Archive | August, 2017

New York University study: Machine learning helps spot counterfeit consumer products

13 Aug

OECD reported in Global trade in fake goods worth nearly half a trillion dollars a year – OECD & EUIPO:

18/04/2016 – Imports of counterfeit and pirated goods are worth nearly half a trillion dollars a year, or around 2.5% of global imports, with US, Italian and French brands the hardest hit and many of the proceeds going to organised crime, according to a new report by the OECD and the EU’s Intellectual Property Office.
“Trade in Counterfeit and Pirated Goods: Mapping the Economic Impact” puts the value of imported fake goods worldwide at USD 461 billion in 2013, compared with total imports in world trade of USD 17.9 trillion. Up to 5% of goods imported into the European Union are fakes. Most originate in middle income or emerging countries, with China the top producer.
The report analyses nearly half a million customs seizures around the world over 2011-13 to produce the most rigorous estimate to date of the scale of counterfeit trade. It points to a larger volume than a 2008 OECD study which estimated fake goods accounted for up to 1.9% of global imports, though the 2008 study used more limited data and methodology.
“The findings of this new report contradict the image that counterfeiters only hurt big companies and luxury goods manufacturers. They take advantage of our trust in trademarks and brand names to undermine economies and endanger lives,” said OECD Deputy Secretary-General Doug Frantz, launching the report with EUIPO Executive Director António Campinos as part of OECD Integrity Week.
Fake products crop up in everything from handbags and perfumes to machine parts and chemicals. Footwear is the most-copied item though trademarks are infringed even on strawberries and bananas. Counterfeiting also produces knockoffs that endanger lives – auto parts that fail, pharmaceuticals that make people sick, toys that harm children, baby formula that provides no nourishment and medical instruments that deliver false readings.\
The report covers all physical counterfeit goods, which infringe trademarks, design rights or patents, and tangible pirated products, which breach copyright. It does not cover online piracy, which is a further drain on the formal economy… http://www.oecd.org/industry/global-trade-in-fake-goods-worth-nearly-half-a-trillion-dollars-a-year.htm

See, Remade In China: Where The World’s Fake Goods Come From [Infographic] https://www.forbes.com/sites/niallmccarthy/2016/04/19/remade-in-china-where-the-worlds-fake-goods-come-from-infographic/#5a56fb5f1b87

SAS described machine learning in Machine Learning: What it is & why it matters:

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.
The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that’s gaining fresh momentum.
Because of new computing technologies, machine learning today is not like machine learning of the past. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications that you may be familiar with:
• The heavily hyped, self-driving Google car? The essence of machine learning.
• Online recommendation offers like those from Amazon and Netflix? Machine learning applications for everyday life.
• Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
• Fraud detection? One of the more obvious, important uses in our world today.
Why the increased interest in machine learning?
Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.
All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. The result? High-value predictions that can guide better decisions and smart actions in real time without human intervention…. https://www.sas.com/en_id/insights/analytics/machine-learning.html

See, What is Machine Learning? https://www.youtube.com/watch?v=f_uwKZIAeM0

Science Daily reported in Machine learning helps spot counterfeit consumer products:

A team of researchers has developed a new mechanism that uses machine-learning algorithms to distinguish between genuine and counterfeit versions of the same product.
The work, led by New York University Professor Lakshminarayanan Subramanian, will be presented on Mon., Aug. 14 at the annual KDD Conference on Knowledge Discovery and Data Mining in Halifax, Nova Scotia….
The system described in the presentation is commercialized by Entrupy Inc., an NYU startup founded by Ashlesh Sharma, a doctoral graduate from the Courant Institute, Vidyuth Srinivasan, and Subramanian.
Counterfeit goods represent a massive worldwide problem with nearly every high-valued physical object or product directly affected by this issue, the researchers note. Some reports indicate counterfeit trafficking represents 7 percent of the world’s trade today.
While other counterfeit-detection methods exist, these are invasive and run the risk of damaging the products under examination.
The Entrupy method, by contrast, provides a non-intrusive solution to easily distinguish authentic versions of the product produced by the original manufacturer and fake versions of the product produced by counterfeiters….
“The classification accuracy is more than 98 percent, and we show how our system works with a cellphone to verify the authenticity of everyday objects,” notes Subramanian.
A demo of the technology may be viewed here: https://www.youtube.com/watch?v=DsdsY8-gljg (courtesy of Entrupy Inc.)
To date, Entrupy, which recently received $2.6 million in funding from a team of investors, has authenticated $14 million worth of goods.

Citation:

Machine learning helps spot counterfeit consumer products
Date: August 11, 2017
Source: New York University
Summary:
A team of researchers has developed a new mechanism that uses machine-learning algorithms to distinguish between genuine and counterfeit versions of the same product.

Here is the NYU press release:

News Release
Researchers Use Machine Learning to Spot Counterfeit Consumer Products
________________________________________
Aug 11, 2017
Engineering, Science and Technology Research Courant Institute of Mathematical Sciences Faculty
New York City
A team of researchers has developed a new mechanism that uses machine-learning algorithms to distinguish between genuine and counterfeit versions of the same product.

A team of researchers has developed a new mechanism that uses machine-learning algorithms to distinguish between genuine and counterfeit versions of the same product. Image courtesy of Entrupy, Inc.
A team of researchers has developed a new mechanism that uses machine-learning algorithms to distinguish between genuine and counterfeit versions of the same product.

The work, led by New York University Professor Lakshminarayanan Subramanian, will be presented on Mon., Aug. 14 at the annual KDD Conference on Knowledge Discovery and Data Mining in Halifax, Nova Scotia.
“The underlying principle of our system stems from the idea that microscopic characteristics in a genuine product or a class of products—corresponding to the same larger product line—exhibit inherent similarities that can be used to distinguish these products from their corresponding counterfeit versions,” explains Subramanian, a professor at NYU’s Courant Institute of Mathematical Sciences.
The system described in the presentation is commercialized by Entrupy Inc., an NYU startup founded by Ashlesh Sharma, a doctoral graduate from the Courant Institute, Vidyuth Srinivasan, and Subramanian.
Counterfeit goods represent a massive worldwide problem with nearly every high-valued physical object or product directly affected by this issue, the researchers note. Some reports indicate counterfeit trafficking represents 7 percent of the world’s trade today.
While other counterfeit-detection methods exist, these are invasive and run the risk of damaging the products under examination.
The Entrupy method, by contrast, provides a non-intrusive solution to easily distinguish authentic versions of the product produced by the original manufacturer and fake versions of the product produced by counterfeiters.
It does so by deploying a dataset of three million images across various objects and materials such as fabrics, leather, pills, electronics, toys and shoes.
“The classification accuracy is more than 98 percent, and we show how our system works with a cellphone to verify the authenticity of everyday objects,” notes Subramanian.
A demo of the technology may be viewed here (courtesy of Entrupy Inc.).
To date, Entrupy, which recently received $2.6 million in funding from a team of investors, has authenticated $14 million worth of goods.
For a copy of the paper, “The Fake vs Real Goods Problem: Microscopy and Machine Learning to the Rescue,” please contact James Devitt, NYU’s Office of Public Affairs, at 212.998.6808 or james.devitt@nyu.edu.

Press Contact
James Devitt
James Devitt
(212) 998-6808

Employment opportunities in machine learning are expected to increase.

UDACITY described machine learning employment opportunities in :5 Skills You Need to Become a Machine Learning Engineer:

To begin, there are two very important things that you should understand if you’re considering a career as a Machine Learning engineer. First, it’s not a “pure” academic role. You don’t necessarily have to have a research or academic background. Second, it’s not enough to have either software engineering or data science experience. You ideally need both.
Data Analyst vs. Machine Learning Engineer
It’s also critical to understand the differences between a Data Analyst and a Machine Learning engineer. In simplest form, the key distinction has to do with the end goal. As a Data Analyst, you’re analyzing data in order to tell a story, and to produce actionable insights. The emphasis is on dissemination—charts, models, visualizations. The analysis is performed and presented by human beings, to other human beings who may then go on to make business decisions based on what’s been presented. This is especially important to note—the “audience” for your output is human. As a Machine Learning engineer, on the other hand, your final “output” is working software (not the analyses or visualizations that you may have to create along the way), and your “audience” for this output often consists of other software components that run autonomously with minimal human supervision. The intelligence is still meant to be actionable, but in the Machine Learning model, the decisions are being made by machines and they affect how a product or service behaves. This is why the software engineering skill set is so important to a career in Machine Learning.
Understanding The Ecosystem
Before getting into specific skills, there is one more concept to address. Being a Machine Learning engineer necessitates understanding the entire ecosystem that you’re designing for.
Let’s say you’re working for a grocery chain, and the company wants to start issuing targeted coupons based on things like the past purchase history of customers, with a goal of generating coupons that shoppers will actually use. In a Data Analysis model, you could collect the purchase data, do the analysis to figure out trends, and then propose strategies. The Machine Learning approach would be to write an automated coupon generation system. But what does it take to write that system, and have it work? You have to understand the whole ecosystem—inventory, catalog, pricing, purchase orders, bill generation, Point of Sale software, CRM software, etc.
Ultimately, the process is less about understanding Machine Learning algorithms—or when and how to apply them—and more about understanding the systemic interrelationships, and writing working software that will successfully integrate and interface. Remember, Machine Learning output is actually working software! http://blog.udacity.com/2016/04/5-skills-you-need-to-become-a-machine-learning-engineer.html

Education guidance counselors should be informed about opportunities in machine learning.

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University of Washington Health Sciences/UW Medicine study: Depression among young teens linked to cannabis use at 18

6 Aug

Often children who evidence signs of a substance abuse problem come from homes where there is a substance abuse problem. That problem may be generational. eMedicineHealth lists some of the causes of substance abuse:

Substance Abuse Causes
Use and abuse of substances such as cigarettes, alcohol, and illegal drugs may begin in childhood or the teen years. Certain risk factors may increase someone’s likelihood to abuse substances.
Factors within a family that influence a child’s early development have been shown to be related to increased risk of drug abuse.
o Chaotic home environment
o Ineffective parenting
o Lack of nurturing and parental attachment
Factors related to a child’s socialization outside the family may also increase risk of drug abuse.
o Inappropriately aggressive or shy behavior in the classroom
o Poor social coping skills
o Poor school performance
o Association with a deviant peer group
o Perception of approval of drug use behavior
http://www.emedicinehealth.com/substance_abuse/article_em.htm
Substance abuse is often a manifestation of other problems that child has either at home or poor social relations including low self-esteem. Dr. Alan Leshner summarizes the reasons children use drugs in why do Sally and Johnny use drugs? http://archives.drugabuse.gov/Published_Articles/Sally.html

Science Daily reported in: Depression among young teens linked to cannabis use at 18:

A study looking at the cumulative effects of depression in youth, found that young people with chronic or severe forms of depression were at elevated risk for developing a problem with cannabis in later adolescence.
The study led by UW Medicine researchers interviewed 521 students recruited from four Seattle public middle schools. Researchers used data from annual assessments when students were ages 12-15 and then again when they were 18. The results were published in the journal Addiction.
“The findings suggest that if we can prevent or reduce chronic depression during early adolescence, we may reduce the prevalence of cannabis use disorder,” said lead author Isaac Rhew, research assistant professor of psychiatry and behavioral sciences at the University of Washington School of Medicine.
What researchers called “a 1 standard deviation increase” in cumulative depression during early adolescence was associated with a 50 percent higher likelihood of cannabis-use disorder.
According to researchers, during the past decade cannabis has surpassed tobacco with respect to prevalence of use among adolescents. Cannabis and alcohol are the two most commonly used substances among youth in the United States. They pointed to one national study showing increases in prevalence of cannabis use disorder and alcohol use disorder in the United States, especially among young adults.
Longitudinal studies looking at the link between depression and later use of alcohol and cannabis, however, have been mixed. Some show a link. Others don’t. But most studies have assessed adolescent depression at a single point in time — not cumulatively, said the researchers. Further, there have been differences in how substance use has been measured ranging from the initiation of any use to heavier problematic forms of use.
The study oversampled for students with depressive and/or conduct problems. The researchers were surprised to see that the prevalence of cannabis and alcohol use disorder in this study was notably higher than national estimates with 21 percent meeting criteria for cannabis use disorder and 20 percent meeting criteria for alcohol use disorder at age 18.
What effect the easing of marijuana laws in Washington state had on the youth is unclear. Researchers said it would be informative to conduct a similar study in a state with more strict marijuana laws to understand whether the relationship between depression and cannabis misuse would still hold in areas where marijuana may be less accessible…. https://www.sciencedaily.com/releases/2017/07/170717151031.htm

Citation:

Depression among young teens linked to cannabis use at 18
Seattle-focused study suggests earlier intervention with depressed youths could reduce rate of cannabis-use disorder
Date: July 17, 2017
Source: University of Washington Health Sciences/UW Medicine
Summary:
Young people with chronic or severe forms of depression were at elevated risk for developing a problem with cannabis in later adolescence, found a study looking at the cumulative effects of depression in youth.
Journal Reference:
1. Isaac C. Rhew, Charles B. Fleming, Ann Vander Stoep, Semret Nicodimos, Cheng Zheng, Elizabeth McCauley. Examination of cumulative effects of early adolescent depression on cannabis and alcohol use disorder in late adolescence in a community-based cohort. Addiction, 2017; DOI: 10.1111/add.13907

Here is the press release from the University of Washington:

07.17.2017
Depression among young teens linked to cannabis use at 18
Seattle-focused study suggests earlier intervention with depressed youths could reduce rate of cannabis-use disorder
By Bobbi Nodell | HSNewsBeat | Updated 10:30 AM, 07.17.2017
Posted in: Research
Young people with chronic or severe depression are at elevated risk for developing a problem with cannabis in later adolescence, new research indicates.

The study, led by UW Medicine investigators, interviewed 521 students recruited from four Seattle public middle schools. Researchers used data from annual assessments when students were ages 12 to 15 and then again when they were 18. The results were published in the journal Addiction.
“The findings suggest that if we can prevent or reduce chronic depression during early adolescence, we may reduce the prevalence of cannabis use disorder,” said lead author Isaac Rhew, research assistant professor of psychiatry and behavioral sciences at the University of Washington School of Medicine.
What researchers called “a 1 standard deviation increase” in cumulative depression during early adolescence was associated with a 50 percent higher likelihood of cannabis-use disorder in the study.
During the past decade, cannabis use among adolescents has surpassed that of tobacco. Cannabis and alcohol are the two most commonly used substances among youth in the United States. They cited one national study showing increases in the prevalence of cannabis-use disorder and alcohol-use disorder in the United States, especially among young adults.
Longitudinal studies of depression and later use of alcohol and cannabis, however, have been mixed. Some show a link, others don’t. Most such studies have assessed adolescent depression at a single point in time – not cumulatively, the researchers noted. Further, previous research has measured substance use differently, ranging from initiation of any use to heavier, problematic use.
The study oversampled for students with depressive and/or conduct problems. The researchers were surprised by data indicating that the prevalence of cannabis- and alcohol-use disorder in this study was notably higher than national estimates, with 21 percent meeting criteria for cannabis-use disorder and 20 percent meeting criteria for alcohol-use disorder at age 18.
What effect the easing of marijuana laws in Washington state had on the youth is unclear. Researchers said it would be informative to conduct a similar study in a state with stricter marijuana laws to understand whether the relationship between depression and later cannabis misuse is similar.
The substance-abuse assessments of 18-year-olds occurred between 2007 and 2010. Washington state legalized medical cannabis in 1998 and its medical cannabis market expanded greatly after 2009, when the U.S. justice department issued a ruling known as the “Ogden Memo.” And in 2003, the city of Seattle made cannabis offenses the lowest enforcement priority for police and the city attorney.
The study was supported by funding from the National Institute of Mental Health and the National Institute on Drug Abuse, as well as funding from the University of Washington Alcohol and Drug Abuse Institute. Other authors include UW Medicine researchers Charles Fleming (psychiatry and the Center for the Study of Health and Risk Behaviors), Ann Vander Stoep (psychiatry and epidemiology), Elizabeth McCauley (psychiatry, pediatrics, psychology), and Semret Nicodimos (psychiatry and the Mental Health Assessment, Research & Training Center). Author Cheng Zheng is with the Ziber School of Public Health at the University of Wisconsin-Milwaukee.
Tagged with: addiction, psychiatry, marijuana

http://hsnewsbeat.uw.edu/story/depression-among-young-teens-linked-cannabis-use-18

The Drug Enforcement Agency (DEA) has a series of questions parents should ask http://www.getsmartaboutdrugs.com/content/default.aspx?pud=a8bcb6ee-523a-4909-9d76-928d956f3f91

If you suspect that your child has a substance abuse problem, you will have to seek help of some type. You will need a plan of action. The Partnership for a Drug Free America lists 7 Steps to Take and each step is explained at the site. http://www.drugfree.org/intervene

If your child has a substance abuse problem, both you and your child will need help. “One day at a time” is a famous recovery affirmation which you and your child will live the meaning. The road to recovery may be long or short, it will have twists and turns with one step forward and two steps back. In order to reach the goal of recovery, both parent and child must persevere.

Related:

University of Washington study: Heroin use among young suburban and rural non-traditional users on the
https://drwilda.com/2013/10/13/university-of-washington-study-heroin-use-among-young-suburban-and-rural-non-traditional-users-on-the-increase/

Resources

Adolescent Substance Abuse Knowledge Base
http://www.crchealth.com/troubled-teenagers/teenage-substance-abuse/adolescent-substance-abuse/signs-drug-use/

Warning Signs of Teen Drug Abuse
http://parentingteens.about.com/cs/drugsofabuse/a/driug_abuse20.htm?r=et

Is Your Teen Using?
http://www.drugfree.org/intervene

Al-Anon and Alateen
http://www.al-anon.alateen.org/

WEBMD: Parenting and Teen Substance Abuse
http://www.webmd.com/mental-health/tc/teen-substance-abuse-choosing-a-treatment-program-topic-overview

The U.S. Department of Health and Human Services has a very good booklet for families What is Substance Abuse Treatment?
http://store.samhsa.gov/home

The National Institute on Drug Abuse (NIDA) has a web site for teens and parents that teaches about drug abuse NIDA for Teens: The Science Behind Drug Abuse
http://teens.drugabuse.gov/

Where information leads to Hope. © Dr. Wilda.com

Dr. Wilda says this about that ©

Blogs by Dr. Wilda:

COMMENTS FROM AN OLD FART©
http://drwildaoldfart.wordpress.com/

Dr. Wilda Reviews ©
http://drwildareviews.wordpress.com/

Dr. Wilda ©
https://drwilda.com/