Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning.

The self-supervised learning (SSL) has emerged as an effective paradigm for deriving general representations from vast amounts of unlabeled data. However, as real-world applications continually integrate new content, the high computational and resource demands of SSL necessitate continual learning (CL) rather than complete retraining. This poses a challenge in balancing between stability and plasticity when adapting to new information.

QDNet: Query-Denoising Network for Visual Traffic Knowledge Graph Generation.

Traffic scene perception underpins essential tasks like map construction and route planning in modern intelligent transportation systems, thus receiving extensive attention. However, existing methods tend to concentrate solely on specific elements, lacking a comprehensive understanding of various traffic scenes. This paper addresses the Visual Traffic Knowledge Graph Generation (VTKGG) task, aiming to extract and represent traffic information from various elements in the traffic scene image as a knowledge graph.

Cross-Modal Causal Representation Learning for Radiology Report Generation.

Radiology Report Generation (RRG) is essential for computer-aided diagnosis and medication guidance, which can relieve the heavy burden of radiologists by automatically generating the corresponding radiology reports according to the given radiology image. However, generating accurate lesion descriptions remains challenging due to spurious correlations from visual-linguistic biases and inherent limitations of radiological imaging, such as low resolution and noise interference. To address these issues, we propose a two-stage framework named Cross-Modal Causal Representation Learning (CMCRL), consisting of the Radiological Cross-modal Alignment and Reconstruction Enhanced (RadCARE) pre-training and the Visual-Linguistic Causal Intervention (VLCI) fine-tuning.

PASS++: A Dual Bias Reduction Framework for Non-Exemplar Class-Incremental Learning.

Class-incremental learning (CIL) aims to continually recognize new classes while preserving the discriminability of previously learned ones. Most existing CIL methods are exemplar-based, relying on the storage and replay of a subset of old data during training. Without access to such data, these methods typically suffer from catastrophic forgetting.

Spatial determinants of antibody-drug conjugate SHR-A1811 efficacy in neoadjuvant treatment for HER2-positive breast cancer.

Selecting optimal candidates for next-generation anti-human epidermal growth factor receptor 2 (HER2) antibody-drug conjugates (ADCs) remains challenging. We conduct a prespecified translational study to identify treatment biomarkers in SHR-A1811-treated HER2-positive breast cancer patients from the phase 2 neoadjuvant FASCINATE-N trial using DNA and RNA sequencing, computational pathology, and single-cell in situ spatial imaging. In the hormone receptor (HR)-negative subgroup, a higher proportion and more infiltration of immune cells (i.

Two Antihypertensive and Antioxidant Peptides Derived from Alaska Pollack () Skin: , , and Investigation.

This study aimed to identify and characterize two novel dual-functional peptides with antihypertensive and antioxidant activities from byproducts of Alaska pollock skin (APS). Results showed that fifty-nine peptides were identified from APS, of which two peptides, GP1 (GSAGPAGPSGPRGP) and GP2 (LGDARNSPAPP), were predicted to exhibit the highest angiotensin-converting enzyme (ACE) inhibitory and antioxidant activities. GP1 and GP2 demonstrated favorable ACE inhibitory activities (IC values of 0.

ProtoGCD: Unified and Unbiased Prototype Learning for Generalized Category Discovery.

Generalized category discovery (GCD) is a pragmatic but underexplored problem, which requires models to automatically cluster and discover novel categories by leveraging the labeled samples from old classes. The challenge is that unlabeled data contain both old and new classes. Early works leveraging pseudo-labeling with parametric classifiers handle old and new classes separately, which brings about imbalanced accuracy between them.

Taxonomic contribution to the knowledge of the genus Indoribates and Lauritzenia (Acari, Oribatida, Haplozetidae), with description of a new species from China.

Oribatid species from China of the genus Indoribates and Lauritzenia are studied, with description of a new species Indoribates (Indoribates) subiasi sp. nov. based on adult materials collected from China.

Average of Pruning: Improving Performance and Stability of Out-of-Distribution Detection.

Detecting out-of-distribution (OOD) inputs has been a critical issue for neural networks in the open world. However, the unstable behavior of OOD detection along the optimization trajectory during training has not been explored clearly. In this article, we first find the performance of OOD detection suffers from overfitting and instability during training: 1) the performance could decrease when the training error is near zero and 2) the performance would vary sharply in the final stage of training.

Two new species of the subgenus () (Acari, Oribatida, Haplozetidae) from Tajikistan.

Two new species of oribatid mites of the subgenus Indoribates (Haplozetes) (Oribatida, Haplozetidae) are described based on adult specimens from Tajikistan. Indoribates (Haplozetes) tajikistanensis differs from I. (H.

The HLA-I landscape confers prognosis and antitumor immunity in breast cancer.

Breast cancer is a highly heterogeneous disease with varied subtypes, prognoses and therapeutic responsiveness. Human leukocyte antigen class I (HLA-I) shapes the immunity and thereby influences the outcome of breast cancer. However, the implications of HLA-I variations in breast cancer remain poorly understood.

ZNF689 deficiency promotes intratumor heterogeneity and immunotherapy resistance in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive disease characterized by remarkable intratumor heterogeneity (ITH), which poses therapeutic challenges. However, the clinical relevance and key determinant of ITH in TNBC are poorly understood. Here, we comprehensively characterized ITH levels using multi-omics data across our center's cohort (n = 260), The Cancer Genome Atlas cohort (n = 134), and four immunotherapy-treated cohorts (n = 109).

Revisiting Confidence Estimation: Towards Reliable Failure Prediction.

Reliable confidence estimation is a challenging yet fundamental requirement in many risk-sensitive applications. However, modern deep neural networks are often overconfident for their incorrect predictions, i.e.

Distributed adaptive sliding-mode control for 2-D plane vehicle platoon with prescribed performance and angle constraint.

This paper focuses on the distributed adaptive sliding-mode control problem for two-dimensional (2-D) plane vehicle platoon with prescribed performance, angle constraints, and actuator faults. The quadratic spacing policy (QSP) is first adopted for the 2-D plane vehicle platoon to adjust the inter-vehicle spacing. The spacing error can converge within a finite time to the small region predetermined by a new finite-time performance function (FTPF).

Antigen-induced chimeric antigen receptor multimerization amplifies on-tumor cytotoxicity.

Ligand-induced receptor dimerization or oligomerization is a widespread mechanism for ensuring communication specificity, safeguarding receptor activation, and facilitating amplification of signal transduction across the cellular membrane. However, cell-surface antigen-induced multimerization (dubbed AIM herein) has not yet been consciously leveraged in chimeric antigen receptor (CAR) engineering for enriching T cell-based therapies. We co-developed ciltacabtagene autoleucel (cilta-cel), whose CAR incorporates two B-cell maturation antigen (BCMA)-targeted nanobodies in tandem, for treating multiple myeloma.

Molecular classification of hormone receptor-positive HER2-negative breast cancer.

Hormone receptor-positive (HR)/human epidermal growth factor receptor 2-negative (HER2) breast cancer is the most prevalent type of breast cancer, in which endocrine therapy resistance and distant relapse remain unmet challenges. Accurate molecular classification is urgently required for guiding precision treatment. We established a large-scale multi-omics cohort of 579 patients with HR/HER2 breast cancer and identified the following four molecular subtypes: canonical luminal, immunogenic, proliferative and receptor tyrosine kinase (RTK)-driven.

Cycle-Consistent Weakly Supervised Visual Grounding With Individual and Contextual Representations.

Visual grounding, aiming to align image regions with textual queries, is a fundamental task for cross-modal learning. We study the weakly supervised visual grounding, where only image-text pairs at a coarse-grained level are available. Due to the lack of fine-grained correspondence information, existing approaches often encounter matching ambiguity.

A New Language-Independent Deep CNN for Scene Text Detection and Style Transfer in Social Media Images.

Due to the adverse effect of quality caused by different social media and arbitrary languages in natural scenes, detecting text from social media images and transferring its style is challenging. This paper presents a novel end-to-end model for text detection and text style transfer in social media images. The key notion of the proposed work is to find dominant information, such as fine details in the degraded images (social media images), and then restore the structure of character information.

Mixed-Supervised Scene Text Detection With Expectation-Maximization Algorithm.

Scene text detection is an important and challenging task in computer vision. For detecting arbitrarily-shaped texts, most existing methods require heavy data labeling efforts to produce polygon-level text region labels for supervised training. In order to reduce the cost in data labeling, we study mixed-supervised arbitrarily-shaped text detection by combining various weak supervision forms (e.