text:
<?php

namespace Symfony\Bridge\PhpUnit;

use Doctrine\Common\Annotations\AnnotationRegistry;

/**
 * Collects and replays skipped tests.
 *
 * @author Nicolas Grekas <p@tchwork.com>
 */
class SymfonyTestsListener extends \PHPUnit_Framework_BaseTestListener
{
    private static $globallyEnabled = false;
    private $state = -1;
    private $skippedFile = false;
    private $wasSkipped = array();
    private $isSkipped = array();

    public function __construct(array $extraClockMockedNamespaces = array())
    {
        if ($extraClockMockedNamespaces) {
            foreach ($extraClockMockedNamespaces as $ns) {
                ClockMock::register($ns.'\DummyClass');
            }
        }
        if (self::$globallyEnabled) {
            $this->state = -2;
        } else {
            self::$globallyEnabled = true;
        }
    }

    public function __destruct()
    {
        if (0 < $this->state) {
            file_put_contents($this->skippedFile, '<?php return '.var_export($this->isSkipped, true).';');
        }
    }

    public function startTestSuite(\PHPUnit_Framework_TestSuite $suite)
    {
        $suiteName = $suite->getName();

        if (-1 === $this->state) {
            echo "Testing $suiteName\n";
            $this->state = 0;

            if (!class_exists('Doctrine\Common\Annotations\AnnotationRegistry', false) && class_exists('Doctrine\Common\Annotations\AnnotationRegistry')) {
                AnnotationRegistry::registerLoader('class_exists');
            }

            if ($this->skippedFile = getenv('SYMFONY_PHPUNIT_SKIPPED_TESTS')) {
                $this->state = 1;

                if (file_exists($this->skippedFile)) {
                    $this->state = 2;

                    if (!$this->wasSkipped = require $this->skippedFile) {
                        echo "All tests already ran successfully.\n";
                        $suite->setTests(array());
                    }
                }
            }
            $testSuites = array($suite);
            for ($i = 0; isset($testSuites[$i]); ++$i) {
                foreach ($testSuites[$i]->tests() as $test) {
                    if ($test instanceof \PHPUnit_Framework_TestSuite) {
                        if (class_exists($test->getName(), false) && in_array('time-sensitive', \PHPUnit_Util_Test::getGroups($test->getName()), true)) {
                            ClockMock::register($test->getName());
                        } else {
                            $testSuites[] = $test;
                        }
                    }
                }
            }
        } elseif (2 === $this->state) {
            $skipped = array();
            foreach ($suite->tests() as $test) {
                if (!$test instanceof \PHPUnit_Framework_TestCase
                    || isset($this->wasSkipped[$suiteName]['*'])
                    || isset($this->wasSkipped[$suiteName][$test->getName()])) {
                    $skipped[] = $test;
                }
            }
            $suite->setTests($skipped);
        }
    }

    public function addSkippedTest(\PHPUnit_Framework_Test $test, \Exception $e, $time)
    {
        if (0 < $this->state) {
            if ($test instanceof \PHPUnit_Framework_TestCase) {
                $class = get_class($test);
                $method = $test->getName();
            } else {
                $class = $test->getName();
                $method = '*';
            }

            $this->isSkipped[$class][$method] = 1;
        }
    }

    public function startTest(\PHPUnit_Framework_Test $test)
    {
        if (-2 < $this->state && $test instanceof \PHPUnit_Framework_TestCase) {
            $groups = \PHPUnit_Util_Test::getGroups(get_class($test), $test->getName());

            if (in_array('time-sensitive', $groups, true)) {
                ClockMock::register(get_class($test));
                ClockMock::withClockMock(true);
            }
        }
    }

    public function endTest(\PHPUnit_Framework_Test $test, $time)
    {
        if (-2 < $this->state && $test instanceof \PHPUnit_Framework_TestCase) {
            $groups = \PHPUnit_Util_Test::getGroups(get_class($test), $test->getName());

            if (in_array('time-sensitive', $groups, true)) {
                ClockMock::withClockMock(false);
            }
        }
    }
}


text:
Q: How to extract filenames with two file extensions? I have a folder containing multiple files which have the double extension ".nii.gz". I want to extract the filenames using os.path.basename but this function returns the filenames only with the first extension ".nii". How can I extract the full filenames with both extensions?

A: Why not use the good old str.split
filename = 'file.nil.gz'
li = filename.split('.')
basename = li[0]
extensions = li[1:]

print(basename)
#file
print(extensions)
#['nil', 'gz']

A: This function extracts the entire name, even with multiple ".":
from os import listdir
from os.path import isfile, join
mypath = 'blabla'
onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]
print(onlyfiles)

if you don't want to see the extention, then here is the add-on:
onlyfiles = [f.split('.')[0] for f in listdir(mypath) if isfile(join(mypath, f))]
print(onlyfiles)

A: I think, best practice is to use pathlib for operations with file path.
Code:
from pathlib import Path

print(Path("C://filename.tar.gz").suffixes)

Result:
['.tar', '.gz']

To extract all suffixes into one string just join them:
print("".join(Path("C://filename.tar.gz").suffixes))

redpajama_set_name: RedPajamaStackExchange


text:
From Huts to Cities: How Changes in Dwellings Have Impacted Microbe Exposure for the Human Immune System
February 12, 2016 (2:00PM)
The shift from living in jungle huts to cities has dramatically changed human exposure to certain microbes, which could have important implications for healthy immune function, according to a study led by researchers from NYU Langone Medical Center and published online February 12 in Science Advances.
"Our study found that urban living spaces increase the number of human-associated microbes we are exposed to, while decreasing our exposure to the outdoor, environmental microbes with which humans co-evolved," says microbiologist Maria Dominguez-Bello, PhD, lead author of the new study, which was funded by the Alfred P. Sloan Foundation.
Microbes inhabit our surroundings as well as our bodies, and the potential health impact of the interplay between bodily and environmental microbial species is worthy of study, say the authors. Thus, their first mission was to detail the microbial changes that occur across a gradient of urbanization.
"The remarkable changes in home microbial content across differing levels of urbanization raise the possibility that the reduced microbial exposure to environmental bacteria seen in modern homes contributes to immune and metabolic disorders, from asthma to obesity, which have become the new disease paradigm in the industrialized world," adds Dominguez-Bello, an associate professor in the Department of Medicine at NYU Langone. "That said, our pilot study was small in size and limited to one geographical region, so larger studies are needed before we can generalize these patterns."
The research team—which included architects, environmental engineers, and microbiologists from the United States, Puerto Rico, Brazil, and Peru—examined microbes on the walls and floors of homes in the Amazon river basin, ranging from an isolated Amerindian community at the Ecuador-Peru border to a small Peruvian village to the large town of Iquitos in Peru to the Westernized, Brazilian city of Manaus. The team collected samples from ten houses in each location, and then used state-of-the-art genomic and statistical techniques to analyze the mix of bacterial DNA from each sample.
Combining their samples and available databases, the team applied statistical tests that accurately tracked the sources of the bacterial microbes on each floor and wall. Specifically, they found more human bacteria in the town and city houses, such as Streptococcaceae and Lactobacillaceae, and decreasing proportions of environmental bacteria.
On the other hand, the bacteria found on the floors and walls of rural and jungle living spaces were proportionally higher in environmental bacterial species, including soil bacteria, like Mesorhizobium and Luteimonas from water sources, as well as Rickettsiella carried by spiders and insects.
Importantly, the researchers also found that the many walls that define urban homes come to reflect the sources of human bacteria, which in turn depend on the function of a given room. Kitchens and bathrooms, for instance, acquire function-dependent microbial signatures, such as oral Streptococcus and gut Enterobacteriaceae in bathrooms, and food-associated bacteria in kitchens.
"Excessively humanized spaces that are poorly ventilated could increase transmission of pathogens," says Dominguez-Bello. "Understanding the consequences of architectural changes on microbial exposures will be important to improving future home design and ultimately to human health."
Other key contributors to the study were from the University of Puerto Rico in Rio Piedras, and included Jean Ruiz-Calderon, a PhD student from the Department of Biology; Humberto Cavallin from the School of Architecture; Luis Pericchi in the Department of Mathematics and Rafael Rios in the Department of Environmental Sciences. Also making important contributions were Se Jin Song at University of Colorado, Boulder; Rob Knight at the University of California, San Diego; Henrique Pereira of the Federal University of Amazonas, Manaus, Brazil; Luciana Paulino of the Center for Natural Sciences and Humanities, Federal University of ABC, Santo André, SP, Brazil; and Atila Novoselac in the Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin.
Greg Williams
gregory.williams@nyumc.org
Adults in Toyland Casino Night Raises $860,000
Surgeon & Scientist Named Director of Gynecologic Oncology
Sleep Changes From Fetal Alcohol Exposure Affect Learning
Language Spoken May Affect Concussion Test Results
Brain Cooling Technique Protects Speech
Bacteria Humans Live With Changed by Westernized, Urban Lifestyle
Origins of 'Rage' Identified in Brains of Male Mice
Renowned Surgeon to Lead New NYU Langone Transplant Institute
Brain Formation Patterns Suggest Why Early Trauma May Leave No Clues
Researchers Partly Restore Baby's Natural Bacteria After C-Section

redpajama_set_name: RedPajamaCommonCrawl


text:
Judicial Watch Sues to Force Damage Assessment of Hillary Clinton's Mishandling of Classified Emails
Lawsuit asks court to declare Defendants' failure to conduct a damage assessment of Clinton's email practices and prepare a report to be unlawful
(Washington, DC) – Judicial Watch today filed suit against the Office of the Director of National Intelligence (ODNI) and the Department of State to require them to conduct, as required by law, an assessment and prepare a report on how and whether Hillary Rodham Clinton's email practices as U.S. Secretary of State damaged national security.
The suit, filed in the U.S. District Court for the District of Columbia, cites the requirement in Intelligence Community Directive ("ICD") 732, issued on June 27, 2014, that a damage assessment be conducted whenever there is "an actual or suspected unauthorized disclosure or compromise of classified national intelligence that may cause damage to U.S. national security" ICD 732(D)(2) (Judicial Watch v. Office of the Director of National Intelligence et al. (No. 1:17-cv-00508)).
The suit specifically refers to FBI Director James Comey's statement on July 5, 2016, concerning Clinton's use of an unsecure email account while conducting official State Department business:
From the group of 30,000 e-mails returned to the State Department, 110 emails in 52 e-mail chains have been determined by the owning agency to contain classified information at the time they were sent or received. Eight of those chains contained information that was Top Secret at the time they were sent; 36 chains contained Secret information at the time; and eight contained Confidential information, which is the lowest level of classification.
Despite the FBI findings and the requirements of Intelligence Community Directive 732, however, the suit argues that: "On or about September 14, 2016, ODNI announced that no Intelligence Community-wide damage assessment into Secretary Clinton's email practices would be conducted and that no individual Intelligence Community member would conduct such an assessment."
In addition to the ODNI and the State Department, the suit names Michael Dempsey in his official capacity as acting director of national intelligence, William Evanina in his official capacity as national counterintelligence executive, and Rex W. Tillerson in his official capacity as U.S. secretary of state.
Judicial Watch has made repeated requests for information concerning any ODNI investigation of Clinton's email practices:
On September 16, 2016, Judicial Watch sent a Freedom of Information Act (FOIA) request to ODNI seeking access to records about the decision not to conduct the required assessment.
When ODNI failed to respond to the request within the time required by FOIA, Judicial Watch filed suit. As of the date of this complaint, ODNI still has not produced any responsive records.
On January 10, 2017, Judicial Watch sent a letter to then-Director Clapper, National Counterintelligence Executive Evanina, and then-Secretary John Kerry formally requesting that "the damage assessment required by ICD 732 be commenced without further delay."
To date Judicial Watch has received no response to its January 10, 2017, request for a damage assessment, and Judicial Watch is not aware of any report or announcement indicating that the assessment and resulting report required by ICD 732 has been conducted or prepared. Judicial Watch is therefore asking the court to:
[O]rder Defendants to conduct the required damage assessment and prepare a report in accordance with ICD 732 …
"The Obama administration conspired with Hillary Clinton regarding her emails, so it is no surprise that Obama officials wouldn't want to hold her to account for her mishandling of classified materials," said Judicial Watch President Tom Fitton. "This lawsuit is an opportunity for the Trump administration to get back to basics on the Clinton email scandal and find out what damage was done to our national security as a result of her illicit email practices."
Standard | Posted in Uncategorized	| Tagged Clinton's Email, Corruption, Judicial Watch	| 3 Comments
Fox News pulls Napolitano after his Trump wiretap claims
Fox News has reportedly pulled Andrew Napolitano from the air indefinitely after he made claims that a British intelligence agency had wiretapped Trump Tower.
Napolitano, a Fox News analyst, is not scheduled to appear on the network in the near future, the Los Angeles Times reported, according to people familiar with the situation.
Last week, Napolitano made the claims about the Government Communications Headquarters (GCHQ) spying on Trump at former President Barack Obama's behest.
White House press secretary Sean Spicer then quoted Napolitano's remarks while defending the president's claims earlier this month that the former president had wiretapped Trump Tower before the election. "Judge Andrew Napolitano made the following statement, quote, 'Three intelligence sources have informed Fox News that President Obama went outside the chain of command,'" Spicer said during a daily press briefing last week.
Standard | Posted in Uncategorized	| Tagged Andrew Napolitano, Cover-up, Fox News, obama Administration, Trump wiretap claims	| 4 Comments


Brave Frontier RPG Android - Free Download Brave Frontier RPG App - gumi Inc.
"Discover the world of Grand Gaia in this strategy game"
Game Goal: You become the "Chosen" in a fantasy world where you must create a team of heroes to battle the God Maxwell and his armies.
How to Play: It is a turn-based role-playing game that pits your army of heroes against monsters and other players teams, all while exploring different scenarios.
Graphics: Good quality, with attention to detail and very attractive designs.
Sound: It has good quality but the music is a bit repetitive.
Content Classification: For all audiences.
Developer: gumi Inc. is based in Japan	- Developer recommended by Google Play.
Game very simple and addictive.
Lots of heroes and powers.
You are not able to play with friends and share heroes.
Do you like Brave Frontier RPG?


text:
Higher Education Fellows
Faculty Contributors
Become a Correspondent
Arizona State has doubled tuition, finds $500K to give to Clintons
The school's tuition has doubled in the past decade.
Arizona State University paid $500,000 to the Clinton Foundation in order to host an event with the Clintons in 2014.
New feminist chemistry class teaches that science is racist
By Shelby Kearns
'EXTREMELY harmful': Pro-life student misrepresented in Gavin Newsom pro-abortion ad…again
By Logan Dubil '23
'Chief experience officer' for students is the newest job in higher ed
PROF. JENKINS: The reasons why DEI is a costly failure
By Rob Jenkins
Woman who faked Native American heritage was awarded university residency
Professors argue sex-segregated sports stem from 'white supremacist,' 'hetero-patriarch…
By Thomas Stevenson '23
Kaitlyn Schallhorn | Former Reporter, Fox News
Thursday, May 28, 2015 9:35 AM
While Arizona State University has almost doubled its tuition over the past 10 years amid claims that it needs more state money, the school somehow had the funds to give half a million dollars to the Clinton Foundation.
According to The Arizona Republic, the public university paid $500,000 to the Clinton Foundation to host the former president-Bill Clinton; former-Secretary of State Hillary Clinton; and their daughter, Chelsea, during a Clinton Global Initiative University (CGI U) event in 2014. Former Secretary of State Hillary Clinton, Bill's wife, is the current front-runner for the Democratic nomination for the 2016 presidential race.
Mark Johnson, an ASU spokesman, told The Republic that the university was a payment for the event—not a contribution to the Clintons.
"ASU played host to the CGI University in March 2014, which featured former President Bill Clinton and former secretary of State Hillary Clinton in a program aimed at bringing together college students to find practical, innovative solutions to global challenges," Johnson told the newspaper. "The report you cited reflects the fact that the university co-invested in this educational and promotional opportunity, which was co-produced for our students, and for students from around the world. No state funds were used for this purpose."
The Republic also reported that while the university has lobbied the State Department, university officials claim the lobbying occurred after Hillary stepped down from the position.
The CGI U website also boasts that more than 1,100 students attended the event to raise $60,000 and participate in a "Day of Action" in the community.
"Student attendees had the opportunity to attend plenary sessions, working sessions, and other special events covering topics across CGI U's five focus areas and allowing them to network with their peers, build skills, and identify potential partnerships," the website for the event states. "Youth organizations, topic experts, and celebrities joined students at the CGI U meeting to help them gain the skills and knowledge needed to take action on their commitments."
Earlier this month Arizona's Board of Regents voted to approve tuition increases for both undergraduate and graduate students at ASU, the University of Arizona, and Northern Arizona University. Arizona Gov. Doug Ducey (R) also recently approved a budget that cut more than $100 million from public universities, The Arizona State Press reported.
"In this regard ASU seems to hold themselves accountable to their own pet projects, as opposed to ASU students," Richard Moorehead, a senior history major at ASU, told Campus Reform. "The money they wasted on the Clinton Foundation event could have been used for scholarships and genuine education."
"I realize bringing high-profile speakers can raise the profile of the university, but no speaker's time is worth $500,000, especially if that money is funneled into a presidential campaign," Moorhead said. He did not attend the CGI U event.
Ryan Hartwig, a recent ASU graduate, told Campus Reform that the $500,000 was "way too much for any celebrity or politician."
"It really makes me question what ASU does with their money, while they continue to complain about state budget cuts," Hartwig said.
The Washington Free Beacon reported that ASU has said it did not use state funds to pay the Clintons, but the university has not disclosed where the money actually came from.
Sen. John McCain (R-Ariz.) participated in the event last year but told The Republic that had he known the price tag, it wouldn't have been worth it.
"Frankly, if I had known that that was the situation, that they were being paid $500,000, I would have spoken up at the time that I thought it was outrageous," McCain said.
Despite both Hillary and Bill receiving huge speaking fees, Hillary claimed last summer that her family left the White House "dead broke and in debt."
The university did not respond to requests for comment from Campus Reform in time for publication.
Follow the author of this article on Twitter: @K_Schallhorn
Kaitlyn Schallhorn
Former Reporter, Fox News
Kaitlyn Schallhorn previously, was a reporter for Fox News, Editor in Chief of the Missouri Times, and a news reporter for the Santa Barbara News-Press. She is currently the City Editor at Orange County Register. Kaitlyn was also a Campus Correspondent with Campus Reform.
What you stand for is what defines you
Conservative students on college campuses are marginalized, threatened, and silenced by threatening students who oppose their views, or radicalized liberal professors or administrators. Campus Reform—and readers like you—are pushing back. Progressives would rather threaten you with violence, silence your conservative views, or call for you to be "canceled" from our society if you oppose them. They say your views are dangerous, hateful, fearful, or racist.
They have it all wrong. What we stand for defines us—it always has.
We can no longer remain silent. It is time for conservatives young and old to unite as a single voice to boldly proclaim what we stand for and oppose the mob.
Will you join with us, select the principles you stand for, and sign your name below?
I stand for the preservation of free speech all across our country—where I'm allowed to express my beliefs without fear of condemnation if my opinion differs from yours.
I stand for protections for conservative students who are illegally being threatened or silenced on college campuses all across our country.
I stand for the federal funding to be pulled from colleges and universities when they silence conservative views or students.
Copyright © 2023 Leadership Institute


text:
Equity Income Opportunity, 20 Ticker: FGQPBX
Distributions | Summary | Pricing | Performance
A unit investment trust that seeks above-average total return through a combination of capital appreciation and dividend income by investing in a fixed portfolio of equity securities.
Please note that there is no assurance the objective will be met.
Product Code: EQIO20
Portfolio Status: Secondary
Initial Offer Date: 03/09/2022
Secondary Date: 06/03/2022
Portfolio Ending Date: 06/09/2023
Tax Structure: Regulated Investment Company
Distributions: Monthly
Initial Offer Price: $10.0000
NAV(*): $9.7139
POP(*): $9.8469
Remaining Deferred Sales Charge: $0.0000
* As of Trade Date: 01/31/2023 4:00pm ET
The Public Offering Price (POP) represents the net asset value per unit plus any applicable organization costs and sales charges. The Net Asset Value (NAV) represents the value per unit of a trust's portfolio securities and other assets reduced by applicable deferred sales charges and other liabilities.
Historical 12-Month Distribution of Trust Holdings:^
Rate (as of 1/31/2023) Per Unit (as of 1/30/2023)
3.51% $0.34560
^ There is no guarantee the issuers of the securities included in the trust will declare dividends or distributions in the future. The historical 12-month distribution per unit and historical 12-month distribution rate of the securities included in the trust are for illustrative purposes only and are not indicative of the trust's distribution or distribution rate. The historical 12-month distribution per unit is based on the weighted average of the trailing twelve month distributions paid by the securities included in the portfolio. The historical 12-month distribution rate is calculated by dividing the historical 12-month distributions by the trust's offering price. The historical 12-month distribution and rate are reduced to account for the effects of fees and expenses, which will be incurred when investing in a trust. Certain of the issuers may have reduced their dividends or distributions over the prior twelve months. The distribution per unit and rate paid by the trust may be higher or lower than the amount shown above due to certain factors that may include, but are not limited to, a change in the dividends or distributions paid by issuers, actual expenses incurred, or the sale of securities in the portfolio. For trusts that include funds, distributions may include realized short term capital gains, realized long-term capital gains and/or return of capital.
Holdings Analysis Click here to Perform a Benchmark Comparison
Sector Weighting
Communication Services 6.30%
Consumer Discretionary 10.25%
Consumer Staples 6.41%
Energy 7.41%
Financials 16.86%
Health Care 17.23%
Industrials 9.57%
Information Technology 15.86%
Materials 6.74%
Real Estate 0.00%
Utilities 3.08%
Large Mid Small
Growth 39.84% 10.59% 0.00%
Value 39.56% 9.61% 0.11%
Style Weighting
Large-Cap Growth 39.84%
Large-Cap Value 39.56%
Mid-Cap Growth 10.59%
Mid-Cap Value 9.61%
Small-Cap Growth 0.00%
Small-Cap Value 0.11%
Undetermined 0.29%
U.S. Stocks 92.53% Other 0.29%
Non-U.S. Stocks 7.18%
The style and sector characteristics of the portfolio are determined as of 01/30/2023 and will likely vary thereafter due to market fluctuations in the underlying securities.
Weighted Average Market Cap $154,992
Price/Earnings 13.59
Price/Book 3.14
All market capitalization numbers are in USD$ Millions. All figures are subject to change.
Holdings Export Current Holdings | View Initial Holdings
Name Symbol Sector Style Weighting
Merck & Co., Inc. MRK Health Care Large-Cap Growth 4.66%
Darden Restaurants, Inc. DRI Consumer Discretionary Mid-Cap Growth 4.01%
Morgan Stanley MS Financials Large-Cap Value 3.89%
The Interpublic Group of Companies, Inc. IPG Communication Services Mid-Cap Growth 3.72%
JPMorgan Chase & Co. JPM Financials Large-Cap Value 3.68%
Devon Energy Corporation DVN Energy Large-Cap Value 3.51%
The Coca-Cola Company KO Consumer Staples Large-Cap Growth 3.46%
Chevron Corporation CVX Energy Large-Cap Value 3.46%
Texas Instruments Incorporated TXN Information Technology Large-Cap Growth 3.44%
Lockheed Martin Corporation LMT Industrials Large-Cap Growth 3.43%
Rio Tinto Plc (ADR) RIO Materials Large-Cap Value 3.40%
The Home Depot, Inc. HD Consumer Discretionary Large-Cap Growth 3.38%
AbbVie Inc. ABBV Health Care Large-Cap Growth 3.37%
Broadcom Inc. AVGO Information Technology Large-Cap Growth 3.37%
BHP Group Ltd (ADR) BHP Materials Large-Cap Value 3.34%
Hewlett Packard Enterprise Company HPE Information Technology Mid-Cap Value 3.34%
Johnson & Johnson JNJ Health Care Large-Cap Growth 3.22%
General Dynamics Corporation GD Industrials Large-Cap Value 3.21%
Fidelity National Financial, Inc. FNF Financials Mid-Cap Value 3.16%
Citizens Financial Group, Inc. CFG Financials Mid-Cap Value 3.11%
Pfizer Inc. PFE Health Care Large-Cap Value 3.10%
Public Service Enterprise Group Incorporated PEG Utilities Large-Cap Value 3.08%
Cisco Systems, Inc. CSCO Information Technology Large-Cap Value 2.99%
Altria Group, Inc. MO Consumer Staples Large-Cap Growth 2.95%
United Parcel Service, Inc. (Class B) UPS Industrials Large-Cap Growth 2.93%
Blackstone Inc. BX Financials Large-Cap Growth 2.91%
CVS Health Corporation CVS Health Care Large-Cap Value 2.88%
Best Buy Co., Inc. BBY Consumer Discretionary Mid-Cap Growth 2.86%
HP Inc. HPQ Information Technology Large-Cap Growth 2.72%
Verizon Communications Inc. VZ Communication Services Large-Cap Value 2.58%
Woodside Energy Group Limited (ADR) WDS Energy Large-Cap Value 0.44%
F&G Annuities & Life, Inc. FG Financials Small-Cap Value 0.11%
Total Number of Holdings: 32
Underlying Securities information represented above is as of 01/30/2023 but will vary with future fluctuations in the market.
Risk Considerations
Equity Risk. An investment in a portfolio containing common stocks is subject to certain risks, such as an economic recession and the possible deterioration of either the financial condition of the issuers of the equity securities or the general condition of the stock market.
COVID-19 Economic Impact Risk. The COVID-19 global pandemic and the ensuing policies enacted by governments and central banks have caused and may continue to cause significant volatility and uncertainty in global financial markets. While the U.S. has resumed "reasonably" normal business activity, many countries continue to impose lockdown measures. Additionally, there is no guarantee that vaccines will be effective against emerging variants of the disease.
Foreign Securities Risk. Securities of non-U.S. issuers are subject to additional risks, including currency fluctuations, political risks, withholding, the lack of adequate financial information, and exchange control restrictions impacting non-U.S. issuers.
Large-Cap Risk. Large capitalization companies may grow at a slower rate than the overall market.
Market Disruption Risk. In February 2022, Russia invaded Ukraine which has caused and could continue to cause significant market disruptions and volatility within the markets in Russia, Europe, and the United States. The hostilities and sanctions resulting from those hostilities could have a significant impact on certain investments as well as performance.
Mid-Cap Risk. An investment in a portfolio containing mid-cap companies is subject to additional risks, as the share prices of certain mid-cap companies are often more volatile than those of larger companies due to several factors, including limited trading volumes, products, financial resources, management inexperience and less publicly available information.
Term Risk - 15 months. Although this unit investment trust terminates in approximately 15 months, the strategy is long-term. Investors should consider their ability to pursue investing in successive portfolios, if available. There may be tax consequences unless units are purchased in an IRA or other qualified plan.
Volatility Risk. The value of the securities held by the trust may be subject to steep declines or increased volatility due to changes in performance or perception of the issuers.
Operational Risk. As the use of Internet technology has become more prevalent in the course of business, the trust has become more susceptible to potential operational risks through breaches in cybersecurity.
The style and capitalization characteristics used to describe the stocks are designed to help you understand how they fit into your overall investment plan. Due to changes in the value of the stocks the characteristics may vary over time. In general, growth stocks have high relative price-to-book ratios while value stocks have low relative price-to-book ratios. In determining market capitalization characteristics, we analyze the market capitalizations of the 3,000 largest stocks in the U.S. (excluding foreign securities, ADRs, limited partnerships and regulated investment companies) on a monthly basis. Companies with market capitalizations among the largest 10% are considered Large-Cap securities, the next 20% are considered Mid-Cap securities and the remaining securities are considered Small-Cap securities.
You should carefully consider the trust's investment objectives, risks, and charges and expenses before investing. Contact your financial professional or call First Trust Portfolios, L.P. at 1.800.621.1675 to request a prospectus, which contains this and other information about the trust. Read it carefully before you invest.
This product information does not constitute an offer to sell, or a solicitation of an offer to buy securities in any state to any person to whom it is not lawful to make such an offer. Sales of any of these securities must include prospectus delivery and the services of a retail broker/dealer duly licensed in the appropriate states.
Not FDIC Insured, Not Bank Guaranteed and May Lose Value.
CUSIP identifiers have been provided by CUSIP Global Services, managed on behalf of the American Bankers Association by FactSet Research Systems Inc. and are not for use or dissemination in a manner that would serve as a substitute for any CUSIP service. The CUSIP Database, ©2023 CUSIP Global Services. "CUSIP" is a registered trademark of the American Bankers Association.
Fund Cusip Information
30324V347 (Cash)
30324V354 (Reinvest)
30324V362 (Cash-Fee)
30324V370 (Reinvest-Fee)


text:
Q: I got errors when I install drivers for opencl and Amd gpu with amdgpu-install in Ubuntu 22.04 To use Davinci Resolve 18. I have to install some drivers.
My Graphic card(amd radeon-620) - AMD® Hainan / Mesa Intel® HD Graphics 620 (KBL GT2)
I install amdgpu-install
   amdgpu-install

Here's my errors.
The following packages have unmet dependencies.
 rocm-llvm : Depends: libstdc++-5-dev but it is not installable or
                      libstdc++-7-dev but it is not installable
             Depends: libgcc-5-dev but it is not installable or
                      libgcc-7-dev but it is not installable
             Recommends: gcc-multilib but it is not going to be installed
             Recommends: g++-multilib but it is not going to be installed
 xserver-xorg-amdgpu-video-amdgpu : Depends: xorg-video-abi-24

E: Unable to correct problems, you have held broken packages.

A: After installing below package, Davinci Resolve works
sudo apt-get install -y mesa-opencl-icd


A couple of weeks ago, Electrolux revealed their plans for creating vacuum cleaners from resin in the various gyres. A challenging project no doubt, but probably doable. Now comes the most over-the-top-pie-in-the-sky-what-were-they-smoking-and-not-sharing proposal yet: take all the plastic and make a floating island with it.
Kinda neat how that the wildlife even likes the island - notice the osprey circling above it.
But this would be far more than just an island; it would be home for living creatures, like people.
Looks like something out of SimCity.
This can't be serious. The first tropical storm through the area would toss this around like the plastic bags it would be made from.


It's the height of winter, and temperatures here are almost sub-zero. For someone with already dry skin and hair, that means consistently slathering on hair and face masks. In fact, I LOVE masking. It's so satisfying to me to have a clean slate of a face and glob on a moisturizer, toner, mask, whatever you like, really. You could say that this act is one of my pet faves – an ordinary thing that brings me extraordinary joy. That being said, I'm still a makeup fanatic, and an even bigger fan of affordable products. As can be seen my many Twitter memes, Sephora isn't the cheapest place to buy your beauty products, but there are still a lot of little goodies that don't break the bank to lift your spirits. I've rounded up reasonably-priced products that might help you start off the weekend right, and they're all under $25!
Posted in BEAUTY, EVERYTHING ELSE. Bookmark the permalink.
If You Do Anything, Follow This One Spring Trend.


text:
\section{Introduction}

The development of advanced robotic technologies has released traditional robots isolated by fences or other protective barriers to environments with human beings \cite{zanchettin2019towards}. Such kinds of robots that are safe and intelligent enough to work alongside or directly interact with humans are called collaborative robots, including lightweight industrial robots, social robots, service robots, etc \cite{stein2020collaborative}. Human motion detection plays a significant role in the motion planning and control of collaborative robots to improve the safety and efficiency of human-robot interaction. On the one hand, the detected human motion will be used as the input information of various real-time motion planning algorithms to avoid the potential collision between a robot and a human subject and guarantee the safety of human-robot interaction \cite{sajedi2022uncertainty}. On the other hand, the detected human motion can be further used for human motion analysis and prediction to enable robots to comprehend human intention and enhance the efficiency of human-robot interaction \cite{unhelkar2018human}. In this paper, we aim to develop an effective human motion detection algorithm with high accuracy and efficiency

Detection of moving objects in a video with static background is usually done by separating foreground from background. The moving objects are typically considered as the foreground. Background modeling plays an important role in moving object detection. Many subspace learning methods such as principal component analysis (PCA) have been developed to model background \cite{bouwmans2009subspace} by reducing the dimensionality and learning the intrinsic low-dimensional subspaces. In practice, a background matrix can be generated by concatenating the vectorized versions of background images in a video, which naturally possesses the low-rank structure. Thus sparsity of singular values is pursued for a background matrix. In one of the most popular methods - robust PCA (RPCA) \cite{candes2011robust}, nuclear-norm regularization is used to enforce the matrix low-rankness as a convex relaxation of the matrix rank. Numerous improved variants of RPCA have been proposed for and a comprehensive review can be found \cite{bouwmans2017decomposition}. To further enhance the low-rankness, some other regularizations based on matrix norms, including the matrix max-norm \cite{javed2015background,javed2016spatiotemporal}, have been developed for modeling the background. More recently, adaptive regularization techniques have been developed to promote sparsity and achieve fast convergence of the regularized algorithms. For example, the weighted nuclear norm (WNN) regularization has shown effectiveness in various image and data processing applications \cite{gu2014weighted}, which can be considered as a natural extension of reweighted L1 \cite{candes2008enhancing} and a more general error function based regularization \cite{guo2021novel}. In this paper, we use the WNN-based regularization imposed on the matrix singular values to enforce the adaptive low-rankness. Moreover, a video usually has complicated geometry and varying smoothness in either the spatial domain or the temporal domain. To preserve those geometrical structures in the background, we create a spatial graph and a temporal graph, which are then embedded in the graph regularizations of the background matrix. Generation of the spatial graph is implemented by comparing the patchwise similarity to exploit the nonlocal similarity. To reduce the computational cost, we only consider the $k$-nearest neighboring pixels in terms of similarity when calculating the pairwise similarity. On the other hand, the $\ell_1$-regularization is imposed on the foreground due to its sparsity. Thus far, by integrating various regularizations, we propose a nonconvex spatiotemporal graph regularized moving object detection model, which is solved by applying the alternating direction method of multipliers (ADMM). By introducing a few auxiliary variables and splitting regularizers, we obtain a sequence of subproblems. One quadratic subproblem is solved by gradient descent, and the other subproblems all have closed-form solutions which can be implemented efficiently. Furthermore, we test our algorithm on the two real RGB videos containing a whole-body motion and an arm motion under a static background, respectively. Performance is compared with other related methods in terms of background recovery and foreground detection accuracy.

The rest of this paper is organized as follows. In Section \ref{sec:LR}, we provide a brief introduction of moving object detection and low-rank based models. In Section \ref{sec:method}, we propose a novel spatiotemporal dual graph regularized moving object detection method based on the WNN regularization. Numerical experiments on two realistic videos with moving objects and the results are reported in Section \ref{sec:exp}. Finally, conclusions of this research and future work are presented in Section \ref{sec:con}.

\section{Low-Rank Models}\label{sec:LR}
Throughout the paper, we use boldface lowercase letters to denote vectors and uppercase letters to denote matrices. For $p\geq1$, the $\ell_p$-norm of a vector $\vx\in\R^n$ is given by $\norm{\vx}_p=(\sum_{i=1}^n|x_i|^p)^{1/p}$. The entry-wise $\ell_1$-norm of a matrix $X\in\R^{n\times m}$ is defined as $\norm{X}_1=\sum_{i,j}|x_{ij}|$ where $x_{ij}$ is the $(i,j)$th entry of $X$. The Frobenious norm of $X$, denoted by $\norm{X}_F$, is defined as $\sqrt{\sum_{i,j}|x_{ij}|^2}$. The nuclear norm of $X$, denoted by $\norm{X}_n$, is defined as the sum of all singular values of $X$. We use the symbol $\diag(\vx)$ to denote a diagonal matrix whose diagonal entries form the vector $\vx$, and $I_n$ as the $n$-by-$n$ identity matrix.

Consider a video with static backgrounds consisting of $m$ frames of gray-scale images with size $n_1\times n_2$. By reshaping each image as a vector, we convert a video to a matrix $D$ of size $n\times m$ where $n=n_1n_2$ is the number of total spatial pixels. Assume that $D$ can be decomposed into the background component $L$ and the foreground component $S$, where $L,S\in\R^{n\times c}$. Here we let $S$ correspond to the moving object. That is, we have $D=L+S$ in the noise-free case. In order to retrieve $L$ and $S$ from $D$ simultaneously, we need to apply regularization techniques on both variables. Due to the static background, the matrix $L$ typically has low-rank structures. In the meanwhile, the object occupies a small portion of each frame and thereby $S$ is sparse. Thus we could consider the problem
\[
\min_{L,S}\mathrm{rank}(L)+\lambda\norm{S}_1\quad \st\quad D=L+S.
\]
Here $\lambda>0$ is a regularization parameter and $\rank(L)$ equals the number of nonzero singular values of $L$. Since this problem is NP-hard, matrix rank is replaced by the nuclear norm which leads to the RPCA model \cite{candes2011robust}
\[
\min_{L,S}\norm{L}_n+\lambda\norm{S}_1\quad\st\quad D=L+S.
\]
In some RPCA variants \cite{shen2014online,javed2015background}, the matrix max-norm based regularizer has been used to replace the nuclear norm
\[
\min_{L,S}\norm{L}_{\max}+\lambda\norm{S}_1\quad \st\quad D=L+S.
\]
Here the max-norm of $L$ is given by $\norm{L}_{\max}=\min_{L=UV'}\norm{U}_{2\to\infty}\norm{V}_{2\to\infty}$ where $V'$ is the transpose of $V$ and $\norm{U}_{2\to\infty}=\max_{\norm{\vx}_2=1}\norm{U\vx}_\infty$. See \cite{srebro2005rank} for the connections between the matrix nuclear norm and the max-norm. They both are convex and can be used to describe the low-rankness of the background matrix.
More recently, adaptive regularizers such as reweighted L1\cite{candes2008enhancing} and its more general version - error function based regularization \cite{guo2021novel} have shown the advantages over the traditional $\ell_1$-regularization in terms of sparsity and convergence speed. They can be naturally extended to the singular values to promote the low-rankness. In this paper, we adopt a weighted nuclear matrix norm \cite{gu2014weighted} as the regularizer
\begin{equation}\label{eqn:matnorm}
\norm{L}_{W,*}:=\sum_{i}w_i\sigma_i(L),
\end{equation}
where $\sigma_i(L)$ is the $i$th singular value of $L$ in the decreasing order and $w_i\geq0$ is the $i$-th weight. Although the weighted nuclear norm minimization (WNNM) is not convex in general, it has shown outstanding performance in a lot of image processing applications \cite{gu2014weighted}. Here we use the exponential function-based weighting scheme that will be detailed in the next section.

\section{Proposed Method}\label{sec:method}
Moving object detection (MOD) is one of the fundamental tasks in robotic applications. The problem is typically cast as the foreground and the background separation. Besides the low-rankness assumption of the background matrix, we can use spatial and temporal graphs to handle the sophisticated geometry. To split multiple regularization terms in the proposed MOD model, we apply the ADMM framework to design an efficient algorithm.

\subsection{Spatial and Temporal Graph Laplacians}\label{sec:lap}
In what follows, we will describe the generation of spatial and temporal graph Laplacians and their corresponding graph regularizers on the background.

For a reshaped video $D\in\mathbb{R}^{n\times m}$, rows and columns of $D$ correspond to the spatial and the temporal samples, respectively. Consider a weighted temporal graph $G_t=(V_t,E_t,A_t)$ where $V_t=\{\vv_i^t\}_{i=1}^m$ is a set of temporal samples, $E_t$ is an edge set and $A_t\in\R^{m\times m}$ is the adjacency matrix which defines the weights. First, we generate the adjacency matrix $A_t$ whose $(i,j)$-th entry is given by
\[
(A_t)_{i,j}=\exp\left(-\frac{\norm{\vv_i^t-\vv_j^t}_2^2}{h_t^2}\right),\quad i,j\in\{1,\ldots,m\}
\]
where $h_t>0$ is a temporal filtering parameter. Let $W_t$ be the degree matrix of $G_t$ where $(W_t)_{i,i}=\sum_{j=1}^m(A_t)_{i,j}$. Next we define the symmetrically normalized temporal graph Laplacian $\Phi_t\in\R^{m\times m}$ given by
\[
\Phi_t=I_m-W_t^{-1/2}A_tW_t^{-1/2}.
\]
Note that $W_t^{-1/2}$ is a diagonal matrix whose $i$-th diagonal entry is $(W_t)_{i,i}^{-1/2}$.

Likewise, we consider a weighted spatial graph $G_s=(V_s,E_s,A_s)$ where $V_s=\{\vv_i^s\}_{i=1}^n$ is a set of spatial samples, $E_s$ is the edge set and $A_s\in\R^{n\times n}$ is a spatial adjacency matrix. Slightly different from the construction of $A_t$, we consider the patchwise similarity in the spatial domain for $A_s$. Specifically, the $(i,j)$-th entry of $A_s$ is given by
\[
(A_s)_{i,j}=\exp\left(-\frac{\norm{\mathcal{N}(\vv_i^s)-\mathcal{N}(\vv_j^s)}_F^2}{h_s^2}\right),\, i,j\in\{1,\ldots,n\}
\]
where $\mathcal{N}(\vv_i^s)\in\R^{p^2\times m}$ is a reshaped version of the video patch centered at the $i$-th pixel and $h_s>0$ is the spatial filtering parameter. To reduce the computational cost, we consider the $k$-nearest neighbors in terms of location for calculating $A_s$. Specifically, we use the four-nearest neighboring spatial pixels to compute the patch-based similarity for generating the spatial adjacency matrix $A_s$. Likewise we use the four-nearest neighboring temporal pixels to compute $A_t$.  Moreover, it is worth noting that Gaussian smoothing could be embedded to the calculation of patchwise similarity in the presence of noise. Now we define the symmetrically normalized graph Laplacian in the spatial domain as
\[
\Phi_s=I_n-W_s^{-1/2}A_sW_s^{-1/2}.
\]
Similar to $W_t$, $W_s$ is the degree matrix corresponding to $G_s$ which can be obtained using $A_s$. Furthermore, we save all graph Laplacians as sparse matrices to circumvent the out-of-memory issue.

\subsection{Robust Dual-Graph Regularized Method}
Let $D\in\R^{n\times m}$ be the reshaped video with $n$ pixels and $m$ frames. Assume that $\Phi_s\in\R^{n\times n}$ and $\Phi_t\in\R^{m\times m}$ are the graph Laplacians in the respective spatial and temporal domains, which are obtained from Section~\ref{sec:lap}. We propose a robust foreground-background separation model of the form
\[\begin{aligned}
&\min_{L,S\in\R^{n\times m}}\norm{D-L-S}_1+\lambda_1\norm{L}_{W,*}+\lambda_2
\norm{S}_1\\
&+\frac{\gamma_1}2\tr(L^T\Phi_s L)+\frac{\gamma_2}2\tr(L\Phi_t L^T).
\end{aligned}\]
Here we adopt the $L_1$-norm in the first data fidelity term to enforce the robustness of the method and suppress the outliers for recovering the low-rank component. The last two terms are the graph regularizations in the respective spatial and the temporal domains, which can be used to enforce the spatiotemporal smoothness for the background. By introducing an auxiliary variables $U$ and $V$, we rewrite the above problem
\begin{multline*}
\min_{L,S,U,V}\norm{V}_1+\lambda_1\norm{U}_{W,*}+\lambda_2
\norm{S}_1+\frac{\gamma_1}2\tr(L^T\Phi_s L)\\
+\frac{\gamma_2}2\tr(L\Phi_t L^T),\quad \st \quad U=L,\,D-L-S=V.
\end{multline*}
Define the augmented Lagrangian
\begin{multline*}
\mathcal{L}
=\norm{V}_1+\lambda_1\norm{U}_{W,*}+\lambda_2
\norm{S}_1+\frac{\gamma_1}2\tr(L^T\Phi_s L)\\
+\frac{\gamma_2}{2}\tr(L\Phi_t L^T)+\frac{\rho_1}2\norm{U-L+\widetilde{U}}_F^2+\frac{\rho_2}2\norm{D-L-S+V+\widetilde{V}}_F^2.
\end{multline*}
Based on the ADMM framework, we derive the algorithm
\[
\left\{\begin{aligned}
L&\leftarrow \argmin_L\frac{\gamma_1}2\tr(L^T\Phi_s L)+\frac{\gamma_2}2\tr(L\Phi_t L^T)\\&
+\frac{\rho_1}2\norm{U-L+\widetilde{U}}_F^2+\frac{\rho_2}2\norm{D-L-S+V+\widetilde{V}}_F^2\\
S&\leftarrow \argmin_S\lambda_2\norm{S}_1+\frac12\norm{D-L-S}_F^2\\
U&\leftarrow \argmin_U\lambda_1\norm{U}_{W,*}+\frac{\rho_1}2\norm{U-L+\widetilde{U}}_F^2\\
&=\argmin_U\frac{\lambda_1}{\rho_1}\norm{U}_{W,*}+\frac12\norm{U-L+\widetilde{U}}_F^2\\
V&\leftarrow\argmin_V\norm{V}_1+\frac{\rho_2}2\norm{D-L-S+V+\widetilde{V}}_F^2\\
\widetilde{U}&\leftarrow \widetilde{U}+(U-L)\\
\widetilde{V}&\leftarrow \widetilde{V}+(D-L-S+V)
\end{aligned}\right.
\]
For the first $L$-subproblem, we apply the gradient descent to solve it.
Note that although the critical equation is a Sylvester equation, the giant matrix $\Phi_s$ will make the standard Sylvester solver very slow.
The gradient of the objective function is
\[\begin{aligned}
\nabla f(L)&=\gamma_1\Phi_sL+\gamma_2 L\Phi_t+\rho_1(L-U-\widetilde{U})\\
&+\rho_2(L-D+S-V-\widetilde{V}).
\end{aligned}
\]
Note that $\frac{d}{dX}\tr(X^TAX)=(A+A^T)X=2AX$ if $A$ is a symmetric matrix
At each inner loop, we update $L$ with fixed $S,U,\widetilde{U},V,\widetilde{V}$ via
\begin{equation}\label{eqn:Lupdate}
L\leftarrow L-dt\cdot \nabla f(L),
\end{equation}
where $dt>0$ is a step size. It can be empirically shown that only a few steps of gradient descent is required here.
The $S$-subproblem has the closed-form solution
\begin{equation}\label{eqn:Supdate}
S\leftarrow \shrink(D-L, \lambda_2).
\end{equation}
Here the shrinkage operator is defined as $(\shrink(A,\mu))_{ij}=\mathrm{sign}(a_{ij})\cdot\max\{|a_{ij}|-\mu,0\}$ where $a_{ij}$ is the $(i,j)$-th entry of $A$. It can be shown that the $U$-subproblem has the closed-form solution via a weighted version of the singular value thresholding operator (SVT)
\begin{equation}\label{eqn:Uupdate}
U\leftarrow A\widetilde{\Sigma}B,\quad \widetilde{\Sigma}=\diag(\shrink(\sigma(\widehat{L}),w_i\lambda_1/\rho_1))
\end{equation}
where $A\Sigma B$ is the singular value decomposition (SVD) form of the matrix $\widehat{L}:=(L-\widetilde{U})$, $\sigma(\widehat{L})$ is the vector containing all the singular values of $\widehat{L}$ with $\sigma_i(\widehat{L})$ as its $i$-th component. Here the weights are constructed iteratively based on the singular values of the matrix $L$ from the previous iteration:
\begin{equation}\label{eqn:Wupdate}
w_i=\exp(-\sigma_i^2(\widehat{L})/{\sigma^2}).
\end{equation}

Finally, the $V$-subproblem is similar to the $S$-subproblem with the closed-form solution and thereby $V$ is updated via
\begin{equation}\label{eqn:Vupdate}
V\leftarrow \shrink(L+S-D-\widehat{V},\rho_2).
\end{equation}

We remove motionless frames in the data set if two consecutive frames have small overall changes, i.e., the $\ell_1$-norm of the difference vector of the two adjacent columns of $D$ is below a threshold. The stopping criteria are based on the relative changes in $L$ and $S$, i.e.,
$
\frac{\norm{L^{i+1}-L^i}_F}{\norm{L^i}_F}<tol
$
and
$
\frac{\norm{S^{i+1}-S^i}_F}{\norm{S^i}_F}<tol
$
where $L^t$ and $S^t$ are the obtained background and foreground matrices at the $i$-th iteration and $tol$ is the preassigned tolerance.
The entire algorithm is summarized in Algorithm~\ref{alg}, which can be extended to handle RGB data sets channelwise and then fuse the results from various color channels. In this work, we focus on gray scale videos by converting all RGB data to gray scale ones.

\begin{algorithm}
\caption{Robust Dual-Graph Regularized Moving Object Detection}\label{alg}
\begin{algorithmic}
\State\textbf{Inputs}: reshaped test video $D\in\R^{n\times m}$, graph filtering parameters $h_s,h_t>0$, parameters $\lambda_1,\lambda_2,\gamma_1,\gamma_2,\rho_1,\rho_2>0$, maximum outer loops $T_{out}$, maximum inner loops $T_{in}$, tolerance $tol$
\State\textbf{Outputs}: background $L$ and foreground $S$
\State Generate graph Laplacians $\Phi_t$ and $\Phi_s$
\State Initialize $L$ and $S$
\For{$i=1,2,\ldots,T_{out}$}
        \For{$j=1,2,\ldots,T_{in}$}
        \State Update $L$ via \eqref{eqn:Lupdate}
        \EndFor
        \State Update $S$ via \eqref{eqn:Supdate}
        \State Update $U$ via \eqref{eqn:Uupdate} and singular values $\sigma_i(\widehat{L})$
        \State Update $W$ via \eqref{eqn:Wupdate}
        \State Update $V$ via \eqref{eqn:Vupdate}
        \State $\widetilde{U}\leftarrow \widetilde{U}+(U-L)$
        \State $\widetilde{V}\leftarrow \widetilde{V}+(D-L-S+V)$
        \State {Exit the loop if the stopping criteria are met.}
\EndFor
\end{algorithmic}
\end{algorithm}

\section{Numerical Experiments}\label{sec:exp}
In this section, we will test the proposed Algorithm~\ref{alg} on two simulated moving object images. For comparison, we include three closely related algorithms based on the fast robust principal component analysis (RPCA) \cite{candes2011robust}: (1) Largangian optimization method for unconstrained RPCA (LAGO) (2) stable principal component pursuit (SPCP) \cite{driggs2019adapting} and (3) SPGL1 \cite{van2009probing} for solving the problem $\min_{L,S}\max\{\norm{L}_*,\lambda\norm{S}_1\}$ subject to $\norm{D-L-S}_F\leq\varepsilon$. Their source codes can be found in fastRPCA \url{https://github.com/stephenbeckr/fastRPCA} \cite{aravkin2014}. There are two groups of metrics for comparing the performance, i.e., comparing the foreground and the background. First, the static background image is extracted from the low-rank component of the given video. We take the mean column of the low-rank matrix $L$ and then reshape it as a matrix. We use the following metrics to evaluate the background recovery quality:
\begin{itemize}
\item relative error (RE): $\mathrm{RE}(\widehat{L},L)=\frac{\norm{L-\widehat{L}}_F}{\norm{L}_F}$;
\item peak signal-to-noise ratio (PSNR): $\mathrm{PSNR}(\widehat{L},L)=20\log({I_{\max}}/\sqrt{\norm{\widehat{L}-L}_F^2/(n_1n_2)})$.
\end{itemize}
Here $\widehat{L}$ is the estimate of the ground truth $L\in\R^{n_1\times n_2}$, and $I_{\max}$ is the maximum image intensity set as 1. In our experiments, all of the videos to be processed are scaled to the range $[0,1]$.

For the foreground assessment, we apply the hard thresholding to extract the foreground masks and then compute the following metrics. Here ground truth foreground masks are manually made. Let $TP$ be the true positive counting the foreground pixels correctly labeled as foreground, $FP$ be the false positive counting the background pixels incorrectly labeled as foreground, and $FN$ be the false negative counting the foreground pixels incorrectly labeled as background. The three metrics are defined as follows.
\begin{itemize}
    \item Precision (Pr): $\mbox{Pr}={TP}/{(TP+FP)}$
    \item Recall (Re):
    $\mbox{Re}={TP}{(TP+FN)}$
    \item F-measure (Fm):
    $\mbox{Fm}=2{\mbox{Re}}/{\mbox{Pr}}$
\end{itemize}
All the three metrics are between 0 and 1. The higher the value is, the more accurate the result is. We also find that various hard thresholding strategies may cause different one or two metrics high while the remaining ones are low.

A Microsoft Azure Kinect Sensor was used to record human motion, including one 1-MP depth sensor, one 7-microphone array, one 12-MP RGB video camera, and one accelerometer and gyroscope (IMU) sensor. Designed to pull together multiple AI sensors in a single device, Azure Kinect sensors have been employed for various applications, such as building telerehabilitation solutions, democratizing home fitness, etc.
In this study, only the RGB video camera was used to record human motion and test the proposed algorithm. All numerical experiments were run in Matlab R2021a on a desktop computer with Intel CPU i9-9960X RAM 64GB and GPU Dual Nvidia Quadro RTX5000 with Windows 10 Pro.

\subsection{Experiment 1: Whole Body Movement Video}
For the first experiment, we consider a video capturing whole-body movement, which was recorded when one student volunteer was walking naturally at an average speed in a lab room. The video of interest consists of 60 frames where each frame has $150\times 200$ pixels. Due to the limited lighting conditions, there are inevitable shadows of the person and brightness variations in the foreground. In Fig.~\ref{fig:exp1bg}, we compare the recovered background from the various methods. For each method, we take the mean column of the recovered $L$ followed by reshaping it as a matrix, i.e., we use the mean of the obtained backgrounds over 60 frames. There are some white spots in the blackboard mistakenly recognized as foreground in the LAGO result and quite a few still exist in the SPCP result. Both SPGL1 and our results can recover the background well except the light shadow on the ground. In Fig.~\ref{fig:exp1fg}, we show the recovered foregrounds at the first and the last frame. The LAGO result has blurry edges for the human body, and both SPGL1 and our results can detect the shadow motion. The quantitative comparison of the recovered foreground and background for all methods is reported in Table~\ref{tab1}. Our method performs best in terms of all the comparison metrics for this video data.

\begin{figure*}[h]
\centering\setlength{\tabcolsep}{2pt}
\begin{tabular}{ccccc}
\includegraphics[width=0.19\textwidth]{exp1_Lg}&
\includegraphics[width=0.19\textwidth]{exp1_lag_Lm}&
\includegraphics[width=0.19\textwidth]{exp1_SPCP_Lm}&
\includegraphics[width=0.19\textwidth]{exp1_SPG_Lm}&
\includegraphics[width=0.19\textwidth]{exp1_our_Lm}\\[-2pt]
 Ground truth &  LAGO &  SPCP &  SPGL1 &  Alg.1
\end{tabular}
\vspace{-2pt}
\caption{Visualization results of various methods on background recovery for the walking video. }\label{fig:exp1bg}
\end{figure*}

\begin{figure*}[h]
\centering\setlength{\tabcolsep}{2pt}
\begin{tabular}{ccccc}
\includegraphics[width=0.19\textwidth]{exp1_D1}&
\includegraphics[width=0.19\textwidth]{exp1_lag_S1}&
\includegraphics[width=0.19\textwidth]{exp1_SPCP_S1}&
\includegraphics[width=0.19\textwidth]{exp1_SPG_S1}&
\includegraphics[width=0.19\textwidth]{exp1_our_S1}\\
\includegraphics[width=0.19\textwidth]{exp1_Dt}&
\includegraphics[width=0.19\textwidth]{exp1_lag_St}&
\includegraphics[width=0.19\textwidth]{exp1_SPCP_St}&
\includegraphics[width=0.19\textwidth]{exp1_SPG_St}&
\includegraphics[width=0.19\textwidth]{exp1_our_St}\\[-2pt]
 Original frame &  LAGO &  SPCP &  SPGL1 &  Alg.1
\end{tabular}
\vspace{-2pt}
\caption{Foreground detection for the walking video. The two rows correspond to the first and the last video frames, respectively.}\label{fig:exp1fg}
\end{figure*}

\begin{table}[h]
\centering
\caption{Quantitative comparison for the walking video}\label{tab1}
\vspace{-4pt}
\begin{tabular}{c|cc|ccc}
\hline \hline
    & RE & PSNR & Pr & Re & Fm\\ \hline
LAGO &0.0377&33.67&0.9796&0.4673&0.6328\\
SPCP& 0.0182&39.99&0.9777&0.6354&0.7703 \\
SPGL1 & 0.0148 & 41.81 & 0.9682 & 0.7180&0.8246 \\
Alg.~1 & 0.0145 & 41.95 & 0.9688 & 0.7187&0.8252\\
\hline\hline
\end{tabular}
\end{table}

\subsection{Experiment 2: Arm Movement Video}
In the second experiment, an arm movement video was recorded when the student volunteer rotated her forearm and hand around the elbow joint slowly.
The tested video is generated by removing motionless frames and cropping the region of interest, which consists of 32 frames and each frame has $180\times 180$ pixels. The visual comparison of foreground and background for all results are shown in Fig.~\ref{fig:exp2bg} and Fig.~\ref{fig:exp1fg}, respectively. In Table~\ref{tab2}, we compare the qualities of the recovered background and foreground. Notice that there is movement still left on the left of the LAGO background and foreground results while some speckle noise exist in the SPCP foreground. Both SPGL1 and our approach can separate the foreground and the background clearly.

In terms of running time, SPCP takes the minimum running time (~0.1 s) while SPGL1 based on Newton's iteration takes about 50 seconds. Both LAGO and our algorithm run about 5 seconds and the graph Laplacian construction can be fast using a small number of neighbors. Overall, our method can keep a good balance in running time and detection accuracy. This phenomenon also applies to the first experiment.

\begin{figure*}[h]
\centering\setlength{\tabcolsep}{2pt}
\begin{tabular}{ccccc}
\includegraphics[width=0.19\textwidth]{exp2_Lg}&
\includegraphics[width=0.19\textwidth]{exp2_LAGO_Lm}&
\includegraphics[width=0.19\textwidth]{exp2_SPCP_Lm}&
\includegraphics[width=0.19\textwidth]{exp2_SPG_Lm}&
\includegraphics[width=0.19\textwidth]{exp2_our_Lm}\\[-2pt]
 Ground truth &  LAGO &  SPCP &  SPGL1 &  Alg.1
\end{tabular}
\vspace{-2pt}
\caption{Visualization results of various methods on background recovery for the arm motion video. }\label{fig:exp2bg}
\end{figure*}

\begin{figure*}[h]
\centering\setlength{\tabcolsep}{2pt}
\begin{tabular}{ccccc}
\includegraphics[width=0.19\textwidth]{exp2_D1}&
\includegraphics[width=0.19\textwidth]{exp2_LAGO_S1}&
\includegraphics[width=0.19\textwidth]{exp2_SPCP_S1}&
\includegraphics[width=0.19\textwidth]{exp2_SPG_S1}&
\includegraphics[width=0.19\textwidth]{exp2_our_S1}\\
\includegraphics[width=0.19\textwidth]{exp2_Dt}&
\includegraphics[width=0.19\textwidth]{exp2_LAGO_St}&
\includegraphics[width=0.19\textwidth]{exp2_SPCP_St}&
\includegraphics[width=0.19\textwidth]{exp2_SPG_St}&
\includegraphics[width=0.19\textwidth]{exp2_our_St}\\[-2pt]
Original frame &  LAGO &  SPCP & SPGL1 &  Alg.1
\end{tabular}
\vspace{-2pt}
\caption{Foreground detection for the arm motion video. The two rows correspond to the first and the last video frames, respectively.}\label{fig:exp2fg}
\end{figure*}

\begin{table}[h]
\centering
\caption{Quantitative comparison for the arm motion video}\label{tab2}
\vspace{-4pt}
\begin{tabular}{c|cc|ccc}
\hline \hline
    & RE & PSNR & Pr & Re & Fm\\ \hline
LAGO &0.0151&20.36&0.9617&0.8305&0.8913\\
SPCP& 0.0132&20.44&0.9666&0.8471&0.9029 \\
SPGL1 & 0.0132 & 35.65 & 0.9665 & 0.8610&0.9107 \\
Alg.~1 & 0.0104 & 35.67 & 0.9704 & 0.8234&0.8909\\
\hline\hline
\end{tabular}
\end{table}

\section{Conclusions and Future Work}\label{sec:con}
Moving object detection is one of the most fundamental tasks in video processing with a wide spectrum of applications, particularly in human-robot interaction. In the case of limited lightening conditions and/or time-varying illuminations, it becomes extremely challenging to separate a moving foreground with shadow from a static background. One classical type of methods is to segment each single frame into foreground and background. However, it usually loses the temporal smoothness and suffers from the intensive computation. In this work, we propose a novel dual-graph regularized motion detection approach. Specifically, we exploit the spatiotemporal geometry of the foreground by constructing the spatial and the temporal graph Laplacians, and adopt a weighted nuclear norm based regularization to utilize adaptive low-rankness of the background. The proposed algorithm is derived by applying the ADMM framework. Numerical results have shown our proposed method performs well on realistic data sets. In the future, we will develop fast methods based on the low-rank tensor decompositions and separate the shadow from the detected moving object under more sophisticated lightening environments.

\section*{ACKNOWLEDGMENTS}

The research of Qin is supported by the NSF grant DMS-1941197.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


A truly unique and unforgettable evening of art and entertainment, Artitude Amplified is a lavish stand-up cocktail event, to raise awareness and funds for children living with speech, hearing and language impairments.
Artitude Amplified 2018 is now complete! Thank you to everyone who supported our premier fundraising event.
Interested in supporting Artitude Amplified 2019? Please contact our marketing and events team at events@tsh.org.au or call 9387 9888 for future opportunities.
View more memorable moments from Artitude on our Facebook and Instagram pages!
By popular demand, sale of this year's collection of art has been extended! This year's collection features highly unique and rarely seen works from International and Australian artists.
Even if you weren't able to join us at Crown Perth on the 16th June you may be interested in purchasing art. Remember part proceeds from all art sold will go back to Telethon Speech & Hearing. To make your purchase of art, please click the link below. Online art sales will close 5pm Friday 29th June 2018.
For a limited time only, Stelios Jewellers is donating 15% of jewellery sales back to TSH.Terms and Conditions apply, please contact info@steliosjewellers.com.au for details.Click here to view the jewellery catalogue.
$10,000 towards jewellery of your choice from Stelios Jewellers. Select from a range of beautifully handcrafted designs using high end diamonds and stones, or custom design your own bespoke piece of jewellery by their master craftsman. The choice is yours.
Includes 2 nights accommodation in a studio room, Crystal Club access for 2 adults, valet parking for 1 vehicle, Crown Spa voucher worth $250 and Crown gift card to the value of $200. A wonderfully indulgent experience.
$1,500 voucher for Total Travel. Total Travel provide customers with exceptional service and first class travel experiences. This voucher can be used towards the holiday destination and flights of your choice.
Two night stay in a Lakeside Room at RAC Karri Valley Holiday Resort for two people.


The Myelin Repair Foundation (MRF) today announced the results of a new peer-reviewed research study published in Nature Neuroscience that demonstrates functional improvement in immune response modulation and myelin repair with factors derived from mesenchymal stem cell (MSC) treatment in animal models of multiple sclerosis (MS).
Funded by the Myelin Repair Foundation, this research conducted by Case Western Reserve University scientists showed positive results with human mesenchymal stem cells in animal models of MS by not only successfully blocking the autoimmune MS response, but also repairing myelin, demonstrating an innovative potential myelin repair treatment for MS.
Multiple sclerosis is a disease of the immune system that attacks the myelin, causing exposed nerves or "lesions" which block brain signals, causing loss of motor skills, coordination and cognitive ability. Compared to the controls, this research study showed fewer and smaller lesions found on the nerves in the MSC treatment group. MSCs were found to block the formation of scar tissue by suppressing the autoimmune response, which would otherwise cause permanent damage to the nerves. Furthermore, the research showed that MSC treatment also repaired myelin, enhancing myelin regeneration of the damaged axon and the rewrapping of the myelin around the axon in animal models of MS. One treatment of MSCs provided long-term protection of the recurring disease.
Led by Myelin Repair Foundation Principal Investigator and Vice President for Research & Technology Management at Case Western Reserve University's Dr. Robert Miller, this study documents a new promising pathway for treating multiple sclerosis that blocks the autoimmune response and reverses the myelin damage in animal models of MS. The human MSCs used in this study were culled from adult stem cells derived from the bone marrow.
"We are thrilled with the publication of this important research study that examines a new pathway to treat multiple sclerosis, one that reverses the damage of the disease," said Dr. Robert Miller. "Since we were just beginning to understand how MSCs provide myelin repair for lesions, with the Myelin Repair Foundation's support, we continue to deepen our knowledge of exploring the next generation of MS treatments that stimulate healing, rather than symptom suppression of the disease."
"We pride ourselves on supporting best-in-class scientists devoted to find new ways to treat multiple sclerosis, advancing highly innovative research projects that otherwise would not have moved forward," said Scott Johnson, president of the Myelin Repair Foundation. "The success of Case Western Reserve University's study and recognition in this prestigious journal furthers our goal to identify new pathways to treat multiple sclerosis by supporting a multi-disciplinary team of the best researchers in the field."
Ja, det med myelin´en er jo ikke nyt. For 10 år siden, da jeg fik min diagnose, var det noget af det mest lovende opdagelse.
Det der nyt er vel, at myelin kan repareres ved hjælp af stamceller (MSC). Forsøget er stadig på dyreforsøgs-stadiet, som jeg læser det. Men come on, vi håber på, at der snart kommer forsøg på mennesker også - selvfølgelig under behørig overvågning.


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I left Iowa early last March for Ironton, Ohio, and six months as a Co-op Forester on Wayne National Forest in the Ohio River Valley. Southern Ohio has a topography similar to northeastern Iowa and has a forest of oak, hickory, tulip-poplar and smokestacks. The land is beautiful, though much of it is abused. The air along the river is often brown and smells of "money."
Caldbeck, Elaine (1979) "Co-op With The Forest Service," Ames Forester: Vol. 66 , Article 16.


text:
package sandeepjoshi1910.arxiv_explore.Utilities;

import android.content.res.Resources;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.os.Build;
import android.support.annotation.RequiresApi;
import android.util.ArraySet;
import android.util.Log;

import java.util.List;
import java.util.Set;
import java.util.regex.Matcher;
import java.util.regex.Pattern;

import sandeepjoshi1910.arxiv_explore.Model.Author;

/**
 * Created by sandeepjoshi on 10/1/17.
 */

public class Utils {

    public static Set<String> savedArticleIds;

    public Boolean isSearchTermValid(String searchTerm) {

        if(searchTerm == null || searchTerm.equals("")) {
            return false;
        }

        Pattern p = Pattern.compile("[^a-z0-9 ]", Pattern.CASE_INSENSITIVE);
        Matcher m = p.matcher(searchTerm);
        if(m.find()) {
            Log.i("Validation", "isSearchTermValid: Search term has special characters");
            return false;
        }

        return true;
    }

    public static String getFinalUrl(String searchTerm, int start, int end) {

        String[] searchTerms = searchTerm.split(" ");

        String finalTerm = "";

        for (String term : searchTerms ) {

            if(finalTerm != "") {
                finalTerm = finalTerm + "+AND+" + "all:" + term;
            } else {
                finalTerm = finalTerm + "all:" + term;
            }

        }

        String initialUrl = "http://export.arxiv.org/api/query?search_query=";

        String url = initialUrl + searchTerm + "&start=" + start + "&max_results=" + end + "&sortBy=relevance&sortOrder=ascending";

        return url;
    }

    public static String getFormattedAuthorNames(List<Author> authors) {

        String fAuthors = "";

        if(authors.size() == 1) {
            return authors.get(0).getAuthorName();
        }

        for (Author author: authors) {
            fAuthors = fAuthors + author.getAuthorName() + " • ";
        }

        return fAuthors;
    }

    // Calculates the height of the image given the width to maintain the Aspect Ratio of 16:9 or 1.7777777778
    public static int getHeight(int width) {

        // Formula : height = 9/16 * width
        float height = Math.round((float) ((9.0/16.0) * (float) width));
        return (int) height;
    }

    /*
    The following two methods 1) decodeSampledBitmapFromResource   2) calculateInSampleSize
                      are sourced from Android documentation on "Loading Large Bitmaps Efficiently"
              Source: https://developer.android.com/topic/performance/graphics/load-bitmap.html
 */
    public static Bitmap decodeSampledBitmapFromResource(Resources res, int resId,
                                                         int reqWidth, int reqHeight) {

        // First decode with inJustDecodeBounds=true to check dimensions
        final BitmapFactory.Options options = new BitmapFactory.Options();
        options.inJustDecodeBounds = true;
        BitmapFactory.decodeResource(res, resId, options);

        // Calculate inSampleSize
        options.inSampleSize = calculateInSampleSize(options, reqWidth, reqHeight);

        // Decode bitmap with inSampleSize set
        options.inJustDecodeBounds = false;
        return BitmapFactory.decodeResource(res, resId, options);
    }

    public static int calculateInSampleSize(
            BitmapFactory.Options options, int reqWidth, int reqHeight) {
        // Raw height and width of image
        final int height = options.outHeight;
        final int width = options.outWidth;
        int inSampleSize = 1;

        if (height > reqHeight || width > reqWidth) {

            final int halfHeight = height / 2;
            final int halfWidth = width / 2;

            // Calculate the largest inSampleSize value that is a power of 2 and keeps both
            // height and width larger than the requested height and width.
            while ((halfHeight / inSampleSize) >= reqHeight
                    && (halfWidth / inSampleSize) >= reqWidth) {
                inSampleSize *= 2;
            }
        }

        return inSampleSize;
    }
}

redpajama_set_name: RedPajamaGithub


text:
Specialized medial prefrontal-amygdala coordination in other-regarding decision preference
View ORCID ProfileOlga Dal Monte, Cheng-Chi J. Chu, Nicholas A. Fagan, View ORCID ProfileSteve W. C. Chang
Olga Dal Monte
Department of Psychology, Yale University, New Haven, CT 06520Department of Psychology, University of Turin, Torino, Italy
ORCID record for Olga Dal Monte
Cheng-Chi J. Chu
Department of Psychology, Yale University, New Haven, CT 06520
Nicholas A. Fagan
Steve W. C. Chang
Department of Psychology, Yale University, New Haven, CT 06520Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT 06510
ORCID record for Steve W. C. Chang
For correspondence: steve.chang@yale.edu
August 31, 2019.
Version 1 (May 17, 2019 - 13:55).
You are viewing Version 2, the most recent version of this article.
Olga Dal Monte1,2,
Cheng-Chi J. Chu1,
Nicholas A. Fagan1 and
Steve W. C. Chang1,3,4,*
1Department of Psychology, Yale University, New Haven, CT 06520
2Department of Psychology, University of Turin, Torino, Italy
3Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510
4Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT 06510
↵*Corresponding Author Steve W. C. Chang, Ph.D. Email: steve.chang{at}yale.edu Tel.: 1-314-307-0498
Posted August 31, 2019.
You are going to email the following Specialized medial prefrontal-amygdala coordination in other-regarding decision preference
Olga Dal Monte, Cheng-Chi J. Chu, Nicholas A. Fagan, Steve W. C. Chang


text:
Legally Blonde 2: Red White & Blonde
Directed by Charles Herman-Wurmfeld
An ebullient comedy that will appeal to all lovers of animal's rights, people's democracy, inner flair, and a heroine who believes in her own true valiant voice.
Film Review by Frederic and Mary Ann Brussat
In the clever and witty opening scenes of this funny sequel to Legally Blonde, friends of Elle Woods (Reese Witherspoon) are looking through a scrapbook they intend to give her at her upcoming wedding shower. Her Delta Nu sorority sisters Margot (Jessica Cauffiel) and Serena (Alanna Ubach) and her manicurist buddy Paulette (Jennifer Coolidge) are excited about Elle's marriage to Emmett (Luke Wilson), a professor at Harvard Law School. They comment on the distinctive benchmarks in her winning fashion sense, such as her being a professional shopper at age three.
Indeed, everything seems to be peachy keen for Elle in the early stages of this perky and winsome story written by Kate Kondell and directed by Charles Herman-Wurmfeld. She's enthusiastically involved in planning a fancy wedding and has even managed to dazzle her fiancé by arranging for the ceremony to be held at Fenway Park baseball stadium. But when she decides to invite the unknown mother of Mr. Bruiser, her feisty Chihuahua, to the wedding and hires a detective to find the dog, a hint of disaster appears on the horizon.
Lo and behold, she discovers that this animal is being used as a subject for tests by a major cosmetics firm. Fired up by this shocking news, she decides she must bring a lawsuit against the company. But her idealism and ardor fall on deaf ears in the board room of her law firm. Before she can blink her pretty eyes, Elle is fired. Undaunted, she decides that the only way to save Bruiser's mom is to make testing on animals against the law. Soon she is on the way to Washington D.C. to work as a legislative assistant to Sally Fields, a liberal Congresswoman.
"Hello patriots!" are the first words out of her mouth to those serving the people in power in the nation's capitol. Needless to say, Elle stands out with her all pink outfit amidst all the corporate gray and black suits. In the Congresswoman's office, she receives a less than enthusiastic welcome. In fact, Grace (Regina King), the African American chief of staff, is downright hostile. When she first sees Elle, she directs her to the orientation room for interns. But the Congresswoman orders her staff to help this newcomer get a bill passed to stop animal testing.
While Elle's disarming honesty and spunk falls flat among this cynical crew of once ardent political idealists, it works in her favor in a crucial friendship with Sid Post (Bob Newhart), who has been a doorman for 30 years at the Wellington Hotel. He identifies with her cause and is impressed with her determination to push ahead in the face of resistance from every quarter. And he has an insider's insight into what works and what doesn't among Washington's power brokers.
Who could have guessed that a summer comedy sequel would make the perfect Fourth of July release with its shining portrait of a Washington outsider who marches to the beat of her own idiosyncratic drum and is convinced that "doing the right thing is in everybody's best interest"? Anyone who has kept up with journalist Bill Moyers' investigative reports knows that the present-day political system in Washington is controlled by wealthy corporate contributors who protect their own interests by buying influence with members of Congress on both sides of the aisle. This process is made clear when the Congresswoman caves into pressure put on her by a big bucks backer who wants the bill against animal testing killed. After Elle tells her "I believe in the people," she replies, "The people believe what you tell them to believe . . . You can't get the people to care." Those are fighting words to an idealist like Elle. It is time to bite the bullet and fly in the face of the established system. The beauty who is convinced of the enduring value of a makeover, decides that Washington needs a makeover.
Using a political maneuver known as a discharge petition, Elle mounts a people's campaign for animal rights, using some unusual strategies: interns as cheerleaders in the halls of legislators, free hair treatments and beauty consultations for members of Congress, a phone tree operated by her sorority sisters, and a Million Dog March. It is a real hoot to witness this grassroots campaign unfold.
In Legally Blonde, Elle proved that a person with inner flair can accomplish almost anything. That motif comes through loud and clear in this wonderful sequel where she champions the cause of her best friend in the world, where warm and fuzzy feelings are celebrated over chilly and cerebral cynicism, and where political change can be brought about by those who are savvy and street-smart enough to buck the system. Best of all, this spunky film gives us a heroine who believes in her own voice and is willing to remain true to those qualities that make her special.
The DVD features deleted scenes.
MGM Home Entertainment, 07/03
DVD Feature
PG-13 - sex-related humor
Dogs on DVD


One of the first things that beginners need to learn are the days of the week. They are useful for understanding the days that shops, restaurants and museums are open and closed. They are useful to make arrangements to meet up with friends, colleagues or relatives and days often come up in general conversation. They are therefore an essential part of any beginner learner's vocabulary.
Unlike in English the days of the week do not have capital letters.
The days of the week are all masculine so if you are using any article (el, los, un, unos) or an adjective with a day of the week you must use the masculine form.
For example, if you want to say "a rainy Tuesday" you would say "un martes llovioso".
"El" is used to say "On" a day of the week.
Also note that you don't need to add anything to the day itself to make it plural – "jueves" stays as "jueves". All you do is change "el" to "los". However, you do add an –s to Saturday (sábado) and Sunday (domingo) as they do not end in –s.
1) Change your phone settings to Spanish so you see the day and the date written in Spanish every day. If you find the idea of having all of your phone settings in Spanish you could start with just having a Spanish calendar.
2) Watch a catchy tune on Youtube to help you learn the days of the week. Find one you really like so that it sticks in your head making it much easier to memorise the days of the week.
3) Write the date down somewhere every day. By writing the date in full it will help you memorise the correct spellings as well as the days of the week and the months of the year. Even if you aren't attending a class you could write the date as a daily diary entry or for your daily to do list.
4) Make up an acronym to memorise the day of the week so that each letter stands for a word. L M M J V S D.


This is the sand spit where I walk at low, low tide!
It is full moon today, so yesterday was an extreme -2.3 tide. The tide pools are filled with clam and mussel shells and hundreds of baby crabs scuttling around. The herons and gulls were out in full force too. My neighbor takes kids out here and calls it Treasure Island as when the tide is in, the spit disappears.
I am trying to adjust to my new circumstances and one thing I am doing is walking more - when the weather is good. Today, August 2 I have the heat on as it is only about 60F outside. Mid-summer!
Newer PostThe Back Yard Defeated Me.


text:
Green Bay man convicted of conspiracy to distribute heroin and fentanyl, resulting in 1 death, 2 overdoses
GREEN BAY, Wis. (CBS 58) -- A federal jury has convicted David Shanks, 37, of Green Bay, of conspiracy to distribute heroin, fentanyl, methamphetamine, and cocaine, conduct which resulted in death.
As a result of his conviction and two prior felony drug convictions, Shanks faces a mandatory sentence of life in prison. The jury also convicted Shanks of five other drug-related counts, including distribution of methamphetamine, distribution of heroin/fentanyl/methamphetamine resulting in death, and distribution of heroin/fentanyl resulting in substantial bodily injury to two overdose victims.
The trial evidence established that between August and October 2017, Shanks was the leader of a conspiracy involved in the daily sale of heroin, laced with fentanyl, and methamphetamine.
Shanks's sale of heroin/fentanyl resulted in the overdose death of one individual on Sept. 10, 2017.
Shanks also was responsible for overdoses by two individuals on Sept. 22, 2017. Fortunately, paramedics and law enforcement were able to provide lifesaving assistance to both individuals. Cooperating witnesses testified that Shanks was responsible for distributing large quantities of heroin and methamphetamine in the greater Green Bay and Iron Mountain, Michigan, areas.
Witnesses described seeing Shanks with baseball-sized quantities of heroin and $5,000 stacks of money generated by his drug sales.
United States Attorney Krueger stated, "This case demonstrates the devastating impact the sale of heroin, fentanyl, and methamphetamine has on communities and families across Wisconsin and across the country. Federal, state, and local law enforcement remain committed to doing everything possible to hold those responsible for this devastation accountable."


text:
Q&A with Chicago Sky's New Head Coach James Wade
WNBA.com Staff November 13, 2018
James Wade was officially named the new Head Coach of the Chicago Sky on the morning of Nov. 13. Wade comes into his first WNBA head coaching role with seven years of experience as an assistant around the league. Wade was most recently a part of the Minnesota Lynx coaching staff under Cheryl Reeve since 2017, and prior to that was an assistant to Dan Hughes' San Antonio Stars from 2012-2016.
Coach Wade discusses his new role as he embarks on the start of his head coaching career.
"Me and my family are very excited. We can't wait to get started, but this is a big deal for us. We're happy about it."
How did this hiring process unfold for you?
"It was an ongoing process – we had several conversations and I came up to visit. I'll tell you what – I've been in the league for seven years and the almost seven years prior have felt like 40 days. The last 40 days have felt like seven years.
It was a job that I wanted. Cheryl Reeves helped me through the process and told me what to expect and I leaned on Dan Hughes a lot having worked with him for a long time. They kinda talked me through everything and gave me the confidence to know that I was ready to take it on. So I was confident going into every interview we had. Actually watching games of the roster and getting even more familiar with the team than a scout, looking at the film from not an opponents point of view, but if I had this team. Looking at the team like that, I was really confident that we could do good things here."
What did working under two WNBA championship winning coaches in Dan Hughes and Cheryl Reeve teach you and what will you look to apply in your first-ever head coaching role?
"I saw two coaches that knew how to get the best out of their players. They taught me to not ever put limits on what players can do – even in times when as a player you might think 'I'm too tired,' or 'this is a tough time for us because I'm not in a rhythm.' Two coaches that hold players to a higher standard and demand it every day. There's no off days in their minds – and that's something that I've learned. When you're a coach, you're responsible for all 12 players and getting them to the right place and those are two of the best in the business."
What excites you the most about this Chicago Sky roster?
"We have some free agency issues that we have to attack, but it's a pretty good and complete roster. We have to keep evaluating and studying how they play together and how they best fit – but there's good leadership on this team going forward. We have to build around that and play to the style of play that I think is where we need to be. That's going to be our focus going forward. We're going to try and get a defensive identity – I think that's something that we've been lacking a little bit from just watching game film. That's something that I want to attack as soon as possible, because I know that defense wins games. The strength of our guard play and the fact that we have post players that play make too is really good as well."
Having worked with many top veterans around the league in the past – how does that help you going into this role?
"It gives me some credibility. It gives me a little bit of a fast track to earning their trust. The last two years have been amazing just because I've coached – in the last two years – arguably seven of the top 15 players of all time, with the starters in Minnesota, and Diana Taurasi and Brittney Griner overseas. You gain things when you coach them every day, when you see how they work. Teaching them and seeing them react and progress gives me confidence as a coach. It gives me an identity of what I want and what I value on the court."
You have a dynamic young talent in Diamond DeShields – what are you looking forward to in having her as a part of your roster?
"Everybody searches for the big next thing. The big dynamic wing that can do everything. For years you can count Maya Moore or Angel McCoughtry in that spot to name a few – and no disrespect to the wings that play in the league, but those kind of players separate themselves. I see a lot of that in Diamond. I think she's the next one. Experience is going to be a big factor in her development and what we do with her in practice is going to be a major factor as well. I think she can be the next wing that takes the league by storm and dominates at her position.
Talking to her – I know she's motivated. I've seen her play since she was 16 years old. She's a great player and has been a great player. The sky's the limit for her and we're going to help her realize that."
What are you looking forward to in the 2019 draft with the No. 4 pick?
"We're going to look for the best player available. A player that can help push us to another level. There's some things we need to address in defense and rebounding, but we're going to try to get the best player available and go from there. I've watched six or seven players that I really really like, but putting a team together is like putting together a puzzle so we have to see what will compliment our team and push us further."
Theme Nights: July 15 – 21
Seven-Time Selection Charles, Dupree, Highlig…
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by Bertrand Benoit, Frankfurt -- "Deutsche Telekom, the German telecommunications group, and Swisscom, the Swiss carrier, on Monday unveiled an agreement allowing Debitel, Swisscom's German mobile unit, to market DT's third-generation, or UMTS, services.
"News of the agreement comes ahead of a June 5 press conference by RegTP, the German telecoms regulator, that should set the rules for co-operation between UMTS licence holders in building and using their networks.
"The smallest of the six German licence holders, which each paid about E8.4bn ($7.4bn) for licences, could save between 20 and 40 per cent on the cost of building networks depending on the extent of co-operation allowed by the regulator.


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Acupuncture for Migraine: Is It Effective?
Shannon Aymes, MD
A significant decrease in migraine frequency was observed, but it is not clear how large a role placebo effect plays in treatment response.
Acupuncture is tied to a persistent reduction in headache frequency, but the placebo effect may still play a role in outcomes, according to results of a study published in JAMA Internal Medicine.
According to the US National Center for Health Statistics, approximately 1 out of 7 Americans is affected by migraine headaches, and migraine treatment is a leading cause of emergency department and outpatient visits.
Pharmacological preventive therapy for migraine can be accompanied by fatigue, sleep interference, gastrointestinal effects, weight gain, and medication overuse headaches. While research is inconsistent, acupuncture is a common treatment for acute treatment and prevention of migraines in China.
Ling Zhao, PhD, from the Acupuncture and Tuina School at Chengdu University of Traditional Chinese Medicine in Sichuan, China, and colleagues sought to assess the long-term prophylactic benefit of acupuncture in patients with migraine without aura.
The investigators conducted a randomized sham-controlled clinical trial over 4 weeks with 20 weeks of follow-up. The 249 participants aged 18 to 65 years were randomly assigned to 20 sessions of true acupuncture (electroacupuncture), sham acupuncture, or to a waitlist group. Prophylactic medications were not permitted in the study protocol but participants could take ibuprofen for acute relief.
Outcome data was obtained from headache diaries to assess change in migraine frequency, severity, and medication use. Participants were also assessed with the Zung self-rating anxiety scale (SAS), the Zung self-rating depression scale (SDS), and the migraine-specific quality-of-life questionnaire (MSQ) at baseline and at week 4.
The change in frequency of migraine attacks was significantly different in the true acupuncture group vs the sham acupuncture group (reduced by 3.2 and 2.1, respectively; difference of 1.1; 95% CI, 0.4-1.9; P =.002). In a similar manner, the change in frequency was significantly different between the true acupuncture group and the waitlist group (difference of 1.8; 95% CI, 1.1-2.5, P <.001).
The true acupuncture group reported sustained improvements in frequency of attacks and migraine days at weeks 4 through 24
(P <.001 for all) and reduced need for acute medication. The true acupuncture group also showed improvements in the MSQ and SAS compared to the waitlist group (P <.05 for all). However, no differences were noted between the true and sham acupuncture groups for SAS, SDS, or MSQ with the exception of the emotional function subscale.
Although the study was limited by a lack of comparison with standard migraine prophylaxis, the investigators noted, "True acupuncture exhibited persistent, superior, and clinically relevant benefits for migraine prophylaxis, reducing the migraine frequency, number of days with migraine, and pain intensity."
In an accompanying editorial, Amy Gelfand, MD, of the Pediatric Headache Program at the University of California in San Francisco, highlighted the study's strengths of utilizing a sham control arm and its lack of significant adverse events. However, Dr Gelfand was concerned about potential unblinding in participants and acupuncturists, leading to a placebo response.
When considering counseling patients on the use of acupuncture for migraine prophylaxis, Dr Gelfand advised, "It is probably safe to try — it is not clear it is effective. The main risks are likely to their pocketbook and their time."
Disclosures: Dr Gelfand reports relationships with Eli Lilly, Zosano, Teva, Allergan, eNeura, MedImmune, and Quest Diagnostics.
Persistent Asthma May Increase Migraine Risk in Children
Migraine Linked to Adverse Events in Pregnancy
Zhao L, Chen J, Li Y, et al. The long-term effect of acupuncture for migraine prophylaxis: a randomized clinical trial [published online February 20, 2017]. JAMA Intern Med. doi:10.1001/jamainternmed.2016.9378
Burch RC, Loder S, Loder E, Smitherman TA. The prevalence and burden of migraine and severe headache in the United States: updated statistics from government health surveillance studies. Headache. 2015;55(1):21-34.
Gelfand AA. Acupuncture for migraine prevention still reaching for convincing evidence [published online February 20, 2017]. JAMA Intern Med. doi:10.1001/jamainternmed.2016.9404
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