Co-evolving Vector Quantization for ID-based Recommendation

Category information plays a crucial role in enhancing the quality and personalization of recommendations. Nevertheless, the availability of item category information is not consistently present, particularly in the context of ID-based recommendations. In this work, we propose an alternative approach to automatically learn and generate entity (i.e., user and item) categorical information at different levels of granularity, specifically for ID-based recommendation. Specifically, we devise a co-evolving vector quantization framework, namely COVE, which enables the simultaneous learning and refinement of code representation and entity embedding in an end-to-end manner, starting from the randomly initialized states. With its high adaptability, COVE can be easily integrated into existing recommendation models. We validate the effectiveness of COVE on various recommendation tasks including list completion, collaborative filtering, and click-through rate prediction, across different recommendation models. We will publish the code and data for other researchers to reproduce our work

Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy

Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels. SSDA tasks need to overcome the domain gap between the source and target graphs. However, to date, this challenging research problem has yet to be formally considered by the existing approaches designed for cross-graph node classification. To tackle the SSDA problem on graphs, a novel method called SemiGCL is proposed, which benefits from graph contrastive learning and minimax entropy training. SemiGCL generates informative node representations by contrasting the representations learned from a graph's local and global views. Additionally, SemiGCL is adversarially optimized with the entropy loss of unlabeled target nodes to reduce domain divergence. Experimental results on benchmark datasets demonstrate that SemiGCL outperforms the state-of-the-art baselines on the SSDA tasks

Steatosis and liver cancer in transgenic mice expressing the structural and nonstructural proteins of hepatitis C virus

Background and Aims: The aim of this study was to determine whether expression of hepatitis C virus proteins alters hepatic morphology or function in the absence of inflammation. Methods: Transgenic C57BL/6 mice with liver-specific expression of RNA encoding the complete viral polyprotein (FL-N transgene) or viral structural proteins (S-N transgene) were compared with nontransgenic littermates for altered liver morphology and function. Results: FL-N transcripts were detectable only by reverse-transcription polymerase chain reaction, and S-N transcripts were identified in Northern blots. The abundance of viral proteins was sufficient for detection only in S-N transgenic animals. There was no inflammation in transgenic livers, but mice expressing either transgene developed age-related hepatic steatosis that was more severe in males. Apoptotic or proliferating hepatocytes were not significantly increased. Hepatocellular adenoma or carcinoma developed in older male animals expressing either transgene, but their incidence reached statistical significance only in FL-N animals. Neither was ever observed in age-matched nontransgenic mice. Conclusions: Constitutive expression of viral proteins leads to common pathologic features of hepatitis C in the absence of specific anti-viral immune responses. Expression of the structural proteins enhances a low background of steatosis in C57BL/6 mice, while additional low level expression of nonstructural proteins increases the risk of cancer

Fabrication and diffraction efficiency enhancement of embedded micro-gratings in a fused glass with fs multi-layer writing technique,

NRC publication: Ye

Packaging of photonic components VII: the process for coupling optical fibers to planar waveguides with thermal curing adhesives

Our developed thermal curing adhesives were reported having excellent performance in coupling optical fibers to waveguides. The fiber-to-waveguide coupling process based on these adhesives was reported in this paper. The process consisted of three major steps in the process, including loading waveguide dies and fiber arrays onto the sample holders, aligning the fibers to waveguides at a constant temperature to reach minimum loss, bonding the fiber arrays to waveguide dies. The sample holders, which used ceramic spaces to isolate heat and springs to damp stress, were specially designed to keep fiber arrays and waveguide dies at a constant temperature up to 120\ub0C with minimum shift. When the fiber arrays and waveguide dies were equilibrated with the set temperature, a rough alignment was conducted manually, followed by an automatic alignment controlled by a Melles Griot system. Then, the adhesives with proper viscosity and curing rate were applied to the gaps between fiber array and waveguide dies to bond them together. The curing temperature was optimized by studying the adhesives\u2019 curing kinetics with a DSC, so that the adhesives could be distributed rapidly and cured at a speed that still allowed a small alignment adjustment during the curing. A post-thermal treatment process was developed to build the maximum bonding strength for the fiber-coupled devices.NRC publication: Ye

Fabrication and stitching of embedded multi-layer micro-gratings in fused silica glass by fs laser pulses

NRC publication: Ye

A Monolithically Integrated Micro Wavelength Interrogator for Fiber Bragg Grating Sensor Alications

NRC publication: Ye

The activity of cuproptosis pathway calculated by AUCell algorithm was employed to construct cuproptosis landscape in lung adenocarcinoma

Abstract Cuproptosis is a recently described copper-dependent cell death pathway. Consequently, there are still few studies on lung adenocarcinoma (LUAD)-related cuproptosis, and we aimed to deepen in this matter. In this study, data from 503 patients with lung cancer from the TCGA-LUAD cohort data collection and 11 LUAD single-cells from GSE131907 as well as from 10 genes associated with cuproptosis were analyzed. The AUCell R package was used to determine the copper-dependent cell death pathway activity for each cell subpopulation, calculate the CellChat score, and display cell communication for each cell subpopulation. The PROGENy score was calculated to show the scores of tumor-related pathways in different cell populations. GO and KEGG analyses were used to calculate pathway activity. Univariate COX and random forest analyses were used to screen prognosis-associated genes and construct models. The ssGSEA and xCell algorithms were used to calculate the immunocyte infiltration score. Based on data from the GDSC database, the drug sensitivity score was calculated using oncoPredict. Finally, in vitro experiments were performed to determine the role of TLE1, the most important gene in the prognostic model. The 11 LUAD single-cell samples were classified into 8 different cell populations, from which epithelial cells showed the highest copper-dependent cell death pathway activity. Epithelial cell subsets were significantly positively correlated with MAKP, hypoxia, and other pathways. In addition, cell subgroup communication showed highly active collagen and APP pathways. Using the Findmark algorithm, differentially expressed genes (DEGs) between epithelial and other cell types were identified. Combined with the bulk data in the TCGA-LUAD database, DEGs were enriched in pathways such as EGFR tyrosine kinase inhibitor resistance, Hippo signaling pathway, and tight junction. Subsequently, we selected 4 genes (out of 112) with prognostic significance, ANKRD29, RHOV, TLE1, and NPAS2, and used them to construct a prognostic model. The high- and low-risk groups, distinguished by the median risk score, showed significantly different prognoses. Finally, we chose TLE1 as a biomarker based on the relative importance score in the prognostic model. In vitro experiments showed that TLE1 promotes tumor proliferation and migration and inhibits apoptosis

A Miniaturized Optical Fiber Bragg Grating Sensor Interrogator Based on Echelle Diffractive Gratings

NRC publication: Ye