arXiv subjects

Wei Cui

Publications and source records attributed to Wei Cui.

At least 73 records · Page 4Linked to original sources

arXiv2024

Evolution and distribution of superbubbles in simulated Milky Way-like galaxies

Stellar feedback plays a crucial role in regulating baryon cycles of a galactic ecosystem, and may manifest itself in the formation of superbubbles in the interstellar medium. In this work, we used a set of high-resolution simulations to systematically study the properties and evolution of superbubbles in galactic environments. The simulations were based on the SMUGGLE galaxy formation framework using the hydrodynamical moving-mesh code Arepo, reaching a spatial resolution of $\sim 4 \, \rm pc$ and mass resolution of $\sim 10^3 \, \rm M_{\odot}$. We identified superbubbles and tracked their time evolution using the parent stellar associations within the bubbles. The X-ray luminosity-size distribution of superbubbles in the fiducial run is largely consistent with the observations of nearby galaxies. The size of superbubbles shows a double-peaked distribution, with the peaks attributed to early feedback (radiative and stellar wind feedback) and supernova feedback. The early feedback tends to suppress the subsequent supernova feedback, and it is strongly influenced by star formation efficiency, which regulates the environmental density. Our results show that the volume filling factor of hot gas ($T > 10^{5.5} ~\mathrm{K}$) is about $12 \%$ averaged over a region of 4 kpc in height and 20 kpc in radius centered on the disk of the galaxy. Overall, the properties of superbubbles are sensitive to the choice of subgrid galaxy formation models and can, therefore, be used to constrain these models.

astro-ph.GA↗

arXiv2024

Quantum-Inspired Machine Learning for Molecular Docking

Molecular docking is an important tool for structure-based drug design, accelerating the efficiency of drug development. Complex and dynamic binding processes between proteins and small molecules require searching and sampling over a wide spatial range. Traditional docking by searching for possible binding sites and conformations is computationally complex and results poorly under blind docking. Quantum-inspired algorithms combining quantum properties and annealing show great advantages in solving combinatorial optimization problems. Inspired by this, we achieve an improved in blind docking by using quantum-inspired combined with gradients learned by deep learning in the encoded molecular space. Numerical simulation shows that our method outperforms traditional docking algorithms and deep learning-based algorithms over 10\%. Compared to the current state-of-the-art deep learning-based docking algorithm DiffDock, the success rate of Top-1 (RMSD<2) achieves an improvement from 33\% to 35\% in our same setup. In particular, a 6\% improvement is realized in the high-precision region(RMSD<1) on molecules data unseen in DiffDock, which demonstrates the well-generalized of our method.

physics.chem-ph↗

arXiv2024

Variational Quantum Circuits Enhanced Generative Adversarial Network

Generative adversarial network (GAN) is one of the widely-adopted machine-learning frameworks for a wide range of applications such as generating high-quality images, video, and audio contents. However, training a GAN could become computationally expensive for large neural networks. In this work, we propose a hybrid quantum-classical architecture for improving GAN (denoted as QC-GAN). The performance was examed numerically by benchmarking with a classical GAN using MindSpore Quantum on the task of hand-written image generation. The generator of the QC-GAN consists of a quantum variational circuit together with a one-layer neural network, and the discriminator consists of a traditional neural network. Leveraging the entangling and expressive power of quantum circuits, our hybrid architecture achieved better performance (Frechet Inception Distance) than the classical GAN, with much fewer training parameters and number of iterations for convergence. We have also demonstrated the superiority of QC-GAN over an alternative quantum GAN, namely pathGAN, which could hardly generate 16$\times$16 or larger images. This work demonstrates the value of combining ideas from quantum computing with machine learning for both areas of Quantum-for-AI and AI-for-Quantum.

quant-ph↗

arXiv2023

Classical Shadow Tomography with Mutually Unbiased Bases

Classical shadow tomography, harnessing randomized informationally complete (IC) measurements, provides an effective avenue for predicting many properties of unknown quantum states with sample-efficient precision. Projections onto $2^n+1$ mutually unbiased bases (MUBs) are widely recognized as minimal and optimal IC measurements for full-state tomography. We study how to use MUBs circuits as the ensemble in classical shadow tomography. For the general observables, the variance to predict their expectation value is shown to be exponential to the number of qubits $n$. However, for a special class termed as appropriate MUBs-average (AMA) observables, the variance decreases to $poly(n)$. Additionally, we find that through biased sampling of MUBs circuits, the variance for non-AMA observables can again be reduced to $poly(n)$ with the MUBs-sparse condition. The performance and complexity of using the MUBs and Clifford circuits as the ensemble in the classical shadow tomography are compared in the end.

quant-ph↗

arXiv2023

High-field THz source centered at 2.6 THz

We demonstrate a table-top high-field terahertz (THz) source based on optical rectification of a collimated near-infrared pulse in gallium phosphide (GaP) to produce peak fields exceeding 300 kV/cm with a spectrum centered at 2.6 THz. The experimental configuration, based on tilted-pulse-front phase matching, is implemented with a phase grating etched directly onto the front surface of the GaP crystal. Although the THz generation efficiency starts showing a saturation onset as the near-infrared pulse energy reaches 0.57 mJ, we can expect our configuration to yield THz peak fields up to 866 kV/cm when a 5 mJ generation NIR pulse is used. This work paves the way towards broadband, high-field THz sources able to access a new class of THz coherent control and nonlinear phenomena driven at frequencies above 2 THz.

physics.optics↗

arXiv2023

Sub-zeptojoule detection of terahertz pulses by parametric frequency upconversion

We combine parametric frequency upconversion with single-photon counting technology to achieve detection sensitivity down to the terahertz (THz) single-photon level. Our relatively simple detection scheme employs a near-infrared ultrafast source, a GaP nonlinear crystal, optical filters, and a single photon avalanche diode. This configuration is capable of detecting a weak THz signal with an energy of 590 zJ contained within a single pulse. Through averaging over 50k data points, the configuration can resolve a 0.5 zJ pulse energy, corresponding to an average of 0.5 photon per pulse. The corresponding noise-equivalent power and THz-to-NIR photon detection efficiency are $4.1 \times 10^{-17} W/\sqrt{Hz}$ and 0.19%, respectively. To test our scheme, we perform spectroscopy of water vapor between 1.0 and 3.7 THz and obtain results in agreement with to those acquired with a standard electro-optic sampling (EOS) method. Our technique provides a 0.2 THz spectral resolution offering a fast alternative to EOS THz detection for monitoring specific spectral components in THz spectroscopy, imaging and communications applications.

physics.optics↗

arXiv2023

Performance analysis of table-top single-pulse terahertz detection up to 1.1 MHz

Slow data acquisition in terahertz time-domain spectroscopy (THz-TDS) has hindered the technique's ability to resolve "fast" dynamics occurring on the microsecond timescale. This timescale, arguably too slow to be accessed via standard optical pump-probe techniques relying on ultrafast sources, hosts a range of phenomena that has been left unexplored due to a lack of proper real-time monitoring techniques. In this work, chirped-pulse spectral encoding, a photonic time-stretch technique, and high-speed electronics are used to demonstrate time-resolved THz detection at a rate up to 1.1 MHz. This configuration relies on a table-top source and a setup able to resolve every THz transient that it can generate. We investigate the performance of this system at different acquisition rates in terms of experimental noise, dynamic range, and signal-to-noise ratio. Our results pave the way towards single-pulse THz-TDS at arbitrarily fast rates to monitor complex dynamics in real-time.

physics.optics↗

arXiv2023

Scientific Objectives of the Hot Universe Baryon Surveyor (HUBS) Mission

The Hot Universe Baryon Surveyor (HUBS) is a proposed space-based X-ray telescope for detecting X-ray emissions from the hot gas content in our universe. With its unprecedented spatially-resolved high-resolution spectroscopy and large field of view, the HUBS mission will be uniquely qualified to measure the physical and chemical properties of the hot gas in the interstellar medium, the circumgalactic medium, the intergalactic medium, and the intracluster medium. These measurements will be valuable for two key scientific goals of HUBS, namely to unravel the AGN and stellar feedback physics that governs the formation and evolution of galaxies, and to probe the baryon budget and multi-phase states from galactic to cosmological scales. In addition to these two goals, the HUBS mission will also help us solve some problems in the fields of galaxy clusters, AGNs, diffuse X-ray backgrounds, supernova remnants, and compact objects. This paper discusses the perspective of advancing these fields using the HUBS telescope.

astro-ph.GA↗

arXiv2023

An XMM-Newton View of the ANdromeda Galaxy as Explored in a Legacy Survey (New-ANGELS) I: the X-ray Source Catalogue

We introduce the New-ANGELS program, an XMM-Newton survey of $\sim7.2\rm~deg^2$ area around M 31, which aims to study the X-ray populations in M 31 disk and the X-ray emitting hot gas in the inner halo of M 31 up to 30 kpc. In this first paper, we report the catalogue of 4506 detected X-ray sources, and attempt to cross-identify or roughly classify them. We identify 352 single stars in the foreground, 35 globular clusters and 27 supernova remnants associated with M 31, as well as 62 AGNs, 59 galaxies, and 1 galaxy clusters in the background. We uniquely classify 236 foreground stars and 17 supersoft sources based on their X-ray colors. X-ray binaries (83 LMXBs, 1 HMXBs) are classified based on their X-ray colors and X-ray variabilities. The remaining X-ray sources either have too low S/N to calculate their X-ray colors or do not have a unique classification, so are regarded as unclassified. The X-ray source catalogue is published online. Study of the X-ray source populations and the contribution of X-ray sources in the unresolved X-ray emissions based on this catalogue will be published in companion papers.

astro-ph.HE↗

arXiv2023

Insight-HXMT Measurements of the Diffuse X-ray Background

We present an X-ray spectrum of the diffuse X-ray background (DXRB) between 1.5 and 120 keV, as measured with the Low-Energy Detector (LE) and the High-Energy Detector (HE) aboard the Insight-HXMT satellite, based on 'blank-sky' observations. LE covers a nominal energy range of 1-15 keV and HE 20-250 keV, but calibration issues and data quality narrowed the energy range for this work. The LE background was directly measured with `blind' detector modules, while the HE background was derived from Earth-occultation data. With the LE data alone, the measured DXRB spectrum can be well described by a power law; fitting the LE and HE data jointly, however, a spectral cut-off must be introduced in the model to account for the measurements above 30 keV. Modelling the combined spectrum with a cut-off power law, the best-fit photon index is 1.40, normalisation $9.57$~$\rm ph~cm^{-2}~s^{-1}~keV^{-1}~sr^{-1} $ (at 1 keV), and cut-off energy 55 keV, after correcting for the effects of the Earth albedo and atmospheric emission (which are significant in the HE band). Based on the best-fit cut-off power law, we derived the spectral energy distribution (SED) of the DXRB. The shape of the SED is in general agreement with the published measurements, but the overall normalization is lower by varying amounts, except for the HEAO-1 result, with which our result is in good agreement.

astro-ph.HE↗

arXiv2023

Tutel: Adaptive Mixture-of-Experts at Scale

Sparsely-gated mixture-of-experts (MoE) has been widely adopted to scale deep learning models to trillion-plus parameters with fixed computational cost. The algorithmic performance of MoE relies on its token routing mechanism that forwards each input token to the right sub-models or experts. While token routing dynamically determines the amount of expert workload at runtime, existing systems suffer inefficient computation due to their static execution, namely static parallelism and pipelining, which does not adapt to the dynamic workload. We present Flex, a highly scalable stack design and implementation for MoE with dynamically adaptive parallelism and pipelining. Flex designs an identical layout for distributing MoE model parameters and input data, which can be leveraged by all possible parallelism or pipelining methods without any mathematical inequivalence or tensor migration overhead. This enables adaptive parallelism/pipelining optimization at zero cost during runtime. Based on this key design, Flex also implements various MoE acceleration techniques. Aggregating all techniques, Flex finally delivers huge speedup at any scale -- 4.96x and 5.75x speedup of a single MoE layer over 16 and 2,048 A100 GPUs, respectively, over the previous state-of-the-art. Our evaluation shows that Flex efficiently and effectively runs a real-world MoE-based model named SwinV2-MoE, built upon Swin Transformer V2, a state-of-the-art computer vision architecture. On efficiency, Flex accelerates SwinV2-MoE, achieving up to 1.55x and 2.11x speedup in training and inference over Fairseq, respectively. On effectiveness, the SwinV2-MoE model achieves superior accuracy in both pre-training and down-stream computer vision tasks such as COCO object detection than the counterpart dense model, indicating the readiness of Flex for end-to-end real-world model training and inference.

cs.DC↗

arXiv2023

SymTFTs and Duality Defects from 6d SCFTs on 4-manifolds

In this work we study particular TQFTs in three dimensions, known as Symmetry Topological Field Theories (or SymTFTs), to identify line defects of two-dimensional CFTs arising from the compactification of 6d $(2,0)$ SCFTs on 4-manifolds $M_4$. The mapping class group of $M_4$ and the automorphism group of the SymTFT switch between different absolute 2d theories or global variants. Using the combined symmetries, we realize the topological defects in these global variants. Our main example is $\mathbb{P}^1 \times \mathbb{P}^1$. For $N$ M5-branes the corresponding 2d theory inherits $\mathbb{Z}_N$ $0$-form symmetries from the SymTFT. We reproduce the orbifold groupoid for theories with $\mathbb{Z}_N$ $0$-form symmetries and realize the duality defects at fixed points of the coupling constant under elements of the mapping class group. We also study other Hirzebruch surfaces, del Pezzo surfaces, as well as the connected sum of $\mathbb{P}^1 \times \mathbb{P}^1$. We find a rich network of global variants connected via automorphisms and realize more interesting topological defects. Finally, we derive the SymTFT on more general 4-manifolds and provide two examples.

hep-th↗

arXiv2023

Correlation Functions in the TsT/$T{\bar T}$ Correspondence

We investigate the proposed holographic duality between the TsT transformation of IIB string theory on AdS$_3\times {\cal N}$ with NS-NS flux and a single-trace $T\bar{T}$ deformation of the symmetric orbifold CFT. We present a non-perturbative calculation of two-point correlation functions using string theory and demonstrate their consistency with those of the $T\bar{T}$ deformation. The two-point correlation function of the deformed theory on the plane, written in momentum space, is obtained from that of the undeformed theory by replacing $h$ with $h+2{\tilde λ\over w} p\bar p$, where $h$ is the spacetime conformal weight, $\tilde λ$ is a deformation parameter, $p$ and $\bar p$ are the momenta, and $w$ labels the twisted sectors in the deformed symmetric product. At $w=1$, the non-perturbative result satisfies the Callan-Symanzik equation for double-trace $T\bar T$ deformed CFT derived in \cite{Cardy:2019qao}. We also perform conformal perturbations on both the worldsheet CFT and the symmetric orbifold CFT as a sanity check. The perturbative and non-perturbative matching between results on the two sides provides further evidence of the conjectured TsT/$T\bar{T}$ correspondence.

hep-th↗

arXiv2023

An Evaluation System for DeFi Lending Protocols

With the development of decentralized finance (DeFi), lending protocols have been increasingly proposed in the market. A comprehensive and in-depth evaluation of lending protocol is essential to the DeFi market participants. Due to the short development time of DeFi, the current evaluation is limited to the single evaluation indicator, such as total locked value (TVL). To our knowledge, we are the first to study the evaluation model for DeFi lending protocol. In this paper, we build a four-layer lending protocol evaluation model based on Analytic Hierarchy Process (AHP). The model contains three first-level indicators and ten second-level indicators, covering various aspects of the performance of lending protocol. We calculated these indicators by obtaining on-chain and off-chain data of lending protocol. Then we evaluated and ranked six mainstream lending protocols utilizing the proposed model during the period 2021/8/20-2022/11/10. Through comparative analysis of evaluation result, we found that the violent decline in the price of ETH increase the market share of stablecoin in the lending protocols. In addition, we also revealed the reasons for various fluctuations.

cs.SI↗

arXiv2023

Uncertainty Injection: A Deep Learning Method for Robust Optimization

This paper proposes a paradigm of uncertainty injection for training deep learning model to solve robust optimization problems. The majority of existing studies on deep learning focus on the model learning capability, while assuming the quality and accuracy of the inputs data can be guaranteed. However, in realistic applications of deep learning for solving optimization problems, the accuracy of inputs, which are the problem parameters in this case, plays a large role. This is because, in many situations, it is often costly or sometime impossible to obtain the problem parameters accurately, and correspondingly, it is highly desirable to develop learning algorithms that can account for the uncertainties in the input and produce solutions that are robust against these uncertainties. This paper presents a novel uncertainty injection scheme for training machine learning models that are capable of implicitly accounting for the uncertainties and producing statistically robust solutions. We further identify the wireless communications as an application field where uncertainties are prevalent in problem parameters such as the channel coefficients. We show the effectiveness of the proposed training scheme in two applications: the robust power loading for multiuser multiple-input-multiple-output (MIMO) downlink transmissions; and the robust power control for device-to-device (D2D) networks.

cs.LG↗

arXiv2023

Numerical Hermitian Yang-Mills Connection for Bundles on Quotient Manifold

We extend the previous computations of Hermitian Yang-Mills connections for bundles on complete intersection Calabi-Yau manifolds to bundles on their free quotients. Bundles on quotient manifolds are often defined by equivariant bundles on corresponding covering spaces. Combining equivariant structure and generalized Donaldson's algorithm, we develop a systematic approach to compute connections of holomorphic poly-stable bundles on quotient manifolds. With it, we construct the connections of an $SU(3)$ bundle on $Z_5$ quotient of quintic and an $SU(5)$ bundle on $Z_3 \times Z_3$ quotient of Bi-cubic. For both of these examples, the algorithm converges as expected and gives a good approximation of Hermitian Yang-Mills connections, which will be important for heterotic model building in Calabi-Yau compactification.

hep-th↗

arXiv2022

AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization

WiFi-based smart human sensing technology enabled by Channel State Information (CSI) has received great attention in recent years. However, CSI-based sensing systems suffer from performance degradation when deployed in different environments. Existing works solve this problem by domain adaptation using massive unlabeled high-quality data from the new environment, which is usually unavailable in practice. In this paper, we propose a novel augmented environment-invariant robust WiFi gesture recognition system named AirFi that deals with the issue of environment dependency from a new perspective. The AirFi is a novel domain generalization framework that learns the critical part of CSI regardless of different environments and generalizes the model to unseen scenarios, which does not require collecting any data for adaptation to the new environment. AirFi extracts the common features from several training environment settings and minimizes the distribution differences among them. The feature is further augmented to be more robust to environments. Moreover, the system can be further improved by few-shot learning techniques. Compared to state-of-the-art methods, AirFi is able to work in different environment settings without acquiring any CSI data from the new environment. The experimental results demonstrate that our system remains robust in the new environment and outperforms the compared systems.

cs.CV↗

arXiv2022

Shadow Celestial Amplitude

We study scattering amplitudes in the shadow conformal primary basis, which satisfies the same defining properties as the original conformal primary basis and has many advantages over it. The shadow celestial amplitudes exhibit locality manifestly on the celestial sphere, and behave like correlation functions in conformal field theory under the operator product expansion (OPE) limit. We study the OPE limits for three-point shadow celestial amplitude, and general $2\to n-2$ shadow celestial amplitudes from a large class of Feynman diagrams. In particular, we compute the conformal block expansion of the $s$-channel four-point shadow celestial amplitude of massless scalars at tree-level, and show that the expansion coefficients factorize as products of OPE coefficients.

hep-th↗