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Yusheng Wu

Publications and source records attributed to Yusheng Wu.

6 recordsLinked to original sources
arXiv2025

Degree-Weighted Social Learning

We study social learning in which agents weight neighbors' opinions differently based on their degrees, capturing situations in which agents place more trust in well-connected individuals or, conversely, discount their influence. We derive asymptotic properties of learning outcomes in large stochastic networks and analyze how the weighting rule affects societal wisdom and convergence speed. We find that assigning greater weight to higher-degree neighbors harms wisdom but has a non-monotonic effect on convergence speed, depending on the diversity of views within high- and low-degree groups, highlighting a potential trade-off between convergence speed and wisdom.

econ.TH↗
arXiv2025

Performance of USTC first batch resistive AC-LGAD sensor

In this paper, the design and characterization of AC-LGAD sensors at the University of Science and Technology of China is introduced. The sensors are characterized with an infrared laser Transient Current Technique (TCT) system for evaluating signal response characteristics and spatial resolution. The temporal resolution was quantified with electrons emitted by a Sr-90 radioactive source. The spatial resolution can reach 4 μm and a temporal resolution of 48 ps is achieved

physics.ins-det↗
arXiv2025

Quantum Frontiers in High Energy Physics

Numerous challenges persist in High Energy Physics (HEP), the addressing of which requires advancements in detection technology, computational methods, data analysis frameworks, and phenomenological designs. We provide a concise yet comprehensive overview of recent progress across these areas, in line with advances in quantum technology. We will discuss the potential of quantum devices in detecting subtle effects indicative of new physics beyond the Standard Model, the transformative role of quantum algorithms and large-scale quantum computers in studying real-time non-perturbative dynamics in the early universe and at colliders, as well as in analyzing complex HEP data. Additionally, we emphasize the importance of integrating quantum properties into HEP experiments to test quantum mechanics at unprecedented high-energy scales and search for hints of new physics. Looking ahead, the continued integration of resources to fully harness these evolving technologies will enhance our efforts to deepen our understanding of the fundamental laws of nature.

hep-ph↗
arXiv2024

SCEP: a Cosmic Magnetic Monopole Search Experiment

Magnetic monopole is a well-motivated class of beyond-Standard-Model particles that could provide insights into the long-standing puzzle of the quantization of electric charge. These hypothetical particles are likely to be super heavy ($\sim$10$^{15}$ GeV) and be produced in the very early stages of the Universe's evolution. We propose a novel detection scenario for the search of such cosmic magnetic monopoles, utilizing a hybrid approach that combines radio-frequency atomic magnetometers and plastic scintillators. Such setup allows for the collection of both the induction and scintillation signals generated by the passage of a magnetic monopole, which provides acceptance to the magnetic monopoles with their velocities larger than about 10$^{-6}$ light speed (assuming a signal-to-noise ratio of $\sim$4) and their masses larger than approximately 10$^7$ GeV (at $β\sim10^{-3}$). The proposed detector design has the potential to scale up to large area, enabling the exploration of the parameter space of the cosmic magnetic monopole beyond the current experimental and astrophysical constraints. It is estimated that such detector can reach current most stringent limits of the flux set by previous searches, with a signal-to-noise ratio of the induction signal larger than about 4.5, assuming an effective exposure being 20000 year$\cdot$m$^2$ and coil layer of 3.

hep-ex↗
arXiv2019

A Method for Arbitrary Instance Style Transfer

The ability to synthesize style and content of different images to form a visually coherent image holds great promise in various applications such as stylistic painting, design prototyping, image editing, and augmented reality. However, the majority of works in image style transfer have focused on transferring the style of an image to the entirety of another image, and only a very small number of works have experimented on methods to transfer style to an instance of another image. Researchers have proposed methods to circumvent the difficulty of transferring style to an instance in an arbitrary shape. In this paper, we propose a topologically inspired algorithm called Forward Stretching to tackle this problem by transforming an instance into a tensor representation, which allows us to transfer style to this instance itself directly. Forward Stretching maps pixels to specific positions and interpolate values between pixels to transform an instance to a tensor. This algorithm allows us to introduce a method to transfer arbitrary style to an instance in an arbitrary shape. We showcase the results of our method in this paper.

cs.CV↗