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Zhou Zhou

Publications and source records attributed to Zhou Zhou.

At least 55 records · Page 3Linked to original sources
arXiv2024

Benchmarking Neural Decoding Backbones towards Enhanced On-edge iBCI Applications

Traditional invasive Brain-Computer Interfaces (iBCIs) typically depend on neural decoding processes conducted on workstations within laboratory settings, which prevents their everyday usage. Implementing these decoding processes on edge devices, such as the wearables, introduces considerable challenges related to computational demands, processing speed, and maintaining accuracy. This study seeks to identify an optimal neural decoding backbone that boasts robust performance and swift inference capabilities suitable for edge deployment. We executed a series of neural decoding experiments involving nonhuman primates engaged in random reaching tasks, evaluating four prospective models, Gated Recurrent Unit (GRU), Transformer, Receptance Weighted Key Value (RWKV), and Selective State Space model (Mamba), across several metrics: single-session decoding, multi-session decoding, new session fine-tuning, inference speed, calibration speed, and scalability. The findings indicate that although the GRU model delivers sufficient accuracy, the RWKV and Mamba models are preferable due to their superior inference and calibration speeds. Additionally, RWKV and Mamba comply with the scaling law, demonstrating improved performance with larger data sets and increased model sizes, whereas GRU shows less pronounced scalability, and the Transformer model requires computational resources that scale prohibitively. This paper presents a thorough comparative analysis of the four models in various scenarios. The results are pivotal in pinpointing an optimal backbone that can handle increasing data volumes and is viable for edge implementation. This analysis provides essential insights for ongoing research and practical applications in the field.

cs.LG↗
arXiv2024

Exposing Text-Image Inconsistency Using Diffusion Models

In the battle against widespread online misinformation, a growing problem is text-image inconsistency, where images are misleadingly paired with texts with different intent or meaning. Existing classification-based methods for text-image inconsistency can identify contextual inconsistencies but fail to provide explainable justifications for their decisions that humans can understand. Although more nuanced, human evaluation is impractical at scale and susceptible to errors. To address these limitations, this study introduces D-TIIL (Diffusion-based Text-Image Inconsistency Localization), which employs text-to-image diffusion models to localize semantic inconsistencies in text and image pairs. These models, trained on large-scale datasets act as ``omniscient" agents that filter out irrelevant information and incorporate background knowledge to identify inconsistencies. In addition, D-TIIL uses text embeddings and modified image regions to visualize these inconsistencies. To evaluate D-TIIL's efficacy, we introduce a new TIIL dataset containing 14K consistent and inconsistent text-image pairs. Unlike existing datasets, TIIL enables assessment at the level of individual words and image regions and is carefully designed to represent various inconsistencies. D-TIIL offers a scalable and evidence-based approach to identifying and localizing text-image inconsistency, providing a robust framework for future research combating misinformation.

cs.CV↗
arXiv2024

Inference for non-stationary time series quantile regression with inequality constraints

We consider parameter inference for linear quantile regression with non-stationary predictors and errors, where the regression parameters are subject to inequality constraints. We show that the constrained quantile coefficient estimators are asymptotically equivalent to the metric projections of the unconstrained estimator onto the constrained parameter space. Utilizing a geometry-invariant property of this projection operation, we propose inference procedures - the Wald, likelihood ratio, and rank-based methods - that are consistent regardless of whether the true parameters lie on the boundary of the constrained parameter space. We also illustrate the advantages of considering the inequality constraints in analyses through simulations and an application to an electricity demand dataset.

stat.ME↗
arXiv2024

Sliding-mediated ferroelectric phase transition in CuInP2S6 under pressure

Interlayer stacking order has recently emerged as a unique degree of freedom to control crystal symmetry and physical properties in two-dimensional van der Waals (vdW) materials and heterostructures. By tuning the layer stacking pattern, symmetry-breaking and electric polarization can be created in otherwise non-polar crystals, whose polarization reversal depends on the interlayer sliding motion. Herein, we demonstrate that in a vdW layered ferroelectric, its existing polarization is closely coupled to the interlayer sliding driven by hydrostatic pressure. Through combined structural, electrical, vibrational characterizations, and theoretical calculations, we clearly map out the structural evolution of CuInP2S6 under pressure. A tendency towards a high polarization state is observed in the low-pressure region, followed by an interlayer-sliding-mediated phase transition from a monoclinic to a trigonal phase. Along the transformation pathway, the displacive-instable Cu ion serves as a pivot point that regulates the interlayer interaction in response to external pressure. The rich phase diagram of CuInP2S6, which is enabled by stacking orders, sheds light on the physics of vdW ferroelectricity and opens an alternative route to tailoring long-range order in vdW layered crystals.

cond-mat.mtrl-sci↗
arXiv2024

Tailoring spatiotemporal wavepackets via two-dimensional space-time duality

Space-time (ST) beams, ultrafast optical wavepackets with customized spatial and temporal characteristics, present a significant contrast to conventional spatial-structured light and hold the potential to revolutionize our understanding and manipulation of light. However, the progress in ST beam research has been constrained by the absence of a universal framework for their analysis and generation. Here, we introduce the concept of "two-dimensional ST duality", establishing a foundational duality between spatial-structured light and ST beams. We show that breaking the exact balance between paraxial diffraction and narrow-band dispersion is crucial for guiding the dynamics of ST wavepackets. Leveraging this insight, we pioneer a versatile complex-amplitude modulation strategy, enabling the precise crafting of ST beams with an exceptional fidelity exceeding 97%. Furthermore, we uncover a new range of ST wavepackets by harnessing the exact one-to-one relationship between scalar spatial-structured light and ST beams. Our findings suggest a paradigm shift opportunity in ST beam research and may apply to a broader range of wave physics systems.

physics.optics↗
arXiv2024

Adaptive Estimation for Non-stationary Factor Models And A Test for Static Factor Loadings

This paper considers the estimation and testing of a class of locally stationary time series factor models with evolutionary temporal dynamics. In particular, the entries and the dimension of the factor loading matrix are allowed to vary with time while the factors and the idiosyncratic noise components are locally stationary. We propose an adaptive sieve estimator for the span of the varying loading matrix and the locally stationary factor processes. A uniformly consistent estimator of the effective number of factors is investigated via eigenanalysis of a non-negative definite time-varying matrix. A possibly high-dimensional bootstrap-assisted test for the hypothesis of static factor loadings is proposed by comparing the kernels of the covariance matrices of the whole time series with their local counterparts. We examine our estimator and test via simulation studies and real data analysis. Finally, all our results hold at the following popular but distinct assumptions: (a) the white noise idiosyncratic errors with either fixed or diverging dimension, and (b) the correlated idiosyncratic errors with diverging dimension.

stat.ME↗
arXiv2024

On the partial autocorrelation function for locally stationary time series: characterization, estimation and inference

For stationary time series, it is common to use the plots of partial autocorrelation function (PACF) or PACF-based tests to explore the temporal dependence structure of such processes. To our best knowledge, such analogs for non-stationary time series have not been fully established yet. In this paper, we fill this gap for locally stationary time series with short-range dependence. First, we characterize the PACF locally in the time domain and show that the $j$th PACF, denoted as $ρ_{j}(t),$ decays with $j$ whose rate is adaptive to the temporal dependence of the time series $\{x_{i,n}\}$. Second, at time $i,$ we justify that the PACF $ρ_j(i/n)$ can be efficiently approximated by the best linear prediction coefficients via the Yule-Walker's equations. This allows us to study the PACF via ordinary least squares (OLS) locally. Third, we show that the PACF is smooth in time for locally stationary time series. We use the sieve method with OLS to estimate $ρ_j(\cdot)$ and construct some statistics to test the PACFs and infer the structures of the time series. These tests generalize and modify those used for stationary time series. Finally, a multiplier bootstrap algorithm is proposed for practical implementation and an $\mathtt R$ package $\mathtt {Sie2nts}$ is provided to implement our algorithm. Numerical simulations and real data analysis also confirm usefulness of our results.

math.ST↗
arXiv2023

Multiscale Jump Testing and Estimation Under Complex Temporal Dynamics

We consider the problem of detecting jumps in an otherwise smoothly evolving trend whilst the covariance and higher-order structures of the system can experience both smooth and abrupt changes over time. The number of jump points is allowed to diverge to infinity with the jump sizes possibly shrinking to zero. The method is based on a multiscale application of an optimal jump-pass filter to the time series, where the scales are dense between admissible lower and upper bounds. For a wide class of non-stationary time series models and trend functions, the proposed method is shown to be able to detect all jump points within a nearly optimal range with a prescribed probability asymptotically under mild conditions. For a time series of length $n$, the computational complexity of the proposed method is $O(n)$ for each scale and $O(n\log^{1+ε} n)$ overall, where $ε$ is an arbitrarily small positive constant. Numerical studies show that the proposed jump testing and estimation method performs robustly and accurately under complex temporal dynamics.

stat.ME↗
arXiv2023

Superhedging duality for multi-action options under model uncertainty with information delay

We consider the superhedging price of an exotic option under nondominated model uncertainty in discrete time in which the option buyer chooses some action from an (uncountable) action space at each time step. By introducing an enlarged space we reformulate the superhedging problem for such an exotic option as a problem for a European option, which enables us to prove the pricing-hedging duality. Next, we present a duality result that, when the option buyers action is observed by the seller up to $l$ periods later, the superhedging price equals the model-based price where the option buyer has the power to look into the future for $l$ periods.

q-fin.MF↗
arXiv2023

Asymptotic analysis of synchrosqueezing transform -- toward statistical inference with nonlinear-type time-frequency analysis

We provide a statistical analysis of a tool in nonlinear-type time-frequency analysis, the synchrosqueezing transform (SST), for both the null and non-null cases. The intricate nonlinear interaction of different quantities in SST is quantified by carefully analyzing relevant multivariate complex Gaussian random variables. Specifically, we provide the quotient distribution of dependent and improper complex Gaussian random variables. Then, a central limit theorem result for SST is established. {As an example}, we provide a block bootstrap scheme based on the established SST theory to test if a given time series contains oscillatory components.

math.ST↗
arXiv2023

Spectral lens enables a minimalist framework for hyperspectral imaging

Conventional lens-based imaging techniques have long been limited to capturing only the intensity distribution of objects, resulting in the loss of other crucial dimensions such as spectral data. Here, we report a spectral lens that captures both spatial and spectral information, and further demonstrate a minimalist framework wherein hyperspectral imaging can be readily achieved by replacing lenses in standard cameras with our spectral lens. As a paradigm, we capitalize on planar liquid crystal optics to implement the proposed framework. Our experiments with various targets show that the resulting hyperspectral camera exhibits excellent performance in both spectral and spatial domains. With merits such as ultra-compactness and strong compatibility, our framework paves a practical pathway for advancing hyperspectral imaging apparatus toward miniaturization, with great potential for portable applications.

physics.optics↗
arXiv2023

Frequency Detection and Change Point Estimation for Time Series of Complex Oscillation

We consider detecting the evolutionary oscillatory pattern of a signal when it is contaminated by non-stationary noises with complexly time-varying data generating mechanism. A high-dimensional dense progressive periodogram test is proposed to accurately detect all oscillatory frequencies. A further phase-adjusted local change point detection algorithm is applied in the frequency domain to detect the locations at which the oscillatory pattern changes. Our method is shown to be able to detect all oscillatory frequencies and the corresponding change points within an accurate range with a prescribed probability asymptotically. This study is motivated by oscillatory frequency estimation and change point detection problems encountered in physiological time series analysis. An application to spindle detection and estimation in sleep EEG data is used to illustrate the usefulness of the proposed methodology. A Gaussian approximation scheme and an overlapping-block multiplier bootstrap methodology for sums of complex-valued high dimensional non-stationary time series without variance lower bounds are established, which could be of independent interest.

stat.ME↗
arXiv2023

Optimal Short-Term Forecast for Locally Stationary Functional Time Series

Accurate curve forecasting is of vital importance for policy planning, decision making and resource allocation in many engineering and industrial applications. In this paper we establish a theoretical foundation for the optimal short-term linear prediction of non-stationary functional or curve time series with smoothly time-varying data generating mechanisms. The core of this work is to establish a unified functional auto-regressive approximation result for a general class of locally stationary functional time series. A double sieve expansion method is proposed and theoretically verified for the asymptotic optimal forecasting. A telecommunication traffic data set is used to illustrate the usefulness of the proposed theory and methodology.

stat.ME↗
arXiv2023

Chloride-induced corrosion of steel in concrete -- insights from bimodal neutron and X-ray microtomography combined with ex-situ microscopy

The steel-concrete interface (SCI) is known to play a major role in corrosion of steel in concrete, but a fundamental understanding is still lacking. One reason is that concrete's opacity complicates the study of internal processes. Here, we report on the application of bimodal X-ray and neutron microtomography as in-situ imaging techniques to elucidate the mechanism of steel corrosion in concrete. The study demonstrates that the segmentation of the specimen components of relevance - steel, cementitious matrix, aggregates, voids, corrosion products - obtained through bimodal X-ray and neutron imaging is more reliable than that based on the results of each of the two techniques separately. Further, we suggest the combination of tomographic in-situ imaging with ex-situ SEM analysis of targeted sections, selected on the basis of the segmented tomograms. These in-situ and ex-situ characterization techniques were applied to study localized corrosion in a very early stage, on reinforced concrete cores retrieved from a concrete bridge. A number of interesting observations were made. First, the acquired images revealed the formation of several corrosion sites close to each other. Second, the morphology of the corrosion pits was relatively shallow. Finally, only about half of the total 31 corrosion initiation spots were in close proximity to interfacial macroscopic air voids, and above 90 percent of the more than 160 interfacial macroscopic air voids were free from corrosion. The findings have implications for the mechanistic understanding of corrosion of steel in concrete and suggest that multimodal in-situ imaging is a valuable technique for further related studies.

physics.app-ph↗
arXiv2023

Oxygen Octahedral Tilt Controlled Topological Hall Effect in Epitaxial and Freestanding SrRuO3/SrIrO3 Heterostructures

The fabrication technique of freestanding oxide flakes by epitaxial lift-off has made significant contributions to the multifunctional oxide thin film research. Several highly impactful work have recently demonstrated the robustness of freestanding oxide flakes retaining their desirable properties after detachment from the substrate by dissolving away the water-soluble Sr3Al2O6 buffer layer. Firstly, in epitaxial SrRuO3/SrIrO3 systems showing Hall-humps reminiescence of Topological Hall Effect, first-principle calculations revealed that octahedral tilts can modify the sign of Dzyaloshinskii-Moriya interaction and avoid cancellation in a symmetric trilayer structure. Secondly, freestanding flakes of the trilayer is also able to retain the Hall-humps across a wide temperature range. The behaviour of humps' peak field does not vary with field direction rotation away from surface normal, consistent to a micromagnetic simulation result of Néel-type magnetic Skyrmions. The layer-resolved octahedral tilts in the freestanding heterostructures also crucially controls the occurrence of Hall-humps, consistent to the insight from the epitaxial ones. This work offers a new perspective to understanding Hall-humps in perovskite oxides, as well as demonstrates the fabrication of oxide heterostructure membranes with high interfacial quality.

cond-mat.mtrl-sci↗
arXiv2023

Auto-Regressive Approximations to Non-stationary Time Series, with Inference and Applications

Understanding the time-varying structure of complex temporal systems is one of the main challenges of modern time series analysis. In this paper, we show that every uniformly-positive-definite-in-covariance and sufficiently short-range dependent non-stationary and nonlinear time series can be well approximated globally by a white-noise-driven auto-regressive (AR) process of slowly diverging order. To our best knowledge, it is the first time such a structural approximation result is established for general classes of non-stationary time series. A high dimensional $\mathcal{L}^2$ test and an associated multiplier bootstrap procedure are proposed for the inference of the AR approximation coefficients. In particular, an adaptive stability test is proposed to check whether the AR approximation coefficients are time-varying, a frequently-encountered question for practitioners and researchers of time series. As an application, globally optimal short-term forecasting theory and methodology for a wide class of locally stationary time series are established via the method of sieves.

math.ST↗
arXiv2023

Kinematic Analysis and Design of a Novel (6+3)-DoF Parallel Robot with Fixed Actuators

A novel kinematically redundant (6+3)-DoF parallel robot is presented in this paper. Three identical 3-DoF RU/2-RUS legs are attached to a configurable platform through spherical joints. With the selected leg mechanism, the motors are mounted at the base, reducing the reflected inertia. The robot is intended to be actuated with direct-drive motors in order to perform intuitive physical human-robot interaction. The design of the leg mechanism maximizes the workspace in which the end-effector of the leg can have a 2g acceleration in all directions. All singularities of the leg mechanism are identified under a simplifying assumption. A CAD model of the (6+3)-DoF robot is presented in order to illustrate the preliminary design of the robot.

cs.RO↗
arXiv2023

AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics

Recent advancements in language-image models have led to the development of highly realistic images that can be generated from textual descriptions. However, the increased visual quality of these generated images poses a potential threat to the field of media forensics. This paper aims to investigate the level of challenge that language-image generation models pose to media forensics. To achieve this, we propose a new approach that leverages the DALL-E2 language-image model to automatically generate and splice masked regions guided by a text prompt. To ensure the creation of realistic manipulations, we have designed an annotation platform with human checking to verify reasonable text prompts. This approach has resulted in the creation of a new image dataset called AutoSplice, containing 5,894 manipulated and authentic images. Specifically, we have generated a total of 3,621 images by locally or globally manipulating real-world image-caption pairs, which we believe will provide a valuable resource for developing generalized detection methods in this area. The dataset is evaluated under two media forensic tasks: forgery detection and localization. Our extensive experiments show that most media forensic models struggle to detect the AutoSplice dataset as an unseen manipulation. However, when fine-tuned models are used, they exhibit improved performance in both tasks.

cs.CV↗