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Father of murdered Bala soldier furious over London memorial unveiling
Reg Keys said he wasn't told or invited to service attended by former Prime Minister Tony Blair
Steve BagnallBreaking News editor
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The father of a soldier killed in the Iraq War was furious he wasn't told about the unveiling of war memorial by The Queen in London today.
Reg Keys son Lance Corporal Tom Keys, from Llanuwchllyn, near Bala, was one of six "Red Caps" murdered when a 400-strong mob descended on a police station in Majar al-Kabir in Iraq in June 2003.
Dad of Bala soldier says 'My son died in vain' in Iraq War
Today Her Majesty The Queen unveiled a sculpture in London dedicated to those that served in the Iraq and Afghanistan conflicts and the contribution made by civilians.
It was followed by a service for 2,500 veterans on Horse Guards Parade.
The Ministry of Defence insisted it had made every effort to include as many people as possible at the ceremony.
But Mr Keys said the first he heard about it was a call from the media last week.
Reg Keys (Image: Daily Mirror)
He was also upset former Prime Minister Tony Blair, who entered the UK into the Iraq war with ex-USA President George W Bush, was at the ceremony.
Last year Mr Blair was heavily criticised in the Chilcott Report - a public inquiry into the nation's role in the 2003 invasion.
Mr Keys said: "The first I heard of this is when I was phoned by the media.
"Usually the Ministry of Defence are very good at keeping us briefed what is going on.
"But this time I knew nothing about it.
Murdered Gwynedd soldier's red cap, belt and whistle were used in acclaimed film
"It would have been nice to at least have received a letter that there was going to be such a memorial, if not an invitation.
"Tony Blair was also invited who got mauled in the Chilcott report and was responsible for a lot of the deaths, which does leave a bitter taste in the mouth.
"There were a lot of top brass and dignitaries, but the people that made the real sacrifices weren't invited."
Lance Corporal Tom Keys, from Bala, was one of six Red Caps murdered when a 400-strong mob descended on a police station in Majar al-Kabir in Iraq in June 2003 (Image: Reg Keys)
A MoD spokesman said: "This monument pays tribute to thousands of former and current members of our Armed Forces as well as civilians who have delivered aid and improved healthcare and education over 25 years.
"We ensured all groups were represented, including bereaved families, which is why we asked charities and groups specifically linked to them to suggest attendees and granted all those applications."
The sculpture by Paul Day, formed of two stone monoliths and a bronze medallion, was unveiled in Victoria Embankment Gardens.
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Australia's superstar mare Winx notched up her 30th successive victory with another dominant display in The Star Apollo Stakes at Randwick.
Making her first appearance since claiming a historic fourth Cox Plate at Moonee Valley in October, Chris Waller's seven-year-old was unsurprisingly a long odds-on favourite to extend an unbeaten run which stretches back to April 2015.
With regular partner Hugh Bowman in the saddle, Winx was dropping from a mile and a quarter to just seven furlongs on her eagerly-awaited return.
The mighty mare was being nudged along in midfield rounding the home turn, but soon engaged her trademark finishing kick to run to the lead and win comfortably by two and a quarter lengths from Happy Clapper.
An emotional Waller told www.racing.com: "It is just a sense of relief – we take it so personally. She can't tell us when she has had enough, we made the decision to push on a bit further. She is pretty good.
The next likely port of call is the Chipping Norton Stakes back at Randwick in Sydney – a Group One prize she has already won three times.
"I think today's race was the most important – seeing her come back like that," Waller added.
"The natural improvement will be there and two weeks' time will be the plan – the Chipping Norton.
"Last year we didn't have the beauty of having a first-up run – she went straight into it.
Bowman said: "The pace of the race was very even, which suited us. I just had to niggle her to hold her position closer than I normally would over a bit further.
"As the race unfolded we settled in to a lovely rhythm – as we always do – and it was a pretty comfortable race for me and it was pretty comfortable for Winx, too.


An intense course with a preparatory textbook and online preparation covering the optimal design of networked automation and control systems. Centred on PROFIBUS technology, but also covers the use of PROFINET, AS-i, HART, 4-20 mA and IO-Link technologies.
The long-awaited accreditation of the PROFIBUS System Design course is now completed. This course has now been accredited by PI and is already recognised by many companies as the minimum requirement for PROFIBUS system designers and those that are associated with PROFIBUS system specification, design and procurement at a technical level.
The course features a substantial amount of online tuition and exams together with a practical design project, all intended to be undertaken over the 2 weeks preceding actual attendance at the Training Centre.
A final Contact Day is dedicated to the actual Certified PROFIBUS System Design part of the course, covering assessment of the Design Project and further tuition followed by final exams.
Good quality PROFIBUS, PROFINET and AS-i training have been widely available for installers, maintenance technicians and engineers for many years. Unfortunately, key decision makers – managers, system designers and system integrators are quite often less well trained than others who are involved in the engineering. Many of the errors that can be seen in installations are traceable to fundamental decisions that were taken at the early stages of the project. For example, use of inappropriate fieldbus for an application, lack of awareness of maintenance and fault-finding facilities, over-complex or inappropriate system architecture, design decisions based on equipment purchasing cost rather than whole life-cycle costs, etc.
This course is aimed at anyone who is dealing with the specification, design or procurement of modern automation and control systems at the engineering or technical level. The course is also suitable for device manufacturers, system integrators and technical sales/marketing people who want to know the best way to put systems together.
Certified PROFIBUS Installer and attendance of the Commissioning & Maintenance course are pre-requisite to taking this course.
The course provides a top-down approach to designing a modern automation and control system and helps managers and designers to make the correct decisions from the project beginning. The course covers the optimal use of networking and communications systems including PROFINET, PROFIBUS DP and PA, AS-i, and IO-Link.
Certified PROFIBUS System Design Contact Day, covering assessment of the Design Project, further tuition followed by final exams.
Note that the online questions and answers account for 50% of the overall marks and the written exam the remaining 50%.
The PROFIBUS System Design Course is an intense course, leading to a valuable professional qualification.
Whilst there is only 1 day of actual classroom work, there is a large element of online work and a textbook (sent free of charge to registered trainees but also available from Amazon) to be studied as an essential part of the course, requiring at least 2 hours dedicated work per day over the 2 weeks before the classroom sessions.
After the initial reading and online study, trainees are required to submit a PROFIBUS network design for a chosen application with network layout drawing and calculations. Students need to allow about 2 hours a day over a period of at least 4 days to think about their application, gather relevant information and plan their PROFIBUS network.
The online questions need at least one day to complete.
People booking themselves or others onto this course need to plan well in advance to ensure that sufficient time is allocated for the student(s) to complete the online studies, questions and design example along with all the documents and calculations required for the Certified Designers qualification.
NB: It is recommended that a student's first sign-on to the online training module should be completed no less than 2 weeks before the date of the course.
Company Management and Training Departments are expected to support their employees in gaining this valuable professional qualification by providing sufficient study time in their work schedules to complete their online and textbook studies, questions and design example.


Enjoying the past with modern conveniences. The historic Lahr Hotel, built in 1831, was renovated and reopened as the Historic Lahr Apartments in 1998. Located in the 500 block between Main and Columbia Streets, it has been a landmark of Lafayette for many years. With extensive renovation, the Lahr and the adjacent Milwaukee block have been transformed into apartments providing downtown with 74 new apartments.
The Historic Fowler Hotel was built in 1915. This neoclassical building is now home to over 100 residents. Renovated into subsidized apartments, the Fowler offers affordable living in the downtown for qualified elderly individuals.
The Central Business District has options to cater to various tastes and needs of the market. Over 2500 people live within a 10-minute walk of downtown. An estimate of downtown employees is 3100. Downtown amenities also draw potential patrons to the area. There are 16 restaurants downtown. Parking availability is a plus with 700 street spaces, 14 city parking lots, and the centrally located garage.
A favorite attraction to downtown is the Farmers Market. It is a great way to shop for fresh fruits, vegetables, plants, and baked goods. The market is open Tuesday, Thursday, and Saturday, May through October from 6:30AM to 12:30PM. The market is located on 5th Street, between Main and Columbia Streets.
In early October 1998, LUEA, along with the Community Development and Redevelopment Departments and Downtown Business Center, hosted a meeting for downtown residents. Topics such as parking, zoning, economic development, and the development of the resident association were discussed.
Although the association is in its infancy, they are taking steps to make improvements in their neighborhood. Three residents have been appointed by Lafayette Mayor Dave Heath to serve on a neighborhood steering committee; formed upon the completion of the Neighborhood Revitalization Strategy Plan.


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redpajama_set_name: RedPajamaWikipedia


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15th July
The stage musical Man Of La Mancha is playing at the ANTA Washington Square Theatre, New York City, USA, during a run of almost two and a half years.
The musical Fiddler On The Roof, starring Zero Mostel, is performed at The Majestic Theatre, Broadway, New York City, USA. It will run continuously (but at three different theatres) until 1972 notching up 3,242 performances.
The musical Hallelujah Baby!, starring Leslie Uggams, is presented at The Martin Beck Theatre, Broadway, New York City, USA, during a run of 293 performances.
The rock musical Your Own Thing is presented at The Orpheum Theatre, New York City, USA, during a run of 937 performances.
The musical comedy The Happy Time is presented at The Broadway Theatre, Broadway, New York City, USA, during a run of 286 performances. The show features music by John Kander and lyrics by Fred Ebb.
The musical Jacques Brel Is Alive And Well And Living In Paris, is performed at The Village Gate, Greenwich Village, New York City, USA, during a run of 1,847 performances.
Pop duo Steve Lawrence and Eydie Gorme are starring in the musical Golden Rainbow, at The Shubert Theater in New York City, USA.
The stage musical Cabaret is presented at The Palace Theatre, London, England, UK, Europe, during a run of 336 performances.
The controversial hippy musical, Hair is playing at the Biltmore Theatre on Broadway, New York City, USA, during a run of 1750 performances.
Roy Orbison is playing during a three-week season at The Club Embassy, Toronto, Canada.
Seatrain and Albert Ayler are playing during thirteen days at The Cafe Au Go Go, Greenwich Village, New York City, USA.
Fats Domino is playing during a series of dates at The Flamingo, Las Vegas, Nevada, USA.
Matt Monro is playing at The Century Plaza, Los Angeles, California, USA.
Louis Prima is playing at The Sands Lounge, Las Vegas, Nevada, USA, with Sam Butera And The Witnesses.
Ten Years After are playing during ten nights at The Golden Bear, Huntington Beach, California, USA.
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Thelma Carpenter is playing at The Apartment, New York City, USA.
Iron Butterfly are playing during four nights at The Sanctuary, Lake Tahoe, Nevada, USA.
Roy Orbison arrives in London, England, UK, Europe, for tv and radio appearances as well as live gigs.
Reparata And The Delrons plays at The Orchid Ballroom, Purley, England, UK, Europe.
Sergio Mendes plays at The Municipal Auditorium, New Orleans, Louisiana, USA.
Pioneering British jazz-rock band Jon Hiseman's Colosseum record a live session for BBC Radio One show Symonds On Sunday at BBC Studio 1, 201 Piccadilly, London England, UK, Europe.
The Foundations play in Tel Aviv, Isael, Asia.
The Moody Blues, including Swindon, Wiltshire-born singer and songwriter Justin Hayward, begin a week of live sessions for David Symonds BBC Radio One show.
Jimi Hendrix and drummer Mitch Mitchell fly in for the opening of Sgt. Pepper's Club, in Majorca, Spain, Europe.
Pink Floyd play the first of three nights at The Scene club, New York City, USA.
The Jimi Hendrix Experience relax on the beach at Lauro Verde, Majorca, Spain, Europe.
Free record a live session BBC radio show Top Gear, in Studio 1, Piccadilly, London, England, UK, Europe.
A new weekday tv series, It's Happening, has its first broadcast in the USA. The show is hosted by Paul Revere and Mark Lindsay of Paul Revere And The Raiders. The show is a spin-off from Happening '68, produced by Dick Clark Productions.
The Beatles begin further work on the songs Ob-La-Di, Ob-La-Da and Revolution, in EMI's Abbey Road Studios, London, England, UK, Europe.
Traffic record a live session for BBC radio show Saturday Club at The Paris Cinema, London, England, UK, Europe.
The Beatles company Apple Corps moves to 3 Savile Row, London, England, UK, Europe, which had been purchased a month earlier for £500,000.
The Who play at the Memorial Centre, Kingston, Canada, supported by The Fifth Column – whose amps they borrow.
The rock musical Hair (which includes a scene with a fully nude cast) is presented during a run of 1,997 performances at the Shaftesbury Theatre, London, England, UK, Europe. The cast includes Marsha Hunt, Paul Nicholas, Sonja Kristina and Jimmy Winston.

redpajama_set_name: RedPajamaCommonCrawl


Because students start new work in their new class at the beginning of the school year, it would be an advantage for your child both socially and educationally, to start school as soon as possible in the new year.
If your child is coming from a non-government school, contact us to make an appointment to discuss your child's potential enrolment.
If your child has a temporary visa or visitor visa you will first need to apply to enrol by contacting the temporary residents program who will give you forms to bring to the school. Forms are also available from the school office.
Enquiries regarding in designated intake area or out of designated intake area enrolment may be made to the school or 97500144.
For years K to 6 enrolment information for students living in the designated intake area for Wiley Park Public School, please contact the school to confirm your address.
Please note that our enrolment criteria changes from time to time.
The current Expression of Interest enrolment form (Form A) must be completed.
No Expression of Interest will be accepted unless it is complete.
Own the property, please read the below form.
2. Rent the property through a Real Estate Agent or Housing Department, please read the below form.
3. Rent the property from the owner with a private lease, please read the below form.
Once you have completed an application to enrol in a NSW public school form and returned it to us, you may be asked to come in for an appointment. We will notify you of the result of your application. Please note that this form must be completed in English. Help is available for parents who do not read English. There are also translations of the enrolment application which may help you fill out the form in English.
For any enquiries, please contact the school on 9750 0144.


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Piedmont Exedra (https://piedmontexedra.com/2019/04/notre-dame-notre-damn)
Featured in Category
Notre Dame? Notre Damn!
By Julie Gardner, Compass | A New Perspective | April 19, 2019
[SPONSORED POST]
Some 13 million people visit Notre Dame a year; one of the world's most iconic and beloved Gothic cathedrals. Not only a notable architectural landmark, Notre Dame also lays claim to countless religious artifacts, including the Crown of Thorns, believed to have been worn by Jesus himself. In spite of its elevated status the world over, it's still very much a working parish, hosting a daily mass, baptisms, weddings, and Easter Sunday services for the citizens of Paris and for anyone who is attracted to its collective history from around the globe, regardless of their religious affiliation or subscribed faith (or lack thereof).
Cliff and I visited Notre Dame just a few years ago. We stayed at a quaint hotel on the island just a few blocks away so this stunning church and sacred monument was part of our daily landscape and view. Quite literally, the centerpiece of Paris and surrounded by the River Seine, Notre Dame has stood as witness to the divine for more than 850 years.
So with the unexpected and tragic news that Notre Dame was on fire on Monday (Notre Damn!) the world watched and waited with bated breath while thousands of Parisians gathered behind the police barricades and sang hymns deep into the night in honor of this magnificent structure and architectural treasure.
The early reports weren't hopeful. The iconic spire had caved in, as had the wooden roof (known as the forest) which had been built from thousands of oak trees felled between 1160 and 1170. Ironically, the wooden beams and hand-carved ceiling fueled the fire as it raged for more than nine harrowing hours. Still, heroic and successful efforts were made to retrieve or to save many of Notre Dame's religious relics, priceless works of art and artifacts, statues, sculptures, furniture and stained-glass windows. Were it not for the stone structure and the herculean efforts of the firefighters, the building might have been a total loss. Certainly, it will cost hundreds of millions (probably more than a billion) to rebuild. Still, once the smoke had cleared, remarkably, much of the structure was still intact . . .
Most of us don't have stone edifices protecting our walls, or well-heeled patrons to donate to their restorations in case of destruction; our beloved abodes our largely built on wooden frames which means they're completely vulnerable to fire, as we've seen the last few years. First, in Sonoma and then in Santa Rosa where fires quickly engulfed and laid waste to whole neighborhoods and communities while firefighters from across the state worked tirelessly to beat back the flames for days, and in some cases, for weeks.
AND while our personal artifacts, arguably, aren't as historically significant as the Crown of Thorns or the Tunic of St. Louis, every home has papers that are critically important to the lives of its residents including birth certificates, deeds of trusts, passports, social security cards, marriage certificates, etc., that would be better stored in a safe-deposit box at the bank. At the very least, they should be in a centralized location where you can easily access them should you need to flee quickly. If you have important jewelry or other like-kind items of real value, a home safe is an excellent investment and one I highly recommend. BTW, when fire victims are evacuated, it's their CHILDREN, their PETS, their important PAPERS, and their PHOTO ALBUMS that they are most likely to load into the car first. Speaking from the heart, everything else, is just "stuff."
So when I am asked to go into homes where people have literally been collecting "stuff" for 30 years and are struggling to extract themselves from their "things," I need to gently remind them that what's really important can usually be held in the palm of our hands. AND more importantly, putting aside ALL of our belongings, it's our memories that hold the most value – and happily, they move with us, no matter where we go. . . .
There's no doubt, given the donations pouring in from around the world that Notre Dame will be rebuilt as soon as feasibly possible, not only as an ongoing symbol of devotion, but as one of the world's most notable attractions. Still, I gotta hand it to the priest who when interviewed, described the church as "just a building," (although historically significant, stunningly beautiful, and religiously important to be sure) and suggested that our sustaining faith depends far more on what happens outside these magnificent religious edifices (and neighborhood churches, mosques, temples, and reading rooms) than what transpires inside them. In short, our faith lies in our actions and in our service to one another.
I couldn't agree more, but P.S., make sure your smoke detectors are working and in good order and that your home is FULLY insured. That never hurts and according to the reports, the fire alarms were the reason NO lives were lost in this horrific blaze. With time and enough money, buildings can be rebuilt (probably faster than it takes to get a new birth certificate); but recreating your important documents may be a much tougher task. Have you seen the line at the Social Security office? (I have.)
Julie Gardener | Compass Realty
Not just a Realtor, but a consultant in all things house and home, I'm here to educate, explore, examine and refer . . . In short, you may count on me to take care of your home as if it were my own and anyone who knows me, knows I take pretty darn good care of my home.


text:
\section{Introduction} \label{sec:intro}

Federated learning (FL) is a distributed machine learning paradigm
whereby a set of {\em learning nodes} cooperate in training a model
(e.g., a neural network) with the assistance of a centralized {\em model
server} and without the need to share their local data.
FL has been introduced~\cite{konen2015federatedOptimization} in 2015 by
Google, with the goal of leveraging the computational power of end-user
devices -- most notably, smartphones -- without the privacy and security
concerns arising from sharing the potentially sensitive information they
own. As discussed in \Sec{fl}, it has since been widely adopted in
edge computing scenarios, owing to its ability to
blend device- and server-based computation, and to enable cooperation
between devices regardless of their location.

FL includes the following high-level steps, summarized in
\Fig{sequence}:
\begin{enumerate}
\item learning nodes train a local
model based on local, on-device data;
\item learning nodes send the model
parameters -- and nothing else -- to the server;
\item the server combines the parameters coming from different learning nodes;
\item the server sends the combined, {\em global} parameters back to the learning nodes;
\item the learning nodes replace their local parameters with the global ones, and move to
a new training iteration (step 1),
till the desired accuracy is achieved.
\end{enumerate}
Step~3 usually takes the
form of a {\em weighted averaging} of the local
parameters~\cite{konen2015federatedOptimization,jointframework}.
Weights are
assigned to individual learners and reflect the magnitude of their
(expected) contribution to the learning process; indeed, as discussed
next, properly assigned model weights is one of the main decisions
learning servers can make.

\begin{figure}[t] \centering
\includegraphics[width=.85\columnwidth]{img/sequence.pdf}
\caption{Main steps of each
iteration of the federated learning paradigm:
learning nodes train their local model (1) and send the local
parameters to the server (2); the server performs a weighted averaging
of the model (3) and sends the global parameters back to the learning
nodes (4).
\label{fig:sequence} }
\end{figure}

Originally envisioned for homogeneous nodes dealing with homogeneous datasets
(e.g., in a classification problem, datasets adequately representing all
classes), FL  has also the potential to deal with {\em heterogeneity},
both in the capabilities of learning
nodes~\cite{kang2020reliable,jointframework} and in their local
data~\cite{infocom20-noniid,jointframework}. In both cases, the key is
to endow the model server with additional responsibilities: the {\em
weights} given to different local models in the averaging phase (step 3
in \Fig{sequence}) can account for the quantity of data they are trained
upon; at the same time, if some nodes are consistently slower than
others, they can be {\em dropped} from the learning
process~\cite{kang2020reliable,client-selection}.

Our key observation is that these two decisions, {\em model-weighting}
and {\em node-dropping}, are deceptively simple, and their impact on the
overall learning process is often misunderstood and underestimated.
Many state-of-the-art works take straightforward approaches to these decisions,
which may result in poor performance under richer, more complex scenarios.
Our contribution is therefore to shed a light on the model-weighting and node-dropping decisions,
studying how they should account for the quantity {\em and quality, e.g., variety,}
of data available to local nodes, as well as for the data processing time.
In so doing, we  focus on an edge network scenario and
leverage a set of experiments using
the popular \path{tensorflow} library and the recent
Fashion-MNIST dataset.

In the remainder of the paper, we describe federated learning in edge
scenarios and the associated challenges in \Sec{fl}, before narrowing
the focus on model-weighting and node-dropping strategies in
\Sec{strats}. We then describe our experiments and results in \Sec{experiments}, and
the main lessons learned in \Sec{takeaway}, along with pointers to
further promising research directions. Finally, \Sec{conclusion}
concludes the paper.

\section{Federated Learning in Edge Scenarios} \label{sec:fl}

Edge computing is a distributed paradigm
predicated upon performing the computation as close as possible to the
user nodes requesting it, i.e., at servers located at the {\em edge} of
the network infrastructure. It also includes scenarios where user nodes themselves have
computational and/or storage capabilities, and require edge support for
coordination, or to offload the heaviest computation tasks. FL has
long been identified as an excellent match for edge computing scenarios, and many
research works aim at making it in such scenarios as efficient as
possible.
At the same time, {\em communication} is a major issue for FL in edge scenarios. Nodes can be connected with the edge-based server in many ways and through different technologies; thus, their connectivity has a major impact on the latency incurred when sending model updates -- and, indeed, on whether or not such updates are received in the first place.

Specifically, as detailed in~\cite{in-edge-ai},
edge computing is more effective than fully-distributed, device-to-device
networks at tackling the main factors hindering the performance of FL, namely, the
different node capabilities, available data, and
unpredictable communication delays and shortages.
Narrowing its focus to node capabilities, \cite{client-selection}~aims
at choosing the set of learning nodes that results in the shortest
learning time, solving a double-edged conundrum. On the one hand, more
nodes mean that convergence can be reached in fewer iterations; on the
other hand, the duration of each iteration is determined by the slowest
node~\cite{neglia}. In a similar spirit, \cite{jointframework}~addresses
the problem of jointly selecting the learning nodes to use for the
learning process and assigning them the wireless resources they need to
communicate effectively. Setting in a fully-decentralized {\em fog}
scenario where no learning server may be present,
\cite{infocom20-fog}~tackles many issues relevant to edge computing, including
device mobility and the possibility of offloading computation from a
node to another.

Shifting the focus towards the major issue of communication between FL nodes and server,
the authors of
\cite{robust-efficient}~seek to reduce the communication overhead of FL
by proposing a compression algorithm suited for federated learning
settings. Their algorithm outperforms existing schemes when local
datasets are heterogeneous, thus making the high-frequency communication
required by FL viable in low-bandwidth scenarios. Heterogeneous datasets
are also identified as a major problem in~\cite{infocom20-noniid}, which
envisions extracting a homogeneous subset from each local dataset in
order to avoid bias and training errors.
\begin{comment}
In a similar spirit,
\cite{jeong2018communication}~proposes to both {\em augment} local
datasets by generating new samples via a generative model, thus making
them homogeneous, and reducing the overhead by aligning ({\em
distilling}) the models being trained, so that periodic updates can be
dispensed with.
\end{comment}

Following an orthogonal, more theoretical approach, several
works~\cite{neglia,wang2019adaptive} aim at characterizing the learning
performance, deriving closed-form expressions for their (expected)
training time. Such a characterization is then exploited to make optimal
or near-optimal decisions on the cooperation among nodes~\cite{neglia}
and the equilibrium between local learning and global
updates~\cite{wang2019adaptive}. In order to obtain manageable
closed-form expressions, some of these works make simplifying
assumptions (e.g., that local datasets be homogeneous), or target
specific parameter optimization algorithms (e.g., stochastic gradient
descent).

One scenario where nontrivial model-weighting decisions are routinely used is {\em asynchronous}
FL~\cite{lu2019differentially}, where nodes may join the learning process at different times and
model-weighting serves the purpose of quickly including newly-arrived nodes in the learning process.
In our case, the purpose is different, namely, to adapt model weights to the contribution each
node can give to the overall learning process, and weed out those nodes that may
have a negative impact on the  learning performance.

\section{Model-weighting and Node-dropping Strategies in Federated
Learning} \label{sec:strats}

Model-weighting and node-dropping are the most fundamental decisions the
learning server can make, and arguably among the simplest to enact. At
the same time, as discussed in the following, these decisions can be
leveraged to address all the main issues of FL, either in combination
with the strategies reviewed in \Sec{fl}, or as an alternative to them.

\paragraph*{Insufficient quantity of data}

In many FL scenarios, some
learning nodes may not have enough
local data, thus
being unable to properly train their local models;
in this case,
a popular solution is {\em augmenting} local
datasets. As an example,
the authors of~\cite{shin2020xor} propose to
combine actual data samples from other learning nodes in a
privacy-preserving way, and adding them to the local dataset.

In both cases, data augmentation is able to increase the quantity of
data available to learning nodes, without jeopardizing FL's privacy
properties. On the negative side, it may increase the complexity of the
system and its overhead; furthermore, the augmented samples come from
processing of already-existing ones, hence, do not increase the total
quantity of information.

Model-weighting decisions represent an additional, simpler way to deal
with learning nodes with small local datasets. The basic
idea~\cite{konen2015federatedOptimization,jointframework} is that model
weights shall account for the quantity of data local models are based
upon; however, as we will demonstrate next, also accounting for the
variety of such data yields even better results. In both cases, relying
on model-weighting to tackle insufficient dataset sizes has the benefit
of reducing complexity and avoid tampering with the nodes' own data.

\paragraph*{Non-homogeneous data}

When it comes to training machine-learning algorithms, the quality of
data is as important as its quantity.
There is no universally-acknowledged definition of data quality, as it is scenario- and application-dependent. For classification applications, a high-quality dataset is expected to adequately represent all existing classes, so that the classifier can be properly trained. In this sense, quality can be expressed as the number of classes existing in a given dataset or, more formally, through entropy~\cite{plos-entropy}.

On the other hand, non-i.i.d. data, where classes are under- or over-represented, is universally characterized as low-quality, and
\begin{comment}
A very important aspect of data
quality is that they represent all aspects of the phenomenon to study,
e.g., in a classification problems, that all labels are represented
proportionally to their frequency -- or, more technically, that they can
be seen as i.i.d. (independent, identically distributed) samples of the
original data. In many FL scenarios this is not the case; indeed, some
or all learning nodes may only have a partial view of the environment,
e.g., only observe some labels.

Non-i.i.d. data
\end{comment}
has immediately been identified as one of the primary
threats to successful FL.
In addition to the augmentation approaches described
earlier~\cite{shin2020xor}, several works propose
training the local model on a subset of the local
data~\cite{infocom20-noniid}, chosen in such a way to be i.i.d.
An alternative to ignoring data is allowing all
learning nodes to use all their data, and then weight their local models
accounting for the quality of such data. Examples of this approach
include entropy~\cite{plos-entropy}, but simpler approaches, e.g.,
counting the labels observed, can yield similarly good performance.

\paragraph*{Nodes with different capabilities}

As with other distributed learning schemes, in FL it is possible to
proceed from an iteration to the next one only when {\em all} learning
nodes have sent their local models, i.e., have performed step~2 in
\Fig{sequence}. It follows that the pace of the learning process as a
whole is determined by the {\em slowest} learning node, which becomes an
issue when different learning nodes take very different times to perform
their iterations~\cite{neglia}. Owing to the limited amount of control
that can be exerted on FL nodes, the most viable solution is often to
exclude overly-slow nodes from the learning
process~\cite{neglia,jointframework,client-selection}.

However, making such node-dropping decisions solely on the basis of
their response times may actually hurt the learning process; indeed,
longer response times can be associated with larger, higher-quality
local datasets, hence, with the nodes that may contribute the most to
the learning. A way to decrease the likelihood of this unwanted outcome
is to consider additional aspects in making node-dropping decisions,
e.g., the quantity and quality or variety of local data. By so doing, it
is possible to differentiate between nodes that are slow due to limited
capabilities (or poor
connectivity~\cite{jointframework,client-selection}) and those that have
simply more data to process.

\section{Experiment Design and Results} \label{sec:experiments}

In this section, we demonstrate how model-weighting and node-dropping
decisions can deal with heterogeneity in the quality and quantity of the
local datasets at learning nodes. Using the set of real-world
experiments described in \Sec{sub-setup}, we obtain the results described
in \Sec{sub-results}.

\subsection{Experiment setup} \label{sec:sub-setup}

{\bf Dataset and neural network structure.}
Fashion-MNIST is a dataset released by Zalando
research and aimed at providing a more challenging, drop-in replacement
for the classic MNIST handwritten digits dataset.
\begin{comment}
It includes 70,000
grayscale, 28-by-28 pixel images of articles of clothing, each
associated with one of ten labels, from ``ankle boot'' to ``pullover''.
Choosing the Fashion-MNIST dataset represents a good balance between
having a sufficiently-challenging learning task (which is not as simple
as with the original MNIST) and obtaining relevant and reproducible
results, which may be harder using less common, domain-specific
datasets.
\end{comment}
Owing to the relative simplicity of the dataset, we use a relatively
small neural network for classification. Specifically, we create a dense
network with four layers, with sizes $[28^2,200,100,200]$~neurons
(notice that the size of the first layer must match the size of the
input, i.e., $28\times 28$~pixels). Neurons use the \path{softmax}
activation function, and parameters are optimized using stochastic
gradient descent (SGD), with a learning rate of~$10^{-2}$. The network
is implemented using the popular \path{tensorflow} library, originally
developed by Google.

\begin{figure}
\centering
\includegraphics[width=.95\columnwidth]{img/commag_1_learning.pdf}
\caption{Relationship between elapsed time and global accuracy for
different model-weighting strategies, when no nodes are dropped from the
learning process.
The color of markers corresponds to the category
of the slowest node in that particular iteration
(gold: yellow, bronze: orange, no other category appears).
\label{fig:learning}
\vspace{-5mm}
}
\end{figure}

{\bf Network scenario and local datasets.}
Our experiments feature a typical medium-scale edge scenario~\cite{in-edge-ai},
with 20~learning nodes connected with, and coordinated by, an edge-based server.
Five out of 20 nodes belong to each of the following
four categories so that the total amount of data remains constant:
\begin{itemize}
\item {\em gold}, having 500~samples each,
representing all 10~classes (i.e., articles of clothing) present in the
Fashion-MNIST dataset;
\item {\em silver}, with 200~samples each, still
belonging to all classes;
\item {\em bronze}, with 500~samples each,
belonging to only two classes per node;
\item {\em garbage}, with
200~samples each, belonging to two classes.
\end{itemize}
With the exception of ``gold'' ones, nodes suffer from either low quantity or
low diversity, hence, low quality,
of local data. Our experiments establish a correlation between
the category of each node and its contribution to the learning process,
thus allowing us to identify the best strategies to decide whether and how to
integrate each node in the learning process.

{\bf Model-weighting and node-dropping strategies.}
As discussed in \Sec{strats}, the weights assigned to local models
during the averaging phase (step~3 of \Fig{sequence}) can account for
the quality and/or quantity of their local data. Specifically, we
consider the following options:
\begin{itemize}
\item {\em uniform}: all
local models are given equal weight;
\item {\em no.\,samples}: weights
are proportional to the number of samples in each local dataset;
\item
{\em no.\,classes}: weights are proportional to the number of classes in
each local dataset;
\item {\em entropy}:
weights are proportional to the {\em entropy} of local data, an information-theoretic metric expressing, intuitively, how difficult it is to predict the class of a randomly-chosen local sample.
\end{itemize}
The ``samples'' strategy
accounts for the quantity of local data, the ``classes'' one for its
quality, and the ``entropy'' one for both. ``Uniform'', where all
weights are equal, is added as a benchmark.

For node-dropping, we assume that five learning nodes are dropped after
iteration~1, and compare the state-of-the-art strategy of dropping the
slowest nodes~\cite{neglia,jointframework,client-selection} against the
alternative one of dropping the nodes with the lowest weight. The
rationale behind the latter strategy is that weights are linked to how
significant the contribution that nodes can give to the learning process is, thus,
dropping the lowest-weight nodes can reduce learning times without
impairing the learning quality.

\begin{figure}
\centering
\includegraphics[width=.95\columnwidth]{img/commag_2_timegain.pdf}
\caption{Relationship between the average per-node learning time and
the local accuracy gain for different categories of nodes (identified by
the color of markers), under different model-weighting strategies (identified
by the shape of markers).\label{fig:timegain}}
\end{figure}

\subsection{Experiment results} \label{sec:sub-results}
The first aspect of interest is the progress of the overall learning,
i.e., the accuracy of the {\em global}, averaged model (step~3 of \Fig{sequence}),
portrayed in \Fig{learning}. Each marker therein represents the state of
the learning process after an iteration: its position along the x- and
y-axis represent (respectively) the elapsed time and the global
classification accuracy, while its color corresponds to the category of the
slowest node in that particular iteration. Different lines correspond to
different model-weighting strategies.

We can immediately observe that iteration times do not differ
substantially across model-weighting strategies, and that they are
usually determined by ``gold'' or ``bronze'' nodes -- which makes
intuitive sense, as those nodes have the largest local datasets. Even
more interestingly, we can observe a clear difference in the
classification accuracy obtained by different weighting strategies:
giving the same weight to all nodes, or only accounting for the size of
the local datasets, results in a lower accuracy than accounting for data
quality. Furthermore, there is little difference between the ``no.
classes'' and ``entropy'' strategies, suggesting that simply counting
the observed classes can be as effective as adopting more complex
metrics.

Next, \Fig{timegain} displays the relationship between the time taken by
local learning iterations and the
local accuracy gain, i.e., the improvement in classification accuracy obtained during local training (step~1 in \Fig{sequence}).
The latter metric can be seen as
a measure of how much individual nodes contribute to the global learning
process.
In the plot, the {\em color} of each marker represents the category of the corresponding node, while the {\em shape} of each marker represents the model-weighting strategy.
It is clear that, for all model-weighting strategies, local
learning times are strongly correlated with the quantity of local data,
while local accuracy gains are more strongly linked with the number of
classes, i.e., the data quality. These results also suggest that only
relying on local learning times for node-dropping decisions may result
in removing nodes with large, hence, potentially valuable, datasets.

\begin{figure}
\centering
\includegraphics[width=.95\columnwidth]{img/commag_3_weightgain.pdf}
\caption{Relationship between the weight assigned to local models and
the local accuracy gain for different categories of nodes (identified by
the color of markers), under different model-weighting strategies (identified
by the shape of markers). Correlation coefficients for the ``uniform'', ``no.
samples'', ``no. classes'' and ``entropy'' strategies are, respectively,
$0$, $0.07$, $0.98$ and $0.99$.\label{fig:weightgain}}
\end{figure}

\Fig{weightgain} shows the relationship between the weight assigned to
each local model and the corresponding local accuracy gain.
Similar to \Fig{timegain}, the color and shape of markers represent, respectively, the node category and model-weighting strategy.
We quantify the relationship between weights and accuracy gains, by {\em correlation coefficients},
expressing to which extent changes in one  quantity are reflected by changes in the other:
values close to~$1$ indicate strong correlation, values close to~$0$ little to no correlation.

In our case,
it is clear that
weights only considering the quantity of data (i.e., the ``no. samples''
strategy) may not be able to identify the nodes that can contribute the
most to the learning process, e.g., it gives similar weights to the
``silver'' and ``garbage'' nodes. On the other hand, ``no. classes'' and
``entropy'' weights are very well correlated with accuracy gains, which
again suggests how the quality of data has a high impact on
the learning effectiveness.
From both \Fig{timegain} and \Fig{weightgain}, it is also possible to see how better local accuracy gains do not necessarily coincide with better global classification accuracy. Indeed, good model-weighting decisions are necessary to consolidate local learning from different nodes into a consistent, high-quality global model.

\begin{figure} \centering
\includegraphics[width=.95\columnwidth]{img/commag_4b_drop.pdf}\\
\includegraphics[width=.95\columnwidth]{img/commag_4c_drop.pdf}
\caption{Relationship between elapsed time and global accuracy for
different node-dropping schemes, under the ``no. samples'' (top) and
``no. classes'' (bottom) model-weighting strategies.
\label{fig:drop}
\vspace{-5mm}

\end{figure}

Last, \Fig{drop} shows the effect of different node-dropping strategies
on the learning process, for the ``no. samples' and ``no. classes''
model-weighting strategies.
Specifically, we wait for the first five iterations, and then drop the five nodes with the lowest score, computed according to each node-dropping strategy.
We can observe that, when weights only
reflect the quantity of local data, dropping the slowest nodes
significantly hurts the learning accuracy. On the other hand, more
sophisticate model-weighting {\em or} node-dropping strategies yield
virtually the same accuracy as keeping all nodes. This also highlights
how  model-weighting and node-dropping decisions interact with one
another, and can represent different, complementary ways to achieve the
same goals.

{\bf Summary.}
In conclusion, our results show that the {\em quantity} of data drives the computation
time of local nodes; however, it is the {\em quality} of data that determines
its usefulness to the global learning process.
It is thus of paramount importance that model-weighting decisions do not solely account
for data quantity or computation time, as that may adversely impact performance.

\section{Take-away Messages and Challenges} \label{sec:takeaway}

Based on both the existing works discussed in \Sec{fl} and \Sec{strats}
and the experiments reported in \Sec{experiments}, we can highlight the
following high-level lessons learned, which also point at interesting
directions for future research.

\paragraph*{Model weights matter}

Assigning the right weights to local nodes during the averaging phase
can have a very significant impact on the learning process, as
highlighted in \Fig{learning} and \Fig{drop}.
Although few works in the literature have explored this option, \Fig{weightgain} shows how
\begin{comment}
Very few existing schemes
have deviated from the simple approach of making weights proportional to
the quantity of available local data; however, our experiments have
shown that more comprehensive metrics can yield much better performance.
Indeed, as highlighted in
\end{comment}
weights accounting for the
quality of data as well as its quantity are much more likely to
identify the nodes that can contribute the most to the learning process.
Importantly, the information needed to compute such weights is either
already available or easy to collect for the learning server, and does
not jeopardize the privacy properties of FL.
\begin{comment}
Defining and measuring data
quality, as well as integrating such figures with FL, are all novel and
potentially promising research challenges.
\end{comment}

\paragraph*{Data quality matters}

Our experiments strongly underline the importance of dataset quality.
An example is provided in \Fig{timegain} and
\Fig{weightgain}, showing how nodes with more diverse data (``gold'' and
``silver'') are able to offer much greater contributions to the overall
learning process.
Quantifying data quality is not a trivial task, however, our experiments show that even simple definitions based on counting the classes present in a given dataset yield very good results. Further research can explore additional
\begin{comment}
This implies that data quality shall be appropriately
estimated -- the number of represented classes is arguably the simplest
metric, but has proven very effective nonetheless -- and accounted for
when making model-weighting and node-dropping decisions. It is almost a
truism to say that both quality and quantity matter, and that both shall
be accounted for. However, our experiments suggest that quality may be
the most significant of the two aspects.

Further research could aim at
narrowing down the
\end{comment}
aspects of data quality, e.g., its freshness~\cite{alaa}.

\paragraph*{Check why nodes straggle before dropping them}

The global learning time of FL depends on the slowest node in each
iteration; therefore, it is often tempting to try and speed learning up
by dropping the slowest nodes~\cite{neglia}. Such a strategy is
appropriate when the slowest nodes are indeed stragglers, with limited
computational capabilities or poor
connectivity~\cite{jointframework,client-selection}; however, this may
result in unduly excluding nodes with valuable, rich datasets.
Privacy concerns often prevent the server from obtaining additional
information on individual learning nodes; however, already-available
data like the size of local datasets can provide significant help to
tell genuine stragglers apart from nodes that simply have a lot of
data. If warranted, the latter can be directed to sample their own
datasets, in a similar spirit to~\cite{infocom20-noniid}, so as to
provide good contributions to the learning process with a smaller
latency. This also points at the exciting research direction of
extending the FL paradigm by allowing additional interaction between the
learning server and learning nodes, striking the right balance between
simplicity, privacy, and effectiveness.

\paragraph*{FL is robust}

This is not very surprising, since FL has been introduced for the very
purpose of exploiting local, potentially heterogeneous, data from
devices that cannot be centrally controlled. It is however interesting
to highlight how the robustness of FL extends beyond tackling
low-quality data, to tackling suboptimal configurations. An example is
provided in \Fig{drop}, where it is sufficient to make high-quality
model-weighting {\em or} node-dropping decisions to obtain very good
learning performance. This suggests that FL is indeed a viable choice in
those environments and scenarios where there is a significant likelihood
that configuration decisions be suboptimal. Robustness to incorrect
configuration is a relevant research area in distributed computing
scenarios, and -- so far -- a neglected one.

\section{Conclusion} \label{sec:conclusion}

In the context of federated learning, we have considered the problems of
model-weighting, i.e., assigning weights to local models during the
averaging phase, and node-dropping, i.e., selecting the nodes to exclude
from the learning process.
After observing how those two decisions can
tackle most of the issues and hurdles of FL, we have reviewed existing
approaches thereto and found them to seldom depart from straightforward
solutions based on the quantity of local data learning nodes have and
their response time. Leveraging a set of real-world experiments, we have
observed how more comprehensive approaches, accounting for the quality
of local data and for the reasons behind longer node latency, can yield
substantially better learning performance.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


One day I'll have scans of all my best Black And White film photography that is being preserved in negative sheets in a rather large file cabinet but for now I just have to photograph the negative and do the processing in photoshop. Would rather have a rad darkroom print but that's not happening. Found this image of Steve Salisian from the Houston SUAS in 1989, hope it looks good on here.


text:
Q: MySQL char vs. int I'm currently having a discussion in my class about the datatypes char and int in MySQL.
We have a phone number of 8 individual numbers. However, we can't find any pros and cons for each of them.
Should we use the char type or the int type, and why?

A: If you have a fixed size then you can use char(8) -> 8B , mysql works much faster with fixed size fields, but if you chose int -> 4B you will save space and also if you will have a index on that field it will work faster then the char.
You can do a simple benchmark to test the speed of writes and reads using char(8) and int
There is no point in using variable length type like varchar or varbinary if you have a fixed size because performance will decrease

A: Depends on how you want to represent the phone number, do you need area codes, country codes and stuff like that, and do you want to save it as a single column or do you want to split it up?
Personally, I would choose to represent area codes, country codes, and the phone number as 3 columns with the datatype int, as this would make it easier to find all phone numbers in one area, and so on. But if it's only purpose is to be a like a string value, the char would be sufficient, however i would consider using the varchar instead, if you have phone numbers for several countries.`

A: Don't know about the US exactly, but in Europe the national access code is a leading 0 , and international access code is a leading 00 or +. So that's a con to using INT, as the leading 0's would be lost. Further more you also have phone numbers that contain names and even though these names can be converted to numbers, it would probably be nice to keep them as a name. That's a second con to using INT. Last con is you can also have extension numbers, etc. All goes into favor of VARCHAR.

A: I started using VARCHAR for storing phone numbers because it gives you more range in formats of different countries.  Storage is cheap.

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Kathmandu's air quality is notorious. Now there's an Air Quality Index to measure just how bad.
At one of the busiest intersections in Kathmandu, business is good for 64-year-old Nucche Khadka. He says he's been working this spot for 40 years, selling crackers, candy and cigarettes. A steady stream of customers buy cigarette boxes decorated with grotesque pictures of cancerous lungs, meant to warn customers of the danger of their contents.
But the air they are breathing may not be much better than the cigarette smoke.
Air quality monitors installed in August are revealing what residents have long suspected: Kathmandu has some of the most toxic air on Earth. In the dead of winter, pollution was at its peak, with the Air Quality Index daily soaring above 200.
To make matters worse, most of the streets have been torn up to lay pipe for a water expansion project, sending dust into the air. Like many of the city's residents, Khadka wears a black cloth mask, but it doesn't stop the most harmful particles, which are smaller than 2.5 microns (pm) wide.
He complains he has a cough and sore throat that keeps returning.
"It's too expensive to go to the doctor, I take medicine from the pharmacy," he says.
Nepal's air quality is the fourth-worst in the world, according to Yale's 2016 Environmental Performance Index. The nation's capital Kathmandu is also one of the fastest-growing metropolitan areas in South Asia and contains a burgeoning middle class, who've funded a boom in construction and auto sales. Until the government recently slowed the rate of lending, residents could get an auto loan with only a 10 percent down payment.
But people here are becoming increasingly aware and concerned with the air pollution. Student protests and a constant stream of articles in local newspapers are pressuring the government to clean up the air. In March, the police began enforcing a law made two years ago that outlaws public transportation vehicles more than 20 years old.
Sabin Pradhan, the deputy superintendent for the Metropolitan Traffic Police Division, said last month they issued tickets to 1,000 overage vehicles in just 10 days.
Langtang valley, north of Kathmandu, Nepal. Glaciers there have been receding.
"In Nepal, if we want to implement something that is forward-thinking, then there is a lot of resistance. But once we catch the vehicles, they've been cooperative," he said.
The pollution comes from many sources besides vehicles, though. Some of the worst air in Nepal is not in Kathmandu, but in the southern plains, where brush fires, brick kilns and cooking stoves produce a haze of smoke.
The pollution could have far-reaching consequences as it floats north into the Himalayas. Smoke from fires and emissions from vehicles produce soot containing black carbon. Black carbon absorbs lots of solar energy. It settles on glaciers and snow, and its dark color causes the snow and ice to absorb more of the sun's radiation. It also warms up the air, changing rainfall patterns.
For the mountains that are called the "water towers of Asia," this could have serious impacts. Over a billion people depend on the monsoon rains and snow melt of the Himalayas for their source of water.
Last week, an Indian court granted "living entity" status to the Gangotri and Yamunotri glaciers, which feed the Hindu holy rivers Ganga and Yamuna. The judges said the glaciers are shrinking at an alarming rate. The Ganga river is also fed by glaciers in the Nepali part of the Himalayas.
Researchers are still studying how much black carbon itself causes glacier melt, and how much is caused by rising global temperatures. So far, scientists haven't been able to pinpoint to what extent the glaciers have melted since there's a lack of historical data and the glaciers themselves can be difficult and expensive to reach. However, a recent paper in The Cryosphere suggested that glaciers in the Everest region could lose 70 to 99 percent of their mass by 2100 due to climate change.
Greenhouse gases are the leading cause of the warming atmosphere, but black carbon is also a major contributor to shrinking glaciers, especially when the sources of the soot are so close to them.
"The closer you are to a glacier, of course, the greater the impact on that glacier," said Dr. Arnico Panday, a researcher at Nepal's International Centre for Integrated Mountain Development.
Kathmandu is only 43 miles away from the closest glaciers, and its black carbon is within striking distance of thousands more.
Stephen Groves reported from Kathmandu, Nepal.


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Table of Contents

Solving SOA pains with patterns

Solving SOA pains with patterns

Defining software architecutre:

Software architecture is the collection of fundamental decisions
about a software product or solution designed to meet the
project's quality attributes (the architectural requirements).

The architecture includes the main components, their main
attributes, and their collaboration to meet the quality attributes.

Arehitecture can, and usually should, be expressed in several
levels of abstraction, where the number of levels depends on the
project's size and complexity.

Some comments about software architecture:

Architecture represents the set of earliest design decisions that
are both hardest to change and most critical to get right.

Architecture is an attribute of every system.

Architecture breaks a system into components and sets boundaries.

Architecture is about relationships and component interactions.

Architecture explains the retionale behind the choices. Also it
can be benefical to understand what alternatives were weighed and
abandoned.

The unit of communicationin SOA is the message.

RPC often requires the caller to have intimate knowledge of the
other system's implementation details. With messaging, this isn't
the case.

Messages have both a header and a body (the payload).

Messages can come in may different forms:

HTTP GET messages,

Simple Object Access Protocol (SOAP) messages,

Java Message Service (JMS) messages,

Simple Mail Transfer Protocol (SMTP) messages.

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