Study detects anomalies in financial markets using GNN and nonextensive entropy.
problem Detecting anomalies in global financial markets with many correlated assets.
method Used Graph Neural Networks (GNN) with nonextensive entropy to measure uncertainty.
result Anomalies are statistically different for nonextensive entropy parameters before, during, and after a crisis.
We analyze global anomalies for elementary Type II strings in the presence of D-branes. Global anomaly cancellation gives a restriction on the D-brane topology. This restriction makes possible the interpretation of D-brane charge as an element of K-theory.
TADA detects anomalies in time series using topological data analysis.
problem Detecting global changes in dependency structure between channels in multivariate time series.
method Topological Data Analysis for detecting anomalies in multivariate time series.
result The approach is more suitable for detecting global changes of correlation structures than existing methods.
Abelian gerbes and twisted bundles describe the topology of the NS-NS 3-form gauge field strength H. We review how they have been usefully applied to study and resolve global anomalies in open string theory. Abelian 2-gerbes and twisted nonabelian gerbes describe the topology of the 4-form field strength G of M-theory.…
Human analysts that use anomaly detection systems in practice want to retain the use of simple and explainable global anomaly detectors. In this paper, we propose a novel human-in-the-loop learning algorithm called GLAD (GLocalized Anomaly Detection) that supports global anomaly detectors. GLAD automatically learns the…
We derive a formula for the global gravitational anomaly of the self-dual field theory on an arbitrary compact oriented Riemannian manifold. Along the way, we uncover interesting links between the theory of determinant line bundles of Dirac operators, Siegel theta functions and a functor constructed by Hopkins and Sing…
The index theorem connects anomalies on a domain wall to global integrals.
problem Relating anomalies on a domain wall to global integrals.
method Formulated and proved an analog of the Atiyah-Patodi-Singer theorem.
result The index is expressed through global chiral and parity anomalies.
A new formulation of the Anomaly flow in the case of vanishing slope parameter is given, where the dependence on the global section of the canonical bundle appears only in the initial data. This allows a natural unification of the Anomaly flow with the Kähler-Ricci flow.
We study global gravitational anomalies in type IIB string theory with nontrivial middle cohomology. This requires the study of the action of diffeomorphisms on this group. Several results and constructions, including some recent vanishing results via elliptic genera, make it possible to consider this problem. Along th…
The main new result here is the cancellation of global anomalies in the Type I superstring, with and without D-branes. Our argument here depends on a precise interpretation of the 2-form abelian gauge field using KO-theory; then the anomaly cancellation follows from a geometric form of the full Atiyah-Singer index theo…
By developing a generalized cobordism theory, we explore the higher global symmetries and higher anomalies of quantum field theories and interacting fermionic/bosonic systems in condensed matter. Our essential math input is a generalization of Thom-Madsen-Tillmann spectra, Adams spectral sequence, and Freed-Hopkins's t…
Study evaluates financial anomaly detection methods on Canadian stock market.
problem Detecting financial anomalies in the Canadian stock market.
method Topological data analysis (TDA), principal component analysis (PCA), and neural network-based approaches.
result Neural network-based methods achieve the strongest performance in detecting financial anomalies.
This paper shows how to construct anomaly free world sheet actions in string theory with D-branes. Our method is to use Deligne cohomology and bundle gerbe theory to define geometric objects which are naturally associated to D-branes and connections on them. The holonomy of these connections can be used to cancel g…
An algorithm detects anomalies based on human perception principles.
problem Anomaly detection in data.
method Inspired by Gestalt psychology and Helmholtz principle, the algorithm models anomalies as unexpected elements in random distributions.
result The algorithm efficiently detects anomalies with minimal user intervention and promising results on multivariate data.
Meta-learning improves anomaly detection with few labeled instances.
problem High requirement of training data for neural network-based anomaly detection.
method Meta-learning framework with one-class classification and generalized eigenvalue problem.
result Meta-learning method achieves better performance than existing methods on various datasets.
Machine-learned anomaly detection in new-physics searches needs calibration and look-elsewhere correction
problem Machine-learned anomaly detection in new-physics searches
method Conformal prediction layer
result Calibrated significance with distribution-free guarantees
New approach reduces shape optimization anomalies and improves design quality.
problem Improving global optimization efficiency and avoiding geometrical anomalies in shape optimization.
method Reducing design variables, modeling generative process via probabilistic models, penalizing anomalous designs.
result Abnormal designs are penalized, leading to high-quality designs and improved convergence.
Extract anomalies from 5D SCFTs using extra-dimensional η-invariants.
problem Anomalies in quantum field theories.
method Use extra-dimensional η-invariants to bypass traditional blowup techniques.
result Anomalies can be determined directly from η-invariants of asymptotic boundaries.
To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with va…
A new algebraic method extracts symmetry anomalies from 5D SCFTs.
problem Extracting global symmetry anomalies from 5D superconformal field theories.
method Path algebra of branes probing Calabi-Yau cones provides a complementary approach.
result Combinatorial approach to symmetry anomalies in 5D SCFTs.
In this note we revisit the subject of anomaly cancelation in string theory and M-theory on manifolds with String structure and give three observations. First, that on String manifolds there is no E8 x E8 global anomaly in heterotic string theory. Second, that the description of the anomaly in the phase of the M-theory…
Cohomological methods are applied for the special set of solutions corresponding to rotating branes in arbitrary dimensions, AdS black holes (which can be embedded in ten or eleven dimensions), and gauge supergravities. A new class of solutions is proposed, the Hilbert modular varieties, which consist of the 2n-fold …
Anomaly detection aims to distinguish observations that are rare and different from the majority. While most existing algorithms assume that instances are i.i.d., in many practical scenarios, links describing instance-to-instance dependencies and interactions are available. Such systems are called attributed networks. …
Study fermionic theories, their anomalies, and modular transformations.
problem Understanding fermionic theories and their anomalies.
method Use spin-cobordisms, surgeries, and invertible topological quantum field theories.
result Explicit combinatorial expressions for spin-bordism invariants.
A new method detects anomalies in multivariate streams without unit dependence.
problem Detect anomalies in multivariate streams without unit dependence.
method Proposes SigMahaKNN combining variance norm and path signature.
result SigMahaKNN detects anomalies better than existing methods.
Tensor networks improve anomaly detection efficiency.
problem Anomaly detection in high-dimensional data.
method Tensor networks for learning a linear transformation over high-dimensional space, penalizing global tendency to normality.
result Tensor networks outperform deep and classical algorithms on various datasets.
CrossAD detects anomalies in time series data by considering cross-scale associations and cross-window modeling.
problem Anomaly detection in time series data is challenging due to varying patterns at different scales and fixed window sizes.
method CrossAD incorporates cross-scale reconstruction and a query library to capture dynamic cross-scale associations and comprehensive context.
result CrossAD achieves state-of-the-art performance in anomaly detection across multiple real-world datasets.
CCE improves anomaly detection metrics by measuring both confidence and consistency.
problem Existing anomaly detection metrics lack discriminative power, hyperparameter dependency, and robustness to perturbations.
method CCE uses Bayesian estimation to quantify uncertainty and constructs global and event-level confidence and consistency scores.
result CCE demonstrates strict boundedness, robustness, and linear time complexity.
Summarizes quantum field theories with discrete symmetry, classifying representations and anomalies.
problem Classifying representations and anomalies in quantum field theories with discrete symmetry.
method Classification of representations and anomalies using the ring of profinite integers.
result Rich and complex classification of representations and anomalies.
Proposes methods to improve interpretability of Isolation Forest for anomaly detection.
problem Lack of interpretability in Isolation Forest.
method Defines feature importance scores and unsupervised feature selection methods.
result Improves interpretability of Isolation Forest for anomaly detection.
A recent paper by Moore and Witten explained that Ramond-Ramond fields in Type II superstring theory have a global meaning in K-theory. In this note we amplify and generalize some points raised in that paper. In particular, we express the coupling of the Ramond-Ramond fields to D-branes in a K-theoretic framework and s…
We survey the Hirzebruch signature theorem as a special case of the Atiyah-Singer index theorem. The family version of the Atiyah-Singer index theorem in the form of the Riemann-Roch-Grothendieck-Quillen (RRGQ) formula is then applied to the complexified signature operators varying along the universal family of ellipti…
Autoencoders misidentify anomalies due to data topology.
problem Autoencoders fail to accurately identify anomalies in data with nontrivial topology.
method Illustrative low-dimensional examples and analysis of autoencoder behavior in latent space.
result Topology of the dataset affects autoencoder performance, leading to misidentification of anomalies.
Investigates how extreme temperature events affect global equity portfolios.
problem Impact of extreme temperature events on global equity portfolios.
method Panel regression analysis and multi-objective portfolio optimization.
result Extreme temperature events negatively impact most sectors' returns.
STRIC detects anomalies in time series by analyzing residual signals.
problem Anomaly detection in multivariate time series data.
method End-to-end differentiable neural network architecture with Sequential Probability Ratio Test on residuals.
result STRIC outperforms state-of-the-art methods on multiple benchmarks.
GAMLA learns manifold structures with auto-encoding for global insights.
problem Limited global insight and lack of interpretable analytical descriptions in manifold learning.
method Two-round auto-encoding process to derive character and complementary representations.
result GAMLA provides global and analytical descriptions of smooth manifolds.
Reformulates mod-two APS index using domain-wall fermion.
problem Non-local APS boundary condition and global anomalies.
method Physicist-friendly reformulation of APS index using domain-wall fermion.
result Equivalence between two formulations of APS index.
Deep neural networks predict CVCM track circuit failures early.
problem Subtle anomalies in CVCM track circuits lead to failures, causing disruptions.
method Deep neural networks classify anomalies before they escalate.
result Deep neural networks achieve 99.31% overall accuracy in detecting CVCM failures.
Enhanced Extended Isolation Forest (EIF+) improves anomaly detection and provides interpretable explanations.
problem Detecting anomalies in complex datasets and explaining model predictions.
method Extended Isolation Forest (EIF) and Extended Isolation Forest Feature Importance (ExIFFI) methods.
result EIF+ outperforms EIF in detecting unseen anomalies and provides better generalization.
This paper discusses challenges and opportunities in vessel behavior detection using machine and deep learning.
problem Real-time analysis of vessel behaviors is crucial for maritime safety and protection.
method Comparison of classical machine learning and deep learning approaches for vessel event and anomaly detection.
result Novel methods and tools are needed to address challenges in vessel behavior detection.
We show that the baryon number of N=2 supersymmetric QCD can be twisted in order to couple the topological field theory of non-abelian monopoles to Spinc-structures. To motivate the construction, we also consider some aspects of the twisting procedure as a gauging of global currents in two and four dimensions, in pa…
Constructs a path integral for fermionic SPTs, solving anomalies in 2+1D topological orders.
problem Anomalies in (2+1)D fermionic topological phases and their computation.
method Combining (2+1)D fermionic topological order with symmetry fractionalization data to construct a (3+1)D path integral.
result Reproduces the Z16 anomaly indicator for time-reversal symmetric topological superconductors. An Ensemble Anomaly Detection Framework for Risk Calculation Integrity
problem Detecting errors in risk valuation outputs
method Ensemble Quality Assessment Framework (EQAF)
result Achieves F1 scores of 61-79% across four datasets
The Seiberg-Witten family of elliptic curves defines a Jacobian rational elliptic surface Z over CP1. We show that for the ∂ˉ-operator along the fiber the logarithm of the regularized determinant −1/2logdet′(∂ˉ∗∂ˉ) satisfies the anomaly equation of the …
Study quantum aspects of 1-form symmetries using BV-BRST cohomology.
problem Quantum aspects of gauging continuous 1-form global symmetries.
method BV-BRST quantization of a U(1) 2-form gauge field, Lie 2-algebroid construction, Čech-de Rham bicomplex.
result Anomaly descent for U(1) 1-form symmetries is naturally set up in the Čech-de Rham bicomplex.
Study quantum aspects of 1-form symmetries using BV-BRST cohomology and gerbes.
problem Quantum aspects of gauging continuous 1-form global symmetries.
method BV-BRST quantization of a U(1) 2-form gauge field, Lie 2-algebroid construction, Čech-de Rham bicomplex.
result Anomaly descent for U(1) 1-form symmetries is naturally set up in the Čech-de Rham bicomplex.
New framework solves string theory's RR-field tadpole cancellation problems.
problem Precise global nature of RR-field tadpole cancellation conditions in string theory.
method Formulated M-theory C-field on flat M-orientifolds using equivariant cohomotopy.
result Equivariant cohomotopy implies anomaly cancellation conditions for M-branes and D-branes.
Improves anomaly detection with contaminated unlabeled data.
problem Weakness in existing semi-supervised anomaly detection methods when unlabeled data contain anomalies.
method Integrates positive-unlabeled learning with deep anomaly detection models.
result Achieves better detection performance on various datasets.