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Source author record

Jing Wang

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

ResearcherUnclaimed source record
cond-mat.mes-hallastro-ph.GAComputer Visioncond-mat.mtrl-sciastro-ph.COMachine Learningquant-phphysics.opticscond-mat.str-elcond-mat.supr-conInformation Theorymath.ITeess.IVastro-ph.HEArtificial Intelligencemath.PRcond-mat.dis-nnhep-thmath.APastro-ph.IMmath.COmath.DGApplicationsComputation and Languagegr-qchep-phmath-phmath.MPNetworking and Internet ArchitectureRoboticscond-mat.stat-mechCryptography and Securityeess.SPmath.DSmath.QAMethodologyMultimediaNeurons and Cognitionphysics.app-phSocial and Information NetworksSystems and Controlastro-ph.SRBiological Physicscond-mat.othercs.CYmath.OCmath.SPphysics.ed-phphysics.flu-dynphysics.med-phphysics.soc-phPopulations and Evolutionastro-phcond-mat.softDistributed, Parallel, and Cluster Computingeess.SYEmerging TechnologiesHardware Architecturehep-exhep-latInformation Retrievalmath.STnlin.CDnlin.PSnlin.SIOther Quantitative Biologyphysics.data-anphysics.geo-phProgramming LanguagesQuantitative MethodsStatistics Theory

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Published work

299 published item(s)

preprint2026arXiv

CURE-OOD: Benchmarking Out-of-Distribution Detection for Survival Prediction

``How long can I live and remain free of cancer?'' is often the first question a patient asks after receiving a cancer diagnosis and treatment. Accurate survival prediction helps alleviate psychological distress and supports risk stratification and personalized treatment planning. Recent survival prediction frameworks have shown strong performance using computed tomography (CT) images. However, variations in imaging acquisition introduce out-of-distribution (OOD) samples caused by covariate shifts that undermine model reliability. Despite this challenge, to our knowledge, no existing benchmark systematically studies OOD detection in cancer survival prediction. To address this gap, we introduce the Cancer sURvival bEnchmark for OOD Detection (CURE-OOD), the first benchmark for systematically evaluating OOD detection in survival prediction under controlled acquisition-induced distribution shifts. CURE-OOD defines scanner-parameter-based training, in-distribution (ID), and OOD test splits across four survival prediction tasks. Our experiments show that covariate shifts notably reduce survival prediction performance. It also shows that mainstream classification-oriented OOD detectors can fail in survival prediction. Finally, we include HazardDev as a simple survival-aware reference baseline for OOD detection. CURE-OOD enables systematic analysis of how distribution shifts affect both downstream survival performance and OOD detectability.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

DBES: A Systematic Benchmark and Metric Suite for Evaluating Expert Specialization in Large-Scale MoEs

Expert specialization in Mixture-of-Experts (MoE) models remains poorly understood, with traditional evaluations conflating architectural load-balancing with functional specialization. We introduce DBES, a comprehensive diagnostic framework combining a multi-domain benchmark with five theoretically grounded metrics: Routing Specialization, Normalized Effective Rank, Domain Isolation, Routing Stiffness Score, and N-gram Expertise measures. Critical findings demonstrate distinct specialization paradigms across models: Qwen-series exhibit modular specialization with high domain isolation, while DeepSeek and GLM employ distributed collaboration. However, we emphasize that specialization is a diagnostic dimension, necessary but not sufficient for downstream performance. Most crucially, interventional evidence validates the actionability of these metrics: by using DBES to identify high-specialization expert paths during domain-specific post-training, we achieved 66% to 94.48% improvement in specialized domains with only 15% of original training resources, demonstrating that these diagnostic tools can be converted into concrete optimization operators. This work provides the first systematic methodology for evaluating expert specialization independently of accuracy metrics, offering crucial insights for the design and post-training optimization of next-generation MoE systems.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Detection of Oscillations in a Type I X-Ray Burst of 4U 0614+091 with SVOM/ECLAIRs

On 2025 January 10, a thermonuclear (Type I) X-ray burst from the neutron star low-mass X-ray binary \textit{4U~0614+091} was detected with the ECLAIRs instrument on board the \textit{SVOM} mission. We present here a time-resolved spectroscopic analysis of the burst, along with the detection of burst oscillations within a 51-second interval during the decay phase. The oscillation frequency is measured to be $ν= 413.674 \pm 0.002\,\mathrm{Hz}$, consistent with previous reports. However, we detect a significant downward frequency drift over the burst duration, characterized by $\dotν = (-4.7 \pm 0.3) \times 10^{-3}\,\mathrm{Hz\,s^{-1}}$. This frequency evolution is atypical compared to those observed in similar burst oscillation sources. We tentatively attribute the observed drift to a Doppler shift induced by orbital motion. Under this interpretation, the inferred orbital period must be shorter than 20 minutes, placing \textit{4U~0614+091} among the most compact known low-mass X-ray binaries.

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preprint2026arXiv

Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL

Generative retrieval offers a promising alternative by unifying the fragmented multi-stage retrieval process into a single end-to-end model. However, its practical adoption in industrial e-commerce search remains challenging, given the massive and dynamic product catalogs, strict latency requirements, and the need to align retrieval with downstream ranking goals. In this work, we propose a retrieval framework tailored for real-world recall scenarios, positioning generative retrieval as a recall-stage supplement rather than an end-to-end replacement. Our method, CQ-SID (Category-and-Query constrained Semantic ID), employs category-aware and query-item contrastive learning along with Residual Quantized VAEs to encode items into hierarchical semantic cluster identifiers, significantly reducing beam search complexity. Additionally, we develop EG-GRPO (Expert-Guided Group Relative Policy Optimization), a reinforcement learning approach that aligns generative recall with downstream ranking under sparse rewards by injecting ground-truth samples to stabilize training. Offline experiments on TmallAPP search logs show that CQ-SID achieves up to 26.76% and 11.11% relative gains in semantic and personalized click hitrate over RQ-VAE baselines, while halving beam search size. EG-GRPO further improves multi-objective performance. Online A/B tests confirm gains in GMV (+1.15%) and UCTCVR (+0.40%). The generative recall channel now contributes substantially in production, accounting for over 50.25% of exposures, 58.96% of clicks, and 72.63% of purchases, demonstrating a viable path for deploying generative retrieval in real-world e-commerce systems.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

FinDeepResearch: Evaluating Deep Research Agents in Rigorous Financial Analysis

Deep Research (DR) agents, powered by advanced Large Language Models (LLMs), have recently garnered increasing attention for their capability in conducting complex research tasks. However, existing literature lacks a rigorous and systematic evaluation of DR Agent's capabilities in critical research analysis. To address this gap, we first propose HisRubric, a novel evaluation framework with a hierarchical analytical structure and a fine-grained grading rubric for rigorously assessing DR agents' capabilities in corporate financial analysis. This framework mirrors the professional analyst's workflow, progressing from data recognition to metric calculation, and finally to strategic summarization and interpretation. Built on this framework, we construct a FinDeepResearch benchmark that comprises 64 listed companies from 8 financial markets across 4 languages, encompassing a total of 15,808 grading items. We further conduct extensive experiments on the FinDeepResearch using 16 representative methods, including 6 DR agents, 5 LLMs equipped with both deep reasoning and search capabilities, and 5 LLMs with deep reasoning capabilities only. The results reveal the strengths and limitations of these approaches across diverse capabilities, financial markets, and languages, offering valuable insights for future research and development. The benchmark and evaluation code is publicly available at https://OpenFinArena.com/.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

HiDream-O1-Image: A Natively Unified Image Generative Foundation Model with Pixel-level Unified Transformer

The evolution of visual generative models has long been constrained by fragmented architectures relying on disjoint text encoders and external VAEs. In this report, we present HiDream-O1-Image, a natively unified generative foundation model via pixel-space Diffusion Transformer, that pioneers a paradigm shift from modular architectures to an end-to-end in-context visual generation engine. By mapping raw image pixels, text tokens, and task-specific conditions into a single shared token space, HiDream-O1-Image achieves a structural unification of multimodal inputs within an Unified Transformer (UiT) architecture. This native encoding paradigm eliminates the need for separate VAEs or disjoint pre-trained text encoders, allowing the model to treat diverse generation and editing tasks as a consistent in-context reasoning process. Extensive experiments show that HiDream-O1-Image excels across various generation tasks, including text-to-image generation, instruction-based editing, and subject-driven personalization. Notably, with only 8B parameters, HiDream-O1-Image (8B) achieves performance parity with or even surpasses established state-of-the-art models with significantly larger parameters (e.g., 27B Qwen-Image). Crucially, to validate the immense scalability of this paradigm, we successfully scale the architecture up to over 200B parameters. Experimental results demonstrate that this massive-scale version HiDream-O1-Image-Pro (200B+) unlocks unprecedented generative capabilities and superior performance, establishing new state-of-the-art benchmarks. Ultimately, HiDream-O1-Image highlights the immense potential of natively unified architectures and charts a highly scalable path toward next-generation multimodal AI.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Immunological Density Shapes Recovery Trajectories in Long COVID

Post-acute sequelae of SARS-CoV-2 infection (Long COVID) frequently persists for months, yet drivers of clinical remission remain incompletely defined. Here we analyzed 97,564 longitudinal PASC assessments from 13,511 participants with linked vaccination histories to disentangle passive temporal progression from vaccine-associated change. Using a clinically validated threshold (PASC $\geq 12$), trajectories separated into three phenotypes: Protected (persistently sub-threshold), Refractory (persistently symptomatic), and Responders (transitioning from symptomatic to recovered). Across the full cohort, symptom severity increased modestly with elapsed time ($r=0.0521$, $P=1.26\times10^{-59}$), whereas cumulative vaccination showed an inverse association with severity ($r=-0.0434$, $P=5.95\times10^{-42}$). In summary, baseline Long COVID severity appears clinically deterministic. In the absence of intervention, symptoms typically persist without spontaneous resolution. Recovery is primarily associated with repeated immunization.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Implicit spatial-frequency fusion of hyperspectral and lidar data via kolmogorov-arnold networks

Hyperspectral image (HSI) classification is challenging in complex scenes due to spectral ambiguity, spatial heterogeneity, and the strong coupling between material properties and geometric structures. Although LiDAR provides complementary elevation information, most HSI-LiDAR fusion methods rely on CNNs or MLPs with fixed activation functions and linear weights. These methods struggle to model structural discontinuities in LiDAR data, intricate spectral features of HSI, and their interactions. In addition, fusion of the two modalities in both spatial and frequency domains with LiDAR guidance remains underexplored. To address these issues, we propose the Implicit Frequency-Geometry Fusion Network (IFGNet), which leverages Kolmogorov-Arnold Networks (KANs) with learnable spline-based functions to adaptively capture highly nonlinear relationships between hyperspectral and LiDAR features. Furthermore, IFGNet introduces a LiDAR-guided implicit aggregation module in both spatial and frequency domains, enhancing geometry-aware spatial representations while capturing global structural patterns. Experiments on the Houston 2013 and MUUFL benchmarks demonstrate that IFGNet consistently outperforms existing fusion methods in overall accuracy, average accuracy, and Cohen's Kappa, while maintaining an efficient architecture.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping

Recently, post-training methods based on reinforcement learning, with a particular focus on Group Relative Policy Optimization (GRPO), have emerged as the robust paradigm for further advancement of text-to-image (T2I) models. However, these methods are often prone to reward hacking, wherein models exploit biases in imperfect reward functions rather than yielding genuine performance gains. In this work, we identify that normalization could lead to miscalibration and directly removing the prompt-level standard deviation term yields an optimal policy ascent direction that is linear in the advantage but still limits the separation of genuine signals from noise. To mitigate the above issues, we propose Super-Linear Advantage Shaping (SLAS) by revisiting the functional update from an information geometry perspective. By extending the Fisher-Rao information metric with advantage-dependent weighting, SLAS introduces a non-linear geometric structure that reshapes the local policy space. This design relaxes constraints along high-advantage directions to amplify informative updates, while tightening those in low-advantage regions to suppress illusory gradients. In addition, batch-level normalization is applied to stabilize training under varying reward scales. Extensive evaluations demonstrate that SLAS consistently surpasses the DanceGRPO baseline across multiple backbones and benchmarks. In particular, it yields faster training dynamics, improved out-of-domain performance on GenEval and UniGenBench++, and enhanced robustness to model scaling, while mitigating reward hacking and preserving semantic and compositional fidelity in generations.

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preprint2026arXiv

Radio AGN feedback sustains quiescence only in a minority of massive galaxies

Radio active galactic nuclei (AGNs) eject a huge amount of energy into the surrounding medium and are thought to potentially prevent gas cooling and maintain the quiescence of massive galaxies. The short-lived, sporadic, and anisotropic nature of radio activities, coupled with the detection of abundant cold gas around some massive quiescent galaxies, raise questions about the efficiency of radio feedback in massive galaxies. Here we present an innovative method rooted in artificial intelligence to separate galaxies in which radio feedback is effective (RFE), regardless of current radio emission, from those in which radio feedback is ineffective (RFI), according to their optical images. Galaxies categorized as RFE are all dynamically hot, whereas quiescent RFI (RFI-Q) galaxies usually have extended cold-disk components. At given stellar mass, dark matter halos hosting RFE galaxies are between four to ten times more massive than those of RFI-Q galaxies. We find, for the first time, that almost all RFE galaxies have scant cold gas, irrespective of AGN activity. In contrast, many RFI-Q galaxies are surrounded by substantial amounts of condensed atomic gas, indicating a different evolutionary path from RFE galaxies. Our finding provides direct and compelling evidence that a radio AGN has gone through about 300 on-off cycles and that radio feedback can prevent gas cooling over a timescale much longer than that of radio activity. Contrary to general belief, our analysis shows that only a small fraction of massive galaxies are influenced by strong radio AGNs, suggesting that current galaxy formation models need serious revision.

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preprint2026arXiv

Revealing Superconducting Chiral Edge Modes via Resistance Distributions

Inducing superconducting correlations in quantum anomalous Hall (QAH) states offers a promising route to realize topological superconductivity with chiral Majorana edge modes. However, the definitive identification of these modes is challenging. Here we propose detecting superconducting chiral edge modes via the probability distribution of the resistance, or equivalently the charge transmission of QAH-superconductor heterojunctions. Remarkably, the distribution for coherent edge exhibits distinct characteristics for different topological superconducting phases in sufficiently long junctions, and this difference remains robust against weak decoherence. These findings provide insights into transport phenomena beyond the clean limit and highlight the resistance distribution as a compelling signature for distinguishing topological superconducting phases.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Revisiting Weighted Strategy for Non-stationary Parametric Bandits and MDPs

Non-stationary parametric bandits have attracted much attention recently. There are three principled ways to deal with non-stationarity, including sliding-window, weighted, and restart strategies. As many non-stationary environments exhibit gradual drifting patterns, the weighted strategy is commonly adopted in real-world applications. However, previous theoretical studies show that its analysis is more involved and the algorithms are either computationally less efficient or statistically suboptimal. This paper revisits the weighted strategy for non-stationary parametric bandits. In linear bandits (LB), we discover that this undesirable feature is due to an inadequate regret analysis, which results in an overly complex algorithm design. We propose a \emph{refined analysis framework}, which simplifies the derivation and, importantly, produces a simpler weight-based algorithm that is as efficient as window/restart-based algorithms while retaining the same regret as previous studies. Furthermore, our new framework can be used to improve regret bounds of other parametric bandits, including Generalized Linear Bandits (GLB) and Self-Concordant Bandits (SCB). For example, we develop a simple weighted GLB algorithm with an $\tilde{O}(k_μ^{5/4} c_μ^{-3/4} d^{3/4} P_T^{1/4}T^{3/4})$ regret, improving the $\tilde{O}(k_μ^{2} c_μ^{-1}d^{9/10} P_T^{1/5}T^{4/5})$ bound in prior work, where $k_μ$ and $c_μ$ characterize the reward model's nonlinearity, $P_T$ measures the non-stationarity, $d$ and $T$ denote the dimension and time horizon. Moreover, we extend our framework to non-stationary Markov Decision Processes (MDPs) with function approximation, focusing on Linear Mixture MDP and Multinomial Logit (MNL) Mixture MDP. For both classes, we propose algorithms based on the weighted strategy and establish dynamic regret guarantees using our analysis framework.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

RTL-BenchMT: Dynamic Maintenance of RTL Generation Benchmark Through Agent-Assisted Analysis and Revision

This paper introduces RTL-BenchMT, an agentic framework for dynamically maintaining RTL generation benchmarks. Large Language Models (LLMs) assisted automated RTL generation is one of the most important directions in EDA research. However, current RTL benchmarks face two critical challenges: (1) flawed cases in the benchmarks and (2) overfitting to the benchmarks. Both challenges are difficult to resolve purely by manual engineering effort. To address these issues and systematically reduce human maintenance costs, we propose an automated agentic framework, RTL-BenchMT. RTL-BenchMT focuses on two key applications: (1) automatically identifying and revising flawed benchmark cases and (2) automatically detecting and updating overfitting cases. With the assistance of RTL-BenchMT, we conduct a thorough, in-depth analysis of flawed and overfitting cases and produce a refined benchmark suite that will be open-sourced to the community.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Towards Understanding Feature Learning in Parameter Transfer

Parameter transfer is a central paradigm in transfer learning, enabling knowledge reuse across tasks and domains by sharing model parameters between upstream and downstream models. However, when only a subset of parameters from the upstream model is transferred to the downstream model, there remains a lack of theoretical understanding of the conditions under which such partial parameter reuse is beneficial and of the factors that govern its effectiveness. To address this gap, we analyze a setting in which both the upstream and downstream models are ReLU convolutional neural networks (CNNs). Within this theoretical framework, we characterize how the inherited parameters act as carriers of universal knowledge and identify key factors that amplify their beneficial impact on the target task. Furthermore, our analysis provides insight into why, in certain cases, transferring parameters can lead to lower test accuracy on the target task than training a new model from scratch. To our best knowledge, our theory is the first to provide a dynamic analysis for parameter transfer and also the first to prove the existence of negative transfer theoretically. Numerical experiments and real-world data experiments are conducted to empirically validate our theoretical findings.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

XekRung Technical Report

We present XekRung, a frontier large language model for cybersecurity, designed to provide comprehensive security capabilities. To achieve this, we develop diverse data synthesis pipelines tailored to the cybersecurity domain, enabling the scalable construction of high-quality training data and providing a strong foundation for cybersecurity knowledge and understanding. Building on this foundation, we establish a complete training pipeline spanning continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL) to further extend the model's capabilities. We further introduce a multi-dimensional evaluation system to guide the iterative improvement of both domain-specific and general-purpose abilities. Extensive experiments demonstrate that XekRung achieves state-of-the-art performance on cybersecurity-specific benchmarks among models of the same scale, while maintaining strong performance on general benchmarks.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

AstroReview: An LLM-driven Multi-Agent Framework for Telescope Proposal Peer Review and Refinement

Competitive access to modern observatories has intensified as proposal volumes outpace available telescope time, making timely, consistent, and transparent peer review a critical bottleneck for the advancement of astronomy. Automating parts of this process is therefore both scientifically significant and operationally necessary to ensure fair allocation and reproducible decisions at scale. We present AstroReview, an open-source, agent-based framework that automates proposal review in three stages: (i) novelty and scientific merit, (ii) feasibility and expected yield, and (iii) meta-review and reliability verification. Task isolation and explicit reasoning traces curb hallucinations and improve transparency. Without any domain specific fine tuning, AstroReview used in our experiments only for the last stage, correctly identifies genuinely accepted proposals with an accuracy of 87%. The AstroReview in Action module replicates the review and refinement loop; with its integrated Proposal Authoring Agent, the acceptance rate of revised drafts increases by 66% after two iterations, showing that iterative feedback combined with automated meta-review and reliability verification delivers measurable quality gains. Together, these results point to a practical path toward scalable, auditable, and higher throughput proposal review for resource limited facilities.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Detection of disk-jet co-precession in a tidal disruption event

Theories and simulations predict that intense spacetime curvature near black holes bends the trajectories of light and matter, driving disk and jet precession under relativistic torques. However, direct observational evidence of disk-jet co-precession remains elusive. Here, we report the most compelling case to date: a tidal disruption event (TDE) exhibiting unprecedented 19.6-day quasi-periodic variations in both X-rays and radio, with X-ray amplitudes exceeding an order of magnitude. The nearly synchronized X-ray and radio variations suggest a shared mechanism regulating the emission regions. We demonstrate that a disk-jet Lense-Thirring precession model successfully reproduces these variations while requiring a low-spin black hole. This study uncovers previously uncharted short-term radio variability in TDEs, highlights the transformative potential of high-cadence radio monitoring, and offers profound insights into disk-jet physics.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Improving the Imaging Performance of Microwave Imaging Systems by Exploiting Virtual Antennas

Starting from the observation that the correlation coefficient defined by the scattered field data tested by two adjacent antennas decreases with the noise, it turns out that the imaging performance can be improved by adding non-redundant scattered field information through more measuring antennas.However, adding more measuring antennas faces practical challenges such as the limited antenna space, high experimental expenses, and a prolonged data collection time. Therefore, the frequency-domain zero-padding (FDZP) interpolation method is proposed to acquire scattered field data on more virtual antennas. To process the data, a linear inversion algorithm based on the modified Born approximation (MBA) and the nonlinear subspace-based optimization method (SOM) are used to image scatterers of moderate and high contrasts, respectively. The effectiveness and the reliability of the proposed approach are then assessed against synthetic data, semi-experimental data from a full-wave simulation software, and experimental data.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Nurse-in-the-Loop Artificial Intelligence for Precision Management of Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive Digital Twin

Background: Type 2 diabetes (T2D) is a prevalent chronic disease with a significant risk of serious health complications and negative impacts on the quality of life. Given the impact of individual characteristics and lifestyle on the treatment plan and patient outcomes, it is crucial to develop precise and personalized management strategies. Artificial intelligence (AI) provides great promise in combining patterns from various data sources with nurses' expertise to achieve optimal care. Methods: This is a 6-month ancillary study among T2D patients (n = 20, age = 57 +- 10). Participants were randomly assigned to an intervention (AI, n=10) group to receive daily AI-generated individualized feedback or a control group without receiving the daily feedback (non-AI, n=10) in the last three months. The study developed an online nurse-in-the-loop predictive control (ONLC) model that utilizes a predictive digital twin (PDT). The PDT was developed using a transfer-learning-based Artificial Neural Network. The PDT was trained on participants self-monitoring data (weight, food logs, physical activity, glucose) from the first three months, and the online control algorithm applied particle swarm optimization to identify impactful behavioral changes for maintaining the patient's glucose and weight levels for the next three months. The ONLC provided the intervention group with individualized feedback and recommendations via text messages. The PDT was re-trained weekly to improve its performance. Findings: The trained ONLC model achieved >=80% prediction accuracy across all patients while the model was tuned online. Participants in the intervention group exhibited a trend of improved daily steps and stable or improved total caloric and total carb intake as recommended.

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preprint2024arXiv

Parabolic Anderson model in bounded domains of recurrent metric measure spaces

A metric measure space equipped with a Dirichlet form is called recurrent if its Hausdorff dimension is less than its walk dimension. In bounded domains of such spaces we study the parabolic Anderson models \[ \partial_{t} u(t,x) = Δu(t,x) + βu(t,x) \, \dot{W}_α(t,x) \] where the noise $W_α$ is white in time and colored in space when $α>0$ while for $α=0$ it is also white in space. Both Dirichlet and Neumann boundary conditions are considered. Besides proving existence and uniqueness in the Itô sense we also get precise $L^p$ estimates for the moments and intermittency properties of the solution as a consequence. Our study reveals new exponents which are intrinsically associated to the geometry of the underlying space and the results for instance apply in metric graphs or fractals like the Sierpiński gasket for which we prove scaling invariance properties of the models.

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preprint2023arXiv

Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detection

Recently, logo detection has received more and more attention for its wide applications in the multimedia field, such as intellectual property protection, product brand management, and logo duration monitoring. Unlike general object detection, logo detection is a challenging task, especially for small logo objects and large aspect ratio logo objects in the real-world scenario. In this paper, we propose a novel approach, named Discriminative Semantic Feature Pyramid Network with Guided Anchoring (DSFP-GA), which can address these challenges via aggregating the semantic information and generating different aspect ratio anchor boxes. More specifically, our approach mainly consists of Discriminative Semantic Feature Pyramid (DSFP) and Guided Anchoring (GA). Considering that low-level feature maps that are used to detect small logo objects lack semantic information, we propose the DSFP, which can enrich more discriminative semantic features of low-level feature maps and can achieve better performance on small logo objects. Furthermore, preset anchor boxes are less efficient for detecting large aspect ratio logo objects. We therefore integrate the GA into our method to generate large aspect ratio anchor boxes to mitigate this issue. Extensive experimental results on four benchmarks demonstrate the effectiveness of our proposed DSFP-GA. Moreover, we further conduct visual analysis and ablation studies to illustrate the advantage of our method in detecting small and large aspect logo objects. The code and models can be found at https://github.com/Zhangbaisong/DSFP-GA.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Steering-induced phase transition in measurement-only quantum circuits

Competing measurements alone can give rise to distinct phases characterized by entanglement entropy$\unicode{x2013}$such as the volume law phase, symmetry-breaking (SB) phase, and symmetry-protected topological (SPT) phase$\unicode{x2013}$that can only be discerned through quantum trajectories, making them challenging to observe experimentally. In another burgeoning area of research, recent studies have demonstrated that steering can give rise to additional phases within quantum circuits. In this work, we show that new phases can appear in measurement-only quantum circuit with steering. Unlike conventional steering methods that rely solely on local information, the steering scheme we introduce requires the circuit's structure as an additional input. These steering induced phases are termed as "informative" phases. They are distinguished by the intrinsic dimension of the bitstrings measured in each circuit run, making them substantially easier to detect in experimental setups. We explicitly show this phase transition by numerical simulation in three circuit models that are previously well-studied: projective transverse field Ising model, lattice gauge-Higgs model and XZZX model. When the informative phase coincides with the SB phase, our steering mechanism effectively serves as a "pre-selection" routine, making the SB phase more experimentally accessible. Additionally, an intermediate phase may manifest, where a discrepancy arises between the quantum information captured by entanglement entropy and the classical information conveyed by bitstrings. Our findings demonstrate that steering not only adds theoretical richness but also offers practical advantages in the study of measurement-only quantum circuits.

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preprint2022arXiv

A Constrained Deformable Convolutional Network for Efficient Single Image Dynamic Scene Blind Deblurring with Spatially-Variant Motion Blur Kernels Estimation

Most existing deep-learning-based single image dynamic scene blind deblurring (SIDSBD) methods usually design deep networks to directly remove the spatially-variant motion blurs from one inputted motion blurred image, without blur kernels estimation. In this paper, inspired by the Projective Motion Path Blur (PMPB) model and deformable convolution, we propose a novel constrained deformable convolutional network (CDCN) for efficient single image dynamic scene blind deblurring, which simultaneously achieves accurate spatially-variant motion blur kernels estimation and the high-quality image restoration from only one observed motion blurred image. In our proposed CDCN, we first construct a novel multi-scale multi-level multi-input multi-output (MSML-MIMO) encoder-decoder architecture for more powerful features extraction ability. Second, different from the DLVBD methods that use multiple consecutive frames, a novel constrained deformable convolution reblurring (CDCR) strategy is proposed, in which the deformable convolution is first applied to blurred features of the inputted single motion blurred image for learning the sampling points of motion blur kernel of each pixel, which is similar to the estimation of the motion density function of the camera shake in the PMPB model, and then a novel PMPB-based reblurring loss function is proposed to constrain the learned sampling points convergence, which can make the learned sampling points match with the relative motion trajectory of each pixel better and promote the accuracy of the spatially-variant motion blur kernels estimation.

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preprint2022arXiv

A Model Predictive Control Functional Continuous Time Bayesian Network for Self-Management of Multiple Chronic Conditions

Multiple chronic conditions (MCC) are one of the biggest challenges of modern times. The evolution of MCC follows a complex stochastic process that is influenced by a variety of risk factors, ranging from pre-existing conditions to modifiable lifestyle behavioral factors (e.g. diet, exercise habits, tobacco use, alcohol use, etc.) to non-modifiable socio-demographic factors (e.g., age, gender, education, marital status, etc.). People with MCC are at an increased risk of new chronic conditions and mortality. This paper proposes a model predictive control functional continuous time Bayesian network, an online recursive method to examine the impact of various lifestyle behavioral changes on the emergence trajectories of MCC and generate strategies to minimize the risk of progression of chronic conditions in individual patients. The proposed method is validated based on the Cameron county Hispanic cohort (CCHC) dataset, which has a total of 385 patients. The dataset examines the emergence of 5 chronic conditions (diabetes, obesity, cognitive impairment, hyperlipidemia, and hypertension) based on four modifiable risk factors representing lifestyle behaviors (diet, exercise habits, tobacco use, alcohol use) and four non-modifiable risk factors, including socio-demographic information (age, gender, education, marital status). The proposed method is tested under different scenarios (e.g., age group, the prior existence of MCC), demonstrating the effective intervention strategies for improving the lifestyle behavioral risk factors to offset MCC evolution.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A neural network model for timing control with reinforcement

How do humans and animals perform trial-and-error learning when the space of possibilities is infinite? In a previous study, we used an interval timing production task and discovered an updating strategy in which the agent adjusted the behavioral and neuronal noise for exploration. In the experiment, human subjects proactively generated a series of timed motor outputs. We found that the sequential motor timing varied at two temporal scales: long-term correlation around the target interval due to memory drifts and short-term adjustments of timing variability according to feedback. We have previously described these features of timing variability with an augmented Gaussian process, termed reward sensitive Gaussian process (RSGP). Here we provide a mechanistic model and simulate the process by borrowing the architecture of recurrent neural networks. While recurrent connection provided the long-term serial correlation in motor timing, to facilitate reward-driven short-term variations, we introduced reward-dependent variability in the network connectivity, inspired by the stochastic nature of synaptic transmission in the brain. Our model was able to recursively generate an output sequence incorporating the internal variability and external reinforcement in a Bayesian framework. We show that the model can learn the key features of human behavior. Unlike other neural network models that search for unique network connectivity for the best match between the model prediction and observation, this model can estimate the uncertainty associated with each outcome and thus did a better job in teasing apart adjustable task-relevant variability from unexplained variability. The proposed artificial neural network model parallels the mechanisms of information processing in neural systems and can extend the framework of brain-inspired reinforcement learning in continuous state control.

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preprint2022arXiv

AlphaVC: High-Performance and Efficient Learned Video Compression

Recently, learned video compression has drawn lots of attention and show a rapid development trend with promising results. However, the previous works still suffer from some criticial issues and have a performance gap with traditional compression standards in terms of widely used PSNR metric. In this paper, we propose several techniques to effectively improve the performance. First, to address the problem of accumulative error, we introduce a conditional-I-frame as the first frame in the GoP, which stabilizes the reconstructed quality and saves the bit-rate. Second, to efficiently improve the accuracy of inter prediction without increasing the complexity of decoder, we propose a pixel-to-feature motion prediction method at encoder side that helps us to obtain high-quality motion information. Third, we propose a probability-based entropy skipping method, which not only brings performance gain, but also greatly reduces the runtime of entropy coding. With these powerful techniques, this paper proposes AlphaVC, a high-performance and efficient learned video compression scheme. To the best of our knowledge, AlphaVC is the first E2E AI codec that exceeds the latest compression standard VVC on all common test datasets for both PSNR (-28.2% BD-rate saving) and MSSSIM (-52.2% BD-rate saving), and has very fast encoding (0.001x VVC) and decoding (1.69x VVC) speeds.

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preprint2022arXiv

Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation

With the development of deep learning techniques, the combination of deep learning with image compression has drawn lots of attention. Recently, learned image compression methods had exceeded their classical counterparts in terms of rate-distortion performance. However, continuous rate adaptation remains an open question. Some learned image compression methods use multiple networks for multiple rates, while others use one single model at the expense of computational complexity increase and performance degradation. In this paper, we propose a continuously rate adjustable learned image compression framework, Asymmetric Gained Variational Autoencoder (AG-VAE). AG-VAE utilizes a pair of gain units to achieve discrete rate adaptation in one single model with a negligible additional computation. Then, by using exponential interpolation, continuous rate adaptation is achieved without compromising performance. Besides, we propose the asymmetric Gaussian entropy model for more accurate entropy estimation. Exhaustive experiments show that our method achieves comparable quantitative performance with SOTA learned image compression methods and better qualitative performance than classical image codecs. In the ablation study, we confirm the usefulness and superiority of gain units and the asymmetric Gaussian entropy model.

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preprint2022arXiv

BIOS: An Algorithmically Generated Biomedical Knowledge Graph

Biomedical knowledge graphs (BioMedKGs) are essential infrastructures for biomedical and healthcare big data and artificial intelligence (AI), facilitating natural language processing, model development, and data exchange. For decades, these knowledge graphs have been developed via expert curation; however, this method can no longer keep up with today's AI development, and a transition to algorithmically generated BioMedKGs is necessary. In this work, we introduce the Biomedical Informatics Ontology System (BIOS), the first large-scale publicly available BioMedKG generated completely by machine learning algorithms. BIOS currently contains 4.1 million concepts, 7.4 million terms in two languages, and 7.3 million relation triplets. We present the methodology for developing BIOS, including the curation of raw biomedical terms, computational identification of synonymous terms and aggregation of these terms to create concept nodes, semantic type classification of the concepts, relation identification, and biomedical machine translation. We provide statistics on the current BIOS content and perform preliminary assessments of term quality, synonym grouping, and relation extraction. The results suggest that machine learning-based BioMedKG development is a viable alternative to traditional expert curation.

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preprint2022arXiv

Centrally Concentrated H I Distribution Enhances Star Formation in Galaxies

We use a sample of 13,511 nearby galaxies from the ALFALFA and SDSS spectroscopic surveys to study the relation between the spatial distribution of H I 21 cm emission and star formation rate (SFR). We introduce a new non-parametric quantity $K$, measured from the curve-of-growth of the line, to describe the shape of the integrated H I profile. The value of $K$ increases from double-horned to single-peaked profiles, depending on projection effects and the spatial and velocity distribution of the gas. Using carefully chosen samples to control for the competing factors that influence the integrated line profile, we argue that useful inferences can be made on the spatial distribution of the gas. We find that galaxies with a high value of $K$ tend to have more centrally concentrated H I distribution within the optical disk of the galaxy at fixed conditions, and that larger values of $K$ are associated with higher levels of total and central SFR. The results suggest that the global concentration of H I plays an important role in facilitating the conversion of neutral atomic hydrogen to molecular hydrogen gas, which, in turn, affects the star formation activity throughout the optical disk. Our sample is biased against quiescent galaxies, and thus the conclusions may not hold for galaxies with low SFR or low H I content.

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preprint2022arXiv

CHANG-ES XXV: HI Imaging of Nearby Edge-on Galaxies -- Data Release 4

We present the HI distribution of galaxies from the Continuum Halos in Nearby Galaxies - an EVLA Survey (CHANG-ES). Though the observational mode was not optimized for detecting HI, we successfully produce HI cubes for 19 galaxies. The moment-0 maps from this work are available on CHANG-ES data release website, i.e., https://www.queensu.ca/changes. Our sample is dominated by star-forming, HI-rich galaxies at distances from 6.27 to 34.1 Mpc. HI interferometric images on two of these galaxies (NGC 5792 and UGC 10288) are presented here for the first time, while 12 of our remaining sample galaxies now have better HI spatial resolutions and/or sensitivities of intensity maps than those in existing publications. We characterize the average scale heights of the HI distributions for a subset of most inclined galaxies (inclination > 80 deg), and compare them to the radio continuum intensity scale heights, which have been derived in a similar way. The two types of scale heights are well correlated, with similar dependence on disk radial extension and star formation rate surface density but different dependence on mass surface density. This result indicates that the vertical distribution of the two components may be governed by similar fundamental physics but with subtle differences.

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preprint2022arXiv

Cold Gas Reservoirs of Low- and High-mass Central Galaxies Differ in Response to Active Galactic Nucleus Feedback

The growth of supermassive black holes, especially the associated state of active galactic nuclei (AGNs), is generally believed to be the key step in regulating star formation in massive galaxies. As the fuel of star formation, the cold gas reservoir is a direct probe of the effect of AGN feedback on their host galaxies. However, in observation, no clear connection has been found between AGN activity and the cold gas mass. In this paper, we find observational signals of significant depletion of the total neutral hydrogen gas reservoir in optically-selected type-2 AGN host central galaxies of stellar mass $10^{9}$--$10^{10}M_\odot$. The effect of AGN feedback on the cold gas reservoir is stronger for higher star formation rates and higher AGN luminosity. But it becomes much weaker above this mass range, consistent with previous findings focusing on massive galaxies. Our result suggests that low-mass and gas-rich AGN host central galaxies would first form dense cores before AGN feedback is triggered, removing their neutral hydrogen gas. More massive central galaxies may undergo a significantly different formation scenario by gradually building up dense cores with less effective and recurrent AGN feedback.

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preprint2022arXiv

Constructing graphs having Laplacian pair state transfer by an edge perturbation

In this paper, we give some sufficient conditions for graphs with an edge perturbation between twin vertices to have Laplacian perfect pair state transfer as well as Laplacian pretty good pair state transfer. By those sufficient conditions, we also construct many new graphs having Laplacian perfect pair state transfer as well as Laplacian pretty good pair state transfer.

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preprint2022arXiv

Content-oriented learned image compression

In recent years, with the development of deep neural networks, end-to-end optimized image compression has made significant progress and exceeded the classic methods in terms of rate-distortion performance. However, most learning-based image compression methods are unlabeled and do not consider image semantics or content when optimizing the model. In fact, human eyes have different sensitivities to different content, so the image content also needs to be considered. In this paper, we propose a content-oriented image compression method, which handles different kinds of image contents with different strategies. Extensive experiments show that the proposed method achieves competitive subjective results compared with state-of-the-art end-to-end learned image compression methods or classic methods.

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preprint2022arXiv

Direct ultrafast parametric amplification pumped by a picosecond thin-disk laser

Optical quadratic nonlinearity is ultrafast in nature, while parametric interaction usually manifests only the broadband characteristic. Enormous progress has been made toward broadband phase-matching for parametric amplification and wide applications. In existing devices of broadband parametric amplification, the power efficiency of conversion is restricted to approximately the energy efficiency, and the desire for the signal power enhancement necessitates additional pulse compression after amplification. Here, we demonstrate ultrafast parametric amplification having an extraordinary power efficiency of 1155%; this allows the generation of intense femtosecond pulses without the need for a pulse stretcher and compressor. Direct femtosecond signal amplification by picosecond pumping is enabled by an ultrafast parametric environment in which the pre-delayed signal of faster speed gradually overtakes and effectively depletes the pump of slower speed as they propagate in a nonlinear crystal. The demonstrated technique should lead to breakthroughs in ultrafast lasers as well as applications.

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preprint2022arXiv

eDIG-CHANGES I: Extended Hα Emission from the Extraplanar Diffuse Ionized Gas (eDIG) around CHANG-ES Galaxies

The extraplanar diffuse ionized gas (eDIG) represents the cool/warm ionized gas reservoir around galaxies. We present a spatial analysis of H$α$ images of 22 nearby edge-on spiral galaxies from the CHANG-ES sample (the eDIG-CHANGES project), taken with the APO 3.5m telescope, in order to study their eDIG. We conduct an exponential fit to the vertical intensity profiles of the sample galaxies, of which 16 can be decomposed into a thin disk plus an extended thick disk component. The median value of the scale height (h) of the extended component is $1.13\pm 0.14$ kpc. We find a tight sublinear correlation between h and the SFR. Moreover, the offset of individual galaxies from the best-fit SFR-h relation shows significant anti-correlation with SFR_SD. This indicates that galaxies with more intense star formation tend to have disproportionately extended eDIG. Combined with data from the literature, we find that the correlations between the eDIG properties and the galaxies' properties extend to broader ranges. We further compare the vertical extension of the eDIG to multi-wavelength measurements of other CGM phases. We find the eDIG to be slightly more extended than the neutral gas (HI 21-cm line), indicating the existence of some extended ionizing sources. Most galaxies have an X-ray scale height smaller than the h, suggesting that the majority of the X-ray emission detected in shallow observations are actually from the thick disk. The h is comparable to the L-band radio continuum scale height, both slightly larger than that at higher frequencies (C-band), where the cooling is stronger and the thermal contribution may be larger. The comparable H$α$ and L-band scale height indicates that the thermal and non-thermal electrons have similar spatial distributions. This further indicates that the thermal gas, the cosmics rays, and the magnetic field may be close to energy equipartition.

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preprint2022arXiv

Effects of Active Galactic Nucleus Feedback on Cold Gas Depletion and Quenching of Central Galaxies

We investigate the influence of active galactic nucleus (AGN) feedback on the galaxy cold gas content and its connection to galaxy quenching in three hydrodynamical simulations of Illustris, IllustrisTNG and SIMBA. By comparing to the observed atomic and molecular neutral hydrogen measurements for central galaxies, we find that Illustris over-predicts the cold gas masses in star-forming galaxies and significantly under-predicts them for quenched galaxies. IllustrisTNG performs better in this comparison than Illustris, but quenched galaxies retain too much cold gas compared with observations. SIMBA shows good agreement with observations, by depleting the global cold gas reservoir for quenched galaxies. We find that the discrepancies in IllustrisTNG are caused by its weak kinetic AGN feedback that only redistributes the cold gas from the inner disks to the outer regions and reduces the inner cold gas densities. It agrees with observations much better when only the cold gas within the stellar disk is considered to infer the star formation rates. From dependences of cold gas reservoir on the black hole mass and Eddington ratio, we find that the cumulative energy release during the black hole growth is the dominant reason for the cold gas depletion and thus the galaxy quenching. We further measure the central stellar surface density within 1 kpc ($Σ_1$) for the high-resolution run of IllustrisTNG and find a tight correlation between $Σ_1$ and black hole mass. It suggests that the observed decreasing trend of cold gas mass with $Σ_1$ is also a reflection of the black hole growth.

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preprint2022arXiv

Electrically tunable second harmonic generation in atomically thin ReS2

Electrical tuning of second-order nonlinearity in optical materials is attractive to strengthen and expand the functionalities of nonlinear optical technologies, though its implementation remains elusive. Here, we report the electrically tunable second-order nonlinearity in atomically thin ReS2 flakes benefiting from their distorted 1T crystal structure and interlayer charge transfer. Enabled by the efficient electrostatic control of the few-atomic-layer ReS2, we show that second harmonic generation (SHG) can be induced in odd-number-layered ReS2 flakes which are centrosymmetric and thus without intrinsic SHG. Moreover, the SHG can be precisely modulated by the electric field, reversibly switching from almost zero to an amplitude more than one order of magnitude stronger than that of the monolayer MoS2. For the even-number-layered ReS2 flakes with the intrinsic SHG, the external electric field could be leveraged to enhance the SHG. We further perform the first-principles calculations which suggest that the modification of in-plane second-order hyperpolarizability by the redistributed interlayer-transferring charges in the distorted 1T crystal structure underlies the electrically tunable SHG in ReS2. With its active SHG tunability while using the facile electrostatic control, our work may further expand the nonlinear optoelectronic functions of two-dimensional materials for developing electrically controllable nonlinear optoelectronic devices.

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preprint2022arXiv

Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation

Recognizing human locomotion intent and activities is important for controlling the wearable robots while walking in complex environments. However, human-robot interface signals are usually user-dependent, which causes that the classifier trained on source subjects performs poorly on new subjects. To address this issue, this paper designs the ensemble diverse hypotheses and knowledge distillation (EDHKD) method to realize unsupervised cross-subject adaptation. EDH mitigates the divergence between labeled data of source subjects and unlabeled data of target subjects to accurately classify the locomotion modes of target subjects without labeling data. Compared to previous domain adaptation methods based on the single learner, which may only learn a subset of features from input signals, EDH can learn diverse features by incorporating multiple diverse feature generators and thus increases the accuracy and decreases the variance of classifying target data, but it sacrifices the efficiency. To solve this problem, EDHKD (student) distills the knowledge from the EDH (teacher) to a single network to remain efficient and accurate. The performance of the EDHKD is theoretically proved and experimentally validated on a 2D moon dataset and two public human locomotion datasets. Experimental results show that the EDHKD outperforms all other methods. The EDHKD can classify target data with 96.9%, 94.4%, and 97.4% average accuracy on the above three datasets with a short computing time (1 ms). Compared to a benchmark (BM) method, the EDHKD increases 1.3% and 7.1% average accuracy for classifying the locomotion modes of target subjects. The EDHKD also stabilizes the learning curves. Therefore, the EDHKD is significant for increasing the generalization ability and efficiency of the human intent prediction and human activity recognition system, which will improve human-robot interactions.

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preprint2022arXiv

Fine-Tuning BERT for Automatic ADME Semantic Labeling in FDA Drug Labeling to Enhance Product-Specific Guidance Assessment

Product-specific guidances (PSGs) recommended by the United States Food and Drug Administration (FDA) are instrumental to promote and guide generic drug product development. To assess a PSG, the FDA assessor needs to take extensive time and effort to manually retrieve supportive drug information of absorption, distribution, metabolism, and excretion (ADME) from the reference listed drug labeling. In this work, we leveraged the state-of-the-art pre-trained language models to automatically label the ADME paragraphs in the pharmacokinetics section from the FDA-approved drug labeling to facilitate PSG assessment. We applied a transfer learning approach by fine-tuning the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model to develop a novel application of ADME semantic labeling, which can automatically retrieve ADME paragraphs from drug labeling instead of manual work. We demonstrated that fine-tuning the pre-trained BERT model can outperform the conventional machine learning techniques, achieving up to 11.6% absolute F1 improvement. To our knowledge, we were the first to successfully apply BERT to solve the ADME semantic labeling task. We further assessed the relative contribution of pre-training and fine-tuning to the overall performance of the BERT model in the ADME semantic labeling task using a series of analysis methods such as attention similarity and layer-based ablations. Our analysis revealed that the information learned via fine-tuning is focused on task-specific knowledge in the top layers of the BERT, whereas the benefit from the pre-trained BERT model is from the bottom layers.

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preprint2022arXiv

Gas dynamics and star formation in NGC 6822

We present H I gas kinematics and star formation activities of NGC 6822, a dwarf galaxy located in the Local Group at a distance of ~ 490 kpc. We perform profile decomposition of line-of-sight velocity profiles of the H I data cube (42.4" x 12.0" spatial, corresponding to ~ 100 pc; 1.6 km s$^{-1}$ spectral) taken with the Australia Telescope Compact Array (ATCA). For this, we use a new tool, the so-called BAYGAUD which is based on Bayesian analysis techniques, allowing us to decompose a line-of-sight velocity profile into an optimal number of Gaussian components in a quantitative manner. We classify the decomposed H I gas components of NGC 6822 into cool-bulk, warm-bulk, cool-non-bulk and warm-non-bulk motions with respect to their centroid velocities and velocity dispersions. We correlate their gas surface densities with corresponding star formation rate densities derived using both the GALEX far-ultraviolet and WISE 22 $μ$m data to examine the resolved Kennicutt-Schmidt (K-S) law for NGC 6822. Of the decomposed H I gas components, the cool-bulk component is likely to better follow the linear extension of the K-S law for molecular hydrogen (H$_2$) at low gas surface densities where H I is not saturated.

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preprint2022arXiv

Gravitational Casimir Effect in Inspiralling Neutron Star Binary

Currently, the discussions and investigations for the vacuum energy is drawing great both theoretical and experimental attention. The vacuum states of variety of fields, subject to special boundary conditions, may contribute to non-trivial macroscopic vacuum energy, i.e. the Casimir effect, which become an interdisciplinary subject and plays an important role in a variety of fields of physics. We adopt Schwinger's source theory and study the Casimir effect due to the quantization of gravitation, i.e. the gravitational Casimir effect (GCE), in inspiraling neutron star (NS) binaries with wide separation of $10^9 \rm m$. By considering gravitoelectromagnetism (GEM) arising from the spiral-in orbital motion and evaluating the contributions of GEM to the vacuum energy of gravitons radiated during the orbital decay, we demonstrate that, when the orbital separation of the binary decay a distance of $L$ in radial direction, the GEM results in a small Casimir correction to the gravitational vacuum energy, which contributes to an attractive gravitational Casimir force to the binary, in addition the gravitational force. The gravitational waves (GWs), emitted from wide inspiraling NS binaries, locate in the low-frequency band of $10^{-4}-1$ Hz. For a characteristic GW frequency of $10^{-3}$ Hz, the gravitational Casimir correction to the signals is estimated as of the order of $\sim10^{-24}$, which corresponds to a force of $10^{-20}$ N. By considering that the sensitivity of space-based gravitational wave observatory, LISA/eLISA and Taiji, can be reduced to $10^{-24}$, we would expect that LISA/eLISA and Taiji with sensitivity improvements give the powerful tool to detect GCE in the near future.

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preprint2022arXiv

HI Vertical Structure of Nearby Edge-on Galaxies from CHANG-ES

We study the vertical distribution of the highly inclined galaxies from the Continuum Halos in Nearby Galaxies - an EVLA Survey (CHANG-ES). We explore the feasibility of photometrically deriving the HI disk scale-heights from the moment-0 images of the relatively edge-on galaxies with inclination >80 deg, by quantifying the systematic broadening effects and thus deriving correction equations for direct measurements. The corrected HI disk scale-heights of the relatively edge-on galaxies from the CHANG-ES sample show trends consistent with the quasi-equilibrium model of the vertical structure of gas disks. The procedure provide a convenient way to derive the scale-heights and can easily be applied to statistical samples in the future.

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preprint2022arXiv

Inspiraling Corrugation-Induced Quantum Effects on Neutron Star Binary Plane

We use the path-integral formula and investigate some dynamical quantum effects induced by the inspiraling lateral corrugation of orbital plane in gravitationally bound neutron star (NS) binaries, with orbital separation of $10^9$ m. Based on Dewitt's approach, we calculate the gravitational Casimir energy cost of the binary plane, which consists of statically gravitational effects and deformation-induced effects. It is found that the static effects include a term coming from the self-gravity of the orbital plane and the contribution of Newtonian gravitational potential of the binary system. While the deformation-induced effect also results from two parts, i.e. the instability of orbital binding energy, scaling as $\frac{1}{(R-r)^2}$, and the dynamically Casimir energy cost of the orbital binding energy, decaying as $\frac{1}{(R-r)^4}$. The dynamically gravitational Casimir phenomena and the corresponding energy cost modify the spiral-in orbital motion of the binary and thus the frequency of released gravitational waves (GWs). We consider the mechanical response of two NS components and qualitatively study the corrections to the orbital motion of the system and the GW frequencies. It is found that the dynamical Casimir effects exert a dissipative force on the binary plane, depending on the frequency of GWs. The resultant dissipation may enhance with the decaying separation and increasing GW frequencies, which subsequently accelerates the orbital decay of the binary. However, the dissipation rate just has an order of $10^{-70}$ eV/s. So the corrections to the dynamics of NS binaries are very marginal, by considering the wide separation, the cosmological coalescence time, and low-frequency GWs of the system.

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preprint2022arXiv

Learning to Socially Navigate in Pedestrian-rich Environments with Interaction Capacity

Existing navigation policies for autonomous robots tend to focus on collision avoidance while ignoring human-robot interactions in social life. For instance, robots can pass along the corridor safer and easier if pedestrians notice them. Sounds have been considered as an efficient way to attract the attention of pedestrians, which can alleviate the freezing robot problem. In this work, we present a new deep reinforcement learning (DRL) based social navigation approach for autonomous robots to move in pedestrian-rich environments with interaction capacity. Most existing DRL based methods intend to train a general policy that outputs both navigation actions, i.e., expected robot's linear and angular velocities, and interaction actions, i.e., the beep action, in the context of reinforcement learning. Different from these methods, we intend to train the policy via both supervised learning and reinforcement learning. In specific, we first train an interaction policy in the context of supervised learning, which provides a better understanding of the social situation, then we use this interaction policy to train the navigation policy via multiple reinforcement learning algorithms. We evaluate our approach in various simulation environments and compare it to other methods. The experimental results show that our approach outperforms others in terms of the success rate. We also deploy the trained policy on a real-world robot, which shows a nice performance in crowded environments.

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preprint2022arXiv

Lesion classification by model-based feature extraction: A differential affine invariant model of soft tissue elasticity

The elasticity of soft tissues has been widely considered as a characteristic property to differentiate between healthy and vicious tissues and, therefore, motivated several elasticity imaging modalities, such as Ultrasound Elastography, Magnetic Resonance Elastography, and Optical Coherence Elastography. This paper proposes an alternative approach of modeling the elasticity using Computed Tomography (CT) imaging modality for model-based feature extraction machine learning (ML) differentiation of lesions. The model describes a dynamic non-rigid (or elastic) deformation in differential manifold to mimic the soft tissues elasticity under wave fluctuation in vivo. Based on the model, three local deformation invariants are constructed by two tensors defined by the first and second order derivatives from the CT images and used to generate elastic feature maps after normalization via a novel signal suppression method. The model-based elastic image features are extracted from the feature maps and fed to machine learning to perform lesion classifications. Two pathologically proven image datasets of colon polyps (44 malignant and 43 benign) and lung nodules (46 malignant and 20 benign) were used to evaluate the proposed model-based lesion classification. The outcomes of this modeling approach reached the score of area under the curve of the receiver operating characteristics of 94.2 % for the polyps and 87.4 % for the nodules, resulting in an average gain of 5 % to 30 % over ten existing state-of-the-art lesion classification methods. The gains by modeling tissue elasticity for ML differentiation of lesions are striking, indicating the great potential of exploring the modeling strategy to other tissue properties for ML differentiation of lesions.

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preprint2022arXiv

Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape

In this paper, we study the sharpness of a deep learning (DL) loss landscape around local minima in order to reveal systematic mechanisms underlying the generalization abilities of DL models. Our analysis is performed across varying network and optimizer hyper-parameters, and involves a rich family of different sharpness measures. We compare these measures and show that the low-pass filter-based measure exhibits the highest correlation with the generalization abilities of DL models, has high robustness to both data and label noise, and furthermore can track the double descent behavior for neural networks. We next derive the optimization algorithm, relying on the low-pass filter (LPF), that actively searches the flat regions in the DL optimization landscape using SGD-like procedure. The update of the proposed algorithm, that we call LPF-SGD, is determined by the gradient of the convolution of the filter kernel with the loss function and can be efficiently computed using MC sampling. We empirically show that our algorithm achieves superior generalization performance compared to the common DL training strategies. On the theoretical front, we prove that LPF-SGD converges to a better optimal point with smaller generalization error than SGD.

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preprint2022arXiv

Magnetic Resonance Imaging study of sheared granular matter

We introduce a Magnetic Resonance Imaging technique to study the geometry of shear zones of soft, low-frictional and hard, frictional granular materials and their mixtures. Hydrogel spheres serve as the soft, low-frictional material component, while mustard seeds represent rigid, frictional grains. Some of the hydrogel spheres are doped with CuSO4 salt to serve as tracers. A split-bottom shear cell is sheared stepwise and the shear profiles are determined from the differences of tomograms after successive shear steps, using Particle Imaging Velocimetry.We find that the shear zone geometry differs considerably between soft grains submersed in water and the same material without the embedding fluid.

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preprint2022arXiv

Massive Galaxy Mergers Have Distinctive Global HI Profiles

The global 21 cm HI emission-line profile of a galaxy encodes valuable information on the spatial distribution and kinematics of the neutral atomic gas. Galaxy interactions significantly influence the HI disk and imprint observable features on the integrated HI line profile. In this work, we study the neutral atomic gas properties of galaxy mergers selected from the Great Observatories All-sky LIRG Survey. The HI spectra come from new observations with the Five-hundred-meter Aperture Spherical Telescope and from a collection of archival data. We quantify the HI profile of the mergers with a newly developed method that uses the curve-of-growth of the line profile. Using a control sample of non-merger galaxies carefully selected to match the stellar mass of the merger sample, we show that mergers have a larger proportion of single-peaked HI profiles, as well as a greater tendency for the HI central velocity to deviate from the systemic optical velocity of the galaxy. By contrast, the HI profiles of mergers are not significantly more asymmetric than those of non-mergers.

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preprint2022arXiv

Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation

For 3D medical image (e.g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly. Previous research on volumetric medical image segmentation in a slice-by-slice manner conventionally use the identical 2D deep neural network to segment all the slices of the same case, ignoring the data heterogeneity among image slices. In this paper, we focus on multi-modal 3D MRI brain tumor segmentation and propose a dynamic architecture network named Med-DANet based on adaptive model selection to achieve effective accuracy and efficiency trade-off. For each slice of the input 3D MRI volume, our proposed method learns a slice-specific decision by the Decision Network to dynamically select a suitable model from the predefined Model Bank for the subsequent 2D segmentation task. Extensive experimental results on both BraTS 2019 and 2020 datasets show that our proposed method achieves comparable or better results than previous state-of-the-art methods for 3D MRI brain tumor segmentation with much less model complexity. Compared with the state-of-the-art 3D method TransBTS, the proposed framework improves the model efficiency by up to 3.5x without sacrificing the accuracy. Our code will be publicly available soon.

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preprint2022arXiv

Moiré Engineering and Topological Flat Bands in Twisted Orbital-Active Bilayers

Topological flat bands at the Fermi level offer a promising platform to study a variety of intriguing correlated phase of matter. Here we present band engineering in the twisted orbital-active bilayers with spin-orbit coupling. The symmetry constraints on the interlayer coupling that determines the effective potential for low-energy physics of moiré electrons are exhaustively derived for two-dimensional point groups. We find the line graph or biparticle sublattice of moiré pattern emerge with a minimal $C_3$ symmetry, which exhibit isolated electronic flat bands with nontrivial topology. The band flatness is insensitive to the twist angle since they come from the interference effect. Armed with this guiding principle, we predict that twisted bilayers of 2H-PbS$_2$ and CdS realize the salient physics to engineer two-dimensional topological quantum phases. At small twist angles, PbS$_2$ heterostructures give rise to an emergent moiré Kagomé lattice, while CdS heterostructures lead to an emergent moiré honeycomb lattice, and both of them host moiré quantum spin Hall insulators with almost flat topological bands. We further study superconductivity of these two systems with local attractive interactions. The superfluid weight and Berezinskii-Kosterlitz-Thouless temperature are determined by multiband processes and quantum geometry of the band in the flat-band limit when the pairing potential exceeds the band width. Our results demonstrate twisted bilayers with multi-orbitals as a promising tunable platform to realize correlated topological phases.

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preprint2022arXiv

MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusion

Measuring the perceptual quality of images automatically is an essential task in the area of computer vision, as degradations on image quality can exist in many processes from image acquisition, transmission to enhancing. Many Image Quality Assessment(IQA) algorithms have been designed to tackle this problem. However, it still remains un settled due to the various types of image distortions and the lack of large-scale human-rated datasets. In this paper, we propose a novel algorithm based on the Swin Transformer [31] with fused features from multiple stages, which aggregates information from both local and global features to better predict the quality. To address the issues of small-scale datasets, relative rankings of images have been taken into account together with regression loss to simultaneously optimize the model. Furthermore, effective data augmentation strategies are also used to improve the performance. In comparisons with previous works, experiments are carried out on two standard IQA datasets and a challenge dataset. The results demonstrate the effectiveness of our work. The proposed method outperforms other methods on standard datasets and ranks 2nd in the no-reference track of NTIRE 2022 Perceptual Image Quality Assessment Challenge [53]. It verifies that our method is promising in solving diverse IQA problems and thus can be used to real-word applications.

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preprint2022arXiv

On a conjecture of spectral extremal problems

For a simple graph $F$, let $\mathrm{Ex}(n, F)$ and $\mathrm{Ex_{sp}}(n,F)$ denote the set of graphs with the maximum number of edges and the set of graphs with the maximum spectral radius in an $n$-vertex graph without any copy of the graph $F$, respectively. The Turán graph $T_{n,r}$ is the complete $r$-partite graph on $n$ vertices where its part sizes are as equal as possible. Cioabă, Desai and Tait [The spectral radius of graphs with no odd wheels, European J. Combin., 99 (2022) 103420] posed the following conjecture: Let $F$ be any graph such that the graphs in $\mathrm{Ex}(n,F)$ are Turán graphs plus $O(1)$ edges. Then $\mathrm{Ex_{sp}}(n,F)\subset \mathrm{Ex}(n,F)$ for sufficiently large $n$. In this paper we consider the graph $F$ such that the graphs in $\mathrm{Ex}(n, F)$ are obtained from $T_{n,r}$ by adding $O(1)$ edges, and prove that if $G$ has the maximum spectral radius among all $n$-vertex graphs not containing $F$, then $G$ is a member of $\mathrm{Ex}(n, F)$ for $n$ large enough. Then Cioabă, Desai and Tait's conjecture is completely solved.

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preprint2022arXiv

On The Design of a Light-weight FPGA Programming Framework for Graph Applications

FPGA accelerators designed for graph processing are gaining popularity. Domain Specific Language (DSL) frameworks for graph processing can reduce the programming complexity and development cost of algorithm design. However, accelerator-specific development requires certain technical expertise and significant effort to devise, implement, and validate the system. For most algorithm designers, the expensive cost for hardware programming experience makes FPGA accelerators either unavailable or uneconomic. Although general-purpose High-Level Synthesis (HLS) tools help to map high-level language to Hardware Description Languages (HDLs), the generated code is often inefficient and lengthy compared with the highly-optimized graph accelerators. One cannot make full use of an FPGA accelerator's capacity with low development cost. To easily program graph algorithms while keeping performance degradation acceptable, we propose a graph programming system named JGraph, which contains two main parts: 1) a DSL for graph atomic operations with a graph library for high-level abstractions including user-defined functions with parameters, 2) a light-weight HLS translator to generate high-performance HDL code, cooperating with a communication manager and a runtime scheduler. To the best of our knowledge, our work is the first graph programming system with DSL and translator on the FPGA platform. Our system can generate up to 300 MTEPS BFS traversal within tens of seconds.

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preprint2022arXiv

Parabolic Anderson model on Heisenberg groups: the Itô setting

In this note we focus our attention on a stochastic heat equation defined on the Heisenberg group $\mathbf{H}^{n}$ of order $n$. This equation is written as $\partial_t u=\frac{1}{2}Δu+u\dot{W}_α$, where $Δ$ is the hypoelliptic Laplacian on $\mathbf{H}^{n}$ and $\{\dot{W}_α; α>0\}$ is a family of Gaussian space-time noises which are white in time and have a covariance structure generated by $(-Δ)^{-α}$ in space. Our aim is threefold: (i) Give a proper description of the noise $W_α$; (ii) Prove that one can solve the stochastic heat equation in the Itô sense as soon as $α>\frac{n}{2}$; (iii) Give some basic moment estimates for the solution $u(t,x)$.

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preprint2022arXiv

Polarization effects on fluorescence emission of zebrafish neurons using light-sheet microscopy

Light-sheet fluorescence microscopy (LSFM) makes use of a thin plane of light to optically section and image transparent tissues or organisms {\it{in vivo}}, which has the advantages of fast imaging speed and low phototoxicity. In this paper, we have employed light-sheet microscopy to investigate the polarization effects on fluorescence emission of zebrafish neurons via modifying the electric oscillation orientation of the excitation light. The intensity of the fluorescence emission from the excited zebrafish larvae follows a cosine square function with respect to the polarization state of the excitation light and reveals a 40$\%$ higher fluorescence emission when the polarization orientation is orthogonal to the illumination and detection axes. Through registration and subtraction of fluorescence images under different polarization states, we have demonstrated that most of the enhanced fluorescence signals are from the nerve cells rather than the extracellular substance. This provides us a way to distinguish the cell boundaries and observe the organism structures with improved contrast and resolution.

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preprint2022arXiv

Preserving Domain Private Representation via Mutual Information Maximization

Recent advances in unsupervised domain adaptation have shown that mitigating the domain divergence by extracting the domain-invariant representation could significantly improve the generalization of a model to an unlabeled data domain. Nevertheless, the existing methods fail to effectively preserve the representation that is private to the label-missing domain, which could adversely affect the generalization. In this paper, we propose an approach to preserve such representation so that the latent distribution of the unlabeled domain could represent both the domain-invariant features and the individual characteristics that are private to the unlabeled domain. In particular, we demonstrate that maximizing the mutual information between the unlabeled domain and its latent space while mitigating the domain divergence can achieve such preservation. We also theoretically and empirically validate that preserving the representation that is private to the unlabeled domain is important and of necessity for the cross-domain generalization. Our approach outperforms state-of-the-art methods on several public datasets.

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preprint2022arXiv

QPanda: high-performance quantum computing framework for multiple application scenarios

With the birth of Noisy Intermediate Scale Quantum (NISQ) devices and the verification of "quantum supremacy" in random number sampling and boson sampling, more and more fields hope to use quantum computers to solve specific problems, such as aerodynamic design, route allocation, financial option prediction, quantum chemical simulation to find new materials, and the challenge of quantum cryptography to automotive industry security. However, these fields still need to constantly explore quantum algorithms that adapt to the current NISQ machine, so a quantum programming framework that can face multi-scenarios and application needs is required. Therefore, this paper proposes QPanda, an application scenario-oriented quantum programming framework with high-performance simulation. Such as designing quantum chemical simulation algorithms based on it to explore new materials, building a quantum machine learning framework to serve finance, etc. This framework implements high-performance simulation of quantum circuits, a configuration of the fusion processing backend of quantum computers and supercomputers, and compilation and optimization methods of quantum programs for NISQ machines. Finally, the experiment shows that quantum jobs can be executed with high fidelity on the quantum processor using quantum circuit compile and optimized interface and have better simulation performance.

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preprint2022arXiv

Quantum criticality of fermion velocities and critical temperature nearby a putative quantum phase transition in the $d$-wave superconductors

Quantum critical behaviors induced by a putative quantum phase transition are vigilantly investigated, which separates a $d$-wave superconducting state and $d$-wave superconducting+$X$ state below the superconducting dome of the $d-$wave superconductors with tuning the non-thermal doping variable. Within the framework of renormalization group approach, we start with a phenomenological effective theory originated from the Landau-Ginzburg-Wilson theory and practice one-loop calculations to construct a set of coupled flows of all interaction parameters. After extracting related physical information from these coupled evolutions, we address that both fermion velocities and critical temperatures exhibit critical behaviors, which are robust enough against the initial conditions due to strong quantum fluctuations. At first, the evolution of Yukawa coupling between $X$-state order parameter and nodal fermions in tandem with quantum fluctuations heavily renormalize fermion velocities and generally drive them into certain finite anisotropic fixed point at the lowest-energy limit, whose concrete value relies upon the very quantum phase transition. In addition, these unique properties of fermion velocities largely reshape the fate of superfluid density, giving rise to either an enhancement or a dip of critical temperature. Moreover, we find that fermion-fermion interactions bring non-ignorable quantitative corrections to quantum critical behaviors despite they are subordinate to quantum fluctuations of order parameters.

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preprint2022arXiv

Quantum relaxed row and column iteration methods based on block-encoding

Iteration method is commonly used in solving linear systems of equations. We present quantum algorithms for the relaxed row and column iteration methods by constructing unitary matrices in the iterative processes, which generalize row and column iteration methods to solve linear systems on a quantum computer. Comparing with the conventional row and column iteration methods, the convergence accelerates when appropriate parameters are chosen. Once the quantum states are efficiently prepared, the complexity of our relaxed row and column methods is improved exponentially and is linear with the number of the iteration steps. In addition, phase estimations and Hamiltonian simulations are not required in these algorithms.

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preprint2022arXiv

Sampling with replacement vs Poisson sampling: a comparative study in optimal subsampling

Faced with massive data, subsampling is a commonly used technique to improve computational efficiency, and using nonuniform subsampling probabilities is an effective approach to improve estimation efficiency. For computational efficiency, subsampling is often implemented with replacement or through Poisson subsampling. However, no rigorous investigation has been performed to study the difference between the two subsampling procedures such as their estimation efficiency and computational convenience. This paper performs a comparative study on these two different sampling procedures. In the context of maximizing a general target function, we first derive asymptotic distributions for estimators obtained from the two sampling procedures. The results show that the Poisson subsampling may have a higher estimation efficiency. Based on the asymptotic distributions for both subsampling with replacement and Poisson subsampling, we derive optimal subsampling probabilities that minimize the variance functions of the subsampling estimators. These subsampling probabilities further reveal the similarities and differences between subsampling with replacement and Poisson subsampling. The theoretical characterizations and comparisons on the two subsampling procedures provide guidance to select a more appropriate subsampling approach in practice. Furthermore, practically implementable algorithms are proposed based on the optimal structural results, which are evaluated through both theoretical and empirical analyses.

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preprint2022arXiv

Spectral extremal graphs for disjoint cliques

The $kK_{r+1}$ is the union of $k$ disjoint copies of $(r+1)$-clique. Moon [Canad. J. Math. 20 (1968) 95--102] and Simonovits [Theory of Graphs (Proc. colloq., Tihany, 1996)] independently showed that if $n$ is sufficiently large, then $K_{k-1}\vee T_{n-k+1,r}$ is the unique extremal graph for $kK_{r+1}$. In this paper, we consider the graph which has the maximum spectral radius among all graphs without $k$ disjoint cliques. We prove that if $G$ attains the maximum spectral radius over all $n$-vertex $kK_{r+1}$-free graphs for sufficiently large $n$, then $G$ is isomorphic to $K_{k-1}\vee T_{n-k+1,r}$.

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preprint2022arXiv

Statistical Analysis of H I Profile Asymmetry and Shape for Nearby Galaxies

We present a uniform analysis of the integrated profile of the H I emission line of 29,958 nearby ($z < 0.06$) galaxies extracted from the ALFALFA 21 cm survey. We apply the curve-of-growth technique to derive a database of spectral parameters and robust estimates of their associated uncertainties. Besides the central velocity and total flux, the main catalog provides new measures of line width, profile asymmetry, and profile shape. For a subsample of 13,511 galaxies with optical properties available from the Sloan Digital Sky Survey, we compute inclination angle-corrected line widths, rotation velocities empirically calibrated from spatially resolved observations, and dynamical masses based on H I sizes estimated from the H I mass. To facilitate subsequent scientific applications of the database, we also compile a number of ancillary physical properties of the galaxies, including their optical morphology, stellar mass, and various diagnostics of star formation activity. We use the homogeneous catalog of H I parameters to examine the statistical properties of profile asymmetry and shape. Across the full sample, which covers a wide range of stellar masses and environments, statistically significant H I profile asymmetry is detected in $\sim 20\%$ of the galaxy population. The global H I profiles are $35.2 \pm 0.3\%$ single-peaked, $26.9 \pm 0.3\%$ flat-topped, and $37.9 \pm 0.3\%$ double-horned. At a given inclination angle, double-horned profiles are preferentially associated with galaxies of higher stellar mass or optical concentration, while galaxies of lower mass or concentration tend to have single-peaked profiles.

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preprint2022arXiv

Strain-tuning Bloch- and Néel-type magnetic skyrmions: a phase-field simulation

Strain manipulation of the magnetic domains, such as the stripe domains and skyrmions, has attracted considerable attention because of its potential applications for magnetic logic and memory devices. Here, utilizing phase-field modeling, we demonstrate the deterministic modulation of the orientation and the configuration of the stripe domains and skyrmions by using a uniaxial strain. The reorientation of the stripe domains can be caused by a suitable strain, and the direction of the reorientated domains is determined by the direction of the applied uniaxial strain and the type of domain walls, including Bloch- and Néel- types. Furthermore, by constructing a phase diagram, we discovered that when the uniaxial tensile strain increases, the ferromagnetic islands undergo a continuous phase transition from a skyrmion to multi-domains or a single domain. The competition between magnetic anisotropy energy and stray field energy leads to the continuous phase transition and the formation of domain patterns under the uniaxial tensile strain. Our research provides a theoretical foundation for the development of strain-controlled magnetic domain designs.

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preprint2022arXiv

Strong Second Harmonic Generation from Bilayer Graphene with Symmetry Breaking by Redox-Governed Charge Doping

Missing second-order nonlinearity in centrosymmetric graphene overshadows its intriguing optical attribute. Here, we report redox-governed charge doping could effectively break the centrosymmetry of bilayer graphene (BLG), enabling a strong second harmonic generation (SHG) with a strength close to that of the well-known monolayer MoS2. Verified from control experiments with in situ electrical current annealing and electrically gate-controlled SHG, the required centrosymmetry breaking of the emerging SHG arises from the charge-doping on the bottom layer of BLG by the oxygen/water redox couple. Our results not only reveal that charge doping is an effective way to break the inversion symmetry of BLG despite its strong interlayer coupling but also indicate that SHG spectroscopy is a valid technique to probe molecular doping on two-dimensional materials.

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preprint2022arXiv

Strong spiral arms drive secular growth of pseudo bulges in disk galaxies

Spiral-driven instabilities may drive gas inflow to enhance central star formation in disk galaxies. We investigate this hypothesis using the Sloan Digital Sky Survey (SDSS) in a sample of 2779 nearby unbarred star-forming main-sequence spiral galaxies. The strength of spiral arms is quantified by their average Fourier amplitude relative to the axisymmetric disk. The star formation properties in the central 1--3\,kpc region were derived from the SDSS spectra. We show that galaxies with stronger spiral arms not only tend to have more intense central specific star formation rate (sSFR), larger Balmer absorption line index, and lower 4000-Å break strength, but also have enhanced central sSFR relative to sSFR measured for the whole galaxy. This link is independent of redshift, stellar mass, surface density, and concentration. There is a lack of evidence for strong spiral arms being associated with a significant fraction of starburst or post-starburst galaxies, implying that the spiral-induced central star formation is likely continuous rather than bursty. We also show that stronger spiral arms tend to have an increasing fraction of pseudo bulges, a relatively unchanged fraction of star-forming classical bulges, and a decreasing fraction of quenched classical bulges. Moreover, the concentration of galaxies hosting pseudo bulges mildly increases with stronger spiral arms, implying that spirals help pseudo bulges grow. The connection between spirals and the bulge type is partly attributed to the suppression of spirals by classical bulges and partly to the enhanced central star formation driven by spirals. We explain our results in a picture where spiral arms transport cold gas inward to trigger continuous central star formation, which facilitates the build-up of pseudo bulges. Spiral arms thus play a role in the secular evolution of disk galaxies.

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preprint2022arXiv

Taxonomy and evolution predicting using deep learning in images

Molecular and morphological characters, as important parts of biological taxonomy, are contradictory but need to be integrated. Organism's image recognition and bioinformatics are emerging and hot problems nowadays but with a gap between them. In this work, a multi-branching recognition framework mediated by genetic information bridges this barrier, which establishes the link between macro-morphology and micro-molecular information of mushrooms. The novel multi-perspective structure is proposed to fuse the feature images from three branching models, which significantly improves the accuracy of recognition by about 10% and up to more than 90%. Further, genetic information is implemented to the mushroom image recognition task by using genetic distance embeddings as the representation space for predicting image distance and species identification. Semantic overfitting of traditional classification tasks and the granularity of fine-grained image recognition are also discussed in depth for the first time. The generalizability of the model was investigated in fine-grained scenarios using zero-shot learning tasks, which could predict the taxonomic and evolutionary information of unseen samples. We presented the first method to map images to DNA, namely used an encoder mapping image to genetic distances, and then decoded DNA through a pre-trained decoder, where the total test accuracy on 37 species for DNA prediction is 87.45%. This study creates a novel recognition framework by systematically studying the mushroom image recognition problem, bridging the gap between macroscopic biological information and microscopic molecular information, which will provide a new reference for intelligent biometrics in the future.

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preprint2022arXiv

The Role of Inner HI Mass in Regulating the Scatter of the Mass-Metallicity Relation

We use 789 disk-like, star-forming galaxies (with 596 HI detections) from HI follow-up observations for the SDSS-IV MaNGA survey to study the possible role of inner HI gas in causing secondary dependences in the mass-gas-phase metallicity relation. We use the gas-phase metallicity derived at the effective radii of the galaxies. We derive the inner HI mass witHIn the optical radius, but also use the total HI mass and star formation rate (SFR) for a comparison. We confirm the anticorrelation between the total HI mass and gas-phase metallicity at fixed stellar mass, but the anticorrelation is significantly strengthened when the total HI mass is replaced by the inner HI mass. Introducing a secondary relation with the inner HI mass can produce a small but noticeable decrease (16%) in the scatter of the mass-gas-phase metallicity relation, in contrast to the negligible effect with the SFR. The correlation with the inner HI mass is robust when using different diagnostics of metallicity, but the correlation with SFR is not. The correlation with the inner HI mass becomes much weaker when the gas-phase metallicity is derived in the central region instead of at the effective radius. These results support the idea that the scatter in the mass-metallicity relation is regulated by gas accretion, and not directly by the SFR, and stress the importance of deriving the gas mass and the metallicity from roughly the same region. The new relation between inner HI mass and gas-phase metallicity will provide new constraints for chemical and galaxy evolution models.

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preprint2022arXiv

TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images

Transformer, benefiting from global (long-range) information modeling using self-attention mechanism, has been successful in natural language processing and computer vision recently. Convolutional Neural Networks, capable of capturing local features, are difficult to model explicit long-distance dependencies from global feature space. However, both local and global features are crucial for dense prediction tasks, especially for 3D medical image segmentation. In this paper, we present the further attempt to exploit Transformer in 3D CNN for 3D medical image volumetric segmentation and propose a novel network named TransBTSV2 based on the encoder-decoder structure. Different from TransBTS, the proposed TransBTSV2 is not limited to brain tumor segmentation (BTS) but focuses on general medical image segmentation, providing a stronger and more efficient 3D baseline for volumetric segmentation of medical images. As a hybrid CNN-Transformer architecture, TransBTSV2 can achieve accurate segmentation of medical images without any pre-training, possessing the strong inductive bias as CNNs and powerful global context modeling ability as Transformer. With the proposed insight to redesign the internal structure of Transformer block and the introduced Deformable Bottleneck Module to capture shape-aware local details, a highly efficient architecture is achieved with superior performance. Extensive experimental results on four medical image datasets (BraTS 2019, BraTS 2020, LiTS 2017 and KiTS 2019) demonstrate that TransBTSV2 achieves comparable or better results compared to the state-of-the-art methods for the segmentation of brain tumor, liver tumor as well as kidney tumor. Code will be publicly available at https://github.com/Wenxuan-1119/TransBTS.

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preprint2022arXiv

Two-Stage Fine-Tuning: A Novel Strategy for Learning Class-Imbalanced Data

Classification on long-tailed distributed data is a challenging problem, which suffers from serious class-imbalance and hence poor performance on tail classes with only a few samples. Owing to this paucity of samples, learning on the tail classes is especially challenging for the fine-tuning when transferring a pretrained model to a downstream task. In this work, we present a simple modification of standard fine-tuning to cope with these challenges. Specifically, we propose a two-stage fine-tuning: we first fine-tune the final layer of the pretrained model with class-balanced reweighting loss, and then we perform the standard fine-tuning. Our modification has several benefits: (1) it leverages pretrained representations by only fine-tuning a small portion of the model parameters while keeping the rest untouched; (2) it allows the model to learn an initial representation of the specific task; and importantly (3) it protects the learning of tail classes from being at a disadvantage during the model updating. We conduct extensive experiments on synthetic datasets of both two-class and multi-class tasks of text classification as well as a real-world application to ADME (i.e., absorption, distribution, metabolism, and excretion) semantic labeling. The experimental results show that the proposed two-stage fine-tuning outperforms both fine-tuning with conventional loss and fine-tuning with a reweighting loss on the above datasets.

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preprint2022arXiv

Undersampled MRI Reconstruction with Side Information-Guided Normalisation

Magnetic resonance (MR) images exhibit various contrasts and appearances based on factors such as different acquisition protocols, views, manufacturers, scanning parameters, etc. This generally accessible appearance-related side information affects deep learning-based undersampled magnetic resonance imaging (MRI) reconstruction frameworks, but has been overlooked in the majority of current works. In this paper, we investigate the use of such side information as normalisation parameters in a convolutional neural network (CNN) to improve undersampled MRI reconstruction. Specifically, a Side Information-Guided Normalisation (SIGN) module, containing only few layers, is proposed to efficiently encode the side information and output the normalisation parameters. We examine the effectiveness of such a module on two popular reconstruction architectures, D5C5 and OUCR. The experimental results on both brain and knee images under various acceleration rates demonstrate that the proposed method improves on its corresponding baseline architectures with a significant margin.

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preprint2021arXiv

A Guide to Global Quantum Key Distribution Networks

We describe systems and methods for the deployment of global quantum key distribution (QKD) networks covering transoceanic, long-haul, metro, and access segments of the network. A comparative study of the state-of-the-art QKD technologies is carried out, including both terrestrial QKD via optical fibers and free-space optics, as well as spaceborne solutions via satellites. We compare the pros and cons of various existing QKD technologies, including channel loss, potential interference, distance, connection topology, deployment cost and requirements, as well as application scenarios. Technical selection criteria and deployment requirements are developed for various different QKD solutions in each segment of networks. For example, optical fiber-based QKD is suitable for access networks due to its limited distance and compatibility with point-to-multipoint (P2MP) topology; with the help of trusted relays, it can be extended to long-haul and metro networks. Spaceborne QKD on the other hand, has much smaller channel loss and extended transmission distance, which can be used for transoceanic and long-haul networks exploiting satellite-based trusted relays.

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preprint2021arXiv

Anomalous gas in ESO 149-G003: A MeerKAT-16 View

ESO 149-G003 is a close-by, isolated dwarf irregular galaxy. Previous observations with the ATCA indicated the presence of anomalous neutral hydrogen (HI) deviating from the kinematics of a regularly rotating disc. We conducted follow-up observations with the MeerKAT radio telescope during the 16-dish Early Science programme as well as with the MeerLICHT optical telescope. Our more sensitive radio observations confirm the presence of anomalous gas in ESO 149-G003, and further confirm the formerly tentative detection of an extraplanar HI component in the galaxy. Employing a simple tilted-ring model, in which the kinematics is determined with only four parameters but including morphological asymmetries, we reproduce the galaxy's morphology, which shows a high degree of asymmetry. By comparing our model with the observed HI, we find that in our model we cannot account for a significant (but not dominant) fraction of the gas. From the differences between our model and the observed data cube we estimate that at least 7%-8% of the HI in the galaxy exhibits anomalous kinematics, while we estimate a minimum mass fraction of less than 1% for the morphologically confirmed extraplanar component. We investigate a number of global scaling relations and find that, besides being gas-dominated with a neutral gas-to-stellar mass ratio of 1.7, the galaxy does not show any obvious global peculiarities. Given its isolation, as confirmed by optical observations, we conclude that the galaxy is likely currently acquiring neutral gas. It is either re-accreting gas expelled from the galaxy or accreting pristine intergalactic material.

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preprint2021arXiv

Confinement and edge effects on atomic collapse in graphene nanoribbons

Atomic collapse in graphene nanoribbons behaves in a fundamentally different way as compared to monolayer graphene, due to the presence of multiple energy bands and the effect of edges. For armchair nanoribbons we find that bound states gradually transform into atomic collapse states with increasing impurity charge. This is very different in zig-zag nanoribbons where multiple quasi-one-dimensional \emph{bound states} are found that originates from the zero energy zig-zag edge states. They are a consequence of the flat band and the electron distribution of these bound states exhibits two peaks. The lowest energy edge state transforms from a bound state into an atomic collapse resonance and shows a distinct relocalization from the edge to the impurity position with increasing impurity charge.

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preprint2021arXiv

Coulomb impurity on a Dice lattice: atomic collapse and bound states

The modification of the quantum states in a Dice lattice due to a Coulomb impurity are investigated. The energy band structure of a pristine Dice lattice consists of a Dirac cone and a flat band at the Dirac point. We use the tight binding formalism and find that the flat band states transform into a set of discrete bound states whose electron density is localized on a ring around the impurity mainly on two of the three sublattices. The energy is proportional to the strength of the Coulomb impurity. Beyond a critical strength of the Coulomb potential atomic collapse states appear that have some similarity with those found in graphene with the difference that the flat band states contribute with an additional ring-like electron density that is spatially decoupled from the atomic collapse part. At large value of the strength of the Coulomb impurity the flat band bound states anti-cross with the atomic collapse states.

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preprint2021arXiv

High-order tensor flow processing using integrated photonic circuits

Tensor analytics lays mathematical basis for the prosperous promotion of multiway signal processing. To increase computing throughput, mainstream processors transform tensor convolutions to matrix multiplications to enhance parallelism of computing. However, such order-reducing transformation produces data duplicates and consumes additional memory. Here, we demonstrate an integrated photonic tensor flow processor without tensor-matrix transformation, which outputs the convolved tensor as the input tensor 'flows' through the processor. The hybrid manipulation of optical dimensions of wavelength, time, and space enables the direct representation and processing of high-order tensors in optical domain. In the proof-of-concept experiment, processing of multi-channel images and videos is accomplished at the frequency of 20 GHz. A convolutional neural network is demonstrated on the processor, which achieves an accuracy of 97.9 percent on action recognition.

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preprint2021arXiv

Hot and counter-rotating star-forming disk galaxies in IllustrisTNG and their real-world counterparts

A key feature of a large population of low-mass, late-type disk galaxies are star-forming disks with exponential light distributions. They are typically also associated with thin and flat morphologies, blue colors, and dynamically cold stars moving along circular orbits within co-planar thin gas disks. However, the latter features do not necessarily always imply the former, in fact, a variety of different kinematic configurations do exist. In this work, we use the cosmological hydrodynamical IllustrisTNG Simulation to study the nature and origin of dynamically hot, sometimes even counter-rotating, star-forming disk galaxies in the lower stellar mass range (between $5\times 10^9\,\mathrm{M_{\odot}}$ and $2\times 10^{10}\,\mathrm{M_{\odot}}$). We find that being dynamically hot arises in most cases as an induced transient state, for example due to galaxy interactions and merger activities, rather than as an age-dependent evolutionary phase of star-forming disk galaxies. The dynamically hot but still actively star-forming disks show a common feature of hosting kinematically misaligned gas and stellar disks, and centrally concentrated on-going star formation. The former is often accompanied by disturbed gas morphologies, while the latter is reflected in low gas and stellar spins in comparison to their dynamically cold, normal disk counterparts. Interestingly, observed galaxies from MaNGA with kinematic misalignment between gas and stars show remarkably similar general properties as the IllustrisTNG galaxies, and therefore are plausible real-world counterparts. In turn, this allows us to make predictions for the stellar orbits and gas properties of these misaligned galaxies.

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preprint2021arXiv

How much we gain by surplus-dependent premiums -- asymptotic analysis of ruin probability

In this paper, we build on the techniques developed in Albrecher et al. (2013), to generate initial-boundary value problems for ruin probabilities of surplus-dependent premium risk processes, under a renewal case scenario, Erlang (2) claim arrivals, and an exponential claims scenario, Erlang (2) claim sizes. Applying the approximation theory of solutions of linear ordinary differential equations developed in Fedoryuk (1993), we derive the asymptotics of the ruin probabilities when the initial reserve tends to infinity. When considering premiums that are {\it linearly} dependent on reserves, representing for instance returns on risk-free investments of the insurance capital, we firstly derive explicit formulas for the ruin probabilities, from which we can easily determine their asymptotics, only to match the ones obtained for general premiums dependent on reserves. We compare them with the asymptotics of the equivalent ruin probabilities when the premium rate is fixed over time, to measure the gain generated by this additional mechanism of binding the premium rates with the amount of reserve own by the insurance company.

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preprint2021arXiv

Intentional Deep Overfit Learning (IDOL): A Novel Deep Learning Strategy for Adaptive Radiation Therapy

In this study, we propose a tailored DL framework for patient-specific performance that leverages the behavior of a model intentionally overfitted to a patient-specific training dataset augmented from the prior information available in an ART workflow - an approach we term Intentional Deep Overfit Learning (IDOL). Implementing the IDOL framework in any task in radiotherapy consists of two training stages: 1) training a generalized model with a diverse training dataset of N patients, just as in the conventional DL approach, and 2) intentionally overfitting this general model to a small training dataset-specific the patient of interest (N+1) generated through perturbations and augmentations of the available task- and patient-specific prior information to establish a personalized IDOL model. The IDOL framework itself is task-agnostic and is thus widely applicable to many components of the ART workflow, three of which we use as a proof of concept here: the auto-contouring task on re-planning CTs for traditional ART, the MRI super-resolution (SR) task for MRI-guided ART, and the synthetic CT (sCT) reconstruction task for MRI-only ART. In the re-planning CT auto-contouring task, the accuracy measured by the Dice similarity coefficient improves from 0.847 with the general model to 0.935 by adopting the IDOL model. In the case of MRI SR, the mean absolute error (MAE) is improved by 40% using the IDOL framework over the conventional model. Finally, in the sCT reconstruction task, the MAE is reduced from 68 to 22 HU by utilizing the IDOL framework.

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preprint2021arXiv

Magnetic moiré surface states and flat chern band in topological insulators

We theoretically study the effect of magnetic moiré superlattice on the topological surface states by introducing a continuum model of Dirac electrons with a single Dirac cone moving in the time-reversal symmetry breaking periodic pontential. The Zeeman-type moiré potentials generically gap out the moiré surface Dirac cones and give rise to isolated flat Chern minibands with Chern number $\pm1$. This result provides a promising platform for realizing the time-reversal breaking correlated topological phases. In a $C_6$ periodic potential, when the scalar $U_0$ and Zeeman $Δ_1$ moiré potential strengths are equal to each other, we find that energetically the first three bands of $Γ$-valley moiré surface electrons are non-degenerate and realize i) an $s$-orbital model on a honeycomb lattice, ii) a degenerate $p_x,p_y$-orbitals model on a honeycomb lattice, and iii) a hybridized $sd^2$-orbital model on a kagome lattice, where moiré surface Dirac cones in these bands emerge. When $U_0\neqΔ_1$, the difference between the two moiré potential serves as an effective spin-orbit coupling and opens a topological gap in the emergent moiré surface Dirac cones.

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preprint2021arXiv

Planar magnetic texture on the surface of a topological insulator

We study the planar magnetic textures in an insulating magnetic film coupled to the Dirac surface state of a topological insulator. It is shown that the radial vortex with winding number $w=\pm1$ leads to the confinement of Dirac states, where an exact mapping to the Schrödinger equation of a two-dimensional hydrogen atom is found. The fully spin polarized zero energy bound state resembles the zeroth Landau level of Dirac electrons in a uniform out-of-plane magnetic field. Interestingly, when the hybrid system is proximity coupled to an $s$-wave superconductor, the existence of Majorana zero modes at Abrikosov vortex depends only on the relative value of the magnetic exchange coupling and the pairing strength. We conclude with a brief discussion on the physical realization with such magnetic textures.

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preprint2021arXiv

Plasmon Coupling Induced Photon Scattering Torque

Bio-compatible Au nanoparticles exhibit great advantages in the application of biomedical researches, such as bio-sensing, medical diagnosis, and cancer therapy. Bio-molecules can even be manipulated by laser tweezers with the optically trapped Au nanoparticles as handles. In this Letter, optical scattering torque arising from the coupled Au nanoparticles driven by circularly polarized light is theoretically presented. The coupled plasmon resonance modes boost the angular momentum transfer from photons to the Au nanoparticle dimers and trimers through light scattering, which does not bring any optical-heating side effect. The generated optical torques on the nanostructures highly depend on the plasmon coupling in the structures. The angular momentum transfer efficiencies from scattered photons to nanostructures can reach around 200\%. The results suggest that coupled plasmonic nanoparticle oligomers are promising candidates to construct optically driven rotary nanomotors that can be applied in biomedical applications.

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preprint2021arXiv

Resolving ultrahigh-contrast ultrashort pulses with single-shot cross-correlator at the photon noise limit

In strong-field physics experiments with intense lasers, it is of paramount importance to single-shot diagnose the temporal contrast between laser pulse peak and its noise pedestal. This allows fast optimization of pulse contrast and meaningful comparison with theory for each pulse shot, and it can help new outcomes from clean laser-plasma interactions. Thus far, high contrast ratios up to ~10^10, required by present petawatt (PW) class lasers, have been accessible in both generation and single-shot characterization. However, ultrahigh contrast ~10^13, required by the planned 200-PW lasers, challenges intense laser technology and remains an open question. This paper reports on the first demonstration of such an ultrahigh-contrast measurement by adapting single-shot cross-correlator (SSCC). We introduce an ultrafast method that enables to determine the SSCC detection limit. Our strategy mimics the test laser having known ultrahigh contrast in the measurement frame of time-to-space mapping. The ultimate contrast-measurement limit of 10^13 is achieved, which corresponds to the highest pulse intensity set by SSCC damage threshold and the lowest noise pedestal set by single-photon detection. As a consequence, photon noise in the detection is observed and increases as the noise pedestal reduces. The demonstrated measurement ability at the photon noise limit is applied to a high-contrast laser system based on second-harmonic generation and optical parametric chirped-pulse amplification, suggesting accessible of ultrahigh contrast pulses.

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preprint2021arXiv

Solutions to integrable space-time shifted nonlocal equations

In this paper we present a reduction technique based on bilinearization and double Wronskians (or double Casoratians) to obtain explicit multi-soliton solutions for the integrable space-time shifted nonlocal equations introduced very recently by Ablowitz and Musslimani in [Phys. Lett. A, 2021]. Examples include the space-time shifted nonlocal nonlinear Schrödinger and modified Korteweg-de Vries hierarchies and the semi-discrete nonlinear Schrödinger equation. It is shown that these nonlocal integrable equations with or without space-time shift(s) reduction share same distributions of eigenvalues but the space-time shift(s) brings new constraints to phase terms in solutions.

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preprint2021arXiv

Some Hopf Algebras related to $\mathfrak{sl}_2$

We define a series of Artin-Schelter Gorenstein Hopf algebras $H_β$ with injective dimensions 3. Radford's Hopf algebra and Gelaki's Hopf algebra are homomorphic images of $H_β$. We determine its Grothendieck ring $G_0(H_β)$. Meanwhile we can obtain Grothendieck rings of Gelaki's Hopf algebras and Radford's Hopf algebras $U_{(N,ν,ω)}$ in \cite{R}, and non-isomorphic Hopf algebras with isomorphic Grothendieck rings.

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preprint2021arXiv

Tight upper bound on the quantum value of Svetlichny operators under local filtering and hidden genuine nonlocality

Nonlocal quantum correlations among the quantum subsystems play essential roles in quantum science. The violation of the Svetlichny inequality provides sufficient conditions of genuine tripartite nonlocality. We provide tight upper bounds on the maximal quantum value of the Svetlichny operators under local filtering operations, and present a qualitative analytical analysis on the hidden genuine nonlocality for three-qubit systems. We investigate in detail two classes of three-qubit states whose hidden genuine nonlocalities can be revealed by local filtering.

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preprint2021arXiv

Universal Undersampled MRI Reconstruction

Deep neural networks have been extensively studied for undersampled MRI reconstruction. While achieving state-of-the-art performance, they are trained and deployed specifically for one anatomy with limited generalization ability to another anatomy. Rather than building multiple models, a universal model that reconstructs images across different anatomies is highly desirable for efficient deployment and better generalization. Simply mixing images from multiple anatomies for training a single network does not lead to an ideal universal model due to the statistical shift among datasets of various anatomies, the need to retrain from scratch on all datasets with the addition of a new dataset, and the difficulty in dealing with imbalanced sampling when the new dataset is further of a smaller size. In this paper, for the first time, we propose a framework to learn a universal deep neural network for undersampled MRI reconstruction. Specifically, anatomy-specific instance normalization is proposed to compensate for statistical shift and allow easy generalization to new datasets. Moreover, the universal model is trained by distilling knowledge from available independent models to further exploit representations across anatomies. Experimental results show the proposed universal model can reconstruct both brain and knee images with high image quality. Also, it is easy to adapt the trained model to new datasets of smaller size, i.e., abdomen, cardiac and prostate, with little effort and superior performance.

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preprint2021arXiv

Unoccupied Topological Surface State in MnBi$_2$Te$_4$

The unoccupied part of the band structure in the magnetic topological insulator MnBi$_2$Te$_4$ is studied by first-principles calculations. We find a second, unoccupied topological surface state with similar electronic structure to the celebrated occupied topological surface state. This state is energetically located approximate $1.6$ eV above the occupied Dirac surface state around $Γ$ point, which permit it to be directly observed by the two-photon angle-resolved photoemission spectroscopy. We propose a unified effective model for the occupied and unoccupied surface states. Due to the direct optical coupling between these two surface states, we further propose two optical effects to detect the unoccupied surface state. One is the polar Kerr effect in odd layer from nonvanishing ac Hall conductance $σ_{xy}(ω)$, and the other is higher-order terahertz-sideband generation in even layer, where the non-vanishining Berry curvature of the unoccupied surface state is directly observed from the giant Faraday rotation of optical emission.

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preprint2021arXiv

WALLABY Pre-Pilot Survey: The effects of tidal interaction on radial distribution of color in galaxies of the Eridanus supergroup

We study the tidal interaction of galaxies in the Eridanus supergroup, using HI data from the pre-pilot survey of WALLABY (Widefield ASKAP L-band Legacy All-sky Blind surveY). We obtain optical photometric measurements and quantify the strength of tidal perturbation using a tidal parameter $S_{sum}$. For low-mass galaxies of $M_* \lesssim 10^9 M_\odot$, we find a dependence of decreasing HI-to-optical disk size ratio with increasing $S_{sum}$, but no dependence of HI spectral line asymmetry with $S_{sum}$. This is consistent with the behavior expected under tidal stripping. We confirm that the color profile shape and color gradient depend on the stellar mass, but there is additional correlation of low-mass galaxies having their color gradients within $2R_{50}$ increasing with higher $S_{sum}$. For these low-mass galaxies, the dependence of color gradients on $S_{sum}$ is driven by color becoming progressively redder in the inner disk when tidal perturbations are stronger. For high-mass galaxies, there is no dependence of color gradients on $S_{sum}$, and we find a marginal reddening throughout the disks with increasing $S_{sum}$. Our result highlights tidal interaction as an important environmental effect in producing the faint end of the star formation suppressed sequence in galaxy groups.

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preprint2020arXiv

A Real-time Automatic Validation System for Optical Transients detected by GWAC

The ground-based wide-angle camera array (GWAC) generates millions of single frame alerts per night. After the complicated and elaborate filters by multiple methods, a couple of dozens of candidates are still needed to be confirmed by follow-up observations in real-time. In order to free scientists from the complex and high-intensity follow-up tasks, we developed a Real-time Automatic transient Validation System (RAVS), and introduce here its system architecture, data processing flow, database schema, automatic follow-up control flow, and mobile message notification solution. This system is capable of automatically carrying out all operations in real-time without human intervention, including the validation of transient candidates, the adaptive light-curve sampling for identified targets in multi-band, and the pushing of observation results to the mobile client. The running of RAVS shows that an M-type stellar flare event can be well sampled by RAVS without a significant loss of the details, while the observing time is only less than one-third of the time coverage. Because the control logic of RAVS is designed to be independent of the telescope hardware, RAVS can be conveniently transplanted to other telescopes, especially the follow-up system of SVOM. Some future improvements are presented for the adaptive light-curve sampling, after taking into account both the brightness of sources and the evolution trends of the corresponding light-curves.

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preprint2020arXiv

Absolutely Continuous Spectrum of Multifrequency Quasiperiodic Schrödinger operator

In this paper, we prove that for any $d$-frequency analytic quasiperiodic Schrödinger operator, if the frequency is weak Liouvillean, and the potential is small enough, then the corresponding operator has absolutely continuous spectrum. Moreover, in the case $d=2$, we even establish the existence of ac spectrum under small potential and some super-Liouvillean frequency, and this result is optimal due to a recent counterexample of Avila and Jitomirskaya.

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preprint2020arXiv

AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevated intraocular pressure and gradually lead to glaucomatous optic neuropathy and eventually to visual impairment and blindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging provides a fast and contactless way to discriminate angle closure from open angle. Although many medical image analysis algorithms have been developed for glaucoma diagnosis, only a few studies have focused on AS-OCT imaging. In particular, there is no public AS-OCT dataset available for evaluating the existing methods in a uniform way, which limits progress in the development of automated techniques for angle closure detection and assessment. To address this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held in conjunction with MICCAI 2019. The AGE challenge consisted of two tasks: scleral spur localization and angle closure classification. For this challenge, we released a large dataset of 4800 annotated AS-OCT images from 199 patients, and also proposed an evaluation framework to benchmark and compare different models. During the AGE challenge, over 200 teams registered online, and more than 1100 results were submitted for online evaluation. Finally, eight teams participated in the onsite challenge. In this paper, we summarize these eight onsite challenge methods and analyze their corresponding results for the two tasks. We further discuss limitations and future directions. In the AGE challenge, the top-performing approach had an average Euclidean Distance of 10 pixels (10um) in scleral spur localization, while in the task of angle closure classification, all the algorithms achieved satisfactory performances, with two best obtaining an accuracy rate of 100%.

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preprint2020arXiv

An algorithm of selection of meteor candidates in GWAC system

With its large field of view, GWAC can record hundreds of meteors every day. These meteors are valuable treasures for some meteor research groups. It is therefore very important to accurately find all of these meteors. To address the challenge of precisely distinguishing meteors from other elongated objects in a GWAC-like sky survey system, we design and implement a meteor candidate recognition algorithm, including the recognizing and morphology analysis of the light curves of the meteor candidates. Although the algorithm may filter out some real meteors, it can provide a sample of meteor with high confidence. After processing the images of Mini-GWAC taken in two months, we detect 109,000 elongated objects in which more than 90 percent of objects are not meteor. Among the elongated objects, about 5.9% objects are identified as meteors with high confidence, after the filters based upon an existence in a single frame, a single peak in the light curves, and a slow variation of the light curves.

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preprint2020arXiv

Anisotropic topological magnetoelectric effect in axion insulators

Three-dimensional topological insulators or axion insulators exhibit the topological magnetoelectric effect, which is isotropic with a universal coefficient of proportionality quantized in units of $e^2/2h$. Here we study the finite-size effect of topological magnetoelectric effect, and find the magnetoelectric coefficients are anisotropic, namely $α_{xx}\neqα_{zz}$. Both of them are shown to converge to a quantized value when the thickness of topological insulator film $d$ increases reaching the three-dimensional bulk limit. The nonzero value of $(α_{xx}-α_{zz})\propto1/d$ could be measured by using the gyrotropic or nonreciprocal birefringence of terahertz light. The unique $1/d$ dependence on film thickness of the rotation angle of optical principle axes is the manifestation of topological magnetoelectric effect, which may also serve as a smoking gun signature for axion insulators.

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preprint2020arXiv

Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing

Deep Reinforcement Learning (RL) is proven powerful for decision making in simulated environments. However, training deep RL model is challenging in real world applications such as production-scale health-care or recommender systems because of the expensiveness of interaction and limitation of budget at deployment. One aspect of the data inefficiency comes from the expensive hyper-parameter tuning when optimizing deep neural networks. We propose Adaptive Behavior Policy Sharing (ABPS), a data-efficient training algorithm that allows sharing of experience collected by behavior policy that is adaptively selected from a pool of agents trained with an ensemble of hyper-parameters. We further extend ABPS to evolve hyper-parameters during training by hybridizing ABPS with an adapted version of Population Based Training (ABPS-PBT). We conduct experiments with multiple Atari games with up to 16 hyper-parameter/architecture setups. ABPS achieves superior overall performance, reduced variance on top 25% agents, and equivalent performance on the best agent compared to conventional hyper-parameter tuning with independent training, even though ABPS only requires the same number of environmental interactions as training a single agent. We also show that ABPS-PBT further improves the convergence speed and reduces the variance.

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preprint2020arXiv

Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network

This research proposes a Ground Penetrating Radar (GPR) data processing method for non-destructive detection of tunnel lining internal defects, called defect segmentation. To perform this critical step of automatic tunnel lining detection, the method uses a CNN called Segnet combined with the Lovász softmax loss function to map the internal defect structure with GPR synthetic data, which improves the accuracy, automation and efficiency of defects detection. The novel method we present overcomes several difficulties of traditional GPR data interpretation as demonstrated by an evaluation on both synthetic and real datas -- to verify the method on real data, a test model containing a known defect was designed and built and GPR data was obtained and analyzed.

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preprint2020arXiv

Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment

In this study, we focus on the unsupervised domain adaptation problem where an approximate inference model is to be learned from a labeled data domain and expected to generalize well to an unlabeled data domain. The success of unsupervised domain adaptation largely relies on the cross-domain feature alignment. Previous work has attempted to directly align latent features by the classifier-induced discrepancies. Nevertheless, a common feature space cannot always be learned via this direct feature alignment especially when a large domain gap exists. To solve this problem, we introduce a Gaussian-guided latent alignment approach to align the latent feature distributions of the two domains under the guidance of the prior distribution. In such an indirect way, the distributions over the samples from the two domains will be constructed on a common feature space, i.e., the space of the prior, which promotes better feature alignment. To effectively align the target latent distribution with this prior distribution, we also propose a novel unpaired L1-distance by taking advantage of the formulation of the encoder-decoder. The extensive evaluations on nine benchmark datasets validate the superior knowledge transferability through outperforming state-of-the-art methods and the versatility of the proposed method by improving the existing work significantly.

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preprint2020arXiv

Dynamical axion state with hidden pseudospin Chern numbers in MnBi$_{2}$Te$_{4}$-based heterostructures

Axion is a hypothetical elementary particle which was initially postulated to solve the charge conjugation-parity problem in particle physics. Interestingly, the axion state has emerged in effective theory of topological insulators and has attracted extensive attention in condensed matter physics. Time-reversal or inversion symmetry constrains the axion field $θ$ to be quantized. When both the time-reversal and inversion symmetries are broken by, say, an antiferromagnetic order, the axion field $θ$ could become unquantized and dynamical along with magnetic fluctuations, which is termed the dynamical axion field. Here, we reveal that a wide class of topological-insulator-based dynamical axion states could be distinguished from the normal-insulator-based ones by a hidden quantity derived from the pseudospin Chern number. Motivated by recent research on MnBi$_{2}$Te$_{4}$-family materials, we further show that such topological-insulator-based dynamical axion states can be hopefully achieved in MnBi$_{2}$Te$_{4}$-based heterostructures, which should greatly facilitate the study of axion electrodynamics in condensed matter physics.

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preprint2020arXiv

Dynamical magnetoelectric coupling in axion insulator thin films

Axion insulator is an exotic magnetic topological insulator with zero Chern number but a nonzero quantized Chern-Simons magnetoelectric coupling. A conclusive experimental evidence for axion insulators is still lacking due to the small signal of topological magnetoelectric effect (TME). Here we show that the dynamical magnetoelectric coupling can be induced by the \emph{out-of-plane} surface magnetization dynamics in axion insulator thin films, which further generates a polarization current in the presence of an external magnetic field in the same direction. Such a current is finite in the bulk and increases as the film thickness $d$ decreases, in opposite to TME current which decreases as $d$ decreases. Remarkably, the current in thin films at magnetic resonance is at least ten times larger than that of TME, and thus may serve as a smoking gun signature for axion insulators.

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preprint2020arXiv

Electric-field-controllable high-spin SrRuO3 driven by a solid ionic junction

Controlling magnetism and spin structures in strongly correlated systems by using electric field is of fundamental importance but challenging. Here, a high-spin ruthenate phase is achieved via a solid ionic chemical junction at SrRuO3/SrTiO3 interface with distinct formation energies and diffusion barriers of oxygen vacancies, analogue to electronic band alignment in semiconductor heterojunction. Oxygen vacancies trapped within this interfacial SrRuO3 reconstruct Ru-4d electronic structure and orbital occupancy, leading to an enhanced magnetic moment. Furthermore, an interfacial magnetic phase can be switched reversibly by electric-field-rectifying oxygen migration in a solid-state ionic gating device, providing a framework for atomic design of functionalities in strongly correlated oxides using a way of solid chemistry.

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preprint2020arXiv

Endpoints for randomized controlled clinical trials for COVID-19 treatments

Introduction: Endpoint choice for randomized controlled trials of treatments for COVID-19 is complex. A new disease brings many uncertainties, but trials must start rapidly. COVID-19 is heterogeneous, ranging from mild disease that improves within days to critical disease that can last weeks and can end in death. While improvement in mortality would provide unquestionable evidence about clinical significance of a treatment, sample sizes for a study evaluating mortality are large and may be impractical. Furthermore, patient states in between "cure" and "death" represent meaningful distinctions. Clinical severity scores have been proposed as an alternative. However, the appropriate summary measure for severity scores has been the subject of debate, particularly in relating to the uncertainty about the time-course of COVID-19. Outcomes measured at fixed time-points may risk missing the time of clinical benefit. An endpoint such as time-to-improvement (or recovery), avoids the timing problem. However, some have argued that power losses will result from reducing the ordinal scale to a binary state of "recovered" vs "not recovered." Methods: We evaluate statistical power for possible trial endpoints for COVID-19 treatment trials using simulation models and data from two recent COVID-19 treatment trials. Results: Power for fixed-time point methods depends heavily on the time selected for evaluation. Time-to-improvement (or recovery) analyses do not specify a time-point. Time-to-event approaches have reasonable statistical power, even when compared to a fixed time-point method evaluated at the optimal time. Discussion: Time-to-event analyses methods have advantages in the COVID-19 setting, unless the optimal time for evaluating treatment effect is known in advance. Even when the optimal time is known, a time-to-event approach may increase power for interim analyses.

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preprint2020arXiv

FaultFace: Deep Convolutional Generative Adversarial Network (DCGAN) based Ball-Bearing Failure Detection Method

Failure detection is employed in the industry to improve system performance and reduce costs due to unexpected malfunction events. So, a good dataset of the system is desirable for designing an automated failure detection system. However, industrial process datasets are unbalanced and contain little information about failure behavior due to the uniqueness of these events and the high cost for running the system just to get information about the undesired behaviors. For this reason, performing correct training and validation of automated failure detection methods is challenging. This paper proposes a methodology called FaultFace for failure detection on Ball-Bearing joints for rotational shafts using deep learning techniques to create balanced datasets. The FaultFace methodology uses 2D representations of vibration signals denominated faceportraits obtained by time-frequency transformation techniques. From the obtained faceportraits, a Deep Convolutional Generative Adversarial Network is employed to produce new faceportraits of the nominal and failure behaviors to get a balanced dataset. A Convolutional Neural Network is trained for fault detection employing the balanced dataset. The FaultFace methodology is compared with other deep learning techniques to evaluate its performance in for fault detection with unbalanced datasets. Obtained results show that FaultFace methodology has a good performance for failure detection for unbalanced datasets.

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preprint2020arXiv

Generating Fundus Fluorescence Angiography Images from Structure Fundus Images Using Generative Adversarial Networks

Fluorescein angiography can provide a map of retinal vascular structure and function, which is commonly used in ophthalmology diagnosis, however, this imaging modality may pose risks of harm to the patients. To help physicians reduce the potential risks of diagnosis, an image translation method is adopted. In this work, we proposed a conditional generative adversarial network(GAN) - based method to directly learn the mapping relationship between structure fundus images and fundus fluorescence angiography images. Moreover, local saliency maps, which define each pixel's importance, are used to define a novel saliency loss in the GAN cost function. This facilitates more accurate learning of small-vessel and fluorescein leakage features.

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preprint2020arXiv

Information-Based Model Discrimination for Digital Twin Behavioral Matching

Digital Twin is a breaking technology that allows creating virtual representations of complex physical systems based on updated information of the system and its physical laws. However, making the Digital Twin behavior matching with the real system can be challenging due to the number of unknown parameters in each twin. Its search can be done using optimization-based techniques, producing a family of models based on different system datasets, so, a discrimination criterion is required to determine the best Digital Twin model. This paper presents an information theory-based discrimination criterion to determine the best Digital Twin model resulting from a behavioral matching process. The information gain of a model is employed as a discrimination criterion. Box-Jenkins models are used to define the family of models for each behavioral matching result. The proposed method is compared with other information-based metrics as well as the $ν$gap metric. As a study case, the discrimination method is applied to the Digital Twin for a real-time vision feedback infrared temperature uniformity control system. Obtained results show that information-based methodologies are useful for selecting an accurate Digital Twin model representing the system among a family of plants

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preprint2020arXiv

Knowledge Consistency between Neural Networks and Beyond

This paper aims to analyze knowledge consistency between pre-trained deep neural networks. We propose a generic definition for knowledge consistency between neural networks at different fuzziness levels. A task-agnostic method is designed to disentangle feature components, which represent the consistent knowledge, from raw intermediate-layer features of each neural network. As a generic tool, our method can be broadly used for different applications. In preliminary experiments, we have used knowledge consistency as a tool to diagnose representations of neural networks. Knowledge consistency provides new insights to explain the success of existing deep-learning techniques, such as knowledge distillation and network compression. More crucially, knowledge consistency can also be used to refine pre-trained networks and boost performance.

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preprint2020arXiv

Large dynamical axion field in topological antiferromagnetic insulator Mn$_2$Bi$_2$Te$_5$

The dynamical axion field is a new state of quantum matter where the magnetoelectric response couples strongly to its low-energy magnetic fluctuations. It is fundamentally different from an axion insulator with a static quantized magnetoelectric response. The dynamical axion field exhibits many exotic phenomena such as axionic polariton and axion instability. However, these effects have not been experimentally confirmed due to the lack of proper topological magnetic materials. Here by combining analytic models and first-principles calculations, we predict a series of van der Waal layered Mn$_2$Bi$_2$Te$_5$-related topological antiferromagnetic materials could host the long-sought dynamical axion field with a topological origin. We also show a large dynamical axion field can be achieved in antiferromagnetic insulating states close to the topological phase transition. We further propose the optical and transport experiments to detect such a dynamical axion field. Our results could directly aid and facilitate the search for topological-origin large dynamical axion field in realistic materials.

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preprint2020arXiv

Large-area printing of ferroelectric surface and super-domains for efficient solar water splitting

Surface electronic structures of the photoelectrodes determine the activity and efficiency of the photoelectrochemical water splitting, but the controls of their surface structures and interfacial chemical reactions remain challenging. Here, we use ferroelectric BiFeO3 as a model system to demonstrate an efficient and controllable water splitting reaction by large-area constructing the hydroxyls-bonded surface. The up-shift of band edge positions at this surface enables and enhances the interfacial holes and electrons transfer through the hydroxyl-active-sites, leading to simultaneously enhanced oxygen and hydrogen evolutions. Furthermore, printing of ferroelectric super-domains with microscale checkboard up/down electric fields separates the distribution of reduction/oxidation catalytic sites, enhancing the charge separation and giving rise to an order of magnitude increase of the photocurrent. This large-area printable ferroelectric surface and super-domains offer an alternative platform for controllable and high-efficient photocatalysis.

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preprint2020arXiv

Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network

The Hyperspectral image (HSI) classification is a standard remote sensing task, in which each image pixel is given a label indicating the physical land-cover on the earth's surface. The achievements of image semantic segmentation and deep learning approaches on ordinary images have accelerated the research on hyperspectral image classification. Moreover, the utilization of both the spectral and spatial cues in hyperspectral images has shown improved classification accuracy in hyperspectral image classification. The use of only 3D Convolutional Neural Networks (3D-CNN) to extract both spatial and spectral cues from Hyperspectral images results in an explosion of parameters hence high computational cost. We propose network architecture called the MixedSN that utilizes the 3D convolutions to modeling spectral-spatial information in the early layers of the architecture and the 2D convolutions at the top layers which majorly deal with semantic abstraction. We constrain our architecture to ResNeXt block because of their performance and simplicity. Our model drastically reduced the number of parameters and achieved comparable classification performance with state-of-the-art methods on Indian Pine (IP) scene dataset, Pavia University scene (PU) dataset, Salinas (SA) Scene dataset, and Botswana (BW) dataset.

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preprint2020arXiv

LogoDet-3K: A Large-Scale Image Dataset for Logo Detection

Logo detection has been gaining considerable attention because of its wide range of applications in the multimedia field, such as copyright infringement detection, brand visibility monitoring, and product brand management on social media. In this paper, we introduce LogoDet-3K, the largest logo detection dataset with full annotation, which has 3,000 logo categories, about 200,000 manually annotated logo objects and 158,652 images. LogoDet-3K creates a more challenging benchmark for logo detection, for its higher comprehensive coverage and wider variety in both logo categories and annotated objects compared with existing datasets. We describe the collection and annotation process of our dataset, analyze its scale and diversity in comparison to other datasets for logo detection. We further propose a strong baseline method Logo-Yolo, which incorporates Focal loss and CIoU loss into the state-of-the-art YOLOv3 framework for large-scale logo detection. Logo-Yolo can solve the problems of multi-scale objects, logo sample imbalance and inconsistent bounding-box regression. It obtains about 4% improvement on the average performance compared with YOLOv3, and greater improvements compared with reported several deep detection models on LogoDet-3K. The evaluations on other three existing datasets further verify the effectiveness of our method, and demonstrate better generalization ability of LogoDet-3K on logo detection and retrieval tasks. The LogoDet-3K dataset is used to promote large-scale logo-related research and it can be found at https://github.com/Wangjing1551/LogoDet-3K-Dataset.

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preprint2020arXiv

Molecular collapse in graphene: sublattice symmetry effect

Atomic collapse can be observed in graphene because of its large "effective" fine structure constant, which enables this phenomenon to occur for an impurity charge as low as $Z_c\sim 1-2$. Here, we investigate the effect of the sublattice symmetry on molecular collapse in two spatially separated charge tunable vacancies, that are located on the same (A-A type) or different (A-B type) sublattices. We find that the broken sublattice symmetry: (1) does not affect the location of the main bonding and anti-bonding molecular collapse peaks, (2) but shifts the position of the satellite peaks, because they are a consequence of the breaking of the local sublattice symmetry, and (3) there are vacancy characteristic collapse peaks that only occur for A-B type vacancies, which can be employed to distinguish them experimentally from the A-A type. As the charge, energy, and separation distance increase, the additional collapse features merge with the main molecular collapse peaks. We show that the spatial distribution around the vacancy site of the collapse states allows us to differentiate the molecular from the frustrated collapse.

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preprint2020arXiv

On the Determination of Rotation Velocity and Dynamical Mass of Galaxies Based on Integrated H I Spectra

The integrated 21 cm H I emission profile of a galaxy encodes valuable information on the kinematics, spatial distribution, and dynamical state of its cold interstellar medium. The line width, in particular, reflects the rotation velocity of the galaxy, which, in combination with a size scale, can be used to constrain the dynamical mass of the system. We introduce a new method based on the concept of the curve of growth to derive a set of robust parameters to characterize the line width, asymmetry, and concentration of the integrated H I spectra. We use mock spectra to evaluate the performance of our method, to estimate realistic systematic uncertainties for the proposed parameters, and to correct the line widths for the effects of instrumental resolution and turbulence broadening. Using a large sample of nearby galaxies with available spatially resolved kinematics, we demonstrate that the newly defined line widths can predict the rotational velocities of galaxies to within an accuracy of $\lesssim 30$ km s$^{-1}$. We use the calibrated line widths, in conjunction with the empirical relation between the size and mass of H I disks, to formulate a prescription for estimating the dynamical mass within the H I-emitting region of gas-rich galaxies. Our formalism yields dynamical masses accurate to $\sim 0.3$ dex based solely on quantities that can be derived efficiently and robustly from current and future extragalactic H I surveys. We further extend the dynamical mass calibration to the scale of the dark matter halo.

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preprint2020arXiv

Optical and electronic properties in amorphous BaSnO3 thin films

Wide-bandgap perovskite stannates are of interest for the emergent all-oxide transparent electronic devices due to their unparalleled room temperature electron mobility. Considering the advantage of amorphous material in integrating with non-semiconductor platforms, we herein reported the optical and electronic properties in the prototypical stannate, amorphous barium stannate (BaSnO3) thin films, which were deposited at room temperature and annealed at various temperatures. Despite remaining amorphous status, with increasing the annealing temperature, the defect level within amorphous BaSnO3 thin films could be suppressed.

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preprint2020arXiv

Performance evaluation of an integrated photonic convolutional neural network based on delay buffering and wavelength division multiplexing

Photonic technologies have shown a promising way to build high-speed and high-energy-efficiency neural network accelerators. In previously presented photonic neural networks, architectures are mainly designed for fully-connected layers. When convolutional layers are executed in such neural networks, the large-scale electrooptic modulation array heavily increases the energy dissipation on chip. To increase the energy efficiency, here we show an integrated photonic architecture specifically for convolutional layer calculations. Optical delay lines replace electronics to execute data manipulations on optical chip, reducing the scale of electro-optic modulation array. Consequently, the energy dissipation of these parts is mitigated. Powered by wavelength division multiplexing, the footprint of delay lines is significantly reduced compared with previous art, thus being practical to fabricate. We evaluate the potential performance of the proposed architecture with respect to component flaws in practical fabrications. According to the results, with well-controlled system insertion loss, energy efficiency of the proposed architecture would surpass previously presented works and the state-of-art electronic processors. We anticipate the proposed architecture is beneficial for future fast and energy-efficient convolutional neural network accelerators.

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preprint2020arXiv

Properties of materials considered for improvised masks

During a pandemic in which aerosol and droplet transmission is possible, the demand for masks that meet medical or workplace standards can prevent most individuals or organizations from obtaining suitable protection. Cloth masks are widely believed to impede droplet and aerosol transmission but most are constructed from materials with unknown filtration efficiency, airflow resistance and water resistance. Further, there has been no clear guidance on the most important performance metrics for the materials used by the general public (as opposed to high-risk healthcare settings). Here we provide data on a range of common fabrics that might be used to construct masks. None of the materials were suitable for masks meeting the N95 NIOSH standard, but many could provide useful filtration (>90%) of 3 micron particles (a plausible challenge size for human generated aerosols), with low pressure drop. These were: nonwoven sterile wraps, dried baby wipes and some double-knit cotton materials. Decontamination of N95 masks using isopropyl alcohol produces the expected increase in particle penetration, but for 3 micron particles, filtration efficiency is still well above 95%. Tightly woven thin fabrics, despite having the visual appearance of a good particle barrier, had remarkably low filtration efficiency and high pressure drop. These differences in filtration performance can be partly explained by the material structure; the better structures expose individual fibers to the flow while the poor materials may have small fundamental fibers but these are in tightly bundled yarns. The fit and use of the whole mask are critical factors not addressed in this work. Despite the complexity of the design of a very good mask, it is clear that for the larger aerosol particles, any mask will provide substantial protection to the wearer and those around them.

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preprint2020arXiv

Quantum Secured Internet Transport

Quantum computing represents an emerging threat to the public key infrastructure underlying transport layer security (TLS) widely used in the Internet. This paper describes how QKD symmetric keys can be used with TLS to provide quantum computing resistant security for existing Internet applications. We also implement and test a general hybrid key delivery architecture with QKD over long distance fibers between secure sites, and wireless key distribution over short distance within each site Finally we show how this same capability can be extended to a TLS cipher scheme with perfect security.

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preprint2020arXiv

Suppressed or enhanced central star formation rates in late-type barred galaxies

Bars in disc-dominated galaxies are able to drive gas inflow inside the corotation radius, thus enhancing the central star formation rate (SFR). Previous work, however, has found that disc-dominated galaxies with centrally suppressed SFR frequently host a bar. Here we investigate possible causes for the suppression of central SFR in such cases. We compare physical properties of a sample of disc-dominated barred galaxies with high central SFR (HC galaxies) with those of a sample of disc-dominated barred galaxies with low central SFR (LC galaxies). We find that the two samples have on average similar HI content and bars of similar strength. But we also find that the HCs have bluer colors than LCs, and that outside the bar region they host stronger spiral arms than the LCs where closed rings are more often seen. We discuss and evaluate the possible causes for the suppression of the central SFR in the LC galaxies as opposed to its enhancement in the HC galaxies.

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preprint2020arXiv

The Breakdown Scale of HI Bias Linearity

The 21 cm intensity mapping experiments promise to obtain the large-scale distribution of HI gas at the post-reionization epoch. In order to reveal the underlying matter density fluctuations from the HI mapping, it is important to understand how HI gas traces the matter density distribution. Both nonlinear halo clustering and nonlinear effects modulating HI gas in halos may determine the scale below which the HI bias deviates from linearity. We employ three approaches to generate the mock HI density from a large-scale N-body simulation at low redshifts, and demonstrate that the assumption of HI linearity is valid at the scale corresponding to the first peak of baryon acoustic oscillations, but breaks down at $k \gtrsim 0.1\,h\, {\rm Mpc}^{-1}$. The nonlinear effects of halo clustering and HI content modulation counteract each other at small scales, and their competition results in a model-dependent "sweet-spot" redshift near $z$=1 where the HI bias is scale-independent down to small scales. We also find that the linear HI bias scales approximately linearly with redshift for $z\le 3$.

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preprint2020arXiv

The reduction of the number of incoherent Kraus operations for qutrit systems

Quantum coherence is a fundamental property that can emerge within any quantum system. Incoherent operations, defined in terms of the Kraus decomposition, take an important role in state transformation. The maximum number of incoherent Kraus operators has been presented in [A. Streltsov, S. Rana, P. Boes, J. Eisert, Phys. Rev. Lett. 119. 140402 (2017)]. In this work, we show that the number of incoherent Kraus operators for a single qubit can be reduced from 5 to 4 by constructing a proper unitary matrix. For qutrit systems we further obtain 32 incoherent Kraus operators, while the upper bound in the research of Sterltsov gives 39 Kraus operators. Besides, we reduce the number of strictly incoherent Kraus operators from more than 15 to 13. And we consider the state transformation problem for these two types of operations in single qutrit systems.

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preprint2020arXiv

xGASS: Cold gas content and quenching in galaxies below the star forming main sequence

We use HI and H2 global gas measurements of galaxies from xGASS and xCOLD GASS to investigate quenching paths of galaxies below the star formation main sequence (SFMS). We show that the population of galaxies below the SFMS is not a 1:1 match with the population of galaxies below the HI and H2 gas fraction scaling relations. Some galaxies in the transition zone (TZ) 1-sigma below the SFMS can be as HI-rich as those in the SFMS, and have on average longer gas depletion timescales. We find evidence for environmental quenching of satellites, but central galaxies in the TZ defy simple quenching pathways. Some of these so-called "quenched" galaxies may still have significant gas reservoirs and be unlikely to deplete them anytime soon. As such, a correct model of galaxy quenching cannot be inferred with SFR (or other optical observables) alone, but must include observations of the cold gas. We also find that internal structure (particularly, the spatial distribution of old and young stellar populations) plays a significant role in regulating the star formation of gas-rich isolated TZ galaxies, suggesting the importance of bulges in their evolution.

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preprint2020arXiv

xGASS: HI fueling of star formation in disk-dominated galaxies

We introduce a method to estimate the HI mass within the optical radius of disk galaxies from integrated HI spectra, with an uncertainty of 0.09 dex. We use these estimates to study how inner HI fuels star formation in late-type disk galaxies. We find that star formation rate (SFR) at a given stellar mass ($M_*$) is well correlated with the inner HI surface density ($Σ_{\rm HI,in}$) and inner HI mass-to-stellar mass ratio. For the massive ($M_*>10^{10} M_{\odot}$) disk galaxies, higher SFR at a given stellar mass is also related with higher efficiency of converting inner HI to molecular gas, but no such correlation is found for the total HI mass. The highest $Σ_{\rm HI,in}$ and the fastest depletion of the total neutral gas within the optical disks are found in the most compact and star-forming disk galaxies at a given stellar mass. These results highlight the important role of inner HI as an intermediate step of fueling star formation in disk galaxies.

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preprint2019arXiv

Detecting causality in multivariate time series via non-uniform embedding

Causal analysis based on non-uniform embedding schemes is an important way to detect the underlying interactions between dynamic systems. However, there are still some obstacles to estimate high-dimensional conditional mutual information and form optimal mixed embedding vector in traditional non-uniform embedding schemes. In this study, we present a new non-uniform embedding method framed in information theory to detect causality for multivariate time series, named LM-PMIME, which integrates the low-dimensional approximation of conditional mutual information and the mixed search strategy for the construction of the mixed embedding vector. We apply the proposed method to simulations of linear stochastic, nonlinear stochastic, and chaotic systems, demonstrating its superiority over partial conditional mutual information from mixed embedding (PMIME) method. Moreover, the proposed method works well for multivariate time series with weak coupling strengths, especially for chaotic systems. In the actual application, we show its applicability to epilepsy multichannel electrocorticographic recordings.

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preprint2019arXiv

Magnetic-field-induced quantized anomalous Hall effect in intrinsic magnetic topological insulator MnBi$_2$Te$_4$

In a magnetic topological insulator, nontrivial band topology conspires with magnetic order to produce exotic states of matter that are best exemplified by quantum anomalous Hall (QAH) insulators and axion insulators. Up till now, such magnetic topological insulators are obtained by doping topological insulators with magnetic atoms. The random magnetic dopants, however, inevitably introduce disorders that hinder further exploration of quantum effects in the material. Here, we resolve this dilemma by probing quantum transport in MnBi$_2$Te$_4$ thin flake - a topological insulator with intrinsic magnetic order. In this layered van der Waals crystal, the ferromagnetic layers couple anti-parallel to each other, so MnBi$_2$Te$_4$ is an antiferromagnet. A magnetic field, however, aligns all the layers and induces an interlayer ferromagnetic order; we show that a quantized anomalous Hall response emerges in atomically thin MnBi$_2$Te$_4$ under a moderate magnetic field. MnBi$_2$Te$_4$ therefore becomes the first intrinsic magnetic topological insulator exhibiting quantized anomalous Hall effect. The result establishes MnBi$_2$Te$_4$ as an ideal arena for further exploring various topological phenomena.

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preprint2019arXiv

Singular low-energy states of tilted Dirac semimetals induced by the fermion-fermion interactions

We attentively investigate the effects of short-range fermion-fermion interactions on the low-energy properties of both two-dimensional type-I and type-II tilted Dirac semimetals by means of the renormalization group framework. Practicing the standard renormalization group procedures via taking into account all one-loop corrections gives rise to the coupled energy-dependent evolutions of all interaction parameters, which are adopted to carefully examine whether and how the fermion-fermion interactions influence the low-energy physical behaviors of tilted Dirac fermions. After carrying out the detailed analysis of coupled flows, we figure out the tilting parameter dictates the low-energy states of tilted Dirac fermions in conjunction with starting values of fermion-fermion couplings. With proper variations of these two kinds of parameters, the tilted Dirac fermions can either flow towards the Gaussian fixed point or undergo certain instability that is conventionally accompanied by a phase transition in the low-energy regime. In addition, all potential instabilities can be clustered into five distinct classes owing to the competitions between the tilting parameter and initial fermionic interactions. Moreover, the dominant phases accompanied by the instabilities are determined via computing and comparing the susceptibilities of eight potential phases.

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preprint2019arXiv

SN 2017cfd: A Normal Type Ia Supernova Discovered Very Young

The Type~Ia supernova (SN~Ia) 2017cfd in IC~0511 (redshift z = 0.01209+- 0.00016$) was discovered by the Lick Observatory Supernova Search 1.6+-0.7 d after the fitted first-light time (FFLT; 15.2 d before B-band maximum brightness). Photometric and spectroscopic follow-up observations show that SN~2017cfd is a typical, normal SN~Ia with a peak luminosity MB ~ -19.2+-0.2 mag, Delta m15(B) = 1.16 mag, and reached a B-band maximum ~16.8 d after the FFLT. We estimate there to be moderately strong host-galaxy extinction (A_V = 0.39 +- 0.03 mag) based on MLCS2k2 fitting. The spectrum reveals a Si~II lambda 6355 velocity of ~11,200 kms at peak brightness. The analysis shows that SN~2017cfd is a very typical, normal SN Ia in nearly every aspect. SN~2017cfd was discovered very young, with multiband data taken starting 2 d after the FFLT, making it a valuable complement to the currently small sample (fewer than a dozen) of SNe~Ia with color data at such early times. We find that its intrinsic early-time (B - V)0 color evolution belongs to the "blue" population rather than to the distinct "red" population. Using the photometry, we constrain the companion star radius to be < 2.5 R_sun, thus ruling out a red-giant companion.

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preprint2019arXiv

Structure fusion based on graph convolutional networks for semi-supervised classification

Suffering from the multi-view data diversity and complexity for semi-supervised classification, most of existing graph convolutional networks focus on the networks architecture construction or the salient graph structure preservation, and ignore the the complete graph structure for semi-supervised classification contribution. To mine the more complete distribution structure from multi-view data with the consideration of the specificity and the commonality, we propose structure fusion based on graph convolutional networks (SF-GCN) for improving the performance of semi-supervised classification. SF-GCN can not only retain the special characteristic of each view data by spectral embedding, but also capture the common style of multi-view data by distance metric between multi-graph structures. Suppose the linear relationship between multi-graph structures, we can construct the optimization function of structure fusion model by balancing the specificity loss and the commonality loss. By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as adjacent matrix to input graph convolutional networks for semi-supervised classification. Experiments demonstrate that the performance of SF-GCN outperforms that of the state of the arts on three challenging datasets, which are Cora,Citeseer and Pubmed in citation networks.

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preprint2019arXiv

The evolution of baryonic mass function of galaxies to z=3

We combine the published stellar mass function (SMF) and gas scaling relations to explore the baryonic (stellar plus cold gas) mass function (BMF) of galaxies to redshift $z=3$. We find evidence that at log$(M_{\rm baryon}/M_{\bigodot})>11.3$, the BMF evolves little since $z\sim 2.2$. With the evolution of BMF and SMF, we investigate the baryon net accretion rate ($\dotρ_{\rm baryon}$) and stellar mass growth rate ($\dotρ_{\rm star}$) for the galaxy population of log($M_{\rm star}/M_{\bigodot}$)>10. The ratio between these two quanties, $\dotρ_{\rm baryon}$/$\dotρ_{\rm star}$, decreases from $\dotρ_{\rm baryon}$/$\dotρ_{\rm star}\sim$2 at $z\sim 2.5$ to $\dotρ_{\rm baryon}$/$\dotρ_{\rm star}<$0.5 at $z\sim 0.5$, suggesting that massive galaxies are transforming from the "accretion dominated" phase to the "depletion dominated" phase from high$-z$ to low$-z$. The transition of these two phases occurs at $z\sim1.5$, which is consistent with the onset redshift of the decline of cosmic star formation rate density. This provides evidence to support the idea that the decline of cosmic star formation rate density since $z\sim1.5$ is mainly resulted from the decline of baryon net accretion rate and star formation quenching in galaxies.

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preprint2019arXiv

The growth of bulges and discs in relatively HI-rich galaxies: indication from HI scaling relations

We study the relation between the properties of the bulge/disc components and the HI mass fraction of galaxies. We find that at fixed stellar mass, disc colours are correlated with the HI mass fraction, while bulge colours are not. The lack of a correlation between the bulge colour and the HI mass fraction is regardless whether the bulges are pseudo, or whether the galaxies host bars or are interacting with a neighbour. There is no strong correlation between the colours of the discs and bulges either. These results suggest that the current total amount of HI is closely related to the formation of discs, but does not necessarily fuel the formation of (pseudo) bulges in an efficient way. We do not find evidence for the star formation in the discs to be quenched by the bulges.

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preprint2019arXiv

The Local Volume HI Survey (LVHIS)

The `Local Volume HI Survey' (LVHIS) comprises deep HI spectral line and 20-cm radio continuum observations of 82 nearby, gas-rich galaxies, supplemented by multi-wavelength images. Our sample consists of all galaxies with Local Group velocities v_LG < 550 km/s or distances D < 10 Mpc that are detected in the HI Parkes All Sky Survey (HIPASS). Using full synthesis observations in at least three configurations of the Australia Telescope Compact Array (ATCA), we obtain detailed HI maps for a complete sample of gas-rich galaxies with Dec < -30 deg. Here we present a comprehensive LVHIS Galaxy Atlas, including the overall gas distribution, mean velocity field, velocity dispersion, and position-velocity diagrams, together with a homogeneous set of measured and derived galaxy properties. Our primary goal is to investigate the HI morphologies, kinematics, and environment at high resolution and sensitivity. LVHIS galaxies represent a wide range of morphologies and sizes; our measured HI masses range from ~10^7 to 10^10 Msun, based on independent distance estimates. The LVHIS Galaxy Atlas (including FITS files) is available on-line.

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preprint2018arXiv

Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features

To predict lung nodule malignancy with a high sensitivity and specificity, we propose a fusion algorithm that combines handcrafted features (HF) into the features learned at the output layer of a 3D deep convolutional neural network (CNN). First, we extracted twenty-nine handcrafted features, including nine intensity features, eight geometric features, and twelve texture features based on grey-level co-occurrence matrix (GLCM) averaged from thirteen directions. We then trained 3D CNNs modified from three state-of-the-art 2D CNN architectures (AlexNet, VGG-16 Net and Multi-crop Net) to extract the CNN features learned at the output layer. For each 3D CNN, the CNN features combined with the 29 handcrafted features were used as the input for the support vector machine (SVM) coupled with the sequential forward feature selection (SFS) method to select the optimal feature subset and construct the classifiers. The fusion algorithm takes full advantage of the handcrafted features and the highest level CNN features learned at the output layer. It can overcome the disadvantage of the handcrafted features that may not fully reflect the unique characteristics of a particular lesion by combining the intrinsic CNN features. Meanwhile, it also alleviates the requirement of a large scale annotated dataset for the CNNs based on the complementary of handcrafted features. The patient cohort includes 431 malignant nodules and 795 benign nodules extracted from the LIDC/IDRI database. For each investigated CNN architecture, the proposed fusion algorithm achieved the highest AUC, accuracy, sensitivity, and specificity scores among all competitive classification models.

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preprint2016arXiv

Bounds on Multipartite Concurrence and Tangle

We present an analytical lower bound of multipartite concurrence based on the generalized Bloch representations of density matrices. It is shown that the lower bound can be used as an effective entanglement witness of genuine multipartite entanglement. Tight lower and upper bounds for multipartite tangles are also derived. Since the lower bounds depend on just part of the correlation tensors, the result is experimentally feasible.

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preprint2016arXiv

Capacity Analysis for Spatially Non-wide Sense Stationary Uplink Massive MIMO Systems

Channel measurements show that significant spatially non-wide-sense-stationary characteristics rise in massive MIMO channels. Notable parameter variations are experienced along the base station array, such as the average received energy at each antenna, and the directions of arrival of signals impinging on different parts of the array. In this paper, a new channel model is proposed to describe this spatial non-stationarity in massive MIMO channels by incorporating the concepts of partially visible clusters and wholly visible clusters. Furthermore, a closed-form expression of an upper bound on the ergodic sum capacity is derived for the new model, and the influence of the spatial non-stationarity on the sum capacity is analyzed. Analysis shows that for non-identically-and-independent-distributed (i.i.d.) Rayleigh fading channels, the non-stationarity benefits the sum capacity by bringing a more even spread of channel eigenvalues. Specifically, more partially visible clusters, smaller cluster visibility regions and a larger antenna array can all help to yield a well-conditioned channel, and benefit the sum capacity. This shows the advantage of using a large antenna array in a non-i.i.d. channel: the sum capacity benefits not only from a higher array gain, but also from a more spatially non-stationary channel. Numerical results demonstrate our analysis and the tightness of the upper bound.

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preprint2016arXiv

Conformal transforms and Doob's h-processes on Heisenberg groups

We study the stochastic processes that are images of Brownian motions on Heisenberg group H2n+1 under conformal maps. In particular, we obtain that Cayley transform maps Brownian paths in H2n+1 to a time changed Brownian motion on CR sphere S2n+1 conditioned to be at its south pole at a random time. We also obtain that the inversion of Brownian motion on H2n+1 started from x\not= 0, is up to time change, a Brownian bridge on H2n+1 conditioned to be at the origin.

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preprint2016arXiv

Edge State Induced Andreev Oscillation in Quantum Anomalous Hall Insulator-Superconductor Junctions

We study the quantum Andreev oscillation induced by interference of the edge chiral Majorana fermions in junctions made of quantum anomalous Hall (QAH) insulators and superconductors (SCs). We show two chiral Majorana fermions on a QAH edge with SC proximity generically have a momentum difference $Δk$, which depends on the chemical potentials of both the QAH insulator and the SC. Due to the spatial interference induced by $Δk$, the longitudinal conductance of QAH-SC junctions oscillates with respect to the edge lengths and the chemical potentials, which can be probed via charge transport. Furthermore, we show the dynamical SC phase fluctuation will give rise to a geometrical correction to the longitudinal conductance of the junctions.

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preprint2016arXiv

Electrically tunable topological superconductivity and Majorana fermions in two dimensions

The external controllability of topological superconductors and Majorana fermions would be important both for fundamental and practical interests. Here we predict the electric-field control of Majorana fermions in two-dimensional topological superconductors utilizing a topological insulator thin film proximity coupled to a conventional $s$-wave superconductor. With ferromagnetic ordering, the tunable structure inversion asymmetry by vertical electric field could induce topological quantum phase transition and realize a chiral topological superconductor state. A zero-energy Majorana bound state appears at the boundary of an applied electric field spot, which can be observed by scanning tunneling microscopy. Furthermore, the structure inversion asymmetry could also enlarge the helical topological superconductor state in the phase diagram, making the realization of such an exotic state more feasible. The electrical control of topological phases could further apply to van der Waals materials such as two-dimensional transition metal dichalcogenides.

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preprint2016arXiv

Generation of arbitrary radially polarized array beams by modulating the correlation structure

We demonstrate a convenient approach for simultaneously manipulating the amplitude and polarization of light beams by means of the modulation of the correlation structure. As an illustration, we constructed a periodic correlation structure that can generate an arbitrary radially polarized array (RPA) beam of a radial or rectangular symmetry array in the focal plane from a radially polarized (RP) beam. The physical realizability conditions for such source and the far-field beam condition are derived. It is illustrated that the beamlet shape and the state of polarization (SOP) can be effectively controlled by the initial correlation structure and the coherence width. Furthermore, by designing the source correlation structure, a tunable OK-shaped RPA beam and an optical cage are demonstrated, which can find widespread applications in non-destructive manipulation of particles and living biological cells. The arbitrariness in the design of correlation structure prompted us to find more convenient approaches for controlling the statistics of light beams in terms of amplitude and polarization.

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preprint2016arXiv

Generation of Spin Currents by Magnetic Field in $\mathcal{T}$- and $\mathcal{P}$-Broken Materials

Pure spin currents carry information in quantum spintronics and could play an essential role in the next generation low-energy-consumption electronics. Here we theoretically predict that the magnetic field can induce a quantum spin current without a concomitant charge current in metals without time reversal symmetry $\mathcal{T}$ and inversion symmetry $\mathcal{P}$ but respect the combined $\mathcal{PT}$ symmetry. It is governed by the magnetic moment of the Bloch states on the Fermi surface, and can be regarded as a spinful generalization of the gyrotropic magnetic effect in $\mathcal{P}$-broken metals. The effect is explicitly studied for a minimal model of an antiferromagnetic Dirac semimetal, where the experimental signature is proposed. We further propose candidate materials, including topological antiferromagnetic Dirac semimetals, Weyl semimetals, and tenary Heusler compounds.

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preprint2016arXiv

Green 5G Heterogeneous Networks through Dynamic Small-Cell Operation

Traditional macro-cell networks are experiencing an upsurge of data traffic, and small-cells are deployed to help offload the traffic from macro-cells. Given the massive deployment of small-cells in a macro-cell, the aggregate power consumption of small-cells (though being low individually) can be larger than that of the macro-cell. Compared to the macro-cell base station (MBS) whose power consumption increases significantly with its traffic load, the power consumption of a small-cell base station (SBS) is relatively flat and independent of its load. To reduce the total power consumption of the heterogeneous networks (HetNets), we dynamically change the operating states (on and off) of the SBSs, while keeping the MBS on to avoid any service failure outside active small-cells. First, we consider that the wireless users are uniformly distributed in the network, and propose an optimal location-based operation scheme by gradually turning off the SBSs closer to the MBS. We then extend the operation problem to a more general case where users are non-uniformly distributed in the network. Although this problem is NP-hard, we propose a joint location and user density based operation scheme to achieve near-optimum (with less than 1\% performance loss in our simulations) in polynomial time.

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preprint2016arXiv

Link Prediction with Node Clustering Coefficient

Predicting missing links in incomplete complex networks efficiently and accurately is still a challenging problem. The recently proposed CAR (Cannistrai-Alanis-Ravai) index shows the power of local link/triangle information in improving link-prediction accuracy. With the information of level-2 links, which are links between common-neighbors, most classical similarity indices can be improved. Nevertheless, calculating the number of level-2 links makes CAR index not efficient enough. Inspired by the idea of employing local link/triangle information, we propose a new similarity index with more local structure information. In our method, local link/triangle structure information can be conveyed by clustering coefficient of common neighbors directly. The reason why clustering coefficient has good effectiveness in estimating the contribution of a common-neighbor is because that it employs links existing between neighbors of the common-neighbor and these links have the same structural position with the candidate link to this common-neighbor. Ten real-world networks drawn from five various fields are used to test the performance of our method against to classical similarity indices and recently proposed CAR index. Two estimators: precision and AUP, are used to evaluate the accuracy of link prediction algorithms. Generally speaking, our new index only performs competitively with CAR, but it is a good complement to CAR for networks with not very high LCP-corr, which is a measure to estimate the correlation between number of common-neighbors and number of links between common-neighbors. Besides, the proposed index is also more efficient than CAR index.

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preprint2016arXiv

Local Codes with Cooperative Repair in Distributed Storage System

Recently, the research on local repair codes is mainly confined to repair the failed nodes within each repair group. But if the extreme cases occur that the entire repair group has failed, the local code stored in the failed group need to be recovered as a whole. In this paper, local codes with cooperative repair, in which the local codes are constructed based on minimum storage regeneration (MSR) codes, is proposed to achieve repairing the failed groups. Specifically, the proposed local codes with cooperative repair construct a kind of mutual interleaving structure among the parity symbols, that the parity symbols of each local code, named as distributed local parity, can be generated by the parity symbols of the MSR codes in its two adjacent local codes. Taking advantage of the structure given, the failed local groups can be repaired cooperatively by their adjacent local groups with lower repair locality, and meanwhile the minimum distance of local codes with cooperative repair is derived. Theoretical analysis and simulation experiments show that, compared with codes with local regeneration (such as MSR-local codes and MBR-local codes), the proposed local codes with cooperative repair have benefits in bandwidth overhead and repair locality for the case of local groups failure.

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preprint2016arXiv

Massive MIMO with Multi-Antenna Users: When are Additional User Antennas Beneficial?

We analyze the performance of massive MIMO systems with $N$-antenna users. The benefit is that $N$ streams can be multiplexed per user, at the price of increasing the channel estimation overhead linearly with $N$. Uplink and downlink spectral efficiency (SE) expressions are derived for any $N$, and these are achievable using estimated channels and per-user-basis MMSE-SIC detectors. Large-system approximations of the SEs are obtained. This analysis shows that MMSE-SIC has similar asymptotic SE as linear MMSE detectors, indicating that the SE increase from having multi-antenna users can be harvested using linear detectors. We generalize the power scaling laws for massive MIMO to handle arbitrary $N$, and show that one can reduce the multiplication of the pilot power and payload power as $\frac{1}{M}$ where $M$ is the number of BS antennas, and still notably increase the SE with $M$ before reaching a non-zero asymptotic limit. Simulations testify our analysis and show that the SE increases with $N$. We also note that the same improvement can be achieved by serving $N$ times more single-antenna users instead, thus the additional user antennas are particular beneficial for SE enhancement when there are few active users in the system.

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preprint2016arXiv

New Lessons from the HI Size-Mass Relation of Galaxies

We revisit the HI size-mass (D$_{\rm HI}$-M$_{\rm HI}$) relation of galaxies with a sample of more than 500 nearby galaxies covering over five orders of magnitude in HI mass and more than ten $B$-band magnitudes. The relation is remarkably tight with a scatter $σ\sim$0.06 dex, or 14%. The scatter does not change as a function of galaxy luminosity, HI richness or morphological type. The relation is linked to the fact that dwarf and spiral galaxies have a homogenous radial profile of HI surface density in the outer regions when the radius is normalised by D$_{\rm HI}$. The early-type disk galaxies typically have shallower HI radial profiles, indicating a different gas accretion history. We argue that the process of atomic-to-molecular gas conversion or star formation cannot explain the tightness of the D$_{\rm HI}$-M$_{\rm HI}$ relation. This simple relation puts strong constraints on simulation models for galaxy formation.

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preprint2016arXiv

NUV Star Catalogue from the Lunar-based Ultraviolet Telescope Survey. First Release

We present a star catalogue extracted from the Lunar-based Ultraviolet Telescope (LUT) survey program. LUT's observable sky area is a circular belt around the Moon's north pole, and the survey program covers a preferred area for about 2400 deg$^2$ which includes a region of the Galactic plane. The data is processed with an automatic pipeline which copes with stray light contamination, artificial sources, cosmic rays, flat field calibration, photometry and so on. In the first release version, the catalogue provides high confidence sources which have been cross-identified with Tycho-2 catalogue. All the sources have signal-to-noise ratio larger than 5, and the corresponding magnitude limit is typically 14.4 mag, which can be deeper as ~16 mag if the stray light contamination is in the lowest level. A total number of 86,467 stars are recorded in the catalogue. The full catalogue in electronic form is available on line.

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preprint2016arXiv

Online Feature Selection with Group Structure Analysis

Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of feature stream. For instance, in image analysis, features are generated in groups which represent color, texture and other visual information. Simply breaking the group structure in feature selection may degrade performance. Motivated by this fact, we formulate the problem as an online group feature selection. The problem assumes that features are generated individually but there are group structure in the feature stream. To the best of our knowledge, this is the first time that the correlation among feature stream has been considered in the online feature selection process. To solve this problem, we develop a novel online group feature selection method named OGFS. Our proposed approach consists of two stages: online intra-group selection and online inter-group selection. In the intra-group selection, we design a criterion based on spectral analysis to select discriminative features in each group. In the inter-group selection, we utilize a linear regression model to select an optimal subset. This two-stage procedure continues until there are no more features arriving or some predefined stopping conditions are met. %Our method has been applied Finally, we apply our method to multiple tasks including image classification %, face verification and face verification. Extensive empirical studies performed on real-world and benchmark data sets demonstrate that our method outperforms other state-of-the-art online feature selection %method methods.

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preprint2016arXiv

Rectifiability of a class of invariant measures with one non-vanishing Lyapunov exponent

We study order-preserving C^1-circle diffeomorphisms driven by irrational rotations with a Diophantine rotation number. We show that there is a non-empty open set of one-parameter families of such diffeomorphisms where the ergodic measures of nearly all family members are one-rectifiable, that is, absolutely continuous with respect to the restriction of the one-dimensional Hausdorff measure to a countable union of Lipschitz graphs.

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preprint2016arXiv

Spatially-resolved NUV-r color of local star-forming galaxies and clues for quenching

Using a sample of ~6,000 local face-on star-forming galaxies (SFGs), we examine the correlations between the NUV-r colors both inside and outside the half-light radius, stellar mass M* and Sérsic index n in order to understand how the quenching of star formation is linked to galaxy structure. For these less dust-attenuated galaxies, NUV-r is found to be linearly correlated with Dn4000, supporting that NUV-r is a good photometric indicator of stellar age (or specific star formation rate). We find that: (1) At M*<10^{10.2}M_{\sun}, the central NUV-r is on average only~ 0.25 mag redder than the outer NUV-r. The intrinsic value would be even smaller after accounting for dust correction. However, the central NUV-r becomes systematically much redder than the outer NUV-r for more massive galaxies at M*>10^{10.2}M_{\sun}. (2) The central NUV-r shows no dependence on Sérsic index n at M*<10^{10.2}M_{\sun}, while above this mass galaxies with a higher n tend to be redder in the central NUV-r color. These results suggest that galaxies with M*<10^{10.2}M_{\sun} exhibit similar star formation activity from the inner R<R_{50} region to the R>R_{50} region. In contrast, a considerable fraction of the M*>10^{10.2}M_{\sun} galaxies, especially those with a high n, have harbored a relatively inactive bulge component.

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preprint2016arXiv

Stochastic areas, Winding numbers and Hopf fibrations

We define and study stochastic areas processes associated with Brownian motions on the complex symmetric spaces $\mathbb{CP}^n$ and $\mathbb{CH}^n$. The characteristic functions of those processes are computed and limit theorems are obtained. In the case $n=1$, we also study windings of the Brownian motion on those spaces and compute the limit distributions. For $\mathbb{CP}^n$ the geometry of the Hopf fibration plays a central role, whereas for $\mathbb{CH}^n$ it is the anti-de Sitter fibration.

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preprint2016arXiv

The subelliptic heat kernel on the anti-de Sitter spaces

We study the subelliptic heat kernel of the sub-Laplacian on a 2n+1-dimensional anti-de Sitter space H2n+1 which also appears as a model space of a CR Sasakian manifold with constant negative sectional curvature. In particular we obtain an explicit and geometrically meaningful formula for the subelliptic heat kernel. The key idea is to work in a set of coordinates that reflects the symmetry coming from the Hopf fibration S1->H2n+1. A direct application is obtaining small time asymptotics of the subelliptic heat kernel. Also we derive an explicit formula for the sub-Riemannian distance on H2n+1

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preprint2016arXiv

Visual Processing by a Unified Schatten-$p$ Norm and $\ell_q$ Norm Regularized Principal Component Pursuit

In this paper, we propose a non-convex formulation to recover the authentic structure from the corrupted real data. Typically, the specific structure is assumed to be low rank, which holds for a wide range of data, such as images and videos. Meanwhile, the corruption is assumed to be sparse. In the literature, such a problem is known as Robust Principal Component Analysis (RPCA), which usually recovers the low rank structure by approximating the rank function with a nuclear norm and penalizing the error by an $\ell_1$-norm. Although RPCA is a convex formulation and can be solved effectively, the introduced norms are not tight approximations, which may cause the solution to deviate from the authentic one. Therefore, we consider here a non-convex relaxation, consisting of a Schatten-$p$ norm and an $\ell_q$-norm that promote low rank and sparsity respectively. We derive a proximal iteratively reweighted algorithm (PIRA) to solve the problem. Our algorithm is based on an alternating direction method of multipliers, where in each iteration we linearize the underlying objective function that allows us to have a closed form solution. We demonstrate that solutions produced by the linearized approximation always converge and have a tighter approximation than the convex counterpart. Experimental results on benchmarks show encouraging results of our approach.

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preprint2016arXiv

Walk and Learn: Facial Attribute Representation Learning from Egocentric Video and Contextual Data

The way people look in terms of facial attributes (ethnicity, hair color, facial hair, etc.) and the clothes or accessories they wear (sunglasses, hat, hoodies, etc.) is highly dependent on geo-location and weather condition, respectively. This work explores, for the first time, the use of this contextual information, as people with wearable cameras walk across different neighborhoods of a city, in order to learn a rich feature representation for facial attribute classification, without the costly manual annotation required by previous methods. By tracking the faces of casual walkers on more than 40 hours of egocentric video, we are able to cover tens of thousands of different identities and automatically extract nearly 5 million pairs of images connected by or from different face tracks, along with their weather and location context, under pose and lighting variations. These image pairs are then fed into a deep network that preserves similarity of images connected by the same track, in order to capture identity-related attribute features, and optimizes for location and weather prediction to capture additional facial attribute features. Finally, the network is fine-tuned with manually annotated samples. We perform an extensive experimental analysis on wearable data and two standard benchmark datasets based on web images (LFWA and CelebA). Our method outperforms by a large margin a network trained from scratch. Moreover, even without using manually annotated identity labels for pre-training as in previous methods, our approach achieves results that are better than the state of the art.

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preprint2015arXiv

A Multi-cell MMSE Detector for Massive MIMO Systems and New Large System Analysis

In this paper, a new multi-cell MMSE detector is proposed for massive MIMO systems. Let $K$ and $B$ denote the number of users in each cell and the number of available pilot sequences in the network, respectively, with $B = βK$, where $β\ge 1 $ is called the pilot reuse factor. The novelty of the multi-cell MMSE detector is that it utilizes all $B$ channel directions that can be estimated locally at a base station, so that intra-cell interference, parts of the inter-cell interference and the noise can all be actively suppressed, while conventional detectors only use the $K$ intra-cell channels. Furthermore, in the large-system limit, a deterministic equivalent expression of the uplink SINR for the proposed multi-cell MMSE is derived. The expression is easy to compute and accounts for power control for the pilot and payload, imperfect channel estimation and arbitrary pilot allocation. Numerical results show that significant sum spectral efficiency gains can be obtained by the multi-cell MMSE over the conventional single-cell MMSE and the recent multi-cell ZF, and the gains become more significant as $β$ and/or $K$ increases. Furthermore, the deterministic equivalent is shown to be very accurate even for relatively small system dimensions.

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preprint2015arXiv

A Multi-cell MMSE Precoder for Massive MIMO Systems and New Large System Analysis

In this paper, a new multi-cell MMSE precoder is proposed for massive MIMO systems. We consider a multi-cell network where each cell has $K$ users and $B$ orthogonal pilot sequences are available, with $B = βK$ and $β\ge 1$ being the pilot reuse factor over the network. In comparison with conventional single-cell precoding which only uses the $K$ intra-cell channel estimates, the proposed multi-cell MMSE precoder utilizes all $B$ channel directions that can be estimated locally at a base station, so that the transmission is designed spatially to suppress both parts of the inter-cell and intra-cell interference. To evaluate the performance, a large-scale approximation of the downlink SINR for the proposed multi-cell MMSE precoder is derived and the approximation is tight in the large-system limit. Power control for the pilot and payload, imperfect channel estimation and arbitrary pilot allocation are accounted for in our precoder. Numerical results show that the proposed multi-cell MMSE precoder achieves a significant sum spectral efficiency gain over the classical single-cell MMSE precoder and the gain increases as $K$ or $β$ grows. Compared with the recent M-ZF precoder, whose performance degrades drastically for a large $K$, our M-MMSE can always guarantee a high and stable performance. Moreover, the large-scale approximation is easy to compute and shown to be accurate even for small system dimensions.

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preprint2015arXiv

Abundance of mode-locking for quasiperiodically forced circle maps

We study the phenomenon of mode-locking in the context of quasiperiodically forced non-linear circle maps. As a main result, we show that under certain C1-open condition on the geometry of twist parameter families of such systems, the closure of the union of modelocking plateaus has positive measure. In particular, this implies the existence of infinitely many mode-locking plateaus (open Arnold tongues). The proof builds on multiscale analysis and parameter exclusion methods in the spirit of Benedicks and Carleson, which were previously developed for quasiperiodic SL(2,R)-cocycles by Young and Bjerklöv. The methods apply to a variety of examples, including a forced version of the classical Arnold circle map.

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preprint2015arXiv

An HI View of Galaxy Conformity: HI-rich Environment around HI-excess Galaxies

Using data taken as part of the Bluedisk project we study the connection between neutral hydrogen (HI) in the environment of spiral galaxies and that in the galaxies themselves. We measure the total HI mass present in the environment in a statistical way by studying the distribution of noise peaks in the HI data cubes obtained for 40 galaxies observed with WSRT. We find that galaxies whose HI mass fraction is high relative to standard scaling relations have an excess HI mass in the surrounding environment as well. Gas in the environment consists of gas clumps which are individually below the detection limit of our HI data. These clumps may be hosted by small satellite galaxies and\or be the high-density peaks of a more diffuse gas distribution in the inter-galactic medium. We interpret this result as an indication for a picture in which the HI-rich central galaxies accrete gas from an extended gas reservoir present in their environment.

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preprint2015arXiv

Blasting away a dwarf galaxy: The "tail" of ESO 324-G024

We present Australia Telescope Compact Array radio data of the dwarf irregular galaxy ESO 324-G024 which is seen in projection against the giant, northern lobe of the radio galaxy Centaurus A (Cen A, NGC 5128). The distorted morphology and kinematics of ESO 324-G024, as observed in the 21 cm spectral line emission of neutral hydrogen, indicate disruptions by external forces. We investigate whether tidal interactions and/or ram pressure stripping are responsible for the formation of the HI tail stretching to the northeast of ESO 324-G024 with the latter being most probable. Furthermore, we closely analyze the sub-structure of Cen A's polarized radio lobes to ascertain whether ESO 324-G024 is located in front, within or behind the northern lobe. Our multi-wavelength, multi-component approach allows us to determine that ESO 324-G024 is most likely behind the northern radio lobe of Cen A. This result helps to constrain the orientation of the lobe, which is likely inclined to our line of sight by approximately 60 degrees if NGC 5128 and ESO 324-G024 are at the same distance.

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preprint2015arXiv

Botnet Detection using Social Graph Analysis

Signature-based botnet detection methods identify botnets by recognizing Command and Control (C\&C) traffic and can be ineffective for botnets that use new and sophisticate mechanisms for such communications. To address these limitations, we propose a novel botnet detection method that analyzes the social relationships among nodes. The method consists of two stages: (i) anomaly detection in an "interaction" graph among nodes using large deviations results on the degree distribution, and (ii) community detection in a social "correlation" graph whose edges connect nodes with highly correlated communications. The latter stage uses a refined modularity measure and formulates the problem as a non-convex optimization problem for which appropriate relaxation strategies are developed. We apply our method to real-world botnet traffic and compare its performance with other community detection methods. The results show that our approach works effectively and the refined modularity measure improves the detection accuracy.

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preprint2015arXiv

Chiral topological superconductor and half-integer conductance plateau from quantum anomalous Hall plateau transition

We propose to realize a two-dimensional chiral topological superconducting (TSC) state from the quantum anomalous Hall plateau transition in a magnetic topological insulator thin film through the proximity effect to a conventional $s$-wave superconductor. This state has a full pairing gap in the bulk and a single chiral Majorana mode at the edge. The optimal condition for realizing such chiral TSC is to have inequivalent superconducting pairing amplitudes on top and bottom surfaces of the doped magnetic topological insulator. We further propose several transport experiments to detect the chiral TSC. One unique signature is that the conductance will be quantized into a half-integer plateau at the coercive field in this hybrid system. In particular, with the point contact formed by a superconducting junction, the conductance oscillates between $e^2/2h$ and $e^2/h$ with the frequency determined by the voltage across the junction. We close by discussing the feasibility of these experimental proposals.

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preprint2015arXiv

Data Processing Pipeline for Pointing Observations of Lunar-based Ultraviolet Telescope

We describe the data processing pipeline developed to reduce the pointing observation data of Lunar-based Ultraviolet Telescope (LUT), which belongs to the Chang'e-3 mission of the Chinese Lunar Exploration Program. The pointing observation program of LUT is dedicated to monitor variable objects in a near-ultraviolet (245-345 nm) band. LUT works in lunar daytime for sufficient power supply, so some special data processing strategies have been developed for the pipeline. The procedures of the pipeline include stray light removing, astrometry, flat fielding employing superflat technique, source extraction and cosmic rays rejection, aperture and PSF photometry, aperture correction, and catalogues archiving, etc. It has been intensively tested and works smoothly with observation data. The photometric accuracy is typically ~0.02 mag for LUT 10 mag stars (30 s exposure), with errors come from background noises, residuals of stray light removing, and flat fielding related errors. The accuracy degrades to be ~0.2 mag for stars of 13.5 mag which is the 5σ detection limit of LUT.

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preprint2015arXiv

Distributed Multilevel Diversity Coding

In distributed multilevel diversity coding, $K$ correlated sources (each with $K$ components) are encoded in a distributed manner such that, given the outputs from any $α$ encoders, the decoder can reconstruct the first $α$ components of each of the corresponding $α$ sources. For this problem, the optimality of a multilayer Slepian-Wolf coding scheme based on binning and superposition is established when $K\leq 3$. The same conclusion is shown to hold for general $K$ under a certain symmetry condition, which generalizes a celebrated result by Yeung and Zhang.

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preprint2015arXiv

Does the 62-day X-ray periodicity come from ULX M82 X-1?

M82 X-1 is the brightest ultraluminous X-ray source in starburst galaxy M82 and is one of the best intermediate mass black hole candidates. Previous studies based on the Rossi X-ray Timing Explorer/Proportional Counter Array (RXTE/PCA) reported a regular X-ray flux modulation of M82 with a period of 62 days, and attributed this periodic modulation to M82 X-1. However, this modulation is not necessarily from M82 X-1 because RXTE/PCA has a very poor spatial resolution of ~1 degree. In this work, we analyzed 1000 days of monitoring data of M82 X-1 from the Swift/X-ray telescope (XRT), which has a much better spatial resolution than RXTE/PCA. The periodicity distribution map of M82 reveals that the 62-day periodicity is most likely not from M82 X-1, but from the summed contributions of several periodic X-ray sources 4 arcsec southeast of M82 X-1. However, Swift/XRT is not able to resolve those periodic sources and locate the precise origin of the periodicity of M82. Thus, more long-term observations with higher spatial resolution are required.

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preprint2015arXiv

Dynamical Axion Field in a Magnetic Topological Insulator Superlattice

We propose that the dynamical axion field can be realized in a magnetic topological insulator superlattice or a topological paramagnetic insulator. The magnetic fluctuations of these systems produce a pseudoscalar field which has an axionic coupling to the electromagnetic field, and thus it gives a condensed-matter realization of the axion electrodynamics. Compared to the previously proposed dynamical axion materials where a long range antiferromagnetic order is required, the systems proposed here have the advantage that only a uniform magnetization or a paramagnetic state is needed for the dynamic axion. We further propose several experiments to detect such a dynamical axion field.

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preprint2015arXiv

Electrically Tunable Magnetism in Magnetic Topological Insulators

The external controllability of the magnetic properties in topological insulators would be important both for fundamental and practical interests. Here we predict the electric-field control of ferromagnetism in a thin film of insulating magnetic topological insulators. The decrease of band inversion by the application of electric fields results in a reduction of magnetic susceptibility, and hence in the modification of magnetism. Remarkably, the electric field could even induce the magnetic quantum phase transition from ferromagnetism to paramagnetism. We further propose a topological transistor device in which the dissipationless charge transport of chiral edge states is controlled by an electric field. In particular, the field-controlled ferromagnetism in magnetic topological insulator can be used for voltage based writing of magnetic random access memories in magnetic tunnel junctions. The simultaneous electrical control of magnetic order and chiral edge transport in such devices may lead to electronic and spintronic applications for topological insulators.

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preprint2015arXiv

Electronic transport through graphene nanoribbons with Stone-Wales reconstruction at edges and interfaces

In this paper, we study the conductance of the graphene nanoribbons(GNRs) in the presence of the Stone-Wales(S-W) reconstruction, using the transfer matrix method. The ribbon is connected with semi-infinite quantum wires as the leads. The S-W reconstruction occurs on the edges and the interfaces between the electrodes and ribbon. When the reconstruction occurs on the edges, the conductance is suppressed considerably if the gate voltage $V_g$ takes intermediate values around $|V_{g}|\sim t_0$($t_0$ being the hopping amplitude of grahene) in both positive and negative energy regions. In contrast, if $V_g$ is close to the Dirac point or the band edges, the conductance is relatively insensitive to the edge reconstruction. The effect of edge reconstruction become less important with increasing ribbon width as expected. The S-W reconstruction occurs also possibly at the interfaces. In this case, the reconstruction suppresses identically the conductance in the entire range of $V_g$ for armchair GNRs. For the zigzag GNRs, the conductance is strongly suppressed in the negative energy region however the change of the conductance is relatively small in the positive energy region. We also analyze the transmission coefficients as functions of the channel index(the transverse momentum $k_y$ of the leads) for the neutral armchair GNRs with interface defects. Interestingly, there are two transmission peaks appearing at $k_{y}=π/3$ and $k_{y}=2π/3$ due to the unit cell doubling.

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preprint2015arXiv

From outside-in to inside-out: galaxy assembly mode depends on stellar mass

In this Letter, we investigate how galaxy mass assembly mode depends on stellar mass $M_{\ast}$, using a large sample of $\sim$10, 000 low redshift galaxies. Our galaxy sample is selected to have SDSS $R_{90}>5\arcsec.0$, which allows the measures of both the integrated and the central NUV$-r$ color indices. We find that: in the $M_{\ast}-($ NUV$-r$) green valley, the $M_{\ast}<10^{10}~M_{\sun}$ galaxies mostly have positive or flat color gradients, while most of the $M_{\ast}>10^{10.5}~M_{\sun}$ galaxies have negative color gradients. When their central $D_{n}4000$ index values exceed 1.6, the $M_{\ast}<10^{10.0}~M_{\sun}$ galaxies have moved to the UV red sequence, whereas a large fraction of the $M_{\ast}>10^{10.5}~M_{\sun}$ galaxies still lie on the UV blue cloud or the green valley region. We conclude that the main galaxy assembly mode is transiting from "the outside-in" mode to "the inside-out" mode at $M_{\ast}< 10^{10}~M_{\sun}$ and at $M_{\ast}> 10^{10.5}~M_{\sun}$. We argue that the physical origin of this is the compromise between the internal and the external process that driving the star formation quenching in galaxies. These results can be checked with the upcoming large data produced by the on-going IFS survey projects, such as CALIFA, MaNGA and SAMI in the near future.

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preprint2015arXiv

Gas-phase metallicity profiles of the Bluedisk galaxies: Is metallicity in a local star-formation regulated equilibrium?

As part of the Bluedisk survey we analyse the radial gas-phase metallicity profiles of 50 late-type galaxies We compare the metallicity profiles of a sample of HI-rich galaxies against a control sample of HI-'normal' galaxies. We find the metallicity gradient of a galaxy to be strongly correlated with its HI mass fraction (M(HI) / Mstar). We note that some galaxies exhibit a steeper metallicity profile in the outer disc than in the inner disc. These galaxies are found in both the HI-rich and control samples. This contradicts a previous indication that these outer drops are exclusive to HI-rich galaxies. These effects are not driven by bars, although we do find some indication that barred galaxies have flatter metallicity profiles. By applying a simple analytical model we are able to account for the variety of metallicity profiles that the two samples present. The success of this model implies that the metallicity in these isolated galaxies may be in a local equilibrium, regulated by star formation. This insight could provide an explanation of the observed local mass-metallicity relation.

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preprint2015arXiv

HI scaling relations of galaxies in the environment of HI-rich and control galaxies observed by the Bluedisk project

Our work is based on the "Bluedisk" project, a program to map the neutral gas in a sample of 25 HI-rich spirals and a similar number of control galaxies with the Westerbork Synthesis Radio Telescope (WSRT). In this paper we focus on the HI properties of the galaxies in the environment of our targeted galaxies. In total, we extract 65 galaxies from the WSRT cubes with stellar masses between $10^8M_{\odot}$ and $10^{11}M_{\odot}$. Most of these galaxies are located on the same HI mass-size relation and "HI-plane" as normal spiral galaxies. We find that companions around HI-rich galaxies tend to be HI-rich as well and to have larger R90,HI/R50,HI. This suggests a scenario of "HI conformity", similar to the colour conformity found by Weinmann et al. (2006): galaxies tend to adopt the HI properties of their neighbours. We visually inspect the outliers from the HI mass-size relation and galaxies which are offset from the HI plane and find that they show morphological and kinematical signatures of recent interactions with their environment. We speculate that these outliers have been disturbed by tidal or ram-pressure stripping processes, or in a few cases, by accretion events.

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preprint2015arXiv

Impact of ordering competition on the global phase diagram of iron pnictides

We consider the impact of the competition among superconductivity, spin density wave, and nematic order in iron pnictides, and show that the ordering competition substantially reshapes the global phase diagram. We perform a detailed renormalization group analysis of an effective field theory of iron pnictides and derive the flow equations of all the physical parameters. Using these results, we find that superconductivity can be strongly suppressed by the ordering competition, and also extract the $T$-dependence of superfluid density. Moreover, the phase transitions may become first order. Interestingly, our RG analysis reveal that the nematic order exists only in an intermediate temperature region $T_{m}< T < T_{n}$, but is destroyed at $T > T_{n}$ by thermal fluctuation and at $T < T_{m}$ by ordering competition. This anomalous existence of nematic order leads to a back-bending of the nematic transition line on the phase diagram, consistent with the observed reentrance of tetragonal structure at low temperatures. A modified phase diagram is obtained based on the RG results.

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preprint2015arXiv

Large-angle quasi-self-collimation effect in a rod-type photonic crystal

A rod-type photonic crystal (PC) with a rectangular lattice shows a large-angle quasi-self-collimation (quasi-SC) effect by changing the symmetry of its rectangular lattice to straighten one of the isofrequency contours. To investigate the straightness of the isofrequency contour as well as the quasi-SC effect, we propose a straightness factor L based on the method of least squares. With L smaller than L0 (L0 = 0.01 is the critical value), the isofrequency contour is sufficiently straight to induce quasi-SC effect with the beam quasi-collimating in the structure. Furthermore, the efficiency of light coupling to the quasi-SC PC is studied, and can be greatly improved by applying a carefully designed antireflection layer.

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preprint2015arXiv

Massive MIMO with Multi-cell MMSE Processing: Exploiting All Pilots for Interference Suppression

In this paper, a new state-of-the-art multi-cell MMSE scheme is proposed for massive MIMO networks, which includes an uplink MMSE detector and a downlink MMSE precoder. The main novelty is that it exploits all available pilots for interference suppression. Specifically, let $K$ and $B$ denote the number of users per cell and the number of orthogonal pilot sequences in the network, respectively, where $β= B/K$ is the pilot reuse factor. Then our multi-cell MMSE scheme utilizes all $B$ channel directions, that can be estimated locally at each base station, to actively suppress both intra-cell and inter-cell interference. The proposed scheme is particularly practical and general, since power control for the pilot and payload, imperfect channel estimation and arbitrary pilot allocation are all accounted for. Simulations show that significant spectral efficiency (SE) gains are obtained over the single-cell MMSE scheme and the multi-cell ZF, particularly for large $β$ and/or $K$. Furthermore, large-scale approximations of the uplink and downlink SINRs are derived, which are asymptotically tight in the large-system limit. The approximations are easy to compute and very accurate even for small system dimensions. Using these SINR approximations, a low-complexity power control algorithm is also proposed to maximize the sum SE.

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preprint2015arXiv

Mechanical Manipulations on Electronic Transport of Graphene Nanoribbons

We study the effects of uniaxial strains on the transport properties of the graphene nanoribbons(GNRs) connected with two metallic leads in heterojunctions, using the transfer matrix method. Two typical GNRs with zigzag and armchair boundaries are considered, and the tension is applied either parallel or perpendicular to the ribbon axis. It turns out that the electron-hole symmetry is missing in the gate voltage dependence of the conductance data of the armchair GNRs, while it persists in the zigzag ribbons under any strains. For an armchair GNR with a vertical tension applied, a sharp drop of conductance is found near the critical value of the strain inducing a quantum phase transition, which allows to determine the critical strain accurately via measuring the conductance. In the zigzag ribbon, there exists a range of gate voltage around zero, where the conductance is insensitive to the small horizontal strains. The band structures and low-energy properties are calculated to elucidate the mechanism on the strain effects in GNRs. We expect that our results can be useful in developing graphene-based strain sensors.

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preprint2015arXiv

Metal-to-Insulator Switching in Quantum Anomalous Hall States

After decades of searching for the dissipationless transport in the absence of any external magnetic field, quantum anomalous Hall effect (QAHE) was recently achieved in magnetic topological insulator (TI) films. However, the universal phase diagram of QAHE and its relation with quantum Hall effect (QHE) remain to be investigated. Here, we report the experimental observation of the giant longitudinal resistance peak and zero Hall conductance plateau at the coercive field in the 6 quintuple-layer (Cr0.12Bi0.26Sb0.62)2Te3 film, and demonstrate the metal-to-insulator switching between two opposite QAHE plateau states up to 0.3 K. Moreover, the universal QAHE phase diagram is realized through the angle-dependent measurements. Our results address that the quantum phase transitions in both QAHE and QHE regimes are in the same universality class, yet the microscopic details are different. In addition, the realization of the QAHE insulating state unveils new ways to explore quantum phase-related physics and applications.

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preprint2015arXiv

New Volleyballenes: Y20C60, La20C60, and Lu20C60

New stable Volleyballenes Y20C60, La20C60, and Lu20C60 molecular clusters have been proposed using first-principles density functional theory studies. In conjunction with recent findings for the scandium system, these findings establish Volleyballene M20C60 molecules as a stable general class of fullerene family. All M20C60 (M=Y, La, and Lu) molecules have Th point group symmetries and relatively large HOMO-LUMO gaps.

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preprint2015arXiv

Observation of Optical Solitons and Abnormal Modulation Instability in Liquid Crystals with Negative Dielectric Anisotropy

We investigate theoretically and experimentally the optical beam propagation in the nematic liquid crystal with negative dielectric anisotropy, which is aligned homeotropically in a $80μm$-thickness planar cell in the presence of an externally voltage. It is predicted that the nonlocal nonlinearity of liquid crystal undergo an oscillatory response function with a negative nonlinear refractive index coefficient. We found that the oscillatory nonlocal nonlinearity can support stable bright solitons, which are observed in experiment. We also found that abnormal modulation instability occurs with infinity gain coefficient at a fixed spatial frequency, which is no depend on the beam intensity. We observed the modulation instability in the liquid crystal at a very low intensity ($0.26W/cm^2$), and the maximum gain frequency were found kept unchange when beam power changes over 2-3 orders of magnitude.

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preprint2015arXiv

Observation of the zero Hall plateau in a quantum anomalous Hall insulator

Quantum anomalous Hall (QAH) effect in magnetic topological insulator (TI) is a novel transport phenomenon in which the Hall resistance reaches the quantum plateau in the absence of external magnetic field. Recently, this exotic effect has been discovered experimentally in an ultrathin film of the Bi2Te3 family TI with spontaneous ferromagnetic (FM) order. An important question concerning the QAH state is whether it is simply a zero-magnetic-field version of the quantum Hall (QH) effect, or if there is new physics beyond the conventional paradigm. Here we report experimental investigations on the quantum phase transition between the two opposite Hall plateaus of a QAH insulator caused by magnetization reversal. We observe a well-defined plateau with zero Hall conductivity over a range of magnetic field around coercivity, consistent with a recent theoretical prediction. The features of the zero Hall plateau are shown to be closely related to that of the QAH effect, but its temperature evolution exhibits quantitative differences from the network model for conventional QH plateau transition. We propose that the chiral edge states residing at the magnetic domain boundaries, which are unique to a QAH insulator, are responsible for the zero Hall plateau. The rich magnetic domain dynamics makes the QAH effect a distinctive class of quantum phenomenon that may find novel applications in spintronics.

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preprint2015arXiv

Optical light curve of GRB 121011A: a textbook for the onset of GRB afterglow in a mixture of ISM and wind-type medium

We reported the optical observations of GRB 121011A by 0.8-m TNT telescope at Xinglong observatory, China. The light curve of optical afterglow shows a smooth and featureless bump during the epoch of $\sim$130 sec and $\sim$5000 sec with a rising index of $1.57\pm0.28$ before the break time of $539\pm44$ sec, and a decaying index of about $1.29\pm0.07$ up to the end of our observations. Meanwhile, the X-ray light curve decays in a single power-law with a slop of about $1.51\pm0.03$ observed by $XRT$ onboard ${\rm} Swift$ from 100 sec to about 10000 sec after the burst trigger. The featureless optical light curve could be understood as an onset process under the external-shock model. The typical frequency has been below or near the optical one before the deceleration time, and the cooling frequency is located between the optical and X-ray wavelengths. The external medium density has a transition from a mixed stage of ISM and wind-type medium before the peak time to the ISM at the later phase. The joint-analysis of X-ray and optical light curves shows that the emission from both frequencies are consistent with the prediction of the standard afterglow model without any energy injections, indicating that the central engine has stopped its activity and does not restart anymore after the prompt phase.

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preprint2015arXiv

Quantized topological magnetoelectric effect of the zero-plateau quantum anomalous Hall state

Topological magnetoelectric effect in a three-dimensional topological insulator is a novel phenomenon, where an electric field induces a magnetic field in the same direction, with a universal coefficient of proportionality quantized in units of $e^2/2h$. Here we propose that the topological magnetoelectric effect can be realized in the zero-plateau quantum anomalous Hall state of magnetic topological insulators or ferromagnet-topological insulator heterostructure. The finite-size effect is also studied numerically, where the magnetoelectric coefficient is shown to converge to a quantized value when the thickness of topological insulator film increases. We further propose a device setup to eliminate the non-topological contributions from the side surface.

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preprint2015arXiv

Quantum anomalous Hall effect in magnetic topological insulators

The search for topologically non-trivial states of matter has become an important goal for condensed matter physics. Here, we give a theoretical introduction to the quantum anomalous Hall (QAH) effect based on magnetic topological insulators in two-dimension (2D) and three-dimension (3D). In 2D topological insulators, magnetic order breaks the symmetry between the counter-propagating helical edge states, and as a result, the quantum spin Hall effect can evolve into the QAH effect. In 3D, magnetic order opens up a gap for the topological surface states, and chiral edge state has been predicted to exist on the magnetic domain walls. We present the phase diagram in thin films of a magnetic topological insulator and review the basic mechanism of ferromagnetic order in magnetically doped topological insulators. We also review the recent experimental observation of the QAH effect. We discuss more recent theoretical work on the coexistence of the helical and chiral edge states, multi-channel chiral edge states, the theory of the plateau transition, and the thickness dependence in the QAH effect.

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preprint2015arXiv

Quantum noise effects with Kerr nonlinearity enhancement in coupled gain-loss waveguides

It is generally difficult to study the dynamical properties of a quantum system with both inherent quantum noises and non-perturbative nonlinearity. Due to the possibly drastic intensity increase of an input coherent light in the gain-loss waveguide couplers with parity-time (PT) symmetry, the Kerr effect from a nonlinearity added into the systems can be greatly enhanced, and is expected to create the macroscopic entangled states of the output light fields with huge photon numbers. Meanwhile, the quantum noises also coexist with the amplification and dissipation of the light fields. Under the interplay between the quantum noises and nonlinearity, the quantum dynamical behaviors of the systems become rather complicated. However, the important quantum noise effects have been mostly neglected in the previous studies about nonlinear PT-symmetric systems. Here we present a solution to this non-perturbative quantum nonlinear problem, showing the real-time evolution of the system observables. The enhanced Kerr nonlinearity is found to give rise to a previously unknown decoherence effect that is irrelevant to the quantum noises, and imposes a limit on the emergence of macroscopic nonclassicality. In contrast to what happen in the linear systems, the quantum noises exert significant impact on the system dynamics, and can create the nonclassical light field states in conjunction with the enhanced Kerr nonlinearity. This first study on the noise involved quantum nonlinear dynamics of the coupled gain-loss waveguides can help to better understand the quantum noise effects in the broad nonlinear systems.

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preprint2015arXiv

Resonant magneto-optic Kerr effect in the magnetic topological insulator Cr:(Sb$_x$,Bi$_{1-x}$)$_2$Te$_3$

We report measurements of the polar Kerr effect, proportional to the out-of-plane component of the magnetization, in thin films of the magnetically doped topological insulator $(\text{Cr}_{0.12}\text{Bi}_{0.26}\text{Sb}_{0.62})_2\text{Te}_3$. Measurements of the complex Kerr angle, $Θ_K$, were performed as a function of photon energy in the range $0.8\text{ eV}<\hbarω<3.0\text{ eV}$. We observed a peak in the real part of $Θ_K(ω)$ and zero crossing in the imaginary part that we attribute to resonant interaction with a spin-orbit avoided crossing located $\approx$ 1.6 eV above the Fermi energy. The resonant enhancement allows measurement of the temperature and magnetic field dependence of $Θ_K$ in the ultrathin film limit, $d\geq2$ quintuple layers. We find a sharp transition to zero remanent magnetization at 6 K for $d<8$~QL, consistent with theories of the dependence of impurity spin interactions on film thickness and their location relative to topological insulator surfaces.

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preprint2015arXiv

Robust Anomaly Detection in Dynamic Networks

We propose two robust methods for anomaly detection in dynamic networks in which the properties of normal traffic are time-varying. We formulate the robust anomaly detection problem as a binary composite hypothesis testing problem and propose two methods: a model-free and a model-based one, leveraging techniques from the theory of large deviations. Both methods require a family of Probability Laws (PLs) that represent normal properties of traffic. We devise a two-step procedure to estimate this family of PLs. We compare the performance of our robust methods and their vanilla counterparts, which assume that normal traffic is stationary, on a network with a diurnal normal pattern and a common anomaly related to data exfiltration. Simulation results show that our robust methods perform better than their vanilla counterparts in dynamic networks.

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preprint2015arXiv

Sc20C60: A Volleyballene

Since the discovery of the fullerene C60, many very interesting structures have been proposed, such as Ti8C12, Au20, Au32, Au42, TM@Sin, Eu@Si20, B80, and B_{40}^{-/0}. Here, an exceptionally stable hollow cage containing 20 scandiums and 60 carbons was identified. This Sc20C60 molecular cluster has a Th point group symmetry and a volleyball-like shape, that we refer to below as Volleyballene. Electronic structure analysis shows that the formation of delocalized π bonds between Sc atoms and neighboring five-membered carbon rings is crucial for stabilizing the cage structure. A relatively large HOMO-LUMO gap was found. The results of vibrational frequency analysis and molecular dynamics simulations also demonstrate that this Volleyballene molecule is exceptionally stable.

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preprint2015arXiv

Simultaneous electrical-field-effect modulation of both top and bottom Dirac surface states of epitaxial thin films of three-dimensional topological insulators

It is crucial for the studies of the transport properties and quantum effects related to Dirac surface states of three-dimensional topological insulators (3D TIs) to be able to simultaneously tune the chemical potentials of both top and bottom surfaces of a 3D TI thin film. We have realized this in molecular beam epitaxy-grown thin films of 3D TIs, as well as magnetic 3D TIs, by fabricating dual-gate structures on them. The films could be tuned between n-type and p-type by each gate alone. Combined application of two gates can reduce the carrier density of a TI film to a much lower level than with only one of them and enhance the film resistance by 10000 %, implying that Fermi level is tuned very close to the Dirac points of both top and bottom surface states without crossing any bulk band. The result promises applications of 3D TIs in field effect devices.

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preprint2015arXiv

Social Trust Prediction via Max-norm Constrained 1-bit Matrix Completion

Social trust prediction addresses the significant problem of exploring interactions among users in social networks. Naturally, this problem can be formulated in the matrix completion framework, with each entry indicating the trustness or distrustness. However, there are two challenges for the social trust problem: 1) the observed data are with sign (1-bit) measurements; 2) they are typically sampled non-uniformly. Most of the previous matrix completion methods do not well handle the two issues. Motivated by the recent progress of max-norm, we propose to solve the problem with a 1-bit max-norm constrained formulation. Since max-norm is not easy to optimize, we utilize a reformulation of max-norm which facilitates an efficient projected gradient decent algorithm. We demonstrate the superiority of our formulation on two benchmark datasets.

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preprint2015arXiv

Statistical models and regularization strategies in statistical image reconstruction of low-dose X-ray CT: a survey

Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose X-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method for various clinical tasks. According to the maximum a posterior (MAP) estimation, the SIR methods can be typically formulated by an objective function consisting of two terms: (1) data-fidelity (or equivalently, data-fitting or data-mismatch) term modeling the statistics of projection measurements, and (2) regularization (or equivalently, prior or penalty) term reflecting prior knowledge or expectation on the characteristics of the image to be reconstructed. Existing SIR methods for low-dose CT can be divided into two groups: (1) those that use calibrated transmitted photon counts (before log-transform) with penalized maximum likelihood (pML) criterion, and (2) those that use calibrated line-integrals (after log-transform) with penalized weighted least-squares (PWLS) criterion. Accurate statistical modeling of the projection measurements is a prerequisite for SIR, while the regularization term in the objective function also plays a critical role for successful image reconstruction. This paper reviews several statistical models on CT projection measurements and various regularization strategies incorporating prior knowledge or expected properties of the image to be reconstructed, which together formulate the objective function of the SIR methods for low-dose X-ray CT.

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preprint2015arXiv

The spatially resolved Kennicutt-Schmidt relation in the HI dominated regions of spiral and dwarf irregular galaxies

We study the Kennicutt-Schmidt relation between average star formation rate and average cold gas surface density in the Hi dominated ISM of nearby spiral and dwarf irregular galaxies. We divide the galaxies into grid cells varying from sub-kpc to tens of kpc in size. Grid-cell measurements of low SFRs using H-alpha emission can be biased and scatter may be introduced because of non-uniform sampling of the IMF or because of stochastically varying star formation. In order to alleviate these issues, we use far-ultraviolet emission to trace SFR, and we sum up the fluxes from different bins with the same gas surface density to calculate the average $Σ_{SFR}$ at a given value of $Σ_{gas}$. We study the resulting Kennicutt-Schmidt relation in 400 pc, 1 kpc and 10 kpc scale grids in nearby massive spirals and in 400 pc scale grids in nearby faint dwarf irregulars. We find a relation with a power law slope of 1.5 in the HI-dominated regions for both kinds of galaxies. The relation is offset towards longer gas consumption timescales compared to the molecular hydrogen dominated centres of spirals, but the offset is an order-of-magnitude less than that quoted by earlier studies. Our results lead to the surprising conclusion that conversion of gas to stars is independent of metallicity in the HI dominated regions of star-forming galaxies. Our observed relations are better fit by a model of star formation based on thermal and hydrostatic equilibrium in the ISM, in which feedback driven turbulence sets the thermal pressure.

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preprint2015arXiv

The subelliptic heat kernels of the quaternionic Hopf fibration

The main goal of this work is to study the sub-Laplacian of the unit sphere which is obtained by lifting with respect to the Hopf fibration the Laplacian of the quaternionic projective space. We obtain in particular explicit formulas for its heat kernel and deduce an expression for the Green function of the conformal sub-Laplacian and small-time asymptotics. As a byproduct of our study we also obtain several results related to the sub-Laplacian of a projected Hopf fibration.

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preprint2015arXiv

Transmission eigenchannels and the densities of states of random media

We show in microwave measurements and computer simulations that the contribution of each eigenchannel of the transmission matrix to the density of states (DOS) is the derivative with angular frequency of a composite phase shift. The accuracy of the measurement of the DOS determined from transmission eigenchannels is confirmed by the agreement with the DOS found from the decomposition of the field into modes. The distribution of the DOS, which underlies the Thouless number, is substantially broadened in the Anderson localization transition. We find a crossover from constant to exponential scaling of fluctuations of the DOS normalized by its average value. These results illuminate the relationships between scattering, stored energy and dynamics in complex media.

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preprint2015arXiv

Transverse Weitzenböck formulas and curvature dimension inequalities on Riemannian foliations with totally geodesic leaves

We prove a family of new Weitzenböck formulas on a Riemannian foliation with totally geodesic leaves. These Weitzenböck formulas are naturally parametrized by the canonical variation of the metric. As a consequence, under natural geometric conditions, the horizontal Laplacian satisfies a generalized curvature dimension inequality. Among other things, this curvature dimension inequality implies Li-Yau estimates for positive solutions of the horizontal heat equation and a sub-Riemannian Bonnet-Myers compactness theorem whose assumptions only rely on the intrinsic geometry of the horizontal distribution.

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preprint2015arXiv

Two-Dimensional Scandium Carbide Monolayer and its Nanotubes

A two-dimensional scandium carbide monolayer with a Sc3C10 primitive cell (Sc3C10 sheet) has been identified using first-principles density functional theory. In the Sc3C10 sheet, there is a similar basic structure to the one in the Volleyballene Sc20C60, the Sc8C10 subunit, in which two connected carbon pentagons are surrounded by one scandium octagon. The hybridization between Sc d orbitals and C s-p orbitals is crucial for stabilizing the Sc3C10 sheet. Ab initio molecular dynamics simulations demonstrate that this Sc3C10 sheet is exceptionally stable. In addition, a series of stable ScC nanotubes have been obtained by rolling up this Sc3C10 sheet. All nanotubes studied have been found to be metallic.

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preprint2015arXiv

Two-loop disorder effects on the nematic quantum criticality in $d$-wave superconductors

The gapless nodal fermions exhibit non-Fermi liquid behaviors at the nematic quantum critical point that is supposed to exist in some $d$-wave cuprate superconductors. This non-Fermi liquid state may be turned into a disorder-dominated diffusive metal if the fermions also couple to a disordered potential that generates a relevant perturbation in the sense of renormalization group theory. It is therefore necessary to examine whether a specific disorder is relevant or not. We study the interplay between critical nematic fluctuation and random chemical potential by performing renormalization group analysis. The parameter that characterizes the strength of random chemical potential is marginal at the one-loop level, but becomes marginally relevant after including the two-loop corrections. Thus even weak random chemical potential leads to diffusive motion of nodal fermions and the significantly critical behaviors of physical implications, since the strength flows eventually to large values at low energies.

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preprint2015arXiv

Unexpected edge conduction in HgTe quantum wells under broken time reversal symmetry

The realization of quantum spin Hall (QSH) effect in HgTe quantum wells (QWs) is considered a milestone in the discovery of topological insulators. The QSH edge states are predicted to allow current to flow at the edges of an insulating bulk, as demonstrated in various experiments. A key prediction of QSH theory that remains to be experimentally verified is the breakdown of the edge conduction under broken time reversal symmetry (TRS). Here we first establish a rigorous framework for understanding the magnetic field dependence of electrostatically gated QSH devices. We then report unexpected edge conduction under broken TRS, using a unique cryogenic microwave impedance microscopy (MIM), on a 7.5 nm HgTe QW device with an inverted band structure. At zero magnetic field and low carrier densities, clear edge conduction is observed in the local conductivity profile of this device but not in the 5.5 nm control device whose band structure is trivial. Surprisingly, the edge conduction in the 7.5 nm device persists up to 9 T with little effect from the magnetic field. This indicates physics beyond simple QSH models, possibly associated with material- specific properties, other symmetry protection and/or electron-electron interactions.

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preprint2014arXiv

Active Learning Via Sequential Design and Uncertainty Sampling

Classification is an important task in many fields including biomedical research and machine learning. Traditionally, a classification rule is constructed based a bunch of labeled data. Recently, due to technological innovation and automatic data collection schemes, we easily encounter with data sets containing large amounts of unlabeled samples. Because to label each of them is usually costly and inefficient, how to utilize these unlabeled data in a classifier construction process becomes an important problem. In machine learning literature, active learning or semi-supervised learning are popular concepts discussed under this situation, where classification algorithms recruit new unlabeled subjects sequentially based on the information learned from previous stages of its learning process, and these new subjects are then labeled and included as new training samples. From a statistical aspect, these methods can be recognized as a hybrid of the sequential design and stochastic approximation procedure. In this paper, we study sequential learning procedures for building efficient and effective classifiers, where only the selected subjects are labeled and included in its learning stage. The proposed algorithm combines the ideas of Bayesian sequential optimal design and uncertainty sampling. Computational issues of the algorithm are discussed. Numerical results using both synthesized data and real examples are reported.

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preprint2014arXiv

An observational and theoretical view of the radial distribution of HI gas in galaxies

We analyze the radial distribution of HI gas for 23 disk galaxies with unusually high HI content from the Bluedisk sample, along with a similar-sized sample of "normal" galaxies. We propose an empirical model to fit the radial profile of the HI surface density, an exponential function with a depression near the center. The radial HI surface density profiles are very homogeneous in the outer regions of the galaxy; the exponentially declining part of the profile has a scale-length of $\sim 0.18$ R1, where R1 is the radius where the column density of the HI is 1 M$_{\odot}$ pc$^{-2}$. This holds for all galaxies, independent of their stellar or HI mass. The homogenous outer profiles, combined with the limited range in HI surface density in the non-exponential inner disk, results in the well-known tight relation between HI size and HI mass. By comparing the radial profiles of the HI-rich galaxies with those of the control systems, we deduce that in about half the galaxies, most of the excess gas lies outside the stellar disk, in the exponentially declining outer regions of the HI disk. In the other half, the excess is more centrally peaked. We compare our results with existing smoothed-particle hydrodynamical simulations and semi-analytic models of disk galaxy formation in a $Λ$ Cold Dark Matter universe. Both the hydro simulations and the semi-analytic models reproduce the HI surface density profiles and the HI size-mass relation without further tuning of the simulation and model inputs. In the semi-analytic models, the universal shape of the outer HI radial profiles is a consequence of the {\em assumption} that infalling gas is always distributed exponentially. The conversion of atomic gas to molecular form explains the limited range of HI surface densities in the inner disk. These two factors produce the tight HI mass-size relation.

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preprint2014arXiv

Competing order in correlated electron systems made simple: Consistent fusion of functional renormalization and mean-field theory

We derive an efficient and unbiased method for computing order parameters in correlated electron systems with competing instabilities. Charge, magnetic and pairing fluctuations above the energy scale of spontaneous symmetry breaking are taken into account by a functional renormalization group flow, while the formation of order below that scale is treated in mean-field theory. The method captures fluctuation driven instabilities such as $d$-wave superconductivity. As a first application we study the competition between antiferromagnetism and superconductivity in the ground state of the two-dimensional Hubbard model.

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preprint2014arXiv

Local Unitary Invariants for Multipartite Quantum Systems

We present an approach of constructing invariants under local unitary transformations for multipartite quantum systems. The invariants constructed in this way can be complement to that in [Science 340 (2013) 1205-1208]. Detailed examples are given to compute such invariant in detail. It is shown that these invariants can be used to detect the local unitary equivalence of degenerated quantum states.

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preprint2014arXiv

Microwave conductance in random waveguides in the crossover to Anderson localization and single parameter scaling

The nature of transport of electrons and classical waves in disordered systems depends upon the proximity to the Anderson localization transition between freely diffusing and localized waves. The suppression of average transport and the enhancement of relative fluctuations in conductance in one-dimensional samples with lengths greatly exceeding the localization length, $L\gg ξ$, are related in the single parameter scaling (SPS) theory of localization. However, the difficulty of producing an ensemble of statistically equivalent samples in which the electron wavefunction is temporally coherent has so-far precluded the experimental demonstration of SPS. Here we demonstrate SPS in random multichannel systems for the transmittance $T$ of microwave radiation, which is the analogue of the dimensionless conductance. We show that for $L\sim4ξ$ a single eigenvalue of the transmission matrix (TM) dominates transmission and the distribution of the $\ln T$ is Gaussian with a variance equal to the average of $-\ln T$, as conjectured by SPS. For samples in the crossover to localization, $L\simξ$, we find a one-sided distribution for $\ln T$. This anomalous distribution is explained in terms of a charge model for the eigenvalues of the transmission matrix $τ$ in which the Coulomb interaction between charges mimics the repulsion between the eigenvalues of transmission matrix. We show in the localization limit that the joint distribution of $T$ and the effective number of transmission eigenvalues determines the probability distributions of intensity and total transmission for a single incident channel.

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preprint2014arXiv

Nematicons in liquid crystals with negative dielectric anisotropy

We report a theoretical and experimental work on the nematicon in the planar cell containing the nematic liquid crystal with negative dielectric anisotropy, aligned homeotropically in the presence of an externally applied voltage. The formation of the soliton is resulted from the balance between the linear difrraction and the nonlocal nonlinearity due to molecular reorientation.

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preprint2014arXiv

Neutron star high mass binaries as the origin of SGR/AXP

A close high-mass binary system consisting of a neutron star (NS) and a massive OB supergiant companion is expected to lead to a TZO structure, which consists of a NS core and a stellar envelope. We use the scenario machine program to calculate the formation tracks of TZOs in close high mass NS binaries and their subsequent evolution. We propose and demonstrate that the explosion and instant contraction of a TZO structure leave its stellar remnant as a soft gamma ray repeater and an anomalous X-ray pulsar respectively.

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preprint2014arXiv

Quantum Anomalous Hall Effect in Magnetic Insulator Heterostructure

Based on ab initio calculations, we predict that a monolayer of Cr-doped (Bi,Sb)2Te3 and GdI2 heterostructure is a quantum anomalous Hall insulator with a non-trivial band gap up to 38 meV. The principle behind our prediction is that the band inversion between two topologically trivial ferromagnetic insulators can result in a non-zero Chern number, which offers a better way to realize the quantum anomalous Hall state without random magnetic doping. In addition, a simple effective model is presented to describe the basic mechanism of spin polarized band inversion in this system. Moreover, we predict that 3D quantum anomalous Hall insulator could be realized in (Bi2/3Cr1/3)2Te3/GdI2 superlattice.

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preprint2014arXiv

Quantum Separability Criteria for Arbitrary Dimensional Multipartite States

We present separability criteria for both bipartite and multipartite quantum states. These criteria include the criteria based on the correlation matrix and its generalized form as special cases. We show by detailed examples that our criteria are more powerful than the positive partial transposition criterion, the realignment criterion and the criteria based on the correlation matrices.

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preprint2014arXiv

Quantum Spin Hall and Quantum Anomalous Hall States Realized in Junction Quantum Wells

Both quantum spin Hall and quantum anomalous Hall states are novel states of quantum matter with promising applications. We propose junction quantum wells comprising II-VI, III-V or IV semiconductors as a large class of new materials realizing the quantum spin Hall state. Especially, we find that the bulk band gap for the quantum spin Hall state can be as large as 0.1 eV. Further more, magnetic doping would induce the ferromagnetism in these junction quantum wells due to band edge singularities in the band-inversion regime and to realize the quantum anomalous Hall state.

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preprint2014arXiv

Quenching depends on morphologies: implications from the ultraviolet-optical radial color distributions in Green Valley Galaxies

In this Letter, we analyse the radial UV-optical color distributions in a sample of low redshift green valley (GV) galaxies, with the Galaxy Evolution Explorer (GALEX)+Sloan Digital Sky Survey (SDSS) images, to investigate how the residual recent star formation distribute in these galaxies. We find that the dust-corrected $u-r$ colors of early-type galaxies (ETGs) are flat out to $R_{90}$, while the colors turn blue monotonously when $r>0.5R_{50}$ for late-type galaxies (LTGs). More than a half of the ETGs are blue-cored and have remarkable positive NUV$-r$ color gradients, suggesting that their star formation are centrally concentrated; the rest have flat color distributions out to $R_{90}$. The centrally concentrated star formation activity in a large portion of ETGs is confirmed by the SDSS spectroscopy, showing that $\sim$50 % ETGs have EW(H$\rm α$)$>6.0$ Å. For the LTGs, 95% of them show uniform radial color profiles, which can be interpreted as a red bulge plus an extended blue disk. The links between the two kinds of ETGs, e.g., those objects having remarkable "blue-cored" and those having flat color gradients, are less known and require future investigations. It is suggested that the LTGs follow a general picture that quenching first occur in the core regions, and then finally extend to the rest of the galaxy. Our results can be re-examined and have important implications for the IFU surveys, such as MaNGA and SAMI.

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preprint2014arXiv

Renormalization group analysis of competition between distinct order parameters

We perform a detailed renormalization group analysis to study a (2+1)-dimensional quantum field theory that is composed of two interacting scalar bosons, which represent the order parameters for two continuous phase transitions. This sort of field theory can describe the competition and coexistence between distinct long-range orders, and therefore plays a vital role in statistical physics and condensed matter physics. We first derive and solve the renormalization group equations of all the relevant physical parameters, and then show that the system does not have any stable fixed point in the lowest energy limit. Interestingly, this conclusion holds in both the ordered and disordered phases, and also at the quantum critical point. Therefore, the originally continuous transitions are unavoidably turned to first-order due to ordering competition. Moreover, we examine the impacts of massless Goldstone boson generated by continuous symmetry breaking on ordering competition, and briefly discuss the physical implications of our results.

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preprint2014arXiv

Robust Face Recognition via Adaptive Sparse Representation

Sparse Representation (or coding) based Classification (SRC) has gained great success in face recognition in recent years. However, SRC emphasizes the sparsity too much and overlooks the correlation information which has been demonstrated to be critical in real-world face recognition problems. Besides, some work considers the correlation but overlooks the discriminative ability of sparsity. Different from these existing techniques, in this paper, we propose a framework called Adaptive Sparse Representation based Classification (ASRC) in which sparsity and correlation are jointly considered. Specifically, when the samples are of low correlation, ASRC selects the most discriminative samples for representation, like SRC; when the training samples are highly correlated, ASRC selects most of the correlated and discriminative samples for representation, rather than choosing some related samples randomly. In general, the representation model is adaptive to the correlation structure, which benefits from both $\ell_1$-norm and $\ell_2$-norm. Extensive experiments conducted on publicly available data sets verify the effectiveness and robustness of the proposed algorithm by comparing it with state-of-the-art methods.

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preprint2014arXiv

Temperature dependent characteristics of GaSb p-channel MOSFETs with Si-implanted source and drain

GaSb p-channel MOSFETs with an atomic layer deposited Al2O3 gate dielectric and a self-aligned Si implanted source/drain are demonstrated. Thermal anneal conditions are optimized for the source/drain impurity activation. Temperature dependent electrical characteristics are investigated. Different electrical behaviors are observed in two different temperature regions and the mechanisms underneath are proposed. Off-state drain current is generation current dominated in the low temperature regions and is diffusion current dominated in the high temperature regions.

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preprint2014arXiv

The nature of obscuration in AGN: II. insights from clustering properties

Based on large optical and mid-infrared (IR) surveys, we investigate the relation between nuclear activity in local Seyfert 2 galaxies and galaxy interactions using a statistical neighbour counting technique. At the same level of host galaxy star formation (SF), we find that active galactic nuclei (AGNs) with stronger [OIII] emission lines do not show an excess of near neighbours, while AGNs with stronger mid-IR emission do have more near neighbours within a projected distance of 100 kpc. The excess neighbour count increases with decreasing projected radius. These results suggest a phase of torus formation during galaxy interactions.

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preprint2014arXiv

Topological States in Ferromagnetic CdO/EuO Superlattices and Quantum Wells

Based on ab initio calculations, we demonstrate that the ferromagnetic CdO/EuO superlattice is a simple Weyl semimetal with two linear Weyl nodes in the Brillouin zone, and the corresponding CdO/EuO quantum well realizes the stichometric quantum anomalous Hall state without random magnetic doping. In addition, a simple effective model is presented to describe the basic mechanism of spin polarized band inversion in this system.

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preprint2014arXiv

Two-dimensional time-reversal-invariant topological superconductivity in a doped quantum spin Hall insulator

Time-reversal-invariant topological superconductors have a full paring gap in the bulk and gapless Majorana states at the edge or on the surface. Here, we theoretically propose topological superconductivity in a doped quantum spin Hall insulator. We study the pairing symmetry of doped Sn$X$ film within a two-orbital model, and show that it realizes a topological superconductor when the intraorbital attractive interaction is stronger than the interorbital interaction, which is generic for a doped quantum spin Hall insulator with strong spin-orbit coupling. Edge channels are studied in a tight-binding model numerically. Finally, we discuss the possible topological superconductivity in Sn$X$ film by comparing to $3d$ superconductivity in bulk Sn.

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preprint2014arXiv

Universal Scaling of Quantum Anomalous Hall Plateau Transition

We study the critical properties of the quantum anomalous Hall (QAH) plateau transition in magnetic topological insulators. We introduce a microscopic model for the plateau transition in QAH effect at the coercive field and then map it to the network model of quantum percolation in the integer quantum Hall effect plateau transition. Generally, an intermediate plateau with zero Hall conductance could occur at the coercive field. $σ_{xx}$ would have double peaks at the coercivity while $ρ_{xx}$ only has single peak. Remarkably, this theoretical prediction is already borne out in experiment. Universal scaling of the transport coefficients $ρ_{xy}$ and $ρ_{xx}$ are predicted.

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preprint2013arXiv

A Contour-Guided Deformable Image Registration Algorithm for Adaptive Radiotherapy

In adaptive radiotherapy, deformable image registration is often conducted between the planning CT and treatment CT (or cone beam CT) to generate a deformation vector field (DVF) for dose accumulation and contour propagation. The auto propagated contours on the treatment CT may contain relatively large errors, especially in low contrast regions. A clinician inspection and editing of the propagated contours are frequently needed. The edited contours are able to meet the clinical requirement for adaptive therapy; however, the DVF is still inaccurate and inconsistent with the edited contours. The purpose of this work is to develop a contour-guided deformable image registration (CG-DIR) algorithm to improve the accuracy and consistency of the DVF for adaptive radiotherapy. Incorporation of the edited contours into the registration algorithm is realized by regularizing the objective function of the original demons algorithm with a term of intensity matching between the delineated structures set pairs. The CG-DIR algorithm is implemented on computer graphics processing units (GPUs) by following the original GPU-based demons algorithm computation framework [Gu et al, Phys Med Biol. 55(1): 207-219, 2010]. The performance of CG-DIR is evaluated on five clinical head-and-neck and one pelvic cancer patient data. It is found that compared with the original demons, CG-DIR improves the accuracy and consistency of the DVF, while retaining similar high computational efficiency.

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preprint2013arXiv

Anomalous Edge Transport in the Quantum Anomalous Hall State

We predict by first-principles calculations that thin films of Cr-doped (Bi,Sb)$_2$Te$_3$ magnetic topological insulator have gapless non-chiral edge states coexisting with the chiral edge state. Such gapless non-chiral states are not immune to backscattering, which would explain dissipative transport in the quantum anomalous Hall (QAH) state observed in this system experimentally. Here we study the edge transport with both chiral and non-chiral states by Landaur-Büttiker formalism, and find that the longitudinal resistance is nonzero whereas Hall resistance is quantized to $h/e^2$. In particular, the longitudinal resistance can be greatly reduced by adding an extra floating probe even if it is not used, while the Hall resistance remains at the quantized value. We propose several transport experiments to detect the dissipative non-chiral edge channels. These results will facilitate the realization of pure dissipationless transport of QAH states in magnetic topological insulators.

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preprint2013arXiv

Calculation of divergent photon absorption in ultrathin films of a topological insulator

We perform linear and non-linear photon absorption calculations in topological insulator ultra-thin films on a substrate. Due to the unique band structure of the coupled topological surface states, novel features are observed for suitable photon frequencies, including a divergent edge singularity in one-photon absorption process and a significantly enhancement in two-photon absorption process. The resonanct frequencies can be controlled by tuning the energy difference and coupling of the top and bottom surface states. Such unique linear and nonlinear optical properties make ultra-thin films of topological insulators promising material building blocks for tunable high-efficiency nanophotonic devices.

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preprint2013arXiv

Competition between superconductivity and nematic order in high-$T_c$ superconductor

We investigate the competition between d-wave superconductivity and nematic order in high-$T_c$ superconductor, and examine the role played by gapless fermionic degrees of freedom. Apart from the competitive interaction with the superconducting order parameter, the nematic order parameter couples strongly to gapless nodal quasiparticles. The interplay of these two kinds of interactions is analyzed by means of renormalization group method. In case the fermionic degree of freedom are entirely neglected, the competitive interaction between two bosonic order parameters is strongly relevant, and can lead to runaway behavior. However, these properties are fundamentally changed once the dynamics of fermions are taken into account. At the nematic quantum critical point where an extreme fermion velocity anisotropy occurs, the superconducting and nematic order parameters are decoupled from each other. Consequently, the phase transitions are continuous, and d-wave superconductivity can coexist with nematic order homogeneously. These results indicate that the gapless fermions can play an important role and should be carefully included in the theoretical description of competing orders.

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preprint2013arXiv

Curvature dimension inequalities and subelliptic heat kernel gradient bounds on contact manifolds

We study curvature dimension inequalities for the sub-Laplacian on contact Riemannian manifolds. This new curvature dimension condition is then used to obtain: 1) Geometric conditions ensuring the compactness of the underlying manifold (Bonnet-Myers type results); 2) Volume estimates of metric balls; 3) Gradient bounds and stochastic completeness for the heat semigroup generated by the sub-Laplacian; 4) Spectral gap estimates.

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preprint2013arXiv

Distributed Power Allocation for Coordinated Multipoint Transmissions in Distributed Antenna Systems

This paper investigates the distributed power allocation problem for coordinated multipoint (CoMP) transmissions in distributed antenna systems (DAS). Traditional duality based optimization techniques cannot be directly applied to this problem, because the non-strict concavity of the CoMP transmission's achievable rate with respect to the transmission power induces that the local power allocation subproblems have non-unique optimum solutions. We propose a distributed power allocation algorithm to resolve this non-strict concavity difficulty. This algorithm only requires local information exchange among neighboring base stations serving the same user, and is thus scalable as the network size grows. The step-size parameters of this algorithm are determined by only local user access relationship (i.e., the number of users served by each antenna), but do not rely on channel coefficients. Therefore, the convergence speed of this algorithm is quite robust to different channel fading coefficients. We rigorously prove that this algorithm converges to an optimum solution of the power allocation problem. Simulation results are presented to demonstrate the effectiveness of the proposed power allocation algorithm.

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preprint2013arXiv

Effects of ozone post deposition treatment on interfacial and electrical characteristics of atomic-layer-deposited Al2O3 and HfO2 films on GaSb substrates

Atomic-layer-deposited Al2O3 and HfO2 films on GaSb substrates were treated by in-situ ozone post deposition treatment (PDT). The effects of ozone PDT on the interfacial and electrical properties of Al2O3 and HfO2 gate dielectric films on GaSb substrates were investigated carefully. It is found that the dielectric quality and the interfacial properties of the Al2O3 and HfO2 films are improved by ozone PDT. After in-situ ozone PDT for 5 min, the Al2O3 and HfO2 films on GaSb substrates exhibit improved electrical and interfacial properties, such as reduced frequency dispersion, gate leakage current, border traps and interface traps. Interface trap density is reduced by ~24% for the Al2O3/GaSb stacks and ~27% for the HfO2/GaSb stacks. In-situ ozone PDT is proved to be a promising technique in improving the quality of high-k gate stacks on GaSb substrates.

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preprint2013arXiv

Elastic scattering of surface states on three-dimensional topological insulators

Topological insulators as new type of quantum matter materials are characterized by a full insulating gap in the bulk and gapless edge/surface states which are protected by time-reversal symmetry. We propose the interference patterns caused by elastic scattering of defects or impurities are dominated by surface states at the extremal points on the constant energy contour. Within such formalism, we summarize our recent theoretical investigations on elastic scattering of topological surface states by various imperfections, including the non-magnetic impurities, magnetic impurities, step-edges, and various other defects, in comparison with recent related experiments in typical topological materials such as BiSb alloys, Bi$_2$Te$_3$ and Bi$_2$Se$_3$ crystals.

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preprint2013arXiv

Fast Approximate $K$-Means via Cluster Closures

$K$-means, a simple and effective clustering algorithm, is one of the most widely used algorithms in multimedia and computer vision community. Traditional $k$-means is an iterative algorithm---in each iteration new cluster centers are computed and each data point is re-assigned to its nearest center. The cluster re-assignment step becomes prohibitively expensive when the number of data points and cluster centers are large. In this paper, we propose a novel approximate $k$-means algorithm to greatly reduce the computational complexity in the assignment step. Our approach is motivated by the observation that most active points changing their cluster assignments at each iteration are located on or near cluster boundaries. The idea is to efficiently identify those active points by pre-assembling the data into groups of neighboring points using multiple random spatial partition trees, and to use the neighborhood information to construct a closure for each cluster, in such a way only a small number of cluster candidates need to be considered when assigning a data point to its nearest cluster. Using complexity analysis, image data clustering, and applications to image retrieval, we show that our approach out-performs state-of-the-art approximate $k$-means algorithms in terms of clustering quality and efficiency.

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preprint2013arXiv

Fast Neighborhood Graph Search using Cartesian Concatenation

In this paper, we propose a new data structure for approximate nearest neighbor search. This structure augments the neighborhood graph with a bridge graph. We propose to exploit Cartesian concatenation to produce a large set of vectors, called bridge vectors, from several small sets of subvectors. Each bridge vector is connected with a few reference vectors near to it, forming a bridge graph. Our approach finds nearest neighbors by simultaneously traversing the neighborhood graph and the bridge graph in the best-first strategy. The success of our approach stems from two factors: the exact nearest neighbor search over a large number of bridge vectors can be done quickly, and the reference vectors connected to a bridge (reference) vector near the query are also likely to be near the query. Experimental results on searching over large scale datasets (SIFT, GIST and HOG) show that our approach outperforms state-of-the-art ANN search algorithms in terms of efficiency and accuracy. The combination of our approach with the IVFADC system also shows superior performance over the BIGANN dataset of $1$ billion SIFT features compared with the best previously published result.

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preprint2013arXiv

Focusing through random media in space and time: a transmission matrix approach

We exploit the evolution in time of the transmission matrix following pulse excitation of a random medium to focus radiation at a selected time delay t' and position r. The temporal profile of a focused microwave pulse is the same as the incident Gaussian pulse. The contrast in space at time t' of the focused wave is determined by the participation number of transmission eigenvalues M' and the size N' of the measured transmission matrix. The initial rise and subsequent decay in contrast observed reflects the distribution of decay rates of the quasi-normal modes within the sample.

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preprint2013arXiv

Full Quantum Theory of ${C_{60}}$ Double-slit Diffraction

In this paper, we apply the full new method of quantum theory to study the double-slit diffraction of ${C_{60}}$ molecules. We calculate the double-slit wave functions of ${C_{60}}$ molecules by Schrödinger equation, and calculate the diffraction wave function behind the slits with the Feynman path integral quantum theory, and then give the relation between the diffraction intensity of double-slit and diffraction pattern position. We compare the calculation results with two different double-slit diffraction experiments. When the decoherence effects are considered, the calculation results are in good agreement with the two experimental data.

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preprint2013arXiv

Galactic accretion and the outer structure of galaxies in the CDM model

We have combined the semi-analytic galaxy formation model of Guo et al. (2011) with the particle-tagging technique of Cooper et al. (2010) to predict galaxy surface brightness profiles in a representative sample of ~1900 massive dark matter haloes (10^12--10^14 M_sol) from the Millennium II Lambda-CDM N-body simulation. Here we present our method and basic results focusing on the outer regions of galaxies, consisting of stars accreted in mergers. These simulations cover scales from the stellar haloes of Milky Way-like galaxies to the 'cD envelopes' of groups and clusters, and resolve low surface brightness substructure such as tidal streams. We find that the surface density of accreted stellar mass around the central galaxies of dark matter haloes is well described by a Sersic profile, the radial scale and amplitude of which vary systematically with halo mass (M_200). The total stellar mass surface density profile breaks at the radius where accreted stars start to dominate over stars formed in the galaxy itself. This break disappears with increasing M_200 because accreted stars contribute more of the total mass of galaxies, and is less distinct when the same galaxies are averaged in bins of stellar mass, because of scatter in the relation between M_star and M_200. To test our model we have derived average stellar mass surface density profiles for massive galaxies at z~0.08 by stacking SDSS images. Our model agrees well with these stacked profiles and with other data from the literature, and makes predictions that can be more rigorously tested by future surveys that extend the analysis of the outer structure of galaxies to fainter isophotes. We conclude that it is likely that the outer structure of the spheroidal components of galaxies is largely determined by collisionless merging during their hierarchical assembly

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preprint2013arXiv

Improved Interfacial and Electrical Properties of GaSb Metal Oxide Semiconductor Devices Passivated with Acidic (NH4)2S Solution

Surface passivation with acidic (NH4)2S solution is shown to be effective in improving the interfacial and electrical properties of HfO2/GaSb metal oxide semiconductor devices. Compared with control samples, those treated with acidic (NH4)2S solution showed great improvements in frequency dispersion, gate leakage current and interface trap density. These improvements were attributed to the acidic (NH4)2S solution enhancing passivation of the substrates, which was analyzed from the perspective of chemical mechanism and confirmed by X-ray photoelectron spectroscopy and high-resolution cross-sectional transmission electron microscopy.

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preprint2013arXiv

Large-gap quantum spin Hall insulators in tin films

The search of large-gap quantum spin Hall (QSH) insulators and effective approaches to tune QSH states is important for both fundamental and practical interests. Based on first-principles calculations we find two-dimensional tin films are QSH insulators with sizable bulk gaps of 0.3 eV, sufficiently large for practical applications at room temperature. These QSH states can be effectively tuned by chemical functionalization and by external strain. The mechanism for the QSH effect in this system is band inversion at the Γpoint, similar to the case of HgTe quantum well. With surface doping of magnetic elements, the quantum anomalous Hall effect could also be realized.

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preprint2013arXiv

Network Anomaly Detection: A Survey and Comparative Analysis of Stochastic and Deterministic Methods

We present five methods to the problem of network anomaly detection. These methods cover most of the common techniques in the anomaly detection field, including Statistical Hypothesis Tests (SHT), Support Vector Machines (SVM) and clustering analysis. We evaluate all methods in a simulated network that consists of nominal data, three flow-level anomalies and one packet-level attack. Through analyzing the results, we point out the advantages and disadvantages of each method and conclude that combining the results of the individual methods can yield improved anomaly detection results.

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preprint2013arXiv

Non-volatile ferroelastic switching of the Verwey transition and resistivity of epitaxial Fe3O4/PMN-PT (011)

A central goal of electronics based on correlated materials or 'Mottronics' is the ability to switch between distinct collective states with a control voltage. Small changes in structure and charge density near a transition can tip the balance between competing phases, leading to dramatic changes in electronic and magnetic properties. In this work, we demonstrate that an electric field induced two-step ferroelastic switching pathway in (011) oriented 0.71Pb(Mg1/3Nb2/3)O3-0.29PbTiO3 (PMN-PT) substrates can be used to tune the Verwey metal-insulator transition in epitaxial Fe3O4 films in a stable and reversible manner. We also observe robust non-volatile resistance switching in Fe3O4 up to room temperature, driven by ferroelastic strain. These results provides a framework for realizing non-volatile and reversible tuning of order parameters coupled to lattice-strain in epitaxial oxide heterostructures over a broad range of temperatures, with potential device applications.

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preprint2013arXiv

Optimal Real-time Spectrum Sharing between Cooperative Relay and Ad-hoc Networks

Optimization based spectrum sharing strategies have been widely studied. However, these strategies usually require a great amount of real-time computation and significant signaling delay, and thus are hard to be fulfilled in practical scenarios. This paper investigates optimal real-time spectrum sharing between a cooperative relay network (CRN) and a nearby ad-hoc network. Specifically, we optimize the spectrum access and resource allocation strategies of the CRN so that the average traffic collision time between the two networks can be minimized while maintaining a required throughput for the CRN. The development is first for a frame-level setting, and then is extended to an ergodic setting. For the latter setting, we propose an appealing optimal real-time spectrum sharing strategy via Lagrangian dual optimization. The proposed method only involves a small amount of real-time computation and negligible control delay, and thus is suitable for practical implementations. Simulation results are presented to demonstrate the efficiency of the proposed strategies.

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preprint2013arXiv

Power Allocation and Time-Domain Artificial Noise Design for Wiretap OFDM with Discrete Inputs

Optimal power allocation for orthogonal frequency division multiplexing (OFDM) wiretap channels with Gaussian channel inputs has already been studied in some previous works from an information theoretical viewpoint. However, these results are not sufficient for practical system design. One reason is that discrete channel inputs, such as quadrature amplitude modulation (QAM) signals, instead of Gaussian channel inputs, are deployed in current practical wireless systems to maintain moderate peak transmission power and receiver complexity. In this paper, we investigate the power allocation and artificial noise design for OFDM wiretap channels with discrete channel inputs. We first prove that the secrecy rate function for discrete channel inputs is nonconcave with respect to the transmission power. To resolve the corresponding nonconvex secrecy rate maximization problem, we develop a low-complexity power allocation algorithm, which yields a duality gap diminishing in the order of O(1/\sqrt{N}), where N is the number of subcarriers of OFDM. We then show that independent frequency-domain artificial noise cannot improve the secrecy rate of single-antenna wiretap channels. Towards this end, we propose a novel time-domain artificial noise design which exploits temporal degrees of freedom provided by the cyclic prefix of OFDM systems {to jam the eavesdropper and boost the secrecy rate even with a single antenna at the transmitter}. Numerical results are provided to illustrate the performance of the proposed design schemes.

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preprint2013arXiv

Quantum Anomalous Hall Effect with Higher Plateaus

Quantum anomalous Hall (QAH) effect in magnetic topological insulators is driven by the combination of spontaneous magnetic moments and spin-orbit coupling. Its recent experimental discovery raises the question if higher plateaus can also be realized. Here we present a general theory for QAH effect with higher Chern numbers, and show by first-principles calculations that thin film magnetic topological insulator of Cr-doped Bi$_2$(Se,Te)$_3$ is a candidate for the C=2 QAH insulator. Remarkably, whereas higher magnetic field leads to lower Hall conductance plateaus in the integer quantum Hall effect, higher magnetic moment leads to higher Hall conductance plateaus in the QAH effect.

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preprint2013arXiv

Quantum theory of light double-slit diffraction

In this paper, we study the light double-slit diffraction experiment with quantum theory approach. Firstly, we calculate the light wave function in slits by quantum theory of photon. Secondly, we calculate the diffraction wave function with Kirchhoff's law. Thirdly, we give the diffraction intensity of light double-slit diffraction, which is proportional to the square of diffraction wave function. Finally, we compare calculation result of quantum theory and classical electromagnetic theory with the experimental data. We find the quantum calculate result is accordance with the experiment data, and the classical calculation result with certain deviation. So, the quantum theory is more accurately approach for studying light diffraction.

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preprint2013arXiv

Scalable $k$-NN graph construction

The $k$-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct $k$-NN graphs remains a challenge, especially for large-scale high-dimensional data. In this paper, we propose a new approach to construct approximate $k$-NN graphs with emphasis in: efficiency and accuracy. We hierarchically and randomly divide the data points into subsets and build an exact neighborhood graph over each subset, achieving a base approximate neighborhood graph; we then repeat this process for several times to generate multiple neighborhood graphs, which are combined to yield a more accurate approximate neighborhood graph. Furthermore, we propose a neighborhood propagation scheme to further enhance the accuracy. We show both theoretical and empirical accuracy and efficiency of our approach to $k$-NN graph construction and demonstrate significant speed-up in dealing with large scale visual data.

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preprint2013arXiv

The Bluedisks project, a study of unusually HI-rich galaxies: I. HI Sizes and Morphology

We introduce the "Bluedisk" project, a large program at the Westerbork Synthesis Radio Telescope (WSRT) that has mapped the HI in a sample of 23 nearby galaxies with unusually high HI mass fractions, along with a similar-sized sample of control galaxies matched in stellar mass, size, inclination and redshift. This paper presents the sample selection, observational set-up, data reduction strategy, and a first analysis of the sizes and structural properties of the HI disks. We find that the HI-rich galaxies lie on the same HI mass versus HI size relation as normal spiral galaxies, extending it to total HI masses of $2 \times 10^{10} M_{\odot}$ and radii R1 of $\sim 100$ kpc (where R1 is defined as the radius where the HI column density reaches 1 $M_{\odot}$ pc$^{-2}$). HI-rich galaxies have significantly larger values of HI-to-optical size ratio at fixed stellar mass, concentration index, stellar and star formation rate surface density compared to the control sample. The disks of HI-rich galaxies are also significantly more clumpy (i.e. have higher HI Gini and $Δ$Area coefficient) than those of normal spirals. There is no evidence that the disks of HI-rich galaxies are more disturbed: HI-rich galaxies exhibit no difference with respect to control samples in their distributions of HI asymmetry indices or optical/HI disk position angle differences. In fact, the center of the HI distribution corresponds more closely with the center of the optical light in the HI-rich galaxies than in the controls. All these results argue against a scenario in which new gas has been brought in by mergers. It is possible that they may be more consistent with cooling from a surrounding quasi-static halo of warm/hot gas.

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preprint2013arXiv

The GALEX Arecibo SDSS Survey. VIII. Final Data Release -- The Effect of Group Environment on the Gas Content of Massive Galaxies

We present the final data release from the GALEX Arecibo SDSS Survey (GASS), a large Arecibo program that measured the HI properties for an unbiased sample of ~800 galaxies with stellar masses greater than 10^10 Msun and redshifts 0.025<z<0.05. This release includes new Arecibo observations for 250 galaxies. We use the full GASS sample to investigate environmental effects on the cold gas content of massive galaxies at fixed stellar mass. The environment is characterized in terms of dark matter halo mass, obtained by cross-matching our sample with the SDSS group catalog of Yang et al. Our analysis provides, for the first time, clear statistical evidence that massive galaxies located in halos with masses of 10^13-10^14 Msun have at least 0.4 dex less HI than objects in lower density environments. The process responsible for the suppression of gas in group galaxies most likely drives the observed quenching of the star formation in these systems. Our findings strongly support the importance of the group environment for galaxy evolution, and have profound implications for semi-analytic models of galaxy formation, which currently do not allow for stripping of the cold interstellar medium in galaxy groups.

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preprint2013arXiv

The Nature of Obscuration in AGN: I. Insights from Host Galaxies

We analyze a sample of 30,000 nearby obscured AGNs with optical spectra from SDSS and mid-IR photometry from WISE. Our aim is to investigate the AGN host galaxy properties with mid-IR luminosities as AGN activity indicator, and to compare with previous studies based on [OIII] emission lines. First we find that the [3.4] - [4.6] colour has weak dependence on host stellar age, but strong dependence on AGN activity. We then use a "pair-matching" technique to subtract the host 4.6 micron contribution. By combining Seyferts with a sample of SDSS quasars at z < 0.7, we show that the [OIII] and the intrinsic AGN 4.6 micron luminosities correlate roughly linearly over 4 orders of magnitude, but with substantial scatter. We also compare the partition functions of the total integrated 4.6 micron and [OIII] line luminosities from Seyferts and a sub-population of LINERs with significant nuclear 4.6 micron emission, as function of a variety of host galaxy properties, finding that they are identical. We conclude, therefore, that [OIII] as an AGN indicator shows no particular biases as compared to the 4.6 micron luminosity. Our results also demonstrate that some LINERs do fit in with the expectations of the simple Unified Model.

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preprint2013arXiv

Topological superconductivity at the edge of transition metal dichalcogenides

Time-reversal breaking topological superconductors are new states of matter which can support Majorana zero modes at the edge. In this paper, we propose a new realization of one-dimensional topological superconductivity and Majorana zero modes. The proposed system consists of a monolayer of transition metal dichalcogenides MX2 (M=Mo, W; X=S, Se) on top of a superconducting substrate. Based on first-principles calculations, we show that a zigzag edge of the monolayer MX2 terminated by metal atom M has edge states with strong spin-orbit coupling and spontaneous magnetization. By proximity coupling with a superconducting substrate, topological superconductivity can be induced at such an edge. We propose NbS2 as a natural choice of substrate, and estimate the proximity induced superconducting gap based on first-principles calculation and low energy effective model. As an experimental consequence of our theory, we predict that Majorana zero modes can be detected at the 120 degree corner of a MX2 flake in proximity with a superconducting substrate.

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preprint2013arXiv

Transmission statistics and focusing in single disordered samples

We show in microwave experiments and random matrix calculations that in samples with a large number of channels the statistics of transmission for different incident channels relative to the average transmission is determined by a single parameter, the participation number of the eigenvalues of the transmission matrix, M. Its inverse, M-1, is equal to the variance of relative total transmission of the sample, while the contrast in maximal focusing is equal to M. The distribution of relative total transmission changes from Gaussian to negative exponential over the range in which M-1 changes from 0 to 1. This provides a framework for transmission and imaging in single samples.

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preprint2013arXiv

Velocity renormalization of nodal quasiparticles in d-wave superconductors

Gapless nodal quasiparticles emerge at a low-energy regime of high-$T_c$ cuprate superconductors due to the $d_{x^2 - y^2}$ gap symmetry. We study the unusual renormalizations of the Fermi velocity $v_F$ and gap velocity $v_Δ$ of these quasiparticles close to various quantum critical points in a superconducting dome. Special attention is paid to the behavior of the velocity ratio, $v_Δ/v_F$, since it determines a number of observable quantities. We perform a renormalization-group analysis and show that the velocity ratio may vanish, approach unity, or diverge at different quantum critical points. The corresponding superfluid densities and critical temperatures are suppressed, slightly increased, or significantly enhanced. The effects of three types of static disorders, namely, random mass, random gauge potential, and random chemical potential, on the stability of the system are also addressed. An analogous analysis reveals that both random mass and random gauge potential are irrelevant. This implies that these fixed points of the velocity ratio are stable, and hence observable effects ignited by them are unchanged. However, the random chemical potential is marginal. As a result, these fixed points are broken, and thus, the instabilities of quantum phase transitions are triggered.

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preprint2012arXiv

A study on quantum mechanical approach for $C_{60}$ diffraction analysis

Diffraction phenomena of large molecules have been studied in many experiments, and these experiments are described by many theoretical works. In this paper, we study $C_{60}$ molecules single and double-slit diffraction with quantum theory approach, and we pay close attention to the $C_{60}$ diffraction experiment carried out by A.Zeilinger et.at in 1999. In double-slit diffraction, we consider the decoherence effect, and find the theoretical results are good agreement with experimental data.

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preprint2012arXiv

An optical diode and magnifier from a general function photonic crystals

We have presented a new general function photonic crystals (GFPCs), which refractive index is a function of space position. Based on Fermat principle, we achieve the motion equations of light in one-dimensional general function photonic crystals, and calculate its transfer matrix. In this paper, we choose the line refractive index function for two mediums $A$ and $B$, and obtain new results: (1) when the line function of refractive indexes is up or down, the transmissivity can be far larger or smaller than 1. (2) when the refractive indexes function increase or decrease at the direction of incident light, the light intensity should be magnified or weaken, which can be made light magnifier or attenuator. (3) The GFPCs can also be made optical diode. The new general function photonic crystals can be applied to design more optical instruments.

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preprint2012arXiv

Correlation Analysis of a Large Sample of Narrow-Line Seyfert 1 Galaxies: Linking Central Engine and Host Properties

We present a statistical study of a large, homogeneously analyzed sample of narrow-line Seyfert 1 (NLS1) galaxies, accompanied by a comparison sample of broad-line Seyfert 1 (BLS1) galaxies. Optical emission-line and continuum properties are subjected to correlation analyses, in order to identify the main drivers of active galactic nuclei (AGN) correlation space, and of NLS1 galaxies in particular. For the first time, we have established the density of the narrow-line region as a key parameter in Eigenvector 1 space, as important as the Eddington ratio L/Ledd. This is important because it links the properties of the central engine with the properties of the host galaxy; i.e., the interstellar medium (ISM). We also confirm previously found correlations involving the line width of Hbeta, and the strength of the FeII and [OIII]5007 emission lines, and we confirm the important role played by L/Ledd in driving the properties of NLS1 galaxies. A spatial correlation analysis shows that large-scale environments of the BLS1 and NLS1 galaxies of our sample are similar. If mergers are rare in our sample, accretion-driven winds on the one hand, or bar-driven inflows on the other hand, may account for the strong dependence of Eigenvector 1 on ISM density.

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preprint2012arXiv

Cosmic Duality and Statefinder Diagnosis of Spinor Quintom

In this paper, we study the possible connections among different Spinor Quintom Dark Energy (DE) models by the aid of duality. Then we apply the statefinder diagnostic to these models. By this diagnostic pair {$\{r,s\}$}, we differentiate one Quintom DE model from the others in a model independent manner. A class of evolutionary trajectories of these Spinor Quintom models are presented in the statefinder parameter planes. We also obtain the current locations of the parameters $r$ and $s$, and these locations correspond to different models in statefinder parameter planes theoretically.

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preprint2012arXiv

Influence of Fermion Velocity Renormalization on Dynamical Mass Generation in QED$_3$

We study dynamical fermion mass generation in (2+1)-dimensional quantum electrodynamics with a gauge field coupling to massless Dirac fermions and non-relativistic scalar bosons. We calculate the fermion velocity renormalization and then examine its influence on dynamical mass generation by using the Dyson-Schwinger equation. It is found that dynamical mass generation takes place even after including the scalar bosons as long as the bosonic compressibility parameter $ξ$ is sufficiently small. In addition, the fermion velocity renormalization enhances the dynamically generated mass.

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preprint2012arXiv

Optical effects of spin currents in semiconductors

A spin current has novel linear and second-order nonlinear optical effects due to its symmetry properties. With the symmetry analysis and the eight-band microscopic calculation we have systematically investigated the interaction between a spin current and a polarized light beam (or the "photon spin current") in direct-gap semiconductors. This interaction is rooted in the intrinsic spin-orbit coupling in valence bands and does not rely on the Rashba or Dresselhaus effect. The light-spin current interaction results in an optical birefringence effect of the spin current. The symmetry analysis indicates that in a semiconductor with inversion symmetry, the linear birefringence effect vanishes and only the circular birefringence effect exists. The circular birefringence effect is similar to the Faraday rotation in magneto-optics but involves no net magnetization nor breaking the time-reversal symmetry. Moreover, a spin current can induce the second-order nonlinear optical processes due to the inversion-symmetry breaking. These findings form a basis of measuring a pure spin current where and when it flows with the standard optical spectroscopy, which may provide a toolbox to explore a wealth of physics connecting the spintronics and photonics.

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preprint2012arXiv

Quantifying the role of bars in the build-up of central mass concentrations in disk galaxies

We analyze the role of bars in the build-up of central mass concentrations in massive, disk galaxies. Our parent sample consists of 3757 face-on disk galaxies with redshifts between 0.01 and 0.05, selected from the seventh Data Release of the Sloan Digital Sky Survey. 1555 galaxies with bars are identified using position angle and ellipticity profiles of the $i$-band light. We compare the ratio of the specific star formation rate measured in the 1-3 kpc central region of the galaxy to that measured for the whole galaxy. Galaxies with strong bars have centrally enhanced star formation; the degree of enhancement depends primarily on the ellipticity of the bar, and not on the size of the bar or on the mass or structure of the host galaxy. The fraction of galaxies with strong bars is highest at stellar masses greater than $3 \times 10^{10} M_{\odot}$, stellar surface densities less than $3 \times 10^8 M_{\odot}$ and concentration indices less than 2.5. In this region of parameter space, galaxies with strong bars either have enhanced central star formation rates, or star formation that is {\em suppressed} compared to the mean. This suggests that bars may play a role in the eventual quenching of star formation in galaxies. Only 50% of galaxies with strongly concentrated star formation have strong bars, indicating that other processes such as galaxy interactions also induce central star-bursts. We also find that the ratio of the size of the bar to that of the disk depends mainly on the colour of the galaxy, suggesting that the growth and destruction of bars are regulated by gas accretion, as suggested by simulations.

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preprint2012arXiv

Quasi-normal modes in random media

We have analyzed spectra of localized microwave transmitted through quasi-1D random samples to obtain the central frequency, linewidth and field speckle pattern of the modes for an ensemble of samples at three lengths. We find strong correlation between modal field speckle patterns. This leads to destructive interference between modes which explain strong suppression of steady state transmission and of pulsed transmission at early times. We have also studied the statistics of mode spacings and widths in localized samples. The distribution of mode spacings between adjacent modes is close to the Wigner surmise predicted for diffusive waves, which exhibit strong level repulsion. However, a deviation from Wigner distribution can be seen in the distribution of spacings beyond the nearest ones. A weakening in the rigidity of the modal spectrum is also observed as the sample length increases because of reduced level repulsion for more strongly localized waves. In contrast to residual diffusive behavior for level spacing statistics, the distribution of level widths are log-normal as predicted for localized waves. We also measured the steady state and dynamic fluctuations and correlation of localized wave transmitted through random waveguides. We find the degree of intensity correlation first increases, and then decays with time delay, before increasing dramatically. The variation in the spatial correlation of intensity with time delay is due to the changing effective number of modes that contribute to transmission. A minimum in correlation is reached when the number of modes contributing appreciably to transmission peaks. At long times, the degree of intensity correlation and the variance of total transmission increase dramatically. This reflects the reduced role of short-lived states and the growing weight of long-lived modes.

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preprint2012arXiv

Statistical Analysis for the Q-factor of twin kHz QPOs

Using the recently published data of twin kHz quasi-period oscillations (QPOs) in neutron star low-mass X-ray binaries (LMXBs), we study the different profiles between bright Z sources and less luminous Atoll sources in a statistical way. We find the quality factors of upper kHz QPOs show a narrow distribution both for Z sources and Atoll sources, which concentrate at 7.98 and 9.75 respectively, the quality factors of lower kHz QPOs show a narrow distribution for Z sources and a broader distribution for Atoll sources, which concentrate at 5.25 and 86.22 respectively. In order to investigate the relation between the quality factor and the peakfrequency of kHz QPOs, we fit the data with power-law, linear and exponential functions, respectively. There is an obvious trend that the quality factors increase with the peak frequencies both for upper and lower QPOs. The implications of our results are discussed.

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preprint2012arXiv

Suppression of superconductivity at nematic critical point in underdoped cuprates

A nematic quantum critical point is anticipated to exist in the superconducting dome of some high-temperature superconductors. The nematic order competes with the superconducting order and hence reduces the superconducting condensate at $T = 0$. Moreover, the critical fluctuations of nematic order can excite more nodal quasiparticles out of the condensate. We address these two effects within an effective field theory and show that superfluid density $ρ^s(T)$ and superconducting temperature $T_c$ are both suppressed strongly by the critical fluctuations. The strong suppression of superconductivity provides a possible way to determine the nematic quantum critical point.

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preprint2012arXiv

Temporal Logic Motion Control using Actor-Critic Methods

In this paper, we consider the problem of deploying a robot from a specification given as a temporal logic statement about some properties satisfied by the regions of a large, partitioned environment. We assume that the robot has noisy sensors and actuators and model its motion through the regions of the environment as a Markov Decision Process (MDP). The robot control problem becomes finding the control policy maximizing the probability of satisfying the temporal logic task on the MDP. For a large environment, obtaining transition probabilities for each state-action pair, as well as solving the necessary optimization problem for the optimal policy are usually not computationally feasible. To address these issues, we propose an approximate dynamic programming framework based on a least-square temporal difference learning method of the actor-critic type. This framework operates on sample paths of the robot and optimizes a randomized control policy with respect to a small set of parameters. The transition probabilities are obtained only when needed. Hardware-in-the-loop simulations confirm that convergence of the parameters translates to an approximately optimal policy.

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preprint2012arXiv

Testing the Accretion-induced Field-decay and Spin-up Model for Recycled Pulsars

Millisecond radio pulsars have long been proposed to form from a spin-up recycling process in a binary system. In this paper we demonstrate that the accretion-induced field-decay and spin-up model for recycled pulsars can indeed produce those millisecond pulsars with relatively weak magnetic fields of $10^8-10^9$ G and short spin periods of a few milliseconds. Our results also suggest that the value of the currently observed highest spin frequency of millisecond pulsars may simply be constrained by the amount of mass available for accretion.

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preprint2012arXiv

The clustering of galaxies as a function of their photometrically-estimated atomic gas content

We introduce a new photometric estimator of the HI mass fraction (M_HI/M_*) in local galaxies, which is a linear combination of four parameters: stellar mass, stellar surface mass density, NUV-r colour, and g-i colour gradient. It is calibrated using samples of nearby galaxies (0.025<z<0.05) with HI line detections from the GASS and ALFALFA surveys, and it is demonstrated to provide unbiased M_HI/M_* estimates even for HI-rich galaxies. We apply this estimator to a sample of ~24,000 galaxies from the SDSS/DR7 in the same redshift range. We then bin these galaxies by stellar mass and HI mass fraction and compute projected two point cross-correlation functions with respect to a reference galaxy sample. Results are compared with predictions from current semi-analytic models of galaxy formation. The agreement is good for galaxies with stellar masses larger than 10^10 M_sun, but not for lower mass systems. We then extend the analysis by studying the bias in the clustering of HI-poor or HI-rich galaxies with respect to galaxies with normal HI content on scales between 100 kpc and ~5 Mpc. For the HI-deficient population, the strongest bias effects arise when the HI-deficiency is defined in comparison to galaxies of the same stellar mass and size. This is not reproduced by the semi-analytic models, where the quenching of star formation in satellites occurs by "starvation" and does not depend on their internal structure. HI-rich galaxies with masses greater than 10^10 M_sun are found to be anti-biased compared to galaxies with "normal" HI content. Interestingly, no such effect is found for lower mass galaxies.

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preprint2012arXiv

The GALEX Arecibo SDSS Survey. VI. Second Data Release and Updated Gas Fraction Scaling Relations

We present the second data release from the GALEX Arecibo SDSS Survey (GASS), an ongoing large Arecibo program to measure the HI properties for an unbiased sample of ~1000 galaxies with stellar masses greater than 10^10 Msun and redshifts 0.025<z<0.05. GASS targets are selected from the Sloan Digital Sky Survey (SDSS) spectroscopic and Galaxy Evolution Explorer (GALEX) imaging surveys, and are observed until detected or until a gas mass fraction limit of a few per cent is reached. This second data installment includes new Arecibo observations of 240 galaxies, and marks the 50% of the complete survey. We present catalogs of the HI, optical and ultraviolet parameters for these galaxies, and their HI-line profiles. Having more than doubled the size of the sample since the first data release, we also revisit the main scaling relations of the HI mass fraction with galaxy stellar mass, stellar mass surface density, concentration index, and NUV-r color, as well as the gas fraction plane introduced in our earlier work.

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preprint2012arXiv

Topological p-n Junction

We consider a junction between surface $p$-type and surface $n$-type on an ideal topological insulator in which carrier type and density in two adjacent regions are locally controlled by composition graded doping or electrical gating. Such junction setting on topological insulators are fundamental for possible device application. A single gapless chiral edge state localized along the junction interface appears in the presence of an external magnetic field, and it can be probed by scanning tunneling microscopy and transport measurements. We propose to realize this topological \emph{p-n} junction in (Bi$_{1-x}$Sb$_x$)$_2$Te$_3$, which has insulating bulk properties and a tunable surface state across the Dirac cone.

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preprint2012arXiv

Transmission Character of General Function Photonic Crystals

In the paper, we present a new general function photonic crystals (GFPCs), which refractive index of medium is a arbitrary function of space position. Unlike conventional photonic crystals (PCs), which structure grow from two mediums $A$ and $B$, with different constant refractive indexes $n_{a}$ and $n_{b}$. Based on Fermat principle, we give the motion equations of light in one-dimensional GFPCs, and calculate its transfer matrix, which is different from the conventional PCs. We choose the linearity refractive index function for two mediums $A$ and $B$, and find the transmissivity of one-dimensional GFPCs can be much larger or smaller than 1 for different slope linearity refractive index function, which is different from the transmissivity of conventional PCs (its transmissivity is in the range of 0 and 1). Otherwise, we study the effect of different incident angles, the number of periods and optical thickness on the transmissivity, and obtain some new results different from the conventional PCs.

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preprint2012arXiv

Transport through modes in random media

We show that the field speckle pattern of transmitted microwave radiation can be decomposed into a sum of patterns of the modes of the medium. We find strong correlation between modal field speckle patterns which leads to destructive interference between modes. This allows us to explain complexities of steady state and pulsed transmission of localized waves and to harmonize wave and particle descriptions of diffusion.

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preprint2012arXiv

Unconventional behavior of Dirac fermions in three-dimensional gauge theory

We study the unconventional behavior of massless Dirac fermions due to interaction with a U(1) gauge field in two spatial dimensions. At zero chemical potential, the longitudinal and transverse components of gauge interaction are both long-ranged. There is no fermion velocity renormalization since the system respects Lorentz invariance. At finite chemical potential, the Lorentz invariance is explicitly broken by the finite Fermi surface. The longitudinal gauge interaction is statically screened and becomes unimportant, whereas the transverse gauge interaction remains long-ranged and leads to singular renormalization of fermion velocity. The anomalous dimension of fermion velocity is calculated by means of the renormalization group method. We then examine the influence of singular velocity renormalization on several physical quantities, and show that they exhibit different behavior at zero and finite chemical potential.

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preprint2011arXiv

A New Class of Boron Nanotube

The configurations, stability and electronic structures of a new class of boron sheet and related boron nanotubes are predicted within the framework of density functional theory. This boron sheet is sparser than those of recent proposals. Our theoretic results show that the stable boron sheet remains flat and is metallic. There are bands similar to the p-bands in graphite near the Fermi level. Stable nanotubes with various diameters and chiral vectors can be rolled from the sheet. Within our study, only the thin (8, 0) nanotube with a band gap of 0.44 eV is semiconducting, while all the other thicker boron nanotubes are metallic, independent of their chirality. It indicates the possibility, in the design of nanodevices, to control the electronic transport properties of the boron nanotube through the diameter.

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preprint2011arXiv

Coherent spin control by electromagnetic vacuum fluctuations

In coherent control, electromagnetic vacuum fluctuations usually cause coherence loss through irreversible spontaneous emission. However, since the dissipation via emission is essentially due to correlation of the fluctuations, when emission ends in a superposition of multiple final states, correlation between different pathways may build up if the "which-way" information is not fully resolved (i.e., the emission spectrum is broader than the transition energy range). Such correlation can be exploited for spin-flip control in a $Λ$-type three-level system, which manifests itself as an all-optical spin echo in nonlinear optics with two orders of optical fields saved as compared with stimulated Raman processes. This finding represents a new class of optical nonlinearity induced by electromagnetic vacuum fluctuations.

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preprint2011arXiv

COLD GASS, an IRAM legacy survey of molecular gas in massive galaxies: I. Relations between H2, HI, stellar content and structural properties

We are conducting COLD GASS, a legacy survey for molecular gas in nearby galaxies. Using the IRAM 30m telescope, we measure the CO(1-0) line in a sample of ~350 nearby (D=100-200 Mpc), massive galaxies (log(M*/Msun)>10.0). The sample is selected purely according to stellar mass, and therefore provides an unbiased view of molecular gas in these systems. By combining the IRAM data with SDSS photometry and spectroscopy, GALEX imaging and high-quality Arecibo HI data, we investigate the partition of condensed baryons between stars, atomic gas and molecular gas in 0.1-10L* galaxies. In this paper, we present CO luminosities and molecular hydrogen masses for the first 222 galaxies. The overall CO detection rate is 54%, but our survey also uncovers the existence of sharp thresholds in galaxy structural parameters such as stellar mass surface density and concentration index, below which all galaxies have a measurable cold gas component but above which the detection rate of the CO line drops suddenly. The mean molecular gas fraction MH2/M* of the CO detections is 0.066+/-0.039, and this fraction does not depend on stellar mass, but is a strong function of NUV-r colour. Through stacking, we set a firm upper limit of MH2/M*=0.0016+/-0.0005 for red galaxies with NUV-r>5.0. The average molecular-to-atomic hydrogen ratio in present-day galaxies is 0.3, with significant scatter from one galaxy to the next. The existence of strong detection thresholds in both the HI and CO lines suggests that "quenching" processes have occurred in these systems. Intriguingly, atomic gas strongly dominates in the minority of galaxies with significant cold gas that lie above these thresholds. This suggests that some re-accretion of gas may still be possible following the quenching event.

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preprint2011arXiv

COLD GASS, an IRAM Legacy Survey of Molecular Gas in Massive Galaxies: II. The non-universality of the Molecular Gas Depletion Timescale

We study the relation between molecular gas and star formation in a volume-limited sample of 222 galaxies from the COLD GASS survey, with measurements of the CO(1-0) line from the IRAM 30m telescope. The galaxies are at redshifts 0.025<z<0.05 and have stellar masses in the range 10.0<log(M*/Msun)<11.5. The IRAM measurements are complemented by deep Arecibo HI observations and homogeneous SDSS and GALEX photometry. A reference sample that includes both UV and far-IR data is used to calibrate our estimates of star formation rates from the seven optical/UV bands. The mean molecular gas depletion timescale, tdep(H2), for all the galaxies in our sample is 1 Gyr, however tdep(H2) increases by a factor of 6 from a value of ~0.5 Gyr for galaxies with stellar masses of 10^10 Msun to ~3 Gyr for galaxies with masses of a few times 10^11 Msun. In contrast, the atomic gas depletion timescale remains contant at a value of around 3 Gyr. This implies that in high mass galaxies, molecular and atomic gas depletion timescales are comparable, but in low mass galaxies, molecular gas is being consumed much more quickly than atomic gas. The strongest dependences of tdep(H2) are on the stellar mass of the galaxy (parameterized as log tdep(H2)= (0.36+/-0.07)(log M* - 10.70)+(9.03+/-0.99)), and on the specific star formation rate. A single tdep(H2) versus sSFR relation is able to fit both "normal" star-forming galaxies in our COLD GASS sample, as well as more extreme starburst galaxies (LIRGs and ULIRGs), which have tdep(H2) < 10^8 yr. Normal galaxies at z=1-2 are displaced with respect to the local galaxy population in the tdep(H2) versus sSFR plane and have molecular gas depletion times that are a factor of 3-5 times longer at a given value of sSFR due to their significantly larger gas fractions.

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preprint2011arXiv

Disorder effects at a nematic quantum critical point in d-wave cuprate superconductor

A d-wave high temperature cuprate superconductor exhibits a nematic ordering transition at zero temperature. Near the quantum critical point, the coupling between gapless nodal quasiparticles and nematic order parameter fluctuation can result in unusual behaviors, such as extreme anisotropy of fermion velocities. We study the disorder effect on the nematic quantum critical behavior and especially on the flow of fermion velocities. The disorders that couple to nodal quasiparticles are divided into three types: random mass, random gauge field, and random chemical potential. A renormalization group analysis shows that random mass and random gauge field are both irrelevant and thus do not change the fixed point of extreme velocity anisotropy. However, the marginal interaction due to random chemical potential destroys this fixed point and makes the nematic phase transition unstable.

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preprint2011arXiv

Evolution of cooperation in multilevel public goods games with community structures

In a community-structured population, public goods games (PGG) occur both within and between communities. Such type of PGG is referred as multilevel public goods games (MPGG). We propose a minimalist evolutionary model of the MPGG and analytically study the evolution of cooperation. We demonstrate that in the case of sufficiently large community size and community number, if the imitation strength within community is weak, i.e., an individual imitates another one in the same community almost randomly, cooperation as well as punishment are more abundant than defection in the long run; if the imitation strength between communities is strong, i.e., the more successful strategy in two individuals from distinct communities is always imitated, cooperation and punishment are also more abundant. However, when both of the two imitation intensities are strong, defection becomes the most abundant strategy in the population. Our model provides insight into the investigation of the large-scale cooperation in public social dilemma among contemporary communities.

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preprint2011arXiv

Least Squares Temporal Difference Actor-Critic Methods with Applications to Robot Motion Control

We consider the problem of finding a control policy for a Markov Decision Process (MDP) to maximize the probability of reaching some states while avoiding some other states. This problem is motivated by applications in robotics, where such problems naturally arise when probabilistic models of robot motion are required to satisfy temporal logic task specifications. We transform this problem into a Stochastic Shortest Path (SSP) problem and develop a new approximate dynamic programming algorithm to solve it. This algorithm is of the actor-critic type and uses a least-square temporal difference learning method. It operates on sample paths of the system and optimizes the policy within a pre-specified class parameterized by a parsimonious set of parameters. We show its convergence to a policy corresponding to a stationary point in the parameters' space. Simulation results confirm the effectiveness of the proposed solution.

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preprint2011arXiv

Optimal Power Allocation for OFDM-Based Wire-Tap Channels with Arbitrarily Distributed Inputs

In this paper, we investigate power allocation that maximizes the secrecy rate of orthogonal frequency division multiplexing (OFDM) systems under arbitrarily distributed inputs. Considering commonly assumed Gaussian inputs are unrealistic, we focus on secrecy systems with more practical discrete distributed inputs, such as PSK, QAM, etc. While the secrecy rate achieved by Gaussian distributed inputs is concave with respect to the transmit power, we have found and rigorously proved that the secrecy rate is non-concave under any discrete inputs. Hence, traditional convex optimization methods are not applicable any more. To address this non-concave power allocation problem, we propose an efficient algorithm. Its gap from optimality vanishes asymptotically at the rate of $O(1/\sqrt{N})$, and its complexity grows in the order of O(N), where $N$ is the number of sub-carriers. Numerical results are provided to illustrate the efficacy of the proposed algorithm.

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preprint2011arXiv

Power-Law Decay of Standing Waves on the Surface of Topological Insulators

We propose a general theory on the standing waves (quasiparticle interference pattern) caused by the scattering of surface states off step edges in topological insulators, in which the extremal points on the constant energy contour of surface band play the dominant role. Experimentally we image the interference patterns on both Bi$_2$Te$_3$ and Bi$_2$Se$_3$ films by measuring the local density of states using a scanning tunneling microscope. The observed decay indices of the standing waves agree excellently with the theoretical prediction: In Bi$_2$Se$_3$, only a single decay index of -3/2 exists; while in Bi$_2$Te$_3$ with strongly warped surface band, it varies from -3/2 to -1/2 and finally to -1 as the energy increases. The -1/2 decay indicates that the suppression of backscattering due to time-reversal symmetry does not necessarily lead to a spatial decay rate faster than that in the conventional two-dimensional electron system. Our formalism can also explain the characteristic scattering wave vectors of the standing wave caused by non-magnetic impurities on Bi$_2$Te$_3$.

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preprint2011arXiv

The GALEX Arecibo SDSS Survey V: The Relation between the HI Content of Galaxies and Metal Enrichment at their Outskirts

We have obtained long-slit spectra of 174 star-forming galaxies with stellar masses greater than 10^10 M_\odot from the GALEX Arecibo SDSS (GASS) survey. These galaxies have both HI and H_2 mass measurements. The average metallicity profile is strikingly flat out to R_90, the radius enclosing 90% of the r-band light. Metallicity profiles which decline steadily with radius are found primarily for galaxies in our sample with low stellar mass (Log(M_*)<10.2), concentration, and/or mean stellar mass density. Beyond ~R_90, however, around 10 percent of the galaxies in our sample exhibit a sharp downturn in metallicity. Remarkably, we find that the magnitude of the outer metallicity drop is well correlated with the total HI content of the galaxy (measured as f_HI=M_HI/M_*). We examine the radial profiles of stellar population ages and star formation rate densities, and conclude that the galaxies with largest outer metallicity drops are actively growing their stellar disks, with mass doubling times across the whole disk only one third as long as a typical GASS galaxy. We also describe a correlation between local stellar mass density and metallicity, which is valid across all galaxies in our sample. We argue that much of the recent stellar mass growth at the edges of these galaxies can be linked to the accretion or radial transport of relatively pristine gas from beyond the galaxies' stellar disks.

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preprint2011arXiv

The GALEX Arecibo SDSS Survey. IV. Baryonic Mass-Velocity-Size Relations of Massive Galaxies

We present dynamical scaling relations for a homogeneous and representative sample of ~500 massive galaxies, selected only by stellar mass (>10^10 Msun) and redshift (0.025<z<0.05) as part of the ongoing GALEX Arecibo SDSS Survey. We compare baryonic Tully-Fisher (BTF) and Faber-Jackson (BFJ) relations for this sample, and investigate how galaxies scatter around the best fits obtained for pruned subsets of disk-dominated and bulge-dominated systems. The BFJ relation is significantly less scattered than the BTF when the relations are applied to their maximum samples, and is not affected by the inclination problems that plague the BTF. Disk-dominated, gas-rich galaxies systematically deviate from the BFJ relation defined by the spheroids. We demonstrate that by applying a simple correction to the stellar velocity dispersions that depends only on the concentration index of the galaxy, we are able to bring disks and spheroids onto the same dynamical relation -- in other words, we obtain a generalized BFJ relation that holds for all the galaxies in our sample, regardless of morphology, inclination or gas content, and has a scatter smaller than 0.1 dex. We find that disks and spheroids are offset in the stellar dispersion-size relation, and that the offset is removed when corrected dispersions are used instead. The generalized BFJ relation represents a fundamental correlation between the global dark matter and baryonic content of galaxies, which is obeyed by all (massive) systems regardless of morphology. [abridged]

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preprint2011arXiv

The Subelliptic Heat Kernel on the CR sphere

We study the heat kernel of the sub-Laplacian L on the CR sphere S2n+1. An explicit and geometrically meaningful formula for the heat kernel is obtained. As a by-product we recover in a simple way the Green function of the conformal sub- Laplacian -L + n2 that was obtained by Geller [12], and also get an explicit formula for the sub-Riemannian distance. The key point is to work in a set of coordinates that reflects the symmetries coming from the fibration S2n+1 \rightarrow CPn.

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preprint2011arXiv

Topological insulators for high performance terahertz to infrared applications

Topological insulators in the Bi2Se3 family have an energy gap in the bulk and a gapless surface state consisting of a single Dirac cone. Low frequency optical absorption due to the surface state is universally determined by the fine structure constant. When the thickness of these three dimensional topological insulators is reduced, they become quasi-two dimensional insulators with enhanced absorbance. The two dimensional insulators can be topologically trivial or non-trivial depending on the thickness, and we predict that the optical absorption is larger for topological non-trivial case compared with the trivial case. Since the three dimensional topological insulator surface state is intrinsically gapless, we propose its potential application in wide bandwidth, high performance photo-detection covering a broad spectrum ranging from terahertz to infrared. The performance of photodetection can be dramatically enhanced when the thickness is reduced to several quintuple layers, with a widely tunable band gap depending on the thickness.

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preprint2010arXiv

A Network Analysis Approach to fMRI Condition-Specific Functional Connectivity

In this work we focus on examination and comparison of whole-brain functional connectivity patterns measured with fMRI across experimental conditions. Direct examination and comparison of condition-specific matrices is challenging due to the large number of elements in a connectivity matrix. We present a framework that uses network analysis to describe condition-specific functional connectivity. Treating the brain as a complex system in terms of a network, we extract the most relevant connectivity information by partitioning each network into clusters representing functionally connected brain regions. Extracted clusters are used as features for predicting experimental condition in a new data set. The approach is illustrated on fMRI data examining functional connectivity patterns during processing of abstract and concrete concepts. Topological (brain regions) and functional (level of connectivity and information flow) systematic differences in the ROI-based functional networks were identified across participants for concrete and abstract concepts. These differences were sufficient for classification of previously unseen connectivity matrices as abstract or concrete based on training data derived from other people.

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preprint2010arXiv

Absorption-line probes of the prevalence and properties of outflows in present-day star-forming galaxies

We analyze star forming galaxies drawn from SDSS DR7 to show how the interstellar medium (ISM) Na I 5890, 5896 (Na D) absorption lines depend on galaxy physical properties, and to look for evidence of galactic winds. We combine the spectra of galaxies with similar geometry/physical parameters to create composite spectra with signal-to-noise ~300. The stellar continuum is modeled using stellar population synthesis models, and the continuum-normalized spectrum is fit with two Na I absorption components. We find that: (1) ISM Na D absorption lines with equivalent widths EW > 0.8A are only prevalent in disk galaxies with specific properties -- large extinction (Av), high star formation rates (SFR), high star formation rate per unit area ($Σ_{\rm SFR}$), or high stellar mass (M*). (2) the ISM Na D absorption lines can be separated into two components: a quiescent disk-like component at the galaxy systemic velocity and an outflow component; (3) the disk-like component is much stronger in the edge-on systems, and the outflow component covers a wide angle but is stronger within 60deg of the disk rotation axis; (4) the EW and covering factor of the disk component correlate strongly with dust attenuation, highlighting the importance that dust shielding may play the survival of Na I. (5) The EW of the outflow component depends primarily on $Σ_{\rm SFR}$ and secondarily on Av; (6) the outflow velocity varies from ~120 to 160km/s but shows little hint of a correlation with galaxy physical properties over the modest dynamic range that our sample probes (1.2 dex in log$Σ_{\rm SFR}$ and 1 dex in log M*).

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preprint2010arXiv

Analyzing Force Concept Inventory with Item Response Theory

Item Response Theory (IRT) is a popular assessment method used in education measurement, which builds on an assumption of a probability framework connecting students' innate ability and their actual performances on test items. The model transforms students' raw test scores through a nonlinear regression process into a scaled proficiency rating, which can be used to compare results obtained with different test questions. IRT also provides a theoretical approach to address ceiling effect and guessing. We applied IRT to analyze the Force Concept Inventory (FCI). The data was collected from 2802 students taking intro level mechanics courses at The Ohio State University. The data was analyzed with a 3-parameter item response model for multiple choice questions. We describe the procedures of the analysis and discuss the results and the interpretations. The analysis outcomes are compiled to provide a detailed IRT measurement metric of the FCI, which can be easily referenced and used by teachers and researchers for a range of assessment applications.

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preprint2010arXiv

Confinement induced by fermion damping in three-dimensional QED

The three-dimensional non-compact QED is known to exhibit weak confinement when fermions acquire a finite mass via the mechanism of dynamical chiral symmetry breaking. In this paper, we study the effect of fermion damping caused by elastic scattering on the classical potential between fermions. By calculating the vacuum polarization function that incorporates the fermion damping effect, we show that fermion damping can induce a weak confinement even when the fermions are massless and the chiral symmetry is not broken.

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preprint2010arXiv

Dust Attenuation in UV-selected Starbursts at High Redshift and their Local Counterparts: Implications for the Cosmic Star Formation Rate Density

We present a new analysis of the dust obscuration in starburst galaxies at low and high redshift. This study is motivated by our unique sample of the most extreme UV-selected starburst galaxies in the nearby universe (z<0.3), found to be good analogs of high-redshift Lyman Break Galaxies (LBGs) in most of their physical properties. We find that the dust properties of the Lyman Break Analogs (LBAs) are consistent with the relation derived previously by Meurer et al. (M99) that is commonly used to dust-correct star formation rate measurements at a very wide range of redshifts. We directly compare our results with high redshift samples (LBGs, BzK, and sub-mm galaxies at z=2-3) having IR data either from Spitzer or Herschel. The attenuation in typical LBGs at z=2-3 and LBAs is very similar. Because LBAs are much better analogs to LBGs compared to previous local star-forming samples, including M99, the practice of dust-correcting the SFRs of high redshift galaxies based on the local calibration is now placed on a much more solid ground. We illustrate the importance of this result by showing how the locally calibrated relation between UV measurements and extinction is used to estimate the integrated, dust-corrected star formation rate density at z=2-6.

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preprint2010arXiv

IRAS F13308+5946: A Possible Transition Phase From Type I ULIRG To Optical Quasar

We present a stellar population synthesis study of a type I luminous infrared galaxy (LIRG): IRAS F13308+5946. It is a quasar with absolute magnitude Mi = -22.56 and has a spectral feature of a Seyfert 1.5 galaxy. Optical images show characteristics of later stages of a merger. With the help of the stellar synthesis code STARLIGHT (Cid Fernandes et al. 2005) and both Calzetti et al. (2000) and Leitherer et al.'s (2002) extinction curves, we estimate the past infrared (IR) luminosities of the host galaxy and find it may have experienced an ultraluminous infrared galaxy (ULIRG) phase for nearly 300 Myr, so this galaxy has probably experienced a type I ULIRG phase. Both nuclear starburst and active galactic nuclei (AGN) contribute to the present IR luminosity budget, and starburst contributes ~70%. The mass of supermassive black-hole (SMBH) is M_BH = 1.8*10^8 M_sun and the Eddington ratio L_bol/L_Edd is 0.12, which both approximate to typical values of PG QSOs. These results indicate that IRAS F13308+5946 is probably at the transitional phase from a type I ULIRG to a classical QSO.

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preprint2010arXiv

Landau Quantization of Massless Dirac Fermions in Topological Insulator

The recent theoretical prediction and experimental realization of topological insulators (TI) has generated intense interest in this new state of quantum matter. The surface states of a three-dimensional (3D) TI such as Bi_2Te_3, Bi_2Se_3 and Sb_2Te_3 consist of a single massless Dirac cones. Crossing of the two surface state branches with opposite spins in the materials is fully protected by the time reversal (TR) symmetry at the Dirac points, which cannot be destroyed by any TR invariant perturbation. Recent advances in thin-film growth have permitted this unique two-dimensional electron system (2DES) to be probed by scanning tunneling microscopy (STM) and spectroscopy (STS). The intriguing TR symmetry protected topological states were revealed in STM experiments where the backscattering induced by non-magnetic impurities was forbidden. Here we report the Landau quantization of the topological surface states in Bi_2Se_3 in magnetic field by using STM/STS. The direct observation of the discrete Landau levels (LLs) strongly supports the 2D nature of the topological states and gives direct proof of the nondegenerate structure of LLs in TI. We demonstrate the linear dispersion of the massless Dirac fermions by the square-root dependence of LLs on magnetic field. The formation of LLs implies the high mobility of the 2DES, which has been predicted to lead to topological magneto-electric effect of the TI.

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preprint2010arXiv

Probing the Nature of High-z Short GRB 090426 with Its Early Optical and X-ray Afterglows

GRB 090426 is a short duration burst detected by Swift ($T_{90}\sim 1.28$ s in the observer frame, and $T_{90}\sim 0.33$ s in the burst frame at $z=2.609$). Its host galaxy properties and some $γ$-ray related correlations are analogous to those seen in long duration GRBs, which are believed to be of a massive-star origin (so-called Type II GRBs). We present the results of its early optical observations with the 0.8-m TNT telescope at Xinglong observatory, and the 1-m LOAO telescope at Mt. Lemmon Optical Astronomy Observatory in Arizona. Our well-sampled optical afterglow lightcurve covers from $\sim 90$ seconds to $\sim 10^4$ seconds post the GRB trigger. It shows two shallow decay episodes that are likely due to energy injection, which end at $\sim 230$ seconds and $\sim 7100$ seconds, respectively. The decay slopes post the injection phases are consistent with each other ($α\simeq 1.22$). The X-ray afterglow lightcurve appears to trace the optical, although the second energy injection phase was missed due to visibility constraints introduced by the {\em Swift} orbit. The X-ray spectral index is $β_X\sim 1.0$ without temporal evolution. Its decay slope is consistent with the prediction of the forward shock model. Both X-ray and optical emission is consistent with being in the same spectral regime above the cooling frequency ($ν_c$). The fact that $ν_c$ is below the optical band from the very early epoch of the observation provides a constraint on the burst environment, which is similar to that seen in classical long duration GRBs. We therefore suggest that death of a massive star is the possible progenitor of this short burst.

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preprint2010arXiv

Quantitative Determination of Eliashberg Function and Evidence of Strong Electron Coupling with Multiple Phonon Modes in Heavily Over doped (Bi,Pb)2Sr2CuO6

Super-high resolution laser-based angle-resolved photoemission spectroscopy measurements have been carried out on a heavily overdoped (Bi,Pb)2Sr2CuO6 (Tc> 5 K) superconductor. Taking advantage of the high-precision data on the subtle change of the quasi-particle dispersion at different temperatures, we develop a general procedure to determine the bare band dispersion and extract the bosonic spectral function quantitatively. Our results show unambiguously that the 70 meV nodal kink is due to the electron coupling with the multiple phonon modes, with a large mass enhancement factor Lamda= 0.42 even in the heavily over-doped regime.

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preprint2010arXiv

Second-order nonlinear optical effects of spin currents

A pure spin current formed by opposite spins moving in opposite directions is a rank-2 axial tensor which breaks the inversion symmetry. Thus a spin current has a second-order optical susceptibility, with unique polarization-dependence determined by the symmetry properties of the current. In particular, a longitudinal spin current, in which the spin polarization directions are parallel or anti-parallel to the moving directions, being a chiral quantity, leads to a chiral sum-frequency effect. Microscopic calculations based on the eight-band model of a III-V compound semiconductor confirm the symmetry analysis and show that the susceptibility is quite measurable under realistic conditions. The second-order nonlinear optical effects may be used for in-situ and non-destructive detection of spin currents, as a standard spectroscopy tool in research of spintronics.

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preprint2010arXiv

Slow light with a swept-frequency source

We introduce a new concept for stimulated-Brillouin-scattering-based slow light in optical fibers that is applicable for broadly-tunable frequency-swept sources. It allows slow light to be achieved, in principle, over the entire transparency window of the optical fiber. We demonstrate a slow light delay of 10 ns at 1550 nm using a 10-m-long photonic crystal fiber with a source sweep rate of 0.4 MHz/ns and a pump power of 200 mW. We also show that there exists a maximal delay obtainable by this method, which is set by the SBS threshold, independent of sweep rate. For our fiber with optimum length, this maximum delay is ~38 ns, obtained for a pump power of 760 mW.

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preprint2010arXiv

The GALEX Arecibo SDSS Survey II: The Star Formation Efficiency of Massive Galaxies

We use measurements of the HI content, stellar mass and star formation rates in ~190 massive galaxies with stellar masses greater than 10^10 Msun, obtained from the Galex Arecibo SDSS Survey (GASS) described in Paper I (Catinella et al. 2010) to explore the global scaling relations associated with the bin-averaged ratio of the star formation rate over the HI mass, which we call the HI-based star formation efficiency (SFE). Unlike the mean specific star formation rate, which decreases with stellar mass and stellar mass surface density, the star formation efficiency remains relatively constant across the sample with a value close to SFE = 10^-9.5 yr^-1 (or an equivalent gas consumption timescale of ~3 Gyr). Specifically, we find little variation in SFE with stellar mass, stellar mass surface density, NUV-r color and concentration. We interpret these results as an indication that external processes or feedback mechanisms that control the gas supply are important for regulating star formation in massive galaxies. An investigation into the detailed distribution of SFEs reveals that approximately 5% of the sample shows high efficiencies with SFE > 10^-9 yr^-1, and we suggest that this is very likely due to a deficiency of cold gas rather than an excess star formation rate. Conversely, we also find a similar fraction of galaxies that appear to be gas-rich for their given specific star-formation rate, although these galaxies show both a higher than average gas fraction and lower than average specific star formation rate. Both of these populations are plausible candidates for "transition" galaxies, showing potential for a change (either decrease or increase) in their specific star formation rate in the near future. We also find that 36+/-5% of the total HI mass density and 47+/-5% of the total SFR density is found in galaxies with stellar mass greater than 10^10 Msun. [abridged]

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preprint2010arXiv

The GALEX Arecibo SDSS survey: III. Evidence for the Inside-Out Formation of Galactic Disks

We analyze a sample of galaxies with stellar masses greater than $10^{10} M_{\odot}$ and with redshifts in the range $0.025<z<0.05$ for which HI mass measurements are available from the GALEX Arecibo SDSS Survey (GASS) or from the Arecibo Legacy Fast ALFA survey (ALFALFA). At a given value of $M_*$, our sample consists primarily of galaxies that are more HI-rich than average. We constructed a series of three control samples for comparison with these HI-rich galaxies. As expected, HI-rich galaxies differ strongly from galaxies of same stellar mass that are selected without regard to HI content. The majority of these differences are attributable to the fact that galaxies with more gas are bluer and more actively star-forming. In order to identify those galaxy properties that are causally connected with HI content, we compare results derived for the HI sample with those derived for galaxies matched in stellar mass, size and NUV-$r$ colour. The only photometric property that is clearly attributable to increasing HI content, is the colour gradient of the galaxy. Galaxies with larger HI fractions have bluer, more actively star-forming outer disks compared to the inner part of the galaxy. HI-rich galaxies also have larger $g$-band radii compared to $i$-band radii. Our results are consistent with the "inside-out" picture of disk galaxy formation, which has commonly served as a basis for semi-analytic models of the formation of disks in the context of Cold Dark Matter cosmologies. The lack of any intrinsic connection between HI fraction and galaxy asymmetry suggests that gas is accreted smoothly onto the outer disk.

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preprint2010arXiv

Thermodynamics of Spinor Quintom

We discuss the thermodynamic properties of dark energy (DE) with Quintom matter in spinor scenario. (1).Using the Cardy-Verlinde formula, we investigate the conditions of validity of the Generalized Second Law of thermodynamics (GSL) in the four evolutionary phases of Spinor Quintom-B model. We also clarify its relation with three cosmological entropy bounds. (2). We take thermodynamic stability of the combination between Spinor Quintom DE and the generalized Chaplygin Gas (GCG) perfect fluid into account, and we find that in the case of $β>0$ and $0<T<T_0$, the system we consider is thermodynamically stable. (3) Making use of the Maxwell Relation and integrability condition, we derive all thermal quantities as functions of either entropy or volume, and present the relation with quantum perturbation stability.

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preprint2010arXiv

Topological Magnetic Insulators with Corundum Structure

Topological insulators are new states of quantum matter in which surface states residing in the bulk insulating gap are protected by time-reversal symmetry. When a proper kind of antiferromagnetic long range order is established in a topological insulator, the system supports axionic excitations. In this paper, we study theoretically the electronic states in a transition metal oxide of corundum structure, in which both spin-orbit interaction and electron-electron interaction play crucial roles. A tight-binding model analysis predicts that materials with this structure can be strong topological insulator. Because of the electron correlation, an antiferromagnetic order may develop, giving rise to a topological magnetic insulator phase with axionic excitations.

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preprint2010arXiv

UGC8802: A Massive Disk Galaxy in Formation

We report new observations of the galaxy UGC8802 obtained through GASS, the GALEX Arecibo SDSS Survey, which show this galaxy to be in a remarkable evolutionary state. UGC8802 (GASS35981) is a disk galaxy with stellar mass M*=2x10^10 Msolar which appears to contain an additional 2.1x10^10 Msolar of HI gas. New millimeter observations with the IRAM 30m telescope indicate a molecular gas mass only a tenth this large. Using deep long-slit spectroscopy, we examine the spatially resolved star formation rate and metallicity profiles of GASS35981 for clues to its history. We find that the star formation surface density in this galaxy is low (Sigma_SFR=0.003 Msolar/yr/kpc^2) and that the star formation is spread remarkably evenly across the galaxy. The low molecular gas masses measured in our three IRAM pointings are largely consistent with the total star formation measured within the same apertures. Our MMT long-slit spectrum reveals a sharp drop in metallicity in the outer disk of GASS35981. The ratio of current star formation rate to existing stellar mass surface density in the outer disk is extremely high, implying that all the stars must have formed within the past ~1Gyr. At current star formation rates, however, GASS35981 will not consume its HI reservoir for another 5-7 Gyr. Despite its exceptionally large gas fraction for a galaxy this massive, GASS35981 has a regular rotation curve and exhibits no sign of a recent interaction or merger. We speculate that GASS35981 may have acquired its gas directly from the inter-galactic medium, and that it and other similar galaxies identified in the GASS survey may provide rare local glimpses of gas accretion processes that were more common during the prime epoch of disk galaxy formation at z~1.

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preprint2010arXiv

Violent Intranight Optical Variability of a Radio-Loud Narrow-Line Seyfert 1 Galaxy: SDSS J094857.3+002225

SDSS J094857.3+002225 is a very radio-loud narrow-line Seyfert 1 (NLS1) galaxy. Here, we report our discovery of the intranight optical variability (INOV) of this galaxy through the optical monitoring in the B and R bands that covered seven nights in 2009. Violent rapid variability in the optical bands was identified in this RL-NLS1 for the first time, and the amplitudes of the INOV reaches 0.5 mag in both the B and R bands on the timescale of several hours. The detection of the INOV provides a piece of strong evidence supporting the fact that the object carries a relativistic jet with a small viewing angle, which confirms the conclusion drawn from the previous multi-wavelength studies.

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preprint2009arXiv

Dynamical Axion Field in Topological Magnetic Insulators

Axions are very light, very weakly interacting particles postulated more than 30 years ago in the context of the Standard Model of particle physics. Their existence could explain the missing dark matter of the universe. However, despite intensive searches, they have yet to be detected. In this work, we show that magnetic fluctuations of topological insulators couple to the electromagnetic fields exactly like the axions, and propose several experiments to detect this dynamical axion field. In particular, we show that the axion coupling enables a nonlinear modulation of the electromagnetic field, leading to attenuated total reflection. We propose a novel optical modulators device based on this principle.

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preprint2009arXiv

The GALEX Arecibo SDSS Survey. I. Gas Fraction Scaling Relations of Massive Galaxies and First Data Release

We introduce the GALEX Arecibo SDSS Survey (GASS), an on-going large program that is gathering high quality HI-line spectra using the Arecibo radio telescope for an unbiased sample of ~1000 galaxies with stellar masses greater than 10^10 Msun and redshifts 0.025<z<0.05, selected from the SDSS spectroscopic and GALEX imaging surveys. The galaxies are observed until detected or until a low gas mass fraction limit (1.5-5%) is reached. This paper presents the first Data Release, consisting of ~20% of the final GASS sample. We use this data set to explore the main scaling relations of HI gas fraction with galaxy structure and NUV-r colour. A large fraction (~60%) of the galaxies in our sample are detected in HI. We find that the atomic gas fraction decreases strongly with stellar mass, stellar surface mass density and NUV-r colour, but is only weakly correlated with galaxy bulge-to-disk ratio (as measured by the concentration index of the r-band light). We also find that the fraction of galaxies with significant (more than a few percent) HI decreases sharply above a characteristic stellar surface mass density of 10^8.5 Msun kpc^-2. The fraction of gas-rich galaxies decreases much more smoothly with stellar mass. One of the key goals of GASS is to identify and quantify the incidence of galaxies that are transitioning between the blue, star-forming cloud and the red sequence of passively-evolving galaxies. Likely transition candidates can be identified as outliers from the mean scaling relations between gas fraction and other galaxy properties. [abridged]

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preprint2009arXiv

The UV-optical colours of brightest cluster galaxies in optically and X-ray selected clusters

Many brightest cluster galaxies (BCGs) at the centers of X-ray selected clusters exhibit clear evidence for recent star formation. However, studies of BCGs in optically-selected clusters show that star formation is not enhanced when compared to control samples of non-BCGs of similar stellar mass. Here we analyze a sample of 113 BCGs in low redshift (z<0.1), optically-selected clusters, a matched control sample of non-BCGs, and a smaller sample of BCGs in X-ray selected clusters. We convolve the SDSS images of the BCGs to match the resolution of the GALEX data and we measure UV-optical colours in their inner and outer regions. We find that optically-selected BCGs exhibit smaller scatter in optical colours and redder inner NUV-r colours than the control galaxies, indicating that they are a homogenous population with very little ongoing star formation. The BCGs in the X-ray selected cluster sample span a similar range in optical colours, but have bluer NUV-r colours. Among X-ray selected BCGs, those located in clusters with central cooling times of less than 1 Gyr are significantly bluer than those located in clusters where the central gas cooling times are long. Our main conclusion is that the location of a galaxy at the centre of its halo is not sufficient to determine whether or not it is currently forming stars. One must also have information about the thermodynamic state of the gas in the core of the halo.

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preprint2009arXiv

Three Spectacular HII-buried-AGN Galaxies from SDSS

We present our analysis of the three HII-buried-AGN: SDSS J091053+333008, SDSS J121837+091324, and SDSS J153002-020415, by studying their optical spectra extracted from SDSS. The location in the BPT diagnostic diagrams of the three galaxies indicates that the narrow emission lines are mainly exited from HII regions. However, after the removal of the host galaxy's stellar emission, the emission lines display the typical feature of Narrow-line Seyfert 1-like. All of the three objects have large Eddington ratio, small black hole mass, and low star formation rate. We propose that the three galaxies are at the transit stage from the starburst-dominated phase to AGN-dominated phase.

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preprint2008arXiv

Learning of Content Knowledge and Development of Scientific Reasoning Ability: A Cross Culture Comparison

Student content knowledge and general reasoning abilities are two important areas in education practice and research. However, there hasn't been much work in physics education that clearly documents the possible interactions between content learning and the development of general reasoning abilities. In this paper, we report one study of a systematic research to investigate the possible interactions between students' learning of physics content knowledge and the development of general scientific reasoning abilities. Specifically, this study seeks to answer the research question of whether and to what extent content learning may affect the development of general reasoning abilities. College entrance testing data of freshman college students in both USA and China were collected using three standardized tests, FCI, BEMA, and Lawson's Classroom Test of Scientific Reasoning (Lawson Test). The results suggest that years of rigorous training of physics knowledge in middle and high schools have made significant impact on Chinese students' ability in solving physics problems, while such training doesn't seem to have direct effects on their general ability in scientific reasoning, which was measured to be at the same level as that of the students in USA. Details of the curriculum structures in the education systems of USA and China are also compared to provide a basis for interpreting the assessment data.

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preprint1999arXiv

A low-energy solution to the mu-problem in gauge mediation

In the gauge-mediation framework the soft supersymmetry breaking mass parameters of the supersymmetric standard model are induced by the gauge interactions of some messenger fields. The parameters exhibit flavor universality which is dictated by the gauge interactions and which efficiently eliminates new dangerous contributions to flavor changing neutral currents. However, the Higgs potential in this framework typically contains an unacceptable hierarchy between its dimensionful parameters (the $μ$-problem of gauge mediation). We show that the problem can be resolved if the Higgs potential arises dynamically once an intermediate U(1)' sector is integrated out rather than arising radiatively from some Yukawa interactions at the messenger scale. As an added benefit, such models may naturally avoid new contribution to CP violating amplitudes. The proposed framework is described, explicit examples are given and its phenomenology is explored. The $μ$ problem is resolved in this case by the low-energy U(1)' dynamics which could be tested in future collider experiments.

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preprint1998arXiv

Towards a Unified Model for the `Diffuse Ionized Medium' in Normal and Starburst Galaxies

We analyze H$α$ images and long-slit spectra of samples of normal and starburst galaxies to better understand the nature of the diffuse, low-surface-brightness gas in these galaxies. We find that in both samples there is a strong inverse correlation between the H$α$ surface-brightness ($Σ_{Hα}$) and the [SII]/H$α$ line ratio at a given location in the galaxy. However, the correlation for the starbursts is offset brightward by an order-of-magnitude in H$α$ surface-brightness at a given line ratio. In contrast, we find that all the galaxies (starburst and normal alike) define a universal relation between line ratio and the relative H$α$ surface brightness ($Σ_{Hα}/Σ_e$, where $Σ_e$ is the mean H$α$ surface brightness within the galaxy half-light radius). We show that such a universal correlation is a natural outcome of a model in which the DIM is photoionized gas that has a characteristic thermal pressure ($P$) that is proportional to the mean rate of star-formation per unit area in the galaxy ($Σ_{SFR}$). Good quantitative agreement with the data follows if we require the constant of proportionality to be consistent with the values of $P$ and $Σ_{SFR}$ in the local disk of the Milky Way. Such a scaling between $P$ and $Σ_{SFR}$ may arise either because feedback from massive stars heats the ISM or because $Σ_{SFR}$ is determined (or limited) by the mean gas pressure.

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