Identifying and evaluating large scale policy interventions : what questions can we answer ?

Using a data-driven empirical case study method, the paper evaluates the impact of one identified reform program on development outcomes. The paper uses the World Bank Country Policy and Institutional Assessment Ratings to identify large scale structural and macro-level policy interventions in the last decade that were seen as being sustained and successful for IDA countries. Robustness checks are performed to show the efficacy of the method in particular cases. It was found that the method attains robustness in the case of Nigeria.Economic Theory&Research,Governance Indicators,Achieving Shared Growth,Poverty Impact Evaluation,Poverty Monitoring&Analysis

Exclusion zones in the law of armed conflict at sea: evolution in law and practice

This article analyses the changes in the law and practice of exclusion zones in the law of armed conflict at sea. It identifies three principal phases. First, it explores the exclusion zones of the Russo-Japanese War of 1904–1905, which were modest in size and defensive in character. Second, it turns to the exclusion zones of the First World War and several subsequent conflicts. The exclusion zones of this period were fundamentally different to those of the Russo-Japanese war: if a vessel was within an exclusion zone, it was deemed susceptible to attack. The article then turns to the third phase of exclusion zone, which can be traced back to the San Remo Manual on International Law Applicable to Armed Conflicts at Sea (1994). The San Remo Manual separated out the establishment of the zone from its enforcement and specified that the same law applies within the zone as outside it. It also set out regulations for the zones should they be created. The practice of States is considered throughout

Exclusion zones in the law of armed conflict at sea: evolution in law and practice

This article analyses the changes in the law and practice of exclusion zones in the law of armed conflict at sea. It identifies three principal phases. First, it explores the exclusion zones of the Russo-Japanese War of 1904–1905, which were modest in size and defensive in character. Second, it turns to the exclusion zones of the First World War and several subsequent conflicts. The exclusion zones of this period were fundamentally different to those of the Russo-Japanese war: if a vessel was within an exclusion zone, it was deemed susceptible to attack. The article then turns to the third phase of exclusion zone, which can be traced back to the San Remo Manual on International Law Applicable to Armed Conflicts at Sea (1994). The San Remo Manual separated out the establishment of the zone from its enforcement and specified that the same law applies within the zone as outside it. It also set out regulations for the zones should they be created. The practice of States is considered throughout

"i have a feeling trump will win..................": Forecasting Winners and Losers from User Predictions on Twitter

Social media users often make explicit predictions about upcoming events. Such statements vary in the degree of certainty the author expresses toward the outcome:"Leonardo DiCaprio will win Best Actor" vs. "Leonardo DiCaprio may win" or "No way Leonardo wins!". Can popular beliefs on social media predict who will win? To answer this question, we build a corpus of tweets annotated for veridicality on which we train a log-linear classifier that detects positive veridicality with high precision. We then forecast uncertain outcomes using the wisdom of crowds, by aggregating users' explicit predictions. Our method for forecasting winners is fully automated, relying only on a set of contenders as input. It requires no training data of past outcomes and outperforms sentiment and tweet volume baselines on a broad range of contest prediction tasks. We further demonstrate how our approach can be used to measure the reliability of individual accounts' predictions and retrospectively identify surprise outcomes.Comment: Accepted at EMNLP 2017 (long paper

QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments

Over the past decade, machine learning techniques have revolutionized how research is done, from designing new materials and predicting their properties to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor QDs are a candidate system for building quantum computers. The present-day tuning techniques for bringing the QD devices into a desirable configuration suitable for quantum computing that rely on heuristics do not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance. To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. We show that the learner's accuracy in recognizing the state of a device is ~96.5 % in both current- and charge-sensor-based training. We also introduce a tool that enables other researchers to use this approach for further research: QFlow lite - a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts.Comment: 18 pages, 6 figures, 3 table