A data-driven game theoretic strategy for developers in software crowdsourcing: a case study

Crowdsourcing has the advantages of being cost-effective and saving time, which is a typical embodiment of collective wisdom and community workers’ collaborative development. However, this development paradigm of software crowdsourcing has not been used widely. A very important reason is that requesters have limited knowledge about crowd workers’ professional skills and qualities. Another reason is that the crowd workers in the competition cannot get the appropriate reward, which affects their motivation. To solve this problem, this paper proposes a method of maximizing reward based on the crowdsourcing ability of workers, they can choose tasks according to their own abilities to obtain appropriate bonuses. Our method includes two steps: Firstly, it puts forward a method to evaluate the crowd workers’ ability, then it analyzes the intensity of competition for tasks at Topcoder.com—an open community crowdsourcing platform—on the basis of the workers’ crowdsourcing ability; secondly, it follows dynamic programming ideas and builds game models under complete information in different cases, offering a strategy of reward maximization for workers by solving a mixed-strategy Nash equilibrium. This paper employs crowdsourcing data from Topcoder.com to carry out experiments. The experimental results show that the distribution of workers’ crowdsourcing ability is uneven, and to some extent it can show the activity degree of crowdsourcing tasks. Meanwhile, according to the strategy of reward maximization, a crowd worker can get the theoretically maximum reward

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

In the era of large language models, Mixture-of-Experts (MoE) is a promising architecture for managing computational costs when scaling up model parameters. However, conventional MoE architectures like GShard, which activate the top-$K$ out of $N$ experts, face challenges in ensuring expert specialization, i.e. each expert acquires non-overlapping and focused knowledge. In response, we propose the DeepSeekMoE architecture towards ultimate expert specialization. It involves two principal strategies: (1) finely segmenting the experts into $mN$ ones and activating $mK$ from them, allowing for a more flexible combination of activated experts; (2) isolating $K_s$ experts as shared ones, aiming at capturing common knowledge and mitigating redundancy in routed experts. Starting from a modest scale with 2B parameters, we demonstrate that DeepSeekMoE 2B achieves comparable performance with GShard 2.9B, which has 1.5 times the expert parameters and computation. In addition, DeepSeekMoE 2B nearly approaches the performance of its dense counterpart with the same number of total parameters, which set the upper bound of MoE models. Subsequently, we scale up DeepSeekMoE to 16B parameters and show that it achieves comparable performance with LLaMA2 7B, with only about 40% of computations. Further, our preliminary efforts to scale up DeepSeekMoE to 145B parameters consistently validate its substantial advantages over the GShard architecture, and show its performance comparable with DeepSeek 67B, using only 28.5% (maybe even 18.2%) of computations

First catalogue of stars with photoelectric astrolabe in San Juan

On the basis of data observed in San Juan of Argentina with the photoelectric astrolabe mark II (PA II) of Beijing Astronomical Observatory from Feb. 23, 1992 to Feb. 28, 1995, residuals of 7200 stars are reduced from about 230000 observations of stars. The mean precision of the residuals is ± 0:046. Using the data, the first catalogue of stars (CPASJ1) has been compiled. There are 2980 stars in this catalogue, including 989 FK5/FK4 Supp stars, 658 FK5 Ext stars, 387 SRS stars, 687 CAMC4 stars, 192 GC stars, and 72 Hipparcos stars. The mean precisions are ±3.2 ms and ± 0:061 in right ascension and declination, respectively. The magnitudes of stars are from 2.0 to 11.3. The declinations are from to -3° to -60°. The mean epoch is 1993.6. Finally, systematic corrections of (CPASJ1-FK5) are given.Facultad de Ciencias Astronómicas y Geofísica

Meta-analysis Followed by Replication Identifies Loci in or near CDKN1B, TET3, CD80, DRAM1, and ARID5B as Associated with Systemic Lupus Erythematosus in Asians

Systemic lupus erythematosus (SLE) is a prototype autoimmune disease with a strong genetic involvement and ethnic differences. Susceptibility genes identified so far only explain a small portion of the genetic heritability of SLE, suggesting that many more loci are yet to be uncovered for this disease. In this study, we performed a meta-analysis of genome-wide association studies on SLE in Chinese Han populations and followed up the findings by replication in four additional Asian cohorts with a total of 5,365 cases and 10,054 corresponding controls. We identified genetic variants in or near CDKN1B, TET3, CD80, DRAM1, and ARID5B as associated with the disease. These findings point to potential roles of cell-cycle regulation, autophagy, and DNA demethylation in SLE pathogenesis. For the region involving TET3 and that involving CDKN1B, multiple independent SNPs were identified, highlighting a phenomenon that might partially explain the missing heritability of complex diseases

Characterization of butyrate-metabolism in colorectal cancer to guide clinical treatment

Abstract Colorectal cancer (CRC) is the third most prevalent one in the world among the most common malignant tumors. Numerous studies have shown that butyrate has demonstrated promise as an antitumor agent in a variety of human cancer types. However, butyrate remains understudied in CRC tumorigenesis and progression. In this study, we explored therapeutic strategies to treat CRC by examining the role of butyrate metabolism. First, from the Molecular Signature Database (MSigDB), we identified 348 butyrate metabolism-related genes (BMRGs). Next, we downloaded 473 CRC and 41 standard colorectal tissue samples from The Cancer Genome Atlas (TCGA) database and the transcriptome data of GSE39582 dataset from Gene Expression Omnibus (GEO) database. Then we evaluated the expression patterns of butyrate metabolism-related genes with difference analysis in CRC. Through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, a prognostic model was constructed based on differentially expressed BMRGs. In addition, we discovered an independent prognostic marker for CRC patients. According to the expression levels and coefficients of identified BMRGs, the risk scores of all CRC samples were calculated. Utilizing differentially expressed genes in the high- and low-risk groups, we also constructed a Protein–Protein Interaction (PPI) network to visualize the interactions between proteins. Through the results of PPI network, we screened out differentially expressed target butyrate metabolism-related genes from ten hub genes. Finally, we performed clinical correlation analysis, immune cell infiltration analysis, and mutation analysis for these target genes. One hundred and seventy three differentially expressed butyrate metabolism-related genes were screened out in all the CRC samples. The prognostic model was established with univariate Cox regression and LASSO regression analysis. CRC patients’ overall survival was significantly lower in the high-risk group than in the low-risk group for both training and validation set. Among the ten hub genes identified from the PPI network, four target butyrate metabolism-related genes were identified containing FN1, SERPINE1, THBS2, and COMP, which might provide novel markers or targets for treating CRC patients. Eighteen butyrate metabolism-related genes were used to develop a risk prognostic model that could be helpful for doctors to predict CRC patients’ survival rate. Using this model, it is beneficial to forecast the response of CRC patients to immunotherapy and chemotherapy, thus making it easier to custom tailor cancer chemotherapy and immunotherapy to the individual patient

WSD-GAN: Word Sense Disambiguation Using Generative Adversarial Networks

Word Sense Disambiguation (WSD), as a tough task in Natural Language Processing (NLP), aims to identify the correct sense of an ambiguous word in a given context. There are two mainstreams in WSD. Supervised methods mainly utilize labeled context to train a classifier which generates the right probability distribution of word senses. Meanwhile knowledge-based (unsupervised) methods which focus on glosses (word sense definitions) always calculate the similarity of context-gloss pair as score to find out the right word sense. In this paper, we propose a generative adversarial framework WSD-GAN which combines two mainstream methods in WSD. The generative model, based on supervised methods, tries to generate a probability distribution over the word senses. Meanwhile the discriminative model, based on knowledge-based methods, focuses on predicting the relevancy of the context-gloss pairs and identifies the correct pairs over the others. Furthermore, in order to optimize both two models, we leverage policy gradient to enhance the performances of the two models mutually. Our experimental results show that WSD-GAN achieves competitive results on several English all-words WSD datasets