In this paper we propose and investigate a wide class of Mirror Descent updates (MD) and associated novel Generalized Exponentiated Gradient (GEG) algorithms by exploiting various trace-form entropies and associated d...
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Federated edge learning (FEL) is a promising paradigm of distributed machine learning that can preserve data privacy while training the global model collaboratively. However, FEL is still facing model confidentiality ...
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In this paper we deal with the problem of characterizing those generalized Mehler semigroups that correspond to càdlàg Markov processes, which has remained open for more than a decade. Our approach is to rec...
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Breast cancer (BC) is the most common cancer in women in Europe and worldwide, with a high prevalence in middle-aged and older women. The last years, the evolution in the existing treatment approaches have contributed...
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作者:
Grzegorz BaronUrszula StańczykDepartment of Graphics
Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland
Discretisation often constitutes a part of initial data preparation stage. It translates continuous domain of features into granular, by assigning a number of intervals to represent attributes’ values by nominal cate...
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Discretisation often constitutes a part of initial data preparation stage. It translates continuous domain of features into granular, by assigning a number of intervals to represent attributes’ values by nominal categories. Typically all real-valued features are subjected to transformations, regardless of their characteristics. The paper presents research on discretisation executed with a discerning approach. To all available attributes, feature selection mechanisms were employed, in the form of rankings that order variables based on their importance. Exploiting this discovered knowledge on attributes, discretisation was then driven by a ranking, and either highest or lowest ranking features were selected for transformation. The influence of selective discretisation on the performance of classification systems was studied for three popular inducers. The procedure was employed in the field of stylometry, and a task of authorship recognition, considered as a binary classification with balanced classes. The experiments show that discretisation based on importance of features can lead to better performance than in the case of transformations applied to all attributes.
Industrial Wireless Sensors and Actuators Networks (IWSANs) are gateway to the Industrial 4.0, which promises to realize smart factory leading to the Industrial Internet of Things (IIoT). It employs Cyber-Physical Sys...
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ISBN:
(纸本)9783885797012
Industrial Wireless Sensors and Actuators Networks (IWSANs) are gateway to the Industrial 4.0, which promises to realize smart factory leading to the Industrial Internet of Things (IIoT). It employs Cyber-Physical systems (CPSs) to enhance operational efficiency and flexibility while reducing cost. IWSANs are delay-sensitive and always require low latency and reliable connection from sensor to actuator to successfully perform a physical action. Reliability and low-latency complement each other to prevent expected failures in wireless medium. In this way, detecting and predicting failure before it actually occurs is key to actually avoid it well in time. Detection and predictions are imperative in locating faults and failures. The causes of failures in a sensor or actuator can include hardware malfunction, poor battery life, interference, accident, and short term wireless connectivity problems. Although, industrial environment mostly undertakes redundant resource to circumvent such issues, yet poor coordination among multiple resources and inaccurately predicting failures may result in losses. In such a scenario, migration of services come to be a rescue, where an intermediary can migrate service from one device, which cannot complete a task due to resource exhaustion, to a more resource-rich device. Thus, in this paper, we focus on wireless connectivity failures caused by interference in the 2.4GHz frequency band. We do it by designing an Multi Channel Sniffing Setup (MCSS) testbed, that acts as a spectrum observer and is deployed in different locations in industrial WSAN. Alongside, we use the concept of Cognitive Radio (CR) to predict interference and noise level in the spectrum by proposing an Intelligent Low-power Wireless Spectrum Prediction (ILPWSP) based on Deep Q Network (DQN). The MCSS testbed and the ILPWSP coordinate in assessing wireless connectivity risks, predict failures in sensor and actuator nodes and then make efficient decisions on the migration o
Because dog noseprints are equivalent to human fingerprints and have unique characteristics, this study proposes a dog identification technology based on dog noseprints for identifying and managing stray animals. This...
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The Ornstein-Uhlenbeck process is interpreted as Brownian motion in a harmonic potential. This Gaussian Markov process has a bounded variance and admits a stationary probability distribution, in contrast to the standa...
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KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to Apr...
KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity.
Branch-and-bound is a typical way to solve combinatorial optimization problems. This paper proposes a graph pointer network model for learning the variable selection policy in the branch-and-bound. We extract the grap...
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