Abstract Genomics data is essential in biological and medical domains, and bioinformatics analysts often manually create circos plots to analyze the data and extract valuable insights. However, creating circos plots is complex, as it requires careful design for multiple track attributes and positional relationships between them. Typically, analysts often seek inspiration from existing circos plots, and they have to iteratively adjust and refine the plot to achieve a satisfactory final design, making the process both tedious and time‐intensive. To address these challenges, we propose IntelliCircos, an AI‐powered interactive authoring tool that streamlines the process from initial visual design to the final implementation of circos plots. Specifically, we build a new dataset containing 4396 circos plots with corresponding annotations and configurations, which are extracted and labeled from published papers. With the dataset, we further identify track combination patterns, and utilize Large Language Model (LLM) to provide domain‐specific design recommendations and configuration references to navigate the design of circos plots. We conduct a user study with 8 bioinformatics analysts to evaluate IntelliCircos, and the results demonstrate its usability and effectiveness in authoring circos plots.

Weather forecast ensembles are commonly used to assess the uncertainty and confidence of weather predictions. Conventional methods in meteorology often employ ensemble mean and standard deviation plots, as well as spaghetti plots, to visualize ensemble data. However, these methods suffer from significant information loss and visual clutter. In this paper, we propose a new approach for uncertainty visualization of weather forecast ensembles, including isovalue selection based on information loss and hierarchical visualization that integrates visual abstraction and detail preservation. Our approach uses non-uniform downsampling to select key-isovalues and provides an interactive visualization method based on hierarchical clustering. Firstly, we sample key-isovalues by contour probability similarity and determine the optimal sampling number using an information loss curve. Then, the corresponding isocontours are presented to guide users in selecting key-isovalues. Once the isovalue is chosen, we perform agglomerative hierarchical clustering on the isocontours based on signed distance fields and generate visual abstractions for each isocontour cluster to avoid visual clutter. We link a bubble tree to the visual abstractions to explore the details of isocontour clusters at different levels. We demonstrate the utility of our approach through two case studies with meteorological experts on real-world data. We further validate its effectiveness by quantitatively assessing information loss and visual clutter. Additionally, we confirm its usability through expert evaluation.

The expected regret of any reinforcement learning algorithm is lower bounded by $\Omega\left(\sqrt{DXAT}\right)$ for undiscounted returns, where $D$ is the diameter of the Markov decision process, $X$ the size of the state space, $A$ the size of the action space and $T$ the number of time steps. However, this lower bound is general. A smaller regret can be obtained by taking into account some specific knowledge of the problem structure. In this article, we consider an admission control problem to an $M/M/c/S$ queue with $m$ job classes and class-dependent rewards and holding costs. Queuing systems often have a diameter that is exponential in the buffer size $S$, making the previous lower bound prohibitive for any practical use. We propose an algorithm inspired by UCRL2, and use the structure of the problem to upper bound the expected total regret by $O(S\log T + \sqrt{mT \log T})$ in the finite server case. In the infinite server case, we prove that the dependence of the regret on $S$ disappears.

The wind farm control problem is challenging, since conventional model-based control strategies require tractable models of complex aerodynamical interactions between the turbines and suffer from the curse of dimension when the number of turbines increases. Recently, model-free and multi-agent reinforcement learning approaches have been used to address this challenge. In this article, we introduce WFCRL (Wind Farm Control with Reinforcement Learning), the first suite of multi-agent reinforcement learning environments for the wind farm control problem. WFCRL frames a cooperative Multi-Agent Reinforcement Learning (MARL) problem: each turbine is an agent and can learn to adjust its yaw, pitch or torque to maximize the common objective (e.g. the total power production of the farm). WFCRL also offers turbine load observations that will allow to optimize the farm performance while limiting turbine structural damages. Interfaces with two state-of-the-art farm simulators are implemented in WFCRL: a static simulator (Floris) and a dynamic simulator (FAST.farm). For each simulator, $10$ wind layouts are provided, including $5$ real wind farms. Two state-of-the-art online MARL algorithms are implemented to illustrate the scaling challenges. As learning online on FAST.Farm is highly time-consuming, WFCRL offers the possibility of designing transfer learning strategies from Floris to FAST.Farm.

Good software engineers are essential to the creation of good software. However, most of what we know about software-engineering expertise are vague stereotypes, such as 'excellent communicators' and 'great teammates'. The lack of specificity in our understanding hinders researchers from reasoning about them, employers from identifying them, and young engineers from becoming them. Our understanding also lacks breadth: what are all the distinguishing attributes of great engineers (technical expertise and beyond)? We took a first step in addressing these gaps by interviewing 59 experienced engineers across 13 divisions at Microsoft, uncovering 53 attributes of great engineers. We explain the attributes and examine how the most salient of these impact projects and teams. We discuss implications of this knowledge on research and the hiring and training of engineers.