arXiv subjects

Yin Liu

Publications and source records attributed to Yin Liu.

35 records · Page 2Linked to original sources

arXiv2024

Fitting ARMA Time Series Models without Identification: A Proximal Approach

Fitting autoregressive moving average (ARMA) time series models requires model identification before parameter estimation. Model identification involves determining the order of the autoregressive and moving average components which is generally performed by inspection of the autocorrelation and partial autocorrelation functions or other offline methods. In this work, we regularize the parameter estimation optimization problem with a non-smooth hierarchical sparsity-inducing penalty based on two path graphs that allow performing model identification and parameter estimation simultaneously. A proximal block coordinate descent algorithm is then proposed to solve the underlying optimization problem efficiently. The resulting model satisfies the required stationarity and invertibility conditions for ARMA models. Numerical results supporting the proposed method are also presented.

stat.CO↗

arXiv2024

Plug-and-Play Latent Feature Editing for Orientation-Adaptive Quantitative Susceptibility Mapping Neural Networks

Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a self-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms.

eess.IV↗

arXiv2024

Highly confined epsilon-near-zero- and surface-phonon polaritons in SrTiO3 membranes

Recent theoretical studies have suggested that transition metal perovskite oxide membranes can enable surface phonon polaritons in the infrared range with low loss and much stronger subwavelength confinement than bulk crystals. Such modes, however, have not been experimentally observed so far. Here, using a combination of far-field Fourier-transform infrared (FTIR) spectroscopy and near-field synchrotron infrared nanospectroscopy (SINS) imaging, we study the phonon-polaritons in a 100 nm thick freestanding crystalline membrane of SrTiO3 transferred on metallic and dielectric substrates. We observe a symmetric-antisymmetric mode splitting giving rise to epsilon-near-zero and Berreman modes as well as highly confined (by a factor of 10) propagating phonon polaritons, both of which result from the deep-subwavelength thickness of the membranes. Theoretical modeling based on the analytical finite-dipole model and numerical finite-difference methods fully corroborate the experimental results. Our work reveals the potential of oxide membranes as a promising platform for infrared photonics and polaritonics.

cond-mat.mtrl-sci↗

arXiv2024

Derivative-Free Optimization for Low-Rank Adaptation in Large Language Models

Parameter-efficient tuning methods such as LoRA could achieve comparable performance to model tuning by tuning a small portion of the parameters. However, substantial computational resources are still required, as this process involves calculating gradients and performing back-propagation throughout the model. Much effort has recently been devoted to utilizing the derivative-free optimization method to eschew the computation of gradients and showcase an augmented level of robustness in few-shot settings. In this paper, we prepend the low-rank modules into each self-attention layer of the model and employ two derivative-free optimization methods to optimize these low-rank modules at each layer alternately. Extensive results on various tasks and language models demonstrate that our proposed method achieves substantial improvement and exhibits clear advantages in memory usage and convergence speed compared to existing gradient-based parameter-efficient tuning and derivative-free optimization methods in few-shot settings.

cs.CL↗

arXiv2023

TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors

Molecular dynamics simulations have emerged as a potent tool for investigating the physical properties and kinetic behaviors of materials at the atomic scale, particularly in extreme conditions. Ab initio accuracy is now achievable with machine learning based interatomic potentials. With recent advancements in high-performance computing, highly accurate and large-scale simulations become feasible. This study introduces TensorMD, a new machine learning interatomic potential (MLIP) model that integrates physical principles and tensor diagrams. The tensor formalism provides a more efficient computation and greater flexibility for use with other scientific codes. Additionally, we proposed several portable optimization strategies and developed a highly optimized version for the new Sunway supercomputer. Our optimized TensorMD can achieve unprecedented performance on the new Sunway, enabling simulations of up to 52 billion atoms with a time-to-solution of 31 ps/step/atom, setting new records for HPC + AI + MD.

physics.comp-ph↗

arXiv2023

Quantitative Susceptibility Mapping through Model-based Deep Image Prior (MoDIP)

The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects. To address this generalization issue in supervised QSM methods, we propose a novel training-free model-based unsupervised method called MoDIP (Model-based Deep Image Prior). MoDIP comprises a small, untrained network and a Data Fidelity Optimization (DFO) module. The network converges to an interim state, acting as an implicit prior for image regularization, while the optimization process enforces the physical model of QSM dipole inversion. Experimental results demonstrate MoDIP's excellent generalizability in solving QSM dipole inversion across different scan parameters. It exhibits robustness against pathological brain QSM, achieving over 32% accuracy improvement than supervised deep learning and traditional iterative methods. It is also 33% more computationally efficient and runs 4 times faster than conventional DIP-based approaches, enabling 3D high-resolution image reconstruction in under 4.5 minutes.

eess.IV↗

arXiv2023

Restricted Holant Dichotomy on Domains 3 and 4

$\operatorname{Holant}^*(f)$ denotes a class of counting problems specified by a constraint function $f$. We prove complexity dichotomy theorems for $\operatorname{Holant}^*(f)$ in two settings: (1) $f$ is any arity-3 real-valued function on input of domain size 3. (2) $f$ is any arity-3 $\{0,1\}$-valued function on input of domain size 4.

cs.CC↗

arXiv2023

Achiral dielectric metasurfaces for spectral and polarization control of valley specific light emission from monolayer MoS2

Excitons in two-dimensional transition metal dichalcogenides have a valley degree of freedom that can be optically accessed and manipulated for quantum information processing. Here, we integrate MoS2 with achiral silicon disk array metasurfaces to enhance and control valley-specific absorption and emission. Through the coupling to the metasurface Mie modes, the intensity and lifetime of the emission of neutral excitons, trions and defect bound excitons can be enhanced, while the spectral shape can be modified. Additionally, we demonstrate the symmetric enhancement of the degree-of-polarization (DOP) of neutral exciton and trions via valley-resolved PL measurements, and find that the DOP can be as high as 24% for exciton emission and 34% for trion emission at 100K. These results can be understood by analyzing the near-field impact of metasurface resonators on both the chiral absorption of MoS2 emitters as well as the enhanced emission from the Purcell effect. Combining Si-compatible photonic design with large-scale (mm-scale) 2D materials integration, our work makes an important step towards on-chip valleytronic applications approaching room-temperature operation.

physics.optics↗

arXiv2023

Stochastic Composition Optimization of Functions without Lipschitz Continuous Gradient

In this paper, we study stochastic optimization of two-level composition of functions without Lipschitz continuous gradient. The smoothness property is generalized by the notion of relative smoothness which provokes the Bregman gradient method. We propose three Stochastic Composition Bregman Gradient algorithms for the three possible relatively smooth compositional scenarios and provide their sample complexities to achieve an $ε$-approximate stationary point. For the smooth of relatively smooth composition, the first algorithm requires $O(ε^{-2})$ calls to the stochastic oracles of the inner function value and gradient as well as the outer function gradient. When both functions are relatively smooth, the second algorithm requires $O(ε^{-3})$ calls to the inner function value stochastic oracle and $O(ε^{-2})$ calls to the inner and outer functions gradients stochastic oracles. We further improve the second algorithm by variance reduction for the setting where just the inner function is smooth. The resulting algorithm requires $O(ε^{-5/2})$ calls to the inner function value stochastic oracle, $O(ε^{-3/2})$ calls to the inner function gradient and $O(ε^{-2})$ calls to the outer function gradient stochastic oracles. Finally, we numerically evaluate the performance of these three algorithms over two different examples.

math.OC↗

arXiv2022

Accelerating quantum materials development with advances in transmission electron microscopy

Quantum materials are driving a technology revolution in sensing, communication, and computing, while simultaneously testing many core theories of the past century. Materials such as topological insulators, complex oxides, quantum dots, color center hosting semiconductors, and other types of strongly correlated materials can exhibit exotic properties such as edge conductivity, multiferroicity, magnetoresistance, single photon emission, and optical-spin locking. These emergent properties arise and depend strongly on the materials detailed atomic scale structure, including atomic defects, dopants, and lattice stacking. In this review, after introduction of different classes of quantum materials and quantum excitations, we describe how progress in the field of electron microscopy, including in situ and in operando EM, can accelerate advances in quantum materials. Our review describes EM methods including: i) principles and operation modes of EM, ii) EM spectroscopies, such as electron energy loss spectroscopy, cathodoluminescence, and electron energy gain spectroscopy, iii) 4D scanning transmission electron microscopy, iv) dynamic and ultrafast EM, v) complimentary ultrafast spectroscopies, and vi) atomic electron tomography. We discuss how these methods inform structure function relations in quantum materials down to the picometer scale and femtosecond time resolution, and how they enable high resolution manipulation of quantum materials. Among numerous results, our review highlights how EM has enabled identification of the 3D structure of quantum defects, measuring reversible and metastable dynamics of quantum excitations, mapping exciton states and single photon emission, measuring nanoscale thermal transport and coupled excitation dynamics, and measuring the internal electric field of quantum heterointerfaces, all at the quantum materials intrinsic atomic and near atomic-length scale.

cond-mat.mtrl-sci↗

arXiv2022

Nanodiamond grain boundaries and lattice expansion drive Silicon vacancy emission heterogeneity

Silicon-vacancy (SiV$^-$) centers in diamond are promising candidates as sources of single-photons in quantum networks due to their minimal phonon coupling and narrow optical linewidths. Correlating SiV$^-$ emission with the defect's atomic-scale structure is important for controlling and optimizing quantum emission, but remains an outstanding challenge. Here, we use cathodoluminescence imaging in a scanning transmission electron microscope (STEM) to elucidate the structural sources of non-ideality in the SiV$^-$ emission from nanodiamonds with sub-nanometer-scale resolution. We show that different crystalline domains of a nanodiamond exhibit distinct zero-phonon line (ZPL) energies and differences in brightness, while near-surface SiV$^-$ emitters remain bright. We correlate these changes with local lattice expansion using 4D STEM and diffraction, and show that associated blue shifts from the ZPL are due to defect density heterogeneity, while red shifts are due to lattice distortions.

physics.app-ph↗

arXiv2021

Bipartite 3-Regular Counting Problems with Mixed Signs

We prove a complexity dichotomy for a class of counting problems expressible as bipartite 3-regular Holant problems. For every problem of the form $\operatorname{Holant}\left(f\mid =_3 \right)$, where $f$ is any integer-valued ternary symmetric constraint function on Boolean variables, we prove that it is either P-time computable or #P-hard, depending on an explicit criterion of $f$. The constraint function can take both positive and negative values, allowing for cancellations. The dichotomy extends easily to rational valued functions of the same type. In addition, we discover a new phenomenon: there is a set $\mathcal{F}$ with the property that for every $f \in \mathcal{F}$ the problem $\operatorname{Holant}\left(f\mid =_3 \right)$ is planar P-time computable but #P-hard in general, yet its planar tractability is by a combination of a holographic transformation by $\left[\begin{smallmatrix} 1 & 1 \\ 1 & -1 \end{smallmatrix}\right]$ to FKT together with an independent global argument.

cs.CC↗

arXiv2020

A first-order optimization algorithm for statistical learning with hierarchical sparsity structure

In many statistical learning problems, it is desired that the optimal solution conforms to an a priori known sparsity structure represented by a directed acyclic graph. Inducing such structures by means of convex regularizers requires nonsmooth penalty functions that exploit group overlapping. Our study focuses on evaluating the proximal operator of the Latent Overlapping Group lasso developed by Jacob et al. (2009). We implemented an Alternating Direction Method of Multiplier with a sharing scheme to solve large-scale instances of the underlying optimization problem efficiently. In the absence of strong convexity, global linear convergence of the algorithm is established using the error bound theory. More specifically, the paper contributes to establishing primal and dual error bounds when the nonsmooth component in the objective function does not have a polyhedral epigraph. We also investigate the effect of the graph structure on the speed of convergence of the algorithm. Detailed numerical simulation studies over different graph structures supporting the proposed algorithm and two applications in learning are provided.

math.OC↗

arXiv2020

Growth and Properties of Dislocated Two-dimensional Layered Materials

Two-dimensional (2D) layered materials hosting dislocations have attracted considerable research attention in recent years. In particular, screw dislocations can result in a spiral topology and an interlayer twist in the layered materials, significantly impacting the stacking order and symmetry of the layers. Moreover, the dislocations with large strain and heavily distorted atomic registry can result in a local modification of the structures around the dislocation. The dislocations thus provide a useful route to engineering optical, electrical, thermal, mechanical and catalytic properties of the 2D layered materials, which show great potential to bring new functionalities. This article presents a comprehensive review of the experimental and theoretical progress on the growth and properties of the dislocated 2D layered materials. It also offers an outlook on the future works in this promising research field.

cond-mat.mtrl-sci↗

arXiv2020

Associating Multi-Scale Receptive Fields for Fine-grained Recognition

Extracting and fusing part features have become the key of fined-grained image recognition. Recently, Non-local (NL) module has shown excellent improvement in image recognition. However, it lacks the mechanism to model the interactions between multi-scale part features, which is vital for fine-grained recognition. In this paper, we propose a novel cross-layer non-local (CNL) module to associate multi-scale receptive fields by two operations. First, CNL computes correlations between features of a query layer and all response layers. Second, all response features are weighted according to the correlations and are added to the query features. Due to the interactions of cross-layer features, our model builds spatial dependencies among multi-level layers and learns more discriminative features. In addition, we can reduce the aggregation cost if we set low-dimensional deep layer as query layer. Experiments are conducted to show our model achieves or surpasses state-of-the-art results on three benchmark datasets of fine-grained classification. Our codes can be found at github.com/FouriYe/CNL-ICIP2020.

cs.CV↗

arXiv2018

Finite groups with coprime fixed-point-free automorphisms and applications

We study a class of finite groups $G$ which behave similarly to elementary abelian $p$-groups with $p$ prime, that is, there exists a subgroup $N$ such that all elements of $G\setminus N$ are conjugate or inverse-conjugate under $\Aut(G)$. In this paper, we show that such groups are solvable. As an application, we characterise a class of normal edge-transitive Cayley graphs of $G$, which are complete multipartite graphs.

math.GR↗

arXiv2017

1.25 GHz sine wave gating InGaAs/InP single-photon detector with monolithically integrated readout circuit

InGaAs/InP single-photon detectors (SPDs) are the key devices for applications requiring near-infrared single-photon detection. Gating mode is an effective approach to synchronous single-photon detection. Increasing gating frequency and reducing module size are important challenges for the design of such detector system. Here we present for the first time an InGaAs/InP SPD with 1.25 GHz sine wave gating using a monolithically integrated readout circuit (MIRC). The MIRC has a size of 15 mm * 15 mm and implements the miniaturization of avalanche extraction for high-frequency sine wave gating. In the MIRC, low-pass filters and a low-noise radio frequency amplifier are integrated based on the technique of low temperature co-fired ceramic, which can effectively reduce the parasitic capacitance and extract weak avalanche signals. We then characterize the InGaAs/InP SPD to verify the functionality and reliability of MIRC, and the SPD exhibits excellent performance with 27.5 % photon detection efficiency, 1.2 kcps dark count rate, and 9.1 % afterpulse probability at 223 K and 100 ns hold-off time. With this MIRC, one can further design miniaturized high-frequency SPD modules that are highly required for practical applications.

quant-ph↗