Search results for "VECTOR"
showing 10 items of 2660 documents
IoT -based adversarial attack's effect on cloud data platform services in a smart building context
2020
IoT sensors and sensor networks are widely employed in businesses. The common problem is a remarkable number of IoT device transactions are unencrypted. Lack of correctly implemented and robust defense leaves the organization's IoT devices vulnerable to numerous cyber threats, such as adversarial and man-in-the-middle attacks or malware infections. A perpetrator can utilize adversarial examples when attacking machine learning (ML) models, such as convolutional neural networks (CNN) or deep neural networks (DNN) used, e.g., in DaaS cloud data platform service of smart buildings. DaaS cloud data platform's function in this study is to connect data from multiple IoT sensors, databases, private…
Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility U…
2020
Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that…
Kohn-Sham Decomposition in Real-Time Time-Dependent Density-Functional Theory An Efficient Tool for Analyzing Plasmonic Excitations
2017
The real-time-propagation formulation of time-dependent density-functional theory (RT-TDDFT) is an efficient method for modeling the optical response of molecules and nanoparticles. Compared to the widely adopted linear-response TDDFT approaches based on, e.g., the Casida equations, RT-TDDFT appears, however, lacking efficient analysis methods. This applies in particular to a decomposition of the response in the basis of the underlying single-electron states. In this work, we overcome this limitation by developing an analysis method for obtaining the Kohn-Sham electron-hole decomposition in RT-TDDFT. We demonstrate the equivalence between the developed method and the Casida approach by a be…
Well-defined polypeptide-based systems as non-viral vectors for cytosolic delivery
2017
A convenient cytosolic drug delivery constitutes a very powerful tool for the treatment and/or prevention of several relevant human diseases. Along with recent advances in therapeutic technologies based on biomacromolecules (e.g. oligonucleotides or proteins), we also require the development of technologies which improve the transport of therapeutic molecules to the cell of choice. This has led to the emergence of a variety of promising methods over the last 20 years. Despite significant progress, these methods still suffer from several shortcomings including low/variable delivery efficiency, high cytotoxicity, and perhaps most importantly, ineffective endosomal/lysosomal escape. In this co…
Operators in Rigged Hilbert Spaces, Gel’fand Bases and Generalized Eigenvalues
2022
Given a self-adjoint operator A in a Hilbert space H, we analyze its spectral behavior when it is expressed in terms of generalized eigenvectors. Using the formalism of Gel’fand distribution bases, we explore the conditions for the generalized eigenspaces to be one-dimensional, i.e., for A to have a simple spectrum.
Maladie du huanglongbing
2019
Maladie du huanglongbing. Analyse du risque phytosanitaire pour l’Union européenne
Robustifying principal component analysis with spatial sign vectors
2012
In this paper, we apply orthogonally equivariant spatial sign covariance matrices as well as their affine equivariant counterparts in principal component analysis. The influence functions and asymptotic covariance matrices of eigenvectors based on robust covariance estimators are derived in order to compare the robustness and efficiency properties. We show in particular that the estimators that use pairwise differences of the observed data have very good efficiency properties, providing practical robust alternatives to classical sample covariance matrix based methods. peerReviewed
Polymeric nanoparticles for siRNA delivery: Production and applications
2017
Gene therapy through the use of siRNA and a polymeric carrier are becoming an efficient therapeutic option to conventional pharmaceutical formulations for the treatment of deadly diseases, such as cancer, pulmonary, ocular and neurodegenerative diseases. However, several considerations regarding the stability, formulation, and efficacy have to be faced up until these systems could be considered to be a marketable pharmaceutical products for to extend siRNA application to clinical practice. This review is focused on the key challenges of siRNA therapeutics, with special attention on the faced obstacles and on the formulation-related difficulties, providing a list of requirements needed for o…
Constructing co-presence through shared VR gameplay
2021
This study analyzes how participants playing VR games construct co-presence and shared gameplay. The analysis focuses on instances of play where one person is wearing the VR equipment, and other participants are located nearby without the ability to directly interact with the game. We first show how the active player using the VR equipment draws on talk and embodied activity to signal their presence in the shared physical environment, while simultaneously conducting actions in the virtual space, and thus creates spaces for the other participants to take part in gameplay. Second, we describe how other participants draw on the contextual configurations of the moment in displaying co-presen…
Signal processing techniques for robust sound event recognition
2019
The computational analysis of acoustic scenes is today a topic of major interest, with a growing community focused on designing machines capable of identifying and understanding the sounds produced in our environment, similar to how humans perform this task. Although these domains have not reached the industrial popularity of other related audio domains, such as speech recognition or music analysis, applications designed to identify the occurrence of sounds in a given scenario are rapidly increasing. These applications are usually limited to a set of sound classes, which must be defined beforehand. In order to train sound classification models, representative sets of sound events are record…