With the rapid rise of generative AI in higher education and the unreliability of current AI detection tools, developing policies that encourage student learning and critical thinking has become increasingly important. This study examines student use and perceptions of generative AI across three proof-based undergraduate mathematics courses: a first-semester abstract algebra course, a topology course and a second-semester abstract algebra course. In each case, course policy permitted some use of generative AI. Drawing on survey responses and student interviews, we analyze how students engaged with AI tools, their perceptions of generative AI's usefulness and limitations, and what implications these perceptions hold for teaching proof-based mathematics. We conclude by discussing future considerations for integrating generative AI into proof-based mathematics instruction.

As unmanned aerial vehicles (UAVs) become increasingly prevalent in both consumer and defense applications, the need for reliable, modality-specific classification systems grows in urgency. This paper addresses the challenge of data scarcity in UAV audio classification by expanding on prior work through the integration of pre-trained deep learning models, parameter-efficient fine-tuning (PEFT) strategies, and targeted data augmentation techniques. Using a custom dataset of 3,100 UAV audio clips (15,500 seconds) spanning 31 distinct drone types, we evaluate the performance of transformer-based and convolutional neural network (CNN) architectures under various fine-tuning configurations. Experiments were conducted with five-fold cross-validation, assessing accuracy, training efficiency, and robustness. Results show that full fine-tuning of the EfficientNet-B0 model with three augmentations achieved the highest validation accuracy (95.95), outperforming both the custom CNN and transformer-based models like AST. These findings suggest that combining lightweight architectures with PEFT and well-chosen augmentations provides an effective strategy for UAV audio classification on limited datasets. Future work will extend this framework to multimodal UAV classification using visual and radar telemetry.

Accurate and interpretable detection of depressive language in social media is useful for early interventions of mental health conditions, and has important implications for both clinical practice and broader public health efforts. In this paper, we investigate the performance of large language models (LLMs) and traditional machine learning classifiers across three classification tasks involving social media data: binary depression classification, depression severity classification, and differential diagnosis classification among depression, PTSD, and anxiety. Our study compares zero-shot LLMs with supervised classifiers trained on both conventional text embeddings and LLM-generated summary embeddings. Our experiments reveal that while zero-shot LLMs demonstrate strong generalization capabilities in binary classification, they struggle with fine-grained ordinal classifications. In contrast, classifiers trained on summary embeddings generated by LLMs demonstrate competitive, and in some cases superior, performance on the classification tasks, particularly when compared to models using traditional text embeddings. Our findings demonstrate the strengths of LLMs in mental health prediction, and suggest promising directions for better utilization of their zero-shot capabilities and context-aware summarization techniques.

M ETHODS A. Multi-Agent Debate Framework We propose a multi-agent debate framework for phishing email detection, composed of three components: two debater agents, a pre-defined and scripted debate procedure, and a judge agent. The debater agents consist of two LLM-based instances, which may be instantiated from the same or different models. The first agent is prompted to argue that the given email is a phishing attempt, while the second agent is prompted to respond to the first agent's output by countering those claims and arguing for the email's legitimacy. The two agents then engage in another round to make sure that the arguments are well-formulated while maintaining computational efficiency. The debate procedure is pre-defined and scripted to generate template prompts for each email in the dataset: 1) Round One: Carefully analyze the following email and argue why it is likely to be a phishing attempt (Agent 1) Carefully analyze the following email and argue why it is likely to be legitimate and not a phishing attempt (Agent 2) 2) Round Two: Given your opponent's rebuttal, reinforce your position that the following email is a phishing attempt (Agent 1) Given your opponent's rebuttal, reinforce your position that the following email is not a phishing attempt (Agent 2) Arguments made by the two agents are logged for subsequent judge evaluation.

Retrieval augmented generation has revolutionized large language model (LLM) outputs by providing factual supports. Nevertheless, it struggles to capture all the necessary knowledge for complex reasoning questions. Existing retrieval methods typically divide reference documents into passages, treating them in isolation. These passages, however, are often interrelated, such as passages that are contiguous or share the same keywords. Therefore, recognizing the relatedness is crucial for enhancing the retrieval process. In this paper, we propose a novel retrieval method, called GNN-Ret, which leverages graph neural networks (GNNs) to enhance retrieval by considering the relatedness between passages. Specifically, we first construct a graph of passages by connecting passages that are structure-related and keyword-related. A graph neural network (GNN) is then leveraged to exploit the relationships between passages and improve the retrieval of supporting passages. Furthermore, we extend our method to handle multi-hop reasoning questions using a recurrent graph neural network (RGNN), named RGNN-Ret. At each step, RGNN-Ret integrates the graphs of passages from previous steps, thereby enhancing the retrieval of supporting passages. Extensive experiments on benchmark datasets demonstrate that GNN-Ret achieves higher accuracy for question answering with a single query of LLMs than strong baselines that require multiple queries, and RGNN-Ret further improves accuracy and achieves state-of-the-art performance, with up to 10.4% accuracy improvement on the 2WikiMQA dataset.

Federated Learning (FL) has emerged as a promising approach for collaborative model training without sharing private data. However, privacy concerns regarding information exchanged during FL have received significant research attention. Gradient Inversion Attacks (GIAs) have been proposed to reconstruct the private data retained by local clients from the exchanged gradients. While recovering private data, the data dimensions and the model complexity increase, which thwart data reconstruction by GIAs. Existing methods adopt prior knowledge about private data to overcome those challenges. In this paper, we first observe that GIAs with gradients from a single iteration fail to reconstruct private data due to insufficient dimensions of leaked gradients, complex model architectures, and invalid gradient information. We investigate a Temporal Gradient Inversion Attack with a Robust Optimization framework, called TGIAs-RO, which recovers private data without any prior knowledge by leveraging multiple temporal gradients. To eliminate the negative impacts of outliers, e.g., invalid gradients for collaborative optimization, robust statistics are proposed. Theoretical guarantees on the recovery performance and robustness of TGIAs-RO against invalid gradients are also provided. Extensive empirical results on MNIST, CIFAR10, ImageNet and Reuters 21578 datasets show that the proposed TGIAs-RO with 10 temporal gradients improves reconstruction performance compared to state-of-the-art methods, even for large batch sizes (up to 128), complex models like ResNet18, and large datasets like ImageNet (224*224 pixels). Furthermore, the proposed attack method inspires further exploration of privacy-preserving methods in the context of FL.

Computer prodigy Hamilton was just 32 years old when Apollo 11 put men on the moon, guided by her innovative software that saved the mission from being aborted minutes before landing on the lunar surface. The Apollo 11 moon landing was one giant leap for womankind. Credit Margaret Hamilton, a 32-year-old mother and computer whiz at the Massachusetts Institute of Technology, who wrote the software that placed Neil Armstrong and Buzz Aldrin on the moon on July 20, 1969. She also worked on the five moon-landing missions that followed. The director of software engineering at MIT's Instrumentation Laboratory, Hamilton was a pioneer of computer science in a transformative era, and on a transformative mission, in human history.

Navigation represents one of the most heavily studied topics in robotics [3]. It is often approached in terms of mapping and planning: constructing a geometric representation of the world from observations, then planning through this model using motion planning algorithms [4-6]. However, such geometric approaches abstract away significant physical and semantic aspects of the navigation problem that in practice leave a range of real-world situations difficult to handle (see Figure 1). These challenges require special handling, resulting in complex systems with many components. Some works have sought to incorporate machine learning techniques to either learn navigational skills from simulation or to learn perception systems for navigation for human-provided labels. In this article, we instead argue that learned navigational models, trained directly on real-world experience rather than human-provided labels or simulators, provide the most promising long-term direction for a general solution to navigation. We refer to such learning approaches as experiential learning, because they learn directly from past experience of performing real-world navigation. As we will discuss in Section 2, such methods relate closely to reinforcement learning.

In a recent filing, Microsoft told New Zealand's Commerce Commission that Blizzard Activision produces no "must-have" games. Weird thing to say when the company plans to spend $68.7 billion to buy the gaming giant behind Call of Duty, Overwatch, Diablo, World of Warcraft and plenty more. In the document, Microsoft said: "There is nothing unique about the video games developed and published by Activision Blizzard that is a'must have' for rival PC and console video game distributors that give rise to a foreclosure concern." Attempting to downplay the importance of Call of Duty is just one of the ways Microsoft has tried to placate regulators. In February, the company pledged it would continue to make the franchise available on PlayStation consoles beyond any existing agreements between Sony and Activision. Apple's 10.2-inch iPad is back on sale for $300 at Amazon Sony is retiring the PlayStation 5's Accolades feature because people aren't nice An e-bike- and scooter-sharing startup co-founded by Olympian Usain Bolt appears to have stopped operations.

His case rests on a viral video he tweeted last month of a goldfish driving a water-tank-equipped robotic vehicle down the side of a street and inside his lab at Ben-Gurion University of the Negev in Israel. The roboride was part of a scientific study to test whether goldfish had the mental acuity to navigate a terrestrial environment toward a target using a machine. The six goldfish that took part in driver's training passed their test. They weren't the first to cross the finish line. Other neuroscientists have taught rats to drive cars as part of experiments testing how experience affects learning.