arXiv subjects

Shan Lin

Publications and source records attributed to Shan Lin.

At least 55 records · Page 3Linked to original sources

arXiv2023

AnyOKP: One-Shot and Instance-Aware Object Keypoint Extraction with Pretrained ViT

Towards flexible object-centric visual perception, we propose a one-shot instance-aware object keypoint (OKP) extraction approach, AnyOKP, which leverages the powerful representation ability of pretrained vision transformer (ViT), and can obtain keypoints on multiple object instances of arbitrary category after learning from a support image. An off-the-shelf petrained ViT is directly deployed for generalizable and transferable feature extraction, which is followed by training-free feature enhancement. The best-prototype pairs (BPPs) are searched for in support and query images based on appearance similarity, to yield instance-unaware candidate keypoints.Then, the entire graph with all candidate keypoints as vertices are divided to sub-graphs according to the feature distributions on the graph edges. Finally, each sub-graph represents an object instance. AnyOKP is evaluated on real object images collected with the cameras of a robot arm, a mobile robot, and a surgical robot, which not only demonstrates the cross-category flexibility and instance awareness, but also show remarkable robustness to domain shift and viewpoint change.

cs.CV↗

arXiv2023

ORRN: An ODE-based Recursive Registration Network for Deformable Respiratory Motion Estimation with Lung 4DCT Images

Deformable Image Registration (DIR) plays a significant role in quantifying deformation in medical data. Recent Deep Learning methods have shown promising accuracy and speedup for registering a pair of medical images. However, in 4D (3D + time) medical data, organ motion, such as respiratory motion and heart beating, can not be effectively modeled by pair-wise methods as they were optimized for image pairs but did not consider the organ motion patterns necessary when considering 4D data. This paper presents ORRN, an Ordinary Differential Equations (ODE)-based recursive image registration network. Our network learns to estimate time-varying voxel velocities for an ODE that models deformation in 4D image data. It adopts a recursive registration strategy to progressively estimate a deformation field through ODE integration of voxel velocities. We evaluate the proposed method on two publicly available lung 4DCT datasets, DIRLab and CREATIS, for two tasks: 1) registering all images to the extreme inhale image for 3D+t deformation tracking and 2) registering extreme exhale to inhale phase images. Our method outperforms other learning-based methods in both tasks, producing the smallest Target Registration Error of 1.24mm and 1.26mm, respectively. Additionally, it produces less than 0.001\% unrealistic image folding, and the computation speed is less than 1 second for each CT volume. ORRN demonstrates promising registration accuracy, deformation plausibility, and computation efficiency on group-wise and pair-wise registration tasks. It has significant implications in enabling fast and accurate respiratory motion estimation for treatment planning in radiation therapy or robot motion planning in thoracic needle insertion.

eess.IV↗

arXiv2023

An Ensemble Learning Approach for Exercise Detection in Type 1 Diabetes Patients

Type 1 diabetes is a serious disease in which individuals are unable to regulate their blood glucose levels, leading to various medical complications. Artificial pancreas (AP) systems have been developed as a solution for type 1 diabetic patients to mimic the behavior of the pancreas and regulate blood glucose levels. However, current AP systems lack detection capabilities for exercise-induced glucose intake, which can last up to 4 to 8 hours. This incapability can lead to hypoglycemia, which if left untreated, could have serious consequences, including death. Existing exercise detection methods are either limited to single sensor data or use inaccurate models for exercise detection, making them less effective in practice. In this work, we propose an ensemble learning framework that combines a data-driven physiological model and a Siamese network to leverage multiple physiological signal streams for exercise detection with high accuracy. To evaluate the effectiveness of our proposed approach, we utilized a public dataset with 12 diabetic patients collected from an 8-week clinical trial. Our approach achieves a true positive rate for exercise detection of 86.4% and a true negative rate of 99.1%, outperforming state-of-the-art solutions.

eess.SP↗

arXiv2023

SemHint-MD: Learning from Noisy Semantic Labels for Self-Supervised Monocular Depth Estimation

Without ground truth supervision, self-supervised depth estimation can be trapped in a local minimum due to the gradient-locality issue of the photometric loss. In this paper, we present a framework to enhance depth by leveraging semantic segmentation to guide the network to jump out of the local minimum. Prior works have proposed to share encoders between these two tasks or explicitly align them based on priors like the consistency between edges in the depth and segmentation maps. Yet, these methods usually require ground truth or high-quality pseudo labels, which may not be easily accessible in real-world applications. In contrast, we investigate self-supervised depth estimation along with a segmentation branch that is supervised with noisy labels provided by models pre-trained with limited data. We extend parameter sharing from the encoder to the decoder and study the influence of different numbers of shared decoder parameters on model performance. Also, we propose to use cross-task information to refine current depth and segmentation predictions to generate pseudo-depth and semantic labels for training. The advantages of the proposed method are demonstrated through extensive experiments on the KITTI benchmark and a downstream task for endoscopic tissue deformation tracking.

cs.CV↗

arXiv2023

Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking

Accurate and robust tracking and reconstruction of the surgical scene is a critical enabling technology toward autonomous robotic surgery. Existing algorithms for 3D perception in surgery mainly rely on geometric information, while we propose to also leverage semantic information inferred from the endoscopic video using image segmentation algorithms. In this paper, we present a novel, comprehensive surgical perception framework, Semantic-SuPer, that integrates geometric and semantic information to facilitate data association, 3D reconstruction, and tracking of endoscopic scenes, benefiting downstream tasks like surgical navigation. The proposed framework is demonstrated on challenging endoscopic data with deforming tissue, showing its advantages over our baseline and several other state-of the-art approaches. Our code and dataset are available at https://github.com/ucsdarclab/Python-SuPer.

eess.IV↗

arXiv2022

CarFi: Rider Localization Using Wi-Fi CSI

With the rise of hailing services, people are increasingly relying on shared mobility (e.g., Uber, Lyft) drivers to pick up for transportation. However, such drivers and riders have difficulties finding each other in urban areas as GPS signals get blocked by skyscrapers, in crowded environments (e.g., in stadiums, airports, and bars), at night, and in bad weather. It wastes their time, creates a bad user experience, and causes more CO2 emissions due to idle driving. In this work, we explore the potential of Wi-Fi to help drivers to determine the street side of the riders. Our proposed system is called CarFi that uses Wi-Fi CSI from two antennas placed inside a moving vehicle and a data-driven technique to determine the street side of the rider. By collecting real-world data in realistic and challenging settings by blocking the signal with other people and other parked cars, we see that CarFi is 95.44% accurate in rider-side determination in both line of sight (LoS) and non-line of sight (nLoS) conditions, and can be run on an embedded GPU in real-time.

cs.NI↗

arXiv2022

Synthesis of functional nitride membranes using sacrificial water-soluble BaO layers

Transition metal nitrides (TMNs) exhibit fascinating physical properties that hold great potential in future device applications. To stack two-dimensional TMNs with other functional materials that have dissimilar orientations and symmetries requires to separate epitaxial TMNs from the growth substrates. However, the lattice constants of TMNs are not compatible with those of most sacrificial layers, leading to a great challenge to fabricate high-quality single crystalline TMN membranes. In this letter, we report the application of a water-soluble BaO sacrificial layer as a general approach to create freestanding TMN membranes. Taken CrN as an example, the relatively small lattice mismatch and identical cubic structure between BaO and CrN ensure the growth of heterostructures. Millimeter-size CrN membrane allows us to directly observe the planar-view of atomic structure and to correlate its electronic state with intrinsic transport properties. Our work provides the opportunity to fabricate freestanding TMN membranes and the ability to transfer them to arbitrary substrates. The integration of TMN membranes with other materials will stimulate further studies in the emergent phenomena at heterointerfaces.

cond-mat.mtrl-sci↗

arXiv2022

An STL-based Approach to Resilient Control for Cyber-Physical Systems

We present ResilienC, a framework for resilient control of Cyber-Physical Systems subject to STL-based requirements. ResilienC utilizes a recently developed formalism for specifying CPS resiliency in terms of sets of $(\mathit{rec},\mathit{dur})$ real-valued pairs, where $\mathit{rec}$ represents the system's capability to rapidly recover from a property violation (recoverability), and $\mathit{dur}$ is reflective of its ability to avoid violations post-recovery (durability). We define the resilient STL control problem as one of multi-objective optimization, where the recoverability and durability of the desired STL specification are maximized. When neither objective is prioritized over the other, the solution to the problem is a set of Pareto-optimal system trajectories. We present a precise solution method to the resilient STL control problem using a mixed-integer linear programming encoding and an a posteriori $ε$-constraint approach for efficiently retrieving the complete set of optimally resilient solutions. In ResilienC, at each time-step, the optimal control action selected from the set of Pareto-optimal solutions by a Decision Maker strategy realizes a form of Model Predictive Control. We demonstrate the practical utility of the ResilienC framework on two significant case studies: autonomous vehicle lane keeping and deadline-driven, multi-region package delivery.

eess.SY↗

arXiv2022

Emergent magnetic states and tunable exchange bias at all 3d nitride heterointerfaces

Interfacial magnetism stimulates the discovery of giant magnetoresistance and spin-orbital coupling across the heterointerfaces, facilitating the intimate correlation between spin transport and complex magnetic structures. Over decades, functional heterointerfaces composed of nitrides are seldomly explored due to the difficulty in synthesizing high-quality and correct composition nitride films. Here we report the fabrication of single-crystalline ferromagnetic Fe3N thin films with precisely controlled thickness. As film thickness decreasing, the magnetization deteriorates dramatically, and electronic state transits from metallic to insulating. Strikingly, the high-temperature ferromagnetism maintains in a Fe3N layer with a thickness down to 2 u. c. (~ 8 Å). The magnetoresistance exhibits a strong in-plane anisotropy and meanwhile the anomalous Hall resistance reserves its sign when Fe3N layer thickness exceeds 5 u. c. Furthermore, we observe a sizable exchange bias at the interfaces between a ferromagnetic Fe3N and an antiferromagnetic CrN. The exchange bias field and saturation moment strongly depend on the controllable bending curvature using cylinder diameter engineering (CDE) technique, implying the tunable magnetic states under lattice deformation. This work provides a guideline for exploring functional nitride films and applying their interfacial phenomena for innovative perspectives towards the practical applications.

cond-mat.mtrl-sci↗

arXiv2022

An STL-based Formulation of Resilience in Cyber-Physical Systems

Resiliency is the ability to quickly recover from a violation and avoid future violations for as long as possible. Such a property is of fundamental importance for Cyber-Physical Systems (CPS), and yet, to date, there is no widely agreed-upon formal treatment of CPS resiliency. We present an STL-based framework for reasoning about resiliency in CPS in which resiliency has a syntactic characterization in the form of an STL-based Resiliency Specification (SRS). Given an arbitrary STL formula $φ$, time bounds $α$ and $β$, the SRS of $φ$, $R_{α,β}(φ)$, is the STL formula $\negφ\mathbf{U}_{[0,α]}\mathbf{G}_{[0,β)}φ$, specifying that recovery from a violation of $φ$ occur within time $α$ (recoverability), and subsequently that $φ$ be maintained for duration $β$ (durability). These $R$-expressions, which are atoms in our SRS logic, can be combined using STL operators, allowing one to express composite resiliency specifications, e.g., multiple SRSs must hold simultaneously, or the system must eventually be resilient. We define a quantitative semantics for SRSs in the form of a Resilience Satisfaction Value (ReSV) function $r$ and prove its soundness and completeness w.r.t. STL's Boolean semantics. The $r$-value for $R_{α,β}(φ)$ atoms is a singleton set containing a pair quantifying recoverability and durability. The $r$-value for a composite SRS formula results in a set of non-dominated recoverability-durability pairs, given that the ReSVs of subformulas might not be directly comparable (e.g., one subformula has superior durability but worse recoverability than another). To the best of our knowledge, this is the first multi-dimensional quantitative semantics for an STL-based logic. Two case studies demonstrate the practical utility of our approach.

cs.LO↗

arXiv2022

Braiding lateral morphotropic grain boundary in homogeneitic oxides

Interfaces formed by correlated oxides offer a critical avenue for discovering emergent phenomena and quantum states. However, the fabrication of oxide interfaces with variable crystallographic orientations and strain states integrated along a film plane is extremely challenge by conventional layer-by-layer stacking or self-assembling. Here, we report the creation of morphotropic grain boundaries (GBs) in laterally interconnected cobaltite homostructures. Single-crystalline substrates and suspended ultrathin freestanding membranes provide independent templates for coherent epitaxy and constraint on the growth orientation, resulting in seamless and atomically sharp GBs. Electronic states and magnetic behavior in hybrid structures are laterally modulated and isolated by GBs, enabling artificially engineered functionalities in the planar matrix. Our work offers a simple and scalable method for fabricating unprecedented innovative interfaces through controlled synthesis routes as well as provides a platform for exploring potential applications in neuromorphics, solid state batteries, and catalysis.

cond-mat.mtrl-sci↗

arXiv2022

Atomically engineered cobaltite layers for robust ferromagnetism

Emergent phenomena at heterointerfaces are directly associated with the bonding geometry of adjacent layers. Effective control of accessible parameters, such as the bond length and bonding angles, offers an elegant method to tailor competing energies of the electronic and magnetic ground states. In this study, we construct unit thick syntactic layers of cobaltites within a strongly tilted octahedral matrix via atomically precise synthesis. The octahedral tilt patterns of adjacent layers propagate into cobaltites, leading to a continuation of octahedral tilting while maintaining significant misfit tensile strain. These effects induce severe rumpling within an atomic plane of neighboring layers triggers the electronic reconstruction between the splitting orbitals. First-principles calculations reveal that the cobalt ions transits to a higher spin state level upon octahedral tilting, resulting in robust ferromagnetism in ultrathin cobaltites. This work demonstrates a design methodology for fine-tuning the lattice and spin degrees of freedom in correlated quantum heterostructures by exploiting epitaxial geometric engineering.

cond-mat.mtrl-sci↗

arXiv2022

Asymmetric Ground States in La$_{0.67}$Sr$_{0.33}$MnO$_3$/BaTiO$_3$ heterostructures Induced by Flexoelectric Bending

Misfit strain delivered from single-crystal substrates typically modifies the ground states of transition metal oxides, generating increasing interests in designing modern transducers and sensors. Here, we demonstrate that magnetotransport properties of La$_{0.67}$Sr$_{0.33}$MnO$_3$ (LSMO) films were continuously tuned by uniaxial strain produced by a home-designed bending jig. The electrical conductivity and Curie temperature of LSMO films are enhanced by bending stresses. The resistivity of a u-shape bended LSMO decays three times faster than that of a n-shape bended LSMO as a response to the same magnitude of strain. The asymmetric magnetic states in uniaxially strained LSMO are attributed to the dual actions of Jahn-Teller distortion and strain gradient mediated flexoelectric fields in an adjacent ferroelectric layer. These findings of multi-field regulation in a single material provide a feasible means for developing flexible electronic and spintronic devices.

cond-mat.mtrl-sci↗

arXiv2022

Adversarial Pairwise Reverse Attention for Camera Performance Imbalance in Person Re-identification: New Dataset and Metrics

Existing evaluation metrics for Person Re-Identification (Person ReID) models focus on system-wide performance. However, our studies reveal weaknesses due to the uneven data distributions among cameras and different camera properties that expose the ReID system to exploitation. In this work, we raise the long-ignored ReID problem of camera performance imbalance and collect a real-world privacy-aware dataset from 38 cameras to assist the study of the imbalance issue. We propose new metrics to quantify camera performance imbalance and further propose the Adversarial Pairwise Reverse Attention (APRA) Module to guide the model learning the camera invariant feature with a novel pairwise attention inversion mechanism.

cs.CV↗

arXiv2021

Room-temperature ferromagnetism at an oxide/nitride interface

Heterointerfaces have led to the discovery of novel electronic and magnetic states because of their strongly entangled electronic degrees of freedom. Single-phase chromium compounds always exhibit antiferromagnetism following the prediction of Goodenough-Kanamori rules. So far, exchange coupling between chromium ions via hetero-anions has not been explored and the associated quantum states is unknown. Here we report the successful epitaxial synthesis and characterizations of chromium oxide (Cr2O3)-chromium nitride (CrN) superlattices. Room-temperature ferromagnetic spin ordering is achieved at the interfaces between these two antiferromagnets, and the magnitude of the effect decays with increasing layer thickness. First-principles calculations indicate that robust ferromagnetic spin interaction between Cr3+ ions via anion-hybridizations across the interface yields the lowest total energy. This work opens the door to fundamental understanding of the unexpected and exceptional properties of oxide-nitride interfaces and provides access to hidden phases at low-dimensional quantum heterostructures.

cond-mat.mtrl-sci↗

arXiv2021

Exchange coupling in synthetic anion-engineered chromia heterostructures

Control of magnetic states by external factors has garnered a mainstream status in spintronic research for designing low power consumption and fast-response information storage and processing devices. Previously, magnetic-cation substitution is the conventional means to induce ferromagnetism in an intrinsic antiferromagnet. Theoretically, the anion-doping is proposed to be another effect means to change magnetic ground states. Here we demonstrate the synthesis of high-quality single-phase chromium oxynitride thin films using in-situ nitrogen doping. Unlike antiferromagnetic monoanionic chromium oxide and nitride phases, chromium oxynitride exhibits a robust ferromagnetic and insulating state, as demonstrated by the combination of multiple magnetization probes and theoretical calculations. With increasing the nitrogen content, the crystal structure of chromium oxynitride transits from trigonal (R3c) to tetragonal (4mm) phase and its saturation magnetization reduces significantly. Furthermore, we achieve a large and controllable exchange bias field in the chromia heterostructures by synthetic anion engineering. This work reflects the anion engineering in functional oxides towards the potential applications in giant magnetoresistance and tunnelling junctions of modern magnetic sensors and read heads.

cond-mat.mtrl-sci↗

arXiv2021

Dynamics of anisotropic oxygen-ion migration in strained cobaltites

Orientation control of oxygen vacancy channel (OVC) is a highly desirable for tailoring oxygen diffusion as it serves fast transport channel in ion conductors, which is widespread exploited in solid-state fuel cells, catalysts, and ion-batteries. Direct observation of oxygen-ions hopping towards preferential vacant sites is a key to clarifying migration pathways. Here we report the anisotropic oxygen-ion migration mediated by strain in ultrathin cobaltites via in-situ thermal activation in an atomic-resolved transmission electron microscopy. Oxygen migration pathways are constructed on the basis of the atomic structure during the OVC switching, which is manifested as the vertical-to-horizontal OVC switching under tensile strain, but the horizontal-to-diagonal switching under compression. We evaluate the topotactic structural changes to OVC, determine the crucial role of tolerance factor for OVC stability and establish the strain-dependent phase diagram. Our work provides a practical guide for engineering OVC orientation that is applicable ionic-oxide electronics.

cond-mat.mtrl-sci↗

arXiv2021

Anisotropic electronic phase transition in CrN epitaxial thin films

Electronic phase transition in strongly correlated materials is extremely sensitive to the dimensionality and crystallographic orientations. Transition metal nitrides (TMNs) are seldom investigated due to the difficulty in fabricating the high-quality and stoichiometric single crystals. In this letter, we report the epitaxial growth and electronic properties of CrN films on different-oriented NdGaO3 (NGO) substrates. Astonishingly, the CrN films grown on (110)-oriented NGO substrates maintain a metallic phase, whereas the CrN films grown on (010)-oriented NGO substrates are semiconducting. We attribute the unconventional electronic transition in the CrN films to the strongly correlation with epitaxial strain. The effective modulation of bandgap by the anisotropic strain triggers the metal-to-insulator transition consequently. This work provides a convenient approach to modify the electronic ground states of functional materials using anisotropic strain and further stimulates the investigations of TMNs.

cond-mat.mtrl-sci↗