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Lin Wang

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preprint2026arXiv

A Curriculum Learning Approach to Reinforcement Learning: Leveraging RAG for Multimodal Question Answering

This paper describes the solutions of the Dianping-Trust-Safety team for the META CRAG-MM challenge. The challenge requires building a comprehensive retrieval-augmented generation system capable for multi-modal multi-turn question answering. The competition consists of three tasks: (1) answering questions using structured data retrieved from an image-based mock knowledge graph, (2) synthesizing information from both knowledge graphs and web search results, and (3) handling multi-turn conversations that require context understanding and information aggregation from multiple sources. For Task 1, our solution is based on the vision large language model, enhanced by supervised fine-tuning with knowledge distilled from GPT-4.1. We further applied curriculum learning strategies to guide reinforcement learning, resulting in improved answer accuracy and reduced hallucination. For Task 2 and Task 3, we additionally leveraged web search APIs to incorporate external knowledge, enabling the system to better handle complex queries and multi-turn conversations. Our approach achieved 1st place in Task 1 with a significant lead of 52.38%, and 3rd place in Task 3, demonstrating the effectiveness of the integration of curriculum learning with reinforcement learning in our training pipeline.

preprint2026arXiv

StableMind: Source-Free Cross-Subject fMRI Decoding with Regularized Adaptation

Existing cross-subject fMRI decoding methods typically train a model on multiple scanned subjects and then adapt it to a new subject using substantial paired fMRI-image data. However, in realistic scenarios, new-subject fMRI data are often limited due to costly data acquisition, and raw data from previous subjects may be inaccessible, leading existing methods to suffer performance degradation during new-subject adaptation. In this paper, we identify that this degradation stems from two key issues: brain-side instability caused by large subject differences in fMRI responses, and image-side supervision unreliability caused by fine-grained visual details that are not reliably supported by limited fMRI signals. To address these challenges, we propose StableMind, a regularized adaptation framework designed to improve brain-side representation stability and image-side supervision reliability. (1) To stabilize brain representations, StableMind reuses ridge projections from the pretrained model as adaptation priors to constrain limited-data new-subject adaptation, and applies Fourier-based feature-level brain augmentation to improve robustness to individual variability. (2) To improve image supervision reliability, StableMind introduces difficulty-aware image blur for brain-image alignment, reducing the influence of fine-grained visual details that are weakly supported by limited fMRI signals while preserving stable visual structure. Experiments on the Natural Scenes Dataset under a unified 1-hour adaptation protocol demonstrate that StableMind achieves 84.02% image retrieval accuracy and 81.66% brain retrieval accuracy averaged over four subjects, surpassing the state-of-the-art method by 5.71% brain retrieval accuracy with fewer trainable adaptation parameters. Our code is available at https://github.com/lingeringlight/StableMind.

preprint2025arXiv

Subgroup Identification and Individualized Treatment Policies: A Tutorial on the Hybrid Two-Stage Workflow

Patients in clinical studies often exhibit heterogeneous treatment effect (HTE). Classical subgroup analyses provide inferential tools to test for effect modification, while modern machine learning methods estimate the Conditional Average Treatment Effect (CATE) to enable individual level prediction. Each paradigm has limitations: inference focused approaches may sacrifice predictive utility, and prediction focused approaches often lack statistical guarantees. We present a hybrid two-stage workflow that integrates these perspectives. Stage 1 applies statistical inference to test whether credible treatment effect heterogeneity exists with the protection against spurious findings. Stage 2 translates heterogeneity evidence into individualized treatment policies, evaluated by cross fitted doubly robust (DR) metrics with Neyman-Pearson (NP) constraints on harm. We illustrate the workflow with working examples based on simulated data and a real ACTG 175 HIV trial. This tutorial provides practical implementation checklists and discusses links to sponsor oriented HTE workflows, offering a transparent and auditable pathway from heterogeneity assessment to individualized treatment policies.

preprint2024arXiv

Hi-Map: Hierarchical Factorized Radiance Field for High-Fidelity Monocular Dense Mapping

In this paper, we introduce Hi-Map, a novel monocular dense mapping approach based on Neural Radiance Field (NeRF). Hi-Map is exceptional in its capacity to achieve efficient and high-fidelity mapping using only posed RGB inputs. Our method eliminates the need for external depth priors derived from e.g., a depth estimation model. Our key idea is to represent the scene as a hierarchical feature grid that encodes the radiance and then factorizes it into feature planes and vectors. As such, the scene representation becomes simpler and more generalizable for fast and smooth convergence on new observations. This allows for efficient computation while alleviating noise patterns by reducing the complexity of the scene representation. Buttressed by the hierarchical factorized representation, we leverage the Sign Distance Field (SDF) as a proxy of rendering for inferring the volume density, demonstrating high mapping fidelity. Moreover, we introduce a dual-path encoding strategy to strengthen the photometric cues and further boost the mapping quality, especially for the distant and textureless regions. Extensive experiments demonstrate our method's superiority in geometric and textural accuracy over the state-of-the-art NeRF-based monocular mapping methods.

preprint2023arXiv

Multi-scale multi-modal micro-expression recognition algorithm based on transformer

A micro-expression is a spontaneous unconscious facial muscle movement that can reveal the true emotions people attempt to hide. Although manual methods have made good progress and deep learning is gaining prominence. Due to the short duration of micro-expression and different scales of expressed in facial regions, existing algorithms cannot extract multi-modal multi-scale facial region features while taking into account contextual information to learn underlying features. Therefore, in order to solve the above problems, a multi-modal multi-scale algorithm based on transformer network is proposed in this paper, aiming to fully learn local multi-grained features of micro-expressions through two modal features of micro-expressions - motion features and texture features. To obtain local area features of the face at different scales, we learned patch features at different scales for both modalities, and then fused multi-layer multi-headed attention weights to obtain effective features by weighting the patch features, and combined cross-modal contrastive learning for model optimization. We conducted comprehensive experiments on three spontaneous datasets, and the results show the accuracy of the proposed algorithm in single measurement SMIC database is up to 78.73% and the F1 value on CASMEII of the combined database is up to 0.9071, which is at the leading level.

preprint2023arXiv

Reconstruction of compressed spectral imaging based on global structure and spectral correlation

In this paper, a convolutional sparse coding method based on global structure characteristics and spectral correlation is proposed for the reconstruction of compressive spectral images. The spectral data is regarded as the convolution sum of the convolution kernel and the corresponding coefficients, using the convolution kernel operates the global image information, preserving the structure information of the spectral image in the spatial dimension. To take full exploration of the constraints between spectra, the coefficients corresponding to the convolution kernel are constrained by the L_(2,1)norm to improve spectral accuracy. And, to solve the problem that convolutional sparse coding is insensitive to low frequency, the global total-variation (TV) constraint is added to estimate the low-frequency components. It not only ensures the effective estimation of the low-frequency but also transforms the convolutional sparse coding into a de-noising process, which makes the reconstructing process simpler. Simulations show that compared with the current mainstream optimization methods, the proposed method can improve the reconstruction quality by up to 4 dB in PSNR and 10% in SSIM, and has a great improvement in the details of the reconstructed image.

preprint2022arXiv

Controllability of Multilayer Networked Sampled-data Systems

This paper explores the state controllability of multilayer networked sampled-data systems with inter-layer couplings, where zero-order holders (ZOHs) are on the control and transmission channels. The effects of both single- and multi-rate sampling on controllability of multilayer networked linear time-invariant (LTI) systems are analyzed, with some sufficient and/or necessary controllability conditions derived. Under specific conditions, the pathological sampling of single node systems could be eliminated by the network structure and inner couplings among different nodes and different layers. The representative drive-response inter-layer coupling mode is studied, and it reveals that the whole system could be controllable due to the inter-layer couplings even if the response layer is uncontrollable itself. Moreover, simulated examples show that the modification of sampling rate on local channels could lay a positive or negative effect on the controllability of the whole system. All the results indicate that the controllability of the multilayer networked sampled-data system is collectively affected by mutually coupled factors.

preprint2022arXiv

Data-driven discovery of high performance layered van der Waals piezoelectric NbOI2

Using high-throughput first-principles calculations to search for layered van der Waals materials with the largest piezoelectric stress coefficients, we discover NbOI2 to be the one among 2940 monolayers screened. The piezoelectric performance of NbOI2 is independent of thickness, and its electromechanical coupling factor of near unity is a hallmark of optimal interconversion between electrical and mechanical energy. Laser scanning vibrometer studies on bulk and few-layer NbOI2 crystals verify their huge piezoelectric responses, which exceed internal references such as In2Se3 and CuInP2S6. Furthermore, we provide insights into the atomic origins of anti-correlated piezoelectric and ferroelectric responses in NbOX2 (X = Cl, Br, I), based on bond covalency and structural distortions in these materials. Our discovery that NbOI2 has the largest piezoelectric stress coefficients among 2D materials calls for the development of NbOI2-based flexible nanoscale piezoelectric devices.

preprint2022arXiv

Deep Learning for Omnidirectional Vision: A Survey and New Perspectives

Omnidirectional image (ODI) data is captured with a 360x180 field-of-view, which is much wider than the pinhole cameras and contains richer spatial information than the conventional planar images. Accordingly, omnidirectional vision has attracted booming attention due to its more advantageous performance in numerous applications, such as autonomous driving and virtual reality. In recent years, the availability of customer-level 360 cameras has made omnidirectional vision more popular, and the advance of deep learning (DL) has significantly sparked its research and applications. This paper presents a systematic and comprehensive review and analysis of the recent progress in DL methods for omnidirectional vision. Our work covers four main contents: (i) An introduction to the principle of omnidirectional imaging, the convolution methods on the ODI, and datasets to highlight the differences and difficulties compared with the 2D planar image data; (ii) A structural and hierarchical taxonomy of the DL methods for omnidirectional vision; (iii) A summarization of the latest novel learning strategies and applications; (iv) An insightful discussion of the challenges and open problems by highlighting the potential research directions to trigger more research in the community.

preprint2022arXiv

Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification

Vehicle re-identification (Re-ID) is urgently demanded to alleviate thepressure caused by the increasingly onerous task of urban traffic management. Multiple challenges hamper the applications of vision-based vehicle Re-ID methods: (1) The appearances of different vehicles of the same brand/model are often similar; However, (2) the appearances of the same vehicle differ significantly from different viewpoints. Previous methods mainly use manually annotated multi-attribute datasets to assist the network in getting detailed cues and in inferencing multi-view to improve the vehicle Re-ID performance. However, finely labeled vehicle datasets are usually unattainable in real application scenarios. Hence, we propose a Discriminative-Region Attention and Orthogonal-View Generation (DRA-OVG) model, which only requires identity (ID) labels to conquer the multiple challenges of vehicle Re-ID.The proposed DRA model can automatically extract the discriminative region features, which can distinguish similar vehicles. And the OVG model can generate multi-view features based on the input view features to reduce the impact of viewpoint mismatches. Finally, the distance between vehicle appearances is presented by the discriminative region features and multi-view features together. Therefore, the significance of pairwise distance measure between vehicles is enhanced in acomplete feature space. Extensive experiments substantiate the effectiveness of each proposed ingredient, and experimental results indicate that our approach achieves remarkable improvements over the state- of-the-art vehicle Re-ID methods on VehicleID and VeRi-776 datasets.

preprint2022arXiv

Efficient Video Deblurring Guided by Motion Magnitude

Video deblurring is a highly under-constrained problem due to the spatially and temporally varying blur. An intuitive approach for video deblurring includes two steps: a) detecting the blurry region in the current frame; b) utilizing the information from clear regions in adjacent frames for current frame deblurring. To realize this process, our idea is to detect the pixel-wise blur level of each frame and combine it with video deblurring. To this end, we propose a novel framework that utilizes the motion magnitude prior (MMP) as guidance for efficient deep video deblurring. Specifically, as the pixel movement along its trajectory during the exposure time is positively correlated to the level of motion blur, we first use the average magnitude of optical flow from the high-frequency sharp frames to generate the synthetic blurry frames and their corresponding pixel-wise motion magnitude maps. We then build a dataset including the blurry frame and MMP pairs. The MMP is then learned by a compact CNN by regression. The MMP consists of both spatial and temporal blur level information, which can be further integrated into an efficient recurrent neural network (RNN) for video deblurring. We conduct intensive experiments to validate the effectiveness of the proposed methods on the public datasets.

preprint2022arXiv

Event-guided Deblurring of Unknown Exposure Time Videos

Motion deblurring is a highly ill-posed problem due to the loss of motion information in the blur degradation process. Since event cameras can capture apparent motion with a high temporal resolution, several attempts have explored the potential of events for guiding deblurring. These methods generally assume that the exposure time is the same as the reciprocal of the video frame rate. However, this is not true in real situations, and the exposure time might be unknown and dynamically varies depending on the video shooting environment(e.g., illumination condition). In this paper, we address the event-guided motion deblurring assuming dynamically variable unknown exposure time of the frame-based camera. To this end, we first derive a new formulation for event-guided motion deblurring by considering the exposure and readout time in the video frame acquisition process. We then propose a novel end-to-end learning framework for event-guided motion deblurring. In particular, we design a novel Exposure Time-based Event Selection(ETES) module to selectively use event features by estimating the cross-modal correlation between the features from blurred frames and the events. Moreover, we propose a feature fusion module to fuse the selected features from events and blur frames effectively. We conduct extensive experiments on various datasets and demonstrate that our method achieves state-of-the-art performance.

preprint2022arXiv

Future Computer Systems and Networking Research in the Netherlands: A Manifesto

Our modern society and competitive economy depend on a strong digital foundation and, in turn, on sustained research and innovation in computer systems and networks (CompSys). With this manifesto, we draw attention to CompSys as a vital part of ICT. Among ICT technologies, CompSys covers all the hardware and all the operational software layers that enable applications; only application-specific details, and often only application-specific algorithms, are not part of CompSys. Each of the Top Sectors of the Dutch Economy, each route in the National Research Agenda, and each of the UN Sustainable Development Goals pose challenges that cannot be addressed without groundbreaking CompSys advances. Looking at the 2030-2035 horizon, important new applications will emerge only when enabled by CompSys developments. Triggered by the COVID-19 pandemic, millions moved abruptly online, raising infrastructure scalability and data sovereignty issues; but governments processing social data and responsible social networks still require a paradigm shift in data sovereignty and sharing. AI already requires massive computer systems which can cost millions per training task, but the current technology leaves an unsustainable energy footprint including large carbon emissions. Computational sciences such as bioinformatics, and "Humanities for all" and "citizen data science", cannot become affordable and efficient until computer systems take a generational leap. Similarly, the emerging quantum internet depends on (traditional) CompSys to bootstrap operation for the foreseeable future. Large commercial sectors, including finance and manufacturing, require specialized computing and networking or risk becoming uncompetitive. And, at the core of Dutch innovation, promising technology hubs, deltas, ports, and smart cities, could see their promise stagger due to critical dependency on non-European technology.

preprint2022arXiv

Go viral or go broadcast? Characterizing the virality and growth of cascades

Quantifying the virality of cascades is an important question across disciplines such as the transmission of disease, the spread of information and the diffusion of innovations. An appropriate virality metric should be able to disambiguate between a shallow, broadcast-like diffusion process and a deep, multi-generational branching process. Although several valuable works have been dedicated to this field, most of them fail to take the position of the diffusion source into consideration, which makes them fall into the trap of graph isomorphism and would result in imprecise estimation of cascade virality inevitably under certain circumstances. In this paper, we propose a root-aware approach to quantifying the virality of cascades with proper consideration of the root node in a diffusion tree. With applications on synthetic and empirical cascades, we show the properties and potential utility of the proposed virality measure. Based on preferential attachment mechanisms, we further introduce a model to mimic the growth of cascades. The proposed model enables the interpolation between broadcast and viral spreading during the growth of cascades. Through numerical simulations, we demonstrate the effectiveness of the proposed model in characterizing the virality of growing cascades. Our work contributes to the understanding of cascade virality and growth, and could offer practical implications in a range of policy domains including viral marketing, infectious disease and information diffusion.

preprint2022arXiv

Impact of disorder on the distribution of gate coupling strengths in a spin qubit device

A scalable spin-based quantum processor requires a suitable semiconductor heterostructure and a gate design, with multiple alternatives being investigated. Characterizing such devices experimentally is a demanding task, with the full development cycle taking at least months. While numerical simulations are more time-efficient, their predictive power is limited due to unavoidable disorder and device-to-device variation. We develop a spin-qubit device simulation for determining the distribution of the coupling strengths between the electrostatic gate potentials and the effective device Hamiltonian in presence of disorder. By comparing our simulation results with the experimental data, we demonstrate that the coupling of the gate voltages to the dot chemical potential and the interdot tunnel coupling match up to disorder-induced variance. To demonstrate the flexibility of our approach, we also analyze an alternative non-planar geometry inspired by FinFET devices.

preprint2022arXiv

Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data

Thyroid cancer is a common endocrine carcinoma that occurs in the thyroid gland. Much effort has been invested in improving its diagnosis, and thyroidectomy remains the primary treatment method. A successful operation without unnecessary side injuries relies on an accurate preoperative diagnosis. Current human assessment of thyroid nodule malignancy is prone to errors and may not guarantee an accurate preoperative diagnosis. This study proposed a machine framework to predict thyroid nodule malignancy based on a novel clinical dataset we collected. The 10-fold cross-validation, bootstrap analysis, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The comparison between model prediction and expert assessment shows the advantage of our framework over human judgment in predicting thyroid nodule malignancy. Our method is accurate, interpretable, and thus useable as additional evidence in the preoperative diagnosis for thyroid cancer.

preprint2022arXiv

Investigating and Modeling the Dynamics of Long Ties

Long ties, the social ties that bridge different communities, are widely believed to play crucial roles in spreading novel information in social networks. However, some existing network theories and prediction models indicate that long ties might dissolve quickly or eventually become redundant, thus putting into question the long-term value of long ties. Our empirical analysis of real-world dynamic networks shows that contrary to such reasoning, long ties are more likely to persist than other social ties, and that many of them constantly function as social bridges without being embedded in local networks. Using a novel cost-benefit analysis model combined with machine learning, we show that long ties are highly beneficial, which instinctively motivates people to expend extra effort to maintain them. This partly explains why long ties are more persistent than what has been suggested by many existing theories and models. Overall, our study suggests the need for social interventions that can promote the formation of long ties, such as mixing people with diverse backgrounds.

preprint2022arXiv

Medical Matting: A New Perspective on Medical Segmentation with Uncertainty

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

preprint2022arXiv

Social physics

Recent decades have seen a rise in the use of physics methods to study different societal phenomena. This development has been due to physicists venturing outside of their traditional domains of interest, but also due to scientists from other disciplines taking from physics the methods that have proven so successful throughout the 19th and the 20th century. Here we dub this field 'social physics' and pay our respect to intellectual mavericks who nurtured it to maturity. We do so by reviewing the current state of the art. Starting with a set of topics that are at the heart of modern human societies, we review research dedicated to urban development and traffic, the functioning of financial markets, cooperation as the basis for our evolutionary success, the structure of social networks, and the integration of intelligent machines into these networks. We then shift our attention to a set of topics that explore potential threats to society. These include criminal behaviour, large-scale migrations, epidemics, environmental challenges, and climate change. We end the coverage of each topic with promising directions for future research. Based on this, we conclude that the future for social physics is bright. Physicists studying societal phenomena are no longer a curiosity, but rather a force to be reckoned with. Notwithstanding, it remains of the utmost importance that we continue to foster constructive dialogue and mutual respect at the interfaces of different scientific disciplines.

preprint2022arXiv

Strong Neel ordering and luminescence correlation in a two-dimensional antiferromagnet

Magneto-optical effect has been widely used in light modulation, optical sensing and information storage. Recently discovered two-dimensional (2D) van der Waals layered magnets are considered as promising platforms for investigating novel magneto-optical phenomena and devices, due to the long-range magnetic ordering down to atomically-thin thickness, rich species and tunable properties. However, majority 2D antiferromagnets suffer from low luminescence efficiency which hinders their magneto-optical investigations and applications. Here, we uncover strong light-magnetic ordering interactions in 2D antiferromagnetic MnPS3 utilizing a newly-emerged near-infrared photoluminescence (PL) mode far below its intrinsic bandgap. This ingap PL mode shows strong correlation with the Neel ordering and persists down to monolayer thickness. Combining the DFT, STEM and XPS, we illustrate the origin of the PL mode and its correlation with Neel ordering, which can be attributed to the oxygen ion-mediated states. Moreover, the PL strength can be further tuned and enhanced using ultraviolet-ozone treatment. Our studies offer an effective approach to investigate light-magnetic ordering interactions in 2D antiferromagnetic semiconductors.

preprint2022arXiv

The spatial dissemination of COVID-19 and associated socio-economic consequences

The ongoing coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc worldwide with millions of lives claimed, human travel restricted and economic development halted. Leveraging city-level mobility and case data, our analysis shows that the spatial dissemination of COVID-19 can be well explained by a local diffusion process in the mobility network rather than a global diffusion process, indicating the effectiveness of the implemented disease prevention and control measures. Based on the constructed case prediction model, it is estimated that there could be distinct social consequences if the COVID-19 outbreak happened in different areas. During the epidemic control period, human mobility experienced substantial reductions and the mobility network underwent remarkable local and global structural changes toward containing the spread of COVID-19. Our work has important implications for the mitigation of disease and the evaluation of the socio-economic consequences of COVID-19 on society.

preprint2022arXiv

The Strength of Structural Diversity in Online Social Networks

Understanding the way individuals are interconnected in social networks is of prime significance to predict their collective outcomes. Leveraging a large-scale dataset from a knowledge-sharing website, this paper presents an exploratory investigation of the way to depict structural diversity in directed networks and how it can be utilized to predict one's online social reputation. To capture the structural diversity of an individual, we first consider the number of weakly and strongly connected components in one's contact neighborhood and further take the coexposure network of social neighbors into consideration. We show empirical evidence that the structural diversity of an individual is able to provide valuable insights to predict personal online social reputation, and the inclusion of a coexposure network provides an additional ingredient to achieve that goal. After synthetically controlling several possible confounding factors through matching experiments, structural diversity still plays a nonnegligible role in the prediction of personal online social reputation. Our work constitutes one of the first attempts to empirically study structural diversity in directed networks and has practical implications for a range of domains, such as social influence and collective intelligence studies.

preprint2022arXiv

The unusual emission from PSR B1859+07 with FAST

We present simultaneous broad-band radio observations on the abnormal emission mode from PSR B1859$+$07 using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). This pulsar shows peculiar emission phenomena, which are occasional shifts of emission to an early rotational phase and mode change of emission at the normal phase. We confirm all these three emission modes with our datasets, including the B (burst) and Q (quiet) modes of the non-shift pulses and the emission shift mode with a quasi-periodicity of 155 pulses. We also identify a new type of emission shift event, which has emission at the normal phase during the event. We studied polarisation properties of these emission modes in details, and found that they all have similar polarisation angle (PA) curve, indicating the emission of all these three modes are from the same emission height.

preprint2022arXiv

Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularization

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

preprint2021arXiv

An embedded multichannel sound acquisition system for drone audition

Microphone array techniques can improve the acoustic sensing performance on drones, compared to the use of a single microphone. However, multichannel sound acquisition systems are not available in current commercial drone platforms. To encourage the research in drone audition, we present an embedded sound acquisition and recording system with eight microphones and a multichannel sound recorder mounted on a quadcopter. In addition to recording and storing locally the sound from multiple microphones simultaneously, the embedded system can connect wirelessly to a remote terminal to transfer audio files for further processing. This will be the first stage towards creating a fully embedded solution for drone audition. We present experimental results obtained by state-of-the-art drone audition algorithms applied to the sound recorded by the embedded system.

preprint2021arXiv

DEFT: Distilling Entangled Factors by Preventing Information Diffusion

Disentanglement is a highly desirable property of representation owing to its similarity to human understanding and reasoning. Many works achieve disentanglement upon information bottlenecks (IB). Despite their elegant mathematical foundations, the IB branch usually exhibits lower performance. In order to provide an insight into the problem, we develop an annealing test to calculate the information freezing point (IFP), which is a transition state to freeze information into the latent variables. We also explore these clues or inductive biases for separating the entangled factors according to the differences in the IFP distributions. We found the existing approaches suffer from the information diffusion problem, according to which the increased information diffuses in all latent variables. Based on this insight, we propose a novel disentanglement framework, termed the distilling entangled factor (DEFT), to address the information diffusion problem by scaling backward information. DEFT applies a multistage training strategy, including multigroup encoders with different learning rates and piecewise disentanglement pressure, to disentangle the factors stage by stage. We evaluate DEFT on three variants of dSprite and SmallNORB, which show low-variance and high-level disentanglement scores. Furthermore, the experiment under the correlative factors shows incapable of TC-based approaches. DEFT also exhibits a competitive performance in the unsupervised setting.

preprint2021arXiv

Locally symmetric lattices for storage ring light sources

In this paper, a new lattice concept called the locally symmetric lattice is proposed for storage ring light sources. In this new lattice, beta functions are made locally symmetric about two mirror planes of the lattice cell, and the phase advances between the two mirror planes satisfy the condition of nonlinear dynamics cancellation. There are two kinds of locally symmetric lattices, corresponding to two symmetric representations of lattice cell. In a locally symmetric lattice, main nonlinear effects caused by sextupoles can be effectively cancelled within one lattice cell, and generally there can also be many knobs of sextupoles available for further optimizing the nonlinear dynamics. Two kinds of locally symmetric lattices are designed for a 2.2 GeV diffraction-limited storage ring to demonstrate the lattice concept.

preprint2021arXiv

Surrogate-assisted cooperative signal optimization for large-scale traffic networks

Reasonable setting of traffic signals can be very helpful in alleviating congestion in urban traffic networks. Meta-heuristic optimization algorithms have proved themselves to be able to find high-quality signal timing plans. However, they generally suffer from performance deterioration when solving large-scale traffic signal optimization problems due to the huge search space and limited computational budget. Directing against this issue, this study proposes a surrogate-assisted cooperative signal optimization (SCSO) method. Different from existing methods that directly deal with the entire traffic network, SCSO first decomposes it into a set of tractable sub-networks, and then achieves signal setting by cooperatively optimizing these sub-networks with a surrogate-assisted optimizer. The decomposition operation significantly narrows the search space of the whole traffic network, and the surrogate-assisted optimizer greatly lowers the computational burden by reducing the number of expensive traffic simulations. By taking Newman fast algorithm, radial basis function and a modified estimation of distribution algorithm as decomposer, surrogate model and optimizer, respectively, this study develops a concrete SCSO algorithm. To evaluate its effectiveness and efficiency, a large-scale traffic network involving crossroads and T-junctions is generated based on a real traffic network. Comparison with several existing meta-heuristic algorithms specially designed for traffic signal optimization demonstrates the superiority of SCSO in reducing the average delay time of vehicles.

preprint2020arXiv

A Framework of Hierarchical Attacks to Network Controllability

Network controllability robustness reflects how well a networked dynamical system can maintain its controllability against destructive attacks. This paper investigates the network controllability robustness from the perspective of a malicious attack. A framework of hierarchical attack is proposed, by means of edge- or node-removal attacks. Edges (or nodes) in a target network are classified hierarchically into categories, with different priorities to attack. The category of critical edges (or nodes) has the highest priority to be selected for attack. Extensive experiments on nine synthetic networks and nine real-world networks show the effectiveness of the proposed hierarchical attack strategies for destructing the network controllability. From the protection point of view, this study suggests that the critical edges and nodes should be hidden from the attackers. This finding helps better understand the network controllability and better design robust networks.

preprint2020arXiv

A wolbachia infection model with free boundary

Scientists have been seeking ways to use Wolbachia to eliminate the mosquitoes that spread human diseases. Could Wolbachia be the determining factor in controlling the mosquito-borne infectious diseases? To answer this question mathematically, we develop a reaction-diffusion model with free boundary in a one-dimensional environment. We divide the female mosquito population into two groups: one is the uninfected mosquito population that grows in the whole region while the other is the mosquito population infected with Wolbachia that occupies a finite small region and invades the environment with a spreading front governed by a free boundary satisfying the well-known one-phase Stefan condition. For the resulting free boundary problem, we establish criteria under which spreading and vanishing occur. Our results provide useful insights on designing a feasible mosquito releasing strategy to invade the whole mosquito population with Wolbachia infection and thus eventually eradicate the mosquito-borne diseases.

preprint2020arXiv

AGL: a Scalable System for Industrial-purpose Graph Machine Learning

Machine learning over graphs have been emerging as powerful learning tools for graph data. However, it is challenging for industrial communities to leverage the techniques, such as graph neural networks (GNNs), and solve real-world problems at scale because of inherent data dependency in the graphs. As such, we cannot simply train a GNN with classic learning systems, for instance parameter server that assumes data parallel. Existing systems store the graph data in-memory for fast accesses either in a single machine or graph stores from remote. The major drawbacks are in three-fold. First, they cannot scale because of the limitations on the volume of the memory, or the bandwidth between graph stores and workers. Second, they require extra development of graph stores without well exploiting mature infrastructures such as MapReduce that guarantee good system properties. Third, they focus on training but ignore the optimization of inference over graphs, thus makes them an unintegrated system. In this paper, we design AGL, a scalable, fault-tolerance and integrated system, with fully-functional training and inference for GNNs. Our system design follows the message passing scheme underlying the computations of GNNs. We design to generate the $k$-hop neighborhood, an information-complete subgraph for each node, as well as do the inference simply by merging values from in-edge neighbors and propagating values to out-edge neighbors via MapReduce. In addition, the $k$-hop neighborhood contains information-complete subgraphs for each node, thus we simply do the training on parameter servers due to data independency. Our system AGL, implemented on mature infrastructures, can finish the training of a 2-layer graph attention network on a graph with billions of nodes and hundred billions of edges in 14 hours, and complete the inference in 1.2 hour.

preprint2020arXiv

An Energy Stable Linear Diffusive Crank-Nicolson Scheme for the Cahn-Hilliard Gradient Flow

We propose and analyze a linearly stabilized semi-implicit diffusive Crank--Nicolson scheme for the Cahn--Hilliard gradient flow. In this scheme, the nonlinear bulk force is treated explicitly with two second-order stabilization terms. This treatment leads to linear elliptic system with constant coefficients and provable discrete energy dissipation. Rigorous error analysis is carried out for the fully discrete scheme. When the time step-size and the space step-size are small enough, second order accuracy in time is obtained with a prefactor controlled by some lower degree polynomial of $1/\varepsilon$. {Here $\varepsilon$ is the thickness of the interface}. Numerical results together with an adaptive time stepping are presented to verify the accuracy and efficiency of the proposed scheme.

preprint2020arXiv

CoAug-MR: An MR-based Interactive Office Workstation Design System via Augmented Multi-Person Collaboration

Digital prototyping and evaluation using 3D modeling and digital human models are becoming more practical for customizing products to the preference of a user. However, the 3D modeling is less accessible to casual users, and digital human models suffer from insufficient body data and less intuitive illustration on how people use the product or how it accommodates to their body. Recently, VR-supported 'Do It Yourself' design has achieved real-time ergonomic evaluation with users themselves by capturing their poses, however, it lacks reliability and quality of design. In this paper, we explore a multi-person interactive design approach that enables designers, users, and even ergonomists to collaborate to achieve effective and reliable design and prototyping tasks. Mixed Reality that utilizes Hololens and motion tracking devices had been developed to provide instant design feedback and evaluation and to experience prototyping in physical space. We evaluate the system based on the usability study, where casual users and designers are engaged in the interactive process of designing items with respect to the body information, the preference, and the environment.

preprint2020arXiv

Comparison of Modeling SPARC spiral galaxies' rotation curves: halo models vs MOND

We investigate a sub-sample of the rotation curves consisting of 45 HSB non-bulgy spiral galaxies selected from SPARC (Spitzer Photometry and Accurate Rotation Curves) database by using two dark halo models (NFW and Burkert) and MOdified Newtonian Dynamics (MOND) theory. Among these three models, the core-dominated Burkert halo model provides a better description of the observed data ($χ_ν^2$ = 0.33) than Navarro, Frenk and White (NFW, $χ_ν^2$= 0.45) and MOND model ($χ_ν^2$ = 0.58). So our results show that, for dark halo models, the selected 45 HSB non-bulgy spiral galaxies prefer a cored density profile to the cuspy one (NFW); We also positively find that there is a correlation between $ρ_0$ and $r_0$ in Burkert model. For MOND fits, when we take $a_0$ as a free parameter, there is no obvious correlation between $a_0$ and disk central surface brightness at 3.6 $μm$ of these HSB spiral galaxies, which is in line with the basic assumption of MOND that $a_0$ should be a universal constant. Interestingly, our fittings gives $a_0$ an average value of $(0.74 \pm 0.45) \times 10^{- 8}\rm {cm\ s^{- 2}}$ if we exclude the three highest values in the sample, which is smaller than the standard value ($1.21 \times 10^{-8}\rm {cm\ s^{- 2}}$).

preprint2020arXiv

Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations

Deep neural networks (DNNs) have achieved impressive performance on handling computer vision problems, however, it has been found that DNNs are vulnerable to adversarial examples. For such reason, adversarial perturbations have been recently studied in several respects. However, most previous works have focused on image classification tasks, and it has never been studied regarding adversarial perturbations on Image-to-image (Im2Im) translation tasks, showing great success in handling paired and/or unpaired mapping problems in the field of autonomous driving and robotics. This paper examines different types of adversarial perturbations that can fool Im2Im frameworks for autonomous driving purpose. We propose both quasi-physical and digital adversarial perturbations that can make Im2Im models yield unexpected results. We then empirically analyze these perturbations and show that they generalize well under both paired for image synthesis and unpaired settings for style transfer. We also validate that there exist some perturbation thresholds over which the Im2Im mapping is disrupted or impossible. The existence of these perturbations reveals that there exist crucial weaknesses in Im2Im models. Lastly, we show that our methods illustrate how perturbations affect the quality of outputs, pioneering the improvement of the robustness of current SOTA networks for autonomous driving.

preprint2020arXiv

Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features

In sentiment classification, the enormous amount of textual data, its immense dimensionality, and inherent noise make it extremely difficult for machine learning classifiers to extract high-level and complex abstractions. In order to make the data less sparse and more statistically significant, the dimensionality reduction techniques are needed. But in the existing dimensionality reduction techniques, the number of components needs to be set manually which results in loss of the most prominent features, thus reducing the performance of the classifiers. Our prior work, i.e., Term Presence Count (TPC) and Term Presence Ratio (TPR) have proven to be effective techniques as they reject the less separable features. However, the most prominent and separable features might still get removed from the initial feature set despite having higher distributions among positive and negative tagged documents. To overcome this problem, we have proposed a new framework that consists of two-dimensionality reduction techniques i.e., Sentiment Term Presence Count (SentiTPC) and Sentiment Term Presence Ratio (SentiTPR). These techniques reject the features by considering term presence difference for SentiTPC and ratio of the distribution distinction for SentiTPR. Additionally, these methods also analyze the total distribution information. Extensive experimental results exhibit that the proposed framework reduces the feature dimension by a large scale, and thus significantly improve the classification performance.

preprint2020arXiv

Dipolar spin waves in uniaxial easy-axis antiferromagnets: A natural topological nodal-line semimetal

The existence of the magnetostatic surface spin waves in ferromagnets, known as Damon-Eshbach mode, was recently demonstrated to originate from the topology of the dipole-dipole interaction. In this work, we study the topological characteristics of magnons in easy-axis antiferromagnets with uniaxial anisotropy. The dipolar spin waves are found to be, driven by the dipole-dipole interaction, in a topological nodal-line semimetal phase, which hosts Damon-Eshbach-type surface modes due to the bulk-edge correspondence. The long wavelength character of dipolar spin waves makes our proposal valid for any natural uniaxial easy-axis antiferromagnet, and thus enriches the candidates of topological magnonic materials. In contrast to the nonreciprocal property in ferromagnetic case, the surface modes with opposite momentum coexist at each surface, but with different chiralities. Such a chirality-momentum or spin-momentum locking, similar to that of electronic surface states in topological insulators, offers the opportunity to design novel chirality-based magnonic devices in antiferromagnets.

preprint2020arXiv

Discovery and timing of pulsars in the globular cluster M13 with FAST

We report the discovery of a binary millisecond pulsar (namely PSR J1641+3627F or M13F) in the globular cluster M13 (NGC 6205) and timing solutions of M13A to F using observations made with the Five-hundred-metre Aperture Spherical radio Telescope (FAST). PSR J1641+3627F has a spin period of 3.00 ms and an orbital period of 1.4 days. The most likely companion mass is 0.16 M$_{\odot}$. M13A to E all have short spin periods and small period derivatives. We also confirm that the binary millisecond pulsar PSR J1641$+$3627E (also M13E) is a black widow with a companion mass around 0.02 M$_{\odot}$. We find that all the binary systems have low eccentricities compared to those typical for globular cluster pulsars and that they decrease with distance from the cluster core. This is consistent with what is expected as this cluster has a very low encounter rate per binary.

preprint2020arXiv

DSSLP: A Distributed Framework for Semi-supervised Link Prediction

Link prediction is widely used in a variety of industrial applications, such as merchant recommendation, fraudulent transaction detection, and so on. However, it's a great challenge to train and deploy a link prediction model on industrial-scale graphs with billions of nodes and edges. In this work, we present a scalable and distributed framework for semi-supervised link prediction problem (named DSSLP), which is able to handle industrial-scale graphs. Instead of training model on the whole graph, DSSLP is proposed to train on the \emph{$k$-hops neighborhood} of nodes in a mini-batch setting, which helps reduce the scale of the input graph and distribute the training procedure. In order to generate negative examples effectively, DSSLP contains a distributed batched runtime sampling module. It implements uniform and dynamic sampling approaches, and is able to adaptively construct positive and negative examples to guide the training process. Moreover, DSSLP proposes a model-split strategy to accelerate the speed of inference process of the link prediction task. Experimental results demonstrate that the effectiveness and efficiency of DSSLP in serval public datasets as well as real-world datasets of industrial-scale graphs.

preprint2020arXiv

EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning

Event cameras sense intensity changes and have many advantages over conventional cameras. To take advantage of event cameras, some methods have been proposed to reconstruct intensity images from event streams. However, the outputs are still in low resolution (LR), noisy, and unrealistic. The low-quality outputs stem broader applications of event cameras, where high spatial resolution (HR) is needed as well as high temporal resolution, dynamic range, and no motion blur. We consider the problem of reconstructing and super-resolving intensity images from LR events, when no ground truth (GT) HR images and down-sampling kernels are available. To tackle the challenges, we propose a novel end-to-end pipeline that reconstructs LR images from event streams, enhances the image qualities and upsamples the enhanced images, called EventSR. For the absence of real GT images, our method is primarily unsupervised, deploying adversarial learning. To train EventSR, we create an open dataset including both real-world and simulated scenes. The use of both datasets boosts up the network performance, and the network architectures and various loss functions in each phase help improve the image qualities. The whole pipeline is trained in three phases. While each phase is mainly for one of the three tasks, the networks in earlier phases are fine-tuned by respective loss functions in an end-to-end manner. Experimental results show that EventSR reconstructs high-quality SR images from events for both simulated and real-world data.

preprint2020arXiv

Predicting Network Controllability Robustness: A Convolutional Neural Network Approach

Network controllability measures how well a networked system can be controlled to a target state, and its robustness reflects how well the system can maintain the controllability against malicious attacks by means of node-removals or edge-removals. The measure of network controllability is quantified by the number of external control inputs needed to recover or to retain the controllability after the occurrence of an unexpected attack. The measure of the network controllability robustness, on the other hand, is quantified by a sequence of values that record the remaining controllability of the network after a sequence of attacks. Traditionally, the controllability robustness is determined by attack simulations, which is computationally time consuming. In this paper, a method to predict the controllability robustness based on machine learning using a convolutional neural network is proposed, motivated by the observations that 1) there is no clear correlation between the topological features and the controllability robustness of a general network, 2) the adjacency matrix of a network can be regarded as a gray-scale image, and 3) the convolutional neural network technique has proved successful in image processing without human intervention. Under the new framework, a fairly large number of training data generated by simulations are used to train a convolutional neural network for predicting the controllability robustness according to the input network-adjacency matrices, without performing conventional attack simulations. Extensive experimental studies were carried out, which demonstrate that the proposed framework for predicting controllability robustness of different network configurations is accurate and reliable with very low overheads.

preprint2020arXiv

Smith-Purcell radiation from a charge moving above a finite-length grating with rectangular profiles

Smith-Purcell radiation is generated by a charged particle beam passing close to the surface of a diffraction grating which has a strong dependency of the emitted radiation intensity on the form of the grating profile. For relativistic electron beam, it is important to take into account the number of grating periods in practical SPR setups. In this paper, the theoretical investigations of Smith-Purcell radiation from a three-dimensional bunch of relativistic electrons that moves at constant speed parallel to an electrically perfectly conducting grating with finite rectangular grooves are carried out by using the modal matching method. This model may offer a new efficient tool for terahertz production by SPR interaction and for nondestructive bunch-length measurements by SPR.

preprint2020arXiv

The FAST discovery of an Eclipsing Binary Millisecond Pulsar in the Globular Cluster M92 (NGC 6341)

We report the discovery of an eclipsing binary millisecond pulsar in the globular cluster M92 (NGC6341) with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). PSR J1717+4308A, or M92A, has a pulse frequency of 316.5~Hz (3.16~ms) and a dispersion measure of 35.45 pc cm$^{-3}$. The pulsar is a member of a binary system with an orbital period of 0.20~days around a low-mass companion which has a median mass of $\sim$0.18~\Ms. From observations so far, at least two eclipsing events have been observed in each orbit. The longer one lasted for ~5000~s in the orbital phase range 0.1--0.5. The other lasted for ~500~s and occurred between 1000--2000~s before or after the longer eclipsing event. The lengths of these two eclipsing events also change. These properties suggest that J1717+4308A is a ``red-back'' system with a low-mass main sequence or sub-giant companion. Timing observations of the pulsar and further searches of the data for additional pulsars are ongoing.

preprint2020arXiv

Towards Optimal Robustness of Network Controllability: An Empirical Necessary Condition

To better understand the correlation between network topological features and the robustness of network controllability in a general setting, this paper suggests a practical approach to searching for optimal network topologies with given numbers of nodes and edges. Since theoretical analysis seems impossible at least in the present time, exhaustive search based on optimization techniques is employed, firstly for a group of small-sized networks that are realistically workable, where \textit{exhaustive} means 1) all possible network structures with the given numbers of nodes and edges are computed and compared, and 2) all possible node-removal sequences are considered. A main contribution of this paper is the observation of an empirical necessary condition (ENC) from the results of exhaustive search, which shrinks the search space to quickly find an optimal solution. ENC shows that the maximum and minimum in- and out-degrees of an optimal network structure should be almost identical, or within a very narrow range, i.e., the network should be extremely homogeneous. Edge rectification towards the satisfaction of the ENC is then designed and evaluated. Simulation results on large-sized synthetic and real-world networks verify the effectiveness of both the observed ENC and the edge rectification scheme. As more operations of edge rectification are performed, the network is getting closer to exactly satisfying the ENC, and consequently the robustness of the network controllability is enhanced towards optimum.

preprint2020arXiv

Weak KAM solutions of Hamilton-Jacobi equations with decreasing dependence on unknown functions

We consider the Hamilton-Jacobi equation \[{H}(x,u,Du)=0,\quad x\in M, \] where $M$ is a connected, closed and smooth Riemannian manifold, ${H}(x,u,p)$ satisfies Tonelli conditions with respect to $p$ and certain decreasing condition with respect to $u$. Based on a dynamical approach developed in \cite{WWY,WWY1,WWY2}, we obtain a series of properties for weak KAM solutions (equivalently, viscosity solutions) of the stationary equation and the long time behavior of viscosity solutions of the evolutionary equation on the Cauchy problem \begin{equation*} \begin{cases} w_t+{H}(x,w,w_x)=0,\quad (x,t)\in M\times (0,+\infty),\\ w(x,0)=φ(x), \quad x\in M. \end{cases} \end{equation*}

preprint2018arXiv

Numerical Method for Free Electron Laser using an Overmoded Rectangular Waveguide

Numerical simulation codes are basic tools for designing Free Electron Lasers (FELs). This paper describes a numerical method for the time-dependent, three-dimensional simulation of the free electron laser (FEL) using a rectangular waveguide within overmoded configuration when the radiation wavelength is much shorter than the waveguide cut-off wavelength. Instead of developing a new code, the GENESIS simulation code is modified for our purpose. This method presented here can be used for extending the capacity of GENESIS to cover this special configuration. The major modification is to apply the metal boundary conditions on the field equations in a limited rectangular region and the full Cartesian mesh using the Alternating Direction Implicit (ADI) integration scheme to solve the field equation remains adopted.

preprint2018arXiv

Toward Stronger Robustness of Network Controllability: A Snapback Network Model

A new complex network model, called q-snapback network, is introduced. Basic topological characteristics of the network, such as degree distribution, average path length, clustering coefficient and Pearson correlation coefficient, are evaluated. The typical 4-motifs of the network are simulated. The robustness of both state and structural controllabilities of the network against targeted and random node- and edge-removal attacks, with comparisons to the multiplex congruence network and the generic scale-free network, are presented. It is shown that the q-snapback network has the strongest robustness of controllabilities due to its advantageous inherent structure with many chain- and loop-motifs.

preprint2016arXiv

Achieving Energy Efficiency in Cloud Brokering

The proliferation of cloud providers has brought substantial interoperability complexity to the public cloud market, in which cloud brokering has been playing an important role. However, energy-related issues for public clouds have not been well addressed in the literature. In this paper, we claim that the broker is also situated in a perfect position where necessary actions can be taken to achieve energy efficiency for public cloud systems, particularly through job assignment and scheduling. We formulate the problem by a mixed integer program and prove its NP-hardness. Based on the complexity analysis, we simplify the problem by introducing admission control on jobs. In the sequel, optimal job assignment can be done straightforwardly and the problem is transformed into improving job admission rate by scheduling on two coupled phases: data transfer and job execution. The two scheduling phases are further decoupled and we develop efficient scheduling algorithm for each of them. Experimental results show that the proposed solution can achieve significant reduction on energy consumption with admission rates improved as well, even in large-scale public cloud systems.

preprint2016arXiv

Design of FELiChEM, the first infrared free-electron laser user facility in China

FELiChEM is a new experimental facility under construction at University of Science and Technology of China (USTC), whose core device is two free electron laser oscillators generating middle-infrared and far-infrared laser and covering the spectral range of 2.5-200 $μ$m. It will be a dedicated infrared light source aiming at energy chemistry research. We present the brief design of FEL oscillators with the emphasis put on the middle-infrared oscillator. Most of the basic parameters are determined and the anticipated performance of the output radiation is given. The first light of FELiChEM is targeted for the end of 2017.

preprint2016arXiv

Direct Observation of Long-range field-effect from gate tuning of non-local conductivity

We report the observation of an unexpected, long-range field-effect in WTe$_2$ devices, leading to large gate-induced changes of the transport properties of crystals much thicker than the electrostatic screening length. The phenomenon --which manifests itself very differently from the conventional field-effect-- originates from the non-local nature of transport in the devices that are thinner than the carrier mean free path, because of the dominant role of surface scattering. We reproduce theoretically the gate dependence of the measured classical and quantum magneto-transport in all detail, and show that the phenomenon is caused by the gate-tuning of the bulk carrier mobility by changing the scattering at the surface. Our results demonstrate the possibility to gate tune the electronic properties deep in the interior of conducting materials, avoiding limitations imposed by electrostatic screening.

preprint2016arXiv

Multi-resource Energy-efficient Routing in Cloud Data Centers with Networks-as-a-Service

With the rapid development of software defined networking and network function virtualization, researchers have proposed a new cloud networking model called Network-as-a-Service (NaaS) which enables both in-network packet processing and application-specific network control. In this paper, we revisit the problem of achieving network energy efficiency in data centers and identify some new optimization challenges under the NaaS model. Particularly, we extend the energy-efficient routing optimization from single-resource to multi-resource settings. We characterize the problem through a detailed model and provide a formal problem definition. Due to the high complexity of direct solutions, we propose a greedy routing scheme to approximate the optimum, where flows are selected progressively to exhaust residual capacities of active nodes, and routing paths are assigned based on the distributions of both node residual capacities and flow demands. By leveraging the structural regularity of data center networks, we also provide a fast topology-aware heuristic method based on hierarchically solving a series of vector bin packing instances. Our simulations show that the proposed routing scheme can achieve significant gain on energy savings and the topology-aware heuristic can produce comparably good results while reducing the computation time to a large extent.

preprint2016arXiv

On viscosity solution of HJB equations with state constraints and reflection control

Motivated by a control problem of a certain queueing network we consider a control problem where the dynamics is constrained in the nonnegative orthant $\mathbb{R}_+$ of the $d$-dimensional Euclidean space and controlled by the reflections at the faces/boundaries. We define a discounted value function associated to this problem and show that the value function is a viscosity solution to a certain HJB equation in $\mathbb{R}_+$ with nonlinear Neumann type boundary condition. Under certain conditions, we also characterize this value function as the unique solution to this HJB equation.

preprint2016arXiv

Probing the Electronic States in Black Phosphorus Vertical Heterostructures

Atomically thin black phosphorus (BP) is a promising two-dimensional material for fabricating electronic and optoelectronic nano-devices with high mobility and tunable bandgap structures. However, the charge-carrier mobility in few-layer phosphorene (monolayer BP) is mainly limited by the presence of impurity and disorders. In this study, we demonstrate that vertical BP heterostructure devices offer great advantages in probing the electron states of monolayer and few-layer phosphorene at temperatures down to 2 K through capacitance spectroscopy. Electronic states in the conduction and valence bands of phosphorene are accessible over a wide range of temperature and frequency. Exponential band tails have been determined to be related to disorders. Unusual phenomena such as the large temperature-dependence of the electron state population in few-layer phosphorene have been observed and systematically studied. By combining the first-principles calculation, we identified that the thermal excitation of charge trap states and oxidation-induced defect states were the main reasons for this large temperature dependence of the electron state population and degradation of the on-off ratio in phosphorene field-effect transistors.

preprint2015arXiv

Broadband Impedance Calculations and Single Bunch Instabilities Estimations of the HLS-II storage ring

The upgrade project of Hefei Light Source storage ring is under way. In this paper, the broadband impedances of resistive wall and coated ceramic vacuum chamber were calculated using the analytic formula, the wake fields and impedances of other designed vacuum chambers have been simulated by the CST code, and then the broadband impedances model was obtained. Using the theoretical formula, longitudinal and transverse single bunch instabilities were discussed. With the carefully-designed vacuum chamber, we find that the thresholds of the beam instabilities are higher than the beam current goal.

preprint2015arXiv

Center Specification Property and Entropy for Partially Hyperbolic Diffeomorphisms

Let $f$ be a partially hyperbolic diffeomorphism on a closed (i.e., compact and boundaryless) Riemannian manifold $M$ with a uniformly compact center foliation $\mathcal{W}^{c}$. The relationship among topological entropy $h(f)$, entropy of the restriction of $f$ on the center foliation $h(f, \mathcal{W}^{c})$ and the growth rate of periodic center leaves $p^{c}(f)$ is investigated. It is first shown that if a compact locally maximal invariant center set $Λ$ is center topologically mixing then $f|_Λ$ has the center specification property, i.e., any specification with a large spacing can be center shadowed by a periodic center leaf with a fine precision. Applying the center spectral decomposition and the center specification property, we show that $ h(f)\leq h(f,\mathcal{W}^{c})+p^{c}(f)$. Moreover, if the center foliation $\mathcal{W}^{c}$ is of dimension one, we obtain an equality $h(f)= p^{c}(f)$.

preprint2015arXiv

Controllability of networked MIMO systems

In this paper, we consider the state controllability of networked systems, where the network topology is directed and weighted and the nodes are higher-dimensional linear time-invariant (LTI) dynamical systems. We investigate how the network topology, the node-system dynamics, the external control inputs, and the inner interactions affect the controllability of a networked system, and show that for a general networked multi-input/multi-output (MIMO) system: 1) the controllability of the overall network is an integrated result of the aforementioned relevant factors, which cannot be decoupled into the controllability of individual node-systems and the properties solely determined by the network topology, quite different from the familiar notion of consensus or formation controllability; 2) if the network topology is uncontrollable by external inputs, then the networked system with identical nodes will be uncontrollable, even if it is structurally controllable; 3) with a controllable network topology, controllability and observability of the nodes together are necessary for the controllability of the networked systems under some mild conditions, but nevertheless they are not sufficient. For a networked system with single-input/single-output (SISO) LTI nodes, we present precise necessary and sufficient conditions for the controllability of a general network topology.

preprint2015arXiv

Dissociation products and structures of solid H2S at strong compression

Hydrogen sulfides have recently received a great deal of interest due to the record high superconducting temperatures of up to 203 K observed on strong compression of dihydrogen sulfide (H2S). A joint theoretical and experimental study is presented in which decomposition products and structures of compressed H2S are characterized, and their superconducting properties are calculated. In addition to the experimentally known H2S and H3S phases, our first-principles structure searches have identified several energetically competitive stoichiometries that have not been reported previously; H2S3, H3S2, and H4S3. In particular, H4S3 is predicted to be thermodynamically stable within a large pressure range of 25-113 GPa. High-pressure room-temperature X-ray diffraction measurements confirm the presence of H3S and H4S3 through decomposition of H2S that emerge at 27 GPa and coexist with residual H2S, at least up to the highest pressure studied in our experiments of 140 GPa. Electron-phonon coupling calculations show that H4S3 has a small Tc of below 2 K, and that H2S is mainly responsible for the observed superconductivity of samples prepared at low temperature (<100K).

preprint2015arXiv

Evolutionary games on multilayer networks: A colloquium

Networks form the backbone of many complex systems, ranging from the Internet to human societies. Accordingly, not only is the range of our interactions limited and thus best described and modeled by networks, it is also a fact that the networks that are an integral part of such models are often interdependent or even interconnected. Networks of networks or multilayer networks are therefore a more apt description of social systems. This colloquium is devoted to evolutionary games on multilayer networks, and in particular to the evolution of cooperation as one of the main pillars of modern human societies. We first give an overview of the most significant conceptual differences between single-layer and multilayer networks, and we provide basic definitions and a classification of the most commonly used terms. Subsequently, we review fascinating and counterintuitive evolutionary outcomes that emerge due to different types of interdependencies between otherwise independent populations. The focus is on coupling through the utilities of players, through the flow of information, as well as through the popularity of different strategies on different network layers. The colloquium highlights the importance of pattern formation and collective behavior for the promotion of cooperation under adverse conditions, as well as the synergies between network science and evolutionary game theory.

preprint2015arXiv

First commissioning of the HLS-II storage ring

To meet the increasing requirements of synchrotron radiation users, the upgrade project to enhance the performance of Hefei Light Source (HLS), named HLS-II, was launched in 2010, and in 2014 the first commissioning of HLS-II was successfully completed. After the commissioning, the main design goals for the HLS-II storage ring have been achieved, with natural emittance of electron beam lower than 40 nm-rad at 800 MeV, five insertion devices installed in straight sections and root mean square (rms) jitter of closed orbit smaller than 4 μm, making HLS-II at a higher level among the same class of machines in the world. This paper reports on the results of the commissioning of the HLS-II storage ring, which includes linear optics correction, compensation of insertion devices effect and closed orbit feedback.

preprint2015arXiv

Identifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history

Spatial spread of infectious diseases among populations via the mobility of humans is highly stochastic and heterogeneous. Accurate forecast/mining of the spread process is often hard to be achieved by using statistical or mechanical models. Here we propose a new reverse problem, which aims to identify the stochastically spatial spread process itself from observable information regarding the arrival history of infectious cases in each subpopulation. We solved the problem by developing an efficient optimization algorithm based on dynamical programming, which comprises three procedures: i, anatomizing the whole spread process among all subpopulations into disjoint componential patches; ii, inferring the most probable invasion pathways underlying each patch via maximum likelihood estimation; iii, recovering the whole process by assembling the invasion pathways in each patch iteratively, without burdens in parameter calibrations and computer simulations. Based on the entropy theory, we introduced an identifiability measure to assess the difficulty level that an invasion pathway can be identified. Results on both artificial and empirical metapopulation networks show the robust performance in identifying actual invasion pathways driving pandemic spread.

preprint2015arXiv

Improved step-by-step chromaticity compensation method for chromatic sextupole optimization

The step-by-step chromaticity compensation method for chromatic sextupole optimization and dynamic aperture increase was proposed by E. Levichev and P. Piminov (E. Levichev and P. Piminov, 2006 [1]). Although this method can be used to enlarge the dynamic aperture of storage ring, it has some drawbacks. In this paper, we combined this method with evolutionary computation algorithms, and proposed an improved version of this method. In the improved method, the drawbacks are avoided, and thus better optimization results can be obtained.

preprint2015arXiv

LazyCtrl: Scalable Network Control for Cloud Data Centers

The advent of software defined networking enables flexible, reliable and feature-rich control planes for data center networks. However, the tight coupling of centralized control and complete visibility leads to a wide range of issues among which scalability has risen to prominence. To address this, we present LazyCtrl, a novel hybrid control plane design for data center networks where network control is carried out by distributed control mechanisms inside independent groups of switches while complemented with a global controller. Our design is motivated by the observation that data center traffic is usually highly skewed and thus edge switches can be grouped according to traffic locality. LazyCtrl aims at bringing laziness to the global controller by dynamically devolving most of the control tasks to independent switch groups to process frequent intra-group events near datapaths while handling rare inter-group or other specified events by the controller. We implement LazyCtrl and build a prototype based on Open vSwich and Floodlight. Trace-driven experiments on our prototype show that an effective switch grouping is easy to maintain in multi-tenant clouds and the central controller can be significantly shielded by staying lazy, with its workload reduced by up to 82%.

preprint2015arXiv

Pseudo-goldstino and electroweakinos via VBF processes at LHC

The multi-sector SUSY breaking predicts pseudo-goldstino which can couple to the visible sector more strongly than the ordinary gavitino and thus induce the decays of the lightest neutralino and chargino (collectively called electroweakinos) inside the detector. In this note we study the electroweakino pair productions via vector boson fusion (VBF) processes followed by decays to pseudo-goldstino at the LHC. Our Monte Carlo simulations show that at the 14 TeV LHC with 3000 fb^{-1} luminosity the dominant production channel pp->chargino+neutralino+2 jets can have a statistical significance above 2-sigma while other production channels are not accessible.

preprint2015arXiv

Raman fingerprint of semi-metal WTe2 from bulk to monolayer

Tungsten ditelluride (WTe2), a layered transition-metal dichalcogenide (TMD), has recently demonstrated an extremely large magnetoresistance effect, which is unique among TMDs. This fascinating feature seems to be correlated with its special electronic structure. Here, we report the observation of 6 Raman peaks corresponding to the A_2^4, A_1^9, A_1^8, A_1^6, A_1^5 and A_1^2 phonons, from the 33 Raman-active modes predicted for WTe2. This provides direct evidence to distinguish the space group of WTe2 from that of other TMDs. Moreover, the Raman evolution of WTe2 from bulk to monolayer is clearly revealed. It is interesting to find that the A_2^4 mode, centered at ~109.8 cm-1, is forbidden in a monolayer, which may be attributable to the transition of the point group from C2v (bulk) to C2h (monolayer). Our work characterizes all observed Raman peaks in the bulk and few-layer samples and provides a route to study the physical properties of two-dimensional WTe2.

preprint2015arXiv

Variational principle for contact Tonelli Hamiltonian systems

We establish an implicit variational principle for the equations of the contact flow generated by the Hamiltonian $H(x,u,p)$ with respect to the contact 1-form $α=du-pdx$ under Tonelli and Osgood growth assumptions. It is the first step to generalize Mather's global variational method from the Hamiltonian dynamics to the contact Hamiltonian dynamics.

preprint2014arXiv

A Dynamical Approach to Viscosity Solutions of Hamilton-Jacobi Equations

In this paper, we consider the following Hamilton-Jacobi equation with initial condition: \begin{equation*} \begin{cases} \partial_tu(x,t)+H(x,t,u(x,t),\partial_xu(x,t))=0, u(x,0)=ϕ(x). \end{cases} \end{equation*} Under some assumptions on the convexity of $H(x,t,u,p)$ w.r.t. $p$, we develop a dynamical approach to viscosity solutions and show that there exists an intrinsic connection between viscosity solutions and certain minimal characteristics.

preprint2014arXiv

A Joint Optimization of Operational Cost and Performance Interference in Cloud Data Centers

Virtual machine (VM) scheduling is an important technique to efficiently operate the computing resources in a data center. Previous work has mainly focused on consolidating VMs to improve resource utilization and thus to optimize energy consumption. However, the interference between collocated VMs is usually ignored, which can result in very worse performance degradation to the applications running in those VMs due to the contention of the shared resources. Based on this observation, we aim at designing efficient VM assignment and scheduling strategies where we consider optimizing both the operational cost of the data center and the performance degradation of running applications and then, we propose a general model which captures the inherent tradeoff between the two contradictory objectives. We present offline and online solutions for this problem by exploiting the spatial and temporal information of VMs where VM scheduling is done by jointly consider the combinations and the life-cycle overlapping of the VMs. Evaluation results show that the proposed methods can generate efficient schedules for VMs, achieving low operational cost while significantly reducing the performance degradation of applications in cloud data centers.

preprint2014arXiv

A Multi-length Bunches Design for Electron Storage Rings with Odd Buckets

A scheme with two superconducting RF cavities (sc-cavities) is designed to upgrade electron storage rings with odd buckets into multi-length bunches. In this paper, Hefei Light Source II (HLS II) is given as an example for odd buckets. In accordance with 45 buckets, which is multiples of 3, three different length of bunches generated simultaneously is proposed in the presently applied user optics. The final result is to, without low-alpha optics, fill HLS II with long bunches of 50 ps length, medium bunches of 23 ps and short bunches of 6 ps. Each third buckets can be filled with short bunches, of which the current limit is up to 6.6 mA, more than 60 times the value of low-alpha mode. Moreover, particles tracking about beam dynamics performed by ELEGANT and calculations about beam instabilities are presented in this paper.

preprint2014arXiv

Anomalous compressibility behavior of chromium monoaresenide under high pressure

CrAs was firstly observed possessing the bulk superconductivity (Tc~2 K) under high pressure (0.8 GPa) in the very recent work (Wei Wu, et al. Nature Communications 5, 5508 (2014)). To explore the correlation between the structure and the superconductivity, the high-pressure structure evolution of CrAs was investigated using angle dispersive X-ray diffraction (XRD) method with small steps of ~0.1 GPa in a diamond anvil cell (DAC) up to 1.8 GPa. In the pressure range covered by our current experiment, the structure of CrAs keeps stable. However, the lattice parameters exhibit anomalous compression behaviors. With the pressure increasing, the lattice parameters a and c both show a process of first increasing and then decreasing, and the lattice parameter b goes through a quick contract at 0.35 GPa, which suggests a pressure-induced isostructural phase transition occurs in CrAs around this pressure point. Above the phase transition pressure, the axial compressibilities of CrAs present remarkable anisotropy. The compressibilities along the a- and c-axis are about an order of magnitude smaller than that along the b-axis, which is closely related to the different stacking modes in CrAs6 octahedron along different crystallographic axes. A schematic band model was used for addressing above anomalous compression behavior in CrAs.

preprint2014arXiv

Degree mixing in multilayer networks impedes the evolution of cooperation

Traditionally, the evolution of cooperation has been studied on single, isolated networks. Yet a player, especially in human societies, will typically be a member of many different networks, and those networks will play a different role in the evolutionary process. Multilayer networks are therefore rapidly gaining on popularity as the more apt description of a networked society. With this motivation, we here consider 2-layer scale-free networks with all possible combinations of degree mixing, wherein one network layer is used for the accumulation of payoffs and the other is used for strategy updating. We find that breaking the symmetry through assortative mixing in one layer and/or disassortative mixing in the other layer, as well as preserving the symmetry by means of assortative mixing in both layers, impedes the evolution of cooperation. We use degree-dependent distributions of strategies and cluster-size analysis to explain these results, which highlight the importance of hubs and the preservation of symmetry between multilayer networks for the successful resolution of social dilemmas.

preprint2014arXiv

Detection of Interlayer Interaction in Few-layer Graphene through Landau Level Spectroscopy

We demonstrate that surface relaxation, which is insignificant in trilayer graphene, starts to manifest in Bernal-stacked tetralayer graphene. Bernal-stacked few-layer graphene has been investigated by analyzing its Landau level spectra through quantum capacitance measurements. We find that in trilayer graphene, the interlayer interaction parameters were similar to that of graphite. However, in tetralayer graphene, the hopping parameters between the bulk and surface bilayers are quite different. This shows a direct evidence for the surface relaxation phenomena. In spite of the fact that the Van der Waals interaction between the carbon layers is thought to be insignificant, we suggest that the interlayer interaction is an important factor in explaining the observed results and the symmetry-breaking effects in graphene sublattice are not negligible.

preprint2014arXiv

Energy-Efficient Flow Scheduling and Routing with Hard Deadlines in Data Center Networks

The power consumption of enormous network devices in data centers has emerged as a big concern to data center operators. Despite many traffic-engineering-based solutions, very little attention has been paid on performance-guaranteed energy saving schemes. In this paper, we propose a novel energy-saving model for data center networks by scheduling and routing "deadline-constrained flows" where the transmission of every flow has to be accomplished before a rigorous deadline, being the most critical requirement in production data center networks. Based on speed scaling and power-down energy saving strategies for network devices, we aim to explore the most energy efficient way of scheduling and routing flows on the network, as well as determining the transmission speed for every flow. We consider two general versions of the problem. For the version of only flow scheduling where routes of flows are pre-given, we show that it can be solved polynomially and we develop an optimal combinatorial algorithm for it. For the version of joint flow scheduling and routing, we prove that it is strongly NP-hard and cannot have a Fully Polynomial-Time Approximation Scheme (FPTAS) unless P=NP. Based on a relaxation and randomized rounding technique, we provide an efficient approximation algorithm which can guarantee a provable performance ratio with respect to a polynomial of the total number of flows.

preprint2014arXiv

Global Spatio-temporal Patterns of Influenza in the Post-pandemic Era

We study the global spatio-temporal patterns of influenza dynamics. This is achieved by analysing and modelling weekly laboratory confirmed cases of influenza A and B from 138 countries between January 2006 and May 2014. The data were obtained from FluNet, the surveillance network compiled by the the World Health Organization. We report a pattern of {\it skip-and-resurgence} behavior between the years 2011 and 2013 for influenza H1N1/09, the strain responsible for the 2009 pandemic, in Europe and Eastern Asia. In particular, the expected H1N1/09 epidemic outbreak in 2011 failed to occur (or"skipped") in many countries across the globe, although an outbreak occurred in the following year. We also report a pattern of {\it well-synchronized} 2010 winter wave of H1N1/09 in the Northern Hemisphere countries, and a pattern of replacement of strain H1N1/77 by H1N1/09 between the 2009 and 2012 influenza seasons. Using both a statistical and a mechanistic mathematical model, and through fitting the data of 108 countries (108 countries in a statistical model and 10 large populations with a mechanistic model), we discuss the mechanisms that are likely to generate these events taking into account the role of multi-strain dynamics. A basic understanding of these patterns has important public health implications and scientific significance.

preprint2014arXiv

High quality sandwiched black phosphorus heterostructure and its quantum oscillations

Two-dimensional (2D) materials, such as graphene and transition metal dichalcogenides have attracted great attention because of the rich physics and potential applications in next-generation nano-sized electronic devices. Recently, atomically thin black phosphorus (BP) has become a new member of the 2D materials family with high theoretical mobility and tunable bandgap structure. However, degradation of properties under atmospheric conditions and high-density charge traps in BP have largely limited its mobility (~400 cm2/Vs at room temperature) and thus restricted its future applications. Here, we report the fabrication of stable BN-BP-BN heterostructures by encapsulating atomically thin BP between hexagonal boron nitride (BN) layers to realize ultraclean BN-BP interfaces which allow a record-high field-effect mobility ~1350 cm2/Vs at room temperature and on-off ratios over 10^5. At low temperatures, the mobility reaches ~2700 cm2/Vs and quantum oscillations in BP 2D hole gas are observed at low magnetic fields. Importantly, the BN-BP-BN heterostructure can effectively avoid the quality degradation of BP in ambient condition.

preprint2014arXiv

Power-efficient Assignment of Virtual Machines to Physical Machines

Motivated by current trends in cloud computing, we study a version of the generalized assignment problem where a set of virtual processors has to be implemented by a set of identical processors. For literature consistency, we say that a set of virtual machines (VMs) is assigned to a set of physical machines (PMs). The optimization criteria is to minimize the power consumed by all the PMs. We term the problem Virtual Machine Assignment (VMA). Crucial differences with previous work include a variable number of PMs, that each VM must be assigned to exactly one PM (i.e., VMs cannot be implemented fractionally), and a minimum power consumption for each active PM. Such infrastructure may be strictly constrained in the number of PMs or in the PMs' capacity, depending on how costly (in terms of power consumption) is to add a new PM to the system or to heavily load some of the existing PMs. Low usage or ample budget yields models where PM capacity and/or the number of PMs may be assumed unbounded for all practical purposes. We study 4 VMA problems depending on whether the capacity or the number of PMs is bounded or not. Specifically, we study hardness and online competitiveness for a variety of cases. To the best of our knowledge, this is the first comprehensive study of the VMA problem for this cost function.

preprint2014arXiv

Probing the Electron States and Metal-Insulator Transition Mechanisms in Atomically Thin MoS2 Based on Vertical Heterostructures

The metal-insulator transition (MIT) is one of the remarkable electrical transport properties of atomically thin molybdenum disulphide (MoS2). Although the theory of electron-electron interactions has been used in modeling the MIT phenomena in MoS2, the underlying mechanism and detailed MIT process still remain largely unexplored. Here, we demonstrate that the vertical metal-insulator-semiconductor (MIS) heterostructures built from atomically thin MoS2 (monolayers and multilayers) are ideal capacitor structures for probing the electron states in MoS2. The vertical configuration of MIS heterostructures offers the added advantage of eliminating the influence of large impedance at the band tails and allows the observation of fully excited electron states near the surface of MoS2 over a wide excitation frequency (100 Hz-1 MHz) and temperature range (2 K- 300 K). By combining capacitance and transport measurements, we have observed a percolation-type MIT, driven by density inhomogeneities of electron states, in the vertical heterostructures built from monolayer and multilayer MoS2. In addition, the valence band of thin MoS2 layers and their intrinsic properties such as thickness-dependence screening abilities and band gap widths can be easily accessed and precisely determined through the vertical heterostructures.

preprint2014arXiv

Pseudo-goldstino and electroweak gauginos at the LHC

The multi-sector SUSY breaking predicts the existence of pseudo-goldstino, which could couple more strongly to visible fields than ordinary gravitino. Then the lightest neutralino and chargino can decay into a pseudo-goldstino plus a Z-boson, Higgs boson or W-boson. In this note we perform a Monte Carlo simulation for the direct productions of the lightest neutralino and chargino followed by the decays to pseudo-goldstino. Considering scenarios with higgsino-like, bino-like or wino-like lightest neutralino, we find that the signal-to-background ratio at the high luminosity LHC is between 6% and 25% and the statistical significance can be above 5-sigma.

preprint2014arXiv

The Roles of a Quantum Channel on a Quantum State

When a quantum state undergoes a quantum channel, the state will be inevitably influenced. In general, the fidelity of the state is reduced, so is the entanglement if the sub- systems go through the channel. However, the influence on the coherence of the state is quite different. Here we present some state-independent quantities to describe to what degree the fidelity, the entanglement and the coherence of the state are influenced. As applications, we consider some quantum channels on a qubit and find that the infidelity ability monotonically depends on the decay rate, but in usual the decoherence ability is not the case and strongly depends on the channel.

preprint2014arXiv

Total destruction of Lagrangian tori

For an integrable Tonelli Hamiltonian with $d\ (d\geq 2)$ degrees of freedom, we show that all of the Lagrangian tori can be destroyed by analytic perturbations which are arbitrarily small in the $C^{d-δ}$ topology.

preprint2014arXiv

Weak KAM theory for general Hamilton-Jacobi equations I: the solution semigroup under proper conditions

We consider the following evolutionary Hamilton-Jacobi equation with initial condition: \begin{equation*} \begin{cases} \partial_tu(x,t)+H(x,u(x,t),\partial_xu(x,t))=0,\\ u(x,0)=ϕ(x). \end{cases} \end{equation*} Under some assumptions on $H(x,u,p)$ with respect to $p$ and $u$, we provide a variational principle on the evolutionary Hamilton-Jacobi equation. By introducing an implicitly defined solution semigroup, we extend Fathi's weak KAM theory to certain more general cases, in which $H$ explicitly depends on the unknown function $u$. As an application, we show the viscosity solution of the evolutionary Hamilton-Jacobi equation with initial condition tends asymptotically to the weak KAM solution of the following stationary Hamilton-Jacobi equation: \begin{equation*} H(x,u(x),\partial_xu(x))=0. \end{equation*}.

preprint2014arXiv

Weak KAM theory for general Hamilton-Jacobi equations II: the fundamental solution under Lipschitz conditions

We consider the following evolutionary Hamilton-Jacobi equation with initial condition: \begin{equation*} \begin{cases} \partial_tu(x,t)+H(x,u(x,t),\partial_xu(x,t))=0,\\ u(x,0)=ϕ(x), \end{cases} \end{equation*} where $ϕ(x)\in C(M,\mathbb{R})$. Under some assumptions on the convexity of $H(x,u,p)$ with respect to $p$ and the uniform Lipschitz of $H(x,u,p)$ with respect to $u$, we establish a variational principle and provide an intrinsic relation between viscosity solutions and certain minimal characteristics. By introducing an implicitly defined {\it fundamental solution}, we obtain a variational representation formula of the viscosity solution of the evolutionary Hamilton-Jacobi equation. Moreover, we discuss the large time behavior of the viscosity solution of the evolutionary Hamilton-Jacobi equation and provide a dynamical representation formula of the viscosity solution of the stationary Hamilton-Jacobi equation with strictly increasing $H(x,u,p)$ with respect to $u$.

preprint2014arXiv

Weak KAM theory for general Hamilton-Jacobi equations III: the variational principle under Osgood conditions

preprint2013arXiv

Analytic modeling of instabilities driven by higher-order modes in HLS II RF system with a higher-harmonic cavity

The utility of a passive fourth-harmonic cavity plays key role in suppressing longitudinal beam instabilities in the electron storage ring and lengthens the bunch by a factor of 2.6 for the phase II project of Hefei Light Source(HLS II). Meanwhile, instabilities driven by higher-order modes(HOM) may limit the performance of the higher-harmonic cavity. In this paper, the parasitic coupled-bunch instability which is driven by narrow band parasitic modes and the microwave instability which is driven by broadband HOM are both modeled analytically. The analytic modeling results are in good agreement with that of our previous simulation study and indicate that the passive fourth-harmonic cavity suppresses parasitic coupled-bunch instabilities and the microwave instability. The modeling suggests that a fourth-harmonic cavity may be successfully used at HLS II.

preprint2013arXiv

GreenDCN: a General Framework for Achieving Energy Efficiency in Data Center Networks

The popularization of cloud computing has raised concerns over the energy consumption that takes place in data centers. In addition to the energy consumed by servers, the energy consumed by large numbers of network devices emerges as a significant problem. Existing work on energy-efficient data center networking primarily focuses on traffic engineering, which is usually adapted from traditional networks. We propose a new framework to embrace the new opportunities brought by combining some special features of data centers with traffic engineering. Based on this framework, we characterize the problem of achieving energy efficiency with a time-aware model, and we prove its NP-hardness with a solution that has two steps. First, we solve the problem of assigning virtual machines (VM) to servers to reduce the amount of traffic and to generate favorable conditions for traffic engineering. The solution reached for this problem is based on three essential principles that we propose. Second, we reduce the number of active switches and balance traffic flows, depending on the relation between power consumption and routing, to achieve energy conservation. Experimental results confirm that, by using this framework, we can achieve up to 50 percent energy savings. We also provide a comprehensive discussion on the scalability and practicability of the framework.

preprint2013arXiv

Higgs decay to goldstini and its observability at the LHC

If supersymmetry is broken independently in multiple sectors with different scales, a number of goldstinos will be generated. One linear combination of these goldstinos is massless and eaten by the gravitino, while the orthogonal combinations acquire a tree level mass and become the physical states named goldstini (G'). Compared to the gravitino, such goldstini could couple more strongly to the visible fields and lead to some exotic phenomenology. In this note we first check the goldstini couplings in some GMSB models and find that the goldstini-photon-neutralino interaction may be very small while the goldstini-Z-neutralino and goldstini-Higgs-neutralino interactions may be sizable. This can induce a new decay mode for the Higgs boson: h-> G'+χ-> Z+2G'. Then in an effective model with conservative fixed parameters we study the observability of this decay channel at the LHC and find that it is not accessible at the finished 8 TeV run (25 /fb) or 14 TeV run with 100 /fb, but might be observed at the high luminosity LHC (14 TeV, 1000-3000 /fb) if the systematics of the backgrounds can be well understood.

preprint2013arXiv

Noise induced enhancement of network reciprocity in social dilemmas

The network reciprocity is an important dynamic rule fostering the emergence of cooperation among selfish individuals. This was reported firstly in the seminal work of Nowak and May, where individuals were arranged on the regular lattice network, and played the prisoner's dilemma game (PDG). In the standard PDG, one often assumes that the players have perfect rationality. However, in reality, we human are far from rational agents, as we often make mistakes, and behave irrationally. Accordingly, in this work, we introduce the element of noise into the measurement of fitness, which is determined by the parameter a controlling the degree of noise. The considered noise-induced mechanism remarkably promotes the behavior of cooperation, which may be conducive to interpret the emergence of cooperation within the population.

preprint2013arXiv

Performance Analysis of Protograph LDPC Codes for Nakagami-$m$ Fading Relay Channels

In this paper, we investigate the error performance of the protograph (LDPC) codes over Nakagami-$m$ fading relay channels. We first calculate the decoding thresholds of the protograph codes over such channels with different fading depths (i.e., different values of $m$) by exploiting the modified protograph extrinsic information transfer (PEXIT) algorithm. Furthermore, based on the PEXIT analysis and using Gaussian approximation, we derive the bit-error-rate (BER) expressions for the error-free (EF) relaying protocol and decode-and-forward (DF) relaying protocol. We finally compare the threshold with the theoretical BER and the simulated BER results of the protograph codes. It reveals that the performance of DF protocol is approximately the same as that of EF protocol. Moreover, the theoretical BER expressions, which are shown to be reasonably consistent with the decoding thresholds and the simulated BERs, are able to evaluate the system performance and predict the decoding threshold with lower complexity as compared to the modified PEXIT algorithm. As a result, this work can facilitate the design of the protograph codes for the wireless communication systems.

preprint2013arXiv

Performance of a Multiple-Access DCSK-CC System over Nakagami-$m$ Fading Channels

In this paper, we propose a novel cooperative scheme to enhance the performance of multiple-access (MA) differential-chaos-shift-keying (DCSK) systems. We provide the bit-error-rate (BER) performance and throughput analyses for the new system with a decode-and-forward (DF) protocol over Nakagami-$m$ fading channels. Our simulated results not only show that this system significantly improves the BER performance as compared to the existing DCSK non-cooperative (DCSK-NC) system and the multiple-input multiple-output DCSK (MIMO-DCSK) system, but also verify the theoretical analyses. Furthermore, we show that the throughput of this system approximately equals that of the DCSK-NC system, both of which have prominent improvements over the MIMO-DCSK system. We thus believe that the proposed system can be a good framework for chaos-modulation-based wireless communications.

preprint2013arXiv

Performance of Joint Channel and Physical Network Coding Based on Alamouti STBC

This work considers the protograph-coded physical network coding (PNC) based on Alamouti space-time block coding (STBC) over Nakagami-fading two-way relay channels, in which both the two sources and relay possess two antennas. We first propose a novel precoding scheme at the two sources so as to implement the iterative decoder efficiently at the relay. We further address a simplified updating rule of the log-likelihood-ratio (LLR) in such a decoder. Based on the simplified LLR-updating rule and Gaussian approximation, we analyze the theoretical bit-error-rate (BER) of the system, which is shown to be consistent with the decoding thresholds and simulated results. Moreover, the theoretical analysis has lower computational complexity than the protograph extrinsic information transfer (PEXIT) algorithm. Consequently, the analysis not only provides a simple way to evaluate the error performance but also facilitates the design of the joint channel-and-PNC (JCNC) in wireless communication scenarios.

preprint2013arXiv

Routing for Energy Minimization with Discrete Cost Functions

Energy saving is becoming an important issue in the design and use of computer networks. In this work we propose a problem that considers the use of rate adaptation as the energy saving strategy in networks. The problem is modeled as an integral demand-routing problem in a network with discrete cost functions at the links. The discreteness of the cost function comes from the different states (bandwidths) at which links can operate and, in particular, from the energy consumed at each state. This in its turn leads to the non-convexity of the cost function, and thus adds complexity to solve this problem. We formulate this routing problem as an integer program, and we show that the general case of this problem is NP-hard, and even hard to approximate. For the special case when the step ratio of the cost function is bounded, we show that effective approximations can be obtained. Our main algorithm executes two processes in sequence: relaxation and rounding. The relaxation process eliminates the non-convexity of the cost function, so that the problem is transformed into a fractional convex program solvable in polynomial time. After that, a randomized rounding process is used to get a feasible solution for the original problem. This algorithm provides a constant approximation ratio for uniform demands and an approximation ratio of $O(\log^{β-1} d)$ for non-uniform demands, where $β$ is a constant and $d$ is the largest demand.

preprint2012arXiv

Converse KAM theory revisited

For an integrable Hamiltonian with $d\ (d\geq 2)$ degrees of freedom, we show the conditions on perturbations, for which invariant tori can be destructed.

preprint2012arXiv

Destruction of Lagrangian torus for positive definite Hamiltonian systems

For an integrable Hamiltonian $H_0=1/2\sum_{i=1}^dy_i^2$ $(d\geq 2)$, we show that any Lagrangian torus with a given unique rotation vector can be destructed by arbitrarily $C^{2d-δ}$-small perturbations. In contrast with it, it has been shown that KAM torus with constant type frequency persists under $C^{2d+δ}$-small perturbations.

preprint2012arXiv

Estimating the value of containment strategies in delaying the arrival time of an influenza pandemic: A case study of travel restriction and patient isolation

With a simple phenomenological metapopulation model, which characterizes the invasion process of an influenza pandemic from a source to a subpopulation at risk, we compare the efficiency of inter- and intra-population interventions in delaying the arrival of an influenza pandemic. We take travel restriction and patient isolation as examples, since in reality they are typical control measures implemented at the inter- and intra-population levels, respectively. We find that the intra-population interventions, e.g., patient isolation, perform better than the inter-population strategies such as travel restriction if the response time is small. However, intra-population strategies are sensitive to the increase of the response time, which might be inevitable due to socioeconomic reasons in practice and will largely discount the efficiency.

preprint2012arXiv

Performance Analysis of Protograph-based LDPC Codes with Spatial Diversity

In wireless communications, spatial diversity techniques, such as space-time block code (STBC) and single-input multiple-output (SIMO), are employed to strengthen the robustness of the transmitted signal against channel fading. This paper studies the performance of protograph-based low-density parity-check (LDPC) codes with receive antenna diversity. We first propose a modified version of the protograph extrinsic information transfer (PEXIT) algorithm and use it for deriving the threshold of the protograph codes in a single-input multiple-output (SIMO) system. We then calculate the decoding threshold and simulate the bit error rate (BER) of two protograph codes (accumulate-repeat-by-3-accumulate (AR3A) code and accumulate-repeat-by-4-jagged-accumulate (AR4JA) code), a regular (3, 6) LDPC code and two optimized irregular LDPC codes. The results reveal that the irregular codes achieve the best error performance in the low signal-to-noise-ratio (SNR) region and the AR3A code outperforms all other codes in the high-SNR region. Utilizing the theoretical analyses and the simulated results, we further discuss the effect of the diversity order on the performance of the protograph codes. Accordingly, the AR3A code stands out as a good candidate for wireless communication systems with multiple receive antennas.

preprint2012arXiv

Performance of MIMO Relay DCSK-CD Systems over Nakagami Fading Channels

A multi-access multiple-input multiple-output (MIMO) relay differential chaos shift keying cooperative diversity (DCSK-CD) system is proposed in this paper as a comprehensive cooperation scheme, in which the relay and destination both employ multiple antennas to strengthen the robustness against signal fading in a wireless network. It is shown that, with spatial diversity gains, the bit error rate (BER) performance of the proposed system is remarkably better than the conventional DCSK non-cooperation (DCSK-NC) and DCSK cooperative communication (DCSK-CC) systems. Moreover, the exact BER and close-form expressions of the proposed system are derived over Nakagami fading channels through the moment generating function (MGF), which is shown to be highly consistent with the simulation results. Meanwhile, this paper illustrates a trade-off between the performance and the complexity, and provides a threshold for the number of relay antennas keeping the user consumed energy constant. Due to the above-mentioned advantages, the proposed system stands out as a good candidate or alternative for energy-constrained wireless communications based on chaotic modulation, especially for low-power and low-cost wireless personal area networks (WPANs).

preprint2012arXiv

Pressure-induced amorphization and polyamorphism in one-dimensional single crystal TiO2 nanomaterials

The structural phase transitions of single crystal TiO2-B nanoribbons were investigated in-situ at high-pressure using the synchrotron X-ray diffraction and the Raman scattering. Our results have shown a pressure-induced amorphization (PIA) occurred in TiO2-B nanoribbons upon compression, resulting in a high density amorphous (HDA) form related to the baddeleyite structure. Upon decompression, the HDA form transforms to a low density amorphous (LDA) form while the samples still maintain their pristine nanoribbon shape. HRTEM imaging reveals that the LDA phase has an α-PbO2 structure with short range order. We propose a homogeneous nucleation mechanism to explain the pressure-induced amorphous phase transitions in the TiO2-B nanoribbons. Our study demonstrates for the first time that PIA and polyamorphism occurred in the one-dimensional (1D) TiO2 nanomaterials and provides a new method for preparing 1D amorphous nanomaterials from crystalline nanomaterials.

preprint2012arXiv

Testing new physics models by top charge asymmetry and polarization at the LHC

As a top quark factory, the LHC can test the new physics models used to explain the top quark forward-backward asymmetry A^t_FB measured at the Tevatron. In this work we perform a comparative study for two such models: the W'-model and the color triplet diquark (ϕ) model. Requiring these models to explain A^t_FB and also satisfy the top pair production rate measured at the Tevatron, we examine their contributions to the LHC observables such as the polarization and charge asymmetry in top quark and W' (or ϕ) productions. We find that these observables can be enhanced to the observable level and the current LHC measurement on the top charge asymmetry can already tightly constrain the W'-model. We also find that each observable shows different characteristics in different models, which can be utilized to discriminate the models.

preprint2012arXiv

Top quark pair production via $e^{+}e^{-}$ collision in the littlest Higgs model with T-parity at the ILC

In the littlest Higgs model with T-parity, we studied the contributions of the new particles to the top-quark pair production via $e^{+}e^{-}$ collision at the International Linear Collider. We calculated the top-quark pair production cross section and found this process can generate significantly relative correction. The result may be a sensitive probe of the littlest Higgs model with T-parity.

preprint2012arXiv

Towards a temporal network analysis of interactive WiFi users

Complex networks are used to depict topological features of complex systems. The structure of a network characterizes the interactions among elements of the system, and facilitates the study of many dynamical processes taking place on it. In previous investigations, the topological infrastructure underlying dynamical systems is simplified as a static and invariable skeleton. However, this assumption cannot cover the temporal features of many time-evolution networks, whose components are evolving and mutating. In this letter, utilizing the log data of WiFi users in a Chinese university campus, we infuse the temporal dimension into the construction of dynamical human contact network. By quantitative comparison with the traditional aggregation approach, we find that the temporal contact network differs in many features, e.g., the reachability, the path length distribution. We conclude that the correlation between temporal path length and duration is not only determined by their definitions, but also influenced by the microdynamical features of human activities under certain social circumstance as well. The time order of individuals' interaction events plays a critical role in understanding many dynamical processes via human close proximity interactions studied in this letter. Besides, our study also provides a promising measure to identify the potential superspreaders by distinguishing the nodes functioning as the relay hub.

preprint2011arXiv

Evolution of scaling emergence in large-scale spatial epidemic spreading

Background: Zipf's law and Heaps' law are two representatives of the scaling concepts, which play a significant role in the study of complexity science. The coexistence of the Zipf's law and the Heaps' law motivates different understandings on the dependence between these two scalings, which is still hardly been clarified. Methodology/Principal Findings: In this article, we observe an evolution process of the scalings: the Zipf's law and the Heaps' law are naturally shaped to coexist at the initial time, while the crossover comes with the emergence of their inconsistency at the larger time before reaching a stable state, where the Heaps' law still exists with the disappearance of strict Zipf's law. Such findings are illustrated with a scenario of large-scale spatial epidemic spreading, and the empirical results of pandemic disease support a universal analysis of the relation between the two laws regardless of the biological details of disease. Employing the United States(U.S.) domestic air transportation and demographic data to construct a metapopulation model for simulating the pandemic spread at the U.S. country level, we uncover that the broad heterogeneity of the infrastructure plays a key role in the evolution of scaling emergence. Conclusions/Significance: The analyses of large-scale spatial epidemic spreading help understand the temporal evolution of scalings, indicating the coexistence of the Zipf's law and the Heaps' law depends on the collective dynamics of epidemic processes, and the heterogeneity of epidemic spread indicates the significance of performing targeted containment strategies at the early time of a pandemic disease.

preprint2011arXiv

Pressure-induced isostructural phase transition and correlation of FeAs coordination with the superconducting properties of 111-type Na1-xFeAs

The effect of pressure on the crystalline structure and superconducting transition temperature (Tc) of the 111-type Na1-xFeAs system using in situ high pressure synchrotron x-ray powder diffraction and diamond anvil cell techniques is studied. A pressure-induced tetragonal to tetragonal isostructural phase transition was found. The systematic evolution of the FeAs4 tetrahedron as a function of pressure based on Rietveld refinements on the powder x-ray diffraction patterns was obtained. The non-monotonic Tc(P) behavior of Na1-xFeAs is found to correlate with the anomalies of the distance between the anion (As) and the iron layer as well as the bond angle between As-Fe-As for the two tetragonal phases. This behavior provides the key structural information in understanding the origin of the pressure dependence of Tc for 111-type iron pnictide superconductors. A pressure-induced structural phase transition is also observed at 20 GPa.

preprint2011arXiv

Top quark forward-backward asymmetry, FCNC decays and like-sign pair production as a joint probe of new physics

The anomaly of the top quark forward-backward asymmetry $A^t_{FB}$ observed at the Tevatron can be explained by the t-channel exchange of a neutral gauge boson (Z') which has sizable flavor changing coupling for top and up quarks. This gauge boson can also induce the top quark flavor-changing neutral-current (FCNC) decays and the like-sign top pair production at the LHC. In this work we focus on two models which predict such a Z', namely the left-right model and the U(1)_X model, to investigate the correlated effects on $A^t_{FB}$, the FCNC decays $t -> u V$ ($V=g,Z,γ$) and the like-sign top pair production at the LHC. We also pay special attention to the most recently measured $A^t_{FB}$ in the large top pair invariant mass region. We find that under the current experimental constraints both models can alleviate the deviation of $A^t_{FB}$ and, meanwhile, enhance the like-sign top pair production to the detectable level of the LHC. We also find that the two models give different predictions for the observables and their correlations, and thus they may even be distinguished by jointly studying these top quark observables.

preprint2011arXiv

Total destruction of invariant tori for the generalized Frenkel-Kontorova model

We consider generalized Frenkel-Kontorova models on higher dimensional lattices. We show that the invariant tori which are parameterized by continuous hull functions can be destroyed by small perturbations in the $C^r$ topology with $r<1$.

preprint2011arXiv

Variational destruction of invariant circles

We construct a sequence of generating functions $(h_n)_{n\in\N}$, arbitrarily close to an integrable system in the $C^r$ topology with $r<4$ for $n$ large enough. With the variational method, we prove that for a given rotation number $ω$ and $n$ large enough, the exact monotone area-preserving twist maps generated by $(h_n)_{n\in\N}$ admit no invariant circles with rotation number $ω$.

preprint2010arXiv

Self organized criticality in an improved Olami-Feder-Christensen model

An improved version of the Olami-Feder-Christensen model has been introduced to consider avalanche size differences. Our model well demonstrates the power-law behavior and finite size scaling of avalanche size distribution in any range of the adding parameter $p_{add}$ of the model. The probability density functions (PDFs) for the avalanche size differences at consecutive time steps (defined as returns) appear to be well approached, in the thermodynamic limit, by q-Gaussian shape with appropriate q values which can be obtained a priori from the avalanche size exponent $τ$. For the small system sizes, however, return distributions are found to be consistent with the crossover formulas proposed recently in Tsallis and Tirnakli, J. Phys.: Conf. Ser. 201, 012001 (2010). Our results strengthen recent findings of Caruso et al. [Phys. Rev. E 75, 055101(R) (2007)] on the real earthquake data which support the hypothesis that knowing the magnitude of previous earthquakes does not make the magnitude of the next earthquake predictable. Moreover, the scaling relation of the waiting time distribution of the model has also been found.

preprint2008arXiv

Generalized transfer matrix theory on electronic transport through graphene waveguide

In the effective mass approximation, electronic property in graphene can be characterized by the relativistic Dirac equation. Within such a continuum model we investigate the electronic transport through graphene waveguides formed by connecting multiple segments of armchair-edged graphene nanoribbons of different widths. By using appropriate wavefunction connection conditions at the junction interfaces, we generalize the conventional transfer matrix approach to formulate the linear conductance of the graphene waveguide in terms of the structure parameters and the incident electron energy. In comparison with the tight-binding calculation, we find that the generalized transfer matrix method works well in calculating the conductance spectrum of a graphene waveguide even with a complicated structure and relatively large size. The calculated conductance spectrum indicates that the graphene waveguide exhibits a well-defined insulating band around the Dirac point, even though all the constituent ribbon segments are gapless. We attribute the occurrence of the insulating band to the antiresonance effect which is intimately associated with the edge states localized at the shoulder regions of the junctions. Furthermore, such an insulating band can be sensitively shifted by a gate voltage, which suggests a device application of the graphene waveguide as an electric nanoswitch.

Lin Wang

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106 published item(s)

A Curriculum Learning Approach to Reinforcement Learning: Leveraging RAG for Multimodal Question Answering

StableMind: Source-Free Cross-Subject fMRI Decoding with Regularized Adaptation

Subgroup Identification and Individualized Treatment Policies: A Tutorial on the Hybrid Two-Stage Workflow

Hi-Map: Hierarchical Factorized Radiance Field for High-Fidelity Monocular Dense Mapping

Multi-scale multi-modal micro-expression recognition algorithm based on transformer

Reconstruction of compressed spectral imaging based on global structure and spectral correlation

Controllability of Multilayer Networked Sampled-data Systems

Data-driven discovery of high performance layered van der Waals piezoelectric NbOI2

Deep Learning for Omnidirectional Vision: A Survey and New Perspectives

Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification

Efficient Video Deblurring Guided by Motion Magnitude

Event-guided Deblurring of Unknown Exposure Time Videos

Future Computer Systems and Networking Research in the Netherlands: A Manifesto

Go viral or go broadcast? Characterizing the virality and growth of cascades

Impact of disorder on the distribution of gate coupling strengths in a spin qubit device

Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data

Investigating and Modeling the Dynamics of Long Ties

Medical Matting: A New Perspective on Medical Segmentation with Uncertainty

Social physics

Strong Neel ordering and luminescence correlation in a two-dimensional antiferromagnet

The spatial dissemination of COVID-19 and associated socio-economic consequences

The Strength of Structural Diversity in Online Social Networks

The unusual emission from PSR B1859+07 with FAST

Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularization

An embedded multichannel sound acquisition system for drone audition

DEFT: Distilling Entangled Factors by Preventing Information Diffusion

Locally symmetric lattices for storage ring light sources

Surrogate-assisted cooperative signal optimization for large-scale traffic networks

A Framework of Hierarchical Attacks to Network Controllability

A wolbachia infection model with free boundary

AGL: a Scalable System for Industrial-purpose Graph Machine Learning

An Energy Stable Linear Diffusive Crank-Nicolson Scheme for the Cahn-Hilliard Gradient Flow

CoAug-MR: An MR-based Interactive Office Workstation Design System via Augmented Multi-Person Collaboration

Comparison of Modeling SPARC spiral galaxies' rotation curves: halo models vs MOND

Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations

Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features

Dipolar spin waves in uniaxial easy-axis antiferromagnets: A natural topological nodal-line semimetal

Discovery and timing of pulsars in the globular cluster M13 with FAST

DSSLP: A Distributed Framework for Semi-supervised Link Prediction

EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning

Predicting Network Controllability Robustness: A Convolutional Neural Network Approach

Smith-Purcell radiation from a charge moving above a finite-length grating with rectangular profiles

The FAST discovery of an Eclipsing Binary Millisecond Pulsar in the Globular Cluster M92 (NGC 6341)

Towards Optimal Robustness of Network Controllability: An Empirical Necessary Condition

Weak KAM solutions of Hamilton-Jacobi equations with decreasing dependence on unknown functions

Numerical Method for Free Electron Laser using an Overmoded Rectangular Waveguide

Toward Stronger Robustness of Network Controllability: A Snapback Network Model

Achieving Energy Efficiency in Cloud Brokering

Design of FELiChEM, the first infrared free-electron laser user facility in China

Direct Observation of Long-range field-effect from gate tuning of non-local conductivity

Multi-resource Energy-efficient Routing in Cloud Data Centers with Networks-as-a-Service

On viscosity solution of HJB equations with state constraints and reflection control

Probing the Electronic States in Black Phosphorus Vertical Heterostructures

Broadband Impedance Calculations and Single Bunch Instabilities Estimations of the HLS-II storage ring

Center Specification Property and Entropy for Partially Hyperbolic Diffeomorphisms

Controllability of networked MIMO systems

Dissociation products and structures of solid H2S at strong compression

Evolutionary games on multilayer networks: A colloquium

First commissioning of the HLS-II storage ring

Identifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history

Improved step-by-step chromaticity compensation method for chromatic sextupole optimization

LazyCtrl: Scalable Network Control for Cloud Data Centers

Pseudo-goldstino and electroweakinos via VBF processes at LHC

Raman fingerprint of semi-metal WTe2 from bulk to monolayer

Variational principle for contact Tonelli Hamiltonian systems

A Dynamical Approach to Viscosity Solutions of Hamilton-Jacobi Equations

A Joint Optimization of Operational Cost and Performance Interference in Cloud Data Centers

A Multi-length Bunches Design for Electron Storage Rings with Odd Buckets

Anomalous compressibility behavior of chromium monoaresenide under high pressure

Degree mixing in multilayer networks impedes the evolution of cooperation

Detection of Interlayer Interaction in Few-layer Graphene through Landau Level Spectroscopy

Energy-Efficient Flow Scheduling and Routing with Hard Deadlines in Data Center Networks

Global Spatio-temporal Patterns of Influenza in the Post-pandemic Era

High quality sandwiched black phosphorus heterostructure and its quantum oscillations