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The suspect in Charlie Kirk's assassination has been captured, FBI director Kash Patel announced MSNBC sparks outrage for'disgusting' Charlie Kirk comments following Utah shooting Tragedy as Charlie Kirk's wife left behind with two young children after conservative activist is fatally shot A DEI mayor, an inconvenient crime and video they never wanted you to see: MAUREEN CALLAHAN knows why the Left has sympathy for that killer... but none for his victim Sweater weather starts here - the cozy, chic pieces from Soft Surroundings you'll actually wear all season We only had one symptom we dismissed... but then we were diagnosed with the rarest form of melanoma Soft-touch prosecutor let felon walk free... before crook'slit Auburn professor's throat in random attack' I tried the 30 cent'miracle chill pill' before a big event.. now I'm taking it for everything Donald Trump and House Republicans lead prayers for Charlie Kirk's family after conservative star is fatally shot Prince Harry says his father King Charles is'great' following their first meeting in 19 months... which was over a cup of tea and just 55 minutes long Liberal media defends thug who killed Ukrainian woman in cold blood: 'This man was hurting' Knifeman accused of stabbing Ukrainian refugee to death gives chilling reason for the attack... as he speaks for the first time from jail on the murder that shocked America Fox News reveals new lineup and elevates star White House reporter who's sparred with Trump Horrific new details of passenger injuries after they were'thrown' around Delta flight during'severe turbulence' Antarctica's'Doomsday Glacier' is on the verge of COLLAPSING: Huge ice sheet the size of Great Britain could cause global sea levels to rise by 2 FEET, study warns READ MORE: 'Doomsday Glacier' melting'much faster' than previously thought With the potential to cause sea levels across the planet to rise, it's no wonder the Thwaites Glacier has earned the nickname the'Doomsday Glacier.' Now, scientists have revealed concerning findings about how and when the glacier could collapse. Researchers from the British Antarctic Survey (BAS) used underwater robots to take new measurements of the glacier, which is the same size as Great Britain. The data indicates that the Thwaites Glacier and much of the West Antarctic Ice Sheet could be lost entirely by the 23rd century. Worryingly, if it collapses entirely, the experts say global sea levels would rise by two feet (65cm) - plunging huge areas underwater. With the potential to cause seas across the planet to rise, it's no wonder the Thwaites Glacier has earned the nickname the'Doomsday Glacier' The Thwaites Glacier is roughly 74.5 miles (120km) across - the same size as Great Britain or Florida - making it the widest glacier on the planet Ice shelf connected to Antarctic's doomsday glacier is CRACKING The Thwaites Glacier is roughly 74.5 miles (120km) across - the same size as Great Britain or Florida.

California is poised to enact the United States' most significant piece of legislation around artificial intelligence, which could help reshape governance of the technology worldwide. The bill, called SB 1047, would be the first binding U.S. legislation aimed squarely at preventing AI-linked catastrophes, representing a significant break from the United States' traditionally hands-off approach to regulating the industry. It recently cleared California's legislature with a large majority and is now entirely in the hands of Gov. Gavin Newsom, who has until Sept. 30 to sign or veto it.

While deep learning has reduced the prevalence of manual feature extraction, transformation of data via feature engineering remains essential for improving model performance, particularly for underwater acoustic signals. The methods by which audio signals are converted into time-frequency representations and the subsequent handling of these spectrograms can significantly impact performance. This work demonstrates the performance impact of using different combinations of time-frequency features in a histogram layer time delay neural network. An optimal set of features is identified with results indicating that specific feature combinations outperform single data features.

Transfer learning is commonly employed to leverage large, pre-trained models and perform fine-tuning for downstream tasks. The most prevalent pre-trained models are initially trained using ImageNet. However, their ability to generalize can vary across different data modalities. This study compares pre-trained Audio Neural Networks (PANNs) and ImageNet pre-trained models within the context of underwater acoustic target recognition (UATR). It was observed that the ImageNet pre-trained models slightly out-perform pre-trained audio models in passive sonar classification. We also analyzed the impact of audio sampling rates for model pre-training and fine-tuning. This study contributes to transfer learning applications of UATR, illustrating the potential of pre-trained models to address limitations caused by scarce, labeled data in the UATR domain.

Managing complex disaster risks requires interdisciplinary efforts. Breaking down silos between law, social sciences, and natural sciences is critical for all processes of disaster risk reduction. This enables adaptive systems for the rapid evolution of AI technology, which has significantly impacted the intersection of law and natural environments. Exploring how AI influences legal frameworks and environmental management, while also examining how legal and environmental considerations can confine AI within the socioeconomic domain, is essential. From a co-production review perspective, drawing on insights from lawyers, social scientists, and environmental scientists, principles for responsible data mining are proposed based on safety, transparency, fairness, accountability, and contestability. This discussion offers a blueprint for interdisciplinary collaboration to create adaptive law systems based on AI integration of knowledge from environmental and social sciences. Discrepancies in the use of language between environmental scientists and decision-makers in terms of usefulness and accuracy hamper how AI can be used based on the principles of legal considerations for a safe, trustworthy, and contestable disaster management framework. When social networks are useful for mitigating disaster risks based on AI, the legal implications related to privacy and liability of the outcomes of disaster management must be considered. Fair and accountable principles emphasise environmental considerations and foster socioeconomic discussions related to public engagement. AI also has an important role to play in education, bringing together the next generations of law, social sciences, and natural sciences to work on interdisciplinary solutions in harmony.

Stance detection is the task of determining the viewpoint expressed in a text towards a given target. A specific direction within the task focuses on cross-target stance detection, where a model trained on samples pertaining to certain targets is then applied to a new, unseen target. With the increasing need to analyze and mining viewpoints and opinions online, the task has recently seen a significant surge in interest. This review paper examines the advancements in cross-target stance detection over the last decade, highlighting the evolution from basic statistical methods to contemporary neural and LLM-based models. These advancements have led to notable improvements in accuracy and adaptability. Innovative approaches include the use of topic-grouped attention and adversarial learning for zero-shot detection, as well as fine-tuning techniques that enhance model robustness. Additionally, prompt-tuning methods and the integration of external knowledge have further refined model performance. A comprehensive overview of the datasets used for evaluating these models is also provided, offering valuable insights into the progress and challenges in the field. We conclude by highlighting emerging directions of research and by suggesting avenues for future work in the task.

Graph convolutional neural networks (GCNNs) have become a machine learning workhorse for screening the chemical space of crystalline materials in fields such as catalysis and energy storage, by predicting properties from structures. Multicomponent materials, however, present a unique challenge since they can exhibit chemical (dis)order, where a given lattice structure can encompass a variety of elemental arrangements ranging from highly ordered structures to fully disordered solid solutions. Critically, properties like stability, strength, and catalytic performance depend not only on structures but also on orderings. To enable rigorous materials design, it is thus critical to ensure GCNNs are capable of distinguishing among atomic orderings. However, the ordering-aware capability of GCNNs has been poorly understood. Here, we benchmark various neural network architectures for capturing the ordering-dependent energetics of multicomponent materials in a custom-made dataset generated with high-throughput atomistic simulations. Conventional symmetry-invariant GCNNs were found unable to discern the structural difference between the diverse symmetrically inequivalent atomic orderings of the same material, while symmetry-equivariant model architectures could inherently preserve and differentiate the distinct crystallographic symmetries of various orderings.

Moral values have deep roots in early civilizations, codified within norms and laws that regulated societal order and the common good. They play a crucial role in understanding the psychological basis of human behavior and cultural orientation. The Moral Foundation Theory (MFT) is a well-established framework that identifies the core moral foundations underlying the manner in which different cultures shape individual and social lives. Recent advancements in natural language processing, particularly Pre-trained Language Models (PLMs), have enabled the extraction and analysis of moral dimensions from textual data. This survey presents a comprehensive review of MFT-informed PLMs, providing an analysis of moral tendencies in PLMs and their application in the context of the MFT. We also review relevant datasets and lexicons and discuss trends, limitations, and future directions. By providing a structured overview of the intersection between PLMs and MFT, this work bridges moral psychology insights within the realm of PLMs, paving the way for further research and development in creating morally aware AI systems.

High-resolution flood probability maps are essential for addressing the limitations of existing flood risk assessment approaches but are often limited by the availability of historical event data. Also, producing simulated data needed for creating probabilistic flood maps using physics-based models involves significant computation and time effort inhibiting the feasibility. To address this gap, this study introduces Flood-Precip GAN (Flood-Precipitation Generative Adversarial Network), a novel methodology that leverages generative machine learning to simulate large-scale synthetic inundation data to produce probabilistic flood maps. With a focus on Harris County, Texas, Flood-Precip GAN begins with training a cell-wise depth estimator using a limited number of physics-based model-generated precipitation-flood events. This model, which emphasizes precipitation-based features, outperforms universal models. Subsequently, a Generative Adversarial Network (GAN) with constraints is employed to conditionally generate synthetic precipitation records. Strategic thresholds are established to filter these records, ensuring close alignment with true precipitation patterns. For each cell, synthetic events are smoothed using a K-nearest neighbors algorithm and processed through the depth estimator to derive synthetic depth distributions. By iterating this procedure and after generating 10,000 synthetic precipitation-flood events, we construct flood probability maps in various formats, considering different inundation depths. Validation through similarity and correlation metrics confirms the fidelity of the synthetic depth distributions relative to true data. Flood-Precip GAN provides a scalable solution for generating synthetic flood depth data needed to create high-resolution flood probability maps, significantly enhancing flood preparedness and mitigation efforts.