The paper bounds abnormal and Goh-abnormal sets for metabelian Lie groups with polarizations.
problem Bounding abnormal and Goh-abnormal sets for metabelian Lie groups.
method Analyzing rank 2 polarizations and sub-Riemannian structures on metabelian Lie groups.
result Metabelian Lie groups with polarizations satisfy the minimizing Sard property.
Proves codimension of abnormal set in step 2 Carnot groups is at least 3.
problem Determining the codimension of abnormal set in Carnot groups.
method Analyzes endpoint map for specific Carnot groups of step 2.
result Codimension of abnormal set is at least 3 for all step 2 Carnot groups up to dimension 7.
New adversarial method detects abnormal trajectories from unlabeled data.
problem Detecting abnormal events from trajectory data without manual threshold setting.
method Generative adversarial network (GAN) framework for unsupervised learning.
result Best accuracy for abnormal trajectory detection on urban traffic videos.
In Carnot-Caratheodory or sub-Riemannian geometry, one of the major open problems is whether the conclusions of Sard's theorem holds for the endpoint map, a canonical map from an infinite-dimensional path space to the underlying finite-dimensional manifold. The set of critical values for the endpoint map is also known …
This study examines abnormal geodesics in 2D-Zermelo navigation problems, revealing their role in separating time minimal and maximal curves.
problem The role of abnormal geodesics in planar Zermelo navigation problems with strong current.
method Geometric time optimal control approach, focusing on the heading angle of the ship.
result Abnormal geodesics separate time minimal and maximal curves, and are both small-time minimizing and maximizing.
Paper classifies heart sound recordings as normal or abnormal.
problem Classifying normal/abnormal heart sound recordings.
method Four steps: preprocessing, feature extraction, training, validation. Back propagation neural network used.
result Optimal threshold determined for distinguishing normal and abnormal.
ODE trajectories become abnormal curves in Carnot groups.
problem Understanding abnormal curves in Carnot groups.
method Explicit construction of covectors for abnormal curves.
result Polynomial ODE trajectories lift to abnormal curves in Carnot groups.
Study Sard problem in step 2 and filiform Carnot groups.
problem Understanding the Sard problem in specific types of Carnot groups.
method Analyzing endpoint maps in step 2 and filiform Carnot groups.
result Characterized abnormal set in filiform groups and provided bounds in step 2 Carnot groups.
We construct normal forms for Lorentzian metrics on Engel distributions under the assumption that abnormal curves are timelike future directed Hamiltonian geodesics. Then we indicate some cases in which the abnormal timelike future directed curve initiating at the origin is geometrically optimal. We also give certain e…
Study finds abnormal paths on specific Lie groups using algebraic structures.
problem Identifying abnormal extremals on Lie groups with quasimetrics.
method Analyzing Lie algebras and seminorms to determine abnormal extremals.
result Established criterion for strong abnormality of extremals.
Improved radiological abnormality detection using LSTM with time-modulated approach.
problem Detect radiological abnormalities in medical images using CNNs on individual exams.
method Used time-modulated LSTM to model entire sequence of radiographs, including reports.
result Improved detection of radiological abnormalities on chest x-rays.
Develops a tool to identify abnormal blood smear results based on CBC tests.
problem Manual review of blood smears by technologists is time-consuming and inconsistent.
method Cost-sensitive Lasso-penalized additive logistic regression combined with stability selection.
result The tool correctly identifies true cutoff values for abnormal smear results.
New algorithm detects outliers from rare abnormal data.
problem Detecting outliers from rare abnormal data in transfer learning.
method Meta-algorithm for transfer learning in outlier detection.
result Meta-algorithm yields strong guarantees and outperforms existing methods.
New integrable structures found with abnormal geodesics.
problem Integrable homogeneous sub-Riemannian structures with abnormal geodesics.
method Analysis of equivalence problem for sub-Riemannian Engel structures.
result First known family of examples of integrable homogeneous sub-Riemannian structures with strictly abnormal geodesics.
Study on abnormal curves in sub-Riemannian manifolds, proving length-minimizing properties.
problem Characterizing abnormal geodesics in sub-Riemannian manifolds.
method Analyzing curves that annihilate Lie brackets and proving minimization properties.
result Strictly abnormal geodesics can cease to be locally length-minimizing.
We prove the smoothness of abnormal minimizers of subriemannian manifolds of step 3 with a nilpotent basis. We prove that rank 2 Carnot groups of step 4 admit no strictly abnormal minimizers. For any subriemannian manifolds of step less than 7, we show all abnormal minimizers have no corner type singularities, which pa…
Researchers found abnormal extremals on specific Lie groups.
problem Identifying abnormal extremals on four-dimensional Lie groups.
method Using left-invariant sub-Finsler quasimetrics and seminorms on Lie algebra.
result Established a criterion for strict abnormality of extremals.
Study of geodesic branching in 2D sub-Riemannian manifolds.
problem Branching of geodesics in sub-Riemannian manifolds of rank two.
method Analysis of geodesic behavior in sub-Riemannian geometry.
result Continuous families of strictly abnormal branching geodesics and accumulation of branching points.
Paper develops a method to detect abnormal road user trajectories using deep autoencoder.
problem Insufficient abnormal data for training machine learning models.
method Used a deep autoencoder network trained on augmented normal data.
result Performs better than classical outlier detection methods on various scenes.
Study of cut locus in Carnot groups disproves conjectures and finds new sets.
problem Disprove conjectures on the shape of cut loci in Carnot groups.
method Analyzing left-invariant Carnot-Carathéodory metric and semi-algebraic sets.
result Found sets of cut points with codimension 2, disproving previous conjectures.
Proves C1 regularity for abnormal minimizers in rank 2 sub-Riemannian structures.
problem Regularity of abnormal minimizers in sub-Riemannian structures.
method Proves C1 regularity using length-minimizers in rank 2 sub-Riemannian structures. result All length-minimizers for rank 2 sub-Riemannian structures of step up to 4 are of class C1. New optimality conditions for sub-Riemannian geodesics derived.
problem Optimality conditions for sub-Riemannian geodesics.
method Geometric translation and ODE derivation.
result New second-order necessary optimality conditions.
Study on sub-Riemannian geometry in 4D, focusing on abnormal geodesics.
problem Understanding sub-Riemannian geometry and its spectrum.
method Wave trace expansion, Weyl laws, propagation of singularities, quantum ergodicity.
result First appearance of abnormal geodesics in sub-Riemannian spectral geometry.
Novel q-space abnormalities detected without labels.
problem Detecting unseen abnormalities in MRI scans.
method Training VAEs on normal data to identify abnormal samples in latent and output spaces.
result Many methods outperform existing q-space novelty detection techniques.
What happens when the Supreme Court of the United States decides a case impacting one or more publicly-traded firms? While many have observed anecdotal evidence linking decisions or oral arguments to abnormal stock returns, few have rigorously or systematically investigated the behavior of equities around Supreme Court…
Media tone around earnings announcements predicts stock returns.
problem Determining if media tone around earnings announcements provides useful information for stock prices.
method Conducted an event study on media tone around earnings announcements for nonfinancial S&P 500 firms.
result Media tone around earnings announcements predicts abnormal stock returns.
Enhanced deep CNNs improve cardiac abnormality diagnosis from ECGs.
problem Diagnosing cardiac abnormalities from 12-lead ECGs.
method Training an enhanced deep convolutional neural network with hand-crafted features, data preprocessing, and augmentation.
result Promising generalization performance in ECG diagnosis.
Given a five dimensional space endowed with a Cartan distribution, the abnormal geodesics form another five dimensional space with a cone structure. Then it is shown, if the cone structure is regarded as a control system, then, the space of abnormal geodesics of the cone structure is naturally identified with the origi…
This paper proposes a novel dynamic Hierarchical Dirichlet Process topic model that considers the dependence between successive observations. Conventional posterior inference algorithms for this kind of models require processing of the whole data through several passes. It is computationally intractable for massive or …
Deep neural network with attention detects multiple ECG abnormalities.
problem Detecting multiple ECG abnormalities from 12-lead recordings.
method Preprocessing, deep residual network with attention, ensemble model.
result Overall F1 score of 0.875 on test set.
Deep learning improves MRI analysis of MSK disorders.
problem Accurate and rapid analysis of musculoskeletal disorders from MRI scans.
method Convolutional neural networks (CNN) for automatic classification of knee abnormalities.
result Multi-view deep learning showed promising performance in classifying MSK abnormalities.
We show that strictly abnormal geodesics arise in graded nilpotent Lie groups. We construct such a group, for which some Carnot geodesics are strictly abnormal; in fact, they are not normal in any subgroup. In the step-2 case we also prove that these geodesics are always smooth. Our main technique is based on the equat…
Novel CNN integrates learnable FIR filters for heart sound detection.
problem Automatic detection of heart sound abnormalities for early diagnosis.
method Proposes a CNN with tConv layers to learn FIR filter-bank parameters.
result Proposed models outperform state-of-the-art systems in heart sound detection.
Learning how to rank multivariate unlabeled observations depending on their degree of abnormality/novelty is a crucial problem in a wide range of applications. In practice, it generally consists in building a real valued "scoring" function on the feature space so as to quantify to which extent observations should be co…
EMODM detects abnormal patterns in complex systems.
problem Detecting abnormal patterns in complex systems.
method Probabilistic models and statistical algorithms.
result EMODM detects abnormal patterns in real-time raw data.
The FCA improved insider trading regulation after 2012, reducing abnormal returns.
problem Regulation of insider trading before and after the UK Financial Services Act 2012.
method Event study methodology using abnormal returns analysis.
result Abnormal returns were reduced after the FCA took over from the FSA.
iSplit LBI predicts individualized partial rankings from ties, outperforming state-of-the-art methods.
problem Predicting partial rankings from pairwise comparisons with ties, considering individual preferences.
method Variable splitting-based algorithm (iSplit LBI) that generates a sequence of estimations with a regularization path, decomposing parameters into abnormal signals, personalized signals, and random noise.
result iSplit LBI significantly outperforms state-of-the-art alternatives in predicting individualized partial rankings.
Optimal search for change point anomaly in multiple processes.
problem Detecting a change point in an anomalous process among multiple normal processes.
method Deterministic search algorithm balancing sample complexity and detection accuracy.
result Asymptotically optimal in minimizing Bayes risk.
We study trade-based manipulation of stock prices from the perspective of complex trading networks constructed by using detailed information of trades. A stock trading network consists of nodes and directed links, where every trader is a node and a link is formed from one trader to the other if the former sells shares …
Paper detects abnormalities in brain activity patterns using unsupervised learning.
problem Detecting abnormalities in resting-state brain activity patterns.
method Two strategies: autoencoder approach and next frame prediction.
result Both approaches can learn useful representations of rs-fMRI data for abnormality detection.
Gradient boosting detects insider purchases predicting abnormal returns in microcap stocks.
problem Predicting abnormal returns in U.S. microcap stocks using insider purchase signals.
method Gradient boosting classifier trained on insider identity, transaction history, and market conditions.
result Gradient boosting classifier achieves AUC of 0.70 on out-of-sample data, with precision and recall of 0.38 and 0.69 respectively.
New model predicts financial market abnormalities using stock index uncertainties.
problem Forecasting abnormal financial fluctuations in the market.
method Quantitative analysis of mean and volatility uncertainties, constructing early warning indicators.
result Established a new abnormal fluctuations warning model.
Unified model identifies and locates thoracic abnormalities with limited annotations.
problem Accurate identification and localization of thoracic abnormalities require large annotated datasets, which are expensive to acquire.
method Unified model that simultaneously identifies and localizes abnormalities using limited location annotations.
result Unified model significantly outperforms baseline in classification and localization tasks.
Generalizes pseudo-product structures with abnormal extremals.
problem Finiteness of symmetry algebras for non-degenerate pseudo-product structures.
method Modified universal prolongation of graded nilpotent Lie algebras and generalized finiteness criterion.
result Distributions with singularly transitive properties have finite-dimensional symmetries.
Co-branding improves stock performance for firms.
problem Little research on co-branding's impact on firm stock value.
method Developed a conceptual framework and tested hypotheses.
result Co-branding events lead to positive abnormal returns.
A 3D almost-Riemannian manifold is a generalized Riemannian manifold defined locally by 3 vector fields that play the role of an orthonormal frame, but could become collinear on some set $\Zz$ called the singular set. Under the Hormander condition, a 3D almost-Riemannian structure still has a metric space structure, wh…
Embeddings of lab test codes improve mortality prediction and preserve ordinality.
problem Improving mortality prediction using lab test embeddings.
method Training embeddings for LOINC codes and their concatenations with abnormality symbols, evaluating performance on mortality prediction tasks.
result Embeddings of lab test codes improve mortality prediction and preserve ordinality.
Detects anomalies in multiple processes using hidden Markov models.
problem Detecting an anomalous process among many with hidden states.
method Sequential search strategy using ADHM algorithm.
result ADHM algorithm effectively leverages temporal correlations.