NMA: Neural Multi-slot Auctions with Externalities for Online Advertising

Online advertising driven by auctions brings billions of dollars in revenue for social networking services and e-commerce platforms. GSP auctions, which are simple and easy to understand for advertisers, have almost become the benchmark for ad auction mechanisms in the industry. However, most GSP-based industrial practices assume that the user click only relies on the ad itself, which overlook the effect of external items, referred to as externalities. Recently, DNA has attempted to upgrade GSP with deep neural networks and models local externalities to some extent. However, it only considers set-level contexts from auctions and ignores the order and displayed position of ads, which is still suboptimal. Although VCG-based multi-slot auctions (e.g., VCG, WVCG) make it theoretically possible to model global externalities (e.g., the order and positions of ads and so on), they lack an efficient balance of both revenue and social welfare. In this paper, we propose novel auction mechanisms named Neural Multi-slot Auctions (NMA) to tackle the above-mentioned challenges. Specifically, we model the global externalities effectively with a context-aware list-wise prediction module to achieve better performance. We design a list-wise deep rank module to guarantee incentive compatibility in end-to-end learning. Furthermore, we propose an auxiliary loss for social welfare to effectively reduce the decline of social welfare while maximizing revenue. Experiment results on both offline large-scale datasets and online A/B tests demonstrate that NMA obtains higher revenue with balanced social welfare than other existing auction mechanisms (i.e., GSP, DNA, WVCG) in industrial practice, and we have successfully deployed NMA on Meituan food delivery platform.Comment: 10 pages, 3figure

Multi-scale distribution of coal fractures based on CT digital core deep learning

In order to realize high-precision and high-efficiency identification of multi-scale distribution characteristics of coal fractures, carry out the study of multi-scale distribution characteristics identification methods based on CT digital core deep learning. Industrial CT scanning system is used to collect a large number of coal original CT digital core information array, the CT digital core information array is converted into a two-dimensional gray-scale image and then it is divided into square images of different scales and the image brightness is enhanced to different levels as training samples, Finally, the construction and optimization of model parameters of AlexNet, ResNet-18, GoogLeNet and Inception-V3 models for the identification of CT-containing fractures are realized by Matlab platform. Study the recognition accuracy and verification accuracy of different model training under different number of training samples; Study the accuracy, calculation efficiency and training time of different models for images with different scales and brightness under the same training sample, obtain the optimal model for calculating the fractal dimension of two-dimensional CT images with fractures, then, the fractal distribution characteristics of each fracture image are calculated according to the statistical method of box-counting dimension, compared with the traditional binarization method and human eye recognition method, The applicability of the multi-scale distribution characteristics identification method of coal fractures based on CT digital core deep learning is verified. The result shows: ① ResNet-18 model is the optimal model for calculating the fractal dimension of two-dimensional CT images with cracks when the image sample is brightness 4 and the scale is 3.5 mm to 21 mm, the model has high accuracy and short training time in calculating the fractal dimension of two-dimensional CT fracture images. ② Compared with the traditional binarization method, the multi-scale recognition method of coal fracture based on CT digital core deep learning has the advantages of fast speed, high accuracy and is not easily affected by impurities in coal

Transfer-free, lithography-free and fast growth of patterned CVD graphene directly on insulators by using sacrificial metal catalyst

Chemical vapor deposited graphene suffers from two problems: transfer from metal catalysts to insulators, and photoresist induced degradation during patterning. Both result in macroscopic and microscopic damages such as holes, tears, doping, and contamination, translated into property and yield dropping. We attempt to solve the problems simultaneously. A nickel thin film is evaporated on SiO2 as a sacrificial catalyst, on which surface graphene is grown. A polymer (PMMA) support is spin-coated on the graphene. During the Ni wet etching process, the etchant can permeate the polymer, making the etching efficient. The PMMA/graphene layer is fixed on the substrate by controlling the surface morphology of Ni film during the graphene growth. After etching, the graphene naturally adheres to the insulating substrate. By using this method, transfer-free, lithography-free and fast growth of graphene realized. The whole experiment has good repeatability and controllability. Compared with graphene transfer between substrates, here, no mechanical manipulation is required, leading to minimal damage. Due to the presence of Ni, the graphene quality is intrinsically better than catalyst-free growth. The Ni thickness and growth temperature are controlled to limit the number of layers of graphene. The technology can be extended to grow other two-dimensional materials with other catalysts

Coronary artery bypass grafting vs. percutaneous coronary intervention in coronary artery disease patients with advanced chronic kidney disease: A Chinese single-center study

ObjectivesAims to compare the contemporary and long-term outcomes of coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) in coronary artery disease (CAD) patients with advanced chronic kidney disease (CKD).Methods823 CAD patients with advanced CKD (eGFR < 30 ml/min/1.73 m2) were collected, including 247 patients who underwent CABG and 576 patients received PCI from January 2014 to February 2021. The primary endpoint was all-cause death. The secondary endpoints included major adverse cardiac and cerebrovascular events (MACCEs), myocardial infarction (MI), stroke and revascularization.ResultsMultivariable Cox regression models were used and propensity score matching (PSM) was also performed. After PSM, the 30-day mortality rate in the CABG group was higher than that in the PCI group but without statistically significant (6.6% vs. 2.4%, p = 0.24). During the first year, patients referred for CABG had a hazard ratio (HR) of 1.42 [95% confidence interval (CI), 0.41–3.01] for mortality compared with PCI. At the end of the 5-year follow-up, CABG group had a HR of 0.58 (95%CI, 0.38–0.86) for repeat revascularization, a HR of 0.77 (95%CI, 0.52–1.14) for survival rate and a HR of 0.88(95%CI, 0.56–1.18) for MACCEs as compared to PCI.ConclusionsAmong patients with CAD and advanced CKD who underwent CABG or PCI, the all-cause mortality and MACCEs were comparable between the two groups in 30 days, 1-year and 5 years. However, CABG was only associated with a significantly lower risk for repeat revascularization compared with PCI at 5 years follow-up

Survivin gene silencing sensitizes prostate cancer cells to selenium growth inhibition

<p>Abstract</p> <p>Background</p> <p>Prostate cancer is a leading cause of cancer-related death in men worldwide. Survivin is a member of the inhibitor of apoptosis (IAP) protein family that is expressed in the majority of human tumors including prostate cancer, but is barely detectable in terminally differentiated normal cells. Downregulation of survivin could sensitize prostate cancer cells to chemotherapeutic agents <it>in vitro </it>and <it>in vivo</it>. Selenium is an essential trace element. Several studies have shown that selenium compounds inhibit the growth of prostate cancer cells. The objective of this study is to investigate whether survivin gene silencing in conjunction with selenium treatment could enhance the therapeutic efficacy for prostate cancer and to elucidate the underlying mechanisms.</p> <p>Methods</p> <p>Expression of survivin was analyzed in a collection of normal and malignant prostatic tissues by immunohistochemical staining. <it>In vitro </it>studies were conducted in PC-3M, C4-2B, and 22Rv1 prostate cancer cells. The effect of selenium on survivin expression was analyzed by Western blotting and semi-quantitative RT-PCR. Survivin gene knockdown was carried out by transfecting cells with a short hairpin RNA (shRNA) designed against survivin. Cell proliferation was quantitated by the 3-(4,5-Dimethylthiazol-2-yl)- 2,5-Diphenyltetrazolium Bromide (MTT) assay and apoptosis by propidium iodide staining followed by flow cytometry analysis. Finally, <it>in vivo </it>tumor growth assay was performed by establishing PC-3M xenograft in nude mice and monitoring tumor growth following transfection and treatment.</p> <p>Results</p> <p>We found that survivin was undetectable in normal prostatic tissues but was highly expressed in prostate cancers. Survivin knockdown or selenium treatment inhibited the growth of prostate cancer cells, but the selenium effect was modest. In contrast to what have been observed in other cell lines, selenium treatment had little or no effect on survivin expression in several androgen-independent prostate cancer cell lines. Survivin knockdown sensitized these cells to selenium growth inhibition and apoptosis induction. In nude mice bearing PC-3M xenografts, survivin knockdown synergizes with selenium in inhibiting tumor growth.</p> <p>Conclusions</p> <p>Selenium could inhibit the growth of hormone-refractory prostate cancer cells both <it>in vitro </it>and <it>in vivo</it>, but the effects were modest. The growth inhibition was not mediated by downregulating survivin expression. Survivin silencing greatly enhanced the growth inhibitory effects of selenium.</p

Influence Mechanism of Geomorphological Evolution in a Tidal Lagoon with Rising Sea Level

A tidal lagoon system has multiple environmental, societal, and economic implications. To investigate the mechanism of influence of the geomorphological evolution of a tidal lagoon, the effect of critical erosion shear stress, critical deposition shear stress, sediment settling velocity, and initial bed elevation were assessed by applying the MIKE hydro- and morpho-dynamic model to a typical tidal lagoon, Qilihai Lagoon. According to the simulation results, without sediment supply, an increase of critical erosion, deposition shear stress, or sediment settling velocity gives rise to tidal networks with a stable terrain. Such an equilibrium state can be defined as when the change of net erosion has little variation, which can be achieved due to counter actions between the erosion and deposition effect. Moreover, the influence of the initial bed elevation depends on the lowest tidal level. When the initial bed elevation is below the lowest tidal level, the tidal networks tend to be fully developed. A Spearman correlation analysis indicated that the geomorphological evolution is more sensitive to critical erosion or deposition shear stress than sediment settling velocity and initial bed elevation. Exponential sea level rise contributes to more intensive erosion than the linear or the parabolic sea level rise in the long-term evolution of a tidal lagoon

Prediction of Acute Kidney Injury Following Isolated Coronary Artery Bypass Grafting in Heart Failure Patients with Preserved Ejection Fraction Using Machine Leaning with a Novel Nomogram

Background: The incidence of postoperative acute kidney injury (AKI) is high due to insufficient perfusion in patients with heart failure. Heart failure patients with preserved ejection fraction (HFpEF) have strong heterogeneity, which can obtain more accurate results. There are few studies for predicting AKI after coronary artery bypass grafting (CABG) in HFpEF patients especially using machine learning methodology. Methods: Patients were recruited in this study from 2018 to 2022. AKI was defined according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria. The machine learning methods adopted included logistic regression, random forest (RF), extreme gradient boosting (XGBoost), gaussian naive bayes (GNB), and light gradient boosting machine (LGBM). We used the receiver operating characteristic curve (ROC) to evaluate the performance of these models. The integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were utilized to compare the prediction model. Results: In our study, 417 (23.6%) patients developed AKI. Among the five models, random forest was the best predictor of AKI. The area under curve (AUC) value was 0.834 (95% confidence interval (CI) 0.80–0.86). The IDI and NRI was also better than the other models. Ejection fraction (EF), estimated glomerular filtration rate (eGFR), age, albumin (Alb), uric acid (UA), lactate dehydrogenase (LDH) were also significant risk factors in the random forest model. Conclusions: EF, eGFR, age, Alb, UA, LDH are independent risk factors for AKI in HFpEF patients after CABG using the random forest model. EF, eGFR, and Alb positively correlated with age; UA and LDH had a negative correlation. The application of machine learning can better predict the occurrence of AKI after CABG and may help to improve the prognosis of HFpEF patients

Risk of temperature, humidity and concentrations of air pollutants on the hospitalization of AECOPD.

AIM:To investigate the effect of temperature, humidity and the concentration of ambient air pollution on the hospitalization of AECOPD. METHOD:Hospitalization record was obtained from Shenyang Medical Insurance Bureau, concluding patient's age, gender, income hospital time, outcome hospital; Generalized additive model was used to analyze the relationship between temperature, humidity, the concentration of ambient air pollution and the hospitalization of AECOPD. RESULT:The effect of ozone on admission rate in male group was higher than that in female group. Ambient air pollution had a weak influence on age≤50 group. It was found that the optimal lag day for daily relative 40 humidity to age≤50 group, 5070 group was on lag5, lag4, lag4 and lag5, respectively. CONCLUSION:Air pollution, relative humidity and temperature can increase the risk of admission for acute exacerbation of COPD, and in this process there was a lag effect

Modulation of Standing Spin Waves in Confined Rectangular Elements

Magnonics is an emerging field within spintronics that focuses on developing novel magnetic devices capable of manipulating information through the modification of spin waves in nanostructures with submicron size. Here, we provide a confined magnetic rectangular element to modulate the standing spin waves, by changing the saturation magnetisation (MS), exchange constant (A), and the aspect ratio of rectangular magnetic elements via micromagnetic simulation. It is found that the bulk mode and the edge mode of the magnetic element form a hybrid with each other. With the decrease in MS, both the Kittel mode and the standing spin waves undergo a shift towards higher frequencies. On the contrary, as A decreases, the frequencies of standing spin waves become smaller, while the Kittel mode is almost unaffected. Moreover, when the length-to-width aspect ratio of the element is increased, standing spin waves along the width and length become split, leading to the observation of additional modes in the magnetic spectra. For each mode, the vibration style is discussed. These spin dynamic modes were further confirmed via FMR experiments, which agree well with the simulation results