Improving antibodies' affinity and specificity has traditionally relied on iterative display selections or structure-based design, both costly and time-intensive. Recent advances in Deep Learning offer data-driven priors that effectively narrow the sequence space before expensive experiments. This paper provides an overview of the progress and challenges of learning -based antibody design. Adopting a pipeline-first perspective, this review organises current methods into three categories: (A) sequence-only protein language models (PLMs); (B) structure-aware strategies, including inverse folding and complex-aware optimisation; and (C) integrated AI-physics workflows. To avoid mixing endpoints, prospective wet-lab outcomes (e.g. hit rates, affinity gains) are reported separately from structure-linked sur-rogates (e.g. region recovery, refold root-mean-square deviation (RMSD), deep mutational scanning (DMS) correlation). Evidence indicates that sequence-only PLMs are effective for low-budget screening, inverse folding methods provide backbone-conditioned ranking and structure-preserving edits, and lightweight AI-physics overlays help prioritise manufacturable candidates. A concise method -selection guide is provided for different data availability scenarios.

Open set recognition (OSR) effectively enhances the reliability of pattern recognition systems by accurately identifying samples of unknown classes. However, the decision-making process in most existing OSR methods adheres to an ill-considered pipeline, where classification probabilities are inferred directly from overall feature representations, neglecting the reasoning about inherent relations. Besides, the handling of identified unknown samples is typically restricted to the assignment of a generic "unknown" class label but fails to explore underlying category information. To tackle the above challenges, we propose a new paradigm for OSR, entitled Reason and Discovery (RAD), which comprises two main modules: the Reason Module and the Discovery Module. Specifically, in the Reason Module, the distinction between known and unknown is performed from the perspective of reasoning the matching relations between topological information and appearance characteristics of discriminative regions. Then, the mixture and recombination of relation representations across classes are employed to provide diverse estimations of unknown distribution, thereby recalibrating OSR decision boundaries. Moreover, in the Discovery Module, the identified unknown samples are semantically grouped through a biased deep clustering process for discovering novel category information. Experimental results on various datasets indicate that the proposed method can achieve outstanding OSR performance and good novel category discovery efficacy.

Infrared small target detection (ISTD) aims to precisely capture the location and morphology of small targets under all-weather conditions. Compared with generic objects, infrared targets in remote fields of view are smaller in size and exhibit lower signal-to-clutter ratios. This poses a significant challenge in simultaneously preserving low-level target details and understanding high-level contextual semantics, forcing a trade-off between reducing miss detection and suppressing false alarms. In addition, most existing ISTD methods are designed for specific target types under certain infrared platforms, rather than as a unified framework broadly applicable across diverse infrared sensing scenarios. To address these challenges, we propose a unified self-prompt learning framework for ISTD under the guidance of the Segment Anything Model (SAM). Specifically, the model is incorporated with SAM in the encoding stage through a consult-guide manner, adapting the general knowledge to facilitate task-specific contextual understanding. Then, shallow-layer features are employed to generate self-derived prompts, which bidirectionally interact with encoded latent representations to complement subtle low-level details. Moreover, the semantic inconsistency during resolution recovery is mitigated by integrating a mutual calibration module into skip connections, ensuring coherent spatial-semantic fusion. Extensive experiments are conducted on four public ISTD datasets, and the results demonstrate that the proposed method consistently achieves superior performance across different infrared sensing platforms and target types. The code is released at https://github.com/fuyimin96/SAM-SPL.

Adversarial attacks serve as an efficient approach to investigating model robustness, providing insights into internal weaknesses. In real-world applications, the model deployment typically adheres to a black-box setting, necessitating the transferability of adversarial examples crafted on a source model to others. Attack methods in the general computer vision field often employ global input transformations in individual spatial or frequency domains to boost adversarial transferability. However, the recognition of remote sensing objects primarily relies on target-related discriminative regions, whose determination exhibits significant model specificity. Besides, the coupling between objects and background further exacerbates the gap between models. Consequently, the transferability of adversarial examples is limited due to overfitting to the source model. To tackle this problem, we propose a spatial-frequency co-transformation (SFCoT) to improve adversarial transferability for remote sensing object recognition. Specifically, the input image is decomposed into blocks and components in the spatial and frequency domains, respectively. Then, a selective frequency transformation (SFT) is performed on the low-frequency components to narrow intermodel gaps. Subsequently, modular spatial transformations (MSTs) are adopted in blocks to enhance target-related diversity. Incorporating transformations across domains effectively mitigates the overfitting to model-specific information, leading to better adversarial transferability. Extensive experiments have been conducted on FGSCR-42 and MTARSI datasets, and the results demonstrate that the proposed method achieves state-of-the-art performance across various model architectures. The code will be released athttps://github.com/fuyimin96/SFCoT.

Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year. Potential depression sufferers usually do not consult psychological doctors because they feel ashamed or are unaware of any depression, which may result in severe delay of diagnosis and treatment. In the meantime, evidence shows that social media data provides valuable clues about physical and mental health conditions. In this paper, we argue that it is feasible to identify depression at an early stage by mining online social behaviours. Our approach, which is innovative to the practice of depression detection, does not rely on the extraction of numerous or complicated features to achieve accurate depression detection. Instead, we propose a novel classifier, namely, Cost-sensitive Boosting Pruning Trees (CBPT), which demonstrates a strong classification ability on two publicly accessible Twitter depression detection datasets. To comprehensively evaluate the classification capability of CBPT, we use additional three datasets from the UCI machine learning repository and CBPT obtains appealing classification results against several state of the arts boosting algorithms. Finally, we comprehensively explore the influence factors for the model prediction, and the results manifest that our proposed framework is promising for identifying Twitter users with depression.