SAIN: Self-Attentive Integration Network for Recommendation

With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where user-item interactions are sparse. To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed. Since these models use auxiliary attributes, they are effective in cold start settings. However, most of the proposed models are either unable to capture complex feature interactions or not properly designed to combine user-item feedback information with content information. In this paper, we propose Self-Attentive Integration Network (SAIN) which is a model that effectively combines user-item feedback information and auxiliary information for recommendation task. In SAIN, a self-attention mechanism is used in the feature-level interaction layer to effectively consider interactions between multiple features, while the information integration layer adaptively combines content and feedback information. The experimental results on two public datasets show that our model outperforms the state-of-the-art models by 2.13%Comment: SIGIR 201

Look at the First Sentence: Position Bias in Question Answering

Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity and effectiveness. In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions. We first illustrate this position bias in popular extractive QA models such as BiDAF and BERT and thoroughly examine how position bias propagates through each layer of BERT. To safely deliver position information without position bias, we train models with various de-biasing methods including entropy regularization and bias ensembling. Among them, we found that using the prior distribution of answer positions as a bias model is very effective at reducing position bias, recovering the performance of BERT from 37.48% to 81.64% when trained on a biased SQuAD dataset.Comment: 13 pages, EMNLP 202

A cancer-associated BRCA2 mutation reveals masked nuclear export signals controlling localization.

Germline missense mutations affecting a single BRCA2 allele predispose humans to cancer. Here we identify a protein-targeting mechanism that is disrupted by the cancer-associated mutation, BRCA2(D2723H), and that controls the nuclear localization of BRCA2 and its cargo, the recombination enzyme RAD51. A nuclear export signal (NES) in BRCA2 is masked by its interaction with a partner protein, DSS1, such that point mutations impairing BRCA2-DSS1 binding render BRCA2 cytoplasmic. In turn, cytoplasmic mislocalization of mutant BRCA2 inhibits the nuclear retention of RAD51 by exposing a similar NES in RAD51 that is usually obscured by the BRCA2-RAD51 interaction. Thus, a series of NES-masking interactions localizes BRCA2 and RAD51 in the nucleus. Notably, BRCA2(D2723H) decreases RAD51 nuclear retention even when wild-type BRCA2 is also present. Our findings suggest a mechanism for the regulation of the nucleocytoplasmic distribution of BRCA2 and RAD51 and its impairment by a heterozygous disease-associated mutation

Tuning orbital-selective phase transitions in a two-dimensional Hund's correlated system

Hund's rule coupling ($\textit{J}$) has attracted much attention recently for its role in the description of the novel quantum phases of multi orbital materials. Depending on the orbital occupancy, $\textit{J}$ can lead to various intriguing phases. However, experimental confirmation of the orbital occupancy dependency has been difficult as controlling the orbital degrees of freedom normally accompanies chemical inhomogeneities. Here, we demonstrate a method to investigate the role of orbital occupancy in $\textit{J}$ related phenomena without inducing inhomogeneities. By growing SrRuO$_3$ monolayers on various substrates with symmetry-preserving interlayers, we gradually tune the crystal field splitting and thus the orbital degeneracy of the Ru \textit{t_2_g$}$ orbitals. It effectively varies the orbital occupancies of two-dimensional (2D) ruthenates. Via in-situ angle-resolved photoemission spectroscopy, we observe a progressive metal-insulator transition (MIT). It is found that the MIT occurs with orbital differentiation: concurrent opening of a band insulating gap in the $\textit{d$_x_y} band and a Mott gap in the \textit{d_x$$_z$$_/$$_y$$_z} bands. Our study provides an effective experimental method for investigation of orbital-selective phenomena in multi-orbital materials

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: the Multi-Targeting Drug DREAM Challenge

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets (‘polypharmacology’). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology

Beyond Fact Verification: Comparing and Contrasting Claims on Contentious Topics

As the importance of identifying misinformation is increasing, many researchers focus on verifying textual claims on the web. One of the most popular tasks to achieve this is fact verification, which retrieves an evidence sentence from a large knowledge source such as Wikipedia to either verify or refute each factual claim. However, while such problem formulation is helpful for detecting false claims and fake news, it is not applicable to catching subtle differences in factually consistent claims which still might implicitly bias the readers, especially in contentious topics such as political, gender, or racial issues. In this study, we propose ClaimDiff, a novel dataset to compare the nuance between claim pairs in both a discriminative and a generative manner, with the underlying assumption that one is not necessarily more true than the other. This differs from existing fact verification datasets that verify the target sentence with respect to an absolute truth. We hope this task assists people in making more informed decisions among various sources of media

Nanoscale Enhancement of the Local Optical Conductivity near Cracks in Metallic SrRuO3 Film

Cracking has been recognized as a major obstacle degrading material properties, including structural stability, electrical conductivity, and thermal conductivity. Recently, there have been several reports on the nanosized cracks (nanocracks), particularly in the insulating oxides. In this work, we comprehensively investigate how nanocracks affect the physical properties of metallic SrRuO3 (SRO) thin films. We grow SRO/SrTiO3 (STO) bilayers on KTaO3 (KTO) (001) substrates, which provide +1.7% tensile strain if the SRO layer is grown epitaxially. However, the SRO/STO bilayers suffer from the generation and propagation of nanocracks, and then, the strain becomes inhomogeneously relaxed. As the thickness increases, the nanocracks in the SRO layer become percolated, and its dc conductivity approaches zero. Notably, we observe an enhancement of the local optical conductivity near the nanocrack region using scanning-type near-field optical microscopy. This enhancement is attributed to the strain relaxation near the nanocracks. Our work indicates that nanocracks can be utilized as promising platforms for investigating local emergent phenomena related to strain effects.11Nsciescopu