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Cryptic crosswords, the dominant crossword variety in the UK, are a promising target for advancing NLP systems that seek to process semantically complex, highly compositional language. Cryptic clues read like fluent natural language but are adversarially composed of two parts: a definition and a wordplay cipher requiring character-level manipulations. Expert humans use creative intelligence to solve cryptics, flexibly combining linguistic, world, and domain knowledge. In this paper, we make two main contributions. First, we present a dataset of cryptic clues as a challenging new benchmark for NLP systems that seek to process compositional language in more creative, human-like ways. After showing that three non-neural approaches and T5, a state-of-the-art neural language model, do not achieve good performance, we make our second main contribution: a novel curriculum approach, in which the model is first fine-tuned on related tasks such as unscrambling words. We also introduce a challenging data split, examine the meta-linguistic capabilities of subword-tokenized models, and investigate model systematicity by perturbing the wordplay part of clues, showing that T5 exhibits behavior partially consistent with human solving strategies. Although our curricular approach considerably improves on the T5 baseline, our best-performing model still fails to generalize to the extent that humans can. Thus, cryptic crosswords remain an unsolved challenge for NLP systems and a potential source of future innovation.

The topic of this final qualification work was chosen due to the importance of developing robotic systems designed to assist people with disabilities. Advances in robotics and automation technologies have opened up new prospects for creating devices that can significantly improve the quality of life for these people. In this context, designing a robotic hand with a control system adapted to the needs of people with disabilities is a major scientific and practical challenge. This work addresses the problem of developing and manufacturing a four-degree-of-freedom robotic hand suitable for practical manipulation. Addressing this issue requires a comprehensive approach, encompassing the design of the hand's mechanical structure, the development of its control system, and its integration with a technical vision system and software based on the Robot Operating System (ROS).

This paper presents an innovative large language model (LLM)-based robotic system for enhancing multi-modal human-robot interaction (HRI). Traditional HRI systems relied on complex designs for intent estimation, reasoning, and behavior generation, which were resource-intensive. In contrast, our system empowers researchers and practitioners to regulate robot behavior through three key aspects: providing high-level linguistic guidance, creating "atomics" for actions and expressions the robot can use, and offering a set of examples. Implemented on a physical robot, it demonstrates proficiency in adapting to multi-modal inputs and determining the appropriate manner of action to assist humans with its arms, following researchers' defined guidelines. Simultaneously, it coordinates the robot's lid, neck, and ear movements with speech output to produce dynamic, multi-modal expressions. This showcases the system's potential to revolutionize HRI by shifting from conventional, manual state-and-flow design methods to an intuitive, guidance-based, and example-driven approach.

As more companies ramp up development of artificial intelligence systems, they are increasingly turning to graphics processing unit (GPU) chips for the computing power they need to run large language models (LLMs) and to crunch data quickly at massive scale. Between video game processing and AI, demand for GPUs has never been higher, and chipmakers are rushing to bolster supply. In new findings released today, though, researchers are highlighting a vulnerability in multiple brands and models of mainstream GPUs--including Apple, Qualcomm, and AMD chips--that could allow an attacker to steal large quantities of data from a GPU's memory. The silicon industry has spent years refining the security of central processing units, or CPUs, so they don't leak data in memory even when they are built to optimize for speed. However, since GPUs were designed for raw graphics processing power, they haven't been architected to the same degree with data privacy as a priority.

Nevertheless, a large class of PPDE is not analytically solvable, and one has to rely on the numerical solution. In Ren and Tan (2017), a probabilistic scheme based on Fahim et al. (2011) has been proposed, and was proved to converge to the viscosity solution of PPDE. However, its practical implementation is far from trivial due to the presence of the conditional expectation. The suggestion of Ren and Tan (2017) to use regression as in Gobet et al. (2005) relies on a careful basis function choice, which may not always be possible, see the discussion at the end of Section 2. Neural networks methods for PDEs have been introduced independently in Han et al. (2018) and Sirignano and Spiliopoulos (2018) using backward stochastic differential equations and the Galerkin method respectively, see also Beck et al. (2019), Huré et al. (2020) for other variants of deep learning-based numerical solutions. A deep neural network algorithm for the numerical solution of PPDEs has also been proposed in Saporito and Zhang (2020) by applying Long Short-Term Memory (LSTM) networks in the framework of the deep Galerkin method for PDEs, see Sirignano and Spiliopoulos (2018). On the other hand, Sabate-Vidales et al. (2020) propose to combine the LSTM net-2 work and the path signature to solve the linear PPDE. Unlike regression methods, deep learning algorithms do not rely on the choice of a basis. In this paper, we propose a deep learning approach to the implementation of the probabilistic scheme of Ren and Tan (2017) for the numerical solution of fully nonlinear PPDEs of the form (1.1). The main idea of Algorithm 4.1 is based on the L

Amazon Prime Day is winding down, ending in just a few hours (11:59 pm PT to be exact). Most of the deals are for Prime subscribers, but there are great discounts for non-members as well. We've spent hours combing through thousands of deals; these are our favorites on WIRED-tested gear, from Alexa-enabled speakers and robot vacs to laptops and tablets. The WIRED Gear team tests products year-round. We sorted through hundreds of thousands of deals by hand to make these picks. Crossed out products are out of stock or no longer discounted. Our Amazon Prime Day coverage page has the latest stories, and our Prime Day Shopping Tips will help you avoid bad deals. You can also get a 1-year subscription to WIRED for $5 here. Updated July 13: We've added new deals, including the OnePlus 9 Pro, Logitech K380 Keyboard, and Bose QuietComfort 45 Headphones. We've also removed expired deals and updated prices. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. We've got a separate roundup of more great home and kitchen deals right here. There are blenders, and then there are Vitamix blenders. I was skeptical, but like my fellow Gear reviewer Joe Ray, the Vitamix made me a blender person.