A Time of Need: Nonprofits Report Poor Communication and Little Help From Foundations During the Economic Downturn

Highlights grantee survey findings on how communicative and helpful foundations have been in response to the recession and the need for foundations to better understand grantees' goals and strategies. Includes interview with Cleveland Foundation staff

Multiple Imputation Using Gaussian Copulas

Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use method for generating multiple imputations using a Gaussian copula. The Gaussian copula for multiple imputation (Hoff, 2007) allows scholars to attain estimation results that have good coverage and small bias. The use of copulas to model the dependence among variables will enable researchers to construct valid joint distributions of the data, even without knowledge of the actual underlying marginal distributions. Multiple imputations are then generated by drawing observations from the resulting posterior joint distribution and replacing the missing values. Using simulated and observational data from published social science research, we compare imputation via Gaussian copulas with two other widely used imputation methods: MICE and Amelia II. Our results suggest that the Gaussian copula approach has a slightly smaller bias, higher coverage rates, and narrower confidence intervals compared to the other methods. This is especially true when the variables with missing data are not normally distributed. These results, combined with theoretical guarantees and ease-of-use suggest that the approach examined provides an attractive alternative for applied researchers undertaking multiple imputations

PFC Topologies for AC to DC Converters in DC Micro-Grid

With increasing dominance of renewable energy resources and DC household appliances, the novelty of DC micro grid is attracting significant attention. The key interface between the main supply grid and DC micro grid is AC to DC converter. The conventional AC to DC converter with large output capacitor introduces undesirable power quality problems in the main supply current. It reduces system efficiency due to low power factor and high harmonic distortion. Power Factor Correction (PFC) circuits are used to make supply currents sinusoidal and in-phase with supply voltages. This paper presents different PFC topologies for single phase AC to DC converters which are analyzed for power factor (PF), total harmonic distortion (THD) and system efficiency by varying output power. Two-quadrant shunt active filter topology attains a power factor of 0.999, 3.03% THD and 98% system efficiency. Output voltage regulation of the presented active PFC topologies is simulated by applying a step load. Two-quadrant shunt active filter achieves better output voltage regulation compared to other topologies and can be used as grid interface

Assassinating “the muscular hook of my cock”: Hayes Condemns White Supremacy

This paper delves into the intricate literary and poetic techniques Terrance Hayes uses in his rhetoric against the state of America’s administration and ideological stance as of 2018, primarily such an ideology’s uncanny reflection of its bleak slave histor

The effects of structural and functional damage to limbic structures on cognitive abilities

Functional and degenerative damage to regions of the limbic system are often associated with cognitive impairments in different aspects of memory. Neuroimaging studies in post-traumatic stress disorder (PTSD) and Alzheimer’s disease (AD) have reported selective hippocampal atrophy. Neuroimaging studies in panic disorder have also suggested reduced functional activity in the right parahippocampal gyrus. It is unclear whether this hippocampal damage is responsible for the emergence of selective neuropsychological deficits. Abnormal activity in limbic structures has also been reported in PTSD patients exposed to trauma-related stimuli. This thesis was concerned with examining the effects of structural and functional damage to the limbic system on selective cognitive abilities. The limbic structures under investigation included the hippocampus, parahippocampal gyrus, anterior cingulate cortex and amygdala. In order to investigate this issue, a series of neuropsychological and neuroimaging experiments were carried out using groups of patient populations, such as panic disorder, PTSD and AD, known to exhibit abnormalities to the limbic structures. An fMRI study, using the Color Stroop and Emotional Stroop task was also administered to PTSD patients and healthy controls.Results from the neuropsychological studies showed greater impairments in topographical/spatial memory compared to verbal memory in all groups of patients. In addition, voxel-based correlation analyses found that both PTSD and AD are associated with neuropsychological deficits in the area of visuo-spatial and topographical memory that may be explained by the regional brain atrophy in limbic structures. Abnormalities of the parahippocampal gyri and cingulate cortex and possibly the amygdalae in the fMRI study also suggested a dysregulation in limbic-cortical networks in PTSD. This thesis has demonstrated that damage to limbic structures might contribute to the cognitive abnormalities of panic disorder, PTSD and AD

Machine Learning with Abstention for Automated Liver Disease Diagnosis

This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can chose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the proposed scheme offers state of the art classification performance.Comment: Preprint version before submission for publication. complete version published in proc. 15th International Conference on Frontiers of Information Technology (FIT 2017), December 18-20, 2017, Islamabad, Pakistan. http://ieeexplore.ieee.org/document/8261064