arXiv subjects

Yu Xu

Publications and source records attributed to Yu Xu.

At least 55 records · Page 3Linked to original sources

arXiv2025

Improving Muon Scattering Tomography Performance With A Muon Momentum Measurement Scheme

Muon imaging, especially muon scattering tomography (MST), has recently garnered significant attention. MST measures the magnitude of muon scattering angles inside an object, which depends not only on the material properties but also on the muon momentum. Due to the difficulty of simultaneous measurement of momentum, it was neglected and taken as a constant in multiple MST reconstruction algorithms. Recently, an experimental measurement scheme has emerged that is feasible in engineering, but it requires many layers of detectors to approach the true momentum. From this, we proposed both an algorithm to incorporating momentum into MST, and a scheme to determine the thresholds of Cherenkov detectors. This novel scheme, termed the "equi-percentage scheme", sets momentum thresholds for Cherenkov detector layers based on cosmic muon momentum distribution. Results showed our approach delivers noticeable enhancement in reconstructed image quality even with only two detector layers, reaching near-saturation performance with four layers. This study proves that momentum measurement significantly enhances short-duration MST, and that substantial improvement can be achieved with relatively coarse momentum measurement using 2-4 layers of Cherenkov detectors.

physics.ins-det↗

arXiv2025

On Kernel Design for Regularized Volterra Series Identification of Wiener-Hammerstein Systems

There have been increasing interests on the Volterra series identification with the kernel-based regularization method. The major difficulties are on the kernel design and efficiency of the corresponding implementation. In this paper, we first assume that the underlying system to be identified is the Wiener-Hammerstein (WH) system with polynomial nonlinearity. We then show how to design kernels with nonzero off-diagonal blocks for Volterra maps by taking into account the prior knowledge of the linear blocks and the structure of WH systems. Moreover, exploring the structure of the designed kernels leads to the same computational complexity as the state-of-the-art result, i.e., $O(N^3)$, where $N$ is the sample size, but with a significant difference that the proposed kernels are designed in a direct and flexible way. In addition, for a special case of the kernel and a class of widely used input signals, further exploring the separable structure of the output kernel matrix can lower the computational complexity from $O(N^3)$ to $O(Nγ^2)$, where $γ$ is the separability rank of the output kernel matrix and can be much smaller than $N$. We finally run Monte Carlo simulations to demonstrate the proposed kernels and the obtained theoretical results.

eess.SY↗

arXiv2025

In-Context Brush: Zero-shot Customized Subject Insertion with Context-Aware Latent Space Manipulation

Recent advances in diffusion models have enhanced multimodal-guided visual generation, enabling customized subject insertion that seamlessly "brushes" user-specified objects into a given image guided by textual prompts. However, existing methods often struggle to insert customized subjects with high fidelity and align results with the user's intent through textual prompts. In this work, we propose "In-Context Brush", a zero-shot framework for customized subject insertion by reformulating the task within the paradigm of in-context learning. Without loss of generality, we formulate the object image and the textual prompts as cross-modal demonstrations, and the target image with the masked region as the query. The goal is to inpaint the target image with the subject aligning textual prompts without model tuning. Building upon a pretrained MMDiT-based inpainting network, we perform test-time enhancement via dual-level latent space manipulation: intra-head "latent feature shifting" within each attention head that dynamically shifts attention outputs to reflect the desired subject semantics and inter-head "attention reweighting" across different heads that amplifies prompt controllability through differential attention prioritization. Extensive experiments and applications demonstrate that our approach achieves superior identity preservation, text alignment, and image quality compared to existing state-of-the-art methods, without requiring dedicated training or additional data collection.

cs.CV↗

arXiv2025

The Feasibility Study of the GeV-Energy Muon Source Based on HIAF

Generating a mono-energetic, high-energy muon beam using accelerator facilities can be very attractive for many purposes, for example, improving muon tomography currently limited by the low flux and wide energy spread of cosmic ray muons, and searching for muon related new physics beyond the Standard Model. One potential accelerator facility is the High Intensity Heavy-Ion Accelerator Facility (HIAF), which is currently under construction in Huizhou City, China. Considering the projectile energy and beamline length, a high-intensity and GeV-energy muon flux could be produced and delivered by the High Energy Fragment Separator beamline of the HIAF facility. In this paper, the flux intensity and purity of muon beam based on HIAF are discussed in detail. For the $μ^+$ beam, the highest muon yield reaches $8.2 \times 10^6 ~ μ$/s with the purity of approximately $2\%$ at a momentum of 3.5 GeV/c; meanwhile, for the $μ^-$ beam, the maximum muon yield is 4.2 $\times 10^6 ~ μ$/s with the purity of around $20\%$ at a momentum of 1.5 GeV/c. The results also indicate that, for muon beams with an energy of several GeV, by applying a suitable purification strategy, we can get a muon beam with a purity of 100\% and an intensity of the order of $10^5 ~ μ$/s.

physics.acc-ph↗

arXiv2025

Impact of Radio Frequency Power on Columnar and Filamentary Modes in Atmospheric Pressure Very Low Frequency Plasma within Pores

The impact of radio frequency (RF) power on columnar and filamentary modes of very low frequency (VLF) plasma within pores is investigated in this work. The 12.5 kHz VLF discharge under various RF powers (13.56 MHz) was analyzed using optical photography and current-voltage measurements. Two-dimensional electron densities were derived using optical emission spectroscopy combined with collisional radiation modeling methods. It is found that RF power and very low frequency voltage (VVLF) significantly influence the plasma and its discharge modes within the 200 μm pore. Under low VVLF conditions, the plasma is more intense within the pore, and the discharge mode is columnar discharge. With increasing RF power, the reciprocal motion of electrons counteracts the local enhancement effect of columnar discharge, the discharge transforms into RF discharge, the pore is completely wrapped by the sheath, and the plasma inside is gradually quenched. Under high VVLF conditions, the electron density within the pore is low and the discharge mode is filamentary discharge. RF introduction reduces plasma intensity within the pores firstly. As RF power increases, more ion trapping in the pore increases the field strength distortion and enhances the plasma intensity inside the pore, this enhancement effects becomes more obvious with increasing RF power. In addition, the above effects were observed for all pore widths from 100 um to 1000 um. These findings provide key insights for controlling plasma in pores and offer new methodologies for plasma technology applications.

physics.plasm-ph↗

arXiv2025

TigerVector: Supporting Vector Search in Graph Databases for Advanced RAGs

In this paper, we introduce TigerVector, a system that integrates vector search and graph query within TigerGraph, a Massively Parallel Processing (MPP) native graph database. We extend the vertex attribute type with the embedding type. To support fast vector search, we devise an MPP index framework that interoperates efficiently with the graph engine. The graph query language GSQL is enhanced to support vector type expressions and enable query compositions between vector search results and graph query blocks. These advancements elevate the expressive power and analytical capabilities of graph databases, enabling seamless fusion of unstructured and structured data in ways previously unattainable. Through extensive experiments, we demonstrate TigerVector's hybrid search capability, scalability, and superior performance compared to other graph databases (including Neo4j and Amazon Neptune) and a highly optimized specialized vector database (Milvus). TigerVector was integrated into TigerGraph v4.2, the latest release of TigerGraph, in December 2024.

cs.DB↗

arXiv2025

Radio dimming associated with filament eruptions in the meter and decimeter wavebands

Filament eruptions are considered to be a common phenomenon on the Sun and other stars, yet they are rarely directly imaged in the meter and decimeter wavebands. Using imaging data from the DAocheng solar Radio Telescope (DART) in the 150-450 MHz frequency range, we present two eruptive filaments that manifest as radio dimmings (i.e., emission depressions). Simultaneously, portions of these eruptive filaments are discernible as dark features in the chromospheric images. The sun-as-a-star flux curves of brightness temperature, derived from the DART images, exhibit obvious radio dimmings. The dimming depths range from 1.5% to 8% of the background level and show a negative correlation with radio frequencies and a positive correlation with filament areas. Our investigation suggests that radio dimming is caused by free-free absorption during filament eruptions obscuring the solar corona. This may provide a new method for detecting stellar filament eruptions.

astro-ph.SR↗

arXiv2025

Simulations of coronal mass ejections on a young solar-type star and their detectability through coronal spectral observations

There is a growing interest in searching for coronal mass ejections (CMEs) in other stellar systems because they are thought to be one of the important factors shaping planetary atmospheres. We investigated the possible spectral signatures related to stellar CMEs using magnetohydrodynamic simulations and spectral synthesis techniques. Blue wing enhancements of the synthetic coronal line profiles caused by the line-of-sight motion of plasma were observed during the simulated CME events. We included instrumental conditions in the spectral synthesis and tested the detectability of the asymmetries under different instrumental broadening conditions. The results show that blue wing asymmetries are visible in some EUV lines with spectral resolutions higher than around 2000, and the line-of-sight velocities of CMEs obtained from asymmetry analysis techniques are comparable to the CME velocities derived from three-dimensional model outputs. However, when the spectral resolution drops below 2000, the asymmetries in the blue wings become barely visible, but blue shifts in the line centroids with velocities around -100 to -200 km/s are observed. We suggest a method of using MHD simulation to synthesize line profiles and analyze their asymmetries which may help to guide future instrument design in terms of detecting stellar CMEs through Doppler shifts or asymmetries of coronal spectral lines.

astro-ph.SR↗

arXiv2025

Transfer learning empowers material Z classification with muon tomography

Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers (Z values), facilitating the identification of various Z-class materials, particularly those radioactive high-Z nuclear elements. Most of the traditional identification methods are based on complex muon event reconstruction and trajectory fitting processes. Supervised machine learning methods offer some improvement but rely heavily on prior knowledge of target materials, significantly limiting their practical applicability in detecting concealed materials. For the first time, transfer learning is introduced into the field of muon tomography in this work. We propose two lightweight neural network models for fine-tuning and adversarial transfer learning, utilizing muon tomography data of bare materials to predict the Z-class of coated materials. By employing the inverse cumulative distribution function method, more accurate scattering angle distributions could be obtained from limited data, leading to an improvement by nearly 4\% in prediction accuracy compared with the traditional random sampling based training. When applied to coated materials with limited labeled or even unlabeled muon tomography data, the proposed method achieves an overall prediction accuracy exceeding 96\%, with high-Z materials reaching nearly 99\%. Simulation results indicate that transfer learning improves prediction accuracy by approximately 10\% compared to direct prediction without transfer. This study demonstrates the effectiveness of transfer learning in overcoming the physical challenges associated with limited labeled/unlabeled data, highlights the promising potential of transfer learning in the field of muon tomography.

physics.ins-det↗

arXiv2025

The Detectability of Coronal Mass Ejections in the Low Corona Using Multi-slit Extreme-ultraviolet Spectroscopy

The spectra of coronal mass ejections (CMEs) in the low corona play a crucial role in understanding their origins and physical mechanism, and enhancing space weather forecasting. However, capturing these spectra faces significant challenges. This paper introduces a scheme of a multi-slit spectrometer design with five slits, acquiring the global spectra of the solar corona simultaneously with a focus on the spectra of CMEs in the low corona. The chosen wavelength range of the spectrometer (170-180 Å) includes four extreme ultraviolet emission lines (Fe x 174.53 Å, Fe ix 171.07 Å, Fe x 175.26 Å, Fe x 177.24 Å), which provides information of plasma velocity, density and temperature. Utilizing a numerical simulation of the global corona for both the on-disk and the off-limb scenarios, we focus on resolving the ambiguity associated with various Doppler velocity components of CMEs, particularly for a fast CME in the low corona. A new application of our decomposition technique is adopted, enabling the successful identification of multiple discrete CME velocity components. Our findings demonstrate a strong correlation between the synthetic model spectra and the inverted results, indicating the robustness of our decomposition method and its significant potential for global monitoring of the solar corona, including CMEs.

astro-ph.SR↗

arXiv2025

Probing and Knocking with Muons

We propose here a set of new methods involving probing and knocking with muons (PKMu). There is a wealth of rich physics to explore with GeV muon beams. Examples include but not limited to: muon scattering can occur at large angles, providing evidence of potential muon-philic dark matter or dark mediator candidates; muon-electron scattering can be used to detect new types of bosons associated with charged lepton flavor violation; precise measurements of GeV-scale muon-electron scattering can be employed to probe quantum correlations.

hep-ph↗

arXiv2025

A Dynamic Model of Private Asset Allocation

We build a state-of-the-art dynamic model of private asset allocation that considers five key features of private asset markets: (1) the illiquid nature of private assets, (2) timing lags between capital commitments, capital calls, and eventual distributions, (3) time-varying business cycle conditions, (4) serial correlation in observed private asset returns, and (5) regulatory constraints on certain institutional investors' portfolio choices. We use cutting-edge machine learning methods to quantify the optimal investment policies over the life cycle of a fund. Moreover, our model offers regulators a tool for precisely quantifying the trade-offs when setting risk-based capital charges.

q-fin.PM↗

arXiv2025

The Sliding Flux Ramp Demodulation Algorithm with High Sampling Rate in Microwave SQUID Multiplexer

Microwave SQUID Multiplexing (uMUX) is a widely used technique in the low temperature detectors community as it offers high capacity of reading out large scale Transition-Edge Sensor (TES) arrays. In this paper, we propose a Sliding Flux Ramp Demodulation (SFRD) algorithm for uMUX readout system. It can achieve a sampling rate in the order of MHz while maintaining a multiplexing ratio about one thousand. Advancing of this large array readout technique makes it possible to observe scientiffc objects with improved time resolution and event count rate. This will be highly helpful for TES calorimeters in X-ray applications, such as X-ray astrophysics missions.

hep-ex↗

arXiv2025

Science objectives of the Einstein Probe mission

The Einstein Probe (EP) is an interdisciplinary mission of time-domain and X-ray astronomy. Equipped with a wide-field lobster-eye X-ray focusing imager, EP will discover cosmic X-ray transients and monitor the X-ray variability of known sources in 0.5-4 keV, at a combination of detecting sensitivity and cadence that is not accessible to the previous and current wide-field monitoring missions. EP can perform quick characterisation of transients or outbursts with a Wolter-I X-ray telescope onboard. In this paper, the science objectives of the Einstein Probe mission are presented. EP is expected to enlarge the sample of previously known or predicted but rare types of transients with a wide range of timescales. Among them, fast extragalactic transients will be surveyed systematically in soft X-rays, which include γ-ray bursts and their variants, supernova shock breakouts, and the predicted X-ray transients associated with binary neutron star mergers. EP will detect X-ray tidal disruption events and outbursts from active galactic nuclei, possibly at an early phase of the flares for some. EP will monitor the variability and outbursts of X-rays from white dwarfs, neutron stars and black holes in our and neighbouring galaxies at flux levels fainter than those detectable by the current instruments, and is expected to discover new objects. A large sample of stellar X-ray flares will also be detected and characterised. In the era of multi-messenger astronomy, EP has the potential of detecting the possible X-ray counterparts of gravitational wave events, neutrino sources, and ultra-high energy γ-ray and cosmic ray sources. EP is expected to help advance the studies of extreme objects/phenomena and their underlying physical processes revealed in the dynamic X-ray universe, as well as studies in other areas of X-ray astronomy.

astro-ph.HE↗

arXiv2024

In situ Qubit Frequency Tuning Circuit for Scalable Superconducting Quantum Computing: Scheme and Experiment

Frequency tunable qubit plays a significant role for scalable superconducting quantum processors. The state-of-the-art room-temperature electronics for tuning qubit frequency suffers from unscalable limit, such as heating problem, linear growth of control cables, etc. Here we propose a scalable scheme to tune the qubit frequency by using in situ superconducting circuit, which is based on radio frequency superconducting quantum interference device (rf-SQUID). We demonstrate both theoretically and experimentally that the qubit frequency could be modulated by inputting several single pulses into rf-SQUID. Compared with the traditional scheme, our scheme not only solves the heating problem, but also provides the potential to exponentially reduce the number of cables inside the dilute refrigerator and the room-temperature electronics resource for tuning qubit frequency, which is achieved by a time-division-multiplex (TDM) scheme combining rf-SQUID with switch arrays. With such TDM scheme, the number of cables could be reduced from the usual $\sim 3n$ to $\sim \log_2{(3n)} + 1$ for two-dimensional quantum processors comprising $n$ qubits and $\sim 2n$ couplers. Our work paves the way for large-scale control of superconducting quantum processor.

quant-ph↗

arXiv2024

HeadRouter: A Training-free Image Editing Framework for MM-DiTs by Adaptively Routing Attention Heads

Diffusion Transformers (DiTs) have exhibited robust capabilities in image generation tasks. However, accurate text-guided image editing for multimodal DiTs (MM-DiTs) still poses a significant challenge. Unlike UNet-based structures that could utilize self/cross-attention maps for semantic editing, MM-DiTs inherently lack support for explicit and consistent incorporated text guidance, resulting in semantic misalignment between the edited results and texts. In this study, we disclose the sensitivity of different attention heads to different image semantics within MM-DiTs and introduce HeadRouter, a training-free image editing framework that edits the source image by adaptively routing the text guidance to different attention heads in MM-DiTs. Furthermore, we present a dual-token refinement module to refine text/image token representations for precise semantic guidance and accurate region expression. Experimental results on multiple benchmarks demonstrate HeadRouter's performance in terms of editing fidelity and image quality.

cs.CV↗

arXiv2024

Improving Pinterest Search Relevance Using Large Language Models

To improve relevance scoring on Pinterest Search, we integrate Large Language Models (LLMs) into our search relevance model, leveraging carefully designed text representations to predict the relevance of Pins effectively. Our approach uses search queries alongside content representations that include captions extracted from a generative visual language model. These are further enriched with link-based text data, historically high-quality engaged queries, user-curated boards, Pin titles and Pin descriptions, creating robust models for predicting search relevance. We use a semi-supervised learning approach to efficiently scale up the amount of training data, expanding beyond the expensive human labeled data available. By utilizing multilingual LLMs, our system extends training data to include unseen languages and domains, despite initial data and annotator expertise being confined to English. Furthermore, we distill from the LLM-based model into real-time servable model architectures and features. We provide comprehensive offline experimental validation for our proposed techniques and demonstrate the gains achieved through the final deployed system at scale.

cs.IR↗

arXiv2024

Large Language Model-driven Multi-Agent Simulation for News Diffusion Under Different Network Structures

The proliferation of fake news in the digital age has raised critical concerns, particularly regarding its impact on societal trust and democratic processes. Diverging from conventional agent-based simulation approaches, this work introduces an innovative approach by employing a large language model (LLM)-driven multi-agent simulation to replicate complex interactions within information ecosystems. We investigate key factors that facilitate news propagation, such as agent personalities and network structures, while also evaluating strategies to combat misinformation. Through simulations across varying network structures, we demonstrate the potential of LLM-based agents in modeling the dynamics of misinformation spread, validating the influence of agent traits on the diffusion process. Our findings emphasize the advantages of LLM-based simulations over traditional techniques, as they uncover underlying causes of information spread -- such as agents promoting discussions -- beyond the predefined rules typically employed in existing agent-based models. Additionally, we evaluate three countermeasure strategies, discovering that brute-force blocking influential agents in the network or announcing news accuracy can effectively mitigate misinformation. However, their effectiveness is influenced by the network structure, highlighting the importance of considering network structure in the development of future misinformation countermeasures.

cs.SI↗