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Received β€” 27 May 2026 ⏭

Remote sensing data imputation using deep learning for multispectral imagery

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Shuang Liua Β· Fiona Johnson Β· Rohitash Chandra
26 May 2026 at 12:00
arXiv:2605.24003v1 Announce Type: cross Abstract: Remote sensing techniques have been increasingly utilised in aquatic applications in recent years. A common challenge in using optical satellite data is the presence of missing observations due to cloud cover. These data gaps can lead to missed detection of critical events, such as algal blooms, in lakes of high interest to water authorities. As a result, enhancing the completeness of optical satellite datasets is crucial for improving the monitoring and prediction of algal blooms. In this study, we compared a traditional data imputation method (i.e., linear interpolation) with deep learning models for reconstructing missing spectral bands across four lakes with historical records of algal blooms. The deep learning models adopted include CNN-based architectures (i.e., CNN, Inception Resnet, and Autoencoder) and CNN-LSTM-based architectures (i.e., CNN-LSTM, Resnet-LSTM, and Autoencoder-LSTM). Our results demonstrated that deep learning models substantially outperformed the baseline linear interpolation method in imputing spectral band values within artificially masked regions. Among these models, CNN delivered the best performance across most lakes. Furthermore, we evaluated the performance of algal bloom indices (i.e., Green/Red and NDCI) derived from the imputed imagery by comparing them with the observed data. Our results demonstrate that deep learning models are effective for imputing missing data in PlanetScope SuperDove imagery, enabling more reliable applications in water monitoring.

Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Xu Yao Β· Siyuan Zhou Β· Zhenbo Wu Β· Chaochuan Hou Β· Shuang Liang Β· Shiping Wang Β· Hailiang Huang Β· Songqiao Han Β· Minqi Jiang
26 May 2026 at 12:00
arXiv:2605.26068v2 Announce Type: cross Abstract: Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision. However, these directions remain isolated, lacking a unified framework to assess whether they address unique challenges or share fundamental mechanics. This paper introduces WSADBench, the first benchmark that unifies evaluation across distinct weakly supervised scenarios, benchmarking diverse approaches from specialized WSAD methods to advanced tabular foundation models. WSADBench establishes standardized protocols to evaluate 36 algorithms across 4 modalities by systematically varying label quantity, granularity, and quality, revealing the performance boundaries of various methods. Based on over 700K experiments, WSADBench reveals four critical insights: (i) Strong intrinsic correlations exist between these weak supervision scenarios, challenging the isolation of current research directions. (ii) Specialized WSAD algorithms excel only in extreme label-scarcity regimes but are quickly dominated by tabular foundation models and general classification methods as supervision increases or in OOD scenarios. (iii) Unlabeled data shows inconsistent utility across settings, with marginal gains compared to label refinement. (iv) Models exhibit asymmetric sensitivity to different types of label noise. We release WSADBench as an open-source benchmark with code and datasets to facilitate future WSAD research: https://github.com/SUFE-AILAB/WSADBench.

SoK: A Comprehensive Security Analysis of Jailbreak Resilience in GPT and DeepSeek Models

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Xiaodong Wu Β· Xiangman Li Β· Qi Li Β· Lingshuang Liu Β· Jianbing Ni
26 May 2026 at 12:00
arXiv:2506.18543v2 Announce Type: replace-cross Abstract: The rapid proliferation of Large Language Models (LLMs) has heightened concerns regarding their exposure to jailbreak attacks, which craft adversarial inputs designed to elicit unsafe content. Although proprietary models such as GPT-4 have been extensively evaluated, the robustness of emerging open-source systems like DeepSeek remains insufficiently examined, despite their growing use in LLM applications. In this paper, we conduct the first comprehensive jailbreak analysis of the DeepSeek model family, comparing it with GPT-3.5 and GPT-4 through the HarmBench benchmark. We investigate seven representative attack methods across 510 harmful behaviors, organized along both functional and semantic dimensions. Findings indicate that DeepSeek provides partial resilience against optimization-driven attacks such as TAP-T, but also results in greater susceptibility to prompt-based and manually engineered adversarial inputs. In contrast, GPT-4 Turbo demonstrates more robust and consistent safety alignment across a wide range of behaviors, likely due to stronger safety optimization and reinforcement learning from human feedback. In addition, fine-grained behavioral analysis and case studies reveal that DeepSeek often fails to consistently apply safety constraints to adversarial prompts, leading to uneven refusal behaviors. Overall, our results highlight an inherent trade-off between model efficiency and alignment generalization, underscoring the importance of targeted safety tuning and robust alignment strategies to ensure secure deployment of open-source LLMs.

An Orally Deliverable, Food-Compatible Lyophilized Recombinant Whole-Cell Catalyst for Alcohol-Associated Liver Injury

βœ‡Omics in Gastric
By: Fan Li Β· Meng-Yue Zhang Β· Xiao-Le Shan Β· Cai-Yun Wang Β· Ying-Ying Wu Β· Shuang Li Β· Shi-Qiao Xu Β· Yi-Xuan Zhang
4 May 2026 at 18:00

Microorganisms. 2026 Mar 26;14(4):746. doi: 10.3390/microorganisms14040746.

ABSTRACT

Effective oral interventions for alcohol-induced metabolic stress and liver injury remain limited. Pre-absorptive gastrointestinal alcohol handling is gaining interest as a non-pharmacological strategy to reduce hepatic burden. In this study, we developed a formulation-integrated, food-compatible lyophilized recombinant whole-cell catalyst based on Escherichia coli Nissle 1917 engineered to express alcohol dehydrogenase and acetaldehyde dehydrogenase. Rather than focusing exclusively on strain-level genetic modification, the engineered cells were protected by lyophilization combined with a food-grade chitosan-alginate layer-by-layer coating, forming an artificial cell wall designed to enhance survivability during oral delivery. The formulation resisted simulated gastric acid, sodium taurocholate, and ethanol, retained enzymatic activity after storage, and demonstrated formulation stability. In alcohol-exposed mice, oral administration reduced blood ethanol and acetaldehyde levels, improved liver biochemical parameters, attenuated hepatic steatosis, and partially restored oxidative stress indicators. Integrated multi-omics analyses indicated coordinated gut-associated metabolic and inflammatory responses to alcohol and intervention, rather than a single dominant pathway. These findings provide hypothesis-generating evidence; causality remains to be established. Overall, this study demonstrates a proof-of-concept, food-compatible lyophilized recombinant whole-cell catalyst that integrates enzymatic function with formulation stability and gastrointestinal resilience, highlighting an applied, food-compatible microbial framework for exploring alcohol-related metabolic stress.

PMID:42075143 | PMC:PMC13119499 | DOI:10.3390/microorganisms14040746

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