Functional magnetic resonance imaging (fMRI) enables noninvasive investigation of brain function, while short clinical scan durations, arising from human and non-human factors, usually lead to reduced data quality and limited statistical power for neuroimaging research. In this paper, we propose BrainCast, a novel spatio-temporal forecasting framework specifically tailored for whole-brain fMRI time series forecasting, to extend informative fMRI time series without additional data acquisition. It formulates fMRI time series forecasting as a multivariate time series prediction task and jointly models temporal dynamics within regions of interest (ROIs) and spatial interactions across ROIs. Specifically, BrainCast integrates a Spatial Interaction Awareness module to characterize inter-ROI dependencies via embedding every ROI time series as a token, a Temporal Feature Refinement module to capture intrinsic neural dynamics within each ROI by enhancing both low- and high-energy temporal components of fMRI time series at the ROI level, and a Spatio-temporal Pattern Alignment module to combine spatial and temporal representations for producing informative whole-brain features. Experimental results on resting-state and task fMRI datasets from the Human Connectome Project demonstrate the superiority of BrainCast over state-of-the-art time series forecasting baselines. Moreover, fMRI time series extended by BrainCast improve downstream cognitive ability prediction, highlighting the clinical and neuroscientific impact brought by whole-brain fMRI time series forecasting in scenarios with restricted scan durations.

By exploiting students' response logs, SCM aims to predict their exercise

Weakly supervised object detection (WSOD) has attracted extensive research attention due to its great flexibility of exploiting large-scale dataset with only image-levelannotations fordetector training.

More and more Chinese adoptees in the US are trying to reunite with their birth parents. For Youxue, it took more than a decade, and a remarkable coincidence. A girl is found on a street in Ma'Anshan, China, in May 1993. Her paternal grandfather, the story goes, set her down and walked away. It's unclear how long she's been outside when somebody arrives and takes her to the orphanage. A white woman adopts the girl and brings her to America in August 1994. She gives her an English name. In spring 2010, when Youxue (her Chinese name) was a high school sophomore in Dallas, Texas, she decided to start searching for her birth parents.

Abstract--Semantic Communication (SC) combined with V e-hicular edge computing (VEC) provides an efficient edge task processing paradigm for Internet of V ehicles (IoV). Focusing on highway scenarios, this paper proposes a Tripartite Cooperative Semantic Communication (TCSC) framework, which enables V e-hicle Users (VUs) to perform semantic task offloading via V ehicle-to-Infrastructure (V2I) and V ehicle-to-V ehicle (V2V) communications. Considering task latency and the number of semantic symbols, the framework constructs a Mixed-Integer Nonlinear Programming (MINLP) problem, which is transformed into two subproblems. First, we innovatively propose a multi-agent proximal policy optimization task offloading optimization method based on parametric distribution noise (MAPPO-PDN) to solve the optimization problem of the number of semantic symbols; second, linear programming (LP) is used to solve offloading ratio. Simulations show that performance of this scheme is superior to that of other algorithms.

Vehicle edge caching is a promising technology that can significantly reduce the latency for vehicle users (VUs) to access content by pre-caching user-interested content at edge nodes. It is crucial to accurately predict the content that VUs are interested in without exposing their privacy. Traditional federated learning (FL) can protect user privacy by sharing models rather than raw data. However, the training of FL requires frequent model transmission, which can result in significant communication overhead. Additionally, vehicles may leave the road side unit (RSU) coverage area before training is completed, leading to training failures. To address these issues, in this letter, we propose a federated distillation-assisted vehicle edge caching scheme based on lightweight denoising diffusion probabilistic model (LDPM). The simulation results demonstrate that the proposed vehicle edge caching scheme has good robustness to variations in vehicle speed, significantly reducing communication overhead and improving cache hit percentage.

U.S. moves to deepen minerals supply chain in AI race with China The U.S. is looking to cut its dependence on China. The U.S. will seek agreements with eight allied nations as part of a fresh effort to strengthen supply chains for the computer chips and critical minerals needed for artificial intelligence technology, according to the top State Department official for economic affairs. The initiative, which builds on efforts dating back to the first administration of President Donald Trump, unfolds as the U.S. looks to cut its dependence on China. It will begin with a meeting at the White House on Dec. 12 between the U.S. and counterparts from Japan, South Korea, Singapore, the Netherlands, the U.K., Israel, the United Arab Emirates and Australia, Jacob Helberg, the undersecretary of state for economic affairs, said in an interview. Helberg, a former adviser at Palantir Technologies, said the summit will focus on reaching agreements across the areas of energy, critical minerals, advanced manufacturing semiconductors, AI infrastructure, and transportation logistics.