Finding relevant tables among databases, lakes, and repositories is the first step in extracting value from data. Such a task remains difficult because assessing whether a table is relevant to a problem does not always depend only on its content but also on the context, which is usually tribal knowledge known to the individual or team. While tools like data catalogs and academic data discovery systems target this problem, they rely on keyword search or more complex interfaces, limiting non-technical users' ability to find relevant data. The advent of large language models (LLMs) offers a unique opportunity for users to ask questions directly in natural language, making dataset discovery more intuitive, accessible, and efficient. In this paper, we introduce Pneuma , a retrieval-augmented generation (RAG) system designed to efficiently and effectively discover tabular data. Pneuma leverages large language models (LLMs) for both table representation and table retrieval. For table representation, Pneuma preserves schema and row-level information to ensure comprehensive data understanding. For table retrieval, Pneuma augments LLMs with traditional information retrieval techniques, such as full-text and vector search, harnessing the strengths of both to improve retrieval performance. To evaluate Pneuma , we generate comprehensive benchmarks that simulate table discovery workload on six real-world datasets including enterprise data, scientific databases, warehousing data, and open data. Our results demonstrate that Pneuma outperforms widely used table search systems (such as full-text search and state-of-the-art RAG systems) in accuracy and resource efficiency.

Course-based Undergraduate Research Experience (CURE) is a pedagogy for engaging an entire class in the scientific exploration of real research problems with unknown solutions that have impact beyond the classroom. Since CUREs originated in biological sciences, there are unique challenges to adapting the CURE model for computer science. We designed a CURE by aligning its principles with entrepreneurial mindset (EM) and software development practices by applying them in a programming project. This experience report introduces the design and adoption of a CURE project for an introductory programming class (CS1).

Many real-world optimisation problems are defined over both categorical and continuous variables, yet efficient optimisation methods such as Bayesian Optimisation (BO) are ill-equipped to handle such mixed-variable search spaces. The optimisation breadth introduced by categorical variables in the mixed-input setting has seen recent approaches operating on local trust regions, but these methods can be greedy in suboptimal regions of the search space. In this paper, we adopt a holistic view and aim to consolidate optimisation of the categorical and continuous sub-spaces under a single acquisition metric. We develop a tree-based method which retains a global view of the optimisation spaces by identifying regions in the search space with high potential candidates which we call value proposals. Our method uses these proposals to make selections on both the categorical and continuous components of the input. We show that this approach significantly outperforms existing mixed-variable optimisation approaches across several mixed-variable black-box optimisation tasks.

We present a fiber-based snapshot imaging spectrometer capable of imaging in VIS and NIR spectral windows. The system's principle is based on the application of a custom imaging fiber bundle to re-organize the input vs. output of the fiber structure. While, the input consists of densely populated fibers, the output provides void spaces between the fibers to accommodate spectral information and non-scanning datacube acquisition. Recently, we presented a proof of concept paper demonstrating the general system's capabilities [1]. The prototype, however, experienced significant light loss while still providing good imaging performance. This paper focuses on the throughput analysis and improvement of the light collection efficiency. We were able to define configurations that improve throughput by over 10+ fold and enable integration times below 1 ms. Here, we briefly describe the system's principle, discuss re-imaging configurations, and present examples of imaging results.

The Thermal Infrared Sensor 2 (TIRS-2) will fly aboard the Landsat 9 spacecraft and leverages the Thermal Infrared Sensor (TIRS) design currently flying on Landsat 8. TIRS-2 will provide similar science data as TIRS, but is not a build-to-print rebuild due to changes in requirements and improvements in absolute accuracy. The heritage TIRS design has been modified to reduce the influence of stray light and to add redundancy for higher reliability over a longer mission life. The TIRS-2 development context differs from the TIRS scenario, adding to the changes. The TIRS-2 team has also learned some lessons along the way.

Vulnerable IoT devices are powerful platforms for building botnets that cause billion-dollar losses every year. In this work, we study Bashlite botnets and their successors, Mirai botnets. In particular, we focus on the evolution of the malware as well as changes in botnet operator behavior. We use monitoring logs from 47 honeypots collected over 11 months. Our results shed new light on those botnets, and complement previous findings by providing evidence that malware, botnet operators, and malicious activity are becoming more sophisticated. Compared to its predecessor, we find Mirai uses more resilient hosting and control infrastructures, and supports more effective attacks.