Long-distance Correlation Research Articles

Guided Nonlocal Patch Regularization and Efficient Filtering-Based Inversion for Multiband Fusion

In multiband fusion, an image with a high spatial and low spectral resolution is combined with an image with a low spatial but high spectral resolution to produce a single multiband image having high spatial and spectral resolutions. This comes up in remote sensing applications such as pansharpening (MS+PAN), hyperspectral sharpening (HS+PAN), and HS-MS fusion (HS+MS). Remote sensing images are textured and have repetitive structures. Motivated by nonlocal patch-based methods for image restoration, we propose a convex regularizer that (i) takes into account long-distance correlations, (ii) penalizes patch variation, which is more effective than pixel variation for capturing texture information, and (iii) uses the higher spatial resolution image as a guide image for weight computation. We come up with an efficient ADMM algorithm for optimizing the regularizer along with a standard least-squares loss function derived from the imaging model. The novelty of our algorithm is that by expressing patch variation as filtering operations and by judiciously splitting the original variables and introducing latent variables, we are able to solve the ADMM subproblems efficiently using FFT-based convolution and soft-thresholding. As far as the reconstruction quality is concerned, our method is shown to outperform state-of-the-art variational and deep learning techniques.

A prison term prediction model based on fact descriptions by capturing long historical information

The legal judgments are always based on the description of the case, the legal document. However, retrieving and understanding large numbers of relevant legal documents is a time-consuming task for legal workers. The legal judgment prediction (LJP) focus on applying artificial intelligence technology to provide decision support for legal workers. The prison term prediction(PTP) is an important task in LJP which aims to predict the term of penalty utilizing machine learning methods, thus supporting the judgement. Long-Short Term Memory(LSTM) Networks are a special type of Recurrent Neural Networks(RNN) that are capable of handling long term dependencies without being affected by an unstable gradient. Mainstream RNN models such as LSTM and GRU can capture long-distance correlation but training is time-consuming, while traditional CNN can be trained in parallel but pay more attention to local information. Both have shortcomings in case description prediction. This paper proposes a prison term prediction model for legal documents. The model adds causal expansion convolution in general TextCNN to make the model not only limited to the most important keyword segment, but also focus on the text near the key segments and the corresponding logical relationship of this paragraph, thereby improving the predicting effect and the accuracy on the data set. The causal TextCNN in this paper can understand the causal logical relationship in the text, especially the relationship between the legal text and the prison term. Since the model uses all CNN convolutions, compared with traditional sequence models such as GRU and LSTM, it can be trained in parallel to improve the training speed and can handling long term. So causal convolution can make up for the shortcomings of TextCNN and RNN models. In summary, the PTP model based on causality is a good solution to this problem. In addition, the case description is usually longer than traditional natural language sentences and the key information related to the prison term is not limited to local words. Therefore, it is crucial to capture substantially longer memory for LJP domains where a long history is required. In this paper, we propose a Causality CNN-based Prison Term Prediction model based on fact descriptions, in which the Causal TextCNN method is applied to build long effective history sizes (i.e., the ability for the networks to look very far into the past to make a prediction) using a combination of very deep networks (augmented with residual layers) and dilated convolutions. The experimental results on a public data show that the proposed model outperforms several CNN and RNN based baselines.

Carboniferous biostratigraphy of rugose corals

Abstract Rugose corals are one of the major fossil groups in shallow-water environments. They played an important role in dividing and correlating Carboniferous strata during the last century, when regional biostratigraphic schemes were established, and may be useful for long-distance correlation. Carboniferous rugose corals document two evolutionary events. One is the Tournaisian recovery event, with abundant occurrences of typical Carboniferous rugose corals such as columellate taxa and a significant diversification of large, dissepimented corals. The other is the changeover of rugose coral composition at the mid-Carboniferous boundary, which is represented by the disappearance of many large dissepimented taxa with complex axial structures and the appearance of typical Pennsylvanian taxa characterized by compound rugose taxa. The biostratigraphic scales for rugose corals show a finer temporal resolution in the Mississippian than in the Pennsylvanian, which was probably caused by the Late Paleozoic Ice Age that resulted in glacial–eustatic changes and a lack of continuous Pennsylvanian carbonate strata. The Pennsylvanian rugose corals are totally missing in the Cimmerian Continent. High-resolution biostratigraphy of rugose corals has so far only been achieved in few regions for the Mississippian timescale. In most regions, more detailed taxonomic work and precise correlations between different fossil groups are needed.

Seq-SetNet: directly exploiting multiple sequence alignment for protein secondary structure prediction.

Accurate prediction of protein structure relies heavily on exploiting multiple sequence alignment (MSA) for residue mutations and correlations as this information specifies protein tertiary structure. The widely used prediction approaches usually transform MSA into inter-mediate models, say position-specific scoring matrix or profile hidden Markov model. These inter-mediate models, however, cannot fully represent residue mutations and correlations carried by MSA; hence, an effective way to directly exploit MSAs is highly desirable. Here, we report a novel sequence set network (called Seq-SetNet) to directly and effectively exploit MSA for protein structure prediction. Seq-SetNet uses an 'encoding and aggregation' strategy that consists of two key elements: (i) an encoding module that takes a component homologue in MSA as input, and encodes residue mutations and correlations into context-specific features for each residue; and (ii) an aggregation module to aggregate the features extracted from all component homologues, which are further transformed into structural properties for residues of the query protein. As Seq-SetNet encodes each homologue protein individually, it could consider both insertions and deletions, as well as long-distance correlations among residues, thus representing more information than the inter-mediate models. Moreover, the encoding module automatically learns effective features and thus avoids manual feature engineering. Using symmetric aggregation functions, Seq-SetNet processes the homologue proteins as a sequence set, making its prediction results invariable to the order of these proteins. On popular benchmark sets, we demonstrated the successful application of Seq-SetNet to predict secondary structure and torsion angles of residues with improved accuracy and efficiency. The code and datasets are available through https://github.com/fusong-ju/Seq-SetNet. Supplementary data are available at Bioinformatics online.

Entanglement Spheres and a UV-IR Connection in Effective Field Theories.

We show that long-distance quantum correlations probe short-distance physics. Two disjoint regions of the latticized, massless scalar field vacuum are numerically demonstrated to become separable at distances beyond the negativity sphere, which extends to infinity in the continuum limit. The size of this quantum coherent volume is determined by the highest momentum mode supported in the identical regions, each of diameter d. More generally, effective field theories (EFTs), describing a system up to a given momentum scale Λ, are expected to share this feature-entanglement between regions of the vacuum depends upon the UV completion beyond a separation proportional to Λ. Through calculations extended to three dimensions, the magnitude of the negativity at which entanglement becomes sensitive to UV physics in an EFT (lattice or otherwise) is conjectured to scale as ∼e^{-Λd}, independent of the number of spatial dimensions. It is concluded that two-region vacuum entanglement at increasing separations depends upon the structure of the theory at increasing momentum scales. This phenomenon may be manifest in perturbative QCD processes.

Cross-scale global attention feature pyramid network for person search

Person search aims to locate the target person in real unconstrained scene images. It faces many challenges such as multi-scale and fine-grained. To address the challenges, a novel cross-scale global attention feature pyramid network (CSGAFPN) is proposed. Firstly, we design a novel multi-head global attention module (MHGAM), which adopts cosine similarity and sparse query location methods to effectively capture cross-scale long-distance dependence. Then, we design the CSGAFPN, which extends top-down feature pyramid network with bottom-up connections and embeds MHGAMs to the connections. CSGAFPN can capture cross-scale long-distance global correlation from multi-scale feature maps, selectively strengthen important features and restrain less important features. CSGAFPN is applied for both person detection and person re-identification (reID) subtasks of person search, it can well handle the multi-scale and fine-grained challenges, and significantly improve person search performance. Furthermore, the output multi-scale feature maps of CSGAFPN are processed by an adaptive feature aggregation with attention (AFAA) layer to further improve the performance. Numerous experiments with two public person search datasets, CUHK-SYSU and PRW, show our CSGAFPN based approach acquires better performance than other state-of-the-art (SOTA) person search approaches.

Global Carboniferous brachiopod biostratigraphy

Abstract We present an updated look at Carboniferous brachiopod biozonation from most of the world framed into a revised Carboniferous palaeogeography, based on a selection of the literature published on Carboniferous brachiopods since the nineteenth century. The biostratigraphic significance of the most important brachiopod taxa is synthesized in seven geographical correlations. The Mississippian is characterized by rich brachiopod faunas, with widespread taxa with a good potential for global correlation, such as Rugosochonetes , Delepinea , Buxtonia , Antiquatonia , Spinocarinifera , Marginatia , Fluctuaria , Ovatia , Rhipidomella , Lamellosathyris , Unispirifer , Tylothyris and Syringothyris . From the mid-Visean to the late Serpukhovian, taxa of gigantoproductidines are biostratigraphically significant, and occur everywhere except South America and Australia, which remain as distinct faunal successions for most of the period. A major turnover occurs at the beginning of the Pennsylvanian, characterized by a higher degree of provincialism. Pennsylvanian brachiopod faunas are diverse in China, Russia and North America, but otherwise they are less developed and are characterized mostly by endemic taxa, hampering long-distance correlation. An exception is the rapid diversification of taxa of the Choristitinae, which were widespread from the Bashkirian to the Moscovian, allowing long-distance correlation.

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer's Disease.

The early diagnosis of Alzheimer’s disease (AD) can allow patients to take preventive measures before irreversible brain damage occurs. It can be seen from cross-sectional imaging studies of AD that the features of the lesion areas in AD patients, as observed by magnetic resonance imaging (MRI), show significant variation, and these features are distributed throughout the image space. Since the convolutional layer of the general convolutional neural network (CNN) cannot satisfactorily extract long-distance correlation in the feature space, a deep residual network (ResNet) model, based on spatial transformer networks (STN) and the non-local attention mechanism, is proposed in this study for the early diagnosis of AD. In this ResNet model, a new Mish activation function is selected in the ResNet-50 backbone to replace the Relu function, STN is introduced between the input layer and the improved ResNet-50 backbone, and a non-local attention mechanism is introduced between the fourth and the fifth stages of the improved ResNet-50 backbone. This ResNet model can extract more information from the layers by deepening the network structure through deep ResNet. The introduced STN can transform the spatial information in MRI images of Alzheimer’s patients into another space and retain the key information. The introduced non-local attention mechanism can find the relationship between the lesion areas and normal areas in the feature space. This model can solve the problem of local information loss in traditional CNN and can extract the long-distance correlation in feature space. The proposed method was validated using the ADNI (Alzheimer’s disease neuroimaging initiative) experimental dataset, and compared with several models. The experimental results show that the classification accuracy of the algorithm proposed in this study can reach 97.1%, the macro precision can reach 95.5%, the macro recall can reach 95.3%, and the macro F1 value can reach 95.4%. The proposed model is more effective than other algorithms.

Characterization of Cenomanian-Turonian oxic facies in Tunisia: sequence stratigraphy and correlation of recorded bioevents

On the southwestern Tethyan Margin of Tunisia, the well-known anoxic black shale facies of the Cenomanian-Turonian transition contrasts with oxic fossiliferous carbonates, first characterized here as lateral equivalents. In terms of sequence stratigraphy, the analysis of four wisely sampled sections in Central and Southern Tunisia led to interpret these deposits as a transgressive interval (TST). This interval spans the Whiteinella archaeocretacea Zone of foraminifera and is capped with a glauconite-rich highly bioturbated maximum flooding surface (MFS). In the reference section of Oued Bahloul and the Kalaat Senan area of North-Central Tunisia, these deposits overlay a Shelf Margin Wedge made of conglomeratic and bioclastic limestones. In South-Central and Southern Tunisia, the TST is characterized by the onset of oxic facies relaying laminated carbonates with local emergence surfaces. The analysis of both oxic and anoxic facies from the Cenomanian-Turonian transition allows identifying five successive bioevent markers, known elsewhere within the Tethyan domain: the extinction of the foraminifera genus Rotalipora (or Thomasinella), the Heterohelix shift, the Whiteinella proliferation, the filament event, and the appearance of Helvetotruncana helvetica. Among these bioevents, the Heterohelix shift coincides with the transgressive surface, while the filament event announces the maximal flooding surface. These Cenomanian-Turonian transition bioevents are of a particular relevance for regional and long-distance high-resolution correlations.

Upper Berriasian chemostratigraphy, clay minerals and calcareous nannofossils of the Barlya section (Western Balkan, Bulgaria): Implications for palaeoclimate and productivity changes, and stratigraphic correlations across the Alpine Tethys

Upper Berriasian chemostratigraphic, clay mineral and calcareous nanofossil data are presented from a precisely dated hemipelagic section of Barlya (Western Balkan, Bulgaria). The section covers an interval from the upper part of the lower Berriasian (Calpionella elliptica Subzone, magnetozone M17r) to the lowermost Valanginian (Calpionellites darderi Subzone, magnetozone M14r). The study aims to reconstruct the major palaeoenvironmental changes (variations in lithogenic input, palaeoredox and palaeoproductivity) and their relation to palaeoclimate and regional tectonic regime, as well as their application to stratigraphic correlations with the Vocontian Basin and Jura Mts. A long-term increase in terrigenous input during the late Berriasian was controlled mostly by the orogenic activity in the NeoTethyan Collision Zone and to a lesser degree by climate humidification, as revealed by variations in kaolinite content and in lithogenic proxies (Ti/K, Th/K, Ti/Al and Zr/Rb ratios). A good correlation is observed between geochemical palaeoproductivity proxies (sedimentation rates of non-detrital (excess or authigenic) portions of P, Zn and Cd) and nannofossil fluxes, determined as the total abundance and species richness. Major calcareous nannofossil peaks, represented by high-diversity and high-abundance nannofossil assemblages, fall within the low-productivity intervals. A smaller peak formed by a low-diversity and high-abundance assemblage, dominated by Watznaueria barnesiae/fossacincta, coincides with the variable, but mostly high-productivity interval, which indicates high plasticity of Watznaueria concerning to environmental conditions. Additionally, trophic changes seem to correspond to bulk rock carbon-isotopic composition, with rising δ13Ccarb values in more oligotrophic intervals. This offers perspectives for long-distance chemostratigraphic correlations between pelagic and platform sections, supplementing traditional schemes based on bio- and sequence stratigraphy. A holostratigraphic correlation is proposed between the Western Balkan (Barlya section), Vocontian Basin (Berrias and Monclus sections) and Jura Mts (La Chambotte section) based on bio-, magnetic and carbon-isotope stratigraphy, as well as climatic and palaeoproductivity proxies.

Altered impact glass from the Devonian Norwood Member of the Antrim Shale, southeast Michigan

The presence of microtektites, indicative of a single short-lived event, may be a useful tool for constraining the timing of barren and monotonous beds thereby enabling more accurate long-distance correlation. While the Paleozoic Laurentian epeiric seas have been studied for more than a century, their geologically complex histories, large areal expanse, and seeming monotony of contained barren stratigraphic units precludes simple extrabasinal correlation. Recent investigations of the Upper Devonian succession in western New York have yielded evidence of extraterrestrial impact events in the form of microtektites. Geothermal well spoils from the coeval Antrim Shale (Norwood Member) of the Michigan Basinwere collected and completely digested. Sediments retrieved from these spoils (shale, clay, and limestone) contain four genera of benthic foraminifera, algal cysts, and other microfossils confirming a Devonian age. The sediments also contain microspherules inferred to be microtektites similar to those recovered from western New York and are potentially evidence of a large impact event. The microspherules may be a useful inter-basin marker event within the Late Devonian beds in North America that has gone previously unreported.

Analyzing In Silico the Relationship Between the Activation of the Edema Factor and Its Interaction With Calmodulin.

Molecular dynamics (MD) simulations have been recorded on the complex between the edema factor (EF) of Bacilllus anthracis and calmodulin (CaM), starting from a structure with the orthosteric inhibitor adefovir bound in the EF catalytic site. The starting structure has been destabilized by alternately suppressing different co-factors, such as adefovir ligand or ions, revealing several long-distance correlations between the conformation of CaM, the geometry of the CaM/EF interface, the enzymatic site and the overall organization of the complex. An allosteric communication between CaM/EF interface and the EF catalytic site, highlighted by these correlations, was confirmed by several bioinformatics approaches from the literature. A network of hydrogen bonds and stacking interactions extending from the helix V of of CaM, and the residues of the switches A, B and C, and connecting to catalytic site residues, is a plausible candidate for the mediation of allosteric communication. The greatest variability in volume between the different MD conditions was also found for cavities present at the EF/CaM interface and in the EF catalytic site. The similarity between the predictions from literature and the volume variability might introduce the volume variability as new descriptor of allostery.

Real-time warm pions from the lattice using an effective theory

Lattice measurements provide adequate information to fix the parameters of long-distance effective field theories in Euclidean time. Using such a theory, we examine the analytic continuation of long-distance correlation functions of composite operators at finite temperature from Euclidean to Minkowski space–time. We show through an explicit computation that the analytic continuation of the pion correlation function is possible and gives rise to nontrivial effects. Among them is the possibility, supported by lattice computations of Euclidean correlators, that long distance excitations can be understood in terms of (very massive) pions even at temperatures higher than the QCD crossover temperature.

Statistical characterization and reconstruction of heterogeneous microstructures using deep neural network

Heterogeneous materials, whether natural or artificial, are usually composed of distinct constituents present in complex microstructures with discontinuous, irregular and hierarchical characteristics. For many heterogeneous materials, such as porous media and composites, the microstructural features are of fundamental importance for their macroscopic properties. This paper presents a novel method to statistically characterize and reconstruct random microstructures through a deep neural network (DNN) model, which can be used to study the microstructure–property relationships. In our method, the digital microstructure image is assumed to be a stationary Markov random field (MRF), and local patterns covering the basic morphological features are collected to train a DNN model, after which statistically equivalent samples can be generated through a DNN-guided reconstruction procedure. Furthermore, to overcome the short-distance limitation associated with the MRF assumption, a multi-level approach is developed to preserve the long-distance morphological features of heterogeneous microstructures. A large number of tests have been conducted to compare the reconstructed and target microstructures in both morphological characteristics and physical properties, and good agreements are observed in all test cases. The proposed method is efficient, accurate, versatile, and especially beneficial to the statistical reconstruction of 2D/3D microstructures with long-distance correlations.

Identification and characterization of topological structures of turbulence in magnetic confined plasmas

Transport in turbulent magnetic plasmas is closely related to the flow topological structures. These are mainly filamentary vortices linked to the magnetic filed lines and zonal flows generated from turbulence. Their identification in the experiments is not yet clear. In this work, resistive magneto-hydrodynamics simulations are used to generate turbulent flows in a plasma. The topological structures obtained from the simulations are analyzed using three different techniques: long-distance correlations, transfer of entropy and intermittence. The combination of the results using these techniques allows us to identify and characterize vortex structures and zonal flows.

Aqueous solutions of hydroxyl-functionalized ionic liquids: Molecular dynamics studies

A series of aqueous solutions of 1-(n-hydroxyalkyl)-3-(n-hydroxyalkyl) imidazolium bromide ([HOCnCmOHIm][Br], with n and m = 2, 6,10 and 14) were studied by atomistic molecular dynamics simulations. Structural properties were characterized by the radial distribution functions between different pairs, angular distributions and aggregation numbers. Dynamics of the system has been investigated by computing the diffusion of the ions and molecules. Structures of the aggregates formed depend upon the length of the hydroxyalkyl chains. The long-distance spatial correlations observed in solutions with cations having long chain substituent are arising due to the formation of intercalated structures. A thin film like structure is formed in solutions having longer hydroxyalkyl chains, with the structure stabilized by the dispersion interactions between the interdigitated alkyl chains and the hydrogen bond formation between the hydroxyl group of a cation with head group of a different cation. Anions are dispersed near the surface of the film.

Berriasian ammonites of supposed Tethyan origin from the type ‘Ryazanian’, Russia: a systematic re-interpretation

For more than a century, a number of ammonite taxa of supposed Tethyan origin or affinity have been reported from the Berriasian condensed deposits of Russia (referred to as ‘Ryazanian’). These occurrences have been used to constrain long-distance correlation and palaeobiogeographic interpretation of the Russian Platform during the earliest Cretaceous. We revise these taxa herein. To accommodate the systematic issues, we introduce a new ammonite genus: Mittaites n. gen. (type species: Mazenoticeras ceccai). We also provide re-assessment for the genera Tauricoceras (= Subriasanites), Riasanella, Riasanites, Prorjasanites, and Karasyazites. Considering the strong affinities between these genera (except for Karasyazites), resctricted palaeobiogeographic distribution and a problematic phyletic origin, we erect a new family Riasanitidae n. fam. Our re-examination suggests that the occurrence of western Tethyan migrants in the type ‘Ryazanian’ should be ruled out. Pending new investigation, correlation of the ammonites of the type ‘Ryazanian’ beds with the Berriasian part of the Standard Mediterranean Ammonite Scale (SMAS) should be treated with caution.

CFSP: a collaborative frequent sequence pattern discovery algorithm for nucleic acid sequence classification.

BackgroundConserved nucleic acid sequences play an essential role in transcriptional regulation. The motifs/templates derived from nucleic acid sequence datasets are usually used as biomarkers to predict biochemical properties such as protein binding sites or to identify specific non-coding RNAs. In many cases, template-based nucleic acid sequence classification performs better than some feature extraction methods, such as N-gram and k-spaced pairs classification. The availability of large-scale experimental data provides an unprecedented opportunity to improve motif extraction methods. The process for pattern extraction from large-scale data is crucial for the creation of predictive models.MethodsIn this article, a Teiresias-like feature extraction algorithm to discover frequent sub-sequences (CFSP) is proposed. Although gaps are allowed in some motif discovery algorithms, the distance and number of gaps are limited. The proposed algorithm can find frequent sequence pairs with a larger gap. The combinations of frequent sub-sequences in given protracted sequences capture the long-distance correlation, which implies a specific molecular biological property. Hence, the proposed algorithm intends to discover the combinations. A set of frequent sub-sequences derived from nucleic acid sequences with order is used as a base frequent sub-sequence array. The mutation information is attached to each sub-sequence array to implement fuzzy matching. Thus, a mutate records a single nucleotide variant or nucleotides insertion/deletion (indel) to encode a slight difference between frequent sequences and a matched subsequence of a sequence under investigation.ConclusionsThe proposed algorithm has been validated with several nucleic acid sequence prediction case studies. These data demonstrate better results than the recently available feature descriptors based methods based on experimental data sets such as miRNA, piRNA, and Sigma 54 promoters. CFSP is implemented in C++ and shell script; the source code and related data are available at https://github.com/HePeng2016/CFSP.

Quantum phases of the 1D anisotropic spin-1/2 frustrated ferromagnetic model: view point of quantum correlations

We have studied the ground state phases of the one-dimensional (1D) spin-1/2 anisotropic frustrated ferromagnetic model, using the numerical Lanczos method. We have focused on the quantum correlations as the concurrence and the quantum discord (QD) between the nearest neighbor (NN) and the next-nearest neighbor (NNN) spins. Numerical results show that the Tomonaga-Luttinger liquid (TLL), the even-parity dimer, and the vector chiral phases can be distinguished from each other using the long-distance quantum correlations. Specially, the critical points are explicitly detected in the first derivative of the concurrence between the nearest neighbor spins with respect to the frustration parameter.

Space-independent community and hub structure of functional brain networks

Coordinated brain activity reflects underlying cognitive processes and can be modeled as a network of inter-regional functional connections. The most costly connections in the network are long-distance correlations that, in the absence of underlying structural connections, are maintained by sustained energetic inputs. Here, we present a spatial modeling approach that amplifies contributions made by long-distance functional connections to whole-brain network architecture, while simultaneously suppressing contributions made by short-range connections. We use this method to characterize the long-distance architecture of functional networks and to identify aspects of community and hub structure that are driven by long-distance correlations and that, we argue, are of greater functional significance. We find that based only on patterns of long-distance connectivity, primary sensory cortices occupy increasingly central positions and appear more “hub-like”. Additionally, we show that the community structure of long-distance connections spans multiple topological levels and differs from the community structure detected in networks that include both short-range and long-distance connections. In summary, these findings highlight the complex relationship between the brain’s physical layout and its functional architecture. The results presented here inform future analyses of community structure and network hubs in health, across development, and in the case of neuropsychiatric disorders.