Optimal (2,δ)(2,\delta) Locally Repairable Codes via Punctured Simplex Codes

Locally repairable codes (LRCs) have attracted a lot of attention due to their applications in distributed storage systems. In this paper, we provide new constructions of optimal $(2, \delta)$-LRCs. Firstly, by the techniques of finite geometry, we present a sufficient condition to guarantee a punctured simplex code to be a $(2, \delta)$-LRC. Secondly, by using characteristic sums over finite fields and Krawtchouk polynomials, we construct several families of LRCs with new parameters. All of our new LRCs are optimal with respect to the generalized Cadambe-Mazumdar bound.Comment: Accepted for publication in ISIT202

Modeling growth of specific spoilage organisms in tilapia: Comparison Baranyi with chi-square automatic interaction detection (CHAID) model

Tilapia is an important aquatic fish, but severe spoilage of tilapia is most likely related to the global aquaculture. The spoilage is mostly caused by specific spoilage organisms (SSO). Therefore, it is very important to use microbial models to predict the growth of SSO in tilapia. This study firstly verified Pseudomonas and Vibrio as the SSO of tilapia, then established microbial growth models based on Baranyi and chi-square automatic interaction detection (CHAID) models and compared their effectiveness. The results showed that both Baranyi model and CHAID model are appropriate for predicting the growth of microorganism. Baranyi model fits the microorganism growth better than CHAID model overall though CHAID model fits well at stationary phase. CHAID model predicts the microorganism growth accurately when the rate of change of the experiment data is big.Key words: Specific spoilage organisms (SSO), tilapia, chi-square automatic interaction detection (CHAID), Baranyi, shelf-life

Context-Aware Sparse Deep Coordination Graphs

Learning sparse coordination graphs adaptive to the coordination dynamics among agents is a long-standing problem in cooperative multi-agent learning. This paper studies this problem and proposes a novel method using the variance of payoff functions to construct context-aware sparse coordination topologies. We theoretically consolidate our method by proving that the smaller the variance of payoff functions is, the less likely action selection will change after removing the corresponding edge. Moreover, we propose to learn action representations to effectively reduce the influence of payoff functions' estimation errors on graph construction. To empirically evaluate our method, we present the Multi-Agent COordination (MACO) benchmark by collecting classic coordination problems in the literature, increasing their difficulty, and classifying them into different types. We carry out a case study and experiments on the MACO and StarCraft II micromanagement benchmark to demonstrate the dynamics of sparse graph learning, the influence of graph sparseness, and the learning performance of our method

Self-Organized Polynomial-Time Coordination Graphs

Coordination graph is a promising approach to model agent collaboration in multi-agent reinforcement learning. It conducts a graph-based value factorization and induces explicit coordination among agents to complete complicated tasks. However, one critical challenge in this paradigm is the complexity of greedy action selection with respect to the factorized values. It refers to the decentralized constraint optimization problem (DCOP), which and whose constant-ratio approximation are NP-hard problems. To bypass this systematic hardness, this paper proposes a novel method, named Self-Organized Polynomial-time Coordination Graphs (SOP-CG), which uses structured graph classes to guarantee the accuracy and the computational efficiency of collaborated action selection. SOP-CG employs dynamic graph topology to ensure sufficient value function expressiveness. The graph selection is unified into an end-to-end learning paradigm. In experiments, we show that our approach learns succinct and well-adapted graph topologies, induces effective coordination, and improves performance across a variety of cooperative multi-agent tasks

Evaluation of the Ecological Quality of the Taishan Region Based on Landsat Series of Satellite Images

The deterioration of ecological environment has seriously restricted regional sustainable development. Taishan region is one of the ecological protection and restoration of life community of mountains-rivers-forests-farmlands-lakes-grasslands in China. Its ecological quality changes are directly related to the overall layout of ecological restoration and protection projects. In this study, the Taishan region of China was taken as study area, and the grade change, spatial distribution, and spatial temporal fluctuation of the ecological environment quality were quantified. Based on the ENVI platform, the Landsat series of three images of the Taishan region in 2005, 2013, and 2017 serve as the data source, and the remote sensing ecological index model (RSEI) was used. According to the change characteristics of land use types, the driving factors of ecological environmental quality change were analyzed. The results showed that: (1) The area ratio of the ecological environment quality above the middle level was in order from large to small: 2005 (97.37%) > 2017 (91.46%) > 2013 (84.64%). (2) The overall quality of the ecological environment declined during the period of 2005-2013. (3) The overall change ranges from 2013 to 2017 are smaller than those from 2005 to 2013. The area of the deteriorating area decreased by 44.90%, and the area of the constant area and the area of the area that improved increased by 16.17% and 28.72%, respectively. During 2013-2017, the general trend is getting better and better. The improved areas were mainly concentrated in the main urban areas (Taishan District, Daiyue District), eastern Ningyang County, and western Xintai City. The research results can provide a scientific basis for the scientific evaluation of the ecological environment quality during the development and construction of the region, and have important value in the design and application of the ecological environment quality optimization path