LOST Stability? Consumption Taxes and the Cyclical Variability of State and Local Revenues

States and localities continue moving towards consumption taxes. Georgia’s local governments displace a portion of their property tax receipts with revenue from the Local Option Sales Tax. This paper employs a panel dataset of Georgia counties across two economic cycles to examine the effects of consumption taxes on the long- and short-run volatility of local own-source revenues. We offer a mean-variance approach for considering correct revenue portfolio shares across tax-instruments. Holding revenues constant we find that permanent substitution towards a consumption tax amplifies variability of own-source revenues, implying that consumption taxes are overweighed in current revenue portfolios

Somebody google a doctor! urgent health information seeking habits of young adults

Introduction: While much scholarship has been done on health information-seeking habits, comparatively little has been done on these habits among young adults. Objective: The primary objective of this study was to determine to which media young adults turn during an urgent health crisis, which factors correspond to their choice, and if information-seeking corresponded to visiting a health professional. Method: A survey method was used, sampling students from two large universities. Results: Credibility was the most consistent factor in predicting respondent media choice for an urgent health matter. Whether respondents were socially conservative or liberal affected media choice, as did perceptions of online and traditional media credibility. Searching for health information online corresponded to more frequently visiting health professionals. Conclusion: This study supports that young adults turn to a variety of media sources, traditional and online, during health crisis and that this information-seeking does correspond to visiting health professionals after

Compact Furniture Dolly

One problem faced by people during moving is how to move large pieces of furniture, i.e. sofa, mattress, table, refrigerator, etc., conveniently without damaging them. A typical small dolly is incapable of carrying such large pieces, and a bulky dolly is inappropriate for an indoor use because it can damage the furniture and the floor. Besides, in either case, the user should lift up and put the load on the dolly. People who want to change positions of their furniture regularly to get a fresh look for the house, it is cumbersome to call a moving company every time. Our team aims to design a dolly system that can load large pieces of furniture onto adjustable frames with multiple wheels so that the user can tow or push furniture without hassle. The lift function of the dolly can jack up the furniture by pushing up the bottom of the furniture. The lift system does not require much power so that most people can easily load furniture onto the system. The frames are extendable for various types of furniture. When not in use, the system can be disassembled and stowed easily

Comparative analysis of electroencephalogram-based classification of user responses to statically vs. dynamically presented visual stimuli

Emotion is an important part of human and it plays important role in human communication. Nowadays, as the use of machine getting more common, the human computer interaction (HCI) has become important. The understanding of user could bring across a better aiding machine. The exploration of using EEG in understanding human is widely studied for benefit in several fields such as neuromarketing and HCI. In this study, we compare the use of 2 different stimuli (3D shapes with motion vs. 2D emotional images that are static) in attempting to classify positive versus negative feelings. A medical-grade 9-electrode Advance Brain Monitoring (ABM) B-alert X10 is used as the brain-computer interface (BCI) acquisition device to obtain the EEG signals. 4 subjects are involved in recording brain signals during viewing 2 types of stimuli. Feature extraction is then applied to the acquired EEG signals to obtain the alpha, beta, gamma, theta and delta rhythms as features using time frequency analysis. Support vector machine (SVM) and K-nearest neighbors (KNN) classifiers are used to train and classify positive and negative feelings for both stimuli using different channels and rhythms. The average accuracy of 3D motion shapes are better than the average accuracy of the 2D static emotional images for both SVM and KNN with 69.88% and 56.35% using SVM for 3D motion shapes and emotional images respectively, and also 65.31% and 55.45% using KNN for 3D motion shapes and emotional images respectively. This study shows that the parietal lobe are more informative in the classification of 3D motion shapes while the Fz channel of the frontal lobe is more informative in classification of 2D static emotional images

Structured Mixture Models

Finite mixture models are a staple of model-based clustering approaches for distinguishing subgroups. A common mixture model is the finite Gaussian mixture model, whose degrees of freedom scales quadratically with increasing data dimension. Methods in the literature often tackle the degrees of freedom of the Gaussian mixture model by sharing parameters between the eigendecomposition of covariance matrices across all mixture components. We posit finite Gaussian mixture models with alternate forms of parameter sharing by imposing additional structure on the parameters, such as sharing parameters with other components as a convex combination of the corresponding parent components or by imposing a sequence of hierarchical clustering structure in orthogonal subspaces with common parameters across levels. Estimation procedures using the Expectation-Maximization (EM) algorithm are derived throughout, with application to simulated and real-world datasets. As well, the proposed model structures have an interpretable meaning that can shed light on clustering analyses performed by practitioners in the context of their data. The EM algorithm is a popular estimation method for tackling issues of latent data, such as in finite mixture models where component memberships are often latent. One aspect of the EM algorithm that hampers estimation is a slow rate of convergence, which affects the estimation of finite Gaussian mixture models. To explore avenues of improvement, we explore the extrapolation of the sequence of conditional expectations admitting general EM procedures, with minimal modifications for many common models. With the same mindset of accelerating iterative algorithms, we also examine the use of approximate sketching methods in estimating generalized linear models via iteratively re-weighted least squares, with emphasis on practical data infrastructure constraints. We propose a sketching method that controls for both data transfer and computation costs, the former of which is often overlooked in asymptotic complexity analyses, and are able to achieve an approximate result in much faster wall-clock time compared to the exact solution on real-world hardware, and can estimate standard errors in addition to point estimates

Weather and Random Forest-based Load Profiling Approximation Models and Their Transferability across Climate Zones

This study is to provide predictive understanding of the associations of weather attributes with electricity load profiles across a variety of climate zones and seasons. Firstly, machine learning (ML) approaches were used to identify and quantify the impacts of various weather attributes on residential and commercial electricity demand and its components across the western United States. Performance and transferability of the developed ML models were then evaluated across different temperate zones (e.g., southern, middle, and northern US) and across coastal, mid-continent, and wet zones, with inputs of weather condition data from the National Oceanic and Atmospheric Administration (NOAA) at representative weather stations. The predictive models were developed based on the ranked and screened factors using the regression tree (RT) and random forest (RF) approaches, for five different scenarios (seasons)

Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network

An automatic system that utilizes data analytics and machine learning to identify adult American eel in data obtained by imaging sonars is created in this study. Wavelet transform has been applied to de-noise the ARIS sonar data and a convolutional neural network model has been built to classify eels and non-eel objects. Because of the unbalanced amounts of data in laboratory and field experiments, a transfer learning strategy is implemented to fine-tune the convolutional neural network model so that it performs well for both the laboratory and field data. The proposed system can provide important information to develop mitigation strategies for safe passage of out-migrating eels at hydroelectric facilities

Advocating a New Approach to Governing Water, Energy, and Food Security: Testing the Effects of Message Inoculation and Conclusion Explicitness in the Case of the WEF Nexus

Message sidedness, including its later format inoculation, and conclusion explicitness have been identified by researchers as two prominent message factors that may influence advocating effects. Two-sided messages, which contain both supporting and opposing information about the issue, particularly those containing inoculation components that refute the negative side, are found to be more effective than one-sided messages. Messages with explicit conclusions are also found to be more persuasive than those that let the audience draw the conclusions themselves. This study tested the persuasion effectiveness of message inoculation and conclusion explicitness on a new scientific concept, the water–energy–food (WEF) nexus, of which the public has little knowledge. This study used five randomly assigned groups (total N = 524) and found that messages with explicit conclusions are more persuasive than those with implicit conclusions; however, it found no difference between the effectiveness of one-sided messages and of refutational two-sided messages. The study suggests that a clear conclusion is necessary to communicate the WEF nexus for a better approach to managing the megacrisis of water, energy, and food security