We are on 22a, 14 lines from the bottom, the last word on the line - "זה". We are starting a new sugya, which first refers back to the mishna, as is customary.
The mishna, after stating the category of days which have four olim for the reading (Rosh Chodesh and Chol HaMoed), gives a rule. This is the only category which has a defining rule. The rule is: "This is the rule - every (day) which has musaf but is not a yom tov, four read."
Are there days, other than Rosh Chodesh and Chol HaMoed, that meet the conditions of the rule, but are not already listed? It is not easy to think of any, but if so, what is the purpose of the rule? The converse question also needs to be asked - are there other days which have four olim but do not meet the conditions of the rule? This is the first question to be addressed in the gemara.
This is the rule, every that has a musaf etc..
or perhaps, here too, there is an addition to the prayer.
זה הכלל כל שיש בו מוסף וכו'.
או דלמא: הכא נמי איכא מוסף תפלה?
In other words, to what does the word musaf, used in the mishna's rule, refer? The word means "addition." We naturally think it refers to the musaf sacrifice, or the musaf prayer (which is in place of the musaf sacrifice), and hence a fast day is not included; but the gemara suggests that perhaps it refers to any addition to the prayers. On a public fast, there is an additional beracha in the prayer, the beracha beginning with the word "aneinu."
Question: In nine out of ten cases, at least, the word musaf refers to the sacrifice or the corresponding prayer. What is the motivation of the gemara to raise the possibility that here the musaf is a single additional beracha in the shemona esrei? Take your time to think about this - I am in no rush and will patiently wait.
I think that the reason the gemara is willing to entertain the possibility that a fast day is called a day that has a musaf, because of the additional beracha in shemona esrei, is because the gemara wants to know why the mishna bothers to formulate a rule for when there are four olim. The obvious utility of a rule like this is to provide us with the means of deciding to have four olim on another day, other than the two listed in the mishna explicitly. We do not need the rule to tell us to have four olim on Rosh Chodesh or Chol HaMoed, since they are listed before the rule. So, it would appear, there must be another day that the rule defines, and that day cannot be any other than a fast day. To include a fast in the rule, we have to redefine the musaf to include even an additional beracha and not just an entire additional sacrifice.
So, the question is: How many olim are there on a fast day?
Come and hear (a proof): "On Rosh Chodesh and on Chol HaMoed, four read." So, on a public fast, three!
Read the first part (of the mishna just quoted) - "On Mondays and Thursdays and Shabbat mincha, three read." So, on a public fast, four! Apparently, we cannot deduce anything from this (mishna).
Come and hear: Rav came to Bavel on a public fast. He arose and read from the sefer (Torah). He began and recited a beracha; he finished and did not recite a beracha. Everybody fell on their faces, but Rav did not fall on his face.
Now Rav was reading the yisrael portion, so why did he not recite a beracha when he finished? Is it not because another needs to read after him?
ת"ש בראשי חדשים ובחולו של מועד קורין ארבעה, הא בתענית צבור - שלשה!
אימא רישא: בשני ובחמישי, ובשבת במנחה קורין שלשה, הא תענית צבור - ארבעה! אלא, מהא ליכא למישמע מינה.
תא שמע, דרב איקלע לבבל בתענית צבור, קם קרא בסיפרא, פתח בריך, חתים ולא בריך. נפול כולי עלמא אאנפייהו. ורב לא נפל על אפיה.
מכדי רב בישראל קרא, מאי טעמא חתם ולא בריך? לאו משום דבעי למיקרי אחרינא בתריה?
In other words, if Rav did not recite a blessing after he finished reading, apparently it is because he was not the last reader. The original custom, you will recall from two weeks ago, was that a closing blessing was recited only after the last reading. Since Rav was neither a kohen or a levi, the earliest aliya he could get was the third, and if that was not the last, then there are apparently four aliyot.
No, Rav read the kohen portion, for Rav Huna used to read the kohen portion.
לא, רב בכהני קרא, דהא רב הונא קרי בכהני.
The fact that even the gemara calls the first aliya the "kohen aliya," does not in fact close the question as to whether a non-kohen can get it. The gemara points out that Rav Huna would regularly be called up first, so apparently a talmid chacham might well have precedence. Hence, it is possible that Rav was the first oleh, and therefore he did not recite a beracha after his aliya, as there were two - but no more than two - additional olim.
Rav Huna could read the kohen portion, for even Rav Ami and Rav Asi, who were the most important kohanim in Eretz Yisrael, were subject to Rav Huna, but Rav - was there not Shemuel, who was a kohen and superior to him?
Shemuel also was subject to Rav, and Rav would merely grant honor to Shemuel. And when would he grant honor to Shemuel, before him, but when not in his presence he would not grant him honor.
בשלמא רב הונא קרי בכהני - דהא אפילו רב אמי ורב אסי, דכהני חשיבי דארעא ישראל, מיכף כייפו ליה לרב הונא. אלא רב, הא איכא שמואל, דכהנא הוה ודבר עליה?
שמואל נמי מיכף הוה כייף ליה לרב, ורב הוא דעבד ליה כבוד. וכי עביד ליה - בפניו, שלא בפניו - לא עביד ליה.
It seems that the first aliya can be properly granted to a talmid chacham, but only to the most outstanding one. Getting the first aliya is a matter of honor, and the question is whether the kohanim are obligated in the superior honor of the outstanding talmid chacham. Rav Huna was such an individual, but it appeared that Rav was subject to the superior status of Shemuel. Yet the gemara answers that this is not the case - Rav would honor Shemuel, but objectively, his status was greater than Shemuel's. hence, when not in the presence of Shemuel, Rav would accept the first aliya.
Aside from the halacha that permits the giving of the first aliya to a talmid chacham, we have just gotten a fascinating glimpse into rabbinic politics and social hierarchy.
This is in fact probable - that Rav was reading the kohen portion - for if he was reading the yisrael portion, why did he recite a beracha before it?
It was after the enactment (that instituted a beracha before and after every aliya).
If so, then he should have recited a beracha after it as well!
Rav's locality was different, as people would come in, but no one would go out.
הכי נמי מסתברא דרב בכהני קרא, דאי סלקא דעתך בישראל קרא - לפניה מאי טעמא בריך?
אי הכי לאחריה נמי לבריך?
שאני היכא דיתיב רב, דמיעל עיילי מיפק לא נפקי.
Rashi - therefore he would recite a beracha before the reading, because of those who would come in late, as we stated above, but afterward he would not recite a beracha, as he did not worry about people leaving.
What is the status of the attempted proof concerning the reading on a fast day at this point? The gemara we have just read has twisted back and forth several times, which is typical - make sure you kept track. Have we proven that there are four olim on a fast day, or not?
This is fairly typical in a sugya. The gemara rejected the proof, which was based on the assumption that Rav had received the third aliya, by raising the possibility that he had in fact received the first aliya, normally reserved for a kohen. This possibility is sufficiently novel that the gemara needs to defend it by citing the example of Rav Huna. The gemara then debates whether the example of Rav Huna is relevant to the case of Rav, as Shemuel, who was a kohen, appears to have a higher status than Rav. The gemara then rejects this perception. All this has merely served to establish the possibility that Rav received the first aliya rather than the third - but that possibility is sufficient to refute the proof that there are four aliyot on a fast day, since a proof must be based on necessity.
But then the gemara goes one step further. It claims that it is in fact necessary to understand that Rav received the first aliya. This does nothing to further our original inquiry, since in any event it is not necessary to understand that he received the third aliya. The gemara has gone off on a tangent concerned with the correct understanding of the story of Rav. This last point is rejected - you cannot prove that Rav received the first aliya. It is possible that he received the third aliya, and was of the opinion that one should recite a beracha before every aliya, but not afterward, at least not in his locality. On the other hand, you cannot prove that he received the third aliya either. Therefore, there still is no proof that there are four aliyot on a fast day.
Which is why the gemara continues its efforts to find a proof to decide the original question.
Come and hear: This is the rule, any day on which there would be interference in work for the people, such as a fast day or Tisha B'Av, three read, but if there is no interference with work for the people, such as Rosh Chodesh or Chol HaMoed, four read.
תא שמע: זה הכלל: כל שיש בו ביטול מלאכה לעם, כגון תענית צבור ותשעה באב - קורין שלשה, ושאין בו ביטול מלאכה לעם, כגון ראשי חדשים וחולו של מועד - קורין ארבעה.
This is pretty straightforward. The beraita states explicitly that on a fast day we have only three olim. What is more, the beraita appears to give the rationale. On a day when people go to work, we are not interested in lengthening the prayers. While one more oleh might not seem significant, apparently there is a principle here at work as well. You lengthen prayers on days when there is no pressure. I would suggest, though I can not prove this, that the consideration may well be psychological rather than economic. On work days, people are under pressure to get to work, and any additional delay causes a problem, which, among other things, would not contribute to the seriousness of their prayers.
Of course, you probably want to ask why Rosh Chodesh does not have a problem of interference with work. After all, it is permitted to work on Rosh Chodesh.
Rashi (naturally) answers this question even before it is asked.
On Rosh Chodesh there is not too much interference with work, for women do not work on them.... And I heard from my old master z"l that this mitzva was given them because they did not take off (and give) their rings for the golden calf.
Tosafot: And I found in the forty-fifth chapter of the Beraita of Rabi Elazar that the women heard and did not want to give their rings to their husbands. Rather they said to them: You want to make a statue and image which has no power to save. And God rewarded the women in this world that they should observe Rosh Chodesh more than the men, and in the next world they are destined to be renewed like the moon.
We now have the beginning of an understanding of the hierarchy of the readings. On work days, there are three olim. On days when there is a limitation on work, it is possible to add more olim. We have to see the rules for when we call four, five, six, and seven - but that will have to wait for next week.


What is going on now with AUD/USD?
AUD/USD: The Australian dollar was supported by improved economic data in yesterday's session and took out all of its long price targets at 0.71790, 0.71944, and 0.72085 as indicated in yesterday's update and on the 30 minutes chart below. The pair retreated from 0.72254 during the European session and finished the session at around 0.71944 support.
AUD/USD: The Australian dollar continues to drift down as it slips below 0.71944 support and trades in a very tight price channel just above its 500 EMA. The pair currently trades at 0.71934 and could fall to its 500 EMA at 0.71790 if it falls below the price channel. The Australian dollar needs to hold above 0.71944 and breakout of the price channel to head back up to 0.72085. The next target after 0.72085 is estimated at 0.72254.
EUR/USD: The Euro retreated to 1.16156 support at the bottom of the price channel in yesterday's session; however, it bounced during the European session and charged to the top, taking out four of its long price targets at 1.16390, 1.16590, 1.16766, and 1.16890 as highlighted in yesterday's update.
The pair could retreat to 1.16766 and 1.16590 if it fails to break at 1.16890.
What is going on now with GBP/USD?
GBP/USD: The Pound bounced in the middle of the price channel and blasted to the top after the UK economic announcement during the European session. The pair surged and took out three of its long targets at 1.30710, 1.30897, and 1.31210 before retreating to 1.30897 support.
What is going on now with USD/JPY?
USD/JPY: The US dollar managed to maintain its uptrend position after bouncing at its 500 EMA. The pair broke through its first long target at 111.430 as highlighted in yesterday's update and later took out the second target at 111.885.
What is going on now with USD/CAD?
USD/CAD: The US dollar still consolidates at 1.30008 and 1.29843 as highlighted in yesterday's update. The pair had numerous attempts to take off above 1.30008, but all failed and remained subdued under 1.30008.
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"Things That Make No Sense" (2006) on the album Field Day(1988).
how can I make this clear ?
Albums has song "Things That Make No Sense"


Step 2: Line two baking trays with baking paper.
Step 3: Beat the butter and sugars together in an electric mixer for 5 minutes or until light and fluffy. Add the treacle and egg and beat again until incorporated. Stir in the remaining biscuit ingredients.
Step 4: Roll the mixture into balls, approximately 2 tablespoons of mixture per ball. Roll the balls in caster sugar and press down with the back of a spoon to flatten slightly. Leave a gap of at least 5 centimetres between each ball.
Step 5: Bake for 8 minutes, or until browned. Remove from oven and cool on wire trays.
Step 6: To serve, place scoops of Burnt Fig, Honeycomb and Caramel Ice Cream between cooled ginger biscuits.


Word of mouth is vital to any small local business that hopes to grow. For a fortunate few, it just happens naturally. But word of mouth can also be planned, executed and nurtured as a specific marketing method. Attending to the basics is important. Word of mouth comes from having happy customers, so that's still Job #1.
 Target the talkers. Makes sense, right? It is word of mouth, after all, so you need to aim your efforts at the people who like to talk about the great products, services and businesses they use. They are your customers, of course. Remember, these might include people who have a recommendation when somebody asks them for one - even if they aren't naturally talkative or write reviews.
 Develop a pitch line that's interesting and easy for people to share. The "interesting" part is vital. Avoid being boring at all costs. Who wants to share something boring? Ask yourself, honestly, why a customer would want to talk about you? Forget your brand statement or product brochure. Try not to think like a marketing person. This is consumer-to-consumer, so your story line should sound like one person telling another about something great they've discovered. Boil it down to one sentence. Maybe two.
 Be amazing. There's no way around this. If you want people to spread the word about you, you have to deliver something exceptional. So in addition to being interesting, you'll need to deliver customer service that's above-and-beyond - and make sure everyone who works for you does the same. Your products and packaging - including your store, office and website - should look great.
 Put it in motion. You already have lots of ways to do this. For example, your business profile, website, newsletter, brochure, e-mails and social media pages should include "tell-a-friend" forms or functions. Consider using postcard mailings, coupons, stickers, buttons and QR codes. When you make it easy for people to share, they are much more likely to do so.
 Create continuous reasons to talk. You can create word-of-mouth opportunities with special sales, great new features or something fun, such as a contest or event. Think distinctive: Do something others aren't. Establish a unique personality for your business that includes certain colors, shapes, a stand-out design or maybe even a mascot.
 Connect to the conversation. Having a presence online - specifically in social media such as Facebook, Twitter and Google+ - is a great way to join and facilitate conversations about your business. The best and most simple thing you can do is simply ask questions. You'll be surprised at how willing consumers are to provide feedback when they're asked what they think. Ask some questions about your business and its products and services.
 Show appreciation. This step too often gets overlooked. When you hear about someone who has recommended your business, say thanks. Send lots of thank-you notes. Keep in mind that people like feeling like they are part of an "insider" group, so share information about things you may be planning or thinking about, and ask for feedback. Reply to e-mails, Facebook messages and blog comments as quickly as possible.


Generate tons of reviews, photos, and other user-generated content and use them to drive qualified traffic, increase conversion rate & boost sales.
Yotpo is a commerce marketing cloud with the most advanced solutions for customer reviews, visual marketing, loyalty, and referrals. With Yotpo, you can effectively leverage social proof to increase trust and sales, cultivate loyal customer advocates, and make better business decisions based on customer feedback. Our suite of integrated solutions for user-generated content marketing, loyalty, and referrals helps you accelerate growth by enabling advocacy and maximizing customer lifetime value.
Yotpo proudly serves thousands of brands including Away Travel, Glossier, and 1-800-Flowers, Inc. An official partner to Google, Facebook and a Premier Technology Partner of Magento, Yotpo has raised $101 million in funding and employs over 300 employees globally. Yotpo is deployed in over 10,000 Magento stores, including on Moroccanoil, Everlast, and Adore Me. Yotpo is also partnered with leading Magento agencies, including Born Group, Vaimo, and Gorilla Group.
Yotpo integrates seamlessly with Magento stores, so you can accelerate growth by leveraging consumer advocacy and maximizing customer lifetime value.
Easily collect and intelligently display customer content like reviews, photos and videos at key touch points throughout the buyer journey to improve trust, social proof, and sales. Our easy-to-use dashboards provide you with access to feedback data and unprecedented insight into consumer behavior and sentiment, so you can continually optimize your site to provide the most value to customers.
Yotpo's extension for Magento requires a Yotpo account. To create your free account click here. For more information on pricing, please click here or scroll down.
Collect reviews, ratings, photos, and Q&A from customers.
Share their best customer content on social networks with a click of a button.
Build a loyal brand community using customer content.
Make better business decisions using analytics and feedback from customers.
In addition, we offer the most integrations in the eCommerce technology ecosystem, with notable Magento partners, such as Nosto, Oracle + Bronto, Listrak, and AdRoll.
What’s Included In Yotpo’s Free Plan?
Installing Yotpo is simple and takes only 5 minutes to set up. For details on installation see our Magento 2.0 installation guide.
Integrate Yotpo to your Magento store in a single click. Migration from other reviews solutions is a breeze, and make it easy to import all of your existing reviews.
- Removed support for Magento versions older than 2.1. Older versions can be installed via Yotpo's Packagist Repository ( yotpo/module-review ).
- Past orders (90 days back) can now be synced with Yotpo by selecting "Sync from"
* Failure of past bulk order submission when one order in the bulk fails.
• Added support for Magento 2.3.
• Added support in PHP 7.2.
• Bug fix : multi-store sending wrong product URL.
-Wrong template path in YotpoReviewRenderer.
-Yotpo settings in single store mode and composer autoload.
-Bug affecting the ability to retrieve configurable product data.
-Bug affecting the abilitiy to enable the default Magento reviews tab option on the Yotpo. configuration page in the Magento Admin.
-Bug affecting the ability to retrieve backend product URL vs. storefront product URL.
2. Added support for additional product specs.
Yotpo is primarily a reviews collection and trust-building software. It lets store administrators collect reviews from past buyers and deploy them on-site. Yotpo also leverages reviews in various ways to boost traffic and drive sales, such as in SEO, posts to social media, and in strategic on-site upselling.
Production compiling fix - removed redundant dependency injections.


Are there Native American characters in fiction/fantasy that don't come off as stereotype?
I was talking about the new Mortal Kombat with someone and how I wanted to see Nightwolf returning. Nightwolf is a Native American who uses a lot of spirit-stuff (magic tomahawks and bows and arrows ) and talks about his ancestors a lot. Someone else said he was a stereotype character and shouldn't return but I believe that he only comes across that way because of his lack of screen time to be fleshed out more. But that got me thinking on other Native characters in similar media. Superheroes or fantasy characters and so on, maybe even normal fiction. Are there any Natives you know about that wouldn't give the same impression and could be pointed to as reference for a good character? Thanks.


How can we control which content is used in our search?
We made the Site Search 360 crawlers as intelligent as possible when it comes to analyzing your website and picking the title, image, and content from the right place. Nonetheless, sometimes it is still necessary to tune the crawlers by pointing them directly to the desired content's position.
This is done via XPath expressions placed in the Site Search 360 control panel. In the following video, we will show you how to configure your search engine to present the content your visitors are searching for.
First we will search for a Google Chrome extension called "XPath Helper", which allows us to easily define XPaths right from your own site.
Navigate to one of your website's content pages. Press the XPath Helper icon in the top right corner of your browser to open the black overlay which will present the currently selected XPath expression.
Now we want to extract the main content. After opening the XPath Helper, hold the [Shift] key and hover your mouse over your website's elements. You will see how the extension highlights them in yellow while displaying the XPath query in the black overlay box. As you move your mouse this XPath query will change. Try to get all your content highlighted in yellow.
You may fine tune your XPath expression by shortening it. There are two ways of shortening an XPath query—you can remove something from the end to match more child nodes or you can leave the tail and cut the head off to make it match more generally. Make sure your XPath always starts with // when shortening from the front. You can find a live example of this step in the video.
Copy the XPath query over to the Site Search 360 control panel and place it under "Indexing Control" > "Crawler Settings" in the appropriate XPath section, e.g. "Include Content XPath(s)" or "Image XPath(s)".
Press the "Test" button and enter your webpage URL to test the XPath query. If everything is fine you will see the extracted content, headline, or image URL below.
Include Content XPath(s): Only content found by these XPaths will be indexed. Leave empty if everything should be indexed.
Title XPath(s): The XPath pointing to the main title of the page. Default is , change if is different for your site.
Exclude Content XPath(s): One XPath per line. Content found by these XPaths will not be indexed. Leave empty if everything should be indexed.
Search Snippet XPath: The XPath pointing to the content that you want to be shown in the search results. Note that you have to change the Search Snippet setting under "Search Settings".
After you set all your XPaths don't forget to save the new settings.
For the new settings to take effect, you have to re-index your entire site under the "Index Control" section in the SS360 control panel.
If you have any questions concerning this process please feel free to contact us. Use our live chat in Gitter or use the chat widget on the Site Search 360 main site. Of course you may also reach us via email at mail[at]sitesearch360.com.


The claimed invention comprises software methods for user continuity and physiological state change assessments through generating biometric profiles of individuals and/or groups of individuals by using physiological input together with biomathematical modeling and machine learning techniques. Intra-individual variation in physiological variables including, but not limited to: heart rate, heart rate variability, pulse waveform and its derivatives, blood pressure, breathing rate, cardiopulmonary coupling, actigraphy and circadian rhythms characteristics, are exploited.
1. A method to generate at least one biometric profile of at least one individual individuals and/or groups of individuals, from physiological data generated during sleep and related stages, with said methods exploiting the intra-individual variation manifesting from one or a combination of two or more physiological features, exemplified by, but not limited to: heart rate (HR), RR intervals from which heart rate variability (HRV) is determined, breathing rate (BR), acceleration (ACC) features, arterial pulse wave form together with its derivatives and associated pulse shape characteristics (for example augmentation index, left ventricular stroke slope (LVSS) LVET, PPGRF, and pulse wave velocity, blood pressure, cardiopulmonary coupling information, actigraphy, and circadian rhythms-derived features.
2. Matching a minimum of one physiological feature obtained from an individual and/or group of individuals to biometric profiles generated by the methods of claim 1, in order to either verify or disprove the identity of the individual/group of individuals from which said biometric profile/s was generated, said physiological data features includes heart rate (HR), RR intervals from which heart rate variability (HRV) is determined, breathing rate (BR), acceleration (ACC) features, arterial pulse wave form together with its derivatives and associated pulse shape characteristics (for example augmentation index, left ventricular stroke slope (LVSS), LVET, PPGRF, and pulse wave velocity), blood pressure, cardiopulmonary coupling information, actigraphy, and circadian rhythms-derived features.
3. The method of claim 1, where biometric profiles are applied to identify users and/or groups of users to authenticate a.) activation and deactivation of electronic devices and/or b.) access and usage of electronic devices, said electronic devices exemplified by, but not limited to, wearable, medical, computing, gaming, smart and implantable devices, by matching one of a combination of two or more physiological features, exemplified by, but not limited to, heart rate (HR), RR intervals from which heart rate variability (HRV) is determined, breathing rate (BR), acceleration (ACC) features, arterial pulse wave form together with its derivatives and associated pulse shape characteristics (for example augmentation index, left ventricular stroke slope (LVSS), LVET, PPGRF, and pulse wave velocity), blood pressure, cardiopulmonary coupling information, actigraphy, and circadian rhythms-derived features, to said biometric profile.
4. The method of claim 1, where biometric profiles of users and/or groups of users are communicated wirelessly from device or platform of origin to remote devices, said devices exemplified by, but not limited to, wearable, medical, computing, gaming, smart and implantable devices and/or cloud-based platforms and databases, exemplified by, but not limited to, medical, insurance and retail databases.
5. A device acquiring one or a combination of two or more physiological sleep stage or related data streams, exemplified by, but not limited to: heart rate, heart rate variability, pulse waveform and its derivatives, augmentation index, pulse wave velocity, blood pressure, breathing rate, cardiopulmonary coupling, actigraphy and circadian rhythm characteristics, and communicating said acquired data streams to a cloud-based platform, where the method of claim 1 is executed to generate biometric profiles, and communicating said profiles and/or condensed profiles back to said device and/or other device(s), database(s), or cloud-based platform(s), exemplified by, but not limited to medical, insurance or retail databases, wearable, computer, smart and implantable devices.
6. A device acquiring one or a combination of two or more physiological sleep stage or related features, exemplified by, but not limited to: heart rate, heart rate variability, pulse waveform and its derivatives, augmentation index, pulse wave velocity, blood pressure, breathing rate, cardiopulmonary coupling, actigraphy and circadian rhythms characteristics, and executing the method of claim 1 on said device to generate biometric profiles using said features, and communicating said biometric profiles to one or a combination of two or more of the following: cloud-based platforms, databases, exemplified by, but not limited to, insurance, medical and retail databases, wearable, computer, smart and implantable devices.
7. The method of claim 1, where intra-individual variation manifesting from physiological features obtained during whole day (24 h) stages, exemplified by, but not limited to, heart rate, heart rate variability, gait analysis, breathing rate and pulse waveform, are utilized together with physiological features obtained during sleep and related stages to generate biometric profiles and/or user continuity and physiological state change assessments in individuals and/or groups of individuals.
8. Software methods generating biometric profiles of individuals and/or groups of individuals from physiological data generated during sleep and related stages, with said methods exploiting the intra-individual variation manifesting from one or a combination of two or more physiological features, exemplified by, but not limited to: heart rate (HR), RR intervals from which heart rate variability (HRV) is determined, breathing rate (BR), acceleration (ACC) features, arterial pulse wave form together with its derivatives and associated pulse shape characteristics (for example augmentation index, left ventricular stroke slope (LVSS), LVET, PPGRF, and pulse wave velocity), blood pressure, cardiopulmonary coupling information, actigraphy, and circadian rhythms-derived features, and utilizing generated biometric profiles to assess acute and/or chronic physiological state changes in an individual/s, exemplified by, but not limited to: pregnancy, growth, or pathophysiological changes, by matching a minimum of one physiological feature to said profile.
9. The method of claim 8, where biometric profiles for assessing physiological state changes of users and/or groups of users, or condensed versions of said profiles, are communicated wirelessly from device or platform of origin to remote devices, said devices exemplified by, but not limited to, wearable, medical, computing, gaming, smart and implantable devices and/or cloud-based platforms and databases, exemplified by, but not limited to, medical, insurance and retail databases.
10. A device acquiring one or a combination of two or more physiological sleep stage or related features, exemplified by, but not limited to: heart rate, heart rate variability, pulse waveform and its derivatives, augmentation index, pulse wave velocity, blood pressure, breathing rate, cardiopulmonary coupling, actigraphy and circadian rhythms characteristics, and communicating said acquired features to a cloud-based platform, where the method of claim 8 is executed, and communicating said profiles and/or condensed profiles back to said device and/or other device(s), database(s), or cloud-based platform(s), exemplified by, but not limited to medical, insurance or retail databases, wearable, computer, smart and implantable devices.
11. A device acquiring one or a combination of two or more physiological sleep state or related features, exemplified by, but not limited to: heart rate, heart rate variability, pulse waveform and its derivatives, augmentation index, pulse wave velocity, blood pressure, breathing rate, cardiopulmonary coupling, actigraphy and circadian rhythms characteristics, and executing the method of claim 8 on said device to generate biometric profiles using said features, and communicating said biometric profiles to one or a combination of two or more of the following: cloud-based platforms, databases, exemplified by, but not limited to, insurance, medical and retail databases, wearable, computer, smart and implantable devices.
12. The method of claim 8, where intra-individual variation manifesting from physiological features obtained during whole day (24 h), exemplified by, but not limited to, heart rate, heart rate variability, gait analysis, breathing rate and pulse waveform, are utilized together with physiological features obtained during sleep stages to generate biometric profiles and/or user continuity and physiological state change assessments of individuals and/or groups of individuals.
13. A system to generate at least one biometric profile of at least one individual from physiological data generated during sleep and related stages, the system comprising: a. at least one device configured to acquire and/or provide data of physiological features from the at least one individual; and b. computing means configured to: i. generate the at least one biometric profile of the at least one individual from the acquired data of the physiological features of the at least one individual; and ii. store the at least one biometric profile for later use.
14. The system of claim 13, wherein the physiological data used to generate the at least one biometric profile is generated from sleep stages of the at least one individual.
15. The system of claim 13, wherein the biometric profile is generated by exploiting intra-individual variation manifesting from the acquired data of the physiological features.
16. The system of claim 15, wherein the acquired data of the physiological features is compiled over a twenty-four hour period.
17. The system of claim 16, wherein the at least biometric profile is generated from the acquired data of at least one physiological features of the individual, wherein the at least one physiological feature comprises at least one of heart rate (HR), RR intervals from which heart rate variability (HRV) is determined, breathing rate (BR), acceleration (ACC) features, arterial pulse wave form together with its derivatives and associated pulse shape characteristics, blood pressure, cardiopulmonary coupling information, actigraphy, circadian rhythms-derived features, or a combination thereof.
18. The system of claim 17, wherein the pulse shape characteristics comprises augmentation index, left ventricular stroke slope (LVSS) LVET, PPGRF, and/or pulse wave velocity.
19. The system of claim 17, wherein the at least one biometric profile is generated from a combination of two or more of the physiological features.
20. The system of claim 13, wherein the computing means is further configured to obtain data of current physiological features of the at least one individual from the at least one device and compare the data of the current physiological features to the at least one biometric profile of the at least one individual to verify the identity of the at least one individual.
21. The system of claim 20, wherein upon verification, the system can allow the at least one individual to use an electronic device associated with the at least one device.
22. The system of claim 13, further comprising a cloud-based platform comprising the computing means, wherein the at least one device is configured to communicate the physiological features to the cloud-based platform.
23. The system of claim 22, wherein the at least one device comprises at least one wearable device.
24. The system of claim 23, wherein the at least one device further comprises a medical device and/or a database.
25. The system of claim 24, wherein the cloud-based platform is further configured to communicate the at least one biometric profile to a plurality of remote devices.
26. The system of claim 13, wherein the computing means is further configured to obtain data of current physiological features of the at least one individual from the at least one device and compare the data of the current physiological features to the generated biometric profile to monitor for a change in a physiological state of the at least one individual.
27. The system of claim 13, wherein the system is further configured to generate biometric profiles for a plurality of individuals.
28. The system of claim 27, wherein the plurality of biometric profiles are applied to identify the plurality of individuals.
29. A method for generating a biometric profile from physiological features of an individual, the method comprising: a. acquiring physiological data of generated during sleep and related states of the individual, the acquired data compiled over a continuous twenty-four hour period; b. generating the biometric profile from the intra-individual variation manifesting from the data of one or a combination of physiological features; c. acquiring data of current physiological features of the individual; and d. comparing the acquired data of current physiological features to the generated biometric profile.
30. The method of claim 29, wherein the physiological comprises at least one of heart rate (HR), RR intervals from which heart rate variability (HRV) is determined, breathing rate (BR), acceleration (ACC) features, arterial pulse wave form together with its derivatives and associated pulse shape characteristics, blood pressure, cardiopulmonary coupling information, actigraphy, circadian rhythms-derived features, or a combination thereof.
31. The method of claim 29, wherein after comparing the acquired data of current physiological features to the generated biometric profile, verifying the individual generating the current physiological features is the same individual associated with the generated biometric profile.
32. The method of claim 29, further comprising monitoring for a change in a physiological state of the individual.
 The present invention claims priority to Provisional Patent Application No. 62/425,977, files on Nov. 23, 2016, which is relied upon and incorporated here in its entirety by reference.
 The present invention relates to the field of biometric modalities and technologies, physiological signal processing and computation of biological data.
 According to the World Health Organization, health care is one of the leading cost categories within the US economy alone. Regardless of the intensity of spending in this sector, when comparing monetary investment to tangible health outcomes, the US is ranked last in the quality of care provided. Moreover, health care and insurance fraud, i.e. the intentional presenting of false information on healthcare claims for the purpose of personal gain by the beneficiary, is associated with estimated losses standing in the billions of dollars annually, with the absorption of the greater part of these costs befalling tax payers.
 Concurrent with recent advances in the collection of physiological data and the application thereof, major advances in the operating of wearable devices have empowered users to not only have a better understanding of their personal health and the requirements for a healthy disposition, but also to manipulate the conditions of data collection for personal gain. For example, users can switch devices between each other to be beneficiaries of medical schemes offering rewards for reaching pre-determined fitness and dietary goals.
 A strong need exists for a simple solution to authenticate patient identity in order to prevent health care and insurance fraud, while strengthening security and privacy as well as convenience, and diminishing costs for beneficiaries, medical facilities and insurance companies alike.
 Biometric identification systems that are currently employed in the health care and insurance sectors are cumbersome, time consuming and expensive, and require access to fingerprints, hand veins and face of an individual in order to create a unique signature.
 Recent advances in technologies enabling physiological data harvesting, as well as how the resultant data is used and/or applied, has enabled researchers to simultaneously accrue multiple data metrics for an individual, including, but not limited to, fitness metrics, sleep patterns, heart rate, and heart rate variability during events such as, but not limited to, sleep, exercise and rest.
 The inventors recognized that existing biometric technologies used for biometric identification are prohibitively expensive, time consuming, invasive and can be, with some innovation from the user, bypassed. Therefore, there is a need for systems and methods used for monitoring physiological state change and assessing biometric user continuity that accurately and reliably monitor and/or identify a user in an unobtrusive and convenient manner.
 The claimed invention is comprised of software systems and methods that use physiological input acquired during sleep, rest and free-living stages to generate biometric profiles of individuals and/or groups of individuals. These systems and methods can acquire such input through the use of wearable devices. In such aspects, the profiles are directed to a set of parameters that describes the state and variation of a user's physiology during sleep, rest, and free-living stages. For example, the profiles can include a user's heart rate and typical high frequency/low frequency HRV ratio and standard deviation means and standard deviations respectively.
 During sleep--a natural, periodically recurring state of immobility during which the nervous system is largely inactive, the eyes are closed, and postural muscles are relaxed--physiology is largely unaffected by conscious behaviour or environmental stimuli. This divide broadens the extent to which cardiopulmonary physiology can be deciphered, as the different sleep stages (for example, REM, stage 1 NREM, stage 2 NREM and stage 3 NREM) also represent different physiological states that manifest in cardiac and pulmonary activity. This activity, and resulting patterns of the vital signs, are generated by a highly similar physiology, unique to an individual, and yet highly varied between individuals. These patterns may be monitored with the use of non-intrusive wearable devices while the subject is asleep in its own surroundings.
 In addition to physiological input acquired from wearable devices, input may also be acquired from, for example, but not limited to, medical devices and databases. The intra-individual variation manifesting from physiological metrics acquired during sleep, rest and free-living stages, is exploited to generate said biometric profiles. The physiological metrics can include, but is not limited to, heart rate, heart rate variability, blood pressure, breathing rate, cardiopulmonary coupling, actigraphy, accelerometry, circadian rhythms characteristics, and pulse waveform, its derivatives, and associated pulse shape characteristics, such as augmentation index, left ventricular ejection time (LVET), photoplethysmographic (PPG) rising front (PPGRF), left ventricular stroke slope (LVSS) and pulse wave velocity.
 The systems and methods of the invention utilize physiological data streams from an individual as input in order to a.) assess user continuity i.e., assess whether the same user is continuously wearing and/or operating a device; and b.) monitor physiological state changes i.e. determine whether there are abnormal physiological changes occurring in a user. The data streams can include data exemplified by, but not limited to, heart rate, heart rate variability, blood pressure, breathing rate, cardiopulmonary coupling, actigraphy, accelerometry, circadian rhythms characteristics, and pulse waveform, its derivatives, and associated pulse shape characteristics, such as augmentation index. LVSS, LVET, PPGRF, and pulse wave velocity. In an aspect, user continuity assessment and physiological state change monitoring is done by matching acquired physiological streams to an individual's generated biometric profile. In an aspect, the system and method includes the initial generation of a biometric profile for an individual that can then be compared to later-acquired physiological streams from the individual. In an aspect, the generation of a biometric profile can occur from only one typical sleep session for the user which includes stretches of continuous data streams of the type of data disclosed above about the user. In an aspect, such data streams are collected by wearable data acquisition device. Moreover, the collection of data streams by the wearable data acquisition device may be relayed between said wearable data acquisition device, a mobile device, and/or a cloud based computing platform, and database for storage of a holistic physiological user profile.
 Generated physiological profiles may be used for physiological state change monitoring and/or biometric user continuity assessment in individuals and/or groups of individuals. In particular embodiments, intra-individual differences observed in the patterns of the vital signs are used to construct unique physiological user profiles. Additionally, methods are presented for real time training of said user profiles against which incoming data streams are compared. In particular embodiments, sleep stage, pulse waveform and circadian rhythm-derived input is used to construct unique user profiles, with said profiles subsequently used to monitor abnormal physiological state changes, for the purpose of detecting underlying disease states and/or assessment of user continuity.
 In an aspect, the invention is directed to software methods to generate biometric profiles of individuals and/or groups of individuals, from physiological data generated during sleep and related stages, with said method exploits the intra-individual variation manifesting from one or a combination of two or more physiological features, exemplified by, but not limited to: heart rate (HR), RR intervals from which heart rate variability (HRV) is determined, breathing rate (BR), acceleration (ACC) features, arterial pulse wave form together with its derivatives and associated pulse shape characteristics (for example augmentation index), left ventricular stroke slope (LVSS), and pulse wave velocity, blood pressure, cardiopulmonary coupling information, actigraphy, and circadian rhythms-derived features to said biometric profiles. In another aspect, the invention is directed to matching a minimum of one physiological feature obtained from an individual and/or group of individuals to the generated biometric profiles in order to either verify or disprove the identity of the individual/group of individuals from which said biometric profile/s was generated. In another aspect, the invention is directed to methods of applying biometric profiles to identify users and/or groups of users to authenticate a.) activation and deactivation of electronic devices and/or b.) access and usage of electronic devices, said electronic devices exemplified by, but not limited to, wearable, medical, computing, gaming, smart and implantable devices, by matching one of a combination of two or more physiological features to said biometric profiles.
 In an aspect, the invention is directed at communicating the created biometric profiles of users and/or groups of users wirelessly from device or platform of origin to remote devices, said devices exemplified by, but not limited to, wearable, medical, computing, gaming, smart and implantable devices and/or cloud-based platforms and databases, exemplified by, but not limited to, medical, insurance and retail databases.
 In an aspect, the invention is directed at a device that acquires one or a combination of two or more physiological sleep stage or related data streams, exemplified by, but not limited to: heart rate, heart rate variability, pulse waveform and its derivatives, augmentation index, pulse wave velocity, blood pressure, breathing rate, cardiopulmonary coupling, actigraphy and circadian rhythm characteristics, and communicating said acquired data streams to a cloud-based platform to generate biometric profiles, and communicating said profiles and/or condensed profiles back to said device and/or other device(s), database(s), or cloud-based platform(s), exemplified by, but not limited to medical, insurance or retail databases, wearable, computer, smart and implantable devices.
 In an aspect, the invention is directed at a device that acquires one or a combination of two or more physiological sleep stage or related features, exemplified by, but not limited to: heart rate, heart rate variability, pulse waveform and its derivatives, augmentation index, pulse wave velocity, blood pressure, breathing rate, cardiopulmonary coupling, actigraphy and circadian rhythms characteristics, and can generate biometric profiles using said features, and can communicate said biometric profiles to one or a combination of two or more of the following: cloud-based platforms, databases, exemplified by, but not limited to, insurance, medical and retail databases, wearable, computer, smart and implantable devices.
 In an aspect, the invention is directed at a method that utilizes intra-individual variations manifesting from physiological features obtained during whole day (24 h) stages with physiological features obtained during sleep and related stages to generate biometric profiles and/or user continuity and physiological state change assessments in individuals and/or groups of individuals.
 In an aspect, the invention is directed to software methods generating biometric profiles of individuals and/or groups of individuals from physiological data generated during sleep and related stages, with said methods exploiting the intra-individual variation manifesting from one or a combination of two or more physiological features, and utilizing generated biometric profiles to assess acute and/or chronic physiological state changes in an individual/s, exemplified by, but not limited to: pregnancy, growth, or pathophysiological changes, by matching a minimum of one physiological feature to said profile. In an another aspect, the invention can utilize the biometric profiles for assessing physiological state changes of users and/or groups of users, or condensed versions of said profiles, are communicated wirelessly from device or platform of origin to remote devices, said devices exemplified by, but not limited to, wearable, medical, computing, gaming, smart and implantable devices and/or cloud-based platforms and databases, exemplified by, but not limited to, medical, insurance and retail databases.
 In an aspect, the invention is directed to a device that can acquire one or a combination of two or more physiological sleep stage or related features and communicate said acquired features to a cloud-based platform where profiles or condensed profiles are formed, and communicate said profiles and/or condensed profiles back to said device and/or other device(s), database(s), or cloud-based platform(s), exemplified by, but not limited to medical, insurance or retail databases, wearable, computer, smart and implantable devices.
 In an aspect, the invention is directed at a device that acquires one or a combination of two or more physiological sleep state or related features, generates biometric profiles using said features, and communicates said biometric profiles to one or a combination of two or more of the following: cloud-based platforms, databases, exemplified by, but not limited to, insurance, medical and retail databases, wearable, computer, smart and implantable devices.
 These and other objects and advantages of the invention will become apparent from the following detailed description of the preferred embodiment of the invention.
 Both the foregoing general description and the following detailed description are exemplary and explanatory only and are intended to provide further explanation of the invention as claimed. The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute part of this specification, illustrate several embodiments of the invention, and together with the description serve to explain the principles of the invention.
 FIG. 1 is a schematic representation of an ecosystem in which preferred embodiments may operate, together with the flow of information with regards to device communication.
 FIG. 2 shows the variability of heart rate distribution during sleep between two different subjects, collected over two nights.
 FIG. 1 illustrates an aspect of a system and method of the present invention. As shown, the system includes a device 101 associated with a user. In an aspect, the device 101 is a wearable device 101. The device 101 can include, but is not limited to, photoplethysmography (PPG), temperature, electrodermal (EDA) and accelerometer (ACC) sensors. In an aspect, such sensors are used to measure cardiac, motion and temperature signals, exemplified by, but not limited to, heart rate, accelerometry, and body temperature, in order to determine patterns of the vital signs presented during sleep, rest, and free-living stages that are unique to the individual. In an aspect, the wearable device 101 comprises processing means that are configured to take the vital signs (captured as signals) to digitally process them into heart rate and related metrics. In an aspect, the wearable device 101 can include the wearable device disclosed in U.S. patent Ser. No. 14/128,675 entitled "Personalized Nutritional and Wellness Assistant", which is fully incorporated in its entirety herein by reference. In other aspects, the wearable device 101 can be any wearable device 101.
 Further biological-based processing may follow, either within the wearable device 101 or sending the heart rate and related metrics over communication networks 102 (e.g., wireless network) to a different device or cloud-based platform 103. In an aspect, on said platform 103, the acquired physiological data streams are segmented into categories. A classifier 104 can segment the data streams into the categories. In an aspect, the classifier 104 can utilize classification algorithm(s), exemplified by, but not limited to, a random forest model, logistic regression, or neural network. In an aspect, the classifier 104 can segment the data streams into biological states. In an exemplary aspect, the biological states include 1. Sleep 105; 2. Rest 106; and 3. Free-living 107.
 In an aspect, a list of features is created for each biological state, including, but not limited to: RR intervals from which heart rate variability (HRV) is determined, breathing rate (BR), acceleration (ACC) features, arterial pulse wave form together with its derivatives and associated pulse shape characteristics (for example augmentation index (i.e., systemic arterial stiffness derived from the ascending aortic pressure waveform), LVSS, LVET, PPGRF, and pulse wave velocity), blood pressure, cardiopulmonary coupling information, actigraphy, and circadian rhythm-derived data. In an aspect, LVET.about..apprxeq.k1/LVSS and LVET.about.=k2/PPGRF, wherein k1 and k2 are constant. The equation shows that there is an inverse linear relationship between LVET and PPGRF. In an exemplary aspect, when PPGRF is calculated on a pulse with normalized amplitude of 1, k2 would have a value of 1 as well. In other aspects, k2 would be the height of a pulse in the unit wherein it was measured.
 Additional input for cloud-based biological processing may also be obtained from other devices, for example, but not limited to, medical devices 108 (e.g. blood pressure devices and pulse oximeters) and databases 109 (e.g., medical and genetic databases which can include information associated to the individual or general information, or both).
 A list of features related to the sleep, rest, and free-living states may be extracted from physiological data streams to create a vector (i.e., an n-dimensional representation of an object) of features. This vector of features and the relationship between features may be modeled using any generative modeling technique 110. An example of such a modeling technique is the fitment of a multivariate normal distribution to the vector of features. Observable patterns of the vital signs, resulting from a highly similar physiology, unique to an individual, and yet highly varied between individuals, may be determined from said model. Distributions may be modeled explaining the typical user specific values of each feature, together with the user specific interaction between said features. Such a model forms a unique user profile 111 for a given individual. Simplistic models, such as the above-mentioned fitting of a multivariate normal distribution may be used, together with more complex approaches, such as kernel methods, to capture non-linear relationships. Investigations of non-linear transformations on said features may also be used to capture non-linear relationships. Unique user profiles 111 may subsequently be constructed from said observable patterns.
 Constructed user profiles may be used to a) verify whether data obtained from a wearable device is associated with the predetermined owner of the device, regardless of it being exchanged between individuals and b) to determine cues that indicate whether changes have taken place in the physiological state of the user, pointing to occurrences such as, but not limited to: pregnancy, growth, or pathophysiological changes. Subsequently, any such cues may be harnessed to motivate the collecting of additional information, prior to a specific diagnosis.
 In particular embodiments of the claimed invention, physiological data streams obtained by, for example, but not limited to, PPG, ACC, EDA, and temperature sensors contained in a wearable device 101, are communicated wirelessly 102 to a cloud-based platform 103. Additional physiological data streams obtained from databases 109 and other devices 108, such as medical devices 108, may also be communicated to the same platform 103. Acquired physiological data streams are passed through a classifier 104, which can use various classification algorithms, including, but not limited to, a random forest model algorithm, logistic regression algorithm, neural network algorithms, and the like, on said platform 103, with data streams being classified into three biological states, namely 1. Sleep 105 2. Rest 106 3. Free-living 107. Further biological-based processing and modeling 110, to create unique user profiles 111, can follow as described below.
 Biological-based processing (either on the wearable device 101 or on a cloud-based platform 103) results in metrics exemplified by, but not limited to, heart rate (HR), RR interval, heart rate variability (HRV), breathing rate (BR) and acceleration (ACC). On said cloud-based platform, several features, including, but not limited to, HR features, HRV features, ACC features, cardio-pulmonary coupling (CPC) and sleep stage features, are derived by a mapping created from measures such as HR, RR intervals and ACC, to an individual's sleep stages by training machine learning models, such as a bidirectional long short term memory (LSTM) neural network. Typical variation of features and interactions between features may be quantified by the fitting of a multivariate normal distribution. The resulting distribution explains the typical values of each of said features, together with the interaction between said features. Such a model forms a unique profile for a given individual. The above-mentioned fitting of a multivariate normal distribution can be improved by incorporating more complex approaches, such as kernel methods, for the capturing of non-linear relationships and/or investigations of non-linear transformations on features.
 FIG. 2 demonstrates an example of how individual biometric user profiles 111 are generated from sleep stage physiological data streams, exemplified by heart rate during sleep, and may be applied towards identification and/or detection of abnormal physiological conditions. The underlying physiology results in highly similar patterns being generated for an individual each night, but which differs greatly between individuals. This figure demonstrates how individual biometric profiles are generated from physiological data streams, exemplified by heart rate, and its application of identification as well as detection of abnormal physiological conditions.
 The variability of heart rate distribution during sleep between two different subjects, collected over two nights, is shown. As observed between subject one 201 and subject two 202, the underlying physiology results in highly similar patterns being generated for an individual each night (201a vs. 201b and 202a vs. 202b), but which differs greatly between individuals (201a and b vs. 202a and b). The heart rate of two individual subjects 201, 202 was measured every second for two nights during sleep (201a, 201b, 202a, 202b), respectively. Thereafter, the range of values is binned (x-axis) and plotted against the number of observations (y-axis) made of a certain heart rate. Similarly, additional features other than heart rate during sleep, together with interactions between said features, may be modeled to contribute to a user specific profile.
 With each heartbeat, a pulse wave propagates through the body's vascular system. The nature of this pulse varies depending on factors such as blood pressure, pulse wave velocity, arterial stiffness and age. These factors vary significantly between individuals, but remain near constant for an individual. In particular embodiments of the invention, these unique pulse waves are detected using photoplethysmogram (PPG) sensors contained in a wearable device 101. In an aspect, the wearable device 101 has internet connectivity capabilities. On-device signal processing results in a typical measured pulse wave form from which features are extracted, exemplified by, but not limited to, the first and second derivatives, augmentation index, LVSS, LVET, PPGRF, amplitude of the original wave, amplitude of the reflected wave and vasodilation. Said extracted features are strongly correlated with factors such as blood pressure, pulse wave velocity, arterial stiffness and age. This renders pulse wave form as an ideal distinguishing factor between individuals. Moreover, when the variation in pulse wave form is compared to heart rate variation, a strong relationship is observed in the sense that varying heart rate exerts a significant yet predictable effect on pulse wave form, and the effect of varying HR on pulse wave can be modeled and compensated for where necessary.
 From all above-mentioned features, a multivariate normal distribution, or more complex non-linear methods, such as kernel methods and investigations of non-linear transformations, may be fitted to produce a unique user specific profile.
 Circadian rhythms are behavioural, physical, and mental changes that adhere to a roughly 24 hour cycle. Through utilizing certain characteristics of several circadian rhythms, such as a) circadian period, b) circadian minimum and maximum values, and c) circadian amplitude, a unique user profile, termed a circadian rhythms profile, can be generated for an individual. As each of these characteristics of circadian rhythms are distinctively different amongst individuals, as well as between physiological processes, multiple circadian rhythm data points may be used to generate user profiles used to identify individuals, or a group of individuals, from a larger group. Said user profiles may also be used to detect underlying physiological state change. For example, the changes can help to determine disease states. Used in combination with sleep and rest stage-generated user profiles, a user may obtain their risk status for clinical disease and/or verification of presence or absence of underlying disease states. An example would be a user with an underlying (and difficult to pick up and diagnose) cardiac electrophysiological disorder. Measured metrics such as RR intervals and heart rate variability may present indications of abnormal cardiac QRS complexes when compared to a normal profile.
 In particular embodiments, circadian-rhythm-related physiological data is collected for 24 hours a day at a specific sampling rate with a wearable device 101 and/or medical device 108. The circadian-rhythm-related physiological data can include, but is not limited to, heart rate, skin temperature, core body temperature and heart rate variability. The recorded data may be directly stored on the device 101 for processing and/or uploaded to a cloud-based platform 103 for biological-based processing. On said cloud-based platform 103, several features, including, but not limited to, circadian period, circadian minimum and maximum, circadian amplitude, HR, HRV features, ACC features, and vasodilation features, are derived by a mapping created from measures such as HR, RR intervals and ACC, to an individual's free-living stage by training machine learning models, such as, but not limited to, bidirectional LSTM neural network models. Features derived from circadian rhythm-related data may be considered over a 24-hour period with time-of-day represented on the X-axis and relative change over one cycle represented on the Y-axis. Generated physiological points of intersection that together make up a circadian profile that is unique to an individual.
 Typical variation of features and interactions between features (i.e., the quantification of more than one set of features on one plot, how two different sets of features interact) may be quantified by the fitting of a multivariate normal distribution. Such a distribution explains the typical values of each of said features, together with the interaction between said features, forming a unique profile for a given individual. From all above-mentioned features, a multivariate normal distribution, or more complex non-linear methods can be fitted to produce a unique user specific profile. Examples of such models include, but are not limited to, a simplistic model such as fitting of a multivariate normal distribution, and more complex approaches, such as kernel methods for the capturing of non-linear relationships and/or investigations of non-linear transformations on features.
 In order to a.) assess user continuity (i.e., inter-individual comparison to assess whether the same user is continuously wearing and/or operating a device) and b.) monitor physiological state changes (i.e., intra-individual comparison to determine whether there are abnormal physiological changes occurring in a user), incoming physiological data streams may be continuously compared against user profiles generated from data obtained during sleep, rest and free-living states (as described in the previous sections) through real time training of generated user profiles. In particular embodiments, incoming physiological data streams are mapped to a generated user profile by creating a list of features from the data streams associated with sleep, rest and free-living states through real time training of machine learning models such as, but not limited to, bidirectional neural networks. The physiological data streams can include, but are not limited to, RR intervals from which HRV is determined, BR, ACC features, arterial pulse wave form together with its derivatives and associated pulse shape characteristics (for example augmentation index, LVSS, HR derivatives such as entropy calculations, LVET, PPGRF, and pulse wave velocity), blood pressure, cardiopulmonary coupling information, actigraphy, and circadian rhythm-derived data. A vector of features associated with sleep, rest, and free-living states is extracted from physiological data streams. Said vector of features and the relationship between features are modeled using any generative modeling technique, for example, but not limited to, simplistic modeling such as the fitment of a multivariate normal distribution of the vector of features, together with more complex approaches, such as kernel methods to capture non-linear relationships. Investigations of non-linear transformations on said features can be used to capture non-linear relationships. By using the above-mentioned techniques, distributions may be modeled explaining the typical user specific values of each feature, together with user specific interaction between said features. Features from incoming physiological data streams are compared to said distributions using Bayesian modeling techniques to determine, for example, but not limited to, a user specific match and/or abnormal physiological state.
 In an aspect, intra-divisional differences illustrate the deviations in a biometric profile/fingerprint currently collected to a previously generated (or baseline) biometric profile/fingerprint that is based upon historic data of the identified user. Such deviations can be used to help identify drift in profiles (i.e., change), which can indicate that the user is experiencing a chronic disease state. For example, the HRV high-frequency:low-frequency ratio of a user can form part of his/her biometric fingerprint and will decrease with age. In a person that experiences an increased sympathetic nervous system tone due to a flu infection, this value might decrease much faster and could be used as an alert that the user's physiology is exiting the normal, personalized range of variation.
 Once an individual's biometric profile has been created, the biometric profile can be used to identify users (inter-individual comparison) for security purposes. In an aspect, various electronic devices are configured to be available to only authorized individuals. Certain biometric profiles can be assigned to the electronic devices. Upon the matching of the biometric profiles, the electronic devices can be activated, deactivated, and/or accessed dependent on the electronic device. In an aspect, the electronic device itself can generate a biometric profile to compare to the stored biometric profile. In other aspects, the electronic device can be in communication with another device that is configured to verify the user. The electronic devices can include, but are not limited to, wearable, medical, computing, gaming, smart and implantable devices.
 Having thus described exemplary embodiments of the present invention, those skilled in the art will appreciate that the within disclosures are exemplary only and that various other alternatives, adaptations, and modifications may be made within the scope of the present invention. Accordingly, the present invention is not limited to the specific embodiments as illustrated herein.


a woman's purse or handbag.
a person's financial resources or means: The price was out of reach of his pocketbook.
Also pocket book. a book, usually paperback, that is small enough to carry in one's coat pocket.
a notebook for carrying in one's pocket.
Seemingly possessed by demons, she would slip objects from the homes of rich people they visited into her pocketbook.
Not many doctors are likely to reach for the pocketbook on that scale without quite a bit of deliberation.
Because they're not limited by their checkbook or pocketbook, they can buy a whole swath of products.
Republicans face increasing difficulty with the Hispanic vote for pocketbook reasons.
Pocketbook issues make Hispanics natural Democrats, not natural Republicans.
Holmes examined it for some time, and then, folding it carefully up, he placed it in his pocketbook.
He gave them his pocketbook, which, he said, contained forty dollars.
After a glance at it I put the crumpled paper in my pocketbook.
Some one must have been here recently and taken the pocketbook.
He took my pocketbook, too, with two dollars and twenty-seven cents in it.
also pocket-book, 1610s, originally a small book meant to be carried in one's pocket, from pocket (n.) + book (n.). Meaning "a booklike leather folder for papers, bills, etc." is from 1722. Meaning "a woman's purse" is from 1816.


I offered no compensation for taking the survey - THANK YOU AUTHORS for donating your time!
1) Cumulative years of pursuing writing as a career before first book sold?
2) What type of house published your debut book?
More than half of the young adult authors participating in the survey were published by Big Five publishers.
I asked authors to rate their experience with their publisher on a scale from 1 (terrible) to 10 (amazing).
Authors at larger and smaller houses reported about the same satisfaction as long as the publisher offered an advance. Authors at houses without advances reported a slightly lower average experience.
As for marketing assistance, authors at large houses reported slightly more marketing assistance than authors at smaller houses.
Did this translate into sales?
I sliced the data a few different ways to see if a few runaway bestsellers were skewing the results (I checked the median and mode, too). The average for large houses is about 20,000 copies no matter how you look at it. The average for small houses is always about half of that.
3) How many drafts did you write of your debut book before it was accepted for publication?
Half of young adult writers wrote four or more drafts of their story before it was accepted for publication.
4) How many hours a week do you devote to writing?
Almost 40% of published young adult writers don't have a regular writing routine.
Most of the authors without a regular writing schedule were published by a Big 5 Publisher (75%). A writer doesn't need a set schedule to be published by a major house, although it doesn't hurt. Most of those working 40+ hours per week were also published by a major house (75%).
5) How many publisher rejections did you receive before your debut sold?
Before you shake a jealous fist at those lucky dogs with no publisher rejections, consider this: all of those authors except one have sold only one book. Their careers sometimes stall after that first book. EVERY AUTHOR EXPERIENCES REJECTION in one way or another.
Also, these numbers don't include agent rejections. One author with no publisher rejections was rejected by agents more than 500 times before selling that debut. Perhaps not coincidentally, he/she is the only one in this set who has gone on to sell books after his/her debut. It pays to have grit.
More on agents later, for now let's look at how many novels a typical young adult author writes before selling something.
6) The average young adult author writes 3.4 "practice stories" before selling one.
In other words, young adult authors write 4.4 stories (including their sell) on average before their debut payday.
7) So how much is the typical young adult author advance?
The average (mean) young adult advance is $38,000. However, this lumps together those with no advance and those making $100,000+. Here are the advance spreads by large and small houses.
The data is clearly a bit patchy. With more data, we'd probably start seeing some nice bell curves. However, according to this data, the most common young adult advance at a smaller house is between $1,000 and $4,999.
The most common advance size at a Big 5 publishers is $10,000 to $19,000, with advances going all the way up to $150,000. Note, all of the advances in the $150,000+ range were titles pitched as series. The highest advance in the survey was over half a million dollars for a three book series.
Wow, some of those advances are HUGE! So what did authors actually earn last year?
8) What was your total writing income last year?
50% of published young adult authors made less than $10,000 last year. Only the top 11% made $50,000 or more.
Wait! Why are most authors earning less than the average advance?
Well, most young adult authors in the survey didn't sell a book last year, and authors don't earn royalties until their books sell enough copies to cover the advance. At least 20% of book never "earn out," and it takes some book several years to earn out. Authors don't earn anything in the meantime.
Some authors only sell one book, while authors with multi-book deals don't receive their offer all at once. Advances for series usually come in chunks over several years at books are accepted for publication.
What helps increase and authors annual income?
Having an agent! The average income for a published young adult author with an agent was $31,000. The average income for one without was $5,000.
Selling a book in that calendar year. The highest earning author said his/her income was 90% advances, 5% royalties, and 5% misc. Note, this author sold 150,000 copies of his/her debut book and has sold 13 trade book titles over a career; however, royalties still only made up a fraction of his/her income. Don't count on royalties to support you. Write something new!
9) What is the typical word count for a young adult novel?
The responses varied from 25,000 to 150,000 words. The average (mean) word count was about 73,000 words.
Most young adult novels fall between 50,000 and 80,000 words.
10) Was your debut young adult novel pitched as a series?
Most debut young adult novels are not pitched as a series.
I asked the authors if their books went on to become series. All of the authors with books pitched as series went on to become one, and twenty-percent of the titles not pitched as a series also transitioned into series. However, overall most young adult novels are stand-alone titles.
11) Do you write in more than one genre?
Most young adult authors (79%) write in more than one genre.
12) Was your young adult debut published with the help of an agent?
73% of young adult novels debuts were published with the help of an agent. This is about the same percent as middle grade novels, but significantly higher than picture book debuts where only about 50% were published with the help of an agent.
I asked the writers if they are currently represented. The percent of representation increased to 76%. Half of the young adult writers who sold their debut without an agent are now represented, and 12% of those with representation for their debut are now without an agent.
This leads into our next question about agent turnover.
13) How many agents have you had over the course of your career?
Most young adult authors (62%) have had one agent, although about a quarter have had two or more agents.
It's somewhat common for a young adult author to shift agents mid-career.
14) How many rejections did you collect before signing with your first agent?
Twenty-five percent of published young adult authors are rejected over 100 times before signing with an agent.
I asked a handful of agents what percent of queries they take on as new clients. Most of the agents take on less than 1% of all queries as clients. If you've been querying agents and receiving A LOT of rejections, you are in good company. It's a tough market, but don't let rejections get you down. Use them as information to improve.
Writers signing with an agent after 30 rejections wrote more drafts of their debut story than those that sold more quickly (4.7 drafts vs 3.1 drafts). They also wrote more stories: 5.5 stories before selling their debut novel vs 2.4 stories for those with fewer rejections. If you've sent your novel to more than 30 agents and haven't had any requests, consider a seeking more feedback and revising before sending it to additional agents.
In other words, if at first you don't succeed, write and write again.
This is why it's so important for an agent to be passionate about a project before they take on additional clients. It can take a lot of energy (and time!) to sell a book.
As for those stories with zero publisher rejections, about half of those were stories where the author came to the agent with an offer in hand. The agent just came on board to negotiate the deal. Most stories on agent submission recieve at least a handful of rejections.
15) How many full requests did you receive before signing with you agent?
Half of young adult authors received ten or more full requests before signing with an agent. Twenty-percent received 100 or more full requests. Note, these requests were probably not all for the same story. Remember, the average young adult author writes more than four novels before selling something.
I also asked authors how often they received personalized feedback from full requests. Authors hear back with specific feedback on less than half of full requests (44%) on average. A full request is a great sign your story is on the right track, but don't be surprised if it doesn't result in a lot of individualized feedback.
16) How long after signing with an agent did it take to sell your first story together?
A third of authors sell their first story more than six months after signing with an agent.
17) If represented by an agent, how many different stories did your agent send on submission last year?
20% of young adult authors had multiple stories out on submission last year. However, most of these authors write in multiple genres and some different age categories. These weren't all necessartily young adult submissions.
18) If represented by an agent, how many stories did you sell last year?
Most young adult authors didn't sell a story last year; however, that's partly because 30% weren't on submission at all. Of the authors on submission, about half sold at least one story.
These numbers are more illuminating than the results of question 16. In question 16, we already know the debut book sold (or they wouldn't be taking the survey). However, in this question it's just: did you sell something last year? According to this question, you have a 57% chance of selling a YA novel over 12 months on submission with an agent...and that's if you've already sold something. It'd also be interesting to know what percent of clients on agent lists have never sold a book.
19) On a scale of 1 (terrible) to 10 (excellent), how much do you like working with your agent?
Most authors really like working with their agent. Only 12.5% of authors rated their agent lower than a 5.
She is always in my corner!
I'm crazy lucky. He's not only a great agent, but a great friend as well.
20) If represented by an agent, what percent of your editor submissions receive responses (rejection or acceptance replies) within four months?
Most agents hear back from 90% or more of their young adult submissions by the four-month mark.
Everyone in this survey heard back from at least half of the editors within four months, although some authors prefer not to be told their submission lists. All of the authors in the survey who wanted a submission list were supplied with one. It's unusual for an agent to withhold a submission list against the author's wishes.
21) If represented by an agent, how many editors did your agent submit to on your most recent round of submitting?
Most agents submitted to less than ten editors on their most recent round of submitting.
22) If represented by an agent, what types of houses did they submit to on your most recent round of submitting?
The majority of agented young adult submissions went to Big Five publishers. According to this survey, no agents submitted exclusively to small houses.
23) How many trade children's book have you sold (with or without an agent) over the course of your career?
Most of the authors in the survey have sold three or fewer books, although one outlier (not pictured below for scale) has sold more than 30 books.
The person selling the most books has been working for more than ten years. Book sales generally increased with years in the industry. However, this isn't guaranteed.
Twenty percent of multi-published young adult authors were not represented. However, all of the authors selling ten or more books had agents.
That said, there isn't a clear correlation between books sold and annual income. An author with three books published won't necessarily earn more money in a year than an author with only one book.
With all these surveys, please remember that data can provide a good overview, but numbers do not define you or your work.
No matter where you are in your creative journey or where life takes you, your work is worth creating! And YOU are worthy of the work.
All the best and happy writing trails!!!


The worker hears a yelled warning over the noise of the jackhammer. The bouncer makes his way towards angry voices across a crowded nightclub floor. The infantryman listens for voices upstairs through the tumult of a helicopter attack outside.
Noise cancellation is nothing new. These devices generate matched frequencies to cancel those from the environment, thus rendering the near side of the headphone quieter. Noice cancelling microphones are also available - these devices get rid of noise not in the frequency of the voice, and allow the microphone to be more effectively used in loud environments.
The NCVA hybridizes these two with some stochastic resonance thrown in to boot. The NCVA device allows people to operate in loud environments with retained perception for human voices. Sound with wavelengths outside the voice are cancelled, but voice wavelengths are allowed through.
One would not want these voice wavelengths amplified, for fear of deafening the user by amplifiying noise components (eg: cymbals) which happened to be in that wavelength as well. The NCVA improves the signal of voice sounds by adding a low background of white noise at these same voice frequencies. This will not make voices louder, but will make them sharper.
I imagine the NCVA as like night vision for noise: hear the important things and not the rest.
I don't think I understand. Is this a noise-canceller that cancels only the noise outside of the human voice range?
Also, a query: what is the spectral width of the human voice (think of the range between a vowel like "ohh" and the hiss of a "th"), and to what extent would it be helpful to cancel out only the noise which was outside of this range?
Finally, I'm not clear about the white- noise benefit. Stochastic resonance helps (sometimes) when the signal is just very very weak, but I'm not sure it helps when the signal is strong but is swamped with noise.
/a noise-canceller that cancels only the noise outside of the human voice range?/ Yes. Like a noise-cancelling microphone.
Yes. After cancellation you will have the remaining frequencies in which there might be a voice. I am not sure if the added noise has to be similar in frequency to the desired signal, but I bet it does. This is a way to improve the perception of voices without simply making them louder. It is really the only new invention in this idea. I lashed it to the noise canceller because only in a situation that was already very noisy would you want to avoid simply amplifying ambient sounds to hear voices better.
(b) that white noise superimposed on top of "voice plus vocal-range-noise" is going to help in any way? Stochastic resonance is mean to help by pushing near-threshold signals over the edge into above-threshold; I don't think it helps with signal-to-noise problems.


I LIKE to collect odd people ... that is, just a little off, not too far out, just offbeat enough to be amusing or interesting. I must tell about Ariadne, a cherub-faced little woman who happens to work as a secretary in an office on the ground floor of my building.
To begin with, I am absolutely captivated by the name Ariadne. If I were ever to introduce her, I am sure I'd be moved to say, ``This is Ariadne auf Naxos,'' for hers has occasioned the only time this rare and fascinating name has welled up from my memory, where it initially was placed during my prewar year in Munich, watching German operas in the little gem of an operahaus, Der Residenz, in the Wittelsbach Palace in 1937.
This morning I happened ruefully to go on a ``dry run.'' I had been told that the secretary of the Hawaii Geographical Society was to give a two-hour talk on Korea this morning at 9; I thought it was to be at the Waikiki-Kapahulu library. After I bathed, dressed, and breakfasted, I steered my little car the longish distance through Waikiki to the Waikiki-Kapahulu library, only to find the auditorium closed tight and not a lecturer in sight.
As I was about to depart for the return trip, I spied a figure on the grass, under a towering palm. Is this a street person? was my first thought. As I walked nearer, I saw that it was someone I but scarcely knew, Ariadne, recumbent on the grass.
Hundreds of cars were driving past on Kapahulu Avenue, and probably every driver, locked intently into his destination, was, on spotting Ariadne, momentarily envious, wishing that he or she could ... but of course never would ... alter his or her time slot or belief structure just long enough to do what this creature was doing, lying prone on a thick grass carpet, gazing up past the lofty palm into the unending blue, complete with clouds as galleons sailing across, propelled by the dependable trades.... At the very least, it could have evoked memories of childhood.
Ariadne responded with alacrity, and as we started driving back along the Ala Wai canal, I asked her politely what she had been doing there, long before the library officially opened.
Not minding my rather nosy question, she said she had been a volunteer there in the past and that the librarian let her photocopy her papers in her office anytime. It seemed to me a little strange for Ariadne to go so far away during business hours, as there is a copy place right across the street from our building.
I immediately grasped that Ariadne was willing to take a bus on her Senior Citizens pass, transfer, and take yet another bus to the place where she felt welcome and comfortable, to get her papers copied, just in order to create a ``fun'' - as well as legitimate, if you stretch a point - outing for herself. And then cap the whole little adventure by flinging herself on the grass under a palm tree before returning.
Hers is a one-woman office, and her boss must be very tolerant.
That Ariadne likes to create outings for herself whenever opportunity arises is illustrated by another arcane bit of Americana, the mysteries of which I was initiated into earlier when I accosted her on a busy street corner below our building.
``Ariadne, where are you going?'' I asked sweetly, knowing again that she would not take offense.
Flash! To the actuality of Ariadne standing there in business clothes, stockings, and street shoes I added the mental picture of her about to trudge through the sand of a Waikiki beach on a blazing Friday afternoon, searching for her boss, rather like a mother seal looking for her baby amid various and assorted recumbent bodies.
There is a space between lagoon and ocean where the several precious few numbered palms spring forth, and happily here she would find him, semi-disguised, no doubt, under dark glasses, his body glistening with suntan oil, prone under palm No. 5, and here he would sign her paper ... an errand surely unique in the annals of secretarial duties, but one upon which she felt not at all imposed in performing.
If this were ``above and beyond the call of duty,'' never mind, she seemed to relish it.
Thus and thus doth Ariadne, not necessarily auf Naxos, create a life not ordinary for herself.


Error Hunt: What is wrong with this statement?
Dylan Minor, a visiting professor in Harvard Business School, and Michael Housman, chief analytics officer in Cornerstone OnDemand, studied just how costly toxic employees are using a large dataset of 60,000 workers across 11 firms from various industries including communications, consumer services, financial services, health care, insurance, and retail.
Answer: There are preposition errors. Prepositions are words that show location in time or space. They are used heavily in business grammar and can be especially confusing to non-native writers.
Examples are in, or, at, to, from, for, between, above, among, around, with, near, along, before, over, under, etc.
At is used with location, while In is used in fields of study or locations.
Dylan Minor, a visiting professor at Harvard Business School, and Michael Housman, chief analytics officer at Cornerstone OnDemand, studied just how costly toxic employees are using a large dataset of 60,000 workers across 11 firms from various industries including communications, consumer services, financial services, health care, insurance, and retail.
Dylan Minor, a visiting professor at Harvard Business School, and Michael Housman, chief analytics officer at Cornerstone OnDemand, studied just how costly toxic employees are. They based their study on a large dataset of 60,000 workers across 11 firms from various industries including communications, consumer services, financial services, health care, insurance, and retail.


So we can add using only NAND gates, but can we subtract? From math, you know that subtracting is just adding a negative. A-B is the same as A+(-B). And making a negative in binary math is really easy: Just take each digit, and invert it, then add one to the resulting number. To invert, we just run each digit though a NOT gate (formed from a NAND with both inputs wired together). Then, adding 1 is easy, since our adder already has a carry input and all a carry in really does is add 1 or 0 to the result. (we will add 1). This "negative of the number" is also called the "additive inverse^" because it's the number you add to make another number go away.
The circuit below is the result, The inputs are from the left. To save space, we just used one number box. The number on the right is subtracted from the number on the left and the result is displayed on the far right. Use the up and down arrows to change the numbers.
If you look back at the adder, you will see that this is almost exactly the same circuit. The only difference (other than using just one input block) is that the wires for one of the numbers goes through that set of NANDs wired as NOTs. And notice the push button at the top. We have that wired into the carry input of the adder, and pre-set to a 1. This adds the one we needed to make our negation into a negative.
If you stare at that long enough, you might start to see the paterns of how the logic works. You can go back to the simple adder to see what is inside each box. Remember: It's all done with NANDs!
The 2 bit to 4 line active low decoder puts things in order.
We aren't /really/ finding the negative number by negating and adding one, but it's close enough. What we are doing is finding the number to add that makes the system exactly overflow.
Notice that the result, 10000 has one more digit, always a 1, and the original 4 digits are now zero. This is what we are doing when we negate and add one to a binary number: We are finding the binary number that makes the original digits zero, with one additional digit. For 0101 (5 decimal), the answer is 1010+1 or 1011 (11 decimal), because 0101+1011=10000 in binary math (5+11=16).
But in digital logic math, we only have so many wires, so that extra digit is lost (or becomes a carry out). So what did we really do if we always ignore the extra top digit? We found the number to ADD which makes the original number zero. And what is a number you add that makes the original zero? It's the negative of that number: A+(-A)=0.
The explanation I found easiest to understand is that of the "additive inverse" or the number you add to something to make it "go away" or rather, to make all but the highest digit zero. This is effectively the negative of the number if you drop the top digit.
So, the inverse of 25 is 75, because 25+75=100 and you drop the 1. So to subtract 25 from, say, 50, you add 75 instead and get 125, drop the 1, 25 is 50-25.
Another example: The inverse of 2 is 8 because 2 and 8 are 10 (drop the 1). So to subtract 2 from 7 for example, just add 7 and 8 which is 15, drop the 1, answer is 5.
When we work with a fixed number of digits, dropping the 1 (the overflow) is easy... and only requires a small adjustment to the way we find the inverse. Let's say we will stick to 4 digits always. That makes the inverse of 25 (actually 0025) become 9975. And the inverse of 0002 is 9998. Everything still works, 0050+9975=(1)0025, and 0007+9998=(1)0005.
A very nice video from Minute Physics that explains this.