Non-local Information Research Articles

Manifestly local formulation of nonlocal approach to the cosmological constant problem

We present a manifestly local and general coordinate invariant formulation of a nonlocal approach to the cosmological constant problem which has been recently proposed by Carroll and Remmen. To do that, we need to introduce a topological term involving a new 3-form gauge field. The equations of motion for this new 3-form gauge field lead to a constant Lagrange multiplier parameter and the resulting action becomes equivalent to that of Carroll and Remmen. In our formulation, nonlocal information is encoded via the procedure of taking the space-time average at the stage of the equations of motion. Consequently, our theory evades a no-go theorem by Weinberg and provides a new solution to the cosmological constant problem in almost exactly the same way as the original proposal by Carroll et al.

Persistence of non-local correlations and quantum information theoretic measures in the thermal state of frustrated molecular wheels

ABSTRACTGenuine quantumness present in quantum systems is the resource for implementing quantum information and computation protocols which can outperform the classical counterparts. These quantumness measures encompass non-local ones known as quantum entanglement (QE) and quantum information theoretic ones, e.g. quantum discord (QD). In this paper, some well-known measures of QE and QD have been studied in two wheel-like frustrated molecular magnetic systems, with special emphasis on the survival of the measures in the thermal states. A molecular magnetic system similar to the first one (with half-integer spins ) has already been synthesised using coordination chemistry, and the other is hypothetical, where the dominant interaction is the spin–spin exchange interaction. Exact numerical diagonalisation methods have been used. Some non-trivial features, like nonmonotonicity of threshold temperatures with external magnetic field, persistence of multipartite entanglement and QD over bipartite entanglement, have been found. The measures, being operational ones, can be used to realise the resource of quantumness in experiments.

Perceptual Ranges, Information Gathering, and Foraging Success in Dynamic Landscapes.

How organisms gather and utilize information about their landscapes is central to understanding land-use patterns and population distributions. When such information originates beyond an individual's immediate vicinity, movement decisions require integrating information out to some perceptual range. Such nonlocal information, whether obtained visually, acoustically, or via chemosensation, provides a field of stimuli that guides movement. Classically, however, models have assumed movement based on purely local information (e.g., chemotaxis, step-selection functions). Here we explore how foragers can exploit nonlocal information to improve their success in dynamic landscapes. Using a continuous time/continuous space model in which we vary both random (diffusive) movement and resource-following (advective) movement, we characterize the optimal perceptual ranges for foragers in dynamic landscapes. Nonlocal information can be highly beneficial, increasing the spatiotemporal concentration of foragers on their resources up to twofold compared with movement based on purely local information. However, nonlocal information is most useful when foragers possess both high advective movement (allowing them to react to transient resources) and low diffusive movement (preventing them from drifting away from resource peaks). Nonlocal information is particularly beneficial in landscapes with sharp (rather than gradual) patch edges and in landscapes with highly transient resources.

Specular Reflection Separation With Color-Lines Constraint.

According to dichromatic reflection model, the previous methods of specular reflection separation in image processing often separate specular reflection from a single image using patch-based priors. Due to lack of global information, these methods often cannot completely separate the specular component of an image and are incline to degrade image textures. In this paper, we derive a global color-lines constraint from dichromatic reflection model to effectively recover specular and diffuse reflection. Our key observation is from that each image pixel lies along a color line in normalized RGB space and the different color lines representing distinct diffuse chromaticities intersect at one point, namely, the illumination chromaticity. For pixels along the same color line, they spread over the entire image and their distances to the illumination chromaticity reflect the amount of specular reflection components. With global (non-local) information from these color lines, our method can effectively separate specular and diffuse reflection components in a pixelwise way for a single image, and it is suitable for real-time applications. Our experimental results on synthetic and real images show that our method performs better than the state-of-the-art methods to separate specular reflection.

Climate reconstruction using data assimilation of water isotope ratios from ice cores

AbstractWater isotope data from ice cores, particularly δ18O, have long been used in paleoclimatology. Although δ18O has been primarily interpreted as a proxy for local air temperature, isotope‐enabled climate models have established that there are many nonlocal and nontemperature‐related climatic influences on isotopic signals at coring locations. Moreover, recent observational studies have linked ice core isotopes to nonlocal patterns of climate variability, particularly to midlatitude atmospheric circulation patterns and to variations in tropical climate. Therefore, paleoclimate reconstructions may better utilize ice core isotope proxies by combining them with isotope‐enabled climate models. Here we employ a data assimilation‐based technique that fuses isotopic proxy information with the dynamical constraints of climate models. Through several idealized and real proxy experiments we assess the spatial and temporal extent to which isotope records can reconstruct surface temperature, 500 hPa geopotential height, and precipitation. We find local reconstruction skill to be most robust across the reconstructions, particularly for temperature and geopotential height, as well as limited nonlocal skill in the tropics. These results are in agreement with long‐held views that isotopes in ice cores have clear value as local climate proxies, particularly for temperature and atmospheric circulation. These results also show that in principle nonlocal climate information may also be inferred from ice cores. However, the spatial range of this information is nonuniform and depends on skillful modeling of the proxy data within the reconstruction process.

Q-Space Upsampling Using x-q Space Regularization.

Acquisition time in diffusion MRI increases with the number of diffusion-weighted images that need to be acquired. Particularly in clinical settings, scan time is limited and only a sparse coverage of the vast q-space is possible. In this paper, we show how non-local self-similar information in the x-q space of diffusion MRI data can be harnessed for q-space upsampling. More specifically, we establish the relationships between signal measurements in x-q space using a patch matching mechanism that caters to unstructured data. We then encode these relationships in a graph and use it to regularize an inverse problem associated with recovering a high q-space resolution dataset from its low-resolution counterpart. Experimental results indicate that the high-resolution datasets reconstructed using the proposed method exhibit greater quality, both quantitatively and qualitatively, than those obtained using conventional methods, such as interpolation using spherical radial basis functions (SRBFs).

A new segmentation method of cerebral MRI images based on the fuzzy c-means algorithm

The aim of this work is to present a new method for cerebral MRI image segmentation based on modification of the fuzzy c-means (FCM) algorithm. We used local and nonlocal information distance in the initial function of the robust FCM model. The obtained results of the classification of MRI images showed the effectiveness of the suggested model. Calculation of the similarity index confirms that our method is well adapted to MRI images even in the presence of noise.

A novel image zooming method based on sparse representation of Weber’s law descriptor

A novel image zooming algorithm based on sparse representation of Weber’s law descriptor is proposed in this article. It is known that features of low resolution can be extracted using four one-dimensional filters convoluting with low resolution patches. Weber’s law descriptor can well deal with local feature, so we extract low-resolution image feature replacing one-dimensional with Weber’s law descriptor in the four filters. In addition, fractional calculus can deal with nonlocal information such as texture. For avoiding small complex component when the size of image is not an odd integer, we modify the extending image method used by Bai, so it can save lots of calculation. The proposed approach combining the Weber’s law descriptor with fractional calculus achieves a very good performance. Experimental results show that our method can well eliminate jagged effect when up-sampling an image and is robustness to noise.

Enhancing entanglement, local and non-local information of accelerated two-qubit and two-qutrit systems via weak-reverse measurements

Weak-reverse measurements (WRM) are employed to recover and protect the entanglement losses of entangled two-qubit and two-qutrit systems that accelerate. It is shown that the upper bounds of entanglement for a partially entangled two-qutrit state are higher than that for a partially entangled two-qubit state. The possibility of protecting local information encoded in partially entangled states using the WRM measurements is much better than that encoded in maximum entangled states. It is shown that the possibility of protecting the local encoded information on an accelerated 2-qutrit state, using the WRM is higher than that for a 2-qubit state.

TENDER: Tensor non-local deconvolution enabled radiation reduction in CT perfusion

Stroke is the leading cause of long-term disability and the second leading cause of mortality in the world, and exerts an enormous burden on the public health. Computed Tomography (CT) remains one of the most widely used imaging modality for acute stroke diagnosis. However when coupled with CT perfusion, the excessive radiation exposure in repetitive imaging to assess treatment response and prognosis has raised significant public concerns regarding its potential hazards to both short- and long-term health outcomes. Tensor total variation has been proposed to reduce the necessary radiation dose in CT perfusion without comprising the image quality by fusing the information of the local anatomical structure with the temporal blood flow model. However the local search in the TTV framework fails to leverage the non-local information in the spatio-temporal data. In this paper, we propose TENDER, an efficient framework of non-local tensor deconvolution to maintain the accuracy of the hemodynamic parameters and the diagnostic reliability in low radiation dose CT perfusion. The tensor total variation is extended using non-local spatio-temporal cubics for regularization, and an efficient algorithm is proposed to reduce the time complexity with speedy similarity computation. Evaluations on clinical data of patients subjects with cerebrovascular disease and normal subjects demonstrate the advantage of non-local tensor deconvolution for reducing radiation dose in CT perfusion.

Robust skull stripping using multiple MR image contrasts insensitive to pathology

Automatic skull-stripping or brain extraction of magnetic resonance (MR) images is often a fundamental step in many neuroimage processing pipelines. The accuracy of subsequent image processing relies on the accuracy of the skull-stripping. Although many automated stripping methods have been proposed in the past, it is still an active area of research particularly in the context of brain pathology. Most stripping methods are validated on T1-w MR images of normal brains, especially because high resolution T1-w sequences are widely acquired and ground truth manual brain mask segmentations are publicly available for normal brains. However, different MR acquisition protocols can provide complementary information about the brain tissues, which can be exploited for better distinction between brain, cerebrospinal fluid, and unwanted tissues such as skull, dura, marrow, or fat. This is especially true in the presence of pathology, where hemorrhages or other types of lesions can have similar intensities as skull in a T1-w image. In this paper, we propose a sparse patch based Multi-cONtrast brain STRipping method (MONSTR),22http://www.nitrc.org/projects/monstr. where non-local patch information from one or more atlases, which contain multiple MR sequences and reference delineations of brain masks, are combined to generate a target brain mask.We compared MONSTR with four state-of-the-art, publicly available methods: BEaST, SPECTRE, ROBEX, and OptiBET. We evaluated the performance of these methods on 6 datasets consisting of both healthy subjects and patients with various pathologies. Three datasets (ADNI, MRBrainS, NAMIC) are publicly available, consisting of 44 healthy volunteers and 10 patients with schizophrenia. Other three in-house datasets, comprising 87 subjects in total, consisted of patients with mild to severe traumatic brain injury, brain tumors, and various movement disorders. A combination of T1-w, T2-w were used to skull-strip these datasets. We show significant improvement in stripping over the competing methods on both healthy and pathological brains. We also show that our multi-contrast framework is robust and maintains accurate performance across different types of acquisitions and scanners, even when using normal brains as atlases to strip pathological brains, demonstrating that our algorithm is applicable even when reference segmentations of pathological brains are not available to be used as atlases.

Identifying local structural states in atomic imaging by computer vision.

The availability of atomically resolved imaging modalities enables an unprecedented view into the local structural states of materials, which manifest themselves by deviations from the fundamental assumptions of periodicity and symmetry. Consequently, approaches that aim to extract these local structural states from atomic imaging data with minimal assumptions regarding the average crystallographic configuration of a material are indispensable to advances in structural and chemical investigations of materials. Here, we present an approach to identify and classify local structural states that is rooted in computer vision. This approach introduces a definition of a structural state that is composed of both local and nonlocal information extracted from atomically resolved images, and is wholly untethered from the familiar concepts of symmetry and periodicity. Instead, this approach relies on computer vision techniques such as feature detection, and concepts such as scale invariance. We present the fundamental aspects of local structural state extraction and classification by application to simulated scanning transmission electron microscopy images, and analyze the robustness of this approach in the presence of common instrumental factors such as noise, limited spatial resolution, and weak contrast. Finally, we apply this computer vision-based approach for the unsupervised detection and classification of local structural states in an experimental electron micrograph of a complex oxides interface, and a scanning tunneling micrograph of a defect-engineered multilayer graphene surface.Electronic supplementary materialThe online version of this article (doi:10.1186/s40679-016-0028-8) contains supplementary material, which is available to authorized users.

Non‐local‐based spatially constrained hierarchical fuzzy C ‐means method for brain magnetic resonance imaging segmentation

Owing to the existence of noise and intensity inhomogeneity in brain magnetic resonance (MR) images, the existing segmentation algorithms are hard to find satisfied results. In this study, the authors propose an improved fuzzy C-mean clustering method (FCM) to obtain more accurate results. First, the authors modify the traditional regularisation smoothing term by using the non-local information to reduce the effect of the noise. Second, inspired by the mechanism of the Gaussian mixture model, the distance function of FCM is defined by using the form of certain exponential function consisting of not only the distance but also the covariance and the prior probability to improve the robustness. Meanwhile, the bias field is modelled by using orthogonal basis functions to reduce the effect of intensity inhomogeneity. Finally, they use the hierarchical strategy to construct a more flexibility function, which considers the improved distance function itself as a sub-FCM, to make the method more robust and accurate. Compared with the state-of-the-art methods, experiment results based on synthetic and real MR images demonstrate its accuracy and robustness.

Dimensionality Reduction Based on Group-Based Tensor Model for Hyperspectral Image Classification

Dimensionality reduction is one of the most important tasks in improving hyperspectral image (HSI) classification performance and has been widely studied. In traditional dimensionality reduction methods, the hyperspectral data cube is first converted to a matrix, and then, the matrix-algebra is employed, in which the spatial structure is usually not taken into consideration. To jointly utilize spectral and spatial information, researches considering HSI as a tensor have attracted more and more attention. In this letter, we propose a group-based tensor model for HIS dimensionality reduction. The local and nonlocal spatial information of HSI cubes is explored by segmenting the original HSI tensors into a lot of small tensors and grouping them into clusters. Finally, the clusters are projected into low-rank subspace to obtain a proper feature space. Experimental results on two real hyperspectral data sets exhibit the effectiveness of the proposed algorithm for HSI-dimensionality reduction.

3D Statistical Shape Models Incorporating Landmark-Wise Random Regression Forests for Omni-Directional Landmark Detection.

3D Statistical Shape Models (3D-SSM) are widely used for medical image segmentation. However, during segmentation, they typically perform a very limited unidirectional search for suitable landmark positions in the image, relying on weak learners or use-case specific appearance models that solely take local image information into account. As a consequence, segmentation errors arise, and results in general depend on the accuracy of a previous model initialization. Furthermore, these methods become subject to a tedious and use-case dependent parameter tuning in order to obtain optimized results. To overcome these limitations, we propose an extension of 3D-SSM by landmark-wise random regression forests that perform an enhanced omni-directional search for landmark positions, thereby taking rich non-local image information into account. In addition, we provide a long distance model fitting based on a multi-scale approach, that allows an accurate and reproducible segmentation even from distant image positions, thus enabling an application without model initialization. Finally, translation of the proposed method to different organs is straightforward and requires no adaptation of the training process. In segmentation experiments on 45 clinical CT volumes, the proposed omni-directional search significantly increased accuracy and displayed great precision regardless of model initialization. Furthermore, for liver, spleen and kidney segmentation in a competitive multi-organ labeling challenge on publicly available data, the proposed method achieved similar or better results than the state of the art. Finally, liver segmentation results were obtained that successfully compete with specialized state-of-the-art methods from the well-known liver segmentation challenge SLIVER.

From paths to blocks: New measures for street patterns

The street network is an important aspect of cities and contains crucial information about their organization and evolution. Characterizing and comparing various street networks could then be helpful for a better understanding of the mechanisms governing the formation and evolution of these systems. Their characterization is however not easy: there are no simple tools to classify planar networks and most of the measures developed for complex networks are not useful when space is relevant. Here, we describe recent efforts in this direction and new methods adapted to spatial networks. We will first discuss measures based on the structure of shortest paths, among which the betweenness centrality. In particular for time-evolving road networks, we will show that the spatial distribution of the betweenness centrality is able to reveal the impact of important structural transformations. Shortest paths are however not the only relevant ones. In particular, they can be very different from those with the smallest number of turns—the simplest paths. The statistical comparison of the lengths of the shortest and simplest paths provides a nontrivial and nonlocal information about the spatial organization of planar graphs. We define the simplicity index as the average ratio of these lengths and the simplicity profile characterizes the simplicity at different scales. Measuring these quantities on artificial (roads, highways, railways) and natural networks (leaves, insect wings) show that there are fundamental differences—probably related to their different function—in the organization of urban and biological systems: there is a clear hierarchy of the lengths of straight lines in biological cases, but they are randomly distributed in urban systems. The paths are however not enough to fully characterize the spatial pattern of planar networks such as streets and roads. Another promising direction is to analyze the statistics of blocks of the planar network. More precisely, we can use the conditional probability distribution of the shape factor of blocks with a given area, and define what could constitute the fingerprint of a city. These fingerprints can then serve as a basis for a classification of cities based on their street patterns. This method applied on more than 130 cities in the world leads to four broad families of cities characterized by different abundances of blocks of a certain area and shape. This classification will be helpful for identifying dominant mechanisms governing the formation and evolution of street patterns.

Optimization-based, QoS-aware distributed traffic control laws for networks with time-varying link capacities

It is a challenge to design optimal, distributed traffic control mechanisms in a network where the link capacities and Class of Service (CoS) requirements may vary with time, such as a virtual network, an overlay network, or a wireless network. In this paper, we develop a family of optimization-based distributed traffic control laws to meet this challenge. This family of control laws enables Quality of Service (QoS), Traffic Engineering (TE), and Failure Recovery (FR) features simultaneously in a network where the link capacities and CoS requirement may vary with time. The approach taken relies on the concept of Sliding Modes to solve the resulting time-varying optimization problem. Running at the edge of a network, a set of control laws selected from this family enables class-of-service-based multi-path load balancing and/or rate adaptation to respond to network congestion, CoS requirement variation and link failures. The only nonlocal information needed as input to the control laws is the number of congested links along a forwarding path. This family of control laws is particularly viable to be implemented in a software-defined network (SDN) where the available underlying resources may not be accurately predictable and vary with time, due to, e.g., network virtualization.

A modified fuzzy C-means method for segmenting MR images using non-local information.

In recent years, MR images have been increasingly used in therapeutic applications such as image-guided radiotherapy (IGRT). However, images with low contrast values and noises present challenges for image segmentation. The objective of this study is to develop a robust method based on fuzzy C-means (FCM) method which can segment MR images polluted with Gaussian noise. A modified FCM algorithm accommodating non-local pixel information via Hausdorff distance was developed for segmenting MR images. The membership and objective functions were modified accordingly. Segmentations with different weights of the Hausdorff distance were compared. Segmentation tests using synthetic and MR images showed that the proposed algorithm was better at resolving boundaries and more robust to Gaussian noise. By segmenting a sample MR image of a tumor, we further showed the capability of the method in capturing the centroid of the target region. The modified FCM algorithm with neighboring information can be used to segment blurry images with potential applications in segmenting motion MR images in image-guided radiotherapy (IGRT).

Across-Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression.

DNA methylation is a key epigenetic mark involved in both normal development and disease progression. Recent advances in high-throughput technologies have enabled genome-wide profiling of DNA methylation. However, DNA methylation profiling often employs different designs and platforms with varying resolution, which hinders joint analysis of methylation data from multiple platforms. In this study, we propose a penalized functional regression model to impute missing methylation data. By incorporating functional predictors, our model utilizes information from nonlocal probes to improve imputation quality. Here, we compared the performance of our functional model to linear regression and the best single probe surrogate in real data and via simulations. Specifically, we applied different imputation approaches to an acute myeloid leukemia dataset consisting of 194 samples and our method showed higher imputation accuracy, manifested, for example, by a 94% relative increase in information content and up to 86% more CpG sites passing post-imputation filtering. Our simulated association study further demonstrated that our method substantially improves the statistical power to identify trait-associated methylation loci. These findings indicate that the penalized functional regression model is a convenient and valuable imputation tool for methylation data, and it can boost statistical power in downstream epigenome-wide association study (EWAS).

Improved fuzzy clustering algorithm with non-local information for image segmentation

Fuzzy C-means(FCM) has been adopted to perform image segmentation due to its simplicity and efficiency. Nevertheless it is sensitive to noise and other image artifacts because of not considering spatial information. Up to now, a series of improved FCM algorithms have been proposed, including fuzzy local information C-means clustering algorithm(FLICM). In FLICM, one fuzzy factor is introduced as a fuzzy local similarity measure, which can control the trade-off between noise and details. However, the fuzzy factor in FLICM cannot estimate the damping extent of neighboring pixels accurately, which will result in poor performance in images of high-level noise. Aiming at solving this problem, this paper proposes an improved fuzzy clustering algorithm, which introduces pixel relevance into the fuzzy factor and could estimate the damping extent accurately. As a result, non-local context information can be utilized in the improved algorithm, which can improve the performance in restraining image artifacts. Experimental results on synthetic, medical and natural images show that the proposed algorithm performs better than current improved algorithms.