Referring Remote Sensing Image Segmentation (RRSIS) aims to segment target objects in aerial imagery based on natural language expressions. Although recent multi-scale feature aggregation methods have improved cross-modal alignment, and existing approaches still struggle with accurate localization and segmentation across scales because interactions between visual scales and language are not sufficiently modeled. To address these challenges, we propose a SAM-based framework termed LaViSE, which incorporates two key modules : the Language-Guided Hierarchical Fusion (LGHF) module integrates cross-modal features at multiple scales for precise object localization by injecting spatial coordinates into visual representations and combining visual features with aligned features; the Language-Attentive Scale-Unified Aggregation (LASA) module globally merges multi-level features while maintaining spatial consistency and boundary fidelity, and further preserves fine-grained structural details through and language-conditioned feature recalibration across scales, which is crucial for segmenting densely distributed and scale-varying targets in complex remote sensing imagery. Experiments on the widely used RefSegRS and RRSIS-D benchmarks demonstrate that LaViSE consistently outperforms state-of-the-art methods, particularly in challenging scenarios with small and densely distributed objects. Our code and pre-trained models will be released upon publication.

The slot attention-based method is widely used in unsupervised object-centric learning, aiming to decompose scenes into interpretable objects and associate them with slots. However, complex backgrounds in the real images can disrupt the model’s focus, leading it to excessively segment background stuff into different regions based on low-level information such as color or texture variations. As a result, the detailed segmentation of foreground objects, which requires shape or geometric information, is often neglected. To address this issue, we introduce a contrastive learning-based indicator designed to differentiate between foreground and background. Integrating this indicator into the existing slot attention-based method enables the model to focus more on segmenting foreground objects while minimizing background distractions. During the testing phase, we utilize a spectral clustering mechanism to refine the results based on the similarity between the slots. Experimental results show that incorporating our method with various state-of-the-art models significantly improves their performance on both simulated data and real-world datasets. Furthermore, multiple sets of ablation experiments confirm the effectiveness of each proposed component. The source code is available at https://github.com/sjyjs09/FG-BG_Indicator.

SUMMARY: The Microbiome Community Detector (MiCoDe) software is a free user-friendly web tool that is designed to cluster a network of microbial taxa into communities using a Bayesian weighted stochastic block model. MiCoDe also filters the data automatically and accounts for the challenges of microbiome high-throughput sequencing data including high-dimensionality, compositionality, zero inflation, and nonlinearity. While MiCoDe is based on a rigorous statistical unsupervised learning model, our web tool can be easily used by any investigator. Users simply upload a csv file that contains their taxonomic abundance data where rows correspond to samples and columns correspond to taxa. Then, users make a few selections regarding data transformation, network estimation, and the number of communities in order to run the online analysis. If users are unsure of what selections to make, then they can opt for the default settings as these are our recommended settings. In this paper, we discuss the motivation, methodology, implementation, and results of MiCoDe. We also discuss how MiCoDe can be adapted by the user and how it may evolve over time. Our software is a valuable tool for microbiome community detection. AVAILABILITY AND IMPLEMENTATION: MiCoDe is freely available online at https://lce.biohpc.swmed.edu/micode/, does not require installation, and is not browser-specific. Users can also work locally using our R code, which is freely available on GitHub at https://github.com/klutz920/MiCoDe.

Generalized Few-shot Segmentation (GFSS) aims to segment both base and novel classes in a query image, conditioning on richly annotated data of base classes and limited exemplars from novel classes. The learning of novel classes undoubtedly faces a disadvantage in this competition due to the highly unbalanced data, which skews the learned feature space toward the base classes. In this article, we present an innovative idea termed as “learning from orthogonal space” to avoid the conflict in the process of learning novel classes. Specifically, we first utilize textual modal information from labels to provide more distinguishable initial prototypes for different categories, ensuring that the prototypes for base and novel classes have distinct initial separations. Then, a simple but effective Feature Separating Module (FSM) is introduced to enhance the model’s ability to differentiate between base and novel classes through learning the novel features from orthogonal space. In addition, we propose a Trigger-Promoting Framework (TPF) during the testing stage to further boost performance. The prediction results from the FSM serve as a multimodal prompt to leverage information residing in large models, such as CLIP and SAM, to enhance performance. Comprehensive experiments on two benchmarks demonstrate that our method achieves superior performance on novel classes without sacrificing accuracy on base classes. Notably, our Feature Separating with Trigger-promoting Network (FS-TPNet) outperforms the current state-of-the-art method by \(12.8\%\) overall IoU on novel classes on PASCAL- \(5^{i}\) under the 1-shot scenario. Our codes will be available at https://github.com/returnZXJ/FS-TPNet .

The emerging hybrid DRAM-NVM architecture is challenging the existing memory management mechanism at the level of the architecture and operating system. In this paper, we introduce Memos, a memory management framework which can hierarchically schedule memory resources over the entire memory hierarchy including cache, channels, and main memory comprising DRAM and NVM simultaneously. Powered by our newly designed kernel-level monitoring module that samples the memory patterns by combining TLB monitoring with page walks, and page migration engine, Memos can dynamically optimize the data placement in the memory hierarchy in response to the memory access pattern, current resource utilization, and memory medium features. Our experimental results show that Memos can achieve high memory utilization, improving system throughput by around 20.0 percent; reduce the memory energy consumption by up to 82.5 percent; and improve the NVM lifetime by up to 34X.

Motivation: The Cancer Genome Atlas (TCGA) program has produced huge amounts of cancer genomics data providing unprecedented opportunities for research. In 2014, we developed TCGA-Assembler, a software pipeline for retrieval and processing of public TCGA data. In 2016, TCGA data were transferred from the TCGA data portal to the Genomic Data Commons (GDCs), which is supported by a different set of data storage and retrieval mechanisms. In addition, new proteomics data of TCGA samples have been generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) program, which were not available for downloading through TCGA-Assembler. It is desirable to acquire and integrate data from both GDC and CPTAC. Results: We develop TCGA-assembler 2 (TA2) to automatically download and integrate data from GDC and CPTAC. We make substantial improvement on the functionality of TA2 to enhance user experience and software performance. TA2 together with its previous version have helped more than 2000 researchers from 64 countries to access and utilize TCGA and CPTAC data in their research. Availability of TA2 will continue to allow existing and new users to conduct reproducible research based on TCGA and CPTAC data. Availability and implementation: http://www.compgenome.org/TCGA-Assembler/ or https://github.com/compgenome365/TCGA-Assembler-2. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.