Many researchers use unit fixed effects regression models as their default methods for causal inference with longitudinal data. We show that the ability of these models to adjust for unobserved ...time-invariant confounders comes at the expense of dynamic causal relationships, which are permitted under an alternative selection-on-observables approach. Using the nonparametric directed acyclic graph, we highlight two key causal identification assumptions of unit fixed effects models: Past treatments do not directly influence current outcome, and past outcomes do not affect current treatment. Furthermore, we introduce a new nonparametric matching framework that elucidates how various unit fixed effects models implicitly compare treated and control observations to draw causal inference. By establishing the equivalence between matching and weighted unit fixed effects estimators, this framework enables a diverse set of identification strategies to adjust for unobservables in the absence of dynamic causal relationships between treatment and outcome variables. We illustrate the proposed methodology through its application to the estimation of GATT membership effects on dyadic trade volume.
Con respecto a la primera de ellas, Silvia Morgades, en el quinto capítulo, detalla cómo la libre circulación es lo que más aprecian los ciudadanos europeos, aunque critica que esa libertad de los ...residentes europeos se traduce en una exclusión de los extranjeros, como pudimos observar durante la crisis de refugiados de 2015, un hecho que puso de manifiesto las debilidades del Espacio Schengen. De esta manera, Klaus-Jürgen Nagel (Capítulo 8) y Pau Bossacoma (Capítulo 9) presentan un recorrido histórico de los movimientos regionalistas de Denis de Rougemont, Guy Héraud y Yann Fouéré, para más tarde explicar de qué modo los deseos frustrados de una mayor participación por parte de las regiones en las instituciones europeas se han cristalizado en movimientos independistas, aunque el funcionamiento y la organización de la Unión Europea dificulta soberanamente que éstos puedan ser fructíferos y les permitan ser Estados miembros de pleno derecho en el caso de alcanzar sus metas independentistas. Para terminar, en el epílogo, Pau Bossacoma ensalza la contribución inequívoca del proyecto europeo en la consolidación de la paz, la cooperación y la democracia, aunque también nos advierte de dos ámbitos sobre los que debe cimentar su acción los próximos años: acercarse a la ciudadanía y otorgar una mayor representación a las minorías.
Grâce au COVID-19 et aux élections américaines, le Brexit a temporairement pâli sur l'écran radar médiatique. A l'approche de la date fatidique du 1er janvier 2021 (limite pour un éventuel accord ...entre Union européenne - UE - et Royaume-Uni), il réapparaít de plus belle. Le dossier qui s'ouvre ici pose (au moins) quatre questions fondamentales.
Fuzzy c-means (FCM) is one of the most frequently used methods for clustering. However, with increasing amount of data, FCM suffers from slow convergence and a large amount of calculation because all ...samples are involved in updating the solutions per iteration without considering the current clustering results. In this article, a new membership scaling FCM (MSFCM) is proposed, based on the observation that the samples, whose nearest cluster center is <inline-formula><tex-math notation="LaTeX">\mathbf {v}</tex-math></inline-formula>, aid the convergence of <inline-formula><tex-math notation="LaTeX">\mathbf {v}</tex-math></inline-formula>, whereas the remaining samples prevent the convergence of <inline-formula><tex-math notation="LaTeX">\mathbf {v}</tex-math></inline-formula>. In the new algorithm, many samples whose nearest cluster centers do not change in the next iteration are chosen by using the triangle inequality. A new scheme for scaling the membership degrees of the chosen samples is suggested to boost the effect of the in-cluster samples and to weaken the effect of the out-of-cluster samples in the clustering process. The new scheme not only accelerates the convergence of the algorithm but also maintains the high clustering quality. Many experimental results on synthetic and real-world data sets have verified the effectiveness of the proposed algorithm in improving the speed of the convergence of the fuzzy clustering. In particular, compared with FCM, MSFCM saves at least two thirds of the total rounds of iterations without significantly increasing the cost per iteration.
COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country ...to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.
•This paper reveals that related tweets failed to guide people on COVID-19 pandemic.•This study analyzes two types of tweets gathered during the pandemic times.•The research demonstrates that no useful words are found in WordCloud or word frequency in tweets.•Claims are validated by a proposed deep learning classifier model yielding accuracy up to 81%.•A designed Gaussian membership based fuzzy rule base correctly identifies sentiments from tweets.
No abstract available. Article truncated after 150 words. There is little doubt that most professional organizations are experiencing a failure to grow. For example, in the early 1950’s, about 75% of ...US physicians were American Medical Association (AMA) members (1). That percentage has steadily decreased over the years. In 2019 there were only 132,133 practicing physicians or about 12.1% of physicians who are AMA members (2). According to Kevin Campbell (2) there are many reasons for this decline including: • The AMA touts itself as speaking for all of us (physicians) -- but rarely listens to any of us -- they work to fill their own pockets with dollars from big pharma and government. • The AMA tends to have a narrow-minded political view and works to stifle any dissenting opinions (in an effort to continue to align with the government agencies that line the pockets of AMA executives with taxpayer money). • The AMA has collaborated with the government to …
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