arXiv subjects

Yan Yin

Publications and source records attributed to Yan Yin.

At least 19 recordsLinked to original sources

arXiv2025

Generation of Polarization-Tunable Hybrid Cylindrical Vector gamma Rays

Cylindrical vector (CV) gamma rays can introduce spatially structured polarization as a new degree of freedom for fundamental research and practical applications. However, their generation and control remain largely unexplored. Here, we put forward a novel method to generate CV gamma rays with tunable hybrid polarization via a rotating electron beam interacting with a solid foil. In this process, the beam generates a coherent transition radiation field and subsequently emits gamma rays through nonlinear Compton scattering. By manipulating the initial azimuthal momentum of the beam, the polarization angle of gamma rays relative to the transverse momentum can be controlled, yielding tunable hybrid CV polarization states. Three-dimensional spin-resolved particle-in-cell simulations demonstrate continuous tuning of the polarization angle across (-90{\deg}, 90{\deg}) with a high polarization degree exceeding 60%. Our work contributes to the development of structured gamma rays, potentially opening new avenues in high-energy physics, nuclear science, and laboratory astrophysics.

physics.plasm-ph↗

arXiv2025

Effects of laser-plasma parameters on sub-nanosecond evolution of cross-beam energy transfer

Cross-beam energy transfer (CBET) between two lasers is investigated through theoretical analysis and two-dimensional hybrid simulations over sub-nanosecond to nanosecond timescales and millimeter spatial scales. A finite frequency-difference range for CBET development is derived. Ion acoustic wave (IAW) damping is found to broaden this range while reducing the growth rate of stimulated Brillouin scattering (SBS). CBET exhibits distinct nonlinear behaviors across different laser-intensity regimes. Denoting $I_{14}=1\times 10^{14} \mathrm{W/cm^2}$ for a laser wavelength of $351 \mathrm{nm}$, at moderate intensities ($1<I/I_{14}<8$), CBET grows weakly and saturates at a low level due to pump depletion. In the strongly coupled SBS regime ($I/I_{14} \gtrsim 8$), harmonic IAW and nonlinear wave-particle interactions emerge. The generation of harmonic IAW reduces the normal IAW mode, while ion-trapping-induced spectral broadening of normal IAW mode causes frequency mismatch, leading to nonlinear reduction of CBET. After the saturation of harmonic mode, ion trapping broadens harmonic IAW spectrum and weakens it, triggering a secondary growth stage of CBET. After approximately $60 \mathrm{ps}$, CBET approaches quasi-steady-state. The maximum total energy transfer occurs at a frequency difference below the linear matching condition due to the trapping-induced IAW redshift. Based on these two intensity regimes, piecewise scalings of the quasi-saturated total energy transfer rate with $I/I_{14}$ are obtained and shown to be robust against spot size. Speckle effects reduce high-intensity overlap and thus the energy transfer rate. The effects of plasma temperature, density, and flow velocity on CBET are also examined.

physics.plasm-ph↗

arXiv2024

$\text{Alpha}^2$: Discovering Logical Formulaic Alphas using Deep Reinforcement Learning

Alphas are pivotal in providing signals for quantitative trading. The industry highly values the discovery of formulaic alphas for their interpretability and ease of analysis, compared with the expressive yet overfitting-prone black-box alphas. In this work, we focus on discovering formulaic alphas. Prior studies on automatically generating a collection of formulaic alphas were mostly based on genetic programming (GP), which is known to suffer from the problems of being sensitive to the initial population, converting to local optima, and slow computation speed. Recent efforts employing deep reinforcement learning (DRL) for alpha discovery have not fully addressed key practical considerations such as alpha correlations and validity, which are crucial for their effectiveness. In this work, we propose a novel framework for alpha discovery using DRL by formulating the alpha discovery process as program construction. Our agent, $\text{Alpha}^2$, assembles an alpha program optimized for an evaluation metric. A search algorithm guided by DRL navigates through the search space based on value estimates for potential alpha outcomes. The evaluation metric encourages both the performance and the diversity of alphas for a better final trading strategy. Our formulation of searching alphas also brings the advantage of pre-calculation dimensional analysis, ensuring the logical soundness of alphas, and pruning the vast search space to a large extent. Empirical experiments on real-world stock markets demonstrates $\text{Alpha}^2$'s capability to identify a diverse set of logical and effective alphas, which significantly improves the performance of the final trading strategy. The code of our method is available at https://github.com/x35f/alpha2.

q-fin.CP↗

arXiv2023

A holistic review on fatigue properties of additively manufactured metals

Additive manufacturing (AM) technology is undergoing rapid development and emerging as an advanced technique that can fabricate complex near-net shaped and light-weight metallic parts with acceptable strength and fatigue performance. A number of studies have indicated that the strength or other mechanical properties of AM metals are comparable or even superior to that of conventionally manufactured metals, but the fatigue performance is still a thorny problem that may hinder the replacement of currently used metallic components by AM counterparts when the cyclic loading and thus fatigue failure dominates. This article reviews the state-of-art published data on the fatigue properties of AM metals, principally including $S$--$N$ data and fatigue crack growth data. The AM techniques utilized to generate samples in this review include powder bed fusion (e.g., EBM, SLM, DMLS) and directed energy deposition (e.g., LENS, WAAM). Further, the fatigue properties of AM metallic materials that involve titanium alloys, aluminum alloys, stainless steel, nickel-based alloys, magnesium alloys, and high entropy alloys, are systematically overviewed. In addition, summary figures or tables for the published data on fatigue properties are presented for the above metals, the AM techniques, and the influencing factors (manufacturing parameters, e.g., built orientation, processing parameter, and post-processing). The effects of build direction, particle, geometry, manufacturing parameters, post-processing, and heat-treatment on fatigue properties, when available, are provided and discussed. The fatigue performance and main factors affecting the fatigue behavior of AM metals are finally compared and critically analyzed, thus potentially providing valuable guidance for improving the fatigue performance of AM metals.

cond-mat.mtrl-sci↗

arXiv2023

Monolayer polar metals with large piezoelectricity derived from MoSi$_2$N$_4$

The advancement of two-dimensional polar metals tends to be limited by the incompatibility between electric polarity and metallicity as well as dimension reduction. Here, we report polar and metallic Janus monolayers of MoSi$_2$N$_4$ family by breaking the out-of-plane (OOP) structural symmetry through Z (P/As) substitution of N. Despite the semiconducting nature of MoSi$_2$X$_4$ (X=N/P/As), four Janus MoSi$_2$N$_{x}$Z$_{4-x}$ monolayers are found to be polar metals owing to the weak coupling between the conducting electrons and electric polarity. The metallicity is originated from the Z substitution induced delocalization of occupied electrons in Mo-d orbitals. The OOP electric polarizations around 10$-$203 pC/m are determined by the asymmetric OOP charge distribution due to the non-centrosymmetric Janus structure. The corresponding OOP piezoelectricity is further revealed as high as 39$-$153 pC/m and 0.10$-$0.31 pm/V for piezoelectric strain and stress coefficients, respectively. The results demonstrate polar metallicity and high OOP piezoelectricity in Janus MoSi$_2$N$_{x}$Z$_{4-x}$ monolayers and open new vistas for exploiting unusual coexisting properties in monolayers derived from MoSi$_2$N$_4$ family.

cond-mat.mtrl-sci↗

arXiv2023

Giant magnetocaloric effect in magnets down to the monolayer limit

Two-dimensional magnets could potentially revolutionize information technology, but their potential application to cooling technology and magnetocaloric effect (MCE) in a material down to the monolayer limit remain unexplored. Herein, we reveal through multiscale calculations the existence of giant MCE and its strain tunability in monolayer magnets such as CrX$_3$ (X=F, Cl, Br, I), CrAX (A=O, S, Se; X=F, Cl, Br, I), and Fe$_3$GeTe$_2$. The maximum adiabatic temperature change ($\Delta T_\text{ad}^\text{max}$), maximum isothermal magnetic entropy change, and specific cooling power in monolayer CrF$_3$ are found as high as 11 K, 35 $\mu$Jm$^{-2}$K$^{-1}$, and 3.5 nWcm$^{-2}$ under a magnetic field of 5 T, respectively. A 2% biaxial and 5% $a$-axis uniaxial compressive strain can remarkably increase $\Delta T_\text{ad}^\text{max}$ of CrCl$_3$ and CrOF by 230% and 37% (up to 15.3 and 6.0 K), respectively. It is found that large net magnetic moment per unit area favors improved MCE. These findings advocate the giant-MCE monolayer magnets, opening new opportunities for magnetic cooling at nanoscale.

cond-mat.mtrl-sci↗

arXiv2022

Emerging versatile two-dimensional MoSi$_2$N$_4$ family

The discovery of two-dimensional (2D) layered MoSi$_2$N$_4$ and WSi$_2$N$_4$ without knowing their 3D parents by chemical vapor deposition in 2020 has stimulated extensive studies of 2D MA$_2$Z$_4$ system due to its structural complexity and diversity as well as versatile and intriguing properties. Here, a comprehensive overview on the state-of-the-art progress of this 2D MA$_2$Z$_4$ family is presented. Starting by describing the unique sandwich structural characteristics of the emerging monolayer MA$_2$Z$_4$, we summarize and anatomize their versatile properties including mechanics, piezoelectricity, thermal transport, electronics, optics/optoelectronics, and magnetism. The property tunability via strain engineering, surface functionalization and layered strategy is also elaborated. Theoretical and experimental attempts or advances in applying 2D MA$_2$Z$_4$ to transistors, photocatalysts, batteries and gas sensors are then reviewed to show its prospective applications over a vast territory. We further discuss new opportunities and suggest prospects for this emerging 2D family. The overview is anticipated to guide the further understanding and exploration on 2D MA$_2$Z$_4$.

cond-mat.mtrl-sci↗

arXiv2021

High and anomalous thermal conductivity in monolayer MSi$_2$Z$_4$ semiconductors

The lattice thermal conductivity ($\kappa$) of newly synthesized two-dimensional (2D) MoSi$_2$N$_4$ family and its associated abnormality is anatomized by $ab$ $initio$ phonon Boltzmann transport calculations. $\kappa$ of MoSi$_2$N$_4$ and WSi$_2$N$_4$ is found over 400 Wm$^{-1}$K$^{-1}$ at 300 K. $\kappa$ of MoSi$_2$Z$_4$ (Z=N,P,As) obeys Slack's rule of thumb, decreasing by one order of magnitude from Z=N to Z=As with 46 Wm$^{-1}$K$^{-1}$. However, in MSi$_2$N$_4$ (M=Mo,Cr,W,Ti,Zr,Hf), the variation of $\kappa$ with respect to M is anomalous, i.e. deviating from Slack's classic rule. For M in the same group, $\kappa$ of MSi$_2$N$_4$ is insensitive to the average atomic mass, Debye temperature, phonon group velocity, and bond strength owing to the similar phonon structure and scattering rates. MSi$_2$N$_4$ with heavy group-VIB M even possesses a three to four times higher $\kappa$ than that with light group-IVB M, due to its much stronger M-N and exterior Si-N bonds and thus one order of magnitude lower phonon scattering rates. Nevertheless, this abnormality could be traced to an interplay of certain basic vibrational properties including the bunching strength and flatness of acoustic branches and their nearby optical branches, which lie outside of the conventional guidelines by Slack. This work predicts high $\kappa$ of 2D MSi$_2$Z$_4$ for thermal management and provides microscopic insight into deciphering the anomalous $\kappa$ of layered 2D structures.

cond-mat.mtrl-sci↗

arXiv2020

Attention-based Transducer for Online Speech Recognition

Recent studies reveal the potential of recurrent neural network transducer (RNN-T) for end-to-end (E2E) speech recognition. Among some most popular E2E systems including RNN-T, Attention Encoder-Decoder (AED), and Connectionist Temporal Classification (CTC), RNN-T has some clear advantages given that it supports streaming recognition and does not have frame-independency assumption. Although significant progresses have been made for RNN-T research, it is still facing performance challenges in terms of training speed and accuracy. We propose attention-based transducer with modification over RNN-T in two aspects. First, we introduce chunk-wise attention in the joint network. Second, self-attention is introduced in the encoder. Our proposed model outperforms RNN-T for both training speed and accuracy. For training, we achieves over 1.7x speedup. With 500 hours LAIX non-native English training data, attention-based transducer yields ~10.6% WER reduction over baseline RNN-T. Trained with full set of over 10K hours data, our final system achieves ~5.5% WER reduction over that trained with the best Kaldi TDNN-f recipe. After 8-bit weight quantization without WER degradation, RTF and latency drop to 0.34~0.36 and 268~409 milliseconds respectively on a single CPU core of a production server.

eess.AS↗

arXiv2019

MIDI-Sandwich: Multi-model Multi-task Hierarchical Conditional VAE-GAN networks for Symbolic Single-track Music Generation

Most existing neural network models for music generation explore how to generate music bars, then directly splice the music bars into a song. However, these methods do not explore the relationship between the bars, and the connected song as a whole has no musical form structure and sense of musical direction. To address this issue, we propose a Multi-model Multi-task Hierarchical Conditional VAE-GAN (Variational Autoencoder-Generative adversarial networks) networks, named MIDI-Sandwich, which combines musical knowledge, such as musical form, tonic, and melodic motion. The MIDI-Sandwich has two submodels: Hierarchical Conditional Variational Autoencoder (HCVAE) and Hierarchical Conditional Generative Adversarial Network (HCGAN). The HCVAE uses hierarchical structure. The underlying layer of HCVAE uses Local Conditional Variational Autoencoder (L-CVAE) to generate a music bar which is pre-specified by the First and Last Notes (FLN). The upper layer of HCVAE uses Global Variational Autoencoder(G-VAE) to analyze the latent vector sequence generated by the L-CVAE encoder, to explore the musical relationship between the bars, and to produce the song pieced together by multiple music bars generated by the L-CVAE decoder, which makes the song both have musical structure and sense of direction. At the same time, the HCVAE shares a part of itself with the HCGAN to further improve the performance of the generated music. The MIDI-Sandwich is validated on the Nottingham dataset and is able to generate a single-track melody sequence (17x8 beats), which is superior to the length of most of the generated models (8 to 32 beats). Meanwhile, by referring to the experimental methods of many classical kinds of literature, the quality evaluation of the generated music is performed. The above experiments prove the validity of the model.

eess.AS↗

arXiv2019

Giant Interfacial Thermal Resistance Arising From Materials With Mismatched Phonon Structures

Previous researches only reported very small interfacial thermal resistances at room temperature due to limitations in sample combinations and methods. Taking cognizance of the importance of mismatched phonon structures, we report values up to $2*10^{-4}W^{-1}m^{2}K$, thousand times larger than highest values reported to date. This enables substantial tuning of the thermal conductivity in composites, and does not constrain other characteristics. Our findings inspire new design strategies, for heat control in integrated circuits and thermoelectric composites, that harness thermal transport at interfaces.

cond-mat.mtrl-sci↗

arXiv2018

Attention-based sequence-to-sequence model for speech recognition: development of state-of-the-art system on LibriSpeech and its application to non-native English

Recent research has shown that attention-based sequence-to-sequence models such as Listen, Attend, and Spell (LAS) yield comparable results to state-of-the-art ASR systems on various tasks. In this paper, we describe the development of such a system and demonstrate its performance on two tasks: first we achieve a new state-of-the-art word error rate of 3.43% on the test clean subset of LibriSpeech English data; second on non-native English speech, including both read speech and spontaneous speech, we obtain very competitive results compared to a conventional system built with the most updated Kaldi recipe.

cs.CL↗

arXiv2018

Stable attosecond electron bunches from a nanofiber driven by Laguerre-Gaussian lasers

Generation of attosecond bunches of energetic electrons offers significant potential from ultrafast physics to novel radiation sources. However, it is still a great challenge to stably produce such electron beams with lasers, since the typical sub-femtosecond electron bunches from laser-plasma interactions either carry low beam charge, or propagate for only several tens of femtoseconds. Here we propose an all-optical scheme for generating dense attosecond electron bunches via the interaction of an intense Laguerre-Gaussian (LG) laser pulse with a nanofiber. The stable bunch train results from the unique field structure of a circularly polarized LG laser pulse, enabling each bunch to be phase-locked and accelerated forward with low divergence, high beam charge and large beam-angular-momentum. This paves the way for wide applications in various fields, e.g., ultrabrilliant attosecond x/$\gamma$-ray emission.

physics.plasm-ph↗

arXiv2017

Beam distribution reconstruction simulation for electron beam probe

Electron beam probe (EBP) is a new principle detector, which makes use of a low-intensity and low-energy electron beam to measure the transverse profile, bunch shape, beam neutralization and beam wake field of an intense beam with small dimensions. While can be applied to many aspects, we limit our analysis to beam distribution reconstruction. This kind of detector is almost non-interceptive for all of the beam and does not disturb the machine environment. In this paper, we present the theoretical aspects behind this technique for beam distribution measurement and some simulation results of the detector involved. First, a method to obtain parallel electron beam is introduced and a simulation code is developed. And then, EBP as a profile monitor for dense beam is simulated using fast scan method under various target beam profile, such as KV distribution, waterbag distribution, parabolic distribution, Gaussian distribution and halo distribution. Profile reconstruction from the deflected electron beam trajectory is implemented and compared with the actual one, and an expected agreement is achieved. Furthermore, Instead of fast scan, a slow scan, i.e. step-by-step scan, is considered, which lows the requirement for hardware, i.e. Radio Frequency deflector. we calculate the three dimensional electric field of Gaussian distribution and simulate the electron motion under this field. In addition, fast scan along the target beam direction and slow scan across the beam is also presented, and can provide a measurement of longitudinal distribution as well as transverse profile simultaneously. Final, simulation results for China Accelerator Driven Sub-critical System (CADS) and High Intensity Heavy Ion Accelerator Facility (HIAF) are given to investigate the quantitative behavior of EBP.

physics.acc-ph↗

arXiv2016

Longitudinal RF capture simulation and BPM signal estimation

In this paper, the theoretical aspects behind longitudinal RF capture are reviewed and the capture process is simulated via a program based on this theory. Four kinds of cases with different initial distribution and capture curve are considered, i.e. uniform distribution with adiabatic capture, uniform distribution with non-adiabatic capture, Gaussian distribution with adiabatic capture and Gaussian distribution with non-adiabatic capture. The simulation results are compared each other and discussed, and Gaussian distribution with adiabatic capture is demonstrated having a higher capture efficiency and leading to a shorter bunch length. In addition, the BPM induced signal is simulated with high input impendence, i.e. $1MΩ$, and low input impendence, i.e. $50Ω$, respectively. Finally, the BPM signal of Heavy Ion Medical Machine (HIMM) is estimated and compared with measured one, and a good agreement is achieved.

physics.acc-ph↗

arXiv2015

Mechanism and modulation of terahertz generation from a semimetal - graphite

Semi-metals might offer a stronger interaction and a better confinement for terahertz wave than semiconductors, while preserve tunability. Particularly, graphene-based materials are envisioned as terahertz modulators, filters and ultra-broadband sources. However, the understanding of terahertz generation from those materials is still not clear, thus limits us recognizing the potential and improving device performances. Graphite, the mother material of graphene and a typical bulk semi-metal, is a good system to study semi-metals and graphene-based materials. Here we experimentally modulate and maximize the terahertz signal from graphite surface, thus reveal the mechanism - surface field driving photon induced carriers into transient current to radiate terahertz wave. We also discuss the differences between graphite and semiconductors; particularly graphite shows no temperature dependency from room temperature to 80C. Above knowledge will help us understand terahertz generations, achieve maximum output and electric modulation, in semi-metal or graphene based devices.

physics.optics↗

arXiv2014

Graphene, a material for high temperature devices; intrinsic carrier density, carrier drift velocity, and lattice energy

Heat has always been a killing matter for traditional semiconductor machines. The underlining physical reason is that the intrinsic carrier density of a device made from a traditional semiconductor material increases very fast with a rising temperature. Once reaching a temperature, the density surpasses the chemical doping or gating effect, any p-n junction or transistor made from the semiconductor will fail to function. Here, we measure the intrinsic Fermi level (|E_F|=2.93k_B*T) or intrinsic carrier density (n_in=3.87*10^6 cm^-2 K^-2*T^2), carrier drift velocity, and G mode phonon energy of graphene devices and their temperature dependencies up to 2400 K. Our results show intrinsic carrier density of graphene is an order of magnitude less sensitive to temperature than those of Si or Ge, and reveal the great potentials of graphene as a material for high temperature devices. We also observe a linear decline of saturation drift velocity with increasing temperature, and identify the temperature coefficients of the intrinsic G mode phonon energy. Above knowledge is vital in understanding the physical phenomena of graphene under high power or high temperature.

cond-mat.mtrl-sci↗

arXiv2007

Scaling of exciton binding energy with external dielectric function in carbon nanotubes

We develop a scaling relationship between the exciton binding energy and the external dielectric function in carbon nanotubes. We show that the electron-electron and electron-hole interaction energies are strongly affected by screening yet largely counteract each other, resulting in much smaller changes in the optical transition energy. The model indicates that the relevant particle interaction energies are reduced by as much as 50 percent upon screening by water and that the unscreened electron-electron interaction energy is larger than the unscreened electron-hole interaction energy, in agreement with explanations of the "ratio problem." We apply the model to measurements of the changes in the optical transistion energies in single, suspended carbon nanotubes as the external dielectric environment is altered.

cond-mat.other↗