arXiv subjects

Bin Liu

Publications and source records attributed to Bin Liu.

At least 361 records · Page 20Linked to original sources

arXiv2021

Role of topological surface states and mirror symmetry in topological crystalline insulator SnTe as an efficient electrocatalyst

The surface orientation dependence on the hydrogen evolution reaction (HER) performance of topological crystalline insulator (TCI) SnTe thin films is studied. Their intrinsic activities are determined by linear sweep voltammetry and cyclic voltammetry measurements. It is found that SnTe (001) and (111) surfaces exhibit intrinsic activities significantly larger than the (211) surface. Density functional theory calculations reveal that pure (001) and (111) surfaces are not good electrocatalysts, while those with Sn vacancies or partially oxidized surfaces, with the latter as evidenced by X-ray photoelectron spectroscopy, have high activity. The calculated overall performance of the (001) and (111) surfaces with robust topological surface states (TSSs) is better than that of the lowly symmetric (211) surface with fragile or without TSSs, which is further supported by their measured weak antilocalization strength. The high HER activity of SnTe (001) and (111) is attributed to the enhanced charge transfer between H atoms and TSSs. We also address the effect of possible surface facets and the contrast of the HER activity of the available active sites among the three samples. Our study demonstrates that the TSSs and mirror symmetry of TCIs expedite their HER activity.

cond-mat.mtrl-sci↗

arXiv2021

Improve Unsupervised Pretraining for Few-label Transfer

Unsupervised pretraining has achieved great success and many recent works have shown unsupervised pretraining can achieve comparable or even slightly better transfer performance than supervised pretraining on downstream target datasets. But in this paper, we find this conclusion may not hold when the target dataset has very few labeled samples for finetuning, \ie, few-label transfer. We analyze the possible reason from the clustering perspective: 1) The clustering quality of target samples is of great importance to few-label transfer; 2) Though contrastive learning is essential to learn how to cluster, its clustering quality is still inferior to supervised pretraining due to lack of label supervision. Based on the analysis, we interestingly discover that only involving some unlabeled target domain into the unsupervised pretraining can improve the clustering quality, subsequently reducing the transfer performance gap with supervised pretraining. This finding also motivates us to propose a new progressive few-label transfer algorithm for real applications, which aims to maximize the transfer performance under a limited annotation budget. To support our analysis and proposed method, we conduct extensive experiments on nine different target datasets. Experimental results show our proposed method can significantly boost the few-label transfer performance of unsupervised pretraining.

cs.CV↗

arXiv2021

Drug-Target Interaction Prediction via an Ensemble of Weighted Nearest Neighbors with Interaction Recovery

Predicting drug-target interactions (DTI) via reliable computational methods is an effective and efficient way to mitigate the enormous costs and time of the drug discovery process. Structure-based drug similarities and sequence-based target protein similarities are the commonly used information for DTI prediction. Among numerous computational methods, neighborhood-based chemogenomic approaches that leverage drug and target similarities to perform predictions directly are simple but promising ones. However, existing similarity-based methods need to be re-trained to predict interactions for any new drugs or targets and cannot directly perform predictions for both new drugs, new targets, and new drug-target pairs. Furthermore, a large amount of missing (undetected) interactions in current DTI datasets hinders most DTI prediction methods. To address these issues, we propose a new method denoted as Weighted k-Nearest Neighbor with Interaction Recovery (WkNNIR). Not only can WkNNIR estimate interactions of any new drugs and/or new targets without any need of re-training, but it can also recover missing interactions (false negatives). In addition, WkNNIR exploits local imbalance to promote the influence of more reliable similarities on the interaction recovery and prediction processes. We also propose a series of ensemble methods that employ diverse sampling strategies and could be coupled with WkNNIR as well as any other DTI prediction method to improve performance. Experimental results over five benchmark datasets demonstrate the effectiveness of our approaches in predicting drug-target interactions. Lastly, we confirm the practical prediction ability of proposed methods to discover reliable interactions that were not reported in the original benchmark datasets.

cs.LG↗

arXiv2021

A simple interpolation-based approach towards the development of an accurate phenomenological constitutive relation for isotropic hyperelastic materials

Soft materials such as rubber and hydrogels are commonly used in industry for their excellent hyperelastic behaviour. There are various types of constitutive models for soft materials, and phenomenological models are very popular for finite element method (FEM) simulations. However, it is not easy to construct a model that can precisely predict the complex behaviours of soft materials. In this paper, we suggest that the strain energy functions should be expressed as functions of ordered principal stretches, which have more flexible expressions and are capable of matching various experimental curves. Moreover, the feasible region is small, and simple experiments, such as uniaxial tension/compression and hydrostatic tests, are on its boundaries. Therefore, strain energy functions can be easily constructed by the interpolation of experimental curves, which does not need initial guessing in the form of the strain energy function as most existing phenomenological models do. The proposed strain energy functions are perfectly consistent with the available experimental curves for interpolation. It is found that for incompressible materials, the function via an interpolation from two experimental curves can already predict other experimental curves reasonably well. To further improve the accuracy, additional experiments can be used in the interpolation.

cond-mat.soft↗

arXiv2021

The Mass Ratio Distribution of Tertiary Induced Binary Black Hole Mergers

Many proposed scenarios for black hole (BH) mergers involve a tertiary companion that induces von Zeipel-Lidov-Kozai (ZLK) eccentricity cycles in the inner binary. An attractive feature of such mechanisms is the enhanced merger probability when the octupole-order effects, also known as the eccentric Kozai mechanism, are important. This can be the case when the tertiary is of comparable mass to the binary components. Since the octupole strength [$\propto (1-q)/(1+q)$] increases with decreasing binary mass ratio $q$, such ZLK-induced mergers favor binaries with smaller mass ratios. We use a combination of numerical and analytical approaches to fully characterize the octupole-enhanced binary BH mergers and provide analytical criteria for efficiently calculating the strength of this enhancement. We show that for hierarchical triples with semi-major axis ratio $a/a_{\rm out}\gtrsim 0.01$-$0.02$, the binary merger fraction can increase by a large factor (up to $\sim 20$) as $q$ decreases from unity to $0.2$. The resulting mass ratio distribution for merging binary BHs produced in this scenario is in tension with the observed distribution obtained by the LIGO/VIRGO collaboration, although significant uncertainties remain about the initial distribution of binary BH masses and mass ratios.

astro-ph.HE↗

arXiv2021

Metal-insulator-like transition, superconducting dome and topological electronic structure in Ga-doped Re$_{3}$Ge$_{7}$

Superconductivity frequently appears by doping compounds that show a collective phase transition. So far, however, this has not been observed in topological materials. Here we report the discovery of superconductivity induced by Ga doping in orthorhombic Re$_{3}$Ge$_{7}$, which undergoes a second-order metal-insulator-like transition at $\sim$58 K and is predicted to have a nontrivial band topology. It is found that the substitution of Ga for Ge leads to hole doping in Re$_{3}$Ge$_{7-x}$Ga$_{x}$. As a consequence, the phase transition is gradually suppressed and disappears above $x$ = 0.2. At this $x$ value, superconductivity emerges and $T_{\rm c}$ exhibits a dome-like doping dependence with a maximum value of 3.37 K at $x$ = 0.25. First-principles calculations suggest that the phase transition in Re$_{3}$Ge$_{7}$ is associated with an electronic instability driven by Fermi surface nesting and the nontrival band topology is preserved after Ga doping. Our results indicate that Ga-doped Re$_{3}$Ge$_{7}$ provides a rare opportunity to study the interplay between superconductivity and competing electronic states in a topologically nontrivial system.

cond-mat.supr-con↗

arXiv2021

Intelligent Decision Method for Main Control Parameters of Tunnel Boring Machine based on Multi-Objective Optimization of Excavation Efficiency and Cost

Timely and reasonable matching of the control parameters and geological conditions of the rock mass in tunnel excavation is crucial for hard rock tunnel boring machines (TBMs). Therefore, this paper proposes an intelligent decision method for the main control parameters of the TBM based on the multi-objective optimization of excavation efficiency and cost. The main objectives of this method are to obtain the most important parameters of the rock mass and machine, determine the optimization objective, and establish the objective function. In this study, muck information was included as an important parameter in the traditional rock mass and machine parameter database. The rock-machine interaction model was established through an improved neural network algorithm. Using 250 sets of data collected in the field, the validity of the rock-machine interaction relationship model was verified. Then, taking the cost as the optimization objective, the cost calculation model related to tunneling and the cutter was obtained. Subsequently, combined with rock-machine interaction model, the objective function of control parameter optimization based on cost was established. Finally, a tunneling test was carried out at the engineering site, and the main TBM control parameters (thrust and torque) after the optimization decision were used to excavate the test section. Compared with the values in the section where the TBM operators relied on experience, the average penetration rate of the TBM increased by 11.10%, and the average cutter life increased by 15.62%. The results indicate that this method can play an effective role in TBM tunneling in the test section.

eess.SY↗

arXiv2021

SpaceHub: A high-performance gravity integration toolkit for few-body problems in astrophysics

We present the open source few-body gravity integration toolkit {\tt SpaceHub}. {\tt SpaceHub} offers a variety of algorithmic methods, including the unique algorithms AR-Radau, AR-Sym6, AR-ABITS and AR-chain$^+$ which we show out-perform other methods in the literature and allow for fast, precise and accurate computations to deal with few-body problems ranging from interacting black holes to planetary dynamics. We show that AR-Sym6 and AR-chain$^+$, with algorithmic regularization, chain algorithm, active round-off error compensation and a symplectic kernel implementation, are the fastest and most accurate algorithms to treat black hole dynamics with extreme mass ratios, extreme eccentricities and very close encounters. AR-Radau, the first regularized Radau integrator with round off error control down to 64 bits floating point machine precision, has the ability to handle extremely eccentric orbits and close approaches in long-term integrations. AR-ABITS, a bit efficient arbitrary precision method, achieves any precision with the least CPU cost compared to other open source arbitrary precision few-body codes. With the implementation of deep numerical and code optimization, these new algorithms in {\tt SpaceHub} prove superior to other popular high precision few-body codes in terms of performance, accuracy and speed.

astro-ph.SR↗

arXiv2021

A Pilot Study of Interplanetary Scintillation with FAST

Observations of Interplanetary Scintillation (IPS) are an efficient remote-sensing method to study the solar wind and inner heliosphere. From 2016 to 2018, some distinctive observations of IPS sources like 3C 286 and 3C 279 were accomplished with the Five-hundred-meter Aperture Spherical radio Telescope (FAST), the largest single-dish telescope in the world. Due to the 270-1620 MHz wide frequency coverage of the Ultra-Wideband (UWB) receiver, one can use both single-frequency and dual-frequency analyses to determine the projected velocity of the solar wind. Moreover, based on the extraordinary sensitivity owing to the large collecting surface area of FAST, we can observe weak IPS signals. With the advantages of both the wider frequency coverage and high sensitivity, also with our radio frequency interference (RFI) mitigation strategy and an optimized model-fitting method developed, in this paper, we analyze the fitting confidence intervals of the solar wind velocity, and present some preliminary results achieved using FAST, which points to the current FAST system being highly capable of carrying out observations of IPS

physics.space-ph↗

arXiv2021

On-Surface Synthesis of Graphene Nanoribbons on Two-Dimensional Rare Earth-Gold Intermetallic Compounds

Here, we demonstrate two reliable routes for the fabrication of armchair-edge graphene nanoribbons (GNRs) on TbAu2/Au(111), belonging to a class of two-dimensional ferromagnetic rare earth-gold intermetallic compounds. On-surface synthesis directly on TbAu2 leads to the formation of GNRs, which are short and interconnected with each other. In contrast, the intercalation approach - on-surface synthesis of GNRs directly on Au(111) followed by rare earth intercalation - yields GNRs on TbAu2/Au(111), where both the ribbons and TbAu2 are of high quality comparable with those directly grown on clean Au(111). Besides, the as-grown ribbons retain the same band gap while changing from p-doping to weak n-doping mainly due to a change in the work function of the substrate after the rare earth intercalation. The intercalation approach might also be employed to fabricate other types of GNRs on various rare earth intermetallic compounds, providing platforms to tailor the electronic and magnetic properties of GNRs on magnetic substrates.

cond-mat.mtrl-sci↗

arXiv2021

"Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?

AI has surpassed humans across a variety of tasks such as image classification, playing games (e.g., go, "Starcraft" and poker), and protein structure prediction. However, at the same time, AI is also bearing serious controversies. Many researchers argue that little substantial progress has been made for AI in recent decades. In this paper, the author (1) explains why controversies about AI exist; (2) discriminates two paradigms of AI research, termed "weak AI" and "strong AI" (a.k.a. artificial general intelligence); (3) clarifies how to judge which paradigm a research work should be classified into; (4) discusses what is the greatest value of "weak AI" if it has no chance to develop into "strong AI".

cs.AI↗

arXiv2021

The GBT Diffuse Ionized Gas Survey (GDIGS): Survey Overview and First Data Release

The Green Bank Telescope (GBT) Diffuse Ionized Gas Survey (GDIGS) traces ionized gas in the Galactic midplane by measuring $4-8$GHz radio recombination line (RRL) emission. The nominal survey zone is $32.3^{\circ}> l >-5^{\circ}$, $|b|<0.5^{\circ}$, but coverage extends above and below the plane in select fields, and additionally includes the areas around W47 ($l \simeq 37.5^{\circ}$) and W49 ($l \simeq 43^{\circ}$). GDIGS simultaneously observes 22 Hn$α$ (15 usable), 25 Hn$β$ (18 usable), and 8 Hn$γ$ RRLs (all usable), as well as multiple molecular line transitions (including of H$_2^{13}$CO, H$_2$CO, and CH$_3$OH). Here, we describe the GDIGS survey parameters and characterize the RRL data, focusing primarily on the Hn$α$ data. We produce sensitive data cubes by averaging the usable RRLs, after first smoothing to a common spectral resolution of 0.5km/s and a spatial resolution of 2.65' for Hn$α$, 2.62' for Hn$β$, and 2.09' for Hn$γ$. The average spectral noise per spaxel in the \hna\ data cubes is $\sim\!10$mK ($\sim\!5$mJy/beam). This sensitivity allows GDIGS to detect RRLs from plasma throughout the inner Galaxy. The GDIGS Hn$α$ data are sensitive to emission measures $EM \gtrsim 1100$cm$^{-6}$pc, which corresponds to a mean electron density $\langle n_e \rangle \gtrsim 30$cm$^{-3}$ for a 1pc path length or $\langle n_e \rangle \gtrsim 1$cm$^{-3}$ for a 1kpc path length.

astro-ph.GA↗

arXiv2021

Diverse Semantic Image Synthesis via Probability Distribution Modeling

Semantic image synthesis, translating semantic layouts to photo-realistic images, is a one-to-many mapping problem. Though impressive progress has been recently made, diverse semantic synthesis that can efficiently produce semantic-level multimodal results, still remains a challenge. In this paper, we propose a novel diverse semantic image synthesis framework from the perspective of semantic class distributions, which naturally supports diverse generation at semantic or even instance level. We achieve this by modeling class-level conditional modulation parameters as continuous probability distributions instead of discrete values, and sampling per-instance modulation parameters through instance-adaptive stochastic sampling that is consistent across the network. Moreover, we propose prior noise remapping, through linear perturbation parameters encoded from paired references, to facilitate supervised training and exemplar-based instance style control at test time. Extensive experiments on multiple datasets show that our method can achieve superior diversity and comparable quality compared to state-of-the-art methods. Code will be available at \url{https://github.com/tzt101/INADE.git}

cs.CV↗

arXiv2021

Spin-Orbit Misalignments in Tertiary-Induced Black-Hole Binary Mergers: Theoretical Analysis

Black-hole (BH) binary mergers driven by gravitational perturbations of tertiary companions constitute an important class of dynamical formation channels for compact binaries detected by LIGO/VIRGO. Recent works have examined numerically the combined orbital and spin dynamics of BH binaries that undergo large Lidov-Kozai (LK) eccentricity oscillations induced by a highly inclined companion and merge via gravitational wave radiation. However, the extreme eccentricity variations make such systems difficult to characterize analytically. In this paper, we develop an analytical formalism for understanding the spin dynamics of binary BHs undergoing LK-induced mergers. We show that, under certain conditions, the eccentricity oscillations of the binary can be averaged over to determine the long-term behavior of the BH spin in a smooth way. In particular, we demonstrate that the final spin-orbit misalignment angle $θ_{\rm sl}$ is often related to the binary's primordial spin orientation through an approximate adiabatic invariant. Our theory explains the "$90^\circ$ attractor" (as found in recent numerical studies) for the evolution of $θ_{\rm sl}$ when the initial BH spin is aligned with the orbital axis and the octupole LK effects are negligible -- such a "$90^\circ$ attractor" would lead to a small binary effective spin parameter $χ_{\rm eff}\sim 0$ even for large intrinsic BH spins. We calculate the deviation from adiabaticity in closed form as a function of the initial conditions. We also place accurate constraints on when this adiabatic invariant breaks down due to resonant spin-orbit interactions. We consider both stellar-mass and supermassive BH tertiary companions, and provide simple prescriptions for determining analytically the final spin-orbit misalignment angles of the merging BH binaries.

gr-qc↗

arXiv2021

Bayesian adversarial multi-node bandit for optimal smart grid protection against cyber attacks

The cybersecurity of smart grids has become one of key problems in developing reliable modern power and energy systems. This paper introduces a non-stationary adversarial cost with a variation constraint for smart grids and enables us to investigate the problem of optimal smart grid protection against cyber attacks in a relatively practical scenario. In particular, a Bayesian multi-node bandit (MNB) model with adversarial costs is constructed and a new regret function is defined for this model. An algorithm called Thompson-Hedge algorithm is presented to solve the problem and the superior performance of the proposed algorithm is proven in terms of the convergence rate of the regret function. The applicability of the algorithm to real smart grid scenarios is verified and the performance of the algorithm is also demonstrated by numerical examples.

cs.CR↗

arXiv2021

Semidiscrete vortex solitons

We demonstrate a possibility of the creation of stable optical solitons combining one continuous and one discrete coordinate, with embedded vorticity, in an array of planar waveguides with intrinsic cubic-quintic nonlinearity. The same system may be realized in terms of the spatiotemporal light propagation in an array of tunnel-coupled optical fibers with the cubic-quintic nonlinearity. In contrast with zero-vorticity states, semidiscrete vortex solitons do not exist without the quintic term in the nonlinearity. Two types of the solitons, \emph{viz.}, intersite- and onsite-centered ones (IC and OC, respectively), with even and odd numbers $N$ of actually excited sites in the discrete direction, are identified. We consider the modes carrying the embedded vorticity $S=1$ and $2$. In accordance with their symmetry, the vortex solitons of the OC type exhibit an intrinsic core, while the IC solitons with a small $N$ may have a coreless structure. Facilitating their creation in the experiment, the modes reported in the present work may be much more compact states than their counterparts considered in other systems, and they feature strong anisotropy. They can be set in motion in the discrete direction, provided that the coupling constant exceeds a certain minimum value. Collisions between moving vortex solitons are considered too.

nlin.PS↗

arXiv2021

A Deep Learning Approach to Quasar Continuum Prediction

We present a novel intelligent quasar continuum neural network (iQNet), predicting the intrinsic continuum of any quasar in the rest-frame wavelength range 1020 Angstroms $\leq λ\leq$ 1600 Angstroms. We train this network using high-resolution Hubble Space Telescope/Cosmic Origin Spectrograph ultraviolet quasar spectra at low redshift ($z \sim 0.2$) from the Hubble Spectroscopic Legacy Archive, and apply it to predict quasar continua from different astronomical surveys. We utilize the HSLA quasar spectra that are well-defined in the rest-frame wavelength range [1020, 1600] Angstroms with an overall median signal-to-noise ratio of at least five. The iQNet achieves a median AFFE of 2.24% on the training quasar spectra, and 4.17% on the testing quasar spectra. We apply iQNet and predict the continua of $\sim$3200 SDSS-DR16 quasar spectra at higher redshift ($2< z \leq 5$) and measure the redshift evolution of mean transmitted flux ($< F >$) in the Ly-$α$ forest region. We measure a gradual evolution of $< F >$ with redshift, which we characterize as a power-law fit to the effective optical depth of the Ly-$α$ forest. Our measurements are broadly consistent with other estimates of $ $ in the literature, but provide a more accurate measurement as we are directly measuring the quasar continuum where there is minimum contamination from the Ly-$α$ forest. This work proves that the deep learning iQNet model can predict the quasar continuum with high accuracy and shows the viability of such methods for quasar continuum prediction.

astro-ph.GA↗

arXiv2021

Hierarchical Black-Hole Mergers in Multiple Systems: Constrain the Formation of GW190412, GW190814 and GW190521-like events

The merging black-hole (BH) binaries GW190412, GW190814 and GW190521 from the third LIGO/VIRGO observing run exhibit some extraordinary properties, including highly asymmetric masses, significant spin, and component mass in the "mass gap". These features can be explained if one or both components of the binary are the remnants of previous mergers. In this paper, we explore hierarchical mergers in multiple stellar systems, taking into account the natal kick and mass loss due to the supernova explosion (SN) on each component, as well as the merger kick received by the merger remnant. The binaries that have survived the SNe and kicks generally have too wide orbital separations to merge by themselves, but can merge with the aid of an external companion that gives rise to Lidov-Kozai oscillations. The BH binaries that consist of second-generation BHs can also be assembled in dense star clusters through binary interactions. We characterize the parameter space of these BH binaries by merger fractions in an analytical approach. Combining the distributions of the survived binaries, we further constrain the parameters of the external companion, using the analytically formulated tertiary perturbation strength. We find that to produce the three LIGO/VIRGO O3 events, the external companions must be at least a few hundreds $M_\odot$, and fall in the intermediate-mass BH and supermassive BH range. We suggest that GW190412, GW190814 and GW190521 could all be produced via hierarchical mergers in multiples, likely in a nuclear star cluster, with the final merger induced by a massive BH.

astro-ph.HE↗