Link Bibliography

1. https://gwern.substack.com

2. “September 2017 News”, Gw­ern Bran­wen (2017-08-16):

   September 2017 gwern.net newsletter with links on genetics (GWAS, engineering, evolution), intelligence, AI, metformin, lithium; 2 book and 6 movie reviews

3. “Gwern.net newsletter archives”, Gw­ern Bran­wen (2013-12-01):

   Newsletter tag: archive of all issues back to 2013 for the gwern.net newsletter (monthly updates, which will include summaries of projects I’ve worked on that month (the same as the changelog), collations of links or discussions from my subreddit, and book/movie reviews.)

4. “Changelog”, Gw­ern Bran­wen (2013-09-15):

   This page is a changelog for Gw­ern.net: a monthly re­verse chrono­log­i­cal list of re­cent ma­jor writings/changes/additions.

   Fol­low­ing my writ­ing can be a lit­tle diffi­cult be­cause it is often so in­cre­men­tal. So every mon­th, in ad­di­tion to my reg­u­lar /r/Gwern sub­red­dit sub­mis­sions, I write up rea­son­ably-in­ter­est­ing changes and send it out to the mail­ing list in ad­di­tion to a com­pi­la­tion of links & re­views (archives).

5. “/r/gwern subreddit”, Gw­ern Bran­wen (2018-10-01):

   A subreddit for posting links of interest and also for announcing updates to gwern.net (which can be used as a RSS feed). Submissions are categorized similar to the monthly newsletter and typically will be collated there.

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

7. “The Neural Net Tank Urban Legend”, Gw­ern Bran­wen (2011-09-20):

   A cau­tion­ary tale in ar­ti­fi­cial in­tel­li­gence tells about re­searchers train­ing an neural net­work (NN) to de­tect tanks in pho­tographs, suc­ceed­ing, only to re­al­ize the pho­tographs had been col­lected un­der spe­cific con­di­tions for tanks/non-tanks and the NN had learned some­thing use­less like time of day. This story is often told to warn about the lim­its of al­go­rithms and im­por­tance of data col­lec­tion to avoid “dataset bias”/“data leak­age” where the col­lected data can be solved us­ing al­go­rithms that do not gen­er­al­ize to the true data dis­tri­b­u­tion, but the tank story is usu­ally never sourced.

   I col­late many ex­tent ver­sions dat­ing back a quar­ter of a cen­tury to 1992 along with two NN-re­lated anec­dotes from the 1960s; their con­tra­dic­tions & de­tails in­di­cate a clas­sic “ur­ban leg­end”, with a prob­a­ble ori­gin in a spec­u­la­tive ques­tion in the 1960s by Ed­ward Fred­kin at an AI con­fer­ence about some early NN re­search, which was sub­se­quently clas­si­fied & never fol­lowed up on.

   I sug­gest that dataset bias is real but ex­ag­ger­ated by the tank sto­ry, giv­ing a mis­lead­ing in­di­ca­tion of risks from deep learn­ing and that it would be bet­ter to not re­peat it but use real ex­am­ples of dataset bias and fo­cus on larg­er-s­cale risks like AI sys­tems op­ti­miz­ing for wrong util­ity func­tions.

8. http://arthurjensen.net/?page_id=9

9. “Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity”, Pe­ter K. Joshi, Nicola Pi­ras­tu, Kather­ine A. Ken­tis­tou, Krista Fis­cher, Edith Hofer, Katha­rina E. Schraut, David W. Clark, Teresa Nu­tile, Catri­ona L. K. Bar­nes, Paul R. H. J. Tim­mers, Xia Shen, Ilaria Gand­in, Aaron F. Mc­Daid, Thomas Folk­mann Hansen, Scott D. Gor­don, Franco Giu­lian­ini, Thibaud S. Boutin, Ab­del Ab­del­laoui, Wei Zhao, Car­olina Med­i­na-Gomez, Traci M. Bartz, Stella Trompet, Leslie A. Lange, Laura Raffield, Ash­ley van der Spek, Tes­sel E. Galesloot, Petroula Proit­si, Lisa R. Yanek, Lawrence F. Bielak, Antony Pay­ton, Fed­erico Mur­gia, Maria Pina Con­cas, Ginevra Bi­ino, Salman M. Tajud­din, Ilkka Sep­pälä, Na­jaf Am­in, Eric Boer­win­kle, An­ders D. Bør­glum, Archie Camp­bell, Ellen W. De­merath, Ilja De­muth, Jes­sica D. Faul, Ian Ford, Alessan­dro Gi­al­luisi, Mar­tin Gögele, Mari­aElisa Graff, Aroon Hin­go­rani, Jouke-Jan Hot­ten­ga, David M. Hougaard, Mikko A. Hurme, M. Ar­fan Ikram, Marja Jyl­hä, Di­ana Kuh, Lan­nie Ligth­art, Christina M. Lill, Ul­man Lin­den­berg­er, Thomas Lum­ley, Reedik Mägi, Pe­dro Mar­ques-Vi­dal, Sarah E. Med­land, Lili Mi­lani, Reka Nagy, William E. R. Ol­lier, Pa­tri­cia A. Pey­ser, Pe­ter P. Pram­staller, Paul M. Rid­ker, Fer­nando Ri­vadeneira, Daniela Rug­giero, Yasaman Saba, Rein­hold Schmidt, He­lena Schmidt, P. Eline Slag­boom, Blair H. Smith, Jen­nifer A. Smith, Nona So­tood­ehnia, Elis­a­beth Stein­hagen-Thiessen, Frank J. A. van Rooij, An­dré L. Ver­beek, Sita H. Ver­meu­len, Pe­ter Vol­len­wei­der, Yun­peng Wang, Thomas Werge, John B. Whit­field, Alan B. Zon­der­man, Terho Lehtimäki, Michele K. Evans, Mario Pi­ras­tu, Chris­t­ian Fuchs­berg­er, Lars Bertram, Neil Pendle­ton, Sharon L. R. Kar­dia, Ma­rina Ciul­lo, Di­ane M. Beck­er, An­drew Wong, Bruce M. Psaty, Cor­nelia M. van Dui­jn, James G. Wilson, J. Wouter Juke­ma, Lam­ber­tus Kiemeney, An­dré G. Uit­ter­lin­den, Nora Frances­chini, Kari E. North, David R. Weir, An­dres Metspalu, Dor­ret I. Booms­ma, Car­o­line Hay­ward, Daniel Chas­man, Nicholas G. Mar­t­in, Naveed Sat­tar, Harry Camp­bell, Tōnu Es­ko, Zoltán Ku­ta­lik & James F. Wil­son (2017-10-13):

   Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents’ survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.

10. “Biological Insights Into Muscular Strength: Genetic Findings in the UK Biobank”, Emmi Tikka­nen, Ste­fan Gustafs­son, David Amar, Anna Shcherbina, Daryl Wag­gott, Euan A. Ash­ley, Erik In­gels­son (2017-10-10):

    Background: Hand grip strength, a simple indicator of muscular strength, has been associated with a range of health conditions, including fractures, disability, cardiovascular disease and premature death risk. Twin studies have suggested a high (50-60%) heritability, but genetic determinants are largely unknown.

    Aims

    In this study, our aim was to study genetic variation associated with muscular strength in a large sample of 334,925 individuals of European descent from the UK Biobank, and to evaluate shared genetic aetiology with and causal effects of grip strength on physical and cognitive health.

    Methods and Results

    In our discovery analysis of 223,315 individuals, we identified 101 loci associated with grip strength at genome-wide significance (P<5×10⁻⁸). Of these, 64 were associated (P<0.01 and consistent direction) also in the replication dataset (n = 111,610). Many of the lead SNPs were located in or near genes known to have a function in developmental disorders (FTO, SLC39A8, TFAP2B, TGFA, CELF1, TCF4, BDNF, FOXP1, KIF1B, ANTXR2), and one of the most significant genes based on a gene-based analysis (ATP2A1) encodes SERCA1, the critical enzyme in calcium uptake to the sarcoplasmic reticulum, which plays a major role in muscle contraction and relaxation. Further, we demonstrated a significant enrichment of gene expression in brain-related transcripts among grip strength associations. Finally, we observed inverse genetic correlations of grip strength with cardiometabolic traits, and positive correlation with parents’ age of death and education; and showed that grip strength was causally related to fitness, physical activity and other indicators of frailty, including cognitive performance scores.

    Conclusion: In our study of over 330,000 individuals from the general population, the genetic findings for hand grip strength suggest an important role of the central nervous system in strength performance. Further, our results indicate that maintaining good muscular strength is important for physical and cognitive performance and healthy aging.

11. “Genome-wide association study of social relationship satisfaction: significant loci and correlations with psychiatric conditions”, Varun War­ri­er, the 23andMe Re­search Team, Thomas Bourg­eron, Si­mon Baron-Co­hen (2017-10-05):

    Dissatisfaction in social relationships is reported widely across many psychiatric conditions. We investigated the genetic architecture of family relationship satisfaction and friendship satisfaction in the UK Biobank. We leveraged the high genetic correlation between the two phenotypes (r_g = 0.87±0.03; p < 2.2×10^-16) to conduct multi-trait analysis of Genome Wide Association Study (GWAS) (N_effective family = 164,112; N_effective friendship = 158,116). We identified two genome-wide significant associations for both the phenotypes: rs1483617 on chromosome 3 and rs2189373 on chromosome 6, a region previously implicated in schizophrenia. eQTL and chromosome conformation capture in neural tissues prioritizes several genes including NLGN1. Gene-based association studies identified several significant genes, with highest expression in brain tissues. Genetic correlation analysis identified significant negative correlations for multiple psychiatric conditions including highly significant negative correlation with cross-psychiatric disorder GWAS, underscoring the central role of social relationship dissatisfaction in psychiatric diagnosis. The two phenotypes were enriched for genes that are loss of function intolerant. Both phenotypes had modest, significant additive SNP heritability of approximately 6%. Our results underscore the central role of social relationship satisfaction in mental health and identify genes and tissues associated with it.

12. “CNV-association meta-analysis in 191,161 European adults reveals new loci associated with anthropometric traits”, Au­rélien Macé, Mar­cus A. Tuke, Patrick Dee­len, Kati Kris­tiansson, Han­nele Mattsson, Mar­git Nõukas, Ya­dav Sap­ko­ta, Ur­sula Schick, Eleonora Por­cu, Sina Rüeger, Aaron F. Mc­Daid, David Por­te­ous, Thomas W. Win­kler, Erika Salvi, Nick Shrine, Xueping Liu, Wei Q. Ang, Wei­hua Zhang, Mary F. Feitosa, Cristina Ven­turini, Pe­ter J. van der Most, An­ders Rosen­gren, An­drew R. Wood, Robin N. Beau­mont, Samuel E. Jones, Kather­ine S. Ruth, Hanieh Yaghootkar, Jes­sica Tyrrell, Aki S. Havulin­na, Har­men Boers, Reedik Mägi, Jen­nifer Kriebel, Mar­tina Müller-Nurasyid, Markus Per­o­la, Markku Niem­i­nen, Mar­ja-Li­isa Lokki, Mika Kähö­nen, Jorma S. Vi­ikari, Frank Geller, Jari Lahti, Aarno Palotie, Päivikki Ko­po­nen, An­na­mari Lundqvist, Harri Ris­sa­nen, Er­win P. Bot­tinger, Saima Afaq, Mary K. Wo­jczyn­ski, Pe­tra Lenzini, Ilja M. Nolte, Thomas Sparsø, Nicole Schupf, Kaare Chris­tensen, Thomas T. Perls, Anne B. New­man, Thomas Werge, Harold Snieder, Tim­o­thy D. Spec­tor, John C. Cham­bers, Seppo Kosk­i­nen, Mads Mel­bye, Olli T. Raitakari, Terho Lehtimäki, Mar­tin D. To­bin, Louise V. Wain, Juha Sin­isa­lo, An­nette Pe­ters, Thomas Meitinger, Nicholas G. Mar­t­in, Naomi R. Wray, Grant W. Mont­gomery, Sarah E. Med­land, Mor­ris A. Swertz, Erkki Var­ti­ainen, Katja Boro­dulin, Satu Män­nistö, Anna Mur­ray, Murielle Bochud, Sébastien Jacque­mont, Fer­nando Ri­vadeneira, Thomas F. Hansen, Al­ber­tine J. Old­e­hinkel, Mas­simo Mangi­no, Michael A. Province, Panos De­loukas, Jas­pal S. Koon­er, Rachel M. Freathy, Craig Pen­nell, Bjarke Feen­stra, David P. Stra­chan, Guil­laume Let­tre, Joel Hirschhorn, Daniele Cusi, Iris M. Heid, Car­o­line Hay­ward, Ka­trin Män­nik, Jacques S. Beck­mann, Ruth J. F. Loos, Dale R. Ny­holt, An­dres Metspalu, Jo­han G. Eriksson, Michael N. Wee­don, Veikko Sa­lo­maa, Lude Franke, Alexan­dre Rey­mond, Tim­o­thy M. Frayling, Zoltán Ku­ta­lik (2017-09-29):

    There are few examples of robust associations between rare copy number variants (CNVs) and complex continuous human traits. Here we present a large-scale CNV association meta-analysis on anthropometric traits in up to 191,161 adult samples from 26 cohorts. The study reveals five CNV associations at 1q21.1, 3q29, 7q11.23, 11p14.2, and 18q21.32 and confirms two known loci at 16p11.2 and 22q11.21, implicating at least one anthropometric trait. The discovered CNVs are recurrent and rare (0.01–0.2%), with large effects on height (>2.4 cm), weight (>5 kg), and body mass index (BMI) (>3.5 kg/m2). Burden analysis shows a 0.41 cm decrease in height, a 0.003 increase in waist-to-hip ratio and increase in BMI by 0.14 kg/m2 for each Mb of total deletion burden (p = 2.5 × 10⁻¹⁰, 6.0 × 10⁻⁵, and 2.9 × 10⁻³). Our study provides evidence that the same genes (e.g., MC4R, FIBIN, and FMO5) harbor both common and rare variants affecting body size and that anthropometric traits share genetic loci with developmental and psychiatric disorders.

13. “Genetic and Environmental Influences on Household Financial Distress”, Yi­lan Xu, Daniel A. Bri­ley, Jeffrey R. Brown, William G. Kar­nes, Brent W. Roberts (2017-01-08):

    Heterogeneity of household financial outcomes emerges from various individual and environmental factors, including personality, cognitive ability, and socioeconomic status (SES), among others. Using a genetically informative data set, we decompose the variation in financial management behavior into genetic, shared environmental and non-shared environmental factors. We find that about half of the variation in financial distress is genetically influenced, and personality and cognitive ability are associated with financial distress through genetic and within-family pathways. Moreover, the genetic influences of financial distress are highest at the extremes of SES, which in part can be explained by neuroticism and cognitive ability being more important predictors of financial distress at low and high levels of SES, respectively.

14. “The molecular genetics of participation in the Avon Longitudinal Study of Parents and Children”, Amy E. Tay­lor, Han­nah J. Jones, Han­nah Sal­lis, Jack Eu­es­den, Evie Ster­giak­ouli, Neil M. Davies, Stan­ley Za­m­mit, Deb­bie A. Lawlor, Mar­cus R. Mu­nafò, George Davey Smith, Kate Till­ing (2017-10-20):

    Background: It is often assumed that selection (including participation and dropout) does not represent an important source of bias in genetic studies. However, there is little evidence to date on the effect of genetic factors on participation.

    Methods: Using data on mothers (n = 7,486) and children (n = 7,508) from the Avon Longitudinal Study of Parents and Children, we 1) examined the association of polygenic risk scores for a range of socio-demographic, lifestyle characteristics and health conditions related to continued participation, 2) investigated whether associations of polygenic scores with body mass index (BMI; derived from self-reported weight and height) and self-reported smoking differed in the largest sample with genetic data and a sub-sample who participated in a recent follow-up and 3) determined the proportion of variation in participation explained by common genetic variants using genome-wide data.

    Results: We found evidence that polygenic scores for higher education, agreeableness and openness were associated with higher participation and polygenic scores for smoking initiation, higher BMI, neuroticism, schizophrenia, ADHD and depression were associated with lower participation. Associations between the polygenic score for education and self-reported smoking differed between the largest sample with genetic data (OR for ever smoking per SD increase in polygenic score:0.85, 95% CI:0.81,0.89) and sub-sample (OR:0.95, 95% CI:0.88,1.02). In genome-wide analysis, single nucleotide polymorphism based heritability explained 17-31% of variability in participation.

    Conclusion: Genetic association studies, including Mendelian randomization, can be biased by selection, including loss to follow-up. Genetic risk for dropout should be considered in all analyses of studies with selective participation.

15. /docs/genetics/correlation/2013-ludeke.pdf

16. http://rstb.royalsocietypublishing.org/content/370/1683/20150015

17. /docs/genetics/heritable/2017-segal-twinmythconceptions-ch12.pdf

18. https://www.gwern.net/docs/genetics/heritable/2013-segal-1.pdf

19. https://www.gwern.net/docs/genetics/heritable/2013-segal-2.pdf

20. /docs/genetics/heritable/2017-hilker.pdf

21. “Censoring (statistics)”, Eng­lish Wikipedia (2020-12-22):

    In statistics, censoring is a condition in which the value of a measurement or observation is only partially known.

22. https://nutritionj.biomedcentral.com/articles/10.1186/s12937-017-0269-y

23. “Brain structure mediates the association between height and cognitive ability”, Eero Vuok­si­maa, Matthew S. Paniz­zon, Carol E. Franz, Chris­tine Fen­nema-Notestine, Don­ald J. Ha­gler, Michael J. Lyons, An­ders M. Dale, William S. Kre­men (2017-09-01):

    Height and general cognitive ability (GCA) are positively associated, but the underlying mechanisms of this relationship are unclear. We used a sample of 515 middle-aged male twins with structural magnetic resonance imaging data to study if the association between height and cognitive ability is mediated by cortical size. We used genetically, ontogenetically and phylogenetically distinct cortical metrics of cortical surface area (SA) and cortical thickness (CT). Our results indicate that the well-replicated height-GCA association is accounted for by individual differences in total cortical SA (highly heritable metric related to global brain size), and not mean CT, and that the genetic association between SA and GCA underlies the phenotypic height-GCA relationship.

24. /docs/genetics/heritable/1987-plomin.pdf

25. https://pdfs.semanticscholar.org/0717/7feac98fc3e751e34db14493bf159ade56d0.pdf

26. https://www.bloomberg.com/news/articles/2017-08-07/horse-clones-start-heading-to-the-races

27. “Mastering the game of Go without human knowledge”, David Sil­ver, Ju­lian Schrit­twieser, Karen Si­monyan, Ioan­nis Antonoglou, Aja Huang, Arthur Guez, Thomas Hu­bert, Lu­cas Bak­er, Matthew Lai, Adrian Bolton, Yut­ian Chen, Tim­o­thy Lil­l­i­crap, Fan Hui, Lau­rent Sifre, George van den Driess­che, Thore Grae­pel, Demis Has­s­abis (2017-10-19):

    A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.

28. https://old.reddit.com/r/reinforcementlearning/comments/778vbk/mastering_the_game_of_go_without_human_knowledge/

29. “Thinking Fast and Slow with Deep Learning and Tree Search”, Thomas An­tho­ny, Zheng Tian, David Bar­ber (2017-05-23):

    Sequential decision making problems, such as structured prediction, robotic control, and game playing, require a combination of planning policies and generalisation of those plans. In this paper, we present Expert Iteration (ExIt), a novel reinforcement learning algorithm which decomposes the problem into separate planning and generalisation tasks. Planning new policies is performed by tree search, while a deep neural network generalises those plans. Subsequently, tree search is improved by using the neural network policy to guide search, increasing the strength of new plans. In contrast, standard deep Reinforcement Learning algorithms rely on a neural network not only to generalise plans, but to discover them too. We show that ExIt outperforms REINFORCE for training a neural network to play the board game Hex, and our final tree search agent, trained tabula rasa, defeats MoHex 1.0, the most recent Olympiad Champion player to be publicly released.

30. “Learning Generalized Reactive Policies using Deep Neural Networks”, Ed­ward Gro­shev, Maxwell Gold­stein, Aviv Tamar, Sid­dharth Sri­vas­tava, Pieter Abbeel (2017-08-24):

    We present a new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem instances. We show that a deep neural network can be used to learn and represent a generalized reactive policy (GRP) that maps a problem instance and a state to an action, and that the learned GRPs efficiently solve large classes of challenging problem instances. In contrast to prior efforts in this direction, our approach significantly reduces the dependence of learning on handcrafted domain knowledge or feature selection. Instead, the GRP is trained from scratch using a set of successful execution traces. We show that our approach can also be used to automatically learn a heuristic function that can be used in directed search algorithms. We evaluate our approach using an extensive suite of experiments on two challenging planning problem domains and show that our approach facilitates learning complex decision making policies and powerful heuristic functions with minimal human input. Videos of our results are available at goo.gl/Hpy4e3.

31. “Progressive Growing of GANs for Improved Quality, Stability, and Variation”, Tero Kar­ras, Timo Aila, Samuli Laine, Jaakko Lehti­nen (2017-10-27):

    We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset.

32. https://github.com/tkarras/progressive_growing_of_gans

33. https://youtu.be/XOxxPcy5Gr4

34. “StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks”, Han Zhang, Tao Xu, Hong­sheng Li, Shaot­ing Zhang, Xi­ao­gang Wang, Xi­aolei Huang, Dim­itris Metaxas (2017-10-19):

    Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of the object based on given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and discriminators in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images.

35. “PassGAN: A Deep Learning Approach for Password Guessing”, Bri­land Hi­taj, Paolo Gasti, Giuseppe Ate­niese, Fer­nando Perez-Cruz (2017-09-01):

    State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes. In addition to performing straightforward dictionary attacks, these tools can expand password dictionaries using password generation rules, such as concatenation of words (e.g., "password123456") and leet speak (e.g., "password" becomes "p4s5w0rd"). Although these rules work well in practice, expanding them to model further passwords is a laborious task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-generated password rules with theory-grounded machine learning algorithms. Instead of relying on manual password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real passwords from actual password leaks, and to generate high-quality password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large password datasets, we were able to surpass rule-based and state-of-the-art machine learning password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on passwords or common password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51 passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of password properties that current state-of-the art rules do not encode.

36. https://hackernoon.com/wheres-waldo-terminator-edition-8b3bd0805741

37. “What does a convolutional neural network recognize in the moon?”, Daigo Shoji (2017-08-18):

    Many people see a human face or animals in the pattern of the maria on the moon. Although the pattern corresponds to the actual variation in composition of the lunar surface, the culture and environment of each society influence the recognition of these objects (i.e., symbols) as specific entities. In contrast, a convolutional neural network (CNN) recognizes objects from characteristic shapes in a training data set. Using CNN, this study evaluates the probabilities of the pattern of lunar maria categorized into the shape of a crab, a lion and a hare. If Mare Frigoris (a dark band on the moon) is included in the lunar image, the lion is recognized. However, in an image without Mare Frigoris, the hare has the highest probability of recognition. Thus, the recognition of objects similar to the lunar pattern depends on which part of the lunar maria is taken into account. In human recognition, before we find similarities between the lunar maria and objects such as animals, we may be persuaded in advance to see a particular image from our culture and environment and then adjust the lunar pattern to the shape of the imagined object.

38. https://www.buzzfeed.com/peteraldhous/hidden-spy-planes

39. https://sappingattention.blogspot.com/2017/09/peer-review-is-younger-than-you-think.html

40. https://www.the-tls.co.uk/articles/public/the-end-of-an-error-peer-review/

41. https://pdfs.semanticscholar.org/437a/902934056604855cde3dcf8dff959ed29b68.pdf

42. http://www.nature.com/news/wikipedia-shapes-language-in-science-papers-1.22656

43. https://projecteuclid.org/download/pdfview_1/euclid.aoas/1372338470

44. http://bwc.thelab.dc.gov/

45. https://www.nytimes.com/2017/10/20/upshot/a-big-test-of-police-body-cameras-defies-expectations.html

46. “The Iron Law Of Evaluation And Other Metallic Rules”, Pe­ter H. Rossi (2012-09-18):

    Problems with social experiments and evaluating them, loopholes, causes, and suggestions; non-experimental methods systematically deliver false results, as most interventions fail or have small effects.

47. https://www.theguardian.com/news/2017/oct/10/the-science-of-spying-how-the-cia-secretly-recruits-academics

48. https://www.newyorker.com/magazine/2017/10/23/hating-on-herbert-hoover

49. /docs/psychology/1969-torrance.pdf

50. http://journals.sagepub.com/doi/full/10.1177/2053168017716547

51. http://rspb.royalsocietypublishing.org/content/284/1851/20162562

52. “Multiplying 10-digit numbers using Flickr: The power of recognition memory”, An­drew Drucker (2011):

    In this informal article, I’ll describe the “recognition method”—a simple, powerful technique for memorization and mental calculation. Compared to traditional memorization techniques, which use elaborate encoding and visualization processes [1], the recognition method is easy to learn and requires relatively little effort…The method works: using it, I was able to mentally multiply two random 10-digit numbers, by the usual grade-school algorithm, on my first attempt! I have a normal, untrained memory, and the task would have been impossible by a direct approach. (I can’t claim I was speedy: I worked slowly and carefully, using about 7 hours plus rest breaks. I practiced twice with 5-digit numbers beforehand.)

    …It turns out that ordinary people are incredibly good at this task [recognizing whether a photograph has been seen before]. In one of the most widely-cited studies on recognition memory, Standing [2] showed participants an epic 10,000 photographs over the course of 5 days, with 5 seconds’ exposure per image. He then tested their familiarity, essentially as described above. The participants showed an 83% success rate, suggesting that they had become familiar with about 6,600 images during their ordeal. Other volunteers, trained on a smaller collection of 1,000 images selected for vividness, had a 94% success rate.

53. https://www.psychologytoday.com/blog/rabble-rouser/201707/why-brilliant-girls-tend-favor-non-stem-careers

54. https://magazine.atavist.com/promethea-unbound-child-genius-montana

55. /docs/iq/1996-jensen.pdf

56. http://blogs.sciencemag.org/pipeline/archives/2017/10/04/a-nobel-for-cryo-em

57. “Predicting Personality from Book Preferences with User-Generated Content Labels”, Ng An­na­lyn, Maarten W. Bos, Leonid Sigal, Boyang Li (2017-07-20):

    Psychological studies have shown that personality traits are associated with book preferences. However, past findings are based on questionnaires focusing on conventional book genres and are unrepresentative of niche content. For a more comprehensive measure of book content, this study harnesses a massive archive of content labels, also known as ’tags’, created by users of an online book catalogue, Goodreads.com. Combined with data on preferences and personality scores collected from Facebook users, the tag labels achieve high accuracy in personality prediction by psychological standards. We also group tags into broader genres, to check their validity against past findings. Our results are robust across both tag and genre levels of analyses, and consistent with existing literature. Moreover, user-generated tag labels reveal unexpected insights, such as cultural differences, book reading behaviors, and other non-content factors affecting preferences. To our knowledge, this is currently the largest study that explores the relationship between personality and book content preferences.

58. http://ordinary-gentlemen.com/2014/01/21/lessons-from-bar-fight-litigation/

59. /docs/biology/2014-fardin.pdf

60. “List of Ig Nobel Prize winners”, Eng­lish Wikipedia (2020-12-22):

    This is a list of Ig Nobel Prize winners from 1991 to the present day.

61. “Ig Nobel Prize”, Eng­lish Wikipedia (2020-12-28):

    

    The Ig Nobel Prize is a satiric prize awarded annually since 1991 to celebrate ten unusual or trivial achievements in scientific research, its stated aim being to "honor achievements that first make people laugh, and then make them think." The name of the award is a pun on the Nobel Prize, which it parodies, and the word ignoble.

62. “Rheology”, Eng­lish Wikipedia (2020-12-22):

    Rheology is the study of the flow of matter, primarily in a liquid or gas state, but also as "soft solids" or solids under conditions in which they respond with plastic flow rather than deforming elastically in response to an applied force. Rheology is a branch of physics, and it is the science that deals with the deformation and flow of materials, both solids and liquids.

63. https://www.youtube.com/watch?v=y37vioBienY

64. “Is That Me or My Twin? Lack of Self-Face Recognition Advantage in Identical Twins”, Mat­teo Mar­tini, Ilaria Bu­falari, Maria An­toni­etta Stazi, Sal­va­tore Maria Agli­oti (2015-01-27):

    Despite the increasing interest in twin studies and the stunning amount of research on face recognition, the ability of adult identical twins to discriminate their own faces from those of their co-twins has been scarcely investigated. One’s own face is the most distinctive feature of the bodily self, and people typically show a clear advantage in recognizing their own face even more than other very familiar identities. Given the very high level of resemblance of their faces, monozygotic twins represent a unique model for exploring self-face processing.

    Herein we examined the ability of monozygotic twins to distinguish their own face from the face of their co-twin and of a highly familiar individual. Results show that twins equally recognize their own face and their twin’s face. This lack of self-face advantage was negatively predicted by how much they felt physically similar to their co-twin and by their anxious or avoidant attachment style.

    We speculate that in monozygotic twins, the visual representation of the self-face overlaps with that of the co-twin. Thus, to distinguish the self from the co-twin, monozygotic twins have to rely much more than control participants on the multisensory integration processes upon which the sense of bodily self is based. Moreover, in keeping with the notion that attachment style influences perception of self and significant others, we propose that the observed self/co-twin confusion may depend upon insecure attachment.

65. /docs/statistics/decision/2010-nutt.pdf

66. https://www.newyorker.com/magazine/2017/10/09/how-the-elderly-lose-their-rights

67. https://helda.helsinki.fi/bof/bitstream/handle/123456789/14908/dp1317.pdf

68. https://www.irishtimes.com/culture/books/in-1973-i-invented-a-girly-drink-called-baileys-1.3240945

69. /docs/psychology/2013-buckwalter.pdf

70. https://www.bbc.com/pidgin

71. http://www.stevenlevy.com/index.php/books/artificial-life

72. /docs/genetics/heritable/1973-dobzhansky-geneticdiversityandhumanequality.pdf

73. “Unforgotten Dreams: Poems by the Zen Monk Shōtetsu”, Shōtet­su, Steven D. Carter (trans­la­tor) (1997):

    [This volume presents translations of over 200 poems by Shōtetsu, who is generally considered to be the last great poet of the uta form. Includes an introduction, a glossary of important names and places and a list of sources of the poems.]

    The Zen monk Shōtetsu (1381–1459) suffered several rather serious misfortunes in his life: he lost all the poems of his first thirty years—more than 30,000 of them—in a fire; his estate revenues were confiscated by an angry shogun; and rivals refused to allow his work to appear in the only imperially commissioned poetry anthology of his time. Undeterred by these obstacles, he still managed to make a living from his poetry and won recognition as a true master, widely considered to be the last great poet of the classical uta, or waka, tradition. Shōtetsu viewed his poetry as both a professional and religious calling, and his extraordinarily prolific corpus comprised more than 11,000 poems—the single largest body of work in the Japanese canon.

    The first major collection of Shōtetsu's work in English, Unforgotten Dreams presents beautifully rendered translations of more than two hundred poems. The book opens with Steven Carter's generous introduction on Shōtetsu's life and work and his significance in Japanese literature, and includes a glossary of important names and places and a list of sources of the poems. Revealing as never before the enduring creative spirit of one of Japan's greatest poets, this fine collection fills a major gap in the English translations of medieval Japanese literature.

74. https://twigserial.wordpress.com/

75. “Blade Runner 2049”, Eng­lish Wikipedia (2020-12-28):

    

    Blade Runner 2049 is a 2017 American neo-noir science fiction film directed by Denis Villeneuve and written by Hampton Fancher and Michael Green. A sequel to the 1982 film Blade Runner, the film stars Ryan Gosling and Harrison Ford, with Ana de Armas, Sylvia Hoeks, Robin Wright, Mackenzie Davis, Carla Juri, Lennie James, Dave Bautista, and Jared Leto in supporting roles. Ford and Edward James Olmos reprise their roles from the original. Gosling plays K, a Nexus-9 replicant "blade runner" who uncovers a secret that threatens to destabilize society and the course of civilization.

76. /Movie-reviews#blade-runner-2049

77. https://www.youtube.com/watch?v=iScU3d35ZLM

78. “Gwern.net newsletter (Substack subscription page)”, Gw­ern Bran­wen (2013-12-01):

    Subscription page for the monthly gwern.net newsletter. There are monthly updates, which will include summaries of projects I’ve worked on that month (the same as the changelog), collations of links or discussions from my subreddit, and book/movie reviews. You can also browse the archives since December 2013.

79. https://old.reddit.com/r/gwern/.rss

80. “Simple statistical gradient–following algorithms for connectionist reinforcement learning”, Ronald J. Williams (1992):

    This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units. These algorithms, called REINFORCE algorithms, are shown to make weight adjustments in a direction that lies along the gradient of expected reinforcement in both immediate-reinforcement tasks and certain limited forms of delayed-reinforcement tasks, and they do this without explicitly computing gradient estimates or even storing information from which such estimates could be computed. Specific examples of such algorithms are presented, some of which bear a close relationship to certain existing algorithms while others are novel but potentially interesting in their own right. Also given are results that show how such algorithms can be naturally integrated with backpropagation. We close with a brief discussion of a number of additional issues surrounding the use of such algorithms, including what is known about their limiting behaviors as well as further considerations that might be used to help develop similar but potentially more powerful reinforcement learning algorithms.

81. “Hex (board game)”, Eng­lish Wikipedia (2020-12-27):

    

    Hex is a two player abstract strategy board game in which players attempt to connect opposite sides of a hexagonal board. Hex was invented by mathematician and poet Piet Hein in 1942 and independently by John Nash in 1948.

82. https://www.sas.upenn.edu/psych/rust-lab/publications/standing_73.pdf