Develops a new method to calculate volumes of manifolds with boundaries.
problem Calculating volumes of manifolds with boundaries, especially those with singularities.
method Introduces a regulated volume expansion with anomaly terms for singular measures.
result Shows how anomaly terms generate invariant pairs for hypersurfaces with boundaries.
OneFlow detects anomalies by finding a minimal volume region, outperforming other methods.
problem Anomaly detection in data with complex outlier structures.
method Flow-based one-class classifier that uses a minimal volume region to define outliers.
result OneFlow outperforms other methods in real-world anomaly detection tasks.
Anomaly detection algorithm using nearest neighbor graphs and max-margin learning.
problem Anomaly detection in high-dimensional data.
method Rank nearest neighbor scores, train max-margin models to imitate, declare anomalies based on percentile.
result Asymptotically optimal decision region converges to minimum volume level set.
Develops a new calculus for volumes of singular metrics.
problem Calculating anomalies and divergences for volumes of singular metrics.
method Distributional calculus and boundary calculus for conformally compact manifolds.
result Explicit formulae for divergences and anomaly of regulated volumes.
A new framework detects abnormal node-level communication activity in networks.
problem Detecting abnormal communication volume at node-level in communication networks.
method Probabilistic framework using clique streams and non-parametric regression.
result The proposed approach outperforms in real-world and synthetic data.
Two new scoring methods improve anomaly detection in Isolation Forest.
problem Anomaly and outlier detection in data.
method Generalized score function and volume-based scoring for individual trees.
result Significant improvement in anomaly detection on 34 datasets.
Survey categorizes time series anomaly detection methods.
problem Need for anomaly detection in time series data.
method Process-centric taxonomy of anomaly detection methods.
result Meta-analysis of time series anomaly detection trends.
Detects crypto pump-and-dump schemes with a thresholding-based model.
problem Detecting genuine anomalies from minor trading fluctuations.
method Combining threshold-based criteria with EWMA and volatility measures.
result Balances high true-positive detection with minimal noise.
New criteria detect anomaly detection algorithms without labeled data.
problem Lack of labeled data for evaluating anomaly detection algorithms.
method Developed two new criteria based on Excess-Mass and Mass-Volume curves, and a feature sub-sampling methodology.
result Empirically validated new criteria outperform classical ROC and PR curves in non-labeled data scenarios.
For a strictly pseudoconvex domain in a complex manifold we define a renormalized volume with respect to the approximately Einstein complete Kähler metric of Fefferman. We compute the conformal anomaly in complex dimension two and apply the result to derive a renormalized Chern--Gauss--Bonnet formula. Relations between…
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.
The paper introduces a new method to rank anomalies in multivariate data.
problem Ranking multivariate unlabeled observations based on their degree of abnormality.
method Formulates the problem as a M-estimation problem using the Mass Volume curve (MV curve).
result Established generalization bounds for the MV curve estimation.
Paper tackles anomaly detection in large-scale networks.
problem Inferring network-level anomalies from indirect link measurements.
method Online subspace tracking of Hankelized traffic tensor using Candecomp/PARAFAC decomposition and RLS algorithm for normal flows; outlier detection for abnormal flows.
result Proposed algorithm achieves faster convergence and better anomaly detection performance.
We propose a non-parametric anomaly detection algorithm for high dimensional data. We score each datapoint by its average K-NN distance, and rank them accordingly. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as an anomaly…
Improved clustering and anomaly detection using latent space conditioning.
problem Anomaly detection on unlabeled data.
method Conditional latent space variational autoencoder (cLSVAE) that separates latent space by conditioning on data labels.
result The method outperforms typical variational autoencoders in clustering and anomaly detection.
This article describes some geometric invariants and conformal anomalies for conformally compact Einstein manifolds and their minimal submanifolds which have recently been discovered via the Anti-de Sitter/Conformal Field Theory correspondence.
Analyzes anomaly detection techniques for real-time streaming data.
problem Challenges in selecting effective anomaly detection techniques for real-time streaming data.
method In-depth analysis of anomaly detection techniques for real-time streaming data.
result First characterization of anomaly detection techniques across diverse fields using production data sets.
Paper extends 2D ZSAD to 3D MRI without training, achieving robust anomaly detection.
problem Challenges in extending zero-shot anomaly detection to 3D medical images.
method Constructs localized volumetric tokens by aggregating 2D slices processed by 2D foundation models.
result Training-free, batch-based ZSAD effectively extends from 2D encoders to full 3D MRI volumes.
Detects BGP routing anomalies using bursty announcement patterns.
problem Detecting disruptive routing updates in the Internet.
method Burstiness measure of inter-arrival times around disruptive update dates and times.
result High recall and better precision in detecting anomalous incidents.
Method detects anomalies on attributed graphs with few labeled instances.
problem Detecting anomalies on connected instances (attributed graphs) with limited labeled data.
method Embed nodes in latent space using GCNs, training to distinguish normal and anomalous nodes.
result Method outperforms existing methods on real-world attributed graph datasets.
TSML tackles anomaly detection and pattern discovery in industrial time series data.
problem Extracting and exploiting information from large industrial data to reduce downtimes and manufacturing errors.
method TSML uses a pipeline of lightweight filters to process industrial time series data in parallel.
result TSML effectively detects anomalies and discovers patterns in industrial time series data.
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.
ZAPs rewards users based on activity, making them less susceptible to bots and sybil attacks.
problem Incentive programs in DeFi are vulnerable to bots and sybil operations.
method ZAPs combines percentile normalization, two-layer weighting, and anomaly detection.
result ZAPs reduces adversarial reward capture by 30-90 percent.
Prototype technique for anomaly detection in industrial big data.
problem Real-time condition monitoring and failure analysis of industrial systems.
method Independent ordinations of repeated bootstrapped partitions and inspection of ordinal distances.
result Prototype technique operates at industrial scale for anomaly detection.
Formula derived for 4D manifolds with boundary involving renormalized volume and boundary integral.
problem Calculating the Euler characteristic of 4D manifolds with boundary.
method Derives a Chern-Gauss-Bonnet formula for a specific type of metric.
result Sum of renormalized volume and boundary integral is a conformal invariant when boundary is umbilic.
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.
Study detects unusual trading patterns on crypto exchanges using complexity measures.
problem Detecting artificial trading activity on cryptocurrency exchanges.
method Complexity and statistical-structure measures derived from high-frequency trade-level data.
result Unusual trading patterns detected on Bitget for BTC and ETH after mid-May 2025.
We produce some explicit examples of conformally compact Einstein manifolds, whose conformal compactifications are foliated by Riemannian products of a closed Einstein manifold with the total space of a principal circle bundle over products of Kahler-Einstein manifolds. We compute the associated conformal invariants, i…
Paper proposes a federated XGBoost for anomaly detection balancing privacy and accuracy.
problem Balancing privacy and accuracy in anomaly detection for unbalanced datasets.
method Proposes a horizontal federated XGBoost algorithm with data aggregation and sparse update processes.
result Demonstrates effectiveness of the proposed scheme compared to state-of-the-arts.
Small trades show unexpected price impact after fee changes.
problem Anomaly in price impact for small transaction volumes post-fee restructuring.
method Reviewed existing master curve for price impact, re-estimated after fee reductions, found anomalies, and rescaled by liquidity proxy.
result Master curve for price impact can be approximated using a liquidity proxy, providing a practical method for practitioners.
We develop a theoretical trading conditioning model subject to price volatility and return information in terms of market psychological behavior, based on analytical transaction volume-price probability wave distributions in which we use transaction volume probability to describe price volatility uncertainty and intens…
We argue that the AdS/CFT calculational prescription for double-trace deformations leads to a holographic derivation of the conformal anomaly, and its conformal primitive, associated to the whole family of conformally covariant powers of the Laplacian (GJMS operators) at the conformal boundary. The bulk side involves a…
RESHAPE explains financial statement anomalies by aggregating explanations from AENNs.
problem Detecting and explaining accounting anomalies in financial audits is challenging.
method Proposes RESHAPE to explain model output on an aggregated attribute-level.
result RESHAPE provides more comprehensible explanations compared to existing methods.
This study evaluates and compares novelty detection algorithms for discrete sequences.
problem Identifying anomalies in temporal data.
method Experimental comparison of state-of-the-art novelty detection methods on various public and industrial datasets.
result Recommendations for efficient and appropriate methods based on extensive experiments and scalability tests.
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.
The paper provides bounds for the empirical angular measure and applies them to improve statistical learning in extreme regions.
problem Estimating the angular measure in high-dimensional data with different distributions.
method Established bounds for the maximal deviations of the empirical angular measure from the true measure, using rank transformation and analyzing the most extreme observations.
result The bounds provide performance guarantees for statistical learning procedures in extreme regions, such as binary classification and anomaly detection.
End-to-end anomaly detection framework using labeled anomalies.
problem Limited deep learning for anomaly detection and inefficiency of existing methods.
method Deviation learning neural network with labeled anomalies and prior probability.
result Significantly better anomaly scoring than state-of-the-art methods.
PReNet detects seen and unseen anomalies using pairwise relations.
problem Detecting unseen anomalies in semi-supervised learning.
method Pairwise Relation prediction Network (PReNet) learns anomaly and normal patterns.
result PReNet significantly outperforms nine competing methods in anomaly detection.
CANARI detects near-anomalies to predict future anomalies proactively.
problem Uncertainty in anomaly detection near distribution boundaries.
method Christoffel-based ANomaly Anticipation for eaRly dIscovery (CANARI) method.
result CANARI outperforms baseline methods in detecting near-anomalies and predicting future anomalies.
Proposes a method for anomaly detection with inexact labels.
problem Handling anomaly detection with inexact labels.
method Trains an anomaly score function using a neural network-based unsupervised method, maximizing the inexact AUC.
result Improves anomaly detection performance with inexact labels and outperforms existing methods.
Detects anomalies relative to typical observations.
problem Common anomaly detection methods fail for frequent anomalies.
method Relative anomaly detection, considering location relative to typical observations.
result Effective for frequent anomalies, computationally feasible, real-time detection.
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.
We study the effect of a relevant double-trace deformation on the partition function (and conformal anomaly) of a CFT at large N and its dual picture in AdS. Three complementary previous results are brought into full agreement with each other: bulk and boundary computations, as well as their formal identity. We show th…
Deep RL detects anomalies from few labeled examples and large unlabeled data.
problem Anomaly detection with limited labeled data and large unlabeled data.
method Deep reinforcement learning to optimize detection of labeled and unlabeled anomalies.
result Significantly outperforms state-of-the-art methods on 48 real-world datasets.
A new method assigns anomaly scores to features for better interpretation.
problem Interpreting anomaly scores from feature attributions.
method Proposes a characteristic function to attribute anomaly scores using Shapley value.
result Demonstrates the potential utility of the proposed attribution methods.
We develop a tractable model of realization utility that studies the role of reference-dependent S-shaped preferences in a dynamic investment setting with reinvestment. Our model generates both voluntarily realized gains and losses. It makes specific predictions about the volume of gains and losses, the holding periods…
Efficient method detects point and collective anomalies in data sequences.
problem Efficiently identifying anomalies in data sequences, especially collective anomalies.
method CAPA: a computationally efficient approach for detecting collective and point anomalies.
result CAPA is consistent at detecting collective anomalies and has close to linear computational cost.
Ensemble learning improves anomaly detection for milder symptoms.
problem Difficulty in detecting incipient anomalies due to similarity to normal conditions.
method Utilize uncertainty information from ensemble learning to identify misclassified incipient anomalies.
result Ensemble learning methods show improved performance on incipient anomaly detection.