Generate DSLR-Like Image With Global Information and Prior Guided ISP

Recent studies argue that strongly-motivated positive emotions (e.g. desire) narrow a scope of attention. This argument is mainly based on an observation that, while humans normally respond faster to global than local information of a visual stimulus (global advantage), positive affects eliminated the global advantage by selectively speeding responses to local (but not global) information. In other words, narrowing of attentional scope was indirectly evidenced by the elimination of global advantage (the same speed of processing between global and local information). No study has directly shown that strongly-motivated positive affects induce faster responses to local than global information while excluding a bias for global information (global advantage) in a baseline (emotionally-neutral) condition. In the present study, we addressed this issue by eliminating the global advantage in a baseline (neutral) state. Induction of positive affects under this state resulted in faster responses to local than global information. Our results provided direct evidence that positive affects in high motivational intensity narrow a scope of attention.

Recent few years have witnessed a growing interest in exploring the dynamical interplay between awareness and epidemic transmission within the framework of multiplex networks. However, both local and global information have significant impacts on individual awareness and behavior, which have not been adequately characterized in the existing works. To this end, we propose a local and global information controlled spreading model to explore the dynamics of two spreading processes. In the upper layer, we construct a threshold model to describe the awareness diffusion process and introduce local and global awareness information as variables into an individual awareness ratio. In the lower layer, we adopt the classical susceptible-infected-susceptible model to represent the epidemic propagation process and introduce local and global epidemic information into individual precaution degree to reflect individual heterogeneity. Using the microscopic Markov chain approach, we theoretically derive the threshold for epidemic outbreaks. Our findings suggest that the local and global information can motivate individuals to increase self-protection awareness and take more precaution measures, thereby reducing disease infection probability and suppressing the spread of epidemics.

Local and global information affect cooperation in networked Prisoner’s dilemma games

When only stereoscopic information is available, the slant of a single isolated surface around a vertical axis is often greatly underestimated. If two small objects (probes), separated horizontally by several degrees, are displayed in front of such a surface, the depth of the probes is perceived relative to the perceived slant of the background surface, leading to systematic misperception of the point of subjective equality (PSE) of the distances of the probes from the observer (Mitchison and Westheimer, 1984 Vision Research24 1063 – 1073; Gillam et al, 1993 Perception22 Supplement, 35). In the present study we found that when we added global perspective information to the background surface, thus increasing its perceived slant, this substantially improved the PSE of the probes. Alternatively, when we added frontal stereoscopic surfaces above and below the background surface, thus providing gradients of disparity discontinuities across the surface boundaries, this also produced an improvement in the perceived slant of the background surface, but produced an even greater improvement in the PSE of the probes. These results imply that the local stereoscopic information specifying the depth of each probe relative to the background surface is integrated with the perceived slant of the background surface, whether specified by global stereoscopic information or by global perspective information, to determine the relative depth of the probes. This integration of local stereoscopic information with global slant information appears to be more complete, however, when the global information is provided by stereopsis rather than by perspective.

We introduce a novel machine learning ensemble architecture for anomaly detection, that exploits global and local information from a 1d time series. A double step validation is performed to decide if a time period is anomalous: from one side a Long Short-Term Memory is trained to be reliable at forecasting, hence a parametric test on the forecasting’s error is used spot the anomalies. Concurrently, a Variational Autoencoder is trained to compress both global and local information from the series to a low-dimensional normal distribution, raising an anomaly if a time step’s likelihood is below a threshold. While anomaly detection with deep learning techniques often comes with the assumption that forecasting error is gaussian, we prove that this is in general a wrong assumption: we show that error function is better approximated by a distribution chosen dynamically. We validate our work on some public physical datasets, outperforming the current deep learning methods in terms of precision and recall.We introduce a novel machine learning ensemble architecture for anomaly detection, that exploits global and local information from a 1d time series. A double step validation is performed to decide if a time period is anomalous: from one side a Long Short-Term Memory is trained to be reliable at forecasting, hence a parametric test on the forecasting’s error is used spot the anomalies. Concurrently, a Variational Autoencoder is trained to compress both global and local information from the series to a low-dimensional normal distribution, raising an anomaly if a time step’s likelihood is below a threshold. While anomaly detection with deep learning techniques often comes with the assumption that forecasting error is gaussian, we prove that this is in general a wrong assumption: we show that error function is better approximated by a distribution chosen dynamically. We validate our work on some public physical datasets, outperforming the current deep learning methods in terms of precision and recall.

GID: Global information distillation for medical semantic segmentation

This study investigated how global and local information about attentional demands influence attentional control, with a special interest in whether one information source dominates when they conflict. In Experiment 1, we manipulated proportion congruence in two blocks (i.e., mostly congruent versus mostly incongruent) of a Stroop task to create different global demands (i.e., low versus high, respectively). Additionally, we created different local demands by embedding 10-trial lists in each block that varied in their proportion congruence (10% to 90% congruent), and half the lists were preceded by a valid precue explicitly informing participants of upcoming attentional demands. Stroop effects were smaller in mostly incongruent compared with mostly congruent blocks demonstrating the influence of global information. Stroop effects also varied according to the proportion congruence of the abbreviated lists and differed between cued and uncued lists (i.e., cueing effect), demonstrating the influence of local information. Critically, we found that global and local information interacted, such that the cueing effect differed between the two blocks. While there was evidence that participants used the precue to relax control for mostly congruent lists within the mostly congruent block, the cueing effect was absent within the mostly incongruent block. In Experiment 2, we replicated the latter pattern and thereby provided further evidence that participants do not use local precues to relax control when attentional demands are globally high. The findings suggest that both global and local information sources influence the control of attention, and global information dominates local expectations when the information sources collide.

Person re-identification has been extensively studied in recent years and has made great progress. Many papers propose a lot of effective methods to improve the accuracy of the person re-identification. However, there are still many problems that remain unsolved. For example, persons are often occluded by obstacles or other persons, leading to loss of the complete person information, and changes in person behaviors or postures make it difficult to identify. In this paper, we propose a person re-identification algorithm that repeatedly uses global feature information and local feature information for mutual supervised learning. The algorithm consists of two parts, person alignment branch and spatial channel feature branch. First, for person alignment branch, we use global feature information and local feature information to correct misaligned person pictures, and calculate the shortest distance to match the right part of the images. For the spatial channel feature branch, the spatial features are segmented to obtain the local feature information of the person image. At the same time, the spatial feature information is extended using the convolution layer and divided to obtain global feature information of the person image. The global feature information and local feature information are used to calculate the spatial channel feature loss. So that the network can learn better discriminative features through the global information and local information repeatedly. The experimental results show that, on the market-1501 and Duke datasets, the algorithm in this paper obtains good experimental results, has strong robustness, and has greatly improved the rate compared with the existing person re-identification algorithms.

Semantic similarity evaluation is used in various fields such as question-and-answering and plagiarism testing, and many studies have been conducted into this problem. In previous studies using neural networks to evaluate semantic similarity, similarity has been measured using global information of sentence pairs. However, since sentences do not only have one meaning but a variety of meanings, using only global information can have a negative effect on performance improvement. Therefore, in this study, we propose a model that uses global information and local information simultaneously to evaluate the semantic similarity of sentence pairs. The proposed model can adjust whether to focus more on global information or local information through a weight parameter. As a result of the experiment, the proposed model can show that the accuracy is higher than existing models that use only global information.

The visual environment consists of global structures (e.g., a forest) made up of local parts (e.g., trees). When compound stimuli are presented (e.g., large global letters composed of arrangements of small local letters), the global unattended information slows responses to local targets. Using a negative priming paradigm, we investigated whether inhibition is required to process hierarchical stimuli when information at the local level is in conflict with the one at the global level. The results show that when local and global information is in conflict, global information must be inhibited to process local information, but that the reverse is not true. This finding has potential direct implications for brain models of visual recognition, by suggesting that when local information is conflicting with global information, inhibitory control reduces feedback activity from global information (e.g., inhibits the forest) which allows the visual system to process local information (e.g., to focus attention on a particular tree).

Due to the complexity of molecules, molecular learning requires a large amount of molecular data. However, labeled data is typically limited, making self-supervised pretraining methods essential. Despite this, current pretraining methods often fail to sufficiently focus on both local and global molecular information. In this study, we propose a multi-modality self-supervised learning framework that simultaneously captures local and global information. Specifically, we encode SMILES sequences and molecular graphs separately and use a unified fusion approach to strengthen the interaction between the two modalities. Moreover, in the molecular graph encoding, we independently capture global and local information, and enhance the attention to bond features through information fusion. Additionally, we introduce the FA-FFN module to aggregate periodic features of the molecule. Experimental results show that MoleTGL exhibits superior performance compared to existing methods on seven classification tasks and six regression tasks related to molecular property prediction, and ablation studies confirm the effectiveness of local and global feature fusion and the superiority of the methods for acquiring local and global information.

Most objects in our environment are organized hierarchically with a global whole embedding its local parts, but the way we recognize these features remains unclear. Using a visual masking paradigm, we examined the temporal dissociation between global and local feature as proposed in Reverse Hierarchy Theory, RHT (), where global and local information are assumed to be processed, respectively, by feed-forward and feedback systems. We found that in a long Stimulus Onset Asynchrony (SOA) condition, both global and local information were recognized adequately. However, in a short SOA condition, global information was recognized correctly while local recognition was critically disrupted. Consistent with RHT, it is suggested that local information is processed in a feedback system; this processing is then interrupted by the mask stimulus presented later at the primary visual area. Global information, by contrast, is transferred via a feed-forward system, and is not disrupted by the mask.

Foreign investors and the delay of information dissemination in the Korean stock market

Nodal spreading influence is the capability of a node to activate the rest of the network when it is the seed of spreading. Combining nodal properties (centrality metrics) derived from local and global topological information respectively has been shown to better predict nodal influence than using a single metric. In this work, we investigate to what extent local and global topological information around a node contributes to the prediction of nodal influence and whether relatively local information is sufficient for the prediction. We show that by leveraging the iterative process used to derive a classical nodal centrality such as eigenvector centrality, we can define an iterative metric set that progressively incorporates more global information around the node. We propose to predict nodal influence using an iterative metric set that consists of an iterative metric from order 1 to K produced in an iterative process, encoding gradually more global information as K increases. Three iterative metrics are considered, which converge to three classical node centrality metrics, respectively. In various real-world networks and synthetic networks with community structures, we find that the prediction quality of each iterative based model converges to its optimal when the metric of relatively low orders (K∼4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$K\\sim 4$$\\end{document}) are included and increases only marginally when further increasing K. This fast convergence of prediction quality with K is further explained by analyzing the correlation between the iterative metric and nodal influence, the convergence rate of each iterative process and network properties. The prediction quality of the best performing iterative metric set with K=4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$K=4$$\\end{document} is comparable with the benchmark method that combines seven centrality metrics: their prediction quality ratio is within the range [91%,106%]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$[91\\%,106\\%]$$\\end{document} across all three quality measures and networks. In two spatially embedded networks with an extremely large diameter, however, iterative metric of higher orders, thus a large K, is needed to achieve comparable prediction quality with the benchmark.

A key issue in current computer-aided diagnostic (CAD) schemes for nodule detection in CT is the large number of false positives, because current schemes use only global three-dimensional (3D) information to detect nodules and discard useful local two-dimensional (2D) information. Thus, the authors integrated local and global information to markedly improve the performance levels of CAD schemes. Our database was obtained from the standard CT lung nodule database created by the Lung Image Database Consortium (LIDC). It consisted of 85 CT scans with 111 nodules of 3 mm or larger in diameter. The 111 nodules were confirmed by at least two of the four radiologists participated in the LIDC. Twenty-six nodules were missed by two of the four radiologists and were thus very difficult to detect. The authors developed five CAD schemes for nodule detection in CT using global 3D information (3D scheme), local 2D information (2D scheme), and both local and global information (2D + 3D scheme, 2D - 3D scheme, and 3D - 2D scheme). The 3D scheme, which was developed previously, used only global 3D information and discarded local 2D information, as other CAD schemes did. The 2D scheme used a uniform viewpoint reformation technique to decompose a 3D nodule candidate into a set of 2D reformatted images generated from representative viewpoints, and selected and used "effective" 2D reformatted images to remove false positives. The 2D + 3D scheme, 2D - 3D scheme, and 3D - 2D scheme used complementary local and global information in different ways to further improve the performance of lung nodule detection. The authors employed a leave-one-scan-out testing method for evaluation of the performance levels of the five CAD schemes. At the sensitivities of 85%, 80%, and 75%, the existing 3D scheme reported 17.3, 7.4, and 2.8 false positives per scan, respectively; the 2D scheme improved the detection performance and reduced the numbers of false positives to 7.6, 2.5, and 1.3 per scan; the 2D + 3D scheme further reduced those to 2.7, 1.9, and 0.6 per scan; the 2D - 3D scheme reduced those to 7.6, 2.1, and 0.8 per scan; and the 3D - 2D scheme reduced those to 17.3, 1.6, and 1.0 per scan. The local 2D information appears to be more useful than the global 3D information for nodule detection, particularly, when it is integrated with 3D information.