Table summarization is a crucial task aimed at condensing information from tabular data into concise and comprehensible textual summaries. However, existing approaches often fall short of adequately meeting users' information and quality requirements and tend to overlook the complexities of real-world queries. In this paper, we propose a novel method to address these limitations by introducing query-focused multi-table summarization. Our approach, which comprises a table serialization module, a summarization controller, and a large language model (LLM), utilizes textual queries and multiple tables to generate query-dependent table summaries tailored to users' information needs. To facilitate research in this area, we present a comprehensive dataset specifically tailored for this task, consisting of 4909 query-summary pairs, each associated with multiple tables. Through extensive experiments using our curated dataset, we demonstrate the effectiveness of our proposed method compared to baseline approaches. Our findings offer insights into the challenges of complex table reasoning for precise summarization, contributing to the advancement of research in query-focused multi-table summarization.

Data-driven, model-free analytics are natural choices for discovery and forecasting of complex, nonlinear systems. Methods that operate in the system state-space require either an explicit multidimensional state-space, or, one approximated from available observations. Since observational data are frequently sampled with noise, it is possible that noise can corrupt the state-space representation degrading analytical performance. Here, we evaluate the synthesis of empirical mode decomposition with empirical dynamic modeling, which we term empirical mode modeling, to increase the information content of state-space representations in the presence of noise. Evaluation of a mathematical, and, an ecologically important geophysical application across three different state-space representations suggests that empirical mode modeling may be a useful technique for data-driven, model-free, state-space analysis in the presence of noise.

Teams of researchers are hoping to give life to a six-foot, 330-pound humanoid robot at the the Robotics Challenge in Homestead, Florida on December 20 and 21. The teams are expected to enable the robot--and others--to autonomously walk, use human tools, and drive a car. The event is sponsored by DARPA, or the Defense Advanced Research Projects Agency, an arm of the U.S. Department of Defense that focuses on advanced research. DARPA said the program at the Homestead Miami Speedway, is aimed at developing robots capable of working hand-in-hand with humans during natural or man-made disasters. "Think of the nuclear plants that were damaged during the tsunami in Japan," said David Conner, a senior research scientist with TORC Robotics, whose team is includes with roboticists from Virginia Polytechnic Institute, better known as Virginia Tech.