes.ConclusionThe analysis of the FAERS database revealed adverse events linked to BDQ, prompting the use of a network pharmacology approach to study the potential molecular mechanism of BDQ-induced liver injury. These findings emphasized the significance of drug safety and offered understanding into the mechanisms behind BDQ-induced liver injury. BDQ demonstrated distinct advantages, including reduced incidence of certain adverse events compared to traditional treatments such as injectable agents and second-line drugs. However, it is important to acknowledge the limitations of this analysis, including potential underreporting and confounding factors. This study provides valuable insights into the safety of BDQ and its role in the management of MDR-TB, emphasizing the need for continued surveillance and monitoring to ensure its safe and effective use

Advancing Model Pruning via Bi-level Optimization

The deployment constraints in practical applications necessitate the pruning of large-scale deep learning models, i.e., promoting their weight sparsity. As illustrated by the Lottery Ticket Hypothesis (LTH), pruning also has the potential of improving their generalization ability. At the core of LTH, iterative magnitude pruning (IMP) is the predominant pruning method to successfully find 'winning tickets'. Yet, the computation cost of IMP grows prohibitively as the targeted pruning ratio increases. To reduce the computation overhead, various efficient 'one-shot' pruning methods have been developed, but these schemes are usually unable to find winning tickets as good as IMP. This raises the question of how to close the gap between pruning accuracy and pruning efficiency? To tackle it, we pursue the algorithmic advancement of model pruning. Specifically, we formulate the pruning problem from a fresh and novel viewpoint, bi-level optimization (BLO). We show that the BLO interpretation provides a technically-grounded optimization base for an efficient implementation of the pruning-retraining learning paradigm used in IMP. We also show that the proposed bi-level optimization-oriented pruning method (termed BiP) is a special class of BLO problems with a bi-linear problem structure. By leveraging such bi-linearity, we theoretically show that BiP can be solved as easily as first-order optimization, thus inheriting the computation efficiency. Through extensive experiments on both structured and unstructured pruning with 5 model architectures and 4 data sets, we demonstrate that BiP can find better winning tickets than IMP in most cases, and is computationally as efficient as the one-shot pruning schemes, demonstrating 2-7 times speedup over IMP for the same level of model accuracy and sparsity.Comment: Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022

The impact of two drying methods on the quality of high-moisture rice: Poster

In this experiment, freshly harvested rice was dried by natural and mechanical methods. For natural drying, paddy rice was spread on a cement floor under a shelter at a thickness of 4cm, and it was turned twice a day. At a temperature of 19.3°C and a relative humidity of 58.8%, a total of 28 days was needed to reduce the water content from 23.11 to 14.38%. For mechanical drying, the Guwang 5HXG-15B circulating dryer was used, drying temperature was set to 42°C, and it took a total of 5 hours to reduce the water content from 23.1 to 11.8%. The changes in spore count, fatty acid value, germination rate, waist burst rate, whole polished rice rate, and taste value of rice mold after drying were studied. The results showed that compared with mechanical drying, the drying rate of air-dried rice was slower, and the number of mold spores increased from 0.65×105/g to 3.05×105/g, a 3.7 times increase. The number of mold spores in dried rice was not significant. Dried rice fatty acid value of 25.1mg/100g for natural drying was higher than the value of 19.9mg/100g for mechanical drying. High temperature affected rice seed vigor: mechanically dried rice germination rate was 58.0%, far lower than the 87.5% for natural drying. The blasting rate, polished rice rate, and taste value of mechanically dried rice were 5.33%, 57.9%, and 83.7, respectively, which was 2.33%, 58.9%, and 89.3 for naturally-dried rice. The processing quality and taste quality were even worse. Therefore, the drying process of the optimized circulation dryer should be further adjusted to reduce its impact on rice processing quality and taste quality.In this experiment, freshly harvested rice was dried by natural and mechanical methods. For natural drying, paddy rice was spread on a cement floor under a shelter at a thickness of 4cm, and it was turned twice a day. At a temperature of 19.3°C and a relative humidity of 58.8%, a total of 28 days was needed to reduce the water content from 23.11 to 14.38%. For mechanical drying, the Guwang 5HXG-15B circulating dryer was used, drying temperature was set to 42°C, and it took a total of 5 hours to reduce the water content from 23.1 to 11.8%. The changes in spore count, fatty acid value, germination rate, waist burst rate, whole polished rice rate, and taste value of rice mold after drying were studied. The results showed that compared with mechanical drying, the drying rate of air-dried rice was slower, and the number of mold spores increased from 0.65×105/g to 3.05×105/g, a 3.7 times increase. The number of mold spores in dried rice was not significant. Dried rice fatty acid value of 25.1mg/100g for natural drying was higher than the value of 19.9mg/100g for mechanical drying. High temperature affected rice seed vigor: mechanically dried rice germination rate was 58.0%, far lower than the 87.5% for natural drying. The blasting rate, polished rice rate, and taste value of mechanically dried rice were 5.33%, 57.9%, and 83.7, respectively, which was 2.33%, 58.9%, and 89.3 for naturally-dried rice. The processing quality and taste quality were even worse. Therefore, the drying process of the optimized circulation dryer should be further adjusted to reduce its impact on rice processing quality and taste quality