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…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,982 papers · 148 categories
Trend · papers per month
48 results for “higher anomalies”
GUIDE detects anomalies in attributed networks by reconstructing node attributes and higher-order structures.
problem Lack of effective mechanisms for detecting anomalies in complex network interactions.
method GUIDE uses attribute and structure autoencoders, graph attention, and reconstruction errors to identify anomalies.
result GUIDE significantly outperforms state-of-the-art methods on multiple real-world datasets.
Develops a new framework for anomaly description in quantum field theories.
problem Anomalies in quantum field theories.
method Extended functorial field theories and symmetric monoidal bicategories.
result Explicit construction of anomaly 2-cocycles and 't Hooft anomalies.
Post-Einsteinian Effects in the General Theory of Relativity from Higher-Order Riemannian Geometrymath.DG
Higher-order geometry modifies Newtonian dynamics and predicts anomalies in spacecraft motion.
problem Observing and understanding higher-order effects in general relativity.
method Generalizing the Einstein-Hilbert action to include higher-order infinitesimals and studying field equations and cosmologies.
result Higher-order corrections predict anomalies like the Pioneer and flyby effects.
Real-time anomaly detection for edge streams using MIDAS and MIDAS-F.
problem Detecting unusual behavior in dynamic graph edge streams.
method MIDAS-F focuses on microcluster anomalies, incorporating anomalies into internal states to reduce poisoning effect.
result MIDAS-F outperforms MIDAS in accuracy, with up to 62% higher ROC-AUC.
Quantum 4d Yang-Mills Theory and Time-Reversal Symmetric 5d Higher-Gauge Topological Field Theoryhep-th
New insights into 4d YM and 5d topological field theories with higher symmetries.
problem Exploring new topological field theories with higher symmetries.
method Dynamic gauging of 1-form symmetry, higher anomalies, and lattice simplicial complex regularizations.
result Discovery of new higher-form gauge fields and exotic anyonic statistics.
Abstract: Generalizes modular forms to family case and finds new anomaly cancellation formulas.
problem Finding anomaly cancellation formulas for determinant line bundles and index gerbes.
method Family index theory applied to modular forms.
result Obtains new anomaly cancellation formulas for determinant line bundles and index gerbes.
We introduce an algorithmic method for population anomaly detection based on gaussianization through an adversarial autoencoder. This method is applicable to detection of `soft' anomalies in arbitrarily distributed highly-dimensional data. A soft, or population, anomaly is characterized by a shift in the distribution o…
Anomaly detection identifies unusual malaria transmission patterns in Ghana.
problem Identifying atypical malaria transmission patterns in Ghana's spatiotemporal surveillance data.
method Consensus-based anomaly detection framework applied to monthly malaria surveillance data.
result High-burden areas are not necessarily those with the most frequent anomalous transmission.
New model detects anomalies without needing clean data.
problem Anomaly detection without clean data.
method Robust denoising diffusion probabilistic models (RDDPM) using robust regression.
result Significantly outperforms existing methods in anomaly segmentation.
ABC uses autoencoders for better anomaly detection.
problem Detecting both known and unknown anomalies accurately.
method Probabilistic binary classifier using Autoencoder for normal data reconstruction.
result ABC outperforms existing methods in anomaly detection.
Enhanced neural networks detect thin boundaries between different types of anomalies.
problem Detecting thin boundaries between different types of anomalies in outlier detection.
method Defined new types of anomalies, enhanced Multi-Layer Perceptron Neural Network with Genetic Algorithm.
result Reduced error in anomaly detection compared to baseline methods.
The purpose of this contribution is to point out connections between recent ideas about gerbes and gerbal actions (as higher categorical extension of representation theory) and old discussion in quantum field theory on commutator anomalies, gauge group extensions, and 3-cocycles. The unifying concept is the classical o…
Fivebrane Structuresmath.AT
We study the cohomological physics of fivebranes in type II and heterotic string theory. We give an interpretation of the one-loop term in type IIA, which involves the first and second Pontrjagin classes of spacetime, in terms of obstructions to having bundles with certain structure groups. Using a generalization of th…
New algorithm detects anomalies by forcing samples to displace mass in low-density regions.
problem Detecting anomalies in datasets.
method Mass Repulsing Optimal Transport (MROT) approach.
result Our algorithm improves anomaly detection over existing methods.
We present a geometrical framework which incorporates higher derivative corrections to the action of N = 2 vector multiplets in terms of an enlarged scalar manifold which includes a complex deformation parameter. This enlarged space carries a deformed version of special Kahler geometry which we characterise. The holomo…
Paper proposes RAN for better anomaly detection in time series data.
problem Anomaly detection algorithms often fail to accurately detect anomalies due to incomplete reconstruction of anomaly data.
method RAN uses adversarial learning and latent vector-constrained Autoencoder to ensure consistent reconstruction of anomaly data.
result RAN outperforms other algorithms in detecting meaningful anomalies with higher AUC-ROC scores.
Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Soundseess.AS
Batch uniformization improves anomaly detection in sound data.
problem Anomaly scores for rare and frequent normal sounds are not uniform.
method Propose batch uniformization to minimize anomaly scores by weighting samples based on their density.
result Improves performance of unsupervised anomaly detection in sound data.
Improved anomaly detection using adversarial mirrored autoencoders.
problem Detecting out-of-distribution samples in machine learning.
method Adversarial Mirrored Autoencoder (AMA) with latent space regularization.
result AMA improves anomaly detection performance on OOD detection benchmarks.
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.
Study of symmetries in 2D Yang-Mills theory, including orbifolds and higher forms.
problem Understanding symmetries and anomalies in 2D Yang-Mills theory.
method Combining continuum methods, topological defects, and higher gauge theory.
result Unified description of higher and lower form gauge fields, identifying spontaneous symmetry breaking.
Improved image reconstruction and anomaly detection using hierarchical VAEs.
problem VAEs struggle with sharp images and high-level features.
method Added a new branch to hierarchical VAEs to separate high-level and low-level features.
result Results in sharper images and better anomaly detection.
We examine the anomalies arising in instanton calculus as detailed by Damiano Anslemi in 1994. Whereas Anselmi uses BRST theory, we use the ADHM construction to arrive at the same conclusions from a differential-geometric way. We observe that Anselmi's TQFT is similar to Donaldson Theory applied to charge 1 instantons …
The Rank Effect for Commoditiesq-fin.GN
We uncover a large and significant low-minus-high rank effect for commodities across two centuries. There is nothing anomalous about this anomaly, nor is it clear how it can be arbitraged away. Using nonparametric econometric methods, we demonstrate that such a rank effect is a necessary consequence of a stationary rel…
Proposes a real-time anomaly detection system using IRL.
problem Real-time anomaly detection in sequential data.
method Uses inverse reinforcement learning to infer reward function and evaluate anomalies.
result Effective in identifying anomalies in real-world data.
It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. At the same time, diverse image and text data are available in enormous quantities. We…
symmetryhep-th
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.
Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluationstat.ML
Study examines how different time series cross-validation methods affect anomaly detection in multivariate time series.
problem Evaluating anomaly detection in multivariate time series requires preserving temporal dependencies, especially for subsequence anomalies.
method Systematically investigates walk-forward and sliding window methods across various validation configurations and classifier types.
result Sliding window method consistently yields higher precision-recall scores and reduced fold-to-fold performance variance, particularly for deep learning models.
MIAEAD detects anomalies in mixed data types.
problem Challenges of heterogeneity in feature subsets for anomaly detection.
method Multiple-Input Variational Auto-Encoder (MIVAE) for simultaneous feature subset anomaly scoring.
result MIVAE outperforms conventional methods and state-of-the-art unsupervised models in AUC score.
Anomalies in (2+1)D fermionic topological phases and (3+1)D path integral state sums for fermionic SPTscond-mat.str-el
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 anomaly indicator for time-reversal symmetric topological superconductors.
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 identifies cancer genes through graph anomaly analysis of protein interactions.
problem Insufficient modeling of biological information in protein interaction networks for cancer gene identification.
method Proposes HIerarchical-Perspective Graph Neural Network (HIPGNN) to detect weight heterogeneity and spectral flattening in cancer gene nodes.
result HIPGNN detects weight heterogeneity and spectral flattening, leading to improved cancer gene identification.
Anomaly cancellation condition for M5-brane action is proven using twisted Cohomotopy.
problem Anomaly in the 6d Wess-Zumino term of M5-brane action.
method Twisted Cohomotopy theory and a twisted/parametrized Hopf invariant.
result Anomaly cancellation condition is implied by charge quantization in twisted Cohomotopy.
Paper introduces a new anomaly detection framework combining density estimation and deep learning.
problem Detecting anomalies in data with varying dimensions.
method Two versions: shallow approach using adaptive Fourier features and density matrices; deep approach using autoencoder.
result Both methods achieve comparable or superior performance compared to state-of-the-art methods.
Physics-informed diffusion model detects anomalous trajectories in GPS data.
problem Detecting fake GPS trajectories in international waters.
method Physics-informed diffusion model integrating kinematic constraints.
result Higher prediction accuracy and lower error rate for anomaly detection.
ATSDLN adapts to time series data for anomaly detection.
problem Challenges in selecting and optimizing anomaly detectors for time series data.
method Adaptive Time Series Detector Learning Network (ATSDLN) that selects and optimizes detectors and parameters.
result ATSDLN outperforms other methods in anomaly detection across various datasets.
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.
A novel semi-supervised outlier detection model detects anomalies with few labels.
problem Efficiently detecting group anomalies with limited labeled data.
method RCC-Dual-GAN model that combines RCC and M-GAN components for semi-supervised outlier detection.
result Significantly improved accuracy in outlier detection with few labeled anomalies.
Bi-directional Curriculum Learning improves graph anomaly detection by considering both homogeneity and heterogeneity.
problem Existing graph anomaly detection methods often ignore the different contributions of nodes to training.
method Introduces Bi-directional Curriculum Learning (BCL) to optimize GAD methods by considering both homogeneity and heterogeneity of nodes.
result Extensive experiments show that BCL significantly improves the performance of GAD anomaly detection models.
A scalable system detects price anomalies in online marketplaces to improve customer experience.
problem Inaccurate prices on online marketplaces lead to poor customer experience and revenue loss.
method MoatPlus uses unsupervised statistical features and an ensemble of models to generate upper price bounds.
result Our approach improves precise anchor coverage by up to 46.6% in high-vulnerability item subsets.
This note investigates the causes of the quality anomaly, which is one of the strongest and most scalable anomalies in equity markets. We explore two potential explanations. The "risk view", whereby investing in high quality firms is somehow riskier, so that the higher returns of a quality portfolio are a compensation …
VAE improves anomaly detection for jet tagging at the LHC.
problem Anomaly detection in jet tagging at the LHC.
method Variational Autoencoder (VAE) trained on background QCD jets, with latent space learning for anomaly detection.
result Outlier Exposed VAE (OE-VAE) achieves excellent results in both sensitivity and decorrelation of jet mass.
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.
Extended symmetries and anomalies in compactified 6d SCFTs on various internal manifolds.
problem Understanding symmetries and anomalies in compactified 6d SCFTs.
method Extended notion of polarizations, focusing on 2D, 3D, and 4D manifolds.
result Recurring themes in compactifications, including Kaluza-Klein modes and symmetries.
Renormalized Volumehep-th
We develop a universal distributional calculus for regulated volumes of metrics that are singular along hypersurfaces. When the hypersurface is a conformal infinity we give simple integrated distribution expressions for the divergences and anomaly of the regulated volume functional valid for any choice of regulator. Fo…
Anomaly detection method tackles hidden adversary actions in real-world scenarios.
problem Detecting hidden adversary actions in real-world scenarios where the defender cannot perfectly observe the attacker's actions.
method Extends existing anomaly detection models to handle continuous action spaces and game-theoretic framework. Proposes two algorithms: direct extension and learning-based approach.
result Learning-based approach produces less exploitable strategies and is scalable to higher dimensions.
There are some statistical anomalies in the Chinese stock market, i.e., positive return skewness, anti-leverage effect (positive returns induce higher volatility than negative returns); and reverse volatility asymmetry (contemporaneous return-volatility correlation is positive). In this paper, we first confirm the exis…
We study 4-dimensional higher-derivative conformal higher spin (CHS) fields generalising Weyl graviton and conformal gravitino. They appear, in particular, as "induced" theories in the AdS/CFT context. We consider their partition function on curved Einstein-space backgrounds like (A)dS or sphere and Ricci-flat spaces. …