Ukraine has said it used sea drones to attack and destroy a Russian warship in the Black Sea near the Russian-annexed Crimean peninsula. The military intelligence agency, known by its Ukrainian acronym GUR, published a video on Thursday that it said depicted a naval drone attack on the missile-armed corvette Ivanovets the night before. The grainy footage, running about 2 and a half minutes and accompanied by a dramatic soundtrack, showed a number of explosions, and the ship eventually listing to one side. It ended with the vessel sinking stern-first into the sea. "As a result of a number of direct hits to the hull, the Russian ship suffered damage incompatible with further movement," the intelligence agency said in a statement accompanying the video, apparently made up of live feeds from the drones.

Oleksandr Prokudin, the governor of the southern Ukrainian region of Kherson, said two French volunteer aid workers were killed after a Russian drone attack on the town of Beryslav. Four people were injured, three of them foreigners. One person was killed and two injured in Russian shelling and rocket attacks on villages in the eastern Donetsk region, the Ukrainian presidential office said. Ukraine said four people were injured in a Russian missile attack on a medical facility in the eastern Kharkiv region, near the front line town of Kupiansk. Ukraine's military intelligence agency GUR, said it attacked and sank the Russian corvette Ivanovets in the Black Sea using undersea drones.

Peers highlighted their'deep concerns' over tech companies hoovering up content from books and news websites on'an absolutely massive scale'. The House of Lords communications and digital committee said ministers had'a duty' to stop tech giants taking control of the multibillion-pound AI industry, warning urgent safeguards were needed. The emergence of ChatGPT has driven demand for the technology, with millions now using the tools every day, from writing school essays to drafting legal opinions. The House of Lords communications and digital committee said ministers had'a duty' to stop tech giants taking control of the multibillion-pound AI industry (File image) News publishers warned AI tools could make it impossible to produce independent journalism. The report said the Government'cannot sit on its hands for the next decade and hope the courts will provide an answer'.

Given there is no UK equivalent of ChatGPT, and in order to avoid another situation in which - as with other areas of tech - all of the industry giants are clustered elsewhere, the Lords committee seems to be essentially warning the country to go easy on the red tape.

If the government is clear that the legislative framework is not adequate then it should update that legislative framework.

Relation extraction aims at inferring structured human knowledge from textual documents. State-of-the-art methods based on language models commonly have two limitations: (1) they require named entities to be either given as input or infer them, which introduces additional noise, and (2) they require human annotations of documents. As a remedy, we present a novel framework for document-level in-context few-shot relation extraction via pre-trained language models. We achieve crucial benefits in that we eliminate the need for both named entity recognition and human annotation of documents. Unlike existing methods based on fine-tuning, our framework is flexible in that it can be easily updated for a new set of relations without re-training. We evaluate our framework using DocRED, the largest publicly available dataset for document-level relation extraction, and demonstrate that our framework achieves state-of-the-art performance. Finally, we show that our framework actually performs much better than the original labels from the development set of DocRED. To the best of our knowledge, we are the first to reformulate the document-level relation extraction task as a tailored in-context few-shot learning paradigm.

Digital Twins (DTs) are a critical technology for digitalizing physical entities in domains ranging from industry to city planning [1, 2]. DTs' ability to continuously adapt to a physical entity's state, simulate future events, and actively influence feedback and decision processes, goes significantly beyond traditional digital models as merely representations [3]. Thus, Industry 4.0 has started using DTs--along with other cutting-edge technologies, such as the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI)--to significantly increase the efficiency and safety of both products and processes [3]. Further, due to DTs' real-time monitoring and simulation capabilities, they are being increasingly adapted to domains such as healthcare to meet demands for individualized diagnostics and treatment [4].

Past, Present and Future are considered to be three temporal and logical concepts which are well defined by human beings for their existence and growth. We, as human beings, have the privilege of using our intelligence to mentally execute an activity before physical occurrence of the same in the real world. Knowledge of the past, aplomb of present and visualisation for the future correspond to three concepts such as look-back, look-at and look-ahead respectively in real life as well as in diversified domains of computing. Look-Ahead(LA) deals with the future prediction of information and processing of input to produce the output in advance. In this article, our main objective is to learn, understand and explore the concept of LA and design novel models as solution for real world problems. We present three well known algorithmic frameworks used in practice based on availability of input information such as offline, online and semi-online. We introduce interesting real life applications and well known computing problems where LA plays a significant role for making a process, system or algorithm efficient. We define new types of LA and propose a taxonomy for LA based on literature review for designing novel LA models in future. Using the concept of LA, We identify and present many interesting and non-trivial research challenges as future potential research directions. Intuitively, we observe that LA can be used as a powerful tool and framework for future researchers in design of efficient computational models and algorithms for solving non-trivial and challenging optimization problems.

Instruction Tuning (IT), the process of training large language models (LLMs) using instruction-response pairs, has emerged as the predominant method for transforming base pre-trained LLMs into open-domain conversational agents. While IT has achieved notable success and widespread adoption, its limitations and shortcomings remain underexplored. In this paper, through rigorous experiments and an in-depth analysis of the changes LLMs undergo through IT, we reveal various limitations of IT. In particular, we show that (1) IT fails to enhance knowledge or skills in LLMs. LoRA fine-tuning is limited to learning response initiation and style tokens, and full-parameter fine-tuning leads to knowledge degradation. (2) Copying response patterns from IT datasets derived from knowledgeable sources leads to a decline in response quality. (3) Full-parameter fine-tuning increases hallucination by inaccurately borrowing tokens from conceptually similar instances in the IT dataset for generating responses. (4) Popular methods to improve IT do not lead to performance improvements over a simple LoRA fine-tuned model. Our findings reveal that responses generated solely from pre-trained knowledge consistently outperform responses by models that learn any form of new knowledge from IT on open-source datasets. We hope the insights and challenges revealed inspire future work.

The challenges in implementing SAE Level 4/5 autonomous vehicles are manifold, with intersection navigation being a pervasive one. We analyze a novel road topology invented by a co-author of this paper, Xiayong Hu. The topology eliminates the need for traditional traffic control and cross-traffic at intersections, potentially improving the safety of autonomous driving systems. The topology, herein called the Zonal Road Topology, consists of unidirectional loops of road with traffic flowing either clockwise or counter-clockwise. Adjacent loops are directionally aligned with one another, allowing vehicles to transfer from one loop to another through a simple lane change. To evaluate the Zonal Road Topology, a one km2 pilot-track near Changshu, China is currently being set aside for testing. In parallel, traffic simulations are being performed. To this end, we conduct a simulation-based comparison between the Zonal Road Topology and a traditional road topology for a generic Electric Vehicle (EV) using the Simulation for Urban MObility (SUMO) platform and MATLAB/Simulink. We analyze the topologies in terms of their travel efficiency, safety, energy usage, and capacity. Drive time, number of halts, progress rate, and other metrics are analyzed across varied traffic levels to investigate the advantages and disadvantages of the Zonal Road Topology. Our results indicate that vehicles on the Zonal Road Topology have a lower, more consistent drive time with greater traffic throughput, while using less energy on average. These results become more prominent at higher traffic densities.