Unified theory explains and mitigates double descent in data reconstruction.
problem Understanding and mitigating double descent in reduced order modeling.
method Data-Noise Averaging theory, sufficient criteria, detailed risk curve prediction, regularization mechanisms.
result Detailed risk curves predicted at reduced computational cost, instability traced to individual sensors.
This work introduces a method to learn dynamical systems from noisy sensor measurements using multiple shooting.
problem Learning dynamical systems from noisy sensor measurements is challenging due to system instability.
method A scalable method based on multiple shooting.
result Robust learning of latent representations of dynamical systems from noisy measurements.
This work builds a sensor graph from DC sensors for anomaly detection.
problem Anomaly detection in data centers with complex sensor relationships.
method Data-driven pipeline (ts2graph) to build a sensor graph from sensor time series.
result Graph neural network (GNN) outperforms existing methods by 2-3 times in anomaly detection.
We improve current instability-based methods for the selection of the number of clusters k in cluster analysis by developing a normalized cluster instability measure that corrects for the distribution of cluster sizes, a previously unaccounted driver of cluster instability. We show that our normalized instability mea…
Modeling HFT interactions reveals market instability.
problem Market instability caused by HFT dynamic coupling.
method Developed a recurrence relations framework to model HFT interactions.
result Unexpected latency and feedback can trigger market instability.
New proof of instability for certain Einstein metrics.
problem Einstein metrics on specific 4-manifolds.
method Proving instability of conformally Kähler, Einstein metrics.
result Proven instability of certain Einstein metrics.
Proposes a neural network for handling multi-sensor time series with varying input dimensions.
problem Handling multi-sensor time series with varying input dimensions.
method Graph neural network conditioning vectors for zero-shot transfer learning.
result Better generalization in activity recognition and equipment prognostics datasets.
RESPIRE calibrates low-cost air-quality sensors for CO levels, resistant to outliers.
problem Calibrating LCAQ sensors against regulatory-grade monitors is expensive and time-consuming.
method PROvably outlier-resistant semi-parametric regression technique.
result RESPIRE offers improved prediction in cross-site, cross-season, and cross-sensor settings.
Study stability and instability of Ricci-flat metrics under generalized Ricci flow.
problem Stability and instability of Ricci-flat metrics under Ricci flow.
method Analysis of generalized Ricci flow for Ricci-flat metrics and vanishing 3-forms.
result Dynamical stability and instability results for Ricci-flat metrics and vanishing 3-forms.
Some exotic compact objects possess evanescent ergosurfaces: timelike submanifolds on which a Killing vector field, which is timelike everywhere else, becomes null. We show that any manifold possessing an evanescent ergosurface but no event horizon exhibits a linear instability of a peculiar kind: either there are solu…
Method learns behavioral states from wearable sensor data.
problem Understanding behavioral patterns from sensor data.
method Non-parametric Bayesian approach to model sensor data.
result Learned behavioral states cluster participants into meaningful groups and predict psychological states.
Interval Neural Networks detect instabilities in image reconstructions.
problem Detecting instabilities in deep learning image reconstructions.
method Employed uncertainty quantification methods with Interval Neural Networks.
result Interval Neural Networks effectively reveal image reconstruction instabilities.
Note on instabilities in super-time-stepping methods for Heston model.
problem Instabilities in super-time-stepping methods applied to Heston model.
method Exploration of explicit super-time-stepping schemes (RK-Chebyshev, RK-Legendre) for Heston model.
result Relevance of stability remarks beyond super-time-stepping schemes.
Low-cost sensors improve air quality prediction accuracy significantly.
problem Improving air quality monitoring networks with affordable sensors.
method Developed a high-resolution air quality prediction engine using low-cost sensors and official data.
result The use of low-cost sensors improves prediction accuracy by 25% and 15% for PM2.5 and PM10 respectively in densely monitored areas.
The paper explores how word embeddings affect the stability of downstream NLP models.
problem Small changes in training data can cause significant changes in model predictions.
method Empirical and theoretical analysis of embedding instability, including the introduction of eigenspace instability measure.
result Increasing embedding memory can reduce the disagreement in predictions by 5% to 37%.
Improved DOA estimation with distributed sensors across multiple frequencies.
problem Sensor gain uncertainties and directional perturbations in multi-frequency scenarios.
method Distributed optimization with local coherence models and iterative exchange of information.
result Advantages in statistical and computational efficiency through parallel iterative technique.
Adversarial approach enhances sensor fusion for robust target detection.
problem Improving target detection and classification using multi-modal sensor fusion.
method Generative network learns latent space from various sensor modalities, then detects damaged sensors and safeguards performance.
result Automatic robustness against noisy/damaged sensors achieved.
Paper presents a WiFi-based indoor sensor localization technique.
problem Indoor localization in wireless sensor networks.
method Zoning-based localization using statistical learning.
result Efficient sensor zone determination in indoor environments.
In this paper, we present a framework to control a self-driving car by fusing raw information from RGB images and depth maps. A deep neural network architecture is used for mapping the vision and depth information, respectively, to steering commands. This fusion of information from two sensor sources allows to provide …
We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query bas…
Regularized recurrent attention filter combines sensor inputs.
problem Combining information from different sensor modalities.
method Regularized recurrent attention filter, co-learning mechanism, probabilistic graphical model.
result Dynamic sensor fusion and latent representation recovery.
FATHOM model improves sensor data analysis with attention and LSTM.
problem Scarcity of training data from multiple sensors.
method Federated multi-task hierarchical attention model (FATHOM) with attention mechanism and LSTM.
result FATHOM outperforms baselines in sensor data classification and regression.
ConvGNP improves sensor placement for climate monitoring.
problem Maximizing informativeness of environmental sensor placements in remote regions.
method Convolutional Gaussian neural processes (ConvGNP) for non-stationary spatial predictions.
result ConvGNP outperforms traditional GP models in predicting sensor performance and reducing uncertainty.
Binary perceptron's instability linked to replica symmetry breaking.
problem Understanding the relationship between algorithmic instability and replica symmetry breaking in binary perceptron learning.
method Established the connection between algorithmic instability and replica symmetry breaking by comparing the instability condition around the fixed point to the instability for breaking the replica symmetric solution of the free energy function.
result The instability condition around the algorithmic fixed point is identical to the instability for breaking the replica symmetric saddle point solution of the free energy function.
A central area of research in nonlinear science is the study of instabilities that drive the emergence of extreme events. Unfortunately, experimental techniques for measuring such phenomena often provide only partial characterization. For example, real-time studies of instabilities in nonlinear fibre optics frequently …
Sensor drift is a well-known issue in the field of sensors and measurement and has plagued the sensor community for many years. In this paper, we propose a sensor drift correction method to deal with the sensor drift problem. Specifically, we propose a discriminative subspace projection approach for sensor drift reduct…
This paper designs sensor arrays for estimating unsteady flows efficiently.
problem Estimating high-dimensional unsteady flow fields with limited sensor placement.
method Combines data-driven modeling, Kalman Filter design, and sparsification for sensor selection.
result Proposed sensor arrays are highly effective for flow-field estimation across various conditions.
Optimizes seismic monitoring networks using Bayesian OED.
problem Improve seismic event identification and location.
method Bayesian optimal experimental design (OED) to configure sensor networks.
result Optimized sensor network improves seismic event identification and location.
Robots rely on sensors to provide them with information about their surroundings. However, high-quality sensors can be extremely expensive and cost-prohibitive. Thus many robotic systems must make due with lower-quality sensors. Here we demonstrate via a case study how modeling a sensor can improve its efficacy when em…
With the rising number of interconnected devices and sensors, modeling distributed sensor networks is of increasing interest. Recurrent neural networks (RNN) are considered particularly well suited for modeling sensory and streaming data. When predicting future behavior, incorporating information from neighboring senso…
Develops a geometric framework for soap film instabilities.
problem Soap film instabilities on minimal surfaces.
method Björling representation of minimal surfaces.
result Jacobi fields are maximally localized with lowest eigenfunction amplitude.
AutoEncoder smooths noisy sensor data and interpolates missing values.
problem Noisy sensor data and missing timepoints require interpolation.
method Uses AutoEncoder to denoise and interpolate data.
result AutoEncoder improves data quality and reveals dynamics.
Sensor fusion has wide applications in many domains including health care and autonomous systems. While the advent of deep learning has enabled promising multi-modal fusion of high-level features and end-to-end sensor fusion solutions, existing deep learning based sensor fusion techniques including deep gating architec…
Self-attention model improves HAR from wearable sensors.
problem Capturing spatio-temporal context from sensor data.
method Proposes a self-attention based neural network model.
result Significant performance improvement over state-of-the-art models.
Adversarial perturbations fool wearable sensor systems, showing transferability across different systems.
problem Adversarial examples fool wearable sensor systems, showing transferability across different systems.
method Study of adversarial transferability in wearable sensor systems from four perspectives: systems, subjects, sensor body locations, and datasets.
result Strong untargeted transferability in most cases, targeted attacks less successful.
Predicts traffic flow using reinforcement learning and sensor data.
problem Accurately predict expanding and evolving long-term streaming traffic networks.
method Formulates the problem as a continuous reinforcement learning task, where the agent predicts future traffic based on sensor data.
result The approach improves accuracy in predicting traffic flow by updating the agent's state representation over time.
Deep RL optimizes sensor placement in digital twins for dynamic data acquisition.
problem Limited applicability of traditional sensor placement techniques for online applications.
method Formulates sensor placement as a Markov decision process and uses deep reinforcement learning.
result Improves predictive accuracy and reliability of digital twins through adaptive sensor repositioning.
Weather balloons deploy sensors to collect stratospheric data.
problem Limited data collection in the stratosphere.
method Modeling forecast deviation as a Gaussian process to determine sensor release times; novel hardware system for optimal sensor release.
result Data engineering framework effectively collects stratospheric data through real flights and simulations.
The paper optimizes sensor selection for network time series data.
problem Optimizing sensor selection for network time series data with minimal error.
method Data-driven strategies to turn off sensors or select a sampling set of nodes.
result Proposes and compares various data-driven strategies for sensor selection.
The study examines stability and instability of Poincaré-Einstein metrics using Ricci flow.
problem Stability and instability of Poincaré-Einstein metrics.
method Variant of expander entropy for asymptotically hyperbolic manifolds, local positive mass theorem, volume comparison.
result Characterization of stability and instability in terms of local positive mass theorem and volume comparison.
Paper proposes methods to localize sources in WSNs without knowing sensor parameters.
problem Source localization in WSNs without sensor parameter knowledge.
method Hitting set approach and feature selection method.
result Effective source localization methods validated through simulations.
Improved sleep apnea detection using sensor fusion and backward shortcut connections.
problem Untreated sleep apnea leads to severe health consequences; automated detection is needed.
method Late sensor fusion using backward shortcut connections to improve deep learning models.
result Significant improvement in predictive performance over single sensor methods.
The problem of information fusion from multiple data-sets acquired by multimodal sensors has drawn significant research attention over the years. In this paper, we focus on a particular problem setting consisting of a physical phenomenon or a system of interest observed by multiple sensors. We assume that all sensors m…
VIDON learns operators with variable sensors, overcoming sensor limitations.
problem Fixed sensor locations restrict operator learning applicability.
method Variable-Input Deep Operator Network (VIDON) with random, varying sensors.
result VIDON efficiently approximates operators in PDEs and is robust to sensor permutations.
Study shows instability of naked singularities in perfect fluid models.
problem Instability of naked singularities in Einstein equations coupled with isothermal perfect fluid.
method Investigated spherically symmetric self-similar naked singularities under C1,α perturbations of an external massless scalar field. result Spherically symmetric self-similar naked singularities are unstable to trapped surface formation.
Study on spectral stability of Riemannian coverings.
problem Stability of eigenvalues in Riemannian coverings.
method Analysis of Laplacian eigenvalues under finite coverings.
result Necessary conditions for spectral stability or instability.
Deep RL predicts equipment maintenance from sensor data.
problem Equipment downtime due to sensor data overload.
method Model-free Deep Reinforcement Learning for optimal maintenance policy.
result Automatic maintenance policy learning from sensor data.
Clinical models can be unstable, leading to unreliable predictions.
problem Stability of clinical prediction models developed using statistical or machine learning methods.
method Simulation and case studies of statistical and machine learning approaches to show instability in model predictions.
result Model instability often leads to miscalibration of predictions in new data.