Tutorials
{{ SITE_NAME }} tutorials are intended to provide hands-on introductions to
the data and software available from this resource. This index lists
currently available {{ SITE_NAME }} tutorials by category, as well as reference
manuals, workshop materials, and links to other tutorials likely to be
of interest to {{ SITE_NAME }} visitors.
About {{ SITE_NAME }}
- About the
PhysioBank Archives. PhysioBank contains well over 36,000 recordings
of annotated, digitized physiologic signals and time series; this
brief tutorial begins with an exploration of these archives using
your web browser, followed by pointers on downloading data, information
about the archives themselves, and recommendations of software available
freely from this site for further study of PhysioBank data.
- An Introduction to
PhysioToolkit. {{ SITE_NAME }}'s collection of software for
viewing, analyzing, and modelling physiologic signals and time
series consists of open-source software that can be studied,
verified, and modified to suit the specific needs of your work.
How to ...
- Finding records in PhysioBank.
This tutorial describes the PhysioBank Index of over 36,000 records that
can be viewed by the PhysioBank ATM, and how to find records with desired
characteristics using the web-based PhysioBank Record Search or via
command-line tools.
- How to obtain PhysioBank data in text
form. Many readers wish to convert binary data from PhysioBank
({{ SITE_NAME }}'s data archive) into text form for further processing. There
are many good reasons
not to do so. If you are determined to do it anyway, here's how.
- Creating PhysioBank (WFDB-compatible)
Records and Data Collections. If you have digital recordings of signals
or time series, perhaps with annotations, that you would like to study using
PhysioToolkit software such as that in the WFDB software package, or that
you would like to contribute to PhysioBank, this tutorial should get you
started on creating PhysioBank-compatible records from your data.
- How to set up a mirror of
{{ SITE_NAME }}. A {{ SITE_NAME }} mirror can run on almost any computer made
in the last ten years, and it can provide local users with fast
access to PhysioBank data and PhysioToolkit software, even in areas
with slow or unreliable Internet connections. {{ SITE_NAME }} mirrors are
easy to set up and essentially self-maintaining. Put an old computer
to good use and help {{ SITE_NAME }} users in your area.
- Using the MIMIC II
Database. The MIMIC II Database
has many types of data that are not available in other PhysioBank
data collections. This tutorial is intended to help you get
started on a project that makes use of this rich data collection.
- An Introduction to Cygwin.
PhysioToolkit includes a large collection of open-source, POSIX-compliant
software that can be useful to most {{ SITE_NAME }} visitors. Since almost all
of the popular platforms are also POSIX-compliant, it's easy to get
PhysioToolkit software running on those platforms, including GNU/Linux,
Mac OS X, and all versions of Unix. Microsoft Windows is not
POSIX-compliant, but a free software package called Cygwin provides a
stable and very complete POSIX layer on top of Windows. By installing
Cygwin on your Windows PC, you will be able to run PhysioToolkit software
on it. This tutorial explains how to do so, without interfering with
any Windows software you may be using.
- Applying {{ SITE_NAME }} tools to manage neurophysiological
signals. How to import and handle data from commercial devices using
PhysioToolkit and other open source software. This tutorial was
contributed by Jesus Olivan Palacios, a neurophysiologist who has written
an excellent introduction to much of the software available from {{ SITE_NAME }}
in the form of a series of hands-on exercises using data provided with the
tutorial. Very readable, and highly recommended for neurophysiologists and
others alike. (Also see Sciteam, below, by the same author).
- RR Intervals, Heart Rate, and HRV Howto. A
brief overview of how to obtain inter-beat (RR) interval and heart rate
time series, and of some basic methods for characterizing heart rate
variability, using freely available PhysioToolkit software.
- Heart Rate Variability Analysis with the
HRV Toolkit: Basic Time and Frequency Domain Measures. This
tutorial describes how to use the HRV toolkit (available here) to
select and prepare time series of inter-beat intervals and to calculate
measurements of the basic time- and frequency-domain HRV
statistics that are widely used in the literature. Particular
attention is given to techniques for identifying and dealing with
outliers, in order to permit reliable determination of
measurements.
- Morphology Representation Using Principal
Components. Using the QRS complex of the ECG as an example,
this tutorial presents practical methods for principal
component analysis of waveforms, including software that can be used as
is or customized as desired.
- Evaluating ECG Analyzers. How
to use PhysioToolkit software and data available from PhysioBank and
other sources to measure the performance of a QRS detector or classifier,
in accordance with protocols prescribed by current ANSI standards and the
US FDA (ANSI/AAMI EC38 and EC57).
- Digitizing Paper ECGs and Other
Plots. A brief survey of resources that may be helpful.
- How to Write HTML pages for
{{ SITE_NAME }}. If you are preparing a contribution of data, software,
or tutorial material for {{ SITE_NAME }}, this guide illustrates how to create
pages with the templates and style sheets used on this web site.
- Distributed Computing with {{ SITE_NAME }} data. A brief tutorial on
how to cloud processing {{ SITE_NAME }} data with WFDB, StarCluster, Amazon EC2, Octave, and Hadoop.
Also see Reference Guides below, on-line books
containing a wealth of additional "how-to" information.
Exploring Data and Novel Analyses
- Variability vs. Complexity. Introduces students
and trainees to the study of complex variability, especially in
physiology and medicine.
- Exploring Patterns in Nature. A set of interactive
tutorials drawn from current research, focussing on emergent phenomena
(random behavior at the smallest scales leading to patterns at larger
scales). Subjects include fractal coastline and dimension, measuring
randomness, physical and chemical branching structures, biological
branching patterns, diffusion, percolation, and motion on a fractal.
These tutorials do not assume extensive knowledge of mathematics.
- Nonlinear Dynamics, Fractals, and Chaos Theory:
Implications for Neuroautonomic Heart Rate Control in Health and
Disease. An introduction to some key concepts of nonlinear
dynamics.
- Exploring Human Gait and Heart Rate Dynamics.
Use two sets of time series derived from human subjects to
study changes in dynamics with age and disease, with a variety of
methods including approximate entropy (ApEn) and detrended fluctuation
analysis (DFA).
- Fractal Mechanisms in Neural Control: Human Heartbeat
and Gait Dynamics in Health and Disease. Beginning with a
definition of fractal dynamics, this tutorial explores how fractal
analysis may reveal information of diagnostic or prognostic value
when applied to two model systems.
- A Brief Overview of Multifractal Time Series.
A concise review of how fractal and multifractal patterns in time can
be quantified, including a short discussion of multifractality in heart
rate.
- Approximate Entropy (ApEn). A brief
description of how to calculate ApEn, a ``regularity statistic'' that
quantifies the unpredictability of fluctuations in a time series,
including a worked-out example.
- Multiscale Entropy (MSE) Analysis.
Introduces the concept of MSE, describes an algorithm for calculating MSE
using sample entropy (SampEn), presents a portable implementation of this
algorithm, and illustrates its application to analysis of interbeat (RR)
interval time series from PhysioBank.
- Generalized Multiscale
Entropy (GMSE) Analysis. Discusses ways to generalize the
concept of MSE by using different coarse-graining functions, and
illustrates the differences between these methods using both
interbeat interval data and simulated data.
- Information Based Similarity Index.
An introduction to a novel linguistic analysis method that has been
successfully applied to studies of inter-beat interval time series, the
origin of the SARS coronavirus, and the authorship of Shakespeare's plays.
Workshops
Materials from these workshops include tutorial presentations and related
materials:
- Electronic Interchange of Polysomnography
Data. Presentations from a workshop to develop guidelines for PSG
transmission and archiving, including presentations on needs of
researchers, standards efforts, and existing formats for PSGs and
other physiologic signals.
- HRV 2006. Materials from our mini-course
about heart rate variability, including presentations on physiologic
mechanisms of HRV, time and frequency domain measures, complexity measures,
clinical applications, and more.
Reference Guides
Books describing the major components of
PhysioToolkit are also available here.
Printed copies of some of these books may be purchased from
the {{ SITE_NAME }}
Bookstore. These books incorporate both tutorial and reference
material:
- WFDB Programmer's Guide.
Essential material for those wishing to read (or create) PhysioBank
data files from their own software. This book includes detailed
descriptions of the application programming interfaces for digitized
signal and annotation files, and sample applications including digital
filters, signal averaging, and a QRS detector.
- WFDB Applications Guide.
How to use several dozen small tools individually and in combination to
view, manipulate, and analyze PhysioBank and similar data. This guide
includes the tutorial on evaluating ECG
analyzers mentioned above.
- WAVE User's Guide. A
comprehensive introduction to WAVE, an interactive graphical interface
to PhysioBank.
- RCVSIM User's
Manual and Software Guide.
This guide introduces the Research Cardiovascular Simulator (RCVSIM),
software for synthesizing realistic human pulsatile hemodynamic waveforms,
cardiac function and venous return curves, and beat-to-beat hemodynamic
variability. The manual includes a description of the cardiovascular
models used by RCVSIM, guides to reading and compiling the RCVSIM source
code, and a tutorial with examples illustrating its use.
plt
Tutorial and
Cookbook.
This book introduces plt
, a highly capable and flexible utility
for making publication-quality 2D plots from text or binary data files.
plt
has a very broad range of applications, and is well-suited for
visualizing the output of many of the PhysioToolkit applications.
Other resources of interest
Tutorials listed in this section are not hosted by {{ SITE_NAME }}. Links
will open in a new browser window.
- ECG Wave-Maven. This is a self-assessment program on
interpretation of 12-lead diagnostic ECGs, with over 400 case studies. Use the
program to test your diagnostic abilities, or browse through the cases in
reference mode. ECG Wave-Maven was developed at Harvard Medical School
and Boston's Beth Israel Deaconess Medical Center. Its creators have written a
paper
describing the goals and technology behind the program, and a survey of its use
during its first 17 months of operation.
- The Alan
E. Lindsay ECG Learning Center in Cyberspace. A comprehensive
introduction to clinical electrocardiography, developed at LDS Hospital, Salt
Lake City.
- A guided tour through TISEAN: Exercises with data sets.
This tutorial introduces a large package of software for nonlinear time series
analysis developed by Rainer Hegger, Holger Kantz, and Thomas Schreiber
(Institut für Physikalische und Theoretische Chemie, Universität
Frankfurt (Main) and Max-Planck-Institut für Physik komplexer Systeme,
Dresden).
- Openeering. This site offers tutorials on Scilab (an
open-source programming environment developed at INRIA for "numerical
computations in a user-friendly environment") and how to use it with
PhysioBank data and PhysioToolkit software. Scilab is similar to Matlab, but
offers many additional features. The tutorials are written by and for clinical
neurophysiologists, and do not assume extensive knowledge of mathematics or
programming.


| [ Team LiB ] |
Chapter 24. Extending and Embedding Classic PythonClassic Python runs on a portable C-coded virtual machine. Python's built-in objects, such as numbers, sequences, dictionaries, and files, are coded in C, as are several modules in Python's standard library. Modern platforms support dynamic-load libraries, with file extensions such as .dll on Windows and .so on Linux, and building Python produces such binary files. You can code your own extension modules for Python in C, using the Python C API covered in this chapter, to produce and deploy dynamic libraries that Python scripts and interactive sessions can later use with the import statement, covered in Chapter 7. Extending Python means building modules that Python code can import to access the features the modules supply. Embedding Python means executing Python code from your application. For such execution to be useful, Python code must in turn be able to access some of your application's functionality. In practice, therefore, embedding implies some extending, as well as a few embedding-specific operations. Embedding and extending are covered extensively in Python's online documentation; you can find an in-depth tutorial at http://www.python.org/doc/ext/ext.html and a reference manual at http://www.python.org/doc/api/api.html. Many details are best studied in Python's extensively documented sources. Download Python's source distribution and study the sources of Python's core, C-coded extension modules and the example extensions supplied for study purposes. This chapter covers the basics of extending and embedding Python with C. It also mentions, but does not cover, other possibilities for extending Python. |
| [ Team LiB ] |


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Load packages
library(tidyverse)
library(hrbrthemes)
library(MASS)
library(moments)
Why you should spell out your model explicitly
Often, assumptions of widely used models, such as linear models, appear opaque. Why is heteroscedasticity important? Where is a list of the model assumptions I need to consider?
As it turns out, there are straight forward answers to these (and similar) questions. The solution is to explicitly spell out your model. All “assumptions” can easily read off from these model specifications.
Example for explicit (and complex) model
Say, we would like to model Covid-19 cases (\(C\)) as a function of tests applied in some population. We could specify our model like this:
\[ C \sim \mathcal{P}(\lambda_i) \\ \text{log}\lambda_i = \alpha + \beta_1\,x_{i}^{s} \\ \alpha \sim \mathcal{E}(.5) \\ \beta_1 \sim \mathcal{E}(0.1) \]
Here, we model \(C\) as Poisson (\(\mathcal{P}\)) distribution variable, because \(C\) is a count variable, and Poisson is a maximum entropy variable for counts. However, this is a apriori reasoning; after seeing the data we might want to consider different model despite the danger of overfitting.
Recall that \(\lambda\) must be positive or null. That’s why we put a “log” before the likelihood (the linear model specification) line. Similarly, we can only take the log of a positive number, so we need to make sure that \((\alpha + \beta_1 \, X )> 0\). Here, the Exponential distribution (\(\mathcal{E}\)) is handy, as it makes sure that \(\alpha\) or \(\beta\) ist positive.
It’s helpful to use standardized predictors, \(x_i^s\), because that allows us to model the intercept \(\alpha\) as the mean value of \(C\), among other things. It would be a fruitful discussion to contemplate the usefulness of the mean for long-tail distributions, see Taleb Incerto, but that’s beyond the scope of this post.
We must take care of the log-normal (\(\mathcal{LN}\)), because log-normal are fat-tailed so the mean will be large. Seeing is believing, so let’s consider \(A \sim \mathcal{LN}(0, 10)\). Draw some samples from that population:
set.seed(42)
a <- rlnorm(n = 1000, meanlog = 0, sdlog = 10)
options(scipen = 3)
mean(a)
#> [1]<PHONE_NUMBER>934
format(mean(a), scientific = T)
#> [1] "1.71638e+12"
For a smaller sd:
set.seed(42)
a <- rlnorm(n = 1000, meanlog = 4, sdlog = 1)
options(scipen = 3)
mean(a)
#> [1] 88.55222
format(mean(a), scientific = T)
#> [1] "8.855222e+01"
Let’s apply the same reasoning for \(\beta_{1}\).
Read off modelling assumptions
Some of the “assumptions” implied by the above model are:
- Certain distributions of the variables involved
- Certain function forms map the input variable to the output variable
- The conditional errors (conditional to \(\hat{Y}\)) are iid - independent and identically distributed, that is, at each value of \(\hat{Y}\) the errors are drawn from the same distribution, and they are drawn without correlation (eg., drawn randomly from an infinite population). This fact incorporated heteroscedasticity, as the residuals have the same variance irrespective to \(\hat{Y}\).
- Our ignorance according to the “true” value of some variable is incorporated. For example, we are not sure about the true value of \(\beta_1\), there is uncertainty involved, which is reflected by the fact that \(\beta_1\) is drawn from a probability distribution.
Simulate data
Say, \(n=1000\)
According to our model:
set.seed(42)
n <- 1000
alpha <- rexp(n = 1000, rate = 1)
beta <- rexp(n = 1000, rate = 0.1)
See:
ggplot(data.frame(alpha)) +
aes(x = alpha) +
geom_density()
ggplot(data.frame(beta)) +
aes(x = beta) +
geom_density()
As we do not have real data \(X\), we need to simulate it. Assuming that \(X\) is again exponentially distributed (to avoid negative values):
set.seed(42)
X <- rexp(1000, rate = .5)
ggplot(data.frame(X)) +
aes(x = X) +
geom_density() +
theme_ipsum_rc()
Compute the likelihood
lambda_log <- (alpha + beta*X) %>% log()
lambda <- exp(lambda_log)
ggplot(data.frame(lambda)) +
aes(x = lambda) +
geom_density()
Oh my, that’s a long tail.
Estimate \(C\)
set.seed(42)
C <- rpois(n = 1000, lambda = lambda)
Put the data together
C_df <- tibble(
C = C,
lambda = lambda,
lambda_log = lambda_log,
X = X,
alpha = alpha,
beta = beta)
ggplot(C_df) +
aes(x= C) +
geom_density() +
theme_ipsum_rc(grid = "Y")
Fit distributions
pois_param <- fitdistr(C_df$C,
densfun = "poisson")$estimate
str(pois_param)
#> Named num 23.2
#> - attr(*, "names")= chr "lambda"
pois_param
#> lambda
#> 23.184
Supported distributions are:
“beta”, “cauchy”, “chi-squared”, “exponential”, “gamma”, “geometric”, “log-normal”, “lognormal”, “logistic”, “negative binomial”, “normal”, “Poisson”, “t” and “weibull”
For comparison, let’s fit another distribution, say a log-normal.
lnorm_param <- fitdistr(C_df$C[C_df$C > 0],
densfun = "lognormal")$estimate
lnorm_param
#> meanlog sdlog
#> 2.389367 1.337632
… or a Gamma:
gamma_param <- fitdistr(C_df$C[C_df$C > 0],
densfun = "gamma")$estimate
gamma_param
#> shape rate
#> 0.71162128 0.02796269
Compare fit
To compare fit, a qqplot can be helpful.
Poisson
C_df %>%
ggplot(aes(sample = C)) +
geom_qq(distribution = qpois,
dparams = as.list(pois_param),
color = "dodgerblue") +
stat_qq_line(distribution = qpois,
dparams = as.list(pois_param),
color = "dodgerblue") +
theme_ipsum_rc()
Gamma
C_df %>%
ggplot(aes(sample = C)) +
geom_qq(distribution = qgamma,
dparams = as.list(gamma_param),
color = "darkseagreen") +
stat_qq_line(distribution = qgamma,
dparams = as.list(gamma_param),
color = "darkseagreen") +
theme_ipsum_rc()
Log-normal
C_df %>%
ggplot(aes(sample = C)) +
geom_qq(distribution = qlnorm,
dparams = as.list(lnorm_param),
color = "firebrick") +
stat_qq_line(distribution = qlnorm,
dparams = as.list(lnorm_param),
color = "firebrick") +
theme_ipsum_rc()
As can be seen, our distributions do not fit well, even given the fact that we simulated the data to be Poisson distributed. This may be due to the fact that we have induced such a long tail. Maybe there are other things going on. Any ideas? Let me know.


Thank You
Your exhibition information has been confirmed! We look forward to seeing your work in person!


scale_x_genomic()
and scale_y_genomic()
are the
default scales for x and y aesthetics defined as representations of genomic
positions such as GPos and GRanges
(sub)-classes.
scale_x_genomic( name = waiver(), breaks = waiver(), minor_breaks = waiver(), n.breaks = NULL, labels = waiver(), minor_labels = waiver(), limits = NULL, expand = waiver(), oob = censorThis, na.value = NA_real_, trans = S4TransIdentity, guide = "genomic_axis", position = "bottom", sec.axis = waiver() ) scale_y_genomic( name = waiver(), breaks = waiver(), minor_breaks = waiver(), n.breaks = NULL, labels = waiver(), minor_labels = waiver(), limits = NULL, expand = waiver(), oob = censorThis, na.value = NA_real_, trans = S4TransIdentity, guide = "genomic_axis", position = "left", sec.axis = waiver() )
| name | The name of the scale. Used as the axis or legend title. If
|
|---|---|
| breaks | One of:
|
| minor_breaks | One of:
|
| n.breaks | An integer guiding the number of major breaks. The algorithm
may choose a slightly different number to ensure nice break labels. Will
only have an effect if |
| labels | One of:
|
| minor_labels | One of:
Keep in mind that displaying the minor labels is dependent on wether the
|
| limits | One of:
Note that by default, setting limits on positional
scales will remove data outside of the limits. Change the
|
| expand | For position scales, a vector of range expansion constants used to add some
padding around the data to ensure that they are placed some distance
away from the axes. Use the convenience function |
| oob | One of:
|
| na.value | Missing values will be replaced with this value. |
| trans | For continuous scales, the name of a transformation object or the object itself. Built-in transformations include "asn", "atanh", "boxcox", "date", "exp", "hms", "identity", "log", "log10", "log1p", "log2", "logit", "modulus", "probability", "probit", "pseudo_log", "reciprocal", "reverse", "sqrt" and "time". A transformation object bundles together a transform, its inverse,
and methods for generating breaks and labels. Transformation objects
are defined in the scales package, and are called |
| guide | A function used to create a guide or its name. See
|
| position | For position scales, The position of the axis.
|
| sec.axis |
|
A ScaleS4
ggproto object.
It is important to note that the scale operates in genomic space
when limits are defined as or inferred to be of the GRanges
class.
This has the following known consequences:
Data represented by unclassed numerical values can not be used on this scale.
The scale limits and breaks are represented by GRanges
and
GPos
objects.
Furthermore, scale expansions as set through the expand
argument,
expand the scale limits per individual seqlevel
.
require(GenomicRanges) df <- DataFrame( x = GRanges(c("chr1:1000-2000", "chr1:1500-2500", "chr2:3000-4000", "chr3:4000-4500")), y = c(1, 2, 3, 2) ) # The default scale for GenomicRanges is scale_(x|y)_genomic() g <- ggplot(df, aes(xmin = x, xmax = x, ymin = y - 0.2, ymax = y + 0.2)) + geom_rect() g# Major labels are parallel to the seqnames g + scale_x_genomic(labels = LETTERS[1:3])


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Object Creation: Int. 2
To build a new character either draw or create shapes. New shapes can be arranged by dragging them to your prefered location.
When you are finished building your character, name and export it to use in your story!
Specify Color:
Specify layer:
Draw a line with a length of:
Draw a rectangle with height:
width:
Draw a circle with radius:
Upload Image:
Add a new layer(layer name):
Save file name as:
Load file of name (ex: "head.png"):
Save total object as (ex: 'person1'):


| MATLAB 文件帮助: cv.seamlessClone | 查看 cv.seamlessClone 的代码 | Index |
seamlessClone Seamless Cloning
blend = cv.seamlessClone(src, dst, mask, p)
blend = cv.seamlessClone(src, dst, mask, p, 'OptionName',optionValue, ...)
[x,y]
in dst
image where object is placed.dst
.src
and mask
and output dst
, between MATLAB's RGB order and
OpenCV's BGR (input: RGB->BGR, output: BGR->RGB). default trueImage editing tasks concern either global changes (color/intensity corrections, filters, deformations) or local changes concerned to a selection. Here we are interested in achieving local changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless manner. The extent of the changes ranges from slight distortions to complete replacement by novel content [PM03].
[PM03]:
Patrick Perez, Michel Gangnet, and Andrew Blake. "Poisson image editing". In ACM Transactions on Graphics (TOG), volume 22, pages 313-318. ACM, 2003.


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Python is the "most powerful language you can still read". - Paul Dubois.
Feel free to provide some precious feedback.


Looking After Topics in Your Forum
Topic size
There is a limit to the size of each topic on CIX; at present this is 2 megabytes, we are intending to increase or abolish this limit at some time in the near future. In the mean time, the Moderate CIX Forums Edit Topics dialog displays the size of each topic in forums that you moderate.
Clearly, this information needs to be periodically updated from CIX. You can configure Moderate to update the topic size for each topic (or for all topics in a forum) either manually only, every 'n' days or every time you blink.
You can also configure Moderate to warn you when a topic gets over a configurable percentage full.
Pruning forums
Withdrawn messages are not physically deleted by CIX so if you are running short of space in a topic, you may wish to physically remove the withdrawn messages in that topic. To do this select the Prune button from the CIX Forums Edit Topics dialog. Pruning a topic on CIX can take a long time and the topic is temporarily made read-only while the topic is reorganised, so you should consider doing this at a non busy time, if possible.
Adding Topics
If there is a need for a new topic in your forum or if an existing topic is full, you can add new topics from the CIX Forums Edit Topics dialog.
By convention, replacements for full topics have the same name prefixed with a digit. For example, queries would be supplanted by 2queries, which in turn would be replaced by 3queries, and so on.
All your participants should be automatically joined to any new topic you create, but occasionally this can fail to happen. Thus it is best to announce the existance of a new topic elsewhere in your forum to make sure that everyone knows about it.
Adding a File List to an Existing Topic
Removing Topics
It is possible to delete topics from a forum, from the CIX Forums Edit Topics dialog. This is usually only done if the topic is either full or obsolete and contains ephemeral data that it is unlikely that anyone will want to refer back to; unless you have asked cixtech to archive your topic first, it is impossible to recover a deleted topic.


Growing up in the early 80’s, Super Mario Bros. was easily one of the greatest joys and annoyances for anyone who dared to own a Nintendo. It was notorious for presenting players with a very difficult challenge with very limited tries. Every stage was increasingly more hair-raising than the last and required more skill to get the red-suited plumber to the end of the stage. At some point, to the frustration of millions, you’d get stuck and after exhausting your extra lives, you’re back to Level 1-1. All that hard work and progress for nothing.
Super Mario Bros. is a metaphor for life: there are obstacles in our way to reach our goal. Sometimes it’s straightforward, but every now and then, there’s that one Goomba that gets in our way, or one falling platform we missed. We’d have to start again. We can find ourselves in the same predicament. There’s always that one struggle, that one recurring sin that messes us up and we have to start again. Sometimes we can’t beat it, and rather not deal with it, we turn off the console and walk away. But walking away doesn’t beat the stage, or clear the challenge. It leaves us unable to move forward.
In Super Mario, there are greater things beyond the challenges on the stage. Ultimately, it’s to beat Bowser and his minions (our enemy), conquer the castle (our challenges) and save the princess (things that are important to us). But none of that happens if we put the controller down. There are greater things beyond the current challenges we face in life, but none of that happens if we give up.
The key is not trying harder, but like Mario, it’s getting the ‘power-ups’: the invincibility stars, the 1-UPs, the mushrooms and the Fire Flowers. As a Christian, our ‘power-ups’ are scattered all over the Word of God. It’s matter of digging in and finding them; and the next time life hits us hard, it’s a matter of using them. That’s what proclaiming God’s Word does to our lives. It’s the power-ups we need to turn the trials into triumphs.
Doing life without proclaiming God’s Word is like finishing Super Mario without power-ups: it’s harder, more complicated and most of all - NOT the point.


MULHOLLAND DRIVE
Reviewed by Harvey Karten Universal Pictures Director: David Lynch Writer: David Lynch Cast: Justin Theroux, Naomi Watts, Laura Elena Harring, Ann Miller, Robert Forster, Dan Hedaya Screened at: Review Screening Rm., NYC 10/1/01
As Burt Bacharach and Hal David advise in the song "Do You Know the Way to San Jose," "L.A. is a great big freeway/ Put a hundred down and buy a car/ In a week maybe two they'll make you a star/ Weeks turn into years and quickly pass/ And all the stars there never were are parking cars and pumping gas..."
"Mulholland Drive" puts those now overly-familiar lyrics into pictures. The movie, by one of the most inventive or American directors, is about a naive, perky, Doris-Day type of woman with short blond hair who travels to Hollywood from her native Ontario in search of stardom. What she finds is pretty much the fate of most women who seek fame and fortune in a industry that chews up women and spits them out. If that were all there is to Lynch's latest film--which blends the surreal with the actual--"Mulholland Drive" would be a pretty simple tale, but this movie, which is bound to be one of the most talked-about puzzlers of the year, is a complex one, one which despite its languid pace and 2-1/2 hour length does not overstay its welcome, and one which lends itself to perhaps four distinct interpretations.
Lynch, whose flamboyant "Blue Velvet," about a kinky nightclub singer and a sadistic drug dealer presents a look behind a Norman Rockwell American town; and whose "Lost Highway," about a jazz musician, suspecting that his wife is having an affair and becomes a suspect in her murder--are also tales that could be spun in simplistic ways but subject themselves to what is now known as Lynchisms. Lynchisms are stylistic touches fabricated by the 55-year-old creator of "Eraserhead," "Dune," and "The Elephant Man" among other unconventional works that make the man's films incomprehensible to some, repulsive to others, and brilliant to still a third group. This split pretty much sums up the reactions of critics who discussed the film after seeing an advance screening, comments running the gamut form "pretentious" to "a master stroke." My own view if that "Mulholland Drive" is a film to respect, one about which perhaps a small segment of the audience can become wowed while others (like me) can admire the originality and stylistic fetishes albeit without great enthusiasm.
"Mulholland Drive," which was chosen as the centerpiece to be presented at the 2001 New York Film Festival at Lincoln Center, focuses on the astonishing Naomi Watts in the role of Betty, a sprightly blonde who arrives in Hollywood with stars in her eyes and a wish of luck from an elderly couple who have befriended her on the flight south but who laugh behind her back when she leaves. Taking a room lent to her by her aunt, she runs into a brunette woman perhaps a little older than she who calls herself Rita (Laura Elena Harring) but is suffering from amnesia which results from the shock of witnessing a fatal car accident which kills the man who is about to execute her. The picture opens with the perhaps most athletic dance contests ever shown on the screen followed by a loud crash of a head-on collision, but from there this character-driven piece becomes increasingly intricate, eerie, suspenseful and sexy as Rita and Betty, who at first share the latter's spacious digs under the watchful eye of manager Coco Lenoix (Ann Miller), become lovers.
To illustrate the way a multi-layered Hollywood power apparatus masticates all in its path, Lynch hones in on a film director, Adam Kosher (Justin Theroux), who at first refuses to cast an actress in his new film and is then persuaded by a man behind the scenes of the studio head (played by Dan Hedaya) to choose that gangster's favorite for the role. When Rita, realizing that she is still in danger and dons a blond wig over her brunette hair and makes love to her roommate Betty, we in the audience see the mutual dependence of the two personalities after which the film sinks arrestingly into a reality-meets-dream world of illusion and actuality. The big kick comes when the director does an about face, changing the identities of the women as the perky Betty's face mirrors her conversion to that of a woman possessed by envy, hatred, and downright evil inclinations. So good is Naomi Watts in the lead role, so convincing is her descent from Canadian small-time naivete into a thoroughly corrupted, even vicious demon, that her very conversion will inevitably cause the film world to debate and ponder what Lynch has in mind. My fondest hope is that Mr. Lynch does not mean for us to take any large part of his story as a character's dream-- in a dream, anything is possible: role reversals, fluid identities--so that we in the audience can accept any and all interpretations given our freedom to discard all logic. My own view is that the picture means primarily to illustrate the nature of power plays: Betty, helping Rita, makes herself beholden to the helpless brunette. The financiers, telling the director, "this is the girl," show their strength in determining how a picture gets made. Adam Kosher's discovery of his wife in bed with another man gives him an edge, but the reaction of the wife's muscular partner changes the picture. Add to this the notion that Betty is no more than name of the blonde than Rita is the name of her new friend. We are led to believe that "Betty" is merely the dream-like fantasy of one Diane Selwyn, who imagines herself an innocent femme seeking her way in Hollywood.
Peter Deming films the action with an emphasis on close-ups, focusing on the eyes of the two principal women as audience clues to their emotions, all backed up by Angelo Badalamenti's non-intrusive and effective score.
Rated R. Running time: 146 minutes. (C) 2001 by Harvey Karten<EMAIL_ADDRESS>========== X-RAMR-ID: 29660 X-Language: en X-RT-ReviewID: 252450 X-RT-TitleID: 1110157 X-RT-SourceID: 570 X-RT-AuthorID: 1123
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Just looking for some gardening tips? We have them
1.Site it right.
- Starting a garden is just like real estate it's all about location.
Place your garden in a part of your yard where you'll see it regularly (out of sight, out of mind definitely applies to gardening).
That way, you'll be much more likely to spend time in it.
2.Follow the sun.
- Misjudging sunlight is a common pitfall when you're first learning to garden.
Pay attention to how sunlight plays through your yard before choosing a spot for your garden.
Most edible plants, including many vegetables, herbs, and fruits, need at least 6 hours of sun in order to thrive.
3.Stay close to water.
- One of the best gardening tips you'll ever get is to plan your new garden near a water source.
Make sure you can run a hose to your garden site, so you don't have to lug water to it each time your plants get thirsty.
The best way to tell if plants need watering is to push a finger an inch down into the soil (that's about one knuckle deep).
If it's dry, it's time to water.
4.Start with great soil.
- When starting a garden, one of the top pieces of advice is to invest in soil that is nutrient-rich and well-drained.
5.Consider containers.
- When space is at a premium, look to containers. You can grow many plants in pots, including vegetables, herbs, flowers, fruit trees, berries, and shrubs.
6.Choose the right plants.
- It's important to select plants that match your growing conditions.
This means putting sun-loving plants into a sunny spot, choosing heat-tolerant plants in warm climates,
and giving ground-gobbling vines like pumpkins and melons ample elbow room (or a trellis to climb).
Do your homework and pick varieties that will grow well where you live and in the space you have.
7.Discover your zone.
- Knowing your "hardiness zone" can help you choose the best plants.
Simply put, it describes the coldest place a plant can grow.
The higher the zone number, the warmer the climate.
So if a plant is "hardy to zone 4" and you garden in zone 5, that plant will survive in your yard.
If, however, you're in zone 3, it's too cold to grow that particular plant.
8.Learn your frost dates.
- Planting too early (or late) in the season can spell disaster for your garden.
You need to know the last average spring frost date for your area so you don't accidentally kill plants by putting them out prematurely.
It's also good to know your first average fall frost date so that you get your plants harvested or moved indoors before late-season cold damages them.
9.Add some mulch.
- Apply a layer of mulch that's 2 to 3 inches deep around each plant.
This will help reduce weeds by blocking out the sun, and reduce moisture loss through evaporation, so you have to water less.
For a polished look, put down a layer of Scotts® bagged mulch. Or, you can put down straw, shredded leaves, pine straw, or some other locally available material.
10.Feed plants regularly.
- We've already talked about the importance of starting with great soil,
but that soil works best in concert with regular boosts of high-quality nutrition for your plants.
In other words, amazing soil + top-notch plant food = super garden success!


I am a student who recognizes the importance of both academic and humanitarian success. As a freshmen, I understand the importance of building relationships by pursing current opportunity. In addition to volunteering, I am committed to competing in several STEM related competitions.
I am a member of the Carnegie Mellon University Girls of Steel robotics team. This team is fortunate enough to have the support of Carnegie Mellon University (CMU) and access to the National Robotics and Engineering Center (NREC). Working alongside female peers and mentors, GOS emphasizes the importance of females I look forward to joining the Girls of Steel FRC team next September.
Girls Who Code (GWC) introduces students to the fun of writing computer code. GWC Club is for any student, but particularly those interested in pursuing the fast pace and ever evolving field of Information Technology.
The Pennsylvania Junior Academy of Science (PJAS) is a statewide organization of junior and senior high school students designed to stimulate and promote interest in science among its members through the development of research projects and investigations.
The goal of the Science Fair is to partner students with industry and academia through mentorships, internships, and scholarships.
death
2016 - Present
GPA: 3.23
GPA: 3.56
Apart from being a web developer, I enjoy most of my time being outdoors. In the winter, I am an avid skiier and novice ice climber. During the warmer months here in Colorado, I enjoy mountain biking, free climbing, and kayaking.
When forced indoors, I follow a number of sci-fi and fantasy genre movies and television shows, I am an aspiring chef, and I spend a large amount of my free time exploring the latest technolgy advancements in the front-end web development world.


Some remarks
The question below are potential questions, in the actual exam the
questions may be changed (however, the changes will not be significant).
Where indicated by (*), further information will be given at the
moment of the exam. This may include figures, further info, etc.
Where present, [...] are intended as tips, comments.Some of the questions do not need to be considered. They are deleted.
Structured Computer Organization
- Explain in your own words the following terms:
- Explain in your own words the term machine language.
- Humans program computers. Yet computers only "talk" machine
language. Does that mean humans write programs in machine language?
Should they? Discuss.
- Explain in your own words the following terms:
- translation,
- interpretation.
- What are the differences between translation and interpretation?
- Explain in your own words the term virtual machine.
- Multilevel machines:
- why are they necessary?
- how does the concept work?
- Explain the connection between a virtual machine and its language.
- Contemporary multilevel machines:
- levels,
- moving between levels [interpretation, compilation],
- brief description of each level.
- Explain in your own words the following terms:
- architecture of a level in a multilevel machine,
- computer architecture.
Evolution of multilevel machines
- Explain in your own words the following terms:
- In what sense are hardware and software equivalent? Not
equivalent?
- Give a short account of the factors that lead to
- the introduction of microprogramming,
- the introduction of the operating system,
- microcode [migration of functionality to microcode],
- the elimination of microprogramming.
Milestones in Computer Architecture
- Give a short account of the computer generations:
- name them,
- explain their main characteristics and driving factors.
- Stop along the road of computer development at 3 of your
favourite [motivated by importance, interestingness, etc.] milestones
and give a short motivation for your choice.
- Describe the von Neumann machine architecture. What makes it so
important for computer development?
- 1965 sees the launch of PDP-8, one of the most successful
minicomputers (50,000 sold). It introduced an important design
innovation. Which is that? Describe its architecture.
- Why did PCs become so popular? Give a short account of the
reasons for this popularity.
The Computer Zoo
- Moore's law.
- Nathan's first law of software.
- Moore's law introduces a virtuous circle. What does it mean?
Describe it. How about Nathan's law? What its consequences? Discuss
benefits, disadvantages.
- Describe the computer spectrum.
- Mainframes are very expensive machines. Why do they still exist?
Are they needed, or are they "dinosaurs"?
- The Intel Pentium family vs. the UltraSPARC family. Discuss pros
and cons. Take into account performance, design, "real world" setting.
Computer Systems Organization
- Explain in your own words the term digital computer.
- Describe your dream PC. Motivate your choice.
- What are the functions of the CPU?
- Describe the organization of a typical CPU.
- The data path cycle.
- The fetch-decode-execute cycle.
- Interpretation in the context of the fetch-decode-execute cycle.
- Computer systems design: discuss interpretation vs. hardware
implementation taking into account performance, cost and
correctness.
- Explain in your own words:
- Enumerate and discuss what are good design principles for modern
computers.
- How can computer performance be increased (from the point of view
of the CPU)?
- Pipelines:
- what are they?
- how do they work?
- give an example.
- Superscalar architectures:
- Parallel architectures:
- types,
- give a brief description of each type.
Memory
- Explain in your own words the term memory.
- Discuss the pros and cons of binary vs. decimal encoding:
- using binary hardware (binary vs. BCD),
- using decimal hardware.
- Explain in your own words the term memory address [words,
examples].
- Usual units for measuring memory.
- Explain in your own words the term cache memory. Give
examples of cache memory architectures.
- How can using caches be made more efficient? [locality principle,
design]
- Memory packaging. RAM types.
- Memory organization: memory hierarchy.
- Describe the characteristics of each memory type in the
hierarchic organization of memory. [access time, storage capacity, cost]
- Magnetic disks:
- description (architecture),
- performance:
- storage capacity
- rotation speeds
- transfer rate
- burst rate
- sustained rate
- controllers.
- IDE disks.
- SCSI disks.
- RAID.
- CD-ROM/R/RW. Architectures.
- DVDs. Architecture. Types. Performance comparison to CDs.
I/O
- Describe the physical structure of a PC.
- Explain in your own words the term controller.
- Buses. Types [CPU, I/O]. Arbiter. Evolution of bus design.
- System buses.
- Bus types. Give a short overview of the ISA, PCI, AGP, USB
architectures.
- User input devices: keyboard and mice.
- Explain in your own words the following terms (in the context of
monitors):
- pixel,
- resolution,
- refresh rate.
- CRT monitors. Organization and functioning principles. Advantages
and disadvantages.
- Flat panel displays. Architecture. Liquid crystals. LCDs.
Advantages and disadvantages vs. CRT.
- Terminals. Types. [character map, bitmap, RS232-C]
- Monochrome printers.
- Color printers. CYMK.
- Modems: data transmission over telephone lines.
- Modems: ISDN, ADSL.
The Digital Logic Level
Gates and Boolean Algebra
- Explain in your own words the terms:
- digital circuit,
- gates,
- transistors.
- (*) Given a description of the transistor, explain the behaviour
of some (given) circuits involving transistors.
- (*) Given a description of the transistor, build circuits that
should have a (required) behaviour. [Example: implement AND, OR, etc.]
- Give a list of the main logical gates, and describe their
behaviour using truth tables.
- Explain using your own words the terms:
- Boolean Algebra,
- Boolean functions,
- truth tables.
- Compact implementation of boolean functions and circuits.
Implementing circuits for boolean functions.
- (*) Given a boolean function, draw its circuit.
- (*) Given the laws of boolean algebra, simplify (given)
circuits to obtain either circuits with simpler gates, or circuits with
only one type of gate.
- Explain in your own words the term integrated circuits.
- Give a classification of integrated circuits (ICs) with respect
to the number of transistors.
- Explain in your own words the term combinatorial circuits.
- Multiplexers. What are they, how do they work, what are
they used for (example)?
- Decoders.What are they, how do they work, what are they
used for (example)?
- Comparators. What are they, how do they work, what are
they used for (example)?
- Programmable Logic Arrays. What are they, how do they
work, what are they used for (example)?
- Shifters. What are they, how do they work, what are they
used for (example)?
- Arithmetic Circuits - Bitwise Adders.
- n-bit Adders. Implementation. How can addition be made
more efficient?
- Arithmetic Logical Units
- Clocks.
Memory Chips
- Latches. Constructive Types. Clocked Latches
- Flip-Flops.
- Latches and Flip-Flops are constructively similar. What is the
essential difference between these devices?
- Registers.
- Memory Chips (inputs, outputs, control).
- Memory Chips Organization.
- CPU Chips. Description [address, data, control, other
pins]. Key parameters for performance.
- Buses. Masters, slaves. Wiring [address/data lines].
- Explain in your own words the terms bus driver, receiver,
transceiver respectively.
- Bus design issues [bus width].
- Bus clocking. Explain on a given (*) figure.
- Explain in your own words how asynchronous buses work.
- Synchronous vs. Asynchronous buses.
- Bus arbitration. Schemes for arbitration.
- Interrupt Controllers.
- Interfacing. I/O Chips.
The Microarchitecture Level
- Explain in your own words the terms:
- microarchitecture,
- microprogram.
- Data path. Given (*) a figure representing the data path, explain
its structure, data path cycle.
- Data path timing. Given (*) a figure explain how operations are
timed through the data path.
- Memory operation on the data path. Data memory and program memory
operations.
- Data path control signals [registers, ALU, memory].
- Memory read. How many data path cycles are necessary? Explain on
a given (*) figure.
- Microinstructions format.
- Describe the role of the control store and that of the
microinstruction control.
- Mic-1 operations. Explain on a given (*) table.
- Microinstruction Sequencing [choosing the next instruction].
- Explain in your own words the terms:
- stack,
- local variable frame.
- The IJVM memory model. [constant pool, local variable frame,
operand stack, method area]
- Explain on a given (*) figure: how the IJVM INVOKEVIRTUAL works.
- Explain on a given (*) figure: how the IJVM RETURN works.
- Stack illustration. Given (*) a code fragment, show the
corresponding state of the stack.
- Explain the improvements of the Mic-2 architecture over Mic-1.
Schematic representation will be given (*).
- Explain in your own words what is an instruction fetch unit.
- Explain in your own words how pipelining of an architecture
works and what are the advantages.
- Explain in your own words the basic approaches to improving
microarchitecture designs.
The Instruction Set Architecture Level
- Explain why the ISA level is sometimes referred to as "the
architecture".
- What is the connection between hardware, the ISA level and
software? [from the point of view of the designers thereof]
- ISA properties. [from the point of view of the designers]
- ISA specification documents. What are they? What do they contain?
- ISA modes.
- Memory models. [cells, words, allignment, address space - linear,
separate]
- Registers.[types]
- The Pentium family. Operating modes, address space, registers.
- UltraSPARC II. Memory, registers.
- UltraSPARC II. Register windows.
- The JVM. Memory model, registers, instruction folding.
- Data types.
- Instructions formats.
- Design criteria for instructions formats.
- Discuss briefly the similarities/differences (with respect to
instructions formats) between the Pentium (IA-32), UltraSPARC and JVM
architectures.
- Addressing.
- Immediate addressing.
- Direct addressing.
- Register addressing.
- Register indirect addressing.
- Indexed addressing.
- Based-indexed addressing.
- Stack addressing.
- Addressing modes for branch instructions.
- Instruction types.
- Data movement instructions.
- Dyadic operations.
- Monadic operations.
- Comparisons and conditional branches.
- Procedure call instructions.
- Loop control.
- I/O instructions. [types]
- Compare the Pentium (IA-32), UltraSPARC and JVM architectures
with respect to the instruction sets.
- Explain in your own words the term flow of control.
- Explain the differences between procedures and coroutines.
- Explain the difference between traps and interrupts.
The Operating System Machine Level
- Explain in your own words the term operating system.
- Explain in your own words the term system calls.
- How are system calls carried out? How about ISA instructions?
- Explain the need for virtual memory.
- Explain in your own words the term overlay.
- Paging. Terminology. Devices. Models.
- Segmentation. Advantages of segmentation.
- Virtual I/O instructions. Motivation. Files. System calls.
Implementation. Directories.
- Parallel processing. Process. States.
- Preemptive scheduling. Preemptive scheduling vs. true parallelism.
The Assembly Language Level
- Are assembly languages interpreted or translated. Describe the
process.
- Advantages and disadvantages of assembly language programming. Is
assembly language programming still used?
- The format of assembly language instructions.
- Pseudoinstructions.
- Macros.
- The assembly process.
- Linking.


Congratulations!
Your email has been verified and your information successfully saved.
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The
pm-table
component can be used in two ways: data driven, or content driven. A driven
table is best used when you have an array of objects that are in a predictable
structure.
For example, an array of users where each user object has a "firstName",
"lastName" and "address" property. You start by telling the table what your
columns are. You do this by passing an array of
TableColumn
objects into the
[columns]
property. You pass the table's data in via the
[rows]
property.
While using a data driven table, you can also use the
pm-paginator
component. Control the page size options via the
[pageSizes]
property like this:
<pm-paginator [table]="tableTemplateRef"
[pageSizes]=[10,20,30]></pm-paginator>
The default page sizes are 5, 10 and 15.
Data driven table example:
<table
#exampleTable="pm-table"
pm-table
[columns]="[
{ key: 'firstName', label: 'First Name' },
{ key: 'lastName', label: 'Second Name' },
{ key: 'address', label: 'Address' }
]"
[rows]="[
{ firstName: 'Mario', lastName: 'Mario', address: '101 Star Kingdom Rd' },
{ firstName: 'Luigi', lastName: 'Mario', address: '103 Star Kingdom Rd' },
{
firstName: 'Princess',
lastName: 'Peach',
address: '325 Royal Mushroom Ln'
},
{ firstName: 'Bowser', lastName: 'Koopa', address: '478 Lava Lake Rd' },
{
firstName: 'Madame',
lastName: 'Flurry',
address: '89 Boggly Woods Dr'
},
{ firstName: 'Don', lastName: 'Pianta', address: '278 W Rogue Port St' },
{
firstName: 'Professor',
lastName: 'Frankly',
address: '2765 E Rogue Port St'
},
{ firstName: 'Prince', lastName: 'Mush', address: '909 Gladiator Blvd' }
]"
>

<pm-paginator [table]="exampleTable"></pm-paginator>
A content driven table is just another way of saying a plain ol' fashion html
table. All you need for that is to add the "pm-table" selector to a table
element like so:

...

Note that you can not use the "pm-paginator" component while using a content
driven table.
Content Driven Table Example:

First Name
Last Name
Address

Mario
Mario
101 Star Kingdom Rd

Luigi
Mario
103 Star Kingdom Rd

Princess
Peach
325 Royal Mushroom Ln

| First Name |
Last Name |
Address |
| Mario |
Mario |
101 Star Kingdom Rd |
| Luigi |
Mario |
103 Star Kingdom Rd |
| Princess |
Peach |
325 Royal Mushroom Ln |


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About Me
|
|
|
|
|
I am a Senior AI/ML Manager at Apple Knowledge Platform where I work on Natural Language Processing and Machine Learning.
Prior to this I was a Senior Staff Applied Scientist and Engineering Manager at IBM Watson where I designed and developed algorithms for IBM's conversational AI product - Watson Assistant. I got my Masters degree at the Language Technologies Institute at Carnegie Mellon University in 2014.
What I’ve been working on
I work on the Apple Knowledge Platform which powers interactions across Siri and Spotlight. I currently work on Knowledge Graphs, Entity Linking, Question Answering and Synthetic Query Generation.
Prior to this I worked on the Natural Language Understanding components of Waston Assistant which includes intent classification, entity recognition, spellcheck and irrelevant detection across multiple languages. The algorithms are designed to be custom-trained for customers globally, deployed at scale with hundreds of thousands of models in production and serves more than 1.9% of the world’s population every month.
Awards & Recognition
- MIT 35 Innovators under 35 - Semi Finalist
- IBM Master Inventor - Title recognizing sustained and outstanding contributions to IP
- Corporate Award 2021 - Watson Assistant
- Outstanding Technical Achievement Award 2021 - State-of-the-art algorithms for intent classification and entity recognition in Watson Assistant
- Research AI Accomplishment (A-level) 2020 - Meta-Learning for Low-Resource NLP
- Corporate Award 2020 - Language enablement for Watson Services
- Women in Leadership Program 2019 - I was one of the 25 women leaders across IBM selected for the fully funded eCornell certificate program
- Corporate Award 2018 - Watson Conversation Service
- Best of IBM 2018 - Awarded to less than 1000 employees globally contributions to IBM’s business
- Eminence and Excellence Award 2017
- Outstanding Technical Achievement Award 2016 - Watson Conversation Service
- Invention Achievement Awards
Selected Patents
Complete list on Google Patents
-
-
-
-
-
US Patent 11120225 Updating an online multi-domain sentence representation generation module of a text classification system
-
US Patent 11068656 Displaying text classification anomalies predicted by a text classification model
Selected Publications
Complete list on Google Scholar
Blog and News
Rising Star Nominee
VentureBeat | July 8, 2022
All the natural language understanding technology that makes Watson Assistant so powerful.
Medium | May 26, 2021
Natural language processing takes chatbots from order takers to true conversational agents. Find out how NLP for chatbots advances interaction.
TechTarget | Apr 19, 2021
While deploying virtual assistants it is important to focus on building conversational experiences which work seamlessly.
Medium | Apr 23, 2021
Watson Assistant has a new and improved intent detection algorithm, which is more accurate versus commercial and open-source solutions.
IBM Watson Blog | Dec 10, 2020
Rising Star Nominee
VentureBeat | Jul 15, 2020
How We Designed the New Zero-Effort Irrelevant Question Detection Feature in Watson Assistant
Medium | Nov 14, 2019
IBM Research AI and IBM Watson worked together to develop a promising method that achieves state-of-the-art performance on relation extraction.
IBM Research Blog | Jul 29, 2019


Photo: Sara Ali Khan looks mesmerising in a red princess outfit
by TIMESOFINDIA.COMSara Ali Khan might be just two films old but the diva has already carved a niche for herself in the industry with her brilliant performances. Not only that but the millennial diva is also an avid social media user and keeps on sharing pictures and videos on Instagram.
With more than 15 million followers on the picture-sharing platform, Sara recently took to the Insta story and shared a picture of herself sipping a coconut drink while dressed in a red princess outfit.
Sara, who is the daughter of Saif Ali Khan and Amrita Singh has not only been making headlines due to her spectacular performances on the celluloid but even her relationship with co-star Kartik Aaryan grabbed a lot of attention.
Meanwhile, on the professional front, Sara will soon be seen opposite Kartik Aaryan in Imtiaz Ali's next and is currently shooting with Varun Dhawan for the remake of 'Coolie No. 1'.


du
Disk usage: estimate and summarize file and folder space usage.
- List the sizes of a folder and any subfolders, in the given unit (B/KB/MB):
du -b|k|m path/to/folder
- List the sizes of a folder and any subfolders, in human-readable form (i.e. auto-selecting the appropriate unit for each size):
du -h path/to/folder
- Show the size of a single folder, in human readable units:
du -sh path/to/folder
- List the human-readable sizes of a folder and of all the files and folders within it:
du -ah path/to/folder
- List the human-readable sizes of a folder and any subfolders, up to N levels deep:
du -h --max-depth=N path/to/folder
- List the human-readable size of all .jpg files in subfolders of the current folder, and show a cumulative total at the end:
du -ch */*.jpg


There are
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words ({{wordPercent}}%) over
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characters in {{totalWordCount}} words
Edit Character Length Here
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Times Appeared
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Introduction
Are you ready to know more about one of the most popular Nigerian cuisines?
First of all, let me introduce you to one of these sumptuous delicacies. Nigeria has many kinds of food that different tribes in Nigeria eat. If you are looking to taste different kinds of food from your usual food, Nigerian cuisine is way to go. You probably might have heard that Nigerian cuisines are spicy. Well, not all are like spicy. The best thing about it is you choose how spicy you want it to be. Below, you will find the recipe and :instructions on how to prepare this special dish: Jollof rice
Now, let's get to it!! 😋
Nigerian Dishes
Jollof Rice Recipe
Find the recipe for jollof rice listed below:
- 1 medium-sized onion roughly chopped
- 5 medium-sized Roma tomatoes
- 1/4 vegetable oil
- 3 tablespoon of tomato paste
- 2 habanero pepper or scotch bonnet
- 1/2 teaspoon curry powder
- 1/2 teaspoon thyme
- 1 reaspoon all purpose seasoning
- 2 cups of parboiled rice
- 1/2 teaspoon salt
- 1 knorr stock cube
- 2 bay leaves
- water, as needed
Preparation
- Blend tomatoes and scotch bonnet peppers in a food processor or blender for about 45 seconds
- Heat vegetable oil in medium-sized pot on medium-high heat. Once the oil is heated, add the roughly chopped onions and stir while frying in order to avoid burnt onions. Make sure you fry till it is golden brown. Once thiis done, add tomato paste, and leave it to fry for 2 minutes. Then add the blended tomato and pepper mixture.Stir consistently while it fries for about 30 minutes, so it does not get burnt
- Then turn the heat down to low-medium heat, and add curry, thyme, all purpose seasoning, salt, and knorr stock cube. Stir and leave it for 5 minutes, and add water to the mixture.
- Add the parboiled rice to the mixture and bay leaves as well. Do not stir it at this point. Leave the rice to cook for about 25 minutes. After leaving it to cook for a while, then stir very well wit the tomato stew mixture.
- Taste the rice to see if it is soft, not too soft though. If it is not soft, add water to it and leave the water to dry up.
- Once the water is dried up, stir for about 1 minute and leave it for another 2 minutes.
- Jollof Rice is ready!!!
Notes: It is important to use parboiled rice, so the jollof rice does not end up baing mushy. You can serve the jollof rice with grilled or fried chicken.
Find a youtube video that will help you in preparing your own jollof rice, below. Cheers!!


Load Click here to add a file to the server. The file can be from your computer or a Youtube link.
Play Play a song using KyleLoop.


pymcuprog.serialupdi package¶
Submodules¶
pymcuprog.serialupdi.application module¶
Application layer for UPDI stack
-
class
pymcuprog.serialupdi.application.
UpdiApplication
(serialport, baud, device=None)¶ Bases:
object
Generic application layer for UPDI
-
enter_progmode
()¶ Enters into NVM programming mode
-
in_prog_mode
()¶ Checks whether the NVM PROG flag is up
-
leave_progmode
()¶ Disables UPDI which releases any keys enabled
-
read_data
(address, size)¶ Reads a number of bytes of data from UPDI
- Parameters
address – address to write to
size – number of bytes to read
-
read_data_words
(address, words)¶ Reads a number of words of data from UPDI
- Parameters
address – address to write to
words – number of words to read
-
read_device_info
()¶ Reads out device information from various sources
-
reset
(apply_reset)¶ Applies or releases an UPDI reset condition
- Parameters
apply_reset – True to apply, False to release
-
unlock
()¶ Unlock by chip erase
-
wait_unlocked
(timeout_ms)¶ Waits for the device to be unlocked. All devices boot up as locked until proven otherwise
- Parameters
timeout_ms – number of milliseconts to wait
-
write_data
(address, data)¶ Writes a number of bytes to memory
- Parameters
address – address to write to
data – data to write
-
write_data_words
(address, data)¶ Writes a number of words to memory
- Parameters
address – address to write to
data – data to write
-
-
pymcuprog.serialupdi.application.
decode_sib
(sib)¶ Turns the SIB into something readable
- Parameters
sib – SIB data to decode
pymcuprog.serialupdi.constants module¶
UPDI protocol constants
pymcuprog.serialupdi.link module¶
Link layer in UPDI protocol stack
-
class
pymcuprog.serialupdi.link.
UpdiDatalink
¶ Bases:
object
UPDI data link class handles the UPDI data protocol within the device
-
LDCS_RESPONSE_BYTES
= 1¶
-
init_datalink
()¶ Init DL layer
-
key
(size, key)¶ Write a key
- Parameters
size – size of key (0=64B, 1=128B, 2=256B)
key – key value
-
ld_ptr_inc
(size)¶ Loads a number of bytes from the pointer location with pointer post-increment
- Parameters
size – number of bytes to load
- Returns
values read
-
ld_ptr_inc16
(words)¶ Load a 16-bit word value from the pointer location with pointer post-increment
- Parameters
words – number of words to load
- Returns
values read
-
ldcs
(address)¶ Load data from Control/Status space
- Parameters
address – address to load
-
read_sib
()¶ Read the SIB
-
repeat
(repeats)¶ Store a value to the repeat counter
- Parameters
repeats – number of repeats requested
-
set_physical
(physical)¶ Inject a serial-port based physical layer for use by this DL
-
st_ptr_inc
(data)¶ Store data to the pointer location with pointer post-increment
- Parameters
data – data to store
-
st_ptr_inc16
(data)¶ Store a 16-bit word value to the pointer location with pointer post-increment
- Parameters
data – data to store
-
stcs
(address, value)¶ Store a value to Control/Status space
- Parameters
address – address to store to
value – value to write
-
-
class
pymcuprog.serialupdi.link.
UpdiDatalink16bit
¶ Bases:
pymcuprog.serialupdi.link.UpdiDatalink
UPDI data link layer in 16-bit version This means that all addresses and pointers contain 2 bytes
-
ld
(address)¶ Load a single byte direct from a 16-bit address
- Parameters
address – address to load from
- Returns
value read
-
ld16
(address)¶ Load a 16-bit word directly from a 16-bit address
- Parameters
address – address to load from
- Returns
values read
-
st
(address, value)¶ Store a single byte value directly to a 16-bit address
- Parameters
address – address to write to
value – value to write
-
st16
(address, value)¶ Store a 16-bit word value directly to a 16-bit address
- Parameters
address – address to write to
value – value to write
-
st_ptr
(address)¶ Set the pointer location
- Parameters
address – address to write
-
-
class
pymcuprog.serialupdi.link.
UpdiDatalink24bit
¶ Bases:
pymcuprog.serialupdi.link.UpdiDatalink
UPDI data link layer in 24-bit version This means that all addresses and pointers contain 3 bytes
-
ld
(address)¶ Load a single byte direct from a 24-bit address
- Parameters
address – address to load from
- Returns
value read
-
ld16
(address)¶ Load a 16-bit word directly from a 24-bit address
- Parameters
address – address to load from
- Returns
values read
-
st
(address, value)¶ Store a single byte value directly to a 24-bit address
- Parameters
address – address to write to
value – value to write
-
st16
(address, value)¶ Store a 16-bit word value directly to a 24-bit address
- Parameters
address – address to write to
value – value to write
-
st_ptr
(address)¶ Set the pointer location
- Parameters
address – address to write
-
pymcuprog.serialupdi.nvm module¶
NVM implementations on various UPDI device families
-
class
pymcuprog.serialupdi.nvm.
NvmUpdi
(readwrite, device)¶ Bases:
object
Base class for NVM
-
chip_erase
()¶ Does a chip erase using the NVM controller
-
erase_eeprom
()¶ Erase EEPROM memory only
-
erase_flash_page
(address)¶ Erasing single flash page using the NVM controller
- Parameters
address (int) – Start address of page to erase
-
erase_user_row
(address, size)¶ Erase User Row memory only
- Parameters
address (int) – Start address of user row
size (int) – Size of user row
-
execute_nvm_command
(command)¶ Executes an NVM COMMAND on the NVM CTRL
- Parameters
command – command to execute
-
wait_flash_ready
()¶ Waits for the NVM controller to be ready
-
write_eeprom
(address, data)¶ Write data to EEPROM
- Parameters
address – address to write to
data – data to write
-
write_flash
(address, data)¶ Writes data to flash
- Parameters
address – address to write to
data – data to write
-
write_fuse
(address, data)¶ Writes one fuse value
- Parameters
address – address to write to
data – data to write
-
write_user_row
(address, data)¶ Writes data to user row
- Parameters
address – address to write to
data – data to write
-
-
class
pymcuprog.serialupdi.nvm.
NvmUpdiAvrDx
(readwrite, device)¶ Bases:
pymcuprog.serialupdi.nvm.NvmUpdi
AKA Version 1 UPDI NVM Present on, for example, AVR-DA and newer
-
chip_erase
()¶ Does a chip erase using the NVM controller Note that on locked devices this it not possible and the ERASE KEY has to be used instead
-
erase_eeprom
()¶ Erase EEPROM memory only (v1)
-
erase_flash_page
(address)¶ Erasing single flash page using the NVM controller (v1)
- Parameters
address (int) – Start address of page to erase
-
erase_user_row
(address, size)¶ Erase User Row memory only (v1)
- Parameters
address (int) – Start address of user row
-
write_eeprom
(address, data)¶ Writes data to NVM (EEPROM)
- Parameters
address – address to write to
data – data to write
-
write_flash
(address, data)¶ Writes data to flash (v1)
- Parameters
address – address to write to
data – data to write
-
write_fuse
(address, data)¶ Writes one fuse value V1 fuses are EEPROM-based
- Parameters
address – address to write to
data – data to write
-
write_nvm
(address, data, use_word_access)¶ Writes data to NVM (version 1) This version of the NVM block has no page buffer, so words are written directly.
- Parameters
address – address to write to
data – data to write
use_word_access – write in whole words?
-
write_user_row
(address, data)¶ Writes data to user row (v1)
- Parameters
address – address to write to
data – data to write
-
-
class
pymcuprog.serialupdi.nvm.
NvmUpdiTinyMega
(readwrite, device)¶ Bases:
pymcuprog.serialupdi.nvm.NvmUpdi
AKA Version 0 UPDI NVM Present on, for example, tiny817 -> mega4809
-
chip_erase
()¶ Does a chip erase using the NVM controller
Note that on locked devices this is not possible and the ERASE KEY has to be used instead, see the unlock method
-
erase_eeprom
()¶ Erase EEPROM memory only (v0)
-
erase_flash_page
(address)¶ Erasing single flash page using the NVM controller (v0)
- Parameters
address (int) – Start address of page to erase
-
erase_user_row
(address, size)¶ Erase User Row memory only (v0)
- Parameters
address (int) – Start address of user row
-
write_eeprom
(address, data)¶ Write data to EEPROM (v0)
- Parameters
address – address to write to
data – data to write
-
write_flash
(address, data)¶ Writes data to flash (v0)
- Parameters
address – address to write to
data – data to write
-
write_fuse
(address, data)¶ Writes one fuse value (v0)
- Parameters
address – address to write to
data – data to write
-
write_nvm
(address, data, use_word_access, nvmcommand=1)¶ Writes a page of data to NVM (v0)
By default the PAGE_WRITE command is used, which requires that the page is already erased. By default word access is used (flash)
- Parameters
address – address to write to
data – data to write
use_word_access – write whole words?
nvmcommand – command to use for commit
-
write_user_row
(address, data)¶ Writes data to user row (v0)
- Parameters
address – address to write to
data – data to write
-
pymcuprog.serialupdi.physical module¶
Serial driver for UPDI stack
-
class
pymcuprog.serialupdi.physical.
UpdiPhysical
(port, baud=115200)¶ Bases:
object
PDI physical driver using a given serial port at a given baud
-
initialise_serial
(port, baud)¶ Standard serial port initialisation
- Parameters
port – serial port to use
baud – baud rate
-
receive
(size)¶ Receives a frame of a known number of chars from UPDI
- Parameters
size – bytes to receive
-
send
(command)¶ Sends a char array to UPDI without inter-byte delay Note that the byte will echo back
- Parameters
command – command to send
-
send_double_break
()¶ Sends a double break to reset the UPDI port
BREAK is actually just a slower zero frame A double break is guaranteed to push the UPDI state machine into a known state, albeit rather brutally
-
sib
()¶ System information block is just a string coming back from a SIB command
-
pymcuprog.serialupdi.readwrite module¶
Read/write access provider for UPDI
-
class
pymcuprog.serialupdi.readwrite.
UpdiReadWrite
(datalink)¶ Bases:
object
Provides various forms of reads and writes for UPDI applications Makes us of the datalink provided
-
read_byte
(address)¶ Read a single byte from UPDI
- Parameters
address – address to read from
- Returns
value read
-
read_cs
(address)¶ Read from Control/Status space
- Parameters
address – address (index) to read
- Returns
value read
-
read_data
(address, size)¶ Reads a number of bytes of data from UPDI
- Parameters
address – address to write to
size – number of bytes to read
-
read_data_words
(address, words)¶ Reads a number of words of data from UPDI
- Parameters
address – address to write to
words – number of words to read
-
read_sib
()¶ Read the SIB from UPDI
- Returns
SIB string (bytearray) read
-
write_byte
(address, value)¶ Writes a single byte to UPDI
- Parameters
address – address to write to
value – value to write
-
write_cs
(address, value)¶ Write to Control/Status space
- Parameters
address – address (index) to write
value – 8-bit value to write
-
write_data
(address, data)¶ Writes a number of bytes to memory
- Parameters
address – address to write to
data – data to write
-
write_data_words
(address, data)¶ Writes a number of words to memory
- Parameters
address – address to write to
data – data to write
-
write_key
(size, key)¶ Write a KEY into UPDI
- Parameters
size – size of key to send
key – key value
-
pymcuprog.serialupdi.timeout module¶
Simple timer helper for UPDI stack


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Maxx G Lehmann
Frontend Software Engineer at Amount. I make stuff and purvey things.
Email
-/-
LinkedIn
-/-
Github


Vista problem.
Posted: Thu Mar 05, 2009 3:22 pm
I cant play because the launcher wont download any updates because apparently the folder is set to read only. I go and set the folder off of read only and it still doesn't work.
Game Development and More
http://unseenstudios.com/community/
http://unseenstudios.com/community/viewtopic.php?f=22&t=1344
Karl G. wrote:If you have a USB drive, you can copy the game to that drive and run "launcher.exe" there. It should work perfectly.
sourceofprovidence wrote:Karl G. wrote:If you have a USB drive, you can copy the game to that drive and run "launcher.exe" there. It should work perfectly.
That worked as well, I just moved the folder on my desktop and that worked.


Love2d Modules
love
When beginning to write games using LÖVE, the most important parts of the API are the callbacks: love.load to do one-time setup of your game, love.update which is used to manage your game’s state frame-to-frame, and love.draw which is used to render the game state onto the screen.
More interactive games will override additional callbacks in order to handle input from the user, and other aspects of a full-featured game.
LÖVE provides default placeholders for these callbacks, which you can override inside your own code by creating your own function with the same name as the callback:
– Load some default values for our rectangle. function love.load() x, y, w, h = 20, 20, 60, 20; end
– Increase the size of the rectangle every frame. function love.update(dt) w = w + 1; h = h + 1; end
– Draw a coloured rectangle. function love.draw() love.graphics.setColor(0, 100, 100); love.graphics.rectangle(‘fill’, x, y, w, h); end ———————————————————————————————–
Modules
love.audio Provides an interface to output sound to the user’s speakers. love.event Manages events, like keypresses. love.filesystem Provides an interface to the user’s filesystem. love.font Allows you to work with fonts. Added since 0.7.0 love.graphics Drawing of shapes and images, management of screen geometry. love.image Provides an interface to decode encoded image data. love.joystick Provides an interface to connected joysticks. love.keyboard Provides an interface to the user’s keyboard. love.math Provides system-independent mathematical functions. Added since 0.9.0 love.mouse Provides an interface to the user’s mouse. love.physics Can simulate 2D rigid body physics in a realistic manner. love.sound This module is responsible for decoding sound files. love.system Provides access to information about the user’s system. Added since 0.9.0 love.thread Allows you to work with threads. Added since 0.7.0 love.timer Provides high-resolution timing functionality. love.window Provides an interface for the program’s window. Added since 0.9.0 ——————————————————————————————–
Third-party modules lua-enet Multiplayer networking module for games. Added since 0.9.0 socket Module for HTTP, TCP, and UDP networking. Added since 0.5.0 utf8 Provides basic support for manipulating UTF-8 strings. Added since 0.9.2 Functions love.getVersion Gets the current running version of LÖVE. Added since 0.9.1 Types Data The superclass of all data. Object The superclass of all LÖVE types. ——————————————————————————————— Callbacks General Config Files Game configuration settings. love.draw Callback function used to draw on the screen every frame. love.errhand The error handler, used to display error messages. love.focus Callback function triggered when window receives or loses focus. Added since 0.7.0 love.keypressed Callback function triggered when a key is pressed. love.keyreleased Callback function triggered when a key is released. love.load This function is called exactly once at the beginning of the game. love.mousefocus Callback function triggered when window receives or loses mouse focus. Added since 0.9.0 love.mousemoved Callback function triggered when the mouse is moved. Added since 0.9.2 love.mousepressed Callback function triggered when a mouse button is pressed. love.mousereleased Callback function triggered when a mouse button is released. love.quit Callback function triggered when the game is closed. Added since 0.7.0 love.resize Called when the window is resized. Added since 0.9.0 love.run The main function, containing the main loop. A sensible default is used when left out. love.textinput Called when text has been entered by the user. Added since 0.9.0 love.threaderror Callback function triggered when a Thread encounters an error. Added since 0.9.0 love.update Callback function used to update the state of the game every frame. love.visible Callback function triggered when window is shown or hidden. Added since 0.9.0 ——————————————————————————————————- Joystick love.gamepadaxis Called when a Joystick’s virtual gamepad axis is moved. Added since 0.9.0 love.gamepadpressed Called when a Joystick’s virtual gamepad button is pressed. Added since 0.9.0 love.gamepadreleased Called when a Joystick’s virtual gamepad button is released. Added since 0.9.0 love.joystickadded Called when a Joystick is connected. Added since 0.9.0 love.joystickaxis Called when a joystick axis moves. Added since 0.9.0 love.joystickhat Called when a joystick hat direction changes. Added since 0.9.0 love.joystickpressed Called when a joystick button is pressed. love.joystickreleased Called when a joystick button is released. love.joystickremoved Called when a Joystick is disconnected. Added since 0.9.0
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