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Former Murderdolls / Dope Drummer Ben Graves Dead at 45
Graham Hartmann Published: May 10, 2018
Mick Hutson, Getty Images
Former Murderdolls / Dope drummer Ben Graves has died at age 45. His death was announced by his fiancee Lydia, who revealed Graves had been battling cancer for the last 11 months.
Graves rose to prominence with the Murderdolls in 2002. Despite not recording drums for Beyond the Valley of the Murderdolls or the Right to Remain Violent EP, Graves was a steadfast member of the Murderdolls until 2004. From there, Ben Graves moved on to Dope, manning the kit from 2005-2006.
Lydia Munster released the official statement regarding Ben Graves' death:
This is incredibly hard for me to write this, but I know it is necessary because Ben has so many friends and fans.
Most of you don't know that Ben has been battling cancer for the last 11 months. It was his decision to keep his health very quiet.
Ben passed away today, May 9th 2018, from complications due to his cancer. His diagnosis was grim but he fought a long and hard battle to try and beat it and overcome the odds. Some battles can't be won no matter how hard you try.
Please respect me, his family and close friends while we grieve. Even though we knew it was eventually coming, the complication was sudden and very unexpected.
It's devastating to lose someone you love more than yourself. My heart is broken, my heart died with him.
We will be taking him home to Massachusetts. I will organize a memorial for Ben's LA friends, details to follow.
Post some photos of Ben for us to see him as he was.
💔Lydia
Rockers We Lost in 2017
Source: Former Murderdolls / Dope Drummer Ben Graves Dead at 45
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INSIDE : Learn how to create an elegant and unique Striped Gold Foil Wooden Bowl. The perfect DIY project to add a little drama to any piece.
Who is with me on gold foil being one of the best things out there? I mean, as far as craft supplies go… gold foil is pretty amazing. I've used it on so many fun projects, like my gold foiled vase, Gold Foiled Brushstroke Clutch and my Gold Foil Succulent Artwork.
It had been almost two weeks since I had added gold foil to something, so I knew it was time to break out another DIY using it. Today, I am sharing how SIMPLE it is to create a Striped Gold Foil Wooden Bowl!
WHAT ARE THE SUPPLIES TO GOLD FOIL A WOODEN BOWL?
You could really create any pattern you would like for this project, but I decided to go for a simple stripe. And if you know me at all, there was absolutely no rulers or measuring involved. Rulers make me cringe, fyi.
So, at random I placed a nice fat piece of Martha Painter's Tape to be my starting point just a little off center. Then, using Martha's super thin patterning tape, I started creating the stripes.
If you don't trust yourself to get fairly straight lines without a ruler, then you could place a then piece of tape. Then, place your next piece of tape and remove the first one. Removing every other piece will create even lines.
Be patient, as you get to the curve of the bowl it takes time to keep a straight but "curved" line. Once all the tape has been installed, go over with your fingernail and make sure all the edges of the tape are pushed down tightly.
Next, apply the gold foil metal adhesive glue. Read the instructions on your glue for dry time, but it is essential to wait the appropriate amount of time. It will be tacky, but that is what you want.
Once the glue is nice and tacky, start applying your gold foil sheets. I simply covered the entire half of my bowl with gold foil sheets and there were only a few small spots here and there that escaped foil. It's easy to add little bits here and there for complete coverage.
Once all my gold foil was adhered, I waited maybe another 15 minutes to really really make sure the glue was good and ready for me. I really wanted to get the straightest lines possible, as often gold foil projects result in a rough unsharp look. Then, I gingerly and slowly removed the tape.
I could not believe the outcome!
For using gold foil sheets and not just painting stripes with a paint pen or gold foil paint, I was so thrilled to see how great these lines turned out!
I've been trying to decide what to use this bowl for now that it has gone such a lovely transformation… but I don't want to put anything in it to hide the gold stripes!!
Does gold foil come off of wood?
This is a great question. I think partially the answer depends on what type of wood item you are applying gold foil to. The bowl I have added my foil to is mostly decorative. I may on occasion put a few items in it but they also are probably decorative, so the wear and tear that it may receive is fairly minimal.
I believe if you were using this bowl daily, over time the foil would rub off… but I gave it a quick scratch test and boy that gold foil appears to really be on there for now.
My best idea is a fruit bowl because we go through a lot of fruit in our house… so maybe I'll put this on our dining room table. Or maybe bigger jewelry? What do you think would be a good use for such a pretty bowl?
Looking for a few other gold foil projects? You might like some of these!
These wooden bowls are absolutely gorgeous! The skinny tape is so helpful in making the tiny gold foil lines. Beautiful work!
Thank you so much, Eden! I was so thrilled with how this bowl turned out.
Just a small touch can have such a huge impact. This is really beautiful and so unique. I am hosting a new link party, Sweet Inspiration and would be happy if you joined us. It runs Friday till Tuesday.
Thank you for the sweet words, Mary! And the link party invite.
This turned out amazingly well! I absolutely love using gold detail when I design a room and pieces like this with a similar design tend to run so expensive so I'm excited to find a great alternative! Thanks for sharing!
Oh yes, this was quite inexpensive to DIY! Thanks for stopping by Johanne!
This is beautiful! How does the gold hold up to daily use? Does it scratch off easily? I'm thinking to use for a empty-my-pockets-bowl in my entry…or should I consider something that is softer or more gentle rather than placing keys, change, etc.?
Thanks Meg! So, I have been using this bowl for softer items… mostly overflow fruit from my main fruit basket. I'm wondering if doing this type of application for a container to hold keys might be too much. Possibly added a clear sealant spray might help? You could just tape it off and use gold foil paint. It would look just as lovely and I'm guessing would scratch less. And a clear sealant spray might be good protection for that one, too.
I love this bowl so much that I have included it in my post on DIY gifts for Mothers Day.
Aw, thank you for including in your roundup!
Great post! This bowl would be super cute to use as part of a centerpiece.
Ah, that's a great idea!! Thanks Serena.
Always love me some gold foil. This is stunning, Amy!
This looks SO classy! I've been wanting to try out gold foil. Maybe I'll give this project a try!
This turned out AMAZING. I love the skinny tape. I have definitely tried to cut tape into thinner strips. Not a good use of time.
This is just gorgeous, Amy! I love the way you styled it too.
Oh my geez!! Don't ever go so long without gold foiling! This is so so pretty and the styling is perfection?
Aw, thanks lady! I know… I've got gold foil in my blood now.


Hotel Terra is a sustainably designed and operated, Silver-LEED certified condominium hotel with modern mountain sophistication located slopeside to Jackson Hole Mountain Resort. It's groundbreaking approach to service and design has earned the property world-renown and set a new standard for mountain resort hospitality. Most notably, the 132 room, 57 condominium unit hotel is consistently ranked top US Hotel and US Ski Hotel by Condé Nast Traveler and has realized excellent press in The New York Times, Town & Country and Outside.


Hi, I'm trying to do a simulation with SimulationXpress but I keep getting an error when I execute the simulation. I attached a picture of the error message. I'm running Solidworks Premium 2012. I already tried to repair the files and to re-install Solidworks.
Usually this means that the simulation can't find or access the file directory that it is set to save results to.
Try making a duplicate study or saving a copy of your part/assembly file and running the sim in that file.
Had some success tricks like that.
Thank's for the answer' I actually tried to do the simulation on 3 different PC so I guess this is not the problem' I also tried to export the file with the ".step" extension wich is nearly the same as duplicate the file because it does create a part file (.sldrpt) when you run the simulation.


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Home Neuroscience Anxiety Effectively Treated With Exercise
Anxiety Effectively Treated With Exercise
Summary: A 12-week program of moderate to strenuous exercise significantly helps relieve anxiety symptoms, a new study reports.
Source: University of Gothenburg
Both moderate and strenuous exercise alleviate symptoms of anxiety, even when the disorder is chronic, a study led by researchers at the University of Gothenburg shows.
The study, now published in the Journal of Affective Disorders, is based on 286 patients with anxiety syndrome, recruited from primary care services in Gothenburg and the northern part of Halland County. Half of the patients had lived with anxiety for at least ten years. Their average age was 39 years, and 70 percent were women.
Through drawing of lots, participants were assigned to group exercise sessions, either moderate or strenuous, for 12 weeks. The results show that their anxiety symptoms were significantly alleviated even when the anxiety was a chronic condition, compared with a control group who received advice on physical activity according to public health recommendations.
Most individuals in the treatment groups went from a baseline level of moderate to high anxiety to a low anxiety level after the 12-week program. For those who exercised at relatively low intensity, the chance of improvement in terms of anxiety symptoms rose by a factor of 3.62. The corresponding factor for those who exercised at higher intensity was 4.88. Participants had no knowledge of the physical training or counseling people outside their own group were receiving.
"There was a significant intensity trend for improvement — that is, the more intensely they exercised, the more their anxiety symptoms improved," states Malin Henriksson, doctoral student at Sahlgrenska Academy at the University of Gothenburg, specialist in general medicine in the Halland Region, and the study's first author.
Importance of strenuous exercise
Previous studies of physical exercise in depression have shown clear symptom improvements. However, a clear picture of how people with anxiety are affected by exercise has been lacking up to now. The present study is described as one of the largest to date.
Both treatment groups had 60-minute training sessions three times a week, under a physical therapist's guidance. The sessions included both cardio (aerobic) and strength training. A warmup was followed by circle training around 12 stations for 45 minutes, and sessions ended with cooldown and stretching.
Most individuals in the treatment groups went from a baseline level of moderate to high anxiety to a low anxiety level after the 12-week program. Image is in the public domain
Members of the group that exercised at a moderate level were intended to reach some 60 percent of their maximum heart rate — a degree of exertion rated as light or moderate. In the group that trained more intensively, the aim was to attain 75 percent of maximum heart rate, and this degree of exertion was perceived as high.
The levels were regularly validated using the Borg scale, an established rating scale for perceived physical exertion, and confirmed with heart rate monitors.
New, simple treatments needed
Today's standard treatments for anxiety are cognitive behavioral therapy (CBT) and psychotropic drugs. However, these drugs commonly have side effects, and patients with anxiety disorders frequently do not respond to medical treatment. Long waiting times for CBT can also worsen the prognosis.
The present study was led by Maria Åberg, associate professor at the University of Gothenburg's Sahlgrenska Academy, specialist in general medicine in Region Västra Götaland's primary healthcare organization, and corresponding author.
"Doctors in primary care need treatments that are individualized, have few side effects, and are easy to prescribe. The model involving 12 weeks of physical training, regardless of intensity, represents an effective treatment that should be made available in primary health care more often for people with anxiety issues," Åberg says.
About this anxiety and exercise research news
Author: Margareta Gustafsson Kubista
Contact: Margareta Gustafsson Kubista – University of Gothenburg
Original Research: Open access.
"Effects of exercise on symptoms of anxiety in primary care patients: A randomized controlled trial" by Maria Åberg et al. Journal of Affective Disorders
Effects of exercise on symptoms of anxiety in primary care patients: A randomized controlled trial
There is a need for high-quality research regarding exercise interventions for persons with anxiety disorders. We investigate whether a 12-week exercise intervention, with different intensities, could reduce anxiety symptoms in patients with anxiety disorders.
286 patients were recruited from primary care in Sweden. Severity of symptoms was self-assessed using the Beck Anxiety Inventory (BAI) and the Montgomery Åsberg Depression Rating Scale (MADRS-S). Participants were randomly assigned to one of two group exercise programs with cardiorespiratory and resistance training and one control/standard treatment non-exercise group, with 1:1:1 allocation.
Patients in both exercise groups showed larger improvements in both anxiety and depressive symptoms compared to the control group. No differences in effect sizes were found between the two groups. To study a clinically relevant improvement, BAI and MADRS-S were dichotomized with the mean change in the control group as reference. In adjusted models the odds ratio for improved symptoms of anxiety after low-intensity training was 3.62 (CI 1.34–9.76) and after moderate/high intensity 4.88 (CI 1.66–14.39), for depressive symptoms 4.96 (CI 1.81–13.6) and 4.36 (CI 1.57–12.08) respectively. There was a significant intensity trend for improvement in anxiety symptoms.
The use of self-rating measures which bears the risk of an under- or overestimation of symptoms.
A 12-week group exercise program proved effective for patients with anxiety syndromes in primary care. These findings strengthen the view of physical exercise as an effective treatment and could be more frequently made available in clinical practice for persons with anxiety issues.
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redpajama_set_name: RedPajamaCommonCrawl


Spent foundry sand may be aliability, or an asset depending on how its handled.
Incoming sand and tramp metal.
Many no-bake foundries have focused their production efforts on castings exclusively, and disposed of leftover sand lumps. But, successful foundries that focus on profit margins are constantly looking for ways to reduce costs. When sales are down, cost-saving projects gain more attention, because the operators have the opportunity to incorporate new equipment. Generally the projects that get approved first are the ones with the fastest payback.
Foundries that dump spent sand mixed with tramp metal realize that someday they will have to clean up the site. This black cloud has become a revenue stream for some foundries. When you can clean up the environment around your foundry and make money doing it, everyone benefits. What some foundries have realized is that over the years a lot of valuable metal has been dumped along with the sand.
Didion International Inc., known for sand reclamation and for separating sand from metal, has developed a system to help foundries recover the sand and metal that has been disposed on-site. Both ferrous and nonferrous foundries have reported excellent savings with the Didion system. Since the equipment can run outside, plant floor space is reserved. The process is very efficient and the savings easily covers the low energy requirements. A variable jet burner with a 10:1 turndown ratio dries the sand and metallics. The sand is scrubbed and screened twice prior to discharge. The patented dual chambered design recirculates the screen-overs, to optimize the yield up to 97%. The metal is separated from the sand and thoroughly cleaned before discharging.
Reclaimed sand going back into the sand system.
For foundries that send their sand to approved landfill sites, the disposal costs and new sand purchases can easily justify an in-house sand reclamation system.
In most cases if a foundry generates 2 tph of disposal sand, then sand reclamation should be investigated. For foundries generating 5 tph, sand reclamation should be considered as a viable cost saving project. For 10 tph and higher, the savings or payback can be less than one year.
Didion offers a full range of rugged, low-operating cost machines sized up to 100 tph for chemically bonded sands.


With First Tutors: Music you can find a Wells piano tutor. Piano lessons are the perfect way to learn a stimulating new hobby, pick up a new talent and gain confidence in piano.
Alison has been coming to our house to teach my children for the past month. She has given then a lovely introduction to music and playing the piano. She is patient, kind and very reliable.
Becky is a good tutor its just that it was a little bit far for us to travel.
I'm a complete beginner so just booked 5 lessons to see how I got on with playing the piano. Alison was a lovely teacher, taught the lessons at just the right pace for me and was very encouraging. I'd definitely go back to her for further lessons or advice in the future.
First Tutors: Music is the best place to find Wells piano teachers in the UK, helping you find Wells piano lessons in your area and Wells tutors to help with any instrument, for example, piano or . We can help you find Wells piano tuition or a Wells piano teacher to prepare for piano grades and improve your piano skills! Our search will order Wells piano tutors in your area according to their lesson charges and distance from you.
If you are a piano tutor wishing to offer piano tuition services in Wells or throughout the UK, please register with First Tutors: Music or learn about us here. Becoming a Wells piano teacher is a brilliant way to re-affirm your own skills through giving piano tuition while helping others, all in Wells!


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How Big are the 10 Largest Cities in Africa by Population and Size?
By Samuel
In recent times, the largest cities in Africa have become quite awe-inspiring even to the most traveled globe-trotting tourist. This is mainly because they have begun to tap into the tech-savviness of the contemporary world and have gone ahead to revolutionize the outlook of their environs and working systems. These big African cities now play hosts to several architectural masterpieces, impressive creations of civil engineering, and complex road networks. Yet, some of these cities still have several ancient architectural monuments that keep pulling the rest of the world to them.
Whether by population or by the size of its landmass, the African continent is a big one. As a matter of fact, Africa is the world's second-largest and second-most-populous continent after Asia, and its most populous cities are homes to millions of people working to better their lives. Impressively, the ten of the largest cities in Africa have become testaments to African excellence, and with the ongoing advancements taking place in these cities, it's not hard to see why.
Size: 1,171.28 square kilometers
Founded: Lagos was founded by the Awori subgroup of the Yoruba tribe in the 15th century.
Time zone: GMT +1
Weather/Temperature Average: 23 -33 degrees Celcius. Usually, the warmest month in Lagos which brings with it the highest average high temperature is February (33.1°C). However, the months that usher in the lowest average high temperature in Lagos are July and August (28.1°C). Additionally, records show that the month with the highest average low temperature is March (23.8°C).
Demonym: Lagosian
The city of Lagos is an incredibly robust place constantly bustling with life; with its 21.3 million people, it is the most populous city in Nigeria and the African continent as a whole. It is also, in fact, a major financial center for all of Africa has the fourth-highest GDP in the continent. It is also reported to be one of the fastest-growing cities in the world. To lend some credence to its robustness, records show that Lagos currently houses one of the largest and busiest seaports in entire Africa.
Many Nigerians and other Africans consider Lagos as the go-to place to achieve success in business and career choices and the city has succeeded in producing megastars in entertainment and sports, and moguls in business.
Fun Fact: The city of Lagos has the current tallest building in West Africa, the NECOM House tower, which stands about 525 ft and is a 32-story building.
2. Kinshasa, Democratic Republic of the Congo
Population: 14.34 million people
Size: 9,965 square kilometers
Founded: Kinshasa was founded by the colonial administrator, Henry Morton Stanley in 1881.
Weather/Temperature Average: 10-38.9 degrees Celcius. Kinshasa usually records the highest temperature in March (38.9°C) and records the lowest recorded temperature in July (10°C).
Area Code: 243 + 9
Demonym: Kinois or Kinshasans.
Kinshasa, which is the capital and the largest city of the Democratic Republic of the Congo, started as a fishing and trading village but has grown into a megacity. In fact, Kinshasa is now the world's largest Francophone urban area, beating Paris to that title (by population). Today, the city boasts of massive structures and is the third-largest urban area after Cairo and Lagos.
Fun fact: It is interesting to know that Kinshasa actually faces Brazzaville, the capital of the neighboring Republic of the Congo. This makes Kinshasa and Brazzaville the world's second-closest pair of capital cities after Rome and Vatican City.
3. Cairo, Egypt
Population: 9.5 million
Founded: Cairo was found by the Fatimid dynasty in 969 AD.
Weather/Temperature Average: 18.9 – 34.7 degrees Celcius. The warmest month in Cairo is usually in July (34.7°C). But, the month with the lowest average high temperature is usually January (18.9°C).
Area Code: (+20) 2
Demonym: Cairene
Cairo, the capital and largest city in Egypt, is one of the most developed cities in Africa and is actually to be considered a World City with a "Beta +" classification. The city boasts of a metro system that ranks amongst the fifteen busiest in the world. It is also reported to be the largest city in the Arab world. Beyond this, Cairo is also home to many culturally significant buildings such as the Bayt Al-Suhaymi and the Citadel of Cairo, among others.
As a majorly Muslim city, Cairo is respected for its abundance of Islamic architecture and has even been titled "the city of a thousand minarets" because of that.
Fun fact: Cairo has the oldest and largest film and music industries in the Arab world.
4. Giza, Egypt
Founded: Giza was founded by ancient Pharaohs in 642 AD.
Weather/Temperature Average: 5- 44.4 degrees Celcius. The highest recorded temperature in Giza is usually recorded in May – 44.4°C; while the lowest recorded temperature in Giza is 5°C, which is often recorded in January.
Demonym: Gizan, Gizanne
Giza is the third-largest city in Egypt and is also one of the most visited places by tourists. This is because it is home to some of the most impressive ancient monuments in the world. Some of the ancients monuments present in Giza today include the Great Sphinx, the Great Pyramid of Giza, a complex of sacred structures and ancient Egyptian royal mortuary, and many more. Giza is undoubtedly rich with culture and history – a legacy that has always been a source of pride to its citizens. Interestingly, the city of Giza has always been considered to be a focal point in Egypt's history.
Fun fact: The city of Giza hosts the first zoo on the entire African continent.
5. Johannesburg, South Africa
Population: 5.64 million
Founded: Johannesburg was founded in 1886 and was named after Christiaan Johannes Joubert and Johannes Rissik, two land surveyors who combined the name they shared and then added 'burg' to it to form the city's name.
Weather/Temperature Average: 4.1 – 25.6 degrees Celcius. The warmest month in Johannesburg (with the highest average high temperature) in January when the temperature is usually at 25.6°C. The coldest months in Johannesburg (with the lowest average low temperature) are June and July when the temperature drops to 4.1°C.
Area Code: 010 and 011
Demonym: Johannesburger
Johannesburg, which is also informally known as Joburg, is the largest city in South Africa and is one of the most intriguing cities in Africa as a whole. The famed city has some of the tallest structures on the continent and is one of the most developed urban areas in Africa.
Johannesburg is classified as a megacity and is, in fact, considered to be one of the 50 largest urban areas in the world. It is also the center of large-scale gold and diamond trade and has some of the largest mines in Africa.
Fun fact: Johannesurg has the busiest airport in Africa, the Tambo airport, which receives over 21 million people annually.
6. Dar es Salaam, Tanzania
Founded: Dar es Salaam was founded by the Majid bin Sayyid, the Sultan of Zanzibar, in 1866.
Weather/Temperature Average: 25 -31 degrees Celcius. Dar es Salaam records its warmest months in March and December when the temperature usually rises to 31°C. It records its coldest months in January and February when the temperature drops to 25°C.
Demonym: Not known
Dar es Salaam, which is an Arabic phrase meaning "haven of peace," was the former capital city of Tanzania and is actually the largest city in Tanzania and East Africa at the moment. It is also considered to be one of the fastest-growing cities in the world.
Though Dar es Salaam is no longer the capital city of Tanzania, the city is now more highly thought of than the capital city of Dodoma. It is also the leading financial center in the country. Dar es Salaam has also become the most prominent city in fashion, music, media, music, film and television, and arts in general in Tanzania. It has the highest concentrations of trade and other services, and manufacturing in Tanzania as well. Being a thriving city, it is easy to see how Dar es Salaam has become a prime destination for tourists and world travelers.
Fun fact: Dar es Salaam is the world's largest and most populous Swahili-speaking city.
7. Alexandria, Egypt
Founder: Alexandria was founded by Alexander the Great in 331 BC.
Weather/Temperature Average: 2.8-42.2 degrees Celcius. Alexandria usually records its highest temperatures in May when the temperature rises to 42.2°C. The city usually records its lowest temperature in January when the temperature drops to 2.8°C.
Demonym: Alexandrine
Alexandria, a major economic center in Egypt, is the second-largest city in Egypt and is also one of its important industrial centres as well as one of the country's most popular tourist destinations. The city is of great historical value especially because it was the intellectual and cultural center of the ancient Mediterranean world. It is also home to the Lighthouse of Alexandria – one of the seven wonders of the world.
Fun fact: Alexandria was once the largest city in the ancient world before being it was overtaken by Rome.
8. Abidjan, Cote d'Ivoire
Founder: Abidjan was founded in 1898 by French colonists.
Weather/Temperature Average: 24 -31 degrees Celcius.
Abidjan's warmest month is April when the temperature usually rises to 31.2°C. March, and occasionally April, are the months with the highest average low temperature as the temperature drops to 24.9°C.
Area Code: +225
Demonym: Abidjanais
Abidjan, which happens to be the economic capital of Côte d'Ivoire, is a highly developed city which is distinguished by an impressively high level of industrialization and urbanization. It is also one of the most populous French-speaking cities in Africa. Abidjan is not the political capital of Cote d'Ivoire (Yamoussoukro is the capital), however, because of the city's impressive growth and urbanization, most of the country's political institutions and even foreign embassies are now located in Abidjan.
Abidjan also has a significant artistic population and as a result, is home to many arts and cultural festivals.
Fun fact: Abidjan is the third-largest French-speaking city in the world
9. Nairobi, Kenya
Size: 696 square kilometers
Founded: Nairobi was founded in 1899 by the colonial authorities in British East Africa.
Weather/Temperature Average: 4.4-35 degrees Celcius. Nairobi usually records its highest temperature in March when the temperature rises to (35°C). The city usually records its lowest temperature in August when the temperature drops to 4.4°C.
Demonym: Nairobian
The name Nairobi comes from Enkare Nairobi, a Maasai phrase which means "cool water". Cool water here is a direct reference to the Nairobi River which flows through the city. Over the years, the city has grown to become an established hub for business and now has one of the largest security exchanges in Africa. Nairobi, which the largest city in Kenya, also plays host to more than 100 major international companies and organizations as well as thousands of local Kenyan businesses. The Kenyan city is also a notably iconic tourist hub and attracts more than one million tourists annually
Fun fact: Nairobi is also referred to as the "Green City in the Sun."
10. Casablanca, Morocco
Founded: Casablanca was founded and settled by Berbers by the seventh century BC.
Weather/Temperature Average: 0-40 degrees Celcius. Casablanca usually records its highest temperature in September when the temperature rises to 40.6°C. It usually records its lowest temperature in January when the temperature can drop to 0°C.
Area Code: 22
Demonym: Casawi, Bidawi, Baydawi, Casablancais
Casablanca is the largest city in Morocco and is also the largest city in Northwest Africa. The city has grown to become one of the largest financial centers in Africa and is the authentic economic and business center of Morocco even though it is not the country's political capital.
Fun fact: The city of Casablanca originally was not called Casablanca; it was originally called Anfa. The name was later changed to Casa Branca by the Portuguese who took control of the city in the 15th century AD. The current name came about when the Portuguese fell under Spanish control.
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It is a tea clipper built in the UK in 1869.
A hull structure in which an outer plate is affixed to the inner iron frame. The deck and the deck house used teak wood, and it was a typical wood-steel mixed ship using iron such as mast.
British shipping industry was a long-standing desire to have a ship that can win in the tea clipper race between China and the UK.
The rapid speed that I showed in the race developed with Thermopylae in 1872 was told through until later generations.
The kit is a hull with a newly designed box structure with little distortion, and the keel frame uses precision laser processed parts. Deck uses full teak wood to give the texture of the actual ship.
All metal items are newly designed. It is newly designed by etching and the bow and stern decoration delicately reproduces sharp edges.
Three original size drawings. A photograph of the manufacturing process is posted in the color manual. The ship's pedestal is newly designed and uses high quality wood rosewood.
*61 kinds of wooden parts and 81 kind of metallic parts use.
*Full sale model by whom even sail of assistance is reproduced.
*Time until completing: For about 200 hours.


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Candace Cameron Bure Inspired by Debates on 'The View' to Prove 'Kindness Isn't Weakness'
CP Current Page: Entertainment | Wednesday, April 25, 2018
By Jeannie Law, Christian Post Reporter | Wednesday, April 25, 2018
Cast member Candace Cameron Bure poses at the premiere for the Netflix television series "Fuller House" at The Grove in Los Angeles, California February 16, 2016. | (Photo: Reuters/Mario Anzuoni)
Actress Candace Cameron Bure, who's known in Hollywood for being an outspoken Christian, just released her new book, Kind Is the New Classy: The Power of Living Graciously, and admits her time as a co-host on "The View" is what inspired her to share this message.
"I wanted to put that spotlight back on kindness and say we can start with the small things and let's change the culture by changing ourselves — one person at a time — and let it have a ripple effect into your community," Bure said in a recent interview on "Fox & Friends."
Bure was the sole conservative Christian voice on "The View" for two seasons before leaving the show, which is taped in New York City, to focus more on her family and acting career, both of which are in California, in 2016. Although she had to constantly defend her beliefs and worldview, it was her time on the daytime talk show that inspired her new book.
"It starts with you and you have a choice to be positive, to be kind, to be respectful no matter what people you encounter," she explained.
"A lot of my time on 'The View' was an inspiration for this book. I really did enjoy being on the show, but it was tough at times. It's tough being the only voice at a table with one specific viewpoint," she added. "I always wanted to be respectful and kind at the end of the day, and I think that's what I heard from people the most, 'How do you stay calm when you feel like people are attacking you?' And so I thought, let me write down the tools that helped."
Watch the latest video at foxnews.com
"In Kind Is the New Classy, Candace reveals the thought patterns and practices that have empowered her to stay centered in who she is while practicing radical graciousness toward others," the book's description reads. "Whether you're navigating major life choices, questions of calling and career, relationships, or personal goals, this book will show you how to:
Keep your cool under pressure
Respond to criticism with grace
Stay grounded yet go places in life
Stay true to who you are despite the expectations of others
Stay centered in what ultimately matters the most
Bure went on to tell the co-hosts of "Fox & Friends" that people should never be reactive or act on emotions when in a heated discussion.
When asked if she feels like being kind will result in people taking advantage of her, the mother of three snapped back quickly and said, "absolutely not."
"Kindness is not weakness, only if you allow it to be. There was nothing weak about me at the table when I sat at 'The View.' I was strong and always kind," she asserted.
Follow Jeannie Law on Twitter: @jlawcp
Candace Cameron Bure Calls Joy Behar's Remarks 'Tone Deaf,' Claims Comedian Is 'Unbeliever'
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Candace Cameron Bure Talks Training Her Kids to Deal With Sexual Predators in Hollywood (Video)
Christian contestant breaks silence on sin and sex in Twitter spat with 'Bachelorette' star
Street preacher rebukes Kirk Franklin for failing to represent Christ at BET Awards; celebrities push back
Hollyn responds to critics claiming she's no longer a Christian artist after 'Sad Girl' EP
Dog the Bounty Hunter shares Bible verse he wants read before he dies in first interview since losing wife
'The Lion King': 'Simba actor Donald Glover reveals how remake differs from 1994 original


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• Progestin-only contraceptives are very effective in preventing pregnancy when used correctly and consistently.
• Women with contraindications to estrogen use may safely use POPs.
• Overall, menstrual blood loss is decreased, but bleeding may be unscheduled or irregular.
• Longer-acting formulations may increase perfect-use compliance by decreasing dosing frequency.
• The effect of DMPA on weight gain is variable.
• Although prolonged DMPA use may decrease bone mineral density, this is reversible after discontinuation, and it is not associated with future fractures or osteoporosis.
All progestin-only contraceptive methods act to thicken cervical mucus, thereby inhibiting sperm access to the upper genital tract. They also cause slowing of tubal motility and atrophy of the endometrium. Some methods act to suppress LH and FSH surges, inhibiting ovulation (Zieman et al., 2007).
The sole progestin-only pill (POP, often called the "minipill") currently available in the United States contains 35 mg of nor-ethindrone. This pill is taken daily, without a pill-free interval. The main contraceptive effect is through thickening of cervical mucus; ovulation suppression occurs in only 50% of cycles.
The POP is very effective in preventing pregnancy when used correctly and consistently. Benefits include decreased menstrual flow and dysmenorrhea, with rapid return to fertility. POPs may be used by women who desire an OC but have a contraindication to estrogen use.
Main contraceptive effect begins to wane after 22 hours and is gone by 27 hours, so women who are more than 3 hours late with dosing need to use backup contraception. Although overall blood loss is decreased, bleeding or spotting may be irregular or unscheduled. POPs offer no protection against STIs, although infections of the ascending genital tract may be decreased because of thickened cervical mucus (Zieman et al., 2007).
A crystalline suspension, DMPA is injected every 3 months either intramuscularly (150 mg) or subcutaneously (104 mg). Its main contraceptive effect is through ovulation suppression.
The convenient dosing schedule of DMPA can increase perfect-use compliance. Advantages include decreased menstrual flow, dysmenorrhea, and elimination of mittelschmerz. DMPA can improve pain from endometriosis and may be used by women with contraindications to estrogen use. It reduces the risk of endometrial and possibly ovarian cancer, as well as significantly decreasing sickle cell crises. DMPA can reduce the number of seizures in women with seizure disorders (Westhoff, 2003). No evidence indicates decreased efficacy in obese women (Goldberg and Grimes, 2007).
Although overall blood loss is decreased, bleeding and spotting may be irregular or unscheduled. The high amenorrhea rate (>50%) may concern some women. Return to fertility may be delayed (average, 10 months from last injection). Patients often express concerns about weight gain, which is actually quite variable, with no studies showing a consistent impact on weight (Westhoff, 2003) (Level A). Although osteoporosis may develop with prolonged DMPA use because of decreased estradiol levels, bone mineral density (BMD) appears to recover completely after discontinuation, and BMD changes during DMPA therapy have not been linked to fractures or postmenopausal osteoporosis (Goldberg and Grimes, 2007).
The only contraceptive implant currently available in the United States is Implanon, a single rod 4 cm long and 2 mm wide made of ethylene vinylacetate (EVA) co-polymer and containing 68 mg of time-release etonogestrel. It is placed subdermally by a health care provider and acts primarily by ovulation inhibition. Although FDA approved for 3 years' use, the implant has proven efficacy to at least 4 years (Isly and Edelman, 2007).
The contraceptive implant is one of the most effective forms of available contraception, with rapid return to fertility; most women ovulate within 4 weeks of removal (Isly and Edelman, 2007). Benefits include decreased menstrual flow, dysmenorrhea, and elimination of mittelschmerz. The implant can improve pain from endometriosis and may be used by women with contraindication to estrogen use. No effect on BMD is seen.
The woman must see a health care provider for implant initiation and discontinuation. Although overall blood loss is decreased, bleeding and spotting may be irregular or unscheduled. Amenorrhea may concern some women. The implant provides no protection against STIs and may be associated with headaches or increased acne.


JCPenney Spa is a complete service salon and spa that administers hair styling, treatments, manicures, pedicures and body waxing. Hair removal and thermal straigthening are also provided. JCPenney Day Spa, of course, is part of the actual parent company itself, JCPenney, which can be found in 49 states and Puerto Rico.
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Fresh and new, the Kaia Wedge Sandals by Jo Mercer are this season must-have piece! The chunky design is elevated by a two-toned platform with an eye-catching silhouette. Crafted from the softest leathers, inside and out, the flatform wedge will take you from day to night effortlessly.


13. The World Is Not Enough, Garbage. This is the best theme of the Pierce Brosnan movie set, if not the best movie. It has that sweeping feel that works well with Bond themes, and I think fits nicely into the "gritty female singer" set of Bond songs, as opposed to the "sultry female singer" set (14 of the 22 songs on the list are sung by female vocalists, which is just under 2/3 of them). In the collection of Bond songs, this one is middle-of-the-road, perfectly serviceable, nothing terribly remarkable.
In some ways, Hashi reflected, military victories might be deemed superior to economic victories. There were conflicts, a playing surface, the outcome in doubt until the enemy surrenders. Rarely does the war endure beyond the decisive battle; both sides are too eager to end the loss of life, the crippling expense that no longer matters. In corporate warfare, like this deal for the exclusive trading rights with the Lopita system, the outcome was often settled weeks or even months before the final contracts were put to paper, and yet his enemies had continued to fight and even now no doubt contemplated the many ways they might attempt to ruin his triumph.
If only such measures carried a stricter price, he thought. If perhaps a negotiator who failed lost his tongue, or if an executive whose initiative failed were publicly executed…well, he reflected, in that case he would be long dead himself. Nobody's life was an unbroken string of successes, and if failure carried such consequences, one would never learn the lessons that would enable future success. Had he not, for example, failed in his previous marriage, he would never have known what was truly worth fighting for.
When the light caught her curves, her nacreous shell shimmered, and though she didn't have eyes, technically, he knew she enjoyed his presence as much as he did hers. They would have another world to explore together, and if that grew boring, well, he could always find another.
So yeah. Not much has happened on the blog or writing front this month, and people at FogCon, if I was perhaps not up to being as social as I would have liked, I hope you understand. But I am back (more or less) and I have some story sale news that I hope to be able to announce soon. I also hope to get the pictures from India up somewhere on a gallery where people can see them because seriously, people. Wow.
Next up on the Bond songs is "The World Is Not Enough." I'm looking forward to it.


Jolla may be just about done selling its own smartphones, but earlier this year the company announced a partnership with India phone maker Intex to launch a new phone powered by Jolla's Sailfish operating system.
Now the Intex Aqua Fish is available for purchase in India for Rs 5,499, or about $82.
That makes the phone one of the first Sailfish OS devices available from a company other than Jolla… although it may not the be last. A Russian phone called the Oysters SF is also on the way, and this month the privacy and security-focused Turing Phone with Sailfish OS began shipping to folks who placed pre-orders.
The Intex Aqua Fish is a phone with a budget price tag and budget specs. It features a 5 inch, 1280 x 720 pixel IPS display, a Qualcomm Snapdragon 212 processor, 2GB of RAM, 16GB of storage, a microSD card slot, an 8MP rear camera, 2MP front camera, and a 2,500 mAh battery.
The phone supports 4G LTE, features dual SIM card slots, and has 802.11b/g/n WiFi, Bluetooth 4.0, GPS, and FM Radio capabilities.
It's the operating system that's the phone's most distinctive feature.
Sailfish OS is a Linux-based operating system with a unique user interface, support for gesture-based navigation, and the ability to run some Android apps as well as native Sailfish apps.
While Jolla hasn't had a lot of success selling its own phones with the software, it's likely that consumer response to phones like the Intex Aqua Fish and Turing Phone will determine whether the operating system has a future as an alternative to Android and iOS (and less popular options including Windows 10 Mobile, Tizen, BlackBerry OS, Ubuntu phone, for that matter).


text:
Walt Wilmot
Born: (1865-10-18)October 18, 1865
Plover, Wisconsin
Died: February 1, 1929(1929-02-01) (aged 65)
Batted: Switch Threw: Right
April 20, 1888, for the Washington Nationals
June 14, 1898, for the New York Giants
Runs batted in
Washington Nationals (1888–1889)
Chicago Colts (1890–1895)
NL home run leader (1890)
Walter Robert Wilmot (October 18, 1863 – February 1, 1929) was a professional baseball player. He played all or part of 10 seasons in Major League Baseball with the Washington Nationals (1888–89), Chicago Colts (1890-95) and New York Giants (1897–98), primarily as an outfielder. Listed at 5 ft 9 in, 165 lb., Wilmot was a switch-hitter and threw right-handed. He was born in Plover, Wisconsin.
While playing for the Nationals in 1889, Wilmot led the league with 19 triples and 139 games played. The following season, he tied with Oyster Burns and Mike Tiernan for the National League lead in home runs with 13, also a career-high. He also set a career best with 76 stolen bases while driving in 99 runs in 1890. On August 22, 1891, he became the first player in major league history to be walked 6 times in 1 game.[1]
Wilmot from 1901 newspaper story
Wilmot's most productive season came in 1894, when he posted career-highs in batting average (.330), runs scored (134), hits (197), RBI (130), doubles (45) and extra-base hits (62) in 133 games.
Overall in his ten-season career, Wilmot was a .276 hitter with 58 home runs and 594 RBI in 962 games, including 727 runs, 152 doubles, 92 triples, 381 stolen bases and a .337 on-base percentage.
Wilmot died in Chicago, at the age of 65.
List of Major League Baseball annual home run leaders
List of Major League Baseball annual triples leaders
List of Major League Baseball career stolen bases leaders
^ Solomon, Abbot Neil, "Baseball Records Illustrated", Quintet Publishing, London, 1988
National League season home run leaders
1876: Hall
1877: Pike
1878: Hines
1879: C. Jones
1880: Stovey & O'Rourke
1881: Brouthers
1882: Wood
1883: Ewing
1884: Williamson
1885: Dalrymple
1886: Brouthers & Richardson
1887: O'Brien
1888: Ryan
1889: Thompson
1890: Burns, Tiernan & Wilmot
1891: Tiernan & Stovey
1892: Holliday
1893: Delahanty
1894: Duffy
1896: Joyce & Delahanty
1898: J. Collins
1899: Freeman
1900: Long
1901: Crawford
1902: Leach
1903: Sheckard
1904: Lumley
1905: Odwell
1906: Jordan
1907: Brain
1909: Murray
1910: Schulte & Beck
1911: Schulte
1912: Zimmerman
1913: Cravath
1916: C. Williams & Robertson
1917: Cravath & Robertson
1920: C. Williams
1921: Kelly
1922: Hornsby
1924: Fournier
1926: Wilson
1927: C. Williams & Wilson
1928: Wilson & Bottomley
1929: Klein
1932: Klein & Ott
1934: Ott & R. Collins
1935: Berger
1936: Ott
1937: Ott & Medwick
1939: Mize
1941: Camilli
1943: Nicholson
1945: Holmes
1946: Kiner
1947: Kiner & Mize
1952: Kiner & Sauer
1953: Mathews
1954: Kluszewski
1955: Mays
1956: Snider
1957: Aaron
1958: Banks
1961: Cepeda
1963: McCovey & Aaron
1968: McCovey
1970: Bench
1971: Stargell
1974: Schmidt
1977: Foster
1979: Kingman
1984: Schmidt & Murphy
1985: Murphy
1987: Dawson
1988: Strawberry
1989: Mitchell
1990: Sandberg
1991: Johnson
1992: McGriff
1993: Bonds
1994: M. Williams
1995: Bichette
1996: Galarraga
1997: Walker
1998: McGwire
2000: Sosa
2003: Thome
2004: Beltré
2005: A. Jones
2006: Howard
2007: Fielder
2009: Pujols
2011: Kemp
2012: Braun
2013: Álvarez & Goldschmidt
2014: Stanton
2015: Arenado & Harper
2016: Arenado & Carter
2018: Arenado
Retrieved from "https://en.wikipedia.org/w/index.php?title=Walt_Wilmot&oldid=906303925"
Major League Baseball left fielders
Washington Nationals (1886–89) players
Chicago Colts players
New York Giants (NL) players
National League home run champions
St. Paul Freezers players
St. Paul Saints (Northwestern League) players
Minneapolis Millers (baseball) players
Grand Rapids Furniture Makers players
Butte Miners players
Butte Fruit Pickers players
Minor league baseball managers
Minneapolis Millers (baseball) managers
Baseball players from Wisconsin
19th-century baseball players
People from Plover, Wisconsin


text:
"""
This tutorial introduces the multilayer perceptron using Theano.

 A multilayer perceptron is a logistic regressor where
instead of feeding the input to the logistic regression you insert a
intermediate layer, called the hidden layer, that has a nonlinear
activation function (usually tanh or sigmoid) . One can use many such
hidden layers making the architecture deep. The tutorial will also tackle
the problem of MNIST digit classification.

.. math::

    f(x) = G( b^{(2)} + W^{(2)}( s( b^{(1)} + W^{(1)} x))),

References:

    - textbooks: "Pattern Recognition and Machine Learning" -
                 Christopher M. Bishop, section 5

"""
__docformat__ = 'restructedtext en'

import os
import sys
import timeit

import numpy

import theano
import theano.tensor as T

import cPickle
import random

from logistic_sgd import LogisticRegression, load_data

# start-snippet-1
class HiddenLayer(object):
    def __init__(self, rng, input, n_in, n_out, W=None, b=None,
                 activation=T.tanh):
        """
        Typical hidden layer of a MLP: units are fully-connected and have
        sigmoidal activation function. Weight matrix W is of shape (n_in,n_out)
        and the bias vector b is of shape (n_out,).

        NOTE : The nonlinearity used here is tanh

        Hidden unit activation is given by: tanh(dot(input,W) + b)

        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights

        :type input: theano.tensor.dmatrix
        :param input: a symbolic tensor of shape (n_examples, n_in)

        :type n_in: int
        :param n_in: dimensionality of input

        :type n_out: int
        :param n_out: number of hidden units

        :type activation: theano.Op or function
        :param activation: Non linearity to be applied in the hidden
                           layer
        """
        self.input = input
        # end-snippet-1

        # `W` is initialized with `W_values` which is uniformely sampled
        # from sqrt(-6./(n_in+n_hidden)) and sqrt(6./(n_in+n_hidden))
        # for tanh activation function
        # the output of uniform if converted using asarray to dtype
        # theano.config.floatX so that the code is runable on GPU
        # Note : optimal initialization of weights is dependent on the
        #        activation function used (among other things).
        #        For example, results presented in [Xavier10] suggest that you
        #        should use 4 times larger initial weights for sigmoid
        #        compared to tanh
        #        We have no info for other function, so we use the same as
        #        tanh.
        if W is None:
            W_values = numpy.asarray(
                rng.uniform(
                    low=-numpy.sqrt(6. / (n_in + n_out)),
                    high=numpy.sqrt(6. / (n_in + n_out)),
                    size=(n_in, n_out)
                ),
                dtype=theano.config.floatX
            )
            if activation == theano.tensor.nnet.sigmoid:
                W_values *= 4

            W = theano.shared(value=W_values, name='W', borrow=True)

        if b is None:
            b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)
            b = theano.shared(value=b_values, name='b', borrow=True)

        self.W = W
        self.b = b

        lin_output = T.dot(input, self.W) + self.b
        self.output = (
            lin_output if activation is None
            else activation(lin_output)
        )
        # parameters of the model
        self.params = [self.W, self.b]

# start-snippet-2
class MLP(object):
    """Multi-Layer Perceptron Class

    A multilayer perceptron is a feedforward artificial neural network model
    that has one layer or more of hidden units and nonlinear activations.
    Intermediate layers usually have as activation function tanh or the
    sigmoid function (defined here by a ``HiddenLayer`` class)  while the
    top layer is a softmax layer (defined here by a ``LogisticRegression``
    class).
    """

    def __init__(self, rng, input, n_in, n_hidden, n_out):
        """Initialize the parameters for the multilayer perceptron

        :type rng: numpy.random.RandomState
        :param rng: a random number generator used to initialize weights

        :type input: theano.tensor.TensorType
        :param input: symbolic variable that describes the input of the
        architecture (one minibatch)

        :type n_in: int
        :param n_in: number of input units, the dimension of the space in
        which the datapoints lie

        :type n_hidden: int
        :param n_hidden: number of hidden units

        :type n_out: int
        :param n_out: number of output units, the dimension of the space in
        which the labels lie

        """

        # Since we are dealing with a one hidden layer MLP, this will translate
        # into a HiddenLayer with a tanh activation function connected to the
        # LogisticRegression layer; the activation function can be replaced by
        # sigmoid or any other nonlinear function
        self.hiddenLayer = HiddenLayer(
            rng=rng,
            input=input,
            n_in=n_in,
            n_out=n_hidden,
            activation=T.tanh
        )

        # The logistic regression layer gets as input the hidden units
        # of the hidden layer
        self.logRegressionLayer = LogisticRegression(
            input=self.hiddenLayer.output,
            n_in=n_hidden,
            n_out=n_out
        )
        # end-snippet-2 start-snippet-3
        # L1 norm ; one regularization option is to enforce L1 norm to
        # be small
        self.L1 = (
            abs(self.hiddenLayer.W).sum()
            + abs(self.logRegressionLayer.W).sum()
        )

        # square of L2 norm ; one regularization option is to enforce
        # square of L2 norm to be small
        self.L2_sqr = (
            (self.hiddenLayer.W ** 2).sum()
            + (self.logRegressionLayer.W ** 2).sum()
        )

        # negative log likelihood of the MLP is given by the negative
        # log likelihood of the output of the model, computed in the
        # logistic regression layer
        self.negative_log_likelihood = (
            self.logRegressionLayer.negative_log_likelihood
        )
        # same holds for the function computing the number of errors
        self.errors = self.logRegressionLayer.errors

        # the parameters of the model are the parameters of the two layer it is
        # made out of
        self.params = self.hiddenLayer.params + self.logRegressionLayer.params
        # end-snippet-3

        # keep track of model input
        self.input = input

    def predict(self,x):

        self.input = x

        x = T.matrix('x')

        hl1 = theano.function(
            inputs=[x],
            outputs=T.tanh(T.dot(x, self.hiddenLayer.W) + self.hiddenLayer.b)
        )

        hl1_output = hl1(self.input)

        lr1_output = theano.function(
            inputs=[x],
            outputs=T.nnet.softmax(T.dot(x, self.logRegressionLayer.W) + self.logRegressionLayer.b)
        )

        p_y_given_x = lr1_output(hl1_output)

        y_pred = theano.function(
            inputs = [x],
            outputs = T.argmax(x, axis=1)
        )

        predictions = y_pred(p_y_given_x)

        return predictions

def get_learned_functions():

    rng = numpy.random.RandomState(1234)
    x = T.matrix('x')

    pkl_file = open( '../data/f1_and_params.pkl', 'rb')
    params = cPickle.load(pkl_file)

    f1_and = MLP(
        rng=rng,
        input=x,
        n_in=2,
        n_hidden=6,
        n_out=2,
    )

    #set up parameters
    f1_and.hiddenLayer.W = params[0]
    f1_and.hiddenLayer.b = params[1]
    f1_and.logRegressionLayer.W = params[2]
    f1_and.logRegressionLayer.b = params[3]

    pkl_file = open( '../data/f1_or_params.pkl', 'rb')
    params = cPickle.load(pkl_file)

    f1_or = MLP(
        rng=rng,
        input=x,
        n_in=2,
        n_hidden=6,
        n_out=2,
    )

    #set up parameters
    f1_or.hiddenLayer.W = params[0]
    f1_or.hiddenLayer.b = params[1]
    f1_or.logRegressionLayer.W = params[2]
    f1_or.logRegressionLayer.b = params[3]
    return f1_and,f1_or

def test_mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=200,
             dataset='mnist.pkl.gz', batch_size=2, n_hidden=6):
    """
    Demonstrate stochastic gradient descent optimization for a multilayer
    perceptron

    This is demonstrated on MNIST.

    :type learning_rate: float
    :param learning_rate: learning rate used (factor for the stochastic
    gradient

    :type L1_reg: float
    :param L1_reg: L1-norm's weight when added to the cost (see
    regularization)

    :type L2_reg: float
    :param L2_reg: L2-norm's weight when added to the cost (see
    regularization)

    :type n_epochs: int
    :param n_epochs: maximal number of epochs to run the optimizer

    :type dataset: string
    :param dataset: the path of the MNIST dataset file from
                 http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz

   """

    f1_and,f1_or = get_learned_functions()

    #datasets = load_data(dataset)

    # test_set_x, test_set_y = datasets[2]
    # print test_set_x.get_value(borrow=True).shape[0]
    # print test_set_y
    datasets = [0,1,2]

    # Go out or not
    X =[
        [0,0,0,0],
        [0,0,0,1],
        [0,0,1,0],
        [0,0,1,1],
        [0,1,0,0],
        [0,1,0,1],
        [0,1,1,0],
        [0,1,1,1],
        [1,0,0,0],
        [1,0,0,1],
        [1,0,1,0],
        [1,0,1,1],
        [1,1,0,0],
        [1,1,0,1],
        [1,1,1,1],
    ]
    # X_reduced = []
    # for x in X:
    #     y_l = f1_or.predict([x[0:2]])[0]
    #     y_r = f1_and.predict([x[2:4]])[0]
    #     X_reduced.append([y_l,y_r])

    # X = X_reduced

    Y = [
        1,
        1,
        1,
        1,
        0,
        0,
        1,
        1,
        0,
        0,
        1,
        1,
        0,
        0,
        0
    ]

    # X = X * 10
    # Y = Y * 10

    #OR
    # X = [
    #     [0,0],
    #     [0,1],
    #     [1,0],
    #     [1,1],
    #     [0,0],
    #     [0,1],
    #     [1,0],
    #     [1,1],
    #     [0,0],
    #     [0,1],
    #     [1,0],
    #     [1,1],
    #     [0,0],
    #     [0,1],
    #     [1,0],
    #     [1,1],
    #     [0,0],
    #     [0,1],
    #     [1,0],
    #     [1,1],
    # ]

    # Y = [
    #     0,
    #     1,
    #     1,
    #     1,
    #     0,
    #     1,
    #     1,
    #     1,
    #     0,
    #     1,
    #     1,
    #     1,
    #     0,
    #     1,
    #     1,
    #     1,
    #     0,
    #     1,
    #     1,
    #     1
    # ]

    #AND
    # X = [
    #     [0,0],
    #     [0,1],
    #     [1,0],
    #     [1,1],
    #     [0,0],
    #     [0,1],
    #     [1,0],
    #     [1,1],
    #     [0,0],
    #     [0,1],
    #     [1,0],
    #     [1,1],
    #     [0,0],
    #     [0,1],
    #     [1,0],
    #     [1,1],
    #     [0,0],
    #     [0,1],
    #     [1,0],
    #     [1,1],
    #     [0,0],
    #     [0,1],
    #     [1,0],
    #     [1,1],

    # ]

    # Y = [
    #     0,
    #     0,
    #     0,
    #     1,
    #     0,
    #     0,
    #     0,
    #     1,
    #     0,
    #     0,
    #     0,
    #     1,
    #     0,
    #     0,
    #     0,
    #     1,
    #     0,
    #     0,
    #     0,
    #     1,
    #     0,
    #     0,
    #     0,
    #     1,
    # ]

    combined = zip(X, Y)
    random.shuffle(combined)

    X[:], Y[:] = zip(*combined)
    # print X
    # print Y
    X = numpy.asarray(X, dtype=theano.config.floatX)

    train_length = int(round(len(X) * 0.60))
    valid_length = int(round(len(X) * 0.20))
    test_length = int(round(len(X) * 0.20))

    X_train = X[0:train_length]
    X_valid = X[train_length: (train_length + valid_length)]
    X_test = X[-test_length:]

    Y_train = Y[0:train_length]
    Y_valid = Y[train_length:(train_length + valid_length)]
    Y_test = Y[-test_length:]

    train_set = [X_train,Y_train]
    valid_set = [X_valid,Y_valid]
    test_set = [X_test,Y_test]

    train_set_x, train_set_y = train_set
    valid_set_x, valid_set_y = valid_set
    test_set_x, test_set_y = test_set

    borrow = True
    train_set_x = theano.shared(numpy.asarray(train_set_x,dtype=theano.config.floatX),borrow=borrow)
    train_set_y = theano.shared(numpy.asarray(train_set_y,dtype=theano.config.floatX), borrow=borrow)

    train_set_y = T.cast(train_set_y, 'int32')

    valid_set_x = theano.shared(numpy.asarray(valid_set_x,dtype=theano.config.floatX),borrow=borrow)
    valid_set_y = theano.shared(numpy.asarray(valid_set_y,dtype=theano.config.floatX),borrow=borrow)

    valid_set_y = T.cast(valid_set_y, 'int32')

    test_set_x = theano.shared(numpy.asarray(test_set_x,dtype=theano.config.floatX),borrow=borrow)
    test_set_y = theano.shared(numpy.asarray(test_set_y,dtype=theano.config.floatX),borrow=borrow)

    test_set_y = T.cast(test_set_y, 'int32')

    # compute number of minibatches for training, validation and testing
    n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
    n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size
    n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size

    ######################
    # BUILD ACTUAL MODEL #
    ######################
    print '... building the model'

    # allocate symbolic variables for the data
    index = T.lscalar()  # index to a [mini]batch
    x = T.matrix('x')  # the data is presented as rasterized images
    y = T.ivector('y')  # the labels are presented as 1D vector of
                        # [int] labels

    rng = numpy.random.RandomState(1234)

    # construct the MLP class
    classifier = MLP(
        rng=rng,
        input=x,
        n_in=2,
        n_hidden=n_hidden,
        n_out=2
    )

    # start-snippet-4
    # the cost we minimize during training is the negative log likelihood of
    # the model plus the regularization terms (L1 and L2); cost is expressed
    # here symbolically
    cost = (
        classifier.negative_log_likelihood(y)
        + L1_reg * classifier.L1
        + L2_reg * classifier.L2_sqr
    )
    # end-snippet-4

    # compiling a Theano function that computes the mistakes that are made
    # by the model on a minibatch
    test_model = theano.function(
        inputs=[index],
        outputs=classifier.errors(y),
        givens={
            x: test_set_x[index * batch_size:(index + 1) * batch_size],
            y: test_set_y[index * batch_size:(index + 1) * batch_size]
        }
    )

    validate_model = theano.function(
        inputs=[index],
        outputs=classifier.errors(y),
        givens={
            x: valid_set_x[index * batch_size:(index + 1) * batch_size],
            y: valid_set_y[index * batch_size:(index + 1) * batch_size]
        }
    )

    # start-snippet-5
    # compute the gradient of cost with respect to theta (sotred in params)
    # the resulting gradients will be stored in a list gparams
    gparams = [T.grad(cost, param) for param in classifier.params]

    # specify how to update the parameters of the model as a list of
    # (variable, update expression) pairs

    # given two lists of the same length, A = [a1, a2, a3, a4] and
    # B = [b1, b2, b3, b4], zip generates a list C of same size, where each
    # element is a pair formed from the two lists :
    #    C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]
    updates = [
        (param, param - learning_rate * gparam)
        for param, gparam in zip(classifier.params, gparams)
    ]

    # compiling a Theano function `train_model` that returns the cost, but
    # in the same time updates the parameter of the model based on the rules
    # defined in `updates`
    train_model = theano.function(
        inputs=[index],
        outputs=cost,
        updates=updates,
        givens={
            x: train_set_x[index * batch_size: (index + 1) * batch_size],
            y: train_set_y[index * batch_size: (index + 1) * batch_size]
        }
    )
    # end-snippet-5

    ###############
    # TRAIN MODEL #
    ###############
    print '... training'

    # early-stopping parameters
    patience = 10000  # look as this many examples regardless
    patience_increase = 2  # wait this much longer when a new best is
                           # found
    improvement_threshold = 0.995  # a relative improvement of this much is
                                   # considered significant
    validation_frequency = min(n_train_batches, patience / 2)
                                  # go through this many
                                  # minibatche before checking the network
                                  # on the validation set; in this case we
                                  # check every epoch

    best_validation_loss = numpy.inf
    best_iter = 0
    test_score = 0.
    start_time = timeit.default_timer()

    epoch = 0
    done_looping = False

    while (epoch < n_epochs) and (not done_looping):
        epoch = epoch + 1
        for minibatch_index in xrange(n_train_batches):

            # update train set X train_set_x[index * batch_size: (index + 1) * batch_size],
            #get current batch
            x_minibatch = train_set_x.get_value()
            y_minibatch = train_set_x.get_value()

            x_minibatch = x_minibatch[minibatch_index * batch_size: (minibatch_index + 1) * batch_size]
            y_minibatch = y_minibatch[minibatch_index * batch_size: (minibatch_index + 1) * batch_size][0]

            X_reduced = []
            Y_reduced = []

            index = 0
            for x in x_minibatch:

                print "x"
                print x

                y_l = f1_or.predict([x[0:2]])[0]
                y_r = f1_or.predict([x[2:4]])[0]

                X_reduced.append([y_l,y_r])
                Y_reduced.append(y_minibatch[index])

                y_l = f1_or.predict([x[0:2]])[0]
                y_r = f1_and.predict([x[2:4]])[0]

                X_reduced.append([y_l,y_r])
                Y_reduced.append(y_minibatch[index])

                y_l = f1_and.predict([x[0:2]])[0]
                y_r = f1_or.predict([x[2:4]])[0]

                X_reduced.append([y_l,y_r])
                Y_reduced.append(y_minibatch[index])

                y_l = f1_and.predict([x[0:2]])[0]
                y_r = f1_and.predict([x[2:4]])[0]

                X_reduced.append([y_l,y_r])
                Y_reduced.append(y_minibatch[index])

                print "current prediction"
                #print classifier.predict([X_reduced[0]])
                print classifier.predict(X_reduced)

                print "X_reduced"
                print X_reduced

                print "Y_reduced"
                print Y_reduced
                # calculate best option
                index += 1
                break

            #print x_minibatch

            minibatch_avg_cost = train_model(minibatch_index)
            # iteration number
            iter = (epoch - 1) * n_train_batches + minibatch_index

            if (iter + 1) % validation_frequency == 0:
                # compute zero-one loss on validation set
                validation_losses = [validate_model(i) for i
                                     in xrange(n_valid_batches)]
                this_validation_loss = numpy.mean(validation_losses)

                print(
                    'epoch %i, minibatch %i/%i, validation error %f %%' %
                    (
                        epoch,
                        minibatch_index + 1,
                        n_train_batches,
                        this_validation_loss * 100.
                    )
                )

                # if we got the best validation score until now
                if this_validation_loss < best_validation_loss:
                    #improve patience if loss improvement is good enough
                    if (
                        this_validation_loss < best_validation_loss *
                        improvement_threshold
                    ):
                        patience = max(patience, iter * patience_increase)

                    best_validation_loss = this_validation_loss
                    best_iter = iter

                    # test it on the test set
                    test_losses = [test_model(i) for i
                                   in xrange(n_test_batches)]
                    test_score = numpy.mean(test_losses)

                    print(('     epoch %i, minibatch %i/%i, test error of '
                           'best model %f %%') %
                          (epoch, minibatch_index + 1, n_train_batches,
                           test_score * 100.))

            if patience <= iter:
                done_looping = True
                break

    end_time = timeit.default_timer()
    print(('Optimization complete. Best validation score of %f %% '
           'obtained at iteration %i, with test performance %f %%') %
          (best_validation_loss * 100., best_iter + 1, test_score * 100.))
    print >> sys.stderr, ('The code for file ' +
                          os.path.split(__file__)[1] +
                          ' ran for %.2fm' % ((end_time - start_time) / 60.))

"""    #classifier.input = datasets[0][0]
    #print X[0]
    #
    #classifier.input = X[0:4]

    #print classifier.predict(X[0:4])

    # minimum hidden units to learn or is 2 and to learn AND is 6
    params = classifier.params

    # save or retrieve saved params
    output = open('../data/f1_and_params.pkl', 'wb')
    cPickle.dump(params, output,protocol=-1)
    output.close()

    pkl_file = open( '../data/f1_and_params.pkl', 'rb')
    params = cPickle.load(pkl_file)

    f1_or = MLP(
        rng=rng,
        input=x,
        n_in=2,
        n_hidden=n_hidden,
        n_out=2,
    )

    #set up parameters
    f1_or.hiddenLayer.W = params[0]
    f1_or.hiddenLayer.b = params[1]
    f1_or.logRegressionLayer.W = params[2]
    f1_or.logRegressionLayer.b = params[3]

    #print f1_or.predict(datasets[0][0])
    print X[0:2]
    print f1_or.predict(X[0:2])"""

    # print numpy.tanh(numpy.dot(classifier.input,params[0].get_value()) + params[1].get_value())
    # print "----------------------------"

    #print classifier_or.hiddenLayer.W.get_value()
    #print theano.printing.pydotprint(predict_or,'../data/predict_or_graph.jpg')
    #print theano.printing.debugprint(predict_or)

if __name__ == '__main__':
    test_mlp()

redpajama_set_name: RedPajamaGithub


Okidata MC362w and MC562w Service Manual (manmc362) at PrinterMalls. Okidata MC362w and MC562w Service Manual (manmc362) at PrinterMalls. Oki Dot Matrix Label Printers POS Printers. Consumables Image Drums Toner Cartridges Transfer Units Select Model More Info Office Manuals and free pdf instructions.
Find the office and computer equipment manual you need at ManualsOnline. The MC562w has been discontinued. Find the support you need for your discontinued OKI Product. View and Download Oki MB472dn user manual online. MB472dn All in One Printer pdf manual download. Also for: Mb472dnw. On this page you find the OKI MC562w manual. Please read the instructions in this operator manual carefully before using the product.
If you have any questions about your Printer that are not answered in the manual, please share your question in the troubleshooting section on the buttom of this page. To get a Manual, Driver or access our Knowledgebase, select your OKI product from the list below User Manual.


Patient privacy is important to us. BloodSTAR has been designed from the ground-up to implement best practice privacy controls and is fully compliant with the Privacy Act (Cwlth).
An analysis of the privacy issues related to the Immunoglobulin Governance National Policy and the supporting online system BloodSTAR was performed prior to implementation, and the privacy impact assessment report, approved by the National Blood Authority (NBA) General Manager provides the detail on governance, management, security and technical governance measures in place for the immunoglobulin governance program and, in particular, the immunoglobulin system development to support it.
The Privacy Statement and Notice explains how the National Blood Authority manages personal and sensitive information it collects about patients and has been developed to support the consent process. The consent form has been designed to allow the patient to voluntarily provide express consent to the collection and storage of their personal and sensitive health information in BloodSTAR and to the usual uses and disclosures as set out in the Privacy Statement and Notice.


text:
The Real Story Of The Dodge Brothers
Pamela Sosnowski
Of all American car companies, I believe Dodge has had the best advertising campaigns. In 2014, in honor of the company's 100th anniversary, they brought their brand's founders — brothers Horace and John Dodge — back to life in a series of commercials that do a nice job paying homage to the auto manufacturer's heritage. The quality of Dodge's cars and customer satisfaction was on the upswing and their models such as the Charger, Challenger, and Dart were helping to preserve the glory days of 1960s and 1970s driving.
Photo By Alden Jewell
They look like fun, these two natty young men who look like they stepped right out of Downton Abbey. But the commercials, of course, can't tell the entire story of the Dodge Brothers, and they may be fudging their reputation and legacy, just a bit.
For starters, both brothers died while in their 50s in the year 1920 — a mere five years after their company started producing complete automobiles. So when you see Horace and John burning rubber and racing each other in classic Dodge vehicles ranging from their very first model up to the present day, obviously that never happened (and the actors chosen to play them in the advertising campaign are younger than the brothers were in 1915). They also were the bad boys of Motor City at the time and developed quite a unfavorable name for themselves – drinking and starting bar room brawls.
Their crude manner actually got so bad, Detroit society eventually shunned them even though they were among the city's wealthiest residents.
John Francis Dodge was born in 1864; his brother, Horace, was born four years later. The two "red-headed urchins" were inseparable both as kids and adults. Biographies about the brothers claim that they dressed exactly alike, well into adulthood, and refused to respond to mail unless it was addressed to both of them. Their father, a boat engine mechanic, fostered the boys' love of transportation and mechanical engineering. John was good at selling and marketing while Horace was a mechanic and gifted inventor. Horace invented the first dirt-proof ball bearing, and he and John ran a bicycle manufacturing business in the late 1800s.
But as the country entered the 20th century, the attention was turning away from bicycles to automobiles. Detroit — which was starting to boom with the likes of Henry Ford — needed auto part suppliers. Seizing the opportunity, the Dodges sold their bike business in 1901 (which was failing anyway due to a lack of royalties from a third party and botched business deal) and set up their own auto part shop in Detroit.
By the following year they were building transmissions for the Olds Motor Vehicle Company. In 1903, they turned down the chance to renew their contract with Olds to instead make chassis and engines for the Ford Motor Company. The deal also included ownership of Ford stock. By 1910, their factory had moved into a larger space in Hamtramck, Michigan, and John Dodge had become the vice president of the Ford company.
Despite their hell-raising reputation during off hours, the brothers treated their employees extremely well. Their plant provided perks that sound like a foreshadowing of Google; their workers had access to a fully staffed on-site medical clinic, and lunch consisted of sandwiches and huge pitchers of beer, paid for by the company. There was also a machine shop called "The Playpen" where the men could tinker with or fix things after their shift.
For ten years, the brothers' company was the main supplier for Ford. John and Horace, however, weren't content to just make car parts; by 1913 they were dreaming of creating their own automobile. Much to Henry Ford's distaste, they terminated their contract with Ford which put their stock earnings in jeopardy when Ford decided to stop paying dividends. They sued Ford, and were awarded $25 million when the court ordered the company to pay special dividends to its shareholders.
Henry Ford had another reason to be disgruntled with the brothers; their first Dodge car was meant to directly compete with Ford's popular Model T. The Dodge Model 30 put out a whopping 35 horsepower compared to the T's 20, and also boasted several innovative standard features such as all-steel body construction, a sliding-gear transmission, and a 12-volt electrical system. Heading into 1916, Dodge Brothers Cars were number two in sales behind Ford.
Dodge Brothers was also making military and medical vehicles, some of which were used during the Pancho Villa Expedition in Mexico.
Both brothers were heavy drinkers. In 1911, John Dodge and a friend beat up a handicapped attorney at a bar in Detroit. Previous to this incident, the brothers were just dismissed as troublemakers, but after this particular brawl made the papers any affection for them was lost and friends and acquaintances stopped inviting them to social functions. Both brothers married and built exclusive homes in one of Detroit's toniest neighborhoods.
By 1919, the Dodge Brothers Company was manufacturing 500 cars daily and still couldn't keep up with customer demand. The following year, however, Horace Dodge got sick. The rumors at the time said he had consumed bad liquor, but his physician said he contracted Spanish flu, which would kill over 500,000 Americans during the epidemic. John Dodge, who slept outside his brother's bedroom while he was ill, soon caught the same influenza. He passed away at age 55 on January 14, 1920. Horace recovered but by many accounts he was a broken man from the loss of the older brother he was so close to. He died at age 52 from cirrhosis of the liver on December 10, 1920.
After the brothers' deaths, the management of their company went to their widows, and then through a few more hands before becoming known simply as Dodge, producing classic models by the mid 20th century such as the Coronet, Charger, Challenger, and more. We'll never know what might have been had the brothers lived a couple of more decades — but no doubt they'd be tickled by their company's advertising campaign…as well as their company's legacy.
Here's a compilation of the Dodge commercials featuring the brothers.
Penny Foolish, Pound Wise: Why Do We Still Use Pennies?
The Biggest Business Blunders
Pamela Sosnowski is a Boston area freelance writer and blogger who blames the Beatles for sparking her fascination with all things nostalgic, particularly from the mid 20th century. When she's not writing, she enjoys exploring the New England area, drawing, and doting on her three spoiled cats. View her writing portfolio at pamelasosnowski.com.
Phrases That Have Gone To Die: Here Are Some Things You Don't Hear Anymore
How Engineering Has Truly Shaped All Aspects Of My Life
Sex Throughout The Ages: From School Days To E.D.
Christmas Miracle – SNL
Let's Talk About Sex: The Reality of the Sexual Pleasure Disparity
Heroic Police Dog Moves The Judges To Tears
Adopt a Career Growth Mindset and Never Think of Yourself as "Unemployed"
Design Your Future Career: Helpful Tips For Men And Women Over 50
Working From Home? Here Is How To Set Yourself Up For Success Part 2
Important Job Search Tip: Stand Up For What You Stand For


Your Photos app has tons of cache files hidden in its library. With CleanMyMac X's Photo Junk, you clean them all up without losing a single photo — in just two clicks.
How does the Photo Junk module help me?
Photo Junk cleans up all the useless files left over by Photos. It's 100% safe to use because it doesn't remove any actual photos. The module scans your Photos library to locate useless cache files and heavy RAWs that take up space.
Is there really that much cache in Photos?
Photos creates cache files almost every time you work with it. Here's an example. If you have iCloud Photo Library enabled, all your pics are stored in the cloud. But when you view these pics on your Mac, Photos creates locally cached copies of them. So, even if you keep all photos in iCloud, their local copies are still wasting space on your Mac.
More cache files pile up when Photos detects faces on your pics — that is, every time you add photos of people. If you import new pics often, you've probably got a lot to clean up.
RAW files take up gigabytes.
When you import RAW files into Photos, the app creates JPEG copies of them. These JPEGs are what you see in Photos — and heavy RAWs just sit on your Mac, forgotten. If you no longer need the RAW originals, CleanMyMac will remove them to save you gigabytes of space.* But don't worry: it will always ask you first.
This feature is not yet available on macOS High Sierra.
What if I'm still using iPhoto?
Just like the Photos app, iPhoto has tons of junk hidden in its library. It's a whole different kind of junk, and CleanMyMac cleans it up, too. Photo Junk searches your iPhoto library to locate useless and hidden copies of your pics, kept by iPhoto after you rotate, crop, or otherwise edit them.


text:
En/football/real-madrid
With Rodrygo, Zidane has 11 forwards in his squad
Real Madrid No shortage of options for the coach
Pablo Polo and Jose Luis Calderon
Adapted by Oliver French
Rodrygo kisses the Real Madrid badge during his presentation. Ángel Rivero
Rodrygo: I had Hazard as my phone wallpaper... now I'm going to play with him!
Rodrygo: I don't want to be the Neymar of Real Madrid, I want to be the Rodrygo of Real Madrid
Few clubs and coaches can boast such an attacking team and it just goes to show what Real Madrid were missing last season that the club have prioritised bringing in attacking reinforcements.
With Rodrygo Goes' presentation on Tuesday, he became the third forward to be presented in seven days, following Luka Jovic and Galactico signing Eden Hazard.
It's true that many of these will be sent out on loan, while Raul de Tomas looks set to move to Benfica on a permanent basis.
Zidane currently boats 11 forwards: Karim Benzema, Gareth Bale, Marco Asensio, Mariano Diaz, Vinicius Junior, Brahim Diaz and Lucas Vazquez, in addition to the three new signings and Borja Mayoral, who returns from loan but has no role to play under the Frenchman.
The Real Madrid boss made it clear from his return that the team wa slacking goals, even in spite of the 30 scored by Benzema in his best season since joinng the club.
Neither Bale nor Asensio lived up to expectations to fill the shoes left by Cristiano Ronaldo and that's why the club's first move this summer was to sign Jovic, an alternative option to Benzema, who comes to compete with him by bringing goals and speed, like Hazard and Rodrygo.
The problem lies in that many of the players feature in the same position, with Zidane already plotting to shift Vinicius to the right wing in order to allow Hazard to play on his preferred left.
Rodrygo made clear in his presentation that he is a wide player, which clashes with Asensio, Lucas and Brahim, whose form towards the end of the season convinced the coach to reconsider whether to allow him to stay this season, though if he does it will force another player out.
So many players mean that Rodrygo's remains unclear, and he has already expressed that he would be open to following in Vinicius' footsteps and featuring for Castilla to start with.
That was what Julen Lopetegui opted to do with Vinicius last season despite having much less competition last year.
Rodrygo will have a point to prove to Zidane and show his capabilities in one position or another, beyond pre-season.
"We'll see, he's a very good player who I've only seen in videos. We'll see next year what we'll do, he's a player for the future," Zidane said of the Brazilian in March.
In any case, Rodrygo is a strategic gamble from the club who continue to focus on bringing the brightest young talents across the world to the Estadio Santiago Bernabeu.
There is no rush to pressure Rodrygo straight into the limelight, but those in charge are convinced that he will be a key player for Los Blancos in years to come and the teenager's attitude has impressed, showing a keen desire to train with the best.
He knows that it won't be easy for him to step up from Santos to Real Madrid, but he has patience and knows that he can never give up.


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The map shows the absolute popularity of the name Dardon as a last name in each of the states. See other popular names in California, New York, or Illinois.
How Popular is the name Dardon? As a last name Dardon was the 44,034th most popular name in 2010.
When was the first name Dardon first recorded in the United States? The oldest recorded birth by the Social Security Administration for the name Dardon is Sunday, June 3rd, 1923.
How unique is the name Dardon? From 1880 to 2017 less than 5 people per year have been born with the first name Dardon. Hoorah! You are a unique individual.
Weird things about the name Dardon: Your name in reverse order is Nodrad. A random rearrangement of the letters in your name (anagram) will give Ndorad. How do you pronounce that?
How many people have the last name Dardon? In 2010, the U.S. Census Bureau surveyed 489 people with the last name Dardon.
How likely are you to meet someone with the last name of Dardon? Chances are, most people haven't met someone with Dardon as their last name since less than 1 person in 588k people have that last name. If you know one, consider yourself lucky!


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Though they are substantial, the VA's benefits for hearing loss are still not well understood - hope this article makes the subject a little easier for you.
Previously a veteran had to gain the status of service-connection for a hearing loss as the result of military duties. Once service-connection was awarded, the vet could get free hearing aids, batteries for life, and maybe some money. That has all changed now, a service connected hearing loss is not needed. All veterans with an honorable discharge can now receive hearing aids, batteries, and assistive care for free – for life. All one must do is register for enrollment at any VA medical facility. Then a referral from the vet's primary health care provider to the audiology services will be needed.
Audiology services provide a standardized hearing exam to determine what kind of hearing loss exists and the severity. Once approved for hearing aids, a device will be ordered, then fitted. After that, all repairs and batteries are free for life.
This is true for all honorably discharged veterans regardless of the reason for the hearing loss. However, for veterans with a hearing loss that is shown by medical evidence to have occurred while the vet was in the military, there may be financial compensation also. This begins with a claim to the VA Regional Benefits Office and is handled like any other claim for benefits.
Medical evidence is first gathered and the Rating Board decides to determine service-connection and the rating percentage (money) to be awarded. Generally, the rating percentage for hearing loss is rarely above 10%. This is because most hearing loss is compensated for by these devices. Other conditions of the ear can also be service connected, such as ringing in the ears, scars, cancers, and chronic infections. Visit http://www.prosthetics.va.gov/psas/Hearing_Aids or www.ebenefits.va.gov for more information.
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Frome man banned from several shops after robbery and theft convictions
He could be jailed for up to five years if he breaks the ban
Max BakerTrainee Digital Reporter
Clive Kemery (Image: Avon and Somerset Constabulary)
A Frome man has been banned from several areas in the town after robbery and theft convictions.
33-year-old Clive Kemery of Buttshill, Frome, was issued with a criminal behaviour order at Taunton Crown Court on July 9, say Avon and Somerset police.
Kemery has also been banned from one property in Shepton Mallet.
A spokesman for Avon and Somerset police said: "At a previous hearing at Taunton Crown Court on 28 May this year, he was convicted of robbery, attempted robbery and two counts of theft and jailed for a total of 27 months.
"The court found that he had engaged in behaviour that caused, or was likely to cause harassment, alarm or distress to one of more persons and considered that an order was necessary to help in preventing the offender from engaging in such behaviour.
"At a hearing at Taunton Crown Court on July 9 a Criminal Behaviour Order was put in place. This prohibited Clive Kemery from certain places in Frome town centre and also at a venue in Shepton Mallet."
B&M, Station Approach
Marks & Spencer, Kingsway
Any Co-op store as defined by the map
To remain on any retail or business premises within Frome, as defined by the map when asked to leave by a member of staff or person of authority
To be on any retail or premises within Frome as defined by the map in the presence of a named individual.
Boots, Townsend Retail Park, Shepton Mallet
The boundary of Kemery's CBO (Image: Avon and Somerset Constabulary)
The spokesman said: "The order runs for four years until July 8, 2023.
"Should Clive Kemery be found guilty of breaching the order the court could grant up to five years imprisonment or a fine or both."
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Understand the underlying psychological principles that drive us toward action.
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