arXiv subjects

Peng Yu

Publications and source records attributed to Peng Yu.

At least 55 records · Page 3Linked to original sources

arXiv2025

Anti-counterfeiting tags with camouflaged QR codes on nanocavities, using polymer-dispersed-liquid-crystals

Counterfeiting poses an evergrowing challenge, driving the need for innovative and sophisticated anti-counterfeiting strategies and technologies. Many solutions focus on tags characterized by optical features that are partially or completely camouflaged to the human eye, thus discouraging scammers. In this paper, a QR code is laser printed on a thin plastic foil previously coated by a specific nanocavity consisting of a metal/insulator/metal/insulator (MIMI) multilayer. This metamaterial possesses unique features in terms of light transmission that are due to the specific design. A thin layer of polymer dispersed liquid crystals, fabricated incorporating specific nematic liquid crystals in a polymer matrix, is able to camouflage the QR code that becomes, then, readable only under specific thermal conditions. Three anti-counterfeiting tags were fabricated, each using a distinct LC with its own nematic-isotropic transition temperature. The peculiar combination of the unique optical properties of nematic liquid crystals and optical nanocavities results in the creation of a novel type of tags showing two different encoding levels. Stress tests including water immersion, bending test, and prolonged heating have been performed ensuring the long-term stability of the tags. The realized two security-level anti-counterfeiting tags are cost-effective, straightforward to manufacture and, thanks to their flexibility, can be easily integrated into packaging and products.

cs.CR↗

arXiv2024

Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models

This paper explores the use of Large Language Models (LLMs) for sequential recommendation, which predicts users' future interactions based on their past behavior. We introduce a new concept, "Integrating Recommendation Systems as a New Language in Large Models" (RSLLM), which combines the strengths of traditional recommenders and LLMs. RSLLM uses a unique prompting method that combines ID-based item embeddings from conventional recommendation models with textual item features. It treats users' sequential behaviors as a distinct language and aligns the ID embeddings with the LLM's input space using a projector. We also propose a two-stage LLM fine-tuning framework that refines a pretrained LLM using a combination of two contrastive losses and a language modeling loss. The LLM is first fine-tuned using text-only prompts, followed by target domain fine-tuning with unified prompts. This trains the model to incorporate behavioral knowledge from the traditional sequential recommender into the LLM. Our empirical results validate the effectiveness of our proposed framework.

cs.IR↗

arXiv2024

Orbital anomalous Hall effect in the few-layer Weyl semimetal TaIrTe4

We report on the observation of the linear anomalous Hall effect (AHE) in the nonmagnetic Weyl semimetal TaIrTe4. This is achieved by applying a direct current Idc and an alternating current Iac (Iac<<Idc) in TaIrTe4, where the former induces time-reversal symmetry breaking and the latter probes the triggered AHE. The anomalous Hall resistance VacH/Iac shows a linear dependence on Idc and changes sign with the polarity of Idc. In temperature-dependent measurements, VacH/Iac also experiences a sign reversal at 100 K, consistent with the temperature-dependent nonlinear Hall effect (NLHE). Furthermore, in measurements involving only dc transport, the dc Hall voltage exhibits a quadratic relationship with Idc. When the Idc direction is reversed, the Hall resistance changes sign, demonstrating a colossal nonreciprocal Hall effect (NRHE). Our theoretical calculations suggest that the observed linear AHE, NLHE, and NRHE all dominantly originate from the current-induced orbital magnetization compared to the minor spin contribution. This work provides deep insights into the orbital magnetoelectric effect and nonlinear Hall response, promising precise electric control of out-of-plane polarized orbit flow.

cond-mat.mes-hall↗

arXiv2024

Room-temperature van der Waals magnetoresistive memories with data writing by orbital current in the Weyl semimetal TaIrTe4

Current-induced out of plane magnetization has been utilized for field-free switching of ferromagnets with perpendicular magnetic anisotropy. Identifying systems capable of energy-efficiently converting charge currents into out of plane orbit- or spin-polarized currents is crucial for advancing magnetic memory technologies. Here we introduce the Berry curvature dipole as a key evaluation factor, directly measurable through nonlinear Hall effects. In the Weyl semimetal TaIrTe4 used in our experiments, applying a current parallel to the Berry curvature dipole results in out of plane orbital magnetization, which governs the field-free perpendicular magnetization switching in TaIrTe4/Fe3GaTe2 heterostructures. Notably, all-electric control of van der Waals magnetoresistive memory at room temperature has been achieved with a low critical current density 2x10^6A/cm2 for data writing. Our findings reveal the connection between nonlinear Hall effects and field-free magnetization switching, highlighting the potential of the Berry curvature dipole in advancing orbitronics.

cond-mat.mes-hall↗

arXiv2024

Vortex shedding and heat transfer from a heated circular cylinder in Bingham plastic fluids

The present study numerically investigates the vortex shedding and heat transfer characteristics of a heated circular cylinder immersed in Bingham plastic fluids.The effects of three parameters, i.e., (i) plastic Reynolds number ($10 \leq Re \leq 180$), (ii) Prandtl number ($1\leq Pr \leq 100$), and (iii) the Bingham number ($0 \leq Bn \leq 10,000$), are evaluated. The Navier-Stokes and energy equations for flow and heat transfer are adopted, along with the incorporation of the Papanastasiou regularization to address the discontinuous-viscosity characteristics of Bingham plastic fluids. To illustrate the impact of fluid yield stress on the flow structure, the study provides comprehensive insights into flow transition, streamlines, shear rate and velocity distributions, the morphology of yielded/unyielded regions, and the drag coefficient ($C_d$). Additionally, the temperature distribution, the local Nusselt number ($\overline{Nu_{local}}$) along the cylinder, and the average Nusselt number on the cylinder ($\overline{Nu}$) are analyzed. The results indicate that the flow transition of Bingham fluids over a circular cylinder is dependent on external disturbances, exhibiting subcritical bifurcation behavior. This leads to abrupt jumps in the $\overline{Cd}$ - $Bn$ curve and the $\overline{Nu}$ - $Bn$ curve near the critical Bingham number $Bn_c$. Furthermore, the heat transfer performance is contingent upon the different distribution of shear strain rate in the boundary layer across various $Bn$ ranges. It is observed that $\overline{Nu}$ and $Bn$ fits well with the Carreau-Yasuda-like non-Newtonian viscosity model. This investigation enhances the understanding of the vortex shedding and heat transfer behaviors in Bingham plastic fluids.

physics.flu-dyn↗

arXiv2024

Strain-tunable Dirac semimetal phase transition and emergent superconductivity in a borophane

A two-dimensional (2D) Dirac semimetal with concomitant superconductivity has been long sought but rarely reported. It is believed that light-element materials have the potential to realize this goal owing to their intrinsic lightweight and metallicity. Here, based on the recently synthesized $β_{12}$ hydrogenated borophene [Science 371, 1143 (2021)], we investigate its counterpart named $β_{12}$-$ \rm {B_5H_3}$. Our first-principles calculations suggest it has good stability. $β_{12}$-$ \rm {B_5H_3}$ is a scarce Dirac semimetal demonstrating a strain-tunable phase transition from three Dirac cones to a single Dirac cone. Additionally, $β_{12}$-$ \rm {B_5H_3}$ is also a superior phonon-mediated superconductor with a superconducting critical temperature of 32.4 K and can be further boosted to 42 K under external strain. The concurrence of Dirac fermions and superconductivity, supplemented with dual tunabilities, reveals $β_{12}$-$ \rm {B_5H_3}$ is an attractive platform to study either quantum phase transition in 2D Dirac semimetal or the superconductivity or the exotic physics brought about by their interplay.

cond-mat.mtrl-sci↗

arXiv2024

Progress in superconductor-semiconductor topological Josephson junctions

Majorana bound states (MBSs) are quasiparticles which are their own antiparticles. They are predicted to emerge as zero-energy modes localized at the boundary of a topological superconductor. No intrinsic topological superconductor is known to date. However, by interfacing conventional superconductors and semiconductors with strong spin--orbit coupling it is possible to create a system hosting topological states. Hence epitaxial superconductors and semiconductors have emerged as an attractive materials system with atomically sharp interfaces and broad flexibility in device fabrications incorporating Josephson junctions. We discuss the basics of topological superconductivity and provide insight on how to go beyond current state-of-the-art experiments. We argue that the ultimate success in realizing MBS physics requires the observation of non-Abelian braiding and fusion experiments.

cond-mat.mes-hall↗

arXiv2024

LTNER: Large Language Model Tagging for Named Entity Recognition with Contextualized Entity Marking

The use of LLMs for natural language processing has become a popular trend in the past two years, driven by their formidable capacity for context comprehension and learning, which has inspired a wave of research from academics and industry professionals. However, for certain NLP tasks, such as NER, the performance of LLMs still falls short when compared to supervised learning methods. In our research, we developed a NER processing framework called LTNER that incorporates a revolutionary Contextualized Entity Marking Gen Method. By leveraging the cost-effective GPT-3.5 coupled with context learning that does not require additional training, we significantly improved the accuracy of LLMs in handling NER tasks. The F1 score on the CoNLL03 dataset increased from the initial 85.9% to 91.9%, approaching the performance of supervised fine-tuning. This outcome has led to a deeper understanding of the potential of LLMs.

cs.CL↗

arXiv2024

Superconducting Diode Effect Sign Change in Epitaxial Al-InAs Josepshon Junctions

There has recently been a surge of interest in studying the superconducting diode effect (SDE) partly due to the possibility of uncovering the intrinsic properties of a material system. A change of sign of the SDE at finite magnetic field has previously been attributed to different mechanisms. Here, we observe the SDE in epitaxial Al-InAs Josephson junctions with strong Rashba spin-orbit coupling (SOC). We show that this effect strongly depends on the orientation of the in-plane magnetic field. In the presence of a strong magnetic field, we observe a change of sign in the SDE. Simulation and measurement of supercurrent suggest that depending on the superconducting widths, $W_\text{S}$, this sign change may not necessarily be related to 0--$π$ or topological transitions. We find that the strongest sign change in junctions with narrow $W_\text{S}$ is consistent with SOC-induced asymmetry of the critical current under magnetic-field inversion, while in wider $W_\text{S}$, the sign reversal could be related to 0--$π$ transitions and topological superconductivity.

cond-mat.mes-hall↗

arXiv2024

Simulation of flow induced vibration of a cylinder in an expansion tube

In this study, a series of simulations are conducted to investigate the motion of a small cylinder in an expansion tube, focusing on two-dimensional dynamics. These simulations are performed on the FLUENT platform employing the Overset function. The collision between the cylinder and the tube wall is modeled as a positive rigid body collision without losing energy. Two key parameters, the dimensionless gravity (Mg*) and the Reynolds number (Re), were explored within the ranges of 4.9-79.4 and 1-300, respectively. Two types of inflow are considered: the invariable inflow or superimposed a sinusoidal inflow of periodical fluctuation. For invariable inflow, three motion modes (drainage mode, balance mode and vibration mode) are found in the phase diagrams of (Mg*, Re). For an invariable inflow, the high-amplitude vibrations of a cylinder is proved to be widespread in the Reynolds number range from 5 to 40. Additionally, the scenario involving Re =300, Mg* =39.25 with sinusoidal periodic fluctuation incoming flow demonstrated intense vibrations of the cylinder. The behavior of the two-dimensional cylinder can be approximated as a simplified version of a three-dimensional sphere when the spherical motion is disregarded. Our research may help to understand this ancient flow problem.

physics.flu-dyn↗

arXiv2024

Strain-driven phonon topological phase transition impedes thermal transport in titanium monoxide

Topological phonon states in crystalline materials have attracted significant research interests due to their importance for fundamental physical phenomena, yet their implication on phonon thermal transport remains largely unexplored. Here, we use density functional theory calculations and symmetry analyses to explore topological phonon phase transitions under uniaxial strains and their tuning effects on thermal transport in titanium monoxide (TiO). Our calculation shows that application of 10% tension significantly diminishes lattice thermal conductivity of TiO by 77% and 66% along the a and c axes, respectively, at room temperature. This suppression is found to result largely from the breaking of symmetry protected degeneracy of acoustic branches, which induces a substantial enhancement of phonon scattering phase space due to the easier fulfillment of scattering selection rules. Our study provides evidence for the importance of phononic band topology in modulating thermal conductivity and offers a promising route towards controlling solid-state heat transport.

physics.comp-ph↗

arXiv2024

Anomalous thermal transport and high thermoelectric performance of Cu-based vanadate CuVO3

Thermoelectric (TE) conversion technology, capable of transforming heat into electricity, is critical for sustainable energy solutions. Many promising TE materials contain rare or toxic elements, so the development of cost-effective and eco-friendly high-performance TE materials is highly urgent. Herein, we explore the thermal transport and TE properties of transition metal vanadate CuVO3 by using first-principles calculation. On the basis of unified theory of heat conduction, we uncover the hierarchical thermal transport feature in CuVO3, where wave-like tunneling makes a significant contribution to the lattice thermal conductivity (\k{appa}l) and result in the anomalously weak temperature dependence of \k{appa}l. This is primarily attributable to the complex phononic band structure caused by the heterogeneity of Cu-O and V-O bonds. Simultaneously, we report a high power factor of 5.45 mW K-2 m-1 realized in hole-doped CuVO3, which arises from a high electrical conductivity and a large Seebeck coefficient enabled by the multiple valleys and large electronic density of states near the valence band edge. Impressively, the low \k{appa}l and the high power factor make p-typed CuVO3 have ZT of up to 1.39, with the excellent average ZT above 1.0 from 300 to 600 K, which is superior to most reported Cu-based TE materials. Our findings suggest that CuVO3 compound is promising candidate for energy conversion applications in innovative TE devices.

physics.comp-ph↗

arXiv2024

Characterizing losses in InAs two-dimensional electron gas-based gatemon qubits

The tunnelling of cooper pairs across a Josephson junction (JJ) allow for the nonlinear inductance necessary to construct superconducting qubits, amplifiers, and various other quantum circuits. An alternative approach using hybrid superconductor-semiconductor JJs can enable superconducting qubit architectures with all electric control. Here we present continuous-wave and time-domain characterization of gatemon qubits and coplanar waveguide resonators based on an InAs two-dimensional electron gas. We show that the qubit undergoes a vacuum Rabi splitting with a readout cavity and we drive coherent Rabi oscillations between the qubit ground and first excited states. We measure qubit relaxation times to be $T_1 =$ 100 ns over a 1.5 GHz tunable band. We detail the loss mechanisms present in these materials through a systematic study of the quality factors of coplanar waveguide resonators. While various loss mechanisms are present in III-V gatemon circuits we detail future directions in enhancing the relaxation times of qubit devices on this platform.

quant-ph↗

arXiv2024

Ubiquitous non-Majorana Zero-Bias Conductance Peaks in Nanowire Devices

We perform tunneling measurements on indium antimonide nanowire/superconductor hybrid devices fabricated for the studies of Majorana bound states. At finite magnetic field, resonances that strongly resemble Majorana bound states, including zero-bias pinning, become common to the point of ubiquity. Since Majorana bound states are predicted in only a limited parameter range in nanowire devices, we seek an alternative explanation for the observed zero-bias peaks. With the help of a self-consistent Poission-Schrödinger multiband model developed in parallel, we identify several families of trivial subgap states which overlap and interact, giving rise to a crowded spectrum near zero energy and zero-bias conductance peaks in experiments. These findings advance the search for Majorana bound states through improved understanding of broader phenomena found in superconductor-semiconductor systems.

cond-mat.mes-hall↗

arXiv2024

Hot electrons and electromagnetic effects in the broadband Au, Ag, and Ag-Au nanocrystals: The UV, visible, and NIR plasmons

Energetic and optical properties of plasmonic nanocrystals strongly depend on their sizes, shapes, and composition. Whereas using plasmonic nanoparticles in biotesting has become routine, applications of plasmonics in energy are still early in development. Here, we investigate hot electron (HE) generation and related electromagnetic effects in both mono- and bi-metallic nanorods (NRs) and focus on one promising type of bi-metallic nanocrystals - core-shell Au-Ag nanorods. The spectra of the NRs are broadband, highly tunable with their geometry, and have few plasmon resonances. In this work, we provide a new quantum formalism describing the HE generation in bi-metallic nanostructures. Interestingly, we observe that the HE generation rate at the UV plasmon resonance of Au-Ag NRs appears to be very high. These HEs are highly energetic and suitable for carbon-fuel reactions. Simultaneously, the HE generation at the longitudinal plasmon (L-plasmon) peaks, which can be tuned from the yellow to near-IR, depends on the near-field and electromagnetic Mie effects, limiting the HE efficiencies for the long and large NRs. These properties of the L-plasmon relate to all kinds of NRs (Au, Ag, and Au-Ag). We also consider the generation of the interband d-holes in Au and Ag, since the involvement of the d-band is crucial for the energetic properties of UV plasmons. The proposed formalism is an important development for the description of bi-metallic (or tri-metallic, or more complex) nanostructures, and it paves the way to the efficient application of the plasmonic HEs and hot holes in sensing, nanotechnology, photocatalysis, and electrophotochemistry.

cond-mat.mes-hall↗

arXiv2023

Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle

In the wake of relentless digital transformation, data-driven solutions are emerging as powerful tools to address multifarious industrial tasks such as forecasting, anomaly detection, planning, and even complex decision-making. Although data-centric R&D has been pivotal in harnessing these solutions, it often comes with significant costs in terms of human, computational, and time resources. This paper delves into the potential of large language models (LLMs) to expedite the evolution cycle of data-centric R&D. Assessing the foundational elements of data-centric R&D, including heterogeneous task-related data, multi-facet domain knowledge, and diverse computing-functional tools, we explore how well LLMs can understand domain-specific requirements, generate professional ideas, utilize domain-specific tools to conduct experiments, interpret results, and incorporate knowledge from past endeavors to tackle new challenges. We take quantitative investment research as a typical example of industrial data-centric R&D scenario and verified our proposed framework upon our full-stack open-sourced quantitative research platform Qlib and obtained promising results which shed light on our vision of automatic evolving of industrial data-centric R&D cycle.

cs.AI↗

arXiv2023

Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems

In this paper, we introduce an innovative approach for addressing Bayesian inverse problems through the utilization of physics-informed invertible neural networks (PI-INN). The PI-INN framework encompasses two sub-networks: an invertible neural network (INN) and a neural basis network (NB-Net). The primary role of the NB-Net lies in modeling the spatial basis functions characterizing the solution to the forward problem dictated by the underlying partial differential equation. Simultaneously, the INN is designed to partition the parameter vector linked to the input physical field into two distinct components: the expansion coefficients representing the forward problem solution and the Gaussian latent noise. If the forward mapping is precisely estimated, and the statistical independence between expansion coefficients and latent noise is well-maintained, the PI-INN offers a precise and efficient generative model for Bayesian inverse problems, yielding tractable posterior density estimates. As a particular physics-informed deep learning model, the primary training challenge for PI-INN centers on enforcing the independence constraint, which we tackle by introducing a novel independence loss based on estimated density. We support the efficacy and precision of the proposed PI-INN through a series of numerical experiments, including inverse kinematics, 1-dimensional and 2-dimensional diffusion equations, and seismic traveltime tomography. Specifically, our experimental results showcase the superior performance of the proposed independence loss in comparison to the commonly used but computationally demanding kernel-based maximum mean discrepancy loss.

math.NA↗

arXiv2023

A Generalized Density Dissipation for Weakly-compressible SPH

The weakly compressible Smoothed Particle Hydrodynamics (SPH) is known to suffer from the pressure oscillation, which would undermine the simulation stability and accuracy. To address this issue, we propose a generalized density dissipation scheme suitable for both single-phase and multiphase flow simulations. Our approach consists of two components. Firstly, we replace the basic density dissipation with the density increment dissipation to enable numerical dissipation crossing the interfaces of different fluids in multiphase flow. Secondly, based on the dissipation volume conservation, we utilize dissipation volume correction factor (VCF) to stabilize the simulations for multiphase flows with large density ratio. We demonstrate the accuracy, stability, and robustness of our method through four three-dimensional benchmarks, i.e., the sloshing under external excitations, the single and double bubbles rising, Rayleigh-Taylor instability, and Kelvin Helmholtz instability. Additionally, our study reveals the relationship between SPH with the density dissipation and the approximate Riemann solver.

physics.flu-dyn↗