Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection

Out-of-distribution (OOD) detection is the key to deploying models safely in the open world. For OOD detection, collecting sufficient in-distribution (ID) labeled data is usually more time-consuming and costly than unlabeled data. When ID labeled data is limited, the previous OOD detection methods are no longer superior due to their high dependence on the amount of ID labeled data. Based on limited ID labeled data and sufficient unlabeled data, we define a new setting called Weakly-Supervised Out-of-Distribution Detection (WSOOD). To solve the new problem, we propose an effective method called Topological Structure Learning (TSL). Firstly, TSL uses a contrastive learning method to build the initial topological structure space for ID and OOD data. Secondly, TSL mines effective topological connections in the initial topological space. Finally, based on limited ID labeled data and mined topological connections, TSL reconstructs the topological structure in a new topological space to increase the separability of ID and OOD instances. Extensive studies on several representative datasets show that TSL remarkably outperforms the state-of-the-art, verifying the validity and robustness of our method in the new setting of WSOOD

Localization and Discrete Beamforming with a Large Reconfigurable Intelligent Surface

In millimeter-wave (mmWave) cellular systems, reconfigurable intelligent surfaces (RISs) are foreseeably deployed with a large number of reflecting elements to achieve high beamforming gains. The large-sized RIS will make radio links fall in the near-field localization regime with spatial non-stationarity issues. Moreover, the discrete phase restriction on the RIS reflection coefficient incurs exponential complexity for discrete beamforming. It remains an open problem to find the optimal RIS reflection coefficient design in polynomial time. To address these issues, we propose a scalable partitioned-far-field protocol that considers both the near-filed non-stationarity and discrete beamforming. The protocol approximates near-field signal propagation using a partitioned-far-field representation to inherit the sparsity from the sophisticated far-field and facilitate the near-field localization scheme. To improve the theoretical localization performance, we propose a fast passive beamforming (FPB) algorithm that optimally solves the discrete RIS beamforming problem, reducing the search complexity from exponential order to linear order. Furthermore, by exploiting the partitioned structure of RIS, we introduce a two-stage coarse-to-fine localization algorithm that leverages both the time delay and angle information. Numerical results demonstrate that centimeter-level localization precision is achieved under medium and high signal-to-noise ratios (SNR), revealing that RISs can provide support for low-cost and high-precision localization in future cellular systems.Comment: 13 page

HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization

Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features. In DG, the prevalent practice of constraining models to a fixed structure or uniform parameterization to encapsulate invariant features can inadvertently blend specific aspects. Such an approach struggles with nuanced differentiation of inter-domain variations and may exhibit bias towards certain domains, hindering the precise learning of domain-invariant features. Recognizing this, we introduce a novel method designed to supplement the model with domain-level and task-specific characteristics. This approach aims to guide the model in more effectively separating invariant features from specific characteristics, thereby boosting the generalization. Building on the emerging trend of visual prompts in the DG paradigm, our work introduces the novel \textbf{H}ierarchical \textbf{C}ontrastive \textbf{V}isual \textbf{P}rompt (HCVP) methodology. This represents a significant advancement in the field, setting itself apart with a unique generative approach to prompts, alongside an explicit model structure and specialized loss functions. Differing from traditional visual prompts that are often shared across entire datasets, HCVP utilizes a hierarchical prompt generation network enhanced by prompt contrastive learning. These generative prompts are instance-dependent, catering to the unique characteristics inherent to different domains and tasks. Additionally, we devise a prompt modulation network that serves as a bridge, effectively incorporating the generated visual prompts into the vision transformer backbone. Experiments conducted on five DG datasets demonstrate the effectiveness of HCVP, outperforming both established DG algorithms and adaptation protocols

Constructing quantum dots@flake g-C3N4 isotype heterojunctions for enhanced visible-light-driven NADH regeneration and enzymatic hydrogenation

The authors thank the financial support from National Natural Science Funds of China (21406163, 91534126, 21621004), Tianjin Research Program of Application Foundation and Advanced Technology (15JCQNJC10000), Open Funding Project of the National Key Laboratory of Biochemical Engineering (2015KF-03), and the Program of Introducing Talents of Discipline to Universities (B06006). X.W. also acknowledges financial support from The Carnegie Trust for the Universities of Scotland (70265) and The Royal Society (RG150001 and IE150611).Peer reviewedPostprin

PuCRZ1, an C2H2 transcription factor from Polyporus umbellatus, positively regulates mycelium response to osmotic stress

Polyporus umbellatus is an edible and medicinal mushroom with the capacity to produce sclerotia. However, the mechanism of P. umbellatus sclerotia formation is unclear. CRZ1 is a C2H2 family transcription factor involved in the Ca2+-calcineurin signaling pathway, which has the function of regulating sclerotia formation, maintaining ion homeostasis, and responding to stress. In this study, we identified 28 C2H2 transcription factors in P. umbellatus genome, 13 of which are differentially expressed between mycelium and sclerotia, including PuCRZ1. Combining DNA affinity purification and sequencing (DAP-seq) and quantitative real-time PCR (qRT-PCR), three genes (PuG10, PuG11, PuG12) were identified as putative PuCRZ1 target genes containing a putative binding motif (GTGGCG) within their promoter. Yeast single hybridization (Y1H) and EMSA further confirmed that PuCRZ1 can bind to the promoter region of PuG10, PuG11, and PuG12. PuCRZ1 gene could reduce the sensitivity of NaCl in yeast cells. Furthermore, overexpression of the PuCRZ1 target gene, especially the FVLY domain containing gene PuG11, could improve the mycelia growth rate and mannitol tolerance in P. umbellatus. These results demonstrate that PuCRZ1 in the Ca2+-calcineurin signaling pathway plays an important role in mycelia growth, as well as osmotic stress tolerance

How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation

In machine learning, generalization against distribution shifts -- where deployment conditions diverge from the training scenarios -- is crucial, particularly in fields like climate modeling, biomedicine, and autonomous driving. The emergence of foundation models, distinguished by their extensive pretraining and task versatility, has led to an increased interest in their adaptability to distribution shifts. GPT-4V(ision) acts as the most advanced publicly accessible multimodal foundation model, with extensive applications across various domains, including anomaly detection, video understanding, image generation, and medical diagnosis. However, its robustness against data distributions remains largely underexplored. Addressing this gap, this study rigorously evaluates GPT-4V's adaptability and generalization capabilities in dynamic environments, benchmarking against prominent models like CLIP, LLaVA, and Gemini. We delve into GPT-4V's zero-shot generalization across 13 diverse datasets spanning natural, medical, and molecular domains. We further investigate its adaptability to controlled data perturbations and examine the efficacy of in-context learning as a tool to enhance its adaptation. Our findings delineate GPT-4V's capability boundaries in distribution shifts, shedding light on its strengths and limitations across various scenarios. Importantly, this investigation contributes to our understanding of how AI foundation models generalize to distribution shifts, offering pivotal insights into their adaptability and robustness. The code is publicly available at https://github.com/jameszhou-gl/gpt-4v-distribution-shift.Comment: added the investigation of Gemini. 66 pages, 41 figure