arXiv subjects

Yang Chen

Publications and source records attributed to Yang Chen.

At least 523 records · Page 29Linked to original sources

arXiv2021

Cellular Automata Simulation of Grain Growth of Powder Metallurgy Nickel-Based Superalloy

Primary γ' phase instead of carbides and borides plays an important role in suppressing grain growth during solution at 1433K of FGH98 nickel-based polycrystalline alloys. Results illustrate that as-fabricated FGH98 has equiaxed grain structure and after heat treatment, grains remain equiaxed but grow larger. In order to clarify the effects of the size and volume fraction of the primary γ' phase on the grain growth during heat treatment, this paper establish a 2D Cellular Automata (CA) model based on the thermal activation and the lowest energy principle. The CA results are compared with the experimental results and show a good fit with an error less than 10%. Grain growth kinetics are depicted and simulations in real time for various sizes and volume fractions of primary γ' particles work out well with the Zener relation. The coefficient n value in Zener relation is theoretically calculated and its minimum value is 0.23 when the radius of primary γ' is 2.8μm.

stat.CO↗

arXiv2021

EnMcGAN: Adversarial Ensemble Learning for 3D Complete Renal Structures Segmentation

3D complete renal structures(CRS) segmentation targets on segmenting the kidneys, tumors, renal arteries and veins in one inference. Once successful, it will provide preoperative plans and intraoperative guidance for laparoscopic partial nephrectomy(LPN), playing a key role in the renal cancer treatment. However, no success has been reported in 3D CRS segmentation due to the complex shapes of renal structures, low contrast and large anatomical variation. In this study, we utilize the adversarial ensemble learning and propose Ensemble Multi-condition GAN(EnMcGAN) for 3D CRS segmentation for the first time. Its contribution is three-fold. 1)Inspired by windowing, we propose the multi-windowing committee which divides CTA image into multiple narrow windows with different window centers and widths enhancing the contrast for salient boundaries and soft tissues. And then, it builds an ensemble segmentation model on these narrow windows to fuse the segmentation superiorities and improve whole segmentation quality. 2)We propose the multi-condition GAN which equips the segmentation model with multiple discriminators to encourage the segmented structures meeting their real shape conditions, thus improving the shape feature extraction ability. 3)We propose the adversarial weighted ensemble module which uses the trained discriminators to evaluate the quality of segmented structures, and normalizes these evaluation scores for the ensemble weights directed at the input image, thus enhancing the ensemble results. 122 patients are enrolled in this study and the mean Dice coefficient of the renal structures achieves 84.6%. Extensive experiments with promising results on renal structures reveal powerful segmentation accuracy and great clinical significance in renal cancer treatment.

eess.IV↗

arXiv2021

Revealing a peculiar supernova remnant G106.3+2.7 as a petaelectronvolt proton accelerator with X-ray observations

Supernova remnants (SNRs) have long been considered as one of the most promising sources of Galactic cosmic rays. In the SNR paradigm, petaelectronvolt (PeV) proton acceleration may only be feasible at the early evolution stage, lasting a few hundred years, when the SNR shock speed is high. While evidence supporting the acceleration of PeV protons in young SNRs has yet to be discovered, X-ray synchrotron emission is an important indicator of fast shock. We here report the first discovery of X-ray synchrotron emission from the possibly middle-aged SNR G106.3+2.7, implying that this SNR is still an energetic particle accelerator despite its age. This discovery, along with the ambient environmental information, multiwavelength observation, and theoretical arguments, supports SNR G106.3+2.7 as a likely powerful PeV proton accelerator.

astro-ph.HE↗

arXiv2021

Multiscale IoU: A Metric for Evaluation of Salient Object Detection with Fine Structures

General-purpose object-detection algorithms often dismiss the fine structure of detected objects. This can be traced back to how their proposed regions are evaluated. Our goal is to renegotiate the trade-off between the generality of these algorithms and their coarse detections. In this work, we present a new metric that is a marriage of a popular evaluation metric, namely Intersection over Union (IoU), and a geometrical concept, called fractal dimension. We propose Multiscale IoU (MIoU) which allows comparison between the detected and ground-truth regions at multiple resolution levels. Through several reproducible examples, we show that MIoU is indeed sensitive to the fine boundary structures which are completely overlooked by IoU and f1-score. We further examine the overall reliability of MIoU by comparing its distribution with that of IoU on synthetic and real-world datasets of objects. We intend this work to re-initiate exploration of new evaluation methods for object-detection algorithms.

cs.CV↗

arXiv2021

Dielectric metasurface for independent complex-amplitude control of arbitrary two orthogonal states of polarization

Metasurfaces are planar structures that can manipulate the amplitude, phase and polarization (APP) of light at subwavelength scale. Although various functionalities have been proposed based on metasurface, a most general optical control, i.e., independent complex-amplitude (amplitude and phase) control of arbitrary two orthogonal states of polarizations, has not yet been realized. Such level of optical control can not only cover the various functionalities realized previously, but also enable new functionalities that are not feasible before. Here, we propose a single-layer dielectric metasurface to realize this goal and experimentally demonstrate several advanced functionalities, such as two independent full-color printing images under arbitrary elliptically orthogonal polarizations and dual sets of printing-hologram integrations. Our work opens the way for a wide range of applications in advanced image display, information encoding, and polarization optics.

physics.optics↗

arXiv2021

Multi-functional microwave photonic radar system for simultaneous distance and velocity measurement and high-resolution microwave imaging

A photonic-assisted multi-functional radar system for simultaneous distance and velocity measurement and high-resolution microwave imaging is proposed and experimentally demonstrated by using a composite transmitted microwave signal of a single-chirped linearly frequency-modulated (LFM) signal and a single-tone microwave signal. In the system, the transmitted signal is generated via photonic frequency up-conversion based on a single integrated dual-polarization dual-parallel Mach-Zehnder modulator (DPol-DPMZM), whereas the echo signals scattered from the target are de-chirped to two low-frequency signals using a microwave photonic frequency mixer. By using the two low-frequency de-chirped signals, the real-time distance and radial velocity of the moving target can be measured accurately according to the round-trip time of the echo signal and its Doppler frequency shift. Compared with the previous reported distance and velocity measurement methods, where two LFM signals with opposite chirps are used, these parameters can be obtained using only a single-chirped LFM signal and a single-tone microwave signal. Meanwhile, high-resolution inverse synthetic aperture radar (ISAR) imaging can also be realized using ISAR imaging algorithms. An experiment is performed to verify the proposed multi-functional microwave photonic radar system. An up-chirped LFM signal from 8.5 to 12.5 GHz and an 8.0 GHz single-tone microwave signal are used as the transmitted signal. The results show that the absolute measurement errors of distance and radial velocity are less than 5.9 cm and 2.8 cm/s, respectively. ISAR imaging results are also demonstrated, which proves the high-resolution and real-time ISAR imaging ability of the proposed system.

eess.SP↗

arXiv2021

The Post-impact Evolution of the X-ray Emitting Gas in SNR 1987A Viewed by XMM-Newton

Since 1996 the blast wave driven by SN 1987A has been interacting with the dense circumstellar material, which provides us with a unique opportunity to study the early evolution of a newborn supernova remnant (SNR). Based on the XMM-Newton RGS and EPIC-pn X-ray observations from 2007 to 2019, we investigated the post-impact evolution of the X-ray emitting gas in SNR 1987A. The hot plasma is represented by two non-equilibrium ionization components with temperature of $\sim0.6$ keV and $\sim2.5$ keV. The low-temperature plasma has a density $\sim2400$ cm$^{-3}$, which is likely dominated by the lower density gas inside the equatorial ring (ER). The high-temperature plasma with a density $\sim550$ cm$^{-3}$ could be dominated by the H II region and the high-latitude material beyond the ring. In the last few years, the emission measure of the low-temperature plasma has been decreasing, indicating that the blast wave has left the main ER. But the blast wave is still propagating into the high-latitude gas, resulting in the steadily increase of the high-temperature emission measure. In the meantime, the average abundances of N, O, Ne, and Mg are found to be declining, which may reflect the different chemical compositions between two plasma components. We also detected the Fe K lines in most of the observations, showing increasing flux and centroid energy. We interpret the Fe K lines as from a third hot component, which may come from the reflected shock-heated gas or originate from Fe-rich ejecta clumps, shocked by the reverse shock.

astro-ph.HE↗

arXiv2021

Towards Synthetic Multivariate Time Series Generation for Flare Forecasting

One of the limiting factors in training data-driven, rare-event prediction algorithms is the scarcity of the events of interest resulting in an extreme imbalance in the data. There have been many methods introduced in the literature for overcoming this issue; simple data manipulation through undersampling and oversampling, utilizing cost-sensitive learning algorithms, or by generating synthetic data points following the distribution of the existing data. While synthetic data generation has recently received a great deal of attention, there are real challenges involved in doing so for high-dimensional data such as multivariate time series. In this study, we explore the usefulness of the conditional generative adversarial network (CGAN) as a means to perform data-informed oversampling in order to balance a large dataset of multivariate time series. We utilize a flare forecasting benchmark dataset, named SWAN-SF, and design two verification methods to both quantitatively and qualitatively evaluate the similarity between the generated minority and the ground-truth samples. We further assess the quality of the generated samples by training a classical, supervised machine learning algorithm on synthetic data, and testing the trained model on the unseen, real data. The results show that the classifier trained on the data augmented with the synthetic multivariate time series achieves a significant improvement compared with the case where no augmentation is used. The popular flare forecasting evaluation metrics, TSS and HSS, report 20-fold and 5-fold improvements, respectively, indicating the remarkable statistical similarities, and the usefulness of CGAN-based data generation for complicated tasks such as flare forecasting.

cs.LG↗

arXiv2021

Agatha: Smart Contract for DNN Computation

Smart contract is one of the core features of Ethereum and has inspired many blockchain descendants. Since its advent, the verification paradigm of smart contract has been improving toward high scalability. It shifts from the expensive on-chain verification to the orchestration of off-chain VM (virtual machine) execution and on-chain arbitration with the pinpoint protocol. The representative projects are TrueBit, Arbitrum, YODA, ACE, and Optimism. Inspired by visionaries in academia and industry, we consider the DNN computation to be promising but on the next level of complexity for the verification paradigm of smart contract. Unfortunately, even for the state-of-the-art verification paradigm, off-chain VM execution of DNN computation has an orders-of-magnitude slowdown compared to the native off-chain execution. To enable the native off-chain execution of verifiable DNN computation, we present Agatha system, which solves the significant challenges of misalignment and inconsistency: (1) Native DNN computation has a graph-based computation paradigm misaligned with previous VM-based execution and arbitration; (2) Native DNN computation may be inconsistent cross platforms which invalidates the verification paradigm. In response, we propose the graph-based pinpoint protocol (GPP) which enables the pinpoint protocol on computational graphs, and bridges the native off-chain execution and the contract arbitration. We also develop a technique named Cross-evaluator Consistent Execution (XCE), which guarantees cross-platform consistency and forms the correctness foundation of GPP. We showcase Agatha for the DNN computation of popular models (MobileNet, ResNet50 and VGG16) on Ethereum. Agatha achieves a negligible on-chain overhead, and an off-chain execution overhead of 3.0%, which represents an off-chain latency reduction of at least 602x compared to the state-of-the-art verification paradigm.

cs.CR↗

arXiv2021

CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval

Video-text retrieval plays an essential role in multi-modal research and has been widely used in many real-world web applications. The CLIP (Contrastive Language-Image Pre-training), an image-language pre-training model, has demonstrated the power of visual concepts learning from web collected image-text datasets. In this paper, we propose a CLIP4Clip model to transfer the knowledge of the CLIP model to video-language retrieval in an end-to-end manner. Several questions are investigated via empirical studies: 1) Whether image feature is enough for video-text retrieval? 2) How a post-pretraining on a large-scale video-text dataset based on the CLIP affect the performance? 3) What is the practical mechanism to model temporal dependency between video frames? And 4) The Hyper-parameters sensitivity of the model on video-text retrieval task. Extensive experimental results present that the CLIP4Clip model transferred from the CLIP can achieve SOTA results on various video-text retrieval datasets, including MSR-VTT, MSVC, LSMDC, ActivityNet, and DiDeMo. We release our code at https://github.com/ArrowLuo/CLIP4Clip.

cs.CV↗

arXiv2021

An Ultrahigh-Q Microresonator on 4H-silicon-carbide-on-insulator Platform for Multiple Harmonics, Cascaded Raman Lasing and Kerr Comb Generations

The realization of ultrahigh quality (Q) resonators regardless of the underpinning material platforms has been a ceaseless pursuit, because the high Q resonators provide an extreme environment of storage of light to enable observations of many unconventional nonlinear optical phenomenon with high efficiencies. Here, we demonstrate an ultra-high Q factor (7.1*10^6) microresonator on the 4H-silicon-carbide-on-insulator (4H-SiCOI) platform in which both \c{hi}^(2) and \c{hi}^(3) nonlinear processes of high efficiencies have been generated. Broadband frequency conversions, including second-, third-, fourth-harmonic generation were observed. Cascaded Raman lasing was demonstrated in the SiC microresonator for the first time to the best of our knowledge. Broadband Kerr frequency combs covering from 1300 to 1700 nm were achieved using a dispersion-engineered SiC microresonator. Our demonstration is a significant milestone in the development of SiC photonic integrated devices.

physics.optics↗

arXiv2021

Local Relation Learning for Face Forgery Detection

With the rapid development of facial manipulation techniques, face forgery detection has received considerable attention in digital media forensics due to security concerns. Most existing methods formulate face forgery detection as a classification problem and utilize binary labels or manipulated region masks as supervision. However, without considering the correlation between local regions, these global supervisions are insufficient to learn a generalized feature and prone to overfitting. To address this issue, we propose a novel perspective of face forgery detection via local relation learning. Specifically, we propose a Multi-scale Patch Similarity Module (MPSM), which measures the similarity between features of local regions and forms a robust and generalized similarity pattern. Moreover, we propose an RGB-Frequency Attention Module (RFAM) to fuse information in both RGB and frequency domains for more comprehensive local feature representation, which further improves the reliability of the similarity pattern. Extensive experiments show that the proposed method consistently outperforms the state-of-the-arts on widely-used benchmarks. Furthermore, detailed visualization shows the robustness and interpretability of our method.

cs.CV↗

arXiv2021

Multiple radio frequency measurement with an improved frequency resolution based on stimulated Brillouin scattering with a reduced gain bandwidth

A photonic-assisted multiple radio frequency (RF) measurement approach based on stimulated Brillouin scattering (SBS) and frequency-to-time mapping with high accuracy and high-frequency resolution is reported. A two-tone signal is single-sideband (SSB) modulated on an optical carrier via a dual-parallel Mach-Zehnder modulator to construct one SBS gain and two SBS losses for SBS gain bandwidth reduction. The unknown RF signal is also SSB modulated on a carrier that has been modulated by a sweep signal, thus the unknown RF signal is converted to a sweep optical signal along with the sweep optical carrier. The bandwidth-reduced SBS gain spectrum is detected by the sweep optical signals at different specific time, mapping the RF frequencies to the time domain. An experiment is performed. RF frequencies from 0.3 to 7.6 GHz are simultaneously measured with a root mean square error of less than 1 MHz. In addition, the frequency resolution of the measurement can be much lower than 10 MHz, which is now the best result in the RF frequency measurement methods employing the SBS effect.

eess.SP↗

arXiv2021

Differential, Difference and Asymptotic Relations for Pollaczek-Jacobi Type Orthogonal Polynomials and Their Hankel Determinants

In this paper, we study the orthogonal polynomials with respect to a singularly perturbed Pollaczek-Jacobi type weight $$ w(x,t):=(1-x^2)^α\mathrm{e}^{-\frac{t}{1-x^{2}}},\qquad x\in[-1,1],\;\;α>0,\;\;t>0. $$ By using the ladder operator approach, we establish the second-order difference equations satisfied by the recurrence coefficient $β_n(t)$ and the sub-leading coefficient $\mathrm{p}(n,t)$ of the monic orthogonal polynomials, respectively. We show that the logarithmic derivative of $β_n(t)$ can be expressed in terms of a particular Painlevé V transcendent. The large $n$ asymptotic expansions of $β_n(t)$ and $\mathrm{p}(n,t)$ are obtained by using Dyson's Coulomb fluid method together with the related difference equations. Furthermore, we study the associated Hankel determinant $D_n(t)$ and show that a quantity $σ_n(t)$, allied to the logarithmic derivative of $D_n(t)$, can be expressed in terms of the $σ$-function of a particular Painlevé V. The second-order differential and difference equations for $σ_n(t)$ are also obtained. In the end, we derive the large $n$ asymptotics of $σ_n(t)$ and $D_n(t)$ from their relations with $β_n(t)$ and $\mathrm{p}(n,t)$.

math.CA↗

arXiv2021

Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model

Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction is one of the most promising ways to compensate for the increased noise due to reduction of photon flux. Rather than most existing prior-driven algorithms that benefit from manually designed prior functions or supervised learning schemes, in this work we integrate the data-consistency as a conditional term into the iterative generative model for low-dose CT. At the stage of prior learning, the gradient of data density is directly learned from normal-dose CT images as a prior. Then at the iterative reconstruction stage, the stochastic gradient descent is employed to update the trained prior with annealed and conditional schemes. The distance between the reconstructed image and the manifold is minimized along with data fidelity during reconstruction. Experimental comparisons demonstrated the noise reduction and detail preservation abilities of the proposed method.

eess.IV↗

arXiv2021

Painlevé V for a Jacobi unitary ensemble with random singularities

In this paper, we focus on the relationship between the fifth Painlevé equation and a Jacobi weight perturbed with random singularities, \begin{equation*} w(z)=\left(1-z^2\right)^α{\rm e}^{-\frac{t}{z^2-k^2}},~~~z,k\in[-1,1],~α,t>0. \end{equation*} By using the ladder operator approach, we obtain that an auxiliary quantity $R_n(t)$, which is closely related to the recurrence coefficients of monic polynomials orthogonal with $w(z)$, satisfies a particular Painlevé V equation.

math-ph↗

arXiv2021

Topologically protected valley-dependent quantum photonic circuits

Topological photonics has been introduced as a powerful platform for integrated optics, since it can deal with robust light transport, and be further extended to the quantum world. Strikingly, valley-contrasting physics in topological photonic structures contributes to valley-related edge states, their unidirectional coupling, and even valley-dependent wave-division in topological junctions. Here, we design and fabricate nanophotonic topological harpoon-shaped beam splitters (HSBSs) based on $120$-deg-bending interfaces and demonstrate the first on-chip valley-dependent quantum information process. Two-photon quantum interference, namely, HongOu-Mandel (HOM) interference with a high visibility of $0.956 \pm 0.006$, is realized with our 50/50 HSBS, which is constructed by two topologically distinct domain walls. Cascading this kind of HSBS together, we also demonstrate a simple quantum photonic circuit and generation of a path-entangled state. Our work shows that the photonic valley state can be used in quantum information processing, and it is possible to realize more complex quantum circuits with valley-dependent photonic topological insulators, which provides a novel method for on-chip quantum information processing.

quant-ph↗

arXiv2021

Painlevé IV, $σ-$Form and the Deformed Hermite Unitary Ensembles

We study the Hankel determinant generated by a deformed Hermite weight with one jump $w(z,t,γ)=e^{-z^2+tz}|z-t|^γ(A+Bθ(z-t))$, where $A\geq 0$, $A+B\geq 0$, $t\in\textbf{R}$, $γ>-1$ and $z\in\textbf{R}$. By using the ladder operators for the corresponding monic orthogonal polynomials, and their relative compatibility conditions, we obtain a series of difference and differential equations to describe the relations among $α_n$, $β_n$, $R_n(t)$ and $r_n(t)$. Especially, we find that the auxiliary quantities $R_n(t)$ and $r_n(t)$ satisfy the coupled Riccati equations, and $R_n(t)$ satisfies a particular Painlevé IV equation. Based on above results, we show that $σ_n(t)$ and $\hatσ_n(t)$, two quantities related to the Hankel determinant and $R_n(t)$, satisfy the continuous and discrete $σ-$form equations, respectively. In the end, we also discuss the large $n$ asymptotic behavior of $R_n(t)$, which produce the expansion of the logarithmic of the Hankel determinant and the asymptotic of the second order differential equation of the monic orthogonal polynomials.

math-ph↗