[Place of publication not identified] : [publisher not identified], [18--?]

[Place of publication not identified] : [publisher not identified], [18--?]

Stuttgart : Franz Steiner Verlag, 2019.

Halle (Saale) : Deutsche Akademie der Naturforscher Leopoldina - Nationale Akademie der Wissenschaften ; Stuttgart : Wissenschaftliche Verlagsgesellschaft, 2019.

São Paulo : Governo do Estado de Sao Paulo, Secretaria da Segurança Pública, 1975.

London : Reproductive Health Matters, 1993-2018.

Arctic sea ice extent (SIE) in September 2019 ranked second-to-lowest in history and is trending downward. The understanding of how internal variability amplifies the effects of external $ ext{CO}_2$ forcing is still limited. We propose the VARCTIC, which is a Vector Autoregression (VAR) designed to capture and extrapolate Arctic feedback loops. VARs are dynamic simultaneous systems of equations, routinely estimated to predict and understand the interactions of multiple macroeconomic time series. Hence, the VARCTIC is a parsimonious compromise between fullblown climate models and purely statistical approaches that usually offer little explanation of the underlying mechanism. Our "business as usual" completely unconditional forecast has SIE hitting 0 in September by the 2060s. Impulse response functions reveal that anthropogenic $ ext{CO}_2$ emission shocks have a permanent effect on SIE - a property shared by no other shock. Further, we find Albedo- and Thickness-based feedbacks to be the main amplification channels through which $ ext{CO}_2$ anomalies impact SIE in the short/medium run. Conditional forecast analyses reveal that the future path of SIE crucially depends on the evolution of $ ext{CO}_2$ emissions, with outcomes ranging from recovering SIE to it reaching 0 in the 2050s. Finally, Albedo and Thickness feedbacks are shown to play an important role in accelerating the speed at which predicted SIE is heading towards 0.

Application and use of deep learning algorithms for different healthcare applications is gaining interest at a steady pace. However, use of such algorithms can prove to be challenging as they require large amounts of training data that capture different possible variations. This makes it difficult to use them in a clinical setting since in most health applications researchers often have to work with limited data. Less data can cause the deep learning model to over-fit. In this paper, we ask how can we use data from a different environment, different use-case, with widely differing data distributions. We exemplify this use case by using single-sensor accelerometer data from healthy subjects performing activities of daily living - ADLs (source dataset), to extract features relevant to multi-sensor accelerometer gait data (target dataset) for Parkinson's disease classification. We train the pre-trained model using the source dataset and use it as a feature extractor. We show that the features extracted for the target dataset can be used to train an effective classification model. Our pre-trained source model consists of a convolutional autoencoder, and the target classification model is a simple multi-layer perceptron model. We explore two different pre-trained source models, trained using different activity groups, and analyze the influence the choice of pre-trained model has over the task of Parkinson's disease classification.

Monte Carlo simulation is useful to compute or estimate expected functionals of random elements if those random samples are possible to be generated from the true distribution. However, when the distribution has some unknown parameters, the samples must be generated from an estimated distribution with the parameters replaced by some estimators, which causes a statistical error in Monte Carlo estimation. This paper considers such a statistical error and investigates the asymptotic distributions of Monte Carlo-based estimators when the random elements are not only the real valued, but also functional valued random variables. We also investigate expected functionals for semimartingales in details. The consideration indicates that the Monte Carlo estimation can get worse when a semimartingale has a jump part with unremovable unknown parameters.

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.

Boosting is one of the most successful ideas in machine learning. The most well-accepted explanations for the low generalization error of boosting algorithms such as AdaBoost stem from margin theory. The study of margins in the context of boosting algorithms was initiated by Schapire, Freund, Bartlett and Lee (1998) and has inspired numerous boosting algorithms and generalization bounds. To date, the strongest known generalization (upper bound) is the $k$th margin bound of Gao and Zhou (2013). Despite the numerous generalization upper bounds that have been proved over the last two decades, nothing is known about the tightness of these bounds. In this paper, we give the first margin-based lower bounds on the generalization error of boosted classifiers. Our lower bounds nearly match the $k$th margin bound and thus almost settle the generalization performance of boosted classifiers in terms of margins.

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.

Data-driven modelling and computational predictions based on maximum entropy principle (MaxEnt-principle) aim at finding as-simple-as-possible - but not simpler then necessary - models that allow to avoid the data overfitting problem. We derive a multivariate non-parametric and non-stationary formulation of the MaxEnt-principle and show that its solution can be approximated through a numerical maximisation of the sparse constrained optimization problem with regularization. Application of the resulting algorithm to popular financial benchmarks reveals memoryless models allowing for simple and qualitative descriptions of the major stock market indexes data. We compare the obtained MaxEnt-models to the heteroschedastic models from the computational econometrics (GARCH, GARCH-GJR, MS-GARCH, GARCH-PML4) in terms of the model fit, complexity and prediction quality. We compare the resulting model log-likelihoods, the values of the Bayesian Information Criterion, posterior model probabilities, the quality of the data autocorrelation function fits as well as the Value-at-Risk prediction quality. We show that all of the considered seven major financial benchmark time series (DJI, SPX, FTSE, STOXX, SMI, HSI and N225) are better described by conditionally memoryless MaxEnt-models with nonstationary regime-switching than by the common econometric models with finite memory. This analysis also reveals a sparse network of statistically-significant temporal relations for the positive and negative latent variance changes among different markets. The code is provided for open access.

French version of Press release about Pablo Hernández de Cos appointed as Chairman of Basel Committee on Banking Supervision

Spanish translation of the BIS press release about the BIS Quarterly Review, March 2018

Spanish translation of the Press Release on the pre-release of two special chapters of the Annual Economic Report of the BIS, 17 June 2018. Trust is the missing link in today's cryptocurrencies - Cryptocurrencies' model of generating trust limits their potential to replace conventional money, the Bank for International Settlements (BIS) writes in its Annual Economic Report (AER), a new title launched this year.

Spanish translation of press release - the Basel Committee on Banking Supervision is finalising stress-testing principles, reviews ways to stop regulatory arbitrage behaviour, agrees on annual G-SIB list, discusses leverage ratio, crypto-assets, market risk framework and implementation, 20 September 2018.

Spanish version of Press release about Pablo Hernández de Cos appointed as Chairman of Basel Committee on Banking Supervision, 7 March 2019. Pablo Hernández de Cos, nombrado Presidente del Comité de Supervisión Bancaria de Basilea.

To view more press releases, please visit http://www.snl.com/irweblinkx/news.aspx?iid=1024452.

Speech by Dr Agustín Carstens at the celebration of 25 years of Bank of Mexico independence, Mexico City, 22 November 2019.

Eine Bibel-App bietet Tausenden von Kurden Zugang zu Gottes Wort.

Este año en el mes en que se llevó a cabo el mantenimiento anual de Logos Hope en Uruguay, la tripulante Cecilia* de Argentina se unió a un pequeño equipo que sirve en Asia Central. Mientras estuvo allí, pudo asistir a dos iglesias subterráneas que desbordaban de esperanza y fe.

Los obreros de OM en la Península Arábiga usan sus empleos de forma intencional como una fuente oportunidades para desarrollar relaciones y compartir la verdad con sus compañeros de trabajo en las naciones menos alcanzadas.

Dan Deming, KKM Financial

Larry Shover, Efficient Advisors

Bob Iaccino, Path Trading Partners

Bayern Munich has secured Canadian soccer star Alphonso Davies for another two years through June 2025, with club officials and the teenager donning surgical masks to ink the deal during the global pandemic.

Larry Shover, Efficient Advisors

Twenty-five years after meeting Johnny Cash, a Newfoundland musician is trying to track down a photo of the pair.

Terry Roggensack, The Hightower Report

Larry Shover, Efficient Advisors

Effective Friday, April 3, 2020, CME Group will implement changes to CME ClearPort Reporting functionality. CME ClearPort Reporting supports the generation of reports based on CME ClearPort trade activity, and CME Account Management information.

These changes include:

• A new look and feel of the user interface
• Improved performance
• Streamlined search features
• New downloadable report format
  • Generated reports will be exportable in Excel (csv) and Adobe (pdf) format.

These changes arecurrently available for customer testing in New Release.

Please Note: User roles and entitlements will remain unchanged.

Access ClearPort Reports from: CME Customer Center>Reports>ClearPort Reporting

Please contact the following support teams for:

• Production Environment Support:
  CME Global Command Center (GCC) in the U.S. +1 800 438 8616, in Europe at +44 20 7623 4747 or in Asia at +65 6532 5010.
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German translation of the BIS Press Release on the presentation of the Annual Report (25 June 2017)

German translation of the speech delivered by Mr Jaime Caruana, General Manager of the BIS, on the occasion of the Bank's Annual General Meeting, Basel, 25 June 2017.

German translation of the BIS press release about the BIS Quarterly Review, September 2017

German translation of the BIS press release about the BIS Quarterly Review, December 2017

German translation of the BIS press release about the BIS Quarterly Review, March 2018

Scotland's crime levels have dropped by around 25% since lockdown measures were introduced.

Former Swiss internationals including Pascal Zuberbühler helped turn the UEFA European Women's Under-17 Championship semi-finals into a family-orientated festival of football.

Source: Ámbito - A dos meses de la cumbre mundial que este año tendrá lugar en Bonn, el cambio climático volvió a irrumpir con toda su fuerza y la temperatura amenaza con convertir a 2017 en el año más caluroso desde que se tiene registro. En nuestro país, la fatídica serie de inundaciones sobre la cuenca del río Salado dejó miles de hectáreas bajo las aguas en La Pampa, el sur de Córdoba y el oeste de Buenos Aires. "El cambio climático está afectando y afectará el desarrollo de los países y Argentina es una de las economías emergentes más vulnerables", alertaron especialistas del Banco Mundial.

Source: El Periódico - Las políticas climáticas que se centran en la agricultura y los bosques podrían llevar al aumento de los precios de los alimentos, pero reducir la deforestación e incrementar la captura de carbono en la agricultura podría reducir significativamente las emisiones de gases de efecto invernadero, evitando riesgos para la seguridad alimentaria, según un nuevo estudio publicado en 'Environmental Research Letters'.