759 research outputs found
Recognition of partially occluded threat objects using the annealed Hopefield network
Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. The neural network approach is suitable for the problems in the sense that the inherent parallelism of neural networks pursues many hypotheses in parallel resulting in high computation rates. Moreover, they provide a greater degree of robustness or fault tolerance than conventional computers. The annealed Hopfield network which is derived from the mean field annealing (MFA) has been developed to find global solutions of a nonlinear system. In the study, it has been proven that the system temperature of MFA is equivalent to the gain of the sigmoid function of a Hopfield network. In our early work, we developed the hybrid Hopfield network (HHN) for fast and reliable matching. However, HHN doesn't guarantee global solutions and yields false matching under heavily occluded conditions because HHN is dependent on initial states by its nature. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybird Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a neural network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from x-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features
Fronto-cerebellar connectivity mediating cognitive processing speed
Processing speed is an important construct in understanding cognition. This study was aimed to control task specificity for understanding the neural mechanisms underlying cognitive processing speed. Forty young adult subjects performed attention tasks of two modalities (auditory and visual) and two levels of task rules (compatible and incompatible). Block-design fMRI captured BOLD signals during the tasks. Thirteen regions of interest were defined with reference to publicly available activation maps for processing speed tasks. Cognitive speed was derived from task reaction times, which yielded six sets of connectivity measures. Mixed-effect LASSO regression revealed six significant paths suggestive of a cerebello-frontal network predicting the cognitive speed. Among them, three are long range (two fronto-cerebellar, one cerebello-frontal), and three are short range (fronto-frontal, cerebello-cerebellar, and cerebello-thalamic). The long-range connections are likely to relate to cognitive control, and the short-range connections relate to rule-based stimulus-response processes. The revealed neural network suggests that automaticity, acting on the task rules and interplaying with effortful top-down attentional control, accounts for cognitive speed
Whole-brain in-vivo measurements of the Axonal G-Ratio in a group of 37 healthy volunteers
The g-ratio, quantifying the ratio between the inner and outer diameters of a fiber, is an important microstructural characteristic of fiber pathways and is functionally related to conduction velocity. We introduce a novel method for estimating the MR g-ratio non-invasively across the whole brain using high-fidelity magnetization transfer (MT) imaging and single-shell diffusion MRI. These methods enabled us to map the MR g-ratio in vivo across the brain's prominent fiber pathways in a group of 37 healthy volunteers and to estimate the inter-subject variability. Effective correction of susceptibility-related distortion artifacts was essential before combining the MT and diffusion data, in order to reduce partial volume and edge artifacts. The MR g-ratio is in good qualitative agreement with histological findings despite the different resolution and spatial coverage of MRI and histology. The MR g-ratio holds promise as an important non-invasive biomarker due to its microstructural and functional relevance in neurodegeneration
Visibility graphs of random scalar fields and spatial data
The family of visibility algorithms were recently introduced as mappings
between time series and graphs. Here we extend this method to characterize
spatially extended data structures by mapping scalar fields of arbitrary
dimension into graphs. After introducing several possible extensions, we
provide analytical results on some topological properties of these graphs
associated to some types of real-valued matrices, which can be understood as
the high and low disorder limits of real-valued scalar fields. In particular,
we find a closed expression for the degree distribution of these graphs
associated to uncorrelated random fields of generic dimension, extending a well
known result in one-dimensional time series. As this result holds independently
of the field's marginal distribution, we show that it directly yields a
statistical randomness test, applicable in any dimension. We showcase its
usefulness by discriminating spatial snapshots of two-dimensional white noise
from snapshots of a two-dimensional lattice of diffusively coupled chaotic
maps, a system that generates high dimensional spatio-temporal chaos. We
finally discuss the range of potential applications of this combinatorial
framework, which include image processing in engineering, the description of
surface growth in material science, soft matter or medicine and the
characterization of potential energy surfaces in chemistry, disordered systems
and high energy physics. An illustration on the applicability of this method
for the classification of the different stages involved in carcinogenesis is
briefly discussed
Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems
Several strategies have been recently proposed in order to improve Monte
Carlo sampling efficiency using machine learning tools. Here, we challenge
these methods by considering a class of problems that are known to be
exponentially hard to sample using conventional local Monte Carlo at low enough
temperatures. In particular, we study the antiferromagnetic Potts model on a
random graph, which reduces to the coloring of random graphs at zero
temperature. We test several machine-learning-assisted Monte Carlo approaches,
and we find that they all fail. Our work thus provide good benchmarks for
future proposals for smart sampling algorithms
Longitudinal Changes of Structural and Functional Connectivity and Correlations with Neurocognitive Metrics
Revealing brain functional and micro-structural changes over a relatively short period at individual levels are especially important given that many risks associated with age including vascular and neuroinflammation increases and could confound the baseline fMRI parametric images. Cellular-level axonal injury and/or demyelination as well as dispersed mesoscopic level substance abnormal aggregation and structural/functional abnormality could occur in short subacute/acute phases, while literatures related to longitudinal changes with age are limited with only our previous fMRI findings. Longitudinal data were used to characterize these multi-parameters including random intercept and interval per individual. No significant age by gender interactions have been found to either DTI fractional anisotropy (FA) or diffusivity metrics. The interval effective regions showed longitudinal change of FA and radial diffusivity (RD)/axial diffusivity (AX) values remained similar to the aging results found with cross-sectional data. Significant correlations between DTI and fMRI metrics as well as between imaging and neurocognitive data including speed and memory were found. Our results indicate significant and consistent age, gender and apolipoprotein E (APOE) genotypic effects on structural and functional connectivity at both short-interval and cross-sectional ranges, together with correlational neurocognitive functions
Unsupervised learning of human motion
An unsupervised learning algorithm that can obtain a probabilistic model of an object composed of a collection of parts (a moving human body in our examples) automatically from unlabeled training data is presented. The training data include both useful "foreground" features as well as features that arise from irrelevant background clutter - the correspondence between parts and detected features is unknown. The joint probability density function of the parts is represented by a mixture of decomposable triangulated graphs which allow for fast detection. To learn the model structure as well as model parameters, an EM-like algorithm is developed where the labeling of the data (part assignments) is treated as hidden variables. The unsupervised learning technique is not limited to decomposable triangulated graphs. The efficiency and effectiveness of our algorithm is demonstrated by applying it to generate models of human motion automatically from unlabeled image sequences, and testing the learned models on a variety of sequences
RCD-SGD: Resource-Constrained Distributed SGD in Heterogeneous Environment via Submodular Partitioning
The convergence of SGD based distributed training algorithms is tied to the
data distribution across workers. Standard partitioning techniques try to
achieve equal-sized partitions with per-class population distribution in
proportion to the total dataset. Partitions having the same overall population
size or even the same number of samples per class may still have Non-IID
distribution in the feature space. In heterogeneous computing environments,
when devices have different computing capabilities, even-sized partitions
across devices can lead to the straggler problem in distributed SGD. We develop
a framework for distributed SGD in heterogeneous environments based on a novel
data partitioning algorithm involving submodular optimization. Our data
partitioning algorithm explicitly accounts for resource heterogeneity across
workers while achieving similar class-level feature distribution and
maintaining class balance. Based on this algorithm, we develop a distributed
SGD framework that can accelerate existing SOTA distributed training algorithms
by up to 32%.Comment: 6 pages and 3 figure
General Adaptive Neighborhood Image Processing. Part II: Practical Applications Issues
23 pagesInternational audienceThe so-called General Adaptive Neighborhood Image Processing (GANIP) approach is presented in a two parts paper dealing respectively with its theoretical and practical aspects. The General Adaptive Neighborhood (GAN) paradigm, theoretically introduced in Part I [20], allows the building of new image processing transformations using context-dependent analysis. With the help of a specified analyzing criterion, such transformations perform a more significant spatial analysis, taking intrinsically into account the local radiometric, morphological or geometrical characteristics of the image. Moreover they are consistent with the physical and/or physiological settings of the image to be processed, using general linear image processing frameworks. In this paper, the GANIP approach is more particularly studied in the context of Mathematical Morphology (MM). The structuring elements, required for MM, are substituted by GAN-based structuring elements, fitting to the local contextual details of the studied image. The resulting morphological operators perform a really spatiallyadaptive image processing and notably, in several important and practical cases, are connected, which is a great advantage compared to the usual ones that fail to this property. Several GANIP-based results are here exposed and discussed in image filtering, image segmentation, and image enhancement. In order to evaluate the proposed approach, a comparative study is as far as possible proposed between the adaptive and usual morphological operators. Moreover, the interests to work with the Logarithmic Image Processing framework and with the 'contrast' criterion are shown through practical application examples
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