Masking: A New Perspective of Noisy Supervision

It is important to learn various types of classifiers given training data with noisy labels. Noisy labels, in the most popular noise model hitherto, are corrupted from ground-truth labels by an unknown noise transition matrix. Thus, by estimating this matrix, classifiers can escape from overfitting those noisy labels. However, such estimation is practically difficult, due to either the indirect nature of two-step approaches, or not big enough data to afford end-to-end approaches. In this paper, we propose a human-assisted approach called Masking that conveys human cognition of invalid class transitions and naturally speculates the structure of the noise transition matrix. To this end, we derive a structure-aware probabilistic model incorporating a structure prior, and solve the challenges from structure extraction and structure alignment. Thanks to Masking, we only estimate unmasked noise transition probabilities and the burden of estimation is tremendously reduced. We conduct extensive experiments on CIFAR-10 and CIFAR-100 with three noise structures as well as the industrial-level Clothing1M with agnostic noise structure, and the results show that Masking can improve the robustness of classifiers significantly.Comment: NIPS 2018 camera-ready versio

The detection of influential subsets in linear regression using an influence matrix.

This paper presents a new method to identify influential subsets in linear regression problems. The procedure uses the eigenstructure of an influence matrix which is defined as the matrix of uncentered covariance of the effect on the whole data set of deleting each observation, normalized to include the univariate Cook's statistics in the diagonal. It is shown that points in an influential subset will appear with large weight in at least one of the eigenvector linked to the largest eigenvalues in this influence matrix. The method is illustrated with several well-known examples in the literature, and in all of them it succeeds in identifying the relevant influential subsets.Eigenvectors; Masking; Multivariate Influence; Outliers;

Single channel speech music separation using nonnegative matrix factorization and spectral masks

A single channel speech-music separation algorithm based on nonnegative matrix factorization (NMF) with spectral masks is proposed in this work. The proposed algorithm uses training data of speech and music signals with nonnegative matrix factorization followed by masking to separate the mixed signal. In the training stage, NMF uses the training data to train a set of basis vectors for each source. These bases are trained using NMF in the magnitude spectrum domain. After observing the mixed signal, NMF is used to decompose its magnitude spectra into a linear combination of the trained bases for both sources. The decomposition results are used to build a mask, which explains the contribution of each source in the mixed signal. Experimental results show that using masks after NMF improves the separation process even when calculating NMF with fewer iterations, which yields a faster separation process

Taste masked thin films printed by jet dispensing

Taste masking of bitter active substances is an emerging area in the pharmaceutical industry especially for paediatric/geriatric medications. In this study we introduce the use of jet – dispensing as a taste masking technology by printing mucosal thin films of three model bitter substances, Cetirizine HCl, Diphenylhydramine HCl and Ibuprofen. The process was used to dispense aqueous drugs/polymer solutions at very high speed where eventually the drugs were embedded in the polymer matrix. The in vivo evaluation of jet – dispensed mucosal films showed excellent taste masking for drug loadings from 20 - 40%. Jet dispensing was proved to make uniform, accurate and reproducible thin films with excellent content uniformity

On the Duality of Probing and Fault Attacks

In this work we investigate the problem of simultaneous privacy and integrity protection in cryptographic circuits. We consider a white-box scenario with a powerful, yet limited attacker. A concise metric for the level of probing and fault security is introduced, which is directly related to the capabilities of a realistic attacker. In order to investigate the interrelation of probing and fault security we introduce a common mathematical framework based on the formalism of information and coding theory. The framework unifies the known linear masking schemes. We proof a central theorem about the properties of linear codes which leads to optimal secret sharing schemes. These schemes provide the lower bound for the number of masks needed to counteract an attacker with a given strength. The new formalism reveals an intriguing duality principle between the problems of probing and fault security, and provides a unified view on privacy and integrity protection using error detecting codes. Finally, we introduce a new class of linear tamper-resistant codes. These are eligible to preserve security against an attacker mounting simultaneous probing and fault attacks

Masking Strategies for Image Manifolds

We consider the problem of selecting an optimal mask for an image manifold, i.e., choosing a subset of the pixels of the image that preserves the manifold's geometric structure present in the original data. Such masking implements a form of compressive sensing through emerging imaging sensor platforms for which the power expense grows with the number of pixels acquired. Our goal is for the manifold learned from masked images to resemble its full image counterpart as closely as possible. More precisely, we show that one can indeed accurately learn an image manifold without having to consider a large majority of the image pixels. In doing so, we consider two masking methods that preserve the local and global geometric structure of the manifold, respectively. In each case, the process of finding the optimal masking pattern can be cast as a binary integer program, which is computationally expensive but can be approximated by a fast greedy algorithm. Numerical experiments show that the relevant manifold structure is preserved through the data-dependent masking process, even for modest mask sizes