I recently switched from an older generic theme to twenty sixteen and am having problems with featured images. Even though the originals are reasonably large (>1000px typically), they show up instead as if they're thumbnail sized.
Removing the featured image and then attempting to add it back doesn't seem to resolve the problem either -- I still get the small thumbnail-sized photo. Similarly, changing it to a new/different photo seems to work fine and it's featured at the correct size, but then changing it in the interface back to the original photo has it ouput again as a thumbnail?!
I've gone back and re-added photos manually which definitely fixes issue, but would prefer not to have to do this for hundreds of posts. Has anyone else moving to this theme had this issue?
As an example, take a peek at: http:/
The output code for the incorrect featured image appears to be:
<code><div class="post-thumbnail"></code>
<pre><img width="120" height="80" src="http://i1.wp.com/boffosocko.com/wp-content/uploads/2015/05/20150509090325.png?fit=120%2C80" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="Firmness of Science vs. # of Schools of Thought" sizes="(max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 984px) 60vw, (max-width: 1362px) 62vw, 840px"> </div></pre>
Why would this happen with old (prior to the theme change) featured photos but not newly uploaded photos?
17w5131: Statistical & Computational Challenges in Large Scale Molecular Biology Workshop @BIRS_Math 3/2017 #ITBio
Arriving in Banff, Alberta Sunday, March 26 and departing Friday March 31, 2017
Organizers
- Barbara Engelhardt (Princeton University)
- Anna Goldenberg (University of Toronto)
- Manolis Kellis (Massachusetts Institute of Technology)
- Jacob Laurent (Centre national de la recherche scientifique)
- Jeff Leek (John Hopkins University)
- Stephen Montgomery (Stanford University)
Objectives
Over the past few years, an increasing number of large scale data sets have been made available in molecular biology. GTEx, for example, produced more than 18,000 RNA-Seq assays for multiple tissues in 900 individuals, Mindact generated gene expression data from about 7000 breast tumors in a single study, and 23andMe claims to have sequenced about 900,000 genomes. This growth in the available genomic data is expected to increase our capacity to identify cancer subtypes, regulatory genes, SNPs associated with phenotypes of interest, and biomarkers for many human traits. It also suggests exploring more complex feature representations when analyzing these datasets.
However, increasing the number of samples and features leads to a set of \textbf{interrelated statistical and computational problems}. Accordingly, the objectives of our workshop will be to:
Systematically identify the statistical and computational
problems arising during the analysis of large scale data in molecular biology;
Bring together experts in computational biology, molecular
biology, computer science, and statistics to propose innovative solutions to these problems, by leveraging recent advances in each of these fields.
Relevance, importance and timeliness
A number of studies generating high throughput molecular data for a large number of biological samples have been completed over the past five years. \textbf{Our workshop is important because the availability of these datasets holds great promises in terms of health improvement and understanding of molecular biology}. First of all, if exploited correctly, larger sample sizes should improve our ability to predict phenotypes of interest from molecular data. This entails very important applications such as improving the survival of cancer patients by better predicting which treatment they should receive, or decreasing bacterial resistances by predicting which antibiotic is efficient against a new strain. Correctly exploiting large scale datasets should also allow us to \textbf{better identify genetic and epigenetic determinants of these phenotypes, yielding a better understanding of human diseases and potentially guiding the development of new treatments and prevention policies}. In particular, more samples should allow the detection of less frequent variants in the human genome, or more complex features involving several modalities (copy number, expression, methylation, etc) associated with diseases. Finally, larger sample sizes should help with essential unsupervised tasks such as the \textbf{inference of regulation networks, or the identification of cancer subtypes}.
Our workshop is relevant because \textbf{all of these promises are conditioned on our solving of new statistical and computational challenges}. First (Challenge 1), we need to build new feature spaces and estimators whose complexity is adapted to these larger sample sizes, which involves designing novel, potentially more complex descriptors of the samples but still controlling the bias/variance trade-off. Second (Challenge 2), we need to build models which correctly integrate different modalities, such as copy number variation and gene expression. Third (Challenge 3), larger scale studies are more prone to unwanted variations, because they typically involve different labs and technical changes which can affect the measurements and become confounders in retrospective analyses. Similar or worse problems arise when trying to combine several existing datasets. We need methods which take this unwanted variation into account. Finally, (Challenge 4), we need new algorithms that make existing statistical tools scalable to the new sample sizes, and make estimation over the larger and more complex features of Challenge 1 tractable.
We also believe our workshop is very timely because \textbf{some of these statistical and computational challenges are starting to be addressed in other application fields} of statistics. It is crucial to recognize that the orders of magnitude are still very different in molecular biology and other data science application fields because of the cost and complexity of the data generation process: current large scale high throughput sequencing data sets typically contain a few thousand of samples but millions of features while computer vision, web, or astronomy datasets can involve billions or trillions of samples and relatively fewer features. A first consequence is that not all recent developments in machine learning are immediately transferable to computational biology. For example, so called deep learning methods have gained a lot of popularity and now represent the state of the art in computer vision but may not be the most appropriate tool for prediction of cancer outcome from molecular data. However, the fact that other fields already have much larger sample sizes also means that they had to develop efficient and scalable algorithms for basic tasks like feature selection, classification or clustering. \textbf{These recent developments are a great source of inspiration for computational biology, where large scale computation is still an emerging challenge}.
We believe \textbf{having a small scale workshop involving international experts in machine learning, statistics, computational biology and molecular biology is of utmost importance} for three main reasons. The first reason is that the technical advances we are referring to are very recent, often unknown to computational biologists and involve paradigms such as online optimization, accelerated gradient methods and network flow optimization, with which they are sometimes unfamiliar. The second reason is that it is not always obvious to non-statisticians which novel methods are appropriate given the current n/p regime. Conversely, the third reason is that statisticians do not know what the recent challenges are in molecular biology. Having them work on abstract versions of the problems is often not satisfactory as it is necessary to be aware of technical realities and of the underlying biology of the problem to come up with useful solutions.
16w5029: Quantum Computer Science Workshop coming up April 17 @BIRS_Math http:/
Brief reply to: Is majoring in liberal arts a mistake for students? https:/
What magisterial sounding pontification! Sadly, it’s not much different than the early philosophies of Socrates and Plato or many of the other early progenitors of the humanities and liberal arts. I get the impression that the author hasn’t read much philosophy and has not much grounding in the liberal arts. While I agree with the spirit in which the piece is written, I find it deplorable that there aren’t what should be obligatory mentions of words like trivium, quadrivium, or philosophy, but rather the corpus of work in which the author seems steeped is that of only modern day authors of popular science (Pinker, Gladwell, Kahneman, et. al.) who have some interesting viewpoints, but ones which require at least a grounding in the liberal arts to pick apart. Several times Khosla demeans the liberal arts and uses the repeated example that a reader should be able to pick apart and think critically about articles in The Economist. To do this requires a knowledge of logic and rhetoric which are two of the pillars of what? — yes, the liberal arts!
He also seems unaware of big movements within the humanities and sciences like Bill Gates and David Christian’s Big History Project which are going a long way towards providing a more balanced education in history, economics, physics, chemistry, biology and evolution. I find here, no prima facie evidence of his knowledge of Thomas Kuhn or Karl Popper, which might help win me to his argument. In all, aside from the passing references to one or two recent works, this entire argument is not much different from many that could have been written at the beginning of the industrial revolution. How blind so many must be to seemingly think there’s something new here.
Most appalling to me here is that the author doesn’t seem to give even a passing nod or small wink to C.P. Snow or “The Two Cultures” [http:/
Yes, we certainly need more emphasis on the quadrivium portion of the liberal arts, and in particular mathematics and critical thinking which seem to have been left by the wayside. It is deplorable that the highest extent of mathematics that 99% of college students are exposed to terminates in the 17th century for the most part. Sadly, many college students are left without the ability to think critically and deeply, not to mention the hordes of students in America who barely make it through high school and don’t attend college. One also only needs to skim through recent issues of Nature [http:/
Yes, we need far, far more, but alas, this poor article only touches the tip of the issue and it sadly only does so with less than half of the picture.
DNA Study of First Ancient African Genome Flawed, Researchers Report http:/
Bedtime problems boost kids’ math performance | Science | AAAS http:/
Amir Aczel, Author of Scientific Cliffhanger, Dies at 65 http:/
The advantage of simple paper abstracts http:/
My reply to ‘Novel, amazing, innovative’: positive words on the rise in science papers | Nature News & Comment
http:/
I can readily posit a potential and very strong motivation for the uptick in the appearance of these words, and particularly for the word "novel", which the paper and this article fail to see.
Beginning in the early 90's, and certainly starting before that time, there has been an intensified interest for both research institutions and researchers writing papers for them to monetize their research outside the halls of the academy. (Particularly with decreased funding for faculty and intensified competition for research dollars - many research faculty either self-fund their salaries or rely heavily on 3rd party income.) Toward this end, individual researchers began more aggressively pursuing patents for their research work and at the institutional level, colleges and universities began very actively pursuing technology transfer to the point of opening up full offices and hiring large numbers of staff to better leverage the return of creating patents and pursuing sales and licensing deals for the resulting research work. The US Patent Office, in making a determination of whether or not to grant patents, (and even moreso when the underlying documents are published scientific research articles) uses words like "novel" and "innovative" as an indicator of their worthiness. Without a patent, it's incredibly difficult to protect the intellectual property contained within a published and public work. Other words like "amazing" which are also cited in the paper are geared (in a business sales sense) more toward potential corporate financial investors who may consider purchasing or licensing the resulting work of a research paper. What corporate bean counter wants to invest in a dull-sounding research paper title which is quite likely one of the only parts of the document they're likely to comprehend? Yet throw in some "excitement words" and you may have a sale!
When one looks at employment contracts of the professoriate (post-docs, graduate students, and the like), which presumably comprises the majority of those publishing papers with these words, one will see that professors prior to the 90's have contracts that don't mention intellectual property rights resulting from their efforts, or which grant them the lion's share. Current contracts after that period will almost necessarily keep either all or most of these types of revenue streams in the institution's pockets rather than the researchers themselves. I'd be willing to bet that this tide began turning in the mid-1970s and has certainly been overwhelming since the mid-90s.
It is these severe economic taskmasters which are almost assuredly the cause of the rise of these buzzwords in research papers, and most particularly in their titles. One need only ask themselves, which university wants to be the next proverbial "Stanford" to not have a piece of revenue from the intellectual property of a proverbial "Page rank" algorithm? Or in biomedical research, to not take a hefty cut out of a drug which could potentially cure cancer or HIV/AIDS? Take a gander at what something as simple as a HELA cell, which was taken and cultured without any notice to Henrietta Lacks, has become from an industrial perspective. If they can avoid it, academia certainly won't let multi-million (or billion) dollar enterprises spring up (again) without them receiving a piece of the pie.
Naturally, there are other factors compounding the issues at hand, particularly when one looks at negative research findings, which are sadly rarely ever published, particularly in an era of big data when what we know isn't true will surely help assist in defining the boundaries of what we do know and what could potentially be determined at that boundary. (Much the way that white space in photographs or in printed pages helps to define an image as much if not more so than the darker portions of the image.) Negative words are likely more difficult to get published as it may reflect poorly on researchers who are reliant on continued future funding and may see their sources disappear when they're not producing groundbreaking work. My hypothesis, however, is that it's more likely a direct economic impact based on technology transfer than it is the increased competitive landscape in academic research.
[1406.1391] ";A Vehicle of Symbols and Nothing More."; George Romanes, Theory of Mind, Information, and Samuel Butler
Donald Forsdyke indicates [at https:/
Let's look into this and see where, if at all, there may be a bridge over to either Claude Shannon or Boolean Algebra.
@fadesingh, While I do agree in part with your comment that the technical side of the math could have been explored a bit more, to me, part of what this story is highlighting (and to great benefit as well) is the "other" personal side of mathematics which is rarely seen by the broader public. Most of math and math history is full of seemingly brilliant solo (read: lone wolf) researchers developing mathematics de novo in dark, smoke and caffeine-filled rooms and emerging with iron clad proofs. This particular story shows the growing more collaborative and friendly side of math in addition to the years of slow development of friendships and theory which have culminated into something potentially interesting. I would suggest that in this case you not take them too hard to task on the subject, particularly as the article was being written contemporaneously with the publication of the journal article itself. (The article here was published <i>just</i> in advance of the arXiv post, such that it didn't even include the link to the paper itself, though it was added the following day.)
I love that Quanta is continually exploring the areas of math and science at the depth and level which they've become accustomed. They're filling a very important gap in science communication between technical journal articles and somewhat sophisticated outlets like National Geographic, Scientific American, and Wired (in which they are also distributed, but yet are still an editorial cut above comparatively. [Cross reference: <a href="http://stream.boffosocko.com/2015/evolution-of-a-scientific-journal-article-title-from-nature-to">Evolution of a Scientific Journal Article Title (from Nature to TMZ)</a>] I'm sure that C.P. Snow himself would praise them for helping to close the gap between the "Two Cultures."
While I'm positive that stories about math at the level of sophistication described here require some technical underpinning which the writer and editors may potentially lack and which therefore make it both more difficult to be aware of them (to know to report on them in the first place) but also to objectively lay out the topic, from a journalistic perspective, this particular story, which references prior work by Benedict Gross, should have at least mentioned, as a caveat, the relationship between Quanta as a publisher and Gross's position as an advisory board member to it.
While Gross's bona fides are certainly not in question given the topic or his distinguished career, what, if any, was his involvement with the piece? Did he suggest it? Did he provide background? (I notice he wasn't directly quoted, which makes me wonder even further). Was he paid? There's certainly the possibility that something potentially not quite kosher may be going on here (even if it is just free publicity for him and his past work, particularly given that the final result discussed here hasn't been published or independently verified), and not providing at least a mention of the relationship from an editorial standpoint is a glaring error.
Aside from this small journalistic oversight, I otherwise laud Quanta for consistently providing interesting, entertaining, and timely coverage of the world of math and science in such a fantastic fashion. Helping to humanize and even idolize mathematicians and scientists should be a more common effort in our society.
Given the somewhat broad nature of the readership and range of backgrounds, for those who may not have the high level of background for reading the related papers (once they're publicly available) yet, many may be interested in knowing that Dr. Gross has an excellent and free online series of lectures on basic abstract algebra available (http:/
Cross posted as: https:/