BZPEERPapers, people, evidence and research paths
Menu

Discover

SearchFind papers, people, topics, institutions and opportunities from one place.GraphSee how papers, authors, topics and evidence connect.

Research tools

HomeContinue from the papers, people and topics that matter now.CollectionsTurn saved papers into reading lists, evidence maps and shareable context.ResearchReconstruct the path that led you from search to paper to question.CollaborationMove from a paper, topic or expert request into a focused thread.PublishCreate full-text research articles with metadata, formulas and versions.

Network

ExpertsFind specialists whose work and reviews explain why they can help.CommunitiesJoin research groups organized around papers, topics and shared evidence.

Opportunities

ListingsBrowse roles, calls and collaborations connected to real research context.

Account

Log inReturn to your saved papers, graph views and active threads.Create accountStart saving papers, building a research trail and joining teams.
Log inCreate account

Source author record

Ying Liu

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

ResearcherUnclaimed source record
cond-mat.mtrl-scicond-mat.mes-hallcond-mat.supr-conastro-ph.SRComputer Visionphysics.soc-phSocial and Information Networkseess.IVMachine Learningquant-phArtificial Intelligenceastro-ph.GAcond-mat.str-elInformation Theorymath.ITMethodologyNetworking and Internet Architecturephysics.app-phphysics.space-phComputationComputation and LanguageCryptography and Securityhep-phphysics.comp-phQuantitative MethodsApplicationsastro-ph.COComputational Complexitycond-mat.softcond-mat.stat-mechcs.CYDistributed, Parallel, and Cluster Computingecon.GNEmerging Technologiesgr-qcGraphicshep-thmath-phmath.MPmath.PRMultimedianlin.PSphysics.ao-phphysics.geo-phphysics.opticsphysics.plasm-phq-fin.ECq-fin.STRobotics

Catalog footprint

What is connected

103works
49topics
4close collaborators

Actions

Connect this record

Log in to claim
Open graphBrowse works

Research graph

See the researcher in context

Open full explorer

Inspect adjacent papers, topics, institutions and collaborators without losing the researcher page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

103 published item(s)

preprint2026arXiv

Adaptive Fused Prior Transfer for Controllable Generative Image Compression

Learned image compression has achieved competitive rate-distortion performance, but very-low-bitrate reconstruction remains difficult because the transmitted representation often cannot preserve fine textures and local structures. Perceptual and generative codecs address this problem by using learned reconstruction priors, and controllable codecs allow one model to cover different bitrate and reconstruction preferences. However, controllability alone does not resolve the decoder-side reconstruction-prior problem: under severe bit constraints, the decoder must infer missing details from limited transmitted information, while existing codebook-based controllable designs generally rely on single-codebook token-based priors. This paper proposes Adaptive Fused Prior Transfer for Controllable Generative Image Compression (AFP-GIC), a controllable codec that transfers an adaptive fused prior from a frozen pretrained AdaCode model. Encoder-side fused-prior features guide latent formation, while the decoder predicts a compatible fused prior from the compressed representation and selected control variables, enabling prior-guided reconstruction without transmitting the fused prior itself. A motivating analysis relates decoder-side fused-prior alignment to a reconstruction-error upper bound and shows that the fused-prior family contains single-codebook choices as special cases. Under the unified benchmark, AFP-GIC reduces decoder latency by 18.1% and the overall parameter count by 31.10 million (20.5%) relative to DC-VIC. Experiments on Kodak, CLIC2020, and DIV2K show competitive PSNR, with the clearest perceptual gains in NIQE scores and very-low-bitrate visual comparisons.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors

Existing diffusion models have made significant progress in generating realistic images. However, their direct adaptation to remote sensing imagery often disregards intrinsic physical laws. This oversight frequently leads to spectral distortion and radiometric inconsistency, severely limiting the scientific utility of generated data. To address this issue, this paper introduces AnyBand-Diff, a novel spectral-prior-guided diffusion framework tailored for robust spectral reconstruction. Specifically, we design a Masked Conditional Diffusion backbone integrated with a dual stochastic masking strategy, empowering the model to recover complete spectral information from arbitrary band subsets. Subsequently, to ensure radiometric fidelity, a Physics-Guided Sampling mechanism is proposed, leveraging gradients from a differentiable physical model to explicitly steer the denoising trajectory toward the manifold of physically plausible solutions. Furthermore, a Multi-Scale Physical Loss is formulated to enforce rigorous constraints across pixel, region, and global levels in a joint manner. Extensive experiments confirm the effectiveness of AnyBand-Diff in generating reliable imagery and achieving accurate spectral reconstruction, contributing to the advancement of physics-aware generative methods for Earth observation.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

D2-CDIG: Controlled Diffusion Remote Sensing Image Generation with Dual Priors of DEM and Cloud-Fog

Remote sensing image generation provides a reliable data foundation for remote sensing large models and downstream tasks. However, existing controllable remote sensing image generation methods typically rely on traditional techniques such as segmentation and edge detection, which do not fully leverage terrain or atmospheric conditions. As a result, the generated images often lack accuracy and naturalness when dealing with complex terrains and atmospheric phenomena. In this paper, we propose a novel remote sensing image generation framework, D2-CDIG, which integrates diffusion models with a dual-prior control mechanism. By incorporating both Digital Elevation Model (DEM) and cloud-fog information as dual prior knowledge, D2-CDIG precisely controls ground features and atmospheric phenomena within the generated images. Specifically, D2-CDIG decouples the terrain and atmospheric generation processes through independent control of ground and atmospheric branches. Additionally, a refined cloud-fog slider is introduced to flexibly adjust cloud thickness and distribution. During training, ground and atmospheric control signals are injected in layers to ensure a seamless transition within the images. Compared to traditional methods based on segmentation or edge detection, D2-CDIG shows significant improvements in image quality, detail richness, and realism. D2-CDIG offers a flexible and precise solution for remote sensing image generation, providing high-quality data for training large remote sensing models and downstream tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Deep RL Dual Sourcing Inventory Management with Supply and Capacity Risk Awareness

In this work, we study how to efficiently apply reinforcement learning (RL) for solving large-scale stochastic optimization problems by leveraging intervention models. The key of the proposed methodology is to better explore the solution space by simulating and composing the stochastic processes using pre-trained deep learning (DL) models. We demonstrate our approach on a challenging real-world application, the multi-sourcing multi-period inventory management problem in supply chain optimization. In particular, we employ deep RL models for learning and forecasting the stochastic supply chain processes under a range of assumptions. Moreover, we also introduce a constraint coordination mechanism, designed to forecast dual costs given the cross-products constraints in the inventory network. We highlight that instead of directly modeling the complex physical constraints into the RL optimization problem and solving the stochastic problem as a whole, our approach breaks down those supply chain processes into scalable and composable DL modules, leading to improved performance on large real-world datasets. We also outline open problems for future research to further investigate the efficacy of such models.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Destination Drone: A Comprehensive Analysis of Japanese Consumer Choice Behavior and Intentions for Drone Delivery Services

The potential for drone delivery services to transform logistics systems and consumer behavior has gained increasing attention. However, comprehensive empirical evidence on consumer delivery choice behavior within the context of transportation and urban air logistics remains limited, particularly in Japan. This study addresses this gap by examining Japanese consumers' preferences and behavioral intentions toward drone delivery services. Using a stated preference (SP) survey and discrete choice modeling approaches, including multinomial logit (MNL) and mixed logit (MMNL) models, the analysis evaluates how delivery cost, delivery time, drop-off location, product type, and social influence affect delivery mode choices across different demographic groups. The results indicate that although consumers express interest in drone delivery, perceived cost and concerns related to reliability continue to constrain adoption. Younger and male consumers exhibit higher preferences for drone delivery, while product type, particularly daily consumer goods and medical or healthcare items, plays a significant role in shaping preferences. Post-estimation willingness-to-pay and elasticity analyses further highlight consumers' sensitivity to delivery pricing and speed attributes. Overall, the findings provide actionable insights for logistics service providers and policymakers regarding pricing strategies, service targeting, and deployment approaches for integrating drone delivery into Japan's evolving logistics system.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate

On-policy distillation (OPD) trains a student on its own trajectories under token-level teacher supervision, but existing methods are capped by a single-teacher capability ceiling: when the teacher errs, the student inherits the error. OPD also remains largely unexplored in agentic tasks, where per-step errors compound across long trajectories and destabilize training. We propose MAD-OPD (Multi-Agent Debate-driven On-Policy Distillation), which breaks this ceiling by recasting the distillation teacher as a deliberative collective of teachers that debate over the student's on-policy state; the debate produces an emergent collective intelligence that supplies token-level supervision, with each teacher's contribution weighted by its post-debate confidence. To extend OPD to agentic tasks, we also introduce On-Policy Agentic Distillation (OPAD), which adds step-level sampling to stabilize training under multi-step error compounding. We additionally derive a task-adaptive divergence principle, selecting JSD (Jensen-Shannon divergence) for agentic stability and reverse KL (Kullback-Leibler) divergence for code generation, and verify it both theoretically and empirically. Across six teacher-student configurations (Qwen3 and Qwen3.5; 1.7B-14B students, 8B-32B teachers) and five agentic and code benchmarks, MAD-OPD ranks first across all six configurations; on the 14B+8B$\to$4B setting it lifts the agentic average by $+2.4\%$ and the code average by $+3.7\%$ over the stronger single-teacher OPD.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

PointSLAM++: Robust Dense Neural Gaussian Point Cloud-based SLAM

Real-time 3D reconstruction is crucial for robotics and augmented reality, yet current simultaneous localization and mapping(SLAM) approaches often struggle to maintain structural consistency and robust pose estimation in the presence of depth noise. This work introduces PointSLAM++, a novel RGB-D SLAM system that leverages a hierarchically constrained neural Gaussian representation to preserve structural relationships while generating Gaussian primitives for scene mapping. It also employs progressive pose optimization to mitigate depth sensor noise, significantly enhancing localization accuracy. Furthermore, it utilizes a dynamic neural representation graph that adjusts the distribution of Gaussian nodes based on local geometric complexity, enabling the map to adapt to intricate scene details in real time. This combination yields high-precision 3D mapping and photorealistic scene rendering. Experimental results show PointSLAM++ outperforms existing 3DGS-based SLAM methods in reconstruction accuracy and rendering quality, demonstrating its advantages for large-scale AR and robotics.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Ramp Josephson junctions of Al/Ti/Sr2RuO4: Observation of single-domain quantum oscillations and the detection of chiral edge current

The determination of how the phase of the superconducting order parameter in a superconductor varies with the spatial direction, which can be done only through the Josephson-effect-based phase-sensitive measurements, is crucial for the establishment of the precise pairing symmetry of the superconductor. So far, such measurements have been done on high-Tc cuprate superconductors but only at a couple of directions for Sr2RuO4 because of the difficulty in preparing Josephson junctions between Sr2RuO4 and an s-wave superconductor with a chosen mutual orientation. Another long-standing issue in Sr2RuO4, which was shown previously to feature a spontaneously broken time-reversal symmetry by muon spin rotation and other measurements, is that the expected presence of chiral surface currents, domains, and domain walls is yet to be explicitly shown experimentally. To address these issues, we have long sought the preparation of high-quality Josephson junctions between Sr2RuO4 and a conventional s-wave with a controllable orientation relative to symmetry axes in Sr2RuO4. We report in this article the successful fabrication of ramp Josephson junctions of Al/Ti/Sr2RuO4 on thin single crystals of Sr2RuO4 obtained by mechanical exfoliation. These junctions were found to show high-quality quantum oscillations consistent with a single-domain Josephson coupling. The normal junction resistance was found to depend extremely sensitively on the supercurrent flowing in the Sr2RuO4 crystal on which the Josephson junction was made. This finding was used in the present work to provide an estimate of the size of the chiral surface current, which is shown to agree with its upper bound established previously.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SRTJ: Self-Evolving Rule-Driven Training-Free LLM Jailbreaking

LLMs are increasingly equipped with safety alignment mechanisms, yet recent studies demonstrate that they remain vulnerable to jailbreaking attacks that elicit harmful behaviors without explicit policy violations. While a growing body of work has explored automated jailbreak strategies, existing methods face several fundamental challenges, including the lack of systematic utilization of both successful and failed attack experiences, as well as the absence of principled mechanisms for composing and selecting reusable attack rules under diverse constraints. As a result, existing methods struggle to accumulate transferable knowledge over time and to reliably adapt attack strategies across different targets and evolving safety mechanisms. To address these issues, we propose a Self-Evolving Rule-Driven Training-Free Jailbreak (SRTJ) framework that systematically discovers, composes, and refines attack strategies through interaction and feedback, without updating model parameters. Specifically, SRTJ couples experience-driven attack generation with answer set programming (ASP)-based rule selection and constraint-aware composition, where iterative verifier feedback is leveraged to jointly refine successful strategies and analyze failure patterns. The resulting rule memory evolves in a hierarchical multi-level manner, explicitly organizing distilled attack knowledge into long-term, middle-term, and short-term rules, thereby capturing both stable transferable strategies and transient adaptive behaviors to effectively balance exploration and exploitation across attack attempts. Extensive experiments on mainstream jailbreak benchmark (HarmBench) demonstrate that SRTJ achieves strong and stable attack performance across different target LLMs, while exhibiting improved robustness and generalization compared to existing jailbreak methods. The code is available at https://github.com/TheSolkatt/SRTJ.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Coexistence of ferroelectric and ferrielectric phases in ultrathin antiferroelectric PbZrO3 thin films

Whereas ferroelectricity may vanish in ultra-thin ferroelectric films, it is expected to emerge in ultra-thin anti-ferroelectric films, sparking people's interest in using antiferroelectric materials as an alternative to ferroelectric ones for high-density data storage applications. Lead Zirconate (PbZrO3) is considered the prototype material for antiferroelectricity, and indeed previous studies indicated that nanoscale PbZrO3 films exhibit ferroelectricity. The understanding of such phenomena from the microstructure aspect is crucial but still lacking. In this study, we fabricated a PbZrO3 film with thicknesses varying from 5 nm to 80 nm. Using Piezoresponse Force Microscopy, we discovered the film displayed a transition from antiferroelectric behaviour in the thicker areas to ferroelectric behaviour in the thinner ones, with a critical thickness between 10 and 15 nm. In this critical thickness range, a 12 nm PZO thin film was chosen for further study using aberration-corrected scanning transmission electron microscopy. The investigation showed that the film comprises both ferroelectric and ferrielectric phases. The ferroelectric phase is characterized by polarisation along the pseudocubic [011] projection direction. The positions of Pb, Zr, and O were determined using the integrated differential phase contrast method. This allowed us to ascertain that the ferroelectric PbZrO3 unit cell is half the size of that in the antiferroelectric phase on the ab plane. The observed unit cell is different from the electric field-induced ferroelectric rhombohedral phases. Additionally, we identified a ferrielectric phase with a unique up-up-zero-zero dipole configuration. The finding is crucial for understanding the performance of ultrathin antiferroelectric thin films and the subsequent design and development of antiferroelectric devices.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Excellent catalytic performance towards the hydrogen evolution reaction in topological semimetals

Topological states of matter and their corresponding properties are classical research topics in condensed matter physics. Quite recently, the application of materials that feature these states has been extended to the field of electrochemical catalysis and become an emerging research topic that is receiving increasing interest. In particular, several recent experimental studies performed on topological semimetals have already revealed high catalytic performance towards hydrogen evolution reaction (HER), strongly promoting acceptance of the fresh concept of the topological catalyst. This emerging concept has experienced rapid developments in the last few years, but these developments have been rarely summarized. Herein, we offer a comprehensive review on the state-of-the-art progress in developing topological catalysts for the HER process through topological semimetals such as Weyl semimetals, Dirac semimetals, nodal line semimetals, etc. The course of development, the general research routes, and the fundamental mechanisms in topological catalysts are also systematically analyzed in this review.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

PACSan: Enforcing Memory Safety Based on ARM PA

Memory safety is a key security property that stops memory corruption vulnerabilities. Existing sanitizers enforce checks and catch such bugs during development and testing. However, they either provide partial memory safety or have overwhelmingly high performance overheads. Our novel sanitizer PACSan enforces spatial and temporal memory safety with no false positives at low performance overheads. PACSan removes the majority of the overheads involved in pointer tracking by sealing metadata in pointers through ARM PA (Pointer Authentication), and performing the memory safety checks when pointers are dereferenced. We have developed a prototype of PACSan and systematically evaluated its security and performance on the Magma, Juliet, Nginx, and SPEC CPU2017 test suites, respectively. In our evaluation, PACSan shows no false positives together with negligible false negatives, while introducing stronger security guarantees and lower performance overheads than state-of-the-art sanitizers, including HWASan, ASan, SoftBound+CETS, Memcheck, LowFat, and PTAuth. Specifically, PACSan has 0.84x runtime overhead and 1.92x memory overhead on average. Compared to the widely deployed ASan, PACSan has no false positives and much fewer false negatives and reduces 7.172% runtime overheads and 89.063%memory overheads.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Physically Explainable CNN for SAR Image Classification

Integrating the special electromagnetic characteristics of Synthetic Aperture Radar (SAR) in deep neural networks is essential in order to enhance the explainability and physics awareness of deep learning. In this paper, we first propose a novel physically explainable convolutional neural network for SAR image classification, namely physics guided and injected learning (PGIL). It comprises three parts: (1) explainable models (XM) to provide prior physics knowledge, (2) physics guided network (PGN) to encode the knowledge into physics-aware features, and (3) physics injected network (PIN) to adaptively introduce the physics-aware features into classification pipeline for label prediction. A hybrid Image-Physics SAR dataset format is proposed for evaluation, with both Sentinel-1 and Gaofen-3 SAR data being experimented. The results show that the proposed PGIL substantially improve the classification performance in case of limited labeled data compared with the counterpart data-driven CNN and other pre-training methods. Additionally, the physics explanations are discussed to indicate the interpretability and the physical consistency preserved in the predictions. We deem the proposed method would promote the development of physically explainable deep learning in SAR image interpretation field.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

RCMNet: A deep learning model assists CAR-T therapy for leukemia

Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treat and cure acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification of CAR-T cells is a big challenge posed by their phenotypic similarity with other blood cells. To address this substantial clinical challenge, herein we first construct a CAR-T dataset with 500 original microscopy images after staining. Following that, we create a novel integrated model called RCMNet (ResNet18 with CBAM and MHSA) that combines the convolutional neural network (CNN) and Transformer. The model shows 99.63% top-1 accuracy on the public dataset. Compared with previous reports, our model obtains satisfactory results for image classification. Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset. Transfer learning is adapted for RCMNet and a maximum of 83.36% accuracy has been achieved, which is higher than other SOTA models. The study evaluates the effectiveness of RCMNet on a big public dataset and translates it to a clinical dataset for diagnostic applications.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Recent Progress of Heterostructures Based on Two Dimensional Materials and Wide Bandgap Semiconductors

Recent progress in the synthesis and assembly of two-dimensional (2D) materials has laid the foundation for various applications of atomically thin layer films. These 2D materials possess rich and diverse properties such as layer-dependent band gaps, interesting spin degrees of freedom, and variable crystal structures. They exhibit broad application prospects in micro-nano devices. In the meantime, the wide bandgap semiconductors (WBS) with an elevated breakdown voltage, high mobility, and high thermal conductivity have shown important applications in high-frequency microwave devices, high-temperature and high-power electronic devices. Beyond the study on single 2D materials or WBS materials, the multi-functional 2D/WBS heterostructures can promote the carrier transport at the interface, potentially providing novel physical phenomena and applications, and improving the performance of electronic and optoelectronic devices. In this review, we overview the advantages of the heterostructures of 2D materials and WBS materials, and introduce the construction methods of 2D/WBS heterostructures. Then, we present the diversity and recent progress in the applications of 2D/WBS heterostructures, including photodetectors, photocatalysis, sensors, and energy related devices. Finally, we put forward the current challenges of 2D/WBS heterostructures and propose the promising research directions in the future.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Remiod: Reference-based Controlled Multiple Imputation of Longitudinal Binary and Ordinal Outcomes with non-ignorable missingness

Missing data on response variables are common in clinical studies. Corresponding to the uncertainty of missing mechanism, theoretical frameworks on controlled imputation have been developed. In practice, it is recommended to conduct a statistically valid analysis under the primary assumptions on missing data, followed by sensitivity analysis under alternative assumptions to assess the robustness of results. Due to the availability of software, controlled multiple imputation (MI) procedures, including delta-based and reference-based approaches, have become popular for analyzing continuous variables under missing-not-at-random assumptions. Similar tools, however, still limit application of these methods to categorical data. In this paper, we introduce the R package \textbf{remiod}, which utilizes the Bayesian framework to perform imputation in regression models on binary and ordinal outcomes. Following outlining theoretical backgrounds, usage and features of \textbf{remiod} are described and illustrated using examples.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment

Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g., artifacts and noise), which severely compromise the exact diagnosis of cardiovascular diseases. The appropriate CCTA Vessel-level Image Quality Assessment (CCTA VIQA) algorithm can be used to reduce the risk of error diagnosis. The primary challenges of CCTA VIQA are that the local part of coronary that determines final quality is hard to locate. To tackle the challenge, we formulate CCTA VIQA as a multiple-instance learning (MIL) problem, and exploit Transformer-based MIL backbone (termed as T-MIL) to aggregate the multiple instances along the coronary centerline into the final quality. However, not all instances are informative for final quality. There are some quality-irrelevant/negative instances intervening the exact quality assessment(e.g., instances covering only background or the coronary in instances is not identifiable). Therefore, we propose a Progressive Reinforcement learning based Instance Discarding module (termed as PRID) to progressively remove quality-irrelevant/negative instances for CCTA VIQA. Based on the above two modules, we propose a Reinforced Transformer Network (RTN) for automatic CCTA VIQA based on end-to-end optimization. Extensive experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the real-world CCTA dataset, exceeding previous MIL methods by a large margin.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Scaling Blockchains with Error Correction Codes: A Survey on Coded Blockchains

This paper reviews and highlights how coding schemes have been used to solve various problems in blockchain systems. Specifically, these problems relate to scaling blockchains in terms of their data storage, computation and communication cost, as well as security. To this end, this paper considers the use of coded blocks or shards that allows participants to store only a fraction of the total blockchain, protect against malicious nodes or erasures due to nodes leaving a blockchain system, ensure data availability in order to promote transparency, and scale the security of sharded blockchains. Further, it helps reduce communication cost when disseminating blocks, which is critical to bootstrapping new nodes and helps speed up consensus of blocks. For each category of solutions, we highlight problems and issues that motivated their designs and use of coding. Moreover, we provide a qualitative analysis of their storage, communication and computation cost.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Self-localized topological states in three dimensions

Three-dimensional (3D) topological materials exhibit much richer phenomena than their lower-dimensional counterparts. Here, we propose self-localized topological states (i.e., topological solitons) in a 3D nonlinear photonic Chern insulator. Despite being in the bulk and self-localized in all 3D, the topological solitons at high-symmetry points K and K' rotate in the same direction, due to the underlying topology. Specifically, under the saturable nonlinearity the solitons are stable over a broad frequency range. Our results highlight how topology and nonlinearity interact with each other and can be extended to other 3D topological systems.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in the real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose DanSmp, a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed DanSmp is capable of improving stock prediction with the constructed MKG.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Exponential-Time Complexity of the complex weighted #CSP

In this paper, I consider a fine-grained dichotomy of Boolean counting constraint satisfaction problem (#CSP), under the exponential time hypothesis of counting version (#ETH). Suppose $\mathscr{F}$ is a finite set of algebraic complex-valued functions defined on Boolean domain. When $\mathscr{F}$ is a subset of either two special function sets, I prove that #CSP($\mathscr{F}$) is polynomial-time solvable, otherwise it can not be computed in sub-exponential time unless #ETH fails. I also improve the result by proving the same dichotomy holds for #CSP with bounded degree (every variable appears at most constant constraints), even for #R$_3$-CSP. An important preparation before proving the result is to argue that pinning (two special unary functions $[1,0]$ and $[0,1]$ are used to reduce arity) can also keep the sub-exponential lower bound of a Boolean #CSP problem. I discuss this issue by utilizing some common methods in proving #P-hardness of counting problems. The proof illustrates the internal correlation among these commonly used methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Universal and Efficient p-Doping of Organic Semiconductors by Electrophilic Attack of Cations

Doping is of great importance to tailor the electrical properties of semiconductors. However, the present doping methodologies for organic semiconductors (OSCs) are either inefficient or can only apply to a small number of OSCs, seriously limiting their general application. Herein, we reveal a novel p-doping mechanism by investigating the interactions between the dopant trityl cation and poly(3-hexylthiophene) (P3HT). It is found that electrophilic attack of the trityl cations on thiophenes results in the formation of alkylated ions that induce electron transfer from neighboring P3HT chains, resulting in p-doping. This unique p-doping mechanism can be employed to dope various OSCs including those with high ionization energy (IE=5.8 eV). Moreover, this doping mechanism endows trityl cation with strong doping ability, leading to polaron yielding efficiency of 100 % and doping efficiency of over 80 % in P3HT. The discovery and elucidation of this novel doping mechanism not only points out that strong electrophiles are a class of efficient p-dopants for OSCs, but also provides new opportunities towards highly efficient doping of OSCs.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Asymmetrically interacting dynamics with mutual confirmation from multi-source on multiplex networks

In the early stage of epidemics, individuals' determination on adopting protective measures, which can reduce their risk of infection and suppress disease spreading, is likely to depend on multiple information sources and their mutual confirmation due to inadequate exact information. Here we introduce the inter-layer mutual confirmation mechanism into the information-disease interacting dynamics on multiplex networks. In our model, an individual increases the information transmission rate and willingness to adopt protective measures once he confirms the authenticity of news and severity of disease from neighbors status in multiple layers. By using the microscopic Markov chain approach, we analytically calculate the epidemic threshold and the awareness and infected density in the stationary state, which agree well with simulation results. We find that the increment of epidemic threshold when confirming the aware neighbors on communication layer is larger than that of the contact layer. On the contrary, the confirmation of neighbors' awareness and infection from the contact layer leads to a lower final infection density and a higher awareness density than that of the communication layer. The results imply that individuals' explicit exposure of their infection and awareness status to neighbors, especially those with real contacts, is helpful in suppressing epidemic spreading.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Ideal fully spin-polarized type-II nodal line state in half-metals X2YZ4 (X=K, Cs, Rb, Y=Cr, Cu, Z=Cl, F)

Lorentz-violating type-II nodal lines exhibit attracting physical properties and have been hot discussed currently. However, their investigations have been mostly limited in nonmagnetic system because of lacking ideal spin-polarized candidates with clean type-II nodal line states. Here, for the first time, we report the family of X2YZ4 (X=K, Cs, Rb, Y=Cr, Cu, Z=Cl, F) compounds are such ideal candidate materials by using the member of K2CuF4 as an example. We show the material is a ferromagnetic half-metal with weak anisotropy, which host fully spin-polarized conducting electrons. In the conducting spin channel, the band crossing form a pair of type-II nodal lines, protected by mirror symmetry. These type-II nodal lines are different with former proposed examples because they have a 100% spin polarization. In addition, we also show the material can realize switchable topological states, which can be easily controlled by external magnetic field. It is noticed that, the material: i) is stable and can be synthesized in experiments; ii) has clear magnetic structure; and iii) manifests clean type-II nodal line state and clear drumhead surface states. Therefore, the proposed X2YZ4 compounds are expected to be an excellent platform to investigate the novel physical properties of both type-II nodal line states with complete

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Identify Influential Spreaders in Asymmetrically Interacting Multiplex Networks

Identifying the most influential spreaders is important to understand and control the spreading process in a network. As many real-world complex systems can be modeled as multilayer networks, the question of identifying important nodes in multilayer network has attracted much attention. Existing studies focus on the multilayer network structure, while neglecting how the structural and dynamical coupling of multiple layers influence the dynamical importance of nodes in the network. Here we investigate on this question in an information-disease coupled spreading dynamics on multiplex networks. Firstly, we explicitly reveal that three interlayer coupling factors, which are the two-layer relative spreading speed, the interlayer coupling strength and the two-layer degree correlation, significantly impact the spreading influence of a node on the contact layer. The suppression effect from the information layer makes the structural centrality on the contact layer fail to predict the spreading influence of nodes in the multiplex network. Then by mapping the coevolving spreading dynamics into percolation process and using the message-passing approach, we propose a method to calculate the size of the disease outbreaks from a single seed node, which can be used to estimate the nodes' spreading influence in the coevolving dynamics. Our work provides insights on the importance of nodes in the multiplex network and gives a feasible framework to investigate influential spreaders in the asymmetrically coevolving dynamics.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Monolayer RhB4: half-auxeticity and almost ideal spin-orbit Dirac point semimetal

Structural-property relationship, the connection between materials' structures and their properties, is central to the materials research. Especially at reduced dimensions, novel structural motifs often generate unique physical properties.Motivated by a recent work reporting a novel half auxetic effect in monolayer PdB4 with a hypercoordinated structure, here, we extensively explore similar 2D transition metal boride structures MB4 with M covering 3d and 4d elements.Our investigation screens out one stable candidate, the monolayer RhB4. We find that monolayer RhB4 also shows half auxeticity, i.e., the material always expands in a lateral in-plane direction in response to an applied strain in the other direction, regardless of whether the strain is positive or negative.We show that this special mechanical character is intimately tied to the hypercoordinated structure with the M\c{opyright}B8 structural motif. Furthermore, regarding electronic properties, monolayer RhB4 is found to be the first example of an almost ideal 2D spin-orbit Dirac point semimetal.The low-energy band structure is clean, with a pair of fourfold degenerate Dirac points robust under spin-orbit coupling located close to the Fermi level. These Dirac points are enforced by the nonsymmorphic space group symmetry which is also determined by the lattice structure. Our work deepens the fundamental understanding of structural-property relationship in reduced dimensions. The half auxeticity and the spin-orbit Dirac points will make monolayer RhB4 a promising platform for nanomechanics and nanoelectronics applications.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Multiple-Fold Fermions and Topological Fermi Arcs Induced Catalytic Enhancement in Nanoporous Electride C12A7

Topological materials are recently regarded as the idea catalysts due to the protected surface metallic states and high carrier mobility, however the fundamental mechanism and the underlying relationship between the catalytic performance and topological states are in debate. Here, by means of symmetry analysis and first-principles calculations, we discover that the electride material of C12A7 hosts the multiple-fold fermions due to the interstitial-electrons, with the sixfold- and fourfold- degenerate points locating at high symmetric points near the Fermi energy, which are identified as the underlying reason of the enhanced catalytic ability in C12A7-based catalysts. The multiple-fold fermions exhibit much longer Fermi arcs on the (001) surface than traditional Weyl/Dirac fermions, the surface is thus highly chemical active and possesses a low Gibbs free energy for the hydrogen evolution reaction. The underlying relationship between catalytic performance and the topological surface state is explicitly verified by artificially hole doping, external strain and similar electride without the Fermi arcs, where the Gibbs free energies are significantly increased when the Fermi arcs is shifted to higher energy level. This work offers a guiding principle for understanding catalytic nature of electrides and the topological quantum catalysts.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Observation of unconventional six-fold, four-fold and three-fold excitations in rare-earth-metal carbide Re2C3

Unconventional fermions, such as three-fold, four-fold, six-fold, and eight-fold fermions have attracted intense attention in recent years. However, the concrete materials hosting unconventional fermions are still in urgent scarcity. In this work, based first-principle calculations and symmetry analysis, we reveal rich unconventional fermions in existing compound Re2C3 (Re = Y, La, Ce, Pr, Nd, Sm, Tb, Dy, Ho, Er, Tm, Yb, Lu). We show that these compounds host quadratic dispersive three-fold (TP), linear dispersive four-fold (FP) and six-fold points (SP) near the Fermi level in their electric band structures when spin-orbital coupling (SOC) is not included. Notably, the FP is charge-2 Dirac-like point. More importantly, among compound Re2C3, the compound Yb2C3 has very clean band structure, and its unconventional fermions are closed to the Fermi level. We also find that a uniaxial strain can transform the unconventional fermions into other types fermions, depending on the directions of strain. When SOC is considered, a SP transform to an eightfold degenerate point and a fourfold degenerate point. Overall, our work provides a family of realistic materials to study the unconventional fermions.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Theoretical realization of hybrid Weyl state and associated high catalytic performance for hydrogen evolution in NiSi

For electrochemical hydrogen evolution reaction (HER), developing high-performance catalysts without containing precious metals has been a major research focus in the current. Herein, we show the feasibility of HER catalytic enhancement in Ni-based materials based on topological engineering from hybrid Weyl states. Via a high-throughput computational screening from 140 000 materials, we identify a chiral compound NiSi is a hybrid Weyl semimetal (WSM) with showing bulk type-I and type-II Weyl nodes and long surface Fermi arcs near the Fermi level. Sufficient evidences verify that topological charge carriers participate in the HER process, and make the certain surface of NiSi highly active with the Gibbs free energy nearly zero (0.07 eV), which is even lower than Pt and locates on the top of the volcano plots. This work opens up a new routine to develop no-precious-metal-containing HER catalysts via topological engineering, rather than traditional defect engineering, doping engineering, or strain engineering.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Topological quantum catalyst: the case of two-dimensional traversing nodal line states associated with high catalytic performance for hydrogen evolution reaction

Topological quantum catalysts (TQCs), where metallic surface states from nontrivial band topology serve as the mechanism to favor heterogeneous catalysis processes, have been well demonstrated in three dimensional (3D) examples but have been rarely discussed in 2D scale. Here, we develop a design scheme to realize 2D TQCs with showing traversing nodal line at the Brillouin zone boundary, large Fermi arc on the edge, and nearly zero Gibbs free energy (ΔGH*) for hydrogen evolution reaction (HER). We demonstrate the 2D Cu2C2N4 sheet is a such example. The material manifests an open nodal line traversing the whole k-path S-Y. It shows a long Fermi arc that spans the entire edge boundary, which is robust against spin-orbit coupling and the H adsorption. As the result, the edge of Cu2C2N4 sheet is relatively active for HER catalysis with possessing a ΔGH* as low as 0.10 eV, which is comparable with that of Pt and superior to other traditional catalysts and 3D TQCs as well. Our work offers an effective route to develop high performance HER catalysis without containing noble metals by utilizing 2D TQCs with traversing nodal line.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Atomic structure of CdS magic-size clusters by X-ray absorption spectroscopy

Magic-size clusters are ultra-small colloidal semiconductor systems that are intensively studied due to their monodisperse nature and sharp UV-vis absorption peak compared with regular quantum dots. However, the small size of such clusters (<2 nm), and the large surface-to-bulk ratio significantly limit characterisation techniques that can be utilised. Here we demonstrate how a combination of EXAFS and XANES can be used to obtain information about sample stoichiometry and cluster symmetry. Investigating two types of clusters that show sharp UV-vis absorption peaks at 311 nm and 322 nm, we found that both samples possess approximately 2:1 Cd:S ratio and have similar nearest-neighbour structural arrangements. However, both samples demonstrate a significant departure from the tetrahedral structural arrangement, with an average bond angle determined to be around 106.1 degree showing a bi-fold bond angle distribution. Our results suggest that both samples are quazi-isomers. Their core structure has identical chemical composition but a different atomic arrangement with distinct bond angle distributions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Electric field induced metallic behavior in thin crystals of ferroelectric α-In2Se3

Ferroelectric semiconductor field effect transistors (FeSmFETs), which employ ferroelectric semiconducting thin crystals of α-In2Se3 as the channel material as opposed to the gate dielectric in conventional ferroelectric FETs (FeFETs) were prepared and measured from room to the liquid-helium temperatures. These FeSmFETs were found to yield evidence for the reorientation of the electrical polarization and an electric field induced metallic state in α-In2Se3. Our findings suggest that FeSmFETs can serve as a platform for the fundamental study of ferroelectric metals as well as the exploration of the integration of data storage and logic operations in the same device.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Fast library-driven approach for implementation of the voxel spread function technique for correcting magnetic field inhomogeneity artifacts

Purpose: Previously-developed Voxel Spread Function (VSF) method (Yablonskiy, et al, MRM, 2013;70:1283) provides means to correct artifacts induced by macroscopic magnetic field inhomogeneities in the images obtained by multi-Gradient-Recalled-Echo (mGRE) techniques. The goal of this study is to develop a library-driven approach for fast VSF implementation. Methods: The VSF approach describes the contribution of the magnetic field inhomogeneity effects on the mGRE signal decay in terms of the F-function calculated from mGRE phase and magnitude images. A pre-calculated library accounting for a variety of background field gradients caused by magnetic field inhomogeneities was used herein to speed up calculation of the F-function and to generate quantitative R2* maps from the mGRE data collected from two healthy volunteers. Results: As compared with direct calculation of the F-function based on a voxel-wise approach, the new library-driven method substantially reduces computational time from several hours to few minutes, while, at the same time, providing similar accuracy of R2* mapping. Conclusion: The new procedure proposed in this study provides a fast post-processing algorithm that can be incorporated in the quantitative analysis of mGRE data to account for background field inhomogeneity artifacts, thus can facilitate the applications of mGRE-based quantitative techniques in clinical practices.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Ferromagnetic hybrid nodal loop and switchable type-I and type-II Weyl fermions in two-dimension

As a novel type of fermionic state, hybrid nodal loop with the coexistence of both type-I and type- II band crossings has attracted intense research interest. However, it remains a challenge to realize hybrid nodal loop in both two-dimensional (2D) materials and in ferromagnetic (FM) materials. Here, we propose the first FM hybrid nodal loop in 2D CrN monolayer. We show that the material has a high Curie temperature (> 600 K) FM ground state, with the out-of-plane [001] magnetization. It shows a half-metallic band structure with two bands in the spin-up channel crossing each other near the Fermi level. These bands produce both type-I and type-II band crossings, which form a fully spin-polarized hybrid nodal loop. We find the nodal loop is protected by the mirror symmetry and robust against spin-orbit coupling (SOC). An effective Hamiltonian characterizing the hybrid nodal loop is established. We further find the configuration of nodal loop can be shifted under external perturbations such as strain. Most remarkably, we demonstrate that both type-I and type-II Weyl nodes can be realized from such FM hybrid nodal loop by simply shifting the magnetization from out-of-plane to in-plane. Our work provides an excellent candidate to realize FM hybrid nodal loop and Weyl fermions in 2D material, and is also promising for related topological applications with their intriguing properties.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

GL-GAN: Adaptive Global and Local Bilevel Optimization model of Image Generation

Although Generative Adversarial Networks have shown remarkable performance in image generation, there are some challenges in image realism and convergence speed. The results of some models display the imbalances of quality within a generated image, in which some defective parts appear compared with other regions. Different from general single global optimization methods, we introduce an adaptive global and local bilevel optimization model(GL-GAN). The model achieves the generation of high-resolution images in a complementary and promoting way, where global optimization is to optimize the whole images and local is only to optimize the low-quality areas. With a simple network structure, GL-GAN is allowed to effectively avoid the nature of imbalance by local bilevel optimization, which is accomplished by first locating low-quality areas and then optimizing them. Moreover, by using feature map cues from discriminator output, we propose the adaptive local and global optimization method(Ada-OP) for specific implementation and find that it boosts the convergence speed. Compared with the current GAN methods, our model has shown impressive performance on CelebA, CelebA-HQ and LSUN datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Graph-Fused Multivariate Regression via Total Variation Regularization

In this paper, we propose the Graph-Fused Multivariate Regression (GFMR) via Total Variation regularization, a novel method for estimating the association between a one-dimensional or multidimensional array outcome and scalar predictors. While we were motivated by data from neuroimaging and physical activity tracking, the methodology is designed and presented in a generalizable format and is applicable to many other areas of scientific research. The estimator is the solution of a penalized regression problem where the objective is the sum of square error plus a total variation (TV) regularization on the predicted mean across all subjects. We propose an algorithm for parameter estimation, which is efficient and scalable in a distributed computing platform. Proof of the algorithm convergence is provided, and the statistical consistency of the estimator is presented via an oracle inequality. We present 1D and 2D simulation results and demonstrate that GFMR outperforms existing methods in most cases. We also demonstrate the general applicability of the method by two real data examples, including the analysis of the 1D accelerometry subsample of a large community-based study for mood disorders and the analysis of the 3D MRI data from the attention-deficient/hyperactive deficient (ADHD) 200 consortium.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Preliminary Study of Connectivity for Quantum Key Distribution Network

Quantum network is fragile to disturbances when qubits are transmitted through quantum channel. Reliability is an essential requirement for a quantum network and even the future quantum internet. A metric is needed to describe the reliability of a quantum network to build a robust infrastructure and communication protocols. In this work, we combined quantum physical parameters with graphic algebraic connectivity to indicate the transmission throughput of a grid quantum network. This metric can be extended to multiple equal point-to-point distance topology. This work also studies how to tune specific physical parameters to maintain or even increase connectivity when nodes or edges are removed from a network. Using this metric, resources consumption are compared.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Quantized Circulation of Anomalous Shift in Interface Reflection

A particle beam may undergo an anomalous spatial shift when it is reflected at an interface. The shift forms a vector field defined in the two-dimensional interface momentum space. We show that, although the shift vector at individual momentum is typically sensitive to the system details, its integral along a close loop, i.e., its circulation, could yield a robust quantized number under certain symmetry conditions of interest. Particularly, this is the case when the beam is incident from a trivial medium, then the quantized circulation of anomalous shift (CAS) directly manifests the topological character of the other medium. We demonstrate that the topological charge of a Weyl medium as well as the unconventional pair potentials of a superconductor can be captured and distinguished by CAS. Our work unveils a hidden quantized feature in a ubiquitous physical process, which may also offer a new approach for probing topological media.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Significant reduced traffic in Beijing failed to relieve haze pollution during the COVID-19 lockdown: implications for haze mitigation

The COVID-19 outbreak greatly limited human activities and reduced primary emissions particularly from urban on-road vehicles, but coincided with Beijing experiencing pandemic haze, raising the public concerns of the validity and effectiveness of the imposed traffic policies to improve the air pollution. Here, we explored the relationship between local vehicle emissions and the winter haze in Beijing before and during the COVID-19 lockdown period based on an integrated analysis framework, which combines a real-time on-road emission inventory, in-situ air quality observations and a localized chemical transport modeling system. We found that traffic emissions decreased substantially affected by the pandemic, with a higher reduction for NOx (75.9%, 125.3 Mg/day) compared to VOCs (53.1%, 52.9 Mg/day). Unexpectedly, our results show that the imbalanced emission abatement of NOx and VOCs from vehicles led to a significant rise of the atmospheric oxidizing capacity in urban areas, but only resulting in modest increases in secondary aerosols due to the inadequate precursors. However, the enhanced oxidizing capacity in the surrounding regions greatly increased the secondary particles with relatively abundant precursors, which is mainly responsible for Beijing haze during the lockdown period. Our results indicate that the winter haze in Beijing was insensitive to the local vehicular emissions reduction due to the complicated nonlinear response of the fine particle and air pollutant emissions. We suggest mitigation policies should focus on accelerating VOC and NH3 emissions reduction and synchronously controlling regional sources to release the benefits on local traffic emission control.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Structure, phase stability, half-metallicity, and fully spin-polarized Weyl states in compound NaV2O4: a new example for topological spintronic material

Here, we systematically investigate the structure, phase stability, half-metallicity, and topological electronic structure for a new topological spintronic material NaV2O4. The material has a tetragonal structure with excellent dynamical and thermal stabilities. It shows a half-metallic ground state, where only the spin-up bands present near the Fermi level. These bands form a Weyl nodal line close to the Fermi level, locating in the kz = 0 plane. The nodal line is robust against SOC, under the protection of the mirror symmetry. The nodal line band structure is very clean, thus the drumhead surface states can be clearly identified. Remarkably, the nodal line and drumhead surface states have the 100% spin polarization, which are highly desirable for spintronics applications. In addition, by shifting the magnetic field in-plane, we find that the Weyl nodal line can transform into single pair of Weyl nodes. The Weyl-line and Weyl-node fermions in the bulk, as well as the drumhead fermions on the surface are all fully spin-polarized, which may generate new physical properties and promising applications.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Three-state quantum walk on the Cayley Graph of the Dihedral Group

The finite dihedral group generated by one rotation and one reflection is the simplest case of the non-abelian group. Cayley graphs are diagrammatic counterparts of groups. In this paper, much attention is given to the Cayley graph of the dihedral group. Considering the characteristics of the elements in the dihedral group, we propose a model of three-state discrete-time quantum walk (DTQW) on the Caylay graph of the dihedral group with Grover coin. We derive analytic expressions for the the position probability distribution and the long-time limit of the return probability starting from the origin. It is shown that the localization effect is governed by the size of the underlying dihedral group, coin operator and initial state. We also numerically investigate the properties of the proposed model via the probability distribution and the time-averaged probability at the designated position. The abundant phenomena of three-state Grover DTQW on the Caylay graph of the dihedral group can help the community to better understand and to develop new quantum algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Two-dimensional antiferromagnetic Dirac fermions in monolayer TaCoTe$_2$

Dirac point in two-dimensional (2D) materials has been a fascinating subject of research. Recently, it has been theoretically predicted that Dirac point may also be stabilized in 2D magnetic systems. However, it remains a challenge to identify concrete 2D materials which host such magnetic Dirac point. Here, based on first-principles calculations and theoretical analysis, we propose a stable 2D material, the monolayers TaCoTe$_2$, as an antiferromagnetic (AFM) 2D Dirac material. We show that it has an AFM ground state with an out-of-plane Néel vector. It hosts a pair of 2D AFM Dirac points on the Fermi level in the absence of spin-orbit coupling (SOC). When the SOC is considered, a small gap is opened at the original Dirac points. Meanwhile, another pair of Dirac points appear on the Brillouin zone boundary below the Fermi level, which are robust under SOC and have a type-II dispersion. Such a type-II AFM Dirac point has not been observed before. We further show that the location of this Dirac point as well as its dispersion type can be controlled by tuning the Néel vector orientation.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Two-dimensional Weyl nodal line semimetal in high Curie temperature d0 ferromagnet K2N monolayer

Nodal line semimetals in two-dimensional (2-D) materials have attracted intense attention currently. From fundamental physics and spintronic applications points of view, high Curie temperature ferromagnetic (FM) ones with nodal lines robust against spin-orbit coupling (SOC) are significantly in desirable. Here, we propose that FM K2N monolayer is such Weyl nodal line semimetal. We show that K2N monolayer is dynamically stable, and has a FM ground magnetic state with the out-of-plane [001] magnetization. It shows two nodal lines in the low-energy band structures. Both nodal lines are robust against SOC, under the protection of mirror symmetry. We construct an effective Hamiltonian, which can well characterize the nodal lines in the system. Remarkably, the nodal line semimetal proposed here is distinct from the previously studied ones in that K2N monolayer is 2-D d0-type ferromagnet with the magnetism arising from the partially filled N-p orbitals, which can bring special advantages in spintronic applications. Besides, the Curie temperature in K2N monolayer is estimated to be 942K, being significantly higher than previous FM nodal lines materials. We also find that, specific tensile strains can transform the nodal line from type-I to a type-II one, making its nodal line characteristics even more interesting.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Deep learning assessment of breast terminal duct lobular unit involution: towards automated prediction of breast cancer risk

Terminal ductal lobular unit (TDLU) involution is the regression of milk-producing structures in the breast. Women with less TDLU involution are more likely to develop breast cancer. A major bottleneck in studying TDLU involution in large cohort studies is the need for labor-intensive manual assessment of TDLUs. We developed a computational pathology solution to automatically capture TDLU involution measures. Whole slide images (WSIs) of benign breast biopsies were obtained from the Nurses' Health Study (NHS). A first set of 92 WSIs was annotated for TDLUs, acini and adipose tissue to train deep convolutional neural network (CNN) models for detection of acini, and segmentation of TDLUs and adipose tissue. These networks were integrated into a single computational method to capture TDLU involution measures including number of TDLUs per tissue area, median TDLU span and median number of acini per TDLU. We validated our method on 40 additional WSIs by comparing with manually acquired measures. Our CNN models detected acini with an F1 score of 0.73$\pm$0.09, and segmented TDLUs and adipose tissue with Dice scores of 0.86$\pm$0.11 and 0.86$\pm$0.04, respectively. The inter-observer ICC scores for manual assessments on 40 WSIs of number of TDLUs per tissue area, median TDLU span, and median acini count per TDLU were 0.71, 95% CI [0.51, 0.83], 0.81, 95% CI [0.67, 0.90], and 0.73, 95% CI [0.54, 0.85], respectively. Intra-observer reliability was evaluated on 10/40 WSIs with ICC scores of >0.8. Inter-observer ICC scores between automated results and the mean of the two observers were: 0.80, 95% CI [0.63, 0.90] for number of TDLUs per tissue area, 0.57, 95% CI [0.19, 0.77] for median TDLU span, and 0.80, 95% CI [0.62, 0.89] for median acini count per TDLU. TDLU involution measures evaluated by manual and automated assessment were inversely associated with age and menopausal status.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Learning epidemic threshold in complex networks by Convolutional Neural Network

Deep learning has taken part in the competition since not long ago to learn and identify phase transitions in physical systems such as many body quantum systems, whose underlying lattice structures are generally regular as they're in euclidean space. Real networks have complex structural features which play a significant role in dynamics in them, and thus the structural and dynamical information of complex networks can not be directly learned by existing neural network models. Here we propose a novel and effective framework to learn the epidemic threshold in complex networks by combining the structural and dynamical information into the learning procedure. Considering the strong performance of learning in Euclidean space, Convolutional Neural Network (CNN) is used and, with the help of confusion scheme, we can identify precisely the outbreak threshold of epidemic dynamics. To represent the high dimensional network data set in Euclidean space for CNN, we reduce the dimensionality of a network by using graph representation learning algorithms and discretize the embedded space to convert it into an image-like structure. We then creatively merge the nodal dynamical states with the structural embedding by multi-channel images. In this manner, the proposed model can draw the conclusion from both structural and dynamical information. A large number of simulations show a great performance in both synthetic and empirical network data set. Our end-to-end machine learning framework is robust and universally applicable to complex networks with arbitrary size and topology.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Machine learning dynamical phase transitions in complex networks

In recent years, machine learning has been adopted to complex networks, but most existing works concern about the structural properties. To use machine learning to detect phase transitions and accurately identify the critical transition point associated with dynamical processes on complex networks thus stands out as an open and significant problem. Here we develop a framework combining supervised and unsupervised learning, incorporating proper sampling of training data set. In particular, using epidemic spreading dynamics on complex networks as a paradigmatic setting, we start from supervised learning alone and identify situations that degrade the performance. To overcome the difficulties leads to the idea of exploiting confusion scheme, effectively a combination of supervised and unsupervised learning. We demonstrate that the scheme performs well for identifying phase transitions associated with spreading dynamics on homogeneous networks, but the performance deteriorates for heterogeneous networks. To strive to meet this challenge leads to the realization that sampling the training data set is necessary for heterogeneous networks, and we test two sampling methods: one based on the hub nodes together with their neighbors and another based on k-core of the network. The end result is a general machine learning framework for detecting phase transition and accurately identifying the critical transition point, which is robust, computationally efficient, and universally applicable to complex networks of arbitrary size and topology. Extensive tests using synthetic and empirical networks verify the virtues of the articulated framework, opening the door to exploiting machine learning for understanding, detection, prediction, and control of complex dynamical systems in general.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Super-Andreev reflection and longitudinal shift of pseudospin-one fermions

Novel fermions with a pseudospin-1 structure can be realized as emergent quasiparticles in condensed matter systems. Here, we investigate its unusual properties during the Andreev reflection at a normal-metal/superconductor (NS) interface. We show that distinct from the previously studied pseudospin-1/2 and two dimensional electron gas models, the pseudospin-1 fermions exhibit a strongly enhanced Andreev reflection probability, and remarkably, can be further tuned to approach perfect Andreev reflection with unit efficiency for all incident angles, exhibiting a previously unknown {super-Andreev reflection effect}. The super-Andreev reflection leads to perfect transparency of the NS interface that strongly promotes charge injection into the superconductor, and directly manifests as a differential conductance peak which can be readily probed in experiment. Additionally, we find that sizable longitudinal shifts exist in the normal and Andreev reflections of pseudospin-1 fermions. Distinct from the pseudospin-1/2 case, the shift is always in the forward direction in the subgap regime, regardless of whether the reflection is of retro- or specular type.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Apply Artificial Noise To Secure Fading Relay Networks: A SER-Based Approach

We apply the concept of artificial and controlled interference in a triangular relay network with an untrusted relay aiming at enhancing the wireless communication secrecy between the source and the destination node. In order to shield the square quadrature amplitude modulated (QAM) signals transmitted from the source node to the relay, the destination node designs and transmits artificial noise (AN) symbols to jam the relay reception. The objective of our considered AN design is to degrade the error probability performance at the untrusted relay, for different types of channel state information (CSI) at the destination. By considering perfect knowledge of the instantaneous CSI of the source-to-relay and relay-to-destination links, we first present an analytical expression for the symbol error rate (SER) performance at the relay. Based on the assumption of an average power constraint at the destination node, we then derive the optimal phase and power distribution of the AN that maximizes the SER at the relay. Furthermore, we obtain the optimal AN design for the case where only statistical CSI is available at the destination node. For both cases, our study reveals that the Gaussian distribution is generally not optimal to generate AN symbols. In particular, the conducted numerical results show that the Gaussian distributed AN is far from optimal. The presented AN design takes into account practical parameters for the communication links, such as QAM signaling and maximum likelihood decoding.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Blue Phosphorene Oxide: Strain-tunable Quantum Phase Transitions and Novel 2D Emergent Fermions

Tunable quantum phase transitions and novel emergent fermions in solid state materials are fascinating subjects of research. Here, we propose a new stable two-dimensional (2D) material, the blue phosphorene oxide (BPO), which exhibits both. Based on first-principles calculations, we show that its equilibrium state is a narrow-bandgap semiconductor with three bands at low energy. Remarkably, a moderate strain can drive a semiconductor-to-semimetal quantum phase transition in BPO. At the critical transition point, the three bands cross at a single point at Fermi level, around which the quasiparticles are a novel type of 2D pseudospin-1 fermions. Going beyond the transition, the system becomes a symmetry-protected semimetal, for which the conduction and valence bands touch quadratically at a single Fermi point that is protected by symmetry, and the low-energy quasiparticles become another novel type of 2D double Weyl fermions. We construct effective models characterizing the phase transition and these novel emergent fermions, and we point out several exotic effects, including super Klein tunneling, supercollimation, and universal optical absorbance. Our result reveals BPO as an intriguing platform for the exploration of fundamental properties of quantum phase transitions and novel emergent fermions, and also suggests its great potential in nanoscale device applications.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Crossover from Shear-Driven to Thermally Activated Drainage of Liquid-Infused Microscale Capillaries

The shear-driven drainage of capillary grooves filled with viscous liquid is a dynamic wetting phenomenon relevant to numerous industrial processes and novel lubricant-infused surfaces. Prior work has reported that a finite length $L_\infty$ of the capillary groove can remain indefinitely filled with liquid even when large shear stresses are applied. The mechanism preventing full drainage is attributed to a balance between the shear-driven flow and a counterflow driven by capillary pressures caused by deformation of the free surface. The final equilibrium length $L_\infty$ is uniquely determined by physical properties of the filling liquid as well as the geometry and wettability of the capillary. In this work, we examine closely the approach to the final equilibrium length $L_\infty$ and report a crossover to a slow drainage regime that cannot be described by conventional dynamic models considering solely hydrodynamic and capillary forces. The slow drainage regime observed in experiments can be instead modeled by a kinetic equation describing a sequence of random thermally activated transitions between multiple metastable states caused by surface defects with nanoscale dimensions. Our findings provide new insights on the critical role that natural or engineered surface roughness with nanoscale dimensions can play in the imbibition and drainage of capillaries and other dynamic wetting processes in microscale systems.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Direct measurement of topological numbers with spins in diamond

Topological numbers can characterize the transition between different topological phases, which are not described by Landau's paradigm of symmetry breaking. Since the discovery of quantum Hall effect, more topological phases have been theoretically predicted and experimentally verified. However, it is still an experimental challenge to directly measure the topological number of various predicted topological phases. In this paper, we demonstrate quantum simulation of topological phase transition of a quantum wire (QW) using a single nitrogen-vacancy (NV) center in diamond. Deploying quantum algorithm of finding eigenvalues, we can reliably extract both the dispersion relations and topological numbers.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Experimental test of Heisenberg's measurement uncertainty relation based on statistical distances

Incompatible observables can be approximated by compatible observables in joint measurement or measured sequentially, with constrained accuracy as implied by Heisenberg's original formulation of the uncertainty principle. Recently, Busch, Lahti, and Werner proposed inaccuracy trade-off relations based on statistical distances between probability distributions of measurement outcomes [Phys. Rev. Lett. 111, 160405 (2013); Phys. Rev. A 89, 012129 (2014)]. Here we reform their theoretical framework, derive an improved relation for qubit measurement, and perform an experimental test on a spin system. The relation reveals that the worst-case inaccuracy is tightly bounded from below by the incompatibility of target observables, and is verified by the experiment employing joint measurement in which two compatible but typically non-commutative observables on one qubit are measured simultaneously.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

On High-Order Capacity Statistics of Spectrum Aggregation Systems over $κ$-$μ$ and $κ$-$μ$ shadowed Fading Channels

The frequency scarcity imposed by fast growing demand for mobile data service requires promising spectrum aggregation systems. The so-called higher-order statistics (HOS) of the channel capacity is a suitable metric on the system performance. While prior relevant works have improved our knowledge on the HOS characterization of spectrum aggregation systems, an analytical framework encompassing generalized fading models of interest is not yet available. In this paper, we pursue a detailed HOS analysis of $κ$-$μ$ and $κ$-$μ$ shadowed fading channels by deriving novel and exact expressions. Furthermore, the simplified HOS expressions for the asymptotically low and high signal-to-noise regimes are derived. Several important statistical measures, such as amount of fading, amount of dispersion, reliability, skewness, and kurtosis, are obtained by using the HOS results. More importantly, the useful implications of system and fading parameters on spectrum aggregation systems are investigated for channel selection. Finally, all derived expressions are validated via Monte-Carlo simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Robust Hybrid Learning for Estimating Personalized Dynamic Treatment Regimens

Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by potentially time-varying patient features and intermediate outcomes observed in previous stages. The complexity, patient heterogeneity and chronicity of many diseases and disorders call for learning optimal DTRs which best dynamically tailor treatment to each individual's response over time. Proliferation of personalized data (e.g., genetic and imaging data) provides opportunities for deep tailoring as well as new challenges for statistical methodology. In this work, we propose a robust hybrid approach referred as Augmented Multistage Outcome-Weighted Learning (AMOL) to integrate outcome-weighted learning and Q-learning to identify optimal DTRs from the Sequential Multiple Assignment Randomization Trials (SMARTs). We generalize outcome weighted learning (O-learning; Zhao et al.~2012) to allow for negative outcomes; we propose methods to reduce variability of weights in O-learning to achieve numeric stability and higher efficiency; finally, for multiple-stage SMART studies, we introduce doubly robust augmentation to machine learning based O-learning to improve efficiency by drawing information from regression model-based Q-learning at each stage. The proposed AMOL remains valid even if the Q-learning model is misspecified. We establish the theoretical properties of AMOL, including the consistency of the estimated rules and the rates of convergence to the optimal value function. The comparative advantage of AMOL over existing methods is demonstrated in extensive simulation studies and applications to two SMART data sets: a two-stage trial for attention deficit and hyperactive disorder (ADHD) and the STAR*D trial for major depressive disorder (MDD).

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Vortex crossing, trapping and pinning in superconducting nanowires of a NbSe$_2$ two-dimensional crystal

Nanowires of two-dimensional (2D) crystals of type-II superconductor NbSe$_2$ prepared by electron-beam lithography were studied, focusing on the effect of the motion of Abrikosov vortices. We present magnetoresistance measurements on these nanowires and show features related to vortex crossing, trapping, and pinning. The vortex crossing rate was found to vary non-monotonically with the applied field, which results in non-monotonic magnetoresistance variations in agreement with theoretical calculations in the London approximation. Above the lower critical field, $H_{c1}$, the crossing rate is also influenced by vortices trapped by sample boundaries or pinning centers, leading to sample-specific magnetoresistance patterns. We show that the local pinning potential can be modified by intentionally introducing surface adsorbates, making the magnetoresistance pattern a "magneto fingerprint" of the sample-specific configuration of vortex pinning centers in a 2D crystal superconducting nanowire.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

All-electric spin transistor based on a side-gate-modulated two-dimensional topological insulator

We propose and investigate a spin transistor device consisting of two ferromagnetic leads connected by a two-dimensional topological insulator as the channel material. It exploits the unique features of the topological spin-helical edge states, such that the injected carriers with a non-collinear spin-direction would travel through both edges and show interference effect. The conductance of the device can be controlled in a simple and allelectric manner by a side-gate voltage, which effectively rotates the spin-polarization of the carrier. At low voltages, the rotation angle is linear in the gate voltage, and the device can function as a good spin-polarization rotator by replacing the drain electrode with a paramagnetic material.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Complex molecules in W51 North region

We present Submillimeter Array (SMA) molecular line observations in two 2 GHz-wide bands centered at 217.5 and 227.5 GHz, toward the massive star forming region W51 North. We identified 84 molecular line transitions from 17 species and their isotopologues. The molecular gas distribution of these lines mainly peaks in the continuum position of W51 North, and has a small tail extending to the west, probably associated with W51 d2. In addition to the commonly detected nitrogen and oxygen-bearing species, we detected a large amount of transitions of the Acetone (CH$_3$COCH$_3$) and Methyl Formate (CH$_3$OCHO), which may suggest that these molecules are present in an early evolutionary stage of the massive stars. We also found that W51 North is an ethanol-rich source. There is no obvious difference in the molecular gas distributions between the oxygen-bearing and nitrogen-bearing molecules. Under the assumption of Local Thermodynamic Equilibrium (LTE), with the XCLASS tool, the molecular column densities, and rotation temperatures are estimated. We found that the oxygen-bearing molecules have considerable higher column densities and fractional abundances than the nitrogen-bearing molecules. The rotation temperatures range from 100 to 200 K, suggesting that the molecular emission could be originated from a warm environment. Finally, based on the gas distributions, fractional abundances and the rotation temperatures, we conclude that CH$_3$OH, C$_2$H$_5$OH, CH$_3$COCH$_3$ and CH$_3$CH$_2$CN might be synthesized on the grain surface, while gas phase chemistry is responsible for the production of CH$_3$OCH$_3$, CH$_3$OCHO and CH$_2$CHCN.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Core-like groups result in invalidation of identifying super-spreader by k-shell decomposition

Identifying the most influential spreaders is an important issue in understanding and controlling spreading processes on complex networks. Recent studies showed that nodes located in the core of a network as identified by the k-shell decomposition are the most influential spreaders. However, through a great deal of numerical simulations, we observe that not in all real networks do nodes in high shells are very influential: in some networks the core nodes are the most influential which we call true core, while in others nodes in high shells, even the innermost core, are not good spreaders which we call core-like group. By analyzing the k-core structure of the networks, we find that the true core of a network links diversely to the shells of the network, while the core-like group links very locally within the group. For nodes in the core-like group, the k-shell index cannot reflect their location importance in the network. We further introduce a measure based on the link diversity of shells to effectively distinguish the true core and core-like group, and identify core-like groups throughout the networks. Our findings help to better understand the structural features of real networks and influential nodes.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Electric control of topological phase transitions in Dirac semimetal thin films

We investigate the effect of a vertical electric field on a Dirac semimetal thin film. We show that through the interplay between the quantum confinement effect and the field-induced coupling between sub-bands, the sub-band gap can be tuned and inverted, during which the system undergoes a topological phase transition between a trivial band insulator and a quantum spin Hall insulator. Consequently, one can electrically switch the topological edge channels on and off, making the system a promising platform for constructing a topological field effect transistor.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Experimental fault-tolerant universal quantum gates with solid-state spins under ambient conditions

Quantum computation provides great speedup over its classical counterpart for certain problems. One of the key challenges for quantum computation is to realize precise control of the quantum system in the presence of noise. Control of the spin-qubits in solids with the accuracy required by fault-tolerant quantum computation under ambient conditions remains elusive. Here, we quantitatively characterize the source of noise during quantum gate operation and demonstrate strategies to suppress the effect of these. A universal set of logic gates in a nitrogen-vacancy centre in diamond are reported with an average single-qubit gate fidelity of 0.999952 and two-qubit gate fidelity of 0.992. These high control fidelities have been achieved at room temperature in naturally abundant 13C diamond via composite pulses and an optimized control method.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Identify influential spreaders in complex networks, the role of neighborhood

Identifying the most influential spreaders is an important issue in controlling the spreading processes in complex networks. Centrality measures are used to rank node influence in a spreading dynamics. Here we propose a node influence measure based on the centrality of a node and its neighbors' centrality, which we call the neighborhood centrality. By simulating the spreading processes in six real-world networks, we find that the neighborhood centrality greatly outperforms the basic centrality of a node such as the degree and coreness in ranking node influence and identifying the most influential spreaders. Interestingly, we discover a saturation effect in considering the neighborhood of a node, which is not the case of the larger the better. Specifically speaking, considering the 2-step neighborhood of nodes is a good choice that balances the cost and performance. If further step of neighborhood is taken into consideration, there is no obvious improvement and even decrease in the ranking performance. The saturation effect may be informative for studies that make use of the local structure of a node to determine its importance in the network.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Image Type Water Meter Character Recognition Based on Embedded DSP

In the paper, we combined DSP processor with image processing algorithm and studied the method of water meter character recognition. We collected water meter image through camera at a fixed angle, and the projection method is used to recognize those digital images. The experiment results show that the method can recognize the meter characters accurately and artificial meter reading is replaced by automatic digital recognition, which improves working efficiency.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Improving the accuracy of the k-shell method by removing redundant links-from a perspective of spreading dynamics

Recent study shows that the accuracy of the k-shell method in determining node coreness in a spreading process is largely impacted due to the existence of core-like group, which has a large k-shell index but a low spreading efficiency. Based on analysis of the structure of core-like groups in real-world networks, we discover that nodes in the core-like group are mutually densely connected with very few out-leaving links from the group. By defining a measure of diffusion importance for each edge based on the number of out-leaving links of its both ends, we are able to identify redundant links in the spreading process, which have a relatively low diffusion importance but lead to form the locally densely connected core-like group. After filtering out the redundant links and applying the k-shell method to the residual network, we obtain a renewed coreness for each node which is a more accurate index to indicate its location importance and spreading influence in the original network. Moreover, we find that the performance of the ranking algorithms based on the renewed coreness are also greatly enhanced. Our findings help to more accurately decompose the network core structure and identify influential nodes in spreading processes.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Local noncentrosymmetricity and possible spin-momentum locking in Sr$_3$Ru$_2$O$_7$

Strong spin-orbital coupling (SOC) was found previously to lead to dramatic effects in quantum materials, such as those found in topological insulators. It was shown theoretically that local noncentrosymmetricity resulting from the rotation of RuO$_6$ octahedral in Sr$_3$Ru$_2$O$_7$ will also give rise to an effective SOC\cite{SocSr327,MicroscopicnematicSr327}. In the presence of a magnetic field applied along a specific in-plane direction, the Fermi surface was predicted to undergo a reconstruction. Here we report results of our in-plane magnetoresistivity and magnetothermopower measurements on single crystals of Sr$_3$Ru$_2$O$_7$ with an electrical or a thermal current applied along specific crystalline directions and a magnetic field rotating in the $ab$ plane (Fig. 1a), showing a minimal value for field directions predicted by the local noncentrosymmetricity theory. Furthermore, the thermopower, and therefore, the electron entropy, were found to be suppressed as the field was applied perpendicular to the thermal current, which suggests that the spin and the momentum in Sr$_3$Ru$_2$O$_7$ are locked over substantial parts of the Fermi surface, likely originating from local noncentrosymmetricity as well.

0 citations0 reviewsNo code linkedOpen access
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.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Properties of the Fast Forward Shock Driven by the July 23 2012 Extreme Coronal Mass Ejection

Late on July 23, 2012, the STEREO-A spacecraft encountered a fast forward shock driven by a coronal mass ejection launched from the Sun earlier that same day. The estimated travel time of the disturbance ($\sim 20$ hrs), together with the massive magnetic field strengths measured within the ejecta ($> 100$nT), made it one of the most extreme events observed during the space era. In this study, we examine the properties of the shock wave. Because of an instrument malfunction, plasma measurements during the interval surrounding the CME were limited, and our approach has been modified to capitalize on the available measurements and suitable proxies, where possible. We were able to infer the following properties. First, the shock normal was pointing predominantly in the radial direction (${\bf n} = 0.97 {\bf e}_r -0.09 {\bf e}_t -0.23 {\bf e}_n$). Second, the angle between ${\bf n}$ and the upstream magnetic field, $θ_{Bn}$, was estimated to be $\approx 34^{\circ}$, making the shock "quasi-parallel," and supporting the idea of an earlier "preconditioning" ICME. Third, the shock speed was estimated to be $\approx 3300$ km s$^{-1}$. Fourth, the sonic Mach number, $M_s$, for this shock was $\sim 28$. We support these results with an idealized numerical simulation of the ICME. Finally, we estimated the change in ram pressure upstream of the shock to be $\sim 5$ times larger than the pressure from the energetic particles, suggesting that this was not a standard "steady-state" cosmic-ray modified shock (CRMS). Instead it might represent an early, transient phase in the evolution of the CRMS.

0 citations0 reviewsNo code linkedOpen access
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.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

SMA observations of the W3(OH) complex: Dynamical differentiation between W3(H$_2$O) and W3(OH)

We present Submillimeter Array (SMA) observations of the HCN\,(3--2) and HCO$^+$\,(3--2) molecular lines toward the W3(H$_2$O) and W3(OH) star-forming complexes. Infall and outflow motions in the W3(H$_2$O) have been characterized by observing HCN and HCO$^+$ transitions. High-velocity blue/red-shifted emission, tracing the outflow, show multiple knots, which might originate in episodic and precessing outflows. `Blue-peaked' line profiles indicate that gas is infalling onto the W3(H$_2$O) dust core. The measured large mass accretion rate, 2.3$\times$10$^{-3}$~M$_{\odot}$~yr$^{-1}$, together with the small free-fall time scale, 5$\times$10$^{3}$~yr, suggest W3(H$_2$O) is in an early evolutionary stage of the process of formation of high-mass stars. For the W3(OH), a two-layer model fit to the HCN and HCO$^+$ spectral lines and Spizter/IRAC images support that the W3(OH) H{\sc ii} region is expanding and interacting with the ambient gas, with the shocked neutral gas being expanding with an expansion timescale of 6.4$\times$10$^{3}$~yr. The observations suggest different kinematical timescales and dynamical states for the W3(H$_2$O) and W3(OH).

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

SMA Observations of W3(OH) Complex: Physical and Chemical Differentiation between W3(H$_2$O) and W3(OH)

We report on the Submillimeter Array (SMA) observations of molecular lines at 270 GHz toward W3(OH) and W3(H$_2$O) complex. Although previous observations already resolved the W3(H$_2$O) into two or three sub-components, the physical and chemical properties of the two sources are not well constrained. Our SMA observations clearly resolved W3(OH) and W3(H$_2$O) continuum cores. Taking the advantage of the line fitting tool XCLASS, we identified and modeled a rich molecular spectrum in this complex, including multiple CH$_3$CN and CH$_3$OH transitions in both cores. HDO, C$_2$H$_5$CN, O$^{13}$CS, and vibrationally excited lines of HCN, CH$_3$CN, and CH$_3$OCHO were only detected in W3(H$_2$O). We calculate gas temperatures and column densities for both cores. The results show that W3(H$_{2}$O) has higher gas temperatures, and larger column densities than W3(OH) as previously observed, suggesting physical and chemical differences between the two cores. We compare the molecular abundances in W3(H$_2$O) to those in the Sgr B2(N) hot core, the Orion KL hot core and the Orion Compact Ridge, and discuss the chemical origin of specific species. An east-west velocity gradient is seen in W3(H$_2$O), and the extension is consistent with the bipolar outflow orientation traced by water masers and radio jets. A north-south velocity gradient across W3(OH) is also observed. However, with current observations we can not assure if the velocity gradients are caused by rotation, outflow or radial velocity differences of the sub-components in W3(OH).

0 citations0 reviewsNo code linkedOpen access
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.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Vortex crossing and trapping in doubly connected mesoscopic loops of a single-crystal type II superconductor

Numerical calculations on a mesoscopic ring of a type II superconductor in the London limit suggest that an Abrikosov vortex can be trapped in such a structure above a critical magnetic field and generate a phase shift in the magnetoresistance oscillations. We prepared submicron-sized superconducting loops of single-crystal, type II superconductor NbSe$_2$ and measured magnetoresistance oscillations resulting from vortices crossing the loops. The free energy barrier for vortex crossing determines the crossing rate and is periodically modulated by the external magnetic flux threading the loop. We demonstrated experimentally that the crossing of vortices can be directed at a pair of constrictions in the loop, leading to more pronounced magnetoresistance oscillations than those in a uniform ring. The vortex trapping in both a simple ring and a ring featuring two constrictions was found to result in a phase shift in the magnetoresistance oscillations as predicted in the numerical calculations. The controlled crossing and trapping of vortices demonstrated in our NbSe$_2$ devices provide a starting point for the manipulation of individual Abrikosov vortices, which is useful for future technologies.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

A Motivation Model of Peer Assessment in Programming Language Learning

Peer assessment is an efficient and effective learning assessment method that has been used widely in diverse fields in higher education. Despite its many benefits, a fundamental problem in peer assessment is that participants lack the motivation to assess others' work faithfully and fairly. Non-consensus is a common challenge that makes the reliability of peer assessment a primary concern in practices. This research proposes a motivation model that uses review deviation and radicalization to identify non-consensus in peer assessment. The proposed model is implemented as a software module in a peer code review system called EduPCR4. EduPCR4 is able to monitor this measure and trigger teacher's arbitration when it detects possible non-consensus. An empirical study conducted in a university-level C programming course showed that the proposed model and its implementation helped to improve the peer assessment practices in many aspects.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Direct CP violation in $τ^\pm\rightarrow K^\pm ρ^0 (ω)ν_τ\rightarrow K^\pm π^+π^-ν_τ$

We study the direct CP violation in the $τ^\pm\rightarrow K^\pm ρ^0 (ω)ν_τ\rightarrow K^\pm π^+π^-ν_τ$ decay process in the Standard Model. An interesting mechanism involving the charge symmetry violating mixing between $ρ^0$ and $ω$ is applied to enlarge the CP asymmetry. With this mechanism, the maximum differential and localized integrated CP asymmetries can reach $-(5.6^{+2.9}_{-1.7})\times10^{-12}$ and $6.3^{+2.4}_{-3.3}\times 10^{-11}$, respectively, which still leave plenty room for CP-violating New Physics to be discovered through this process.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Super-elastic Collision of Large-scale Magnetized Plasmoids in The Heliosphere

Super-elastic collision is an abnormal collisional process, in which some particular mechanisms cause the kinetic energy of the system increasing. Most studies in this aspect focus on solid-like objects, but they rarely consider gases or liquids, as the collision of the latter is primarily a mixing process. With cross-field diffusion being effectively prohibited, magnetized plasmoids are different from ordinary gases. But it remains unclear how they act during a collision. Here we present the global picture of a unique collision between two coronal mass ejections in the heliosphere, which are the largest magnetized plasmoids erupting from the Sun. Our analysis for the first time reveals that these two magnetized plasmoids collided like solid-like objects with a 73% likelihood of being super-elastic. Their total kinetic energy surprisingly increased by about 6.6% through the collision, which significantly influenced the dynamics of the plasmoids.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Superconductivity in single-layer films of FeSe with a transition temperature above 100 K

Recently, interface has been employed to enhance superconductivity in the single-layer FeSe films grown on SrTiO3(001)(STO) with a possible Tc of ~ 80 K, which is nearly ten times of the Tc of bulk FeSe and is above the Tc record of 56 K for the bulk Fe-based superconductors. This work together with those on superconducting oxides interfaces revives the long-standing idea that electron pairing at a two-dimensional (2D) interface between two different materials is a potential path to high transition temperature (Tc) superconductivity. Subsequent angle-resolved photoemission spectroscopy (ARPES) measurements revealed different electronic structure from those of bulk FeSe with a superconducting-like energy gap closing at around 65K. However, previous ex situ electrical transport measurements could only detect the zero-resistance below ~30 K. Here we report the observation of high Tc superconductivity in the FeSe/STO system. By in situ 4-point probe (4PP) electrical transport measurement that can be conducted at an arbitrary position of the FeSe film on STO, we detected superconductivity above 100 K. Our finding makes FeSe/STO the exciting and ideal research platform for higher Tc superconductivity.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Topological Basis Associated with B-M-W algebra: Two Spin-1/2 Realization

In this letter, we study the two-spin-1/2 realization for the Birman-Murakami-Wenzl (B-M-W) algebra and the corresponding Yang-Baxter $\breve{R}(θ,ϕ)$ matrix. Based on the two-spin-1/2 realization for the B-M-W algebra, the three-dimensional topological space, which is spanned by topological basis, is investigated. By means of such topological basis realization, the four-dimensional Yang-Baxter $\breve{R}(θ,ϕ)$ can be reduced to Wigner $D^{J}$ function with $J=1$. The entanglement and Berry phase in the spectral parameter space are also explored. The results show that one can obtain a set of entangled basis via Yang-Baxter $\breve{R}(θ,ϕ)$ matrix acting on the standard basis, and the entanglement degree is maximum when the $\breve{R}_{i}(θ,ϕ)$ turns to the braiding operator.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Trending Videos: Measurement and Analysis

Unlike popular videos, which would have already achieved high viewership numbers by the time they are declared popular, YouTube trending videos represent content that targets viewers attention over a relatively short time, and has the potential of becoming popular. Despite their importance and visibility, YouTube trending videos have not been studied or analyzed thoroughly. In this paper, we present our findings for measuring, analyzing, and comparing key aspects of YouTube trending videos. Our study is based on collecting and monitoring high-resolution time-series of the viewership and related statistics of more than 8,000 YouTube videos over an aggregate period of nine months. Since trending videos are declared as such just several hours after they are uploaded, we are able to analyze trending videos time-series across critical and sufficiently-long durations of their lifecycle. In addition, we analyze the profile of users who upload trending videos, to potentially identify the role that these users profile plays in getting their uploaded videos trending. Furthermore, we conduct a directional-relationship analysis among all pairs of trending videos time-series that we have monitored. We employ Granger Causality (GC) with significance testing to conduct this analysis. Unlike traditional correlation measures, our directional-relationship analysis provides a deeper insight onto the viewership pattern of different categories of trending videos. Trending videos and their channels have clear distinct statistical attributes when compared to other YouTube content that has not been labeled as trending. Our results also reveal a highly asymmetric directional-relationship among different categories of trending videos. Our directionality analysis also shows a clear pattern of viewership toward popular categories, whereas some categories tend to be isolated.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

A controllable valley polarization in Graphene

The electron transport of different conical valleys is investigated in graphene with extended line-defects. Intriguingly, the electron with a definite incident angle can be completely modulated into one conical valley by a resonator which consists of several paralleling line-defects. The related incident angle can be controlled easily by tuning the parameters of the resonator. Therefore, a controllable 100% valley polarization, as well as the detection of the valley polarization, can be realized conveniently by tuning the number of line-defects and the distance between two nearest neighbouring line-defects. This fascinating finding opens a way to realize the valley polarization by line-defects. With the advancement of experimental technologies, this resonator is promising to be realized and thus plays a key role in graphene valleytronics.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Bounds on the Number of Huffman and Binary-Ternary Trees

Huffman coding is a widely used method for lossless data compression because it optimally stores data based on how often the characters occur in Huffman trees. An $n$-ary Huffman tree is a connected, cycle-lacking graph where each vertex can have either $n$ "children" vertices connecting to it, or 0 children. Vertices with 0 children are called \textit{leaves}. We let $h_n(q)$ represent the total number of $n$-ary Huffman trees with $q$ leaves. In this paper, we use a recursive method to generate upper and lower bounds on $h_n(q)$ and get $h_2(q) \approx (0.1418532)(1.7941471)^q+(0.0612410)(1.2795491)^q$ for $n=2$. This matches the best results achieved by Elsholtz, Heuberger, and Prodinger in August 2011. Our approach reveals patterns in Huffman trees that we used in our analysis of the Binary-Ternary (BT) trees we created. Our research opens a completely new door in data compression by extending the study of Huffman trees to BT trees. Our study of BT trees paves the way for designing data-specific trees, minimizing possible wasted storage space from Huffman coding. We prove a recursive formula for the number of BT trees with $q$ leaves. Furthermore, we provide analysis and further proofs to reach numeric bounds. Our discoveries have broad applications in computer data compression. These results also improve graphical representations of protein sequences that facilitate in-depth genome analysis used in researching evolutionary patterns.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Electrical Tuning of Surface Plasmon Polariton Propagation in Graphene-Nanowire Hybrid Structure

We demonstrate a dynamic surface plasmonic modulation of graphene-nanowire hybrid structures in visible light range, which was thought to be a tough task for graphene based field effect transistor modulator previously. Static modulation depth of as high as 0.07 dB/μm has been achieved experimentally. Carefully simulation indicates the strongly focused electromagnetic field and dramatically enhanced electric field at the interface between a silver NW and a graphene sheet are key roles for bringing the optical response of the device to the visible range. Furthermore, the modulation behaviors near the Dirac point of monolayer graphene and the singularity of gap-induced bilayer graphene are investigated.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Learning Gaussian Graphical Models with Observed or Latent FVSs

Gaussian Graphical Models (GGMs) or Gauss Markov random fields are widely used in many applications, and the trade-off between the modeling capacity and the efficiency of learning and inference has been an important research problem. In this paper, we study the family of GGMs with small feedback vertex sets (FVSs), where an FVS is a set of nodes whose removal breaks all the cycles. Exact inference such as computing the marginal distributions and the partition function has complexity $O(k^{2}n)$ using message-passing algorithms, where k is the size of the FVS, and n is the total number of nodes. We propose efficient structure learning algorithms for two cases: 1) All nodes are observed, which is useful in modeling social or flight networks where the FVS nodes often correspond to a small number of high-degree nodes, or hubs, while the rest of the networks is modeled by a tree. Regardless of the maximum degree, without knowing the full graph structure, we can exactly compute the maximum likelihood estimate in $O(kn^2+n^2\log n)$ if the FVS is known or in polynomial time if the FVS is unknown but has bounded size. 2) The FVS nodes are latent variables, where structure learning is equivalent to decomposing a inverse covariance matrix (exactly or approximately) into the sum of a tree-structured matrix and a low-rank matrix. By incorporating efficient inference into the learning steps, we can obtain a learning algorithm using alternating low-rank correction with complexity $O(kn^{2}+n^{2}\log n)$ per iteration. We also perform experiments using both synthetic data as well as real data of flight delays to demonstrate the modeling capacity with FVSs of various sizes.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Majorana mode in vortex core of Bi2Te3/NbSe2 topological insulator-superconductor heterostructure

Majorana fermions have been intensively studied in recent years for their importance to both fundamental science and potential applications in topological quantum computing1,2. Majorana fermions are predicted to exist in a vortex core of superconducting topological insulators3. However, they are extremely difficult to be distinguished experimentally from other quasiparticle states for the tiny energy difference between Majorana fermions and these states, which is beyond the energy resolution of most available techniques. Here, we overcome the problem by systematically investigating the spatial profile of the Majorana mode and the bound quasiparticle states within a vortex in Bi2Te3/NbSe2. While the zero bias peak in local conductance splits right off the vortex center in conventional superconductors, it splits off at a finite distance ~20nm away from the vortex center in Bi2Te3/NbSe2, primarily due to the Majorana fermion zero mode. While the Majorana mode is destroyed by reducing the distance between vortices, the zero bias peak splits as a conventional superconductor again. This work provides strong evidences of Majorana fermions and also suggests a possible route to manipulating them.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Simulation-efficient shortest probability intervals

Bayesian highest posterior density (HPD) intervals can be estimated directly from simulations via empirical shortest intervals. Unfortunately, these can be noisy (that is, have a high Monte Carlo error). We derive an optimal weighting strategy using bootstrap and quadratic programming to obtain a more compu- tationally stable HPD, or in general, shortest probability interval (Spin). We prove the consistency of our method. Simulation studies on a range of theoret- ical and real-data examples, some with symmetric and some with asymmetric posterior densities, show that intervals constructed using Spin have better cov- erage (relative to the posterior distribution) and lower Monte Carlo error than empirical shortest intervals. We implement the new method in an R package (SPIn) so it can be routinely used in post-processing of Bayesian simulations.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Single-crystal superconducting nanowires of NbSe$_2$ fabricated by reactive plasma etching

We present the preparation and measurements of nanowires of single-crystal NbSe$_2$. These nanowires were prepared on ultrathin ($\lesssim10\text{ nm}$) flakes of NbSe$_2$ mechanically exfoliated from a bulk single crystal using a process combining electron beam lithography and reactive plasma etching. The electrical contacts to the nanowires were prepared using Ti/Au. Our technique, which overcomes several limitations of methods developed previously for fabricating superconducting nanowires, also allows for the preparation of complex superconducting nanostructures with a desired geometry. Current-voltage characteristics of individual superconducting single-crystal nanowires with widths down to 30~nm and cross-sectional areas as low as 270 nm$^2$ were measured for the first time.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Structural and metal-insulator transitions in ionic liquid-gated Ca3Ru2O7 surface

We report the fabrication and measurements of ionic liquid gated Hall bar devices prepared on thin Ca$_3$Ru$_2$O$_7$ flakes exfoliated from bulk single crystals that were grown by a floating zone method. Two types of devices with their electrical transport properties dominated by $c$-axis transport in Type A or that of the in-plane in Type B devices, were prepared. Bulk physical phenomena, including a magnetic transition near 56 K, a structural and metal-insulator transition at a slightly lower temperature, as well as the emergence of a highly unusual metallic state as the temperature is further lowered, were found in both types of devices. However, the Shubnikov-de Haas oscillations were found in Type A but not Type B devices, most likely due to enhanced disorder on the flake surface. Finally, the ionic liquid gating of a Type B device revealed a shift in critical temperature of the structural and metal-insulator transitions, suggesting that such transitions can be tuned by the electric field effect.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Topological Superconductor Bi2Te3/NbSe2 heterostructures

Topological superconductors (TSCs) have a full gap in the bulk and gapless surface states consisting of Majorana fermions, which have potential applications in fault-tolerant topological quantum computation. Because TSCs are very rare in nature, an alternative way to study the TSC is to artificially introduce superconductivity into the surface states of a topological insulator (TI) through proximity effect (PE)1-4. Here we report the first experimental realization of the PE induced TSC in Bi2Te3/NbSe2 thin films as demonstrated by the density of states probed using scanning tunneling microscope. We observe Abrikosov vortices and lower energy bound states on the surface of topological insulator and the dependence of superconducting coherence length on the film thickness and magnetic field, which are attributed to the superconductivity in the topological surface states. This work demonstrates the practical feasibility of fabricating a TSC with individual Majorana fermions inside superconducting vortex as predicted in theory and accomplishes the pre-requisite step towards searching for Majorana fermions in the PE induced TSCs.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Characteristics of kinematics of a coronal mass ejection during the 2010 August 1 CME-CME interaction event

We study the interaction of two successive coronal mass ejections (CMEs) during the 2010 August 1 events using STEREO/SECCHI COR and HI data. We obtain the direction of motion for both CMEs by applying several independent reconstruction methods and find that the CMEs head in similar directions. This provides evidence that a full interaction takes place between the two CMEs that can be observed in the HI1 field-of-view. The full de-projected kinematics of the faster CME from Sun to Earth is derived by combining remote observations with in situ measurements of the CME at 1 AU. The speed profile of the faster CME (CME2; ~1200 km/s) shows a strong deceleration over the distance range at which it reaches the slower, preceding CME (CME1; ~700 km/s). By applying a drag-based model we are able to reproduce the kinematical profile of CME2 suggesting that CME1 represents a magnetohydrodynamic obstacle for CME2 and that, after the interaction, the merged entity propagates as a single structure in an ambient flow of speed and density typical for quiet solar wind conditions. Observational facts show that magnetic forces may contribute to the enhanced deceleration of CME2. We speculate that the increase in magnetic tension and pressure, when CME2 bends and compresses the magnetic field lines of CME1, increases the efficiency of drag.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Effects of Weak Ties on Epidemic Predictability in Community Networks

Weak ties play a significant role in the structures and the dynamics of community networks. Based on the susceptible-infected model in contact process, we study numerically how weak ties influence the predictability of epidemic dynamics. We first investigate the effects of different kinds of weak ties on the variabilities of both the arrival time and the prevalence of disease, and find that the bridgeness with small degree can enhance the predictability of epidemic spreading. Once weak ties are settled, compared with the variability of arrival time, the variability of prevalence displays a diametrically opposed changing trend with both the distance of the initial seed to the bridgeness and the degree of the initial seed. More specifically, the further distance and the larger degree of the initial seed can induce the better predictability of arrival time and the worse predictability of prevalence. Moreover, we discuss the effects of weak tie number on the epidemic variability. As community strength becomes very strong, which is caused by the decrease of weak tie number, the epidemic variability will change dramatically. Compared with the case of hub seed and random seed, the bridgenss seed can result in the worst predictability of arrival time and the best predictability of prevalence. These results show that the variability of arrival time always marks a complete reversal trend of that of prevalence, which implies it is impossible to predict epidemic spreading in the early stage of outbreaks accurately.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Epidemic Variability in Hierarchical Geographical Networks with Human Activity Patterns

Recently, some studies have revealed that non-Poissonian statistics of human behaviors stem from the hierarchical geographical network structure. On this view, we focus on epidemic spreading in the hierarchical geographical networks, and study how two distinct contact patterns (i. e., homogeneous time delay (HOTD) and heterogeneous time delay (HETD) associated with geographical distance) influence the spreading speed and the variability of outbreaks. We find that, compared with HOTD and null model, correlations between time delay and network hierarchy in HETD remarkably slow down epidemic spreading, and result in a upward cascading multi-modal phenomenon. Proportionately, the variability of outbreaks in HETD has the lower value, but several comparable peaks for a long time, which makes the long-term prediction of epidemic spreading hard. When a seed (i. e., the initial infected node) is from the high layers of networks, epidemic spreading is remarkably promoted. Interestingly, distinct trends of variabilities in two contact patterns emerge: high-layer seeds in HOTD result in the lower variabilities, the case of HETD is opposite. More importantly, the variabilities of high-layer seeds in HETD are much greater than that in HOTD, which implies the unpredictability of epidemic spreading in hierarchical geographical networks.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Holographic Λ(t)CDM model in a non-flat universe

The holographic $Λ(t)$CDM model in a non-flat universe is studied in this paper. In this model, to keep the form of the stress-energy of the vacuum required by general covariance, the holographic vacuum is enforced to exchange energy with dark matter. It is demonstrated that for the holographic model the best choice for the IR cutoff of the effective quantum field theory is the event horizon size of the universe. We derive the evolution equations of the holographic $Λ(t)$CDM model in a non-flat universe. We constrain the model by using the current observational data, including the 557 Union2 type Ia supernovae data, the cosmic microwave background anisotropy data from the 7-yr WMAP, and the baryon acoustic oscillation data from the SDSS. Our fit results show that the holographic $Λ(t)$CDM model tends to favor a spatially closed universe (the best-fit value of $Ω_{k0}$ is -0.042), and the 95% confidence level range for the spatial curvature is $-0.101<Ω_{k0}<0.040$. We show that the interaction between the holographic vacuum and dark matter induces an energy flow of which the direction is first from vacuum to dark matter and then from dark matter to vacuum. Thus, the holographic $Λ(t)$CDM model is just a time-varying vacuum energy scenario in which the interaction between vacuum and dark matter changes sign during the expansion of the universe.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Radio signatures of CME-streamer interaction and source diagnostics of type II radio burst

It has been suggested that type II radio bursts are due to energetic electrons accelerated at coronal shocks. Radio observations, however, have poor or no spatial resolutions to pinpoint the exact acceleration locations of these electrons. In this paper, we discuss a promising approach to infer the electron acceleration location by combining radio and white light observations. The key assumption is to relate specific morphological features (e.g. spectral bumps) of the dynamic spectra of type II radio bursts, to imaging features (e.g. CME going into a streamer) along the CME (and its driven shock) propagation. In this study, we examine the CME-streamer interaction for the solar eruption dated on 2003 November 1. The presence of spectral bump in the relevant type II radio burst is identified, which is interpreted as a natural result of the shock-radio emitting region entering the dense streamer structure. The study is useful for further determinations of the location of type II radio burst and the associated electron acceleration by CME-driven shock.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

The 2010 August 01 type II burst: A CME-CME Interaction, and its radio and white-light manifestations

We present observational results of a type II burst associated with a CME-CME interaction observed in the radio and white-light wavelength range. We applied radio direction-finding techniques to observations from the STEREO and Wind spacecraft, the results of which were interpreted using white-light coronagraphic measurements for context. The results of the multiple radio-direction finding techniques applied were found to be consistent both with each other and with those derived from the white-light observations of coronal mass ejections (CMEs). The results suggest that the Type II burst radio emission is causally related to the CMEs interaction.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

A Large Family of Multi-path Dual Congestion Control Algorithms

The goal of traffic management is efficiently utilizing network resources via adapting of source sending rates and routes selection. Traditionally, this problem is formulated into a utilization maximization problem. The single-path routing scheme fails to react to instantaneous network congestion. Multi-path routing schemes thus have been proposed aiming at improving network efficiency. Unfortunately, the natural optimization problem to consider is concave but not strictly concave. It thus brings a huge challenge to design stable multi-path congestion control algorithms. In this paper, we propose a generalized multi-path utility maximization model to consider the problem of routes selection and flow control, and derive a family of multi-path dual congestion control algorithms. We show that the proposed algorithms are stable in the absence of delays. We also derive decentralized and scalable sufficient conditions for a particular scheme when propagation delays exist in networks. Simulations are implemented using both Matlab and NS2, on which evaluation of the proposed multi-path dual algorithms is exerted. The comparison results, between the proposed algorithms and the other two existing algorithms, show that the proposed multi-path dual algorithms with appropriate parameter settings can achieve a stable aggregated throughput while maintaining fairness among the involved users.

0 citations0 reviewsNo code linkedOpen access
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.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Exploring geometrical constraint effects of Cooper pairs

We report low-temperature electrical transport measurements on filled Al squares prepared by e-beam lithography featuring a sample size ranging from 130 nm to 530 nm. The values of the superconducting coherence length, $ξ$(0), were found to range from $\sim$100-210 nm. We found that phase diagrams in the magnetic field and temperature space feature quantized steps in samples with a size comparable with $ξ$(0), suggesting the emergence of quantized states in these smallest superconducting structures. These quantized states, which are not anticipated in Ginzburg-Landau theory, may be associated with the quantum size effects of Cooper pairs.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Feedback Message Passing for Inference in Gaussian Graphical Models

While loopy belief propagation (LBP) performs reasonably well for inference in some Gaussian graphical models with cycles, its performance is unsatisfactory for many others. In particular for some models LBP does not converge, and in general when it does converge, the computed variances are incorrect (except for cycle-free graphs for which belief propagation (BP) is non-iterative and exact). In this paper we propose {\em feedback message passing} (FMP), a message-passing algorithm that makes use of a special set of vertices (called a {\em feedback vertex set} or {\em FVS}) whose removal results in a cycle-free graph. In FMP, standard BP is employed several times on the cycle-free subgraph excluding the FVS while a special message-passing scheme is used for the nodes in the FVS. The computational complexity of exact inference is $O(k^2n)$, where $k$ is the number of feedback nodes, and $n$ is the total number of nodes. When the size of the FVS is very large, FMP is intractable. Hence we propose {\em approximate FMP}, where a pseudo-FVS is used instead of an FVS, and where inference in the non-cycle-free graph obtained by removing the pseudo-FVS is carried out approximately using LBP. We show that, when approximate FMP converges, it yields exact means and variances on the pseudo-FVS and exact means throughout the remainder of the graph. We also provide theoretical results on the convergence and accuracy of approximate FMP. In particular, we prove error bounds on variance computation. Based on these theoretical results, we design efficient algorithms to select a pseudo-FVS of bounded size. The choice of the pseudo-FVS allows us to explicitly trade off between efficiency and accuracy. Experimental results show that using a pseudo-FVS of size no larger than $\log(n)$, this procedure converges much more often, more quickly, and provides more accurate results than LBP on the entire graph.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Internet Resource Pricing Models, Mechanisms, and Methods

With the fast development of video and voice network applications, CDN (Content Distribution Networks) and P2P (Peer-to-Peer) content distribution technologies have gradually matured. How to effectively use Internet resources thus has attracted more and more attentions. For the study of resource pricing, a whole pricing strategy containing pricing models, mechanisms and methods covers all the related topics. We first introduce three basic Internet resource pricing models through an Internet cost analysis. Then, with the evolution of service types, we introduce several corresponding mechanisms which can ensure pricing implementation and resource allocation. On network resource pricing methods, we discuss the utility optimization in economics, and emphasize two classes of pricing methods (including system optimization and entities' strategic optimizations). Finally, we conclude the paper and forecast the research direction on pricing strategy which is applicable to novel service situation in the near future.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Manipulating superconducting fluctuations by the Little-Parks-de Gennes effect in ultrasmall Al loops

The destruction of superconducting phase coherence by quantum fluctuations and the control of these fluctuations have been a problem of long-standing interest, with recent impetus provided by its relevance to the pursuit of very high temperature superconductivity. Building on the work of Little and Parks, de Gennes predicted more than three decades ago that superconductivity could be destroyed near half-integer-flux quanta in ultrasmall loops with a side branch, resulting in a destructive regime. We report the experimental observation of this Little-Parks-de Gennes effect in Al loops prepared by advanced e-beam lithography. We show that the effect can be used to restore the lost phase coherence through side branches.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Technology ready use of single layer graphene as a transparent electrode for hybrid photovoltaic devices

Graphene has been used recently as a replacement for indium tin oxide (ITO) for the transparent electrode of an organic photovoltaic device. Due to its limited supply, ITO is considered as a limiting factor for the commercialization of organic solar cells. We explored the use of large-area graphene grown on copper by chemical vapor deposition (CVD) and then transferred to a glass substrate as an alternative transparent electrode. The transferred film was shown by scanning Raman spectroscopy measurements to consist of >90% single layer graphene. Optical spectroscopy measurements showed that the layer-transferred graphene has an optical absorbance of 1.23% at a wavelength of 532 nm. We fabricated organic hybrid solar cells utilizing this material as an electrode and compared their performance with ITO devices fabricated using the same procedure. We demonstrated power conversion efficiency up to 3.98%, higher than that of the ITO device (3.86%), showing that layer-transferred graphene promises to be a high quality, low-cost, flexible material for transparent electrodes in solar cell technology.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Geometric Triangulation of Imaging Observations to Track Coronal Mass Ejections Continuously Out to 1 AU

We describe a geometric triangulation technique, based on time-elongation maps constructed from imaging observations, to track coronal mass ejections (CMEs) continuously in the heliosphere and predict their impact on the Earth. Taking advantage of stereoscopic imaging observations from STEREO, this technique can determine the propagation direction and radial distance of CMEs from their birth in the corona all the way to 1 AU. The efficacy of the method is demonstrated by its application to the 2008 December 12 CME, which manifests as a magnetic cloud (MC) from in situ measurements at the Earth. The predicted arrival time and radial velocity at the Earth are well confirmed by the in situ observations around the MC. Our method reveals non-radial motions and velocity changes of the CME over large distances in the heliosphere. It also associates the flux-rope structure measured in situ with the dark cavity of the CME in imaging observations. Implementation of the technique, which is expected to be a routine possibility in the future, may indicate a substantial advance in CME studies as well as space weather forecasting.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

STEREO and Wind observations of a fast ICME flank triggering a prolonged geomagnetic storm on 5-7 April 2010

On 5 April 2010 an interplanetary (IP) shock was detected by the Wind spacecraft ahead of Earth, followed by a fast (average speed 650 km/s) IP coronal mass ejection (ICME). During the subsequent moderate geomagnetic storm (minimum Dst = -72 nT, maximum Kp=8-), communication with the Galaxy 15 satellite was lost. We link images from STEREO/SECCHI to the near-Earth in situ observations and show that the ICME did not decelerate much between Sun and Earth. The ICME flank was responsible for a long storm growth phase. This type of glancing collision was for the first time directly observed with the STEREO Heliospheric Imagers. The magnetic cloud (MC) inside the ICME cannot be modeled with approaches assuming an invariant direction. These observations confirm the hypotheses that parts of ICMEs classified as (1) long-duration MCs or (2) magnetic-cloud-like (MCL) structures can be a consequence of a spacecraft trajectory through the ICME flank.

0 citations0 reviewsNo code linkedOpen access