Hound introduces a relation-first graph engine that improves system-level reasoning across interrelated components in complex codebases. The agent designs flexible, analyst-defined views with compact annotations (e.g., monetary/value flows, authentication/authorization roles, call graphs, protocol invariants) and uses them to anchor exact retrieval: for any question, it loads precisely the code that matters (often across components) so it can zoom out to system structure and zoom in to the decisive lines. A second contribution is a persistent belief system: long-lived vulnerability hypotheses whose confidence is updated as evidence accrues. The agent employs coverage-versus-intuition planning and a QA finalizer to confirm or reject hypotheses. On a five-project subset of ScaBench[1], Hound improves recall and F1 over a baseline LLM analyzer (micro recall 31.2% vs. 8.3%; F1 14.2% vs. 9.8%) with a modest precision trade-off. We attribute these gains to flexible, relation-first graphs that extend model understanding beyond call/dataflow to abstract aspects, plus the hypothesis-centric loop; code and artifacts are released to support reproduction.

This paper introduces a scalar numerical differentiator, represented as a system of nonlinear differential equations of any high order. We derive the explicit solution for this system and demonstrate that, with a suitable choice of differentiator order, the error converges to zero for polynomial signals with additive white noise. In more general cases, the error remains bounded, provided that the highest estimated derivative is also bounded. A notable advantage of this numerical differentiation method is that it does not require tuning parameters based on the specific characteristics of the signal being differentiated. We propose a discretization method for the equations that implements a cumulative smoothing algorithm for time series. This algorithm operates online, without the need for data accumulation, and it solves both interpolation and extrapolation problems without fitting any coefficients to the data.

Text-attributed graph (TAG) is an important type of graph structured data with text descriptions for each node. Few- and zero-shot node classification on TAGs have many applications in fields such as academia and social networks. However, the two tasks are challenging due to the lack of supervision signals, and existing methods only use the contrastive loss to align graph-based node embedding and language-based text embedding. In this paper, we propose Hound to improve accuracy by introducing more supervision signals, and the core idea is to go beyond the node-text pairs that come with data. Specifically, we design three augmentation techniques, i.e., node perturbation, text matching, and semantics negation to provide more reference nodes for each text and vice versa. Node perturbation adds/drops edges to produce diversified node embeddings that can be matched with a text. Text matching retrieves texts with similar embeddings to match with a node. Semantics negation uses a negative prompt to construct a negative text with the opposite semantics, which is contrasted with the original node and text. We evaluate Hound on 5 datasets and compare with 13 state-of-the-art baselines. The results show that Hound consistently outperforms all baselines, and its accuracy improvements over the best-performing baseline are usually over 5%.

Off-road vehicles are susceptible to rollovers in terrains with large elevation features, such as steep hills, ditches, and berms. One way to protect them against rollovers is ruggedization through the use of industrial-grade parts and physical modifications. However, this solution can be prohibitively expensive for academic research labs. Our key insight is that a software-based rollover-prevention system (RPS) enables the use of commercial-off-the-shelf hardware parts that are cheaper than their industrial counterparts, thus reducing overall cost. In this paper, we present HOUND, a small-scale, inexpensive, off-road autonomy platform that can handle challenging outdoor terrains at high speeds through the integration of an RPS. HOUND is integrated with a complete stack for perception and control, geared towards aggressive offroad driving. We deploy HOUND in the real world, at high speeds, on four different terrains covering 50 km of driving and highlight its utility in preventing rollovers and traversing difficult terrain. Additionally, through integration with BeamNG, a state-of-the-art driving simulator, we demonstrate a significant reduction in rollovers without compromising turning ability across a series of simulated experiments. Supplementary material can be found on our website, where we will also release all design documents for the platform: https://sites.google.com/view/prl-hound .

The idea of a robotic dog that you can carry on your chest like a baby may sound like a concept from the latest episode of Black Mirror. But the idea has become a reality with the launch of Sony's latest accessory for its robotic dog, Aibo. The tech giant has unveiled the'Aibo Thong' – a 9,790 yen (£63) carrying strap designed to allow owners to hold their robo-dog while they're out and about. 'We propose a new lifestyle to all people who live with aibo with a carrying strap that pays attention to details so that you can hold it safely and comfortably,' Sony explained. Sony has unveiled the'Aibo Thong' – a 9,790 yen (£63) carrying strap designed to allow owners to hold their robo-dog while they're out and about Aibo is Sony's robotic dog, which costs an eye-watering £2,145, and features artificial intelligence technology.

ATTRIBUTING AUTHORSHIP WITH STYLOMETRY Stylometry is the quantitative study of literary style through computational text analysis. It's based on the idea that we all have a unique, consistent, and recognizable style to our writing. This includes our vocabulary, our use of punctuation, the average length of our sentences and words, and so on. A common application of stylometry is authorship attribution. Do you ever wonder if Shakespeare really wrote all his plays?

If you've ever thought turning on your microwave or vacuum cleaner was too hard, the solution may be as easy as spending $2,900 on a robotic dog that will do it for you. That's the operating theory behind Aibo, a robotic pet canine created by Sony, which was released last year. Sony has been continually adding features to Aibo and the latest features will allow the small robotic dog to communicate with a range of household smart appliances to help make life easier for its owners. According to a report from Gizmodo, Sony hosted a demonstration of the new features at the CEATEC show in Tokyo, Japan's largest IT and electronics trade show. One example showed Aibo communicating wirelessly with a smart microwave, telling it to start cooking a snack as soon as its owners come home from a long day.

Artificial intelligence is not the future anymore since this technology is already widely used in our lives. Most sci-fi movies predict rather tragic outcome of using the artificial intelligence. You probably remember the plots of Matrix and Terminator – quite depressive vista is shown in these masterpieces of cinema. However, it is the fantasy of scriptwriter and film directors. In reality, the AI has already gained its supporters.

Disney Research has unveiled an amazing prototype of a robot that moves just like a real dog would. The company has released a new robotic kit that is capable of creating a variety of'robotic manipulators and legged robots'. A video shows the development of the robots from the very first conceptual stages. Disney has unveiled a new robotic kit that can create a'Puppybot' machine. The computer system uses a library of components to create potential robotic designs.

"Listen to me as I speak, do what I say and give me what I demand", – the best way to depict the ancient times of kings, where everyone was bound to obey them. Not while ago, the time has come for everyone to say the command and let the courtiers do the rest. However, the courtiers are not humans anymore as it was in the ancient times – machines have replaced the humans and the kings are common people. All I am talking about is Artificial Intelligence- where machines can think and work like humans. The popularity of artificial intelligence is no longer new as everyone whether using iPhone or not would be well aware of SIRI.