Comprehensive Information Integration Modeling Framework for Video Titling

In e-commerce, consumer-generated videos, which in general deliver consumers' individual preferences for the different aspects of certain products, are massive in volume. To recommend these videos to potential consumers more effectively, diverse and catchy video titles are critical. However, consumer-generated videos seldom accompany appropriate titles. To bridge this gap, we integrate comprehensive sources of information, including the content of consumer-generated videos, the narrative comment sentences supplied by consumers, and the product attributes, in an end-to-end modeling framework. Although automatic video titling is very useful and demanding, it is much less addressed than video captioning. The latter focuses on generating sentences that describe videos as a whole while our task requires the product-aware multi-grained video analysis. To tackle this issue, the proposed method consists of two processes, i.e., granular-level interaction modeling and abstraction-level story-line summarization. Specifically, the granular-level interaction modeling first utilizes temporal-spatial landmark cues, descriptive words, and abstractive attributes to builds three individual graphs and recognizes the intra-actions in each graph through Graph Neural Networks (GNN). Then the global-local aggregation module is proposed to model inter-actions across graphs and aggregate heterogeneous graphs into a holistic graph representation. The abstraction-level story-line summarization further considers both frame-level video features and the holistic graph to utilize the interactions between products and backgrounds, and generate the story-line topic of the video. We collect a large-scale dataset accordingly from real-world data in Taobao, a world-leading e-commerce platform, and will make the desensitized version publicly available to nourish further development of the research community...Comment: 11 pages, 6 figures, to appear in KDD 2020 proceeding

GLP-1RAs caused gastrointestinal adverse reactions of drug withdrawal: a system review and network meta-analysis

BackgroundGlucagon-like peptide-1 receptor agonists (GLP-1RAs) significantly reduce postprandial blood glucose, inhibit appetite, and delay gastrointestinal emptying. However, it is controversial that some patients are intolerant to GLP-1RAs.MethodsPubMed, Embase, Web of Science, and Cochrane Library were searched for randomized controlled trials (RCTs) using GLP-1RAs with documented withdrawal due to gastrointestinal adverse reactions (GI AEs) from their inception to September 28, 2022. After extracting the information incorporated into the studies, a random-effects network meta-analysis was performed within a frequentist framework.Results64 RCTs were finally enrolled, which included six major categories of the GLP-1RA. The sample size of the GLP-1RAs treatment group was 16,783 cases. The risk of intolerable gastrointestinal adverse reactions of Liraglutide and Semaglutide was higher than that of Dulaglutide. Meanwhile, the higher the dose of the same GLP-1RA preparation, the more likely to cause these adverse reactions. These intolerable GI AEs were not significantly related to drug homology or formulations and may be related to the degree of suppression of the appetite center.ConclusionDulaglutide caused the lowest intolerable GI AEs, while Liraglutide and Semaglutide were the highest. For Semaglutide, the higher the dose, the more likely it is to drive GI AEs. Meanwhile, the risk of these GI AEs is independent of the different formulations of the drug. All these findings can effectively guide individualized treatment.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022359346, identifier CRD42022359346

Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case

Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML). However, there has been limited research that applies KGE for industrial problems in manufacturing. This paper investigates whether and to what extent KGE can be used for an important problem: quality monitoring for welding in manufacturing industry, which is an impactful process accounting for production of millions of cars annually. The work is in line with Bosch research of data-driven solutions that intends to replace the traditional way of destroying cars, which is extremely costly and produces waste. The paper tackles two very challenging questions simultaneously: how large the welding spot diameter is; and to which car body the welded spot belongs to. The problem setting is difficult for traditional ML because there exist a high number of car bodies that should be assigned as class labels. We formulate the problem as link prediction, and experimented popular KGE methods on real industry data, with consideration of literals. Our results reveal both limitations and promising aspects of adapted KGE methods.Comment: Paper accepted at ISWC2023 In-Use trac

Q-switching of waveguide lasers based on graphene/WS_2 van der Waals heterostructure

We report on the operation of passively -switched waveguide lasers at 1 μm wavelength based on a graphene/WS2 heterostructure as a saturable absorber (SA). The gain medium is a crystalline Nd:YVO4 cladding waveguide produced by femtosecond laser writing. The nanosecond waveguide laser operation at 1064 nm has been realized with the maximum average output power of 275 mW and slope efficiency of 37%. In comparison with the systems based on single WS2 or graphene SA, the lasing -switched by a graphene/WS2 heterostructure SA possesses advantages of a higher pulse energy and enhanced slope efficiency, indicating the promising applications of van der Waals heterostructures for ultrafast photonic device111 Project of China (B13029); Strategic Priority Research Program of CAS (XDB16030700); Key Research Program of Frontier Science of CAS (QYZDB-SSW-JSC041); National Natural Science Foundation of China (NSFC) (11274203, 61522510); STCSM Excellent Academic Leader of Shanghai (17XD1403900)

Experimental investigation of kinetic instabilities driven by runaway electrons in the EXL-50 spherical torus

In this study, the first observation of high-frequency instabilities driven by runaway electrons has been reported in the EXL-50 spherical torus using a high-frequency magnetic pickup coil. The central frequency of these instabilities is found to be exponentially dependent on the plasma density, similar to the dispersion relation of the whistler wave. The instability frequency displays chirping characteristics consistent with the Berk-Breizman model of beam instability. Theoretically, the excitation threshold of the instability driven by runaway electrons is related to the ratio of the runaway electron density to the background plasma density, and such a relationship is first demonstrated experimentally in this study. The instability can be stabilized by increasing the plasma density, consistent with the wave-particle resonance mechanism. This investigation demonstrates the controlled excitation of chirping instabilities in a tokamak plasma and reveals new features of these instabilities, thereby advancing the understanding of the mechanisms for controlling and mitigating runaway electrons

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.Comment: 20 pages, Transactions on Knowledge and Data Engineerin

In situ interface engineering for probing the limit of quantum dot photovoltaic devices.

Quantum dot (QD) photovoltaic devices are attractive for their low-cost synthesis, tunable band gap and potentially high power conversion efficiency (PCE). However, the experimentally achieved efficiency to date remains far from ideal. Here, we report an in-situ fabrication and investigation of single TiO2-nanowire/CdSe-QD heterojunction solar cell (QDHSC) using a custom-designed photoelectric transmission electron microscope (TEM) holder. A mobile counter electrode is used to precisely tune the interface area for in situ photoelectrical measurements, which reveals a strong interface area dependent PCE. Theoretical simulations show that the simplified single nanowire solar cell structure can minimize the interface area and associated charge scattering to enable an efficient charge collection. Additionally, the optical antenna effect of nanowire-based QDHSCs can further enhance the absorption and boost the PCE. This study establishes a robust 'nanolab' platform in a TEM for in situ photoelectrical studies and provides valuable insight into the interfacial effects in nanoscale solar cells