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Zhiyong Liu

Publications and source records attributed to Zhiyong Liu.

At least 37 records · Page 2Linked to original sources
arXiv2023

Convergence of one-level and multilevel unsymmetric collocation for second order elliptic boundary value problems

Thepaperprovesconvergenceofone-levelandmultilevelunsymmetriccollocationforsecondorderelliptic boundary value problems on the bounded domains. By using Schaback's linear discretization theory,L2 errors are obtained based on the kernel-based trial spaces generated by the compactly supported radial basis functions. For the one-level unsymmetric collocation case, we obtain convergence when the testing discretization is finer than the trial discretization. The convergence rates depend on the regularity of the solution, the smoothness of the computing domain, and the approximation of scaled kernel-based spaces. The multilevel process is implemented by employing successive refinement scattered data sets and scaled compactly supported radial basis functions with varying support radii. Convergence of multilevel collocation is further proved based on the theoretical results of one-level unsymmetric collocation. In addition to having the same dependencies as the one-level collocation, the convergence rates of multilevel unsymmetric collocation especially depends on the increasing rules of scattered data and the selection of scaling parameters.

math.NA↗
arXiv2023

FIT: Frequency-based Image Translation for Domain Adaptive Object Detection

Domain adaptive object detection (DAOD) aims to adapt the detector from a labelled source domain to an unlabelled target domain. In recent years, DAOD has attracted massive attention since it can alleviate performance degradation due to the large shift of data distributions in the wild. To align distributions between domains, adversarial learning is widely used in existing DAOD methods. However, the decision boundary for the adversarial domain discriminator may be inaccurate, causing the model biased towards the source domain. To alleviate this bias, we propose a novel Frequency-based Image Translation (FIT) framework for DAOD. First, by keeping domain-invariant frequency components and swapping domain-specific ones, we conduct image translation to reduce domain shift at the input level. Second, hierarchical adversarial feature learning is utilized to further mitigate the domain gap at the feature level. Finally, we design a joint loss to train the entire network in an end-to-end manner without extra training to obtain translated images. Extensive experiments on three challenging DAOD benchmarks demonstrate the effectiveness of our method.

cs.CV↗
arXiv2023

Refined Pseudo labeling for Source-free Domain Adaptive Object Detection

Domain adaptive object detection (DAOD) assumes that both labeled source data and unlabeled target data are available for training, but this assumption does not always hold in real-world scenarios. Thus, source-free DAOD is proposed to adapt the source-trained detectors to target domains with only unlabeled target data. Existing source-free DAOD methods typically utilize pseudo labeling, where the performance heavily relies on the selection of confidence threshold. However, most prior works adopt a single fixed threshold for all classes to generate pseudo labels, which ignore the imbalanced class distribution, resulting in biased pseudo labels. In this work, we propose a refined pseudo labeling framework for source-free DAOD. First, to generate unbiased pseudo labels, we present a category-aware adaptive threshold estimation module, which adaptively provides the appropriate threshold for each category. Second, to alleviate incorrect box regression, a localization-aware pseudo label assignment strategy is introduced to divide labels into certain and uncertain ones and optimize them separately. Finally, extensive experiments on four adaptation tasks demonstrate the effectiveness of our method.

cs.CV↗
arXiv2023

Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation

Segmenting unseen objects is a crucial ability for the robot since it may encounter new environments during the operation. Recently, a popular solution is leveraging RGB-D features of large-scale synthetic data and directly applying the model to unseen real-world scenarios. However, the domain shift caused by the sim2real gap is inevitable, posing a crucial challenge to the segmentation model. In this paper, we emphasize the adaptation process across sim2real domains and model it as a learning problem on the BatchNorm parameters of a simulation-trained model. Specifically, we propose a novel non-parametric entropy objective, which formulates the learning objective for the test-time adaptation in an open-world manner. Then, a cross-modality knowledge distillation objective is further designed to encourage the test-time knowledge transfer for feature enhancement. Our approach can be efficiently implemented with only test images, without requiring annotations or revisiting the large-scale synthetic training data. Besides significant time savings, the proposed method consistently improves segmentation results on the overlap and boundary metrics, achieving state-of-the-art performance on unseen object instance segmentation.

cs.CV↗
arXiv2023

Zero-shot object goal visual navigation

Object goal visual navigation is a challenging task that aims to guide a robot to find the target object based on its visual observation, and the target is limited to the classes pre-defined in the training stage. However, in real households, there may exist numerous target classes that the robot needs to deal with, and it is hard for all of these classes to be contained in the training stage. To address this challenge, we study the zero-shot object goal visual navigation task, which aims at guiding robots to find targets belonging to novel classes without any training samples. To this end, we also propose a novel zero-shot object navigation framework called semantic similarity network (SSNet). Our framework use the detection results and the cosine similarity between semantic word embeddings as input. Such type of input data has a weak correlation with classes and thus our framework has the ability to generalize the policy to novel classes. Extensive experiments on the AI2-THOR platform show that our model outperforms the baseline models in the zero-shot object navigation task, which proves the generalization ability of our model. Our code is available at: https://github.com/pioneer-innovation/Zero-Shot-Object-Navigation.

cs.CV↗
arXiv2022

Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning

This paper tackles the problem of novel category discovery (NCD), which aims to discriminate unknown categories in large-scale image collections. The NCD task is challenging due to the closeness to the real-world scenarios, where we have only encountered some partial classes and images. Unlike other works on the NCD, we leverage the prototypes to emphasize the importance of category discrimination and alleviate the issue of missing annotations of novel classes. Concretely, we propose a novel adaptive prototype learning method consisting of two main stages: prototypical representation learning and prototypical self-training. In the first stage, we obtain a robust feature extractor, which could serve for all images with base and novel categories. This ability of instance and category discrimination of the feature extractor is boosted by self-supervised learning and adaptive prototypes. In the second stage, we utilize the prototypes again to rectify offline pseudo labels and train a final parametric classifier for category clustering. We conduct extensive experiments on four benchmark datasets and demonstrate the effectiveness and robustness of the proposed method with state-of-the-art performance.

cs.CV↗
arXiv2022

Weakly Aligned Feature Fusion for Multimodal Object Detection

To achieve accurate and robust object detection in the real-world scenario, various forms of images are incorporated, such as color, thermal, and depth. However, multimodal data often suffer from the position shift problem, i.e., the image pair is not strictly aligned, making one object has different positions in different modalities. For the deep learning method, this problem makes it difficult to fuse multimodal features and puzzles the convolutional neural network (CNN) training. In this article, we propose a general multimodal detector named aligned region CNN (AR-CNN) to tackle the position shift problem. First, a region feature (RF) alignment module with adjacent similarity constraint is designed to consistently predict the position shift between two modalities and adaptively align the cross-modal RFs. Second, we propose a novel region of interest (RoI) jitter strategy to improve the robustness to unexpected shift patterns. Third, we present a new multimodal feature fusion method that selects the more reliable feature and suppresses the less useful one via feature reweighting. In addition, by locating bounding boxes in both modalities and building their relationships, we provide novel multimodal labeling named KAIST-Paired. Extensive experiments on 2-D and 3-D object detection, RGB-T, and RGB-D datasets demonstrate the effectiveness and robustness of our method.

cs.CV↗
arXiv2019

Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection

Multispectral pedestrian detection has shown great advantages under poor illumination conditions, since the thermal modality provides complementary information for the color image. However, real multispectral data suffers from the position shift problem, i.e. the color-thermal image pairs are not strictly aligned, making one object has different positions in different modalities. In deep learning based methods, this problem makes it difficult to fuse the feature maps from both modalities and puzzles the CNN training. In this paper, we propose a novel Aligned Region CNN (AR-CNN) to handle the weakly aligned multispectral data in an end-to-end way. Firstly, we design a Region Feature Alignment (RFA) module to capture the position shift and adaptively align the region features of the two modalities. Secondly, we present a new multimodal fusion method, which performs feature re-weighting to select more reliable features and suppress the useless ones. Besides, we propose a novel RoI jitter strategy to improve the robustness to unexpected shift patterns of different devices and system settings. Finally, since our method depends on a new kind of labelling: bounding boxes that match each modality, we manually relabel the KAIST dataset by locating bounding boxes in both modalities and building their relationships, providing a new KAIST-Paired Annotation. Extensive experimental validations on existing datasets are performed, demonstrating the effectiveness and robustness of the proposed method. Code and data are available at https://github.com/luzhang16/AR-CNN.

cs.CV↗
arXiv2019

Knowledge Network System (KNS) by Evolutionary Collective Intelligence (ECI): Model, Algorithm and Applications

Aiming at overcoming some inherent drawbacks and bottlenecks encountered by the conventional Knowledge, Recommendation, Search, and Social Systems, in this article we introduce the Knowledge Network System (KNS), a novel type of knowledge graph which is constructed by a new proposed algorithm, the Evolutionary Collective Intelligence (ECI) algorithm. The ECI, an agent-machine interactive algorithm, constructs the KNS by iteratively recommending interesting/matched samples/files to the agents, and meanwhile taking advantages of the collective intelligence of the agents. The ECI based KNS, to the best of our knowledge, is the first attempt in literature that integrates the functions of knowledge network construction, high-quality recommendation, new types of search and social in a same framework. Some real and potential applications of KNS and ECI are discussed, and a real system named VISVA is provided to demonstrate their efficacy. Some open problems for future works are also summarized in the end.

cs.SI↗
arXiv2018

SegET: Deep Neural Network with Rich Contextual Features for Cellular Structures Segmentation in Electron Tomography Image

Electron tomography (ET) allows high-resolution reconstructions of macromolecular complexes at nearnative state. Cellular structures segmentation in the reconstruction data from electron tomographic images is often required for analyzing and visualizing biological structures, making it a powerful tool for quantitative descriptions of whole cell structures and understanding biological functions. However, these cellular structures are rather difficult to automatically separate or quantify from view owing to complex molecular environment and the limitations of reconstruction data of ET. In this paper, we propose a single end-to-end deep fully-convolutional semantic segmentation network dubbed SegET with rich contextual features which fully exploitsthe multi-scale and multi-level contextual information and reduces the loss of details of cellular structures in ET images. We trained and evaluated our network on the electron tomogram of the CTL Immunological Synapse from Cell Image library. Our results demonstrate that SegET can automatically segment accurately and outperform all other baseline methods on each individual structure in our ET dataset.

cs.CV↗
arXiv2018

PABO: Mitigating Congestion via Packet Bounce in Data Center Networks

In today's data center, a diverse mix of throughput-sensitive long flows and delay-sensitive short flows are commonly presented in shallow-buffered switches. Long flows could potentially block the transmission of delay-sensitive short flows, leading to degraded performance. Congestion can also be caused by the synchronization of multiple TCP connections for short flows, as typically seen in the partition/aggregate traffic pattern. While multiple end-to-end transport-layer solutions have been proposed, none of them have tackled the real challenge: reliable transmission in the network. In this paper, we fill this gap by presenting PABO -- a novel link-layer design that can mitigate congestion by temporarily bouncing packets to upstream switches. PABO's design fulfills the following goals: i) providing per-flow based flow control on the link layer, ii) handling transient congestion without the intervention of end devices, and iii) gradually back propagating the congestion signal to the source when the network is not capable to handle the congestion.Experiment results show that PABO can provide prominent advantage of mitigating transient congestions and can achieve significant gain on end-to-end delay.

cs.NI↗
arXiv2018

Screening of Fungi for the Application of Self-Healing Concrete

Concrete is susceptible to cracking owing to drying shrinkage, freeze-thaw cycles, delayed ettringite formation, reinforcement corrosion, creep and fatigue, etc. Since maintenance and inspection of concrete infrastructure require onerous labor and high costs, self-healing of harmful cracks without human interference or intervention could be of great attraction. The goal of this study is to explore a new self-healing approach in which fungi are used as a self-healing agent to promote calcium carbonate precipitation to fill the cracks in concrete structures. Recent research results in the field of geomycology have shown that many species of fungi could play an important role in promoting calcium carbonate mineralization, but their application in self-healing concrete has not been reported. Therefore, a screening of different species of fungi has been conducted in this study. Our results showed that, despite the drastic pH increase owing to the leaching of calcium hydroxide from concrete, Aspergillus nidulans (MAD1445), a pH regulatory mutant, could grow on concrete plates and promote calcium carbonate precipitation.

q-bio.OT↗
arXiv2018

Interstellar Scintillation observations for PSR B0355+54

In this paper, we report our investigation of pulsar scintillation phenomena by monitoring PSR B0355$+$54 at 2.25 GHz for three successive months using \emph{Kunming 40-m radio telescope}. We have measured the dynamic spectrum, the two-dimensional correlation function, and the secondary spectrum. In those observations with high signal-to-noise ratio ($S/N\ge100$), we have detected the scintillation arcs, which are rarely observable using such a small telescope. The sub-microsecond scale width of the scintillation arc indicates that the transverse scale of structures on scattering screen is as compact as AU size. Our monitoring has also shown that both the scintillation bandwidth, timescale, and arc curvature of PSR B0355$+$54 were varying temporally. The plausible explanation would need to invoke multiple-scattering-screen or multiple-scattering-structure scenario that different screens or ray paths dominate the scintillation process at different epochs.

astro-ph.GA↗
arXiv2018

No bursts detected from FRB121102 in two 5-hour observing campaigns with the Robert C. Byrd Green Bank Telescope

Here, we report non-detection of radio bursts from Fast Radio Burst FRB 121102 during two 5-hour observation sessions on the Robert C. Byrd 100-m Green Bank Telescope in West Virginia, USA, on December 11, 2017, and January 12, 2018. In addition, we report non-detection during an abutting 10-hour observation with the Kunming 40-m telescope in China, which commenced UTC 10:00 January 12, 2018. These are among the longest published contiguous observations of FRB 121102, and support the notion that FRB 121102 bursts are episodic. These observations were part of a simultaneous optical and radio monitoring campaign with the the Caltech HIgh- speed Multi-color CamERA (CHIMERA) instrument on the Hale 5.1-m telescope.

astro-ph.HE↗
arXiv2017

Spontaneous antiferromagnetic order and strain effect on electronic properties of $α$-graphyne

Using hybrid exchange-correlation functional in ab initio density functional theory calculations, we study magnetic properties and strain effect on the electronic properties of $α$-graphyne monolayer. We find that a spontaneous antiferromagnetic (AF) ordering occurs with energy band gap ($\sim$ 0.5 eV) in the equilibrated $α$-graphyne. Bi-axial tensile strain enhances the stability of AF state as well as the staggered spin moment and value of the energy gap. The antiferromagnetic semiconductor phase is quite robust against moderate carrier filling with threshold carrier density up to 1.7$\times$10$^{14}$ electrons/cm$^2$ to destabilize the phase. The spontaneous AF ordering and strain effect in $α$-graphyne can be well described by the framework of the Hubbard model. Our study shows that it is essential to consider the electronic correlation effect properly in $α$-graphyne and may pave an avenue for exploring magnetic ordering in other carbon allotropes with mixed hybridization of s and p orbitals.

physics.comp-ph↗
arXiv2016

Green Data Centers: A Survey, Perspectives, and Future Directions

At present, a major concern regarding data centers is their extremely high energy consumption and carbon dioxide emissions. However, because of the over-provisioning of resources, the utilization of existing data centers is, in fact, remarkably low, leading to considerable energy waste. Therefore, over the past few years, many research efforts have been devoted to increasing efficiency for the construction of green data centers. The goal of these efforts is to efficiently utilize available resources and to reduce energy consumption and thermal cooling costs. In this paper, we provide a survey of the state-of-the-art research on green data center techniques, including energy efficiency, resource management, thermal control and green metrics. Additionally, we present a detailed comparison of the reviewed proposals. We further discuss the key challenges for future research and highlight some future research issues for addressing the problem of building green data centers.

cs.DC↗
arXiv2016

Single top and Higgs associated production in the minimal $B-L$ model at the LHC

In this paper, we study the single top production in association with a Higgs boson in the $U(1)_{B-L}$ extension of the Standard Model at the LHC. We calculate the production cross sections of the processes $pp\rightarrow thX(h=H_{1},H_{2};X=j,b,W)$ in this model. Then we further study the observability of the process $pp\rightarrow tH_{2}j$ through $pp\rightarrow t(\rightarrow q\bar{q'} b)H_{2}(\rightarrow 4\ell)j$. We find that the systematic significance can be improved obviously, but it is still challenging for the 14 TeV LHC with high-luminosity to detect this signal.

hep-ph↗