125 research outputs found
Inside-Out: Perception of Key Finance Professionals about Theory and Practice of Islamic Banking
Islamic banking has shown tremendous growth in the first decade of 21st century. By the end of 2008, global volume of assets under Islamic banking reached US$951 billion. In Pakistan, Islamic banking has displayed a tremendous annual average growth of 76% during the last seven years and accounts for 6% of the market share (SBP-2010). However, keeping in view the religious ideologies of the significant majority of Pakistanis, Islamic banking industry has expanded at less than its expected potential. This study documents the perception of key players in finance industry about Islamic banking, to highlight the underlying issues directly responsible for slower-than-potential-expansion of this industry. Findings suggest, although theoretically, that the industry perceives Islamic banking correctly, however professionals do not feel content with its practice
autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components
Approximate computing is an emerging paradigm for developing highly
energy-efficient computing systems such as various accelerators. In the
literature, many libraries of elementary approximate circuits have already been
proposed to simplify the design process of approximate accelerators. Because
these libraries contain from tens to thousands of approximate implementations
for a single arithmetic operation it is intractable to find an optimal
combination of approximate circuits in the library even for an application
consisting of a few operations. An open problem is "how to effectively combine
circuits from these libraries to construct complex approximate accelerators".
This paper proposes a novel methodology for searching, selecting and combining
the most suitable approximate circuits from a set of available libraries to
generate an approximate accelerator for a given application. To enable fast
design space generation and exploration, the methodology utilizes machine
learning techniques to create computational models estimating the overall
quality of processing and hardware cost without performing full synthesis at
the accelerator level. Using the methodology, we construct hundreds of
approximate accelerators (for a Sobel edge detector) showing different but
relevant tradeoffs between the quality of processing and hardware cost and
identify a corresponding Pareto-frontier. Furthermore, when searching for
approximate implementations of a generic Gaussian filter consisting of 17
arithmetic operations, the proposed approach allows us to identify
approximately highly important implementations from possible
solutions in a few hours, while the exhaustive search would take four months on
a high-end processor.Comment: Accepted for publication at the Design Automation Conference 2019
(DAC'19), Las Vegas, Nevada, US
Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks
Spiking Neural Networks (SNNs) claim to present many advantages in terms of
biological plausibility and energy efficiency compared to standard Deep Neural
Networks (DNNs). Recent works have shown that DNNs are vulnerable to
adversarial attacks, i.e., small perturbations added to the input data can lead
to targeted or random misclassifications. In this paper, we aim at
investigating the key research question: ``Are SNNs secure?'' Towards this, we
perform a comparative study of the security vulnerabilities in SNNs and DNNs
w.r.t. the adversarial noise. Afterwards, we propose a novel black-box attack
methodology, i.e., without the knowledge of the internal structure of the SNN,
which employs a greedy heuristic to automatically generate imperceptible and
robust adversarial examples (i.e., attack images) for the given SNN. We perform
an in-depth evaluation for a Spiking Deep Belief Network (SDBN) and a DNN
having the same number of layers and neurons (to obtain a fair comparison), in
order to study the efficiency of our methodology and to understand the
differences between SNNs and DNNs w.r.t. the adversarial examples. Our work
opens new avenues of research towards the robustness of the SNNs, considering
their similarities to the human brain's functionality.Comment: Accepted for publication at the 2020 International Joint Conference
on Neural Networks (IJCNN
Risk and Returns of Shar??ah Compliant Stocks on the Karachi Stock Exchange: A CAPM and SCAPM Approach
This study documents the asset pricing mechanism of Shar??ah compliant securities listed on the the Karachi Stock Exchange. We select the CAPM market model to test for the impact in variations of stock returns on a sample of Shar??ah-compliant companies on ten years monthly data (2001-10). We first test the basic CAPM (Capital Asset Pricing Model) and its modified form known as the Shar??ah-compliant asset pricing model (SCAPM). We also analyse return differences due to size (market capitalization), book to market (B/M) value, price-earning ratio (PER), and cash-flow yield (CFY). Our results find a strong impact of the market index on stock returns (adj-R2 70%) and confirm the anomalies of size, B/M, CFY, and PER, while SCAPM is slightly better in explaining variations in cross-sectional stock returns
Impact of Real Sector Variables on Shari’a Compliant Stock Returns
Shari’a compliant stocks are a recent development under Islamic finance, whereby stocks
are screened through Shari’a compliance filters. This study is conducted to understand
and document the important Real Sector macroeconomic factors contributing in
determining stock prices of Shari’a compliant companies in Pakistan. Our sample
includes all 97 non-financial companies screened by Al-Meezan Investment Management
Ltd, based on financial results of 2009. We have included Six Macroeconomic variables
in addition to market index in our study for ten years period (2001-10). Results identified
Zero real sector variable in pricing, however, with the inclusion of market index in
analysis, the single important variable in pricing of Shari’a compliant securities is market
Beta. Evidence favors CAPM for pricing of securities in local market as market index
captures the risk of macroeconomic variables
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