arXiv subjects

Fan Zhang

Publications and source records attributed to Fan Zhang.

At least 775 records · Page 43Linked to original sources

arXiv2020

Video compression with low complexity CNN-based spatial resolution adaptation

It has recently been demonstrated that spatial resolution adaptation can be integrated within video compression to improve overall coding performance by spatially down-sampling before encoding and super-resolving at the decoder. Significant improvements have been reported when convolutional neural networks (CNNs) were used to perform the resolution up-sampling. However, this approach suffers from high complexity at the decoder due to the employment of CNN-based super-resolution. In this paper, a novel framework is proposed which supports the flexible allocation of complexity between the encoder and decoder. This approach employs a CNN model for video down-sampling at the encoder and uses a Lanczos3 filter to reconstruct full resolution at the decoder. The proposed method was integrated into the HEVC HM 16.20 software and evaluated on JVET UHD test sequences using the All Intra configuration. The experimental results demonstrate the potential of the proposed approach, with significant bitrate savings (more than 10%) over the original HEVC HM, coupled with reduced computational complexity at both encoder (29%) and decoder (10%).

eess.IV↗

arXiv2020

Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization

ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is one of the most authoritative academic competitions in the field of Computer Vision (CV) in recent years. But applying ILSVRC's annual champion directly to fine-grained visual categorization (FGVC) tasks does not achieve good performance. To FGVC tasks, the small inter-class variations and the large intra-class variations make it a challenging problem. Our attention object location module (AOLM) can predict the position of the object and attention part proposal module (APPM) can propose informative part regions without the need of bounding-box or part annotations. The obtained object images not only contain almost the entire structure of the object, but also contains more details, part images have many different scales and more fine-grained features, and the raw images contain the complete object. The three kinds of training images are supervised by our multi-branch network. Therefore, our multi-branch and multi-scale learning network(MMAL-Net) has good classification ability and robustness for images of different scales. Our approach can be trained end-to-end, while provides short inference time. Through the comprehensive experiments demonstrate that our approach can achieves state-of-the-art results on CUB-200-2011, FGVC-Aircraft and Stanford Cars datasets. Our code will be available at https://github.com/ZF1044404254/MMAL-Net

cs.CV↗

arXiv2020

Active Fuzzing for Testing and Securing Cyber-Physical Systems

Cyber-physical systems (CPSs) in critical infrastructure face a pervasive threat from attackers, motivating research into a variety of countermeasures for securing them. Assessing the effectiveness of these countermeasures is challenging, however, as realistic benchmarks of attacks are difficult to manually construct, blindly testing is ineffective due to the enormous search spaces and resource requirements, and intelligent fuzzing approaches require impractical amounts of data and network access. In this work, we propose active fuzzing, an automatic approach for finding test suites of packet-level CPS network attacks, targeting scenarios in which attackers can observe sensors and manipulate packets, but have no existing knowledge about the payload encodings. Our approach learns regression models for predicting sensor values that will result from sampled network packets, and uses these predictions to guide a search for payload manipulations (i.e. bit flips) most likely to drive the CPS into an unsafe state. Key to our solution is the use of online active learning, which iteratively updates the models by sampling payloads that are estimated to maximally improve them. We evaluate the efficacy of active fuzzing by implementing it for a water purification plant testbed, finding it can automatically discover a test suite of flow, pressure, and over/underflow attacks, all with substantially less time, data, and network access than the most comparable approach. Finally, we demonstrate that our prediction models can also be utilised as countermeasures themselves, implementing them as anomaly detectors and early warning systems.

cs.SE↗

arXiv2020

Managing Recurrent Virtual Network Updates in Multi-Tenant Datacenters: A System Perspective

With the advent of software-defined networking, network configuration through programmable interfaces becomes practical, leading to various on-demand opportunities for network routing update in multi-tenant datacenters, where tenants have diverse requirements on network routings such as short latency, low path inflation, large bandwidth, high reliability, etc. Conventional solutions that rely on topology search coupled with an objective function https:// www.overleaf.com/project/5beb742041ab9c0e3caec84f to find desired routings have at least two shortcomings: (i) they run into scalability issues when handling consistent and frequent routing updates and (ii) they restrict the flexibility and capability to satisfy various routing requirements. To address these issues, this paper proposes a novel search and optimization decoupled design, which not only saves considerable topology search costs via search result reuse, but also avoids possible sub-optimality in greedy routing search algorithms by making decisions based on the global view of all possible routings. We implement a prototype of our proposed system, OpReduce, and perform extensive evaluations to validate its design goals.

cs.CR↗

arXiv2020

MediaPipe Hands: On-device Real-time Hand Tracking

We present a real-time on-device hand tracking pipeline that predicts hand skeleton from single RGB camera for AR/VR applications. The pipeline consists of two models: 1) a palm detector, 2) a hand landmark model. It's implemented via MediaPipe, a framework for building cross-platform ML solutions. The proposed model and pipeline architecture demonstrates real-time inference speed on mobile GPUs and high prediction quality. MediaPipe Hands is open sourced at https://mediapipe.dev.

cs.CV↗

arXiv2020

BlazePose: On-device Real-time Body Pose tracking

We present BlazePose, a lightweight convolutional neural network architecture for human pose estimation that is tailored for real-time inference on mobile devices. During inference, the network produces 33 body keypoints for a single person and runs at over 30 frames per second on a Pixel 2 phone. This makes it particularly suited to real-time use cases like fitness tracking and sign language recognition. Our main contributions include a novel body pose tracking solution and a lightweight body pose estimation neural network that uses both heatmaps and regression to keypoint coordinates.

cs.CV↗

arXiv2020

Integrating global spatial features in CNN based Hyperspectral/SAR imagery classification

The land cover classification has played an important role in remote sensing because it can intelligently identify things in one huge remote sensing image to reduce the work of humans. However, a lot of classification methods are designed based on the pixel feature or limited spatial feature of the remote sensing image, which limits the classification accuracy and universality of their methods. This paper proposed a novel method to take into the information of remote sensing image, i.e., geographic latitude-longitude information. In addition, a dual-branch convolutional neural network (CNN) classification method is designed in combination with the global information to mine the pixel features of the image. Then, the features of the two neural networks are fused with another fully neural network to realize the classification of remote sensing images. Finally, two remote sensing images are used to verify the effectiveness of our method, including hyperspectral imaging (HSI) and polarimetric synthetic aperture radar (PolSAR) imagery. The result of the proposed method is superior to the traditional single-channel convolutional neural network.

cs.LG↗

arXiv2020

MultiResolution Attention Extractor for Small Object Detection

Small objects are difficult to detect because of their low resolution and small size. The existing small object detection methods mainly focus on data preprocessing or narrowing the differences between large and small objects. Inspired by human vision "attention" mechanism, we exploit two feature extraction methods to mine the most useful information of small objects. Both methods are based on multiresolution feature extraction. We initially design and explore the soft attention method, but we find that its convergence speed is slow. Then we present the second method, an attention-based feature interaction method, called a MultiResolution Attention Extractor (MRAE), showing significant improvement as a generic feature extractor in small object detection. After each building block in the vanilla feature extractor, we append a small network to generate attention weights followed by a weighted-sum operation to get the final attention maps. Our attention-based feature extractor is 2.0 times the AP of the "hard" attention counterpart (plain architecture) on the COCO small object detection benchmark, proving that MRAE can capture useful location and contextual information through adaptive learning.

cs.CV↗

arXiv2020

Exact Simulation for Multivariate Itô Diffusions

We provide the first generic exact simulation algorithm for multivariate diffusions. Current exact sampling algorithms for diffusions require the existence of a transformation which can be used to reduce the sampling problem to the case of a constant diffusion matrix and a drift which is the gradient of some function. Such transformation, called Lamperti transformation, can be applied in general only in one dimension. So, completely different ideas are required for exact sampling of generic multivariate diffusions. The development of these ideas is the main contribution of this paper. Our strategy combines techniques borrowed from the theory of rough paths, on one hand, and multilevel Monte Carlo on the other.

math.PR↗

arXiv2020

Quasi-One-Dimensional Higher-Order Topological Insulators

Quasi-1D materials Bi$_{4}$X$_{4}$ (X=Br,I) are prototype weak topological insulators (TI) in the $β$ phase. For the $α$ phases, recent high-throughput database screening suggests that Bi$_{4}$Br$_{4}$ is a rare higher-order TI (HOTI) whereas Bi$_{4}$I$_{4}$ has trivial symmetry indicators. Here we show that in fact the two $α$ phases are both pristine HOTIs yet with distinct termination-dependent hinge state patterns by performing first-principles calculations, analyzing coupled-edge dimerizations, inspecting surface lattice structures, constructing tight-binding models, and establishing boundary topological invariants. We reveal that the location of inversion center dictates Bi$_{4}$Br$_{4}$ (Bi$_{4}$I$_{4}$) to feature opposite (the same) dimerizations of a surface or intrinsic (bulk or extrinsic) origin at two side cleavage surfaces. We propose a variety of experiments to examine our predictions. Given the superior hinges along atomic chains, the structural transition at room temperature, and the extreme anisotropies in three axes, our results not only imply the possible existence of many topological materials beyond the scope of symmetry indicators but also establish a new TI paradigm and a unique material platform for exploring the interplay of geometry, symmetry, topology, and interaction.

cond-mat.mes-hall↗

arXiv2020

Encoding in the Dark Grand Challenge: An Overview

A big part of the video content we consume from video providers consists of genres featuring low-light aesthetics. Low light sequences have special characteristics, such as spatio-temporal varying acquisition noise and light flickering, that make the encoding process challenging. To deal with the spatio-temporal incoherent noise, higher bitrates are used to achieve high objective quality. Additionally, the quality assessment metrics and methods have not been designed, trained or tested for this type of content. This has inspired us to trigger research in that area and propose a Grand Challenge on encoding low-light video sequences. In this paper, we present an overview of the proposed challenge, and test state-of-the-art methods that will be part of the benchmark methods at the stage of the participants' deliverable assessment. From this exploration, our results show that VVC already achieves a high performance compared to simply denoising the video source prior to encoding. Moreover, the quality of the video streams can be further improved by employing a post-processing image enhancement method.

eess.IV↗

arXiv2020

Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting

Unsupervised domain adaptation (UDA) for nuclei instance segmentation is important for digital pathology, as it alleviates the burden of labor-intensive annotation and domain shift across datasets. In this work, we propose a Cycle Consistency Panoptic Domain Adaptive Mask R-CNN (CyC-PDAM) architecture for unsupervised nuclei segmentation in histopathology images, by learning from fluorescence microscopy images. More specifically, we first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images. Secondly, a semantic branch with a domain discriminator is designed to achieve panoptic-level domain adaptation. Thirdly, in order to avoid the influence of the source-biased features, we propose a task re-weighting mechanism to dynamically add trade-off weights for the task-specific loss functions. Experimental results on three datasets indicate that our proposed method outperforms state-of-the-art UDA methods significantly, and demonstrates a similar performance as fully supervised methods.

cs.CV↗

arXiv2020

TRAKO: Efficient Transmission of Tractography Data for Visualization

Fiber tracking produces large tractography datasets that are tens of gigabytes in size consisting of millions of streamlines. Such vast amounts of data require formats that allow for efficient storage, transfer, and visualization. We present TRAKO, a new data format based on the Graphics Layer Transmission Format (glTF) that enables immediate graphical and hardware-accelerated processing. We integrate a state-of-the-art compression technique for vertices, streamlines, and attached scalar and property data. We then compare TRAKO to existing tractography storage methods and provide a detailed evaluation on eight datasets. TRAKO can achieve data reductions of over 28x without loss of statistical significance when used to replicate analysis from previously published studies.

eess.IV↗

arXiv2020

AdaNN: Adaptive Neural Network-based Equalizer via Online Semi-supervised Learning

The demand for high speed data transmission has increased rapidly, leading to advanced optical communication techniques. In the past few years, multiple equalizers based on neural network (NN) have been proposed to recover signal from nonlinear distortions. However, previous experiments mainly focused on achieving low bit error rate (BER) on certain dataset with an offline-trained NN, neglecting the generalization ability of NN-based equalizer when the properties of optical link change. The development of efficient online training scheme is urgently needed. In this paper, we've proposed an adaptive online training scheme, which can fine-tune parameters of NN-based equalizer without the help of an online training sequence. By introducing data augmentation and virtual adversarial training, the convergence speed has been accelerated by 4.5 times, compared with decision-directed self-training. The proposed adaptive NN-based equalizer is called "AdaNN". Its BER has been evaluated under two scenarios: a 56 Gb/s PAM4-modulated VCSEL-MMF optical link (100-m), and a 32 Gbaud 16QAM-modulated Nyquist-WDM system (960-km SSMF). In our experiments, with the help of AdaNN, BER values can be quickly stabilized below 1e-3 after trained with 10^5 unlabeled symbols. AdaNN shows great performance improvement compared with non-adaptive NN and conventional MLSE.

eess.SP↗

arXiv2020

First-principles study of metal-graphene edge contact for ballistic Josephson junction

Edge-contacted superconductor-graphene-superconductor Josephson junction have been utilized to realize topological superconductivity, which have shown superconducting signatures in the quantum Hall regime. We perform the first-principles calculations to interpret electronic couplings at the superconductor-graphene edge contacts by investigating various aspects in hybridization of molybdenum d orbitals and graphene $π$ orbitals. We also reveal that interfacial oxygen defects play an important role in determining the doping type of graphene near the interface.

cond-mat.supr-con↗

arXiv2020

Design and Calibration of the High Energy Particle Monitor onboard the Insight-HXMT

Three high energy particle monitors (HPMs) employed onboard the Hard X-ray Modulation Telescope Insight-HXMT) can detect the charged particles from South Atlantic Anomaly (SAA) and hence provide the alert trigger for switch-on/off of the main detectors. Here a typical design of HPM with high stability and reliability is adopted by taking a plastic scintillator coupled with a small photomultiplier tube (PMT). The window threshold of HPM is designed as 1 MeV and 20 MeV for the incident electron and proton, respectively. Before the launch of Insight-HXMT, we performed in details the ground calibration of HPM. The measured energy response and its dependence on temperature are taken as essential input of Geant4 simulation for estimating the HPM count rate given with an incident particle energy spectrum. This serves as a guidance for choosing a reasonable working range of the PMT high voltage once the real SAA count rate is measured by HPM in orbit. So far the three HPMs have been working in orbit for more than two years. Apart from providing reliable alert trigger, the HPMs data are used as well to map the SAA region.

physics.ins-det↗

arXiv2020

MIMO Amplify-and-Forward Precoding for Networked Control Systems

In this paper, we consider a MIMO networked control system (NCS) in which a sensor amplifies and forwards the observed MIMO plant state to a remote controller via a MIMO fading channel. We focus on the MIMO amplify-and-forward (AF) precoding design at the sensor to minimize a weighted average state estimation error at the remote controller subject to an average communication power gain constraint of the sensor. The MIMO AF precoding design is formulated as an infinite horizon average cost Markov decision process (MDP). To deal with the curse of dimensionality associated with the MDP, we propose a novel continuous-time perturbation approach and derive an asymptotically optimal closed-form priority function for the MDP. Based on this, we derive a closed-form first-order optimal dynamic MIMO AF precoding solution, and the solution has an event-driven control structure. Specifically, the sensor activates the strongest eigenchannel to deliver a dynamically weighted combination of the plant states to the controller when the accumulated state estimation error exceeds a dynamic threshold. We further establish technical conditions for ensuring the stability of the MIMO NCS, and show that the mean square error of the plant state estimation is $\mathcal{O}\left(\frac{1}{\bar{F}}\right)$, where $\bar{F}$ is the maximum AF gain of the MIMO AF precoding.

cs.IT↗

arXiv2020

Inelastic electron tunneling in 2H-Ta$_x$Nb$_{1-x}$Se$_2$ evidenced by scanning tunneling spectroscopy

We report a detailed study of tunneling spectra measured on 2H-Ta$_x$Nb$_{1-x}$Se$_2$ ($x=0\sim 0.1$) single crystals using a low-temperature scanning tunneling microscope. The prominent gap-like feature unintelligible for a long time was found to be accompanied by some "in-gap" fine structures. By investigating the second-derivative spectra and their temperature and magnetic field dependencies, we were able to prove that inelastic electron tunneling is the origin of these features and obtain the Eliashberg function of 2H-Ta$_x$Nb$_{1-x}$Se$_2$ at atomic scale, providing a potential way to study the local Eliashberg function and phonon spectra of the related transition-metal dichalcogenides.

cond-mat.mtrl-sci↗