# “measure concentration”

### The Separation Capacity of Random Neural Networks

Abstract: Neural networks with random weights appear in a variety of machine learning applications, most prominently as the initialization of many deep learning algorithms and as a computationally cheap alternative to fully learned neural networks.

### The Separation Capacity of Random Neural Networks

Abstract: Neural networks (NNs) with random weights appear in a variety of machine learning applications, perhaps most prominently as initialization of many deep learning algorithms. We take one step closer to their theoretical foundation by addressing the following data separation problem: Under what conditions can a random NN make two classes $$\mathcal X^{-}, \mathcal X^{\plus} \subset \mathbb R^{d}$$ (with positive distance) linearly separable?

### Quantized Compressive Sensing with RIP Matrices: The Benefit of Dithering

Abstract: Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals with quantized, finite precision representations, i.e., a mandatory process involved in any practical sensing model.

### STIM map: detection map for exoplanets imaging beyond asymptotic Gaussian residual speckle noise

Abstract: Direct imaging of exoplanets is a challenging task as it requires to reach a high contrast at very close separation to the star. Today, the main limitation in the high-contrast images is the quasi-static speckles that are created by residual instrumental aberrations.

### Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors

Abstract: The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse signals. Practical ADCs not only sample but also quantize each measurement to a finite number of bits; moreover, there is an inverse relationship between the achievable sampling rate and the bit-depth.

### Stabilizing Nonuniformly Quantized Compressed Sensing with Scalar Companders

Abstract: This paper studies the problem of reconstructing sparse or compressible signals from compressed sensing measurements that have undergone nonuniform quantization. Previous approaches to this Quantized Compressed Sensing (QCS) problem based on Gaussian models (bounded l2-norm) for the quantization distortion yield results that, while often acceptable, may not be fully consistent: re-measurement and quantization of the reconstructed signal do not necessarily match the initial observations.

### Randomly Driven Fuzzy Key Extraction of Unclonable Images

Abstract: In this paper, we develop an adjustable Fuzzy Extractor using the Physical Unclonable Functions (PUF) obtained by a common laser engraving method to sign physical objects. In particular, a string (or helper data) is generated by XORing a binary reduction of the PUF observation with the encoding of a randomly generated key, or identifier.