arXiv subjects

Bowen Li

Publications and source records attributed to Bowen Li.

At least 217 records · Page 12Linked to original sources

arXiv2021

Ferroelectric-HfO2/Oxide Interfaces, Oxygen Distribution Effect and Implications for Device Performance

Atomic-scale understanding of HfO2 ferroelectricity is important to help address many challenges in developing reliable and high-performance ferroelectric HfO2 (fe-HfO2) based devices. Though investigated from different angles, a factor that is real device-relevant and clearly deserves more attention has largely been overlooked by previous research, namely, the fe-HfO2/dielectric interface. Here, we investigate the electronic structures of several typical interfaces formed between ultrathin fe-HfO2 and oxide dielectrics in the sub-3-nm region. We find that interface formation introduces strong depolarizing fields in fe-HfO2, which is detrimental for ferroelectric polarization but can be a merit if tamed for tunneling devices, as recently demonstrated. Asymmetric oxygen distribution-induced polarity, intertwined with ferroelectric polarization or not, is also investigated as a relevant interfacial effect in real device. Though considered detrimental from certain aspects, such as inducing build-in field (independent of ferroelectric polarization) and exacerbating depolarization (intertwined with ferroelectric polarization), it can be partly balanced out by other effects, such as annealing (extrinsic) and polarity-induced defect formation (intrinsic). This work provides insights into ferroelectric-HfO2/dielectric interfaces and some useful implications for the development of devices.

cond-mat.mtrl-sci↗

arXiv2021

Deep-3D Microscope: 3D volumetric microscopy of thick scattering samples using a wide-field microscope and machine learning

Confocal microscopy is the standard approach for obtaining volumetric images of a sample with high axial and lateral resolution, especially when dealing with scattering samples. Unfortunately, a confocal microscope is quite expensive compared to traditional microscopes. In addition, the point scanning in a confocal leads to slow imaging speed and photobleaching due to the high dose of laser energy. In this paper, we demonstrate how the advances in machine learning can be exploited to "teach" a traditional wide-field microscope, one that's available in every lab, into producing 3D volumetric images like a confocal. The key idea is to obtain multiple images with different focus settings using a wide-field microscope and use a 3D Generative Adversarial Network (GAN) based neural network to learn the mapping between the blurry low-contrast image stack obtained using wide-field and the sharp, high-contrast images obtained using a confocal. After training the network with widefield-confocal image pairs, the network can reliably and accurately reconstruct 3D volumetric images that rival confocal in terms of its lateral resolution, z-sectioning and image contrast. Our experimental results demonstrate generalization ability to handle unseen data, stability in the reconstruction results, high spatial resolution even when imaging thick ($\sim40$ microns) highly-scattering samples. We believe that such learning-based-microscopes have the potential to bring confocal quality imaging to every lab that has a wide-field microscope.

physics.optics↗

arXiv2021

Learning from Subjective Ratings Using Auto-Decoded Deep Latent Embeddings

Depending on the application, radiological diagnoses can be associated with high inter- and intra-rater variabilities. Most computer-aided diagnosis (CAD) solutions treat such data as incontrovertible, exposing learning algorithms to considerable and possibly contradictory label noise and biases. Thus, managing subjectivity in labels is a fundamental problem in medical imaging analysis. To address this challenge, we introduce auto-decoded deep latent embeddings (ADDLE), which explicitly models the tendencies of each rater using an auto-decoder framework. After a simple linear transformation, the latent variables can be injected into any backbone at any and multiple points, allowing the model to account for rater-specific effects on the diagnosis. Importantly, ADDLE does not expect multiple raters per image in training, meaning it can readily learn from data mined from hospital archives. Moreover, the complexity of training ADDLE does not increase as more raters are added. During inference each rater can be simulated and a 'mean' or 'greedy' virtual rating can be produced. We test ADDLE on the problem of liver steatosis diagnosis from 2D ultrasound (US) by collecting 46 084 studies along with clinical US diagnoses originating from 65 different raters. We evaluated diagnostic performance using a separate dataset with gold-standard biopsy diagnoses. ADDLE can improve the partial areas under the curve (AUCs) for diagnosing severe steatosis by 10.5% over standard classifiers while outperforming other annotator-noise approaches, including those requiring 65 times the parameters.

cs.CV↗

arXiv2021

Accurate and Generalizable Quantitative Scoring of Liver Steatosis from Ultrasound Images via Scalable Deep Learning

Background & Aims: Hepatic steatosis is a major cause of chronic liver disease. 2D ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated diagnoses are highly subjective. We developed a scalable deep learning (DL) algorithm for quantitative scoring of liver steatosis from 2D ultrasound images. Approach & Results: Using retrospectively collected multi-view ultrasound data from 3,310 patients, 19,513 studies, and 228,075 images, we trained a DL algorithm to diagnose steatosis stages (healthy, mild, moderate, or severe) from ultrasound diagnoses. Performance was validated on two multi-scanner unblinded and blinded (initially to DL developer) histology-proven cohorts (147 and 112 patients) with histopathology fatty cell percentage diagnoses, and a subset with FibroScan diagnoses. We also quantified reliability across scanners and viewpoints. Results were evaluated using Bland-Altman and receiver operating characteristic (ROC) analysis. The DL algorithm demonstrates repeatable measurements with a moderate number of images (3 for each viewpoint) and high agreement across 3 premium ultrasound scanners. High diagnostic performance was observed across all viewpoints: area under the curves of the ROC to classify >=mild, >=moderate, =severe steatosis grades were 0.85, 0.90, and 0.93, respectively. The DL algorithm outperformed or performed at least comparably to FibroScan with statistically significant improvements for all levels on the unblinded histology-proven cohort, and for =severe steatosis on the blinded histology-proven cohort. Conclusions: The DL algorithm provides a reliable quantitative steatosis assessment across view and scanners on two multi-scanner cohorts. Diagnostic performance was high with comparable or better performance than FibroScan.

eess.IV↗

arXiv2021

Manipulating the anisotropic phase separation in strained VO2 epitaxial films by nanoscale ion-implantation

Manipulating the strain induced poly-domains and phase transition in correlated oxide material are important for high performance devices fabrication. Though the electronic transport in the strained oxide film at macroscopic scales can be directly measured, the anisotropic electronic state and the controllable phase separation cross the insulator-to-metal transition within nanoscale size are still elusive. Here, we selected VO2 crystal film as a prototypical oxide and achieved the manipulation of anisotropy electronic phase separation via injecting He+ nanobeam into VO2 film at room temperature. In addition, this nanoscale phase separation was directly visualized by infrared near-field imaging measurements, showing the pronounced and unique cR-axis dependent anisotropy on VO2 surface. Our results offered new insights towards understanding the anisotropic nanoscale phase separation in strained metal oxide films.

cond-mat.mtrl-sci↗

arXiv2021

HiFT: Hierarchical Feature Transformer for Aerial Tracking

Most existing Siamese-based tracking methods execute the classification and regression of the target object based on the similarity maps. However, they either employ a single map from the last convolutional layer which degrades the localization accuracy in complex scenarios or separately use multiple maps for decision making, introducing intractable computations for aerial mobile platforms. Thus, in this work, we propose an efficient and effective hierarchical feature transformer (HiFT) for aerial tracking. Hierarchical similarity maps generated by multi-level convolutional layers are fed into the feature transformer to achieve the interactive fusion of spatial (shallow layers) and semantics cues (deep layers). Consequently, not only the global contextual information can be raised, facilitating the target search, but also our end-to-end architecture with the transformer can efficiently learn the interdependencies among multi-level features, thereby discovering a tracking-tailored feature space with strong discriminability. Comprehensive evaluations on four aerial benchmarks have proven the effectiveness of HiFT. Real-world tests on the aerial platform have strongly validated its practicability with a real-time speed. Our code is available at https://github.com/vision4robotics/HiFT.

cs.CV↗

arXiv2021

Federated Deep Learning with Bayesian Privacy

Federated learning (FL) aims to protect data privacy by cooperatively learning a model without sharing private data among users. For Federated Learning of Deep Neural Network with billions of model parameters, existing privacy-preserving solutions are unsatisfactory. Homomorphic encryption (HE) based methods provide secure privacy protections but suffer from extremely high computational and communication overheads rendering it almost useless in practice . Deep learning with Differential Privacy (DP) was implemented as a practical learning algorithm at a manageable cost in complexity. However, DP is vulnerable to aggressive Bayesian restoration attacks as disclosed in the literature and demonstrated in experimental results of this work. To address the aforementioned perplexity, we propose a novel Bayesian Privacy (BP) framework which enables Bayesian restoration attacks to be formulated as the probability of reconstructing private data from observed public information. Specifically, the proposed BP framework accurately quantifies privacy loss by Kullback-Leibler (KL) Divergence between the prior distribution about the privacy data and the posterior distribution of restoration private data conditioning on exposed information}. To our best knowledge, this Bayesian Privacy analysis is the first to provides theoretical justification of secure privacy-preserving capabilities against Bayesian restoration attacks. As a concrete use case, we demonstrate that a novel federated deep learning method using private passport layers is able to simultaneously achieve high model performance, privacy-preserving capability and low computational complexity. Theoretical analysis is in accordance with empirical measurements of information leakage extensively experimented with a variety of DNN networks on image classification MNIST, CIFAR10, and CIFAR100 datasets.

cs.LG↗

arXiv2021

Internal reverse-biased p-n junctions: a possible origin of the high resistance in phase change superlattice

Phase change superlattice is one of the emerging material technologies for ultralow-power phase change memories. However, the resistance switching mechanism of phase change superlattice is still hotly debated. Early electrical measurements and recent materials characterizations have suggested that the Kooi phase is very likely to be the as-fabricated low-resistance state. Due to the difficulty in in-situ characterization at atomic resolution, the structure of the electrically switched superlattice in its high-resistance state is still unknown and mainly investigated by theoretical modellings. So far, there has been no simple model that can unify experimental results obtained from device-level electrical measurements and atomic-level materials characterizations. In this work, we carry out atomistic transport modellings of the phase change superlattice device and propose a simple mechanism accounting for its high resistance. The modeled high-resistance state is based on the interfacial phase changed superlattice that has previously been mistaken for the low-resistance state. This work advances the understanding of phase change superlattice for emerging memory applications.

cond-mat.mtrl-sci↗

arXiv2021

Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models

As it has been unveiled that pre-trained language models (PLMs) are to some extent capable of recognizing syntactic concepts in natural language, much effort has been made to develop a method for extracting complete (binary) parses from PLMs without training separate parsers. We improve upon this paradigm by proposing a novel chart-based method and an effective top-K ensemble technique. Moreover, we demonstrate that we can broaden the scope of application of the approach into multilingual settings. Specifically, we show that by applying our method on multilingual PLMs, it becomes possible to induce non-trivial parses for sentences from nine languages in an integrated and language-agnostic manner, attaining performance superior or comparable to that of unsupervised PCFGs. We also verify that our approach is robust to cross-lingual transfer. Finally, we provide analyses on the inner workings of our method. For instance, we discover universal attention heads which are consistently sensitive to syntactic information irrespective of the input language.

cs.CL↗

arXiv2021

Mathematical analysis of electromagnetic scattering by dielectric nanoparticles with high refractive indices

In this work, we are concerned with the mathematical modeling of the electromagnetic (EM) scattering by arbitrarily shaped non-magnetic nanoparticles with high refractive indices. When illuminated by visible light, such particles can exhibit a very strong isotropic magnetic response, resulting from the coupling of the incident wave with the circular displacement currents of the EM fields. The main aim of this work is to mathematically illustrate this phenomenon. We shall first introduce the EM scattering resolvent and the concept of dielectric subwavelength resonances. Then we derive the a priori estimates for the subwavelength resonances and the associated resonant modes. We also show the existence of resonances and obtain their asymptotic expansions in terms of the small particle size and the high contrast parameter. After that, we investigate the enhancement of the scattering amplitude and the cross sections when the resonances occur. In doing so, we develop a novel multipole radiation framework that directly separates the electric and magnetic multipole moments and allows us to clearly see their orders of magnitude and blow--up rates. We prove that at the dielectric subwavelength resonant frequencies, the nanoparticles with high refractive indices behave like the sum of the electric dipole and the resonant magnetic dipole. Some explicit calculations and numerical experiments are also provided to validate our general results and formulas.

math-ph↗

arXiv2021

SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking

Recently, the Siamese-based method has stood out from multitudinous tracking methods owing to its state-of-the-art (SOTA) performance. Nevertheless, due to various special challenges in UAV tracking, \textit{e.g.}, severe occlusion and fast motion, most existing Siamese-based trackers hardly combine superior performance with high efficiency. To this concern, in this paper, a novel attentional Siamese tracker (SiamAPN++) is proposed for real-time UAV tracking. By virtue of the attention mechanism, we conduct a special attentional aggregation network (AAN) consisting of self-AAN and cross-AAN for raising the representation ability of features eventually. The former AAN aggregates and models the self-semantic interdependencies of the single feature map via spatial and channel dimensions. The latter aims to aggregate the cross-interdependencies of two different semantic features including the location information of anchors. In addition, the anchor proposal network based on dual features is proposed to raise its robustness of tracking objects with various scales. Experiments on two well-known authoritative benchmarks are conducted, where SiamAPN++ outperforms its baseline SiamAPN and other SOTA trackers. Besides, real-world tests onboard a typical embedded platform demonstrate that SiamAPN++ achieves promising tracking results with real-time speed.

cs.CV↗

arXiv2021

Unity Perception: Generate Synthetic Data for Computer Vision

We introduce the Unity Perception package which aims to simplify and accelerate the process of generating synthetic datasets for computer vision tasks by offering an easy-to-use and highly customizable toolset. This open-source package extends the Unity Editor and engine components to generate perfectly annotated examples for several common computer vision tasks. Additionally, it offers an extensible Randomization framework that lets the user quickly construct and configure randomized simulation parameters in order to introduce variation into the generated datasets. We provide an overview of the provided tools and how they work, and demonstrate the value of the generated synthetic datasets by training a 2D object detection model. The model trained with mostly synthetic data outperforms the model trained using only real data.

cs.CV↗

arXiv2021

An adaptive edge element method and its convergence for an electromagnetic constrained optimal control problem

In this work, an adaptive edge element method is developed for an H(curl)-elliptic constrained optimal control problem. We use the lowest-order Nedelec's edge elements of first family and the piecewise (element-wise) constant functions to approximate the state and the control, respectively, and propose a new adaptive algorithm with error estimators involving both residual-type error estimators and lower-order data oscillations. By using a local regular decomposition for H(curl)-functions and the standard bubble function techniques, we derive the a posteriori error estimates for the proposed error estimators. Then we exploit the convergence properties of the orthogonal $L^2$-projections and the mesh-size functions to demonstrate that the sequences of the discrete states and controls generated by the adaptive algorithm converge strongly to the exact solutions of the state and control in the energy-norm and $L^2$-norm, respectively, by first achieving the strong convergence towards the solution to a limiting control problem. Three-dimensional numerical experiments are also presented to confirm our theoretical results and the quasi-optimality of the adaptive edge element method.

math.NA↗

arXiv2021

Gravitational wave cosmology I: high frequency approximation

In this paper, we systematically study gravitational waves (GWs) produced by remote compact astrophysical sources. To describe such GWs properly, we introduce three scales, $λ, \; L_c$ and $L$, denoting, respectively, the typical wavelength of GWs, the scale of the cosmological perturbations, and the size of the observable universe. For GWs to be detected by the current and foreseeable detectors, the condition $λ\ll L_c \ll L$ holds, and such GWs can be well approximated as high-frequency GWs. In order for the backreaction of the GWs to the background to be negligible, we must assume that $\left|h_{μν}\right| \ll 1$, in addition to the condition $ε\ll 1$, which are also the conditions for the linearized Einstein field equations for $h_{μν}$ to be valid, where $g_{μν} = γ_{μν} + εh_{μν}$, and $γ_{μν}$ denotes the background. To simplify the field equations, we show that the spatial, traceless, and Lorentz gauge conditions can be imposed simultaneously, even when the background is not vacuum, as long as the high-frequency GW approximation is valid. However, to develop the formulas that can be applicable to as many cases as possible, we first write down explicitly the linearized Einstein field equations by imposing only the spatial gauge. Applying the general formulas together with the geometrical optics approximation to such GWs, we find that they still move along null geodesics and its polarization bi-vector is parallel-transported, even when both the cosmological scalar and tensor perturbations are present. In addition, we also calculate the gravitational integrated Sachs-Wolfe effects, whereby the dependences of the amplitude, phase and luminosity distance of the GWs on these two kinds of perturbations are read out explicitly.

astro-ph.CO↗

arXiv2021

ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking

Prior correlation filter (CF)-based tracking methods for unmanned aerial vehicles (UAVs) have virtually focused on tracking in the daytime. However, when the night falls, the trackers will encounter more harsh scenes, which can easily lead to tracking failure. In this regard, this work proposes a novel tracker with anti-dark function (ADTrack). The proposed method integrates an efficient and effective low-light image enhancer into a CF-based tracker. Besides, a target-aware mask is simultaneously generated by virtue of image illumination variation. The target-aware mask can be applied to jointly train a target-focused filter that assists the context filter for robust tracking. Specifically, ADTrack adopts dual regression, where the context filter and the target-focused filter restrict each other for dual filter learning. Exhaustive experiments are conducted on typical dark sceneries benchmark, consisting of 37 typical night sequences from authoritative benchmarks, i.e., UAVDark, and our newly constructed benchmark UAVDark70. The results have shown that ADTrack favorably outperforms other state-of-the-art trackers and achieves a real-time speed of 34 frames/s on a single CPU, greatly extending robust UAV tracking to night scenes.

cs.CV↗

arXiv2021

Programming and Deployment of Autonomous Swarms using Multi-Agent Reinforcement Learning

Autonomous systems (AS) carry out complex missions by continuously observing the state of their surroundings and taking actions toward a goal. Swarms of AS working together can complete missions faster and more effectively than single AS alone. To build swarms today, developers handcraft their own software for storing, aggregating, and learning from observations. We present the Fleet Computer, a platform for developing and managing swarms. The Fleet Computer provides a programming paradigm that simplifies multi-agent reinforcement learning (MARL) -- an emerging class of algorithms that coordinate swarms of agents. Using just two programmer-provided functions Map() and Eval(), the Fleet Computer compiles and deploys swarms and continuously updates the reinforcement learning models that govern actions. To conserve compute resources, the Fleet Computer gives priority scheduling to models that contribute to effective actions, drawing a novel link between online learning and resource management. We developed swarms for unmanned aerial vehicles (UAV) in agriculture and for video analytics on urban traffic. Compared to individual AS, our swarms achieved speedup of 4.4X using 4 UAV and 62X using 130 video cameras. Compared to a competing approach for building swarms that is widely used in practice, our swarms were 3X more effective, using 3.9X less energy.

cs.MA↗

arXiv2021

Time-magnified photon counting with a 550fs resolution

Time-resolved photon-counting plays an indispensable role in precision metrology in both classical and quantum regimes. In particular, time-correlated single-photon counting (TCSPC) has been the key enabling technology for applications such as low-light fluorescence lifetime spectroscopy and photon counting time-of-flight (ToF) 3D imaging. However, state-of-the-art TCSPC single-photon timing resolution (SPTR) is limited in the range of 10-100 ps by the available single-photon detector technology. In this paper, we experimentally demonstrate a time-magnified TCSPC (TM-TCSPC) that achieves an unprecedentedly short SPTR of 550 fs for the first time with an off-the-shelf single-photon detector. The TM-TCSPC can resolve ultrashort pulses with a 130-fs pulsewidth difference at a 22-fs accuracy. When applied to photon counting ToF 3D imaging, the TM-TCSPC greatly suppresses the range walk error that limits all photon counting ToF 3D imaging systems by 99.2 % (130 times) and thus provides unprecedentedly high depth measurement accuracy and precision of 26 μm and 3 μm, respectively.

physics.optics↗

arXiv2021

Spatially-resolved insulator-metal transition for rewritable optical gratings

Doping is an effective way to tune the property of metal oxides1-5, for achieving functional oxide electronics6-8. Previously we developed a controllable hydrogen doping technology at ambient conditions by use of electron-proton synergistic doping strategy, which enables one to get rid of high-temperature/pressure treatments required by traditional technologies9. Here, based on this facile doping route, we achieve a visual and reversible insulator-metal transition (MIT) for tungsten trioxide (WO3) film. Its outstanding spatial selection is comparable to standard UV lithography, which shows the potential of becoming a viable way for rewritable WO3 grating device fabrication. Furthermore, the period of the obtained WO3 structural grating can also be easily changed for requirement by doping area selection. This advanced doping technology opens up alternative approaches for developing not only optical devices, but also rewritable ions devices and integrated circuits for various oxide electronics.

physics.app-ph↗