arXiv subjects

Yue Yang

Publications and source records attributed to Yue Yang.

At least 181 records · Page 10Linked to original sources

arXiv2021

Diagnostics for ultrashort X-ray pulses using silicon trackers

The spectrum of laser-plasma generated X-rays is very important, it characterizes electron dynamics in plasma and is basic for applications. However, the accuracies and efficiencies of existing methods to diagnose the spectrum of laser-plasma based X-ray pulse are not very high, especially in the range of several hundred keV. In this study, a new method based on electron tracks detection to measure the spectrum of laser-plasma produced X-ray pulses is proposed and demonstrated. Laser-plasma generated X-rays are scattered in a multi-pixel silicon tracker. Energies and scattering directions of Compton electrons can be extracted from the response of the detector, and then the spectrum of X-rays can be reconstructed. Simulations indicate that the energy resolution of this method is approximately 20% for X-rays from 200 to 550 keV for a silicon-on-insulator pixel detector with 12 $\rm μ$m pixel pitch and 500 $\rm μ$m depletion region thickness. The results of a proof-of-principle experiment based on a Timepix3 detector are also shown.

physics.ins-det↗

arXiv2021

Economic prospects of the Russian-Chinese partnership in the logistics projects of the Eurasian Economic Union and the Silk Road Economic Belt: a scientific literature review

The authors of the article have reviewed the scientific literature on the development of the Russian-Chinese cooperation in the field of combining economic and logistics projects of the Eurasian Economic Union and the Silk Road Economic Belt. The opinions of not only Russian, but also Chinese experts on these projects are indicated, which provides the expansion of the vision of the concept of the New Silk Road in both countries.

econ.GN↗

arXiv2021

Optimal Entry and Consumption under Habit Formation

This paper studies a composite problem involving the decision making of the optimal entry time and dynamic consumption afterwards. In stage-1, the investor has access to full market information subjecting to some information costs and needs to choose an optimal stopping time to initiate stage-2; in stage-2, the investor terminates the costly full information acquisition and starts dynamic investment and consumption under partial observations of free public stock prices. The habit formation preference is employed, in which the past consumption affects the investor's current decisions. By using the stochastic Perron's method, the value function of the composite problem is proved to be the unique viscosity solution of some variational inequalities.

math.OC↗

arXiv2021

High-Throughput Virtual Screening of Small Molecule Inhibitors for SARS-CoV-2 Protein Targets with Deep Fusion Models

Structure-based Deep Fusion models were recently shown to outperform several physics- and machine learning-based protein-ligand binding affinity prediction methods. As part of a multi-institutional COVID-19 pandemic response, over 500 million small molecules were computationally screened against four protein structures from the novel coronavirus (SARS-CoV-2), which causes COVID-19. Three enhancements to Deep Fusion were made in order to evaluate more than 5 billion docked poses on SARS-CoV-2 protein targets. First, the Deep Fusion concept was refined by formulating the architecture as one, coherently backpropagated model (Coherent Fusion) to improve binding-affinity prediction accuracy. Secondly, the model was trained using a distributed, genetic hyper-parameter optimization. Finally, a scalable, high-throughput screening capability was developed to maximize the number of ligands evaluated and expedite the path to experimental evaluation. In this work, we present both the methods developed for machine learning-based high-throughput screening and results from using our computational pipeline to find SARS-CoV-2 inhibitors.

cs.LG↗

arXiv2021

A predictive model of the turbulent burning velocity for planar and Bunsen flames over a wide range of conditions

We propose a predictive model of the turbulent burning velocity over a wide range of conditions. The model consists of sub models of the stretch factor and the turbulent flame area. The stretch factor characterizes the flame response of turbulence stretch and incorporates effects of detailed chemistry and transport with a lookup table of laminar counterflow flames. The flame area model captures the area growth based on Lagrangian statistics of propagating surfaces, and considers effects of turbulence length scales and fuel characteristics. The present model predicts the turbulent burning velocity via an algebraic expression without free parameters. It is validated against 285 cases of the direct numerical simulation or experiment reported from various research groups on planar and Bunsen flames over a wide range of conditions, covering fuels from hydrogen to n-dodecane, pressures from 1 to 20 atm, lean and rich mixtures, turbulence intensity ratios from 0.35 to 110, and turbulence length ratios from 0.5 to 80. The comprehensive comparison shows that the proposed turbulent burning velocity model has an overall good agreement over the wide range of conditions, with the averaged modeling error of 25.3%. Furthermore, the model prediction involves the uncertainty quantification for model parameters and chemical kinetics to extend the model applicability.

physics.flu-dyn↗

arXiv2021

Deceptive Reinforcement Learning for Privacy-Preserving Planning

In this paper, we study the problem of deceptive reinforcement learning to preserve the privacy of a reward function. Reinforcement learning is the problem of finding a behaviour policy based on rewards received from exploratory behaviour. A key ingredient in reinforcement learning is a reward function, which determines how much reward (negative or positive) is given and when. However, in some situations, we may want to keep a reward function private; that is, to make it difficult for an observer to determine the reward function used. We define the problem of privacy-preserving reinforcement learning, and present two models for solving it. These models are based on dissimulation -- a form of deception that `hides the truth'. We evaluate our models both computationally and via human behavioural experiments. Results show that the resulting policies are indeed deceptive, and that participants can determine the true reward function less reliably than that of an honest agent.

cs.LG↗

arXiv2020

Brilliant attosecond γ-ray emission and high-yield positron production from intense laser-irradiated Nano-Micro array

We investigate a novel scheme for brilliant attosecond γ-ray emission and high-yield positron production, which is accomplished with an ultra-intense laser pulse incident upon a Nano-Micro array (NMA) with substrate incorporated. This scheme is able to realize effectively electron acceleration and colliding geometry. Both the γ-ray flash and positron bunch are then generated with high conversion efficiency. At laser intensity of I_0 = 8 times 10^{23} W/cm^2, ~27% of the laser energy is transferred successfully into the γ-rays, and ~0.7% of the laser energy into the positrons. As a consequence, ultra-short (~440 as) and ultra-brilliant (~10^{24} photons s^{-1} mm^{-2} mrad^{-2} per 0.1%BW @ 15 MeV) γ-ray burst, and high-yield (1.48 times 10^{11}) and overdense (~10^{22} cm^{-3}) positron bunch are generated. We found a sub-linear scaling of laser-to-photon conversion efficiency (proportional to I_0^{0.75}) and a super-linear scaling of laser-to-positron conversion efficiency (proportional to I_0^{2.5}) with the laser intensity. Multi-dimensional particle-in-cell simulations show that particle (γ photon and positron) generation can be manipulated by laser-focusing position, and NMA's length and spacing. Optimal conditions for particle generation in NMAs are obtained, indicating that microwire array has the advantage over nanowire array in particle generation in the extreme laser fields. Furthermore, positron annihilation effect in high-energy-density (HED) environment is discussed. The scheme using NMAs would provide effective avenues toward investigating attosecond nuclear science and HED physics with the coming 10 PW laser facilities.

physics.plasm-ph↗

arXiv2020

Optimal Dividend Strategy for an Insurance Group with Contagious Default Risk

This paper studies the optimal dividend for a multi-line insurance group, in which each subsidiary runs a product line and is exposed to some external credit risk. The default contagion is considered such that one default event may increase the default probabilities of all surviving subsidiaries. The total dividend problem for the insurance group is investigated and we find that the optimal dividend strategy is still of the barrier type. Furthermore, we show that the optimal barrier of each subsidiary is modulated by the default state. That is, how many and which subsidiaries have defaulted will determine the dividend threshold of each surviving subsidiary. These conclusions are based on the analysis of the associated recursive system of Hamilton-Jacobi-Bellman variational inequalities (HJBVIs). The existence of the classical solution is established and the verification theorem is proved. In the case of two subsidiaries, the value function and optimal barriers are given in analytical forms, allowing us to conclude that the optimal barrier of one subsidiary decreases if the other subsidiary defaults.

q-fin.RM↗

arXiv2020

Real-time and Large-scale Fleet Allocation of Autonomous Taxis: A Case Study in New York Manhattan Island

Nowadays, autonomous taxis become a highly promising transportation mode, which helps relieve traffic congestion and avoid road accidents. However, it hinders the wide implementation of this service that traditional models fail to efficiently allocate the available fleet to deal with the imbalance of supply (autonomous taxis) and demand (trips), the poor cooperation of taxis, hardly satisfied resource constraints, and on-line platform's requirements. To figure out such urgent problems from a global and more farsighted view, we employ a Constrained Multi-agent Markov Decision Processes (CMMDP) to model fleet allocation decisions, which can be easily split into sub-problems formulated as a 'Dynamic assignment problem' combining both immediate rewards and future gains. We also leverage a Column Generation algorithm to guarantee the efficiency and optimality in a large scale. Through extensive experiments, the proposed approach not only achieves remarkable improvements over the state-of-the-art benchmarks in terms of the individual's efficiency (arriving at 12.40%, 6.54% rise of income and utilization, respectively) and the platform's profit (reaching 4.59% promotion) but also reveals a time-varying fleet adjustment policy to minimize the operation cost of the platform.

cs.LG↗

arXiv2020

Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications

While deep learning methods have shown great success in medical image analysis, they require a number of medical images to train. Due to data privacy concerns and unavailability of medical annotators, it is oftentimes very difficult to obtain a lot of labeled medical images for model training. In this paper, we study cross-modality data augmentation to mitigate the data deficiency issue in the medical imaging domain. We propose a discriminative unpaired image-to-image translation model which translates images in source modality into images in target modality where the translation task is conducted jointly with the downstream prediction task and the translation is guided by the prediction. Experiments on two applications demonstrate the effectiveness of our method.

eess.IV↗

arXiv2020

A recursion theoretic foundation of computation over real numbers

We define a class of computable functions over real numbers using functional schemes similar to the class of primitive and partial recursive functions defined by Gödel and Kleene. We show that this class of functions can also be characterized by master-slave machines, which are Turing machine like devices. The proof of the characterization gives a normal form theorem in the style of Kleene. Furthermore, this characterization is a natural combination of two most influential theories of computation over real numbers, namely, the type-two theory of effectivity (TTE) (see, for example, Weihrauch) and the Blum-Shub-Smale model of computation (BSS). Under this notion of computability, the recursive (or computable) subsets of real numbers are exactly effective $Δ^0_2$ sets.

cs.LO↗

arXiv2020

Variational approximations of empirical Bayes posteriors in high-dimensional linear models

In high-dimensions, the prior tails can have a significant effect on both posterior computation and asymptotic concentration rates. To achieve optimal rates while keeping the posterior computations relatively simple, an empirical Bayes approach has recently been proposed, featuring thin-tailed conjugate priors with data-driven centers. While conjugate priors ease some of the computational burden, Markov chain Monte Carlo methods are still needed, which can be expensive when dimension is high. In this paper, we develop a variational approximation to the empirical Bayes posterior that is fast to compute and retains the optimal concentration rate properties of the original. In simulations, our method is shown to have superior performance compared to existing variational approximations in the literature across a wide range of high-dimensional settings.

stat.ME↗

arXiv2020

MedDialog: Two Large-scale Medical Dialogue Datasets

Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. To facilitate the research and development of medical dialogue systems, we build two large-scale medical dialogue datasets: MedDialog-EN and MedDialog-CN. MedDialog-EN is an English dataset containing 0.3 million conversations between patients and doctors and 0.5 million utterances. MedDialog-CN is an Chinese dataset containing 1.1 million conversations and 4 million utterances. To our best knowledge, MedDialog-(EN,CN) are the largest medical dialogue datasets to date. The dataset is available at https://github.com/UCSD-AI4H/Medical-Dialogue-System

cs.LG↗

arXiv2020

Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigms

Pretraining has become a standard technique in computer vision and natural language processing, which usually helps to improve performance substantially. Previously, the most dominant pretraining method is transfer learning (TL), which uses labeled data to learn a good representation network. Recently, a new pretraining approach -- self-supervised learning (SSL) -- has demonstrated promising results on a wide range of applications. SSL does not require annotated labels. It is purely conducted on input data by solving auxiliary tasks defined on the input data examples. The current reported results show that in certain applications, SSL outperforms TL and the other way around in other applications. There has not been a clear understanding on what properties of data and tasks render one approach outperforms the other. Without an informed guideline, ML researchers have to try both methods to find out which one is better empirically. It is usually time-consuming to do so. In this work, we aim to address this problem. We perform a comprehensive comparative study between SSL and TL regarding which one works better under different properties of data and tasks, including domain difference between source and target tasks, the amount of pretraining data, class imbalance in source data, and usage of target data for additional pretraining, etc. The insights distilled from our comparative studies can help ML researchers decide which method to use based on the properties of their applications.

cs.CV↗

arXiv2020

Microwave Photonic Imaging Radar with a Millimeter-level Resolution

Microwave photonic radars enable fast or even real-time high-resolution imaging thanks to its broad bandwidth. Nevertheless, the frequency range of the radars usually overlaps with other existed radio-frequency (RF) applications, and only a centimeter-level imaging resolution has been reported, making them insufficient for civilian applications. Here, we propose a microwave photonic imaging radar with a millimeter-level resolution by introducing a frequency-stepped chirp signal based on an optical frequency shifting loop. As compared with the conventional linear-frequency modulated (LFM) signal, the frequency-stepped chirp signal can bring the system excellent capability of anti-interference. In an experiment, a frequency-stepped chirp signal with a total bandwidth of 18.2 GHz (16.9 to 35.1 GHz) is generated. Postprocessing the radar echo, radar imaging with a two-dimensional imaging resolution of ~8.5 mm$\times$~8.3 mm is achieved. An auto-regressive algorithm is used to reconstruct the disturbed signal when a frequency interference exists, and the high-resolution imaging is sustained.

eess.SP↗

arXiv2020

Sex Differences in Severity and Mortality Among Patients With COVID-19: Evidence from Pooled Literature Analysis and Insights from Integrated Bioinformatic Analysis

Objective: To conduct a meta-analysis of current studies that examined sex differences in severity and mortality in patients with COVID-19, and identify potential mechanisms underpinning these differences. Methods: We performed a systematic review to collate data from observational studies examining associations of sex differences with clinical outcomes of COVID-19. PubMed, Web of Science and four preprint servers were searched for relevant studies. Data were extracted and analyzed using meta-analysis where possible, with summary data presented otherwise. Publicly available bulk RNA sequencing (RNA-seq), single-cell RNA sequencing (scRNA-seq), and chromatin immunoprecipitation sequencing (ChIP-seq) data were analyzed to explore the potential mechanisms underlying the observed association. Results: 39 studies met inclusion criteria, representing 77932 patients, of which 41510 (53.3%) were males. Men were at a markedly increased risk of developing severe cases compared with women. Furthermore, the pooled odds ratio (OR) of mortality for male group compared with the female group indicated significant higher mortality rate for male. Data from scRNA-seq suggest that men have a higher amount of ACE2-expressing pulmonary alveolar type II cells than women. Sex-based immunological differences exist. The expression of androgen receptor (AR) is positively correlated with ACE2, and there is evidence that AR may directly regulate the expression of ACE2. Conclusions: This meta-analysis detected an increased severity and mortality rate in the male populations with COVID-19, which might be attributable to the sex-based differences in cellular compositions and immunological microenvironments of the lung. The host cell receptor ACE2 is likely regulated by AR signaling pathway, which is identified as a potential target for prevention and treatment of SARS-Cov-2 infections in men.

q-bio.PE↗

arXiv2020

An Intelligent Quaternion SVDCKF AHRS Estimation with Variable Adaptive Methods in Complex Conditions

Aimed at solving the problem of Attitude and Heading Reference System(AHRS) in the complex and dynamic conditions for small-UAV, An intelligent Singular Value Decomposition Cubature Kalman Filter(SVDCKF) combined with the Variable Adaptive Methods(VAM) is proposed in this paper. Considering the nonlinearity of quaternion AHRS model and non-positive definite of the state covariance matrix, the SVDCKF algorithm is presented with both the SVD and CKF in order to better obtain the filter accuracy and reliability. Additionally, there are the different changes of the values in the accelerometer measurement resulting from the complex flying conditions. Thus, the VAM is designed to deal with three-axis values of the acceleration and tune intelligently the measurement noise matrix Ra. Moreover, the heading measurement from the three-axis values of the magnetometer is calculated according to the whether to use the three-axis values of the acceleration in the special situations. The simulation and experiment results demonstrate that the proposed filter algorithm has the more excellent attitude solution accuracy and robustness than both the Complementary Filter(CF) and the Error State Kalman Filter(ESKF).

cs.RO↗

arXiv2020

An improved nonlinear FastEuler AHRS estimation based on the SVDCKF algorithm

In this paper, we present a Singular Value Decomposition Cubature Kalman Filter(SVDCKF) fusion algorithm based on the improved nonlinear FastEuler Attitude and Heading Reference and System(AHRS) estimation model for small-UAV attitude. The contributions of this work are the derivation of the low-cost IMU/MAG integrated AHRS model combined with the quaternion attitude determination, and use the FastEuler to correct the gyroscope attitude update, which can increase the real-time solution. In addition, the SVDCKF algorithm is fused the various raw sensors data in order to improve the filter accuracy compared with the CKF. The simulation and experiment results demonstrate the proposed algorithm has the more excellent attitude solution accuracy compared with the CKF in the low and high dynamic flight conditions.

cs.RO↗