Detects handlebodies and mapping class extensions using bordered Floer homology.
problem Detecting handlebodies and mapping class extensions over compression bodies.
method Combining bordered Floer homology with ideas from Casson-Long and train tracks.
result Algorithm to detect mapping class extensions over any compression body.
UAV uses RL to navigate, map, and detect targets in unknown environments.
problem Optimizing UAV trajectory for accurate mapping and target detection in unknown environments.
method Formulated as an MDP, UAV uses RL to infer navigation policy.
result UAV autonomously explores high target detection areas while reconstructing the environment.
FCDD explains deep anomaly detection by mapping anomalies away and providing heatmap explanations.
problem Deep one-class classification's non-linear transformation makes it hard to interpret.
method FCDD learns a mapping that concentrates nominal samples, maps anomalies away, and provides heatmap explanations.
result FCDD sets a new state of the art in unsupervised anomaly detection on MVTec-AD.
CMDNet simplifies MAP detection for large systems with probabilistic relaxation.
problem High complexity of MAP detection in large systems.
method Probabilistic Continuous relaxation of discrete variables, iterative CMD algorithm, CMDNet with online optimization.
result CMDNet achieves a promising accuracy-complexity trade-off in MIMO systems.
A novel method detects multiple mitosis events and mitigates annotation gaps in phase-contrast microscopy.
problem Detecting multiple mitosis events and handling annotation gaps in closely placed cells.
method Estimating a spatiotemporal likelihood map via 3DCNN to detect multiple mitosis events and mitigate annotation gaps.
result Our method outperformed compared methods in terms of F1-score using a challenging dataset.
We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies.The information in the maps is compressed…
Proposes SOMDAGMM for more accurate network intrusion detection.
problem Inaccurate network intrusion detection in secure network environments.
method Integrates self-organizing map with deep autoencoding Gaussian mixture model.
result SOMDAGMM outperforms state-of-the-art DAGMM with up to 15.58% improvement in F1 score.
This is a survey paper of author's results on cobordism groups and semigroups of fold maps and simple fold maps. The results include: establishing a relation between fold maps and immersions through geometrical invariants of cobordism classes of fold maps and simple fold maps in terms of immersions with prescribed norm…
Much of the community detection literature studies structural communities, communities defined solely by the connectivity patterns of the network. Often, networks contain additional metadata which can inform community detection such as the grade and gender of students in a high school social network. In this work, we i…
Bayesian methods detect clusters in noisy data more reliably.
problem Noisy data distorts traditional clustering methods, leading to unreliable results.
method Bayesian community detection using Minimum Description Length principle.
result Bayesian methods identify more robust clusters in noisy data.
Standard methods for anomaly detection assume that all features are observed at both learning time and prediction time. Such methods cannot process data containing missing values. This paper studies five strategies for handling missing values in test queries: (a) mean imputation, (b) MAP imputation, (c) reduction (redu…
KPCA improves OoD detection by separating InD and OoD data.
problem Insufficiency of PCA in detecting OoD data from InD data.
method Kernel PCA (KPCA) with task-specific kernels.
result KPCA achieves superior OoD detection performance.
Improved YOLOv5 model detects mask-wearing with enhanced accuracy.
problem Detecting mask-wearing in high traffic public places.
method Improved YOLOv5l with Multi-Head Attentional Self-Convolution, Swin Transformer Block, I-CBAM module, and enhanced feature fusion.
result 1.1% improvement in mAP(0.5) and 1.3% improvement in mAP(0.5:0.95) compared to YOLOv5l.
A novel method using saliency maps detects adversarial attacks effectively.
problem Vulnerability of neural networks to adversarial attacks.
method Combining saliency maps with additional noises and inconsistency strategy.
result High detection success rate of adversarial attacks on various datasets.
We show that the detection of geometric intersection in an arbitrary representation of the mapping class group of surface implies the injectivity of that representation up to center, and vice versa. As an application, we discuss the geometric intersection in the Johnson filtration. Also, we further consider the problem…
Structural variants compose the majority of human genetic variation, but are difficult to assess using current genomic sequencing technologies. Optical mapping technologies, which measure the size of chromosomal fragments between labeled markers, offer an alternative approach. As these technologies mature towards becom…
Detecting and mapping informal settlements encompasses several of the United Nations sustainable development goals. This is because informal settlements are home to the most socially and economically vulnerable people on the planet. Thus, understanding where these settlements are is of paramount importance to both gove…
Proposes a novel anomaly detection method for echocardiogram videos.
problem Anomaly detection in echocardiogram videos.
method Dynamic Variational Trajectory Models (TVAE-C, TVAE-R, TVAE-S) trained on healthy infant echocardiogram videos.
result Superior performance in detecting congenital heart defects and pulmonary hypertension.
CMC method detects causal relationships in time series data.
problem Understanding causal relationships in nonlinear systems.
method Cross-Mapping Coherence method, based on nonlinear state-space reconstruction and coherence metrics.
result CMC accurately identifies causal connections in various systems.
Contagion maps detect network structure in noisy data.
problem Detecting underlying manifold structure in noisy data.
method Using activation times in threshold contagions to map network nodes to high-dimensional space.
result Contagion maps reliably detect manifold structure in noisy data, while Isomap fails.
CHAODA detects anomalies in high-dimensional data.
problem Anomaly detection in high-dimensional spaces.
method Hierarchical clustering, manifold mapping, transfer learning.
result CHAODA outperforms other algorithms on 16 out of 18 datasets.
Algorithm detects free products in disk mapping class groups.
problem Detecting free products in mapping class groups of punctured disks.
method Algorithm based on Dynnikov coordinates to verify completeness and reveal free product structure.
result Algorithm determines exact structure of free products generated by Dehn twists.
Autonomous driving requires 3D perception of vehicles and other objects in the in environment. Much of the current methods support 2D vehicle detection. This paper proposes a flexible pipeline to adopt any 2D detection network and fuse it with a 3D point cloud to generate 3D information with minimum changes of the 2D d…
This paper improves anomaly detection in lane rendering images for safer navigation.
problem Anomalies in lane rendering images can mislead drivers, posing safety risks.
method Proposes a four-phase pipeline using Transformer models, self-supervised pre-training, and fine-tuning.
result The pipeline enhances detection accuracy and reduces training time.
A new method detects communities in multi-relational networks.
problem Detect communities in heterogeneous multi-relational networks.
method Message passing based algorithm on a hierarchical structure of homogeneous networks.
result Effectiveness of the proposed method confirmed on synthetic and real-world networks.
We describe a birational map between subvarieties in the character varieties of mutative 3-manifolds. By studying the birational map, one can decide in certain circumstances whether a mutation surface is detected by an ideal point of the character variety.
Study uncovers bias in image classification models using attribution maps.
problem Data bias in image classification models.
method Created an artificial dataset with known bias, trained CNNs, and used attribution maps to inspect decisions.
result Different attribution map techniques highlight bias better than others, and metrics support bias identification.
Fastest video anomaly detection via teacher-student distillation.
problem Anomaly detection in video at high speed.
method Adversarial knowledge distillation from object-level teacher models.
result 7-62 times faster than state-of-the-art methods.
We prove asymptotic faithfulness for the quantum Sp(4) mapping class group representation. This provides the first example of asymptotic faithfulness lying outside of the An family. The methods used are generalized from the proof of asymptotic faithfulness for skein SU(2)k mapping class group represent…
We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder …
Balanced Activation improves object detection performance on long-tailed datasets.
problem Mismatch between training and testing label distributions in object detection.
method Introduces Balanced Activation (Balanced Softmax and Balanced Sigmoid) to address label distribution shift.
result Balanced Activation provides ~3% gain in mAP on LVIS-1.0 compared to state-of-the-art methods.
Universal approach combines OOD detection scores for robustness.
problem Combining diverse OOD detection scores for robustness.
method Quantile normalization to p-values, meta-analysis, probabilistic interpretation.
result Significantly improved robustness and performance across diverse OOD detection scenarios.
MethaneMapper detects methane emissions with high accuracy and reduced model size.
problem Challenges in detecting and quantifying methane emissions from AVIRIS-NG data.
method Spectral absorption wavelength aware transformer network, introducing two novel modules.
result Achieves 0.63 mAP in detection and reduces model size by 5x.
It is shown that Schneiderman and Teichner's invariant τ detects the example due to Kirk of a link map f:S+2∪S−2→S4 for which the restricted map f∣S+2:S+2→S4−f(S−2) has vanishing Wall self-intersection but is not homotopic to an embedding.
Ensemble unsupervised anomaly detection using IRT for hidden ground truth.
problem Challenges in constructing an ensemble from unsupervised anomaly detection methods.
method Use Item Response Theory to compute latent traits and construct an ensemble that downplays noisy methods.
result Demonstrated effectiveness of IRT ensemble on extensive data repository.
Detecting and mapping informal settlements encompasses several of the United Nations sustainable development goals. This is because informal settlements are home to the most socially and economically vulnerable people on the planet. Thus, understanding where these settlements are is of paramount importance to both gove…
CEBMs learn flexible latent mappings from data.
problem Learning flexible latent mappings from data.
method CEBMs decompose joint density into tractable posterior over latent variables.
result CEBMs achieve competitive results in image modeling and latent space predictive power.
Detect changes in noisy dynamical systems using empirical approximations and finite-sample bounds.
problem Change detection in noisy dynamical systems
method Partition-based empirical approximations and finite-state stationary distribution stability
result Finite-sample bound for empirical stationary density
Algorithm speeds up search for stationary targets with guaranteed accuracy.
problem Minimize search time while ensuring high detection accuracy of stationary targets.
method Multi-fidelity Gaussian process model and EMTS algorithm.
result Guaranteed performance in target detection accuracy and search time.
New method detects RNA modifications without prior training, revealing novel sites.
problem Detecting RNA modifications with high accuracy and sensitivity.
method Anomaly detection using nanopore raw ionic current signals and nearest neighbor comparison.
result Detects diverse RNA modifications without prior training, including a novel 2'-O-methylated site in DENV.
Deep neural features identify unique vehicles from dash-cam feeds.
problem Identifying unique vehicles in dash-cam feeds for self-driving cars.
method Used pretrained YOLO network feature maps to create deep integrated feature signatures (DIFS) for 700 images of 35 vehicles and 340 images of 17 vehicles.
result Correctly identified unique vehicles at 96.7% for high resolution data and 86.8% for lower resolution data.
We study the action of the mapping class group on the real homology of finite covers of a topological surface. We use the homological representation of the mapping class to construct a faithful infinite-dimensional representation of the mapping class group. We show that this representation detects the Nielsen-Thurston …
CST-YOLO improves blood cell detection with YOLOv7 and CNN-Swin Transformer.
problem Small-scale object detection in blood cells.
method YOLOv7 architecture enhanced with CNN-Swin Transformer, W-ELAN, MCS, CatConv.
result CST-YOLO achieves 92.7%, 95.6%, and 91.1% mAP@0.5 on three blood cell datasets.
New PCA method detects faults using occupation kernels.
problem Fault detection in dynamical systems.
method Occupation kernel PCA for irregularly sampled data.
result Validation of reconstruction error approach for fault detection.
We propose a technique to develop (and localize in) topological maps from light detection and ranging (Lidar) data. Localizing an autonomous vehicle with respect to a reference map in real-time is crucial for its safe operation. Owing to the rich information provided by Lidar sensors, these are emerging as a promising …
New algorithm detects changes in high-dimensional data with mean and variance.
problem Challenges in detecting changes in high-dimensional data with mean and variance.
method Complete graph-based approach to detect changes of mean and variance from low to high-dimensional online data.
result The proposed method outperforms existing methods in terms of detection power.
PRESTO maps latent representations across diverse ML models.
problem Understanding variability in latent representations across different ML models.
method Uses persistent homology to characterize latent spaces and measure their pairwise similarity.
result Preserves desirable properties and enables sensitivity analysis of latent representations.
In the present paper, we study the Sp-module structure of the cokernel of the Johnson homomorphism of the mapping class groups of surfaces. We detect the Sp-irreducible components with highest weight [1^k] (and [k]) in the cokernel. We also show that the multiplicities of them is equal to one.