A system for the simulation of hardware to software allocation and performance evaluation

Imperial Users onl

Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets

The superconducting LHC magnets are coupled with an electronic monitoring system which records and analyses voltage time series reflecting their performance. A currently used system is based on a range of preprogrammed triggers which launches protection procedures when a misbehavior of the magnets is detected. All the procedures used in the protection equipment were designed and implemented according to known working scenarios of the system and are updated and monitored by human operators. This paper proposes a novel approach to monitoring and fault protection of the Large Hadron Collider (LHC) superconducting magnets which employs state-of-the-art Deep Learning algorithms. Consequently, the authors of the paper decided to examine the performance of LSTM recurrent neural networks for modeling of voltage time series of the magnets. In order to address this challenging task different network architectures and hyper-parameters were used to achieve the best possible performance of the solution. The regression results were measured in terms of RMSE for different number of future steps and history length taken into account for the prediction. The best result of RMSE=0.00104 was obtained for a network of 128 LSTM cells within the internal layer and 16 steps history buffer

The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection. Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets was intended for real-life experiments and model training. Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art OC-SVM reference model operating on the same data. The OC-SVM model was equipped with a rich set of feature extractors accounting for a range of the input signal properties. It was determined in the course of the experiments that the detector, along with its supporting design methodology, reaches F1 equal or very close to 1 for almost all test sets. Due to the profile of the data, the best_length setup of the detector turned out to perform the best among all five tested configuration schemes of the detection system. The quantization parameters have the biggest impact on the overall performance of the detector with the best values of input/output grid equal to 16 and 8, respectively. The proposed solution of the detection significantly outperformed OC-SVM-based detector in most of the cases, with much more stable performance across all the datasets.Comment: Related to arXiv:1702.0083

Władza i rodowód. O wizerunku władcy w staroserbskiej literaturze

The article refers to a problem of relationship between authority and genealogy, and to their specific functioning in the Old Serbian literature, that is in other words, in the hagiographical, historiographical, and hymnic texts. In all those works, the combination of the both ideas undoubtedly serves the ideological creation, regarding the image of sovereign together with the exposition of his extra-ordinary lineage and destiny which are tied to his place and role in the space of history, politics, religion, and culture. In the literature, this peculiar relationship is expressed by the two characteristic constructions of dynasty and genealogy. With regard to this representation, the dynastic construction is based on the model of biblical motif concerning Jesse’s stem and functions as the saint osier of the family of Nemanjić. As for the second construction, of genealogy, it reveals itself as a component of the classical myth of historiography that determines the beginnings namely, the Serbian sovereign’s mythical genesis that also concerns all his charismatic, saint family.The article refers to a problem of relationship between authority and genealogy, and to their specific functioning in the Old Serbian literature, that is in other words, in the hagiographical, historiographical, and hymnic texts. In all those works, the combination of the both ideas undoubtedly serves the ideological creation, regarding the image of sovereign together with the exposition of his extra-ordinary lineage and destiny which are tied to his place and role in the space of history, politics, religion, and culture. In the literature, this peculiar relationship is expressed by the two characteristic constructions of dynasty and genealogy. With regard to this representation, the dynastic construction is based on the model of biblical motif concerning Jesse’s stem and functions as the saint osier of the family of Nemanjić. As for the second construction, of genealogy, it reveals itself as a component of the classical myth of historiography that determines the beginnings namely, the Serbian sovereign’s mythical genesis that also concerns all his charismatic, saint family

Проблема рецепции средневековой южнославянской письменности – о переводах древнесербских текстов на польский язык

Перевел с польского: Иван Н. ПетровPublikacja powstała w związku z projektem naukowo-badawczym pt. "Recepcja piśmiennictwa oraz literatury ludowej kręgu Slavia Orthodoxa w Polsce – historia i bibliografia twórczości przekładowej", realizowanym w Centrum Ceraneum Uniwersytetu Łódzkiego. Projekt został sfinansowany ze środków Narodowego Centrum Nauki przyznanych na podstawie decyzji numer DEC – 2012/05/E/HS2/03827

Upiorne oblicze systemu

Recenzja książki Božidara Jezernika pt. "Naga wyspa. Gułag Tity".635335725Poznańskie Studia Slawistyczn

Assessing Dataset Quality Through Decision Tree Characteristics in Autoencoder-Processed Spaces

In this paper, we delve into the critical aspect of dataset quality assessment in machine learning classification tasks. Leveraging a variety of nine distinct datasets, each crafted for classification tasks with varying complexity levels, we illustrate the profound impact of dataset quality on model training and performance. We further introduce two additional datasets designed to represent specific data conditions - one maximizing entropy and the other demonstrating high redundancy. Our findings underscore the importance of appropriate feature selection, adequate data volume, and data quality in achieving high-performing machine learning models. To aid researchers and practitioners, we propose a comprehensive framework for dataset quality assessment, which can help evaluate if the dataset at hand is sufficient and of the required quality for specific tasks. This research offers valuable insights into data assessment practices, contributing to the development of more accurate and robust machine learning models