class: inverse, middle, left, my-title-slide, title-slide # Prediction & Model Uncertainty ### Gavin L. Simpson ### August 13, 2020 --- # Recap — shrimp species richness .row[ .col-6[ ```r shrimp <- read.csv(here('data', 'trawl_nl.csv')) ``` ```r m_rich <- gam(richness ~ s(year), family = poisson, method = "REML", data = shrimp) ``` ] .col-6[ ![](04-predictions-and-variance_files/figure-html/richness-violin-1.svg)<!-- --> ] ] --- # Recap — spatio-temporal ![](04-predictions-and-variance_files/figure-html/biom-space-time-plot-1.svg)<!-- --> --- # Recap — spatio-temporal model ```r m_spt <- gam(shrimp ~ te(x, y, year, d = c(2,1), bs = c('tp', 'cr'), k = c(20, 5)), data = shrimp, family = tw, method = "REML") ``` --- # Confidence bands .row[ .col-6[ `plot()` ```r plot(m_rich) ``` ![](04-predictions-and-variance_files/figure-html/plot-richness-model-1.svg)<!-- --> ] .col-6[ `gratia::draw()` ```r draw(m_rich) ``` ![](04-predictions-and-variance_files/figure-html/draw-richness-model-1.svg)<!-- --> ] ] What do the bands represent? --- # Confidence intervals for smooths Bands are a bayesian 95% credible interval on the smooth `plot.gam()` draws the band at ± **2** std. err. `gratia::draw()` draws them at `\((1 - \alpha) / 2\)` upper tail probability quantile of `\(\mathcal{N}(0,1)\)` `gratia::draw()` draws them at ~ ±**1.96** std. err. & user can change `\(\alpha\)` via argument `ci_level` -- So `gratia::draw()` draws them at ~ ±**2** st.d err --- # Across the function intervals The *frequentist* coverage of the intervals is not pointwise — instead these credible intervals have approximately 95% coverage when *averaged* over the whole function Some places will have more than 95% coverage, other places less -- Assumptions yielding this result can fail, where estimated smooth is a straight line -- Correct this with `seWithMean = TRUE` in `plot.gam()` or `overall_uncertainty = TRUE` in `gratia::draw()` This essentially includes the uncertainty in the intercept in the uncertainty band --- # Correcting for smoothness selection The defaults assume that the smoothness parameter(s) `\(\lambda_j\)` are *known* and *fixed* -- But we estimated them -- Can apply a correction for this extra uncertainty via argument `unconditional = TRUE` in both `plot.gam()` and `gratia::draw()` --- # But still, what do the bands represent? ```r sm_fit <- evaluate_smooth(m_rich, 's(year)') # tidy data on smooth sm_post <- smooth_samples(m_rich, 's(year)', n = 20, seed = 42) # more on this later draw(sm_fit) + geom_line(data = sm_post, aes(x = .x1, y = value, group = draw), alpha = 0.3, colour = 'red') ``` ![](04-predictions-and-variance_files/figure-html/plot-conf-band-plus-posterior-smooths-1.svg)<!-- --> --- class: inverse middle center subsection # Your turn! --- class: inverse middle center subsection # Using your shiny new model --- # Predicting with `predict()` `plot.gam()` and `gratia::draw()` show the component functions of the model on the link scale Prediction allows us to evaluate the model at known values of covariates on the response scale Use the standard function `predict()` Provide `newdata` with a data frame of values of covariates --- # `predict()` ```r new_year <- with(shrimp, tibble(year = seq(min(year), max(year), length.out = 100))) pred <- predict(m_rich, newdata = new_year, se.fit = TRUE, type = 'link') pred <- bind_cols(new_year, as_tibble(as.data.frame(pred))) pred ``` ``` ## # A tibble: 100 x 3 ## year fit se.fit ## <dbl> <dbl> <dbl> ## 1 2005 3.05 0.0100 ## 2 2005. 3.05 0.00901 ## 3 2005. 3.06 0.00830 ## 4 2005. 3.06 0.00792 ## 5 2005. 3.06 0.00786 ## 6 2005. 3.06 0.00807 ## 7 2006. 3.07 0.00844 ## 8 2006. 3.07 0.00887 ## 9 2006. 3.07 0.00926 ## 10 2006. 3.08 0.00955 ## # … with 90 more rows ``` --- # `predict()` → response scale ```r ilink <- inv_link(m_rich) # inverse link function crit <- qnorm((1 - 0.89) / 2, lower.tail = FALSE) # or just `crit <- 2` pred <- mutate(pred, richness = ilink(fit), lwr = ilink(fit - (crit * se.fit)), # lower... upr = ilink(fit + (crit * se.fit))) # upper credible interval pred ``` ``` ## # A tibble: 100 x 6 ## year fit se.fit richness lwr upr ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 2005 3.05 0.0100 21.1 20.8 21.4 ## 2 2005. 3.05 0.00901 21.2 20.9 21.5 ## 3 2005. 3.06 0.00830 21.2 20.9 21.5 ## 4 2005. 3.06 0.00792 21.3 21.0 21.6 ## 5 2005. 3.06 0.00786 21.4 21.1 21.6 ## 6 2005. 3.06 0.00807 21.4 21.1 21.7 ## 7 2006. 3.07 0.00844 21.5 21.2 21.8 ## 8 2006. 3.07 0.00887 21.6 21.3 21.9 ## 9 2006. 3.07 0.00926 21.6 21.3 22.0 ## 10 2006. 3.08 0.00955 21.7 21.4 22.0 ## # … with 90 more rows ``` --- # `predict()` → plot Tidy objects like this are easy to plot with `ggplot()` ```r ggplot(pred, aes(x = year)) + geom_ribbon(aes(ymin = lwr, ymax = upr), alpha = 0.2) + geom_line(aes(y = richness)) + labs(y = "Species richness", x = NULL) ``` ![](04-predictions-and-variance_files/figure-html/plot-predictions-richness-1.svg)<!-- --> --- # `predict()` for space and time This idea is very general; spatiotemporal model needs a grid of x,y coordinates for each year ```r sp_new <- with(shrimp, expand.grid(x = seq_min_max(x, n = 100), y = seq_min_max(y, n = 100), year = unique(year))) sp_pred <- predict(m_spt, newdata = sp_new, se.fit = TRUE) # link scale is default sp_pred <- bind_cols(as_tibble(sp_new), as_tibble(as.data.frame(sp_pred))) sp_pred ``` ``` ## # A tibble: 100,000 x 5 ## x y year fit se.fit ## <dbl> <dbl> <int> <dbl> <dbl> ## 1 428238. 5078244. 2005 3.34 1.06 ## 2 436886. 5078244. 2005 3.32 1.06 ## 3 445535. 5078244. 2005 3.31 1.05 ## 4 454183. 5078244. 2005 3.29 1.04 ## 5 462831. 5078244. 2005 3.26 1.03 ## 6 471479. 5078244. 2005 3.24 1.02 ## 7 480127. 5078244. 2005 3.21 1.01 ## 8 488775. 5078244. 2005 3.18 1.00 ## 9 497423. 5078244. 2005 3.15 0.994 ## 10 506071. 5078244. 2005 3.12 0.985 ## # … with 99,990 more rows ``` --- # `predict()` → response scale ```r ilink <- inv_link(m_spt) too_far <- exclude.too.far(sp_pred$x, sp_pred$y, shrimp$x, shrimp$y, dist = 0.1) sp_pred <- sp_pred %>% mutate(biomass = ilink(fit), biomass = case_when(too_far ~ NA_real_, TRUE ~ biomass)) sp_pred ``` ``` ## # A tibble: 100,000 x 6 ## x y year fit se.fit biomass ## <dbl> <dbl> <int> <dbl> <dbl> <dbl> ## 1 428238. 5078244. 2005 3.34 1.06 NA ## 2 436886. 5078244. 2005 3.32 1.06 NA ## 3 445535. 5078244. 2005 3.31 1.05 NA ## 4 454183. 5078244. 2005 3.29 1.04 NA ## 5 462831. 5078244. 2005 3.26 1.03 NA ## 6 471479. 5078244. 2005 3.24 1.02 NA ## 7 480127. 5078244. 2005 3.21 1.01 NA ## 8 488775. 5078244. 2005 3.18 1.00 NA ## 9 497423. 5078244. 2005 3.15 0.994 NA ## 10 506071. 5078244. 2005 3.12 0.985 NA ## # … with 99,990 more rows ``` --- # `predict()` → plot ```r ggplot(sp_pred, aes(x = x, y = y, fill = biomass)) + geom_raster() + scale_fill_viridis_c(option = "plasma") + facet_wrap(~ year, ncol = 5) + coord_equal() ``` ![](04-predictions-and-variance_files/figure-html/spt-example-plot-1.svg)<!-- --> --- # Visualizing the trend? We have this model .smaller[ ```r m_spt ``` ``` ## ## Family: Tweedie(p=1.686) ## Link function: log ## ## Formula: ## shrimp ~ te(x, y, year, d = c(2, 1), bs = c("tp", "cr"), k = c(20, ## 5)) ## ## Estimated degrees of freedom: ## 70.4 total = 71.38 ## ## REML score: 19102.91 ``` ] How would you visualize the average change in biomass over time? --- # Welcome back old friend One way is to decompose the spatio-temporal function in main effects plus interaction ```r m_ti <- gam(shrimp ~ ti(x, y, year, d = c(2, 1), bs = c("tp", "cr"), k = c(20, 5)) + s(x, y, bs = "tp", k = 20) + s(year, bs = "cr", k = 5), data = shrimp, family = tw, method = "REML") ``` and predict from the model using only the marginal effect of `s(year)` --- # `predict()` with `exclude` .row[ .col-6[ We can exclude the spatial & spatiotemporal terms from predictions using `exclude` **Step 1** run `summary()` on model & note the names of the smooth you *don't* want → ] .col-6[ .smaller[ ```r summary(m_ti) ``` ``` ## ## Family: Tweedie(p=1.686) ## Link function: log ## ## Formula: ## shrimp ~ ti(x, y, year, d = c(2, 1), bs = c("tp", "cr"), k = c(20, ## 5)) + s(x, y, bs = "tp", k = 20) + s(year, bs = "cr", k = 5) ## ## Parametric coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 3.89948 0.03129 124.6 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Approximate significance of smooth terms: ## edf Ref.df F p-value ## ti(x,y,year) 48.139 59.401 7.824 <2e-16 *** ## s(x,y) 18.885 18.997 179.974 <2e-16 *** ## s(year) 3.873 3.979 110.646 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## R-sq.(adj) = 0.13 Deviance explained = 45.1% ## -REML = 19087 Scale est. = 9.3859 n = 4152 ``` ] ] ] --- # `predict()` with `exclude` — Step 2 *predict* Prediction data only need dummy values for `x` and `y` ```r ti_new <- with(shrimp, expand.grid(x = mean(x), y = mean(y), year = seq_min_max(year, n = 100))) ti_pred <- predict(m_ti, newdata = ti_new, se.fit = TRUE, * exclude = c("ti(x,y,year)", "s(x,y)")) ti_pred <- bind_cols(as_tibble(ti_new), as_tibble(as.data.frame(ti_pred))) %>% mutate(biomass = ilink(fit), lwr = ilink(fit - (crit * se.fit)), upr = ilink(fit + (crit * se.fit))) ``` `exclude` takes a character vector of terms to exclude — `predict()` sets the contributions of those terms to 0 Could also use `terms = "s(year)"` to select only the named smooths ```r predict(m_ti, newdata = ti_new, se.fit = TRUE, terms = "s(year)") ``` --- # `predict()` with `exclude`— Step 3 *plot it!* ```r ggplot(ti_pred, aes(x = year)) + geom_ribbon(aes(ymin = lwr, ymax = upr), alpha = 0.3) + geom_line(aes(y = biomass)) + labs(y = "Biomass", x = NULL) ``` ![](04-predictions-and-variance_files/figure-html/plot-ti-marginal-trend-1.svg)<!-- --> --- class: inverse middle center subsection # Your turn! --- class: inverse middle center subsection # Posterior simulation --- # Remember this? ![](04-predictions-and-variance_files/figure-html/plot-conf-band-plus-posterior-smooths-1.svg)<!-- --> -- Where did the red lines come from? --- # Posterior distributions Each red line is a draw from the *posterior distribution* of the smooth Remember the `\(\beta_j\)` from Eric's opening lecture yesterday? Together they are distributed *multivariate normal* with * mean vector given by `\(\hat{\beta}_j\)` * covariance matrix `\(\boldsymbol{\hat{V}}_{\beta}\)` `$$\text{MVN}(\boldsymbol{\hat{\beta}}, \boldsymbol{\hat{V}}_{\beta})$$` -- The model as a whole has a posterior distribution too -- We can simulate data from the model by taking draws from the posterior distribution --- # Posterior simulation for a smooth Sounds fancy but it's only just slightly more complicated than using `rnorm()` To do this we need a few things: 1. The vector of model parameters for the smooth, `\(\boldsymbol{\hat{\beta}}\)` 2. The covariance matrix of those parameters, `\(\boldsymbol{\hat{V}}_{\beta}\)` 3. A matrix `\(\boldsymbol{X}_p\)` that maps parameters to the linear predictor for the smooth `$$\boldsymbol{\hat{\eta}}_p = \boldsymbol{X}_p \boldsymbol{\hat{\beta}}$$` -- Let's do this for `m_rich` --- # Posterior sim for a smooth — step 1 The vector of model parameters for the smooth, `\(\boldsymbol{\hat{\beta}}\)` ```r sm_year <- get_smooth(m_rich, "s(year)") # extract the smooth object from model idx <- gratia:::smooth_coefs(sm_year) # indices of the coefs for this smooth idx ``` ``` ## [1] 2 3 4 5 6 7 8 9 10 ``` ```r beta <- coef(m_rich) # vector of model parameters ``` --- # Posterior sim for a smooth — step 2 The covariance matrix of the model parameters, `\(\boldsymbol{\hat{V}}_{\beta}\)` ```r Vb <- vcov(m_rich) # default is the bayesian covariance matrix ``` --- # Posterior sim for a smooth — step 3 A matrix `\(\boldsymbol{X}_p\)` that maps parameters to the linear predictor for the smooth We get `\(\boldsymbol{X}_p\)` using the `predict()` method with `type = "lpmatrix"` ```r new_year <- with(shrimp, tibble(year = seq_min_max(year, n = 100))) Xp <- predict(m_rich, newdata = new_year, type = 'lpmatrix') dim(Xp) ``` ``` ## [1] 100 10 ``` --- # Posterior sim for a smooth — step 4 Take only the columns of `\(\boldsymbol{X}_p\)` that are involved in the smooth of `year` ```r Xp <- Xp[, idx, drop = FALSE] dim(Xp) ``` ``` ## [1] 100 9 ``` --- # Posterior sim for a smooth — step 5 Simulate parameters from the posterior distribution of the smooth of `year` ```r set.seed(42) beta_sim <- rmvn(n = 20, beta[idx], Vb[idx, idx, drop = FALSE]) dim(beta_sim) ``` ``` ## [1] 20 9 ``` Simulating many sets (20) of new model parameters from the estimated parameters and their uncertainty (covariance) Result is a matrix where each row is a set of new model parameters, each consistent with the fitted smooth --- # Posterior sim for a smooth — step 6 .row[ .col-6[ Form `\(\boldsymbol{\hat{\eta}}_p\)`, the posterior draws for the smooth ```r sm_draws <- Xp %*% t(beta_sim) dim(sm_draws) ``` ``` ## [1] 100 20 ``` ```r matplot(sm_draws, type = 'l') ``` A bit of rearranging is needed to plot with `ggplot()` ] .col-6[ ![](04-predictions-and-variance_files/figure-html/richness-posterior-draws-1.svg)<!-- --> ] ] -- Or use `smooth_samples()` --- # Posterior sim for a smooth — steps 1–6 ```r sm_post <- smooth_samples(m_rich, 's(year)', n = 20, seed = 42) draw(sm_post) ``` ![](04-predictions-and-variance_files/figure-html/plot-posterior-smooths-1.svg)<!-- --> --- # Posterior simulation from the model Simulating from the posterior distribution of the model requires 1 modification of the recipe for a smooth and one extra step We want to simulate new values for all the parameters in the model, not just the ones involved in a particular smooth -- Additionally, we could simulate *new response data* from the model and the simulated parameters (**not shown** below) --- # Posterior simulation from the model ```r beta <- coef(m_rich) # vector of model parameters Vb <- vcov(m_rich) # default is the bayesian covariance matrix Xp <- predict(m_rich, type = 'lpmatrix') set.seed(42) beta_sim <- rmvn(n = 1000, beta, Vb) # simulate parameters eta_p <- Xp %*% t(beta_sim) # form linear predictor values mu_p <- inv_link(m_rich)(eta_p) # apply inverse link function mean(mu_p[1, ]) # mean of posterior for the first observation in the data ``` ``` ## [1] 21.09487 ``` ```r quantile(mu_p[1, ], probs = c(0.025, 0.975)) ``` ``` ## 2.5% 97.5% ## 20.67821 21.51736 ``` --- # Posterior simulation from the model ```r ggplot(tibble(richness = mu_p[587, ]), aes(x = richness)) + geom_histogram() + labs(title = "Posterior richness for obs #587") ``` ![](04-predictions-and-variance_files/figure-html/posterior-sim-model-hist-1.svg)<!-- --> --- # Posterior simulation from the model Or easier using `fitted_samples()` ```r rich_post <- fitted_samples(m_rich, n = 1000, newdata = shrimp, seed = 42) ggplot(filter(rich_post, row == 587), aes(x = fitted)) + geom_histogram() + labs(title = "Posterior richness for obs #587", x = "Richness") ``` ![](04-predictions-and-variance_files/figure-html/richness-fitted-samples-1.svg)<!-- --> --- # Why is this of interest? Say you wanted to get an estimate for the total biomass of shrimp over the entire region of the trawl survey for 2007 You could predict for the spatial grid for `year == 2007` using code shown previously and sum the predicted biomass values over all the grid cells -- **Easy** -- But what if you also wanted the uncertainty in that estimate? -- **Hard** -- **Math** 😱😱😱 "something, something, delta method, something" 😱😱😱 --- # Posterior simulation makes this easy 1. Take a draw from the posterior distribution of the model 2. Use the posterior draw to predict biomass for each grid cell 3. Sum the predicted biomass values over all grid cells 4. Store the total biomass value 5. Repeat 1–4 a lot of times to get posterior distribution of total biomass 6. Summarize the total biomass posterior * Estimated total biomass is the mean of the total biomass posterior * Uncertainty is some lower/upper tail probability quantiles of the posterior --- # Let's do it ```r sp_new <- with(shrimp, expand.grid(x = seq_min_max(x, n = 100), y = seq_min_max(y, n = 100), year = 2007)) Xp <- predict(m_spt, newdata = sp_new, type = "lpmatrix") ## work out now which points are too far now too_far <- exclude.too.far(sp_new$x, sp_new$y, shrimp$x, shrimp$y, dist = 0.1) beta <- coef(m_spt) # vector of model parameters Vb <- vcov(m_spt) # default is the bayesian covariance matrix set.seed(42) beta_sim <- rmvn(n = 1000, beta, Vb) # simulate parameters eta_p <- Xp %*% t(beta_sim) # form linear predictor values mu_p <- inv_link(m_spt)(eta_p) # apply inverse link function ``` Columns of `mu_p` contain the expected or mean biomass for each grid cell per area trawled Sum the columns of `mu_p` and summarize --- # Summarize the expected biomass ```r mu_copy <- mu_p # copy mu_p mu_copy[too_far, ] <- NA # set cells too far from data to be NA total_biomass <- colSums(mu_copy, na.rm = TRUE) # total biomass over the region mean(total_biomass) ``` ``` ## [1] 1564741 ``` ```r quantile(total_biomass, probs = c(0.025, 0.975)) ``` ``` ## 2.5% 97.5% ## 1394264 1757706 ``` --- # Summarize the expected biomass ![](04-predictions-and-variance_files/figure-html/total-biomass-histogram-1.svg)<!-- --> --- # With `fitted_samples()` .row[ .col-7[ ```r bio_post <- fitted_samples(m_spt, n = 1000, newdata = sp_new[!too_far, ], seed = 42) %>% group_by(draw) %>% summarise(total = sum(fitted), .groups = "drop_last") with(bio_post, mean(total)) ``` ``` ## [1] 1561014 ``` ```r with(bio_post, quantile(total, probs = c(0.025, 0.975))) ``` ``` ## 2.5% 97.5% ## 1403512 1753195 ``` ] .col-5[ ```r ggplot(bio_post, aes(x = total)) + geom_histogram() + labs(x = "Total biomass") ``` ![](04-predictions-and-variance_files/figure-html/biomass-fitted-samples-plot-1.svg)<!-- --> ] ] --- class: inverse middle center subsection # Your turn!


--- layout: page title: About header-img: "img/about-bg.png" ---
27 yr old Mother of one, who lives in NYC.


Man in hospital after being stabbed in town centre
by Katy SandallsA man was stabbed in the early hours of Sunday morning outside a town centre pub.
Police were called to the High Street in Colchester at 1am on Sunday, November 17 after reports that a 21-year-old man had been stabbed.
The man was found with a wound to the chest following a disturbance outside the After Office Hours pub near to the taxi rank.
He was taken to hospital but his injury is not believed to be life-threatening at this stage.
A 20-year-old man from Colchester was arrested on suspicion of grievous bodily harm with intent at the scene.
He remains in custody for questioning.
A spokesman for Essex Police said: "We are treating the attack as targeted and we must stress that while the incident happened outside After Office Hours, we do not believe this incident is linked to the venue in any way."
Those with any information about the incident - including mobile footage or dash cam footage - should contact Colchester CID on 101 quoting incident 70 of 17/11.
Alternatively you can contact Crimestoppers anonymously on 0800 555 11.


It allows you to do various tasks such as rooting, backup and more.
Allows you to flash IMEI to your MTK and SpreadTrum Devices.
King Root, Framaroot, Kingo Root, and more root.
It allows you to remove pattern, Pin and Password Lock.
Allows you to easily remove MI Lock on your devices.
Samsung, Coolpad, Micromax etc.


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SphinxBase
5prealpha
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Apply Cepstral Mean Normalization (CMN) to the set of input mfc frames. More...
#include <sphinxbase/sphinxbase_export.h>
#include <sphinxbase/prim_type.h>
#include <sphinxbase/fe.h>
Go to the source code of this file.
Data Structures | |
| struct | cmn_t |
| wrapper of operation of the cepstral mean normalization. More... | |
Macros | |
| #define | CMN_WIN_HWM 800 /* #frames after which window shifted */ |
| #define | CMN_WIN 500 |
Typedefs | |
| typedef enum cmn_type_e | cmn_type_t |
| Types of cepstral mean normalization to apply to the features. | |
Enumerations | |
| enum | cmn_type_e { CMN_NONE = 0, CMN_BATCH, CMN_LIVE } |
| Types of cepstral mean normalization to apply to the features. | |
Functions | |
| SPHINXBASE_EXPORT cmn_type_t | cmn_type_from_str (const char *str) |
| Convert string representation (from command-line) to cmn_type_t. | |
| SPHINXBASE_EXPORT cmn_t * | cmn_init (int32 veclen) |
| SPHINXBASE_EXPORT void | cmn (cmn_t *cmn, mfcc_t **mfc, int32 varnorm, int32 n_frame) |
| CMN for the whole sentence. More... | |
| SPHINXBASE_EXPORT void | cmn_live (cmn_t *cmn, mfcc_t **incep, int32 varnorm, int32 nfr) |
| CMN for one block of data, using live mean. More... | |
| SPHINXBASE_EXPORT void | cmn_live_update (cmn_t *cmn) |
| Update live mean based on observed data. | |
| SPHINXBASE_EXPORT void | cmn_live_set (cmn_t *cmn, mfcc_t const *vec) |
| Set the live mean. | |
| SPHINXBASE_EXPORT void | cmn_live_get (cmn_t *cmn, mfcc_t *vec) |
| Get the live mean. | |
| SPHINXBASE_EXPORT void | cmn_free (cmn_t *cmn) |
Variables | |
| SPHINXBASE_EXPORT const char * | cmn_type_str [] |
| String representations of cmn_type_t values. More... | |
Apply Cepstral Mean Normalization (CMN) to the set of input mfc frames.
By subtractingthe mean of the input from each frame. C0 is also included in this process. This function operates on an entire utterance at a time. Hence, the entire utterance must be available beforehand (batchmode).
Definition in file cmn.h.
| SPHINXBASE_EXPORT void cmn | ( | cmn_t * | cmn, |
| mfcc_t ** | mfc, | ||
| int32 | varnorm, | ||
| int32 | n_frame | ||
| ) |
CMN for the whole sentence.
| cmn | In/Out: cmn normalization, which contains the cmn_mean and cmn_var) |
| mfc | In/Out: mfc[f] = mfc vector in frame f |
| varnorm | In: if not FALSE, variance normalize the input vectors to have unit variance (along each dimension independently); Irrelevant if no cmn is performed |
| n_frame | In: Number of frames of mfc vectors |
| SPHINXBASE_EXPORT void cmn_live | ( | cmn_t * | cmn, |
| mfcc_t ** | incep, | ||
| int32 | varnorm, | ||
| int32 | nfr | ||
| ) |
CMN for one block of data, using live mean.
| cmn | In/Out: cmn normalization, which contains the cmn_mean and cmn_var) |
| incep | In/Out: mfc[f] = mfc vector in frame f |
| varnorm | This flag should always be 0 for live |
| nfr | Number of incoming frames |
Definition at line 145 of file cmn_live.c.
References cmn_t::cmn_mean, E_FATAL, cmn_t::nframe, cmn_t::sum, and cmn_t::veclen.
| SPHINXBASE_EXPORT const char* cmn_type_str[] |
String representations of cmn_type_t values.
Definition at line 59 of file cmn.c.
Referenced by cmn_type_from_str(), and feat_init().


Class GLGE.FilePreloader
Extends
GLGE.Events.
FilePreloader class
Defined in: glge_filepreloader.js.
| Constructor Attributes | Constructor Name and Description |
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| Field Attributes | Field Name and Description |
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0 - 100
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Specifies how many file sizes has been collected
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| Method Attributes | Method Name and Description |
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addFile(url, type, callback)
Add a file which has to be loaded
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addFileRef(file)
Same as addFile.
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getFile(url)
This function returns information about one file.
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This function returns a list (an array) of all loaded files.
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start()
Start loading
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- Methods borrowed from class GLGE.Events:
- addEventListener, fireEvent, removeEventListener
Field Detail
files
0 - 100
progress
Specifies how many file sizes has been collected
Method Detail
addFile(url, type, callback)
Add a file which has to be loaded
- Parameters:
- {string} url
- The url of the file.
- {string} type
- Defines the type of the requested file. "image" or "xml"
- {function} callback Optional
- Call this function when the file is loaded and pass the loaded content.
addFileRef(file)
Same as addFile. But instead of creating a new file object use an existing one.
- Parameters:
- {object} file
- The file to add.
getFile(url)
This function returns information about one file.
- Parameters:
- {string} url
- The url of the file.
getLoadedFiles()
This function returns a list (an array) of all loaded files.
start()
Start loading


@PrepareForTest(value={Scenario.class,Fragment.class,DomainModel.class}) public class ScenarioTest extends TestSetUp
CREATE_FRAGMENT_METHOD, FETCH_DOMAIN_MODEL_METHOD, FETCH_VERSION_METHOD, SET_DOMAIN_MODEL_METHOD, TRUNCATE_TABLES_FILE
| Constructor and Description |
|---|
ScenarioTest() |
| Modifier and Type | Method and Description |
|---|---|
void |
setUp()
Before each Test, create an empty Scenario and mock necessary methods.
|
void |
testConstructorWithURL()
Tests if the url is set correctly inside the constructor.
|
void |
testDataObjectCreation()
This Methods Tests if DataObjects are created correctly.
|
void |
testDomainModelVersionImpactOnSaving()
Test that a scenario is newly saved both when the version of the datamodel has been changed or the datamodel is a new one.
|
void |
testInitializeFromXMLRunsWithoutException()
This Test asserts parsing without exceptions.
|
void |
testMetaData()
This Test asserts that MetaInformation about the Scenario are Set
correctly.
|
void |
testMigration()
Assure that migration is initiated when a scenario is written to the database and a new one is initialized
which is only changed in so far, that new fragments are addded.
|
void |
testModificationInFragmentScenarioNewlySaved()
Assure that when a fragment of a scenario has been modified and the older version of the scenario
is already in the database, it gets saved as a new scenario.
|
void |
testModificationInScenarioScenarioNewlySaved()
Assure that when a scenario itself has been modified and the older version of the scenario
is already in the database, it gets saved as a new scenario.
|
void |
testNoMigrationWhenFragmentAddedAndModificationsExist()
Assure that when a fragment of a scenario has been modified and the older version of the scenario
is already in the database, it gets saved as a new scenario, even when fragments were added (no migration!).
|
void |
testNoMigrationWhenFragmentDeletedDespiteNewFragment()
Assure that when a fragment of a scenario has been deleted and the older version of the scenario
is already in the database, it gets saved as a new scenario, even when fragments were added (no migration!).
|
void |
testSameScenarioNotDeletedSavedAgain()
Assure that a unchanged scenario is written to the database once again, if the old scenario in the database which
seems to be the same has been deleted.
|
void |
testSameScenarioNotSavedTwice()
Assure that a unchanged scenario is not written to the database once again.
|
void |
testSaveCompleteScenario()
Test if the scenario is created and initialized correctly.
|
void |
testSetDomainURL()
This method tests if the given URL of an XML is correctly set.
|
void |
testTerminationCondition()
This Methods Tests if the TerminationCondition is set correctly.
|
void |
testVersion()
This Methods tests whether the version is set correctly or not.
|
emptyDatabase, getDocumentFromXmlFile, initializeCompleteScenario, initializeDomainModel, initializeFragment, resetDatabase
public void setUp() throws java.lang.Exception
java.lang.Exception
- java.lang.Exceptionpublic void testInitializeFromXMLRunsWithoutException() throws java.lang.Exception
java.lang.Exception
- java.lang.Exceptionpublic void testSetDomainURL() throws java.lang.Exception
java.lang.Exception
- java.lang.Exceptionpublic void testConstructorWithURL()
public void testMetaData() throws java.lang.Exception
java.lang.Exception
- java.lang.Exceptionpublic void testVersion() throws java.lang.Exception
java.lang.Exception
- occurs while creating the MockObject.public void testDataObjectCreation() throws java.lang.Exception
java.lang.Exception
- occurs if creation of MockObject failed.public void testTerminationCondition() throws java.lang.Exception
java.lang.Exception
- occurs if creation of MockObject failed.public void testSaveCompleteScenario() throws java.lang.Exception
java.lang.Exception
- java.lang.Exceptionpublic void testSameScenarioNotSavedTwice() throws java.lang.Exception
java.lang.Exception
- java.lang.Exceptionpublic void testSameScenarioNotDeletedSavedAgain() throws java.lang.Exception
java.lang.Exception
- java.lang.Exceptionpublic void testModificationInFragmentScenarioNewlySaved() throws java.lang.Exception
java.lang.Exception
- java.lang.Exceptionpublic void testModificationInScenarioScenarioNewlySaved() throws java.lang.Exception
java.lang.Exception
public void testNoMigrationWhenFragmentAddedAndModificationsExist() throws java.lang.Exception
java.lang.Exception
- java.lang.Exceptionpublic void testNoMigrationWhenFragmentDeletedDespiteNewFragment() throws java.lang.Exception
java.lang.Exception
- java.lang.Exceptionpublic void testMigration() throws java.lang.Exception
java.lang.Exception
- java.lang.Exceptionpublic void testDomainModelVersionImpactOnSaving() throws java.lang.Exception
java.lang.Exception
- java.lang.Exception


About Me
WHO I AM
I'm a Toronto based enthusiastic full stack software developer and hobbyists / control engineer from Nigeria, very passionate about software design and development, engineering and technology. I am self driven, focused, and hard-working with 9+ years of software development experience and a proven ability to lead others and oversee daily operations within fast-paced environments.
A little of an introvert but I socialize well when not busy. Although my career path has taken many twists and turns — from studying electrical engineering, working as a software engineer and later proceeding to obtain a masters degree in control science and engineering — I've always had a passion for solving problems by implementing hardware and software automated systems.
WHAT I DO
I create desktop, mobile and web based software applications. Experienced in managing large-scale technical projects, creating innovative applications, maintaining internal application systems, and making appropriate recommendations based on organizational technical needs. Equipped with an ability to improve operational efficiency levels in addition to maintaining strong communication with others.
FREE TIME
In my free time I travel a lot, read tech related journals, listen to good music and watch movies.
Skills
Experience
The interface to commercial insurance for developers.
Senior Software Engineer
- Solution architecture and RFC development to define and streamline product/feature development.
- Develop libraries and systems with organization-wide impact.
- Expedite third party API integrations by developing open api specifications (OAS) tools for code auto generation in typescript.
- Adequate PCI compliance mechanisms to ensure secure payment collection over multiple (custom) processors (stripe, adyen, chase etc).
- Custom API client authentication and permission rules using revokable keys.
- API performance monitoring and instrumentation (using datadog) to identify and optimize endpoint responses in accordance with client SLAs.
- Accountable for code running on production including monitoring for errors and hotfixes.
- Co-write and review design documentation.
- Technical mentor for junior engineers.
- Participate in cross-team technical meetings, learning and demo sessions.
- Conduct and help define technical interview sessions for new potential hires.
- Advocate and promote software development best practices.
AWS Infrastructure Engineer
- Update infrastructure as a code using terraform to support feature rollouts.
- Refactor AWS resources to enhance scaling.
- Integrate AWS to application and performance monitoring (datadog) as well as Opsgenie pager for oncall engineer rotation.
- Speed up github action (GHA) CI test runners by batching and parallel execution of unit and integration tests.
- VGS integration management in code.
Stack: Typescript / NodeJS / Vue / AWS
Making subscription commerce work.
AWS Infrastructure Engineer
- Create infrastructure as a code for all client projects to aid creation and update.
- Improve product performance by integrating and updating resources (CDN, caching, code optimization, distributed computing).
- Restructure product AWS resources to support horizontal scaling.
- Setup dev, sandbox and production environments to aid product development and testing.
- Setup infrastructure to support continuous integration and delivery for over 40 clients
- Implement client frontend and backend features from requirement specifications.
Stack: Typescript / NodeJS / Angular / AWS
First in Air Quality.
Core AirVisual Backend Development
- Successful migration of Airvisual's monolithic backend to amazon web service's serverless architecture.
- Designed an AI system which boosted the quality of air pollution measurement AQI accuracy to 86%.
- Designed a pollution forecast system using advanced machine learning techniques.
- Low cost pollution sensor (purpleair and airvisual pro devices) measurement calibration using artificial neural networks.
- Time series analysis of pollution measurement quality - anomaly detection and validation
- Pollution forecast scrapping from government sources.
- Image manipulation and masked image merging based on source region accuracy.
- Maptiles generation and serving from merged forecast maps. Used for rendering of the airvisual world pollution map and airvisual earth.
Stack: Javascript / NodeJS / Python / AWS / Serverless / Machine Learning
Designed, developed, deployed and currently maintaining the freebe utility application (Frontend and backend).
Product Features
- Quick Pay to send money anywhere.
- QRCode scan to receive money anytime.
- Bank accounts, ATM cards and virtual wallet management.
- Easy money transfer to freebe users, phone contacts and direct bank transfers.
- Mobile airtime topup.
- DSTV, Startimes, GoTv, LCC etc utility bill payments.
- Mobile enabled enterprise resource management (ERP) module for business management.
- Uber-like order taxi.
- Hotel booking reservations.
- Purchase cinema movie tickets.
- Purchase flight tickets.
- Home delivery services.
- Much more.
Stack: Docker / NodeJS / Microservices / React-Native / AngularJS / MongoDB / AWS / Javascript / CSS
Delivering business value through custom enterprise solutions.
Developed Core Product
- Verify, prioritize, and fix software bugs
- Build new features, both via product roadmap and self-designed
- Manage feature sprint life cycle
- process product requirements
- assess current functionality
- estimate total work time and merge dates
- break down feature into smaller deliverables
- solicit feedback regularly
- continuously measure and document progress
- analyze sprint execution retrospectively
Stack: Microsoft C# .NET / Java / SharePoint / Liferay / PostgreSQL / Javascript / jQuery / CSS
Accomplishments
- Team lead in the development of a product distribution and fleet management automation ERP solution for a Nigerian oil and gas company (Oando PLC). Increased fleet scheduling efficiency, delivery time and overall productivity.
- Backend developer (team member) in the implemention of an online high school management grid system
- Android developer in a social messaging application (Shuffle).
- Airtel Know You Customer development team contributor.
- Backend developer (team member) in the design of a payroll application PrimePay.
Portfolio
Explore the air quality anywhere in the world. Air quality solution for organizations for healthy buildings and happy occupants. Improve your health with air quality knowledge and forecasting.
Accomplishments
- Web based and mobile client console
- Inventory module
- Sales Module
- Purchase Module
- Human resource management Module
- Finance and account management Module
- Integrated to the Moneywave API for fund transfer (Account credit/debit)
Freebe is an instant fund transfer service backed with an enteprise resource planning backend. It's mobile application enables a payer/initiator perform mobile fund transfer by simply scanning a payee generated QR code or by entering its destination bank account details.
Accomplishments
- Online product purchase and delivery module.
- Fleet management module.
- Integrated to Oando PLC Oracle ERP.
- Custom J2ME developed mobile application for fleet management and inspection.
Design and development of a shopping cart, called EVAP (Electronic Value Added peddling) in 2011. This has become a fast, convenient and efficient way to get Oando products delivered to customers at their doorsteps!
Accomplishments
- Integrated to Oando PLC Oracle ERP.
- Custom J2ME developed mobile application for fleet management and inspection.
KYC captures essential subscriber/customer bio-data (Name, DOB, contact info, photo & fingerprint etc), which is temporarily stored on the client device and pushed to a central database for record purpose.
Accomplishments
-
Employee Features
Employee Information Management, Employee Self Onboarding, Employee Self Service Accounts and Email Payslips For Employees
-
Automated Tasks
Payroll on Autopilot, Deductions & Bonuses, Supports Multiple Tax laws (PIT/PAYE), Contributory Pensions & Health Insurance
-
General Features
Dashboards & Charts, Detailed Reports at Your Fingertips, Flexible Salary Structure, Electronic Payments Ready
-
Support & Security
24/7 Mobile & Web Access, Highly Secure System, Efficient Customer Support
PrimePayroll™ is an enterprise cloud service for Human resource and payroll management, tailored to the needs of small and medium enterprises in Nigeria. It adopts best practices while conforming to a high degree of flexibility in its architecture...
Designed to ease local hookups, find hot spots, bars and restaurants. Back parkings will find this application very interesting as meeting new friends as never been easier.
An online mimic of a library, users can register and borrow books from others. It is a demo application based on a FreeCodeCamp courseware. Integrated with Twitter and Facebook.
Education
- PhD Control Science and Engineering (In view)
- M.Sc Control Science and Engineering
- B.Eng Electrical and Electronics Engineering (Magnum Cum Laude)
Certificates and Awards
Contact
Currently, I am available for hire/employement and open to any ideas of cooperation.


Stephanie loves hiking and camping in the great outdoors with her family, reading and watching science fiction, and sleeping in as much as is allowed by her four-year-old son. She has yet to grow carrots longer than an inch, but has had surprising success with citrus trees in the pacific northwest. Someday she hopes to build the educational game of her dreams.
Easily manipulated by dogs and fantastic at killing house plants. Nerds out over Python.
Mark is a technology and product management executive with a passion for creating products that delight customers and drive revenue growth. When he’s not at work, Mark loves to surf, cycle, skateboard, play volleyball and spend as much time as possible with his two children, Milo and Violet.
Eats salad for breakfast. Philosophizes over beer. Dreams of finding time to play Viva Pinata.


See: Description
| Interface | Description |
|---|---|
| Action |
Interface describing how a SAML1.1
Action element behaves. |
| Advice |
This interface defines how the object representing a SAML 1
Advice element behaves. |
| Assertion |
This interface defines how the object representing a SAML 1
Assertion element behaves. |
| AssertionArtifact |
This interface is for the SAML1
AssertionArtifact element. |
| AssertionIDReference |
This interface defines how the object representing a SAML 1
AssertionIDReference element behaves. |
| Attribute |
This interface defines how the object representing a SAML 1
Attribute element behaves. |
| AttributeDesignator |
This interface defines how the object representing a SAML 1
AttributeDesignator element behaves. |
| AttributeQuery |
Description of the behaviour of the
AttributeQuery element. |
| AttributeStatement |
This interface defines how the object representing a SAML 1
AttributeStatement element behaves. |
| AttributeValue |
This interface defines how the object representing a SAML 1
AttributeValue element behaves. |
| Audience |
This interface describes how an object representing a SAML1
Audience element will behave. |
| AudienceRestrictionCondition |
This interface describes how ab object representing a SAML1
AudienceRestrictionCondition element will
behave. |
| AuthenticationQuery |
Description of the behaviour of the
AuthenticationQuery element. |
| AuthenticationStatement |
This interface defines how the object representing a SAML1
AuthenticationStatment element behaves. |
| AuthorityBinding |
Interface to define how a
AuthorityBinding element behaves. |
| AuthorizationDecisionQuery |
Description of the behaviour of the
AuthorizationDecisionQuery element. |
| AuthorizationDecisionStatement |
This interface defines how the object representing a SAML1
AuthorizationDecisionStatement element
behaves. |
| Condition |
This interface describes how a object representing a SAML1
Condition element will behave. |
| Conditions |
This interface defines how the object representing a SAML1
Conditions element behaves. |
| ConfirmationMethod |
This interface defines how the object representing a SAML1
ConfirmationMethod element behaves. |
| DoNotCacheCondition |
This interface describes how ab object representing a SAML1
DoNotCacheCondition element will behave. |
| Evidence |
Interface describing how a SAML1.1
Evidence element behaves. |
| Evidentiary |
Marker interface for element types that can constitute evidence within a
Evidence object. |
| NameIdentifier |
Interface to define how a NameIdentifier element behaves.
|
| Query |
This interface is for the SAML1
Query extention point. |
| Request |
This interface defines how the SAML1
Request objects behave. |
| RequestAbstractType |
This interface describes the base class for types derived from
RequestAbstractType . |
| RespondWith |
This interface defines how the (deprecated) RespondWith element would behave.
|
| Response |
This interface defines how the object representing a SAML1
Response element behaves. |
| ResponseAbstractType |
This interface defines the base class for type derived from the SAML1
ResponseAbstractType . |
| Statement |
This interface defines how the object representing a SAML1
Statement element behaves. |
| Status |
This interface defines how the object representing a SAML 1
Status element behaves. |
| StatusCode |
This interface defines how the object representing a SAML 1
StatusCode element behaves. |
| StatusDetail |
Interface to describe how a
StatusDetail element behaves. |
| StatusMessage |
This interface defines how the object representing a SAML1
StatusMessage element behaves. |
| Subject |
Interface to describe how the
Subject elements work. |
| SubjectConfirmation |
Interface to define how a SubjectConfirmation element behaves.
|
| SubjectConfirmationData |
This interface defines how the object representing a SAML1
SubjectConfirmationData element behaves. |
| SubjectLocality |
Interface to define how a SubjectLocality element behaves.
|
| SubjectQuery |
This interface is for the SAML1
SubjectQuery extention point. |
| SubjectStatement |
Interface to descibe how the anchor point SubjectStatement would work.
|
| Class | Description |
|---|---|
| DecisionTypeEnumeration |
A type safe "enumeration" of
AuthorizationDecisionStatement Decision types. |
Copyright © 1999–2016 Shibboleth Consortium. All rights reserved.


8nook, based on 8values, is an Animal Crossing personality test that attempts to assign percentages for eight different personality values. You will be presented with a statement, and then you will answer with your opinion on the statement, from Strongly Agree to Strongly Disagree, with each answer slightly affecting your scores. At the end of the quiz, your answers will be compared to the maximum possible for each value, thus giving you a percentage. Answer honestly!
There are questions in the test.
There are four independent axes — Intensity, Desire, Style, and Organization — and each has two opposing values assigned to them. They are:
Ambition (Intensity)
Players with high Ambition scores tend to play efficiently and vigorously. They aim to achieve as much as possible and strive to "complete" the game, whatever that may mean to them. They may choose to indulge in time travel and glitch exploitation. They derive enjoyment from playing the game to its fullest extent.
Serenity (Intensity)
Players with high Serenity scores take the game at a slow pace. To these players, the game is just a game. There is little to no sense of "completion", and they may ignore certain aspects of the game entirely. They value relaxation over achievement.
Material (Desire)
Players with high Material scores derive enjoyment from obtaining items and completing milestones. When playing with others, it is not so much the multiplayer interaction that matters, but the mutual material benefit that can be realized. They may prefer to keep to themselves, focusing on personal progress.
Social (Desire)
Players with high Social scores adore interaction with other human players. It is not the goods exchanged between players that matters, but the act of trading itself. They may compare their island to others'. To them, the game is truly a multiplayer experience. Isolating oneself is severely limiting.
Experience (Style)
Players with high Experience scores see the game as a social simulation. These players enjoy cultivating friendships with villagers, eagerly embracing their dialogue. They especially appreciate the relaxing ambiance, dynamic environments, and seasonal events of the game. They want to see and explore everything. They may enjoy taking photos and visiting Photopia. Their island is desirable as-is, and changes are only icing on the cake.
Creation (Style)
Players with high Creation scores see the game as a construction sandbox. These players enjoy terraforming, creating custom designs, and furnishing their homes. Their enjoyment of the game stems from having ideas and painting them into reality. Their island is not desirable as-is, but must be made so through their creative energy.
Flow (Organization)
Players with high Flow scores play through spontaneity and momentary passions. They find enjoyment in starting up the game without pre-planned commitments. They may jump between activities frequently. To them, the game is best played by doing exactly what they want in the moment without regard to the past or future.
Structure (Organization)
Players with high Structure scores play through plans and routines. They desire an island that is orderly both in form and function. They may have a list of tasks for a given day and schedule time to complete them. In creative endeavours, they strive to design neatly and to an excellent standard.
In addition to matching you to the eight personality values, the quiz also attempts to match you to a proper personality type.
A for Ambition, S for Serenity, M for Material, O for sOcial, E for Experience, C for Creation, F for Flow, and T for sTructure.
The dominant value for each axis will yield a letter for a total of four. In some cases, an x will be rendered indicating that you are close to the middle.
Simply sharing your four-letter personality type is the quickest, but most reductive, way to share your results.
My name is Brian. I've loved Animal Crossing since I played it on the GameCube as a kid. So I thought I'd screw around a bit and make this personality quiz.
My college roommates gave me valuable feedback during the development process. You can find out more about them by clicking the obnoxious banner below.
If there's interest, I might publish a video talking about my interpretations of the values, and what you can glean from them.
Thanks for playing!
This test is imperfect, but that's OK because this is a seaplane.
If you have any suggestions or constructive criticism, feel free to get in contact with myself and my roommates in our Discord channel.
You're wrong if you think I'm done developing the test! Based on your feedback, it will be tweaked to be more accurate, or simply more fun.


Please Choose Game Mode
Choose Mode
Computer Vs Human
Human Vs Human
Computer Vs Computer (comming soon)
Tic Tac Toe
Draw!
You Lost!
You Won!
Player 1 (O)Won!
Player 2 (X)Won!
Reset
Player2's (X)Turn
Player1's (O)Turn
Computer's' Turn
Your Turn


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/ordering.20natural.20numbers.html ---
It appears that lean can't synthesize what appears to be a natural order on natural numbers, and neither can I.
I'm a bit lost on what to do. I think I need something: linear_ordered_field nat
but not sure how to get that something
. Any help would be greatly appreciated. The terms I'm trying to produce are called DOESNTWORK
and DOESNTWORKEITHER
and are at the bottom of the second lemma.
def gt1or0 (a : nat) : a = 0 ∨ a > 0 := sorry def multiply_is_no_less (a b : nat) (P : b ≠ 0) : a <= a * b := or.elim (gt1or0 a) (λ (H : a = 0), have A : 0 <= 0, from dec_trivial, have B : 0 = 0 * b, from eq.symm (zero_mul b), have C : 0 <= 0 * b, from eq.subst B A, have D : 0 = a, from eq.symm H, show a <= a * b, from eq.subst D C ) (λ (H : a > 0), have A : b > 1, from sorry, -- this won't be automatically true, but we can get it from appropriate or.elim or similarly. have DOESNTWORK : a < a * b, from lt_mul_of_gt_one_right H A, have DOESNTWORKEITHER : a < a * b, from @lt_mul_of_gt_one_right nat _ b a H A, sorry )
In maths, a field is something with + - * /
like the rationals or reals.
nat is no good because no -
and no /
In particular you can't use the general result lt_mul_of_gt_one_right
on nat
Ah of course. It's a bad lemma then.
You might well find though, that the lemma you want is there anyway
because when it comes to nat, the library seems to me to be fairly robust and complete
In fact it would not surprise me if pretty much anything like this that you wanted to prove was either there (although you already found a counterexample to that with the div thing) or was easily provable from what is there.
I know what each of the words linear
, field
and ordered
mean, but of course typing was faster than thinking this time :D. I'll have a look, thanks!
For gt1or0
(which maybe should be called gt0or0
) my instinct (in VS Code) is to type #check nat.pos_of
and then hit esc, ctrl-space, esc,ctrl-space (the lean plugin is a bit buggy in this regard)
and to see what comes up.
nat.pos_of...
means "proof, specific to nat, that something is positive if..."
and then we find nat.pos_of_ne_zero
Similarly, #check nat.le_mul
(ctrl-space dance) leads you to nat.mul_le_mul_right
Yup, looks like nat.pos_of_ne_zero
will work (I can't reproduce your problems with the plugin, it works for me no problem, which is strange, since we're both using ubuntu 16.04 if I recall correctly).
To prove a = a * 1
(I am suggesting proving a <= a * b
by proving b >= 1
and a = a * 1
and the lemma) that's just mul_one
The issue with the plugin, which you might run into at some point, is that sometimes ctrl-space gives you a list of possibilities, and then esc and ctrl-space again gives you a bigger list!
It's difficult to reproduce reliably
nat.succ_le_of_lt
will get you from b > 0
to b >= 1
, and you can guess how I found this
In fact I could have guessed the name of that lemma without even searching.
(note that le and lt are preferred to ge and gt in the naming convention)
One naming convention gotcha -- div
is /
and and dvd
is |
(or more precisely \|
)
and sub
and neg
are different -- one is binary, one unary
but the rigorous naming conventions make looking through the library much easier and if you're proving stuff about nat then it should be really helpful for you.
Last thing: example : decidable_linear_order ℕ := by apply_instance
would have been the answer if the instance had been there. But stuff like decidable_linear_order
is part of the type class system, so should all happen magically (indeed apply_instance
is a tactic which invokes the magic).
great, I think things will be much easier from now on. Thank you! I'm trying to show there are infinitely many primes, which I think is a good exercise, although it's already in mathlib:
https://github.com/leanprover/mathlib/blob/08f19fde695d20cf1bd899969a1c59b350dd9e43/data/nat/prime.lean#L201
Last updated: Apr 24 2020 at 19:02 UTC


Pomodoro Clock
Break Length
Session Length
-
05
+
-
25
+
Session
Restart
Session and break length can only be changed while paused, and cannot be less than zero.


--- layout: post status: publish published: true title: 'NLCS Game #1 Preview - Episode 8.65' author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 12126 wordpress_url: http://ivyenvy.com/?p=12126 date: '2016-10-14 01:07:19 +0000' date_gmt: '2016-10-14 06:07:19 +0000' categories: - Podcast tags: [] comments: [] ---
On this episode, we preview the 2016 NLCS. The series starts on Saturday at Wrigley Field, as the Cubs host the Dodgers. We talk about how these two teams fared against each other in 2016.
We also look at the Game #1 starting pitchers and how the opposing teams have recently performed against Lester and Maeda.
Save 20% off Ivy Envy shirts by using the coupon code NLCS at http://ivyenvy.com/store


SS - book your trip today
Traveling agency for a trip between Helsinki and Turku
My fares
Book a fare
We are offering a great travel package between Turku and Helsinki on our new ship MS Monsoon.
Book a trip


WTime stands for "WorldTime".
This is a C++ and Java time library for representing date/time and timespans with support and conversions to/from local, solar, and GMT/UTC times. Corrections for daylight savings, etc. (are also included but are used less frequently in software for which this package was developed).
This utility provides the following features:
Microsecond accuracy. Not all libraries and representations provide accurate arithmetic. If floating point representations are used, then standard error propagation can occur over repeated operations. Thus, a 64-bit integer representation is used.
Useful over long period of times. The Gregorian calendar was introduced in 1582, so this library arbitrarily starts in the year 1600. Conversions from the prior calendar require overhead and aren't needed for the intent of this library. With a 64-bit representation, it is useful for centuries into the future.
various printing and parsing options for presentation and extraction of date and time fields. Note there's always room for further improvement here.
Functionality to determine when the sun rises, sets, and is at solar noon, given a specific location and date.
Basic serialization.
Consistency w/r to usage, regardless of the precise time, or timezone, being requested.
Conversion routines to/from some (not all) other existing time formats.
The C++ version includes project files for use in Visual Studio 2010 or newer (tested with both MSVC 2010 and Intel Studio 13.0) and autotools files which have been tested with GCC 4.5.3.
The Java version contains Eclipse project files as well as an Ant build script. It requires at least JRE7. The Java library depends on the HSS_Java library.
//Create a time for someone in New York City
WTime time(2016, 2, 11, 6, 0, 0, new TimeManager(WorldLocation(40.7127, -74.0059, true)));
//Construct a time from a string of known format
time.ParseDateTime("2016/02/12 22:30:00", NEWTIME_FORMAT_STRING_YYYYhMMhDD | NEWTIME_FORMAT_TIME);
//Print a time to a string
std::string str = time.ToString(NEWTIME_FORMAT_AS_LOCAL | NEWTIME_FORMAT_WITHDST | NEWTIME_FORMAT_DATE | NEWTIME_FORMAT_TIME | NEWTIME_FORMAT_YEAR | NEWTIME_FORMAT_ABBREV);
//Output from example 2: Feb 12, 2016 22:30:00
//Java Calendar to WTime
Calendar c = Calendar.getInstance();
WTime time = new WTime(c.get(Calendar.YEAR), c.get(Calendar.MONTH) + 1, c.get(Calendar.DAY_OF_MONTH), c.get(Calendar.HOUR_OF_DAY), c.get(Calendar.MINUTE), c.get(Calendar.SECOND), new WTimeManager(new WorldLocation()));
//Create a time span of 5 hours and add it to the current time
WTimeSpan span = new WTimeSpan(0, 5, 0, 0);
time.Add(span);
WTime time = new WTime(2016, 2, 11, 9, 30, 0, new WTimeManager(new WorldLocation()));
System.out.println(time.toString(NEWTIME_FORMAT_AS_LOCAL | NEWTIME_FORMAT_WITHDST | NEWTIME_FORMAT_DATE | NEWTIME_FORMAT_TIME | NEWTIME_FORMAT_YEAR | NEWTIME_FORMAT_ABBREV));
//Output: Feb 11, 2016 9:30:00
Times are stored as the number of microseconds since January 1, 1600. It is not possible to store times before January 1, 1600.
No concern for leap seconds. There is discussion on the utility of these in the real world, so until the two camps come to a conclusion, there's no need for this library to handle them, at this time.
Although many time zones are accounted for, the list may never be deemed complete, nor is there a way to express a preference for one time zone or another, when more than one exists for a given correction from UTC.
Arctic and Antarctic circles may or may not have time zones, depending on the source. This library hasn't been tested for this range.
The library assumes that the client code will configure it appropriately when used for the timezone and settings of the computer's configuration. The library was built for projects where a computer may be located in a different timezone than the project data.
Please use GitHub issues and Pull Requests for support.
Copyright 2016 Heartland Software Solutions Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
| ca.hss.times | A time library to replace the built in Calendar and Date classes. |


About Me (Mamoun Hussein)
- Eduction: Physics from BAU With 3.3 GPA (2016-2020)1
- Staff at Texas chicken(06/2014-09/2014)
- Part time sales and social media manager Acrylic&More (08/2017-2/2019)
- Call center 3 Weeks Projects It beep (01/2020-02/2020)
- 1.Rainbow Six Siege
- 2.Rocket League
- 3.League Of Legends
- 4.Mine Craft
- 5.Fortinet
- 6.Brawlhalla
- 7.Among Us
- 8.Horizon Zero Dawn
- 9.Shadow Of Coleuses
- 10.Apex Legend
Top Ten Video Games I like to Play:


{% comment %} This is where we should place scripts that need to load last on the page {% endcomment %}


Deterministic Pena-Yohai initial estimator for robust S estimators of regression. The procedure is described in detail in Pena, D., & Yohai, V. (1999) <doi:10.2307/2670164>.
| Version: | 1.0.2 |
| Imports: | robustbase |
| Published: | 2018-03-14 |
| Author: | David Kepplinger [aut, cre], Matias Salibian-Barrera [aut], Gabriela Cohen Freue [aut] |
| Maintainer: | David Kepplinger <david.kepplinger at gmail.com> |
| BugReports: | https://github.com/dakep/pyinit/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Copyright: | See the file COPYRIGHTS for copyright details on some of the
functions. pyinit copyright details |
| URL: | https://github.com/dakep/pyinit |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | pyinit results |
| Reference manual: | pyinit.pdf |
| Package source: | pyinit_1.0.2.tar.gz |
| Windows binaries: | r-devel: pyinit_1.0.2.zip, r-release: pyinit_1.0.2.zip, r-oldrel: pyinit_1.0.2.zip |
| OS X binaries: | r-release: pyinit_1.0.2.tgz, r-oldrel: pyinit_1.0.2.tgz |
Please use the canonical form https://CRAN.R-project.org/package=pyinit to link to this page.


Check out my google scholar or semantic scholar pages for a list of publications.
My undergrad theses can be found below:


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About Me
Thomas is a fledgling Full Stack Web Developer with a background in Philosophy, French and Music Production. A teacher and student both professionally and as a hobby, he finds learning and teaching to be mirror images of what it means to have a deep engagement with knowledge and information. He excels in abstract thinking and synthesizing ideas from disparate disciplines into effective solutions, regardless of obstacles. Friendly and personable, he is capable of boosting morale or creating an environment for critical analysis as needed. It is also possible that he is a cat.


Expose a resource as a new Kubernetes service.
Looks up a deployment, service, replica set, replication controller or pod by name and uses the selector for that resource as the selector for a new service on the specified port. A deployment or replica set will be exposed as a service only if its selector is convertible to a selector that service supports, i.e. when the selector contains only the matchLabels component. Note that if no port is specified via --port and the exposed resource has multiple ports, all will be re-used by the new service. Also if no labels are specified, the new service will re-use the labels from the resource it exposes.
Possible resources include (case insensitive):
pod (po), service (svc), replicationcontroller (rc), deployment (deploy), replicaset (rs)
Examples:
# Create a service for a replicated nginx, which serves on port 80 and connects to the containers on port 8000.
kubectl expose rc nginx --port=80 --target-port=8000
# Create a service for a replication controller identified by type and name specified in "nginx-controller.yaml", which serves on port 80 and connects to the containers on port 8000.
kubectl expose -f nginx-controller.yaml --port=80 --target-port=8000
# Create a service for a pod valid-pod, which serves on port 444 with the name "frontend"
kubectl expose pod valid-pod --port=444 --name=frontend
# Create a second service based on the above service, exposing the container port 8443 as port 443 with the name "nginx-https"
kubectl expose service nginx --port=443 --target-port=8443 --name=nginx-https
# Create a service for a replicated streaming application on port 4100 balancing UDP traffic and named 'video-stream'.
kubectl expose rc streamer --port=4100 --protocol=UDP --name=video-stream
# Create a service for a replicated nginx using replica set, which serves on port 80 and connects to the containers on port 8000.
kubectl expose rs nginx --port=80 --target-port=8000
# Create a service for an nginx deployment, which serves on port 80 and connects to the containers on port 8000.
kubectl expose deployment nginx --port=80 --target-port=8000
Options:
--allow-missing-template-keys=true:
If true, ignore any errors in templates when a field or map key is missing in the template. Only applies to golang and jsonpath output formats.
--cluster-ip='':
ClusterIP to be assigned to the service. Leave empty to auto-allocate, or set to 'None' to create a headless service.
--dry-run='none':
Must be "none", "server", or "client". If client strategy, only print the object that would be sent, without sending it. If server strategy, submit server-side request without persisting the resource.
--external-ip='':
Additional external IP address (not managed by Kubernetes) to accept for the service. If this IP is routed to a node, the service can be accessed by this IP in addition to its generated service IP.
-f,
--filename=[]: Filename, directory, or URL to files identifying the resource to expose a service
--generator='service/v2':
The name of the API generator to use. There are 2 generators: 'service/v1' and 'service/v2'. The only difference between them is that service port in v1 is named 'default', while it is left unnamed in v2. Default is 'service/v2'.
-k,
--kustomize='': Process the kustomization directory. This flag can't be used together with -f or -R.
-l,
--labels='': Labels to apply to the service created by this call.
--load-balancer-ip='':
IP to assign to the LoadBalancer. If empty, an ephemeral IP will be created and used (cloud-provider specific).
--name='':
The name for the newly created object.
-o,
--output='': Output format. One of: json|yaml|name|go-template|go-template-file|template|templatefile|jsonpath|jsonpath-file.
--overrides='':
An inline JSON override for the generated object. If this is non-empty, it is used to override the generated object. Requires that the object supply a valid apiVersion field.
--port='':
The port that the service should serve on. Copied from the resource being exposed, if unspecified
--protocol='':
The network protocol for the service to be created. Default is 'TCP'.
--record=false:
Record current kubectl command in the resource annotation. If set to false, do not record the command. If set to true, record the command. If not set, default to updating the existing annotation value only if one already exists.
-R,
--recursive=false: Process the directory used in -f, --filename recursively. Useful when you want to manage related manifests organized within the same directory.
--save-config=false:
If true, the configuration of current object will be saved in its annotation. Otherwise, the annotation will be unchanged. This flag is useful when you want to perform kubectl apply on this object in the future.
--selector='':
A label selector to use for this service. Only equality-based selector requirements are supported. If empty (the default) infer the selector from the replication controller or replica set.)
--session-affinity='':
If non-empty, set the session affinity for the service to this; legal values: 'None', 'ClientIP'
--target-port='':
Name or number for the port on the container that the service should direct traffic to. Optional.
--template='':
Template string or path to template file to use when -o=go-template, -o=go-template-file. The template format is golang templates [http://golang.org/pkg/text/template/#pkg-overview].
--type='':
Type for this service: ClusterIP, NodePort, LoadBalancer, or ExternalName. Default is 'ClusterIP'.
Usage:
kubectl expose (-f FILENAME | TYPE NAME) [--port=port] [--protocol=TCP|UDP|SCTP] [--target-port=number-or-name] [--name=name] [--external-ip=external-ip-of-service] [--type=type] [options]
Use "kubectl options" for a list of global command-line options (applies to all commands).


{% extends "layout.html" %} {% block content %}
I was really pissed off with this sem's results and also with the fact that some third party bcsociety is hosting our results(seriously, WHY BUT???) so I made this. [Though all results are crawled from there].


WITNESS LIST Government Organization Committee April 13, 2005 -10:00A SB 1651 FOR: Waring, Colleen Auditor (Institute of Internal Auditors), Austin, TX ON: Anderson, Urton Professor of Internal Auditing (The Institute of Internal Auditors), Austin, TX Crain, Christina Melton Chairman (Texas Board of Criminal Justice), Dallas, TX Flood, Brian OIG (HHSC), Austin, TX Moriarty, John Inspector General (TDCG- OIG), Austin, TX Registering, but not testifying: On: Betts, Albert Chief of Staff (Texas Department of Insurance), Austin, TX Hawkins, Albert Commissioner (Health and Human Services Commission), Austin, TX Livingston, Brad Executive Director (Texas Department of Criminal Justice), Houston, TX Moore, John Director, Regulatory Enforcement (Texas Workforce Commission), Austin, TX Pompa, Dennis Associate Commissioner, Fraud Unit (Texas Department of Insurance), Austin, TX


Mind is a movement. It has to flow either towards samsara or towards Ishvara. There is no 3rd flow. As it contemplates upon Ishvara there is a reasonless joy.
Mind becomes what it things upon. When mind contemplates upon Shiva that is enough to bring out inherent joy as wave after wave.
Bhakti is an emotion. It is not knowledge, it is emotion. It prepares the mind for higher knowledge by purifying.
Shivas feet - lotus like - aravinda. Aravinda is the lotus which blossoms as the sun rises in the east. Sun stands for knowledge, for atma. Aravidma stands for the mind which is ready to receive that knowledge. Isvaras feet are always compared with the lotus.
If you take literally that becomes religion. If you take spirit, it becomes philosophy.
Spirit of holding on to the feet of Shiva is total self-surrender (sharanagati).
Always contemplate: consistently, perseveringly. Not every second of the waking state. Instead of contemplating things of the world we may better contemplate on Lord Shiva.
A heavy laden mind cannot go towards Isvara. The mind should be simple, subtle, light. Story in Purana: when a person dies there is a devaduta standing there with a balance. The suksha sharira (mind) is put into one pan and in the other weights are put.  Therefore our emotions should be simple.
The river, no matter where it has taken birth, it has only one goal - ocean. The mind should overcome the absorbing power of samsara and ultimately reach the ocean.
It is Isvara who makes the first step, then it must be our effort. Like a creeper. It chooses a tree and then starts growing in that direction.
Inching towards Isvara is equivalent to inching away from samsara. Ramakrishna: Love towards Isvara grows in direct proportion to dispassion to the things of the world. You go towards east and west simultaneously. Dispassion towards things of the world and motiveless devotion to Isvara progress simultaneously. The more we get entangled in samsara, the more devotion gets less and less.
There is no chaos in the world except the chaos you have in your mind.
The devotion should not be seasonal (when things go well).
Devotion is entirely our effort. Isvara made his will very clear - come near me and I will protect you. Now it is for us to go near.
The river just relinquishes names and form. It doesn’t give up its true individuality of being (water). It becomes one with the infinite. Bhakti becomes one with the infinite but it must be in such a way that egoity must be neutralized.
Egoic state - where there is an effort to preserve the identity.
Love for the world is called attachment. The same love directed towards Ishvara is called boundless.
Love is such a thing, unless we put bounds in ignorance, it has no bounds. Just like space. Mahatmas don’t restrict the love.
Samsari’s love is a vector. Jnanis love is a scalar.
There is notion that beauty has to come from outside things by reorganizing things. Same for happiness. Societies across the globe are moved by this notion. Because I’m disenchanted in the heart and I want some respite from it.
Doing is not intrinsic to atma. Atma is being and knowing. Doing is there and there is purpose for it - antahkarana shuddhi.
Mind is limited in the sense that whatever it can think of is always limited.
What is the essence of a tree? Fruit. What is the essence of cow? Milk.  Ishvara bhakti and jnanam.
Except bhakti there is nothing else in the heart. Then you have the crop, which is self-knowledge. That life only is fruitful. Any other life is wasted.
As the creation ends the devatas resolve also in Parameshvara. They are also part of samsara only. Even a little peace of real happiness one won’t get from worship of devatas. Ananda devata cannot give.
Devote: “Sri Krisha make my mind pure and make me happy” - he knows how to and can grant. If you ask for a new car, he’ll consult his prarabdha …
There is no real happiness in pursuit of all this. I don’t want all this. I’m done with that. I want only one thing.
Having created duality out of language now you have to negate it.
Shakti resolves in the being becomes Shiva. The becoming aspect of Shiva is Shakti. The resolution of Shakti to being is Shiva.
Isvara being ananda, how can there be dukha in the creation? Ananda is the upadana of the universe. Why there is dukha? That’s our creation. Isvara doesn’t know how to create a dukha. Dukha is created by jiva. Not god, thus god will not be able to remove it.
Pleasure and pain always go together. Stomach pain I create. By overeating in the night. Why? Because I want … pleasure. As you pursue one you create the opposite.
Everything is wrong with accumulation. Cups and saucers behind a glass in a cupboard!? I’m looking for some meaning it it. Then we come to Vedanta. And we accumulate emotions. A child does not. As the emotions are accumulated we become heavy laden like a ship.
What we are accumulating is terminology. Vedanta is all about unlearning. Nothing new you learn. Nothing new you get. Good Vedanta teaching is that demolishes every possible concept that a person has in the mind and leaves him free and open so he can be the truth which he already is.
People have become linguistic. They are no more existential. Biodata is my linguistic “being”, all mithya. What I’m truly cannot be put in the biodata, even one word.
We’re totally deluded thinking that literature and language is the truth.
We accumulate formulas. Will they eliminate this horrible samsara? Are they helping us? Will they remove it? We must honestly examine it.
How long will it take for you to come to the truth? Stop the accumulation. Collect all the formula in one place. Make it simple. Develop devotion. Purify your heart.
Collect and showcase formulas. It will not save. Ultimately there is only one way - sharanagatih. Oh, intelligent person, you’ve learned enough of new things. What you’ve learned start practising and develop that total inner devotion to Ishvara.
A time comes when we’ve accumulated enough. Now it is time to take stock. Keep what is required and take proper steps.
First step is freedom from the concern of the future. It is horrible egoic state. The only way is Ishvara sharanagati. Then be open minded, don’t have too many expectations. Dismantle the entire structure of expectations. Then let the future unfold itself. Allow the life to unfold itself
“Did you take lunch? When are you leaving?” Simple and practical. What is there to discuss about the concern of future. Whenever there is concern about the future enjoy the glorious anxiety. Allow it to unfold. Devote to Ishvara. I pray only one thing: give me the strength to face the future. Ability to surrender to Ishvara. Only a strong mind can surrender.
All self-seeking is a violence. We take the same self-seeking to selfish pity-mindedness to the religion. That’s why it doesn’t have any transformative power. It seems to have become dumb. Public show is not religion. Private communion!
We should not try to impress god. We should surrender to god. Spirit of self-surrender alone bridges the gap between jiva and Ishvara.
Devotion is not an easy thing. We’re more devoted to the world than to Ishvara.
Mind offering is meditation. No more karma. No object is offered. That is the greatest worship. This wisdom, entirely by your grace, has taken root in my heart. I’m fulfilled. I surrender day after day at your lotus feet. Whatever wealth, resources … are needed, you take care of it. Devotee won’t suffer.


Kanav Petkar
Student @uWaterloo
Backend Engineering Intern @Splunk
Previously:
@Autonomic
@Toyota
@Capgemini
1493, Leonardo, The Black Swan, Zero-to-One
Favourite Fiction:Exhalation, Siddhartha, Kafka on the Shore, Cryptonomicon
Favourite TV Shows:Sopranos, Better Call Saul, Archer


Interested in Contributing?
- Check out available resources.
- Create an account and start submitting your own systems.
Scored Systems
| System | Submitter | System Notes | Constraint | Run Notes | BLEU | BLEU (11b) | BLEU-cased | BLEU-cased (11b) | TER | z BLEU-cased-norm | zz BEER 2.0 | zzz CharactTER |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| uedin-wmt13-en-es (Details) | Nadir University of Edinburgh |
Phrase-based Moses | yes | [1304-11] |
31.6 |
31.6 |
30.4 |
30.4 |
0.566 |
|||
| DCU prompsit (Details) | dcuprompsit DCU-Prompsit |
Monolingual tuning-typed combination method. | yes |
30.3 |
30.3 |
29.0 |
29.0 |
0.572 |
||||
| MES:en-es (Details) | Nadir Durrani University of Edinburgh |
MES: Munich-Edinburgh-Stuttgart | yes | OSM (Operation Sequence Model) |
29.5 |
29.5 |
28.4 |
28.4 |
0.589 |
|||
| dcu-prompsit-pbsmt (Details) | dcuprompsit DCU-Prompsit |
Moses PB-SMT trained on news commentary, common crawl and a selection of europarl and un. Interpolated LM with all available data (except giga). | yes |
29.5 |
29.5 |
28.4 |
28.4 |
0.592 |
||||
| TALP-UPC (Details) | lluis.formiga Universitat Politècnica de Catalunya |
Two different runs were submitted: - Our primary (contrastive) run is a internal system combination of different training approaches: (a) Moses Baseline (w/o commoncrawl) (b) Moses Baseline+Morphology Generation (w/o commoncrawl) (c) Moses Baseline+News Adaptation through pivot based alignment (w/o commoncrawl) (d) Moses Baseline + News Adaptation (b) + Morphology Generation (c) (e) Moses Baseline + News Adaptation (b) + Filtered CommonCrawl Adaptation The System selection models are trained from training material from WMT'13QE task. - Our secondary run is the full training strategy marked as (e) on the system combination: Moses Baseline + News Adaptation (b) + Filtered CommonCrawl Adaptation | yes | Our primary (contrastive) run is a internal system combination of different training approaches: <br/> (a) Moses Baseline (w/o commoncrawl) (b) Moses Baseline+Morphology Generation (w/o commoncrawl) (c) Moses Baseline+News Adaptation through pivot based alignment (w/o commoncrawl) (d) Moses Baseline + News Adaptation (b) + Morphology Generation (c) (e) Moses Baseline + News Adaptation (b) + Filtered CommonCrawl Adaptation |
29.5 |
29.5 |
28.3 |
28.3 |
0.586 |
|||
| TALP-UPC (Details) | lluis.formiga Universitat Politècnica de Catalunya |
Two different runs were submitted: - Our primary (contrastive) run is a internal system combination of different training approaches: (a) Moses Baseline (w/o commoncrawl) (b) Moses Baseline+Morphology Generation (w/o commoncrawl) (c) Moses Baseline+News Adaptation through pivot based alignment (w/o commoncrawl) (d) Moses Baseline + News Adaptation (b) + Morphology Generation (c) (e) Moses Baseline + News Adaptation (b) + Filtered CommonCrawl Adaptation The System selection models are trained from training material from WMT'13QE task. - Our secondary run is the full training strategy marked as (e) on the system combination: Moses Baseline + News Adaptation (b) + Filtered CommonCrawl Adaptation | yes | Our secondary run is the full training strategy marked as (e) on the system combination: (e) Moses Baseline + News Adaptation (b) + Filtered CommonCrawl Adaptation |
29.4 |
29.4 |
28.2 |
28.2 |
0.586 |
|||
| PROMT Hybrid (Details) | Alex Molchanov PROMT LLC |
no | primary |
29.2 |
29.2 |
28.0 |
28.0 |
0.586 |
||||
| FDA (Details) | bicici |
Instance selection with Feature Decay Algorithms (FDA) and phrase-based translation with Moses. | yes |
29.0 |
29.0 |
27.9 |
27.9 |
0.598 |
||||
| LIMSI-Ncode (Details) | LIMSI-CNRS LIMSI-CNRS |
An open source statistical machine translation system based on bilingual n-grams. | yes | Filtered Common-Crawl |
29.0 |
29.0 |
27.9 |
27.9 |
0.602 |
|||
| cu-zeman (Details) | zeman Charles University in Prague, ÚFAL |
yes | secondary; not for manual evaluation; normalized; constrained; s.evaluator.50ac1e3b.20130430-1144; A:en-lemma-es-lemma DEV:wmt2012 LM:gigaword LM:news8all LM:news8euro LMA:es+stc LMO:6 S:en T:es TM:news8euro.es-en TST:wmt2013; 0.2856 [0.2793,0.2924] |
28.8 |
28.8 |
27.1 |
27.1 |
0.604 |
||||
| JHU En-Es (Details) | jhushua Johns Hopkins University |
Joshua Hiero model. LMs interpolated. Feature weights averaged from multiple runs | yes |
28.4 |
28.4 |
24.0 |
24.0 |
0.601 |
||||
| Test1 (Details) | rrubino Saarland University & DFKI |
yes |
28.3 |
28.3 |
27.2 |
27.2 |
0.604 |
|||||
| cu-zeman (Details) | zeman Charles University in Prague, ÚFAL |
yes | primary; unnormalized; constrained; s.evaluator.50ac1e3b.20130430-1144; A:en-lemma-es-lemma DEV:wmt2012 LM:gigaword LM:news8all LM:news8euro LMA:es+stc LMO:6 S:en T:es TM:news8euro.es-en TST:wmt2013; 0.2856 [0.2793,0.2924] |
27.0 |
27.0 |
25.4 |
25.4 |
0.611 |
||||
| Shef-wproa (Details) |<EMAIL_ADDRESS>University of Sheffield |
weighted training | yes |
24.2 |
24.2 |
23.1 |
23.1 |
0.625 |


Agile ASIC™ Solution Touchscreen Driver Receiver
Agile ASIC™ Solution – Projected Capacitive Touchscreen Receiver


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Dashboard
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Kevin Ciarniello
Skills
Programming: Swift
Kotlin
Java
Bash
Python
Mobile: iOS
Android
iOS: Combine
async/await
SwiftUI
Swift 5.7.1
Databases: MySQL
sqlite
MSSQL
DevOps: Docker
AWS
Kubernetes
Back-End: node.js
Vapor
Automation: Jenkins
Bash/Python Scripts
Xcode Cloud
Basics: YAML
XML
JSON
Git
Gitflow
Test-Driven Development
system architecture & design
Music Production: Logic Pro X
Work Experience
MeisterLabs
Senior
iOS Developer • Sept.2020 – Present • Vienna, Austria
- Develop features and refactor legacy code for their new MindMapping
application
- Created CI/CD, updated packages to new Apple Frameworks, helped
support old legacy features, wrote tests
- Refactored Objective-C to Swift/SwiftUI, keep the project up-to-date
with new technologies,
Core-Data
,
Combine
Apple
Software
Development Engineer • Jan 2018 – Jan 2019 • Vancouver, Canada
- Develop technologies for the 1st trillion dollar company
- Engineer on
Xcode Cloud
Buddybuild
Engineer • June
2017 – Jan 2018 • Vancouver, Canada
Developed and maintained CI/CD
solution for both the iOS
and Android Platforms
- Buddybuild
SDK
support for the Android Platform
- Android
CI/CD
development and integration from start to
deployment of Android projects
- technical support with engineers
Eventbase Technology
Senior
Software Developer • Sept 2016 – May 2017 • Vancouver, Canada
Helped assist in Android
and iOS Development, while I
enjoyed automating things using Python to service over 100+ Event
Apps
- iOS and Android development on the
core product that facilitates more than 100+
apps/year
- Made tools that could be used to help speed-up QA processes
- Created a private Python Module for aiding in developer/tester and
automation
- Setup a Continuous Integration Environment to deploy builds and get
configuration running faster
Vonigo
Senior
Full-stack Developer • Nov 2015 – Aug 2016 • Vancouver, Canada
When they needed help, I switched to being a .NET
Developer. I started to understand the whole paradigm
MSSQL
/.NET
developer working on back-end
features for the company
- Developed the Vonigo API (<api.vonigo.com>), helped with the
test harness for automated API testing
- Setup the CI/CD Environment
- Optimizations of current systems, store procedures and the web
application which service more than 1000 work orders a day
Senior
Android Developer • Mar 2014 – Nov 2015 • Vancouver, Canada
- Developed the Vonigo application on Android
XSLT/XML
to HTML
development
- Cross-platform hybrid architecture implementing
JavaScript
/CSS
/HTML
and native
functionality
QuickMobile (cEvent)
Senior
Mobile Developer, Lead • Marc 2010 – Nov 2013 • Vancouver, Canada
Became a lead developer in the apps industry that serviced many
Fortune 500 companies
- Lead Developer for Android/BlackBerry in start-up
environment
- Worked on over 100+ iPhone, Android and BlackBerry
Applications
- Worked extensively on iPhone and Android Framework
- Created Automation and test scripts for
iPhone/Android/BlackBerry
- Developed BlackBerry Framework
- Traveled internationally to interface with and support key clients
(McDonald’s, Intel, Google)
- Helped grow the company from 7 to over 200 Employees
Redback Networks (Ericcson)
Software
Developer • January 2008 – January 2009 • Burnaby, BC, Canada
- Data driven development by creating analytics of all calls to the
SmartEdge Routers
- Automation and test scripts using
perl
and
bash
- Implemented cross-communication using
SOAP
,
XML/XSD/XSLT
and Java
BlackBerry
Security
Software Developer (Intern) • May 2006 – September 2006 • Waterloo, ON,
Canada
- Helped fix bugs and understand security at BB
- Bug fixes, device-side PGP/SSP implementation in
Java
Retail
Applications Developer (Intern) • September 2005 – January 2006 •
Waterloo, ON, Canada
- Designing and fixing bugs related to Customer display UI demos in
Java
Education
B.A.Sc.
Engineering Science Electronics Option
Simon Fraser University
Sep 2002 – Jan 2009
Hobbies
- Brazilian Jiu-Jitsu (Brown
Belt)
- Logic Pro X
Languages


Ariat Boots - Size 10 EE
North Face Vest - Medium
Oasis - (What's The Story) Morning Glory Vinyl LP
Green Day - Dookie Vinyl LP
Queen - Greatest Hits Volume 1 Vinyl LP
The Essential Johnny Cash Vinyl LP
Musician's Gear A Frame Electric Guitar Stand (2-Pack)
Meinl Snare Cajon with Free Bag


Curiosity Rover @MarsCuriosity
The journey of 352,000,000 miles begins with a single launch. One year ago today, I left Earth for Mars twitpic.com/bgq1vn
26 Nov 12
Curiosity Rover @MarsCuriosity
From chemist to explorer to mayor, I am one busy bot. Just became the mayor of Mars' Gale Crater on @foursquare
7 Oct 12


{% load staticfiles %}
Informal Science
Conducted at youth centres and schools, focusing on young people aged between 7-10. Each workshop centres around a particular science theme, e.g ‘When will the sun burn out?’ . The hacking education mentor gives a short introduction, explaining the fundamental science related to the theme. During the introduction, the young people are encouraged to ask questions and are posed questions. The young people then use computers to independently learn and research the answer to their own questions, with guidance from the undergraduate. The aim of these science workshops is to spark scientific curiosity and cultivate the young person’s questioning mind in an informal learning environment.
Intersection of science, technology and creativity.
Project based learning conducted in schools, for young people aged between 13-16. Taking place over a 12 week period, young people start off with practical coding exercises and then decide on a project, where they create something of their choice. hackingEducation mentors provide assistance and guidance when needed. E.g One group is creating a revision tool using a Raspberry Pi and the Google Assistant API, giving them practical experience with machine learning and Unix based systems. Young people are then given the opportunity to expand on their project, in an academic setting, during a 3 day workshop hosted at UCL. The aim of the programming projects is to give young people freedom to choose what they learn, and to cultivate the skills at the intersection of science, technology and creativity.
Issues & Solutions
Health inequity between people of different class, race and gender is a direct result of the effects of low air quality. Humans on average spend 70% of their time indoors, with that number being on the higher side for women especially women of colour. In the mapToBreathe project we’re developing low-cost high accuracy air quality measuring devices to map air quality, specifically indoor air quality in areas of low ventilation. We’re going to find the pollutant hotspots and place specific plants in those hotspots that could reduce the concentration of these pollutants. The project is going to be completed by a group of 8 sixth form students under the supervision of PhD students. A mapToBreathe will contribute to active research in improving air quality, but we also seek to create agents of change who will further the fight to protect the environment. Please visit www.mapToBreathe.org to find out more
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Lecture Series & festival of culture
hackingEducation is in the process of planning a history of race project in collaboration with Consented. "A History of Race & Resistance in Britain" aims to create a free lecture series on black history in Britain. A curriculum has been devised, and will be delivered by renowned speakers in the field. The history of Race lecture series will take place over 2 days, on the 9th and 10th of August (see our events), where they will be recorded to create a public online learning resource.The lectures will be interlaced with workshops and performances, producing a festival of culture. As well as being recorded, the event will be open to school children as well as the general public. Register for the event here!
Young People & Parents
Following our science workshops, we are using the content to create a science magazine. The magazine will be a collection of science posters, alongside illustrations created by the young people, to produce a public magazine. Each poster contains information on a science topic, its current areas of research, and poses further questions for the reader to explore. The posters act as self contained science lessons, helping to facilitate independent learning. The magazine will be distributed to schools and families, and will be used for science workshops at youth centers and libraries. The first edition of the magazine is expected to be in print and ready for distribution by November 2018.
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Class Controller
- java.lang.Object
-
- Controller
-
public class Controller extends java.lang.Object
Manipulates and controls the scene builder containers through individual fx:id's and methods.- Author:
- Jaysson Balbuena
-
-
Field Summary
Fields Modifier and Type Field Description private javafx.scene.control.Label
acctCreatedLbl
private javafx.scene.control.TextField
createName
private javafx.scene.control.TextField
createPassword
private javafx.scene.control.Label
errorMessage
private javafx.scene.control.Label
lblCreateName
private javafx.scene.control.Label
lblCreatePassword
private javafx.scene.control.Label
lblPassword
private javafx.scene.control.Label
lblUsername
private javafx.scene.control.TextField
passwordTxt
(package private) javafx.scene.layout.AnchorPane
rootPane
private javafx.scene.control.TextField
userNameTxt
-
Constructor Summary
Constructors Constructor Description Controller()
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description (package private) void
backHyperlink(javafx.event.ActionEvent event)
allows user to return to the login page.(package private) void
createAccount(javafx.event.ActionEvent event)
calls loadCreateAcctScreen() method which loads the create account screen.(package private) void
createAcctButton(javafx.event.ActionEvent event)
call registerUser() method where allows a user to register into the system.(package private) void
disableErrorMsgForName(javafx.scene.input.KeyEvent event)
disables error message shown when user tries to create an account with an improper name.(package private) void
disableErrorMsgForPassword(javafx.scene.input.KeyEvent event)
disables name error message shown when user tries to create an account with an improper name.private void
loadCreateAcctScreen()
Once the user click on create account, the create account window pops up.(package private) void
loginBtn(javafx.event.ActionEvent event)
Once loginBtn is clicked, loginScreen method verifies user input and logs in if valid.void
loginScreen(javafx.event.ActionEvent event)
When this method is called, it checks for valid input and it logs in if input is corrected.(package private) void
passwordKeyReleased(javafx.scene.input.KeyEvent event)
erases the password error message shown when incorrect password is entered.private void
registerUser()
prompts user to enter valid input to create a new account.(package private) void
usernameKeyReleased(javafx.scene.input.KeyEvent event)
erases the username error message shown when incorrect username is entered.
-
-
-
Field Detail
-
rootPane
javafx.scene.layout.AnchorPane rootPane
-
userNameTxt
private javafx.scene.control.TextField userNameTxt
-
passwordTxt
private javafx.scene.control.TextField passwordTxt
-
lblUsername
private javafx.scene.control.Label lblUsername
-
lblPassword
private javafx.scene.control.Label lblPassword
-
createName
private javafx.scene.control.TextField createName
-
createPassword
private javafx.scene.control.TextField createPassword
-
lblCreateName
private javafx.scene.control.Label lblCreateName
-
lblCreatePassword
private javafx.scene.control.Label lblCreatePassword
-
errorMessage
private javafx.scene.control.Label errorMessage
-
acctCreatedLbl
private javafx.scene.control.Label acctCreatedLbl
-
-
Method Detail
-
backHyperlink
void backHyperlink(javafx.event.ActionEvent event) throws java.io.IOException
allows user to return to the login page.- Parameters:
event
- offers a series of ActionEvents such as "On mouse clicked" etc.- Throws:
java.io.IOException
- is thrown if something goes wrong when reading the fxml file.
-
loginBtn
void loginBtn(javafx.event.ActionEvent event) throws java.io.IOException
Once loginBtn is clicked, loginScreen method verifies user input and logs in if valid.- Parameters:
event
- event offers a series of ActionEvents such as "On mouse clicked" etc.- Throws:
java.io.IOException
- is thrown if something goes wrong when reading the fxml file.
-
usernameKeyReleased
void usernameKeyReleased(javafx.scene.input.KeyEvent event)
erases the username error message shown when incorrect username is entered.- Parameters:
event
- executes an action when the user type something into the TextField.
-
passwordKeyReleased
void passwordKeyReleased(javafx.scene.input.KeyEvent event)
erases the password error message shown when incorrect password is entered.- Parameters:
event
- executes an action when the user type something into the TextField.
-
disableErrorMsgForName
void disableErrorMsgForName(javafx.scene.input.KeyEvent event)
disables error message shown when user tries to create an account with an improper name.- Parameters:
event
- executes an action when the user type something into the TextField.
-
disableErrorMsgForPassword
void disableErrorMsgForPassword(javafx.scene.input.KeyEvent event)
disables name error message shown when user tries to create an account with an improper name.- Parameters:
event
- executes an action when the user type something into the TextField.
-
createAcctButton
void createAcctButton(javafx.event.ActionEvent event) throws java.io.IOException
call registerUser() method where allows a user to register into the system.- Parameters:
event
- executes an action when the user type something into the TextField.- Throws:
java.io.IOException
- is thrown if something goes wrong when reading the fxml file.
-
createAccount
void createAccount(javafx.event.ActionEvent event)
calls loadCreateAcctScreen() method which loads the create account screen.- Parameters:
event
- executes an action when the user type something into the TextField.
-
loginScreen
public void loginScreen(javafx.event.ActionEvent event) throws java.io.IOException
When this method is called, it checks for valid input and it logs in if input is corrected.- Parameters:
event
- contains an action that is executed when- Throws:
java.io.IOException
- is thrown if something goes wrong when reading the fxml file.
-
loadCreateAcctScreen
private void loadCreateAcctScreen()
Once the user click on create account, the create account window pops up.
-
registerUser
private void registerUser() throws java.io.IOException
prompts user to enter valid input to create a new account.- Throws:
java.io.IOException
- is thrown if something goes wrong when reading the fxml file.
-
-


--- date: 2010-02-25T00:00:00Z published: true title: Belmont Club » The Last Bubble url: /2010/02/25/belmont-club-the-last-bubble/ ---
The only way to prevent a catastrophe, according to Johnson and Boone, is to cut deficits back and reform the financial system to avoid future hanky-panky. But neither is very optimistic it can be done. The actors benefiting from the short term bubbles are simply too powerful to keep anyone from wresting control of the wheel. And if the reformers ever succeed the money men will simply corrupt them all over again.
The real danger is that as this cycle continues, the scale of the problem is getting bigger. If each cycle requires greater and greater public intervention, we will surely eventually collapse….
In our view, the long-term failure of regulation to check financial collapses reflects deep political difficulties in creating regulation. The banks have the money, they have the best lawyers and they have the funds to finance the political system. Politicians rarely want strong regulators – except after a major collapse. So politics rarely favours regulation.