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Home Practice areas AI, Machine Learning & Big Data Laws and Regulations 2021 USA
AI, Machine Learning & Big Data Laws and Regulations 2021 | USA
Artificial intelligence trends and considerations under U.S. law
Legal trends in the AI industry
AI considerations for IP transactions
The antitrust implications of big data and algorithmic pricing
Pricing algorithms can facilitate collusion
Financial services regulation for AI systems
Although autonomous machine technology is still in the nascent stages of development and implementation, the relatively recent emergence of "big data" aggregation and machine learning ("ML") analytics has nevertheless already presented numerous questions of how businesses and society writ large should address the myriad implications of truly autonomous systems. As legislators and regulators from around the globe seek to strike the right balance between encouraging innovation and protecting individual rights, it is becoming increasingly incumbent upon business leaders to ensure their operations and policies are nimble enough to adapt to a regulatory landscape that is as dynamic and unpredictable as autonomous machine technology itself. In order to mitigate the risks associated with entering the artificial intelligence ("AI"), big data analytics or ML industries, it is imperative to develop a nuanced understanding of the commercial and regulatory developments in that space to date. In order to avoid the pitfalls of AI and other autonomous technologies, each business must cater its approach based on its own unique considerations and circumstances. For businesses owned and operated within the United States ("U.S."), a good starting point is to examine the recent legal trends in the AI industry, the unique intellectual property ("IP") ownership considerations presented by AI technologies, and the application of antitrust and financial services regulations to AI systems and other autonomous technologies. The purpose of this chapter is to establish a foundational understanding of the issues, considerations and legal frameworks that businesses have thus far encountered when developing and commercialising autonomous technologies within the U.S., in order to enable business leaders and other stakeholders to discern, anticipate and adapt to future developments in this space.
From automotive and transportation,1 to supply chain management,2 human resource functions,3 and financial services,4 there are few industry sectors which have not been impacted by the evolution of AI technologies. The relatively rapid growth and adoption of this new technology has left legislators in an increasingly reactive position as new issues and potential risks materialise alongside the ever-growing compendium of AI applications. The U.S., in particular, has been slow to provide industry participants with guidance on the legal and regulatory landscape that is developing with respect to AI technologies. For instance, the first official statement from the White House5 regarding how companies can leverage AI for economic and technological growth did not come until the final months of the Obama Administration. This initial report outlined recommendations related to AI regulations, security, ethics and fairness, and automation. The Obama Administration followed up that initial report with two companion reports, National Artificial Intelligence Research and
Development Strategic Plan6 and Artificial Intelligence, Automation, and the Economy,7 to expound upon the recommendations set forth in the initial report. The former set forth a strategic plan for providing publicly-funded research and development of AI technologies, while the latter analysed the economic and societal effects of automation in order to discern how public policy should be construed to maximise the benefits of AI technologies, while mitigating the costs of implementing AI systems. In May 2018, the Trump Administration held a summit on AI technologies8 for industry, academia and government participants. At this conference, White House officials outlined the following four core goals of the U.S. government with respect to AI technologies: (i) maintaining American AI leadership; (ii) supporting American workers; (iii) an increased focus on research and development; and (iv) removing barriers to innovation. In February 2019, President Trump followed up on these previously stated goals by signing an executive order to create the "American AI Initiative", which, amongst other things, directs heads of federal agencies to budget an "appropriate" amount of funding for AI research and development.9
While these promulgations are instructive as to how the future of AI technologies will be fostered and supported by the U.S. government, they nevertheless fall short of actual policy implementation or providing new funding for AI development.10 While the U.S. may be lagging behind other countries such as China when it comes to legislating and regulating AI systems, the task of constructing a viable legislative regime for AI systems is a veritable minefield of complexities and unknown variables. The challenge of holding AI systems accountable for the automated decisions they make comes from the fact that AI systems, and even the engineers who built them, are unable to explain the rationale or process by which an automated system reaches a decision. As noted by the Berkman Klein Center for Internet & Society at Harvard University, "[g]ood choices about when to demand explanation can help prevent negative consequences from AI systems, while poor choices may not only fail to hold AI systems accountable but also hamper the development of much-needed beneficial AI systems".11 For legislation to be effective, AI and the systems they are incorporated into need to be able to give the reasons or justification for a particular outcome, not just a description of the steps taken throughout the decision-making process.
With the current state of ML technology, it can be difficult or even impossible to discern the reasons as to why a specific algorithm "chose" to take a certain action.12 How an AI system weighed certain factors, exercised judgment, and adjusted its actions in anticipation of certain undesirable outcomes are all crucial components of enforcing laws against AI systems. And when even the engineers who built the AI cannot provide these explanations, crafting appropriate legislation around AI can become a Sisyphean task. Furthermore, while other jurisdictions have made their first attempts to regulate decisions made by automated processes,13 the U.S. has yet to see any such similar attempts.
As U.S. legislators struggle with these complexities, AI-based businesses are having to come up with creative solutions in the transactional context to account for the unsettled state of the law. In particular, the traditional models for licensing and commercialising IP in the software industry have required certain adjustments in order to account for the differences between software and AI-based systems.
Due to the technical aspects of how ML algorithms and models intersect with AI, the usual software licensing constructs either do not apply or require material adjustments to transactions involving ML systems. The most significant distinction between traditional software and ML is the manner in which ML algorithms ingest and learn from data inputs As a result, the IP rights to the input data is a crucial component of any transaction involving ML algorithms. Similarly, the output of the ML algorithm – whether that output is data or something else, such as training parameters – has also increased value in AI-based transactions. As a result, parties negotiating a contract for an AI-based service or product need to carefully analyse the various components of IP embodied by the AI system in order to determine how to apportion the IP rights accordingly.
The first step in the transactional process is making sure the parties have a complete understanding of the automated system itself. In order to address how the IP rights should be apportioned, it is necessary for the parties to make a clear distinction upfront as to whether they are contracting over an ML algorithm, an ML model, AI software, or the input and/or output data of an automated system, or any combination of the same. Without this distinction, the parties will not be able to clearly define the licence scope, which needs to account for various configurations, modifications, enhancements and parameters of the technology.
Each of the aforementioned components of an automated system are variable in value based on the needs and goals of the parties. For instance, the rights to the input data may contain sensitive customer info which the data licensor will want to retain ownership over, whereas the data licensee may want to retain ownership over a proprietary ML algorithm which is the core of their business model. Since the input data will train the ML algorithm and improve its efficiency and the accuracy of its output, it is imperative that parties in this sort of transaction clearly delineate the scope of rights and the type of rights needed for each component. Then, the parties will need to determine who gets what rights to the output data, as both parties have equally contributed to the creation of the output. These scenarios are highly fact-dependent, and the ultimate outcome of negotiations in a given AI-related transaction will vary widely; however, the following considerations should be taken into account regardless of the specific circumstances of a given transaction:
for vendors of AI-based systems, being able to retrain, modify and improve their ML algorithm or model is a core component of creating a long-term revenue model – as a result, the vendor's rights to the learnings and algorithmic optimisations generated by the transaction are highly valuable to the vendor;
parties need to be careful of entering into "gain sharing" arrangements, whereby the financial gains of AI-optimised processes are shared by the parties – the increased scale afforded by AI-optimised systems can still come with increased costs and fees, as accounting and administrative efforts to track the gains can be costly;
since AI systems can become deeply engrained in a licensee's business, the parties need to understand how they can comply with any post-termination requirements, particularly with respect to the return or destruction of confidential information which may be irrevocably intertwined with a party's business; and
residual rights to the learnings of an ML system, any algorithmic and parameter optimisations generated by a transaction, and the input or output data need to be carefully constructed so each party retains its freedom to operate once the contractual relationship ends.
The commercial and regulatory complexities posed by the IP considerations in a transaction regarding AI systems are not the only concerns that companies conducting business in the AI field need to be cognisant of in a given instance. Sometimes, the implications of a given transaction could raise antitrust concerns as well.
In recent years, AI and ML have drastically changed how businesses are able to utilise big data to more effectively compete for new customers. Businesses currently use machines to store massive amounts of economic data, including pricing information, consumer shopping patterns, and consumer address information. These machines can then use algorithms to process this raw data into information that the business can use to estimate consumer demand and forecast price changes in its relevant market, enabling it to react almost instantaneously to price movements by competitors. The impact has already been felt on Wall Street, where "algorithmic trading is a ubiquitous phenomenon across the financial markets today",14 as well as in the halls of Congress, where lawmakers are considering proposed legislation such as the Algorithmic Accountability Act of 2019, which would direct the Federal Trade Commission ("FTC") to require companies to affirmatively evaluate and minimise the risks of flawed computer algorithms that result in inaccurate, unfair, biased, or discriminatory decisions.14B
While utilising algorithmic pricing can lead to several pro-competitive benefits for consumers, there is also the dangerous potential for businesses to share pricing information collected through these algorithms with their competitors to fix prices or engage in other anticompetitive conduct. Antitrust enforcers have become increasingly wary of this potential for collusion, noting that algorithms "might facilitate cartel formation and maintenance… [or] tacit collusion between competitors".15 Below, we analyse several potential antitrust concerns arising from big data and algorithmic pricing.
Businesses can use pricing algorithms to collect and ultimately share competitively-sensitive information with their competitors, leading to illegal price fixing and market allocations. There are two possible types of collusion – overt collusion and tacit collusion.
Overt collusion: explicit agreements
Overt collusion occurs when humans use pricing algorithms as an instrument to facilitate a pre-arranged price-fixing conspiracy. For example, in United States v. Topkins,16 the U.S. Department of Justice ("DOJ") prosecuted two e-commerce sellers for agreeing to align their pricing algorithms to increase online prices for posters. Here, the parties' agreement to violate the antitrust laws was explicit, and the application of antitrust law to the agreement was equally straightforward. As Margrethe Vestager, the European Commissioner for Competition, recently remarked, "no one should imagine they can get away with price-fixing by allowing software to make those agreements for them".17
Tacit collusion: what happens when machines "collude?"
The more complex issue could arise where pricing algorithms are the source of the collusion rather than simply an instrument used to further an already-existing agreement. Scholars have observed that algorithmic pricing has become so advanced that it has surpassed humans' ability to analyse market data and adjust pricing. Professors Ariel Ezrachi and Maurice E. Stucke have written that "as competitors' prices shift online, their algorithms can assess and adjust prices… for thousands of products… within milliseconds… [and] can swiftly match a rival's discount, thus eliminating its incentive to discount in the first place."18 Similarly, Former Acting FTC Chairman Maureen K. Ohlhausen has remarked that tacit collusion through algorithmic pricing can be "extremely hard to detect", as computers can "react almost instantaneously" to changes in any of several variables.19
A recent study by four economists at the University of Bologna appears to validate some of these concerns, with the results suggesting that AI-powered algorithms "may be better than humans at colluding tacitly".20 The study found that even relatively simple pricing algorithms, operating in repeated price competition, would systematically learn to "collude" and charge supracompetitive prices, enforced by "punishing" defectors from the scheme. Notably, they learned to play these strategies "by trial and error", and "leave no trace whatever of concerted action", as the pricing algorithms were not designed to collude, nor were they able to communicate with one another. As the authors conclude, more research is needed, but "[f]rom the standpoint of competition policy, these findings should clearly ring a bell".
While no antitrust regulator has brought an enforcement action on the basis of tacit collusion using pricing algorithms, in 2015, private plaintiffs sued Uber, alleging that the pricing and payments mechanism at the heart of the Uber app violated the Sherman Act.21 Plaintiffs argued that the pricing mechanism supported a hub-and-spoke conspiracy, whereby each driver used the mechanism to compare rates and ultimately ensure that other drivers would not undercut their prices. The court found the allegations in the complaint sufficient to withstand a motion to dismiss, finding that drivers would sign up for Uber understanding that all Uber drivers were agreeing to the same pricing algorithm. While the case was ultimately removed to arbitration, the court's rejection of Uber's argument that drivers had made independent decisions to enter into a vertical agreement with Uber in order to take advantage of the payment processing and rider matching services could influence the success of certain pro-competitive defences used in future antitrust cases involving pricing algorithms.
Algorithmic pricing as a factor in merger analysis
In addition to the threat of hub-and-spoke conspiracies, increased use of algorithmic pricing may have a significant bearing on Sherman Act Section 7 antitrust merger analysis. Former FTC Commissioner Terrell McSweeny has observed that advanced pricing algorithms can enable companies to engage in sophisticated price discrimination involving a combination of differential "list" prices and targeted discounts, without ever reaching an explicit agreement.22 In McSweeny's view, increasingly nuanced and profitable price discrimination strategies by sellers could also lead to narrower product markets in the future.23
Former Director of the FTC's Bureau of Competition D. Bruce Hoffman has also suggested that autonomous machines may be able to achieve oligopoly outcomes more quickly or more sustainably than can humans, given their ability to quickly process, compare, and modify prices.24 As "one of the fundamental principles of merger policy is the prevention of mergers that would allow firms to acquire the ability to achieve an oligopoly outcome",25 to the extent that algorithmic pricing could reach and/or sustain such an outcome more easily than humans, enforcers may become more aggressive in challenging a broader set of mergers.
Practical considerations for companies
Despite these recent advancements in technology, U.S. antitrust regulators continue to take the view that pricing algorithms are not all that novel from an antitrust enforcement perspective. Officials from both the DOJ and FTC have remarked that tacit collusion through pricing algorithms does not call for a new theory of competitive harm, and that the antitrust laws are "demonstrably capable of evolving with the times".26 When considering collusion under Sherman Act Section 1 violations, for example, proof of agreement is key to determining whether parallel conduct amounts to an antitrust violation under U.S. law, whether that agreement is verbal, written, or reached through a pricing algorithm.27
As FTC Commissioner Rebecca Kelly Slaughter recently explained, "while many of the problems of AI—bad data, failure to test, proxy discrimination—have longstanding analogs, AI can simultaneously obscure the problems and amplify them, all while giving the impression that they don't or couldn't possibly exist".27B Thus, in her view, "the starting point of nearly all discussions about AI ethics and the focal point of many regulatory responses is to require increased transparency and accountability in order to mitigate discriminatory effects". She observed that this emphasis on transparency and accountability is reflected in a number of pending legislative proposals, but is perhaps best illustrated by the Algorithmic Accountability Act, noted above. According to the Commissioner, "[t]he core insight of the proposed bill, through required impact assessments (IAs), is that vigilant testing and iterative improvements are the fair and necessary cost of outsourcing decisions to algorithms. Or, as [she] would put it, you can't have AI without IA".
Despite the regulators' hands-off approach to potential tacit collusion through the use of pricing algorithms to date, companies should still take the appropriate precautions in how they manage big data. Companies should create antitrust compliance programmes which include training specific to the use of pricing algorithms, and should instruct employees that algorithms contain competitively sensitive information that should not be shared with competitors. Further, companies should be very clear that employees must avoid discussing the use of pricing algorithms with their competitors, just as they would avoid any discussion of prices. Finally, while pre-deployment testing of any algorithm is critical, there is an emerging consensus that monitoring, evaluating, and retraining algorithms on an ongoing basis is an equally essential component of any algorithm-focused compliance programme.
While these antitrust concerns can apply regardless of the application for which an AI-based system is utilised, further complexities arise when the system is used in the financial services industry.
The financial services industry covers a broad scope encompassing banking, money transmission, lending and finance, underwriting, brokerage, insurance, investment management and related sectors. The industry serves retail customers, high-net-worth customers and institutions and can be packaged as anything from extremely "low touch" to extremely "high touch". It is also among the most highly supervised industries, with a multiplicity of regulators at the federal and state level. Financial services regulators tend to both make rules and to carry out ongoing inspections and risk analyses, typically with a combination of goals that include customer protection, market integrity, and safe and sound operation of the supervised institutions.
Automated and AI-based applications are used throughout the industry. Marketing applications ingest social media and other source data to identify and profile customers. Chatbots interact with customers in service and marketing capacities. Quantitative programmes trade in securities and derivatives markets, often at speeds and volumes far in excess of human trading; automated underwriting processes make lending and insurance decisions. Other automated programmes identify and research anomalies to support risk management, fraud detection, anti-money laundering ("AML") profiling, and other control processes. Banking regulators and the U.S. Treasury Department's Financial Crimes Enforcement Network have generally encouraged the use of innovative technologies to meet AML requirements.28
Both financial services firms and their regulators appear to view these developments in the same way, namely that they are an inevitable reflection of an industry in flux. Most profoundly, the industry is awash in – and hungry for – data from many different sources, at a level of volume and complexity that cannot be efficiently managed without sophisticated technology. Firms that are not constantly re-examining how they can deploy technology and data-driven processes are at a real competitive disadvantage in the industry, and a real disadvantage in identifying problems and maintaining their compliance with regulatory requirements before being identified by regulators using advanced technology.
How has the government responded?
As previously noted, the White House, both under President Obama and President Trump, has made AI a top national priority, publishing white papers and holding summits that, at bottom, call for the U.S. to be a leader in AI and cautioning that excessive governmental encumbrances should be avoided.29
Congress has organised an AI caucus, which proposed legislation on December 12, 2017 in the FUTURE of AI Act (Fundamentally Understanding the Usability and Realistic Evolution of AI Act). The primary purpose of both the caucus and the bill appears to be ensuring that Congress is familiar with AI and taking its potential into account when developing public policy.30
The U.S. Treasury Department issued, as part of a series of reports designed to identify regulations that are inconsistent with core principles for the regulation of the financial industry, a broad and comprehensive discussion specific to AI in financial services (see "A Financial System that Creates Opportunities: Nonbank Financials, Fintech and Innovation" (July 2018)). The report observed that AI investment by financial services firms is accelerating and that AI innovations drive efficiencies for firms and improve outcomes and choices for customers. The Treasury cautioned, however, that in other contexts, industries that rely heavily on technology and data-based platforms tend towards concentration, with attendant long-term risks to levels of innovation and choice.
Turning to specific challenges presented by AI, the Treasury suggests that AI is a double-edged sword in many respects. As automated processes replace human judgment, opportunities for unlawful discrimination are reduced (for example, automated lending decisions should be more neutral than human decisions) – that is, unless the AI encodes or learns prejudice of its own, the risk of which increases as powerful, data-rich AIs may identify correlations to target characteristics that are also correlated to a discriminatory or impermissible characteristic. Powerful new risk and fraud detection tools can be used to block and root out rogue traders, money launderers, cyber criminals and other bad actors. But bad actors likewise might deploy AI of their own to circumvent existing controls. Massive investment in AI will lead to a boom in demand for engineers, data scientists and other specialists. But layoffs will follow in employment sectors where AI replaces existing staff.
Finally, the Treasury notes the concern that "black box" systems are inconsistent with traditional regulatory norms that expect transparency and auditability for industry activities. Opaque decisions risk poor consumer outcomes; e.g., when AI makes an inappropriate financial recommendation to a customer. Opaque decisions are most concerning, of course, when the stakes are highest and involve matters such as institutional solvency or financial stability. In other words, the Treasury is most concerned about possibilities like these: AI roiling financial markets with volatile trading; AI misrouting large money transfers; AI mispricing assets or accounts; or AI causing an institution or regulator to misunderstand risks.
U.S. Federal Reserve
The U.S. Federal Reserve, in addition to its role setting monetary policy as the nation's central bank, is also a regulator for many U.S. banks. In a thoughtful and widely cited speech, Lael Brainard, a member of the Federal Reserve's Board of Governors, described the regulatory approach to AI as one that should start with "existing regulatory and supervisory guardrails". Governor Brainard then described two Federal Reserve guidance notes as directly applicable, the first being the Fed's guidance on risk management when using complex models (SR Letter 11-7), and the second being guidance on vendor risk management (SR 13-19/CA 13-21).
Regarding models, Governor Brainard noted "maker-checker" type controls that empower unbiased, qualified individuals separated from the model's development, implementation, and use as a "second set of eyes", as well as the potential for circuit breakers or other controls that would come into force to deal with unexplained or unexpected outcomes. Regarding vendor risk management, she noted due diligence, selection, and contracting processes, oversight and monitoring throughout the relationship with the vendor, and considerations about business continuity and contingencies for a firm to consider before the termination of any such relationship.
Speaking to questions of opacity and explainability, Governor Brainard agreed that existing guidance "recognizes that not all aspects of a model may be fully transparent, as with proprietary vendor models, for instance". In the absence of full transparency, however, upfront and ongoing risk monitoring efforts are heightened. Principles of proportionality also apply, with more caution required when AI will be used for major decisions or across a broad customer base. Finally, Governor Brainard referred to risks associated with invalid or improper data sets leading to potentially cascading failures in an AI's algorithms and outputs. Controls around how an AI system will source and consume data are critical.
The U.S. Securities and Exchange Commission ("SEC") is the primary U.S. regulator for public securities markets, investment advisers, and broker-dealers. The agency has not issued direct guidance as to how regulated firms should consider or review their use of AI, but has provided consistent principles around the evaluation of risk through exam results and in speeches that make clear the industry should be considering these issues carefully. Historically, the agency has brought a number of enforcement actions involving failures by firms to properly vet and implement complex investment models – generally also alleging related failures to disclose weaknesses or limitations in the models – which are obvious analogues to how faults in AI-driven models and systems may be considered. In particular, the SEC expects firms to carefully test and document technology before it is rolled out, and to continue testing technology over time as conditions change. A firm should understand and be able to explain the core operations and individual outcomes of their technology both to internal and external governance bodies (senior management, compliance and control functions, and regulators) and be able to provide documentation of its deliberative processes around both the evaluation of the technology and the individual outcomes. Risks that might be presented by reliance on the technology need to be accurately identified and disclosed to clients.31 The SEC confirmed its interest in AI when it brought an enforcement action in 2020 against a registered investment adviser for, among other violations, failing to properly disclose the use of a semi-systematic trading system to investors in due diligence questionnaires and marketing literature. The investment adviser paid over $100 million in disgorgement and an additional civil penalty of over $37 million.32
The U.S. Commodity Futures Trading Commission ("CFTC") is the primary U.S. regulator for derivatives markets and their participants, including trading facilities, clearing organisations and market intermediaries such as swap dealers, futures commission merchants and commodity trading advisors. The agency has not formally spoken on how its regulated firms should consider their use of AI,32B but the agency has considered a number of issues related to automated trading activity more generally. For example, the agency has long taken the position that provision of software that provides automated trading signals or directions may constitute a form of commodity trading advice, which in some circumstances may be subject to regulation and registration. The CFTC has also looked for a number of years at the impact of automated trading on regulated markets, culminating in the issuance in 2021 of Electronic Trading Risk Principles applicable to regulated futures exchanges.33 The CFTC had previously issued a more comprehensive and prescriptive, and controversial 2015 regulatory proposal aimed at high-frequency and other electronic trading on regulated futures exchanges, referred to as Regulation Automated Trading ("Reg AT"). Aside from the breadth of its proposed impact, one of the major stumbling blocks with respect to Reg AT was a proposed requirement that the proprietary automated trading source code of registered traders be subject to inspection by the CFTC and DOJ, in some cases without requiring a subpoena. In the face of significant opposition, the CFTC withdrew proposed Reg AT33B in 2020 and adopted the narrower set of Electronic Trading Risk Principles, which focus on the following: (1) exchanges rules and controls to prevent, detect and mitigate market disruptions and system anomalies associated with electronic trading; (2) implementation of pre-trade risk controls to prevent, detect and mitigate such disruptions and anomalies; and (3) notice to the CFTC of significant disruptions to electronic trading. The CFTC has also continued to monitor developments with respect to automation of trading practices, releasing a report in March 2019 concerning the "Impact of Automated Orders in Futures Markets" that presented findings with respect to the amount and impact of orders generated or routed without human intervention, and the manner in which those orders are employed.
More generally, although the effects of the change in administration are not yet certain, the CFTC continues to engage with innovators and the broader financial technology community to foster "responsible innovation" through its Technology Advisory Committee public meetings, its LabCFTC office and the related CFTC 2.0 initiative. Regarding enforcement and interpretative activity, the CFTC has made clear through various actions and no action positions that it remains focused on the manner in which automated trading systems, which could implement some form of AI, are accessed and used by and offered to market participants. Firms should understand that AI, like other automated systems, does not fall outside of the bounds of the CFTC's remit – the agency can be expected to use its authority to regulate derivatives markets and police fraudulent and manipulative activity in the derivatives markets, regardless of the underlying technology.
The Financial Industry Regulatory Authority, Inc. ("FINRA") is the largest self-regulatory organisation for securities firms operating in the U.S., providing regulatory oversight for broker-dealers and registered securities representatives, under the supervision of the SEC. For FINRA, technology applications to the securities markets has become a central regulatory priority.34 FINRA has recently requested comment on emerging technologies and become a frequent convener of industry and government thought leaders to discuss not only the use of financial technology ("FinTech") by member companies, but also the use of technology in regulating the industry and enhancing member firm regulatory compliance mechanisms ("RegTech").35 Indeed, FINRA has, itself, implemented AI and ML in its market surveillance operations, noting its ability to enhance the detection of market manipulation and collusion.36 In April 2019, FINRA created the Office of Financial Innovation (an outgrowth of its Innovation Outreach Initiative), designed to coordinate issues related to significant financial innovations, particularly the use of FinTech. The establishment of the new office follows years of active monitoring of and engagement on technology developments, including the creation of a FinTech Industry Committee, publishing reports on FinTech and RegTech applications in the securities industry, and the hosting of four regional FinTech and RegTech conferences.37
In June 2020, FINRA published a report regarding AI in the securities industry.38 In that report, FINRA emphasised that broker-dealers' use of AI applications related to communications with customers, investment processes, and operational functions pose unique compliance considerations, including those related to model explainability and data bias. As further noted in FINRA's 2020 Risk Monitoring and Exam Priorities Letter,39 broker-dealers increasingly rely on technology for customer-facing activities. FINRA noted such reliance can expose firms to operational failures and accordingly, firms should review its technology governance programmes to ensure that AI applications are carefully tested and that there are back-up plans in the event the technology fails. FINRA further provided that firms should ensure that its written supervisory procedures regarding AI-based tools and services are regularly updated and tested.
Battles over source code
Different regulators have taken different tacks with respect to demanding access to sensitive source code when supervising businesses deploying AI or other sophisticated software applications. As noted above, the CFTC released, but ultimately withdrew, a controversial proposal, Reg AT, which would have required that source code be subject to regulatory inspection, without a subpoena. In light of the strong negative response, and as an indication of how concerned some parties are that source code will be mishandled by the government (the highest order concern is that a company's intellectual property "crown jewels" might be stolen by hackers or even bad actors inside the government), Congress has considered (but not adopted) bills that would have prohibited the SEC and CFTC from accessing source code at their regulated firms without obtaining a subpoena. This effectively means that source code could not be accessed during an ordinary course examination of a regulated firm.
Regulation of data
Given the importance of large data sets to the effective operation of most AI, a discussion of AI is always linked to a discussion of data. There is no comprehensive legal and regulatory approach to data that applies across the U.S. Moreover, none of the federal financial regulators have put forth regulations on data that can be said to address the scope and diversity of today's data practices. Instead, a patchwork of often conflicting laws and regulations apply. Here are a few of them:
Privacy and protected classes. One constant is that many jurisdictions seek to protect "personal data" or "privacy" associated with individuals, especially names, addresses, government identification numbers, and the like. Closely related to privacy, populations deemed especially vulnerable, such as children or the elderly, are often given special data protections. Likewise, personal health and financial records, gender orientation information, political and religious affiliations and other special categories of personal information often have heightened protection.
Governmental data. Much governmental data, especially in democratic societies, is intended to be "open" and freely accessible to the public. However, it should not be assumed that any use of governmental data, even when it can be readily accessed, is permissible without consideration of the specific circumstances. Some public data sources may include restrictions that they are intended for or limited to research or other non-commercial purposes. Such restrictions may appear as disclaimers on the data itself, or may only be evident in the background laws or regulations, including criminal laws, of such governmental body. There is also a variety of instances when governmental data are explicitly non-public or restricted; e.g., in connection with governmental contracts, studies and approvals that have not yet been announced.
Website data. "Web scraping", also called crawling or spidering, is the automated gathering of data from a third-party website. Scraped data has become a vital component of the investment research programmes of many asset managers, and is critical to many business processes generally throughout the industry; and, accordingly, it is in wide use. But the permissibility of the practice – and associated legal risk – remains unclear. A variety of legal claims may apply under U.S. law to unauthorised scraping, including breach of contract, copyright infringement, trespass and other torts, and causes of action or even sanctions under state and federal laws specific to website access. Perhaps most significantly, federal law – enforceable both criminally and civilly – specifically protects websites from unauthorised access, with that phrase potentially extending the law's protections to any website whose terms of use forbid or limit automated scraping of data.40
It has been common over many years for firms that make heavy use of data to speak of their "data ethics". This is sometimes referred to as embodying the principle that the question for a firm is not whether it can (operationally or legally) put data to a particular use, but whether it should (whether doing so is "right"). Data ethics policies are intended to ensure that an organisation has a governance framework to answer that question and, in doing so, considers a broad range of factors (e.g., legal and contractual requirements, technical capacity, social expectations, reputational considerations, etc.).
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(Hyperlink). Exec. Order No. 13859, 3 C.F.R. 3967 (2019).
9. Exec. Order No. 13859, 3 C.F.R. 3967 (2019).
10. See Metz, Cade, Trump Signs Executive Order Promoting Artificial Intelligence, The New York Times, February. 11, 2019, available at (Hyperlink) business/ai-artificial-intelligence-trump.html (last accessed April 24, 2019) (stating that "the [Trump] administration provided few details on how it planned [to] put its new policies into effect").
11. Accountability of AI Under the Law: The Role of Explanation, Berkman Klein Center Working Group on Explanation and the Law, Harvard University, November 27, 2017, available at (Hyperlink) (last accessed April 24, 2019).
12. Knight, Will, The Dark Secret at the Heart of AI, MIT Technology Review, April. 11, 2017, available at (Hyperlink) heart-of-ai/ (last accessed April 24, 2019).
13. See Article 22(1) of the General Data Protection Regulation (EU) 2016/679 (2018), stating: "The data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her."
14. Ohlhausen, Maureen, Remarks to the Concurrences Conference on Antitrust in the Financial Sector: "Should We Fear The Things That Go Beep In the Night? Some Initial Thoughts on the Intersection of Antitrust Law and Algorithmic Pricing" (New York, May 23, 2017) at 2.
14B. Algorithmic Accountability Act of 2019, H.R. 2231, 116th Cong. (as referred to S. Comm. On Consumer Prot. & Commerce, April 11, 2019); S. 1108, 116th Cong. (as referred to Comm. On Commerce, Sci., & Transp., April 10, 2019), available at www. congress.gov/bill/116th-congress/senate-bill/1108/all-info.
15. Terrell McSweeny & Brian O'Dea, The Implications of Algorithmic Pricing for Coordinated Effects Analysis and Price Discrimination Markets in Antitrust Enforcement, Antitrust, Fall 2017 at 75–76.
16. No. CR 15-00201 (N.D. Cal. 2015).
17. Margrethe Vestager, Comm'r, Eur. Comm'n, Algorithms and Competition, Remarks at the Bundeskartellamt 18th Conference on Competition, Berlin (March 16, 2017).
18. Ariel Ezrachi & Maurice Stucke, VIRTUAL COMPETITION: THE PROMISE AND PERILS OF THE ALGORITHM-DRIVEN ECONOMY 62 (2016).
19. Ohlhausen, supra note 1 at 1.
20. Emilio Calvano, Giacomo Calzolari, Vincenzo Denicolò, & Sergio Pastorello, Artificial Intelligence, Algorithmic Pricing and Collusion (April 2019), available at (Hyperlink)
21. Meyer v. Kalanick, No. 1:15-cv-09796-JSR (S.D.N.Y.).
22. McSweeny & Brian O'Dea, supra note 2 at 75.
23. Id. at 77.
24. D. Bruce Hoffman, Remarks at Competition and Consumer Protection in the 21st Century (November 14, 2018).
25. Id.
26. D. Bruce Hoffman, Remarks at Computer & Communications Industry Association (April 12, 2018); see also GCRI, US DOJ Deputy: Algorithmic Cartel Requires Agreement (Miami, Feb. 3, 2018); Ohlhausen, supra note 1 at 11.
27. Ohlhausen, supra note 1 at 3; see Andrew Finch, Remarks at the 44th Annual Conference on International Antitrust Law and Policy (New York, September 14, 2017).
27B. Rebecca Kelly Slaughter, Comm'r, FTC, Algorithms and Economic Justice, Remarks at UCLA School of Law (January. 24, 2020), available at (Hyperlink).
28. See Joint Statement on Innovative Efforts to Combat Money Laundering and Terrorist Financing (December 3, 2018), available at (Hyperlink).
29. Preparing for the Future of AI, National Science and Technology Council, October. 2016, available at (Hyperlink) files/ microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf. AI, Automation, and the Economy, Executive Office of the President, Dec. 2016, available at https:// (Hyperlink) 20Economy%20Report.pdf . Summary of the 2018 White House Summit on AI for American Industry, May 2018, The White House Office of Science and Technology Policy, available at (Hyperlink) Summary-Report-of-White-House-AI-Summit.pdf.
30. Available at (Hyperlink). Congress Takes Aim at the FUTURE of Artificial Intelligence, Blank Rome, January 2018, available at (Hyperlink)
31. For a discussion specific to regulator expectations of quantitative trading techniques, see SEC Enforcements Against Quant Managers Show a Pattern, Shearman & Sterling FinTech Blog (January 15, 2019), available at (Hyperlink) enforcements-against-quant-managers-show-a-pa.
32. In the Matter of BlueCrest Capital Management Limited, File No. 3-20162 (December 8, 2020), available at (Hyperlink).
32B. The CFTC's LabCFTC division has published a Primer on Artificial Intelligence in Financial Markets, available at (Hyperlink)
33. See Electronic Trading Risk Principles, 86 Fed Reg. 2048 (January 11, 2021).
33B. See Regulation Automated Trading; Withdrawal, 85 Fed. Reg. 42755 (July 15, 2020).
34. FINRA, 2019 Risk Monitoring and Examination Priorities Letter (January. 22, 2019), available at: (Hyperlink) Examination_Priorities_Letter.pdf.
35. FINRA Requests Comment on FinTech Innovation in the Broker-Dealer Industry (June 30, 2018), available at (Hyperlink).
36. FINRA, How the Cloud and Machine Learning Have Transformed FINRA Market Surveillance (July 16, 2018), available at (Hyperlink).
37. (Hyperlink), Technology Based Innovations for Regulatory Compliance ("RegTech") in the Securities Industry (September 2018), available at (Hyperlink) files/2018_RegTech_Report.pdf.
38. FINRA, Artificial Intelligence in the Securities Industry (June 2020), available at (Hyperlink).
39. FINRA, 2020 Risk Monitoring and Examination Priorities Letter (January 2020), available at (Hyperlink).
40. The most widely cited federal law in this area is the Computer Fraud and Abuse Act ("CFAA"), which makes it unlawful to "intentionally access" a computer or website without authorisation or in a manner that "exceeds authorized access". There are also numerous U.S. state law analogues.
Shearman & Sterling LLP
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Specifies the shapes and dimensions of ferrite cores for inductive components (transformers and chokes). Their coil is typically constructed by multi-layer board or is part of the motherboard. Also provides general considerations upon which the design of this range of cores is based.


When your church is growing you might reach a point where the building starts to feel small and cramped. When members of a church staff reach this point they often start to consider building a new church. However, long before you consider this expensive option, you should make sure that you are using your smaller building to its absolute capacity.
Most churches don't use space well, especially since the traditional way of using space is not efficient. The typical church sets up rooms for singular purposes, then is frustrated when it seems like there's never enough room.
Flexibility of space is the way to go for the modern small church. Here are a few tips to help you stop daydreaming and start recreating your space into one that you love for years to come.
There are so many areas in a church that aren't well-utilized. Each space can be used more effectively, giving you extra square footage you didn't know you had! For example, the foyer is often just a space for hanging coats and wiping feet off before entering the main part of the building. Instead, turn it into a welcome station.
Set up tables and stock them with muffins, coffee, and tea. Have church welcomers stand next to the tables to invite people in and make them feel comfortable. Set up a few comfortable chairs to invite people to stay a while and chat with one another. Not only will you use the space more effectively, you'll also help to build fellowship.
The days of bolted down wooden bench pews are in the past for most churches. While they have an old-fashioned feel that many like, they aren't practical or efficient. Other types of furniture throughout the building might be restricting your ability to effectively use the space, too. Always pick chairs and tables that can be moved around, stacked, and perhaps even folded up for effective storage. Don't skimp on quality or comfort, just make sure the furniture is useful in many areas.
3. Make the sanctuary more adaptable.
The sanctuary is the "home" of the church building and many feel that it should only be used for Sunday worship. If your church is open to the idea, try to use that space for more than just Sunday services. The sanctuary can provide the space you need for activities throughout the week, as well. By choosing flexible furniture for the space, you also make it possible to rearrange the space to fit any event.
4. Set up rooms for multipurpose use.
Every classroom and other functional space should be set up for use by many types of groups. Change the way you store items in each room so as to maximize space and use storage in better ways. Make it possible for everyone to use each space and you'll never run out of room for an event.
5. Stay open minded and continue to assess usage.
Some of your members might feel a little resistant to making the space more flexible and instilling big changes. Be patient with them and involve them in the process of transforming the building. Designate a team to continually assess how the church is using the building and find ways to maximize use. Strategize the way you use furniture and develop innovative storage solutions. Be open to new ideas, visit other small churches to glean new suggestions, and daydream together about the best ways to build a thriving congregation.
When you need to update your furnishings and make your church space more flexible, reach out to Advantage Church Chairs. We offer a wide selection of chairs and tables, customization options, and excellent customer service. You can find us online at advantagechurchchairs.com and by calling 1 (855) 294-0532.


In Sanskrit "Karna" means "Ear", "Pida" means "pressure", "Asana" means "Posture". The English name of this asana is "Ear Pressure Posture". It is also known as "Knee-to-ear pose". Position : Supine Type : Inversion Spiritual Awareness : Vishuddhi and Manipurs chakras Physical Awareness : Neck, ling, shoulders, vertebral column Dosha Suitability : Kapha Introducery Asanas : Sarvangasana, Setu Bandha Asana Procedure: From Halasana, exhale, bend the knees and place them next to the ears. If possible keep the hands interlaced on the floor behind the back, if not support your back as in salamba sarvangasana (supported shoulder stand) whichever way you choose, be sure not to hurt the spine. You can keep the palms flat, fingers interlaced, or hug your legs with the knees next to the ears.
How To Do Santolanasana (Balancing pose)?


text:
Beatrice Hulon Morrow Cannady (1889-1974)
Posted on January 18, 2007 September 11, 2019 by contributed by: Kimberley Mangun
Beatrice Hulon Morrow Cannady
Courtesy Oregon Historical Society
Civil rights activist, ambassador of interracial goodwill, editor and publisher of the (Portland) Advocate, Oregon's first African American female to practice law — Beatrice Morrow Cannady spent nearly 25 years working for equal rights for Oregon's two thousand black citizens.
Born in 1889 in Littig, Texas, Cannady graduated from Wiley College in Marshall, Texas, in 1908. She continued her education at the University of Chicago, but left in 1912 for Portland where she married Edward Daniel Cannady and became editor of the Advocate. Her activities included writing for the weekly newspaper, lecturing to high school and college students, broadcasting during Negro History Week, and holding interracial, interreligious teas to educate whites about black history and eliminate stereotypes. Cannady protested showings of the controversial film The Birth of a Nation, kept readers informed of acts of terror committed by the Oregon Ku Klux Klan, worked to desegregate a school in Vernonia, Oregon, and filed a lawsuit against the Portland School Board for its practice of segregating swimming pools. She graduated from Portland's Northwestern College of Law in 1922.
In 1929, the Portland Council of Churches nominated her for the Harmon Foundation's annual award for Distinguished Achievement Among Negroes in the field of race relations. Although the honor went to Tuskegee Institute President Robert R. Moton, contemporaries called her an "outstanding citizen," and a "leader among the people of her race." Cannady continued to be active in Portland in the early 1930s, and ran unsuccessfully for the state legislature in 1932. At the end of 1936, she moved to Southern California where she spent the next four decades pursuing the things she was passionate about, though in a far less public way. She wrote for the (Perris, Calif.) Precinct Reporter, and held informal interracial gatherings – "fireside meetings" – at the Perris ranch she shared with her third husband. Cannady died in Los Angeles in 1974.
Subjects: African American History, PeopleTerms: 20th Century (1900-1999), Gender - Women, United States - California, United States - Illinois, United States - Texas, United States - Oregon, Occupation-Attorney, Occupation-Newspaper Owner
Mangun, K. (2007, January 18) Beatrice Hulon Morrow Cannady (1889-1974). Retrieved from https://www.blackpast.org/african-american-history/cannady-beatrice-hulon-morrow-1889-1974/
Kimberley Ann Mangun, "Beatrice Morrow Cannady and the Advocate: Building and Defending Oregon's African American Community, 1912-1933" (Ph.D. Dissertation, University of Oregon, 2005).
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redpajama_set_name: RedPajamaCommonCrawl


They get to the root cause and help alleviate any shame associated with their sexual orientation. In 1973, The American Psychiatric Association changed the classification and reported that homosexuality was a normal and positive variation of human sexuality.
Traditional rehab centers are often ineffective at treating the LGBT community.
Although studying creativity is considered a legitimate scientific discipline nowadays, it is still a very young one. One of Guilford's most famous studies was the nine-dot puzzle.They may be negative towards LGBT individuals and may have their own prejudices towards them.LGBT rehab centers are aware of these issues and offer a nonjudgmental, understanding approach that a traditional rehab center may not.Gay drug rehab facilities that specialize in treating the LGBT community can help.Call These feelings are separate from their addiction issues but interrelated.
Click a link and chat with someone living near Sudbury now.


text:
Festus Dollas Davis[1, 2, 3]
Name Festus Dollas Davis
Born 13 Oct 1891 Doddridge, West Virginia, United States [3]
Birth 14 Oct 1891 Doddridge, West Virginia, United States [1, 2, 3, 4]
Columbia Mine
Occupation Between 1910 and 1914 [3, 4]
Driver, coal mine
Died 29 Mar 1914 Clarksburg, Harrison, West Virginia, United States [1, 3]
Buried Clarksburg, Harrison, West Virginia, United States [1]
Elkview Masonic Cemetery
Father Sylvanis Davis, b. 24 Jun 1846, Doddridge, West Virginia, United States
, d. 13 Aug 1929, Coal City, Harrison, West Virginia, United States
Mother Elizabeth Ann Smith, b. 1859, Upshur, West Virginia, United States
, d. 1 Nov 1945, Harrison, West Virginia, United States
Married 18 Jul 1887 Tucker, West Virginia, United States [3, 4]
Born - Address:
Long Run - 13 Oct 1891 - Doddridge, West Virginia, United States
Birth - ,Address:
Columbia Mine - 14 Oct 1891 - Doddridge, West Virginia, United States
Died - 29 Mar 1914 - Clarksburg, Harrison, West Virginia, United States
Elkview Masonic Cemetery - - Clarksburg, Harrison, West Virginia, United States
[S1634] West Virginia Vital Research Records .


Glad it worked out for you, Jim. I didn't think you'd have any issues.
I use Uber Assist all the time, works great.
United Spinal was recently contacted by a company in New Jersey that wants to create a new Uber-like system that would benefit the disabled. He's looking for feedback from those with disabilities that have had bad experiences with Uber.
Any wheelchair user Uber drivers out there???
Uber And Accessibility: What's Going On And What's Next?


text:
<?php
namespace Bee\Security\XmlNamespace;

use Bee\Context\Xml\XmlNamespace\HandlerSupport;
use Bee\Security\Elements;

/**
 * Created by IntelliJ IDEA.
 * User: mp
 * Date: Feb 17, 2010
 * Time: 6:35:32 PM
 * To change this template use File | Settings | File Templates.
 */

class Handler extends HandlerSupport {

    const INTERCEPT_METHODS = "intercept-methods";
    const GLOBAL_METHOD_SECURITY = 'global-method-security';

    public function init() {
//        $this->registerBeanDefinitionParser(Elements.LDAP_PROVIDER, new LdapProviderBeanDefinitionParser());
//        $this->registerBeanDefinitionParser(Elements.LDAP_SERVER, new LdapServerBeanDefinitionParser());
//        $this->registerBeanDefinitionParser(Elements.LDAP_USER_SERVICE, new LdapUserServiceBeanDefinitionParser());
		$this->registerBeanDefinitionParser(Elements::HTTP, new HttpSecurityBeanDefinitionParser());
//        $this->registerBeanDefinitionParser(Elements.USER_SERVICE, new UserServiceBeanDefinitionParser());
//        $this->registerBeanDefinitionParser(Elements.JDBC_USER_SERVICE, new JdbcUserServiceBeanDefinitionParser());
//        $this->registerBeanDefinitionParser(Elements.AUTHENTICATION_PROVIDER, new AuthenticationProviderBeanDefinitionParser());
        $this->registerBeanDefinitionParser(self::GLOBAL_METHOD_SECURITY, new GlobalMethodSecurityBeanDefinitionParser());
//        $this->registerBeanDefinitionParser(Elements.AUTHENTICATION_MANAGER, new AuthenticationManagerBeanDefinitionParser());
//        $this->registerBeanDefinitionParser(Elements.FILTER_INVOCATION_DEFINITION_SOURCE, new FilterInvocationDefinitionSourceBeanDefinitionParser());

        // Decorators
//        $this->registerBeanDefinitionDecorator(self::INTERCEPT_METHODS, new Bee_Security_Namespace_InterceptMethodsBeanDefinitionDecorator());
//        $this->registerBeanDefinitionDecorator(Elements.FILTER_CHAIN_MAP, new FilterChainMapBeanDefinitionDecorator());
//        $this->registerBeanDefinitionDecorator(Elements.CUSTOM_FILTER, new OrderedFilterBeanDefinitionDecorator());
//        $this->registerBeanDefinitionDecorator(Elements.CUSTOM_AUTH_PROVIDER, new CustomAuthenticationProviderBeanDefinitionDecorator());
//        $this->registerBeanDefinitionDecorator(Elements.CUSTOM_AFTER_INVOCATION_PROVIDER, new CustomAfterInvocationProviderBeanDefinitionDecorator());
    }
}
?>

redpajama_set_name: RedPajamaGithub


I choose to order this abacus from KApalan because I knew it would be well made. I am not disappointed.
I really liked this abacus because it is well made and very colorful. The kids will have a hard time breaking this one!
This abacus has helped my students very much during Math time. They love it and are very excited to use it every day. It is very sturdy and it is a great item for the price. I would recommend it to any teacher.


text:
Press Release -- April 3rd, 2013
Source: Digital Realty Trust
Digital Realty Trust, Inc. Schedules First Quarter 2013 Earnings Release And Conference Call
Company Release - 04/03/2013 16:30
SAN FRANCISCO, April 3, 2013 /PRNewswire/ -- Digital Realty Trust (NYSE:DLR, news, filings) today announced that it will hold a conference call on Friday, April 26, 2013 at 1:00 pm ET/10:00 am PT to discuss its first quarter 2013 financial results and operating performance. The conference call will feature Chief Executive Officer, Michael Foust, and Chief Financial Officer and Chief Investment Officer, A. William Stein. The Company will release its financial results for the first quarter 2013 before the market opens on Friday, April 26, 2013.
To participate in the live call, investors are invited to dial +1 (888) 701-6680 (for domestic callers) or +1 (706) 634-5758 (for international callers) and quote the conference ID # 30119432 at least five minutes prior to start time. A live webcast of the call will be available via the Investors section of Digital Realty's website at www.digitalrealty.com. Please go to the website at least 15 minutes early to register and download and install any necessary audio software. If you are unable to listen to the live conference call, a telephone and webcast replay will be available until 11:59 pm ET on Friday, May 10, 2013. The telephone replay can be accessed two hours after the call by dialing +1 (855) 859-2056 (for domestic callers) or +1 (404) 537-3406 (for international callers) and using the conference ID # 30119432. The webcast replay can be accessed on Digital Realty's website immediately after the live call has concluded.
About Digital Realty
Digital Realty Trust, Inc. focuses on delivering customer driven data center solutions by providing secure, reliable and cost effective facilities that meet each customer's unique data center needs. Digital Realty's customers include domestic and international companies across multiple industry verticals ranging from information technology and Internet enterprises, to manufacturing and financial services. Digital Realty's 119 properties, excluding three properties held as investments in unconsolidated joint ventures, comprise approximately 22.2 million square feet as of February 28, 2013, including 2.4 million square feet of space held for development. Digital Realty's portfolio is located in 32 markets throughout Europe, North America, Asia and Australia. Additional information about Digital Realty is included in the Company Overview, which is available on the Investors page of Digital Realty's website at www.digitalrealty.com.
A. William Stein Pamela Matthews Garibaldi
Chief Financial Officer and Vice President, Investor Relations and
Chief Investment Officer Corporate Marketing
Digital Realty Trust, Inc. Digital Realty Trust, Inc.
SOURCE Digital Realty Trust, Inc.
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Digital Realty Trust, Inc. Schedules Second Quarter 2011 Earnings Release and Conference Call
Digital Realty Trust, Inc. Schedules Fourth Quarter And Full Year 2012 Earnings Release And Conference Call
Digital Realty Trust, Inc. Schedules Fourth Quarter 2011 Earnings Release and Conference Call


text:
using Azure.Messaging.ServiceBus;

namespace Duracellko.PlanningPoker.Azure.ServiceBus
{
    /// <summary>
    /// When implemented, then object is able to convert messages of type <see cref="T:NodeMessage"/> to ServiceBusMessage and vice versa.
    /// </summary>
    public interface IMessageConverter
    {
        /// <summary>
        /// Converts <see cref="T:NodeMessage"/> message to ServiceBusMessage  object.
        /// </summary>
        /// <param name="message">The message to convert.</param>
        /// <returns>Converted message of ServiceBusMessage type.</returns>
        ServiceBusMessage ConvertToServiceBusMessage(NodeMessage message);

        /// <summary>
        /// Converts ServiceBusReceivedMessage message to <see cref="T:NodeMessage"/> object.
        /// </summary>
        /// <param name="message">The message to convert.</param>
        /// <returns>Converted message of NodeMessage type.</returns>
        NodeMessage ConvertToNodeMessage(ServiceBusReceivedMessage message);
    }
}


text:
using Metrics.PerfCounters;

namespace Metrics
{
    public static class PerformanceCountersConfigExtensions
    {
        public const string DefaultMachineCountersContext = "System";
        public const string DefaultApplicationCountersContext = "Application";

        /// <summary>
        /// Register all pre-defined performance counters as Gauge metrics.
        /// This includes System Counters, CLR Global Counters and CLR App Counters.
        /// </summary>
        public static MetricsConfig WithAllCounters(this MetricsConfig config, string systemContext = DefaultMachineCountersContext,
            string applicationContext = DefaultApplicationCountersContext)
        {
            return config.WithSystemCounters(systemContext)
                .WithAppCounters(applicationContext);
        }

        /// <summary>
        /// Register all pre-defined system performance counters as Gauge metrics.
        /// This includes: Available RAM, CPU Usage, Disk Writes/sec, Disk Reads/sec
        /// </summary>
        public static MetricsConfig WithSystemCounters(this MetricsConfig config, string context = DefaultMachineCountersContext)
        {
            config.WithConfigExtension((ctx, hs) => PerformanceCounters.RegisterSystemCounters(ctx.Context(context)));
            return config;
        }

        /// <summary>
        /// Register application level, CLR related performance counters as Gauge metrics.
        /// This includes: Mb in all heaps, time in GC, exceptions per sec, Threads etc.
        /// </summary>
        public static MetricsConfig WithAppCounters(this MetricsConfig config, string context = DefaultApplicationCountersContext)
        {
            config.WithConfigExtension((ctx, hs) => PerformanceCounters.RegisterAppCounters(ctx.Context(context)));
            return config;
        }
    }
}


Mr. BBBB doesn't have a personal statement currently.
Read This Before You Buy Any Speakers!!!!
Take the time to skim over this. I hope it will help some people to avoid some traps!
1/ What are good monitors ?
2/ " Closed cabinet " vs. " Bass –reflex "
That is the positive aspect of " active monitors " the negative is you carry around heavy speakers, and to help you out the manufacturers have very kindly given you a " bass-flex " design. Great, you think. But…..
Put your hand up to those ports/holes in the cabinet and you get nice little puffs of air (and sound) everytime there is a bass signal. Are they in phase with the signals coming from the woofer? The constructor will swear they are…..but are they? They are not.
Do a comparison between a " closed-cabinet (non-ported)" design and a " bass reflex " and you will hear the bass smear and spread of the sounds coming from the bass-flex cabinet. And while most speaker systems are of a similar design, especially hi-fi that love to give an exaggerated bass, it won't tell you what your bass frequencies really are.
Listen to a bass-reflex monitor system, can you pinpoint the bass? Or is it all wide and floppy?
Now do the same on closed monitor; the bass is tighter and more focused as there are only 2 sources, the woofers, no holes front or back. The transients will be better defined as they are correctly aligned giving you the accurate "in focus"picture.
Some manufacturers use "tranmission-line" design to partially overcome this. They build in a labyrinth for the sound to run through and thesound comes out 180 degrees out of phase. Your fundamental freqencies are 1 cycle behind,,,, but the attacks are better, because theyare ligned up again.
You can mix and record on Bass-Reflex lots of people do. But you run a much higher risk of NOT having the bass content you thought you did. And when you playback on a friend's hi-fi the sound can be without all the lovely bass you were sure was there! !
3/ Proximity vs. " a good room "
If you want to spend a lot of money fast, buy huge speakers, run them loud, and do an acoustic treatment of your listening room, You might impress your girl-friend.
Save money by buying small near-field monitors, run them at a low or comfortable level and don't treat your room. You will impress your wife and the master engineer!
If you are a Pro studio this won't be possible, some deaf musician will always ask you to turn it up, and most client still confuse LOUD with better, usually it is the opposite.
As you turn up your monitors, the room acoustics come into play. And your room is a swimming pool of full of little waves reinforcing or cancelling each other out. Each space has a resonance and it will be apparent in your mixes, as it will be the frequency that will be missing in your mix. If you mix with the vast majority of the sound coming directly to your ears from near-fields, you stand a better chance of not screwing it up.
Once again if you could only SEE the sound, the problems would very apparent.
I recently heard some very impressive Altec-Lansing " Voice of the Theater " speakers with 30cm woofers made of paper with very low mass and we got 109db with 2 watts of power! The resulting dynamics were incredible. The drums and bass were so present.
Only drawback, they are very fragile.
A boomer pushs air forward, brakes, stops, then moves backward, brakes, stops. ETC… The more mass or weight the Boomer has, the more inertia it has, the more difficulty it has to stop and reverse direction. The lighter the truck, the faster it will brake, try it with a load of bricks in a van.
Obviously the less weight or mass a boomer has, the easier it is to respond to the task of reproducing the sound wave asked of it.
The test for judging this is to listen at LOW level for the dynamics of the music. If the speaker has a low mass the impression of having dynamics will be there, if you have to turn up the volume to get a " punchy " sound the boomer is a fat, lazy guy who will never move like he should and will eat up all your amp's power to get dancing.
Here again "closed speakers" have an advantage over "bass-reflex". The expanding and compressing of the trapped air behind the woofer helps to overcome the inertia of getting the woofer moving and then getting it to brake, instead of overshooting. The "bass-reflex" models cannot brake as fast because they are sucking or pushing air out the port. They are free to overshoot it is only the amp calling it back magnetically that is operating.
Personally I mistrust them and would only mix with them if I were required to do a 5.1 surround mix where the playback system would include a sub-woofer.
First there is the problem of the phase relationship of the third source of the bass frequencies, and then there is the fact that all the bass frequencies will be accentuated. The risk again is that you get an artificial boost in the bass, and you do a bass-light mix. Where did all my lovely bass go? This is what you will be asking yourself when you listen to a friend's system WITHOUT a sub-woofer.
And then there is that "swimming pool" of a room to think about.
Imagine you are 40 years old sitting at a bar, and 6 feet away is a beautiful woman discussing not too discreetly with a friend how attractive you are, but you can't hear her.
Other users have left no comments for Mr. BBBB.


Hello there! I'm Emilie, a Minnesota mom of three boys who loves getting creative in the kitchen, cheering on my football team, traveling to new places and enjoying the simple things in life. I hope you'll stick around for easy recipes, fun projects and fabulous product finds to help you find the zest in your every day. Be sure to sign up for my free newsletter so you don't miss out on any updates!
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text:
FROM debian:sid

# Install ffserver.
RUN \
  apt-get update && \
  apt-get install -y ffmpeg libvpx1 && \
  rm -rf /var/lib/apt/lists/*

EXPOSE 8090

VOLUME ["/ffserver"]

WORKDIR /ffserver

CMD ffserver -f ffserver.conf


Hashgard is a distributed, trusted asset management protocol and a high functionality next generation digital finance public chain. Hashgard provides a one-stop blockchain solution for asset management in digital finance. Hashgard has a large number of business modules, including operational-level on-chain data, advanced versions of asset management autonomous organization, a smart contract system tailored for asset management, able to systematically support the issuance, management, trusteeship, settlement, audit, process control, and dispute arbitration of decentralized assets. Hashgard is an open, highly-expansive ecosystem. We will have in-depth collaboration with other ecosystems in terms of identity authentication, content distribution, predicting markets, asset lending, trusted data validation, cross-chain collaboration and so on, to jointly build infrastructure in the digital finance field and provide complete functional modules for decentralized applications on the chain. We will continue to expand new modules based on the needs of applications. Hashgard will solve the pain points in digital finance and protect investors' assets through technological innovation and ecological construction in an effort to ultimately achieve distributed inclusive finance.


MLS Now: Who is the frontrunner for the Coach of the Year?
MLS Now: Who deserves the Volkswagen MVP award?
Is Gyasi Zardes the best American striker in MLS?
MLS Now: Can Jones carry the Revs to the playoffs?


text:
using System;
using System.Data.Entity;
using Abp.Dependency;
using Abp.Domain.Entities;
using Abp.Domain.Repositories;
using Abp.EntityFramework.Repositories;
using Abp.Tests;
using Castle.MicroKernel.Registration;
using Shouldly;
using Xunit;

namespace Abp.EntityFramework.Tests.Repositories
{
    public class EntityFrameworkGenericRepositoryRegistrar_Tests : TestBaseWithLocalIocManager
    {
        public EntityFrameworkGenericRepositoryRegistrar_Tests()
        {
            var fakeBaseDbContextProvider = NSubstitute.Substitute.For<IDbContextProvider<MyBaseDbContext>>();
            var fakeMainDbContextProvider = NSubstitute.Substitute.For<IDbContextProvider<MyMainDbContext>>();
            var fakeModuleDbContextProvider = NSubstitute.Substitute.For<IDbContextProvider<MyModuleDbContext>>();

            LocalIocManager.IocContainer.Register(
                Component.For<IDbContextProvider<MyBaseDbContext>>().UsingFactoryMethod(() => fakeBaseDbContextProvider),
                Component.For<IDbContextProvider<MyMainDbContext>>().UsingFactoryMethod(() => fakeMainDbContextProvider),
                Component.For<IDbContextProvider<MyModuleDbContext>>().UsingFactoryMethod(() => fakeModuleDbContextProvider),
                Component.For<EntityFrameworkGenericRepositoryRegistrar>().LifestyleTransient()
                );

            using (var repositoryRegistrar = LocalIocManager.ResolveAsDisposable<EntityFrameworkGenericRepositoryRegistrar>())
            {
                repositoryRegistrar.Object.RegisterForDbContext(typeof(MyModuleDbContext), LocalIocManager);
                repositoryRegistrar.Object.RegisterForDbContext(typeof(MyMainDbContext), LocalIocManager);
            }
        }

        [Fact]
        public void Should_Resolve_Generic_Repositories()
        {
            //Entity 1 (with default PK)
            var entity1Repository = LocalIocManager.Resolve<IRepository<MyEntity1>>();
            entity1Repository.ShouldNotBe(null);
            (entity1Repository is EfRepositoryBase<MyBaseDbContext, MyEntity1>).ShouldBe(true);

            //Entity 1 (with specified PK)
            var entity1RepositoryWithPk = LocalIocManager.Resolve<IRepository<MyEntity1, int>>();
            entity1RepositoryWithPk.ShouldNotBe(null);
            (entity1RepositoryWithPk is EfRepositoryBase<MyBaseDbContext, MyEntity1, int>).ShouldBe(true);

            //Entity 2
            var entity2Repository = LocalIocManager.Resolve<IRepository<MyEntity2, long>>();
            (entity2Repository is EfRepositoryBase<MyMainDbContext, MyEntity2, long>).ShouldBe(true);
            entity2Repository.ShouldNotBe(null);

            //Entity 3
            var entity3Repository = LocalIocManager.Resolve<IMyModuleRepository<MyEntity3, Guid>>();
            (entity3Repository is EfRepositoryBase<MyModuleDbContext, MyEntity3, Guid>).ShouldBe(true);
            entity3Repository.ShouldNotBe(null);
        }

        public class MyMainDbContext : MyBaseDbContext
        {
            public virtual DbSet<MyEntity2> MyEntities2 { get; set; }

            public virtual DbSet<MyNonEntity> MyNonEntities { get; set; }
        }

        [AutoRepositoryTypes(
            typeof(IMyModuleRepository<>),
            typeof(IMyModuleRepository<,>),
            typeof(MyModuleRepositoryBase<>),
            typeof(MyModuleRepositoryBase<,>)
            )]
        public class MyModuleDbContext : MyBaseDbContext
        {
            public virtual DbSet<MyEntity3> MyEntities3 { get; set; }
        }

        public abstract class MyBaseDbContext : AbpDbContext
        {
            public virtual IDbSet<MyEntity1> MyEntities1 { get; set; }
        }

        public class MyEntity1 : Entity
        {

        }

        public class MyEntity2 : Entity<long>
        {

        }

        public class MyEntity3 : Entity<Guid>
        {

        }

        public class MyNonEntity
        {

        }

        public interface IMyModuleRepository<TEntity> : IRepository<TEntity>
            where TEntity : class, IEntity<int>
        {

        }

        public interface IMyModuleRepository<TEntity, TPrimaryKey> : IRepository<TEntity, TPrimaryKey>
            where TEntity : class, IEntity<TPrimaryKey>
        {

        }

        public class MyModuleRepositoryBase<TEntity, TPrimaryKey> : EfRepositoryBase<MyModuleDbContext, TEntity, TPrimaryKey>, IMyModuleRepository<TEntity, TPrimaryKey>
            where TEntity : class, IEntity<TPrimaryKey>
        {
            public MyModuleRepositoryBase(IDbContextProvider<MyModuleDbContext> dbContextProvider)
                : base(dbContextProvider)
            {
            }
        }

        public class MyModuleRepositoryBase<TEntity> : MyModuleRepositoryBase<TEntity, int>, IMyModuleRepository<TEntity>
            where TEntity : class, IEntity<int>
        {
            public MyModuleRepositoryBase(IDbContextProvider<MyModuleDbContext> dbContextProvider)
                : base(dbContextProvider)
            {
            }
        }
    }
}


text:
Free Business Forms
Fiverr Tips for Buyers: How Do You Find a Good Seller?
How to Turn Down Low-Pay Work as a Professional Freelancer
Realize Your Business Dreams With SMART Goals
5 Things to Know Before Working at a Start-Up
What Are the Best Free Online Logo Makers in 2022?
Target Market vs Demographic Market: What are the Differences?
How to Effectively Measure Your Digital Marketing Results
10 Quality Business Blogs that Accept Guest Posts in 2018
What We Can Learn from the Rise and Fall of Uber
How to Move a Small Business Out of the House
How to Start Building a Business and Stop Spinning Your Wheels
How To Be More Productive When Working at Home
5 Practical Ways to Motivate Employees in Your Small Business
All Posts by Susan Brown
Susan Brown is a serial entrepreneur and business and finance writer. After running and owning several small businesses, she decided to use her experiences to help other small and home-based business owners better manage their resources and get through those first tentative years of operation.
By: Susan Brown
Here is our rundown of the top free online logo makers and logo generators for small businesses, websites and ecommerce. Why Use an Online Logo Maker? A logo is not just a symbol that helps your audience remember your brand, it represents your brand's core values, mission, and unique style. But what can you do if… (0 comment)
Small Business Forex Trading: The Benefits of Offshore Forex Brokers
As the business world becomes increasingly competitive and global, access to an adequate amount of liquidity when dealing with foreign currencies has never been more critical. But achieving that liquidity is not so easy. Regulation and taxation sit on top of a forex market that is experiencing an unprecedented level of volatility and uncertainty. For… (0 comment)
In this guide, we're going to get into the nitty-gritty of what you need to know and do to find a good seller on Fiverr. I'll mention here at the beginning that I've been both an active buyer and seller on the site for close to seven years. So, these Fiverr tips for buyers are… (1 comment)
The difference between a target market vs demographic market is more than just a war of marketing buzzwords. It's the difference between making the most of your marketing budget versus throwing a large portion of your hard-earned money to the wind. If you've been involved in the marketing for your small business lately, then you… (0 comment)
Business Ideas for the Internet Savvy Mom in 2020
Thinking of starting your own business as a work-at-home mom, but are unsure what you should do? If pet sitting, grant writing and making gift baskets doesn't exactly float your boat, then check out these real business opportunities for Internet savvy moms in 2020. How to Be Successful in Business as a Work-at-Home Mom But… (2 comments)
27 Alternatives to eBay for Online Sellers and Buyers in 2022
10 Fiverr Alternatives in 2022 (That Aren't a Waste of Time)
27 Alternatives to Paypal in 2022 for Online Payments and Money Transfers
10 Powerful Public Speaking Tips for Introverts
Best Accounting Software for Freelancers & Small Business in 2022
One of the BEST Resume Guides Out There
Tanvi Punia on How To Be More Productive When Working at Home: "I love this article! Paying attention to your health and nutrition, including a balanced diet, is sooo important to mood,…"
Cs executive on 10 Fiverr Alternatives in 2022 (That Aren't a Waste of Time): "thanks for sharing this"
monika Wilsone on 27 Alternatives to eBay for Online Sellers and Buyers in 2022: "you can try druzza.com"
Carol Smith on Fiverr Tips for Buyers: How Do You Find a Good Seller?: "Thank you for this!!! I've heard a lot of horror stories from friends who bought gigs and the buyers kept…"
Tanvi Punia on 27 Alternatives to eBay for Online Sellers and Buyers in 2022: "TrueGether is a no-fee marketplace with features and functionality similar to eBay that makes it a much better and cheaper…"
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text:
package cache

import (
	"testing"

	"k8s.io/kubernetes/pkg/api"
	"k8s.io/kubernetes/pkg/labels"
	"k8s.io/kubernetes/pkg/util"
)

func TestStoreToMinionLister(t *testing.T) {
	store := NewStore(MetaNamespaceKeyFunc)
	ids := util.NewStringSet("foo", "bar", "baz")
	for id := range ids {
		store.Add(&api.Node{ObjectMeta: api.ObjectMeta{Name: id}})
	}
	sml := StoreToNodeLister{store}

	gotNodes, err := sml.List()
	if err != nil {
		t.Fatalf("Unexpected error: %v", err)
	}
	got := make([]string, len(gotNodes.Items))
	for ix := range gotNodes.Items {
		got[ix] = gotNodes.Items[ix].Name
	}
	if !ids.HasAll(got...) || len(got) != len(ids) {
		t.Errorf("Expected %v, got %v", ids, got)
	}
}

func TestStoreToReplicationControllerLister(t *testing.T) {
	store := NewStore(MetaNamespaceKeyFunc)
	lister := StoreToReplicationControllerLister{store}
	testCases := []struct {
		inRCs      []*api.ReplicationController
		list       func() ([]api.ReplicationController, error)
		outRCNames util.StringSet
		expectErr  bool
	}{
		// Basic listing with all labels and no selectors
		{
			inRCs: []*api.ReplicationController{
				{ObjectMeta: api.ObjectMeta{Name: "basic"}},
			},
			list: func() ([]api.ReplicationController, error) {
				return lister.List()
			},
			outRCNames: util.NewStringSet("basic"),
		},
		// No pod lables
		{
			inRCs: []*api.ReplicationController{
				{
					ObjectMeta: api.ObjectMeta{Name: "basic", Namespace: "ns"},
					Spec: api.ReplicationControllerSpec{
						Selector: map[string]string{"foo": "baz"},
					},
				},
			},
			list: func() ([]api.ReplicationController, error) {
				pod := &api.Pod{
					ObjectMeta: api.ObjectMeta{Name: "pod1", Namespace: "ns"},
				}
				return lister.GetPodControllers(pod)
			},
			outRCNames: util.NewStringSet(),
			expectErr:  true,
		},
		// No RC selectors
		{
			inRCs: []*api.ReplicationController{
				{
					ObjectMeta: api.ObjectMeta{Name: "basic", Namespace: "ns"},
				},
			},
			list: func() ([]api.ReplicationController, error) {
				pod := &api.Pod{
					ObjectMeta: api.ObjectMeta{
						Name:      "pod1",
						Namespace: "ns",
						Labels:    map[string]string{"foo": "bar"},
					},
				}
				return lister.GetPodControllers(pod)
			},
			outRCNames: util.NewStringSet(),
			expectErr:  true,
		},
		// Matching labels to selectors and namespace
		{
			inRCs: []*api.ReplicationController{
				{
					ObjectMeta: api.ObjectMeta{Name: "foo"},
					Spec: api.ReplicationControllerSpec{
						Selector: map[string]string{"foo": "bar"},
					},
				},
				{
					ObjectMeta: api.ObjectMeta{Name: "bar", Namespace: "ns"},
					Spec: api.ReplicationControllerSpec{
						Selector: map[string]string{"foo": "bar"},
					},
				},
			},
			list: func() ([]api.ReplicationController, error) {
				pod := &api.Pod{
					ObjectMeta: api.ObjectMeta{
						Name:      "pod1",
						Labels:    map[string]string{"foo": "bar"},
						Namespace: "ns",
					},
				}
				return lister.GetPodControllers(pod)
			},
			outRCNames: util.NewStringSet("bar"),
		},
	}
	for _, c := range testCases {
		for _, r := range c.inRCs {
			store.Add(r)
		}

		gotControllers, err := c.list()
		if err != nil && c.expectErr {
			continue
		} else if c.expectErr {
			t.Fatalf("Expected error, got none")
		} else if err != nil {
			t.Fatalf("Unexpected error %#v", err)
		}
		gotNames := make([]string, len(gotControllers))
		for ix := range gotControllers {
			gotNames[ix] = gotControllers[ix].Name
		}
		if !c.outRCNames.HasAll(gotNames...) || len(gotNames) != len(c.outRCNames) {
			t.Errorf("Unexpected got controllers %+v expected %+v", gotNames, c.outRCNames)
		}
	}
}

func TestStoreToPodLister(t *testing.T) {
	store := NewStore(MetaNamespaceKeyFunc)
	ids := []string{"foo", "bar", "baz"}
	for _, id := range ids {
		store.Add(&api.Pod{
			ObjectMeta: api.ObjectMeta{
				Name:   id,
				Labels: map[string]string{"name": id},
			},
		})
	}
	spl := StoreToPodLister{store}

	for _, id := range ids {
		got, err := spl.List(labels.Set{"name": id}.AsSelector())
		if err != nil {
			t.Errorf("Unexpected error: %v", err)
			continue
		}
		if e, a := 1, len(got); e != a {
			t.Errorf("Expected %v, got %v", e, a)
			continue
		}
		if e, a := id, got[0].Name; e != a {
			t.Errorf("Expected %v, got %v", e, a)
			continue
		}

		exists, err := spl.Exists(&api.Pod{ObjectMeta: api.ObjectMeta{Name: id}})
		if err != nil {
			t.Errorf("unexpected error: %v", err)
		}
		if !exists {
			t.Errorf("exists returned false for %v", id)
		}
	}

	exists, err := spl.Exists(&api.Pod{ObjectMeta: api.ObjectMeta{Name: "qux"}})
	if err != nil {
		t.Errorf("unexpected error: %v", err)
	}
	if exists {
		t.Errorf("Unexpected pod exists")
	}
}

func TestStoreToServiceLister(t *testing.T) {
	store := NewStore(MetaNamespaceKeyFunc)
	store.Add(&api.Service{
		ObjectMeta: api.ObjectMeta{Name: "foo"},
		Spec: api.ServiceSpec{
			Selector: map[string]string{},
		},
	})
	store.Add(&api.Service{ObjectMeta: api.ObjectMeta{Name: "bar"}})
	ssl := StoreToServiceLister{store}

	pod := &api.Pod{
		ObjectMeta: api.ObjectMeta{
			Name:   "foopod",
			Labels: map[string]string{"role": "foo"},
		},
	}

	services, err := ssl.GetPodServices(pod)
	if err != nil {
		t.Errorf("Unexpected error: %v", err)
	}

	if len(services) != 1 {
		t.Fatalf("Expected 1 service, got %v", len(services))
	}
	if e, a := "foo", services[0].Name; e != a {
		t.Errorf("Expected service %q, got %q", e, a)
	}
}


What is the maximum round-trip time between Cisco Unified Contact Center Express servers in a WAN deployment?
Apply a quot;WAN Standbyquot; license.
Reinstall a second Cisco Unified CCX node and add it to the cluster over WAN.
Do a fresh installation of the whole system as high availability over WAN.
Apply a quot;Warm Standbyquot; license.
What is the maximum number of contacts that Cisco Finesse supports in a phone book?
Which facility is provided to debug a Cisco Unified Contact Center Express script live with a real voice call?
In Cisco Unified Contact Center Express Serviceability, what is the status of the database subsystem on the Control Center page, if the database subsystem in not configured?
The name of the layout is passed to the agent desktop via a keyword variable that is named user layout.
The layout is associated to the team under Team Resources.
The layout is associated to the CSQ definition.
The layout is associated to the desktop layout under Team Resources.
Ifyou use skills-based routing, where is the agent selection criteria defined?


text:
Home / HERBS / Superfoods
Spirulina platensis (Spirulina)
Raw, Organic, Algae, Powder, 50 Gr from China (SKU 4332)
Nutritional Value Per 100g: Energy 1712KJ / 409kcal; Protein 63,5 gr; Fat 8,2g; Of which saturated fat 3,42g; Carbohydrates 16,1g; Of which sugars 0,52g; Fibre 7g; Sodium 450,5mg
Allergen Statement: We hereby certify that Spirulina powder we sell does not contain any substances causing allergies or intolerances as ingredients or by possibility of cross contamination. The list of allergens which are likely to cause adverse reactions in susceptible individuals is provided in Annex IIIa of Directive 2007/68/EC. Please note that this statement is based on the information provided by the manufacturer of the product.
Show all options at once
raw, organic
US Dollar Euro British Pound Japanese Yen Australian Dollar Canadian Dollar Chinese Yuan Danish Krone Hong Kong Dollar New Zealand Dollar Norwegian Krone Philippine Peso Polish Zloty Russian Rouble Singapore Dollar South African Rand Swedish Krona Swiss Franc
Show one option at a time
Sort by: --default-- Price Unit SKU VarietyVarietyPartPartFormFormWeightWeight
Variety: raw, organic
Part: algae
Unit:1 pc
A modern day "superfood", Spirulina is is a wealthy source of vitamins, proteins and amino acids.
Harvested in cultivation ponds, Spirulina is a microscopic plant cultivated on an island in the South China Sea.
Spirulina is dried using a natural process of spray evaporation to create Spirulina powder. This process preserves its nutritional content.
This is a food product. It is packaged and delivered as bulk herb product, for further processing and repackaging, and is not intended as a consumer package.
The buyer is responsible for appropriate handling, repacking and labeling this product according your local regulations.
Please read our Terms & Conditions before placing your order.
Related items ...
Reishi has been used for more than 4000 years in China. It is believed to be the oldest mushroom used and is also one of the most respected mushrooms.
Most often Ginkgo is associated with improved circulation and memory retention. Ginkgo has strong antioxidant properties, protecting both the central nervous system and the cardiovascular system from aging.
The Chaga Mushroom is most definitely one of the superior medicinal mushrooms and one of the most powerful antioxidants around.
Methyl-Sulfonyl-Methane
MSM is a non-animal-based nutritional supplement derived from Pine trees. Many researchers believe that sulfur, the eighth most abundant element in the body, is a sorely neglected mineral nutrient and plays an indispensable role in human nutrition.
Mucuna, also known as Velvet Bean, contains the very powerful neurotransmitter precursor L-Dopa. L-Dopa is precursor of the neurotransmitter Dopamine, an important brain chemical involved in mood, sexuality, and movement.
Myrciaria dubia
Camu Camu has an extraordinarily high vitamin C content and though this is the most important property of the Camu Camu fruit it also contains several other essential antioxidants.
Paullinia cupana
Guarana has been used for hundreds of years by Brazilian Indians as a general tonic for the body and as a source of energy.
Pouteria lucuma
The Lucuma is a subtropical fruit native to the Andean coastal valleys of Peru. The fruit is very nutritious, having high levels of carotene and vitamin B3.
Chocolate is a natural product made from the bean of the cacao tree Theobroma cacao.
Wheatgrass is the young grass of the common wheat plant, and provides a wide variety of natural health benefits. It's a great source of amino acids, minerals and vitamins.


text:
Q: How to get kendo window content from input text I have a question about kendo window.
I have a input like this

Your name

    <input type="text" id="name" >
    <button type="button" id="btnSend">Send</button>

And when I click on button, a window will pop-up. Here, I use Kendo window.
$('#btnSend').click(createAndShowPopup);
   var kendoWindowAssign = $("#windowForAssign");
   var title = "Sample title";
   var url = "";

   function createAndShowPopup(){
     kendoWindowAssign.kendoWindow({
       width: "650px",
       modal: true,
       height: '120px',
       iframe: true,
       resizable: false,
       title: title,
       content: null,
       visible: false
     });

     var popup = $("#windowForAssign").data('kendoWindow');
     popup.open();
     popup.center();
   }

But I don't know how to get data from input for content.
Thank you for your help!

A: You can use the content method for this. From Telerik docs:

Gets or sets the content of a Window.

Just pass in the input's value by using popup.content($("#name").val());
Dojo example here

Your name

  <input type="text" id="name" />
  <button type="button" id="btnSend">Send</button>

A: To get the input value, you can use jQuery as follows:
$("#name").val();

Learn more about jQuery's .val() method here
This will get you the desired value from the input field that you require to set the content for your window.
Now, set your content property that you are specifying in your initialization parameters for your kendo window.
Set the content property to following:
content: { template: kendo.template($("#name").val()) }

instead of: content: null
that you have set.
Learn more about kendo.Window's content method here
This should get you the input value for your kendo window on button click event.
Here I am using kendo.template method that kendo has to set the content for the kendo window and you can read more about that here

redpajama_set_name: RedPajamaStackExchange