645 research outputs found
Abstract Learning Frameworks for Synthesis
We develop abstract learning frameworks (ALFs) for synthesis that embody the
principles of CEGIS (counter-example based inductive synthesis) strategies that
have become widely applicable in recent years. Our framework defines a general
abstract framework of iterative learning, based on a hypothesis space that
captures the synthesized objects, a sample space that forms the space on which
induction is performed, and a concept space that abstractly defines the
semantics of the learning process. We show that a variety of synthesis
algorithms in current literature can be embedded in this general framework.
While studying these embeddings, we also generalize some of the synthesis
problems these instances are of, resulting in new ways of looking at synthesis
problems using learning. We also investigate convergence issues for the general
framework, and exhibit three recipes for convergence in finite time. The first
two recipes generalize current techniques for convergence used by existing
synthesis engines. The third technique is a more involved technique of which we
know of no existing instantiation, and we instantiate it to concrete synthesis
problems
Cost implication analysis of concrete and Masonry waste in construction project
Concrete and masonry waste are the main types of waste typically generated at a construction project. There is a lack of studies in the country regarding the cost implication of managing these types of construction waste To address this need in Malaysia, the study is carried out to measure the disposal cost of concrete and masonry waste. The study was carried out by a site visit method using an indirect measurement approach to quantify the quantity of waste generated at the project. Based on the recorded number of trips for waste collection, the total expenditure to dispose the waste were derived in three construction stages. Data was collected four times a week for the period July 2014 to July 2015. The total waste generated at the study site was 762.51 m3 and the cost incurred for the 187 truck trips required to dispose the waste generated from the project site to the nearby landfill was RM22,440.00. The findings will be useful to both researchers and policy makers concerned with construction waste
Cooperation and Contagion in Web-Based, Networked Public Goods Experiments
A longstanding idea in the literature on human cooperation is that
cooperation should be reinforced when conditional cooperators are more likely
to interact. In the context of social networks, this idea implies that
cooperation should fare better in highly clustered networks such as cliques
than in networks with low clustering such as random networks. To test this
hypothesis, we conducted a series of web-based experiments, in which 24
individuals played a local public goods game arranged on one of five network
topologies that varied between disconnected cliques and a random regular graph.
In contrast with previous theoretical work, we found that network topology had
no significant effect on average contributions. This result implies either that
individuals are not conditional cooperators, or else that cooperation does not
benefit from positive reinforcement between connected neighbors. We then tested
both of these possibilities in two subsequent series of experiments in which
artificial seed players were introduced, making either full or zero
contributions. First, we found that although players did generally behave like
conditional cooperators, they were as likely to decrease their contributions in
response to low contributing neighbors as they were to increase their
contributions in response to high contributing neighbors. Second, we found that
positive effects of cooperation were contagious only to direct neighbors in the
network. In total we report on 113 human subjects experiments, highlighting the
speed, flexibility, and cost-effectiveness of web-based experiments over those
conducted in physical labs
Invariant Synthesis for Incomplete Verification Engines
We propose a framework for synthesizing inductive invariants for incomplete
verification engines, which soundly reduce logical problems in undecidable
theories to decidable theories. Our framework is based on the counter-example
guided inductive synthesis principle (CEGIS) and allows verification engines to
communicate non-provability information to guide invariant synthesis. We show
precisely how the verification engine can compute such non-provability
information and how to build effective learning algorithms when invariants are
expressed as Boolean combinations of a fixed set of predicates. Moreover, we
evaluate our framework in two verification settings, one in which verification
engines need to handle quantified formulas and one in which verification
engines have to reason about heap properties expressed in an expressive but
undecidable separation logic. Our experiments show that our invariant synthesis
framework based on non-provability information can both effectively synthesize
inductive invariants and adequately strengthen contracts across a large suite
of programs
Building nonparametric -body force fields using Gaussian process regression
Constructing a classical potential suited to simulate a given atomic system
is a remarkably difficult task. This chapter presents a framework under which
this problem can be tackled, based on the Bayesian construction of
nonparametric force fields of a given order using Gaussian process (GP) priors.
The formalism of GP regression is first reviewed, particularly in relation to
its application in learning local atomic energies and forces. For accurate
regression it is fundamental to incorporate prior knowledge into the GP kernel
function. To this end, this chapter details how properties of smoothness,
invariance and interaction order of a force field can be encoded into
corresponding kernel properties. A range of kernels is then proposed,
possessing all the required properties and an adjustable parameter
governing the interaction order modelled. The order best suited to describe
a given system can be found automatically within the Bayesian framework by
maximisation of the marginal likelihood. The procedure is first tested on a toy
model of known interaction and later applied to two real materials described at
the DFT level of accuracy. The models automatically selected for the two
materials were found to be in agreement with physical intuition. More in
general, it was found that lower order (simpler) models should be chosen when
the data are not sufficient to resolve more complex interactions. Low GPs
can be further sped up by orders of magnitude by constructing the corresponding
tabulated force field, here named "MFF".Comment: 31 pages, 11 figures, book chapte
Interprofessional communication with hospitalist and consultant physicians in general internal medicine : a qualitative study
This study helps to improve our understanding of the collaborative environment in GIM, comparing the communication styles and strategies of hospitalist and consultant physicians, as well as the experiences of providers working with them. The implications of this research are globally important for understanding how to create opportunities for physicians and their colleagues to meaningfully and consistently participate in interprofessional communication which has been shown to improve patient, provider, and organizational outcomes
Computational complexity analysis of decision tree algorithms
YesDecision tree is a simple but powerful learning technique that is considered as one of the famous learning algorithms that have been successfully used in practice for various classification tasks. They have the advantage of producing a comprehensible classification model with satisfactory accuracy levels in several application domains. In recent years, the volume of data available for learning is dramatically increasing. As a result, many application domains are faced with a large amount of data thereby posing a major bottleneck on the computability of learning techniques. There are different implementations of the decision tree using different techniques. In this paper, we theoretically and experimentally study and compare the computational power of the most common classical top-down decision tree algorithms (C4.5 and CART). This work can serve as part of review work to analyse the computational complexity of the existing decision tree classifier algorithm to gain understanding of the operational steps with the aim of optimizing the learning algorithm for large datasets
Computational complexity analysis of decision tree algorithms
YesDecision tree is a simple but powerful learning technique that is considered as one of the famous learning algorithms that have been successfully used in practice for various classification tasks. They have the advantage of producing a comprehensible classification model with satisfactory accuracy levels in several application domains. In recent years, the volume of data available for learning is dramatically increasing. As a result, many application domains are faced with a large amount of data thereby posing a major bottleneck on the computability of learning techniques. There are different implementations of the decision tree using different techniques. In this paper, we theoretically and experimentally study and compare the computational power of the most common classical top-down decision tree algorithms (C4.5 and CART). This work can serve as part of review work to analyse the computational complexity of the existing decision tree classifier algorithm to gain understanding of the operational steps with the aim of optimizing the learning algorithm for large datasets
Anaesthesia Choice for Creation of Arteriovenous Fistula (ACCess) study protocol : a randomised controlled trial comparing primary unassisted patency at 1 year of primary arteriovenous fistulae created under regional compared to local anaesthesia
INTRODUCTION: Arteriovenous fistulae (AVF) are the 'gold standard' vascular access for haemodialysis. Universal usage is limited, however, by a high early failure rate. Several small, single-centre studies have demonstrated better early patency rates for AVF created under regional anaesthesia (RA) compared with local anaesthesia (LA). The mechanistic hypothesis is that the sympathetic blockade associated with RA causes vasodilatation and increased blood flow through the new AVF. Despite this, considerable variation in practice exists in the UK. A high-quality, adequately powered, multicentre randomised controlled trial (RCT) is required to definitively inform practice. METHODS AND ANALYSIS: The Anaesthesia Choice for Creation of Arteriovenous Fistula (ACCess) study is a multicentre, observer-blinded RCT comparing primary radiocephalic/brachiocephalic AVF created under regional versus LA. The primary outcome is primary unassisted AVF patency at 1 year. Access-specific (eg, stenosis/thrombosis), patient-specific (including health-related quality of life) and safety secondary outcomes will be evaluated. Health economic analysis will also be undertaken. ETHICS AND DISSEMINATION: The ACCess study has been approved by the West of Scotland Research and ethics committee number 3 (20/WS/0178). Results will be published in open-access peer-reviewed journals within 12 months of completion of the trial. We will also present our findings at key national and international renal and anaesthetic meetings, and support dissemination of trial outcomes via renal patient groups. TRIAL REGISTRATION NUMBER: ISRCTN14153938. SPONSOR: NHS Greater Glasgow and Clyde GN19RE456, Protocol V.1.3 (8 May 2021), REC/IRAS ID: 290482
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