Deep learning improves neutrino-nucleus interaction vertex reconstruction.
problem Vertex reconstruction of neutrino-nucleus interaction events.
method Combining energy and timing data for classification and regression tasks using deep learning.
result The model achieves 4.00% higher classification accuracy and 0.9919 higher regression accuracy than previous methods.
We introduce a novel implementation in ANSI C of the MINE family of algorithms for computing maximal information-based measures of dependence between two variables in large datasets, with the aim of a low memory footprint and ease of integration within bioinformatics pipelines. We provide the libraries minerva (with th…
This letter presents the sparse vector signal detection from one bit compressed sensing measurements, in contrast to the previous works which deal with scalar signal detection. In this letter, available results are extended to the vector case and the GLRT detector and the optimal quantizer design are obtained. Also, a …
Adversarial perturbations fool deepfake detectors with high accuracy.
problem Improving deepfake detection accuracy against adversarial attacks.
method Used adversarial perturbations and two defenses: Lipschitz regularization and Deep Image Prior (DIP).
result Deepfake detectors achieved 27% accuracy on perturbed images, compared to 95% on unperturbed.
This paper studies concept drift detectors for financial time series.
problem Improving accuracy on financial time series with concept drifts.
method Three simple concept drift detectors tailored to financial time series.
result Two of the detectors are as effective as state-of-the-art detectors.
Graph networks improve particle reconstruction in irregular detectors.
problem Handling irregular particle-detector geometries in particle reconstruction.
method Introduce distance-weighted graph network architectures (GarNet, GravNet layers) for irregular geometry detectors.
result The proposed graph networks provide equally performing or less resource-demanding solutions compared to existing methods.
In this paper, deep neural network (DNN) is utilized to improve the belief propagation (BP) detection for massive multiple-input multiple-output (MIMO) systems. A neural network architecture suitable for detection task is firstly introduced by unfolding BP algorithms. DNN MIMO detectors are then proposed based on two m…
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.
New method uses eigenspectrum to optimize object detector architectures.
problem Understanding the effects of ImageNet pre-training on object detectors.
method Analysis of eigenspectrum dynamics of feature maps in object detectors.
result Object detectors trained from scratch and ImageNet pre-trained models behave differently.
New method uses neural networks to estimate parameters without needing detector simulations.
problem Estimating parameters in high-energy physics with detector effects.
method Two-level fitting approach: SRGN (Simulation-level fit based on Reweighting Generator-level events with Neural networks).
result Demonstrated using simulated datasets, SRGN can estimate parameters without detector effects.
We introduce a Bayesian defect detector to facilitate the defect detection on the motion blurred images on rough texture surfaces. To enhance the accuracy of Bayesian detection on removing non-defect pixels, we develop a class of reflected non-local prior distributions, which is constructed by using the mode of a distr…
Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, such an approach suffers from electricity theft cyberattacks. Different from the existing research that utilizes shallow, static, and customer-specific-based electricity theft detectors, this paper p…
TomOpt optimizes muon detector designs using differentiable programming.
problem Designing efficient particle detectors for muon tomography.
method Differentiable programming for muon interaction modeling, inference, and optimisation.
result Demonstrated end-to-end differentiable and inference-aware optimisation of particle physics instruments.
DCSO dynamically selects top-performing base detectors for outlier ensembles.
problem Challenges in selecting and combining outlier scores from different detectors.
method DCSO dynamically selects top-performing base detectors based on local k-nearest neighbors.
result DCSO provides consistent performance improvement over static combination approaches.
Improves relevancy of black-box anomaly detectors with user feedback.
problem Users often ignore many detected anomalies, requiring a method to identify and prioritize relevant ones.
method Uses user feedback to adjust anomaly selection process based on identified anomaly types.
result Significant improvements in precision and recall over various anomaly detectors.
Variational autoencoders model water Cherenkov detector data.
problem Modeling generative distribution of water Cherenkov detector data.
method Variational autoencoders and normalizing flows.
result Demonstrated capability of variational autoencoders for generative modelling.
In this paper, we consider signal detection algorithms in a multiple-input multiple-output (MIMO) decode-forward (DF) relay channel with one source, one relay, and one destination. The existing suboptimal near maximum likelihood (NML) detector and the NML with two-level pair-wise error probability (NMLw2PEP) detector a…
Novel unsupervised MIG detectors improve signal detection in cluttered environments.
problem Signal detection in nonhomogeneous clutter environments.
method Developed novel discriminative MIG detectors using HPD matrices and geometric measures.
result Improved signal detection performance compared to conventional methods.
Unified detector calibration and simulation using MLE from generative models.
problem Combining detector calibration and simulation using traditional methods.
method Maximum likelihood estimation from conditional generative models.
result Prior-independent and non-Gaussian resolutions possible.
This paper proposes a generic classification system designed to detect security threats based on the behavior of malware samples. The system relies on statistical features computed from proxy log fields to train detectors using a database of malware samples. The behavior detectors serve as basic reusable building block…
Detect anomalies in complex networks using topological subspace detectors.
problem Detect anomalies in complex networks defined by simplicial complexes.
method Formulate a hypothesis testing framework using Neyman-Pearson matched topological subspace detectors.
result Effective detection of anomalies in foreign currency exchange networks and other real-world data.
Continuous dropout mimics brain neuron firing rates to prevent feature detector co-adaptation.
problem Preventing overfitting in deep neural networks.
method Extending binary dropout to continuous dropout, inspired by brain neuron firing rates.
result Continuous dropout improves test performance by preventing feature detector co-adaptation.
DALC customizes LSTM models for detectors in large-scale traffic networks.
problem Fine-grained traffic prediction for large-scale transportation networks.
method Formulated as a finite Markov decision process, introduced ALC algorithm for automatic customization, and developed DALC for distributed customization.
result DALC provides higher prediction accuracy than Apache Spark MLlib approaches.
DistPre predicts traffic speeds efficiently for large networks.
problem Fine-grained, accurate speed prediction for large-scale transportation networks.
method Customizes LSTM models on a cluster, sharing trained models between detectors.
result Efficient and accurate fine-grained traffic-speed prediction.
An adversarial detector identifies anomalous sequences in sequential data.
problem Detecting anomalous sequences in one-class settings with limited data.
method Solves a minimax problem to find an optimal detector against the worst-case sequences from a generator, using marked point process model.
result Demonstrated good performance on simulations and real credit card fraud datasets.
A new OOD detector using an overlap index improves accuracy without high computational costs.
problem Effective OOD detection for machine learning models in open-world scenarios.
method Proposes an overlap index-based confidence score function for OOD detection.
result The proposed method achieves competitive accuracy with lower computational costs compared to state-of-the-art detectors.
Improved histogram-based anomaly detector using extended principal component features.
problem Challenges in detecting anomalies in large, correlated datasets.
method Extended histogram-based anomaly detection using principal components.
result Significant improvement in anomaly detection accuracy with no significant increase in runtime.
Physical patches can fool object detectors anywhere in an image.
problem Physical adversarial attacks on object detection systems.
method Demonstrated a patch that suppresses all detected objects regardless of its position.
result A patch can be placed anywhere in an image to suppress all detected objects, including those far away.
New method uses SHapley Additive Explanations to identify anomaly detectors with complementary behaviors.
problem Challenges in unsupervised anomaly detection due to diverse data distributions and lack of labels.
method Characterize anomaly detectors using SHapley Additive Explanations to measure feature importance and similarity.
result Detectors with similar explanations produce correlated anomaly scores, while those with divergent explanations are complementary.
Neural network detects spectrum signals under uncertain parameters.
problem Spectrum sensing under uncertain primary user signal parameters.
method Trained neural network to detect modulated signals robustly.
result Neural network gains robustness under carrier frequency, phase, and symbol time offsets.
Study complex-valued VAEs for radar OOD detection.
problem Detecting out-of-distribution signals in complex radar environments.
method Proposed and compared several detection metrics for CVAE.
result CVAE-MSE and latent-based scores outperform ANMF-Tyler.
A novel UNet detector detects sheep in UAV imagery.
problem Detecting small objects (sheep) in UAV imagery.
method Developed a novel dataset, used various object detectors, and evaluated their performance.
result UNet detector with weighted Hausdorff distance is best for sheep detection.
Paper studies autoencoder-based anomaly detectors' robustness to adversarial poisoning attacks.
problem Adversarial poisoning attacks on online-trained autoencoder-based anomaly detectors in ICSs.
method Proposes two algorithms for generating poison samples and evaluates them on synthetic and real-world ICS data.
result Autoencoder detectors are resilient to poisoning in the face of all relevant attacks in the SWaT dataset.
Machine learning and deep learning in particular has advanced tremendously on perceptual tasks in recent years. However, it remains vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to a human. In this work, we propose to aug…
Deep learning speeds up gravitational wave analysis.
problem Computational challenge in analyzing gravitational wave data.
method Trained a neural-network to model posterior probability distributions over 15-dimensional system parameters.
result Generated accurate posterior samples at high speed.
GSI improves efficiency of large language model inference.
problem Efficiently guiding test-time alignment in large language models.
method Combines soft best-of-n scaling with a reward model and speculative samples. result Achieves higher accuracy and reduced latency compared to standard methods.
It is widely believed that the backpropagation algorithm is essential for learning good feature detectors in early layers of artificial neural networks, so that these detectors are useful for the task performed by the higher layers of that neural network. At the same time, the traditional form of backpropagation is bio…
AutoIDS uses auto-encoders to detect network intrusions efficiently.
problem Detecting network intrusions with high accuracy and efficiency.
method Semi-supervised machine learning with cascading encoder-decoder neural networks.
result AutoIDS achieves 90.17% accuracy on NSL-KDD dataset, superior to other methods.
DSDE improves OoD detection by estimating model library proportions.
problem Uncertainty quantification and balanced error rates in model selection for OoD detection.
method Inverted sequential p-value strategies, change-point detection, automatic hyperparameter selection.
result DSDE reduces FPR from 11.07% to 3.31% on CIFAR10.
With the proliferation of social media, fashion inspired from celebrities, reputed designers as well as fashion influencers has shortened the cycle of fashion design and manufacturing. However, with the explosion of fashion related content and large number of user generated fashion photos, it is an arduous task for fas…
Reviews spams are prevalent in e-commerce to manipulate product ranking and customers decisions maliciously. While spams generated based on simple spamming strategy can be detected effectively, hardened spammers can evade regular detectors via more advanced spamming strategies. Previous work gave more attention to evas…
Develops a nonparametric framework for detecting changes in sequential data.
problem Detecting changes in nonparametrically specified distributions.
method Introduces e-detectors based on e-processes for nonnegative supermartingales.
result Provides bounds on average run length and detection delay.
One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles leave when they pass through a detector among a huge number of points, so-called hi…
Method explains anomaly detection by generating normal modifications.
problem Complexity of deep learning methods in anomaly detection.
method Generates multiple alternative modifications for anomalies.
result High-quality semantic explanations provided for anomaly detection.
Methods for unsupervised anomaly detection suffer from the fact that the data is unlabeled, making it difficult to assess the optimality of detection algorithms. Ensemble learning has shown exceptional results in classification and clustering problems, but has not seen as much research in the context of outlier detecti…
Specialists tolerate defects to gain flexibility, which can be removed when needed.
problem The economic benefits and limitations of deliberately tolerating defects in decision-making.
method Analyzes the conditions under which defects can be kept and removed, using economic models and structural analysis.
result A defect is profitably removable if certain conditions are met, and the premium is the support function of the class's ROC set.
Specialists tolerate defects to gain flexibility, which can be removed when needed.
problem The economic benefits and limits of deliberately tolerating defects in decision-making.
method Analyzes the economic position of keeping and removing defects, using a coupling lemma and structural economic models.
result A defect is profitably removable if the detector-relevant distinction survives a restriction and the advantage condition holds.
Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomal…