Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels

With the proliferation of edge computing, efficient AI inference on edge devices has become essential for intelligent applications such as autonomous vehicles and VR/AR. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an online optimization framework to address the challenge of dynamic channel conditions and device mobility in an end-to-end communication system. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multi-level feature transmission, accounting for temporal factors and dynamic elements throughout the transmission process. To solve the online optimization problem, we design a novel soft actor-critic-based deep reinforcement learning system with a carefully designed reward function for real-time decision-making, overcoming the optimization difficulty of the NP-hard problem and achieving the minimization of semantic loss while respecting latency constraints. Numerical results showcase the superiority of our approach compared to traditional greedy methods under various system setups.Comment: 6 page

EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE

Building scalable vision-language models to learn from diverse, multimodal data remains an open challenge. In this paper, we introduce an Efficient Vision-languagE foundation model, namely EVE, which is one unified multimodal Transformer pre-trained solely by one unified pre-training task. Specifically, EVE encodes both vision and language within a shared Transformer network integrated with modality-aware sparse Mixture-of-Experts (MoE) modules, which capture modality-specific information by selectively switching to different experts. To unify pre-training tasks of vision and language, EVE performs masked signal modeling on image-text pairs to reconstruct masked signals, i.e., image pixels and text tokens, given visible signals. This simple yet effective pre-training objective accelerates training by 3.5x compared to the model pre-trained with Image-Text Contrastive and Image-Text Matching losses. Owing to the combination of the unified architecture and pre-training task, EVE is easy to scale up, enabling better downstream performance with fewer resources and faster training speed. Despite its simplicity, EVE achieves state-of-the-art performance on various vision-language downstream tasks, including visual question answering, visual reasoning, and image-text retrieval.Comment: Accepted by AAAI 202

Unprotected quadratic band crossing points and quantum anomalous Hall effect in FeB2 monolayer

Quadratic band crossing points (QBCPs) and quantum anomalous Hall effect (QAHE) have attracted the attention of both theoretical and experimental researchers in recent years. Based on first-principle calculations, we find that the FeB$_2$ monolayer is a nonmagnetic semimetal with QBCPs at $K$. Through symmetry analysis and $\mathbf{k}\cdot\mathbf{p}$ invariant theory, we find that the QBCP is not protected by rotation symmetry and consists of two Dirac points with same chirality (Berry phase of $2\pi$). Once introducing Coulomb interactions, we find that there is a spontaneous-time-reversal-breaking instability of the spinful QBCPs, which gives rise to a $C=2$ QAH insulator with orbital moment ordering

Shear Stress-mediated Angiogenesis Through Id1 Relevant to Atherosclerosis

Abnormal shear stress in the blood vessel is an important stimulating factor for the formation of angiogenesis and vulnerable plaques. This paper intended to explore the role of shear stress-regulated Id1 in angiogenesis. First, we applied a carotid artery ring ligation to create local stenosis in ApoE-/- mice. Then, 3D geometry of the vessel network was reconstructed based on MRI, and our analysis of computational fluid dynamics revealed that wall shear stress of the proximal region was much higher than that of the distal region. In addition, results from histological staining of the proximal region found more vulnerable-probe plaques with new capillary formation, the presence of macrophages and collagen fibers degradation. Our in vitro and in vivo experiments further indicated high shear stress can induce endothelial cell-mediated angiogenesis and high expression of Id1. Id1-overexpression promoted endothelial cells migration and angiogenesis through collagen degradation mediated by MT-MMPs. Together, our results support a biomechanical role for Id1 in angiogenesis, suggesting manipulation of the Id1 activity may offer a novel anti-angiogenic therapeutic strategy in vulnerable plaques

Effects of shading on photosynthetic characteristics of wax apple leaves

The wax apple (Syzygium samarangense) is a highly valuable fruit species in Southeast Asia. To regulate the fruiting season, shading is commonly used to induce flowering in wax apple. However, the effects of shading on the growth of wax apple is not well understood. To address this, we conducted a study analyzing the photosynthetic characteristics of wax apple leaves under 40% and 90% shading rates. Our findings revealed that shading had a significant impact on the photosynthesis and branching tip development of wax apple. During shading treatments, the chlorophyll contents of the leaves increased to enhance light absorption efficiency. In the 40% shading treatment, the primary factor causing the decrease in net photosynthetic rate was stomatal limitation, while in the 90% shading treatment, both stomatal and non-stomatal limitations contributed to the decrease in net photosynthetic rate. These results are indications that sheading plays a key role in chlorophyll and photosynthesis in wax apple. These results will have led to a new research direction for genetic crop improvement

Polycomb group proteins EZH2 and EED directly regulate androgen receptor in advanced prostate cancer

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149265/1/ijc32118.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149265/2/ijc32118_am.pd

RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

Despite Retrieval-Augmented Generation (RAG) showing promising capability in leveraging external knowledge, a comprehensive evaluation of RAG systems is still challenging due to the modular nature of RAG, evaluation of long-form responses and reliability of measurements. In this paper, we propose a fine-grained evaluation framework, RAGChecker, that incorporates a suite of diagnostic metrics for both the retrieval and generation modules. Meta evaluation verifies that RAGChecker has significantly better correlations with human judgments than other evaluation metrics. Using RAGChecker, we evaluate 8 RAG systems and conduct an in-depth analysis of their performance, revealing insightful patterns and trade-offs in the design choices of RAG architectures. The metrics of RAGChecker can guide researchers and practitioners in developing more effective RAG systems. This work has been open sourced at https://github.com/amazon-science/RAGChecker.Under Review. Github Repo: https://github.com/amazon-science/RAGChecke