arXiv subjects

Yufeng Liu

Publications and source records attributed to Yufeng Liu.

At least 37 records · Page 2Linked to original sources

arXiv2025

A Preliminary Exploration of the Differences and Conjunction of Traditional PNT and Brain-inspired PNT

Developing universal Positioning, Navigation, and Timing (PNT) is our enduring goal. Today's complex environments demand PNT that is more resilient, energy-efficient and cognitively capable. This paper asks how we can endow unmanned systems with brain-inspired spatial cognition navigation while exploiting the high precision of machine PNT to advance universal PNT. We provide a new perspective and roadmap for shifting PNT from "tool-oriented" to "cognition-driven". Contributions: (1) multi-level dissection of differences among traditional PNT, biological brain PNT and brain-inspired PNT; (2) a four-layer (observation-capability-decision-hardware) fusion framework that unites numerical precision and brain-inspired intelligence; (3) forward-looking recommendations for future development of brain-inspired PNT.

cs.RO↗

arXiv2025

Minimax Regret Learning for Data with Heterogeneous Subgroups

Modern complex datasets often consist of various sub-populations with known group information. In the presence of sub-population heterogeneity, it is crucial to develop robust and generalizable learning methods that (1) can enjoy robust performance on each of the training populations, and (2) is generalizable to an unseen testing population. While various min-max formulations have been proposed to achieve (1) in the robust learning literature, their generalization to an unseen testing is less explored. Moreover, a general min-max formulation can be sensitive to the noise heterogeneity, and, in the extreme case, be degenerate such that a single high-noise population dominates. The min-max-regret (MMR) can mitigate these challenges. In this work, we consider a distribution-free robust hierarchical model for the generalization from multiple training populations to an unseen testing population. Under the robust hierarchical model, the empirical MMR can enjoy the regret guarantees on each of the training populations as well as the unseen testing population. We further specialize the general MMR framework to linear regression and generalized linear model, where we characterize the geometry of MMR and its distinction from other robust methods. We demonstrate the effectiveness of MMR through extensive simulation studies and an application to image recognition.

stat.ME↗

arXiv2025

Do You Need Proprioceptive States in Visuomotor Policies?

Imitation-learning-based visuomotor policies have been widely used in robot manipulation, where both visual observations and proprioceptive states are typically adopted together for precise control. However, in this study, we find that this common practice makes the policy overly reliant on the proprioceptive state input, which causes overfitting to the training trajectories and results in poor spatial generalization. On the contrary, we propose the State-free Policy, removing the proprioceptive state input and predicting actions only conditioned on visual observations. The State-free Policy is built in the relative end-effector action space, and should ensure the full task-relevant visual observations, here provided by dual wide-angle wrist cameras. Empirical results demonstrate that the State-free policy achieves significantly stronger spatial generalization than the state-based policy: in real-world tasks such as pick-and-place, challenging shirt-folding, and complex whole-body manipulation, spanning multiple robot embodiments, the average success rate improves from 0% to 85% in height generalization and from 6% to 64% in horizontal generalization. Furthermore, they also show advantages in data efficiency and cross-embodiment adaptation, enhancing their practicality for real-world deployment. Discover more by visiting: https://statefreepolicy.github.io.

cs.RO↗

arXiv2025

Hub Detection in Gaussian Graphical Models

Graphical models are popular tools for exploring relationships among a set of variables. The Gaussian graphical model (GGM) is an important class of graphical models, where the conditional dependence among variables is represented by nodes and edges in a graph. In many real applications, we are interested in detecting hubs in graphical models, which refer to nodes with a significant higher degree of connectivity compared to non-hub nodes. A typical strategy for hub detection consists of estimating the graphical model, and then using the estimated graph to identify hubs. Despite its simplicity, the success of this strategy relies on the accuracy of the estimated graph. In this paper, we directly target on the estimation of hubs, without the need of estimating the graph. We establish a novel connection between the presence of hubs in a graphical model, and the spectral decomposition of the underlying covariance matrix. Based on this connection, we propose the method of inverse principal components for hub detection (IPC-HD). Both consistency and convergence rates are established for IPC-HD. Our simulation study demonstrates the superior performance and fast computation of the proposed method compared to existing methods in the literature in terms of hub detection. Our application to a prostate cancer gene expression dataset detects several hub genes with close connections to tumor development.

stat.ME↗

arXiv2025

Imaging moiré flat bands and Wigner molecular crystals in twisted bilayer MoTe2

Two-dimensional semiconducting moiré materials have emerged as a highly tunable platform for exploring novel quantum phenomena. Recently, tMoTe2 has attracted significant attentions due to the observation of the long-sought fractional quantum anomalous Hall effect. However, a comprehensive microscopic understanding of the tMoTe2 moiré superlattice remains elusive. Here, we report STM/STS studies in dual-gated tMoTe2 moiré devices with twist angles ranging from 2.3 to 3.8 deg. The device consists of two independent back-gates, one enables an ohmic contact for tMoTe2, while the other fine-tunes the Fermi level of tMoTe2. This dual-gate control enables direct measurement of the electronic structure in tMoTe2 under varied displacement fields and moiré filling factors, by fine tuning the gate voltage and the tip bias. Our STS spectra and spatial imaging reveal that the low-energy moiré flat bands are predominantly localized in the XM and MX regions of the moiré superlattice. At zero E-field, these bands form a honeycomb lattice with non-trivial topology, whereas an applied E-field drives a transition into two distinct triangular lattices with trivial topology. The spatial distributions align with large-scale first-principle calculations, demonstrating that the topological flat bands arise from the K-valley hybridization between the top and bottom MoTe2 layers. Furthermore, we show that the effective moiré potential depth can be controlled via gate and tip biases. At sufficient potential depths, we observe the emergence of Wigner molecular crystals, transitioning MX triangular lattice into a Kagome lattice at MX moiré filling factor 3. These results elucidate the microscopic origin of topological flat bands in tMoTe2 and demonstrate electric-field control of topology and correlated electronic orders, paving the way to engineer exotic quantum phases in moiré simulators.

cond-mat.mes-hall↗

arXiv2025

Experimental Measurement-Device-Independent Quantum Cryptographic Conferencing

Quantum cryptographic conferencing (QCC) allows sharing secret keys among multiple distant users and plays a crucial role in quantum networks. Because of the fragility and low generation rate of genuine multipartite entangled states required in QCC, realizing and extending QCC with the entanglement-based protocol is challenging. Measurement-device-independent (MDI) QCC, which removes all detector side channels, is a feasible long-distance quantum communication scheme to practically generate multipartite correlation with multiphoton projection measurement. Here we experimentally realize the three-user MDIQCC protocol with four-intensity decoy-state method, in which we employ the polarization encoding and the Greenberger-Horne-Zeilinger state projection measurement. Our work demonstrates the experimental feasibility of the MDI QCC, which lays the foundation for the future realization of quantum networks with multipartite communication tasks.

quant-ph↗

arXiv2025

Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection

Dynamic treatment regimes or policies are a sequence of decision functions over multiple stages that are tailored to individual features. One important class of treatment policies in practice, namely multi-stage stationary treatment policies, prescribes treatment assignment probabilities using the same decision function across stages, where the decision is based on the same set of features consisting of time-evolving variables (e.g., routinely collected disease biomarkers). Although there has been extensive literature on constructing valid inference for the value function associated with dynamic treatment policies, little work has focused on the policies themselves, especially in the presence of high-dimensional feature variables. We aim to fill the gap in this work. Specifically, we first estimate the multi-stage stationary treatment policy using an augmented inverse probability weighted estimator for the value function to increase asymptotic efficiency, and further apply a penalty to select important feature variables. We then construct one-step improvements of the policy parameter estimators for valid inference. Theoretically, we show that the improved estimators are asymptotically normal, even if nuisance parameters are estimated at a slow convergence rate and the dimension of the feature variables increases with the sample size. Our numerical studies demonstrate that the proposed method estimates a sparse policy with a near-optimal value function and conducts valid inference for the policy parameters.

stat.ML↗

arXiv2024

Real-space study of zero-field correlation in tetralayer rhombohedral graphene

Rhombohedral graphene (RG) has emerged as a promising platform for exploring exotic quantum phenomena, such as quantum magnetism, unconventional superconductivity, and fractional quantum anomalous Hall effects. Despite its potential, atomic-scale investigations of RG remain limited, hindering a detailed microscopic understanding of the origins of these correlated states. In this study, we employ scanning probe microscopy and spectroscopy to probe the intrinsic electronic states in trilayer and tetralayer RG. We identify a correlated insulating state with a 17 meV gap at the charge neutrality point in tetralayer RG, which is absent in the trilayer configuration. This gap is suppressed by applying a perpendicular magnetic field or doping the charge carrier density and does not exhibit inter-valley coherence patterns. We attribute this phenomenon to a symmetry-broken layer antiferromagnetic state, characterized by ferrimagnetic ordering in the outermost layers and antiferromagnetic coupling between them. To further investigate this magnetic correlated state, we conduct local scattering experiments. Within the correlated regime, a bound state emerges around a non-magnetic impurity but is absent near magnetic impurities, suggesting that non-magnetic doping induces a spin texture in the ferrimagnetic surface layers. Outside the correlated regime, Friedel oscillations are observed, allowing precise determination of the band dispersion in tetralayer RG. These findings provide atomic-scale evidences of zero-field correlations in RG and may be extended to study other exotic phases in RG.

cond-mat.mes-hall↗

arXiv2024

Coupling of Electronic Transitions to Ferroelectric Order in a 2D Semiconductor

A ferroelectric material often exhibits a soft transvers optical (TO) phonon mode which governs it phase transition. Charge coupling to this ferroelectric soft mode may further mediate emergent physical properties, including superconductivity and defect tolerance. However, direct experimental evidence for such coupling is scarce. Here we show that a photo-launched coherent phonon couples strongly to electronic transitions across the bandgap in the van der Waals (vdW) two-dimensional (2D) ferroelectric semiconductor NbOI2. Using terahertz time-domain spectroscopy and first-principles calculations, we identify this mode as the TO phonon responsible for ferroelectric order. This exclusive coupling occurs only with above-gap electronic transition and is absent in the valence band as revealed by resonant inelastic X-ray scattering. Our findings suggest a new role of the soft TO phonon mode in electronic and optical properties of ferroelectric semiconductors.

cond-mat.mtrl-sci↗

arXiv2024

Atomic-Scale Imaging of Fractional Spinon Quasiparticles in Open-Shell Triangulene Spin-$\frac{1}{2}$ Chains

The emergence of spinon quasiparticles, which carry spin but lack charge, is a hallmark of collective quantum phenomena in low-dimensional quantum spin systems. While the existence of spinons has been demonstrated through scattering spectroscopy in ensemble samples, real-space imaging of these quasiparticles within individual spin chains has remained elusive. In this study, we construct individual Heisenberg antiferromagnetic spin-$\frac{1}{2}$ chains using open-shell [2]triangulene molecules as building blocks. Each [2]triangulene unit, owing to its sublattice imbalance, hosts a net spin-$\frac{1}{2}$ in accordance with Lieb's theorem, and these spins are antiferromagnetically coupled within covalent chains with a coupling strength of $J = 45$ meV. Through scanning tunneling microscopy and spectroscopy, we probe the spin states, excitation gaps, and their spatial excitation weights within covalent spin chains of varying lengths with atomic precision. Our investigation reveals that the excitation gap decreases as the chain length increases, extrapolating to zero for long chains, consistent with Haldane's gapless prediction. Moreover, inelastic tunneling spectroscopy reveals an m-shaped energy dispersion characteristic of confined spinon quasiparticles in a one-dimensional quantum box. These findings establish a promising strategy for exploring the unique properties of excitation quasiparticles and their broad implications for quantum information.

cond-mat.mtrl-sci↗

arXiv2024

Artificially built Kondo chains with organic radicals on metallic surfaces: new model system of heavy fermion quantum criticality

Heavy fermion quantum criticality is an extremely rich domain of research which represents a framework to understand strange metals as a consequence of a Kondo breakdown transition. Here we provide an experimental realization of such systems in terms of organic radicals on a metallic surface. The ground state of organic radicals is a Kramer's doublet that can be modeled by a spin 1/2 degree of freedom. Using on-surface synthesis and scanning tunneling microscopy (STM) tip manipulation, one can controllably engineer and characterize chains of organic radicals on a Au(111) surface. The spatial-resolved differential conductance reveals site-dependent low-energy excitations, which support the picture of emergent many-body Kondo physics. Using quantum Monte Carlo simulations, we show that a Kondo lattice model of spin chains on a metallic surface reproduces accurately the experimental results. This allows us to interpret the experimental results in terms of a heavy fermion metal, below the coherence temperature. We foresee that the tunability of these systems will pave the way to realize quantum simulators of heavy fermion criticality.

cond-mat.mes-hall↗

arXiv2024

Change Point Detection for High-dimensional Linear Models: A General Tail-adaptive Approach

We propose a novel approach for detecting change points in high-dimensional linear regression models. Unlike previous research that relied on strict Gaussian/sub-Gaussian error assumptions and had prior knowledge of change points, we propose a tail-adaptive method for change point detection and estimation. We use a weighted combination of composite quantile and least squared losses to build a new loss function, allowing us to leverage information from both conditional means and quantiles. For change point testing, we develop a family of individual testing statistics with different weights to account for unknown tail structures. These individual tests are further aggregated to construct a powerful tail-adaptive test for sparse regression coefficient changes. For change point estimation, we propose a family of argmax-based individual estimators. We provide theoretical justifications for the validity of these tests and change point estimators. Additionally, we introduce a new algorithm for detecting multiple change points in a tail-adaptive manner using the wild binary segmentation. Extensive numerical results show the effectiveness of our method. Lastly, an R package called ``TailAdaptiveCpt" is developed to implement our algorithms.

stat.ME↗

arXiv2024

Simultaneous Change Point Detection and Identification for High Dimensional Linear Models

In this article, we consider change point inference for high dimensional linear models. For change point detection, given any subgroup of variables, we propose a new method for testing the homogeneity of corresponding regression coefficients across the observations. Under some regularity conditions, the proposed new testing procedure controls the type I error asymptotically and is powerful against sparse alternatives and enjoys certain optimality. For change point identification, an argmax based change point estimator is proposed which is shown to be consistent for the true change point location. Moreover, combining with the binary segmentation technique, we further extend our new method for detecting and identifying multiple change points. Extensive numerical studies justify the validity of our new method and an application to the Alzheimer's disease data analysis further demonstrate its competitive performance.

stat.ME↗

arXiv2023

Mutual-Learning Knowledge Distillation for Nighttime UAV Tracking

Nighttime unmanned aerial vehicle (UAV) tracking has been facilitated with indispensable plug-and-play low-light enhancers. However, the introduction of low-light enhancers increases the extra computational burden for the UAV, significantly hindering the development of real-time UAV applications. Meanwhile, these state-of-the-art (SOTA) enhancers lack tight coupling with the advanced daytime UAV tracking approach. To solve the above issues, this work proposes a novel mutual-learning knowledge distillation framework for nighttime UAV tracking, i.e., MLKD. This framework is constructed to learn a compact and fast nighttime tracker via knowledge transferring from the teacher and knowledge sharing among various students. Specifically, an advanced teacher based on a SOTA enhancer and a superior tracking backbone is adopted for guiding the student based only on the tight coupling-aware tracking backbone to directly extract nighttime object features. To address the biased learning of a single student, diverse lightweight students with different distillation methods are constructed to focus on various aspects of the teacher's knowledge. Moreover, an innovative mutual-learning room is designed to elect the superior student candidate to assist the remaining students frame-by-frame in the training phase. Furthermore, the final best student, i.e., MLKD-Track, is selected through the testing dataset. Extensive experiments demonstrate the effectiveness and superiority of MLKD and MLKD-Track. The practicality of the MLKD-Track is verified in real-world tests with different challenging situations. The code is available at https://github.com/lyfeng001/MLKD.

cs.CV↗

arXiv2023

A Scalable Arrangement Method for Aperiodic Array Antennas to Reduce Peak Sidelobe Level

Peak sidelobe level reduction (PSLR) is crucial in the application of large-scale array antenna, which directly determines the radiation performance of array antenna. We study the PSLR of subarray level aperiodic arrays and propose three array structures: dislocated subarrays with uniform elements (DSUE), uniform subarrays with random elements (USRE), dislocated subarrays with random elements (DSRE). To optimize the dislocation position of subarrays and random position of elements, the improved Bat algorithm (IBA) is applied. To draw the comparison of PSLR effect among these three array structures, we take three size of array antennas from small to large as examples to simulate and calculate the redundancy and peak sidelobe level (PSLL) of them. The results show that DSRE is the optimal array structure by analyzing the dislocation distance of subarray, scanning angle and applicable frequency. The proposed design method is a universal and scalable method, which is of great application value to the design of large-scale aperiodic array antenna.

eess.SP↗

arXiv2023

Robust Low-rank Tensor Decomposition with the $\operatorname{L_2}$ Criterion

The growing prevalence of tensor data, or multiway arrays, in science and engineering applications motivates the need for tensor decompositions that are robust against outliers. In this paper, we present a robust Tucker decomposition estimator based on the $\operatorname{L_2}$ criterion, called the Tucker-$\operatorname{L_2E}$. Our numerical experiments demonstrate that Tucker-$\operatorname{L_2E}$ has empirically stronger recovery performance in more challenging high-rank scenarios compared with existing alternatives. The appropriate Tucker-rank can be selected in a data-driven manner with cross-validation or hold-out validation. The practical effectiveness of Tucker-$\operatorname{L_2E}$ is validated on real data applications in fMRI tensor denoising, PARAFAC analysis of fluorescence data, and feature extraction for classification of corrupted images.

stat.ME↗

arXiv2022

Edge-based Monocular Thermal-Inertial Odometry in Visually Degraded Environments

State estimation in complex illumination environments based on conventional visual-inertial odometry is a challenging task due to the severe visual degradation of the visual camera. The thermal infrared camera is capable of all-day time and is less affected by illumination variation. However, most existing visual data association algorithms are incompatible because the thermal infrared data contains large noise and low contrast. Motivated by the phenomenon that thermal radiation varies most significantly at the edges of objects, the study proposes an ETIO, which is the first edge-based monocular thermal-inertial odometry for robust localization in visually degraded environments. Instead of the raw image, we utilize the binarized image from edge extraction for pose estimation to overcome the poor thermal infrared image quality. Then, an adaptive feature tracking strategy ADT-KLT is developed for robust data association based on limited edge information and its distance distribution. Finally, a pose graph optimization performs real-time estimation over a sliding window of recent states by combining IMU pre-integration with reprojection error of all edge feature observations. We evaluated the performance of the proposed system on public datasets and real-world experiments and compared it against state-of-the-art methods. The proposed ETIO was verified with the ability to enable accurate and robust localization all-day time.

cs.RO↗

arXiv2022

Joint and Individual Component Regression

Multi-group data are commonly seen in practice. Such data structure consists of data from multiple groups and can be challenging to analyze due to data heterogeneity. We propose a novel Joint and Individual Component Regression (JICO) model to analyze multi-group data. In particular, our proposed model decomposes the response into shared and group-specific components, which are driven by low-rank approximations of joint and individual structures from the predictors respectively. The joint structure has the same regression coefficients across multiple groups, whereas individual structures have group-specific regression coefficients. Moreover, the choice of global and individual ranks allows our model to cover global and group-specific models as special cases. For model estimation, we formulate this framework under the representation of latent components and propose an iterative algorithm to solve for the joint and individual scores under the new representation. To construct the latent scores, we utilize the Continuum Regression (CR), which provides a unified framework that covers the Ordinary Least Squares (OLS), the Partial Least Squares (PLS), and the Principal Component Regression (PCR) as its special cases. We show that JICO attains a good balance between global and group-specific models and remains flexible due to the usage of CR. Finally, we conduct simulation studies and analysis of an Alzheimer's disease dataset to further demonstrate the effectiveness of JICO. R implementation of JICO is available online at https://github.com/peiyaow/JICO.

stat.ME↗