Antecedents of Dividend Policy: Empirical Evidence from Banking Sector of Pakistan

This paper explores the determinants of dividend policy of commercial banks operating in Pakistan. Dividend decision of any bank primarily depends upon its profitability, retained earnings, cash flows, corporate taxes and leverage. This study is an attempt to find out key determinants and their impact on cash payout and total payout ratios. It also aims to test the implication of dividend theories on Pakistani banks using data for a period of 8 years ranging from 2006 to 2013. Balanced panel data regression with fixed effects model has been used in this study. All independent variables - PAT, SLACK, EPS, CTA and TD[1] reported significant results. We found significant role of profitability theory, packing order theory, free cash flow theory and agency cost theory in determining dividend policies whereas, tax effect and financial slack has no effect in banking sector of Pakistan. [1] Profitability, retained earnings, earnings per share, cash flows, and leverag

Deep reinforcement learning based Evasion Generative Adversarial Network for botnet detection

Botnet detectors based on machine learning are potential targets for adversarial evasion attacks. Several research works employ adversarial training with samples generated from generative adversarial nets (GANs) to make the botnet detectors adept at recognising adversarial evasions. However, the synthetic evasions may not follow the original semantics of the input samples. This paper proposes a novel GAN model leveraged with deep reinforcement learning (DRL) to explore semantic aware samples and simultaneously harden its detection. A DRL agent is used to attack the discriminator of the GAN that acts as a botnet detector. The agent trains the discriminator on the crafted perturbations during the GAN training, which helps the GAN generator converge earlier than the case without DRL. We name this model RELEVAGAN, i.e. [“relieve a GAN” or deep REinforcement Learning-based Evasion Generative Adversarial Network] because, with the help of DRL, it minimises the GAN's job by letting its generator explore the evasion samples within the semantic limits. During the GAN training, the attacks are conducted to adjust the discriminator weights for learning crafted perturbations by the agent. RELEVAGAN does not require adversarial training for the ML classifiers since it can act as an adversarial semantic-aware botnet detection model. The code will be available at https://github.com/rhr407/RELEVAGAN

Preparing GMAT for Operational Maneuver Planning of the Advanced Composition Explorer (ACE)

The General Mission Analysis Tool (GMAT) is an open-source space mission design, analysis and trajectory optimization tool. GMAT is developed by a team of NASA, private industry, public and private contributors. GMAT is designed to model, optimize and estimate spacecraft trajectories in flight regimes ranging from low Earth orbit to lunar applications, interplanetary trajectories and other deep space missions. GMAT has also been flight qualified to support operational maneuver planning for the Advanced Composition Explorer (ACE) mission. ACE was launched in August, 1997 and is orbiting the Sun-Earth L1 libration point. The primary science objective of ACE is to study the composition of both the solar wind and the galactic cosmic rays. Operational orbit determination, maneuver operations and product generation for ACE are conducted by NASA Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF). This paper discusses the entire engineering lifecycle and major operational certification milestones that GMAT successfully completed to obtain operational certification for the ACE mission. Operational certification milestones such as gathering of the requirements for ACE operational maneuver planning, gap analysis, test plans and procedures development, system design, pre-shadow operations, training to FDF ACE maneuver planners, shadow operations, Test Readiness Review (TRR) and finally Operational Readiness Review (ORR) are discussed. These efforts have demonstrated that GMAT is flight quality software ready to support ACE mission operations in the FDF

Expression analysis of cyclooxygenase-2 in patients suffering from esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the aggressive malignancies and mechanisms underlying its pathogenesis remain unclear. Cyclooxygenase-2 (COX-2) enzyme system plays a crucial role in many gastrointestinal malignancies and is an important regulator of cell growth, proliferation, apoptosis, differentiation and transformation. More precise outcome of COX-2 in ESCC is less investigated. In this study we investigated the risk factors of ESCC and expression of COX-2 in Carcinoma in situ (CIS) and ESCC compared to normal esophageal mucosa. ESCC relationship to clinico-pathological parameters using immunohistochemistry was also part of this investigation. Current study was conducted in the Institute of Basic Medical Sciences, Khyber Medical University, Peshawar, Pakistan. A total of 69 diagnosed patients of ESCC, both Pakistanis and Afghans were enrolled. Various risk factors associated with ESCC were recorded. Mean age at the time of diagnosis was 55 years. Out of 69 patients of ESCC 46 (67%) were users of dipping tobacco (Naswar). Expression of COX-2 was determined in normal esophageal mucosa, CIS and invasive ESCC using Immunohistochemistry (IHC). Differences of mean were computed using ANOVA followed by applying Post Hoc test. Patients were categorized as positive with high expression or negative with low to nil expression. ANOVA showed large differences in expression of COX-2 in normal healthy mucosa compared with CIS and ESCC with the mean difference of -9.529 and -7.370 respectively, p-value being.05 at 95% CI. Our complete cohort (23-85 years) showed statistically significant difference in the expression of COX-2 gene in ESCC and CIS tissue samples compared with normal healthy mucosa. Results of this study indicate that over-expression of COX-2 is positively associated with ESCC

Maxillary sinus mucocele in a 20-year-old male: a case report of a rare occurrence

Mucocele of Maxillary sinus is a rare entity comprising 2-10% of all mucoceles and develops due to obstruction of drainage ostium. Here, we present a case of maxillary sinus mucocele in a 20-year-old male who presented with diffuse swelling on the left side of his face. Provisional diagnosis of mucocele was made on a computed tomography scan, which was later confirmed on histopathology. The lesion was managed surgically with uneventful healing at 2 weeks and 3 months follow-up. Mucoceles are often misdiagnosed as cysts or tumours of odontogenic origin on the conventional radiograph. Delay in diagnosis can result in complications due to the expansion of mucocele towards adjacent structures such as the nose and orbit. Therefore, it becomes crucial to diagnose it appropriately with the help of higher imaging modalities so that it can be managed well in time

Abnormal Trafficking of Endogenously Expressed BMPR2 Mutant Allelic Products in Patients with Heritable Pulmonary Arterial Hypertension

More than 200 heterozygous mutations in the type 2 BMP receptor gene, BMPR2, have been identified in patients with Heritable Pulmonary Arterial Hypertension (HPAH). More severe clinical outcomes occur in patients with BMPR2 mutations by-passing nonsense-mediated mRNA decay (NMD negative mutations). These comprise 40% of HPAH mutations and are predicted to express BMPR2 mutant products. However expression of endogenous NMD negative BMPR2 mutant products and their effect on protein trafficking and signaling function have never been described. Here, we characterize the expression and trafficking of an HPAH-associated NMD negative BMPR2 mutation that results in an in-frame deletion of BMPR2 EXON2 (BMPR2ΔEx2) in HPAH patient-derived lymphocytes and in pulmonary endothelial cells (PECs) from mice carrying the same in-frame deletion of Exon 2 (Bmpr2 (ΔEx2/+) mice). The endogenous BMPR2ΔEx2 mutant product does not reach the cell surface and is retained in the endoplasmic reticulum. Moreover, chemical chaperones 4-PBA and TUDCA partially restore cell surface expression of Bmpr2ΔEx2 in PECs, suggesting that the mutant product is mis-folded. We also show that PECs from Bmpr2 (ΔEx2/+) mice have defects in the BMP-induced Smad1/5/8 and Id1 signaling axis, and that addition of chemical chaperones restores expression of the Smad1/5/8 target Id1. These data indicate that the endogenous NMD negative BMPRΔEx2 mutant product is expressed but has a folding defect resulting in ER retention. Partial correction of this folding defect and restoration of defective BMP signaling using chemical chaperones suggests that protein-folding agents could be used therapeutically in patients with these NMD negative BMPR2 mutations

Machine learning-based prediction of specific energy consumption for cut-off grinding

Cut-off operation is widely used in the manufacturing industry and is highly energy-intensive. Prediction of specific energy consumption (SEC) using data-driven models is a promising means to understand, analyze and reduce energy consumption for cut-off grinding. The present article aims to put forth a novel methodology to predict and validate the specific energy consumption for cut-off grinding of oxygen-free copper (OFC–C10100) using supervised machine learning techniques. State-of-the-art experimental setup was designed to perform the abrasive cutting of the material at various cutting conditions. First, energy consumption values were predicted on the bases of input process parameters of feed rate, cutting thickness, and cutting tool type using the three supervised learning techniques of Gaussian process regression, regression trees, and artificial neural network (ANN). Among the three algorithms, Gaussian process regression performance was found to be superior, with minimum errors during validation and testing. The predicted values of energy consumption were then exploited to evaluate the specific energy consumption (SEC), which turned out to be highly accurate, with a correlation coefficient of 0.98. The relationship of the predicted specific energy consumption (SEC) with material removal rate agrees well with the relationship depicted in physical models, which further validates the accuracy of the prediction models.Peer ReviewedPostprint (published version

Spontaneous off-stoichiometry as the knob to control dielectric properties of gapped metals

Using the first-principles calculations and La3Te4 as an example of an n-type gapped metal, we demonstrate that gapped metals can develop spontaneous defect formation resulting in off-stoichiometric compounds. Importantly, these compounds have different free carrier concentrations and can be realized by optimizing synthesis conditions. The ability to manipulate the free carrier concentration allows to tailor intraband and interband transitions, thus controlling the optoelectronic properties of materials in general. Specifically, by realizing different off-stochiometric La3-xTe4 compounds, it is possible to reach specific crossings of the real part of the dielectric function with the zero line, reduce plasma frequency contribution to absorption spectra, or, more generally, induce metal-to-insulator transition. This is particularly important in the context of optoelectronic, plasmonic, and epsilon-near-zero materials, as it enables materials design with a target functionality. While this work is limited to the specific gapped metal, we demonstrate that the fundamental physics is transferable to other gapped metals and can be generally used to design a wide class of new optoelectronic/plasmonic materials.Comment:

An Investigation on Fragility of Machine Learning Classifiers in Android Malware Detection

Machine learning (ML) classifiers have been increasingly used in Android malware detection and countermeasures for the past decade. However, ML based solutions are vulnerable to adversarial evasion attacks. An attacker can craft a malicious sample carefully to fool an underlying pre-trained classifier. In this paper, we highlight the fragility of the ML classifiers against adversarial evasion attacks. We perform mimicry attacks based on Oracle and Generative Adversarial Network (GAN) against these classifiers using our proposed methodology. We use static analysis on Android applications to extract API-based features from a balanced excerpt of a well-known public dataset. The empirical results demonstrate that among ML classifiers, the detection capability of linear classifiers can be reduced as low as 0 by perturbing only up to 4 out of 315 extracted API features. As a countermeasure, we propose TrickDroid, a cumulative adversarial training scheme based on Oracle and GAN-based adversarial data to improve evasion detection. The experimental results of cumulative adversarial training achieves a remarkable detection accuracy of up to 99.46 against adversarial samples

Antecedents of Dividend Policy: Empirical Evidence from Banking Sector of Pakistan

This paper explores the determinants of dividend policy of commercial banks operating in Pakistan. Dividend decision of any bank primarily depends upon its profitability, retained earnings, cash flows, corporate taxes and leverage. This study is an attempt to find out key determinants and their impact on cash payout and total payout ratios. It also aims to test the implication of dividend theories on Pakistani banks using data for a period of 8 years ranging from 2006 to 2013. Balanced panel data regression with fixed effects model has been used in this study. All independent variables - PAT, SLACK, EPS, CTA and TD[1] reported significant results. We found significant role of profitability theory, packing order theory, free cash flow theory and agency cost theory in determining dividend policies whereas, tax effect and financial slack has no effect in banking sector of Pakistan. [1] Profitability, retained earnings, earnings per share, cash flows, and leverag