'Four dead and 12 injured' in Mississippi shooting after people descend on town for homecoming game Joe Biden, 82, receiving new treatment after'aggressive' cancer spread to his bones REVEALED: The secret George Soros network'behind America's street chaos'... and the dossier that shows how to stop it Tinnitus destroyed Peter's life but doctors dismissed him. Then he tried an extraordinary drug-free University of Cambridge-backed treatment that gives instant relief - no wonder medics say it's so'exciting' KENNEDY: Obama's bitter post about Trump's Gaza peace deal proves what I've long suspected about Barry... and it would make Sigmund Freud blush Gold is soaring... here's what the pros say you should do with your 401(k) before it's too late Model dubbed'the world's most beautiful girl' when she was six is now all grown up and looks VERY different as she poses up a storm at Paris Fashion Week Teacher was'so high on cocaine she thought one of her students was her dog' But now, a royal insider claims they're'just as entitled as their parents' with'shady friends' Heartbreaking moment NFL reporter makes brutal comment about player Xavier Legette's dead father in locker room interview Experts reveal the surprising TRUTH behind RFK Jr's link between circumcision and autism Bombshell records that damn Letitia James and show Trump was RIGHT... and the staggering sum she was swindling Trump starts DOGE 2.0 as mass layoffs take place across federal government amid shutdown Famed'Big Short' investor gives terrifying verdict on Trump hammering China with 100 PERCENT tariff... and issues doomsday warning to Wall Street Jennifer Aniston, you've betrayed every woman with your selfish admission about not having children: CAROLINE BULLOCK Meteorologist's stark warning to Americans to brace for a harsh winter with less snow but more nor'easters Meteorologists are already predicting what the winter months will bring, with some regions of the US expected to see less snow than last year, and nor'easters anticipated to ravage parts of the Northeast. Paul Pastelok, chief meteorologist for AccuWeather's long-range forecasting team, told the Daily Mail that while he didn't expect above normal snowfall for the winter season, he warned that those in the Northeast should brace for nor'easters and it would still be a harsh winter. Pastelok explained that the nor'easter over this weekend is on trend with what is to come, as rapidly developing storms come in off the East Coast. 'People may say, Well, you're forecasting less snow, so it doesn't look like a harsh winter.

Peer-to-peer trading and the move to decentralized grids have reshaped the energy markets in the United States. Notwithstanding, such developments lead to new challenges, mainly regarding the safety and authenticity of energy trade. This study aimed to develop and build a secure, intelligent, and efficient energy transaction system for the decentralized US energy market. This research interlinks the technological prowess of blockchain and artificial intelligence (AI) in a novel way to solve long-standing challenges in the distributed energy market, specifically those of security, fraudulent behavior detection, and market reliability. The dataset for this research is comprised of more than 1.2 million anonymized energy transaction records from a simulated peer-to-peer (P2P) energy exchange network emulating real-life blockchain-based American microgrids, including those tested by LO3 Energy and Grid+ Labs. Each record contains detailed fields of transaction identifier, timestamp, energy volume (kWh), transaction type (buy/sell), unit price, prosumer/consumer identifier (hashed for privacy), smart meter readings, geolocation regions, and settlement confirmation status. The dataset also includes system-calculated behavior metrics of transaction rate, variability of energy production, and historical pricing patterns. The system architecture proposed involves the integration of two layers, namely a blockchain layer and artificial intelligence (AI) layer, each playing a unique but complementary function in energy transaction securing and market intelligence improvement. The machine learning models used in this research were specifically chosen for their established high performance in classification tasks, specifically in the identification of energy transaction fraud in decentralized markets.

In tackling frequent batch anomalies in high-speed stamping processes, this study introduces a novel semi-supervised in-process anomaly monitoring framework, utilizing accelerometer signals and physics information, to capture the process anomaly effectively. The proposed framework facilitates the construction of a monitoring model with imbalanced sample distribution, which enables in-process condition monitoring in real-time to prevent batch anomalies, which helps to reduce batch defects risk and enhance production yield. Firstly, to effectively capture key features from raw data containing redundant information, a hybrid feature extraction algorithm is proposed to utilize data-driven methods and physical mechanisms simultaneously. Secondly, to address the challenge brought by imbalanced sample distribution, a semi-supervised anomaly detection model is established, which merely employs normal samples to build a golden baseline model, and a novel deviation score is proposed to quantify the anomaly level of each online stamping stroke. The effectiveness of the proposed feature extraction method is validated with various classification algorithms. A real-world in-process dataset from stamping manufacturing workshop is employed to illustrate the superiority of proposed semi-supervised framework with enhance performance for process anomaly monitoring.

The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) has significantly transformed logistics and supply chain management, particularly in the pursuit of sustainability and eco-efficiency. This study explores AI-based methodologies for optimizing logistics operations in the USA, focusing on reducing environmental impact, improving fuel efficiency, and minimizing costs. Key AI applications include predictive analytics for demand forecasting, route optimization through machine learning, and AI-powered fuel efficiency strategies. Various models, such as Linear Regression, XGBoost, Support Vector Machine, and Neural Networks, are applied to real-world logistics datasets to reduce carbon emissions based on logistics operations, optimize travel routes to minimize distance and travel time, and predict future deliveries to plan optimal routes. Other models such as K-Means and DBSCAN are also used to optimize travel routes to minimize distance and travel time for logistics operations. This study utilizes datasets from logistics companies' databases. The study also assesses model performance using metrics such as mean absolute error (MAE), mean squared error (MSE), and R2 score. This study also explores how these models can be deployed to various platforms for real-time logistics and supply chain use. The models are also examined through a thorough case study, highlighting best practices and regulatory frameworks that promote sustainability. The findings demonstrate AI's potential to enhance logistics efficiency, reduce carbon footprints, and contribute to a more resilient and adaptive supply chain ecosystem.

Open-source large language models are becoming increasingly available and popular among researchers and practitioners. While significant progress has been made on open-weight models, open training data is a practice yet to be adopted by the leading open-weight models creators. At the same time, there researchers are working to make language models safer. We propose a data curation pipeline to reduce harmful outputs by models trained on public domain data. There are unique challenges to working with public domain data, as these sources differ from web text in both form and content. Many sources are historical documents and are the result of Optical Character Recognition (OCR). Consequently, current state-of-the-art approaches to toxicity filtering are often infeasible or inappropriate for open data models. In this paper, we introduce a new fully open-source pipeline for open-data toxicity filtering. Our contributions are threefold. We create a custom training dataset, ToxicCommons, which is composed of texts which have been classified across five different dimensions (racial/origin-based, gender/sex-based, religious, ability-based discrimination, and violence). We use this dataset to train a custom classifier, Celadon, that can be used to detect toxic content in open data more efficiently at a larger scale. Finally, we describe the balanced approach to content filtration that optimizes safety filtering with respect to the filtered data available for training.

The advent of Large Language Models (LLMs) has revolutionized various domains of artificial intelligence, including the realm of software engineering. In this research, we evaluate the efficacy of pre-trained LLMs in replicating the tasks traditionally performed by developers in response to code review comments. We provide code contexts to five popular LLMs and obtain the suggested code-changes (patch sets) derived from real-world code-review comments. The performance of each model is meticulously assessed by comparing their generated patch sets against the historical data of human-generated patch-sets from the same repositories. This comparative analysis aims to determine the accuracy, relevance, and depth of the LLMs' feedback, thereby evaluating their readiness to support developers in responding to code-review comments. Novelty: This particular research area is still immature requiring a substantial amount of studies yet to be done. No prior research has compared the performance of existing Large Language Models (LLMs) in code-review comments. This in-progress study assesses current LLMs in code review and paves the way for future advancements in automated code quality assurance, reducing context-switching overhead due to interruptions from code change requests.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder known for its impact on motor neurons, causing symptoms like tremors, stiffness, and gait difficulties. This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. This research aims to predict the onset of Parkinson's disease. Utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine, among others, the study evaluates the predictive performance of these models using metrics such as accuracy, area under the curve (AUC), sensitivity, and specificity. The findings of this comprehensive analysis highlight LightGBM as the most effective model, achieving an impressive accuracy rate of 96% alongside a matching AUC of 96%. LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores. Given the complexities of Parkinson's disease and its challenges in early diagnosis, this study underscores the significance of leveraging vocal biomarkers coupled with advanced machine-learning techniques for precise and timely PD detection.

Artificial intelligence's (AI) progress holds great promise in tackling pressing societal concerns such as health and climate. Large Language Models (LLM) and the derived chatbots, like ChatGPT, have highly improved the natural language processing capabilities of AI systems allowing them to process an unprecedented amount of unstructured data. However, the ensuing excitement has led to negative sentiments, even as AI methods demonstrate remarkable contributions (e.g. in health and genetics). A key factor contributing to this sentiment is the misleading perception that LLMs can effortlessly provide solutions across domains, ignoring their limitations such as hallucinations and reasoning constraints. Acknowledging AI fallibility is crucial to address the impact of dogmatic overconfidence in possibly erroneous suggestions generated by LLMs. At the same time, it can reduce fear and other negative attitudes toward AI. This necessitates comprehensive AI literacy interventions that educate the public about LLM constraints and effective usage techniques, i.e prompting strategies. With this aim, a pilot educational intervention was performed in a high school with 21 students. It involved presenting high-level concepts about intelligence, AI, and LLMs, followed by practical exercises involving ChatGPT in creating natural educational conversations and applying established prompting strategies. Encouraging preliminary results emerged, including high appreciation of the activity, improved interaction quality with the LLM, reduced negative AI sentiments, and a better grasp of limitations, specifically unreliability, limited understanding of commands leading to unsatisfactory responses, and limited presentation flexibility. Our aim is to explore AI acceptance factors and refine this approach for more controlled future studies.

Ensembling BERT models often significantly improves accuracy, but at the cost of significantly more computation and memory footprint. In this work, we propose Multi-CLS BERT, a novel ensembling method for CLS-based prediction tasks that is almost as efficient as a single BERT model. Multi-CLS BERT uses multiple CLS tokens with a parameterization and objective that encourages their diversity. Thus instead of fine-tuning each BERT model in an ensemble (and running them all at test time), we need only fine-tune our single Multi-CLS BERT model (and run the one model at test time, ensembling just the multiple final CLS embeddings). To test its effectiveness, we build Multi-CLS BERT on top of a state-of-the-art pretraining method for BERT (Aroca-Ouellette and Rudzicz, 2020). In experiments on GLUE and SuperGLUE we show that our Multi-CLS BERT reliably improves both overall accuracy and confidence estimation. When only 100 training samples are available in GLUE, the Multi-CLS BERT_Base model can even outperform the corresponding BERT_Large model. We analyze the behavior of our Multi-CLS BERT, showing that it has many of the same characteristics and behavior as a typical BERT 5-way ensemble, but with nearly 4-times less computation and memory.

This paper describes a new approach for approximating the inverse kinematics of a manipulator using an Ant Colony Optimization (ACO) based RBFN (Radial Basis Function Network). In this paper, a training solution using the ACO and the LMS (Least Mean Square) algorithm is presented in a two-phase training procedure. To settle the problem that the cluster results of k-mean clustering Radial Basis Function (RBF) are easy to be influenced by the selection of initial characters and converge to a local minimum, Ant Colony Optimization (ACO) for the RBF neural networks which will optimize the center of RBF neural networks and reduce the number of the hidden layer neurons nodes is presented. The result demonstrates that the accuracy of Ant Colony Optimization for the Radial Basis Function (RBF) neural networks is higher, and the extent of fitting has been improved.