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  1. Kumar, Sudhir (Ed.)

    Phylogenetic models have become increasingly complex, and phylogenetic data sets have expanded in both size and richness. However, current inference tools lack a model specification language that can concisely describe a complete phylogenetic analysis while remaining independent of implementation details. We introduce a new lightweight and concise model specification language, ‘LPhy’, which is designed to be both human and machine-readable. A graphical user interface accompanies ‘LPhy’, allowing users to build models, simulate data, and create natural language narratives describing the models. These narratives can serve as the foundation for manuscript method sections. Additionally, we present a command-line interface for converting LPhy-specified models into analysis specification files (in XML format) compatible with the BEAST2 software platform. Collectively, these tools aim to enhance the clarity of descriptions and reporting of probabilistic models in phylogenetic studies, ultimately promoting reproducibility of results.

     
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    Free, publicly-accessible full text available July 18, 2024
  2. null (Ed.)
    In this article, we propose a novel semicentralized deep deterministic policy gradient (SCDDPG) algorithm for cooperative multiagent games. Specifically, we design a two-level actor-critic structure to help the agents with interactions and cooperation in the StarCraft combat. The local actor-critic structure is established for each kind of agents with partially observable information received from the environment. Then, the global actor-critic structure is built to provide the local design an overall view of the combat based on the limited centralized information, such as the health value. These two structures work together to generate the optimal control action for each agent and to achieve better cooperation in the games. Comparing with the fully centralized methods, this design can reduce the communication burden by only sending limited information to the global level during the learning process. Furthermore, the reward functions are also designed for both local and global structures based on the agents' attributes to further improve the learning performance in the stochastic environment. The developed method has been demonstrated on several scenarios in a real-time strategy game, i.e., StarCraft. The simulation results show that the agents can effectively cooperate with their teammates and defeat the enemies in various StarCraft scenarios. 
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  3. null (Ed.)
    Increasingly, individuals and companies adopt a cloud service provider as a primary data and IT infrastructure platform. The remote access of the data inevitably brings the issue of trust. Data encryption is necessary to keep sensitive information secure and private on the cloud. Yet adversaries can still learn valuable information regarding encrypted data by observing data access patterns. To solve such problem, Oblivious RAMs (ORAMs) are proposed to completely hide access patterns. However, most ORAM constructions are expensive and not suitable to deploy in a database for supporting query processing over large data. Furthermore, an ORAM processes queries synchronously, hence, does not provide high throughput for concurrent query processing. In this work, we design a practical oblivious query processing framework to enable efficient query processing over a cloud database. In particular, we focus on processing multiple range and kNN queries asynchronously and concurrently with high throughput. The key idea is to integrate indices into ORAM which leverages a suite of optimization techniques (e.g., oblivious batch processing and caching). The effectiveness and efficiency of our oblivious query processing framework is demonstrated through extensive evaluations over large datasets. Our construction shows an order of magnitude speedup in comparison with other baselines. 
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  4. Serverless computing has gained attention due to its fine-grained provisioning, large-scale multi-tenancy, and on-demand scaling. However, it also forces applications to externalize state in remote storage, adding substantial overheads. To fix this "data shipping problem" we built Shredder, a low-latency multi-tenant cloud store that allows small units of computation to be performed directly within storage nodes. Storage tenants provide Shredder with JavaScript functions (or WebAssembly programs), which can interact directly with data without moving them over the network. The key challenge in Shredder is safely isolating thousands of tenant storage functions while minimizing data interaction costs. Shredder uses a unique approach where its data store and networking paths are implemented in native code to ensure performance, while isolated tenant functions interact with data using a V8-specific intermediate representation that avoids expensive cross-protection-domain calls and data copying. As a result, Shredder can execute 4 million remotely-invoked tenant functions per second spread over thousands of tenants with median and 99th-percentile response latencies of less than 50 μs and 500 μs, respectively. Our evaluation shows that Shredder achieves a 14% to 78% speedup against conventional remote storage when fetching items with just one to three data dependencies between them. We also demonstrate Shredder's effectiveness in accelerating data-intensive applications, including a k-hop query on social graphs that shows orders of magnitude gain. 
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  5. This paper proposes an intelligent multi-agent approach in a real-time strategy game, StarCraft, based on the deep deterministic policy gradients (DDPG) techniques. An actor and a critic network are established to estimate the optimal control actions and corresponding value functions, respectively. A special reward function is designed based on the agents' own condition and enemies' information to help agents make intelligent control in the game. Furthermore, in order to accelerate the learning process, the transfer learning techniques are integrated into the training process. Specifically, the agents are trained initially in a simple task to learn the basic concept for the combat, such as detouring moving, avoiding and joining attacking. Then, we transfer this experience to the target task with a complex and difficult scenario. From the experiment, it is shown that our proposed algorithm with transfer learning can achieve better performance. 
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