2,814 research outputs found
A foundation for synthesising programming language semantics
Programming or scripting languages used in real-world systems are seldom designed
with a formal semantics in mind from the outset. Therefore, the first step for developing well-founded analysis tools for these systems is to reverse-engineer a formal
semantics. This can take months or years of effort.
Could we automate this process, at least partially? Though desirable, automatically reverse-engineering semantics rules from an implementation is very challenging,
as found by Krishnamurthi, Lerner and Elberty. They propose automatically learning
desugaring translation rules, mapping the language whose semantics we seek to a simplified, core version, whose semantics are much easier to write. The present thesis
contains an analysis of their challenge, as well as the first steps towards a solution.
Scaling methods with the size of the language is very difficult due to state space
explosion, so this thesis proposes an incremental approach to learning the translation
rules. I present a formalisation that both clarifies the informal description of the challenge by Krishnamurthi et al, and re-formulates the problem, shifting the focus to the
conditions for incremental learning. The central definition of the new formalisation is
the desugaring extension problem, i.e. extending a set of established translation rules
by synthesising new ones.
In a synthesis algorithm, the choice of search space is important and non-trivial,
as it needs to strike a good balance between expressiveness and efficiency. The rest
of the thesis focuses on defining search spaces for translation rules via typing rules.
Two prerequisites are required for comparing search spaces. The first is a series of
benchmarks, a set of source and target languages equipped with intended translation
rules between them. The second is an enumerative synthesis algorithm for efficiently
enumerating typed programs. I show how algebraic enumeration techniques can be applied to enumerating well-typed translation rules, and discuss the properties expected
from a type system for ensuring that typed programs be efficiently enumerable.
The thesis presents and empirically evaluates two search spaces. A baseline search
space yields the first practical solution to the challenge. The second search space is
based on a natural heuristic for translation rules, limiting the usage of variables so that
they are used exactly once. I present a linear type system designed to efficiently enumerate translation rules, where this heuristic is enforced. Through informal analysis
and empirical comparison to the baseline, I then show that using linear types can speed
up the synthesis of translation rules by an order of magnitude
On the real world practice of Behaviour Driven Development
Surveys of industry practice over the last decade suggest that Behaviour Driven Development is a popular Agile practice. For example, 19% of respondents to the 14th State of Agile annual survey reported using BDD, placing it in the top 13 practices reported. As well as potential benefits, the adoption of BDD necessarily involves an additional cost of writing and maintaining Gherkin features and scenarios, and (if used for acceptance testing,) the associated step functions. Yet there is a lack of published literature exploring how BDD is used in practice and the challenges experienced by real world software development efforts. This gap is significant because without understanding current real world practice, it is hard to identify opportunities to address and mitigate challenges. In order to address this research gap concerning the challenges of using BDD, this thesis reports on a research project which explored: (a) the challenges of applying agile and undertaking requirements engineering in a real world context; (b) the challenges of applying BDD specifically and (c) the application of BDD in open-source projects to understand challenges in this different context.
For this purpose, we progressively conducted two case studies, two series of interviews, four iterations of action research, and an empirical study. The first case study was conducted in an avionics company to discover the challenges of using an agile process in a large scale safety critical project environment. Since requirements management was found to be one of the biggest challenges during the case study, we decided to investigate BDD because of its reputation for requirements management. The second case study was conducted in the company with an aim to discover the challenges of using BDD in real life. The case study was complemented with an empirical study of the practice of BDD in open source projects, taking a study sample from the GitHub open source collaboration site.
As a result of this Ph.D research, we were able to discover: (i) challenges of using an agile process in a large scale safety-critical organisation, (ii) current state of BDD in practice, (iii) technical limitations of Gherkin (i.e., the language for writing requirements in BDD), (iv) challenges of using BDD in a real project, (v) bad smells in the Gherkin specifications of open source projects on GitHub. We also presented a brief comparison between the theoretical description of BDD and BDD in practice. This research, therefore, presents the results of lessons learned from BDD in practice, and serves as a guide for software practitioners planning on using BDD in their projects
Less is More: Restricted Representations for Better Interpretability and Generalizability
Deep neural networks are prevalent in supervised learning for large amounts of tasks such as image classification, machine translation and even scientific discovery.
Their success is often at the sacrifice of interpretability and generalizability. The increasing complexity of models and involvement of the pre-training process make the inexplicability more imminent. The outstanding performance when labeled data are abundant while prone to overfit when labeled data are limited demonstrates the difficulty of deep neural networks' generalizability to different datasets.
This thesis aims to improve interpretability and generalizability by restricting representations. We choose to approach interpretability by focusing on attribution analysis to understand which features contribute to prediction on BERT, and to approach generalizability by focusing on effective methods in a low-data regime.
We consider two strategies of restricting representations: (1) adding bottleneck, and (2) introducing compression. Given input x, suppose we want to learn y with the latent representation z (i.e. x→z→y), adding bottleneck means adding function R such that L(R(z)) < L(z) and introducing compression means adding function R so that L(R(y)) < L(y) where L refers to the number of bits. In other words, the restriction is added either in the middle of the pipeline or at the end of it.
We first introduce how adding information bottleneck can help attribution analysis and apply it to investigate BERT's behavior on text classification in Chapter 3.
We then extend this attribution method to analyze passage reranking in Chapter 4, where we conduct a detailed analysis to understand cross-layer and cross-passage behavior.
Adding bottleneck can not only provide insight to understand deep neural networks but can also be used to increase generalizability.
In Chapter 5, we demonstrate the equivalence between adding bottleneck and doing neural compression. We then leverage this finding with a framework called Non-Parametric learning by Compression with Latent Variables (NPC-LV), and show how optimizing neural compressors can be used in the non-parametric image classification with few labeled data.
To further investigate how compression alone helps non-parametric learning without latent variables (NPC), we carry out experiments with a universal compressor gzip on text classification in Chapter 6.
In Chapter 7, we elucidate methods of adopting the perspective of doing compression but without the actual process of compression using T5.
Using experimental results in passage reranking, we show that our method is highly effective in a low-data regime when only one thousand query-passage pairs are available.
In addition to the weakly supervised scenario, we also extend our method to large language models like GPT under almost no supervision --- in one-shot and zero-shot settings. The experiments show that without extra parameters or in-context learning, GPT can be used for semantic similarity, text classification, and text ranking and outperform strong baselines, which is presented in Chapter 8.
The thesis proposes to tackle two big challenges in machine learning --- "interpretability" and "generalizability" through restricting representation. We provide both theoretical derivation and empirical results to show the effectiveness of using information-theoretic approaches. We not only design new algorithms but also provide numerous insights on why and how "compression" is so important in understanding deep neural networks and improving generalizability
The Distributional Impacts of Economic Development Incentives: Three Essays
Economic Development Incentives (EDIs) are among the most common and costly tools used by state and local governments in the United States to promote economic development. While literature has predominantly focused on the efficiency of EDIs, comparatively less focus has been paid to the distributional impacts of business attraction or the impact on individual welfare. In three papers, this dissertation seeks to better understand and evaluate how business attraction supported by EDIs impacts current residents. The first paper critiques EDI evaluations that focus on bottom-line growth instead of metrics that can show changes to the quality of life and welfare of current resident. A new framework, Distributive Welfare Evaluations, is proposed. A second paper examines how business attraction impacts wages and employment rates in local economies, using large warehouses as a natural experiment. Analysis shows that jobs were filled by shifting commuting patterns and had minimal benefits for incumbent workers. Finally, a third paper, co-authored with Jeremy Moulton and Scott Wentland, measures the impacts of EDI announcements on housing markets. We find highly variable results across 114 cases but demonstrate significant increases in prices when many jobs are promised. Together, evidence contributes to a growing body of work arguing that EDIs have limited or even negative impacts on the welfare of current residents and can contribute to growing inequalityDoctor of Philosoph
A Theistic Critique of Secular Moral Nonnaturalism
This dissertation is an exercise in Theistic moral apologetics. It will be developing both a critique of secular nonnaturalist moral theory (moral Platonism) at the level of metaethics, as well as a positive form of the moral argument for the existence of God that follows from this critique. The critique will focus on the work of five prominent metaethical theorists of secular moral non-naturalism: David Enoch, Eric Wielenberg, Russ Shafer-Landau, Michael Huemer, and Christopher Kulp. Each of these thinkers will be critically examined. Following this critique, the positive moral argument for the existence of God will be developed, combining a cumulative, abductive argument that follows from filling in the content of a succinct apagogic argument. The cumulative abductive argument and the apagogic argument together, with a transcendental and modal component, will be presented to make the case that Theism is the best explanation for the kind of moral, rational beings we are and the kind of universe in which we live, a rational intelligible universe
Evaluating a potential gender gap, disparities in residency, and factors affecting financial literacy in Norway
This paper analyses a questionnaire measuring the financial literacy of the Norwegian respondents. There is a consistent gender gap in investments, and as this paper concluded, in financial literacy as well. Variables such as education, income, gender, and risk aversion have significant impact on financial literacy. There are some significant impacts of where the respondent’s resident, whether it is a central area, a specific county, or municipalities containing a large proportion of investors. The survey revealed how females are more risk averse, but also more affected by other factors such as environment and upbringing than males. Another interesting result is the distinctive difference in what the respondents consider a relevant investment product, and what they currently possess. Our recommendation to reduce the gender gap in financial literacy is by incorporate and evolve the school’s curriculum within personal finance as well as encourage openness about the subject
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
"Le present est plein de l’avenir, et chargé du passé" : Vorträge des XI. Internationalen Leibniz-Kongresses, 31. Juli – 4. August 2023, Leibniz Universität Hannover, Deutschland. Band 3
[No abstract available]Deutschen Forschungsgemeinschaft (DFG)/Projektnr. 517991912VGH VersicherungNiedersächsisches Ministerium für Wissenschaft und Kultur (MWK
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