GET: Global envelopes in R. (arXiv:1911.06583v1 [stat.ME])

This work describes the R package GET that implements global envelopes, which can be employed for central regions of functional or multivariate data, for graphical Monte Carlo and permutation tests where the test statistic is multivariate or functional, and for global confidence and prediction bands. Intrinsic graphical interpretation property is introduced for global envelopes, and…

Deep learning methods in speaker recognition: a review. (arXiv:1911.06615v1 [eess.AS])

This paper summarizes the applied deep learning practices in the field of speaker recognition, both verification and identification. Speaker recognition has been a widely used field topic of speech technology. Many research works have been carried out and little progress has been achieved in the past 5-6 years. However, as deep learning techniques do advance…

Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images. (arXiv:1911.06616v1 [eess.IV])

Diagnosing basal cell carcinomas (BCC), one of the most common cutaneous malignancies in humans, is a task regularly performed by pathologists and dermato-pathologists. Improving histological diagnosis by providing diagnosis suggestions, i.e. computer-assisted diagnoses is actively researched to improve safety, quality and efficiency. Increasingly, machine learning methods are applied due to their superior performance. However, typical…

Feedback Linearization based on Gaussian Processes with event-triggered Online Learning. (arXiv:1911.06565v1 [eess.SY])

Combining control engineering with nonparametric modeling techniques from machine learning allows to control systems without analytic description using data-driven models. Most existing approaches separate learning, i.e. the system identification based on a fixed dataset, and control, i.e. the execution of the model-based control law. This separation makes the performance highly sensitive to the initial selection…

Smart transformer Modelling in Optimal Power Flow Analysis. (arXiv:1911.06614v1 [eess.SY])

The smart transformer (ST) implemented using power electronics converters, has the capability of independent voltage control and reactive power isolation between its primary and secondary terminals. This capability provides a flexibility in the power system to support the voltage at the primary side and control the demand at the secondary side. Using this flexibility, the…

Deep learning methods in speaker recognition: a review. (arXiv:1911.06615v1 [eess.AS])

This paper summarizes the applied deep learning practices in the field of speaker recognition, both verification and identification. Speaker recognition has been a widely used field topic of speech technology. Many research works have been carried out and little progress has been achieved in the past 5-6 years. However, as deep learning techniques do advance…

Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images. (arXiv:1911.06616v1 [eess.IV])

Diagnosing basal cell carcinomas (BCC), one of the most common cutaneous malignancies in humans, is a task regularly performed by pathologists and dermato-pathologists. Improving histological diagnosis by providing diagnosis suggestions, i.e. computer-assisted diagnoses is actively researched to improve safety, quality and efficiency. Increasingly, machine learning methods are applied due to their superior performance. However, typical…

Long-range Prediction of Vital Signs Using Generative Boosting via LSTM Networks. (arXiv:1911.06621v1 [cs.LG])

Vital signs including heart rate, respiratory rate, body temperature and blood pressure, are critical in the clinical decision making process. Effective early prediction of vital signs help to alert medical practitioner ahead of time and may prevent adverse health outcomes. In this paper, we suggest a new approach called generative boosting, in order to effectively…

A System Theoretical Perspective to Gradient-Tracking Algorithms for Distributed Quadratic Optimization. (arXiv:1911.06665v1 [eess.SY])

In this paper we consider a recently developed distributed optimization algorithm based on gradient tracking. We propose a system theory framework to analyze its structural properties on a preliminary, quadratic optimization set-up. Specifically, we focus on a scenario in which agents in a static network want to cooperatively minimize the sum of quadratic cost functions.…

Sparse associative memory based on contextual code learning for disambiguating word senses. (arXiv:1911.06415v1 [cs.CL])

In recent literature, contextual pretrained Language Models (LMs) demonstrated their potential in generalizing the knowledge to several Natural Language Processing (NLP) tasks including supervised Word Sense Disambiguation (WSD), a challenging problem in the field of Natural Language Understanding (NLU). However, word representations from these models are still very dense, costly in terms of memory footprint,…