arXiv research

One-dimensional CNNs improve signal recovery from sparse measurements.

This work uses encoder-decoder networks to denoise one-dimensional signals by aligning clean and noisy signal latent representations.

We present the group fused Lasso for detection of multiple change-points shared by a set of co-occurring one-dimensional signals. Change-points are detected by approximating the original signals with a constraint on the multidimensional total variation, leading to piecewise-constant approximations. Fast algorithms are …

1DCNN detects OSA from ECG signals with high accuracy.

VPNet uses variable projection for efficient neural network training.

Efficiently estimates common mean in high-dimensional data with noisy subset.

Two complementary approaches have been extensively used in signal and image processing leading to novel results, the sparse representation methodology and the variational strategy. Recently, a new sparsity based model has been proposed, the cosparse analysis framework, which may potentially help in bridging sparse appr…

Studying a softmax-attention model, we show that the learned query converges to the latent signal subspace spanned by the informative direction.

We exploit a recently derived inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework that applies to a wide range of systems and problems. The approach outperforms current state-of-the-art methods on MNIST reaching $99.14\%$ of test set accuracy while using $5$ labeled e…

Adaptive sampling theory has shown that, with proper assumptions on the signal class, algorithms exist to reconstruct a signal in $\mathbb{R}^{d}$ with an optimal number of samples. We generalize this problem to the case of spatial signals, where the sampling cost is a function of both the number of samples taken and t…

Study causal financial signals for non-stationary markets, improving short-term forecasts.

Study on detecting and recovering hidden dense cycles in random graphs.

Convolutional networks struggle with repeating patterns in ECGs.

A new distance metric derived from information theory and estimation theory.

Novel CTG analysis splits signals into balanced windows and uses 1DCNN for automatic feature extraction.

Positive--unlabeled (PU) learning considers two samples, a positive set P with observations from only one class and an unlabeled set U with observations from two classes. The goal is to classify observations in U. Class mixture proportion estimation (MPE) in U is a key step in PU learning. Blanchard et al. [2010] showe…

Study one-dimensional topological theories with linear generating functions.

Novel CNN array for sign language recognition using wearable IMUs.

Novel method segments heart sound signals using LSTMs with attention.

LatentNN corrects neural network attenuation bias in astronomical data.

We found a new simple family of Cantor sets whose projections are one-dimensional.

We show that finite-width deep ReLU neural networks yield rate-distortion optimal approximation (Bölcskei et al., 2018) of polynomials, windowed sinusoidal functions, one-dimensional oscillatory textures, and the Weierstrass function, a fractal function which is continuous but nowhere differentiable. Together with thei…

Study of one-dimensional non-Hausdorff manifolds and their quotient to CW complexes.

A new metric-based principal curve method learns 1D manifolds from spatial data.

The Brasselet number helps calculate function germs with one-dimensional critical sets.

Overparameterized linear model shows strong classification but weak regression, susceptible to adversarial perturbations.

We prove that for compact, non-contractible, one dimensional geodesic spaces, a version of the marked length spectrum conjecture holds. For a compact one dimensional geodesic space X, we define a subspace Conv(X). When X is non-contractible, we show that X deformation retracts to Conv(X). If two such spaces X, Y have t…

Paper proposes a new neural machine translation method for wave data.

Deep learning detects arrhythmias from ECGs using multidimensional representations.

One-dimensional crystals have convex shapes under certain conditions.

Proves metric measure spaces with certain properties are one-dimensional.

New method uses machine learning to estimate sensitivity without binning.

Smooth algebra analysis for one-dimensional singular foliations.

We classify the harmonic morphisms with one-dimensional fibres (1) from real-analytic conformally-flat Riemannian manifolds of dimension at least four, and (2) between conformally-flat Riemannian manifolds of dimensions at least three.

Study local topology of a function-germ deformation with a one-dimensional critical set.

We obtain a deterministic characterisation of the \emph{no free lunch with vanishing risk}, the \emph{no generalised arbitrage} and the \emph{no relative arbitrage} conditions in the one-dimensional diffusion setting and examine how these notions of no-arbitrage relate to each other.

In this paper we continue our studies of the one dimensional conformal metric flows, which were introduced in [8]. In this part we mainly focus on evolution equations involving fourth order derivatives. The global existence and exponential convergence of metrics for the 1-Q and 4-Q flows are obtained.

We prove that, from an Einstein manifold of dimension greater than or equal to five, there are just two types of harmonic morphism with one-dimensional fibres. This generalizes a result of R.L. Bryant who obtained the same conclusion under the assumption that the domain has constant curvature.

The study examines different types of equilibria for stopping problems in one-dimensional diffusion processes.

Suppose that f and g are Markov surjections, each defined on a wedge of circles, each fixing the branch point and having the branch point as the only critical value. We show that if the points in the inverse limit spaces associated with f and g corresponding to the branch point are distinguished then these inverse limi…

Transform drift of diffusions without knowing if measure change is a martingale.

In their study of fundamental groups of one-dimensional path-connected compact metric spaces, Cannon and Conner have asked: Is there a tree-like object that might be considered the topological Cayley graph? We answer this question in the positive and provide a combinatorial description of such an object.

Deep-AER system uses lightweight CNN models for EEG-based emotion recognition.

Study shows one-dimensional location-scale-shape models are flat in Wasserstein geometry.

This paper optimizes Gaussian mixture model learning with optimal sampling complexity.

The paper analyzes how the one-dimensional Wasserstein distance captures pointwise density differences in finite samples.

Any two compact, complete, one-dimensional geodesic spaces with identical marked length spectrum have isometric $π_1$-hull. The present version contains errors, notably in Lemmas 2.2 and 2.3 (path cancellations can be more complicated), which then propagate through the paper. The main result is correct as stated, and a…

The paper explores global index formulas for one-dimensional holomorphic foliations.