When the U.S. first embraced "de-risking" to get Europe on board with measures to deny key technology to China, officials in Beijing dismissed the term as no different than decoupling. Now they are trying a new strategy: redefining the concept. Chinese Premier Li Qiang last week acknowledged the legitimacy of de-risking while speaking to CEOs on a trip to Germany, but said it should be decided by business leaders instead of governments. He also warned that risks shouldn't be "exaggerated" -- opening a discussion on what exactly poses a serious threat to national security. Li touched on the theme again Tuesday at a high-profile economic forum in China known as "Summer Davos," where he told delegates "if there is risk in a certain industry, it's not the call or decision of a particular organization or a single government.

The multitude of data generated by sensors available on users' mobile devices, combined with advances in machine learning techniques, support context-aware services in recognizing the current situation of a user (i.e., physical context) and optimizing the system's personalization features. However, context-awareness performances mainly depend on the accuracy of the context inference process, which is strictly tied to the availability of large-scale and labeled datasets. In this work, we present a framework developed to collect datasets containing heterogeneous sensing data derived from personal mobile devices. The framework has been used by 3 voluntary users for two weeks, generating a dataset with more than 36K samples and 1331 features. We also propose a lightweight approach to model the user context able to efficiently perform the entire reasoning process on the user mobile device. To this aim, we used six dimensionality reduction techniques in order to optimize the context classification. Experimental results on the generated dataset show that we achieve a 10x speed up and a feature reduction of more than 90% while keeping the accuracy loss less than 3%.

We investigate the role of attention and memory in complex reasoning tasks. We analyze Transformer-based self-attention as a model and extend it with memory. By studying a synthetic visual reasoning test, we refine the taxonomy of reasoning tasks. Incorporating self-attention with ResNet50, we enhance feature maps using feature-based and spatial attention, achieving efficient solving of challenging visual reasoning tasks. Our findings contribute to understanding the attentional needs of SVRT tasks. Additionally, we propose GAMR, a cognitive architecture combining attention and memory, inspired by active vision theory. GAMR outperforms other architectures in sample efficiency, robustness, and compositionality, and shows zero-shot generalization on new reasoning tasks.

Deep R Programming is a comprehensive and in-depth introductory course on one of the most popular languages for data science. It equips ambitious students, professionals, and researchers with the knowledge and skills to become independent users of this potent environment so that they can tackle any problem related to data wrangling and analytics, numerical computing, statistics, and machine learning. This textbook is a non-profit project. Its online and PDF versions are freely available at .

The mother of the 19-year-old killed aboard the Titan submersible said the plan had been for her to accompany her husband on a trip to see the wreck of the Titanic at the bottom of the sea. She "stepped back" from going on the trip because of her son's enthusiasm, Christine Dawood told the BBC, and he boarded the ill-fated craft carrying a Rubik's Cube and dreaming of setting a world record. He and his father, Shahzada Dawood, died when the vessel imploded. Christine Dawood told the news outlet the original plan was for her to accompany her husband on the underwater trek roughly 12,500 feet below the surface to view the Titanic. The original trip, however, was canceled because of the COVID-19 pandemic.

Human beings are social creatures. We routinely reason about other agents, and a crucial component of this social reasoning is inferring people's goals as we learn about their actions. In many settings, we can perform intuitive but reliable goal inference from language descriptions of agents, actions, and the background environments. In this paper, we study this process of language driving and influencing social reasoning in a probabilistic goal inference domain. We propose a neuro-symbolic model that carries out goal inference from linguistic inputs of agent scenarios. The "neuro" part is a large language model (LLM) that translates language descriptions to code representations, and the "symbolic" part is a Bayesian inverse planning engine. To test our model, we design and run a human experiment on a linguistic goal inference task. Our model closely matches human response patterns and better predicts human judgements than using an LLM alone.

Suleman Dawood, the 19-year-old who died aboard OceanGate's Titan submersible last week, hoped to set the world record for solving a Rubik's Cube in the deep ocean, his mother said Monday. Dawood and his father, Shahzada, had finished the process of applying to the Guinness World Records and entered the submersible equipped with a camera to record the achievement. Christine Dawood and her daughter remained aboard the Polar Prince mother ship while the submersible descended toward the wreck of the Titanic earlier this month, she told the BBC in an interview. Christine spoke of the moment the crew of the Prince informed her they had lost communications with the submersible. "I didn't comprehend at that moment what it meant – and then it just went downhill from there," she said.

As well as Suleman and his father Shahzada Dawood, three other people died on board: Stockton Rush, the 61-year-old CEO of OceanGate which owned the Titan, British businessman Hamish Harding, 58, and Paul-Henry Nargeolet, 77, a former French navy diver and renowned explorer.

This work in progress paper provides an example to show a detouring procedure through knowledge representation and reasoning. When a human manager requests a detouring, this should affect the related agents. Through non-monotonic reasoning process, we verify each step to be proceeded and provide all the successful connections of the reasoning. Following this progress and continuing this idea development, we expect that this simulated scenario can be a guideline to build the traffic management system in real. After a brief introduction including related works, we provide our problem formulation, primary work, discussion, and conclusions.

We automate deep step-by step reasoning in an LLM dialog thread by recursively exploring alternatives (OR-nodes) and expanding details (AND-nodes) up to a given depth. Starting from a single succinct task-specific initiator we steer the automated dialog thread to stay focussed on the task by synthesizing a prompt that summarizes the depth-first steps taken so far. Our algorithm is derived from a simple recursive descent implementation of a Horn Clause interpreter, except that we accommodate our logic engine to fit the natural language reasoning patterns LLMs have been trained on. Semantic similarity to ground-truth facts or oracle advice from another LLM instance is used to restrict the search space and validate the traces of justification steps returned as answers. At the end, the unique minimal model of a generated Horn Clause program collects the results of the reasoning process. As applications, we sketch implementations of consequence predictions, causal explanations, recommendation systems and topic-focussed exploration of scientific literature.