Computational drug repositioning aims to discover new therapeutic diseases for marketed drugs and has the advantages of low cost, short development cycle, and high controllability compared to traditional drug development. The matrix factorization mod
el has become a mainstream cornerstone technique for computational drug repositioning due to its ease of implementation and excellent scalability. However, the matrix factorization model uses the inner product operation to represent the association between drugs and diseases, which is lacking in expressive ability. Moreover, the degree of similarity of drugs or diseases could not be implied on their respective latent factor vectors, which is not satisfy the common sense of drug discovery. Therefore, a neural metric factorization model for computational drug repositioning is proposed in this work. We novelly consider the latent factor vector of drugs and diseases as a point in a high-dimensional coordinate system and propose a generalized Euclidean distance to represent the association between drugs and diseases to compensate for the shortcomings of the inner product operation. Furthermore, by embedding multiple drug and disease metrics information into the encoding space of the latent factor vector, the latent factor vectors of similar drugs or diseases are made closer. Finally, we conduct wide analysis experiments on two real datasets to demonstrate the effectiveness of the above improvement points and the superiority of the NMF model.
Quantifying the heterogeneity is an important issue in meta-analysis, and among the existing measures, the $I^2$ statistic is the most commonly used measure in the literature. In this paper, we show that the $I^2$ statistic was, in fact, defined as p
roblematic or even completely wrong from the very beginning. To confirm this statement, we first present a motivating example to show that the $I^2$ statistic is heavily dependent on the study sample sizes, and consequently it may yield contradictory results for the amount of heterogeneity. Moreover, by drawing a connection between ANOVA and meta-analysis, the $I^2$ statistic is shown to have, mistakenly, applied the sampling errors of the estimators rather than the variances of the study populations. Inspired by this, we introduce an Intrinsic measure for Quantifying the heterogeneity in meta-analysis, and meanwhile study its statistical properties to clarify why it is superior to the existing measures. We further propose an optimal estimator, referred to as the IQ statistic, for the new measure of heterogeneity that can be readily applied in meta-analysis. Simulations and real data analysis demonstrate that the IQ statistic provides a nearly unbiased estimate of the true heterogeneity and it is also independent of the study sample sizes.
Modern polarization theory yields surface bound charge associated with spontaneous polarization of bulk. However, understanding polarization in nano systems also requires a proper treatment of charge transfer between surface dangling bonds. Here, we
develop a real-space approach for total polarization and apply it to wurtzite semiconductors and BaTiO3 perovskite. First-principles calculations utilizing this approach not only yield spontaneous bulk polarization in agreement with Berry phase calculations, but also uncover phenomena specific to nano systems. As an example, we show surface passivation leads to a complete quenching of the piezoelectric effect, which reemerges only at larger length scale and/or spontaneous polarization.
Recent advances have enabled a single neural network to serve as an implicit scene representation, establishing the mapping function between spatial coordinates and scene properties. In this paper, we make a further step towards continual learning of
the implicit scene representation directly from sequential observations, namely Continual Neural Mapping. The proposed problem setting bridges the gap between batch-trained implicit neural representations and commonly used streaming data in robotics and vision communities. We introduce an experience replay approach to tackle an exemplary task of continual neural mapping: approximating a continuous signed distance function (SDF) from sequential depth images as a scene geometry representation. We show for the first time that a single network can represent scene geometry over time continually without catastrophic forgetting, while achieving promising trade-offs between accuracy and efficiency.
Two-dimensional (2D) ferromagnets have recently drawn extensive attention, and here we study the electronic structure and magnetic properties of the bulk and monolayer of CrSBr, using first-principles calculations and Monte Carlo simulations. Our res
ults show that bulk CrSBr is a magnetic semiconductor and has the easy magnetization b-axis, hard c-axis, and intermediate a-axis. Thus, the experimental triaxial magnetic anisotropy (MA) is well reproduced here, and it is identified to be the joint effects of spin-orbit coupling (SOC) and magnetic dipole-dipole interaction. We find that bulk CrSBr has a strong ferromagnetic (FM) intralayer coupling but a marginal interlayer one. We also study CrSBr monolayer in detail and find that the intralayer FM exchange persists and the shape anisotropy has a more pronounced contribution to the MA. Using the parameters of the FM exchange and the triaxial MA, our Monte Carlo simulations show that CrSBr monolayer has Curie temperature Tc = 175 K. Moreover, we find that a uniaxial tensile (compressive) strain along the a (b) axis would further increase the Tc.
The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1), making the DNNs-based systems collapsed byadding the inconspicuous perturbations to the images. Most of the existing works for adversarial attack are gradient-based and suf-fe
r from the latency efficiencies and the load on GPU memory. Thegenerative-based adversarial attacks can get rid of this limitation,and some relative works propose the approaches based on GAN.However, suffering from the difficulty of the convergence of train-ing a GAN, the adversarial examples have either bad attack abilityor bad visual quality. In this work, we find that the discriminatorcould be not necessary for generative-based adversarial attack, andpropose theSymmetric Saliency-based Auto-Encoder (SSAE)to generate the perturbations, which is composed of the saliencymap module and the angle-norm disentanglement of the featuresmodule. The advantage of our proposed method lies in that it is notdepending on discriminator, and uses the generative saliency map to pay more attention to label-relevant regions. The extensive exper-iments among the various tasks, datasets, and models demonstratethat the adversarial examples generated by SSAE not only make thewidely-used models collapse, but also achieves good visual quality.The code is available at https://github.com/BravoLu/SSAE.
Cobaltates have rich spin-states and diverse properties. Using spin-state pictures and firstprinciples calculations, here we study the electronic structure and magnetism of the mixed-valent double perovskite YBaCo2O6. We find that YBaCo2O6 is in the
formal intermediate-spin (IS) Co3+/low-spin (LS) Co4+ ground state. The hopping of eg electron from IS-Co3+ to LS-Co4+ via double exchange gives rise to a ferromagnetic half-metallicity, which well accounts for the recent experiments. The reduction of both magnetization and Curie temperature by oxygen vacancies is discussed, aided with Monte Carlo simulations. We also explore several other possible spin-states and their interesting electronic/magnetic properties. Moreover, we predict that a volume expansion more than 3% would tune YBaCo2O6 into the high-spin (HS) Co3+/LS Co4+ ferromagnetic state and simultaneously drive a metal-insulator transition. Therefore, spin-states are a useful parameter for tuning the material properties of cobaltates.
Two-dimensional ferromagnetic (2D FM) half-metal holds great potential for quantum magnetoelectronics and spintronic devices. Here, using density functional calculations and magnetic pictures, we study the electronic structure and magnetic properties
of the novel van der Waals (vdW) metal-organic framework (MOF), CrCl2(N2C4H4)2, i.e. CrCl2(pyrazine)2. Our results show that CrCl2(pyrazine)2 is a 2D FM half-metal, having a strong intralayer FM coupling but a much weak interlayer one due to the vdW spacing. Its spin-polarized conduction bands are formed by the pyrazine molecular orbitals and are polarized by the robust Cr3+ local spin = 3/2. These results agree with the recent experiments [Pedersen et al., Nature Chemistry, 2018, 10, 1056]. More interestingly, CrCl2(pyrazine)2 monolayer has a strong doping tunability of the FM half-metallicity, and the FM coupling would be significantly enhanced by electron doping. Our work highlights a vital role of the organic ligand and suggests that vdW MOF is also worth exploration for new 2D magnetic materials.
We introduce an information criterion, PCIC, for predictive evaluation based on quasi-posterior distributions. It is regarded as a natural generalisation of the widely applicable information criterion (WAIC) and can be computed via a single Markov ch
ain Monte Carlo run. PCIC is useful in a variety of predictive settings that are not well dealt with in WAIC, including weighted likelihood inference and quasi-Bayesian prediction
Nowadays, Graph Neural Networks (GNNs) following the Message Passing paradigm become the dominant way to learn on graphic data. Models in this paradigm have to spend extra space to look up adjacent nodes with adjacency matrices and extra time to aggr
egate multiple messages from adjacent nodes. To address this issue, we develop a method called LinkDist that distils self-knowledge from connected node pairs into a Multi-Layer Perceptron (MLP) without the need to aggregate messages. Experiment with 8 real-world datasets shows the MLP derived from LinkDist can predict the label of a node without knowing its adjacencies but achieve comparable accuracy against GNNs in the contexts of semi- and full-supervised node classification. Moreover, LinkDist benefits from its Non-Message Passing paradigm that we can also distil self-knowledge from arbitrarily sampled node pairs in a contrastive way to further boost the performance of LinkDist.
