scholarly journals Kernel Two-Sample and Independence Tests for Nonstationary Random Processes

2021 ◽  
Vol 5 (1) ◽  
pp. 31
Author(s):  
Felix Laumann ◽  
Julius von Kügelgen ◽  
Mauricio Barahona
Keyword(s):  
Synthetic Data ◽  
Random Processes ◽  
Superior Performance ◽  
Test Power ◽  
Independence Tests ◽  
Current State ◽  
Scientific Disciplines ◽  
Multivariate Probability Distribution ◽  
Stationary Random Processes ◽  
Prevalent Form

Two-sample and independence tests with the kernel-based mmd and hsic have shown remarkable results on i.i.d. data and stationary random processes. However, these statistics are not directly applicable to nonstationary random processes, a prevalent form of data in many scientific disciplines. In this work, we extend the application of mmd and hsic to nonstationary settings by assuming access to independent realisations of the underlying random process. These realisations—in the form of nonstationary time-series measured on the same temporal grid—can then be viewed as i.i.d. samples from a multivariate probability distribution, to which mmd and hsic can be applied. We further show how to choose suitable kernels over these high-dimensional spaces by maximising the estimated test power with respect to the kernel hyperparameters. In experiments on synthetic data, we demonstrate superior performance of our proposed approaches in terms of test power when compared to current state-of-the-art functional or multivariate two-sample and independence tests. Finally, we employ our methods on a real socioeconomic dataset as an example application.

scholarly journals Nursing Interventions to Prevent Delirium in Critically Ill Patients in the Intensive Care Unit during the COVID19 Pandemic—Narrative Overview

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 578
Author(s):  
Dorota Ozga ◽  
Sabina Krupa ◽  
Paweł Witt ◽  
Wioletta Mędrzycka-Dąbrowska
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Critically Ill Patients ◽  
Current Knowledge ◽  
Personalised Medicine ◽  
Nursing Interventions ◽  
Evidence Based ◽  
Current State ◽  
Scientific Disciplines ◽  
Reliable Methods

It has become a standard measure in recent years to utilise evidence-based practice, which is associated with a greater need to implement and use advanced, reliable methods of summarising the achievements of various scientific disciplines, including such highly specialised approaches as personalised medicine. The aim of this paper was to discuss the current state of knowledge related to improvements in “nursing” involving management of delirium in intensive care units during the SARS-CoV-2 pandemic. This narrative review summarises the current knowledge concerning the challenges associated with assessment of delirium in patients with COVID-19 by ICU nurses, and the role and tasks in the personalised approach to patients with COVID-19.

scholarly journals A Review of Algorithms and Hardware Implementations for Spiking Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 23
Author(s):  
Duy-Anh Nguyen ◽  
Xuan-Tu Tran ◽  
Francesca Iacopi
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Superior Performance ◽  
Training Algorithms ◽  
Current State ◽  
Hardware Implementations ◽  
Neuromorphic Hardware ◽  
High Level ◽  
Event Based ◽  
Hardware Platforms

Deep Learning (DL) has contributed to the success of many applications in recent years. The applications range from simple ones such as recognizing tiny images or simple speech patterns to ones with a high level of complexity such as playing the game of Go. However, this superior performance comes at a high computational cost, which made porting DL applications to conventional hardware platforms a challenging task. Many approaches have been investigated, and Spiking Neural Network (SNN) is one of the promising candidates. SNN is the third generation of Artificial Neural Networks (ANNs), where each neuron in the network uses discrete spikes to communicate in an event-based manner. SNNs have the potential advantage of achieving better energy efficiency than their ANN counterparts. While generally there will be a loss of accuracy on SNN models, new algorithms have helped to close the accuracy gap. For hardware implementations, SNNs have attracted much attention in the neuromorphic hardware research community. In this work, we review the basic background of SNNs, the current state and challenges of the training algorithms for SNNs and the current implementations of SNNs on various hardware platforms.

Heuristische Textpraktiken in den Wissenschaften

2020 ◽  
Vol 48 (1) ◽  
pp. 1-46
Author(s):  
Michael Bender ◽  
Marcus Müller
Keyword(s):  
Science Research ◽  
Annotation Scheme ◽  
Scientific Debate ◽  
Domain Specific ◽  
Current State ◽  
Scientific Disciplines ◽  
Textual Practices ◽  
Collaborative Annotation ◽  
The Subject ◽  
State Of Research

AbstractThis article contains a comparative study of heuristic textual practices in various scientific disciplines. By this we mean formulation practices with which new knowledge is generated in institutionally influenced routines and connected to existing knowledge, e. g. ‚highlighting the relevance of a research topic‘, ‚defining a concept‘ or ‚supporting a statement argumentatively‘.The aim is to find out to what extent such textual practices occur in different scientific disciplines, how they are distributed and combined. Furthermore, we study the effects domain-specific contexts have on heuristic textual practices. The data basis of our study is a corpus of 65 dissertations from the 13 different faculties of the TU Darmstadt. In the pilot study we report here, we examined the introductory chapters of the dissertations. Methodologically, it is an annotation study: Based on the current state of research on the subject, we have derived a basic annotation scheme, which we have developed and refined in a collaborative process of guideline creation. Our study affiliates on socio-pragmatic research on text production and formulation routines in the sciences. It is theoretically informed by the philosophy of science research on heuristics, methodically we make a contribution to the scientific debate on collaborative annotation procedures.

scholarly journals AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays

2021 ◽  
Vol 7 ◽  
pp. e495
Author(s):  
Saleh Albahli ◽  
Hafiz Tayyab Rauf ◽  
Abdulelah Algosaibi ◽  
Valentina Emilia Balas
Keyword(s):  
Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Synthetic Data ◽  
X Rays ◽  
Deep Convolutional Neural Networks ◽  
Current State ◽  
Pathology Classification ◽  
Wide Range ◽  
Multi Class Classification

Artificial intelligence (AI) has played a significant role in image analysis and feature extraction, applied to detect and diagnose a wide range of chest-related diseases. Although several researchers have used current state-of-the-art approaches and have produced impressive chest-related clinical outcomes, specific techniques may not contribute many advantages if one type of disease is detected without the rest being identified. Those who tried to identify multiple chest-related diseases were ineffective due to insufficient data and the available data not being balanced. This research provides a significant contribution to the healthcare industry and the research community by proposing a synthetic data augmentation in three deep Convolutional Neural Networks (CNNs) architectures for the detection of 14 chest-related diseases. The employed models are DenseNet121, InceptionResNetV2, and ResNet152V2; after training and validation, an average ROC-AUC score of 0.80 was obtained competitive as compared to the previous models that were trained for multi-class classification to detect anomalies in x-ray images. This research illustrates how the proposed model practices state-of-the-art deep neural networks to classify 14 chest-related diseases with better accuracy.

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