Accurately modeling weather variation pattern from large amount of meteorological variables sequences is increasingly vital for providing efficient weather analysis support for disaster warning.

Objective: Robot-assisted minimally invasive surgery (RMIS) has become the gold standard for a variety of surgical procedures, but the optimal method of training surgeons for RMIS is unknown. We hypothesized that real-time, rather than post-task, error feedback would better increase learning speed and reduce errors. Methods: Forty-two surgical novices learned a virtual version of the ring-on-wire task, a canonical task in RMIS training. We investigated the impact of feedback timing with multi-sensory (haptic and visual) cues in three groups: (1) real-time error feedback, (2) trial replay with error feedback, and (3) no error feedback. Results: Participant performance was evaluated based on the accuracy of ring position and orientation during the task. Participants who received real-time feedback outperformed other groups in ring orientation. Additionally, participants who received feedback in replay outperformed participants who did not receive any error feedback on ring orientation during long, straight path sections. There were no significant differences between groups for ring position overall, but participants who received real-time feedback outperformed the other groups in positional accuracy on tightly curved path sections. Conclusion: The addition of real-time haptic and visual error feedback improves learning outcomes in a virtual surgical task over error feedback in replay or no error feedback at all. Significance: This work demonstrates that multi-sensory error feedback delivered in real time leads to better training outcomes as compared to the same feedback delivered after task completion. This novel method of training may enable surgical trainees to develop skills with greater speed and accuracy.

Accurately modeling weather variation pattern from large amount of meteorological variables sequences is increasingly vital for providing efficient weather analysis support for disaster warning.

Campaigners say a vessel, part of a flotilla carrying aid to Gaza, has been struck in a suspected drone attack. It's the second such suspected attack in two days. Swedish campaigner Greta Thunberg is amongst the activists travelling to Gaza with the flotilla to try and break Israel's naval blockade. BBC Verify has been analysing footage of the incident and has spoken to two weapons experts who say a device found on board after the attack appears to be a grenade. 'I witnessed war crimes' in Gaza, former worker at GHF aid site tells BBC A retired US soldier reveals why he quit working at Israel and US-backed Gaza Humanitarian Foundation aid hubs.

Decision trees are a fundamental tool in machine learning for representing, classifying, and generalizing data. It is desirable to construct ``small'' decision trees, by minimizing either the \textit{size} ($s$) or the \textit{depth} $(d)$ of the \textit{decision tree} (\textsc{DT}). Recently, the parameterized complexity of \textsc{Decision Tree Learning} has attracted a lot of attention. We consider a generalization of \textsc{Decision Tree Learning} where given a \textit{classification instance} $E$ and an integer $t$, the task is to find a ``small'' \textsc{DT} that disagrees with $E$ in at most $t$ examples. We consider two problems: \textsc{DTSO} and \textsc{DTDO}, where the goal is to construct a \textsc{DT} minimizing $s$ and $d$, respectively. We first establish that both \textsc{DTSO} and \textsc{DTDO} are W[1]-hard when parameterized by $s+δ_{max}$ and $d+δ_{max}$, respectively, where $δ_{max}$ is the maximum number of features in which two differently labeled examples can differ. We complement this result by showing that these problems become \textsc{FPT} if we include the parameter $t$. We also consider the kernelization complexity of these problems and establish several positive and negative results for both \textsc{DTSO} and \textsc{DTDO}.

We examine an approval-based model of Liquid Democracy with a budget constraint on voting and delegating costs, aiming to centrally select casting voters ensuring complete representation of the electorate. From a computational complexity perspective, we focus on minimizing overall costs, maintaining short delegation paths, and preventing excessive concentration of voting power. Furthermore, we explore computational aspects of strategic control, specifically, whether external agents can change election components to influence the voting power of certain voters.

On a long-haul flight, there's nothing worse than being sat next to a chatty stranger. But songbirds don't seem to mind, as a new study suggests they are likely to'talk' to other species as they migrate. Last year, a team of scientists discovered that birds seem to'buddy up' with other species at stopover sites during migration, but there was no evidence that different species pair up or communicate vocally on the wing. But now it's been found that the birds may even chat to gather important information about the journey they are on. For their new study the researchers, from the University of Illinois, analysed more than 18,000 hours of recorded flight calls made over three years in eastern North America.

Language agents perform complex tasks by using tools to execute each step precisely. However, most existing agents are based on proprietary models or designed to target specific tasks, such as mathematics or multi-hop question answering. We introduce Husky, a holistic, open-source language agent that learns to reason over a unified action space to address a diverse set of complex tasks involving numerical, tabular, and knowledge-based reasoning. Husky iterates between two stages: 1) generating the next action to take towards solving a given task and 2) executing the action using expert models and updating the current solution state. We identify a thorough ontology of actions for addressing complex tasks and curate high-quality data to train expert models for executing these actions. Our experiments show that Husky outperforms prior language agents across 14 evaluation datasets. Moreover, we introduce HuskyQA, a new evaluation set which stress tests language agents for mixed-tool reasoning, with a focus on retrieving missing knowledge and performing numerical reasoning. Despite using 7B models, Husky matches or even exceeds frontier LMs such as GPT-4 on these tasks, showcasing the efficacy of our holistic approach in addressing complex reasoning problems. Our code and models are available at https://github.com/agent-husky/Husky-v1.

This paper demonstrates that pre-trained language models (PLMs) are strong foundation models for on-device meteorological variables modeling. We present LM-Weather, a generic approach to taming PLMs, that have learned massive sequential knowledge from the universe of natural language databases, to acquire an immediate capability to obtain highly customized models for heterogeneous meteorological data on devices while keeping high efficiency. Concretely, we introduce a lightweight personalized adapter into PLMs and endows it with weather pattern awareness. During communication between clients and the server, low-rank-based transmission is performed to effectively fuse the global knowledge among devices while maintaining high communication efficiency and ensuring privacy. Experiments on real-wold dataset show that LM-Weather outperforms the state-of-the-art results by a large margin across various tasks (e.g., forecasting and imputation at different scales). We provide extensive and in-depth analyses experiments, which verify that LM-Weather can (1) indeed leverage sequential knowledge from natural language to accurately handle meteorological sequence, (2) allows each devices obtain highly customized models under significant heterogeneity, and (3) generalize under data-limited and out-of-distribution (OOD) scenarios.

Generative deep learning models are able to create realistic audio and video. This technology has been used to impersonate the faces and voices of individuals. These ``deepfakes'' are being used to spread misinformation, enable scams, perform fraud, and blackmail the innocent. The technology continues to advance and today attackers have the ability to generate deepfakes in real-time. This new capability poses a significant threat to society as attackers begin to exploit the technology in advances social engineering attacks. In this paper, we discuss the implications of this emerging threat, identify the challenges with preventing these attacks and suggest a better direction for researching stronger defences.