Split learning preserves privacy in 1D CNN models for detecting heart abnormalities.
problem Privacy leakage in 1D CNN models under split learning.
method Implemented and validated an 1D CNN model under split learning, applied privacy leakage mitigation techniques.
result Split learning alone is insufficient to maintain raw data privacy in 1D CNN models.
The paper classifies soil texture using 1D CNNs on hyperspectral data.
problem Classifying soil texture from hyperspectral data.
method Developed and implemented three 1D CNNs: LucasCNN, LucasResNet, and LucasCoordConv.
result LucasCoordConv achieves the best performance in accuracy.
Improved robustness of 1D CNNs for heart arrhythmia classification.
problem Improving the robustness of 1D CNNs for classification tasks.
method Parameterization using Cayley transform and controllability Gramian for Lipschitz-bounded CNNs.
result Improved robustness of trained Lipschitz-bounded 1D CNNs for heart arrhythmia classification.
Study uses 1D-CNNs to forecast mortality in ELSA survey.
problem Forecasting mortality in the middle-aged and older population.
method 1D-CNNs applied to longitudinal data with various over/undersampling and activation functions.
result Swish nonlinearity outperforms other functions in forecasting mortality.
iSTFTNet2 improves iSTFTNet's speed and lightness with 1D-2D CNN.
problem Efficiently synthesizing high-fidelity speech.
method Improved iSTFTNet using 1D-2D CNNs for temporal and spectrogram structures.
result iSTFTNet2 is faster and more lightweight with comparable speech quality.
Simple 1D-CNN network predicts electricity loads 36 hours ahead.
problem Forecasting electricity loads for future time periods.
method Used a one-dimensional CNN with parameter scanning to optimize kernel size, filters, and dense size.
result Good forecast quality achieved with basic CNN architectures.
A new deep learning benchmark reduces resource needs.
problem Understanding and training deep learning models requires significant resources.
method Introducing MNIST-1D, a minimalist benchmark.
result MNIST-1D enables efficient deep learning research and prototyping.
Estimates reliability of nuclear fuel using advanced modeling techniques.
problem Determining the reliability of TRISO-coated particle fuel, which has small failure probabilities and expensive computational models.
method Coupled active learning, multifidelity modeling, and subset simulation.
result Multifidelity modeling strategies consistently reduce the number of high-fidelity model calls.
Paper proposes PiPs for non-stationary 1D signal analysis.
problem Handling non-stationary oscillatory data in signal analysis.
method Kernel-based optimization using Gaussian Process and Pattern-inducing Points (PiPs).
result PiPs improve signal reconstruction accuracy and robustness.
Gradient descent trains shallow neural networks to approximate functions in 1D.
problem Approximating functions in 1D with shallow neural networks trained by gradient descent.
method Gradient descent optimization of non-convex weight space for finite width networks in 1D.
result Gradient descent can approximate functions in 1D with a minimal number of weights, balancing practical performance and theoretical capabilities.
Capsule networks improve temporal data understanding, achieving 96.21% ECG accuracy.
problem Improving temporal data understanding with capsule networks.
method Generated capsules along temporal and channel dimensions, learning contrasting relationships.
result Achieved 96.21% accuracy on ECG signal beat categories, surpassing state-of-the-art.
New method trains generative models without discriminators, improving stability and accuracy.
problem Training implicit generative models with adversarial discriminators leads to instability and mode-dropping.
method Invariant statistical loss function, avoiding discriminators.
result Successfully trains generative models for various complex distributions without mode-dropping.
This paper improves GANs for 1D data using reparameterizations.
problem Improving GAN performance for 1D data.
method Identifies and uses reparameterizations of Taylor and Fourier series.
result Reparameterized critics outperform standard GANs.
Estimates latent positions in 1D torus from noisy pairwise affinities.
problem Estimating latent positions in a 1D torus from noisy pairwise affinities.
method Introduced an estimation procedure with provable localization error of O ( log ( n ) / n ) O(\sqrt{\log(n)/n}) O ( log ( n ) / n ) . result The estimation procedure provably localizes latent positions with a maximum error of O ( log ( n ) / n ) O(\sqrt{\log(n)/n}) O ( log ( n ) / n ) . Study on the complexity of 1D ReLU neural networks, proving growth in linear regions.
problem Understanding the complexity and expressivity of 1D ReLU neural networks.
method Analyzing the number of linear regions in randomly initialized, fully connected 1D ReLU networks in the infinite-width limit.
result The expected number of linear regions grows as a function of the number of neurons in each layer.
The paper proposes an ensemble of convolution-based methods for fault detection in gearboxes.
problem Fault detection in planetary gearboxes using vibration signals.
method Ensemble of three convolution kernel-based methods (ROCKET, 1D CNN with ResNet, FCN).
result Outperforms other approaches with over 98.8% accuracy.
This paper reconstructs equivalence classes in 1D neural networks.
problem Reconstructing equivalence classes in neural networks.
method Singular Riemannian geometry approach.
result Algorithm to build the set of points on the same equivalence class.
Study shows how 3+1D cosmologies can evolve to de Sitter space under certain conditions.
problem Understanding the evolution of 3+1D cosmologies with specific symmetry constraints.
method Mean Curvature Flow methods applied to cosmologies with positive cosmological constant and specific symmetry groups.
result Asymptotically, 3+1D cosmologies evolve to de Sitter space under certain conditions.
End-to-end CNN classifies environmental sounds from audio signals.
problem Classifying environmental sounds from audio signals.
method 1D Convolutional Neural Network (CNN) with overlapped frames and Gammatone filterbank initialization.
result 89% mean accuracy on UrbanSound8k dataset.
PAC-Bayes framework fails on simple 1D linear classification task.
problem Proving the learnability of simple 1D linear classification tasks using PAC-Bayes bounds.
method Demonstrated a specific 1D linear classification task that PAC-Bayes cannot analyze.
result PAC-Bayes framework cannot prove learnability of simple 1D linear classification tasks.
Discovering topological quantum field theories in 2+1 and 3+1 dimensions.
problem Exploring topological orders in condensed matter lattice models.
method Calculating braiding statistics and link invariants of anyon excitations.
result Identifying new spin topological quantum field theories with specific knot/link invariants.
Uniqueness of 1D bi-Schrödinger flow proven from flat torus to compact space.
problem Proving uniqueness of a smooth flow from flat torus to compact space.
method Extrinsic approach using isometric embedding into Euclidean space, modifying classical H 2 H^2 H 2 -energy to handle loss of derivatives. result Uniqueness of the generalized bi-Schrödinger flow established.
A simple block configures optimal kernel sizes for time series classification.
problem Choosing the right kernel size for time series classification.
method Proposes Omni-Scale block (OS-block) with kernel sizes determined by prime numbers.
result Models with OS-block achieve state-of-the-art performance on time series benchmarks.
New method uses neural networks to interpolate stellar atmospheres with high precision.
problem Recover precise stellar model atmospheres from grids of models.
method Deep neural network with 1D convolutional auto-encoder for feature extraction.
result Higher precision compared to traditional methods.
NTS-NOTEARS learns DBNs from time-series data with prior knowledge.
problem Learning dynamic Bayesian networks from time-series data with nonlinear and lagged relationships.
method Uses 1D CNNs to model DBNs, incorporating prior knowledge as constraints.
result Achieves state-of-the-art DAG structure quality compared to parametric and nonparametric methods.
FSNet compresses CNNs by sharing weights in 1D vectors.
problem Compressing deep CNNs while maintaining performance.
method Learning a 1D vector (Filter Summary) to share weights in convolutional filters.
result FSNet reduces parameter space and accelerates computation.
CNN improves OSA diagnosis accuracy from PSG data.
problem Manual PSG analysis by specialists is tedious, time-consuming, and prone to errors.
method 1D CNN architecture with convolutional and FCN layers for OSA severity classification.
result Proposed CNN model achieves excellent classification results without manual preprocessing.
Study shows inflation in 3+1D cosmologies with bounded scalar potential and specific symmetry.
problem Understanding inflation in 3+1D cosmologies with specific constraints.
method Mean curvature flow and asymptotic analysis of metric variations, stress-energy tensor, and inflaton field dynamics.
result Inflation occurs in 3+1D cosmologies with specific constraints, demonstrating it is possible with inhomogeneous initial conditions.
Paper proposes a method to reduce hallucinations in diffusion models using Laplacian score sharpening.
problem Hallucinations in diffusion models create incoherent or unrealistic samples.
method Post-hoc adjustment to the score function during inference using Laplacian approximation.
result Significantly reduces the rate of hallucinated samples across various data types.
A new method for efficient optimal partial transport in 1D.
problem Limitation of equal mass assumption in optimal transport.
method Sliced Optimal Partial Transport (Sliced-OPT) algorithm.
result Sliced-OPT demonstrates computational and accuracy benefits.
Logistic Gaussian process (LGP) priors provide a flexible alternative for modelling unknown densities. The smoothness properties of the density estimates can be controlled through the prior covariance structure of the LGP, but the challenge is the analytically intractable inference. In this paper, we present approximat…
In 1D, optimal double bubbles are intervals or spheres.
problem Finding the least-perimeter way to enclose two volumes with a log-convex density.
method Analyzing the density function's log-convexity to determine the optimal configuration.
result In 1D, the optimal configuration can be intervals or spheres.
This study analyzes a non-orientable spacetime model in 1+1D quantum gravity.
problem Analyzing a non-orientable spacetime model in 1+1D quantum gravity.
method Formulated a Jackiw-Teitelboim gravity toy model on the Möbius band, computed Stiefel-Whitney classes, and analyzed the Dirac operator.
result Half-integer momentum quantization, spectral symmetry, vanishing mod-2 index, and η_D(0) = 0 follow.
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.
This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled independently. We can handle missing data naturally, infer latent structure, and cope with large numbers of observed processes. A further novel …
Study improves mortality prediction in ICU patients using feature engineering and 1D CNN.
problem Improving mortality prediction in ICU patients with high-dimensional, imbalanced, and missing data.
method Feature engineering, 1D Convolutional Neural Network (1D CNN), traditional machine learning algorithms.
result Best AUC of 0.848 achieved with 1D CNN model.
Master thesis applies deep learning to sEMG hand gesture recognition, improving accuracy.
problem Reliability issues in sEMG-based hand gesture recognition due to motion artefacts and variability.
method Used deep learning on Unibo-INAIL dataset, collecting data over 8 sessions of 7 subjects.
result Deep learning architecture yields 81.2% inter-posture test accuracy and 75.9% inter-day test accuracy.
NNs accurately predict energy eigenvalues and other physical phenomena in 1D quantum mechanics.
problem Understanding how neural networks interpret physics.
method Training NNs to predict energy eigenvalues from potentials and testing their ability to generalize.
result NNs can predict physical phenomena not learned during training, indicating a new way of understanding physics.
A new metric-based principal curve method learns 1D manifolds from spatial data.
problem Learning 1D manifolds from spatial data.
method Metric-based Principal Curve (MPC) approach.
result The method effectively learns the shape of 1D manifolds from synthetic and real datasets.
Paper models Bitcoin market dynamics using 1+1D field theory.
problem Understanding stylized facts in Bitcoin markets.
method Collects order-book datasets, applies KPZ-like stochastic equations.
result Predicts order book dynamics with high precision.
SCORE technique reduces BO's high-dimensional search costs.
problem Bayesian optimization's high computational costs in high-dimensional spaces.
method 1D reparametrization trick to maintain linear time complexity.
result Successfully finds global minimum in high-dimensional optimization.
Linear RNNs exhibit a bias towards shorter memory due to initialization variance.
problem Understanding the performance limitations of RNNs, especially linear ones.
method Kernel regime analysis to show equivalence to 1D-convolutional networks and analyze weightings.
result Linear RNNs with random initialization have a bias towards shorter memory periods.
Study no-arbitrage conditions in 1D diffusion markets with interest rates.
problem Determining no-arbitrage conditions in 1D diffusion markets with interest rates.
method Established deterministic criteria for no-arbitrage notions in terms of scale function and speed measure.
result Revealed various effects, e.g., NIP not excluded by reflecting boundaries.
New method reduces PDE model parameters by 30% with sparsity.
problem Redundant parameters in neural network projections.
method Bregman iterations for sparsity, POD compression, bias propagation.
result 30% fewer parameters with similar accuracy.
In this paper, we introduce an inclined curves according to parallel transport frame. Also, we define a vector field called Darboux vector field of an inclined curve in and we give a new characterization such as: "α: I \subset R \rightarrow E^4 is an inclined curve \Leftrightarrow k_1 \int k_1ds + k_2 \int \k_2 +k_3ds …
Paper proposes a faster RAE with sequence-aware encoding.
problem Training recurrent autoencoders is challenging and time-consuming.
method Introduces a recurrent autoencoder with sequence-aware encoding using 1D convolutional layers.
result The proposed autoencoder trains faster than standard RAE.
The author studies regions foliated by 1D families of functions and their applications.
problem Understanding regions represented as foliated forms and natural smooth maps onto them.
method Investigates natural smooth maps respecting canonical projections and moment maps, focusing on foliated regions.
result Discusses the 1st derivative of functions and critical sets in foliated regions.
Ridgeless ReLU networks interpolate datasets and extrapolate based on curvature signs.
problem Interpolating and extrapolating 1D datasets with ReLU networks.
method Minimizes ℓ 2 \ell_2 ℓ 2 -norm of weights, extrapolates based on curvature signs. result Ridgeless ReLU interpolants extrapolate as nearest neighbor curvature extrapolation.