Amorphous procedure extraction

The procedure extraction problem is concerned with the meaning preserving formation of a procedure from a (not necessarily contiguous) selected set of statements. Previous approaches to the problem have used dependence analysis to identify the non-selected statements which must be 'promoted' (also selected) in order to preserve semantics. All previous approaches to the problem have been syntax preserving. This work shows that by allowing transformation of the program's syntax it is possible to extract both procedures and functions in an amorphous manner. That is, although the amorphous extraction process is meaning preserving it is not necessarily syntax preserving. The amorphous approach is advantageous in a variety of situations. These include when it is desirable to avoid promotion, when a value-returning function is to be extracted from a scattered set of assignments to a variable, and when side effects are present in the program from which the procedure is to be extracted

Amorphous procedure extraction

The procedure extraction problem is concerned with the meaning preserving formation of a procedure from a (not necessarily contiguous) selected set of statements. Previous approaches to the problem have used dependence analysis to identify the non-selected statements which must be 'promoted' (also selected) in order to preserve semantics. All previous approaches to the problem have been syntax preserving. This work shows that by allowing transformation of the program's syntax it is possible to extract both procedures and functions in an amorphous manner. That is, although the amorphous extraction process is meaning preserving it is not necessarily syntax preserving. The amorphous approach is advantageous in a variety of situations. These include when it is desirable to avoid promotion, when a value-returning function is to be extracted from a scattered set of assignments to a variable, and when side effects are present in the program from which the procedure is to be extracted

How good is the orthopaedic literature?

Randomized trials constitute approximately 3% of the orthopaedic literature Concerns regarding quality of the orthopaedic literature stem from a widespread notion that the overall quality of the surgical literature is in need of improvement. Limitations in surgical research arises primarily from two pervasive issues: 1) A reliance on low levels of evidence to advance surgical knowledge, and 2) Poor reporting quality among the high level surgical evidence that is available. The scarcity of randomized trials may be largely attributable to several unique challenges which make them difficult to conduct. We present characteristics of the orthopaedic literature and address the challenges of conducting randomized trials in surgery

Digital technologies and information translucence in healthcare management: An institutional theory perspective for adopting electronic incidence reporting systems

The purpose of this study was to provide an institutional theory perspective on the adoption of electronic IRS technology for healthcare management. This research employs institutional theory to investigate the adoption of electronic IRS for healthcare management. The study’s conceptual analysis demonstrates that coercive, normative, and imitative forces influence the adoption of electronic IRS for healthcare management. International healthcare regulations and standards reflect the presence of coercive forces. International healthcare societies and professional networks mirror normative forces. Imitative forces exert pressure on smaller enterprises and developing nations to adopt electronic IRS. This research contributes to the literature and theory by extending the application of institutional theory to the adoption of digital technologies such as the electronic IRS. In addition, the study has practical implications because it demonstrates the importance of digital technologies such as electronic IRS for information translucence and healthcare management. Small businesses in developing nations can learn from large businesses in developed nations to adopt electronic IRS for efficient and effective healthcare management

The educational technology's impact on youth creativity and innovation: A case of Ha’il region of Saudi Arabia

Educational technology can play a prominent role in fostering youth creativity and innovation. However, only limited studies have examined the impact of educational technology on youth creativity and innovation. This assessment is especially critical in the context of Saudi Arabia, which has been investing significant resources in higher education and digital transformation initiatives as part of the Saudi Vision 2030. Thus, this research examines the effect of educational technology on university students' creative and innovative capabilities. We used quantitative methodology to accomplish the study's objectives and employed questionnaires. The questionnaires were conducted at the University of Ha’il, Saudi Arabia. The study's findings establish critical parameters for utilizing educational technology to promote university students' creativity and innovativeness throughout their learning process in Saudi Arabia, particularly in developing regions such as Ha'il. The research contributes to the body of knowledge about youth innovation and creativity, particularly in developing countries. Additionally, the study's findings contribute to the realization of Saudi Vision 2030 by raising awareness of the value of accessibility and effective utilization of educational technology in fostering youth creativity and innovation

Intelligent Ranking for Dynamic Restoration in Next Generation Wireless Networks

Emerging 5G and next generation 6G wireless are likely to involve myriads of connectivity, consisting of a huge number of relatively smaller cells providing ultra-dense coverage. Guaranteeing seamless connectivity and service level agreements in such a dense wireless system demands efficient network management and fast service recovery. However, restoration of a wireless network, in terms of maximizing service recovery, typically requires evaluating the service impact of every network element. Unfortunately, unavailability of real-time KPI information, during an outage, enforces most of the existing approaches to rely significantly on context-based manual evaluation. As a consequence, configuring a real-time recovery of the network nodes is almost impossible, thereby resulting in a prolonged outage duration. In this article, we explore deep learning to introduce an intelligent, proactive network recovery management scheme in anticipation of an eminent network outage. Our proposed method introduces a novel utilization-based ranking scheme of different wireless nodes to minimize the service downtime and enable a fast recovery. Efficient prediction of network KPI (Key Performance Index), based on actual wireless data demonstrates up to ~54% improvement in service outage

Density-functional theory study of small Fe, Co, Ni and Pt clusters on graphene

Master'sMASTER OF SCIENC

Ab Initio Phonon Dispersions for PbTe

We report first principles calculations of the phonon dispersions of PbTe both for its observed structure and under compression. At the experimental lattice parameter we find a near instability of the optic branch at the zone center, in accord with experimental observations.This hardens quickly towards the zone boundary. There is also a very strong volume dependence of this mode, which is rapidly driven away from an instability by compression. These results are discussed inrelation to the thermal conductivity of the material.Comment: 3 figures; typos corrected. Figure 1 replaced to correct labe

Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality

Contrastively trained vision-language models have achieved remarkable progress in vision and language representation learning, leading to state-of-the-art models for various downstream multimodal tasks. However, recent research has highlighted severe limitations of these models in their ability to perform compositional reasoning over objects, attributes, and relations. Scene graphs have emerged as an effective way to understand images compositionally. These are graph-structured semantic representations of images that contain objects, their attributes, and relations with other objects in a scene. In this work, we consider the scene graph parsed from text as a proxy for the image scene graph and propose a graph decomposition and augmentation framework along with a coarse-to-fine contrastive learning objective between images and text that aligns sentences of various complexities to the same image. Along with this, we propose novel negative mining techniques in the scene graph space for improving attribute binding and relation understanding. Through extensive experiments, we demonstrate the effectiveness of our approach that significantly improves attribute binding, relation understanding, systematic generalization, and productivity on multiple recently proposed benchmarks (For example, improvements upto $18\%$ for systematic generalization, $16.5\%$ for relation understanding over a strong baseline), while achieving similar or better performance than CLIP on various general multimodal tasks.Comment: 16 pages, 12 figures, 7 Tables. Pre-prin