text:
CWCI Adelaide Committee has been a part of the South Australian scene for over 50 years and for many years arranged a city day convention and a weekend convention. While the weekend convention was traditionally held annually in the beautiful south coast town of Victor Harbor, the 2016 Convention was located at Westminster School.
We have been blessed, over the years, with excellent speakers from overseas as well as our own home-grown Aussie speakers.
After 21 years of CWCI in Australia, the founder, Grace Collins, recorded the events leading to the establishment of the ministry in South Australia…
In my mail one September morning in 1963, there were two letters from SA which were to start the ripples moving even further out in Convention work. One came from a young woman and the other from a minister’s wife.
The young woman wrote, “For some time now several of us in this area have wondered whether there was any possibility of a small Convention weekend being held here in South Australia, similar to those you have described in the magazine, ‘Christian Woman’. Distance is a problem, but perhaps one or two of your personnel may be able to come over.” She gave the names and addresses of three other women who were interested and prepared to do some organising.
The minister’s wife, who lived in another area, said, “I have in my possession two addresses given by Miss Cook and Mrs Chambers at your Sydney convention. I have never read anything so vital and alive as these two messages for women by women. I am convinced that such conventions with their special ministry provide the answer to women’s needs. So many speakers at women’s meetings give topical and travel talks, but there is a real seeking for the things that count most. Your conventions are on our daily prayer list. We are praying that God will raise up someone in South Australia to whom he will give a vision of what conventions could do for women here.”
I replied to both women that CWCI would be praying with them about this.
Two years later I wrote again saying I was now able to come to Adelaide to explain convention work to any women who were interested. The young woman replied, “We are still vitally interested, although may not be able to work on a committee. Since writing to you two years ago, three of us have had babies and we all have young families. However we are keen to meet you and have friends who would like to also.”
As it turned out, two of them were on the first committee of seven in Adelaide, as was the minister’s wife. A missionary-hearted couple at Victor Harbor took the trouble to write a list of more than thirty names and addresses of women they thought would be interested. The secretary of the Evangelisation Society of SA gladly gave permission to use their meeting room in the city.
On my arrival in Adelaide, the women who gathered were very interested in the possibilities of CWCI, but not all were sure that this new idea would take off in SA as it had in NSW and Qld. One woman said to me, “We don’t get as excited about things here as you do in the eastern states,” I had not realised we were excitable, but I have smiled to myself many times since, on hearing of the enthusiasm and wonderful attendances at SA conventions in the years that followed.
The committee decided to start in a small way. They chose Port Elliot as the venue, booked a Christian boarding house and the smaller of two halls in the town, and had brochures printed to advertise the convention. After several weeks there were none left to give to women who were still asking for them. 78 women lived in for that first convention, and extra beds were put up everywhere. They encountered other difficulties. They had to clean the hall, and there was no microphone for the speakers. The following year they booked four guest houses and the Town Hall, and the registrations more than doubled.
A report of the first committee meeting after the convention in 1966 says, ‘time was spent reflecting on the wonderful convention held at Amerta Guest House and the RSL hall, Port Elliot, when Mrs Chambers and Miss Cook spoke on the theme, Christ pre-eminent in all things.’
At the first convention the chairman suggested that the women put aside twenty cents each week to help pay for their next weekend away. Shortly afterwards she wrote, “The word is spreading around. The other day I was walking along the street and a woman called out to me, “I’ve put aside my first twenty cents.’ She missed out the first time but is determined to come next year.”
From the first convention in 1966, there has been a focus on missions. This programme included a missionary session with a speaker from Borneo. Another year a panel of missionaries took part, answering questions from the women about their work and witness in various missions fields. And so it has continued.
A day Convention was launched in 1968 in a suburban area at Unley Park Baptist Church. It was a great day and the outreach was encouraging. This Convention moved in 1973 into Maugham Methodist Church in the city, and large numbers attended each year.
In the same year, 1973, the Central Weekend Convention moved to Victor Harbor for more accommodation. The auditorium at the Bible College of South Australia was so crowded with women that committee members were informed that not only did the meeting create a fire hazard, but that if any more were packed in the floor would probably give way! The following year they booked the theatre in Victor Harbor, which was another venture of faith, because they had no idea whether or not it would be filled. In fact, the theatre was almost full that first time, and was packed to capacity in the years that followed.
A valuable outreach developed through large sales of books at the Conventions. Recordings of addresses were also popular and now many thousands, including men, have been reached by the messages recorded on cassettes and CDs. Two women at least are known to have been converted through listening to these recordings.
In 1971 there was a general move into country areas – Mount Gambier, Port Wakefield, Mannum, and later to the Riverland, Naracoorte, Port Lincoln and Whyalla, and in 1976 to Murray Bridge. These country conventions became a great blessing to many women and their families.
One of the country Weekend Conventions was held in a picturesque spot at Melrose in the Flinders Ranges, looking out onto glorious mountains. Here, far removed from other distractions, women were able to listen to what God had to say to them.
In the early days, bringing speakers from another state was a big step in faith. When budgeting for expenses at one of the first conventions the committee quite forgot to include the speaker’s travel. After all, none of them had ever arranged anything like this before. After a hasty consultation the committee decided to take up a thank offering and were full of praise when this covered all the expenses.
And so conventions in SA went on from strength to strength. People have been converted, homes have been transformed, missionary interest has been increased, and women have attempted great things for God. We have traced the many avenues into which God has led CWCI in South Australia. They thought in the beginning, “How can we start?” They stepped out bravely with God, and proved that working in this way with him was certainly beyond expectations.
Excerpts from “Beyond Expectations”
by Grace Collins and Jean Raddon
Published by CWCI ©1977

domain: history


A man claiming to be a former Syrian intelligence officer was found guilty of entering the UAE on a false passport.
Jail and deportation for false passport
ABU DHABI // A purported former Syrian intelligence officer who was once a key witness in the 2005 assassination of the former Lebanese prime minister Rafiq Hariri was found guilty of entering the UAE on a false passport and sentenced to six months in jail today followed by deportation. Mohammed Zuhair al Siddiq was arrested in Sharjah last April, and had been held by the State Security authority for "entering the UAE on false documents". Since Siddiq was incarcerated in April, his six-month sentence will end on October 15.
Siddiq became known for testifying at a UN tribunal that Syria ordered the assassination of Mr Hariri, helping create a damning case against Syria before he was himself discredited. Siddiq testified before the State Security Court that he had been given a passport by French intelligence, and claimed he entered the country with the knowledge and permission of the Ministry of Interior. State Security, however, denied any knowledge of Siddiq's presence in the country. Siddiq fled Damascus sometime after the February 14, 2005, bombing that killed Mr Hariri and 22 others in Beirut. He later testified to the UN tribunal that Syria had ordered the assassination. He was labelled a key witness by the tribunal but Syria adamantly denied the allegations and questioned his claim of being an intelligence officer. The tribunal later called Siddiq's reliability as a witness into question.
In October 2005, Lebanese prosecutors charged him in absentia with murder in connection with the assassination of Mr Hariri and 21 others in the car bombing in February that year. He was arrested under an international warrant in a Paris suburb in October 2005, but the French government released him a few months later when neither Lebanon nor Syria would guarantee he would not be executed if extradited. His whereabouts were unknown until his arrest was reported in Dubai in April of this year. It remains unclear to which country he will be deported. In cases in which a foreigner enters the country using a false passport, the UAE deports the person to the country of origin. If the country of origin cannot be determined, the respective embassies are contacted to verify nationality.
Siddiq's lawyer is appealing deportation on the grounds that he faces the execution if he is returned to Syria.


text:
I have managed to read an average of 2 books per month in 2016. Taking into consideration that this year I wrote my bachelor thesis, I have been working almost daily and I moved out of Denmark in October, I think 25 books is a decent number*.
Let me tell you 10 short ideas about reading and the way I do it.
1. Everyone can read a book a month
No excuses! If the CEO of Facebook can do it, you can do it too. Most of us waste at least 2 hours on social media. Even if you spend 2-3 hours a week to read, you can still finish 500 pages a month.
2. I don’t like asking people for book recommendations
We all love to give some recommendations. I know what to read, I don’t need any tips. However, I do look for suggestions by studying people who I admire and who read as well. For example, I’ve read almost every top recommendation from Mikael.
3. The more well-known a book, the least I like it
I usually hate best sellers. I feel like most of them are catered to the masses and do not have any practical information. This idea takes me to the next point:
4. I read only books with real life examples and tips
For some reason, I do not have the patience to read or watch fiction. It’s hard for me to enjoy or learn from something which is not real. I rarely consume something (articles / books / movies) for entertainment purposes.
5. Books are 10x more insightful than movies
Movies are full of drama and romanticism. Otherwise, nobody would watch them nowadays. Unfortunately, movies do not reflect reality. It is hard to include enough details in a movie in order to capture real life situations and feelings. Thus, movie directors go with what drives more emotion, not what is more real.
Although as an author you get commercial pressure as well from the publisher / editor, you still get more freedom to deliver your thoughts and to keep it real than a filmmaker.
6. The author reputation is as important as the book (maybe even more)
Walk the talk. If you write a book about public speaking, but you have no references or videos with your speeches, I see no value in your thoughts. If you write about how to make money, but you have no real business we know about or other proofs, I see no value in your thoughts.
We all know how to give advice, but if it doesn’t come from someone who applied it himself, it is pointless for us.
7. I try to get 2-3 ideas out of each book
Seth Godin said he only reads a book until he gets its main idea (typically the first 20 pages), because the rest is just an iteration of that idea.
I usually try to extract 2-3 main ideas from each reading.
Marcus Aurelius says that life is a river and that you can control how you react to external events / factors. Cal Newport explains the importance of practicing deep work in a world in which automatized processes and robotics will play an increasing role. Jim Rickards talks about the financial crisis coming upon and the dangers of negative interest rates.
8. I continue researching about those ideas after finishing a book
What I usually do is watch some interviews with the author or read summaries of that book. Thus, I get a more comprehensive understanding of the author and his/her views and ideas.
9. I read multiple books on the same topic (usually one after another)
I do this because I don’t want to switch between too many topics until I understand one clear enough. For example, this year I got interested in 3 topics:
The first one was Sales & Public Speaking, as I had to give some speeches and I needed some practical advice. Thus, I’ve read „How to talk so people listen”, „Secrets of closing the sale” and „The quick and easy way to effective speaking”. The authors lived in different periods and the books were written in completely different styles, but all gave real life examples and practical tips. Thus, I was able to select and apply the advice that I felt most comfortable with.
The second one was Stoicism. I started with „A guide to the good life” (a very nice introduction and synthesis of stoics’ lessons) and then continued with “Meditations” (I enjoyed it a lot) and “Letters” (although most people liked it, I found it very challenging to go through). All 3 books follow similar concepts, which helped me to get a basic understanding of stoicism. On top of this, I have read “A guide to rational living”, which had lots of practical advice as well, most of it stoic-related.
The third one was Economy. I have read “How an economy grows” and “The new case for gold”. I have read “Bull!” as well, which is not really on the same topic, but it has insightful info on how economic bubbles develop. The first 2 share similar views because both authors think a huge financial crisis is coming upon. I have watched interviews with them and I continued to study the topic by researching about the top investors / hedgefund managers view on the current economy. It is funny and strange that a lot of them have been saying for 1-2 years that SUA is in a bubble, yet the S&P just got to an all-time high of 2200 today.
10. I read new-released books
The reason I do it is because I want to follow the latest trends in business and tech. For example, “Chaos Monkeys” tells you about Facebook’s culture around 2014-2015, which I find somehow more relevant than reading what Jack Welch learned working for GE in the nineties.
Bonus: I read summaries
Besides the 25 books finished this year, I have read summaries of another 10. I usually do it for the books which I do not find interesting enough, yet I think it is still good to take some ideas from them. Thus, summaries are a good shortcut for books which do not make a “must-read list”.
*On the flip slide, I attended few events / parties this year and I haven’t watched more than 5 movies. I guess my priorities were messed at 21.

domain: literature


Hardly a smartphone that has not fallen over 4 inches tall. A tribute to the increasingly better resolution and continuous transformation from a simple phone towards mobile entertainment machine. Since you want to ever have a larger display. HTC madness drives the size of the tip and gives the latest model in the family, the name probably best that such a block can meet.
HTC Titan and the battle for the spasm
A 4.7-inch Super-LCD screen graces the front of the new HTC Titan, whose body from the typical aluminum, is that we know now from HTC. The display makes it far away from a very good impression. Are we moving closer to the screen, disillusionment is spreading, because due to the low resolution, we can identify one or two pixels with the naked eye. The Titan uses 800 x 480 pixels and is thus comparable to the level of 4-inch models. The resolution is poor even with the Samsung S2 Galaxy as one of the criticisms, which with a 4.3 inch screen, not just the small phone is next door.
The rest you have not saved, so in turn drives the HTC Titan on the back of an 8 megapixel camera. If they do not differ from the others, then it is only weak mediocrity, since HTC little stingy with megapixels but the light is sometimes so bad that the pictures kriseln easily even in low light. For one or the other snapshot it will still suffice. For something more light there is then also the good old LED flash lamp, if only for women.
Windows Phone Mango on today's largest smartphone
With a 1.5 GHz single-core chip and 512 MB of RAM fitted, running on the HTC Titan, the freshly harvested mango. For memory, there are fixed 16 GB. In addition, a 1.3 megapixel camera comes on the front side of the screen on the right side, which is ideal for video chats. Now that Microsoft, Skype has brought on board, the more important aspect.
One advantage is the size in terms of productivity, because that can, for example Office documents much easier to edit MS Office pre-installed on the smartphone. The device is already available from about 545 €.


text:
Ipswich Spy has seen the light in regard to wind turbines.
Question time at Ipswich Borough Council’s Full Council meeting last night proved interesting in a number of areas, not least when the consultation process regarding the building of wind turbines on land at Thorington Hall was raised by a member of the public.
Labour’s Environment Portfolio Holder, Cllr Sandy Martin, explained the process of consultation, which started back in 2008 with a report to Executive Committee by the then portfolio holder, Liberal Democrat former councillor Louise Gooch.
The impression given by Cllr Martin is one of a seemless progression of consultation, with all three parties fully behind the proposals. But that isn’t quite true. Reading the minutes of that original decision shows that Cllr Gooch emphasised the importance of public consultation. Tory Cllr Steven Wells reiterated the the need for consultation.
And for those trying to suggest that the Tories have “blood on their hands” over this decision, Tory…
View original post 366 more words

domain: news


Hillary Clinton and the Al Capone factor
WASHINGTON, October 20, 2015 – When Al Capone was convicted for tax evasion on October 17, 1931, a reporter asked the Chicago gangster for a comment. “It was a blow to the belt,” said Capone of the jury’s verdict, “but what can you expect when the whole community is prejudiced against you?”
Al never saw it coming. He assumed that payoffs and intimidation were his two most impenetrable shields. That the corruption, incompetence and stupidity of local and federal agencies placed him beyond the law’s reach.
It was Al, not Eliot Ness and his Treasury agents, who was “untouchable.”
But Al’s mistake was not giving much thought to the guy staying at Chicago’s Sheridan Plaza Hotel. Frank J. Wilson was on a mission to “find gross income in excess of $5,000 accruing to [Alphonse] Capone for any one year,” noting that hiding the fruits of his nefarious criminal enterprises required Capone’s “production department” to turn out “fifty corpses a year.”
Three years of forensic accounting eventually led Treasury agent Wilson to the ledger books with entries proving Capone clearly failed to declare his taxable annual income above $5,000. In 1929 alone, he netted $26,000,000. Adjusted for inflation, in today’s dollars, that’s roughly $357,024,093.56.
“I never heard of anyone getting more than five years for income tax trouble,” complained Capone, “but when they’re prejudiced, what can you do even if you’ve got good lawyers?”
Tax evasion seemed such a petty beef to a guy who personally bludgeoned three disloyal associates to death with a baseball bat.
I thought of Capone when leading Democrats on the House Select Committee on Benghazi issued a report exonerating former Secretary of State Hillary Clinton regarding her role in the Obama administration’s disastrous handling of the 2012 terrorist attack on the U.S diplomatic mission in Benghazi, which left four Americans dead, including U.S. Ambassador Chris Stevens.
A major component of the affair was the crafting of a false cover story blaming the attack on a mob angry over an American-made anti-Islamic video, which culminated in the arrest and imprisonment of the filmmaker.
The 124-page report’s bullet points claims there is:
- No evidence that Secretary Clinton ordered the military to stand down on the night of the attacks;
- No evidence that Secretary Clinton personally approved or ordered a reduction of security in Benghazi prior to the attacks;
- No evidence that Secretary Clinton pressed the United States into supporting the United Nations campaign in Libya under false pretenses;
- No evidence that Secretary Clinton or her aides oversaw an operation at the State Department to destroy or scrub embarrassing documents; and
- No evidence that Secretary Clinton or any other U.S. official directed or authorized the U.S. Mission in Benghazi to transfer weapons from Libya to another country.
And Hillary hasn’t even testified before the committee yet.
Rep. Elijah Cummings, the Benghazi committee’s top Democrat, told CNN that “Republicans have been abusing millions of taxpayer dollars for the illegitimate purpose of damaging Hillary Clinton’s bid for president.”
He could just as easily have said the FBI is targeting Hillary at the expense of U.S. taxpayers by recovering secret State Department documents illegally stored on her home email server – before she wiped it clean.
Rep. Trey Gowdy is in hot pursuit of Hillary because, like the FBI, the evidence compiled by his committee compels him to believe wrongdoing has occurred.
It’s said that one of Capone’s knuckle-draggers recognized a Treasury agent that worked for Eliot Ness while walking the city’s streets and asked, “Everyone here in Chicago gets along good with each other. Why not you?”
After all, Chicago’s mayor, William “Big Bill” Thompson, provided passports of a sort to Capone’s gang, telling police to “extend the courtesies of this department to the bearer.”
Trey Gowdy might need to think outside the box in pursuing the criminal Clinton mob in the manner of accountant Frank J. Wilson. It’s time Republicans get their act together and rescind Hillary’s get-out-of-jail card.


text:
He's going to run like the wind — for victims of Hurricane Sandy.
The police chief of Fairview, N.J., Frank Del Vecchio, will begin an epic 30-hour, 100-mile run Thursday that will take him from Moonachie, N.J., into Staten Island, then down the Jersey Shore, ending in Seaside Heights.
Whenever his energy fades, Del Vecchio, 44, said he'll think of the people still reeling from the storm who he is honoring.
"What they've endured for six months I can endure for them in a 100-mile run," Del Vecchio said.
So far he has raised $20,000, to be distributed to Sandy victims through his Run for Hope Foundation, and he expects more money will pour in once he begins his one-man ultra-marathon.
Del Vecchio was inspired to do the run after speaking with Mayor Mauro Raguseo of Little Ferry, N.J., in the aftermath of the storm.
"I'm not concerned with all the help we have now — what makes me nervous is that in six months, people are going to forget," Del Vecchio said the mayor told him.
So Del Vecchio began training Jan. 2 and lost about 15 pounds. He hopes the run will serve as a reminder that people are still struggling in Sandy-stricken areas.
He plans to structure his journey by running 5 miles, walking 1 mile while refueling on peanut butter sandwiches, then resuming his brisk pace. Local law enforcement will join him for various legs of the journey.
He added that he'd be running rain or shine.
"Even if a storm hits, it's not going to stop me," he said.


text:
Dr. Ben Staiger is a highly versatile surgeon with more than 15 years of experience working in academia and at a variety of high-volume veterinary hospitals. He routinely performs a wide range of specialty surgeries. Besides his extensive orthopedic training, strong interest in traumatology, and love for soft tissue surgery, Dr. Staiger has a particular passion for minimally invasive surgery (MIS) and interventional radiology.
Inspired by the exceptional level of veterinary care and quality of specialty training in the United States, Dr. Staiger pursued surgical specialization at Michigan State University after graduating from the Ludwig-Maximilian University in Munich, Germany in 2004.
After an Internship in General Medicine and Surgery, he completed a Residency in Small Animal Surgery, a Surgery Specialty Fellowship, and a Fellowship in Interventional Radiology (IR).
As a Fear Free Certified Professional, Dr. Staiger emphasizes the importance of a personal approach to each individual patient and their family.
Dr. Staiger enjoys a number of downtime activities including traveling, hiking, sailing, skiing, motorcycling, home improvements, and lately, gardening. He loves good food, great company, and above all, quality time with his friends and family, which naturally includes the ubiquitous furry section.

domain: medical


I have a question relating to a classic engine (3.3L straight six, Vauxhall engine). Most of the straight six cylinder engines of the '60's used a hot spot on the intake manifold (typically from a connection to the exhaust, or maybe water heated). As I understand, this was for cold running, and also to help keep the fuel in vapour form, travelling along the length of the manifold. Of course the manifold does pick up a good amount of heat, just from being bolted to the cylinder head. With the volatility of modern fuels, do you think it is really necessary to keep the hot spot? It certainly doesn't help hot starts, or the volumetric efficiency.
Typically the manifold would have a centrally mounted carburettor, the longest runner length from the carb to the cylinder head is of the order 300mm, the shortest runner length about 80mm (these values need the intake port length adding to get to the back of the valve).
This is a very interesting question. Hot-spots or heated inlet manifolds are another example where an engine does not behave as one may expect.
Thinking about the 3.3L engine. During one full revolution 3 cylinders draw in mixture, i.e. 1.65L. This has to travel through the inlet manifold. Assuming the approximate diameter of the inlet manifold is 50mm (2”) then at 2,500 rpm, the mixture will take approximately 8ms (0.008s) to travel down the longest 300mm runing length. This is far too short a time for heat to be transferred from the hot-spot to the bulk of the mixture.
A heated manifold or hot-spot has virtually no effect on the temperature of the mixture in a running engine. So why are they there?
Engines can suffer from a problem called “pooling” where liquid petrol collects in the inlet manifold. Its flow into the engine is uncontrolled. When it does, it makes the mixture temporarily richer and causes uneven running. Pooling is worse when an engine has just been started or is running cold on the choke. To address this, the carburettors on the XPAG are semi-downdraft. The carburettors and inlet manifold slope downwards towards the engine to allow the petrol to drain into the cylinder. The hotspots or heated exhaust are an alternative way of addressing this problem. They vapourise any liquid petrol as it collects in the inlet manifold.


text:
Towards continual task learning in artificial neural networks
Towards continual task learning in artificial neural networks: current approaches and insights from neuroscience
Introduction & historical context
The innate capacity of humans and other animals to learn a diverse, and often interfering, range of knowledge and skills throughout their lifespan is a hallmark of natural intelligence, with obvious and irrefutable evolutionary motivations (Legg & Hutter 2007; Tenenbaum et al., 2011). In parallel, the ability of artificial neural networks (ANNs) to learn across a range of tasks and domains, combining and re-using learned representations where required, is a clear goal of artificial intelligence. This capacity, widely described as continual learning, has become a prolific subfield of research in machine learning (Hadsell et al., 2020). Despite the numerous successes of deep learning in recent years, across domains ranging from image recognition to machine translation (Krizhevsky et al., 2012; Vaswani et al., 2017), such continual task learning has proved challenging. Neural networks trained on multiple tasks in sequence with stochastic gradient descent often suffer from representational interference, whereby the learned weights for a given task effectively overwrite those of previous tasks in a process termed catastrophic forgetting (French, 1999). This represents a major impediment to the development of more generalised artificial learning systems, capable of accumulating knowledge over time and task space, in a manner analogous to humans (Hinton et al., 1986; Hassabis et al., 2017; Marblestone et al., 2016).
A proliferation of research seeking to alleviate the catastrophic forgetting problem has emerged in recent years, motivated by the requirement for machine learning pipelines to accumulate and analyse vast data streams in real-time (Parisi et al., 2019). Despite significant progress being made through such research, both in theory and application, the sub-field of continual learning research is vast, and therefore benefits from clarification and unifying critical appraisal. Simple categorisation of these approaches according to network architecture, training paradigm, and regularisation proves useful in structuring the literature of this increasingly important sub-field. Furthermore, many existing approaches to alleviating catastrophic forgetting in neural networks draw inspiration from neuroscience (Hassabis et al., 2017). This review will address both of these issues, providing a broad critical appraisal of current approaches to continual learning, while interrogating the extent to which insight might be provided by the rich literature of learning and memory in neuroscience.
The influence of network architecture on task performance has been widely described in machine learning (LeCun et al., 2015), and represents a fruitful area of continual learning research (Parisi et al., 2019). In particular, attention has focussed on network architectures which dynamically reconfigure in response to increasing training data availability, primarily by recruiting the training of additional neural network units or layers.
Progressive neural networks are one such example, where a dynamically expanding neural network architecture is employed (Rusu et al., 2016). For training on each subsequent task, this model recruits additional neural networks which are trained on these tasks, while transfer of learned knowledge across tasks is facilitated by learned ‘lateral’ connections between the constituent networks (Figure 1A). Together, this alleviates catastrophic forgetting in a range of reinforcement learning benchmarks, such as Atari games, and compares favourably with baseline methods which leverage pre-training or fine-tuning of model parameters (Figure 1B,C). Despite the empirical successes of progressive networks, an obvious conceptual limitation is that the number of network parameters grows with the number of tasks experienced. For sequential task training (on n tasks), the broader applicability of this method, as n tends to infinity, remains unclear.
Figure 1. A) Schematic of the Progressive neural network architecture, where each of the three columns represents a constituent neural network instantiated to solve a given task. Concretely, the first two columns represent networks trained on tasks 1 and 2, respectively. The third column on the right represents a third network added to solve a novel task, and can draw on previously learned features via lateral connections between the networks (as indicated by the arrows). B) An example task domain (adapted from Rusu et al., 2016), in which a range of 3-dimensional labyrinths are navigated to attain reward. The structural diversity of these environments makes them an ideal test of continual task learning. C) Atari 2600 games similarly offer a paradigm for testing continual learning, but with a richer and more complex set of tasks and environments.
An alternative approach was proposed by Draelos et al. (2017), which takes direct inspiration from hippocampal neurogenesis, a well-described phenomenon from neuroscience in which new neurons are formed in adulthood, raising intriguing questions of learning and plasticity in both natural and artificial intelligence settings (Amione et al., 2014). The method proposed in this paper, termed neurogenesis deep learning, is conceptually similar to that of progressive neural networks, but in this instance involves additional neurons in deep neural network layers being recruited as the network is trained on subsequent tasks. Draelos et al. implement this as an autoencoder trained on the MNIST dataset of handwritten digits. As a greater range of digits is added incrementally to the training distribution, units are added in parallel to the autoencoder, thereby giving rise to the ‘neurogenesis’ in this dynamic network architecture. The autoencoder network in this instance preserves weights associated with previously learned tasks using a form of replay, while the reconstruction error provides an indication of how well representations of previously learned digits are preserved across learning of subsequent digits (that is, subsequent task learning). This paper presents an elegant idea for mitigating catastrophic forgetting, but further experiments are required to fully appraise its potential. For instance, the incremental training data used in this paper is solely in the form of discrete, one-hot categories, rather than the more challenging (and more naturalistic) scenario of novel data accumulating gradually, or without clear boundaries.
Both of the approaches discussed so far have involved dynamic network architectures, but nonetheless ones in which networks or units are recruited and incorporated in response to subsequent tasks. An alternative method has been advanced by Cortes et al. (2016), in which no network architecture is explicitly encoded. Instead, the proposed AdaNet algorithm adaptively selects both the optimal network architecture and weights for the given task. When tested on binary classification tasks drawn from the popular CIFAR-10 image recognition dataset, this approach performed well, with the algorithm automatically learning appropriate network architectures for the given task. Although AdaNet has not been tested exhaustively in the context of continual learning, it represents an appealing method of dynamically reconfiguring the network to mitigate catastrophic forgetting with subsequent tasks.
Overall, some combination of these approaches – a dynamic network architecture and an algorithm for automatically inferring the optimal architecture for newly encountered tasks – is likely to offer potential solutions to continual learning.
Imposing constraints on the neural network weight updates is another major area of continual learning research. Such regularisation approaches have proved popular in recent years, and many derive inspiration from models of memory consolidation in theoretical neuroscience (Fusi et al., 2005; Losonczy et al., 2008).
- Learning without forgetting: Li & Hoiem, 2016
Learning without forgetting (LwF) is one such proposed regularisation method for continual learning (Li & Hoiem, 2016), and draws on knowledge distillation (Hinton et al., 2015). Proposed by Hinton and colleagues, knowledge distillation is a technique in which the learned knowledge from a large, regularised model (or ensemble of models) is distilled into a model with many fewer parameters (the details of this technique, however, are beyond the scope of this review). This concept was subsequently employed in the LwF algorithm to provide a form of functional regularisation, whereby the weights of the network trained on previous tasks or training data are enforced to remain similar to the weights of the new network trained on novel tasks. Informally, LwF aims to effectively take a representation of the network before training on new tasks. In Li & Hoiem, this was implemented as a convolutional neural network, in which only novel task data was used to train the network, while the 'snapshot' of the prior network weights preserved good performance on previous tasks. This approach has garnered significant attention in recent years, and offers a novel perspective on the use of knowledge distillation techniques in alleviating catastrophic forgetting. However, Learning without Forgetting has some notable limitations. Firstly, it is highly influenced by task history, and is thus susceptible to forming sub-optimal representations for novel tasks. Indeed, balancing stability of existing representations with the plasticity required to efficiently learn new ones is a major unresolved topic of research in continual learning. A further limitation of LwF is that, due to the nature of the distillation protocol, training time for each subsequent task increases linearly with the number of tasks previously learned. For broad applicability, this practically limits the capacity of this technique to handle pipelines of training data for which novel tasks are encountered regularly.
- Elastic weight consolidation: Kirkpatrick et al., 2017
In recent years, one of the most prominent regularisation approaches to prevent catastrophic forgetting is that of elastic weight consolidation (EWC) (Kirkpatrick et al., 2017). EWC, which is suitable for supervised and reinforcement learning paradigms, takes direct inspiration from neuroscience, where synaptic consolidation is thought to preserve sequential task performance by consolidating the most important features of previously encountered tasks (Yang et al., 2009). Intuitively, EWC works by slowing learning of the network weights which are most relevant for solving previously encountered tasks. This is achieved by applying a quadratic penalty to the difference between the parameters of the prior and current network weights, with the objective of preserving or consolidating the most task-relevant weights. It is this quadratic penalty, with its ‘elastic’ preservation of existing network weights, which takes inspiration from synaptic consolidation in neuroscience, and is schematically represented in Figure 2A. More formally, the loss function of EWC, L(θ), is given by:
Where θ represents the parameters of the network, L_B(θ) represents the loss for task B, λ is a hyperparameter indicating the relative importance of previously encountered tasks compared to new tasks, F is the Fisher information matrix, and finally θA* represents the trainable parameters of the network important for solving previously encountered tasks. Intuitively, this loss function can be understood as penalising large differences between previous and current network weights (the term within the brackets). In EWC, the Fisher information matrix is used to give an estimation of the importance of weights for solving tasks, by using an importance weighting proportional to the diagonal of the Fisher information metric over the old parameters for the previous task. While conceptually elegant, this presents a notable limitation of EWC: exact computation of this Fisher diagonal has complexity linear with the number of outputs, limiting the applicability of this method to low-dimensional output spaces.
Empirically, EWC performs well on supervised and reinforcement learning tasks, such as MNIST digit classification and sequential Atari 2600 games, respectively. However, the suitability of the quadratic penalty term (which is only derived for the two-task case in the original paper) has been questioned for cases of multi-task learning (Huszár, 2018). Additionally, building on the strong empirical results presented in the paper, Kemker et al. (2018) demonstrated in further experiments that EWC fails to learn new classes incrementally in response to accumulating training data (Figure 2C). Overall, EWC is a promising method for alleviating catastrophic forgetting, but several details regarding its broader applicability (and theoretical underpinning) remain unresolved.
Figure 2. A) Schematic of the analogy between synaptic consolidation (left) and the regularisation of EWC (right), in which network weights important for solving previous tasks are preserved in an ‘elastic’ manner. Adapted from Hassabis et al., 2017. B) Schematic showing the parameter spaces of tasks A and B, for which EWC finds the optimal balance of weights for preserving performance of task A after training on task B. Unlike other regularisation approaches (such as L2 regularisation, as depicted), EWC does so by explicitly calculating the importance of weights in the network for solving a given task. Adapted from Kirkpatrick et al., 2017. C) Following on from the impressive empirical results described in the original paper, Kemker et al. provided more thorough testing of EWC. Here, they report that this method is unable to learn new task classes incrementally (in this case, adding new MNIST digit classes incrementally).
- Synaptic intelligence: Zenke et al., 2017
An approach closely related to EWC is that of synaptic intelligence (Zenke et al., 2017). In this method, however, individual synapses (the connections between neurons or units) estimate their importance in solving a given task. Such intelligent synapses can then be preserved for subsequent tasks by penalising weight updates, thereby mitigating catastrophic forgetting of previously learned tasks. Intuitively, synaptic intelligence can be considered a mechanism of anchoring network weights relevant for previous tasks to their existing values, and decelerating updates of these weights to prevent over-writing of previous task performance.
In summary, numerous regularisation methods have been developed to aid continual learning. By modulating gradient-based weight updates, these methods aim to preserve the performance of the model across multiple tasks trained in sequence. Many such regularisation methods have garnered interest from the research community, both due to both their theoretical appeal and strong empirical validation. Ultimately, however, none of these approaches has offered a comprehensive solution to the continual learning problem, and it is likely that a deeper understanding of credit assignment within neural networks will drive this research further.
Beyond the model itself, the training regime employed is critical to sequential task performance, and represents a rich avenue of continual learning research. Although numerous training paradigms have been described, this review will focus on those most directly developed to alleviate catastrophic forgetting:
- Transfer learning:
Historically, limitations of dataset size have motivated the study of transfer learning for machine learning systems, with the aim of initially training ANNs on large datasets before transferring the trained network parameters to other tasks (Bengio et al., 2012; Higgins et al., 2016). The recent successes of transfer learning in fields such as computer vision and natural language processing are well-documented. For instance, Yosinski et al. (2014) demonstrated the efficacy of transfer learning for computer vision in a range of benchmarking datasets. It was then found that subsequent layers in the hierarchy of the neural network display representational features which are reminiscent of human visual cognition (such as edge detection in lower layers and image-specific feature detection in higher layers).
Successes of transfer learning in few-shot learning paradigms, which aim to perform well upon first presentation of a novel task, have been well-described in recent years (Palatucci et al., 2009; Vinyals et al., 2016). However, translation of this potential into alleviating catastrophic forgetting has proved more challenging. One of the earliest attempts to leverage transfer for continual learning was implemented in the form of a hierarchical neural network, termed CHILD, trained to solve increasingly challenging reinforcement learning problems (Ring, 1997). This model was not only capable of learning to solve complex tasks, but also demonstrated a degree of continual learning – after learning nine task structures, the agent could still successfully perform the first task when returned to it. The impressive (and perhaps overlooked) performance of CHILD draws on two main principles: firstly, transfer of previously learned knowledge to novel tasks; secondly, incremental addition of network units as more tasks are learned. In many ways, this model serves as a precursor of progressive neural networks (Rusu et al., 2016), and offers an appealing account of transfer learning in aiding continual task learning.
- Curriculum learning:
In parallel to this, curriculum learning has gained attention of improving continual learning capacities in ANNs (Bengio et al., 2009; Graves et al., 2017). Broadly, curriculum learning can be defined as the phenomenon by which both natural and artificial intelligence agents learn more efficiently when the training dataset contains some inherent structure, and when exemplars provided to the learning system are organised in a meaningful way (for instance, progressing in difficulty) (Bengio et al., 2009). When trained on datasets such as MNIST, curriculum learning both accelerates the learning process (as measured by time or training steps to reach the global minimum – that is, the optimum of the model) and helps prevent catastrophic forgetting (Bengio et al., 2009). However, one limitation of curriculum learning in ANNs is the assumption that task difficulty can be represented in a linear and uniform manner (often described as a ‘single axis of difficulty’), disregarding the nuances of each task structure. Nevertheless, curriculum learning is a promising, and underexplored, avenue of research for better continual learning performance in neural networks.
- Generative replay: Shin et al. (2017)
The neural phenomenon of hippocampal (or, more generally, memory) replay has recently garnered attention with respect to the design of ANNs (Shin et al., 2017; Kemker & Kanan, 2018). Shin et al. (2017) designed the model most obviously inspired by hippocampal replay, with a training regime termed Deep Generative Replay. This comprises a generative model and a task-directed model, the former of which is used to generate representative data from previous tasks, from which a sample is selected and interspersed with the dataset of the new task. In this way, the model mimics hippocampal replay, drawing on statistical regularities from previous experiences when completing novel tasks (Liu et al., 2019). This approach, which shares some conceptual similarities to the model proposed by Draelos et al. (2017), displays a substantial improvement in continual learning compared to complexity-matched models lacking replay.
Several other implementations of replay in neural networks have also been described, including a straightforward experience replay buffer of all prior events for a reinforcement learning agent (Rolnick et al., 2018). This method, called CLEAR, attempts to address the stability-plasticity tradeoff of sequential task learning, using off-policy learning and replay-based behavioural cloning to enhance stability, while maintaining plasticity via on-policy learning. This outperforms existing deep reinforcement learning approaches with respect to catastrophic forgetting, but might prove unsuitable in cases where storage of a complete memory buffer is intractable.
Figure 3. Sub-panels A and B demonstrate the efficacy of deep generative replay in alleviating catastrophic forgetting. A) When a neural network is trained on sequential tasks (in this case, from the permuted MNIST dataset) with vanilla gradient descent, catastrophic forgetting of previous task performance occurs due to overwriting of the weights associated with these prior tasks. As multiple tasks in sequence are encountered, performance on previous tasks can decrease dramatically, as the network weights optimised for these prior tasks are ‘catastrophically forgotten’. B) Conversely, when the network is trained with deep generative replay, continual learning across tasks is much improved. By sampling training data from previous tasks and interleaving this with the current task, this method enables multiple tasks to be learned in sequence with the same network, without catastrophic forgetting of earlier task performance (adapted from a PyTorch implementation of deep generative replay). C) Schematic of the REMIND model, which proposes the replay of compressed data representations in a more biologically plausible framework. REMIND takes an input image (denoted X in the schematic), and compresses this into low-dimensional tensor representations of that training data by passing it through the neural network layers labelled G. These compressed representations can then be efficiently stored in memory (as shown by the lower box in the schematic), and indexed for replay as required. The compressed tensor representations can be reconstructed by the neural network layers labelled F, and interleaved with current training data. A more comprehensive description can be found here.
- REMIND: Hayes et al. (2019)
Drawing on the successes of replay in alleviating catastrophic forgetting, Hayes et al. (2019) proposed a novel method which more accurately reflects the role of memory replay as described by modern neuroscience, implemented as a convolutional neural network. Standard replay approaches for CNNs rely on presenting raw pixel input from previously encountered data interleaved with novel data. Although effective, this approach is both biologically implausible and memory-intensive. By contrast, the REMIND (replay using memory indexing) algorithm proposed in this paper replays a compressed representation of previously encountered training data, thereby enabling efficient replay and online training. This approach is directly inspired by hippocampal indexing theory (Teyler & Rudy, 2007), and proposes a solution to the issue with prior replay implementations of having to store raw training data from previous tasks. In REMIND, this compression of input data is achieved using layers of the neural network, where the raw input data is compressed into a lower-dimensional tensor representation (for instance, in the context of CNNs, a feature map) for replay (see Figure 3C). Hayes et al. implement this using product quantization, a technique for compressing data which has a significantly lower reconstruction error compared to other methods (the details of product quantization are beyond the scope of this review, but see Jegou et al., 2010 for a comprehensive account). This compression proves highly effective in maximising memory efficiency: REMIND can store 1 million compressed representations compared to just 20,000 when raw data input is stored in alternative models, matched for memory capacity. Empirically, replay of compressed training data was shown to confer strong benefits, whereby REMIND outperforms constraint-matched methods on incremental class learning tasks derived from the ImageNet dataset.
Can inspiration be drawn from neuroscience?
Neuroscience (in particular, cognitive neuroscience) and artificial intelligence have long been intertwined both in aetiology and research methodologies (Hassabis et al., 2017; Hinton et al., 1986), and this intersection has already inspired numerous approaches to continual learning research. Many of the methods described previously in this review draw inspiration from neuroscience, either implicitly or explicitly (for instance, generative replay, REMIND algorithm, and transfer learning are all conceptually indebted to decades of neuroscience research).
One line of justification for this approach is that there are studies demonstrating a phenomenon analogous to catastrophic forgetting in humans, suggesting that a shared mechanistic framework might underlie continual learning (and its inherent limitations) in both humans and ANNs (Pallier et al., 2003; Mareschal et al., 2007). The first of these studies, Pallier et al. (2003), examined language acquisition and overwriting in Korean-born subjects whose functional mother tongue was French (due to being adopted before the age of 8), and had no conscious knowledge of the Korean language, as verified by behavioural testing. Functional neuroimaging (fMRI) demonstrated that the Korean-born francophone subjects displayed no greater (cortical) response to the Korean language in the setting of passive listening compared to French subjects with no exposure to Korean. This was interpreted as a form of over-writing, or catastrophic forgetting, of the first language by the second. The significance of these results is unclear, particularly given the limited literature on human catastrophic forgetting, but represents an interesting mandate for the use of cognitive neuroscience as a source of inspiration for continual learning research.
Replay in humans and neural networks:
From the perspective of neuroscience, several mechanistic features underpinning human continual learning have been dissected, such as memory replay (perhaps as a means of transferring learned knowledge from short-term to long-term storage), curriculum and transfer learning paradigms, structural plasticity, and the integration of multiple sensory modalities to provide rich sensory context for memories (Parisi et al., 2019).
One phenomenon widely considered to contribute to continual learning is memory replay or hippocampal replay, defined as the re-activation (hence, replay) of patterns of activity in hippocampal neurons during states of slow-wave sleep and passive, resting awake (Skaggs et al., 1996; Schönauer et al., 2017; Dave & Margoliash 2000; Rudoy et al., 2009). Such replay episodes are thought to provide additional trials serving to rehearse task learning and generalise knowledge during so-called ‘offline’ learning, and were first identified by recording hallmarks of brain activity during learning and mapping these onto covarying activity patterns identified during sleep (Rasch et al., 2018). An elegant demonstration of this phenomenon in humans was provided by Rudoy et al. (2009), whereby subjects learned the position of arbitrary objects on a computer screen, with each object presented in association with a unique and characteristic sound. The participants then slept for a short period of time, with electroencephalography (EEG) used to identify different stages of sleep. During slow-wave sleep, the characteristic sounds for half of the objects were played at an audible but unobtrusive volume. It was found that the participants subsequently recalled the positions of these sound-consolidated objects with greater accuracy. Replay approaches in machine learning have already proved fruitful, and studies such as these from neuroscience only serve to further motivate replay as a topic of continual learning research.
Replay, as well as the lesser-understood hippocampal pre-play (whereby hippocampal neurons display what is thought to represent simulated activity which can be mapped onto future environments) might aid continual learning through this ‘offline’ consolidation (Dragoi & Tonegawa, 2011; Bendor & Spiers, 2016). Although the mechanism is incompletely described, it has been proposed that replay could contribute to continual learning by promoting the consolidation of previous task knowledge (Ólafsdóttir et al., 2018). In some ways, this can be considered analogous to pre-training neural networks with task-relevant data, and might prove an interesting research avenue for continual learning.
Indeed, the value of neuroscience-inspired research was recently underlined by a study from van de Ven et al. (2020). Here, a more biologically plausible form of replay was implemented, whereby instead of storing previous task data, a learned generative model was trained to replay internal, compressed representations of that data. These internal representations for replay were then interleaved with current training data in a context-dependent manner, modulated by feedback connections. This approach also overcomes a potential issue with existing replay methods in machine learning – cases where storing previously encountered data is not permissable due to safety or privacy concerns. Replay of compressed representations, or replay in conjunction with some form of federated learning, might offer a solution in these instances.
Complementary learning systems for continual learning:
Complementary learning systems (CLS) theory, first advanced by McClelland et al. (1995) and recently updated by Kumaran et al. (2016), delineates two distinct structural and functional circuits underlying human learning (McClelland et al., 1995; Kumaran et al., 2016; O’Reilly et al., 2004; Girardeau et al., 2009). The hippocampus serves as the substrate for short-term memory and the rapid, ‘online’ learning of knowledge relevant to the present task; in parallel, the neocortex mediates long-term, generalised memories, structured over experience. Transfer of knowledge from the former to the latter occurs with replay, and it is intuitive that the catastrophic forgetting of previously learned knowledge in machine learning systems could be mitigated to some extent by such complementary learning systems. This has proved an influential theory in neuroscience, offering an account of the mechanisms by which humans accumulate task performance over time, and has started to provide ideas for how complementary learning systems might aid continual learning in artificial agents.
Transfer & curriculum learning:
Furthermore, the learning strategies employed by humans, of which transfer learning and curriculum learning have come into recent focus, are themselves likely to contribute further to continual learning (Barnett & Ceci, 2002; Holyoak & Thagard, 1997). Humans are capable of transferring knowledge between domains with minimal interference, and this capability derives from both continual task learning and generalisation of previously learned knowledge. A human learning to play tennis, for instance, can generalise some features of this task learning to a different racquet sport (Goode & Magill, 1986). Such transfer learning is poorly understood at the level of neural computations, and has perhaps been neglected by the neuroscience research community until recently, when attempts to endow artificial agents with these abilities has re-focussed attention on the mechanisms underpinning transfer learning in humans (Weiss et al., 2016).
Attempts to explain this abstract transfer of generalised knowledge in humans have themselves recapitulated many features of continual learning (Doumas et al., 2008; Barnett & Ceci, 2002; Pan & Yang, 2010; Holyoak & Thagard, 1997). For instance, Doumas et al. (2008) proposed that this is achieved by the neural encoding of relational information between objects comprising a sensory environment. Critically, such relational information would be invariant to nuances and specific features in these objects, and this could aid continual learning by providing a generalised task learning framework. Although the neural coding for such a relational framework has not yet been elicited, an intriguing recent paper by Constantinescu et al. (2016) proposed abstract concepts are encoded by grid cells in the human entorhinal cortex in a similar way to maps of physical space. Just as replay and memory consolidation have already led approaches to continual learning, such research might inspire novel approaches to alleviating catastrophic forgetting.
A related, and perhaps lesser-studied, learning paradigm is that of curriculum learning (Bengio et al., 2009; Elman, 1993). Intuitively, curriculum learning states that agents (both natural and artificial) learn more effectively when learning examples are structured and presented in a meaningful manner. The most obvious instantiation of this is to increase the difficulty of the learning rules throughout the sequence of examples presented; indeed, this is consistent with the structure of most human educational programmes (Krueger & Dayan, 2009; Goldman & Kearns, 1995; Khan et al., 2011). It has been appreciated for some time that this form of non-random learning programme aids human continual learning (Elman, 1993; Krueger & Dayan, 2009); however, the theoretical underpinnings of this are only starting to be elicited. Curriculum learning has the potential to enhance continual learning in neural networks by providing more structured training regimes, which emphasise the features of the training dataset which are most relevant to the tasks. Ultimately, however, more work is required to explore the promise of this.
Multi-sensory integration & attention:
It has been appreciated for some time in the field of cognitive neuroscience that humans receive a stream of rich multi-sensory input from the environment, and that the ability to integrate this information into a multi-modal representation is critical for cognitive functions ranging from reasoning to memory (Spence, 2010; Spence, 2014; Stein & Meredith, 1993; Stein et al., 2014). By enriching the context and representation of individual memories, multi-sensory integration is thought to aid human continual learning.
There is also evidence that attention is a cognitive process contributing to continual learning in humans (Flesch et al., 2018). Here, when the task learning curriculum was designed in a manner permitting greater attention (namely, with tasks organised in blocks for training, rather than ‘interleaved’ task training), continual learning of the task in the (human) subjects was enhanced. Even if optimal training regimes differ across biological and artificial agents, this underlines the importance of curriculum and attention in addressing catastrophic forgetting.
Future perspective: bridging neuroscience and machine learning to inspire continual learning research:
The inherent efficacy of human continual learning and its cognitive substrates is perhaps most impressive when contrasted with the current inability to endow AI agents with similar properties. With a projected increase in global data generation from 16 zettabytes annually in 2018 to over 160 zettabytes annually by 2025 (and the consequent impossibility of comprehensive storage), there is a clear motivation for developing machine learning systems capable of continual learning in a manner analogous to humans (IDC White Paper, 2017; Tenenbaum et al., 2011).
The super-human performance of deep reinforcement learning agents on a range of complex learning tasks, from Atari 2600 video games to chess, has been well-publicised in recent years (Mnih et al., 2015; Mnih et al., 2016; Silver et al., 2018; Kasparov, 2018; LeCun et al., 2015). However, these successes conceal a profound limitation of such machine learning systems: an inability to sustain performance when trained sequentially over a range of tasks. Traditionally, approaches to the issue of catastrophic forgetting have focussed on training regime, and often remain tangential to the cause of such forgetting. In the future, bridging the conceptual gap between continual learning research and the rich literature of learning and memory in neuroscience might prove fruitful, as motivated by several of the examples already discussed in this review.
Parallels of CLS theory in machine learning systems:
For example, with respect to CLS theory, biologically inspired neural network architectures involving two different network parameters (a ‘plastic’ parameter for slow-changing information, and a rapidly updating parameter) have existed for decades (Hinton & Plaut, 1987). Indeed, these networks outperformed state-of-the-art ANNs in continual learning-related tasks at their time of development. When considered from the perspective of CLS theory, this suggests that the parallel and complementary functions of the hippocampus and neocortex in human memory contribute to continual learning.
More recent models, such as the Differentiable Neural Computer (DNC), also support the view that having complementary memory systems supports continual learning (Graves et al., 2016). The DNC architecture consists of an artificial neural network and an external memory matrix, to which the network has access to store and manipulate data structures (broadly analogous to random-access memory). As such, a DNC can be interpreted as having ‘short-term’ and ‘long-term’ memory proxies, and the capacity to relay information between them. This model is capable of solving complex RL problems, and answering natural language questions constructed to mimic reasoning, lending further support to the contribution of complementary learning systems in human continual learning. The true potential of such approaches, however, remains unclear, and requires further investigation.
Emerging significance of multi-sensory integration & attention in artificial intelligence agents for continual learning:
In the context of continual learning in machine learning, multi-sensory integration (often called multi-modal integration in this context) has an obvious benefit of conferring additional information from different modalities when the environment is uncertain or has high entropy. Indeed, multi-modal machine learning has demonstrated efficacy in a range of task learning paradigms, such as lip reading, where the presence of both audio (phoneme) and visual (viseme) information improves performance compared to a uni-sensory training approach (Ngiamet et al., 2011). Ultimately, greater investigation of multi-modal machine learning could unravel the value of such integration across domains, and offer approaches to aiding continual learning in settings where the environment is unpredictable or multimodal.
The role of attention in human continual learning was underlined by a recent study endowing ANNs with a ‘hard attention mask’, an attentional gating mechanism directly inspired by human cognitive function (Serrà et al., 2018). This substantially decreased catastrophic forgetting in this model when trained on image classification tasks, thereby emphasising attention as an important contribution to continual learning.
Advances in deep learning have accelerated in recent years, capturing the imagination of researchers and the public alike with their capacity to achieve superhuman performance on tasks, and offer novel scientific insights. However, if machine learning pipelines are ever going to dynamically learn new tasks in real time, with interfering goals and multiple input datasets, the continual learning problem must be addressed. In this review, several of the most promising avenues of research have been appraised, with many of these deriving inspiration from neuroscience. Although much progress has been made, no existing approach adequately solves the continual learning problem. This review argues that bridging continual learning research with neuroscience might offer novel insights and inspiration, ultimately guiding the development of novel approaches to catastrophic forgetting which bring the performance of artificial agents closer to that of humans – assimilating knowledge and skills over time and experience.
Links to selected references of interest are additionally included in the main text
Aimone, J.B., Li, Y., Lee, S.W., Clemenson, G.D., Deng, W. and Gage, F.H., 2014. Regulation and function of adult neurogenesis: from genes to cognition. Physiological reviews, 94(4), pp.991-1026. (https://pubmed.ncbi.nlm.nih.gov/25287858/)
Barnett S.M., Ceci, S.J. When and where do we apply what we learn?: A taxonomy for far transfer. Psychol. Bull. 128, 612-637 (2002). (https://pubmed.ncbi.nlm.nih.gov/12081085/)
Bengio, Y. 2012. Practical recommendations for gradient-based training of deep architectures. In Tricks of the Trade, Second Edition, volume 7700 of Theoretical Computer Science and General Issues. Springer-Verlag. 437–478. (https://arxiv.org/abs/1206.5533)
Bengio, Y., Louradour, J., Collobert, R. & Weston, J. (2009), Curriculum learning, pp. 41–48 (https://ronan.collobert.com/pub/matos/2009_curriculum_icml.pdf)
Cortes, C., Gonzalvo, X., Kuznetsov, V., Mohri, M. and Yang, S., 2017, July. Adanet: Adaptive structural learning of artificial neural networks. In International conference on machine learning (pp. 874-883). PMLR. (https://arxiv.org/abs/1607.01097)
C. Wu, L. Herranz, X. Liu, J. van de Weijer, and B. Raducanu, ‘‘Memory replay GANs: Learning to generate new categories without forgetting,’’ in Proc. Adv. Neural Inf. Process. Syst. (NIPS), Montreal, QC, Canada, 2018, pp. 5964–5974. (https://arxiv.org/abs/1809.02058)
Constantinescu, A.O., J.X. O’Reilly, T.E. Behrens. Organizing conceptual knowledge in humans with a gridlike code. Science, 352 (2016), pp. 1464-1468. (https://science.sciencemag.org/content/352/6292/1464)
D. Bendor, H.J. Spiers. Does the hippocampus map out the future? Trends Cogn. Sci., 20 (2016), pp. 167-169. (https://www.sciencedirect.com/science/article/abs/pii/S1364661316000218)
Dave AS, Margoliash D. Song replay during sleep and computational rules for sensorimotor vocal learning. Science 290: 812–816, 2000. (https://science.sciencemag.org/content/290/5492/812)
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis. A general reinforcement learning algorithm that masters chess, shogi, and go through self-play. Science, 362(6419):1140–1144, 2018. (https://science.sciencemag.org/content/362/6419/1140)
Doumas, L., Hummel, J. & Sandhofer, C. (2008), ‘A theory of the discovery and predication of relational concepts’, Psychological Review 115, 1–43. (https://pubmed.ncbi.nlm.nih.gov/18211183/)
Draelos, T.J., Miner, N.E., Lamb, C.C., Cox, J.A., Vineyard, C.M., Carlson, K.D., Severa, W.M., James, C.D. and Aimone, J.B., 2017, May. Neurogenesis deep learning: Extending deep networks to accommodate new classes. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 526-533). IEEE. (https://ieeexplore.ieee.org/document/7965898)
Dragoi G., Tonegawa S. Preplay of future place cell sequences by hippocampal cellular assemblies. Nature. 2011; 469: 397-40. (https://pubmed.ncbi.nlm.nih.gov/21179088/)
Elman, J. L. (1993), ‘Learning and development in neural networks: The importance of starting small’, Cognition 48 (1), 71–99. (https://www.sciencedirect.com/science/article/abs/pii/0010027793900584)
F. Khan, X. Zhu, and B. Mutlu. How do humans teach: On curriculum learning and teaching dimension. NIPS, 2011. (https://proceedings.neurips.cc/paper/2011/hash/f9028faec74be6ec9b852b0a542e2f39-Abstract.html)
Flesch, T. et al. Comparing continual task learning in minds and machines. PNAS 115 (44): E10313- E10322 (2018). (https://www.pnas.org/content/115/44/E10313)
French, R. M. (1999), ‘Catastrophic forgetting in connectionist networks’, Trends in Cognitive Sciences 3 (4), 128–135. (https://www.sciencedirect.com/science/article/abs/pii/S1364661399012942)
Fusi, S., Drew, P.J. and Abbott, L.F., 2005. Cascade models of synaptically stored memories. Neuron, 45(4), pp.599-611. (https://www.sciencedirect.com/science/article/pii/S0896627305001170)
G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter. Continual lifelong learning with neural networks: A review. arXiv preprint arXiv:1802.07569, 2018. (https://www.sciencedirect.com/science/article/pii/S0893608019300231)
G. Kasparov. Chess, a drosophila of reasoning. Science (New York, NY), 362(6419):1087, 2018. (https://science.sciencemag.org/content/362/6419/1087)
Gepperth, A. & Karaoguz, C. (2015), ‘A bio-inspired incremental learning architecture for applied perceptual problems’, Cognitive Computation 8 (5), 924–934. (https://hal.archives-ouvertes.fr/hal-01418123/document)
Girardeau G., Benchenane K., Wiener S.I., Buzsaki G., Zugaro M.B. Selective suppression of hippocampal ripples impairs spatial memory. Nat. Neurosci. 2009; 12: 1222-1223 (https://www.nature.com/articles/nn.2384)
Goode, S., Magill, R.A. Contextual interference effects in three badminton serves. Res. Q. Exerc. Sport (1986). (https://www.tandfonline.com/doi/abs/10.1080/02701367.1986.10608091)
Goodfellow, I.J., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A.C. Courville, and Y. Bengio. Generative adversarial nets. In Proceedings of NIPS, pages 2672–2680, 2014. (https://arxiv.org/abs/1406.2661)
Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwinska, A., Colmenarejo, S. G., Grefenstette, E., Ramalho, T. & Agapiou, J. e. a. (2016), ‘Hybrid computing using a neural network with dynamic external memory’, Nature 538, 471–476. (https://www.nature.com/articles/nature20101)
Graves, A., Bellemare, M. G., Menick, J., Munos, R. & Kavukcuoglu, K. (2017), Automated curriculum learning for neural networks, arXiv:1704.03003. (https://arxiv.org/abs/1704.03003)
H.F. Olafsdottir, D. Bush, C. Barry. The role of hippocampal replay in memory and planning. Curr. Biol., 28 (2018), pp. R37-R50. (https://www.sciencedirect.com/science/article/pii/S0960982217314410)
Hadsell, R., Rao, D., Rusu, A.A. and Pascanu, R., 2020. Embracing Change: Continual Learning in Deep Neural Networks. Trends in Cognitive Sciences. (https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(20)30219-9)
Hassabis, D., Kumaran, D., Summerfield, C. & Botvinick, M. (2017), ‘Neuroscience-inspired artificial intelligence’, Neuron Review 95 (2), 245–258. (https://www.cell.com/neuron/pdf/S0896-6273(17)30509-3.pdf)
Hayes, T.L., Kafle, K., Shrestha, R., Acharya, M. and Kanan, C., 2020, August. Remind your neural network to prevent catastrophic forgetting. In European Conference on Computer Vision (pp. 466-483). Springer, Cham. (https://arxiv.org/abs/1910.02509)
Higgins, I., Matthey, L., Glorot, X., Pal, A., Uria, B., Blundell, C., Mohamed, S., and Lerchner, A. (2016). Early visual concept learning with unsupervised deep learning. arXiv, arXiv:160605579. (https://arxiv.org/abs/1606.05579)
Hinton, G. E. & Plaut, D. C. (1987), Using fast weights to deblur old memories, Proceedings of the Annual Conference of the Cognitive Science Society, pp. 177–186. (https://www.cs.toronto.edu/~hinton/absps/fastweights87.pdf)
Hinton, G.E. (1986). Learning Distributed Representations of Concepts. In Proceedings of the Eighth Annual Conference of the Cognitive Science Society (Amherst 1986), Hillsdale: Erlbaum. (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.408.7684&rep=rep1&type=pdf)
Hinton, G., Vinyals, O. and Dean, J., 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531. (https://arxiv.org/abs/1503.02531)
Holyoak, K. J. , & Thagard, P. (1997). The analogical mind. American Psychologist, 52, 35–44. (https://mitpress.mit.edu/books/analogical-mind)
Huszár, F., 2018. Note on the quadratic penalties in elastic weight consolidation. Proceedings of the National Academy of Sciences, p.201717042. (https://www.pnas.org/content/115/11/E2496)
IDC Whitepaper, 2017. https://www.import.io/wp-content/uploads/2017/04/Seagate-WP-DataAge2025-March-2017.pdf
Jegou, H., Douze, M. and Schmid, C., 2010. Product quantization for nearest neighbor search. IEEE transactions on pattern analysis and machine intelligence, 33(1), pp.117-128. (https://lear.inrialpes.fr/pubs/2011/JDS11/jegou_searching_with_quantization.pdf)
Kemker, R., McClure, M., Abitino, A., Hayes, T. and Kanan, C., 2018, April. Measuring catastrophic forgetting in neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1). (https://arxiv.org/abs/1708.02072)
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A. and Hassabis, D., 2017. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, 114(13), pp.3521-3526. (https://arxiv.org/abs/1612.00796)
Krueger, K. A. & Dayan, P. (2009), ‘Flexible shaping: how learning in small steps helps’, Cognition 110, 380–394. (https://www.sciencedirect.com/science/article/abs/pii/S0010027708002850)
Kumaran, D., Hassabis, D. & McClelland, J. L. (2016), ‘What learning systems do intelligent agents need? complementary learning systems theory updated’, Trends in Cognitive Sciences 20 (7), 512– 534. (https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(16)30043-2)
LeCun, Y., Bengio, Y. & Hinton, G. (2015), ‘Deep learning’, Nature 521, 436–444. (https://www.nature.com/articles/nature14539)
LeCun, Y., Cortes, C.: The MNIST database of handwritten digits (1998). (http://yann.lecun.com/exdb/mnist/)
Legg, S., Hutter, M. A collection of definitions of intelligence. arXiv:10.1207/s15327051hci0301_2. Preprint, posted June 25th, 2007. (https://arxiv.org/abs/0706.3639)
Li, Z. and Hoiem, D., 2017. Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence, 40(12), pp.2935-2947. (https://arxiv.org/abs/1606.09282)
Liu, Y., Dolan, R.J., Kurth-Nelson, Z. and Behrens, T.E., 2019. Human replay spontaneously reorganizes experience. Cell, 178(3), pp.640-652. (https://www.sciencedirect.com/science/article/pii/S0092867419306403)
Losonczy, A., Makara, J.K. and Magee, J.C., 2008. Compartmentalized dendritic plasticity and input feature storage in neurons. Nature, 452(7186), pp.436-441. (https://www.nature.com/articles/nature06725)
M. Schönauer, S. Alizadeh, H. Jamalabadi, A. Abraham, A. Pawlizki, S. Gais. Decoding material-specific memory reprocessing during sleep in humans. Nat. Comm., 8 (2017). (https://www.nature.com/articles/ncomms15404)
Marblestone A.H., Wayne G., Kording K.P. Toward an integration of deep learning and neuroscience. Front. Comput. Neurosci. 2016; 10: 94 (https://www.frontiersin.org/articles/10.3389/fncom.2016.00094/full)
Mareschal, D., Johnson, M., Sirios, S., Spratling, M., Thomas, M. & Westermann, G. (2007), Neuroconstructivism: How the brain constructs cognition, Oxford: Oxford University Press.
Masse, N.Y. et al. Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization. arXiv preprint arXiv:1802.01569 (2018). (https://www.pnas.org/content/115/44/E10467)
McClelland, J. L., McNaughton, B. L. & O’Reilly, R. C. (1995), ‘Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory’, Psychological Review 102, 419–457. (https://pubmed.ncbi.nlm.nih.gov/7624455/)
McCloskey, M. & Cohen, N. J. (1989), ‘Catastrophic interference in connectionist networks: The sequential learning problem’, The Psychology of Learning and Motivation 24, 104–169. (https://www.sciencedirect.com/science/article/pii/S0079742108605368)
Mnih, V. et al. Asynchronous methods for deep reinforcement learning. arXiv: 1602.01783v2. (https://arxiv.org/abs/1602.01783)
Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518: 529-533 (2015). (https://www.nature.com/articles/nature14236)
Ngiam, J. et al. Multimodal deep learning. In International Conference on Machine Learning (ICML), Bellevue, USA, June 2011. (https://dl.acm.org/doi/10.5555/3104482.3104569)
O’Reilly, R. C. (2004), The Division of Labor Between the Neocortex and Hippocampus. Connectionist Modeling in Cognitive (Neuro-)Science, George Houghton, Ed., Psychology Press. (https://www.taylorfrancis.com/chapters/division-labor-neocortex-hippocampus-randall-reilly/e/10.4324/9780203647110-24)
Palatucci, M.M., Pomerleau, D.A., Hinton, G.E. and Mitchell, T., 2009. Zero-shot learning with semantic output codes. (https://www.cs.toronto.edu/~hinton/absps/palatucci.pdf)
Pallier, C., Dehaene, S., Poline, J.-B., LeBihan, D., Argenti, A.-M., Dupoux, E. & Mehler, J. (2003), ‘Brain imaging of language plasticity in adopted adults: can a second language replace a first?’, Cerebral Cortex 13, 155–161. (https://academic.oup.com/cercor/article/13/2/155/270786)
Pan, S. J. & Yang, Q. (2010), ‘A survey on transfer learning’, IEEE Transactions on Knowledge and Data Engineering 22 (10), 1345–1359. (https://journalofbigdata.springeropen.com/articles/10.1186/s40537-016-0043-6)
Parisi, G. I., Tani, J., Weber, C. & Wermter, S. (2017), ‘Lifelong learning of humans actions with deep neural network self-organization’, Neural Networks 96, 137–149. (https://www.sciencedirect.com/science/article/pii/S0893608017302034)
Parisi, G., Tani, J., Weber, C. & Wermter, S. (2018), Lifelong learning of spatiotemporal representations with dual-memory recurrent self-organization, arXiv:1805.10966. (https://arxiv.org/abs/1805.10966)
PyTorch implementation of Deep Generative Replay. https://github.com/kuc2477/pytorch-deep-generative-replay
Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015. (https://arxiv.org/abs/1511.06434)
Rasch, B. Memory Formation: Let’s Replay. Elife e43832 (2018). (https://elifesciences.org/articles/43832)
Rebuffi, S.-A., Kolesnikov, A., Sperl, G. & Lampert, C. H. (2016), iCaRL: Incremental classifier and representation learning, arXiv:1611.07725. (https://arxiv.org/abs/1611.07725)
Ring, M.B., 1998. CHILD: A first step towards continual learning. In Learning to learn (pp. 261-292). Springer, Boston, MA. (https://link.springer.com/article/10.1023/A:1007331723572)
Robins, A. V. (1995), ‘Catastrophic forgetting, rehearsal and pseudorehearsal’, Connection Science 7 (2), 123–146. (https://www.tandfonline.com/doi/abs/10.1080/09540099550039318)
Rudou, JD et al. Strengthening individual memories by reactivating them during sleep. Science 326L 1079, 2009. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2990343/)
Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R. & Hadsell, R. (2016), Progressive neural networks, arXiv:1606.04671. (https://arxiv.org/abs/1606.04671)
S. Goldman and M. Kearns. On the complexity of teaching. Journal of Computer and Systems Sciences, 50(1):20–31, 1995. (https://dl.acm.org/doi/10.1006/jcss.1995.1003)
Serrà, J. et al. Overcoming Catastrophic Forgetting with Hard Attention to the task. arXiv preprint arXiv:1801.01423 (2018). (https://arxiv.org/abs/1801.01423)
Shin, H., Lee, J. K., Kim, J. & Kim, J. (2017), Continual learning with deep generative replay, NIPS’17, Long Beach, CA. (https://arxiv.org/abs/1705.08690)
Spence, C. (2010), ‘Crossmodal spatial attention’, Annals of the New York Academy of Sciences 1191, 182–200. (https://pubmed.ncbi.nlm.nih.gov/20392281/)
Spence, C. (2014), ‘Orienting attention: A crossmodal perspective’, The Oxford Handbook of Attention. Oxford, UK: Oxford University Press pp. 446–471.
Stein, B. E. & Meredith, M. A. (1993), The merging of the senses , The MIT Press, Cambridge, MA, US.
Stein, B. E., Stanford, T. R. & Rowland, B. A. (2014), ‘Development of multisensory integration from the perspective of the individual neuron’, Nature Reviews Neuroscience 15 (8), 520–535. (https://www.nature.com/articles/nrn3742)
Tenenbaum, J.B., Kemp, C., Griffiths, T.L. and Goodman, N.D., 2011. How to grow a mind: Statistics, structure, and abstraction. science, 331(6022), pp.1279-1285. (https://science.sciencemag.org/content/331/6022/1279)
Teyler, T.J. and Rudy, J.W., 2007. The hippocampal indexing theory and episodic memory: updating the index. Hippocampus, 17(12), pp.1158-1169. (https://pubmed.ncbi.nlm.nih.gov/17696170/)
Thrun, S. & Mitchell, T. (1995), ‘Lifelong robot learning’, Robotics and Autonomous Systems 15, 25–46. (https://www.sciencedirect.com/science/article/abs/pii/092188909500004Y)
van de Ven, G.M., Siegelmann, H.T. and Tolias, A.S., 2020. Brain-inspired replay for continual learning with artificial neural networks. Nature communications, 11(1), pp.1-14. (https://www.nature.com/articles/s41467-020-17866-2)
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K. and Wierstra, D., 2016. Matching networks for one shot learning. arXiv preprint arXiv:1606.04080. (https://arxiv.org/abs/1606.04080)
W.E. Skaggs, B.L. McNaughton, M.A. Wilson, C.A. Barnes. Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences. Hippocampus, 6 (1996), pp. 149- 173. (https://pubmed.ncbi.nlm.nih.gov/8797016/)
Weiss, K., Khoshgoftaar, T. M. & Wang, D.-D. (2016), ‘A survey of transfer learning’, Journal of Big Data 3 (9). (https://journalofbigdata.springeropen.com/articles/10.1186/s40537-016-0043-6)
Yang, G., Pan, F. and Gan, W.B., 2009. Stably maintained dendritic spines are associated with lifelong memories. Nature, 462(7275), pp.920-924. (https://www.nature.com/articles/nature08577)
Yosinski, J. et al. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems, pages 3320–3328, 2014. (https://arxiv.org/abs/1411.1792)
Zenke, F., Poole, B. and Ganguli, S., 2017, July. Continual learning through synaptic intelligence. In International Conference on Machine Learning (pp. 3987-3995). PMLR. (https://arxiv.org/abs/1703.04200)
Recommended for you
Manipulative Attacks in Group Identification
Manipulative Attacks in Group Identification
This review provides an introduction to the group identification problem and gives an overview of the feasibility and computational complexity of manipulative attacks in group identification.


text:
With an autumnal palette and a classic check print, the Gig Knitted Scarf is an instant classic for cold weather dressing. The super soft scarf has a subtle check pattern throughout, punctuated by a block hem in a rich blue tone. A sleek accessory, this scarf will complete workwear style. description
With a knack for feminine, everyday style with an edge, this quintessentially British brand is still going strong after over 50 years. Expect love-forever coats, dresses and, of course, denim.

domain: design


text:
Prep roundup: East tennis goes 2-1 on weekend
WAUSAU – The Wausau East boys tennis team posted a 2-1 record in their home tournament over the weekend.
The Lumberjacks defeated Madison East and Green Bay East both by 6-1 scores, and fell to Stevens Point Area Senior High 6-1.
Wausau East lost No. 1 singles player Matt LaViolette, who suffered an injury against SPASH and did not play against either Madison East or Green Bay East.
Adam Schneider, East’s No. 4 singles player, went 2-0 with wins over Madison East’s Tito Perez and Green Bay East’s Evan Lafeyice.
Schneider paired with Dakota Osborn at No. 1 doubles against SPASH and lost in straight sets to the Panthers’ Jon Peck/Blaine Bancker.


text:
Considerations To Know About How Can I Make Bitcoins
Perfectly, he required to make a constant movement of latest bitcoins in to the process. In a sense, this was done to maintain inflation in Verify. Mining problems is set to make sure that, on typical, a new block might be additional each and every ten minutes (i.e., the variety is going to be guessed every single ten minutes on ordinary).
emolument 'the returns arising from Workplace or work generally in the form of payment or perquisites' troll 'to harass, criticize, or antagonize Specifically by disparaging or mocking general public statements' exculpatory 'tending or serving to exculpate' ideologue 'an usually blindly partisan advocate or adherent of a specific ideology' socialism 'a strategy for Arranging a Culture where main industries are owned and controlled by the government' SEE ALL
Moreover, all Bitcoin transactions are irreversible, protected, and have no particular information linked to the buyer, which makes it the perfect currency for merchants who will be in search of protection and balance.
A Secret Weapon For Cryptocurrency List ValuesBitcoins are an item of pure mathematics and Uncooked algorithmic calculations, and therefore are only influenced by the level of trust that its people put in the currency and how effectively it adapts to being used around the globe.
The 5-Second Trick For Crypton CurrencyEnhanced publicity and push protection may even improve the price of Bitcoins, which might be motivated by demand from customers or dread of uncertainty. Bitcoin will behave just like any other currency, minus The federal government control and susceptibility to monetary criminal offense.
The current stance of the united kingdom govt could be considered as “wait and see” because no certain regulation has long been established in place. The bitcoin is considered as private income and no VAT or another taxes implement if you are getting or marketing the BTC.
Indicators on Exodus Exodus You Should KnowDespite the fact that Bitcoin has put an enormous dent during the currency earth, fairly there continues to be a small sum of companies and other people using it.
If anyone forgets their wallet handle, it doesn’t expire; instead it remains to be Energetic around the network with no transactions. So incorporating up every one of the wallets won’t give us an accurate amount of buyers.
Top latest Five Cryptocurrency List Values Urban newsSome acknowledge credit rating and debit cards; Some others only lender transfers. Opening an account has a tendency to be fairly onerous with regards to paperwork, these is the bureaucratic entire world where we Stay, but That may be a journey you must vacation.
This is a Yet one read what he said more controversial subject. As a result of independence along with the degree of anonymity that using Bitcoin provides, lots of customers who have been looking for to invest in or solicit unlawful goods or products and services in the beginning turned to the use of Bitcoin being a method of payment.
Details, Fiction and Crypton CurrencyAlthough Bitcoin has proven alone to be a fairly trusted currency over the past eight a long time and there is plenty of incentive and possible for Bitcoin to continue expanding steadily in a optimistic way, it isn’t feasible to forecast how profitable the lifespan of Bitcoins will be.
There’s also Bittylicious, where you pays by financial institution transfer: you happen to be presented the bank aspects of the seller, you transfer The cash to them, the bitcoins are transferred to you personally. It’s tough and ready but it works – nevertheless, once more, small amounts only Whilst you’re familiarising your self with the tech.
The smart Trick of How To Buy Bitcoin Uk That Nobody is DiscussingMost cryptocurrencies are meant to minimize in production as time passes like Bitcoin, which creates a market cap on them. That’s different from fiat currencies where by monetary establishments can generally make a lot more, consequently inflation.
Having said that, A number of folks are nonetheless relatively bewildered In regards to Bitcoins. They know the general notion of what this currency process is, but generally brush it off like a gimmick or by no means choose it significantly.

domain: finance


text:
Editor, Political News Chosun, Republic of Korea
China's new geography of power: the who's who in the new leadership
Woosuk Kenneth Choi, is the Editor of Political News of the TV Chosun. He is the Blue House correspondent, covering the President of Korea. He has been traveling with the President to the US and China recently for the summit meetings.

domain: geography


text:
Beryl Markham – aviator, horse trainer, writer
A marvelous book. I have read so much about Karen Blixen/Isak Dinesen. Elspeth Huxley, Beryl Matkham and others who made Kenya their home and played a special part in the history of this country. I lived in Kenya for 2 years in the 1960’s. This was well within the lifetime of Beryl Markham – but alas I never met her.
However I brushed history when I had tea at Karen Blixen’s home near the Ngong Hills. This was long after Karen had sold her farm and left Kenya. Her former home was temporarily being occupied by a Danish couple whom I knew through the University. Little did I realize the role that house had played in the lives of the early settlers.
But when I lived in Kenya again in the early 1990’s, I did appreciate the history of the golf course I played on – Karen Country Club had once been Karen Blixen’s coffee plantation.


text:
Maintenance sex may not sound like the most titillating thing in the world, but it is crucial to the health of your relationship.
When we say maintenance sex, we mean having sex just to have it, even if you donâ€™t feel like doing it. It means keeping the sex up to ensure both people in the relationship are sexually satisfied.
Sometimes your partner wants to get it on and all you want to do is binge your latest Netflix obsession. Sex sounds time-consuming and annoying. Or maybe when you really want to have sex, your partner always seems to be too tired or stressed. In long-term relationships, itâ€™s important to have sex even if youâ€™re not necessarily randy. Maintenance sex shouldnâ€™t be the only kind of sex youâ€™re having, but it should absolutely be on the menu.
There will always be two different sex drives in a relationship
There will inevitably be times in a relationship when one partner wants to have sex and the other doesnâ€™t. Itâ€™s rare for two people to have matching sex drives. It happens occasionally, but to expect that your partner will always want the exact amount of sex as you do is not particularly practical.
Weâ€™re fed this idea that we should only have sex when weâ€™re â€œin the mood.â€ This is how unfulfilled, sexless marriages happen. One partner isnâ€™t â€œin the moodâ€ and doesnâ€™t think he or she needs to be, and therefore the partner with the higher libido feels ashamed for wanting sex. This leaves one partner feeling hounded for sex all the time and the other pathetic for wanting itâ€”not a healthy relationship.
You wonâ€™t regret having it
Maintenance sex is designed to keep both partners content. When youâ€™re in a long-term relationship or marriage, you wonâ€™t regret putting in the extra effort to have sex. If your partner really wants you and you just go for it, youâ€™ll feel better afterwards.
Obviously, if this is the only way youâ€™re having sex, there is a problem. If you feel fully sexually unsatisfied or are experiencing an unusually prolonged dip in libido, that is a separate issue and you should address it with your therapist or healthcare provider. Sex is like going to the gym: You donâ€™t want to do it before, but once you suck it up and hit the elliptical, you feel amazing afterwards.
You need to show up for your partner and vice versa
In relationships, you need to show up for your partner. This goes back to that â€œshameâ€ thing; we think if one person doesnâ€™t want to have sex, we shouldnâ€™t have sex. Sometimes you need to show up for your partner and be engaged. If you need your partner to make you feel desired, he or she should be willing to do that for you. Sex is a principal part of happy relationships.
It breeds intimacy
Sex breeds intimacy. Orgasms release oxytocin, the feel-good bonding hormone in your body. Sex helps you feel closer to your partner. It is the most intimate physical activity you and your partner can experience. Without it, itâ€™s easy to lose sight of your connection. Relationships and a healthy sex life take constant work. You have to check in with each other and take the temperature of your relationship on a regular basis.
Having sex, feeling your partner on top of you, the scent of his or her skin brings you back into that loving mindset. Donâ€™t skip out on it just because youâ€™re tired from work and would rather watch television. Having maintenance sex will remind both of you what you mean to each other. Always take an opportunity to work sex into your schedule. The more you have it, the happier youâ€™ll be.

domain: psychology


text:
Posture PT is the first full-back ergonomic posture device that can be utilized in virtually any chair or car. Turn your chair or car into an orthopedic workstation for 1/10th the cost and enjoy all the benefits of better posture.
Posture PT Portable Posture Trainer
- Uses memory foam to comfortably train you to sit with great posture
- With just 30 minutes of use per day, you will even notice improvements in your posture while standing
- Great for people who need lower back support, upper back support, lower lumbar support, mid back support, or have back pains
- Medical studies suggest great posture will increase your confidence, boost your energy, and reduce your stress
- Successfully tested on users that range from 5’1” to 6’11”
Included In The Box
- 1x Posture PT Posture Personal Trainer
- 1x Strap
Estimated Delivery Date
Monday, July 13th - Wednesday, July 15th. Due to a high volume of orders, delivery times are longer than usual.


text:
Nexon announces MapleStory 2 alpha test in Korea
Nexon Korea Corporation is glad to announce the dates for Alpha Testing with their upcoming free-to-play MMORPG MapleStory 2. From 17th to 21st September, Nexon Korea can tell you a major portion of the sport to Buy MapleStory 2 Mesos selected players from South Korea.
MapleStory 2 is usually a new, online, full 3D MMORPG which has been in development since 2010. Set in the era before MapleStory, players can begin to play a completely new a higher level freedom within their experience.
The Alpha Test lets players to participate in MapleStory 2 Mesos development because they select from different character classes and explore the rich landscape of mountains, oceans, valleys, and cities.

domain: game


text:
Cute Little Clown Costume - Child's
Dazzle your fellow party goers whilst making a vibrant appearance with this stunning Kids' Cute Little Clown costume.
Included is a bright yellow dress with blue skirt, and a yellow hat on a black headband.
The neck ruffle, sleeves and petticoat covering the skirt are a multicoloured rainbow of transparent netting. The yellow top also features bright red buttons to complete the fun and funky look.
Available in sizes:
Small, Medium, Large