Gothic Elements in Representations of a Pandemic: Borislav Pekic’s Rabies

The paper deals with the Gothic elements in the representation of a pandemic based on the 1983 novel Besnilo (‘Rabies’) by Serbian author Borislav Pekic. The authors start from the premise that the elements ‘borrowed’ from the Gothic genre play a key role in creating the main plot of the novel: a catastrophe caused by an extremely contagious and deadly man-manipulated version of the rabies virus. The theoretical framework is based on Fred Botting’s (1995) and Jerrold E. Hogle’s (2002) views of Gothic writing as a diffused mode that exceeds genres and categories and contributes its various elements to various literary forms. Furthermore, Gothic elements characteristic of Gothic science fiction, such as madness, monstrosity, the Mad Scientist, people meddling with nature with catastrophic consequences, the apocalyptic vision of human future and “the removal of man from his natural, living state and entry instead into a state of being neither completely human or monster, and neither fully alive or completely dead” (MacArthur 2015: 79) are traced in the novel and analysed in the context of literary representations of a pandemic. As Pekic’s novel is a mixture of various genres and is often defined and described as a horror thriller novel, an attempt is made to offer a new reading that would consider its constituent Gothic elements against a backdrop of the deeply and inherently human drama of the everlasting struggle between good and evil. Thus, pandemics are represented as a kind of catalyst that exposes both deeply human and rational, and deeply inhuman and irrational, impulses, leaving the final outcome of that struggle uncertain

Nostalgia in John Cheever’s The Housebreaker of Shady Hill and Other Stories

The paper deals with the notion and function of nostalgia in John Cheever’s short story cycle The Housebreaker of Shady Hill and Other Stories (1958). The analysis relies on Svetlana Boym’s definition of nostalgia as “a longing for a home that no longer exists or has never existed” (2001: xiii) and on Jennifer Smith’s (2017) study of nostalgia in the American short story cycle. The objective is to examine how and for what purposes Cheever’s cycle employs nostalgia in individual stories, and to show whether different evocations of nostalgia present partial or false versions of the past. Having in mind the recursive and accretive nature of the cycle as a genre, the authors intend to examine how individual stories come together to render a more collective sense of nostalgia, whether coherent or ambivalent, that might be in a correlation with a specific middle-class suburban locality of the American Northeast

Empirical control system development for intelligent mobile robot based on the elements of the reinforcement machine learning and axiomatic design theory

Ovaj rad predstavlja istraživanje autora u domenu koncepcijskog projektovanja upravljačkog sistema koji može da uči na osnovu sopstvenog iskustva. Sposobnost adaptivnog ponašanja pri izvršavanju postavljenog zadatka u realnim, nepredvidivim uslovima, jedan je od ključnih zadataka svakog inteligentnog robotskog sistema. U funkciji rešavanja ovog problema, predlaže se pristup baziran na učenju, i to kombinovanjem empirijske upravljačke strategije, mašinskog učenja ojačavanjem i aksiomatske teorije projektovanja. Predloženi koncept koristi najbolje osobine pomenutih teorijskih pristupa u cilju ostvarivanja optimalne odluke mobilnog robota za trenutno stanje sistema. Empirijska upravljačka teorija se, u ovom radu, a priori koristi u utvrđivanju idejnog rešenja za rešavanje problema navigacije mobilnog robota. Učenje ojačavanjem realizuje mehanizme koji memorišu i ažuriraju odgovore okruženja, a u kombinaciji sa empirijskom upravljačkom teorijom određuje najbolju moguću odluku u skladu sa trenutnim okolnostima. Aksiomatska teorija projektovanja se koristi pri definisanju upravljačkog problema, kao i pri uspostavljanju koncepcijskog rešenja za dati zadatak, sa aspekta primene pomenutih pristupa. Deo predloženog algoritma empirijskog upravljanja realizovan je pomoću LEGO Mindstorms NXT mobilnog robota, tretirajući problem navigacije u nepoznatom okruženju. Ostvareni eksperimentalni rezultati nagoveštavaju dobru perspektivu za realizaciju efikasnog upravljanja baziranog na iskustvu, čiji dalji razvoj može da dovede do ostvarenja autonomnog ponašanja mobilnog robota pri izbegavanju prepreka u tehnološkom okruženju, što je i očekivani naučni cilj.This paper presents the authors' efforts to conceptual design of control system that can learn from its own experience. The ability of adaptive behaviour regarding the given task in real, unpredictable conditions is one of the main demands for every intelligent robotic system. To solve this problem, the authors suggest a learning approach that combines empirical control strategy, reinforcement learning and axiomatic design theory. The proposed concept uses best features of mentioned theoretical approaches to produce optimal action in the current state of the mobile robot. In this paper empirical control theory imparts the basis of conceptual solution for the navigation problem of mobile robot. Reinforcement learning enables the mechanisms that memorize and update environment responses, and combining with the empirical control theory determines best possible action according to the present circumstances. Axiomatic design theory accurately defines the problem and possible solution for the given task in terms of the elements defined by two previously mentioned approaches. Part of the proposed algorithm was implemented on the LEGO Mindstorms NXT mobile robot for the navigation task in an unknown manufacturing environment. Experimental results have shown good perspective for development of efficient and adaptable control system, which could lead to autonomous mobile robot behaviour

Empirical control system development for intelligent mobile robot based on the elements of the reinforcement machine learning and axiomatic design theory

Ovaj rad predstavlja istraživanje autora u domenu koncepcijskog projektovanja upravljačkog sistema koji može da uči na osnovu sopstvenog iskustva. Sposobnost adaptivnog ponašanja pri izvršavanju postavljenog zadatka u realnim, nepredvidivim uslovima, jedan je od ključnih zadataka svakog inteligentnog robotskog sistema. U funkciji rešavanja ovog problema, predlaže se pristup baziran na učenju, i to kombinovanjem empirijske upravljačke strategije, mašinskog učenja ojačavanjem i aksiomatske teorije projektovanja. Predloženi koncept koristi najbolje osobine pomenutih teorijskih pristupa u cilju ostvarivanja optimalne odluke mobilnog robota za trenutno stanje sistema. Empirijska upravljačka teorija se, u ovom radu, a priori koristi u utvrđivanju idejnog rešenja za rešavanje problema navigacije mobilnog robota. Učenje ojačavanjem realizuje mehanizme koji memorišu i ažuriraju odgovore okruženja, a u kombinaciji sa empirijskom upravljačkom teorijom određuje najbolju moguću odluku u skladu sa trenutnim okolnostima. Aksiomatska teorija projektovanja se koristi pri definisanju upravljačkog problema, kao i pri uspostavljanju koncepcijskog rešenja za dati zadatak, sa aspekta primene pomenutih pristupa. Deo predloženog algoritma empirijskog upravljanja realizovan je pomoću LEGO Mindstorms NXT mobilnog robota, tretirajući problem navigacije u nepoznatom okruženju. Ostvareni eksperimentalni rezultati nagoveštavaju dobru perspektivu za realizaciju efikasnog upravljanja baziranog na iskustvu, čiji dalji razvoj može da dovede do ostvarenja autonomnog ponašanja mobilnog robota pri izbegavanju prepreka u tehnološkom okruženju, što je i očekivani naučni cilj.This paper presents the authors' efforts to conceptual design of control system that can learn from its own experience. The ability of adaptive behaviour regarding the given task in real, unpredictable conditions is one of the main demands for every intelligent robotic system. To solve this problem, the authors suggest a learning approach that combines empirical control strategy, reinforcement learning and axiomatic design theory. The proposed concept uses best features of mentioned theoretical approaches to produce optimal action in the current state of the mobile robot. In this paper empirical control theory imparts the basis of conceptual solution for the navigation problem of mobile robot. Reinforcement learning enables the mechanisms that memorize and update environment responses, and combining with the empirical control theory determines best possible action according to the present circumstances. Axiomatic design theory accurately defines the problem and possible solution for the given task in terms of the elements defined by two previously mentioned approaches. Part of the proposed algorithm was implemented on the LEGO Mindstorms NXT mobile robot for the navigation task in an unknown manufacturing environment. Experimental results have shown good perspective for development of efficient and adaptable control system, which could lead to autonomous mobile robot behaviour

Variational inference for robust sequential learning of multilayered perceptron neural network

U radu je prikazan i izveden novi sekvencijalni algoritam za obučavanje višeslojnog perceptrona u prisustvu autlajera. Autlajeri predstavljaju značajan problem, posebno ukoliko sprovodimo sekvencijalno obučavanje ili obučavanje u realnom vremenu. Linearizovani Kalmanov filtar robustan na autlajere (LKF-RA), je statistički generativni model u kome je matrica kovarijansi šuma merenja modelovana kao stohastički proces, a apriorna informacija usvojena kao inverzna Višartova raspodela. Izvođenje svih jednakosti je bazirano na prvim principima Bajesovske metodologije. Da bi se rešio korak modifikacije primenjen je varijacioni metod, u kome rešenje problema tražimo u familiji raspodela odgovarajuće funkcionalne forme. Eksperimentalni rezultati primene LKF-RA, dobijeni korišćenjem stvarnih vremenskih serija, pokazuju da je LKF-RA bolji od konvencionalnog linearizovanog Kalmanovog filtra u smislu generisanja niže greške na test skupu podataka. Prosečna vrednost poboljšanja određena u eksperimentalnom procesu je 7%.We derive a new sequential learning algorithm for Multilayered Perceptron (MLP) neural network robust to outliers. Presence of outliers in data results in failure of the model especially if data processing is performed on-line or in real time. Extended Kalman filter robust to outliers (EKF-OR) is probabilistic generative model in which measurement noise covariance is modeled as stochastic process over the set of symmetric positive-definite matrices in which prior is given as inverse Wishart distribution. Derivation of expressions comes straight form first principles, within Bayesian framework. Analytical intractability of Bayes' update step is solved using Variational Inference (VI). Experimental results obtained using real world stochastic data show that MLP network trained with proposed algorithm achieves low error and average improvement rate of 7% when compared directly to conventional EKF learning algorithm

Variational inference for robust sequential learning of multilayered perceptron neural network

U radu je prikazan i izveden novi sekvencijalni algoritam za obučavanje višeslojnog perceptrona u prisustvu autlajera. Autlajeri predstavljaju značajan problem, posebno ukoliko sprovodimo sekvencijalno obučavanje ili obučavanje u realnom vremenu. Linearizovani Kalmanov filtar robustan na autlajere (LKF-RA), je statistički generativni model u kome je matrica kovarijansi šuma merenja modelovana kao stohastički proces, a apriorna informacija usvojena kao inverzna Višartova raspodela. Izvođenje svih jednakosti je bazirano na prvim principima Bajesovske metodologije. Da bi se rešio korak modifikacije primenjen je varijacioni metod, u kome rešenje problema tražimo u familiji raspodela odgovarajuće funkcionalne forme. Eksperimentalni rezultati primene LKF-RA, dobijeni korišćenjem stvarnih vremenskih serija, pokazuju da je LKF-RA bolji od konvencionalnog linearizovanog Kalmanovog filtra u smislu generisanja niže greške na test skupu podataka. Prosečna vrednost poboljšanja određena u eksperimentalnom procesu je 7%.We derive a new sequential learning algorithm for Multilayered Perceptron (MLP) neural network robust to outliers. Presence of outliers in data results in failure of the model especially if data processing is performed on-line or in real time. Extended Kalman filter robust to outliers (EKF-OR) is probabilistic generative model in which measurement noise covariance is modeled as stochastic process over the set of symmetric positive-definite matrices in which prior is given as inverse Wishart distribution. Derivation of expressions comes straight form first principles, within Bayesian framework. Analytical intractability of Bayes' update step is solved using Variational Inference (VI). Experimental results obtained using real world stochastic data show that MLP network trained with proposed algorithm achieves low error and average improvement rate of 7% when compared directly to conventional EKF learning algorithm

The study of Renner-Teller effect in linear polyatomic molecules with variational method

Ренер-Телеров ефекат представља вибрационо-електронску (вибронску) спрегу и последица jе нарушавања Борн-Опенхаjмерове апроксимациjе приликом савиjања линеарних молекула са просторно дегенерисаним електронским стањем. Услед савиjаjућих вибрациjа молекула, електронско стање коjе jе просторно дегенерисано при линеарноj геометриjи се цепа на два блиска електронска стања коjа међусобно интерагуjу. Вибронска спрега тада одређуjе ровибрациону структуру спектра молекула, као и могуће понашање молекула у хемиjским реакциjама. У овоj докторскоj дисертациjи проучаван jе Ренер-Телеров ефекат код вишеатомских молекула са произвољним броjем jезгара (више од три jезгра) у Π електронским стањима и линеарном равнотежном геометриjом применом вариjационе методе. За разлику од троатомских молекула где се поjављуjе класични Ренер-Телеров ефекат коjи jе описан у претходном пасусу, код четвороатомских молекула и молекула са већим броjем jезгара уочава се комбинациjа Ренер-Телеровог ефекта и поjаве коничних пресека потенциjалних површи, односно Jан-Телеровог ефекта. Детаљно jе представљен модел за третирање Ренер-Телеровог ефекта и његова поузданост jе тестирана на примеру молекула C2H+ 2 у X 2Π u електронском стању ab initio рачунањем електронских потенциjалних површи и неадиjабатских матричних елемената. Проучена jе топологиjа (избегнутих) пресецања потенциjалних површи и неадиjабатских матричних елемената. У програмском jезику Python написан jе програм за вариjационо рачунање ниско-енергетских вибронских нивоа вишеатомских молекула са произвољним броjем jезгара, линеарном равнотежном геометриjом, Π електронским стањима и произвољне спинске мултиплетности. Конкретно, проучаван jе четвороатомски молекул C2S− 2 у X 2Πu стању. Вариjационо су израчунати вибронски нивои овог молекула и упоређени са доступним експерименталним подацима, при чему jе предложена асигнациjа трака коjе нису биле експериментално асигниране. Даље, топологиjа (избегнутих) пресецања потенциjалних површи и неадиjабатских матричних елемената проучена jе код петоатомских молекула, а на примеру молекула C5− у X 2Πu електронском стању. Први пут jе израчунат вибронски спектар петоатомског молекула, коjи испољава Ренер-Телеров ефекат, применом вариjационе методе. Такође, вибронски нивои молекула C6− у X 2Πu електронском стању су први пут израчунати у вариjационом формализму, а резултати упоређени са резултатима добиjени применом пертурбационе теориjе.The Renner-Teller effect represents а vibrational-electronic (vibronic) coupling and arises as a consequence of violation of the Born-Oppenheimer approximation in linear molecules with spatial degenerate electronic states upon bending vibrations. During bending vibrations of the molecule, a spatially degenerate electronic state at linear geometry splits into two energetically close electronic states that mutually interact. Vibronic coupling could lead to complicated rovibrational spectra of molecules, and also determine their reaction pathways. In this doctoral thesis, we have studied the Renner-Teller effect in linear polyatomic molecules with arbitrary number of nuclei (more than three) in Π electronic states and linear equilibrium geometry by using the variational method. In comparison to the classical Renner-Teller effect in triatomic molecules that was defined in the previous paragraph, in tetra- and any-atomic molecules a combination of classical Renner-Teller effect and appearance of conical intersections of potential energy surfaces occur, i.e. the Jahn-Teller effect. We describe in details a model for treatment of the Renner-Teller effect and test its plausibility on example of C2H+ 2 molecule in the X 2Πu electronic state by ab initio calculations of its electronic potential surface and nonadiabatic matrix elements. Topology structure of (avoided) crossings of potential surfaces and nonadiabatic matrix elements was studied. The program for variational calculation of low-lying vibrionic levels of polyatomic molecules with arbitrary number of nuclei and linear equilibrium geometry in Π electronic states and arbitrary spin multiplicity has been written in Python programming language. We have studied the tetra-atomic molecule C2S− 2 in its X 2Π u electronic state. Vibronic levels of this molecule have been variationally calculated and the obtained results were compared with available experimental data. The assignation of several previously unassigned spectral lines in the previous experimental investigation was proposed according to the obtained results. Further, topology structure of (avoided) crossings of potential surfaces and nonadiabatic matrix elements was studied in five-atomic molecules, on example of the C5− molecule in the X 2Πu electronic state. For the first time, the low-lying vibronic spectrum of five-atomic species which exhibit the Renner-Teller effect was calculated by applying the variational method. Additionally, the vibonic levels of C6− molecule in its X 2Π u electronic state have been calculated for the first time in variational manner, and the obtained results were compared to the perturbational theory results

Indicators of Entrepreneurial Behavior and Attitudes for Entrepreneurs of Slovenia and Croatia, in 2018/2019

Abstract. Following the theoretical presentation of entrepreneurship, that is, the determinants that determine the success of entrepreneurial ventures extracted from various scientific studies and research, the paper defines through research the indicators of entrepreneurial behavior and attitudes using the Global Entrepreneurship Monitor 2018/2019 Global Report database. Using the methods of comparison, induction and deduction, an analysis of indicators of Entrepreneurial Behavior and Attitudes for Slovenia and Croatia was conducted. 

LEADERSHIP AND MANAGEMENT AND ITS ROLE IN STRATEGIC CHANGE

Leadership and management, as two important processes in organizations, have a significant impact on all interactions and other processes that occur within an organization. The process of strategic change, important for the survival of organizations in turbulent times, is also conditioned by the roles of managers and leaders and their coordination of resources, vision, and the like.The aim of the paper is to demonstrate the influence of managers and leaders on strategic change (strategy implementation), through secondary and primary research.

Empirical Control System Development for Intelligent Mobile Robot Based on the Elements of the Reinforcement Machine Learning and Axiomatic Design Theory

This paper presents the authors’ efforts to conceptual design of control system that can learn from its own experience. The ability of adaptive behaviour regarding the given task in real, unpredictable conditions is one of the main demands for every intelligent robotic system. To solve this problem, the authors suggest a learning approach that combines empirical control strategy, reinforcement learning and axiomatic design theory. The proposed concept uses best features of mentioned theoretical approaches to produce optimal action in the current state of the mobile robot. In this paper empirical control theory imparts the basis of conceptual solution for the navigation problem of mobile robot. Reinforcement learning enables the mechanisms that memorize and update environment responses, and combining with the empirical control theory determines best possible action according to the present circumstances. Axiomatic design theory accurately defines the problem and possible solution for the given task in terms of the elements defined by two previously mentioned approaches. Part of the proposed algorithm was implemented on the LEGO Mindstorms NXT mobile robot for the navigation task in an unknown manufacturing environment. Experimental results have shown good perspective for development of efficient and adaptable control system, which could lead to autonomous mobile robot behaviour