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 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.
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.
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.
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.
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.
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.
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.
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.
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…
We provide the first known family of examples of integrable homogeneous sub-Riemannian structures admitting strictly abnormal geodesics. These examples were obtained through the analysis of the equivalence problem for sub-Riemannian Engel structures. We formulate a criterion of strict abnormality in terms of structure …
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 …
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…
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.
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 …
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.
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.
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.
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.
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.
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.
We prove the C1 regularity for a class of abnormal length-minimizers in rank 2 sub-Riemannian structures. As a consequence of our result, all length-minimizers for rank 2 sub-Riemannian structures of step up to 4 are of class C1.
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.
Valid inference method for DTW distance for abnormal time-series detection.
problem Statistical inference on DTW distance under uncertain conditions.
method Conditional selective inference framework to derive valid p-values.
result First method to provide valid p-values for DTW distance.
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…
Twitter promotes cryptocurrency pump-and-dumps, affecting trading behavior and returns.
problem The influence of Twitter on cryptocurrency pump-and-dump events.
method Analysis of abnormal returns, trading volume, and tweet activity.
result Investors relying on Twitter information sell later, leading to significant losses.
Deep neural network predicts ECG abnormalities from short-duration exams.
problem Improving accuracy of ECG diagnosis from short-duration exams.
method Residual neural network with 9 convolutional layers trained on large dataset.
result Model outperformed medical doctors on ECG abnormalities.
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.
In this article we prove that the codimension of the abnormal set of the endpoint map for certain classes of Carnot groups of step 2 is at least three. Our result applies to all step 2 Carnot groups of dimension up to 7 and is a generalisation of a previous analogous result for step 2 free nilpotent groups.
Research examines how foreign direct investment in Vietnam affects stock returns.
problem Impact of foreign direct investment on stock returns in Vietnam.
method Analyzes data from 1995 to 2015 focusing on M&A deals.
result Foreign direct investment in Vietnam, particularly M&A, influences abnormal stock returns.
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.
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.
Lung segmentation from abnormal CXRs using data imputation.
problem Segmenting lungs from CXRs with high opacity caused by respiratory ailments.
method Modified CNN-based segmentation network with deep generative model for data imputation.
result The model can segment lungs from abnormal CXRs, extending to cases with extreme abnormalities.
ACA identifies and explains anomalies in data.
problem Explaining anomalies in non-supervised data analysis.
method Abnormal Component Analysis (ACA) using data depth.
result ACA provides a linear explanation for anomalies.
Proves Sard conjecture for specific distributions, controlling divergence of vector fields.
problem Proving the Sard conjecture for certain types of distributions.
method Constructs a singular distribution capturing essential abnormal lifts, proving the conjecture for rank 3 distributions in dimension 4 and generic corank 1 distributions.
result Proves the Sard conjecture for generic co-rank one distributions.
Many radiological studies can reveal the presence of several co-existing abnormalities, each one represented by a distinct visual pattern. In this article we address the problem of learning a distance metric for plain radiographs that captures a notion of "radiological similarity": two chest radiographs are considered …
We use the notion of generalized connection over a bundle map in order to present an alternative approach to sub-Riemannian geometry. Known concepts, such as normal and abnormal extremals, will be studied in terms of this new formalism. In particular, some necessary and sufficient conditions for the existence of abnorm…