text:
Q: "Thread 1: Fatal error, index out of range" I tried to create an app in which random colours are being displayed in a random order. The challenge should be to memorise their order and click the buttons underneath in the right order. Now when I tried to check the order with an array, the error "Thread 1: Fatal error, index out of range" appeared. I'm new to coding and app development, but figured I have to start somewhere. I would really appreciate your help :)
//
//  ContentView.swift
//  randomColorAufKnopfdruck
//
//  Created by Jeff on 13.05.20.
//  Copyright © 2020 Jeff. All rights reserved.
//

import SwiftUI

class TestColor{
    var chosenColor = Color.black
    var colors = [Color.green, .blue, .red, .pink, .orange, .purple]
    var id: Int
    var numbers = [0,1,2,3,4,5]

    init(){
        id = numbers.randomElement()!
    }

    func generateRDMColor() -> Color{
        if id<=5{
            switch id{
            case 0:
                chosenColor = colors[id]
            case 1:
                chosenColor = colors[id]
            case 2:
                chosenColor = colors[id]
            case 4:
                chosenColor = colors[id]
            case 5:
                chosenColor = colors[id]
            default:
                break;
            }
        }
        return chosenColor
    }
}

class ColorButtons{
    var colorRepresenting: Color
    enum Colors
    {
        case red
        case blue
        case green
        case purple
        case orange
        case pink
    }

    init(color: Colors){
        switch color{
        case .red:
            colorRepresenting = Color.red
        case .blue:
            colorRepresenting = Color.blue
        case .green:
            colorRepresenting = Color.green
        case .purple:
            colorRepresenting = Color.purple
        case .orange:
            colorRepresenting = Color.orange
        case .pink:
            colorRepresenting = Color.pink
        }

    }

}

public var outputColor = Color.black
public var numberCache = [0]
public var cache = 0
public var score = 0

struct ContentView: View {
    @State private var didTapstart:Bool = false
    var body: some View {
        VStack{
            Spacer()
            RoundedRectangle(cornerRadius: 3)
            .foregroundColor(self.didTapstart ? outputColor : outputColor)
            Spacer()
            HStack{
                Spacer()
                Button(action: {
                    if self.didTapstart == false{
                        self.didTapstart = true
                    }
                    else{
                        self.didTapstart = false
                    }
                    outputColor = TestColor.init().generateRDMColor()
                    if numberCache.count <= 1{

                        switch outputColor{
                        case .green:
                            numberCache[0] = 0
                        case .blue:
                            numberCache[0] = 1
                        case .red:
                            numberCache[0] = 2
                        case .pink:
                            numberCache[0] = 3
                        case .orange:
                            numberCache[0] = 4
                        case .purple:
                            numberCache[0] = 5
                        default:
                            numberCache[0] = 0
                        }
                    }
                    else if numberCache.count > 1 && numberCache.count <= 6{

                        switch outputColor{
                        case .green:
                            numberCache.append(0)
                        case .blue:
                            numberCache.append(1)
                        case .red:
                            numberCache.append(2)
                        case .pink:
                            numberCache.append(3)
                        case .orange:
                            numberCache.append(4)
                        case .purple:
                            numberCache.append(5)
                        default:
                            numberCache.removeAll()
                            numberCache[0] = 0
                        }
                    }
                    else{
                        numberCache.removeAll()
                        outputColor = Color.gray

                    }
                }) {
                    Text("press me")
                }
                Spacer()
            }
            Spacer()
            Text(String(score))
            HStack{
                Spacer()
                Button(action: {
                    if cache <= 6{
                        cache += 1
                    }
                    else{
                        cache = 0
                    }
                    if numberCache[cache] == 0{
                        score += 1
                    }
                    else{
                        score = 0
                    }
                }) {
                    RoundedRectangle(cornerRadius: 3)
                        .foregroundColor(ColorButtons.init(color: .green).colorRepresenting)
                }
                Spacer()
                Button(action: {
                    if cache <= 5{
                        cache += 1
                    }
                    else{
                        cache = 0
                    }
                    if numberCache[cache] == 1{
                        score += 1
                    }
                    else{
                        score = 0
                    }
                }) {
                    RoundedRectangle(cornerRadius: 3)
                        .foregroundColor(ColorButtons.init(color: .blue).colorRepresenting)
                }
                Spacer()
                Button(action: {
                    if cache <= 5{
                        cache += 1
                    }
                    else{
                        cache = 0
                    }
                    if numberCache[cache] == 2{
                        score += 1
                    }
                    else{
                        score = 0
                    }
                }) {
                    RoundedRectangle(cornerRadius: 3)
                        .foregroundColor(ColorButtons.init(color: .red).colorRepresenting)
                }
                Spacer()
            }
            HStack{
                Spacer()
                Button(action: {
                    if cache <= 5{
                        cache += 1
                    }
                    else{
                        cache = 0
                    }
                    if numberCache[cache] == 3{
                        score += 1
                    }
                    else{
                        score = 0
                    }
                }) {
                    RoundedRectangle(cornerRadius: 3)
                        .foregroundColor(ColorButtons.init(color: .pink).colorRepresenting)
                }
                Spacer()
                Button(action: {
                    if cache <= 5{
                        cache += 1
                    }
                    else{
                        cache = 0
                    }
                    if numberCache[cache] == 4{
                        score += 1
                    }
                    else{
                        score = 0
                    }
                }) {
                    RoundedRectangle(cornerRadius: 3)
                        .foregroundColor(ColorButtons.init(color: .orange).colorRepresenting)
                }
                Spacer()
                Button(action: {
                    if cache <= 5{
                        cache += 1
                    }
                    else{
                        cache = 0
                    }
                    if numberCache[cache] == 5{
                        score += 1
                    }
                    else{
                        score = 0
                    }
                }) {
                    RoundedRectangle(cornerRadius: 3)
                        .foregroundColor(ColorButtons.init(color: .purple).colorRepresenting)
                }
                Spacer()
            }
            Spacer()
        }
    }
}

struct ContentView_Previews: PreviewProvider {
    static var previews: some View {
        ContentView()
    }
}

A: I think you never reach your
                    else if numberCache.count > 1 && numberCache.count <= 6{...

statement in your "tap me"-button to append to your numberCache before the app already tries to check your if numberCache[cache] == 0{
statement. Because of that there is only array 0 available.
You can check this, if your app stopps crashing, when you change the code just for test purpose to if numberCache[0] == 0{ .
Despite of that use self.didTapstart.toggle() to change a Bool-value from true to false or vice versa instead of your complex if statement at the action of your tap-button.
`

redpajama_set_name: RedPajamaStackExchange


text:
Viewing Record 3 of 24 tree
El Capitan, Winter, Yosemite National Park, California
image/paper: 9 3/4 in x 7 1/4 in; mat: 20 in x 16 in; paper: 9 3/4 in x 7 1/4 in
Gift of Arnold and Thelma Gilbert
positive space
view camera
American, b. 1902 San Francisco, CA, d. 1984
Ansel Adams's documentation of the western landscape has taken on iconic significance as one of the defining purist visions of both the American West and of the photographic medium. Images taken within the National Park System have frequently been used to promote tourism and preservation of the landscapes they portray. Compositionally, Adams tends to frame monuments of nature so that their iconic character is evoked, and to this aim, he avoids including the tourists and signs of habitation that surround the sites.
Born in San Francisco in 1902, Adams began working as an official photographer for the Sierra Club in 1928. In 1932 he and other California-based photographers, including Edward Weston, founded the group f64, which maintained an interest in the technically perfect photographic print. The name f64 is a reference to the smallest standardized aperture setting on the camera's lens; photographs taken on this setting have the greatest depth of field meaning that nearly every object in the picture plane is in perfect focus. Adams developed a means of explaining exposure and development control known as the "zone system," publishing his first book on how to master photographic technique in 1935. Over the next several years, Adams published a number of books and articles detailing his photographic approach; his titles include The Camera and the Lens (1948), The Negative (1948), The Print (1950), Natural Light Photography (1952), and Artificial Light Photography (1956). In the 1930s, Adams printed the photographs of Dorothea Lange, with whom he would collaborate with on several assignments including a study of the wartime shipyards in Richmond, California (1945), a report for Life on the Mormons in Utah, and a project on agriculture in the San Joaquin valley for Fortune (both published in the 1950s). His photographs of Yosemite Valley – a lifelong inspiration to Adams – and other subjects have been frequently reproduced, published as portfolios, and exhibited internationally. Adams was elected a Fellow of the American Academy of Arts and Sciences, and was critical in the founding of The Friends of Photography, the Ansel Adams Center for Photography, San Francisco, which existed from 1967 to 2001. In 2002, John Szarkowski organized a traveling exhibition of Adams's work commemorating the 100th Anniversary of his birth. In 1985, a year after Adams's death, an 11,760 foot mountain in Yosemite National Park was named Mt. Ansel Adams.
Ansel Adams: Images 1923-1974, Copy #204Adams, Ansel1974
Bridalveil Fall, Yosemite National Park, CaliforniaAdams, Ansel1927

redpajama_set_name: RedPajamaCommonCrawl


Stroke is a medical condition where the body of a person suffers disability like paralysis of half of his body (hemiplegic), difficulty in speaking, immobility, weakness of muscles and inaccuracy in remembering. This is caused by the depletion of oxygen supply in some parts of the brain because some nerves where blood is supposed to pass become clogged. The risk factors that contribute to this are: hypertension, diabetes mellitus, smoking and cardiac conditions.
Patients who suffer stroke should be rushed to a hospital as soon as possible so that they can immediately undergo medical treatments and procedures such as the administration of anti-coagulant drugs and CT-Scan. Time is critical when it comes to stroke victims, because people whose central nervous system is injured like in the case of stroke have a six-month golden period. In the first six months after the stroke, the patient has the highest rate of recovery, hence they should exhaust all options and do what must be done like take medications prescribed by specialists and undergo rehabilitation.
We at REHAB EXPERTS are here to offer our services in the field of physical therapy at the comfort of your home. We have more than 11 years of experience in giving quality service to people who suffered stroke in Quezon City, Marikina, Pasig, Mandaluyong, San Juan, Makati, Taguig, Pasay and Manila. We have high recovery record of stroke patients. We have equipment that we use in order to provide our patients the best possible treatment at their home similar to those found in rehab clinics. All our physical therapists are duly licensed.


Strong vibrations from a surprisingly small package deliver the ultimate pleasure! This powerful, rechargeable and waterproof vibrating wand is finished in luxuriously smooth body-safe silicone.
Strong vibrations from a surprisingly small package deliver the ultimate pleasure! This powerful, rechargeable and waterproof vibrating wand is finished in luxuriously smooth body-safe silicone. The flexible head is designed to enhance stimulation, allow for easy positioning, and heighten vibration levels. Plus, check our available attachments for maximum versatility!


text:
Полиацетилен је органски полимер и органски полупроводник, који се припрема или синтетички или као један облик биолошког пигмента меланина. Неки фунгални меланини су чисти полиацетилен. Полиацетилен ме може претварити у водљиви полимер. Припрема се у посебном Циглер-Ната катализатору. Оксидација са јодом повећава проводљивост 108 пута. За тај рад добијена је 2000. Нобелова награда за хемију. Полимеризацијом ацетилена хемијске формуле -{C2H2}-, структурне формуле: -{HC≡CH}-, добија се дугачак ланац атома угљеника са алтернирајућим двоструким везама. Сваки атом водоника може се заменити функционалном везом. Схематски се то приказује -{...C=C-C=C-C....}- Полиацетилен, који се различито припрема зове се и црни ацетилен или меланин.

Референце

Спољашње везе

Органски полимери
Молекулска електроника
Органски полупроводници

redpajama_set_name: RedPajamaWikipedia


I never really noticed this little book floating around our house, until a friend recommended it. I picked it up once I had finished a couple of others. It didn't take too long to finish (even considering that I only read it during breakfast), but it is packed with theology.
The point of the book is contained in the subtitle – "learning to see the glories of God's love." It's a gospel primer for Christians. We often tend to see the gospel as something that is in our past – it got us saved, and now its usefulness is done. But that couldn't be further from the truth. According to Vincent, we need to be "preaching the gospel to ourselves" every day.
The book is divided into three parts, with a fourth section recounting Vincent's own testimony. The first part (also the longest) contains thirty-one reasons to rehearse the gospel to yourself, spanning pretty much every area of the Christian walk. Each one will open your eyes still further to how much we need the gospel in our daily lives. Some of them I'd thought of before, but many I'd never even considered.
The second part is simply the story of the gospel, in prose form. It's composed of several sections, beginning with, "The Glory of God" and ending with, "My Salvation." It's not long, and it's written in simple language; but it's packed with truth. One thing I appreciated about this section (and the next), is that for every sentence, even every phrase, there's a footnote containing the verses from which that particular truth is taken. You don't have to take the author's word for it; everything he says is directly from the Scriptures.
The third part is actually the gospel in poetic form. Honestly, I was a little wary of this at first – not because I don't think the gospel can be put into poetic form (it can and should), but because if an author lacks the skill it can end up sounding forced or unnatural. But it was actually quite good. The poetry isn't complex, but it flows well and is easy to understand. Sometimes poetry can make more of an impact or lasting impression, so I loved that he added that.
In the introduction, Vincent advises that you read the book slowly. That's probably good advice. Reading only a little every day allowed me to really process what I was taking in. I hadn't really thought before about how important it is to preach the gospel to myself, but the book makes it very clear. We need the gospel – every day, even every hour. If we lose sight of this truth we lose one of the greatest tools available for our walk with Christ. A day-to-day awareness and rehearsal of these glorious truths isn't optional for us – it's essential. I'll definitely be reading this book again, hopefully many times, as I grow in my own knowledge and understanding of the gospel.
Have you read this book? What did you think? Please let me know! I'd love to hear your thoughts.


Skip the food, ignore the shares.
It is no secret that there some groups of stocks, most notably community banks and REITs, that I love as an investor. There are also some that I just hate. I can't stand the business and I just hate everything about the stock and the group.
For me, the casual-dining group fits this bill very nicely. I can't stand the places and rarely will consent to enter one. While my good friend and fellow contributor Jon Heller noted in his recent restaurant review that he and I were the only Real Money folks who frequented Cracker Barrel (CBRL) , I have to say that while that was true when I lived up north, and Cracker Barrel was the only place to get something resembling a good plate of chicken fried steak and eggs, this is no longer the case. Living down south as I do now, there are numerous local joints that have a far superior product and now I even avoid the country-style chain.
I am not a big fan of the business model either, as it depends almost entirely on the opening of new locations to grow, and eventually that game runs out of steam. There are only so many corners that need some variation of the casual-dining chain, as we know. While they are perceived to be growing, these chains will frequently have high multiples, and this usually does not work out well for investors.
My least-favorite chain today is Buffalo Wild Wings (BWLD) . I have never liked the stock, though I love sports bars and have long thought that sports bars that aren't in a hotel should be named Ernie's or Lou's with local owners who are fans of a particular team and decorate the joint accordingly. The beer should include local brands, and the food should be regional favorites. In Maryland, I should be able to get crab cakes, and in Wisconsin, I should be feasting on brats. The homogenized, something-for-everybody, straight-down-the-middle-of-the-road approach of Buffalo Wild Wings just offends my highly developed sense of a good sports bar.
The company has been a Wall Street darling with good earnings growth and a continued stream of store openings. Since 2008, the company has had one of the top three F-score rankings as fundamentals were improving year after year, much to my dismay, but that is changing. Last year, the F-score slipped all the way down to 3 and is still just a 4, indicating that fundamentals are slipping for the company. Even after declining by 23% over the past year, the stock still trades at almost 30x earnings, so there could be further to fall.
During the stocks' great run from 2010 to 2013, the company had an F-score most of the time of 7 or higher. In 2013, it started to trade between 5 and 6 and the stock has been pretty much stalled since then. For the last year, the F-score had moved between 4 and 6 as the fundamentals are flatlining here. As long as the economy remains soft, I have a hard time seeing the chain accelerating growth to the point it becomes a growth darling again. Uncertain consumers will find it increasingly difficult to indulge in $9 sandwiches and $5 coffees if the growth rate doesn't pick up and the quality of jobs being created does not improve.
I think we will see consumers continue to back away from casual-dining chains and investors will probably be well served by doing the same with the stocks.
At the time of publication, Melvin had no positions in the stocks mentioned.


text:
El brutal death metal (o simplemente llamado brutal death), es un subgénero musical del death metal. Este subgénero, es el más pesado, fuerte y rápido del death metal y de la música en general.

Características
El brutal death metal, se basa en baterías extremadamente pesadas y extremadamente rápidas, con una gran predilección por el blast beat, lo que lo caracteriza y guitarras zumbantes, muy encasilladas en una 7.ª cuerda, o una de 6 cuerdas donde estas están afinadas varios tonos por debajo de la afinación normal o "estándar", combinándose, frecuentemente, con tonos agudos en parte de los riffs, que las hacen más estridentes. El bajo, se caracteriza por la concordancia que presenta con el doble bombo o doble pedal de la batería. Las pesadas guitarras y las voces se destacan por los gritos graves con intervenciones agudas, haciéndolo el género más pesado y brutal entre la música en general. Las letras, generalmente, tratan sobre el gore y trastornos o enfermedades humanas clínicas. En este estilo también se observan influencias jazzísticas, patentes en las líneas de bajo y en los trabajos baterísticos.

El brutal death metal, aparece a principios de los años 1990 como una corriente más que se ramifica del death metal. Empieza con bandas como Cannibal Corpse y continúa con bandas como Suffocation, seguidos por una segunda generación más "bestial" y gutural liderada por Dying Fetus.

Bandas notables
 Cryptopsy - Canadá
 Suffocation - Estados Unidos
 Dying Fetus - Estados Unidos
 Deeds of Flesh - Estados Unidos
 Devourment - Estados Unidos
 Skinless - Estados Unidos
 Nile - Estados Unidos

Enlaces externos
Brutal death metal en morbidofest.com
 El sonido más intenso de propio brutal death metal

Subgéneros del metal
Death metal


About Us - Greater Greenville Housing & Revitalizatioin Association, Inc.
Greater Greenville Housing and Revitalization Association, Inc. (GGHRA) is an independent 501(c)(3) nonprofit, Community Development Corporation (CDC)/Organization with over twenty-three years of affordable housing development and support experience to the greater Greenville, Mississippi community. Our offices are located inside the Wetherbee House at 503 Washington Avenue Greenville, Mississippi 38701.
The mission of our organization is to develop, provide and promote safe, affordable, and decent housing in conjunction with a focus on designing, researching, and/or promoting revitalization activities in the community of Greenville, Mississippi.
With an impeccable reputation within the industry, our organization has successfully administered numerous community based programs, which assisted thousands of families within the region find and retain adequate housing.
GGHRA has a rich history in developing subsidized multi-family housing, and the organization currently owns and manages Seventy-Six (76) low-income units through five (5) separate complexes within the Greenville community. The scope of these developments/redevelopments range from the substantial rehabilitation of Greenville's first multi-family building (originally constructed in 1938) to new construction on vacant land. In addition, our organization has also been successful in implementing and marketing other programs, such as the redevelopment of scattered-site single family units, home-buyers assistance, and homeowner rehabilitation programs.


text:
Q: How to check if a private method was called with expected argument in unit test? I am writing unit for a class which looks like following using nunit and Rhino mock.
Class MyClass
{
  private void M()
  {
    N("Hi");
  }

  private void N(string text)
  {
    ........ do something
  }
}

For the unit test for method M I want to check if method N was called with argument "Hi". How do I do it?

A: +1 to Brian's response.
The alternative is to split "N" out to a different class, and then use a mocked instance of that class in your test. You can then set up the mock to expect a call with a specific parameter. But, it might not actually be appropriate to split it out. Depends on your exact scenario.

A: It seems to me that from the point of view of testing, you're delving into the implementation details of your object. Can't you perform your tests by checking the ultimate result of your method call ? That is, presumably these method calls have some effect. So rather than checking the arguments being passed, you should be checking the final result.
That way you can change your underlying code at a later date, and your unit tests will confirm that the ultimate result is the same, independent of your implementation.

A: Use mocking on your method N().
http://www.mockobjects.com/
However, Brian solution is better -- think it's a good direction for good unit testing.

A: Following code can help you.
var mock = new Mock<IFoo>();
bool called=false;
string test=string.empty;
mock.Setup(foo => foo.Execute(It.IsAny<string>())).Callback((string s) => { test = s; called = true;});
Assert.IsTrue(called, "Execute() was not called");


text:
Q: How do I support Serbian locale in Unity? I'm currently trying to support Serbian localization within Unity (2017.2.1f1) using.Net 3.5. I'm aware of the fact that this version of .net apparently doesn't support Serbian locale, however, I've seen numerous sources suggest that you can make a culture if it's not officially supported. I'm unsure if this is possible using Unity, and the documentation is of very limited help.
I've been considering upgrading to .net 4, as this apparently supports Serbian, however, this is quite a large cost to incur currently so I'd rather do that as a last resort. Additionally, if the 4 platforms I'm deploying to (Android, Windows, Mac, and iOS) all support this locale, then I'm wondering if there's a way to do it natively alongside Unity? Further to this, my code doesn't rely too much on localization, only pluralization in a few places, so as a less robust option, I wonder if there's a locale I could use in place of Serbian?
I don't believe using CultureAndRegionInfoBuilder is an option here, due to my use case of Unity across multiple platforms.


It's been a very long while since I have blogged something here and it's time to come back and continue sharing our field experiences with the IT community hoping to shed light for similar problems.
I was tasked to deal with a customer problem where the end users were reporting various problems like "cannot access the file server, getting authentication prompts" and the IT admins were also observing various problems like the server wasn't properly applying GPOs, Netlogon service complaining about DC access issues and etc. At times, they were even able to manually reproduce the issue by issuing a "telnet DC-IP 389" command from the affected server.
03E0.5214::01/04/18-15:07:37.5237622 [Microsoft-Windows-TCPIP/Diagnostic] TCP: endpoint (sockaddr=0.0.0.0) bind failed: port-acquisition status = The transport address could not be opened because all the available addresses are in use..
58F0.4558::01/04/18-15:07:51.8242042 [Microsoft-Windows-TCPIP/Diagnostic] TCP: endpoint (sockaddr=0.0.0.0) bind failed: port-acquisition status = The transport address could not be opened because all the available addresses are in use..
Actually that clearly explained why the outbound connections were failing: PORT EXHAUSTION.
Value exists by default? No, needs to be added.
Please note that the same techniques could be applied to virtually any Windows versions as of Windows 7/Windows 2008 R2 onwards easily.


text:
tubular bells instrument facts
There are over 12,000 parts in a piano, 10,000 which are moving. It was the first time a "tune" was filed with that office. In the 16th cent. Welcome to Hand Chimes & Chime Bars ... We sell a collection of hand chimes and chime bars, which are perfect for educational and professional purposes. Tubular bells, also known as chimes, are a percussion instrument. Print and download sheet music for Tubular Bells (Theme from The Exorcist) by Mike Oldfield. Get it as soon as Wed, Dec 2. 4.7 out of 5 stars 29. Since starting with Yamaha in 2003, Troy has guided numerous product development activities and has managed over 50 events a year. Here are seven facts about chimes that you may not know. All rights reserved. As orchestral chimes, tubular bells can attain greater rhythmic precision than true bells, and their tone is clearer, for it emphasizes fewer higher harmonics. Mike's debut album 'Tubular Bells' was an acclaimed multimillion seller out of left field, and its successor, 'Hergest Ridge', sold prodigiously too, despite a critical backlash which Mike took hard. The medieval glockenspiel was a sort of miniature carillon (see bell [2]), sometimes played mechanically by means of a rotating cylinder with protruding pins. It was the success of this album that established Virgin Record's edge making it the world's biggest independent label. Tubular bells, also known as chimes, are a percussion instrument. The cor anglais. glockenspiel (glŏk´ənspēl) [Ger.,=bell-play], percussion instrument [1]. They make a bell sound when hit with a mallet. Prior to 1900, chime bells typically lacked dynamic variation and the inner tuning (the mathematical balance of a bell's complex sound) required to permit the use of harmony. It is also used for locks, gears, bearings, doorknobs, ammunition casings, valves, zippers, plumbing and electrical applications. The tubes have various lengths tuned to musical scales. https://hub.yamaha.com/wp-content/uploads/2020/07/NBC-chimes.mp3. Back in 1975 Mike Oldfield should have been on top of the world. In 1950, the National Broadcasting Company (NBC) filed with the U.S. Patent Office to make their three-note chime a registered audible service mark. This pedal affects all the chimes at the same time and works the same way as on a piano. $37.99 $ 37. Tubular bells as a substitute for church bells were first used by Giuseppe Verdi in his operas Il trovatore (1853) and Un ballo in maschera (1859). Wintergatan - Marble Machine (music instrument using 2000 marbles) - Duration: 4:33. 99. Oldfield claims to have played 20 instruments on "Tubular Bells", which was the first release of Virgin Records. Here are seven facts about chimes that you may not know. Untuned percussion instruments include: bass drum, side drum (snare drum), maracas, castanets, cymbals, tambourine, claves and many more. The first bronze bells of a large size were cast in Mesopotamia and Egypt in the 9th century BC. Unlike most percussion instruments, they produce a pitch when struck. Tuned percussion instruments include: xylophone, glockenspiel, vibraphone, tubular bells and timpani. it was given a keyboard. This instrument is one of the few melodic instruments on which percussionists perform. Steel and copper, for instance, are both popular materials for tubular bells. Arthur Sullivan is believed to have been the first composer to score for tubular bells, for an 1886 orchestral performance of the cantata Golden Legend. Provide feedback on this article in our Tubular bells Forum, https://wiki.kidzsearch.com/w/index.php?title=Tubular_bells&oldid=4586625. It is made from a small, round, steel tube It is a Medieval instrument It is hit with a metal beater There were also flutes, a mandolin, and of course, tubular bells. Notes Mike Oldfield's original demos for what was to become Tubular Bells were recorded in his flat in Tottenham, London, using a Bang & Olufsen Beocord 1/4" tape machine, which he had borrowed from Kevin Ayers, leader of 'The Whole World', the band that Mike had just left. ENNBOM Desk Bells Hand Bells 8 Notes Music Bells Percussion Instrument Musical Teaching Chromatic. Copyright © 2016 Yamaha Corporation of America and Yamaha Corporation. They are used in Mike Oldfield's album Tubular Bells . This instrument is one of the few melodic instruments on which percussionists perform. These tubes are struck with mallets to produce a sound. The piano is believed to have been invented in approximately 1700. During the late 1970s, Peart augmented his acoustic setup with diverse percussion instruments including orchestra bells, tubular bells, wind chimes, crotales, timbales, timpani, gong, temple blocks, bell tree, triangle, and melodic cowbells. In addition, Rush drummer Neil Peart used tubular bells on the songs "Xanadu" and "Closer to the Heart.". Glockenspiel is translated literally as "bell play." These tubes are struck with mallets to produce a sound. $37.99 $ 37. But alongside the pipes, a full set of tubular bells and a bass drum can be operated by the pedals. What the brass section consists of. 1 comment: Judi Kartu Poker Onlline 16 February 2018 at 10:14. But behind the scenes he was suffering. A softer mallet brings out the fundamentals (the lower tones) and higher partials are not so noticeable, making for a darker tonality.
Pacific Elkhorn Coral Price, James Burton And The Animals, Raspberry Ketone Solubility, Late Hatching Chicken Eggs, Portugal Weather March, Outdoor Floor Tiles Design Pictures, Lg Ultra Heat Power Boil, Zulu Surname Praises, Vmware Nfs Best Practices, Tresemme Curl Locking Finishing Spray,


Begin with pickup at your centrally-located hotel or cruise ship by 2:30pm: the exact time of departure depends on your location, and will be confirmed by the local supplier after booking. Transfer to a jetty on the Nile River, where you'll board a boat for Sohail Island. Depending on river conditions and the weather, you'll travel in a traditional felucca sail boat, or in a modern motorboat. Take in views of the Nile riverbanks on the way to Sohail Island, passing Elephantine Island along the way. Disembark on Sohail Island, and visit a traditional Nubian house to learn about the history and culture of the Nubian people over a glass of welcome tea. After your time at the house, take a walk to explore the island on your own, strolling through brightly-painted villages, and chatting with the locals. At the end of the afternoon, meet back up at the boat jetty, where you'll begin the return trip to Aswan. This tour concludes with drop-off at your hotel or cruise ship at roughly 5:30pm.
Overview An amazing Nile Cruise from Aswan to Luxor to discover the ancient temples and tombs. Visit Philae Temple, the Karnak Temple, Valley of the Kings and Hatshepsut Temple at the West Bank of the Nile.
Anakato Beach Chill This day package is available all year around. Advantages of this relaxation are privacy and seclusion in this unique oasis. You can combine this with water sports, reading, yoga & meditation. Customized meals can be prepared on this beach. Anakato guests Anakato Kayak Cardio Raise your metabolism with Kayaking. Tour around the beach; combine it with the mud musk and sand burying - in this cleanest part of the Nile. Anakato Sand/Mud Earthing Take a Nile mineral mud mask, to ionize your body. Following this, you can earth to correct your pulse with the ancient Sand Burying technique, to heal many disorders and ailments. Meet us at the dock at 11:30 sharp. Bring your swimsuits. NUBIAN CUISINE We specialize in Nubian food made by the village ladies. In this way, we empower the women and bring forward their tradition. Love is the main ingredient in this community cuisine.


text:
American Medium
Muffy
Ann Hirsch
…chained by her neck to a blob creature with a thick, worm-like tongue, a petite woman in a gold bikini top and flimsy purple loin cloth is held against her will...when the blob pulls the shackle around her neck, the woman's slight body violently jerks back and presses taut against the slimy ridges of the blob creature's skin…she closes her eyes……somewhere in a galaxy far, far away, a young girl in white underwear opens her eyes…she is sitting in her lavender bedroom, enjoying the stirring feeling she's noticing in her body…the girl ties her neck to a giant stuffed bear with a long ribbon…she thinks, "there's nothing i can do now…i am his slave forever…"
For her debut solo exhibition, Ann Hirsch explores the inchoate state of early adolescent sexuality, in which one intuits the sexual nature of their bodies, but doesn't yet know what sex is. Her drawings, prints, and sculptures transport viewers to the surreal psychological space of this often contradictory life stage. The self is presented in states of dreamlike transformation informed by both biological imperatives and, in a series of works with autobiographical dimensions, the social and cultural influence of Internet communities and pop culture.
…oh, the pleasure she feels at being naked, stuffing toys down her underwear, imagining she is a beautiful princess trapped in a monster's maze... *****************ANN HIRSCH (b. 1985) is a video and performance artist who looks at the ways technology has influenced popular culture and gender. Her research has included becoming a YouTube camwhore, amassing over two million views on her videos, as well as making appearances on some popular reality television shows including as a romantic contestant on Vh1's Frank the Entertainer...In a Basement Affair. She was awarded a 2012–13 Rhizome commission for her two person play Playground, which debuted at the New Museum and was recently awarded a 2014 WaveFarm Media Arts grant to continue producing this show. Her companion ebook,Twelve was censored from the iTunes store but is now available through Klaus von Nichtssagend Gallery. Hirsch has performed at Performa 13, Vox Populi, Eyebeam Art + Technology Center and exhibited at Abrons Art Center, KANSAS Gallery, ABC No Rio and Arcadia Missa. She has been an artist in residence at Yaddo, Atlantic Center for the Arts and the Lower Manhattan Cultural Council. Her work been written about for NYmag.com, The New York Times, Rhizome.org, Artforum.com and more. www.therealannhirsch.com
Ann Hirsch: Muffy is the inaugural exhibition of American Medium's Brooklyn location.
(L to R)
My Little Guy and My Big Man, 2014
plushie doll, iPad mini, Twelve censored app
36 x 24 x 16 inches and 60 x 24 x 16 inches
Photos for jobe #1 1998, 2013
archival inkjet print on mirror
That She Devil Jew Lady, 2014
8.5 x 11 inches
Truly Exotic, 2014
oil pastel, colored pencil on paper
Scary Daddy, 2014
Sexy Baby in Repose, 2014
oil pastel, gouache, colored pencil on paper
Reclining Slutty Grandpa, 2014
oil pastel and gouache on paper
Slave For You/My Spank Bank, 2014
mixed media (video/sound installation)
size variable
Madonna, 2014
My Starving Public 1998, 2014


text:
Scraptia ougandensis es una especie de coleóptero de la familia Scraptiidae.

Distribución geográfica
Habita en el este de  África.

Referencias

Ougandensis
Coleópteros de África


In shallow water, the echo from the bottom or surface can dominate the return from the water column. The risk of getting a strong echo from the bottom increases as the acoustic energy pulse moves further away from the instrument, therefore impacting range. The maximum profiling range, R, can be estimated as R <15*h, where h is the distance from the instrument to the bottom. It is advised to mount the instrument in 2/3 of the water depth from the bottom for best range.


Editors's note: This the fourth in a week-long series WKSU is doing on the integration of Bhutanese-Nepali refugees, who began their migration to Akron a decade ago. This story also is part of a collaboration with the Huffington Post.
Ohio has one of the highest infant mortality rates in the country, and the effort to get more babies to their first birthday is focusing increasingly on a kind of support-group model for pregnancy. Most of the groups are formed around common due dates of the expectant moms. As part of our collaboration this week with the Huffington Post, WKSU's M.L. Schultze explored one of the groups formed around a different experience, that of being Bhutanese refugees.
Sandchu Rai's first son was born in one of the camps in Nepal set up after the refugees were expelled from Bhutan. The other two are American born.
Nine pregnant women get together on Friday mornings on Summa Health Systems' Akron campus – in what looks more like a living room than a medical office. It's time for blood pressure, urine and belly checks; time to compare swollen ankles, back pain and sales on diaper bags.
But nine times, the chatter grows hushed.
The women stop to listen to the fetal monitor detecting a heartbeat coming from the examination table behind a screen.
Ohio's infant mortality rate last year was 7.1 deaths of children before their first birthday for every 1,000 live births. Centering-pregnancy is one effort to fight premature births.
Unlike other centering-pregnancy groups, this one is not formed around due dates. It's formed around language. So, for example, Sanchu Rai shares the lessons learned from three pregnancies with a shy newcomer who is in the early stages of her first. Rai had her first son in the camps in Nepal where she and about a hundred thousand other Bhutanese refugees lived for two decades. Her second boy was born here, as his baby brother will be.
That means two-hour group visits with physicians, nurses, case managers and social workers. The first hour is all about the physical, "doing the traditional medical examination, where we measure the belly, listen to the baby's heartbeat and get vitals. Things everyone's familiar with.
Summa's Dr. Dante Roulette says this group differs from other centered pregnancy efforts because the woman are at all stages of pregnancy. The group has grown to as many as 19.
Michelle Kusic is the nurse practitioner who works with the group. Beyond the medical appointments, the Friday gatherings include education. There could be a lactation consultant one week, a hospital tour another.
She says in many ways, the Nepali women are made for centering pregnancy.
Kusic works behind the screen with each woman. But she's not alone. Her questions are all asked and answered through Laxmi Dhimal Dhakal. She moved from Nepal to Atlanta in 2008, and, like thousands of other Bhutanese refugees, migrated to Akron four years later. As a medical assistant and translator, she's the bridge between the Summa staff and the women.
She says camaraderie is key for this group.
Dhakal notes that healthcare was limited in the camps, so many of the newcomers are more used to sharing questions about their pregnancies with each other than with doctors. So she says the centering group combines the best elements of each.
Dr. Roulette says some in the groups initially had a mistrust, especially when it came to talking about related issues such as intrauterine devices and other birth control. He said IUDS were almost a cultural taboo.
He says it was a moment for the caregivers here to understand real fears and to discuss ethical and moral parameters in U.S. medicine. Did it help?
Roulette notes that studies increasingly link premature birth, infant mortality and health over a lifetime to stress. And, he acknowledges, living decades in refugee camps -- with lives largely on-hold -- is a huge stressor – as is resettling halfway across the world. So he says, the centering-prenancy groups like Summa's are key to building health, as well as trust, over the long term.
In the last of this week-long series, tomorrow, we'll have the story of the ultimate blending of the two cultures – what's believed to be Akron's first mixed marriage.


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

Deep neural networks are an extremely powerful tool for object recognition \cite{krizhevsky2012imagenet,szegedy2015going,he:15,schroff:15} and image segmentation \cite{long2015fully}. More recently,
they have also shown uncanny abilities to generate images \cite{radford:15,dosovitskiy:14,gregor2015draw}. In particular style transfer \cite{gatys:15,Li2016Combining}, deep dream \cite{mordvintsev:15},
generative adversarial networks \cite{radford:15}, all have been producing highly compelling results. In this work we explore our ability to control the images deep neural networks produce.

For the purposes of this work we use FaceNet \cite{schroff:15}, a face-recognition network that has been trained to distinguish between people, as our
test bench. We address the problem of inverting the network output, or the {\em embedding vector}, i.e., provided with the embedding vector $e$, we generate a
realistic face image, which after being passed through the FaceNet produces $e$.
One interesting aspect of this problem is the fact the space of distinct acceptable solutions is huge, in particular
different orientations and poses of the same person should in theory produce the same embedding.
Furthermore, that space by itself is dominated by the space of
{\it unacceptable} solutions -- the images with glimpses of faces in various orientations, or simply random-noise \cite{szegedy:13}
looking solutions.
All of these unacceptable images are mapped into a given embedding and are thus proper inversions, just not particularly interesting ones.
One approach to solve this is to employ adversarial learning algorithms \cite{radford:15, goodfellow2014generative} where a pair of networks e.g. generator and classifier
are training in parallel. However this somewhat limits our ability to control what is produced by generator.
Our goal for this work is to produce consistent inverse solutions that look like faces in the prescribed position and orientation.
In this paper, perhaps somewhat surprisingly, we show that several very simple regularization techniques worked well in enforcing the consistency of the output images.
In the rest of the section we provide an overview of our results.

\subsection{Image Embedding Network}

  For our experiments we use a Facenet model \cite{schroff:15} mapping a $224\times 224$ RGB face image to a normalized
  128-dimensional ``embedding'' vector. This network was trained to have embeddings of different photographs of the same
  person to be closer to each other than to those of a different person. This network achieves comparable to human-level of face recognition
  performance \cite{schroff:15}.

  \subsection{Overview of results and paper structure}

  The contents of this paper can be roughly separated into two parts.
  First, in \Secs{sec:minimization} and \ref{sec:iterative}, we introduce a general problem of face reconstruction and propose a loss function, using which a gradient-descent style algorithm can reconstruct highly recognizable faces using only the target embedding vector.
  The orientation and facial expression of the produced image match that of a provided guiding image.
  \omtnips{
  The main idea of the method is based on attaching additional regularization losses that enforce face consistency and orientation to the optimized embedding loss function.
  More specifically, we use total-variation loss \cite{mahendran:15} and Laplacian pyramid graident normalization \cite{burt:83}
  to ensure the image is smooth.
  We also use $\ell_2$ distance on intermediate layers with the guiding image to enforce a specific face orientation and position.
  The minimization of the combined loss function is approached by using gradient descent starting at random noise or an apriori chosen initial state.
  }

  In the second part, as outlined in \Sec{sec:ff}, we introduce a feed-forward neural network, which can be trained to produce face images that minimize the loss function used previously for iterative reconstruction.
  We believe this approach to be of independent interest since it allows one to solve the minimization problem in a single step.

  Finally, our experimental results are presented in \Sec{sec:experiments}.
  An interesting observation made while studying the reconstructions, which might be of independent interest, is that
  even faces that look remarkably similar, can still be recognized despite sharing virtually identical macro charactertics.
  We show several examples of this phenomenon in \Sec{sec:experiments}.

\section{Face reconstruction as a minimization problem}
\label{sec:minimization}

  The face reconstruction problem discussed in \Sec{sec:introduction} can be formalized as follows.
  Let $\mc{F}$ be a function defined by a trained deep neural network, mapping a photo $p_s$ of a person $s$ to a lower-dimensional embedding $e_s = \mc{F}(p_s)$.
  In the following, considering FaceNet, we use two definitions of the embedding vector: an unnormalized embedding obtained in an intermediate FaceNet node and the normalized embedding calulated by applying a softmax activation function to the unnormalized vector.

  Naively, given an embedding $e\in \mc{E}$, the reconstruction could be accomplished by finding an image $p$ minimizing $d\left[\mc{F}(p),e\right]$, where $d:\mc{E}\times\mc{E}\to \mathbb{R}$ is some metric on the embedding space $\mc{E}$.
  However, since in practical applications the space of all possible images $\mc{P}$ has a much greater dimension that the embedding space $\mc{E}$, the inverse $\mc{F}^{-1}(e)$ of arbitrary $e\in \mc{E}$ is generally a high-dimensional manifold containing a rich variety of images only a small fraction of which could be considered realisitic face images.
  A more sensible definition of the face reconstruction problem could thus be written as:
  \begin{equation}
       \label{eq:minp}
       p_* = \mathop{\mathrm{arg\,min}}_{p\in \mc{P}^*} d\left[\mc{F}(p),e\right]
       = \mathop{\mathrm{arg\,min}}_{p\in \mc{P}} \mc{L},
  \end{equation}
  where $\mc{L} = d\left[\mc{F}(p),e\right] + R(p)$ and the regularizer $R(p)$ vanishes for all images within a subset of ``realistic'' face images $\mc{P}^* \subset \mc{P}$ and $R(p)=\infty$ otherwise.
  Since the set $\mc{P}^*$ is generally very difficult to define, we solve the minimization problem~\eqref{eq:minp} for other classes of regularization functions $R(p)$ which only ``favour'' face-like images.

  One of the approaches to characterizing the set $\mc{P}^*$ is based on using a single reference, or a ``guiding'' image $\Img_G$.
  Since the trained convolutional neural network defining $\mc{F}$ contains lower-level ``edge'' and ``shape'' filters as well as more complex features relevant for face recognition, the guiding image regularization function $\Reg_G(p;\Img_G)$ can be constructed using the intermediate nodes of this network.
  For example, $\Reg_G$ could naturally be chosen as $w_G \|a(p)-a(\Img_G)\|_r$, where $w_G$ is the regularizer weight and $a$ is a vector of activations in a specific network layer $\ell$.
  When $\ell$ is chosen amongst the lowest network layers, the regularizer $\Reg_G$ effectively pulls $p_*$ towards the image $\Img_G$.
  For higher layers $\ell$, the regularizer $\Reg_G$ introduces restrictions on the higher-order features of $p_*$ without necesserily forcing specific textures or colors \cite{gatys:15}.

  The advantage of using a single image $\Img_G$ to condition the reconstruction is the possibility to enforce a specific pose, facial expression and background.
  The disadvantage, is of course, the fact that the final image may contain  facial features corresponding to both the embedding and the guiding image.
  For very low values of the guiding image regularizer weight $w_G$, the produced image does not look realistic and frequently consists of numerous face fragments.
  In contrast, for large $w_G$, the reconstruction may be almost indistinguishable from the guiding image.
  By tuning the value $w_G$, it is, however, possible to produce realistic looking faces with barely any facial features ``leaked'' from the guiding image (see \Sec{sec:iterative}).
  \omtnips{
  In Appendix~\ref{sec:gaussian}, we also discuss an alternative approach, in which the regularizer uses a collection of images (with faces sharing a common pose) instead of a single guiding image.
  This regularizer does not force any specific facial features, but generally results in lower-quality images.}

  Numerical optimization of \Eq{eq:minp} frequently produces noisy and distorted images.
  This problem can be alleviated by introducing additional regularizers.
  We use the {\em total-variation} (TV) regularizer \cite{mahendran:15}:
  \begin{equation}
    \label{eq:rtv}
    \Reg_{\rm TV}(p) = \left[ \| p - \op{S}_x p \|_2^2 + \| p - \op{S}_y p \|_2^2 \right]^{\alpha/2},
  \end{equation}
  which can be seen to penalize images with high-frequency noise, large gradients and sharp boundaries.
  Here, $\op{S}_x$ and $\op{S}_y$ are operators shifting the entire image by $1$ pixel in $x$ or $y$ direction correspondingly and $\alpha$ is a constant parameter.

  The choice of the optimization function $d$ can have a strong impact on the produced face reconstructions.
  In this paper, we consider two families of loss functions defined on normalized or unnormalized embedding spaces.
  The first one is based on $\ell_2$ metric in the embedding space, {\mbox{\textit{i.\hairsp{}e.},~}} $d\left[\mc{F}(p),e\right]=\|\mc{F}(p)-e\|_2^2$.
  Another approach, which was shown to frequently result in higher-quality images, employs a dot-product: $d\left[\mc{F}(p),e\right] = - \mc{F}(p) \cdot e$.

\section{Iterative face reconstruction}
\label{sec:iterative}

  Provided with an embedding $e\in\mc{E}$ and a chosen set of regularizer parameters, the minimization problem \eqref{eq:minp} can
  be solved numerically using stochastic gradient descent (SGD), Adam \cite{kingma:14},
  or another optimization method starting from a random noise or the guiding image entering $\Reg_G$.

  Without any regularizers, the iterative process converges to an image from within a small neighborhood of the initial state \cite{mahendran:15,szegedy:13}.
  Performing an optimization with the guiding image regularization alone was also unsuccessful at reconstructing a realistic face image.
  A significant improvement was observed once the total-variation regularizer \eqref{eq:rtv} was introduced in \Eq{eq:minp} (see \Figs{fig:l2}, \ref{fig:dot}).
  The initial state of the reconstruction was also shown to play an important role: starting with the guiding image instead of a random noise frequently improved both the
  stability and the quality of the produced images (see \Figs{fig:rand}, \ref{fig:l2}).

  Interestingly, using a sufficiently high $w_G$ allowed us to generate realistic images with facial features of the embedding $e$ and the facial expression of the guiding image.
  By running the algorithm on a sequence of video frames, we were able to perform a ``face transfer'' from the embedding onto the face shown in the video.
  This result is particularly impressive given that the embedding can be produced from just a single photo of a person.

  The positive effect of the TV regularizer has been previously observed, for example, in Ref.~\cite{mahendran:15}.
  One can speculate that it can be attributed to suppresion of high-frequency harmonics leading to the search for a local minimum in a subspace of smooth images.
  \omtnips{
  Indeed, $\pld{\Reg_{\rm TV}}{p}$ can be shown to be proportional to $\Laplace p$, where $\Laplace$ is the discrete Laplacian operator defined as $(\Laplace p)_{x,y} = p_{x+1,y} + p_{x-1,y} + p_{x,y+1} + p_{x,y-1} - 4 p_{x,y}$.
  If the gradient descent step size is sufficiently small, the expression for the SGD update $\Delta p = -\mu \, \pld{\mc{L}}{p}$, can be viewed as a discretization of a dynamical system with a continuous step number $n$, where the regularizer plays a role of the diffusion term:
  \begin{equation}
    \label{eq:cont}
    \pd{p}{n} = -\mu \left( \pd{L}{p} - \alpha w_{\rm TV} \Reg_{\rm TV}^{1-\frac{2}{\alpha}} \Laplace p \right).
  \end{equation}
  Here $L(p)$ is the sum of embedding and guiding image loss functions, which we ultimately need to minimize.
  }
  \omtnips{
  Notice that as shown in \App{sec:minima}, in the limit $w_{\rm TV}\to \infty$, there are generally a finite number of local minima of $\mc{L}$ and they no longer form high-dimensional submanifolds of $\mc{P}$.}

  The total-variation regularizer proved to be essential essential for converging towards a realistic face image, however, it also blurs the output images.
  We reduced the blurring effect in the produced reconstruction by reducing $w_{\rm TV}$ with the iteration number.
  Further we also used laplacian pyramid normalization~\cite{mordvintsev:15} applied to intermediate gradients.
  This improved overall contrast of the image.

  The choice of the guiding image had also proven to be very important for a high quality face reconstruction.
  When the guiding image and the embedding corresponded to people of different gender or nationality, the produced images could resemble the guiding image with only some facial features ``borrowed'' from the embedding (see \Figs{fig:femmale}, \ref{fig:femfem}).
  This effect was less noticeable for very large values of $w_{\rm TV}$, but in this case, the reconstructions had worse quality and were unstable, {\mbox{\textit{i.\hairsp{}e.},~}} could be drastically different for different random initial conditions (sometimes producing images with percievably wrong gender or age).
  The problem of the guiding image choice can be solved by either building a classifier which predicts the gender and nationality of the face corresponding to a given embedding (and thus prediciting a proper guiding image to be used in the algorithm), or attempting to produce reconstructions with different guiding images and choosing the result with the smallest embedding space distance.

  \begin{figure}
  \centering
  \captionsetup[subfigure]{labelformat=empty}
  \captionsetup[subfigure]{labelformat=parens}
    \begin{subfigure}{.12\textwidth}
        \centering
        \label{fig:guiding}
        \includegraphics[width=.9\linewidth]{f1_guide}
        \caption{}
    \end{subfigure}
    \begin{subfigure}{.14\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{f1_target1}
      \includegraphics[width=.9\linewidth]{f1_target2}
      \caption{}
      \label{fig:emb1}
    \end{subfigure}
  \captionsetup[subfigure]{labelformat=parens}
  \begin{subfigure}{.14\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{f1_0_no_guide_no_tv}
      \includegraphics[width=.9\linewidth]{f1_1_no_guide_no_tv}
      \caption{}
      \label{fig:noguide_no_tv}
    \end{subfigure}
    \begin{subfigure}{.14\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{f1_0_no_guide_tv}
      \includegraphics[width=.9\linewidth]{f1_1_no_guide_tv}
      \caption{}
      \label{fig:no_guide_tv}
    \end{subfigure}
    \begin{subfigure}{.14\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{f1_rand_init}
      \includegraphics[width=.9\linewidth]{f1_rand_init2}
      \caption{}
      \label{fig:rand}
    \end{subfigure}
    \begin{subfigure}{.14\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{f1_l2}
      \includegraphics[width=.9\linewidth]{f1_2_l2}
      \caption{}
      \label{fig:l2}
    \end{subfigure}
    \begin{subfigure}{.14\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{f1_dot}
      \includegraphics[width=.9\linewidth]{f1_2_dot}
      \caption{}
      \label{fig:dot}
    \end{subfigure}
    \caption{Original images and face reconstructions obtained using different techniques:
      (a) Guiding image;
      (b) Source of the embedding used for reconstruction;
      (c) Minimizes $d[e_1,e_2] = \| e_1 - e_2 \|_2^2$ starting from random noise;
      (d) Total Variation added for regularization;
      (e) Total Variation and guiding image regularization added on an intermediate layer;
      (f) Same metric as (e), but with the guiding image initialization;
      (g) Same as (f), but with $d\left[e_1,e_2\right] = - e_1 \cdot e_2$.
    }
  \label{fig:set1}
  \end{figure}

  \begin{figure}[b]
  \centering
    \begin{subfigure}{.19\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{guide_3}
      \caption{}
      \label{fig:guide1}
    \end{subfigure}
    \begin{subfigure}{.19\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{guide_4}
      \caption{}
      \label{fig:guide2}
    \end{subfigure}
    \begin{subfigure}{.19\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{f2_target}
      \caption{}
      \label{fig:emb4}
    \end{subfigure}
    \begin{subfigure}{.19\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{f2_male_rec}
      \caption{}
      \label{fig:femmale}
    \end{subfigure}
    \begin{subfigure}{.19\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{f2_female_rec}
      \caption{}
      \label{fig:femfem}
    \end{subfigure}
    \caption{Original images and face reconstructions:
      (a) generic ``male'' guiding image;
      (b) generic ``female'' guiding image;
      (c) image used for calculating the target embedding $e$;
      (d) reconstruction of $e$ with the guiding image (a);
      (e) reconstruction of $e$ with the guiding image (b).}
  \label{fig:set2}
  \end{figure}

\section{Feed-forward network for face reconstruction}
\label{sec:ff}

    Each reconstruction obtained using the methods discussed in \Sec{sec:iterative}, requires hundreds or even thousands of iterations.
    Training a feed-forward neural network capable of reconstructing an image in a single pass could have a significant performance advantage.
    The main idea behind training such a network is based on using the same loss function, which we employed for the iterative face reconstruction.
    Specifically, on each training step, given a random input embedding $e$, the network weights are updated to minimize the loss \eqref{eq:minp} calculated on $e$ and the image produced by the network.

\subsection{Feed-forward network taking embedding as an input}
\label{sec:simple-ff}

  The feed-forward network we used for face reconstruction took a $128$-dimensional vector $e$ as an input and produced a $224 \times 224$ image with 3 channels (see \Fig{fig:arch1}).
  Within the network, a fully-connected layer (followed by a ReLU nonlinearity) was first used to transform the embedding vector into a $8\times 8 \times 16$ tensor (with 16 being the number of ``filters'').
  This tensor had then been passed through a sequence of ReLU deconvolutions, each of which took an input tensor of size $2^n \times 2^n \times L_n$ and producing a tensor of the size $2^{n+1} \times 2^{n+1} \times L_{n+1}$.
  In our experiments, the deconvolution kernel had a size $5\times 5$ and all $L_n$ except for the last one with $L_8=3$ were equal.
  The final $256\times 256\times 3$ tensor had been cropped to the fit the FaceNet dimensions of $224\times 224$.
  Using the same loss function which we used for iterative reconstruction, we could then train such a network to produce a face-like image with a desired FaceNet embedding.

\subsection{Feed-forward network with an embedding and a guiding image as inputs}
\label{sec:image-ff}

  A feed-forward network described in \Sec{sec:simple-ff} can be trained to perform face reconstruction with a set of guiding images instead of one.
  In this case, a sparse guiding image index can be passed to the network as one of its inputs.
  Unfortunately, due to a finite capacity of the network and a need to somehow encode all guiding images in the network weights, this could only be demonstrated for a few, but not even dozens of similar guiding images (taken from frames of a video clip).
  One of the approaches to alleviating this restriction and potentially performing face reconstruction with an arbitrary guiding image is based on passing the guiding image as one of the inputs to the feed-forward neural network.

  \begin{figure}
  \centering
  \captionsetup[subfigure]{labelformat=parens}
    \begin{subfigure}{.49\textwidth}
      \includegraphics[width=0.95\linewidth]{arch_01}
      \centering
      \caption{}
      \label{fig:arch1}
    \end{subfigure}
    \begin{subfigure}{.49\textwidth}
      \includegraphics[width=0.95\linewidth]{arch_02}
      \centering
      \caption{}
      \label{fig:arch2}
    \end{subfigure}
    \caption{
      (a) The architecture of a feed-forward network taking an embedding $e$ as an input and producing an output image $I$ (blue).
          The loss function used to train the weights of this network depended on $e'$ obtained by passing the output image $I$ through the FaceNet model ($\mc{F}$; red).
      (b) The architecture of a feed-forward network taking an embedding $e$ and the guiding image $G$ as inputs.
          The intermediate tensors obtained by applying a sequence of convolutions with stride 2 to $G$ (green) are concatenated to the intermediate tensors in the feed-forward network (blue).
          The output image $I$ is passed through the FaceNet (red) in order to produce $e'$ entering the loss function.
    }
  \end{figure}

  In our experiments, the input guiding image was first padded to the size of $256\times 256$ and then passed through series of convolutional layers of stride $2$ (see \Fig{fig:arch2}).
  Obtained tensors of size $2^n \times 2^n \times \bar{L}_n$ were then depth-concatenated with the tensors of the same spatial dimensions produced through series of deconvolutions as described above.
  In other words, starting with a $8\times 8\times (16 + \bar{L}_3)$ tensor generated from the embedding vector and the final convolution of the guiding image, each deconvolution consumed a $2^n \times 2^n \times (L_n + \bar{L}_n)$ tensor and produced a $2^{n+1} \times 2^{n+1} \times L_{n+1}$ tensor, which was then concatenated with a $2^{n+1} \times 2^{n+1} \times \bar{L}_{n+1}$ tensor obtained from the guiding image.
  The last $256\times 256 \times (L_8 + 3)$ tensor was finally transformed by a convolution operation to produce an output $256\times 256\times 3$ image.

\section{Experiments}
\label{sec:experiments}

  In this section we explore our ability to generate face images using iterative reconstruction with SGD and feed-forward networks.
  The quality of reconstruction measures the quality of our loss function in finding recognizable faces.
  Once we are satisfied with the loss function, that is: we are reasonably confident that the gradient
  descent with such a loss function produces recognizable faces, we turn our attention to feed-forward networks, which are trained to find the optimum of the same loss, but do it in a single pass.

  For our experiments where we want to illustrate the recognizability of people, we choose to use famous people in order to maximize recognizability of the reconstructed images.
  For our guiding images we use publicly available cartoon images from \Ref{public-domain-vectors} as well as averaged images from \Ref{face-research-org}.

  \subsection{Iterative Reconstruction}

  For our experiments we use pre-normalized embedding as an input. Even though, the original FaceNet was trained to differentiate between normalized embeddings and thus ignore
  the difference in $\ell_2$ norm, we find that optimizing a match to pre-normalized embedding produces better results. We conjecture that with normalization, SGD favors smaller embedding values, which
  essentially results in more generic looking image, as illustrated in \Fig{fig:scaling}.
  For our experiments, we use both $\ell_2$ and dot product. Dot product produces significantly sharper, but slightly less
  recognizable images, as demonstrated in \Figs{fig:set1}. Somewhat surprisingly, using normalized $\ell_2$ distance (or, equilvalently, normalized dot product),
  results in much worse reconstructions.
  \begin{figure}
  \centering
  \captionsetup[subfigure]{labelformat=parens}
    \begin{subfigure}{.16\textwidth}
        \centering
        \includegraphics[width=.9\linewidth]{arnold}
        \includegraphics[width=.9\linewidth]{einstein}
        \caption{}
    \end{subfigure}
    \begin{subfigure}{.16\textwidth}
      \centering
        \includegraphics[width=.9\linewidth]{arnold_to_einstein}
        \includegraphics[width=.9\linewidth]{einstein_to_arnold}
      \caption{}
      \label{fig:arn_x_ein}
   \end{subfigure}
   \begin{subfigure}{.16\textwidth}
        \centering
        \includegraphics[width=.9\linewidth]{clinton}
        \includegraphics[width=.9\linewidth]{watson}
        \caption{}
    \end{subfigure}
    \begin{subfigure}{.16\textwidth}
      \centering
        \includegraphics[width=.9\linewidth]{watson_to_clinton}
        \includegraphics[width=.9\linewidth]{clinton_to_watson}
      \caption{}
      \label{fig:watson_x_clinton}
   \end{subfigure}
  \caption{Face transfer: An embedding of one person transferred over to the photograph of another. In all cases network used one photo as guide, and embedding of the other as target.}
  \end{figure}

  \begin{figure}
  \centering
    \includegraphics[width=.85\linewidth]{manypeople}
    \caption{Face transfer examples. It is noteworthy that different people have different degree of recognizability, and some are strong enough to change the
    gender of the guiding image, and some are not.}
    \label{fig:manypeople}
  \end{figure}

  For the example in \Fig{fig:set1} we have used activation target on a single intermediate layer.
  But in the remainder of this section, we attach $\ell_2$-distance loss to multiple intermediate layers as well as use
  Laplacian Pyramid Gradient Normalization~\cite{mordvintsev:15} to change spectral characteristics of the image gradients.
  We find that this technique improves the quality of reconstructed images.
  In figure \Fig{fig:manypeople} we show face reconstructions for multiple
  celebrities. The guiding images that we use are either cartoon-like faces, or average faces from \Ref{face-research-org}. For more face transfer examples including face transfer in a video see supplementary materials.

  We then turn our attention to the total-variation regularizer. In figures \Fig{fig:impact_of_tv}, we show how increasing TV weight affects the image quality. To demonstrate the impact of high TV we did not perform
  any normalization of the image, which results in very subdued images.
  It is interesting to note that
  the embedding $e^*$ of the reconstructed image gets further away from the target as shown in \Fig{fig:loss_for_high_tv} and yet the
  image becomes more recognizable. Another observation that might be of independent interest
  is that images in the right-most column of \Fig{fig:impact_of_tv} (the lowest total variation), are extremely similar, and yet each exhibit some traits of the person whose embedding they minimize.

  \begin{figure}
      \centering
      \begin{subfigure}{.68\textwidth}
        \includegraphics[width=0.9\linewidth]{tv_impact}
        \caption{}
      \end{subfigure}
      \begin{subfigure}{.3\textwidth}
      \centering
      \includegraphics[width=0.99\linewidth]{loss_for_tv}
      \caption{}
      \label{fig:loss_for_high_tv}
    \end{subfigure}
      \caption{
      The impact of changing the TV weight. The first column contains the original images, whose embeddings we used.
      To highlight the difference we did not apply brightness correction. \omtnips{On figure \Fig{fig:normalized} we show the first three images with adjusted brighness and intensity.}
      (b) Distance to the embedding for different values as a function of gradient descent iteration.
      Note: more recognizable images are further away from the target embedding.}
      \label{fig:impact_of_tv}
  \end{figure}
  \omtnips{
  \begin{figure}
      \centering
      \includegraphics[width=0.9\linewidth]{high_tv_normalized}
      \caption{Same as \Fig{fig:impact_of_tv} with adjusted brightness and contrast.}
      \label{fig:normalized}
  \end{figure}
  }

  \begin{figure}
      \centering
      \includegraphics[width=0.9\linewidth]{scaling_l2}
      \caption{Impact on reconstruction using $\ell_2$ as target metric when the target embedding has been scaled by a given factor.
      Notably, the result obtained with $\ell_2$ metric when the target is scaled is very similar to the result obtained with the dot-product loss (the rightmost image).}
      \label{fig:scaling}
  \end{figure}

  \subsection{Feed-forward network}

  Being trained on a set of embeddings and a single guiding image, the feed-forward network described in \Sec{sec:simple-ff} taking a FaceNet embedding as an input and producing an image as its output, succesfully converged.
  The images produced by the network on several embeddings never seen during the training are shown in \Fig{fig:simple-ff}.

  The model was observed to converge faster if the network weights were initialized as follows.
  The deconvolution weights were chosen to produce smooth spatial interpolation with random Xaiver filter-to-filter connections and the final deconvolution was tuned to produce grayscale output image.

  \begin{figure}[b]
    \begin{minipage}{0.6\textwidth}
      \centering
      \begin{subfigure}{.24\textwidth}
        \centering
        \includegraphics[width=.9\linewidth]{ff_01x}
        \caption{}
      \end{subfigure}
      \begin{subfigure}{.24\textwidth}
        \centering
        \includegraphics[width=.9\linewidth]{ff_02x}
        \caption{}
      \end{subfigure}
      \begin{subfigure}{.24\textwidth}
        \centering
        \includegraphics[width=.9\linewidth]{ff_03x}
        \caption{}
      \end{subfigure}
      \begin{subfigure}{.24\textwidth}
        \centering
        \includegraphics[width=.9\linewidth]{ff_04x}
        \caption{}
      \end{subfigure}
      \captionof{figure}{Face reconstructions obtained using a feed-forward network trained with a generic male image from \Fig{fig:guide1}:
    (a) Arnold Schwarzenegger (b) Albert Einstein; (c) Barack Obama; (d) Ronald Reagan.
     Top row uses a dot-product embedding loss and the bottom row uses $\ell_2$ distance.}
        \label{fig:simple-ff}
    \end{minipage}
   \hspace{0.3cm}
    \begin{minipage}{.4\textwidth}
      \centering
      \includegraphics[width=0.98\linewidth]{scatter}
      \captionof{figure}{Scatter plot obtained for 100 embeddings, showing a feed-forward loss function $\mc{L}$ against  iterative loss achieved by minimizing $\mc{L}$ using SGD.}
      \label{fig:scatter}
    \end{minipage}
  \end{figure}

  It is remarkable that seemingly similar images produced with the feed-forward network are still recognizible reconstructions of the provided embeddings.
  This is a demonstration of the fact that only a subtle change of facial features is frequently sufficient to distinguish one person from another.

  \omt{For example one could compare the pixel difference between different images.
  Averaged over a set of pairs of different embeddings, this difference could characterize the extent to which the network modifies the guiding image in order to match the input embedding.}

  \omtnips{While in case of iterative reconstruction, there is generally no expectation of the reconstructed image to be smooth, unless we apply a regularizer, for convolutional networks it is
  smoothness is faciliatedby the fact that lots of parameters are shared across the entire image. One approach quantifying the quality of the constructed feed-forward network is to use the
  average distance between reconstructed and target embeddings.}
  Note that in contrast with iterative reconstruction, the feed-forward network is unlikely to match input embeddings almost exactly by means of adding just a small perturbation to the guiding image.
  Indeed, since this additive perturbation is expected to strongly depend on the input embedding, ``memorizing'' this complicated dependence may require much greater network information capacity
  than producing accurate smooth reconstructions. On the other hand, the final embedding space loss calculated for images produced by the feed-forward network can be used for choosing optimal
  model parameters.
  Table~\ref{table:filters} shows average values of the total loss function, $\ell_2$ embedding space distance and the dot product for the normalized embeddings calculated for several trained feed-forward networks (with $\ell_2$ distance optimization) on a set of 100 embedding vectors.
  Even though the feed-forward network seems to perform best with the largest number of filters, using just $50$ filters already results in $\avr{\tilde{e}_1\cdot \tilde{e}_2} \approx 0.75$, which is greater than $0.6$, the average value generally obtained for different real photos of a single person.

  \newcommand\Tstrut{\rule{0pt}{2.6ex}}
  \newcommand\Bstrut{\rule[-0.9ex]{0pt}{0pt}}

  \begin{table}[b]
    \centering
    \begin{tabular}{ c | c | c | c | c | c | c |}
        \cline{2-7}
        &
        \multicolumn{3}{|c|}{$w_{\rm TV} = 10^{-4}$\Tstrut\Bstrut} &
        \multicolumn{3}{|c|}{$w_{\rm TV} = 10^{-3}$} \\
        \hline
        \multicolumn{1}{|c|}{Number of filters $(L_n)$\Tstrut\Bstrut} &
        \multicolumn{1}{|c|}{$\avr{\mc{L}}$} &
        \multicolumn{1}{|c|}{$\avr{\|e_1-e_2\|_2^2}$} &
        \multicolumn{1}{|c|}{$\avr{\tilde{e}_1\cdot \tilde{e}_2}$} &
        \multicolumn{1}{|c|}{$\avr{\mc{L}}$} &
        \multicolumn{1}{|c|}{$\avr{\|e_1-e_2\|_2^2}$} &
        \multicolumn{1}{|c|}{$\avr{\tilde{e}_1\cdot \tilde{e}_2}$} \\
        \hline
        \multicolumn{1}{|c|}{50\Tstrut} & 15.098 & 0.0544 & 0.752 & 16.927 & 0.0533 & 0.751 \\
        \multicolumn{1}{|c|}{112} & 12.089 & 0.0407 & 0.804 & 14.798 & 0.0431 & 0.788 \\
        \multicolumn{1}{|c|}{250\Bstrut} & 11.142 & 0.0352 & 0.832 & 11.615 & 0.0400 & 0.808 \\
        \hline
    \end{tabular}
    \vskip .2cm
    \caption{
      Dependence of the average total loss $\mc{L}$, average $\ell_2$ distance $\avr{\|e_1-e_2\|_2^2}$ and average dot product $\avr{\tilde{e}_1\cdot \tilde{e}_2}$ calculated for a
     feed-forward network on the number of filters $L_n$ and the TV weight $w_{\rm TV}$.
      Here $e_1$,  $e_2$ are the unnormalized embeddings of the target and reconstruction respectively. Tilde denotes normalized value.
    }
    \label{table:filters}
  \end{table}
  The extent to which the trained feed-forward network optimizes the loss function $\mc{L}$ can be compared to that of the iterative reconstruction algorithm.
  Figure~\ref{fig:scatter} shows a scatter plot comparing the values of $\mc{L}$ obtained using two approaches.
  As one would expect, the average of the full loss function calculated for the feed-forward solutions (for $\ell_2$ embedding space loss and a set of 100 random embeddings) is by a factor of $1.6$ greater than the average loss obtained via iterative reconstruction for the same embeddings.
  Interestingly, the results of the iterative reconstructions are percieved to be worse than those produced by the feed-forward network.
  At the same time, the average embedding space distance between the input and output is significantly smaller for the iterative reconstruction.

  \paragraph{Feed-forward network with an embedding and a guiding image as inputs}
  In our experiments, we trained a feed-forward network described in \Sec{sec:image-ff} on random embedding vectors and $70$ frames of a short video clip treated as a set of independent guiding images.
  After the network had been trained, we used an embedding vector not seen during the training stage and the same frame sequence to produce an animation.
  This procedure is generally sucessfull at performing face transfer from the embedding to the target video clip.
  A few frames from the resulting animation are shown in \Fig{fig:image-ff}.

  \begin{figure}[t]
  \centering
    \begin{subfigure}{.14\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{ffwi_1_z}
      \caption{}
    \end{subfigure}
    \begin{subfigure}{.14\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{ffwi_1_a}
      \caption{}
    \end{subfigure}
    \begin{subfigure}{.14\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{ffwi_1_b}
      \caption{}
    \end{subfigure}
    \begin{subfigure}{.14\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{ffwi_2_z}
      \caption{}
    \end{subfigure}
    \begin{subfigure}{.14\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{ffwi_2_a}
      \caption{}
    \end{subfigure}
    \begin{subfigure}{.14\textwidth}
      \centering
      \includegraphics[width=.9\linewidth]{ffwi_2_b}
      \caption{}
    \end{subfigure}
    \caption{Original guiding images [(a) and (d)] and face reconstructions obtained using a feed-forward network that takes the guiding image as one of the inputs. The network has been trained on 70 frames of a short video clip:
    (a), (b) and (c) 1st orientation; (d), (e) and (f) 2nd orientation.}
  \label{fig:image-ff}
  \end{figure}

\section{Conclusions and future work}

  We have demonstrated that a FaceNet embedding loss coupled with simple regularization functions can be used to succesfully reconstruct realistic looking faces.
  Both gradient descent and simple deep neural networks were shown to produce high-quality results.
  In a way, our work defines two distinct areas that should guide future
  research.
  On one hand, we can explore better regularization functions that might improve the quality of the generated images
  and combine multiple networks for more precise control of the reconstructions.
  For example, it would be interesting to explore the capability of controlling facial expression of the generated image using a network that was
  trained to recognize facial expressions (such as anger, satisfaction, smile etc.).

  On the other hand, in order to achieve fast generation we need to train a deep network that would solve the minimization problem in
  one pass.
  It is interesting that we were able to achieve this without using adversarial learning.
  This suggests that embedding produced by unrelated network is mostly ``complete'' in the sense
  that it contains enough information to reconstruct a recognizable image that matches the original in human understandable sense. An interesting further extension would be to employ more advanced architectures
  including those utilizing recurrent networks. Also, combining our techniques and adversarial learning is a very promising direction.

\paragraph{Acknowledgments.}
  The authors thank Alexander Mordvintsev, Blaise Ag\"uera y Arcas, Eider Moore and Florian Schroff for valueable discussions.

\small

\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


The first Repair Cafe was a success!
What do you do with a broken toaster? Or with a bike when the wheel runs out of true? Or a pair of jeans with a hole? Toss it? No way! Sustainable Saratoga and the Saratoga Springs Public Library are organizing the first Repair Café in Saratoga Springs on June 16th from 11-2.


text:
Home ⁄ Periodic Fables ⁄ GMOs ⁄ Chipotle Sued for False and Misleading GMO…
Chipotle Sued for False and Misleading GMO Claims
Categories: GMOs, Public Policy
Chipotle's announcement it was switching to a "GMO-free" menu earned the Mexican food chain a spate of negative press coverage. News outlets from the Washington Post to NPR pointed out that the move was a "gimmick"—since there's no scientific evidence genetically modified foods are less safe or nutritious, there's no health reason for the switch. Now, Chipotle is facing a lawsuit from a California woman who accuses the chain of misleading consumers with its GMO-free advertising.
In making the switch, Chipotle disclosed on its website that since most animal feed contains GMOs, it's meat offerings will still mostly come from animals fed GMOs. The chain also serves sodas made with GMO ingredients. But you wouldn't know the chain still serves GMOs from its large "G-M-Over it," signs at all its restaurants. The lawsuit contends that since most customers are unlikely to see the disclaimers, Chipotle's advertising is false and misleading.
Chipotle is far from the only company to mislead consumers with its GMO-free labeling. As the Wall Street Journal points out, a number of food companies are putting GMO-free on products that would never actually contain GMOs in the first place. Even brands of salt—a mineral impossible to genetically modify—are labeled "GMO-free." The article states:
While the U.S. government and most major science groups say evidence shows that GMOs are safe, consumer concern has grown so strong that some vendors of products such as blueberries and lettuce are paying for non-GMO labeling even though their products aren't among the small number of crops that are genetically modified in the U.S.
Labeling foods as "GMO-free," especially foods that have no genetically modified counterparts, adds to the misleading narrative that GMOs aren't safe or as healthy. To read more about the marketing gimmicks behind GMO labeling, read my recent op-ed in the Charleston Gazette Mail here.
California Potholes May Soon Be Repaired With Recycled Plastic
Are Environmental Activists Anti-Science? Green Groups Push to Eliminate Key European Science Post
UK Says No Amount of Drinking is Safe. Science Says Otherwise.
Non-Dairy Milk Alternatives Will Not Save the Planet


text:
Voici la liste des maires de Montréal depuis la constitution de la ville de Montréal, au Québec, en 1832.

|Première femme élue mairesse de Montréal

Notes et références

 2
Montreal


text:
Home News Local News Rallying behind storm victims
Rallying behind storm victims
By Cassandra Tanti - October 7, 2020
French President Emmanuel Macron has toured the towns devastated by storm Alex as Monaco continues to do its part for the victims in Vésubie, Tinée and Roya valleys.
Five days after disaster truck, and relief operations continue in the Alpes Maritimes and Liguria, with a total of five bodies recovered since Sunday.
More than 1,000 rescuers are involved in search operations and delivering aid to communities devastated by the floods.
Eight people are still missing while the number of suspected disappearances – people who have not been heard from since Friday – was at least 14 on Wednesday.
Three towns remain inaccessibly by land – Tende, La Brigue, Saorge and Fontan.
French President Emmanuel Macron visited Tende and Breil on Wednesday afternoon, before heading to Saint-Martin-Vesubie.
The government has confirmed a state of natural disaster for the affected towns, facilitating compensation claims.
After a request by the Alpes-Maritimes Regional Health Agency, the Monaco government said on Wednesday that it will be sending 45 nurses and caregivers from the Cardio-Thoracique Center and the Monaco Red Cross to support the Tende Hospital.
https://www.facebook.com/721217880/videos/10158532466712881/
On Tuesday, Minister of State Pierre Dartout announced to the National Council that Monaco would be giving €4 million to relief efforts, to be allocated evenly to each affected French town, as well as to Ventimiglia and the Italian side of the Roya river.
Mayor Georges Marsan and members of the Council will also be making a financial donation to the Department of the Alpes Maritimes to contribute to the reconstruction of infrastructure.
Numerous support efforts are currently underway in Monaco, and the Mairie is now setting up a special collection point at the Espace Léo Ferré on Thursday and Friday welcoming all foodstuffs, hygiene products and other everyday items.
The Unité Légère d'Intervention et de Secours (ULIS) will be responsible for distributing the items to affected communities.
The collection point will be open from 9am to 5pm on Thursday, and 9am to 2pm on Friday.
It is requested that only the following items be donated:
Foodstuffs, Flour, Salt, Sugar, Yeast, Preserves (tuna, pâté, peas …), Pasta, Rice, Lentils, Compotes, Sweets / chips / peanuts, Cereal bars, Cereals, Biscuits, Tea, Instant coffee, Chocolate powder, Jam, Water (still & sparkling), Milk, Fruit juice boxes (large and small), Hygiene products, Soap, Shower gel, Shampoo, Baby & adult diapers, Female hygiene products, Toothbrush and toothpaste, Tissues, Deodorant, Toilet paper, Hairbrush – comb, Candles – lighters, Matches, Camping tableware (cutlery – plates – glasses), Flashlights with batteries, Dry food for animals, Notebooks / Coloured pencils Coloring pages, Scotch, Markers, Ballpoint pens, Pencils, Erasers, Scissors
People wishing to donate other types of food and non-food products other than those listed above can contact the Ulis association directly on 06 60 10 87 68 / http://ulisfrance.fr/contact.php
Top photo: Prefecture des Alpes Maritimes
Previous article3D printed panels to improve marine biodiversity
Next articleASM gets new performance department and director
January 8, 2021 | Local News
Prince Albert: "This is not the America I know"
Prince Albert says that President Trump "has to be held accountable" for his role in the riots at the United States Capitol that left five dead, including a police officer.
Sun. Aug 6. – Symphony concert by...
Local News	Staff Writer - July 1, 2017
Sunday 6 August, 9.30 pm, Prince's Palace – Royal Courtyard Symphony concert by the Monte-Carlo Philharmonic Orchestra, conducted by Marc Albrecht with David Guerrier, trumpet. The programme features Johann Nepomuk Hummel and Ludwig van Beethoven Information: +377 98 06 28 28
Spiderman burglar sentenced in absence
Local News	Staff Writer - May 11, 2016
A serial thief who burgled an apartment in Monaco in November 2014 has been given a stiff sentence, in his absence…
Already the subject of an exclusion order, the native of Marseille was given a two year jail term. He is thought to be hiding from justice in Lebanon.
A resident of The Bermuda apartment block, on the Monaco side of the border with France on avenue Hector Otto, reported the theft of her baggage shortly after returning from a foreign trip. She was alerted to the theft by the slamming of a door.
Although he left by the main entrance, the thief had gained entry by climbing up the lift shaft and climbing on to the roof, from where he accessed the flat by a terrace. His booty has been valued at 50,000 euros.
Article first published May 11, 2016.


by entering the On the House Great Backyard Sweepstakes!
Hey Yardmax fans! Interested in a chance to win the Yardmax Dual Rotating Rear Tine Tiller shown above? Now's your chance! Our friends over at On the House have launched "The Great Backyard Makeover SweepstakesGreat Backyard Sweepstakes," and our tiller is one of the prizes you could win.


We are living in a know-how pushed society. GM announced at CES that the corporate plans to check driverless car expertise by 2015 and have cars on the street round 2018. Generally, dedicated tech media carry out news on enterprise management software program and database administration system. Other companies similar to Cisco and Microsoft have made related moves during the past few months, as pure language processing and voice recognition software are in a position to talk with enterprise software program.
Many information items assist us make our life extra comfy- as we begin utilizing these applied sciences in our own purposes. The latest tech information about the world's finest (and generally worst) hardware, apps, and far more. The Bits newsletter will preserve you up to date on the most recent from Silicon Valley and the expertise industry. You're already utilizing your smartphone, laptop computer or tablet to share or work on recordsdata-now you can print from them using the HP 1200w Cell Print Accessory for NFC-enabled gadgets.
Products such as MaaS360 present the control and safety organizations must manage units and mobile platforms with a real 360 view of who's utilizing what, where and how. Connecting resolution makers to a dynamic network of information, folks and ideas, Bloomberg rapidly and accurately delivers business and financial information, news and perception world wide. Users are reminded that they're absolutely accountable for their very own created content and their very own posts, feedback and submissions and totally and successfully warrant and indemnify Journal Media in relation to such content and their potential to make such content material, posts, comments and submissions available.
Whether employees are utilizing their very own or firm-issued devices, IT managers should have a approach to handle all the users, gadgets and data from a central console. When you run an IT company, subscribing to blogs on technology might help you study the latest trends in the market. Know-how information is for everyone. It is a contactless payment technology that permits customers to pay with their iPhone, Apple Watch, or iPad (Air 2 or mini 3)-no actual wallet or credit cards required.
Boasting innovative structure design inspired by Google Materials Design, TechNews is a multi-proficient Joomla template that can be utilized as a base for a spread of magazine portal web sites, overflowing with options together with three header kinds to choose from, publish previews and slideshows in a responsive structure that appears amazing on every type of modern machine.