arXiv subjects

Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 523 records · Page 29Linked to original sources

arXiv2022

Experimental observation of one-dimensional motion of interstitial skyrmion in FeGe

The interplay between dimensionality and topology manifests in magnetism via both exotic texture morphology and novel dynamics. A free magnetic skyrmion exhibits the skyrmion Hall effect under electric currents. Once it is confined in one-dimensional (1D) channels, the skyrmion Hall effect would be suppressed, and the current-driven skyrmion speed should be boosted by the non-adiabatic spin transfer torque \b{eta}. Here, we experimentally demonstrate that stripes of a spatially modulated spin helix serve as natural 1D channels to restrict skyrmion. Using FeGe as a benchmark, an interstitial skyrmion is created by geometry notch and further moves steadily without the skyrmion Hall effect. The slope of the current-velocity curve for 1D skyrmion is enhanced almost by an order of magnitude owing to a large \b{eta} in FeGe. This feature is also observed in other topological defects. Utilizing the 1D skyrmion dynamics would be a highly promising route to implement topological spintronic devices.

cond-mat.mes-hall↗

arXiv2022

Efficient simulation of open quantum systems coupled to a reservoir through multiple channels

The simulation of open quantum systems coupled to a reservoir through multiple channels remains a substantial challenge. This kind of open quantum system arises when considering the radiationless decay of excited states that are coupled to molecular vibrations, for example. We use the chain mapping strategy in the interaction picture to study systems linearly coupled to a harmonic bath through multiple interaction channels. In the interaction picture, the bare bath Hamiltonian is removed by a unitary transformation (the system-bath interactions remain), and a chain mapping transforms the bath modes to a new basis. The transformed Hamiltonian contains time-dependent local system-bath couplings. The open quantum system is coupled to a limited number of (transformed) bath modes in the new basis. As such, the entanglement generated by the system-bath interactions is local, making efficient dynamical simulations possible with matrix product states. We use this approach to simulate singlet fission, using a generalized spin-boson Hamiltonian. The electronic states are coupled to a vibrational bath both diagonally and off-diagonally. This approach generalizes the chain mapping scheme to the case of multi-channel system-bath couplings, enabling the efficient simulation of this class of open quantum systems using matrix product states.

quant-ph↗

arXiv2022

Measurement of Double Beta Decay Half-life of $^{136}$Xe with the PandaX-4T Detector

Precise measurement of two-neutrino double beta decay (DBD) half-life is an important step for the searches of Majorana neutrinos with neutrinoless double beta decay. We report the measurement of DBD half-life of $^{136}$Xe using the PandaX-4T dual-phase Time Projection Chamber (TPC) with 3.7-tonne natural xenon and the first 94.9-day physics data release. The background model in the fiducial volume is well constrained in situ by events in the outer active region. With a $^{136}$Xe exposure of 15.5 kg-year, we establish the half-life as $2.27 \pm 0.03 (stat.)\pm 0.10 (syst.)\times 10^{21}$ years. This is the first DBD half-life measurement with natural xenon and demonstrates the physics capability of a large-scale liquid xenon TPC in the field of rare event searches.

nucl-ex↗

arXiv2022

Explore of exfoliable multifunctional high-k two-dimensional oxides

As the continuing down-scaling of field-effect transistors (FETs) in more-than-Moore integrated circuits, finding new functional two-dimensional (2D) materials with a higher dielectric constant (high-k) serve as gate dielectrics is critical. Here, we identify dozens of binary 2D oxides by screening potentially exfoliable bulk metal oxides despite of their non-layered structures followed by simulation of the exfoliation process. For dynamically stable materials, we fully characterize their static dielectric constants and electronic structures, among which GeO2(011)/(101)/(1-11) 2D oxides exhibit unusually high k values (85-99), being much higher than the k of the currently highly regarded 2D dielectrics CaF2 (k ~6) and \b{eta}-Bi2SeO5 (k ~22), together with band gap of 3.3 eV. We further design 2D high-k oxides/2D semiconductors (such as MoS2) heterostructures, and determine by DFT calculations whether they can form Van der Waals interfaces to evaluate their compatibility as gate dielectrics in 2D FETs. In addition to dielectric properties, we also explore magnetic and mechanical properties of potentially exfoliable 2D oxides, revealing a number of functional materials that can be studied experimentally, notably including ferromagnetic half semiconductors, non-magnetic spintronic materials, flexible high-k 2D oxides, and auxetic monolayers.

cond-mat.mtrl-sci↗

arXiv2022

CL4CTR: A Contrastive Learning Framework for CTR Prediction

Many Click-Through Rate (CTR) prediction works focused on designing advanced architectures to model complex feature interactions but neglected the importance of feature representation learning, e.g., adopting a plain embedding layer for each feature, which results in sub-optimal feature representations and thus inferior CTR prediction performance. For instance, low frequency features, which account for the majority of features in many CTR tasks, are less considered in standard supervised learning settings, leading to sub-optimal feature representations. In this paper, we introduce self-supervised learning to produce high-quality feature representations directly and propose a model-agnostic Contrastive Learning for CTR (CL4CTR) framework consisting of three self-supervised learning signals to regularize the feature representation learning: contrastive loss, feature alignment, and field uniformity. The contrastive module first constructs positive feature pairs by data augmentation and then minimizes the distance between the representations of each positive feature pair by the contrastive loss. The feature alignment constraint forces the representations of features from the same field to be close, and the field uniformity constraint forces the representations of features from different fields to be distant. Extensive experiments verify that CL4CTR achieves the best performance on four datasets and has excellent effectiveness and compatibility with various representative baselines.

cs.IR↗

arXiv2022

Trace the Accretion Geometry of H 1743--322 with Type C Quasi-periodic Oscillations in Multiple Outbursts

We present a systematic analysis of type C quasi-periodic oscillation (QPO) observations of H 1743--322 throughout the Rossi X-ray Timing Explorer (RXTE) era. We find that, while different outbursts have significant flux differences, they show consistent positive correlations between the QPO fractional root-mean-square (rms) amplitude and non-thermal fraction of the emission, which indicate an independence of the intrinsic QPO rms on individual outburst brightness in H 1743--322. However, the dependence of the QPO rms on frequency is different between the outburst rise and decay phases, where QPO fractional rms of the decay phase is significantly lower than that of the rise phase at low frequencies. The spectral analysis also reveals different ranges of coronal temperature between the two outburst stages. A semi-quantitative analysis shows that the Lense-Thirring precession model could be responsible for the QPO rms differences, requiring a variable coronal geometric shape. However, the variable-Comptonization model could also account for the findings. The fact that the rms differences and the hysteresis traces in the hardness-intensity diagram (HID) accompany each other indicates a connection between the two phenomena. By correlating the findings with QPO phase lags and the quasi-simultaneous radio flux previously published, we propose there could be corona-jet transitions in H 1743--322 similar to those that have been recently reported in GRS 1915+105.

astro-ph.HE↗

arXiv2022

Hardness Results for Minimizing the Covariance of Randomly Signed Sum of Vectors

Given vectors $\mathbb{v}_1, \ldots, \mathbb{v}_n \in \mathbb{R}^d$ with Euclidean norm at most $1$ and $\mathbb{x}_0 \in [-1,1]^n$, our goal is to sample a random signing $\mathbb{x} \in \{\pm 1\}^n$ with $\mathbb{E}[\mathbb{x}] = \mathbb{x}_0$ such that the operator norm of the covariance of the signed sum of the vectors $\sum_{i=1}^n \mathbb{x}(i) \mathbb{v}_i$ is as small as possible. This problem arises from the algorithmic discrepancy theory and its application in the design of randomized experiments. It is known that one can sample a random signing with expectation $\mathbb{x}_0$ and the covariance operator norm at most $1$. In this paper, we prove two hardness results for this problem. First, we show it is NP-hard to distinguish a list of vectors for which there exists a random signing with expectation ${\bf 0}$ such that the operator norm is $0$ from those for which any signing with expectation ${\bf 0}$ must have the operator norm $Ω(1)$. Second, we consider $\mathbb{x}_0 \in [-1,1]^n$ whose entries are all around an arbitrarily fixed $p \in [-1,1]$. We show it is NP-hard to distinguish a list of vectors for which there exists a random signing with expectation $\mathbb{x}_0$ such that the operator norm is $0$ from those for which any signing with expectation ${\bf 0}$ must have the operator norm $Ω((1-|p|)^2)$.

cs.CC↗

arXiv2022

Generation of nonparaxial self-accelerating beams using pendant droplets

We propose and demonstrate the effectual generation and control of nonparaxial self-accelerating beams by using UV-resin pendant droplets. We show that the geometrical shape of the hanging droplets formed as a result of the interplay between surface tension and gravity offers a natural curvature enabling the generation of nonparaxial self-accelerating beams. By simply adjusting the tilt angle of the surface where the droplets reside, a passing light beam is set to propagate along different curved trajectories, bending into large angles with non-diffracting features superior to a conventional Airy beam. Such self-accelerating beams are directly traced experimentally through the scattered light in yeast-cell suspensions, along with extensive ray tracing and numerical simulations. Furthermore, by modifying the shape of uncured pendant resin droplets in real time, we showcase the dynamical trajectory control of the self-accelerating beams. Our scheme and experimental method may be adopted for droplet-based shaping of other waves such as microfluidic jets and surface acoustic waves.

physics.optics↗

arXiv2022

Recovering Fine Details for Neural Implicit Surface Reconstruction

Recent works on implicit neural representations have made significant strides. Learning implicit neural surfaces using volume rendering has gained popularity in multi-view reconstruction without 3D supervision. However, accurately recovering fine details is still challenging, due to the underlying ambiguity of geometry and appearance representation. In this paper, we present D-NeuS, a volume rendering-base neural implicit surface reconstruction method capable to recover fine geometry details, which extends NeuS by two additional loss functions targeting enhanced reconstruction quality. First, we encourage the rendered surface points from alpha compositing to have zero signed distance values, alleviating the geometry bias arising from transforming SDF to density for volume rendering. Second, we impose multi-view feature consistency on the surface points, derived by interpolating SDF zero-crossings from sampled points along rays. Extensive quantitative and qualitative results demonstrate that our method reconstructs high-accuracy surfaces with details, and outperforms the state of the art.

cs.CV↗

arXiv2022

Noise-Resilient Quantum Power Flow

Quantum power flow (QPF) provides inspiring directions for tackling power flow's computational burdens leveraging quantum computing. However, existing QPF methods are mainly based on noise-sensitive quantum algorithms, whose practical utilization is significantly hindered by the limited capability of today's noisy-intermediate-scale quantum (NISQ) devices. This paper devises a NISQ-QPF algorithm, which enables power flow calculation on noisy quantum computers. The main contributions include: (1) a variational quantum circuit (VQC)-based AC power flow formulation, which enables QPF using short-depth quantum circuits; (2) noise-resilient QPF solvers based on the variational quantum linear solver (VQLS) and modified fast decoupled power flow; (3) a practical NISQ-QPF framework for implementable and reliable power flow analysis on noisy quantum machines. Promising case studies validate the effectiveness and accuracy of NISQ-QPF on IBM's real, noisy quantum devices.

quant-ph↗

arXiv2022

Coherence-Assisted Superradiant Laser with Hz Linewidth and $10^{-10}$W Power

The superradiant laser, based on the clock transition between the electric ground state $^1$S$_0$ and the metastable state $^3$P$_0$ of fermionic alkaline-earth(-like) atoms, has been proposed to be a new promising light source with linewidth being the order of millihertz. However, due to the small $^1$S$_0$-to-$^3$P$_0$ transition strength, the steady-state power in that system is relatively low ($\sim 10^{-12}$W). In this work, we propose an alternative superradiant laser scheme based on a Raman-transition-induced coupling between the $^3$P$_0$ and $^3$P$_1$ states in bosonic alkaline-earth(-like) atoms, and achieve a laser with linewidth $\lesssim 2π\times1$Hz and power $\gtrsim 10^{-10}$W ($\sim 10^{3}$ photons in steady state) at a small pumping cost. The Raman beams play two significant roles in our scheme. First, the coherence between the dark and bright states induced by the Raman beams produce a new local minimum in the pumping-linewidth curve with pumping rate lower than $2π\times 10$kHz, which is beneficial for continuous output. Second, the Raman beams mix the long-lived $^3$P$_0$ state into the lasing state and thus reduce the linewidth. Our work greatly improves the output performance of the superradiant laser system with coherence induced by Raman transitions and may offer a firm foundation for its practical use in future.

quant-ph↗

arXiv2022

Measured potential profile in a quantum anomalous Hall system suggests bulk-dominated current flow

Ideally, quantum anomalous Hall systems should display zero longitudinal resistance. Yet in experimental quantum anomalous Hall systems elevated temperature can make the longitudinal resistance finite, indicating dissipative flow of electrons. Here, we show that the measured potentials at multiple locations within a device at elevated temperature are well-described by solution of Laplace's equation, assuming spatially-uniform conductivity, suggesting non-equilibrium current flows through the two-dimensional bulk. Extrapolation suggests that at even lower temperatures current may still flow primarily through the bulk rather than, as had been assumed, through edge modes. An argument for bulk current flow previously applied to quantum Hall systems supports this picture.

cond-mat.mes-hall↗

arXiv2022

Knowing the Past to Predict the Future: Reinforcement Virtual Learning

Reinforcement Learning (RL)-based control system has received considerable attention in recent decades. However, in many real-world problems, such as Batch Process Control, the environment is uncertain, which requires expensive interaction to acquire the state and reward values. In this paper, we present a cost-efficient framework, such that the RL model can evolve for itself in a Virtual Space using the predictive models with only historical data. The proposed framework enables a step-by-step RL model to predict the future state and select optimal actions for long-sight decisions. The main focuses are summarized as: 1) how to balance the long-sight and short-sight rewards with an optimal strategy; 2) how to make the virtual model interacting with real environment to converge to a final learning policy. Under the experimental settings of Fed-Batch Process, our method consistently outperforms the existing state-of-the-art methods.

cs.LG↗

arXiv2022

Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations

Point cloud segmentation with scene-level annotations is a promising but challenging task. Currently, the most popular way is to employ the class activation map (CAM) to locate discriminative regions and then generate point-level pseudo labels from scene-level annotations. However, these methods always suffer from the point imbalance among categories, as well as the sparse and incomplete supervision from CAM. In this paper, we propose a novel weighted hypergraph convolutional network-based method, called WHCN, to confront the challenges of learning point-wise labels from scene-level annotations. Firstly, in order to simultaneously overcome the point imbalance among different categories and reduce the model complexity, superpoints of a training point cloud are generated by exploiting the geometrically homogeneous partition. Then, a hypergraph is constructed based on the high-confidence superpoint-level seeds which are converted from scene-level annotations. Secondly, the WHCN takes the hypergraph as input and learns to predict high-precision point-level pseudo labels by label propagation. Besides the backbone network consisting of spectral hypergraph convolution blocks, a hyperedge attention module is learned to adjust the weights of hyperedges in the WHCN. Finally, a segmentation network is trained by these pseudo point cloud labels. We comprehensively conduct experiments on the ScanNet and S3DIS segmentation datasets. Experimental results demonstrate that the proposed WHCN is effective to predict the point labels with scene annotations, and yields state-of-the-art results in the community. The source code is available at http://zhiyongsu.github.io/Project/WHCN.html.

cs.CV↗

arXiv2022

Biological free energy transduction is an Achilles heel of mean-field transport theory

Studies of nanoscale biological transport often use a mean-field approximation that is exact only when the system is at equilibrium and there are no interactions between particles on different sites in the network. We explore the limitations of this approximation to describe many-particle transport in the context of enzyme function and biological transport networks. Our focus is on three bioenergetic networks: a linear electron transfer chain (as found in bacterial nanowires), a redox-coupled proton pump (as in complex IV of respiration), and a near reversible electron bifurcation network (as in complex III of respiration and other recently discovered structures). Away from equilibrium and with typical site-site interactions, we find that the mean-field approximation adequately describes linear transport chains. However, the mean-field approximation fails catastrophically to describe energy-transducing systems, as in the redox coupled proton pump and reversible electron bifurcation reactions. The mean-field approximation fails to capture the essential correlations that are needed to prevent slippage events and to produce efficient energy transduction.

physics.bio-ph↗

arXiv2022

MorphTE: Injecting Morphology in Tensorized Embeddings

In the era of deep learning, word embeddings are essential when dealing with text tasks. However, storing and accessing these embeddings requires a large amount of space. This is not conducive to the deployment of these models on resource-limited devices. Combining the powerful compression capability of tensor products, we propose a word embedding compression method with morphological augmentation, Morphologically-enhanced Tensorized Embeddings (MorphTE). A word consists of one or more morphemes, the smallest units that bear meaning or have a grammatical function. MorphTE represents a word embedding as an entangled form of its morpheme vectors via the tensor product, which injects prior semantic and grammatical knowledge into the learning of embeddings. Furthermore, the dimensionality of the morpheme vector and the number of morphemes are much smaller than those of words, which greatly reduces the parameters of the word embeddings. We conduct experiments on tasks such as machine translation and question answering. Experimental results on four translation datasets of different languages show that MorphTE can compress word embedding parameters by about 20 times without performance loss and significantly outperforms related embedding compression methods.

cs.CL↗

arXiv2022

Programming Bare-Metal Accelerators with Heterogeneous Threading Models: A Case Study of Matrix-3000

As the hardware industry moves towards using specialized heterogeneous many-cores to avoid the effects of the power wall, software developers are finding it hard to deal with the complexity of these systems. This article shares our experience when developing a programming model and its supporting compiler and libraries for Matrix-3000, which is designed for next-generation exascale supercomputers but has a complex memory hierarchy and processor organization. To assist its software development, we developed a software stack from scratch that includes a low-level programming interface and a high-level OpenCL compiler. Our low-level programming model offers native programming support for using the bare-metal accelerators of Matrix-3000, while the high-level model allows programmers to use the OpenCL programming standard. We detail our design choices and highlight the lessons learned from developing systems software to enable the programming of bare-metal accelerators. Our programming models have been deployed to the production environment of an exascale prototype system.

cs.PL↗

arXiv2022

Iteratively Reweighte Least Squares Method for Estimating Polyserial and Polychoric Correlation Coefficients

An iteratively reweighted least squares (IRLS) method is proposed for estimating polyserial and polychoric correlation coefficients in this paper. It iteratively calculates the slopes in a series of weighted linear regression models fitting on conditional expected values. For polyserial correlation coefficient, conditional expectations of the latent predictor is derived from the observed ordinal categorical variable, and the regression coefficient is obtained using weighted least squares method. In estimating polychoric correlation coefficient, conditional expectations of the response variable and the predictor are updated in turns. Standard errors of the estimators are obtained using the delta method based on data summaries instead of the whole data. Conditional univariate normal distribution is exploited and a single integral is numerically evaluated in the proposed algorithm, comparing to the double integral computed numerically based on the bivariate normal distribution in the traditional maximum likelihood (ML) approaches. This renders the new algorithm very fast in estimating both polyserial and polychoric correlation coefficients. Thorough simulation studies are conducted to compare the performances of the proposed method with the classical ML methods. Real data analyses illustrate the advantage of the new method in computation speed.

stat.ME↗