This research project delves into the intricacies of road traffic accidents severity in the UK, employing a potent combination of machine learning algorithms, econometric techniques, and traditional statistical methods to analyse longitudinal historical data. Our robust analysis framework includes descriptive, inferential, bivariate, and multivariate methodologies, correlation analysis: Pearson's and Spearman's Rank Correlation Coefficient, multiple and logistic regression models, Multicollinearity Assessment, and Model Validation. In addressing heteroscedasticity or autocorrelation in error terms, we've advanced the precision and reliability of our regression analyses using the Generalized Method of Moments (GMM). Additionally, our application of the Vector Autoregressive (VAR) model and the Autoregressive Integrated Moving Average (ARIMA) models have enabled accurate time-series forecasting. With this approach, we've achieved superior predictive accuracy, marked by a Mean Absolute Scaled Error (MASE) of 0.800 and a Mean Error (ME) of -73.80 compared to a naive forecast.

New York City's busiest subway station has a new crew member -- an almost 400-pound robocop unveiled by NYC Mayor Eric Adams. 'We're committed to exploring innovative tools to continue to make this city the safest big city in America, and this robot K5, it has the potential to serve as an important tool in our toolbox,' Adams said Friday as he unveiled the machine. The robot, a product of California-based autonomous security robots developer Knightscope, has four cameras it can use to shoot video and moves at 3mph. It will roam the Times Square -- 42 street subway station alongside a human officer for two weeks as part of a test run from city hall. After that, it is expected to patrol the mezzanine level of the station for two months.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. U.S. Ambassador to Japan Rahm Emanuel accused China on Friday of using "economic coercion" against Japan by banning imports of Japanese seafood in response to the release of treated wastewater from the damaged Fukushima nuclear plant into the ocean, while Chinese boats continue to fish off Japan's coasts. "Economic coercion is the most persistent and pernicious tool in their economic toolbox," Emanuel said in a speech Friday in Tokyo, calling China's ban on Japanese seafood the latest example. China is the biggest market for Japanese seafood, and the ban has badly hurt Japan's fishing industry.

Its cargo is so precious it could help answer some of humanity's biggest existential questions. That's why there is so much excitement about the return of the OSIRIS-REx spacecraft, which will drop a capsule full of 4.5 billion-year-old space dust back to Earth on Sunday. The 8.8oz (250g) sample, audaciously grabbed from the mountain-sized asteroid Bennu in October 2020, could shed light on how life emerged on Earth and whether we are alone in the universe. OSIRIS-REx began its two-year, four-month journey home in May 2021, having been powered down to conserve energy during the trip. In the early hours of Sunday, however, the probe will be woken from this low-power mode ahead of its all-important delivery.

The internal combustion engine ruled the 20th century. In the 21st, electric motors and automation are reshaping the way stuff and people get around. Transportation workers aren't entirely thrilled about how it's going. On Tuesday, a caravan of big rig trucks roared into Sacramento as the Teamsters union rallied support for a bill banning driverless trucks in California. Meanwhile, newly expanded fleets of robotaxis tootled around San Francisco collecting fares, despite the objections of city leaders and unions concerned about the vehicles obstructing emergency vehicles and transit.

Israel touts the capabilities of its new AI-enhanced tank, the "Barak" (or "Lightning"), which provides a full 360-degree awareness of the environment, as demonstrated in the video with animation. Israel unveiled the "Barak" tank as its newest artificial intelligence (AI)-powered military advancement, promising a "new era" in combat capabilities. "The Barak tank is very innovative – it takes our maneuvering capabilities to another level, and it is a clear expression of our technological capabilities," Israeli Minister of Defense (IMOD) Yoav Gallant said of the tank, whose name translates to "lightning." "I would like to express my appreciation to the engineers of the Tank and APC Administration, the Israeli Ground Forces and all those involved in the professional process," he added. The fifth-generation Barak tank introduces a 360-degree awareness capability, giving a total vision of the battlefield. In a video that features animated segments to demonstrate the tank's potential, an IDF operator is able to identify targets both in front and behind his tank thanks to a specially designed helmet that helps him filter battlefield data.

Assurance cases can be used to argue for the safety of products in safety engineering. In safety-critical areas, the construction of assurance cases is indispensable. Trustworthiness Derivation Trees (TDTs) enhance assurance cases by incorporating formal methods, rendering it possible for automatic reasoning about assurance cases. We present Trustworthiness Derivation Tree Analyzer (Trusta), a desktop application designed to automatically construct and verify TDTs. The tool has a built-in Prolog interpreter in its backend, and is supported by the constraint solvers Z3 and MONA. Therefore, it can solve constraints about logical formulas involving arithmetic, sets, Horn clauses etc. Trusta also utilizes large language models to make the creation and evaluation of assurance cases more convenient. It allows for interactive human examination and modification. We evaluated top language models like ChatGPT-3.5, ChatGPT-4, and PaLM 2 for generating assurance cases. Our tests showed a 50%-80% similarity between machine-generated and human-created cases. In addition, Trusta can extract formal constraints from text in natural languages, facilitating an easier interpretation and validation process. This extraction is subject to human review and correction, blending the best of automated efficiency with human insight. To our knowledge, this marks the first integration of large language models in automatic creating and reasoning about assurance cases, bringing a novel approach to a traditional challenge. Through several industrial case studies, Trusta has proven to quickly find some subtle issues that are typically missed in manual inspection, demonstrating its practical value in enhancing the assurance case development process.

Large Language Models (LLMs), acting as a powerful reasoner and generator, exhibit extraordinary performance across various natural language tasks, such as question answering (QA). Among these tasks, Multi-Hop Question Answering (MHQA) stands as a widely discussed category, necessitating seamless integration between LLMs and the retrieval of external knowledge. Existing methods employ LLM to generate reasoning paths and plans, and utilize IR to iteratively retrieve related knowledge, but these approaches have inherent flaws. On one hand, Information Retriever (IR) is hindered by the low quality of generated queries by LLM. On the other hand, LLM is easily misguided by the irrelevant knowledge by IR. These inaccuracies, accumulated by the iterative interaction between IR and LLM, lead to a disaster in effectiveness at the end. To overcome above barriers, in this paper, we propose a novel pipeline for MHQA called Furthest-Reasoning-with-Plan-Assessment (FuRePA), including an improved framework (Furthest Reasoning) and an attached module (Plan Assessor). 1) Furthest reasoning operates by masking previous reasoning path and generated queries for LLM, encouraging LLM generating chain of thought from scratch in each iteration. This approach enables LLM to break the shackle built by previous misleading thoughts and queries (if any). 2) The Plan Assessor is a trained evaluator that selects an appropriate plan from a group of candidate plans proposed by LLM. Our methods are evaluated on three highly recognized public multi-hop question answering datasets and outperform state-of-the-art on most metrics (achieving a 10%-12% in answer accuracy).

Large language models (LLMs) have demonstrated great potential in natural language processing tasks within the financial domain. In this work, we present a Chinese Financial Generative Pre-trained Transformer framework, named CFGPT, which includes a dataset~(CFData) for pre-training and supervised fine-tuning, a financial LLM~(CFLLM) to adeptly manage financial texts, and a deployment framework~(CFAPP) designed to navigate real-world financial applications. The CFData comprising both a pre-training dataset and a supervised fine-tuning dataset, where the pre-training dataset collates Chinese financial data and analytics, alongside a smaller subset of general-purpose text with 584M documents and 141B tokens in total, and the supervised fine-tuning dataset is tailored for six distinct financial tasks, embodying various facets of financial analysis and decision-making with 1.5M instruction pairs and 1.5B tokens in total. The CFLLM, which is based on InternLM-7B to balance the model capability and size, is trained on CFData in two stage, continued pre-training and supervised fine-tuning. The CFAPP is centered on large language models (LLMs) and augmented with additional modules to ensure multifaceted functionality in real-world application. Our codes are released at https://github.com/TongjiFinLab/CFGPT.

We expose a surprising failure of generalization in auto-regressive large language models (LLMs). If a model is trained on a sentence of the form "A is B", it will not automatically generalize to the reverse direction "B is A". This is the Reversal Curse. For instance, if a model is trained on "Olaf Scholz was the ninth Chancellor of Germany", it will not automatically be able to answer the question, "Who was the ninth Chancellor of Germany?". Moreover, the likelihood of the correct answer ("Olaf Scholz") will not be higher than for a random name. Thus, models exhibit a basic failure of logical deduction and do not generalize a prevalent pattern in their training set (i.e. if "A is B'' occurs, "B is A" is more likely to occur). We provide evidence for the Reversal Curse by finetuning GPT-3 and Llama-1 on fictitious statements such as "Uriah Hawthorne is the composer of 'Abyssal Melodies'" and showing that they fail to correctly answer "Who composed 'Abyssal Melodies?'". The Reversal Curse is robust across model sizes and model families and is not alleviated by data augmentation. We also evaluate ChatGPT (GPT-3.5 and GPT-4) on questions about real-world celebrities, such as "Who is Tom Cruise's mother? [A: Mary Lee Pfeiffer]" and the reverse "Who is Mary Lee Pfeiffer's son?". GPT-4 correctly answers questions like the former 79% of the time, compared to 33% for the latter. This shows a failure of logical deduction that we hypothesize is caused by the Reversal Curse. Code is available at https://github.com/lukasberglund/reversal_curse.