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Zhi Li

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preprint2026arXiv

Data-Driven Exploration and Insights into Temperature-Dependent Phonons in Inorganic Materials

Phonons, quantized vibrations of the atomic lattice, are fundamental to understanding thermal transport, structural stability, and phase behavior in crystalline solids. Despite advances in computational materials science, most predictions of vibrational properties in large materials databases rely on the harmonic approximation and overlook crucial temperature-dependent anharmonic effects. Here, we present a scalable computational framework that combines machine learning interatomic potentials, anharmonic lattice dynamics, and high-throughput calculations to investigate temperature-dependent phonons across thousands of materials. By fine-tuning the universal M3GNet interatomic potential using high-quality phonon data, we improve phonon prediction accuracy by a factor of four while preserving computational efficiency. Integrating this refined model into a high-throughput implementation of the stochastic self-consistent harmonic approximation, we compute temperature-dependent phonons for 4,669 inorganic compounds. Our analysis identifies systematic elemental and structural trends governing anharmonic phonon renormalization, with particularly strong manifestations in alkali metals, perovskite-derived frameworks, and related systems. Machine learning models trained on this dataset identify key atomic-scale features driving strong anharmonicity, including weak bonding, large atomic radii, and specific coordination motifs. First-principles validation confirms that anharmonic effects can dramatically alter lattice thermal conductivity by factors of two to four in some materials. This work establishes a robust and efficient data-driven approach for predicting finite-temperature phonon behavior, offering new pathways for the design and discovery of materials with tailored thermal and vibrational properties.

preprint2026arXiv

HydroAgent: Closing the Gap Between Frontier LLMs and Human Experts in Hydrologic Model Calibration via Simulator-Grounded RL

Calibrating distributed hydrologic models is a critical bottleneck across operational water resources management - streamflow prediction, reservoir operation, drought monitoring, infrastructure design, and flood forecasting all depend on it. Each basin demands an expert to translate hydrograph signatures into adjustments of a high-dimensional parameter vector, and the resulting workflow does not transfer between watersheds. We ask: can frontier large language model (LLM) agents replace the human hydrologic modeler, and if not, what would it take? We benchmark nine frontier LLM agents - Claude Opus 4.6/4.7, Sonnet 4.6, GPT-5/5.4/5.4-pro, and Gemini 2.5-pro/3.1-pro/3-flash - on the operational CREST distributed hydrologic model used by the U.S. National Weather Service for flash-flood forecasting. Best-of-twenty-rounds Nash-Sutcliffe Efficiency (NSE) across four held-out gauges spanning 329-40,792 km2 ranges from -0.16 (GPT-5.4) to 0.75 (Sonnet 4.6); the ceiling reproduces across all three vendors and capability tiers, with the strongest models concentrating in the 0.65-0.75 band, and no model reaches the human-expert reference except Opus-4.7 on one gauge. We argue this gap is not a parameter-count problem but a domain-grounding problem. We then propose HYDROAGENT, fine-tuning open-weight Qwen3-4B with supervised fine-tuning on 2,576 expert calibration trajectories and Group-Relative Policy Optimization using NSE as a verifiable reward from online CREST simulations - reinforcement learning with simulation feedback (RLSF). For Earth system science, a small domain-tuned policy with simulator-in-the-loop RL is a more compute-efficient and physically faithful path than scaling generic frontier models, and the multi-modal richness of Earth data - remote sensing, in-situ time series, and forecaster narrative - makes domain agents a leveraged direction for AI in physical science.

preprint2026arXiv

Lightweight Yet Secure: Secure Scripting Language Generation via Lightweight LLMs

The security of scripting languages such as PowerShell is critical given their powerful automation and administration capabilities, often exercised with elevated privileges. Today, securing these languages still demands substantial human effort to craft and enforce rules, imposing heavy burdens on typical administrators and creating critical production risks (e.g., misoperations that shut down servers).Large language models (LLMs) have demonstrated strong capabilities in code generation, vulnerability detection, and automated repair for languages like Python and JavaScript. However, their ability to assist with generating secure scripting-language code remains largely underexplored. In this paper, we present SecGenEval-PS, a benchmark designed to systematically evaluate LLMs on secure scripting generation, security analysis, and automated repair. Our results show that both proprietary and open-source models fall short in these areas. For instance, over 60% of PowerShell scripts produced by GPT-4o and o3-mini are insecure without structured guidance.To bridge this gap, we propose PSSec, a framework that combines data synthesis with fine-tuning to enhance model security capabilities. We develop a self-debugging agent that integrates static analyzers with the reasoning abilities of advanced LLMs to synthesize large-scale structured triplets of insecure scripts, violation analyses, and corresponding repairs. We then fine-tune lightweight LLMs (as small as 1.7B parameters) using supervised fine-tuning (SFT) and reinforcement learning (RL), enabling security-aware reasoning and the generation of secure PowerShell code.Across multiple LLM families, including GPT and Qwen, \textit{PSSec}-trained models match or surpass general-purpose large models on PowerShell security tasks while reducing inference cost by more than an order of magnitude.

preprint2026arXiv

Physically-Grounded Manifold Projection Model for Generalizable Metal Artifact Reduction in Dental CBCT

Metal artifacts in Dental CBCT severely obscure anatomical structures, hindering diagnosis. Current deep learning for Metal Artifact Reduction (MAR) faces limitations: supervised methods suffer from spectral blurring due to "regression-to-the-mean", while unsupervised ones risk structural hallucinations. Denoising Diffusion Models (DDPMs) offer realism but rely on slow, stochastic iterative sampling, unsuitable for clinical use. To resolve this, we propose the Physically-Grounded Manifold Projection (PGMP) framework. First, our Anatomically-Adaptive Physics Simulation (AAPS) pipeline synthesizes high-fidelity training pairs via Monte Carlo spectral modeling and patient-specific digital twins, bridging the synthetic-to-real gap. Second, our DMP-Former adapts the Direct x-Prediction paradigm, reformulating restoration as a deterministic manifold projection to recover clean anatomy in a single forward pass, eliminating stochastic sampling. Finally, a Semantic-Structural Alignment (SSA) module anchors the solution using priors from medical foundation models (MedDINOv3), ensuring clinical plausibility. Experiments on synthetic and multi-center clinical datasets show PGMP outperforms state-of-the-art methods on unseen anatomy, setting new benchmarks in efficiency and diagnostic reliability. Code and data: https://github.com/ricoleehduu/PGMP.

preprint2026arXiv

Tunable chiral spiral phases in a non-Hermitian Ising-Gamma spin chain

We study the influence of dissipation on the Ising-Gamma model. Through observables such as ground-state energy, order parameters, entanglement entropy, etc., we identify each phase region and provide the global phase diagram of the system. The results show that the region of the spiral phase will continuously expand with the increase of dissipation, gradually squeezing the original paramagnetic and antiferromagnetic phase regions. Remarkably, unlike the conservative system, the introduction of dissipation will cause two spiral phases with opposite chiralities to emerge simultaneously in the system, which provides a possibility for the manipulation of spiral chirality in cold atomic experiments. Moreover, we reveal the mechanism of the dependence of the transformation between these two spiral phases with distinct chirality on the strength of the relative coefficient of off-diagonal Gamma interactions in the Ising-Gamma model. Since both the relevant order parameters and dissipation can be well controlled within a detectable range, these phenomena can be observed in ultracold atomic experiments.

preprint2024arXiv

Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues

Face anti-spoofing (FAS) and face forgery detection play vital roles in securing face biometric systems from presentation attacks (PAs) and vicious digital manipulation (e.g., deepfakes). Despite promising performance upon large-scale data and powerful deep models, the generalization problem of existing approaches is still an open issue. Most of recent approaches focus on 1) unimodal visual appearance or physiological (i.e., remote photoplethysmography (rPPG)) cues; and 2) separated feature representation for FAS or face forgery detection. On one side, unimodal appearance and rPPG features are respectively vulnerable to high-fidelity face 3D mask and video replay attacks, inspiring us to design reliable multi-modal fusion mechanisms for generalized face attack detection. On the other side, there are rich common features across FAS and face forgery detection tasks (e.g., periodic rPPG rhythms and vanilla appearance for bonafides), providing solid evidence to design a joint FAS and face forgery detection system in a multi-task learning fashion. In this paper, we establish the first joint face spoofing and forgery detection benchmark using both visual appearance and physiological rPPG cues. To enhance the rPPG periodicity discrimination, we design a two-branch physiological network using both facial spatio-temporal rPPG signal map and its continuous wavelet transformed counterpart as inputs. To mitigate the modality bias and improve the fusion efficacy, we conduct a weighted batch and layer normalization for both appearance and rPPG features before multi-modal fusion. We find that the generalization capacities of both unimodal (appearance or rPPG) and multi-modal (appearance+rPPG) models can be obviously improved via joint training on these two tasks. We hope this new benchmark will facilitate the future research of both FAS and deepfake detection communities.

preprint2023arXiv

Complete space-like self-expanders in the Minkovski space

It is our purpose to study complete space-like self-expanders in the Minkovski space. By use of maximum principle of Omori-Yau type, we can obtain the rigidity theorems on $n$-dimensional complete space-like self-expanders in the Minkovski space $\mathbb R^{n+1}_{1}$. For complete space-like self-expanders of dimension $2$, we give a classification of them under assumption of constant squared norm of the second fundamental form.

preprint2022arXiv

A Preliminary Research on Space Situational Awareness Based on Event Cameras

Event camera is a new type of sensor that is different from traditional cameras. Each pixel is triggered asynchronously by an event. The trigger event is the change of the brightness irradiated on the pixel. If the increment or decrement is higher than a certain threshold, the event is output. Compared with traditional cameras, event cameras have the advantages of high temporal resolution, low latency, high dynamic range, low bandwidth and low power consumption. We carried out a series of observation experiments in a simulated space lighting environment. The experimental results show that the event camera can give full play to the above advantages in space situational awareness. This article first introduces the basic principles of the event camera, then analyzes its advantages and disadvantages, then introduces the observation experiment and analyzes the experimental results, and finally, a workflow of space situational awareness based on event cameras is given.

preprint2022arXiv

ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving Cooperative Learning

Cooperative learning, that enables two or more data owners to jointly train a model, has been widely adopted to solve the problem of insufficient training data in machine learning. Nowadays, there is an urgent need for institutions and organizations to train a model cooperatively while keeping each other's data privately. To address the issue of privacy-preserving in collaborative learning, secure outsourced computation and federated learning are two typical methods. Nevertheless, there are many drawbacks for these two methods when they are leveraged in cooperative learning. For secure outsourced computation, semi-honest servers need to be introduced. Once the outsourced servers collude or perform other active attacks, the privacy of data will be disclosed. For federated learning, it is difficult to apply to the scenarios where vertically partitioned data are distributed over multiple parties. In this work, we propose a multi-party mixed protocol framework, ABG$^n$, which effectively implements arbitrary conversion between Arithmetic sharing (A), Boolean sharing (B) and Garbled-Circuits sharing (G) for $n$-party scenarios. Based on ABG$^n$, we design a privacy-preserving multi-party cooperative learning system, which allows different data owners to cooperate in machine learning in terms of data security and privacy-preserving. Additionally, we design specific privacy-preserving computation protocols for some typical machine learning methods such as logistic regression and neural networks. Compared with previous work, the proposed method has a wider scope of application and does not need to rely on additional servers. Finally, we evaluate the performance of ABG$^n$ on the local setting and on the public cloud setting. The experiments indicate that ABG$^n$ has excellent performance, especially in the network environment with low latency.

preprint2022arXiv

Amorphous alloys surpass E/10 strength limit at extreme strain rates

Theoretical predictions of the ideal strength of materials range from E/30 to E/10 (E is Young's modulus). However, despite intense interest over the last decade, the value of the ideal strength that can be attained experimentally for metals remains a mystery (1-5). In this study, we demonstrated the unprecedented strength of an amorphous Cu-Zr alloy that surpassed the E/10 limit. Laser-induced shock experiments were conducted on Cu50Zr50 to explore its strength and failure mechanisms at ultrahigh strain rates. The material demonstrated a high spall strength of 9.8 GPa, approximately 1/13 of its P-wave modulus (~ E/6), at strain rates greater than 10^7 s^-1, which sets a new record for the elastic limit of metallic materials. Electron microscopy and large-scale molecular dynamics simulations revealed that void nucleation and growth, not shear-banding, comprised the major failure mechanism for metallic glasses at extremely fast strain rates. A new model for void formation under the control of surface energy explained the rate dependence of the material strength. The results of this study reveal new possible ways to use the amorphous phase in nanostructured metals in future applications under demanding mechanical conditions.

preprint2022arXiv

An immersed $S^{n}$ $λ$-hypersurface

In this paper, we construct an immersed, non-embedded $S^{n}$ $λ$-hypersurface in Euclidean spaces $\mathbb{R}^{n+1}$.

preprint2022arXiv

Banding vs. Quality: Perceptual Impact and Objective Assessment

Staircase-like contours introduced to a video by quantization in flat areas, commonly known as banding, have been a long-standing problem in both video processing and quality assessment communities. The fact that even a relatively small change of the original pixel values can result in a strong impact on perceived quality makes banding especially difficult to be detected by objective quality metrics. In this paper, we study how banding annoyance compares to more commonly studied scaling and compression artifacts with respect to the overall perceptual quality. We further propose a simple combination of VMAF and the recently developed banding index, CAMBI, into a banding-aware video quality metric showing improved correlation with overall perceived quality.

preprint2022arXiv

Dynamics in an exact solvable quantum magnet: benchmark for quantum computer

Quantum magnets are never short of novel and fascinating dynamics, yet its simulation by classical computers requires exponentially-scaled computation resources, which renders the research on large-scale many-body dynamics fiendishly difficult. In this letter, we explore the dynamic behavior of 2D large-scale ferromagnetic J1-J2 Heisenberg model both theoretically and experimentally. First, the analytical solution of magnon dynamics is obtained to show an obvious ballistic propagation of magnon, which is typical for quantum walk. Then, we verify the dynamic behavior of the system through numerical approach of exact diagonalization and tensor network method. We also calculate out-of-time ordered correlators and butterfly velocities among different lattice points, finding that they can well depict the competition between different couplings. Finally, a quantum walk experiment is designed and conducted on the basis of IBM programmable quantum processors, and the experimental results are in consistence with our theoretical predictions. Since the analytical results can be used, in principle, to predict the behavior of large-scale quantum many-body systems and even those infinitely large, this work will help facilitate further research on quantum walk and quantum many-body dynamics in large-scale lattice systems, guide future design of quantum computers, as well as popularize quantum computers until they are known and available to every household in the world.

preprint2022arXiv

Emergent phase transition in Cluster Ising model with dissipation

We study a cluster Ising model with non-Hermitian external field which can be exactly solved in the language of free fermions. By investigating the second derivative of energy density and fidelity, the possible new critical points are tentatively located. String order parameter and staggered magnetization are then detected to reveal emergent phases of brand new characteristics. To categorize the exotic phases and phase transitions induced by non-Hermiticity, we calculate the variation mode of spin correlation function as well as string parameter, which characterize the emergent phases and critical points with different patterns of decay and critical exponents. With the help of string order parameter and staggered magnetization, we find that there are four phases after introducing the non-Hermiticity -- the cluster phase, the gapless phase, the paramagnetic (PM) phase and the antiferromagnetic (AF) phase. A phase diagram is then presented to graphically illustrate, based on two "KT-like" phase transitions and an Ising phase transition, respectively, the generation of three critical lines as non-Hermitian strength increases. Our theoretical work is expected to be realized in the experiment of ultra-cold atoms, pushing for progress in exploring novel phases and phase transitions.

preprint2022arXiv

Estimating the Resize Parameter in End-to-end Learned Image Compression

We describe a search-free resizing framework that can further improve the rate-distortion tradeoff of recent learned image compression models. Our approach is simple: compose a pair of differentiable downsampling/upsampling layers that sandwich a neural compression model. To determine resize factors for different inputs, we utilize another neural network jointly trained with the compression model, with the end goal of minimizing the rate-distortion objective. Our results suggest that "compression friendly" downsampled representations can be quickly determined during encoding by using an auxiliary network and differentiable image warping. By conducting extensive experimental tests on existing deep image compression models, we show results that our new resizing parameter estimation framework can provide Bjøntegaard-Delta rate (BD-rate) improvement of about 10% against leading perceptual quality engines. We also carried out a subjective quality study, the results of which show that our new approach yields favorable compressed images. To facilitate reproducible research in this direction, the implementation used in this paper is being made freely available online at: https://github.com/treammm/ResizeCompression.

preprint2022arXiv

FourNetFlows: An efficient model for steady airfoil flows prediction

FourNetFlows, the abbreviation of Fourier Neural Network for Airfoil Flows, is an efficient model that provides quick and accurate predictions of steady airfoil flows. We choose the Fourier Neural Operator (FNO) as the backbone architecture and utilize OpenFOAM to generate numerical solutions of airfoil flows for training. Our results indicate that FourNetFlows matches the accuracy of the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) integrated with the Spalart-Allmaras turbulence model, one of the numerical algorithms. FourNetFlows is also used to predict flows around an oval whose shape is definitely different from samples in the training set. We note that both qualitative and quantitative results are consistent with the numerical results. Meanwhile, FourNetFlows solves thousands of solutions in seconds, orders of magnitude faster than the classical numerical method. Surprisingly, FourNetFlows achieves model flows with zero-shot super-resolution when it is trained under a lower resolution. And the inferring time is almost constant when the resolution of solutions is increasing.

preprint2022arXiv

HULC: 3D Human Motion Capture with Pose Manifold Sampling and Dense Contact Guidance

Marker-less monocular 3D human motion capture (MoCap) with scene interactions is a challenging research topic relevant for extended reality, robotics and virtual avatar generation. Due to the inherent depth ambiguity of monocular settings, 3D motions captured with existing methods often contain severe artefacts such as incorrect body-scene inter-penetrations, jitter and body floating. To tackle these issues, we propose HULC, a new approach for 3D human MoCap which is aware of the scene geometry. HULC estimates 3D poses and dense body-environment surface contacts for improved 3D localisations, as well as the absolute scale of the subject. Furthermore, we introduce a 3D pose trajectory optimisation based on a novel pose manifold sampling that resolves erroneous body-environment inter-penetrations. Although the proposed method requires less structured inputs compared to existing scene-aware monocular MoCap algorithms, it produces more physically-plausible poses: HULC significantly and consistently outperforms the existing approaches in various experiments and on different metrics. Project page: https://vcai.mpi-inf.mpg.de/projects/HULC/.

preprint2022arXiv

Improving Maximum Likelihood Difference Scaling method to measure inter content scale

The goal of most subjective studies is to place a set of stimuli on a perceptual scale. This is mostly done directly by rating, e.g. using single or double stimulus methodologies, or indirectly by ranking or pairwise comparison. All these methods estimate the perceptual magnitudes of the stimuli on a scale. However, procedures such as Maximum Likelihood Difference Scaling (MLDS) have shown that considering perceptual distances can bring benefits in terms of discriminatory power, observers' cognitive load, and the number of trials required. One of the disadvantages of the MLDS method is that the perceptual scales obtained for stimuli created from different source content are generally not comparable. In this paper, we propose an extension of the MLDS method that ensures inter-content comparability of the results and shows its usefulness especially in the presence of observer errors.

preprint2022arXiv

Large-scale Hydrodynamical Shocks as the Smoking Gun Evidence for a Bar in M31

The formation and evolutionary history of M31 are closely related to its dynamical structures, which remain unclear due to its high inclination. Gas kinematics could provide crucial evidence for the existence of a rotating bar in M31. Using the position-velocity diagram of [OIII] and HI, we are able to identify clear sharp velocity jump (shock) features with a typical amplitude over 100 km/s in the central region of M31 (4.6 kpc X 2.3 kpc, or 20 arcmin X 10 arcmin). We also simulate gas morphology and kinematics in barred M31 potentials and find that the bar-induced shocks can produce velocity jumps similar to those in [OIII]. The identified shock features in both [OIII] and HI are broadly consistent, and they are found mainly on the leading sides of the bar/bulge, following a hallmark pattern expected from the bar-driven gas inflow. Shock features on the far side of the disk are clearer than those on the near side, possibly due to limited data coverage on the near side, as well as obscuration by the warped gas and dust layers. Further hydrodynamical simulations with more sophisticated physics are desired to fully understand the observed gas features and to better constrain the parameters of the bar in M31.

preprint2022arXiv

Learning Meta Pattern for Face Anti-Spoofing

Face Anti-Spoofing (FAS) is essential to secure face recognition systems and has been extensively studied in recent years. Although deep neural networks (DNNs) for the FAS task have achieved promising results in intra-dataset experiments with similar distributions of training and testing data, the DNNs' generalization ability is limited under the cross-domain scenarios with different distributions of training and testing data. To improve the generalization ability, recent hybrid methods have been explored to extract task-aware handcrafted features (e.g., Local Binary Pattern) as discriminative information for the input of DNNs. However, the handcrafted feature extraction relies on experts' domain knowledge, and how to choose appropriate handcrafted features is underexplored. To this end, we propose a learnable network to extract Meta Pattern (MP) in our learning-to-learn framework. By replacing handcrafted features with the MP, the discriminative information from MP is capable of learning a more generalized model. Moreover, we devise a two-stream network to hierarchically fuse the input RGB image and the extracted MP by using our proposed Hierarchical Fusion Module (HFM). We conduct comprehensive experiments and show that our MP outperforms the compared handcrafted features. Also, our proposed method with HFM and the MP can achieve state-of-the-art performance on two different domain generalization evaluation benchmarks.

preprint2022arXiv

MoCapDeform: Monocular 3D Human Motion Capture in Deformable Scenes

3D human motion capture from monocular RGB images respecting interactions of a subject with complex and possibly deformable environments is a very challenging, ill-posed and under-explored problem. Existing methods address it only weakly and do not model possible surface deformations often occurring when humans interact with scene surfaces. In contrast, this paper proposes MoCapDeform, i.e., a new framework for monocular 3D human motion capture that is the first to explicitly model non-rigid deformations of a 3D scene for improved 3D human pose estimation and deformable environment reconstruction. MoCapDeform accepts a monocular RGB video and a 3D scene mesh aligned in the camera space. It first localises a subject in the input monocular video along with dense contact labels using a new raycasting based strategy. Next, our human-environment interaction constraints are leveraged to jointly optimise global 3D human poses and non-rigid surface deformations. MoCapDeform achieves superior accuracy than competing methods on several datasets, including our newly recorded one with deforming background scenes.

preprint2022arXiv

One-Class Knowledge Distillation for Face Presentation Attack Detection

Face presentation attack detection (PAD) has been extensively studied by research communities to enhance the security of face recognition systems. Although existing methods have achieved good performance on testing data with similar distribution as the training data, their performance degrades severely in application scenarios with data of unseen distributions. In situations where the training and testing data are drawn from different domains, a typical approach is to apply domain adaptation techniques to improve face PAD performance with the help of target domain data. However, it has always been a non-trivial challenge to collect sufficient data samples in the target domain, especially for attack samples. This paper introduces a teacher-student framework to improve the cross-domain performance of face PAD with one-class domain adaptation. In addition to the source domain data, the framework utilizes only a few genuine face samples of the target domain. Under this framework, a teacher network is trained with source domain samples to provide discriminative feature representations for face PAD. Student networks are trained to mimic the teacher network and learn similar representations for genuine face samples of the target domain. In the test phase, the similarity score between the representations of the teacher and student networks is used to distinguish attacks from genuine ones. To evaluate the proposed framework under one-class domain adaptation settings, we devised two new protocols and conducted extensive experiments. The experimental results show that our method outperforms baselines under one-class domain adaptation settings and even state-of-the-art methods with unsupervised domain adaptation.

preprint2022arXiv

PF4Microservices: A decomposion scheme for microservices based on Problem Frames

In recent years, microservice architecture has become a popular architectural style in software engineering, with its natural support for DevOps and continuous delivery, as well as its scalability and extensibility, which drive industry practitioners to migrate to microservice architecture. However, there are many challenges in adopting a microservice architecture, the most important of which is how to properly decomposition a monolithic system into microservices. Currently, microservice decomposition decisions for monolithic systems rely on subjective human experience, which is a costly, time-consuming process with high uncertainty of results. To address this problem, this paper proposes a method for microservice decomposition using Jackson Problem Frames. In this method, requirements of the system are analysed, descriptions of the interactions between the proposed software and its environment is obtained, multiple problem diagrams are constructed, and then the problem diagrams are merged by analyzing the correlation and similarity between them, resulting in a microservice decomposition scheme. A case study is also conducted based on a smart parking system. The results of the study show that the method can perform microservice decomposition based on requirements and the software environment, resulting in reducing the decisionmaking burden of developers, with reasonable decomposition results.

preprint2022arXiv

Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction

Network-aware cascade size prediction aims to predict the final reposted number of user-generated information via modeling the propagation process in social networks. Estimating the user's reposting probability by social influence, namely state activation plays an important role in the information diffusion process. Therefore, Graph Neural Networks (GNN), which can simulate the information interaction between nodes, has been proved as an effective scheme to handle this prediction task. However, existing studies including GNN-based models usually neglect a vital factor of user's preference which influences the state activation deeply. To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i.e., preference topics generation, preference shift modeling, and social influence activation. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate. Extensive experiments on two large-scale real-world datasets have clearly demonstrated the effectiveness of our proposed model compared to state-of-the-art baselines.

preprint2022arXiv

Short Proofs of Linear Growth of Quantum Circuit Complexity

The complexity of a quantum gate, defined as the minimal number of elementary gates to build it, is an important concept in quantum information and computation. It is shown recently that the complexity of quantum gates built from random quantum circuits almost surely grows linearly with the number of building blocks. In this article, we provide two short proofs of this fact. We also discuss a discrete version of quantum circuit complexity growth.

preprint2022arXiv

Topological classification of non-Hermitian bands

We proposed a framework for the topological classification of non-Hermitian systems. Different from previous $K$-theoretical approaches, our approach is a homotopy classification, which enables us to see more topological invariants. Specifically, we considered the classification of non-Hermitian systems with separable band structures. We found that the whole classification set is decomposed into several sectors based on the braiding of energy levels and characterized by some braid group data. Each sector can be further classified based on the topology of eigenstates (wave functions). Due to the interplay between energy levels braiding and eigenstates topology, we found some torsion invariants, which only appear in the non-Hermitian world via homotopical approach. We further proved that these new topological invariants are unstable (fragile), in the sense that adding more bands will trivialize these invariants.

preprint2021arXiv

Direct and Indirect Communication in Multi-Human Multi-Robot Interaction

How can multiple humans interact with multiple robots? The goal of our research is to create an effective interface that allows multiple operators to collaboratively control teams of robots in complex tasks. In this paper, we focus on a key aspect that affects our exploration of the design space of human-robot interfaces -- inter-human communication. More specifically, we study the impact of direct and indirect communication on several metrics, such as awareness, workload, trust, and interface usability. In our experiments, the participants can engage directly through verbal communication, or indirectly by representing their actions and intentions through our interface. We report the results of a user study based on a collective transport task involving 18 human subjects and 9 robots. Our study suggests that combining both direct and indirect communication is the best approach for effective multi-human / multi-robot interaction.

preprint2021arXiv

Gas Dynamics in the Galaxy: Total Mass Distribution and the Bar Pattern Speed

Gas morphology and kinematics in the Milky Way contain key information for understanding the formation and evolution of our Galaxy. We present a high resolution hydrodynamical simulation based on a realistic barred Milky Way potential constrained by recent observations. Our model can reproduce most features in the observed longitude-velocity diagram, including the Central Molecular Zone, the Near and Far 3-kpc arms, the Molecular Ring, and the spiral arm tangents. It can also explain the non-circular motions of masers obtained by the recent BeSSeL2 survey. The central gas kinematics are consistent with a mass of $6.9\times10^8\; {\rm M}_{\odot}$ in the Nuclear Stellar Disk. Our model predicts the formation of an elliptical gaseous ring surrounding the bar, which is composed of the 3-kpc arms, Norma arm, and the bar-spiral interfaces. This ring is similar to those "inner" rings in some Milky Way analogs with a boxy/peanut-shaped bulge. The kinematics of gas near the solar neighbourhood are governed by the Local arm, which is induced by the four major stellar spiral arms. The bar pattern speed constrained by our gas model is $37.5-40\; {\rm km}\;{\rm s}^{-1}\;{\rm kpc}^{-1}$, corresponding to a corotation radius of $R_{\rm CR}=6.0-6.4\;{\rm kpc}$. The rotation curve of our model rises gently within the central $\sim5\;{\rm kpc}$, which is significantly less steep than those predicted by modern zoom-in cosmological simulations such as Auriga.

preprint2021arXiv

Learning Skill Equivalencies Across Platform Taxonomies

Assessment and reporting of skills is a central feature of many digital learning platforms. With students often using multiple platforms, cross-platform assessment has emerged as a new challenge. While technologies such as Learning Tools Interoperability (LTI) have enabled communication between platforms, reconciling the different skill taxonomies they employ has not been solved at scale. In this paper, we introduce and evaluate a methodology for finding and linking equivalent skills between platforms by utilizing problem content as well as the platform's clickstream data. We propose six models to represent skills as continuous real-valued vectors and leverage machine translation to map between skill spaces. The methods are tested on three digital learning platforms: ASSISTments, Khan Academy, and Cognitive Tutor. Our results demonstrate reasonable accuracy in skill equivalency prediction from a fine-grained taxonomy to a coarse-grained one, achieving an average recall@5 of 0.8 between the three platforms. Our skill translation approach has implications for aiding in the tedious, manual process of taxonomy to taxonomy mapping work, also called crosswalks, within the tutoring as well as standardized testing worlds.

preprint2021arXiv

Learning the Implicit Semantic Representation on Graph-Structured Data

Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole, while the implicit semantic associations behind highly complex interactions of graphs are largely unexploited. In this paper, we propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs. In previous work, there are explorations of graph semantics via meta-paths. However, these methods mainly rely on explicit heterogeneous information that is hard to be obtained in a large amount of graph-structured data. SGCN first breaks through this restriction via leveraging the semantic-paths dynamically and automatically during the node aggregating process. To evaluate our idea, we conduct sufficient experiments on several standard datasets, and the empirical results show the superior performance of our model.

preprint2021arXiv

New convergence analysis of a primal-dual algorithm with large stepsizes

We consider a primal-dual algorithm for minimizing $f(x)+h\square l(Ax)$ with Fréchet differentiable $f$ and $l^*$. This primal-dual algorithm has two names in literature: Primal-Dual Fixed-Point algorithm based on the Proximity Operator (PDFP$^2$O) and Proximal Alternating Predictor-Corrector (PAPC). In this paper, we prove its convergence under a weaker condition on the stepsizes than existing ones. With additional assumptions, we show its linear convergence. In addition, we show that this condition (the upper bound of the stepsize) is tight and can not be weakened. This result also recovers a recently proposed positive-indefinite linearized augmented Lagrangian method. In addition, we apply this result to a decentralized consensus algorithm PG-EXTRA and derive the weakest convergence condition.

preprint2021arXiv

Ultrafast Parallel LiDAR with Time-encoding and Spectral Scanning: Breaking the Time-of-flight Limit

Light detection and ranging (LiDAR) has been widely used in autonomous driving and large-scale manufacturing. Although state-of-the-art scanning LiDAR can perform long-range three-dimensional imaging, the frame rate is limited by both round-trip delay and the beam steering speed, hindering the development of high-speed autonomous vehicles. For hundred-meter level ranging applications, a several-time speedup is highly desirable. Here, we uniquely combine fiber-based encoders with wavelength-division multiplexing devices to implement all-optical time-encoding on the illumination light. Using this method, parallel detection and fast inertia-free spectral scanning can be achieved simultaneously with single-pixel detection. As a result, the frame rate of a scanning LiDAR can be multiplied with scalability. We demonstrate a 4.4-fold speedup for a maximum 75-m detection range, compared with a time-of-flight-limited laser ranging system. This approach has the potential to improve the velocity of LiDAR-based autonomous vehicles to the regime of hundred kilometers per hour and open up a new paradigm for ultrafast-frame-rate LiDAR imaging.

preprint2020arXiv

A New Quasi One-Dimensional Compound Ba3TiTe5 and Superconductivity Induced by Pressure

We report systematical studies of a new quasi-one-dimensional (1D) compound Ba3TiTe5 and the high-pressure induced superconductivity therein. Ba3TiTe5 was synthesized at high pressure and high temperature. It crystallizes into a hexagonal structure (P63/mcm), which consists of infinite face-sharing octahedral TiTe6 chains and Te chains along the c axis, exhibiting a strong 1D characteristic structure. The first-principles calculations demonstrate that Ba3TiTe5 is a well-defined 1D conductor and thus, it can be considered a starting point to explore the exotic physics induced by pressure via enhancing the interchain hopping to move the 1D conductor to a high dimensional metal. For Ba3TiTe5, high-pressure techniques were employed to study the emerging physics dependent on interchain hopping, such as the Umklapp scattering effect, spin/charge density wave (SDW/CDW), superconductivity and non-Fermi Liquid behavior. Finally, a complete phase diagram was plotted. The superconductivity emerges from 8.8 GPa, near which the Umklapp gap is mostly suppressed. Tc is enhanced and reaches the maximum ~6 K at about 36.7 GPa, where the spin/charge density wave (SDW/CDW) is completely suppressed, and a non-Fermi Liquid behavior appears. Our results suggest that the appearance of superconductivity is associated with the fluctuation due to the suppression of Umklapp gap and the enhancement of Tc is related with the fluctuation of the SDW/CDW.

preprint2020arXiv

A substantial increase of Curie temperature in a new type of diluted magnetic semiconductors via effects of chemical pressure

Chemical pressure is an effective method to tune physical properties, particularly for diluted magnetic semiconductors (DMS) of which ferromagnetic ordering is mediated by charge carriers. Via substitution of smaller Ca for larger Sr, we introduce chemical pressure on (Sr,Na)(Cd,Mn)2As2 to fabricate a new DMS material (Ca,Na)(Cd,Mn)2As2. Carriers and spins are introduced by substitutions of (Ca,Na) and (Cd,Mn) respectively. The unit cell volume reduces by 6.2% after complete substitution of Ca for Sr, suggesting a subsistent chemical pressure. Importantly the local geometry of [Cd/MnAs4] tetrahedron is optimized via chemical compression that increases the Mn-As hybridization leading to enhanced ferromagnetic interactions. As a result, the maximum Curie temperature (TC) is increased by about 50% while the the maximum saturation moment increases by over 100% from (Sr,Na)(Cd,Mn)2As2 to (Ca,Na)(Cd,Mn)2As2. The chemical pressure estimated from the equation of state is equal to an external physical pressure of 3.6 GPa.

preprint2020arXiv

Complete $3$-dimensional $λ$-translators in the Minkowski space $\mathbb{R}^{4}_{1}$

In this paper, we obtain the classification theorem for three-dimensional complete space-like $λ$-translators $x:M^{3} \rightarrow \mathbb R^{4}_{1}$ with constant norm of the second fundamental form and constant $f_{4}$ in the Minkowski space $\mathbb R^{4}_{1}$.

preprint2020arXiv

Complete self-shrinkers with constant norm of the second fundamental form

In this paper, we classify $3$-dimensional complete self-shrinkers in Euclidean space $\mathbb R^{4}$ with constant squared norm of the second fundamental form $S$ and constant $f_{4}$.

preprint2020arXiv

Do nuclear rings in barred galaxies form at the shear minimum of the rotation curve?

It has been recently suggested that (i) nuclear rings in barred galaxies (including our own Milky Way) form at the radius where the shear parameter of the rotation curve reaches a minimum; (ii) the acoustic instability of Montenegro et al. is responsible for driving the turbulence and angular momentum transport in the central regions of barred galaxies. Here we test these suggestions by running simple hydrodynamical simulations in a logarithmic barred potential. Since the rotation curve of this potential is scale-free, the shear minimum theory predicts that no ring should form. We find that in contrast to this prediction, a ring does form in the simulation, with morphology consistent with that of nuclear rings in real barred galaxies. This proves that the presence of a shear-minimum is not a necessary condition for the formation of a ring. We also find that perturbations that are predicted to be acoustically unstable wind up and eventually propagate off to infinity, so that the system is actually stable. We conclude that (i) the shear-minimum theory is an unlikely mechanism for the formation of nuclear rings in barred galaxies; (ii) the acoustic instability is a spurious result and may not be able to drive turbulence in the interstellar medium, at least for the case without self-gravity. The question of the role of turbulent viscosity remains open.

preprint2020arXiv

GPM: A Generic Probabilistic Model to Recover Annotator's Behavior and Ground Truth Labeling

In the big data era, data labeling can be obtained through crowdsourcing. Nevertheless, the obtained labels are generally noisy, unreliable or even adversarial. In this paper, we propose a probabilistic graphical annotation model to infer the underlying ground truth and annotator's behavior. To accommodate both discrete and continuous application scenarios (e.g., classifying scenes vs. rating videos on a Likert scale), the underlying ground truth is considered following a distribution rather than a single value. In this way, the reliable but potentially divergent opinions from "good" annotators can be recovered. The proposed model is able to identify whether an annotator has worked diligently towards the task during the labeling procedure, which could be used for further selection of qualified annotators. Our model has been tested on both simulated data and real-world data, where it always shows superior performance than the other state-of-the-art models in terms of accuracy and robustness.

preprint2020arXiv

Hamiltonian Tomography via Quantum Quench

We show that it is possible to uniquely reconstruct a generic many-body local Hamiltonian from a single pair of initial and final states related by time evolution with the Hamiltonian. We then propose a practical version of the protocol involving multiple pairs of such initial/final states. Using the eigenstate thermalization hypothesis, we provide bounds on the protocol's performance and stability against errors from measurements and in the ansatz of the Hamiltonian. The protocol is efficient (requiring experimental resources scaling polynomially with system size in general and constant with system size given translation symmetry) and thus enables analog and digital quantum simulators to verify implementation of a putative Hamiltonian.

preprint2020arXiv

Learning the Compositional Visual Coherence for Complementary Recommendations

Complementary recommendations, which aim at providing users product suggestions that are supplementary and compatible with their obtained items, have become a hot topic in both academia and industry in recent years. %However, it is challenging due to its complexity and subjectivity. Existing work mainly focused on modeling the co-purchased relations between two items, but the compositional associations of item collections are largely unexplored. Actually, when a user chooses the complementary items for the purchased products, it is intuitive that she will consider the visual semantic coherence (such as color collocations, texture compatibilities) in addition to global impressions. Towards this end, in this paper, we propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents. Specifically, we first propose a \textit{Global Coherence Learning} (GCL) module based on multi-heads attention to model the global compositional coherence. Then, we generate the semantic-focal representations from different semantic regions and design a \textit{Focal Coherence Learning} (FCL) module to learn the focal compositional coherence from different semantic-focal representations. Finally, we optimize the CANN in a novel compositional optimization strategy. Extensive experiments on the large-scale real-world data clearly demonstrate the effectiveness of CANN compared with several state-of-the-art methods.

preprint2020arXiv

Nonlinear Bloch-Zener oscillations for Bose-Einstein condensates in a Lieb optical lattice

We investigate Bloch-Zener oscillations and mean-field Bloch bands of a Bose-Einstein condensate (BEC) in a Lieb optical lattice. We find that the atomic interaction will break the point group symmetry of the system, leading to the destruction of the Dirac cone structure, while the flat band is preserved on the highly symmetric lines. Due to the nonlinear effect, a tubular band structure with a flat band will appear in the system. Furthermore, comparing with that the tight-binding (TB) model fails to describe the interacting bosonic systems in the honeycomb lattice, we show that the TB model is applicable to study the nonlinear energy band structures for the Lieb lattice. In addition, we show that the loop structure can be determined by the observation of the chaos of the state in the Bloch-Zener oscillations.

preprint2020arXiv

Nonlinear optical response from quantum kinetic equation

Motivated by the nonlinear Hall effect observed in topological semimetals, we studied the photocurrent by the quantum kinetic equation. We recovered the shift current and injection current discovered by Sipe et al., and the nonlinear Hall current induced by Berry curvature dipole (BCD) proposed by Inti Sodemann and Liang Fu. Especially, we further proposed that 3-form tensor can also induce photocurrent, in addition to the Berry curvature and BCD. This work will supplement the existing mechanisms for photocurrent. In contrast to the shift current induced by shift vector, all photocurrents induced by gradient/curl of Berry curvature, and high rank tensor require circularly polarized light and topologically non-trivial band structure, viz. non-vanishing Berry curvature.

preprint2020arXiv

Perceptual Video Quality Prediction Emphasizing Chroma Distortions

Measuring the quality of digital videos viewed by human observers has become a common practice in numerous multimedia applications, such as adaptive video streaming, quality monitoring, and other digital TV applications. Here we explore a significant, yet relatively unexplored problem: measuring perceptual quality on videos arising from both luma and chroma distortions from compression. Toward investigating this problem, it is important to understand the kinds of chroma distortions that arise, how they relate to luma compression distortions, and how they can affect perceived quality. We designed and carried out a subjective experiment to measure subjective video quality on both luma and chroma distortions, introduced both in isolation as well as together. Specifically, the new subjective dataset comprises a total of $210$ videos afflicted by distortions caused by varying levels of luma quantization commingled with different amounts of chroma quantization. The subjective scores were evaluated by $34$ subjects in a controlled environmental setting. Using the newly collected subjective data, we were able to demonstrate important shortcomings of existing video quality models, especially in regards to chroma distortions. Further, we designed an objective video quality model which builds on existing video quality algorithms, by considering the fidelity of chroma channels in a principled way. We also found that this quality analysis implies that there is room for reducing bitrate consumption in modern video codecs by creatively increasing the compression factor on chroma channels. We believe that this work will both encourage further research in this direction, as well as advance progress on the ultimate goal of jointly optimizing luma and chroma compression in modern video encoders.

preprint2020arXiv

Perceptually Optimizing Deep Image Compression

Mean squared error (MSE) and $\ell_p$ norms have largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties. However, when used to assess visual information loss, these simple norms are not highly consistent with human perception. Here, we propose a different proxy approach to optimize image analysis networks against quantitative perceptual models. Specifically, we construct a proxy network, which mimics the perceptual model while serving as a loss layer of the network.We experimentally demonstrate how this optimization framework can be applied to train an end-to-end optimized image compression network. By building on top of a modern deep image compression models, we are able to demonstrate an averaged bitrate reduction of $28.7\%$ over MSE optimization, given a specified perceptual quality (VMAF) level.

preprint2020arXiv

Skin superfluid, topological Mott insulators, and asymmetric dynamics in interacting non-Hermitian Aubry-Andre-Harper models

Non-Hermitian quantum many-body systems are a fascinating subject to be explored. Using the generalized density matrix renormalisation group method and complementary exact diagonalization, we elucidate the many-body ground states and dynamics of a 1D interacting non-Hermitian Aubry-Andre-Harper model for bosons. We find stable ground states in the superfluid and Mott insulating regimes under wide range of conditions in this model. We reveal a skin superfluid state induced by the non-Hermiticity from the nonreciprocal hopping. We investigate the topology of the Mott insulating phase and find its independence of the non-Hermiticity. The topological Mott insulators in this non-Hermitian system are characterized by four equal Chern numbers and a quantized shift of biorthogonal many-body polarizations. Furthermore, we show generic asymmetric expansion and correlation dynamics in the system.

preprint2020arXiv

Spin gapless semiconductors

Spin gapless semiconductors (SGSs) are a new class of zero gap materials which have a fully spin polarised electrons and holes. They bridge zero gap materials and half-metals. The band structures of the SGSs can have two types of energy dispersions: Dirac linear dispersion and parabolic dispersion. The Dirac type SGSs exhibit fully spin polarized Dirac cones, and offer a platform for massless and fully spin polarized spintronics as well as dissipationless edge state via quantum anomalous Hall effect. Due to its fascinating spin and charge states, they hold great potential application in spintronics. There have been tremendous efforts worldwide on searching for suitable candidates of SGSs. In particularly, there is an increasing interest in searching for Dirac type SGSs. In the past decade, a large number of Dirac or parabolic type SGSs have been predicted by density functional theory and some of parabolic SGSs have been experimentally demonstrated. The SGSs hold great potential for high speed and low-energy consumption spintronics, electronics and optoelectronics. Here, we review both Dirac and parabolic types of SGSs in different materials systems and outline the concepts of SGSs, novel spin and charge states, and potential applications of SGSs in next generation spintronic devices.

preprint2020arXiv

Statistically related many-body localization in the one-dimensional anyon Hubbard model

Many-body localization (MBL) has been widely investigated for both fermions and bosons, it is, however, much less explored for anyons. Here we numerically calculate several physical characteristics related to MBL of a one-dimensional disordered anyon-Hubbard model in both localized and delocalized regions. We figure out a logarithmically slow growth of the half-chain entanglement entropy and an area-law rather than volume-law obedience for the highly excited eigenstates in the MBL phase. The adjacent energy level gap-ratio parameter is calculated and is found to exhibit a Poisson-like probability distribution in the deep MBL phase. By studying a hybridization parameter, we reveal an intriguing effect that the statistics can induce localization-delocalization transition. Several physical quantities, such as the half-chain entanglement, the adjacent energy level gap-ratio parameter, {\color{black} the long-time limit of the particle imbalance}, and the critical disorder strength, are shown to be non-monotonically dependent on the anyon statistical angle. Furthermore, a feasible scheme based on the spectroscopy of energy levels is proposed for the experimental observation of these statistically related properties.

preprint2020arXiv

Testing the Prediction of Fuzzy Dark Matter Theory in the Milky Way Center

The fuzzy dark matter model (FDM, also known as quantum wave dark matter model) argues that light bosons with a mass of $\sim10^{-22}{\;{\rm eV}}$ are a possible candidate for dark matter in the Universe. One of the most important predictions of FDM is the formation of a soliton core instead of a density cusp at the center of galaxies. If FDM is the correct theory of dark matter, then the predicted soliton core can help to form the Central Molecular Zone (CMZ) in the Milky Way. We present high-resolution hydrodynamical simulations of gas flow patterns to constrain the properties of the soliton core based on a realistic Milky Way potential. We find that a dense center is required to form a reasonable CMZ. The size and kinematics of the CMZ offer a relatively strong constraint on the inner enclosed mass profile of the Galaxy. If a soliton core is not considered, a compact nuclear bulge alone with a radially varying mass-to-light ratio can match the observed size and kinematics of the CMZ. A soliton core model with a mass of $\approx4.0\times10^8{\; {\rm M}_{\odot}}$ and a core radius of $\approx0.05{\;{\rm kpc}}$, together with a less massive nuclear bulge with a constant mass-to-light ratio, also agrees nicely with the current data. Such a FDM soliton core corresponds to a boson mass of $\sim2-7\times10^{-22}{\;{\rm eV}}$, which could be further constrained by the improved determination of the mass-to-light ratio in the Galactic center.

preprint2020arXiv

Weak localization and anti-localization in rare earth doped topological insulators

We study magneto-transport phenomena in two rare-earth doped topological insulators, SmxFexSb2-2xTe3 and SmxBi2-xTe2Se single crystals. The magneto-transport behaviours in both compounds exhibit a systematic crossover between weak anti-localization (positive magnetoresistance) and weak localization (negative magnetoresistance) with changes in temperatures and magnetic fields. The weak localization is caused by rare-earth-doping induced magnetization, and the weak anti-localization originates from topologically protected surface states. The transition between weak localization and weak anti-localization demonstrates a gap opening at the Dirac point of surface states in the quantum diffusive regime. This work demonstrates an effective way to manipulate the magneto-transport properties of the topological insulators by rare-earth element doping. Magnetometry measurements indicate that the Sm-dopant alone is paramagnetic, whereas the co-doped Fe-Sm state has short-range antiferromagnetic order. Our results hold potential for the realization of exotic topological effects in gapped topological insulator surface states.

preprint2019arXiv

A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates

This paper proposes a novel proximal-gradient algorithm for a decentralized optimization problem with a composite objective containing smooth and non-smooth terms. Specifically, the smooth and nonsmooth terms are dealt with by gradient and proximal updates, respectively. The proposed algorithm is closely related to a previous algorithm, PG-EXTRA \cite{shi2015proximal}, but has a few advantages. First of all, agents use uncoordinated step-sizes, and the stable upper bounds on step-sizes are independent of network topologies. The step-sizes depend on local objective functions, and they can be as large as those of the gradient descent. Secondly, for the special case without non-smooth terms, linear convergence can be achieved under the strong convexity assumption. The dependence of the convergence rate on the objective functions and the network are separated, and the convergence rate of the new algorithm is as good as one of the two convergence rates that match the typical rates for the general gradient descent and the consensus averaging. We provide numerical experiments to demonstrate the efficacy of the introduced algorithm and validate our theoretical discoveries.

preprint2019arXiv

Landau-Zener-Stückelberg Interferometry in $\mathcal{PT}$-symmetric Non-Hermitian models

We systematically investigate the non-Hermitian generalisations of the Landau-Zener (LZ) transition and the Landau-Zener-Stückelberg (LZS) interferometry. The LZ transition probabilities, or band populations, are calculated for a generic non-Hermitian model and their asymptotic behaviour analysed. We then focus on non-Hermitian systems with a real adiabatic parameter and study the LZS interferometry formed out of two identical avoided level crossings. Four distinctive cases of interferometry are identified and the analytic formulae for the transition probabilities are calculated for each case. The differences and similarities between the non-Hermitian case and its Hermitian counterpart are emphasised. In particular, the geometrical phase originated from the sign change of the mass term at the two level crossings is still present in the non-Hermitian system, indicating its robustness against the non-Hermiticity. We further apply our non-Hermitian LZS theory to describing the Bloch oscillation in one-dimensional parity-time $(\mathcal{PT})$ reversal symmetric non-Hermitian Su-Schrieffer-Heeger model and propose an experimental scheme to simulate such dynamics using photonic waveguide arrays. The Landau-Zener transition, as well as the LZS interferometry, can be visualised through the beam intensity profile and the transition probabilitiess measured by the centre of mass of the profile.

preprint2019arXiv

Novel Trotter formulas for digital quantum simulation

Quantum simulation promises to address many challenges in fields ranging from quantum chemistry to material science, and high-energy physics, and could be implemented in noisy intermediate-scale quantum devices. A challenge in building good digital quantum simulators is the fidelity of the engineered dynamics given a finite set of elementary operations. Here we present a framework for optimizing the order of operations based on a geometric picture, thus abstracting from the operation details and achieving computational efficiency. Based on this geometric framework, we provide two alternative second-order Trotter expansions, one with optimal fidelity at a short time scale, and the second robust at a long time scale. Thanks to the improved fidelity at different time scale, the two expansions we introduce can form the basis for experimental-constrained digital quantum simulation.

preprint2016arXiv

A View of Fog Computing from Networking Perspective

With smart devices, particular smartphones, becoming our everyday companions, the ubiquitous mobile Internet and computing applications pervade people's daily lives. With the surge demand on high-quality mobile services at anywhere, how to address the ubiquitous user demand and accommodate the explosive growth of mobile traffics is the key issue of the next generation mobile networks. The Fog computing is a promising solution towards this goal. Fog computing extends cloud computing by providing virtualized resources and engaged location-based services to the edge of the mobile networks so as to better serve mobile traffics. Therefore, Fog computing is a lubricant of the combination of cloud computing and mobile applications. In this article, we outline the main features of Fog computing and describe its concept, architecture and design goals. Lastly, we discuss some of the future research issues from the networking perspective.

preprint2016arXiv

Dynamics of Weyl quasiparticles emerged in an optical lattice

We investigate the dynamics of the Weyl quasiparticles emerged in an optical lattice where the topological Weyl semimental and trivial band insulator phases can be adjusted with the on-site energy. The evolution of the density distribution is demonstrated to have an anomalous velocity in Weyl semimental but a steady Zitterbewegung effect in the band insulator. Our analysis demonstrates that the topological Chern number and the chirality of the system can be directly determined from the positions of the atomic center-of-mass. Furthermore, the amplitude and the period of the relativistic Zitterbewegung oscillations are shown to be observable with the time-of-flight experiments.

preprint2016arXiv

Fog Computing: Focusing on Mobile Users at the Edge

With smart devices, particular smartphones, becoming our everyday companions, the ubiquitous mobile Internet and computing applications pervade people daily lives. With the surge demand on high-quality mobile services at anywhere, how to address the ubiquitous user demand and accommodate the explosive growth of mobile traffics is the key issue of the next generation mobile networks. The Fog computing is a promising solution towards this goal. Fog computing extends cloud computing by providing virtualized resources and engaged location-based services to the edge of the mobile networks so as to better serve mobile traffics. Therefore, Fog computing is a lubricant of the combination of cloud computing and mobile applications. In this article, we outline the main features of Fog computing and describe its concept, architecture and design goals. Lastly, we discuss some of the future research issues from the networking perspective.

preprint2016arXiv

Gas dynamics in the Milky Way: a low pattern speed model

We present gas flow models for the Milky Way based on high-resolution grid-based hydrodynamical simulations. The basic galactic potential we use is from a N-body model constrained by the density of red clump giants in the Galactic bulge. We augment this potential with a nuclear bulge, two pairs of spiral arms and additional mass at the bar end to represent the long bar component. With this combined model we can reproduce many features in the observed ($l,v$) diagram with a bar pattern speed of $33\; {\rm km}\;{\rm s}^{-1}\;{\rm kpc}^{-1}$ and a spiral pattern speed of $23\; {\rm km}\;{\rm s}^{-1}\;{\rm kpc}^{-1}$. The shape and kinematics of the nuclear ring, Bania's Clump 2, the Connecting arm, the Near and Far 3-kpc arms, the Molecular Ring, and the spiral arm tangent points in our simulations are comparable to those in the observations. Our results imply that a low pattern speed model for the bar in our Milky Way reproduces the observations for a suitable Galactic potential. Our best model gives a better match to the ($l,v$) diagram than previous high pattern speed hydrodynamical simulations.

preprint2016arXiv

Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification

In this paper, we propose a multi-scale deep feature learning method for high-resolution satellite image classification. Specifically, we firstly warp the original satellite image into multiple different scales. The images in each scale are employed to train a deep convolutional neural network (DCNN). However, simultaneously training multiple DCNNs is time-consuming. To address this issue, we explore DCNN with spatial pyramid pooling (SPP-net). Since different SPP-nets have the same number of parameters, which share the identical initial values, and only fine-tuning the parameters in fully-connected layers ensures the effectiveness of each network, thereby greatly accelerating the training process. Then, the multi-scale satellite images are fed into their corresponding SPP-nets respectively to extract multi-scale deep features. Finally, a multiple kernel learning method is developed to automatically learn the optimal combination of such features. Experiments on two difficult datasets show that the proposed method achieves favorable performance compared to other state-of-the-art methods.

preprint2016arXiv

Upper bounds on the first eigenvalue for the $p$-Laplacian

In this paper, we establish gradient estimates for positive solutions to the following equation with respect to the $p$-Laplacian $$Δ_{p}u=-λ|u|^{p-2}u$$ with $p>1$ on a given complete Riemannian manifold. Consequently, we derive upper bound estimates of the first nontrivial eigenvalue of the $p$-Laplacian.

preprint2016arXiv

Visualizing the elongated vortices in $γ$-Ga nanostrips

We study the magnetic response of superconducting $γ$-Ga via low temperature scanning tunneling microscopy and spectroscopy. The magnetic vortex cores rely substantially on the Ga geometry, and exhibit an unexpectedly-large axial elongation with aspect ratio up to 40 in rectangular Ga nano-strips (width $l$ $<$ 100 nm). This is in stark contrast with the isotropic circular vortex core in a larger round-shaped Ga island. We suggest that the unusual elongated vortices in Ga nanostrips originate from geometric confinement effect probably via the strong repulsive interaction between the vortices and Meissner screening currents at the sample edge. Our finding provides novel conceptual insights into the geometrical confinement effect on magnetic vortices and forms the basis for the technological applications of superconductors.

preprint2015arXiv

Detection of a superconducting phase in a two-atom layer of hexagonal Ga film grown on semiconducting GaN(0001)

The recent observation of superconducting state at atomic scale has motivated the pursuit of exotic condensed phases in two-dimensional (2D) systems. Here we report on a superconducting phase in two-monolayer crystalline Ga films epitaxially grown on wide band-gap semiconductor GaN(0001). This phase exhibits a hexagonal structure and only 0.552 nm in thickness, nevertheless, brings about a superconducting transition temperature Tc as high as 5.4 K, confirmed by in situ scanning tunneling spectroscopy, and ex situ electrical magneto-transport and magnetization measurements. The anisotropy of critical magnetic field and Berezinski-Kosterlitz-Thouless-like transition are observed, typical for the 2D superconductivity. Our results demonstrate a novel platform for exploring atomic-scale 2D superconductor, with great potential for understanding of the interface superconductivity.

preprint2015arXiv

GdN thin-film: Chern Insulating State on Square Lattice

Using first-principles calculations, we predict a Chern insulating phase in thin films of the ferromagnetic semi-metal GdN. In contrast to previously proposed Chern insulator candidates, which mostly rely on honeycomb lattices, this system affords a great chance to realize the quantum anomalous Hall Effect on a square lattice without either a magnetic substrate or transition metal doping, making synthesis easier. The band inversion between 5d-orbitals of Gd and 2p-orbitals of N is verified by first-principles calculation based on density functional theory, and the band gap can be as large as 100 meV within GdN trilayer. With further increase of film thickness, the band gap tends to close and the metallic bulk property becomes obvious.

preprint2015arXiv

Hydrodynamical Simulations of Nuclear Rings in Barred Galaxies

Dust lanes, nuclear rings, and nuclear spirals are typical gas structures in the inner region of barred galaxies. Their shapes and properties are linked to the physical parameters of the host galaxy. We use high-resolution hydrodynamical simulations to study 2D gas flows in simple barred galaxy models. The nuclear rings formed in our simulations can be divided into two groups: one group is nearly round and the other is highly elongated. We find that roundish rings may not form when the bar pattern speed is too high or the bulge central density is too low. We also study the periodic orbits in our galaxy models, and find that the concept of inner Lindblad resonance (ILR) may be generalized by the extent of $x_2$ orbits. All roundish nuclear rings in our simulations settle in the range of $x_2$ orbits (or ILRs). However, knowing the resonances is insufficient to pin down the exact location of these nuclear rings. We suggest that the backbone of round nuclear rings is the $x_2$ orbital family, i.e. round nuclear rings are allowed only in the radial range of $x_2$ orbits. A round nuclear ring forms exactly at the radius where the residual angular momentum of infalling gas balances the centrifugal force, which can be described by a parameter $f_{\rm ring}$ measured from the rotation curve. The gravitational torque on gas in high pattern speed models is larger, leading to a smaller ring size than in the low pattern speed models. Our result may have important implications for using nuclear rings to measure the parameters of real barred galaxies with 2D gas kinematics.

preprint2015arXiv

Metal-Silicene Interaction Studied by Scanning Tunneling Microscopy

Ag atoms have been deposited on 3x3 silicene and R3xR3 silicene films by molecular beam epitaxy method in ultrahigh vacuum. Using scanning tunneling microscopy and Raman spectroscopy, we found that Ag atoms do not form chemical bonds with both 3x3 silicene and R3xR3 silicene films,which is due to chemically inert surface of silicene. On 3x3 silicene films, Ag atoms mostly form into stable flat top Ag islands. In contrast, Ag atoms form nanoclusters and glide on silicene films, suggesting more inert nature. Raman spectroscopy suggests that there is more sp2 hybridization in R3xR3 than in R7xR7/3x3silicene films

preprint2015arXiv

Monotonicity formulas of eigenvalues and energy functionals along the rescaled List's extended Ricci flow

In this paper, we study monotonicity formulas of eigenvalues and entropies along the rescaled List's extended Ricci flow. We derive some monotonicity formulas of eigenvalues of Laplacian which generalize those of Li in [8] and Cao-Hou-Ling in [3]. Moreover, we also consider monotonicity formulas of $\mathcal{F}_k$-functional which can be seen as a generalized $\mathcal{F}$-functional corresponding with steady Ricci breathers, and $\mathcal{W}_k$-functional which generalizes $\mathcal{W}$-functional corresponding with expanding Ricci breathers.

preprint2015arXiv

Observation of van Hove Singularities in Twisted Silicene Multilayers

Interlayer interactions perturb the electronic structure of two-dimensional materials and lead to new physical phenomena, such as van Hove singularities and Hofstadter's butterfly pattern. Silicene, the recently discovered two-dimensional form of silicon, is quite unique, in that silicon atoms adopt competing sp2 and sp3 hybridization states leading to a low-buckled structure promising relatively strong interlayer interaction. In multilayer silicene, the stacking order provides an important yet rarely explored degree of freedom for tuning its electronic structures through manipulating interlayer coupling. Here, we report the emergence of van Hove singularities in the multilayer silicene created by an interlayer rotation. We demonstrate that even a large-angle rotation (> 20o) between stacked silicene layers can generate a Moire pattern and van Hove singularities due to the strong interlayer coupling in multilayer silicene. Our study suggests an intriguing method for expanding the tunability of the electronic structure for electronic applications in this two-dimensional material.

preprint2015arXiv

Pressure-Driven Topological Phase Transition in the Yb Chalcogenides YbO and YbS

By first-principles calculation based on the density functional theory (DFT) with the modified Becke-Johnson local density approximation plus Hubbard U (MBJLDA+U), we studied the band structures of the Yb chalcogenides YbO and YbS under ambient and high pressures. It was revealed that both YbO and YbS have a trivial band topology under ambient pressure, and a nontrivial band topology under high pressure. The topological phase transition is reduced by the pressure-driven single-band inversion between 5d- and 4f -orbitals at the time-reversal invariant momentum (TRIM) point X. A bulk Dirac cone coexisting with a pair of metallic surface states on the [001] surface determined by tight binding model calculation with a slab geometry also demonstrates the nontrivial band topology of YbO and YbS under high pressure.

preprint2015arXiv

Stochastic Duty Cycling for Heterogenous Energy Harvesting Networks

In recent years, there have been several kinds of energy harvesting networks containing some tiny devices, such as ambient backscatter, ring and renewable sensor networks. During energy harvesting, such networks suffer from the energy heterogeneity, dynamics and prediction hardness because the access to natural resources is often spatiotemporal different and timely changing among the devices. Meanwhile, the charging efficiency is quite low especially when the power of the harvested energy is weak. It results in the energy waste to store the harvested energy indirectly. These features bring challenging and interesting issues on efficient allocation of the harvested energy. This paper studies the \emph{stochastic duty cycling} by considering these features with the objective characterized by maximizing the common active time. We consider two cases: offline and online stochastic duty cycling. For the offline case, we design an optimal solution: offline duty cycling algorithm. For the online case, we design an online duty cycling algorithm, which achieves the approximation ratio with at least $1-e^{-γ^2}$, where $γ$ is the probability able to harvest energy. We also evaluate our algorithms with the experiment on a real energy harvesting network. The experiment results show that the performance of the online algorithm can be very close to the offline algorithm.

preprint2014arXiv

ARPES insights on the metallic states of YbB6(001): E(k) dispersion, temporal changes and spatial variation

We report high resolution Angle Resolved PhotoElectron Spectroscopy (ARPES) results on the (001) cleavage surface of YbB$_{6}$, a rare-earth compound which has been recently predicted to host surface electronic states with topological character. We observe two types of well-resolved metallic states, whose Fermi contours encircle the time-reversal invariant momenta of the YbB$_{6}$(001) surface Brillouin zone, and whose full (E,$k$)-dispersion relation can be measured wholly unmasked by states from the rest of the electronic structure. Although the two-dimensional character of these metallic states is confirmed by their lack of out-of-plane dispersion, two new aspects are revealed in these experiments. Firstly, these states do not resemble two branches of opposite, linear velocity that cross at a Dirac point, but rather straightforward parabolas which terminate to high binding energy with a clear band bottom. Secondly, these states are sensitive to time-dependent changes of the YbB$_{6}$ surface under ultrahigh vacuum conditions. Adding the fact that these data from cleaved YbB$_{6}$ surfaces also display spatial variations in the electronic structure, it appears there is little in common between the theoretical expectations for an idealized YbB$_{6}$(001) crystal truncation on the one hand, and these ARPES data from real cleavage surfaces on the other.

preprint2014arXiv

Baryon spectrum in a finite-temperature AdS/QCD model

We propose a model for baryons at finite temperature using AdS/QCD correspondence. In this model, we modify the AdS spacetime into the AdS-Schwarzchild spacetime and consider a couple of bulk spinors in this background. Solving this model, we get an eigenvalue equation for the baryon masses. We then investigate the effects of temperature on the baryon masses numerically, and find an interesting temperature dependence. Our result is consistent with the results from lattice QCD and other phenomenological models.

preprint2014arXiv

Dichotomy of Electronic Structure and Superconductivity between Single-Layer and Double-Layer FeSe/SrTiO3 Films

The latest discovery of possible high temperature superconductivity in the single-layer FeSe film grown on a SrTiO3 substrate, together with the observation of its unique electronic structure and nodeless superconducting gap, has generated much attention. Initial work also found that, while the single-layer FeSe/SrTiO3 film exhibits a clear signature of superconductivity, the double-layer FeSe/SrTiO3 film shows an insulating behavior. Such a dramatic difference between the single-layer and double-layer FeSe/SrTiO3 films is surprising and the underlying origin remains unclear. Here we report our comparative study between the single-layer and double-layer FeSe/SrTiO3 films by performing a systematic angle-resolved photoemission study on the samples annealed in vacuum. We find that, like the single-layer FeSe/SrTiO3 film, the as-prepared double-layer FeSe/SrTiO3 film is insulating and possibly magnetic, thus establishing a universal existence of the magnetic phase in the FeSe/SrTiO3 films. In particular, the double-layer FeSe/SrTiO3 film shows a quite different doping behavior from the single-layer film in that it is hard to get doped and remains in the insulating state under an extensive annealing condition. The difference originates from the much reduced doping efficiency in the bottom FeSe layer of the double-layer FeSe/SrTiO3 film from the FeSe-SrTiO3 interface. These observations provide key insights in understanding the origin of superconductivity and the doping mechanism in the FeSe/SrTiO3 films. The property disparity between the single-layer and double-layer FeSe/SrTiO3 films may facilitate to fabricate electronic devices by making superconducting and insulating components on the same substrate under the same condition.

preprint2014arXiv

Electronic Evidence of an Insulator-Superconductor Transition in Single-Layer FeSe/SrTiO3 Films

In high temperature cuprate superconductors, it is now generally agreed that the parent compound is a Mott insulator and superconductivity is realized by doping the antiferromagnetic Mott insulator. In the iron-based superconductors, however, the parent compound is mostly antiferromagnetic metal, raising a debate on whether an appropriate starting point should go with an itinerant picture or a localized picture. It has been proposed theoretically that the parent compound of the iron-based superconductors may be on the verge of a Mott insulator, but so far no clear experimental evidence of doping-induced Mott transition has been available. Here we report an electronic evidence of an insulator-superconductor transition observed in the single-layer FeSe films grown on the SrTiO3 substrate. By taking angle-resolved photoemission measurements on the electronic structure and energy gap, we have identified a clear evolution of an insulator to a superconductor with the increasing doping. This observation represents the first example of an insulator-superconductor transition via doping observed in the iron-based superconductors. It indicates that the parent compound of the iron-based superconductors is in proximity of a Mott insulator and strong electron correlation should be considered in describing the iron-based superconductors.

preprint2014arXiv

Imaging the Electron-Boson Coupling in Superconducting FeSe

Scanning tunneling spectroscopy has been used to reveal signatures of a bosonic mode in the local quasiparticle density of states of superconducting FeSe films. The mode appears below Tc as a 'dip-hump' feature at energy Ω~ 4.7 kBTc beyond the superconducting gap Δ. Spectra on strained regions of the FeSe films reveal simultaneous decreases in Δand Ω. This contrasts with all previous reports on other high-Tc superconductors, where Δlocally anti-correlates with Ω. A local strong coupling model is found to reconcile the discrepancy well, and to provide a unified picture of the electron-boson coupling in unconventional superconductors.

preprint2014arXiv

New Vacuum Solar Telescope and Observations with High Resolution

The New Vacuum Solar Telescope (NVST) is a 1 meter vacuum solar telescope that aims to observe the fine structures on the Sun. The main tasks of NVST are high resolution imaging and spectral observations, including the measurements of solar magnetic field. NVST is the primary ground-based facility of Chinese solar community in this solar cycle. It is located by the Fuxian Lake of southwest China, where the seeing is good enough to perform high resolution observations. In this paper, we first introduce the general conditions of Fuxian Solar Observatory and the primary science cases of NVST. Then, the basic structures of this telescope and instruments are described in detail. Finally, some typical high resolution data of solar photosphere and chromosphere are also shown.

preprint2014arXiv

Streaming Video over HTTP with Consistent Quality

In conventional HTTP-based adaptive streaming (HAS), a video source is encoded at multiple levels of constant bitrate representations, and a client makes its representation selections according to the measured network bandwidth. While greatly simplifying adaptation to the varying network conditions, this strategy is not the best for optimizing the video quality experienced by end users. Quality fluctuation can be reduced if the natural variability of video content is taken into consideration. In this work, we study the design of a client rate adaptation algorithm to yield consistent video quality. We assume that clients have visibility into incoming video within a finite horizon. We also take advantage of the client-side video buffer, by using it as a breathing room for not only network bandwidth variability, but also video bitrate variability. The challenge, however, lies in how to balance these two variabilities to yield consistent video quality without risking a buffer underrun. We propose an optimization solution that uses an online algorithm to adapt the video bitrate step-by-step, while applying dynamic programming at each step. We incorporate our solution into PANDA -- a practical rate adaptation algorithm designed for HAS deployment at scale.

preprint2013arXiv

Direct observation of high temperature superconductivity in one-unit-cell FeSe films

Heterostructure based interface engineering has been proved an effective method for finding new superconducting systems and raising superconductivity transition temperature (TC). In previous work on one unit-cell (UC) thick FeSe films on SrTiO3 (STO) substrate, a superconducting-like energy gap as large as 20 meV, was revealed by in situ scanning tunneling microscopy/spectroscopy (STM/STS). Angle resolved photoemission spectroscopy (ARPES) further revealed a nearly isotropic gap of above 15 meV, which closes at a temperature of ~ 65 K. If this transition is indeed the superconducting transition, then the 1-UC FeSe represents the thinnest high TC superconductor discovered so far. However, up to date direct transport measurement of the 1-UC FeSe films has not been reported, mainly because growth of large scale 1-UC FeSe films is challenging and the 1-UC FeSe films are too thin to survive in atmosphere. In this work, we successfully prepared 1-UC FeSe films on insulating STO substrates with non-superconducting FeTe protection layers. By direct transport and magnetic measurements, we provide definitive evidence for high temperature superconductivity in the 1-UC FeSe films with an onset TC above 40 K and a extremely large critical current density JC ~ 1.7*106 A/cm2 at 2 K. Our work may pave the way to enhancing and tailoring superconductivity by interface engineering.

preprint2013arXiv

Mass acquisition of Dirac fermions in Cr-doped topological insulator Sb2Te3 films

We introduce time-reversal-symmetry breaking by doping Cr atoms into the topmost quintuple layer or into the bulk of Sb2Te3 thin films. We find that even at a high Cr-doping level the Landau level spectrum keeps a good quality, enabling the first demonstration of deviation of the zero-mode Landau level, induced by the acquisition of a mass term in the surface states in the presence of surface or bulk magnetic doping. The magnitude of the mass term in the surface states increases with increasing Cr-doping level. Our observation suggests Cr-doped Sb2Te3 is a promising candidate for the realization of the proposed novel magnetoelectric effects.

preprint2013arXiv

Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale

Today, the technology for video streaming over the Internet is converging towards a paradigm named HTTP-based adaptive streaming (HAS). HAS comes with two unique flavors. First, by riding on top of HTTP/TCP, it leverages the network-friendly TCP to achieve firewall/NATS traversal and bandwidth sharing. Second, by pre-encoding and storing the video in a number of discrete bitrate levels, it introduces video bitrate adaptivity in a scalable way that the video encoding is excluded from the closed-loop adaptation. A conventional wisdom is that the TCP throughput observed by a HAS client indicates the available network bandwidth, thus can be used as a reliable reference for the video bitrate selection. We argue that this no longer holds true when HAS becomes a substantial fraction of the Internet traffic. We show that when multiple HAS clients compete at a network bottleneck, the presence of competing clients and the discrete nature of the video bitrates would together create confusion for a client to correctly perceive its fair-share bandwidth. Through analysis and real experiments, we demonstrate that this fundamental limitation would lead to, for example, video rate oscillation that negatively impacts the video watching experiences. We therefore argue that it is necessary to implement at the application layer a "probe-and-adapt" mechanism for HAS video rate adaptation, which is akin but orthogonal to the transport-layer network rate adaptation achieved by TCP. We present PANDA -- a client-side rate adaptation algorithm for HAS -- as an embodiment of this idea. Our testbed results show that compared to conventional algorithms, PANDA is able to reduce the instability of video rate by 60%, at a given risk of buffer underrun.

preprint2013arXiv

Spin Dynamics and Resonant Inelastic X-ray Scattering in Chromium with Commensurate Spin-Density Wave Order

We theoretically investigate spin dynamics and $L_3$-edge resonant inelastic X-ray scattering (RIXS) of Chromium with commensurate spin-density wave (SDW) order, based on a multi-band Hubbard model composed of 3$d$ and 4$s$ orbitals. Obtaining the ground state with the SDW mean-field approximation, we calculate the dynamical transverse and longitudinal spin susceptibility by using random-phase approximation. We find that a collective spin-wave excitation seen in inelastic neutron scattering hardly damps up to $\sim$0.6 eV. Above the energy, the excitation overlaps individual particle-hole excitations as expected, leading to broad spectral weight. On the other hand, the collective spin-wave excitation in RIXS spectra has a tendency to be masked by large spectral weight coming from particle-hole excitations with various orbital channels. This is in contrast with inelastic neutron scattering, where only selected diagonal orbital channels contribute to the spectral weight. However, it may be possible to detect the spin-wave excitation in RIXS experiments in the future if resolution is high enough.

preprint2012arXiv

Electronic Origin of High Temperature Superconductivity in Single-Layer FeSe Superconductor

The latest discovery of high temperature superconductivity signature in single-layer FeSe is significant because it is possible to break the superconducting critical temperature ceiling (maximum Tc~55 K) that has been stagnant since the discovery of Fe-based superconductivity in 2008. It also blows the superconductivity community by surprise because such a high Tc is unexpected in FeSe system with the bulk FeSe exhibiting a Tc at only 8 K at ambient pressure which can be enhanced to 38 K under high pressure. The Tc is still unusually high even considering the newly-discovered intercalated FeSe system A_xFe_{2-y}Se_2 (A=K, Cs, Rb and Tl) with a Tc at 32 K at ambient pressure and possible Tc near 48 K under high pressure. Particularly interesting is that such a high temperature superconductivity occurs in a single-layer FeSe system that is considered as a key building block of the Fe-based superconductors. Understanding the origin of high temperature superconductivity in such a strictly two-dimensional FeSe system is crucial to understanding the superconductivity mechanism in Fe-based superconductors in particular, and providing key insights on how to achieve high temperature superconductivity in general. Here we report distinct electronic structure associated with the single-layer FeSe superconductor. Its Fermi surface topology is different from other Fe-based superconductors; it consists only of electron pockets near the zone corner without indication of any Fermi surface around the zone center. Our observation of large and nearly isotropic superconducting gap in this strictly two-dimensional system rules out existence of node in the superconducting gap. These results have provided an unambiguous case that such a unique electronic structure is favorable for realizing high temperature superconductivity.

preprint2012arXiv

First Principles Calculation of Helical Spin Order in Iron Perovskite SrFeO3 and BaFeO3

Motivated by recent discovery of ferromagnetism in cubic perovskite BaFeO3 under small magnetic field, we investigate spin order in BaFeO3 and isostructual SrFeO3 by the first principles calculation. The on-site Coulomb and exchange interactions are necessary for the helical spin order consistent with experiments. SrFeO3 exhibits stable G-type helical order, while A- and G-type helical orders in BaFeO3 are almost degenerate at short propagating vector with tiny energetic barrier with respect to ferromagnetic spin order, explaining ferromagnetism under small field. The results are consistent with model calculation where negative charge-transfer energy is explicitly taken into account.

preprint2012arXiv

Interface induced high temperature superconductivity in single unit-cell FeSe films on SrTiO3

Searching for superconducting materials with high transition temperature (TC) is one of the most exciting and challenging fields in physics and materials science. Although superconductivity has been discovered for more than 100 years, the copper oxides are so far the only materials with TC above 77 K, the liquid nitrogen boiling point. Here we report an interface engineering method for dramatically raising the TC of superconducting films. We find that one unit-cell (UC) thick films of FeSe grown on SrTiO3 (STO) substrates by molecular beam epitaxy (MBE) show signatures of superconducting transition above 50 K by transport measurement. A superconducting gap as large as 20 meV of the 1 UC films observed by scanning tunneling microcopy (STM) suggests that the superconductivity could occur above 77 K. The occurrence of superconductivity is further supported by the presence of superconducting vortices under magnetic field. Our work not only demonstrates a powerful way for finding new superconductors and for raising TC, but also provides a well-defined platform for systematic study of the mechanism of unconventional superconductivity by using different superconducting materials and substrates.

preprint2012arXiv

KFe_2Se_2 is the parent compound of K-doped iron selenide superconductors

We elucidate the existing controversies in the newly discovered K-doped iron selenide (KxFe2-ySe2-z) superconductors. The stoichiometric KFe2Se2 with \surd2\times\surd2 charge ordering was identified as the parent compound of KxFe2-ySe2-z superconductor using scanning tunneling microscopy and spectroscopy. The superconductivity is induced in KFe2Se2 by either Se vacancies or interacting with the anti-ferromagnetic K2Fe4Se5 compound. Totally four phases were found to exist in KxFe2-ySe2-z: parent compound KFe2Se2, superconducting KFe2Se2 with \surd2\times\surd5 charge ordering, superconducting KFe2Se2-z with Se vacancies and insulating K2Fe4Se5 with \surd5\times\surd5 Fe vacancy order. The phase separation takes place at the mesoscopic scale under standard molecular beam epitaxy condition.

preprint2012arXiv

Phase Diagram and High Temperature Superconductivity at 65 K in Tuning Carrier Concentration of Single-Layer FeSe Films

Superconductivity in the cuprate superconductors and the Fe-based superconductors is realized by doping the parent compound with charge carriers, or by application of high pressure, to suppress the antiferromagnetic state. Such a rich phase diagram is important in understanding superconductivity mechanism and other physics in the Cu- and Fe-based high temperature superconductors. In this paper, we report a phase diagram in the single-layer FeSe films grown on SrTiO3 substrate by an annealing procedure to tune the charge carrier concentration over a wide range. A dramatic change of the band structure and Fermi surface is observed, with two distinct phases identified that are competing during the annealing process. Superconductivity with a record high transition temperature (Tc) at ~65 K is realized by optimizing the annealing process. The wide tunability of the system across different phases, and its high-Tc, make the single-layer FeSe film ideal not only to investigate the superconductivity physics and mechanism, but also to study novel quantum phenomena and for potential applications.

preprint2012arXiv

Pressure Induced Ferromagnetism in Cubic Perovskite SrFeO3 and BaFeO3

The spin order in cubic perovskite SrFeO3 and BaFeO3 under high pressure is studied by density functional theory (DFT) calculation with local spin density approximation plus Hubbard U (LSDA+U). At ambient pressure, A-type and G-type helical spin orders are almost degenerate in BaFeO3 whose lattice constant is 3.97Å . When the lattice constant is reduced to 3.85 Å which is same as the lattice constant of SrFeO3 at ambient pressure, G-type helical spin order becomes stable, being consistent with SrFeO3. This is because superexchange interaction is enhanced as compared with double exchange interaction. Phase transition from helical spin state to ferromagnetic state in both SrFeO3 and BaFeO3 takes place if the lattice constant is further reduced to 3.70 Å. This is because reduced local spin moment weakens the contribution from superexchange interaction. Our result agrees with recent experimental result of BaFeO3 under high pressure. Additionally, our calculation predicts that half-metal BaFeO3 at ambient pressure will become a good metal under high pressure

preprint2011arXiv

Fermi Level Tuning of Epitaxial Sb2Te3 Thin Films on Graphene by Regulating Intrinsic Defects and Substrate Transfer Doping

High-quality Sb2Te3 films are obtained by molecular beam epitaxy on graphene substrate and investigated by in situ scanning tunneling microscopy/spectroscopy. Intrinsic defects responsible for the natural p-type conductivity of Sb2Te3 are identified to be the Sb vacancies and SbTe antisites in agreement with first-principles calculations. By minimizing defect densities, coupled with a transfer doping by the graphene substrate, the Fermi level of Sb2Te3 thin films can be tuned over the entire range of the bulk band gap. This establishes the necessary condition to explore topological insulator behaviors near the Dirac point.

preprint2011arXiv

Landau quantization and the thickness limit of topological insulator thin films of Sb2Te3

We report the experimental observation of Landau quantization of molecular beam epitaxy grown Sb2Te3 thin films by a low-temperature scanning tunneling microscope. Different from all the reported systems, the Landau quantization in Sb2Te3 topological insulator is not sensitive to the intrinsic substitutional defects in the films. As a result, a nearly perfect linear energy dispersion of surface states as 2D massless Dirac fermion system is achieved. We demonstrate that 4 quintuple layers are the thickness limit for Sb2Te3 thin film being a 3D topological insulator. The mechanism of the Landau level broadening is discussed in terms of enhanced quasiparticle lifetime.

preprint2011arXiv

Reactive processing of formaldehyde and acetaldehyde in aqueous aerosol mimics: Surface tension depression and secondary organic products

The reactive uptake of carbonyl-containing volatile organic compounds (cVOCs) by aqueous atmospheric aerosols is a likely source of particulate organic material. The aqueous-phase secondary organic products of some cVOCs are surface-active. Therefore, cVOC uptake can lead to organic film formation at the gas-aerosol interface and changes in aerosol surface tension. We examined the chemical reactions of two abundant cVOCs, formaldehyde and acetaldehyde, in water and aqueous ammonium sulfate (AS) solutions mimicking tropospheric aerosols. Secondary organic products were identified using Aerosol Chemical Ionization Mass Spectrometry (Aerosol-CIMS), and changes in surface tension were monitored using pendant drop tensiometry. Hemiacetal oligomers and aldol condensation products were identified using Aerosol-CIMS. Acetaldehyde depresses surface tension to 65(\pm2) dyn/cm in pure water (a 10% surface tension reduction from that of pure water) and 62(\pm1) dyn/cm in AS solutions (a 20.6% reduction from that of a 3.1 M AS solution). Surface tension depression by formaldehyde in pure water is negligible; in AS solutions, a 9% reduction in surface tension is observed. Mixtures of these species were also studied in combination with methylglyoxal in order to evaluate the influence of cross-reactions on surface tension depression and product formation in these systems. We find that surface tension depression in the solutions containing mixed cVOCs exceeds that predicted by an additive model based on the single-species isotherms.

preprint2010arXiv

Electronic Structure of Weakly Correlated Antiferromagnetic Metal SrCrO3: First-principles calculations

By systematic first-principles calculations, we study the electronic structure and magnetic property of SrCrO$_3$. Our results suggest that SrCrO$_3$ is a weakly correlated antiferromagnetic (AF) metal, a very rare situation in transition-metal oxides. Among various possible AF states, the C-type spin ordering with small amount of orbital polarization (dxy orbital is more occupied than the d_{yz/zx} orbital) is favored. The detailed mechanism to stabilize the C-type AF state is analyzed based on the competition between the itinerant Stoner instability and superexchange, and our results suggest that the magnetic instability rather than the orbital or lattice instabilities plays an important role in this system. The experimentally observed compressive tetragonal distortion can be naturally explained from the C-type AF state. By applying the LDA+$U$ method to study this system, we show that wrong ground state will be obtained if $U$ is large.

Zhi Li

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88 published item(s)

Data-Driven Exploration and Insights into Temperature-Dependent Phonons in Inorganic Materials

HydroAgent: Closing the Gap Between Frontier LLMs and Human Experts in Hydrologic Model Calibration via Simulator-Grounded RL

Lightweight Yet Secure: Secure Scripting Language Generation via Lightweight LLMs

Physically-Grounded Manifold Projection Model for Generalizable Metal Artifact Reduction in Dental CBCT

Tunable chiral spiral phases in a non-Hermitian Ising-Gamma spin chain

Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues

Complete space-like self-expanders in the Minkovski space

A Preliminary Research on Space Situational Awareness Based on Event Cameras

ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving Cooperative Learning

Amorphous alloys surpass E/10 strength limit at extreme strain rates

An immersed $S^{n}$ $λ$-hypersurface

Banding vs. Quality: Perceptual Impact and Objective Assessment

Dynamics in an exact solvable quantum magnet: benchmark for quantum computer

Emergent phase transition in Cluster Ising model with dissipation

Estimating the Resize Parameter in End-to-end Learned Image Compression

FourNetFlows: An efficient model for steady airfoil flows prediction

HULC: 3D Human Motion Capture with Pose Manifold Sampling and Dense Contact Guidance

Improving Maximum Likelihood Difference Scaling method to measure inter content scale

Large-scale Hydrodynamical Shocks as the Smoking Gun Evidence for a Bar in M31

Learning Meta Pattern for Face Anti-Spoofing

MoCapDeform: Monocular 3D Human Motion Capture in Deformable Scenes

One-Class Knowledge Distillation for Face Presentation Attack Detection

PF4Microservices: A decomposion scheme for microservices based on Problem Frames

Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction

Short Proofs of Linear Growth of Quantum Circuit Complexity

Topological classification of non-Hermitian bands

Direct and Indirect Communication in Multi-Human Multi-Robot Interaction

Gas Dynamics in the Galaxy: Total Mass Distribution and the Bar Pattern Speed

Learning Skill Equivalencies Across Platform Taxonomies

Learning the Implicit Semantic Representation on Graph-Structured Data

New convergence analysis of a primal-dual algorithm with large stepsizes

Ultrafast Parallel LiDAR with Time-encoding and Spectral Scanning: Breaking the Time-of-flight Limit

A New Quasi One-Dimensional Compound Ba3TiTe5 and Superconductivity Induced by Pressure

A substantial increase of Curie temperature in a new type of diluted magnetic semiconductors via effects of chemical pressure

Complete $3$-dimensional $λ$-translators in the Minkowski space $\mathbb{R}^{4}_{1}$

Complete self-shrinkers with constant norm of the second fundamental form

Do nuclear rings in barred galaxies form at the shear minimum of the rotation curve?

GPM: A Generic Probabilistic Model to Recover Annotator's Behavior and Ground Truth Labeling

Hamiltonian Tomography via Quantum Quench

Learning the Compositional Visual Coherence for Complementary Recommendations

Nonlinear Bloch-Zener oscillations for Bose-Einstein condensates in a Lieb optical lattice

Nonlinear optical response from quantum kinetic equation

Perceptual Video Quality Prediction Emphasizing Chroma Distortions

Perceptually Optimizing Deep Image Compression

Skin superfluid, topological Mott insulators, and asymmetric dynamics in interacting non-Hermitian Aubry-Andre-Harper models

Spin gapless semiconductors

Statistically related many-body localization in the one-dimensional anyon Hubbard model

Testing the Prediction of Fuzzy Dark Matter Theory in the Milky Way Center

Weak localization and anti-localization in rare earth doped topological insulators

A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates

Landau-Zener-Stückelberg Interferometry in $\mathcal{PT}$-symmetric Non-Hermitian models

Novel Trotter formulas for digital quantum simulation

A View of Fog Computing from Networking Perspective

Dynamics of Weyl quasiparticles emerged in an optical lattice

Fog Computing: Focusing on Mobile Users at the Edge

Gas dynamics in the Milky Way: a low pattern speed model

Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification

Upper bounds on the first eigenvalue for the $p$-Laplacian

Visualizing the elongated vortices in $γ$-Ga nanostrips

Detection of a superconducting phase in a two-atom layer of hexagonal Ga film grown on semiconducting GaN(0001)

GdN thin-film: Chern Insulating State on Square Lattice

Hydrodynamical Simulations of Nuclear Rings in Barred Galaxies

Metal-Silicene Interaction Studied by Scanning Tunneling Microscopy

Monotonicity formulas of eigenvalues and energy functionals along the rescaled List's extended Ricci flow

Observation of van Hove Singularities in Twisted Silicene Multilayers

Pressure-Driven Topological Phase Transition in the Yb Chalcogenides YbO and YbS

Stochastic Duty Cycling for Heterogenous Energy Harvesting Networks

ARPES insights on the metallic states of YbB6(001): E(k) dispersion, temporal changes and spatial variation

Baryon spectrum in a finite-temperature AdS/QCD model

Dichotomy of Electronic Structure and Superconductivity between Single-Layer and Double-Layer FeSe/SrTiO3 Films

Electronic Evidence of an Insulator-Superconductor Transition in Single-Layer FeSe/SrTiO3 Films

Imaging the Electron-Boson Coupling in Superconducting FeSe

New Vacuum Solar Telescope and Observations with High Resolution

Streaming Video over HTTP with Consistent Quality