Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model

In this paper we consider the problem of computing an $\epsilon$-optimal policy of a discounted Markov Decision Process (DMDP) provided we can only access its transition function through a generative sampling model that given any state-action pair samples from the transition function in $O(1)$ time. Given such a DMDP with states $S$, actions $A$, discount factor $\gamma\in(0,1)$, and rewards in range $[0, 1]$ we provide an algorithm which computes an $\epsilon$-optimal policy with probability $1 - \delta$ where \emph{both} the time spent and number of sample taken are upper bounded by $$O\left[\frac{|S||A|}{(1-\gamma)^3 \epsilon^2} \log \left(\frac{|S||A|}{(1-\gamma)\delta \epsilon} \right) \log\left(\frac{1}{(1-\gamma)\epsilon}\right)\right] ~.$$ For fixed values of $\epsilon$, this improves upon the previous best known bounds by a factor of $(1 - \gamma)^{-1}$ and matches the sample complexity lower bounds proved in Azar et al. (2013) up to logarithmic factors. We also extend our method to computing $\epsilon$-optimal policies for finite-horizon MDP with a generative model and provide a nearly matching sample complexity lower bound.Comment: 31 pages. Accepted to NeurIPS, 201

Cardinal Optimizer (COPT) User Guide

Cardinal Optimizer is a high-performance mathematical programming solver for efficiently solving largescale optimization problem. This documentation provides basic introduction to the Cardinal Optimizer

Chinese Fine-Grained Financial Sentiment Analysis with Large Language Models

Entity-level fine-grained sentiment analysis in the financial domain is a crucial subtask of sentiment analysis and currently faces numerous challenges. The primary challenge stems from the lack of high-quality and large-scale annotated corpora specifically designed for financial text sentiment analysis, which in turn limits the availability of data necessary for developing effective text processing techniques. Recent advancements in large language models (LLMs) have yielded remarkable performance in natural language processing tasks, primarily centered around language pattern matching. In this paper, we propose a novel and extensive Chinese fine-grained financial sentiment analysis dataset, FinChina SA, for enterprise early warning. We thoroughly evaluate and experiment with well-known existing open-source LLMs using our dataset. We firmly believe that our dataset will serve as a valuable resource to advance the exploration of real-world financial sentiment analysis tasks, which should be the focus of future research. The FinChina SA dataset is publicly available at https://github.com/YerayL/FinChina-SAComment: FinLLM Symposium at IJCAI 202

Identification of the Genes Involved in Riemerella anatipestifer Biofilm Formation by Random Transposon Mutagenesis

Riemerella anatipestifer causes epizootics of infectious disease in poultry that result in serious economic losses to the duck industry. Our previous studies have shown that some strains of R. anatipestifer can form a biofilm, and this may explain the intriguing persistence of R. anatipestifer on duck farms post infection. In this study we used strain CH3, a strong producer of biofilm, to construct a library of random Tn4351 transposon mutants in order to investigate the genetic basis of biofilm formation by R. anatipestifer on abiotic surfaces. A total of 2,520 mutants were obtained and 39 of them showed a reduction in biofilm formation of 47%–98% using crystal violet staining. Genetic characterization of the mutants led to the identification of 33 genes. Of these, 29 genes are associated with information storage and processing, as well as basic cellular processes and metabolism; the function of the other four genes is currently unknown. In addition, a mutant strain BF19, in which biofilm formation was reduced by 98% following insertion of the Tn4351 transposon at the dihydrodipicolinate synthase (dhdps) gene, was complemented with a shuttle plasmid pCP-dhdps. The complemented mutant strain was restored to give 92.6% of the biofilm formation of the wild-type strain CH3, which indicates that the dhdp gene is associated with biofilm formation. It is inferred that such complementation applies also to other mutant strains. Furthermore, some biological characteristics of biofilm-defective mutants were investigated, indicating that the genes deleted in the mutant strains function in the biofilm formation of R. anatipestifer. Deletion of either gene will stall the biofilm formation at a specific stage thus preventing further biofilm development. In addition, the tested biofilm-defective mutants had different adherence capacity to Vero cells. This study will help us to understand the molecular mechanisms of biofilm development by R. anatipestifer and to study the pathogenesis of R. anatipestifer further

Regulation of Hemogenic Endothelial Cell Specification by microRna-223

Embryonic definitive hematopoiesis generates hematopoietic stem and progenitor cells (HSPCs) essential for the establishment and maintenance of the adult blood system. This process requires the specification of a subset of vascular endothelial cells to become blood-forming, or hemogenic, and the subsequent endothelial-to-hematopoietic transition (EHT) to generate HSPCs therefrom. The mechanisms that regulate these processes are under intensive investigation, as their recapitulation in vitro from human pluripotent stem cells has the potential to generate autologous HSPCs for clinical applications. In these studies, we identified microRNA-223 (miR-223) as a novel negative regulator of hemogenic endothelial cell (EC) specification and HSPC production. We found that miR-223 is enriched in hemogenic ECs and HSPCs in the mouse aorta-gonad-mesonephros region (AGM), and represses the generation of these cells during definitive hematopoiesis. Thus, loss of miR-223 leads to increased formation of hemogenic ECs and their generation of HSPCs, and this is associated with increased retinoic acid (RA) signaling in the AGM endothelium, which we previously found promotes hemogenic EC specification. Loss of miR-223 also promotes the generation of myeloid-biased hemogenic ECs and HSPCs, which results in an increased proportion of myeloid cells throughout embryonic development and postnatally into adulthood. We also found that chemical inhibition of N-glycome phenocopies miR-223-deficiency and miR-223 restrains hemogenic specification potentially by targeting N-glycogenes to regulate N-glycan biosynthesis. Collectively, our findings improve our understanding of hematopoiesis and may yield new targets for clinical therapy

A Note on Quadratic Convergence of the Homogeneous and Self-Dual Linear Programming Algorithm

: In this note we show that Ye-Todd-Mizuno's O( p nL)-iteration homogeneous and self-dual linear programming (LP) algorithm possesses quadratic convergence of the duality gap to zero. In the case of infeasibility, this result shows that a homogenizing variable quadratically converges to zero and implies that the iterates of the (original) LP variable quadratically diverge. Thus, we have established a complete asymptotic convergence result for interior-point algorithms without any assumption on the LP problem. Key words: Linear Programming, interior point algorithms, homogeneity, self-dual, quadratic convergence. The Institute of Applied Mathematics, Academia Sinica, Beijing, China. y Department of Management Sciences, The University of Iowa, Iowa City, Iowa 52242, USA. This research was supported in part by NSF grant DDM-9207347 and the K.C. WONG Education Foundation, Hong Kong, through Academia Sinica, Beijing, China. 1 Introduction Consider linear programs in the following..

Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model

In this paper we consider the problem of computing an $\epsilon$-optimal policy of a discounted Markov Decision Process (DMDP) provided we can only access its transition function through a generative sampling model that given any state-action pair samples from the transition function in $O(1)$ time. Given such a DMDP with states $S$, actions $A$, discount factor $\gamma\in(0,1)$, and rewards in range $[0, 1]$ we provide an algorithm which computes an $\epsilon$-optimal policy with probability $1 - \delta$ where \emph{both} the time spent and number of sample taken are upper bounded by $$O\left[\frac{|S||A|}{(1-\gamma)^3 \epsilon^2} \log \left(\frac{|S||A|}{(1-\gamma)\delta \epsilon} \right) \log\left(\frac{1}{(1-\gamma)\epsilon}\right)\right] ~.$$ For fixed values of $\epsilon$, this improves upon the previous best known bounds by a factor of $(1 - \gamma)^{-1}$ and matches the sample complexity lower bounds proved in Azar et al. (2013) up to logarithmic factors. We also extend our method to computing $\epsilon$-optimal policies for finite-horizon MDP with a generative model and provide a nearly matching sample complexity lower bound