arXiv subjects

Kevin Zhang

Publications and source records attributed to Kevin Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2023

Fractionalization in Fractional Correlated Insulating States at $n\pm 1/3$ filled twisted bilayer graphene

Fractionalization without time-reversal symmetry breaking is a long-sought-after goal in the study of correlated phenomena. The earlier proposal of correlated insulating states at $n \pm 1/3$ filling in twisted bilayer graphene and recent experimental observations of insulating states at those fillings strongly suggest that moiré graphene systems provide a new platform to realize time-reversal symmetric fractionalized states. However, the nature of fractional excitations and the effect of quantum fluctuation on the fractional correlated insulating states are unknown. We show that excitations of the fractional correlated insulator phases in the strong coupling limit carry fractional charges and exhibit fractonic restricted mobility. Upon introduction of quantum fluctuations, the resonance of ``lemniscate" structured operators drives the system into ``quantum lemniscate liquid (QLL)" or ``quantum lemniscate solid (QLS)". We find an emergent $U(1)\times U(1)$ 1-form symmetry unifies distinct motions of the fractionally charged excitations in the strong coupling limit and in the QLL phase while providing a new mechanism for fractional excitations in two-dimension. We predict emergent Luttinger liquid behavior upon dilute doping in the strong coupling limit due to restricted mobility and discuss implications at a general $n \pm 1/3$ filling.

cond-mat.str-el↗

arXiv2023

PAniC-3D: Stylized Single-view 3D Reconstruction from Portraits of Anime Characters

We propose PAniC-3D, a system to reconstruct stylized 3D character heads directly from illustrated (p)ortraits of (ani)me (c)haracters. Our anime-style domain poses unique challenges to single-view reconstruction; compared to natural images of human heads, character portrait illustrations have hair and accessories with more complex and diverse geometry, and are shaded with non-photorealistic contour lines. In addition, there is a lack of both 3D model and portrait illustration data suitable to train and evaluate this ambiguous stylized reconstruction task. Facing these challenges, our proposed PAniC-3D architecture crosses the illustration-to-3D domain gap with a line-filling model, and represents sophisticated geometries with a volumetric radiance field. We train our system with two large new datasets (11.2k Vroid 3D models, 1k Vtuber portrait illustrations), and evaluate on a novel AnimeRecon benchmark of illustration-to-3D pairs. PAniC-3D significantly outperforms baseline methods, and provides data to establish the task of stylized reconstruction from portrait illustrations.

cs.CV↗

arXiv2023

PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection

Masked Autoencoders learn strong visual representations and achieve state-of-the-art results in several independent modalities, yet very few works have addressed their capabilities in multi-modality settings. In this work, we focus on point cloud and RGB image data, two modalities that are often presented together in the real world, and explore their meaningful interactions. To improve upon the cross-modal synergy in existing works, we propose PiMAE, a self-supervised pre-training framework that promotes 3D and 2D interaction through three aspects. Specifically, we first notice the importance of masking strategies between the two sources and utilize a projection module to complementarily align the mask and visible tokens of the two modalities. Then, we utilize a well-crafted two-branch MAE pipeline with a novel shared decoder to promote cross-modality interaction in the mask tokens. Finally, we design a unique cross-modal reconstruction module to enhance representation learning for both modalities. Through extensive experiments performed on large-scale RGB-D scene understanding benchmarks (SUN RGB-D and ScannetV2), we discover it is nontrivial to interactively learn point-image features, where we greatly improve multiple 3D detectors, 2D detectors, and few-shot classifiers by 2.9%, 6.7%, and 2.4%, respectively. Code is available at https://github.com/BLVLab/PiMAE.

cs.CV↗

arXiv2023

Roadmap on Deep Learning for Microscopy

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

physics.optics↗

arXiv2022

T-SEA: Transfer-based Self-Ensemble Attack on Object Detection

Compared to query-based black-box attacks, transfer-based black-box attacks do not require any information of the attacked models, which ensures their secrecy. However, most existing transfer-based approaches rely on ensembling multiple models to boost the attack transferability, which is time- and resource-intensive, not to mention the difficulty of obtaining diverse models on the same task. To address this limitation, in this work, we focus on the single-model transfer-based black-box attack on object detection, utilizing only one model to achieve a high-transferability adversarial attack on multiple black-box detectors. Specifically, we first make observations on the patch optimization process of the existing method and propose an enhanced attack framework by slightly adjusting its training strategies. Then, we analogize patch optimization with regular model optimization, proposing a series of self-ensemble approaches on the input data, the attacked model, and the adversarial patch to efficiently make use of the limited information and prevent the patch from overfitting. The experimental results show that the proposed framework can be applied with multiple classical base attack methods (e.g., PGD and MIM) to greatly improve the black-box transferability of the well-optimized patch on multiple mainstream detectors, meanwhile boosting white-box performance. Our code is available at https://github.com/VDIGPKU/T-SEA.

cs.CV↗

arXiv2022

A Rogue Planet Helps Populate the Distant Kuiper Belt

The orbital distribution of transneptunian objects (TNOs) in the distant Kuiper Belt (with semimajor axes beyond the 2:1 resonance, roughly $a$=50-100 au) provides constraints on the dynamical history of the outer solar system. Recent studies show two striking features of this region: 1) a very large population of objects in distant mean-motion resonances with Neptune, and 2) the existence of a substantial detached population (non-resonant objects largely decoupled from Neptune). Neptune migration models are able to implant some resonant and detached objects during the planet migration era, but many fail to match a variety of aspects of the orbital distribution. In this work, we report simulations carried out using an improved version of the GPU-based code GLISSE, following 100,000 test particles per simulation in parallel while handling their planetary close encounters. We demonstrate for the first time that a 2 Earth-mass rogue planet temporarily present during planet formation can abundantly populate both the distant resonances and the detached populations, surprisingly even without planetary migration. We show how weak encounters with the rogue greatly increase the efficiency of filling the resonances, while also dislodging TNOs out of resonance once they reach high perihelia. The rogue's secular gravitational influence simultaneously generates numerous detached objects observed at all semimajor axes. These results suggest that the early presence of additional planet(s) reproduces the observed TNO orbital structure in the distant Kuiper Belt.

astro-ph.EP↗

arXiv2022

MetaDIP: Accelerating Deep Image Prior with Meta Learning

Deep image prior (DIP) is a recently proposed technique for solving imaging inverse problems by fitting the reconstructed images to the output of an untrained convolutional neural network. Unlike pretrained feedforward neural networks, the same DIP can generalize to arbitrary inverse problems, from denoising to phase retrieval, while offering competitive performance at each task. The central disadvantage of DIP is that, while feedforward neural networks can reconstruct an image in a single pass, DIP must gradually update its weights over hundreds to thousands of iterations, at a significant computational cost. In this work we use meta-learning to massively accelerate DIP-based reconstructions. By learning a proper initialization for the DIP weights, we demonstrate a 10x improvement in runtimes across a range of inverse imaging tasks. Moreover, we demonstrate that a network trained to quickly reconstruct faces also generalizes to reconstructing natural image patches.

cs.CV↗

arXiv2022

Mission-level Robustness with Rapidly-deployed, Autonomous Aerial Vehicles by Carnegie Mellon Team Tartan at MBZIRC 2020

For robotic systems to succeed in high risk, real-world situations, they have to be quickly deployable and robust to environmental changes, under-performing hardware, and mission subtask failures. These robots are often designed to consider a single sequence of mission events, with complex algorithms lowering individual subtask failure rates under some critical constraints. Our approach utilizes common techniques in vision and control, and encodes robustness into mission structure through outcome monitoring and recovery strategies. In addition, our system infrastructure enables rapid deployment and requires no central communication. This report also includes lessons in rapid field robotic development and testing. We developed and evaluated our systems through real-robot experiments at an outdoor test site in Pittsburgh, Pennsylvania, USA, as well as in the 2020 Mohamed Bin Zayed International Robotics Challenge. All competition trials were completed in fully autonomous mode without RTK-GPS. Our system placed fourth in Challenge 2 and seventh in the Grand Challenge, with notable achievements such as popping five balloons (Challenge 1), successfully picking and placing a block (Challenge 2), and dispensing the most water onto an outdoor, real fire with an autonomous UAV (Challenge 3).

cs.RO↗

arXiv2022

Sequential Models in the Synthetic Data Vault

The goal of this paper is to describe a system for generating synthetic sequential data within the Synthetic data vault. To achieve this, we present the Sequential model currently in SDV, an end-to-end framework that builds a generative model for multi-sequence, real-world data. This includes a novel neural network-based machine learning model, conditional probabilistic auto-regressive (CPAR) model. The overall system and the model is available in the open source Synthetic Data Vault (SDV) library {https://github.com/sdv-dev/SDV}, along with a variety of other models for different synthetic data needs. After building the Sequential SDV, we used it to generate synthetic data and compared its quality against an existing, non-sequential generative adversarial network based model called CTGAN. To compare the sequential synthetic data against its real counterpart, we invented a new metric called Multi-Sequence Aggregate Similarity (MSAS). We used it to conclude that our Sequential SDV model learns higher level patterns than non-sequential models without any trade-offs in synthetic data quality.

cs.LG↗

arXiv2021

Fractional correlated insulating states at $n \pm 1/3$ filled magic angle twisted bilayer graphene

Although much progress has been made on the physics of magic angle twisted bilayer graphene at integer fillings, little attention has been given to fractional fillings. Here we show that the three-peak structure of Wannier orbitals, dictated by the symmetry and topology of flat bands, facilitates the emergence of a novel state at commensurate fractional filling of $ν= n \pm 1/3$. We dub this state a "fractional correlated insulator". Specifically for the filling of $\pm 1/3$ electrons per moiré unit cell, we show that short-range interactions alone imply an approximate extensive entropy due to the "breathing" degree of freedom of an irregular honeycomb lattice that emerges through defect lines. The leading further-range interaction lifts this degeneracy and selects a novel ferromagnetic nematic state that breaks AB/BA sublattice symmetry. The proposed fractional correlated insulating state might underlie the suppression of superconductivity at $ν= 2-1/3$ filling observed in arXiv:2004.04148. Further investigation of the proposed fractional correlated insulating state would open doors to new regimes of correlation effects in MATBG.

cond-mat.mes-hall↗

arXiv2021

Efficient detection of emission line galaxies in the Cl0016+1609 and MACSJ1621.4+3810 supercluster filaments using SITELLE

We observe a system of filaments and clusters around Cl0016+1609 and MACSJ1621.4+3810 using the SITELLE Fourier transform spectrograph at the Canada France Hawaii Telescope. For Cl0016+1609 (z=0.546), the observations span an 11.8 Mpc x 4.3 Mpc region along an eastern filament which covers the main cluster core, as well as two 4.3 Mpc x 4.3 Mpc regions which each cover southern subclumps. For MACSJ1621.4+3810 (z= 0.465), 3.9 Mpc x 3.9 Mpc around the main cluster core is covered. We present the frequency and location of the emission line galaxies, their emission line images, and calculate the star formation rates, specific star formation rates and merger statistics. In Cl0016+1609, we find thirteen [OII]~3727 Angstrom emitting galaxies with star formation rates between 0.2 and 14.0 M$_{\odot}$ yr$^{-1}$. 91$^{+3}_{-10}$% are found in regions with moderate local galaxy density, avoiding the dense cluster cores. These galaxies follow the main filament of the superstructure, and are mostly blue and disky, with several showing close companions and merging morphologies. In MACSJ1621.4+3810, we find ten emission line sources. All are blue (100$^{+0}_{-15}$%), with 40$^{+16}_{-12}$% classified as disky and 60$^{+12}_{-16}$% as merging systems. Eight avoid the cluster core (80$^{+7}_{-17}$%), but two (20$^{+17}_{-7}$%) are found near high density regions, including the brightest cluster galaxy (BCG). These observations push the spectroscopic study of galaxies in filaments beyond z~ 0.3 to z~ 0.5. Their efficient confirmation is paramount to their usefulness as more galaxy surveys come online.

astro-ph.GA↗

arXiv2021

Memory-efficient Learning for High-Dimensional MRI Reconstruction

Deep learning (DL) based unrolled reconstructions have shown state-of-the-art performance for under-sampled magnetic resonance imaging (MRI). Similar to compressed sensing, DL can leverage high-dimensional data (e.g. 3D, 2D+time, 3D+time) to further improve performance. However, network size and depth are currently limited by the GPU memory required for backpropagation. Here we use a memory-efficient learning (MEL) framework which favorably trades off storage with a manageable increase in computation during training. Using MEL with multi-dimensional data, we demonstrate improved image reconstruction performance for in-vivo 3D MRI and 2D+time cardiac cine MRI. MEL uses far less GPU memory while marginally increasing the training time, which enables new applications of DL to high-dimensional MRI.

eess.IV↗

arXiv2021

Hamiltonian reconstruction as metric for variational studies

Variational approaches are among the most powerful modern techniques to approximately solve quantum many-body problems. These encompass both variational states based on tensor or neural networks, and parameterized quantum circuits in variational quantum eigensolvers. However, self-consistent evaluation of the quality of variational wavefunctions is a notoriously hard task. Using a recently developed Hamiltonian reconstruction method, we propose a multi-faceted approach to evaluating the quality of neural-network based wavefunctions. Specifically, we consider convolutional neural network (CNN) and restricted Boltzmann machine (RBM) states trained on a square lattice spin-1/2 $J_1$-$J_2$ Heisenberg model. We find that the reconstructed Hamiltonians are typically less frustrated, and have easy-axis anisotropy near the high frustration point. Furthermore, the reconstructed Hamiltonians suppress quantum fluctuations in the large $J_2$ limit. Our results highlight the critical importance of the wavefunction's symmetry. Moreover, the multi-faceted insight from the Hamiltonian reconstruction reveals that a variational wave function can fail to capture the true ground state through suppression of quantum fluctuations.

cond-mat.str-el↗

arXiv2021

Playing with Food: Learning Food Item Representations through Interactive Exploration

A key challenge in robotic food manipulation is modeling the material properties of diverse and deformable food items. We propose using a multimodal sensory approach to interact and play with food that facilitates the ability to distinguish these properties across food items. First, we use a robotic arm and an array of sensors, which are synchronized using ROS, to collect a diverse dataset consisting of 21 unique food items with varying slices and properties. Afterwards, we learn visual embedding networks that utilize a combination of proprioceptive, audio, and visual data to encode similarities among food items using a triplet loss formulation. Our evaluations show that embeddings learned through interactions can successfully increase performance in a wide range of material and shape classification tasks. We envision that these learned embeddings can be utilized as a basis for planning and selecting optimal parameters for more material-aware robotic food manipulation skills. Furthermore, we hope to stimulate further innovations in the field of food robotics by sharing this food playing dataset with the research community.

cs.RO↗

arXiv2020

GLISSE: A GPU-optimized planetary system integrator with application to orbital stability calculations

We present a GPU-accelerated numerical integrator specifically optimized for stability calculations of small bodies in planetary systems. Specifically, the integrator is designed for cases when large numbers of test particles (tens or hundreds of thousands) need to be followed for long durations (millions of orbits) to assess the orbital stability of their initially "close-encounter free" orbits. The GLISSE (Gpu Long-term Integrator for Solar System Evolution) code implements several optimizations to achieve a roughly factor of 100 speed increase over running the same code on a CPU. We explain how various hardware speed bottlenecks can be avoided by the careful code design, although some of the choices restrict the usage to specific types of application. As a first application, we study the long-term stability of small bodies initially on orbits between Uranus and Neptune. We map out in detail the small portion of the phase space in which small bodies can survive for 4.5 billion years of evolution; the ability to integrate large numbers of particles allow us to identify for the first time how instability-inducing mean-motion resonance overlaps sharply define the stable regions. As a second application, we map the boundaries of 4 Gyr stability or transneptunian objects in the 5:2 and 3:1 mean-motion resonances, demonstrating that long-term perturbations remove the initially stable Neptune-crossing members.

astro-ph.EP↗

arXiv2020

A Modular Robotic Arm Control Stack for Research: Franka-Interface and FrankaPy

We designed a modular robotic control stack that provides a customizable and accessible interface to the Franka Emika Panda Research robot. This framework abstracts high-level robot control commands as skills, which are decomposed into combinations of trajectory generators, feedback controllers, and termination handlers. Low-level control is implemented in C++ and runs at $1$kHz, and high-level commands are exposed in Python. In addition, external sensor feedback, like estimated object poses, can be streamed to the low-level controllers in real time. This modular approach allows us to quickly prototype new control methods, which is essential for research applications. We have applied this framework across a variety of real-world robot tasks in more than $5$ published research papers. The framework is currently shared internally with other robotics labs at Carnegie Mellon University, and we plan for a public release in the near future.

cs.RO↗

arXiv2020

Chip-Firing and Fractional Bases

We study a particular chip-firing process on an infinite path graph. At any time when there are at least $a+b$ chips at a vertex, $a$ chips fire to the left and $b$ chips fire to the right. We describe the final state of this process when we start with $n$ chips at the origin.

math.CO↗

arXiv2020

Fomite transmission and disinfection strategies for SARS-CoV-2 and related viruses

Contaminated objects or surfaces, referred to as fomites, play a critical role in the spread of viruses, including SARS-CoV-2, the virus responsible for the COVID-19 pandemic. The long persistence of viruses (hours to days) on surfaces calls for an urgent need for surface disinfection strategies to intercept virus transmission and the spread of the disease. Elucidating the physicochemical processes and surface science underlying the adsorption and transfer of virus between surfaces, as well as their inactivation, are important in understanding how the disease is transmitted, and in developing effective interception strategies. This review aims to summarize the current knowledge and underlying physicochemical processes of virus transmission, in particular via fomites, and common disinfection approaches. Gaps in knowledge and needs for further research are also identified. The review focuses on SARS-CoV-2, but will supplement the discussions with related viruses.

q-bio.OT↗