newsletter/2018/11 (Link Bibliography)

“newsletter/​2018/​11” links:

  1. https://gwern.substack.com

  2. 10

  3. newsletter

  4. Changelog

  5. https://www.patreon.com/gwern

  6. Cat-Sense

  7. Embryo-selection#faq-frequently-asked-questions

  8. Embryo-selection#multi-stage-selection

  9. Embryo-selection#gamete-selection

  10. Embryo-selection#optimal-stoppingsearch

  11. Embryo-selection#robustness-of-utility-weights

  12. Statistical-notes#oh-deer-could-deer-evolve-to-avoid-car-accidents

  13. Notes#urban-area-cost-of-living-as-big-tech-moats-employee-golden-handcuffs

  14. Links#uses-this

  15. https://onlinelibrary.wiley.com/doi/pdf/10.1111/gbb.12441

  16. ⁠, Rona J. Strawbridge, Joey Ward, Amy Ferguson, Nicholas Graham, Richard J. Shaw, Breda Cullen, Robert Pearsall, Laura M. Lyall, Keira J. A. Johnston, Claire L. Niedzwiedz, Jill P. Pell, Daniel Mackay, Julie Langan Martin, Donald M. Lyall, Mark E. S. Bailey, Daniel J. Smith (2018-10-25):

    Background: Suicide is a major issue for global public health. ‘Suicidality’ describes a broad clinical spectrum of thoughts and behaviours, some of which are common in the general population.

    Methods: recruited ~0·5 million middle age individuals from the UK, of whom 157,000 completed an assessment of suicidality. Mutually exclusive groups were assessed in an ordinal of suicidality: ‘no suicidality’ controls (n = 83,557); ‘thoughts that life was not worth living’ (n = 21,063); ‘ever contemplated self-harm’ (n = 13,038); ‘an act of deliberate self-harm in the past’ (n = 2,498); and ‘a previous suicide attempt’ (n = 2,666). Linkage of UK to death certification records identified a small sub-group of ‘completed suicide’ (n = 137).

    Outcomes: We identified three novel genome-wide statistically-significant loci for suicidality (on Chromosomes 9, 11 and 13) and moderate-to-strong between suicidality and a range of psychiatric disorders, most notably depression (rg 0·81). Higher polygenic risk scores for suicidality were associated with increased risk of completed suicide relative to controls in an independent sub-group (n = 137 vs n = 5,330, OR 1·23, 95% 1·06 to 1·41, p = 0.03). Rs598046-G (chromosome 11) demonstrated a similar and direction (p = 0·05) within a Danish suicidality study.

    Interpretation: These findings have significant implications for our understanding of genetic vulnerability to suicidal thoughts and behaviours. Future work should assess the extent to which polygenic risk scores for suicidality, in combination with non-genetic risk factors, may be useful for stratified approaches to suicide prevention at a population level.

    Funding: UKRI Innovation-HDR-UK Fellowship (⁠/​​​​S003061/​​​​1). MRC Mental Health Data Pathfinder Award (MC_PC_17217).

  17. https://www.sciencemag.org/news/2018/10/giant-study-links-dna-variants-same-sex-behavior

  18. ⁠, Lucy Riglin, Ajay K. Thapar, Beate Leppert, Joanna Martin, Alexander Richards, Richard Anney, George Davey Smith, Kate Tilling, Evie Stergiakouli, Benjamin B. Lahey, Michael C. O’Donovan, Stephan Collishaw, Anita Thapar (2018-11-21):

    Background: Psychiatric disorders show phenotypic as well as genetic overlaps. Factor analyses of child and adult psychopathology have found that phenotypic overlaps largely can be explained by a general “p” factor that reflects general liability to psychopathology. We investigated whether shared genetic liability across disorders would be reflected in associations between multiple different psychiatric polygenic risk scores () and a ‘general psychopathology’ factor in childhood.

    Methods: The sample was a UK, prospective, population-based cohort (ALSPAC), including data on psychopathology at age 7 (n = 8161) years. PRS were generated from large published genome-wide association studies.

    Outcomes

    The general psychopathology factor was associated with both schizophrenia PRS and attention-deficit/​​​​hyperactivity disorder () PRS, whereas there was no strong evidence of association with major depressive disorder and autism spectrum disorder PRS. Schizophrenia PRS was also associated with a specific “emotional” problems factor.

    Interpretation

    Our findings suggest that genetic liability to and ADHD may contribute to shared genetic risks across childhood psychiatric diagnoses at least partly via the ‘general psychopathology’ factor. However, the pattern of observations could not be explained by a general “p” factor on its own.

    Funding

    This work was supported by the Wellcome Trust (204895/​​​​Z/​​​​16/​​​​Z).Introduction

  19. 2018-walters.pdf: “Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders”⁠, Raymond K. Walters, Renato Polimanti, Emma C. Johnson, Jeanette N. McClintick, Mark J. Adams, Amy E. Adkins, Fazil Aliev, Silviu-Alin Bacanu, Anthony Batzler, Sarah Bertelsen, Joanna M. Biernacka, Tim B. Bigdeli, Li-Shiun Chen, Toni-Kim Clarke, Yi-Ling Chou, Franziska Degenhardt, Anna R. Docherty, Alexis C. Edwards, Pierre Fontanillas, Jerome C. Foo, Louis Fox, Josef Frank, Ina Giegling, Scott Gordon, Laura M. Hack, Annette M. Hartmann, Sarah M. Hartz, Stefanie Heilmann-Heimbach, Stefan Herms, Colin Hodgkinson, Per Hoffmann, Jouke Hottenga, Martin A. Kennedy, Mervi Alanne-Kinnunen, Bettina Konte, Jari Lahti, Marius Lahti-Pulkkinen, Dongbing Lai, Lannie Ligthart, Anu Loukola, Brion S. Maher, Hamdi Mbarek, Andrew M. McIntosh, Matthew B. McQueen, Jacquelyn L. Meyers, Yuri Milaneschi, Teemu Palviainen, John F. Pearson, Roseann E. Peterson, Samuli Ripatti, Euijung Ryu, Nancy L. Saccone, Jessica E. Salvatore, Sandra Sanchez-Roige, Melanie Schwandt, Richard Sherva, Fabian Streit, Jana Strohmaier, Nathaniel Thomas, Jen-Chyong Wang, Bradley T. Webb, Robbee Wedow, Leah Wetherill, Amanda G. Wills, Michelle Agee, Babak Alipanahi, Adam Auton, Robert K. Bell, Katarzyna Bryc, Sarah L. Elson, Pierre Fontanillas, Nicholas A. Furlotte, David A. Hinds, Karen E. Huber, Aaron Kleinman, Nadia K. Litterman, Jennifer C. McCreight, Matthew H. McIntyre, Joanna L. Mountain, Elizabeth S. Noblin, Carrie A. M. Northover, Steven J. Pitts, J. Fah Sathirapongsasuti, Olga V. Sazonova, Janie F. Shelton, Suyash Shringarpure, Chao Tian, Joyce Y. Tung, Vladimir Vacic, Catherine H. Wilson, Jason D. Boardman, Danfeng Chen, Doo-Sup Choi, William E. Copeland, Robert C. Culverhouse, Norbert Dahmen, Louisa Degenhardt, Benjamin W. Domingue, Sarah L. Elson, Mark A. Frye, Wolfgang Gäbel, Caroline Hayward, Marcus Ising, Margaret Keyes, Falk Kiefer, John Kramer, Samuel Kuperman, Susanne Lucae, Michael T. Lynskey, Wolfgang Maier, Karl Mann, Satu Männistö, Bertram Müller-Myhsok, Alison D. Murray, John I. Nurnberger, Aarno Palotie, Ulrich Preuss, Katri Räikkönen, Maureen D. Reynolds, Monika Ridinger, Norbert Scherbaum, Marc A. Schuckit, Michael Soyka, Jens Treutlein, Stephanie Witt, Norbert Wodarz, Peter Zill, Daniel E. Adkins, Joseph M. Boden, Dorret I. Boomsma, Laura J. Bierut, Sandra A. Brown, Kathleen K. Bucholz, Sven Cichon, E. Jane Costello, Harriet Wit, Nancy Diazgranados, Danielle M. Dick, Johan G. Eriksson, Lindsay A. Farrer, Tatiana M. Foroud, Nathan A. Gillespie, Alison M. Goate, David Goldman, Richard A. Grucza, Dana B. Hancock, Kathleen Mullan Harris, Andrew C. Heath, Victor Hesselbrock, John K. Hewitt, Christian J. Hopfer, John Horwood, William Iacono, Eric O. Johnson, Jaakko A. Kaprio, Victor M. Karpyak, Kenneth S. Kendler, Henry R. Kranzler, Kenneth Krauter, Paul Lichtenstein, Penelope A. Lind, Matt McGue, James MacKillop, Pamela A. F. Madden, Hermine H. Maes, Patrik Magnusson, Nicholas G. Martin, Sarah E. Medland, Grant W. Montgomery, Elliot C. Nelson, Markus M. Nöthen, Abraham A. Palmer, Nancy L. Pedersen, Brenda W. J. H. Penninx, Bernice Porjesz, John P. Rice, Marcella Rietschel, Brien P. Riley, Richard Rose, Dan Rujescu, Pei-Hong Shen, Judy Silberg, Michael C. Stallings, Ralph E. Tarter, Michael M. Vanyukov, Scott Vrieze, Tamara L. Wall, John B. Whitfield, Hongyu Zhao, Benjamin M. Neale, Joel Gelernter, Howard J. Edenberg, Arpana Agrawal

  20. https://www.nature.com/articles/s41598-018-34713-z

  21. https://www.economist.com/science-and-technology/2018/11/17/a-newly-discovered-tea-plant-is-caffeine-free

  22. 2018-jin.pdf

  23. https://www.wired.com/story/whole-genome-sequencing-cost-200-dollars/

  24. https://www.businesswire.com/news/home/20181129005208/en/Ancestry-Breaks-November-Sales-Record

  25. https://www.prnewswire.com/news-releases/400-million-investment-programme-positions-ireland-for-global-leadership-in-genomic-research-and-advanced-life-sciences-300755716.html

  26. https://www.quantamagazine.org/neutral-theory-of-evolution-challenged-by-evidence-for-dna-selection-20181108/

  27. ⁠, Luke J. O’Connor, Armin P. Schoech, Farhad Hormozdiari, Steven Gazal, Nick Patterson, Alkes L. Price (2018-09-18):

    Complex traits and common disease are highly polygenic: thousands of common variants are causal, and their effect sizes are almost always small. Polygenicity could be explained by negative selection, which constrains common-variant effect sizes and may reshape their distribution across the genome. We refer to this phenomenon as flattening, as genetic signal is flattened relative to the underlying biology. We introduce a mathematical definition of polygenicity, the effective number of associated SNPs, and a robust statistical method to estimate it. This definition of polygenicity differs from the number of causal SNPs, a standard definition; it depends strongly on SNPs with large effects. In analyses of 33 complex traits (average n = 361k), we determined that common variants are ~4× more polygenic than low-frequency variants, consistent with pervasive flattening. Moreover, functionally important regions of the genome have increased polygenicity in proportion to their increased heritability, implying that heritability enrichment reflects differences in the number of associations rather than their magnitude (which is constrained by selection). We conclude that negative selection constrains the genetic signal of biologically important regions and genes, reshaping genetic architecture.

  28. 1999-bradshaw.pdf: ⁠, J. W. S. Bradshaw, G. F. Horsfield, J. A. Allen, I. H. Robinson (1999-12; cat⁠, genetics  /​ ​​ ​selection  /​ ​​ ​dysgenics):

    The so-called occupies a unique position within the truly domestic animals since it freely interbreeds with feral populations, and there is considerable gene flow in both directions. This is possible because the likelihood of an individual cat forming a relationship with people is strongly affected by its experiences during the socialisation period (3–8 weeks of age), although this does not preclude differences between owned and feral populations in the relative frequencies of alleles which affect social behaviour towards humans.

    We suggest a hitherto unconsidered reason why a separate domesticated population of cats (apart from pedigree breeds) has not yet emerged: the unusual and stringent nutrient requirements of the cat may historically have militated against successful breeding on a completely human-provided diet, and led to the retention of the ability to achieve a nutritionally complete diet by scavenging and/​​​​or hunting. More recently, the widespread availability of nutritionally complete manufactured foods and veterinary care in western countries appears to be leading towards a rapid change in the population dynamics and population genetics of both owned and feral cats.

    [Keywords: domestication, feral populations, population dynamics, cat]

  29. http://www.ratbehavior.org/DumboRatMutation.htm

  30. 2015-harpending.pdf

  31. https://www.cambridge.org/core/services/aop-cambridge-core/content/view/A8887CDB53100F6A4D3AD0E5AECA6440/S002193201600002Xa.pdf/some_uses_of_models_of_quantitative_genetic_selection_in_social_science.pdf

  32. https://apnews.com/4997bb7aa36c45449b488e19ac83e86d

  33. https://apnews.com/13303d99c4f849829e98350301e334a9

  34. https://www.statnews.com/2018/11/28/chinese-scientist-defends-creating-gene-edited-babies/

  35. https://blogs.plos.org/synbio/2018/11/30/opinion-the-first-crispred-babies-are-here-whats-next/

  36. https://www.technologyreview.com/s/612494/despite-crispr-baby-controversy-harvard-university-will-begin-gene-editing-sperm/

  37. 2018-mullaart.pdf: “Embryo Biopsies for Genomic Selection”⁠, Erik Mullaart, David Wells

  38. https://bioengineeringcommunity.nature.com/channels/541-behind-the-paper/posts/37262-engineering-yeast-megachromosomes

  39. http://www.genetics.org/content/202/3/877

  40. 2015-gianola.pdf: ⁠, Daniel Gianola, Guilherme J. M. Rosa (2014-11-03; genetics  /​ ​​ ​selection):

    Statistical methodology has played a key role in scientific animal breeding. Approximately one hundred years of statistical developments in animal breeding are reviewed. Some of the scientific foundations of the field are discussed, and many milestones are examined from historical and critical perspectives. The review concludes with a discussion of some future challenges and opportunities arising from the massive amount of data generated by livestock, plant, and human genome projects.

  41. ⁠, Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, Dario Amodei (2018-11-15):

    To solve complex real-world problems with ⁠, we cannot rely on manually specified reward functions. Instead, we can have humans communicate an objective to the agent directly. In this work, we combine two approaches to learning from human feedback: expert demonstrations and trajectory preferences. We train a deep neural network to model the reward function and use its predicted reward to train an -based deep reinforcement learning agent on 9 Atari games. Our approach beats the imitation learning baseline in 7 games and achieves strictly superhuman performance on 2 games without using game rewards. Additionally, we investigate the goodness of fit of the reward model, present some reward hacking problems, and study the effects of noise in the human labels.

  42. 2018-poplin.pdf: ⁠, Ryan Poplin, Avinash V. Varadarajan, Katy Blumer, Yun Liu, Michael V. McConnell, Greg S. Corrado, Lily Peng, Dale R. Webster (2018-01-01; ai):

    Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses. However, with medical images, observing and quantifying associations can often be difficult because of the wide variety of features, patterns, colours, values and shapes that are present in real data. Here, we show that deep learning can extract new knowledge from retinal fundus images. Using deep-learning models trained on data from 284,335 patients and validated on 2 independent datasets of 12,026 and 999 patients, we predicted cardiovascular risk factors not previously thought to be present or quantifiable in retinal images, such as age (mean absolute error within 3.26 years), gender (area under the receiver operating characteristic curve (AUC) = 0.97), smoking status (AUC = 0.71), systolic blood pressure (mean absolute error within 11.23 mmHg) and major adverse cardiac events (AUC = 0.70). We also show that the trained deep-learning models used anatomical features, such as the optic disc or blood vessels, to generate each prediction. [Sex detection replicated in ⁠.]

  43. ⁠, Lvmin Zhang, Chengze Li, Tien-Tsin Wong, Yi Ji, Chunping Liu (2018-05-04):

    Github repo with screenshot samples of style2paints, a neural network for colorizing anime-style illustrations (trained on Danbooru2018), with or without user color hints, which was available as an online service in 2018. style2paints produces high-quality colorizations often on par with human colorizations. Many examples can be seen on Twitter or the repo:

    Example style2paints colorization of a character from Prison School

    style2paints has been described in more detail in ⁠, Zhang et al 2018:

    Sketch or line art colorization is a research field with substantial market demand. Different from photo colorization which strongly relies on texture information, sketch colorization is more challenging as sketches may not have texture. Even worse, color, texture, and gradient have to be generated from the abstract sketch lines. In this paper, we propose a semi-automatic learning-based framework to colorize sketches with proper color, texture as well as gradient. Our framework consists of two stages. In the first drafting stage, our model guesses color regions and splashes a rich variety of colors over the sketch to obtain a color draft. In the second refinement stage, it detects the unnatural colors and artifacts, and try to fix and refine the result.Comparing to existing approaches, this two-stage design effectively divides the complex colorization task into two simpler and goal-clearer subtasks. This eases the learning and raises the quality of colorization. Our model resolves the artifacts such as water-color blurring, color distortion, and dull textures.

    We build an interactive software based on our model for evaluation. Users can iteratively edit and refine the colorization. We evaluate our learning model and the interactive system through an extensive user study. Statistics shows that our method outperforms the state-of-art techniques and industrial applications in several aspects including, the visual quality, the ability of user control, user experience, and other metric

  44. 2018-zhang.pdf: ⁠, Lvmin Zhang, Chengze Li, Tientsin Wong, Yi Ji, Chunping Liu (2018; anime):

    Sketch or line art colorization is a research field with substantial market demand. Different from photo colorization which strongly relies on texture information, sketch colorization is more challenging as sketches may not have texture. Even worse, color, texture, and gradient have to be generated from the abstract sketch lines. In this paper, we propose a semi-automatic learning-based framework to colorize sketches with proper color, texture as well as gradient. Our framework consists of two stages. In the first drafting stage, our model guesses color regions and splashes a rich variety of colors over the sketch to obtain a color draft. In the second refinement stage, it detects the unnatural colors and artifacts, and try to fix and refine the result. Comparing to existing approaches, this two-stage design effectively divides the complex colorization task into two simpler and goal-clearer subtasks. This eases the learning and raises the quality of colorization. Our model resolves the artifacts such as water-color blurring, color distortion, and dull textures. We build an interactive software based on our model for evaluation. Users can iteratively edit and refine the colorization. We evaluate our learning model and the interactive system through an extensive user study. Statistics shows that our method outperforms the state-of-art techniques and industrial applications in several aspects including, the visual quality, the ability of user control, user experience, and other metrics.

  45. Danbooru2020#danbooru2017

  46. https://reddit.com/r/anime/comments/9yjl41/ocfanart_created_a_bot_to_colorize_this_line/

  47. https://reddit.com/r/MachineLearning/comments/9xy4j0/p_style2paints_v4_finally_released_help_artists/

  48. https://nitter.hu/hashtag/style2paints?f=tweets&vertical=default

  49. http://manikvarma.org/pubs/kumar17.pdf

  50. ⁠, Francisco Raposo, David Martins de Matos, Ricardo Ribeiro, Suhua Tang, Yi Yu (2017-12-14):

    Modeling of music audio semantics has been previously tackled through learning of mappings from audio data to high-level tags or latent unsupervised spaces. The resulting semantic spaces are theoretically limited, either because the chosen high-level tags do not cover all of music semantics or because audio data itself is not enough to determine music semantics. In this paper, we propose a generic framework for semantics modeling that focuses on the perception of the listener, through EEG data, in addition to audio data. We implement this framework using a novel end-to-end 2-view Neural Network (NN) architecture and a Deep Canonical Correlation Analysis (DCCA) that forces the semantic embedding spaces of both views to be maximally correlated. We also detail how the EEG dataset was collected and use it to train our proposed model. We evaluate the learned semantic space in a transfer learning context, by using it as an audio feature extractor in an independent dataset and proxy task: music audio-lyrics cross-modal retrieval. We show that our embedding model outperforms Spotify features and performs comparably to a state-of-the-art embedding model that was trained on 700 times more data. We further discuss improvements to the model that are likely to improve its performance.

  51. ⁠, Pouya Bashivan, Kohitij Kar, James J. DiCarlo (2018-11-04):

    Particular deep artificial neural networks (ANNs) are today’s most accurate models of the primate brain’s ventral visual stream. Here we report that, using a targeted -driven image synthesis method, new luminous power patterns (i.e. images) can be applied to the primate retinae to predictably push the spiking activity of targeted V4 neural sites beyond naturally occurring levels. More importantly, this method, while not yet perfect, already achieves unprecedented independent control of the activity state of entire populations of V4 neural sites, even those with overlapping receptive fields. These results show how the knowledge embedded in today’s ANN models might be used to non-invasively set desired internal brain states at neuron-level resolution, and suggest that more accurate ANN models would produce even more accurate control.

  52. https://80000hours.org/podcast/episodes/brian-christian-algorithms-to-live-by/

  53. ⁠, Richard Klein, Michelangelo, Vianello, Fred, Hasselman, Byron, Adams, Reginald B. Adams, Jr., Sinan Alper, Mark Aveyard, Jordan Axt, Mayowa Babalola, Š těpán Bahník, Mihaly Berkics, Michael Bernstein, Daniel Berry, Olga Bialobrzeska, Konrad Bocian, Mark Brandt, Robert Busching, Huajian Cai, Fanny Cambier, Katarzyna Cantarero, Cheryl Carmichael, Zeynep Cemalcilar, Jesse Chandler, Jen-Ho Chang, Armand Chatard, Eva CHEN, Winnee Cheong, David ⁠, Sharon Coen, Jennifer Coleman, Brian Collisson, Morgan Conway, Katherine Corker, Paul Curran, Fiery Cushman, Ilker Dalgar, William Davis, Maaike de Bruijn, Marieke de Vries, Thierry Devos, Canay Doğulu, Nerisa Dozo, Kristin Dukes, Yarrow Dunham, Kevin Durrheim, Matthew Easterbrook, Charles Ebersole, John Edlund, Alexander English, Anja Eller, Carolyn Finck, Miguel-Ángel Freyre, Mike Friedman, Natalia Frankowska, Elisa Galliani, Tanuka Ghoshal, Steffen Giessner, Tripat Gill, Timo Gnambs, Angel Gomez, Roberto Gonzalez, Jesse Graham, Jon Grahe, Ivan Grahek, Eva Green, Kakul Hai, Matthew Haigh, Elizabeth Haines, Michael Hall, Marie Heffernan, Joshua Hicks, Petr Houdek, Marije van, der Hulst, Jeffrey Huntsinger, Ho Huynh, Hans IJzerman, Yoel Inbar, Åse Innes-Ker, William Jimenez-Leal, Melissa-Sue John, Jennifer Joy-Gaba, Roza Kamiloglu, Andreas Kappes, Heather Kappes, Serdar Karabati, Haruna Karick, Victor Keller, Anna Kende, Nicolas Kervyn, Goran Knezevic, Carrie Kovacs, Lacy Krueger, German Kurapov, Jaime Kurtz, Daniel Lakens, Ljiljana Lazarevic, Carmel Levitan, Neil Lewis, Samuel Lins, Esther Maassen, Angela Maitner, Winfrida Malingumu, Robyn Mallett, Satia Marotta, Jason McIntyre, Janko Međedović, Taciano Milfont, Wendy Morris, Andriy Myachykov, Sean Murphy, Koen Neijenhuijs, Anthony Nelson, Felix Neto, Austin Nichols, Susan O'Donnell, Masanori Oikawa, Gabor Orosz, Malgorzata Osowiecka, Grant Packard, Rolando Pérez, Boban Petrovic, Ronaldo Pilati, Brad Pinter, Lysandra Podesta, Monique Pollmann, Anna Dalla Rosa, Abraham Rutchick, Patricio Saavedra, Airi Sacco, Alexander Saeri, Erika Salomon, Kathleen Schmidt, Felix Schönbrodt, Maciek Sekerdej, David Sirlopu, Jeanine Skorinko, Michael Smith, Vanessa Smith-Castro, Agata Sobkow, Walter Sowden, Philipp Spachtholz, Troy Steiner, Jeroen Stouten, Chris Street, Oskar Sundfelt, Ewa Szumowska, Andrew Tang, Norbert Tanzer, Morgan Tear, Jordan Theriault, Manuela Thomae, David Torres-Fernández, Jakub Traczyk, Joshua Tybur, Adrienn Ujhelyi, Marcel van Assen, Anna van 't Veer, Alejandro, Vásquez-Echeverría Leigh, Ann Vaughn, Alexandra Vázquez, Diego Vega, Catherine Verniers, Mark Verschoor, Ingrid Voermans, Marek Vranka, Cheryl Welch, Aaron Wichman, Lisa Williams, Julie Woodzicka, Marta Wronska, Liane Young, John Zelenski, Brian Nosek (2019-11-19):

    We conducted replications of 28 classic and contemporary published findings with protocols that were peer reviewed in advance to examine variation in effect magnitudes across sample and setting. Each protocol was administered to approximately half of 125 samples and 15,305 total participants from 36 countries and territories. Using conventional statistical-significance (p < 0.05), fifteen (54%) of the replications provided evidence in the same direction and statistically-significant as the original finding. With a strict statistical-significance criterion (p < 0.0001), fourteen (50%) provide such evidence reflecting the extremely high powered design. Seven (25%) of the replications had effect sizes larger than the original finding and 21 (75%) had effect sizes smaller than the original finding. The median comparable Cohen’s d effect sizes for original findings was 0.60 and for replications was 0.15. Sixteen replications (57%) had small effect sizes (< 0.20) and 9 (32%) were in the opposite direction from the original finding. Across settings, 11 (39%) showed statistically-significant heterogeneity using the Q statistic and most of those were among the findings eliciting the largest overall effect sizes; only one effect that was near zero in the aggregate showed heterogeneity. Only one effect showed a Tau > 0.20 indicating moderate heterogeneity. Nine others had a Tau near or slightly above 0.10 indicating slight heterogeneity. In moderation tests, very little heterogeneity was attributable to task order, administration in lab versus online, and exploratory WEIRD versus less WEIRD culture comparisons. Cumulatively, variability in observed effect sizes was more attributable to the effect being studied than the sample or setting in which it was studied.

  54. https://80000hours.org/podcast/episodes/eva-vivalt-social-science-generalizability/

  55. https://leapsmag.com/a-star-surgeon-left-a-trail-of-dead-patients-and-his-whistleblowers-were-punished/

  56. https://www.nature.com/articles/d41586-018-07351-8

  57. http://nickchk.com/causalgraphs.html

  58. 2018-salminen.pdf: “Five-Year Follow-up of Antibiotic Therapy for Uncomplicated Acute Appendicitis in the APPAC Randomized Clinical Trial⁠, Paulina Salminen, Risto Tuominen, Hannu Paajanen, Tero Rautio, Pia Nordström, Markku Aarnio, Tuomo Rantanen, Saija Hurme, Jukka-Pekka Mecklin, Juhani Sand, Johanna Virtanen, Airi Jartti, Juha M. Grönroos

  59. https://arstechnica.com/science/2018/09/after-century-of-removing-appendixes-docs-find-antibiotics-can-be-enough/

  60. https://www.weeklystandard.com/gary-saul-morson/the-history-of-russian-terrorism-dagger-and-swagger

  61. ⁠, James C. Davie (1962-02):

    Revolutions are most likely to occur when a prolonged period of objective economic and social development is followed by a short period of sharp reversal. People then subjectively fear that ground gained with great effort will be quite lost; their mood becomes revolutionary. The evidence from Dorr’s Rebellion, the Russian Revolution, and the Egyptian Revolution supports this notion; tentatively, so do data on other civil disturbances. Various statistics—as on rural uprisings, industrial strikes, unemployment, and cost of living—may serve as crude indexes of popular mood. More useful, though less easy to obtain, are direct questions in cross-sectional interviews. The goal of predicting revolution is conceived but not yet born or mature

  62. https://www.politico.com/magazine/story/2018/11/11/republican-party-anti-pornography-politics-222096

  63. https://reason.com/archives/2018/11/24/legalizing-marijuana-and-gay-m

  64. 1972-page.pdf: “How We *All* Failed In Performance Contracting”⁠, Ellis B. Page

  65. http://cabinetmagazine.org/issues/48/pendle.php

  66. https://aeon.co/essays/why-fake-miniatures-depicting-islamic-science-are-everywhere

  67. 1980-metzger.pdf

  68. https://gizmodo.com/when-a-stranger-decides-to-destroy-your-life-1827546385

  69. ⁠, Scott Alexander (2018-10-30):

    [Contemporary SF short story; inspired by NN text generation, social media dynamics, clickbait, and debates like ⁠; imagines AI natural language processing systems run amok after being trained to maximize user reactions to create clickbait, leading to learning ‘scissor statements’, claims which are maximally controversial and divide the population 50-50 between those who find the statement obviously correct and moral, and those who find it equally obviously false and immoral, leading to intractable polarizing debates, contempt, and warfare.]

  70. https://www.theatlantic.com/magazine/archive/2018/12/the-sex-recession/573949/

  71. https://www.newyorker.com/magazine/2018/11/26/how-to-control-a-machine-with-your-brain

  72. https://www.nature.com/articles/srep30516

  73. 1993-jensen.pdf

  74. ⁠, Drupad K. Trivedi, Eleanor Sinclair, Yun Xu, Depanjan Sarkar, Camilla Liscio, Phine Banks, Joy Milne, Monty Silverdale, Tilo Kunath, Royston Goodacre, Perdita Barran (2018-11-15):

    Parkinson’s disease (PD) is a progressive, neurodegenerative disease that presents with significant motor symptoms, for which there is no diagnostic test (1–3). We have serendipitously identified a hyperosmic individual, a ‘Super Smeller’ that can detect PD by odor alone, and our early pilot studies have indicated that the odor was present in the sebum from the skin of PD subjects(4). Here, we have employed an unbiased approach to investigate the volatile metabolites of sebum samples obtained non-invasively from the upper back of 64 participants in total (21 controls and 43 PD subjects). Our results, validated by an independent cohort, identified a distinct volatiles-associated signature of PD, including altered levels of perillic aldehyde and eicosane, the smell of which was then described as being highly similar to the scent of PD by our ‘Super Smeller’.

    1 sentence summary

    Metabolomics identifies volatile odorous compounds from patient sebum that associate with the smell of Parkinson’s.

  75. https://psyarxiv.com/4q9gv/

  76. https://www.sapiens.org/culture/madagascar-vanilla-boom/

  77. https://www.lesspenguiny.com/articles/best-article-on-bragging

  78. https://www.wired.com/1997/05/ff-well/

  79. https://www.newyorker.com/magazine/2018/11/12/why-doctors-hate-their-computers

  80. https://www.newyorker.com/magazine/2018/11/26/do-proteins-hold-the-key-to-the-past

  81. http://antonhowes.tumblr.com/post/128190471139/the-great-british-industrial-bake-off

  82. https://www.richardcarrier.info/archives/14660

  83. https://www.wired.com/story/margit-wennmachers-is-andreessen-horowitzs-secret-weapon/

  84. https://www.gq.com/story/memory-of-mankind-time-capsule

  85. https://www.nytimes.com/interactive/2018/10/24/magazine/candy-kit-kat-japan.html

  86. https://www.texasmonthly.com/food/i-believe-i-can-fry/

  87. https://features.propublica.org/palm-oil/palm-oil-biofuels-ethanol-indonesia-peatland/

  88. https://features.propublica.org/brazil-carbon-offsets/inconvenient-truth-carbon-credits-dont-work-deforestation-redd-acre-cambodia/

  89. https://www.rollingstone.com/culture/culture-features/drug-war-mexico-gas-oil-cartel-717563/

  90. https://goo.gl/1AfwZG

  91. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.453.7194&rep=rep1&type=pdf

  92. https://www.theringer.com/movies/2018/11/15/18091620/box-office-futures-dodd-frank-mpaa-recession

  93. https://www.alexirpan.com/2018/11/10/neopets-economy.html

  94. https://old.reddit.com/r/SilkRoad/comments/3a2xqp/sr1_sales_data_6_feb_2011_2_oct_2013/

  95. https://www.bloomberg.com/news/articles/2018-11-08/the-curse-of-the-honeycrisp-apple

  96. https://www.wnycstudios.org/story/91725-words#ember29180666

  97. ⁠, Jeffery A. Martin (2013):

    Persistent forms of nondual awareness, enlightenment, mystical experience, and so forth (Persistent Non-Symbolic Experience) have been reported since antiquity. Though sporadic research has been performed on them, the research reported here represents the initial report from the first larger scale cognitive psychology study of this population.

    Method: Assessment of the subjective experience of fifty adult participants reporting persistent non-symbolic experience was undertaken using 6–12 hour semi-structured interviews and evaluated using thematic analysis. Additional assessment was performed using psychometric measures, physiological measurement, and experimentation.

    Results: Five core, consistent categories of change were uncovered: sense-of-self, cognition, emotion, perception, and memory. Participants’ reports formed clusters in which the types of change in each of these categories were consistent. Multiple clusters were uncovered that formed a range of possible experiences. The variety of these experiences and their underlying categories may inform the debate between constructivist, common core, and participatory theorists.

    …Over the course of a week, his father died followed very rapidly by his sister. He was also going through a major issue with one of his children. Over dinner I asked him about his internal state, which he reported as deeply peaceful and positive despite everything that was happening. Having known that the participant was bringing his longtime girlfriend, I’d taken an associate researcher with me to the meeting to independently collect the observations from her. My fellow researcher isolated the participant’s girlfriend at the bar and interviewed her about any signs of stress that the participant might be exhibiting. I casually asked the same questions to the participant as we continued our dinner conversation. Their answers couldn’t have been more different. While the participant reported no stress, his partner had been observing many telltale signs: he wasn’t sleeping well, his appetite was off, his mood was noticeably different, his muscles were much tenser than normal, his sex drive was reduced, his health was suffering, and so forth…It was not uncommon for participants to state that they had gained increased bodily awareness upon their transition into PNSE. I arranged and observed private yoga sessions with a series of participants as part of a larger inquiry into their bodily awareness. During these sessions it became clear that participants believed they were far more aware of their body than they actually were…Many participants discussed the thought, just after their transition to PNSE, that they would have to go to work and explain the difference in themselves to co-workers. They went on to describe a puzzled drive home after a full day of work when no one seemed to notice anything different about them. Quite a few chose to never discuss the change that had occurred in them with their families and friends and stated that no one seemed to notice much of a difference.

    There was also a progressively decreasing sense of agency. In the final stage, Location 4, he reports: “These participants reported having no sense of agency or any ability to make a decision. It felt as if life was simply unfolding and they were watching the process happen. Severe memory deficits were common in these participants, including the inability to recall scheduled events that were not regular and ongoing.” And yet, almost all of the subjects reported it as a positive experience. The subjects, at whatever point they were in the scale, were often completely certain about the nature of the experience: “PNSE was often accompanied by a tremendous sense of certainty that participants were experiencing a ‘deeper’ or ‘more true’ reality. As time passed, this often increased in strength.” They also tended to be dogmatic about their PNSE being the real thing (whichever location they were at) and descriptions of other people’s different PNSEs as not the real thing. Another way to say “completely certain” is “unable to doubt”.

  98. https://plato.stanford.edu/entries/chinese-legalism/

  99. https://www.newyorker.com/magazine/2012/03/12/the-hours-daniel-zalewski

  100. http://msls.net/2018/10/17/the-clock-part-1-introduction/

  101. http://msls.net/2018/11/03/the-clock-part-2-matinee/

  102. https://vastabrupt.com/2018/08/07/time-war-briefing-for-neolemurian-agents/

  103. https://animekritik.wordpress.com/2011/11/22/imperialism-translation-gunbuster-introduction/

  104. https://animekritik.wordpress.com/2011/11/25/imperialism-translation-gunbuster-episode-one/

  105. https://animekritik.wordpress.com/2011/11/27/imperialism-translation-gunbuster-episode-two-nsfw/

  106. https://animekritik.wordpress.com/2011/11/29/imperialism-translation-gunbuster-episode-three/

  107. https://animekritik.wordpress.com/2011/12/01/imperialism-translation-gunbuster-episode-four/

  108. https://animekritik.wordpress.com/2011/12/03/imperialism-translation-gunbuster-episode-five/

  109. https://animekritik.wordpress.com/2011/12/05/imperialism-translation-gunbuster-episode-six/

  110. https://believermag.com/oulipo-ends-where-the-work-begins/

  111. https://archiveofourown.org/works/137185

  112. https://www.newyorker.com/recommends/read/no-reservations-narnia-a-triumph-of-anthony-bourdain-fan-fiction

  113. https://www.amazon.com/Cat-Sense-Feline-Science-Better/dp/0465031013

  114. #gwern-cat-sense

  115. http://press.etc.cmu.edu/index.php/product/well-played-3-0/

  116. http://press.etc.cmu.edu/index.php/product/well-played-2-0-video-games-value-and-meaning/

  117. http://press.etc.cmu.edu/index.php/product/well-played-vol-4-no-1/

  118. Movies#kedi

  119. MLP#music

  120. https://www.youtube.com/watch?v=NBkEfIzu_Ig

  121. https://www.youtube.com/watch?v=9jqs-5S5joU

  122. https://www.youtube.com/watch?v=jXTYkuEWlVM

  123. https://www.youtube.com/watch?v=UHj-prkyex0

  124. https://www.youtube.com/watch?v=rg-P8zMlThk