arXiv subjects

Wei Cui

Publications and source records attributed to Wei Cui.

At least 55 records · Page 3Linked to original sources

arXiv2025

Probing the Hot Gaseous Halo of the Low-mass Disk Galaxy NGC 7793 with eROSITA and Chandra

Galaxy formation models predict that local galaxies are surrounded by hot X-ray-emitting halos, which are technically difficult to detect due to their extended and low surface brightness nature. Previous X-ray studies have mostly focused on disk galaxies more massive than the Milky Way, with essentially no consensus on the halo X-ray properties at the lower mass end. We utilize the early-released eROSITA and archival Chandra observations to analyze the diffuse X-ray emission of NGC7793, a nearby spiral galaxy with an estimated stellar mass of only $3.2\times 10^9$ $M_{\odot}$. We find evidence for extraplanar hot gas emission from both the radial and vertical soft X-ray intensity profiles, which spreads up to a galactocentric distance of $\sim$ 6 kpc, nearly 30 $\%$ more extended than its stellar disk. Analysis of the eROSITA spectra indicates that the hot gas can be characterized by a temperature of $0.18^{+0.02}_{-0.03}$ keV, with 0.5--2 keV unabsorbed luminosity of $1.3\times 10^{38}$ erg $s^{-1}$. We compare our results with the IllustrisTNG simulations and find overall consistence on the disk scale, whereas excessive emission at large radii is predicted by TNG50. This work provides the latest detection of hot corona around a low-mass galaxy, putting new constrains on state-of-the-art cosmological simulations. We also verify the detectability of hot circumgalactic medium around even low-mass spirals with future high-resolution X-ray spectrometer such as the Hot Universe Baryon Surveyor.

astro-ph.GA↗

arXiv2025

Characterization of Terahertz Spectral Bands for Next Generation of Wireless Communications

The ever-increasing demand for high-speed data transmission continues to motivate research and development efforts towards the sixth generation (6G) of wireless communication technologies and beyond. The use of terahertz (THz) carrier frequencies is considered to achieve faster data transmission rates, with the potential to reach terabits per second. However, there is a necessity to study the impact of environmental factors affecting the signal transmission at these frequencies. One key challenge of THz signal propagation through air is water vapor absorption, which can severely limit the broadcast distance of THz wireless communications. In this work, we investigate the propagation distance of several THz channels under 35 % relative humidity atmospheric conditions using a table-top THz spectroscopy apparatus equipped with a sensitive THz detection system based on nonlinear parametric upconversion. Our results show seven transmission bands between 1 and 3 THz with negligible signal absorption at different propagation lengths with potential application in short-, mid- and long-range wireless communications.

physics.optics↗

arXiv2025

The warm-hot intergalactic medium in inter-cluster filaments -- A forecast for HUBS observations based on eRASS1 superclusters

Cosmological simulations indicate that nearly half of the baryons in the nearby Universe are in the warm-hot intergalactic medium (WHIM) phase, and about a half them reside in cosmic filaments connecting galaxy clusters. Recent observational studies using stacked survey data and deep exposures of galaxy cluster outskirts have detected soft X-ray excesses associated with optically identified filaments. However, the physical characteristics of WHIM in filaments remain largely undetermined due to a lack of direct spectral diagnostics of individual targets, which are limited by the spectral resolution of current instruments in the soft X-ray band. We aim to select appropriate targets for WHIM characterization through pointing observations with the future Hot Universe Baryon Surveyor (HUBS) mission, which is designed with eV-level energy resolution in the 0.1-2.0 keV band and a one-square-degree field of view, thus complementing other planned microcalorimetry missions such as Athena. We built a sample of 1577 inter-cluster filaments based on the eRASS1 supercluster catalog and estimated their soft X-ray emission. Their modeled emission and geometrical properties were used to select candidate targets for HUBS observations. Four inter-cluster filaments were selected as the most appropriate candidates. By simulating and analyzing their mock observations, we demonstrated that with 200 ks HUBS exposure for each candidate, the gas properties of individual filaments can be accurately determined, with the temperature constrained to $\pm0.01$ keV, metallicity constrained to $\leq\pm0.03$ solar, and density constrained to $<\pm10\%$. Elemental abundances of O, Ne, Mg, and Fe can be measured separately, providing unprecedented insights into the chemical history of the filament gas. We also show that direct mapping of the WHIM distribution is promising with narrowband imaging of the Oviii line.

astro-ph.GA↗

arXiv2024

Non-Invertible Surface Defects in 2+1d QFTs from Half Spacetime Gauging

We study duality defects in 2+1d theories with $\bZ^{(0)}_N\times\bZ^{(1)}_N$ global symmetry and trivial mixed 't Hooft anomaly. By gauging these symmetries simultaneously in half of the spacetime, we define duality defects for theories that are self-dual under gauging. We calculate the fusion rules involving duality defects and show that they obey a fusion 2-category. We also construct the corresponding symmetry topological field theory, a four-dimensional BF theory on a slab which realizes the duality defects on the boundary upon shrinking the interval. Furthermore, we provide explicit examples of such duality defects in $U(1)\times U(1)$ gauge theories and in more general product theories. Finally, we find duality defects in non-Lagrangian theories obtained by compactification of 6d $\cN=(2,0)$ SCFTs of type $A_{N-1}$ on various three-manifolds.

hep-th↗

arXiv2024

High detectivity terahertz radiation sensing using frequency-noise-optimized nanomechanical resonators

We achieve high detectivity terahertz sensing using a silicon nitride nanomechanical resonator functionalized with a metasurface absorber. High performances are achieved by striking a fine balance between the frequency stability of the resonator, and its responsivity to absorbed radiation. Using this approach, we demonstrate a detectivity $D^*=3.4\times10^9~\mathrm{cm\cdot\sqrt{Hz}/W}$ and a noise equivalent power $\mathrm{NEP}=36~\mathrm{pW/\sqrt{Hz}}$ that outperform the best room-temperature on-chip THz detectors (i.e., pyroelectrics). Our optical absorber consists of a 1-mm diameter metasurface, which currently enables a 0.5-3 THz detection range but can easily be scaled to other frequencies in the THz and infrared ranges. In addition to demonstrating high-performance terahertz sensing, our work unveils an important fundamental trade-off between high frequency stability and high responsivity in thermal-based nanomechanical radiation sensors.

physics.optics↗

arXiv2024

MSW-type compactifications of 6d $(1,0)$ SCFTs on 4-manifolds

In this work, we study compactifications of 6d $(1,0)$ SCFTs, in particular those of conformal matter type, on Kähler 4-manifolds. We show how this can be realized via wrapping M5 branes on 4-cycles of non-compact Calabi-Yau fourfolds with ADE singularity in the fiber. Such compactifications lead to domain walls in 3d $\mathcal{N}=2$ theories which flow to 2d $\mathcal{N}=(0,2)$ SCFTs. We compute the central charges of such 2d CFTs via 6d anomaly polynomials by employing a particular topological twist along the 4-manifold. Moreover, we study compactifications on non-compact 4-manifolds leading to coupled 3d-2d systems. We show how these can be glued together consistently to reproduce the central charge and anomaly polynomial obtained in the compact case. Lastly, we study concrete CFT proposals for some special cases.

hep-th↗

arXiv2024

Modularity of Vafa-Witten Partition Functions from SymTFT

The 6d (2,0) theory of $N$ M5 branes compactified on the product geometry $T^2\times S$, where $S$ is a Kähler 4-manifold, can be studied in two different limits. In one limit, the size of $T^2$ is taken to zero and together with a topological twist one arrives at the Vafa-Witten partition function on $S$. On the other hand, taking the size of $S$ to zero leads to a 2d $\mathcal{N}=(0,4)$ theory. This gives rise to a 2d-4d correspondence where the Vafa-Witten partition functions are identified with the characters of the 2d theory. In this paper, we test this conjecture for Hirzebruch and Del Pezzo surfaces by employing the technique of SymTFT to show that the modular transformation properties of the two sides match. Moreover, we construct modular invariant 2d absolute partition functions and verify that they are invariant under gauging of a discrete symmetry at the self-dual point in coupling space. This provides further hints for the presence of duality defects in the 2d SCFT.

hep-th↗

arXiv2024

Simpler Gradient Methods for Blind Super-Resolution with Lower Iteration Complexity

We study the problem of blind super-resolution, which can be formulated as a low-rank matrix recovery problem via vectorized Hankel lift (VHL). The previous gradient descent method based on VHL named PGD-VHL relies on additional regularization such as the projection and balancing penalty, exhibiting a suboptimal iteration complexity. In this paper, we propose a simpler unconstrained optimization problem without the above two types of regularization and develop two new and provable gradient methods named VGD-VHL and ScalGD-VHL. A novel and sharp analysis is provided for the theoretical guarantees of our algorithms, which demonstrates that our methods offer lower iteration complexity than PGD-VHL. In addition, ScalGD-VHL has the lowest iteration complexity while being independent of the condition number. Furthermore, our novel analysis reveals that the blind super-resolution problem is less incoherence-demanding, thereby eliminating the necessity for incoherent projections to achieve linear convergence. Empirical results illustrate that our methods exhibit superior computational efficiency while achieving comparable recovery performance to prior arts.

cs.IT↗

arXiv2024

Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel Factorization

Current spectral compressed sensing methods via Hankel matrix completion employ symmetric factorization to demonstrate the low-rank property of the Hankel matrix. However, previous non-convex gradient methods only utilize asymmetric factorization to achieve spectral compressed sensing. In this paper, we propose a novel nonconvex projected gradient descent method for spectral compressed sensing via symmetric factorization named Symmetric Hankel Projected Gradient Descent (SHGD), which updates only one matrix and avoids a balancing regularization term. SHGD reduces about half of the computation and storage costs compared to the prior gradient method based on asymmetric factorization. {Besides, the symmetric factorization employed in our work is completely novel to the prior low-rank factorization model, introducing a new factorization ambiguity under complex orthogonal transformation}. Novel distance metrics are designed for our factorization method and a linear convergence guarantee to the desired signal is established with $O(r^2\log(n))$ observations. Numerical simulations demonstrate the superior performance of the proposed SHGD method in phase transitions and computation efficiency compared to state-of-the-art methods.

cs.IR↗

arXiv2024

Parallel Ising Annealer via Gradient-based Hamiltonian Monte Carlo

Ising annealer is a promising quantum-inspired computing architecture for combinatorial optimization problems. In this paper, we introduce an Ising annealer based on the Hamiltonian Monte Carlo, which updates the variables of all dimensions in parallel. The main innovation is the fusion of an approximate gradient-based approach into the Ising annealer which introduces significant acceleration and allows a portable and scalable implementation on the commercial FPGA. Comprehensive simulation and hardware experiments show that the proposed Ising annealer has promising performance and scalability on all types of benchmark problems when compared to other Ising annealers including the state-of-the-art hardware. In particular, we have built a prototype annealer which solves Ising problems of both integer and fraction coefficients with up to 200 spins on a single low-cost FPGA board, whose performance is demonstrated to be better than the state-of-the-art quantum hardware D-Wave 2000Q and similar to the expensive coherent Ising machine. The sub-linear scalability of the annealer signifies its potential in solving challenging combinatorial optimization problems and evaluating the advantage of quantum hardware.

quant-ph↗

arXiv2024

MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework

We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on the design and implementation of noisy intermediate-scale quantum (NISQ) algorithms. Leveraging the robust support of MindSpore, an advanced open-source deep learning training/inference framework, MindSpore Quantum exhibits exceptional efficiency in the design and training of variational quantum algorithms on both CPU and GPU platforms, delivering remarkable performance. Furthermore, this framework places a strong emphasis on enhancing the operational efficiency of quantum algorithms when executed on real quantum hardware. This encompasses the development of algorithms for quantum circuit compilation and qubit mapping, crucial components for achieving optimal performance on quantum processors. In addition to the core framework, we introduce QuPack, a meticulously crafted quantum computing acceleration engine. QuPack significantly accelerates the simulation speed of MindSpore Quantum, particularly in variational quantum eigensolver (VQE), quantum approximate optimization algorithm (QAOA), and tensor network simulations, providing astonishing speed. This combination of cutting-edge technologies empowers researchers and practitioners to explore the frontiers of quantum computing with unprecedented efficiency and performance.

quant-ph↗

arXiv2024

Empowering In-Browser Deep Learning Inference on Edge Devices with Just-in-Time Kernel Optimizations

Web is increasingly becoming the primary platform to deliver AI services onto edge devices, making in-browser deep learning (DL) inference more prominent. Nevertheless, the heterogeneity of edge devices, combined with the underdeveloped state of Web hardware acceleration practices, hinders current in-browser inference from achieving its full performance potential on target devices. To address this issue, this paper presents the pioneering inbrowser inference system, nnJIT, which enables just-in-time (JIT) auto-generation of optimized computing kernels for edge devices. nnJIT is built upon two novel techniques that significantly reduce kernel search and compilation overhead while improving performance firmly: Tensor-Web Compiling Co-Design lowers compiling costs by around 100X through eliminating redundant and ineffective compiling passes; Web-Specific Lite Kernel Optimization Space reduces kernel tuning costs by focusing on Web programming requirements and efficient device resource utilization, pruning the optimization space from millions to only dozens. nnJIT is evaluated for modern models, e.g., BART, T5, and Llama 2, on a range of edge devices including laptops and smartphones using different browsers and hardware from ARM, Intel, AMD and Nvidia. The results show that nnJIT can achieve up to 8.2X faster within 30 seconds compared to the existing baselines.

cs.AI↗

arXiv2024

Diffuse X-ray Explorer: a high-resolution X-ray spectroscopic sky surveyor on the China Space Station

DIffuse X-ray Explorer (DIXE) is a proposed high-resolution X-ray spectroscopic sky surveyor on the China Space Station (CSS). DIXE will focus on studying hot baryons in the Milky Way. Galactic hot baryons like the X-ray emitting Milky Way halo and eROSITA bubbles are best observed in the sky survey mode with a large field of view. DIXE will take advantage of the orbital motion of the CSS to scan a large fraction of the sky. High-resolution X-ray spectroscopy, enabled by superconducting microcalorimeters based on the transition-edge sensor (TES) technology, will probe the physical properties (e.g., temperature, density, elemental abundances, kinematics) of the Galactic hot baryons. This will complement the high-resolution imaging data obtained with the eROSITA mission. Here we present the preliminary design of DIXE. The payload consists mainly of a detector assembly and a cryogenic cooling system. The key components of the detector assembly are a microcalorimeter array and frequency-domain multiplexing readout electronics. To provide a working temperature for the detector assembly, the cooling system consists of an adiabatic demagnetization refrigerator and a mechanical cryocooler system.

astro-ph.IM↗

arXiv2024

Hybrid Quantum-Classical Normalizing Flow

With the rapid development of quantum computing technology, we have entered the era of noisy intermediate-scale quantum (NISQ) computers. Therefore, designing quantum algorithms that adapt to the hardware conditions of current NISQ devices and can preliminarily solve some practical problems has become the focus of researchers. In this paper, we focus on quantum generative models in the field of quantum machine learning, and propose a hybrid quantum-classical normalizing flow (HQCNF) model based on parameterized quantum circuits. Based on the ideas of classical normalizing flow models and the characteristics of parameterized quantum circuits, we cleverly design the form of the ansatz and the hybrid method of quantum and classical computing, and derive the form of the loss function in the case that quantum computing is involved. We test our model on the image generation problem. Experimental results show that our model is capable of generating images of good quality. Compared with other quantum generative models, such as quantum generative adversarial networks (QGAN), our model achieves lower (better) Fréchet inception distance (FID) score, and compared with classical generative models, we can complete the image generation task with significantly fewer parameters. These results prove the advantage of our proposed model.

quant-ph↗

arXiv2024

Preliminary Design of Detector Assembly for DIXE

Diffuse X-ray Explorer (DIXE) is a proposed X-ray spectroscopic survey experiment for the China Space Station. Its detector assembly (DA) contains the transition edge sensor (TES) microcalorimeter and readout electronics based on the superconducting quantum interference device (SQUID) on the cold stage. The cold stage is thermally connected to the ADR stage, and a Kevlar suspension is used to stabilize and isolate it from the 4 K environment. TES and SQUID are both sensitive to the magnetic field, so a hybrid shielding structure consisting of an outer Cryoperm shield and an inner niobium shield is used to attenuate the magnetic field. In addition, IR/optical/UV photons can produce shot noise and thus degrade the energy resolution of the TES microcalorimeter. A blocking filter assembly is designed to minimize the effects. In it, five filters are mounted at different temperature stages, reducing the probability of IR/optical/UV photons reaching the detector through multiple reflections between filters and absorption. This paper will describe the preliminary design of the detector assembly and its optimization.

astro-ph.IM↗

arXiv2024

Tabular Data Contrastive Learning via Class-Conditioned and Feature-Correlation Based Augmentation

Contrastive learning is a model pre-training technique by first creating similar views of the original data, and then encouraging the data and its corresponding views to be close in the embedding space. Contrastive learning has witnessed success in image and natural language data, thanks to the domain-specific augmentation techniques that are both intuitive and effective. Nonetheless, in tabular domain, the predominant augmentation technique for creating views is through corrupting tabular entries via swapping values, which is not as sound or effective. We propose a simple yet powerful improvement to this augmentation technique: corrupting tabular data conditioned on class identity. Specifically, when corrupting a specific tabular entry from an anchor row, instead of randomly sampling a value in the same feature column from the entire table uniformly, we only sample from rows that are identified to be within the same class as the anchor row. We assume the semi-supervised learning setting, and adopt the pseudo labeling technique for obtaining class identities over all table rows. We also explore the novel idea of selecting features to be corrupted based on feature correlation structures. Extensive experiments show that the proposed approach consistently outperforms the conventional corruption method for tabular data classification tasks. Our code is available at https://github.com/willtop/Tabular-Class-Conditioned-SSL.

cs.LG↗

arXiv2024

Reinforcement Learning with Non-Cumulative Objective

In reinforcement learning, the objective is almost always defined as a \emph{cumulative} function over the rewards along the process. However, there are many optimal control and reinforcement learning problems in various application fields, especially in communications and networking, where the objectives are not naturally expressed as summations of the rewards. In this paper, we recognize the prevalence of non-cumulative objectives in various problems, and propose a modification to existing algorithms for optimizing such objectives. Specifically, we dive into the fundamental building block for many optimal control and reinforcement learning algorithms: the Bellman optimality equation. To optimize a non-cumulative objective, we replace the original summation operation in the Bellman update rule with a generalized operation corresponding to the objective. Furthermore, we provide sufficient conditions on the form of the generalized operation as well as assumptions on the Markov decision process under which the globally optimal convergence of the generalized Bellman updates can be guaranteed. We demonstrate the idea experimentally with the bottleneck objective, i.e., the objectives determined by the minimum reward along the process, on classical optimal control and reinforcement learning tasks, as well as on two network routing problems on maximizing the flow rates.

cs.LG↗

arXiv2024

Transfer Learning with Reconstruction Loss

In most applications of utilizing neural networks for mathematical optimization, a dedicated model is trained for each specific optimization objective. However, in many scenarios, several distinct yet correlated objectives or tasks often need to be optimized on the same set of problem inputs. Instead of independently training a different neural network for each problem separately, it would be more efficient to exploit the correlations between these objectives and to train multiple neural network models with shared model parameters and feature representations. To achieve this, this paper first establishes the concept of common information: the shared knowledge required for solving the correlated tasks, then proposes a novel approach for model training by adding into the model an additional reconstruction stage associated with a new reconstruction loss. This loss is for reconstructing the common information starting from a selected hidden layer in the model. The proposed approach encourages the learned features to be general and transferable, and therefore can be readily used for efficient transfer learning. For numerical simulations, three applications are studied: transfer learning on classifying MNIST handwritten digits, the device-to-device wireless network power allocation, and the multiple-input-single-output network downlink beamforming and localization. Simulation results suggest that the proposed approach is highly efficient in data and model complexity, is resilient to over-fitting, and has competitive performances.

cs.LG↗