Trying to fit a Paranormal Distribution. LOL.

Guess what's the function to fit the above Paranormal Distribution. LOL.

What Usually The Fitted Curves Tries to Say. LOL.

Adsense. Meh.

After reactivated this blog site, I moved on to submit site registration to AdSense hoping to serve Ads to fund a bit on the site expenditures. However, to my amazement, even though the submission process was relatively friction-less, my submission kept rejected by the AdSense system and only received an email with some generic pointer to the ghostly issues I supposed to fix. I guess I have to either randomly changing something or moving on to other advertisers.

Java v. Python

Drone Holographics Light Show in China

Amazing Drone Use Case. 

How alcohol addiction occurs: the brain 'learns' alcohol

Running into a former drinking buddy or passing a once-favourite bar can cause recovering alcoholics to relapse.

But the biology behind why those setbacks happen was a mystery to scientists until just recently.

Researchers from the Perelman School of Medicine at the University of Pennsylvania (Penn), Uni-ted States, found that alcohol directly affects the brain’s learning and memory system through a test on mice.

Drinking actually helps people’s brains “learn” alcohol – the bar they’re drinking in, the street they’re on and the people they’re with – said Dr Shelley Berger, a cell and developmental biology professor, who is the study’s senior author.

This learning can make people want to drink more when they’re in certain environments or with groups of people.

And unravelling how the learning happens opens the possibility of someday having treatments to block that action.

Prof Berger’s research is the latest addition to a growing body of research that alcohol and drugs can cause genetic changes that may reinforce addiction and affect future generations.

Earlier this year (2019), researchers from Rutgers University found that binge drinking can trigger genetic changes that make people crave alcohol even more.

In the new study, published recently in the journal Nature, researchers found that when alcohol is consumed, it goes to the liver, where it’s processed into the by-product acetate.

Acetate then travels to the brain, where it turns certain genes on and off in a cell by attaching to histones, the proteins that package DNA in a cell’s nucleus, said Prof Berger.

“DNA is about two feet long in every cell and it’s all folded up into the nucleus,” she said.

“There’s a packaging material that organises it in a really orderly way, and it turns the right genes on and off through these little chemical groups. Acetate is one of those chemical groups.”

It turns out that the enzyme responsible for depositing acetate into cells, called ACSS2, activates key memory genes important for learning.

To test this, researchers exposed mice to two differently patterned chambers – one with alcohol and one without.

After a “learning period”, the mice were allowed to roam freely between the two. They preferred the chamber that was paired with alcohol.

But after lowering the levels of ACSS2 in the mice’s brains, the researchers found that they spent an equal amount of time in both chambers.

Researchers also found that when a pregnant mouse consumed alcohol, acetate was delivered through the placenta and into the foetus’ developing brain.

This finding could lead to better understanding of foetal alcohol syndrome, said Prof Berger.

She said that this was the first empirical evidence showing that alcohol consumption affects how genes are expressed in the brain.

She’s now interested in using the information to make a drug that could be used to stop people from “learning” alcohol.

“When we learn something, there’s a process called reconsolidation,” Prof Berger said.

“That’s when something is really solidified and we feel that we really understand it now.

“We want to disrupt that after someone has been exposed to alcohol, but before they reconsolidate the information.”

Penn’s Center for Studies on Addiction director Dr Henry Kranzler said Prof Berger’s research is “intriguing” because it offers the potential of “filling in a missing piece in our understanding of the development and maintenance of heavy drinking”.

“Her research is consistent with the idea that addiction is a learned process,” he said.

“Importantly, it opens up the potential to identify specific targets in the brain to be modified therapeutically with medications, and perhaps also genetically, at some point.”

In other words, researchers may be able to make drugs to treat addiction that target specific areas of the brain that are affected by alcohol.

However, the next step is to conduct more research using animals before eventually moving on to humans.

“This is a really exciting, unexpected finding, that alcohol consumed would end up in the brain,” Prof Berger said.

“What this tells us is that acetate may be a very good thing for memory, but a lot might not be good, especially if you’re getting it in the context of something that’s not good for you.” – The Philadelphia Inquirer/Tribune News Service

Source: https://www.thestar.com.my/lifestyle/health/2019/11/13/this-is-how-alcohol-addiction-occurs#6obX73K09hkxaVGq.99

Data Science, The Fourth Industrial Revolution And The Future Of Entrepreneurship

As an accountant and entrepreneur, I’m always trying to keep track of the newest developments in my professional fields. I believe that it’s important to follow present-day trends in order to gain insight into the potential future of these industries. I’ve written previously about some of these major developments, including blockchain technology, the internet of things and the Tax Cuts and Jobs Act.

However, the next big thing I’ve been following has to do with the combination of two large trends. Currently, I believe that the intersection of two recent developments in business and technology will have a monumental impact on my career: the fledgling field of data science, and the Fourth Industrial Revolution.

The Intersection Of Data Science And The Fourth Industrial Revolution

For the sake of clarification, here’s a brief primer on both of these concepts.

As a relatively new field, data science seeks to improve the way we interact with the massive amount of information provided to us in the modern era. In the age of the Internet, smartphones and social media, the sheer volume of data available to businesses, politicians and researchers can be difficult to parse. Through a combination of previously existing methodologies such as statistics, programming and mathematics, this new scientific field has grown into a highly lucrative career path.

The term “Fourth Industrial Revolution” was coined by World Economic Forum (WEF) chairman Klaus Schwab in 2015. In an article he wrote for the WEF, he describes it as an exponential growth of several key technological fields that were previously born out of the digital-based Third Industrial Revolution. Some specific fields he cites as examples of this forthcoming revolution include artificial intelligence, quantum computing, the internet of things and 3-D printing, among others. Subsequent descriptions of the Fourth Industrial Revolution have gone on to include technological concepts, such as smart materials and blockchain technology.

These two emerging concepts interact in a symbiotic manner. What I mean by that is that the Fourth Industrial Revolution requires the proper implementation of data science to exist. At the same time, the effects of technologies and business ventures created through the Fourth Industrial Revolution provide new avenues of data, which can then be harnessed through the power of data science.

From Theory To Practice

Everything mentioned above makes sense in theory, but what does any of this actually mean in practice? As with any newly emerging trends and technologies, it can be difficult to cut through the buzzwords to understand what any of these concepts are actually capable of. Here are some real-world examples to help illustrate these terms in greater detail.

One of the major decisions Netflix made that helped it reach its current level of success, both as a popular streaming service and content creator, was the production and release of its first original show, House of Cards. A 2013 New York Times article (registration required) goes into more detail about the data-based research that went into this program. Essentially, the large quantities of data gathered by the streaming platform allowed Netflix to use data science and identify key factors (visual elements, subject matter, actors) that would result in a popular TV show. Consequently, incorporating these elements into their original programming essentially guaranteed its success, both among audiences and at awards shows.

When discussing the Fourth Industrial Revolution, there’s a particular product that illustrates its current and future potential in the same way as the steam engine encapsulated the potential of the first Industrial Revolution. That product is Amazon’s Alexa — an AI virtual assistant powered by machine learning and able to connect with the Internet of Things. As a 2018 Amazon Developer blog post puts it, Alexa “is about removing friction in our customers’ interactions with the physical and digital world,” which can be interpreted as the central thesis of the current data-driven revolution. The way it — and Amazon by extension — accomplishes this monumental task would simply not be possible without the previous three industrial revolutions, making it a true product of its time.

The Future Of Entrepreneurship

With an understanding of what these lofty concepts are and how they’re currently being implemented by businesses, what can we discern about the future? If you’re an entrepreneur looking to benefit from this unique intersection of science and technology, here are some of my personal suggestions based on extensive research and past experience.

First, it’s important to learn as much about these fields as you can. If you don’t have someone on your team who’s knowledgeable in data science, you should fix that. Many colleges now offer courses and entire major programs around this fledgling field of study, making it easier to recruit people with a solid academic foundation in this area. Alternatively, you can sponsor current employees to pursue data science certifications through online education providers like edX.

Additionally, look for professionals who have experience with projects or businesses relating to the Fourth Industrial Revolution. This is the strategy that Google and Amazon have been taking with their respective acquisitions of IoT companies Nest and Ring. However, you also need to be careful of existing regulations when planning your business of the future. For an excellent example of what can go wrong when attempting to disrupt current industries and laws, look into the story of Defense Distributed and its Ghost Gunner 3-D printer.

Although it’s never a bad idea to look at the actions taken by these companies for inspiration, it’s also important to attempt to innovate and take bold risks by pursuing new partnerships and business ventures. There’s a popular quote from mathematician and entrepreneur Clive Humby that sums up the current state of business: “Data is the new oil.” Ultimately, I believe that the path to success in the current age is to take this statement to heart and approach the intersection of these two big ideas with the aspirations of a modern oil baron. I believe that the company that successfully does this will become the next Google or Amazon.

Source: https://www.forbes.com/sites/theyec/2019/11/07/data-science-the-fourth-industrial-revolution-and-the-future-of-entrepreneurship/#6a87b1a31a6d

Behind the Scenes as Alibaba Revs Up For 11.11 2019

After five years away from its Hangzhou headquarters, Alibaba is bringing much of its 11.11 Global Shopping Festival festivities back to Xixi campus.

Advertisers emerge as buyers for ad tech

With venture capital money for ad tech and marketing technology businesses scarce and strategic buyers also hard to find, marketers are emerging as potential acquirers.

The slump in optimism around ad tech and martech businesses hasn’t been enough to deter a growing number of advertisers. So far this year, several high-profile advertisers have swooped for companies that sat on the periphery of the ad tech and mar tech sectors. McDonald’s bought personalization platform in March, Walmart snapped up ad tech startup Polymorph Labs to deliver more relevant ads to online shoppers in April, Nike bet on predictive analytics company Celect in August; travel startup OYO Hotels and Homes-bought Danamica, a Copenhagen-based startup that specializes in dynamic pricing through machine learning in September; and the same month MasterCard cut a deal for customer data platform SessionM.

As different as the acquired businesses are to one another, none of them can be described as a core part of an ad tech or martech stack — i.e., a demand-side platform, an ad server, a content marketing platform or search engine optimization tools. None of those types of businesses are easy to manage. Agencies and publishers have tried to make those deals work in the past and struggled to varying degrees. When mobile phone operator Three kicked off a search to acquire a DSP in the first quarter of the year, for example, the procurement process quickly ground to a halt, said one consultant with knowledge of the plan on condition of anonymity. With the latest wave of acquisitions, however, advertisers aren’t trying to shoehorn sprawling ad businesses that weren’t developed with them in mind. Instead, they’re eyeing smaller but arguably more strategic vendors that can turbocharge specific objectives. 

“We’re not talking about premium ad tech and martech businesses that are attracting the interest from advertisers,” said Tristan Rice, partner at M&A advisory SI Partners.

The motivations for these deals aren’t too dissimilar to why advertisers have bought agencies, built their own technology and hired internal specialists over the years. Data-driven personalization is a common thread that draws together many of the deals that have been completed by brands. Owning those types of companies directly could save time and money while also provide greater control over results.

Take Walmart’s acquisition of Polymorph Labs and McDonald’s deal for Dynamic Yield, for example. Walmart is able to serve highly targeted ads, and McDonald’s can offer diners individually tailored menus. The idea of using data and technology to enable a better customer experience and ultimately drive higher conversion rates is a key factor in many of these deals. 

Growing interest in ad tech and martech from advertisers is viewed as something of a lifeboat for entrepreneurs worried they weren’t going to see a payday for their businesses, which provide solutions to specific problems rather than the full-scale propositions of larger players in the ad tech and martech space. For a start, the companies that have caught the eye of advertisers are valued on the tech solutions needed rather than the strength of their financial models. The likes of Dynamic Yield are smaller vendors that haven’t been able to grow at the same rate as larger, more traditional ad tech and martech players have. The valuations between the two types of ad tech and martech businesses are worlds apart. McDonald’s bought Dynamic Yield for $300 million (£234 million), whereas AT&T acquired ad tech vendor AppNexus for $1.6 billion (£1.3 billion, for example).

“Brands and retailers at risk of being disintermediated by Google, Amazon, Facebook and Apple are actively fighting back,” said Julie Langley, partner at M&A advisory firm Results International. “We will undoubtedly see a lot of interest in tech businesses that can help physical retailers bridge the gap, and turn physical stores into a positive as part of their broader digital transformation.”

Source: https://digiday.com/marketing/advertisers-emerge-buyers-ad-tech/