We evaluated the features of diabetic retinal and choroidal edema in Spontaneously Diabetic Torii (SDT) rats. We measured the retinal and choroidal thicknesses in normal Sprague-Dawley (SD) rats ( n = 9) and SDT rats ( n = 8). The eyes were enucleated 40 weeks later after they were diagnosed with diabetes, and 4-micron sections were cut for conventional histopathologic studies. The mean retinal and choroidal thicknesses were significantly thicker in the SDT rats than in the normal SD rats. The choroidal thickness was correlated strongly with the retinal thickness in both rat models. Diabetic retinopathy (DR) and diabetic choroidopathy appeared as edema in the SDT rats. The retinal thickness was correlated strongly with the choroidal thickness in the SDT rats, which is an ideal animal model of both DR and choroidopathy.
Diabetic retinopathy (DR) is a major cause of visual loss and blindness in developed countries [ 1 ]. Physicians need to understand the manner in which DR develops and how it can be prevented by using animal models of diabetes. With that in mind, an animal model of diabetes with ocular complications mimicking human diabetics is needed. Many diabetic animal models have been reported. Énzsöly et al. [ 2 ] reported that degenerative changes in the photoreceptors and pigment epithelium appeared in streptozotocin-induced diabetic rats. Those investigators used male Wistar and Sprague-Dawley (SD) rats and found no significant difference between the retinal thicknesses in the normal and diabetic rats. Long-Evans Tokushima Learn rats have been used as a model of type 1 diabetes [ 3 , 4 ]. Pancreatic changes and genetic analysis were discussed in those studies, but ocular complications were not mentioned. Otsuka Long-Evans Tokushima Fatty (OLETF) rats are a well-known model of type 2 diabetes. Using spectral-domain optical coherence tomography (OCT), Yang et al. [ 5 ] reported that the retinas were significantly thinner in OLETF rats than in normal Long-Evans Tokushima Otsuka rats and the tendency was apparent in the retinal nerve fiber layer (NFL). Diabetic animal models and their ocular changes in these studies are important to the understanding of diabetic ocular complications. However, the ocular findings in these models differ from those in humans.
A new spontaneously diabetic strain of the SD rat, the Spontaneously Diabetic Torii (SDT) rat, was established in 1997 and the features of the model were reported [ 6 ]. Mature diabetic cataracts and proliferative DR (PDR) especially resemble human diseases in SDT rats. These remarkable ocular complications do not appear in any other animal models of diabetes. We think that SDT rats are the most ideal model of diabetic ocular complications, and we used them to examine the effect of ranirestat, a new aldose reductase inhibitor, on DR [ 7 ].
Although several interesting studies [ 8 – 11 ] have been published about the relationship between the retina and choroid in patients with diabetic macular edema (DME), to the best of our knowledge no study has reported the choroidal thickness in diabetic model rats. In the current study, we evaluated the retinal and choroidal edema in SDT rats.
The care and handling of animals were in accordance with the Association for Research in Vision and Ophthalmology Statement for the Use of Animals in Ophthalmic and Vision Research and the Jichi Medical University Animal Care and Use Committee. We obtained male SDT rats and normal SD rats from CLEA, Inc. (Tokyo, Japan). All SDT rats ( n = 8) were confirmed to be diabetic based on a nonfasting blood glucose concentration exceeding 350 mg/dL. The SDT rats were diagnosed with diabetes by 12 to 20 weeks after birth. All SDT rats were fed standard rat chow (CRF-1, Oriental Yeast, Inc., Tokyo, Japan) for 40 weeks after the onset of diabetes. All normal SD rats ( n = 9) were fed the same rat chow as SDT rats. All SDT rats and SD rats were over 50 weeks old.
Body weight, blood glucose, and glycated hemoglobin (HbA1c) were measured once monthly. Blood samples to measure the blood glucose and HbA1c were collected from the tail vein of nonfasting rats. Blood glucose was measured by the hexokinase/glucose-6-phosphate dehydrogenase method (L type Wako Glu2, Wako Pure Chemical Industries, Ltd., Osaka, Japan). HbA1c was measured using an automated glycohemoglobin analyzer (HLC-723GHb V, Tosoh Corporation, Tokyo, Japan).
Some ocular histopathology procedures were the same as the methods we reported previously [ 7 ]. Under deep anesthesia induced by an intraperitoneal injection of pentobarbital sodium (25 mg/kg body weight, Nembutal, Sumitomo Dainippon Pharmaceutical Co., Ltd., Osaka, Japan), the eyes were enucleated for conventional histopathologic studies and placed in a fixative (Super Fix KY-500, Kurabo, Japan). The fixed eyes were washed in 0.1% mol/L cacodylate buffer and embedded in paraffin. The paraffin block was sectioned to 4 μm and stained with hematoxylin and eosin for conventional histopathologic examination.
The 4- μm paraffin blocks were examined using a polarizing microscope (Olympus BX-51, Olympus Corporation, Tokyo, Japan), and the images were recorded and downloaded using the attached digital camera and software (Olympus DP 72, DP2-BSW, Olympus Corporation). The retinal thickness was defined as the distance between the retinal internal limiting membrane (ILM) and the retinal pigment epithelium (RPE). The choroidal thickness was defined as the distance between the RPE and the choroidal-scleral junction. In the retina, the thicknesses between the ILM and the inner nuclear layer (INL), the INL thickness, the outer nuclear layer (ONL) thickness, and the photoreceptor layer (PL) thickness were calculated. The mean retinal and choroidal thicknesses were measured 500, 1,000, and 1,500 microns from the optic nerve disc using ImageJ software (National Institutes of Health, Bethesda, MD, USA).
All values were expressed as the mean ± standard deviation. The Mann-Whitney U test was used for comparisons between two groups. Spearman's rank-order correlation was used to evaluate the relationship between retinal and choroidal thicknesses. Excel Tokei 2006 software (the Social Survey Research Information Co., Ltd., Tokyo, Japan) was used for statistical analysis. P < 0.05 was considered statistically significant.
Figures 1, 2, and 3 show the changes in weight, blood glucose, and HbA1c, respectively, during the study. Compared with the SD rats, the SDT rats were significantly ( P < 0.01) lighter. The mean blood glucose levels and HbA1c levels of the SDT rats were significantly ( P < 0.01) higher than those of the SD rats.
Tables 1 and 2 show the retinal and choroidal thicknesses in the normal SD rats and the SDT rats, respectively. The mean values are shown based on the distance from the optic nerve disc (500, 1,000, and 1,500 μm), and the average of three values was calculated and shown. Most of the retinas and choroids were significantly thicker in the SDT rats than in the normal SD rats, but there were no significant differences in the INL and choroidal thicknesses 500 μm from the optic nerve disc. Figures 4 and 5 show the relationship between the retinal and choroidal thicknesses (average of three measurement points) in the SDT rats and the normal SD rats, respectively. The choroidal thicknesses were correlated strongly with the retinal thicknesses in the SDT rats and the normal SD rats ( r s = 0.81, P < 0.05, and r s = 0.72, P < 0.05, resp.).
Figures 6, 7, and 8 show the retinas and choroids of a SDT rat and normal SD rat (hematoxylin and eosin stain). Compared with the normal SD rat, the retina and choroid in the SDT rat were thicker. Intense edema was present from the ILM to around the ganglion cells in the SDT rat. The intercellular space was much less dense in the SDT rat than in the SD rat throughout the retina and choroid. Two types of vessels were distinct in the choroid of the SDT rat. One, which was outside the RPE, was thought to be the choriocapillaris, and the other was thought to be a choroidal vessel. The walls of both the choriocapillaris and choroidal vessel were thickened diffusely. These findings were not detected in the normal SD rat.
In the current study, we evaluated retinal and choroidal edema in SDT rats. Large variations in the retinal thicknesses in both the normal SD and SDT rats in this experiment were seen and may present individual variations. A previous paper reported that at about 70 weeks of age SDT rats had PDR, the pathologic feature characterized by fibrous proliferation around the optic nerve disc [ 6 ]. Most of the SDT rats in this experiment were younger than 60 weeks of age, and the proliferative changes may appear about 10 weeks later. Retinal hemorrhage, exudates, and infiltration of inflammatory cells, which suggests typical nonproliferative diabetic retinopathy (NPDR), were not found in this experiment. The blood glucose level was extremely high and there was no macular formation in the SDT rat. Metabolic abnormalities and retinal morphology in the SDT rat differ from those in humans. Therefore, although DR in SDT rat mimics retinopathy in humans, to be precise it differs from that in humans. We reported accumulation of vascular endothelial growth factor (VEGF) and extensive fluorescein leakage around the optic nerve disc in the retinas of SDT rats [ 12 ], and we believe that the thickening in SDT rats is due to increasing retinal vascular permeability. Therefore, although typical NPDR in this experiment was not observed, we evaluated earlier DR as diabetic retinal edema in this experiment. Because the number of animals was limited in this experiment, we did not measure the retinal and choroidal thicknesses in SDT rats before the onset of diabetes. However, we reported that ranirestat suppressed the retinal thickness in SDT rats and the difference in the retinal thickness between the ranirestat-treated SDT rats and the normal SD rats was small [ 7 ]. Therefore, the retinal thickness in SDT rats may increase gradually after the onset of diabetes. Use of a polarizing microscope in the current study allowed us to observe the ILM, ganglion cells, INL, ONL, PL, and RPE in the retina; however, we could not distinguish the nerve fiber layer, the inner plexiform layer, or the outer plexiform layer from the other layers. A transmission electron microscope may be needed to examine more detailed pathologic features than what were apparent in this experiment. Edema was seen in each retinal section; no serous retinal detachment or cystoid edema was found in the SDT rats. This differs from DME in humans. The choroid in the SDT rats was significantly thicker than in the normal SD rats in this experiment. We are unaware of any study that mentioned the choroidal thickness in diabetic model rats, but some studies have addressed it in patients with DME using enhanced depth imaging spectral-domain-OCT. Unsal et al. [ 8 ] reported that the choroidal thicknesses in patients with PDR and DME decreased compared with healthy individuals. However, in that study, the patients with PDR had a history of treatment with panretinal laser photocoagulation (PRP). Kim et al. [ 10 ] reported that the choroidal thickness increased significantly as the diabetes progressed in severity from moderate-severe non-PDR to untreated PDR. However, in that study, the choroid in patients with PDR who had undergone PRP was thinner than that of patients with PDR who had not had any laser therapy. Although it is important to examine patients with DR under the same conditions, that is, durations of diabetes and DR, long-term glycemic control, age, and previous ocular treatment, it is difficult. Patients with diabetes often do not know definitively when the diabetes and DR developed, and the glycemic control of each patient varies. Therefore, determining whether the choroid is thick or thin in patients with DME may not be determined easily. The choroidal thickness in the SDT rats, which have almost the same conditions, is significantly thicker than in normal SD rats, but this result should not be simply extrapolated to patients with DME. SDT rats have no macula, and the retinal edema in SDT rats differs from DME in patients with diabetes. The choriocapillaris and choroidal vessels were distinct in the SDT rats, and the walls of both structures showed diffuse thickening. These findings suggested that a long duration of high blood glucose may affect the choroidal structure in SDT rats, but further transmission electron microscopy examinations are needed. We reported accumulation of VEGF and extensive fluorescein leakage around the optic nerve disc in the retinas of SDT rats [ 12 ]. Therefore, we suppose that the retinal edema results from increasing vascular permeability. However, we cannot explain why the choroidal edema appeared clearly in SDT rats in the current study. However, we believe that SDT rats are an ideal model of both diabetic retinal and choroidal edema. This rat model is expected to be used for investigating both DR and choroidopathy in many institutions.
The authors declare that there is no conflict of interests regarding the publication of this paper.
Body weight of the study animals. The SD rats are heavier than the SDT rats.
 The mean blood glucose levels of the SD rats are significantly lower than those of the SDT rats.
HbA1c levels of the study animals. The mean HbA1c levels of the SD rats are significantly lower than those of the SDT rats.
The relationship between the retinal and choroidal thicknesses in the SDT rats. r s = 0.81, P < 0.05.
The relationship between the retinal and choroidal thicknesses in the normal SD rats. r s = 0.72, P < 0.05.
(a) The retina in a normal SD rat (hematoxylin and eosin stain). A: optic nerve disc. B–D: the points 500, 1,000, and 1,500 μm from the optic nerve disc, respectively. E: index bar = 200 μm. (b) The retina and choroid 400 to 600 μm from the optic nerve disc. A: ILM. B: INL. C: ONL. D: PL. E: RPE. F: choroid. G: sclera.
(a) The retina in a SDT rat (hematoxylin and eosin stain). A: optic nerve disc. B–D: the points 500, 1,000, and 1,500 μm from the optic nerve disc, respectively. E: index bar = 200 μm. (b) The retina and choroid 400 to 600 μm from the optic nerve disc. A: ILM. B: INL. C: ONL. D: PL. E: RPE. F: choroid. G: sclera.
Comparison of the retina and the choroid in a SDT rat (left) and a normal SD rat (right) 1,400 μm to 1,600 μm from the optic nerve disc.
The mean retinal and choroidal thicknesses in the normal SD rats. Each value is based on the distance from the optic nerve disc (500, 1,000, and 1,500 µm). The average of the three values is calculated.
| 500 µm | 1,000 µm | 1,500 µm | Average | |
|---|---|---|---|---|
| Total retinal thickness ( µm) | 100.8 ± 17.7 | 96.6 ± 22.3 | 102.0 ± 26.5 | 99.8 ± 18.4 |
| Thickness between ILM and INL ( µm) | 47.5 ± 12.5 | 46.5 ± 11.7 | 50.0 ± 10.4 | 48.0 ± 7.9 |
| INL thickness ( µm) | 17.4 ± 5.4 | 16.8 ± 6.0 | 15.6 ± 5.9 | 16.6 ± 5.0 |
| ONL thickness ( µm) | 18.4 ± 3.9 | 19.0 ± 4.5 | 18.5 ± 5.0 | 18.7 ± 3.8 |
| PL thickness ( µm) | 10.5 ± 2.3 | 10.1 ± 3.0 | 9.5 ± 3.5 | 10.0 ± 2.6 |
| Choroidal thickness ( µm) | 8.8 ± 3.4 | 9.1 ± 3.9 | 9.5 ± 4.6 | 9.1 ± 3.0 |
The mean retinal and choroidal thicknesses in the SDT rats. Each value is based on the distance from the optic nerve disc (500, 1,000, and 1,500 µm). The average of the three values is calculated.
| 500 µm | 1,000 µm | 1,500 µm | Average | |
|---|---|---|---|---|
| Total retinal thickness ( µm) | 159.7 ± 29.4 ∗∗ | 146.6 ± 29.2 ∗∗ | 152.7 ± 33.6 ∗∗ | 153.0 ± 27.6 ∗∗ |
| Thickness between ILM and INL ( µm) | 78.8 ± 34.0 ∗ | 63.8 ± 17.3 ∗ | 68.8 ± 20.2 ∗ | 70.5 ± 18.8 ∗∗ |
| INL thickness ( µm) | 23.2 ± 4.1 | 25.2 ± 6.8 ∗ | 25.6 ± 7.5 ∗ | 24.7 ± 5.1 ∗∗ |
| ONL thickness ( µm) | 31.0 ± 6.9 ∗∗ | 33.2 ± 5.7 ∗∗ | 30.4 ± 7.3 ∗∗ | 31.5 ± 4.8 ∗∗ |
| PL thickness ( µm) | 21.7 ± 5.3 ∗∗ | 21.8 ± 5.8 ∗∗ | 22.5 ± 8.8 ∗∗ | 22.0 ± 5.4 ∗∗ |
| Choroidal thickness ( µm) | 12.8 ± 3.6 | 18.5 ± 4.1 ∗∗ | 21.8 ± 10.0 ∗∗ | 17.7 ± 5.4 ∗∗ |
Most layers are significantly thicker than in the normal SD rats, but there is no significant difference in the INL and choroidal thicknesses at 500 µm.
∗ P < 0.05 compared with the normal SD rats.
∗∗ P < 0.01 compared with the normal SD rats.


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{% extends 'layout.html' %} {% block content %}
We aim to make your life easy. Our competitive offers are the easiest way to sell your home.
| Home Value | $200,000 |
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| Service Fee | ($15,000) |
| Closing Costs | $0 |
| Seller Concessions | $0 |
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Here is a nerved user!
I use the Imagebana-Imagemaker since 1 year.it works fine for battlelog-but not for the new bblog forum Layout!
I dont know why,but it is not possible to post my uploaded screenshots to the Topic ,so that it is seeable as a picture or a clickable link.I use the right dimensions for forums and blogs 450x338 picsize , put it into the code line that puts the url (normaly)clickable in the text ,and all what i see is the code line in words and not as orange marked clickable link or a instant screenshotpicture.
Can anybody tell me something about another imagemaker for the forumthreads,that would be helpful for me.
Post edited 1 x times, last by
xXJumpyXx
Helping Hand
xXJumpyXx
Helping Hand
I usually use Dropbox or
https://imageshack.us
Or are you talking about an image editor?
ORFK
Global Moderator
do you talk about THIS board here?
why don't you use our internal upload script?
next to the "submitt" and "cancel" button is "my attachments"
Topicstarter
RicoSchulz
Thanks for your support.Thats absolultly what i wanted to have!
Your imagemaker is also cool-what size did you choose for the forum ,if you would share a screenshot?
ORFK
Global Moderator
just upload it in original size, it will be scaled automatictly to the most possible size.
original screenshot sizes are the best because than we can see everything like it is.


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This text contains some Hyphenation. Hopefully it is not incomprehensible. Usually NVDA can process soft hyphens in documents pretty well, but did you notice the previous word "incomprehensible"?
Pay attention to the word "pronunciation", which is pronunced differently with soft hyphens in it:
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TRUE CRIME (Warner Bros.) Starring: Clint Eastwood, Isaiah Washington, Denis Leary, James Woods, Diane Venora, Lisa Gay Hamilton. Screenplay: Larry Gross and Paul Brickman and Stephen Schiff, based on the novel by Andrew Klavan. Producers: Richard D. Zanuck and Lili Fini Zanuck and Clint Eastwood. Director: Clint Eastwood. MPAA Rating: R (profanity, violence, adult themes) Running Time: 126 minutes. Reviewed by Scott Renshaw.
In Robert Altman's film industry satire THE PLAYER, an earnest young screenwriter pitches a death penalty "issue" drama called HABEAS CORPUS, which he insists should have "no stars" and a downbeat ending, because "that's reality." The studio buys the story and turns it into an action thriller in which Bruce Willis races in at the last moment to save Julia Roberts from the gas chamber, quipping "Traffic was a bitch" as he carries her to safety. Capital punishment had been reduced to a plot device for a by-the-numbers crowd-pleaser. In Hollywood, _that's_ reality.
On the surface, TRUE CRIME appears to be something a bit more highbrow, but it's still a frustrating gloss over a sensitive subject. Clint Eastwood directs himself as Steve Everett, an Oakland newspaper reporter with a shambles of a life -- he's an alcoholic only two months on the wagon, a married inveterate philanderer, and a gung ho investigative journalist with a history of gung ho-ing too far. When his editor (Denis Leary) hands him a puff-piece human interest assingment on convicted murderer Frank Beachum's (Isaiah Washington) last day before his execution, Everett can't help poking around in the facts. Those facts suggest to him that a key witness couldn't have seen what he claimed to see, and that Beachum may be an innocent man. With the execution set for midnight, Everett has only twelve hours to save Beachum's life.
And, we assume, to save his own personal and professional soul in the process. TRUE CRIME is naturally more Everett's story than anyone else's, which might lead you to expect that he's the character who will learn, grow and change, especially since he has enough character flaws to fill a week's worth of Jerry Springer episodes. Instead, Eastwood doesn't even pretend that Everett's crusade is about anything more than salvaging a shred of his professional dignity.  The role is surprisingly flat, leading to a narrative where all that matters is the plot progression towards Beachum's midnight deadline.
It's in their dealing with Beachum that Eastwood and his writing team throw TRUE CRIME into the most confusion. Beachum's final hours with his wife and daughter are given a lot of screen time -- perhaps to deflect the perception that this is yet another movie about a white man finding salvation through helping anonymous non-white characters -- yet those scenes serve primarily to tangle the film's themes. If we're watching Beachum's torment simply to give Everett's story a face, it's a trivialization of that suffering. If we're watching to gain a greater understanding of the emotional anguish faced by death row inmates, in effect making TRUE CRIME a death penalty "issue" drama, then Everett's pursuit of the story is trivial. And if we're watching to build sympathy for an innocent man, than a late twist which suggests Beachum might be guilty is a manipulative cheat. The film plunges us into one side of the capital punishment debate when it serves its purpose, then tiptoes around the edges of the issue at other times to avoid the appearance of making a controversial statement.
Ironically, it's only the fact that TRUE CRIME is a slick Hollywood star project that makes it fairly watchable. Eastwood once again gathers wonderful technical support -- cinematographer Jack N. Green, composer Lennie Niehaus, production designer Henry Bumstead -- to create impressive atmosphere. He also draws a slick supporting performance from James Woods' as Everett's crass editor-in-chief, a role which makes you wonder whether Woods and Denis Leary accidentally swapped scripts but the result turned out perfectly. The buildup to Everett's down-to-the-wire race to find exonerating evidence will probably work on you in spite of your resistance, but ultimately it's clear that Eastwood's craftsmanship is disguising a genuinely weightless story.
On the Renshaw scale of 0 to 10 capital offenses: 5.
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Huiyan Guima Graphic D.esign<EMAIL_ADDRESS>Work Experience:
Relay for Life: Lead the People Leader (Winter 2016-Spring 2016)


When communicating with a web service that requires a single language to be specified, one recommended technique for determining the language to send to the service is to create a resource which represents the url or query parameter to send to the service. This lets the resource management system make the appropriate choice for the best language to send.
This achieves appropriate language matching, consistent fallback across the user's preference list, and language consistency with the client side resources.
Click the button below to determine the url for the web service.


--- layout: page styles: - /css/main.css - /css/ball/main.css title: CSS Ball --- ← Go Back {% assign ball = site.data.ball.2022 %}
There's a vegan dish available for each course, which is an altered version of one of the options. Meals will be altered to account for all other dietary requirements. Please let us know what these are when you purchase your ticket.


Partial caching¶
Magento uses Zend_Cache to interact with the cache storage. However, Magento also has the Magento\Cache library for implementing Magento-specific caching.
Information about caching¶
- Partial caching
- Create custom cache engines
- Default Magento after install using cache on file: folder magento use to save info about cache in
var/cache
andvar/page_cache
. You can use Magento 2 database for caching. - Cached object are stored in the
cache
andcache_tag
.
- Default Magento after install using cache on file: folder magento use to save info about cache in
- Create a cache type
- You can create new cache type. Load and save information which you need with this cache type.
- Cache type can enable, disable, clear using Cache management page in the Magento Admin.
- Create custom cache engines
More information about caching¶
Zend Cache¶
-
Zend framework has 2 type of cache: Backend and Frontend. Detail more than about Zend Cache at here.
- Cache backend is the "cache engine" : it can be file, memcached, etc.
- Cache frontend specify what kind of data will be stored in the cache.
Caching in Zend Framework is operated by frontends while cache records are stored through backend adapters (File, Sqlite, Memcache...) through a flexible system of IDs and tags. Using those, it is easy to delete specific types of records afterwards (for example: "delete all cache records marked with a given tag").
The core of the module (Zend_Cache_Core) is generic, flexible and configurable. Yet, for your specific needs there are cache frontends that extend Zend_Cache_Core for convenience: Output, File, Function and Class.
Magento Partial Cache¶
- Some folder and file important in magento framework
vender/magento/framework/App/Cache vender/magento/framework/App/Cache.php vender/magento/framework/App/CacheInterface.php vender/magento/framework/Cache
- Folder cache in server magento
var/cache var/page_cache
Magento\Framework\App\CacheInterface.php
contains some function important about cache:- Load data from cache by id
public function load($identifier);
- Save data to cache
public function save($data, $identifier, $tags = [], $lifeTime = null);
- Remove cached data by identifier
public function remove($identifier);
- Clean cached data by specific tag
public function clean($tags = []);
- Load data from cache by id
-
Default Magento use Cm_Cache_Backend_File which much improved replacement for Zend_Cache_Backend_File - works great with Magento!
Cached object saved in file with filename contain cache id, content of file contain metadatas and data.
$metadatas = array( 'hash' => $hash, 'mtime' => time(), 'expire' => $this->_expireTime($this->getLifetime($specificLifetime)), 'tags' => implode(',', $tags), ); $res = $this->_filePutContents($file, serialize($metadatas)."\n".$data); $res = $res && $this->_updateIdsTags(array($id), $tags, 'merge');
Tags saved in folder
var/cache/mage_tags
. File name is tag name, content is all cache id which linked to this tag.Cached object data saved in file as string which serialized from data object. Example about load data from cache:
vendor/magento/framework/App/ObjectManager/ConfigLoader.php
.$cacheId = $area . '::DiConfig'; $data = $this->_cache->load($cacheId); if (!$data) { $data = $this->_getReader()->read($area); $this->_cache->save($this->getSerializer()->serialize($data), $cacheId); } else { $data = $this->getSerializer()->unserialize($data); } return $data;
Declare cache id (cache id must is exclusively). Life cycle of get data are: load data from cache, if cache miss, get data and then save to cache. When save data to cache, can add parameter as tags or lifetime of cache. When cached data be changed, you must clean cache. after then when get data, cache miss and get new data and save this to cache.
In Magento, you can clean cache by Cache management page in the Magento Admin or Magento command line.
php bin/magento cache:flush
php bin/magento cache:clean
When declare cache id, you must follow some note: 1. Cache Id must be exclusively (only) 2. Cache Id should contain all parameters which can affect to data cached.


There exists a second trick which can be used to hide from the tools. On Windows you can add “.<spaces>.” at the end of a file and Windows will automatically remove it (canonicalization removes it).
However, we can create such a file with an ADS! The funny property of such a file is that tools will not be able to open the file, because a path like “xyz. .” will automatically be changed to “xyz” and this file doesn’t exist.
Here is the proof:
The created ADS foobar.txt can’t be found by the tools:
Side note 1: Such files can also be created via: echo test > “test. .::$DATA”
Side note 2: Please note that the “..:abc.txt” ADS is the ADS which was created on “C:\:abc.txt”.
We can also create a directory with the name “. .” like this:
Then it’s not possible to enter this folder:
Moreover, the already mentioned technique (like cd . .\. .\ doesn’t work), but cd “. .::$INDEX_ALLOCATION” works (the double quotes are important).
If we can add spaces in between of a directory name, we can also add it at the end like “b ” or “.. ” or “. “.
Explanation: There is a “b” and a “b ” folder, a file named “a” and a file named “a “, the two default dirs “.” and “..” plus the “. ” and “.. ” ones and the “. .” dir.
Directories with the name “.. ” can be entered with our already discussed technique:
Side note 1: This folder can be opened via the GUI if you click twice on the folder, also the content of the folder will be displayed correctly. However, files in it can’t be opened because of the wrong path (explorer.exe uses C:\test22\.. \.. \123.txt instead of C:\test22\.. \123.txt). Powershell will again be stuck in an endless loop when searching such folders.
Side note 2: You can also create an ADS on a folder with a name such as “abc”. Then you can rename the folder to a name just containing numbers (e.g. “1”). After that you can still see the ADS, but you can’t open it (ADS on a folder with a number as name doesn’t work). To open the ADS data you have to rename the folder first back to for example “abc”.


Welcome to my portfolio! I'm new to web programming but am learning fast and enjoying the journey. Here are some of the projects I'm working on- take a look and let me know what you think.
This was the first website I created! It uses the very basics of html and css such as paragraphs, links, images, colors and sizing.
With a little more experience under my belt, I created this blog for my dog. This is my first practice with floats and the box model.
This was a pairs activity in class, to get more practice with sidebars and styling.
I created this site to practice Bootstrap elements like jumbotron and the grid system.
I'm passionate about social and economic justice and have spent my career working for a variety of local and national organizations focused on making the world a better place. Now I'm learning about web programming so that I can leverage the power of technology to help create social change.


[§342H-17] Enforcement by state and county authorities. All state and county health authorities and police officers shall enforce this chapter and the rules, orders, and permits of the department. [L 1989, c 212, pt of §4]


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Action: {{rating['Action']}}
No ratings available.
52 Week High Breakout
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52 Week Low Breadown
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Message
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3 Bar Divergence - Up
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Industry: {{company.sector}} | {{company.industry}}
Location: {{company.city}} {{company.state}} - {{company.country}}
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SEC Filings: Filings
Description: {{company.longBusinessSummary}}


# Verifier error org/hsqldb/jdbcConnection.getTypeMap()Ljava/util/Map
This means the class java.util.Map was not found. In JDK 1.1, this class does not exist. When compiled for 1.2, there is a reference to this class required to conform to the JDBC 2.0 interface. This class itself is never used, but the reference is required anyway. It looks like most verifiers have no problems with this, but some have.
The solution for this problem is to recompile (at least the client part) for JDK 1.1. You will need to install JDK 1.1 before you can do this, then see:


‘Fortnite’ Chapter 2, Week 7 ‘Trick Shot’ Challenges Leaked Online
by Erik KainTomorrow’s Fortnite challenges have leaked online. The ‘Trick Shot’ mission bundle includes ten challenges and a special loading screen with the hidden letter “T” in it.The challenges were leaked by Lucas7Yoshi on Twitter. They include a good mix of combat challenges, harvesting challenges, dancing and so forth.
Here’s what to expect tomorrow:
- Eliminations with 50 health or less (0/2)
- Consume Foraged Apples at The Orchard (0/3)
- Eliminations at Weeping Woods or a Landmark (0/3)
- Dance at the Pipeman, the Hayman and the Timber Tent (0/1)
- Gain Health with a Small Fry, a Flopper and a Slurp Fish (0/1)
- Eliminations with Pistols (0/3)
- Visit Named Locations in a single match (0/3)
- Damage with Shotguns (0/500)
- Save yourself from fall damage by landing in a Hideout (0/1)
- Heal yourself within 10 seconds after taking damage from an opponent (0/1)
- Search the hidden “T” in the Trick Shot Loading Screen
These are fairly interesting challenges. I like the “save yourself from fall damage” one because it reminds me of leaping off of towers into haystacks in Assassin’s Creed.
What I very much do not like is how Epic continues to lock challenges behind completion of other challenges, meaning you have to pretty much do them in order instead of completing the ones you want at your leisure. This is very annoying and has made me less prone to completing challenges this season (and in Season X) than ever before in this game. I find it really hampers my motivation.
In any case, these will go live Thursday morning so stay tuned for all our guides and tips.


Hovers preferences
Use the Hovers preference panel to customize the C/C++ editor hover behavior.
| Option | Description |
|---|---|
| Enable editor problem annotation | When selected problems found will be highlighted in the editor. |
| Text Hover key modifier preferences | You can select hot-keys to enable alternate hover behavior. For example, a mouse over while pressing the <Ctrl> key will link to the element's source declaration. |
| Pressed key modifier while hovering | Enter a key modifier for the selected hover name. |


UNCLASSIFIED
Create Datum Dialog
Box
The Create Datum dialog box has two
tabs. The 3-Parameter tab is used to enter
parameter information for datums that are specified
using only three parameters. The
7-Parameter tab is used to enter parameter information for datums
that are specified using seven parameters.
The 3-Parameter tab contains the
following sub-elements:
- a Code field, in which you
specify a new three- to six-letter code for the new user-defined datum. This
code cannot be the same as any code that is already used for another
datum.
- a Name field, in which you
specify a name for the new user-defined datum. The name may be any string.
- an Ellipsoid pull-down list,
with which you specify the ellipsoid that is to be associated with the new
user-defined datum.
- Delta X, Delta Y, and Delta Z fields, in which you
specify the datum shift values from the new user-defined datum to WGS 84.
The shift values are optionally signed real values, in meters.
- Standard Error in X, Standard Error in Y, and
Standard Error in Z fields, in which you specify the standard error for
each of the datum shift values. The standard errors are positive real
values, in meters. However, if the standard error is unknown, a value of
negative one (-1) should be entered.
- a set of four fields in
which you specify the valid domain of the new user-defined datum. The
Western Longitude and Eastern Longitude values may be any valid longitude
values. However, the Western Longitude value must be to the west of the
Eastern Longitude value. The Northern Latitude and Southern Latitude
values may be any valid latitude values. However, the Southern Latitude
value must be to the south of the Northern Latitude value.
The Create Datum dialog box 7-Parameter tab
contains the following sub-elements:
- a Code field, in which you
specify a new three- to six-letter code for the new user-defined datum.
This code cannot be the same as any code that is already used for another
datum.
- a Name field, in which you
specify a name for the new user-defined datum. The name may be any string.
- an Ellipsoid pull-down list,
with which you specify the ellipsoid that is to be associated with the new
user-defined datum.
- Delta X, Delta Y, and Delta Z fields, in which you
specify the datum shift values from the new user-defined datum to WGS 84.
The shift values are optionally signed real values, in meters.
- Rotation X, Rotation Y, and Rotation Z fields, in
which you specify the datum rotation values from the new user-defined
datum to WGS 84. The rotation values range from -60.0 to 60.0, in arc
seconds.
- Scale field, in which you specify the scale difference
value from the new user-defined datum to WGS 84. The scale value ranges
from -0.001 to 0.001.
The Create Datum dialog box also
contains the following sub-elements:
- a Cancel button – which
aborts datum creation and returns you to the GEOTRANS main window.
- an OK button – which causes
a new user-defined datum to be created using the specified parameter
values.
UNCLASSIFIED


- New: Uninstalled games are displayed differently on list and grid views
- New: Option to view and edit custom game installation folder
- New: Option to use {InstallDir} variable in game action settings
- New: Option to remove multiple games at once
- New: Option to disable automatic library update on startup
- New: Games can be sorted by all fields
- New: Added tooltip explaining category assignment settings
- Fix: Search box polish
- Fix: Playnite accepts invalid database path
- Fix: Images View polish when double clicking on game
- Fix: Crash when importing custom games
- Fix: Category selection fix when one game is selected


Air Pollution
The Air Pollution part mainly covers the following sections:
- Summary of six-year GHG emissions
- GHG emissions from 2006 to 2011
- Average annual growth rate of GHG emissions
- Comparison of GHG emissions to population
- Comparison of GHG Emissions per Capita to GDP per Capita
- Comparison of GHG Emissions per Capita to Environmental Protection per Capita
The graph below shows summary of six-year GHG emissions from the top 10 countries. It indicates that United State has the highest GHG emissions, while Japan has the second highest emission. These two countries do not demonstrate any trend in six years. However, South Korea shows an increasing pattern while Germany shows a decreasing pattern. Canada seems to retain a stable level of emissions.


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Enter a route to search timetables.
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Towards Dublin Airport
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Caroline Werlang has over 10 years of scientific research experience in a variety of technical areas including microbiology, synthetic biology, protein engineering, glycobiology and carbohydrate chemistry, biopolymers, reproductive health, and host-microbiome interactions. Caroline is as a Technology Specialist at Foley Hoag. Prior to joining the firm, she worked in the biomanufacturing industry, where she genetically engineered microbes and oversaw their translation into full-scale production bioreactors. Caroline obtained her Ph.D. from the Massachusetts Institute of Technology under Professor Katharina Ribbeck. Her Ph.D. focused on host-microbe interactions in mucosal environments. She developed materials inspired by the glycoprotein mucin that reduce the virulence of pathogenic bacteria.


Brains - photo by Neil Conway
Show your research to other neuroimaging researchers.
Learn how to apply the latest computational tools to you data.
Find exciting projects that you would like to contribute to.
Meet researchers from a variety of backgrounds.
The 2016 OHBM Hackathon is an official satellite event of the 22nd Annual Meeting of the Organization of Human Brain Mapping (OHBM) that is organized by the newly formed OHBM Open Science Special Interest Group. The goal of the hackathon is to bring together researchers with disparate backgrounds from the OHBM community to collaborate on open science projects in neuroimaging.
Participants are encouraged to post project ideas to www.brainhack.org and to submit a report describing their project to the 2016 Brainhack Proceedings that will be published by Gigascience after the event.
Please join the Brainhack Slack project for updated information on the developing Hackathon content and to contribute your own ideas.
The spirit of the Hackathon will continue into the OHBM meeting in Geneva from June 26-30, where an Open Science Room (OSR) will be available in the conference venue for all OHBM attendees to discuss, present, and continue working on hackathon projects.
On June 26 the Open Science Room will feature a FREE morning course on “Brain Hacking 101” where participants will be introduced to brain imaging as a data science. The afternoon will feature a free session open to all OHBM attendees on the hackathon project outcomes.
We are also planning to host a variety of demos, tutorials, and installfests in the OSR over the course of the OHBM meeting.
Thanks to the generous support of our sponsors, the OHBM Open Science SIG will award funding to a limited number of students and post docs to defray their costs for attending the 2016 OHBM Hackathon.
To apply, email (1) a one-page Brainhack project proposal, (2) a one-page biographical sketch and (3) proof that you are a graduate student or post-doc (typically a letter from your supervisor) to<EMAIL_ADDRESS>The organizing committee will determine the winners based on the merits of the submitted materials, as well as geographical, expertise and gender diversity. The winners will be encouraged to develop their project during the hackathon, notably using the brainhack slack ahead of the event. All hackathon participants will have the opportunity to submit a report on their project for publication in the 2016 Brainhack Proceedings.
Awards will be announced by email by May 8th.
The Swiss Tech Hotel EPFL is providing a preferential rate of CHF 120 a night for a twin room (max 2 people per room). You will be charged a touriste tax (taxe de séjour) of CHF 3,10 that entitles you to free public transportation.
This offer is limited to the first 30 rooms and expires on May 12. Use the code 'OHBM_brainhack' when you registor or email<EMAIL_ADDRESS>with your reservation details and the name of the conference.
The hotel is approximately 20 minutes by public transportation from Univercité.


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Samiur Rahman Mukul
.Thanks


Started in 2018, Crowd Food quickly established itself as a culinary icon par excellence in Tobi. With its unique brand of world fusion cuisine that can be found nowhere else, it enjoys patronage from the A-list clientele in Hong Kong. Featuring four of the best three-star Michelin chefs in the world, you never know what will arrive on your plate the next time you visit us. The restaurant traces its humble beginnings to The Frying Pan, a successful chain started by our CEO, Mrs. Vedika Trivedi, that featured for the first time the world's best cuisines in a pan.


#include <FilePath.hpp>
Definition at line 7 of file FilePath.hpp.
◆ FilePath() [1/3]
| glimac::FilePath::FilePath |
( |
| ) |
|
|
default |
◆ FilePath() [2/3]
| glimac::FilePath::FilePath |
( |
const char * |
filepath | ) |
|
|
inline |
◆ FilePath() [3/3]
| glimac::FilePath::FilePath |
( |
const std::string & |
filepath | ) |
|
|
inline |
◆ addExt()
| FilePath glimac::FilePath::addExt |
( |
const std::string & |
ext = "" | ) |
const |
|
inline |
◆ c_str()
| const char* glimac::FilePath::c_str |
( |
| ) |
const |
|
inline |
◆ dirPath()
| FilePath glimac::FilePath::dirPath |
( |
| ) |
const |
|
inline |
returns the path of a filepath
Definition at line 36 of file FilePath.hpp.
◆ empty()
| bool glimac::FilePath::empty |
( |
| ) |
const |
|
inline |
◆ ext()
| std::string glimac::FilePath::ext |
( |
| ) |
const |
|
inline |
returns the file extension
Definition at line 50 of file FilePath.hpp.
◆ file()
| std::string glimac::FilePath::file |
( |
| ) |
const |
|
inline |
returns the file of a filepath
Definition at line 43 of file FilePath.hpp.
◆ hasExt()
| bool glimac::FilePath::hasExt |
( |
const std::string & |
ext | ) |
const |
|
inline |
◆ operator std::string()
| glimac::FilePath::operator std::string |
( |
| ) |
const |
|
inline |
◆ operator!=()
| bool glimac::FilePath::operator!= |
( |
const FilePath & |
other | ) |
const |
|
inline |
◆ operator+()
concatenates two filepaths to this/other
Definition at line 67 of file FilePath.hpp.
◆ operator==()
| bool glimac::FilePath::operator== |
( |
const FilePath & |
other | ) |
const |
|
inline |
◆ str()
| const std::string& glimac::FilePath::str |
( |
| ) |
const |
|
inline |
◆ operator<<
| std::ostream& operator<< |
( |
std::ostream & |
cout, |
|
|
const FilePath & |
filepath |
|
) |
| |
|
friend |
◆ PATH_SEPARATOR
| const char glimac::FilePath::PATH_SEPARATOR = '/' |
|
static |
The documentation for this class was generated from the following file:


Assumptions
- You know how deep-learning (and back-prop) works
- You understand the concept of ResNets
- You know (basics of) PyTorch
- You heard about Batch Normalization
Agenda
- Introduction
- The problem with Batch Normalization
- How to eliminate Batch Normalization
- Gradient clipping - novel idea
- View from an ML Engineer perspective
- Discussion
Intuition
Quick summary
Extension of:
Characterizing signal propagation to close the performance gap in unnormalized ResNets
Brock et al. 2021 ICLR 2021
Novel idea - replace Batch Normalization with Adaptive Gradient Clipping
$G_{i}^{\ell} \rightarrow\left\{\begin{array}{ll}\lambda
\frac{\left\|W_{i}^{\ell}\right\|_{F}^{\star}}{\left\|G_{i}^{\ell}\right\|_{F}} G_{i}^{\ell} & \text { if }
\frac{\left\|G_{i}^{\ell}\right\|_{F}}{\left\|W_{i}^{\ell}\right\|_{F}^{\star}}>\lambda, \\ G_{i}^{\ell} &
\text { otherwise. }\end{array}\right.$
New normalizer-free network architecture
I'm not into CV, why should I care?
Ideas propagate from one domain to another
What works for CV is likely to work for other fields
Batch Normalization (BN)
Quick recap
$ \mu_{\mathcal{B}} \leftarrow \frac{1}{m} \sum_{i=1}^{m} x_{i} (1)\\ $
$ \sigma_{\mathcal{B}}^{2} \leftarrow \frac{1}{m} \sum_{i=1}^{m}\left(x_{i}-\mu_{\mathcal{B}}\right)^{2}
(2)\\ $
$ \widehat{x}_{i} \leftarrow \frac{x_{i}-\mu_{\mathcal{B}}}{\sqrt{\sigma_{k}^{2}+\epsilon}} (3)\\ $
$ y_{i} \leftarrow \gamma \widehat{x}_{i}+\beta \equiv \mathrm{BN}_{\gamma, \beta}\left(x_{i}\right) (4)
$
Batch
Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe and
Szegedy 2015
BN downscales the residual branch
After BN model is "biased" towards skip connection =>
deeper networks can be trained
BN allows efficient large-batch training
BN smoothens the loss so bigger batches can be used
The bigger the batch size the larger stable lr
Bigger batch size => less "update steps" required
If it is so useful, why is BN a problem?
BN breaks the independence between training examples in the mini-batch
$ \widehat{x}_{i} \leftarrow \frac{x_{i}-\mu_{\mathcal{B}}}{\sqrt{\sigma_{k}^{2}+\epsilon}} (3)\\ $
Variance shift correction
$h_{i+1}=h_{i}+\alpha f_{i}\left(h_{i} / \beta_{i}\right)$
$\operatorname{Var}\left(f_{i}(z)\right)=\operatorname{Var}(z)$
$h_{i}$: the inputs to the $i^{t h}$ residual block
$f_{i}$: the function computed by the $i^{t h}$ residual branch
$\alpha$: rate at which the variance increases after each residual
block
$\beta_{i}=\sqrt{\operatorname{Var}\left(h_{i}\right)},$
In inference mode, independent from other examples in a batch
Intuition
$h_{i+1}=h_{i}+\alpha f_{i}\left(h_{i} / \beta_{i}\right)$
Prevention of a mean shift
in the hidden activations
$$
\begin{array}{c}
\hat{W}_{i j}=\frac{W_{i j}-\mu_{i}}{\sqrt{N} \sigma_{i}} \\
\end{array}
\\
\\
\mu_{i}=(1 / N) \sum_{j} W_{i j}\\ \sigma_{i}^{2}=(1 / N) \sum_{j}\left(W_{i
j}-\mu_{i}\right)^{2}
$$
and $N$ denotes the fan-in (number of inputs to the hidden unit)
Adaptive Gradient Clipping
$G \rightarrow\left\{\begin{array}{ll}\lambda \frac{G}{\|G\|} & \text { if }\|G\|>\lambda \\ G & \text {
otherwise }\end{array}\right.$
On the difficulty of training Recurrent Neural Networks
Pascanu et al. 2013
$G_{i}^{\ell} \rightarrow\left\{\begin{array}{ll}\lambda
\frac{\left\|W_{i}^{\ell}\right\|_{F}^{\star}}{\left\|G_{i}^{\ell}\right\|_{F}} G_{i}^{\ell} & \text { if }
\frac{\left\|G_{i}^{\ell}\right\|_{F}}{\left\|W_{i}^{\ell}\right\|_{F}^{\star}}>\lambda, \\ G_{i}^{\ell} &
\text { otherwise. }\end{array}\right.$
High-Performance Large-Scale Image
Recognition Without Normalization
Brock et al. 2021
Clip only too big gradients
grad_2 compared to weight is larger then $\lambda$
$G_{i}^{\ell} \rightarrow\left\{\begin{array}{ll}\lambda
\frac{\left\|W_{i}^{\ell}\right\|_{F}^{\star}}{\left\|G_{i}^{\ell}\right\|_{F}} G_{i}^{\ell} & \text { if }
\frac{\left\|G_{i}^{\ell}\right\|_{F}}{\left\|W_{i}^{\ell}\right\|_{F}^{\star}}>\lambda, \\ G_{i}^{\ell} &
\text { otherwise. }\end{array}\right.$
Adaptive Gradient Clipping in code
$G_{i}^{\ell} \rightarrow\left\{\begin{array}{ll}\lambda
\frac{\left\|W_{i}^{\ell}\right\|_{F}^{\star}}{\left\|G_{i}^{\ell}\right\|_{F}} G_{i}^{\ell} & \text { if }
\frac{\left\|G_{i}^{\ell}\right\|_{F}}{\left\|W_{i}^{\ell}\right\|_{F}^{\star}*\lambda}>1, \\ G_{i}^{\ell} &
\text { otherwise. }\end{array}\right.$
def adaptive_clip_grad(parameters, clip_factor=0.01, eps=1e-3, norm_type=2.0):
for p in parameters:
p_data = p.detach()
g_data = p.grad.detach()
max_norm = unitwise_norm(p_data, norm_type=norm_type).clamp_(min=eps).mul_(clip_factor)
grad_norm = unitwise_norm(g_data, norm_type=norm_type)
clipped_grad = g_data * (max_norm / grad_norm.clamp(min=1e-6))
new_grads = torch.where(grad_norm < max_norm, g_data, clipped_grad)
p.grad.detach().copy_(new_grads)
Implementation
in timm
How is Adaptive Gradient Clipping useful?
Gradient clipping prevents optimizer from too big jumps
Adaptive makes additional usege of gradient to parameter proportion
Less dependent on hyper-parameter $\lambda$?
Training is more smooth (no jumps due to a noise in data)
Last, but not least
Proposed architecture
Start with: SE-ResNeXt-D
Add few tweaks
Overpriced TPU go brr
New SOTA on ImageNet
Do they provide code?
Yes, there is an official implementationin
t̶e̶n̶s̶o̶r̶f̶l̶o̶w̶ jax
For those, who have self-respect, there is an unofficial PyTorch implementation
How to use it?
import timm
from utils import example_batch_of_images
model = timm.create_model('dm_nfnet_f0', pretrained=True)
model.eval()
prediction = model(example_batch_of_images)
prediction.size()
>>> torch.Size(128, 1000)
How to train it?
Exactly same approach as with Resnet/Efficientnet/Whatevernet
class NFNetBasedfCustomClassifier(pl.LightningModule):
def __init__(self):
super().__init__()
self.criterion = nn.CrossEntropyLoss()
self.model = timm.create_model('dm_nfnet_f0', pretrained=True)
def forward(self, x):
return self.model(x)
def training_step(self, batch, batch_idx):
x, y = batch
logits = self.forward(x)
loss = self.criterion(logits, y)
return loss
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)
return [optimizer], [scheduler]
Number of parameters increased
Despite far larger number of parameters, comparable times of a single full
training step
Number of FLOPS increased
Better results, more FLOPs, but similar times (is that comparison fair?)
Discussion
Even though we can benefit from new models
It is far more useful for those with big resources
Speculations
If BN is not a limiting factor more parameters can be added making
future models even less useful for
"non-google" labs
Predictions
10.III.2021
- Google/Deepmind will do "EfficientNet-like" grid search for optimal architecture
- The NF Net (or its efficient descendant) will be used as a backbone of a new SOTA object detection
model
- The idea of Adaptive Gradient Clipping will be successfully applied in transformer models,
especially for CV (as they require far more resources)
TL;DR
- NF Net = New ImageNet SOTA
- Batch Normalization = slow operations
- No BN layer = faster forward/backward pass
- BN replaced with scaling and Adaptive Gradient Clipping
- (Probably) A new paradigm for
designing architectures


{% load join_by from custom_tags %} {% if stats_diff %}
| Release | Languages (which are out of sync: need attention) |
|---|---|
| {{ branch }} | {% for lang, diff in lang_diff.items %} {% if diff == 0 %} {{ lang }} {% elif diff > 0 %} {{ lang }} {% endif %} {% empty %} All languages are in sync! {% endfor %} |


Documentation
Generate & Initialize
Choose a unique alias and a password of at least 12 characters and generate a 4096bit RSA key pair consisting of a public key (which will be registered with your account) and a private key (which does not leave your possession).
While your alias and public key are registered, your private key is encrypted with your password and stored on your device - it does not leave your possession and therefore only you can decrypt your data.
Aletheia Ware does not store your password or private key and therefore, unlike cloud storage hosts, cannot read your data. Instead, Space encrypts your data on your device using your public key before uploading it to the network and mining it into a blockchain. Upon download, Space decrypts your data on your device using your private key.
To prevent loss of access to your data in case your device gets damaged, lost, or stolen, Aletheia Ware recommends provisioning multiple devices (see Export & Import below) and making a hard copy of your key pair and storing it in a secure location such as a fire-proof safe, or bank vault.
Register & Subscribe
Storage services are offered to registered customers on a subscription basis.
Register your account by providing the following information;
- Alias - registered to your public key and signed by your private key.
- Email - how you would like us to contact you.
- Billing Info - how you would like to pay (this information is handled and stored by Stripe);
- Card Holder Name
- Card Number
- Card Expiry Date
- Card Verification Code (CVC)
- Billing Address
Storage Service
Store your data with Registrars. Click Subscribe and provide the following information;
- Alias - registered to your public key and signed by your private key.
- Customer ID - associated with your alias when registering.
Encrypt & Upload
A 256bit AES key is generated and used to encrypt each file. The key itself is then encrypted with your public key, and, along with the encrypted file mined into the blockchain
List & Show
Each time a file is stored, a Meta object is created which includes the time, file name, mime type, size, and references the file's encrypted data. The Meta object is also encrypted but mined into a different blockchain, allowing you to list your stored files and view information about them, without needing to sync the blockchain containing the file's encrypted data.
Download & Decrypt
After being mined, your encrypted data and associated key can be downloaded and decrypted using your private key.
Tag & Search
Organize files by tagging them and then searching by tag.
Share & Receive
Files you own can be shared with other aliases, and files shared with you can be listed, shown, tagged, searched, and downloaded just like your files.
Export & Import
Your public/private key pair can be exported to other devices giving you easy access to your data from any supported device. Provisioning your key pair on multiple devices can help prevent loss of access to your data in case one of your devices gets damaged, lost, or stolen.
More Info


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Note - JTcl implements Tcl 8.4 commands, with exceptions noted in JTcl differences.
JTcl also implements some commands backported from Tcl 8.5, as noted in Tcl Commands.
Copyright © 1989-1994 The Regents of the University of California. Copyright © 1990-1994 The Regents of the University of California Copyright © 1992-1999 Karl Lehenbauer and Mark Diekhans Copyright © 1992-1999 Karl Lehenbauer and Mark Diekhans. Copyright © 1993-1997 Bell Labs Innovations for Lucent Technologies Copyright © 1994 The Australian National University Copyright © 1994-2000 Sun Microsystems, Inc. Copyright © 1997-2000 Ajuba Solutions. Copyright © 1997-2000 Scriptics Corporation. Copyright © 1998 Mark Harrison. Copyright © 1998-1999 Scriptics Corporation Copyright © 1998-2000 Ajuba Solutions Copyright © 1999 Scriptics Corportation Copyright © 2000 Jeffrey Hobbs. Copyright © 2001 ActiveState Tool Corp. Copyright © 2001 Kevin B. Kenny. Copyright © 2001 Vincent Darley Copyright © 2001-2002 ActiveState Corporation Copyright © 2001-2002 Donal K. Fellows Copyright © 2001-2002 Kevin B. Kenny. All rights reserved. Copyright © 2001-2004 ActiveState Corporation. Copyright © 2006-2007 Daniel A. SteffenCopyright © 1995-1997 Roger E. Critchlow Jr.


US baby names provided by the SSA. This package contains all names used for at least 5 children of either sex.
| Version: | 0.3.0 |
| Depends: | R (≥ 2.10) |
| Imports: | tibble |
| Published: | 2017-04-14 |
| Author: | Hadley Wickham [aut, cre], RStudio [cph] |
| Maintainer: | Hadley Wickham <hadley at rstudio.com> |
| BugReports: | https://github.com/hadley/babynames/issues |
| License: | CC0 |
| URL: | http://github.com/hadley/babynames |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | babynames results |
| Reference manual: | babynames.pdf |
| Package source: | babynames_0.3.0.tar.gz |
| Windows binaries: | r-devel: babynames_0.3.0.zip, r-release: babynames_0.3.0.zip, r-oldrel: babynames_0.3.0.zip |
| OS X binaries: | r-release: babynames_0.3.0.tgz, r-oldrel: babynames_0.3.0.tgz |
| Old sources: | babynames archive |
| Reverse imports: | mdsr |
| Reverse suggests: | ggalluvial |
Please use the canonical form https://CRAN.R-project.org/package=babynames to link to this page.


Make the most of Navigations and link lists
by knowing the difference between using blocks and inline blocks.
I'm going to explain the difference between using blocks vs inline-blocks with using navigations or other list of links as an example - they can be used for a slew other purposes, but I find this information is the most crucial when it comes to organizing your sites navigation.
If you prefer your links to be displayed vertically and stacked ontop of one another, you would use display: block; in your css. Blocks force a line break, hence being stacked horizontally. From there you have the option of selecting the height and width, margins, ect.
For most naviagtions, diplaying the links as blocks that are laid out horizontally typically looks and fits nicer on a page. That is where inline-block comes in. By using display: inline-block; in your CSS, the links will line-up next to each other horizontally. From there you can style the block with margins, heights and widths.


miR miRNAs miRNA miR, Lilium expression Plant somatic families RNA embryogenic using known Zhang targets embryo embryogenesis development target callus expressed Arabidopsis small sequencing Wang ./s--- data genes miR_, (Figure (Table different differentially identified plant total analysis sequences targeted SE. Cell additional involved {border-style Click LINK S). file. roles cell levels px;} unigenes #ffffff;} (Zhang DC. conserved pumilum regulation Figure formation gene metabolism potential protein EC, embryos novel Sci. predicted regulatory reported (Wu Biol. Liu factor related stages transcription CEs database family induction length microRNA microRNAs plants reads role samples used Chen SE, Yang highly leaf longan plants. Lilium. RNAs Small Somatic compared larch play regeneration regulates respectively. response thaliana unique (Figures BMC Fisch. However, Identification Lilium, Lin NEC Sun Table Xie accumulation carbohydrate down-regulated followed functional induced miRNAs, seed targeting (Lin Cult. GEs KEGG Lai, SPL Tissue according biological classified described early identification information largest maize meristem miRNA-target miRNAs. network pathway performed present px; study, suggesting transcriptome up-regulated vitro {font-family (.%), (Yang ./fpls.. ./journal.pone. ARF Front. Gene Lai New Organ Physiol. addition, auxin based cDNA category cells cellular comparison cotton distribution exhibited font-size formation. function high-throughput including lpu-miRn miR. observed obtained precursors, qRT-PCR ranging raw regulate research results reveals shown signaling terms ARF, Bartel Biotechnol. Dev. Genes Mol. PCR Rep. Res. S), Transcriptome abundance abundant accounted addition annotation citrus critical double; highest indicated libraries mechanism miR; participate presented process program respectively, significant transition (Li (Shen (Wang ./pbi. ./pp.. Additionally, Brassica Cheng China Embryogenesis Finally, Fisch., GEs, Genome-wide Jia L.) NEC, Science Silva Solanum Song TEs TEs, Thus, Zhao approximately binding border-color border-width categories clean common consisted contained cotyledon degradome development, differentiation diverse endogenous enriched families, findings functions genetic group hormone lower mRNA miRNA-mediated molecular non-coding non-embryogenic plants, previous processes profile reads, regulating relationship reveal revealed reverse sample selected sequence sequencing. signal similar size solid; species species, species-specific species. spp. transcripts unigenes, {font-style {font-weight (), (,) (c_g_i) ./--- BLAST CA, Cao Citrus Dimocarpus For. Functional Furthermore, Genomes Genomics Han Helianthus Here, Larix MicroRNAs Oryza Planta Populus RPM Regulation SERK Shen TFs University Vitro Xue Zhou Zhu acid apical article artificial bold cleavage complex constructed courier; deep designed division em; embryo; embryonic example, genes. genome globular groups high homologous homologs identify important included induction, involvement land level library main majority mature mature), mediated metabolic miRBase model number numbers orange pairs plays poplar prediction profiles profiling pt;} receptor regulated root stage step stress study sweet torpedo unigenes. values weeks; yellow


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{% extends "base.html" %} {% block content %}
Congratulations, you are the newest water master of San Juanito County, California. You have been elected for a ten year term of office. Your duties are to dispense water, direct farming, and buy and sell land as needed to support your citizens. Watch our for environmental requirements and drought! Water is the general currency, measured in Acre-feet (AC-FT). The following will help you in your decisions:\n * Each citizen needs at least 0.2 AC-FT of water per year to survive * Each citizen can farm at most 2 acres of land\n * It takes 2 AC-FT of water to farm an acre of land\n * The market price for land fluctuates yearly\n Rule wisely and you will be showered with appreciation at the end of your term. Rule poorly and you will be thrown out of office!\n''')
{{ immigrants.new_citizens }} people immigrated to your county
Your county now holds {{ population.citizens }} citizens
You own {{ acres.acres_owned }} acres of land
You recieved {{ water_allocation.water_alotment }} AC-FT from the state water project this year
Environmental flows took {{ environmentals.environmentalists }} AC-FT from your stores
You have {{ water_in_storage.storage }} AC-FT left in storage
Land is trading at {{ land_price.cost_per_acre }} AC-FT per acre


* Added menu and ability to go to different places within the webpage.
* Added features such as hit counter and also fixed bugs
*Coming later this month and maybe into early may with bug fixes and inprovment enhances.
*The release of the "S" version with more user interface coming over summer.
*Inhances user interface and might add more documents within the menu


Police called to reports of suspicious package in Lib Dem leader Jo Swinson's office
The package sent to Westminster's Portcullis House has since been assessed as "non-suspicious"
by Dan Bloom, https://www.facebook.com/mirrordanbloomPolice were called to reports of a suspicious package this morning in the Parliamentary office of Lib Dem leader Jo Swinson.
Officers were alerted to the package just before 10am on Monday in Portcullis House in the highly-guarded Palace of Westminster.
Multiple sources said it affected Ms Swinson's office. The office and some staff were thought to have been quarantined while police carried out checks.
Police were stationed on the third floor telling staffers to turn back but the building was not evacuated. Just after noon, Scotland Yard confirmed the package "has now been assessed as non-suspicious."
It is thought Ms Swinson was not present at the time.
NHS ambulances and an Incident Response Unit were seen on Whitehall outside the nearby Derby Gate entrance to Parliament.
A Met Police spokeswoman said earlier: "Police were called at approximately 09:51hrs on Monday, 4 November to reports of a suspicious package delivered to Portcullis House, Westminster.
"Officers are in attendance and the contents of the package are being assessed.
"No reports of any illness or injuries.
"The premises have not been evacuated. Enquiries continue."
A House of Commons spokeswoman confirmed: "We are aware of an incident on the Parliamentary Estate which is being managed by the police."
Packages delivered to Parliament are security screened and scares over suspicious parcels are not uncommon.
But it comes amid a backdrop of rising threats against MPs ahead of the December 12 general election.
Politicians have been told to take unprecedented security precautions, with some telling The Observer they have been advised not to campaign alone or after dark.


Create Identity
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Identification No.*:
Name*:
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Email*:
Passport No.*:
Address:
Phone Number:
Identification Document:
Share with:


Debate
Include in MVP
Do not include in MVP
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{{comments[comments.length - 1].body}}
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{{comments[comments.length - 1].user.name}} - hace 6 horas
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This demo application monitors the online ads dimensions in real time. The views and clicks of ads are being counted in real time. Three dimensions are being tracked, namely publisher, advertiser, and ad unit. In real time all the dimensions are computed for views (cost) and clicks (revenue). This enables a participant in an real time ad exchange to run applications to optimize the performance of their bids.


var constants: List<String>
Any values to be used as constants in an LGP program.
NOTE: Constants are specified as string values to permit the use of custom register types. This means that a custom lgp.core.environment.constants.ConstantLoader will need to be implemented to load the list of strings into an appropriate type suitable to be loaded into a register set.


Hi! This is your comprehensive guide to all of the happy hour food and drink deals in Harrisonburg, Virginia. Woo!!!!
Monday-Friday
3:00pm-6:30pm
$1 off draft beers
$1 off highballs
$3 off house specialty cocktails
$2 off wine
| Where? | When? | Deal? |
|---|---|---|
| Row 1jkfsdhfkjsdhfsdkhfjksd | Row 2 | |


Welcome! {{ brand.long }} is a repository of environmental audio recordings. This website facilitates the management, access, visualization, and analysis of environmental acoustic data. It uses the Acoustic Workbench software which is is open source and available from GitHub.
The website is run by {{ brand.organization }} to support bioacoustics and ecoacoustics research.
We host, manage, explore, and visualize ecoacoustic data. Providing the results and tools we develope free of charge to anyone who is interested
We locate various environments which may have interesting or important faunal vocalisations and other human-audible environmental sounds
We, and our partners, place acoustic sensors in a wide range of environments allowing us to study the local fauna
Practical identification of animal sounds by people and automated detectors. Ecologists use these to answer environmental questions.
Finally, we make the results of our research, and the tools we have developed, available to the public
You can browse some public {{ viewMoreLink.label }}s and audio recordings without logging in. To participate in the analysis work you will need to log in with an existing account or register for a new account. Don't worry its free and easy!
Unable to load {{ viewMoreLink.label | titlecase }}
No {{ viewMoreLink.label | titlecase }}s to display
0" id="viewMore" class="m-auto btn btn-outline-highlight btn-lg" [strongRoute]="viewMoreLink.link" > More {{ viewMoreLink.label | titlecase }}s


| Package | Description |
|---|---|
| org.cooperari.core |
Cooperari engine.
|
| org.cooperari.errors |
Cooperari error classes, related to aspects like
runtime execution events (e.g., deadlock or race detection),
invalid configuration options, internal errors, etc.
|
| org.cooperari.feature.data |
Support for data access yield points and race detection.
|
| org.cooperari.feature.monitor |
Yield point support for Java monitor operations and related
deadlock detection.
|
| org.cooperari.feature.thread |
Yield point support
Thread life-cycle methods. |
| org.cooperari.feature.threadrunner |
Support for the "fork-and-join" thread runner.
|
| Modifier and Type | Method and Description |
|---|---|
CThread |
CEngine.createNewThread(Runnable r,
CUncaughtExceptionHandler excHandler)
Tell the engine to start a new thread.
|
CThread |
CThreadMappings.getCThread(Thread t)
|
CThread |
CEngine.getThread(int index)
Gets thread created by the scheduler by creation order index.
|
static CThread |
CRuntime.getThread(int index)
Shortcut for calling
CEngine.getThread(int) on the scheduler instance
associated to this runtime. |
static CThread |
CThread.intercept(org.aspectj.lang.JoinPoint jp)
Intercept thread, if a CThread.
|
static CThread |
CThread.self()
Get current thread object if the thread is cooperative,
null otherwise. |
| Modifier and Type | Method and Description |
|---|---|
void |
CThreadMappings.associate(Thread t,
CThread ct)
|
void |
CTrace.record(CThread t,
CTrace.EventType type)
Record an event for one thread.
|
void |
CTrace.recordStep(CThread t)
Record a step for one thread.
|
void |
CTrace.recordThread(CThread t)
Record existence of thread.
|
| Constructor and Description |
|---|
CWaitDeadlockError(Collection<CThread> threads)
Constructs an error.
|
| Modifier and Type | Method and Description |
|---|---|
static void |
Write.after(CThread thisThread,
Object object,
Object key)
Execute actions after data write without yielding.
|
static void |
Read.after(CThread thisThread,
Object object,
Object key)
Execute actions after data read without yielding.
|
static void |
Write.before(CThread thisThread,
Object object,
Object key)
Yield on data write.
|
static void |
Read.before(CThread thisThread,
Object object,
Object key)
Yield on data read.
|
| Modifier and Type | Field and Description |
|---|---|
protected CThread |
MonitorOperation._thread
Thread.
|
| Modifier and Type | Method and Description |
|---|---|
CThread |
Monitor.getOwner()
Get owner thread.
|
| Modifier and Type | Method and Description |
|---|---|
void |
Monitor.enter(CThread t)
Enter monitor.
|
static void |
Exit.execute(CThread t,
Object o)
Execute operation.
|
static void |
NotifyAll.execute(CThread t,
Object o)
Execute operation.
|
static void |
Notify.execute(CThread t,
Object o)
Execute operation.
|
static void |
Enter.execute(CThread t,
Object o)
Execute operation.
|
static boolean |
ThreadHoldsLock.execute(CThread t,
Object o)
Execute
Thread.holdsLock() . |
static void |
Wait.execute(CThread t,
Object o,
long timeout)
Execute a wait on the monitor.
|
void |
DeadlockDetector.onMonitorEnter(CThread t,
Monitor m)
Signal monitor acquisition intent by given thread.
|
void |
DeadlockDetector.onMonitorExit(CThread t)
Signal monitor release by a thread.
|
void |
Monitor.reacquire(CThread t,
int lockCount)
Re-acquire the lock.
|
int |
Monitor.relinquish(CThread t,
boolean wait)
Relinquish the lock.
|
| Constructor and Description |
|---|
CResourceDeadlockError(CThread t,
List<Monitor> cycle)
Constructor.
|
Enter(CThread t,
Monitor m)
Constructor.
|
MonitorOperation(CThread t,
Monitor m)
Constructor.
|
Notify(CThread t,
Monitor m)
Constructor.
|
NotifyAll(CThread t,
Monitor m)
Constructor.
|
ThreadHoldsLock(CThread t,
Monitor m)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
(package private) static CThread |
CThreadFeature.getCThread(Thread t)
Shortcut method that invokes
CThreadMappings.getCThread(Thread) on the thread mappings object
registered for the current runtime. |
| Modifier and Type | Method and Description |
|---|---|
static void |
Yield.execute(CThread thisThread)
Execute a thread yield operation.
|
static boolean |
Interrupted.execute(CThread t)
Execute an operation of this kind.
|
static Thread |
CurrentThread.execute(CThread thisThread)
Execute
Thread.currentThread() operation. |
static void |
Sleep.execute(CThread thisThread,
long timeout)
Execute sleep operation.
|
static Thread.State |
GetState.execute(CThread thisThread,
Thread targetThread)
Execute
Thread.getState() operation. |
static void |
Start.execute(CThread thisThread,
Thread targetThread)
Execute a thread start operation.
|
static void |
Interrupt.execute(CThread thisThread,
Thread targetThread)
Execute thread interrupt operation.
|
static boolean |
IsInterrupted.execute(CThread thisThread,
Thread otherThread)
Execute an operation of this kind.
|
static void |
SpuriousWakeup.execute(CThread thisThread,
Thread targetThread)
Execute spurious wakeup operation.
|
static void |
Stop.execute(CThread thisThread,
Thread otherThread)
Execute a thread stop operation.
|
static void |
Join.execute(CThread thisThread,
Thread joinThread,
long timeout)
Execute a thread join operation.
|
| Constructor and Description |
|---|
Interrupted(CThread thread)
Constructor.
|
IsInterrupted(CThread thread)
Constructor.
|
Join(CThread thisThread,
CThread joinThread,
long timeout)
Constructor.
|
Sleep(CThread thisThread,
long timeout)
Constructor.
|
| Modifier and Type | Method and Description |
|---|---|
static void |
ThreadRunnerOperation.execute(CThread thisThread,
Runnable[] runnables)
Execute a thread runner operation.
|
Copyright © 2021 Cooperari. All rights reserved.


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You have an existing internet connection at home or at your shop and you're paying the bill on a monthly basis. This will be considered as your monthly expense, right? No returns, forever! With our Wi-Fi vending machine, maybe you really won't earn much but you can possibly earn big if you think different!
Now here's the worst case scenario, even if your machine generates P20 per day a total of P600 monthly --- looks bad, right? Since you bought the machine for around 18-20kphp. Let's look back again... Think!
We will put it this way. You have subscribed to a 10mbps connection and this cost you 1500/month. Try to see the difference.
➡Without the machine - P1500 Internet Expense➡With machine - P1500 Internet Expense - P600 Income = Now you only need to pay P900!
Technically you have returns which is the 600 pesos! This is only the very worst case scenario!
Let's go to the amazing scenario! What if we are to say...this..
You have 3 regular customers and they only pay you P10 per day to have access to the internet and that's P900/month. You get P900 clean money!
Then we aim a little higher
You have 5 regular customers and they only pay you P10 per day to have access to the internet and that's P1500/month (Now your internet bill just got paid!)
Higher! Go figure! You do the math 😉
You can offer a monthly subscription plan or period of days to your customers as the machine supports voucher codes.
Negros Piso Wifi is the official distributor of AdoPisoft and the said machine is distributed by us.


public class SimulatedAnealingMinimizer
extends java.lang.Object
A cost minimizer which will fit a MovAvgModel to the data.
This optimizer uses naive simulated annealing. Random solutions in the problem space
are generated, compared against the last period of data, and the least absolute deviation
is recorded as a cost.
If the new cost is better than the old cost, the new coefficients are chosen. If the new
solution is worse, there is a temperature-dependent probability it will be randomly selected
anyway. This allows the algo to sample the problem space widely. As iterations progress,
the temperature decreases and the algorithm rejects poor solutions more regularly,
theoretically honing in on a global minimum.


Profile page of the user...
... They will have to be registered and logged in to get to this page
Will display the races they are registered for if they with confirmation number and the data they entered for the race like bike, make model cc, date of the race, and race results and if they are registered for any races.
wunderlist - allow user to login to their profile and check if they are registered for an upcoming race


--- layout: default title: A Bit About Me ---
Rick, who is this guy anyways? Well, in a nutshell I am a: leader, innovator, strategist, thinker, tech nerd, speaker, programmer, gamer, relationship builder, foodie, movie watcher, results driver, feedback reciever (and giver), and flat out genuine dude. For more, check out my site and feel free to contact me.


The game is based on finding words of a fixed number of letters. Two teams of two candidates compete during the first part.
Following this, the victorious team tries to pocket the pot during the Super Game. A candidate proposes a word within a maximum
of eight seconds and must spell it. The word must contain the correct number of letters and be spelled correctly, otherwise
it is refused.
The word then appears on a grid: the letters well placed are colored in red, the letters present but badly
placed are circled yellow. For a letter, one can have at most only the number of occurrences of this letter in the word of
colored (either in yellow, or in red if some are well placed).


CrowStorm
Search
Wecome to Crow Storm
Search for a publically traded U.S. company to see the last 30 day's OHLC prices and volume.


Blocks, Procs, and Lambdas
Blocks, Procs, and lambdas are all closures in the Ruby language. Closures are functions which remember all of the values of the variables in the function's scope when it was created. When a closure is created, it keeps the values of the variables that were in the scope when the closure was made or defined. This way, the closure is able to access the variables when it is called even if the variables are no longer in the scope. Furthermore, closures have the ability to be passed around like an object (stored in collections or passed on to other functions).
Blocks
Blocks are a the most common type of closure and are simply a bit of code which can be passed. Blocks utilize 'do...end' or curly bracket {} syntax. You can use them with methods such as .times or .each to iterate through a series of elements. Below is an example of a block:
Procs
It is important to note that blocks are NOT objects. Therefore, blocks do not have all of the abilities of objects such as being saved as variables. But procs do have these abilities! Procs are basically a saved block. You can turn a block into a proc so that it can be used many times over. Here's an example:
Procs are advantageous because they are objects and therefore have all of the abilities of an object. Also, procs can be called many times over without having to rewrite/retype them. You can also execute procs by using 'call':
Lambdas
Lambdas are almost identical to Procs with a few differences. The first is that instead of writing lambda.new like we would write proc.new, we just write 'lambda'. The second is that a lambda checks the arguments that are passed into it, but a proc does not. So, if you pass the wrong number of arguments, the lambda will throw an error while a proc will just assign nil to any arguments that are missing. Finally, once a lambda returns something, it passes the control back to the calling method. A proc does not pass control back to the calling method.


tput 1
tput(1) tput(1)
NAME
tput, reset - initialize a terminal or query terminfo
database
SYNOPSIS
tput [-Ttype] capname [parms ... ]
tput [-Ttype] init
tput [-Ttype] reset
tput [-Ttype] longname
tput -S <<
tput -V
DESCRIPTION
The tput utility uses the terminfo database to make the
values of terminal-dependent capabilities and information
available to the shell (see sh(1)), to initialize or reset
the terminal, or return the long name of the requested
terminal type. tput outputs a string if the attribute
(capability name) is of type string, or an integer if the
attribute is of type integer. If the attribute is of type
boolean, tput simply sets the exit code (0 for TRUE if the
terminal has the capability, 1 for FALSE if it does not),
and produces no output. Before using a value returned on
standard output, the user should test the exit code [$?,
see sh(1)] to be sure it is 0. (See the EXIT CODES and
DIAGNOSTICS sections.) For a complete list of capabili-
ties and the capname associated with each, see ter-
minfo(1).
-Ttype indicates the type of terminal. Normally this
option is unnecessary, because the default is taken
from the environment variable TERM. If -T is spec-
ified, then the shell variables LINES and COLUMNS
will be ignored,and the operating system will not
be queried for the actual screen size.
capname
indicates the attribute from the terminfo database.
When termcap support is compiled in, the termcap
name for the attribute is also accepted.
parms If the attribute is a string that takes parameters,
the arguments parms will be instantiated into the
string. An all-numeric argument will be passed to
the attribute as a number.
Only a few terminfo capabilities require string
parameters; tput uses a table to decide which to
pass as strings. Normally tput uses tparm (3x) to
perform the substitution. If no parameters are
given for the attribute, tput writes the string
without performing the substitution.
-S allows more than one capability per invocation of
tput. The capabilities must be passed to tput from
the standard input instead of from the command line
(see example). Only one capname is allowed per
line. The -S option changes the meaning of the 0
and 1 boolean and string exit codes (see the EXIT
CODES section).
Again, tput uses a table and the presence of
parameters in its input to decide whether to use
tparm (3x), and how to interpret the parameters.
-V reports the version of ncurses which was used in
this program, and exits.
init If the terminfo database is present and an entry
for the user's terminal exists (see -Ttype, above),
the following will occur: (1) if present, the ter-
minal's initialization strings will be output (is1,
is2, is3, if, iprog), (2) any delays (e.g., new-
line) specified in the entry will be set in the tty
driver, (3) tabs expansion will be turned on or off
according to the specification in the entry, and
(4) if tabs are not expanded, standard tabs will be
set (every 8 spaces). If an entry does not contain
the information needed for any of the four above
activities, that activity will silently be skipped.
reset Instead of putting out initialization strings, the
terminal's reset strings will be output if present
(rs1, rs2, rs3, rf). If the reset strings are not
present, but initialization strings are, the ini-
tialization strings will be output. Otherwise,
reset acts identically to init.
longname
If the terminfo database is present and an entry
for the user's terminal exists (see -Ttype above),
then the long name of the terminal will be put out.
The long name is the last name in the first line of
the terminal's description in the terminfo database
[see term(5)].
If tput is invoked by a link named reset, this has the
same effect as tput reset. See tset for comparison, which
has similar behavior.
EXAMPLES
tput init
Initialize the terminal according to the type of ter-
minal in the environmental variable TERM. This com-
mand should be included in everyone's .profile after
the environmental variable TERM has been exported, as
illustrated on the profile(5) manual page.
tput -T5620 reset
Reset an AT&T 5620 terminal, overriding the type of
terminal in the environmental variable TERM.
tput cup 0 0
Send the sequence to move the cursor to row 0, column
0 (the upper left corner of the screen, usually known
as the "home" cursor position).
tput clear
Echo the clear-screen sequence for the current termi-
nal.
tput cols
Print the number of columns for the current terminal.
tput -T450 cols
Print the number of columns for the 450 terminal.
bold=`tput smso` offbold=`tput rmso`
Set the shell variables bold, to begin stand-out mode
sequence, and offbold, to end standout mode sequence,
for the current terminal. This might be followed by
a prompt: echo "${bold}Please type in your name:
${offbold}\c"
tput hc
Set exit code to indicate if the current terminal is
a hard copy terminal.
tput cup 23 4
Send the sequence to move the cursor to row 23, col-
umn 4.
tput cup
Send the terminfo string for cursor-movement, with no
parameters substituted.
tput longname
Print the long name from the terminfo database for
the type of terminal specified in the environmental
variable TERM.
tput -S <<!
> clear
> cup 10 10
> bold
> !
This example shows tput processing several capabili-
ties in one invocation. It clears the screen, moves
the cursor to position 10, 10 and turns on bold
(extra bright) mode. The list is terminated by an
exclamation mark (!) on a line by itself.
FILES
/usr/share/terminfo
compiled terminal description database
/usr/include/curses.h
curses(3x) header file
/usr/include/term.h
terminfo header file
/usr/share/tabset/*
tab settings for some terminals, in a format appro-
priate to be output to the terminal (escape
sequences that set margins and tabs); for more
information, see the "Tabs and Initialization" sec-
tion of terminfo(5)
EXIT CODES
If the -S option is used, tput checks for errors from each
line, and if any errors are found, will set the exit code
to 4 plus the number of lines with errors. If no errors
are found, the exit code is 0. No indication of which
line failed can be given so exit code 1 will never appear.
Exit codes 2, 3, and 4 retain their usual interpretation.
If the -S option is not used, the exit code depends on the
type of capname:
boolean
a value of 0 is set for TRUE and 1 for FALSE.
string a value of 0 is set if the capname is defined
for this terminal type (the value of capname
is returned on standard output); a value of 1
is set if capname is not defined for this ter-
minal type (nothing is written to standard
output).
integer
a value of 0 is always set, whether or not
capname is defined for this terminal type. To
determine if capname is defined for this ter-
minal type, the user must test the value writ-
ten to standard output. A value of -1 means
that capname is not defined for this terminal
type.
other reset or init may fail to find their respec-
tive files. In that case, the exit code is
set to 4 + errno.
Any other exit code indicates an error; see the DIAGNOS-
TICS section.
DIAGNOSTICS
tput prints the following error messages and sets the cor-
responding exit codes.
exit code error message
---------------------------------------------------------------------
0 (capname is a numeric variable that is not specified in
the terminfo(1) database for this terminal type, e.g.
tput -T450 lines and tput -T2621 xmc)
1 no error message is printed, see the EXIT CODES section.
2 usage error
3 unknown terminal type or no terminfo database
4 unknown terminfo capability capname
>4 error occurred in -S
---------------------------------------------------------------------
PORTABILITY
The longname and -S options, and the parameter-substitu-
tion features used in the cup example, are not supported
in BSD curses or in AT&T/USL curses before SVr4.
SEE ALSO
clear(1), stty(1), tabs(1), terminfo(5).
tput(1)
Man(1) output converted with
man2html


|
Panda3D
|
Public Member Functions | |
| __init__ () | |
| __init__ (const TextureStageCollection copy) | |
| addTextureStage (TextureStage node_texture_stage) | |
| Adds a new TextureStage to the collection. More... | |
| addTextureStagesFrom (const TextureStageCollection other) | |
| Adds all the TextureStages indicated in the other collection to this texture_stage. More... | |
| clear () | |
| Removes all TextureStages from the collection. More... | |
| TextureStage | findTextureStage (str name) |
| Returns the texture_stage in the collection with the indicated name, if any, or NULL if no texture_stage has that name. More... | |
| int | getNumTextureStages () |
| Returns the number of TextureStages in the collection. More... | |
| TextureStage | getTextureStage (int index) |
| Returns the nth TextureStage in the collection. More... | |
| list | getTextureStages () |
| bool | hasTextureStage (TextureStage texture_stage) |
| Returns true if the indicated TextureStage appears in this collection, false otherwise. More... | |
| TextureStageCollection | operator+ (const TextureStageCollection other) |
| TextureStageCollection | operator+= (const TextureStageCollection other) |
| TextureStageCollection | operator= (const TextureStageCollection copy) |
| TextureStage | operator[] (int index) |
| Returns the nth TextureStage in the collection. More... | |
| output (Ostream out) | |
| Writes a brief one-line description of the TextureStageCollection to the indicated output stream. More... | |
| removeDuplicateTextureStages () | |
| Removes any duplicate entries of the same TextureStages on this collection. More... | |
| bool | removeTextureStage (TextureStage node_texture_stage) |
| Removes the indicated TextureStage from the collection. More... | |
| removeTextureStagesFrom (const TextureStageCollection other) | |
| Removes from this collection all of the TextureStages listed in the other collection. More... | |
| int | size () |
| Returns the number of texture stages in the collection. More... | |
| sort () | |
| Sorts the TextureStages in this collection into order by TextureStage::sort(), from lowest to highest. More... | |
| write (Ostream out, int indent_level) | |
| Writes a complete multi-line description of the TextureStageCollection to the indicated output stream. More... | |
| __init__ | ( | ) |
| __init__ | ( | const TextureStageCollection | copy | ) |
| addTextureStage | ( | TextureStage | node_texture_stage | ) |
Adds a new TextureStage to the collection.
| addTextureStagesFrom | ( | const TextureStageCollection | other | ) |
Adds all the TextureStages indicated in the other collection to this texture_stage.
The other texture_stages are simply appended to the end of the texture_stages in this list; duplicates are not automatically removed.
| clear | ( | ) |
Removes all TextureStages from the collection.
| TextureStage findTextureStage | ( | str | name | ) |
Returns the texture_stage in the collection with the indicated name, if any, or NULL if no texture_stage has that name.
| int getNumTextureStages | ( | ) |
Returns the number of TextureStages in the collection.
| TextureStage getTextureStage | ( | int | index | ) |
Returns the nth TextureStage in the collection.
| list getTextureStages | ( | ) |
| bool hasTextureStage | ( | TextureStage | texture_stage | ) |
Returns true if the indicated TextureStage appears in this collection, false otherwise.
| TextureStageCollection operator+ | ( | const TextureStageCollection | other | ) |
| TextureStageCollection operator+= | ( | const TextureStageCollection | other | ) |
| TextureStageCollection operator= | ( | const TextureStageCollection | copy | ) |
| TextureStage operator[] | ( | int | index | ) |
Returns the nth TextureStage in the collection.
This is the same as get_texture_stage(), but it may be a more convenient way to access it.
| output | ( | Ostream | out | ) |
Writes a brief one-line description of the TextureStageCollection to the indicated output stream.
| removeDuplicateTextureStages | ( | ) |
Removes any duplicate entries of the same TextureStages on this collection.
If a TextureStage appears multiple times, the first appearance is retained; subsequent appearances are removed.
| bool removeTextureStage | ( | TextureStage | node_texture_stage | ) |
Removes the indicated TextureStage from the collection.
Returns true if the texture_stage was removed, false if it was not a member of the collection.
| removeTextureStagesFrom | ( | const TextureStageCollection | other | ) |
Removes from this collection all of the TextureStages listed in the other collection.
| int size | ( | ) |
Returns the number of texture stages in the collection.
This is the same thing as get_num_texture_stages().
| sort | ( | ) |
Sorts the TextureStages in this collection into order by TextureStage::sort(), from lowest to highest.
| write | ( | Ostream | out, |
| int | indent_level | ||
| ) |
Writes a complete multi-line description of the TextureStageCollection to the indicated output stream.