It has been 113 years since the Titanic sank beneath the waves, claiming the lives of more than 1,500 passengers and crew. But new evidence has finally cleared the tragedy's Scottish scapegoat: First Officer William Murdoch. For years, Officer Murdoch has been accused of taking bribes, abandoning his post, and was even depicted shooting a passenger in the James Cameron movie. Now, more than a century later, 3D scans show that Officer Murdoch did not flee his position, but died while helping passengers escape until the very end. Deep sea scanning company Magellan has snapped 715,000 photos of the Titanic wreck 12,500 feet beneath the Atlantic.

Federated Learning (FL), introduced in 2016, was designed to enhance data privacy in collaborative model training environments. Among the FL paradigm, horizontal FL, where clients share the same set of features but different data samples, has been extensively studied in both centralized and decentralized settings. In contrast, Vertical Federated Learning (VFL), which is crucial in real-world decentralized scenarios where clients possess different, yet sensitive, data about the same entity, remains underexplored. Thus, this work introduces De-VertiFL, a novel solution for training models in a decentralized VFL setting. De-VertiFL contributes by introducing a new network architecture distribution, an innovative knowledge exchange scheme, and a distributed federated training process. Specifically, De-VertiFL enables the sharing of hidden layer outputs among federation clients, allowing participants to benefit from intermediate computations, thereby improving learning efficiency. De-VertiFL has been evaluated using a variety of well-known datasets, including both image and tabular data, across binary and multiclass classification tasks. The results demonstrate that De-VertiFL generally surpasses state-of-the-art methods in F1-score performance, while maintaining a decentralized and privacy-preserving framework.

An autonomous drone fleet overseen by Ocean Infinity has rediscovered the USS Stewart, the only US Navy destroyer ever captured by Japanese forces during World War II. The marine robotics company's trio of orange, 20-foot-long underwater robots found the historic vessel while mapping what is now the 1,286-square-mile Cordell Bank national marine sanctuary off the California coast. Also known as the "Ghost Ship of the Pacific," the 314-foot-long ship has spent the past 78 years resting roughly 3,500 feet below the ocean's surface, and appears to remain almost completely intact and upright. "This level of preservation is exceptional for a vessel of its age and makes it potentially one of the best-preserved examples of a US Navy'four-piper' destroyer known to exist," Maria Brown, superintendent for both Cordell Bank and Greater Farallones national marine sanctuaries, said in a statement to The New York Times on October 1. The USS Stewart's story is unique in US maritime history, making it one of the most sought-after wrecks for decades.

Even after a century beneath the water, the Titanic's bow remains one of the most magnificent and haunting sights in the ocean. However, a new survey of the wreck site has revealed that the railing, made famous by Jack and Rose, has now collapsed into rust. Haunting images snapped by underwater robots through the years show the great ship's bow has gradually eroded. Experts say that its metal construction and frequent human visits mean it is only a matter of time before the Titanic collapses. Dr Rodrigo Pacheco-Ruiz, archaeological data manager for HMS Victory and maritime archaeologist from the University of Southampton, told MailOnline: 'The realistic view is that because she's such a big metal object, she won't be there for very long.' Haunting pictures reveal how the Titanic's iconic bow has decayed in the 37 years between 1987 and 2010 Earlier this week, RMS Titanic Inc, the company which holds the salvage rights for the ship, released new images and footage of the sunken liner.

For more than 100 years, Titanic's famous bow has remained just as recognisable as it was before it sank beneath the waves. But shocking footage now shows that the iconic front rail of the great liner has collapsed onto the seafloor. Videos and photos captured by robotic submarines reveal just how fast the Titanic is deteriorating, as it lies 3,800 metres (12,500ft) beneath the surface. The expedition team who made the discovery now warn that it is only a matter of time before the entire ship collapses for good. Tomasina Ray, director of collections at RMS Titanic Inc, which led the expedition, said: 'People ask all the time: "How long is Titanic going to be there?"

Jack and Rose stood on Titanic's bow might be one of the most recognisable shots in movie history. But shocking new photos have revealed that this iconic structure has now crumbled into rust. Images captured by robotic submarines this summer found that 4.5m (14.7ft) of the ship's bow has collapsed onto the sea floor. The expedition team who made the discovery now warn that it is only a matter of time before the entire ship collapses for good. Tomasina Ray, director of collections at RMS Titanic Inc, which led the expedition, said: 'People ask all the time: "How long is Titanic going to be there?"

The development of large language models (LLMs), such as GPT, has enabled the construction of several socialbots, like ChatGPT, that are receiving a lot of attention for their ability to simulate a human conversation. However, the conversation is not guided by a goal and is hard to control. In addition, because LLMs rely more on pattern recognition than deductive reasoning, they can give confusing answers and have difficulty integrating multiple topics into a cohesive response. These limitations often lead the LLM to deviate from the main topic to keep the conversation interesting. We propose AutoCompanion, a socialbot that uses an LLM model to translate natural language into predicates (and vice versa) and employs commonsense reasoning based on Answer Set Programming (ASP) to hold a social conversation with a human. In particular, we rely on s(CASP), a goal-directed implementation of ASP as the backend. This paper presents the framework design and how an LLM is used to parse user messages and generate a response from the s(CASP) engine output. To validate our proposal, we describe (real) conversations in which the chatbot's goal is to keep the user entertained by talking about movies and books, and s(CASP) ensures (i) correctness of answers, (ii) coherence (and precision) during the conversation, which it dynamically regulates to achieve its specific purpose, and (iii) no deviation from the main topic.

Adventurers set sail for the wreck of the Titanic on Friday – marking the first expedition since the doomed OceanGate mission over a year ago that tragically killed five men. The Georgia-based firm that owns the salvage rights to Titanic, called RMS Titanic Inc, is probing the sunken ocean liner using two remotely operated vehicles (ROVs). According to the BBC, the robotic vehicles will reach 12,000ft (3,700 metres) down – the bottom of the North Atlantic, where the remains of the Titanic lie. They will capture millions of high-resolution photos to make a new 3D model of the debris, which is quickly deteriorating and could be lost in a few decades. A plaque will also be laid on the seabed in honour of Frenchman Paul-Henri Nargeolet, director of research at RMS Titanic Inc, who was one of the five Titan victims.

Generative AI such as those with large language models have created opportunities for innovative assessment design practices. Due to recent technological developments, there is a need to know the limits and capabilities of generative AI in terms of simulating cognitive skills. Assessing student critical thinking skills has been a feature of assessment for time immemorial, but the demands of digital assessment create unique challenges for equity, academic integrity and assessment authorship. Educators need a framework for determining their assessments vulnerability to generative AI to inform assessment design practices. This paper presents a framework that explores the capabilities of the LLM ChatGPT4 application, which is the current industry benchmark. This paper presents the Mapping of questions, AI vulnerability testing, Grading, Evaluation (MAGE) framework to methodically critique their assessments within their own disciplinary contexts. This critique will provide specific and targeted indications of their questions vulnerabilities in terms of the critical thinking skills. This can go on to form the basis of assessment design for their tasks.

Vertical Federated Learning (VFL) has emerged as a popular machine learning paradigm, enabling model training across the data and the task parties with different features about the same user set while preserving data privacy. In production environment, VFL usually involves one task party and one data party. Fair and economically efficient feature trading is crucial to the commercialization of VFL, where the task party is considered as the data consumer who buys the data party's features. However, current VFL feature trading practices often price the data party's data as a whole and assume transactions occur prior to the performing VFL. Neglecting the performance gains resulting from traded features may lead to underpayment and overpayment issues. In this study, we propose a bargaining-based feature trading approach in VFL to encourage economically efficient transactions. Our model incorporates performance gain-based pricing, taking into account the revenue-based optimization objectives of both parties. We analyze the proposed bargaining model under perfect and imperfect performance information settings, proving the existence of an equilibrium that optimizes the parties' objectives. Moreover, we develop performance gain estimation-based bargaining strategies for imperfect performance information scenarios and discuss potential security issues and solutions. Experiments on three real-world datasets demonstrate the effectiveness of the proposed bargaining model.