If your SAS Visual Analytics report contains many sections and objects, you might encounter performance problems when you are editing the report.   A hot fix is planned for this issue.

Airbnb says it is laying off 25% of its workforce as it confronts a steep decline in global travel due to the new coronavirus pandemic. It’s a serious setback for the 12-year-old home-sharing company, which just a few months ago was...

A discussion marking the 25th anniversary of the 1990 Immigration Act, where experts examinine the history of the legislation, how it was accomplished politically, and the stakeholders and issues that were critical to its passage.

Clarke Island is a windswept place in Eastern Bass Strait. It had no residents and could only be accessed by light plane or boat when weather permitted. Having never seen the place, Dion and his family move there in March of 1984. They arrived with few provisions and few possessions to an old farm house in a poor state of repair. To survive they had to learn to live in harmony with the island by forging a semi self-sufficient life style in which they not only grew a garden and kept milking goats, but learned to live off the bounty of the land and sea.

Australia -- History -- 1901-1945.

Popular Medicine in America presents materials from the Library Company of Philadelphia’s extensive collection. The resource documents the history of ‘popular’ medicine in America during the nineteenth century, featuring a wide variety of material that was aimed at the general public rather than medical professionals, and which enabled the ordinary person to treat himself and his family at home using an array of inventive methods and fashionable techniques

British newspapers from the 19th century selected by the British Library's editorial board. Includes both national and regional newspapers. All newspapers are full text and fully searchable. Full runs are available where possible.

Berlin : E.S. Mittler, 1890.

Leipzig : S. Hirzel, 1892.

London : printed for the Council by Spottiswoode, 1903.

London : Layton, 1903.

[Edinburgh], [between 1788 and 1797]

[Edinburgh], [between 1788 and 1797]

A major difficulty of solving continuous POMDPs is to infer the multi-modal distribution of the unobserved true states and to make the planning algorithm dependent on the perceived uncertainty. We cast POMDP filtering and planning problems as two closely related Sequential Monte Carlo (SMC) processes, one over the real states and the other over the future optimal trajectories, and combine the merits of these two parts in a new model named the DualSMC network. In particular, we first introduce an adversarial particle filter that leverages the adversarial relationship between its internal components. Based on the filtering results, we then propose a planning algorithm that extends the previous SMC planning approach [Piche et al., 2018] to continuous POMDPs with an uncertainty-dependent policy. Crucially, not only can DualSMC handle complex observations such as image input but also it remains highly interpretable. It is shown to be effective in three continuous POMDP domains: the floor positioning domain, the 3D light-dark navigation domain, and a modified Reacher domain.

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.

We study the problem of parameter estimation for a non-ergodic Gaussian Vasicek-type model defined as $dX_t=(mu+ heta X_t)dt+dG_t, tgeq0$ with unknown parameters $ heta>0$ and $muinR$, where $G$ is a Gaussian process. We provide least square-type estimators $widetilde{ heta}_T$ and $widetilde{mu}_T$ respectively for the drift parameters $ heta$ and $mu$ based on continuous-time observations ${X_t, tin[0,T]}$ as $T ightarrowinfty$.

Our aim is to derive some sufficient conditions on the driving Gaussian process $G$ in order to ensure that $widetilde{ heta}_T$ and $widetilde{mu}_T$ are strongly consistent, the limit distribution of $widetilde{ heta}_T$ is a Cauchy-type distribution and $widetilde{mu}_T$ is asymptotically normal. We apply our result to fractional Vasicek, subfractional Vasicek and bifractional Vasicek processes. In addition, this work extends the result of cite{EEO} studied in the case where $mu=0$.

We study the interplay of two important issues on Bayesian model selection (BMS): censoring and model misspecification. We consider additive accelerated failure time (AAFT), Cox proportional hazards and probit models, and a more general concave log-likelihood structure. A fundamental question is what solution can one hope BMS to provide, when (inevitably) models are misspecified. We show that asymptotically BMS keeps any covariate with predictive power for either the outcome or censoring times, and discards other covariates. Misspecification refers to assuming the wrong model or functional effect on the response, including using a finite basis for a truly non-parametric effect, or omitting truly relevant covariates. We argue for using simple models that are computationally practical yet attain good power to detect potentially complex effects, despite misspecification. Misspecification and censoring both have an asymptotically negligible effect on (suitably-defined) false positives, but their impact on power is exponential. We portray these issues via simple descriptions of early/late censoring and the drop in predictive accuracy due to misspecification. From a methods point of view, we consider local priors and a novel structure that combines local and non-local priors to enforce sparsity. We develop algorithms to capitalize on the AAFT tractability, approximations to AAFT and probit likelihoods giving significant computational gains, a simple augmented Gibbs sampler to hierarchically explore linear and non-linear effects, and an implementation in the R package mombf. We illustrate the proposed methods and others based on likelihood penalties via extensive simulations under misspecification and censoring. We present two applications concerning the effect of gene expression on colon and breast cancer.

Adaptive importance samplers are adaptive Monte Carlo algorithms to estimate expectations with respect to some target distribution which extit{adapt} themselves to obtain better estimators over a sequence of iterations. Although it is straightforward to show that they have the same $mathcal{O}(1/sqrt{N})$ convergence rate as standard importance samplers, where $N$ is the number of Monte Carlo samples, the behaviour of adaptive importance samplers over the number of iterations has been left relatively unexplored. In this work, we investigate an adaptation strategy based on convex optimisation which leads to a class of adaptive importance samplers termed extit{optimised adaptive importance samplers} (OAIS). These samplers rely on the iterative minimisation of the $chi^2$-divergence between an exponential-family proposal and the target. The analysed algorithms are closely related to the class of adaptive importance samplers which minimise the variance of the weight function. We first prove non-asymptotic error bounds for the mean squared errors (MSEs) of these algorithms, which explicitly depend on the number of iterations and the number of samples together. The non-asymptotic bounds derived in this paper imply that when the target belongs to the exponential family, the $L_2$ errors of the optimised samplers converge to the optimal rate of $mathcal{O}(1/sqrt{N})$ and the rate of convergence in the number of iterations are explicitly provided. When the target does not belong to the exponential family, the rate of convergence is the same but the asymptotic $L_2$ error increases by a factor $sqrt{ ho^star} > 1$, where $ ho^star - 1$ is the minimum $chi^2$-divergence between the target and an exponential-family proposal.

The Rosenbrock function is an ubiquitous benchmark problem for numerical optimisation, and variants have been proposed to test the performance of Markov Chain Monte Carlo algorithms. In this work we discuss the two-dimensional Rosenbrock density, its current $n$-dimensional extensions, and their advantages and limitations. We then propose a new extension to arbitrary dimensions called the Hybrid Rosenbrock distribution, which is composed of conditional normal kernels arranged in such a way that preserves the key features of the original kernel. Moreover, due to its structure, the Hybrid Rosenbrock distribution is analytically tractable and possesses several desirable properties, which make it an excellent test model for computational algorithms.

Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, but need to overcome the challenge of maintaining high accuracy as precision decreases. Here, we present a method for training such networks, Learned Step Size Quantization, that achieves the highest accuracy to date on the ImageNet dataset when using models, from a variety of architectures, with weights and activations quantized to 2-, 3- or 4-bits of precision, and that can train 3-bit models that reach full precision baseline accuracy. Our approach builds upon existing methods for learning weights in quantized networks by improving how the quantizer itself is configured. Specifically, we introduce a novel means to estimate and scale the task loss gradient at each weight and activation layer's quantizer step size, such that it can be learned in conjunction with other network parameters. This approach works using different levels of precision as needed for a given system and requires only a simple modification of existing training code.

The ability of intelligent agents to learn and remember multiple tasks sequentially is crucial to achieving artificial general intelligence. Many continual learning (CL) methods have been proposed to overcome catastrophic forgetting. Catastrophic forgetting notoriously impedes the sequential learning of neural networks as the data of previous tasks are unavailable. In this paper we focus on class incremental learning, a challenging CL scenario, in which classes of each task are disjoint and task identity is unknown during test. For this scenario, generative replay is an effective strategy which generates and replays pseudo data for previous tasks to alleviate catastrophic forgetting. However, it is not trivial to learn a generative model continually for relatively complex data. Based on recently proposed orthogonal weight modification (OWM) algorithm which can keep previously learned input-output mappings invariant approximately when learning new tasks, we propose to directly generate and replay feature. Empirical results on image and text datasets show our method can improve OWM consistently by a significant margin while conventional generative replay always results in a negative effect. Our method also beats a state-of-the-art generative replay method and is competitive with a strong baseline based on real data storage.

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

Once the search has begun, something will be found

Don Shula, who won the most games of any NFL coach and led the Miami Dolphins to the only perfect season in league history, died Monday at his home, the team said. He was 90.

A proposed class action lawsuit has been filed in B.C. Supreme Court which, if successful, could mean every ICBC-insured motorist and crash victim will be in line for a share of almost $1 billion. 

PLEASE CLICK ON LINK TO VIEW PDF OF SER-8590:

Effective Sunday, June 2 (trade date Monday, June 3), CME STP and CME STP FIX will support values entered in the Notes field for CME ClearPort UI and API (TrdCaptRpt/RptSide/@Notes) deals.

This enhancement will be supported in CME STP and CME STP FIX in the New Release environment today, Wednesday, May 15.

Batteries, especially button batteries, are an important source of pediatric injury. Recent reports suggest that fatal and severe button battery ingestions are increasing.

An estimated 3289 battery-related ED visits occurred annually among US children <18 years of age, averaging 1 visit approximately every 3 hours. The number and rate of visits increased significantly during the 20-year study period, driven by increases during the last 8 study years. (Read the full article)

A previous study of inflatable bouncer–related fractures has shown that upper extremity fractures are most common, and many fractures are caused by collisions; however, no study has examined nonfracture injuries or used nationally representative data to investigate inflatable bouncer–related injuries.

This is the first study to use nationally representative data to calculate national injury rates, assess risk factors, and examine trends for pediatric inflatable bouncer–related injuries treated in US emergency departments over a 21-year period (1990–2010). (Read the full article)

Introduction of the pneumococcal conjugate vaccine was associated with decreased invasive pneumococcal disease (IPD) in children. Few data exist on the impact in infants aged 1 to 90 days, who are too young to be fully immunized.

The incidence and proportion of IPD in Utah infants aged 1–90 days remained stable after vaccine introduction. IPD caused by PCV7 serotypes decreased significantly in the post-vaccine period. Serotype 7F emerged as the predominant serotype and commonly resulted in meningitis. (Read the full article)

Previous research has demonstrated that pediatric injuries associated with television (TV) tip-overs are increasing, children aged ≤4 years are at highest risk for injury, and the head and neck are most commonly injured.

We analyzed a nationally representative sample comprising 22 years of data. On average, 17 313 children receive emergency treatment of a TV-related injury annually in the United States. The rate of injury attributable to falling TVs increased by 95% over 22 years. (Read the full article)

Various low-risk criteria have been developed to guide management of the febrile young infant (<90 days), but they differ in age criteria, recommendations, and implementation. Therefore, variation in care is likely but has not been previously studied.

There is wide variation in testing, treatment, and overall resource utilization in management of the febrile young infant across all 3 age groups: ≤28, 29 to 56, and 57 to 89 days. There may be opportunities to improve care variation without compromising outcomes. (Read the full article)

Thanks to its compact, fanless design, the Lenovo ThinkCentre M90n Nano IoT is a tiny and silent mini PC with enough power for basic office and media-streaming tasks, as well as vertical-market and digital-signage use.

In our review, we called this TV 'among the brightest we've tested.' It comes in five different sizes, all of which are currently between 18 and 39 percent off at Amazon.

Paul L. Aronson
Oct 1, 2014; 134:667-677
ARTICLES

A new Penn State study found that life may be more stressful now than it was in the 1990s, especially for people between the ages of 45 and 64.

At around $500, neither of these high-performance CPUs comes cheap, and both are exceptionally good as the brains of a gaming or content-creation PC. So which one should you go with?

Not content to dominate the high-end desktop (HEDT) market with its new 32-core CPU, today AMD announced the upcoming launch of its most powerful Threadripper yet: the 64-core, 128-thread Ryzen Threadripper 3990X.

To view more press releases, please visit http://ir.wintrust.com/news.aspx?iid=1024452.

A 25-year-old woman has revealed how she has cut her food waste and managed to travel.

मराठवाड्याची राजधानी असलेल्या औरंगाबादमध्ये करोनामुळे चिंताजनक स्थिती निर्माण झाली आहे

एसईझेड जमिनीत नुकतीच एक सुरक्षारक्षकासाठी बांधकाम करण्यात आलेले आहे.

Anand Marriage Act 1909

Ancient Monuments Preservation Act 1904

To honor October as “Fire Prevention Month,” Smokey Bear will visit over 90 Delaware schools to teach children that “only you can prevent wildfires.” Ashley Peebles, trainer-educator for the Delaware Forest Service, oversees the annual Smokey Bear program, which reached almost 8,000 children last year. 2014 marks the 70th Anniversary of Smokey Bear's introduction in 1944, which has evolved into the longest running public service campaign in U.S. history.