New algorithm localizes spoofing attackers using tropical geometry.
problem Localizing spoofing attackers in digital systems.
method Adaptive pruning algorithm based on tropical geometry.
result Adaptive pruning improves localisation accuracy.
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48 results for “Localisation”
Equivalent bicategories constructed from action Lie groupoids.
problem Equivalence of bicategories constructed from action Lie groupoids.
method Localizing at equivariant weak equivalences, surjective submersive equivariant weak equivalences, and all weak equivalences.
result Weak equivalences between action Lie groupoids are isomorphic to compositions of nice forms of equivariant weak equivalences.
The paper applies -localization to symplectic cohomology.
problem Equivariant symplectic cohomology relations.
method Localisation by pseudocycles and moduli space lifting.
result Relations between equivariant symplectic classes and Gromov-Witten invariants.
Convolutional neural network localizes OD and fovea in UWFoV-SLO images.
problem Localizing optic disc and fovea centers in ultra-widefield retinal images.
method Convolutional neural network trained on reflectance and autofluorescence images.
result 99.4% OD localisation accuracy and 99.1% fovea localisation accuracy.
Improved vehicle classification using ResNets and spatial pooling.
problem Fine-grained vehicle classification using ResNet architectures.
method Training ResNet-18, -34, and -50 on Comprehensive Cars dataset. Adding Spatially Weighted Pooling and localisation.
result Combining Spatially Weighted Pooling and localisation increases top-1 accuracy to 96.351%.
Solves open problem on Lie groupoids equivalence.
problem Whether Lie groupoids Morita equivalent are diffeologically Morita equivalent.
method Localisation of 2-categories, anafunctors, Lie groupoids, diffeological groupoids.
result Two Lie groupoids diffeologically Morita equivalent are Morita equivalent in the Lie sense.
We solve 6-DoF localisation and 3D reconstruction using deep state-space models.
problem 6-DoF localisation and dense 3D reconstruction in spatial environments.
method Approximate Bayesian inference in a deep state-space model combining learning and domain knowledge.
result Near state-of-the-art performance on UAV flight data.
Geometric interpretations and localisation theory for Kane-Mele invariant.
problem Understanding the Kane-Mele invariant in three-dimensional fermionic systems.
method Homotopy theory, geometric interpretations, Mayer-Vietoris Theorem, bundle gerbes.
result Unified cohomological explanation for equivalence between discrete Pfaffian and local geometric computations.
The study explores stable diffeomorphism groups in 4-manifolds using localisation and invariants.
problem Understanding stable diffeomorphism groups in 4-manifolds.
method Localisation of n-manifolds, inverting connected sum construction, using Bauer--Furuta invariants.
result K3-stable Bauer--Furuta invariants determine S^2xS^2-stable invariants.
Develops a new theory of localization in algebraic geometry.
problem Understanding localizations in cohomological theories with open-closed structures.
method Categorical and algebro-geometric approach, focusing on torsors and refinements.
result Establishes compatibility with various algebraic operations and recovers classical results.
CNNs help diagnose diabetic retinopathy by localizing lesions.
problem Diabetic retinopathy diagnosis requires identifying lesions in fundus images.
method Post-attention technique (Grad-CAM) on deep learning models' penultimate layer.
result InceptionV3 model achieves best performance and localizes lesions better.
We propose a method that performs anomaly detection and localisation within heterogeneous data using a pairwise undirected mixed graphical model. The data are a mixture of categorical and quantitative variables, and the model is learned over a dataset that is supposed not to contain any anomaly. We then use the model o…
Formula for fixed points on noncompact spaces.
problem Calculating fixed points on noncompact manifolds.
method Equivariant index theorem, localised functional, asymptotically local operators.
result Obtained a new Lefschetz fixed-point formula.
The paper calculates invariants for projective surfaces using Higgs pairs and virtual localisation.
problem Calculating invariants for projective surfaces with positive canonical bundle.
method Using Higgs pairs and virtual localisation, the paper defines invariants constant under deformations.
result The invariants can be rational and contribute to the Euler characteristic of the moduli space of instantons.
New approach to -stability and critical metrics on Kähler manifolds.
problem Determining -stability and existence of -critical metrics on Kähler manifolds.
method Equivariant localisation applied to integrals over test configurations.
result Existence of -critical metrics is equivalent to -stability.
Localised analytic torsion and relative analytic torsion for non compact Lie groups of type Imath.FA
Analytic torsion defined for non-compact Lie groups and discrete subgroups.
problem Defining and calculating analytic torsion for non-compact Lie groups and their discrete subgroups.
method Localised analytic torsion and relative analytic torsion defined for Lie groups of type I, using representations and discrete subgroups.
result Relative analytic torsion of coincides with Lott analytic torsion of a covering space.
Paper tackles spooky effect in OSPA estimation, showing GOSPA solves it.
problem Spooky effect in optimal estimation of multiple targets with OSPA metric.
method Introduces GOSPA metric () to penalize false and missed targets.
result GOSPA avoids the spooky effect and optimally lowers target estimation errors.
Extends coarse index theory to locally compact groups for Callias operators.
problem Developing an equivariant coarse index theory for non-cocompact actions.
method Using admissible modules and localised -theory of group -algebras.
result Equivariant index for Callias operators is a special case of the localised index.
New ensemble methods improve time series forecasting accuracy.
problem Global Forecasting Models (GFM) lack localisation for heterogeneous datasets.
method Ensemble techniques with clustering and varied GFM models.
result Significantly higher accuracy achieved compared to baseline models.
Meta-models predict model hyperparameters for NDT experiments.
problem Non-destructive testing experiments are isolated; this work connects them.
method Bayesian multilevel approach, capturing inter-task relationships.
result Meta-models encode knowledge between and within tasks for transfer learning.
This paper explores estimating chaotic dynamics and parameters using local ensemble Kalman filters.
problem Estimating chaotic dynamics and parameters from observations.
method Local ensemble Kalman filters with covariance and local domain localisation.
result Rigorously updating global parameters using a local domain ensemble Kalman filter.
Study connections on Seifert-fibered spaces using gauge theory.
problem Counting connections on Seifert-fibered spaces.
method Introduced perturbations of the Chern--Simons functional and proved a localisation result.
result Formulae for the Euler characteristic and Poincaré polynomial of the stable locus of the character variety of a Seifert-fibered homology 3-sphere.
Defining Vafa-Witten invariants for semistable Higgs pairs on polarised surfaces.
problem Counting semistable Higgs pairs on projective surfaces.
method Virtual localisation applied to Mochizuki/Joyce-Song pairs, proving for deg .
result Invariants for K3 surfaces calculated in terms of modular forms.
We present a simple one-parameter model for spatially localised evolving agents competing for spatially localised resources. The model considers selling agents able to evolve their pricing strategy in competition for a fixed market. Despite its simplicity, the model displays extraordinarily rich behavior. In addition t…
Paper presents an energy-efficient RL method for sensor networks.
problem Energy consumption in sensor networks for health monitoring.
method Adaptive Reinforcement Learning framework using SARSA algorithm.
result Achieves performance enhancement and energy savings over time.
Improved reinforcement learning for 3D games using SLAM and object detection.
problem Challenges in 3D game environments, especially partial observability and combinatorial spaces.
method Augmented Deep Q-Learning Network with SLAM and object detection for better policy learning.
result Our approach consistently learns better policies in 3D games like Doom.
This article is a first step in establishing a link between the Donaldson polynomials and Seiberg-Witten invariants of a smooth 4-manifold.
Localizes smooth spaces to study their homotopy properties.
problem Understanding the homotopy theory of smooth spaces.
method Model category localization, Quillen equivalences, fibrant replacement.
result Localisation of smooth spaces agrees with motivic-style -localisation.
Improved particle filters enhance vehicle tracking accuracy.
problem Particle filters struggle with frequent, informative observations.
method Proposes particle filters that sample around recent observations.
result Significant improvement in accuracy and efficiency.
ALP outperforms other data descriptors in one-class classification.
problem Challenges in one-class classification using data descriptors.
method Determined optimal default hyperparameters for data descriptors, proposed ALP, evaluated using leave-one-dataset-out procedure.
result ALP outperforms other data descriptors, including IF and SVM.
Study uses stacked hourglass networks to improve facial landmark detection for medical diagnosis.
problem Improving accuracy of facial landmark detection for medical diagnosis.
method Conducted a study on landmark localisation methods using stacked hourglass networks.
result State-of-the-art stacked hourglass architecture outperforms traditional methods.
Fast saliency detection method for any differentiable classifier.
problem Real-time interpretation of black box classifiers.
method Training a masking model to manipulate classifier scores.
result Produces interpretable, sharp saliency maps.
Optimizes one-class classification methods for better performance.
problem Improving one-class classification accuracy through hyperparameter optimization.
method Hyperparameter optimization for five one-class classification methods (SVM, NND, LNND, LOF, ALP) using various datasets.
result ALP and SVM perform best after hyperparameter optimization, with ALP being more efficient.
Advocates against over-smoothing and over-squashing in GNNs, suggesting they are less critical than previously thought.
problem Over-smoothing and over-squashing in Graph Neural Networks (GNNs).
method Challenged the prevailing focus on these phenomena, proposing that performance decreases are due to uninformative receptive fields and localised information distribution.
result Performance decreases are mostly uncorrelated with over-smoothing and over-squashing, and optimal model depths remain small.
The paper extends localisation technique to multiple constraints in Euclidean spaces.
problem Proving log-concavity of conditional measures in decomposed convex sets.
method Defining partitions of maximal closed convex sets and proving log-concavity of conditional measures.
result Existence of a partition and log-concavity of conditional measures for almost every set of the partition.
We record various properties of twisted Becker-Gottlieb transfer maps and study their multiplicative properties analogous to Becker-Gottlieb transfer. We show these twisted transfer maps factorise through Becker-Schultz-Mann-Miller-Miller transfer; some of these might be well known. We apply this to show that $BSO(2n+1…
Improving the Accuracy of Marginal Approximations in Likelihood-Free Inference via Localisationstat.ME
Improved likelihood-free inference by localizing and refining low-dimensional approximations.
problem Poor performance of common likelihood-free methods in high-dimensional models.
method Localisation followed by refinement of low-dimensional summaries.
result Improved accuracy in marginal posteriors through localized and refined approximations.
Deep learning detects building defects from images.
problem Time-consuming, laborious, and expensive traditional building condition assessment.
method Convolutional Neural Networks (CNN) with class activation mapping (CAM) for object localisation.
result Robust model accurately detects and localises building defects.
On a five dimensional simply connected Sasaki-Einstein manifold, one can construct Yang-Mills theories coupled to matter with at least two supersymmetries. The partition function of these theories localises on the contact instantons, however the contact instanton equations are not elliptic. It turns out that these equa…
In four-dimensional gauge theory there exists a well-known correspondence between instantons and holomorphic curves, and a similar correspondence exists between certain octonionic instantons and triholomorphic curves. We prove that this latter correspondence stems from the dynamics of various dimensional reductions of …
Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detectionstat.ML
Improved trading strategy using deep learning and changepoint detection for market changes.
problem Traditional momentum strategies struggle with rapid market changes, especially after trend reversals.
method Inserted an online changepoint detection module into a Deep Momentum Network (DMN) pipeline.
result Improvement in Sharpe ratio by one-third over 1995-2020 period, especially beneficial in nonstationary periods.
Paper develops efficient DML estimators for multiway clustered data without cross-fitting.
problem Efficient inference in models with multiway clustered dependence.
method Neyman-orthogonal moment conditions combined with localisation-based empirical process approach.
result Valid inference achieved without cross-fitting, showing debiased GMM estimators are asymptotically linear and normal.
Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimatorcs.LG
Softmax cross-entropy optimizes mutual information in neural networks.
problem Understanding the relationship between mutual information and classification neural networks.
method Demonstrated that optimizing softmax cross-entropy maximizes mutual information between inputs and labels.
result Softmax cross-entropy can approximate mutual information and highlight relevant image regions.
Uncovering Locally Low-dimensional Structure in Networks by Locally Optimal Spectral Embeddingstat.ML
LASE improves local network structure visualization by targeting locally low-dimensional regions.
problem Global spectral embedding fails to capture local geometric features in sparse, transitive networks.
method Local Adjacency Spectral Embedding (LASE) using weighted spectral decomposition.
result LASE reveals locally low-dimensional structure, improving local reconstruction and visualization.
A new method uses deep learning to efficiently solve complex physics equations in high dimensions.
problem Efficiently solving high-dimensional time-dependent PDEs with dynamic solutions.
method Deep adaptive sampling framework for PINNs extended to spacetime domains using normalizing flows.
result The method effectively identifies and tracks high-residual regions in both space and time.
Simplifies complex RL policies by ranking important decisions.
problem Complexity in RL policies makes them hard to analyze and interpret.
method Statistical fault localisation to rank states and prune unimportant decisions.
result Pruned policies can perform similarly to original policies, improving interpretability.
Link projections and flypesmath.GT
Let Πbe a link projection in S^2. John Conway and later Francis Bonahon and Larry Siebenmann undertook to split into canonical pieces. These pieces received different names: basic or polyhedral diagrams on one hand, rational, algebraic, bretzel, arborescent diagrams on the other hand. This paper proposes a thorough…
Study quantisation of geometric operators on manifolds with group actions.
problem Quantisation of geometric operators on manifolds with group actions.
method Use maximal versions of equivariant localised Roe algebras to define and compute indices.
result Recover an index defined earlier by integrating over the group action.