The Whole Pathological Slide Classification via Weakly Supervised Learning

Due to its superior efficiency in utilizing annotations and addressing gigapixel-sized images, multiple instance learning (MIL) has shown great promise as a framework for whole slide image (WSI) classification in digital pathology diagnosis. However, existing methods tend to focus on advanced aggregators with different structures, often overlooking the intrinsic features of H\&E pathological slides. To address this limitation, we introduced two pathological priors: nuclear heterogeneity of diseased cells and spatial correlation of pathological tiles. Leveraging the former, we proposed a data augmentation method that utilizes stain separation during extractor training via a contrastive learning strategy to obtain instance-level representations. We then described the spatial relationships between the tiles using an adjacency matrix. By integrating these two views, we designed a multi-instance framework for analyzing H\&E-stained tissue images based on pathological inductive bias, encompassing feature extraction, filtering, and aggregation. Extensive experiments on the Camelyon16 breast dataset and TCGA-NSCLC Lung dataset demonstrate that our proposed framework can effectively handle tasks related to cancer detection and differentiation of subtypes, outperforming state-of-the-art medical image classification methods based on MIL. The code will be released later

Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses

For vehicular metaverses, one of the ultimate user-centric goals is to optimize the immersive experience and Quality of Service (QoS) for users on board. Semantic Communication (SemCom) has been introduced as a revolutionary paradigm that significantly eases communication resource pressure for vehicular metaverse applications to achieve this goal. SemCom enables high-quality and ultra-efficient vehicular communication, even with explosively increasing data traffic among vehicles. In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool. The global and local metaverses are brand-new concepts from the metaverse's distribution standpoint. Considering the QoS of users, this article explores the potential security vulnerabilities of the proposed framework. To that purpose, this study highlights a specific security risk to the framework's SemCom module and offers a viable defense solution, so encouraging community researchers to focus more on vehicular metaverse security. Finally, we provide an overview of the open issues of secure SemCom in the vehicular metaverses, notably pointing out potential future research directions

DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs

Existing nighttime unmanned aerial vehicle (UAV) trackers follow an "Enhance-then-Track" architecture - first using a light enhancer to brighten the nighttime video, then employing a daytime tracker to locate the object. This separate enhancement and tracking fails to build an end-to-end trainable vision system. To address this, we propose a novel architecture called Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night by efficiently learning to generate darkness clue prompts. Without a separate enhancer, DCPT directly encodes anti-dark capabilities into prompts using a darkness clue prompter (DCP). Specifically, DCP iteratively learns emphasizing and undermining projections for darkness clues. It then injects these learned visual prompts into a daytime tracker with fixed parameters across transformer layers. Moreover, a gated feature aggregation mechanism enables adaptive fusion between prompts and between prompts and the base model. Extensive experiments show state-of-the-art performance for DCPT on multiple dark scenario benchmarks. The unified end-to-end learning of enhancement and tracking in DCPT enables a more trainable system. The darkness clue prompting efficiently injects anti-dark knowledge without extra modules. Code and models will be released.Comment: Under revie

Impaired function of dendritic cells within the tumor microenvironment

Dendritic cells (DCs), a class of professional antigen-presenting cells, are considered key factors in the initiation and maintenance of anti-tumor immunity due to their powerful ability to present antigen and stimulate T-cell responses. The important role of DCs in controlling tumor growth and mediating potent anti-tumor immunity has been demonstrated in various cancer models. Accordingly, the infiltration of stimulatory DCs positively correlates with the prognosis and response to immunotherapy in a variety of solid tumors. However, accumulating evidence indicates that DCs exhibit a significantly dysfunctional state, ultimately leading to an impaired anti-tumor immune response due to the effects of the immunosuppressive tumor microenvironment (TME). Currently, numerous preclinical and clinical studies are exploring immunotherapeutic strategies to better control tumors by restoring or enhancing the activity of DCs in tumors, such as the popular DC-based vaccines. In this review, an overview of the role of DCs in controlling tumor progression is provided, followed by a summary of the current advances in understanding the mechanisms by which the TME affects the normal function of DCs, and concluding with a brief discussion of current strategies for DC-based tumor immunotherapy

Frisson Waves: Exploring Automatic Detection, Triggering and Sharing of Aesthetic Chills in Music Performances

Frisson is the feeling and experience of physical reactions such as shivers, tingling skin, and goosebumps. Using entrainment through facilitating interpersonal transmissions of embodied sensations, we present "Frisson Waves" with the aim to enhance live music performance experiences. "Frisson Waves" is an exploratory real-time system to detect, trigger and share frisson in a wave-like pattern over audience members during music performances. The system consists of a physiological sensing wristband for detecting frisson and a thermo-haptic neckband for inducing frisson. In a controlled environment, we evaluate detection (n=19) and triggering of frisson (n=15). Based on our findings, we conducted an in-the-wild music concert with 48 audience members using our system to share frisson. This paper summarizes a framework for accessing, triggering and sharing frisson. We report our research insights, lessons learned, and limitations of "Frisson Waves". Yan He, George Chernyshov, Jiawen Han, Dingding Zheng, Ragnar Thomsen, Danny Hynds, Muyu Liu, Yuehui Yang, Yulan Ju, Yun Suen Pai, Kouta Minamizawa, Kai Kunze, and Jamie A War

Design of the PMT underwater cascade implosion protection system for JUNO

Photomultiplier tubes (PMTs) are widely used underwater in large-scale neutrino experiments. As a hollow glass spherelike structure, implosion is unavoidable during long-term operation under large water pressure. There is a possibility of cascade implosion to neighbor PMTs due to shockwave. Jiangmen Underground Neutrino Observatory designed a protection structure for each 20-inch PMT, consisting of a top cover, a bottom cover, and their connection. This paper introduces the requirement and design of the PMT protection system, including the material selection, investigation of manufacture technology, and prototyping. Optimization and validation by simulation and underwater experiments are also presented.Comment: 10 pages, 15 figure