The Transmission of Mathematics into Greek Education, 1800-1840: From Individual Initiatives to Institutionalization

19. yüzyıl başlarında birtakım Rum cemaatleri matematik alanında göz kamaştırıcı bir eğitim öğretim geliştirdiler. Bu eğitimin ana konusu, bazı öğretmenler Prusya’nın matematik kaynaklarını tercih etmelerine karşın ağırlıklı olarak Fransızca ders kitaplarından şekillendirildi. Lakin bu çabalar o dönemin Ortodoks kilisesinin dinî tutuculuğu tarafından engellendi, ki kilise bir Rum matematik anlatım metodunun ortaya çıkmasını tercih etmemekteydi. Sonuç olarak matematiksel bilginin dışarıdan içeriye aktarımı parçalı ve gelişigüzel bir süreçti; bir bütünlükten, uyumdan ve geçişlilikten yoksundu. Bu durum 1820’ler ve 1830’larda kökten bir şekilde değişti. Korfu’da 1824’te kurulan İonya Akademisi ile Nafplio’da 1828’de kurulan Military School (Askeri Okul) bir Rum eğitimi için ilk kurumsal çerçeveyi yarattılar. Bu eğitim kurumlarında Rum matematik anlatım metodunun temeli olarak İhtilal sonrası Fransız matematiği tesis edildi. Rum matematik eğitiminin Fransız kaynaklı arka planı, 1837 sonrasında orta öğretimin kurumsallaştırılması ve 1836-1837’de Atina Üniversitesi’nin kurulmasını müteakiben daha da güçlendirildi. Aynı zamanda Rum matematiksel anlatım metodunun içerisine Fransız etkisi zerk edilirken bir yandan da önemli bazı Prusya ders kitaplarının çevrilmesi teşvik edildi.19. yüzyılın ilk yarısı o dönemin epistemolojik eğilimlerinin, örneğin analitik model, Fransız matematikçilerinde hakim olan pozitivizm, Prusya matematiğinin tümleşik “paradigmasının” Rum matematik eğitiminin tarihsel oluşumuna aktarılmasına şahit oldu.In the early 19th century, a number of Greek communities developed a remarkable education in mathematics. The subject matter for this instruction was drawn mainly from French textbooks, although some teachers displayed a preference for Prussian mathematical sources. These efforts, however, were thwarted by the religious conservatism of the Greek establishment of the time, which did not favor the emergence of a Greek mathematical discourse. As a consequence, the reception of mathematical knowledge was a fragmented, random process lacking cohesion, collectivity and transitivity. The situation radically changed during the second and third decades of the 19th century. The Ionian Academy in Corfu, and the Military School in Nafplio, founded in 1824 and 1828 respectively, created the first institutional frame for a Greek education in which post-revolutionary French mathematics was established as the basis of Greek mathematical discourse. The French background of Greek mathematical education was further reinforced after 1837, subsequent to the institutionalization of secondary education, and to the founding of the University of Athens in 1836-1837. At the same time, along with this French infusion into Greek mathematical discourse, some noteworthy translations of Prussian textbooks were promoted as well. The first half of the 19th century also witnessed the transmission of the respective epistemological trends of that era, i.e. of the analytical- model, of the positivism dominating French mathematics, and of the combinatorial “paradigm” of Prussian mathematics, to the historical setting of Greek mathematical education

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. Efficiently detecting whether an industrial component has deviated from its normal operating condition or predicting when a fault will occur are the main challenges these systems aim at addressing. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. The increasing availability of data and the stunning progress of Machine Learning (ML) and Deep Learning (DL) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. On the other hand, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. In this work, we explore the intersection of Artificial Intelligence (AI) methods and PHM applications. We present a thorough review of existing works both in the contexts of fault diagnosis and fault prognosis, highlighting the benefits and the drawbacks introduced by the adoption of AI techniques. Our goal is to highlight potentially fruitful research directions along with characterizing the main challenges that need to be addressed in order to realize the promises of AI-based PHM systems.ISSN:2624-821

Reinforcement learning utilizes proxemics: an avatar learns to manipulate the position of people in immersive virtual reality

A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals

Reinforcement learning utilizes proxemics: an avatar learns to manipulate the position of people in immersive virtual reality

A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals

Uncertainty-Aware Prognosis via Deep Gaussian Process

The task of predicting how long a certain industrial asset will be able to operate within its nominal specifications is called Remaining Useful Life (RUL) estimation. Efficient methods of performing this task promise to drastically transform the world of industrial maintenance, paving the way for the so-called Industry 4.0 revolution. Given the abundance of data resulting from the advent of the digitalization era, Machine Learning (ML) models are the ideal candidates for tackling the RUL estimation problem in a fully data-driven fashion. However, given the safety-critical nature of maintenance operations on industrial assets, it's crucial that such ML-based methods be designed such that their levels of transparency and reliability are maximized. Modern ML algorithms, however, are often employed as black-box methods, which do not provide any clue regarding the confidence level associated with their output. In this paper, we address this limitation by investigating the performance of a recently proposed class of algorithms, Deep Gaussian Processes, which provide uncertainty estimates associated with their RUL prediction, yet retain the expressive power of modern ML techniques. Contrary to standard approaches to uncertainty quantification, such methods scale favourably with the size of the available datasets, allowing their usage in the "big data" setting. We perform a thorough evaluation and comparison of several variants of DGPs applied to RUL predictions. The performance of the algorithms is evaluated on the NASA N-CMAPSS (New Commercial Modular Aero-Propulsion System Simulation) dataset for aircraft engines. The results show that the proposed methods are able to yield very accurate RUL predictions along with sensible uncertainty estimates, providing more reliable solutions for (safety-critical) real-life industrial applications.ISSN:2169-353