Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation

Click-Through Rate (CTR) prediction plays a core role in recommender systems, serving as the final-stage filter to rank items for a user. The key to addressing the CTR task is learning feature interactions that are useful for prediction, which is typically achieved by fitting historical click data with the Empirical Risk Minimization (ERM) paradigm. Representative methods include Factorization Machines and Deep Interest Network, which have achieved wide success in industrial applications. However, such a manner inevitably learns unstable feature interactions, i.e., the ones that exhibit strong correlations in historical data but generalize poorly for future serving. In this work, we reformulate the CTR task -- instead of pursuing ERM on historical data, we split the historical data chronologically into several periods (a.k.a, environments), aiming to learn feature interactions that are stable across periods. Such feature interactions are supposed to generalize better to predict future behavior data. Nevertheless, a technical challenge is that existing invariant learning solutions like Invariant Risk Minimization are not applicable, since the click data entangles both environment-invariant and environment-specific correlations. To address this dilemma, we propose Disentangled Invariant Learning (DIL) which disentangles feature embeddings to capture the two types of correlations separately. To improve the modeling efficiency, we further design LightDIL which performs the disentanglement at the higher level of the feature field. Extensive experiments demonstrate the effectiveness of DIL in learning stable feature interactions for CTR. We release the code at https://github.com/zyang1580/DIL.Comment: 11 pages, 6 Postscript figures, to be published in SIGIR202

Numerical Investigation on the Urban Heat Island Effect by Using a Porous Media Model

The urban heat island (UHI) effect resulted from urbanization as well as industrialization has become a major environmental problem. UHI effect aggravates global warming and endangers human health. Thus, mitigating the UHI effect has become a primary task to address these challenges. This paper verifies the feasibility of a three-dimensional turbulent porous media model. Using this model, the authors simulate the urban canopy wind-heat environment. The temperature and flow field over a city with a concentric circular structure are presented. The impact of three factors (i.e., anthropogenic heat, ambient crosswind speed, and porosity in the central area) on turbulent flow and heat transfer in the central business district of a simplified city model with a concentric circular structure were analyzed. It is found that the three-dimensional turbulent porous media model is suitable for estimating the UHI effect. The UHI effect could be mitigated by reducing the artificial heat and improving the porosity of the central city area

Risk factors for deep vein thrombosis in patients with pelvic or lower-extremity fractures in the emergency intensive care unit

IntroductionThis study aimed to investigate the incidence of deep vein thrombosis (DVT) in patients with pelvic or lower-extremity fractures in the emergency intensive care unit (EICU), explore the independent risk factors for DVT, and investigate the predictive value of the Autar scale for DVT in these patients.MethodsThe clinical data of patients with single fractures of the pelvis, femur, or tibia in the EICU from August 2016 to August 2019 were retrospectively examined. The incidence of DVT was statistically analyzed. Logistic regression was used to analyze the independent risk factors for DVT in these patients. The receiver-operating characteristic (ROC) curve was used to evaluate the predictive value of the Autar scale for the risk of DVT.ResultsA total of 817 patients were enrolled in this study; of these, 142 (17.38%) had DVT. Significant differences were found in the incidence of DVT among the pelvic fractures, femoral fractures, and tibial fractures (P < 0.001). The multivariate logistic regression analysis showed multiple injuries (OR = 2.210, 95% CI: 1.166–4.187, P = 0.015), fracture site (compared with tibia fracture group, femur fracture group OR = 4.839, 95% CI: 2.688–8.711, P < 0.001; pelvic fracture group OR = 2.210, 95% CI: 1.225–3.988, P = 0.008), and Autar score (OR = 1.198, 95% CI: 1.016–1.353, P = 0.004) were independent risk factors for DVT in patients with pelvic or lower-extremity fractures in the EICU. The area under the ROC curve (AUROC) of the Autar score for predicting DVT was 0.606. When the Autar score was set as the cutoff value of 15.5, the sensitivity and specificity for predicting DVT in patients with pelvic or lower-extremity fractures were 45.1% and 70.7%, respectively.DiscussionFracture is a high-risk factor for DVT. Patients with a femoral fracture or multiple injuries have a higher risk of DVT. In the case of no contraindications, DVT prevention measures should be taken for patients with pelvic or lower-extremity fractures. Autar scale has a certain predictive value for the occurrence of DVT in patients with pelvic or lower-extremity fractures, but it is not ideal

An epigraphene platform for coherent 1D nanoelectronics

Exceptional edge state ballistic transport, first observed in graphene nanoribbons grown on the sidewalls of trenches etched in electronics grade silicon carbide even at room temperature, is shown here to manifest in micron scale epigraphene structures that are conventionally patterned on single crystal silicon carbide substrates. Electronic transport is dominated by a single electronic mode, in which electrons travel large distances without scattering, much like photons in an optical fiber. In addition, robust quantum coherence, non-local transport, and a ground state with half a conductance quantum are also observed. These properties are explained in terms of a ballistic edge state that is pinned at zero energy. The epigraphene platform allows interconnected nanostructures to be patterned, using standard microelectronics methods, to produce phase coherent 1D ballistic networks. This discovery is unique, providing the first feasible route to large scale quantum coherent graphene nanoelectronics, and a possible inroad towards quantum computing

Protected transport in the epigraphene edge state

The graphene edge state has long been predicted to be a zero energy, one-dimensional electronic waveguide mode that dominates transport in neutral graphene nanostructures, with potential application to graphene devices. However, its exceptional properties have been observed in only a few cases, each employing novel fabrication methods without a clear path to large-scale integration. We show here that interconnected edge-state networks can be produced using non-conventional facets of electronics grade silicon carbide wafers and scalable lithography, which cuts the epitaxial graphene and apparently fuses its edge atoms to the silicon carbide substrate. Measured epigraphene edge state (EGES) conduction is ballistic with mean free paths exceeding tens of microns, thousands of times greater than for the diffusive 2D bulk. It is essentially independent of temperature, decoupled from the bulk and substantially immune to disorder. Remarkably, EGES transport involves a non-degenerate conductance channel that is pinned at zero energy, yet it does not generate a Hall voltage, implying balanced electron and hole components. These properties, observed at all tested temperatures, magnetic fields, and charge densities, are not predicted by present theories, and point to a zero-energy spin one-half quasiparticle, composed of half an electron and a half a hole moving in opposite directions