Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification

This paper introduces a novel approach to leverage the knowledge of existing expert models for training new Convolutional Neural Networks, on domains where task-specific data are limited or unavailable. The presented scheme is applied in offline handwritten signature verification (OffSV) which, akin to other biometric applications, suffers from inherent data limitations due to regulatory restrictions. The proposed Student-Teacher (S-T) configuration utilizes feature-based knowledge distillation (FKD), combining graph-based similarity for local activations with global similarity measures to supervise student's training, using only handwritten text data. Remarkably, the models trained using this technique exhibit comparable, if not superior, performance to the teacher model across three popular signature datasets. More importantly, these results are attained without employing any signatures during the feature extraction training process. This study demonstrates the efficacy of leveraging existing expert models to overcome data scarcity challenges in OffSV and potentially other related domains

Hypoxic regulation of RIOK3 is a major mechanism for cancer cell invasion and metastasis.

Hypoxia is a common feature of locally advanced breast cancers that is associated with increased metastasis and poorer survival. Stabilisation of hypoxia-inducible factor-1α (HIF1α) in tumours causes transcriptional changes in numerous genes that function at distinct stages of the metastatic cascade. We demonstrate that expression of RIOK3 (RIght Open reading frame kinase 3) was increased during hypoxic exposure in an HIF1α-dependent manner. RIOK3 was localised to distinct cytoplasmic aggregates in normoxic cells and underwent redistribution to the leading edge of the cell in hypoxia with a corresponding change in the organisation of the actin cytoskeleton. Depletion of RIOK3 expression caused MDA-MB-231 to become elongated and this morphological change was due to a loss of protraction at the trailing edge of the cell. This phenotypic change resulted in reduced cell migration in two-dimensional cultures and inhibition of cell invasion through three-dimensional extracellular matrix. Proteomic analysis identified interactions of RIOK3 with actin and several actin-binding factors including tropomyosins (TPM3 and TPM4) and tropomodulin 3. Depletion of RIOK3 in cells resulted in fewer and less organised actin filaments. Analysis of these filaments showed reduced association of TPM3, particularly during hypoxia, suggesting that RIOK3 regulates actin filament specialisation. RIOK3 depletion reduced the dissemination of MDA-MB-231 cells in both a zebrafish model of systemic metastasis and a mouse model of pulmonary metastasis. These findings demonstrate that RIOK3 is necessary for maintaining actin cytoskeletal organisation required for migration and invasion, biological processes that are necessary for hypoxia-driven metastasis

Subscripto multiplex: A Riemannian symmetric positive definite strategy for offline signature verification

International audienceThe human handwritten signature is considered to be a significant biometric trait. In the case of offline signatures, the problem is addressed as an image recognition task. On the other hand, the visual representation of symmetric positive definitive matrices, usually by means of the covariance descriptor of the image feature maps, forms a specific Riemannian manifold with a widespread usage and a favorable performance in a plethora of applications. Surprisingly, no records of offlinesignature-verification-oriented research in the space of symmetric positive definitive matrix have been found up to now. In this work, we propose, for the first time in offline signature-verification literature, mapping of handwritten signature images in points of the tangent space of a connected symmetric positive definitive manifold for verification purposes. Furthermore, based on the principles of differential geometry, we address the notorious limited training problem of offline signature verification in this manifold by proposing two different feature augmentation methods. The efficiency of the proposed method is evaluated using three popular datasets of Western and Asian origin. Error rates against skilled and random forgery in both baselines as well augmentation scenarios are strong indicators of the informative and highly discriminative nature of symmetric positive definitive manifold oriented representation

A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons

The increasing penetration of renewable energy sources tends to redirect the power systems community’s interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons constitutes an essential electric utility task in network planning, operation, and management. This paper presents a novel mixed power-load forecasting scheme for multiple prediction horizons ranging from 15 min to 24 h ahead. The proposed approach makes use of a pool of models trained by several machine-learning methods with different characteristics, namely neural networks, linear regression, support vector regression, random forests, and sparse regression. The final prediction values are calculated using an online decision mechanism based on weighting the individual models according to their past performance. The proposed scheme is evaluated on real electrical load data sensed from a high voltage/medium voltage substation and is shown to be highly effective, as it results in R2 coefficient values ranging from 0.99 to 0.79 for prediction horizons ranging from 15 min to 24 h ahead, respectively. The method is compared to several state-of-the-art machine-learning approaches, as well as a different ensemble method, producing highly competitive results in terms of prediction accuracy