arXiv subjects

Chang Liu

Publications and source records attributed to Chang Liu.

At least 829 records · Page 46Linked to original sources

arXiv2022

Self-driven water or polarized liquid based ultraviolet photodetector

Traditionally, photodetector is based on solid materials constructed PN junction, which needs many delicate growth technologies. Herein, we demonstrate the feasibility of polarized liquid triggered photodetector where the liquid is sandwiched between P-type or N-type semiconductor which can be chosen freely according to the requirement of the specific response wavelength. Under the multiple cycles of optical switching, transient photo-polarized current, steady state photo-polarized current and depolarized current are repeatably observed in semiconductor/polar liquids/semiconductor structure. The responsivity and specific detectivity of transient photo-polarized current in N-GaN/water/P-GaN reach values of 104.2 mA/W and 4.4*10^12 Jones at 365 nm, and 52.8 mA/W and 1.9*10^12 Jones at 254 nm illumination under zero voltage bias. We anticipate that our research will have a profound impact on integrating self-powered photodetector with freely selectable wavelength bands.

physics.app-ph↗

arXiv2022

Chiral Assemblies of Pinwheel Superlattices on Substrates

The unique topology and physics of chiral superlattices make their self-assembly from nanoparticles a holy grail for (meta)materials. Here we show that tetrahedral gold nanoparticles can spontaneously transform from a perovskite-like low-density phase with corner-to-corner connections into pinwheel assemblies with corner-to-edge connections and denser packing. While the corner-sharing assemblies are achiral, pinwheel superlattices become strongly mirror-asymmetric on solid substrates as demonstrated by chirality measures. Liquid-phase transmission electron microscopy and computational models show that van der Waals and electrostatic interactions between nanoparticles control thermodynamic equilibrium. Variable corner-to-edge connections among tetrahedra enable fine-tuning of chirality. The domains of the bilayer superlattices display strong chiroptical activity identified by photon-induced near-field electron microscopy and finite-difference time-domain simulations. The simplicity and versatility of the substrate-supported chiral superlattices facilitate manufacturing of metastructured coatings with unusual optical, mechanical and electronic characteristics.

cond-mat.mtrl-sci↗

arXiv2022

Tensor Recovery Based on A Novel Non-convex Function Minimax Logarithmic Concave Penalty Function

Non-convex relaxation methods have been widely used in tensor recovery problems, and compared with convex relaxation methods, can achieve better recovery results. In this paper, a new non-convex function, Minimax Logarithmic Concave Penalty (MLCP) function, is proposed, and some of its intrinsic properties are analyzed, among which it is interesting to find that the Logarithmic function is an upper bound of the MLCP function. The proposed function is generalized to tensor cases, yielding tensor MLCP and weighted tensor $Lγ$-norm. Consider that its explicit solution cannot be obtained when applying it directly to the tensor recovery problem. Therefore, the corresponding equivalence theorems to solve such problem are given, namely, tensor equivalent MLCP theorem and equivalent weighted tensor $Lγ$-norm theorem. In addition, we propose two EMLCP-based models for classic tensor recovery problems, namely low-rank tensor completion (LRTC) and tensor robust principal component analysis (TRPCA), and design proximal alternate linearization minimization (PALM) algorithms to solve them individually. Furthermore, based on the Kurdyka-Łojasiwicz property, it is proved that the solution sequence of the proposed algorithm has finite length and converges to the critical point globally. Finally, Extensive experiments show that proposed algorithm achieve good results, and it is confirmed that the MLCP function is indeed better than the Logarithmic function in the minimization problem, which is consistent with the analysis of theoretical properties.

eess.IV↗

arXiv2022

Hot-tail electrons' impact on assimilation and injection penetration of D2 Shattered Pellet Injections

The fragment ablation rate plays significant roles in the mitigation efficiency of Shattered Pellet Injection (SPI) as a Disruption Mitigation System (DMS). Current mainstream 3D MHD codes modelling SPIs mostly assume instantaneous thermalization between the previously hot ambient electrons and the newly released cold electrons, which results in underestimation of the ablation rate if the hot electron thermalization time is comparable or even longer than the fragment flying time. To resolve this doubt, we hereby investigate the thermalization dynamics and the overall hot-electron impact. The finite-time collisional thermalization of hot-tail electrons in a rapidly cooling plasma, as well as the so-called ``self-limiting'' effect are considered. The former effect tends to deplete the colder population within a hot-tail species, while the latter is found to preferentially deplete the higher energy population. The combined result is found to cause an almost self-similar decay of the hot electron distribution function, while its shape does not deviate much from that of Maxwellian distribution and the mean energy does not change much during the thermalization process. Based on this observation, axisymmetric JOREK D2 SPI simulations were carried out with additional hot-tail contribution to evaluate their overall impact onto the injection assimilation and penetration. It is found that the hot-tail effect indeed causes enhanced assimilation and shallower penetration, although the overall effect depends on the exact injection configuration, with the slow injection showing negligible hot-tail effect while the fast single non-shattered pellet case shows drastic hot-tail ablation enhancement. For ITER-like SPI parameters, there is no significant deviation in the total assimilation, but some deviation in the injection penetration is observed for the fast injection velocity cases.

physics.plasm-ph↗

arXiv2022

Nonlinear inhomogeneous Fokker-Planck models: energetic-variational structures and long time behavior

Inspired by the modeling of grain growth in polycrystalline materials, we consider a nonlinear Fokker-Plank model, with inhomogeneous diffusion and with variable mobility parameters. We develop large time asymptotic analysis of such nonstandard models by reformulating and extending the classical entropy method, under the assumption of periodic boundary condition. In addition, illustrative numerical tests are presented to highlight the essential points of the current analytical results and to motivate future analysis.

math.AP↗

arXiv2022

Electromagnetic Follow-up Observations of Binary Neutron Star Mergers with Early Warnings from Decihertz Gravitational-wave Observatories

We investigate the prospects of electromagnetic follow-up observations for binary neutron star (BNS) mergers, with the help of early warnings from decihertz gravitational-wave (GW) observatories, B-DECIGO and DO-Optimal. Extending the previous work, we not only give quick assessments of joint short $γ$-ray burst (sGRB) detection rates for different $γ$-ray satellites and BNS population models, but also elaborate on the analyses and results on multi-band kilonova detections for survey telescopes with different limiting magnitudes. During an assumed 4-year mission time for decihertz GW observatories, we find that for the goals of electromagnetic follow-ups, DO-Optimal performs better than B-DECIGO as a whole on the detection rate, and has a larger detectable distance for joint sGRB/kilonova searches. Taking the log-normal population model for BNS mergers and a one-day early-warning time as an example, we discuss the accuracy in localization and timing, as well as the redshift distributions for various synergy observations with electromagnetic facilities and decihertz GW detectors. Based on our analyses, we propose a feasible "wait-for" pattern as a novel detecting mode for future multi-messenger astrophysics.

astro-ph.HE↗

arXiv2022

Realistic Detection and Early Warning of Binary Neutron Stars with Decihertz Gravitational-wave Observatories

We investigated the detection rates and early warning parameters of binary neutron star (BNS) populations with decihertz gravitational-wave observatories in a realistic detecting strategy. Assuming 4 years' operation of B-DECIGO, we based on parameter precision to classify the detectable BNSs into three categories: (a) sources that merge within 1 year, which could be localized with an uncertainty of $ΔΩ\sim 10^{0}$ deg$^2$; (b) sources that merge in 1-4 years, which take up three quarters of the total events and yield the most precise angular resolution with $ΔΩ\sim 10^{-2}$ deg$^2$ and time-of-merger accuracy with $Δt_c\sim 10^{-1}$ s; and (c) sources that do not merge during the 4-yr mission window, which enable possible early warnings, with $ΔΩ\sim 10^{-1}$ deg$^2$ and $Δt_c\sim 10^{0}$ s. Furthermore, we compared the pros and cons of B-DECIGO with the third-generation ground-based detectors, and explored the prospects of detections using 3 other decihertz observatories and 4 BNS population models. In realistic observing scenarios, we found that decihertz detectors could even provide early-warning alerts to a source decades before its merger while their localizations are still as accurate as ground-based facilities. Finally we found a decrease of events when considering the confusion noise, but this could be partially solved by a proper noise subtraction.

astro-ph.HE↗

arXiv2022

Infrared Radiation of Graphene Electrothermal Film Triggered Alpha and Theta Brainwaves

The alpha and theta frequency brainwave activity in Electroencephalogram (EEG) signal has been correlated with attention, inhibitory processes, memory, perceptual abilities, and sleep. The enhanced alpha and theta brainwave activity may bring positive behavioral modifications such as promoting creativity and a quick sleep. Herein, we discover that infrared radiation from multilayer graphene electrothermal film can obviously promote the appearance of alpha and theta brainwave in human mind. In particular, the occurrence frequency of the alpha and theta waves in EEG can be effectively enhanced up to 2.3 and 3.0 times, respectively. And the duration time of the alpha and theta waves in EEG can also be effectively extended. The mechanism may be attributed to the efficient infrared radiation caused by graphene mainly focused on the range from 7 to 14 micron, coinciding with the radiation wavelength of natural human body, which can be effectively absorbed by the human skin and speed up the blood microcirculation and metabolism. The comparative effect of different working temperature and heating materials such as water, Cu and even monolayer graphene are systematically investigated, indicating the infrared radiation from the multilayer graphene electrothermal film at 50 degrees has the largest enhancement effect of alpha and theta brainwaves. The multilayer graphene film electrical heater represents a convenient and surprising way for triggering the alpha and theta brainwaves, which has many potential applications in the area of enlarged health cerements.

physics.app-ph↗

arXiv2022

Resource Allocation and 3D Trajectory Design for Power-Efficient IRS-Assisted UAV-NOMA Communications

In this paper, an intelligent reflecting surface (IRS) is introduced to assist an unmanned aerial vehicle (UAV) communication system based on non-orthogonal multiple access (NOMA) for serving multiple ground users. We aim to minimize the average total system energy consumption by jointly designing the resource allocation strategy, the three dimensional (3D) trajectory of the UAV, as well as the phase control at the IRS. The design is formulated as a non-convex optimization problem taking into account the maximum tolerable outage probability constraint and the individual minimum data rate requirement. To circumvent the intractability of the design problem due to the altitude-dependent Rician fading in UAV-to-user links, we adopt the deep neural network (DNN) approach to accurately approximate the corresponding effective channel gains, which facilitates the development of a low-complexity suboptimal iterative algorithm via dividing the formulated problem into two subproblems and address them alternatingly. Numerical results demonstrate that the proposed algorithm can converge to an effective solution within a small number of iterations and illustrate some interesting insights: (1) IRS enables a highly flexible UAV's 3D trajectory design via recycling the dissipated radio signal for improving the achievable system data rate and reducing the flight power consumption of the UAV; (2) IRS provides a rich array gain through passive beamforming in the reflection link, which can substantially reduce the required communication power for guaranteeing the required quality-of-service (QoS); (3) Optimizing the altitude of UAV's trajectory can effectively exploit the outage-guaranteed effective channel gain to save the total required communication power enabling power-efficient UAV communications.

cs.IT↗

arXiv2022

Thermal ion kinetic effects and Landau damping in fishbone modes

The kinetic-MHD hybrid simulation approach for macroscopic instabilities in plasmas can be extended to include the kinetic effects of both thermal ions and energetic ions. The new coupling scheme includes synchronization of density and parallel velocity between thermal ions and MHD, in addition to pressure coupling, to ensure the quasineutrality condition and avoid numerical errors. The new approach has been implemented in the kinetic-MHD code M3D-C1-K, and was used to study the thermal ion kinetic effects and Landau damping in fishbone modes in both DIII-D and NSTX. It is found that the thermal ion kinetic effects can cause an increase of the frequencies of the non-resonant $n=1$ fishbone modes driven by energetic particles for $q_\mathrm{min}>1$, and Landau damping can provide additional stabilization effects. A nonlinear simulation for $n=1$ fishbone mode in NSTX is also performed, and the perturbation on magnetic flux surfaces and the transport of energetic particles are calculated.

physics.plasm-ph↗

arXiv2022

On the $α$-spectral radius of the $k$-uniform supertrees

Let $G$ be a $k$-uniform hypergraph with vertex set $V(G)$ and edge set $E(G)$. A connected and acyclic hypergraph is called a supertree. For $0\leqα<1$, the $α$-spectral radius of $G$ is the largest $H$-eigenvalue of $αD(G)+(1-α)A(G)$, where $D(G)$ and $A(G)$ are the diagonal tensor of the degrees and the adjacency tensor of $G$, respectively. In this paper, we determine the unique supertrees with the maximum $α$-spectral radius among all $k$-uniform supertrees with $m$ edges and independence number $β$ for $\lceil\frac{m(k-1)+1}{k}\rceil\leqβ\leq m$, among all $k$-uniform supertrees with given degree sequences, and among all $k$-uniform supertrees with $m$ edges and matching number $μ$ for $1\leqμ\leq\lfloor\frac{m(k-1)+1}{k}\rfloor$, respectively.

math.CO↗

arXiv2022

Pronunciation Dictionary-Free Multilingual Speech Synthesis by Combining Unsupervised and Supervised Phonetic Representations

This paper proposes a multilingual speech synthesis method which combines unsupervised phonetic representations (UPR) and supervised phonetic representations (SPR) to avoid reliance on the pronunciation dictionaries of target languages. In this method, a pretrained wav2vec 2.0 model is adopted to extract UPRs and a language-independent automatic speech recognition (LI-ASR) model is built with a connectionist temporal classification (CTC) loss to extract segment-level SPRs from the audio data of target languages. Then, an acoustic model is designed, which first predicts UPRs and SPRs from texts separately and then combines the predicted UPRs and SPRs to generate mel-spectrograms. The results of our experiments on six languages show that the proposed method outperformed the methods that directly predicted mel-spectrograms from character or phoneme sequences and the ablated models that utilized only UPRs or SPRs.

eess.AS↗

arXiv2022

Strain-driven valley states and phase transitions in Janus VSiGeN4 monolayer

The interplay between topology and valley degree of freedom has attracted much interest because it can realize new phenomena and applications. Here, based on first-principles calculations, we demonstrate intrinsically valley-polarized quantum anomalous Hall effect in monolayer ferrovalley material: Janus VSiGeN4, of which the edge states are chiral-spin-valley locking. Besides, a small tensile or compressive strain can drive phase transition in the material from valley-polarized quantum anomalous Hall state to half-valley-metal state. With the increase of the strain, the material turns into ferrovalley semiconductor with valley anomalous Hall effect. The origin of phase transition is sequent band inversion of V d orbital at K valley. Moreover, we find that phase transition causes the sign reversal of Berry curvature and induces different polarized light absorption in different valley states. Our work provides an ideal material platform for practical applications and experimental exploration of the interplay between topology, spintronics, and valleytronics.

cond-mat.mes-hall↗

arXiv2022

Crystal structure prediction with machine learning-based element substitution

The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of assembled atoms can be determined by optimizing the energy surface, which in turn can be evaluated using first-principles calculations. However, performing the iterative gradient descent on the potential energy surface using first-principles calculations is prohibitively expensive for complex systems, such as those with many atoms per unit cell. Here, we present a unique methodology for crystal structure prediction (CSP) that relies on a machine learning algorithm called metric learning. It is shown that a binary classifier, trained on a large number of already identified crystal structures, can determine the isomorphism of crystal structures formed by two given chemical compositions with an accuracy of approximately 96.4\%. For a given query composition with an unknown crystal structure, the model is used to automatically select from a crystal structure database a set of template crystals with nearly identical stable structures to which element substitution is to be applied. Apart from the local relaxation calculation of the identified templates, the proposed method does not use ab initio calculations. The potential of this substation-based CSP is demonstrated for a wide variety of crystal systems.

cond-mat.mtrl-sci↗

arXiv2022

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations, which might suffer from under- or over-exposed regions and different sources of noise. The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds).

cs.CV↗

arXiv2022

Generalizing to Unseen Domains: A Survey on Domain Generalization

Machine learning systems generally assume that the training and testing distributions are the same. To this end, a key requirement is to develop models that can generalize to unseen distributions. Domain generalization (DG), i.e., out-of-distribution generalization, has attracted increasing interests in recent years. Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain. Great progress has been made in the area of domain generalization for years. This paper presents the first review of recent advances in this area. First, we provide a formal definition of domain generalization and discuss several related fields. We then thoroughly review the theories related to domain generalization and carefully analyze the theory behind generalization. We categorize recent algorithms into three classes: data manipulation, representation learning, and learning strategy, and present several popular algorithms in detail for each category. Third, we introduce the commonly used datasets, applications, and our open-sourced codebase for fair evaluation. Finally, we summarize existing literature and present some potential research topics for the future.

cs.LG↗

arXiv2022

Increased Phase Coherence Length in a Porous Topological Insulator

The surface area of Bi2Te3 thin films was increased by introducing nanoscale porosity. Temperature dependent resistivity and magnetotransport measurements were conducted both on as-grown and porous samples (23 and 70 nm). The longitudinal resistivity of the porous samples became more metallic, indicating the increased surface area resulted in transport that was more surface-like. Weak antilocalization (WAL) was present in all samples, and remarkably the phase coherence length doubled in the porous samples. This increase is likely due to the large Fermi velocity of the Dirac surface states. Our results show that the introduction of nanoporosity does not destroy the topological surface states but rather enhances them, making these nanostructured materials promising for low energy electronics, spintronics and thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2022

Test-time Fourier Style Calibration for Domain Generalization

The topic of generalizing machine learning models learned on a collection of source domains to unknown target domains is challenging. While many domain generalization (DG) methods have achieved promising results, they primarily rely on the source domains at train-time without manipulating the target domains at test-time. Thus, it is still possible that those methods can overfit to source domains and perform poorly on target domains. Driven by the observation that domains are strongly related to styles, we argue that reducing the gap between source and target styles can boost models' generalizability. To solve the dilemma of having no access to the target domain during training, we introduce Test-time Fourier Style Calibration (TF-Cal) for calibrating the target domain style on the fly during testing. To access styles, we utilize Fourier transformation to decompose features into amplitude (style) features and phase (semantic) features. Furthermore, we present an effective technique to Augment Amplitude Features (AAF) to complement TF-Cal. Extensive experiments on several popular DG benchmarks and a segmentation dataset for medical images demonstrate that our method outperforms state-of-the-art methods.

cs.CV↗