The Stability and Growth Pact: A European Answer to the Political Budget Cycle?

The existing literature on political budget cycles looks at the temptation for incumbent governments to run a greater deficit before an election by considering the characteristics of the incumbent. We propose here to look at the signals the incumbent receives from the voters. For this purpose, we consider the votes from the previous national elections and see whether they may influence the incumbent government to run a sound fiscal policy or an expansionary fiscal policy. However, since 1993 Europe has been equipped with two fiscal rules: a deficit and a debt ceiling. In this context, can we find evidence of a “political budget cycle” before 1993, and did the fiscal rules prevent the existence of a “political budget cycle” afterwards? To address these questions, we use a cross-sectional time series analysis of European countries from 1979 to 2005.Stability and Growth Pact, Political Business Cycle, Political budget Cycle, Partisan Theory

Multilateralism cursed by bilateralism: Japan’s Role at the International Whaling Commission

We propose a new categorization of international organizations to account for the fact that within multilateral international organizations, states may engage in “enticement” strategies in order to advance their policy preferences. Thus, to the traditional multilateral/bilateral categorizations we substitute a hard multilateral/soft multilateral and reciprocal bilateral/bilateral taxonomy. For illustration purposes, we use the well-known case study of Japan and the International Whaling Commission (IWC). Using a modified gravity model to analyze Japan’s Official Development Assistance from 1973-2005, we find that Japan has a very traditional – and generous – assistance policy broadly defined, but when it comes to the IWC, some of the general principles driving the aid policy are put aside to possibly influence vote outcomes. Given this finding, we conclude that the IWC is best categorized as a soft multilateral organization.

Development in helicopter tail boom strake applications in the US

The use of a strake or spoiler on a helicopter tail boom to beneficially change helicopter tail boom air loads was suggested in the United States in 1975. The anticipated benefits were a change of tail boom loads to reduce required tail rotor thrust and power and improve directional control. High tail boom air loads experienced by the YAH-64 and described in 1978 led to a wind tunnel investigation of the usefullness of strakes in altering such loads on the AH-64, UH-60, and UH-1 helicopters. The wind tunnel tests of 2-D cross sections of the tail boom of each demonstrated that a strake or strakes would be effective. Several limited test programs with the U.S. Army's OH-58A, AH-64, and UH-60A were conducted which showed the effects of strakes were modest for those helicopters. The most recent flight test program, with a Bell 204B, disclosed that for the 204B the tail boom strake or strakes would provide more than a modest improvement in directional control and reduction in tail rotor power

Passive object recognition using intrinsic shape signatures

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 53).The Agile Robotics Group (AR) at MIT's Computer Science and Artificial Intelligence Laboratories (CSAIL) has an autonomous, forklift capable of doing task planning, navigation, obstacle detection and avoidance, focused object detection, etc. The goal of the project is to have a completely autonomous robot that is safe to use in a human environment. One aspect of the project which would be very beneficial to moving on to more complicated tasks is passive object recognition. The forklift is capable of doing a focused scan and looking for very particular things. The forklift is constantly scanning its vicinity with its Light Detection and Ranging (LiDAR) sensors to ensure that it avoids obstacles; instead of only using that information for hazard avoidance, that information can be used to not only passively notice objects but also classify them. This will be useful later when the team starts implementing various higher-level processes, such as localization and/or mapping. This paper will discuss various modules that were integrated into the Agile Robotics infrastructure that made object recognition possible. These modules were 1) a data segmentation module, 2) an object recognition module using Intrinsic Shape Signature[10] (ISS) to find feature points in our LiDAR data, and 3) various visualization modules to ensure that each module was behaving properly.by Kenneth M. Donahue.M.Eng

#17 - Detecting the Presence of GMOs in Tortilla Chips Served U.S. Major Restaurant Chains

Today, consumers are more aware and demand more information about the foods they consume. Transgenic crops, such as BT-corn, are a popular topic of concern among an ever-growing population of health-conscious consumers. Additionally, many U.S. consumers are regularly going out to eat at restaurants instead of cooking at home, with about half of all U.S. food expenditures accounting for food-away-from home. Specifically, chips are becoming a staple of the current American diet and are popular amongst diverse demographics. This study is an attempt to discover if tortilla chips served in common U.S. restaurant chains are genetically modified. Tortilla chips from some of the largest restaurant chains: Moe’s, Taco Bell, Chilis, and Chipotle, were screened for the presence of the most common transgenes in corn: Cry1Ab, ESPS4, as well as the GMO marker gene nptII. Additionally, the corn samples were tested for evidence of genetic modification using ELISA. Since detailed information on foods served in restaurants is either unavailable or not easily accessible, the findings of this experiment should provide consumers with valuable information about the presence and/or prevalence of GMOs served in major restaurant chains

Bandpass Dependence of X-ray Temperatures in Galaxy Clusters

We explore the band dependence of the inferred X-ray temperature of the intracluster medium (ICM) for 192 well-observed galaxy clusters selected from the Chandra Data Archive. If the hot ICM is nearly isothermal in the projected region of interest, the X-ray temperature inferred from a broad-band (0.7-7.0 keV) spectrum should be identical to the X-ray temperature inferred from a hard-band (2.0-7.0 keV) spectrum. However, if unresolved cool lumps of gas are contributing soft X-ray emission, the temperature of a best-fit single-component thermal model will be cooler for the broad-band spectrum than for the hard-band spectrum. Using this difference as a diagnostic, the ratio of best-fitting hard-band and broad-band temperatures may indicate the presence of cooler gas even when the X-ray spectrum itself may not have sufficient signal-to-noise to resolve multiple temperature components. To test this possible diagnostic, we extract X-ray spectra from core-excised annular regions for each cluster in our archival sample. We compare the X-ray temperatures inferred from single-temperature fits when the energy range of the fit is 0.7-7.0 keV (broad) and when the energy range is 2.0/(1+z)-7.0 keV (hard). We find that the hard-band temperature is significantly higher, on average, than the broad-band temperature. Upon further exploration, we find this temperature ratio is enhanced preferentially for clusters which are known merging systems. In addition, cool-core clusters tend to have best-fit hard-band temperatures that are in closer agreement with their best-fit broad-band temperatures. We show, using simulated spectra, that this diagnostic is sensitive to secondary cool components (TX = 0.5-3.0 keV) with emission measures >10-30% of the primary hot component.Comment: Accepted for publication in Ap

Star Formation, Radio Sources, Cooling X-ray Gas, and Galaxy Interactions in the Brightest Cluster Galaxy in 2A0335+096

We present deep emission-line imaging taken with the SOAR Optical Imaging Camera of the brightest cluster galaxy (BCG) in the nearby (z=0.035) X-ray cluster 2A0335+096. We analyze long-slit optical spectroscopy, archival VLA, Chandra X-ray, and XMM UV data. 2A0335+096 is a bright, cool-core X-ray cluster, once known as a cooling flow. Within the highly disturbed core revealed by Chandra X-ray observations, 2A0335+096 hosts a highly structured optical emission-line system. The redshift of the companion is within 100 km/s of the BCG and has certainly interacted with the BCG, and is likely bound to it. The comparison of optical and radio images shows curved filaments in H-alpha emission surrounding the resolved radio source. The velocity structure of the emission-line bar between the BCG nucleus and the companion galaxy provides strong evidence for an interaction between the two in the last ~50 Myrs. The age of the radio source is similar to the interaction time, so this interaction may have provoked an episode of radio activity. We estimate a star formation rate of >7 solar mass/yr based on the Halpha and archival UV data, a rate similar to, but somewhat lower than, the revised X-ray cooling rate of 10-30 solar masses/year estimated from XMM spectra by Peterson & workers. The Halpha nebula is limited to a region of high X-ray surface brightness and cool X-ray temperature. The detailed structures of H-alpha and X-ray gas differ. The peak of the X-ray emission is not the peak of H-alpha emission, nor does it lie in the BCG. The estimated age of the radio lobes and their interaction with the optical emission-line gas, the estimated timescale for depletion and accumulation of cold gas, and the dynamical time in the system are all similar, suggesting a common trigger mechanism.Comment: Accepted AJ, July 2007 publication. Vol 134, p. 14-2

Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks

With the rapid development of fashion market, the customers' demands of customers for fashion recommendation are rising. In this paper, we aim to investigate a practical problem of fashion recommendation by answering the question "which item should we select to match with the given fashion items and form a compatible outfit". The key to this problem is to estimate the outfit compatibility. Previous works which focus on the compatibility of two items or represent an outfit as a sequence fail to make full use of the complex relations among items in an outfit. To remedy this, we propose to represent an outfit as a graph. In particular, we construct a Fashion Graph, where each node represents a category and each edge represents interaction between two categories. Accordingly, each outfit can be represented as a subgraph by putting items into their corresponding category nodes. To infer the outfit compatibility from such a graph, we propose Node-wise Graph Neural Networks (NGNN) which can better model node interactions and learn better node representations. In NGNN, the node interaction on each edge is different, which is determined by parameters correlated to the two connected nodes. An attention mechanism is utilized to calculate the outfit compatibility score with learned node representations. NGNN can not only be used to model outfit compatibility from visual or textual modality but also from multiple modalities. We conduct experiments on two tasks: (1) Fill-in-the-blank: suggesting an item that matches with existing components of outfit; (2) Compatibility prediction: predicting the compatibility scores of given outfits. Experimental results demonstrate the great superiority of our proposed method over others.Comment: 11 pages, accepted by the 2019 World Wide Web Conference (WWW-2019