Deep Landscape Forecasting for Real-time Bidding Advertising

The emergence of real-time auction in online advertising has drawn huge attention of modeling the market competition, i.e., bid landscape forecasting. The problem is formulated as to forecast the probability distribution of market price for each ad auction. With the consideration of the censorship issue which is caused by the second-price auction mechanism, many researchers have devoted their efforts on bid landscape forecasting by incorporating survival analysis from medical research field. However, most existing solutions mainly focus on either counting-based statistics of the segmented sample clusters, or learning a parameterized model based on some heuristic assumptions of distribution forms. Moreover, they neither consider the sequential patterns of the feature over the price space. In order to capture more sophisticated yet flexible patterns at fine-grained level of the data, we propose a Deep Landscape Forecasting (DLF) model which combines deep learning for probability distribution forecasting and survival analysis for censorship handling. Specifically, we utilize a recurrent neural network to flexibly model the conditional winning probability w.r.t. each bid price. Then we conduct the bid landscape forecasting through probability chain rule with strict mathematical derivations. And, in an end-to-end manner, we optimize the model by minimizing two negative likelihood losses with comprehensive motivations. Without any specific assumption for the distribution form of bid landscape, our model shows great advantages over previous works on fitting various sophisticated market price distributions. In the experiments over two large-scale real-world datasets, our model significantly outperforms the state-of-the-art solutions under various metrics.Comment: KDD 2019. The reproducible code and dataset link is https://github.com/rk2900/DL

Answer Acquisition for Knowledge Base Question Answering Systems Based on Dynamic Memory Network

In recent years, with the rapid growth of Artificial Intelligence (AI) and the Internet of Things (IoT), the question answering systems for human-machine interaction based on deep learning have become a research hotspot of the IoT. Different from the structured query method in traditional Knowledge Base Question Answering (KBQA) systems based on templates or rules, representation learning is one of the most promising approaches to solving the problems of data sparsity and semantic gaps. In this paper, an answer acquisition method for KBQA systems based on a dynamic memory network is proposed, in which representation learning is employed to represent the natural language questions that are raised by users and the knowledge base subgraphs of the related entities. These representations are taken as inputs of the dynamic memory network. The correct answers are obtained by utilizing the memory and inferential capabilities. The experimental results demonstrate the effectiveness of the proposed approach. - 2013 IEEE.This work was supported by the National Science Foundation of China under Grant 61365010.Scopu

Estimating cancer incidence based on claims data from medical insurance systems in two areas lacking cancer registries in China.

BACKGROUND: We aimed to establish a Medical-Insurance-System-based Cancer Surveillance System (MIS-CASS) in China and evaluate the completeness and timeliness of this system through reporting cancer incidence rates using claims data in two regions in northern and southern China. METHODS: We extracted claims data from medical insurance systems in Hua County of Henan Province, and Shantou City in Guangdong Province in China from Jan 1, 2012 to Jun 30, 2019. These two regions have been considered to be high risk regions for oesophageal cancer. We developed a rigorous procedure to establish the MIS-CASS, which includes data extraction, cleaning, processing, case ascertainment, privacy protection, etc. Text-based diagnosis in conjunction with ICD-10 codes were used to determine cancer diagnosis. FINDINGS: In 2018, the overall age-standardised (Segi population) incidence rates (ASR World) of cancer in Hua County and Shantou City were 167·39/100,000 and 159·78/100,000 respectively. In both of these areas, lung cancer and breast cancer were the most common cancers in males and females respectively. Hua County is a high-risk region for oesophageal cancer (ASR World: 25·95/100,000), whereas Shantou City is not a high-risk region for oesophageal cancer (ASR World: 11·43/100,000). However, Nanao island had the highest incidence of oesophageal cancer among all districts and counties in Shantou (ASR World: 36·39/100,000). The age-standardised male-to-female ratio for oesophageal cancer was lower in Hua County than in Shantou (1·69 vs. 4·02). A six-month lag time was needed to report these cancer incidences for the MIS-CASS. INTERPRETATION: MIS-CASS efficiently reflects cancer burden in real-time, and has the potential to provide insight for improvement of cancer surveillance in China. FUNDING: The National Key R&D Program of China (2016YFC0901404), the Digestive Medical Coordinated Development Center of Beijing Municipal Administration of Hospitals (XXZ0204), the Sanming Project of Shenzhen (SZSM201612061), and the Shantou Science and Technology Bureau (190829105556145, 180918114960704)

The N-terminal Phosphodegron Targets TAZ/WWTR1 Protein for SCF β-TrCP -dependent Degradation in Response to Phosphatidylinositol 3-Kinase Inhibition

The Hippo tumor suppressor pathway plays a major role in development and organ size control, and its dysregulation contributes to tumorigenesis. TAZ (transcriptional co-activator with PDZ-binding motif; also known as WWTR1) is a transcription co-activator acting downstream of the Hippo pathway, and increased TAZ protein levels have been associated with human cancers, such as breast cancer. Previous studies have shown that TAZ is inhibited by large tumor suppressor (LATS)-dependent phosphorylation, leading to cytoplasmic retention and ubiquitin-dependent degradation. The LATS kinase, a core component of the Hippo pathway, phosphorylates the C-terminal phosphodegron in TAZ to promote its degradation. In this study, we have found that the N-terminal phosphodegron of TAZ also plays a role in TAZ protein level regulation, particularly in response to different status of cellular PI3K signaling. GSK3, which can be inhibited by high PI3K via AKT-dependent inhibitory phosphorylation, phosphorylates the N-terminal phosphodegron in TAZ, and the phosphorylated TAZ binds to β-TrCP subunit of the SCFβ-TrCP E3 ubiquitin ligase, thereby leading to TAZ ubiquitylation and degradation. We observed that the TAZ protein level is elevated in tumor cells with high PI3K signaling, such as in PTEN mutant cancer cells. This study provides a novel mechanism of TAZ regulation and suggests a role of TAZ in modulating tissue growth and tumor development in response to PI3K signaling

Acetylation Targets the M2 Isoform of Pyruvate Kinase for Degradation through Chaperone-Mediated Autophagy and Promotes Tumor Growth

Most tumor cells take up more glucose than normal cells but metabolize glucose via glycolysis even in the presence of normal levels of oxygen, a phenomenon known as the Warburg effect. Tumor cells commonly express the embryonic M2 isoform of pyruvate kinase (PKM2) that may contribute to the metabolism shift from oxidative phosphorylation to aerobic glycolysis and tumorigenesis. Here we show that PKM2 is acetylated on lysine 305 and that this acetylation is stimulated by high glucose concentration. PKM2 K305 acetylation decreases PKM2 enzyme activity and promotes its lysosomal-dependent degradation via chaperone-mediated autophagy (CMA). Acetylation increases PKM2 interaction with HSC70, a chaperone for CMA, and association with lysosomes. Ectopic expression of an acetylation mimetic K305Q mutant accumulates glycolytic intermediates and promotes cell proliferation and tumor growth. These results reveal an acetylation regulation of pyruvate kinase and the link between lysine acetylation and CMA

A Non-Canonical Function of Gβ as a Subunit of E3 Ligase in Targeting GRK2 Ubiquitylation

G protein-coupled receptors (GPCRs) comprise the largest family of cell-surface receptors, regulate a wide range of physiological processes, and are the major targets of pharmaceutical drugs. Canonical signaling from GPCRs is relayed to intracellular effector proteins by trimeric G proteins, composed of α, β, and γ subunits (Gαβγ). Here, we report that G-protein β subunits (Gβ) bind to DDB1 and that Gβ2 targets GRK2 for ubiquitylation by the DDB1-CUL4A-ROC1 ubiquitin ligase. Activation of GPCR results in PKA-mediated phosphorylation of DDB1 at Ser645 and its dissociation from Gβ2, leading to increase of GRK2 protein. Deletion of Cul4a results in cardiac hypertrophy in male mice that can be partially rescued by the deletion of one Grk2 allele. These results reveal a non-canonical function of the Gβ protein as a ubiquitin ligase component and a mechanism of feedback regulation of GPCR signaling