arXiv subjects

Ye Chen

Publications and source records attributed to Ye Chen.

At least 91 records · Page 5Linked to original sources

arXiv2021

Evolution of superconductivity and charge order in pressurized RbV$_3$Sb$_5$

The kagome metals $A$V$_3$Sb$_5$ ($A=$ K, Rb, Cs) under ambient pressure exhibit an unusual charge order, from which superconductivity emerges. In this work, by applying hydrostatic pressure using a liquid pressure medium and carrying out electrical resistance measurements for RbV$_3$Sb$_5$, we find the charge order becomes suppressed under a modest pressure $p_{\rm c}$ ($1.4<p_{\rm c}<1.6$ GPa), while the superconducting transition temperature $T_{\rm c}$ is maximized. $T_{\rm c}$ is then gradually weakened with further increase of pressure and reaches a minimum around 14.3 GPa, before exhibiting another maximum around 22.8 GPa, signifying the presence of a second superconducting dome. Distinct normal state resistance anomalies are found to be associated with the second superconducting dome, similar to KV$_3$Sb$_5$. Our findings point to qualitatively similar temperature-pressure phase diagrams in KV$_3$Sb$_5$ and RbV$_3$Sb$_5$, and suggest a close link between the second superconducting dome and the high-pressure resistance anomalies.

cond-mat.supr-con↗

arXiv2021

CW Operation of the European XFEL: SC-Gun Injector Optimization, S2E Calculations and SASE Performance

This note presents optimization results for an injector with a super-conducting gun for the cw operation of the European XFEL, the corresponding start-to-end simulation calculations for the beam transport to the undulators and SASE calculations of the X-ray intensities for the achievable photon energies. The optimizations for an injector with a super-conducting gun are done mainly for an already experimentally shown accelerating gradient of 40 MV/m. Different working points with respect to transverse emittance and bunch length are chosen to see whether the lower transverse emittance of the longer bunches survives the necessary stronger bunch compression to achieve comparable peak currents in the undulators. Also, results for possible further improvement of the gun gradient to 50 MV/m and a modified transverse laser profile (truncated Gaussian) are shown. The S2E calculations are taking into account all relevant collective effects, including a thorough treatment of the impact of the so called Micro-Bunching Instability. For that purpose, tracking with the real number of particles is done, with a resolution in space and time fine enough to calculate instability growth model-free.

physics.acc-ph↗

arXiv2021

Stacked polarimeters with twisted black phosphorus

The real-time, in-line analysis of light polarization is critical in optical communication networks, which suffers from the complex systems with numerous bulky opto-electro-mechanical elements tandemly arranged along optical path. Here, we propose a fiber-integrated polarimeter with nano-thickness by vertically stacking three two-dimensional (2D) materials based photodetection units. We demonstrate a self-power-calibrated, ultrafast, unambiguous detection of linear (LP) and circular polarized (CP) light according to the symmetry broken induced linear photogalvanic effects (LPGE) and circular photogalvanic effects (CPGE) in black phosphorous (BP) units, which are twistedly stacked to substitute traditional mechanical rotation of polarizers. As a demonstration, we achieve Hadamard single-pixel polarimetric imaging by the polarimeter to recognize the polarization distributions, showing potential in high-speed polarization-division-multiplexed imaging and real-time polarized endoscopy. This work provides a new strategy for next-generation ultracompact optical and optoelectronic systems, and guides a way for developing high-resolution arrayed devices with multifunctional pixels.

physics.optics↗

arXiv2021

Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit

Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g., numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters.

q-bio.QM↗

arXiv2021

High-fidelity Prediction of Megapixel Longitudinal Phase-space Images of Electron Beams using Encoder-Decoder Neural Networks

Modeling of large-scale research facilities is extremely challenging due to complex physical processes and engineering problems. Here, we adopt a data-driven approach to model the longitudinal phase-space diagnostic beamline at the photoinector of the European XFEL with an encoder-decoder neural network model. A deep convolutional neural network (decoder) is used to build images measured on the screen from a small feature map generated by another neural network (encoder). We demonstrate that the model trained only with experimental data can make high-fidelity predictions of megapixel images for the longitudinal phase-space measurement without any prior knowledge of photoinjectors and electron beams. The prediction significantly outperforms existing methods. We also show the scalability and interpretability of the model by sharing the same decoder with more than one encoder used for different setups of the photoinjector, and propose a pragmatic way to model a facility with various diagnostics and working points. This opens the door to a new way of accurately modeling a photoinjector using neural networks and experimental data. The approach can possibly be extended to the whole accelerator and even other types of scientific facilities.

cs.LG↗

arXiv2021

Interplay between charge order and superconductivity in the kagome metal KV$_3$Sb$_5$

The kagome metal KV$_3$Sb$_5$ hosts charge order, topologically nontrivial Dirac band crossings, and a superconducting ground state with unconventional characteristics, providing an ideal platform to investigate the interplay between different electronic states on the kagome lattice. Here we study the evolution of charge order and superconductivity in KV$_3$Sb$_5$ under hydrostatic pressure using electrical resistivity measurements. With the application of pressure, the superconducting transition temperature $T_{\rm c}=0.9$ K under ambient pressure quickly increases to 3.1 K at $p=0.4$ GPa, as charge order progressively weakens. Upon further increasing pressure, signatures of charge order disappears at $p_{\rm c1}\approx0.5$ GPa and $T_{\rm c}$ is gradually suppressed, forming a superconducting dome that terminates at $p\approx10$ GPa. Beyond $p\approx10$ GPa, a second superconducting dome emerges with maximum $T_{\rm c}\approx1.0$ K at $p_{\rm c2}\approx22$ GPa, which becomes fully suppressed at $p\approx28$ GPa. The suppression of superconductivity for the second superconducting dome is associated with the appearance of a unique high-pressure phase , possibly a distinct charge order.

cond-mat.supr-con↗

arXiv2021

Nodeless superconductivity in the kagome metal CsV$_3$Sb$_5$

The recently discovered kagome metal series $A$V$_3$Sb$_5$ ($A$=K, Rb, Cs) exhibits topologically nontrivial band structures, chiral charge order and superconductivity, presenting a unique platform for realizing exotic electronic states. The nature of the superconducting state and the corresponding pairing symmetry are key questions that demand experimental clarification. Here, using a technique based on the tunneling diode oscillator, the magnetic penetration depth $Δλ(T)$ of CsV$_3$Sb$_5$ was measured down to 0.07 K. A clear exponential behavior in $Δλ(T)$ with marked deviations from a $T$ or $T^2$ temperature dependence is observed at low temperatures, indicating a deficiency of nodal quasiparticles. Temperature dependence of the superfluid density and electronic specific heat can be described by two-gap $s$-wave superconductivity, consistent with the presence of multiple Fermi surfaces in CsV$_3$Sb$_5$. These results evidence nodeless superconductivity in CsV$_3$Sb$_5$ under ambient pressure, and constrain the allowed pairing symmetry.

cond-mat.supr-con↗

arXiv2021

A one-dimensional flow model enhanced by machine learning for simulation of vocal fold vibration

We describe a one-dimensional (1D) unsteady and viscous flow model that is derived from the momentum and mass conservation equations, and to enhance this physics-based model, we use a machine learning approach to determine the unknown modeling parameters. Specifically, we first construct an idealized larynx model and perform ten cases of three-dimensional (3D) fluid--structure interaction (FSI) simulations. The flow data are then extracted to train the 1D flow model using a sparse identification approach for nonlinear dynamical systems. As a result of training, we obtain the analytical expressions for the entrance effect and pressure loss in the glottis, which are then incorporated in the flow model to conveniently handle different glottal shapes due to vocal fold vibration. We apply the enhanced 1D flow model in the FSI simulation of both idealized vocal fold geometries and subject-specific anatomical geometries reconstructed from the MRI images of rabbits' larynges. The 1D flow model is evaluated in both of these setups and is shown to have robust performance. Therefore, it provides a fast simulation tool superior to the previous 1D models.

physics.flu-dyn↗

arXiv2021

FamDroid: Learning-Based Android Malware Family Classification Using Static Analysis

Android is currently the most extensively used smartphone platform in the world. Due to its popularity and open source nature, Android malware has been rapidly growing in recent years, and bringing great risks to users' privacy. The malware applications in a malware family may have common features and similar behaviors, which are beneficial for malware detection and inspection. Thus, classifying Android malware into their corresponding families is an important task in malware analysis. At present, the main problem of existing research works on Android malware family classification lies in that the extracted features are inadequate to represent the common behavior characteristics of the malware in malicious families, and leveraging a single classifier or a static ensemble classifier is restricted to further improve the accuracy of classification. In this paper, we propose FamDroid, a learning-based Android malware family classification scheme using static analysis technology. In FamDroid, the explicit features including permissions, hardware components, app components, intent filters are extracted from the apk files of a malware application. Besides, a hidden feature generated from the extracted APIs is used to represents the API call relationship in the application. Then, we design an adaptive weighted ensemble classifier, which considers the adaptability of the sample to each base classifier, to carry out accurate malware family classification. We conducted experiments on the Drebin dataset which contains 5560 Android malicious applications. The superiority of FamDroid is demonstrated through comparing it with 5 traditional machine learning models and 4 state-of-the-art reference schemes. FamDroid can correctly classify 98.92% of malware samples into their families and achieve 99.12% F1-Score.

cs.CR↗

arXiv2021

Localized 4f-electrons in the quantum critical heavy fermion ferromagnet CeRh$_6$Ge$_4$

Ferromagnetic quantum critical points were predicted to be prohibited in clean itinerant ferromagnetic systems, yet such a phenomenon was recently revealed in CeRh$_6$Ge$_4$, where the Curie temperature can be continuously suppressed to zero under a moderate hydrostatic pressure. Here we report the observation of quantum oscillations in CeRh$_6$Ge$_4$ from measurements using the cantilever and tunnel-diode oscillator methods in fields up to 45 T, clearly demonstrating that the ferromagnetic quantum criticality occurs in a clean system. In order to map the Fermi surface of CeRh$_6$Ge$_4$, we performed angle-dependent measurements of quantum oscillations at ambient pressure, and compared the results to density functional theory calculations. The results are consistent with the Ce 4f electrons remaining localized, and not contributing to the Fermi surface, suggesting that localized ferromagnetism is a key factor for the occurrence of a ferromagnetic quantum critical point in CeRh$_6$Ge$_4$.

cond-mat.str-el↗

arXiv2020

Experimental validation of collective effects modeling at injector section of x-ray free-electron laser

We consider the collective beam dynamics at the injector section of the European XFEL. The results of the measurements for the longitudinal phase space (LPS) of the electron beam are compared with those obtained from numerical modeling. A new approach is proposed for the analysis of the self-field effects in the LPS measurements. It allows to determine accurately the synchronous RF phase in an accelerating module and to subtract properly the RF curvature imprinted in the LPS. A further incorporation with the simulation made it possible to separate the collective effects originating from different sources and thus to quantify, individually, the impact of these effects on the beam dynamics. This includes the space-charge dominated beam dynamics in the RF gun as well as the collective effects dominated by the wake fields after the gun up to the end of the injector section. A new analytical model is also proposed for the short-range wake function of a finite chain of RF cavities and verified by both numerical analysis and experimental results. It is shown that the physical models used in the simulation of the beam dynamics after the gun allow to reduce the absolute error in the modeling of the correlated energy chirp by order of magnitude in comparison with the case when the collective effects after the gun are neglected.

physics.acc-ph↗

arXiv2020

Limitation of Acyclic Oriented Graphs Matching as Cell Tracking Accuracy Measure when Evaluating Mitosis

Multi-object tracking (MOT) in computer vision and cell tracking in biomedical image analysis are two similar research fields, whose common aim is to achieve instance level object detection/segmentation and associate such objects across different video frames. However, one major difference between these two tasks is that cell tracking also aim to detect mitosis (cell division), which is typically not considered in MOT tasks. Therefore, the acyclic oriented graphs matching (AOGM) has been used as de facto standard evaluation metrics for cell tracking, rather than directly using the evaluation metrics in computer vision, such as multiple object tracking accuracy (MOTA), ID Switches (IDS), ID F1 Score (IDF1) etc. However, based on our experiments, we realized that AOGM did not always function as expected for mitosis events. In this paper, we exhibit the limitations of evaluating mitosis with AOGM using both simulated and real cell tracking data.

cs.CV↗

arXiv2020

Self-Prediction for Joint Instance and Semantic Segmentation of Point Clouds

We develop a novel learning scheme named Self-Prediction for 3D instance and semantic segmentation of point clouds. Distinct from most existing methods that focus on designing convolutional operators, our method designs a new learning scheme to enhance point relation exploring for better segmentation. More specifically, we divide a point cloud sample into two subsets and construct a complete graph based on their representations. Then we use label propagation algorithm to predict labels of one subset when given labels of the other subset. By training with this Self-Prediction task, the backbone network is constrained to fully explore relational context/geometric/shape information and learn more discriminative features for segmentation. Moreover, a general associated framework equipped with our Self-Prediction scheme is designed for enhancing instance and semantic segmentation simultaneously, where instance and semantic representations are combined to perform Self-Prediction. Through this way, instance and semantic segmentation are collaborated and mutually reinforced. Significant performance improvements on instance and semantic segmentation compared with baseline are achieved on S3DIS and ShapeNet. Our method achieves state-of-the-art instance segmentation results on S3DIS and comparable semantic segmentation results compared with state-of-the-arts on S3DIS and ShapeNet when we only take PointNet++ as the backbone network.

cs.CV↗

arXiv2020

CircleNet: Anchor-free Detection with Circle Representation

Object detection networks are powerful in computer vision, but not necessarily optimized for biomedical object detection. In this work, we propose CircleNet, a simple anchor-free detection method with circle representation for detection of the ball-shaped glomerulus. Different from the traditional bounding box based detection method, the bounding circle (1) reduces the degrees of freedom of detection representation, (2) is naturally rotation invariant, (3) and optimized for ball-shaped objects. The key innovation to enable this representation is the anchor-free framework with the circle detection head. We evaluate CircleNet in the context of detection of glomerulus. CircleNet increases average precision of the glomerulus detection from 0.598 to 0.647. Another key advantage is that CircleNet achieves better rotation consistency compared with bounding box representations.

cs.CV↗

arXiv2020

Single shot cathode transverse momentum imaging in high brightness photoinjectors

In state of the art photoinjector electron sources, thermal emittance from photoemission dominates the final injector emittance. Therefore, low thermal emittance cathode developments and diagnostics are very important. Conventional thermal emittance measurements for the high gradient gun are time-consuming and thus thermal emittance is not measured as frequently as quantum efficiency during the lifetime of photocathodes, although both are important properties for the photoinjector optimizations. In this paper, a single shot measurement of photoemission transverse momentum, i.e., thermal emittance per rms laser spot size, is proposed for photocathode RF guns. By tuning the gun solenoid focusing, the electrons transverse momenta at the cathode are imaged to a downstream screen, which enables a single shot measurement of both the rms value and the detailed spectra of the photoelectrons transverse momenta. Both simulations and proof of principle experiments are reported.

physics.acc-ph↗

arXiv2020

Sequential Selection for Accelerated Life Testing via Approximate Bayesian Inference

Accelerated life testing (ALT) is typically used to assess the reliability of material's lifetime under desired stress levels. Recent advances in material engineering have made a variety of material alternatives readily available. To identify the most reliable material setting with efficient experimental design, a sequential test planning strategy is preferred. To guarantee a tractable statistical mechanism for information collection and update, we develop explicit model parameter update formulas via approximate Bayesian inference. Theories show that our explicit update formulas give consistent parameter estimates. Simulation study and a case study show that the proposed sequential selection approach can significantly improve the probability of identifying the material alternative with best reliability performance over other design approaches.

stat.ME↗

arXiv2019

Strange metal behavior in a pure ferromagnetic Kondo lattice

The strange metal phases found to develop in a wide range of materials near a quantum critical point (QCP), have posed a long-standing mystery. The frequent association of strange metals with unconventional superconductivity and antiferromagnetic QCPs has led to a belief that they are highly entangled quantum states. Ferromagnets, by contrast are regarded as an unlikely setting for strange metals, for they are weakly entangled and their QCPs are often interrupted by competing phases or first order phase transitions. Here, we provide compelling evidence that the stoichiometric heavy fermion ferromagnet CeRh$_6$Ge$_4$ becomes a strange metal at a pressure-induced QCP: specific heat and resistivity measurements demonstrate that the FM transition is continuously suppressed to zero temperature revealing a strange metal phase. We argue that strong magnetic anisotropy plays a key role in this process,injecting entanglement, in the form of triplet resonating valence bonds (tRVBs) into the ordered ferromagnet. We show that the singular transformation from tRVBs into Kondo singlets that occurs at the QCP causes a jump in the Fermi surface volume: a key driver of strange metallic behavior. Our results open up a new direction for research into FM quantum criticality, while also establishing an important new setting for the strange metal problem. Most importantly, strange metallic behavior at a FM quantum critical point suggests that it is quantum entanglement rather than the destruction of antiferromagnetism that is the common driver of the many varied examples of strange metallic behavior.

cond-mat.str-el↗

arXiv2019

All fiber ultrafast laser generating gigahertz pulse based on a hybrid plasmonic microfiber resonator

Ultrafast lasers generating high repetition rate ultrashort pulses through various mode-locking methods can benefit many important applications including communication, materials processing, astronomical observation, etc. For decades, mode-locking based on dissipative four-wave-mixing (DFWM) has been fundamental in producing pulses with repetition rates on the order of gigahertz (GHz), where multiwavelength comb filters and long nonlinear components are elemental. Recently, this method has been improved using filter-driven DFWM, which exploits both the filtering and nonlinear features of silica microring resonators. However, the fabrication complexity and coupling loss between waveguides and fibers are problematics. In this study, we demonstrate a tens to hundreds of gigahertz stable pulsed all-fiber laser based on the hybrid plasmonic microfiber knot resonator device. Unlike previously reported pulse generation mechanisms, the operation utilizes the nonlinear-polarization-rotation (NPR) effect introduced by the polarization-dependent feature of the device to increase intracavity power for boosting DFWM mode-locking, which we term NPR -stimulated DFWM. The easily-fabricated versatile device acts as a polarizer, comb filter, and nonlinear component simultaneously, thereby introducing a novel application of microfiber resonator devices in ultrafast and nonlinear photonics. We believe that our work underpins a significant improvement in achieving practical low-cost ultrafast light sources.

physics.optics↗