Programme At A Glance
Monday
| Time | Monday 11th | |||||
|---|---|---|---|---|---|---|
| 845 | Housekeeping, 098 Lecture Theatre (260-098) | |||||
| 850 | Opening Ceremony | |||||
| 910 |
TBA-LT Luke Tierney, University of Iowa, USA 098 Lecture Theatre (260-098) |
|||||
| 1000 | Morning Tea (30 minutes) | |||||
| 098 Lecture Theatre(260-098) | OGGB4(260-073) | OGGB5(260-051) | Case Room 2(260-057) | Case Room 3(260-055) | Case Room 4(260-009) | |
| 1030 | NA |
Effect Of Area Level Deprivation On Body Mass Index: Analysis Of NZ Health Surveys Andrew Adiguna Halim |
Calendar-Based Graphics For Visualising People’s Daily Schedules Earo Wang |
Nonparametric Test For Volatility In Clustered Multiple Time Series Abstract Paolo Victor Redondo |
IGESS: A Statistical Approach To Integrating Individual Level Genotype Data And Summary Statistics In Genome Wide Association Studies Xiang Wan |
Author Name Identification For Evaluating Research Performance Of Institutes Tomokazu Fujino |
| 1050 | NA |
Clustering Using Nonparametric Mixtures And Mode Identification Shengwei Hu |
Bayesian Curve Fitting For Discontinuous Function Using Overcomplete Representation With Multiple Kernels Youngseon Lee |
Estimation Of A Semiparametric Spatiotemporal Models With Mixed Frequency Erniel Barrios |
Lsmm: A Statistical Approach To Integrating Functional Annotations With Genome-Wide Association Studies Jingsi Ming |
A Study Of The Influence Of Articles In The Large-Scale Citation Network Frederick Kin Hing Phoa |
| 1110 | NA |
Estimation Of A High-Dimensional Covariance Matrix Xiangjie Xue |
Innovative Bayesian Estimation In The von Mises Distribution Yuta Kamiya |
Evidence Of Climate Change From Nonparametric Change-Point Analysis Angela Nalica |
Joint Analysis Of Individual Level Genotype Data And Summary Statistics By Leveraging Pleiotropy Can Yang |
An Advanced Approach For Time Series Forecasting Using Deep Learning Balaram Panda |
| 1130 | NA |
Genetic Map Estimation Using Hidden Markov Models In The Presence Of Partially Observed Information Timothy Bilton |
A Simple Method For Grouping Patients Based On Historical Doses Shengli Tzeng |
Semiparametric Mixed Analysis Of Covariance Model Joseph Ryan Lansangan |
Adaptive False Discovery Rate Regression With Application In Integrative Analysis Of Large-Scale Genomic Data Can Yang |
Structure Of Members In The Organization To Induce Innovation: Quantitatively Analyze The Capability Of The Organization Yuji Mizukami |
| 1150 | NA |
Vector Generalized Linear Time Series Models Victor Miranda |
Local Canonical Correlation Analysis For Multimodal Labeled Data Seigo Mizutani |
A Practitioners Guide To Deep Learning For Predictive Analytics On Structured Data Balaram Panda |
NA |
Clustering Of Research Subject Based On Stochastic Block Model Keisuke Honda |
| 1210 | Group photo, followed by Lunch (1 hour 10 minutes) | |||||
| 1320 |
TBA-SU Simon Urbanek, AT&T Labs, USA 098 Lecture Theatre (260-098) |
|||||
| 098 Lecture Theatre(260-098) | OGGB4(260-073) | OGGB5(260-051) | Case Room 2(260-057) | Case Room 3(260-055) | Case Room 4(260-009) | |
| 1410 |
Canonical Covariance Analysis For Mixed Numerical And Categorical Three-Way Three-Mode Data Jun Tsuchida |
Variable Selection Algorithms Fangyao Li |
Estimating Causal Structures For Continuous And Discrete Variables Mako Yamayoshi |
Incorporating Genetic Networks Into Case-Control Association Studies With High-Dimensional DNA Methylation Data Hokeun Sun |
Adaptive Model Checking For Functional Single-Index Models Zhenghui Feng |
Testing For Genetic Associations In Arbitrarily Structured Populations Minsun Song |
| 1430 |
On Optimal Group Testing Designs: Prevalence Estimation, Cost Considerations, And Dilution Effects Shih-Hao Huang |
The Use Of Bayesian Networks In Grape Yield Prediction Rory Ellis |
Pattern Prediction For Time Series Data With Change Points Satoshi Goto |
Test For Genomic Imprinting Effects On The X Chromosome Wing Kam Fung |
Fluctuation Reduction Of Value-At-Risk Estimation And Its Applications Shih-Feng Huang |
Meta-Analytic Principal Component Analysis In Integrative Omics Application George Tseng and SungHwan Kim |
| 1450 |
Estimation Of Animal Density From Acoustic Detections Ben Stevenson |
Mixed Models For Complex Survey Data Xudong Huang |
Regression With Random Effects For Analysing Correlated Survival Data: Application To Disease Recurrences Richard Tawiah |
Genetic Predictors Underlying Long-Term Cognitive Recovery Following Mild Traumatic Brain Injury Priya Parmar |
Bayesian Structure Selection For Vector Autoregression Model Ray-Bing Chen |
Phylogenetic Tree-Based Microbiome Association Test Sungho Won |
| 1510 |
Talk Data To Me Lisa Hall |
Smooth Nonparametric Regression Under Shape Restrictions Hongbin Guo |
Elastic-Band Transform: A New Approach To Multiscale Visualization Hee-Seok Oh |
A Computational Tool For Detecting Copy Number Variations From Whole Genome And Targeted Exome Sequencing Yu-Chung Wei |
Flight To Relative Safety: Learning From A No-Arbitrage Network Of Yield Curves Model Of The Euro Area Linlin Niu |
Wavelet-Based Power Transformation Of Non-Gaussian Long Memory Time Series Kyungduk Ko |
| 1530 | Afternoon tea (30 minutes) | |||||
| 098 Lecture Theatre(260-098) | OGGB4(260-073) | OGGB5(260-051) | Case Room 2(260-057) | Case Room 3(260-055) | Case Room 4(260-009) | |
| 1600 | NA |
Robustness Of Temperature Reconstruction For Past 500 Years Matthew Schofield |
Nonparametric Causal Inference By The Kernel Method Yuchi Matsuoka |
A Unified Regularized Group Pls Algorithm Scalable To Big Data Pierre Lafaye de Micheaux |
Evaluation Of Spatial Cluster Detection Method Based On All Geographical Linkage Patterns Fumio Ishioka |
Scoring Rules For Prediction And Classification Challenges Matt Parry |
| 1620 | NA |
Real-Time Transit Network Modelling For Improved Arrival Time Predictions Tom Elliott |
Sparse Group-Subgroup Partial Least Squares With Application To Genomic Data Matthew Sutton |
BIG-SIR A Sliced Inverse Regression Approach For Massive Data Benoit Liquet |
Genetic Approach And Statistical Approach For Association Study On DNA Data Makoto Tomita |
Modeling Of Document Abstraction Using Association Rule Based Characterization Ken Nittono |
| 1640 | NA |
Bayesian Survival Analysis Of Batsmen In Test Cricket Oliver Stevenson |
Covariate Discretisation On Big Data Hon Hwang |
Visualization And Statistical Modeling Of Financial Big Data Masayuki Jimichi |
Symbolic Data Analytical Approach To Unauthorized-Access Logs Hiroyuki Minami |
My Knee Still Hurts; The Statistical Pathway To The Development Of A Clinical Decision Aid Robert Borotkanics |
Tuesday
| Time | Tuesday 12th | |||||
|---|---|---|---|---|---|---|
| 900 | Housekeeping, 098 Lecture Theatre (260-098) | |||||
| 910 |
Could Do Better &Hellip; A Report Card For Statistical Computing Ross Ihaka, University of Auckland 098 Lecture Theatre (260-098) |
|||||
| 1000 | Morning Tea (30 minutes) | |||||
| 098 Lecture Theatre(260-098) | OGGB4(260-073) | OGGB5(260-051) | Case Room 2(260-057) | Case Room 3(260-055) | Case Room 4(260-009) | |
| 1030 |
R&D Policy Regimes In France: New Evidence From A Spatio-Temporal Analysis Benjamin Montmartin |
Analysing Scientific Collaborations Of New Zealand Institutions Using Scopus Bibliometric Data Samin Aref |
Family Structure And Academic Achievements Of High School Students In Tonga Losana Vao Latu Latu |
Analysis Of Multivariate Binary Longitudinal Data: Metabolic Syndrome During Menopausal Transition Geoff Jones |
Clustering Of Curves On A Spatial Domain Using A Bayesian Partitioning Model Chae Young Lim |
The Uncomfortable Entrepreneurs: Bad Working Conditions And Entrepreneurial Commitment Catherine Laffineur |
| 1050 |
Spatial Surveillance With Scan Statistics By Controlling The False Discovery Rate Xun Xiao |
Statistical Models For The Source Attribution Of Zoonotic Diseases: A Study Of Campylobacteriosis Sih-Jing Liao |
Latent Variable Models And Multivariate Binomial Data John Holmes |
Identifying Clusters Of Patients With Diabetes Using A Markov Birth-Death Process Mugdha Manda |
Bayesian Temporal Density Estimation Using Autoregressive Species Sampling Models Seongil Jo |
How Does The Textile Set Describe Geometric Structures Of Data? Ushio Tanaka |
| 1110 |
Intensity Estimation Of Spatial Point Processes Based On Area-Aggregated Data Hsin-Cheng Huang |
Bayesian Inference For Population Attributable Measures Sarah Pirikahu |
An Information Criterion For Prediction With Auxiliary Variables Under Covariate Shift Takahiro Ido |
Analysis Of A Brief Telephone Intervention For Problem Gambling And Examining The Impact On Co-Existing Depression? Nick Garrett |
Prior-Based Bayesian Information Criterion Woncheol Jang |
Correlated Defaults With A Distance To Default Chu-Lan Kao |
| 1130 |
Geographically Weighted Principal Component Analysis For Spatio-Temporal Statistical Dataset Narumasa Tsutsumida |
Dimensionality Reduction Of Multivariate Data For Bayesian Analysis Anjali Gupta |
NA |
Adjusting For Linkage Bias In The Analysis Of Record-Linked Data Patrick Graham |
Bayesian Semiparametric Hierarchical Models For Longitudinal Data Analysis With Application To Dose-Response Studies Taeryon Choi |
Optimizing Junior Rugby Weight Limits Emma Campbell |
| 1150 |
Spatial Scan Statistics For Matched Case-Control Data Inkyung Jung |
Whitebait In All Its Varieties: One Fish, Two Fish, Three, Four, Five Fish. Bridget Armstrong |
NA |
Asking About Sex In General Health Surveys: Comparing The Methods And Findings Of The 2010 Health Survey For England With Those Of The Third National Survey Of Sexual Attitudes And Lifestyles Philip Prah |
NA |
Tolerance Limits For The Reliability Of Semiconductor Devices Using Longitudinal Data Thomas Nowak |
| 1210 | Lunch (1 hour 10 minutes) | |||||
| 1320 |
Session In Memory Of Alastair John Scott 1320hrs-1530hrs Speakers: Professor John Neuhaus (UCSF), Professor Chris Wild, Professor Alan Lee, Professor Thomas Lumley |
|||||
| 1530 | Afternoon tea (30 minutes) | |||||
| 098 Lecture Theatre(260-098) | OGGB4(260-073) | OGGB5(260-051) | Case Room 2(260-057) | Case Room 3(260-055) | Case Room 4(260-009) | |
| 1600 |
Model-Checking For Regressions: A Local Smoothing-Based Global Smoothing Test Lingzhu Li |
Breeding Value Estimation In Partially-Genotyped Populations Alastair Lamont |
BIVAS: A Scalable Bayesian Method For Bi-Level Variable Selection Mingxuan Cai |
Ranking Potential Shoplifters In Real Time Barry McDonald |
Two Stage Approach To Data-Driven Subgroup Identification In Clinical Trials Toshio Shimokawa |
Estimation And Prediction For A Distribution With Bathtub Shape Under Progressive First Failure Censoring Tanmay Kayal |
| 1620 |
Inverse Regression For Multivariate Functional Data Ci-Ren Jiang |
Including Covariate Estimation Error When Predicting Species Distributions: A Simulation Exercise Using Template Model Builder Andrea Havron |
Adjusted Adaptive Index Model For Binary Response Ke Wan |
Factors Influencing On Growth Of Garments Industry In Bangladesh Md. Shahidul Islam |
Comparison Of Exact And Approximate Testing Procedures In Clinical Trials With Multiple Binary Endpoints Takuma Ishihara |
Bayesian Optimum Warranty Length Under Type-II Unified Hybrid Censoring Scheme Tanmay Sen |
| 1640 |
Multiple Function-On-Function Linear Regression With Application To Weather Forecast Calibration Lu-Hung Chen |
Modelling The Distribution Of Lifetime Using Compound Time-Homogenous Poisson Process Kien Tran |
Detecting Change-Points In The Stress-Strength Reliability P(X<Y) Hang Xu |
New Zealand Crime And Victims Survey: Filling The Knowledge Gap Michael Slyuzberg |
Missing Data In Randomised Control Trials: Stepped Multiple Imputation Rose Sisk |
NA |
| 1700 | NA |
NZSA Annual General Meeting 1700 - 1815 |
IASC General Assembly 1700-1815 |
NA | NA | NA |
| 1830 |
Conference Dinner Owen G Glenn Building Foyer Level 1, 20 Grafton Road 1830-2230 |
Wednesday
| Time | Wednesday 13th | |||||
|---|---|---|---|---|---|---|
| 900 | Housekeeping | |||||
| 910 |
Professor Michael Ferris, University Of Wisconsin - Madison, USA ORSNZ Keynote 098 Lecture Theatre (260-098) |
|||||
| 1000 | Morning Tea (30 minutes) | |||||
| 098 Lecture Theatre(260-098) | OGGB4(260-073) | OGGB5(260-051) | Case Room 2(260-057) | Case Room 3(260-055) | Case Room 4(260-009) | |
| 1030 |
Promoting Your R Package Hadley Wickham |
A Smoothing Filter Modelling Approach For Time Series Marco Reale |
Online Learning For Bayesian Nonparametrics: Weakly Conjugate Approximation Yongdai Kim |
Improving The Production Cycle At Stats NZ With Rstudio Gareth Minshall |
Sparse Common Component Analysis Heewon Park |
Random Search Global Optimization Using Random Forests Blair Robertson |
| 1050 |
gridSVG: Then And Now Paul Murrell |
Probabilistic Outlier Detection And Visualization Of Smart Meter Data Rob Hyndman |
The Joint Models For Nonlinear Longitudinal And Time-To-Event Data Using Penalized Splines: A Bayesian Approach Thi Thu Huong Pham |
R – A Powerful Analysis Tool To Improve Official Statistics In Romania Nicoleta Caragea |
Robust Principal Expectile Component Analysis Liang-Ching Lin |
Dimension Reduction For Classification Of High-Dimensional Data By Stepwise SVM Elizabeth Chou |
| 1110 |
Bringing Multimix From Fortran To R Murray Jorgensen |
Specification Of GARCH Model Under Asymmetric Error Innovations Oyebimpe Adeniji |
The Joint Models For Nonlinear Longitudinal And Time-To-Event Data Using Penalized Splines: A Bayesian Approach Darfiana Nur |
Statistical Disclosure Control With R: Traditional Methods And Synthetic Data Matthias Templ |
A Max-Type Multivariate Two-Sample Baumgartner Statistic Hidetoshi Murakami |
Deep Learning High-Dimensional Covariance Matrices Philip Yu |
| 1130 |
R In Industry – Application On Pipe Renewal Planning Glenn Thomas |
Empirical Comparison Of Some Algorithms For Automatic Univariate ARMA Modeling Using Rcmdrplugin.SPSS Dedi Rosadi |
Bayesian Analyses Of Non-Homogeneous Gaussian Hidden Markov Models Shin Sato |
Imputation Of The 2016 Economic Census For Business Activity In Japan Kazumi Wada |
Simultaneous Test For Mean Vectors And Covariance Matrices In High-Dimensional Settings Takahiro Nishiyama |
Applying Active Learning Procedure To Drug Consumption Data Yuan-Chin Chang |
| 1150 |
R For Everything Jared Lander |
R Package For New Two-Stage Methods In Forecasting Time Series With Multiple Seasonality Shubhabrata Das |
Performance Of Bayesian Credible Interval For Binomial Proportion Using Logit Transformation Toru Ogura |
Analysis Of Official Microdata Using Secure Statistical Computation System Kiyomi Shirakawa |
High Dimensional Asymptotics For The Naive Canonical Correlation Coefficient Mitsuru Tamatani |
Presenting Flexi, A Statistical Program For Fitting Variance Models Martin Upsdell |
| 1210 | Lunch (1 hour 10 minutes) | |||||
| 1320 |
Space And Circular Time Log Gaussian Cox Processes With Application To Crime Event Data Alan Gelfand, Duke University 098 Lecture Theatre (260-098) |
|||||
| 098 Lecture Theatre(260-098) | Case Room 1(260-005) | OGGB5(260-051) | Case Room 2(260-057) | Case Room 3(260-055) | Case Room 4(260-009) | |
| 1410 |
Cluster-Wise Regression Models Combined By A Quasi-Linear Function Kenichi Hayashi |
Hierarchical Structural Component Analysis Of Gene-Environment Interactions Taesung Park |
Wavelet-Based Power Transformation Of Non-Gaussian Long Memory Time Series Kyungduk Ko |
Cross Covariance Estimation For Integration Of Multi-Omics Data Hyungwon Choi |
Relationships Between Linguistic Characteristics And The Use Of MāOri Loanwords In New Zealand English. Steven Miller |
Transfer Regression And Predictive Distributions Jinfang Wang |
| 1430 |
“Bayesian Structure Selection For Vector Autoregression Model Ray-Bing Chen” |
Testing For Genetic Associations In Arbitrarily Structured Populations Minsun Song |
Bayesian Analysis For Fitting Zero-Inflated Count Data With Data Augmentation Beomseuk Hwang |
Regularized Noise-Reduction Methodology For High-Dimensional Data Kazuyoshi Yata |
Computation Of Influence Functions For Robust Statistics Maheswaran Rohan |
Adaptive Model Averaging In High-Dimensional Linear Regression Tzu-Chang Forrest Cheng |
| 1450 |
Flight To Relative Safety: Learning From A No-Arbitrage Network Of Yield Curves Model Of The Euro Area Linlin Niu |
Meta-Analytic Principal Component Analysis In Integrative Omics Application George Tseng |
Model-Based Clustering For Multivariate Categorical Data With Dimension Reduction Michio Yamamoto |
Selecting The Number Of Principal Components Yunjin Choi |
Rolling Survival Extrapolation Algorithm For Estimating Life Years Lost Among Subjects Exposed To Long-Term Air Pollution Jing-Shiang Hwang |
Enhancing The Flexibility Of Regression Modeling By Liquid Association Ker-Chau Li |
| 1510 | NA |
Phylogenetic Tree-Based Microbiome Association Test Sungho Won |
NA |
Towards A Sparse, Scalable, And Stably Positive Definite (Inverse) Covariance Estimator Joong-Ho Won |
NA |
High Mortality Predictions With Lines Or Curves Fitted To Over-Dispersed Exposure-Mortality Data John Maindonald |
| 1530 | Afternoon tea (30 minutes) | |||||
| 098 Lecture Theatre(260-098) | Case Room 4(260-009) | OGGB5(260-051) | Case Room 2(260-057) | Case Room 3(260-055) | Case Room 1(260-005) | |
| 1600 |
Lattice Polytope Samplers Martin Hazelton |
Statistical Modelling And Analysis Of Cosmic Microwave Background Data Andriy Olenko |
Measure Of Departure From Marginal Average Point-Symmetry For Two-Way Contingency Tables With Ordered Categories Kiyotaka Iki |
Sparse Estimates From Dense Precision Matrix Posteriors Beatrix Jones |
Dimension Reduction Strategies For Modeling Bi-Clustered High Dimensional Data Michael Van Supranes |
NA |
| 1620 |
Computing Entropies With Nested Sampling Brendon Brewer |
Spline-Based Drift Models For High Temperature Operating Life Tests Thomas Nowak |
A New Approach To Distribution Free Tests In Contingency Tables Thuong Nguyen |
Bayesian Continuous Space-Time Model Of Burglaries Chaitanya Joshi |
Modified Gene Shaving Algorithm - A Dimension Reduction And Clustering Method Donna Mae Santos |
NA |
| 1640 | NA |
A Simple Method To Construct Confidence Bands In Functional Linear Regression Kengo Kato |
Separation Of Symmetry For Square Contingency Tables With Ordinal Categories Kouji Tahata |
An Overview Of The Correspondence Analysis Family Eric Beh |
Testing For Presence Of Clustering Effect In Multilevel Model With High-Dimensional Predictors Frances Claire San Juan |
NA |
Thursday
| Time | Thursday 14th | |||||
|---|---|---|---|---|---|---|
| 900 | Housekeeping | |||||
| 910 |
TBA-JB Jenny Bryan, University of British Columbia 098 Lecture Theatre (260-098) |
|||||
| 1000 | Morning Tea (30 minutes) | |||||
| 098 Lecture Theatre(260-098) | OGGB4(260-073) | OGGB5(260-051) | Case Room 2(260-057) | Case Room 3(260-055) | Case Room 4(260-009) | |
| 1030 |
Penalized Vector Generalized Additive Models Thomas Yee |
NA |
Cross Covariance Estimation For Integration Of Multi-Omics Data Hyungwon Choi |
Meta-Analysis With Symbolic Data Analysis And Its Application For Clinical Data Ryo Takagi |
Bayesian Static Parameter Inference For Partially Observed Stochastic Systems Yaxian Xu |
Testing For Genetic Associations In Arbitrarily Structured Populations Minsun Song |
| 1050 |
Consistency Of Linear Mixed-Effects Model Selection With Inconsistent Covariance Parameter Estimators Chihhao Chang |
NA |
Regularized Noise-Reduction Methodology For High-Dimensional Data Kazuyoshi Yata |
Comparison Of Tests Of Mean Difference In Longitudinal Data Based On Block Resampling Methods Hirohito Sakurai |
SSREM: A Summary-Statistics-Based Random Effect Model To Estimating Heritability, Co-Heritability And Effect Sizes In GWAS Data Analysis Jin Liu |
Meta-Analytic Principal Component Analysis In Integrative Omics Application George Tseng |
| 1110 |
Selecting Generalised Linear Models Under Inequality Constraints Daniel Gerhard |
NA |
Selecting The Number Of Principal Components Yunjin Choi |
Analysis Of Spatial Data With A Gaussian Mixture Markov Random Field Model Wataru Sakamoto |
Forward Selection In Regression Models Based On Robust Estimation Shan Luo |
Phylogenetic Tree-Based Microbiome Association Test Sungho Won |
| 1130 |
Statistical Generalized Derivative Applied To The Profile Likelihood Estimation In A Mixture Of Semiparametric Models Yuichi Hirose |
NA |
Towards A Sparse, Scalable, And Stably Positive Definite (Inverse) Covariance Estimator Joong-Ho Won |
NA |
Feature Selection In High-Dimensional Models With Complex Block Structures Zehua Chen |
Wavelet-Based Power Transformation Of Non-Gaussian Long Memory Time Series Kyungduk Ko |
| 1200 | Lunch (1 hour 10 minutes) | |||||
| 098 Lecture Theatre(260-098) | OGGB4(260-073) | OGGB5(260-051) | Case Room 2(260-057) | Case Room 3(260-055) | Case Room 4(260-009) | |
| 1310 | NA |
Dissimilarities Between Groups Of Data Nobuo Shimizu |
A Package For Multiple Precision Floating-Point Computation On R Junji Nakano |
NA |
Cluster-Wise Regression Models Combined By A Quasi-Linear Function Kenichi Hayashi |
Mobile Learning In Teaching Bioinformatics For Medical Doctors Taerim Lee |
| 1330 | NA |
Interactive Visualization Of Aggregated Symbolic Data Yoshikazu Yamamoto |
An Incomplete-Data Fisher Scoring With An Acceleration Method Keiji Takai |
NA |
Clusterwise Low-Rank Correlation Analysis Based On Majorization Kensuke Tanioka |
Towards An Informal Test For Goodness-Of-Fit Anna Fergusson |
| 1350 | NA |
An EWMA Chart For Monitoring Covariance Matrix Based On Dissimilarity Index Longcheen Huwang |
Improvement Of Computation For Nonlinear Multivariate Methods Yuichi Mori |
NA | NA |
Three-Dimensional Data Visualization Education With Virtual Reality Dae-Heung Jang |
| 1410 |
Closing Ceremony 098 Lecture Theatre (260-098) |


CP Holiday Train will be here in two weeks
It will stop in both Cambridge and Ayr
by Blair AdamsIt's always a big draw when it rolls on through.
The CP Holiday Train will be in Waterloo Region on Friday, November 29.
It will first stop in Cambridge at the former Galt Station at 10 Malcolm Street at 4 p.m. that day - Alan Doyle and Beautiful Band will then perform from 4:15 to 4:45.
The train will then head to Ayr (Inglis Street and Willison Street crossing - with a performance by Alan Doyle and Beautiful Band happening between 5:30 and 6.
Cash and food donations are collected along the route for area food banks.
Since the inaugural journey back in 1999, almost $16 million has been raised and 4.5 million pounds of food has been collected.


Methods
-
Builds an array that can be passed to Checkout Finland as POST parameters. Sends the data. Returns the response body
-
Poll payment, returns with xml containing payment info or error message
-
Posts data, tries to use stream context if allow_url_fopen is on in php.ini or CURL if not. If neither option is available throws exception.
Methods Details
-
public function sendPayment(variable $payment)Builds an array that can be passed to Checkout Finland as POST parameters. Sends the data. Returns the response bodythat contains payment options xml or error message.
-
public function poll(variable $poll)Poll payment, returns with xml containing payment info or error message
-
protected function postData(variable $url, variable $postData)Posts data, tries to use stream context if allow_url_fopen is on in php.ini or CURL if not. If neither option is available throws exception.


If you have problems playing videos inline, please refresh your page.
This slide deck is a single web-page with lots of linked videos which can confuse the browser cache.
12.1.1: queries-caching-vs-stream-queries
12.1.2: queries-caching-and-the-web
12.1.3: queries-data-mining
12.1.4: replay
12.1.5: queries-caching-queries


Certbot - No module named '_cffi_backend'
Today, one of the server manage by me has failed to auto renew the https certificate.
Btw, i’m using certificate from R3 by Let’s Encrypt. When i tried to manual renew using sudo certbot renew
, i got this error message (see below for the error message).
ubuntu@ip-172-26-4-157:~$ sudo certbot renew
Traceback (most recent call last):
File "/usr/bin/certbot", line 11, in <module>
load_entry_point('certbot==0.31.0', 'console_scripts', 'certbot')()
File "/usr/lib/python3/dist-packages/pkg_resources/__init__.py", line 542, in load_entry_point
return get_distribution(dist).load_entry_point(group, name)
File "/usr/lib/python3/dist-packages/pkg_resources/__init__.py", line 2569, in load_entry_point
return ep.load()
File "/usr/lib/python3/dist-packages/pkg_resources/__init__.py", line 2229, in load
return self.resolve()
File "/usr/lib/python3/dist-packages/pkg_resources/__init__.py", line 2235, in resolve
module = __import__(self.module_name, fromlist=['__name__'], level=0)
File "/usr/lib/python3/dist-packages/certbot/main.py", line 10, in <module>
import josepy as jose
File "/usr/lib/python3/dist-packages/josepy/__init__.py", line 44, in <module>
from josepy.interfaces import JSONDeSerializable
File "/usr/lib/python3/dist-packages/josepy/interfaces.py", line 8, in <module>
from josepy import errors, util
File "/usr/lib/python3/dist-packages/josepy/util.py", line 4, in <module>
import OpenSSL
File "/usr/lib/python3/dist-packages/OpenSSL/__init__.py", line 8, in <module>
from OpenSSL import crypto, SSL
File "/usr/lib/python3/dist-packages/OpenSSL/crypto.py", line 12, in <module>
from cryptography import x509
File "/usr/lib/python3/dist-packages/cryptography/x509/__init__.py", line 9, in <module>
from cryptography.x509.base import (
File "/usr/lib/python3/dist-packages/cryptography/x509/base.py", line 16, in <module>
from cryptography.x509.extensions import Extension, ExtensionType
File "/usr/lib/python3/dist-packages/cryptography/x509/extensions.py", line 18, in <module>
from cryptography.hazmat.primitives import constant_time, serialization
File "/usr/lib/python3/dist-packages/cryptography/hazmat/primitives/constant_time.py", line 9, in <module>
from cryptography.hazmat.bindings._constant_time import lib
ModuleNotFoundError: No module named '_cffi_backend'
On the Stack Overflow, someone suggested installing the cffi
package using python pip and i tried to install the package, but i got the error message again. No problem for me, this is challenge when tried to solve the issue but got a new issue. wkwk.
See below for a new error message.
ubuntu@ip-172-26-4-157:~$ pip3 install -U cffi
Exception:
Traceback (most recent call last):
File "/usr/lib/python3/dist-packages/pip/basecommand.py", line 209, in main
status = self.run(options, args)
File "/usr/lib/python3/dist-packages/pip/commands/install.py", line 267, in run
with self._build_session(options) as session:
File "/usr/lib/python3/dist-packages/pip/basecommand.py", line 66, in _build_session
session = PipSession(
File "/usr/lib/python3/dist-packages/pip/download.py", line 321, in __init__
self.headers["User-Agent"] = user_agent()
File "/usr/lib/python3/dist-packages/pip/download.py", line 93, in user_agent
zip(["name", "version", "id"], platform.linux_distribution()),
AttributeError: module 'platform' has no attribute 'linux_distribution'
Traceback (most recent call last):
File "/usr/bin/pip3", line 11, in <module>
sys.exit(main())
File "/usr/lib/python3/dist-packages/pip/__init__.py", line 217, in main
return command.main(cmd_args)
File "/usr/lib/python3/dist-packages/pip/basecommand.py", line 242, in main
with self._build_session(
File "/usr/lib/python3/dist-packages/pip/basecommand.py", line 66, in _build_session
session = PipSession(
File "/usr/lib/python3/dist-packages/pip/download.py", line 321, in __init__
self.headers["User-Agent"] = user_agent()
File "/usr/lib/python3/dist-packages/pip/download.py", line 93, in user_agent
zip(["name", "version", "id"], platform.linux_distribution()),
AttributeError: module 'platform' has no attribute 'linux_distribution'
I think python3 pip on the server is crash. So i delete the python3 pip and re-install it using command curl -L get-pip.io | python3
. After that, i tried again to install cffi
package and successfull install the package.
ubuntu@ip-172-26-4-157:~$ pip3 install -U cffi
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: cffi in /usr/lib/python3/dist-packages (1.10.0)
Collecting cffi
Downloading cffi-1.14.5-cp38-cp38-manylinux1_x86_64.whl (411 kB)
|████████████████████████████████| 411 kB 33.2 MB/s
Requirement already satisfied: pycparser in /usr/lib/python3/dist-packages (from cffi) (2.14)
Installing collected packages: cffi
Successfully installed cffi-1.14.5


The Root of All Tex
Hi everyone!
Recently we talked about how to set-up a delightful working environment for LaTeX using Sublime Text 2.
Today I want to share with you a trick that will help you while dealing with multi-part LaTeX documents using the \input
or the \include
commands.
The scenario
Suppose you’re working on a large and structured LaTeX document, maybe a paper for an upcoming conference, maybe a thesis or similar stuff.
Being a tidy person you correctly decided to split your document in several parts, each one in a separate .tex
file.
So you end up having a main.tex
document that looks like this
(note: I used the \input
command in the above example. There’s a difference between \input
and \include
which is well explained here).
The problem
The structure looks great but let’s take a look at the annoying problem we have here.
Suppose you’re working on the abstract.tex
file and you want to compile it to see how the document looks like. As we learned in the previous pill we press Cmd+B
(or Ctrl+B
on Windows and Linux) in our beautiful Sublime Text editor and we are done, right?
Unfortunately that’s not the case and we’ll get an error looking more or less like
1
|
|
The LaTeX compiler cannot figure out by itself that the current document is included in another one (in our case main.tex
) so it complains about the missing \begin{document}
command.
If you want to compile your document you should go back to the main.tex
file and do it from there.
Kind of annoying, isn’t it?
The solution
It turns out that there’s a very simple and elegant way to help the compiler to figure out that a file is a part of a larger document.
Simply add this line at the top of abstract.tex
:
1
|
|
This will tell the compiler that the root of our document is called main.tex
and it will compile that one even when we are running the command while working on abstract.tex
.
Productivity for the win!
Stay tuned for more pills
Bye
Comments


Sung Park
Front End Ninja
Featured Work
Project One
my project 1
Project Two
my project 2
Project Three
my project 3


MENTAL STRATEGIES -
do these in your head
| Q | Question | Answer |
|---|---|---|
| {{question.number}} | {{question.question}} |
{{question.answer.template.postfix}}
|
TIMESTABLES -
do these in your head
| Q | Question | Answer |
|---|---|---|
| {{question.number}} | {{question.question}} | {{question.answers}} |
KEY SKILLS - you may use written calculations for these questions
| Q | Question | Answer |
|---|---|---|
| {{question.number}} | {{question.question}} | {{question.answers}} |
| What's your score? | Mental Strategies | Timestables | Key Skills | Score | |||
|---|---|---|---|---|---|---|---|
| Fill in your scores in the boxes and calculate your score. |
☐ | + | ☐ | + | ☐ | = | ☐ |


Protocols
The following protocols are available globally.
-
A Place that is showable on map
See moreDeclaration
Swift
protocol PlaceMappable
-
Actions that a Mappable displayer can handle
See moreDeclaration
Swift
protocol Mapper
-
Information common to all place types
See moreDeclaration
Swift
protocol PlaceInfo
-
Abstraction for displaying avatars or placeholders
See moreDeclaration
Swift
protocol UserAvatar
-
Abstraction for logged in or other users
See moreDeclaration
Swift
protocol UserInfo : UserAvatar
-
Application navigation, external, and global state functions
See moreDeclaration
Swift
protocol ApplicationService
-
Provides stored data functionality
See moreDeclaration
Swift
protocol DataService : AnyObject, Observable, ServiceProvider
-
Currently synonym for the Nuke protocol
See moreDeclaration
Swift
protocol ImageService : Nuke_ImageDisplaying
-
Provides location-related functionality
See moreDeclaration
Swift
protocol LocationService : Mapper, ServiceProvider
-
Wraps a single logging statement implmentation
See moreDeclaration
Swift
protocol LoggingService
-
Provides network-related functionality
See moreDeclaration
Swift
protocol NetworkService : Observable, ServiceProvider
-
Provides alerts, modal messages, and background notifications
See moreDeclaration
Swift
protocol NotificationService
-
Analytics and crash reporting
See moreDeclaration
Swift
protocol ReportingService : AnyObject
-
Provider of application-wide services
See moreDeclaration
Swift
protocol ServiceProvider
-
Manage AddPhoto controller
See moreDeclaration
Swift
protocol AddPhotoDelegate : AnyObject
-
Notification of title change
See moreDeclaration
Swift
protocol TitleChangeDelegate : AnyObject
-
Common accessors for single and cluster views
See moreDeclaration
Swift
protocol MappingAnnotationView
-
Notify of display state changes
See moreDeclaration
Swift
protocol CountCellGroupDelegate : AnyObject
-
Notify of display state changes
See moreDeclaration
Swift
protocol CountInfoHeaderDelegate : AnyObject
-
Notify of display state changes
See moreDeclaration
Swift
protocol CountSectionHeaderDelegate : AnyObject
-
Tagging protocol for count cell models
Declaration
Swift
protocol CountCellModel
-
Abstraction of two and three level hierarchy models
See moreDeclaration
Swift
protocol CountsViewModel
-
Receive notification of a Country or Location selection
See moreDeclaration
Swift
protocol LocationSearchDelegate : AnyObject
-
Notifies of scroll for menu updating
See moreDeclaration
Swift
protocol MyCountsPageVCDelegate : AnyObject
-
Actions triggered by photo cell
See moreDeclaration
Swift
protocol PhotoCellDelegate : AnyObject
-
Actions triggered by photo header buttons
See moreDeclaration
Swift
protocol PhotosHeaderDelegate : AnyObject
-
Photo selection notification
See moreDeclaration
Swift
protocol PhotoSelectionDelegate : AnyObject
-
Actions triggered by post cell
See moreDeclaration
Swift
protocol PostCellDelegate : AnyObject
-
Actions triggered by post header buttons
See moreDeclaration
Swift
protocol PostHeaderDelegate : AnyObject
-
Convenience for injecting a User model
See moreDeclaration
Swift
protocol UserInjectable
-
Actions triggered by ranking cell
See moreDeclaration
Swift
protocol RankingCellDelegate : AnyObject
-
Notifies of header taps
See moreDeclaration
Swift
protocol RankingHeaderDelegate : AnyObject
-
Notifies of scroll for menu updating
See moreDeclaration
Swift
protocol RankingsPageVCDelegate : RankingCellDelegate
-
Adopt to provide scorecard and content state
See moreDeclaration
Swift
protocol UserCountsPageDataSource : AnyObject
-
Abstract Facebook SDK for testing
See moreDeclaration
Swift
protocol FacebookSDKClient
-
Abstract Facebook SDK for testing
See moreDeclaration
Swift
protocol FacebookLoginManager
-
Abstract Facebook SDK for testing
See moreDeclaration
Swift
protocol FacebookInfoRequest
-
Abstract UNUserNotificationCenter for testing
See moreDeclaration
Swift
protocol UNUserNotificationCenterProtocol : AnyObject
-
Protocol for data displaying things to adopt
See moreDeclaration
Swift
protocol ContentStateMessaging
-
UIViews display a text string in their background view
See moreDeclaration
Swift
protocol ContentStateMessagingView : UIView, ContentStateMessaging
-
Can be
See moreUIView
orUIBarButtonItem
.Declaration
Swift
@objc protocol AnchorView : AnyObject
-
CellFactoryProtocol
See moreDeclaration
Swift
protocol CellFactoryProtocol
-
FeedbackEditingItemsRepositoryProtocol
See moreDeclaration
Swift
protocol FeedbackEditingItemsRepositoryProtocol
-
FeedbackEditingEventProtocol
See moreDeclaration
Swift
protocol FeedbackEditingEventProtocol
-
FeedbackEditingServiceProtocol
See moreDeclaration
Swift
protocol FeedbackEditingServiceProtocol
-
FeedbackItemProtocol
See moreDeclaration
Swift
protocol FeedbackItemProtocol
-
FeedbackWireframeProtocol
See moreDeclaration
Swift
protocol FeedbackWireframeProtocol
-
AttachmentCellEventProtocol
See moreDeclaration
Swift
protocol AttachmentCellEventProtocol
-
BodyCellEventProtocol
See moreDeclaration
Swift
protocol BodyCellEventProtocol
-
topic
See moreDeclaration
Swift
protocol TopicProtocol
-
UserEmailCellEventProtocol
See moreDeclaration
Swift
protocol UserEmailCellEventProtocol
-
UserPhoneCellEventProtocol
See moreDeclaration
Swift
protocol UserPhoneCellEventProtocol
-
AXPhotosTransitionAnimatorDelegate
See moreDeclaration
Swift
protocol AXPhotosTransitionAnimatorDelegate : AnyObject
-
AXPhotosTransitionControllerDelegate
See moreDeclaration
Swift
protocol AXPhotosTransitionControllerDelegate : AnyObject
-
AXPhotoViewControllerDelegate
See moreDeclaration
Swift
protocol AXPhotoViewControllerDelegate : AnyObject, NSObjectProtocol
-
AXPhotosViewControllerDelegate
See moreDeclaration
Swift
protocol AXPhotosViewControllerDelegate : AnyObject, NSObjectProtocol
-
AXStackableViewContainerDelegate
See moreDeclaration
Swift
protocol AXStackableViewContainerDelegate : AnyObject
-
AXZoomingImageViewDelegate
See moreDeclaration
Swift
protocol AXZoomingImageViewDelegate : AnyObject
-
AXPhotoProtocol - @objc required for NSMapTable
See moreDeclaration
Swift
@objc protocol AXPhotoProtocol : AnyObject, NSObjectProtocol
-
AXCaptionViewProtocol
See moreDeclaration
Swift
protocol AXCaptionViewProtocol : NSObjectProtocol
-
AXLoadingViewProtocol
See moreDeclaration
Swift
protocol AXLoadingViewProtocol : NSObjectProtocol
-
AXNetworkIntegrationProtocol
See moreDeclaration
Swift
protocol AXNetworkIntegrationProtocol : AnyObject, NSObjectProtocol
-
AXNetworkIntegrationDelegate
See moreDeclaration
Swift
protocol AXNetworkIntegrationDelegate : AnyObject, NSObjectProtocol
-
AXOverlayTitleViewProtocol
See moreDeclaration
Swift
protocol AXOverlayTitleViewProtocol : NSObjectProtocol
-
AXPageableViewControllerProtocol
See moreDeclaration
Swift
protocol AXPageableViewControllerProtocol : AnyObject
-
IBOutlet injection template
See moreDeclaration
Swift
protocol InterfaceBuildable
-
Dependency injection template
See moreDeclaration
Swift
protocol Injectable
-
Adopt to handle location related events
See moreDeclaration
Swift
protocol LocationTracker : Mapper, ServiceProvider
-
Wrapper to produce type names for identifers and the like
See moreDeclaration
Swift
protocol Namable
-
Simple implementation of Observer pattern
See moreDeclaration
Swift
@objc protocol Observer
-
Adopt tp publish changes
See moreDeclaration
Swift
protocol Observable
-
Protocol for objects that can be converted to and from Dictionaries
See moreDeclaration
Swift
protocol DictionaryRepresentable
-
Protocol for objects enqueued in OfflineRequestManager to perform operations
See moreDeclaration
Swift
protocol OfflineRequest : AnyObject, DictionaryRepresentable
-
Protocol for receiving callbacaks from OfflineRequestManager and reconfiguring a new OfflineRequestManager from dictionaries saved to disk in the case of previous requests that never completed
See moreDeclaration
Swift
protocol OfflineRequestManagerDelegate : AnyObject
-
RealmSearchViewController data source
See moreDeclaration
Swift
protocol RealmSearchResultsDataSource
-
RealmSearchViewController results
See moreDeclaration
Swift
protocol RealmSearchResultsDelegate : AnyObject
-
Adopt to be constructed at app startup
Declaration
Swift
protocol AppHandler
-
Adopt to have launch notifications routed
See moreDeclaration
Swift
protocol AppLaunchHandler : AppHandler
-
Adopt to have state notifications routed
See moreDeclaration
Swift
protocol AppStateHandler : AppHandler
-
Adopt to have open URL notifications routed
See moreDeclaration
Swift
protocol AppOpenURLHandler : AppHandler
-
Adopt to have memory warning notifications routed
See moreDeclaration
Swift
protocol AppMemoryHandler : AppHandler
-
Adopt to have time change notifications routed
See moreDeclaration
Swift
protocol AppTimeChangeHandler : AppHandler
-
Adopt to have remote notifications routed
See moreDeclaration
Swift
protocol AppNotificationsHandler : AppHandler
-
Adopt to have background URL session notifications routed
See moreDeclaration
Swift
protocol AppBackgroundURLSessionHandler : AppHandler
-
Adopt to have shortcut notifications routed
See moreDeclaration
Swift
protocol AppShortcutHandler : AppHandler
-
Adopt to have watch notifications routed
See moreDeclaration
Swift
protocol AppWatchHandler : AppHandler
-
Adopt to have health notifications routed
See moreDeclaration
Swift
protocol AppHealthHandler : AppHandler
-
Adopt to have Siri notifications routed
See moreDeclaration
Swift
protocol AppSiriHandler : AppHandler
-
Adopt to have data notifications routed
See moreDeclaration
Swift
protocol AppContentHandler : AppHandler
-
Adopt to have extension notifications routed
See moreDeclaration
Swift
protocol AppExtensionHandler : AppHandler
-
Adopt to have restoration notifications routed
See moreDeclaration
Swift
protocol AppRestorationHandler : AppHandler
-
Adopt to have continuity notifications routed
See moreDeclaration
Swift
protocol AppContinuityHandler : AppHandler
-
Adopt to have CloudKit notifications routed
See moreDeclaration
Swift
protocol AppCloudKitHandler : AppHandler
-
Adopt to have UISceneSession notifications routed
See more -
Service wrapper for a Styler type instance
See moreDeclaration
Swift
protocol StyleService
-
Provide scroll view for keyboard avoidance
See moreDeclaration
Swift
protocol KeyboardListener
-
Delegate functions
See moreDeclaration
Swift
protocol WKWebViewControllerDelegate : AnyObject
-
Streamline configuration closures/functions
See moreDeclaration
Swift
protocol Withable
-
Exposes an item to UI tests Intended as base class for screen based enums
See moreDeclaration
Swift
protocol Exposable
-
Tagging protocol for view controllers
See moreDeclaration
Swift
protocol Exposing
-
Tagging protocol for collection data sources
See moreDeclaration
Swift
protocol CollectionCellExposing : AnyObject
-
Tagging protocol for table data sources
See moreDeclaration
Swift
protocol TableCellExposing : AnyObject


{{define "root"}} {{template "header"}}
LoginThis site uses cookies for authentication. This page by itself does not use any cookies. By logging in you have agreed to use cookies because I'm too lazy to implement other forms of authentication.
{{template "resource"}} {{end}}


Powerful Incantation
问题描述 :
Some dangerous Witches invaded the garden city! As a righteous magician, james0zan want to find the most powerful incantation so he can beat those dangerous witches easily.
After consulted one hundred and eight thousand Magic Books, james0zan find a way to calculate the power of an incantation. There is a very short incantation called “magic index” which contains the magic power, and each incantation’s power can be calculated by the times the “magic index” appearance in the incantation. Notice that if two “magic index” overlapped, the power only should be calculated once. And we just want the incantation more powerful. That is to say, if the “magic index” is “aa”, the power of incantation “aaaa” is 2(“aa”+”aa”), not 1(“a”+”aa”+”a”) or 3.
After consulted one hundred and eight thousand Magic Books, james0zan find a way to calculate the power of an incantation. There is a very short incantation called “magic index” which contains the magic power, and each incantation’s power can be calculated by the times the “magic index” appearance in the incantation. Notice that if two “magic index” overlapped, the power only should be calculated once. And we just want the incantation more powerful. That is to say, if the “magic index” is “aa”, the power of incantation “aaaa” is 2(“aa”+”aa”), not 1(“a”+”aa”+”a”) or 3.
输入:
The first line is an integer t (t<=30), the number of test cases. Each of the next t lines contains two strings, represent the incantation (length<=10^6) and the “magic index” (length<=5). Every char in the incantation and the magic index is lowercase.
输出:
The first line is an integer t (t<=30), the number of test cases. Each of the next t lines contains two strings, represent the incantation (length<=10^6) and the “magic index” (length<=5). Every char in the incantation and the magic index is lowercase.
样例输入:
3 aaaa aa bsdjfassdiifo sd papapapapapapap ap
样例输出:
2 2 7
#include<iostream> #include<cstring> #include<cstdio> using namespace std; char a[1111111], b[11]; int main() { int t, i, j, ans, lena, lenb; scanf("%d", &t); while(t--) { scanf("%s%s", a, b); lena = strlen(a); lenb = strlen(b); ans = 0; for(i = 0; i < lena; i++) { for(j = 0; j < lenb; j++) if(a[i + j] != b[j]) break; if(j == lenb) ans++, i += j - 1; } printf("%d\n", ans); } }
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还有一种差不多的叫“少胡”听说过吗？
右一你在咬什么
大哥，你复制也看看是不是这题的答案才复制吧。
学算法中的数据结构学到一定程度会乐此不疲的，比如其中的2－3树，类似的红黑树，我甚至可以自己写个逻辑文件系统结构来。
学习了
分析的很专业！
不错，值得借鉴
第一题还有个解释：没说桌子有多大，可以假设桌子和硬币一样大，肯定是先手胜
从数学角度确实是C(n,r)，能给个递推关系式吗，从分治的角度不好理解
位运算真赞
为什么暴力求解方法循环到sqrt(n) 就可以了呢
Gucci New Fall Arrivals
This is really nice to know. I hope it will be successful in the future. Good job on this and keep up the good work.
正确
这一题的代码这样提交时错误的~少考虑了很多情况


Mental Health is a passion of mine. Journaling is somthing that is essential to mental health. My project proposal is to create a website that allows users to write different journal enteries based on prompts that I will come up with.


Thankyou for registering with Cake Shop
Hi Ravi Kumar!
Thank you for registering with us. We're really happy to have you!
Best Regards,Team Cake Shop


About CCF
As we look to the future, the California Community Foundation’s Board of Directors and staff remain fully committed to making a difference through meaningful advocacy, outreach and community investments. Ultimately, we understand that every dollar we grant represents a donor’s legacy, a dream for lasting change and a chance to bring that dream to life.
Our mission is to lead positive systemic change that strengthens Los Angeles communities. We envision a future where all Angelenos have the opportunity to contribute to the productivity, health and well-being of our region. And we believe that our common fate will be determined by how successfully we improve the quality of life for all of our residents. The impact we help create is of, by and for Los Angeles, because the community is our foundation.
A Billion-Dollar Pledge to L.A.
In 2015, the California Community Foundation celebrated 100 years of service by making a billion-dollar pledge to the people of Los Angeles County.Discover the progress we and our donors have made and how this pledge is already transforming L.A. communities.
What we Fund
We are driven to find long-term, systemic solutions addressing the root causes of the most pressing issues facing Los Angeles County. Since 2000, we have given more than $200 million in discretionary grants, impacting hundreds of thousands of lives and helping to transform entire communities. Every one of those grants represented the legacy of a donor who was dedicated to creating a healthier, more prosperous and empowered L.A.
How We Work
Every year, we and our donors invest tens of millions in transforming Los Angeles, but real change takes more than money. Through partnerships and innovative approaches, we use our tools to maximize impact, empower communities and strengthen the nonprofit sector for generations to come.
Staff & Leadership
CCF’s leadership team, Board of Directors and staff are among the most diverse of any foundation in the country. We are uniquely qualified and experienced to address the important challenges we face here in Los Angeles, and we support CCF’s long-standing tradition of excellence in governance, stability and fiduciary responsibility.
Careers
Our commitment to building a strong and diverse community is reflected not only in our work, but also in our staffing. We understand that our fates and futures are interwoven, so we actively strive to be an employer of choice – offering a competitive compensation package, worthwhile development opportunities and a rewarding workplace environment.
We are committed to the principle of equal employment opportunity for all applicants and employees, and we make employment decisions without regard to race, ethnicity, religion, gender, sexual orientation or preference, gender identity, gender expression, age or any other protected status. We will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable federal, state and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance.
VISIT OUR LINKEDIN PAGE
All applicants with an existing account should log in under the “Previous Visitors” section to complete/submit an employment application.


Selector in Android is one of problems confusing developers. The reason is that it is very hard for developers to control attributes of selector, for ListView and GridView in particular. The paddings of selector, for instance, are very confusing. If you use Android's resources, such as android.R.drawable.gallery_thumb and set it as selector of GridView, the paddings are 16px(in WVGA). These paddings will be adding around the view in GridView but inside the selector, which means the selector will be stretched to contain your view and the paddings. But, if you put the same resources in your own package's resource folder /drawable/my_selector.xml , the images used in selector are the same. Set it as selector for GridView, the paddings are 0 and, what's worse, the selector won't be stretched to contain your view, which means the selector stays in its own size and might be smaller than your view. This is rather wierd and I wish the upcoming SDK release can fix such an issue.
In addition to the difference between Android's resources and developer defined, such paddings of selector are unable to change for developers. Sometimes, our layout is a little bigger which almost reaches screen's edges. In such condition, after adding selector(Android's resource), the selector will stretch out of screen. This huants many developers a lot. It haunts me for a great while at least.
To control the paddings, there are two ways:
1, also is the simpliest one, that is re-design the image resources to exactly fit your view. But this will limit the compatbility which will cause nightmare when maintaining.
2. Another way is implement desired GridView by yourself which enables you open interface to control these paddings when setting selectors. Note: simply inherit GridView or ListView and override method setSelector is not enough, because the fields controlling paddings inside AbsListView are package access control, which prevents classes in other packages including child classes. One referencable example is GridViewSpecial in Andorid's Gallery, which inherits View drectly and implement all it needs by itself.
3. Third way is use shape instead of images to implement desired selector, which enables you to control everything. Let's take a look at an example first:
This is a simple selector. For this selector, you can control everything: stroke width, strok color , most important the paddings. There are many other attributes of shape such as gradient, corner enabling you to create more impressive selectors.
Although you can control the selector in more ideally way, it still causes troubles. The selector set to GridView, however, will affect your layout, even if you have set the vertical spacings and horizontal spacings. This needs you should adjust layout after setting selectors. So, implement your own GridView might be the best solution which is much easier to maintain, costly at first much easily after first pain, though.


{% load i18n %}
{% url 'contact:landing' as contact_url %} {% blocktrans trimmed %} We would be delighted if you would like to join us as a mentor! Contact us if you're interested ❤ {% endblocktrans %}
{% blocktrans trimmed %} This workshop is a part of bigger initiative: Django Girls. It is a non-profit organization and events are organized by volunteers in different places of the world. {% endblocktrans %}
{% blocktrans trimmed %} To see the source for the program find us on Github: github.com/DjangoGirls. {% endblocktrans %}
{% blocktrans trimmed %} If you want to bring Django Girls to your city, drop us a line {% endblocktrans %}


Its time to put your sleuthing skills to the test as you try and guess which of your fellow players is actually a chat bot.
Answer 16 questions in just 50 characters each and then ask 4 of your own.
But be careful, if you own answers are too vague, you might just be the one bumped off the chat...
Are you up for the challenge?


Implements the SISAL algorithm by Tikka and Hollmén. It is a sequential backward selection algorithm which uses a linear model in a cross-validation setting. Starting from the full model, one variable at a time is removed based on the regression coefficients. From this set of models, a parsimonious (sparse) model is found by choosing the model with the smallest number of variables among those models where the validation error is smaller than a threshold. Also implements extensions which explore larger parts of the search space and/or use ridge regression instead of ordinary least squares.
| Version: | 0.46 |
| Depends: | R (≥ 3.1.2) |
| Imports: | graphics, grDevices, grid, methods, stats, utils, boot, lattice, mgcv, digest, R.matlab, R.methodsS3 |
| Suggests: | graph, Rgraphviz, testthat (≥ 0.8) |
| Published: | 2015-10-10 |
| Author: | Mikko Korpela [aut, cre] |
| Maintainer: | Mikko Korpela <mvkorpel at iki.fi> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Copyright: | Aalto University |
| NeedsCompilation: | no |
| Citation: | sisal citation info |
| CRAN checks: | sisal results |
| Reference manual: | sisal.pdf |
| Package source: | sisal_0.46.tar.gz |
| Windows binaries: | r-devel: sisal_0.46.zip, r-release: sisal_0.46.zip, r-oldrel: sisal_0.46.zip |
| OS X binaries: | r-release: sisal_0.46.tgz, r-oldrel: sisal_0.46.tgz |
Please use the canonical form https://CRAN.R-project.org/package=sisal to link to this page.


you have answered correctly
0 time
Short Biography
Hi, my name is Naeem Musamh, I work for many companies in Jordan all of them in Irbid city, and one of them Issa Mohammad AL-Rjoub company and it the first one I have worked for it when I was graduated Remained at home one week and then I get the job can you imagine that something cool yeah, I learn a lot of things there.
Education History
I study in yarmouk university at 2010, and graduated from it at 2014, it was harder but i have some fun with my friend. By the way i studied accounting and i love this Subject from the High school, i have fun when the teacher came and said we well take accounting.
Your Job Experience
Like i said before i work for many company and ok lets me show you this one
| Name The Company | Major | Start | End |
| Issa Mohammad AL_Rjoub company | Junior Accountant | 2014 | 2018 |
| Phillyz company | Junior accountant | 2018 | 2019 |
| ADAM Resturant | Cashier and Financial Accountant | 2019 | 2020 |
| TCHE TCHE Cafe | Cost Accountant and auditor | 2020 | 2020 |
| LTUC | Trainee Web Development | 2020 | For Now |
The Goals
I have many goal to reach it but at this moment i trainee in LTUC web Development so i dream to create some program to help the accountant in there job.
Top ten singers
- 1- Will.I.Am
- 2- Tayler Swift
- 3- BOB Marley
- 4- Adam Lambert
- 5- Eminem
- 6- Rihanna
- 7- Kendrick Lamar
- 8- Logic
- 9- Zayn
- 10- Drake


When you've played games for as long as I have, eventually you get to the
point where a lot of the finer details of game design start to stick out.
I'm here to catalouge some of the more interesting decisions I've come
across in hopes of inspiring new ways to think about building games. I can't
guarantee that all my thoughts here will be complete, coherent, or written
grammatically correctly, but I'll be exploring a lot of different topics
in a bunch of random games. If that sounds interesting at all, get to
reading below!
{% for post in site.categories.ramblings %}
Games mentioned:
{% for tag in post.tags %}
{% capture tag_name %}{{ tag }}{% endcapture %}
{{ tag_name }}
{% endfor %}
{{ post.excerpt }}
Read More
{% endfor %}


Get into the spirit with the Monasterevin Christmas Market
December 15
by Paula CampbellMonasterevin Youth Action and The People's Market have teamed up once again to bring to town a Christmas Market to remember focusing on local traders, local produce and local talent at Monasterevin Community Centre on December 15 from 11am to 5pm.
According to organisers there will be an astonishing array of stalls that is guaranteed to suit all tastes and pockets.
Special performances from the Morrissey School of Dance and Monasterevin Gospel Choir will also take place throughout the day.
Santa Clause will also make an appearance with presents to speak with all the girls and boys.
All welcome.


 Scream revitalized a genre the studios are now intent on burying into the ground again-the serial killers in these new slasher movies have nothing on Sony and Miramax in the "relentless" department. I Still Know What You Did Last Summer is a terrible film in many respects, but in the wake of the stupefyingly bad Urban Legend, it's Citizen Hook.
Jennifer Love-Hewitt reprises her role as buxom teenager Julie James-who apparently escaped certain doom at the end of the last movie by...waking up. She lives in fear of Ben Willis, the vengeful fisherman-victim of a hit and run by Julie and her pals. Paranoid and beat, she accepts a free trip to the Bahamas from her friend Karla (Norwood), winner of the local radio station's 4th of July getaway giveaway. Bikini-ready Julie invites boyfriend Ray (Prinze, Jr.), who-and here's the movie's biggest mystery-TURNS HER DOWN, but changes his mind and plans to surprise her before take-off. Until he gets a roadside visit from Captain Hell-liner himself, that is. Unaware of this and feeling shunned, Julie goes on vacation, anyway, with Karla, Karla's boyfriend (Pfeiffer), and Will (Matthew Settle), a real boy-next-door type who's sweet on Julie.
To make a long story short-I can't believe it took a paragraph to describe the set-up for this gratuitous sequel-the trip is a disaster. Not only is it storm season, not only is the desk clerk (The Frighteners' Jeffrey Combs) a jerk, not only are the few island residents and our heroic vacationers getting picked off by the resourceful Willis one-by-one, but the Karaoke machine isn't working properly! (You think killing is hard? Try reprogramming a LaserDisc so that Gloria Gaynor's "I Will Survive" now contains the lyric "I still know what you did last summer!") All is not lost-Ray is on his way to save the day, and a helpful witch doctor is saying little prayers for Julie and co.
This picture is really about breasts: two of them. Julie, like a good horror heroine, never does up her shirt to the collar, always wears white in the rain, and keeps sexy underwear on in case of a sudden desire to tan. Based on the hormonal charge I got out of the movie, I can't imagine what it was doing to the ten year old boy who sat next to me-he gets Jennifer Love-Hewitt, and my generation got Heather Langenkamp!
I didn't like I Know What You Did Last Summer and I can't say I liked this continuation any more or less. The pacing in both films is languid-how is it that so much time passes with neither murder nor character development? I Still Know What You Did Last Summer has a better sense of humour than the first one, though, and at least it explains away Willis's random selection of victims. (I don't think the hotel maid or the stoner dude had the slightest idea what Julie did last summer.) Director Cannon (Judge Dredd) is a competent filmmaker but not a particularly imaginative one-if there is a part three (what on Earth would they call it?), and the fun denouement suggests there will be, here's my suggestion: hire a filmmaker with flair, someone who can really energize this stillborn series-someone who won't rely on so many shock notes. And let that person run wild with the camera. (Aside: if Blandy [sic] must appear in the next one, try to keep the number of times she says "baby" to a minimum. Thanks in advance.)
-November, 1998
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


In doing so, an attacker has managed to add the additional action=edit to the href URL, which could lead to a vulnerability depending on how the server handles receiving two action parameters.
In doing so, an attacker has managed to add the additional action=edit to the href URL, which could lead to a vulnerability depending on how the server handles receiving two action parameters.
In doing so, an attacker has managed to add the additional action=edit to the href URL, which could lead to a vulnerability depending on how the server handles receiving two action parameters.
In doing so, an attacker has managed to add the additional action=edit to the href URL, which could lead to a vulnerability depending on how the server handles receiving two action parameters.
In doing so, an attacker has managed to add the additional action=edit to the href URL, which could lead to a vulnerability depending on how the server handles receiving two action parameters.
In doing so, an attacker has managed to add the additional action=edit to the href URL, which could lead to a vulnerability depending on how the server handles receiving two action parameters.
Des disciples qui se chargent de la prestation des poèmes (Khassida) de cheikh Ahmadou Bamba.
Ils garantissent la sécurité du CHEIKH et ses condisciples, gèrent l’installation logistique et nettoient les lieux culte de rencontre.
Des disciples qui se chargent de la prestation des poèmes (Khassida) de cheikh Ahmadou Bamba.
Des disciples qui se chargent de la prestation des poèmes (Khassida) de cheikh Ahmadou Bamba.
Des disciples qui se chargent de la prestation des poèmes (Khassida) de cheikh Ahmadou Bamba.
Des disciples qui se chargent de la prestation des poèmes (Khassida) de cheikh Ahmadou Bamba.


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#document


The aim of this puzzle, is to minimize the length of cable used to connect a business park to the network, given the coordinates of all the building. There must be a main horizontal cable, and for each building a dedicated vertical cable from the building to the main cable.
If there are A buildings above it and B buildings below it, shifting it up increases the total length of vertical cables by B-A, and shifting it down increases this number by A-B. So to minimize the length:
Finally, the optimal positions for the main cable are the positions where A = B. That is why, the y-coordinate of the main cable must be a median of the set of y-coordinates of the building.
Once the array Ys
of the y-coordinates of the N buildings, sorted (once again thanks to qsort, but you could use your own sorting function), the median is simply Ys[N/2].
#include <stdlib.h> #include <stdio.h> #include <string.h> #include <limits.h> /* Comparison function between two long long integers return -1 if a<b, 1 if a>b, 0 if a=b */ int cmp (const void * a, const void * b){ long long aa = *(long long*)a; long long bb = *(long long*)b; return aa<bb?-1:aa>bb?1:0; } int main() { int N; scanf("%d", &N); long long xMin = LLONG_MAX; long long xMax = 0LL; long long x; long long Ys[N]; // reading coordinates, storing Ys, computing xMin and xMax for (int i = 0; i < N; i++) { long x; scanf("%Ld %Ld", &x, Ys+i); if (x < xMin) xMin = x; if (x > xMax) xMax = x; } // sorting Ys in ascending order qsort(Ys, N, sizeof(long long), cmp); /* The main horizontal cable length is xMax-xMin To minimize the total length of vertical cables, its y-coordinate of the main cable must be a median of Ys, Ys[N/2]*/ long long res = xMax-xMin; for (int i = 0 ; i < N ; i++) res += abs(Ys[N/2]-Ys[i]); printf("%Ld\n", res); }


I want my Matrix back!
Here. Seriously, how did one of the best movies in a generation disintegrate into Matrix: Revolutions?
Anyway, I'm still reeling. Scott says it better than I can.


Shaun Yan - President
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The protein "{{$ctrl.gene.proteinLabel}}"is
involved in
"{{$ctrl.goFormModel.go.goterm_label.value}}". This claim was stated in
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Prince Vladimir
Unseen and mysterious ancient Russia, immersed in the colors of pristine nature and ancient holidays. Wooden cities and vast spaces, fierce battles and generous feasts, noisy fairs and grandiose hunt. Against all these historical events and amazing adventures, true friendship grows and first love is born ...


| ff_draftpicks {ffscrapr} | R Documentation |
Returns all draft picks (current and future) that belong to a specific franchise and have not yet been converted into players (i.e. selected.)
ff_draftpicks(conn, ...) ## S3 method for class 'espn_conn' ff_draftpicks(conn, ...) ## S3 method for class 'flea_conn' ff_draftpicks(conn, franchise_id = NULL, ...) ## S3 method for class 'mfl_conn' ff_draftpicks(conn, ...) ## S3 method for class 'sleeper_conn' ff_draftpicks(conn, ...)
conn |
the list object created by |
... |
other arguments (currently unused) |
franchise_id |
A list of franchise IDs to pull, if NULL will return all franchise IDs |
Returns a dataframe with current and future draft picks for each franchise
espn_conn
: ESPN: does not support future/draft pick trades - for draft results, please use ff_draft.
flea_conn
: Fleaflicker: retrieves current and future draft picks, potentially for a specified team.
mfl_conn
: MFL: returns current and future picks
sleeper_conn
: Sleeper: retrieves current and future draft picks
try({ # try only shown here because sometimes CRAN checks are weird conn <- espn_connect( season = 2018, league_id = 1178049, espn_s2 = Sys.getenv("TAN_ESPN_S2"), swid = Sys.getenv("TAN_SWID") ) ff_draftpicks(conn) }) # end try try({ # try only shown here because sometimes CRAN checks are weird conn <- fleaflicker_connect(2020, 206154) ff_draftpicks(conn, franchise_id = 1373475) }) # end try try({ # try only shown here because sometimes CRAN checks are weird dlf_conn <- mfl_connect(2020, league_id = 37920) ff_draftpicks(conn = dlf_conn) }) # end try try({ # try only shown here because sometimes CRAN checks are weird jml_conn <- ff_connect(platform = "sleeper", league_id = "522458773317046272", season = 2020) ff_draftpicks(jml_conn) }) # end try


---
title: Testing
date: 2010-05-20
comments: true
layout: post
categories:
- Travel
ref_url:
---
In preparation for the big upcoming trip to Europe I wanted to try out a couple of blogging apps for my iPad. I will be on the road for almost a month without my laptop.
Since I am writing this from home here is a picture of the sunset from a couple days ago.


Market Analysis
Ether (ETH) Looks to Break Nearly 2-Year Bear Market Against Bitcoin
by Horus HughesThis past week, Bitcoin (BTC) price had many investors biting their nails as the top crypto asset turned bearish and is currently fighting to stay above crucial support at $8,300.
Meanwhile, altcoin investors are growing increasingly bullish sentiment wise and a number of lesser-known altcoins rallied impressively throughout the week. Ether price (ETH) is also heating up and the ETH/BTC pair is continuing to flash bullish signals.
Crypto market data daily view. Source: Coin360
The Nov. 15 drop from $186.59 brought Ether price below the ascending trendline and below the $177.57 area where the altcoin has bounced on 3 previous occasions. Ether price eventually found support at $176.79 and has since reversed course and begun to recover lost territory.
ETH USD daily chart. Source: TradingView
The Bollinger Bands and underlying support at $177.57 and $176.79 suggest that Ether has temporarily bottomed and the price is likely to make an attempt to return to the middle Bollinger Band moving average, which is directly aligned with the ascending trendline at $185.28
ETH USD 6-hour chart. Source: TradingView
Based on Ether’s price action since Oct. 27, the current bounce off support appears to be a good entry point for 3.5% to 3.94% gain. Risk-averse traders will probably wait for the price to break above the ascending trendline at $185.28 before considering an entry.
Aggressive traders may have concluded that if Ether price returns to its most recent range an entry at $179.50 represents a 7.08% profit opportunity.
The moving average confluence divergence (MACD) on the 6-hour timeframe shows the MACD line beginning to curve up toward the signal line and the histogram bars have shortened and flipped from red to pink as buying volume increases.
As mentioned earlier, a move to $185.28 would place Ether back above the 50-DMA and the ascending trendline. The volume profile visible range shows a lot of selling pressure at $180 to $186.93. But if the price can clear this node, Ether could run to the upper Bollinger Band arm at $191.33 followed by the 200-DMA at $197.25.
Reclaiming the 200-DMA restores the previously discussed profit-taking estimates starting at $204.
ETH-BTC grows increasingly bullish
ETH BTC daily chart. Source: TradingView
As suggested last week, Ether continues to flash bullish signals on its ETH/BTC pairing. In the daily timeframe, one can clearly see that Ether price continues to press against the descending trendline. The daily MACD on the pair is also bullish and the RSI is currently pushing above 57.
ETH BTC daily MACD and RSI. Source: TradingView
Furthermore, crypto analyst Michaël van de Poppe spotted a series of bullish divergences on the daily RSI, possibly hinting that ETH/BTC has bottomed.
ETH BTC daily chart. Source: TradingView
ETH BTC daily chart. Source: TradingView
Despite Friday’s pullback, Ether price remains in an ascending channel with the 12 exponential moving average (EMA) above the 26-EMA.
Breaking above the descending trendline would be notable as Ether price has been trapped below the trendline for 22-months. Although, traders will certainly remember Ether’s December 2018 rally from $80 to $364 on the ETH/USD pair.
Bullish scenario
Traders will be closely watching to see if Ether can cross above the descending trendline. This would be significant as Ether leads altcoins by market capitalization and at the moment handfuls of altcoins are showing identical chart setups or have already crossed above the 200-DMA and the descending trendline.
Over the weekend and the coming week, traders will be looking for Ether price to continue climbing in the ascending channel. A move above 0.021581 (sats) would clear the high volume node shown on the volume profile visible range (VPVR) and bring Ether price right to the long-term resistance at 0.021999 (sats).
This point has served as resistance since July 14, 2019, and moving above it would position Ether to set a monthly higher high at 0.023063 (sats).
More importantly, the VPVR shows that there is minimal selling pressure above this level and Ether would be en-route to take out the 200-DMA. Assuming Ether is able to remain in the ascending channel and overcome the resistance levels mentioned above, the altcoin could be potentially gain 18.15% before encountering resistance at 0.025346 (sats).
The views and opinions expressed here are solely those of the author (@HorusHughes) and do not necessarily reflect the views of Cointelegraph. Every investment and trading move involves risk. You should conduct your own research when making a decision.


|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface MetaBean
A meta-bean, defining those aspects of a bean which are not specific to a particular instance, such as the type and set of meta-properties.
This interface can be thought of as the equivalent of Class
but for beans.
In most cases the meta-bean will be code generated and the concrete class will have additional methods.
| Method Summary | ||
|---|---|---|
String |
beanName()
Gets the bean name, which is normally the fully qualified class name of the bean. |
|
Class<? extends Bean> |
beanType()
Get the type of the bean, represented as a Class . |
|
BeanBuilder<? extends Bean> |
builder()
Creates a bean builder that can be used to create an instance of this bean. |
|
PropertyMap |
createPropertyMap(Bean bean)
Creates a map of properties for the specified bean. |
|
|
metaProperty(String propertyName)
Gets a meta-property by name. |
|
int |
metaPropertyCount()
Counts the number of properties. |
|
boolean |
metaPropertyExists(String propertyName)
Checks if a property exists. |
|
Iterable<MetaProperty<?>> |
metaPropertyIterable()
Gets an iterator of meta-properties. |
|
Map<String,MetaProperty<?>> |
metaPropertyMap()
Gets the map of meta-properties, keyed by property name. |
| Method Detail |
|---|
BeanBuilder<? extends Bean> builder()
The builder is used in two main ways. The first is to allow immutable beans to be constructed. The second is to enable automated tools like serialization/deserialization.
The builder can be thought of as a Map
of MetaProperty
to value.
Note that the implementation is not necessarily an actual map.
UnsupportedOperationException
- if the bean cannot be createdPropertyMap createPropertyMap(Bean bean)
This allows the entire set of properties of the bean to be exposed as a Map
.
The map is keyed by the property name and has Property
instances as values.
Call PropertyMap.flatten()
to convert the map to hold the actual values from the bean.
bean
- the bean to create the map for, not null
String beanName()
This is primarily used for human-readable output.
Class<? extends Bean> beanType()
Class
.
A MetaBean
can be thought of as the equivalent of Class
but for beans.
This method allows the actual Class
instance of the bean to be obtained.
int metaPropertyCount()
Each meta-bean manages a single bean with a known set of properties. This method returns the count of properties.
boolean metaPropertyExists(String propertyName)
Each meta-bean manages a single bean with a known set of properties. This method checks whether there is a property with the specified name.
propertyName
- the property name to check, null returns false
<R> MetaProperty<R> metaProperty(String propertyName)
Each meta-bean manages a single bean with a known set of properties. This method returns the property with the specified name.
The base interface throws an exception if the name is not recognised.
By contrast, the DynamicMetaBean
subinterface creates the property on demand.
R
- the property type, optional, enabling auto-castingpropertyName
- the property name to retrieve, not null
NoSuchElementException
- if the property name is invalidIterable<MetaProperty<?>> metaPropertyIterable()
This method returns an Iterable
, which is simpler than a Map
.
As a result, implementations may be able to optimise, and so this method should be
preferred to metaPropertyMap()
where a choice is possible.
Map<String,MetaProperty<?>> metaPropertyMap()
Where possible, use metaPropertyIterable()
instead as it typically has better performance.
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


DAMAGE
A film review by Gerard Foley
Copyright 1996 Gerard Foley
Partly moved by Louis Malle's recent death and partly because it was on the library shelf, I looked again at "Damage", the 1992 film which he produced and directed. The screenplay is by David Hare from a novel by Josephine Hart (I have not read the novel. I believe David Hare writes fiction about British politics).
I think the film is extraordinary on several levels. It is the most convincing tale of eroticism that I have ever seen. It also maintains a moderately high level of suspense, not enough to distract from my enjoyment of other aspects of it. The performances Malle has obtained from his three stars are outstanding. Jeremy Irons and Juliette Binoche have, properly enough, top billing. In addition to her surprising delivery of her lines, without the least sign of a French acccent, as Malle says in his epilog, she tells volumes without speaking at all. So, for that matter, does Irons. I do not remember him giving a performance of such range in any other film or TV vehicle. He convinces me that he is an upper level politician and technocrat, an even smoother performance than we have grown accustomed to from other British actors playing politicians and civil servants. Part of this, of course, must be credited to Hare, the screenwriter, and Hart, the novelist, but some credit should go to Irons and Malle too.
The story is of a middle aged politician whose instant infatuation with his sons girl friend is as instantly reciprocated. Miranda Richardson plays the wife-mother, a much smaller and less flashy role than that of the other two actors but equally convincing. Malle makes you perfectly aware, by the end of the film, that a woman like the one Binoche plays is an all around menace, but you still mourn Irons characters loss of her as much as you do his loss of his wife and son.
As I write this, I am reminded of Polanskis "Bitter Moon". Superficially one might want to compare the male villain of "Bitter Moon" with the female protagonist of "Damage", but to me Polanski reveals a really deep hatred and contempt for all of his characters, while I think Malle really loves all of his.
The video includes a very warm interview-epilogue with Malle. Weve lost one of the great film makers.
Gerry
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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Final year student in JKUAT studying Public Management and Development
I decided to take on progreamming because the world is becoming a global village everyday and we should as well join the bandwagon
New website This is a trial HTML website i tried creating
More practiceThis also another website i created for practice as well
Languages used were HTML and CSS


I ranted recently about the never-ending slew of books entitled Flapdoodle's Pin-Cushion and variations thereof (see Rant, 5th April). During the unfortunate technical trauma that closed down Hooting Yard for a couple of weeks, a friend reminded me that I had got myself into a similar tizz some years ago. My bugbear then was that the Booker Prize shortlist always seemed to be limited to novels with pompous, portentous titles like The Redundancy of Courage—was that by Timothy Mo? (I could check, but I really, really can't be bothered.) Titles, in any case, which announced: “This is a serious work. It may be ill-written, tedious, and simple-minded, but it addresses big themes. This is literature.” My plan at the time—never fulfilled, alas, but you know how it is—was to write a thumping great tome entitled The Consistency of Porridge. I may still do so.


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| Services |
| AccessibleCellView |
The accessible view of a cell in a text document or in the page preview
of a spreadsheet document. See
::com::sun::star::sheet::AccessibleCell for cells in
the edit view of a spreadsheet.
|
| AccessibleTableView |
The accessible view of a table in a text document or in the page preview
of a spreadsheet document. See
::com::sun::star::sheet::AccessibleSpreadsheet for
tables in the edit view of a spreadsheet.
|
| Cell |
represents a singe cell within a table.
|
| CellCursor |
represents a cell cursor in a table.
|
| CellProperties |
contains the properties of a table cell.
|
| CellRange |
represents a range of of cells within a table.
|
| CellRangeListSource |
defines the a source of list entries coming from a cell range in a tbale document
|
| CellValueBinding |
defines the binding to a single cell in a table document
|
| ListPositionCellBinding |
defines the binding to a single cell in a table document, which can be used to
transfer a list position into the cell.
|
| TableChart |
represents a chart based on data in a table or spreadsheet.
|
| TableCharts |
represents a collection of all charts based on data in a table.
|
| TableChartsEnumeration |
represents an enumeration of table charts.
|
| TableColumn |
represents a special cell range containing all cells of a single
specific column in a table or spreadsheet.
|
| TableColumns |
represents a collection of all columns of a table or spreadsheet.
|
| TableColumnsEnumeration |
represents an enumeration of table columns.
|
| TableRow |
represents a special cell range containing all cells of a single
specific row in a table or spreadsheet.
|
| TableRows |
represents a collection of all rows of a table or spreadsheet.
|
| TableRowsEnumeration |
represents an enumeration of table rows.
|
| TableSortDescriptor |
[ DEPRECATED ]
contains properties which describe sorting of rows or columns in a table.
|
| TableSortDescriptor2 |
specifies properties which describe sorting of fields (rows or columns)
in a table.
|
| Interfaces |
| XAutoFormattable |
provides a method to apply TableAutoFormats to a cell range.
|
| XCell |
provides methods to access the contents of a cell in a table.
|
| XCellCursor |
provides methods to control the position of a cell cursor.
|
| XCellRange |
provides access to the cells or to sub-ranges of a cell range.
|
| XColumnRowRange |
provides methods to access the collections of columns and rows
of a cell range.
|
| XMergeableCell |
provides methods to access information about a cell that is mergeable with
other sells.
|
| XMergeableCellRange |
represents a range of cells that can possibly be merged or unmerged.
|
| XTable |
|
| XTableChart |
provides access to the settings of a chart object in a table or
spreadsheet.
|
| XTableCharts |
provides methods to access charts via name and to insert and remove
charts.
|
| XTableChartsSupplier |
provides a method to access a collection of charts in a table or
spreadsheet.
|
| XTableColumns |
provides methods to access columns via index and to insert and remove
columns.
|
| XTableRows |
provides methods to access rows via index and to insert and remove rows.
|
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


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(model for expressions, data...)
vtl-parser
(parser for expressions)
vtl-parser
(parser for expressions)
Trevas architecture
Features
- Base parser generated by Antlr (visitor pattern)
- Engine extensibility (JSR 223)
Trevas roadmap
Current work
- Documentation
- Logging
- Logo
Trevas roadmap
Version 0.2
- Cast
- Join
- Aggregation
- Packaging
Trevas roadmap
Version 0.3
- Extensibility
- Process metadata
- Optimization
Trevas roadmap
Other activities
- Client application
- Research on SparkSQL
- Coverage extension
- R wrapping


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{include file="public/adminmenu" /} {include file="public/adminnav" /}
博客后台
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{$author} 位作者
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Usage¶
This section covers how to use Go Elasticsearch Alerts.
Startup¶
Once you have installed the binary, simply execute it in order to start the daemon running.
$ ./go-elasticsearch-alerts
Distributed Operation¶
Go Elasticserach Alerts can be run in a distributed fashion without sending duplicate alerts. Distributed operation can be enabled in the main configuration file. It uses the lock feature of Hashicorp’s Consul for synchronization across nodes and as such requires a functioning Consul server in order to use this feature.
Specifically, each instance of this process will attempt to acquire the lock, but only one node can have the lock at any given time. If the instance holding the lock is killed, another instance will acquire the lock and become the leader. Only the instance holding the lock will execute queries. However, all instances will continue to maintain state regardless of whether or not they have the lock.
Reloading Rules¶
Go Elasticsearch Alerts allows you to change your rule configuration files without having to restart the process. If you change your rules and wish to update the process to use the updated rules, simply send the process a SIGHUP signal. It will then stop the currently- running query handlers, parse the rules, create new query handlers with the new rules, and start query handlers. You can send a SIGHUP signal to the process with the following command:
$ kill -SIGHUP $(ps aux | grep '[g]o-elasticsearch-alerts' | awk '{print $2}')
Nomad¶
Because Go Elasticsearch Alerts can be run in a distributed fashion and allows live rule updates it is highly compatible with HashiCorp’s Nomad application scheduler. See the code block below for an example Nomad file.
Example¶
job "go-elasticsearch-alerts" {
datacenters = ["us-east-1"]
region = "us-east"
type = "service"
update {
max_parallel = 1
canary = 1
min_healthy_time = "30s"
healthy_deadline = "2m"
auto_revert = true
}
migrate {
max_parallel = 1
health_check = "checks"
min_healthy_time = "30s"
healthy_deadline = "5m"
}
meta {
GO_ELASTICSEARCH_ALERTS_VERSION = "0.0.21"
}
group "alerters" {
count = 2
constraint {
distinct_hosts = true
}
ephemeral_disk {
sticky = true
}
restart {
interval = "20s"
attempts = 1
delay = "20s"
mode = "delay"
}
task "daemon" {
driver = "docker"
config {
image = "alpine:3.8"
command = "/local/go-elasticsearch-alerts"
volumes = [
"/etc/key:/etc/key"
]
dns_servers = [
"${attr.unique.network.ip-address}",
]
}
artifact {
source = "https://github.com/morningconsult/go-elasticsearch-alerts/releases/download/v${NOMAD_META_GO_ELASTICSEARCH_ALERTS_VERSION}/go-elasticsearch-alerts_${NOMAD_META_GO_ELASTICSEARCH_ALERTS_VERSION}_Linux_x86_64.tar.gz"
destination = "local/"
options {
checksum = "sha256:471f879ed2f31c030832553c6d9cb878dac5d413892ecad9b05a7446bdf3c807"
}
}
resources {
memory = 400
cpu = 300
}
env {
GO_ELASTICSEARCH_ALERTS_CONFIG_FILE = "/local/alerts-config.json"
GO_ELASTICSEARCH_ALERTS_RULES_DIR = "/local/rules"
}
template {
data = <<EOH
{
"elasticsearch": {
"server": {
"url": "https://elasticsearch.service.consul:9200"
},
"client": {
"tls_enabled": true,
"ca_cert": "/etc/key/elastic-ca-chain.pem",
"client_cert": "/etc/key/elastic-cert.pem",
"client_key": "/etc/key/elastic-key.pem",
"server_name": "node.elasticsearch.service.consul"
}
},
"distributed": true,
"consul": {
"consul_lock_key": "go-elasticsearch-alerts/leader",
"consul_http_addr": "http://{{ env "attr.unique.network.ip-address" }}:8500"
}
}
EOH
destination = "local/alerts-config.json"
change_mode = "restart"
}
template {
data = <<EOH
{{ key "go-elasticsearch-alerts/rules/apm-errors" }}
EOH
destination = "local/rules/apm-errors.json"
change_mode = "signal"
change_signal = "SIGHUP"
}
}
}
}
According to this job definition, when the job is executed Nomad will download
the Go Elasticsearch Binary from the Github releases page since it was defined
as an artifact and insert it into the container and execute it. Also, because
the rule (called "apm-errors"
in the example) is stored as a template in
Consul, if you change the template in Consul then Nomad will send a SIGHUP to
the process and update the rule without you having to restart the job. This
definition avoids the need to create custom Docker images for your job.


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