6,030 research outputs found
BUDDHIST EQUILIBRIUM: THE THEORY OF MIDDLE PATH FOR SUSTAINABLE DEVELOPMENT
Environmental Economics and Policy,
CAPITALISM IN HUMAN SCALE: ARE THERE "VIRTUOUS CIRCLES" IN ECONOMIC GROWTH AND HUMAN DEVELOPMENT IN ACHIEVING A NEWLY INDUSTRIALIZED COUNTRY STATUS?
The purpose of this paper is two-fold: the first part is to understand the "virtuous circles" of economic growth and human development; the second part is to review the development literature related to this discussion. For this, the evolving development strategies of Sri Lanka and South Korea and the seemingly convergent two schools of thinking are briefly outlined in Section II. Section III surveys the pace of economic and human progress to give a broad background of the debate. Section IV analyzes the economic growth and human development in selected Industrialized Countries, Newly Industrialized Countries (NICs), Emerging NICs, and Aspiring South Asian neighbors of Sri Lanka...In the second part of the paper, a discussion of economic development literature and the experience of NICs are reviewed to learn from their policies and strategies in Section V.International Development,
THE POLITICAL ECONOMY OF POVERTY ALLEVIATION IN DEVELOPING COUNTRIES: IS SRI LANKA REALLY AN EXCEPTION?
Food Security and Poverty, Political Economy,
goSLP: Globally Optimized Superword Level Parallelism Framework
Modern microprocessors are equipped with single instruction multiple data
(SIMD) or vector instruction sets which allow compilers to exploit superword
level parallelism (SLP), a type of fine-grained parallelism. Current SLP
auto-vectorization techniques use heuristics to discover vectorization
opportunities in high-level language code. These heuristics are fragile, local
and typically only present one vectorization strategy that is either accepted
or rejected by a cost model. We present goSLP, a novel SLP auto-vectorization
framework which solves the statement packing problem in a pairwise optimal
manner. Using an integer linear programming (ILP) solver, goSLP searches the
entire space of statement packing opportunities for a whole function at a time,
while limiting total compilation time to a few minutes. Furthermore, goSLP
optimally solves the vector permutation selection problem using dynamic
programming. We implemented goSLP in the LLVM compiler infrastructure,
achieving a geometric mean speedup of 7.58% on SPEC2017fp, 2.42% on SPEC2006fp
and 4.07% on NAS benchmarks compared to LLVM's existing SLP auto-vectorizer.Comment: Published at OOPSLA 201
IMPACT OF GLOBALIZATION: THE INCIDENCE OF POVERTY AND FOOD SECURITY POLICIES IN SRI LANKA
Food Security and Poverty,
Response to the consultation ‘Regulating On-line Gambling in the EU: Recent Developments and Current Challenges from the Internal Market Standpoint'
This is a collaborative submission from a group of academics based in the UK with expertise in information technology law and related areas. The preparation of this response has been funded by the Information Technology Think Tank, which is supported by the Arts and Humanities Research Council and led by the SCRIPT/AHRC Centre for Research in Intellectual Property and Technology, University of Edinburgh. This response has been prepared by Abhilash Nair and Dinusha Mendis
The Application of Airtraq (fibreoptic intubation device) to Otolaryngology
The anaesthetic laryngoscope Airtraq is designed for the difficult airway. This disposable laryngoscope requires minimal cervical manipulation and unlike other common anaesthetic larynmgoscopes contains a channel for the guidance of an endotracheal tube. This could also be used for diagnosis and biopsy under a general anaesthetic or potentially under a local anaesthetic in an outpatient setting for biopsies or the removal of hypopharyngeal foreign bodies via flexible biopsy forceps obviating the need for a general anaesthetic. Thus Airtraq could be included in the armoury of pre-existing direct laryngoscopes because of its virtue of minimal airway manipulation
On the origin of comets
Physico-chemical processes leading to the dynamic formation and physical evolution of comets are reviewed in relationship to the various theories that propose solar origins, protoplanetary origins, planetary origins and interstellar origins. Evidence points to the origins of comets by the growth and agglomeration of small particles from gas and dust at very low temperatures at undetermined regions in space
Spectral Attention-Driven Intelligent Target Signal Identification on a Wideband Spectrum
This paper presents a spectral attention-driven reinforcement learning based
intelligent method for effective and efficient detection of important signals
in a wideband spectrum. In the work presented in this paper, it is assumed that
the modulation technique used is available as a priori knowledge of the
targeted important signal. The proposed spectral attention-driven intelligent
method is consists of two main components, a spectral correlation function
(SCF) based spectral visualization scheme and a spectral attention-driven
reinforcement learning mechanism that adaptively selects the spectrum range and
implements the intelligent signal detection. Simulations illustrate that the
proposed method can achieve high accuracy of signal detection while observation
of spectrum is limited to few ranges via effectively selecting the spectrum
ranges to be observed. Furthermore, the proposed spectral attention-driven
machine learning method can lead to an efficient adaptive intelligent spectrum
sensor designs in cognitive radio (CR) receivers.Comment: 6 pages, 11 figure
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Predicting the number of clock cycles a processor takes to execute a block of
assembly instructions in steady state (the throughput) is important for both
compiler designers and performance engineers. Building an analytical model to
do so is especially complicated in modern x86-64 Complex Instruction Set
Computer (CISC) machines with sophisticated processor microarchitectures in
that it is tedious, error prone, and must be performed from scratch for each
processor generation. In this paper we present Ithemal, the first tool which
learns to predict the throughput of a set of instructions. Ithemal uses a
hierarchical LSTM--based approach to predict throughput based on the opcodes
and operands of instructions in a basic block. We show that Ithemal is more
accurate than state-of-the-art hand-written tools currently used in compiler
backends and static machine code analyzers. In particular, our model has less
than half the error of state-of-the-art analytical models (LLVM's llvm-mca and
Intel's IACA). Ithemal is also able to predict these throughput values just as
fast as the aforementioned tools, and is easily ported across a variety of
processor microarchitectures with minimal developer effort.Comment: Published at 36th International Conference on Machine Learning (ICML)
201
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