Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of macroeconomics forecast errors on volatility smile

In the academic world, the Black, Scholes and Merton model is the most famous and taught option-pricing model. However, this model assumes the volatility to be constant, and a short analysis of the historical volatility proves that this assumption is wrong: the volatility depends both on the option’s strike and maturity. In the professional world, the success of this model is due to its ability to obtain the implied volatilities. The aim of this dissertation is to analyse the dynamics of the volatility smile on S&P 500 3-months options. More precisely, the objective is to measure the impact of macroeconomic surprises on the level, slope, and convexity of the volatility smile. It results that many macroeconomic indexes influence these aspects. Moreover, these dimensions will react differently according to the signs of the macroeconomic surprises and the index itself. Thus, an underestimation of the inflation will expand the general level of the volatility smile, increase its trend, and diminish its convexity. In contrast, a negative surprise of the level of importation will reduce the trend and convexity. Consequently, it is possible to invest and build volatility portfolios in order to take advantages of the distorted volatility smile.
On Time and Money Donations (preliminary)

Tutt’e tre stesero la mano verso colui che usciva [dall’osteria] con passo franco, e con l’aspetto rianimato: nessuno parlò; che poteva dir di più una preghiera? <<La c’è la Provvidenza! >> disse Renzo; e, cacciata subito la mano in tasca, la votò di que’ pochi soldi; li mise nella mano che si trovò più vicina, e riprese la sua strada. La refezione e l’opera buona (giacchè siam composti d’anima e di corpo) avevano riconfortati e rallegrati tutti i suoi pensieri. [A. Manzoni, I promessi sposi, Cap. XVII, 1840-42]
Self-Attentive Hawkes Process

Capturing the occurrence dynamics is crucial to predicting which type of events will happen next and when . A common method to do this is through Hawkes processes. To enhance their capacity, recurrent neural networks (RNNs) have been incorporated due to RNNs’ successes in processing sequential data such as languages. Recent evidence suggests that self-attention is more competent than RNNs in dealing with languages. However, we are unaware of the effectiveness of self-attention in the context of Hawkes processes. This study aims to ﬁll the gap by designing a self-attentive Hawkes process (SAHP). SAHP employs self-attention to summarise the inﬂuence of history events and compute the probability of the next event. One deﬁcit of the conventional self-attention, when applied to event sequences, is that its positional encoding only considers the order of a sequence ignoring the time intervals between events. To overcome this deﬁcit, we modify its encoding by translating time intervals into phase shifts of sinusoidal functions. Experiments on goodness-of-ﬁt and prediction tasks show the improved capability of SAHP. Furthermore, SAHP is more interpretable than RNN-based counter-parts because the learnt attention weights reveal contributions of one event type to the happening of another type. To the best of our knowledge, this is the ﬁrst work that studies the effectiveness of self-attention in Hawkes processes.
Streaming Nonlinear Bayesian Tensor Decomposition

Despite the success of the recent nonlinear tensor decomposition models based on Gaussian processes (GPs), they lack an effective way to deal with streaming data, which are important for many applications. Using the standard streaming variational Bayes framework or the recent streaming sparse GP approximations will lead to intractable model evidence lower bounds; although we can use stochastic gradient descent for incremental updates, they are unreliable and often yield poor estimations. To address this problem, we propose Streaming Nonlinear Bayesian Tensor Decomposition (SNBTD) that can conduct high-quality, closed-form and iteration-free updates upon receiving new tensor entries. Speciﬁcally, we use random Fourier features to build a sparse spectrum GP decomposition model to dispense with complex kernel/matrix operations and to ease posterior inference. We then extend the assumed-density-ﬁltering framework by approximating all the data likelihoods in a streaming batch with a single factor to perform one-shot updates. We use conditional moment matching and Taylor approximations to fulﬁll efﬁcient, analytical factor calculation. We show the advantage of our method on four real-world applications.
BUILDING IT ARCHITECTURE FOR DEVELOPING NATIONS USING TOGAF

Developing nations vary in data usage techniques with respect to developed nations because of lack of standard information technology architecture. With the concept of globalization in the modern times, there is a necessity of information sharing between different developing nations for better advancements in socio-economic and science and technology fields. A robust IT architecture is needed and has to be built between different developing nations which eases information sharing and other data usage methods. A framework like TOGAF may work in this case as a normal IT framework may not fit to meet the requirements of an enterprise architecture. The intention of the thesis is to build an enterprise architecture between different developing nations using a framework TOGAF.
EAFIT UNIVERSITY COLLEGE OF ENGINEERING MASTER PROGRAM IN ENGINEERING MEDELLIN, COLOMBIA MAY 2013 MASTER PUBLICATION Compendium of M.Sc. Publications on Assessment of Effective Properties of Multi-phase Materials MASTER STUDENT

The present paper deals with the formulation of minimal loading conditions for the application of numerical homogenisation techniques, namely the FE2 methodology. Based on the set of volume averaging rules connecting the heterogeneous micro and the homogeneous macro scale, the minimal constraints on the deformation of a micro volume are derived for a classical Cauchy continuum as well as for a micromorphic continuum theory. For both cases, numerical studies are included highlighting the main aspects of the proposed procedure within the context of small deformations.
Forecasting the Volatility of Philippine Inflation using GARCH Models

The study highlights the statistical procedure employed in developing a shortterm forecasting model that explores the volatility feature of Philippine inflation from 1995 up to August 2007. To build such a model, we identify first the stationary series. Second, we specify the Autoregressive Moving Average (ARMA) model then include the Seasonal ARMA (SARMA) model if seasonality is present, to represent the mean component using the past values of inflation. Next, we incorporate the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to represent its volatility. Diagnostic tests and examination of forecast accuracy measures indicate that the specifications D(IR) (the first difference of monthon-month inflation) as the stationary series, AR(1) and SMA(12) for the mean, GARCH(0,1) or ARCH(1) for the variance with Student’s t distribution having fixed degrees of freedom v = 10 for the errors, performs best in forecasting the volatility of the inflation rate for the Philippines. Lastly, out of sample forecasts for the mean and error variance of Philippine inflation from September 2007 to October 2007 are achieved using dynamic forecasting.
Capital Structure and Impact of Debt Maturity Structure on Firms’ Leverage Level in Malaysia

DOI: 10.21276/sjebm.2018. 5.6. 9 Abstract: The main objective of a firm’s manager is maximizing shareholders’ wealth while trying to achieve optimum capital structure. Having too much of debts could increase the firm’s risk level while it might help to take up good investment opportunities and bring in positive cash flow in future. On the other hand, holding low leverage level to maintain lower liquidity risk might cause the firm to sacrifice any great investment opportunity. This causing underinvestment and agency problem between the firm’s managers and shareholders. Thus, various issues and factors need to be considered while deciding on a firm’s capital structure and financing decisions. Firms debt maturity structure can affect the liquidity risk in a firm as well as the firm’s borrowing power. Having too much short term debts will reduce the firm’s borrowing power and causing a higher liquidity risk, whereby firm’s managers need to ensure they have enough cash flow for debt repayments while the debt is due for payment in short term. Long term debts could reduce the liquidity risk but causing a higher cost to the firm. Thus, this study is to investigate the firms’ capital structure while focusing on the impact of debt maturity on firm’s leverage level together with other factors. Results indicated that firms in Malaysia are following pecking order theory whereby firms prefer to use internal funds compare to debts. The firms’ debt maturity structure, tax shields, investment activities and firms size was found with significant positive impact on firms’ leverage level. Conversely, firms’ profitability, dividend payout ratio and economics conditions were found with significant negative impact on the firms’ debt level. This paper provides a better understanding on the Malaysian firms’ capital structure and guiding the firms’ manager in managing firms’ debt level and avoiding bankruptcy.
Cloning and Expression Analysis of Sugarcane Alkaline/Neutral Invertase Gene SoNIN1

Neutral/alkaline invertase irreversibly catalyzes sucrose into glucose and fructose, and plays an important regulating role in plant growth and development. In the present study, the whole length of neutral/alkaline invertase gene cDNA was cloned and sequenced from leaf of sugarcane variety GT28 using RT-PCR(reverse transcription-polymerase chain reaction) and RACE(rapid amplification of cDNA ends), and named as SoNIN1. The gene consists of 2289 bp with an ORF(open reading frame) of 1812 bp encoding a polypeptide of 603 amino acids with calculated protein molecular weight of 67.79 kD, and an isoelectric point of pH 6.41. SoNIN1 gene encodes a protein that is close to that of Zea mays, Oryza sativa, and Lolium perenne, belonging to the same evolutionary branch. The putative protein contains the conserved domain of alkaline/neutral invertase, and no membrane-spanning domain and signal peptide at N-end. The gene promoter sequence was cloned by using genome walking, which contains the basic components of CAAT-boxes and TATA-boxes, as well as specific acting elements such as cis-regulatory element involved in meristem activation, methyl jasmonate response and circadian rhythm, plus drought-induced MYB binding sites, abscisic acid response element, gibberellin response element, etc. Real-time PCR results showed that expression of SoNIN1 could be detected in the roots, stalks, leaves, flowers and buds, among which the highest was found in leaves, the medium in buds, and the lowest in internode +1(enrolled by the top visible dewlap leaf). The expression of SoNIN1 gene in roots and leaves of sugarcane seedling was induced by PEG and NaCl.
Research on Applications of Solid 鄄 state RF Power Supply Based on GaN Devices

Along with the development of power electronics technology, RF power has been developed from the tube power supply to the transistor RF power supply at present. As a typical representative of the third鄄generation wide bandgap semiconductor materials, gallium nitride ( GaN ) has excellent properties such as wide bandgap, high critical br鄄 eakdown field strength, high electron saturation drift velocity, and highly conductive AlGaN/GaN heterojunction two鄄di鄄 mensional electrons gas ( 2DEG ) . Compared with silicon ( Si ) power devices, GaN power devices have characteristics such as lower on鄄resistance, and smaller input and output capacitances, leading to their high switching speed and low loss. Based on the Class鄄E power RF power supply, a 4 MHz, power鄄adjustable, and all鄄solid鄄state RF power supply prototype was designed and manufactured using GaN power devices. Through the circuit design and optimization, the prototype ’ s efficiency reached 96.7% when its output power was 21.4 W. Meanwhile, the GaN device on the prototype was replaced by a Si power device, which was specifically produced for the RF power supply. Experimental data verified that the GaN device had a fast switching speed and low loss, thus greatly improving the efficiency of the RF power supply.
Functional activation changes at ultra-high field related to upper and lower limb impairments in multiple sclerosis

Upper and lower limb impairments are common in people with multiple sclerosis (pwMS), yet difficult to clinically identify in early stages of disease progression. Tasks involving complex motor control can potentially reveal more subtle deficits in early stages, and can be performed during functional MRI acquisition, to investigate underlying neural mechanisms, providing markers for early motor progression. We investigated brain activation during visually-guided force-matching of hand or foot in 28 minimally disabled pwMS and 17 healthy controls (HC) using ultra-high field 7-Tesla fMRI, allowing us to visualise sensorimotor network activity in high detail. Task activations and performance (tracking lag and error) were compared between groups, and correlations were performed. PwMS showed delayed (+124 s, p=0.002) and more erroneous (+0.15 N, p=0.001) lower limb tracking, together with higher primary motor and premotor cortex activation, and lower cerebellar activation compared to HC. No differences were seen in upper limb performance or activation. Functional activation levels of cerebellar, visual and motor areas correlated with task performance. These results demonstrate that ultra-high field fMRI during complex hand and foot tracking can identify subtle impairments in movement and brain activity, and differentiates upper and lower limb impairments in minimally disabled pwMS.
Antibacterial effect of Surfacen and surfactant protein A (SP-A) against a clinical isolate of Streptococcus pneumoniae.

Surfacen®, a clinical preparation of exogenous pulmonary surfactant, and pulmonary surfactant protein A (SP-A) were evaluated in relation to their in vitro effect on the growth of a clinical isolate of Streptococcus pneumoniae. The bacterium was incubated in a normal saline solution for 5 h at 37oC, together with Surfacen® and SP-A. Surfacen® showed antibacterial activity against the clinical isolate of Streptococcus pneumonia at 10 or 25 mg/ml with one logarithm of reduction of the bacterial growth; also SP-A had the same effect at concentrations of 0.25, 0.5 or 1 mg/ml. Porcine SP-A addition at the highest Surfacen® concentration (25 mg/ml) showed a higher antibacterial activity by reducing two logarithms of bacterial growth. Key word: lung surfactant, Surfacen®, antibacterial, Surfactant Protein A (SP-A), Acute Respiratory Distress Syndrome. *Autor para la correspondencia: Dra. Odalys Blanco Hidalgo. Centro Nacional de Sanidad Agropecuaria (CENSA) Autopista Nacional y Carretera de Tapaste, San José de Las Lajas, Apartado 10, CP 32 700, Mayabeque, Cuba. Tel: 5347-863014. Fax: 5347-861104. E-mail: oblanco@censa.edu. cu y odalysbh@infomed.sld. cu. El sistema surfactante pulmonar, contiene fosfolípidos y proteínas específicas que permiten la apertura estable de los espacios respiratorios pulmonares (1). Su falta o disfunción está asociada al desarrollo de patologías severas tales como el Síndrome de Distrés Respiratorio Agudo (SDRA), neumonías, etc (2). El sistema surfactante integra también componentes encargados de establecer una barrera de defensa innata que previene la entrada de patógenos a través de la superficie respiratoria, la mayor área de contacto del organismo con su entorno. Diversos estudios han revelado la capacidad antibiótica del
Intelligent Recognition of Ferrographic Images Combining Optimal CNN With Transfer Learning Introducing Virtual Images

Ferrography analysis(FA) is an important approach to detect the wear state of mechanical equipment. Ferrographic image recognition based on deep learning needs a large number of image samples. However, the ferrographic images of mechanical equipment are difficult to obtain enough high-quality samples in a short time due to the complexity and low efficiency of the ferrogram making. Therefore, the recognition method for small sample ferrographic images based on the convolutional neural network(CNN) and transfer learning(TL) is proposed. Based on the similarity of samples, the virtual ferrographic image set is designed as the source data of the pretraining model, the tested CNN model is constructed by using the TL. Based on the AlexNet frame, this paper studies the influence of the CNN internal factors including network structure, convolution parameters, activation function, optimization mode, learning rate and the external factors on the classification effect of test samples, and the L2 regularizer is added to solve the overfitting. According to the classification result of test samples, an optimal parameter combination is obtained to establish an intelligent recognition model of ferrographic images based on CNN and TL with the recognition accuracy of 93.75%. Moreover, the t-SNE is used to realize the wear particle recognition process visualization, which proves the effectiveness of the proposed algorithm. This work provides an effective way for the ferrographic image recognition of wear particles under small samples.
Information Flow Analysis between EPU and Other Financial Time Series

We investigate the strength and direction of information flow among economic policy uncertainty (EPU), US imports and exports to China, and the CNY/US exchange rate by using the novel concept of effective transfer entropy (ETE) with a sliding window methodology. We verify that this new method can capture dynamic orders effectively by validating them with the linear transfer entropy (TE) and Granger causality methods. Analysis shows that since 2016, US economic policy has contributed substantially to China-US bilateral trade and that China is making passive adjustments based on this trade volume. Unlike trade market conditions, China’s economic policy has significantly influenced the exchange rate fluctuation since 2016, which has, in turn, affected US economic policy.
Compliant Motion Adaptation with Dynamical System during Robot-Environment Interaction

Compliant control ability is essential for physical robot-environment interaction. This paper presents a novel framework for robots to achieve active compliant behaviour through Dynamical System (DS). In contrast to the existing research works that aim to improve the compliant performance by introducing an appropriate impedance model or force control scheme, in our framework, no prerequisite for predefined robot impedance parameters or environmental model is required. The proposed method can generate a continuous and adaptive trajectory in terms of the contacting force by designing a globally asymptotically stable DS to guarantee a safe interaction with unknown environment. The method is not only strictly derived from theoretical study, but also verified by extensive simulations and experiments on different shapes of contacting object and different types of DS model.
MADFU: An Improved Malicious Application Detection Method Based on Features Uncertainty

Millions of Android applications (apps) are widely used today. Meanwhile, the number of malicious apps has increased exponentially. Currently, there are many security detection technologies for Android apps, such as static detection and dynamic detection. However, the uncertainty of the features in detection is not considered sufficiently in these technologies. Permissions play an important role in the security detection of Android apps. In this paper, a malicious application detection model based on features uncertainty (MADFU) is proposed. MADFU uses logistic regression function to describe the input (permissions) and output (labels) relationship. Moreover, it uses the Markov chain Monte Carlo (MCMC) algorithm to solve features’ uncertainty. After experimenting with 2037 samples, for malware detection, MADFU achieves an accuracy of up to 95.5%, and the false positive rate (FPR) is 1.2%. MADFU’s Android app detection accuracy is higher than the accuracy of directly using 24 dangerous permission. The results also indicate that the method for an unknown/new sample’s detection accuracy is 92.7%. Compared to other state-of-the-art approaches, the proposed method is more effective and efficient, by detecting malware.
Design of a Variable Stiffness Joint Module to Quickly Change the Stiffness and to Reduce the Power Consumption

Variable stiffness actuators (VSA) are finding wide applications in robotics to enhance safety during interactions with stiff environments. Researchers have proposed various design architectures like antagonistic actuation, which requires both the motors to be powered simultaneously for varying the stiffness or equilibrium position. In this paper, the design of a novel joint module, named as variable stiffness joint module (VSJM), is proposed, which consists of a lead-screw arrangement for varying the stiffness range and a cam based mechanism to change the stiffness within the set range quickly. The cam profile has been synthesized to maximize the stiffness variation as well as to maintain the cam and cam follower in static equilibrium when the output link is in the equilibrium position. This was achieved by properly positioning and orienting the friction cones at the contact points. By mechanically compensating the moment due to unbalanced forces at the contact points, the continuous usage of stiffness motor has been eliminated, leading to reduced power consumption. Details of the proposed mechanism are presented along with the mathematical model for cam profile synthesis and static analysis. A simplified prototype of the proposed design has been fabricated to perform the experiments. A hammering-a-nail experiment has been conducted to show the capability of the mechanism, and the results are presented.
Comprehensive Review of Cybercrime Detection Techniques

Cybercrimes are cases of indictable offences and misdemeanors that involve computers or communication tools as targets and commission instruments or are associated with the prevalence of computer technology. Common forms of cybercrimes are child pornography, cyberstalking, identity theft, cyber laundering, credit card theft, cyber terrorism, drug sale, data leakage, sexually explicit content, phishing, and other forms of cyber hacking. They mostly lead to a privacy breach, security violation, business loss, financial fraud, or damage in public and government properties. Thus, this study intensively reviews cybercrime detection and prevention techniques. It first explores the different types of cybercrimes and discusses their threats against privacy and security in computer systems. Then, it describes the strategies that cybercriminals may utilize in committing these crimes against individuals, organizations, and societies. It also reviews the existing techniques of cybercrime detection and prevention. It objectively discusses the strengths and critically analyzes the vulnerabilities of each technique. Finally, it provides recommendations for the development of a cybercrime detection model that can detect cybercrimes effectively compared with the existing techniques.