Schopenhauer’s Philosophy Of Love In Flaubert’s Madam Bovary And Fitzgerald’s The Great Gatsby

This study examines the works of two writers F. Scott Fitzgerald’s The Great Gatsby and Flaubert’s Madam Bovary in relation to the concept of Schopenhauer’s philosophy of the ‚instinct of sex‛ as a subjective necessity and ‚love‛ as an objective point. The study determines whether the concepts of Schopenhauer, specifically on love, desire, and suffering, influence the life of the major characters in the two novels.This research is based on how Schopenhauer’s concept of ‚love as physical attraction‛ is portrayed in the characters of two novels. The study also attempts to find out what part the instinct of sex as a subjective point and the elements of desire, suffering and love as an objective point, plays in the lives of the characters. The research concludes that love among the characters is merely based on physical attraction, which leads them to have a strong desire which in turn, causes them to suffer

A philosophical approach towards the concept of freedom in Henry James’s The Portrait of a Lady

Freedom is one of the major elements in Henry James’s The Portrait of a Lady. In an age when American women were usually engaged or married, James’s heroine, Isabel, was somewhat ahead of her time in hoping for a marriage in which she could still be independent. She was very fond of her liberty and afraid of losing it, but does her return to her husband, Osmond, at the end of the novel suggest that she has put an end to her eagerness for freedom? It is an underlying argument of this study that James’s novel, in its last scene, covers a different aspect of freedom which, through a Schopenhauerian approach, delivers a different insight into liberty. This perception of freedom in The Portrait of a Lady has never been considered in connection with Arthur Schopenhauer’s view on the experience of freedom. The implicit critical point on which the paper is founded is that, although Schopenhauer is not conspicuously mentioned in James’s notes, there is important evidence that shows the convergence between the thoughts of Schopenhauer and Henry James; that is, that evidence through which this study aims to analyse the heroine’s final decision in the last scene

Relative Entropy (RE) Based LTI System Modeling Equipped with time delay Estimation and Online Modeling

This paper proposes an impulse response modeling in presence of input and noisy output of a linear time-invariant (LTI) system. The approach utilizes Relative Entropy (RE) to choose the optimum impulse response estimate, optimum time delay and optimum impulse response length. The desired RE is the Kulback-Lielber divergence of the estimated distribution from its unknown true distribution. A unique probabilistic validation approach estimates the desired relative entropy and minimizes this criterion to provide the impulse response estimate. Classical methods have approached this system modeling problem from two separate angles for the time delay estimation and for the order selection. Time delay methods focus on time delay estimate minimizing various proposed criteria, while the existing order selection approaches choose the optimum impulse response length based on their proposed criteria. The strength of the proposed RE based method is in using the RE based criterion to estimate both the time delay and impulse response length simultaneously. In addition, estimation of the noise variance, when the Signal to Noise Ratio (SNR) is unknown is also concurrent and is based on optimizing the same RE based criterion. The RE based approach is also extended for online impulse response estimations. The online method reduces the model estimation computational complexity upon the arrival of a new sample. The introduced efficient stopping criteria for this online approaches is extremely valuable in practical applications. Simulation results illustrate precision and efficiency of the proposed method compared to the conventional time delay or order selection approaches.Comment: 13 pages, 11 figure

Learnability, Sample Complexity, and Hypothesis Class Complexity for Regression Models

The goal of a learning algorithm is to receive a training data set as input and provide a hypothesis that can generalize to all possible data points from a domain set. The hypothesis is chosen from hypothesis classes with potentially different complexities. Linear regression modeling is an important category of learning algorithms. The practical uncertainty of the target samples affects the generalization performance of the learned model. Failing to choose a proper model or hypothesis class can lead to serious issues such as underfitting or overfitting. These issues have been addressed by alternating cost functions or by utilizing cross-validation methods. These approaches can introduce new hyperparameters with their own new challenges and uncertainties or increase the computational complexity of the learning algorithm. On the other hand, the theory of probably approximately correct (PAC) aims at defining learnability based on probabilistic settings. Despite its theoretical value, PAC does not address practical learning issues on many occasions. This work is inspired by the foundation of PAC and is motivated by the existing regression learning issues. The proposed approach, denoted by epsilon-Confidence Approximately Correct (epsilon CoAC), utilizes Kullback Leibler divergence (relative entropy) and proposes a new related typical set in the set of hyperparameters to tackle the learnability issue. Moreover, it enables the learner to compare hypothesis classes of different complexity orders and choose among them the optimum with the minimum epsilon in the epsilon CoAC framework. Not only the epsilon CoAC learnability overcomes the issues of overfitting and underfitting, but it also shows advantages and superiority over the well known cross-validation method in the sense of time consumption as well as in the sense of accuracy.Comment: 14 pages,10 figure

A Schopenhauerinan Reading of Henry James's The Portrait of a Lady and D. H. Lawrence's The White Peacock

My study aims to offer a Schopenhauerian reading of Henry James's The Portrait of a Lady and D. H. Lawrence's The White Peacock. Throughout the dissertation, I am driven by two goals. First, I aim to examine the selected novels by considering Schopenhauer's philosophy. Secondly, I shall investigate why characters, especially the heroines, having recognised that their marriage was basically a mistake, still remained in their tormented relationships. Why it is important to answer this question and what makes this a unique concern, especially in James's novel, is the possibility that previous studies and many other critiques have questioned the destiny of these heroines in regard to the novelists' anti-feminist tendencies or their social and personal concerns, while I believe that by using Schopenhauer's philosophy I can provide a deeper conceptualisation of the novels' ending. In so doing, in the second chapter I will describe the reception of Schopenhauer's philosophy in England, and the direct and indirect presence of his philosophy in Lawrence's and James's Works. In the third chapter, I concentrate on Schopenhauer's concept of freedom, morality and the will in James's novel. My fourth chapter considers Lawrence's philosophy of love and reveals how his philosophy differs from Schopenhauer's. Furthermore, it draws his readers' attention to the Schopenhauerian notion of the will-to-live, acknowledged in Lawrence's novel

Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning

Price movement prediction has always been one of the traders' concerns in financial market trading. In order to increase their profit, they can analyze the historical data and predict the price movement. The large size of the data and complex relations between them lead us to use algorithmic trading and artificial intelligence. This paper aims to offer an approach using Twin-Delayed DDPG (TD3) and the daily close price in order to achieve a trading strategy in the stock and cryptocurrency markets. Unlike previous studies using a discrete action space reinforcement learning algorithm, the TD3 is continuous, offering both position and the number of trading shares. Both the stock (Amazon) and cryptocurrency (Bitcoin) markets are addressed in this research to evaluate the performance of the proposed algorithm. The achieved strategy using the TD3 is compared with some algorithms using technical analysis, reinforcement learning, stochastic, and deterministic strategies through two standard metrics, Return and Sharpe ratio. The results indicate that employing both position and the number of trading shares can improve the performance of a trading system based on the mentioned metrics

Association of IFN-γ and P2X7 Receptor Gene Polymorphisms in Susceptibility to Tuberculosis Among Iranian Patients

Interferon-gamma (IFN-γ) and P2X7 receptor are crucial for host defence against mycobacterial infections. Recent studies have indicated that IFN-γ, IFN-γ receptor 1 (IFN-γR1) andP2X7 gene polymorphisms are associated with susceptibility to pulmonary tuberculosis (TB). However, the relationship between IFN-γ and P2X7 polymorphism and TB susceptibility remains inconclusive in Iranian population. For this reason, single nucleotide polymorphisms (SNPs) in IFN-γ (G+2109A), IFN-γR1 (G-611A) and P2X7 genes (at –762, 1513 position) in patients (n = 100) were assessed using PCR-RFLP. Data were analysed with SPSS version 18. For the 2109 loci of IFN-γ gene, the frequency of mutant alleles between patients and controls were not statistically significant. However, there was a significant difference between the TB patient and controls for –611 alleles of IFN-γR1 (P = 0.01). Additionally, the frequency of P2X7 gene polymorphisms (SNP-762 and 1513) between patients and controls was statistically significant. In conclusions, our study revealed a significant association of IFN-γR1 and P2X7 genes polymorphisms with risk of developing TB in Iranian population

The Effect of Intellectual Capital on Corporate Performance

The primary goal of this study is to investigate the relationship between intellectual capital and corporate performance by focusing on the characteristics of board members. For this purpose, the diversity in the educational background, and the education level of board members, were utilized as indicators of intellectual capital, while gender diversity was also used as a characteristic of members on the board of directors.The study population consisted of companies listed on the Tehran Stock Exchange in the period from 2011 to 2017. The research method was descriptive-correlational and the relationship between research variables was explained using regression models based on the panel data.The findings suggested that the intellectual capital of the board of directors in companies listed on the Tehran Stock Exchange did not have any effect on their performance in practice. Therefore, according to the results of the study, managers should be appointed irrespective of their gender, because gender diversity has no effect on the performance of companies competing in Iran business environment