arXiv subjects

Jeffrey Huang

Publications and source records attributed to Jeffrey Huang.

13 recordsLinked to original sources

arXiv2026

PtyRANNOSAUR: Ptychography with Robust Artificial Neural Networks Optimized for Sub-Angstrom Accuracy and Ultrafast Reconstruction

We present PtyRANNOSAUR, a data-driven neural network code that reconstructs atomic resolution electron ptychography data in seconds, 10-100x faster than standard methods. PtyRANNOSAUR uses convolutional autoencoders to map 4D-scanning transmission electron microscopy data to 2D phase images. Each model is trained on a large database of crystal structures and is tailored for a range of experimental parameters, such as accelerating voltage, convergence angle, defocus, and sample thickness. This approach yields high quality reconstructions without requiring any fine-tuning of hyperparameters. In addition, the code handles spatial partial coherence, multiple scattering, and scan position errors, which are critical for state-of-the-art electron ptychography reconstructions. By testing PtyRANNOSAUR on experimental and simulated data, we show that the neural networks accurately reconstruct atomic structures of a broad range of materials systems and can achieve high resolutions of $<0.5$ {\AA}, comparable to the best iterative reconstructions of the same data. These advances enable near-live, state-of-the-art electron ptychography reconstructions.

cond-mat.mtrl-sci↗

arXiv2025

Dyadic Neural Dynamics: Extending Representation Learning to Social Neuroscience

Social communication fundamentally involves at least two interacting brains, creating a unique modeling problem. We present the first application of Contrastive Embedding for Behavioral and Neural Analysis (CEBRA) to dyadic EEG hyperscanning data, extending modeling paradigms to interpersonal neural dynamics. Using structured social interactions between participants, we demonstrate that CEBRA can learn meaningful representations of joint neural activity that captures individual roles (speaker-listener) and other behavioral metrics. Our approach to characterizing interactions, as opposed to individual neural responses to stimuli, addresses the key principles of foundational model development: scalability and cross-subject generalization, opening new directions for representation learning in social neuroscience and clinical applications.

q-bio.NC↗

arXiv2025

3D Mapping of Defects and Moir\'e Corrugations via Electron Ptychography Atomic Coordinate Retrieval

Defects and reconstructions in 2D moir\'e materials cause out-of-plane deformations which strongly modify their electronic properties but are difficult to experimentally access. Here, we solve the 3D atomic coordinates of twisted bilayer WSe$_2$ with picometer-scale accuracy using multislice electron ptychography (MEP) acquired from a single orientation. The resulting atomic models individually visualize each of the six atomic planes, revealing the curvature of each WSe$_2$ layer, variations in the interlayer spacing, and the 3D locations of individual vacancies -- which lie exclusively in the outer Se planes. We also observe a new, unexpected type of structural disorder consisting of mixed bending -- and breathing-type moir\'e-induced corrugations that should strongly impact the emergent electronic properties. Broadly, our methods generate 3D atom-by-atom models of a 2D heterointerface from data acquired in about 30 seconds, methods that should unlock routine access to 3D atomic information in 2D systems and catalyze design methods to control out-of-plane deformations.

cond-mat.mtrl-sci↗

arXiv2025

Atom-by-atom Imaging of Moir\'e Phasons using Electron Ptychography

Twisted 2D materials exhibit unique vibrational modes called moir\'e phonons, which arise from the moir\'e superlattice. Here, we demonstrate atom-by-atom imaging of phasons, an ultrasoft class of moir\'e phonons in twisted bilayer WSe2. Using ultrahigh-resolution (<15 pm) electron ptychography, we image the size and shape of each atom to extract time-averaged vibrational amplitudes as a function of twist angle and position. We observe several signature properties of moir\'e phasons, such as increased vibrational amplitudes at solitons and AA-stacked regions. By correlating experiments with molecular dynamics simulations and lattice dynamics calculations, we show phasons dominate the thermal vibrations in low-angle twisted bilayers. These results represent a powerful route to image thermal vibrations at atomic resolution, unlocking experimental studies of a thus-far hidden branch of moir\'e phonon physics.

cond-mat.mtrl-sci↗

arXiv2024

Design and Performance Evaluation of an Elbow-Based Biomechanical Energy Harvester

Carbon emissions have long been attributed to the increase in climate change. With the effects of climate change escalating in the past few years, there has been an increased effort to find green alternatives to power generation, which has been a major contributor to carbon emissions. One prominent way that has arisen is biomechanical energy, or harvesting energy based on natural human movement. This study will evaluate the feasibility of electric generation using a gear and generator-based biomechanical energy harvester in the elbow joint. The joint was chosen using kinetic arm analysis through MediaPipe, in which the elbow joint showed much higher angular velocity during walking, thus showing more potential as a place to construct the harvester. Leg joints were excluded to not obstruct daily movement. The gear and generator type was decided to maximize energy production in the elbow joint. The device was constructed using a gearbox and a generator. The results show that it generated as much as 0.16 watts using the optimal resistance. This demonstrates the feasibility of electric generation with an elbow joint gear and generator-type biomechanical energy harvester.

cs.RO↗

arXiv2024

Assessing the Impact of Disorganized Background Noise on Timed Stress Task Performance Through Attention Using Machine-Learning Based Eye-Tracking Techniques

Noise pollution has been rising alongside urbanization. Literature shows that disorganized background noise decreases attention. Timed testing, an attention-demanding stress task, has become increasingly important in assessing students' academic performance. However, there is insufficient research on how background noise affects performance in timed stress tasks by impacting attention, which this study aims to address. The paper-based SAT math test under increased time pressure was administered twice: once in silence and once with conversational and traffic background noise. Attention is negatively attributed to increasing blink rate, measured using eye landmarks from dLib's machine-learning facial-detection model. First, the study affirms that background noise detriments attention and performance. Attention, through blink rate, is established as an indicator of stress task performance. Second, the study finds that participants whose blink rates increased due to background noise differed in performance compared to those whose blink rates decreased, possibly correlating with their self-perception of noise's impact on attention. Third, using a case study, the study finds that a student with ADHD had enhanced performance and attention from background noise. Fourth, the study finds that although both groups began with similar blink rates, the group exposed to noise had significantly increased blink rate near the end, indicating that noise reduces attention over time. While schools can generally provide quiet settings for timed stress tasks, the study recommends personalized treatments for students based on how noise affects them. Future research can use different attention indices to consolidate this study's findings or conduct this study with different background noises.

cs.CY↗

arXiv2024

Formation of Quasi-Bound States in the Continuum in a Single Deformed Microcavity

Bound states in the continuum (BIC) holds significant promise in manipulating electromagnetic fields and reducing losses in optical structures, leading to advancements in both fundamental research and practical applications. Despite their observation in various optical systems, the behavior of BIC in whispering-gallery-modes (WGMs) optical microcavities, essential components of photonic integrated chips, has yet to be thoroughly explored. In this study, we propose and experimentally identify a robust mechanism for generating quasi-BIC in a single deformed microcavity. By introducing boundary deformations, we construct stable unidirectional radiation channels as leaking continuum shared by different resonant modes and experimentally verify their external strong mode coupling. This results in drastically suppressed leaking loss of one originally long-lived resonance, manifested as more than a 3-fold enhancement of its quality (Q) factor, while the other short-lived resonance becomes more lossy, demonstrating the formation of Friedrich-Wintgen quasi-BICs as corroborated by both the theoretical model and the experimental data. This research will provide a practical approach to enhance the Q factor of optical microcavities, opening up potential applications in the area of deformed microcavities, nonlinear optics, quantum optics, and integrated photonics.

physics.optics↗

arXiv2023

Disorder and diffuse scattering in single-chirality (TaSe$_4$)$_2$I crystals

The quasi-one-dimensional chiral compound (TaSe$_4$)$_2$I has been extensively studied as a prime example of a topological Weyl semimetal. Upon crossing its phase transition temperature $T_\textrm{CDW}$ $\approx$ 263 K, (TaSe$_4$)$_2$I exhibits incommensurate charge density wave (CDW) modulations described by the well-defined propagation vector $\sim$(0.05, 0.05, 0.11), oblique to the TaSe$_4$ chains. Although optical and transport properties greatly depend on chirality, there is no systematic report about chiral domain size for (TaSe$_4$)$_2$I. In this study, our single-crystal scattering refinements reveal a bulk iodine deficiency, and Flack parameter measurements on multiple crystals demonstrate that separate (TaSe$_4$)$_2$I crystals have uniform handedness, supported by direct imaging and helicity dependent THz emission spectroscopy. Our single-crystal X-ray scattering and calculated diffraction patterns identify multiple diffuse features and create a real-space picture of the temperature-dependent (TaSe$_4$)$_2$I crystal structure. The short-range diffuse features are present at room temperature and decrease in intensity as the CDW modulation develops. These transverse displacements, along with electron pinning from the iodine deficiency, help explain why (TaSe$_4$)$_2$I behaves as an electronic semiconductor at temperatures above and below $T_\textrm{CDW}$, despite a metallic band structure calculated from density functional theory of the ideal structure.

cond-mat.str-el↗

arXiv2022

Angstrom-Scale Imaging of Magnetization in Antiferromagnetic Fe$_2$As via 4D-STEM

We demonstrate a combination of computational tools and experimental 4D-STEM methods to image the local magnetic moment in antiferromagnetic Fe$_2$As with 6 angstrom spatial resolution. Our techniques utilize magnetic diffraction peaks, common in antiferromagnetic materials, to create imaging modes that directly visualize the magnetic lattice. Using this approach, we show that center-of-mass analysis can determine the local magnetization component in the plane perpendicular to the path of the electron beam. Moreover, we develop Magnstem, a quantum mechanical electron scattering simulation code, to model electron scattering of an angstrom-scale probe from magnetic materials. Using these tools, we identify optimal experimental conditions for separating weak magnetic signals from the much stronger interactions of an angstrom-scale probe with electrostatic potentials. Our techniques should be useful for characterizing the local magnetic order in systems such in thin films, interfaces, and domain boundaries of antiferromagnetic materials, which are difficult to probe with existing methods.

cond-mat.mtrl-sci↗

arXiv2022

High-resolution diffraction reveals magnetoelastic coupling and coherent phase separation in tetragonal CuMnAs

Tetragonal CuMnAs was the first antiferromagnet where reorientation of the Néel vector was reported to occur by an inverse spin galvanic effect. A complicating factor in the formation of phase-pure tetragonal CuMnAs is the formation of an orthorhombic phase with nearly the same stoichiometry. Pure-phase tetragonal CuMnAs has been reported to require an excess of Cu to maintain a single phase in traditional solid state synthesis reactions. Here we show that subtle differences in diffraction patterns signal pervasive inhomogeneity and phase separation, even in Cu-rich Cu$_{1.18}$Mn$_{0.82}$As. From calorimetry and magnetometry measurements, we identify two transitions corresponding to the Néel temperature (T$_N$) and an antiferromagnet to weak ferromagnet transition in Cu$_{1.18}$Mn$_{0.82}$As and CuMn$_{0.964}$As$_{1.036}$. These transitions have clear crystallographic signatures, directly observable in the lattice parameters upon in-situ heating and cooling. The immiscibility and phase separation could arise from a spinoidal decomposition that occurs at high temperatures, and the presence of a ferromagnetic transition near room temperature warrants further investigation of its effect on the electrical switching behavior.

cond-mat.mtrl-sci↗

arXiv2022

Positional Bias in Long-Document Ranking: Impact, Assessment, and Mitigation

We tested over 20 Transformer models for ranking long documents (including recent LongP models trained with FlashAttention and RankGPT models "powered" by OpenAI and Anthropic cloud APIs). We compared them with the simple FirstP baseline, which applied the same model to truncated input (up to 512 tokens). On MS MARCO, TREC DL, and Robust04 no long-document model outperformed FirstP by more than 5% (on average). We hypothesized that this lack of improvement is not due to inherent model limitations, but due to benchmark positional bias (most relevant passages tend to occur early in documents), which is known to exist in MS MARCO. To confirm this, we analyzed positional relevance distributions across four long-document corpora (with six query sets) and observed the same early-position bias. Surprisingly, we also found bias in six BEIR collections, which are typically categorized as short-document datasets. We then introduced a new diagnostic dataset, MS MARCO FarRelevant, where relevant spans were deliberately placed beyond the first 512 tokens. On this dataset, many long-context models (including RankGPT) performed at random-baseline level, suggesting overfitting to positional bias. We also experimented with debiasing training data, but with limited success. Our findings (1) highlight the need for careful benchmark design in evaluating long-context models for document ranking, (2) identify model types that are more robust to positional bias, and (3) motivate further work on approaches to debias training data. We release our code and data to support further research.

cs.IR↗

arXiv2019

DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation

Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a fundamental way researchers study the brain in a host of neurological conditions including normal maturation and aging. Despite the availability of open-source brain segmentation software, widespread clinical adoption of volumetric analysis has been hindered due to processing times and reliance on manual corrections. Here, we extend the use of deep learning models from proof-of-concept, as previously reported, to present a comprehensive segmentation of cortical and deep gray matter brain structures matching the standard regions of aseg+aparc included in the commonly used open-source tool, Freesurfer. The work presented here provides a real-life, rapid deep learning-based brain segmentation tool to enable clinical translation as well as research application of quantitative brain segmentation. The advantages of the presented tool include short (~1 minute) processing time and improved segmentation quality. This is the first study to perform quick and accurate segmentation of 102 brain regions based on the surface-based protocol (DMK protocol), widely used by experts in the field. This is also the first work to include an expert reader study to assess the quality of the segmentation obtained using a deep-learning-based model. We show the superior performance of our deep-learning-based models over the traditional segmentation tool, Freesurfer. We refer to the proposed deep learning-based tool as DARTS (DenseUnet-based Automatic Rapid Tool for brain Segmentation). Our tool and trained models are available at https://github.com/NYUMedML/DARTS

q-bio.QM↗

arXiv2018

SOFIA - HIRMES: Looking forward to the HIgh-Resolution Mid-infrarEd Spectrometer

The HIgh-Resolution Mid-infrarEd Spectrometer (HIRMES) is the 3rd Generation Instrument for the Stratospheric Observatory For Infrared Astronomy (SOFIA), currently in development at the NASA Goddard Space Flight Center (GSFC), and due for commissioning in 2019. By combining direct-detection Transition Edge Sensor (TES) bolometer arrays, grating-dispersive spectroscopy, and a host of Fabry-Perot tunable filters, HIRMES will provide the ability for High Resolution (R~100,000), Mid-Resolution (R~10,000), and Low-Resolution (R~600) slit-spectroscopy, and 2D Spectral Imaging (R~2000 at selected wavelengths) over the 25 - 122 μm mid-far infrared waveband. The driving science application is the evolution of proto-planetary systems via measurements of water-vapor, water-ice, deuterated hydrogen (HD), and neutral oxygen lines. However, HIRMES has been designed to be as flexible as possible to cover a wide range of science cases that fall within its phase-space, all whilst reaching sensitivities and observing powers not yet seen thus far on SOFIA, providing unique observing capabilities which will remain unmatched for decades.

astro-ph.IM↗