arXiv subjects

Jieun Lee

Publications and source records attributed to Jieun Lee.

At least 37 records · Page 2Linked to original sources

arXiv2024

Development of a Chinese Human-Automation Trust Scale

The development of a reliable and valid assessment tool of human-automation trust is an important topic. This study aimed to develop a Chinese version of human-automation trust scale (C-HATS) with reasonable reliability and validity based on Lee and See (2004)'s trust model. After three phases of assessments including exploratory factor analysis, item analysis, and confirmatory factor analysis, different dimensions and items were considered for initial and posttask human-automation trust. For post-task trust, the scale had three dimensions and 11 items and reflected Lee and See (2004)'s model, whereas different from Lee and See (2004)'s model, the final scale had 14 items but only two dimensions for initial trust. Nevertheless, for both initial and post-task trust, reasonable reliability and validity of the scale were verified with various consumer automation products. Although further verification is still necessary, the developed C-HATS could be used to effectively assess human-automation trust in the Chinese context.

cs.HC↗

arXiv2024

Transferring Ultrahigh-Field Representations for Intensity-Guided Brain Segmentation of Low-Field Magnetic Resonance Imaging

Ultrahigh-field (UHF) magnetic resonance imaging (MRI), i.e., 7T MRI, provides superior anatomical details of internal brain structures owing to its enhanced signal-to-noise ratio and susceptibility-induced contrast. However, the widespread use of 7T MRI is limited by its high cost and lower accessibility compared to low-field (LF) MRI. This study proposes a deep-learning framework that systematically fuses the input LF magnetic resonance feature representations with the inferred 7T-like feature representations for brain image segmentation tasks in a 7T-absent environment. Specifically, our adaptive fusion module aggregates 7T-like features derived from the LF image by a pre-trained network and then refines them to be effectively assimilable UHF guidance into LF image features. Using intensity-guided features obtained from such aggregation and assimilation, segmentation models can recognize subtle structural representations that are usually difficult to recognize when relying only on LF features. Beyond such advantages, this strategy can seamlessly be utilized by modulating the contrast of LF features in alignment with UHF guidance, even when employing arbitrary segmentation models. Exhaustive experiments demonstrated that the proposed method significantly outperformed all baseline models on both brain tissue and whole-brain segmentation tasks; further, it exhibited remarkable adaptability and scalability by successfully integrating diverse segmentation models and tasks. These improvements were not only quantifiable but also visible in the superlative visual quality of segmentation masks.

cs.CV↗

arXiv2024

New twisted van der Waals fabrication method based on strongly adhesive polymer

Observations of emergent quantum phases in twisted bilayer graphene prompted a flurry of activities in van-der-Waals (vdW) materials beyond graphene. Most current twisted experiments use a so-called tear-and-stack method using a polymer called PPC. However, despite the clear advantage of the current PPC tear-and-stack method, there are also technical limitations, mainly a limited number of vdW materials that can be studied using this PPC-based method. This technical bottleneck has been preventing further development of the exciting field beyond a few available vdW samples. To overcome this challenge and facilitate future expansion, we developed a new tear-and-stack method using a strongly adhesive polycaprolactone (PCL). With similar angular accuracy, our technology allows fabrication without a capping layer, facilitating surface analysis and ensuring inherently clean interfaces and low operating temperatures. More importantly, it can be applied to many other vdW materials that have remained inaccessible with the PPC-based method. We present our results on twist homostructures made with a wide choice of vdW materials - from two well-studied vdW materials (graphene and MoS$_2$) to the first-ever demonstrations of other vdW materials (NbSe$_2$, NiPS$_3$, and Fe$_3$GeTe$_2$). Therefore, our new technique will help expand $moir\acute{e}$ physics beyond few selected vdW materials and open up more exciting developments.

cond-mat.mtrl-sci↗

arXiv2023

FusionLoc: Camera-2D LiDAR Fusion Using Multi-Head Self-Attention for End-to-End Serving Robot Relocalization

As technology advances in autonomous mobile robots, mobile service robots have been actively used more and more for various purposes. Especially, serving robots have been not surprising products anymore since the COVID-19 pandemic. One of the practical problems in operating a serving robot is that it often fails to estimate its pose on a map that it moves around. Whenever the failure happens, servers should bring the serving robot to its initial location and reboot it manually. In this paper, we focus on end-to-end relocalization of serving robots to address the problem. It is to predict robot pose directly from only the onboard sensor data using neural networks. In particular, we propose a deep neural network architecture for the relocalization based on camera-2D LiDAR sensor fusion. We call the proposed method FusionLoc. In the proposed method, the multi-head self-attention complements different types of information captured by the two sensors to regress the robot pose. Our experiments on a dataset collected by a commercial serving robot demonstrate that FusionLoc can provide better performances than previous end-to-end relocalization methods taking only a single image or a 2D LiDAR point cloud as well as a straightforward fusion method concatenating their features.

cs.RO↗

arXiv2022

Moral Hazard on Productivity Among Work-From-Home Workers Amid the COVID-19 Pandemic

After the outbreak of COVID 19, firms appear to monitor Work From Home (WFH) workers more than ever out of anxiety that workers may shirk at home or implement moral hazard at home. Using the Survey of Working Arrangements and Attitudes (SWAA, Barrero et al., 2021), the evidence of WFH workers' ex post moral hazard as well as its specific aspects are examined. The results show that the ex post moral hazard among the WFH workers is generally found. Interestingly, however, the moral hazard on specific type of productivity, efficiency, is not detected for the workers at firms with WFH friendly policy for long term. Moreover, the advantages & challenges for the WFH culture report that workers with health or disability issues improve their productivity, whereas certain conditions specific to the WFH environment must be met.

econ.GN↗

arXiv2022

Evidence and Strategy on Economic Distance in Spatially Augmented Solow-Swan Growth Model

Economists' interests in growth theory have a very long history (Harrod, 1939; Domar, 1946; Solow, 1956; Swan 1956; Mankiw, Romer, and Weil, 1992). Recently, starting from the neoclassical growth model, Ertur and Koch (2007) developed the spatially augmented Solow-Swan growth model with the exogenous spatial weights matrices ($W$). While the exogenous $W$ assumption could be true only with the geographical/physical distance, it may not be true when economic/social distances play a role. Using Penn World Table version 7.1, which covers year 1960-2010, I conducted the robust Rao's score test (Bera, Dogan, and Taspinar, 2018) to determine if $W$ is endogeonus and used the maximum likelihood estimation (Qu and Lee, 2015). The key finding is that the significance and positive effects of physical capital externalities and spatial externalities (technological interdependence) in Ertur and Koch (2007) were no longer found with the exogenous $W$, but still they were with the endogenous $W$ models. I also found an empirical strategy on which economic distance to use when the data recently has been under heavy shocks of the worldwide financial crises during year 1996-2010.

econ.EM↗

arXiv2022

Testing Endogeneity of Spatial Weights Matrices in Spatial Dynamic Panel Data Models

I propose Robust Rao's Score (RS) test statistic to determine endogeneity of spatial weights matrices in a spatial dynamic panel data (SDPD) model (Qu, Lee, and Yu, 2017). I firstly introduce the bias-corrected score function since the score function is not centered around zero due to the two-way fixed effects. I further adjust score functions to rectify the over-rejection of the null hypothesis under a presence of local misspecification in contemporaneous dependence over space, dependence over time, or spatial time dependence. I then derive the explicit forms of our test statistic. A Monte Carlo simulation supports the analytics and shows nice finite sample properties. Finally, an empirical illustration is provided using data from Penn World Table version 6.1.

econ.EM↗

arXiv2022

Electrical Charge Control of h-BN Single Photon Sources

Colour centres of hexagonal boron nitride (h-BN) have been discovered as promising and practical single photon sources due to their high brightness and narrow spectral linewidth at room-temperature. In order to realize h-BN based photonic quantum communications, the ability to electrically activate the single photon fluorescence using an external electric field is crucial. In this work, we show the electrical switching of the photoluminescence from h-BN quantum emitters, enabled by the controllable electron transfer from the nearby charge reservoir. By tuning the Fermi level of graphene next to the h-BN defects, we observed luminescence brightening of a quantum emitter upon the application of a voltage due to the direct charge state manipulation. In addition, the correlation measurement of the single photon sources with the graphene's Raman spectroscopy allows us to extract the exact charge transition level of quantum emitters, providing the information on the crystallographic nature of the defect structure. With the complete on-off switching of emission intensity of h-BN quantum emitters using a voltage, our result paves the way for the van der Waals colour centre based photonic quantum information processing, cryptography and memory applications.

physics.optics↗

arXiv2022

Personalization Trade-offs in Designing a Dialogue-based Information System for Support-Seeking of Sexual Violence Survivors

The lack of reliable, personalized information often complicates sexual violence survivors' support-seeking. Recently, there is an emerging approach to conversational information systems for support-seeking of sexual violence survivors, featuring personalization with wide availability and anonymity. However, a single best solution might not exist as sexual violence survivors have different needs and purposes in seeking support channels. To better envision conversational support-seeking systems for sexual violence survivors, we explore personalization trade-offs in designing such information systems. We implement a high-fidelity prototype dialogue-based information system through four design workshop sessions with three professional caregivers and interviewed with four self-identified survivors using our prototype. We then identify two forms of personalization trade-offs for conversational support-seeking systems: (1) specificity and sensitivity in understanding users and (2) relevancy and inclusiveness in providing information. To handle these trade-offs, we propose a reversed approach that starts from designing information and inclusive tailoring that considers unspecified needs, respectively.

cs.HC↗

arXiv2021

Air Stable and Layer Dependent Ferromagnetism in Atomically Thin van der Waals CrPS$_{4}$

Ferromagnetism in two-dimensional materials presents a promising platform for the development of ultrathin spintronic devices with advanced functionalities. Recently discovered ferromagnetic van der Waals crystals such as CrI$_{3}$, readily isolated two-dimensional crystals, are highly tunable through external fields or structural modifications. However, there remains a challenge because of material instability under air exposure. Here, we report the observation of an air stable and layer dependent ferromagnetic (FM) van der Waals crystal, CrPS$_{4}$, using magneto-optic Kerr effect microscopy. In contrast to the antiferromagnetic (AFM) bulk, the FM out-of-plane spin orientation is found in the monolayer crystal. Furthermore, alternating AFM and FM properties observed in even and odd layers suggest robust antiferromagnetic exchange interactions between layers. The observed ferromagnetism in these crystals remains resilient even after the air exposure of about a day, providing possibilities for the practical applications of van der Waals spintronics.

cond-mat.mes-hall↗

arXiv2021

Microsphere-coupled light emission control of van der Waals heterostructures

Two-dimensional transition metal dichalcogenides (TMDCs) integrated to photonic structures provide an intriguing playground for the development of novel optoelectronic devices with improved performance. Here, we show the enhanced light emission from TMDC based van der Waals heterostructures through coupling with microsphere cavities. We observe cavity-induced emission enhancement of TMDC materials which varies by an order of magnitude, depending on the size of the microsphere and thickness of the supporting oxide substrate. Furthermore, we demonstrate microsphere cavity-enhanced electroluminescence of van der Waals light emitting transistor, showing the potential of 2D material based hybrid optoelectronic structures.

physics.optics↗

arXiv2020

Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse using Root-Flipping: DeepRF_SLR

A novel approach of applying deep reinforcement learning to an RF pulse design is introduced. This method, which is referred to as DeepRF_SLR, is designed to minimize the peak amplitude or, equivalently, minimize the pulse duration of a multiband refocusing pulse generated by the Shinar Le-Roux (SLR) algorithm. In the method, the root pattern of SLR polynomial, which determines the RF pulse shape, is optimized by iterative applications of deep reinforcement learning and greedy tree search. When tested for the designs of the multiband factors of three and seven RFs, DeepRF_SLR demonstrated improved performance compared to conventional methods, generating shorter duration RF pulses in shorter computational time. In the experiments, the RF pulse from DeepRF_SLR produced a slice profile similar to the minimum-phase SLR RF pulse and the profiles matched to that of the computer simulation. Our approach suggests a new way of designing an RF by applying a machine learning algorithm, demonstrating a machine-designed MRI sequence.

cs.LG↗

arXiv2020

Polarization and localization of single-photon emitters in hexagonal boron nitride wrinkles

Color centers in 2-dimensional hexagonal boron nitride (h-BN) have recently emerged as stable and bright single-photon emitters (SPEs) operating at room temperature. In this study, we combine theory and experiment to show that vacancy-based SPEs selectively form at nano-scale wrinkles in h-BN with its optical dipole preferentially aligned to the wrinkle direction. By using density functional theory calculations, we find that the wrinkle curvature plays a crucial role in localizing vacancy-based SPE candidates and aligning the defects symmetry plane to the wrinkle direction. By performing optical measurements on SPEs created in h-BN single-crystal flakes, we experimentally confirm the wrinkle-induced generation of SPEs and their polarization alignment to the wrinkle direction. Our results not only provide a new route to controlling the atomic position and the optical property of the SPEs but also revealed the possible crystallographic origin of the SPEs in h-BN, greatly enhancing their potential for use in solid-state quantum photonics and quantum information processing.

cond-mat.mes-hall↗

arXiv2019

Existence of standing pulse solutions to a skew-gradient system

Reaction-diffusion systems have been primary tools for studying pattern formation. A skew-gradient system is well known to encompass a class of activator-inhibitor type reaction-diffusion systems that exhibit localized patterns such as fronts and pulses. While there is a substantial literature for the case of a linear inhibitor equation, the study of nonlinear inhibitor effect is still limited. To fill this research gap, we investigate standing pulse solutions to a skew-gradient system in which both activator and inhibitor reaction terms inherit nonlinear structures. Using a variational approach that involves several nonlocal terms, we establish the existence of standing pulse solutions with a sign change. In addition, we explore some qualitative properties of the standing pulse solutions.

math.AP↗

arXiv2019

Artificial neural network for myelin water imaging

Purpose: To demonstrate the application of artificial-neural-network (ANN) for real-time processing of myelin water imaging (MWI). Methods: Three neural networks, ANN-IMWF, ANN-IGMT2, and ANN-II, were developed to generate MWI. ANN-IMWF and ANN-IGMT2 were designed to output myelin water fraction (MWF) and geometric mean T2 (GMT2,IEW), respectively whereas ANN-II generates a T2 distribution. For the networks, gradient and spin echo data from 18 healthy controls (HC) and 26 multiple sclerosis patients (MS) were utilized. Among them, 10 HC and 12 MS had the same scan parameters and were used for training (6 HC and 6 MS), validation (1 HC and 1 MS), and test sets (3 HC and 5 HC). The remaining data had different scan parameters and were applied to exam the effects of the scan parameters. The network results were compared with those of conventional MWI in the white matter mask and regions of interest (ROI). Results: The networks produced highly accurate results, showing averaged normalized root-mean-squared error under 3% for MWF and 0.4% for GMT2,IEW in the white matter mask of the test set. In the ROI analysis, the differences between ANNs and conventional MWI were less than 0.1% in MWF and 0.1 ms in GMT2,IEW (no statistical difference and R2 > 0.97). Datasets with different scan parameters showed increased errors. The average processing time was 0.68 sec in ANNs, gaining 11,702 times acceleration in the computational speed (conventional MWI: 7,958 sec). Conclusion: The proposed neural networks demonstrate the feasibility of real-time processing for MWI with high accuracy.

eess.IV↗

arXiv2019

Strain-engineering of Berry curvature dipole and valley magnetization in monolayer MoS$_2$

The Berry curvature dipole is a physical quantity that is expected to allow various quantum geometrical phenomena in a range of solid-state systems. Monolayer transition metal dichalcogenides provide an exceptional platform to modulate and investigate the Berry curvature dipole through strain. Here we theoretically demonstrate and experimentally verify for monolayer MoS$_\rm{2}$ the generation of valley orbital magnetization as a response to an in-plane electric field due to the Berry curvature dipole. The measured valley orbital magnetization shows excellent agreement with the calculated Berry curvature dipole which can be controlled by the magnitude and direction of strain. Our results show that the Berry curvature dipole acts as an effective magnetic field in current-carrying systems, providing a novel route to generate magnetization.

cond-mat.mes-hall↗

arXiv2019

Pose estimator and tracker using temporal flow maps for limbs

For human pose estimation in videos, it is significant how to use temporal information between frames. In this paper, we propose temporal flow maps for limbs (TML) and a multi-stride method to estimate and track human poses. The proposed temporal flow maps are unit vectors describing the limbs' movements. We constructed a network to learn both spatial information and temporal information end-to-end. Spatial information such as joint heatmaps and part affinity fields is regressed in the spatial network part, and the TML is regressed in the temporal network part. We also propose a data augmentation method to learn various types of TML better. The proposed multi-stride method expands the data by randomly selecting two frames within a defined range. We demonstrate that the proposed method efficiently estimates and tracks human poses on the PoseTrack 2017 and 2018 datasets.

cs.CV↗

arXiv2018

Stark Tuning of Single-Photon Emitters in Hexagonal Boron Nitride

Single-photon emitters play an essential role in quantum technologies, including quantum computing and quantum communications. Atomic defects in hexagonal boron nitride (h-BN) have recently emerged as new room-temperature single-photon emitters in solid-state systems, but the development of scalable and tunable h-BN single-photon emitters requires external methods that can control the emission energy of individual defects. Here, by fabricating van der Waals heterostructures of h-BN and graphene, we demonstrate the electrical control of single-photon emission from atomic defects in h-BN via the Stark effect. By applying an out-of-plane electric field through graphene gates, we observed Stark shifts as large as 5.4 nm per GV/m. The Stark shift generated upon a vertical electric field suggests the existence of out-of-plane dipole moments associated with atomic defect emitters, which is supported by first-principles theoretical calculations. Furthermore, we found field-induced discrete modification and stabilization of emission intensity, which were reversibly controllable with an external electric field.

physics.app-ph↗