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Yilin Chen

Publications and source records attributed to Yilin Chen.

At least 37 records · Page 2Linked to original sources
arXiv2025

Pseudo Empirical Likelihood Inference for Non-Probability Survey Samples

In this paper, the authors first provide an overview of two major developments on complex survey data analysis: the empirical likelihood methods and statistical inference with non-probability survey samples, and highlight the important research contributions to the field of survey sampling in general and the two topics in particular by Canadian survey statisticians. The authors then propose new inferential procedures on analyzing non-probability survey samples through the pseudo empirical likelihood approach. The proposed methods lead to asymptotically equivalent point estimators that have been discussed in the recent literature but possess more desirable features on confidence intervals such as range-respecting and data-driven orientation. Results from a simulation study demonstrate the superiority of the proposed methods in dealing with binary response variables.

stat.ME↗
arXiv2025

Operator dependent para-controlled calculus, periodic homogenisation and singular PDEs

We develop a variant of the para-controlled distributions framework based on operator dependent (generalised) Besov spaces. These spaces were introduced by Kerkyacharian-Petrushev ([KP15]). In contrast to the Fourier decomposition in the classical setting, they are built from spectral decomposition of general self-adjoint elliptic operators. A major difference is that products of two spectrally localised functions in general have ``frequencies" spread over the whole spectrum. We obtain a decay estimate on ``high frequency" component of the product of two spectrally-localised functions. This enables us to obtain uniform bounds for the naturally associated para-product and commutator operations over a class of operators. This in particular includes the family of periodic homogenisation operators uniform in the oscillation parameter. Next, we show convergence properties of these operations in the periodic homogenisation setting. A key ingredient is the convergence of the generalised Littlewood-Paley block operators with quantitative dependence on the spectrum level. The proof combines a novel clustering argument to group nearby eigenvalues, Weyl's asymptotic formula that implies quantitative bounds on the sizes and locations of the eigenvalue clusters, together with a result by Kenig-Lin-Shen ([KLS13]) on convergence of individual eigenvalues. Finally, we apply this framework to periodic homogenisation problems for dynamical $Φ^4_3$ and KPZ equations. Assuming the convergence of stochastic objects to their homogenised limit, we show that the solutions and fluxes also converge to the corresponding limits. The convergence of the explicit stochastic terms with suitable renormalisations will be treated in a separate note.

math.AP↗
arXiv2025

Vertical Profile Corrected Satellite NH3 Retrievals Enable Accurate Agricultural Emission Characterization in China

Ammonia (NH3) emissions significantly contribute to atmospheric pollution, yet discrepancies exist between bottom-up inventories and satellite-constrained top-down estimates, with the latter typically one-third higher. This study quantifies how assumptions about NH3 vertical distribution in satellite retrievals contribute to this gap. By implementing spatially and temporally resolved vertical profiles from the Community Multiscale Air Quality model to replace steep gradients in Infrared Atmospheric Sounding Interferometer (IASI) retrievals, we reduced satellite-model column discrepancies from 71% to 18%. We subsequently constrained NH3 emissions across China using a hybrid inversion framework combining iterative mass balance and four-dimensional variational methods. Our posterior emissions showed agreement with the a priori inventory (7.9% lower), suggesting that discrepancies between inventory approaches were amplified by overestimation of near-surface NH3 in baseline satellite retrievals, potentially causing a 43% overestimation of growing season emissions. Evaluation against ground-based measurements confirmed improved model performance, with normalized root-mean-square error reductions of 1-27% across six months. These findings demonstrate that accurate representation of vertical profiles in satellite retrievals is critical for robust NH3 emission estimates and can reconcile the long-standing discrepancy between bottom-up and top-down approaches. Our hybrid inversion methodology, leveraging profile-corrected satellite data, reveals that China's NH3 emissions exhibit greater spatial concentration than previously recognized, reflecting agricultural intensification. This advancement enables timely and accurate characterization of rapidly changing agricultural emission patterns, critical for implementing effective nitrogen pollution control measures.

physics.ao-ph↗
arXiv2025

PQCache: Product Quantization-based KVCache for Long Context LLM Inference

As the field of Large Language Models (LLMs) continues to evolve, the context length in inference is steadily growing. Key-Value Cache (KVCache), the intermediate representations of tokens within LLM inference, has now become the primary memory bottleneck due to limited GPU memory. Current methods selectively determine suitable keys and values for self-attention computation in LLMs to address the issue. However, they either fall short in maintaining model quality or result in high serving latency. Drawing inspiration from advanced embedding retrieval techniques prevalent in the data management community, we consider the storage and retrieval of KVCache as a typical embedding retrieval problem. We propose PQCache, which employs Product Quantization (PQ) to manage KVCache, maintaining model quality while ensuring low serving latency. During the prefilling phase, we apply PQ to tokens' keys for each LLM layer and head. During the autoregressive decoding phase, we use PQ codes and centroids to approximately identify important preceding tokens, then fetch the corresponding key-value pairs for self-attention computation. Through meticulous design of overlapping and caching, we minimize any additional computation and communication overhead during both phases. Extensive experiments demonstrate that PQCache achieves both effectiveness and efficiency, with 4.60% score improvement over existing methods on InfiniteBench and low system latency in both prefilling and decoding.

cs.CL↗
arXiv2024

PointSCNet: Point Cloud Structure and Correlation Learning Based on Space Filling Curve-Guided Sampling

Geometrical structures and the internal local region relationship, such as symmetry, regular array, junction, etc., are essential for understanding a 3D shape. This paper proposes a point cloud feature extraction network named PointSCNet, to capture the geometrical structure information and local region correlation information of a point cloud. The PointSCNet consists of three main modules: the space-filling curve-guided sampling module, the information fusion module, and the channel-spatial attention module. The space-filling curve-guided sampling module uses Z-order curve coding to sample points that contain geometrical correlation. The information fusion module uses a correlation tensor and a set of skip connections to fuse the structure and correlation information. The channel-spatial attention module enhances the representation of key points and crucial feature channels to refine the network. The proposed PointSCNet is evaluated on shape classification and part segmentation tasks. The experimental results demonstrate that the PointSCNet outperforms or is on par with state-of-the-art methods by learning the structure and correlation of point clouds effectively.

cs.CV↗
arXiv2024

Multi-Constitutive Neural Network for Large Deformation Poromechanics Problem

In this paper, we study the problem of large-strain consolidation in poromechanics with deep neural networks (DNN). Given different material properties and different loading conditions, the goal is to predict pore pressure and settlement. We propose a novel method "multi-constitutive neural network" (MCNN) such that one model can solve several different constitutive laws. We introduce a one-hot encoding vector as an additional input vector, which is used to label the constitutive law we wish to solve. Then we build a DNN which takes $(\hat{X}, \hat{t})$ as input along with a constitutive law label and outputs the corresponding solution. It is the first time, to our knowledge, that we can evaluate multi-constitutive laws through only one training process while still obtaining good accuracies. We found that MCNN trained to solve multiple PDEs outperforms individual neural network solvers trained with PDE in some cases.

cs.LG↗
arXiv2023

Periodic homogenisation for $P(ϕ)_2$

We consider the periodic homogenisation problem for dynamical $P(ϕ)_2$, a toy model that combines both renormalisation in singular stochastic PDEs and homogenisation. Our result shows that the two limiting procedures commute in this case.

math.AP↗
arXiv2023

What determines the rest frame of bubble nucleation?

We revisit the question addressed in recent papers by Garriga et al: What determines the rest frame of pair nucleation in a constant electric field? The conclusion reached in these papers is that pairs are observed to nucleate at rest in the rest frame of the detector which is used to detect the pairs. A similar conclusion should apply to bubble nucleation in a false vacuum. This conclusion however is subject to doubt due to the unphysical nature of the model of a constant eternal electric field that was used by Garriga et al. The number density of pairs in such a field would be infinite at any finite time. Here we address the same question in a more realistic model where the electric field is turned on at a finite time $t_0$ in the past. The process of turning on the field breaks the Lorentz invariance of the model and could in principle influence the frame of pair nucleation. We find however that the conclusion of Garriga et al still holds in the limit $t_0 \to -\infty$. This shows that the setup process of the electric field does not have a lasting effect on the observed rest frame of pair nucleation. {On the other hand, the electric current and charge density due to the pairs are determined by the way in which the electric field was turned on.

hep-th↗
arXiv2023

Graphic characterization and clustering configuration descriptors of determinant space for molecules

Quantum Monte Carlo approaches based on the stochastic sampling of the determinant space have evolved to be powerful methods to compute the electronic states of molecules. These methods not only calculate the correlation energy at an unprecedented accuracy but also provides insightful information on the electronic structure of computed states, e.g. the population, connection, and clustering of determinants, which have not been fully explored. In this work, we devise a configuration graph for visualizing the determinant space, revealing the nature of the molecule's electronic structure. In addition, we propose two analytical descriptors to quantify the extent of configuration clustering of multi-determinant wave functions. The graph and descriptors provide us with a fresh perspective of the electronic structure of molecules and can assist the further development of configuration interaction based electronic structure methods.

physics.chem-ph↗
arXiv2023

Multi-configurational nature of electron correlation within nitrogen vacancy centers in diamond

Diamond is a solid-state platform to develop quantum technologies, but it has been a long-standing problem that the current understanding of quantum states in diamond is mostly limited to single-electron pictures. Here, we combine the full configuration interaction quantum Monte Carlo method and the density-matrix functional embedding theory, to achieve unprecedented accuracy in describing the many-body quantum states of nitrogen vacancy (NV) centers in diamond. More than 30 electrons and 130 molecular orbitals are correlated, which reveals the multi-configurational wavefunction of the many-body quantum states in diamond. The multi-configurational description explains puzzling experimental measurements in intersystem crossing and charge state transition in NV centers in diamond. The calculations not only reproduce the available experimental measurements of the energy gaps between quantum states but also provide new benchmarks for states that are still subject to considerable uncertainty. This study highlights the importance of multi-configurational wavefunction in the many-body quantum states in solids.

physics.chem-ph↗
arXiv2022

Coupled Metaball Discrete Element Lattice Boltzmann Method for Fluid-Particle Systems with non-spherical particle shapes: A sharp interface coupling scheme

Fluid-particle systems are very common in many natural processes and engineering applications. However, accurately and efficiently modelling fluid-particle systems with complex particle shapes is still a challenging task. Here, we present a numerical model that combines the advantages of Lattice Boltzmann Method (LBM) in solving complex flow problems and the capability of the recently introduced Metaball Discrete Element Method (MDEM) in handling non-spherical particle shapes. A sharp interface coupling scheme is developed and the numerical instability issues due to the discontinuity of interfaces are carefully addressed. A local refilling algorithm for new fluid nodes is proposed and special treatments are introduced to reduce numerical noises when two particles are close. The proposed model is validated by simulations of settling of a single sphere (with metaball representation) as well as a non-spherical particle in a viscous fluid. Good agreements are found comparing the simulations with experimental results which are also carried out in this study. The coupling scheme is also tested by multiple particle simulations which clearly illustrated the stability of the proposed model. Finally, numerical examples with complex particle shapes demonstrated that the proposed model can be a powerful tool for future applications such as shape-induced segregation in riverbeds, and phase transition of dense suspension.

physics.flu-dyn↗
arXiv2021

Signal Processing on the Permutahedron: Tight Spectral Frames for Ranked Data Analysis

Ranked data sets, where m judges/voters specify a preference ranking of n objects/candidates, are increasingly prevalent in contexts such as political elections, computer vision, recommender systems, and bioinformatics. The vote counts for each ranking can be viewed as an n! data vector lying on the permutahedron, which is a Cayley graph of the symmetric group with vertices labeled by permutations and an edge when two permutations differ by an adjacent transposition. Leveraging combinatorial representation theory and recent progress in signal processing on graphs, we investigate a novel, scalable transform method to interpret and exploit structure in ranked data. We represent data on the permutahedron using an overcomplete dictionary of atoms, each of which captures both smoothness information about the data (typically the focus of spectral graph decomposition methods in graph signal processing) and structural information about the data (typically the focus of symmetry decomposition methods from representation theory). These atoms have a more naturally interpretable structure than any known basis for signals on the permutahedron, and they form a Parseval frame, ensuring beneficial numerical properties such as energy preservation. We develop specialized algorithms and open software that take advantage of the symmetry and structure of the permutahedron to improve the scalability of the proposed method, making it more applicable to the high-dimensional ranked data found in applications.

stat.ML↗
arXiv2021

A Summation of Series Involving Bessel Functions and Order Derivatives of Bessel Functions

In this note, we derive the closed-form expression for the summation of series $\sum_{n=0}^{\infty}nJ_n(x)\partial J_n/\partial n$, which is found in the calculation of entanglement entropy in 2-d bosonic free field, in terms of $Y_0$, $J_0$ and an integral involving these two Bessel functions. Further, we point out the integral can be expressed as a Meijer G function.

math-ph↗
arXiv2021

A full configuration interaction quantum Monte Carlo study of ScO, TiO and VO molecules

Accurate ab initio calculations of 3d transition metal monoxide molecules have attracted extensive attention because of its relevance in physical and chemical science, as well as theoretical challenges in treating strong electron correlation. Meanwhile, recent years have witnessed the rapid development of full configuration interaction quantum Monte Carlo (FCIQMC) method to tackle electron correlation. In this study, we carry out FCIQMC simulations to ScO, TiO and VO molecules and obtain accurate descriptions of 13 low-lying electronic states (ScO $^2Σ^+$, $^2Δ$, $^2Π$; TiO $^3Δ$, $^1Δ$, $^1Σ^+$, $^3Π$, $^3Φ$; VO $^4Σ^-$, $^4Φ$, $^4Π$, $^2Γ$, $^2Δ$), including states that have significant multi-configurational character. The FCIQMC results are used to assess the performance of several other wave function theory and density functional theory methods. Our study highlights the challenging nature of electronic structure of transition metal oxides and demonstrates FCIQMC as a promising technique going forward to treat more complex transition metal oxide molecules and materials.

cond-mat.str-el↗
arXiv2020

Bubble nucleation as seen by different observers

Pair production in a constant electric field is closely analogous to bubble nucleation in a false vacuum. The classical trajectories of the pairs are Lorentz invariant, but this invariance should be broken by the nucleation process. Garriga et al. used a model detector, consisting of other particles interacting with the pairs, to investigate how pair production is seen by different Lorentzian observers. They found that particles (antiparticles) of the pair are predominantly observed moving in the direction of (opposite to) the electric field and concluded that observers see pairs nucleating preferentially in the detector's rest frame. Here, we apply this approach to the case where two detectors moving relative to one another are used to observe the particle and antiparticle of the same pair. We find that each detector will still observe nucleation to occur in its rest frame, regardless of the motion of the other detector. However, if the relative velocity of the two detectors is sufficiently high, the particle and antiparticle of the pair can be observed moving towards one another with arbitrarily large momenta, contrary to the usual expectation.

hep-th↗
arXiv2020

Combining Non-probability and Probability Survey Samples Through Mass Imputation

This paper presents theoretical results on combining non-probability and probability survey samples through mass imputation, an approach originally proposed by Rivers (2007) as sample matching without rigorous theoretical justification. Under suitable regularity conditions, we establish the consistency of the mass imputation estimator and derive its asymptotic variance formula. Variance estimators are developed using either linearization or bootstrap. Finite sample performances of the mass imputation estimator are investigated through simulation studies and an application to analyzing a non-probability sample collected by the Pew Research Centre.

stat.ME↗
arXiv2020

Predicting Geographic Information with Neural Cellular Automata

This paper presents a novel framework using neural cellular automata (NCA) to regenerate and predict geographic information. The model extends the idea of using NCA to generate/regenerate a specific image by training the model with various geographic data, and thus, taking the traffic condition map as an example, the model is able to predict traffic conditions by giving certain induction information. Our research verified the analogy between NCA and gene in biology, while the innovation of the model significantly widens the boundary of possible applications based on NCAs. From our experimental results, the model shows great potentials in its usability and versatility which are not available in previous studies. The code for model implementation is available at https://redacted.

cs.CV↗