101 Proposals to reform the Stability and Growth Pact. Why so many? A Survey

The failure of key EU Member States to respect the requirements of the Stability and Growth Pact (SGP) a few years after its inception triggered a heated debate on how to reform the framework of fiscal policy coordination in the Economic and Monetary Union (EMU). This paper systematically analyzes 101 reform proposals presented by professional academic and non-academic economists prior to March 2005, when the Council of the European Union adopted a revised version of the SGP. Each proposal is characterized by a set of variables reflecting features such as the degree of modification of the SGP, the background of its author(s), the main aim attached to fiscal policy coordination in the EMU, the timing of the proposal and the type of proposal made. Using multivariate statistical analysis, roughly four different schools of thought concerning the reform of the SGP are identified. In line with the main findings of the political economy literature, all four schools of thought share the view that in the absence of specific rules fiscal policy would lead to excessive deficits and hence affect the conduct of the common monetary policy. However, beyond this common denominator, there is no consensus on how best to co-ordinate fiscal policy.Several explanations for the multitude of proposals are presented, the most important being the present lack of a consensus in the economics profession concerning the role of fiscal policy.Monetary union, euro, fiscal policy, Stability and Growth Pact, international policy coordination, EMU, European Union, Europe, Fischer, Jonung, Larch

101 proposals to reform the Stability and Growth Pact. Why so many? A survey.

The failure of key EU Member States to respect the provisions of the Stability and Growth Pact (SGP) a few years after its inception triggered a heated debate on how to reform the framework of fiscal policy coordination in the Economic and Monetary Union (EMU). This paper analyzes 101 reform proposals presented by professional academic and non-academic economists prior to March 2005, when the Council of the European Union adopted a revised version of the SGP. Roughly four different schools of thought concerning the reform of the SGP are identified. In line with the main findings of the political economy literature, all four schools of thought share the view that in the absence of specific rules fiscal policy would lead to excessive deficits and hence affect the conduct of the common monetary policy. However, beyond this common denominator, there is no consensus on how best to co-ordinate fiscal policy. We present several explanations for the multitude of proposals, the most important being the present lack of a consensus in the economics profession concerning the role of fiscal policy. Economists hold diverging views on the goals, instruments, efficiency and institutions for fiscal policy-making. This state of affairs is in sharp contrast to the case of monetary policy. Also, the euro area is the first case where monetary policy-making is centralized while fiscal policy-making is decentralized to national governments.Monetary union, euro, fiscal policy, Stability and Growth Pact, international policy coordination, EMU, European nion, Europe

Bidirectional Text Compression in External Memory

Bidirectional compression algorithms work by substituting repeated substrings by references that, unlike in the famous LZ77-scheme, can point to either direction. We present such an algorithm that is particularly suited for an external memory implementation. We evaluate it experimentally on large data sets of size up to 128 GiB (using only 16 GiB of RAM) and show that it is significantly faster than all known LZ77 compressors, while producing a roughly similar number of factors. We also introduce an external memory decompressor for texts compressed with any uni- or bidirectional compression scheme

Code for all: a case study in entrepreneurial finance - evaluation of a social impact start-up from a venture capital firm-s perspective

While the many examples of start-up funding journeys offer a large basis for analysis, the scarcity of social impact ventures leaves many pending questions about funding these types of ventures. We review the history of a Portuguese start-up named “Code For All” by creating a case study. We draw conclusions on which typology of financing fits best with the social impact start-up, analyze how a venture capital firm would assess and value Code For All, and lastly determine a rationale of how such a venture could quantify their impact on society

More than the sum of its parts – pattern mining, neural networks, and how they complement each other

In this thesis we explore pattern mining and deep learning. Often seen as orthogonal, we show that these fields complement each other and propose to combine them to gain from each other’s strengths. We, first, show how to efficiently discover succinct and non-redundant sets of patterns that provide insight into data beyond conjunctive statements. We leverage the interpretability of such patterns to unveil how and which information flows through neural networks, as well as what characterizes their decisions. Conversely, we show how to combine continuous optimization with pattern discovery, proposing a neural network that directly encodes discrete patterns, which allows us to apply pattern mining at a scale orders of magnitude larger than previously possible. Large neural networks are, however, exceedingly expensive to train for which ‘lottery tickets’ – small, well-trainable sub-networks in randomly initialized neural networks – offer a remedy. We identify theoretical limitations of strong tickets and overcome them by equipping these tickets with the property of universal approximation. To analyze whether limitations in ticket sparsity are algorithmic or fundamental, we propose a framework to plant and hide lottery tickets. With novel ticket benchmarks we then conclude that the limitation is likely algorithmic, encouraging further developments for which our framework offers means to measure progress.In dieser Arbeit befassen wir uns mit Mustersuche und Deep Learning. Oft als gegensätzlich betrachtet, verbinden wir diese Felder, um von den Stärken beider zu profitieren. Wir zeigen erst, wie man effizient prägnante Mengen von Mustern entdeckt, die Einsichten über konjunktive Aussagen hinaus geben. Wir nutzen dann die Interpretierbarkeit solcher Muster, um zu verstehen wie und welche Information durch neuronale Netze fließen und was ihre Entscheidungen charakterisiert. Umgekehrt verbinden wir kontinuierliche Optimierung mit Mustererkennung durch ein neuronales Netz welches diskrete Muster direkt abbildet, was Mustersuche in einigen Größenordnungen höher erlaubt als bisher möglich. Das Training großer neuronaler Netze ist jedoch extrem teuer, für das ’Lotterietickets’ – kleine, gut trainierbare Subnetzwerke in zufällig initialisierten neuronalen Netzen – eine Lösung bieten. Wir zeigen theoretische Einschränkungen von starken Tickets und wie man diese überwindet, indem man die Tickets mit der Eigenschaft der universalen Approximierung ausstattet. Um zu beantworten, ob Einschränkungen in Ticketgröße algorithmischer oder fundamentaler Natur sind, entwickeln wir ein Rahmenwerk zum Einbetten und Verstecken von Tickets, die als Modellfälle dienen. Basierend auf unseren Ergebnissen schließen wir, dass die Einschränkungen algorithmische Ursachen haben, was weitere Entwicklungen begünstigt, für die unser Rahmenwerk Fortschrittsevaluierungen ermöglicht

Macroscopic Floquet topological crystalline steel pump

The transport of a steel sphere on top of two dimensional periodic magnetic patterns is studied experimentally. Transport of the sphere is achieved by moving an external permanent magnet on a closed loop around the two dimensional crystal. The transport is topological i.e. the steel sphere is transported by a primitive unit vector of the lattice when the external magnet loop winds around specific directions. We experimentally determine the set of directions the loops must enclose for nontrivial transport of the steel sphere into various directions

Linear Time Runs Over General Ordered Alphabets

A run in a string is a maximal periodic substring. For example, the string $\texttt{bananatree}$ contains the runs $\texttt{anana} = (\texttt{an})^{3/2}$ and $\texttt{ee} = \texttt{e}^2$. There are less than $n$ runs in any length-$n$ string, and computing all runs for a string over a linearly-sortable alphabet takes $\mathcal{O}(n)$ time (Bannai et al., SODA 2015). Kosolobov conjectured that there also exists a linear time runs algorithm for general ordered alphabets (Inf. Process. Lett. 2016). The conjecture was almost proven by Crochemore et al., who presented an $\mathcal{O}(n\alpha(n))$ time algorithm (where $\alpha(n)$ is the extremely slowly growing inverse Ackermann function). We show how to achieve $\mathcal{O}(n)$ time by exploiting combinatorial properties of the Lyndon array, thus proving Kosolobov's conjecture.Comment: This work has been submitted to ICALP 202

Lyndon Words Accelerate Suffix Sorting

Suffix sorting is arguably the most fundamental building block in string algorithmics, like regular sorting in the broader field of algorithms. It is thus not surprising that the literature is full of algorithms for suffix sorting, in particular focusing on their practicality. However, the advances on practical suffix sorting stalled with the emergence of the DivSufSort algorithm more than 10 years ago, which, up to date, has remained the fastest suffix sorter. This article shows how properties of Lyndon words can be exploited algorithmically to accelerate suffix sorting again. Our new algorithm is 6-19% faster than DivSufSort on real-world texts, and up to three times as fast on artificial repetitive texts. It can also be parallelized, where similar speedups can be observed. Thus, we make the first advances in practical suffix sorting after more than a decade of standstill

The Effectiveness of Security Interventions on GitHub

In 2017, GitHub was the first online open source platform to show security alerts to its users. It has since introduced further security interventions to help developers improve the security of their open source software. In this study, we investigate and compare the effects of these interventions. This offers a valuable empirical perspective on security interventions in the context of software development, enriching the predominantly qualitative and survey-based literature landscape with substantial data-driven insights. We conduct a time series analysis on security-altering commits covering the entire history of a large-scale sample of over 50,000 GitHub repositories to infer the causal effects of the security alert, security update, and code scanning interventions. Our analysis shows that while all of GitHub's security interventions have a significant positive effect on security, they differ greatly in their effect size. By comparing the design of each intervention, we identify the building blocks that worked well and those that did not. We also provide recommendations on how practitioners can improve the design of their interventions to enhance their effectiveness