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|
department_id |
department |
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1 |
frozen |
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2 |
other |
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3 |
bakery |
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4 |
produce |
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5 |
alcohol |
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6 |
international |
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7 |
beverages |
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8 |
pets |
|
9 |
dry goods pasta |
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10 |
bulk |
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11 |
personal care |
|
12 |
meat seafood |
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13 |
pantry |
|
14 |
breakfast |
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15 |
canned goods |
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16 |
dairy eggs |
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17 |
household |
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18 |
babies |
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19 |
snacks |
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20 |
deli |
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21 |
missing |


#TiM
#Trends
The Current Trends in the Research World
Our algorithm quickly and efficiently compiles over 100 different articles from various journals to track research trends in the medical field
Surgery
Oncology
Psychiatry
Pathology
Dentistry
More
More
More


You are viewing an historical archive of past issues. Please report new issues to the appropriate project issue tracker on GitHub.
Issues
| # | Summary | Product | Status | Kind | When |
|---|---|---|---|---|---|
| 1871 |
Sync up EBS snapshot scripts across hosts |
wincent.com | closed | feature request | Created , updated |
| 1870 |
Don't post issue annotations for moderation events |
wincent.com | closed | bug | |
| 1869 |
Investigate pre-processing as a means of speeding up matching algorithm |
Command-T | closed | feature request | Created , updated |
| 1868 |
Shortcut to close a buffer in :CommandTBuffer popup menu |
Command-T | closed | feature request | Created , updated |
| 1867 |
not working |
Synergy | closed | bug | Created , updated |
| 1866 |
Rakefile dependencies don't do the right thing |
mkdtemp | open | bug | |
| 1864 |
"Update" to Ruby Enterprise Edition, or a later 1.8.7 patch level |
wincent.com | closed | bug | Created , updated |
| 1863 |
Update to jQuery 1.6.3 |
wincent.com | closed | feature request | |
| 1862 |
Moved to new machine |
Synergy | open | support ticket | Created , updated |
| 1861 |
Update to Rails 3.1.0 |
wincent.com | closed | feature request | Created , updated |


Many tasks require robots to operate in long-duration missions with minimal interruption for recharging or replenishing resources. However, robot power and resources are limited, and without a continuous supply of these resources, robots would have significant downtime for recharging or refreshing other resources, which could be severely disruptive to their mission.
In this project, we are developing a theoretical framework to deploy teams of robots (task robots) for exploration and surveillance while taking into account their energy and resource requirements. We envision a distribution center with replenishable resources (batteries, cameras etc.) that receives or predicts requests for fresh resources from deployed robots, and dispatches agile delivery robots (e.g., quadrotors) to deliver them in a timely manner.
We will address the scheduling and prediction problem underlying this distribution task and propose solutions which generate near-optimal schedules for resource redistribution with multiple incoming requests from deployed robots. The framework will incorporate priorities on task robots which can be changed over time, and allow a relaxed delivery schedule if there are not enough delivery robots available. Delivery robots can also be dynamically re-routed to make efficient use of time and resources to sustain long-duration missions for robotic teams operating in a given region.


Please find my ongoing and completed projects My interest lies in Deep Learning, Machine learning and Self Driving Cars
Find my research, paper notes and Deep learning projects related blogs Medium
Find my Tutorial and project videos on my Youtube Channel my youtube channel
Find my Research Paper and Publications here


PexelSpace
--Site Construction in Progress--
Website Design?
Website Development?
Mobile Application?
We can build that, Just not yet
(We're working on it)


In the enterprise private domain knowledge management setting, topic representation is critical for knowledge conflation, retrieval and related items discovery. Topics are the main entities in the knowledge graph, under which there are attributes like definitions, related people and related documents. Topic representation should accurately reflect the topic’s relationship with others in semantic space even when they have ambiguous names. It is important to enforce topic representations are consistent with all other word token representations.
For topic conflation tasks, conflation algorithm could avoid topic over merge by comparing the similarity of their embeddings, while in retrieval tasks, K-NN algorithm can effectively discover topics by using query context. When documents and people in the corpus also consistently encoded with the representation in the same topic representation space, they can attach to related topics based on the embedding distance as well.
There are in general 2 ways to generate topic embeddings - relation based and content based.
The relation based approach generates topic embeddings based on the topic relations with its neighbor entities and attributes. For example, we can build bi-part graph for topic and people entities, and each topic can be represented by its neighbor people. The representation can be learnt independent of the topic content or any other prior knowledge, but the accuracy of the learnt representation largely depends on the density and quality of its attributes, and it is especially challenge during cold start. The content based approaches takes the definition and surrounding context of the topic for generating its embedding. It is usually more flexible and information rich, but require focused language model training on the target domain.
This work based on the content representation learning, but requires much fewer training samples and no human labeling.
Private domain topic representation learning majorly poses following 3 challenges: 1. Large volume of topics with various length of tokens needs to be jointly optimized for generating fixed size embeddings, under the same embedding space of word tokens. 2. Content summarization if needed for generating topic embeddings from their related documents, given topic definition text may be not widely available on all topics. Large domain refined language model shall be used for quality summarization. 3. Topic representation training and language model refinement may be impossible on small tenant with very limited documents or user labels.
In our method, we use pre-trained language model (BERT) to generate topic representation in private domain by joint masked LM continues training and topic embedding training. The masked LM with topic are predicating probabilities of following distribution:
P(xm|xi, xt, zt)
where xm represents all masked tokens, xt represents unmasked topic tokens, xi represents all unmasked normal tokens, and zt represents the fixed length topic embedding associated with topic tokens xt. There could be multiple tokens xt maps to a single topic embedding zt.
The fixed size topic embedding were assigned with a position embedding very close to the topic tokens xt to take advantage of the sub-word information, while the topic embedding zt itself carries over in-ambiguously in multiple documents. Our observations in the preliminary experiment supports the claim of single token fixed-size topic embedding reduces topic ambiguities while also captures certain sub-word information.
There are also different design choices about how to encode the document context into the topic. One obvious alternative is to use the “Sentence-BERT” to encode the topic definition or the summary of the topic related documents. The idea is to capture the topic semantic information by encoding its definition or related documents, but less practical than our approach because: 1. topic definitions are not always available; 2. related documents summary may not directly support the topic but containing other information that may reduce the accuracy of the topic representation; 3. not be able to continue training the model to improve the topic representation.
The topic embeddings zt are initialized with prompt-assistant masked LM topic prediction from its related document contexts. Prompt text " ( aka [MASK]) " is inserted immediately after the topic tokens in the context of the document. For example, for topic “Who Knows What”, we will generate following annotated sentence:
Viva topics utilize multiple sources of topics from Alexandria, Yukon and Who Knows What (aka [MASK]) for building the knowledge base.
The mask token after prompt “( aka )” pulls attentions from the multi-tokens topic words and its surrounding context text. By averaging all the mask token predictions for the topic in different documents, it generates topic embedding initialization.
This method naturally initialize the topic embedding into the pre-trained word token embedding space, with such, continues domain specific topic infused masked LM fine-tuning become possible. However, based on our experiments, this initialization approach is very effective even without continues fine tuning, which makes it possible to directly generate topic embeddings on small private domain settings, where personalized training is in-practical.
While topic embeddings are not picked from a fixed close token set, it is still desirable to keep the model size (word embeddings and token decoders) fixed, to minimize the complexity impact brought by the fast increasing topic counts. It is important for GPU based accelerations as VRAM size is limited. We developed a technique called “dynamic embedding swap” that supports dynamically expanding the topic embeddings while keep the model fix-sized. It assumes there are limited number of topics appears in each training epoch, though the total number of the topics across epochs are unbounded.
Our model reserves 1000 empty slots in word embedding and decoder layers to support dynamic embeddings loading in the middle of the training epochs. During the data-loading stage of each epoch, system will dynamically discover all topics occurred in the sentences, and load previously initialized topic embeddings into reserved 1000 empty slots in the embedding matrix and decoder matrix. At the end of each epoch, topic embeddings are again swapped back to external storage for future reference. As the ADEM optimizer is stateful against parameters, it is necessary to re-initialize the optimizer before each new epoch of training.
Based on our observation, the dynamic embedding swap process does not cause training accuracy deterioration while managing the fixed size model parameters loaded in the VRAM.
We experimented the topic representation generation over small subset of the stack-overflow domain documents, and evaluated the performance on the related topic task.
We downloaded and down-sampled 1% of the achieved stack-overflow data, with questions, answers as well as tags, and only the best answer for each question is kept. We use tags occurred in more than 500 of the questions and answers as topics to extract semantic representations (embeddings). Google news pretrained case-folded small BERT model is used to perform prompt assisted masked LM topic prediction as well as further joint embedding fine tuning based on the stack-overflow data. To enable dynamic topic embedding swapping, we take advantage of the 1000 unused token in the BERT model, which saves additional effort of expanding the BERT model structure.
For masked topic prediction, we use the BERT mask token output right before token decoder layer as the single instance of raw topic embedding. The embedding vector is then normalized into the average norm2 size of all other tokens in the BERT vocabulary, and gets averaged over all instances of the documents. We directly take the average bias of BERT vocabulary as the topic decoding bias. During the fine tuning stage, we set the batch size to be 64 paragraphs, and we limits 5000 paragraph and 1000 unique topics in each epoch.
We experimented 2 different settings on the masked LM training: 1. set the prompt text and the artificial topic token as token type 1, and avoid predicting them in the final loss computation; 2. treat prompt text and artificial topic token as normal token type 0, and predict them in the final loss computation just like any other tokens. The second approach gets slightly better result in our experiments.
We setup 3 different topic embedding generation approaches for comparison: 1. baseline: use domain fine-tuned BERT model directly generate embeddings for every tokens in each topic, and average them over the topic; 2. prediction: use pretrained BERT model to predict the topic embedding with prompt based approach (covering 3 epochs); 3. fine-tuned: use #2 as the initialization of the topic embeddings, and perform joint masked LM training (covering 3 epochs).
SBS Surplus score and P-value are computed between 1-2 and 2-3 comparison.
We randomly pick 50 topics from the generated related topic list from each experiments, and assign SBS scores. Win or Loss were assigned if right or left (respectively) side have equal or more than 2 topics in the top 5 positions are significantly better than the other side, and we assign 2 points. WeakWin or WeakLoss were assigned if the right or left (respectively) side have less than 2 topics in the topic 5 positions are significantly better than the other side, and we assign 1 point. For all other cases, we assign the result as neutral with 0 credit.
| Metrics | Exp 1 - 2 | Exp 2 - 3 |
|---|---|---|
| Neutral | 11 | 32 |
| Win | 20 | 1 |
| Loss | 1 | 0 |
| WeakWin | 12 | 11 |
| WeakLoss | 6 | 6 |
| Surplus | 0.88 | 0.14 |
| P-Value | < 0.0001 | 0.026 |
With the limitation of the computing resource during FHL, we only finished the experiment 3 on 3 epochs training (less than 0.03% of the stack overflow dataset). Nevertheless, early observations already shown the trends of improvements over experiment 2 with prediction only approach. To view the complete list of the result topic output result, please refer to the files in the result folder.
We proposed a novel approach to generate topic embeddings in small private domains. Topic embeddings are initialized by prompt assisted masked LM with a small documents set, and then jointly fine tuned in the domain with all other token embeddings. Our preliminary experiments successfully demonstrated the effectiveness of the prompt based initialization, and the improvements of fine tuning even with extremely small set of the data.
For future explorations, it is interested to see the embedding performance under different fine tuning tasks, e.g. contrastive training based on topics occurs in the same or different documents. More experiments with different NLP tasks are also needed to verify the performance of the topic embeddings.


Preparation time
less than 30 mins
Cooking time
1 to 2 hours
Serves
Serves 4-6
Pot-roasting chicken makes it incredibly moist. This hearty recipe is perfect for a cold evening.
For the chicken, preheat the oven to 180C/350F/Gas 4.
Season the chicken with salt and freshly ground black pepper.
Heat a large casserole dish until hot, and the oil and brown the chicken on both sides. Remove the chicken from the pan and set aside.
Add the onion and fry for a minute, return the chicken to the pan then pour over the stout and stock.
Place in the oven, uncovered, for 1½ hours; basting every 30 minutes or so with the juices.
Remove from the oven, cover and set aside to rest for 15 minutes.
For the potato ragù, heat a large frying pan until hot, add the butter and potatoes and fry gently for 4-5 minutes, or until just softened.
Add the shallots, garlic, cabbage and thyme and fry for 3-4 minutes, or until the potatoes and cabbage are just coloured.
Add a couple of ladles of the cooking liquor from the chicken then bring to a simmer and cook for 2-3 minutes, or until the vegetables are tender.
Season with salt and freshly ground black pepper then stir in the parsley.
To serve, carve the chicken. Ladle the potato mixture into serving bowls then top with the chicken.


Enables the use of byte calculations and declarations, like 45.bytes + 2.6.megabytes
Methods
- byte
- bytes
- exabyte
- exabytes
- gigabyte
- gigabytes
- kilobyte
- kilobytes
- megabyte
- megabytes
- petabyte
- petabytes
- terabyte
- terabytes
Public Instance methods
Alias for bytes
This method is also aliased as
byte
[ show source ]
# File vendor/rails/activesupport/lib/active_support/core_ext/numeric/bytes.rb, line 6 6: def bytes 7: self 8: end
Alias for exabytes
This method is also aliased as
exabyte
[ show source ]
# File vendor/rails/activesupport/lib/active_support/core_ext/numeric/bytes.rb, line 36 36: def exabytes 37: self * 1024.petabytes 38: end
Alias for gigabytes
This method is also aliased as
gigabyte
[ show source ]
# File vendor/rails/activesupport/lib/active_support/core_ext/numeric/bytes.rb, line 21 21: def gigabytes 22: self * 1024.megabytes 23: end
Alias for kilobytes
This method is also aliased as
kilobyte
[ show source ]
# File vendor/rails/activesupport/lib/active_support/core_ext/numeric/bytes.rb, line 11 11: def kilobytes 12: self * 1024 13: end
Alias for megabytes
This method is also aliased as
megabyte
[ show source ]
# File vendor/rails/activesupport/lib/active_support/core_ext/numeric/bytes.rb, line 16 16: def megabytes 17: self * 1024.kilobytes 18: end
Alias for petabytes
This method is also aliased as
petabyte
[ show source ]
# File vendor/rails/activesupport/lib/active_support/core_ext/numeric/bytes.rb, line 31 31: def petabytes 32: self * 1024.terabytes 33: end
Alias for terabytes
This method is also aliased as
terabyte
[ show source ]
# File vendor/rails/activesupport/lib/active_support/core_ext/numeric/bytes.rb, line 26 26: def terabytes 27: self * 1024.gigabytes 28: end


| PERLAIX(1) | Perl Programmers Reference Guide | PERLAIX(1) |
When compiling Perl, you must use an ANSI C compiler. AIX does not ship an ANSI compliant C compiler with AIX by default, but binary builds of gcc for AIX are widely available. A version of gcc is also included in the AIX Toolbox which is shipped with AIX.
If you plan to link Perl to any module that requires thread-support, like DBD::Oracle, it is better to use the _r version of the compiler. This will not build a threaded Perl, but a thread-enabled Perl. See also "Threaded Perl" later on.
As of writing (2010-09) only the IBM XL C for AIX or IBM XL C/C++ for AIX compiler is supported by IBM on AIX 5L/6.1/7.1.
The following compiler versions are currently supported by IBM:
IBM XL C and IBM XL C/C++ V8, V9, V10, V11
The XL C for AIX is integrated in the XL C/C++ for AIX compiler and therefore also supported.
If you choose XL C/C++ V9 you need APAR IZ35785 installed otherwise the integrated SDBM_File do not compile correctly due to an optimization bug. You can circumvent this problem by adding -qipa to the optimization flags (-Doptimize='-O -qipa'). The PTF for APAR IZ35785 which solves this problem is available from IBM (April 2009 PTF for XL C/C++ Enterprise Edition for AIX, V9.0).
If you choose XL C/C++ V11 you need the April 2010 PTF (or newer) installed otherwise you will not get a working Perl version.
Perl can be compiled with either IBM's ANSI C compiler or with gcc. The former is recommended, as not only it can compile Perl with no difficulty, but also can take advantage of features listed later that require the use of IBM compiler-specific command-line flags.
If you decide to use gcc, make sure your installation is recent and complete, and be sure to read the Perl INSTALL file for more gcc-specific details. Please report any hoops you had to jump through to the development team.
Perl | AIX Level | Compiler Level | w th | w/o th -------+---------------------+-------------------------+------+------- 5.12.2 |5.1 TL9 32 bit | XL C/C++ V7 | OK | OK 5.12.2 |5.1 TL9 64 bit | XL C/C++ V7 | OK | OK 5.12.2 |5.2 TL10 SP8 32 bit | XL C/C++ V8 | OK | OK 5.12.2 |5.2 TL10 SP8 32 bit | gcc 3.2.2 | OK | OK 5.12.2 |5.2 TL10 SP8 64 bit | XL C/C++ V8 | OK | OK 5.12.2 |5.3 TL8 SP8 32 bit | XL C/C++ V9 + IZ35785 | OK | OK 5.12.2 |5.3 TL8 SP8 32 bit | gcc 4.2.4 | OK | OK 5.12.2 |5.3 TL8 SP8 64 bit | XL C/C++ V9 + IZ35785 | OK | OK 5.12.2 |5.3 TL10 SP3 32 bit | XL C/C++ V11 + Apr 2010 | OK | OK 5.12.2 |5.3 TL10 SP3 64 bit | XL C/C++ V11 + Apr 2010 | OK | OK 5.12.2 |6.1 TL1 SP7 32 bit | XL C/C++ V10 | OK | OK 5.12.2 |6.1 TL1 SP7 64 bit | XL C/C++ V10 | OK | OK 5.13 |7.1 TL0 SP1 32 bit | XL C/C++ V11 + Jul 2010 | OK | OK 5.13 |7.1 TL0 SP1 64 bit | XL C/C++ V11 + Jul 2010 | OK | OK w th = with thread support w/o th = without thread support OK = tested
Successfully tested means that all "make test" runs finish with a result of 100% OK. All tests were conducted with -Duseshrplib set.
All tests were conducted on the oldest supported AIX technology level with the latest support package applied. If the tested AIX version is out of support (AIX 4.3.3, 5.1, 5.2) then the last available support level was used.
It is highly recommended to use the new interface.
IBM uses the AIX system Perl (V5.6.0 on AIX 5.1 and V5.8.2 on AIX 5.2 / 5.3 and 6.1; V5.8.8 on AIX 5.3 TL11 and AIX 6.1 TL4; V5.10.1 on AIX 7.1) for some AIX system scripts. If you switch the links in /usr/bin from the AIX system Perl (/usr/opt/perl5) to the newly build Perl then you get the same features as with the IBM AIX system Perl if the threaded options are used.
The threaded Perl build works also on AIX 5.1 but the IBM Perl build (Perl v5.6.0) is not threaded on AIX 5.1.
Perl 5.12 an newer is not compatible with the IBM fileset perl.libext.
If you need more memory (larger data segment) for your Perl programs you can set:
/etc/security/limits default: (or your user) data = -1 (default is 262144 * 512 byte)
With the default setting the size is limited to 128MB. The -1 removes this limit. If the "make test" fails please change your /etc/security/limits as stated above.
There seem to be various problems in this long double implementation. If Configure detects this brokenness, it will disable the long double support. This can be overridden with explicit "-Duselongdouble" (or "-Dusemorebits", which enables both long doubles and 64 bit integers). If you decide to enable long doubles, for most of the broken things Perl has implemented workarounds, but the handling of the special values infinity and NaN remains badly broken: for example infinity plus zero results in NaN.
rm config.sh ./Configure \ -d \ -Dcc=cc_r \ -Duseshrplib \ -Dusethreads \ -Dprefix=/usr/opt/perl5_32
The -Dprefix option will install Perl in a directory parallel to the IBM AIX system Perl installation.
rm config.sh ./Configure \ -d \ -Dcc=cc_r \ -Duseshrplib \ -Dprefix=/usr/opt/perl5_32
The -Dprefix option will install Perl in a directory parallel to the IBM AIX system Perl installation.
export OBJECT_MODE=64 / setenv OBJECT_MODE 64 (depending on your shell) rm config.sh ./Configure \ -d \ -Dcc=cc_r \ -Duseshrplib \ -Dusethreads \ -Duse64bitall \ -Dprefix=/usr/opt/perl5_64
export OBJECT_MODE=64 / setenv OBJECT_MODE 64 (depending on your shell) rm config.sh ./Configure \ -d \ -Dcc=cc_r \ -Duseshrplib \ -Duse64bitall \ -Dprefix=/usr/opt/perl5_64
The -Dprefix option will install Perl in a directory parallel to the IBM AIX system Perl installation.
If you choose gcc to compile 64-bit Perl then you need to add the following option:
-Dcc='gcc -maix64'
When compiling Perl, you must use an ANSI C compiler. AIX does not ship an ANSI compliant C-compiler with AIX by default, but binary builds of gcc for AIX are widely available.
At the moment of writing, AIX supports two different native C compilers, for which you have to pay: xlC and vac. If you decide to use either of these two (which is quite a lot easier than using gcc), be sure to upgrade to the latest available patch level. Currently:
xlC.C <IP_ADDRESS> or <IP_ADDRESS> or <IP_ADDRESS> or <IP_ADDRESS> or <IP_ADDRESS> vac.C <IP_ADDRESS> or <IP_ADDRESS> or <IP_ADDRESS>
note that xlC has the OS version in the name as of version <IP_ADDRESS>, so you will find xlC.C for AIX-5.0 as package
xlC.aix50.rte <IP_ADDRESS> or <IP_ADDRESS>
subversions are not the same "latest" on all OS versions. For example, the latest xlC-5 on aix41 is <IP_ADDRESS>, while on aix43, it is <IP_ADDRESS>.
Perl can be compiled with either IBM's ANSI C compiler or with gcc. The former is recommended, as not only can it compile Perl with no difficulty, but also can take advantage of features listed later that require the use of IBM compiler-specific command-line flags.
The IBM's compiler patch levels <IP_ADDRESS> and <IP_ADDRESS> have compiler optimization bugs that affect compiling perl.c and regcomp.c, respectively. If Perl's configuration detects those compiler patch levels, optimization is turned off for the said source code files. Upgrading to at least <IP_ADDRESS> is recommended.
If you decide to use gcc, make sure your installation is recent and complete, and be sure to read the Perl INSTALL file for more gcc-specific details. Please report any hoops you had to jump through to the development team.
# oslevel <IP_ADDRESS> # lslpp -l | grep 'bos.rte ' bos.rte <IP_ADDRESS> COMMITTED Base Operating System Runtime bos.rte <IP_ADDRESS> COMMITTED Base Operating System Runtime #
The same might happen to AIX 5.1 or other OS levels. As a side note, Perl cannot be built without bos.adt.syscalls and bos.adt.libm installed
# lslpp -l | egrep "syscalls|libm" bos.adt.libm <IP_ADDRESS> COMMITTED Base Application Development bos.adt.syscalls <IP_ADDRESS> COMMITTED System Calls Application #
Note that starting from Perl 5.7.2 (and consequently 5.8.0) and AIX 4.3 or newer Perl uses the AIX native dynamic loading interface in the so called runtime linking mode instead of the emulated interface that was used in Perl releases 5.6.1 and earlier or, for AIX releases 4.2 and earlier. This change does break backward compatibility with compiled modules from earlier Perl releases. The change was made to make Perl more compliant with other applications like Apache/mod_perl which are using the AIX native interface. This change also enables the use of C++ code with static constructors and destructors in Perl extensions, which was not possible using the emulated interface.
If you've chosen to use vac 4, be sure to run <IP_ADDRESS>. Older versions will turn up nasty later on. For vac 5 be sure to run at least <IP_ADDRESS>, but vac <IP_ADDRESS> or up is highly recommended. Note that since IBM has removed vac <IP_ADDRESS> through <IP_ADDRESS> from the software depot, these versions should be considered obsolete.
Here's a brief lead of how to upgrade the compiler to the latest level. Of course this is subject to changes. You can only upgrade versions from ftp-available updates if the first three digit groups are the same (in where you can skip intermediate unlike the patches in the developer snapshots of Perl), or to one version up where the "base" is available. In other words, the AIX compiler patches are cumulative.
vac.C.<IP_ADDRESS> => vac.C.<IP_ADDRESS> is OK (vac.C.<IP_ADDRESS> not needed) xlC.C.<IP_ADDRESS> => xlC.C.<IP_ADDRESS> is NOT OK (xlC.C.<IP_ADDRESS> is not available) # ftp ftp.software.ibm.com Connected to service.boulder.ibm.com. : welcome message ...  Password: ... accepted login stuff ftp> cd /aix/fixes/v4/ ftp> dir other other.ll output to local-file: other.ll? y 200 PORT command successful. 150 Opening ASCII mode data connection for /bin/ls. 226 Transfer complete. ftp> dir xlc xlc.ll output to local-file: xlc.ll? y 200 PORT command successful. 150 Opening ASCII mode data connection for /bin/ls. 226 Transfer complete. ftp> bye ... goodbye messages # ls -l *.ll -rw-rw-rw- 1 merijn system 1169432 Nov 2 17:29 other.ll -rw-rw-rw- 1 merijn system 29170 Nov 2 17:29 xlc.ll
On AIX 4.2 using xlC, we continue:
# lslpp -l | fgrep 'xlC.C ' xlC.C <IP_ADDRESS> COMMITTED C for AIX Compiler xlC.C <IP_ADDRESS> COMMITTED C for AIX Compiler # grep 'xlC.C.3.1.4.*.bff' xlc.ll -rw-r--r-- 1 45776101 1 6286336 Jul 22 1996 xlC.C.<IP_ADDRESS>.bff -rw-rw-r-- 1 45776101 1 6173696 Aug 24 1998 xlC.C.<IP_ADDRESS>.bff -rw-r--r-- 1 45776101 1 6319104 Aug 14 1996 xlC.C.<IP_ADDRESS>.bff -rw-r--r-- 1 45776101 1 6316032 Oct 21 1996 xlC.C.<IP_ADDRESS>.bff -rw-r--r-- 1 45776101 1 6315008 Dec 20 1996 xlC.C.<IP_ADDRESS>.bff -rw-rw-r-- 1 45776101 1 6178816 Mar 28 1997 xlC.C.<IP_ADDRESS>.bff -rw-rw-r-- 1 45776101 1 6188032 May 22 1997 xlC.C.<IP_ADDRESS>.bff -rw-rw-r-- 1 45776101 1 6191104 Sep 5 1997 xlC.C.<IP_ADDRESS>.bff -rw-rw-r-- 1 45776101 1 6185984 Jan 13 1998 xlC.C.<IP_ADDRESS>.bff -rw-rw-r-- 1 45776101 1 6169600 May 27 1998 xlC.C.<IP_ADDRESS>.bff # wget ftp://ftp.software.ibm.com/aix/fixes/v4/xlc/xlC.C.<IP_ADDRESS>.bff #
On AIX 4.3 using vac, we continue:
# lslpp -l | grep 'vac.C ' vac.C <IP_ADDRESS> COMMITTED C for AIX Compiler vac.C <IP_ADDRESS> COMMITTED C for AIX Compiler # grep 'vac.C.5.0.2.*.bff' other.ll -rw-rw-r-- 1 45776101 1 13592576 Apr 16 2001 vac.C.<IP_ADDRESS>.bff -rw-rw-r-- 1 45776101 1 14133248 Apr 9 2002 vac.C.<IP_ADDRESS>.bff -rw-rw-r-- 1 45776101 1 14173184 May 20 2002 vac.C.<IP_ADDRESS>.bff -rw-rw-r-- 1 45776101 1 14192640 Nov 22 2002 vac.C.<IP_ADDRESS>.bff # wget ftp://ftp.software.ibm.com/aix/fixes/v4/other/vac.C.<IP_ADDRESS>.bff #
Likewise on all other OS levels. Then execute the following command, and fill in its choices
# smit install_update -> Install and Update from LATEST Available Software * INPUT device / directory for software [ vac.C.<IP_ADDRESS>.bff ] [ OK ] [ OK ]
Follow the messages ... and you're done.
If you like a more web-like approach, a good start point can be <http://www14.software.ibm.com/webapp/download/downloadaz.jsp> and click "C for AIX", and follow the instructions.
cc -o miniperl ... miniperlmain.o opmini.o perl.o ... -lm -lc ...
causes error like this
ld: 0711-317 ERROR: Undefined symbol: .aintl ld: 0711-317 ERROR: Undefined symbol: .copysignl ld: 0711-317 ERROR: Undefined symbol: .syscall ld: 0711-317 ERROR: Undefined symbol: .eaccess ld: 0711-317 ERROR: Undefined symbol: .setresuid ld: 0711-317 ERROR: Undefined symbol: .setresgid ld: 0711-317 ERROR: Undefined symbol: .setproctitle ld: 0711-345 Use the -bloadmap or -bnoquiet option to obtain more information.
you could retry with
make realclean rm config.sh ./Configure -Dusenm ...
which makes Configure to use the "nm" tool when scanning for library symbols, which usually is not done in AIX.
Related to this, you probably should not use the "-r" option of Configure in AIX, because that affects of how the "nm" tool is used.
http://www.ibm.com/servers/aix/products/aixos/linux/
You may get a warning when doing a threaded build:
"pp_sys.c", line 4640.39: 1506-280 (W) Function argument assignment between types "unsigned char*" and "const void*" is not allowed.
The exact line number may vary, but if the warning (W) comes from a line line this
hent = PerlSock_gethostbyaddr(addr, (Netdb_hlen_t) addrlen, addrtype);
in the "pp_ghostent" function, you may ignore it safely. The warning is caused by the reentrant variant of gethostbyaddr() having a slightly different prototype than its non-reentrant variant, but the difference is not really significant here.
| 2019-10-21 | perl v5.30.3 |


{% assign posts = site.categories['typescript'] %} {% if posts.size > 1 %} > This post is part of an ongoing series on TypeScript. In it, I try to explain in a detailed way different situations I've faced while building relatively big React applications using TypeScript, and moving them from JavaScript. Here's a list of other posts I've written about TypeScript:
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effect-custom-points
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gl-line-strip
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Welcome to gdp_growth_forecast’s documentation!¶
GDP Growth Forecast¶
GPD Growth Forecast is a software that provides a command line interface tool to train a model and make predictions about GDP Growth for the different countries in the world.
The way the software works is:
It processes the database data by removing columns with too many NaN and replacing the remaining with the mean of other samples, accoeding to different criterias (mean by year, mean by income group, …).
The data is also organized in a convenient way for manipulation, with one column for each indicator.
To reduce the number of features of the model, it is computed the shap values to select the most important features.
Then, a gradient boosting regressor based model is trained with the selected features.
Finally, the software allows to make predictions of GDP growth with the trained model.
- Notes:
Selected features and the model are stored in persistent files so that they can be used again.


LOOP FISH
Daniel
1685mm x 1020mm x 700mm
steel frame + abs + pp
2017.10.09
3M
Windhover沉思中心，由艺术家Nathan Oliveria系列作品命名，是斯坦福大学校园的精神避难所。 使用Oliveira的艺术作为个人更新的载体，教堂式的中心提供了从日常生活的强度的喘息。 与建筑相结合，景观提供了一系列宁静的空间，旨在全天安静的反思。 任何斯坦福大学的学生，教师或工作人员以及较大社区的成员都可以使用该中心。
I wanted the final design to be focused on people-centeredness because human factor and usability should not be afterthoughts on a hand-held devise. I nailed down a few keywords for the development themes such as personalized, expandable, and professional. In order to overcome the palm products, I had to emphasize on multi-tasking and multi-capability of the Pocket PC and make it like a mobile Swiss knife than just an electronic organizer or an address book. The final design had to look compact, elegant, intelligent and sophisticated. The final design had to work seamlessly between the hardware and the software to be able to operate the Pocket PC intuitively. To reduce the weight and size of the product, the core team came out with the expandable jacket concept . The curtail benefit of the feature jackets were designed to fit in different users in different usage scenarios without burdening too much technology into the small devise.


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School SelectR
April 16, 2015To get more experience with recommendation engines, I decided to try and create one using publicly available data from the National Center of Education statistics on college institutional characteristics. The end product is a content-based recommendation engine designed to take user input and output a table listing 4-year private and public colleges that best match this input. The recommendations are based on the Gower dissimilarity index, which can take variables of multiple types and also allows unique variable weights. The final result is an index on a scale from 0 to 1, where 1 represents a perfect match.
Data were taken from the publicly available IPEDS, from the National Center for Education Statistics. Only a small subset of variables were used in the matching process for simplicity, and are the following:
- geographic region
- size
- sector (i.e., public or private)
- total price for out-of-state students living off campus (not with family)
- percent admitted - total
- Graduation rate total cohort after 6 years
This application was built using R and Shiny, and can be seen here. The defaults were set according to my preferences back in the day when I was thinking about what colleges to apply to. All the schools I applied to are actually shown in the top 10 with my alma mater coming in at number 8, which is a nice little validation check with respect to the matching algorithm. As always, the relevant code and data for this application can be seen on GitHub.


Pine Jewelry
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Every Pine Jewelry piece is created with great attention to detail using a combination of both new items. antiques, and recycled materials. This means every piece is unique.
Every Pine Jewelry piece is created with great attention to detail using a combination of both new items. antiques, and recycled materials. This means every piece is unique.
Every Pine Jewelry piece is created with great attention to detail using a combination of both new items. antiques, and recycled materials. This means every piece is unique.
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Plan for Swarm in production
Estimated reading time: 14 minutesYou are viewing docs for legacy standalone Swarm. These topics describe standalone Docker Swarm. In Docker 1.12 and higher, Swarm mode is integrated with Docker Engine. Most users should use integrated Swarm mode — a good place to start is Getting started with swarm mode, Swarm mode CLI commands, and the Get started with Docker walkthrough). Standalone Docker Swarm is not integrated into the Docker Engine API and CLI commands.
This article provides guidance to help you plan, deploy, and manage Docker swarm clusters in business critical production environments. The following high level topics are covered:
Security
There are many aspects to securing a Docker Swarm cluster. This section covers:
- Authentication using TLS
- Network access control
These topics are not exhaustive. They form part of a wider security architecture that includes: security patching, strong password policies, role based access control, technologies such as SELinux and AppArmor, strict auditing, and more.
Configure Swarm for TLS
All nodes in a swarm cluster must bind their Docker Engine daemons to a network port. This brings with it all of the usual network related security implications such as man-in-the-middle attacks. These risks are compounded when the network in question is untrusted such as the internet. To mitigate these risks, Swarm and the Engine support Transport Layer Security (TLS) for authentication.
The Engine daemons, including the swarm manager, that are configured to use TLS only accepts commands from Docker Engine clients that sign their communications. Engine and Swarm support external 3rd party Certificate Authorities (CA) as well as internal corporate CAs.
The default Engine and Swarm ports for TLS are:
- Engine daemon: 2376/tcp
- Swarm manager: 3376/tcp
For more information on configuring Swarm for TLS, see the Overview Docker Swarm with TLS page.
Network access control
Production networks are complex, and usually locked down so that only allowed traffic can flow on the network. The list below shows the network ports and protocols that the different components of a Swam cluster listen on. You should use these to configure your firewalls and other network access control lists.
- Swarm manager.
- Inbound 80/tcp (HTTP). This allows
docker pull
commands to work. If you plan to pull images from Docker Hub, you must allow Internet connections through port 80. - Inbound 2375/tcp. This allows Docker Engine CLI commands direct to the Engine daemon.
- Inbound 3375/tcp. This allows Engine CLI commands to the swarm manager.
- Inbound 22/tcp. This allows remote management via SSH.
- Inbound 80/tcp (HTTP). This allows
- Service Discovery:
- Inbound 80/tcp (HTTP). This allows
docker pull
commands to work. If you plan to pull images from Docker Hub, you must allow Internet connections through port 80. - Inbound Discovery service port. This needs setting to the port that the backend discovery service listens on (consul, etcd, or zookeeper).
- Inbound 22/tcp. This allows remote management via SSH.
- Inbound 80/tcp (HTTP). This allows
- Swarm nodes:
- Inbound 80/tcp (HTTP). This allows
docker pull
commands to work. If you plan to pull images from Docker Hub, you must allow Internet connections through port 80. - Inbound 2375/tcp. This allows Engine CLI commands direct to the Docker daemon.
- Inbound 22/tcp. This allows remote management via SSH.
- Inbound 80/tcp (HTTP). This allows
- Custom, cross-host container networks:
- Inbound 7946/tcp Allows for discovering other container networks.
- Inbound 7946/udp Allows for discovering other container networks.
- Inbound
<store-port>
/tcp Network key-value store service port. - 4789/udp For the container overlay network.
- ESP packets For encrypted overlay networks.
If your firewalls and other network devices are connection state aware, they allow responses to established TCP connections. If your devices are not state aware, you need to open up ephemeral ports from 32768-65535. For added security you can configure the ephemeral port rules to only allow connections from interfaces on known swarm devices.
If your swarm cluster is configured for TLS, replace 2375
with 2376
, and
3375
with 3376
.
The ports listed above are just for swarm cluster operations such as cluster creation, cluster management, and scheduling of containers against the cluster. You may need to open additional network ports for application-related communications.
It is possible for different components of a swarm cluster to exist on separate networks. For example, many organizations operate separate management and production networks. Some Docker Engine clients may exist on a management network, while swarm managers, discovery service instances, and nodes might exist on one or more production networks. To offset against network failures, you can deploy swarm managers, discovery services, and nodes across multiple production networks. In all of these cases you can use the list of ports above to assist the work of your network infrastructure teams to efficiently and securely configure your network.
High Availability (HA)
All production environments should be highly available, meaning they are continuously operational over long periods of time. To achieve high availability, an environment must survive failures of its individual component parts.
The following sections discuss some technologies and best practices that can enable you to build resilient, highly-available swarm clusters. You can then use these cluster to run your most demanding production applications and workloads.
Swarm manager HA
The swarm manager is responsible for accepting all commands coming in to a swarm cluster, and scheduling resources against the cluster. If the swarm manager becomes unavailable, some cluster operations cannot be performed until the swarm manager becomes available again. This is unacceptable in large-scale business critical scenarios.
Swarm provides HA features to mitigate against possible failures of the swarm manager. You can use Swarm’s HA feature to configure multiple swarm managers for a single cluster. These swarm managers operate in an active/passive formation with a single swarm manager being the primary, and all others being secondaries.
Swarm secondary managers operate as warm standby’s, meaning they run in the background of the primary swarm manager. The secondary swarm managers are online and accept commands issued to the cluster, just as the primary swarm manager. However, any commands received by the secondaries are forwarded to the primary where they are executed. Should the primary swarm manager fail, a new primary is elected from the surviving secondaries.
When creating HA swarm managers, you should take care to distribute them over as many failure domains as possible. A failure domain is a network section that can be negatively affected if a critical device or service experiences problems. For example, if your cluster is running in the Ireland Region of Amazon Web Services (eu-west-1) and you configure three swarm managers (1 x primary, 2 x secondary), you should place one in each availability zone as shown below.
In this configuration, the swarm cluster can survive the loss of any two availability zones. For your applications to survive such failures, they must be architected across as many failure domains as well.
For swarm clusters serving high-demand, line-of-business applications, you should have 3 or more swarm managers. This configuration allows you to take one manager down for maintenance, suffer an unexpected failure, and still continue to manage and operate the cluster.
Discovery service HA
The discovery service is a key component of a swarm cluster. If the discovery service becomes unavailable, this can prevent certain cluster operations. For example, without a working discovery service, operations such as adding new nodes to the cluster and making queries against the cluster configuration fail. This is not acceptable in business critical production environments.
Swarm supports four backend discovery services:
- Hosted (not for production use)
- Consul
- etcd
- Zookeeper
Consul, etcd, and Zookeeper are all suitable for production, and should be configured for high availability. You should use each service’s existing tools and best practices to configure these for HA.
For swarm clusters serving high-demand, line-of-business applications, it is recommended to have 5 or more discovery service instances. This due to the replication/HA technologies they use (such as Paxos/Raft) requiring a strong quorum. Having 5 instances allows you to take one down for maintenance, suffer an unexpected failure, and still maintain a strong quorum.
When creating a highly available swarm discovery service, you should take care to distribute each discovery service instance over as many failure domains as possible. For example, if your cluster is running in the Ireland Region of Amazon Web Services (eu-west-1) and you configure three discovery service instances, you should place one in each availability zone.
The diagram below shows a swarm cluster configured for HA. It has three swarm managers and three discovery service instances spread over three failure domains (availability zones). It also has swarm nodes balanced across all three failure domains. The loss of two availability zones in the configuration shown below does not cause the swarm cluster to go down.
It is possible to share the same Consul, etcd, or Zookeeper containers between the swarm discovery and Engine container networks. However, for best performance and availability you should deploy dedicated instances – a discovery instance for Swarm and another for your container networks.
Multiple clouds
You can architect and build swarm clusters that stretch across multiple cloud providers, and even across public cloud and on premises infrastructures. The diagram below shows an example swarm cluster stretched across AWS and Azure.
While such architectures may appear to provide the ultimate in availability, there are several factors to consider. Network latency can be problematic, as can partitioning. As such, you should seriously consider technologies that provide reliable, high speed, low latency connections into these cloud platforms – technologies such as AWS Direct Connect and Azure ExpressRoute.
If you are considering a production deployment across multiple infrastructures like this, make sure you have good test coverage over your entire system.
Isolated production environments
It is possible to run multiple environments, such as development, staging, and
production, on a single swarm cluster. You accomplish this by tagging swarm
nodes and using constraints to filter containers onto nodes tagged as
production
or staging
etc. However, this is not recommended. The recommended
approach is to air-gap production environments, especially high performance
business critical production environments.
For example, many companies not only deploy dedicated isolated infrastructures for production – such as networks, storage, compute and other systems. They also deploy separate management systems and policies. This results in things like users having separate accounts for logging on to production systems etc. In these types of environments, it is mandatory to deploy dedicated production swarm clusters that operate on the production hardware infrastructure and follow thorough production management, monitoring, audit and other policies.
Operating system selection
You should give careful consideration to the operating system that your Swarm infrastructure relies on. This consideration is vital for production environments.
It is not unusual for a company to use one operating system in development environments, and a different one in production. A common example of this is to use CentOS in development environments, but then to use Red Hat Enterprise Linux (RHEL) in production. This decision is often a balance between cost and support. CentOS Linux can be downloaded and used for free, but commercial support options are few and far between. Whereas RHEL has an associated support and license cost, but comes with world class commercial support from Red Hat.
When choosing the production operating system to use with your swarm clusters, choose one that closely matches what you have used in development and staging environments. Although containers abstract much of the underlying OS, some features have configuration requirements. For example, to use Docker container networking with Docker Engine 1.10 or higher, your host must have a Linux kernel that is version 3.10 or higher. Refer to the change logs to understand the requirements for a particular version of Docker Engine or Swarm.
You should also consider procedures and channels for deploying and potentially patching your production operating systems.
Performance
Performance is critical in environments that support business critical line of business applications. The following sections discuss some technologies and best practices that can help you build high performance swarm clusters.
Container networks
Docker Engine container networks are overlay networks and can be created across multiple Engine hosts. For this reason, a container network requires a key-value (KV) store to maintain network configuration and state. This KV store can be shared in common with the one used by the swarm cluster discovery service. However, for best performance and fault isolation, you should deploy individual KV store instances for container networks and swarm discovery. This is especially so in demanding business critical production environments.
Beginning with Docker Engine 1.9, Docker container networks require specific Linux kernel versions. Higher kernel versions are usually preferred, but carry an increased risk of instability because of the newness of the kernel. Where possible, use a kernel version that is already approved for use in your production environment. If you can not use a 3.10 or higher Linux kernel version for production, you should begin the process of approving a newer kernel as early as possible.
Scheduling strategies
Scheduling strategies are how Swarm decides which nodes in a cluster to start containers on. Swarm supports the following strategies:
- spread
- binpack
- random (not for production use)
You can also write your own.
Spread is the default strategy. It attempts to balance the number of containers evenly across all nodes in the cluster. This is a good choice for high performance clusters, as it spreads container workload across all resources in the cluster. These resources include CPU, RAM, storage, and network bandwidth.
If your swarm nodes are balanced across multiple failure domains, the spread strategy evenly balance containers across those failure domains. However, spread on its own is not aware of the roles of any of those containers, so has no intelligence to spread multiple instances of the same service across failure domains. To achieve this you should use tags and constraints.
The binpack strategy runs as many containers as possible on a node, effectively filling it up, before scheduling containers on the next node.
This means that binpack does not use all cluster resources until the cluster fills up. As a result, applications running on swarm clusters that operate the binpack strategy might not perform as well as those that operate the spread strategy. However, binpack is a good choice for minimizing infrastructure requirements and cost. For example, imagine you have a 10-node cluster where each node has 16 CPUs and 128GB of RAM. However, your container workload across the entire cluster is only using the equivalent of 6 CPUs and 64GB RAM. The spread strategy would balance containers across all nodes in the cluster. However, the binpack strategy would fit all containers on a single node, potentially allowing you turn off the additional nodes and save on cost.
Ownership of Swarm clusters
The question of ownership is vital in production environments. It is therefore vital that you consider and agree on all of the following when planning, documenting, and deploying your production swarm clusters.
- Whose budget does the production swarm infrastructure come out of?
- Who owns the accounts that can administer and manage the production swarm cluster?
- Who is responsible for monitoring the production swarm infrastructure?
- Who is responsible for patching and upgrading the production swarm infrastructure?
- On-call responsibilities and escalation procedures?
The above is not a complete list, and the answers to the questions vary depending on how your organization’s and team’s are structured. Some companies are along way down the DevOps route, while others are not. Whatever situation your company is in, it is important that you factor all of the above into the planning, deployment, and ongoing management of your production swarm clusters.
Related information
- Try Swarm at scale
- Swarm and container networks
- High availability in Docker Swarm
- Docker Data Center


SUSE Manager started as a product based on Spacewalk (as developed by Red Hat as the upstream of their Satellite product). During recent years the SUSE Manager team extended the Spacewalk backend to be based on Salt, the open source configuration management software (as developed by SaltStack Inc.). The SUSE Manager team is steadily making contributions to both of these projects as well as to many other smaller projects.


Hello! Welcome to Diabetastic! It is our goal to help you manage you diabetes. We will help you monitor your stress, sugar intake, and exercise throughout the day to help you understand when and why your peaks are occuring.


The blockquote element is used to present content from another source:
For 50 years, WWF has been protecting the future of nature. The world's leading conservation organization, WWF works in 100 countries and is supported by 1.2 million members in the United States and close to 5 million globally.
The blockquote element is used to present content from another source:
For 50 years, WWF has been protecting the future of nature. The world's leading conservation organization, WWF works in 100 countries and is supported by 1.2 million members in the United States and close to 5 million globally.


Live
A few clips from live performances
LUFF Festival, Lausanne, Switzerland 19/10/17
Digital synthesis, prepared speaker and stylus manipulation, modular synthesiser
Cafe Kino, Bristol 30/09/15
“bounce” synthesis algorithm
prepared speaker & stylus
modular synth
Cafe Kino, Bristol 20/06/14
computer, prepared speaker & stylus
Computer generates saw waves, randomised pulse trains and raw voltage offsets; controls playback of a recording of a discharging 9v battery and routing of fedback from a phono cartridge on the prepared speaker. The speaker and phono cartridge are manipulated, sounds are mixed and distributed between prepared speaker and PA.


Speed Vs Velocity: Understanding the Difference
What makes velocity different from speed?
by Kashyap VyasVelocity is the rate of change of motion in a specific direction. The definition seems very simple, yet this vector quantity is complex in more ways than one.
The equation of average velocity is as follows:
Where,
Δx = position or displacement
Δt = Time interval
The line or sometimes an arrow over ν signifies that this value is a vector.
RELATED: UNDERSTANDING THE PHYSICS OF FIDGET SPINNERS
According to the equation, the value of velocity increases as the time required for displacement decreases. So, we say high velocity when the time required to cross a distance is low.
Likewise, low velocity is a result of more time required to make a displacement.
Displacement or distance
You might have noticed that we use the term displacement with velocity rather than distance. There is a reason why we chose to do so!
Distance is a scalar quantity and is a measure of the magnitude of distance covered. It is just a number that specifies how much an object has traveled from point A to point B.
Now imagine two points, separated by a distance. There are infinite ways to connect them.
The shortest distance would be a single line connecting these two points.
However, you can also connect two points using curved lines. But the distance will be different for every type of line you choose to connect the two points.
We cannot say the same for displacement because it is the measure of how far the second point is from the reference point.
Therefore, displacement has both magnitude and direction, making it a vector quality. When defining velocity, you are defining not just the rate of motion, but also its direction.
Speed vs Velocity
This is another comparison that leaves many scratching their heads. People often use the term velocity and speed interchangeably, but they both have very distinct qualities.
Let us examine the equation of speed.
There’s a striking similarity between the equation of speed and velocity. The equation tells us that speed is the rate of distance covered in a specific time frame.
But here, we are concerned only about the distance. Hence, speed is a scalar quantity that gives us the rate of motion without any information about the direction.
However, when we re-examine velocity, we can see that distance is replaced by displacement. This gives us an idea about the direction of the object.
Therefore, the velocity and speed of an object are both defined differently.
For example, let us imagine that a car travels at 20kmph. If you ask a physicist, he/she would say that the speed of the car is 20kmph but the velocity is unknown.
But if you know that the car is traveling at 20kmph to the North, then the physicist would say that the speed of the car is 20kmph and its velocity is 20kmph in the North direction.
Can the speed and velocity of an object be different?
There is always a comparison drawn between speed and velocity regarding the motion of an object. These two entities are constantly pitted against each other to see which is superior.
But there is no point comparing vector and scalar quantities because they are distinct in their ways.
To give a rough idea, consider an object traveling in a perfect circle at 20 kmph. Since this is a perfect circle and we have defined a constant rate of motion for it, speed and velocity should be the same, right?
Wrong! In this case, the speed of the object is constant, but the velocity is always changing.
This is because we consider direction when defining velocity. So when an object travels in a circle, we find that its direction is always in a state of change.
If we find out the direction of that object at any given point, it will be tangential to the outline edge of that circle.
Instantaneous Velocity and Instantaneous Speed
When we talk about the standard equation, almost always, we are talking about average speed and average velocity. However, sometimes it is necessary to know the velocity or speed at an instant.
The velocity of an object at a specific point in time is known as instantaneous velocity. The general equation for instantaneous velocity is:
The speed of an object at a specific point in time is known as instantaneous speed. The general equation for instantaneous speed is:
Where,
x = Displacement
s = Distance
The most common example of the measurement of instantaneous speed is a speedometer. Speedometers show the speed of an object in a particular instant, not its average value.
Why is velocity important?
Why bother having two equations for the rate of motion with respect to the time when you can simply use speed, which has a simpler equation?
The reason why velocity is of high importance is that, in physics, the direction of an object carries great importance.
Consider an airplane en route to its destination. To get detailed information on the airplane, air control terminals use velocity instead of the speed.
Vectors also play a very important role in helping us determine how the velocity of one object may influence another object. For example, if two objects collide in a nonlinear fashion, understanding their velocity before impact will help us understand their behavior (direction and speed) after impact.
This is not possible if you are using only the speed of the two objects. There are many more examples that you can draw when it comes to the importance of velocity in physics.
RELATED: THE MOST MISUNDERSTOOD THEORIES IN PHYSICS - EXPLAINED
Conclusion
Velocity often gets overlooked because it doesn’t serve major roles outside of physics. However, within the realm of physics, it takes the center stage while speed is regarded with a lesser importance.
Irrespective of how important both these quantities are, understanding the differences between velocity and speed gives us great insight into the different aspects of science and its applications.


Showfields / LUKSTUDIO
- Curated by 韩爽 - HAN Shuang
- about 4 hours
Interior Design · New York, United States
- Interiors DesignersAuthors of this architecture project LUKSTUDIO
- AreaArea of this architecture project 1366.0 m2
- Project YearBrands with products used in this architecture project 2019
- PhotographerCreated with Sketch. Ines Leong of L-INES Photo
- Director Christina Luk
- Design Team Alba Beroiz Blazquez, Ray Ou, Munyee Ng & Edoardo Nieri
- Local Architect Marin Architects
- Lighting Fixtures Expressive Lighting
- Client Showfields
More Specs Less Specs
Text description provided by the architects. Inside a 4-storey heritage brownstone in the popular NoHo neighborhood in New York City, Showfields launches its first location and brings upon a new platform for both brands and customers. The business model aims to streamline the process for online brands going offline and deliver a customer-centric experience: half "show" and half "fields", that requires a more dynamic and adaptive retail environment. Lukstudio was brought onboard to create a flexible infrastructure that adapts to different events and vendors.
On the first floor, white walls form a zigzag gallery that reveals and conceals as one moves along the space, luring the curious minds to explore within. The white enclosure opens to different stores at strategic locations and frames these colourful worlds beyond. The second floor shares the same layout as the first one, except the solid and grid walls are swapped, so the space feels more open at first glance.
On the third floor that is dedicated to fashion, we opt for a parallel wall layout where aligned openings form a corridor and frame a spiral slide at the end. This floor could host catwalk shows where models slide away after walking down the aisle. The last floor is equipped with an open kitchen, a long dining table and a living area. Together with the roof garden, the homey loft provides many possibilities for private events.
The wall partition not only serves as a visual buffer between different immersive stores, but also encases all the audiovisual equipment and wiring, avoiding unsightly clutter. The grid allows each brand to customize its shop easily by simply combining different kit-of-parts ranging from shelving, cabinet and panels that come in different sizes and materiality. The resulting interiors deliver a sense of serendipity with a modular order.
The overall experience in Showfields is reminiscent of walking in a white village and discovering something new at every corner, may it be art exhibitions, interesting boutique shops or a festival event. In the era of new retail, a hybrid project like Showfields presents an opportunity to rethink our social experience in a physical store.
In the era of new retail, a hybrid project like Showfields presents an opportunity to rethink our social experience in a physical store. A meaningful shopping experience is a lot more than picking something, pay and go; it should be an exchange of ideas with new people, a discovery of what’s out there and who we are. Therefore, the physical platform for a successful hybrid space should cater to different social activities where everyone can find a spot of interest.


Nobert's
cafe
TM
Home
Cakes
Wedding cakes
Birthday cakes
Aniversary cakes
Chocolate cakes
Black forest
Snacks
Crisps and chips
sausages
Chinese burger
pizza
Fried chicken
cart
0
Contact us
Item
Price
Quantity
Purchase
Total:
Thankyou for the purchase
:)
×


Class RandomGenerator
Random class
Inheritance
Namespace: TorchSharp
Assembly: TorchSharp.dll
Syntax
public class RandomGenerator : IDisposable
Remarks
Behind the scenes this is the THGenerator API and THRandom combined into one as THRandom are just convenience methods on top of THGenerator.
Constructors
RandomGenerator()
Initializes a new instance of the RandomGenerator class.
Declaration
public RandomGenerator ();
Properties
InitialSeed
Returns the starting seed used.
Declaration
public ulong InitialSeed { get; }
Property Value
| Type | Description |
|---|---|
| System.UInt64 | The initial seed. |
IsValid
Gets a value indicating whether this RandomGenerator is valid.
Declaration
public bool IsValid { get; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
|
Methods
Dispose()
Releases all resource used by the RandomGenerator object.
Declaration
public void Dispose ();
Remarks
Call Dispose when you are finished using the RandomGenerator. This method leaves the RandomGenerator in an unusable state. After calling this method, you must release all references to the RandomGenerator so the garbage collector can reclaim the memory that the RandomGenerator was occupying.
Dispose(Boolean)
Dispose the specified disposing.
Declaration
protected virtual void Dispose (bool disposing);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Boolean | disposing | If set to |
Finalize()
Releases unmanaged resources and performs other cleanup operations before the RandomGenerator is reclaimed by garbage collection.
Declaration
~RandomGenerator ();
InitRandomSeed()
Initializes the random number generator from /dev/urandom or in Windows with the current time.
Declaration
public ulong InitRandomSeed ();
Returns
| Type | Description |
|---|---|
| System.UInt64 | The random seed. |
InitWithSeed(UInt64)
Initializes the random number generator with the given seed.
Declaration
public void InitWithSeed (ulong seed);
Parameters
| Type | Name | Description |
|---|---|---|
| System.UInt64 | seed | Seed. |
NextBernoulliDouble(Double, Single)
Returns true with double probability $p$ and false with probability 1-p (p > 0).
Declaration
public float NextBernoulliDouble (double mean, float p);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Double | mean | To be added. |
| System.Single | p | To be added. |
Returns
| Type | Description |
|---|---|
| System.Single | To be added. |
NextBernoulliDouble(Double, Double)
Returns true with double probability $p$ and false with probability 1-p (p > 0).
Declaration
public double NextBernoulliDouble (double mean, double p);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Double | mean | To be added. |
| System.Double | p | To be added. |
Returns
| Type | Description |
|---|---|
| System.Double | To be added. |
NextCauchyDouble(Double, Double)
Returns a random number from a Cauchy distribution.
Declaration
public double NextCauchyDouble (double median, double sigma);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Double | median | To be added. |
| System.Double | sigma | To be added. |
Returns
| Type | Description |
|---|---|
| System.Double | To be added. |
Remarks
The Cauchy density is $p(x) = sigma/(pi*(sigma^2 + (x-median)^2))$
NextDouble()
Generates a uniform random double on [0,1).
Declaration
public double NextDouble ();
Returns
| Type | Description |
|---|---|
| System.Double | Generates a uniform random double on [0,1). |
NextDouble(Double, Double)
Generates a uniform random double on [a,b).
Declaration
public double NextDouble (double a, double b);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Double | a | To be added. |
| System.Double | b | To be added. |
Returns
| Type | Description |
|---|---|
| System.Double | Generates a uniform random double on [a, b). |
NextExponentialDouble(Double)
Generates a random number from an exponential distribution.
Declaration
public double NextExponentialDouble (double lambda);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Double | lambda | Must be a positive number |
Returns
| Type | Description |
|---|---|
| System.Double | To be added. |
Remarks
The density is $p(x) = lambda * exp(-lambda * x)$, where lambda is a positive number.
NextFloat(Single, Single)
Generates a uniform random float on [a,b).
Declaration
public double NextFloat (float a, float b);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Single | a | To be added. |
| System.Single | b | To be added. |
Returns
| Type | Description |
|---|---|
| System.Double | Generates a uniform random float on [a, b). |
NextGeometricDouble(Double, Double)
Generates a random number from a geometric distribution.
Declaration
public double NextGeometricDouble (double mean, double p);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Double | mean | To be added. |
| System.Double | p | To be added. |
Returns
| Type | Description |
|---|---|
| System.Double | To be added. |
Remarks
It returns an integer i, where p(i) = (1-p) * p^(i-1). p must satisfy $0 < p < 1
NextLogNormalDouble(Double, Double)
Generates a random number from a log-normal distribution.
Declaration
public double NextLogNormalDouble (double mean, double stddev);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Double | mean |
|
| System.Double | stddev | is its standard deviation. |
Returns
| Type | Description |
|---|---|
| System.Double | To be added. |
NextNormalDouble(Double, Double)
Generates a random number from a normal distribution.
Declaration
public double NextNormalDouble (double mean, double stdev);
Parameters
| Type | Name | Description |
|---|---|---|
| System.Double | mean | Mean for the distribution |
| System.Double | stdev | Stanard deviation for the distribution, > 0 |
Returns
| Type | Description |
|---|---|
| System.Double | Generates a number for the normal distribution. |
NextUInt32()
Generates a uniform 32 bits integer.
Declaration
public uint NextUInt32 ();
Returns
| Type | Description |
|---|---|
| System.UInt32 | UInt32 random value. |
NextUInt64()
Generates a uniform 64 bits integer.
Declaration
public ulong NextUInt64 ();
Returns
| Type | Description |
|---|---|
| System.UInt64 | UInt64 random value. |


Q. 41. Can any one be saved through the covenant of works? A. None can be saved through the covenant of works.
Q. 42. Why can none be saved through the covenant of works? A. Because all have broken it, and are condemned by it


Ottessa Moshfegh is a New York Times–bestselling and award winning author known for her satirical prose and unreliable narrators. Her newest book, Death in Her Hands, is centered around an elderly widow who finds herself in the center of an unfolding mystery—or so she thinks.
As a designer at Penguin Random House, I worked with the marketing team at Penguin Press to create materials for a promotional mailing. Leaning into the murder-mystery theme and gothic feel of the book, we created a “make your own murder-mystery kit” akin to Clue or roleplaying games. We provided a note—a replica of the one the protagonist discovers on the first page—as well as a map of the land, and designs of the main suspects.
Jacket designed by Oliver Munday.


| lightning power tools | hosted analytics for lightning network nodes |
| ssss | simple demo for submarine swaps from bitcoin to lightning network |
| riemann-keys | implementation of bip32 HD Wallets using libsecp256k1 for signing, verification, and derivation |
| frankensteinbike | an unholy marraige between scooter and bike |
| lnt | command line tool for lightning network node operators |
| lightninghat | a helmet with fans that spin when lightning payments are made to it |
| hacking-the-maketimeclock | reprogrammed an old wooden cube to work |
| hail | multicoin cyptocurrency wallet |
| pantherbot | a chat bot for slack with dedicated analytics |


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| Swizzle< ST, 0, 1, 0, 0 > | xyxx |
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| Swizzle< ST, 0, 1, 0, 1 > | xyxy |
| |
| Swizzle< ST, 0, 1, 0, 2 > | xyxz |
| |
| Swizzle< ST, 0, 1, 0, 3 > | xyxw |
| |
| Swizzle< ST, 0, 1, 1, 0 > | xyyx |
| |
| Swizzle< ST, 0, 1, 1, 1 > | xyyy |
| |
| Swizzle< ST, 0, 1, 1, 2 > | xyyz |
| |
| Swizzle< ST, 0, 1, 1, 3 > | xyyw |
| |
| Swizzle< ST, 0, 1, 2, 0 > | xyzx |
| |
| Swizzle< ST, 0, 1, 2, 1 > | xyzy |
| |
| Swizzle< ST, 0, 1, 2, 2 > | xyzz |
| |
| Swizzle< ST, 0, 1, 2, 3 > | xyzw |
| |
| Swizzle< ST, 0, 1, 3, 0 > | xywx |
| |
| Swizzle< ST, 0, 1, 3, 1 > | xywy |
| |
| Swizzle< ST, 0, 1, 3, 2 > | xywz |
| |
| Swizzle< ST, 0, 1, 3, 3 > | xyww |
| |
| Swizzle< ST, 0, 2, 0, 0 > | xzxx |
| |
| Swizzle< ST, 0, 2, 0, 1 > | xzxy |
| |
| Swizzle< ST, 0, 2, 0, 2 > | xzxz |
| |
| Swizzle< ST, 0, 2, 0, 3 > | xzxw |
| |
| Swizzle< ST, 0, 2, 1, 0 > | xzyx |
| |
| Swizzle< ST, 0, 2, 1, 1 > | xzyy |
| |
| Swizzle< ST, 0, 2, 1, 2 > | xzyz |
| |
| Swizzle< ST, 0, 2, 1, 3 > | xzyw |
| |
| Swizzle< ST, 0, 2, 2, 0 > | xzzx |
| |
| Swizzle< ST, 0, 2, 2, 1 > | xzzy |
| |
| Swizzle< ST, 0, 2, 2, 2 > | xzzz |
| |
| Swizzle< ST, 0, 2, 2, 3 > | xzzw |
| |
| Swizzle< ST, 0, 2, 3, 0 > | xzwx |
| |
| Swizzle< ST, 0, 2, 3, 1 > | xzwy |
| |
| Swizzle< ST, 0, 2, 3, 2 > | xzwz |
| |
| Swizzle< ST, 0, 2, 3, 3 > | xzww |
| |
| Swizzle< ST, 0, 3, 0, 0 > | xwxx |
| |
| Swizzle< ST, 0, 3, 0, 1 > | xwxy |
| |
| Swizzle< ST, 0, 3, 0, 2 > | xwxz |
| |
| Swizzle< ST, 0, 3, 0, 3 > | xwxw |
| |
| Swizzle< ST, 0, 3, 1, 0 > | xwyx |
| |
| Swizzle< ST, 0, 3, 1, 1 > | xwyy |
| |
| Swizzle< ST, 0, 3, 1, 2 > | xwyz |
| |
| Swizzle< ST, 0, 3, 1, 3 > | xwyw |
| |
| Swizzle< ST, 0, 3, 2, 0 > | xwzx |
| |
| Swizzle< ST, 0, 3, 2, 1 > | xwzy |
| |
| Swizzle< ST, 0, 3, 2, 2 > | xwzz |
| |
| Swizzle< ST, 0, 3, 2, 3 > | xwzw |
| |
| Swizzle< ST, 0, 3, 3, 0 > | xwwx |
| |
| Swizzle< ST, 0, 3, 3, 1 > | xwwy |
| |
| Swizzle< ST, 0, 3, 3, 2 > | xwwz |
| |
| Swizzle< ST, 0, 3, 3, 3 > | xwww |
| |
| Swizzle< ST, 1, 0, 0, 0 > | yxxx |
| |
| Swizzle< ST, 1, 0, 0, 1 > | yxxy |
| |
| Swizzle< ST, 1, 0, 0, 2 > | yxxz |
| |
| Swizzle< ST, 1, 0, 0, 3 > | yxxw |
| |
| Swizzle< ST, 1, 0, 1, 0 > | yxyx |
| |
| Swizzle< ST, 1, 0, 1, 1 > | yxyy |
| |
| Swizzle< ST, 1, 0, 1, 2 > | yxyz |
| |
| Swizzle< ST, 1, 0, 1, 3 > | yxyw |
| |
| Swizzle< ST, 1, 0, 2, 0 > | yxzx |
| |
| Swizzle< ST, 1, 0, 2, 1 > | yxzy |
| |
| Swizzle< ST, 1, 0, 2, 2 > | yxzz |
| |
| Swizzle< ST, 1, 0, 2, 3 > | yxzw |
| |
| Swizzle< ST, 1, 0, 3, 0 > | yxwx |
| |
| Swizzle< ST, 1, 0, 3, 1 > | yxwy |
| |
| Swizzle< ST, 1, 0, 3, 2 > | yxwz |
| |
| Swizzle< ST, 1, 0, 3, 3 > | yxww |
| |
| Swizzle< ST, 1, 1, 0, 0 > | yyxx |
| |
| Swizzle< ST, 1, 1, 0, 1 > | yyxy |
| |
| Swizzle< ST, 1, 1, 0, 2 > | yyxz |
| |
| Swizzle< ST, 1, 1, 0, 3 > | yyxw |
| |
| Swizzle< ST, 1, 1, 1, 0 > | yyyx |
| |
| Swizzle< ST, 1, 1, 1, 1 > | yyyy |
| |
| Swizzle< ST, 1, 1, 1, 2 > | yyyz |
| |
| Swizzle< ST, 1, 1, 1, 3 > | yyyw |
| |
| Swizzle< ST, 1, 1, 2, 0 > | yyzx |
| |
| Swizzle< ST, 1, 1, 2, 1 > | yyzy |
| |
| Swizzle< ST, 1, 1, 2, 2 > | yyzz |
| |
| Swizzle< ST, 1, 1, 2, 3 > | yyzw |
| |
| Swizzle< ST, 1, 1, 3, 0 > | yywx |
| |
| Swizzle< ST, 1, 1, 3, 1 > | yywy |
| |
| Swizzle< ST, 1, 1, 3, 2 > | yywz |
| |
| Swizzle< ST, 1, 1, 3, 3 > | yyww |
| |
| Swizzle< ST, 1, 2, 0, 0 > | yzxx |
| |
| Swizzle< ST, 1, 2, 0, 1 > | yzxy |
| |
| Swizzle< ST, 1, 2, 0, 2 > | yzxz |
| |
| Swizzle< ST, 1, 2, 0, 3 > | yzxw |
| |
| Swizzle< ST, 1, 2, 1, 0 > | yzyx |
| |
| Swizzle< ST, 1, 2, 1, 1 > | yzyy |
| |
| Swizzle< ST, 1, 2, 1, 2 > | yzyz |
| |
| Swizzle< ST, 1, 2, 1, 3 > | yzyw |
| |
| Swizzle< ST, 1, 2, 2, 0 > | yzzx |
| |
| Swizzle< ST, 1, 2, 2, 1 > | yzzy |
| |
| Swizzle< ST, 1, 2, 2, 2 > | yzzz |
| |
| Swizzle< ST, 1, 2, 2, 3 > | yzzw |
| |
| Swizzle< ST, 1, 2, 3, 0 > | yzwx |
| |
| Swizzle< ST, 1, 2, 3, 1 > | yzwy |
| |
| Swizzle< ST, 1, 2, 3, 2 > | yzwz |
| |
| Swizzle< ST, 1, 2, 3, 3 > | yzww |
| |
| Swizzle< ST, 1, 3, 0, 0 > | ywxx |
| |
| Swizzle< ST, 1, 3, 0, 1 > | ywxy |
| |
| Swizzle< ST, 1, 3, 0, 2 > | ywxz |
| |
| Swizzle< ST, 1, 3, 0, 3 > | ywxw |
| |
| Swizzle< ST, 1, 3, 1, 0 > | ywyx |
| |
| Swizzle< ST, 1, 3, 1, 1 > | ywyy |
| |
| Swizzle< ST, 1, 3, 1, 2 > | ywyz |
| |
| Swizzle< ST, 1, 3, 1, 3 > | ywyw |
| |
| Swizzle< ST, 1, 3, 2, 0 > | ywzx |
| |
| Swizzle< ST, 1, 3, 2, 1 > | ywzy |
| |
| Swizzle< ST, 1, 3, 2, 2 > | ywzz |
| |
| Swizzle< ST, 1, 3, 2, 3 > | ywzw |
| |
| Swizzle< ST, 1, 3, 3, 0 > | ywwx |
| |
| Swizzle< ST, 1, 3, 3, 1 > | ywwy |
| |
| Swizzle< ST, 1, 3, 3, 2 > | ywwz |
| |
| Swizzle< ST, 1, 3, 3, 3 > | ywww |
| |
| Swizzle< ST, 2, 0, 0, 0 > | zxxx |
| |
| Swizzle< ST, 2, 0, 0, 1 > | zxxy |
| |
| Swizzle< ST, 2, 0, 0, 2 > | zxxz |
| |
| Swizzle< ST, 2, 0, 0, 3 > | zxxw |
| |
| Swizzle< ST, 2, 0, 1, 0 > | zxyx |
| |
| Swizzle< ST, 2, 0, 1, 1 > | zxyy |
| |
| Swizzle< ST, 2, 0, 1, 2 > | zxyz |
| |
| Swizzle< ST, 2, 0, 1, 3 > | zxyw |
| |
| Swizzle< ST, 2, 0, 2, 0 > | zxzx |
| |
| Swizzle< ST, 2, 0, 2, 1 > | zxzy |
| |
| Swizzle< ST, 2, 0, 2, 2 > | zxzz |
| |
| Swizzle< ST, 2, 0, 2, 3 > | zxzw |
| |
| Swizzle< ST, 2, 0, 3, 0 > | zxwx |
| |
| Swizzle< ST, 2, 0, 3, 1 > | zxwy |
| |
| Swizzle< ST, 2, 0, 3, 2 > | zxwz |
| |
| Swizzle< ST, 2, 0, 3, 3 > | zxww |
| |
| Swizzle< ST, 2, 1, 0, 0 > | zyxx |
| |
| Swizzle< ST, 2, 1, 0, 1 > | zyxy |
| |
| Swizzle< ST, 2, 1, 0, 2 > | zyxz |
| |
| Swizzle< ST, 2, 1, 0, 3 > | zyxw |
| |
| Swizzle< ST, 2, 1, 1, 0 > | zyyx |
| |
| Swizzle< ST, 2, 1, 1, 1 > | zyyy |
| |
| Swizzle< ST, 2, 1, 1, 2 > | zyyz |
| |
| Swizzle< ST, 2, 1, 1, 3 > | zyyw |
| |
| Swizzle< ST, 2, 1, 2, 0 > | zyzx |
| |
| Swizzle< ST, 2, 1, 2, 1 > | zyzy |
| |
| Swizzle< ST, 2, 1, 2, 2 > | zyzz |
| |
| Swizzle< ST, 2, 1, 2, 3 > | zyzw |
| |
| Swizzle< ST, 2, 1, 3, 0 > | zywx |
| |
| Swizzle< ST, 2, 1, 3, 1 > | zywy |
| |
| Swizzle< ST, 2, 1, 3, 2 > | zywz |
| |
| Swizzle< ST, 2, 1, 3, 3 > | zyww |
| |
| Swizzle< ST, 2, 2, 0, 0 > | zzxx |
| |
| Swizzle< ST, 2, 2, 0, 1 > | zzxy |
| |
| Swizzle< ST, 2, 2, 0, 2 > | zzxz |
| |
| Swizzle< ST, 2, 2, 0, 3 > | zzxw |
| |
| Swizzle< ST, 2, 2, 1, 0 > | zzyx |
| |
| Swizzle< ST, 2, 2, 1, 1 > | zzyy |
| |
| Swizzle< ST, 2, 2, 1, 2 > | zzyz |
| |
| Swizzle< ST, 2, 2, 1, 3 > | zzyw |
| |
| Swizzle< ST, 2, 2, 2, 0 > | zzzx |
| |
| Swizzle< ST, 2, 2, 2, 1 > | zzzy |
| |
| Swizzle< ST, 2, 2, 2, 2 > | zzzz |
| |
| Swizzle< ST, 2, 2, 2, 3 > | zzzw |
| |
| Swizzle< ST, 2, 2, 3, 0 > | zzwx |
| |
| Swizzle< ST, 2, 2, 3, 1 > | zzwy |
| |
| Swizzle< ST, 2, 2, 3, 2 > | zzwz |
| |
| Swizzle< ST, 2, 2, 3, 3 > | zzww |
| |
| Swizzle< ST, 2, 3, 0, 0 > | zwxx |
| |
| Swizzle< ST, 2, 3, 0, 1 > | zwxy |
| |
| Swizzle< ST, 2, 3, 0, 2 > | zwxz |
| |
| Swizzle< ST, 2, 3, 0, 3 > | zwxw |
| |
| Swizzle< ST, 2, 3, 1, 0 > | zwyx |
| |
| Swizzle< ST, 2, 3, 1, 1 > | zwyy |
| |
| Swizzle< ST, 2, 3, 1, 2 > | zwyz |
| |
| Swizzle< ST, 2, 3, 1, 3 > | zwyw |
| |
| Swizzle< ST, 2, 3, 2, 0 > | zwzx |
| |
| Swizzle< ST, 2, 3, 2, 1 > | zwzy |
| |
| Swizzle< ST, 2, 3, 2, 2 > | zwzz |
| |
| Swizzle< ST, 2, 3, 2, 3 > | zwzw |
| |
| Swizzle< ST, 2, 3, 3, 0 > | zwwx |
| |
| Swizzle< ST, 2, 3, 3, 1 > | zwwy |
| |
| Swizzle< ST, 2, 3, 3, 2 > | zwwz |
| |
| Swizzle< ST, 2, 3, 3, 3 > | zwww |
| |
| Swizzle< ST, 3, 0, 0, 0 > | wxxx |
| |
| Swizzle< ST, 3, 0, 0, 1 > | wxxy |
| |
| Swizzle< ST, 3, 0, 0, 2 > | wxxz |
| |
| Swizzle< ST, 3, 0, 0, 3 > | wxxw |
| |
| Swizzle< ST, 3, 0, 1, 0 > | wxyx |
| |
| Swizzle< ST, 3, 0, 1, 1 > | wxyy |
| |
| Swizzle< ST, 3, 0, 1, 2 > | wxyz |
| |
| Swizzle< ST, 3, 0, 1, 3 > | wxyw |
| |
| Swizzle< ST, 3, 0, 2, 0 > | wxzx |
| |
| Swizzle< ST, 3, 0, 2, 1 > | wxzy |
| |
| Swizzle< ST, 3, 0, 2, 2 > | wxzz |
| |
| Swizzle< ST, 3, 0, 2, 3 > | wxzw |
| |
| Swizzle< ST, 3, 0, 3, 0 > | wxwx |
| |
| Swizzle< ST, 3, 0, 3, 1 > | wxwy |
| |
| Swizzle< ST, 3, 0, 3, 2 > | wxwz |
| |
| Swizzle< ST, 3, 0, 3, 3 > | wxww |
| |
| Swizzle< ST, 3, 1, 0, 0 > | wyxx |
| |
| Swizzle< ST, 3, 1, 0, 1 > | wyxy |
| |
| Swizzle< ST, 3, 1, 0, 2 > | wyxz |
| |
| Swizzle< ST, 3, 1, 0, 3 > | wyxw |
| |
| Swizzle< ST, 3, 1, 1, 0 > | wyyx |
| |
| Swizzle< ST, 3, 1, 1, 1 > | wyyy |
| |
| Swizzle< ST, 3, 1, 1, 2 > | wyyz |
| |
| Swizzle< ST, 3, 1, 1, 3 > | wyyw |
| |
| Swizzle< ST, 3, 1, 2, 0 > | wyzx |
| |
| Swizzle< ST, 3, 1, 2, 1 > | wyzy |
| |
| Swizzle< ST, 3, 1, 2, 2 > | wyzz |
| |
| Swizzle< ST, 3, 1, 2, 3 > | wyzw |
| |
| Swizzle< ST, 3, 1, 3, 0 > | wywx |
| |
| Swizzle< ST, 3, 1, 3, 1 > | wywy |
| |
| Swizzle< ST, 3, 1, 3, 2 > | wywz |
| |
| Swizzle< ST, 3, 1, 3, 3 > | wyww |
| |
| Swizzle< ST, 3, 2, 0, 0 > | wzxx |
| |
| Swizzle< ST, 3, 2, 0, 1 > | wzxy |
| |
| Swizzle< ST, 3, 2, 0, 2 > | wzxz |
| |
| Swizzle< ST, 3, 2, 0, 3 > | wzxw |
| |
| Swizzle< ST, 3, 2, 1, 0 > | wzyx |
| |
| Swizzle< ST, 3, 2, 1, 1 > | wzyy |
| |
| Swizzle< ST, 3, 2, 1, 2 > | wzyz |
| |
| Swizzle< ST, 3, 2, 1, 3 > | wzyw |
| |
| Swizzle< ST, 3, 2, 2, 0 > | wzzx |
| |
| Swizzle< ST, 3, 2, 2, 1 > | wzzy |
| |
| Swizzle< ST, 3, 2, 2, 2 > | wzzz |
| |
| Swizzle< ST, 3, 2, 2, 3 > | wzzw |
| |
| Swizzle< ST, 3, 2, 3, 0 > | wzwx |
| |
| Swizzle< ST, 3, 2, 3, 1 > | wzwy |
| |
| Swizzle< ST, 3, 2, 3, 2 > | wzwz |
| |
| Swizzle< ST, 3, 2, 3, 3 > | wzww |
| |
| Swizzle< ST, 3, 3, 0, 0 > | wwxx |
| |
| Swizzle< ST, 3, 3, 0, 1 > | wwxy |
| |
| Swizzle< ST, 3, 3, 0, 2 > | wwxz |
| |
| Swizzle< ST, 3, 3, 0, 3 > | wwxw |
| |
| Swizzle< ST, 3, 3, 1, 0 > | wwyx |
| |
| Swizzle< ST, 3, 3, 1, 1 > | wwyy |
| |
| Swizzle< ST, 3, 3, 1, 2 > | wwyz |
| |
| Swizzle< ST, 3, 3, 1, 3 > | wwyw |
| |
| Swizzle< ST, 3, 3, 2, 0 > | wwzx |
| |
| Swizzle< ST, 3, 3, 2, 1 > | wwzy |
| |
| Swizzle< ST, 3, 3, 2, 2 > | wwzz |
| |
| Swizzle< ST, 3, 3, 2, 3 > | wwzw |
| |
| Swizzle< ST, 3, 3, 3, 0 > | wwwx |
| |
| Swizzle< ST, 3, 3, 3, 1 > | wwwy |
| |
| Swizzle< ST, 3, 3, 3, 2 > | wwwz |
| |
| Swizzle< ST, 3, 3, 3, 3 > | wwww |
| |


NATO Kills More Than a Dozen of Marine Mammals During Drills in Germany - Watchdog
Estimates say that over 1,6 mln of tonnes of unexploded World War II ordnance remains on the bottom of the Baltic and the North Seas near Germany, according to environmental watchdog Naturschutzbund (NABU).
According to NABU, the Bundeswehr (German army) detonated 42 WWII mines during its preparations for the August 2019 NATO drills, dubbed "Northern Coasts". Every explosion is reported to have killed every living thing within a radius оf 10-30 meters.
After German forces cleared the maritime areas, it was noted that at least 18 porpoises were found dead. The exact number, however, of killed or injured sea mammals remains unknown and could be higher as these creatures have a lengthy breeding period, NABU stressed in the statement.
Another issue is the unexploded ammunition lying dormant underwater. NABU warns that gradually corroding shells emit dangerous toxic chemicals that poison the environment and are harmful to people, who eat the fish caught from the vast maritime areas.
NABU has urged German authorities to address the urgent issue.


Have a cat? Make sure they are the best dressed on the block with one of our most popular Fancy Cat collars and/or accessories.
Every 1st of the month, we have a giveaway with 10 winners! Winners are annuonced on the 15th, and may choose any 1 Fancy Cat collar of their choice.
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He loves his new bow tie and can't wait until uncle and auntie buys him a another one so he can be the most dapper man on the block.
I usually always remove the bells off from collars because my cats freak out but I didn't have to with this one. It has a very delicate and soft-sounding bell and my kitties don't mind it. Also love that it has a breakaway collar!
He was posing after I put it on him. Fred, per usual, won best dressed and put all the humans to shame. Love love love all of Fancy Cat products!


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If one could conclude as to the nature of the Creator from a study of his creation it would appear that God has a special fondness for stars and beetles. -J. B. S. Haldane


Deployment & GitHub Repository
This app helps you stay organize on your job search.
● HTML
● CSS
● Javascript
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● jquery
● Undraw
● Cryptjs
● Dragula.js
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● Sequelize
Deployment & GitHub Repository
This app helps you stay informed and engaged in politics.
● HTML
● CSS
● Javascript
● Google Civil Information API
● ProPublica API
● Typed.js
● Bulma
● jQuery
● Twitter Developer Tools
● Baloon CSS


This is System.out.
clear it
Add ?j2snocore to URL to see full class list; ?j2sdebug to use uncompressed j2s/core files
get _j2sClassList.txt