Learning Fashion Compatibility with Bidirectional LSTMs

The ubiquity of online fashion shopping demands effective recommendation services for customers. In this paper, we study two types of fashion recommendation: (i) suggesting an item that matches existing components in a set to form a stylish outfit (a collection of fashion items), and (ii) generating an outfit with multimodal (images/text) specifications from a user. To this end, we propose to jointly learn a visual-semantic embedding and the compatibility relationships among fashion items in an end-to-end fashion. More specifically, we consider a fashion outfit to be a sequence (usually from top to bottom and then accessories) and each item in the outfit as a time step. Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM) model to sequentially predict the next item conditioned on previous ones to learn their compatibility relationships. Further, we learn a visual-semantic space by regressing image features to their semantic representations aiming to inject attribute and category information as a regularization for training the LSTM. The trained network can not only perform the aforementioned recommendations effectively but also predict the compatibility of a given outfit. We conduct extensive experiments on our newly collected Polyvore dataset, and the results provide strong qualitative and quantitative evidence that our framework outperforms alternative methods.Comment: ACM MM 1

Innate immune mechanisms of atherosclerosis

Atherosclerosis is a multi-factorial immune mediated disease in arterial wall characterized by lipid driven inflammation through activation of the immune system. Chronic vascular inflammation is an important component that modulates atherosclerosis evolution and its complications. The interaction of innate immune activators from both host and environment with innate immune receptors has been considered as one of fundamental mechanisms accounting for the inflammatory responses that affect multiple pathogenic processes during atherosclerosis. The aim of the thesis is to improve our understanding of innate immune mechanisms in atherosclerosis. The objective of the thesis is to investigate the cellular mechanism of NOD1 and TRIM21, to identify the innate immune phenotype of intimal vascular smooth muscle cells (SMC) and to elucidate the activity and clinical relevance of inflammasome-IL-1 signaling in atherosclerosis. Paper I addresses how the local NOD1 signaling in vascular wall contributes to atherosclerosis and vascular inflammation. We report that a phenotypically distinct subpopulation of VSMC imprinted by NOD1high, a member of NOD-like receptor family, have unique functions in promoting vascular inflammation and lesion development. Paper II reports the identification of a SMC subpopulation with typical innate immune features in human atherosclerosis lesion and rat neointimal lesion. Functional studies and numerical quantifications further establish that these SMCs as important source of arterial resident innate immune effector cells. Paper III investigates inflammasome function and IL-1 generation in human atherosclerosis lesion. IL-1α/β production is a common feature of advanced lesion, and is linked with the regulation of multiple canonical and non-canonical inflammasome. Plaque IL-1β increases in complex plaques and in the patients with hyperlipidemia and no or low-dose statin therapy. Paper IV elucidates the mechanisms of Trim21, an ubiquitin E3 ligase with potent regulatory function in innate immune responses, in the pathogenesis of atherosclerosis. TRIM21 deficiency drives the generation of non-pathogenic Th17 in a cell-intrinsic manner and leads to a more stable plaque phenotype with higher collagen content. This thesis illustrates the involvement and regulation of different modules in innate immunity in the pathogenesis of atherosclerosis. These notions may provide novel understandings in the inflammatory hypothesis of atherosclerosis and lead to new therapeutic strategies

Dual Relation Alignment for Composed Image Retrieval

Composed image retrieval, a task involving the search for a target image using a reference image and a complementary text as the query, has witnessed significant advancements owing to the progress made in cross-modal modeling. Unlike the general image-text retrieval problem with only one alignment relation, i.e., image-text, we argue for the existence of two types of relations in composed image retrieval. The explicit relation pertains to the reference image & complementary text-target image, which is commonly exploited by existing methods. Besides this intuitive relation, the observations during our practice have uncovered another implicit yet crucial relation, i.e., reference image & target image-complementary text, since we found that the complementary text can be inferred by studying the relation between the target image and the reference image. Regrettably, existing methods largely focus on leveraging the explicit relation to learn their networks, while overlooking the implicit relation. In response to this weakness, We propose a new framework for composed image retrieval, termed dual relation alignment, which integrates both explicit and implicit relations to fully exploit the correlations among the triplets. Specifically, we design a vision compositor to fuse reference image and target image at first, then the resulted representation will serve two roles: (1) counterpart for semantic alignment with the complementary text and (2) compensation for the complementary text to boost the explicit relation modeling, thereby implant the implicit relation into the alignment learning. Our method is evaluated on two popular datasets, CIRR and FashionIQ, through extensive experiments. The results confirm the effectiveness of our dual-relation learning in substantially enhancing composed image retrieval performance

MotionEditor: Editing Video Motion via Content-Aware Diffusion

Existing diffusion-based video editing models have made gorgeous advances for editing attributes of a source video over time but struggle to manipulate the motion information while preserving the original protagonist's appearance and background. To address this, we propose MotionEditor, a diffusion model for video motion editing. MotionEditor incorporates a novel content-aware motion adapter into ControlNet to capture temporal motion correspondence. While ControlNet enables direct generation based on skeleton poses, it encounters challenges when modifying the source motion in the inverted noise due to contradictory signals between the noise (source) and the condition (reference). Our adapter complements ControlNet by involving source content to transfer adapted control signals seamlessly. Further, we build up a two-branch architecture (a reconstruction branch and an editing branch) with a high-fidelity attention injection mechanism facilitating branch interaction. This mechanism enables the editing branch to query the key and value from the reconstruction branch in a decoupled manner, making the editing branch retain the original background and protagonist appearance. We also propose a skeleton alignment algorithm to address the discrepancies in pose size and position. Experiments demonstrate the promising motion editing ability of MotionEditor, both qualitatively and quantitatively.Comment: 18 pages, 15 figures. Project page at https://francis-rings.github.io/MotionEditor

Effect of Vegetation on the Flow of a Partially-Vegetated Channel

Abstract A vegetated channel commonly exists in the natural environment. Over recent decades, many researchers have taken an interest in this field. The hydraulic characteristics of flow over vegetated channels are complex. Vegetation significantly affects the flow resistance and turbulence, resulting in sediments, nutrients, and contaminants transportation. Thus, understanding the impact of vegetation on flow structures is important for river and environment management. However, most attention on vegetated channel flow focuses on single-layered vegetated channels. There are few studies on the impact of double-layered, partially placed vegetation on open channel flow. To fill this research gap, this paper aims to investigate the impact of vegetation on the flow velocity of a double-layered, partially placed vegetated channel.</jats:p

FBXO5 acts as a novel prognostic biomarker for patients with cervical cancer

Background: Cervical cancer (CC) remains one of the most common and deadly malignancies in women worldwide. FBXO5, a protein-coding gene, is highly expressed in a variety of primary tumors and promotes tumor progression, however, its role and prognostic value in CC remain largely unknown.Methods: A key differential gene, FBXO5, was screened according to WGCNA based on immunohistochemical assays of clinical samples, multiple analyses of the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, including survival analysis, tumor mutational burden, GO, KEGG, tumor immune infiltration, and chemotherapeutic drug sensitivity, to explore the expression and prognostic value of FBXO5 in CC. The migration and invasiveness of cervical cancer cells following FBXO5 knockdown and overexpression were examined using wound healing and transwell assays, and the viability of cancer cells was assessed using CCK8 and EdU assays.Results:FBXO5 was discovered to be substantially expressed in CC tissues using data from our CC cohort and the TCGA database, and a survival analysis indicated FBXO5 as a predictive factor for poor overall survival in CC patients. In vitro, CC cells were more inclined to proliferate, migrate, and invade when FBXO5 was upregulated as opposed to when it was knocked down

Modeling of senescence-related chemoresistance in ovarian cancer using data analysis and patient-derived organoids

BackgroundOvarian cancer (OC) is a malignant tumor associated with poor prognosis owing to its susceptibility to chemoresistance. Cellular senescence, an irreversible biological state, is intricately linked to chemoresistance in cancer treatment. We developed a senescence-related gene signature for prognostic prediction and evaluated personalized treatment in patients with OC.MethodsWe acquired the clinical and RNA-seq data of OC patients from The Cancer Genome Atlas and identified a senescence-related prognostic gene set through differential and cox regression analysis in distinct chemotherapy response groups. A prognostic senescence-related signature was developed and validated by OC patient-derived-organoids (PDOs). We leveraged gene set enrichment analysis (GSEA) and ESTIMATE to unravel the potential functions and immune landscape of the model. Moreover, we explored the correlation between risk scores and potential chemotherapeutic agents. After confirming the congruence between organoids and tumor tissues through immunohistochemistry, we measured the IC50 of cisplatin in PDOs using the ATP activity assay, categorized by resistance and sensitivity to the drug. We also investigated the expression patterns of model genes across different groups.ResultsWe got 2740 differentially expressed genes between two chemotherapy response groups including 43 senescence-related genes. Model prognostic genes were yielded through univariate cox analysis, and multifactorial cox analysis. Our work culminated in a senescence-related prognostic model based on the expression of SGK1 and VEGFA. Simultaneously, we successfully constructed and propagated three OC PDOs for drug screening. PCR and WB from PDOs affirmed consistent expression trends as those of our model genes derived from comprehensive data analysis. Specifically, SGK1 exhibited heightened expression in cisplatin-resistant OC organoids, while VEGFA manifested elevated expression in the sensitive group (P&lt;0.05). Intriguingly, GSEA results unveiled the enrichment of model genes in the PPAR signaling pathway, pivotal regulator in chemoresistance and tumorigenesis. This revelation prompted the identification of potential beneficial drugs for patients with a high-risk score, including gemcitabine, dabrafenib, epirubicin, oxaliplatin, olaparib, teniposide, ribociclib, topotecan, venetoclax.ConclusionThrough the formulation of a senescence-related signature comprising SGK1 and VEGFA, we established a promising tool for prognosticating chemotherapy reactions, predicting outcomes, and steering therapeutic strategies. Patients with high VEGFA and low SGK1 expression levels exhibit heightened sensitivity to chemotherapy