Miracle-dz/UniFAR-FTUs huggingface dataset:
An Equilibrium Analysis of the Long-Term Care Insurance Market

This paper uses a model of family interactions to explain why the long-term care insurance market has not been growing. Coverage rates are low and premiums have risen sharply in recent years. I develop and estimate a dynamic non-cooperative model of the family in which parents and children interact over long-term care decisions. Competitive equilibrium analyses of the insurance market show that private information about the availability of informal care limits the size of the market by creating substantial adverse selection. In equilibrium, the market only serves high-risk individuals with limited access to informal care. I also find that children strategically reduce informal care in response to their parents’ insurance coverage. This family moral hazard effect of insurance reduces the insurance demand and increases the formal care risk of the insured, both of which limit the size of the insurance market. I demonstrate that the initial neglect of adverse selection and family moral hazard resulted in substantial underpricing of insurance products. I further show that the decreasing availability of informal care for more recent birth cohorts puts upward pressure on the equilibrium premium. I propose child demographic-based pricing as an alternative risk adjustment that could decrease the average premium, invigorate the market, and generate welfare gains. ∗Department of Economics, Georgetown University, 3700 O St NW ICC 557, Washington DC 20009. E-mail: ami.ko@georgetown.edu. I am extremely grateful to my advisors, Hanming Fang, Holger Sieg, and Petra Todd for their guidance and support throughout this project. I also thank seminar participants at Arizona State University, University of Rochester, Boston College, Georgetown University, American Society of Health Economists Biennial Meetings, Canadian Health Economics Study Group Meetings, Center for Retirement Research at Boston College, Econometric Society North American Summer Meetings, Social Security Administration RRC Annual Meetings, and University of Pennsylvania Empirical Micro Workshop for helpful comments and discussions. I gratefully acknowledge financial support from the Korea Foundation for Advanced Studies and the Center for Retirement Research at Boston College (BC Grant 5002099) sponsored by the Social Security Administration.
Detecting rooftop available surface for installing PV modules in aerial images using Deep Learning

Mapping the location and size of potential surface on rooftops to install photovoltaics (PV) panels can be a valuable input for policymakers and for investing in distributed energy infrastructures. Machine Learning techniques, combined with satellite and aerial imagery, allow to overcome the limitations of surveys and sparse databases in providing this mapping at large scale. In this project we apply a supervised method based on convolutional neural networks to delineate available rooftop area for the installation of PV panels by means of pixel-wise image segmentation. We created the dataset from scratch, using satellite images of Geneve and labelling them manually. We explored different data augmentation and we varied network parameters in order to maximize model performance. First we trained our model on the whole dataset, then we focused on only on a speciﬁc class of images, residential area: our hypothesis is that in order to generalize our solution on new different geographical areas, it is better, in a ﬁrst step, splitting the dataset in classes (i.e. residential area and industrial area) in a supervised or unsupervised way and then train a different model for each one. Preliminary results show that, training a different model for each area, at the cost of having less data, increases anyway the performance of our detection. In particular we are able to automatically detect, on the residential areas, the available rooftop area at pixel level with an accuracy on the test set of about 0.97 and an Intersection over Union index of 0.77 using only 244 images in the training. The scalability of the trained model should allow to predict the available rooftop area at the Swiss national scale, while new classes (in add to residential and industrial area) could be found to improve and adapt our solution to other country with different geographic settlement,
AGRICULTURAL IRRIGATION MATHEMATICAL MODELING USING GAME THEORY FOR NEXT GENERATION FARMING

Scientific transform is a next source of skepticism. Transformation in production approaches, improvement associated with new products or advices, and benefits of various other innovative developments cannot be precisely forecasted. These kinds of advancements may possibly have an impact on the desirability of substitute ideas which farmers may make. Because of the time lag among expenditure and benefit, the price‐taking characteristics of farming, stochastic conditions factors along with other elements, the uncertainty confronted by decision makers in farming is higher than and various from that facing supervisors of most some other industries of economic system. Within this paper, game theory model is used on the issue of decision making within uncertainness in farming irrigation system. In this study, applications are designed to the decision of sort of irrigation system and through this the best possible serving of fertilizer too. Farmers must monitor, consider suggestions, come to a decision, and carry out their particular plan.
Cough Classiﬁer CS-433 Machine Learning: Project 2

—Cough sound analysis is commonly performed by doctors to diagnose respiratory conditions (i.e. asthma, pneumonia, or COVID-19). An important property that can be detected from cough sounds is whether mucus is produced (”wet” cough) or not (”dry” cough). The goal of this project is to perform cough classiﬁcation based on cough sounds. This could enable quick and cheap diagnoses without requiring medical professionals. The Embedded Systems Lab [1] has published the COUGHVID dataset [2], a collection of crowd-sourced cough sounds, 2,200 of which have been annotated by doctors. The lab provided a set of around 70 features computed on each cough recording, which we used to perform automated cough type classiﬁcation. The ﬁnal results were computed using a private test set that has been excluded from publishing. Our best classical model, the KNN classiﬁer, achieved an AUC score of 0.61, and the deep learning model an AUC score of 0.63.
Quantification of Herbal Drug Hypoxoside from the Roots of South African Hypoxis hemerocallidea Using Cost-Effective High-Performance Thin-Layer Chromatography–Densitometry Validated Method

Hypoxis (Hypoxidaceae) consists of about 90 species of plants reported worldwide, of which 76 occur in Africa. As many as 41 species are indigenous to countries belonging to the Southern African Development Community (SADC), including South Africa. Of all the Hypoxis species, Hypoxis hemerocallidea has versatile application in traditional health care system of over 85% of South Africans and is regarded as one of the most ethnomedicinally important and most marketed species in South Africa. H. hemerocallidea corm’s water or alcoholic extract is widely used as traditional medicine for the treatment of benign hypertrophy and urinary tract infections as well as for boosting the immune system of people living with human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS) among others. However, the use of other parts of Hypoxis plant as medicine is vital for conservation purposes. The roots attached on the corm of H. hemerocallidea contain hypoxoside, but the roots are usually ripped off during the preparation of Hypoxis-containing traditional medicines and other herbal products. A developed and validated, affordable but reliable high-performance thin-layer chromatography (HPTLC) densitometry for the rapid and repeatable visualization and quantitative determination of hypoxoside from the roots of H. hemerocallidea was performed. After thin-layer chromatography analysis, the hypoxoside resolved and was visualized at RF of 0.30 in CHCl3–MeOH–H20 (70:30:2 v/v). The method was linear with R2 of 0.9876 over a calibration range of 0.20 × 10−4–1.80 × 10−3 mg mL−1. The limits of detection (LOD) and quantification (LOQ) were 5.08 × 10−4 and 1.65 × 10−3 mg mL−1, respectively, while the percentage recovery and the method repeatability (%RSD) were 84.10 and 4.98, respectively. The roots of H. hemerocallidea were found to contain 4.101 × 10−4 mg mL−1 of hypoxoside.
The African Growth Experience and Tourism Receipts: A Threshold Analysis and Quantile Regression Approach

Using a linear estimation model, most previous stud ies have found a positive and statistically signifi cant linear relationship between tourism receipts and ec onomic growth. In this study, we apply a Threshold analysis and Quantile regression to investigate if he relationship between tourism receipts and econo mic growth may be nonlinear. We find the existence of a nonlinear relationship between tourism receipts an d economic growth and that tourism receipts tend to c ontribute to economic growth relatively more below a threshold of 2.59% of the tourism/ GDP per capita r atio and less so above this threshold of the ratio. The Quantile regression results also suggest that count ries tend to benefit more from tourism at the lower end than at the upper end of their GDP per capita distr ibution. A policy implication which may be drawn fr om the study is that African countries which heavily r ely on tourism receipts for their economic growth h ave to understand that the impact of tourism receipts o n gr wth wanes beyond the threshold. Consequently, it may be important to diversify their growth sources and to enhance tourism by committing their resource s to building reliable infrastructure and security fo r t urist arrivals in order to realize maximum impa ct on their economic growth, particularly in the initial stage of their economic growth.
Appendix for Data Diversiﬁcation: A Simple Strategy For Neural Machine Translation

We continue to differentiate our method from other existing works. First, Shen et al. [12] also seek to generate diverse set of translations using mixture of experts, not to improve translation quality like ours. In this method, multiple experts are tied into a single NMT model to be trained to generate diverse translations through EM optimization. It does not employ data augmentation, neither forward nor backward translations. Our method does not train multiple peer models with EM training either.
OFFICE OF MANAGEMENT AND BUDGET Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and Integrity of Information Disseminated by Federal Agencies

General Services Administration . Tom Fitzpatrick, 202–501–0324 Website: www.cfo. gsa.gov. Housing and Urban Development . Janice Blake-Green, 202–708–0638 Website: www.hud. gov/cfo/ cforept.html. Justice . Larry Silvis, 202–616–3754 Website: www.usdoj. gov. Labor . Kathy Alejandro, 202–693–4026 Website: www.dol. gov. National Capital Planning Commission . Connie Harshaw, 202–482–7200 Website: www.ncpc. gov. National Labor Relations Board (OIG) . Emil George, 202–273–1960 Website: www.nlrb. gov/active.html. Social Security Administration . Phil Kelly, 410–965–4656 Website: www.ssa. gov/budget. Smithsonian Institution . Bruce Dauer, 202–357–2917 Website: www.si. edu. Transportation . Bill Moga, 202–366–9666 Website: www.dot. gov. Treasury . Kevin Whitfield, 202–622–0248 Website: www.treas. gov/fair. U.S. Commission on Civil Rights . George Harbison, 202–376–8356 Website: www.usccr. gov. Woodrow Wilson Center . Ronnie Dempsey, 202–691–4216 Website: www.wilsoncenter. org.
Almost Ricci-Bourguignon solitons and geometrical structure in a relativistic perfect fluid spacetime

The present study is based on the geometrical bearing of relativistic perfect fluid spacetime and GRW -spacetime in terms of almost Ricci-Bourguignon solitons with torse-forming vector field ξ. A condition for the almost Ricci-Bourguignon solitons to be steady, expanding or shrinking is also given. In particular, when the potential vector field ξ of the soliton is of gradient type, we derive a Poisson-Laplacian equation from the almost η-Ricci-Bourguignon soliton. Finally, we provide an example of 4-dimensional relativistic spacetime admitting the almost Ricci-Bourguignon and almost η-Ricci-Bourguignon solitons. M.S.C. 2010: 53B50, 53C44, 53C50, 83C02.
TNF in Female C57BL/6J Mice Peripheral Blood Immunophenotype Are Not Mediated by Effects of Obesity-Associated Chronic Inflammation on

Chronic low-grade systemic inflammation in obesity contributes to the development and progression of aspects of metabolic syndrome. In obese male mice, expanded adipose tissue releases proinflammatory cytokines, including TNF, which promotes an increase in immature, proinflammatory, peripheral blood Ly-6C monocytes. The aim of this study was to characterize how TNF alters circulating cellular immunity in female mice with diet-induced obesity. We initially quantified peripheral blood immune cells by flow cytometry in female wild-type C57BL/6J mice after 3–30 wk of allocation to a high-fat (HF) or standard chow diet. We assessed effects of diet and time on neutrophil, monocyte, B cell, NK cell, CD4 T cell, and CD8 T cell populations. There was a significant interaction of the effects of diet type and time on the numbers and prevalence of circulating total monocytes and Ly-6C, Ly-6C, and Ly-6C subsets. Circulating monocytes, in particular Ly-6C monocytes, were increased in HF-fed mice compared with chowfed mice. Ly-6C monocytes from HF-fed mice also had a more immature phenotype yet were highly responsive to the chemotactic ligand CCL2 and had greater intracellular production of TNF. Comparisons of the effects of HF diet feeding in littermate wild-type (TNF) and TNF female mice showed that genetic ablation of TNF did not protect from higher adiposity or an increase in circulating, immature, proinflammatory Ly-6C monocytes during HF diet–induced obesity. These data emphasize the importance of considering biological sex when determining the mechanisms of TNF action in obesity-induced cellular inflammation and in other chronic inflammatory conditions. ImmunoHorizons, 2021, 5: 370–383.
Applications of Deep Learning into Computer Visions

: The Deep learning has recently turn out to be one of the most famous sub-fields of device mastering as a result of its distributed information illustration with more than one tiers of abstraction. A numerous variety of deep gaining knowledge of algorithms is being employed to solve traditional synthetic intelligence issues. Deep neural network (DNN) are a successful into computer vision task. Though, the much accurate DNN requires million of the parameter & the operation, making energy to them, computations & the memory intensive. Impedes deployment for large DNN in lower-power device alongwith limited computing resource. Recent researches improved DNNs model from reducing memory requirements, energy consumptions & some of the operation without significantly decrease in the accuracy. The paper addresses the numerous methods of deep learning algorithms, followed by way of a description in their applications in image classifications, object identifications, face recognitions, images graph extractions and semantic segmentations inside the presence of noise. This assessment gives a review of a number of the maximum significantly deep learning scheme used into computer visions which, Convolution ally Neural Network, Deep’s Boltzmann Machine & Deep Beliefs Network, & the Stacked Denoised Autoencoder.
in Critically-Ill COVID-19 Patients

How the Pandemic Changed Telemedicine, V. Herasevich, J. Clain, B. Pickering Rethinking Critical Care Use and Challenges of Artificial Intelligence, L. Martin, A. Peine, G. Marx et al. Prone Position in Awake, Non-Intubated Patients with ARDS: From Physiology to the Bedside, O. Perez-Neito, E. Zamarron-Lopez, R. Soriano-Orozco et al. Cardiorespiratory Compromise in the Perioperative Environment Prediction, Quality, Analytics and AI, A. Khanna, P. Mathur, J. Cywinski et al. Mouth Care Challenges and the Use of the COVID-19 Oral Grading System, J. Allen, G. Rossano, J. McRae. SPECIAL SUPPLEMENT Sedation in Critically-Ill COVID-19 Patients ICU
Overlapping Clustering of Contextual Bandits with NMF techniques

We introduce a novel approach to recommendation based on item clustering using non-negative matrix factorization (NMF) techniques. We propose a new algorithm, OCB (Overlapping Clustering Bandits), that groups items into latent clusters using online user feedbacks and uses learned clusters to make recommendations. By making recommendation at cluster-level instead of at item-level, the algorithm can overcome scalability issues associated with a large number of items without compensating for long-term reward maximization. Also, by performing online clustering of items, the algorithm can learn latent topics associated with items based on user feedbacks.
Deconvolution in Auger Electron Spectroscopy

Deconvolution calculations have been applied in Auger Electron Spectroscopy to increase resolution and/or to eliminate loss features. We present: i) A short review of the methodology; ii) Recent results obtained in our laboratory in spectroscopy of Al, Ni, Cu, Ag and Te; iii) A discussion on th e conditions for the appearance of artefacts originating either in the calculation or the physical processes (emission anisotropy, distribution of electron path lengths, and intrinsic losses).
The Impact of Online Competition on Local Newspapers: Evidence from the Introduction of Craigslist

How does competition from online platforms affect the organization, performance, and editorial choices of newspapers? And what are the implications of these changes for the information voters are exposed to and for their political choices? We study these questions using the staggered introduction of Craigslist – the world’s largest online platform for classified advertising – across US counties between 1995 and 2009. This setting allows us to separate the effect of competition for classified advertising from other changes brought about by the Internet, and to compare newspapers that relied more or less heavily on classified ads ex ante. We find that, following the entry of Craigslist, local papers experienced a significant decline in the number of newsroom and management staff. Cuts in editorial staff disproportionately affected editors covering politics. These organizational changes led to a reduction in news coverage of politics and resulted in a decline in newspaper readership, which was not compensated by increased news consumption online or in other media. Finally, we document that reduced exposure to political news was associated with more partisan voting and increased support for ideologically extreme candidates.
New York City Taxis: Demand and Revenue in an Uber World

We empirically examine the effect of Uber presence on (medallion) taxi trip demand in NYC. We estimate Uber elasticities of demand for the Yellow cab and Green cab using NYC medallion taxi trip records and Uber pick-up records from April to September 2014, and January to June 2015. The elasticities are estimated along with instrumental variable estimations of a taxi trip demand model. We find an empirical evidence that, in overall, Uber is a complement, rather than a substitute, for both Yellow cab and Green cab passengers. For Yellow cab. the positive and significant elasticity is estimated only in Manhattan below 110th street, where 91% of daily Yellow cab trip and 72% of daily Uber trip were occurred. In addition, we find different elasticity estimate in different time and day at different area. Negative Uber elasticity of Yellow cab estimate in Manhattan below 110th street during the morning rush hour implies that; Uber turn out to be a substitute during the morning rush hour in the central business district of Manhattan.
Conformance Evaluation of Topic Modeling Approaches on Web-Based Short Text Dynamic Graph Databases

Topic modeling is a popular analytical approach for extracting topics from textual data and clustering them. There are many methods for topic modeling that consider the types of relationships and constraints for different types of datasets. Many researchers have been interested in Dirichlet's Latent Analysis (LDA) modeling method because of its flexibility and compatibility. But choosing this method on complex and dynamic datasets brings many challenges. Due to the rapid development of social networks and the existence of dynamic and short text databases, the aim of this research is to investigate the feasibility of using the best topic modeling appeoach based on evaluation criteria such as topic coherence, runtime, exclusivity and perplexity. In this paper, various approaches for topic modeling on dynamic short text datasets are analyzed. Such datasets can have a variety of applications. Article-related datasets, social media datasets and user feedback databases about a product offered by a business are among them. Due to the graph-based nature of the dataset used in this study (DBLP dataset), the results obtained from our experiments, helps a lot in the challenging problem of identifying communities in the field of graph analysis. Proper identification of communities can be effective in finding appropriate influential nodes in research areas based on customer based graph analysis such as viral marketing. The results of employing various topic modeling methods on the DBLP database and the node type of article title and the evaluation of the results with the mentioned topic evaluation criteria show the stability and compatibility of the Biterm method on this database.
Reply to RC2 on acp-2020-461

Major comments Comparison to observations is lacking which limits the sense I get that the changes are in anyway in the right direction. For example, the changes in surface ozone and NO2 in EAS are large and I would imagine significant. It would be good to see how these compare with observations. Whilst I agree with the previous reviewers comments about model resolution and whilst there may well be structural errors in EMAC that mean that even with a better representation of the chemistry the comparison to observations is worse, I feel some comparison to observations is warranted.
Scavenging Properties of Plant-Derived Natural Biomolecule Para-Coumaric Acid in the Prevention of Oxidative Stress-Induced Diseases

Para-coumaric acid (p-CA) is a plant derived secondary metabolite belonging to the phenolic compounds. It is widely distributed in the plant kingdom and found mainly in fruits, vegetables, and cereals. Various in vivo and in vitro studies have revealed its scavenging and antioxidative properties in the reduction of oxidative stress and inflammatory reactions. This evidence-based review focuses on the protective role of p-CA including its therapeutic potential. p-CA and its conjugates possesses various bioactivities such as antioxidant, anti-inflammatory, anti-cancer, anti-diabetic, and anti-melanogenic properties. Due to its potent free radical scavenging activity, it can mitigate the ill effects of various diseases including arthritis, neurological disorders, and cardio-vascular diseases. Recent studies have revealed that p-CA can ameliorate the harmful effects associated with oxidative stress in the reproductive system, also by inhibiting enzymes linked with erectile function.