2,804 research outputs found
Replication Data for: Downscaling approach to compare COVID-19 count data from databases aggregated atdifferent spatial scales
No full textThis file contains the code and data required to reproduce the study
Local Church Week Pastor Andre Mitchell
No full textPastor Andre Mitchell, Pastor, Author, CEO, Deliverance Temple/Andre Mitchell Ministries, Muncie, IN, speaks on how connecting with other believers is important to your faith for Local Church Week
Replication Data for: Predicting non-state terrorism worldwide
No full textThis folder contains all files required to replicate the work "Predicting non-state terrorism worldwide"
Data for: A Bayesian approach to modelling subnational spatial dynamics of worldwide non-state terrorism, 2010 - 2016
No full textA Bayesian Approach to Modelling Subnational Spatial Dynamics of Worldwide Non-State Terrorism, 2010 - 2016
A. Python, J. Illian, C. Jones-Todd, M. Blangiardo
J. R. Statist. Soc. A, (2018)
All results are obtained using R statistical software. We provide the R code and data needed to reproduce the research work.
For full replication, the R code listed below should be run in this order:
-first: from (1) to (2): generate input data for the statistical models;
-second: (4), (5) and (6): the L,S, and F model, respectively;
-third: (7): the results and plots of the L,S, and F model, respectively.
Since fitting the models are computationally intense, we also provide the outputs of the models as .RData files along with the R code used to produce them.
The R code and data are provided in "R_code.zip", which contains the following folders:
(1) GTD
GTD.R: extraction of terrorism data from the Global Terrorism database (GTD)
Input: GTDsource.csv (file downloaded Jan 2017 from GTD)
Output: GTDworld.csv used in R code (2):
(2) DATA
Data_paper.R: extraction of covariate data based on GTD events locations
Input: covariates data with different formats in covariate.zip
Output: paper_data.RData used in R code (4) to (9)
(3) FUNCTIONS
functions.R: functions to facilitate running the spatio-temporal models in R-INLA
used in in R code (4) to (9)
(4) L-MODEL
L_model.R: the Bernoulli space-time models of the lethality of terrorism
Inputs: paper_data.RData (2) and functions.R (3)
Outputs:
-models with different sets of covariates: L0.Rdata to L7.RData ;
-selected final model: L7.Rdata
-models for plotting: L7.Rdata
-robustness test models: Lrob1.Rdata, Lmesh2.RData, Lmesh3.RData
-predictive models with different sets of covariates: L7pred.Rdata, L0pred.Rdata
(5) S-MODEL
S_model.R: the Poisson space-time models of the severity of lethal terrorism
Inputs: paper_data.RData (2) and functions.R (3)
Outputs:
-models with different sets of covariates: S0.Rdata to S7.RData ;
-selected final model: S4.Rdata
-models for plotting: S4plot.Rdata
-robustness test models: bernrob1.Rdata, bernmesh2.RData, bernmesh3.RData
-predictive models with different sets of covariates: bern0pred.Rdata, bern4pred.Rdata
(6) F-MODEL
F_model.R: the Poisson space-time models of the frequency of lethal terrorism
Inputs: paper_data.RData (2) and functions.R (3)
Outputs:
-models with different sets of covariates: F0.Rdata to F7.RData ;
-selected final model: Ffinal.Rdata
-models for plotting: F3plot.Rdata
-robustness test models: Frob1.Rdata, Ffinalagg075.RData, Ffinalagg025.RData,Ffinalagg100.RData, Ffinalagg150.RData
-predictive models with different sets of covariates: F0pred.Rdata, F6pred.Rdata
(7) RESULTS
-Results_Lmodel.R: generate the results of the L-models and the graphics in the manuscript
Inputs: paper_data.RData (2), functions.R (3), L-model outputs (4)
Outputs: plots and figures
-Results_Smodel.R: generate the results of the S-models and the graphics in the manuscript
Inputs: paper_data.RData (2), functions.R (3), S-model outputs (5)
Outputs: plots and figures
-Results_Fmodel.R: generate the results of the F-models and the graphics in the manuscript
Inputs: paper_data.RData (2), functions.R (3), F-model outputs (6), and country.shp
Outputs: plots and figures
Contact (first author):
Dr Andre Python
Malaria Atlas Project | University of Oxford
Big Data Institute | Li Ka Shing Centre for Health Information and Discovery
Old Road Campus | Headington | Oxford | OX3 7LF | United Kingdom
E: [email protected]
W: www.map.ox.ac.uk | www.bdi.ox.ac.u
kivy/python-for-android: v2022.09.04
No full textHighlights:
This is the last release that defaults to Python 3.9 when building hostpython3 and python3. The next one will target Python 3.10
This is the last release that uses Android NDK 23b by default, the next one will use Android NDK 25
This is the last release that defaults to target API 27, the next one will default to target API 31, following the new requirement from Google for apps that need to be distributed on Play Store.
In order to fully support API 31 and as a propedeutic change for new features in Kivy, in the next release, python-for-android will use the new SDL2 releases.
Full changelog:
liblzma: Use p4a_install instead of install, as a file named INSTALL is already present. #2663 (misl6)
Force --platform=linux/amd64 in Dockerfile #2660 (misl6)
Remove six and enum34 dependency #2657 (misl6)
Update supported Python versions #2656 (misl6)
Fixes some E275 - assert is a keyword. #2647 (misl6)
Updates matplotlib, fixes an issue related to shared libc++ #2645 (misl6)
RTSP support for ffmpeg #2644 (alicakici1234)
Fixes TypeError: str.join() takes exactly one argument (2 given) in hostpython3/__init__.py", line 69 #2642 (Furtif)
Resolve absolute path to local recipes #2640 (dbnicholson)
Merges master into develop after release 2022.07.20 #2639 (misl6)
Fix webview Back button behaviour #2636 (interlark)
Add icon-bg and icon-fg to fix_args #2633 (danigm)
Remove stray - in output file name #2581 (dbnicholson)
Add option for adding files to res/xml without touching manifest #2330 (rambo
Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data
No full textAbstract
Topic models are a useful and popular method to find latent topics of documents. However, the short and sparse texts in social media micro-blogs such as Twitter are challenging for the most commonly used Latent Dirichlet Allocation (LDA) topic model. We compare the performance of the standard LDA topic model with the Gibbs Sampler Dirichlet Multinomial Model (GSDMM) and the Gamma Poisson Mixture Model (GPM), which are specifically designed for sparse data. To compare the performance of the three models, we propose the simulation of pseudo-documents as a novel evaluation method. In a case study with short and sparse text, the models are evaluated on tweets filtered by keywords relating to the Covid-19 pandemic. We find that standard coherence scores that are often used for the evaluation of topic models perform poorly as an evaluation metric. The results of our simulation-based approach suggest that the GSDMM and GPM topic models may generate better topics than the standard LDA model
Pearl Andre, Political Activist and Author from Bismarck
No full textAn undated photograph of Pearl Andre, an author and political activist from Bismarck. She wrote the book Women on the Move about the Nonpartisan League in North Dakota in 1975.https://commons.und.edu/nd-politics-photos/1254/thumbnail.jp
The deadly facets of terrorism
No full textCan Bayesian models reveal the underlying processes that drive the lethality of non‐state terrorism at a local level? Andre Python, Janine B. Illian, Charlotte M. Jones‐Todd and Marta Blangiardo investigate
Andre Gide and the Negro, 1940
No full textBecause it is generally known that Andre Gide is one of France's most influential contemporary writers, there is no need to justify a study based on his works. Desirous of obtaining the opinion of an influential white author concerning Negroid people and learning his activities in their behalf, the writer of this thesis undertook the study, Andre Gide end the Negro. The value of such a study to the American Negro lies primarily in (1) a better acquaintance with and appreciation for one whose interest in darker people has resulted in a tangible contribution; (2) a knowledge of the condition of a people, who, though distant in territory, are kindred in race, and (3) a challenge for scholastic accomplishment. The method of procedure was a careful analysis of Voyage au Congo and Retour du Tchad supplemented by collateral readings. To secure information concerning Mr. Gide's official investigation in 1938 of the natives' educational facilities in Senegal, a letter was sent him. Despite the anxiety which must be his because of the present war in which his country is involved, the eminent author found time to respond. His reply is quoted on page thirty-one of this study. Grateful acknowledgment is hereby given both Mr. Gide for his amicable letter and Professor Cook, who made such a contact possible. The various factors for the evolution of Gide's interest in Negroid people are shown in Chapter I. Chapter II contains a discussion of the colonial abuses existing in French Equatorial Africa exposed by Gide and his efforts to eradicate these. Through Chapter III one learns the author's personal impression of the morale and intelligence of Negroid people. In Chapter IV an interpretation of Gide's literary art in Voyage au Gang and Retour du Tchad is presented. The results are summarized in the conclusion
Mapping ex ante risks of COVID‐19 in Indonesia using a Bayesian geostatistical model on airport network data
No full textA rapid response to global infectious disease outbreaks is crucial to protect public health. Ex ante information on the spatial probability distribution of early infections can guide governments to better target protection efforts. We propose a two‐stage statistical approach to spatially map the ex ante importation risk of COVID‐19 and its uncertainty across Indonesia based on a minimal set of routinely available input data related to the Indonesian flight network, traffic and population data, and geographical information. In a first step, we use a generalised additive model to predict the ex ante COVID‐19 risk for 78 domestic Indonesian airports based on data from a global model on the disease spread and covariates associated with Indonesian airport network flight data prior to the global COVID‐19 outbreak. In a second step, we apply a Bayesian geostatistical model to propagate the estimated COVID‐19 risk from the airports to all of Indonesia using freely available spatial covariates including traffic density, population and two spatial distance metrics. The results of our analysis are illustrated using exceedance probability surface maps, which provide policy‐relevant information accounting for the uncertainty of the estimates on the location of areas at risk and those that might require further data collection
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