Joint Neural Collaborative Filtering for Recommender Systems

We propose a J-NCF method for recommender systems. The J-NCF model applies a joint neural network that couples deep feature learning and deep interaction modeling with a rating matrix. Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix. Deep interaction modeling captures non-linear user-item interactions with a deep neural network using the feature representations generated by the deep feature learning process as input. J-NCF enables the deep feature learning and deep interaction modeling processes to optimize each other through joint training, which leads to improved recommendation performance. In addition, we design a new loss function for optimization, which takes both implicit and explicit feedback, point-wise and pair-wise loss into account. Experiments on several real-word datasets show significant improvements of J-NCF over state-of-the-art methods, with improvements of up to 8.24% on the MovieLens 100K dataset, 10.81% on the MovieLens 1M dataset, and 10.21% on the Amazon Movies dataset in terms of HR@10. NDCG@10 improvements are 12.42%, 14.24% and 15.06%, respectively. We also conduct experiments to evaluate the scalability and sensitivity of J-NCF. Our experiments show that the J-NCF model has a competitive recommendation performance with inactive users and different degrees of data sparsity when compared to state-of-the-art baselines.Comment: 30 page

A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

Generating multi-contrasts/modal MRI of the same anatomy enriches diagnostic information but is limited in practice due to excessive data acquisition time. In this paper, we propose a novel deep-learning model for joint reconstruction and synthesis of multi-modal MRI using incomplete k-space data of several source modalities as inputs. The output of our model includes reconstructed images of the source modalities and high-quality image synthesized in the target modality. Our proposed model is formulated as a variational problem that leverages several learnable modality-specific feature extractors and a multimodal synthesis module. We propose a learnable optimization algorithm to solve this model, which induces a multi-phase network whose parameters can be trained using multi-modal MRI data. Moreover, a bilevel-optimization framework is employed for robust parameter training. We demonstrate the effectiveness of our approach using extensive numerical experiments.Comment: 12 page

Improving End-to-End Sequential Recommendations with Intent-aware Diversification

Sequential Recommendation (SRs) that capture users' dynamic intents by modeling user sequential behaviors can recommend closely accurate products to users. Previous work on SRs is mostly focused on optimizing the recommendation accuracy, often ignoring the recommendation diversity, even though it is an important criterion for evaluating the recommendation performance. Most existing methods for improving the diversity of recommendations are not ideally applicable for SRs because they assume that user intents are static and rely on post-processing the list of recommendations to promote diversity. We consider both recommendation accuracy and diversity for SRs by proposing an end-to-end neural model, called Intent-aware Diversified Sequential Recommendation (IDSR). Specifically, we introduce an Implicit Intent Mining module (IIM) into SRs to capture different user intents reflected in user behavior sequences. Then, we design an Intent-aware Diversity Promoting (IDP) loss to supervise the learning of the IIM module and force the model to take recommendation diversity into consideration during training. Extensive experiments on two benchmark datasets show that IDSR significantly outperforms state-of-the-art methods in terms of recommendation diversity while yielding comparable or superior recommendation accuracy

Microencapsulated 3-Dimensional Sensor for the Measurement of Oxygen in Single Isolated Pancreatic Islets

Background: Oxygen consumption reflects multiple processes in pancreatic islets including mechanisms contributing to insulin secretion, oxidative stress and viability, providing an important readout in studies of islet function, islet viability and drug testing. Due to the scarcity, heterogeneity, and intrinsic kinetic properties of individual islets, it would be of great benefit to detect oxygen consumption by single islets. We present a novel method we have developed to image oxygen in single islets. Methodology/Principal Findings: Using a microfluidics system, individual islets and a fluorescent oxygen-sensitive dye were encased within a thin alginate polymer layer. Insulin secretion by the encapsulated islets was normal. Fluorescent signal from the encased dye, detected using a standard inverted fluorescence microscope and digital camera, was stable and proportional to the amount of oxygen in the media. When integrated into a perifusion system, the sensing system detected changes in response to metabolic substrates, mitochondrial poisons, and induced-oscillations. Glucose responses averaged 30.167.1 % of the response to a metabolic inhibitor (cyanide), increases were observed in all cases (n = 6), and the system was able to resolve changes in oxygen consumption that had a period greater than 0.5 minutes. The sensing system operated similarly from 2–48 hours following encapsulation, and viability and function of the islets were not significantly affected by the encapsulation process

Vehicle currency pricing and exchange rate pass-through

Using detailed firm-level transactions data for UK imports, we find that invoicing in a vehicle currency is pervasive, with more than half of transactions in our sample invoiced in neither sterling nor the exporter's currency. We then study the relationship between invoicing currency choices and the response of import prices to exchange rate changes. We find that for transactions invoiced in a vehicle currency, import prices are much more sensitive to changes in the vehicle currency than in the bilateral exchange rate. Pass-through therefore substantially increases once we account for vehicle currencies. Our results help to explain the higher-thanexpected pass-through into import prices during the Great Recession and after the EU referendum. Finally, within a theoretical framework we conceptualize an omitted variable bias arising in estimating pass-through with only bilateral exchange rates under vehicle currency pricing. Overall, our results contribute to understanding the disconnect between exchange rates and prices

The Influence of an EPS Concrete Buffer Layer Thickness on Debris Dams Impacted by Massive Stones in the Debris Flow

The failure of debris dams impacted by the massive stones in a debris flow represents a difficult design problem. Reasonable materials selection and structural design can effectively improve the resistance impact performance of debris dams. Based on the cushioning properties of expanded polystyrene (EPS) concrete, EPS concrete as a buffer layer poured on the surface of a rigid debris dam was proposed. A three-dimensional numerical calculation model of an EPS concrete buffer layer/rigid debris dam was established. The single-factor theory revealed change rules for the thickness of the buffer layer concerning the maximal impact force of the rigid debris dam surface through numerical simulation. Moreover, the impact force-time/history curves under different calculation conditions for the rigid debris dam surface were compared. Simulation results showed that the EPS concrete buffer layer can not only effectively extend the impact time of massive stones affecting the debris dam but also reduce the impact force of the rigid debris dam caused by massive stones in the debris flow. The research results provide theoretical guidance for transferring the energy of the massive stone impact, creating a structural design and optimizing debris dams

IL28B is associated with outcomes of chronic HBV infection

Purpose The role of IL28B gene variants and expression in hepatitis B virus (HBV) infections are not well understood. Here, we evaluated whether IL28B gene expression and rs12979860 variations are associated with HBV outcomes. Materials and Methods IL28B genetic variations (rs12979860) were genotyped by pyrosequencing of DNA samples from 137 individuals with chronic HBV infection [50 inactive carriers (IC), 34 chronic hepatitis B (CHB), 27 cirrhosis, 26 hepatocellular carcinoma (HCC)], and 19 healthy controls. IL28A/B mRNA expression in peripheral blood mononuclear cells was determined by qRT-PCR, and serum IL28B protein was measured by ELISA. Results Patients with IL28B C/C genotype had greater IL28A/B mRNA expression and higher IL28B protein levels than C/T patients. Within the various disease stages, compared to IC and healthy controls, IL28B expression was reduced in the CHB, cirrhosis, and HCC cohorts (CHB vs. IC, p=0.02; cirrhosis vs. IC, p=0.01; HCC vs. IC, p=0.001; CHB vs. controls, p&#60;0.01; cirrhosis vs. controls, p&#60;0.01; HCC vs. controls, p&#60;0.01). When stratified with respect to serum HBV markers in the IC and CHB cohorts, IL28B mRNA and protein levels were higher in HBeAg-positive than negative individuals (p=0.01). Logistic regression analysis revealed that factors associated with high IL28B protein levels were C/C versus C/T genotype [p=0.016, odds ratio (OR)=0.25, 95% confidence interval (CI)=0.08-0.78], high alanine aminotransferase values (p&#60;0.001, OR=8.02, 95% CI=2.64-24.4), and the IC stage of HBV infection (p&#60;0.001). Conclusion Our data suggest that IL28B genetic variations may play an important role in long-term development of disease in chronic HBV infections.</p

Modeling and model calibration for model predictive occupants comfort control in buildings

Mathematical models are essential in Model-Predictive Control (MPC) for building automation and control (BAC) application, which must be precise and computationally efficient for realtime optimization and control. However, building models are of high complexity because of the nonlinearities of heat and mass transfer processes in buildings and their air-conditioning and mechanical ventilation (ACMV) systems. This paper proposes a method to develop an integrated linear model for indoor air temperature, humidity and Predicted Mean Vote (PMV) index suitable for fast real-time multiple objectives optimization. A linear dynamic model is developed using SIMSCAPE language based on the BCA SkyLab test bed facility in Singapore as a case study. Experimental data is used to calibrate the model using trust-region-reflective least squares optimization method. The results show that the mean absolute percentage errors (MAPE) of predicted room temperature and humidity ratio are 1.25% and 4.98%, compared to measurement, respectively

An efficient low-density grating setup for monochromatization of XUV ultrafast light sources

Ultrafast light sources have become an indispensable tool to access and understand transient phenomenon in material science. However, a simple and easy-to-implement method for harmonic selection, with high transmission efficiency and pulse duration conservation, is still a challenge. Here we showcase and compare two approaches for selecting the desired harmonic from a high harmonic generation source while achieving the above goals. The first approach is the combination of extreme ultraviolet spherical mirrors with transmission filters and the second approach uses a normal-incidence spherical grating. Both solutions target time- and angle-resolved photoemission spectroscopy with photon energies in the 10-20 eV range but are relevant for other experimental techniques as well. The two approaches for harmonic selection are characterized in terms of focusing quality, efficiency, and temporal broadening. It is demonstrated that a focusing grating is able to provide much higher transmission as compared to the mirror+filter approach (3.3 times higher for 10.8 eV and 12.9 times higher for 18.1 eV), with only a slight temporal broadening (6.8% increase) and a somewhat larger spot size (∼30% increase). Overall, our study establishes an experimental perspective on the trade-off between a single grating normal incidence monochromator design and the use of filters. As such, it provides a basis for selecting the most appropriate approach in various fields where an easy-to-implement harmonic selection from high harmonic generation is needed