newsletter/2016/05 (Link Bibliography)

“newsletter/​2016/​05” links:

  1. https://gwern.substack.com

  2. 04

  3. Changelog

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

  5. Candy-Japan

  6. 2016-okbay-2.pdf: ⁠, Aysu Okbay, Jonathan P. Beauchamp, Mark Alan Fontana, James J. Lee, Tune H. Pers, Cornelius A. Rietveld, Patrick Turley, Guo-Bo Chen, Valur Emilsson, S. Fleur W. Meddens, Sven Oskarsson, Joseph K. Pickrell, Kevin Thom, Pascal Timshel, Ronald de Vlaming, Abdel Abdellaoui, Tarunveer S. Ahluwalia, Jonas Bacelis, Clemens Baumbach, Gyda Bjornsdottir, Johannes H. Brandsma, Maria Pina Concas, Jaime Derringer, Nicholas A. Furlotte, Tessel E. Galesloot, Giorgia Girotto, Richa Gupta, Leanne M. Hall, Sarah E. Harris, Edith Hofer, Momoko Horikoshi, Jennifer E. Huffman, Kadri Kaasik, Ioanna P. Kalafati, Robert Karlsson, Augustine Kong, Jari Lahti, Sven J. van der Lee, Christiaan de Leeuw, Penelope A. Lind, Karl-Oskar Lindgren, Tian Liu, Massimo Mangino, Jonathan Marten, Evelin Mihailov, Michael B. Miller, Peter J. van der Most, Christopher Oldmeadow, Antony Payton, Natalia Pervjakova, Wouter J. Peyrot, Yong Qian, Olli Raitakari, Rico Rueedi, Erika Salvi, Börge Schmidt, Katharina E. Schraut, Jianxin Shi, Albert V. Smith, Raymond A. Poot, Beate St Pourcain, Alexander Teumer, Gudmar Thorleifsson, Niek Verweij, Dragana Vuckovic, Juergen Wellmann, Harm-Jan Westra, Jingyun Yang, Wei Zhao, Zhihong Zhu, Behrooz Z. Alizadeh, Najaf Amin, Andrew Bakshi, Sebastian E. Baumeister, Ginevra Biino, Klaus Bønnelykke, Patricia A. Boyle, Harry Campbell, Francesco P. Cappuccio, Gail Davies, Jan-Emmanuel De Neve, Panos Deloukas, Ilja Demuth, Jun Ding, Peter Eibich, Lewin Eisele, Niina Eklund, David M. Evans, Jessica D. Faul, Mary F. Feitosa, Andreas J. Forstner, Ilaria Gandin, Bjarni Gunnarsson, Bjarni V. Halldórsson, Tamara B. Harris, Andrew C. Heath, Lynne J. Hocking, Elizabeth G. Holliday, Georg Homuth, Michael A. Horan, Jouke-Jan Hottenga, Philip L. de Jager, Peter K. Joshi, Astanand Jugessur, Marika A. Kaakinen, Mika Kähönen, Stavroula Kanoni, Liisa Keltigangas-Järvinen, Lambertus A. L. M. Kiemeney, Ivana Kolcic, Seppo Koskinen, Aldi T. Kraja, Martin Kroh, Zoltan Kutalik, Antti Latvala, Lenore J. Launer, Maël P. Lebreton, Douglas F. Levinson, Paul Lichtenstein, Peter Lichtner, David C. M. Liewald, LifeLines Cohort Study, Anu Loukola, Pamela A. Madden, Reedik Mägi, Tomi Mäki-Opas, Riccardo E. Marioni, Pedro Marques-Vidal, Gerardus A. Meddens, George McMahon, Christa Meisinger, Thomas Meitinger, Yusplitri Milaneschi, Lili Milani, Grant W. Montgomery, Ronny Myhre, Christopher P. Nelson, Dale R. Nyholt, William E. R. Ollier, Aarno Palotie, Lavinia Paternoster, Nancy L. Pedersen, Katja E. Petrovic, David J. Porteous, Katri Räikkönen, Susan M. Ring, Antonietta Robino, Olga Rostapshova, Igor Rudan, Aldo Rustichini, Veikko Salomaa, Alan R. Sanders, Antti-Pekka Sarin, Helena Schmidt, Rodney J. Scott, Blair H. Smith, Jennifer A. Smith, Jan A. Staessen, Elisabeth Steinhagen-Thiessen, Konstantin Strauch, Antonio Terracciano, Martin D. Tobin, Sheila Ulivi, Simona Vaccargiu, Lydia Quaye, Frank J. A. van Rooij, Cristina Venturini, Anna A. E. Vinkhuyzen, Uwe Völker, Henry Völzke, Judith M. Vonk, Diego Vozzi, Johannes Waage, Erin B. Ware, Gonneke Willemsen, John R. Attia, David A. Bennett, Klaus Berger, Lars Bertram, Hans Bisgaard, Dorret I. Boomsma, Ingrid B. Borecki, Ute Bültmann, Christopher F. Chabris, Francesco Cucca, Daniele Cusi, Ian J. Deary, George V. Dedoussis, Cornelia M. van Duijn, Johan G. Eriksson, Barbara Franke, Lude Franke, Paolo Gasparini, Pablo V. Gejman, Christian Gieger, Hans-Jörgen Grabe, Jacob Gratten, Patrick J. F. Groenen, Vilmundur Gudnason, Pim van der Harst, Caroline Hayward, David A. Hinds, Wolfgang Hoffmann, Elina Hyppönen, William G. Iacono, Bo Jacobsson, Marjo-Riitta Järvelin, Karl-Heinz Jöckel, Jaakko Kaprio, Sharon L. R. Kardia, Terho Lehtimäki, Steven F. Lehrer, Patrik K. E. Magnusson, Nicholas G. Martin, Matt McGue, Andres Metspalu, Neil Pendleton, Brenda W. J. H. Penninx, Markus Perola, Nicola Pirastu, Mario Pirastu, Ozren Polasek, Danielle Posthuma, Christine Power, Michael A. Province, Nilesh J. Samani, David Schlessinger, Reinhold Schmidt, Thorkild I. A. Sørensen, Tim D. Spector, Kari Stefansson, Unnur Thorsteinsdottir, A. Roy Thurik, Nicholas J. Timpson, Henning Tiemeier, Joyce Y. Tung, André G. Uitterlinden, Veronique Vitart, Peter Vollenweider, David R. Weir, James F. Wilson, Alan F. Wright, Dalton C. Conley, Robert F. Krueger, George Davey Smith, Albert Hofman, David I. Laibson, Sarah E. Medland, Michelle N. Meyer, Jian Yang, Magnus Johannesson, Tõnu Esko, Peter M. Visscher, Philipp D. Koellinger, David Cesarini, Daniel J. Benjamin (2016-05-11; iq):

    Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals. Here we report the results of a (GWAS) for educational attainment that extends our earlier discovery sample1, of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.

  7. Everything

  8. ⁠, Eva Krapohl, Robert Plomin (2015-03-10):

    One of the best predictors of children’s educational achievement is their family’s (SES), but the degree to which this association is genetically mediated remains unclear. For 3000 UK-representative unrelated children we found that genome-wide single-nucleotide polymorphisms could explain a third of the of scores on an age-16 UK national examination of educational achievement and half of the correlation between their scores and family SES. Moreover, genome-wide polygenic scores based on a previously published genome-wide association meta-analysis of total number of years in education accounted for ~3.0% variance in educational achievement and ~2.5% in family SES. This study provides the first molecular evidence for substantial genetic influence on differences in children’s educational achievement and its association with family SES.

  9. ⁠, Jie Zheng, A. Mesut Erzurumluoglu, Benjamin L. Elsworth, Laurence Howe, Philip C. Haycock, Gibran Hemani, Katherine Tansey, Charles Laurin, Early Genetics, Lifecourse Epidemiology (EAGLE) Eczema Consortium, Beate St. Pourcain, Nicole M. Warrington, Hilary K. Finucane, Alkes L. Price, Brendan K. Bulik-Sullivan, Verneri Anttila, Lavinia Paternoster, Tom R. Gaunt, David M. Evans, Benjamin M. Neale (2016-05-03):

    Motivation

    LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the between different phenotypes. Because the method relies on summary level results data, score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously.

    Results: In this manuscript, we describe LD Hub—a centralized database of summary-level GWAS results for 177 diseases/​​​​traits from different publicly available resources/​​​​consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/​​​​diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies.

    Availability and implementation

    The web interface and instructions for using LD Hub are available at http:/​​​​/​​​​ldsc.broadinstitute.org/​​​​

  10. http://ldsc.broadinstitute.org/about/

  11. ⁠, Bulik-Sullivan, Brendan K. Loh, Po-Ru Finucane, Hilary K. Ripke, Stephan Yang, Jian Patterson, Nick Daly, Mark J. Price, Alkes L. Neale, Benjamin M (2015):

    Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

  12. 2016-pickrell.pdf: “Detection and interpretation of shared genetic influences on 42 human traits”⁠, Tomaz Berisa, Jimmy Z. Liu, Laure Ségurel, Joyce Y. Tung, David A. Hinds, Joseph K. Pickrell

  13. ⁠, Brendan Bulik-Sullivan, Hilary K. Finucane, Verneri Anttila, Alexander Gusev, Felix R. Day, ReproGen Consortium, Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Consortium 3, Laramie Duncan, John R. B. Perry, Nick Patterson, Elise B. Robinson, Mark J. Daly, Alkes L. Price, Benjamin M. Neale (2015-04-06):

    Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use our method to estimate 300 genetic correlations among 25 traits, totaling more than 1.5 million unique phenotype measurements. Our results include genetic correlations between anorexia nervosa and ⁠, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no genome-wide statistically-significant SNPs for anorexia nervosa and only three for educational attainment.

  14. ⁠, Vincent Deary, Saskia P. Hagenaars, Sarah E. Harris, W. David Hill, Gail Davies, David CM Liewald, International Consortium for Blood Pressure GWAS, CHARGE consortium Aging, Longevity Group, Andrew M. McIntosh, Catharine R. Gale, Ian J. Deary (2016-04-05):

    Self-reported tiredness and low energy, often called fatigue, is associated with poorer physical and mental health. Twin studies have indicated that this has a heritability between 6% and 50%. In the sample (n = 108 976) we carried out a genome-wide association study of responses to the question, “Over the last two weeks, how often have you felt tired or had little energy?” Univariate -GREML found that the proportion of variance explained by all common SNPs for this tiredness question was 8.4% (SE = 0.6%). GWAS identified one genome-wide statistically-significant hit (Affymetrix id 1:64178756_C_T; p = 1.36 x 10−11). LD score regression and polygenic profile analysis were used to test for pleiotropy between tiredness and up to 28 physical and mental health traits from GWAS consortia. Significant genetic correlations were identified between tiredness and ⁠, HDL cholesterol, forced expiratory volume, grip strength, HbA1c, longevity, obesity, self-rated health, smoking status, triglycerides, type 2 diabetes, waist-hip ratio, ⁠, bipolar disorder, major depressive disorder, neuroticism, schizophrenia, and verbal-numerical reasoning (absolute rg effect sizes between 0.11 and 0.78). Significant associations were identified between tiredness phenotypic scores and polygenic profile scores for BMI, HDL cholesterol, LDL cholesterol, coronary artery disease, HbA1c, height, obesity, smoking status, triglycerides, type 2 diabetes, and waist-hip ratio, childhood cognitive ability, neuroticism, bipolar disorder, major depressive disorder, and schizophrenia (standardised β’s between −0.016 and 0.03). These results suggest that tiredness is a partly-heritable, heterogeneous and complex phenomenon that is phenotypically and genetically associated with affective, cognitive, personality, and physiological processes.

    "Hech, sirs! But I’m wabbit, I’m back frae the toon;

    I ha’ena dune pechin’—jist let me sit doon.

    From Glesca’

    By William Dixon Cocker (1882-1970)

  15. ⁠, Field, Yair Boyle, Evan A. Telis, Natalie Gao, Ziyue Gaulton, Kyle J. Golan, David Yengo, Loic Rocheleau, Ghislain Froguel, Philippe McCarthy, Mark I. Pritchard, Jonathan K (2016):

    Detection of recent is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2000 to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans.

  16. ⁠, Kevin J. Galinsky, Po-Ru Loh, Mallick Swapan, Nick J. Patterson, Alkes L. Price (2016-05-27):

    Analyzing genetic differences between closely related populations can be a powerful way to detect recent adaptation. The very large sample size of the UK Biobank is ideal for detecting selection using population differentiation, and enables an analysis of UK population structure at fine resolution. In analyses of 113,851 UK Biobank samples, population structure in the UK is dominated by 5 principal components (PCs) spanning 6 clusters: Northern Ireland, Scotland, northern England, southern England, and two Welsh clusters. Analyses with ancient Eurasians show that populations in the northern UK have higher levels of Steppe ancestry, and that UK population structure cannot be explained as a simple mixture of Celts and Saxons. A scan for unusual population differentiation along top PCs identified a genome-wide statistically-significant signal of selection at the coding variant rs601338 in FUT2 (p = 9.16 × 10−9). In addition, by combining evidence of unusual differentiation within the UK with evidence from ancient Eurasians, we identified new genome-wide statistically-significant (p < 5 × 10−8) signals of recent selection at two additional loci: CYP1A2/​​​​CSK and F12. We detected strong associations to diastolic blood pressure in the UK Biobank for the variants with new selection signals at CYP1A2/​​​​CSK (p = 1.10 × 10−19)) and for variants with ancient Eurasian selection signals in the ATXN2/​​​​SH2B3 locus (p = 8.00 × 10−33), implicating recent adaptation related to blood pressure.

  17. ⁠, Joseph Christopher Lee (2013-06-12):

    Quantitative genetics is primarily concerned with two subjects: the correlation between relatives and the response to selection. The correlation between relatives is used to determine the heritability of a trait—the key quantity that addresses the question of nature vs. nurture. Heritability, in turn, is used to predict the response to selection—the main driver of improvements in crops and livestock. The theory of quantitative genetics has been thoroughly tested and applied in plants and animals, but heritability and selection remain open questions in humans due to limited natural experimental designs.

    The Donor Sibling Registry (DSR) is an organization that helps individuals conceived as a result of sperm, egg, or embryo donation make contact with genetically related individuals. Families who conceived children via anonymous sperm donation join the DSR and match with other families who used the same donor ID at the same sperm bank. The resulting donor pedigree consists of heterosexual, lesbian, and single mother families who are connected through the common anonymous sperm donor used to conceive their children.

    Here, we introduce a new quantitative genetic study design based on the unprecedented family relationships found in the donor pedigree. We surveyed 945 individual families constituting 159 donor pedigrees from the Donor Sibling Registry and used their demographic, physical, and behavioral characteristics to conduct a quantitative genetic study of selection and heritability. A direct measurement of phenotypic assortment showed mothers actively selected mates for height, eye color, and religion. Artificial selection for donor height increased mean child height in a manner consistent with the selection differential. Reared-apart donor-conceived paternal half-siblings provided unbiased heritability estimates for traits influenced by maternal and contrast effects. Maternal effects were important in determining the variance of birth weight while eliminating contrast effects revealed sociability to be a highly heritable childhood temperament. Thus, the unprecedented family relationships in the donor pedigree enable a universal model for quantitative genetics.

  18. ⁠, Jonathan Beauchamp (2016-05-05):

    Recent findings from molecular genetics now make it possible to test directly for natural selection by analyzing whether genetic variants associated with various phenotypes have been under selection. I leverage these findings to construct polygenic scores that use individuals’ genotypes to predict their body mass index, educational attainment (EA), glucose concentration, height, schizophrenia, total cholesterol, and (in females) age at menarche. I then examine associations between these scores and fitness to test whether natural selection has been occurring. My study sample includes individuals of European ancestry born between 1931 and 1953 in the Health and Retirement Study, a representative study of the US population. My results imply that natural selection has been slowly favoring lower EA in both females and males, and are suggestive that natural selection may have favored a higher age at menarche in females. For EA, my estimates imply a rate of selection of about -1.5 months of education per generation (which pales in comparison with the increases in EA observed in contemporary times). Though they cannot be projected over more than one generation, my results provide additional evidence that humans are still evolving—albeit slowly, especially when compared to the rapid secular changes that have occurred over the past few generations due to cultural and environmental factors.

  19. 2011-ciani.pdf

  20. https://www.nature.com/articles/tp201662

  21. 2016-simonti.pdf

  22. 2016-liu.pdf: “The MC1R Gene and Youthful Looks”⁠, Fan Liu, Merel A. Hamer, Joris Deelen, Japal S. Lall, Leonie Jacobs, Diana van Heemst, Peter G. Murray, Andreas Wollstein, Anton J. M. de Craen, Hae-Won Uh, Changqing Zeng, Albert Hofman, André G. Uitterlinden, Jeanine J. Houwing-Duistermaat, Luba M. Pardo, Marian Beekman, P. Eline Slagboom, Tamar Nijsten, Manfred Kayser, David A. Gunn

  23. ⁠, Sarah E. Harris, Saskia P. Hagenaars, Gail Davies, W. David Hill, David CM Liewald, Stuart J. Ritchie, Riccardo E. Marioni, METASTROKE consortium, International Consortium for Blood Pressure, CHARGE consortium Aging, Longevity Group, CHARGE consortium Cognitive Group, Cathie LM Sudlow, Joanna M. Wardlaw, Andrew M. McIntosh, Catharine R. Gale, Ian J. Deary (2016-04-12):

    Background: Poorer self-rated health (SRH) predicts worse health outcomes, even when adjusted for objective measures of disease at time of rating. Twin studies indicate SRH has a heritability of up to 60% and that its genetic architecture may overlap with that of personality and cognition.

    Methods: We carried out a genome-wide association study (GWAS) of SRH on 111 749 members of the UK Biobank sample. Univariate genome-wide complex trait analysis (GCTA)-GREML analyses were used to estimate the proportion of variance explained by all common autosomal SNPs for SRH. Linkage Disequilibrium (LD) score regression and polygenic risk scoring, two complementary methods, were used to investigate pleiotropy between SRH in UK Biobank and up to 21 health-related and personality and cognitive traits from published GWAS consortia.

    Results: The GWAS identified 13 independent signals associated with SRH, including several in regions previously associated with diseases or disease-related traits. The strongest signal was on chromosome 2 (rs2360675, p = 1.77×10−10) close to KLF7, which has previously been associated with obesity and type 2 diabetes. A second strong peak was identified on chromosome 6 in the major histocompatibility region (rs76380179, p = 6.15×10−10). The proportion of variance in SRH that was explained by all common genetic variants was 13%. Polygenic scores for the following traits and disorders were associated with SRH: cognitive ability, education, neuroticism, BMI, longevity, ADHD, major depressive disorder, schizophrenia, lung function, blood pressure, coronary artery disease, large vessel disease stroke, and type 2 diabetes.

    Conclusion: Individual differences in how people respond to a single item on SRH are partly explained by their genetic propensity to many common psychiatric and physical disorders and psychological traits.

    Key Messages

    Genetic variants associated with common diseases and psychological traits are associated with self-rated health.

    The SNP-based heritability of self-rated health is 0.13 (SE 0.006).

    There is pleiotropy between self-rated health and psychiatric and physical diseases and psychological traits.

  24. 2016-badran.pdf: “Continuous evolution of Bacillus thuringiensis toxins overcomes insect resistance”⁠, Ahmed H. Badran, Victor M. Guzov, Qing Huai, Melissa M. Kemp, Prashanth Vishwanath, Wendy Kain, Autumn M. Nance, Artem Evdokimov, Farhad Moshiri, Keith H. Turner, Ping Wang, Thomas Malvar, David R. Liu

  25. http://www.rifters.com/crawl/?p=6697

  26. http://rifters.com/real/articles/God-has-sent-me-to-you-Right-temporal-epilepsy-left-prefrontal-psychosis.pdf

  27. http://www.tampabay.com/projects/2016/food/farm-to-fable/restaurants/

  28. ⁠, Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap (2016-05-19):

    Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of “one-shot learning.” Traditional gradient-based networks require a lot of data to learn, often through extensive iterative training. When new data is encountered, the models must inefficiently relearn their parameters to adequately incorporate the new information without catastrophic interference. Architectures with augmented memory capacities, such as Neural Turing Machines (NTMs), offer the ability to quickly encode and retrieve new information, and hence can potentially obviate the downsides of conventional models. Here, we demonstrate the ability of a memory-augmented neural network to rapidly assimilate new data, and leverage this data to make accurate predictions after only a few samples. We also introduce a new method for accessing an external memory that focuses on memory content, unlike previous methods that additionally use memory location-based focusing mechanisms.

  29. ⁠, Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky, Sebastian Riedel (2016-05-21):

    Given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. In this paper, we consider the case of prior procedural knowledge for neural networks, such as knowing how a program should traverse a sequence, but not what local actions should be performed at each step. To this end, we present an end-to-end differentiable interpreter for the programming language Forth which enables programmers to write program sketches with slots that can be filled with behaviour trained from program input-output data. We can optimise this behaviour directly through gradient descent techniques on user-specified objectives, and also integrate the program into any larger neural computation graph. We show empirically that our interpreter is able to effectively leverage different levels of program structure and learn complex behaviours such as sequence sorting and addition. When connected to outputs of an and trained jointly, our interpreter achieves state-of-the-art accuracy for end-to-end reasoning about quantities expressed in natural language stories.

  30. ⁠, Gustav Larsson, Michael Maire, Gregory Shakhnarovich (2016-05-24):

    We introduce a design strategy for neural network macro-architecture based on self-similarity. Repeated application of a simple expansion rule generates deep networks whose structural layouts are precisely truncated fractals. These networks contain interacting subpaths of different lengths, but do not include any pass-through or residual connections; every internal signal is transformed by a filter and nonlinearity before being seen by subsequent layers. In experiments, fractal networks match the excellent performance of standard on both CIFAR and classification tasks, thereby demonstrating that residual representations may not be fundamental to the success of extremely deep convolutional neural networks. Rather, the key may be the ability to transition, during training, from effectively shallow to deep. We note similarities with student-teacher behavior and develop drop-path, a natural extension of dropout, to regularize co-adaptation of subpaths in fractal architectures. Such regularization allows extraction of high-performance fixed-depth subnetworks. Additionally, fractal networks exhibit an anytime property: shallow subnetworks provide a quick answer, while deeper subnetworks, with higher latency, provide a more accurate answer.

  31. ⁠, Manuel Ruder, Alexey Dosovitskiy, Thomas Brox (2016-04-28):

    In the past, manually re-drawing an image in a certain artistic style required a professional artist and a long time. Doing this for a video sequence single-handed was beyond imagination. Nowadays computers provide new possibilities. We present an approach that transfers the style from one image (for example, a painting) to a whole video sequence. We make use of recent advances in in still images and propose new initializations and loss functions applicable to videos. This allows us to generate consistent and stable stylized video sequences, even in cases with large motion and strong occlusion. We show that the proposed method clearly outperforms simpler baselines both qualitatively and quantitatively.

  32. https://www.youtube.com/watch?v=vQk_Sfl7kSc

  33. https://karpathy.github.io/2016/05/31/rl/

  34. https://slatestarcodex.com/2016/05/28/book-review-age-of-em/

  35. https://www.openphilanthropy.org/focus/global-catastrophic-risks/potential-risks-advanced-artificial-intelligence/what-should-we-learn-past-ai-forecasts

  36. http://www.sumsar.net/blog/2014/05/jeffreys-substitution-posterior/

  37. 2016-makel.pdf: ⁠, Matthew C. Makel, Harrison J. Kell, David Lubinski, Martha Putallaz, Camilla P. Benbow (2016-07-01; iq  /​ ​​ ​smpy):

    The educational, occupational, and creative accomplishments of the profoundly gifted participants (IQs ⩾ 160) in the Study of Mathematically Precocious Youth (SMPY) are astounding, but are they representative of equally able 12-year-olds? Duke University’s Talent Identification Program (TIP) identified 259 young adolescents who were equally gifted. By age 40, their life accomplishments also were extraordinary: Thirty-seven percent had earned doctorates, 7.5% had achieved academic tenure (4.3% at research-intensive universities), and 9% held patents; many were high-level leaders in major organizations. As was the case for the SMPY sample before them, differential ability strengths predicted their contrasting and eventual developmental trajectories—even though essentially all participants possessed both mathematical and verbal reasoning abilities far superior to those of typical Ph.D. recipients. Individuals, even profoundly gifted ones, primarily do what they are best at. Differences in ability patterns, like differences in interests, guide development along different paths, but ability level, coupled with commitment, determines whether and the extent to which noteworthy accomplishments are reached if opportunity presents itself.

    [Keywords: intelligence, creativity, giftedness, replication, blink comparator]

  38. https://www.nytimes.com/2016/05/17/us/aging-research-disease-dogs.html

  39. 2016-martin.pdf

  40. ⁠, Eric Jonas, Konrad Paul Kording (2016-11-14):

    There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.

    Author Summary

    Neuroscience is held back by the fact that it is hard to evaluate if a conclusion is correct; the complexity of the systems under study and their experimental inaccessability make the assessment of algorithmic and data analytic technqiues challenging at best. We thus argue for testing approaches using known artifacts, where the correct interpretation is known. Here we present a microprocessor platform as one such test case. We find that many approaches in neuroscience, when used na•vely, fall short of producing a meaningful understanding.

  41. 2002-lazebnik.pdf

  42. https://www.frontiersin.org/articles/10.3389/fpsyg.2016.00642/full

  43. https://www.lesswrong.com/r/discussion/lw/nkz/a_second_year_of_spaced_repetition_software_in/

  44. https://eprint.iacr.org/2016/464.pdf

  45. https://mastermind.atavist.com/

  46. http://nautil.us/issue/36/aging/the-father-of-modern-metal

  47. https://haveibeenpwned.com/

  48. 2016-baade.pdf: ⁠, Robert Baade, Victor A. Matheson (2016-01-01; economics):

    In this paper, we explore the costs and benefits of hosting the Olympic Games. On the cost side, there are three major categories: general infrastructure such as transportation and housing to accommodate athletes and fans; specific sports infrastructure required for competition venues; and operational costs, including general administration as well as the opening and closing ceremony and security. Three major categories of benefits also exist: the short-run benefits of tourist spending during the Games; the long-run benefits or the “Olympic legacy” which might include improvements in infrastructure and increased trade, foreign investment, or tourism after the Games; and intangible benefits such as the “feel-good effect” or civic pride. Each of these costs and benefits will be addressed in turn, but the overwhelming conclusion is that in most cases the Olympics are a money-losing proposition for host cities; they result in positive net benefits only under very specific and unusual circumstances. Furthermore, the cost–benefit proposition is worse for cities in developing countries than for those in the industrialized world. In closing, we discuss why what looks like an increasingly poor investment decision on the part of cities still receives significant bidding interest and whether changes in the bidding process of the International Olympic Committee (IOC) will improve outcomes for potential hosts.

  49. 2016-caudevilla-2.pdf: ⁠, Fernando Caudevilla (2016-09-01; silk-road):

    Introduction: User surveys indicate that expectations of higher drug purity are a key reason for use. In 2014–2015, Spain’s NGO Energy Control conducted a 1-year pilot project to provide a testing service to cryptomarket drug users using the Transnational European Drug Information (TEDI) guidelines. In this paper, we present content and purity data from the trial.

    Methods: 219 samples were analyzed by gas chromatography associated with mass spectrometry (⁠/​​​​MS). Users were asked to report what substance they allegedly purchased.

    Results: 40 different advertised substances were reported, although 77.6% were common recreational drugs (cocaine, ⁠, amphetamines, LSD, ketamine, cannabis). In 200 samples (91.3%), the main result of analysis matched the advertised substance. Where the advertised compound was detected, purity levels (m ± SD) were: cocaine 71.6 ± 19.4%; MDMA (crystal) 88.3 ± 1.4%; MDMA (pills) 133.3 ± 38.4 mg; Amphetamine (speed) 51.3 ± 33.9%; LSD 123.6 ± 40.5 μg; Cannabis resin THC: 16.5 ± 7.5% CBD: 3.4 ± 1.5%; Ketamine 71.3 ± 38.4%. 39.8% of cocaine samples contained the adulterant (11.6 ± 8%). No adulterants were found in MDMA and LSD samples.

    Discussion: The largest collection of test results from drug samples delivered from cryptomarkets are reported in this study. Most substances contained the advertised ingredient and most samples were of high purity. The representativeness of these results is unknown.

    [Keywords: cryptomarkets, drug markets, purity, adulterants, drug checking, drug trend monitoring]

    [See also ⁠.]

    Table 1: Advertised substance and purities in samples from International Drug Testing Service (March 2014–March 2015).
  50. http://www.collectorsweekly.com/articles/railway-paradise/

  51. https://www.poetryfoundation.org/poems-and-poets/poems/detail/44400

  52. https://www.amazon.com/Titan-Life-John-Rockefeller-Sr/dp/1400077303

  53. Books#titan-chernow-2004

  54. https://www.gutenberg.org/ebooks/22403

  55. Books#the-poems-of-gerard-manley-hopkins-hopkins-1976

  56. https://archiveofourown.org/works/6178036/chapters/14154868

  57. http://www.anarchyishyperbole.com/p/significant-digits.html

  58. https://www.youtube.com/watch?v=sUPoqaL-vs4

  59. https://www.youtube.com/watch?v=XG36h8Ow04g

  60. https://www.dropbox.com/s/ncmdj5w2d0px8k5/pastry-plusieursfleur-monologremix.ogg

  61. https://www.youtube.com/watch?v=XYyUlY1UW1s

  62. https://www.youtube.com/watch?v=aU7O_EEqXmU

  63. https://www.dropbox.com/s/pi12ujp98o287rn/escarmew-secretsealingmoratorium-likethegeese.ogg

  64. https://www.youtube.com/watch?v=Rw9bT4LerMY

  65. https://www.youtube.com/watch?v=uXrD-AVr0pE&t=302s

  66. https://www.dropbox.com/s/3tqu27k52hqwqtm/%E3%81%A8%E3%82%89%E3%81%A3%E3%81%97%E3%82%85-phantasmasoundarchiveno53-%E4%BF%AF%E7%9E%B0%E3%81%99%E3%82%8B%E8%92%BC%E7%84%B6%E6%9A%AE%E8%89%B2.ogg

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  68. https://www.dropbox.com/s/uxjqkxxcnzfc5hw/%E3%81%A8%E3%82%89%E3%81%A3%E3%81%97%E3%82%85-phantasmasoundarchiveno53-%E9%A3%9B%E6%9D%A5%E3%81%99%E3%82%8B%E8%93%AC%E8%8E%B1%E7%89%A9%E8%B3%AA.ogg

  69. https://www.dropbox.com/s/pwltss6lzcj6o71/%E3%81%A8%E3%82%89%E3%81%A3%E3%81%97%E3%82%85-phantasmasoundarchiveno53-%E3%82%A2%E3%83%B3%E3%83%A9%E3%82%AF%E3%83%88%E3%83%BB%E3%83%95%E3%82%A3%E3%83%AA%E3%83%B3%E3%82%B0.ogg

  70. https://www.dropbox.com/s/anbjh2hjujbeus1/%E3%81%AF%E3%81%A1%E3%81%BF%E3%81%A4%E3%82%8C%E3%82%82%E3%82%93xaftergrow-touhousixstring02%E5%B0%81-%E3%82%AD%E3%83%9F%E3%83%8E%E3%82%BB%E3%82%AB%E3%82%A4yourworld.ogg

  71. https://www.youtube.com/watch?v=fzHb608BOT0

  72. https://adidkh.bandcamp.com/track/autumn-beat

  73. https://www.youtube.com/watch?v=T4-UwJctlJ0