AEW and Ring of Honor star Lee Moriarty welcomes Katsuyori Shibata's challenge for Pure Championship Giants vs. Red Sox betting preview: Logan Webb and Sonny Gray both locked in for under play NCAA wins Tenth Circuit'stay', but faces more eligibility lawsuits from 2022 class athletes in chaotic time Jacob Misiorowski's MLB-leading ERA and WHIP make Milwaukee Brewers the pick against Atlanta Braves Texas AG Ken Paxton sends message to WNBA amid'concerns' over treatment of Sophie Cunningham supporters NFL to NCAA? Courtroom chaos Is giving pro athletes a bizarre path back to college, and it's only just begun Another sex toy was thrown during a WNBA game, and Dream's Angel Reese absolutely lost it AEW star Willow Nightingale shares how she's able to keep her joyful personality going Peyton Manning's freshman son makes his debut, but freak running back steals the show How Kirk Cousins and Fernando Mendoza are building'great' relationship through Raiders work, faith and more IndyCar star Alex Palou reflects on White House, Oval Office visit with Trump: 'incredible experience' I was a sports columnist at the Seattle Times for 11 years. BREAKING: Iran's president calls for END of war Sophie Cunningham calls for'bold' commissioner amid women's sports debate Are American workers really using AI? Gen. Jack Keane details US military strategy as Trump warns of Iran's nuclear threat ISIS indoctrinating Americans with'ease,' ex-NYPD detective warns One of the biggest nights in pro wrestling will take place on Friday in Philadelphia. Ring of Honor (ROH) will hold Death Before Dishonor at the 2300 Arena - the home of the influential promotion known as Extreme Championship Wrestling. Death Before Dishonor is known as one of the premier events on the Ring of Honor schedule and the match card that is set for the event underscores that notion. There are several matches set to take place with a championship on the line.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Decision-making models for individuals, particularly in high-stakes scenarios like vaccine uptake, often diverge from population optimal predictions. This gap arises from the uniqueness of the individual decision-making process, shaped by numerical attributes (e.g., cost, time) and linguistic influences (e.g., personal preferences and constraints). Developing upon Utility Theory and leveraging the textualreasoning capabilities of Large Language Models (LLMs), this paper proposes an Adaptive Textual-symbolic Human-centric Reasoning framework (ATHENA) to address the optimal information integration. ATHENA uniquely integrates two stages: First, it discovers robust, group-level symbolic utility functions via LLMaugmented symbolic discovery; Second, it implements individual-level semantic adaptation, creating personalized semantic templates guided by the optimal utility to model personalized choices. Validated on real-world travel mode and vaccine choice tasks, ATHENA consistently outperforms utility-based, machine learning, and other LLM-based models, lifting F1 score by at least 6.5% over the strongest cutting-edge models. Further, ablation studies confirm that both stages of ATHENA are critical and complementary, as removing either clearly degrades overall predictive performance. By organically integrating symbolic utility modeling and semantic adaptation, ATHENA provides a new scheme for modeling human-centric decisions. The project page can be found at https://yibozh.github.io/Athena.

Decision-making models for individuals, particularly in high-stakes scenarios like vaccine uptake, often diverge from population optimal predictions. This gap arises from the uniqueness of the individual decision-making process, shaped by numerical attributes (e.g., cost, time) and linguistic influences (e.g., personal preferences and constraints). Developing upon Utility Theory and leveraging the textual-reasoning capabilities of Large Language Models (LLMs), this paper proposes an Adaptive Textual-symbolic Human-centric Reasoning framework (ATHENA) to address the optimal information integration. ATHENA uniquely integrates two stages: First, it discovers robust, group-level symbolic utility functions via LLM-augmented symbolic discovery; Second, it implements individual-level semantic adaptation, creating personalized semantic templates guided by the optimal utility to model personalized choices. Validated on real-world travel mode and vaccine choice tasks, ATHENA consistently outperforms utility-based, machine learning, and other LLM-based models, lifting F1 score by at least 6.5\% over the strongest cutting-edge models. Further, ablation studies confirm that both stages of ATHENA are critical and complementary, as removing either clearly degrades overall predictive performance. By organically integrating symbolic utility modeling and semantic adaptation, ATHENA provides a new scheme for modeling human-centric decisions. The project page can be found at https://yibozh.github.io/Athena.

In this work, we study the problem pertaining to personalized classification of subclinical atherosclerosis by developing a hierarchical graph neural network framework to leverage two characteristic modalities of a patient: clinical features within the context of the cohort, and molecular data unique to individual patients. Current graph-based methods for disease classification detect patient-specific molecular fingerprints, but lack consistency and comprehension regarding cohort-wide features, which are an essential requirement for understanding pathogenic phenotypes across diverse atherosclerotic trajectories. Furthermore, understanding patient subtypes often considers clinical feature similarity in isolation, without integration of shared pathogenic interdependencies among patients. To address these challenges, we introduce ATHENA: Atherosclerosis Through Hierarchical Explainable Neural Network Analysis, which constructs a novel hierarchical network representation through integrated modality learning; subsequently, it optimizes learned patient-specific molecular fingerprints that reflect individual omics data, enforcing consistency with cohort-wide patterns. With a primary clinical dataset of 391 patients, we demonstrate that this heterogeneous alignment of clinical features with molecular interaction patterns has significantly boosted subclinical atherosclerosis classification performance across various baselines by up to 13% in area under the receiver operating curve (AUC) and 20% in F1 score. Taken together, ATHENA enables mechanistically-informed patient subtype discovery through explainable AI (XAI)-driven subnetwork clustering; this novel integration framework strengthens personalized intervention strategies, thereby improving the prediction of atherosclerotic disease progression and management of their clinical actionable outcomes.

This paper focuses on the design of a parallel robot designed for robotic assisted minimally invasive pancreatic surgery. T wo alternative architectures, called ATHENA - 1 and ATHENA - 2, each with 4 degrees of freedom (DOF) are proposed. T heir kinematic schemes are presented, and the conceptual 3D CAD models are illustrated. Based on these, two F inite E lement M ethod (FEM) simulations were performed to determine which architecture has the higher stiffness. A workspace quantitative analysis is performed to further assess the usability of the two proposed parallel architectures related to the medical tasks . The obtained results are used to select the architecture which fit the required design criteria and will be used to develop the experimental model of the surgical robot.

Moon exploration is undergoing a potentially transformative moment. Over the course of six weeks, three different lunar landers began a rocket-fueled space journey to learn more about Earth's nearest neighbor. All three landers are operated by private, and relatively newly-formed companies. That's a marked shift away from space exploration of the 20th century, which was dominated by state-backed, public institutions like NASA. If they complete their missions, these space upstarts could help pave the way for future planned human moon missions, and possibly, even a not-too distant lunar economy.

Recently, large language models (LLMs) like ChatGPT, LLaMA, and Claude have prevailed in countless domains, including legal scenarios. With LLMs' rapid technological progress, the development of prompt engineering (PE) as an interface between the LLMs and real-world applications has drawn the attention of all developers. Various PE methods have been proposed to overcome real-world challenges, such as few-shot prompting, chain-of-thought, and retrieval-augmented generation (RAG). However, RAG for legal judgment prediction (LJP) is still underexplored. To address this, we propose "Athena", a novel framework cultivating RAG as a core preprocess component to enhance LLMs' performance on specialized tasks. Athena constructs a knowledge base for accusations, attached with a semantic retrieval mechanism through vectorization. Our experiments show that Athena's overall performance has improved significantly, achieving state-of-the-art results on the CAIL2018 dataset. Our ablation study on the in-context window size parameter further reproduces LLMs' "lost-in-the-middle" phenomenon with a relative positional variation. And with moderate hyper-parameter-tuning, we can achieve at most 95% of accuracy accordingly. We also study the impact of query rewriting and data distribution, providing possible directions for future research based on former analyses.

Large Language Models (LLMs) have significantly advanced natural language processing tasks such as machine translation, text generation, and sentiment analysis. However, their large size, often consisting of billions of parameters, poses challenges for storage, computation, and deployment, particularly in resource-constrained environments like mobile devices and edge computing platforms. Effective compression and quantization techniques are crucial for addressing these issues, reducing memory footprint and computational requirements without significantly compromising performance. Traditional methods that uniformly map parameters to compressed spaces fail to account for the uneven distribution of parameters, leading to substantial accuracy loss. In this work, we propose Athena, a novel algorithm for efficient block-wise post-training quantization of LLMs. Athena leverages Second-Order Matrix Derivative Information to guide the quantization process using the curvature information of the loss landscape. By grouping parameters by columns or rows and iteratively optimizing the quantization process, Athena updates the model parameters and Hessian matrix to achieve significant compression while maintaining high accuracy. This makes Athena a practical solution for deploying LLMs in various settings.