This paper presents a sound source localization strategy that relies on a microphone array embedded in an unmanned ground vehicle and an asynchronous close-talking microphone near the operator. A signal coarse alignment strategy is combined with a time-domain acoustic echo cancellation algorithm to estimate a time-frequency ideal ratio mask to isolate the target speech from interferences and environmental noise. This allows selective sound source localization, and provides the robot with the direction of arrival of sound from the active operator, which enables rich interaction in noisy scenarios. Results demonstrate an average angle error of 4 degrees and an accuracy within 5 degrees of 95% at a signal-to-noise ratio of 1dB, which is significantly superior to the state-of-the-art localization methods.

Recently, Large Language Models (LLMs) have made substantial progress in code generation, but they still frequently generate code containing logic errors or syntax bugs. While research has focused on improving performance through fine-tuning and data collection, less attention has been given to analyzing error patterns and employing prompt-engineering to address these issues. Existing benchmarks primarily assess LLMs on easy to intermediate-level coding tasks, often neglecting more complex challenges involving advanced algorithms and data structures. Additionally, data contamination in these benchmarks limits their ability to accurately measure the capability of LLMs in code generation. In this paper, we present the new USACO Benchmark, derived from the USA Computing Olympiad (USACO) competition, to evaluate 11 closed and open-source LLMs. Through a detailed analysis, we identify common code generation errors across the models and propose Hint-Driven Prompts to address logic errors, alongside the Syntax Mitigation Prompt to reduce syntax bugs. Our results demonstrate that the Hint-Driven Prompt boosts pass rates for DBRX 132B, Deepseek-Coder 33B, Codegemma 7B, Codellama 7B, Llama 3, and GPT-4o by 6.6×, 4.7×, 3×, 2.5×, 2.1×, and 25%, respectively. Additionally, the Syntax Mitigation Prompt significantly reduces syntax errors, with reductions of 71.32% for Codegemma 7B, 25.56% for Deepseek-Coder 33B, 23.39% for Llama 3, and 11.19% for Codellama 70B.

The use of computational electromagnetics (CEM) techniques has greatly advanced nanophotonics. The applications of nanophotonics in turn motivates the development of efficient highly tailored algorithms for specific application domains. In this paper, we will discuss some specific considerations in seeking to advance CEM for nanophotonic design and discovery, with examples drawn from the design of aperiodic nanophotonic structures for on-chip information processing applications.

This paper presents an adaptable service assurance system that can be easily integrated into an operation support system for the telecommunication business. It adopts technologies like CORBA, Java, and workflow management. It also adopts off-the-shelf products to shorten the development cycle.