Royal Navy sailors begin palace guard duties
British Army guards have been temporarily replaced by Royal Navy sailors as guardians of the Royal residences.
Royal Navy sailors have taken over as guards at four royal residences for the second time in history.
The sailors have stepped in to help the Army as many of its Foot Guards Battalions are busy on overseas operations or in training.
The Royal Navy’s time as guardians started on Monday at Buckingham Palace where 1 Battalion Grenadier Guards were replaced by a Royal Navy contingent of 35 during the Changing of the Guard.
They will mount guards at each of the Royal palaces and the Tower of London until December 19 when they march out of Windsor Castle and hand back the duties to the Army.
Royal Navy Guard commanding officer Lieutenant Commander Richard Dobson said the start of their duties follows weeks of training.
“This will be an intense and memorable experience for all of us at the end of several weeks of ceremonial duties training, just eight or nine days of it specifically on palace guarding, and could be regarded as the pinnacle of my professional career which began as a rating in 1990,” he said in a statement.
“The instructors from the guards could not have been better, for those eight or nine days they really put us through our paces and prepared us thoroughly for our Fit For Role inspection last week enabling us to mount guards today.”
After completing their training, Brigade Major of the Household Division Lieutenant Colonel Guy Stone praised their efforts.
“The sailors were outstanding and had clearly put a huge amount into their preparation, assisted by the Company Sergeant Major of Number 7 Company, Coldstream Guards and his team,” he said in a statement.
“They were well turned out, confident of all guard change formats and clearly enjoying the experience.“
The Senior Service sailors are from three of the five fighting arms – the Surface Fleet, Submarine Service and Fleet Air Arm.
The only other time they mounted guards was in 2017 – the Year of the Navy.


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Daily: Bookings: 7.30am - 10.00am
Sunday: Bookings: 7.30am - 11.00am
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PART I. GENERAL
§183-1 Definitions. As used in this chapter, unless the context indicates otherwise:
"Board" means the board of land and natural resources.
"Department" means the department of land and natural resources. [L 1981, c 85, §2; am L 1985, c 174, §1]
Revision Note
This section is new. Former §183-1 renumbered as 183-1.5.


When you think of the word troll you may think of a ugly dimwitted creature that lives under a bridge. More recently, with the invention of social media and the internet, the word troll has been used to describe people who saying outrageous or derogatory things on the internet to get a rise out of people and start arguments. Now we get to worry about Patent Trolls
.
Patent Trolls are persons or companies who accuse others of using their patents, in an attempt to collect licensing fees, even though they are not using the patent themselves for production or services. The patent trolls accusations of patent infringement can often be broad or unfounded. So why does this matter? Just defending this accusation in court can cost a company up to $2.5 million in legal fees even if successful defended. This is something that can kill your business. There is a reason that Patent trolling is big business, Patent trolling in 2011 alone caused business $29 billion in direct costs.
This has been an issue that spans across all business but has been much more prevalent within the tech community. The issue with tech and software patents is that the legal jargon within these patents are often conceptual and not precise enough, making them four times more likely to face trolls as compared to other patent like chemical patents. While this has been a big issue for larger tech companies such as Google, Apple and Amazon for some time, they have the resources to ward off these troll while many smaller startups do not.
The good news is we are taking steps in the right direction. On April 29th, 2014 the Supreme Court made a ruling that makes it easier for defendants to receive reimbursement from the patent trolls in cases of frivolous patent lawsuits.


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Hello, I am writing because I had a trouble.
On my raspberry I created a small web server with emoncms I shows the measured temperature and brightness in my room.
When you start doing from 4 programs in python who ran all the functions, the boot file was as follows:
GPIO_start.sh
#! / bin / bash /
modprobe i2c-dev
cd / home / LR /
nohup python /home/LR/LEDServer.py> / dev / null / &
nohup python /home/LR/GPIOInt_ff.py> / dev / null / &
nohup python /home/LR/GPIOInt_AUX.py> / dev / null / &
nohup python / home / LR / GPIOInt_MESS py> / dev / null / &
I wanted to edit the boot file in this way:
GPIO_start.sh
#! / bin / bash /
modprobe i2c-dev
cd / home / LR /
python /home/LR/LEDServer.py
python /home/LR/GPIOInt_FF.py
python /home/LR/GPIOInt_AUX.py
python / home / LR / py GPIOInt_MESS
because the execution of the program LEDServer.py there are some mistakes that I wanted to see.
The problem is that now when I start my program raspberry GPIOInt_FF.py I occupies the terminal and I can no longer get out of the program's running, not even by pressing ctrl + c.
I also do not work with the free LAN because the software kitty I can no longer connect to my raspberry card, in fact I had to connect the monitor.
How do I get out of this situational?
There is no way to modify the startup files GPIO_start.sh?
I report the last message I see on the terminal Raspberri.
Code: Select all
INIT: Entering runlevel: 2
[info] Using makefile-style concurrent boot in runlevel 2
[ok] Network interface plugging Deamon ... skip eth0 ... done.
[ok] Starting enhanced syslogd: rsyslogd.
error opening '/ dev / input / event *': No such file or directory
Traceback (most recent call last):
File "/home/LR/LEDServer.py", line 45, in <module>
with mdb.connect = (DbServer, dbuser, DbPasswd, 'RaspiBase');
File "/usr/lib/python2.7/dist-packages/MySQLdb/_init_.py", line 187, in _init_
super (Connection, self) init ._ _ (* args, ** kwargs2)
_mysql_exceptions.OperationalError: (2002, "cant't connect to local MySQL server through socket
pin initial value = '1'
_
Thanks for your help.
Marcello
Re: startup problems
I've come up with a solution but not if it is feasible.
I can not remove the SD card from my raspberry and a reader usb connect to pc with windows, so I could edit the script that I created (GPIO_start.sh) in /etc/init.d.
Thank you
Re: startup problems
If the file is located in /etc/init.d you are unlikely to be able to access it in windows.
You could either use a "linux live cd" to reboot your windows PC as linux or if your SD card reader is a portable usb device and have another working SD card, boot the Pi using the good SD card and access the problem SD card using your card reader connected to the Pi.
Re: startup problems
I am a beginner with linux environment, it is difficult to browse and edit the files in a usb card with linux?
Re: startup problems
It maybe difficult but at least it is possible. I do not think you will be able to access the files you want on a windows PC as they are in a linux file system which windows cannot see.
If you struggle with the command line you could boot to a graphical user interface where there is a desktop and a file manager which is not whole lot different to windows, point and click.
If you connect a screen, keyboard and mouse to the Pi and type xstart (or maybe sudo xstart, I'm not sure which) it should start the desktop GUI.
The issues you have are not specific to this project and you would actually get more suitable advice on the raspberry pi forum.
Paul
Re: startup problems
Ok, thanks for the tip
This evening I make some attempt.
Re: startup problems
You have been my help with your last suggestion, I got a new SD card, I uploaded an image of the distribution debian, I did restart my raspberry with the new card, and from there I connected with the old going to modify the boot file.
Now I work as before.
Thank you all for the help.


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Fast Bayesian inference of marginal and conditional independence structures between variables from high-dimensional data (Leday and Richardson (2018) <arXiv:1803.08155>).
| Version: | 1.0.2 |
| Depends: | R (≥ 3.1.0) |
| Imports: | stats, methods, grDevices, graphics, Matrix, fdrtool, igraph, knitr |
| Published: | 2018-05-03 |
| Author: | Gwenael G.R. Leday [cre, aut], Ilaria Speranza [aut], Harry Gray [ctb] |
| Maintainer: | Gwenael G.R. Leday <gwenael.leday at mrc-bsu.cam.ac.uk> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)] |
| NeedsCompilation: | yes |
| CRAN checks: | beam results |
| Reference manual: | beam.pdf |
| Package source: | beam_1.0.2.tar.gz |
| Windows binaries: | r-devel: beam_1.0.2.zip, r-release: beam_1.0.2.zip, r-oldrel: beam_1.0.2.zip |
| OS X binaries: | r-release: beam_1.0.2.tgz, r-oldrel: beam_1.0.2.tgz |
| Old sources: | beam archive |
Please use the canonical form https://CRAN.R-project.org/package=beam to link to this page.


I love programming and making ideas real. I can code HTML, CSS and Javascript. Also know how to use web development frameworks for example AngularJS and Bootstrap.
Collaboration is what I love, getting together with customers and peers to make ideas a reality it's what drives me.
Extensive experience setting up and maintaining environments for development and prototyping.


Our team develops statistical methods and analytical tools to help people make sense of data. Building on close collaborative relationships with public health practitioners from across the world -- from Denver to San Juan to Bangkok -- we use modern statistical and machine learning tools to gain insights into complex disease systems.
We build software packages, maintain code repositories, develop SQL databases, create interactive data visualizations, and run computationally intensive simulation studies. We use the R programming language for most of our work, but also use some C, C++, and Python.
Check out our work on GitHub.
Our team develops models for understanding complex and dynamic systems of infectious disease. We have developed real-time forecasts of dengue fever in Thailand, estimated the duration of cross-protection between serotypes of dengue, and predicted the trajectory of the flu season in the US.
Posted on {{ post.date | date: '%d %B, %Y' }}
{% endfor %}


TABLE OF INTERACTIONS WITH
ANTIEMETIC NEUROLEPTICS
From the French ANSM drug interactions document of September 2016, p. 163
ANTIEMITIC NEUROLEPTICS CLASS CODE: A03FA-001 |
DOPAMINERGICS CLASS CODE: N04B-002 |
Reciprocal antagonism between the dopaminergic and the neuropleptic |
CONTRAINDICATION Use an antiemetic without extrapyramidal effects. |
ANTIEMITIC NEUROLEPTICS CLASS CODE: A03FA-001 |
LEVODOPA RxNorm: 6375 ATC: N04BA01 N04BA02 N04BA03 |
Reciprocal antagonism between the levodopa and the neuroleptic |
CONTRAINDICATION Use an antiemitic without extrapyramidal effects. |


The Dialogs interface is designed to display standard dialogs windows. Its createOptionDialog() method is the entry point to the fluent API that allows you to construct and show option dialogs.
Dialogs
createOptionDialog()


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Rosalisa K. Alindahao - Diagnosed at age 42
Born on 14 December 1961 in Paco, Manila, her menarche was at age 12. At age 4, she suffered from asthma and received continuous medications. With an M.S. Degree in Economics, Shalee has been in the banking industry ever since. She started taking birth control pills intermittently at age 27 over a 10-year period. She gave birth by cesarean section to all of her four children at ages 29, 30, 37 and 39. At the onset of her 2nd pregnancy, she experienced gestational diabetes. The longest breastfed was her 3rd child for a period of six months and the shortest was her eldest at only two weeks. Her first born died at an early age and deeply depressed her for a long period.


After reading some discussions on edu-sig, and with a little inspiration from Try Ruby, I've been playing around more with a web-based console, HTConsole. (Like several people now, I am lazily using the Cheese Shop page as the homepage.) (I should note also, I'm not really trying to recreate the publically-available interpreter aspect of Try Ruby, it's more that I'm trying to further explore the HTML-based interpreter that the later parts of the Try Ruby tutorial introduces)
This isn't intended to be Yet Another Python Console; I'm really thinking more about this as a learning tool. And since I haven't really thought about the security implications, you'll have to install it yourself to try it. Maybe some of this later.
But really, the idea isn't to duplicate (or even improve ala ipython) the console experience (ignoring the name), except to use that as a starting point.
So far I've mostly just achieved that starting point, but if you poke around there's a couple places where I'm expanding on that. If you add something to the local scope (e.g., through an import or assignment), it shows up in a table at the bottom of the screen. (Though I'm going to have to figure out how to keep the screen from getting too long.) Objects can also be displayed in an interactive manner. A function, for instance, has an edit button; if you hit it, you can edit the function in place. I want to add self-editing features to more objects. Well, at least to classes and functions. And dicts. And lists too I guess. And property.
On edu-sig there's also been talk of doctesting, and that's another thing I want to add, just to select a doctest and run it against the environment.
Anyway, it's kind of fun I think, and thoroughly non-enterprise; not even suitable for serious programming. That lends a sense of freedom. You install it like:
easy_install htconsole ... lots of junk passes by ... htconsole ... web browser pops up with console ...
At least, I think this is all it should take to install. Note: I'm pretty sure you'll need the latest version of setuptools to get it to install nicely (with support for dependency_links) -- grab ez_setup.py and run it to get the latest version.
Now to figure out some of the tricky Python magic to edit more objects in place...
How come you wrote your own serializeJSON, when MochiKit already has one?
The keyboard handling code would be a lot cleaner and more correct if you used the MochiKit.Signal API. Check the latest interpreter example sources.
It's also kind of weird that you're using the exported functions for base and async stuff, but fully qualified DOM functions. I'd pick one or the other style and stick to it.
Could definitely use a status indicator and a way to interrupt.. I guess via some kind of trace func of a C extension to send exceptions across threads.
How come you wrote your own serializeJSON, when MochiKit already has one?Because I could not find it. I have a really hard time finding things in MochiKit sometimes.
Could definitely use a status indicator and a way to interrupt.. I guess via some kind of trace func of a C extension to send exceptions across threads.I haven't particularly noticed that, but then there's lots of conditions I haven't tried. Was there a particular thing you were doing that caused a problem (something I might be able to reproduce)?
# Ian Bicking
How can the MochiKit docs change such that you'd have an easier time finding things? I mean shit, if you had looked for a function named serializeJSON you'd have found it... you picked the same name I did.
I did "while True: pass" to see how it would be handled.
Huh, yeah it's right in Base. I guess I have a problem figuring out what module things are in -- specifically Base, Format, and Async, though Base and Iter are also hard to tell apart at times. Now that I look at it, I realize I could just expand all the function lists on the front page of the documentation; instead I kept looking on individual pages. Maybe an "expand list of all available functions" link on the front page would help?
For the while True thing... damn, stopping a runaway thread is hard. Certainly on the client side some indication of command-sent-waiting-for-response can be added easily enough, but unfortunately I don't know how to actually handle the problem of infinite loops.
# Ian Bicking
There are two ways to handle an infinite loop.. you can set a trace func that checks some variable to see if it should raise an exception or not, or you can hack into some semi-private C API which lets you send an exception to another thread. The former is probably better for this.
I'll see about adding that expand all button, should be easy enough.
Keep in mind that Turing proved (before computers existed in fact) that it is impossible to accurately detect infinte loops -- it's called the 'Halting problem' FYI.
# Silas Snider
A single page with the entire documentation would also help (easier to search). On the topic of docs, links to the implementations from the documentation would also be really nice; the docs can be a little terse at times, but usually the implementation is clear enough.
# Ian Bicking
I'd rather fix the doc bugs than try and link to the source from the docs (non-trivial). Please file tickets when you find documentation that is insufficient.


Apparently, there was a reason the last king forbade the squirrels from eating anything other than acorns. The acorns were the only thing keeping the squirrels weak. They become super evil super mutants
-
create an army of your own super GOOD super mutants
-
hide from your sins


Cavicchia, C., Sarnacchiaro, P., Vichi, M. and Zaccaria, G.
A model-based ultrametric composite indicator for studying waste management in Italian municipalities
Computational Statistics, (2023+)
Vichi, M., Cavicchia, C. and Groenen, P.J.F.
Hierarchical Means Clustering
Journal of Classification, 39(3), pp.553–577 (2022)
Cavicchia, C., Vichi, M. and Zaccaria, G.
Hierarchical Disjoint Principal Component Analysis
AStA Advances in Statistical Analysis, (2022)
Cavicchia, C., Vichi, M. and Zaccaria, G.
Gaussian mixture model with an extended ultrametric covariance structure
Advances in Data Analysis and Classification, 16(2), pp.399–427 (2022)
De Rosa, E., Montuori, P., Sarnacchiaro, P., Di Duca, F., Giovinetti, M. C., Provvisiero, D. P., Cavicchia, C. and Triassi, M.
Spatiotemporal estimation of heavy metals Pollution in the Mediterranean Sea from Volturno River, Southern Italy: Distribution, Risk Assessment and Loads
Chemistry and Ecology, 38(4), pp.327–355 (2022)
Cavicchia, C. and Sarnacchiaro, P.
A multi-group higher-order factor analysis for studying the gender-effect in Teacher Job Satisfaction
METRON, 80(1), pp.23–38 (2022)
Cavicchia, C. and Vichi, M.
Second-Order Disjoint Factor Analysis
Psychometrika, 87(1), pp.289–309 (2022)
Cavicchia, C. and Sarnacchiaro, P.
The effect of a new e-tivity on students’ performance and satisfaction in an online course
Statistica Applicata - Italian Journal of Applied Statistics, 33(2), pp.163–175 (2021)
Cavicchia, C., Sarnacchiaro, P. and Vichi, M.
A composite indicator for the waste management in the EU via Hierarchical Disjoint Non-Negative Factor Analysis
Socio-Economic Planning Sciences, 73, 100832, (2021)
Cavicchia, C. and Vichi, M.
Statistical Model-based Composite Indicators for tracking coherent policy conclusions
Social Indicators Research, 156(2), pp.449–479 (2021)
Cavicchia, C., Vichi, M. and Zaccaria, G.
The ultrametric correlation matrix for modelling hierarchical latent concepts
Advances in Data Analysis and Classification, 14(4), pp.837–853 (2020)
Cavicchia, C. and Vichi, M.
Building hierarchies of factors with disjoint factor analysis
Davino, C., Palumbo, F., Wilhelm, A., Kestler, A. (eds) Recent Trends and Future Challenges in Learning from Data (ECDA 2022). Studies in Classification, Data Analysis, and Knowledge Organization., (2023+)
Cavicchia, C., Sarnacchiaro, P. and Montuori, P.
How much do Knowledge about and Attitude toward mobile phone use affect Behavior while driving? An empirical study using a Structural Equation Model
Salvati, N., Perna, C., Marchetti, S., Chambers, R. (eds) Studies in Theoretical and Applied Statistics. SIS 2021. Springer Proceedings in Mathematics & Statistics, vol 406. pp.263–280 (2023)
Cavicchia, C., Vichi, M. and Zaccaria, G.
Exploring Hierarchical Concepts: Theoretical and Application Comparisons
Imaizumi, T., Nakayama, A.,Yokoyama, S. (eds) Advanced Studies in Behaviormetrics and Data Science: Essays in Honor of Akinori Okada., pp.315–328 (2020)
Cavicchia, C., Sarnacchiaro, P. and Vichi, M.
Cost and performance: a composite indicator for separated waste collection in Italy
International Statistical Institute 2021. Proceedings of the 63rd ISI World Statistics Congress, pp.819–824 (2022)
Cavicchia, C., Sarnacchiaro, P., Vichi, M. and Zaccaria, G.
An ultrametric model to build a Composite Indicators system
Book of Short papers. 10th International Conference IES 2022: Innovation and Society 5.0: Statistical and Economic Methodologies for Quality Assessment., pp.208–211 (2022)
Cavicchia, C., Vichi, M. and Zaccaria, G.
A parsimonious parameterization of a nonnegative correlation matrix
Book of Short Papers of the 5th international workshop on Models and Learning for Clustering and Classification MBC2 2020., pp.21–26 (2021)
Cavicchia, C., Vichi, M. and Zaccaria, G.
Model-Based Clustering with Parsimonious Covariance Structure
CLADAG 2021. Book of abstracts and short papers., pp.296–299 (2021)
Cavicchia, C., Vichi, M. and Zaccaria, G.
The ultrametric covariance model for modelling teachers’ job satisfaction
SIS 2021. Book of short papers., pp.1319–1324 (2021)
Cavicchia, C. and Sarnacchiaro, P.
Mobile phone use while driving: a Structural Equation Model to analyze the Behavior behind the wheel
SIS 2021. Book of short papers., pp.835–840 (2021)
Cavicchia, C., Vichi, M. and Zaccaria, G.
Dimensionality reduction via hierarchical factorial structure
CLADAG 2019. Book of Short Papers., pp.116–119 (2019)
Cavicchia, C., Sarnacchiaro, P. and Vichi, M.
A composite indicator via hierarchical disjoint factor analysis for measuring the Italian football teams’ performances
ASA Conference 2019. Statistics for Health and Well-being. Book of short papers., pp.65–68 (2019)
Cavicchia, C., Sarnacchiaro, P. and Vichi, M.
Hierarchical Disjoint Non-Negative Factor Analysis: environment and waste management in the EU
Statistical methods for service quality evaluation. Book of short papers of IES 2019., pp.154–157 (2019)
Cavicchia, C., Vichi, M. and Zaccaria, G.
A new hierarchical model-based composite indicator on climate change
Statistical methods for service quality evaluation. Book of short papers of IES 2019., pp.346–349 (2019)
Cavicchia, C., Vichi, M. and Zaccaria, G.
Hierarchical clustering and dimensionality reduction for big data
Smart statistics for smart applications. Book of short papers SIS2019., pp.173–180 (2019)


'Cheers, ladies': Andreescu and Oleksiak spotted courtside at Raptors game
by CTV News TorontoTORONTO -- What happens when a tennis superstar and an Olympic swimmer sit side-by-side at a Toronto Raptors game? The two 19-year-olds clink drinks, lock arms and take a big gulp.
Mississauga’s Bianca Andreescu, Canada’s first Grand Slam singles champion, and Toronto’s Penny Oleksiak, a four-time Olympic medallist, were spotted courtside celebrating their success at Scotiabank Arena on Monday night.
The Raptors’ Twitter account shared a GIF of the athletes on Monday night with the caption “Cheers, ladies.”
Thousands of Twitter users have liked the post and many responded to the tweet expressing their excitement.
The pair was watching the Raptors take on the Charlotte Hornets, which led to a 132-96 victory for Toronto.


Date: {{wellness.body.effective_time_frame.date_time}}
Fatigue: {{wellness.body.fatigue}}, Mood: {{wellness.body.mood}}, Readiness: {{wellness.body.readiness}}, Sleep Duration: {{wellness.body.sleep.duration.value}} {{wellness.body.sleep.duration.unit}}


Video summarization is the process of providing a short trailer so that a viewer can get the gist without investing much time in watching the entire video. This is done by selecting the frames which collectively represent the video in a concise way. This service can be integrated with online video browsing platforms so that it can be helpful for a viewer to select a video.
There are several supervised approaches available for video summarization. They use human generated summaries as labels. Also, reinforcement learning has been used by Song et al. 2016 to make use of keyframe-labels and category information of training videos to train their agent. However human labels are subjected to opinions therefore these approaches may not guarantee an unbiased summary.
Our solution is based on Zhou, K., & Qiao, Y. (2018) where a Deep Summmarization Network is used to summarize unlabelled videos. This appproach is completely unsupervised. On top of that we used augmentations and alternate reinforcement learning algorithm to evaluate any performance improvement.
Click here to summarize videoThe solution uses a Deep Summarization Network that consists of an Encoder and a Decoder to select a frame for summarization based on predicted probabilities. And there is a reward function that evaluates the generated summary by determining how diverse the selected frames are and how much they represent the entire video.
Encoder
The encoder is a pretrained CNN used to extract features from the video frames. In this case we used ResNet50/ResNet101.
Decoder
The decoder is a bidirectional RNN (we used LSTM) which accepts the extracted features and predicts the probability distribution for a frame to be selected. This component is trainable.
Reward Function
The reward funtion calculates the diversity among the video frames and how representative are the selected frames of the entire video. The diversity part aims for dissimilarities of the frames in feature space although allowing similar features among frames which are temporaly distant and the representative part encourages selection of frames that are closest to the centroids of clusters of feature space.
This reward is used for training the decoder using REINFORCE/PPO with the aim of maximizing the rewards.
Augmentation
We use the following augmentation techniques on the videos:
- GaussianBlur
- Cutout
- Cutout Color
- Rotate
- Flip
- Center Crop
- Grayscale
We ensure that these augmentations are applied uniformly along the temporal dimension, i.e. in the Cutout augmentation, we ensure that the same patch is blacked out in all frames of the video to ensure that the temporal information is not affected.
Dataset
We train our model architectures on the TVSum dataset using the cross-validation method.  We also evaluate our model the SumMe dataset to test the generalization of our models. Note that SumMe is an out-of-distribution data and our model is not trained on this dataset.
Results on TVSum dataset
| Encoder - Backbone CNN | Encoder - Backbone CNN | RL Algorithm | Trained with augmented data | F1 Score (mean ± std) |
|---|---|---|---|---|
| Resnet50 | Bidirectional LSTM | REINFORCE | False | 0.5700 ± 0.003 |
| Resnet50 | Bidirectional LSTM | PPO | False | 0.5736 ± 0.004 |
| Resnet50 | Bidirectional LSTM | PPO | True | 0.5756 ± 0.002 |
| Resnet101 | Bidirectional LSTM | REINFORCE | False | 0.5695 ± 0.004 |
| Resnet101 | Bidirectional LSTM | PPO | False | 0.5712 ± 0.004 |
| Resnet101 | Bidirectional LSTM | PPO | True | 0.5741 ± 0.003 |
Results on SumMe dataset
| Encoder - Backbone CNN | Encoder - Backbone CNN | RL Algorithm | Trained with augmented data | F1 Score |
|---|---|---|---|---|
| Resnet50 | Bidirectional LSTM | PPO | False | 0.1780 |
| Resnet50 | Bidirectional LSTM | PPO | True | 0.1887 |
| Resnet101 | Bidirectional LSTM | PPO | False | 0.1817 |
| Resnet101 | Bidirectional LSTM | PPO | True | 0.1830 |
Discussion
We train our models on 5 different seeds and report mean and standard deviation of the F1 score. We were able to observe that augmentations improve the F1 score of our model on the TVSum and SumMe dataset. We also observed that the PPO algorithm performs better than REINFORCE algorithm. The smaller backbone, which is the Resnet50 performs better than the larger backbone, i.e. Resnet101. The result of our model the SumMe dataset also improves with augmentations. Note that this score is not comparable to models which are trained on this dataset.
References:
- Zhou, K., & Qiao, Y. (2018). Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward. ArXiv, abs/1801.00054.
- [Song et al. 2016] Song, X.; Chen, K.; Lei, J.; Sun, L.; Wang, Z.; Xie, L.; and Song, M. 2016. Category driven deep recurrent neural network for video summarization. In ICMEW, 1–6. IEEE.
- [Song et al. 2015] Song, Y.; Vallmitjana, J.; Stent, A.; andJaimes, A. 2015. Tvsum: Summarizing web videos using titles. In CVPR, 5179–5187.
- [Gygli et al. 2014] Gygli, M.; Grabner, H.; Riemenschneider, H.; and Van Gool, L. 2014. Creating summaries from user videos. In ECCV, 505–520. Springer.
- [Williams 1992] Williams, R. J. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning 8(3-4):229–256.
- Schulman, J. et al.: Proximal Policy Optimization Algorithms. CoRR. abs/1707.06347, (2017).
- He, K. et al.: Deep Residual Learning for Image Recognition. Presented at the (2015).
We are Computer Science graduate students at New York University. This project is a part of our course on Deep Reinforcement Learning.


Fiori 3/Next
Icon semantic 'design' variants
Icon with aria-hidden="true"
Icon with tooltip
Interactive Icon
Tests "click" and "ui5-click" events
"click"
"ui5-click"
"click"
"ui5-click"
Some icons can display default tooltip
Auto, SAP-icons, horizon
SAP Icons
SAP Fiori Next Icons
SAP Fiori Tools Icons
SAP business-suite Icons
Icons in RTL, but not mirrored
Icons in RTL and mirrored


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ICSNP: Tools for Multivariate Nonparametrics
Tools for multivariate nonparametrics, as location tests based on marginal ranks, spatial median and spatial signs computation, Hotelling's T-test, estimates of shape are implemented.
| Version: |
1.1-1 |
| Depends: |
R (≥ 2.4.0), mvtnorm, ICS |
| Published: |
2018-03-10 |
| Author: |
Klaus Nordhausen, Seija Sirkia, Hannu Oja, David E. Tyler |
| Maintainer: |
Klaus Nordhausen <klaus.nordhausen at tuwien.ac.at> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| Materials: |
ChangeLog |
| In views: |
Multivariate |
| CRAN checks: |
ICSNP results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=ICSNP
to link to this page.


Image Effects Summary
The jQuery Image Effects project is a plugin that adds simple effects to your images for various events.
Grow In
Grow Out
Blur
Un-Blur
Please read the image effects documentation for instructions on how to use the plugin.
Options
- classmodifier
- Used to apply class names to the elements created by the plugin. You only need to specify a class modifier if you have a name-clash.
- startevent
- The event used to trigger the effect, such as "mouseover", the "focus" event is also automatically added for keyboard accessibility.
- resetevent
- The event used to reset the effect, such as "mouseout", the "blur" event is also automatically added for keyboard accessibility.
- effect
- "growin", "growout".
- speed
- The number of milliseconds to animate, default 200
- easing
- Either "swing" or "linear" easing.
- colour
- Default is "#FFFFFF". The colour of the feature.
- size
- The number representing the size, default 25.


D:/Project_Keil/STM32F4DISCOVERY/Src/usbd_conf.c File Reference
: This file implements the board support package for the USB device library
More...
#include "stm32f4xx.h"
#include "stm32f4xx_hal.h"
#include "usbd_def.h"
#include "usbd_core.h"
|
Functions |
| void | Error_Handler (void) |
| | This function is executed in case of error occurrence.
|
|
void | SystemClock_Config (void) |
| void | HAL_PCD_MspInit (PCD_HandleTypeDef *pcdHandle) |
| void | HAL_PCD_MspDeInit (PCD_HandleTypeDef *pcdHandle) |
| void | HAL_PCD_SetupStageCallback (PCD_HandleTypeDef *hpcd) |
| | Setup stage callback.
|
| void | HAL_PCD_DataOutStageCallback (PCD_HandleTypeDef *hpcd, uint8_t epnum) |
| | Data Out stage callback.
|
| void | HAL_PCD_DataInStageCallback (PCD_HandleTypeDef *hpcd, uint8_t epnum) |
| | Data In stage callback.
|
| void | HAL_PCD_SOFCallback (PCD_HandleTypeDef *hpcd) |
| | SOF callback.
|
| void | HAL_PCD_ResetCallback (PCD_HandleTypeDef *hpcd) |
| | Reset callback.
|
| void | HAL_PCD_SuspendCallback (PCD_HandleTypeDef *hpcd) |
| | Suspend callback. When Low power mode is enabled the debug cannot be used (IAR, Keil doesn't support it).
|
| void | HAL_PCD_ResumeCallback (PCD_HandleTypeDef *hpcd) |
| | Resume callback. When Low power mode is enabled the debug cannot be used (IAR, Keil doesn't support it).
|
| void | HAL_PCD_ISOOUTIncompleteCallback (PCD_HandleTypeDef *hpcd, uint8_t epnum) |
| | ISOOUTIncomplete callback.
|
| void | HAL_PCD_ISOINIncompleteCallback (PCD_HandleTypeDef *hpcd, uint8_t epnum) |
| | ISOINIncomplete callback.
|
| void | HAL_PCD_ConnectCallback (PCD_HandleTypeDef *hpcd) |
| | Connect callback.
|
| void | HAL_PCD_DisconnectCallback (PCD_HandleTypeDef *hpcd) |
| | Disconnect callback.
|
| USBD_StatusTypeDef | USBD_LL_Init (USBD_HandleTypeDef *pdev) |
| | Initializes the low level portion of the device driver.
|
| USBD_StatusTypeDef | USBD_LL_DeInit (USBD_HandleTypeDef *pdev) |
| | De-Initializes the low level portion of the device driver.
|
| USBD_StatusTypeDef | USBD_LL_Start (USBD_HandleTypeDef *pdev) |
| | Starts the low level portion of the device driver.
|
| USBD_StatusTypeDef | USBD_LL_Stop (USBD_HandleTypeDef *pdev) |
| | Stops the low level portion of the device driver.
|
| USBD_StatusTypeDef | USBD_LL_OpenEP (USBD_HandleTypeDef *pdev, uint8_t ep_addr, uint8_t ep_type, uint16_t ep_mps) |
| | Opens an endpoint of the low level driver.
|
| USBD_StatusTypeDef | USBD_LL_CloseEP (USBD_HandleTypeDef *pdev, uint8_t ep_addr) |
| | Closes an endpoint of the low level driver.
|
| USBD_StatusTypeDef | USBD_LL_FlushEP (USBD_HandleTypeDef *pdev, uint8_t ep_addr) |
| | Flushes an endpoint of the Low Level Driver.
|
| USBD_StatusTypeDef | USBD_LL_StallEP (USBD_HandleTypeDef *pdev, uint8_t ep_addr) |
| | Sets a Stall condition on an endpoint of the Low Level Driver.
|
| USBD_StatusTypeDef | USBD_LL_ClearStallEP (USBD_HandleTypeDef *pdev, uint8_t ep_addr) |
| | Clears a Stall condition on an endpoint of the Low Level Driver.
|
| uint8_t | USBD_LL_IsStallEP (USBD_HandleTypeDef *pdev, uint8_t ep_addr) |
| | Returns Stall condition.
|
| USBD_StatusTypeDef | USBD_LL_SetUSBAddress (USBD_HandleTypeDef *pdev, uint8_t dev_addr) |
| | Assigns a USB address to the device.
|
| USBD_StatusTypeDef | USBD_LL_Transmit (USBD_HandleTypeDef *pdev, uint8_t ep_addr, uint8_t *pbuf, uint16_t size) |
| | Transmits data over an endpoint.
|
| USBD_StatusTypeDef | USBD_LL_PrepareReceive (USBD_HandleTypeDef *pdev, uint8_t ep_addr, uint8_t *pbuf, uint16_t size) |
| | Prepares an endpoint for reception.
|
| uint32_t | USBD_LL_GetRxDataSize (USBD_HandleTypeDef *pdev, uint8_t ep_addr) |
| | Returns the last transfered packet size.
|
| void | USBD_LL_Delay (uint32_t Delay) |
| | Delays routine for the USB Device Library.
|
Variables |
|
PCD_HandleTypeDef | hpcd_USB_OTG_FS |
Detailed Description
: This file implements the board support package for the USB device library
- Version:
- : v1.0_Cube This notice applies to any and all portions of this file that are not between comment pairs USER CODE BEGIN and USER CODE END. Other portions of this file, whether inserted by the user or by software development tools are owned by their respective copyright owners.
Copyright (c) 2019 STMicroelectronics International N.V. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted, provided that the following conditions are met:
1. Redistribution of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of STMicroelectronics nor the names of other contributors to this software may be used to endorse or promote products derived from this software without specific written permission. 4. This software, including modifications and/or derivative works of this software, must execute solely and exclusively on microcontroller or microprocessor devices manufactured by or for STMicroelectronics. 5. Redistribution and use of this software other than as permitted under this license is void and will automatically terminate your rights under this license.
 IN NO EVENT SHALL STMICROELECTRONICS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Function Documentation
| void Error_Handler |
( |
void |
|
) |
|
This function is executed in case of error occurrence.
- Return values:
-
| void HAL_PCD_ConnectCallback |
( |
PCD_HandleTypeDef * |
hpcd |
) |
|
Connect callback.
- Parameters:
-
- Return values:
-
| void HAL_PCD_DataInStageCallback |
( |
PCD_HandleTypeDef * |
hpcd, |
|
|
uint8_t |
epnum | |
|
) |
| | |
Data In stage callback.
- Parameters:
-
| hpcd,: | PCD handle |
| epnum,: | Endpoint number |
- Return values:
-
| void HAL_PCD_DataOutStageCallback |
( |
PCD_HandleTypeDef * |
hpcd, |
|
|
uint8_t |
epnum | |
|
) |
| | |
Data Out stage callback.
- Parameters:
-
| hpcd,: | PCD handle |
| epnum,: | Endpoint number |
- Return values:
-
| void HAL_PCD_DisconnectCallback |
( |
PCD_HandleTypeDef * |
hpcd |
) |
|
Disconnect callback.
- Parameters:
-
- Return values:
-
| void HAL_PCD_ISOINIncompleteCallback |
( |
PCD_HandleTypeDef * |
hpcd, |
|
|
uint8_t |
epnum | |
|
) |
| | |
ISOINIncomplete callback.
- Parameters:
-
| hpcd,: | PCD handle |
| epnum,: | Endpoint number |
- Return values:
-
| void HAL_PCD_ISOOUTIncompleteCallback |
( |
PCD_HandleTypeDef * |
hpcd, |
|
|
uint8_t |
epnum | |
|
) |
| | |
ISOOUTIncomplete callback.
- Parameters:
-
| hpcd,: | PCD handle |
| epnum,: | Endpoint number |
- Return values:
-
| void HAL_PCD_MspDeInit |
( |
PCD_HandleTypeDef * |
pcdHandle |
) |
|
USB_OTG_FS GPIO Configuration PA9 ------> USB_OTG_FS_VBUS PA10 ------> USB_OTG_FS_ID PA11 ------> USB_OTG_FS_DM PA12 ------> USB_OTG_FS_DP
| void HAL_PCD_MspInit |
( |
PCD_HandleTypeDef * |
pcdHandle |
) |
|
USB_OTG_FS GPIO Configuration PA9 ------> USB_OTG_FS_VBUS PA10 ------> USB_OTG_FS_ID PA11 ------> USB_OTG_FS_DM PA12 ------> USB_OTG_FS_DP
| void HAL_PCD_ResetCallback |
( |
PCD_HandleTypeDef * |
hpcd |
) |
|
Reset callback.
- Parameters:
-
- Return values:
-
| void HAL_PCD_ResumeCallback |
( |
PCD_HandleTypeDef * |
hpcd |
) |
|
Resume callback. When Low power mode is enabled the debug cannot be used (IAR, Keil doesn't support it).
- Parameters:
-
- Return values:
-
| void HAL_PCD_SetupStageCallback |
( |
PCD_HandleTypeDef * |
hpcd |
) |
|
Setup stage callback.
- Parameters:
-
- Return values:
-
| void HAL_PCD_SOFCallback |
( |
PCD_HandleTypeDef * |
hpcd |
) |
|
SOF callback.
- Parameters:
-
- Return values:
-
| void HAL_PCD_SuspendCallback |
( |
PCD_HandleTypeDef * |
hpcd |
) |
|
Suspend callback. When Low power mode is enabled the debug cannot be used (IAR, Keil doesn't support it).
- Parameters:
-
- Return values:
-
| USBD_StatusTypeDef USBD_LL_ClearStallEP |
( |
USBD_HandleTypeDef * |
pdev, |
|
|
uint8_t |
ep_addr | |
|
) |
| | |
Clears a Stall condition on an endpoint of the Low Level Driver.
- Parameters:
-
| pdev,: | Device handle |
| ep_addr,: | Endpoint number |
- Return values:
-
| USBD_StatusTypeDef USBD_LL_CloseEP |
( |
USBD_HandleTypeDef * |
pdev, |
|
|
uint8_t |
ep_addr | |
|
) |
| | |
Closes an endpoint of the low level driver.
- Parameters:
-
| pdev,: | Device handle |
| ep_addr,: | Endpoint number |
- Return values:
-
| USBD_StatusTypeDef USBD_LL_DeInit |
( |
USBD_HandleTypeDef * |
pdev |
) |
|
De-Initializes the low level portion of the device driver.
- Parameters:
-
- Return values:
-
| void USBD_LL_Delay |
( |
uint32_t |
Delay |
) |
|
Delays routine for the USB Device Library.
- Parameters:
-
- Return values:
-
| USBD_StatusTypeDef USBD_LL_FlushEP |
( |
USBD_HandleTypeDef * |
pdev, |
|
|
uint8_t |
ep_addr | |
|
) |
| | |
Flushes an endpoint of the Low Level Driver.
- Parameters:
-
| pdev,: | Device handle |
| ep_addr,: | Endpoint number |
- Return values:
-
| uint32_t USBD_LL_GetRxDataSize |
( |
USBD_HandleTypeDef * |
pdev, |
|
|
uint8_t |
ep_addr | |
|
) |
| | |
Returns the last transfered packet size.
- Parameters:
-
| pdev,: | Device handle |
| ep_addr,: | Endpoint number |
- Return values:
-
| USBD_StatusTypeDef USBD_LL_Init |
( |
USBD_HandleTypeDef * |
pdev |
) |
|
Initializes the low level portion of the device driver.
- Parameters:
-
- Return values:
-
| uint8_t USBD_LL_IsStallEP |
( |
USBD_HandleTypeDef * |
pdev, |
|
|
uint8_t |
ep_addr | |
|
) |
| | |
Returns Stall condition.
- Parameters:
-
| pdev,: | Device handle |
| ep_addr,: | Endpoint number |
- Return values:
-
| USBD_StatusTypeDef USBD_LL_OpenEP |
( |
USBD_HandleTypeDef * |
pdev, |
|
|
uint8_t |
ep_addr, |
|
|
uint8_t |
ep_type, |
|
|
uint16_t |
ep_mps | |
|
) |
| | |
Opens an endpoint of the low level driver.
- Parameters:
-
| pdev,: | Device handle |
| ep_addr,: | Endpoint number |
| ep_type,: | Endpoint type |
| ep_mps,: | Endpoint max packet size |
- Return values:
-
| USBD_StatusTypeDef USBD_LL_PrepareReceive |
( |
USBD_HandleTypeDef * |
pdev, |
|
|
uint8_t |
ep_addr, |
|
|
uint8_t * |
pbuf, |
|
|
uint16_t |
size | |
|
) |
| | |
Prepares an endpoint for reception.
- Parameters:
-
| pdev,: | Device handle |
| ep_addr,: | Endpoint number |
| pbuf,: | Pointer to data to be received |
| size,: | Data size |
- Return values:
-
| USBD_StatusTypeDef USBD_LL_SetUSBAddress |
( |
USBD_HandleTypeDef * |
pdev, |
|
|
uint8_t |
dev_addr | |
|
) |
| | |
Assigns a USB address to the device.
- Parameters:
-
| pdev,: | Device handle |
| dev_addr,: | Device address |
- Return values:
-
| USBD_StatusTypeDef USBD_LL_StallEP |
( |
USBD_HandleTypeDef * |
pdev, |
|
|
uint8_t |
ep_addr | |
|
) |
| | |
Sets a Stall condition on an endpoint of the Low Level Driver.
- Parameters:
-
| pdev,: | Device handle |
| ep_addr,: | Endpoint number |
- Return values:
-
| USBD_StatusTypeDef USBD_LL_Start |
( |
USBD_HandleTypeDef * |
pdev |
) |
|
Starts the low level portion of the device driver.
- Parameters:
-
- Return values:
-
| USBD_StatusTypeDef USBD_LL_Stop |
( |
USBD_HandleTypeDef * |
pdev |
) |
|
Stops the low level portion of the device driver.
- Parameters:
-
- Return values:
-
| USBD_StatusTypeDef USBD_LL_Transmit |
( |
USBD_HandleTypeDef * |
pdev, |
|
|
uint8_t |
ep_addr, |
|
|
uint8_t * |
pbuf, |
|
|
uint16_t |
size | |
|
) |
| | |
Transmits data over an endpoint.
- Parameters:
-
| pdev,: | Device handle |
| ep_addr,: | Endpoint number |
| pbuf,: | Pointer to data to be sent |
| size,: | Data size |
- Return values:
-


-
Eclectic Fall Bowl Centerpiece
This decoration can be fashioned easily with a fall nature hunt and a halved globe used as a bowl. There's no need to plan the arrangement -- simply mix the treasures together: pinecones, bittersweet, acorns, and branches make the perfect fall centerpiece.
-
Fall Branches in Vase
Bring fall's burst of colors indoors. A simple glass vase transforms a foliage display into naturally beautiful art. Choose a bouquet of maple, oak, sweetgum, dogwood, or persimmon leaves, which typically develop the brightest hues.
-
Bouquet of Fall Flowers and Vegetables
Going simple is the best decorating solution for fall centerpieces and table toppers. Here, deeply hued roses and dahlias play off the more muted collection of faux tomatoes and fall foliage.
Editor's Tip: Choose a vase color that recedes and doesn't compete with the blooms.
-
Video: Fall Centerpiece Ideas
-
Fall Flowers in Gourd Vases
Display these pretty fall flowers in hollowed-out gourds for a unique twist on a fall centerpiece. Select flowers in a variety of autumn hues, such as bright purple, orange, and yellow, and accent the small bouquets with sprigs of orange berries.
-
White Pumpkins Tabletop Decoration
Color-coordinate your tabletop decoration with your serving dishes to create a classy, monochromatic fall display. Place white gourds at different levels in white ceramic dishes, and accent the table with sprigs of red fall berries in a white ceramic vase.
-
Decorative Tabletop Pumpkins
Without carved faces, pumpkins make a dramatic tabletop. Start with lengths of grapevine wrapped around and up and down pumpkins in different patterns. Display smaller gourds under glass and scatter a base of leaves underneath, and include a tall vase filled with wheat or dried lavender for subtle color complement.
-
Fall Leaf Table Runner
It's easy to craft a no-sew table runner in the shapes and colors of fall. Find and enlarge a fallen leaf shape and cut it out from a piece of felt. Glue ribbon or single-fold bias tape to the edge and place on a contrasting piece of felt or complementary table runner.
-
Tiered Centerpiece of China and Roses
This simple addition to your holiday table requires only household items. Using a large china plate as the base, create a layered decoration with stacked teacups and smaller plates. Adorn with orange flowers on each level, and tuck a small amount of foliage in the top teacup.
-
Vegetable Vases with Flowers Tabletop Decoration
These colorful vegetables are the perfect fall tabletop decorations when stuffed with flowers and foliage. Cut each pepper lengthwise an inch from the top to make an opening (don't cut the top completely off), hollow out the pepper and fill with different types of flowers. Set the peppers on small plates to finish the decoration.
-
Traced-Leaf Pumpkin Centerpiece
Inexpensive tools make this uniquely carved pumpkin easy to recreate. Use a large leaf to trace onto a pumpkin and use a scraping tool to cut away only the outermost layer. Set it on a bed of moss and a pedestal for a centerpiece that will last all season.
-
Indian Corn Candleholder
The vibrant, diverse colors of Indian corn perfectly represent the color palette of fall, and you can put them to use in a no-fuss candleholder. Place a candle at the center and hot-glue corncobs together in a circle around it to make this pretty tabletop decoration.
-
Indian Corn-and-Wheat Centerpiece
Pumpkins typically are front and center at doorways and on tables, but this elegant collection of corn and wheat stalks fits in perfectly with the season. Include a variety of colors of corn and a few different sizes and include an individual cob at each place setting.
-
Small Pumpkin Plate Centerpiece
Small gourds and pumpkins are nearly sculptural, and a cluster of two or three on a pretty plain plate resembles a still life. Tuck in a pinecone and a few leaves, and wrap a bit of raffia or twine around one of the stems.
-
Tall Grass-and-Feathers Tabletop Decoration
This decorative arrangement of cattails, pheasant feathers (found at crafts stores), and Purple Majesty ornamental millet doesn't need water, so it will last all season long.
Editor's Tip: Place chicken wire or floral netting inside an urn to create structure within the vase. Mold the wire into a ball to fit the bottom of the vase and stick the stems into the holes to complete the arrangement.
-
Pumpkin Flower Arrangement
Pumpkins are plentiful and inexpensive in fall. For a pretty centerpiece, use a large pumpkin to display richly colored blooms. Cut the top off of a pumpkin or squash, scoop out the flesh, and tuck in a vase. Display a variety of flowers including dahlias, sunflowers, and zinnias.
-
Flowers-and-Candles Tabletop Decoration
A rustic rectangular container disguises a few small flower vases, making a great way to create an eclectic collection of blooms, fall foliage, and candles. Tie long grass or fall berries around the container to make the decoration pop.
-
Pretty Pumpkins Centerpiece
Pumpkins like the ones pictured can be used as decorations all season long. Elevate a few -- chosen for the beauty of their contrasting colors -- on a pedestal. Underneath, use a table runner or cloth that picks up on the hues, and tuck in few bittersweet branches for texture.
-
Tabletop Pumpkin Display
Repetition of color -- whether with pumpkins, foliage, or flowers -- creates a unified fall decorating scheme that accents all of fall's bounty. Dominant tones of orange and yellow are present in a vase of blooms and a cluster of branches on a sideboard, in addition to the main pumpkin display.
-
Seasonal Bounty Basket Centerpiece
A wicker basket contains a lush arrangement of the growth of autumn. Put a square of floral foam into the container and add a collection of kale, dill, tomatoes, broccoli, and other harvest finds for a colorful, edible centerpiece.
-
Colorful Fall Leaf Vase and Garland
This easy decoration can brighten a living or dining room in just a few minutes, where a vase filled with colorful branches brings the beauty of the outdoors in.
-
Pumpkin Tree Tabletop Decoration
Simple decorations can make a dressed-up tabletop for fall. A few cylindrical vases filled with birdseed provide a sturdy base for long branches. Small pumpkins -- suspended with bits of twine -- hover above a beautiful assortment of branches, berries, and pinecones. To craft the candle-displaying pumpkins, hollow out a few gourds and tuck in candles and berries.
-
Fall Foliage Centerpiece
Brightly colored foliage gathered at its prime brings the perfect fall color accent to this room. Snip a few long branches with a variety of colored leaves still attached and tuck them in a cluster of tall, clear vases on a tabletop.
-
Video: Creative Thanksgiving Centerpiece
-
Just For You


src/state.js
import { Maybe } from "./maybe";
import { ErrorM } from "./error";
import { clone } from "./services/clone";
import { equality } from "./services/equality";
import { Monad } from './monad';
/**
* Class State - it takes a state and returns an intermediate value and some new state value.
* @extends {Monad}
*/
export class State extends Monad {
/**
* Creates an instance of the class State with an initialization or not, the initialization can be occur late with bind method.
* @param {Object} [state] - the initial state of app.
*/
constructor(state) {
super();
/**
* Keeps the state of application variables.
* @type {any}
*/
this.state = clone(state);
/**
* The instance of Maybe.
* @type {Maybe}
*/
this.maybe = new Maybe();
/**
* The instance of ErrorM.
* @type {ErrorM}
*/
this.err = new ErrorM();
}
/**
* It takes an initial state of the monad if monad has initialized in the constructor then function assigns Error to underlying value.
* @method bind
* @param {Function} f
* @param [v] - underlying value for the monad, it can be null.
*/
bind(f, v) {
const state = !!this.state;
const vL = !!v;
switch (true) {
case (state && vL):
this.state = this.fail('State.bind() - underlying value of the monad have defined in the constructor!');
break;
case (!state && !vL):
this.state = this.fail('State.bind() - underlying value of the monad have not defined!');
break;
case (!state && vL):
this.state = v;
break;
}
}
/**
* Changes the state of application variables, if you try add new key with put() to state object it'll be assigned
* with Error instance.
* @method put
* @param {function(v: T)=> T} f - app. state transformation function.
*/
put(f) {
const buffer = clone(this.state);
this.state = this.err.bind((v) => equality(Object.getOwnPropertyNames(buffer), Object.getOwnPropertyNames(v))
? v
: this.fail('State.put()._maybeErrorT() - after init we can not add / remove keys in state obj.'), this.maybe.bind((v) => f(v), this.state));
}
/**
* Extracts the state of app.
* @method get
* @return {Pr<T> | Error}
*/
get() {
return this.state;
}
}
//Copyright (c) 2017 Alex Tranchenko. All rights reserved.


The
JarClassLoader class extends
java.net.URLClassLoader. As its name implies,
URLClassLoader is designed to be
used for loading classes and resources that are accessed by searching a
set of URLs. The URLs can refer either to directories or to JAR files.
In addition to subclassing URLClassLoader, JarClassLoader
also makes use of features in two other new JAR-related APIs, the
java.util.jar package and the java.net.JarURLConnection
class. In this section, we'll look in detail at the constructor and two
methods of JarClassLoader.
The JarClassLoader Constructor
The constructor takes an instance of
java.net.URL as an
argument. The URL passed to this constructor will be used elsewhere in
JarClassLoader to find the JAR file from which classes are to
be loaded.
public JarClassLoader(URL url) {
super(new URL[] { url });
this.url = url;
}
The
URL object is passed to the constructor of the
superclass,
URLClassLoader, which takes a
URL[] array,
rather than a single
URL instance, as an argument.
The getMainClassName Method
Once a
JarClassLoader object is constructed with the URL of
a JAR-bundled application, it's going to need a way to determine which
class in the JAR file is the application's entry point. That's the
job of the
getMainClassName method:
public String getMainClassName() throws IOException {
URL u = new URL("jar", "", url + "!/");
JarURLConnection uc = (JarURLConnection)u.openConnection();
Attributes attr = uc.getMainAttributes();
return attr != null
? attr.getValue(Attributes.Name.MAIN_CLASS)
: null;
}
You may recall from a
previous lesson
that a JAR-bundled application's entry point is specified by the
Main-Class header of the JAR file's manifest. To understand how
getMainClassName accesses the
Main-Class header value,
let's look at the method in detail, paying special attention to the
new JAR-handling features that it uses:
The JarURLConnection class and JAR URLs
The
getMainClassName method uses the JAR URL format specified
by the
java.net.JarURLConnection class. The syntax for the URL
of a JAR file is as in this example:
jar:http://www.example.com/jarfile.jar!/
The terminating
!/ separator indicates that the URL refers to
an entire JAR file. Anything following the separator refers to
specific JAR-file contents, as in this example:
jar:http://www.example.com/jarfile.jar!/mypackage/myclass.class
The first line in the getMainClassName method is:
URL u = new URL("jar", "", url + "!/");
This statement constructs a new
URL object representing a JAR URL,
appending the
!/ separator to the URL that was used in creating the
JarClassLoader instance.
The java.net.JarURLConnection class
This class represents a communications
link between an application and a JAR file. It has methods for accessing
the JAR file's manifest. The second line
of
getMainClassName is:
JarURLConnection uc = (JarURLConnection)u.openConnection();
In this statement,
URL instance created in the first line opens a
URLConnection. The
URLConnection instance is then cast
to
JarURLConnection so it can take advantage of
JarURLConnection's JAR-handling features.
Fetching Manifest Attributes: java.util.jar.Attributes
With a
JarURLConnection open to a JAR file, you can access
the header information in the JAR file's manifest by using
the
getMainAttributes method of
JarURLConnection. This
method returns an instance of
java.util.jar.Attributes,
a class that maps header names in JAR-file manifests
with their associated string values. The third line in
getMainClassName creates an
Attributes object:
Attributes attr = uc.getMainAttributes();
To get the value of the manifest's Main-Class header,
the fourth line of getMainClassName invokes the
Attributes.getValue method:
return attr != null
? attr.getValue(Attributes.Name.MAIN_CLASS)
: null;
The method's argument,
Attributes.Name.MAIN_CLASS, specifies
that it's the value of the
Main-Class header that you want.
(The
Attributes.Name class also provides static fields such
as
MANIFEST_VERSION,
CLASS_PATH, and
SEALED
for specifying other standard manifest headers.)
The invokeClass Method
We've seen how
JarURLClassLoader can identify the main class
in a JAR-bundled application. The last method to consider,
JarURLClassLoader.invokeClass, enables that main
class to be invoked to launch the JAR-bundled application:
public void invokeClass(String name, String[] args)
throws ClassNotFoundException,
NoSuchMethodException,
InvocationTargetException
{
Class c = loadClass(name);
Method m = c.getMethod("main", new Class[] { args.getClass() });
m.setAccessible(true);
int mods = m.getModifiers();
if (m.getReturnType() != void.class || !Modifier.isStatic(mods) ||
!Modifier.isPublic(mods)) {
throw new NoSuchMethodException("main");
}
try {
m.invoke(null, new Object[] { args });
} catch (IllegalAccessException e) {
// This should not happen, as we have disabled access checks
}
}
The invokeClass method takes two arguments: the name of the
application's entry-point class and an array of string arguments to
pass to the entry-point class's main method. First, the
main class is loaded:
Class c = loadClass(name);
The
loadClass method is inherited from
java.lang.ClassLoader.
Once the main class is loaded, the reflection API of the
java.lang.reflect package is used to pass the arguments to
the class and launch it. You can refer to the tutorial on
The Reflection API for a
review of reflection.


This example shows how to create an asynchronous test with the YUI Test framework for testing browser-based JavaScript code.
A Y.Test.Case
object is created with a test that waits for a
few seconds before continuing. The Y.Test.Runner
is then used to run the tests once the page has loaded.


Partition By Expression
Partition By Expression distributes data records to its output partitions based on evaluation of expression specified. It is present under Transform category in the component palette.
By default it contains one input ports, two output port and one unused ports. The number of output ports can be increased in the Partition By Expression configuration property. All the records at the input that satisfy partition condition are available at its output partition. Those records that do not satisfy the condition are transmitted through the unused port.
User is provided with options to create Java class or specify expressions. Java class includes the specified partition conditions which is then applied on the input records. Optionally, user can also use the pre-defined standard Operation Classes, more details are provided in the Operation Class section Operation Class
User can manually create Expressions or use the Predefined Expressions provided in the Expression Editor. Based on the expression provided the records are Partitioned by the Partition By Expression component.
The Partition By Expression component can be easily distinguished in the component palette. An enlarged version of the same is depicted when dragged on the canvas.
For further reference click on the links below:


Status: Beds Baths
Half Baths
Quarter Baths
Partial Baths
on
Listed on
Listed at
Days on Market:
Days on Market:
Days on Market:
Style: , Built in
Living Area:
Square Feet
Leasable:
Square Feet
Building:
Square Feet
Lot:
Square Feet
Lot: Acres
Lot: SqFt
With Pool Stories:


class: center, middle, inverse, title-slide # STA 235 - Causal Inference: Regression Discontinuity Design (Cont.) ## Spring 2021 ### McCombs School of Business, UT Austin ---  # Reminders .box-3LA[In-class midterm March 29th] -- - **.darkorange[What will the midterm look like?]** - Shorter version of a homework: Examples/cases with data (conceptual + R code) -- - **.darkorange[Where should I study from?]** - R code posted for class (includes conceptual questions), R code example questions for midterm, examples/questions seen in class, JITTs, etc. - Other resources posted on the bookmark section (websites have *a lot* of data exercises) - **.darkorange[Don't memorize anything.]** --- # Today .pull-left[ - Quick recap and finish with **.darkorange[regression discontinuity design]**: - How do we estimate an effect in an RD? - **.darkorange[Instrumental variables]**: - Noncompliance in RCTs. - Fuzzy regression discontinuity designs. ] .pull-right[ .center[ ![](https://media.giphy.com/media/l2Sq8cCzDCwhbFr1e/giphy.gif)] ] --- background-position: 50% 50% class: left, bottom, inverse .big[ Let's recap ] --- # Behind the scenes of RDs - Basically, regression discontinuities work under an **.darkorange[asymptotic assumption]**: -- - Let `\(Y_i\)` be the outcome of interest, `\(Z_i\)` the treatment assignment, `\(R_i\)` the running variable, and `\(c\)` the cutoff score: `$$Z_i = \left\{ \begin{array}{ll} 0 & R_i\leq c \\ 1 & R_i>c \\ \end{array} \right.$$` -- - Then, we can define the treatment effect `\(\delta\)` as: `$$\delta = \lim_{\epsilon \rightarrow 0^+}E[Y_i| R_i = c + \epsilon] - \lim_{\epsilon \rightarrow 0^-}E[Y_i| R_i = c + \epsilon]$$` --- # What does the limit expression mean?  --- # What does the limit expression mean?  --- # What does the limit expression mean?  --- # Conditions required for identification -- - Threshold rule **.darkorange[exists]** and cutoff point is **.darkorange[known]** -- - The running variable `\(R_i\)` is **.darkorange[continuous]** near `\(c\)`. -- - Key assumption: .box-5LA[Continuity of E[Y(1)|R] and E[Y(0)|R] at R=c] --- # Potential outcomes need to be smooth across the threshold  --- # Potential outcomes need to be smooth across the threshold  ---

 .box-4LA[How can I check if this assumption holds?] --
 .box-4t[You can't! (it's an assumption)] --

 **.darkorange[Robustness checks:]** - Check density across the cutoff - Check RD for covariates --- background-position: 50% 50% class: left, bottom, inverse .big[ Estimation in practice ] --- # How do we actually estimate an RD? - The simplest way to do this is to fit a regression: `$$Y_i = \beta_0 + \beta_1(R_i-c) + \beta_2\mathrm{I}[R_i>c] + \beta_3(R_i-c)\mathrm{I}[R_i> c]$$` --- # How do we actually estimate an RD? - The simplest way to do this is to fit a regression: `$$Y_i = \beta_0 + \beta_1\color{#900DA4}{\underbrace{(R_i-c)}_\text{Distance to the cutoff}} + \beta_2\mathrm{I}[R_i>c] + \beta_3\color{#900DA4}{\overbrace{(R_i-c)}^\text{Distance to the cutoff}}\mathrm{I}[R_i> c]$$` --- # How do we actually estimate an RD? - The simplest way to do this is to fit a regression: `$$Y_i = \beta_0 + \beta_1(R_i-c) + \beta_2\color{#F89441}{\underbrace{\mathrm{I}[R_i>c]}_\text{Treatment}} + \beta_3(R_i-c)\color{#F89441}{\overbrace{\mathrm{I}[R_i> c]}^\text{Treatment}}$$` -- - You want to add **.darkorange[flexibility]** for each side of the cutoff. -- .box-2LA[Can you identify these parameters in a plot?] --- # Let's see some examples: Sales using a linear model ```r sales <- sales %>% mutate(dist = c-time) lm(sales ~ dist + treat + dist*treat, data = sales) ```  --- # Let's see some examples: Sales using a linear model .small[ ```r summary(lm(sales ~ dist + treat + dist*treat, data = sales)) ``` ``` ## ## Call: ## lm(formula = sales ~ dist + treat + dist * treat, data = sales) ## ## Residuals: ## Min 1Q Median 3Q Max ## -65.738 -13.940 0.051 13.538 76.515 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 178.640954 1.300314 137.38 <2e-16 *** ## dist 0.205355 0.008882 23.12 <2e-16 *** ## treat 31.333952 1.842338 17.01 <2e-16 *** ## dist:treat -0.200845 0.012438 -16.15 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 20.52 on 1996 degrees of freedom ## Multiple R-squared: 0.6939, Adjusted R-squared: 0.6934 ## F-statistic: 1508 on 3 and 1996 DF, p-value: < 2.2e-16 ``` ] --- # We can be more flexible - The previous example just included linear terms, but you can also be more flexible: `$$Y_i = \beta_0 + \beta_1f(R_i-c) + \beta_2\mathrm{I}[R_i>c] + \beta_3f(R_i-c)\mathrm{I}[R_i> c]$$` - Where `\(f\)` is any function you want. --- # What happens if we fit a quadratic model? ```r lm(sales ~ dist + I(dist^2) + treat + dist*treat + treat*I(dist^2), data = sales) ```  --- # What happens if we fit a quadratic model? .small[ ```r summary(lm(sales ~ dist + I(dist^2) + treat + dist*treat + treat*I(dist^2), data = sales)) ``` ``` ## ## Call: ## lm(formula = sales ~ dist + I(dist^2) + treat + dist * treat + ## treat * I(dist^2), data = sales) ## ## Residuals: ## Min 1Q Median 3Q Max ## -66.090 -13.979 0.239 13.154 76.656 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 1.698e+02 1.937e+00 87.665 < 2e-16 *** ## dist -4.302e-03 3.556e-02 -0.121 0.903725 ## I(dist^2) -8.288e-04 1.363e-04 -6.083 1.41e-09 *** ## treat 3.308e+01 2.747e+00 12.041 < 2e-16 *** ## dist:treat 1.713e-01 4.964e-02 3.452 0.000569 *** ## I(dist^2):treat 2.034e-04 1.877e-04 1.084 0.278554 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 20.23 on 1994 degrees of freedom ## Multiple R-squared: 0.7029, Adjusted R-squared: 0.7021 ## F-statistic: 943.5 on 5 and 1994 DF, p-value: < 2.2e-16 ``` ] --- # What happens if we only look at observations close to c? ```r sales_close <- sales %>% filter(dist>-100 & dist<100) lm(sales ~ dist + treat + dist*treat + treat, data = sales_close) lm(sales ~ dist + I(dist^2) + treat + dist*treat + treat*I(dist^2), data = sales_close) ```  --- # How do they compare? .small[ ```r summary(lm(sales ~ dist + treat + dist*treat + treat, data = sales_close)) ``` ``` ## ## Call: ## lm(formula = sales ~ dist + treat + dist * treat + treat, data = sales_close) ## ## Residuals: ## Min 1Q Median 3Q Max ## -53.241 -14.764 0.268 12.938 57.811 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 170.84457 2.05528 83.125 <2e-16 *** ## dist 0.06345 0.03542 1.791 0.0736 . ## treat 32.21243 2.93614 10.971 <2e-16 *** ## dist:treat 0.06909 0.05047 1.369 0.1714 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 20.25 on 782 degrees of freedom ## Multiple R-squared: 0.5261, Adjusted R-squared: 0.5243 ## F-statistic: 289.4 on 3 and 782 DF, p-value: < 2.2e-16 ``` ] --- # How do they compare? .small[ ```r summary(lm(sales ~ dist + I(dist^2) + treat + dist*treat + treat*I(dist^2), data = sales_close)) ``` ``` ## ## Call: ## lm(formula = sales ~ dist + I(dist^2) + treat + dist * treat + ## treat * I(dist^2), data = sales_close) ## ## Residuals: ## Min 1Q Median 3Q Max ## -50.080 -14.238 -0.463 12.740 54.231 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 163.550012 3.001833 54.483 < 2e-16 *** ## dist -0.375526 0.136936 -2.742 0.006240 ** ## I(dist^2) -0.004415 0.001331 -3.317 0.000951 *** ## treat 38.757140 4.316684 8.978 < 2e-16 *** ## dist:treat 0.552254 0.195847 2.820 0.004927 ** ## I(dist^2):treat 0.003975 0.001894 2.099 0.036121 * ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 20.13 on 780 degrees of freedom ## Multiple R-squared: 0.5328, Adjusted R-squared: 0.5298 ## F-statistic: 177.9 on 5 and 780 DF, p-value: < 2.2e-16 ``` ] --- # Potential problems - There are **.darkorange[many potential problems]** with the previous examples: - Which polynomial function should we choose? Linear, quadratic, other? - What bandwidth should we choose? Whole sample? [-100,100]? .center[ ![:scale 20%](https://raw.githubusercontent.com/maibennett/sta235/main/exampleSite/content/Classes/Week8/1_RD/images/confusion.svg) ] -- - There are some ways to address these concerns.

 .bottom[ .source[ .tiny[
Icons made by Freepik from www.flaticon.com
]]] --- # Package `rdrobust` - Robust Regression Discontinuity introduced by Cattaneo, Calonico, Farrell & Titiunik (2014). - Use of **.darkorange[local polynomial]** for fit. - **.darkorange[Data-driven optimal bandwidth]** (bias vs variance). -- - `rdrobust`: Estimation of LATE and opt. bandwidth - `rdplot`: Plotting RD with nonparametric local polynomial. --- # Let's compare with previous parametric results ```r rdplot(y = sales$sales, x = sales$time, c = c, title = "RD plot", x.label = "time", y.label = "sales") ```  --- # Let's compare with previous parametric results ```r rdplot(y = sales$sales, x = sales$time, c = c, title = "RD plot", x.label = "time", y.label = "sales") ``` .pull-left[  ] .pull-right[  ] --- # Let's compare with previous parametric results .small[ ```r summary(rdrobust(y = sales$sales, x = sales$time, c = c)) ``` ``` ## Call: rdrobust ## ## Number of Obs. 2000 ## BW type mserd ## Kernel Triangular ## VCE method NN ## ## Number of Obs. 1000 1000 ## Eff. Number of Obs. 202 213 ## Order est. (p) 1 1 ## Order bias (q) 2 2 ## BW est. (h) 54.304 54.304 ## BW bias (b) 87.787 87.787 ## rho (h/b) 0.619 0.619 ## Unique Obs. 1000 1000 ## ## ============================================================================= ## Method Coef. Std. Err. z P>|z| [ 95% C.I. ] ## ============================================================================= ## Conventional -37.434 4.344 -8.618 0.000 [-45.948 , -28.921] ## Robust - - -7.610 0.000 [-48.596 , -28.691] ## ============================================================================= ``` ] --- # How do we weight observations? - `rdrobust` uses `rdbwselect()` function (by default) to estimate a data-driven bandwidth (i.e. what observations we are going to use for estimation). - If we use a bandwidth, does this mean that the RD is estimating an effect for that population within the bandwidth? -- - **.darkorange[Kernels]** are also important in this context: - How do I weight observations within the bandwidth (e.g. uniform, triangle) --- # Observing kernels  --- .center2[ .box-4LA[Now it's your turn] ] --- .center2[ .box-2[Example: Tutoring program according to entrance exam]

 .box-7[Outcome: Exit exam] ] --- # In-class exercise **Instructions**: - You will be sorted into groups. - Download the R Script for the course website or link provided. - **.darkorange[Discuss with your group]**. You will need to talk to each other. - Write down your answers in your script, and upload it to Canvas if you want to submit it. --- # What did you find?  --- # What did you find? .small[ ```r summary(rdrobust(y = tutoring$exit_exam, x = tutoring$entrance_exam, c = 70)) ``` ``` ## Call: rdrobust ## ## Number of Obs. 1000 ## BW type mserd ## Kernel Triangular ## VCE method NN ## ## Number of Obs. 238 762 ## Eff. Number of Obs. 133 212 ## Order est. (p) 1 1 ## Order bias (q) 2 2 ## BW est. (h) 8.540 8.540 ## BW bias (b) 12.946 12.946 ## rho (h/b) 0.660 0.660 ## Unique Obs. 238 762 ## ## ============================================================================= ## Method Coef. Std. Err. z P>|z| [ 95% C.I. ] ## ============================================================================= ## Conventional -6.984 1.891 -3.692 0.000 [-10.691 , -3.277] ## Robust - - -3.234 0.001 [-11.619 , -2.850] ## ============================================================================= ``` ] --- # Takeaway points .pull-left[ - RD designs are **.darkorange[great]** for causal inference! - Strong internal validity - Number of robustness checks - **.darkorange[Limited]** external validity. - Make sure to check your data: - Discontinuity in treatment assignment - Density across the cutoff - Smoothness of covariates ] .pull-right[ ![](https://media.giphy.com/media/yQF1MwG7u9OvgVAjmr/giphy.gif) ] --- # References - Angrist, J. and S. Pischke. (2015). "Mastering Metrics". *Chapter 4*. - Calonico, Cattaneo and Titiunik. (2015). "rdrobust: An R Package for Robust Nonparametric Inference in Regression-Discontinuity Designs". R Journal 7(1): 38-51. - Heiss, A. (2020). "Program Evaluation for Public Policy". *Class 10: Regression Discontinuity I, Course at BYU*. - Lee, D. and T. Lemieux. (2010). "Regression Discontinuity in Economics". *Journal of Economic Literature 48, pp 281-355*.


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|
JavaTM 2 Platform Standard Ed. 5.0 |
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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface ListSelectionModel
This interface represents the current state of the selection for any of the components that display a list of values with stable indices. The selection is modeled as a set of intervals, each interval represents a contiguous range of selected list elements. The methods for modifying the set of selected intervals all take a pair of indices, index0 and index1, that represent a closed interval, i.e. the interval includes both index0 and index1.
DefaultListSelectionModel
| Field Summary | |
|---|---|
static int |
MULTIPLE_INTERVAL_SELECTION
A value for the selectionMode property: select one or more contiguous ranges of indices at a time. |
static int |
SINGLE_INTERVAL_SELECTION
A value for the selectionMode property: select one contiguous range of indices at a time. |
static int |
SINGLE_SELECTION
A value for the selectionMode property: select one list index at a time. |
| Method Summary | |
|---|---|
void |
addListSelectionListener(ListSelectionListener x)
Add a listener to the list that's notified each time a change to the selection occurs. |
void |
addSelectionInterval(int index0,
int index1)
Change the selection to be the set union of the current selection and the indices between index0 and index1 inclusive. |
void |
clearSelection()
Change the selection to the empty set. |
int |
getAnchorSelectionIndex()
Return the first index argument from the most recent call to setSelectionInterval(), addSelectionInterval() or removeSelectionInterval(). |
int |
getLeadSelectionIndex()
Return the second index argument from the most recent call to setSelectionInterval(), addSelectionInterval() or removeSelectionInterval(). |
int |
getMaxSelectionIndex()
Returns the last selected index or -1 if the selection is empty. |
int |
getMinSelectionIndex()
Returns the first selected index or -1 if the selection is empty. |
int |
getSelectionMode()
Returns the current selection mode. |
boolean |
getValueIsAdjusting()
Returns true if the value is undergoing a series of changes. |
void |
insertIndexInterval(int index,
int length,
boolean before)
Insert length indices beginning before/after index. |
boolean |
isSelectedIndex(int index)
Returns true if the specified index is selected. |
boolean |
isSelectionEmpty()
Returns true if no indices are selected. |
void |
removeIndexInterval(int index0,
int index1)
Remove the indices in the interval index0,index1 (inclusive) from the selection model. |
void |
removeListSelectionListener(ListSelectionListener x)
Remove a listener from the list that's notified each time a change to the selection occurs. |
void |
removeSelectionInterval(int index0,
int index1)
Change the selection to be the set difference of the current selection and the indices between index0 and index1 inclusive. |
void |
setAnchorSelectionIndex(int index)
Set the anchor selection index. |
void |
setLeadSelectionIndex(int index)
Set the lead selection index. |
void |
setSelectionInterval(int index0,
int index1)
Change the selection to be between index0 and index1 inclusive. |
void |
setSelectionMode(int selectionMode)
Set the selection mode. |
void |
setValueIsAdjusting(boolean valueIsAdjusting)
This property is true if upcoming changes to the value of the model should be considered a single event. |
| Field Detail |
|---|
static final int SINGLE_SELECTION
setSelectionMode(int)
,
Constant Field Valuesstatic final int SINGLE_INTERVAL_SELECTION
setSelectionMode(int)
,
Constant Field Valuesstatic final int MULTIPLE_INTERVAL_SELECTION
setSelectionMode(int)
,
Constant Field Values| Method Detail |
|---|
void setSelectionInterval(int index0, int index1)
index0
- one end of the interval.index1
- other end of the intervaladdListSelectionListener(javax.swing.event.ListSelectionListener)
void addSelectionInterval(int index0, int index1)
index0
- one end of the interval.index1
- other end of the intervaladdListSelectionListener(javax.swing.event.ListSelectionListener)
void removeSelectionInterval(int index0, int index1)
index0
- one end of the interval.index1
- other end of the intervaladdListSelectionListener(javax.swing.event.ListSelectionListener)
int getMinSelectionIndex()
int getMaxSelectionIndex()
boolean isSelectedIndex(int index)
int getAnchorSelectionIndex()
getLeadSelectionIndex()
,
setSelectionInterval(int, int)
,
addSelectionInterval(int, int)
void setAnchorSelectionIndex(int index)
getAnchorSelectionIndex()
int getLeadSelectionIndex()
getAnchorSelectionIndex()
,
setSelectionInterval(int, int)
,
addSelectionInterval(int, int)
void setLeadSelectionIndex(int index)
getLeadSelectionIndex()
void clearSelection()
addListSelectionListener(javax.swing.event.ListSelectionListener)
boolean isSelectionEmpty()
void insertIndexInterval(int index, int length, boolean before)
void removeIndexInterval(int index0, int index1)
void setValueIsAdjusting(boolean valueIsAdjusting)
valueIsAdjusting
- The new value of the property.getValueIsAdjusting()
boolean getValueIsAdjusting()
setValueIsAdjusting(boolean)
void setSelectionMode(int selectionMode)
SINGLE_SELECTION
Only one list index can be selected at a time. In this
mode the setSelectionInterval and addSelectionInterval
methods are equivalent, and only the second index
argument (the "lead index") is used.
SINGLE_INTERVAL_SELECTION
One contiguous index interval can be selected at a time.
In this mode setSelectionInterval and addSelectionInterval
are equivalent.
MULTIPLE_INTERVAL_SELECTION
In this mode, there's no restriction on what can be selected.
getSelectionMode()
int getSelectionMode()
setSelectionMode(int)
void addListSelectionListener(ListSelectionListener x)
x
- the ListSelectionListenerremoveListSelectionListener(javax.swing.event.ListSelectionListener)
,
setSelectionInterval(int, int)
,
addSelectionInterval(int, int)
,
removeSelectionInterval(int, int)
,
clearSelection()
,
insertIndexInterval(int, int, boolean)
,
removeIndexInterval(int, int)
void removeListSelectionListener(ListSelectionListener x)
x
- the ListSelectionListeneraddListSelectionListener(javax.swing.event.ListSelectionListener)
|
JavaTM 2 Platform Standard Ed. 5.0 |
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


--- layout: default ---
This GitHub repository was created for me to manage and keep track of my Processing projects, which are usually called sketches. It contains reproductions of known mathematical and scientific beauties, some visualisations of my own and, of course, games. These games can be found as built and compressed .exe files here.


--- layout: post title: Incense date: 2011-01-02 09:00:24.000000000 +05:30 categories: - kiranoncemore - Nikon D3000 tags: - incense - Ladakh status: publish type: post published: true meta: _edit_last: '2' author: login: kiranoncemore email<EMAIL_ADDRESS>display_name: kiranoncemore first_name: Kiran last_name: '' ---
Tv: 1/1000s; Av:F/11.0; f:55mm.
Post-processing using RAW Therapee
This was shot at Thiksay monastery in Ladakh. I had seen people get some pretty amazing shots of the burning incense, and figured I'd have a shot at it. I'm not exactly pleased with the outcome!
I think better positioning could have helped improve this snap. The incense itself (the part the burns) is okay, but the bowl is in its own shadow. Secondly, the background in the left portion of the photo is dark and that kind of masks the "spoon" jutting out of the bowl.
What I am most interested in knowing is - how do people capture the smoke in such cases? I mean, I know the theory - but how close do you get to the burning object?


AkCoffeeSlideshow Demo
animate: "fade" - autoplay: 5000 - all images
animate: "slideHorizontal" - all html
animate: "slideVertical" - mix of html and images


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