Object Discovery via Contrastive Learning for Weakly Supervised Object Detection

Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations. Current state-of-the-art models benefit from self-supervised instance-level supervision, but since weak supervision does not include count or location information, the most common ``argmax'' labeling method often ignores many instances of objects. To alleviate this issue, we propose a novel multiple instance labeling method called object discovery. We further introduce a new contrastive loss under weak supervision where no instance-level information is available for sampling, called weakly supervised contrastive loss (WSCL). WSCL aims to construct a credible similarity threshold for object discovery by leveraging consistent features for embedding vectors in the same class. As a result, we achieve new state-of-the-art results on MS-COCO 2014 and 2017 as well as PASCAL VOC 2012, and competitive results on PASCAL VOC 2007.Comment: Accepted at ECCV 2022. For project page, see https://jinhseo.github.io/research/wsod.html For code, see https://github.com/jinhseo/OD-WSC

GScluster: Network-weighted gene-set clustering analysis

Background: Gene-set analysis (GSA) has been commonly used to identify significantly altered pathways or functions from omics data. However, GSA often yields a long list of gene-sets, necessitating efficient post-processing for improved interpretation. Existing methods cluster the gene-sets based on the extent of their overlap to summarize the GSA results without considering interactions between gene-sets. Results: Here, we presented a novel network-weighted gene-set clustering that incorporates both the gene-set overlap and protein-protein interaction (PPI) networks. Three examples were demonstrated for microarray gene expression, GWAS summary, and RNA-sequencing data to which different GSA methods were applied. These examples as well as a global analysis show that the proposed method increases PPI densities and functional relevance of the resulting clusters. Additionally, distinct properties of gene-set distance measures were compared. The methods are implemented as an R/Shiny package GScluster that provides gene-set clustering and diverse functions for visualization of gene-sets and PPI networks. Conclusions: Network-weighted gene-set clustering provides functionally more relevant gene-set clusters and related network analysis

Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets

We present a novel approach to identify human microRNA (miRNA) regulatory modules (mRNA targets and relevant cell conditions) by biclustering a large collection of mRNA fold-change data for sequence-specific targets. Bicluster targets were assessed using validated messenger RNA (mRNA) targets and exhibited on an average 17.0% (median 19.4%) improved gain in certainty (sensitivity + specificity). The net gain was further increased up to 32.0% (median 33.4%) by incorporating functional networks of targets. We analyzed cancer-specific biclusters and found that the PI3K/Akt signaling pathway is strongly enriched with targets of a few miRNAs in breast cancer and diffuse large B-cell lymphoma. Indeed, five independent prognostic miRNAs were identified, and repression of bicluster targets and pathway activity by miR-29 was experimentally validated. In total, 29 898 biclusters for 459 human miRNAs were collected in the BiMIR database where biclusters are searchable for miRNAs, tissues, diseases, keywords and target genes

Self-assembled nanocomplex between polymerized phenylboronic acid and doxorubicin for efficient tumor-targeted chemotherapy

Since the discovery that nano-scaled particulates can easily be incorporated into tumors via the enhanced permeability and retention (EPR) effect, such nanostructures have been exploited as therapeutic small molecule delivery systems. However, the convoluted synthetic process of conventional nanostructures has impeded their feasibility and reproducibility in clinical applications. Herein, we report an easily prepared formulation of self-assembled nanostructures for systemic delivery of the anti-cancer drug doxorubicin (DOX). Phenylboronic acid (PBA) was grafted onto the polymeric backbone of poly(maleic anhydride). pPBA-DOX nanocomplexes were prepared by simple mixing, on the basis of the strong interaction between the 1,3-diol of DOX and the PBA moiety on pPBA. Three nanocomplexes (1, 2, 4) were designed on the basis of [PBA]:[DOX] molar ratios of 1: 1, 2: 1, and 4: 1, respectively, to investigate the function of the residual PBA moiety as a targeting ligand. An acid-labile drug release profile was observed, owing to the intrinsic properties of the phenylboronic ester. Moreover, the tumor-targeting ability of the nanocomplexes was demonstrated, both in vitro by confocal microscopy and in vivo by fluorescence imaging, to be driven by an inherent property of the residual PBA. Ligand competition assays with free PBA pre-treatment demonstrated the targeting effect of the residual PBA from the nanocomplexes 2 and 4. Finally, the nanocomplexes 2 and 4, compared with the free DOX, exhibited significantly greater anti-cancer effects in vitro and even in vivo. Our pPBA-DOX nanocomplex enables a new paradigm for self-assembled nanostructures with potential biomedical applications.115Ysciescopu

On the compression of shallow non-causal ASR models using knowledge distillation and tied-and-reduced decoder for low-latency on-device speech recognition

Recently, the cascaded two-pass architecture has emerged as a strong contender for on-device automatic speech recognition (ASR). A cascade of causal and shallow non-causal encoders coupled with a shared decoder enables operation in both streaming and look-ahead modes. In this paper, we propose shallow cascaded model by combining various model compression techniques such as knowledge distillation, shared decoder, and tied-and-reduced transducer network in order to reduce the model footprint. The shared decoder is changed into a tied-and-reduced network. The cascaded two-pass model is further compressed using knowledge distillation using a Kullback-Leibler divergence loss on the model posteriors. We demonstrate a 50% reduction in the size of a 41 M parameter cascaded teacher model with no noticeable degradation in ASR accuracy and a 30% reduction in latenc