Threading the Needle: T est and Evaluation of Early Stage UAS Capabilities to Autonomously Navigate GPS-Denied Environments in the DARPA Fast Lightweight Autonomy (FLA) Program Adam Norton 1 and Holly A. Y anco 1 Abstract -- The DARPA Fast Lightweight Autonomy (FLA) program (2015-2018) served as a significant milestone in the development of UAS, particularly for autonomous navigation through unknown GPS-denied environments. Three performing teams developed UAS using a common hardware platform, focusing their contributions on autonomy algorithms and sensing. Several experiments were conducted that spanned indoor and outdoor environments, increasing in complexity over time. This paper reviews the testing methodology developed in order to benchmark and compare the performance of each team, each of the FLA Phase 1 experiments that were conducted, and a summary of the Phase 1 results. I NTRODUCTION The past 25 years of research and development in aerial robotics has seen tremendous growth in the adoption of systems as well as the advancement of capabilities including increased speed, more reliable autonomy, and powerful onboard computing.

MLLM agents demonstrate potential for complex embodied tasks by retrieving multimodal task-relevant trajectory data. However, current retrieval methods primarily focus on surface-level similarities of textual or visual cues in trajectories, neglecting their effectiveness for the specific task at hand. To address this issue, we propose a novel method, MLLM as ReTriever (MART), which enhances the performance of embodied agents by utilizing interaction data to fine-tune an MLLM retriever based on preference learning, such that the retriever fully considers the effectiveness of trajectories and prioritize them for unseen tasks. We also introduce Trajectory Abstraction, a mechanism that leverages MLLMs' summarization capabilities to represent trajectories with fewer tokens while preserving key information, enabling agents to better comprehend milestones in the trajectory. Experimental results across various environments demonstrate our method significantly improves task success rates in unseen scenes compared to baseline methods. This work presents a new paradigm for multimodal retrieval in embodied agents, by fine-tuning a general-purpose MLLM as the retriever to assess trajectory effectiveness. All benchmark task sets and simulator code modifications for action and observation spaces will be released.

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Serving the international community as the official, exclusive supplier of automated translation software for both the 2022 Beijing Winter Olympic and Paralympic Games and the 2022 World Team Table Tennis Championships; Launching the Super Brain 2030 Plan, a series of strategic initiatives to advance the quality and availability of personal robotics and artificial intelligence solutions to assist families and communities; Achieving significant and sustainable growth amid the ongoing pandemic and challenging economic trends as the only Chinese A-share company to achieve an annual revenue growth rate of over 25% for 10 consecutive years; Earning recognition for our outstanding performance in making environmental, social, and governance (ESG) policies central to the company's advancement by the Hang Seng Corporate Responsibility Benchmark Index.

An artificial intelligence known as'Dragonfly' has been used by researchers to design more efficient batteries. Scientists at Carnegie Mellon have used the tool to design better electrolytes for lithium-ion batteries, which would allow the batteries to charge faster. An electrolyte moves ions – atoms that have been charged by either gaining or losing an electron – between the two electrodes in a battery. Lithium ions are created at the negative electrode, the anode, and flow to the cathode where they gain electrons. When a battery charges, the ions move back to the anode.

SUNNYVALE, Calif., April 18, 2017 –Socionext Inc. and SOINN Inc. today announced initial results of collaboration started in 2016, in which Socionext extracts and delivers biometrics data to the "Artificial Brain SOINN". The companies achieved initial results in reading ultrasound images from Socionext's viewphii mobile ultrasound solution by Artificial Brain SOINN. The results will be introduced at Medtec Japan, held in Tokyo Big Sight, April 19-21, at booths 4505 & 4507. In this initial trial, SOINN learned to read subcutaneous fat thickness from abdominal ultrasound images. The estimations by SOINN were then compared with the reading results by ultrasound technicians.

Datacenters, especially the really big guys known as the Super 7 (Alibaba,, Baidu, Facebook, Google, Microsoft and Tencent), are experiencing significant growth in key workloads that require more performance than can squeezed out of even the fastest CPUs. Applications such as Deep Neural Networks (DNN) for Artificial Intelligences (AIs), complex data analytics, 4K live streaming video and advanced networking and security features are increasingly being offloaded to super-fast accelerators that can provide 10X or more the performance of a CPU. NVIDIA GPUs in particular have benefited enormously from the training portion of machine learning, reporting a 193% Y/Y last quarter in their datacenter segment, which is now approaching a $1B run-rate business. Microsoft has recently announced that Field Programmable Gate Array (FPGA) accelerators have become pervasive in their datacenters. Soon after, announced that Baidu is using their devices for acceleration of machine learning applied to speech processing and autonomous vehicles.