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Daily Paper Digest

Track new papers by area, author & keyword, and receive a one sentence summary for each paper on a daily basis.

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Every day, we crawl new papers published on major academic paper websites (like arxiv, medrxiv) as well as hundreds of conferences/journals, and then generate a one sentence summary for each paper to capture the paper highlight. We are eager to share the results with our subscribers on a daily basis!

Sign up for the Daily Paper Digest to get the daily paper update straight to your inbox. You can also choose not to receive emails and instead read the daily updates online.

Our daily paper digest service started in May, 2018, and has been very well received in the community. Currently, more than 200 areas under the following subjects are tracked: BiologyComputer ScienceEE and System Science, Finance, Health ScienceMathPhysics,  and Statistics.

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Daily digest will be sent to your email daily and can also be accessed online.

Advanced Setting: Filter by Author and Keyword


Our default setting is to send you all papers under the categories you signed up with. Many users can browse hundreds of papers everyday using the one-sentence highlight.


If you do not have time to browse all papers, you can either filter papers using keywords on our console, or update the tracking list by including some authors and keywords.


When time is limited, users can choose to read the papers that can pass the filters. Such papers are marked with * in email and put in a separate list (called tracking results) online.


John Smith signed up with 6 categories: cs.AI; cs.CL; cs.CV; cs.IR; cs.LG; cs.SI, and receive >100 papers under them. He also added two authors and two keywords to tracking list: Michael I. Jordan ; Huan Liu ; multi-task learning ; reinforcement learning’ and receive only ~10 papers in the tracking results.


Machine Learning
Computer Vision
Language Processing
Infectious Diseases

Daily Digest Samples

After signing up, you will receive daily update emails, and here are some samples:

1, TITLE: MUSE unravels the ionisation and origin of metal enriched absorbers in the gas halo of a z = 2.92 radio galaxy
AUTHORS: S. Kolwa ; J. Vernet ; C. De Breuck ; M. Villar-Martin ; A. Humphrey ; F. Arrigoni-Battaia ; B. Gullberg ; T. Falkendal ; G. Drouart ; M. Lehnert ; D. Wylezalek ; A. Man
COMMENTS: 21 pages, 20 figures, accepted for publication in A&A
HIGHLIGHT: To do this, we obtain photoionisation grid models in \pkg{cloudy} which show that AGN radiation is capable of ionising the gas and producing the observed column densities in a gas of metallicity of Z/Z$_\odot \simeq$ 0.01 with a nitrogen abundance a factor of 10 greater than that of hydrogen.
2, TITLE: The stellar halo of the Milky Way traced by blue horizontal-branch stars in the Subaru Hyper Suprime-Cam Survey
AUTHORS: Tetsuya Fukushima ; Masashi Chiba ; Mikito Tanaka ; Kohei Hayashi ; Daisuke Homma ; Sakurako Okamoto ; Yutaka Komiyama ; Masayuki Tanaka ; Nobuo Arimoto ; Tadafumi Matsuno
COMMENTS: 15 pages, 10 figures, 3 tables, accepted for publication in PASJ
HIGHLIGHT: We report on the global structure of the Milky Way (MW) stellar halo up to its outer boundary based on the analysis of blue-horizontal branch stars (BHBs).
53, TITLE: Evidence for the third stellar population in the Milky Way’s disk
AUTHORS: Daniela Carollo ; Masashi Chiba ; Miho Ishigaki ; Ken Freeman ; Chiara Battistini ; Tim Beers ; Patricia Tissera ; Francesca Primas ; Y. S. Lee
COMMENTS: 23 pages, 5 figures, submitted to Nature on April 9, 2019
HIGHLIGHT: In this article, we analyze the kinematics, spatial distribution, and chemistry of a large number of stars in the Solar Neighborhood, where all of the main Galactic components are well-represented.
Digests on Physics
1, TITLE: Approximation in $L^p(μ)$ with deep ReLU neural networks
AUTHORS: Felix Voigtlaender ; Philipp Petersen
COMMENTS: Accepted for presentation at SampTA 2019
HIGHLIGHT: After reviewing these findings, we show that the results concerning networks with fixed depth— which up to now only consider approximation in $L^p(\lambda)$ for the Lebesgue measure $\lambda$— can be generalized to approximation in $L^p(\mu)$, for any finite Borel measure $\mu$.
2, TITLE: On the approximation of the solution of partial differential equations by artificial neural networks trained by a multilevel Levenberg-Marquardt method
AUTHORS: Henri Calandra ; Serge Gratton ; Elisa Riccietti ; Xavier Vasseur
HIGHLIGHT: This paper is concerned with the approximation of the solution of partial differential equations by means of artificial neural networks.
107, TITLE: On the Adaptivity of Stochastic Gradient-Based Optimization
AUTHORS: Lihua Lei ; Michael I. Jordan
COMMENTS: 41 pages
HIGHLIGHT: We present the stochastically controlled stochastic gradient (SCSG) method for composite convex finite-sum optimization problems and show that SCSG is adaptive to both strong convexity and target accuracy.
Digests on Artificial Intelligence

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