Framework transfers inertial motion across domains without labeled data.
problem Inertial measurements are sensitive to sensor placement and motion dynamics.
method Extracts domain-invariant features and transfers them to new domains.
result Framework converts raw IMU sequences into accurate inertial trajectories.
Jointly estimates flow fields and particle properties from Lagrangian data.
problem Estimating flow fields and particle properties from sparse, noisy Lagrangian data.
method Data assimilation framework coupling Eulerian and Lagrangian models.
result Joint estimation of flow fields and particle properties in various flow regimes.
A deep learning model improves pedestrian tracking accuracy.
problem Pedestrian tracking accuracy is low, especially with inertial measurement unit.
method Deep learning model using IMU and LIDAR data, attention mechanism.
result Preliminary results show improved accuracy.
Human inertial thinking schemes can be formed through learning, which are then applied to quickly solve similar problems later. However, when problems are significantly different, inertial thinking generally presents the solutions that are definitely imperfect. In such cases, people will apply creative thinking, such a…
RIO uses rotation-equivariance to train robust inertial odometry models.
problem Training robust inertial odometry models with limited labeled data.
method Rotation-equivariance as self-supervisor, adaptive Test-Time Training (TTT).
result RIO-trained models achieve on-par performance with full-labeled data models.
Method infers depth from sparse points and camera motion.
problem Depth inference from limited sparse data.
method Constructs a planar scaffolding and uses predictive cross-modal criterion.
result State-of-the-art performance on depth completion benchmark.
This paper proposes IMU preintegrated features for efficient deep inertial odometry.
problem Efficient odometry from IMU data is challenging due to sensor imperfections and noise.
method Proposes IMU preintegrated features exploiting IMU motion model's manifold structure.
result Improves odometry performance and reduces computational burdens.
Inertial methods solve non-convex non-smooth optimization problems efficiently.
problem Non-convex non-smooth optimization problems.
method Inertial block proximal methods for solving these problems.
result The methods converge globally under certain conditions and perform well in applications like NMF.
We study the Whitehead torsions of inertial h-cobordisms, and identify various types representing a nested sequence of subsets of the Whitehead group. A number of examples are given to show that these subsets are all different in general.
Neural networks outperform conventional filters in inertial sensor-based attitude estimation.
problem Limited accuracy in inertial sensor-based attitude estimation due to dynamic and static motion.
method Investigated neural networks versus conventional filters for improving accuracy.
result Neural networks outperform conventional filters only with domain-specific optimizations.
The proximal inertial gradient descent is efficient for the composite minimization and applicable for broad of machine learning problems. In this paper, we revisit the computational complexity of this algorithm and present other novel results, especially on the convergence rates of the objective function values. The no…
This work uses scientific constraints to validate neural network predictions in fusion physics.
problem Verifying the scientific plausibility of neural network predictions in fusion physics.
method Using known scientific constraints as a validation tool.
result Validated neural network predictions in fusion physics using scientific constraints.
Improved UAV navigation and landing using deep learning.
problem Autonomous navigation and landing of UAVs with high accuracy.
method Multimodal fusion of visual and inertial sensor data using deep neural networks.
result 25% improvement in pose estimation accuracy compared to traditional methods.
New deep fusion methods improve human action recognition using depth and inertial sensor data.
problem Existing multimodal HAR frameworks lack mid-level feature fusion.
method Proposes three deep multilevel multimodal fusion frameworks, transforming depth and inertial sensor data into images and using convolution with Prewitt filter to create modality within modality.
result Supremacy of proposed fusion frameworks over existing methods on three publicly available datasets.
New method learns routines from inertial data without privacy concerns.
problem Recognizing human activities with limited sets of specific activities.
method Metric learning problem defined for time series, SS2S architecture.
result Clustering recovers daily routines from learned distance.
Classically time is kept fixed for infinitesimal variations in problems in mechanics. Apparently, there appears to be no mathematical justification in the literature for this standard procedure. This can be explained canonically by unveiling the intrinsic mathematical structure of time in Lagrangian mechanics. Moreover…
Study classifies surface types for autonomous indoor robots using inertial data.
problem Classifying surface types for wheeled robots in indoor environments.
method Prepared a time series dataset of inertial measurements, used deep learning and ensemble machine learning models.
result Baseline model achieved over 68% accuracy on a nine-category surface type dataset.
New method improves matrix factorization speed and accuracy.
problem Matrix factorization optimization problems suffer from biased solutions and lack of convergence guarantees.
method Proposes a novel Bregman distance for matrix factorization, enabling non-alternating schemes with convergence proof.
result Convergence to a stationary point proved for matrix factorization problems.
Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts and unknown initial states in real applications, which renders the estimated trajectories inaccurate and not obtained in a well defined coordinate system for matching trajectories of different users. In this paper, an approach adopting receiv…
SympFormer accelerates attention blocks using inertial dynamics on density spaces.
problem Improving the efficiency of self-attention blocks in Transformers.
method Introduced accelerated attention blocks derived from inertial Nesterov dynamics on density spaces.
result Accelerated attention blocks converge faster than classical blocks while preserving oracle calls.
Accelerates coordinate descent methods for machine learning problems.
problem Slowness of coordinate descent methods in machine learning.
method Extrapolation-based accelerated coordinate descent.
result Significant speed-up in practice compared to existing methods.
Paper proposes a deep learning method for better IMU gyroscope data.
problem Improving accuracy of IMU gyroscope data for robot orientation estimation.
method Dilated convolution neural network, proper loss function, key points identification.
result Algorithm outperforms state-of-the-art on unseen test sequences.
CNNs classify human activities from IMU data.
problem Automatic identification of physical activities using motion sensors.
method Used Convolutional Neural Networks (CNNs) with raw IMU data.
result CNNs perform well in classifying 16 lower-limb activities.
Improved neural network surrogates for ICF using manifold and cycle consistency.
problem Modeling and predicting complex physical processes in inertial confinement fusion.
method Training neural network surrogates that are consistent with the physical manifold and cyclically consistent.
result Surrogates are superior in predictive performance, more resilient to sampling artifacts, and more data efficient.
TransFall uses transfer learning to improve activity recognition from mobile sensors.
problem Performance degradation due to platform and user movement differences.
method Two-tier data transformation, label estimation, and model generation layers.
result TransFall enhances activity recognition accuracy for new scenarios.
Novel AI-IMU method accurately estimates vehicle position and orientation.
problem Accurate dead-reckoning for wheeled vehicles using only IMU.
method Kalman filter and deep neural networks for noise adaptation.
result Average 1.10% translational error, competitive with LiDAR or stereo vision methods.
Seq2seq models predict complex multi-physics systems' time evolution.
problem Predicting the time-evolution of complex multi-physics systems.
method Sequence-to-sequence models applied to multi-physics simulations.
result Seq2seq models accurately emulate complex systems and predict their evolution.
Transfer learning improves fusion simulation accuracy.
problem Calibrate fusion simulation models to experimental data.
method Hierarchical transfer learning using deep neural networks.
result Calibrated models predict Omega experiments more accurately.
We prove that the essential smoothness of the gravitational metric at shock waves in GR, a PDE regularity issue for weak solutions of the Einstein equations, is determined by a geometrical condition which we introduce and name the {\it Riemann-flat condition}. The Riemann-flat condition determines whether or not the es…
Solves memorization in diffusion models for manifold data.
problem Memorization effect in diffusion models for manifold data.
method Inertia update at the end of empirical diffusion simulation.
result Approximates true data distribution on a C2 manifold. Deep learning gait recognition from smartphone data in unconstrained settings.
problem Gait recognition in unconstrained environments using smartphones.
method Hybrid deep neural network combining convolutional and recurrent neural networks for gait feature extraction.
result Achieves over 93% accuracy in person identification and authentication.
Robotics: Rolling robots on a moving platform can be controlled.
problem Controlling the motion of rolling robots atop a moving platform.
method Developed a mathematical model and demonstrated simulations.
result Platform acceleration can control robot's heading and motion.
The Schroedinger operator on the Newtonian space-time is defined in a way which is independent on the class of inertial observers. In this picture the Schroedinger operator acts not on functions on the space-time but on sections of certain one-dimensional complex vector bundle over space-time. This bundle, constructed …
INNA is a new second-order optimization method for deep learning.
problem Training deep neural networks efficiently and accurately.
method INNA combines gradient-descent and Newton-like behaviors with inertia, proven to converge for most deep learning problems.
result INNA proves sublinear convergence for deep learning loss functions and uses aggressive learning rates.
Personalized activity recognition improves performance for diverse users.
problem Poor performance of impersonal algorithms for individual users.
method Personalized activity recognition using deep embeddings from a fully convolutional neural network with triplet loss.
result Novel subject triplet loss provides the best performance overall.
In this paper, we develop a new sequential regression modeling approach for data streams. Data streams are commonly found around us, e.g in a retail enterprise sales data is continuously collected every day. A demand forecasting model is an important outcome from the data that needs to be continuously updated with the …
The Schroedinger operators on the Newtonian space-time are defined in a way which make them independent on the class of inertial observers. In this picture the Schroedinger operators act not on functions on the space-time but on sections of certain one-dimensional complex vector bundle -- the Schroedinger line bundle. …
Revisits Finsler spacetimes from inertial observer perspective.
problem Physical foundations of relativistic spacetimes.
method Inertial observers and double linear approximation.
result Finsler spacetimes are defined by dropping the second linearization.
A frame independent formulation of analytical mechanics in the Newtonian space-time is presented. The differential geometry of affine values i.e., the differential geometry in which affine bundles replace vector bundles and sections of one dimensional affine bundles replace functions on manifolds, is used. Lagrangian a…
Develops a gradient flow for Muon optimizer, a method for optimization.
problem Optimization of complex systems with matrix-valued parameters.
method Gradient flow on probability measures induced by regularized Muon optimizer.
result Derives continuous-time limits and proves Hamiltonian dissipation.
The Wall surgery obstruction groups have two interesting geometrically defined subgroups, consisting of the surgery obstructions between closed manifolds, and the inertial elements. We show that the inertia group In+1(π,w) and the closed manifold subgroup Cn+1(π,w) are equal in dimensions n+1≥6, for any…
Automates feature extraction for IMU-based activity recognition.
problem Manual feature engineering is time-consuming and limits IMU-based applications.
method FRESH algorithm for automated feature extraction.
result Workflow automates feature extraction from synchronized IMU sensors.
New derivation shows spacetime interval is quadratic without light.
problem Deriving spacetime geometry from fundamental principles.
method Formalizing axioms of smoothness, homogeneity, isotropy, and determinism.
result Invariant spacetime interval is quadratic, independent of light.
RL approach for target tracking with unknown dynamics and sensor control.
problem Tracking an unknown target with sensor control.
method Track-MDP formulation for RL, compared with POMDP.
result Optimal RL policy tracks all target paths with certainty.
This work uses SVM to identify track component failures in AC Track Circuits.
problem Detecting and identifying specific track component failures in AC Track Circuits.
method Applied SVM classifier to STDS track circuit data.
result Successfully classified 15 different track component failures.
New train tracks for complex homeomorphisms found.
problem Existence of irreducible train tracks for pseudo-Anosov homeomorphisms.
method Starting from a veering triangulation, identify and modify branches to bypass obstructions.
result Construction of invariant train tracks with irreducible transition matrix.
New method uses cluster shapes to improve track finding in particle collisions.
problem Combining timing and additional detector information for efficient track finding.
method Neural networks to analyze cluster shapes for track seeding.
result Cluster shapes reduce fake combinatorial backgrounds while maintaining high track efficiency.
Value-tracking in financial markets breaks down when non-valuation-based traders dominate.
problem Understanding the threshold for value-tracking in financial markets.
method Simple discrete-time model to show how non-valuation-based traders can cause tracking errors.
result A threshold above which value-tracking breaks down without changes in asset value.