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Source author record

Xin Zhao

Xin Zhao appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Computer VisionArtificial Intelligencecond-mat.mtrl-sciGraphicsMachine Learningphysics.opticscond-mat.mes-hallphysics.ins-detcond-mat.supr-conmath-phmath.DSmath.MPmath.SPphysics.comp-phCryptography and Securityeess.ASeess.IVInformation RetrievalInformation Theorymath.ITMultimediaNeural and Evolutionary Computingnucl-exPerformancephysics.gen-phphysics.med-phQuantitative MethodsSocial and Information NetworksSound

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Published work

49 published item(s)

preprint2026arXiv

Decoupling Amplitude and Phase Attention in Frequency Domain for RGB-Event based Visual Object Tracking

Existing RGB-Event visual object tracking approaches primarily rely on conventional feature-level fusion, failing to fully exploit the unique advantages of event cameras. In particular, the high dynamic range and motion-sensitive nature of event cameras are often overlooked, while low-information regions are processed uniformly, leading to unnecessary computational overhead for the backbone network. To address these issues, we propose a novel tracking framework that performs early fusion in the frequency domain, enabling effective aggregation of high-frequency information from the event modality. Specifically, RGB and event modalities are transformed from the spatial domain to the frequency domain via the Fast Fourier Transform, with their amplitude and phase components decoupled. High-frequency event information is selectively fused into RGB modality through amplitude and phase attention, enhancing feature representation while substantially reducing backbone computation. In addition, a motion-guided spatial sparsification module leverages the motion-sensitive nature of event cameras to capture the relationship between target motion cues and spatial probability distribution, filtering out low-information regions and enhancing target-relevant features. Finally, a sparse set of target-relevant features is fed into the backbone network for learning, and the tracking head predicts the final target position. Extensive experiments on three widely used RGB-Event tracking benchmark datasets, including FE108, FELT, and COESOT, demonstrate the high performance and efficiency of our method. The source code of this paper will be released on https://github.com/Event-AHU/OpenEvTracking

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Exposing Functional Fusion: A New Class of Strategic Backdoor in Dynamic Prompt Architectures

Existing ViT backdoor attacks based on backbone-overwriting full-tuning are computationally expensive and inflict performance degradation. This has forced adversaries towards the Visual Parameter-Efficient Fine-Tuning (PEFT) paradigm, dominated by adapter-based (e.g., LoRA) and prompt-based (e.g., VPT) approaches. While adapter security has seen initial study, the risks of the burgeoning prompt-based ecosystem remain critically unexplored. We fill this critical gap, exposing how the evolution of VPT towards dynamic and context-aware architectures can facilitate a far more dangerous and emergent threat. This vulnerability arises even though these dynamic modules unlock superior benign performance. We propose VIPER, an attack framework built on a lightweight, dynamic Visual Prompt Generator (VPG) that demonstrates this vulnerability. Critically, this dynamic architecture enables Functional Fusion: an emergent phenomenon where malicious logic and benign task utility are tightly fused into the same sparse, high-magnitude parameter core. This fusion creates a formidable ``hostage" dilemma, as pruning the attack necessarily destroys the benign performance. Comprehensive evaluations show VIPER effectively addresses the attacker's trilemma: VIPER not only achieves state-of-the-art performance on clean data, but also maintains near-100% ASR even under 90% VPG-module pruning (where LoRA attacks collapse), while adding only an imperceptible 0.06ms (1.16%) of inference latency. VIPER's results, driven by Functional Fusion, expose a new, paradigm-level risk in dynamic prompt architectures.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SoDa2: Single-Stage Open-Set Domain Adaptation via Decoupled Alignment for Cross-Scene Hyperspectral Image Classification

Cross-scene hyperspectral image (HSI) classification stands as a fundamental research topic in remote sensing, with extensive applications spanning various fields. Owing to the inclusion of unknown categories in the target domain and the existence of domain shift across different scenes, open-set domain adaptation techniques are commonly employed to address cross-scene HSI classification. However, existing open-set cross-scene HSI classification methods still face two critical challenges: (1) domain shift issues arising from the direct alignment of mixed spectral-spatial features; (2) high computational costs caused by two-stage training strategies. To address these issues, this paper proposes a single-stage open-set domain adaptation method with decoupled alignment (SoDa$^2$) for cross-scene HSI classification. A contribution-aware dual-modality feature extraction is customized to disentangle the characteristics from spectral sequence signals and spatial details, selectively and adaptively enhancing discriminative features. The decoupled alignment module minimizes the Maximum Mean Discrepancy to independently reduce the spectral discrepancy and the spatial discrepancy between the source and target domains, extracting more fine-grained domain-invariant features. A cost-effective single-stage dual-branch framework is designed to learn MMD-constrainted aligned features and constraint-free intrinsic features for adaptive distinction between known and unknown classes. This framework employs a Gaussian Mixture Model to model the squared cosine similarity distribution between the two feature types, enabling open-set recognition without prior knowledge of unknown classes. Extensive experiments on three groups of HSI datasets demonstrate that SoDa$^2$ outperforms state-of-the-art methods, achieving superior classification accuracy and model transferability for open-set cross-scene tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Robust single-particle cryo-EM image denoising and restoration

Cryo-electron microscopy (cryo-EM) has achieved near-atomic level resolution of biomolecules by reconstructing 2D micrographs. However, the resolution and accuracy of the reconstructed particles are significantly reduced due to the extremely low signal-to-noise ratio (SNR) and complex noise structure of cryo-EM images. In this paper, we introduce a diffusion model with post-processing framework to effectively denoise and restore single particle cryo-EM images. Our method outperforms the state-of-the-art (SOTA) denoising methods by effectively removing structural noise that has not been addressed before. Additionally, more accurate and high-resolution three-dimensional reconstruction structures can be obtained from denoised cryo-EM images.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

InsPro: Propagating Instance Query and Proposal for Online Video Instance Segmentation

Video instance segmentation (VIS) aims at segmenting and tracking objects in videos. Prior methods typically generate frame-level or clip-level object instances first and then associate them by either additional tracking heads or complex instance matching algorithms. This explicit instance association approach increases system complexity and fails to fully exploit temporal cues in videos. In this paper, we design a simple, fast and yet effective query-based framework for online VIS. Relying on an instance query and proposal propagation mechanism with several specially developed components, this framework can perform accurate instance association implicitly. Specifically, we generate frame-level object instances based on a set of instance query-proposal pairs propagated from previous frames. This instance query-proposal pair is learned to bind with one specific object across frames through conscientiously developed strategies. When using such a pair to predict an object instance on the current frame, not only the generated instance is automatically associated with its precursors on previous frames, but the model gets a good prior for predicting the same object. In this way, we naturally achieve implicit instance association in parallel with segmentation and elegantly take advantage of temporal clues in videos. To show the effectiveness of our method InsPro, we evaluate it on two popular VIS benchmarks, i.e., YouTube-VIS 2019 and YouTube-VIS 2021. Without bells-and-whistles, our InsPro with ResNet-50 backbone achieves 43.2 AP and 37.6 AP on these two benchmarks respectively, outperforming all other online VIS methods.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Simulation of environmental impacts on the synthesis of carbyne with more than 6000 atoms for emerging continuously tunable energy barriers in CNT-based transistors

Transistors made up of carbon nanotubes CNT have demonstrated excellent current-voltage characteristics which outperform some high-grade silicon-based transistors. A continuously tunable energy barrier across semiconductor interfaces is desired to make the CNT-based transistors more robust. Despite the direct band gap of carbyne inside a CNT can be widely tuned by strain, the size of carbyne cannot be controlled easily. The production of a monoatomic chain with more than 6000 carbon atoms is an enormous technological challenge. To predict the optimal chain length of a carbyne in different molecular environments, we have developed a Monte Carlo model in which a finite-length carbyne with a size of 4000-15000 atoms is encapsulated by a CNT at finite temperatures. Our simulation shows that the stability of the carbyne@nanotube is strongly influenced by the nature and porosity of the CNT, the external pressure, the temperature and the chain length. We have observed an initiation of chain-breaking process in a compressed carbyne@nanotube. Our work provides much needed input for optimising the carbyne length to produce carbon chains much longer than 6000 atoms at ~300K. Design rules are proposed for synthesizing ~1% strained carbyne@(6,5)CNT as a component in CNT-based transistors to tune the energy barriers continuously.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

With the acceleration of urbanization and living standards, microorganisms play increasingly important roles in industrial production, bio-technique, and food safety testing. Microorganism biovolume measurements are one of the essential parts of microbial analysis. However, traditional manual measurement methods are time-consuming and challenging to measure the characteristics precisely. With the development of digital image processing techniques, the characteristics of the microbial population can be detected and quantified. The changing trend can be adjusted in time and provided a basis for the improvement. The applications of the microorganism biovolume measurement method have developed since the 1980s. More than 62 articles are reviewed in this study, and the articles are grouped by digital image segmentation methods with periods. This study has high research significance and application value, which can be referred to microbial researchers to have a comprehensive understanding of microorganism biovolume measurements using digital image analysis methods and potential applications.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images

This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end convolutional neural network (CNN) model with an encoder--decoder architecture. The pixel interval down-sampling operations are concatenated with max-pooling operations to combine the sparse and dense features. This addresses the limitation of contour conglutination of dense objects while counting. The evaluation was conducted using classical segmentation metrics (the Dice, Jaccard and Hausdorff distance) as well as counting metrics. The experimental results show that the proposed PID-Net had the best performance and potential for dense tiny object counting tasks, which achieved 96.97\% counting accuracy on the dataset with 2448 yeast cell images. By comparing with the state-of-the-art approaches, such as Attention U-Net, Swin U-Net and Trans U-Net, the proposed PID-Net can segment dense tiny objects with clearer boundaries and fewer incorrect debris, which shows the great potential of PID-Net in the task of accurate counting.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Attribute Controllable Beautiful Caucasian Face Generation by Aesthetics Driven Reinforcement Learning

In recent years, image generation has made great strides in improving the quality of images, producing high-fidelity ones. Also, quite recently, there are architecture designs, which enable GAN to unsupervisedly learn the semantic attributes represented in different layers. However, there is still a lack of research on generating face images more consistent with human aesthetics. Based on EigenGAN [He et al., ICCV 2021], we build the techniques of reinforcement learning into the generator of EigenGAN. The agent tries to figure out how to alter the semantic attributes of the generated human faces towards more preferable ones. To accomplish this, we trained an aesthetics scoring model that can conduct facial beauty prediction. We also can utilize this scoring model to analyze the correlation between face attributes and aesthetics scores. Empirically, using off-the-shelf techniques from reinforcement learning would not work well. So instead, we present a new variant incorporating the ingredients emerging in the reinforcement learning communities in recent years. Compared to the original generated images, the adjusted ones show clear distinctions concerning various attributes. Experimental results using the MindSpore, show the effectiveness of the proposed method. Altered facial images are commonly more attractive, with significantly improved aesthetic levels.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Global Instance Tracking: Locating Target More Like Humans

Target tracking, the essential ability of the human visual system, has been simulated by computer vision tasks. However, existing trackers perform well in austere experimental environments but fail in challenges like occlusion and fast motion. The massive gap indicates that researches only measure tracking performance rather than intelligence. How to scientifically judge the intelligence level of trackers? Distinct from decision-making problems, lacking three requirements (a challenging task, a fair environment, and a scientific evaluation procedure) makes it strenuous to answer the question. In this article, we first propose the global instance tracking (GIT) task, which is supposed to search an arbitrary user-specified instance in a video without any assumptions about camera or motion consistency, to model the human visual tracking ability. Whereafter, we construct a high-quality and large-scale benchmark VideoCube to create a challenging environment. Finally, we design a scientific evaluation procedure using human capabilities as the baseline to judge tracking intelligence. Additionally, we provide an online platform with toolkit and an updated leaderboard. Although the experimental results indicate a definite gap between trackers and humans, we expect to take a step forward to generate authentic human-like trackers. The database, toolkit, evaluation server, and baseline results are available at http://videocube.aitestunion.com.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System

A combinatorial recommender (CR) system feeds a list of items to a user at a time in the result page, in which the user behavior is affected by both contextual information and items. The CR is formulated as a combinatorial optimization problem with the objective of maximizing the recommendation reward of the whole list. Despite its importance, it is still a challenge to build a practical CR system, due to the efficiency, dynamics, personalization requirement in online environment. In particular, we tear the problem into two sub-problems, list generation and list evaluation. Novel and practical model architectures are designed for these sub-problems aiming at jointly optimizing effectiveness and efficiency. In order to adapt to online case, a bootstrap algorithm forming an actor-critic reinforcement framework is given to explore better recommendation mode in long-term user interaction. Offline and online experiment results demonstrate the efficacy of proposed JDRec framework. JDRec has been applied in online JD recommendation, improving click through rate by 2.6% and synthetical value for the platform by 5.03%. We will publish the large-scale dataset used in this study to contribute to the research community.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Medical Matting: A New Perspective on Medical Segmentation with Uncertainty

It is difficult to accurately label ambiguous and complex shaped targets manually by binary masks. The weakness of binary mask under-expression is highlighted in medical image segmentation, where blurring is prevalent. In the case of multiple annotations, reaching a consensus for clinicians by binary masks is more challenging. Moreover, these uncertain areas are related to the lesions' structure and may contain anatomical information beneficial to diagnosis. However, current studies on uncertainty mainly focus on the uncertainty in model training and data labels. None of them investigate the influence of the ambiguous nature of the lesion itself.Inspired by image matting, this paper introduces alpha matte as a soft mask to represent uncertain areas in medical scenes and accordingly puts forward a new uncertainty quantification method to fill the gap of uncertainty research for lesion structure. In this work, we introduce a new architecture to generate binary masks and alpha mattes in a multitasking framework, which outperforms all state-of-the-art matting algorithms compared. The proposed uncertainty map is able to highlight the ambiguous regions and a novel multitasking loss weighting strategy we presented can improve performance further and demonstrate their concrete benefits. To fully-evaluate the effectiveness of our proposed method, we first labelled three medical datasets with alpha matte to address the shortage of available matting datasets in medical scenes and prove the alpha matte to be a more efficient labeling method than a binary mask from both qualitative and quantitative aspects.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multi-target Tracking of Zebrafish based on Particle Filter

Zebrafish is an excellent model organism, which has been widely used in the fields of biological experiments, drug screening, and swarm intelligence. In recent years, there are a large number of techniques for tracking of zebrafish involved in the study of behaviors, which makes it attack much attention of scientists from many fields. Multi-target tracking of zebrafish is still facing many challenges. The high mobility and uncertainty make it difficult to predict its motion; the similar appearances and texture features make it difficult to establish an appearance model; it is even hard to link the trajectories because of the frequent occlusion. In this paper, we use particle filter to approximate the uncertainty of the motion. Firstly, by analyzing the motion characteristics of zebrafish, we establish an efficient hybrid motion model to predict its positions; then we establish an appearance model based on the predicted positions to predict the postures of every targets, meanwhile weigh the particles by comparing the difference of predicted pose and observation pose ; finally, we get the optimal position of single zebrafish through the weighted position, and use the joint particle filter to process trajectory linking of multiple zebrafish.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multimodal foundation models are better simulators of the human brain

Multimodal learning, especially large-scale multimodal pre-training, has developed rapidly over the past few years and led to the greatest advances in artificial intelligence (AI). Despite its effectiveness, understanding the underlying mechanism of multimodal pre-training models still remains a grand challenge. Revealing the explainability of such models is likely to enable breakthroughs of novel learning paradigms in the AI field. To this end, given the multimodal nature of the human brain, we propose to explore the explainability of multimodal learning models with the aid of non-invasive brain imaging technologies such as functional magnetic resonance imaging (fMRI). Concretely, we first present a newly-designed multimodal foundation model pre-trained on 15 million image-text pairs, which has shown strong multimodal understanding and generalization abilities in a variety of cognitive downstream tasks. Further, from the perspective of neural encoding (based on our foundation model), we find that both visual and lingual encoders trained multimodally are more brain-like compared with unimodal ones. Particularly, we identify a number of brain regions where multimodally-trained encoders demonstrate better neural encoding performance. This is consistent with the findings in existing studies on exploring brain multi-sensory integration. Therefore, we believe that multimodal foundation models are more suitable tools for neuroscientists to study the multimodal signal processing mechanisms in the human brain. Our findings also demonstrate the potential of multimodal foundation models as ideal computational simulators to promote both AI-for-brain and brain-for-AI research.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Neural Architecture Search for Inversion

Over the year, people have been using deep learning to tackle inversion problems, and we see the framework has been applied to build relationship between recording wavefield and velocity (Yang et al., 2016). Here we will extend the work from 2 perspectives, one is deriving a more appropriate loss function, as we now, pixel-2-pixel comparison might not be the best choice to characterize image structure, and we will elaborate on how to construct cost function to capture high level feature to enhance the model performance. Another dimension is searching for the more appropriate neural architecture, which is a subset of an even bigger picture, the automatic machine learning, or AutoML. There are several famous networks, U-net, ResNet (He et al., 2016) and DenseNet (Huang et al., 2017), and they achieve phenomenal results for certain problems, yet it's hard to argue they are the best for inversion problems without thoroughly searching within certain space. Here we will be showing our architecture search results for inversion.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation

This paper presents a unified framework for depth-aware panoptic segmentation (DPS), which aims to reconstruct 3D scene with instance-level semantics from one single image. Prior works address this problem by simply adding a dense depth regression head to panoptic segmentation (PS) networks, resulting in two independent task branches. This neglects the mutually-beneficial relations between these two tasks, thus failing to exploit handy instance-level semantic cues to boost depth accuracy while also producing sub-optimal depth maps. To overcome these limitations, we propose a unified framework for the DPS task by applying a dynamic convolution technique to both the PS and depth prediction tasks. Specifically, instead of predicting depth for all pixels at a time, we generate instance-specific kernels to predict depth and segmentation masks for each instance. Moreover, leveraging the instance-wise depth estimation scheme, we add additional instance-level depth cues to assist with supervising the depth learning via a new depth loss. Extensive experiments on Cityscapes-DPS and SemKITTI-DPS show the effectiveness and promise of our method. We hope our unified solution to DPS can lead a new paradigm in this area. Code is available at https://github.com/NaiyuGao/PanopticDepth.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Physical Mechanism of Superconductivity Part II Superconductivity and Superfluidity

The transition mechanism of metal-insulator in metal oxides is discussed in detail, which is a part of the mechanism of superconductivity. Through the study of magic angle twisted bilayer graphene superconductor and other new findings on superconductivity, we further demonstrate that the physical mechanism of superconductivity proposed in Part I is the only correct way to handle the properties of superconductivity in various materials. We propose that superfluid helium consists of normal liquid helium mixed with high-energy helium atoms. Based on this new model, all peculiar features discovered in superfluid helium can be truly understood, such as the climb of superfluid helium on the container's wall, the fountain effect, the discontinuity of specific heat capacity at phase transition point, and the maintaining mass current in a ring-shaped container. We demonstrate that the high-energy particles play a driving force role in both superconductors and superfluid helium, and therefore dominate their properties.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Nature of Schrodinger Equation -- On Quantum Physics Part I

We propose that the Schrodinger equation results from applying the classical wave equation to describe the physical system in which subatomic particles play random motion, thereby leading to quantum mechanics. The physical reality described by the wave function is subatomic particle moving randomly. Therefore, the characteristics of quantum mechanics have a dual nature, one of them is the deterministic nature carried on from classical physics, and the other is the probabilistic nature coined by particle's random motion. Based on this model, almost all of open questions in quantum mechanics can be explained consistently, which include the particle-wave duality, the principle of quantum superposition, the interference pattern of double-slit experiments, and the boundary between the classical world and the quantum world. The current quantum mechanics is a mixture of matrix mechanics and wave mechanics, which are sharply conflicting in principle. Matrix mechanics treats quantum particles as classical particles with fixed relation between the particle's position and its momentum. The matrix mechanics, in fact, belongs to the old quantum theory. Both Born's non-commutative relation and Heisenberg uncertainty relation originate from matrix mechanics. However, in wave mechanics, there is no any fixed relation between the particle's position and its momentum, and the particle's position and its momentum belong to immeasurable physical quantities. Therefore, there is no need for non-commutative relation and uncertainty relation in wave mechanics.

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preprint2022arXiv

Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularization

Existing unsupervised domain adaptation methods based on adversarial learning have achieved good performance in several medical imaging tasks. However, these methods focus only on global distribution adaptation and ignore distribution constraints at the category level, which would lead to sub-optimal adaptation performance. This paper presents an unsupervised domain adaptation framework based on category-level regularization that regularizes the category distribution from three perspectives. Specifically, for inter-domain category regularization, an adaptive prototype alignment module is proposed to align feature prototypes of the same category in the source and target domains. In addition, for intra-domain category regularization, we tailored a regularization technique for the source and target domains, respectively. In the source domain, a prototype-guided discriminative loss is proposed to learn more discriminative feature representations by enforcing intra-class compactness and inter-class separability, and as a complement to traditional supervised loss. In the target domain, an augmented consistency category regularization loss is proposed to force the model to produce consistent predictions for augmented/unaugmented target images, which encourages semantically similar regions to be given the same label. Extensive experiments on two publicly fundus datasets show that the proposed approach significantly outperforms other state-of-the-art comparison algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A New Pairwise Deep Learning Feature For Environmental Microorganism Image Analysis

Environmental microorganism (EM) offers a high-efficient, harmless, and low-cost solution to environmental pollution. They are used in sanitation, monitoring, and decomposition of environmental pollutants. However, this depends on the proper identification of suitable microorganisms. In order to fasten, low the cost, increase consistency and accuracy of identification, we propose the novel pairwise deep learning features to analyze microorganisms. The pairwise deep learning features technique combines the capability of handcrafted and deep learning features. In this technique we, leverage the Shi and Tomasi interest points by extracting deep learning features from patches which are centered at interest points locations. Then, to increase the number of potential features that have intermediate spatial characteristics between nearby interest points, we use Delaunay triangulation theorem and straight-line geometric theorem to pair the nearby deep learning features. The potential of pairwise features is justified on the classification of EMs using SVMs, k-NN, and Random Forest classifier. The pairwise features obtain outstanding results of 99.17%, 91.34%, 91.32%, 91.48%, and 99.56%, which are the increase of about 5.95%, 62.40%, 62.37%, 61.84%, and 3.23% in accuracy, F1-score, recall, precision, and specificity respectively, compared to non-paired deep learning features.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Leveraging Regular Fundus Images for Training UWF Fundus Diagnosis Models via Adversarial Learning and Pseudo-Labeling

Recently, ultra-widefield (UWF) 200\degree~fundus imaging by Optos cameras has gradually been introduced because of its broader insights for detecting more information on the fundus than regular 30 degree - 60 degree fundus cameras. Compared with UWF fundus images, regular fundus images contain a large amount of high-quality and well-annotated data. Due to the domain gap, models trained by regular fundus images to recognize UWF fundus images perform poorly. Hence, given that annotating medical data is labor intensive and time consuming, in this paper, we explore how to leverage regular fundus images to improve the limited UWF fundus data and annotations for more efficient training. We propose the use of a modified cycle generative adversarial network (CycleGAN) model to bridge the gap between regular and UWF fundus and generate additional UWF fundus images for training. A consistency regularization term is proposed in the loss of the GAN to improve and regulate the quality of the generated data. Our method does not require that images from the two domains be paired or even that the semantic labels be the same, which provides great convenience for data collection. Furthermore, we show that our method is robust to noise and errors introduced by the generated unlabeled data with the pseudo-labeling technique. We evaluated the effectiveness of our methods on several common fundus diseases and tasks, such as diabetic retinopathy (DR) classification, lesion detection and tessellated fundus segmentation. The experimental results demonstrate that our proposed method simultaneously achieves superior generalizability of the learned representations and performance improvements in multiple tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

NumaPerf: Predictive and Full NUMA Profiling

Parallel applications are extremely challenging to achieve the optimal performance on the NUMA architecture, which necessitates the assistance of profiling tools. However, existing NUMA-profiling tools share some similar shortcomings, such as portability, effectiveness, and helpfulness issues. This paper proposes a novel profiling tool - NumaPerf - that overcomes these issues. NumaPerf aims to identify potential performance issues for any NUMA architecture, instead of only on the current hardware. To achieve this, NumaPerf focuses on memory sharing patterns between threads, instead of real remote accesses. NumaPerf further detects potential thread migrations and load imbalance issues that could significantly affect the performance but are omitted by existing profilers. NumaPerf also separates cache coherence issues that may require different fix strategies. Based on our extensive evaluation, NumaPerf is able to identify more performance issues than any existing tool, while fixing these bugs leads to up to 5.94x performance speedup.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-task Learning

The need for comprehensive and automated screening methods for retinal image classification has long been recognized. Well-qualified doctors annotated images are very expensive and only a limited amount of data is available for various retinal diseases such as age-related macular degeneration (AMD) and diabetic retinopathy (DR). Some studies show that AMD and DR share some common features like hemorrhagic points and exudation but most classification algorithms only train those disease models independently. Inspired by knowledge distillation where additional monitoring signals from various sources is beneficial to train a robust model with much fewer data. We propose a method called synergic adversarial label learning (SALL) which leverages relevant retinal disease labels in both semantic and feature space as additional signals and train the model in a collaborative manner. Our experiments on DR and AMD fundus image classification task demonstrate that the proposed method can significantly improve the accuracy of the model for grading diseases. In addition, we conduct additional experiments to show the effectiveness of SALL from the aspects of reliability and interpretability in the context of medical imaging application.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Transition space for the continuity of the Lyapunov exponent of quasiperiodic Schrödinger cocycles

We construct discontinuous point of the Lyapunov exponent of quasiperiodic Schrödinger cocycles in the Gevrey space $G^{s}$ with $s>2$. In contrast, the Lyapunov exponent has been proved to be continuous in the Gevrey space $G^{s}$ with $s<2$ \cite{klein,cgyz}. This shows that $G^2$ is the transition space for the continuity of the Lyapunov exponent.

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preprint2020arXiv

A Lite Microphone Array Beamforming Scheme with Maximum Signal-to-Noise Ratio Filter

Since space-domain information can be utilized, microphone array beamforming is often used to enhance the quality of the speech by suppressing directional disturbance. However, with the increasing number of microphone, the complexity would be increased. In this paper, a concise beamforming scheme using Maximum Signal-to-Noise Ratio (SNR) filter is proposed to reduce the beamforming complexity. The maximum SNR filter is implemented by using the estimated direction-of-arrival (DOA) of the speech source localization (SSL) and the solving method of independent vector analysis (IVA). Our experiments show that when compared with other widely-used algorithms, the proposed algorithm obtain higher gain of signal-to-interference and noise ratio (SINR).

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation

To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, "mU-Net-B3", with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel "buffer" strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.

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preprint2020arXiv

Hölder regularity of the integrated density of states for quasi-periodic long-range operators on $\ell^2(\Z^d)$

We prove the Hölder continuity of the integrated density of states for a class of quasi-periodic long-range operators on $\ell^2(\Z^d)$ with large trigonometric polynomial potentials and Diophantine frequencies. Moreover, we give the Hölder exponent in terms of the cardinality of the level sets of the potentials, which improves, in the perturbative regime, the result obtained by Goldstein and Schlag \cite{gs2}. Our approach is a combination of Aubry duality, generalized Thouless formula and the regularity of the Lyapunov exponents of analytic quasi-periodic $GL(m,\C)$ cocycles which is proved by quantitative almost reducibility method.

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preprint2020arXiv

Hybrid Transceiver Optimization for Multi-Hop Communications

Multi-hop communication with the aid of large-scale antenna arrays will play a vital role in future emergence communication systems. In this paper, we investigate amplify-and-forward based and multiple-input multiple-output assisted multi-hop communication, in which all nodes employ hybrid transceivers. Moreover, channel errors are taken into account in our hybrid transceiver design. Based on the matrix-monotonic optimization framework, the optimal structures of the robust hybrid transceivers are derived. By utilizing these optimal structures, the optimizations of analog transceivers and digital transceivers can be separated without loss of optimality. This fact greatly simplifies the joint optimization of analog and digital transceivers. Since the optimization of analog transceivers under unit-modulus constraints is non-convex, a projection type algorithm is proposed for analog transceiver optimization to overcome this difficulty. Based on the derived analog transceivers, the optimal digital transceivers can then be derived using matrix-monotonic optimization. Numeral results obtained demonstrate the performance advantages of the proposed hybrid transceiver designs over other existing solutions.

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preprint2020arXiv

PET Quantification of Ultra Low Activity via Inhomogeneous Poisson Process Parameters Estimation Directly from Listmode Data

Metabolic imaging with PET/CT using $^{18}$F-Fludeoxyglucose ($^{18}$F-FDG) as well as other imaging biomarkers has achieved wide acceptance in oncology, cardiology and neurology not only because of the unique metabolic information generated by this modality, but also because of its ability to quantify biological processes. However, PET quantification is affected by many technical and physiologic factors, and then recognized as an important problem for diagnosis, determination of prognosis, and response monitoring in oncology. In this work, we investigated the effect of reduced PET emission count statistics on the accuracy and precision of tracer quantification, and proposed Inhomogeneous Poisson Process Parameter Estimation (I3PE) method. In I3PE method, we modelled the coincidence event as Inhomogeneous Poisson Process, and estimate its parameter directly from the streaming listmode data. To evaluate the effectiveness, a experiment using $^{18}$F-FDG was implemented on LIGHTNING. The results not only demonstrate I3PE method, but also evaluated the minimal detectable activity of the using PET machine. According $0.3\%$ mean error rate criterion, LIGHTNING can detect several nano-Curie, cooperated with I3PE method.

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preprint2019arXiv

Comb-mode-resolved adaptive sampling terahertz dual-comb spectroscopy with a free-running single-cavity fiber laser

Mode-resolved dual-comb spectroscopy (DCS) is an emerging spectroscopic tool with the potential to simultaneously achieve a broad spectral coverage and ultrahigh spectral resolution in terahertz (THz) spectroscopy. However, the need for two independently stabilized ultrafast lasers significantly hampers the potential application of DCS techniques. In this article, we demonstrate mode-resolved DCS in the THz region based on a free-running single-cavity dual-comb fiber laser with adaptive sampling. Low-pressure spectroscopy of acetonitrile gas with absorption features approaching the Doppler limit is demonstrated by comb-mode-resolved measurements with a spectral sampling spacing of 48.8 MHz, a spectral resolution of less than 5 MHz and a signal-to-noise ratio of ~50 dB. The successful demonstration of the proposed method clearly indicates the great potential for the realization of low-complexity, MHz-resolution THz spectroscopy instrumentation.

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preprint2019arXiv

Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field

To improve the classification performance in the context of hyperspectral image processing, many works have been developed based on two common strategies, namely the spatial-spectral information integration and the utilization of neural networks. However, both strategies typically require more training data than the classical algorithms, aggregating the shortage of labeled samples. In this letter, we propose a novel framework that organically combines the spectrum-based deep metric learning model and the conditional random field algorithm. The deep metric learning model is supervised by the center loss to produce spectrum-based features that gather more tightly in Euclidean space within classes. The conditional random field with Gaussian edge potentials, which is firstly proposed for image segmentation tasks, is introduced to give the pixel-wise classification over the hyperspectral image by utilizing both the geographical distances between pixels and the Euclidean distances between the features produced by the deep metric learning model. The proposed framework is trained by spectral pixels at the deep metric learning stage and utilizes the half handcrafted spatial features at the conditional random field stage. This settlement alleviates the shortage of training data to some extent. Experiments on two real hyperspectral images demonstrate the advantages of the proposed method in terms of both classification accuracy and computation cost.

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preprint2018arXiv

Spatially-correlated Site Occupancy in the Nonstoichiometric Meta-stable ε-Al60Sm11 Phase during Devitrification of Al-10.2 at.% Sm Glasses

A metastable ε-Al60Sm11 phase appears during the initial devitrification of as-quenched Al-10.2 at.% Sm glasses. The ε phase is nonstoichiometric in nature since Al occupation is observed on the 16f Sm lattice sites. Scanning transmission electron microscopic images reveal profound spatial correlation of Sm content on these sites, which cannot be explained by the "average crystal" description from Rietveld analysis of diffraction data. Thermodynamically favorable configurations, established by Monte Carlo (MC) simulations based on a cluster-expansion model, also give qualitatively different correlation functions from experimental observations. On the other hand, molecular dynamics simulations of the growth of ε-Al60Sm11 in undercooled liquid show that when the diffusion range of Sm is limited to ~ 4 Å, the correlation function of the as-grown crystal structure agrees well with that of the STEM images. Our results show that kinetic effects, especially the limited diffusivity of Sm atoms plays the fundamental role in determining the nonstoichiometric site occupancies of the ε-Al60Sm11 phase during the crystallization process.

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preprint2016arXiv

Destructive quantum interference in electron transport: A reconciliation of the molecular orbital and the atomic orbital perspective

Destructive quantum interference (DQI) in single molecule electronics is a purely quantum mechanical effect and entirely defined by inherent properties of the molecule in the junction such as its structure and symmetry. This definition of DQI by molecular properties alone suggests its relation to other more general concepts in chemistry as well as the possibility of deriving simple models for its understanding and molecular device design. Recently, two such models have gained wide spread attention, where one was a graphical scheme based on visually inspecting the connectivity of carbon sites in conjugated pi systems in an atomic orbital (AO) basis and the other one put the emphasis on the amplitudes and signs of the frontier molecular orbitals (MOs). There have been discussions on the range of applicability for these schemes, but ultimately conclusions from topological molecular Hamiltonians should not depend on whether they are drawn from an AO or a MO representation, as long as all the orbitals are taken into account. In this article we clarify the relation between both models in terms of the zeroth order Green's function and compare their predictions for a variety of systems. From this comparison we conclude that for a correct description of DQI from a MO perspective it is necessary to include the contributions from all MOs rather than just those from the frontier orbitals. The cases where DQI effects can be successfully predicted within a frontier orbital approximation we show to be limited to alternant even-membered hydrocarbons, as a a direct consequence of the Coulson-Rushbrooke pairing theorem in quantum chemistry.

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preprint2016arXiv

Large magnetic anisotropy predicted for rare-earth free Fe16-xCoxN2 alloys

Structures and magnetic properties of Fe16-xCoxN2 are studied using adaptive genetic algorithm and first-principles calculations. We show that substituting Fe by Co in Fe16N2 with Co/Fe ratio smaller than 1 can greatly improve the magnetic anisotropy of the material. The magnetocrystalline anisotropy energy from first-principles calculations reaches 3.18 MJ/m3 (245.6 μeV per metal atom) for Fe12Co4N2, much larger than that of Fe16N2 and is one of the largest among the reported rare-earth free magnets. From our systematic crystal structure searches, we show that there is a structure transition from tetragonal Fe16N2 to cubic Co16N2 in Fe16-xCoxN2 as the Co concentration increases, which can be well explained by electron counting analysis. Different magnetic properties between the Fe-rich (x < 8) and Co-rich (x > 8) Fe16-xCoxN2 is closely related to the structural transition.

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preprint2016arXiv

Picometer-resolution dual-comb spectroscopy with a free-running fibre laser

Dual-comb spectroscopy utilizes two sets of comb lines with slightly different comb-tooth-spacings, and optical spectral information is acquired by measuring the radio-frequency beat notes between the sets of comb lines. It holds the promise as a real-time, high-resolution analytical spectroscopy tool for a range of applications. However, the stringent requirement on the coherence between comb lines from two separate lasers and the sophisticated control system to achieve that have confined the technology to the top metrology laboratories. By replacing control electronics with an all-optical dual-comb lasing scheme, a simplified dual-comb spectroscopy scheme is demonstrated using just one dual-wavelength, passively mode-locked fiber laser. Dual-comb pulses with a repetition-frequency difference determined by the intracavity dispersion are shown to be sufficiently stable against common-mode cavity drifts and noises. As sufficiently low relative linewidth is maintained between two sets of comb lines, capability to resolve RF beat notes between comb teeth and picometer-wide optical spectral features is demonstrated using a simple data acquisition and processing system in an all-fiber setup. Possibility to use energy-efficient, free-running fiber lasers with a small comb-tooth-spacing could enable the realization of low-cost dual-comb spectroscopy systems affordable to more applications.

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preprint2016arXiv

Three Magnetic Rotation Bands in 84^Rb

High-spin states in $^{84}$Rb are studied by using the $^{70}$Zn($^{18}$O, p3n)$^{84}$Rb reaction at beam energy of 75 MeV. Three high-lying negative-parity bands are established, whose level spacings are very regular, i.e., there don't exist signature splitting. The dipole character of the transitions of these three bands is assigned by the $γ$-$γ$ directional correlations of oriented states (DCO) intensity ratios and the multipolarity M1 is suggested by the analogy to multiparticle excitations in neighboring nuclei. The strong M1 and weak or no E2 transitions are observed. All these characteristic features show they are magnetic rotation bands.

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preprint2016arXiv

Unidirectional, dual-comb lasing under multiple pulse formation mechanisms in a passively mode-locked fiber ring laser

Dual-comb lasers from which asynchronous ultrashort pulses can be simultaneously generated have recently become an interesting research subject. They could be an intriguing alternative to the current dual-laser optical-frequency-comb source with highly sophisticated electronic control systems. If generated through a common light path traveled by all pulses, the common-mode noises between the spectral lines of different pulse trains could be significantly reduced. Therefore, coherent dual-comb generation from a completely common-path, unidirectional lasing cavity would be an interesting territory to explore. In this paper, we demonstrate such a dual-comb lasing scheme based on a nanomaterial saturable absorber with additional pulse narrowing and broadening mechanisms concurrently introduced into a mode-locked fiber laser. The interactions between multiple soliton formation mechanisms result in unusual bifurcation into two-pulse states with quite different characteristics. Simultaneous oscillation of pulses with four-fold difference in pulsewidths and tens of Hz repetition rate difference is observed. The coherence between these spectral-overlapped, picosecond and femtosecond pulses is further verified by the corresponding asynchronous cross-sampling and dual-comb spectroscopy measurements.

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preprint2015arXiv

Exploration of tetrahedral structures in silicate cathodes using a motif-network scheme

Using a motif-network search scheme, we studied the tetrahedral structures of the dilithium/disodium transition metal orthosilicates A2MSiO4 with A = Li or Na and M = Mn, Fe or Co. In addition to finding all previously reported structures, we discovered many other different tetrahedral-network-based crystal structures which are highly degenerate in energy. These structures can be classified into structures with 1D, 2D and 3D M-Si-O frameworks. A clear trend of the structural preference in different systems was revealed and possible indicators that affect the structure stabilities were introduced. For the case of Na systems which have been much less investigated in the literature relative to the Li systems, we predicted their ground state structures and found evidence for the existence of new structural motifs.

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preprint2015arXiv

Structures and magnetic properties of Co-Zr-B magnets studied by first-principles calculations

The structures and magnetic properties of the Co-Zr-B alloys near the Co5Zr composition were studied using adaptive genetic algorithm and first-principles calculations to guide further experimental effort on optimizing their magnetic performances. Through extensive structural searches, we constructed the contour maps of the energetics and magnetic moments of the Co-Zr-B magnet alloys as a function of composition. We found that the Co-Zr-B system exhibits the same structural motif as the "Co11Zr2" polymorphs, which plays a key role in achieving high coercivity. Boron atoms can either substitute selective cobalt atoms or occupy the interstitial sites. First-principles calculation shows that the magnetocrystalline anisotropy energies can be significantly improved through proper boron doping.

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preprint2015arXiv

Temporal Identification of Latent Communities on Twitter

User communities in social networks are usually identified by considering explicit structural social connections between users. While such communities can reveal important information about their members such as family or friendship ties and geographical proximity, they do not necessarily succeed at pulling like-minded users that share the same interests together. In this paper, we are interested in identifying communities of users that share similar topical interests over time, regardless of whether they are explicitly connected to each other on the social network. More specifically, we tackle the problem of identifying temporal topic-based communities from Twitter, i.e., communities of users who have similar temporal inclination towards the current emerging topics on Twitter. We model each topic as a collection of highly correlated semantic concepts observed in tweets and identify them by clustering the time-series based representation of each concept built based on each concept's observation frequency over time. Based on the identified emerging topics in a given time period, we utilize multivariate time series analysis to model the contributions of each user towards the identified topics, which allows us to detect latent user communities. Through our experiments on Twitter data, we demonstrate i) the effectiveness of our topic detection method to detect real world topics and ii) the effectiveness of our approach compared to well-established approaches for community detection.

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preprint2013arXiv

4-Dimensional Geometry Lens: A Novel Volumetric Magnification Approach

We present a novel methodology that utilizes 4-Dimensional (4D) space deformation to simulate a magnification lens on versatile volume datasets and textured solid models. Compared with other magnification methods (e.g., geometric optics, mesh editing), 4D differential geometry theory and its practices are much more flexible and powerful for preserving shape features (i.e., minimizing angle distortion), and easier to adapt to versatile solid models. The primary advantage of 4D space lies at the following fact: we can now easily magnify the volume of regions of interest (ROIs) from the additional dimension, while keeping the rest region unchanged. To achieve this primary goal, we first embed a 3D volumetric input into 4D space and magnify ROIs in the 4th dimension. Then we flatten the 4D shape back into 3D space to accommodate other typical applications in the real 3D world. In order to enforce distortion minimization, in both steps we devise the high dimensional geometry techniques based on rigorous 4D geometry theory for 3D/4D mapping back and forth to amend the distortion. Our system can preserve not only focus region, but also context region and global shape. We demonstrate the effectiveness, robustness, and efficacy of our framework with a variety of models ranging from tetrahedral meshes to volume datasets.

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preprint2013arXiv

A New 3D Geometric Approach to Focus and Context Lens Effect Simulation

We present a novel methodology based on geometric approach to simulate magnification lens effects. Our aim is to promote new applications of powerful geometric modeling techniques in visual computing. Conventional image processing/visualization methods are computed in two dimensional space (2D). We examine this conventional 2D manipulation from a completely innovative perspective of 3D geometric processing. Compared with conventional optical lens design, 3D geometric method are much more capable of preserving shape features and minimizing distortion. We magnify an area of interest to better visualize the interior details, while keeping the rest of area without perceivable distortion. We flatten the mesh back into 2D space for viewing, and further applications in the screen space. In both steps, we devise an iterative deformation scheme to minimize distortion around both focus and context region, while avoiding the noncontinuous transition region between the focus and context areas. Particularly, our method allows the user to flexibly modify the ROI shapes to accommodate complex feature. The user can also easily specify a spectrum of metrics for different visual effects. Various experimental results demonstrate the effectiveness, robustness, and efficiency of our framework.

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preprint2013arXiv

Anatomical Feature-guided Volumeric Registration of Multimodal Prostate MRI

Radiological imaging of prostate is becoming more popular among researchers and clinicians in searching for diseases, primarily cancer. Scans might be acquired at different times, with patient movement between scans, or with different equipment, resulting in multiple datasets that need to be registered. For this issue, we introduce a registration method using anatomical feature-guided mutual information. Prostate scans of the same patient taken in three different orientations are first aligned for the accurate detection of anatomical features in 3D. Then, our pipeline allows for multiple modalities registration through the use of anatomical features, such as the interior urethra of prostate and gland utricle, in a bijective way. The novelty of this approach is the application of anatomical features as the pre-specified corresponding landmarks for prostate registration. We evaluate the registration results through both artificial and clinical datasets. Registration accuracy is evaluated by performing statistical analysis of local intensity differences or spatial differences of anatomical landmarks between various MR datasets. Evaluation results demonstrate that our method statistics-significantly improves the quality of registration. Although this strategy is tested for MRI-guided brachytherapy, the preliminary results from these experiments suggest that it can be also applied to other settings such as transrectal ultrasound-guided or CT-guided therapy, where the integration of preoperative MRI may have a significant impact upon treatment planning and guidance.

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preprint2013arXiv

Evolution of photo-excited carrier distribution from anisotropic to isotropic and isotropic photon absorption in graphene

Femtosecond time-resolved spectroscopy using 400 nm-pump and 800 nm-probe in CVD-grown multilayer graphene provides strong evidence for isotropic distribution of photoexcited carrier after initial relaxation. Indicative of such isotropic distribution is a pump polarization independence of differential reflectivity (\DeltaR/R) and transmittance (\DeltaT/T) from pump-probe measurements. Combined with results using 800 nm-pump in [arXiv. 1301.1743v3 (2013)], these pump polarization dependences of time-resolved spectroscopy corroborates the evolution of photo-excited carrier distribution from anisotropic to isotropic with carrier relaxation. And, the absorbance of graphene is identical for in-plane and out-of-plane optical fields. No matter the carrier distribution in momentum space, the influence of carrier on in-plane and out-of-plane optical fields from state filling effect is identical. The sign reversing of ps dynamics signal in graphene/graphite should not directly relate to carrier.

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preprint2013arXiv

Experimental observation of polarization-dependent ultrafast carrier dynamics in multi-layer graphene

Polarization characteristic of ultrafast carrier dynamics in multi-layer CVD-grown graphene is probed with tilted beams (with respected to the graphene plane). The graphene ultrafast carrier dynamics measurement greatly depends on both polarization (i.e., orientation of linear polarization) and wave vector of probe beam. The differential reflectivity ΔR=R signal of picosecond dynamics could be continuously altered from positive to negative by changing the probe polarization from P to S when the dynamics is probed by a total internal reflected beam. The polarization dependent ΔR=R signal around 0 delay time is positive. It could be altered to negative by changing the probe polarization if the probe beam is non-total internal reflected beam. However, no sign reversal was observed for differential transmittance ΔT=T . These extremely unexpected results indicate the anisotropy of graphene carrier dynamics. Thus the ultrafast carrier dynamics should be further studied with consideration of the anisotropic structure (in- and out-of-graphene plane) of graphene.

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preprint2013arXiv

Parallel Coordinates Guided High Dimensional Transfer Function Design

High-dimensional transfer function design is widely used to provide appropriate data classification for direct volume rendering of various datasets. However, its design is a complicated task. Parallel coordinate plot (PCP), as a powerful visualization tool, can efficiently display high-dimensional geometry and accurately analyze multivariate data. In this paper, we propose to combine parallel coordinates with dimensional reduction methods to guide high-dimensional transfer function design. Our pipeline has two major advantages: (1) combine and display extracted high-dimensional features in parameter space; and (2) select appropriate high-dimensional parameters, with the help of dimensional reduction methods, to obtain sophisticated data classification as transfer function for volume rendering. In order to efficiently design high-dimensional transfer functions, the combination of both parallel coordinate components and dimension reduction results is necessary to generate final visualization results. We demonstrate the capability of our method for direct volume rendering using various CT and MRI datasets.

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preprint2013arXiv

Pattern Recognition and Revealing using Parallel Coordinates Plot

Parallel coordinates plot (PCP) is an excellent tool for multivariate visualization and analysis, but it may fail to reveal inherent structures for datasets with a large number of items. In this paper, we propose a suite of novel clustering, dimension ordering and visualization techniques based on PCP, to reveal and highlight hidden structures. First, we propose a continuous spline based polycurves design to extract and classify different cluster aspects of the data. Then, we provide an efficient and optimal correlation based sorting technique to reorder coordinates, as a helpful visualization tool for data analysis. Various results generated by our framework visually represent much structure, trend and correlation information to guide the user, and improve the efficacy of analysis, especially for complex and noisy datasets.

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preprint2010arXiv

Physical Mechanism of Superconductivity

The physical mechanism of superconductivity is proposed on the basis of carrier-induced dynamic strain effect. By this new model, superconducting state consists of the dynamic bound state of superconducting electrons, which is formed by the high-energy nonbonding electrons through dynamic interaction with their surrounding lattice to trap themselves into the three - dimensional potential wells lying in energy at above the Fermi level of the material. The binding energy of superconducting electrons dominates the superconducting transition temperature in the corresponding material. Under an electric field, superconducting electrons move coherently with lattice distortion wave and periodically exchange their excitation energy with chain lattice, that is, the superconducting electrons transfer periodically between their dynamic bound state and conducting state. Thus, the intrinsic feature of superconductivity is to generate an oscillating current under a dc voltage. The coherence lengths in cuprates must have the value equal to an even number times the lattice constant. A superconducting material must simultaneously satisfy three criteria required by superconductivity. Almost all of the puzzling behavior of the cuprates can be uniquely understood under this new model. We demonstrate that the factor 2 in Josephson current equation, in fact, is resulting from 2V, the voltage drops across the two superconductor sections on both sides of a junction, not from the Cooper pair, and the magnetic flux is quantized in units of h/e, postulated by London, not in units of h/2e. The central features of superconductivity, such as Josephson effect, the tunneling mechanism in multijunction systems, and the origin of the superconducting tunneling phenomena, are all physically reconsidered under this superconductivity model.

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