The meaning isn't neutral when the Motion Picture Association of America (MPAA) uses it. Beginning in the early 1990s, the MPAA started giving the R rating to movies with "pervasive language." Most movies have language throughout, of course. The MPAA is using the phrase "pervasive language" to refer to the frequent use of a particular kind of language: profanity.
We provide a systematic review of epidemiological surveys of autistic disorder and pervasive developmental disorders (PDDs) worldwide. A secondary aim was to consider the possible impact of geographic, cultural/ethnic, and socioeconomic factors on prevalence estimates and on clinical presentation of PDD. Based on the evidence reviewed, the median of prevalence estimates of autism spectrum disorders was 62/10 000. While existing estimates are variable, the evidence reviewed does not support differences in PDD prevalence by geographic region nor of a strong impact of ethnic/cultural or socioeconomic factors. However, power to detect such effects is seriously limited in existing data sets, particularly in low-income countries. While it is clear that prevalence estimates have increased over time and these vary in different neighboring and distant regions, these findings most likely represent broadening of the diagnostic concets, diagnostic switching from other developmental disabilities to PDD, service availability, and awareness of autistic spectrum disorders in both the lay and professional public. The lack of evidence from the majority of the world's population suggests a critical need for further research and capacity building in low- and middle-income countries.
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The New York State Department of Health Early Intervention Program especially appreciates the advice and assistance of the New York State Early Intervention Coordinating Council and Clinical Practice Guidelines Project Steering Committee on all aspects of this important effort to improve the quality of early intervention services for young children with autism/pervasive developmental disorders and their families.
This document provides an extraordinarily thoughtful and balanced presentation of the critical issues in assessment and intervention for this group of children. There is no doubt in my mind that readers will find the Guideline to be a valuable resource, as it will allow numerous individuals with different levels of expertise to gain a firm understanding and make highly informed decisions with respect to assessment and intervention for young children with autism and pervasive developmental disorders.
Harassment is unwelcome conduct that is based on race, color, religion, sex (including sexual orientation, gender identity, or pregnancy), national origin, older age (beginning at age 40), disability, or genetic information (including family medical history). Harassment becomes unlawful where 1) enduring the offensive conduct becomes a condition of continued employment, or 2) the conduct is severe or pervasive enough to create a work environment that a reasonable person would consider intimidating, hostile, or abusive. Anti-discrimination laws also prohibit harassment against individuals in retaliation for filing a discrimination charge, testifying, or participating in any way in an investigation, proceeding, or lawsuit under these laws; or opposing employment practices that they reasonably believe discriminate against individuals, in violation of these laws.
When investigating allegations of harassment, the EEOC looks at the entire record: including the nature of the conduct, and the context in which the alleged incidents occurred. A determination of whether harassment is severe or pervasive enough to be illegal is made on a case-by-case basis.
While Canadians often believe that our country is a model when it comes to inclusion, the hard truth is we have a long way to go toward achieving equity for the Black population in Canada. A new review and compilation of the available data by BCG and CivicAction demonstrate the depth and pervasiveness of anti-Black racism in Canada, and how systemic racism against Black individuals appears across their full lifecycle in areas like education, employment, healthcare, and policing.
This report is a summary of the research completed by BCG in partnership with CivicAction during the summer of 2020. The work was initiated through a mutual goal to shed a light on the pervasive and systemic biases that are experienced by Black communities in Canada, with the collective goal to drive to meaningful action.
Scores of recent stories have exposed the pervasiveness of sexual harassment in industries such as Hollywood, tech, politics, and academia. Less attention has been given to lower-paying jobs, such as those in the service and hospitality industry, where the problem runs rampant.
Pervasive Intelligences is related to artificial intelligence (AI), but it is a much broader concept. Any individual EDA tool can use AI techniques such as ML for better or faster results, but this is not pervasive. Just connecting a series of such tools in a development flow does not qualify. Pervasive Intelligence requires true collaboration between the tools in the flow, including sharing of data useful when passed from one tool or step to another.
The digitization of practically everything coupled with the mobile Internet, the automation of knowledge work, and advanced robotics promises a future with democratized use of machines and wide-spread use of robots and customization. However, pervasive use of robots remains a hard problem. Where are the gaps that we need to address in order to advance toward a future where robots are common in the world and they help reliably with physical tasks? What is the role of computation along this trajectory?
In this talk I will discuss challenges toward pervasive use of robots and recent developments in algorithms for customizing robots. I will focus on a suite of algorithms for automatically designing, fabricating, and tasking robots using a print-and-fold approach. I will also describe how computation can play a role in creating robots more capable of reasoning in the world. By enabling on-demand creation of functional robots from high-level specifications, we can begin to imagine a world with one robot for every physical task.
Bio:
Daniela Rus is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Rus's research interests are in robotics, mobile computing, and data science. Rus is a Class of 2002 MacArthur Fellow, a fellow of ACM, AAAI and IEEE, and a member of the National Academy of Engineering. She earned her PhD in Computer Science from Cornell University. Prior to joining MIT, Rus was a professor in the Computer Science Department at Dartmouth College.
Before we even try to answer these questions, we need to establish how pervasive corporate fraud is. Is the fraud we observe the whole iceberg or just its visible tip? The answer to this question requires an estimate of the ratio of the exposed tip to the submerged portion, also known as the detection likelihood. Thus far in the literature, approaches to estimating the detection likelihood (and by implication the hidden prevalence of fraud) have included (i) accounting misconduct prediction models (Beneish 1999; Dechow et al. 2011), (ii) corporate surveys (Dichev et al. 2013), and (iii) structural, partial-observability approaches (Wang 2013; Wang et al. 2010; Hahn et al. 2016; Zakolyukina 2018). In this paper, we introduce a fourth approach, which relies on a natural experiment: the 2001 demise of Arthur Andersen (AA).
The simple idea is that after the AA demise, former AA clients were subject to vastly increased scrutiny. They found themselves in the spotlight of the media, investment intermediaries, short-sellers, and their internal gatekeepers. In addition, they were forced to seek a different audit firm. Given the extreme cloud of suspicion that was covering AA clients immediately after the Enron scandal exploded (Chaney and Philipich 2002; Krishnamurthy et al. 2006), the new auditors, as well as all other fraud detectors, had strong incentives to uncover any fraud committed by former AA clients. Even if this increased scrutiny does not reveal all the existing fraud, the Kolmogorov axiom of conditional probability allows us to derive an upper-bound (and thereby conservative) estimate of the detection likelihood, which in turn provides us with a lower-bound estimate of the pervasiveness of corporate fraud.
With our estimates of the detection likelihood, we can calculate the pervasiveness of corporate fraud as a function of the definition of fraud we adopt. Accounting violations are widespread: in an average year, 41% of companies misrepresent their financial reports, even when we ignore simple clerical errors. Fortunately, securities fraud is less pervasive. In an average year, 10% of all large public corporations commit (alleged) securities fraud, with a 95% confidence interval between 7 and 14%.
The rest of the paper proceeds as follows. Section 2 describes the data and presents summary statistics on caught frauds. Section 3 explains our methodology. Section 4 presents the detection likelihood and the pervasiveness of corporate fraud results. Section 5 provides estimates of the cost of such fraud, and Sect. 6 concludes.
A more general concern is that the increased attention on corporate fraud that followed the Enron and WorldCom scandals might have prompted the SEC (or the other auditors not affected by the turnover) to become more active in detecting fraud. In the limit, if this enhanced scrutiny exposed all fraud in all firms, there will be no difference between the amount of fraud revealed in AA clients and the amount of fraud revealed in clients of other audit firms, invalidating our experiment. Yet, as long as some enhanced scrutiny affects all firms but AA firms are affected more, our methodology will work, but will underestimate undetected fraud. Thus, our results should be interpreted as a lower bound on the pervasiveness of fraud. be457b7860
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