BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Zhe Yuan

Zhe Yuan contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.mes-hallcond-mat.mtrl-scihep-exComputation and Languagecond-mat.dis-nnphysics.app-ph

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
7works
0followers
6topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

7 published item(s)

preprint2026arXiv

GPS-Synchronized Monitoring of Core-collapse Supernova Bursts with PandaX-4T via Coherent Elastic Neutrino Nuclear Scattering

The landmark detection of neutrinos from SN1987A marked the dawn of neutrino astrophysics. The neutrino burst provided essential insights into fundamental properties of neutrinos, and served as key probes of stellar evolution and supernova dynamics. The recent advancement in coherent elastic neutrino-nucleus scattering enables the detection of core-collapse supernova burst neutrinos using tonne-scale liquid xenon detectors originally designed for dark matter direct detection. Leveraging this capability, we developed and deployed an online supernova monitoring system for the PandaX-4T experiment. This system features a GPS module with millisecond-level timing precision, a low false-alarm rate, and high sensitivity to galactic core-collapse supernova explosion events. The methodology is robust, directly scalable, and planned for implementation in the next-generation PandaX-20T experiment.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

LambdaPO: A Lambda Style Policy Optimization for Reasoning Language Models

Group Relative Policy Optimization(GRPO) has become a cornerstone of modern reinforcement learning alignment, prized for its efficacy in foregoing an explicit value-critic by leveraging reward normalization across sampled trajectory cohorts. However, the method's reliance on a monolithic statistical baseline, such as the group mean, collapses the relational topology of the trajectory space into a single scalar, thereby erasing the fine-grained preference information essential for navigating complex, rank-sensitive reward landscapes. To address this issue, we introduce a novel framework, Lambda Policy Optimization (LambdaPO), that addresses this information-theoretic bottleneck by re-conceptualizing advantage estimation from a scalar value to a decomposed, pairwise preference structure. Specifically, the advantage for any given trajectory is formulated as the integrated sum of reward differentials against all peers in its cohort, where each pairwise comparison is dynamically attenuated by the policy's own probabilistic confidence in the established preference. To further mitigate the sparsity of binary outcome supervision, we augment the objective with a semantic density reward, derived from the precision-recall alignment between generated reasoning traces and ground-truth solutions. As a result, our method can mine more fine-grained optimization signals from a group of rollouts, guiding the LLM to a better optima. Experimental results across challenging math reasoning and question-answering tasks demonstrates that LambdaPO improves performance compared to the baseline methods.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

A First Search for Solar $^8$B Neutrino in the PandaX-4T Experiment using Neutrino-Nucleus Coherent Scattering

A search for interactions from solar $^8$B neutrinos elastically scattering off xenon nuclei using PandaX-4T commissioning data is reported. The energy threshold of this search is further lowered compared with the previous search for dark matter, with various techniques utilized to suppress the background that emerges from data with the lowered threshold. A blind analysis is performed on the data with an effective exposure of 0.48 tonne$\cdot$year, and no significant excess of events is observed. Among results obtained using the neutrino-nucleus coherent scattering, our results give the best constraint on the solar $^8$B neutrino flux. We further provide a more stringent limit on the cross section between dark matter and nucleon in the mass range from 3 to 9 GeV/c$^2$.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Calculating the spin memory loss at Cu$|$metal interfaces from first principles

The role played by interfaces in metallic multilayers is not only to change the momenta of incident electrons; their symmetry lowering also results in an enhancement of the effects of spin-orbit coupling, in particular the flipping of the spins of conduction electrons. This leads to a significant reduction of a spin current through a metallic interface that is quantitatively characterized by a dimensionless parameter $δ$ called the spin memory loss (SML) parameter, the interface counterpart of the spin-flip diffusion length for bulk metals. In this paper we use first-principles scattering calculations that include temperature-induced lattice and spin disorder to systematically study three parameters that govern spin transport through metallic interfaces of Cu with Pt, Pd, Py (permalloy) and Co: the interface resistance, spin polarization and the SML. The value of $δ$ for a Cu$|$Pt interface is found to be comparable to what we recently reported for a Au$|$Pt interface [Gupta {\it et al.}, Phys. Rev. Lett. 124, 087702 (2020)]. For Cu$|$Py and Cu$|$Co interfaces, $δ$ decreases monotonically with increasing temperature to become negligibly small at room temperature. The calculated results are in good agreement with currently available experimental values in the literature. Inserting a Cu layer between Pt and the Py or Co layers slightly increases the total spin current dissipation at these "compound" interfaces.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Integrated Plasmonics: Broadband Dirac Plasmons in Borophene

The past decade has witnessed numerous discoveries of two-dimensional (2D) semimetals and insulators, whereas 2D metals are rarely identified. Borophene, a monolayer boron sheet, has recently emerged as a perfect 2D metal with unique structure and electronic properties. Here we study collective excitations in borophene, which exhibit two major plasmon modes with low damping rates extending from infrared to ultraviolet regime. The anisotropic 1D plasmon originates from electronic excitations of tilted Dirac cones in borophene, analogous to that in heavily doped Dirac semimetals. These features make borophene promising to realize directional polariton transportation and broadband optical communications for next-generation optoelectronic devices.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Recent progress in antiferromagnetic dynamics

Spintronics, since its inception, has mainly focused on ferromagnetic materials for manipulating the spin degree of freedom in addition to the charge degree of freedom, whereas much less attention has been paid to antiferromagnetic materials. Thanks to the advances of micro-nano-fabrication techniques and the electrical control of the Néel order parameter, antiferromagnetic spintronics is booming as a result of abundant room temperature materials, robustness against external fields and dipolar coupling, and rapid dynamics in the terahertz regime. For the purpose of applications of antiferromagnets, it is essential to have a comprehensive understanding of the antiferromagnetic dynamics at the microscopic level. Here, we first review the general form of equations that govern both antiferromagnetic and ferrimagnetic dynamics. This general form unifies the previous theories in the literature. We also provide a survey for the recent progress related to antiferromagnetic dynamics, including the motion of antiferromagnetic domain walls and skyrmions, the spin pumping and quantum antiferromagnetic spintronics. In particular, open problems in several topics are outlined. Furthermore, we discuss the development of antiferromagnetic quantum magnonics and its potential integration with modern information science and technology.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Recurrent Neural Networks Made of Magnetic Tunnel Junctions

Artificial intelligence based on artificial neural networks, which are originally inspired by the biological architectures of human brain, has mostly been realized using software but executed on conventional von Neumann computers, where the so-called von Neumann bottleneck essentially limits the executive efficiency due to the separate computing and storage units. Therefore, a suitable hardware platform that can exploit all the advantages of brain-inspired computing is highly desirable. Based upon micromagnetic simulation of the magnetization dynamics, we demonstrate theoretically and numerically that recurrent neural networks consisting of as few as 40 magnetic tunnel junctions can generate and recognize periodic time series after they are trained with an efficient machine-learning algorithm. With ultrahigh operating speed, nonvolatile memory and high endurance and reproducibility, spintronic devices are promising hardware candidates for neuromorphic computing.

0 citations0 reviewsNo code linkedOpen access