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Sen Yang

Publications and source records attributed to Sen Yang.

At least 127 records · Page 7Linked to original sources
arXiv2023

SNR-based beaconless multi-scan link acquisition model with vibration for LEO-to-ground laser communication

We propose a link acquisition time model deeply involving the process from the transmitted power to received signal-to-noise ratio (SNR) for LEO-to-ground laser communication for the first time. Compared with the conventional acquisition models founded on geometry analysis with divergence angle threshold, utilizing SNR as the decision criterion is more appropriate for practical engineering requirements. Specially, under the combined effects of platform vibration and turbulence, we decouple the parameters of beam divergence angle, spiral pitch, and coverage factor at a fixed transmitted power for a given average received SNR threshold. Then the single-scan acquisition probability is obtained by integrating the field of uncertainty (FOU), probability distribution of coverage factor, and receiver field angle. Consequently, the closed-form analytical expression of acquisition time expectation adopting multi-scan, which ensures acquisition success, with essential reset time between single-scan is derived. The optimizations concerning the beam divergence angle, spiral pitch, and FOU are presented. Moreover, the influence of platform vibration is investigated. All the analytical derivations are confirmed by Monte Carlo simulations. Notably, we provide a theoretical method for designing the minimum divergence angle modulated by the laser, which not only improves the acquisition performance within a certain vibration range, but also achieves a good trade-off with the system complexity.

eess.SP↗
arXiv2023

Merging toroidal dipole bound states in the continuum without up-down symmetry in Lieb lattice metasurfaces

The significance of bound states in the continuum (BICs) lies in their potential for theoretically infinite quality factors. However, their actual quality factors are limited by imperfections in fabrication, which lead to coupling with the radiation continuum. In this study, we present a novel approach to address this issue by introducing a merging BIC regime based on a Lieb lattice. By utilizing this approach, we effectively suppress the out-of-plane scattering loss, thereby enhancing the robustness of the structure against fabrication artifacts. Notably, unlike previous merging systems, our design does not rely on the up-down symmetry of metasurfaces. This characteristic grants more flexibility in applications that involve substrates and superstrates with different optical properties, such as microfluidic devices. Furthermore, we incorporate a lateral band gap mirror into the design to encapsulate the BIC structure. This mirror serves to suppress the in-plane radiation resulting from finite-size effects, leading to a remarkable ten-fold improvement in the quality factor. Consequently, our merged BIC metasurface, enclosed by the Lieb lattice photonic crystal mirror, achieves an exceptionally high-quality factor of 105 while maintaining a small footprint of 26.6X26.6 um. Our findings establish an appealing platform that capitalizes on the topological nature of BICs within compact structures. This platform holds great promise for various applications, including optical trapping, optofluidics, and high-sensitivity biodetection, opening up new possibilities in these fields.

physics.optics↗
arXiv2023

Towards rapid extracellular vesicles colorimetric detection using optofluidics-enhanced color-changing optical metasurface

Efficient transportation and delivery of analytes to the surface of optical sensors are crucial for overcoming limitations in diffusion-limited transport and analyte sensing. In this study, we propose a novel approach that combines metasurface optics with optofluidics-enabled active transport of extracellular vesicles (EVs). By leveraging this combination, we show that we can rapidly capture EVs and detect their adsorption through a color change generated by a specially designed optical metasurface that produces structural colors. Our results demonstrate that the integration of optofluidics and metasurface optics enables robust colorimetric read-out for EV concentrations as low as 107 EVs/ml, achieved within a short incubation time of two minutes, while using a CCD camera or naked eye for the read-out. This approach offers the potential for rapid sensing without the need for spectrometers and provides a short response time. Our findings suggest that the synergy between optofluidics and metasurface platforms can enhance the detection efficiency of low concentration bioparticle samples by overcoming the diffusion limits.

physics.optics↗
arXiv2023

Enhancing Grammatical Error Correction Systems with Explanations

Grammatical error correction systems improve written communication by detecting and correcting language mistakes. To help language learners better understand why the GEC system makes a certain correction, the causes of errors (evidence words) and the corresponding error types are two key factors. To enhance GEC systems with explanations, we introduce EXPECT, a large dataset annotated with evidence words and grammatical error types. We propose several baselines and analysis to understand this task. Furthermore, human evaluation verifies our explainable GEC system's explanations can assist second-language learners in determining whether to accept a correction suggestion and in understanding the associated grammar rule.

cs.CL↗
arXiv2023

Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types

Tumor mutational burden (TMB) is a potential genomic biomarker of immunotherapy. However, TMB detected through whole exome sequencing lacks clinical penetration in low-resource settings. In this study, we proposed a multi-scale deep learning framework to address the detection of TMB status from routinely used whole slide images for a multiple cancer TMB prediction model (MC- TMB). The MC-TMB achieved a mean area under the curve (AUC) of 0.818 (0.804-0.831) in the cross-validation cohort, which showed superior performance to each single-scale model. The improvements of MC-TMB over the single-tumor models were also confirmed by the ablation tests on x10 magnification, and the highly concerned regions typically correspond to dense lymphocytic infiltration and heteromorphic tumor cells. MC-TMB algorithm also exhibited good generalization on the external validation cohort with an AUC of 0.732 (0.683-0.761), and better performance when compared to other methods. In conclusion, we proposed a deep learning-based approach to reveal tumor mutational burden status from routinely used pathological slides across multiple cancer types.

cs.CV↗
arXiv2023

MISO: Legacy-compatible Privacy-preserving Single Sign-on using Trusted Execution Environments

Single sign-on (SSO) allows users to authenticate to third-party applications through a central identity provider. Despite their wide adoption, deployed SSO systems suffer from privacy problems such as user tracking by the identity provider. While numerous solutions have been proposed by academic papers, none were adopted because they require modifying identity providers, a significant adoption barrier in practice. Solutions do get deployed, however, fail to eliminate major privacy issues. Leveraging Trusted Execution Environments (TEEs), we propose MISO, the first privacy-preserving SSO system that is completely compatible with existing identity providers (such as Google and Facebook). This means MISO can be easily integrated into existing SSO ecosystem today and benefit end users. MISO also enables new functionality that standard SSO cannot offer: MISO allows users to leverage multiple identity providers in a single SSO workflow, potentially in a threshold fashion, to better protect user accounts. We fully implemented MISO based on Intel SGX. Our evaluation shows that MISO can handle high user concurrency with practical performance.

cs.CR↗
arXiv2023

Testing Super-Eddington Accretion onto a Supermassive Black Hole: Reverberation Mapping of PG 1119+120

We measure the black hole mass and investigate the accretion flow around the local ($z=0.0502$) quasar PG 1119+120. Spectroscopic monitoring with Calar Alto provides H$β$ lags and linewidths from which we estimate a black hole mass of $\log \left(M_{\bullet}/\mathrm{M}_{\odot} \right) = 7.0$, uncertain by $\sim0.4$ dex. High cadence photometric monitoring over two years with the Las Cumbres Observatory provides lightcurves in 7 optical bands suitable for intensive continuum reverberation mapping. We identify variability on two timescales. Slower variations on a 100-day timescale exhibit excess flux and increased lag in the $u'$ band and are thus attributable to diffuse bound-free continuum emission from the broad line region. Faster variations that we attribute to accretion disc reprocessing lack a $u'$-band excess and have flux and delay spectra consistent with either $τ\propto λ^{4/3}$, as expected for a temperature structure of $T(R) \propto R^{-3/4}$ for a thin accretion disc, or $τ\propto λ^{2}$ expected for a slim disc. Decomposing the flux into variable (disc) and constant (host galaxy) components, we find the disc SED to be flatter than expected with $f_ν \sim \rm{const}$. Modelling the SED predicts an Eddington ratio of $λ_{\rm Edd} > 1$, where the flat spectrum can be reproduced by a slim disc with little dust extinction or a thin disc which requires more dust extinction. While this accretion is super-Eddington, the geometry is still unclear, however a slim disc is expected due to the high radiation pressure at these accretion rates, and is entirely consistent with our observations.

astro-ph.GA↗
arXiv2023

MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicine

METHODS: First, a set of evaluation criteria is designed based on a comprehensive literature review. Second, existing candidate criteria are optimized for using a Delphi method by five experts in medicine and engineering. Third, three clinical experts design a set of medical datasets to interact with LLMs. Finally, benchmarking experiments are conducted on the datasets. The responses generated by chatbots based on LLMs are recorded for blind evaluations by five licensed medical experts. RESULTS: The obtained evaluation criteria cover medical professional capabilities, social comprehensive capabilities, contextual capabilities, and computational robustness, with sixteen detailed indicators. The medical datasets include twenty-seven medical dialogues and seven case reports in Chinese. Three chatbots are evaluated, ChatGPT by OpenAI, ERNIE Bot by Baidu Inc., and Doctor PuJiang (Dr. PJ) by Shanghai Artificial Intelligence Laboratory. Experimental results show that Dr. PJ outperforms ChatGPT and ERNIE Bot in both multiple-turn medical dialogue and case report scenarios.

cs.CL↗
arXiv2023

Axial gravitational quasinormal modes of a self-dual black hole in loop quantum gravity

We study the axial gravitational quasinormal modes of a self-dual black hole in loop quantum gravity. Considering the axial perturbation of the background spacetime, we obtain the Schrödinger-like master equation. Then we calculate the quasinormal frequencies with the Wentzel-Kramers-Brillouin approximation and the asymptotic iteration method. We also investigate the numerical evolution of an initial wave packet on the self-dual black hole spacetime. We find the quantum correction parameter $P$ positively affects the absolute values of both the real and imaginary parts of quasinormal frequencies. We derive the relation between the parameters of the circular null geodesics and quasinormal frequencies in the eikonal limit for the self-dual black hole, and numerically verify this relation.

gr-qc↗
arXiv2023

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery.

cs.CV↗
arXiv2023

ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies

Designing neural architectures requires immense manual efforts. This has promoted the development of neural architecture search (NAS) to automate the design. While previous NAS methods achieve promising results but run slowly, zero-cost proxies run extremely fast but are less promising. Therefore, it is of great potential to accelerate NAS via those zero-cost proxies. The existing method has two limitations, which are unforeseeable reliability and one-shot usage. To address the limitations, we present ProxyBO, an efficient Bayesian optimization (BO) framework that utilizes the zero-cost proxies to accelerate neural architecture search. We apply the generalization ability measurement to estimate the fitness of proxies on the task during each iteration and design a novel acquisition function to combine BO with zero-cost proxies based on their dynamic influence. Extensive empirical studies show that ProxyBO consistently outperforms competitive baselines on five tasks from three public benchmarks. Concretely, ProxyBO achieves up to 5.41x and 3.86x speedups over the state-of-the-art approaches REA and BRP-NAS.

cs.LG↗
arXiv2023

Capturing the motion of every joint: 3D human pose and shape estimation with independent tokens

In this paper we present a novel method to estimate 3D human pose and shape from monocular videos. This task requires directly recovering pixel-alignment 3D human pose and body shape from monocular images or videos, which is challenging due to its inherent ambiguity. To improve precision, existing methods highly rely on the initialized mean pose and shape as prior estimates and parameter regression with an iterative error feedback manner. In addition, video-based approaches model the overall change over the image-level features to temporally enhance the single-frame feature, but fail to capture the rotational motion at the joint level, and cannot guarantee local temporal consistency. To address these issues, we propose a novel Transformer-based model with a design of independent tokens. First, we introduce three types of tokens independent of the image feature: \textit{joint rotation tokens, shape token, and camera token}. By progressively interacting with image features through Transformer layers, these tokens learn to encode the prior knowledge of human 3D joint rotations, body shape, and position information from large-scale data, and are updated to estimate SMPL parameters conditioned on a given image. Second, benefiting from the proposed token-based representation, we further use a temporal model to focus on capturing the rotational temporal information of each joint, which is empirically conducive to preventing large jitters in local parts. Despite being conceptually simple, the proposed method attains superior performances on the 3DPW and Human3.6M datasets. Using ResNet-50 and Transformer architectures, it obtains 42.0 mm error on the PA-MPJPE metric of the challenging 3DPW, outperforming state-of-the-art counterparts by a large margin. Code will be publicly available at https://github.com/yangsenius/INT_HMR_Model

cs.CV↗
arXiv2023

Detection of quantum signals free of classical noise via quantum correlation

Extracting useful signals is key to both classical and quantum technologies. Conventional noise filtering methods rely on different patterns of signal and noise in frequency or time domains, thus limiting their scope of application, especially in quantum sensing. Here, we propose a signal-nature-based (not signal-pattern-based) approach which singles out a quantum signal from its classical noise background by employing the intrinsic quantum nature of the system. We design a novel protocol to extract the Quantum Correlation signal and use it to single out the signal of a remote nuclear spin from its overwhelming classical noise backgrounds, which is impossible to be accomplished by conventional filter methods. Our work demonstrates the quantum/classical nature as a new degree of freedom in quantum sensing. The further generalization of this quantum nature-based method opens a new direction in quantum research.

quant-ph↗
arXiv2023

Broad-line region in NGC 4151 monitored by two decades of reverberation mapping campaigns. I. Evolution of structure and kinematics

We report the results of long-term reverberation mapping (RM) campaigns of the nearby active galactic nuclei (AGN) NGC 4151, spanning from 1994 to 2022, based on archived observations of the FAST Spectrograph Publicly Archived Programs and our new observations with the 2.3m telescope at the Wyoming Infrared Observatory. We reduce and calibrate all the spectra in a consistent way, and derive light curves of the broad H$β$ line and 5100\,Å continuum. Continuum light curves are also constructed using public archival photometric data to increase sampling cadences. We subtract the host galaxy contamination using {\it HST} imaging to correct fluxes of the calibrated light curves. Utilizing the long-term archival photometric data, we complete the absolute flux-calibration of the AGN continuum. We find that the H$β$ time delays are correlated with the 5100\,Å luminosities as $τ_{\rm Hβ}\propto L_{5100}^{0.46\pm0.16}$. This is remarkably consistent with Bentz et al. (2013)'s global size-luminosity relationship of AGNs. Moreover, the data sets for five of the seasons allow us to obtain the velocity-resolved delays of the H$β$ line, showing diverse structures (outflows, inflows and disks). Combining our results with previous independent measurements, we find the measured dynamics of the H$β$ broad-line region (BLR) are possibly related to the long-term trend of the luminosity. There is also a possible additional $\sim$1.86 years time lag between the variation in BLR radius and luminosity. These results suggest that dynamical changes in the BLR may be driven by the effects of radiation pressure.

astro-ph.GA↗
arXiv2023

Spectroscopy Study on NV Sensors in Diamond-based High-pressure Devices

Recently, the negatively charged nitrogen-vacancy (NV) center has emerged as a robust and versatile quantum sensor in pressurized environments. There are two popular ways to implement NV sensing in a diamond anvil cell (DAC), which is a conventional workhorse in the high-pressure community: create implanted NV centers (INVs) at the diamond anvil tip or immerse NV-enriched nano-diamonds (NDs) in the pressure medium. Nonetheless, there are limited studies on comparing the local stress environments experienced by these sensor types as well as their performances as pressure gauges. In this work, by probing the NV energy levels with the optically detected magnetic resonance (ODMR) method, we experimentally reveal a dramatic difference in the partially reconstructed stress tensors of INVs and NDs incorporated in the same DAC. Our measurement results agree with computational simulations, concluding that INVs perceive a more non-hydrostatic environment dominated by a uniaxial stress along the DAC axis. This provides insights on the suitable choice of NV sensors for specific purposes and the stress distribution in a DAC. We further propose some possible methods, such as using NDs and nanopillars, to extend the maximum working pressure of quantum sensing based on ODMR spectroscopy, since the maximum working pressure could be restricted by non-hydrostaticity of the pressure environment. Moreover, we explore more sensing applications of the NV center by studying how pressure modifies different aspects of the NV system. We perform a photoluminescence study using both INVs and NDs to determine the pressure dependence of the zero-phonon line, which helps developing an all-optical pressure sensing protocol with the NV center. We also characterize the spin-lattice relaxation ($T_1$) time of INVs under pressure to lay a foundation for robust pulsed measurements with NV centers in pressurized environments.

cond-mat.mes-hall↗
arXiv2023

Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study

Background: Clear cell renal cell carcinoma (ccRCC) is the most common renal-related tumor with high heterogeneity. There is still an urgent need for novel diagnostic and prognostic biomarkers for ccRCC. Methods: We proposed a weakly-supervised deep learning strategy using conventional histology of 1752 whole slide images from multiple centers. Our study was demonstrated through internal cross-validation and external validations for the deep learning-based models. Results: Automatic diagnosis for ccRCC through intelligent subtyping of renal cell carcinoma was proved in this study. Our graderisk achieved aera the curve (AUC) of 0.840 (95% confidence interval: 0.805-0.871) in the TCGA cohort, 0.840 (0.805-0.871) in the General cohort, and 0.840 (0.805-0.871) in the CPTAC cohort for the recognition of high-grade tumor. The OSrisk for the prediction of 5-year survival status achieved AUC of 0.784 (0.746-0.819) in the TCGA cohort, which was further verified in the independent General cohort and the CPTAC cohort, with AUC of 0.774 (0.723-0.820) and 0.702 (0.632-0.765), respectively. Cox regression analysis indicated that graderisk, OSrisk, tumor grade, and tumor stage were found to be independent prognostic factors, which were further incorporated into the competing-risk nomogram (CRN). Kaplan-Meier survival analyses further illustrated that our CRN could significantly distinguish patients with high survival risk, with hazard ratio of 5.664 (3.893-8.239, p < 0.0001) in the TCGA cohort, 35.740 (5.889-216.900, p < 0.0001) in the General cohort and 6.107 (1.815 to 20.540, p < 0.0001) in the CPTAC cohort. Comparison analyses conformed that our CRN outperformed current prognosis indicators in the prediction of survival status, with higher concordance index for clinical prognosis.

eess.IV↗
arXiv2022

Determinant functors and deformation of Chow groups

Using determinant functor, we describe a natural transformation from local Hilbert functor to K-theoretic cycle groups of codimension one, which were variants of Balmer's tensor triangular Chow groups. This enables us to answers a question of Bloch on infinitesimal deformation of Chow groups of divisors.

math.AG↗
arXiv2022

Probing the Evolution of Electron Spin Wavefunction of NV Center in diamond via Pressure Tuning

Understanding the profile of a qubit's wavefunction is key to its quantum applications. Unlike conducting systems, where a scanning tunneling microscope can be used to probe the electron distribution, there is no direct method for solid-state-defect based qubits in wide-bandgap semiconductors. In this work, we use pressure as a tuning method and a nuclear spin as an atomic scale probe to monitor the hyperfine structure of negatively charged nitrogen vacancy (NV) centers in diamonds under pressure. We present a detailed study on the nearest-neighbor $^{13}C$ hyperfine splitting in the optically detected magnetic resonance (ODMR) spectrum of NV centers at different pressures. By examining the $^{13}C$ hyperfine interaction upon pressurizing, we show that the NV hyperfine parameters have prominent changes, resulting in an increase in the NV electron spin density and rehybridization from $sp^3$ to $sp^2$ bonds. The $ab$ $initio$ calculations of strain dependence of the NV center's hyperfine levels are done independently. The theoretical results qualitatively agree well with experimental data without introducing any fitting parameters. Furthermore, this method can be adopted to probe the evolution of wavefunction in other defect systems. This potential capability could play an important role in developing magnetometry and quantum information processing using the defect centers.

cond-mat.mes-hall↗