I've written and said it many times. AEC firms have many different paths to success. There is no ONE single formula that always works better. Nor is there one magic bullet – a single thing a firm does or way it does something that makes it successful. Usually, it is a unique combination of many different things that makes up the "success" formula.
Spend your time on what's working versus what isn't. Our tendency is to do the exact opposite. We spend time and energy and resources trying to fix what isn't working instead of spending the same time, energy, and resources on what IS working. As a result, we get frustrated and fail to capitalize on the opportunities that are right there in front of us. And remember – if something isn't working – maybe there is a reason outside of your control.
If you are the boss, one of, if not the single most, important things you do is recruit. You have to constantly build your team. You cannot just delegate this crucial activity to others in your organization. It will take your active personal involvement to find the truly great people you need to fill vacancies as well as those you need to add because they will bring new opportunities to the rest of your team. Recruiting takes selling. You have to be selling people on why your firm is a great place to work. It also takes creativity. You have to find new people using methods that everyone else isn't using. If you think that means going to LinkedIn, you're wrong!
Getting your second-in-command in place is crucial! It will ensure continuity of the firm if something happens to you. It is also the only chance you have at personal liberation. And it cannot happen overnight. The person you select has to earn the respect of everyone else in your firm. If they can't, they won't ever be able to succeed you.
Everyone who works for you needs their second-in-command on board as well. Because if they don't have them, you're vulnerable. Someone could get sick or die or suddenly leave for a new opportunity. You cannot afford to be setback by a problem that you should have anticipated. That's just stupid.
Marketing takes a continuous and increasing investment if you want your firm to grow. Show me a firm that isn't growing over a couple-year period and I will show you one that hasn't increased its marketing budget during that time frame. There is a direct link between what you spend and what comes out the other end. Not to say all marketing expenditures are good. Some work and some don't. That said, NOT spending more and expecting revenues to go up is unrealistic.
Get someone good answering your phones. This is no place for someone who sounds like they've had a lobotomy or cannot pronounce your company name or always gets phone numbers and names confused. You need someone intelligent and motivated on that front desk! So be equipped to pay better than starting wages for a McDonald's worker if that's what you want. Have a high standard for this crucial front door to your company!
I could go on but I'm out of time. Have a wonderful week and make it happen!


Yesterday, STAWP was ranked #43 which was a-ma-zing!
If you like the story, please help me get to #25 by remembering to vote for any chapters you enjoyed!
It hurts to even consider the possibility of Logan and Jess together. A lump forms in my throat and I feel a stabbing pain in the vicinity of my heart. It can't be true. Logan said he liked me. The passion between us, I didn't imagine that. It meant something and there's no way Logan would be doing that with another girl.
I turn to Logan, begging his to tell me I got it all wrong. He has to have an explanation. Maybe Jess is angry about something else, like homework. I just need Logan to explain. To say something. Anything!
He doesn't. Logan doesn't even look at me, he's too busy staring Jess with obvious regret. "I—I didn't mean..." he says, shooting a guilty look my way and then quickly looking away. "It's not what it looks like."
He can't even look at me. I think. Why would he? I'm just the new girl he made the mistake of hooking up with, and Jess is his... Jess is his... I can't even finish that thought.
Logan opens his mouth to tell Jess something—make up some stupid excuse is my best guess—and then closes it. He doesn't even spare me a glance.
I take a step back, but Jess's got my arm in a death grip. I yank my arm out of her grip and that startles her out of her trance. She stops glaring at Logan and grabs my arm again. This time, she gives my arm a yank, drags me into Zara's and my room, and slams the door shut behind us.
I'm too hurt at Logan's betrayal to protest. I keep waiting for him to rush in after us—after me—but he doesn't. Jess stares at the door too, as if waiting for the same thing. One second passes, then five, then ten and eventually a full minute. Logan isn't coming.
My lower lips trembles and I look at Jess, who's openly crying. Tears spill down her cheeks, her face contorted with pain. There's no doubt in my mind now that he cheated on her. Cheated on both of us. How  could I have thought he was a good guy? How could I have trusted him?
Seeing Jess crying prompts tears to spring to my own eyes. I wipe them away with the back of my hand, which catches Jess's attention.
"I'm sorry," she whispers, pulling up the hem of her shirt to wipe away her tears. Her stomach is flat and tanned, and her waist narrow and girly, so unlike my own, which is pale and not very feminine. It's hard not to have muscle living with dad, hunting for him, fighting him off, and constantly being attacked by Rogues. "I'm so sorry."
"It's not your fault," I tell Jess. It's not like she's responsible for what Logan does, even if they are together. If anyone should be apologizing, it's Logan. He's the cheater.
"I should have warned you." Jess growls.
"Warned me?" Has he cheated on her before? Does he make a pattern of hooking up with new girls? The thought of him making out on the bathroom floor with someone else hurts.
"Logan and I..." Jess takes my hand and pulls me to sit on the bed next to her.
I wince at her words—at the thought of there being a Logan and her, or a Logan and anyone, unless that someone is me—and then cringe at how pathetic I am. I actually fell for Logan's act. I thought we shared something special, even if that something would end when I ran away. Instead, all I've been is the other girl. A random hookup. Someone Logan won't think about or remember. He'll probably be relieved when I'm gone. He definitely won't miss me.
"We dated for a year," Jess tells me.
I nod for her to continue, trying to ignore the pain in my chest. It's bad enough knowing there's a her and Logan, and I'm no one to him... but that they've been together for a whole year? No wonder he didn't spare me a second glance. I'm just the girl he cheated on his long-term girlfriend with.
"He broke up with me fifty-three days ago," Jess adds.
"Fifty-three days..." I trail off. Not two months. Not eight weeks. Fifty-three days. I'm so focused on her odd phrasing that it takes a second for it to really sink in. Jess and Logan aren't together.
"Right before his birthday. He turned seventeen." A single tear sliding down her cheek.
"Oh." I feel like an idiot for not figuring it out sooner. It should have been obvious. I mean, I'm a wolf! I should auto-associate mates and birthdays. I guess I've spent so much with humans, sometimes I forget.
Logan's that guy. The one Zara told me about. The one who broke up with her on his birthday, when he found his mate. Wait! He cheated on his mate? With me? Who is his mate and why the hell would he do that?
"I loved him," Jess continues, oblivious of my racing mind. "I was so sure he'd be...," she trails off and shakes her head, "but he wasn't. He didn't even care." She grimaces. "He's heartless."
"So he found his mate and..." I trail off, trying to put the pieces together.
"He didn't." Jess shakes her head.
"So he doesn't have a mate and you two aren't together?" I ask, relieved but very confused. If Logan didn't cheat on her, and he doesn't have a mate, then why is it such a big deal that I hooked up with him?
Jess shakes her head. "Don't do it, Sofie," she warns me, her expression turning intense.
"Do what?" I ask. What's going on? Why did Jess pull me away from Logan? And why did he look so guilty?
"Don't fall for him," Jess warns.
"No!" Jess stands up, hands on hips, and glares at me. "This is what he does," her voice grows louder with every word. "He'll make you fall in love with him and then he'll break your heart and stomp all over it." I open my mouth to speak, but Jess continues shouting, her face and neck turning an angry red. "I hate him for what he did to me. He's a cold, heartless, player. I'm not going to let him do this again! I'm not going—"
Someone throws open the room door, interrupting Jess mid-shout, and we both turn to look.
Who's at the door, you guys?!?!?!?!
Remember, if you enjoyed this chapter, fill the star orange !!
Enough weirdness for today! Expect a new chapter tomorrow: Thursday, November 16th.


ACLS or Advanced Cardiovascular Life Support is a certification required by health care providers who deal with acute care or emergency services. This certification will give you the skills and knowledge needed in administering a set of extremely important interventions in the urgent treatment of life-threatening medical emergencies (such as cardiac arrest or stroke).
Besides being a great way to expand your knowledge base and getting prepared to perfume advanced resuscitation, this certification is also required by most hospitals and emergency services in the US. Even more, it is important that you obtain the certification through the American Heart Association or AHA.
It is important to make sure that your certification is supported by AHA because otherwise the certification won't be valid. An AHA approved certification requires two steps: a written exam and a practical test where you prove your skills.
There are two main paths to study for an ACLS certification: go to classes in your area or study at home using resources you can find online.
If you decide to go with classes, you should look for hospitals or medical education facilities that provide the knowledge and first aid training. Of course, you will have to pay to attend these classes.
If you don't have the time to go to classes, you can always study on your own, but you must know how to choose your resources. There are many books and online courses that promise to get you through both the exam and practical test but only a few of them are the real deal.
To help you choose, we put together a top 5 of the best ACLS study guides on the market. Below you can read our reviews and pros & cons on each resource.
A great book for both certification and re-certification in ACLS, this guide of 208 pages is a must-have. The book is approved by the American Heart Association and the authors are the ones who actually put together the topics for the exam.
It is also recommended for all ACLS classes so you can see why it must be your number one resource for your preparation.
The guide offers detailed information on what you need to know for the exam and all the explanations are clear and to the point. Nothing is left out and you don't have to learn extra like with some guides.
We like that it comes with the ACLS Pocket Reference Card Set, a foldable insert that contains a condensed version of ACLS protocols. With this guide you'll get the ACLS Acute Coronary Syndromes and Stroke card, and the ACLS Cardiac Arrest, Arrhythmias, and Their Treatment card.
However, it has a weird structure so it may take you some time to organize everything in a more effective manner. Also, on the downside, we should mention that there are no practice tests or questions to help you organize your learning process and identify possible weaknesses.
The ACLS Study Guide from ECC is one of the most complete and well-organized guides out there. In 424 pages, it teaches students about providing the best possible emergency cardiovascular care in an easy-to-read approach.
The guide comes with hundreds of colorful photos and illustrations that make learning and understanding difficult concepts a lot easier. You'll find everything you need, starting with airway management and ending with cardiac arrest rhythms and how to deal with the situation. There is also useful information on acute strokes and coronary syndromes, and electrical therapy which comes in handy when you have to prove your skills.
The guide is updated with the latest ACLS algorithms and includes case studies to help you apply the theoretical knowledge to a real-life situation. Through case studies you'll learn how to apply your skills in cardiac rhythm interpretation, administering the correct medication, and more. The best part is that the guide integrated case studies on each chapter. This way, you can apply the theoretical knowledge as soon as you learn it.
The ACLS Study Guide, 5e also includes ACLS pearl boxes where you can read brief explanations on complex topics. This way, a difficult topic is broken into simple ideas, making it easier to understand and process.
We also like the end-of-chapter quizzes. These are paramount for students who want to make sure they remember the information correctly. These quizzes will also help you learn about your weaknesses and assess your current state of preparedness.
Finally, this guide is based on practice recommendations from the American Heart Association for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care the 2015 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science (with treatment recommendations).
If you're looking for a great guide for your ACLS preparation, the ACLS Review Made Incredibly Easy should be the perfect choice. The guide is organized in such a way that difficult concepts seem easy to understand and boring definitions are easy to remember.
Such a fantastic content organization is possible due to step-by-step directions on treatment algorithms (the latest in the field) and easy to read reviews of the exam content. Even more, the content is addressing both first-time students and healthcare professionals.
2) Updated information on interventions such as basic life support skills, defibrillation and the adult cardiac arrest algorithm.
Of course, this is a summary, but the information is a lot more comprehensive. Even more, the content is enhanced with colorful diagrams and illustrations that describe various interventions and core concepts.
Besides reviewing the content for the exam, the guide also offers valuable information on how the exam works (to get you prepared for the atmosphere) and offers useful study strategies. The strategies include end-of-chapter quick quizzes and there is a practice test at the end of the book.
At the end of the day, the ACLS Review Made Incredibly Easy guide is a valuable resource in your kit.
This is a small guide (only 104 pages) and it will only be useful in combination with one of the guides discussed above. First, you should know that this book is not supported by the American Heart Association (which is against our recommendations). However, it is in top 5 resources we propose because it comes in handy when you want to review the knowledge you gathered.
Please keep in mind that the information mentioned above is in a compacted form and the guide won't be helpful if you're not already familiar with the concepts.
Finally, the guide ends with 160 review questions, which is, probably, the most appreciated part.
This 130-pages guide for ACLS exam preparation is up to date with the latest procedures and treatment algorithms. The content is well-organized and the concepts are well explained, but not everything is covered. This is what makes this guide better for review sessions than for learning from scratch.
Many professionals find it useful due to the well-designed reviews and the updates on the latest guidelines and research in the field. Even more, the price is quite affordable and besides the necessary material, the guide comes with 101 practice questions.
All the questions are designed to simulate the exam to make sure all students will be prepared for the atmosphere. Still, if you're already familiar with the exam, you can use these questions in identifying your weaknesses and assessing the situation of your preparedness.
The 'ACLS Certification Exam Q & A with Explanations' is mostly oriented towards healthcare professionals (physicians, nurses, paramedics, residents, dentists) who need to re-certify their ACLS. Still, it will be just as helpful to people who are taking the exam for the first time and went through all the necessary resources.
The author likes to keep in touch with the students so he created a YouTube channel where you can watch more in-depth explanations as for how to use the guide and how it can help (more information about this in the guide). Also, you can email the author with various questions about the book or about life as an ACLS graduate.
On the downside, many students complained about the amount of typos throughout the book. However, this can be put aside if you consider this guide's value for both students and healthcare professionals.
ACLS is no joke and the exam is among the most difficult ones you'll ever have to take. After all, you will have to know how to react in emergency situations and how to apply the correct procedure when your patient's life is hanging by a thread. There will be no room for mistakes!
Still, with the right resources, you will pass the exam with astonishing results and you'll get to practice saving lives every day. If this is what you want, we recommend starting with the first item on our list (Advanced Cardiovascular Life Support (ACLS) Provider Manual 2015 Guidelines). It may be a bit more expensive, but it is AHA approved and comes with everything you need to know.
If you feel adventurous and want to explore more in-depth, we recommend the ACLS Study Guide, 5e (second item on our list). It is affordable and comes with more in-depth information (compared to what you need for the exam).
Finally, if you're looking for guides to help you review what you already know, take a look at the ACLS Provider Manual Study Guide and the ACLS Certification Exam Q & A with Explanations. These are both easy to browse and quite affordable.


Since 1973, The ArQuives (The ArQuives) has been acquiring and preserving material documenting LGBTQ+ history. The ArQuives's mandate is to acquire, preserve, organize, and provide public access to information and materials in any medium, by and about LGBTQ+ people, primarily produced in or concerning Canada.
The ArQuives houses papers and records documenting many Canadian LGBTQ+ individuals and organizations. More detailed information about fonds and collections at The ArQuives can be found on the Finding Aids and Guides page.
The ArQuives houses the world's largest collection of LGBTQ+ periodicals. We currently have over 9,700 unique titles in the collection. The guide "Our Own Voices: A Directory of Lesbian and Gay Periodicals, 1890s to 2000s", compiled by long-time The ArQuives volunteer Alan Miller, lists more than 7,200 titles from all over the world, most of them held at The ArQuives.
Our collection of books and monographs, named for The ArQuives's first volunteer archivist, James Fraser, contains over 11,000 titles from both Canadian and international authors. The collection consists of both non-fiction and fiction, including a large collection of "pulp" fiction. Material in the James Fraser Library is non-circulating and available for reference only.
The ArQuives maintains a collection of vertical (subject) files relating to people, organizations, and events. The majority of these files relate to subjects in Canada, while the remainder are organized by the United States and other international countries. Guides to the over 33,000 vertical files can be found on the Finding Aids and Guides page.
The ArQuives's collection of photographs includes prints, negatives, slides, and digital files, documenting LGBTQ+ people, locations, and events, such as demonstrations, conferences, parades, parties, plays and shows. Many of the photographs form part of an archival fonds or collection.
The poster collection consists of thousands of posters, of all sizes, relating to parties, dances, concerts, bars, plays, films, and art exhibitions. A guide to the posters can be found on the Finding Aids and Guides page.
The Music and Audio collections contain reel-to-reel tapes, vinyl LPs, audiocassettes, and digital media. The collections consist of all genres including, but not limited to, commercially produced music, oral histories, radio broadcasts, and recordings of events or political protests.
The Moving Images collection includes films, filmstrips, videocassettes, DVDs, and digital media. The collection consists of all genres, including, but not limited to: feature films, documentaries, footage recorded from television programs, original productions, and erotica.
In addition to records and published material, The ArQuives also collects artifacts relating to the LGBTQ+ communities. The artifact collection includes buttons and pins, t-shirts, matchbook covers, trophies and plaques, and leather gear.
The Archives accepts paintings, sculptures, drawings, and other creative works that are created by or have historical and cultural significance for LGBTQ+ Canada. For more details, see the Art Collection Policy.
More detailed information about what we collect can be found in our Acquisitions Policy.


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Home » Key EU Privacy & Cybersecurity Highlights, November 16 – November 22, 2015
Key EU Privacy & Cybersecurity Highlights, November 16 – November 22, 2015
By Lisa Weinert on November 25, 2015
Posted in Cybersecurity / Data Security, Data Breach, Government Agencies, Privacy, Public Sectors
Record Fine: Belgium's Court orders Facebook to stop Data Protection law violation under forfeiture of a penalty of € 250,000 per day; Big Data: Opinion of The European Data Protection Supervisor; Safe Harbor Topic 1: Hamburg DPA actively preparing enforcement actions; Data Protection vs. Terrorism: Belgium to push for Passenger Records Law following Paris attacks; Safe Harbor Topic 2: EU Chief Jourova confident about ongoing Safe Harbor negotiations; Safe Harbor Topic 3: Norwegian DPA requires authorization of US data transfers.
Penalties and Fines: Belgium's Court orders Facebook to stop violations of Belgium Data Protection Act under forfeiture of a penalty of €250,000 per day
A Belgian Court has fined Facebook €250.000 per day for violations of the Belgian Data Protection Act.
Facebook had collected web data of millions of Belgians who are not members of Facebook's social network page, but were simply visiting websites. The Court in its judgment of 9 November 2015 found that this way of collecting data is a "manifest" violation of Belgian data protection law. According to the court, this applies irrespective of the purposes Facebook uses this data after having collecting it. Facebook argued that European users of its social network are subject to the Irish Data Protection Law (instead of Belgian law). The court disagreed citing the well-known Google Spain case that ruled that a Member State law applies if the activities of a local establishment are inextricably linked to the activities of the data controller.
The Court ordered to stop the violations under forfeiture of a penalty of €250.000 per day. The court based this on the consideration that the penalty's amount needs to be sufficiently deterrent. The Court pointed out that Facebook in 2014 realized a turnover of US-$ 12.4 billion and a profit of US-$ 2.9 billion, so that the amount of € 250,000 per day was considered adequate. Facebook has announced that it will file an appeal against the judgment, which however does suspend the initial judgment.
Big Data: Opinion of The European Data Protection Supervisor (EDPS)
The European Data Protection Supervisor (EDPS) on Nov. 19 issued an Opinion regarding the challenges of Big Data. It calls for transparency, user control and data protection by design and accountability, principles which are enshrined in the draft General Data Protection Regulation.
The EDPS discusses the opportunities, risks and challenges of Big Data and Big Data Analytics, provides recommendations, and discusses next steps to put the principles into practice, with reference to the General Data Protection Regulation.
Adequacy: Hamburg DPA is Engaged in a Process to Identify and Prosecute Data Transfers to the US
The German state DPA of Hamburg is actively examining which companies are or plan to send data to the US over the next two months. In the aftermath of the October 6 European Court of Justice ("ECJ") decision invalidating the original safe harbor agreement that had been in effect for fifteen years, the DPA therefore keeps taking an aggressive approach among all DPAs of the European Member States.
The DPA has announced that companies, who are not making efforts to comply with the ECJ decision, will be subject to enforcement in form of fines, starting at the end of January 2016.
The German state authority has also commented on the "consent solution" currently used by Google. In that regard, the DPA of Hamburg has apparently sent "orders" to Google to i.a. provide documentation in order to allow it to investigate whether the "consent solution" of Google results in a valid consent that would allow the transfer of personal data to the US.
Belgium to Push Ahead With Passenger Records Law as a result to the Paris attacks
Belgium is pushing ahead the finalization of a Passenger Records Law as a result of the recent terrorist attacks in Paris. This law is aimed to apply to international trains as well as aviation, Belgian Prime Minister Charles Michel said on Nov. 19.
The release of this law is just one of many steps the Belgian government is taking in order to try and increase the security level.
EU Justice Chief Vera Jourova Updates on New Safe Harbor Pact Negotiations
Safe Harbor Invalidation, the 2nd:
In the aftermath of the October 6 European Court of Justice ("ECJ") decision invalidating the Commission Decision that put in place the EU-US Safe Harbor agreement that had been in effect for fifteen years, the EU and the US continue negotiations on a solution for the EU-US data transfers.
On 16 November, EU Justice Chief Vera Jourova gave an interview to the Wall Street Journal, in which she stated to be confident about coming to an agreement prior to the end of the so called "grace period" (i.e. the period during which Data Protection Authorities agreed not to take enforcement measures in the aftermath of the ECJ Safe Harbor judgment), ending on 31 January 2016. Jourova stressed that "given the sense of urgency, which both [sides] understand — it [was] high time and …very useful and necessary that [the EU and the US] meet on the highest possible level."
Items that are still under negotiation are those that the court had emphasized in its October 6 ruling]. However, Jourova is positive about things being achievable and negotiable. "Of course when you go to technical details, you see that we will still need some time. We are under huge pressure because we mean it when we say that we need to finalize this within three months. I would like to have it earlier for the sake of urgency and necessity to guarantee the legal certainty for the businesses."
Adequacy: Norwegian Organisations Must Obtain the Authorisation of the DPA Prior to Conducting Data Transfers to the US
Safe Harbor Invalidation, the 3rd:
In the ongoing discussions about the future of EU-US data transfers after the October 6 decision of the European Court of Justice, the Data Protection Authorities of the Member States keep testing their newly discovered powers and independence.
The Norwegian DPA announced that it discourages the transfer of personal data to the US based on the consent of the individual alone, but rather recommends obtaining the DPA's approval prior to conducting any transfers to the US. But it seems that if the data transfers are based on the so-called "standard contractual clauses" recognized by the EU Commission, the approval of the DPA should be obtained.
The Norwegian DPA therefore follows the approach of many European DPAs who require an application for authorization, or at least a filing of the contract used for conformity check with the "Standard Contractual Clauses".
Earlier, some German DPA's also already declared that consent might only serve as a justification for data transfers "in exceptional cases."
Tags: Cybersecurity / Data Security, Data Breach, Government Agencies, Privacy, Public Sectors
Comment Period Extended for NIST SP 800-171 Assessment Guide
Key EU Privacy & Cybersecurity Highlights, November 30 – December 6, 2015
Privacy-Cybersecurity Weekly News Update November 29- December 4, 2015
Key Privacy & Cybersecurity Highlights, November 2 – November 8, 2015
Key Privacy & Cybersecurity Highlights, October 26, 2015 – November 1, 2015
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I thought i'd do a little blog about each of the girls just giving some highlights, details, personality, and extra info you don't get everyday on each of the quads. That way you guys get a little extra info on their personalities, and i get to kind of journal things so i don't forget them! Hope you enjoy!
Today we will start with Indie!
Indie was the first of our daughters that was named. A little detail behind the reasoning; we knew when we traveling to Los Angeles to have surgery on her and Esme, that they needed a name. We wanted to be able to address them by name when praying to our Father in Heaven at that point. Both twins were given their names, Indie first, as she was in the most danger at that time!
Indie's middle name Mae comes from my Grandmother on my mother's side, Edna Mae. She was one of the most incredible people i've ever known. She could be the most loving, caring, and thoughtful person, and at the same time whip you into shape in the blink of an eye if she felt it was needed. She was a great role model to me of love, faith, hard-work, conviction, truth, kindness, and strength. I spent many days with her growing up, developing a bond. I have the fondest memories of my grandma, and i am grateful for that relationship. We knew when we were naming Indie under the circumstances we were, that it was only fitting her middle name be Mae, after Edna Mae. So far, Indie has lived up to her namesake as she has been one tough cookie from day one!!
Indie was our first born, and also our smallest baby. She was born at 1 lb and 13 ounces, but got down to 1 lb and 6 ounces after birth.
Indie was at the bottom of the pile during pregnancy, holding all of her sisters in as long as she could! Ashley sometimes affectionately refers to her as "the plug".
Although our smallest, Indie has consistently been one of our most talkative, and at times loudest baby. Size makes no difference for this little spitfire. She'll let you know just hows she's feeling at any given moment. She has consistently let her sisters know who's the oldest!
Indie is our most Independent daughter at this point. She loves to do her own thing. When we let the girls run wild, you'll usually find Indie by herself in the corner with a book, or in front of the tv listening to her songs. While the other girls are running wild, Indie is doing her thing!
Indie is a momma's girl. She loves her momma. She loves to snuggle her momma. When she is sad, she looks for her momma. She was our first to really het jealous when mom would hold one of the other girls. It was almost sad, but more cute.
Indie was the last to walk, but first to talk. She's very smart. I just watch her sometimes, and i can see her just studying everything, including people. She watches her sisters, and then reacts. She literally thinks about what she's going to do before she does it. It has been very interesting to watch.
Indie is so sweet. I think that is why she has the nickname "Pie". She's just as sweet as pie. She'll give you a smile if you want one. She's cuddle with you, if the mood is right. She always plays nice with her sisters, and she loves everyone. She's just a sweet girl!
Hope you enjoyed a few extra stories about our Indie Mae!


text:
Šibeniční vrch je táhlý zalesněný dvojvrcholový hřbet (590 a 573 m) v nejsevernější části České republiky, v okrese Děčín, v Lužických horách, vybíhající z masívu Jedlové do údolí Lužničky k Hornímu Podluží. Nad okolní droby, ležící při lužickém zlomu, zde pronikl výlev znělce. Pod skálou na severním úpatí stála popravní šibenice města Jiřetína pod Jedlovou.

Dnes je na severním svahu asi 600 metrů dlouhá upravená lyžařská sjezdovka s převýšením 120 m s vlekem.

Popis
Šibeniční vrch korunovaný šibenicí je zakreslen v plánu z Jiřetína a okolí z roku 1745. Je zde nazván "das Gerich". Jak byl používám ve starší době není doloženo. Jsou známé tři rozsudky smrti, o kterých v konečné instanci rozhodoval tzv. apelační soud v Praze. První rozsudek pochází ze dne 19. prosince 1724, a týká se Cathariny Gärtnerové, která měla být popravena mečem. Pro hrdelní svatokrádežní zločin byl odsouzen Friedrich Rauch. Ortel apelačního soudu v Praze, kterému byl případ postoupen soudem v Oudolu svatého Jiří zněl, že delikvent má být kvůli svému těžkému zločinu, jiným na postrach a za příklad, jemu samému pak zasloužený trest, provazem jej od života k smrti odpraviti a potom pod šibenicí spáliti. Poslední rozsudek smrti byl vynesen z důvodu lotrovství nad Georgem Ulbrichtem. Ten byl postižen těžším koncem. Nejdříve měl být před radnicí zašit do volské kůže, v ní vláčen na popraviště, tam mu měl kat zad vyříznout pruh kůže a konečně měl být od shora dolů kolem přiveden od života k smrti, jeho tělo vpleteno do kola a tak vyzvednuto do vzduchu. Za exekuci muselo město Jiřetín pod Jedlovou zaplatit 84 tolarů, 4 groše a 7 feniků.

Při jižním úbočí je vedena červeně značená turistická trasa k rozhledně na nedaleké Jedlové.

Odkazy

Reference

Literatura

Externí odkazy
 Podrobný popis Šibeničního vrchu

Hory a kopce v Lužických horách
Hory a kopce v okrese Děčín
Jiřetín pod Jedlovou

redpajama_set_name: RedPajamaWikipedia


text:
\section{Introduction}

The $\Lambda$CDM of cosmology has been very successful in describing observations of
various  data. While it is the simplest model that is
consistent with the data, there is a plethora of other viable
cosmological models (for reviews see \cite{Copeland:2006wr,
  Clifton:2011jh}). A common approach is to fit these models to the
data and apply model selection techniques to determine the most likely
theory.

An alternative approach is to perform model-independent
reconstructions, which can be used to determine deviations from
$\Lambda$CDM. This is particularly important when physical models are poorly motivated from a fundamental theory, as is the case for most dark energy models. A range of consistency tests have been introduced to
probe various aspects of $\Lambda$CDM \cite{Clarkson:2007pz,
  Zunckel:2008ti, Sahni:2008xx, Shafieloo:2009hi, Nesseris:2010ep,
  Seikel:2012cs}. For these tests it is crucial to use a
non-parametric method to reconstruct functions such as the luminosity
distance $d_{L}(z)$ or the Hubble rate $H(z)$. Gaussian processes (GP)
provide such a method, which is also capable of reliably
reconstructing derivatives of a function. GP have been applied to this problem in
cosmology in \cite{Seikel:2012uu, Seikel:2012cs, Yahya:2013xma,
  Holsclaw:2010nb, Holsclaw:2010sk, Holsclaw:2011wi, Shafieloo:2012ht, Shafieloo:2012ms, Nair:2013sna}.

In this work, we focus on some aspects of GP reconstructions of dark
energy from supernova (SN) data. The luminosity distance is given by
\begin{equation}
d_{L}(z) = \frac{c(1+z)}{H_0 \sqrt{-\Omega_k}}
\sin{\left(
\sqrt{-\Omega_k}\int_0^z{\mathrm{d}z'\frac{H_0}{H(z')}}
\right)}\,,
\end{equation}
where
\begin{eqnarray}
&& H(z)^2 =  H_0^2 \left\{ \Omega_{m} (1+z)^3 + \Omega_{k}(1+z)^2 \right. \\
&&\qquad{} + \left. (1 - \Omega_m - \Omega_k)\exp{\left[3\int_0^z
\frac{1+w(z')}{1+z'}\mathrm{d}z'\right]} \right\}\,. \nonumber
\end{eqnarray}
Using the dimensionless comoving distance
\begin{equation}
D(z)=(H_0/c)(1+z)^{-1}d_L(z)\,,
\end{equation}
we can write the equation of state $w(z)$ as
\begin{eqnarray}
&&\!\!\!w(z)=\{2(1+z)(1+\Omega_kD^2)D''-[(1+z)^2\Omega_kD'^2\nonumber\\&&
+2(1+z)\Omega_kDD'-3(1+\Omega_kD^2)]D'\}/ \label{w} \\&&
\{3\{(1+z)^2[\Omega_k+(1+z)\Omega_m]D'^2-(1+\Omega_kD^2)\}D'\}\,,\nonumber
\end{eqnarray}
where $'=d/dz$.

Reconstructing $w(z)$ from distance data requires the knowledge of the
density parameters $\Omega_m$ and $\Omega_k$. As there are
degeneracies between the density parameters and the equation of state
\cite{Kunz:2007rk, Clarkson:2007bc, Hlozek:2008mt, Seikel:2012uu},
these quantities cannot be determined by considering distance
measurements only. In this work, we only use mock data sets and thus
have the luxury of being able to fix the values for the density
parameters. We use $\Omega_m=0.3$ and $\Omega_k=0$ throughout the
paper. In practice, these degeneracies are
problematic.  Despite these concerns, we
focus on the reconstruction of $w(z)$ with fixed density parameters in
this work because our main interest here is to analyze how well the
input model (which is determined by its equation of state) can be
recovered by GP reconstructions.
While we focus on the reconstruction of $w$, we also reconstruct the
deceleration parameter,
\begin{equation}
q(z) = - (1+z) D''(z)/D'(z) - 1\,,
\end{equation}
for some tests.

For a non-parametric regression method such as GP, our aim is to produce confidence limits such that we trap the true function appropriately. For example, for repeated realisations of a data set for a given model we require that our confidence limit of 95\% traps the correct model in 95\% of realisations. For~$w$  we are not just trying to reconstruct a function from data, but a complicated function of its first and second derivatives as well. This makes it a much more complicated problem for which we need to identify appropriate covariance functions which can reproduce expected models accurately.
We introduce a family of 4 $w(z)$'s in addition to LCDM of increasing
complexity which we attempt to reconstruct with a fixed number of SNIa
(about 500). These models are not intended to be physical, but rather to
illustrate the types of features we might be able to constrain.  We
analyse 4 different covariance functions which each have one amplitude
parameter and one length parameter. These are a simple Gaussian, and 3
functions of the Mat\'ern class.
The main difference between these is the widths of their peaks which
influence the strength of the correlations between function points at
different redshifts.
We identify the one which gives the most accurate confidence limits for the number of SNIa we consider. Clearly, for 500 SNIa we can only expect to reconstruct one `feature' in $w$, but it is still important that for more complicated $w$ we trap the function appropriately.

This work is structured as follows: The basics of GP reconstruction
are summarized in
section~\ref{gaussianprocesses}. Section~\ref{mockdata} lists the
models for which we create mock data sets.
Section~\ref{examples} provides several example reconstructions. In
section~\ref{coverage}, we perform coverage tests which determine the
reliability of the error bars of the reconstructions. These tests
allow us to choose a suitable covariance function. We introduce a
method to determine deviations of a model from the GP reconstructions
in section~\ref{consistency} and conclude in section~\ref{conclusions}.

\section{Gaussian processes}\label{gaussianprocesses}
GP provide a technique to reconstruct a function from observational
data without assuming a specific parameteriziation.  Derivatives of
the function can also be reliably reconstructed. A detailed
description of GP can be found in the book by Rasmussen and Williams
\cite{Rasmussen}, which we will follow throughout this work.  We use
GaPP\footnote{\href{http://www.acgc.uct.ac.za/~seikel/GAPP/index.html}{\url{http://www.acgc.uct.ac.za/~seikel/GAPP/index.html}}}
(Gaussian Processes in Python), which is a freely available GP package developed by
Seikel. It was introduced in \cite{Seikel:2012uu} and has recently
been expanded to allow for marginalization over hyperparameters using
the MCMC package
emcee\footnote{\href{http://github.com/dfm/emcee}{\url{http://github.com/dfm/emcee}}}
\cite{ForemanMackey:2012ig}.

\subsection{Gaussian process reconstructions}\label{gpr}
Assume we have $N$ observational data points $\bm y=(y_1, \dots, y_N)$
at redshifts $\bm Z=(z_1, \dots, z_N)$. The errors of the observations
are given in the covariance matrix $C$. We want to reconstruct the
function underlying the data at points $\bm Z^*=(z^*_1, \dots, z^*_N)$
and denote the function values at these points as $\bm
f^*=(f(z^*_1), \dots, f(z^*_N))$.

GP can be thought of as a generalization of a Gaussian distribution:
At each point $z^*$, the reconstructed function $f(z^*)$ is described
by normal distribution. Function values at different points $z^*$ and
$\tilde{z}^*$ are not independent from each other, but are related by
a covariance function $k(z^*, \tilde{z}^*)$ (see section
\ref{covfunctions} for some choices of $k$). Covariance functions
depend on hyperparameters, such as the signal variance $\sigma_f$ and
the characteristic length scale $\ell$. (More complicated covariance
functions can depend on additional hyperparameters; but in this work,
we will focus on covariance functions that only include $\sigma_f$ and
$\ell$.) In contrast to regular parameters, hyperparameters do not
specify the form of a function; they only characterize typical
changes of the reconstructed function.  $\ell$ roughly corresponds to
the scale in $z$ over which significant changes occur in the function
values and $\sigma_f$ denotes the typical size of these changes.

For a given covariance function and hyperparameters, the reconstructed
function is determined by the covariances between the data and the
points $\bm Z^*$, where the function is to be reconstructed. The
relevant covariances are given by matrices $K(\bm X,\tilde{\bm X})$
with $\{K(\bm X,\tilde{\bm X})\}_{ij} = k(x_i, \tilde{x}_j)$, where
$\bm X, \tilde{\bm X} \in \{\bm Z, \bm Z^*\}$.

The mean and covariance of the GP reconstruction of the
$n$th derivative of a function are given by
\begin{equation}
\overline{{\bm f}^{*(n)}} = K^{(n,0)}(\bm Z^*,\bm Z) \left[K(\bm Z,\bm Z) +
  C\right]^{-1} {\bm y}
\end{equation}
and
\begin{eqnarray}
&&\hspace{-1em}\text{cov}\left({\bm f}^{*(n)}\right) = K^{(n,n)}(\bm Z^*,\bm Z^*) \\
&&{}- K^{(n,0)}(\bm Z^*,\bm Z) \left[K(\bm Z,\bm Z) +
  C\right]^{-1}  K^{(0,n)}(\bm Z,\bm Z^*) \nonumber
\end{eqnarray}
respectively. Here, $k^{(n,m)}$ denotes the $n$th derivative of $k$
with respect to the first argument and the $m$th derivative with
respect to the second argument.

Besides the data, the above equations also depend on the covariance
function and the values of the hyperparameters. While the covariance
function needs to be chosen by hand, one can either determine the
optimal hyperparameters by maximizing the marginal likelihood
$\mathcal{L} = p({\bm y}|\bm Z,\sigma_f,\ell)$, or one can marginalize
over this likelihood. (The term ``marginal likelihood'' refers to the
fact that one marginalizes over all possible functions $f(z)$.) In
both cases, the hyperparameters are determined by the data. The log
marginal likelihood is given by:
\begin{eqnarray}\label{log-marginal-p}
\ln \mathcal{L} &=& \ln p({\bm y}|\bm Z,\sigma_f,\ell) \nonumber\\
&=& -\frac{1}{2}\bm y^T \left[K(\bm Z,\bm Z) +
  C\right]^{-1} {\bm y} \nonumber \\
&& {} - \frac{1}{2}\ln
  \left|K(\bm Z,\bm Z)+C\right|
  - \frac{n}{2}\ln 2\pi \,.
\end{eqnarray}
Recall that $K$ depends on the hyperparameters $\sigma_f$ and
$\ell$.

From a Bayesian point of view, marginalizing over the hyperparameters
is the correct approach. This is, however, computationally much more
expensive than optimizing the hyperparameters. In most cases, the
optimization approach is a good approximation. Problems with this
approximation can occur if the likelihood $\mathcal{L}$ has multiple
(equally high) peaks. However, we have not encountered these problems
when reconstructing dark energy. We show below that the difference between the two is close, and so justify using
the optimization approach.

\subsection{Reconstructing $g(f(z),f'(z),\dots)$}

Often we are not only interested in a function $f(z)$ for which we
have observational data, but also in functions $g(f(z),f'(z),\dots)$
which depend on $f(z)$ and its derivatives. It is important to note
that $f(z)$ and its derivatives are not independent
quantities. Therefore, we need to consider the covariances
\begin{eqnarray}
&&\hspace{-1em}\text{cov}\left({\bm f}^{*(n)}, {\bm f}^{*(m)}\right) = K^{(n,m)}(\bm Z^*,\bm Z^*) \\
&&{}- K^{(n,0)}(\bm Z^*,\bm Z) \left[K(\bm Z,\bm Z) +
  C\right]^{-1}  K^{(0,m)}(\bm Z,\bm Z^*) \nonumber
\end{eqnarray}
when reconstructing $g(f(z),f'(z),\dots)$.

At each point $z^*$, we perform a Monte Carlo sampling to obtain the
distribution of function values for $g^*=g(f^*,f^*{}',\dots)$, where
$f^*=f(z^*)$. In each sampling step, we draw values for
$f^*,f^*{}',\dots$ from a multivariate normal distribution:
\begin{equation}
\begin{bmatrix} {f^*} \\ {f^*{}'} \\ \vdots \end{bmatrix} \sim
\mathcal{N} \left(
\begin{bmatrix}
\overline{f^*}\\
\overline{f^*{}'}\\
\vdots
\end{bmatrix}
,
\begin{bmatrix}
\text{cov}(f^*,f^*)     & \text{cov}(f^*,f^*{}')    & \cdots \\
\text{cov}(f^*,f^*{}')  & \text{cov}(f^*{}',f^*{}') & \cdots \\
\vdots                  & \vdots                   & \ddots
\end{bmatrix}
\right) \,.
\end{equation}
These values are used to calculate $g^*$. Performing many of these
MCMC steps, we obtain a distribution of function values for $g$ at
each redshift $z^*$.

\subsection{Covariance functions}\label{covfunctions}

There is a large variety of covariance functions. The optimal choice
of covariance function depends on the problem at hand (see
\cite{Rasmussen} for some examples). Here, we consider the squared
exponential, which is considered to be a general purpose covariance
function, and the Mat\'ern class, from which individual covariance
functions can be obtained by choosing the parameter $\nu$, which
governs the width of the peak of the covariance function for a given
$\ell$.

The squared exponential and the Mat\'ern class are given by:
\begin{description}
\item[Squared exponential]
\begin{equation}
k(z,\tilde{z}) =
  \sigma_f^2 \exp\left( - \frac{(z -
    \tilde{z})^2}{2\ell^2} \right)
\end{equation}
\item[Mat\'ern]
\begin{eqnarray}
k(z,\tilde{z}) &=&
  \sigma_f^2\, \frac{2^{1-\nu}}{\Gamma(\nu)}
  \left(\frac{\sqrt{2\nu(z - \tilde{z})^2}}{\ell}\right)^\nu
  \nonumber\\
  && {}\times K_\nu \left(\frac{\sqrt{2\nu(z - \tilde{z})^2}}{\ell}\right)
  \,,
\end{eqnarray}
\end{description}
where $K_\nu$ is a modified Bessel function. $\nu$ is a positive
parameter which defines the shape of the covariance
function. While $\nu=1/2$ corresponds to an exponential
$k(z,\tilde{z})=\sigma_f^2 \exp\left( -|z - \tilde{z}|/\ell \right)$,
we recover the squared exponential covariance function for
$\nu\to\infty$.  The Mat\'ern covariance function is $n$-times mean
square differentiable (i.e. the derivative $\partial^{2n}
k(z,\tilde{z})/\partial z^n\partial\tilde{z}^n$ exists and is finite)
if and only if $\nu>n$. A covariance function that is $n$-times mean
square differentiable can be used to reconstuct up to the $n$th
derivative of a function.

For half-integer values of $\nu$, the Mat\'ern covariance function can
be simplified. Here we consider $\nu=5/2$, $\nu=7/2$ and $\nu=9/2$.
The reconstruction of the equation of state from distance data
requires the reconstruction of the second derivative $D''(z)$ (see
Eq. \eqref{w}). Therefore, the smoothness parameter $\nu$ needs to be
larger than 2.
In the following, the Mat\'ern functions with $\nu=5/2$, $\nu=7/2$ and
$\nu=9/2$ will be refered to as Mat\'ern(5/2), Mat\'ern(7/2) and
Mat\'ern(9/2), respectively. These functions are given by:

\begin{description}
\item[Mat\'ern(5/2)]\begin{eqnarray}
&& k(z,\tilde{z}) = \sigma_f^2
  \exp\left[-\frac{\sqrt{5}\,|z-\tilde{z}|}{\ell}\right] \\
&&\qquad \times \left(1 +
  \frac{\sqrt{5}\,|z-\tilde{z}|}{\ell} +
  \frac{5(z-\tilde{z})^2}{3\ell^2} \right) \nonumber
\end{eqnarray}
\item[Mat\'ern(7/2)]
\begin{eqnarray}
&& k(z,\tilde{z}) = \sigma_f^2
  \exp\left[-\frac{\sqrt{7}\,|z-\tilde{z}|}{\ell}\right] \\
  &&\qquad \times \left(1 +
  \frac{\sqrt{7}\,|z-\tilde{z}|}{\ell} + \frac{14(z-\tilde{z})^2}{5\ell^2} +
  \frac{7\sqrt{7}\,|z-\tilde{z}|^3}{15\ell^3} \right) \nonumber
\end{eqnarray}
\item[Mat\'ern(9/2)]
\begin{eqnarray}
k(z,\tilde{z}) &=& \sigma_f^2
  \exp\left[-\frac{3\,|z-\tilde{z}|}{\ell}\right]  \\
  && \times \left(1 +
  \frac{3\,|z-\tilde{z}|}{\ell} + \frac{27(z-\tilde{z})^2}{7\ell^2}\right. \nonumber\\
&&\qquad \left. {}+ \frac{18\,|z-\tilde{z}|^3}{7\ell^3} +
  \frac{27(z-\tilde{z})^4}{35\ell^4} \right)\nonumber
\end{eqnarray}
\end{description}

Figure~\ref{covfig} shows all four covariance functions considered in
this work. Increasing the parameter $\nu$ of the Mat\'ern class widens
the peak of the function. We get the widest peak for the squared
exponential, which corresponds to Mat\'ern($\nu\to\infty$).

\begin{figure}
\includegraphics[width=0.45\textwidth]{Plots/covfunctions}
\caption{Different covariance functions $k(r)/\sigma_f^2$ as a
  function of $r=(z-\tilde{z})/\ell$. Shown are the squared
  exponential and three functions of the Mat\'ern class with $\nu =
  9/2$, $7/2$, $5/2$.}
\label{covfig}
\end{figure}

\section{Mock data}\label{mockdata}

We create mock SN data catalogues for different cosmological models.
Each model represents a specific smoothness of the equation of
state, and is not supposed to represent a specific model. $w(z)$ for the five test models is listed in table
\ref{modelstable} and shown in figure \ref{modelsfig}. Also shown are
the dimensionless comoving distance $D(z)$ and the residual distance
$D(z)-D_{\Lambda\text{CDM}}(z)$. For easier reference, we have
assigned the names ``smooth'', ``peak'', ``bumpy'' and ``noisy'' to
the non-$\Lambda$CDM models. We assume all models to be spatially flat
($\Omega_k=0$) and fix the matter density to $\Omega_m=0.3$.

We use the scatter anticipated for the Dark Energy Survey (DES)
\cite{Bernstein:2011zf} to create the mock data. However, instead of
using the anticipated number of $\sim 4000$ SNe, we reduced this
number to 546 to speed up the analysis. For real life observations the
errors are Gaussian in magnitude. Here, we also assume the errors to be
Gaussian in distance $D$ as GP require the observational errors to be
Gaussian. This assumption is justified because the actual errors in $D$
deviate only slightly from Gaussianity for the considered scatter.

\begin{table}
\begin{ruledtabular}
\begin{tabular}{ll}
$\Lambda$CDM & $w(z) = -1$ \\
smooth       & $w(z) = \frac{1}{2} \left(-1 + \tanh\left[3\left(z- \frac{1}{2}\right)\right]\right)$  \\
peak         & $w(z) = -1 + 0.7  \exp\left[-\left(\frac{z-0.5}{0.2}\right)^2\right]$  \\
bumpy        & $w(z) = -0.9 - 0.1 \cos(3.5 z) + 0.1\sin(10 z)$  \\
noisy        & $w(z) = -0.9+0.05 \sin(2.5 z)-0.055 \cos(5.7 z)$ \\
             & \hspace{3em} ${}-0.06 \sin(8.9 z)+0.03 \cos(14.2 z)$ \\
             & \hspace{3em} ${}-0.07 \cos(19.4 z) +0.06 \sin(27.1 z)$ \\
             & \hspace{3em} ${}+0.034 \cos(33.8 z)-0.047 \sin(47.2 z)$ \\
             & \hspace{3em} ${}-0.027 \cos(77.8 z)+0.032 \sin(117.8 z)$
\end{tabular}
\end{ruledtabular}
\caption{\label{modelstable}$w(z)$ for the different test models. From top to bottom the models become increasingly complex, and serve as suitable benchmark models to quantify the sorts of behaviour that can be constrained for a given number of SNIa.}
\end{table}

\begin{figure*}
\subfloat[$w(z)$]{
\includegraphics*[width=0.3\textwidth]{Plots/models}} \hfill
\subfloat[$D(z)$]{
\includegraphics*[width=0.3\textwidth]{Plots/Dmodels}} \hfill
\subfloat[$D(z)-D_{\Lambda\text{CDM}}(z)$]{
\includegraphics*[width=0.3\textwidth]{Plots/Dresmodels}}
\caption{\label{modelsfig}
  $w(z)$, $D(z)$ and $D(z)-D_{\Lambda\text{CDM}}(z)$ for the
  different test models.}
\end{figure*}

\section{Example reconstructions}\label{examples}

In this section, we show some example reconstructions. This gives some intuition of how different
methods can affect the result of a reconstruction. However, isolated examples
are not suitable to determine which approach leads to the most
reliable results for a given problem. In order to determine that one
needs to analyze many reconstructions instead of just a few
examples. This is done in sections~\ref{coverage} and
\ref{consistency}.

\subsection{Marginalization vs. optimization}

As mentioned in section~\ref{gpr}, marginalization over the
hyperparameters would be the correct Bayesian approach. As this method
is computationally much more expensive than optimizing the
hyperparameters by maximizing the marginal likelihood
\eqref{log-marginal-p}, it can become prohibitive for large data sets.
Optimization is much faster and in most cases provides good results.
While we focus on the optimization method in this work, we show some
example reconstructions to compare the two different approaches in this
section.

Figure~\ref{mcmcfig} shows reconstructions of the equation of state
$w(z)$ for two realisations of mock $\Lambda$CDM data sets. Here, we
have used the squared exponential covariance function. The
reconstructions have once been performed by optimizing the
hyperparameters and once by marginalizing over them. In all cases, the
value of the input model, $w=-1$, is captured within the 95\%
confidence limits (CL) of the reconstruction. Note that
marginalization does not necessarily lead to increased errors, which
can be clearly seen for realisation B.

For a given realisation, it is not obvious which approach leads to a
reconstruction that resembles the input model more closely than the
other approach. For realisation A, the reconstructed mean is very
close to the theoretical value up to $z\sim 1$ when marginalizing over
the hyperparameter. Using the optimisation approach, the mean seems
less stable. But for realisation B, the mean and 68\% CL indicate an
increase in $w$ for the marginalisation approach (the 95\% CL still
captures $w=-1$), while optimization resembles $\Lambda$CDM more
closely.

However note that we do expect the input model to leave the 68\% CL
limits for a part of the redshift range for at least some of the
realisations. Therefore, a closer resemblance of the input model does
not necessarily indicate a better method.
See sections~\ref{coverage} and \ref{consistency} for
a detailed analysis.

\begin{figure*}
\subfloat[Realisation A \newline Optimization]{
  \includegraphics*[width=0.3\textwidth]{Plots/w_lcdm_squex_ex1.eps}}
\subfloat[Realisation A \newline Marginalization]{
  \includegraphics*[width=0.3\textwidth]{Plots/mcmc_w_lcdm_squex_ex1.eps}}\\
\subfloat[Realisation B \newline Optimization]{
  \includegraphics*[width=0.3\textwidth]{Plots/w_lcdm_squex_ex2.eps}}
\subfloat[Realisation B \newline Marginalization]{
  \includegraphics*[width=0.3\textwidth]{Plots/mcmc_w_lcdm_squex_ex2.eps}}
\caption{Gaussian process reconstructions of $w(z)$ for two
  realisations of a mock data set. The blue (dotted) line is the mean
  of the reconstruction and the blue shaded regions indicate the 68\%
  and 95\% confidence limits, respectively.  {\em Left:} The
  hyperparameters were optimized for the reconstruction. {\em Right:}
  Marginalization over the hyperparameters using MCMC methods.}
\label{mcmcfig}
\end{figure*}

\subsection{Optimization using different covariance functions}

Figure~\ref{examplesfig} shows example reconstructions of $w(z)$ for all
models and covariance functions. The different covariance functions
have been applied to the same realisation of the data for each model.
While the reconstructions using different covariance functions show
similar features for a given realisation, we can also see some
differences. For our examples, these differences are most obvious for
the ``peak'' model. The squared exponential clearly fails to
reconstruct the model, whereas the Mat\'ern functions capture it
within the 95\% CL.

\begin{figure*}
\subfloat[Squared exponential\newline $\Lambda$CDM]{
  \includegraphics*[width=0.24\textwidth]{Plots/w_lcdm_squex_ex.eps}}\hfill
\subfloat[Mat\'ern($9/2$)\newline $\Lambda$CDM]{
  \includegraphics*[width=0.24\textwidth]{Plots/w_lcdm_mat92_ex.eps}}\hfill
\subfloat[Mat\'ern($7/2$)\newline $\Lambda$CDM]{
  \includegraphics*[width=0.24\textwidth]{Plots/w_lcdm_mat72_ex.eps}}\hfill
\subfloat[Mat\'ern($5/2$)\newline $\Lambda$CDM]{
  \includegraphics*[width=0.24\textwidth]{Plots/w_lcdm_mat52_ex.eps}}\\
\subfloat[Squared exponential\newline ``smooth'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_smooth_squex_ex.eps}}\hfill
\subfloat[Mat\'ern($9/2$)\newline ``smooth'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_smooth_mat92_ex.eps}}\hfill
\subfloat[Mat\'ern($7/2$)\newline ``smooth'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_smooth_mat72_ex.eps}}\hfill
\subfloat[Mat\'ern($5/2$)\newline ``smooth'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_smooth_mat52_ex.eps}}\\
\subfloat[Squared exponential\newline ``peak'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_peak_squex_ex.eps}}\hfill
\subfloat[Mat\'ern($9/2$)\newline ``peak'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_peak_mat92_ex.eps}}\hfill
\subfloat[Mat\'ern($7/2$)\newline ``peak'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_peak_mat72_ex.eps}}\hfill
\subfloat[Mat\'ern($5/2$)\newline ``peak'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_peak_mat52_ex.eps}}\\
\subfloat[Squared exponential\newline ``bumpy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_bumpy_squex_ex.eps}}\hfill
\subfloat[Mat\'ern($9/2$)\newline ``bumpy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_bumpy_mat92_ex.eps}}\hfill
\subfloat[Mat\'ern($7/2$)\newline ``bumpy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_bumpy_mat72_ex.eps}}\hfill
\subfloat[Mat\'ern($5/2$)\newline ``bumpy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_bumpy_mat52_ex.eps}}\\
\subfloat[Squared exponential\newline ``noisy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_noisy_squex_ex.eps}}\hfill
\subfloat[Mat\'ern($9/2$)\newline ``noisy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_noisy_mat92_ex.eps}}\hfill
\subfloat[Mat\'ern($7/2$)\newline ``noisy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_noisy_mat72_ex.eps}}\hfill
\subfloat[Mat\'ern($5/2$)\newline ``noisy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/w_noisy_mat52_ex.eps}}
\caption{Gaussian process reconstructions of $w(z)$ from a mock data
  set for different cosmological models and using different covariance
  functions. The blue line is the mean of the reconstruction and the
  blue shaded regions indicate the 68\% and 95\% confidence limits,
  respectively. The input model is given by the red line.}
\label{examplesfig}
\end{figure*}

\section{Coverage tests}\label{coverage}

When applying GP to a large variety of problems, the true function is
{\em on average} captured within the 1-$\sigma$ (2-$\sigma$) limits
for 68\% (95\%) of the input range. However, when applying GP to a
specific problem (in our case the reconstruction of dark energy), it
is not guaranteed that we obtain the same amount of coverage. For a
specific problem~-- and in our case any given EOS~-- this coverage depends on the choice of covariance
function.

For each model, we created 500 mock data sets and performed
reconstructions of $w(z)$ for all data sets and covariance
functions. Averaging over all realisations for each model, we then
determined the fraction of input function points lying between the
reconstructed 1- and 2-$\sigma$ limits, respectively, for each
covariance function and each redshift point. The result is shown in
figure~\ref{coveragefig}.

Table~\ref{coveragetab} lists these fractions averaged over redshift
and also shows the results for the reconstructions of $D$, $D'$, $D''$
and $q$. The covariance functions are listed in the order of
decreasing peak width, i.e.\ decreasing $\nu$. Recall that the squared
exponential corresponds to Mat\'ern($\infty$). GP are a distribution
over functions, which consists of functions of different
smoothness. Covariance functions with wider peak lead to a stronger
preference of smooth functions in this distribution due to the
stronger correlation of function points for $|z-\tilde{z}|\lesssim
2\ell$ (see figure~\ref{covfig}). Scales larger than $2\ell$ are not
relevant for our purposes as the considered redshift range is much
smaller than $2\ell$ (using the optimized value of $\ell$) in all
cases.

The preference of smooth functions in the GP distribution subsequently
leads to smaller error bars for the reconstruction. Thus, for a given
input model, we expect lower coverage for covariance functions with
wider peaks due to the smaller error bars of the reconstruction. This
effect can indeed be seen in the results of table~\ref{coveragetab}.

For $\Lambda$CDM and the ``smooth'' model, Mat\'ern($9/2$) leads to
the most realistic error bars in the sense that the coverage obtained
by the simulation is very close to the theoretically expected values
of 68\% and 95\% for the 1- and 2-$\sigma$ limits, respectively. When
using the squared exponential, we obtain a smaller coverage,
indicating that the errors are underestimated. In contrast, the errors
are overestimated for Mat\'ern($7/2$) and Mat\'ern($5/2$).

For the models with stronger redshift dependence (``peak'', ``bumpy''
and ``noisy''), Mat\'ern($9/2$) and Mat\'ern($7/2$) come closest to
the theoretically expected values, with Mat\'ern($9/2$)
underestimating and Mat\'ern($7/2$) overestimating the error bars.

\begin{table*}
\begin{ruledtabular}
\begin{tabular}{lllll | llll}
       & Squared exp. & Mat\'ern($9/2$) & Mat\'ern($7/2$) &
Mat\'ern($5/2$) & Squared exp. & Mat\'ern($9/2$) & Mat\'ern($7/2$) &
Mat\'ern($5/2$) \\
\hline
$\Lambda$CDM &&&& &&&& \\
$D$    & 0.636   & {\bf 0.676} & 0.685   & 0.728  & 0.924    & {\bf 0.950} & 0.952   & 0.968 \\
$D'$   & 0.580   & {\bf 0.661} & 0.712   & 0.793  & 0.899    & {\bf 0.947} & 0.963   & 0.985 \\
$D''$  & 0.518   & {\bf 0.684} & 0.770   & 0.934  & 0.851    & {\bf 0.960} & 0.989   & 1.000 \\
$w$    & 0.542   & {\bf 0.676} & 0.776   & 0.929  & 0.863    & {\bf 0.953} & 0.985   & 1.000 \\
$q$    & 0.521   & {\bf 0.678} & 0.774   & 0.937  & 0.850    & {\bf 0.954} & 0.987   & 1.000  \\
\hline
Smooth &&&& &&&& \\
$D$    & 0.663   & 0.703       & {\bf 0.696} & 0.723  & 0.935  & 0.955  & {\bf 0.952} & 0.966  \\
$D'$   & 0.595   & {\bf 0.695} & 0.727       & 0.794  & 0.904  & {\bf 0.953}  & 0.966  & 0.985  \\
$D''$  & 0.505   & {\bf 0.707} & 0.784       & 0.940  & 0.823  & {\bf 0.963}  & 0.988  & 1.000  \\
$w$    & 0.524   & {\bf 0.702} & 0.785       & 0.945  & 0.836  & {\bf 0.956}  & 0.987  & 1.000  \\
$q$    & 0.516   & {\bf 0.702} & 0.787       & 0.945  & 0.833  & {\bf 0.957}  & 0.987  & 1.000  \\
\hline
Peak &&&& &&&&\\
$D$    & 0.544   & 0.604   & {\bf 0.691}   & 0.724  & 0.868  & 0.912   & {\bf 0.950} & 0.965  \\
$D'$   & 0.437   & 0.539   & {\bf 0.720}   & 0.788  & 0.739  & 0.850   & {\bf 0.961} & 0.980  \\
$D''$  & 0.337   & 0.480   & {\bf 0.770}   & 0.927  & 0.568  & 0.773   & {\bf 0.973} & 1.000  \\
$w$    & 0.361   & 0.491   & {\bf 0.774}   & 0.926  & 0.598  & 0.781   & {\bf 0.973} & 0.999  \\
$q$    & 0.344   & 0.482   & {\bf 0.775}   & 0.932  & 0.575  & 0.770   & {\bf 0.973} & 0.999  \\
\hline
Bumpy &&&& &&&&\\
$D$    & 0.621  & 0.663  & {\bf 0.679}   & 0.717  & 0.925  &  0.931   & {\bf 0.950} & 0.968 \\
$D'$   & 0.569  & 0.638  & {\bf 0.707}   & 0.783  & 0.890  &  0.929   & {\bf 0.961} & 0.983  \\
$D''$  & 0.479  & 0.605  & {\bf 0.751}   & 0.931  & 0.793  &  0.902   & {\bf 0.981} & 1.000  \\
$w$    & 0.506  & {\bf 0.605}  & 0.756   & 0.929  & 0.812  &  0.906   & {\bf 0.978} & 1.000  \\
$q$    & 0.482  & 0.601  & {\bf 0.755}   & 0.936  & 0.795  &  0.899   & {\bf 0.979} & 1.000  \\
\hline
Noisy &&&& &&&&\\
$D$    & 0.621  & {\bf 0.671}  & 0.693  & 0.718  & 0.927  & {\bf 0.953} & 0.956   & 0.964  \\
$D'$   & 0.565  & {\bf 0.649}  & 0.720  & 0.793  & 0.875  & {\bf 0.941} & 0.964   & 0.981  \\
$D''$  & 0.406  & 0.544  & {\bf 0.752}  & 0.934  & 0.698  & 0.853   & {\bf 0.977} & 1.000  \\
$w$    & 0.440  & 0.559  & {\bf 0.760}  & 0.929  & 0.733  & 0.865   & {\bf 0.975} & 0.999  \\
$q$    & 0.414  & 0.547  & {\bf 0.757}  & 0.937  & 0.704  & 0.852   & {\bf 0.975} & 1.000  \\
\end{tabular}
\end{ruledtabular}
\caption{Coverage test of the Gaussian process for different models
  and covariance functions. {\em Left:} Fraction of function points
  lying between the 1-$\sigma$ limits. In each line, the fraction
  closest to the theoretical value 0.68 has been highlighted.
  {\em Right:} Fraction of function points lying between the
  2-$\sigma$ limits. In each line, the fraction closest to the
  theoretical value 0.95 has been highlighted.}
\label{coveragetab}
\end{table*}

\begin{figure*}
\subfloat[Squared exponential\newline $\Lambda$CDM]{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_lcdm_squex.eps}}\hfill
\subfloat[Mat\'ern($9/2$)\newline $\Lambda$CDM]{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_lcdm_mat92.eps}}\hfill
\subfloat[Mat\'ern($7/2$)\newline $\Lambda$CDM]{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_lcdm_mat72.eps}}\hfill
\subfloat[Mat\'ern($5/2$)\newline $\Lambda$CDM]{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_lcdm_mat52.eps}}
\\
\subfloat[Squared exponential\newline ``smooth'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_smooth_squex.eps}}\hfill
\subfloat[Mat\'ern($9/2$)\newline ``smooth'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_smooth_mat92.eps}}\hfill
\subfloat[Mat\'ern($7/2$)\newline ``smooth'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_smooth_mat72.eps}}\hfill
\subfloat[Mat\'ern($5/2$)\newline ``smooth'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_smooth_mat52.eps}}
\\
\subfloat[Squared exponential\newline ``peak'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_peak_squex.eps}}\hfill
\subfloat[Mat\'ern($9/2$)\newline ``peak'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_peak_mat92.eps}}\hfill
\subfloat[Mat\'ern($7/2$)\newline ``peak'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_peak_mat72.eps}}\hfill
\subfloat[Mat\'ern($5/2$)\newline ``peak'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_peak_mat52.eps}}
\\
\subfloat[Squared exponential\newline ``bumpy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_bumpy_squex.eps}}\hfill
\subfloat[Mat\'ern($9/2$)\newline ``bumpy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_bumpy_mat92.eps}}\hfill
\subfloat[Mat\'ern($7/2$)\newline ``bumpy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_bumpy_mat72.eps}}\hfill
\subfloat[Mat\'ern($5/2$)\newline ``bumpy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_bumpy_mat52.eps}}
\\
\subfloat[Squared exponential\newline ``noisy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_noisy_squex.eps}}\hfill
\subfloat[Mat\'ern($9/2$)\newline ``noisy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_noisy_mat92.eps}}\hfill
\subfloat[Mat\'ern($7/2$)\newline ``noisy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_noisy_mat72.eps}}\hfill
\subfloat[Mat\'ern($5/2$)\newline ``noisy'']{
  \includegraphics*[width=0.24\textwidth]{Plots/coverage_noisy_mat52.eps}}
\caption{Coverage test for $w(z)$ using 500 mock data sets for
  different cosmological models and using different covariance
  functions. Shown is the fraction of input model points captured with
  the 1-$\sigma$ (red) and 2-$\sigma$ (blue) limits of the
  reconstructions. The constant red and blue lines indicate the
  theoretically expected values 0.68 and 0.95, respectively.}
\label{coveragefig}
\end{figure*}

\section{Consistency with a model}\label{consistency}

GP provide a method for non-parametric reconstructions directly from
observational data. But often one would like to know whether a
specific model is consistent with the GP reconstruction. In this
section, we will describe how deviations of a model from the GP
reconstructions can be quantified. We explain the method by means of
an example, namely $\Lambda$CDM. The technique can be applied to other
models analogously.  For the reconstructions, we use the
Mat\'ern($9/2$) covariance function, which was shown in
section~\ref{coverage} to provide the most reliable results for our
purposes.

We create 1000 mock $\Lambda$CDM data sets, using the scatter and
redshift distributions as described in section~\ref{mockdata}. For
each data set, we reconstruct $w$ and determine the fraction of the
redshift range, in which the fiducial $\Lambda$CDM model is captured
by the 1-, 2- and 3-$\sigma$ limits, corresponding to 68.3\%, 95.4\% and
99.7\% CL, respectively of the reconstruction. Note that here
we determine this fraction for each realisation of the data
individually, which is in contrast to the averaging over all mock data
sets that has been done in Section~\ref{coverage}.

Figure~\ref{coveragehist} summarizes the obtained fractions as
histograms.  For roughly one third of the mock data sets, the fiducial
model is captured within the 1-$\sigma$ limits of the reconstruction
over the complete redshift range. For 983 out of 1000 data sets, it is
captured within the 3-$\sigma$ limits over the whole redshift
range. While we expect the fiducial model to lie within the 1-$\sigma$
limits {\em on average} over 68\% of the redshift range, the histgram
shows a very large spread of the actual percentages for the individual
realisations of the data. For a given data set, it is not unusual to
find that the fiducial model is captured within the 1-$\sigma$ limits
anywhere between 10\% and 100\% of the redshift range. When we
consider the 2-$\sigma$ limits, small percentages become less
likely. For the 3-$\sigma$ limits, we can expect a coverage close to
100\%.

We can use these histograms to quantify deviations of the
reconstructions from $\Lambda$CDM when applying GP to a data set,
which may or may not represent $\Lambda$CDM. As the 1-$\sigma$ limits
allow for a large range of percentages and thus offer very little
constraints, the 2- and 3-$\sigma$ limits are more suitable for this
test. For example, if we find that $\Lambda$CDM is captured within the
3-$\sigma$ limits of the reconstruction over 95\% of the redshift
range, we can exclude this model at a 99.6\% CL as we only found
smaller coverage values for 4 out of 1000 mock data sets. Or if
$\Lambda$CDM is captured within the 2-$\sigma$ limits over only 50\% of
the redshift range, then $\Lambda$CDM can be ruled out at a 98.3\% CL.

In order to quantify deviations for a different model, we would need
to produce histograms using mock data sets which assume exactly that
model. Also note that when applying the method to a real world
problem, it is necessary to create mock data sets using the scatter
and redshifts of the actual data set.

\begin{figure*}
\subfloat[1-$\sigma$]{
  \includegraphics*[width=0.32\textwidth]{Plots/w-1sigma.eps}} \hfill
\subfloat[2-$\sigma$]{
  \includegraphics*[width=0.32\textwidth]{Plots/w-2sigma.eps}} \hfill
\subfloat[3-$\sigma$]{
  \includegraphics*[width=0.32\textwidth]{Plots/w-3sigma.eps}}\\
\subfloat[1-$\sigma$]{
  \includegraphics*[width=0.32\textwidth]{Plots/w-1sigma_zoom.eps}} \hfill
\subfloat[2-$\sigma$]{
  \includegraphics*[width=0.32\textwidth]{Plots/w-2sigma_zoom.eps}} \hfill
\subfloat[3-$\sigma$]{
  \includegraphics*[width=0.32\textwidth]{Plots/w-3sigma_zoom.eps}}
\caption{Histograms of the percentages of the redshift range, where
  the fiducial $\Lambda$CDM model is captured by the 1-, 2- and
  3-$\sigma$ limits of the reconstructions of $w$ for a given
  realisation of the data. In total, 1000 mock data sets have been
  used. The bottom row shows a zoomed-in version of the plots in the
  top row.}
\label{coveragehist}
\end{figure*}

\section{Conclusions}\label{conclusions}

We analyzed various aspects of using GP to reconstruct dark energy
from distance measurements. The main questions addressed in this paper
were how to choose a suitable covariance function and how to quantify
deviations of a model from the GP reconstruction. The presented
methods can be applied to other problems analogously.

In theory, the fiducial model that was used to create a mock data set
should be captured within the 1-$\sigma$ (2-$\sigma$) limits of the GP
reconstruction for 68\% (95\%) of the input range. These are average
values when GP are applied to a variety of different problems. These
values do not necessarily apply if we use GP for a specific problem ---
in our case the reconstruction of dark energy from SN data. We tested
various covariance functions on mock data sets for different
cosmological models to determine which function recovers the
theoretical coverage values for the reconstruction of $w$.

While Mat\'ern($7/2$) provides very good results for the
reconstruction of $D$ for all models, it overestimates the errors for
the reconstructions of the derivatives of $D$. If one is only
interested in $D$ itself, but not in its derivatives or any derived
quantities, then Mat\'ern($7/2$) is the best choice.

We found that the Mat\'ern($9/2$) covariance function leads to
excellent results for $\Lambda$CDM and the ``smooth'' model for all
reconstructed quantities, but tends to underestimate the errors for
the other models. This is caused by the fact that GP in general prefer
the reconstructed functions to be smooth if there is no sufficient
evidence for rapid variations. The strength of this preference depends
on the choice of covariance function: the wider the peak of the covariance
function, the stronger the preference for a smooth reconstruction.

We recommend the Mat\'ern($9/2$) covariance function for the
reconstruction of dark energy with current supernovae data as it provides reliable results for the
smoothest reconstructions that are consistent with the data. However,
keep in mind that it does not pick up rapid variations in $w$ if there
is insufficient evidence for these variations. In such a case, the
errors will be underestimated, and for a more conservative estimate one should use a smaller $\nu$.

In general these results are indicative of a general trend for the reconstruction of $w$, but for substantially increased numbers of SNIa, very different errors or distributions, and different considerations in the families of EOS one might be interested in, this analysis should be extended. Once the numbers of SNIa becomes sufficiently large, more complicated covariance functions with different length scale parameters could also be considered for complicated EOS.

We introduced a way to quantify deviations of a model from the GP
reconstructions. The coverage obtained for the actual data set needs
to be compared to the distribution of coverages of many mock data
sets. The input model for the mock data sets needs to be identical to
the model one wishes to test. The number of mock data sets with a
larger coverage than the one for the observed data determines the CL
at which the model can be rejected.

\acknowledgments

MS was supported by the South Africa Square Kilometre Array Project
and the South African National Research Foundation (NRF). CC was
supported by the NRF.

redpajama_set_name: RedPajamaArXiv


Memes and humor aside, here's another look at Initial D.
Street Racing was incredibly popular in the 80's and 90's. It was no surprise that Initial D gained popularity when Shuichi Shigeno started producing this masterfully-storyboarded adrenaline-packed seinen.
Initial D is set in Japan's illegal touge street racing scene; the story revolves around a young driver named Fujiwara Takumi (18), who's been driving (drifting) up and down Mt. Akina's touge delivering tofu for their family's shop since he was fourteen years old. As you go along, you watch Takumi and his friends' pursuit of higher levels of skill, as well as his growth as a young man.
Spilling water means breaking the tofu, with this as training, Takumi acquires great driving skills.
The formula was perfect. (it's easy to say that in hindsight) Imagine you're a teenager in the 90's; you and your friends just got, or maybe about to get your driver's license, there's not much to keep you busy at home and your parents just bought you a car. What do you do? You go out for drives! Some of you might even race (Illegally, because you're young and rebellious). Takumi and his friends even get several love interests throughout the series, what's not relatable about it?
You trashed your R32 GT-R, might as well try and look cool, right?
Not everyone can drive like Takumi, though, not immediately anyway (please race on the track).
Cars and racing, eh? I wonder what else they could use to reel in young men?
Now, this is an anime review; but I just have to point out that while the character art style might not have been the best, Shuichi Shigeno was remarkable at drawing cars for the manga.
Here you'll also see a bit of that storyboarding I mentioned earlier, It worked well when it came to making it look fast and action-packed in the manga; this kind of storyboarding worked even better for the anime.
Being an anime, there are plenty of notions and/or actions that they do with cars that are nigh-impossible or too risky to do in real life; aside from those, the anime is actually quite technically sound (thanks to supervision and inputs from a real racer).
The racing scenes are almost exclusively 3d CGI; were you to watch the first stage of Initial D today you'd be surprised at how bad and buggy it looks, but remember, this came out in 1998! You would also have to pardon any mis-spelling (probably a legal measure?) errors on the badges of cars.
As the years went by, graphics got better and more refined; and by the fourth and fifth stage, they went for cell-shading graphics that looked more like an anime/manga compared to the realistic-looking (and overly shiny) approach they had in the first and second stages.
It is in this exact scene where we hear "Gas Gas Gas" by Manuel.
I'm a fan of all kinds of music from metal, jazz, funk, classical- all the way to eurobeat. It's okay to like eurobeat. Just throwing it out there. It's fast-paced with rapid beats, melodious and oftentimes complex; undeniably catchy, a fellow writer of this page couldn't stop humming deja vu for a week when the memes started coming out.
Eurobeat is sure to get you pumped.
Eurobeat's fast-paced beats and melody make it perfect for a racing anime and has become an integral part of Initial D. you can't have one without the other. So it kind of stressed me out when I realized that the new Initial D legend movies (and newer arcade games) don't have eurobeat! More on that issue in another article.
Initial D had a big impact, to say the least. Countless anime have referenced it. It's driven up the price of a certain 1980's Toyota into the sky. It's turned car and racing lovers into anime fans and vice versa. For many, (including me) Initial D is a two-way gateway into two very different interests. Actually, make it three-ways; If you're not a fan of either of them, this might get you into both.
and you're looking for an anime that's jam-packed with action and details, humorous and heartbreaking moments, give this 90's gem a chance.
Wow, Initial D is a blast from my past. I remember the old Tokyopop DVDs and collecting 1st Stage up to Extra Stage 1. I've seen 3rd Stage and some of 4th stage and I thought it was super cool. It's so weird seeing these Running in the 90s memes when I knew that song because of that anime a long time ago.
I love Initial D. It's my favourite anime EVER. My brother introduced it to me after he got me into racing when I was a teenager and it became our thing, something we watched to celebrate racing victories, or birthdays, haha. I re-watch it every year or so, and it always makes me so nostalgic for the racing scene. I definitely miss it, it was such an amazing experience.


There are a variety of reasons that an individual may pick to upgrade his or her resume. Potentially one might not be making development in their job or else desires another profession. Perhaps an individual constantly loses on employment deals. For whatever reason, there will certainly be times a person need to transform their occupation resume. So, we will certainly check into methods to upgrade one's resume like expert support, volunteering or to discover Arabic to ensure that one has the ability to talk 2 languages.
Depending on what a person will certainly delight in assists an individual develop which place to volunteer. Also, a person should consider their profession. For instance, in case an individual needs a task in the clinical field she or he may uncover using their assistance in a senior treatment center takes place to be far better than offering their assistance in an academic establishment. Nonetheless, in the event one likes a position in a school they might understand giving their solutions in an instructional facility will be a lot more beneficial in contrast to volunteering at a hospital. For this reason, one might pick to try to find someplace to volunteer that is connected to her or his job.
One will certainly discover several methods to be able to obtain understanding of the Arabic language as an example traditional courses, private tutors or net based programs. Which strategy utilized will certainly depend upon a great deal of variables. An individual needs to think about just how much leisure she or he carries hand. In case an individual has a full timetable they could find classroom training courses are not a suitable technique. In situation one often tends to be fretted about expense they might find private instructors are by no suggests the very best approach. When one does not have inspiration they might find internet software application applications often tend not to be a terrific strategy. However, no matter if an individual decides on a traditional course, an individual tutor otherwise online programs to discover Arabic she or he is particular to talk 2 languages. This particular skill has a tendency to be excellent for consisting of in the occupation resume.
Sometimes, a work resume simply will certainly require a couple fine tweaks. Possibly there contain inaccurate margins, a couple misspellings or grammar mistakes. One has the capability to take care of these problems by themselves. Though, whenever an individual is incapable ahead across anything incorrect in relation to his/her occupation resume they perhaps will require professional assistance.
That specialist is able to aid a private adjust his or her work resume. One will certainly want his or her resume to contain excellent details plus proper design to make sure that when a potential employer views and also reads the career resume the employer will certainly wish to pick up a phone and then organize a meeting rather than placing it with the paper shredder.


A new trailer has appeared online for black comedy Kill Me Three Times in which Simon Pegg stars as a hitman whose latest job leads him to a remote Australian surf town.
He's charged with taking out Alice Braga, but as it turns out, he's not the only one planning to bump her off, with half of the local population proving to be gun-wielding loons.
Directed by Kriv Stenders and co-starring Sullivan Stapleton, Teresa Palmer and Luke Hemsworth, Kill Me Three Times will open in the US on 10 April 2016, with a UK release date yet to be confirmed.


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Q: Why is my popular ☺ smileyface ☺ question not listed among my top questions? On my top questions list, my very top question isn't showing up. What's going on here?

A: Our service responsible for aggregating posts across the network failed to record that the question was re-opened almost immediately after being closed. As we look into exactly what happened, I've manually corrected this entry in the database, so your question is correctly appearing in your top questions now.

redpajama_set_name: RedPajamaStackExchange


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Filed under: Movies, Reviews | Tags: Daryl Haney, Friday the 13th, John Carl Buechler, Kane Hodder, Lar Park-Lincoln, Paramount, Slasher Movies, Susan Blu
By the time I'd made it all the way to tonight's piece I was going on 14 and even then Younger Me could sense the hurk and jerk of a movie series tottering on its last legs, begging for a fresh idea to blow through its sagging sails. At the time, Young Me found it odd that this series of films, centered as they are around a homicidal pseudo-zombie, could be so lacking in life.
Older and (I like to think) Wiser Me is nonplussed at this. He (I) no longer finds anything odd in the progressive degeneration of the American horror film – or film in general, for that matter. Older Me (I) possesses enough insight to see these films clearly, both as perpetrators and victims of their own perpetuation. Their downfall and degrading "quality" were as inevitable as a teenage death inside the Crystal Lake Woods, the result of a mass market system geared, not to telling stories, but to making the good people at Gulf Western (who at the time owned Paramount Pictures, and thus Jason Voorhees) richer than they already were. And they've no one to blame but themselves. {More}
Filed under: Movies, Reviews | Tags: Casey Kasem, Corey Burton, Eric Idle, Frank Welker, John Moschitta Jr., Leonard Nimoy, Nelson Shin, Orson Welles, Peter Cullen, Robert Stack, Ron Friedman, Scatman Crothers, Susan Blu, Transformers
The Transformers: The Movie is no place to begin your journey through the myriad maze of the Cybertronian Wars. A link between the second and third seasons of its eponymous TV show, The Movie will leave uninitiated peoples confused, annoyed, and potentially hostile to a set of stories that continue to fascinate hardcore fans of this stupid little cartoon. The trauma it caused us and its shinning, brilliant disdain for outsiders makes this the high water mark of 1980s toy commercial animation. Screw you, the film says. This is for the fans. And if you don't like it, just wait 'til will kill off all the main characters.
The excellent (if muddled) Transformers FAQ provides a good enough primer (pun most certainly intended) for our purposes. For those too lazy to scroll down to the appropriate section of text, we present this origin story: {More}


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Trump plans to designate Iranian military unit as a terrorist group
On Monday, the Trump administration announced that the Iranian Revolutionary Guard Corps (IRGC) is now considered to be a terrorist organization. The move was made to tighten existing economic and political pressures on Iran. This is the first time the United States has labeled any part of a foreign government as a terrorist group. And it came despite pleas for caution by military and intelligence officials in the U.S. "This action sends a clear message to Tehran that its support for terrorism has serious consequences," said President Trump. "We will continue to increase financial pressure and raise the costs on the Iranian regime for its support of terrorist activity until it abandons its malign and outlaw behavior." The designation will officially take effect April 16, and that means criminal action can be taken against individuals or groups who are proven to be aiding the IRGC in any way. Upon hearing the news in Tehran, members of Parliament have promised that any action taken against the IRGC by the Trump administration will be met with swift retaliation.
The United States moved Monday to list Iran's elite military Revolutionary Guard Corps as a foreign terrorist organization as the Trump administration looks for new ways to increase economic and political pressure on the Islamic regime in Tehran.
The designation marks the first time Washington has branded a foreign government entity a terrorist group and came despite warnings from U.S. military and intelligence officials that other nations could use the designation as a precedent against U.S. action abroad.
The announcement also comes one day before Israeli elections in which Prime Minister Benjamin Netanyahu is seeking a fifth term with hawkish promises to battle threatening Iranian behavior across the Middle East.
"This action sends a clear message to Tehran that its support for terrorism has serious consequences," President Trump said in a statement. "We will continue to increase financial pressure and raise the costs on the Iranian regime for its support of terrorist activity until it abandons its malign and outlaw behavior."
The terrorist designation, which takes effect April 16, will allow the Trump administration to seek criminal penalties against elements of the military agency and foreign officials deemed to be aiding it.
Designating the IRGC, as the sprawling agency is known, a terrorist organization will also allow Washington to ban travel to the United States for individuals associated with the IRGC.
The White House statement said the unprecedented step "recognizes the reality that Iran is not only a State Sponsor of Terrorism, but that the IRGC actively participates in, finances, and promotes terrorism as a tool of statecraft."
Trump called the IRGC "the Iranian government's primary means of directing and implementing its global terrorist campaign."
U.S. officials have long said that the group's opaque structure and responsibilities provided a mask for terrorist activities that threaten Israelis, Europeans and U.S. forces.
The new State Department designation "underscores the fact that Iran's actions are fundamentally different from those of other governments," Trump's statement said.
This action will significantly expand the scope and scale of our maximum pressure on the Iranian regime. It makes crystal clear the risks of conducting business with, or providing support to, the IRGC. If you are doing business with the IRGC, you will be bankrolling terrorism."
The action has been debated for years, including a renewed consideration last year that included warnings from Director of National Intelligence Daniel Coats and others about the potential for backlash.
Iranian parliamentarians warned of reciprocal action if the Trump administration acted against the IRGC, Reuters reported, citing state news agency IRNA.
Secretary of State Mike Pompeo said the designation makes clear that the IRGC is not just a behind-the-scenes enabler of terrorism but a direct participant in planning and carrying out terrorist attacks.
The IRGC is a military unit originally set up as security for Iran's clerical rulers. It has grown to be the country's most powerful security organization, with nearly unchecked political influence and interests in business, real estate and other areas of the economy. The United States blames the IRGC for facilitating U.S. service member deaths in Iraq and elsewhere, via financing, training and weapons support to terrorist networks.
Mark Dubowitz, chief executive of the Foundation for the Defense of Democracies, said the designation is welcome.
"It is fitting that the most dangerous terrorist group in the world, responsible for the deaths of tens of thousands of innocents and backed by a massive state apparatus and vast energy wealth, is being designated finally as a foreign terrorist organization," Dubowitz said.
Source: The Washington Post, Anne Gearan and Carol Morello, Apr. 8, 2019. Photo credit to Alex Brandon/AP.
| Tags: Economic, Iran, Iranian Revolutionary Guard Corps, military, Parliament, terror, terrorist, Trump, United States
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