Feature Ranking in Credit-Risk with Qudit-Based Networks

Nvidia Corp. has seen a dramatic surge in its stock value, with shares of its AI circuit board rising 530%, resulting in a $9 billion profit for investors. This increase follows a private banquet hosted by CEO Jensen Huang in Taiwan, attended by key figures from the electronics industry, including leaders from Taiwan Semiconductor Manufacturing Co. and Hon Hai Precision Industry Co.

Nvidia shares fell following reports that Meta is negotiating to invest billions in Google’s AI chips, intensifying the competition between Nvidia and Google in the chip market. This development comes amid broader concerns regarding the stability of the AI sector and its impact on stock markets.

Cornell Tech has secured a $7 million investment from NASA and Schmidt Sciences aimed at modernizing arXiv, a preprint repository for scientific papers. This funding will facilitate the migration of arXiv to cloud infrastructure, upgrade its outdated codebase, and develop new tools to enhance the discovery of relevant preprints for researchers.

A new framework named Reading Comprehension Exercise Generation (RCEG) has been proposed to leverage large language models (LLMs) for automatically generating personalized English reading comprehension exercises. This framework utilizes fine-tuned LLMs to create content candidates, which are then evaluated by a discriminator to select the highest quality output, significantly enhancing the educational content generation process.

A recent study highlights the limitations of evaluating machine unlearning (MU) by relying solely on a single training seed, revealing that results can vary significantly based on the random number seed used during model training. This finding emphasizes the need for more robust empirical comparisons in MU algorithms, particularly those that are deterministic in nature.

A novel approach named PocketLLM has been introduced to address the challenges of compressing large language models (LLMs) for efficient storage and transmission on edge devices. This method utilizes meta-networks to project LLM weights into discrete latent vectors, achieving significant compression ratios, such as a 10x reduction for Llama 2-7B, while maintaining accuracy.

A new framework called Task-aware Virtual View Exploration (TVVE) has been introduced to enhance robotic manipulation by integrating virtual view exploration with task-specific representation learning. This approach addresses limitations in existing vision-language-action models that rely on static viewpoints, improving 3D perception and reducing task interference.

A recent analysis has been conducted on semi-supervised learning within hypergraphs, revealing that variational learning on random geometric hypergraphs can achieve asymptotic consistency. This study introduces Higher-Order Hypergraph Learning (HOHL), which utilizes Laplacians from skeleton graphs to enhance multiscale smoothness and converges to a higher-order Sobolev seminorm, demonstrating strong empirical performance on standard benchmarks.