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AMD Zen 4 Ryzen 9 7950X and Ryzen 5 7600X Review: Retaking The High-End
by Ryan Smith & Gavin Bonshor on September 26, 2022 9:00 AM EST
Zen 4
Ryzen 9 7950X
205 Comments + Add A
AMD Zen 4 Ryzen 9 7950X and Ryzen 5 7600X Review: Retaking The High-End Socket AM5: The New Platform for Consumer AMD More I/O For AM5: PCIe 5, Additional PCIe Lanes, & More Displays AM5 Chipsets: X670 and B650, Built by ASMedia DDR5 & AMD EXPO Memory: Memory Overclocking, AMD's Way Ryzen 7000 I/O Die: TSMC & Integrated Graphics At Last Zen 4 Architecture: Power Efficiency, Performance, & New Instructions Zen 4 Execution Pipeline: Familiar Pipes With More Caching Test Bed and Setup Core-to-Core Latency SPEC2017 Single-Threaded Results SPEC2017 Multi-Threaded Results CPU Benchmark Performance: Power, Web, And Science CPU Benchmark Performance: Simulation And Encoding CPU Benchmark Performance: Rendering CPU Benchmark Performance: Legacy Tests Gaming Performance: 720p and Lower Gaming Performance: 1080p Gaming Performance: 4K Closing Thoughts
Socket AM5: The New Platform for Consumer AMD
While the star for most CPU launches is typically the titular CPU, there's a good argument to be had that for the Ryzen 7000 desktop launch, AMD's overall platform warrants just as much attention, if not a bit more. After all, the company's up-to-now current AM4 platform was introduced back in 2016, alongside Bristol Ridge, the last of the Bulldozers. A lot has changed for AMD in the last 6 years – not the least being the movement to the Zen core family – and now their underlying platform is getting a major overhaul as well.
The first new consumer platform and associated socket designed by AMD since the start of the Zen era, AM5 is designed to bring three major updates to AMD's desktop ecosystem. These can be roughly broken down as improving power delivery, adding DDR5 memory support, and boosting the amount of bandwidth and flexibility for I/O devices downstream of the CPU. All three of these things require (or at least are best accomplished) with CPU pin changes, so this is AMD's first chance in 6 years to overhaul everything that flows in and out of their desktop CPUs.
Physically, the most obvious change is of course the socket itself. AMD held on to using pin grid array (PGA) sockets for its mainstream consumer platforms for over a decade after Intel made the switch to land grid array (LGA), but now with AM5 the company is making the switch as well.
As was the case when Intel switched, the shift to LGA brings a couple of benefits, and a couple of trade-offs. Overall LGA allows for greater pin density; whereas socket AM4 was 1331 pins, socket AM5 is 1718 pins in the same package size. This switch also means that the relatively vulnerable pins are moved from the CPU package to the motherboard, which is modestly safer overall. The good news is that this means the days of trying to fix pins on an AMD CPU with a mechanical pencil are coming to an end; the bad news is that if LGA pins are damaged, the motherboard is essentially done for.
This also means that AM5 gets the vastly improved retention mechanisms required by LGA setups, as opposed to the zero-insertion force (ZIF) sockets of the last 30+ years. So CPUs are now much more securely locked down into their sockets, and the errant cases where trying to pull off a cooler and having the CPU come with it is no longer a possibility.
It's notable here that AMD's new socket actually has slightly more pins than Intel's current LGA-1700 socket, at 1718 vs. 1700. Though sharp-eyed power users will note that Intel and AMD have pretty distinct pin configurations (never mind package sizes), with Intel reserving a decent amount of the of the pin pad for placing capacitors and other electronics on the underside of the CPU package, whereas AMD has placed everything on top.
But despite the significant changes in CPU sockets, AMD has worked to preserve backwards compatibility with standard socket AM4 coolers. This is part of the reason for AM5 chips' odd heatspreader layout, and while I have some personal misgivings about keeping the clips around, there's no arguing that it will help get the AM5 ecosystem immediately bootstrapped, as it won't require entirely new coolers. With that said, this compatibility does not extend to coolers that need to replace the stock AMD backplate, so the compatibility rate is not 100%. Builders looking to reuse an old cooler in a system upgrade will want to look into matters to make sure that their cooler is indeed compatible with AM5.
AM5 Power Delivery: Higher Limits & SVI3 Power Management
A big portion of AMD's new pin budget for AM5 has, in turn, been invested into power delivery. The AM4 platform was designed for CPUs at TDPs up to 105W – or more specifically, a maximum socket power of 142W. As seasoned overclockers already know, this isn't a hard limit and it's long been possible to drive more power than this through AM4. But doing so does exceed the design parameters of the socket, the trade-offs of which aren't advisable for commercial off-the-shelf systems that may need to be supported for several years.
So for AM5, AMD has significantly increased the maximum power they can drive through their socket. The official TDP limit is 170 Watts, which maps out to a total socket power of 230 Watts, a 62% increase in stock power limits. And AMD is going to be taking full advantage of these higher power limits right out of the gate, with the Ryzen 9 7950X and other high-end processors slated to ship with 170W TDPs. In fact this is where a significant chunk of the multi-threaded performance gains from the new Ryzen chips are coming from, as previously AMD's highest core count CPUs were TDP-constrained when trying to light up all of those cores.
Stock AMD Socket AM5 TDP Groups
65W TDP 105W TDP 170W TDP
Socket Power (PPT) Watts 88W 142W 230W
Peak Current (EDC) Amps 150A 170A 225S
Sustained Current (TDC) Amps 75A 110A 160A
Do note that this also means that cooling requirements are going up. For their 2 CCD parts with 170W TDPs, the 7900X and 7950X, AMD is outright recommending a 240mm (or larger) AIO cooler for keeping those chips under control. AMD only suggests tower coolers for the 1 CCD parts, as these have the same 105W TDP as AMD's former top-end AM4 parts. With that said, this doesn't mean an air cooled setup isn't going to be viable with the higher TDP parts, but you will definitely need a good air cooler if you want to keep ahead on dissipating all of the heat a fully loaded 16C processor can produce.
And while AMD's peak power consumption is going higher, AMD is also implementing additional technologies to better manage power consumption, and to keep their chips from fully ramping up unless they need to. The big change here is that AMD has ported a lot of their power savings technologies from the Ryzen 6000 Mobile family over to the AM5 platform (and Ryzen 7000 IOD), most notably Scalable Voltage Interface 3 (SVI3), which as in the case of the mobile parts, is designed to allow faster and more discrete control over the voltage required from the chip.
SVI3 is a high-speed, 2-way communications protocol between the CPU and the voltage regulators, allowing the CPU to not only better direct the increasingly large number of VRM phases on high-end motherboards, but to keep an eye on VRM health, monitoring things such as power delivery and VRM temperature. This allows an AM5 CPU to make better and more aggressive decisions about power management, as opposed to having to make more conservative assumptions about the state of the VRMs.
Of particular note here, these upgrades allow for a larger number of overall power states the CPU can be in, which in turn allows for more optimal use of the VRMs. For example, this allows automatic phase shedding to shut off some of the VRM phases feeding the CPU when they're not needed, which boosts the overall power efficiency of the system (extra phases are great when you need a lot of power, but waste power themselves at low loads).
Altogether, AM5 supplies CPUs with 3 variable power rails, which is up from the 2 rails supplied by AM4. The CPU cores and graphics cores get a shared rail, and there's a separate SoC rail to power most of the rest of the IOD. Finally, there's a miscellaneous VDD rail that provides power to the GMI/Infinity Fabric interconnects, along with some other, smaller IPs. Separate from that, there are still fixed rails for things like the memory, 3.3v rail, etc.
Native BIOS Flashback Support
With AMD slated to support the AM5 platform through at least 2025, this time around the company is more actively planning around the future of the platform. In particular, AMD is taking a stab at motherboard forward compatibility, which was increasingly an issue for AM4 over its six-year reign.
The big breaker there was that, even when supported by the chipset, older boards required newer BIOSes to support newer generations of CPUs. Thus the only way to make an old board work with a newer CPU was to flash it with a newer BIOS, which in turn required a working CPU to begin with – a catch 22 situation that ultimately resulted in AMD developing a loaner kit program which rented out older AM4 CPUs that customers could use to flash their older boards if they didn't already have a new CPU for it (e.g. they just bought it off the shelf).
Ultimately, the more elegant solution to the problem is to allow BIOS flashing without a working CPU (or BIOS), which does an end-run around the whole problem. That exists today in the form of USB BIOS Flashback features; however flashback has been a motherboard-level feature that's normally only found on select high-end motherboards and has to be implemented by the motherboard vendor itself. So for the AM4 generation, it was not a universal (or even widely available) option.
For the AM5 platform, AMD is taking matters into their own hands to make USB BIOS Flashback a universal feature. Ryzen 7000 chips will be able to support USB BIOS Flashback mode across the board and regardless of the BIOS currently installed. As a result, users will always be able to flash an updated BIOS to their AM5 boards, regardless of the CPUs supported by or the operational status of the current motherboard.
The net impact is that when AMD releases future chips on the AM5 platform – say, a Zen 5 chip in 2024 – it will be possible to install a compatible BIOS without first having to use a Zen 4 chip. Though this does assume, of course, that Zen 5 chips will work in current X/B-600 series boards, which is likely but should not be taken as a given. Otherwise, this change also offers a moderate improvement to the reliability of the AM5 platform, by making it so that the BIOS on any board can be restored should it end up corrupted for whatever reason.
AMD Zen 4 Ryzen 9 7950X and Ryzen 5 7600X Review: Retaking The High-End More I/O For AM5: PCIe 5, Additional PCIe Lanes, & More Displays AMD Zen 4 Ryzen 9 7950X and Ryzen 5 7600X Review: Retaking The High-End Socket AM5: The New Platform for Consumer AMD More I/O For AM5: PCIe 5, Additional PCIe Lanes, & More Displays AM5 Chipsets: X670 and B650, Built by ASMedia DDR5 & AMD EXPO Memory: Memory Overclocking, AMD's Way Ryzen 7000 I/O Die: TSMC & Integrated Graphics At Last Zen 4 Architecture: Power Efficiency, Performance, & New Instructions Zen 4 Execution Pipeline: Familiar Pipes With More Caching Test Bed and Setup Core-to-Core Latency SPEC2017 Single-Threaded Results SPEC2017 Multi-Threaded Results CPU Benchmark Performance: Power, Web, And Science CPU Benchmark Performance: Simulation And Encoding CPU Benchmark Performance: Rendering CPU Benchmark Performance: Legacy Tests Gaming Performance: 720p and Lower Gaming Performance: 1080p Gaming Performance: 4K Closing Thoughts
Freeb!rd - Monday, September 26, 2022 - link
This paragraph reads like someone having a stroke while writing it...
"Although this is overridable through manually overclocking with a maximum TJ Max of up to 115°C, it's key tovitalte that users will need to use more premium and aggressive cooling types to squeeze every last drop of performance from ZAMD intended thistended when designing Zen 4, and as such, has opted not to bundls own CPU coolers with the retail packages."
and someone's spell checker is broken. Reply
gryer7421 - Monday, September 26, 2022 - link
It reads like a GTP-3 BOT .... :( Reply
Threska - Monday, September 26, 2022 - link
We now know "Zencally " is a word. Reply
Gavin Bonshor - Monday, September 26, 2022 - link
Hi, yeah something screwy happened, but it's fixed now. Apologies. I think it may be time to update to a new system, and software. This isn't the first time it's jumbled stuff up for me. Reply
Cow86 - Monday, September 26, 2022 - link
I wish I could say that all the errors in the article are fixed, but that very paragraph even still has several (big) errors in it... A missing letter is one thing, half a sentence just missing and going into the next is another. Reply
herozeros - Monday, September 26, 2022 - link
Keep the copy editor awake, or fire them. Grammar/syntax/CMS error, it doesn't matter if it gives me a headache reading this. Reply
Ryan Smith - Monday, September 26, 2022 - link
Unfortunately we're having to do this kind of live. It's been a very busy past two weeks and we haven't had as much time to prepare as we like. So most of what you're seeing is first-draft copy, which I'll get around to editing as I can.
Digital publications do not employ dedicated copy editors any more. They have all been let go for cost efficiency reasons. Reply
flyingpants265 - Monday, September 26, 2022 - link
What? Come on now. Reply
Hifihedgehog - Monday, September 26, 2022 - link
@flypants265: It's kind of like Microsoft who got rid of their QA team and made all of their developers honorary QA tests. They can't help it that their leadership is being stupid. Don't blame Ryan or Gavin. Blame these greedy cheapskates that likewise didn't want to pay Ian Cutress enough to want to stay. Reply
*QA testers Reply


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Q: Why does accessing a non-existent object property result in `undefined` instead of throwing a `ReferenceError`? When I try to use an undeclared variable I get ReferenceError:
console.log(a); // Uncaught ReferenceError: a is not defined

I could use a variable first and define it later and it won't be a problem due to hoisting.
console.log(a); // undefined

var a;

But when I declare an object why would the execution context let me use any property of it?
var obj = {};

console.log(obj.a); // undefined
console.log(obj.why); // undefined

Why are these allowed even though a and why are never declared anywhere?

A: Because object properties are not variables. The rules are different. Accessing an object property that doesn't exist gives you undefined, not an error. It's just the way the language is designed.
One possible explanation for the difference, other than just that "that's how Eich designed it," is that you don't declare object properties. You just use them. But variables must be declared (other than The Horror of Implicit Globals, and we don't have that now we have strict mode).

A: The language design has specified that trying to access a non existent property on an object will simply return undefined.
var obj = {};
console.log(obj.a);   // undefined

But, that trying to access a variable that is not defined anywhere within the current or parent scopes is a ReferenceError.
console.log(b);       // ReferenceError

Why are these allowed even though a and why are never declared
  anywhere?

It's just the way the language is designed.  I can see some reasons for it to be this way, but the real reason that it was decided to make it this way is only in the heads of some of the original designers.  Our job at this point is to understand how it works and write code that is compatible with the current design (since we can't change the current language design).

There are ways to work-around this issue by testing for these undefined variables such as:
if (typeof b === "undefined")

Or, if you're in a browser and you're expecting b to be a global, you can use the fact that globals are attached to the window object and that missing object properties don't create ReferenceErrors like this:
console.log(window.b);   // undefined

redpajama_set_name: RedPajamaStackExchange


Due to "kimchi and chips", an experimental art & technology Seoul (South Korea) studio, we can watch a real miracle of living lights. This composition called "assembly" uses a set of 5,500 white blocks hanging from above and 5 high-res cameras connected to a computer sequence, scanning the cubes with structured light. The combination of these technologies produces a habitat for digital forms to exist in our real world. Presence in such room gives an affect of mystery and mystic. The philosophy of "kimchi and chips" can be seen from the next statement: we create installations, products and services that bridge the gap between people and people, people and technologies, people and nature.


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Enforex - Almuñecar
If you find this site helpful, please tell a friend!
http://www.enforex.com
Almunecar, Spain
Students Referred: 1178
view evaluations | rate school
Instructions & Pricing
Levels: Beginner to Intermediate
School Size: Medium
Deposit or Fee: US $100.00
Individual Instruction
- Hourly Cost: US $36.00
Class Instruction
- Class Size: 5 to 9 students
- Hours per Week: 18
- Weekly Cost: US $100 - $199
- Min. Weeks Study: 2
Live w/ Family
Share Apartment
Private Apartment
Dorm or B&B
Internet at School
Excursions (fees)
Mature Adults (over 50), Teen Programs, Business
Learn Spanish in Spain, a country that offers everything you could possibly look for in a place to live: good weather, a vibrant social life, excellent cuisine, friendly inhabitants, rich culture and traditions, a fascinating history, and a varied landscape from the lush mountains of the north to the beautiful beaches of the Mediterranean.
Spanish has grown over the past decades to become the most popular and widely spoken language in the western world, bar English. With statistics like these it is easy to understand why one might choose to learn Spanish in Spain. Close to half a billion people speak it as their first language, with a population close to that of the United States speaking a variety of forms across South America and just short of 50 million people in Spanish speaking the peninsular strand.
While different environments, heritages and political alignments have evolved into different accents and vocabularies both within each of the Spanish-speaking countries and, most markedly, from one country to another, standard Spanish (as in the traditional sort developed in the kingdom of Castile over the course of centuries) still remains the overwhelmingly dominating material underpinning all forms of modern Spanish, to the point where the rule is common understanding between people stemming from places as distant as Argentina and Catalonia, for instance, with the occasional instances of incomprehension standing out as recurrent but utterly solvable exceptions.
NOTE: students should verify details with school in case they have changed since this listing was last modified.
More language schools in Spain
Return to the Spanish Language Schools Directory
Popular Phrase: fascinar | Conjugated Verb: hipnotizar - to hypnotize [ click for full conjugation ]

redpajama_set_name: RedPajamaCommonCrawl


text:
Gardening can help people living with Alzheimer's says Alan Titchmarsh
Alan Titchmarsh. Photo: PA.
Rebecca Marano
Published: 20:29 Friday 23 August 2019
Alan Titchmarsh said gardening can help people living with Alzheimer's after he featured one of the youngest people in the country to be diagnosed with dementia on his show.
The broadcaster welcomed 32-year-old Daniel Bradbury, who was diagnosed with a rare form of the disease in 2017, onto the upcoming series of Love Your Garden.
Mr Bradbury is a landscape gardener and had long dreamed of building a green space for children Lola and Jasper, and wife Jordan.
As his dementia progresses, the father of two is facing increasing difficulties with his mobility, speech and coordination, leading Titchmarsh and his team to step in and build the garden.
Titchmarsh, a long-time supporter of the Alzheimer's Society, said: "I loved working with Daniel and Jordan on this very special episode of Love Your Garden. A garden can help people living with dementia take part in physical activity and stimulate the senses and memories, all of which greatly improves their well-being.
"Dementia is one of the biggest challenges that our society faces today - by 2021 there are set to be one million people living with it.
"We knew that Daniel, as a landscape gardener, really wanted to build a garden for his family but was sadly no longer able to do so because of his condition, so we jumped at the opportunity to help."
Mr Bradbury, who got married last year, inherited dementia from his father who passed away in 1990, aged 36. There is a 50% chance that Lola and Jasper have also inherited the rare gene from Mr Bradbury.
Mrs Bradbury, 31, added: "We feel incredibly lucky to have met Alan and the team and watch Daniel's gardening dreams come true before our very eyes.
"Working with Alzheimer's Society, the team were able to make sure that the final result was able to accommodate Daniel's needs, as well as giving Lola and Jasper somewhere spectacular to play in.
"I hope others enjoy watching our little garden get such a wonderful makeover as much as we did!"
Former Ground Force presenter Titchmarsh is backing the Alzheimer's Society's Memory Walk campaign, raising vital funds to beat dementia.
The Memory Walks take place across England, Wales and Northern Ireland during September and October.
Walking festival takes fundraiser's widower back to Yorkshire Dales family roots
You can register for the Memory Walks at memorywalk.org.uk. The Love Your Garden episode featuring the Bradburys will air on ITV on August 27.


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National supplier of technical training equipment and systems. The business sources products from global suppliers in order to provide market leading training solutions to clients within the education, defence and civil sectors.
The purchaser, Bestech Sensors and Instrumentation, a national distributer of sensor and testing equipment, was identified within the researched primary market.


I'm not singling out a single doctor or condition or even specialty practice. But I do often see mistakes (from western and eastern points of view) when patients come in. I'd like to address the top 5 that you – as the OWNERS of your health – need to be aware of.
Not touching, observing, palpating, performing physical exams, screening, or otherwise practicing old school western medicine.
This is a head-scratcher. The basics in physical exam are basic, but they are also probably 90% of western medicine's best tricks. I have so many patients come in and claim their doctor doesn't know if they have a Rotator Cuff, UTI, broken bone (until they Xray it) etc… I'm thinking: "exam, screen, palpate, per-cuss, and use a tuning fork!!" I don't understand why they are allowed to get away with missing easy diagnoses and sending people to scans just so they can file a claim on the insurance. I agree: do the scan. But before you do, and before the patient runs off seeking all kinds of care: do a physical exam. TOUCH the patient! I'm not a chiropractor and I can tell if a bone is out by touching the patient. This enables me to make proper referrals. Just this past Tuesday I used our neighbor, Dr. Harmon, in Berea for two separate cases of subluxation. I didn't need a scan, I can just tell. And UTI screening is so cheap. It really can clear up a lot of confusion for people. But instead they just often end up doing number 2.
Antibiotics just kill bacteria. But they also push pathogens deeper inside (by causing the body to react to a biotoxin and squeeze in the intestines and channels) and killing good flora. What returns after napalm and nuclear bombs? Roaches and bad bacteria. Why are hospitals full of MRSA? Bleaching and overuse of antibiotics. The reality of our Yin/Yang universe is not going to stop working just because we discovered a mighty useful drug. It will adapt and evolve. Our medicine, all medicine, needs to realize that if you push on one end of a lever, the other end will move, too.
It's one thing to stop an inflammatory or ischemic attack. It's another thing to suppress energy into the nerves, encourage hormone imbalances and weight gain, and make a person dependent on a substance with diminishing returns. In the end, after all the docs have done everything and now the pain meds are being taken away… only then are they referring out to us. Rather we should have been the first referral after the scan, and after visiting us the patient should go to the chiropractor now that muscles are relaxed. Bass ackwards, as they say.
What's worse is the over-prescription of corticosteroids. I've even had one patient who developed Meniere's Disease iatrogenically from use of asthma inhalers. It was killing him. Finally I revealed it to him when he admitted his over-reliance.
Remember folks: your doctor isn't going to live with his/her mistakes, you are.
3) after all, if the wrong scan or blood lab is ordered, and a person is "cleared" as "negative"… they can become hurt by lack of diagnosis. The only thing worse is giving a limited, bad prognosis.
Treating the person, body, and the whole country in dissected, segmented, disconnected sections.
It's amazing to me with all the plethora of pscyhological, neurological, and of course spiritual research of the last 10, 100, and 3000 years that doctors still cling to the "mechanistic" view of the body. I agree, the body is a machine: an energetic, liquid, biological, and most importantly spirituo-psychological machine.
You can't separate a pancake into its ingredients, and you can't divide a body and expect to create the world's most effective medicine. You can treat individual cases, but you'll never reach the roots of disease: stress, diet, lifestyle, behavior, thoughts, habits, and trauma. You just won't.
I know some doctors won't want to hear advice from an acupuncturist – ever. But ironically I'm helping people kick literal poisons out of their bodies, heal wounds that they can't heal, and finding conditions (like hypogonadicism, Cushing's, or hyperparathyroidism) before, during, and after they manifest. More importantly I can explain using more than numbers on a page but actual signs and symptoms the person can recognize the causes and conditions, test the energetic body, the glands, and prescribe solutions that slowly separate people from bad situations getting worse.
So what do I know? At least I can diagnose using the tongue: the ONLY muscle visible to the naked eye without some expensive CT-scan or MRI.


text:
Have you seen our new Bookmark manager already?
By Stefan van Dierendonck | December 20, 2021
Our users know full well, that we're in the business of bookmarks. Once upon a time, we even called ourselves a Bookmark manager. Even though we offer lot of of dashboard features too. Recently, we've added an advanced tool to help you to keep your large collections of bookmarks up-to-date. We've called it Bookmark manager. Here's…
Discover our Widget Gallery today
By Stefan van Dierendonck | October 21, 2021
The Widget Gallery is our latest improvement of our Discover section. Leaf through our essential widgets, PRO widgets and other custom widgets. With over six thousand custom news feeds you're bound to discover something new. Here's what you need to know.
Add social logins to your account to login even faster
By Stefan van Dierendonck | September 9, 2021
Social logins provide a form of single sign-on using a social networking such as Facebook or Twitter. When you connect one to your existing start.me account, you'll be able to login faster. No separate password to remember! In this article, you'll learn how to set it up.
Import web pages to jumpstart your link collection in start.me
With start.me you can quickly build a valuable collection of bookmarks. Now, you can also import bookmarks from external web pages with a few clicks. We recommend this feature when you switch bookmark managers or if you want to collect links for your OSINT or academic research. Here's why you'll love it.
DIY Single Sign-On (SAML2) now available for all Teams and Enterprises
By Stefan van Dierendonck | June 14, 2021
Single Sign-On offers ease of use, faster access, and more security online. Up until today, you had to ask our support team to set it up. Finally, we've put this option in the hands of all team or Enterprise owners. Do It Yourself!
Introducing our latest (PRO) feature: detect and remove duplicate links
By Stefan van Dierendonck | April 15, 2021
Exactly a month ago, we teased you with a soon to be developed Duplicate links checker feature. Today the time has come for the big reveal!
Beyond the innovative design: here's what to expect next!
By Stefan van Dierendonck | March 15, 2021
Last week, we've introduced a major redesign of our service. We consider it the next step towards the best possible bookmarking experience. Fortunately, many of our users love our redesign, as you can see in the comments. But we didn't stop there. Are you ready for the next steps?
Have you noticed? We made start.me simpler, faster, and better looking!
By Stefan van Dierendonck | March 4, 2021
As you may have noticed, today we launched a complete redesign for start.me. We wanted to make your workflow simpler and faster. It's also a lot better looking, we think. Continue reading, as we'll talk you through all the major changes and minor improvements.
How do you make your shared pages more secure? Add a password!
By Stefan van Dierendonck | January 28, 2021
Have you ever shared a start.me page? Then you'll know you've got several options. As of today, you can add a password too. Here's why we thought this new feature would be helpful.


text:
Building memory stability through rehearsal
Dr Piotr A. Wozniak, SuperMemo R&D, SuperMemo World, Poznan, Poland Dr Edward J. Gorzelanczyk, Assistant Professor, University of Bydgoszcz, Poland Dr Janusz A. Murakowski, Assistant Professor, University of Delaware, USA; Spring 2005
New article by Dr. Piotr Wozniak: The true history of spaced repetition
This article is a popularized rewrite of a paper "The two-component model of long-term memory. Symbolic formula describing the increase in memory stability for varying levels of recall" presented at Cybernetic Modelling of Biological Systems. A particular emphasis is placed on highlighting the role of SuperMemo in collecting data that helped refine the two-component model of long-term memory. The article also adds some practical conclusions that might affect learning strategies among users of SuperMemo
1. S/R Model of memory
10 years ago we published a paper that delineated a distinction between the two components of long-term memory: stability and retrievability (Wozniak, Gorzelanczyk, Murakowski, 1995). The paper provided the first outline of the so-called S/R Model that makes it easier to build molecular models of long-term memory. Until now, the stability-retrievability distinction has not made a major impact on research in the field. Here we re-emphasize the importance of the S/R Model and sum up the findings of the last decade that refine the S/R Model and highlight its importance in the deconvolution of seemingly contradictory research in the field of memory and learning.
2. Why do we need two variables to describe long-term memory?
In the literature on memory and learning, we often encounter an ill-defined term: strength of memory (or strength of synaptic connections). However, if we look at how information is assimilated by the human mind, we notice that there must be at least two independent variables that could stand for the strength of memory. On one hand, after a review of a learned piece of knowledge, we remember things well. We may say that "after a review, memory is strong". On the other hand, after years of keeping a piece of information in memory, we say that "memory of the fact is strong" even though, at times, we may hesitate while trying to recall the fact. In the two said cases, we speak of two different phenomena that are both labeled as memory strength. Disentangling the two is vital if we are to avoid contradictory findings in memory research. We proposed two distinct names for the two components of long-term memory (Wozniak, Gorzelanczyk, Murakowski, 1995):
memory retrievability determines how easy it is to retrieve a memory trace (i.e. recall it)
memory stability determines how long a memory trace can last in memory (i.e. not be forgotten)
3. Intuitive interpretation of the memory variables: retrievability and stability
Both retrievability and stability of long-term memory can be correlated with a number of molecular or behavioral variables. However, for the purpose of further analysis, we propose two intuitive measures of the two components:
Let us express retrievability R as the probability of retrieving a piece of information from memory. Although, the actual probability as measured for a given piece of information is usually close to 1 or close to 0 (approaching "all or nothing" characteristic of low-level neural transmission), the present definition is useful in investigating populations of memory traces. In a population of independent memories, the probability of forgetting is constant per engram per unit time. This results in a continual decline of memory engrams. If we know the decay function, we can estimate the probability of recall of a piece of information in a given uniform population at a given time. We will show later that retrievability as a function of time is well approximated by the exponential decay R=e-d*t where t is time and d is a decay constant for a given homogenous population of memory traces
Let us express stability S as the inter-repetition interval that produces retrievability of 0.9. Although, at the molecular level, stability might be subject to a slow drift, our intuitive definition sets it at a constant level between reviews. This assumption is valid for we are only interested in stability and its increase at the time of review
These two intuitive interpretations of retrievability and stability will make it easier to develop further mathematical descriptions of changes in long-term memory variables over time depending on the rehearsal patterns (i.e. the timing of repetitions).
We express the changes in retrievability as:
(3.1) R=e-d*t
t - time
R - probability of recall at time t (retrievability)
d - decay constant dependent on the stability
We can replace the constant d dependent on stability, with a constant k that is independent of stability:
(3.2) R=e-k*t/S
R - probability of recall at time t
S - stability expressed by the inter-repetition interval that results in retrievability of 90% (i.e. R=0.9)
k - constant independent of stability
Now we can obtain the value of k. According to the definition of stability S, at time t=S, retrievability is R=0.9:
(3.3) 0.9=e-k*S/S
(3.4) ln(0.9)=-k
(3.5) k=ln(10/9)
We will use Eqns. (3.2) and (3.5) frequently throughout this article.
4. Memory is optimized to meet probabilistic properties of the environment
It has been proposed in the 1980s, that properties of memory can be derived from the study of the events in the environment (Anderson&Schooler, 1991). The guiding light of this proposition is the intuition that memory should reflect probabilistic properties of the environment. Such a reflection is likely considering a relatively easy evolutionary adjustment of molecular memory properties targeted at optimum survival through maximizing the storage of useful information without abusing storage capacity.
Interesting conclusions have been drawn, among others, from studying the patterns of occurrence of selected keywords in New York Times. If memory "strength" was to reflect the probability of a re-encounter with a given word, we might conclude that the forgetting, reflecting the decline in memory strength, would optimally be a power function of time (in contrast to the exponential nature of forgetting concluded or assumed in a majority of publications).
Let us have a closer look at the New York Times example to illustrate how understanding of the distinction between retrievability and stability of memories could help us interpret Anderson's work.
If we look at each memory episode as a Bernoulli trial, we can apply the binomial distribution to investigate the links between the episode frequency, re-occurrence interval, and the encounter probability. That would apply to the occurrence of words in New York Times as well. For a large number of trials and a low episode probability (here low keyword frequency), we can apply a Poisson distribution to approximate the binomial distribution and notice that Anderson's observation on the power relationship describing the probability of word re-occurrence in New York Times as a function of interval since the last occurrence must be correct. In other words, probability of re-occurrence is proportional to the reciprocal of the interval since the last occurrence. Seemingly, the power function would adequately reflect the probability of episode re-occurrence. Negative power curve would then seem a solid evolutionary candidate to reflect forgetting in a well-optimized memory system.
However, Anderson, as most researchers then and now, rely on an ill-defined concept of memory strength. What if we make a clear-cut distinction between R and S. Which one, R or S, would reflect the probabilistic properties of the environment? R is a "delete timer", which measures the interval since the last occurrence of the stimulus evoking a memory that is to be consolidated. If the average occurrence rate is inversely proportional to the interval since the last occurrence, probability of recall should show a negative power decline. However, there seems to be no correlation between the occurrence rate and retrievability. After each encounter (or repetition), probability of recall climbs back to 1. This climb-back is independent of the actual frequency of the occurrence of the stimulus! Moreover, for recall proportional to 1/t, we would be approaching infinity as t approaches 0. Retrievability definitely does not reflect the probability of re-encounter with the stimulus. Retrievability is used by the memory system only to time out the lifespan of memory traces. This span is longer for more relevant (frequent) stimuli, but the forgetting curve expressing R is not a reflection of the probability of re-encounter. As we will argue later, the forgetting curve is negatively exponential in nature despite the expectation of it being a negative power function.
Can then memory stability meet Anderson's postulates? Stability of memory is a very solid measure of the occurrence rate. Each re-encounter with the stimulus boosts stability. If again we view the stream of stimuli as a Poisson process, we could expect both the probability of a re-encounter and the memory stability to increase in the r-th repetition as follows:
(4.1) Pr(Nt=0)=e-ar*t
(4.2) Pr+1(Nt=0)=exp(-ar*(r/(r-1))*t)
(4.3) ln(Pr+1(Nt=0))=(ln(Pr(Nt=0))/t)*r/(r-1)*t=ln(Pr(Nt=0))*r/(r-1)
(4.4) Pr+1(Nt=0)=Pr(Nt=0)^(r/(r-1))
(4.5) Pr+1(Nt>0)=1-(1-Pr(Nt>0))^(r/(r-1))
Pr(Nt=0) - probability of no encounter in time t before the r-th repetition
Pr+1(Nt>0) - probability of encounter in time t after the r-th repetition
ar - average encounter rate before the r-th repetition
If we denote the increase in stability as SInc, and expect it to match the increase in probability of the encounter, then
(4.6) SInc=Pr+1(Nt>0)/Pr(Nt>0)
(4.7) SInc=(1-(1-Pr)^(r/(r-1)))/Pr
For r=2 we have:
(4.8) SInc=(1-(Pr2-2Pr+1))/Pr=(-Pr2+2Pr)/Pr=-Pr+2
For Pr approaching 0 we then have SInc approaching 2. This is precisely the intuitive increase in memory stability proposed for the first formulation of spaced repetition techniques that later evolved into SuperMemo. Also, for low values of Pr, and r>2, successive decline in the value of SInc mirrors the power decline of R-factors in SuperMemo.
In conclusion, what Anderson proposed in reference to memory strength, should be addresses to memory stability, not to memory retrievability. Instead of investigating forgetting curves, we should measure and analyze memory stability in successive reviews. The finding that forgetting is exponential in nature should not undermine Anderson's postulates.
However, a observation that partly unravels the simplified model presented above, and severs the straightforward connection between memory and environment is that of the spacing effect. Clearly memory refuses to view the stream of environmental stimuli as a Poisson process. It does not treat individual events as independent. Memory takes into account clustering of events in time. This is to prevent clusters of probabilistically dependent events being equivalent in memory stability-building power to a numerically equivalent stream of independent events.
Andersons postulates are bound to be a solid guideline to understanding memory. The full elucidation of the link between environment and memory is a question of time. The S/R distinction should be monumentally helpful in avoiding confusion resulting from the superposition of the two properties of long-term memory.
5. The memory decay function is negatively exponential
Although it has always been suspected that forgetting is exponential in nature, proving this fact has never been simple. Exponential decay appears standardly in biological and physical systems from radioactive decay to drying wood; everywhere where expected decay count is proportional to the size of the sample, and where the probability of a single particle decay is constant. The following problems have hampered the effort of modeling forgetting since the years of Ebbinghaus (Ebbinghaus, 1885):
small sample size
sample heterogeneity
confusion between forgetting curves, re-learning curves, practise curves, savings curves, trials to learn curves, error curves, and others in the family of learning curves
By employing SuperMemo, we can overcome all these obstacles to study the nature of memory decay. As a popular commercial application, SuperMemo provides virtually unlimited access to huge bodies of data collected from students all over the world. The forgetting curve graphs available to every user of the program (Tools : Statistics : Analysis : Forgetting curves) are plotted on relatively homogenic data samples and are bona fide reflection of memory decay in time (as opposed to other forms of learning curves). The quest for heterogeneity significantly affects the sample size though. It is important to note that the forgetting curves for material with different memory stability and different knowledge difficulty differ. Whereas memory stability affects the decay rate, heterogeneous learning material produces a superposition of individual forgetting curves each characterized by a different decay rate. Consequently, even in bodies with hundreds of thousands of individual pieces of information participating in the learning process, only relatively small homogenous samples of data can be filtered out. These samples rarely exceed several thousands of repetition cases. Even then, these bodies of data go far beyond sample quality available to researchers studying the properties of memory in controlled conditions. Yet the stochastic nature of forgetting, still makes it hard to make an ultimate stand on the mathematical nature of the decay function (see two examples below). Having analyzed several hundred thousands samples we have come closest yet to show that the forgetting is a form of exponential decay.
Fig. 5.1. Exemplary forgetting curve sketched by SuperMemo. The database sample of nearly a million repetition cases have been sifted for average difficulty and low stability (A-Factor=3.9, S in [4,20]), resulting in 5850 repetition cases (less than 1% of the entire sample). The red line is a result of regression analysis with R=e-kt/S. Curve fitting with other elementary functions demonstrates that the exponential decay provides the best match to data. The measure of time used in the graph is the so-called U-Factor defined as the quotient of the present and the previous inter-repetition interval. Note that the exponential decay in the range of R from 1 to 0.9 can plausibly be approximated with a straight line, which would not be the case had the decay been characterized by a power function.
Fig. 5.2. Exemplary forgetting curve sketched by SuperMemo. The database sample of nearly a million repetition cases have been sifted for average difficulty and medium stability (A-Factor=3.3, S > 1 year) resulting in 1082 repetition cases. The red line is a result of regression analysis with R=e-kt/S
To illustrate the importance of homogenous samples in studying forgetting curves, let us see the effect of mixing difficult knowledge with easy knowledge on the shape of the forgetting curve. Fig. 5.3 shows why heterogeneous samples may lead to wrong conclusions about the nature of forgetting. The heterogeneous sample in this demonstration is best approximated with a power function! The fact that power curves emerge through averaging of exponential forgetting curves has earlier been reported by others (Anderson&Tweney 1997; Ritter&Schooler, 2002).
Fig. 5.3. Superposition of forgetting curves may result in obscuring the exponential nature of forgetting. A theoretical sample of two types of memory traces has been composed: 50% of the traces in the sample with stability S=1 (thin yellow line) and 50% of the traces in the sample with stability S=40 (thin violet line). The superimposed forgetting curve will, naturally, exhibit retrievability R=0.5*Ra+0.5*Rb=0.5*(e-k*t+e-k*t/40). The forgetting curve of such a composite sample is shown in granular black in the graph. The thick blue line shows the exponential approximation (R2=0.895), and the thick red line shows the power approximation of the same curve (R2=0.974). In this case, it is the power function that provides the best match to data, even though the forgetting of sample subsets is negatively exponential.
6. The magnitude of the increase in memory stability through rehearsal
Until now, we have not been able to formulate a universal formula that would link a repetition with an increase in memory stability. Repetition spacing algorithms proposed by the authors (Wozniak, Gorzelanczyk, 1992) are based on a general understanding on how stability increases when so-called optimum inter-repetition intervals are used (defined as intervals that produce a known recall rate that usually exceeds 90%). The term optimum interval is used for interval's applicability in learning. The said repetition spacing algorithms also allow of determining an accurate stability increase function for optimum intervals in a matrix form. However, little has been known about the increase in stability for low retrievability levels (i.e. when intervals are not optimum). With data collected with the help of SuperMemo, we can now attempt to fill in this gap. Although SuperMemo has been designed to apply optimum intervals in learning, in real-life situations users are often forced to delay repetitions for various reasons (such as holiday, illness, etc.). This provides for a substantial dose of repetitions with lower retrievability in nearly every body of learning material. In addition, in 2002, SuperMemo introduced the concept of a mid-interval repetition that makes it possible to shorten inter-repetition intervals. Although the proportion of mid-interval repetitions in any body of data is very small, for sufficiently large data samples, the number of repetition cases with very low and very high retrievability should make it possible to generalize the finding on the increase in memory stability from the retrievability of 0.9 to the full retrievability range.
To optimally build memory stability through learning, we need to know the function of optimum intervals, or, alternatively, the function of stability increase (SInc). These functions take three arguments: memory stability (S), memory retrievability (R) and difficulty of knowledge (D). Traditionally, SuperMemo has always focused on dimensions S and D, as keeping retrievability high is the chief criterion of the optimization procedure used in computing inter-repetition intervals. The focus on S and D was dictated by practical applications of the stability increase function. In the presented article, we focus on S and R, as we attempt to eliminate the D dimension by analyzing "pure knowledge", i.e. non-composite memory traces that characterize knowledge that is easy to learn. Eliminating the D dimension makes our theoretical divagations easier, and the conclusions can later be extended to composite memory traces and knowledge considered difficult to learn. In other words, as we move from practice to theory, we shift our interest from the (S,D) pair to the (S,R) pair. In line with this reasoning, all data sets investigated in this article have been filtered for item difficulty (in SuperMemo: A-factor ranging from 3.0 to 3.5 to balance a large number of items of knowledge versus a large number of repetitions). At the same time, we looked for possibly largest sets in which representation of items with low retrievability would be large enough as a result of delays in rehearsal (in violation of the optimum spacing of repetitions).
Below we describe a two step procedure that was used to propose a symbolic formula for the increase in stability for different retrievability levels in data sets characterized by low and uniform difficulty (so called well-formulated knowledge data sets that are easy to retain in memory). Well-formulated and uniform learning material makes it easy to distill a pure process of long-term memory consolidation through rehearsal. As discussed elsewhere in this article, ill-formulated knowledge results in superposition of independent consolidation processes and is unsuitable for the presented analysis.
The two-step procedure for determining the function of the increase in memory stability SInc:
Step 1: Using a matrix representation of SInc and an iterative procedure to minimize the deviation Dev between the grades in a real learning process (data) and the grades predicted by SInc. Dev is defined as a sum of R-Pass over a sequence of repetitions of a given piece of knowledge, where R is retrievability and Pass is 1 for passing grades and 0 for failing grades
Step 2: Using a hill-climbing algorithm to solve a least square problem to evaluate symbolic candidates for SInc that provide the best fit to the matrix SInc derived in Step 1
Let us define a procedure for computing stability of memory for a given rehearsal pattern. This procedure can be used to compute stability on the basis of known grades scored in learning (practical variant) and to compute stability on the basis of repetition timing only (theoretical variant). The only difference between the two is that the practical variant allows of the correction of stability as a result of stochastic forgetting reflected by failing grades.
In the following passages we will use the following notation:
S(t) - memory stability at time t
S[r] - memory stability after the rth repetition (e.g. with S[1] standing for memory stability after learning a new piece of knowledge)
R(S,t) - memory retrievability for stability S and time t (we know that R=exp(-k*t/S) and that k=ln(10/9))
SInc(R,S) - increase in stability as a result of a rehearsal for retrievability R and stability S such that SInc(R(S,t),S(t))=S(t'')/S(t')=S[r]/S[r-1] (where: t' and t'' stand for the time of rehearsal as taken before and after memory consolidation with t''-t' being indistinguishable from zero)
Our goal is to find the function of stability increase for any valid level of R and S: SInc=f(R,S).
If we can find SInc(R,S) and use S[1]=S1, where S1 is the stability derived from the memory decay function after the first-contact review (for optimum inter-repetition interval), then for each repetition history we can compute S using the following iteration:
r:=1;
S[r]:=S1
t:=Interval[r]; // where: Interval[r] is taken from a learning process (practical variant) or
// from the investigated review pattern (theoretical variant)
Pass:=(Grade[r]>=3); // where: Grade[r] is the grade after the r-th interval (practical variant) or
// 4 (theoretical variant)
R:=Ret(S[r],t);
if Pass then
S[r+1]:=S[r]*SInc[R,S[r]]
r:=r+1;
S[r]:=S1;
until (r is the last repetition)
Note that SuperMemo uses a first-interval graph to determine S1, which is progressively shorter after each failing grade. In the future, this more precise measure of S1 should be used for increased accuracy.
Step 1 - Finding SInc as a matrix
If we start off with an unknown matrix SInc[R,S], e.g. with all entries arbitrarily set to 2 (see Eqn. (4.8)) then we can use the above procedure on the existing repetition history data to compute SInc[R,S] that minimizes the deviation Dev=R-Pass. This is possible if we observe that:
if Pass=true and S[r]<Interval[r] then SInc[R,S[r-1]] entry is underestimated (and can be corrected towards Interval[r]/S[r]*SInc[R,S[r-1]])
if Pass=false and S[r]>Interval[r] then SInc[R,S[r-1]] entry is overestimated
This approach makes it possible to arrive at the same final SInc[R,S] independent of the original value of SInc[R,S] set at initialization. The application and the source code used to compute SInc[R,S] from repetition history data in SuperMemo is available from the authors upon request.
In Step 2, we will use the SInc[R,S] matrix obtained here to obtain a symbolic formula for SInc.
Step 2 - Finding SInc as a symbolic formula
We can now use any gradient descent algorithm to evaluate symbolic candidates for SInc that provide the best fit to the matrix SInc derived above.
When inspecting the SInc matrix, we immediately see that SInc as a function of S for constant R is excellently described with a negative power function as in the exemplary data set below:
Which is even more clear in the log-log version of the same graph:
The conclusion on the power dependence between SInc and S above confirms previous findings. In particular, the decline of R-Factors along repetition categories in SuperMemo has always been best approximated with a power function.
When we look for the function reflecting the relationship of SInc and R for constant S, we see more noise in data due to the fact that SuperMemo provides far fewer hits at low R (its algorithm usually attempts to achieve R>0.9). Nevertheless, upon inspecting multiple data sets we have concluded that, somewhat surprisingly, SInc increases exponentially when R decreases (see later to show how this increase results in a nearly linear relationship between SInc and time). The magnitude of that increase is higher than expected, and should provide further evidence of the power of the spacing effect. That conclusion should have a major impact on learning strategies.
Here is an exemplary data set of SInc as a function of R for constant S. We can see that SInc(R) can be quite well approximated with a negative exponential function:
And the semi-log version of the same graph with a linear approximation trendline intercept set at 1:
Interestingly, stability increase for retrievability of 100% may be less than 1. Some molecular research indicates increased lability of memories at review time. This is one more piece of evidence that repetitive cramming can hurt you not only by costing you extra time.
7. Symbolic formula for memory stability increase
For constant knowledge difficulty, we need to apply two-dimensional surface-fitting to obtain the symbolic formula for SInc. We have used a modified Levenberg-Marquardt algorithm with a number of possible symbolic function candidates that might accurately describe SInc as a function of S and R. The algorithm has been enhanced with a persistent random-restart loop to ensure that the global maxima be found. We have obtained the best results with the following formula:
(7.1) SInc=aS-b*ecR+d;
SInc - increase in memory stability as a result of a repetition (quotient of stability S before and after the repetition)
R - retrievability of memory at the moment of repetition expressed as the probability of recall in percent
S - stability of memory before the repetition expressed as an interval generating R=0.9
a, b, c, d - parameters that may differ slightly for different data sets
e - base of the natural logarithm
The parameters a, b, c, d would vary slightly for different data sets, and this might reflect user-knowledge interaction variability (i.e. different sets of learning material presented to different users may result in a different distribution of difficulty as well as with a different grading criteria that may all affect the ultimate measurement).
For illustration, an average value of a, b, c, d taken from several data sets has been found to be: a=76, b=0.023, c=-0.031, d:=-2, with c varying little from set to set, and with a and d showing relatively higher variance.
The above formula produced SInc values that differed on average by 15% from those obtained from data in the form of the SInc matrix on homogenous data sets (i.e. repetition history sets selected for: a single student, single type of knowledge, low difficulty, and a small range of A-Factors).
As inter-repetition interval increases, despite double exponentiation over time, SInc increases along a nearly-linear sigmoid curve (both negative exponentiation operations canceling each other):
Fig 7.1. The graph of changes of SInc in time. This graph was generated for S=240 using Eqn. 7.1
The nearly linear dependence of SInc on time is reflected in SuperMemo by computing the new optimum interval by multiplying the O-Factor by the actually used inter-repetition interval, not by the previously computed optimum interval (in SuperMemo, O-Factors are entries of a two-dimensional matrix OF[S,D] that represent SInc for R=0.9).
8. Expected value of the increase in memory stability depending on retrievability
Let's define the expected value of the increase in memory stability as:
(8.1) E(SInc)=SInc*R
R - retrievability
SInc - increase in stability as per Eqn (7.1)
E(SInc) - expected probabilistic increase in stability (i.e. the increase defined by SInc and diminished by forgetting)
From Eqn (7.1) we have E(SInc)=(aS-b*ecR+d)*R. By finding the derivative dE(SInc)/dR, and equating it with zero, we can find retrievability that maximizes the expected increase in stability for various levels of stability. Figure 8.1. shows the graph of E(SInc) as a function of R for S<15. Using terminology known to users of SuperMemo, the maximum expected increase in memory stability for short intervals occurs for the forgetting index equal to 60%! This also means that the maximum forgetting index allowed in SuperMemo (20%) results in the expected increase in stability that is nearly 80% less than the maximum possible (if we were only ready to sacrifice high retention levels).
Fig. 8.1. Expected increase in memory stability E(SInc) as a function of retrievability R for stability S<15.
9. Knowledge complexity affects the forgetting rate
We believe that the difficulty in learning is determined by complexity of remembered information. Complex knowledge results in two effects:
increased interference with other pieces of information
difficulty in uniform stimulation of memory trace sub-components at review time
Both components of difficulty can be counteracted with the application of appropriate representation of knowledge in the learning process.
Let us see how complexity of knowledge affects build up of memory stability.
Imagine we would like to learn the following: Marie Sklodowska-Curie was a sole winner of the 1911 Nobel Prize for Chemistry. We can take two approaches: one in which knowledge is kept complex, and one with easy formulations. In a complex variant, a double cloze might have been formulated for the purpose of learning the name of Marie Curie and the year in which she received the Nobel Prize.
Q: [...] was a sole winner of the [...] Nobel Prize for Chemistry
A: Marie Sklodowska-Curie, 1911
In a simple variant, this double cloze would be split and the Polish maiden name would be made optional and used to create a third cloze:
Q: [...] was a sole winner of the 1911 Nobel Prize for Chemistry
A: Marie (Sklodowska-)Curie
Q: Marie Sklodowska-Curie was a sole winner of the [...](year) Nobel Prize for Chemistry
A: 1911
Q: Marie [...]-Curie was a sole winner of the 1911 Nobel Prize for Chemistry
A: Sklodowska
In addition, in the simple variant, a thorough approach to learning would require formulating yet two cloze deletions, as Marie Curie was also a winner of 1903 Nobel Prize for Physics (as well as other awards):
Q: Marie Sklodowska-Curie was a sole winner of the 1911 Nobel Prize for [...]
A: Chemistry
Q: Marie Sklodowska-Curie was a sole winner of the 1911 [...]
A: Nobel Prize (for Chemistry)
Let us now consider the original composite double cloze. For the sake of the argument, let's assume that remembering the year 1911 and the name Curie is equally difficult. The retrievability of the composite memory trace (i.e. the entire double cloze) will be a product of the retrievability for its subtraces. This comes from the general rule that memory traces, in most cases, are largely independent. Although forgetting one trace may increase the probability of forgetting the other, in a vast majority of cases, as proved by experience, separate and different questions pertaining to the same subject can carry an entirely independent learning process, in which recall and forgetting are entirely unpredictable. Let us see how treating probabilities of recall as independent events affects the stability of a composite memory trace:
(9.1) R=Ra*Rb
R - retrievability of a binary composite memory trace
Ra and Rb - retrievability of two independent memory trace subcomponents (subtraces): a and b
(9.2) R=exp(-kt/Sa)*exp(-kt/Sb)=exp(-kt/S)
k - ln(10/9)
S - stability of the composite memory trace
Sa and Sb - stabilities of memory subtraces a and b
(9.3) -kt/S=-kt/Sa-kt/Sb=-kt(1/Sa+1/Sb)
(9.4) S=Sa*Sb/(Sa+Sb)
We will use the Eqn. (9.4) if further analysis of composite memory traces. We might expect, that if initially, stability of memory subtraces Sa and Sb differs substantially, subsequent repetitions, optimized for maximizing S (i.e. with the criterion R=0.9) might worsen the stability of subcomponents due to sub-optimal timing of review. The next section shows this is not the case.
10. Convergence of sub-stabilities for composite memory traces
We have seen earlier than R=exp(-k*t/S). If we generate a double-cloze, we are not really sure if a single repetition generates a uniform activation of both memory circuits responsible for storing the two distinct pieces of knowledge. Let us assume that the first repetition is the only differentiating factor for the two memory traces, and that the rest of the learning process proceeds along the formulas presented above.
To investigate the behavior of stability of memory subtraces under a rehearsal pattern optimized for composite stability with the criterion R=0.9, let us take the following:
Sa=1
Sb=30
S=Sa*Sb/(Sa+Sb) (from Eqn. 9.4)
SInc=aS-b*ecR+d (from Eqn. 7.1)
composite memory trace is consolidated through rehearsal with R=0.9 so that both subtraces are equally well re-consolidated (i.e. the review of the composite trace is to result in no neglect of subtraces)
As can be seen in Fig. 10.1., memory stability for the composite trace will always be less than the stability for individual subtraces; however, the stabilities of subtraces converge.
Fig. 10.1. Convergence of stability for memory sub-traces rehearsed with the same review pattern optimized for the entire composite memory trace (i.e. review occurs when the composite retrievability reaches 0.9). The horizontal axis represents the number of reviews, while the vertical axis shows the logarithm of stability. Blue and red lines correspond with the stability of two sub-traces which substantially differed in stability after the original learning. The black line corresponds with the composite stability (S=Sa*Sb/(Sa+Sb)). The disparity between Sa and Sb self-corrects if each review results in a uniform activation of the underlying synaptic structure.
11. Composite stability increase is the same as the increase in stability of sub-traces
Let us now figure out how much SInc differs for composite stability S and for subtrace stabilities Sa and Sb? If we assume the identical stimulation of memory subtraces, and denote SInca and SIncb as i, then for repetition number r, we have:
SInca=SIncb=i
Sa[r]=Sa[r-1]*i
Sb[r]=Sb[r-1]*i
S[r]=Sa[r]*Sb[r]/(Sa[r]+Sb[r])=
=Sa[r-1]*Sb[r-1]*i2/(Sa[r-1]*i+Sb[r-1]*i)=
=i*(Sa[r-1]*Sb[r-1])/(Sa[r-1]+Sb[r-1)=i*S[r-1]
(11.1) SInc=i=SInca=SIncb
The above demonstrates that with the presented model, the increase in memory stability is independent of the complexity of knowledge assuming equal re-consolidation of memory subtraces.
12. Molecular model of memory based on the distinction between memory stability and memory retrievability
At the molecular level, the distinction between stability and retrievability of memory may help resolve seeming paradoxes in attempts to answer questions such as "Do ampakines improve memory?". If ampakines activate AMPA, they may increase retrievability, yet they might worsen conditions for unblocking NMDA and memory consolidation. Ampakines might paradoxically improve recall and worsen learning. The distinctions of the S/R Model are central for similar analyses. Most memory research deal with short time scales and would only produce R-relevant results. This emphasizes the importance of the S/R Model in disentangling experimental contradictions.
Let us demonstrate the value of the S/R Model for building molecular models of long-term memory. In these models, we use the guidelines formulated in two-component model of long-term memory (Wozniak, Gorzelanczyk, Murakowski, 1995):
R should be related to the probability of recall; forgetting should be understood as the drop of R to zero
R should reach a high value as early as after the first repetition, and decline rapidly in the matter of days
S determines the rate of decline of R (the higher the stability of memories, the slower the decrease of retrievability)
With each review, as S gets higher, R declines at a slower rate
S should assume a high value only after a larger number of repetitions
S should not change (significantly) during the inter-repetition interval
If the value of R is high, repetitions do not affect S significantly (spacing effect)
R and S increase only as a result of an effective repetition (i.e. repetition that takes place after a sufficiently long interval)
Forgetting should result in the drop of S to zero
Exemplary model of long-term memory based on the S/R guidelines:
R is phosphorylation of AMPA
S is the number of AMPA receptors inserted in the membrane
Guidelines (cf. Wozniak, Gorzelanczyk, 1998):
Phosphorylated AMPA improves neural transmission, hence better recall
At learning, calcium flowing through NMDA activates a cascade (incl. CaMKII) that leads to phosphorylation of AMPA; initially, spontaneous dephosphorylation proceeds fast
The more AMPA gets inserted in the membrane, the more stable the phosphorylated AMPA is (perhaps through structural changes in the PSD); dephosphorylation proceeds more slowly
Stability changes in the membrane lead to more AMPA being stably incorporated; the stability increases on each effective review
The longer the synapse survives in the stabilized state, the more it gets stabilized on each review
When there is no recall, AMPA dephosphorylation gradually reduces R, but AMPA remains in the membrane and the stability does not change much
When there is a high phosphorylation of AMPA, the synapse transmits easily and NMDA does not get Mg-unblocked due to hyperpolarization; there is no increase in stability; only when dephosphorylation reduces R (AMPA transmission), can NMDA Mg block be relieved and produce an increase in stability through calcium currents
When NMDA gets unblocked new AMPA gets incorporated in the membrane and it gets fully phosphorylated (maximum retrievability). Synthesis of more AMPA receptors can also play a part
As S increases, its further increase becomes easier through changes in the PSD structure
Representing stability of memory as an inter-repetition interval that results in retrievability of 0.9 makes further theoretical fruitful investigations easier
Stability of memory can reflect the probability of re-occurrence of events in the environment. However, the spacing effect precludes treating the occurrence of events as a Poisson process
With each successive review, the increase of stability is less in magnitude. This can be explained if environmental events are treated as a Poisson process. For low occurrence rates, the increase in stability in the first review as predicted by the Poisson process approaches 2
Forgetting is a form of negative exponential decay. Large datasets collected with SuperMemo confirm this observation. Small size and heterogeneity of samples explains why other researchers might have arrived at other conclusions. In addition, not all forms of learning curves can be used to represent retrievability
Increase in stability due to rehearsal is lower for higher levels of stability. This relationship is expressed by a negative power function
Increase in stability due to rehearsal is far more pronounced for lower levels of retrievability. This relationship seems to be best expressed by a negative exponential function
A symbolic formula for the increase in stability due to rehearsal has been found. This finding was possible with a large datasets collected with SuperMemo. The formula may open rich vistas towards further progress in modeling long-term memory. It also opens the field to researchers without a background in programming
Increase in stability due to rehearsal depends on the timing of the rehearsal. The relationship between time and the increase in stability is sigmoid in nature, yet in practical applications for limited time ranges can well be approximated with linear functions
The expected increase in stability understood as the product of retrievability and the increase in stability is maximized at retrievability levels around 0.4
The stability of binary composite memories can be found from subtrace stabilities using the formula: S=Sa*Sb/(Sa+Sb). Similar formulas can be derived for composite memories built of more memory subtraces
If the re-consolidation of memory subtraces is equal, stabilities of memory subtraces converge in the review process
If the re-consolidation of memory subtraces is equal, the increase in memory stability of the composite trace is equal to the increase in memory stability of individual subtraces
The S/R Model of memory can be helpful in the deconvolution of contradictory findings in research on molecular basis of long-term memory
Anderson, J. R., & Schooler, L. J. (1991). Reflections of the environment in memory. Psychological Science, 2, 396 - 408
Anderson, R. B. (1997). The power law as the emergent property. Bowling Green State University.
Anderson, R. B., & Tweney, R. (1997). Artifactual power curves in forgetting. Memory & Cognition, 25,724-730
Ebbinghaus H. (1885). Memory: A Contribution to Experimental Psychology
Ritter, F.E., & Schooler, L. J. (2002). The learning curve. Encyclopedia of the Social and Behavioral Sciences. 8602-8605
Wozniak, P.A., Gorzela?k, E.J. (1992). Optimal scheduling of repetitions in paired-associate learning. Acta Neurobiologiae Experimentalis, Vol. 52, p. 189
Wozniak P.A, Gorzela?k, E.J., Murakowski J. (1995). Two components of long-term memory. Acta Neurobiologiae Experimentalis, Vol. 55, p. 301-305
Wozniak P.A, Gorzela?k, E.J., (1998). Hypothetical molecular correlates of the two-component model of long-term memory. Symposium of the Polish Network of Molecular and Cellular Biology


A group meeting where each person shits into a cup,then they all eat or drink each other's shots till they are sick .
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Compounding of Steam Turbines
Compounding of steam turbines is the method in which energy from the steam is extracted in a number of stages rather than a single stage in a turbine. In all turbines the rotating blade velocity is proportional to the steam velocity passing over the blade. If the steam is expanded only in a single stage from the boiler pressure to the exhaust pressure, its velocity must be extremely high.
A compounded steam turbine has multiple stages i.e. it has more than one set of nozzles and rotors, in series, keyed to the shaft or fixed to the casing, so that either the steam pressure or the jet velocity is absorbed by the turbine in number of stages. For example, large HP Turbine used in nuclear power plants can be double-flow reaction turbine with about 10 stages with shrouded blades. Large LP turbines used in nuclear power plants are usually double-flow reaction turbines with about 5-8 stages (with shrouded blades and with free-standing blades of last 3 stages).
In an impulse steam turbine compounding can be achieved in the following three ways:
pressure compounding
velocity compounding
pressure-velocity compounding
Impulse Turbine – velocity compounding
A velocity-compounded impulse stage consist of a row of fixed nozzles followed by two or more rows of moving blades and fixed blades (without expansion). This divides the velocity drop across the stage into several smaller drops. In this type, the total pressure drop (expansion) of the steam take place only in the first nozzle ring. This produces very high velocity steam, which flows through multiple stages of fixed and moving blades. At each stage, only a portion of the high velocity is absorbed, the remainder is exhausted on to the next ring of fixed blades. The function of the fixed blades is to redirect the steam (without appreciably altering the velocity) leaving from the first ring of moving blades to the second ring of moving blades. The jet then passes on to the next ring of moving blades, the process repeating itself until practically all the velocity of the jet has been absorbed.
This method of velocity compounding is used to solve the problem of single stage impulse turbine for use of high pressure steam (i.e. required velocity of the turbine), but they are less efficient due to high friction losses.
Pressure Compounding - Rateau Turbine - Zoelly Turbine
Rateau Turbine – pressure compounding
A pressure-compounded impulse stage is a row of fixed nozzles followed by a row of moving blades, with multiple stages for compounding. In this type, the total pressure drop of the steam does not take place in the first nozzle ring, but is divided up between all the nozzle rings. The effect of absorbing the pressure drop in stages is to reduce the velocity of the steam entering the moving blades. The steam from the boiler is passed through the first nozzle ring in which it is only partially expanded. It then passes over the first moving blade ring where nearly all of its velocity (momentum) is absorbed. From this ring it exhausts into the next nozzle ring and is again partially expanded. This method of pressure compounding is used in Rateau and Zoelly turbines, but such turbines are bigger and bulkier in size.
Pressure-Velocity Compounding - Curtis Turbine
Curtis Turbine – pressure-velocity compounding
Impulse stages may be either pressure-compounded, velocity-compounded, or pressure-velocity compounded. The pressure-velocity compounding is a combination of the above two types of compounding. In fact, a series of velocity-compounded impulse stages is called a pressure-velocity compounded turbine. Each stage consists of rings of fixed and moving blades. Each set of rings of moving blades is separated by a single ring of fixed nozzles. In each stage there is one ring of fixed nozzles and 3-4 rings of moving blades (with fixed blades between them). Each stage acts as a velocity compounded impulse turbine.
The steam coming from the steam generator is passed to the first ring of fixed nozzles, where it gets partially expanded. The pressure partially decreases and the velocity rises correspondingly. It then passes over the 3-4 rings of moving blades (with fixed blades between them) where nearly all of its velocity is absorbed. From the last ring of the stage it exhausts into the next nozzle ring and is again partially expanded.
This has the advantage of allowing a bigger pressure drop in each stage and, consequently, less stages are necessary, resulting in a shorter turbine for a given pressure drop. It may be seen that the pressure is constant during each stage; the turbine is, therefore, an impulse turbine. The method of pressure-velocity compounding is used in the Curtis turbine.
Pressure Compounding of Reaction Turbine
A compounded steam turbine has multiple stages i.e. it has more than one set of nozzles and blades, in series, keyed to the shaft or fixed to the casing, so that either the steam pressure or the jet velocity is absorbed by the turbine in number of stages. For example, large HP Turbine used in nuclear power plants can be double-flow reaction turbine with about 10 stages with shrouded blades. Large LP turbines used in nuclear power plants are usually double-flow reaction turbines with about 5-8 stages (with shrouded blades and with free-standing blades of last 3 stages).
In a reaction steam turbine compounding can be achieved only in the pressure compounding. In fact, it is not exactly the same as what it was discussed in impulse turbines. Note that, there is steam expansion in both the fixed and moving blades.
Reactor Physics and Thermal Hydraulics:
J. R. Lamarsh, Introduction to Nuclear Reactor Theory, 2nd ed., Addison-Wesley, Reading, MA (1983).
J. R. Lamarsh, A. J. Baratta, Introduction to Nuclear Engineering, 3d ed., Prentice-Hall, 2001, ISBN: 0-201-82498-1.
W. M. Stacey, Nuclear Reactor Physics, John Wiley & Sons, 2001, ISBN: 0- 471-39127-1.
Glasstone, Sesonske. Nuclear Reactor Engineering: Reactor Systems Engineering, Springer; 4th edition, 1994, ISBN: 978-0412985317
Todreas Neil E., Kazimi Mujid S. Nuclear Systems Volume I: Thermal Hydraulic Fundamentals, Second Edition. CRC Press; 2 edition, 2012, ISBN: 978-0415802871
Zohuri B., McDaniel P. Thermodynamics in Nuclear Power Plant Systems. Springer; 2015, ISBN: 978-3-319-13419-2
Moran Michal J., Shapiro Howard N. Fundamentals of Engineering Thermodynamics, Fifth Edition, John Wiley & Sons, 2006, ISBN: 978-0-470-03037-0
Kleinstreuer C. Modern Fluid Dynamics. Springer, 2010, ISBN 978-1-4020-8670-0.
U.S. Department of Energy, THERMODYNAMICS, HEAT TRANSFER, AND FLUID FLOW. DOE Fundamentals Handbook, Volume 1, 2 and 3. June 1992.
U.S. NRC. NUREG-0800, Standard Review Plan for the Review of Safety Analysis Reports for Nuclear Power Plants: LWR Edition
See above:
Advantages and Disadvantages of Steam Turbines
Back-pressure Steam Turbine – Process Steam
Classification of Turbines – Steam supply and exhaust conditions
Condensing Steam Turbine
Extraction Turbine – Turbine with Steam Extraction
How does a steam turbine work
HP Turbine – High-pressure Steam Turbine
Impulse Turbine – Impulse Blading
Impulse Turbine and Reaction Turbine
Losses in Steam Turbines
LP Turbine – Low-pressure Steam Turbine
Pressure Compounding – Rateau Turbine – Zoelly Turbine
Pressure-Velocity Compounding – Curtis Turbine
Reaction Turbine – Parsons Turbine
Reheat Steam Turbine
Types of Steam Turbines


Roast the chiles directly over a flame, turning, until lightly charred all over; transfer to a blender.
In a small saucepan, combine the vinegar with the honey, ginger and a pinch of salt and bring to a boil. Pour the vinegar into the blender and let cool completely, then puree until smooth. Transfer the habanero vinegar to a jar and season with salt.
The habanero vinegar can be refrigerated for up to 2 weeks.
Review Body: I made this vinegar as part of the pulled pork meal. It was awesome and everything came together with a nice ginger beer to wash it all down.


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In the Pipeline – How do you find new drugs?
I'd like to direct you to another blog called "In the Pipeline" hosted by Corante. It is written by a man called Derek Lowe. In the Pipeline is the first blog that I started reading regularly and I've been following it for a few years now.


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Могрен:

 Могрен, Торгни — шведский лыжник, олимпийский чемпион, четырёхкратный чемпион мира
 Могрен — черногорский футбольный клуб из города Будва.

redpajama_set_name: RedPajamaWikipedia


Ten-year-old Tracy Taylor lays down her crutches and with a little help from her brother Robby climbs atop a skateboard, folding her toothpick-thin legs under her tiny 35-pound frame. With a little push, she is off—speeding down the street, squealing with delight. Again and again she rides this asphalt roller coaster, her face illuminated by a 100-watt grin. And then, as she zooms down again, the board gets away from her, and she tumbles into the street. "Ouch!" she cries, holding up a scraped finger for inspection by her mother, Anne, 40, a diabetic who is standing on the sidewalk, leaning on her own crutches. Tracy collects a kiss from her mother and a Band-Aid from her brother, and then she is off again, the fall forgotten, wrestling with her golden retriever, Kelly.
Ten years ago, when Tracy was born premature, deformed and battered by a cruel array of diseases, doctors predicted that she would never live to see her 5th birthday. If Tracy had merely survived her years of pain, recurrent surgery and frustrating setbacks, her story would be extraordinary enough. But there's a twist—a dark side to her victory over suffering that must be familiar to many more families in similar distress than will ever be known. At the depths of his child's pain, his family buffeted by incalculable physical, emotional and financial strains, Tracy's father, college counselor Bob Taylor, 40, once considered the offer of a mercy killing to end her misery. Could the parents of any severely handicapped child not be brushed by such thoughts?
Tracy and her family not only survived, but thrived. Now she plays, swims, rides horses and makes public speeches as a California March of Dimes poster child. And she does it all with a smile that belies any experience of pain and despair. "Many times I've been unhappy and frustrated," Tracy says. "Sometimes, I just go, 'Why me?' I think God knew that if He gave it to somebody else who didn't have as much—what do you call it?—courage and a positive attitude, they wouldn't have been able to make it. So He gave it to me, thinking maybe I could handle it. Maybe God sent me down here to do a job, to make other people understand that handicapped people are just the same. The message He wants me to give is, 'Don't just sit there when you can fly. If you try, you can do anything.' If somebody says, 'I can't get up,' my message to them is just try and try and you can overcome that." Her story is not unique, but her family's telling of their anguish may be.
Tracy's myriad ailments were not the Taylor family's only problem. Anne was also in dire health. Her severe diabetes, which had already caused blindness in her left eye, had begun to threaten her kidneys and her legs. Recognizing the difficulty for a mother so sick to provide the constant care that Tracy required, Anne's doctors suggested that the baby be institutionalized. Anne refused. "I couldn't accept it," she says.
So after seven weeks in intensive care, Tracy came home to the Taylors' Glendale, Calif. home. As the doctors had predicted, her life was a series of traumas. In her first year Tracy fell off a bed and broke her leg. Several months later she broke her leg again, falling from a changing table. Although her heart and lung ailments cleared up, her kidneys were chronically infected, necessitating a catheter and four operations.
But Tracy's frail body housed a will of iron and a tenacious drive to live and to move. At the age of 1, she rolled over, which surprised her doctors. Before 2, she sat up, a feat the doctors said she would never accomplish. Then, shortly before her 3rd birthday, she began to walk independently on crutches.
And she didn't stop there. Tracy had become particularly close to her step-grandfather, Happle Cartwright, an ex-rodeo rider who always encouraged her to walk. He promised her a ride on a fine white pony someday, when she could manage it. But before Hap could make good on his promise, he died at 72, when Tracy was 7. The child grieved enormously. Then, the next Christmas, she came downstairs and spotted, hanging on the tree, a little wooden horse. "Oh, Happle," Tracy cried. Without thinking, she took her first two steps without her crutches to reach it.
Despite Tracy's tenacity, her battle was far from won. In the summer of 1981 she traveled to the Mayo Clinic for the second time to undergo another urinary tract operation. Two years later an operation to correct her painful foot deformity was unsuccessful. Meanwhile, her mother's condition was worsening. In 1981 Anne had an operation to repair the tendons in her wrists, which were painfully swollen as a result of her diabetes. She was also undergoing emergency laser surgery on her one good eye. Shortly before, her diabetes-ravaged kidneys had failed—requiring dialysis for up to five hours every other day. "The idea of being hooked up to a machine," she says, "was almost more than I could bear." The diabetes had also crippled her legs. "I had to crawl on my knees to make a bed," she says. "I couldn't go up a threshold more than an inch without a crutch or a cane." With the medical problems came financial worries. Even after health insurance coverage, the bills (totaling some $250,000 to date) have been at least $10,000 a year, more than Bob's $38,000 salary could cover.
Coldly, analytically, Bob weighed the anonymous offer until the horror of it hit him. "What am I doing? What am I thinking of? I can't do this." About 10 days later the man called again and asked Bob if he had considered the offer. "I told him that we would have to work it out some other way," Bob says.
These days Tracy's doctors are issuing optimistic predictions about her future. "Given the fact that she will always be small and have some vision difficulty, I think she can have a very good life," says Dr. Robert demons of the Children's Medical Group in Glendale. "She can certainly attend college and certainly have a career." Tracy agrees. She just can't decide which career. She wants to be a horseback gymnast or maybe a teacher or a swimming instructor or a scientist—one who "finds cures for stuff like diabetes." She may first become an actress; a producer has optioned the movie rights to her story and may star Tracy.
But Tracy doesn't talk for very long. Like most kids, she is too full of pep to sit and philosophize. She'd much rather move. "Let me show you my forward roll," she says, with that glorious smile. And suddenly she is somersaulting across the living room.


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/ Met Gala: 2019
Met Gala: 2019
Today, Anna Wintour announced the theme of the Met Gala - Camp: Notes On Fashion. The first Monday in May will see the inspiration of Susan Sontag's essay written in 1964 Notes on "Camp" which suggested ways in which the concept of camp could be constructed. Met's curator of the Costume Institute, Andrew Bolton said that he "felt it would have a lot of cultural resonance" – referring to our current cultural and political climate.
The exhibition will feature 200 fine art and fashion objects and will trace the origins of the subject from the 17th century to the present day. In her announcement Anna Wintour said, "basically, we go from sun kings to drag queens". In terms of designers, we can expect to see Moschino, Gucci, Off-White and Schiaparelli on show.
Gucci creative director Alessandro Michele said: "This is a very important moment because we will collaborate on the creation of this fantastic exhibition that has a DNA that is related to my work, working to the expression of human nature... Camp is a beautiful word." Paying tribute to Karl Lagerfeld, Michele went on to say, "I would like to seize this opportunity to thank Karl, who is no longer with us, who has been a great representative of fashion, and who had a great love for life and this kind of work."
The 71st event will be co-chaired by Lady Gaga, Alessandro Michele, Harry Styles and Serena Williams. We can only assume ateliers and design housees are getting to work as we type.


This playful necklace by Bibi van der Velden features two diamond-set Sterling Silver monkeys with 18k Rose Gold faces and tails, swinging from an 18k Rose Gold chain. Whilst one monkey clutches onto a bunch of 18k yellow gold bananas, the other climbs towards the tiny 18k yellow gold banana at the centre of the necklace. The monkeys also feature white diamond eyes. The necklace chain measures 50 cm long, and is adjustable to 45 cm.
Brand new piece in beautiful Bibi van der Velden packaging.
Brown and white diamonds in rose gold art-deco adjustable choker necklace.
Beautiful designer necklace from Pomellato. 18k gold with diamonds. Clasp fully set with diamonds.


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James Murdoch Blasts "Media Outlets That Propagate Lies" In FT Interview
Van Halen Jumps On 'Jimmy Kimmel Live', 'Ellen' – Update
By Erik Pedersen
Erik Pedersen
More Stories By Erik
'The Vaccine: Conquering Covid' Special With Dr. Anthony Fauci & Others Set At Discovery & Discovery+
'Top Class' Docuseries About High School Basketball Dynasty Set At IMDb TV From Amazon Studios & Uninterrupted – Trailer & Premiere Date
Kevin Costner Sues Former 'Dances With Wolves' Business Partner For $15 Million
UPDATED, 6:07 PM: Van Halen also will appear on Ellen on April 2.
PREVIOUSLY, 3:07 PM: Van Halen just announced a big tour, and it'll sorta kick off on Hollywood Boulevard next week. Jimmy Kimmel Live has booked the notoriously TV-shy band for a concert that it will air over
two nights. The gig, set for March 30 and airing that night and the next, will mark Van Halen's first U.S. television appearance with once and current singer David Lee Roth. Will we get a glimpse of the revered/reviled "Diamond Dave" persona that helped launch the group to megastardom and eventually contributed to its demise?
The revived band will be promoting its 38-date North American tour, which kicks off in earnest July 5 in Auburn, WA, and also has a new record out next week. Tokyo Dome Live In Concert is Van Halen's first concert album with Roth, who left the group in 1985 and rejoined 22 years later. The Kimmel date will be a warmup for the tour — reminiscent of the Los Angeles-bred band's playing a free show at the Forum in advance of its Roth-reunion jaunt in 2007.