arXiv subjects

Minghao Li

Publications and source records attributed to Minghao Li.

At least 73 records · Page 4Linked to original sources

arXiv2023

Shallow Silicon Vacancy Centers with lifetime-limited optical linewidths in Diamond Nanostructures

The negatively charged silicon vacancy center (SiV$^-$) in diamond is a promising, yet underexplored candidate for single-spin quantum sensing at sub-kelvin temperatures and tesla-range magnetic fields. A key ingredient for such applications is the ability to perform all-optical, coherent addressing of the electronic spin of near-surface SiV$^-$ centers. We present a robust and scalable approach for creating individual, $\sim$50nm deep SiV$^-$ with lifetime-limited optical linewidths in diamond nanopillars through an easy-to-realize and persistent optical charge-stabilization scheme. The latter is based on single, prolonged 445nm laser illumination that enables continuous photoluminescence excitation spectroscopy, without the need for any further charge stabilization or repumping. Our results constitute a key step towards the use of near-surface, optically coherent SiV$^-$ for sensing under extreme conditions, and offer a powerful approach for stabilizing the charge-environment of diamond color centers for quantum technology applications.

quant-ph↗

arXiv2023

Theoretical bound of the efficiency of learning with coarse-graining

A thermodynamic formalism describing the efficiency of information learning is proposed, which is applicable for stochastic thermodynamic systems with multiple internal degree of freedom. The learning rate, entropy production rate (EPR), and entropy flow from the system to the environment under coarse-grained dynamics are derived. The Cauchy-Schwarz inequality has been applied to demonstrate the lower bound on the EPR of an internal state. The inequality of EPR is tighter than the Clausius inequality, leading to the derivative of the upper bound on the efficiency of learning. The results are verified in cellular networks with information processes.

cond-mat.stat-mech↗

arXiv2023

CPNet: Exploiting CLIP-based Attention Condenser and Probability Map Guidance for High-fidelity Talking Face Generation

Recently, talking face generation has drawn ever-increasing attention from the research community in computer vision due to its arduous challenges and widespread application scenarios, e.g. movie animation and virtual anchor. Although persevering efforts have been undertaken to enhance the fidelity and lip-sync quality of generated talking face videos, there is still large room for further improvements of synthesis quality and efficiency. Actually, these attempts somewhat ignore the explorations of fine-granularity feature extraction/integration and the consistency between probability distributions of landmarks, thereby recurring the issues of local details blurring and degraded fidelity. To mitigate these dilemmas, in this paper, a novel CLIP-based Attention and Probability Map Guided Network (CPNet) is delicately designed for inferring high-fidelity talking face videos. Specifically, considering the demands of fine-grained feature recalibration, a clip-based attention condenser is exploited to transfer knowledge with rich semantic priors from the prevailing CLIP model. Moreover, to guarantee the consistency in probability space and suppress the landmark ambiguity, we creatively propose the density map of facial landmark as auxiliary supervisory signal to guide the landmark distribution learning of generated frame. Extensive experiments on the widely-used benchmark dataset demonstrate the superiority of our CPNet against state of the arts in terms of image and lip-sync quality. In addition, a cohort of studies are also conducted to ablate the impacts of the individual pivotal components.

cs.MM↗

arXiv2023

Microscopic analysis of spin-momentum locking on a geometric phase metasurface

We revisit spin-orbit coupling in a plasmonic Berry metasurface comprised of rotated nanoapertures, which is known to imprint a robust far-field polarization response. We present a scattering formalism that shows how that spin-momentum locking emerges from the geometry of the unit cell without requiring global rotation symmetries. We find and confirm with Mueller polarimetry measurements that spin-momentum locking is an approximate symmetry. The symmetry breakdown is ascribed to the elliptical projection of circularly polarized light into the planar surface. This breakdown is maximal when surface waves are excited, and a new set of spin-momentum locking rules is presented for this case.

physics.optics↗

arXiv2022

TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models

Text recognition is a long-standing research problem for document digitalization. Existing approaches are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments show that the TrOCR model outperforms the current state-of-the-art models on the printed, handwritten and scene text recognition tasks. The TrOCR models and code are publicly available at \url{https://aka.ms/trocr}.

cs.CL↗

arXiv2022

Understanding the Challenges of Team-Based Live Streaming for First-person Shooter Games

First-person shooter (FPS) game tournaments take place across the globe. A growing number of people choose to watch FPS games online instead of attending the game events in person. However, live streaming might miss critical highlight moments in the game, including kills and tactics. We identify how and why the live streaming team fails to capture highlight moments to reduce such live streaming mistakes. We named such mistakes jarring observations. We conducted a field study of live streaming competitions of Game For Peace, a popular FPS mobile game, to summarize five typical jarring observations and identify three primary reasons that caused the issues. We further studied how to improve the live streaming system to prevent jarring observations from happening by doing semi-structured interviews with two professional streaming teams for Game For Peace. The study showed that a better system should (1) add a new sub-team role to share the director's responsibility of managing observers; (2) provide interfaces customized for three roles of live streamers in the team; (3) abstract more geographical info; (4) predict the priority of observation targets; and (5) provide non-verbal interfaces for sync-up between sub-teams. Our work provides insights for esports streaming system researchers and developers to improve the system for a smoother audience experience.

cs.HC↗

arXiv2022

Neutral silicon vacancy centers in undoped diamond via surface control

Neutral silicon vacancy centers (SiV0) in diamond are promising candidates for quantum networks because of their long spin coherence times and stable, narrow optical transitions. However, stabilizing SiV0 requires high purity, boron doped diamond, which is not a readily available material. Here, we demonstrate an alternative approach via chemical control of the diamond surface. We use low-damage chemical processing and annealing in a hydrogen environment to realize reversible and highly stable charge state tuning in undoped diamond. The resulting SiV0 centers display optically detected magnetic resonance and bulk-like optical properties. Controlling the charge state tuning via surface termination offers a route for scalable technologies based on SiV0 centers, as well as charge state engineering of other defects.

quant-ph↗

arXiv2022

Automation Slicing and Testing for in-App Deep Learning Models

Intelligent Apps (iApps), equipped with in-App deep learning (DL) models, are emerging to offer stable DL inference services. However, App marketplaces have trouble auto testing iApps because the in-App model is black-box and couples with ordinary codes. In this work, we propose an automated tool, ASTM, which can enable large-scale testing of in-App models. ASTM takes as input an iApps, and the outputs can replace the in-App model as the test object. ASTM proposes two reconstruction techniques to translate the in-App model to a backpropagation-enabled version and reconstruct the IO processing code for DL inference. With the ASTM's help, we perform a large-scale study on the robustness of 100 unique commercial in-App models and find that 56\% of in-App models are vulnerable to robustness issues in our context. ASTM also detects physical attacks against three representative iApps that may cause economic losses and security issues.

cs.SE↗

arXiv2021

Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach

The problem of multi-robot navigation of connectivity maintenance is challenging in multi-robot applications. This work investigates how to navigate a multi-robot team in unknown environments while maintaining connectivity. We propose a reinforcement learning (RL) approach to develop a decentralized policy, which is shared among multiple robots. Given range sensor measurements and the positions of other robots, the policy aims to generate control commands for navigation and preserve the global connectivity of the robot team. We incorporate connectivity concerns into the RL framework as constraints and introduce behavior cloning to reduce the exploration complexity of policy optimization. The policy is optimized with all transition data collected by multiple robots in random simulated scenarios. We validate the effectiveness of the proposed approach by comparing different combinations of connectivity constraints and behavior cloning. We also show that our policy can generalize to unseen scenarios in both simulation and holonomic robots experiments.

cs.RO↗

arXiv2021

Free energies of triply and quadruply degenerate electron ladder spectra

The electronic free energies in metal clusters are calculated based on a simple ladder spectrum for the level degeneracies three and four, and compared with the known results for degeneracies one and two. The low temperature and the asymptotic high temperature results for free energy differences are generalized to ladder spectra of any degeneracy. Remarkably, free energy differences do not approach zero at high energies for size-independent Fermi energies.

cond-mat.mes-hall↗

arXiv2021

Planar chirality and optical spin-orbit coupling for chiral Fabry-Perot cavities

We design, in a most simple way, Fabry-Perot cavities with longitudinal chiral modes by sandwiching between two smooth metallic silver mirrors a layer of polystyrene made planar chiral by torsional shear stress. We demonstrate that the helicity-preserving features of our cavities stem from a spin-orbit coupling mechanism seeded inside the cavities by the specific chiroptical features of planar chirality. Planar chirality gives rise to an extrinsic source of three-dimensional chirality under oblique illumination that endows the cavities with enantiomorphic signatures measured experimentally and simulated with excellent agreement. The simplicity of our scheme is particularly promising in the context of chiral cavity QED and polaritonic asymmetric chemistry.

physics.optics↗

arXiv2021

Galilean relativity for Brownian dynamics and energetics

We study experimentally the impact of inertial reference frame changes on overdamped Brownian motion. The reference frame changes are implemented by inducing, with a laser, laminar convection flows in a column of fluid where Brownian microspheres are dispersed. The convection flow plays the role of the relative velocity between the laboratory and the fluid comoving frames, and enables us to analyse the consequences of Galilean transformations on Brownian diffusion. We verify in particular how the Brownian dynamics remains "weakly" Galilean invariant, in agreement with recent discussions. We also carefully look at the consequences of Galilean relativity on the Brownian energetics. This leads us to derive a Galilean invariant expression of the stochastic thermodynamic first law, consistent with existing theoretical results. We finally discuss a potential ambiguity of the Galilean relativistic features of diffusive systems that has obvious practical implications in the context of force measurements in external flows.

cond-mat.stat-mech↗

arXiv2020

DocBank: A Benchmark Dataset for Document Layout Analysis

Document layout analysis usually relies on computer vision models to understand documents while ignoring textual information that is vital to capture. Meanwhile, high quality labeled datasets with both visual and textual information are still insufficient. In this paper, we present \textbf{DocBank}, a benchmark dataset that contains 500K document pages with fine-grained token-level annotations for document layout analysis. DocBank is constructed using a simple yet effective way with weak supervision from the \LaTeX{} documents available on the arXiv.com. With DocBank, models from different modalities can be compared fairly and multi-modal approaches will be further investigated and boost the performance of document layout analysis. We build several strong baselines and manually split train/dev/test sets for evaluation. Experiment results show that models trained on DocBank accurately recognize the layout information for a variety of documents. The DocBank dataset is publicly available at \url{https://github.com/doc-analysis/DocBank}.

cs.CL↗

arXiv2020

Noise and ergodic properties of Brownian motion in an optical tweezer: looking at the crossover between Wiener and Ornstein-Uhlenbeck processes

We characterize throughout the spectral range of an optical trap the nature of the noise at play and the ergodic properties of the corresponding Brownian motion of an overdamped trapped single microsphere, comparing experimental, analytical and simulated data. We carefully analyze noise and ergodic properties $(i)$ using the Allan variance for characterizing the noise and $(ii)$ exploiting a test of ergodicity tailored for experiments done over finite times. We derive these two observables in the low-frequency Ornstein-Uhlenbeck trapped-diffusion regime and study analytically their evolution towards the high-frequency Wiener free-diffusion regime, in a very good agreement with simulated and experimental results. This leads to reveal noise and ergodic spectral signatures associated with the distinctive features of both regimes.

cond-mat.stat-mech↗

arXiv2020

TableBank: A Benchmark Dataset for Table Detection and Recognition

We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet. Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousand human-labeled examples, which is difficult to generalize on real-world applications. With TableBank that contains 417K high quality labeled tables, we build several strong baselines using state-of-the-art models with deep neural networks. We make TableBank publicly available and hope it will empower more deep learning approaches in the table detection and recognition task. The dataset and models are available at \url{https://github.com/doc-analysis/TableBank}.

cs.CV↗

arXiv2020

LayoutLM: Pre-training of Text and Layout for Document Image Understanding

Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the widespread use of pre-training models for NLP applications, they almost exclusively focus on text-level manipulation, while neglecting layout and style information that is vital for document image understanding. In this paper, we propose the \textbf{LayoutLM} to jointly model interactions between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into LayoutLM. To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for document-level pre-training. It achieves new state-of-the-art results in several downstream tasks, including form understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification (from 93.07 to 94.42). The code and pre-trained LayoutLM models are publicly available at \url{https://aka.ms/layoutlm}.

cs.CL↗

arXiv2020

SegAttnGAN: Text to Image Generation with Segmentation Attention

In this paper, we propose a novel generative network (SegAttnGAN) that utilizes additional segmentation information for the text-to-image synthesis task. As the segmentation data introduced to the model provides useful guidance on the generator training, the proposed model can generate images with better realism quality and higher quantitative measures compared with the previous state-of-art methods. We achieved Inception Score of 4.84 on the CUB dataset and 3.52 on the Oxford-102 dataset. Besides, we tested the self-attention SegAttnGAN which uses generated segmentation data instead of masks from datasets for attention and achieved similar high-quality results, suggesting that our model can be adapted for the text-to-image synthesis task.

cs.CV↗

arXiv2019

Ship resistance when operating in floating ice floes: a combined CFD&DEM approach

Whilst climate change is transforming the Arctic into a navigable ocean where small ice floes are floating on the sea surface, the effect of such ice conditions on ship performance has yet to be understood. The present work combines a set of numerical methods to simulate the ship-wave-ice interaction in such ice conditions. Particularly, Computational Fluid Dynamics is applied to provide fluid solutions for the floes and it is incorporated with the Discrete Element Method to govern ice motions and account for ship-ice/ice-ice collisions, by which, the proposed approach innovatively includes wave effects in the interaction. In addition, this work introduces two algorithms that can implement computational models with natural ice-floe fields, which takes randomness into consideration thus achieving high-fidelity modelling of the problem. Following validation against experiments, the model is shown accurate in predicting the ice-floe resistance of a ship, and then a series of simulations are performed to investigate how the resistance is influenced by ship speed, ice concentration, ice thickness and floe diameter. This paper presents a useful approach that can provide power estimates for Arctic shipping and has the potential to facilitate other polar engineering purposes.

physics.flu-dyn↗