Environmental Health

BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF

Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning text-to-image (T2I) models with human preferences. However, RLHF's feedback mechanism also opens new pathways for adversaries. This paper demonstrates the feasibility of hijacking T2I models by poisoning a small fraction of preference data with natural-appearing examples. Specifically, we propose BadReward, a stealthy clea...

Transport Network, Graph, and Air Pollution

Air pollution can be studied in the urban structure regulated by transport networks. Transport networks can be studied as geometric and topological graph characteristics through designed models. Current studies do not offer a comprehensive view as limited models with insufficient features are examined. Our study finds geometric patterns of pollution-indicated transport networks through 0.3 milli...

Neuro-Symbolic Generative Diffusion Models for Physically Grounded, Robust, and Safe Generation

Despite the remarkable generative capabilities of diffusion models, their integration into safety-critical or scientifically rigorous applications r...

anyECG-chat: A Generalist ECG-MLLM for Flexible ECG Input and Multi-Task Understanding

The advent of multimodal large language models (MLLMs) has sparked interest in their application to electrocardiogram (ECG) analysis. However, exist...

Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction.

Heart failure (HF) poses a significant public health challenge, with a rising global mortality rate. Early detection and prevention of HF could signif...

A Comprehensive Exploration of Groundwater Quality of Ambagarh Chowki Region, Chhattisgarh, India: Water Quality Index, Health Risk, and ANN Predictive Modeling.

Access to safe and clean drinking water remains a critical global challenge, with groundwater as a primary source for billions of people. Further, tox...

Improving real-time forecasting of bay water quality by integrating in-situ monitoring, machining learning, and process-based modeling.

Frequent occurrences of disasters such as red tides significantly threaten bay ecosystems, making near real-time water quality forecasting crucial for...

Prediction of enhanced coagulation with varied pre-oxidations for seasonal variations of cyanobacteria-dominated algae-laden water quality.

Enhanced coagulation with pre-oxidation is a cost-effective approach for managing seasonal water quality pollution caused by algal outbreaks and death...

Exploring the link between grandmaternal air pollution exposure and Grandchild's ASD risk: A multigenerational population-based study in California.

BACKGROUND: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with increasing prevalence. While genetics play a strong causal role, amon...

Chitosan-based adsorbents for remediation of toxic dyes from wastewater: A review on adsorption mechanism, reusability, machine learning based modeling and future perspectives.

The disposal of recalcitrant dyes in aquatic environments from various industrial sectors is a threat to both the plant and animal kingdom. The presen...

Faster R-CNN approach for estimating global QRS duration in electrocardiograms with a limited quantity of annotated data.

In electrocardiography (ECG), measurement of QRS duration (QRSd) is crucial for diagnosing conditions such as left bundle branch block. To address the...

A multi-objective optimization model integrating machine learning and time-frequency analysis for supporting nitrogen and phosphorus pollution reduction in Guangzhou city, China.

The unbridled discharge of nitrogen and phosphorus (NP) pollutants is believed to have surpassed ecosystem resilience limits for many regions, which i...

Developmental toxicity: artificial intelligence-powered assessments.

Regulatory agencies require comprehensive toxicity testing for prenatal drug exposure, including new drugs in development, to reduce concerns about de...

Artificial neural network-based fungal chitin production for submicron-chitosan synthesis: effects on bioremediation for heavy metal pollution.

This study focused on optimizing fungal chitin (CT) production from a newly identified Fusarium incarnatum (GenBank: OL314753) for subsequent synthesi...

Research on the influencing factors of PM in China at different spatial scales based on machine learning algorithm.

PM pollution is one of the prominent environmental issues currently faced in China, influenced by various factors and showed significant spatial diffe...

XenoBug: machine learning-based tool to predict pollutant-degrading enzymes from environmental metagenomes.

Application of machine learning-based methods to identify novel bacterial enzymes capable of degrading a wide range of xenobiotics offers enormous pot...

Integrating surface chemistry properties and machine learning to map the toxicity landscape of superparamagnetic iron oxide nanoparticles.

The relationship between Superparamagnetic Iron Oxide Nanoparticles (SPIONs) surface chemistry and their toxicological outcomes is crucial for biomedi...

Comparing neural network architectures for simulating pollutant loads and first flush events in urban watersheds: Balancing specialization and generalization.

This study investigates the effectiveness of artificial neural networks (ANNs) models in predicting urban water quality, specifically focusing on firs...

Refining source-specific lung cancer risk assessment from PM-bound PAHs: Integrating component-based potency factors and machine learning in Ningbo, China.

The component-based potency factor approach, combined with benzo[a]pyrene (BaP) unit risk values from the World Health Organization (WHO), is commonly...

Improved surface NO Retrieval: Double-layer machine learning model construction and spatio-temporal characterization analysis in China (2018-2023).

As an important atmospheric pollutant causing serious harm to human health and the natural environment, monitoring of surface NO (SNO) level is of cri...