Social psychology -- Australia.

Climatic changes -- Effect of human beings on -- Religious aspects -- Christianity.

Menopause.

Young people across the globe marched, protested, and signaled calls to action today in the name of climate change. Here's what it looked like.

Paris : Rueff, 1896.

Paris : G. Steinheil, 1891.

Paris : Doin, 1896.

Berlin : A. Hirschwald, 1875.

London : C. Griffin, 1899.

London : H.K. Lewis, 1903.

London : Cassell, 1898.

London : Cassell, 1902.

Paris : Pihan Delaforest (Morinval), 1831.

New York : W. Wood, 1887.

Paris : G. Steinheil, 1888.

Paris : O. Doin, 1887.

Paris : Steinheil, 1896.

Paris : Société d’éditions scientifiques, 1896.

Paris : Steinheil, 1896.

Paris : J.-B. Baillière, 1905.

Londres : Paris, 1866.

Paris : G. Steinheil, 1895.

Nice : A.M.J. Canis, 1822.

South Dakota lawmakers have rejected a bill that would have allowed science teachers to discuss strengths and weaknesses of evolutionary and climate change theories.

[London?]

The justices declined to hear the appeal of the Ferguson-Florissant district over its at-large board elections, which lower courts invalidated as violating the Voting Rights Act.

Rockville, Maryland : National Institute on Drug Abuse, 1975.

Rockville, Maryland : National Institute on Drug Abuse, 1981.

Rockville, Maryland : National Institute on Drug Abuse, 1985.

Rockville, Maryland : National Institute on Drug Abuse, 1988.

Rockville, Maryland : National Institute on Drug Abuse, 1986.

Rockville, Maryland : National Institute on Drug Abuse, 1979.

Rockville, Maryland : National Institute on Drug Abuse, 1980.

Oleksandr Sverdlov, Weng Kee Wong, Yevgen Ryeznik.

Source: Statistics Surveys, Volume 8, 2--44.

Abstract:
Adaptive clinical trials are becoming increasingly popular research designs for clinical investigation. Adaptive designs are particularly useful in phase I cancer studies where clinical data are scant and the goals are to assess the drug dose-toxicity profile and to determine the maximum tolerated dose while minimizing the number of study patients treated at suboptimal dose levels. In the current work we give an overview of adaptive design methods for phase I cancer trials. We find that modern statistical literature is replete with novel adaptive designs that have clearly defined objectives and established statistical properties, and are shown to outperform conventional dose finding methods such as the 3+3 design, both in terms of statistical efficiency and in terms of minimizing the number of patients treated at highly toxic or nonefficacious doses. We discuss statistical, logistical, and regulatory aspects of these designs and present some links to non-commercial statistical software for implementing these methods in practice.

Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where the path leading to individual predictions cannot be followed in detail. In order to address this issue, we propose the novel "Cyclic Boosting" machine learning algorithm, which allows to efficiently perform accurate regression and classification tasks while at the same time allowing a detailed understanding of how each individual prediction was made.

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.