arXiv subjects

Ling Li

Publications and source records attributed to Ling Li.

At least 127 records · Page 7Linked to original sources

arXiv2021

Active Terahertz Imaging Dataset for Concealed Object Detection

Concealed object detection in Terahertz imaging is an urgent need for public security and counter-terrorism. In this paper, we provide a public dataset for evaluating multi-object detection algorithms in active Terahertz imaging resolution 5 mm by 5 mm. To the best of our knowledge, this is the first public Terahertz imaging dataset prepared to evaluate object detection algorithms. Object detection on this dataset is much more difficult than on those standard public object detection datasets due to its inferior imaging quality. Facing the problem of imbalanced samples in object detection and hard training samples, we evaluate four popular detectors: YOLOv3, YOLOv4, FRCN-OHEM, and RetinaNet on this dataset. Experimental results indicate that the RetinaNet achieves the highest mAP. In addition, we demonstrate that hiding objects in different parts of the human body affect detection accuracy. The dataset is available at https://github.com/LingLIx/THz_Dataset.

cs.CV↗

arXiv2021

Product semantics translation from brain activity via adversarial learning

A small change of design semantics may affect a user's satisfaction with a product. To modify a design semantic of a given product from personalised brain activity via adversarial learning, in this work, we propose a deep generative transformation model to modify product semantics from the brain signal. We attempt to accomplish such synthesis: 1) synthesising the product image with new features corresponding to EEG signal; 2) maintaining the other image features that irrelevant to EEG signal. We leverage the idea of StarGAN and the model is designed to synthesise products with preferred design semantics (colour & shape) via adversarial learning from brain activity, and is applied with a case study to generate shoes with different design semantics from recorded EEG signals. To verify our proposed cognitive transformation model, a case study has been presented. The results work as a proof-of-concept that our framework has the potential to synthesis product semantic from brain activity.

cs.MM↗

arXiv2021

Verifying Design through Generative Visualization of Neural Activities

Current neuroscience focused approaches for evaluating the effectiveness of a design do not use direct visualisation of mental activity. A recurrent neural network is used as the encoder to learn latent representation from electroencephalogram (EEG) signals, recorded while subjects looked at 50 categories of images. A generative adversarial network (GAN) conditioned on the EEG latent representation is trained for reconstructing these images. After training, the neural network is able to reconstruct images from brain activity recordings. To demonstrate the proposed method in the context of the mental association with a design, we performed a study that indicates an iconic design image could inspire the subject to create cognitive associations with branding and valued products. The proposed method could have the potential in verifying designs by visualizing the cognitive understanding of underlying brain activity.

q-bio.NC↗

arXiv2021

CANet: Context Aware Network for 3D Brain Glioma Segmentation

Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning. Based on deep neural networks, previous studies have shown promising technologies for brain glioma segmentation. However, these approaches lack powerful strategies to incorporate contextual information of tumor cells and their surrounding, which has been proven as a fundamental cue to deal with local ambiguity. In this work, we propose a novel approach named Context-Aware Network (CANet) for brain glioma segmentation. CANet captures high dimensional and discriminative features with contexts from both the convolutional space and feature interaction graphs. We further propose context guided attentive conditional random fields which can selectively aggregate features. We evaluate our method using publicly accessible brain glioma segmentation datasets BRATS2017, BRATS2018 and BRATS2019. The experimental results show that the proposed algorithm has better or competitive performance against several State-of-The-Art approaches under different segmentation metrics on the training and validation sets.

cs.CV↗

arXiv2021

Daisy: Data analysis integrated software system for X-ray experiments

Daisy (Data Analysis Integrated Software System) has been designed for the analysis and visualization of the X-ray experiments. To address an extensive range of Chinese radiation facilities community's requirements from purely algorithmic problems to scientific computing infrastructure, Daisy sets up a cloud-native platform to support on-site data analysis services with fast feedback and interaction. The plugs-in based application is convenient to process the expected high throughput data flow in parallel at next-generation facilities such as the High Energy Photon Source (HEPS). The objectives, functionality and architecture of Daisy are described in this article.

hep-ex↗

arXiv2021

Using the Split Bregman Algorithm to Solve the Self-repelling Snake Model

Preserving contour topology during image segmentation is useful in many practical scenarios. By keeping the contours isomorphic, it is possible to prevent over-segmentation and under-segmentation, as well as to adhere to given topologies. The Self-repelling Snake model (SR) is a variational model that preserves contour topology by combining a non-local repulsion term with the geodesic active contour model (GAC). The SR is traditionally solved using the additive operator splitting (AOS) scheme. In our paper, we propose an alternative solution to the SR using the Split Bregman method. Our algorithm breaks the problem down into simpler sub-problems to use lower-order evolution equations and a simple projection scheme rather than re-initialization. The sub-problems can be solved via fast Fourier transform (FFT) or an approximate soft thresholding formula which maintains stability, shortening the convergence time, and reduces the memory requirement. The Split Bregman and AOS algorithms are compared theoretically and experimentally.

cs.CV↗

arXiv2021

A General Framework for Revealing Human Mind with auto-encoding GANs

Addressing the question of visualising human mind could help us to find regions that are associated with observed cognition and responsible for expressing the elusive mental image, leading to a better understanding of cognitive function. The traditional approach treats brain decoding as a classification problem, reading the mind through statistical analysis of brain activity. However, human thought is rich and varied, that it is often influenced by more of a combination of object features than a specific type of category. For this reason, we propose an end-to-end brain decoding framework which translates brain activity into an image by latent space alignment. To find the correspondence from brain signal features to image features, we embedded them into two latent spaces with modality-specific encoders and then aligned the two spaces by minimising the distance between paired latent representations. The proposed framework was trained by simultaneous electroencephalogram and functional MRI data, which were recorded when the subjects were viewing or imagining a set of image stimuli. In this paper, we focused on implementing the fMRI experiment. Our experimental results demonstrated the feasibility of translating brain activity to an image. The reconstructed image matches image stimuli approximate in both shape and colour. Our framework provides a promising direction for building a direct visualisation to reveal human mind.

q-bio.NC↗

arXiv2021

Causal Factors, Benefits and Challenges of Test-Driven Development: Practitioner Perceptions

This report describes the experiences of one organization's adoption of Test Driven Development (TDD) practices as part of a medium-term software project employing Extreme Programming as a methodology. Three years into this project the team's TDD experiences are compared with their non-TDD experiences on other ongoing projects. The perceptions of the benefits and challenges of using TDD in this context are gathered through five semi-structured interviews with key team members. Their experiences indicate that use of TDD has generally been positive and the reasons for this are explored to deepen the understanding of TDD practice and its effects on code quality, application quality and development productivity. Lessons learned are identified to aid others with the adoption and implementation of TDD practices, and some potential further research areas are suggested.

cs.SE↗

arXiv2020

Electrically Tunable Multifunctional All-Dielectric Metasurfaces Integrated with Liquid Crystals in the Visible

As two-dimensional metamaterials, metasurfaces open up new avenues for designing static planar optics. However, the dynamic modulation of metasurfaces in the optical band is required for practical applications. The existing dynamic devices rarely utilized the polarization manipulation capability of metasurfaces. Here, we demonstrate an electrically tunable multifunctional metasurface in the visible range by integrating birefringent liquid crystals (LCs) with all-dielectric metasurfaces based on a novel packaging scheme. By combining the helicity-dependent geometric phase of the metasurface and the polarization control ability of LC molecules, continuous intensity tuning and switching of two helicity channels are realized. Electrically tunable single-channel switchable metaholograms, multicolor multiplexed metaholograms, and dynamic varifocal metalenses are designed to verify the concept. The exploration of polarization control in dynamic tuning can pave the way for dynamic metasurface devices in various applications, such as space light modulators, light detection and ranging systems, and holographic displays.

physics.optics↗

arXiv2020

Few-layer hyperbolic multilayer for spontaneous emission enhancement

Multilayer hyperbolic metamaterials consisting of alternating metal and dielectric layers have important applications in spontaneous emission enhancement. In contrast to the conventional choice of at least dozens of layers in multilayer structures to achieve tunable Purcell effect on quantum emitters, our numerical calculations reveal that multilayers with fewer layers and thinner layers would outperform in Purcell effect. These discoveries are attributed to the negative contributions by an increasing layer number to the imaginary part of the reflection coefficient, and the stronger coupling between surface plasmon polariton modes on a thinner metal layer. This work could provide fundamental insights and practical guide for optimizing the local density of optical states enhancement functionality of ultrathin and even two-dimensional photon sources.

physics.optics↗

arXiv2020

A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19

Epidemic models play a key role in understanding and responding to the emerging COVID-19 pandemic. Widely used compartmental models are static and are of limited use to evaluate intervention strategies with the emerging pandemic. Applying the technology of data assimilation, we propose a Bayesian updating approach for estimating epidemiological parameters using observable information for the purpose of assessing the impacts of different intervention strategies. We adopt a concise renewal model and propose new parameters by disentangling the reduction of instantaneous reproduction number Rt into mitigation and suppression factors for quantifying intervention impacts at a finer granularity. Then we developed a data assimilation framework for estimating these parameters including constructing an observation function and developing a Bayesian updating scheme. A statistical analysis framework is then built to quantify the impact of intervention strategies by monitoring the evolution of these estimated parameters. By Investigating the impacts of intervention measures of European countries, the United States and Wuhan with the framework, we reveal the effects of interventions in these countries and the resurgence risk in the USA.

q-bio.PE↗

arXiv2020

Mesoscale investigations of fluid-solid interaction: Liquid slip flow in a parallel-plate microchannel

Liquid slip flow with a Knudsen number Kn = 0.001-0.1 plays a dominant role in confined flow channels. Its physical origin can be attributed to the intermolecular fluid-solid (F-S) interaction force. To this end, we propose herein continuous force functions (decaying either exponentially or by a power law) between fluid particles and two confined flat walls in the framework of the mesoscopic lattice Boltzmann model (LBM). The analytical solutions for density profile, velocity profile, slip length, and permeability ratio are derived and related to the mesoscale F-S interaction parameters and the size of the gap of the flow channel. Through nondimensionalization of the analytical solutions, we obtain the dimensionless numbers that indicate the key feature of slip-flow systems for each of the proposed force functions. The analytical solutions are strictly consistent with the LBM solutions. We suggest reasonable ranges for the F-S interaction parameters based on the observed range of density ratio (film fluid to bulk fluid) and the increasing permeability ratios with narrowing gap size. Within the given range of interaction parameters, simple relationships between permeability ratios and dimensionless numbers are provided by fitting. The curves for continuous F-S interaction force with two free parameters are calibrated for a hydrophobic surface by using LBM simulations, which were validated a priori by comparison with the slip velocity profile measured in a benchmark flow experiment. The mesoscopic LBM model based on the proposed F-S interaction force functions provides a robust framework to elucidate the physical process of liquid slip flow.

physics.flu-dyn↗

arXiv2020

Regularizing Semi-supervised Graph Convolutional Networks with a Manifold Smoothness Loss

Existing graph convolutional networks focus on the neighborhood aggregation scheme. When applied to semi-supervised learning, they often suffer from the overfitting problem as the networks are trained with the cross-entropy loss on a small potion of labeled data. In this paper, we propose an unsupervised manifold smoothness loss defined with respect to the graph structure, which can be added to the loss function as a regularization. We draw connections between the proposed loss with an iterative diffusion process, and show that minimizing the loss is equivalent to aggregate neighbor predictions with infinite layers. We conduct experiments on multi-layer perceptron and existing graph networks, and demonstrate that adding the proposed loss can improve the performance consistently.

cs.LG↗

arXiv2019

A Novel General Compact Model Approach for 7nm Technology Node Circuit Optimization from Device Perspective and Beyond

This work presents a novel general compact model for 7nm technology node devices like FinFETs. As an extension of previous conventional compact model that based on some less accurate elements including one-dimensional Poisson equation for three-dimensional devices and analytical equations for short channel effects, quantum effects and other physical effects, the general compact model combining few TCAD calibrated compact models with statistical methods can eliminate the tedious physical derivations. The general compact model has the advantages of efficient extraction, high accuracy, strong scaling capability and excellent transfer capability. As a demo application, two key design knobs of FinFET and their multiple impacts on RC control ESD power clamp circuit are systematically evaluated with implementation of the newly proposed general compact model, accounting for device design, circuit performance optimization and variation control. The performance of ESD power clamp can be improved extremely. This framework is also suitable for pathfinding researches on 5nm node gate-all-around devices, like nanowire (NW) FETs, nanosheet (NSH) FETs and beyond.

cs.ET↗

arXiv2019

Separation Effect of Early Visual Cortex V1 Under Different Crowding Conditions A TMS Study

The visual crowding makes it difficult to identify the patterns in peripheral vision, but the neural mechanism for this phenomenon is still unclear because of different opinions. In order to study the separation effect of V1 under different crowding conditions, single-pulse transcranial magnetic stimulation is applied within the right V1. The experimental design includes two factors: TMS intensity (10%, 65%, and 90% of the phosphene threshold) and crowding (high and low) conditions. The accuracy results show that there is a strong interaction between crowding condition and TMS condition. When the TMS stimulation intensity is lower than the phosphene threshold, more crowding will be perceived under the high crowding condition, and less crowding will be perceived under the low crowding condition. The above results conclude that the high and low crowding condition separate by TMS stimulation. The results support the assumption that the crowding is related to V1 and occurs in the visual coding phase.

q-bio.NC↗

arXiv2019

A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images

In recent years, with the development of aerospace technology, we use more and more images captured by satellites to obtain information. But a large number of useless raw images, limited data storage resource and poor transmission capability on satellites hinder our use of valuable images. Therefore, it is necessary to deploy an on-orbit semantic segmentation model to filter out useless images before data transmission. In this paper, we present a detailed comparison on the recent deep learning models. Considering the computing environment of satellites, we compare methods from accuracy, parameters and resource consumption on the same public dataset. And we also analyze the relation between them. Based on experimental results, we further propose a viable on-orbit semantic segmentation strategy. It will be deployed on the TianZhi-2 satellite which supports deep learning methods and will be lunched soon.

cs.CV↗

arXiv2019

A Research and Strategy of Remote Sensing Image Denoising Algorithms

Most raw data download from satellites are useless, resulting in transmission waste, one solution is to process data directly on satellites, then only transmit the processed results to the ground. Image processing is the main data processing on satellites, in this paper, we focus on image denoising which is the basic image processing. There are many high-performance denoising approaches at present, however, most of them rely on advanced computing resources or rich images on the ground. Considering the limited computing resources of satellites and the characteristics of remote sensing images, we do some research on these high-performance ground image denoising approaches and compare them in simulation experiments to analyze whether they are suitable for satellites. According to the analysis results, we propose two feasible image denoising strategies for satellites based on satellite TianZhi-1.

eess.IV↗

arXiv2019

Velocity interpolation based Bounce-Back scheme for non-slip boundary condition in Lattice Boltzmann Method

Lattice Boltzmann Method(LBM) has achieved considerable success on simulating complex flows. However, how to impose correct boundary conditions on the fluid-solid interface with complex geometries is still an open question. Here we proposed a velocity interpolation based bounce-back scheme where the ideas of interpolated bounce-back and non-equilibrium extrapolation are combined. The proposed scheme is validated by several well-defined benchmark cases. It is shown that the proposed scheme offers a better accuracy at high Reynolds number and less dependency on solids positions which may crucial in many engineering and science applications.

physics.flu-dyn↗