Search

The new-found two-dimensional antiferromagnetic GdTe$_3$ is attractive owing to its highest carrier mobility among all known layered magnetic materials, as well as its potential application for novel magnetic twistronic and spintronic devices. Here, we have used high-resolution angle-resolved photoemission spectroscopy to investigate its Fermi surface topology and low-lying electronic band structure. The Fermi surface is partially gapped by charge density wave below the transition temperature, the residual part reconstructs making GdTe$_3$ metallic. The high carrier mobility can be attributed to the sharp and nearly linear band dispersions near the Fermi energy. We find that the scattering rate of the linear band near the Fermi energy is almost linear within a wide energy range, indicating that GdTe$_3$ is a non-Fermi liquid metal. Our results in this paper provide a fundamental understanding of this layered Van der Waals antiferromagnetic materials to guide future studies on it.

The knockoff filter is a recent false discovery rate (FDR) control method for high-dimensional linear models. We point out that knockoff has three key components: ranking algorithm, augmented design, and symmetric statistic, and each component admits multiple choices. By considering various combinations of the three components, we obtain a collection of variants of knockoff. All these variants guarantee finite-sample FDR control, and our goal is to compare their power. We assume a Rare and Weak signal model on regression coefficients and compare the power of different variants of knockoff by deriving explicit formulas of false positive rate and false negative rate. Our results provide new insights on how to improve power when controlling FDR at a targeted level. We also compare the power of knockoff with its propotype - a method that uses the same ranking algorithm but has access to an ideal threshold. The comparison reveals the additional price one pays by finding a data-driven threshold to control FDR., Comment: 67 pages, 13 figures

Deep neural networks (DNNs) have become increasingly popular and achieved outstanding performance in predictive tasks. However, the DNN framework itself cannot inform the user which features are more or less relevant for making the prediction, which limits its applicability in many scientific fields. We introduce neural Gaussian mirrors (NGMs), in which mirrored features are created, via a structured perturbation based on a kernel-based conditional dependence measure, to help evaluate feature importance. We design two modifications of the DNN architecture for incorporating mirrored features and providing mirror statistics to measure feature importance. As shown in simulated and real data examples, the proposed method controls the feature selection error rate at a predefined level and maintains a high selection power even with the presence of highly correlated features.

As a sub-array of the Large High Altitude Air Shower Observatory (LHAASO), KM2A is mainly designed to cover a large fraction of the northern sky to hunt for gamma-ray sources at energies above 10 TeV. Even though the detector construction is still underway, a half of the KM2A array has been operating stably since the end of 2019. In this paper, we present the pipeline of KM2A data analysis and the first observation on the Crab Nebula, a standard candle in very high energy gamma-ray astronomy. We detect gamma-ray signals from the Crab Nebula in both energy ranges of 10$-$100 TeV and $>$100 TeV with high significance, by analyzing the KM2A data of 136 live days between December 2019 and May 2020. With the observations, we test the detector performance including angular resolution, pointing accuracy and cosmic ray background rejection power. The energy spectrum of the Crab Nebula in the energy range 10-250 TeV fits well with a single power-law function dN/dE =(1.13$\pm$0.05$_{stat}$$\pm$0.08$_{sys}$)$\times$10$^{-14}$$\cdot$(E/20TeV)$^{-3.09\pm0.06_{stat}\pm0.02_{sys}}$ cm$^{-2}$ s$^{-1}$ TeV$^{-1}$. It is consistent with previous measurements by other experiments. This opens a new window of gamma-ray astronomy above 0.1 PeV through which ultrahigh-energy gamma-ray new phenomena, such as cosmic PeVatrons, might be discovered., Comment: 13 pages, 15 figures,submitted to CPC

Topological superconductors have long been predicted to host Majorana zero modes which obey non-Abelian statistics and have potential for realizing non-decoherence topological quantum computation. However, material realization of topological superconductors is still a challenge in condensed matter physics. Utilizing high-resolution angle-resolved photoemission spectroscopy and first-principles calculations, we predict and then unveil the coexistence of topological Dirac semimetal and topological insulator states in the vicinity of Fermi energy ($E_F$) in the titanium-based oxypnictide superconductor BaTi$_2$Sb$_2$O. Further spin-resolved measurements confirm its spin-helical surface states around $E_F$, which are topologically protected and give an opportunity for realization of Majorana zero modes and Majorana flat bands in one material. Hosting dual topological superconducting states, the intrinsic superconductor BaTi$_2$Sb$_2$O is expected to be a promising platform for further investigation of topological superconductivity., Comment: 6 pages, 4 figures

A topologically equivalent tight binding model is proposed to study the quantum phase transitions of dimer chain driven by an imaginary ac field. I demonstrate how the partner Hamiltonian is constructed by a similarity transformation to fulfil the $\mathcal{PT}$ symmetry. The $\mathcal{PT}$ symmetry of the partner model allows us to study the topological properties of the original non-Hermitian model as the Bloch bands of the Hermitian system. The quantum phase transitions are discussed in different frequency regime. The approach has the potential applications to investigate the topological states of matter driven by the complex external parameters., Comment: 7 pages, 3 figures

A link between magnetars and fast radio burst (FRB) sources has finally been established. In this context, one of the open issues is whether/which sources of extra galactic FRBs exhibit X/gamma-ray outbursts and whether it is correlated with radio activity. We aim to constrain possible X/gamma-ray burst activity from one of the nearest extragalactic FRB sources currently known over a broad energy range, by looking for bursts over a range of timescales and energies that are compatible with being powerful flares from extragalactic magnetars. We followed up the as-yet nearest extragalactic FRB source at a mere 149 Mpc distance, the periodic repeater FRB180916.J0158+65, during the active phase on February 4-7, 2020, with the Insight-Hard X-ray Modulation Telescope (HXMT). Taking advantage of the combination of broad band, large effective area, and several independent detectors available, we searched for bursts over a set of timescales from 1 ms to 1.024 s with a sensitive algorithm, that had previously been characterised and optimised. Moreover, through simulations we studied the sensitivity of our technique in the released energy-duration phase space for a set of synthetic flares and assuming different energy spectra. We constrain the possible occurrence of flares in the 1-100 keV energy band to E<10^46 erg for durations <0.1 s over several tens of ks exposure. We can rule out the occurrence of giant flares similar to the ones that were observed in the few cases of Galactic magnetars. The absence of reported radio activity during our observations does not allow us to make any statements on the possible simultaneous high-energy emission., Comment: 10 pages, 3 figures, accepted by A&A

We report on the observation of the accreting pulsar GRO J1008-57 performed by Insight-HXMT at the peak of the source's 2017 outburst. Pulsations are detected with a spin period of 93.283(1) s. The pulse profile shows double peaks at soft X-rays, and only one peak above 20 keV. The spectrum is well described by the phenomenological models of X-ray pulsars. A cyclotron resonant scattering feature is detected with very high statistical significance at a centroid energy of $E_{\rm cyc}=90.32_{-0.28}^{+0.32}$ keV, for the reference continuum and line models, HIGHECUT and GABS respectively. Detection is very robust with respect to different continuum models. The line energy is significantly higher than what suggested from previous observations, which provided very marginal evidence for the line. This establishes a new record for the centroid energy of a fundamental cyclotron resonant scattering feature observed in accreting pulsars. We also discuss the accretion regime of the source during the Insight-HXMT observation., Comment: 8 pages, 3 figures, accepted for publication in ApJL

We report on a novel class of nanocrystalline/amorphous Gd$_3$Ni/Gd$_{65}$Ni$_{35}$ composite microwires, which was created directly by melt-extraction through controlled solidification. X-ray diffraction (XRD) and transmission electron microscopy (TEM) confirmed the formation of a biphase nanocrystalline/amorphous structure in these wires. Magnetic and magnetocaloric experiments indicate the large magnetic entropy change (-$\Delta$SM ~9.64 J/kg K) and the large refrigerant capacity (RC ~742.1 J/kg) around the Curie temperature of ~120 K for a field change of 5 T. These values are ~1.5 times larger relative to its bulk counterpart, and are superior to other candidate materials being considered for active magnetic refrigeration in the liquid nitrogen temperature range.

The success of deep learning has inspired recent interests in applying neural networks in statistical inference. In this paper, we investigate the use of deep neural networks for nonparametric regression with measurement errors. We propose an efficient neural network design for estimating measurement error models, in which we use a fully connected feed-forward neural network (FNN) to approximate the regression function $f(x)$, a normalizing flow to approximate the prior distribution of $X$, and an inference network to approximate the posterior distribution of $X$. Our method utilizes recent advances in variational inference for deep neural networks, such as the importance weight autoencoder, doubly reparametrized gradient estimator, and non-linear independent components estimation. We conduct an extensive numerical study to compare the neural network approach with classical nonparametric methods and observe that the neural network approach is more flexible in accommodating different classes of regression functions and performs superior or comparable to the best available method in nearly all settings., Comment: 37 pages, 8 figures

Bi-clustering is a useful approach in analyzing biological data when observations come from heterogeneous groups and have a large number of features. We outline a general Bayesian approach in tackling bi-clustering problems in moderate to high dimensions, and propose three Bayesian bi-clustering models on categorical data, which increase in complexities in their modeling of the distributions of features across bi-clusters. Our proposed methods apply to a wide range of scenarios: from situations where data are cluster-distinguishable only among a small subset of features but masked by a large amount of noise, to situations where different groups of data are identified by different sets of features or data exhibit hierarchical structures. Through simulation studies, we show that our methods outperform existing (bi-)clustering methods in both identifying clusters and recovering feature distributional patterns across bi-clusters. We apply our methods to two genetic datasets, though the area of application of our methods is even broader.

The generalized linear models (GLM) have been widely used in practice to model non-Gaussian response variables. When the number of explanatory features is relatively large, scientific researchers are of interest to perform controlled feature selection in order to simplify the downstream analysis. This paper introduces a new framework for feature selection in GLMs that can achieve false discovery rate (FDR) control in two asymptotic regimes. The key step is to construct a mirror statistic to measure the importance of each feature, which is based upon two (asymptotically) independent estimates of the corresponding true coefficient obtained via either the data-splitting method or the Gaussian mirror method. The FDR control is achieved by taking advantage of the mirror statistic's property that, for any null feature, its sampling distribution is (asymptotically) symmetric about 0. In the moderate-dimensional setting in which the ratio between the dimension (number of features) p and the sample size n converges to a fixed value, we construct the mirror statistic based on the maximum likelihood estimation. In the high-dimensional setting where p is much larger than n, we use the debiased Lasso to build the mirror statistic. Compared to the Benjamini-Hochberg procedure, which crucially relies on the asymptotic normality of the Z statistic, the proposed methodology is scale free as it only hinges on the symmetric property, thus is expected to be more robust in finite-sample cases. Both simulation results and a real data application show that the proposed methods are capable of controlling the FDR, and are often more powerful than existing methods including the Benjamini-Hochberg procedure and the knockoff filter., Comment: 60 pages, 13 pages

To overcome the computational bottleneck of various data perturbation procedures such as the bootstrap and cross validations, we propose the Generative Multiple-purpose Sampler (GMS), which constructs a generator function to produce solutions of weighted M-estimators from a set of given weights and tuning parameters. The GMS is implemented by a single optimization without having to repeatedly evaluate the minimizers of weighted losses, and is thus capable of significantly reducing the computational time. We demonstrate that the GMS framework enables the implementation of various statistical procedures that would be unfeasible in a conventional framework, such as the iterated bootstrap, bootstrapped cross-validation for penalized likelihood, bootstrapped empirical Bayes with nonparametric maximum likelihood, etc. To construct a computationally efficient generator function, we also propose a novel form of neural network called the \emph{weight multiplicative multilayer perceptron} to achieve fast convergence. Our numerical results demonstrate that the new neural network structure enjoys a few orders of magnitude speed advantage in comparison to the conventional one. An R package called GMS is provided, which runs under Pytorch to implement the proposed methods and allows the user to provide a customized loss function to tailor to their own models of interest.

As a principled dimension reduction technique, factor models have been widely adopted in social science, economics, bioinformatics, and many other fields. However, in high-dimensional settings, conducting a 'correct' Bayesianfactor analysis can be subtle since it requires both a careful prescription of the prior distribution and a suitable computational strategy. In particular, we analyze the issues related to the attempt of being "noninformative" for elements of the factor loading matrix, especially for sparse Bayesian factor models in high dimensions, and propose solutions to them. We show here why adopting the orthogonal factor assumption is appropriate and can result in a consistent posterior inference of the loading matrix conditional on the true idiosyncratic variance and the allocation of nonzero elements in the true loading matrix. We also provide an efficient Gibbs sampler to conduct the full posterior inference based on the prior setup from Rockova and George (2016)and a uniform orthogonal factor assumption on the factor matrix.

Fast radio bursts (FRBs) are short pulses observed in radio band from cosmological distances. One class of models invoke soft gamma-ray repeaters (SGRs), or magnetars, as the sources of FRBs. Some radio pulses have been observed from some magnetars, however, no FRB-like events had been detected in association any magnetar burst, including one giant flare. Recently, a pair of FRB-like bursts (FRB 200428 hereafter) separated by milliseconds (ms) were detected from the general direction of the Galactic magnetar SGR J1935+2154. Here we report the detection of a non-thermal X-ray burst in the 1-250 keV energy band with the Insight-HXMT satellite, which we identify as emitted from SGR J1935+2154. The burst showed two hard peaks with a separation of 34 ms, broadly consistent with that of the two bursts in FRB 200428. The delay time between the double radio and X-ray peaks is about 8.57 s, fully consistent with the dispersion delay of FRB 200428. We thus identify the non-thermal X-ray burst is associated with FRB 200428 whose high energy counterpart is the two hard peaks in X-ray. Our results suggest that the non-thermal X-ray burst and FRB 200428 share the same physical origin in an explosive event from SGR J1935+2154., Comment: 24 pages, 9 figures, 6 tables; initial submission to a journal on May 9th, 2020. Significant changes include updated localization and detailed spectral evolution of the X-ray burst, and better determination of the two narrow X-ray peaks corresponding to the two radio pulses. Conclusions are strengthened. Nature Astronomy online on Feb. 18, 2021

Hydrogen (H) induced damage in metals has been a long-standing woe for many industrial applications. One form of such damage is linked to H clustering, for which the atomic origin remains contended, particularly for non-hydride forming metals. In this work, we systematically studied H clustering behavior in bcc metals represented by W, Fe, Mo, and Cr, combining first-principles calculations, atomistic and Monte Carlo simulations. H clustering has been shown to be energetically favorable, and can be strongly facilitated by anisotropic stress field, dominated by the tensile component along one of the <001> crystalline directions. We showed that the stress effect can be well predicted by the continuum model based on H formation volume tensor, and that H clustering is thermodynamically possible at edge dislocations, evidenced by nanohydride formation at rather low levels of H concentration. Moreover, anisotropy in the stress effect is well reflected in nanohydride morphology around dislocations, with nanohydride growth occurring in the form of thin platelet structures that maximize one <001> tension. In particular, the <001> type edge dislocation, with the <001> tensile component maximized, has been shown to be highly effective in facilitating H aggregation, thus expected to play an important role in H clustering in bcc metals, in close agreement with recent experimental observations. This work explicitly and quantitatively clarifies the anisotropic nature of stress effect on H energetics and H clustering behaviors, offering mechanistic insights critical towards understanding H-induced damages in metals.

We use the In data collected during the 2019 outburst from X-ray pulsar 4U 1901+03 to complement the orbital parameters reported by Fermi/GBM. Using the Insight-HXMT, we examine the correlation between the derivative of the intrinsic spin frequency and bolometric flux based on accretion torque models. It was found that the pulse profiles significantly evolve during the outburst. The existence of two types of the profile's pattern discovered in the Insight-HXMT data indicates that this source experienced transition between a super-critical and a sub-critical accretion regime during its 2019 outburst. Based on the evolution of the pulse profiles and the torque model, we derive the distance to 4U 1901+03 as 12.4+-0.2 kpc., Comment: 8 pages, 5 figures, accepted by JHEAP

Sequential Monte Carlo (SMC), also known as particle filters, has been widely accepted as a powerful computational tool for making inference with dynamical systems. A key step in SMC is resampling, which plays the role of steering the algorithm towards the future dynamics. Several strategies have been proposed and used in practice, including multinomial resampling, residual resampling (Liu and Chen 1998), optimal resampling (Fearnhead and Clifford 2003), stratified resampling (Kitagawa 1996), and optimal transport resampling (Reich 2013). We show that, in the one dimensional case, optimal transport resampling is equivalent to stratified resampling on the sorted particles, and they both minimize the resampling variance as well as the expected squared energy distance between the original and resampled empirical distributions; in the multidimensional case, the variance of stratified resampling after sorting particles using Hilbert curve (Gerber et al. 2019) in $\mathbb{R}^d$ is $O(m^{-(1+2/d)})$, an improved rate compared to the original $O(m^{-(1+1/d)})$, where $m$ is the number of resampled particles. This improved rate is the lowest for ordered stratified resampling schemes, as conjectured in Gerber et al. (2019). We also present an almost sure bound on the Wasserstein distance between the original and Hilbert-curve-resampled empirical distributions. In light of these theoretical results, we propose the stratified multiple-descendant growth (SMG) algorithm, which allows us to explore the sample space more efficiently compared to the standard i.i.d. multiple-descendant sampling-resampling approach as measured by the Wasserstein metric. Numerical evidence is provided to demonstrate the effectiveness of our proposed method.

No robust detection of prompt electromagnetic counterparts to fast radio bursts (FRBs) has yet been obtained, in spite of several multi-wavelength searches carried out so far. Specifically, X/gamma-ray counterparts are predicted by some models. We planned on searching for prompt gamma-ray counterparts in the Insight-Hard X-ray Modulation Telescope (Insight-HXMT) data, taking advantage of the unique combination of large effective area in the keV-MeV energy range and of sub-ms time resolution. We selected 39 FRBs that were promptly visible from the High-Energy (HE) instrument aboard Insight-HXMT. After calculating the expected arrival times at the location of the spacecraft, we searched for a significant excess in both individual and cumulative time profiles over a wide range of time resolutions, from several seconds down to sub-ms scales. Using the dispersion measures in excess of the Galactic terms, we estimated the upper limits on the redshifts. No convincing signal was found and for each FRB we constrained the gamma-ray isotropic-equivalent luminosity and the released energy as a function of emission timescale. For the nearest FRB source, the periodic repeater FRB180916.J0158+65, we find $L_{\gamma,iso}<5.5\times 10^{47}$ erg/s over 1 s, whereas $L_{\gamma,iso}<10^{49}-10^{51}$ erg/s for the bulk of FRBs. The same values scale up by a factor of ~100 for a ms-long emission. Even on a timescale comparable with that of the radio pulse itself no keV-MeV emission is observed. A systematic association with either long or short GRBs is ruled out with high confidence, except for subluminous events, as is the case for core-collapse of massive stars (long) or binary neutron star mergers (short) viewed off axis. Only giant flares from extra-galactic magnetars at least ten times more energetic than Galactic siblings are ruled out for the nearest FRB., Comment: 15 pages, 3 figures, 6 tables, accepted by A&A

Selecting relevant features associated with a given response variable is an important issue in many scientific fields. Quantifying quality and uncertainty of a selection result via false discovery rate (FDR) control has been of recent interest. This paper introduces a way of using data-splitting strategies to asymptotically control the FDR while maintaining a high power. For each feature, the method constructs a test statistic by estimating two independent regression coefficients via data splitting. FDR control is achieved by taking advantage of the statistic's property that, for any null feature, its sampling distribution is symmetric about zero. Furthermore, we propose Multiple Data Splitting (MDS) to stabilize the selection result and boost the power. Interestingly and surprisingly, with the FDR still under control, MDS not only helps overcome the power loss caused by sample splitting, but also results in a lower variance of the false discovery proportion (FDP) compared with all other methods in consideration. We prove that the proposed data-splitting methods can asymptotically control the FDR at any designated level for linear and Gaussian graphical models in both low and high dimensions. Through intensive simulation studies and a real-data application, we show that the proposed methods are robust to the unknown distribution of features, easy to implement and computationally efficient, and are often the most powerful ones amongst competitors especially when the signals are weak and the correlations or partial correlations are high among features.

Simultaneously finding multiple influential variables and controlling the false discovery rate (FDR) for linear regression models is a fundamental problem. We here propose the Gaussian Mirror (GM) method, which creates for each predictor variable a pair of mirror variables by adding and subtracting a randomly generated Gaussian perturbation, and proceeds with a certain regression method, such as the ordinary least-square or the Lasso (the mirror variables can also be created after selection). The mirror variables naturally lead to test statistics effective for controlling the FDR. Under a mild assumption on the dependence among the covariates, we show that the FDR can be controlled at any designated level asymptotically. We also demonstrate through extensive numerical studies that the GM method is more powerful than many existing methods for selecting relevant variables subject to FDR control, especially for cases when the covariates are highly correlated and the influential variables are not overly sparse.

Knowledge on structures and energetics of nanovoids is fundamental to understand defect evolution in metals. Yet there remain no reliable methods able to determine essential structural details or to provide accurate assessment of energetics for general nanovoids. Here, we performed systematic first-principles investigations to examine stable structures and energetics of nanovoids in bcc metals, explicitly demonstrated the stable structures can be precisely determined by minimizing their Wigner-Seitz area, and revealed a linear relationship between formation energy and Wigner-Seitz area of nanovoids. We further developed a new physics-based model to accurately predict stable structures and energetics for arbitrary-sized nanovoids. This model was well validated by first-principles calculations and recent nanovoid annealing experiments, and showed distinct advantages over the widely used spherical approximation. The present work offers mechanistic insights that crucial for understanding nanovoid formation and evolution, being a critical step towards predictive control and prevention of nanovoid related damage processes in structural metals.

We consider the problem of comparing probability densities between two groups. A new probabilistic tensor product smoothing spline framework is developed to model the joint density of two variables. Under such a framework, the probability density comparison is equivalent to testing the presence/absence of interactions. We propose a penalized likelihood ratio test for such interaction testing and show that the test statistic is asymptotically chi-square distributed under the null hypothesis. Furthermore, we derive a sharp minimax testing rate based on the Bernstein width for nonparametric two-sample tests and show that our proposed test statistics is minimax optimal. In addition, a data-adaptive tuning criterion is developed to choose the penalty parameter. Simulations and real applications demonstrate that the proposed test outperforms the conventional approaches under various scenarios.

By mixing the target posterior distribution with a surrogate distribution, of which the normalizing constant is tractable, we propose a method for estimating the marginal likelihood using the Wang-Landau algorithm. We show that a faster convergence of the proposed method can be achieved via the momentum acceleration. Two implementation strategies are detailed: (i) facilitating global jumps between the posterior and surrogate distributions via the Multiple-try Metropolis; (ii) constructing the surrogate via the variational approximation. When a surrogate is difficult to come by, we describe a new jumping mechanism for general reversible jump Markov chain Monte Carlo algorithms, which combines the Multiple-try Metropolis and a directional sampling algorithm. We illustrate the proposed methods on several statistical models, including the Log-Gaussian Cox process, the Bayesian Lasso, the logistic regression, and the g-prior Bayesian variable selection.

We show that the Wang-Landau algorithm can be formulated as a stochastic gradient descent algorithm minimizing a smooth and convex objective function, of which the gradient is estimated using Markov chain Monte Carlo iterations. The optimization formulation provides us a new way to establish the convergence rate of the Wang-Landau algorithm, by exploiting the fact that almost surely, the density estimates (on the logarithmic scale) remain in a compact set, upon which the objective function is strongly convex. The optimization viewpoint motivates us to improve the efficiency of the Wang-Landau algorithm using popular tools including the momentum method and the adaptive learning rate method. We demonstrate the accelerated Wang-Landau algorithm on a two-dimensional Ising model and a two-dimensional ten-state Potts model., Comment: 10 pages, 3 figures

We report on the highest energy photons from the Crab Nebula observed by the Tibet air shower array with the underground water-Cherenkov-type muon detector array. Based on the criterion of muon number measured in an air shower, we successfully suppress 99.92% of the cosmic-ray background events with energies $E>100$ TeV. As a result, we observed 24 photon-like events with $E>100$ TeV against 5.5 background events, which corresponds to 5.6$\sigma$ statistical significance. This is the first detection of photons with $E>100$ TeV from an astrophysical source., Comment: April 4, 2019; Submitted to the Physical Review Letters

Uncertainty quantification has been a core of the statistical machine learning, but its computational bottleneck has been a serious challenge for both Bayesians and frequentists. We propose a model-based framework in quantifying uncertainty, called predictive-matching Generative Parameter Sampler (GPS). This procedure considers an Uncertainty Quantification (UQ) distribution on the targeted parameter, which matches the corresponding predictive distribution to the observed data. This framework adopts a hierarchical modeling perspective such that each observation is modeled by an individual parameter. This individual parameterization permits the resulting inference to be computationally scalable and robust to outliers. Our approach is illustrated for linear models, Poisson processes, and deep neural networks for classification. The results show that the GPS is successful in providing uncertainty quantification as well as additional flexibility beyond what is allowed by classical statistical procedures under the postulated statistical models.

Whereas many studies have examined stabilization of emulsions and foams in low salinity aqueous phases with nanoparticles (NPs) with and without added surfactants, interest has grown recently in much higher salinities relevant to subsurface oil and gas applications. It is shown for the first time that NPs grafted with well-defined low molecular weight ligands colloidally stable in concentrated brine (in particular, API brine, 8% NaCl + 2% CaCl2) and are interfacially active at the brine-air interface. These properties were achieved for three types of ligands: a nonionic diol called GLYMO and two short poly(ethylene glycol) (PEG) oligomers with 6-12 EO repeat units. Carbon dioxide-in-water (C/W) foams could be formed only with modified NPs with higher surface pressures at the A/W interface. Furthermore, these ligands were sufficiently CO2-philic that the hydrophilic/CO2-philic balance of silica NPs was low enough for stabilization of CO2-in-water (C/W) foam with API brine. Additionally, NPs with these three ligands formed stable dispersions with various free molecular surfactants in DI water and even API brine (8% NaCl + 2% CaCl2) at room temperature. A wide variety of mixtures of NPs plus anionic, nonionic, or cationic mixtures that formed stable dispersions were also found to stabilize C/W foams in porous media at high salinity. These results provide a basis for future studies of the mechanism of foam stabilization with NPs and NP/surfactant mixtures at high salinity.

In this paper, we develop a low than character feature embedding called radical embedding, and apply it on LSTM model for sentence segmentation of pre modern Chinese texts. The datasets includes over 150 classical Chinese books from 3 different dynasties and contains different literary styles. LSTM CRF model is a state of art method for the sequence labeling problem. Our new model adds a component of radical embedding, which leads to improved performances. Experimental results based on the aforementioned Chinese books demonstrates a better accuracy than earlier methods on sentence segmentation, especial in Tang Epitaph texts., Comment: The Journal of China Universities of Posts and Telecommunications, 2019

Relief based algorithms have often been claimed to uncover feature interactions. However, it is still unclear whether and how interaction terms will be differentiated from marginal effects. In this paper, we propose IMMIGRATE algorithm by including and training weights for interaction terms. Besides applying the large margin principle, we focus on the robustness of the contributors of margin and consider local and global information simultaneously. Moreover, IMMIGRATE has been shown to enjoy attractive properties, such as robustness and combination with Boosting. We evaluate our proposed method on several tasks, which achieves state-of-the-art results significantly., Comment: R package ('Immigrate') available on CRAN

The nudged elastic band (NEB) method is a commonly used approach for the calculation of minimum energy pathways of kinetic processes. However, the final paths obtained rely heavily on the nature of the initially chosen path. This often necessitates running multiple calculations with differing starting points in order to obtain accurate results. Recently, it has been shown that the NEB algorithm can only conserve or raise the distortion symmetry exhibited by an initial pathway. Using this knowledge, symmetry-adapted perturbations can be generated and used as a tool to systematically lower the initial path symmetry, enabling the exploration of other low-energy pathways that may exist. The group and representation theory details behind this process are presented and implemented in a standalone piece of software (DiSPy). The method is then demonstrated by applying it to the calculation of ferroelectric switching pathways in LiNbO3. Previously reported pathways are more easily obtained, with new paths also being found which involve a higher degree of atomic coordination.

Although Bayesian variable selection methods have been intensively studied, their routine use in practice has not caught up with their non-Bayesian counterparts such as Lasso, likely due to difficulties in both computations and flexibilities of prior choices. To ease these challenges, we propose the neuronized priors to unify and extend some popular shrinkage priors, such as Laplace, Cauchy, horseshoe, and spike-and-slab priors. A neuronized prior can be written as the product of a Gaussian weight variable and a scale variable transformed from Gaussian via an activation function. Compared with classic spike-and-slab priors, the neuronized priors achieve the same explicit variable selection without employing any latent indicator variables, which results in both more efficient and flexible posterior sampling and more effective posterior modal estimation. Theoretically, we provide specific conditions on the neuronized formulation to achieve the optimal posterior contraction rate, and show that a broadly applicable MCMC algorithm achieves an exponentially fast convergence rate under the neuronized formulation. We also examine various simulated and real data examples and demonstrate that using the neuronization representation is computationally more or comparably efficient than its standard counterpart in all well-known cases. An R package NPrior is provided in the CRAN for using neuronized priors in Bayesian linear regression.

Interplay between hydrogen and nanovoids, despite long-recognized as a central aspect in hydrogen-induced damages in structural materials, remains poorly understood. Focusing on tungsten as a model BCC system, the present study, for the first time, explicitly demonstrated sequential adsorption of hydrogen adatoms on Wigner-Seitz squares of nanovoids with distinct energy levels. Interaction between hydrogen adatoms on the nanovoid surface is shown to be dominated by pairwise power law repulsion. A predictive model was established for quantitative prediction of configurations and energetics of hydrogen adatoms in nanovoids. This model, further combined with equation of states of hydrogen gas, enables prediction of hydrogen molecule formation in nanovoids. Multiscale simulations based on the predictive model were performed, showing excellent agreement with experiments. This work clarifies fundamental physics and provides full-scale predictive model for hydrogen trapping and bubbling in nanovoids, offering long-sought mechanistic insights crucial for understanding hydrogen-induced damages in structural materials.