When one agent interacts with a multi-agent environment, it is challenging to deal with various opponents unseen before.

According to Bayes' theorem, as we update the posterior probability as The changing trends of α are diverse when against different opponents. IOP to accurately model the opponent policy. Figure 7: Performance against different types of opponents, i.e., fixed policy, naïve learner, and Figure 8: Performance against different types of opponents, i.e., fixed policy, naïve learner, and Note that M = 1 is MBOM w/o IOPs. Figure 9: Performance against different types of opponents, i.e., fixed policy, naïve learner, and reasoning learner in Predator-Prey, where x -axis is joint opponent index. Figure 9 shows the performance when against different types of opponents compared with the baselines. For each type, there are ten test joint opponent policies.

Recent advances in reasoning-focused large language models (LLMs) mark a shift from general LLMs toward models designed for complex decision-making, a crucial aspect in medicine. However, their performance in specialized domains like ophthalmology remains underexplored. This study comprehensively evaluated and compared the accuracy and reasoning capabilities of four newly developed reasoning-focused LLMs, namely DeepSeek-R1, OpenAI o1, o3-mini, and Gemini 2.0 Flash-Thinking. Each model was assessed using 5,888 multiple-choice ophthalmology exam questions from the MedMCQA dataset in zero-shot setting. Quantitative evaluation included accuracy, Macro-F1, and five text-generation metrics (ROUGE-L, METEOR, BERTScore, BARTScore, and AlignScore), computed against ground-truth reasonings. Average inference time was recorded for a subset of 100 randomly selected questions. Additionally, two board-certified ophthalmologists qualitatively assessed clarity, completeness, and reasoning structure of responses to differential diagnosis questions.O1 (0.902) and DeepSeek-R1 (0.888) achieved the highest accuracy, with o1 also leading in Macro-F1 (0.900). The performance of models across the text-generation metrics varied: O3-mini excelled in ROUGE-L (0.151), o1 in METEOR (0.232), DeepSeek-R1 and o3-mini tied for BERTScore (0.673), DeepSeek-R1 (-4.105) and Gemini 2.0 Flash-Thinking (-4.127) performed best in BARTScore, while o3-mini (0.181) and o1 (0.176) led AlignScore. Inference time across the models varied, with DeepSeek-R1 being slowest (40.4 seconds) and Gemini 2.0 Flash-Thinking fastest (6.7 seconds). Qualitative evaluation revealed that DeepSeek-R1 and Gemini 2.0 Flash-Thinking tended to provide detailed and comprehensive intermediate reasoning, whereas o1 and o3-mini displayed concise and summarized justifications.

Understanding human instructions to identify the target objects is vital for perception systems. In recent years, the advancements of Large Language Models (LLMs) have introduced new possibilities for image segmentation. In this work, we delve into reasoning segmentation, a novel task that enables segmentation system to reason and interpret implicit user intention via large language model reasoning and then segment the corresponding target. Our work on reasoning segmentation contributes on both the methodological design and dataset labeling. For the model, we propose a new framework named LLM-Seg. LLM-Seg effectively connects the current foundational Segmentation Anything Model and the LLM by mask proposals selection. For the dataset, we propose an automatic data generation pipeline and construct a new reasoning segmentation dataset named LLM-Seg40K. Experiments demonstrate that our LLM-Seg exhibits competitive performance compared with existing methods. Furthermore, our proposed pipeline can efficiently produce high-quality reasoning segmentation datasets. The LLM-Seg40K dataset, developed through this pipeline, serves as a new benchmark for training and evaluating various reasoning segmentation approaches. Our code, models and dataset are at https://github.com/wangjunchi/LLMSeg.

Multiobjective optimization is a hot topic in the artificial intelligence and operations research communities. The design and development of multiobjective methods is a frequent task for researchers and practitioners. As a result of this vibrant activity, a myriad of techniques have been proposed in the literature to date, demonstrating a significant effectiveness for dealing with situations coming from a wide range of real-world areas. This paper is focused on a multiobjective problem related to optimizing Infrastructure-as-Code deployment configurations. The system implemented for solving this problem has been coined as IaC Optimizer Platform (IOP). Despite the fact that a prototypical version of the IOP has been introduced in the literature before, a deeper analysis focused on the resolution of the problem is needed, in order to determine which is the most appropriate multiobjective method for embedding in the IOP. The main motivation behind the analysis conducted in this work is to enhance the IOP performance as much as possible. This is a crucial aspect of this system, deeming that it will be deployed in a real environment, as it is being developed as part of a H2020 European project. Going deeper, we resort in this paper to nine different evolutionary computation-based multiobjective algorithms. For assessing the quality of the considered solvers, 12 different problem instances have been generated based on real-world settings. Results obtained by each method after 10 independent runs have been compared using Friedman's non-parametric tests. Findings reached from the tests carried out lad to the creation of a multi-algorithm system, capable of applying different techniques according to the user's needs.

In the last years, one of the fields of artificial intelligence that has been investigated the most is nature-inspired computing. The research done on this specific topic showcases the interest that sparks in researchers and practitioners, who put their focus on this paradigm because of the adaptability and ability of nature-inspired algorithms to reach high-quality outcomes on a wide range of problems. In fact, this kind of methods has been successfully applied to solve real-world problems in heterogeneous fields such as medicine, transportation, industry, or software engineering. Our main objective with this paper is to describe a tool based on nature-inspired computing for solving a specific software engineering problem. The problem faced consists of optimizing Infrastructure as Code deployment configurations. For this reason, the name of the system is IaC Optimizer Platform. A prototypical version of the IOP was described in previous works, in which the functionality of this platform was introduced. With this paper, we take a step forward by describing the final release of the IOP, highlighting its main contribution regarding the current state-of-the-art, and justifying the decisions made on its implementation. Also, we contextualize the IOP within the complete platform in which it is embedded, describing how a user can benefit from its use. To do that, we also present and solve a real-world use case.

Evolving technology, best practice and landmark evidence in glaucoma care were reviewed by an international expert faculty in session presentations and debates during the 11th Moorfields International Glaucoma Symposium 2019. The authors were meeting chairs and provide an overview of symposium proceedings. Hans Lemij, Rotterdam Eye Hospital, the Netherlands, discussed glaucoma optical coherence tomography (OCT) imaging and automated segmentation issues, noting several common image artefacts. Paul Foster highlighted research by the UK Biobank Eye and Vision Consortium related to cognitive function and the expanding use of OCT imaging in dementia and neurodegeneration research. Findings show that a thinner retinal nerve fibre layer (RNFL) is associated with worse cognitive function in individuals without known neurodegenerative disease, as well as a greater likelihood of future cognitive decline [1]. The Rotterdam Study also revealed an association of retinal neurodegeneration on OCT with an increased risk of dementia, including Alzheimer's disease [2].

SAN FRANCISCO, September 17, 2019 -- Oracle Exadata Database Machine X8M, available today, sets a new bar and changes the dynamics of the database infrastructure market. Exadata X8M combines Intel Optane DC persistent memory and 100 gigabit remote direct memory access (RDMA) over Converged Ethernet (RoCE) to remove storage bottlenecks and dramatically increase performance for the most demanding workloads such as Online Transaction Processing (OLTP), analytics, IoT, fraud detection, and high frequency trading. "With Exadata X8M, we deliver in-memory performance with all the benefits of shared storage for both OLTP and analytics," said Juan Loaiza, executive vice president, mission-critical database technologies, Oracle. "Reducing response times by an order of magnitude using direct database access to shared persistent memory accelerates every OLTP application, and is a game changer for applications that need real-time access to large amounts of data such as fraud detection and personalized shopping." Exadata X8M helps customers perform existing tasks faster and accelerates time-to-insight, while also enabling deeper and more frequent analyses.