arXiv subjects

Peng Liu

Publications and source records attributed to Peng Liu.

At least 397 records · Page 22Linked to original sources

arXiv2019

Circuit architecture of a sub-GHz resolution panoramic C band on-chip spectral sensor

Monitoring the state of the optical network is a key enabler for programmability of network functions, protocols and efficient use of the spectrum. A particular challenge is to provide the SDN-EON controller with a panoramic view of the complete state of the optical spectrum. This paper describes the architecture for compact on-chip spectrometry targeting high resolution across the entire C-band to reliably and accurately measure the spectral profile of WDM signals in fixed and flex-grid architectures. An industry standard software tool is used to validate the performance of the spectrometer. The fabrication of the proposed design is found to be practical.

physics.app-ph↗

arXiv2019

KRNET: Image Denoising with Kernel Regulation Network

One popular strategy for image denoising is to design a generalized regularization term that is capable of exploring the implicit prior underlying data observation. Convolutional neural networks (CNN) have shown the powerful capability to learn image prior information through a stack of layers defined by a combination of kernels (filters) on the input. However, existing CNN-based methods mainly focus on synthetic gray-scale images. These methods still exhibit low performance when tackling multi-channel color image denoising. In this paper, we optimize CNN regularization capability by developing a kernel regulation module. In particular, we propose a kernel regulation network-block, referred to as KR-block, by integrating the merits of both large and small kernels, that can effectively estimate features in solving image denoising. We build a deep CNN-based denoiser, referred to as KRNET, via concatenating multiple KR-blocks. We evaluate KRNET on additive white Gaussian noise (AWGN), multi-channel (MC) noise, and realistic noise, where KRNET obtains significant performance gains over state-of-the-art methods across a wide spectrum of noise levels.

eess.IV↗

arXiv2019

SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT Perfusion

With substantial public concerns on potential cancer risks and health hazards caused by the accumulated radiation exposure in medical imaging, reducing radiation dose in X-ray based medical imaging such as Computed Tomography Perfusion (CTP) has raised significant research interests. In this paper, we embrace the deep Convolutional Neural Networks (CNN) based approaches and introduce Smoothed Dense-Convolution Neural Network (SDCNet) to recover high-dose quality CTP images from low-dose ones. SDCNet is composed of sub-network blocks cascaded by skip-connections to infer the noise (differentials) from paired low/high-dose CT scans. SDCNet can effectively remove the noise in real low-dose CT scans and enhance the quality of medical images. We evaluate the proposed architecture on thousands of CT perfusion frames for both reconstructed image denoising and perfusion map quantification including cerebral blood flow (CBF) and cerebral blood volume (CBV). SDCNet achieves high performance in both visual and quantitative results with promising computational efficiency, comparing favorably with state-of-the-art approaches. \textit{The code is available at \url{https://github.com/cswin/RC-Nets}}.

eess.IV↗

arXiv2019

CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation

Recently, deep neural networks have demonstrated comparable and even better performance with board-certified ophthalmologists in well-annotated datasets. However, the diversity of retinal imaging devices poses a significant challenge: domain shift, which leads to performance degradation when applying the deep learning models to new testing domains. In this paper, we propose a novel unsupervised domain adaptation framework, called Collaborative Feature Ensembling Adaptation (CFEA), to effectively overcome this challenge. Our proposed CFEA is an interactive paradigm which presents an exquisite of collaborative adaptation through both adversarial learning and ensembling weights. In particular, we simultaneously achieve domain-invariance and maintain an exponential moving average of the historical predictions, which achieves a better prediction for the unlabeled data, via ensembling weights during training. Without annotating any sample from the target domain, multiple adversarial losses in encoder and decoder layers guide the extraction of domain-invariant features to confuse the domain classifier and meanwhile benefit the ensembling of smoothing weights. Comprehensive experimental results demonstrate that our CFEA model can overcome performance degradation and outperform the state-of-the-art methods in segmenting retinal optic disc and cup from fundus images. \textit{Code is available at \url{https://github.com/cswin/AWC}}.

eess.IV↗

arXiv2019

Deep learning enables extraction of capillary-level angiograms from single OCT volume

Optical coherence tomography angiography (OCTA) has drawn numerous attentions in ophthalmology. However, its data acquisition is time-consuming, because it is based on temporal-decorrelation principle thus requires multiple repeated volumetric OCT scans. In this paper, we developed a deep learning algorithm by combining a fovea attention mechanism with a residual neural network, which is able to extract capillary-level angiograms directly from a single OCT scan. The segmentation results of the inner limiting membrane and outer plexiform layers and the central $1\times1$ mm$^2$ field of view of the fovea are employed in the fovea attention mechanism. So the influences of large retinal vessels and choroidal vasculature on the extraction of capillaries can be minimized during the training of the network. The results demonstrate that the proposed algorithm has the capacity to better-visualizing capillaries around the foveal avascular zone than the existing work using a U-Net architecture.

physics.med-ph↗

arXiv2019

REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs

Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (\url{https://refuge.grand-challenge.org}), held in conjunction with MICCAI 2018. The challenge consisted of two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.

cs.CV↗

arXiv2019

Recalibration of the binding energy of hypernuclei measured in emulsion experiments and its implications

The $Λ$ separation energy for $Λ$-hypernuclei, denoted $B_Λ$, measured in 1967, 1968, and 1973 are recalibrated using the current best mass estimates for particles and nuclei. The recalibrated $B_Λ$ are systematically larger (except in the case of $^6_Λ$He) than the original published values by about 100 keV. The effect of this level of recalibration is very important for light hypernuclei, especially for the hypertriton. The early $B_Λ$ values measured in 1967, 1968, and 1973 are widely used in theoretical research, and the new results provide better constraints on the conclusions from such studies.

nucl-ex↗

arXiv2019

Entanglement of Purification in Holographic Systems

The holographic entanglement of purification (EoP) in AdS$_{4}$ and AdS-RN black hole backgrounds is studied. We develop an algorithm to compute the EoP for bipartite configuration with infinitely long strips. The temperature behavior of EoP is revealed for small, intermediate and large configurations: EoP monotonically increases with the temperature for small configurations; while for intermediate configurations, EoP is configuration-dependent; EoP vanishes for large configurations. Our numerical results verify some important inequalities of EoP, which we also prove geometrically in Poincaré coordinate.

hep-th↗

arXiv2019

The Effect of Anisotropy on Holographic Entanglement Entropy and Mutual Information

We study the effect of anisotropy on holographic entanglement entropy (HEE) and holographic mutual information (MI) in the Q-lattice model, by exploring the HEE and MI for infinite strips along arbitrary directions. We find that the lattice always enhances the HEE. The MI, however, is enhanced by lattice for large sub-regions; while for small sub-regions, the MI is suppressed by the lattice. We also discuss how these phenomena result from the deformation of geometry caused by Q-lattices.

hep-th↗

arXiv2019

A speculation about a puzzled result in energy spectrum of cosmic-ray electrons around TeV energies

Nuclear Coulomb potential at completely ionized and extremely thin atmosphere can leaks to a macroscopic spatial scale. This effect is used to explain the difference between the energy spectra of cosmic-ray electrons around $1 TeV$ measured by different experimental groups. The result inspires us to review the traditional electromagnetic shower theory at the extreme conditions. It is also reminds us that the energy spectrum of cosmic-ray electrons, which are measured by Fermi-LAT and DAMPE at a higher altitude is more closer to a true signature.

astro-ph.HE↗

arXiv2019

Bias induced spin state transition mediated by electron excitations

Recent experiments reported that spin-state transitions were realized by applying bias voltages. But these bias-induced spin state transitions (BISSTs) are not fully understood, especially the mechanism. It is well known that the metal-to-ligand charge transfer excitation (MLCT) and the metal-centered excitation (MC) activated by light radiation can induce the transition from low spin (LS) to high spin (HS) and that from HS to LS. Moreover, electronic excitations are accessible by inelastic cotunneling in molecular junctions with bias voltages applied. Based on these two experimental facts, we propose the MLCT basically leads to the BISST from LS to HS, and the MC results in the BISST from HS to LS. The rationality of the mechanism is demonstrated by comparing first-principles results and experimental observations. The calculated voltage threshold for activating the MLCT (MC) is close to the experimental voltage for observing the BISST from LS to HS (from HS to LS). The activation of MLCT (MC) depends on the bias polarity, which can explain the bias-polarity dependence of BISST in the experiment. Our study is important for further design of molecular spintronic devices working on spin state transition.

cond-mat.mes-hall↗

arXiv2019

DurIAN: Duration Informed Attention Network For Multimodal Synthesis

In this paper, we present a generic and robust multimodal synthesis system that produces highly natural speech and facial expression simultaneously. The key component of this system is the Duration Informed Attention Network (DurIAN), an autoregressive model in which the alignments between the input text and the output acoustic features are inferred from a duration model. This is different from the end-to-end attention mechanism used, and accounts for various unavoidable artifacts, in existing end-to-end speech synthesis systems such as Tacotron. Furthermore, DurIAN can be used to generate high quality facial expression which can be synchronized with generated speech with/without parallel speech and face data. To improve the efficiency of speech generation, we also propose a multi-band parallel generation strategy on top of the WaveRNN model. The proposed Multi-band WaveRNN effectively reduces the total computational complexity from 9.8 to 5.5 GFLOPS, and is able to generate audio that is 6 times faster than real time on a single CPU core. We show that DurIAN could generate highly natural speech that is on par with current state of the art end-to-end systems, while at the same time avoid word skipping/repeating errors in those systems. Finally, a simple yet effective approach for fine-grained control of expressiveness of speech and facial expression is introduced.

cs.CL↗

arXiv2019

Sojourn times of Gaussian processes with trend

We derive exact tail asymptotics of sojourn time above the level $u\geq 0$ $$ \mathbb{P}\left(v(u)\int_0^T \mathbb{I}(X(t)-ct>u)d t>x\right), \quad x\geq 0 $$ as $u\to\infty$, where $X$ is a Gaussian process with continuous sample paths, $c>0$, $v(u)$ is a positive function of $u$ and $T\in (0,\infty]$. Additionally, we analyze asymptotic distributional properties of $$τ_u(x):=\inf\left\{t\geq 0: v(u) \int_0^t \mathbb{I}(X(s)-cs>u)d s>x\right\}, $$ as $u\to\infty$, $x\geq 0$, where $\inf\emptyset=\infty$. The findings of this contribution are illustrated by a detailed analysis of a class of Gaussian processes with stationary increments and a family of self-similar processes.

math.PR↗

arXiv2019

Good Motive but Bad Design: Why ARM MPU Has Become an Outcast in Embedded Systems

As more and more embedded devices are connected to the Internet, leading to the emergence of Internet-of-Things (IoT), previously less tested (and insecure) devices are exposed to miscreants. To prevent them from being compromised, the memory protection unit (MPU), which is readily available on many devices, has the potential to become a free lunch for the defenders. To our surprise, the MPU is seldom used by real-world products. The reasons are multi-fold. While there are non-technical reasons such as compatibility issues, more importantly, we found that MPU brings virtually no security enhancement at the expense of decreased performance and responsiveness. In this work, we investigate the MPU adoption in major real-time operating systems (RTOSs), in particular, the FreeRTOS, and try to pinpoint the fundamental reasons to explain why MPU is not favored. We hope our findings can inspire new remedial solutions to change the situation. We also review the latest MPU design and provide technical suggestions to build more secure embedded systems.

cs.CR↗

arXiv2019

Learning Privately over Distributed Features: An ADMM Sharing Approach

Distributed machine learning has been widely studied in order to handle exploding amount of data. In this paper, we study an important yet less visited distributed learning problem where features are inherently distributed or vertically partitioned among multiple parties, and sharing of raw data or model parameters among parties is prohibited due to privacy concerns. We propose an ADMM sharing framework to approach risk minimization over distributed features, where each party only needs to share a single value for each sample in the training process, thus minimizing the data leakage risk. We establish convergence and iteration complexity results for the proposed parallel ADMM algorithm under non-convex loss. We further introduce a novel differentially private ADMM sharing algorithm and bound the privacy guarantee with carefully designed noise perturbation. The experiments based on a prototype system shows that the proposed ADMM algorithms converge efficiently in a robust fashion, demonstrating advantage over gradient based methods especially for data set with high dimensional feature spaces.

cs.LG↗

arXiv2019

Tail Asymptotic Behavior of the supremum of a class of chi-square processes

In this paper, we analyze a multivariate counterpart of the generalized weighted Kolmogorov-Smirnov statistic, which is the supremum of weighted locally stationary chi-square process over non-compact interval. The boundedness and the exact tail asymptotic behavior of the statistics are derived. We illustrate our findings by two examples where the statistic is defined by Brownian bridge and fractional Brownian motion respectively.

math.PR↗

arXiv2019

Discovering and Understanding the Security Hazards in the Interactions between IoT Devices, Mobile Apps, and Clouds on Smart Home Platforms

A smart home connects tens of home devices to the Internet, where an IoT cloud runs various home automation applications. While bringing unprecedented convenience and accessibility, it also introduces various security hazards to users. Prior research studied smart home security from several aspects. However, we found that the complexity of the interactions among the participating entities (i.e., devices, IoT clouds, and mobile apps) has not yet been systematically investigated. In this work, we conducted an in-depth analysis of five widely-used smart home platforms. Combining firmware analysis, network traffic interception, and blackbox testing, we reverse-engineered the details of the interactions among the participating entities. Based on the details, we inferred three legitimate state transition diagrams for the three entities, respectively. Using these state machines as a reference model, we identified a set of unexpected state transitions. To confirm and trigger the unexpected state transitions, we implemented a set of phantom devices to mimic a real device. By instructing the phantom devices to intervene in the normal entity-entity interactions, we have discovered several new vulnerabilities and a spectrum of attacks against real-world smart home platforms.

cs.CR↗

arXiv2019

SvTPM: A Secure and Efficient vTPM in the Cloud

Virtual Trusted Platform Modules (vTPMs) have been widely used in commercial cloud platforms (e.g. Google Cloud, VMware Cloud, and Microsoft Azure) to provide virtual root-of-trust for virtual machines. Unfortunately, current state-of-the-art vTPM implementations are suffering from confidential data leakage and high performance overhead. In this paper, we present SvTPM, a secure and efficient software-based vTPM implementation based on hardware-rooted Trusted Execution Environment (TEE), providing a whole life cycle protection of vTPMs in the cloud. SvTPM offers strong isolation protection, so that cloud tenants or even cloud administrators cannot get vTPM's private keys or any other sensitive data. In SvTPM, we identify and solve a couple of critical security challenges for vTPM protection with SGX, such as NVRAM replacement attack, rollback attacks, trust establishment, and a fine-grained trusted clock. We implement a prototype of SvTPM on both QEMU and KVM. Performance evaluation results show that SvTPM achieves orders of magnitude of performance gains comparing to the vTPMs protected with physical TPM. The launch time of SvTPM is 2600$\times$ faster than vTPMs built upon hardware TPM. In the micro-benchmarks evaluation, we find that the command execution latency of SvTPM is smaller than or equal to the existing schemes.

cs.CR↗