Compared to DSLR cameras, smartphone cameras have smaller sensors, which limits their spatial resolution; smaller apertures, which limits their light gathering ability; and smaller pixels, which reduces their signal-to-noise ratio. The use of color filter arrays (CFAs) requires demosaicing, which further degrades resolution. In this paper, we supplant the use of traditional demosaicing in single-frame and burst photography pipelines with a multi-frame super-resolution algorithm that creates a complete RGB image directly from a burst of CFA raw images. We harness natural hand tremor, typical in handheld photography, to acquire a burst of raw frames with small offsets. These frames are then aligned and merged to form a single image with red, green, and blue values at every pixel site. This approach, which includes no explicit demosaicing step, serves to both increase image resolution and boost signal to noise ratio.

Further indications that mobile photography is disrupting the digital imaging industry thanks to a tighter integration of hardware and software.

It’ll be some time before computational notebooks replace PDFs in scientific journals, because that would mean changing the incentive structure of science itself. Until journals require scientists to submit notebooks, and until sharing your work and your data becomes the way to earn prestige, or funding, people will likely just keep doing what they’re doing.

It is incredibly depressing that we live in a world where scientific knowledge is still shared mostly by means of PDF documents, but the title of this article is misleading at best. The future of science communication will not be built on yet another proprietary document format. On the other hand, the Web platform has all the technical capabilities needed to create any sort of “computational” papers, but it still lacks appropriate authoring tools to empower scientists to do it by themselves.
The reports of the scientific paper’s death have been (unfortunately) greatly exaggerated.

Complexity bias is a logical fallacy that leads us to give undue credence to complex concepts.
Faced with two competing hypotheses, we are likely to choose the most complex one. That’s usually the option with the most assumptions and regressions. As a result, when we need to solve a problem, we may ignore simple solutions — thinking “that will never work” — and instead favor complex ones.
To understand complexity bias, we need first to establish the meaning of three key terms associated with it: complexity, simplicity, and chaos.

Nice piece on the risks of being seduced by unnecessary complexity, especially in the broader context of language. It reminded me of an old essay by Italo Calvino, “L’antilingua”—literally: “the anti-language”—in which he comically shows the effects of replacing simple words with increasingly grotesque jargon. To paraphrase Calvino, the anti-language is the language of people who prefer saying “utilize” instead of “use”, people who are scared of showing familiarity with the subject of their talk. According to him, speaking the anti-language is a sign of being out of touch with life, and ultimately represents the death of language itself.