Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks

The delayed feedback problem is one of the most pressing challenges in predicting the conversion rate since users' conversions are always delayed in online commercial systems. Although new data are beneficial for continuous training, without complete feedback information, i.e., conversion labels, training algorithms may suffer from overwhelming fake negatives. Existing methods tend to use multitask learning or design data pipelines to solve the delayed feedback problem. However, these methods have a trade-off between data freshness and label accuracy. In this paper, we propose Delayed Feedback Modeling by Dynamic Graph Neural Network (DGDFEM). It includes three stages, i.e., preparing a data pipeline, building a dynamic graph, and training a CVR prediction model. In the model training, we propose a novel graph convolutional method named HLGCN, which leverages both high-pass and low-pass filters to deal with conversion and non-conversion relationships. The proposed method achieves both data freshness and label accuracy. We conduct extensive experiments on three industry datasets, which validate the consistent superiority of our method

Decentralized Graph Neural Network for Privacy-Preserving Recommendation

Building a graph neural network (GNN)-based recommender system without violating user privacy proves challenging. Existing methods can be divided into federated GNNs and decentralized GNNs. But both methods have undesirable effects, i.e., low communication efficiency and privacy leakage. This paper proposes DGREC, a novel decentralized GNN for privacy-preserving recommendations, where users can choose to publicize their interactions. It includes three stages, i.e., graph construction, local gradient calculation, and global gradient passing. The first stage builds a local inner-item hypergraph for each user and a global inter-user graph. The second stage models user preference and calculates gradients on each local device. The third stage designs a local differential privacy mechanism named secure gradient-sharing, which proves strong privacy-preserving of users' private data. We conduct extensive experiments on three public datasets to validate the consistent superiority of our framework

Text-Video Retrieval via Variational Multi-Modal Hypergraph Networks

Text-video retrieval is a challenging task that aims to identify relevant videos given textual queries. Compared to conventional textual retrieval, the main obstacle for text-video retrieval is the semantic gap between the textual nature of queries and the visual richness of video content. Previous works primarily focus on aligning the query and the video by finely aggregating word-frame matching signals. Inspired by the human cognitive process of modularly judging the relevance between text and video, the judgment needs high-order matching signal due to the consecutive and complex nature of video contents. In this paper, we propose chunk-level text-video matching, where the query chunks are extracted to describe a specific retrieval unit, and the video chunks are segmented into distinct clips from videos. We formulate the chunk-level matching as n-ary correlations modeling between words of the query and frames of the video and introduce a multi-modal hypergraph for n-ary correlation modeling. By representing textual units and video frames as nodes and using hyperedges to depict their relationships, a multi-modal hypergraph is constructed. In this way, the query and the video can be aligned in a high-order semantic space. In addition, to enhance the model's generalization ability, the extracted features are fed into a variational inference component for computation, obtaining the variational representation under the Gaussian distribution. The incorporation of hypergraphs and variational inference allows our model to capture complex, n-ary interactions among textual and visual contents. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on the text-video retrieval task

The Correlation between Thyrotropin and Dyslipidemia in a Population-based Study

This study investigated the relationship between serum thyrotrophin levels and dyslipidemia in subclinical hypothyroid and euthyroid subjects. A total of 110 subjects with subclinical hypothyroidism and 1,240 euthyroid subjects enrolled in this study. Patients with subclinical hypothyroidism had significantly lower high density lipoprotein cholesterol (HDL-C) levels than those who were euthyroid. The lipid profiles were each categorized and mean thyrotrophin levels were higher in subjects in the dyslipidemia subclasses than subjects in the normal subclasses. Thyrotrophin was positively associated with serum triglyceride and negatively associated with serum HDL-C in women. Thyrotrophin was also positively associated with total cholesterol (TC) in the overweight population along with TC and LDL-C in overweight women. In the euthyroid population, thyrotrophin was positively associated with TC in the overweight population. In conclusion, serum thyrotrophin was correlated with dyslipidemia in subclinical hypothyroid and euthyroid subjects; the correlation was independent of insulin sensitivity

Etiologic Diagnosis of Lower Respiratory Tract Bacterial Infections Using Sputum Samples and Quantitative Loop-Mediated Isothermal Amplification

Etiologic diagnoses of lower respiratory tract infections (LRTI) have been relying primarily on bacterial cultures that often fail to return useful results in time. Although DNA-based assays are more sensitive than bacterial cultures in detecting pathogens, the molecular results are often inconsistent and challenged by doubts on false positives, such as those due to system- and environment-derived contaminations. Here we report a nationwide cohort study on 2986 suspected LRTI patients across P. R. China. We compared the performance of a DNA-based assay qLAMP (quantitative Loop-mediated isothermal AMPlification) with that of standard bacterial cultures in detecting a panel of eight common respiratory bacterial pathogens from sputum samples. Our qLAMP assay detects the panel of pathogens in 1047(69.28%) patients from 1533 qualified patients at the end. We found that the bacterial titer quantified based on qLAMP is a predictor of probability that the bacterium in the sample can be detected in culture assay. The relatedness of the two assays fits a logistic regression curve. We used a piecewise linear function to define breakpoints where latent pathogen abruptly change its competitive relationship with others in the panel. These breakpoints, where pathogens start to propagate abnormally, are used as cutoffs to eliminate the influence of contaminations from normal flora. With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients. In conclusion, qLAMP is a reliable method in quantifying bacterial titer. Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe

A Coarse-to-Fine Model for Geolocating Chinese Addresses

Address geolocation aims to associate address texts to the geographic locations. In China, due to the increasing demand for LBS applications such as take-out services and express delivery, automatically geolocating the unstructured address information is the key issue that needs to be solved first. Recently, a few approaches have been proposed to automate the address geolocation by directly predicting geographic coordinates. However, such point-based methods ignore the hierarchy information in addresses which may cause poor geolocation performance. In this paper, we propose a hierarchical region-based approach for geolocating Chinese addresses. We model the address geolocation as a Sequence-to-Sequence (Seq2Seq) learning task, that is, the input sequence is a textual address, and the output sequence is a GeoSOT grid code which exactly represents multi-level regions covered by the address. A novel coarse-to-fine model, which combines BERT and LSTM, is designed to learn the task. The experimental results demonstrate that our model correctly understands the Chinese addresses and achieves the highest geolocation accuracy among all the baselines

SiC Trench MOSFET with Depletion-Mode pMOS for Enhanced Short-Circuit Capability and Switching Performance

A novel 4H-SiC trench metal-oxide-semiconductor field-effect transistor (TMOS) with depletion-mode pMOS (D-pMOS) is proposed and investigated via TCAD simulation. It has an auxiliary gate electrode that controls the electrical connections of P-shield layers under the trench bottom through the D-pMOS. In linear operation, the D-pMOS is turned off and then the potential of the P-shield layers is raised with the auxiliary gate, which shrinks the width of the depletion region of the P-shield/N-drift junction to reduce the resistance of the JFET region. In the saturation operation, the saturation current density of the proposed TMOS is reduced, benefiting from its relatively large cell pitch. The design concept eases the tension between specific on-resistance and short circuit capabilities. Numerical simulation results show that the proposed TMOS exhibits a short circuit withstand time that is 1.92 times longer than that of the conventional TMOS. In addition, a drive tactic is introduced and optimized for the proposed TMOS, which requires only one set of gate drivers. Compared with the conventional TMOS, the switching performance is improved and the switching loss is reduced by 40%

Modeling of Charge-to-Breakdown with an Electron Trapping Model for Analysis of Thermal Gate Oxide Failure Mechanism in SiC Power MOSFETs

The failure mechanism of thermal gate oxide in silicon carbide (SiC) power metal oxide semiconductor field effect transistors (MOSFETs), whether it is field-driven breakdown or charge-driven breakdown, has always been a controversial topic. Previous studies have demonstrated that the failure time of thermally grown silicon dioxide (SiO2) on SiC stressed with a constant voltage is indicated as charge driven rather than field driven through the observation of Weibull Slope β. Considering the importance of the accurate failure mechanism for the thermal gate oxide lifetime prediction model of time-dependent dielectric breakdown (TDDB), charge-driven breakdown needs to be further fundamentally justified. In this work, the charge-to-breakdown (QBD) of the thermal gate oxide in a type of commercial planar SiC power MOSFETs, under the constant current stress (CCS), constant voltage stress (CVS), and pulsed voltage stress (PVS) are extracted, respectively. A mathematical electron trapping model in thermal SiO2 grown on single crystal silicon (Si) under CCS, which was proposed by M. Liang et al., is proven to work equally well with thermal SiO2 grown on SiC and used to deduce the QBD model of the device under test (DUT). Compared with the QBD obtained under the three stress conditions, the charge-driven breakdown mechanism is validated in the thermal gate oxide of SiC power MOSFETs

An Investigation of Body Diode Reliability in Commercial 1.2 kV SiC Power MOSFETs with Planar and Trench Structures

The body diode degradation in SiC power MOSFETs has been demonstrated to be caused by basal plane dislocation (BPD)-induced stacking faults (SFs) in the drift region. To enhance the reliability of the body diode, many process and structural improvements have been proposed to eliminate BPDs in the drift region, ensuring that commercial SiC wafers for 1.2 kV devices are of high quality. Thus, investigating the body diode reliability in commercial planar and trench SiC power MOSFETs made from SiC wafers with similar quality has attracted attention in the industry. In this work, current stress is applied on the body diodes of 1.2 kV commercial planar and trench SiC power MOSFETs under the off-state. The results show that the body diodes of planar and trench devices with a shallow P+ depth are highly reliable, while those of the trench devices with the deep P+ implantation exhibit significant degradation. In conclusion, the body diode degradation in trench devices is mainly influenced by P+ implantation-induced BPDs. Therefore, a trade-off design by controlling the implantation depth/dose and maximizing the device performance is crucial. Moreover, the deep JFET design is confirmed to further improve the body diode reliability in planar devices