“Morphometricity as a measure of the neuroanatomical signature of a trait”, Sabuncu et al 2016 (Heritability/variance component estimation generalized to brain volume/thickness: demonstrates that total brain structure—as opposed to just the predictors estimated using underpowered samples, which can only predict like r = 0.3—can predict a large fraction of variance among Alzheimers & aging (~1), IQ (0.95), etc, and so those traits have causal relationships (of some sort) with brain volume/thickness. A common mistake in interpreting brain imaging studies is to argue that, since the state-of-the-art study can predict only a relatively small amount of a trait from their brain imaging data, neuroanatomy is not important; the fallacy here is to treat an extremely loose lower bound as being close to the true total value. By using variance components, however, the total can be directly estimated, and the true total turns out to be far larger. While the causal relationships may not turn out to be interesting (we already knew brain volumes and thicknesses are catastrophically affected by aging and Alzheimer’s), it does at least imply that as brain imaging datasets get larger, they will get ever better at predicting whether a subject has Alzheimers or how intelligent a person is. Hopefully we’ll see variance components taken seriously outside of genetics. If power analysis tells you whether you have enough light to find the needles in the haystack, variance components can tell you whether there are even any needles to look for. See also Seidlitz et al 2018, Bessadok & Rekik 2018, Rincent et al 2018)
“Capacity-approaching DNA storage”, Erlich & Zielinski 2016 (If DNA storage gets real-world usage, it might help accelerate the DNA synthesis cost-curve, and we could get whole genome synthesis years before I project!)
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).
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.
Not all cats respond to the catnip stimulant; the rate of responders is generally estimated at ~70% of cats. A meta-analysis of catnip response experiments since the 1940s indicates the true value is ~62%. The low quality of studies and the reporting of their data makes examination of possible moderators like age, sex, and country difficult. Catnip responses have been recorded for a number of species both inside and outside the Felidae family; of them, there is evidence for a catnip response in the Felidae, and, more uncertainly, the Paradoxurinae, and Herpestinae.
To extend the analysis, I run large-scale online surveys measuring catnip response rates globally in domestic cats, finding high heterogeneity but considerable rates of catnip immunity worldwide.
As a piece of practical advice for cat-hallucinogen sommeliers, I treat catnip response & finding catnip substitutes as a decision problem, modeling it as a Markov decision process where one wishes to find a working psychoactive at minimum cost. Bol et al 2017 measured multiple psychoactives simultaneously in a large sample of cats, permitting prediction of responses conditional on not responding to others. (The solution to the specific problem is to test in the sequence catnip → honeysuckle → silvervine → Valerian.)
For discussion of cat psychology in general, see my Cat Sense review.
The pathophysiology of antisocial personality disorder (ASPD) remains unclear. Although the most consistent biological finding is reduced grey matter volume in the frontal cortex, about 50% of the total liability to developing ASPD has been attributed to genetic factors. The contributing genes remain largely unknown. Therefore, we sought to study the genetic background of ASPD. We conducted a genome-wide association study (GWAS) and a replication analysis of Finnish criminal offenders fulfilling DSM-IV criteria for ASPD (n = 370, n = 5850 for controls, GWAS; n = 173, n = 3766 for controls and replication sample). The GWAS resulted in suggestive associations of two clusters of single-nucleotide polymorphisms at 6p21.2 and at 6p21.32 at the human leukocyte antigen (HLA) region. Imputation of HLA alleles revealed an independent association with DRB1*01:01 (odds ratio (OR)=2.19 (1.53–3.14), p = 1.9 × 10-5). Two polymorphisms at 6p21.2 LINC00951–LRFN2 gene region were replicated in a separate data set, and rs4714329 reached genome-wide significance (OR = 1.59 (1.37–1.85), p = 1.6 × 10−9) in the meta-analysis. The risk allele also associated with antisocial features in the general population conditioned for severe problems in childhood family (β = 0.68, p = 0.012). Functional analysis in brain tissue in open access GTEx and Braineac databases revealed eQTL associations of rs4714329 with LINC00951 and LRFN2 in cerebellum. In humans, LINC00951 and LRFN2 are both expressed in the brain, especially in the frontal cortex, which is intriguing considering the role of the frontal cortex in behavior and the neuroanatomical findings of reduced gray matter volume in ASPD. To our knowledge, this is the first study showing genome-wide significant and replicable findings on genetic variants associated with any personality disorder.
Educated people are generally healthier, have fewer comorbidities and live longer than people with less education. Previous evidence about the effects of education come from observational studies many of which are affected by residual confounding. Legal changes to the minimum school leave age is a potential natural experiment which provides a potentially more robust source of evidence about the effects of schooling. Previous studies have exploited this natural experiment using population-level administrative data to investigate mortality, and relatively small surveys to investigate the effect on mortality. Here, we add to the evidence using data from a large sample from the UK Biobank. We exploit the raising of the school-leaving age in the UK in September 1972 as a natural experiment and regression discontinuity and instrumental variable estimators to identify the causal effects of staying on in school. Remaining in school was positively associated with 23 of 25 outcomes. After accounting for multiple hypothesis testing, we found evidence of causal effects on twelve outcomes, however, the associations of schooling and intelligence, smoking, and alcohol consumption may be due to genomic and socioeconomic confounding factors. Education affects some, but not all health and socioeconomic outcomes. Differences between educated and less educated people may be partially due to residual genetic and socioeconomic confounding.
On average people who choose to stay in education for longer are healthier, wealthier, and live longer. We investigated the causal effects of education on health, income, and well-being later in life. This is the largest study of its kind to date and it has objective clinic measures of morbidity and aging. We found evidence that people who were forced to remain in school had higher wages and lower mortality. However, there was little evidence of an effect on intelligence later in life. Furthermore, estimates of the effects of education using conventionally adjusted regression analysis are likely to suffer from genomic confounding. In conclusion, education affects some, but not all health outcomes later in life.
The Medical Research Council (MRC) and the University of Bristol fund the MRC Integrative Epidemiology Unit [MC_UU_12013/1, MC_UU_12013/9]. NMD is supported by the Economics and Social Research Council (ESRC) via a Future Research Leaders Fellowship [ES/N000757/1]. The research described in this paper was specifically funded by a grant from the Economics and Social Research Council for Transformative Social Science. No funding body has influenced data collection, analysis or its interpretations. This publication is the work of the authors, who serve as the guarantors for the contents of this paper. This work was carried out using the computational facilities of the Advanced Computing Research Centre -http://www.bris.ac.uk/acrc/ and the Research Data Storage Facility of the University of Bristol — http://www.bris.ac.uk/acrc/storage/. This research was conducted using the UK Biobank Resource.
The statistical code used to produce these results can be accessed here: (https://github.com/nmdavies/UKbiobankROSLA). The final analysis dataset used in this study is archived with UK Biobank, which can be accessed by contacting UK Biobank email@example.com.
“Genomic analyses for age at menarche identify 389 independent signals and indicate BMI-independent effects of puberty timing on cancer susceptibility”, Felix R. Day, Deborah J. Thompson, Hannes Helgason, Daniel I. Chasman, Hilary Finucane, Patrick Sulem, Katherine S. Ruth, Sean Whalen, Abhishek K. Sarkar, Eva Albrecht, Elisabeth Altmaier, Marzyeh Amini, Caterina M. Barbieri, Thibaud Boutin, Archie Campbell, Ellen Demerath, Ayush Giri, Chunyan He, Jouke J. Hottenga, Robert Karlsson, Ivana Kolcic, Po-Ru Loh, Kathryn L. Lunetta, Massimo Mangino, Brumat Marco, George McMahon, Sarah E. Medland, Ilja M. Nolte, Raymond Noordam, Teresa Nutile, Lavinia Paternoster, Natalia Perjakova, Eleonora Porcu, Lynda M. Rose, Katharina E. Schraut, Ayellet V. Segrè, Albert V. Smith, Lisette Stolk, Alexander Teumer, Irene L. Andrulis, Stefania Bandinelli, Matthias W. Beckmann, Javier Benitez, Sven Bergmann, Murielle Bochud, Eric Boerwinkle, Stig E. Bojesen, Manjeet K. Bolla, Judith S. Brand, Hiltrud Brauch, Hermann Brenner, Linda Broer, Thomas Brüning, Julie E. Buring, Harry Campbell, Eulalia Catamo, Stephen Chanock, Georgia Chenevix-Trench, Tanguy Corre, Fergus J. Couch, Diana L. Cousminer, Angela Cox, Laura Crisponi, Kamila Czene, George Davey-Smith, Eco J.C.N de Geus, Renée de Mutsert, Immaculata De Vivo, Joe Dennis, Peter Devilee, Isabel dos-Santos-Silva, Alison M. Dunning, Johan G. Eriksson, Peter A. Fasching, Lindsay Fernández-Rhodes, Luigi Ferrucci, Dieter Flesch-Janys, Lude Franke, Marike Gabrielson, Ilaria Gandin, Graham G. Giles, Harald Grallert, Daniel F. Gudbjartsson, Pascal Guénel, Per Hall, Emily Hallberg, Ute Hamann, Tamara B. Harris, Catharina A. Hartman, Gerardo Heiss, Maartje J. Hooning, John L. Hopper, Frank Hu, David Hunter, M. Arfan Ikram, Hae Kyung Im, Marjo-Riitta Järvelin, Peter K. Joshi, David Karasik, Zoltan Kutalik, Genevieve LaChance, Diether Lambrechts, Claudia Langenberg, Lenore J. Launer, Joop S.E. Laven, Stefania Lenarduzzi, Jingmei Li, Penelope A. Lind, Sara Lindstrom, YongMei Liu, Jian'an Luan, Reedik Mägi, Arto Mannermaa, Hamdi Mbarek, Mark I. McCarthy, Christa Meisinger, Thomas Meitinger, Cristina Menni, Andres Metspalu, Kyriaki Michailidou, Lili Milani, Roger L. Milne, Grant W. Montgomery, Anna M. Mulligan, Mike A. Nalls, Pau Navarro, Heli Nevanlinna, Dale R. Nyholt, Albertine J. Oldehinkel, Tracy A. O'Mara, Aarno Palotie, Nancy Pedersen, Annette Peters, Julian Peto, Paul D.P. Pharoah, Anneli Pouta, Paolo Radice, Iffat Rahman, Susan M. Ring, Antonietta Robino, Frits R. Rosendaal, Igor Rudan, Rico Rueedi, Daniela Ruggiero, Cinzia F. Sala, Marjanka K. Schmidt, Robert A. Scott, Mitul Shah, Rossella Sorice, Melissa C. Southey, Ulla Sovio, Meir Stampfer, Maristella Steri, Konstantin Strauch, Toshiko Tanaka, Emmi Tikkanen, Nicholas J. Timpson, Michela Traglia, Thérèse Truong, Jonathan P. Tyrer, André G. Uitterlinden, Digna R. Velez Edwards, Veronique Vitart, Uwe Völker, Peter Vollenweider, Qin Wang, Elisabeth Widen, Ko Willems van Dijk, Gonneke Willemsen, Robert Winqvist, Bruce H.R Wolffenbuttel, Jing Hua Zhao, Magdalena Zoledziewska, Marek Zygmunt, Behrooz Z. Alizadeh, Dorret I. Boomsma, Marina Ciullo, Francesco Cucca, Tõnu Esko, Nora Franceschini, Christian Gieger, Vilmundur Gudnason, Caroline Hayward, Peter Kraft, Debbie A. Lawlor, Patrik K.E Magnusson, Nicholas G. Martin, Dennis O. Mook-Kanamori, Ellen A. Nohr, Ozren Polasek, David Porteous, Alkes L. Price, Paul M. Ridker, Harold Snieder, Tim D. Spector, Doris Stöckl, Daniela Toniolo, Sheila Ulivi, Jenny A. Visser, Henry Völzke, Nicholas J. Wareham, James F. Wilson, The LifeLines Cohort Study, The InterAct Consortium, kConFab/AOCS Investigators, Endometrial Cancer Association Consortium, Ovarian Cancer Association Consortium, PRACTICAL consortium, Amanda B. Spurdle, Unnur Thorsteindottir, Katherine S. Pollard, Douglas F. Easton, Joyce Y. Tung, Jenny Chang-Claude, David Hinds, Anna Murray, Joanne M. Murabito, Kari Stefansson, Ken K. Ong, John R.B Perry (2016-09-23):
The timing of puberty is a highly polygenic childhood trait that is epidemiologically associated with various adult diseases. Here, we analyse 1000-Genome reference panel imputed genotype data on up to ~370,000 women and identify 389 independent signals (all p < 5×10−8) for age at menarche, a notable milestone in female pubertal development. In Icelandic data from deCODE, these signals explain ~7.4% of the population variance in age at menarche, corresponding to one quarter of the estimated heritability. We implicate over 250 genes via coding variation or associated gene expression, and demonstrate enrichment across genes active in neural tissues. We identify multiple rare variants near the imprinted genes MKRN3 and DLK1 that exhibit large effects on menarche only when paternally inherited. Disproportionate effects of variants on early or late puberty timing are observed: single variant and heritability estimates are larger for early than late puberty timing in females. The opposite pattern is seen in males, with larger estimates for late than early puberty timing. Mendelian randomization analyses indicate causal inverse associations, independent of BMI, between puberty timing and risks for breast and endometrial cancers in women, and prostate cancer in men. In aggregate, our findings reveal new complexity in the genetic regulation of puberty timing and support new causal links with adult cancer risks.
Susceptibility to obesity in today’s environment has a strong genetic component. Lower socioeconomic position (SEP) is associated with a higher risk of obesity but it is not known if it accentuates genetic susceptibility to obesity. We aimed to use up to 120,000 individuals from the UK Biobank study to test the hypothesis that measures of socioeconomic position accentuate genetic susceptibility to obesity. We used the Townsend deprivation index (TDI) as the main measure of socioeconomic position, and a 69-variant genetic risk score (GRS) as a measure of genetic susceptibility to obesity. We also tested the hypothesis that interactions between BMI genetics and socioeconomic position would result in evidence of interaction with individual measures of the obesogenic environment and behaviours that correlate strongly with socioeconomic position, even if they have no obesogenic role. These measures included self-reported TV watching, diet and physical activity, and an objective measure of activity derived from accelerometers. We performed several negative control tests, including a simulated environment correlated with BMI but not TDI, and sun protection use. We found evidence of gene-environment interactions with TDI (pinteraction = 3×10−10) such that, within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. We also observed evidence of interaction between sun protection use and BMI genetics, suggesting that residual confounding may result in evidence of non-causal interactions. Our findings provide evidence that relative social deprivation best captures aspects of the obesogenic environment that accentuate the genetic predisposition to obesity in the UK.
“The Iron Law Of Evaluation And Other Metallic Rules” is a classic review paper by American “sociologistPeter Rossi, a dedicated progressive and the nation’s leading expert on social program evaluation from the 1960s through the 1980s”; it discusses the difficulties of creating a useful social program, and proposed some aphoristic summary rules, including most famously:
The Iron law: “The expected value of any net impact assessment of any large scale social program is zero”
the Stainless Steel law: “the better designed the impact assessment of a social program, the more likely is the resulting estimate of net impact to be zero.”
Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new target goals, and (2) data inefficiency i.e., the model requires several (and often costly) episodes of trial and error to converge, which makes it impractical to be applied to real-world scenarios. In this paper, we address these two issues and apply our model to the task of target-driven visual navigation. To address the first issue, we propose an actor-critic model whose policy is a function of the goal as well as the current state, which allows to better generalize. To address the second issue, we propose AI2-THOR framework, which provides an environment with high-quality 3D scenes and physics engine. Our framework enables agents to take actions and interact with objects. Hence, we can collect a huge number of training samples efficiently.
We show that our proposed method (1) converges faster than the state-of-the-art deep reinforcement learning methods, (2) generalizes across targets and across scenes, (3) generalizes to a real robot scenario with a small amount of fine-tuning (although the model is trained in simulation), (4) is end-to-end trainable and does not need feature engineering, feature matching between frames or 3D reconstruction of the environment.
The supplementary video can be accessed at the following link: https://youtu.be/SmBxMDiOrvs.
YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a separate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact. [Keywords: recommender system; deep learning; scalability]
Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4× upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method.
This work explores hypernetworks: an approach of using a one network, also known as a hypernetwork, to generate the weights for another network. Hypernetworks provide an abstraction that is similar to what is found in nature: the relationship between a genotype—the hypernetwork—and a phenotype—the main network. Though they are also reminiscent of HyperNEAT in evolution, our hypernetworks are trained end-to-end with backpropagation and thus are usually faster. The focus of this work is to make hypernetworks useful for deep convolutional networks and long recurrent networks, where hypernetworks can be viewed as relaxed form of weight-sharing across layers. Our main result is that hypernetworks can generate non-shared weights for LSTM and achieve near state-of-the-art results on a variety of sequence modelling tasks including character-level language modelling, handwriting generation and neural machine translation, challenging the weight-sharing paradigm for recurrent networks. Our results also show that hypernetworks applied to convolutional networks still achieve respectable results for image recognition tasks compared to state-of-the-art baseline models while requiring fewer learnable parameters.
Some risks have extremely high stakes. For example, a worldwide pandemic or asteroid impact could potentially kill more than a billion people. Comfortingly, scientific calculations often put very low probabilities on the occurrence of such catastrophes. In this paper, we argue that there are important new methodological problems which arise when assessing global catastrophic risks and we focus on a problem regarding probability estimation. When an expert provides a calculation of the probability of an outcome, they are really providing the probability of the outcome occurring, given that their argument is watertight. However, their argument may fail for a number of reasons such as a flaw in the underlying theory, a flaw in the modeling of the problem, or a mistake in the calculations. If the probability estimate given by an argument is dwarfed by the chance that the argument itself is flawed, then the estimate is suspect. We develop this idea formally, explaining how it differs from the related distinctions of model and parameter uncertainty. Using the risk estimates from the Large Hadron Collider as a test case, we show how serious the problem can be when it comes to catastrophic risks and how best to address it.
Genomic selection—the prediction of breeding values using DNA polymorphisms—is a disruptive method that has widely been adopted by animal and plant breeders to increase crop, forest and livestock productivity and ultimately secure food and energy supplies. It improves breeding schemes in different ways, depending on the biology of the species and genotyping and phenotyping constraints. However, both genomic selection and classical phenotypic selection remain difficult to implement because of the high genotyping and phenotyping costs that typically occur when selecting large collections of individuals, particularly in early breeding generations. To specifically address these issues, we propose a new conceptual framework called phenomic selection, which consists of a prediction approach based on low-cost and high-throughput phenotypic descriptors rather than DNA polymorphisms. We applied phenomic selection on two species of economic interest (wheat and poplar) using near-infrared spectroscopy on various tissues. We showed that one could reach accurate predictions in independent environments for developmental and productivity traits and tolerance to disease. We also demonstrated that under realistic scenarios, one could expect much higher genetic gains with phenomic selection than with genomic selection. Our work constitutes a proof of concept and is the first attempt at phenomic selection; it clearly provides new perspectives for the breeding community, as this approach is theoretically applicable to any organism and does not require any genotypic information.
When syphilis first appeared in Europe in 1495, it was an acute and extremely unpleasant disease. After only a few years it was less severe than it once was, and it changed over the next 50 years into a milder, chronic disease. The severe early symptoms may have been the result of the disease being introduced into a new host population without any resistance mechanisms, but the change in virulence is most likely to have happened because of selection favouring milder strains of the pathogen. The symptoms of the virulent early disease were both debilitating and obvious to potential sexual partners of the infected, and strains that caused less obvious or painful symptoms would have enjoyed a higher transmission rate.
Humanity produces data at exponential rates, creating a growing demand for better storage devices. DNA molecules are an attractive medium to store digital information due to their durability and high information density. Recent studies have made large strides in developing DNA storage schemes by exploiting the advent of massive parallel synthesis of DNA oligos and the high throughput of sequencing platforms. However, most of these experiments reported small gaps and errors in the retrieved information. Here, we report a strategy to store and retrieve DNA information that is robust and approaches the theoretical maximum of information that can be stored per nucleotide. The success of our strategy lies in careful adaption of recent developments in coding theory to the domain specific constrains of DNA storage. To test our strategy, we stored an entire computer operating system, a movie, a gift card, and other computer files with a total of 2.14×106 bytes in DNA oligos. We were able to fully retrieve the information without a single error even with a sequencing throughput on the scale of a single tile of an Illumina sequencing flow cell. To further stress our strategy, we created a deep copy of the data by PCR amplifying the oligo pool in a total of nine successive reactions, reflecting one complete path of an exponential process to copy the file 218×1012 times. We perfectly retrieved the original data with only five million reads. Taken together, our approach opens the possibility of highly reliable DNA-based storage that approaches the information capacity of DNA molecules and enables virtually unlimited data retrieval.
In industry, models of the learning or experience curve effect express the relationship between experience producing a good and the efficiency of that production, specifically, efficiency gains that follow investment in the effort. The effect has large implications for costs and market share, which can increase competitive advantage over time.
Ted Chiang is an American science fiction writer. His work has won four Nebula awards, four Hugo awards, the John W. Campbell Award for Best New Writer, and four Locus awards. His short story "Story of Your Life" was the basis of the film Arrival (2016). He is also artist in residence at the University of Notre Dame.
This fantasy short story by Ted Chiang follows Fuwaad ibn Abbas, a fabric merchant in the ancient city of Baghdad. It begins when he is searching for a gift to give a business associate and happens to discover a new shop in the marketplace. The shop owner, who makes and sells a variety of very interesting items, invites Fuwaad into the back workshop to see a mysterious black stone arch which serves as a gateway into the future, which the shop owner has made by the use of alchemy. Fuwaad is intrigued, and the shop owner tells him 3 stories of others who have traveled through the gate to meet and have conversation with their future selves. When Fuwaad learns that the shop keeper has another gate in Cairo that will allow people to travel even into the past, he makes the journey there to try to rectify a mistake he made 20 years earlier. [Summary adapted from Wikipedia]
Ringing Bell is a 1978 Japanese anime adventure-drama short film adaption of the storybook of the same name written by Takashi Yanase, the creator of Anpanman. It is most notable by fans and critics as a family film which makes a sharp sudden turn into a dark and violent story that criticizes and reflects upon the theme of revenge and war. It is also recognized as one of the only Japanese shock films directed towards children and families.
Soul Eater is a Japanese manga series written and illustrated by Atsushi Ōkubo. Set at the "Death Weapon Meister Academy", the series revolves around three teams, each consisting of a weapon meister and weapon that can transform into a humanoid. Trying to make the latter a "death scythe" and thus fit for use by the academy's headmaster Shinigami, the personification of death, they must collect the souls of 99 evil humans and one witch, in that order; otherwise, they will have to start all over again.
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.
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.)
I review John Bradshaw’s book on cat psychology, Cat Sense, after difficulties dealing with my own cat. Bradshaw reviews the history of domestic cats from their apparent Middle Eastern origins as a small solitary desert predator to their domestication in Ancient Egypt where breeding millions of cats for sacrifice may have played a critical role (as opposed to any unique role as a vermin exterminator) through to the modern day and psychological studies of the learning abilities and personalities of cats, with particular emphasis on cat social skills in “cat colonies” & plasticity in kittenhood. As Bradshaw diagnoses it, these are responsible for what ability they have to modern pet life, even though they are not bred for this like dogs; every tame cat still has the feral cat in them, and are in many ways unsuited for contemporary living, with disturbing hints that human lack of selective breeding plus recent large-scale spay/neuter population control efforts may be producing a subtle dysgenic effect on domestication, and this double neglect & backfire may be responsible for disturbingly high rates of cat maladaptation & chronic stress diseases.
Sociology is the study of human behavior. Sociology refers to social behavior, society, patterns of social relationships, social interaction, and culture that surrounds everyday life. It is a social science that uses various methods of empirical investigation and critical analysis to develop a body of knowledge about social order and social change. Sociology can also be defined as the general science of society. While some sociologists conduct research that may be applied directly to social policy and welfare, others focus primarily on refining the theoretical understanding of social processes. Subject matter can range from micro-level analyses of society to macro-level analyses.
Welfare is a type of government support intended to ensure that members of a society can meet basic human needs such as food and shelter. Social security may either be synonymous with welfare, or refer specifically to social insurance programs, which provide support only to those who have previously contributed, as opposed to social assistance programs, which provide support on the basis of need alone. The International Labour Organization defines social security as covering support for those in old age, support for the maintenance of children, medical treatment, parental and sick leave, unemployment and disability benefits, and support for sufferers of occupational injury.
Long-standing problems in standard scientific methodology have exploded as the “Replication Crisis”: the discovery that many results in fields as diverse as psychology, economics, medicine, biology, and sociology are in fact false or quantitatively highly inaccurately measured. I cover here a handful of the issues and publications on this large, important, and rapidly developing topic up to about 2013, at which point the Replication Crisis became too large a topic to cover more than cursorily.
The crisis is caused by methods & publishing procedures which interpret random noise as important results, far too small datasets, selective analysis by an analyst trying to reach expected/desired results, publication bias, poor implementation of existing best-practices, nontrivial levels of research fraud, software errors, philosophical beliefs among researchers that false positives are acceptable, neglect of known confounding like genetics, and skewed incentives (financial & professional) to publish ‘hot’ results.
Thus, any individual piece of research typically establishes little. Scientific validation comes not from small p-values, but from discovering a regular feature of the world which disinterested third parties can discover with straightforward research done independently on new data with new procedures—replication.
The replication crisis is, as of 2020, an ongoing methodological crisis in which it has been found that many scientific studies are difficult or impossible to replicate or reproduce. The replication crisis affects the social sciences and medicine most severely. The crisis has long-standing roots; the phrase was coined in the early 2010s as part of a growing awareness of the problem. The replication crisis represents an important body of research in the field of metascience.