arXiv subjects

Yuanyuan Li

Publications and source records attributed to Yuanyuan Li.

At least 55 records · Page 3Linked to original sources

arXiv2024

Conditional Denoising Diffusion Probabilistic Model for Ground-roll Attenuation

Ground-roll attenuation is a challenging seismic processing task in land seismic survey. The ground-roll coherent noise with low frequency and high amplitude seriously contaminate the valuable reflection events, corrupting the quality of seismic data. The transform-based filtering methods leverage the distinct characteristics of the ground roll and seismic reflections within the transform domain to attenuate the ground-roll noise. However, the ground roll and seismic reflections often share overlaps in the transform domain, making it challenging to remove ground-roll noise without also attenuating useful reflections. We propose to apply a conditional diffusion denoising probabilistic model (c-DDPM) to attenuate the ground-roll noise and recover the reflections efficiently. We prepare the training dataset by using the finite-difference modelling method and the convolution modelling method. After the training process, the c-DDPM can generate the clean data given the seismic data as condition. The ground roll obtained by subtracting the clean data from the seismic data might contain some residual reflection energy. Thus, we further improve the c-DDPM to allow for generating the clean data and ground roll simultaneously. We then demonstrate the feasibility and effectiveness of our proposed method by using the synthetic data and the field data. The methods based on the local time-frequency (LTF) transform and U-Net are also applied to these two examples for comparing with our proposed method. The test results show that the proposed method perform better in attenuating the ground-roll noise from the seismic data than the LTF and U-Net methods.

physics.geo-ph↗

arXiv2024

Distributed Experimental Design Networks

As edge computing capabilities increase, model learning deployments in diverse edge environments have emerged. In experimental design networks, introduced recently, network routing and rate allocation are designed to aid the transfer of data from sensors to heterogeneous learners. We design efficient experimental design network algorithms that are (a) distributed and (b) use multicast transmissions. This setting poses significant challenges as classic decentralization approaches often operate on (strictly) concave objectives under differentiable constraints. In contrast, the problem we study here has a non-convex, continuous DR-submodular objective, while multicast transmissions naturally result in non-differentiable constraints. From a technical standpoint, we propose a distributed Frank-Wolfe and a distributed projected gradient ascent algorithm that, coupled with a relaxation of non-differentiable constraints, yield allocations within a $1-1/e$ factor from the optimal. Numerical evaluations show that our proposed algorithms outperform competitors with respect to model learning quality.

cs.NI↗

arXiv2023

Distribution-free risk assessment of regression-based machine learning algorithms

Machine learning algorithms have grown in sophistication over the years and are increasingly deployed for real-life applications. However, when using machine learning techniques in practical settings, particularly in high-risk applications such as medicine and engineering, obtaining the failure probability of the predictive model is critical. We refer to this problem as the risk-assessment task. We focus on regression algorithms and the risk-assessment task of computing the probability of the true label lying inside an interval defined around the model's prediction. We solve the risk-assessment problem using the conformal prediction approach, which provides prediction intervals that are guaranteed to contain the true label with a given probability. Using this coverage property, we prove that our approximated failure probability is conservative in the sense that it is not lower than the true failure probability of the ML algorithm. We conduct extensive experiments to empirically study the accuracy of the proposed method for problems with and without covariate shift. Our analysis focuses on different modeling regimes, dataset sizes, and conformal prediction methodologies.

cs.LG↗

arXiv2023

Discover governing differential equations from evolving systems

Discovering the governing equations of evolving systems from available observations is essential and challenging. In this paper, we consider a new scenario: discovering governing equations from streaming data. Current methods struggle to discover governing differential equations with considering measurements as a whole, leading to failure to handle this task. We propose an online modeling method capable of handling samples one by one sequentially by modeling streaming data instead of processing the entire dataset. The proposed method performs well in discovering ordinary differential equations (ODEs) and partial differential equations (PDEs) from streaming data. Evolving systems are changing over time, which invariably changes with system status. Thus, finding the exact change points is critical. The measurement generated from a changed system is distributed dissimilarly to before; hence, the difference can be identified by the proposed method. Our proposal is competitive in identifying the change points and discovering governing differential equations in three hybrid systems and two switching linear systems.

physics.comp-ph↗

arXiv2023

Nonuniqueness of solutions to the $L_p$ chord Minkowski problem

This paper explores the nonuniqueness of solutions to the $L_p$ chord Minkowski problem for negative $p.$ The $L_p$ chord Minkowski problem was recently posed by Lutwak, Xi, Yang and Zhang, which seeks to determine the necessary and sufficient conditions for a given finite Borel measure such that it is the $L_p$ chord measure of a convex body, and it includes the chord Minkowski problem and the $L_p$ Minkowski problem.

math.AP↗

arXiv2023

The $Lp$ chord Minkowski problem for negative $p$

In this paper, we solve the $L_p$ chord Minkowski problem in the case of discrete measures whose supports are in general position for negative $p$ and $q>0.$ As for general Borel measure with a density, we also give a proof but need $p\in(-n,0)$ and $n+1>q\geqslant 1.$ The $L_p$ chord Minkowski problem was recently posed by Lutwak, Xi, Yang and Zhang, which seeks to determine the necessary and sufficient conditions for a given finite Borel measure such that it is the $L_p$ chord measure of a convex body, and it includes the chord Minkowski problem and the $L_p$ Minkowski problem.

math.AP↗

arXiv2023

Jointly Optimal Routing and Caching with Bounded Link Capacities

We study a cache network in which intermediate nodes equipped with caches can serve requests. We model the problem of jointly optimizing caching and routing decisions with link capacity constraints over an arbitrary network topology. This problem can be formulated as a continuous diminishing-returns (DR) submodular maximization problem under multiple continuous DR-supermodular constraints, and is NP-hard. We propose a poly-time alternating primal-dual heuristic algorithm, in which primal steps produce solutions within $1-\frac{1}{e}$ approximation factor from the optimal. Through extensive experiments, we demonstrate that our proposed algorithm significantly outperforms competitors.

cs.NI↗

arXiv2022

A note on the singular set of the no-sign obstacle problem

In this note, we prove the uniqueness of blowups at singular points of the no-sign obstacle problem $Δu=χ_{_{B_1\backslash \{u=|Du|=0\}}}\ \text{in}\ B_1,$ thus give a positive answer to a problem raised in \cite[Notes of Chaper 7, page 149]{PSU12}.

math.AP↗

arXiv2022

Revealing the CO2 emission reduction of ridesplitting and its determinants based on real-world data

Ridesplitting, which is a form of pooled ridesourcing service, has great potential to alleviate the negative impacts of ridesourcing on the environment. However, most existing studies only explored its theoretical environmental benefits based on optimization models and simulations. By contrast, this study aims to reveal the real-world emission reduction of ridesplitting and its determinants based on the observed data of ridesourcing in Chengdu, China. Integrating the trip data with the COPERT model, this study calculates the CO2 emissions of shared rides (ridesplitting) and their substituted single rides (regular ridesourcing) to estimate the CO2 emission reduction of each ridesplitting trip. The results show that not all ridesplitting trips reduce emissions from ridesourcing in the real world. The CO2 emission reduction rate of ridesplitting varies from trip to trip, averaging at 43.15g/km. Then, interpretable machine learning models, gradient boosting machines, are applied to explore the relationship between the CO2 emission reduction rate of ridesplitting and its determinants. Based on the SHapley Additive exPlanations (SHAP) method, the overlap rate and detour rate of shared rides are identified to be the most important factors that determine the CO2 emission reduction rate of ridesplitting. Increasing the overlap rate, the number of shared rides, average speed, and ride distance ratio while decreasing the detour rate, actual trip distance, and ride distance gap can increase the CO2 emission reduction rate of ridesplitting. In addition, nonlinear effects and interactions of the determinants are examined through the partial dependence plots. To sum up, this study provides a scientific method for the government and ridesourcing companies to better assess and optimize the environmental benefits of ridesplitting.

cs.LG↗

arXiv2022

Irrational self-similar sets

Let $K\subset\mathbb{R}$ be a self-similar set defined on $\mathbb{R}$. It is easy to prove that if the Lebesgue measure of $K$ is zero, then for Lebesgue almost every $t$, $$K+t=\{x+t:x\in K\}$$ only consists of irrational or transcendental numbers. In this note, we shall consider some classes of self-similar sets, and explicitly construct such $t$'s. Our main idea is from the $q$-expansions.

math.NT↗

arXiv2022

RGB Image Classification with Quantum Convolutional Ansaetze

With the rapid growth of qubit numbers and coherence times in quantum hardware technology, implementing shallow neural networks on the so-called Noisy Intermediate-Scale Quantum (NISQ) devices has attracted a lot of interest. Many quantum (convolutional) circuit ansaetze are proposed for grayscale images classification tasks with promising empirical results. However, when applying these ansaetze on RGB images, the intra-channel information that is useful for vision tasks is not extracted effectively. In this paper, we propose two types of quantum circuit ansaetze to simulate convolution operations on RGB images, which differ in the way how inter-channel and intra-channel information are extracted. To the best of our knowledge, this is the first work of a quantum convolutional circuit to deal with RGB images effectively, with a higher test accuracy compared to the purely classical CNNs. We also investigate the relationship between the size of quantum circuit ansatz and the learnability of the hybrid quantum-classical convolutional neural network. Through experiments based on CIFAR-10 and MNIST datasets, we demonstrate that a larger size of the quantum circuit ansatz improves predictive performance in multiclass classification tasks, providing useful insights for near term quantum algorithm developments.

quant-ph↗

arXiv2022

First Lattice QCD determination of semileptonic decays of charmed-strange baryons $Ξ_c$

While the standard model is the most successfully theory to describe all interactions and constituents in elementary particle physics, it has been constantly examined for over four decades. Weak decays of charm quarks can measure the coupling strength of quarks in different families and serve as an ideal probe for CP violation. As the lowest charm-strange baryons with three different flavors, $Ξ_c$ baryons (made of $csu$ or $csd$) have been extensively studied in experiments at the large hadron collider and in electron-positron collision. However the lack of reliable knowledge in theory becomes the unavoidable obstacle in the way. In this work, we use the state-of-the-art Lattice QCD techniques, and generate 2+1 clover fermion ensembles with two lattice spacings, $a=(0.108{\rm fm},0.080{\rm fm})$. We then present the first {\it ab-initio} lattice QCD determination of form factors governing $Ξ_{c}\to Ξ\ell^+ν_{\ell}$, analogous with the notable $β$-decay of nuclei. Our theoretical results for decay widths are consistent with and about two times more precise than the latest measurements by ALICE and Belle collaborations. Together with experimental measurements, we independently determine the quark-mixing matrix element $|V_{cs}|$, which is found in good agreement with other determinations.

hep-lat↗

arXiv2022

Experimental Design Networks: A Paradigm for Serving Heterogeneous Learners under Networking Constraints

Significant advances in edge computing capabilities enable learning to occur at geographically diverse locations. In general, the training data needed in those learning tasks are not only heterogeneous but also not fully generated locally. In this paper, we propose an experimental design network paradigm, wherein learner nodes train possibly different Bayesian linear regression models via consuming data streams generated by data source nodes over a network. We formulate this problem as a social welfare optimization problem in which the global objective is defined as the sum of experimental design objectives of individual learners, and the decision variables are the data transmission strategies subject to network constraints. We first show that, assuming Poisson data streams, the global objective is a continuous DR-submodular function. We then propose a Frank-Wolfe type algorithm that outputs a solution within a 1-1/e factor from the optimal. Our algorithm contains a novel gradient estimation component which is carefully designed based on Poisson tail bounds and sampling. Finally, we complement our theoretical findings through extensive experiments. Our numerical evaluation shows that the proposed algorithm outperforms several baseline algorithms both in maximizing the global objective and in the quality of the trained models.

cs.NI↗

arXiv2021

In Situ Phase-Transition Crystallization of All-Inorganic Water-Resistant Exciton-Radiative Heteroepitaxial CsPbBr3-CsPb2Br5 Core-Shell Perovskite Nanocrystals

The instability of metal halide perovskites upon exposure to moisture or heat strongly hampers their applications in optoelectronic devices. Here, we report the large-yield synthesis of highly water-resistant total-inorganic green luminescent CsPbBr3/CsPb2Br5 core/shell heteronanocrystals (HNCs) by developing an in situ phase transition approach. It is implemented via water-driven phase transition of the original monoclinic CsPbBr3 nanocrystal and the resultant tetragonal CsPb2Br5 nanoshell has small lattice mismatch with the CsPbBr3 core, which ensures formation of an epitaxial interface for the yielded CsPbBr3/CsPb2Br5 HNCs. These HNCs maintain nearly 100% of the original luminescence intensity after immersion in water for eleven months and the luminescence intensity drops only to 81.3% at 100 °C. The transient luminescence spectroscopy and density functional theory calculation reveal that there are double radiative recombination channels in the core CsPbBr3 nanocrystal, and the electron potential barrier provided by the CsPb2Br5 nanoshell significantly improves the exciton recombination rate. A prototype quasi-white light-emitting device based on these robust CsPbBr3/CsPb2Br5 HNCs is realized, showing their strong competence in solid-state lighting and wide color-gamut displays.

cond-mat.mtrl-sci↗

arXiv2021

Role of polyhedron unit in distinct photodynamics of zero-dimensional organic-inorganic hybrid tin halide compounds

The zero-dimensional (0D) metal halides comprise periodically distributed and isolated metal-halide polyhedra, which act as the smallest inorganic quantum systems and can accommodate quasi-localized Frenkel excitons. These excitons exhibit unique photophysics including broadband photon emission, huge Stokes shift, and long decay lifetime. The polyhedra can have different symmetries due to the coordination degree of the metal ions. Little is known about how the polyhedron type affects the characteristics of the 0D metal halide crystals. We synthesize and comparatively study three novel kinds of 0D organic-inorganic hybrid tin halide compounds. They are efficient light emitters with a highest quantum yield of 92.3%. Although they have the same compositional organic group, the most stable phases are composed of octahedra for the bromide and iodide but disphenoids (see-saw structures) for the chloride. They separately exhibit biexponential and monoexponential luminescence decays due to different symmetries (Ci group for octahedra and C2 group for disphenoids) and corresponding different electronic structures. The chloride has the largest absorption photon energy among the three halides, but it has the smallest emission photon energy. A model regarding the unoccupied energy band degeneracy is proposed based on the experiments and density functional theory calculations, which explains well the experimental phenomena and reveals the crucial role of polyhedron type in determining the optical properties of the 0D tin halide compounds.

cond-mat.mtrl-sci↗

arXiv2021

From Impurity Doping to Metallic Growth in Diffusion Doping: Properties and Structure of Ag Doped InAs Nanocrystals

Tuning of the electronic properties of pre-synthesized colloidal semiconductor nanocrystals (NCs) by doping plays a key role in the prospect of implementing them in printed electronics devices such as transistors, and photodetectors. While such impurity doping reactions have already been introduced, the understanding of the doping process, the nature of interaction between the impurity and host atoms, and the conditions affecting the solubility limit of impurities in nanocrystals are still unclear. Here, we used a post-synthesis diffusion based doping reaction to introduce Ag impurities into InAs NCs. Optical absorption spectroscopy along with analytical inductively coupled plasma mass-spectroscopy (ICP-MS) were used to present a two stage doping model consisting of a "doping region" and a "growth region", depending on the concentration of the impurities in the reaction vessel. X-ray absorption fine-structure (XAFS) spectroscopy was employed to determine the impurity location and correlate between the structural and electronic properties for different sizes of InAs NCs and dopant concentrations. The resulting structural model describes a heterogeneous system where the impurities initially dope the NC, by substituting for In atoms near the surface of the NC, until the "solubility limit" is reached, after which the rapid growth and formation of metallic structures are identified.

cond-mat.mtrl-sci↗

arXiv2021

Multi-modal Spectroscopic Study of Surface Termination Evolution in Cr2TiC2Tx MXene

Control of surface functionalization of MXenes holds great potential, and in particular, may lead to tuning of magnetic and electronic order in the recently reported magnetic Cr2TiC2Tx. Here, vacuum annealing experiments of Cr2TiC2Tx are reported with in situ electron energy loss spectroscopy and novel in situ Cr K-edge extended energy loss fine structure analysis, which directly tracks the evolution of the MXene surface coordination environment. These in situ probes are accompanied by benchmarking synchrotron X-ray absorption fine structure measurements and density functional theory calculations. With the etching method used here, the MXene has an initial termination chemistry of Cr2TiC2O1.3F0.8. Annealing to 600 C results in the complete loss of -F, but -O termination is thermally stable up to (at least) 700 C. These findings demonstrate thermal control of -F termination in Cr2TiC2Tx and offer a first step towards termination engineering this MXene for magnetic applications. Moreover, this work demonstrates high energy electron spectroscopy as a powerful approach for surface characterization in 2D materials.

cond-mat.mtrl-sci↗

arXiv2020

On continuous images of self-similar sets

Let $(\mathcal{M}, c_k, n_k,κ)$ be a class of homogeneous Moran sets. Suppose $f(x,y)\in C^3$ is a function defined on $\mathbb{R}^2$. Given $E_1, E_2\in(\mathcal{M}, c_k, n_k,κ) $, in this paper, we prove, under some checkable conditions on the partial derivatives of $f(x,y)$, that $$f(E_1,E_2)=\{f(x,y):x\in E_1,y\in E_2\}$$ is exactly a closed interval or a union of finitely many closed intervals. Similar results for the homogeneous self-similar sets with arbitrary overlaps can be obtained. Further generalization is available for some inhomogeneous self-similar sets if we utilize the approximation theorem.

math.MG↗