Breakthroughs, discoveries, and DIY tips sent every weekday. Oyster larvae that grow in water with traces of common drugs such as cocaine, ketamine, and fentanyl are slower swimmers that appear more stressed. This new research indicates that the common drugs do have an effect on oyster larvae that are found in contaminated water. The results were presented this week at the Society for Risk Analysis' annual conference and published in the journal All sorts of pharmaceuticals, from pain relievers to illegal drugs, can make it into the water supply via human excretion, manufacturing plants, or if they are flushed down the toilet . While that water does go through wastewater treatment, pharmaceuticals can pass right through.

As dawn spread over San Antonio on September 9, 2021, almond-colored smoke began to fill the sky above the city's Far West Side. The plumes were whorling off the top of a 132-foot-tall cell tower that overshadows an office park just north of SeaWorld. At a hotel a mile away, a paramedic snapped a photo of the spectacle and posted it to the r/sanantonio subreddit. "Cell tower on fire around 1604 and Culebra," he wrote. In typical Reddit fashion, the comments section piled up with corny jokes. "Blazing 5G speeds," quipped one user. "I hope no one inhales those fumes, the Covid transmission via 5G will be a lot more potent that way," wrote another, in a swipe at the conspiracy theorists who claim that radiation from 5G towers caused the Covid-19 pandemic. The wisecracks went on: "Can you hear me now?" "Great, some hero trying to save us from 5G." That self-styled hero was actually lurking in the comments. As he followed the thread on his phone, Sean Aaron Smith delighted in the sheer volume of attention the tower fire was receiving, even if most of it dripped with sarcasm. A lean, tattooed--and until recently, entirely apolitical--27-year-old, Smith had come to view 5G as the linchpin of a globalist plot to zombify humanity. To resist that supposed scheme, he'd spent the past five months setting Texas cell towers ablaze. Smith's crude and quixotic campaign against 5G was precisely the sort of security threat that was fast becoming one of the US government's top concerns in 2021.

Tree ensembles are widely recognized for their effectiveness in classification tasks, achieving state-of-the-art performance across diverse domains, including bioinformatics, finance, and medical diagnosis. With increasing emphasis on data privacy and the \textit{right to be forgotten}, several unlearning algorithms have been proposed to enable tree ensembles to forget sensitive information. However, existing methods are often tailored to a particular model or rely on the discrete tree structure, making them difficult to generalize to complex ensembles and inefficient for large-scale datasets. To address these limitations, we propose FUTURE, a novel unlearning algorithm for tree ensembles. Specifically, we formulate the problem of forgetting samples as a gradient-based optimization task. In order to accommodate non-differentiability of tree ensembles, we adopt the probabilistic model approximations within the optimization framework. This enables end-to-end unlearning in an effective and efficient manner. Extensive experiments on real-world datasets show that FUTURE yields significant and successful unlearning performance.

Threading the Needle: T est and Evaluation of Early Stage UAS Capabilities to Autonomously Navigate GPS-Denied Environments in the DARPA Fast Lightweight Autonomy (FLA) Program Adam Norton 1 and Holly A. Y anco 1 Abstract -- The DARPA Fast Lightweight Autonomy (FLA) program (2015-2018) served as a significant milestone in the development of UAS, particularly for autonomous navigation through unknown GPS-denied environments. Three performing teams developed UAS using a common hardware platform, focusing their contributions on autonomy algorithms and sensing. Several experiments were conducted that spanned indoor and outdoor environments, increasing in complexity over time. This paper reviews the testing methodology developed in order to benchmark and compare the performance of each team, each of the FLA Phase 1 experiments that were conducted, and a summary of the Phase 1 results. I NTRODUCTION The past 25 years of research and development in aerial robotics has seen tremendous growth in the adoption of systems as well as the advancement of capabilities including increased speed, more reliable autonomy, and powerful onboard computing.

The robot manipulation ecosystem currently faces issues with integrating open-source components and reproducing results. This limits the ability of the community to benchmark and compare the performance of different solutions to one another in an effective manner, instead relying on largely holistic evaluations. As part of the COMPARE Ecosystem project, we are developing modular grasping and manipulation pipeline infrastructure in order to streamline performance benchmarking. The infrastructure will be used towards the establishment of standards and guidelines for modularity and improved open-source development and benchmarking. This paper provides a high-level overview of the architecture of the pipeline infrastructure, experiments conducted to exercise it during development, and future work to expand its modularity.

Buffer is All Y ou Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering Xingyu Lyu, Ning Wang, Y ang Xiao, Shixiong Li, Tao Li, Danjue Chen, Yimin Chen Miner School of Computer and Information Sciences, University of Massachusetts Lowell, USA, Department of Computer Science and Engineering, University of South Florida, USA, Department of Computer Science, University of Kentucky, Department of Computer and Information Technology, Purdue University, USA, Department of Civil, Construction, and Environmental Engineering, North Carolina State University, USA {xingyu_lyu, shixiong_li, ian_chen}@uml.edu, Abstract --Federated Learning (FL) is a popular paradigm enabling clients to jointly train a global model without sharing raw data. However, FL is known to be vulnerable towards backdoor attacks due to its distributed nature. Here we propose FLBuff for tackling backdoor attacks even under non-iids. The main challenge for such defenses is that non-iids bring benign and malicious updates closer, hence harder to separate. FLBuff is inspired by our insight that non-iids can be modeled as omni-directional expansion in representation space while backdoor attacks as uni-directional. Comprehensive evaluations demonstrate that FLBuff consistently outperforms state-of-the-art defenses.

Federated Learning is a popular paradigm that enables remote clients to jointly train a global model without sharing their raw data. However, FL has been shown to be vulnerable towards model poisoning attacks due to its distributed nature. Particularly, attackers acting as participants can upload arbitrary model updates that effectively compromise the global model of FL. While extensive research has been focusing on fighting against these attacks, we find that most of them assume data at remote clients are under iid while in practice they are inevitably non-iid. Our benchmark evaluations reveal that existing defenses generally fail to live up to their reputation when applied to various non-iid scenarios. In this paper, we propose a novel approach, GeminiGuard, that aims to address such a significant gap. We design GeminiGuard to be lightweight, versatile, and unsupervised so that it aligns well with the practical requirements of deploying such defenses. The key challenge from non-iids is that they make benign model updates look more similar to malicious ones. GeminiGuard is mainly built on two fundamental observations: (1) existing defenses based on either model-weight analysis or latent-space analysis face limitations in covering different MPAs and non-iid scenarios, and (2) model-weight and latent-space analysis are sufficiently different yet potentially complementary methods as MPA defenses. We hence incorporate a novel model-weight analysis component as well as a custom latent-space analysis component in GeminiGuard, aiming to further enhance its defense performance. We conduct extensive experiments to evaluate our defense across various settings, demonstrating its effectiveness in countering multiple types of untargeted and targeted MPAs, including adaptive ones. Our comprehensive evaluations show that GeminiGuard consistently outperforms SOTA defenses under various settings.

Large language models (LLMs) are the foundation of many AI applications today. However, despite their remarkable proficiency in generating coherent text, questions linger regarding their ability to perform fine-grained linguistic annotation tasks, such as detecting nouns or verbs, or identifying more complex syntactic structures like clauses in input texts. These tasks require precise syntactic and semantic understanding of input text, and when LLMs underperform on specific linguistic structures, it raises concerns about their reliability for detailed linguistic analysis and whether their (even correct) outputs truly reflect an understanding of the inputs. In this paper, we empirically study the performance of recent LLMs on fine-grained linguistic annotation tasks. Through a series of experiments, we find that recent LLMs show limited efficacy in addressing linguistic queries and often struggle with linguistically complex inputs. We show that the most capable LLM (Llama3-70b) makes notable errors in detecting linguistic structures, such as misidentifying embedded clauses, failing to recognize verb phrases, and confusing complex nominals with clauses. Our results provide insights to inform future advancements in LLM design and development.

As compliance with privacy regulations becomes increasingly critical, the growing demand for data privacy has highlighted the significance of machine unlearning in many real world applications, such as social network and recommender systems, many of which can be represented as graph-structured data. However, existing graph unlearning algorithms indiscriminately modify edges or nodes from well-trained models without considering the potential impact of such structural modifications on fairness. For example, forgetting links between nodes with different genders in a social network may exacerbate group disparities, leading to significant fairness concerns. To address these challenges, we propose a novel approach that jointly optimizes the graph structure and the corresponding model for fair unlearning tasks. Specifically,our approach rewires the graph to enhance unlearning efficiency by removing redundant edges that hinder forgetting while preserving fairness through targeted edge augmentation. Additionally, we introduce a worst-case evaluation mechanism to assess the reliability of fair unlearning performance. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed approach in achieving superior unlearning outcomes.

Language models are prone to dataset biases, known as shortcuts and spurious correlations in data, which often result in performance drop on new data. We present a new debiasing framework called ``FairFlow'' that mitigates dataset biases by learning to be undecided in its predictions for data samples or representations associated with known or unknown biases. The framework introduces two key components: a suite of data and model perturbation operations that generate different biased views of input samples, and a contrastive objective that learns debiased and robust representations from the resulting biased views of samples. Experiments show that FairFlow outperforms existing debiasing methods, particularly against out-of-domain and hard test samples without compromising the in-domain performance