Russia's creeping advance in Donetsk has captured 4,168 square kilometres (1,609sq miles) of territory at the cost of 430,000 soldiers, according to a new analysis. Ukraine will reportedly receive its first French Mirage 2000-5F multirole fighters this month, according to French magazine Avions Legendaires. A Russian court has ordered the largest search engine in Russia, Yandex, to hide maps and photos of one of the country's biggest oil refineries after repeated attacks by Ukrainian drones, state news agency TASS reports. Russia's creeping advance in Donetsk has captured 4,168 square kilometres (1,609sq miles) of territory at the cost of 430,000 soldiers, according to a new analysis. Ukraine will reportedly receive its first French Mirage 2000-5F multirole fighters this month, according to French magazine Avions Legendaires.

In this paper, we present the design and benchmark of an innovative sensor, ViTacTip, which fulfills the demand for advanced multi-modal sensing in a compact design. A notable feature of ViTacTip is its transparent skin, which incorporates a `see-through-skin' mechanism. This mechanism aims at capturing detailed object features upon contact, significantly improving both vision-based and proximity perception capabilities. In parallel, the biomimetic tips embedded in the sensor's skin are designed to amplify contact details, thus substantially augmenting tactile and derived force perception abilities. To demonstrate the multi-modal capabilities of ViTacTip, we developed a multi-task learning model that enables simultaneous recognition of hardness, material, and textures. To assess the functionality and validate the versatility of ViTacTip, we conducted extensive benchmarking experiments, including object recognition, contact point detection, pose regression, and grating identification. To facilitate seamless switching between various sensing modalities, we employed a Generative Adversarial Network (GAN)-based approach. This method enhances the applicability of the ViTacTip sensor across diverse environments by enabling cross-modality interpretation.

The ability to automatically encircle boundaries with mobile robots is crucial for tasks such as border tracking and object enclosing. Previous research has primarily focused on regular boundaries, often assuming that their geometric equations are known in advance, which is not often the case in practice. In this paper, we investigate a more general case and propose an algorithm that addresses geometric irregularities of boundaries without requiring prior knowledge of their analytical expressions. To achieve this, we develop a Fourier-based curve fitting method for boundary approximation using sampled points, enabling parametric characterization of general 2-D boundaries. This approach allows star-shaped boundaries to be fitted into polar-angle-based parametric curves, while boundaries of other shapes are handled through decomposition. Then, we design a vector field (VF) to achieve the encirclement of the parameterized boundary, wherein a polar radius error is introduced to measure the robot's ``distance'' to the boundary. The controller is finally synthesized using a control barrier function and quadratic programming to mediate some potentially conflicting specifications: boundary encirclement, obstacle avoidance, and limited actuation. In this manner, the VF-guided reference control not only guides the boundary encircling action, but can also be minimally modified to satisfy obstacle avoidance and input saturation constraints. Simulations and experiments are presented to verify the performance of our new method, which can be applied to mobile robots to perform practical tasks such as cleaning chemical spills and environment monitoring.

Verifying the provenance of content is crucial to the function of many organizations, e.g., educational institutions, social media platforms, firms, etc. This problem is becoming increasingly difficult as text generated by Large Language Models (LLMs) becomes almost indistinguishable from human-generated content. In addition, many institutions utilize in-house LLMs and want to ensure that external, non-sanctioned LLMs do not produce content within the institution. In this paper, we answer the following question: Given a piece of text, can we identify whether it was produced by LLM $A$ or $B$ (where $B$ can be a human)? We model LLM-generated text as a sequential stochastic process with complete dependence on history and design zero-shot statistical tests to distinguish between (i) the text generated by two different sets of LLMs $A$ (in-house) and $B$ (non-sanctioned) and also (ii) LLM-generated and human-generated texts. We prove that the type I and type II errors for our tests decrease exponentially in the text length. In designing our tests, we derive concentration inequalities on the difference between log-perplexity and the average entropy of the string under $A$. Specifically, for a given string, we demonstrate that if the string is generated by $A$, the log-perplexity of the string under $A$ converges to the average entropy of the string under $A$, except with an exponentially small probability in string length. We also show that if $B$ generates the text, except with an exponentially small probability in string length, the log-perplexity of the string under $A$ converges to the average cross-entropy of $B$ and $A$. Lastly, we present preliminary experimental results to support our theoretical results. By enabling guaranteed (with high probability) finding of the origin of harmful LLM-generated text with arbitrary size, we can help fight misinformation.

Anthropic has partly resolved a legal disagreement that saw the AI startup draw the ire of the music industry. The group alleged that the company had trained its Claude AI model on at least 500 songs to which they held rights and that, when promoted, Claude could reproduce the lyrics of those tracks either partially or in full. Among the song lyrics the publishers said Anthropic had infringed on included Beyoncé's "Halo" and "Moves Like Jagger" by Maroon 5. In cases where the company intends not to address an issue, it must clearly state its intent to do so. "Our decision to enter into this stipulation is consistent with those priorities.

Has your Google smart speaker been giving you the silent treatment lately? Over the past several days, owners of Google's Nest and Nest Hub devices have been reporting that Google Assistant has stopped responding to basic commands such as "What's the weather" and "What time is it?" Perplexed Nest users had been turning to Google's support team for possible solutions, but with little success. Luckily, Google just told Android Authority that it's deployed a fix and that "all users should be up and running now." The problems appear to have begun earlier this week, with Nest users on Reddit and other forums complaining that their smart speakers were going silent when asked the most basic commands.

But Google's global search dominance makes it the most important player, and the company has already rolled out AI Overviews to more than a billion people worldwide. The result is searches that feel more like conversations. Google and OpenAI both report that people interact differently with generative search--they ask longer questions and pose more follow-ups. This new application of AI has serious implications for online advertising and (gulp) media. Because these search products often summarize information from online news stories and articles in their responses, concerns abound that generative search results will leave little reason for people to click through to the original sources, depriving those websites of potential ad revenue.

Have you ever wondered what's really happening out there in the dark? With 4K night-vision digital binoculars, you can explore the unseen and uncover mysteries like the star of your own sci-fi movie. Equipped with 4K resolution, these binoculars let you capture vivid details, even in total darkness. Whether you're observing nocturnal wildlife, navigating trails at night, or just stargazing, the 8X digital zoom ensures you never miss a thing. And with a 3-inch LCD screen, you can easily view what you're tracking without squinting through tiny eyepieces--talk about futuristic vibes.

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This thesis presents Abstractive Text Summarization models for contemporary Sanskrit prose. The first chapter, titled Introduction, presents the motivation behind this work, the research questions, and the conceptual framework. Sanskrit is a low-resource inflectional language. The key research question that this thesis investigates is what the challenges in developing an abstractive TS for Sanskrit. To answer the key research questions, sub-questions based on four different themes have been posed in this work. The second chapter, Literature Review, surveys the previous works done. The third chapter, data preparation, answers the remaining three questions from the third theme. It reports the data collection and preprocessing challenges for both language model and summarization model trainings. The fourth chapter reports the training and inference of models and the results obtained therein. This research has initiated a pipeline for Sanskrit abstractive text summarization and has reported the challenges faced at every stage of the development. The research questions based on every theme have been answered to answer the key research question.