München [Munich] : Fr. Hanfstaengl, [between 1870 and 1879?]

London : Published by J.B. Virtue & Co. Ltd. : Jean Boussod Manzi Joyant & Co., [1897]

London (6 Seymour Place, Euston Square) : Charles Heath ; [London] (Poultry) : Robert Jennings, May 15 1827 ([London] : Printed by McQueen)

[London] (Haymarket) : Published ... for the proprietors, by T McLean, Septr. 1. 1836 ([London?] : Printed by Lahee & Co.)

[Paris] : [publisher not identified], 1782.

Education Week catches you up on the week gone by with a thoughtful look at recent news in K-12 education.

A collection of short news stories from the last week.

There have been so many great hockey images taken by photographers. We choose our top three shots ranging from Stanley Cup celebations to iconic moments.

Melissa Tebbenkamp, the director of instructional technology for the Raytown Quality Schools near Kansas City, says her district's biggest cybersecurity risk is "ourselves." She outlines what it takes to teach educators how to help protect schools and districts against cyberattacks.

[United Kingdom] : Dame Vera Lynn Children's Charity, 2018.

2019

Calcutta, India : Cleanair, Campaign for Smoke-free Environment, [198-?]

Rockville, Maryland : National Institute on Drug Abuse, 1976.

[London] : [publisher not identified], [2019]

[London] : [publisher not identified], [2019]

[Rockville, Md.?] : U.S. Dept. of Health and Human Services, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, National Institute on Drug Abuse, 1983.

[Dorchester] : Dorset Community NHS Trust, 1997.

Watch the debut of "Our Stories - Natalie Chou" on Sunday, May 10 at 12:30 p.m. PT/ 1:30 p.m. MT on Pac-12 Network.

Over the past decades, numerous loss functions have been been proposed for a variety of supervised learning tasks, including regression, classification, ranking, and more generally structured prediction. Understanding the core principles and theoretical properties underpinning these losses is key to choose the right loss for the right problem, as well as to create new losses which combine their strengths. In this paper, we introduce Fenchel-Young losses, a generic way to construct a convex loss function for a regularized prediction function. We provide an in-depth study of their properties in a very broad setting, covering all the aforementioned supervised learning tasks, and revealing new connections between sparsity, generalized entropies, and separation margins. We show that Fenchel-Young losses unify many well-known loss functions and allow to create useful new ones easily. Finally, we derive efficient predictive and training algorithms, making Fenchel-Young losses appealing both in theory and practice.

In Canada, financial advisors and dealers by provincial securities commissions, and those self-regulatory organizations charged with direct regulation over investment dealers and mutual fund dealers, respectively to collect and maintain Know Your Client (KYC) information, such as their age or risk tolerance, for investor accounts. With this information, investors, under their advisor's guidance, make decisions on their investments which are presumed to be beneficial to their investment goals. Our unique dataset is provided by a financial investment dealer with over 50,000 accounts for over 23,000 clients. We use a modified behavioural finance recency, frequency, monetary model for engineering features that quantify investor behaviours, and machine learning clustering algorithms to find groups of investors that behave similarly. We show that the KYC information collected does not explain client behaviours, whereas trade and transaction frequency and volume are most informative. We believe the results shown herein encourage financial regulators and advisors to use more advanced metrics to better understand and predict investor behaviours.

For millions of Americans, working at home isn't an option. NBC News identified seven occupations in which employees are at especially high risk of COVID-19.

Delta said it is making the move to protect employees amid the coronavirus pandemic, but planes have been flying near empty

Susan Gruber, Mark J. van der Laan.

Source: Statistical Science, Volume 34, Number 1, 82--85.

Abstract:
Dorie and co-authors (DHSSC) are to be congratulated for initiating the ACIC Data Challenge. Their project engaged the community and accelerated research by providing a level playing field for comparing the performance of a priori specified algorithms. DHSSC identified themes concerning characteristics of the DGP, properties of the estimators, and inference. We discuss these themes in the context of targeted learning.

London (33 Stillness Rd, London, SE23 1NG) : Cleanair, Campaign for a Smoke-free Environment, [198-?]

London (33 Stllness Rd, London, SE23 1NG) : Cleanair, Campaign for a Smoke-free Environment, [198-?]

London (33 Stillness Rd, London, SE23 1NG) : Cleanair, Campaign for a Smoke-free Environment, [198-?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

London (33 Stillness Rd, London, SE23 1NG) : Cleanair, Campaign for a Smoke-free Environment, [198-?]

London : Cleanair, [1988?]

London : Cleanair, [1988?]

London (33 Stillness Rd, London, SE23 1NG) : Cleanair, Campaign for a Smoke-free Environment, [198-?]

London : Cleanair, Smoke-free Environment (33 Stillness Rd, London, SE23 1NG), [198-?]

[London?], [199-?]