Joint Deep Modeling of Users and Items Using Reviews for Recommendation

A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of recommendations. In this paper, we present a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. One of the networks focuses on learning user behaviors exploiting reviews written by the user, and the other one learns item properties from the reviews written for the item. A shared layer is introduced on the top to couple these two networks together. The shared layer enables latent factors learned for users and items to interact with each other in a manner similar to factorization machine techniques. Experimental results demonstrate that DeepCoNN significantly outperforms all baseline recommender systems on a variety of datasets.Comment: WSDM 201

Filaggrin gene defects and risk of developing allergic sensitisation and allergic disorders: systematic review and meta-analysis

Objective To investigate whether filaggrin gene defects, present in up to one in 10 western Europeans and North Americans, increase the risk of developing allergic sensitisation and allergic disorders

Neural Collaborative Filtering

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -- collaborative filtering -- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.Comment: 10 pages, 7 figure

The Patterns of High-Level Magnetic Activity Occurring on the Surface of V1285 Aql: The OPEA Model of Flares and DFT Models of Stellar Spots

Statistically analyzing Johnson UBVR observations of V1285 Aql during the three observing seasons, both activity level and behavior of the star are discussed in respect to obtained results. We also discuss the out-of-flare variation due to rotational modulation. Eighty-three flares were detected in the U-band observations of season 2006 . First, depending on statistical analyses using the independent samples t-test, the flares were divided into two classes as the fast and the slow flares. According to the results of the test, there is a difference of about 73 s between the flare-equivalent durations of slow and fast flares. The difference should be the difference mentioned in the theoretical models. Second, using the one-phase exponential association function, the distribution of the flare-equivalent durations versus the flare total durations was modeled. Analyzing the model, some parameters such as plateau, half-life values, mean average of the flare-equivalent durations, maximum flare rise, and total duration times are derived. The plateau value, which is an indicator of the saturation level of white-light flares, was derived as 2.421{\pm}0.058 s in this model, while half-life is computed as 201 s. Analyses showed that observed maximum value of flare total duration is 4641 s, while observed maximum flare rise time is 1817 s. According to these results, although computed energies of the flares occurring on the surface of V1285 Aql are generally lower than those of other stars, the length of its flaring loop can be higher than those of more active stars.Comment: 44 pages, 10 figures, 5 tables, 2011PASP..123..659

SchNet - a deep learning architecture for molecules and materials

Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning in general and deep learning in particular is ideally suited for representing quantum-mechanical interactions, enabling to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for \emph{molecules and materials} where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study of the quantum-mechanical properties of C$_{20}$-fullerene that would have been infeasible with regular ab initio molecular dynamics

Neonatal Blood Methylation Marks Associated with Obstetric Pain Relief

The placenta, responsible for intrauterine development, can facilitate modifications within the placental epigenome in response to changes in the mother. In turn these changes have the potential to also influence the neonate1. Pain relief during delivery is widely used and frequently involves the use of nitrous oxide (N2O, commonly referred to as laughing gas), and pudendal blocks. These treatments, alone or in combination, are generally accepted as safe methods of providing pain relief to mothers. However, laughing gas and local anesthetics such as the ones used during pudendal blocks have been known to cross the placental barrier from mother to child2,3. Furthermore, although current literature about the effects of laughing gas and pudendal blocks on the epigenome, when used as maternal pain relief, is very limited, some evidence implicates effects of obstetric anesthesia on the neonatal methylome2,4,5. Thus, it is reasonable to hypothesize that obstetric pain relief administered to the mother during childbirth may affect the methylome of the child. In conclusion, we detected methylome-wide significantly associated loci for laughing gas and pudendal block treatment when studied in combination, but not for either of the treatments separately.https://scholarscompass.vcu.edu/uresposters/1421/thumbnail.jp

Post-Mortem Brain Nuclei Isolation for Single Nucleus RNA Sequencing

Abstract Post-Mortem Brain Nuclei Isolation for Single Nucleus RNA Sequencing Charles Tran, Dept. of Biology, with Dr. Karolina Aberg, VCU School of Pharmacy When tissue samples are studied in bulk without consideration for different cell proportions and types, results can be biased due to the attenuation of unique cellular expressions. In order to study cell type specific RNA expression profiles within tissue, single cell RNA sequencing (scRNA-seq) is used. For scRNA-seq studies it is critical to have intact cells. However, when investigating frozen post-mortem brain tissue, it is often challenging to isolate intact whole cells. An alternative solution is to instead isolate nuclei (which have similar, but not identical, transcriptomes to cells) and then perform single-nucleus RNA sequencing (snRNA-seq). In this study we have carefully optimized a protocol for nuclei extraction from post-mortem brain cells suitable for downstream snRNA-seq analysis. We found that adjusting our protocol to include less aggressive methods of tissue homogenization and sample-retaining lab techniques has resulted in the successful removal of cell debris and myelin alongside providing a workable sample size. In conclusion we have successfully evaluated and prepared enough high-quality nuclei for downstream scRNA-seq using our optimized protocol.https://scholarscompass.vcu.edu/uresposters/1398/thumbnail.jp

Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms

Neural network based models for collaborative filtering have started to gain attention recently. One branch of research is based on using deep generative models to model user preferences where variational autoencoders were shown to produce state-of-the-art results. However, there are some potentially problematic characteristics of the current variational autoencoder for CF. The first is the too simplistic prior that VAEs incorporate for learning the latent representations of user preference. The other is the model's inability to learn deeper representations with more than one hidden layer for each network. Our goal is to incorporate appropriate techniques to mitigate the aforementioned problems of variational autoencoder CF and further improve the recommendation performance. Our work is the first to apply flexible priors to collaborative filtering and show that simple priors (in original VAEs) may be too restrictive to fully model user preferences and setting a more flexible prior gives significant gains. We experiment with the VampPrior, originally proposed for image generation, to examine the effect of flexible priors in CF. We also show that VampPriors coupled with gating mechanisms outperform SOTA results including the Variational Autoencoder for Collaborative Filtering by meaningful margins on 2 popular benchmark datasets (MovieLens & Netflix)

X-Ray Spectroscopy of II Pegasi: Coronal Temperature Structure, Abundances, and Variability

We have obtained high resolution X-ray spectra of the coronally active binary, II Pegasi (HD 224085), covering the wavelength range of 1.5-25 Angstroms. For the first half of our 44 ksec observation, the source was in a quiescent state with constant X-ray flux, after which it flared, reaching twice the quiescent flux in 12 ksec, then decreasing. We analyze the emission-line spectrum and continuum during quiescent and flaring states. The differential emission measure derived from lines fluxes shows a hot corona with a continuous distribution in temperature. During the non-flare state, the distribution peaks near log T = 7.2, and when flaring, near 7.6. High-temperature lines are enhanced slightly during the flare, but most of the change occurs in the continuum. Coronal abundance anomalies are apparent, with iron very deficient relative to oxygen and significantly weaker than expected from photospheric measurements, while neon is enhanced relative to oxygen. We find no evidence of appreciable resonant scattering optical depth in line ratios of iron and oxygen. The flare light curve is consistent with Solar two-ribbon flare models, but with a very long reconnection time-constant of about 65 ks. We infer loop lengths of about 0.05 stellar radii, to about 0.25 in the flare, if the flare emission originated from a single, low-density loop.Comment: 25 pages, 5 figures, 3 tables, accepted by ApJ (scheduled for the v559 n2 p1 Oct 1, 2001 issue