New brain atlas method improves classification accuracy.
problem Creating accurate brain atlases from connectomes.
method Connectivity-based hierarchical clustering and consensus aggregation.
result Consensus parcellation outperforms existing atlases in classification tasks.
Detects parking spaces in parcels using satellite images.
problem Locating parking spaces in urban areas using satellite imagery.
method Used Feature Pyramid based Mask RCNN for localizing parking spaces and vehicles in parking lots.
result Average class accuracy of 97.56% for parking spaces and vehicles.
Paper detects anomalies in wheat and rapeseed crops using satellite data.
problem Detecting anomalies in crop development at parcel-level.
method Unsupervised outlier detection using SAR and multispectral features.
result Best performance with a 10% outlier ratio, achieving 94.1% true positives for rapeseed and 95.5% for wheat.
Ensemble learning improves rs-fMRI predictions using 3D CNNs.
problem Improving specificity and sensitivity of rs-fMRI measurements through better parcellation schemes.
method Ensemble learning with 3D CNNs to combine predictions from different parcellations.
result Ensemble learning with 3D CNNs outperforms traditional methods in rs-fMRI classification and regression tasks.
New method tackles parcel routing with AI.
problem Routing parcels efficiently through a network of hubs.
method Combines graph neural networks with model-free RL.
result Extracts small feature graphs from the environment state.
Paper uses robust GMM to reconstruct missing data in Sentinel-2 images for crop monitoring.
problem Missing data in remote sensing images, especially from multispectral and SAR sensors.
method Robust Gaussian Mixture Models (GMM) with outlier detection using isolation forest.
result Robust GMM outperforms standard GMM in reconstructing imputed values, reducing errors.
To define oscillatory movements of securities market, we put in the non-local extension of Ito- equation for wavelet-images of random processes. It is proposed an algorithm of creation of evolutionary equation and a model of prediction of the most probable price movement path. It is carried out experimental validation …
First European crop map created using satellite data.
problem Need for detailed parcel-level crop type mapping for EU.
method Used Sentinel-1 radar observations and LUCAS in-situ data.
result 80.3% overall accuracy for 19 crop types, highest for rape and turnip rape.
Essential dimension of a family of complex manifolds is the dimension of the image of its base in the Kuranishi space of the fiber. We prove that any family of hyperkähler manifolds over a compact simply connected base has essential dimension not greater than 1. A similar result about families of complex tori is also…
From the point of view of index theory, we give a simple proof of a Gauss-Bonnet-Chern formula for all Finsler manifolds by the Cartan connection. Based on this, we establish a Gauss-Bonnet-Chern formula for any metric-compatible connection and also derive the Gauss-Bonnet-Chern formula of Lackey.
The aim of the present paper is to investigate new classes of symplectically fat fibre bundles. We prove a general existence theorem for fat vectors with respect to the canonical invariant connections. Based on this result we give new proofs of some constructions of symplectic structures. This includes twistor bundles …
Manifold learning offers nonlinear dimensionality reduction of high-dimensional datasets. In this paper, we bring geometry processing to bear on manifold learning by introducing a new approach based on metric connection for generating a quasi-isometric, low-dimensional mapping from a sparse and irregular sampling of an…
Einstein metrics on homogeneous torus bundles
problem Einstein metrics on total space of homogeneous torus bundles
method Descend to common base and establish estimates
result Prove precompactness theorem for Einstein manifolds
We study the topology of the space of positive scalar curvature metrics on high dimensional spheres and other spin manifolds. Our main result provides elements of infinite order in higher homotopy and homology groups of these spaces, which, in contrast to previous approaches, are of infinite order and survive in the (o…
We consider learning a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But, the estimation results could be distorted if some assumptions actually a…
A general formulation of zero curvature connections in a principle bundle is presented and some applications are discussed. It is proved that a related connection based on a prolongation in an associated bundle remains zero curvature as well. It is also shown that the connection coefficients can be defined so that the …
We show that all vector bundles over CP^2 which are not spin admit a complete metric with nonnegative sectional curvature. In the proof we construct a nonnegatively curved metric on the corresponding principle bundle by showing that it admits a cohomogeneity one action with singular orbits of codimension 2. This is clo…
A {1}-structure on a Banach manifold M (with model space E) is an E-valued 1-form on M that induces on each tangent space an isomorphism onto E. Given a Banach principal bundle P with connected base space and a {1}-structure on P, we show that its automorphism group can be turned into a Banach-Lie group acting smoothly…
rags2ridges simplifies graphical modeling of high-dimensional data.
problem Graphical modeling of high-dimensional precision matrices.
method Modular framework for extraction, visualization, and analysis of Gaussian graphical models.
result Provides a one-stop-shop for graphical modeling of high-dimensional precision matrices.
Real-time crime forecasting is important. However, accurate prediction of when and where the next crime will happen is difficult. No known physical model provides a reasonable approximation to such a complex system. Historical crime data are sparse in both space and time and the signal of interests is weak. In this wor…
Following the previous authors works (joint with I.A.Dynnikov) we develop a theory of the discrete analogs of the differential-geometrical (DG) connections in the triangulated manifolds. We study a nonstandard discretization based on the interpretation of DG Connection as linear first order (''triangle'') difference eq…
Characterizes Kähler-Berwald metrics on complex manifolds.
problem Identifying Kähler-Berwald metrics among strongly convex complex Finsler metrics.
method Geometric characterization using Cartan and Chern-Finsler connections.
result Characterizes Kähler-Berwald metrics in terms of parallelism of the canonical complex structure.
Early classification improves satellite-based crop type identification.
problem Accurate early identification of crop types from satellite imagery.
method End-to-end trainable recurrent neural network with an additional stopping probability based on previously seen data.
result The model can distinguish crop types before the end of the vegetative period.
StackNet predicts fluid intelligence from brain images of adolescents.
problem Predicting fluid intelligence in adolescents using brain imaging.
method Feature extraction, normalization, denoising, selection, StackNet architecture, 11 models, 3 layers, 10-fold cross-validation.
result StackNet achieves mean squared errors of 82.42 on training/validation and 94.25 on testing.
Novel risk matrix for optimal portfolio choice with tail risk considerations.
problem Optimal portfolio choice with tail risk events.
method Risk matrix with Value-at-Risk and Delta-CoVaR measures, derived conditions for closed-form solution, examination of portfolio risk and centrality, demonstration of asset centrality's impact on optimal weight allocation.
result Portfolio risk is not necessarily increasing with stock centrality and can be improved by high connectivity.
Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a c…
A CNN integrates with Markov random fields for brain segmentation.
problem Combining CNNs and probabilistic models for robust brain segmentation.
method Backpropagation-based recurrent CNN for spatial interactions in Markov random fields.
result Model generalizes across different imaging protocols.
Study holonomy in pseudo-Hermitian geometry structures.
problem Holonomy classification in pseudo-Hermitian geometry.
method Analyzes sub-Riemannian structures, torsion, and holonomy algebras.
result Holonomy groups of Schouten and adapted connections are related under certain conditions.
A new graph embedding method using Hebbian learning for improved vector representations.
problem Creating accurate vector representations for nodes in graphs.
method Hebbian learning with non-convex Gaussian mixture model for node embeddings.
result The method outperforms state-of-the-art methods on benchmark data sets and generates relevant recommendations.
A new framework detects forecast model inadequacies using online monitoring of forecast errors.
problem Inaccurate forecasts lead to poor decision-making in complex models.
method Sequential changepoint techniques on forecast errors for real-time identification of process changes.
result The framework identifies shifts in forecast errors faster than in the original models, indicating process changes.
eDCF estimates intrinsic dimension using local connectivity.
problem Challenges in estimating intrinsic dimension due to scale dependence.
method eDCF: a novel, scalable, and parallelizable method based on Connectivity Factor (CF).
result eDCF consistently matches leading estimators with comparable MAE and higher exact intrinsic dimension match rates.
Studying neural connectivity is considered one of the most promising and challenging areas of modern neuroscience. The underpinnings of cognition are hidden in the way neurons interact with each other. However, our experimental methods of studying real neural connections at a microscopic level are still arduous and cos…
New algorithm extracts shared latent space for cortico-muscular interactions.
problem Challenges of high dimensionality and limited sample sizes in multivariate cortico-muscular analysis.
method Structured and sparse partial least squares coherence (ssPLSC) algorithm.
result ssPLSC achieves competitive or better performance in scenarios with limited sample sizes and high noise levels.
Proves path connectedness of asymptotically flat metrics with boundary.
problem Proving path connectedness of asymptotically flat metrics with boundary.
method Generalization of Marques' result to compact manifolds with boundary, differential topology, and a new proof.
result Space of asymptotically flat metrics with nonnegative scalar curvature and mean convex boundary on R^3\B^3 is path connected.
Study uses MTD model to optimize portfolios by capturing complex financial asset relationships.
problem Capturing nonlinear and directional relationships in financial markets.
method Directed and weighted financial networks using Mixture Transition Distribution (MTD) model.
result Portfolio optimization with network-based assortativity measures outperforms classical methods.
Synaptic pruning reduces CNNs by 96% on CIFAR-10.
problem Memory and computation constraints in CNNs for mobile devices.
method Synaptic Pruning: data-driven method to prune connections based on Synaptic Strength.
result Significant size reduction and computation saving with up to 96% pruning on CIFAR-10.
Let X be a closed, four-dimensional, oriented, smooth manifold with a Riemannian metric, g, let G be a compact Lie group, and P be a principal G bundle over X. D. Groisser and T. Parker (1987, 1989) and S. K. Donaldson (1990) conjectured that the moduli space of g-anti-self-dual connections on P, endowe…
New method identifies key channels for extreme brain events.
problem Identifying channels responsible for extreme brain events like seizures.
method Extends canonical correlation to tail dependence, developing TPDM for clustering.
result Tail connectivity provides additional discriminatory power for seizure risk.
Proposes neuron alignment to optimize mode connectivity in neural networks.
problem Understanding and optimizing mode connectivity in deep neural networks.
method Introduces neuron alignment to approximate optimal weight permutations and improve mode connectivity.
result Neuron alignment significantly alleviates robust loss barriers and improves model robustness and accuracy.
Geometric quantization often produces not one Hilbert space to represent the quantum states of a classical system but a whole family Hs of Hilbert spaces, and the question arises if the spaces Hs are canonically isomorphic. [ADW] and [Hi] suggest to view Hs as fibers of a Hilbert bundle H, introduce a connec…
ADAVI tackles variational inference for large HBM models in neuroimaging.
problem Large, pyramidally-organized HBM models in neuroimaging studies.
method Automatic dual amortized variational inference using neural networks and attention-based hierarchical encoding.
result Significantly reduced parameterization of the variational family, maintaining expressivity.
Generalizes classifying spaces for topological groups with torsion.
problem Classifying spaces for topological group actions with non-Hausdorff spaces.
method Generalizes Milnor's, Gelfand-Fuks', and Segal's theorems to non-Hausdorff spaces.
result Existence and uniqueness theorems for G-spaces over metric spaces. New complete panel dataset for LMICs helps analyze innovation and development.
problem Lack of complete data for empirical analyses in LMICs.
method Predictive Mean Matching multiple imputation technique.
result Created a large dataset of 47 variables for 82 LMICs from 2005-2019.
NAT optimizes neural architectures to improve performance without extra cost.
problem Redundant operations in neural architectures consume memory and degrade performance.
method Transformed Markov Decision Process (MDP) and reinforcement learning to replace redundant operations with more efficient ones.
result Transformed architectures outperform original and existing methods on CIFAR-10 and ImageNet datasets.
NTK-SAP improves neural network pruning by aligning training dynamics.
problem Improving neural network pruning to reduce training time and memory.
method Prune connections based on the spectrum of the Neural Tangent Kernel (NTK), using multiple random weight realizations and random inputs.
result Empirically, NTK-SAP achieves better performance than all baselines on multiple datasets.
Method predicts brain regions for neuroimaging phenotypes.
problem Predicting phenotypes from brain networks without static community structure.
method Supervised community detection using block-structured regularization and ADMM optimization.
result The method identifies task-specific brain regions that improve phenotype prediction.
ST-UNet models spatio-temporal graphs by pooling and unpooling operations.
problem Lack of effective means to extract dynamic features from spatio-temporal graphs.
method Designing a multi-scale architecture, Spatio-Temporal U-Net (ST-UNet), with paired sampling operations.
result Achieves substantial improvements in spatio-temporal prediction tasks.
Recent empirical results on long-term dependency tasks have shown that neural networks augmented with an external memory can learn the long-term dependency tasks more easily and achieve better generalization than vanilla recurrent neural networks (RNN). We suggest that memory augmented neural networks can reduce the ef…