text:
The Honourable Judith Seidman
Statements & Speeches
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Room 147, East Block
Senate of Canada
Ottawa, Ontario K1A 0A4
Judith.Seidman@sen.parl.gc.ca
OPED: It's past time to boost federal vaping regulations
By: Sen. Chantal Petitclerc & Sen. Judith G. Seidman
Alarming evidence of the risks associated with the use of e-cigarettes makes the news almost every week.
The harms — especially to young people — are becoming increasingly clear: there is a strong relationship between e-cigarette use and the start of cigarette smoking, and vaping-linked illnesses are sending people to hospital.
Vaping products are relatively new to the Canadian market. Since their arrival in 2007, there has been spirited debate involving health experts, legislators, educators and the public about the potential harms and benefits of these products.
Researchers have come a long way in their understanding of how these products are used. They have potential value in helping smokers quit cigarettes — but we have also seen their use by teenagers rapidly increase.
To address and reduce the harms of tobacco use in Canada, the federal government was pressed to establish a strong regulatory framework that would allow adults access to legal alternatives to tobacco products while keeping harmful products away from underage users.
This was the main intent of the Tobacco and Vaping Products Act, which became law in May 2018. This legislation was designed to give the federal government new powers to regulate e-cigarettes and vaping products as the market for these products expanded rapidly.
As legislators, we looked forward to the government's timely use of these new powers to ensure vaping products were carefully regulated.
The evidence we have today is clear. It compels us to take immediate action to regulate vaping products.
There is a strong relationship between youth vaping and smoking, and the data also show that the long-term effects of high-dose nicotine exposure on the adolescent brain are similar to those of alcohol or cannabis: memory loss and decreased attention span.
Even more alarming, the Public Health Agency of Canada has confirmed 14 cases of severe lung illness related to vaping in Canada, with one life-threatening case linked to flavoured compounds found in the e-liquid.
Time is of the essence. In the 20th century, decades of weak rules for cigarette advertising meant that far too many young people became addicted to smoking, with long-term health consequences that are still playing out today.
We cannot afford to make the same mistakes we made then.
This leaves us with the question: What should be done?
Health Canada is in the process of finalizing regulations on vaping products. This is a perfect opportunity for the government to introduce further safeguards.
Health experts and advocates such as the Canadian Medical Association, the Heart and Stroke Foundation and the Canadian Cancer Society urge that the following measures be adopted:
1. A ban on the sale of flavoured vaping products marketed to youth, as recent evidence suggests that these flavoured products are driving the increase in vaping by teenagers. (Nova Scotia has already announced that flavoured vaping products will be banned as of April 1.)
2. A ban on vaping and e-cigarette advertising in places where young people gather, such as malls and corner stores, and on social media. The rules should match those that already exist for tobacco.
3. Lower the maximum concentration of nicotine found in e-liquids from 66 mg/ml to 20 mg/ml, as recent studies show that high concentrations of nicotine have resulted in higher nicotine dependence levels, especially among teenage users.
Provinces and municipalities across the country are already addressing this growing concern.
Now is the time for the federal government to join them.
Our children's health depends on it.
Senator Chantal Petitclerc is a Paralympic champion and health advocate. Senator Judith G. Seidman is an epidemiologist and health researcher. They served as sponsor and critic of Bill S-5, the Tobacco and Vaping Products Act.
This OPED was published in the Ottawa Citizen on Tuesday, December 17, 2019
Advertising of Vaping Products (Question Period)
2019-2020 Parliamentary Intern Programme (September 2019)
Opinion: Where are the vaping regulations to protect teens from targeted ads?
Health Canada's Clinical Trial Database (Question Period)
Speech and Hearing Month (Statement)
Bill C-81 – Accessible Canada Bill (Report of Committee)
Cannabis Regulations (Question Period)
© The Honourable Judith Seidman All rights reserved. Website designed & built by Robin Jessome


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text:
Europa Universalis 2 Wiki β
A vassal is a country which has assumed a subordinate relationship to a dominant country, called the "suzerain". Historically, being a vassal almost always implied providing military assistance to the suzerain when called, and sometimes the payment of tribute.
In EU2, vassalization is a kind of diplomatic agreement in which the obligations of military assistance and tribute are reversed from history. A vassal always pays tribute from its province taxes, but is not necessarily a part of the suzerain's alliance.
Effects of Vassaldom
Any time a vassalization exists, it has effects on the vassal and on the suzerain. In addition, there are a few effects on suzerains that obtain not based on any specific vassal.
Effects on Vassals
A country which is a vassal has the following limitations:
It cannot enter any war on the suzerain without breaking its vassaldom.
It gives the suzerain one half of its province taxes.
It cannot join any alliance except for the one which its suzerain is in. Note that it can create a new alliance, and it can also be in alliances not containing the suzerain, if the suzerain dishonors the alliance and it does not.
It cannot make any separate peace treaty in any war which its suzerain is also in. This includes force annexation.
Effects on Suzerains of Specific Vassals
The suzerain has these benefits and effects:
It cannot enter any war on the vassal without breaking the vassaldom.
It gains one half of the vassal's province taxes.
It may gain a fraction of the vassal's manpower.
The manpower effect varies with the suzerain's Centralization domestic policy: at max centralization, a country gets no bonus techspeed or manpower from any vassal. At max decentralization, it gets 50% of the vassal´s manpower. Also, the vassal must be on the same continent: vassals on other continents do not give any manpower bonus regardless of centralization.
Effects on Suzerains Unrelated to Specific Vassals
The following effects happen to any country that is a suzerain of one or more vassals:
It cannot be diplomatically vassalized.
It may gain a tiny bonus to techspeed.
It may gain a stability cost reduction up to 20%, depending on its centralization domestic policy, and the number of vassals it has on the its continent. See the article on stability cost for details.
It gets a manpower multiplier on its own manpower.
Due to a bug in EU2 1.09, when a country has at least one vassal (regardless of where the vassal is), the country gains the bonus to the manpower based on its own manpower, as if it were its own vassal. (This is in addition to whatever it gets from vassal(s).) At full decentralization, this gives it in effect a huge +50% manpower bonus.
Gaining a Vassal
There are several ways in which a country can gain a vassal. These are reviewed in this section.
Releasing a Vassal
When a country owns a province which is a "minimum" province of a country defined in revolt.txt, it may be allowed to release the new country as a vassal. A country can release a vassal if these conditions are true:
it owns and controls at least one "minimum" province of the vassal
the vassal does not currently exist
the other conditions encoded in revolt.txt hold: the dates allow the vassal to exist, no other countries exist which disallow it, etc.
When a country releases a vassal, all provinces which it owns and controls which are "minimum" provinces of the vassal are granted to the vassal. (If your country's capital province is granted, your capital will be moved to the lowest numbered province that you still own. If you grant all of your provinces to a vassal, you lose.) The vassal starts out with +200 relations to the suzerain; other aspects of the new country are done according to the normal process of new country creation. The new vassal does not start out allied with you. If you want a newly released vassal in your alliance, you'll have to spend a diplomat to do that.
AI countries never release vassals voluntarily.
There are events which force a country to release a vassal. (The command is called "independence".) There are no random events which do this, and few scripted events, but a few countries have them. For example, the Burgundy inheritance event begins with the freeing of Gelre, Friesen, Kleves, Hanover, Cologne, Lorraine, and the Palinat, if Burgundy owns the appropriate provinces.
Force Vassalization
Forced vassalization (abbreviated FV) is possible in certain conditions when a country offers peace:
It must control the enemy capital
It must have at least 70% in warscore against the enemy.
The enemy must not be someone else's vassal.
If the enemy country accepts a peace proposal with a force vassalization, it becomes the vassal of the country which made the peace (which had to be the one controlling its capital). In addition, it breaks any alliance it is in, and all royal marriages. Relations between the two countries will also rise.
AIs will demand vassalization in war if they have the warscore for it, but this is somewhat unusual since they'll normally make peace long before they get 80+ warscore. Usually AIs only end up vassalizing 2-province countries.
Diplomatic Vassalization
Diplomatic vassalization (abbreviated DV), or "diplovassalization" as players sometimes call it, is a diplomatic action. A country can attempt to diplomatically vassalize a second country only when all of the following conditions hold:
The countries are members of the same alliance.
The countries have a royal marriage.
Relations between the countries are at +190 or higher.
The target country is not currently at war.
The target country is not a suzerain (in this case the "offer vassalization" menu will be active, but the attempt will automatically fail).
Even when all conditions above hold, diplovassalization does not succeed automatically. Chances are (or seem to be) increased for the would-be suzerain by the following conditions:
Having a superior monarch diplomatically.
Having a superior economy (this seems to be done by monthly income, so, try to vassalize in a month in which you doing a lot of looting).
Having a superior military (morale seems to matter, so put your maintenance slider to 100% in the month you try to vassalize).
High stability.
Low badboy value.
If a diplovassalization fails, relations will decline by 30 points. There is no other bad effect, so if you've got a great diplomatic monarch and money, you can keep sending gifts and trying until you succeed.
Ending Vassalization
There are several ways in which a country can cease to be a vassal. They are:
If a suzerain gets annexed. Any vassals it has at the time are freed.
If a suzerain and its vassal enter the state of war against each other.
Either party may choose the "Cancel Vassalization" diplomatic action, so long as its stability is at least 0. AIs will do this seemingly based only on low relations, not size. Thus, for example, if Nippon can vassalize China, it can maintain China as a vassal throughout the game. A force vassalized country cannot cancel the vassalization during the consequent truce.
If the suzerain attempts diplomatic annexation of the vassal, and the attempt is a failure, there is a chance that the vassal will break vassaldom.
The country that cancels the vassalization will get -50 relations to countries it shares state religion with, in addition to -3 stability.
Retrieved from "https://eu2.paradoxwikis.com/index.php?title=Vassal&oldid=1342"
Europa Universalis 2 Wiki
Content is available under Attribution-ShareAlike 3.0 unless otherwise noted.

redpajama_set_name: RedPajamaCommonCrawl


text:
HER BEST MISTAKE
Buy in eBook Now!
PUBLISHER: DONNA MCDONALD
RELEASE DATE (EBOOK): MARCH 4, 2014
Maybe other forty year old divorced women were different, but Lisa Dennison was not the kind of woman to climb into bed with every guy she dated. Actually, she should never even have agreed to date younger man, Eric Roberts at all. She was flattered by the attention, but as a person, Eric never really interested her.
So how can she rationalize sleeping with Eric's Harvard Archeology Professor twin? What she did with Finn was a mistake–a big mistake–even if it doesn't feel like one.
Lisa tapped on the door of the suite. When there was no answer, she almost turned to leave but then reminded herself she was not that kind of person. She had made a mistake and now she had to try to make it right.
Taking the key card from her wallet, the key card that she was happy now she had neglected to turn back in, she slid it through the lock and stepped into the suite.
You just want to see him again<, a voice inside her insisted. You want to see if he's naked and waiting for you.
The first thing she saw inside the room was the object of her thoughts. This time he was stretched out on the sofa, feet hanging over it while sound asleep. But unfortunately he was not naked. Lisa sighed with regret as she closed the door softly behind her.
Then she smelled him again. That damn shower gel or whatever it was.
Her toes curled and arousal hit instantly. Moisture gathered and this time her nipples pressed against the rough lace of bra. She looked down at her breasts in disgust.
Hello, girls. Where the hell have you been hiding for the last few years?
She dropped silently and dejectedly into a chair across from the man she now realized was Finn Roberts. She watched him sleep while she tried to decide what to do.
Finn looked like Eric in many ways, but now she could see there were some significant differences. They were semi-identical twins, Eric had said. Finn was a lot taller, probably three inches.
Then there was the way he stood, the way he spoke, the way he looked at her. The way he was looking at her now.
Shit. I'm busted.
Lisa sighed as his curious gaze met her guilty one.
Maybe, Finn thought, he should be shocked at her boldness in coming into his room uninvited, but instead he was just happy as hell Lisa Dennison had returned. The illogical thought had him smiling.
"Hi," Finn said, surprised at both the excitement and the happiness in his voice.
"Hi," Lisa replied reluctantly, mad that all the butterflies in her stomach lifted into flight at his smiling hello.
They stayed like that for a few minutes, looking at each other in shock. Lisa shook her head again, mostly because she was still at a loss for the right words to explain what had happened.
"So should I apologize?" Finn finally asked, figuring one of them needed to say something.
"I don't know. Have you done something you regret?" Lisa asked, trying to make a joke and failing.
"Well, last night I made love to an incredibly beautiful woman who it turns out is my brother's girlfriend. I guess I should be sorry for that. But seeing you sitting across from me and wanting you all over again, I can't seem to work up the proper guilt," Finn explained.
Well, that was certainly putting the truth out there, Lisa decided, feeling her heartbeat double. The inner voice inside her was doing a happy dance. She ordered it to calm down and grow up.
"I don't know what your brother told you, but one dinner and a chaste kiss goodnight last weekend doesn't make me Eric's girlfriend," Lisa declared, sounding more defensive than she'd meant to be.
She sighed in irritation at herself for her rationalization of her mistake, realizing the same could be said about what she'd done with Finn. Crawling all over him in bed didn't necessarily mean anything either.
But then what did sleeping with any man have to mean anyway? Nothing, Lisa told herself fiercely. It could mean nothing but the need to scratch an old itch. It could be just one of those casual, temporary things. People did that all the time.
Yet no matter how casual she told herself it had been, Lisa still couldn't tear her eyes away as Finn Roberts sat up and looked at her, all his muscles fluidly moving at once. He was certainly graceful for a tall man. And incredibly strong, she remembered, sighing for a different reason as she watched him move around. She shook her head just to make herself stop looking at him.
"I admit that I did think you were Eric at first. You look so much alike, or at least you did to me yesterday. Not so much right now," she co rrected.
She ran a hand through her hair, and then looked at Finn to see his reaction to what she had said. He was staring at her with an intense gaze that made her feel like she was being doubted. Out poured her thoughts without her permission.
"Okay fine. You want honest. Well honestly, after you put your hands on me, I didn't care who you were because I hadn't felt that way, been able to feel that way, in a really, really long time. Shit—maybe I should be the one apologizing to you," Lisa said unhappily, crossing her arms. "Are you happy now?"
Finn thought the woman facing him now might just be the most honest woman he'd ever met. He could like her, he decided swiftly. And since she had told him a hard truth, he told her one in return.
"I've been divorced for two years, and you're the first woman I've been with since my ex-wife," he said flatly, enjoying the shock that came into her face. Her reaction almost made the last two years of abstaining worth it.
Lisa stared. Words dried up in her throat. She shook her head because she truly had trouble believing that this incredibly sexy guy had gone two years without female companionship.
How could she believe such a thing when Finn made her wet and aroused just by looking at her with that gaze that seemed to strip her naked?
She cleared her throat to speak, chagrined when no words came out. She tried again, finally managing a squeaky, nervous explanation of her own situation. "I've been divorced for several years myself, a little longer than you. I haven't really felt any interest in sex for a while."
Then appalled at herself for telling him all her secrets, Lisa leaned back and closed her eyes. "What I mean is that I have dated, but I haven't. . .been with anyone. . .either. I'm sure that was really clear to you last night."
She blushed at her confession. Why was she admitting more to Finn Roberts than she had even told her freaking OB/GYN doctor? The voice inside her jumped to her aid. Shut up, Lisa. Just stop talking.
Finn rubbed his nose and tried to smother his grin. "I tried to go as slow as I could. Did I hurt you?" he asked quietly.
He didn't think he had or Lisa wouldn't be here, but he wanted to see if she would answer him. He wanted to see if she would answer intimate questions just as honestly as she did simple ones.
"Hurt me?" Lisa said. She heard her voice squeaking again as she confronted the serious concern that was reflected in his face. "You're serious, aren't you? Okay—no. No, you didn't hurt me. You were . . ."
She stopped a moment to consider her words. What could she say? How could she describe the experience of being with Finn without gushing about how amazing it had been?
"You were just the right amount of careful," Lisa finally said at last, her need to reassure him winning out over her urge to protect her pride.
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An improved system and method for importing update data in a distributed column chunk data store is provided. A distributed column chunk data store may be provided by multiple storage servers operably coupled to a network. A storage server provided may include a database engine for partitioning a data table into the column chunks for distributing across multiple storage servers, a storage shared memory for storing the column chunks during processing of semantic operations performed on the column chunks, and a storage services manager for striping column chunks of a partitioned data table across multiple storage servers. Any data table may be flexibly partitioned into column chunks using one or more columns with various partitioning methods. Update data may then be incrementally imported as separate column chunks that may later be merged with the column chunks of the partitioned data table.
"Method Using a Hierarchy of Servers for Query Processing of Column Chunks in a Distributed Column Chunk Data Store," U.S. patent application Ser. No. 11/305,958.
"System and Method for Compression in a Distributed Column Chunk Data Store," U.S. patent application Ser. No. 11/226,668.
The invention relates generally to computer systems, and more particularly to an improved system and method for updating data in a distributed column chunk data store.
Distributed storage systems implemented either as a distributed database or a distributed file system fail to scale well for data mining and business intelligence applications that may require fast and efficient retrieval and processing of large volumes of data. Distributed databases for large volumes of data, perhaps on the order of terabytes, may be traditionally implemented across several servers, each designed to host a portion of a database and typically storing a particular table of data. In some implementations, such a system may also store a horizontally partitioned table of data on one or more servers. For instance, the technique known as horizontal partitioning may be used to store a subset of rows of data in a table resident on a storage server. Queries for retrieving data from the distributed storage system may then be processed by retrieving rows of data having many associated columns of datum for which only one or few columns may be needed to process the query. As a result, the storage and retrieval of data in these types of systems is inefficient, and consequently such systems do not scale well for handling terabytes of data.
Typical transaction processing systems using a distributed database likewise fail to scale well for data mining and business intelligence applications. Such systems may characteristically have slower processing speed during a failed transaction. During transaction processing a failed transaction may become abandoned and the database may be rolled back to a state prior to the failed transaction. Such database implementations prove inefficient for updating large data sets on the order of gigabytes or terabytes.
What is needed is a way for providing data storage, query processing and retrieval for large volumes of data perhaps in the order of hundreds of terabytes for data warehousing, data mining and business intelligence applications. Any such system and method should allow the use of common storage components without requiring expensive fault-tolerant equipment.
Briefly, the present invention may provide a system and method for updating data into a distributed column chunk data store. A distributed column chunk data store may be provided by multiple storage servers operably coupled to a network. A client executing an application may also be operably coupled to the network. A storage server provided may include a database engine for partitioning a data table into column chunks for distributing across multiple storage servers, a storage shared memory for storing the column chunks during processing of semantic operations performed on the column chunks, and a storage services manager for striping column chunks of a partitioned data table across multiple storage servers.
The invention may partition any type of data table into column chunks for distributing across multiple storage servers. Update data for a partitioned data table may then be imported into the distributed column chunk data store. To do so, update data for the partitioned data table may be incrementally stored as one or more column chunks separate from the column chunks of the partitioned data table. As additional update data to the partitioned data table may be incrementally imported into the distributed column chunk store, a column chunk of update data imported previously may be merged with a column chunk of update data being imported. Subsequently, such a merged column chunk of update data may be split into multiple column chunks using fewer partitions than the column chunks of the partitioned data table. If the cost of retrieving the column chunks of update data may exceed the cost of merging the column chunks of update data with the column chunks of the partitioned data table, then the column chunks of update data may be merged with the column chunks of the partitioned data table.
FIG. 9 is a flowchart generally representing the steps undertaken in an embodiment for merging column chunks of update data with the column chunks of the partitioned data table, in accordance with an aspect of the present invention.
The present invention is generally directed towards a system and method for importing update data into a distributed column chunk data store. More particularly, the present invention provides multiple storage servers operably coupled by a network for storing distributed column chunks of partitioned data tables. Any data table may be partitioned into column chunks and the column chunks may then be distributed for storage among multiple storage servers. To do so, a data table may be flexibly partitioned into column chunks by applying various partitioning methods using one or more columns as a key, including range partitioning, list partitioning, hash partitioning, and/or combinations of these partitioning methods. Update data for a partitioned data table may then be incrementally imported into the distributed column chunk data store by storing one or more column chunks of update data separate from the column chunks of the partitioned data table.
When additional update data to the partitioned data table may be incrementally imported into the distributed column chunk store, a column chunk of update data imported previously may be merged with a column chunk of update data being imported. As will be seen, if the cost of retrieving the column chunks of update data may exceed the cost of merging the column chunks of update data with the column chunks of the partitioned data table, then the column chunks of update data may be merged with the column chunks of the partitioned data table. As will be understood, the various block diagrams, flow charts and scenarios described herein are only examples, and there are many other scenarios to which the present invention will apply.
The database engine 208 may be responsible, in general, for communicating with an application 202, communicating with the storage server to satisfy client requests, accessing the column chunk data store, and communicating with the storage services manager 226 for execution of storage operations, including accessing column chunks 224 in storage shared memory 220. The database engine 208 may include load services 210, query services 212, metadata services 214, transaction services 216 and a storage services proxy 218. Load services 210 may be used for importing data into the data tables. Query services 212 may process received queries by retrieving the data from the storage services manager 226 and processing the retrieved data. The load services 210 and query services 212 may communicate with the metadata services 214 and transaction services 216 using a communication mechanism such as inter-process communication. Each of these services may in turn communicate with the storage services proxy 218 to request services such as retrieving and loading column chunks into storage shared memory 220. The storage services proxy 218 may receive storage read and write requests and pass the requests off to the storage services manager 226 to execute the request.
The metadata services 214 may provide services for the configuration of the storage servers and may manage metadata for the database engine and the column chunk data store. The metadata may include, for example, data tables that reflect the current state of the system including the name of each server configured in the system, the load on each server, the bandwidth between servers, and many other variables maintained in the data tables. There may be dynamically updated tables and static tables of data. Static tables of data may include configuration tables, the defined logical tables, policies that may apply for partitioning the data table and storage distribution, and so forth. Some tables, such as configuration tables, may be generated dynamically by the system based upon system configuration. The metadata services 214 may include services to dynamically update metadata, such as configuration tables. In addition, metadata services 214 may include services to add or update fixed metadata such as adding new logical data table definitions or updating an existing logical data table definition.
The transaction services 216 may be responsible for maintaining active transactions in the system and may provide various services such as identifying and loading the appropriate version of column chunks. The transaction services 216 can also notify metadata services to update or commit metadata relating to a specific transaction. Generally, a transaction may include semantic operations that modify the system or that may be performed on data, including data loading, data optimization, data retrieval, updating existing data table, creating new tables, modifying the data schema, creating a new storage policy, partitioning data tables, recording the column chunk distribution in storage servers, and so forth. For each transaction such as incrementally updating a data table, there may be an indication of a start of a transaction and end of transaction when the update of the data table completes. Other examples of transactions may be executing a query, including generating intermediate data tables or other data tables, or optimizing storage of column chunks. To do so, the query services may use transaction services to process a query and the storage services manager may use transactions services while optimizing column chunk storage.
The storage shared memory 220 of the storage server 206 may include low level metadata 222 and column chunks 224. The low level metadata may include information about physical storage, such as the file name and server name where a column chunk may be located, what the compressed size of a column chunk may be, what the uncompressed size of a column chunk may be, what the checksum on a column chunk may be for verifying that the column chunk is not corrupted on the disk storage, and so forth. The storage services manager 226 may generate low level metadata 222 by using the metadata such as policies, server configurations, resources available in metadata to generate physical storage for column chunks.
Once the data table may be partitioned into column chunks, the storage server may distribute the column chunks among multiple storage servers at step 304. For example, the column chunks of the data table may be striped across multiple storage servers. In one embodiment, each column chunk of the data table may be assigned to an available storage server using any assignment method including round robin order. In various embodiments, column chunks of a data table may be striped across multiple storage servers. As used herein, column chunk striping means striping column chunks of a data table across multiple storage servers. Any level of redundancy may be implemented in distributing the column chunks for recovery of one or more failed servers. For example, column chunk parity may be calculated and stored to enable recovery from failure of one server. In an embodiment, a bitwise XOR operation may be performed on two column chunks to create a parity column chunk. Additional bitwise XOR operations may be performed with a parity column chunk and another binary representation of a column chunk to compute a parity column chunk for three column chunks. The resulting parity column chunk may then be assigned to an available server that does not store one of the three column chunks used to make the parity column chunk. In this way, any number of parity column chunks may be calculated and assigned to storage servers for recovery from failure of one or more storage servers. It should be noted that prior to performing a bitwise XOR operation on two column chunks of unequal length, the shorter column chunk may be padded with 0's until it become of equal length with the other column chunk.
Any policy for partitioning the data table may then be applied at step 404 to create the column chunks. In an embodiment, partitioning may be performed on the data table by first partitioning the data table into multiple tables using range partitioning and then partitioning each of the multiple tables by applying column-wise partitioning. In various other embodiments, list partitioning, hash partitioning, or combinations of list, hash, and/or range partitioning may be applied to partition the data table into multiple tables and then column wise partitioning may be subsequently applied to each of the multiple data tables.
Once data tables may be partitioned, distributed and stored in the column chunks data store, data updating the partitioned data tables may be incrementally imported into the column chunks data store. FIG. 5 presents a flowchart generally representing the steps undertaken in one embodiment for importing update data to the column chunk data store. At step 502, one or more column chunks may be created for the update data. Depending upon the size of the update data, any number of partitions for creating one or more column chunks for the update data may be determined. For example, one column chunk may be created for the update data; several column chunks may be created for the update data; or the same number of column chunks may be created as the number created for the partitioned data table. As additional update data to the partitioned data table may be incrementally imported into the distributed column chunk store, a column chunk of update data imported previously may be merged with a column chunk of update data being imported, if, for example, the cost of retrieving the column chunks of update data may exceed the cost of merging the column chunks of update data.
Accordingly, the column chunks created for the update data may be then be merged at step 504. In an embodiment, one or more column chunks of update data may be merged with one or more column chunks from a previous data update. After several such merges of column chunks of update data, the size of the column chunks of update data may grow large enough that the input/output cost of transferring the column chunks between storage servers in the column chunk data store may exceed the cost of splitting the column chunks and using those multiple column chunks. When this may occur, the merged column chunks may be split into multiple column chunks in various embodiments. In various other embodiments, one or more column chunks of update data may be merged with the column chunks of the partitioned data table, if, for example, the cost of retrieving the column chunks of update data may exceed the cost of merging the column chunks of update data with the column chunks of the partitioned data table. After merging, the merged column chunks may be stored at step 506 across multiple storage servers. Once the column chunks have been stored, processing may be finished for importing update data to the column chunk data store.
FIG. 6 presents a flowchart generally representing the steps undertaken in one embodiment for determining the number of partitions for creating one or more column chunks for the update data. The size of the data update for the partitioned data table may be determined at step 602. In an embodiment, this may be determined by multiplying the size of a row of data in the data table by the number of rows in the data update. It may then be determined at step 604 whether the size of the data update exceeds a predefined threshold. In an embodiment, the predefined threshold may be 20% of the data stored in the column chunks of the partitioned data table. If the size of the data update may not exceed a predefined threshold, then column chunks may be created for the update data at step 606 using a fewer number of partitions than the partitioned data table. In an embodiment, this may be one column chunk. In another embodiment, this may be one-tenth of the number of partitions used for the partitioned data table. In either embodiment, the partition numbers created may be associated with the group of update column chunks so that whenever column chunks of the partitioned data table are retrieved from the column chunk data store, any associated incremental column chunks of update data may be identified and also retrieved.
Returning to step 604 of FIG. 6, if it may be determined that the size of the data update may exceed a predefined threshold, then column chunks may be created for the update data at step 608 using the number of partitions created for the partitioned data table. For instance, there may be many business intelligence and data mining applications where a very large data table may be imported daily that may use the same number of partitions created for the partitioned data table. After creating the column chunks for the update data using the determined number of partitions, processing may be finished for determining the number of partitions for creating one or more column chunks for the update data.
Where the size of the data update may not exceed a predefined threshold, FIG. 7 presents a flowchart generally representing the steps undertaken in an embodiment for creating column chunks for the update data using a fewer number of partitions than the partitioned data table. At step 702, the number of partitions may be determined for creating column chunks for the update data. Where the update data may be relatively small, one data chunk may be created for the update data in an embodiment. In this case, the column chunk partitions may be determined and stored as part of the data chunk. In another embodiment, the number of partitions for the update data may be a percentage of number of column chunks of the partitioned data table that may be determined by the ratio of the number of rows of update data compared to the average number of rows in a partition of the partitioned data table. For example, if the number of rows of update data may be 25% of the average number of rows in a column chunk of a partitioned data table, then the number of partitions determined for the update data may be 25% of the number of column chunks for the partitioned data table. In another exemplary method the column chunks may be created based on the usage pattern of columns. For example, there may be a few columns of the partitioned data table that are frequently retrieved together for queries and column chunks for update data may be created that include such data in columns frequently retrieved together.
After determining the number of partitions for creating column chunks for the update data, the update data may be partitioned into column chunks at step 704. Typically, the update data may be partitioned into column chunks by using the same method used to create the column chunks of the partitioned data table. For instance, if a data table was partitioned into column chunks using range partitioning and then hash partitioning, then the update data may likewise be partitioned using range partitioning and then hash partitioning. For example, the update data table may first be partitioned in one embodiment by using a date, taken from a column of data that represents a date in the update data, as a key for partitioning the update data. Once the update data has been partitioned by date, then the columns of each partition of update data may then be partitioned by hashing into column chunks. A column key may be chosen to generate a hash and use the resulting hash to partition columns into column chunks. Any hashing may be used such as Message Digest Algorithm 5 ("MD5"), R. J. Cichelli's perfect hash function generation algorithm, or other hashing technique.
Although the update data may typically be partitioned into column chunks by using the same method used to create the column chunks of the partitioned data table, those skilled in the art will appreciate that another method may be used to partition the update data into column chunks. In one embodiment, the update data may be partitioned into column chunks by performing column-wise partitioning whereby a partition may be specified by a set of columns. In another embodiment, a combination of some partitioning technique and column-wise partitioning may be performed. In this embodiment, the update data may be first partitioned into several initial partitions and then column-wise partitioning may be performed on the initial partitions to create column chunks. To do so, any number of partitioning techniques may be used such as range partitioning by specifying a range of values for a partitioning key, list partitioning by specifying a list of values for a partitioning key, hash partitioning by applying hashing to a partitioning key, combinations of these partitioning techniques, and other partitioning techniques known to those skilled in the art.
Once the update data may be partitioned into column chunks, the partition numbers for each column chunk may be then stored at step 706 in a column chunk, so that the partition number for an update column chunk may be looked up when that update column chunk may need to be used. Upon storing the partition numbers in a column chunk, the column chunks may be distributed among multiple storage servers at step 708. Where there may be fewer partitions, or even one data chunk of update data, data mirroring may be used in an embodiment to provide redundancy for the column chunks of update data. In this case, a copy of each column chunk of update data may be assigned to an available storage server that does not store the respective column chunk of update data from which the copy was made. In various embodiments, a storage policy may also be provided that may specify the level of redundancy desired for the update column chunks. If so, then parity of the column chunks may be calculated for the level of redundancy specified in the storage policy and the parity column chunks may be assigned to available storage servers. After the distribution of column chunks of update data among the multiple storage servers may be determined, the column chunks of update data may be stored on their assigned servers at step 710 and processing may be finished for creating column chunks for the update data using a fewer number of partitions than the partitioned data table.
FIG. 8 presents a flowchart generally representing the steps undertaken in an embodiment for merging column chunks created for the update data. As one or more column chunks may be created during an incremental update of data to the partitioned data table, the column chunks of update data may be merged at step 802 with one or more column chunks of update data previously imported. The column chunks of update data previously imported may have also been previously merged with other column chunks of update data imported during an incremental update of the partitioned data table. At some point, the size of the column chunks of update data may grow large enough that the input/output cost of transferring the column chunks between storage servers in the column chunk data store may exceed the cost of splitting the column chunks. Accordingly, it may then be determined at step 804 whether to split the merged column chunks of update data. In an embodiment, the merged column chunks of update data may be split if the size of a merged column chunk exceeds a predefined size. If it may be determined to split the merged column chunks of update data, then the merged column chunks may be split at step 806. In an embodiment, the merged column chunks may be split using the steps presented in FIG. 7 for creating column chunks for the update data using a fewer number of partitions than the partitioned data table.
It may then be determined at step 808 whether to merge the column chunks created from incremental updates of data with the column chunks of the partitioned data table. If so, then the column chunks created from incremental updates of data may be merged with the column chunks of the partitioned data table at step 810. In an embodiment, if the cost of retrieving the column chunks of update data may exceed the cost of merging the column chunks of update data with the column chunks of the partitioned data table, then the column chunks of update data may be merged with the column chunks of the partitioned data table. After merging the column chunks of update data with the column chunks of the partitioned data table, processing may be finished for merging column chunks created for the update data.
FIG. 9 presents a flowchart generally representing the steps undertaken in an embodiment for merging column chunks of update data with the column chunks of the partitioned data table. At step 902, the column chunks of the partitioned data table may be identified for applying changes of each row of update data. In an embodiment, the column chunks for applying the update data may be identified by the key for each row of update data stored in the column chunks of update data. Next, each column chunk of the partitioned data table identified for applying changes of the update data may be decompressed at step 904. Then the changes for each row of update data may be applied to the respective column chunk identified by the key determined from that row of update data. The changes may include adding a row of update data to the partitioned data table, modifying a row of data in the partitioned data table, or deleting a row of data in the partitioned data table.
Upon applying the changes to the column chunks of the partitioned data table, then data domain compression may be applied to the column chunks at step 908. Finally, the column chunks may be stored at step 910 among the multiple storage servers. In an embodiment, the column chunks may use the same assignment made to storage servers when the data table was partitioned and the column chunks were initially distributed. After the column chunks may be stored on their assigned servers, processing may be finished for merging column chunks of update data with the column chunks of the partitioned data table.
Thus the present invention may advantageously support importing incremental update data to column chunks of a partitioned data table. As additional update data may be incrementally imported into the distributed column chunk store, a column chunk of update data may be merged with a column chunk of update data previously imported until the column chunk grows to a size where the costs of retrieving the column chunks exceeds the cost of splitting the column chunk. Then the column chunk of update data may be split. When the number of column chunks of update data increases to a point where the costs of retrieving the column chunks of update data exceeds the cost of merging the column chunks of update data with the column chunks of the partitioned table, then the column chunks of update data may be merged with the column chunks of the partitioned data table. Advantageously, merging of column chunks may be done during system idle time or as part of garbage collection. By supporting such incremental updating of data to the column chunk data store, update data for a data table may be efficiently imported and retrieved for use whenever the data for the respective data table may be retrieved.
As can be seen from the foregoing detailed description, the present invention provides an improved system and method for importing update data into a distributed column chunk data store. Any data table may be flexibly partitioned into column chunks by applying various partitioning methods using one or more columns as a key, including range partitioning, list partitioning, hash partitioning, and/or combinations of these partitioning methods. Furthermore, domain specific compression may be applied to a column chunk to reduce storage requirements of column chunks and decrease transmission delays for transferring column chunks between storage servers. Update data may then be incrementally imported as separate column chunks that may later be merged with the column chunks of the partitioned data table. Such a system and method support storing detailed data needed by data mining, segmentation and business intelligence applications over long periods of time. As a result, the system and method provide significant advantages and benefits needed in contemporary computing, and more particularly in data mining and business intelligence applications.
storing the at least one column chunk of the update data to the partitioned data table on the one or more storage servers.
2. The method of claim 1 wherein creating at least one additional column chunk for importing update data to the partitioned data table comprises determining a partition number associated with the at least one additional column chunk.
3. The method of claim 1 wherein creating at least one additional column chunk for importing update data to the partitioned data table comprises partitioning the update data into column chunks.
4. The method of claim 3 wherein partitioning the update data into column chunks comprises using the same number of partitions as the partitioned data table.
5. The method of claim 3 wherein partitioning the update data into column chunks comprises using fewer partitions than the partitioned data table.
6. The method of claim 3 further comprising creating a column chunk for storing the partition numbers keys from partitioning the update data into column chunks.
7. The method of claim 3 further comprising distributing the column chunks from partitioning the update data among the one or more storage servers.
8. The method of claim 1 further comprising merging the at least one column chunk of the update data with another column chunk of update data to the partitioned data table.
9. The method of claim 8 further comprising determining whether to partition the merged column chunks of update data.
10. The method of claim 9 further comprising partitioning the merged column chunks of update data.
11. The method of claim 10 further comprising determining whether to merge the merged column chunks of update data with the column chunks of the partitioned data table.
12. The method of claim 11 further comprising merging the merged column chunks of update data with the column chunks of the partitioned data table.
storing the column chunks of the partitioned data table with the changes applied on the one or more storage servers.
storing the at least one column chunk with the changes applied on the one or more storage servers.
16. The method of claim 15 wherein applying changes to at least one column chunk for importing update data to the partitioned data table comprises determining a key associated with a row of update data for the partitioned data table.
17. The method of claim 15 wherein applying changes to at least one column chunk for importing update data to the partitioned data table comprises decompressing at least one column chunk of the partitioned data table that stores data indexed by the key associated with the row of update data for applying changes indicated in the row of the update data.
18. The method of claim 17 further comprising applying data domain compression to the at least one column chunk of the partitioned data table after applying changes in the row of the update data.
19. A computer-readable medium having computer-executable instructions for performing the method of claim 15.
storing the column chunks with the applied update data on the one or more storage servers.
Eda Baykan, Recent Research on Database System Performance, Jun. 28, 2005, published on Internet at www.csd.uoc.gr/~hy460/0506-fall/instructional-material.html pp. 1-9.
George Copeland, Setreg Khoshafian, a Decomposition Storage Model. ACM SIGMOD International Conference on Management of Data, pp. 268-279, ACM Press, 1985.
Jingren Zhou, Kenneth A. Ross, A Multi-resolution Block Storage Model for Database Design, ideas, p. 22, Seventh International Database Engineering and Applications Symposium pp. 22-33, 2003, IEEE.


text:
Get Beginning Theory: An Introduction to Literary and Cultural Theory from Amazon.com
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Theory before theory - liberal humanism
Post-structuralism and deconstruction
Psychoanalytic criticism
Feminist criticism
Lesbian/gay criticism
Marxist criticism
New historicism and cultural materialism
Postcolonial criticism
Ecocriticism
Beginning Theory: An Introduction to Literary and Cultural Theory - Postmodernism Summary & Analysis
Peter Barry
This Study Guide consists of approximately 32 pages of chapter summaries, quotes, character analysis, themes, and more - everything you need to sharpen your knowledge of Beginning Theory.
Postmodernism Summary and Analysis
Modernism dominates art and culture through the first part of the twentieth century until postmodernism develops in the 1980s. Modernism originates in Vienna to counter the structuralism in painting, music, literature and architecture and spreads to all of Europe and England. Modernist trends appear in painting styles like Cubism and Surrealism. A key element of modernism rejects fundamentals for abstraction. For example, the use of pitched roofs, wood and domes is replaced architecturally with simple geometrical forms of glass and concrete. Writers like T.S. Eliot, James Joyce and Virginia Woolf represent modernism in literature from 1910 to 1930. Five features of modernist writing includes an emphasis on how rather than what is seen, trends away from objectivity, the tendency towards mixing poetry and prose in a work, and the fragmentary forms and reflexivity about nature, status and role of poems, drama and...
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Any gig in Glasgow is worth filming cause we're the best fans in the world....and most be true cause there's a sign at the SSE Hydro above the door that leads to the stage that says "Time to meet the best fans in the world"
About this DVD live in Berlin to be honest I expected more ! Her Barrowlands Shows in december deserve more to be released as her first live album !
Here is my collection so far.
Today I picked up a free magazine called I-on Amy is on the front cover and inside is a interview with her.
I wonder what the 2 other Cds look like (booklet, CD etc.).
I'm so jealous about the people who have Life In A Beuatiful Light Box Set.
I only have her 4 standard albums.
Oh nice collection and pic !
"This Is What Happiness Means To Me..."
Beautiful collection Anne ! Congratulations !
wow i want to see all!
including a signed Fender-Acoustic-Guitar, a few live DVDs from germany, 2 Fan-Shop Posters, 4 magazine-Posters & 1 Tourposter.
Waouh ! you finally managed to put them !
Beautiful collection Daphne ! Thank you very much for sharing !!!
I must post my own collection too !
Yes I have this version ! The live at thez barrowlands is so great !
Whaou ! Congrats Markus !


Amazon made good on their promise to launch a content delivery network by the end of the year this morning with the release of CloudFront.
Pricing in the U.S. and Europe starts at $.17/GB transferred, ranging down to $.09/GB for transfers over 150 TB/month. They're somewhat higher in Asia. This isn't the lowest CDN pricing out there by far, but it's pay for usage only, giving smaller players a price advantage over large competitors like Akamai. And the service promises to work seamlessly with Amazon S3 and their other web services.
Amazon CTO Werner Vogels has more.


About 8 or 9 years ago I did a series of photographs of old houses and barns from the south, mostly from North Carolina. It was called Southern Comfort. Living in Barcelona allows me to really photograph some great new and old architecture but I still have a love for the back roads of the south.
On my current visit to the US I am trying to expand on Southern Comfort to include some abandoned treasures and few buildings from small towns. After taking the picture of Harrell's yesterday I realized that these small town streets are also a very important part of the old south and must be included.
Edwin where are you photographing? In NC?
I just love this picture, so I had to share it.


Land and attached structures. Interest in real estate can be protected through various insurance policies.
What is a qualifying ratio? What is an annuity? How to close a real estate deal without an agent What is a housing ratio? What is a negative amortization?


text:
Outdoor & Camping Gear Reviews
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text:
Rethinking the diagnosis of mental disorders: data-driven psychological dimensions, not categories, as a framework for mental-health research, treatment, and training
Christopher C. Conway*, Robert F. Krueger, HiTOP Consortium Executive Board
Generations of psychologists have been taught that mental disorder can be carved into discrete categories, each qualitatively different from the others and from normality. This model is now outdated. A preponderance of evidence indicates that (a) individual differences in mental health (health vs. illness) are a matter of degree, not kind, and (b) broad mental-health conditions (e.g., internalizing) account for the tendency of narrower ones (e.g., depression, social anxiety, panic) to co-occur. With these observations in mind, we discuss an alternative diagnostic system, called the Hierarchical Taxonomy of Psychopathology (HiTOP), that describes the broad and specific components of mental disorder. It deconstructs traditional diagnostic categories, such as those listed in the Diagnostic and Statistical Manual of Mental Disorders, and recasts them in terms of profiles of dimensions. Recent findings support the utility of this approach for mental-health research and intervention efforts. HiTOP has the potential to put mental-health research, training, and treatment on a much sounder scientific footing.
Current Directions in Psychological Science
Hierarchical Taxonomy of Psychopathology (HiTOP)
Dive into the research topics of 'Rethinking the diagnosis of mental disorders: data-driven psychological dimensions, not categories, as a framework for mental-health research, treatment, and training'. Together they form a unique fingerprint.
Mental Disorders Medicine & Life Sciences 100%
Mental Health Medicine & Life Sciences 84%
Psychopathology Medicine & Life Sciences 52%
Panic Medicine & Life Sciences 33%
Individuality Medicine & Life Sciences 25%
Diagnostic and Statistical Manual of Mental Disorders Medicine & Life Sciences 24%
Anxiety Medicine & Life Sciences 19%
Conway, C. C., Krueger, R. F., & HiTOP Consortium Executive Board (2021). Rethinking the diagnosis of mental disorders: data-driven psychological dimensions, not categories, as a framework for mental-health research, treatment, and training. Current Directions in Psychological Science, 30(2), 151-158. https://doi.org/10.1177/0963721421990353
Conway, Christopher C. ; Krueger, Robert F. ; HiTOP Consortium Executive Board. / Rethinking the diagnosis of mental disorders : data-driven psychological dimensions, not categories, as a framework for mental-health research, treatment, and training. In: Current Directions in Psychological Science. 2021 ; Vol. 30, No. 2. pp. 151-158.
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title = "Rethinking the diagnosis of mental disorders: data-driven psychological dimensions, not categories, as a framework for mental-health research, treatment, and training",
abstract = "Generations of psychologists have been taught that mental disorder can be carved into discrete categories, each qualitatively different from the others and from normality. This model is now outdated. A preponderance of evidence indicates that (a) individual differences in mental health (health vs. illness) are a matter of degree, not kind, and (b) broad mental-health conditions (e.g., internalizing) account for the tendency of narrower ones (e.g., depression, social anxiety, panic) to co-occur. With these observations in mind, we discuss an alternative diagnostic system, called the Hierarchical Taxonomy of Psychopathology (HiTOP), that describes the broad and specific components of mental disorder. It deconstructs traditional diagnostic categories, such as those listed in the Diagnostic and Statistical Manual of Mental Disorders, and recasts them in terms of profiles of dimensions. Recent findings support the utility of this approach for mental-health research and intervention efforts. HiTOP has the potential to put mental-health research, training, and treatment on a much sounder scientific footing.",
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Conway, CC, Krueger, RF & HiTOP Consortium Executive Board 2021, 'Rethinking the diagnosis of mental disorders: data-driven psychological dimensions, not categories, as a framework for mental-health research, treatment, and training', Current Directions in Psychological Science, vol. 30, no. 2, pp. 151-158. https://doi.org/10.1177/0963721421990353
Rethinking the diagnosis of mental disorders : data-driven psychological dimensions, not categories, as a framework for mental-health research, treatment, and training. / Conway, Christopher C.; Krueger, Robert F.; HiTOP Consortium Executive Board.
In: Current Directions in Psychological Science, Vol. 30, No. 2, 04.2021, p. 151-158.
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AU - Forbes, Miriam K.
AU - Hallquist, Michael N.
AU - Kotov, Roman
AU - Latzman, Robert D.
AU - Ruggero, Camilo J.
AU - Simms, Leonard J.
AU - Waldman, Irwin D.
AU - Waszczuk, Monika A.
AU - Watson, David
AU - Widiger, Thomas A.
AU - Wright, Aidan G. C.
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JO - Current Directions in Psychological Science
JF - Current Directions in Psychological Science
Conway CC, Krueger RF, HiTOP Consortium Executive Board. Rethinking the diagnosis of mental disorders: data-driven psychological dimensions, not categories, as a framework for mental-health research, treatment, and training. Current Directions in Psychological Science. 2021 Apr;30(2):151-158. https://doi.org/10.1177/0963721421990353


Zebras, horses, and other herd animals frighten Donald Trump! Trump also fears germs (mysophobia) but usually limits his Mal-adaptive behavior to avoiding handshakes (see cell phones below).
Equinophobia or hippophobia is a psychological fear of horses. Equinophobia is a mix of Greek φόβος (phóbos), meaning "fear" and Latin word equus, meaning "horse". Hippophobia is derived from the two Greek words fear (phóbos) and horse, ἵππος (híppos).
Last week, when a federal judge lifted a temporary restraining order clearing the way for the sale of 149 unbranded wild horses captured by the Fort McDermitt Paiute and Shoshone Tribe in northern Nevada, wild mustang advocates reacted swiftly.
I grew up in the Reno area... we used to say "no amount of beer could make Wild Horse Annie look good..."
Mustangs are a living part of America's History. Not the "white washed" history Trump is promoting. (Maybe Trump also fears educated "other than white" voters.) BTW: Xenophobia is the fear of that which is perceived to be foreign - Mexicans? Islamophobia is the "fear of Islam." Beware: "The Silent Rise of Christophobia & Afrophobia!"
For perspective, Coronado's expedition through present-day Arizona and New Mexico took place eighty years before the Mayflower landed on Plymouth Rock.
Mustangs are an American horse breed descended from abandoned horses originating brought from Spain during the early SPANISH conquest of the Americas. They are classified within the larger grouping of the Colonial Spanish horse, a type that today is now rare in Spain. Oh, mustangs are from Spanish Conquistadors! Mustangs are from Spanish Conquistadors!
Horses are MORE than political!
Simple: "How wild horses of the West would die under Trump budget"
In Hindu Brihadaranyaka, the horse is linked to Varuna and as such, is equated to the cosmos. Additionally, a white (pale) horse is believed to be the final incarnation of Vishnu.
In Buddhism the horse is symbolic of energy and effort in the practice of dharma.
Now I am no "End-timer." (What do 'End Timers' believe about all this religious nonsense?" They happily believe that we sinners left behind will suffer the Tribulation.) From a fish to a pale horse... sound familiar?


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text:
Q: Node child_processes not executing concurrently Background
I want to navigate to multiple websites in separate browser tabs programmatically. Ideally, I want to be able to have these websites loading in parallel, when the code is triggered.
Problem
As a proof of concept, I have the following code to open a browser through a command.
const { exec } = require("child_process");

exec('xdg-open https://www.google.com').on('exit', () => {console.log('child 1 exited')})
exec('xdg-open https://www.github.com').on('exit', () => {console.log('child 2 exited')})

What happens when I run the code above:

*

*Both child 1 exited and child 2 exited were logged almost immediately after I run the code

*A browser tab opens and starts loading one of the pages. I had to wait about 3 seconds after the first page is loaded, before the 2nd tab opens and starts loading the second page.

++
I expected both tabs to open and start loading the pages almost at the same time, but this is not happening.
Other Observations
I tried running xdg-open https://www.google.com and xdg-open https://www.github.com in separate terminals (executing both at around the same time), and I am able to have 2 tabs open and start loading pages at around the same time (this is the behavior I am aiming for)
However, the code above seems to behave similarly to running xdg-open https://www.google.com && xdg-open https://www.github.com
Question
If exec() spawns a new child_process, why is the code above not triggering multiple page navigations simultaneously? How do I achieve this behavior?

A: I would suggest you to use open from npm. It will be a good option to just open multiple URLs from the Node.js APP.
const open = require('open');

const main = async () => {
  await open('https://www.github.com');
  await open('https://google.com');
}

main();

This code will open these URLs in parallel and it will open in the default browser selected. However, you can change the configuration in open to pass dynamic configuration to it. Please check here for more details.
You should not use the child_process to open such task as it doesn't require any computation. These are generally called when CPU is unable to handle the blocking requests as Node.js main loop runs on single thread. You can check here for more details when to use child_process.

A: I found this issue to only occur when my default browser is set to Firefox. If my default browser is set to chrome, both browser tabs will open at the same time and start fetching data from both websites (at the same time).
I'm posting this observation here in case someone might find it useful, but I have not come across a solution to make this work with Firefox.
EDIT:
More Observations
When I tried to open more websites in the same way (e.g. 10 websites), firefox will open in 2 batches (with some delay in between each batch). In each batch, multiple websites will open concurrently. So far, it seems like it will always open in 2 batches.

redpajama_set_name: RedPajamaStackExchange


The Minister for Information and Communication Technology (ICT), Frank Tumwebaze has admitted that the implementation of the levy on Over The Top services widely interpreted as Social Media Tax has had negative effects on the sector.
Tumwebaze said that the Finance Ministry "hoodwinked" Parliament to pass the controversial tax on July last year on the hope that it would widen the country's tax base which has not been the case.
The minister now agrees with the Parliament's ICT Committee which has started an impact assessment exercise on the tax that mandates internet users to cough a daily Shs200 to access social media sites and applications on their smartphones, tablets and computers.
According to the Committee chairman, Tororo North MP Annet Nyakecho, the Over The Top tax seems to a negative effect on the consumption of ICT services and products in Uganda and as a result, needs to be streamlined.
Her comments are music to the ears of activists under the movement #ThisTaxMustGo, that have been calling for the scrapping of the tax since it came into force.
Journalists Joel Ssenyonyi, Raymond Mujuni and lawyer Silver Kayondo even petitioned Court against the social media and mobile money taxes. Although the latter was halved to 0.5% from the initial 1%, government insisted that the OTT tax would remain unchanged.
"I wonder why our leaders tend to shoot first and aim later. We told them and they listened not," Ssenyonyi said welcoming the move by Parliament to review the tax.
Although the Uganda Revenue Authority (URA) claimed to have collected Shs20.5bn from the tax in the first one month, the long-term effects are seemingly catching up with the government as most Ugandans use VPN services to access the sites unfettered.


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Kibwe could be the clubmaster's more daring cousin. The familiar brow-heavy style of those sunglasses is re-imagined with a chunky matt/gloss black combination angular frame, the subtle gold flash lenses continuing the angled design. Metal accents complete the look that.


text:
Q: Remove a group from data frame if all values across are a certain number? I have a data frame where I'd like to remove entire groups if their y value is the same across 6 time points.

Patients
Time
Status

1
a
5

1
b
5

1
c
5

1
d
5

1
e
5

1
f
5

2
a
4

2
b
4

2
c
5

2
d
5

2
e
5

2
f
5

Basically, I'd like to remove all patients from this data frame who have a status of "5" at ALL time points. If a patient has any value apart from 5 at any point in time I'd like to include them.
I tried
df <- df %>%

filter(a !=5 & b !=5 & c !=5 & d !=5 & e !=5 & f !=5)

To no avail, unfortunately. Would appreciate any help. Thank you!

A: You can use any/all :
library(dplyr)
df %>% group_by(Patients) %>% filter(any(Status != 5))
#With `all`
#df %>% group_by(Patients) %>% filter(!all(Status == 5))

#  Patients Time  Status
#     <int> <chr>  <int>
#1        2 a          4
#2        2 b          4
#3        2 c          5
#4        2 d          5
#5        2 e          5
#6        2 f          5

This can be also be written with base R :
subset(df, ave(Status != 5, Patients, FUN = any))

#and `data.table` :
library(data.table)
setDT(df)[, .SD[any(Status != 5)], Patients]

Without grouping by Patients you can do :
subset(df, Patients %in% unique(Patients[Status != 5]))

data
df <- structure(list(Patients = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L), Time = c("a", "b", "c", "d", "e", "f", "a", "b",
"c", "d", "e", "f"), Status = c(5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L,
5L, 5L, 5L, 5L)), row.names = c(NA, -12L), class = "data.frame")

A: Something like this?
df <- data.frame(
  patients = c(rep(1,6),rep(2,6)),
  time = rep(letters[1:6],2),
  status = c(rep(5,6),rep(4,2),rep(5,4))
)

df %>%
  group_by(patients) %>%
  dplyr::filter(status*6 != sum(status))

A: if I understood your problem correctly one of these two solutions should help:
library(dplyr)
library(data.table)

# your test data
df <- data.table::fread("Patients   Time    Status
1   a   5
1   b   5
1   c   5
1   d   5
1   e   5
1   f   5
2   a   4
2   b   4
2   c   5
2   d   5
2   e   5
2   f   5")

# one option to get all rows diferent than 5
df %>%
  # exclude everything where Status is 5
  dplyr::filter(Status != 5)

Patients Time Status
1:        2    a      4
2:        2    b      4

# one option to get all distinct patients
df %>%
  # exclude everything where Status is 5
  dplyr::filter(Status != 5) %>%
  # unique values per column or column combination
  dplyr::distinct(Patients)

Patients
1:        2

# on option to get all data of patien with at least one status != 5
df %>%
  # exclude everything where Status is 5
  dplyr::filter(Status != 5) %>%
  # unique values per column or column combination
  dplyr::distinct(Patients) %>%
  # join back on original data to get all values for specific patients
  dplyr::inner_join(df, by = "Patients")

Patients Time Status
1:        2    a      4
2:        2    b      4
3:        2    c      5
4:        2    d      5
5:        2    e      5
6:        2    f      5


text:
Discover the contemporary impressionist who is inspiring millions: Erin Hanson, the founder of Open Impressionism, has captured the imagination of art enthusiasts around the world. Her passion for natural beauty is seen in her work as she transforms vistas familiar and rare into stunning interpretations of bold color, playful rhythms, and raw emotional impact. As an iconic, driving force in the rebirth of contemporary Impressionism, Hanson is quickly recognized as a prolific, modern master.
Click here to view Erin Hanson's portfolio of original oil paintings. Erin Hanson paintings sell quickly, often before they are dry. Here is a link to Hanson's currently available works.
Carmel-by-the-Sea
Visit our retail gallery location in beautiful Carmel-by-the-Sea:
The Erin Hanson Gallery
Carmel, California, 93921
Walk-in hours: Thursday - Sunday, 10am to 6pm
Inspired by a lifetime of backpacking and exploring the national parks and monuments of the West, Hanson captures the iconic scenery of the Grand Canyon, Canyon de Chelly, Arches National Park, Monument Valley, Mojave Desert, Saguaro National Park, and more.
Thursday 1 pm – 9 pm (Artwalk from 7 pm - 9 pm!)
Visit Erin Hanson's 18,000-square foot studio and art gallery in McMinnville, located in Oregon wine country.
Walk-in hours: Wednesday - Saturday, 10am - 6pm
What is Open Impressionism?
Erin Hanson is the creator of the contemporary painting style "Open Impressionism," which is now being taught in art schools and colleges worldwide. Open Impressionism is a blend of classic impressionism and modern expressionism, with a dash of plein air style.
Hanson has spent 20 years developing the unique techniques of Open Impressionism, which involve minimal brush strokes and the impasto application of paint. Unlike traditional oil painters, Hanson does not build up the painting layer by layer; instead, she lays her paint strokes side by side without overlapping and works to get each stroke "right the first time." The clean brush strokes give her paintings a mosaic or stained-glass appearance, while also conveying a sense of movement and spontaneity. She uses a limited palette of only five pigments to create vivid, un-muddied colors that ignite the imagination.
Open Impressionism continues the work of impressionists and post-impressionists like Monet and Van Gogh. The purpose of each painting is to capture the fleeting and momentary light seen out of doors, especially during early dawn and sunset. Like other impressionist painters, Hanson's work appears more abstract when viewed up close and "goes into focus" when seen from a few feet away. <CLICK TO LEARN MORE>
Join Erin Hanson's newsletter to see her newest paintings fresh from the easel, read articles and insider-only blogs written by the artist, and get inspired!
J O I N E R I N ' S W E E K L Y N E W S L E T T E R
F R E E F L I P B O O K
To receive a free copy of Erin's 50-page "Design Inspirations" flipbook, please include your mailing address below (U.S. only).
SHOP ERIN HANSON
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Erin Hanson has developed a unique style of painting known as Open Impressionism. This coffee table book shows the progression of Erin's style over 300 pages of full-color images. Explore the beautiful landscapes and sun-drenched colors of the West through the eyes of contemporary impressionist Erin Hanson, and experience the evolution of Open Impressionism.
Wine Country Book
Erin's newest coffee table book has now been released! Wine Country: Impressions in Oil (2018) contains over 300 paintings of lush vineyards, rolling hills, and iconic oak trees from around the United States.
This year we have 6 options for 2023 wall calendars -- available while supplies last! You can purchase Erin Hanson 2023 calendars from our giftshop on this website.
The Impressionist Magazine
Read the premier issue of The Impressionist and learn more about the advent of impressionism in Europe, as well as the development of post-impressionism and contemporary impressionism. The authors searched for fascinating and little-known facts about the impressionists, and we hope you enjoy the articles!
Purchase fine art prints directly from Erin Hanson. We offer stretched canvas prints in a variety of floater frames. These open-edition prints are based on photos that Erin Hanson takes herself, to ensure accurate reproductions of her works. They are designed to capture the texture of an original oil painting through the illusion of photography.
3-D Textured Replicas
Discover the cutting-edge technology of 3-D printing. Each 3-D Textured Replica captures the same brush strokes and colors as the original, using micro-layered 3-D modeling and 48-bit color to create the most realistic painting reproductions available today.
Click here to view Erin Hanson's guide to collecting prints and originals. Explore the possibilities!
Click here to download Erin Hanson's 50-page booklet filled with interior design tips and inspirations.
LEARN ABOUT ERIN HANSON
Erin Hanson Blog
Visit Erin's blog for a wealth of articles written by Erin Hanson and guest authors including biographies, interviews, adventure stories, in-depth painting looks, interior design tips, events and collection releases, news articles, and much more!
Artist Adventure Series
Erin explores the wilderness as a rock climber, backpacker, and photographer, gathering reference material to use in her paintings. She seeks out the isolation of deserted locations, surrounding herself in natural beauty, and she also finds inspiration by exploring our National Parks and wine country regions. Read about her adventures in the Adventure Series Blogs.
Museum Shows
Erin Hanson's paintings have been shown in ten art museums across the United States. Along with several group exhibitions, she has had two solo exhibitions (both were sell-out shows), with two more solo shows scheduled for 2021 and 2023.
Erin recently launched her Artist Mentorship Program! This study-at-home program will guide artists in building their basics, developing mastery, and creating their own style. The purpose of the program is to help artists learn how to create paintings the way they've always wanted to create.
ERIN HANSON is a life-long painter, beginning her study of oils as a young child. Her passion for natural beauty is seen in her work as she transforms vistas familiar and rare into stunning interpretations of bold color, playful rhythms and raw emotional impact. Her frequent forays into National Parks and other recesses of nature include backpacking expeditions, rock climbing, and photo safaris. Hanson's unique painting style has become known as Open Impressionism, with hundreds of collectors eagerly anticipating her work. As an iconic, driving force in the rebirth of contemporary impressionism, Hanson is quickly recognized as a prolific, modern master.


… what can you really do to make a difference?
When you sense the accumulated energy of humanity, does it seem chaotic, confused, or just plain negative? Of course it does, at least on the surface. And without denying any of the problems, let's look at some solutions — vibrational solutions — choices in consciousness which you can make, if you let yourself.
Here is the key to making a difference for people, and for yourself. When you scan the energies of humanity, you will notice, at first, the broader and seemingly chaotic part of humanity's frequency spectrum. However, know that this chaos is actually a smaller part of humanity's energy spectrum. Use your imagination to let yourself sense below the surface of reality. Let yourself tune into the clearer, brighter part of humanity's vibrational spectrum. You then realize that the brighter energies had been there all the time. But why would your noticing this happier vibration actually make a difference?
A secret of consciousness is that when you bring your attention to something, it becomes stronger. It is reinforced through the natural power that consciousness has, and through your coming into relationship with what you're bringing your attention to. And in terms of yourself, when you bring your attention to the healthier and brighter parts of humanity's vibrational spectrum, you strengthen those positive aspects of yourself as well. And you are allowed and encouraged to do just that.
But does this mean that you must deny the real problems? Not at all… This technique invites you to participate in a simple expansion of your consciousness. You are now choosing to include the positive range of the spectrum in your awareness, and that means that you can feel a resonance with the solutions to problems. And you are sensing your relationship or resonance with those positive possibilities. At first it will seem that it is fleeting, or that perhaps you are imagining it. This is ok.
This simple, yet profound perceptual shift lets you look at a problem, and discover a solution, even if you hadn't seen one before. This is because sometimes the way to start finding solutions is to get a sense, even if it seems that you're making it up, that a solution can exist. And when you tap into that possibility, you are opening up an energy doorway that resonates with that possibility.
Just take a moment, here and there throughout the day, to find the positive range in the spectrum of humanity. And feel your relationship with that positive energy. You just raised your vibration — made a subtle shift in your reality, and you did a good thing for the planet as well. Yes, it's subtle. But deeply meaningful, because you gently reinforced a positive trend that uplifts everyone, and you have brought yourself into alignment with the world of positive possibilities.
This entry was posted in Uncategorized and tagged consciousness, energy, make a difference, positive possibilities, subtle shift, vibrations, world. Bookmark the permalink.


text:
Q: What is the equation for the right-side branch of $Axy + Bx + C y = D$? I would like to know how to derive the formula for the right-side branch of $Axy + Bx + Cy = D$, where the constant and coefficients are positive integers, and expressed as an equation and in terms of $A$, $B$, $C$ and $D$.

A: The equation for the right side or the left side is the same.
The only difference is the domain.
We need to find the  vertical asymptote and select the domain accordingly.
For example if you have  $$3 xy+2x-2 y=5.$$
Solving for $y$ we get $$ y=\frac {5-2x}{3x-2}$$
The vertical asymptote is $x=2/3$, therefore the right branch occurs at $$y=\frac {5-2x}{3x-2},   x>2/3.    $$

A: Take
$$|A|xy+|B|x+|C|y=|D|$$
and let's assume $x$ and $y$ are real.
The solution is
$$\\y=\frac{-|B|x+|D|}{|A|x+|C|}.$$
The graph of the function $f$ with $y=f(x)$ is a hyperbola. It has a pole at $x=-\frac{|C|}{|A|}$. The right-side branch is therefore
$$f\colon (-\frac{|C|}{|A|},\infty)\to\mathbb{C},x\mapsto f(x)=\frac{-|B|x+|D|}{|A|x+|C|}.$$


text:
Q: Mixed number class istream issues I assigned myself some homework over the summer, and the project I am 98% finished with has come to a standstill due to this one problem.
I have a class called Mixed. It contains member data for a whole number, a numerator, and a denominator. I need to overload all of the common operators to allow multiplication, addition, comparison and streaming of objects of type Mixed. I have all the operators overloaded except for >> (the extraction operator).
All mixed numbers read in will be of format:
whole numerator/denominator
ex: 1 2/3, 0 7/8, -3 18/5, 0 -1/89
Header: friend istream& operator>> (istream &, Mixed);
CPP file: istream& operator>> (istream &in, Mixed m) {...}
For the assignment, I am limited to the iostream and iomanip libraries. My plan was to read in the values from the stream and assign them to temporary int variables (w, n, d) which I would then use with the Mixed constructor to create object m. Unfortunately, I cannot think of a way to separate the numerator and denominator. They are both ints, but they have a char (/) between them.

*

*I cannot use getline() with its delimiter, because it assigns data to a char array, which I do not believe I can convert to an int without another library.

*I cannot use a char array and then segment it for the same reason.

*I cannot use a while loop with get() and peek() because, again, I do not think I will be able to convert a char array into an int.

*I cannot use a string or c-string and then segment it because that requires external libraries.

Once again, I need to split a value like "22/34" into 22 and 34, using only iostream and iomanip. Is there some fairly obvious method I am overlooking? Is there a way to implicitly convert using pointers?

A: You could first extract the nominator, then the separating character, and then the denominator.
Example for illustration:
istream& operator>> (istream &in, Mixed &m) {
    int num, denom;
    char separ;

    in >> num;
    in.get(separ);
    if (separ != '/')
      in.setstate(ios::failbit);
    in >> denom;

    if (in) {
      // All extraction worked
      m.numerator = num;
      m.denominator = denom;
    }

    return in;
}

A:
Once again, I need to split a value like "22/34" into 22 and 34, using
  only iostream and iomanip.

Couldn't you just read in the first integer, use get to get the next character, and then read the second integer?  Something like this:
#include <iostream>

int main() {
    using std::cin;
    using std::cout;

    int num;
    int den;

    while(cin) {
        cin >> num;
        if (cin.get() != '/') {
            // handle error
        }
        cin >> den;

        cout << num << "/" << den << std::endl;
    }

    return 0;
}

You can then make sure that the character read between the two integers was a '/' and handle appropriately if it isn't.