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Hao Yan

Publications and source records attributed to Hao Yan.

At least 109 records · Page 6Linked to original sources
arXiv2023

Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering

Passenger clustering based on trajectory records is essential for transportation operators. However, existing methods cannot easily cluster the passengers due to the hierarchical structure of the passenger trip information, including multiple trips within each passenger and multi-dimensional information about each trip. Furthermore, existing approaches rely on an accurate specification of the clustering number to start. Finally, existing methods do not consider spatial semantic graphs such as geographical proximity and functional similarity between the locations. In this paper, we propose a novel tensor Dirichlet Process Multinomial Mixture model with graphs, which can preserve the hierarchical structure of the multi-dimensional trip information and cluster them in a unified one-step manner with the ability to determine the number of clusters automatically. The spatial graphs are utilized in community detection to link the semantic neighbors. We further propose a tensor version of Collapsed Gibbs Sampling method with a minimum cluster size requirement. A case study based on Hong Kong metro passenger data is conducted to demonstrate the automatic process of cluster amount evolution and better cluster quality measured by within-cluster compactness and cross-cluster separateness. The code is available at https://github.com/bonaldli/TensorDPMM-G.

stat.ML↗
arXiv2023

Influence of EOM sideband modulation noise on space-borne gravitational wave detection

Clock noise is one of the dominant noises in the space-borne gravitational wave (GW) detection. To suppress this noise, the clock noise-calibrated time-delay-interferometry (TDI) technique is proposed. In this technique, an inter-spacecraft clock tone transfer chain is necessary to obtain the comparison information of the clock noises in two spacecraft, during which an electro-optic-modulator (EOM) is critical and used to modulate the clock noise to the laser phase. Since the EOM sideband modulation process introduces modulation noise, it is significant to put forward the corresponding requirements and assess whether the commercial EOM meets. In this work, based on the typical Michelson TDI algorithm and the fundamental noise requirement of GW detectors, the analytic expression of the modulation noise requirement is strictly derived, which relax the component indicator need compared to the existing commonly used rough assessments. Furthermore, a commercial EOM (iXblue-NIR-10 GHz) is tested, and the experimental results show that it can meet the requirement of the typical GW detection mission LISA in whole scientific bandwidth by taking the optimal combination of the data stream. Even when the displacement measurement accuracy of LISA is improved to 1 pm/ $\mathrm{Hz^{1/2}}$ in the future, it still meets the demand.

astro-ph.IM↗
arXiv2023

Inverse design of a pyrochlore lattice of DNA origami through model-driven experiments

Sophisticated statistical mechanics approaches and human intuition have demonstrated the possibility to self-assemble complex lattices or finite size constructs, but have mostly only been successful in silico. The proposed strategies quite often fail in experiment due to unpredicted traps associated to kinetic slowing down (gelation, glass transition), as well as to competing ordered structures. An additional challenge that theoretical predictions face is the difficulty to encode the desired inter-particle interaction potential with the currently available library of nano- and micron-sized particles. To overcome these issues, we conjugate here SAT-assembly -- a patchy-particle interaction design algorithm based on constrained optimization solvers -- with coarse-grained simulations of DNA nanotechnology to experimentally realize trap-free self-assembly pathways. As a proof of concept we investigate the assembly of the pyrochlore (also known as tetrastack) lattice, a highly coveted 3D crystal lattice due to its promise in construction of optical metamaterials. We confirm the successful assembly with two different patchy DNA origami designs via SAXS as well as SEM visualization of the silica-coated lattice. Our approach offers a versatile modeling pipeline that starts from patchy particles designed in silico and ends with wireframe DNA origami that self-assemble into the desired structure.

cond-mat.soft↗
arXiv2023

Precessing jet nozzle connecting to a spinning black hole in M87

The nearby radio galaxy M87 offers a unique opportunity to explore the connections between the central supermassive black hole and relativistic jets. Previous studies of the inner region of M87 revealed a wide opening angle for the jet originating near the black hole. The Event Horizon Telescope resolved the central radio source and found an asymmetric ring structure consistent with expectations from General Relativity. With a baseline of 17 years of observations, there was a shift in the jet's transverse position, possibly arising from an eight to ten-year quasi-periodicity. However, the origin of this sideways shift remains unclear. Here we report an analysis of radio observations over 22 years that suggests a period of about 11 years in the position angle variation of the jet. We infer that we are seeing a spinning black hole that induces the Lense-Thirring precession of a misaligned accretion disk. Similar jet precession may commonly occur in other active galactic nuclei but has been challenging to detect owing to the small magnitude and long period of the variation.

astro-ph.HE↗
arXiv2023

Fully parallel optical matrix-matrix multiplication

In recent years, with the rapid development of electro-optic modulators, optical computing has become a potential excellent candidate for various computing tasks. New structures and devices for optical computing are emerging one after another, but the computing method is still the optical vector-matrix multiplication method that was decades ago. Here, we propose a novel optical computing paradigm that can parallelly implement matrix-matrix multiplication operation, which can directly replace existing vector-matrix multiplication, greatly improving computational efficiency. This preprint presents theoretical analysis, and we will supplement experimental results and conclusions in the future.

physics.optics↗
arXiv2023

Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data

We propose personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets. perTucker decomposes tensor data into shared global components and personalized local components. We introduce a mode orthogonality assumption and develop a proximal gradient regularized block coordinate descent algorithm that is guaranteed to converge to a stationary point. By learning unique and common representations across datasets, we demonstrate perTucker's effectiveness in anomaly detection, client classification, and clustering through a simulation study and two case studies on solar flare detection and tonnage signal classification.

cs.LG↗
arXiv2023

A construction method of the quasi-monolithic compact interferometer based on UV-adhesives bonding

Quasi-monolithic interferometers play a crucial role in high-precision measurement experiments, including gravitational wave detection, inertial sensing, vibrometry, and seismology. Achieving high stability and accuracy in such interferometers requires a method for bonding optical components to a baseplate. While optical contact bonding and silicate bonding are common methods, UV adhesives offer advantages such as controlled curing and low geometrical requirements for optical components and baseplates. This paper presents a detailed construction method for a quasi-monolithic compact interferometer based on UV-adhesive bonding. We built two types of interferometers using this method: a $100\,{\rm mm} \times 100\,{\rm mm}\times 20\,{\rm mm}$ Mach-Zender homodyne interferometer with unequal arm lengths of about $100\,{\rm mm}$ for laser frequency noise monitoring, and a heterodyne interferometer as a displacement sensing head sizing $20\,{\rm mm} \times 30\,{\rm mm}\times 20\,{\rm mm}$. Our Mach-Zender interferometer achieved a phase noise level of $2\,μ{\rm rad}\sqrt{\rm Hz}$ at $1\,{\rm Hz}$ and a equivalent laser frequency noise monitoring sensitivity of about $1\,{\rm kHz}/\sqrt{\rm Hz}$ at $1\,{\rm Hz}$. The compact heterodyne interferometer sensing head showed a sensitivity level of $1\,{\rm pm}/\sqrt{\rm Hz}$ in translation and $0.2\,{\rm nrad}/\sqrt{\rm Hz}$ in two tilts above $0.4\,{\rm Hz}$. Our tests demonstrate that quasi-monolithic compact interferometers based on UV-adhesive bonding can achieve high sensitivity levels at the pico-meter and nano-radian scales.

physics.ins-det↗
arXiv2023

Minimax rates for latent position estimation in the generalized random dot product graph

Latent space models play an important role in the modeling and analysis of network data. Under these models, each node has an associated latent point in some (typically low-dimensional) geometric space, and network formation is driven by this unobserved geometric structure. The random dot product graph (RDPG) and its generalization (GRDPG) are latent space models under which this latent geometry is taken to be Euclidean. These latent vectors can be efficiently and accurately estimated using well-studied spectral embeddings. In this paper, we develop a minimax lower bound for estimating the latent positions in the RDPG and the GRDPG models under the two-to-infinity norm, and show that a particular spectral embedding method achieves this lower bound. We also derive a minimax lower bound for the related task of subspace estimation under the two-to-infinity norm that holds in general for low-rank plus noise network models, of which the RDPG and GRDPG are special cases. The lower bounds are achieved by a novel construction based on Hadamard matrices.

math.ST↗
arXiv2023

Extremal statistics for a resetting Brownian motion before its first-passage time

We study the extreme value statistics of a one-dimensional resetting Brownian motion (RBM) till its first passage through the origin starting from the position $x_0$ ($>0$). By deriving the exit probability of RBM in an interval $\left[0, M \right] $ from the origin, we obtain the distribution $P_r(M|x_0)$ of the maximum displacement $M$ and thus gives the expected value $\langle M \rangle$ of $M$ as functions of the resetting rate $r$ and $x_0$. We find that $\langle M \rangle$ decreases monotonically as $r$ increases, and tends to $2 x_0$ as $r \to \infty$. In the opposite limit, $\langle M \rangle$ diverges logarithmically as $r \to 0$. Moreover, we derive the propagator of RBM in the Laplace domain in the presence of both absorbing ends, and then leads to the joint distribution $P_r(M,t_m|x_0)$ of $M$ and the time $t_m$ at which this maximum is achieved in the Lapalce domain by using a path decomposition technique, from which the expected value $\langle t_m \rangle$ of $t_m$ is obtained explicitly. Interestingly, $\langle t_m \rangle$ shows a nonmonotonic dependence on $r$, and attains its minimum at an optimal $r^{*} \approx 2.71691 D/x_0^2$, where $D$ is the diffusion coefficient. Finally, we perform extensive simulations to validate our theoretical results.

cond-mat.stat-mech↗
arXiv2023

Tensor Dirichlet Process Multinomial Mixture Model for Passenger Trajectory Clustering

Passenger clustering based on travel records is essential for transportation operators. However, existing methods cannot easily cluster the passengers due to the hierarchical structure of the passenger trip information, namely: each passenger has multiple trips, and each trip contains multi-dimensional multi-mode information. Furthermore, existing approaches rely on an accurate specification of the clustering number to start, which is difficult when millions of commuters are using the transport systems on a daily basis. In this paper, we propose a novel Tensor Dirichlet Process Multinomial Mixture model (Tensor-DPMM), which is designed to preserve the multi-mode and hierarchical structure of the multi-dimensional trip information via tensor, and cluster them in a unified one-step manner. The model also has the ability to determine the number of clusters automatically by using the Dirichlet Process to decide the probabilities for a passenger to be either assigned in an existing cluster or to create a new cluster: This allows our model to grow the clusters as needed in a dynamic manner. Finally, existing methods do not consider spatial semantic graphs such as geographical proximity and functional similarity between the locations, which may cause inaccurate clustering. To this end, we further propose a variant of our model, namely the Tensor-DPMM with Graph. For the algorithm, we propose a tensor Collapsed Gibbs Sampling method, with an innovative step of "disband and relocating", which disbands clusters with too small amount of members and relocates them to the remaining clustering. This avoids uncontrollable growing amounts of clusters. A case study based on Hong Kong metro passenger data is conducted to demonstrate the automatic process of learning the number of clusters, and the learned clusters are better in within-cluster compactness and cross-cluster separateness.

stat.ML↗
arXiv2023

Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing

Interactive semantic parsing based on natural language (NL) feedback, where users provide feedback to correct the parser mistakes, has emerged as a more practical scenario than the traditional one-shot semantic parsing. However, prior work has heavily relied on human-annotated feedback data to train the interactive semantic parser, which is prohibitively expensive and not scalable. In this work, we propose a new task of simulating NL feedback for interactive semantic parsing. We accompany the task with a novel feedback evaluator. The evaluator is specifically designed to assess the quality of the simulated feedback, based on which we decide the best feedback simulator from our proposed variants. On a text-to-SQL dataset, we show that our feedback simulator can generate high-quality NL feedback to boost the error correction ability of a specific parser. In low-data settings, our feedback simulator can help achieve comparable error correction performance as trained using the costly, full set of human annotations.

cs.CL↗
arXiv2023

On the noise effect of test mass surface roughness in spaceborne gravitational wave detectors

Spaceborne gravitational wave detection mission has a demanding requirement for the precision of displacement sensing, which is conducted by the interaction between the laser field and test mass. However, due to the roughness of the reflecting surface of the test mass, the displacement measurement along the sensitive axis suffers a coupling error caused by the residue motion of other degrees of freedom. In this article, we model the coupling of the test mass residue random motion to the displacement sensing along the sensitive axis and derived an analytical formula of the required precision of the surface error for the spaceborne gravitational wave detectors. Our result shows that this coupling error will not contaminate the picometer displacement sensing for the test masses in the LISA pathfinder.

physics.ins-det↗
arXiv2023

Interpretation and visualization of distance covariance through additive decomposition of correlations formula

Distance covariance is a widely used statistical methodology for testing the dependency between two groups of variables. Despite the appealing properties of consistency and superior testing power, the testing results of distance covariance are often hard to be interpreted. This paper presents an elementary interpretation of the mechanism of distance covariance through an additive decomposition of correlations formula. Based on this formula, a visualization method is developed to provide practitioners with a more intuitive explanation of the distance covariance score.

stat.ME↗
arXiv2023

GAN-Based Multi-View Video Coding with Spatio-Temporal EPI Reconstruction

The introduction of multiple viewpoints in video scenes inevitably increases the bitrates required for storage and transmission. To reduce bitrates, researchers have developed methods to skip intermediate viewpoints during compression and delivery, and ultimately reconstruct them using Side Information (SI). Typically, depth maps are used to construct SI. However, their methods suffer from inaccuracies in reconstruction and inherently high bitrates. In this paper, we propose a novel multi-view video coding method that leverages the image generation capabilities of Generative Adversarial Network (GAN) to improve the reconstruction accuracy of SI. Additionally, we consider incorporating information from adjacent temporal and spatial viewpoints to further reduce SI redundancy. At the encoder, we construct a spatio-temporal Epipolar Plane Image (EPI) and further utilize a convolutional network to extract the latent code of a GAN as SI. At the decoder side, we combine the SI and adjacent viewpoints to reconstruct intermediate views using the GAN generator. Specifically, we establish a joint encoder constraint for reconstruction cost and SI entropy to achieve an optimal trade-off between reconstruction quality and bitrates overhead. Experiments demonstrate significantly improved Rate-Distortion (RD) performance compared with state-of-the-art methods.

eess.IV↗
arXiv2023

The effect of loops on the mean square displacement of Rouse-model chromatin

Many researchers have been encouraged to describe the dynamics of chromosomal loci in chromatin using the classical Rouse model of polymer dynamics by the agreement between the measured mean square displacement (MSD) versus time of fluorescently-labelled loci and the Rouse-model predictions. However, the discovery of intermediate-scale chromatin organization, known as topologically associating domains (TADs), together with the proposed explanation of TADs in terms of chromatin loops and loop extrusion, is at odds with the classical Rouse model, which does not contain loops. Accordingly, we introduce an extended Rouse model that incorporates chromatin loop configurations from loop-extrusion-factor-model simulations. Specifically, we extend the classical Rouse model by modifying the polymer's dynamical matrix to incorporate extra springs that represent loop bases. We also theoretically generalize the friction coefficient matrix so that the Rouse beads with non-uniform friction coefficients are compatible with our Rouse model simulation method. This extended Rouse model allowes us to investigate the impact of loops and loop extrusion on the dynamics of chromatin. We show that loops significantly suppress the averaged MSD of a chromosomal locus, consistent with recent experiments that track fluorescently-labelled chromatin loci in fission yeast [M. L. P. Bailey, I. Surovtsev, J. F. Williams, H. Yan, T. Yuan, S. G. Mochrie, and M. C. King, Mol. Biol. Cell (in press)]. We also find that loops slightly reduce the MSD's stretching exponent from the classical Rouse-model value of 0.5 to a loop-density-dependent value in the 0.45-0.40 range. Remarkably, stretching exponent values in this range have also been reported in recent experiments [S. C. Weber, A. J. Spakowitz, and J. A. Theriot, Phys. Rev. Lett. 104, 238102 (2010) and Bailey et al., Mol. Biol. Cell (in press)].

physics.bio-ph↗
arXiv2023

Learning on Large-scale Text-attributed Graphs via Variational Inference

This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for such a problem would be integrating both the text and graph structure information with large language models and graph neural networks (GNNs). However, the problem becomes very challenging when graphs are large due to the high computational complexity brought by training large language models and GNNs together. In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization (EM) framework, called GLEM. Instead of simultaneously training large language models and GNNs on big graphs, GLEM proposes to alternatively update the two modules in the E-step and M-step. Such a procedure allows training the two modules separately while simultaneously allowing the two modules to interact and mutually enhance each other. Extensive experiments on multiple data sets demonstrate the efficiency and effectiveness of the proposed approach.

cs.LG↗
arXiv2022

Ultrasmall InGa(As)P dielectric and plasmonic nanolasers

Nanolasers have great potential as both on-chip light sources and optical barcoding particles. We demonstrate ultrasmall InGaP and InGaAsP disk lasers with diameters down to 360 nm (198 nm in height) in the red spectral range. Optically pumped, room-temperature, single-mode lasing was achieved from both disk-on-pillar and isolated particles. When isolated disks were placed on gold, plasmon polariton lasing was obtained with Purcell-enhanced stimulated emission. UV lithography and plasma ashing enabled the fabrication of nanodisks on a wafer-scale, with intended random size variation. Silica-coated nanodisk particles generated stable sub-nanometer spectra from within biological cells across an 80 nm bandwidth from 635 to 715 nm.

physics.optics↗
arXiv2022

Dual Moving Average Pseudo-Labeling for Source-Free Inductive Domain Adaptation

Unsupervised domain adaptation reduces the reliance on data annotation in deep learning by adapting knowledge from a source to a target domain. For privacy and efficiency concerns, source-free domain adaptation extends unsupervised domain adaptation by adapting a pre-trained source model to an unlabeled target domain without accessing the source data. However, most existing source-free domain adaptation methods to date focus on the transductive setting, where the target training set is also the testing set. In this paper, we address source-free domain adaptation in the more realistic inductive setting, where the target training and testing sets are mutually exclusive. We propose a new semi-supervised fine-tuning method named Dual Moving Average Pseudo-Labeling (DMAPL) for source-free inductive domain adaptation. We first split the unlabeled training set in the target domain into a pseudo-labeled confident subset and an unlabeled less-confident subset according to the prediction confidence scores from the pre-trained source model. Then we propose a soft-label moving-average updating strategy for the unlabeled subset based on a moving-average prototypical classifier, which gradually adapts the source model towards the target domain. Experiments show that our proposed method achieves state-of-the-art performance and outperforms previous methods by large margins.

cs.LG↗