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Learning AI If You Suck at Math - Part Eight - The Musician in the Machine

The wavelike movement effecting the economic system, the recurrence of periods of boom which are followed by periods of depression is the unavoidable outcome of the attempts, repeated again and again, to lower the gross market rate of interest by means of credit expansion. -Ludwig von Mises Global trade wars have escalated primarily between the US and China, but have spilled out to other economies as well. Tariffs placed on Turkey by the US is the catalyst for the recent volatility in the Lira, which has now declined 40% against the dollar in 2018. Though the Lira decline is recent, Turkey has experienced wider economic woes since 2013 when protestors claimed Erdogan’s regime was setting authoritarian policies while in the midst of a $100 billion scandal. Turkey in 2013 had a GDP of $950 billion which has since fallen to $850 billion, which still places Turkey is the 13th largest economy in the world in purchase price parity and the economic gateway between Europe, West Asia, and Africa. The vulnerabilities of Turkey’s currency volatility, inflation, and geopolitical risk are potentially of a much larger scale than the Thai Baht Crisis of 1997, which had complex ripple effects through a world highly interconnected at the time. Thai Baht Crisis of 1997 Thailand experienced an average year-over-year economic growth rate of 9% from 1985–1996, fueled in part by substantial foreign lending. Despite high growth, inflation remained low due to the Thai Baht peg to the U.S. dollar at 25 Baht. A result of this strong growth, and key to the Crisis, was continued significant foreign investment through the 1990’s made in the form of US Dollar-denominated debt, both into Thai local banks and companies. On May 14, 1997, the Baht experienced speculative currency shorting that crippled the federal reserve of Thailand. Unable to support the Baht peg, the Thai government began to float the price of the Baht. July 2, 1997, following the Baht free-float, not only did the currency collapse, the inevitable rapid increase in cost of borrowing critically weakened all such lenders and borrowers. High inflation ensued with the Baht falling to a low of 56 points per U.S. dollar in January of 1998. Instability in the finance, construction, and real estate sectors led to the end of economic growth in Thailand. The stock market fell 75% when the largest Thai finance company collapsed. Banks refused to issue bonds and loans to struggling companies, which subsequently began to fail. A critical piece to this story is that Thailand’s economy has never fully recovered. Asia Crisis Spreads Asia in general was suffering from the irrational binge of massive borrowing of U.S. dollars to reinvest into local markets during the 1990’s, which had accounted for GDP growth in the region. Large scale lending into emerging markets led to an inevitable burden of foreign debt. Due to the interconnected nature of trade economies, Thailand, at the time a tiny $180 billion GDP economy, caused a ripple effect across Asia and Russia. Foreign investors became hesitant to lend to other developing countries, inhibiting economic growth. Commodities such as oil faced sharp price declines, spreading contagion to oil exporting countries. Russia, whose federal reserves were severely diminished as a direct result of the declining demand for crude oil and metals, defaulted on domestic debt to workers, halted payments of foreign debt, and were also forced to float the ruble, being unable to maintain the peg of 5.3–7.1 rubles to the U.S. dollar. Within one month, the value of the ruble plummeted to 21 rubles to the U.S. dollar, leading to an economic collapse and inflation of over 80% in 1998. Argentina followed suit with sovereign defaults that were not settled until 2016. Likewise, US, Japanese and European banks who had made these loans, felt the contagion spread into the West. Most notably a US hedge fund, Long-Term Capital Management, LP, backed by no less than 16 market leading US investment banks, collapsed after losing $4.6 billion in 4 months. The Federal Reserve Bank of New York organized a $3.6 billion bailout amongst many other government level bailouts, including IMF intervention. Turkey Could Overshadow Thailand Turkey’s economy has been struggling now for five years, beginning in 2013 when Erogan quelled protests against his unconventional rule, religious persecution, authoritarian policies, and a $100 billion fraud scandal. Since 2013, Turkey’s GDP has dropped by $100 billion to $850 billion today. When the Thai Baht Crisis began in 1997, Thailand’s GDP was only $150 billion. Looking at the size of GDP alone, Turkey’s economic fallout has the potential to spread on a much larger scale. The Baht lost 40% in 1997, as has the Lira already lost 40% in 2018, half of the loss occurring in the last seven days. Contagion is already quickly occurring around Turkey, with a 10% drop in the South African Rand, and volatility in the Asian Forex markets. Fed rate hikes have already crippled many emerging markets, as higher interest rates are typically felt by less developed countries first, as their sovereign and corporate borrowing costs move higher. ( ) Couple higher rates with tariffs, which cause sudden inflation to already struggling economies, and a contagion quickly spreads across the global economy. Read more about rates causing next recession Digital Currencies The word cryptocurrency used broadly is a bit of a misnomer, which is why I prefer “digital assets”. What is commonly referred to as cryptocurrencies can represent anything from a decentralized or distributed digital currency, mainnet gas (the cost of using a blockchain or network), asset-backed tokens that represent securities or sovereign fiat, or even in-platform rewards or tokens such as gold bars in Candy Crush. Digital currencies such as Bitcoin, Bitcoin Cash, Litecoin, or Cardano are meant to be mediums of exchange. They are valuable in that emerging market economies can purchase and hold these currencies and use as a medium of exchange when their own localized fiat currencies are volatile and devalued, such as in a hyperinflationary environment, such as the Weimar Republic in 1920s, Zimbabwe in 1990, or recently in Turkey, Venezuela and Iran. Another use case of digital currencies is in countries where there is strict government control or totalitarianism, such as China or North Korea. In China, the RMB is kept to a strict peg, and Chinese nationals face many restrictions on how much RMB can leave the country or be exchanged for other currencies. Bitcoin and other digital currencies have become used as a store of value or transaction ability for other assets outside of China. network All of this is important historical context when discussing currency without borders. The efficacy of Bitcoin, for example, in countries experiencing hyperinflation due to lack of confidence in the underlying currency is enormous, and will play a role in the evolution of international economic proceedings in the years to come. With other cross-border digital currency options, there exist safety nets for potential local currency volatility, which could effectively mitigate events like Turkey Lira volatility or the Thai Baht Crisis. Although many of the underlying problems that led to these financial crises cannot be solved by the introduction of a borderless digital currency, the economic fallout that followed may have been dampened, if such a digital currency existed. Greek Debt Crisis Similar to countries facing crisis due to their own currency, a country may also face economic depression by relying on a stronger shared currency. Early in my career I was a sovereign debt and emerging market credit analyst. One of the first things I learned about investing in smaller countries is the risk of less diversified economies. They may specialize in only a few economic sectors, such as oil, fishing, farming, or tourism, which can lead to shorter economic cycles and higher occurrence and likelihood of recessions and inflation. One of the hairiest sovereign creditors I have looked at was Greece in 2009. Greece was a member of the EU, arguably a EU political move for a country not meeting many of the admission criteria that were waived by the EU, and had relinquished its Drachmas in exchange for Euros with the promise of being part of a stronger economic union and shared currency. The larger more industrial economies in the EU such as Germany, France, Italy, and Belgium have more predictable GDP growth due to steady year-round production of exportable goods. Greece, however, is highly tied to tourism, which peaks in the summers but simmers the rest of the year. Prior to joining the EU, Greece could control its own currency by inflating rates during tourist season, then deflating the rest of the year. Upon adopting the Euro, they were now subject to German “stability”. For the last 8 years Greece has been on the verge of default with a suffering economy. From 2009 until now, when the rest of the world was experiencing the biggest bull market in a generation, Greece GDP has lost over 40%, falling from $350 billion to $200 billion. Compare Greece to Croatia, another Balkan country with a similar economic makeup. Croatia is in the EU, but still uses its own currency, the Kuna. Croatia’s GDP only dropped from $63 billion to $55 billion over the same period due to its ability to float the Kuna. , with further negative consequences to Greece. The ECB is likely to raise rates to battle potential inflation in Europe Does a Global Currency Work? Many proponents of Bitcoin or other digital currencies have expressed the need for a single digital currency that is globally adopted and out of control of the governments to solve central currency problems, often stating that issues arise out of free floating currencies. Undoubtedly a country trying to break free of the collective problems of Forex pegged investment and intra-governmental organization imposed austerity and other control, which while admirable at a global level can be devastating on a country basis, might legitimately turn to a cryptocurrency. However, there are several flaws in this argument. The first is demonstrated by the above example of Greece. Multiple economies operating on a single currency can be dangerous for smaller economies that cannot control their own fiscal policy. As demonstrated by the Thai Baht, free floating currencies are not the issue either; rather, a currency that has been pegged too long that begins to immediately free float becomes volatile. Finally, an issue many people have with existing digital currencies is transferring control out of the hands of the governments but into a small group of early adopters who hold the majority of Bitcoin or one of the altcoins like Zcash or Cordano. It is like taking the power away from the tzars and giving it to the Bolsheviks, our new “benevolent” dictators ruling the global economy from Puerto Rico, Malta, and China (specifically Bitmain). Of course, everyone who has created a cryptocurrency along with their early adopters want the world to adopt their Blockchain, subsequently creating wealth for themselves and their constituents. I prefer democracy and liberty over Bolshevik rule; therefore, I am a proponent of continued work to create multiple digital currencies with multiple use, function, and decentralized (or distributed) control. I would like to extend a special thanks to Emma Channing of Satis Group for her contributions to this piece. Satis Research Team has committed extensive work on how borderless digital currencies may function. Disclosure: This commentary is provided as general information only and is in no way intended as investment advice, investment research, a research report or a recommendation. Any decision to invest or take any other action with respect to the securities discussed in this commentary may involve risks not discussed herein and such decisions should not be based solely on the information contained in this document. Statements in this communication may include forward-looking information and/or may be based on various assumptions. The forward-looking statements and other views or opinions expressed herein are made as of the date of this publication. Actual future results or occurrences may differ significantly from those anticipated and there is no guarantee that any particular outcome will come to pass. The statements made herein are subject to change at any time. Theseus Capital disclaims any obligation to update or revise any statements or views expressed herein. In considering any performance information included in this commentary, it should be noted that past performance is not a guarantee of future results and there can be no assurance that future results will be realized. Some or all of the information provided herein may be or be based on statements of opinion. In addition, certain information provided herein may be based on third-party sources, which information, although believed to be accurate, has not been independently verified. Theseus Capital and/or certain of its affiliates and/or clients hold and may, in the future, hold a financial interest in securities that are the same as or substantially similar to the securities discussed in this commentary. No claims are made as to the profitability of such financial interests, now, in the past or in the future and Theseus Capital and/or its clients may sell such financial interests at any time. The information provided herein is not intended to be, nor should it be construed as an offer to sell or a solicitation of any offer to buy any securities. This commentary has not been reviewed or approved by any regulatory authority and has been prepared without regard to the individual financial circumstances or objectives of persons who may receive it. The appropriateness of a particular investment or strategy will depend on an investor’s individual circumstances and objectives.

Trade Wars, Turkey Destabilization, and Forex Contagion: The Importance of Cryptocurrencies to…

12 Tips to Manage Your Energy and Skyrocket Your Productivity

Remember the good old times? When humanity thought the world was flat? When we thought intelligence was a completely fixed trait? When we thought our brains couldn’t physically be altered? When we thought saturated fats were the public health enemy #1? When we thought taking breaks was a waste of time? Wait, what was that last one again?! You heard me right. I believe in a few years we will mostly agree on the fact that frequent recovery breaks are NOT a waste of time, but in fact the key to . productivity Skeptical? Keep reading. Our Physiological Need for Breaks We live in a world characterized by rhythmic, wavelike movements. Think about the daily rising and setting of the sun, the ebb and flow of the tides, and the movement between seasons. Similarly, all organisms on this planet, including human beings, follow rhythms. You and I, and every other human being on this planet, we are constantly moving between periods of activity (spending energy) and periods of rest (renewing energy). This is because we adhere to rhythms, most notably the circadian rhythm. dictates that we live our lives in 24 hour cycles. We’re ‘on’, awake, and spending energy for approximately 16 hours. And then we’re ‘off’, asleep, and renewing energy for approximately 8 hours. The circadian rhythm If you’ve ever pulled an all-nighter (who hasn’t?) or tried to stay awake for as long as possible as a kid, then you know overriding your circadian rhythm is a terrible idea and a productivity no-go. And the circadian rhythm is by no means the only rhythm influencing our lives. During our 16 hours of wake time for example, our energy also heavily fluctuates — anyone who’s ever experienced the infamous ‘afternoon slump’ can surely attest to that. One rhythm that is partly responsible for that is **the ultradian rhythm.†**Tony Schwartz and Jim Loehr explain how it works in their book The Power of Full Engagement These ultradian rhythms help to account for the ebb and flow of our energy throughout the day. Physiological measures such as heart rate, hormonal levels, muscle tension and brain-wave activity all increase during the first part of the cycle — and so does alertness. After an hour or so, these measures start to decline. Somewhere between 90 and 120 minutes, the body begins to crave a period of rest and recovery. Signals include a desire to yawn and stretch, hunger pangs, increased tension, difficulty concentrating, an inclination to procrastinate or fantasize, and a higher incidence of mistakes. So the ultradian rhythm is a cycle that repeats itself multiple times during the day. For approximately 90 minutes you are in high performance mode. Your alertness, concentration, creativity, emotional resilience, and mental stamina are all at the top of their game. Then, for a period of approximately 20 minutes, your body needs time to rest and renew its energy stores. The point I’m trying to make is this: We are NOT super computers. We are not built to run at high speeds, continuously, for long periods of time. Instead we work best when we adhere to our natural rhythms, which dictate periods of intense activity (expending energy) followed by periods of rest (renewing energy). Science Agrees: More Breaks = Higher Productivity Time-tracking app Desktime found that the most productive people work for 52 minutes, and then take a break for 17 minutes. They also note that employees with the highest productivity ratings for the most part don’t even work 8 hours a day. The secret to retaining the highest level of productivity over the span of a work day is NOT working longer, but working more efficiently and with frequent breaks. They explain: The reason the 10% most productive employees are able to get the most done during the comparatively short periods of working time is that they’re treated as sprints for which they’re well-rested. They make the most of the 52 working minutes, in other words, they work with purpose. According to their study, the key to maximizing your productivity lies in working hard for short periods of time and then taking frequent breaks to reload the guns (so to speak). Sounds like they’re following a certain rhythm, huh? Researcher , who has studied elite performers, including musicians, athletes, actors and chess players, found a similar pattern. K. Anders Ericsson Turns out the best performers in each of these fields typically practice in uninterrupted sessions that last no more than 90 minutes. They begin in the morning, take a break between sessions, and rarely work for more than four and a half hours in any given day. They aren’t spending more time practicing their craft, but they engage in more so-called deliberate practice — intentional, high effort, high concentration practice beyond ones comfort zone. High performance isn’t about working for longer, but smarter. How to Work Like an Expert and Maximize Your Productivity If we want to work like an expert and maximize our productivity, we need to learn from elite performers. Here’s the recipe: Give it all you’ve got for short periods of time. Get as much done during that time as possible. Push hard. Work in sprints: In-between work sprints you need to recover and replenish some of your energy. Take frequent recovery breaks: That’s it. You’re either working super hard — deeply focused, fully engaged, highly concentrated, at full speed — or you’re taking a recovery break. There’s no more in-between. There’s no more ‘doing some work’. There’s no more preserving energy when you’re working. There’s no more half-assed working because you’re tired or whatever. You either work (you’re super productive) or you don’t (you’re taking a break). You’re either expending energy (work sprint) or replenishing energy (recovery break). That’s working according to your natural rhythms and it’s exactly how the highest performing people in the world do it. Now, how long should those work sprints and recovery breaks ideally be? If you want to copy the best, then the ideal length seems to be working for 90 minutes and recovering for 15 minutes. (That way you also adhere to the ultradian rhythm.) For most people, however, that’s not very practical. You may have a meeting coming up, co-workers interrupting you, or whatever. What’s important is that you either work or recover. Don’t do some half-working/half-chilling stuff. Sometimes you may have a work sprint that only lasts for 30 minutes. Or one that lasts for 80 minutes. Or heck, even one that goes on for 2 hours. Similarly, some breaks may only last 10 minutes, or 5 minutes, or 40 minutes. Don’t overcomplicate it. The key is to make waves and follow periods of intense activity with periods of intense recovery. If you work, work hard. If you take a break, recover properly. Do that and your productivity go through the roof. will How to Get the Most Out of Your Work Sprints There are a few strategies that’ll help you make your work sprints as productive as possible. 1) Give it all you’ve got Work sprints are called ‘sprints’ for a reason. During these relatively short periods of time, you really want to give it your all. Work like a maniac. Completely plunge in. Focus deeply. Get fully engaged. Do NOT try to pace yourself and preserve energy. Remember, you’ll get a break soon. Working in this sprint-like fashion accomplishes two things: 1) You get a massive amount of work done in a short period of time. 2) Your ability to work this way increases. Because you’re pushing yourself hard, you’ll actually get better at it. Your energy levels, concentration, and focus will all improve over time (and so will your productivity). This is exactly what elite performers do when they train with deliberate practice. 2) Eliminate all distractions This means: no phone no emails no notifications no calls no conversations no Facebook no news The single goal during your work sprints is to get as much done as possible. You can handle everything else later on, but you can’t have any distractions during your work sprints whatsoever. If you’re off checking email every 5 minutes, chances are you won’t get anything done and you certainly won’t improve your concentration, focus, and ability to work hard for sustained periods of times. 3) Single-task and work on one task, uninterruptedly, for long periods of time Haven’t heard the news yet? . (It also SHRINKS your brain, temporarily lowers your IQ, and creates unnecessary stress for the brain. More about the downfalls of multitasking here.) Multitasking makes you 40% less productive Even better than just not multitasking: Work on ONE and the same project for long periods of time. While switching between projects isn’t as bad as multitasking, it still isn’t ideal. Every time you switch to a new project, your attention doesn’t immediately follow. Instead, a residue of your attention remains stuck thinking and wrestling with the previous project. This is called a term that was originally coined by Sophie Leroy. She explains why switching between projects isn’t a smart idea: attention residue , “People experiencing attention residue after switching tasks are likely to demonstrate poor performance on that next task”. 4) Take mini breaks every 20 minutes Turns out our brains aren’t able to focus on one thing, without interruption, for more than 15–20 minutes. any A study in the journal showed that people can maintain their focus or vigilance much longer when their brains are given something else to think about every 20 minutes. Cognition Neuroscientist Mark Waldman confirms this: Our research has found that taking 2–3 breaks during each hour to consciously relax, stretch, meditate, or do something pleasurable–even for 10 seconds–will reduce stress, enhance your awareness, and significantly boost your concentration and productivity. So, every approximately 20 minutes, take a mini break of 10–120 seconds. Here are some ideas: Take a few deep breaths Yawn and stretch Look at nature (outside the window or even just on a picture) These three activities are all shown to be highly relaxing and rejuvenating for the brain. How to Get the Most Out of Your Breaks Between Work Sprints Do you know the difference between some of the best tennis players and average tennis players? One difference is that top players are maximizing their recovery between points. Tony Schwartz and Jim Loehr, authors of have found that the top players are able to lower their heart rates between points by up to twenty beats per minute. The average player’s heart rate stays the same, because he hasn’t optimized his recovery/rest period. The Power of Full Engagement If a match goes into the 4thor 5thset, guess who’s going to have more energy left? The point of this story is that getting the most out of your recovery breaks is crucial if you’re looking to maximize your productivity. Yes, taking break is better than not taking breaks at all. But there are better and worse ways to spend your time during breaks. any Watching TV, reading the news, or checking your Facebook newsfeed won’t give you the highest possible returns from your recovery breaks. Instead, you may want to choose one of the following activities: Exercise Get in nature Boost your mood Meditate What’s so special about these activities? They are all shown to increase your productivity. It’s like killing two birds with one stone: You get the benefits and enhanced productivity from taking a break You get the benefits and enhanced productivity from doing something that itself is highly beneficial If you watch TV during your breaks, you only get the benefits of taking a break. The End Quick summary: We work best when we adhere to our natural rhythms, which dictate switching between periods of expending energy and periods of renewing energy Elite musicians, actors, and athletes know this well enough and rarely practice for longer than 90 minutes in one session. They take breaks between sessions, and rarely work/practice for more than 4.5 hours in any given day. To maximize your productivity you need to copy their blueprint: Work hard for approximately 60–120 minutes and then take a recovery break for approximately 10–30 minutes. And that’s the science behind why taking frequent breaks makes you more productive. Now I’d love to hear from you. What’s your experience with taking regular breaks at work? Let me know in the comments below, and thanks for reading! P.S. Want More Productivity Tips? If you enjoyed these productivity tips, you’ll probably enjoy my free productivity guide as well. You can download it for free by clicking the link below: ==> (free PDF) My Top 7 Productivity Hacks to Get More Done https://upscri.be/hackernoon/

Why Taking Frequent Breaks Is the Key to Productivity

Newsflash: We are human beings, NOT high-speed computers. We are not meant to run at high speeds, continuously, for long periods of time. Instead, we are at our best when we move between expending energy and intermittently renewing . performing energy We live in a so-called oscillatory universe that is characterized by rhythmic, wavelike movement between activity and rest. Think about the daily rising and setting of the sun, the ebb and flow of the tides, and the movement between seasons. Similarly, all organisms on this planet follow rhythms. And that includes human beings. You and I, we are guided by rhythms. The most famous rhythm which we adhere to is the circadian rhythm. We live our lives in 24 hour periods. We’re on and awake for 16–18 hours (spending energy) and then we’re off and asleep for 7–9 hours (renewing energy). A period of activity is followed by a period of rest. A period of energy expenditure (activity) is followed by a period of energy renewal (rest). Our energy resources aren’t endless. We can’t sprint 100% for more than 10–20 seconds. We can’t concentrate for hours and hours on end. We can’t stay awake for much more than 18–20 hours before our performance plummets. Sooner or later we need to refuel our energy. That means, if we want to be as productive as possible, we need to live a rhythmic life with periods of intense activity followed by periods of intense rest. We need to live life as a series of sprints, not a never-ending marathon. Either Fully Engage Or Strategically Disengage Imagine for a second the look of a typical long-distance runner: skinny, sallow, gaunt, unhealthy, and not very energetic. Now imagine a sprinter such as Usain Bolt: powerful, healthy, strong, bursting with energy, and determined. The sprinter looks much better. Why? Because he’s oscillating. He’s on and then he’s off. He fully exerts himself and then follows that by a period of rest. He’s either fully engaged or strategically disengaged. He adheres to nature’s rhythms. Like the sprinter, we want to live our lives as a series of sprints, oscillating between periods of intense engagement and equally intense renewal. Makes total sense, right? It is, however, not how most of us live. Instead, most of us are in a state of constant energy preservation. We’re never fully on and never fully off. Never fully engaged and never fully disengaged. Instead of living oscillatory lives, we live linear lives (which is the complete opposite). Think about a typical work day for example. Do you ever completely relax? Do you ever fully disengage from what you’re doing during the day? Do you take frequent breaks? Do you ever fully concentrate and give 100% of all you’ve got to a given task? Chances are you do none of these things. For example, most of us view breaks as a sign of weakness and instead work in a state of constant energy preservation for hours and hours on end. Even if we’re tired and can barely concentrate anymore, we just keep going and going… …until eventually the clock hits 5pm and we get to go home where we watch TV for the rest of the day. (Watching TV by the way doesn’t count as either full engagement or full disengagement. It’s just a state of low, “blah”-type of energy.) This is neither our natural nor our optimal way of living. So remember, you’re much better off when you’re oscillating between activity and recovery than when you’re living in a linear fashion with your brakes on all the time. You want to be either fully engaged or strategically disengaged. The Ultradian Rhythm Tony Schwartz and Jim Loehr are the authors of . I’ve learned most of what I’m writing about in this article in that book (you can check out my summary ). In it, they describe a phenomenon called : The Power of Full Engagement here ultradian rhythms “These ultradian rhythms help to account for the ebb and flow of our energy throughout the day. Physiological measures such as heart rate, hormonal levels, muscle tension and brain-wave activity all increase during the first part of the cycle — and so does alertness. After an hour or so, these measures start to decline. Somewhere between 90 and 120 minutes, the body begins to crave a period of rest and recovery. Signals include a desire to yawn and stretch, hunger pangs, increased tension, difficulty concentrating, an inclination to procrastinate or fantasize, and a higher incidence of mistakes.” The ultradian rhythm is a cycle that repeats itself countless times during the day. For about 90 minutes you are in high performance mode. Your alertness, concentration, creativity, emotional resilience, and mental stamina are all at the top of their game. Then, for a period of about 20 minutes, your body needs time to rest and renew its energy stores. This gives us an easy prescription for optimizing our energy and productivity throughout the day: To get the most out of your working day, go full out for 90 to 120 minutes (fully engage) and then take a break for 15 to 20 minutes (strategically disengage). Simple as that. You work hard and on your limit for 90 to 120 minutes. Then you take a break and relax for 15 to 20 minutes. Repeat. (NOTE: We are able to override these natural cycles by summoning the fight-or-flight response and flooding our bodies with stress hormones that are designed to help us handle emergencies. Over the long-run, however, that’s a terrible idea. Toxins and stress hormones will build up in our system and over time take a toll on our bodies. Over-relying on caffeine, nicotine, cocaine, and amphetamines is NOT a long-term solution.) Source: http://www.deeperdish.com/90-minute-energy-advice-i-cant-usealone No More “Doing Some Work” I feel like I haven’t been clear enough so far, so let me state it more strongly: You are either working like a maniac — going all out, giving 100%, fully engaged, intensely focused, completely absorbed in your task — or you are taking a break. True productivity means either being fully on or being fully off. It means either fully engaging and getting a boatload of work done, or strategically disengaging to renew energy. There’s no in-between. There’s no more “doing some work”. There’s no more preserving energy when you’re working. There’s no more half-assed working because you’re tired or whatever. You either work (you’re super productive) or you don’t (you’re taking a break). You’re either in a work sprint or you’re recovering energy. Believe me: You will get more done in a single 90 to 120 minute work sprint than most people do in an entire day… (NOTE: Work sprints can differ in length depending on how your energy is doing. If you’re in a great work flow, still have good concentration and focus, then you can go beyond 90–120 minutes. You can also go longer if you’re using artificial help such as caffeine and other smart drugs. The key is to realize when you’re getting a bit tired or can’t focus anymore… that’s when you want to take a break and renew your energy and attention.) This is an excerpt from a short e-book on my 7 best productivity hacks. You can get the whole thing here for free.

Why Working in Sprints Maximizes Human Productivity

Lockheed P-80A airplane (1946). Credit: NASA Commons. — A reminder to test your code. After my short piece, appeared in , I received several emails and Twitter mentions asking for more specific tips — both about tools and documents we use in the group to train the team about reproducibility. “A hard road to reproducibility,” Science In answer to popular demand, then, I have collected here what we could call the “Barba-group Reproducibility Syllabus.” Top-10 Readings in Reproducibility Early this year, my student Olivier and I were getting started writing a book chapter and later a full-length journal article; the first was about our reproducible-research workflow and the second on our . These represented about three years of work, not exclusively on this project, but taking most of the graduate student’s time. As part of our “pre-writing” tasks, we decided to build — collectively as a group — our list of Top 10 papers discussing reproducible research in computational science. Here’s our current reading list (modified from our first version of Feb. 2016): CFD replication study Schwab, M., Karrenbach, N., Claerbout, J. (2000) Making scientific computations reproducible, . 2(6):61–67, doi: Comp. Sci. Eng 10.1109/5992.881708 Donoho, D. et al. (2009), Reproducible research in computational harmonic analysis, 11(1):8–18, doi: Comp. Sci. Eng. 10.1109/MCSE.2009.15 Reproducible Research, by the Yale Law School Roundtable on Data and Code Sharing, 12(5): 8–13 (Sept.-Oct. 2010), doi: Comp. Sci. Eng. 10.1109/mcse.2010.113 Peng, R. D. (2011), Reproducible research in computational science, 334(6060): 1226–1227, doi: Science 10.1126/science.1213847 Diethelm, Kai (2012) The limits of reproducibility in numerical simulation, 14(1): 64–72, doi: Comp. Sci. Eng. 10.1109/MCSE.2011.21 Setting the default to reproducible (2013), ICERM report of the Workshop on Reproducibility in Computational and Experimental Mathematics (Providence, Dec. 10–14, 2012), Stodden et al. (eds.), // https://icerm.brown.edu/tw12-5-rcem/ report PDF Sandve, G. K. et al. (2013), Ten simple rules for reproducible computational research, (editorial), Vol. 9(10):1–4, doi: PLOS Comp. Bio. 10.1371/journal.pcbi.1003285 Leek, J. and Peng, R (2015), Opinion: Reproducible research can still be wrong: Adopting a prevention approach, 112(6):1645–1646, doi: PNAS 10.1073/pnas.1421412111 M. Liberman, “Replicability vs. reproducibility — or is it the other way around?,” Oct. 2015, http://languagelog.ldc.upenn.edu/nll/?p=21956 Goodman, S. N., Fanelli, D., & Ioannidis, J. P. (2016). What does research reproducibility mean? 8(341), 341ps12–341ps12, doi: Science Translational Medicine 10.1126/scitranslmed.aaf5027 et al. (2000) report on the pioneering example of reproducible research in the lab (Exploration Geophysics, Stanford University). The first public communication of this group’s approach that we could find goes back to 1992 [1]. That paper describes tools and processes in more detail, but for the same reason it is quite dated. So, we start with the summary account in . The Claerbout group developed an automatic build system for their published papers, including all the analyses and figures plus the typeset document. They used GNU make, certain standardized commands (burn, build, view, clean), and a notion of the file set or research compendium associated with the paper (data sets, programs, scripts, parameter files, makefiles). They report having used the system to-date for 14 papers involving 15 authors and hundreds of files. It’s remarkable to read about their careful methods for reproducible documents, given that more than two decades later we’re still struggling to adopt similar standards more widely. Schwab Claerbout CiSE Jump to Donoho et al. (2009). This could be the first group to explicitly associate reproducible research with open code and data: Reproducible computational research, in which all details of computations — code and data — are made conveniently available to others, is a necessary response to [the credibility] crisis. Donoho et al. admonish that computation cannot claim to be the third branch of science because most computational results cannot be verified. In the two traditional branches, standards of practice already exist for managing the ubiquity of error: deductive science uses formal logic and the mathematical proof, while empirical science uses statistical hypothesis testing and detailed methods reporting. Ouch! “Many users of scientific computing aren’t even trying to follow a systematic, rigorous discipline that would in principle allow others to verify the claims they make.” Donoho et al. cite strong influences from Claerbout’s methods, and lament that these are still not widely practiced. This paper also repeats the classic paraphrase of Claerbout: (First appearing in a 1995 paper from this group [2].) My favorite quote from Donoho et al. (2009) is: The middle sections of the paper describe the various computational libraries developed to date in the Donoho group; those sections can be skimmed according to the reader’s interest. Towards the end, an interesting passage — written in the format of a Q&A — addresses the typical objections of researchers to working reproducibly. Many such objections are still hot topics today: it takes time and effort, we get no credit for it, competition, and so on. Notably, the final hypothetical objection is that “true reproducibility” should mean starting from scratch to re-create the results (rather than from the author-provided code and data). The rebuttal: “an article about computational science … is not the scholarship itself, it’s merely scholarship advertisement. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures.” “… if everyone on a research team knows that everything they do is going to someday be published for reproducibility, they’ll behave differently from day one.” “…it proves nothing if your implementation fails to give my results because we won’t know why it fails. The only way we’d ever get to the bottom of such a discrepancy is if we both worked reproducibly…” The jointly authored paper of the Yale Law Roundtable participants ( , 2010) expanded on the theme of transparency via open code and data. They defined reproducible computational research unambiguously as that making available all details (code and data) of the computations. Their additional recommendations include: assigning a unique identifier to every version of the data and code, describing within each publication the computing environment used, using open licenses and non-proprietary formats, and publishing under open-access conditions (or posting pre-prints). The rationale behind linking open access with reproducibility was absent, and some have criticized this aspect of the Roundtable recommendations. It may have grown out of the idea in Donoho et al. (2009) that “reproducibility means publication over the Internet,” and that authors should maintain a Web presence to facilitate discovery and access to their research. The connection between reproducible research and open-access publishing is, however, questionable. On the other hand, open code and data are valid components of reproducible computational research. Among future goals, the Yale Roundtable recognized the importance of enabling citation of code and data, of developing tools to facilitate versioning, testing and tracking, and of standardizing various aspects like terminology, ownership, policy. CiSE Peng (2011) introduced the idea of a reproducibility spectrum. He says that reproducible research is a Here we find an explicit distinction in terminology — something that continues to muddle the field — where full replication of a study involves collecting new data, with a different method (and code), and arriving at the same or equivalent final findings. (The distinction previously appeared in [3]) Peng mentions the Sloan Digital Sky Survey as an example of a project that would require formidable resources to fully replicate, and therefore proposes that reproducibility is a lesser standard that is more attainable. Other domains exist where full replication is unrealistic or extremely expensive. Reproducibility, says Peng, Nevertheless, it is a desirable minimum standard to assess the quality of the scientific claims. It requires that But, Peng laments, “minimum standard for judging scientific claims when full independent replication of a study is not possible.” “falls short of full replication because the same data are analyzed again.” “the data and the computer code used to analyze the data be made available to others.” “the biggest barrier to reproducible research is the lack of a deeply ingrained culture that simply requires reproducibility for all scientific claims.” Number 5 on our list (ordered chronologically) shifts to a different concern: numerical reproducibility in computations that involve parallel processing. In the discussion up until now, the concept of reproducible research assumed that running the same code twice with identical input will produce the same output. If the computation is done in serial, this assumption is good; but with parallel computing, it is not always the case. Diethelm (2012) ran an experiment using an application of finite-element analysis in computational mechanics. Executing the same simulation (same code, same input data) with varying number of processors gave different results! Investigating the differences and the source code pinpointed the cause of non-deterministic behavior: a direct solver for sparse linear systems (an external library). Diethelm goes through an example that illustrates how this can happen: a vector dot-product, computed in parallel over several partial sums. On each execution, individual processors may complete their portion of the sum in different order. In finite precision, addition is not associative and the final sum depends on the order of the partial sums. Under these conditions, ensuring numerical reproducibility involves introducing artificial synchronization points in the program, at the cost of additional run time. More elaborate techniques are available, but the conclusion is that in high-performance computing “lack of reproducibility is typically a price that must be paid for speeding up the algorithm.” The ICERM Workshop Report (2013) builds on the contributions of the Yale Roundtable by placing particular focus on: (1) changing the culture and reward structure; (2) role of funding agencies, journals and employers; (3) teaching the skills for reproducible research. The required culture change includes valuing openness and transparency. But the academic reward structure sets critical barriers: Moreover, software development and data management are not valued scientific activities. The report addresses several ways to introduce incentives, requiring leadership from funders, journals and employers. Many of those discussions continue to this day in different venues (workshops, journal editorials, blogs, etc.). “The current system, which places a great deal of emphasis on the number of journal publications and virtually none on reproducibility …penalizes authors who spend extra time on a publication.” I should add that I participated in the ICERM Workshop, giving a short talk titled “Reproducibility PI Manifesto.” The slides of this talk have been widely shared and commented [4]. Sandve et al. (2013) give us ten concrete actions we can take to make our research reproducible: For every result, keep track of how it was produced Avoid manual data-manipulation steps Archive the exact versions of all external programs used Version-control all custom scripts Record all intermediate results, when possible in standard formats For analyses that include randomness, note underlying random seeds Always store raw data behind plots Generate hierarchical analysis output, allowing layers of increasing detail to be inspected Connect textual statements to underlying results Provide public access to scripts, runs, and results Some common threads run through most of these recommendations. First, recognizing that a final result is the product of a sequence of intermediate steps (the analysis workflow), a key device for reproducibility is automation. Second, the central technology for dealing with software as a living, changing thing, is version control. And finally, archive and document everything with the best tools at hand. The one, inescapable corollary for the purposes of training researchers is that command-line skills are essential. Item 8 of our reading list (Leek and Peng, 2015) expands on the purpose of reproducible research: to protect the integrity of science and build the public’s trust on scientific results. Although a reproducible study can still suffer from poor study design, missing data, or confounding factors, reproducibility increases the rate at which we can uncover these flaws. Even so, the key is prevention via the training of more people on techniques for data analysis. Leek and Peng contribute to this goal via their massive online courses, and they also recognize the value of crowd-sourced workshops like and . Software Carpentry Data Carpentry Next on the list is an essay by Mark Liberman, Christopher H. Browne Distinguished Professor of Linguistics at the University of Pennsylvania. He teaches introductory linguistics, as well as big data in linguistics, and computational analysis and modeling of biological signals and systems (among other topics). The subject of his essay is the big confusion of terminology that has spread on the reproducibility literature. He traces the confusion to a machine-learning workshop contribution, where the terms reproducible and replicable are swapped completely, compared to previous papers. Liberman concludes: “Since the technical term ‘reproducible research’ has been in use since 1990, and the technical distinction between reproducible and replicable at least since 2006, we should reject [the] attempt to re-coin technical terms reproducible and replicable in senses that assign the terms to concepts nearly opposite to those used in the definitions by Claerbout, Peng and others.” Our final item on the reading list is from earlier this year. Goodman et al. (2016) note that the various terms used in the field (e.g., reproducible vs. replicable) are not standardized. The importance of corroborating a previous study’s results is widely recognized. But, the authors note, [mistake: geophysicist] [used in] [references provided]. Goodman et al. uphold the Claerbout/Donoho/Peng terminology, but propose a new lexicon as a way out of the confusion reigning the literature: methods reproducibility (original meaning of reproducibility), results reproducibility (previously called replication), and inferential reproducibility. Who knows if this new lexicon will stick, but what I like of this paper is its skillful discussion of differences among scientific domains that affect how each addresses reproducibility. In computational research, we’re used to a degree of determinism, for example, so methods reproducibility and results reproducibility are linked. Other fields have to deal with major stochastic variability. For most computational scientists, the second half of this paper will be alien, because it focuses on issues of statistical significance testing, clinical and pre-clinical research, and so on. It is good, however, to get a glimpse into this other world of science, where p-hacking and HARKing (hypothesis after results are known) are a thing. “ … the modern use of ‘reproducible research’ was originally applied not to corroboration, but to transparency, with application in the computational sciences. Computer scientist Jon Claerbout coined the term and associated it with a software platform and set of procedures that permit the reader of a paper to see the entire processing trail from the raw data and code to figures and tables. …This concept has been epidemiology, computational biology, economics and clinical trials…” Additional References [1] Claerbout, Jon and Martin Karrenbach (1992). Electronic documents give reproducible research a new meaning, Proc. 62nd Ann. Int. Meeting of the Soc. of Exploration Geophysics, pp. 601–604, doi: 10.1190/1.1822162 http://library.seg.org/doi/abs/10.1190/1.1822162 [2] Buckheit, J. B. and D. Donoho (1995), WaveLab and reproducible research, In Wavelets and Statistics, edited by A. Antoniadis and G. Oppenheim, Lecture Notes in Statistics 103: pp. 55–81, doi: 10.1007/978–1–4612–2544–7_5 [3] Roger D. Peng, Francesca Dominici and Scott L. Zeger (2006), Reproducible epidemiologic research, Am. J. Epidemiol. 163 (9): 783–789. doi: 10.1093/aje/kwj093, http://aje.oxfordjournals.org/content/163/9/783.short [4] Barba, Lorena A. (2012): Reproducibility PI Manifesto. figshare, doi: 10.6084/m9.figshare.104539.v1 Originally published at lorenabarba.com on October 31, 2016.

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