7,495 research outputs found
Implicit Loss of Surjectivity and Facial Reduction: Theory and Applications
Facial reduction, pioneered by Borwein and Wolkowicz, is a preprocessing method that is commonly used to obtain strict feasibility in the reformulated, reduced constraint system.
The importance of strict feasibility is often addressed in the context of the convergence results for interior point methods.
Beyond the theoretical properties that the facial reduction conveys, we show that facial reduction, not only limited to interior point methods, leads to strong numerical performances in different classes of algorithms.
In this thesis we study various consequences and the broad applicability of facial reduction.
The thesis is organized in two parts.
In the first part, we show the instabilities accompanied by the absence
of strict feasibility through the lens of facially reduced systems.
In particular, we exploit the implicit redundancies, revealed by each nontrivial facial reduction step, resulting in the implicit loss of surjectivity.
This leads to the two-step facial reduction and two novel related notions of singularity.
For the area of semidefinite programming, we use these singularities to strengthen a known bound on the solution rank, the Barvinok-Pataki bound.
For the area of linear programming, we reveal degeneracies caused by the implicit redundancies.
Furthermore, we propose a preprocessing tool that uses the simplex method.
In the second part of this thesis, we continue with the semidefinite programs that do not have strictly feasible points.
We focus on the doubly-nonnegative relaxation of the binary quadratic program and a semidefinite program with a nonlinear objective function.
We closely work with two classes of algorithms, the splitting method and the Gauss-Newton interior point method.
We elaborate on the advantages in building models from facial reduction. Moreover, we develop algorithms for real-world problems including the quadratic assignment problem, the protein side-chain positioning problem, and the key rate computation for quantum key distribution.
Facial reduction continues to play an important role for
providing robust reformulated models in both the theoretical and the practical aspects, resulting in successful numerical performances
Resilience and food security in a food systems context
This open access book compiles a series of chapters written by internationally recognized experts known for their in-depth but critical views on questions of resilience and food security. The book assesses rigorously and critically the contribution of the concept of resilience in advancing our understanding and ability to design and implement development interventions in relation to food security and humanitarian crises. For this, the book departs from the narrow beaten tracks of agriculture and trade, which have influenced the mainstream debate on food security for nearly 60 years, and adopts instead a wider, more holistic perspective, framed around food systems. The foundation for this new approach is the recognition that in the current post-globalization era, the food and nutritional security of the worldâs population no longer depends just on the performance of agriculture and policies on trade, but rather on the capacity of the entire (food) system to produce, process, transport and distribute safe, affordable and nutritious food for all, in ways that remain environmentally sustainable. In that context, adopting a food system perspective provides a more appropriate frame as it incites to broaden the conventional thinking and to acknowledge the systemic nature of the different processes and actors involved. This book is written for a large audience, from academics to policymakers, students to practitioners
The LDBC social network benchmark: Business intelligence workload
The Social Network Benchmarkâs Business Intelligence workload (SNB BI) is a comprehensive graph OLAP benchmark targeting analytical data systems capable of supporting graph workloads. This paper marks the finalization of almost a decade of research in academia and industry via the Linked Data Benchmark Council (LDBC). SNB BI advances the state-of-the art in synthetic and scalable analytical database benchmarks in many aspects. Its base is a sophisticated data generator, implemented on a scalable distributed infrastructure, that produces a social graph with small-world phenomena, whose value properties follow skewed and correlated distributions and where values correlate with structure. This is a temporal graph where all nodes and edges follow lifespan-based rules with temporal skew enabling realistic and consistent temporal inserts and (recursive) deletes. The query workload exploiting this skew and correlation is based on LDBCâs âchoke pointâ-driven design methodology and will entice technical and scientific improvements in future (graph) database systems. SNB BI includes the first adoption of âparameter curationâ in an analytical benchmark, a technique that ensures stable runtimes of query variants across different parameter values. Two performance metrics characterize peak single-query performance (power) and sustained concurrent query throughput. To demonstrate the portability of the benchmark, we present experimental results on a relational and a graph DBMS. Note that these do not constitute an official LDBC Benchmark Result â only audited results can use this trademarked term
Blockchain Technology: Disruptor or Enhnancer to the Accounting and Auditing Profession
The unique features of blockchain technology (BCT) - peer-to-peer network, distribution ledger, consensus decision-making, transparency, immutability, auditability, and cryptographic security - coupled with the success enjoyed by Bitcoin and other cryptocurrencies have encouraged many to assume that the technology would revolutionise virtually all aspects of business. A growing body of scholarship suggests that BCT would disrupt the accounting and auditing fields by changing accounting practices, disintermediating auditors, and eliminating financial fraud. BCT disrupts audits (Lombard et al.,2021), reduces the role of audit firms (Yermack 2017), undermines accountants' roles with software developers and miners (Fortin & Pimentel 2022); eliminates many management functions, transforms businesses (Tapscott & Tapscott, 2017), facilitates a triple-entry accounting system (Cai, 2021), and prevents fraudulent transactions (Dai, et al., 2017; Rakshit et al., 2022). Despite these speculations, scholars have acknowledged that the application of BCT in the accounting and assurance industry is underexplored and many existing studies are said to lack engagement with practitioners (Dai & Vasarhelyi, 2017; Lombardi et al., 2021; Schmitz & Leoni, 2019).
This study empirically explored whether BCT disrupts or enhances accounting and auditing fields. It also explored the relevance of audit in a BCT environment and the effectiveness of the BCT mechanism for fraud prevention and detection. The study further examined which technical skillsets accountants and auditors require in a BCT environment, and explored the incentives, barriers, and unintended consequences of the adoption of BCT in the accounting and auditing professions. The current COVID-19 environment was also investigated in terms of whether the pandemic has improved BCT adoption or not.
A qualitative exploratory study used semi-structured interviews to engage practitioners from blockchain start-ups, IT experts, financial analysts, accountants, auditors, academics, organisational leaders, consultants, and editors who understood the technology. With the aid of NVIVO qualitative analysis software, the views of 44 participants from 13 countries: New Zealand, Australia, United States, United Kingdom, Canada, Germany, Italy, Ireland, Hong Kong, India, Pakistan, United Arab Emirates, and South Africa were analysed.
The Technological, Organisational, and Environmental (TOE) framework with consequences of innovation context was adopted for this study. This expanded TOE framework was used as the theoretical lens to understand the disruption of BCT and its adoption in the accounting and auditing fields. Four clear patterns emerged. First, BCT is an emerging tool that accountants and auditors use mainly to analyse financial records because technology cannot disintermediate auditors from the financial system. Second, the technology can detect anomalies but cannot prevent financial fraud. Third, BCT has not been adopted by any organisation for financial reporting and accounting purposes, and accountants and auditors do not require new skillsets or an understanding of the BCT programming language to be able to operate in a BCT domain. Fourth, the advent of COVID-19 has not substantially enhanced the adoption of BCT. Additionally, this study highlights the incentives, barriers, and unintended consequences of adopting BCT as financial technology (FinTech). These findings shed light on important questions about BCT disrupting and disintermediating auditors, the extent of adoption in the accounting industry, preventing fraud and anomalies, and underscores the notion that blockchain, as an emerging technology, currently does not appear to be substantially disrupting the accounting and auditing profession.
This study makes methodological, theoretical, and practical contributions. At the methodological level, the study adopted the social constructivist-interpretivism paradigm with an exploratory qualitative method to engage and understand BCT as a disruptive innovation in the accounting industry. The engagement with practitioners from diverse fields, professions, and different countries provides a distinctive and innovative contribution to methodological and practical knowledge. At the theoretical level, the findings contribute to the literature by offering an integrated conceptual TOE framework. The framework offers a reference for practitioners, academics and policymakers seeking to appraise comprehensive factors influencing BCT adoption and its likely unintended consequences. The findings suggest that, at present, no organisations are using BCT for financial reporting and accounting systems. This study contributes to practice by highlighting the differences between initial expectations and practical applications of what BCT can do in the accounting and auditing fields. The study could not find any empirical evidence that BCT will disrupt audits, eliminate the roles of auditors in a financial system, and prevent and detect financial fraud. Also, there was no significant evidence that accountants and auditors required higher-level skillsets and an understanding of BCT programming language to be able to use the technology. Future research should consider the implications of an external audit firm as a node in a BCT network on the internal audit functions. It is equally important to critically examine the relevance of including programming languages or codes in the curriculum of undergraduate accounting students. Future research could also empirically evaluate if a BCT-enabled triple-entry system could prevent financial statements and management fraud
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The Go-GN Open Research Handbook
This Handbook draws together work done between 2020 and 2023 by members of the Global OER Graduate Network (GO-GN). GO-GN is a network of PhD candidates around the world whose research projects include a focus on open education. GO-GN is currently funded through the OER programme of The William and Flora Hewlett Foundation and administered by the Open Education Research Hub from the Institute of Educational Technology at The Open University, UK.
In our current phase of activity, we began these collaborative writing efforts with a Research Methods Handbook which was created during the depths of the Covid-19 pandemic. Working together at distance provided an important way to strengthen community links when meeting in person was not possible. The Research Methods Handbook was well received by a much larger audience than we anticipated, and went on to win an Open Research Award. We followed this up with a sister publication, our Conceptual Frameworks Guide. This explores a less well traversed (but nonetheless important) area of scholarly focus. Together, these two explore open approaches to the theory and practice of research in open education. One distinctive feature of our presentation is to foreground the authentic experiences of doctoral researchers who have used specific approaches in researching open education. While it is not possible to cover all approaches in this detail, we hope that important insights are presented in this form of open practice.
Throughout 2020-2022 we also regularly engaged our membership through collective reviews of recently published papers and articles. The Research Reviews serve as an overview of recent research but also as a snapshot of the critical responses recorded by doctoral and post-doctoral researchers working in relevant areas.
No one volume can claim to comprehensively contain the diversity and variety of open approaches, and this is no exception. But one virtue of openness is that we can draw on the openly licensed works of others to increase our coverage of relevant areas. The Additional Resources at the end of this volume bring together a range of openly licensed texts on open education research and suggests places for further reading and research.
Consequently, the information contained here represents a wide range of contributors and collaborators. The original and intended audience for this volume is the doctoral student working on an open education research project - in short, the typical student member of GO-GN and the profile the network exists to support.
However, weâve learned through feedback and analytics that the potential audience for works like this is much larger. Many people who wouldnât describe themselves as researchers still do research and evaluation. Presenting accessible insights into research foundations and practices helps with this and can be understood as a form of open practice
Efficient Centrality Maximization with Rademacher Averages
The identification of the set of k most central nodes of a graph, or
centrality maximization, is a key task in network analysis, with various
applications ranging from finding communities in social and biological networks
to understanding which seed nodes are important to diffuse information in a
graph. As the exact computation of centrality measures does not scale to
modern-sized networks, the most practical solution is to resort to rigorous,
but efficiently computable, randomized approximations. In this work we present
CentRA, the first algorithm based on progressive sampling to compute
high-quality approximations of the set of k most central nodes. CentRA is based
on a novel approach to efficiently estimate Monte Carlo Rademacher Averages, a
powerful tool from statistical learning theory to compute sharp data-dependent
approximation bounds. Then, we study the sample complexity of centrality
maximization using the VC-dimension, a key concept from statistical learning
theory. We show that the number of random samples required to compute
high-quality approximations scales with finer characteristics of the graph,
such as its vertex diameter, or of the centrality of interest, significantly
improving looser bounds derived from standard techniques. We apply CentRA to
analyze large real-world networks, showing that it significantly outperforms
the state-of-the-art approximation algorithm in terms of number of samples,
running times, and accuracy.Comment: Accepted to KDD '2
Partitioning algorithms for induced subgraph problems
This dissertation introduces the MCSPLIT family of algorithms for two closely-related NP-hard problems that involve finding a large induced subgraph contained by each of two input graphs: the induced subgraph isomorphism problem and the maximum common induced subgraph problem.
The MCSPLIT algorithms resemble forward-checking constrant programming algorithms, but use problem-specific data structures that allow multiple, identical domains to be stored without duplication. These data structures enable fast, simple constraint propagation algorithms and very fast calculation of upper bounds. Versions of these algorithms for both sparse and dense graphs are described and implemented. The resulting algorithms are over an order of magnitude faster than the best existing algorithm for maximum common induced subgraph on unlabelled graphs, and outperform the state of the art on several classes of induced subgraph isomorphism instances.
A further advantage of the MCSPLIT data structures is that variables and values are treated identically; this allows us to choose to branch on variables representing vertices of either input graph with no overhead. An extensive set of experiments shows that such two-sided branching can be particularly beneficial if the two input graphs have very different orders or densities. Finally, we turn from subgraphs to supergraphs, tackling the problem of finding a small graph that contains every member of a given family of graphs as an induced subgraph. Exact and heuristic techniques are developed for this problem, in each case using a MCSPLIT algorithm as a subroutine. These algorithms allow us to add new terms to two entries of the On-Line Encyclopedia of Integer Sequences
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The History of Periodicals in Hungarian Secondary Mathematics Education Between 1867 and 1956
The purpose of this study was to determine how secondary mathematics education changes in Hungary between 1867 and 1956 were reflected in journal articles of that time. In an attempt to accomplish this purpose, the researcher sought to identify which major political and socioeconomic factors affected the role and content of periodicals, how the content and approach of the topics changed, and who were the most prominent and influential authors of the periodicals between 1867 and 1956. This research investigates Journal of the National Association of Secondary School Teachers, the first periodical devoted to Hungarian secondary education published between 1868 and 1944, and Teaching of Mathematics, the first Hungarian periodical dedicated to mathematic education published between 1953 and 1956. The researcher employed historical-research methodology to examine the articles of the periodicals and categorize them based on similar content such as curriculum, teaching methods, school mathematics, and book/textbook reviews. The study also provides brief summaries of several articles.
This research has shown that the history of Hungarian education in general was often influenced by foreign and domestic politics and ideologies. Studying journal articles provides a unique opportunity to observe real-time communication between educators and administrators and to analyze the effect of social and political changes which influenced mathematics education.
Between 1867 and 1956, Hungary underwent major political and social changesâa dual Monarchy with Austria, independence as a truncated state, and occupation by Germany and later the Soviet Union. These changes significantly altered Hungary as a country and impacted its education system. While every country has undergone political and ideological influences in its educational history, Hungary was particularly affected by neighboring countries such as Germany and later the Soviet Union.
Taking the broader perspective of the evolution of periodicals, this study demonstrated that the history of periodicals as a general form of scientific communication has passed through several stages. The journals, in some respects, are a bridge between educators and were affected by the political atmosphere of the country.
In general, this study has shown that Journal of the National Association of Secondary School Teachers and Teaching of Mathematics were heavily influenced by social and political changes in Hungary, as well as foreign influences from countries such as Germany and the Soviet Union. These factors collectively formed Hungarian mathematics education between 1867 and 1956
Feature Papers in Compounds
This book represents a collection of contributions in the field of the synthesis and characterization of chemical compounds, natural products, chemical reactivity, and computational chemistry. Among its contents, the reader will find high-quality, peer-reviewed research and review articles that were published in the open access journal Compounds by members of the Editorial Board and the authors invited by the Editorial Office and Editor-in-Chief
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