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gwern branwen
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"GWAS of habitual physical activity in over 277,000 UK Biobank participants identifies novel variants and genetic correlations with chronotype and obesity-related traits", Klimentidis et al 2017 http://www.biorxiv.org/content/early/2017/08/22/179317 "Physical activity (PA) protects against a wide range of diseases. Engagement in habitual PA has been shown to be heritable, motivating the search for specific genetic variants that explain variation in habitual PA and may ultimately improve efforts to promote PA and target the best type of PA for each individual. We used data from the UK Biobank to perform the largest genome-wide association study of PA, using four measures based on self-report (n=277,656) and accelerometry (n=67,808). Replication was then sought in the Atherosclerosis Risk in Communities (ARIC) study (n=8,556). In the UK Biobank, we identified 17 genome-wide loci across the four PA measures. Interestingly, rs429358 of the APOE gene was the most strongly associated variant with any single PA measure and was at least nominally associated with three of the four PA measures examined. We also identified three loci (DNAJC1, DCAF5, and PML) consistently associated with PA across all four measures. Tissue enrichment analyses implicate the brain and pituitary gland as locations where PA-associated loci may exert their actions. Genetic correlation analyses suggest a positive genetic correlation of PA with early-morning chronotype and psychiatric traits, and a negative genetic correlation of PA with obesity-related traits. Using data from the GIANT consortium, we identify several loci that are associated with both increased waist circumference and decreased PA. Although very small effect sizes precluded replication of individual loci in ARIC, we found consistent overall genetic correlations of PA with other traits. These results provide new insight into the genetic basis of habitual PA, and the genetic links connecting PA and obesity."

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"The genetic basis of human brain structure and function: 1,262 genome-wide associations found from 3,144 GWAS of multimodal brain imaging phenotypes from 9,707 UK Biobank participants", Elliott et al 2017: http://www.biorxiv.org/content/early/2017/08/21/178806

"The genetic basis of brain structure and function is largely unknown. We carried out genome-wide association studies (GWAS) of 3,144 distinct functional and structural brain imaging derived phenotypes (IDPs), using imaging and genetic data from a total of 9,707 participants in UK Biobank. All subjects were imaged on a single scanner, with 6 distinct brain imaging modalities being acquired. We show that most of the IDPs are heritable and we identify patterns of co-heritability within and between IDP sub-classes. We report 1,262 SNP associations with IDPs, based on a discovery sample of 8,426 subjects. Notable significant and interpretable associations include: spatially specific changes in T2* in subcortical regions associated with several genes related to iron transport and storage; spatially extended changes in white matter micro-structure associated with genes coding for proteins of the extracellular matrix and the epidermal growth factor; variations in pontine crossing tract neural organization associated with genes that regulate axon guidance and fasciculation during development; and variations in brain connectivity associated with 14 genes that contribute broadly to brain development, patterning and plasticity. Our results provide new insight into the genetic architecture of the brain with relevance to complex neurological and psychiatric disorders, as well as brain development and aging...Over the next few years the number of UK Biobank participants with imaging data will gradually increase to 100,000, which will allow a much more complete discovery of the genetic basis of human brain structure, function and connectivity. A potential avenue of research will involve attempting to uncover causal pathways that link genetic variants to IDPs and then onto a range of neurological, psychiatric and developmental disorders."

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Topically: "What does a convolutional neural network recognize in the moon?", Shoji 2017:

"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."

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It's amazing this works (is there anything convolutions can't do?): "SMASH: One-Shot Model Architecture Search through HyperNetworks", Brock et al 2017: https://arxiv.org/abs/1708.05344

"Designing architectures for deep neural networks requires expert knowledge and substantial computation time. We propose a technique to accelerate architecture selection by learning an auxiliary HyperNet that generates the weights of a main model conditioned on that model's architecture. By comparing the relative validation performance of networks with HyperNet-generated weights, we can effectively search over a wide range of architectures at the cost of a single training run. To facilitate this search, we develop a flexible mechanism based on memory read-writes that allows us to define a wide range of network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as special cases. We validate our method (SMASH) on CIFAR-10 and CIFAR-100, STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with similarly-sized hand-designed networks. Our code is available at this https URL"

Video: https://www.youtube.com/watch?v=79tmPL9AL48
Code: https://github.com/ajbrock/SMASH
Discussion: https://www.reddit.com/r/MachineLearning/comments/6uesw7/r_smash_oneshot_model_architecture_search_through/

Basically, at each training step, generate the schematics of a random NN architecture; feed the skeleton into the hypernetwork, which will directly spit out numbers for each neuron (as a convolutional hypernetwork it can handle big and small NNs the same way); with the fleshed out NN, train 1 minibatch on the image classification task as usual, and update its parameters; use that update as the 'error' for the hypernetwork to train it to spit out weights for that skeleton which are slightly closer to what it was after 1 minibatch. After training the hypernetwork many times on many random NN architectures, its generated weights will be close to what training each random NN architecture from scratch would have been. Now you can simply generate lots of random NN architectures, fill them in, run them on a small validation set, and see their 'final' performance without ever actually training them fully (which would be like 10,000x more expensive). So this runs on 1 GPU in a day or two versus papers you may remember like Zoph which used 800 GPUs for a few weeks... It's amazing this works, and like synthetic gradients it troubles me a little because it implies that even complex highly sophisticated NNs are in some sense very simple & predictable and incredibly wasteful in both training & parameter size.

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Translation request: Japanese/French/Portuguese versions of my one-question catnip survey would be very helpful. The question:

"Have you ever given catnip to your cat?

- No: I do not have a cat
- No: I have a cat but have not tried catnip
- Yes: but they did not respond to catnip
- Yes: they responded to catnip"

I am surveying hundreds of cat owners internationally using Google Surveys to try to estimate how common catnip responses are, and how it differs between countries (probably genetically). The results are already interesting as response rates range from as low as 19% (Canada) to as high as 50% (Spain/Mexico). I would particularly appreciate a Japanese translation since the research literature suggests catnip response may be highly unusually common in Japanese cats, but French & Portuguese translations would also be helpful - a French translation, for example, would help me see if France's cats are intermediate the UK's relatively common catnip response rate and Spain's relatively rare catnip response rate.

Alternately, does anyone know where I could buy a translation? I don't want to risk using Google Translate because I've already discovered a serious error in the German translation and I want it checked for comprehensibility by native (or at least fluent) speakers, otherwise the money will be wasted or the data misleading.

(Catnip: https://ja.wikipedia.org/wiki/%E3%82%A4%E3%83%8C%E3%83%8F%E3%83%83%E3%82%AB https://fr.wikipedia.org/wiki/Cataire https://fr.wikipedia.org/wiki/Cataire )

Also asked on Twitter: https://twitter.com/gwern/status/894378155716345856

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Speaking of subjecting many thousands of American children annually to useless human experimentation and unproven treatments of dubious efficacy...

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>35000 children subjected to useless human experimentation annually in the USA: "Discontinuation and Nonpublication of Randomized Clinical Trials Conducted in Children", Pica et al 2016: https://www.dropbox.com/s/dxz42icqvkbhvbb/2016-pica.pdf?dl=0

"BACKGROUND: Trial discontinuation and nonpublication represent potential waste in research resources and lead to compromises in medical evidence. Pediatric trials may be particularly vulnerable to these outcomes given the challenges encountered in conducting trials in children. We aimed to determine the prevalence of discontinuation and nonpublication of randomized clinical trials (RCTs) conducted in pediatric populations.
METHODS: Retrospective, cross-sectional study of pediatric RCTs registered in ClinicalTrials. gov from 2008 to 2010. Data were collected from the registry and associated publications identified (final search on September 1, 2015).
RESULTS: Of 559 trials, 104 (19%) were discontinued early, accounting for an estimated 8369 pediatric participants. Difficulty with patient accrual (37%) was the most commonly cited reason for discontinuation. Trials were less likely to be discontinued if they were funded by industry compared with academic institutions (odds ratio [OR] 0.46, 95% confidence interval [CI] 0.27–0.77). Of the 455 completed trials, 136 (30%) were not published, representing 69 165 pediatric participants [35,000/year]. Forty-two unpublished trials posted results on ClinicalTrials.gov. Trials funded by industry were more than twice as likely to result in nonpublication at 24 and 36 months (OR 2.21, 95% CI 1.35–3.64; OR 3.12, 95% CI 1.6–6.08, respectively) and had a longer mean time to publication compared with trials sponsored by academia (33 vs 24 months, P < .001).
CONCLUSIONS: In this sample of pediatric RCTs, discontinuation and nonpublication were common, with thousands of children exposed to interventions that did not lead to informative or published findings. Trial funding source was an important determinant of these outcomes, with both academic and industry sponsors contributing to inefficiencies."

I'm glad the bioethicists and IRBs are tackling the real problems, like what happens to leftover embryos or whether genetic engineering might insult the disabled or whether enough community meetings have been held & all 'stakeholders' properly informed.

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"Genes, psychological traits and civic engagement", Dawes et al 2015 http://rstb.royalsocietypublishing.org/content/370/1683/20150015

"Civic engagement is a classic example of a collective action problem: while civic participation improves life in the community as a whole, it is individually costly and thus there is an incentive to free ride on the actions of others. Yet, we observe significant inter-individual variation in the degree to which people are in fact civically engaged. Early accounts reconciling the theoretical prediction with empirical reality focused either on variation in individuals’ material resources or their attitudes, but recent work has turned to genetic differences between individuals. We show an underlying genetic contribution to an index of civic engagement (0.41), as well as for the individual acts of engagement of volunteering for community or public service activities (0.33), regularly contributing to charitable causes (0.28) and voting in elections (0.27). There are closer genetic relationships between donating and the other two activities; volunteering and voting are not genetically correlated. Further, we show that most of the correlation between civic engagement and both positive emotionality and verbal IQ can be attributed to genes that affect both traits. These results enrich our understanding of the way in which genetic variation may influence the wide range of collective action problems that individuals face in modern community life."

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The wind is rising.
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