Molecular property prediction has attracted substantial attention recently. Accurate prediction of drug properties relies heavily on effective molecular representations. The structures of chemical compounds are commonly represented as graphs or SMILES sequences. Recent advances in learning drug properties commonly employ Graph Neural Networks (GNNs) based on the graph representation. For the SMILES representation, Transformer-based architectures have been adopted by treating each SMILES string as a sequence of tokens. Because each representation has its own advantages and disadvantages, combining both representations in learning drug properties is a promising direction. We propose a method named Dual-Modality Cross-Attention (DMCA) that can effectively combine the strengths of two representations by employing the cross-attention mechanism. DMCA was evaluated across eight datasets including both classification and regression tasks. Results show that our method achieves the best overall performance, highlighting its effectiveness in leveraging the complementary information from both graph and SMILES modalities.

Large-N nationally representative surveys, which have profoundly shaped American politics scholarship, represent related but distinct domains -a key condition for transfer learning applications. These surveys are related through their shared demographic, party identification, and ideological variables, yet differ in that individual surveys often lack specific policy preference questions that researchers require. Our study introduces a novel application of transfer learning (TL) to address these gaps, marking the first systematic use of TL paradigms in the context of survey data. Specifically, models pre-trained on the Cooperative Election Study (CES) dataset are fine-tuned for use in the American National Election Studies (ANES) dataset to predict policy questions based on demographic variables. Even with a naive architecture, our transfer learning approach achieves approximately 92 percentage accuracy in predicting missing variables across surveys, demonstrating the robust potential of this method. Beyond this specific application, our paper argues that transfer learning is a promising framework for maximizing the utility of existing survey data. We contend that artificial intelligence, particularly transfer learning, opens new frontiers in social science methodology by enabling systematic knowledge transfer between well-administered surveys that share common variables but differ in their outcomes of interest.

Self-supervised speech models (S3Ms) have become a common tool for the speech processing community, leveraging representations for downstream tasks. Clustering S3M representations yields discrete speech units (DSUs), which serve as compact representations for speech signals. DSUs are typically obtained by k-means clustering. Using DSUs often leads to strong performance in various tasks, including automatic speech recognition (ASR). However, even with the high dimensionality and redundancy of S3M representations, preprocessing S3M representations for better clustering remains unexplored, even though it can affect the quality of DSUs. In this paper, we investigate the potential of linear preprocessing methods for extracting DSUs. We evaluate standardization, principal component analysis, whitening, and independent component analysis (ICA) on DSU-based ASR benchmarks and demonstrate their effectiveness as preprocessing for k-means. We also conduct extensive analyses of their behavior, such as orthogonality or interpretability of individual components of ICA.

Safety is a critical aspect of the air transport system given even slight operational anomalies can result in serious consequences. To reduce the chances of aviation safety occurrences, accidents and incidents are reported to establish the root cause, propose safety recommendations etc. However, analysis narratives of the pre-accident events are presented using human-understandable, raw, unstructured, text that a computer system cannot understand. The ability to classify and categorise safety occurrences from their textual narratives would help aviation industry stakeholders make informed safety-critical decisions. To classify and categorise safety occurrences, we applied natural language processing (NLP) and AI (Artificial Intelligence) models to process text narratives. The study aimed to answer the question. How well can the damage level caused to the aircraft in a safety occurrence be inferred from the text narrative using natural language processing. The classification performance of various deep learning models including LSTM, BLSTM, GRU, sRNN, and combinations of these models including LSTM and GRU, BLSTM+GRU, sRNN and LSTM, sRNN and BLSTM, sRNN and GRU, sRNN and BLSTM and GRU, and sRNN and LSTM and GRU was evaluated on a set of 27,000 safety occurrence reports from the NTSB. The results of this study indicate that all models investigated performed competitively well recording an accuracy of over 87.9% which is well above the random guess of 25% for a four-class classification problem. Also, the models recorded high precision, recall, and F1 scores above 80%, 88%, and 85%, respectively. sRNN slightly outperformed other single models in terms of recall (90%) and accuracy (90%) while LSTM reported slightly better performance in terms of precision (87%).

Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Framework, fall short of addressing the complexities of these agents, which are capable of independent decision-making, learning, and adaptation. To bridge these gaps, we propose the textbfETHOS (Ethical Technology and Holistic Oversight System) framework--a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs). ETHOS establishes a global registry for AI agents, enabling dynamic risk classification, proportional oversight, and automated compliance monitoring through tools like soulbound tokens and zero-knowledge proofs. Furthermore, the framework incorporates decentralized justice systems for transparent dispute resolution and introduces AI-specific legal entities to manage limited liability, supported by mandatory insurance to ensure financial accountability and incentivize ethical design. By integrating philosophical principles of rationality, ethical grounding, and goal alignment, ETHOS aims to create a robust research agenda for promoting trust, transparency, and participatory governance. This innovative framework offers a scalable and inclusive strategy for regulating AI agents, balancing innovation with ethical responsibility to meet the demands of an AI-driven future.

Reliable and interpretable traffic crash modeling is essential for understanding causality and improving road safety. This study introduces a novel approach to predicting collision types by utilizing a comprehensive dataset fused from multiple sources, including weather data, crash reports, high-resolution traffic information, pavement geometry, and facility characteristics. Central to our approach is the development of a Feature Group Tabular Transformer (FGTT) model, which organizes disparate data into meaningful feature groups, represented as tokens. These group-based tokens serve as rich semantic components, enabling effective identification of collision patterns and interpretation of causal mechanisms. The FGTT model is benchmarked against widely used tree ensemble models, including Random Forest, XGBoost, and CatBoost, demonstrating superior predictive performance. Furthermore, model interpretation reveals key influential factors, providing fresh insights into the underlying causality of distinct crash types.

Experts say saltwater isn't a fire department's first choice, but is sometimes necessary to battle out-of-control flames. Federal authorities and California police are investigating after someone flew a drone into the wing of a firefighting aircraft as it carried water to battle the raging wildfires across Los Angeles – causing a "fist-sized hole" and knocking it out of service for days at a crucial time. It happened as the plane, the Quebec 1 Super Scooper that flew down from Canada to help, was working to contain the Palisades Fire, a Federal Aviation Administration spokesperson told Fox News Digital. It was one of only two Super Scooper aircraft in use in Southern California at the time. Around 1 p.m. Thursday, a civilian drone flew into its wing, according to Los Angeles Fire Department spokesman Erik Scott.

Please do not pilot your drones over the deadly wildfires raging across portions of Southern California. None of the footage is worth grounding emergency response planes--or the potential jail time. The Federal Aviation Administration was forced to issue a reminder on January 9th, shortly after an unidentified civilian drone collided with a Canadair CL-415 Super Scooper at approximately 1PM PST over the Palisades firestorm. "Anyone who interferes with emergency response operations may face severe fines and criminal prosecution," the FAA also posted on Thursday evening to social media. "If you fly, emergency responders can't," they added, echoing a similar motto from the US Forestry Department.

The Consumer Financial Protection Bureau proposed a new measure on Friday that could protect your Robux from scammers and hackers. The proposed rule would interpret terms in the Electronic Fund Transfer Act, or EFTA, which has traditionally been used to protect consumers from unauthorized debit transactions, to include some virtual currencies supplied by gaming and cryptocurrency companies. "Gamers--or in some cases their parents and guardians--have reported issues such as trouble when converting dollars to in-game currency, unauthorized transactions, account hacks and takeovers, theft, scams, and loss of assets," reads the CFPB's post announcing the proposal. "They have also described receiving limited to no help from gaming companies and the banks or digital wallets involved. Refunds are often denied, people are finding their gaming accounts suspended by the video game company after a player tries to get a refund from their financial institution, or people are left caught in doom loops with AI-powered customer service representatives while they're just trying to get straight answers."

A drone collided with a firefighting aircraft flying over the Palisades fire on Thursday, the Federal Aviation Administration said in a statement. The aircraft landed safely and the incident will be investigated, an FAA official said. "It's a federal crime, punishable by up to 12 months in prison, to interfere with firefighting efforts on public lands," the statement said. "Additionally, the FAA can impose a civil penalty of up to 75,000 against any drone pilot who interferes with wildfire suppression, law enforcement or emergency response operations" during a temporary flight restriction. "We hit a drone this afternoon -- first one," said L.A. County Fire Chief Anthony Marrone.