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'No Limit' ending explained: Is it based on a true story? What happened to Roxanne?
September 10, 2022 by bamagz
"No Limit" is beautiful underwater but shallow above. The actual story could have been a captivating watch, but it only became interesting in the last few minutes. Roxana Aubrey's character failed to make an impact; she has a tragic past, but we never get to know her in her entirety. The film is visually stunning, but it doesn't invoke the multitude of emotions that one might experience upon seeing what led to the tragic death of freediver Audrey Mestre.
spoilers ahead
Plot Summary: What is the movie about?
As a child, Roxana went to the beach with her grandfather, who taught her to dive. The peace and tranquility that she experienced underwater was incomparable with worldly pleasures. She studied to be a marine biologist in college. One day, she noticed a poster of the world famous freediver, Pascal Gautier, who would be giving diving lessons to those interested. Without thinking twice, she Roxana paid the course fee and arrived at the venue on time. There she met Tom, a diving instructor who worked with Pascal. The two shared unmissable chemistry on the first day of school, but the moment Roxana set eyes on Pascal, she knew who she wanted. Pascal had an incredible pain tolerance capacity, so he could withstand pressure and dive deep into the water. His calm and ability impressed Roxana, and she couldn't take her eyes off him during dinner after the practice session. Pascal was a womanizer. Even with his girlfriend by his side, he continued to flirt with Roxana. They kissed passionately in the bathroom, and when he returned, his girlfriend broke up with him.
Pascal taught Roxana the techniques to stay underwater. Her shared passion for diving brought them closer together. Roxana felt at peace in Pascal's presence, and she wouldn't trade it for the world. She soon joined Pascal's team in a practice session. From day one she understood how risky the profession was. While Pascal was aiming to set a world record by diving 172 meters underwater, his team was spread out over several levels to monitor him. Although Pascal managed to complete the dive safely, one of his team members lost his life. Stephane, a member of the team, blamed Pascal for sending the rookie 40 meters down. The team needed a new member and Roxana stepped in to fill the position. Stephane was against her, knowing how risky it could be for her, but Pascal trusted Roxana's ability. He believed that she was natural and could easily adapt. Roxana learned the security protocol and prepared to help Pascal. On the day of the event, Pascal successfully dove 172m and the team celebrated the new world record. Roxana was on top of the world. She had never felt so alive. She informed her mother that she would be leaving her studies to accompany Pascal on her journey. Her mother did not support her decision, but she refused to listen to her advice. A few days after setting a new world record, another freediver managed to descend 174m. Pascal had to compete now and was aiming for 176m. But this time, luck was not on his side. He fainted and the doctor advised him to stop diving. Pascal wasn't about to give up and Roxana couldn't change his mind, knowing how passionate he was about her profession. While taking a break, he introduced Roxana to the world of freediving. She soon set a new record for French women by diving 56m in the CWF category. The media celebrated her achievement and Pascal watched her from a distance. "No Limit" traces the relationship between Pascal and Roxana. A romance that soon turned sour as a result of envy and mistrust.
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Explanation of the ending of 'No Limit': Who was responsible for Roxana's death?
Pascal could not accept defeat when a woman created another new world record. He was determined to dive 180m and create a new record, but his team and the organizers knew he was not fit enough to do it. As Roxana rose to fame, Pascal could feel her importance slipping away. Roxana looked at him; he was her lover and her mentor, but she could no longer bear the celebration. He was proud of Roxana but disappointed in himself. He went so far as to state that he would be happy to see her set the new world record rather than allow anyone else to do so. It was important for him to be involved either as a participant or as a coach.
Roxana could feel the growing distance between her and Pascal. She often left her at night and she was left alone. Roxana's childhood was not easy. Her father committed suicide as a result of depression when she was 12 years old. She always wondered why her father left her and little by little she became obsessed with the idea of death. She attempted suicide hoping to join her father, but her grandfather saved her life. We get a glimpse of how Roxana had been searching for the meaning of her existence, and it was only after meeting Pascal that she realized who she was meant to be. She emphasized how important Pascal was in his life and how trophies and titles were worthless without him in his life. But he didn't feel the same. He wasn't about to give up. For him, the competition mattered more than what he shared with Roxana. He decided that Roxana would compete in the no limit category even though his entire team thought it was a bad idea, but Roxana accepted the challenge. While she was training to prove Pascal right, he cheated on her. Roxana soon found out about her affair and sought solace from Tom.
The next morning, she decided to leave Pascal, but he was adamant about not letting her go. She broke the mirror with her fist and Roxana couldn't help but sympathize with him. Later, during their training session, Pascal read a text message Tom sent to Roxana and found out about her relationship. At a press conference, Roxana noticed the woman Pascal cheated with in the audience. Looking at him, she knew that he never made amends and she abruptly left the conference. She kept to herself, and she could feel the loneliness creeping in again.
The next day, Roxana was ready to compete in the No Limit category and go down 180m to create a new world record. Pascal talked to Tom and informed him that although they had some setbacks, she was still counting on him. Pascal went over to Roxana to spend a few minutes with her, but realized that she no longer cared about him. Tom noticed that Pascal was checking the air tanks before the dive. A few seconds before Roxana submerged, Pascal planted a kiss on her lips. She dove to 180 m, but when she tried to open the valve on the air tank to inflate the lifting bag, he noticed that it was not working. The cylinder was devoid of air, which prevented it from rising quickly to the surface. Though Tom tried to fill up the tank, it wasn't enough to pull her up that fast. She was supposed to stay under the water for three minutes, but she stayed there for more than eight minutes. He realized that it was a futile attempt to revive himself; her body couldn't take any more. He heard the whale sing and, in the calm depths, he lost consciousness. By the time her security team dove in to save her, it was too late. She was pronounced dead upon reaching the surface. When police questioned the team, no one revealed any knowledge of what went wrong. Though Tom knew that Pascal had a role to play in Roxana's death, as he was last seen manning the air tank lift bag. As Tom was about to enter the airport, he noticed Pascal watching him from a distance.
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Pascal was involved in Roxana's death. He maybe he couldn't tolerate her indifference and wanted revenge on her. He knew that she could leave him, and he would no longer be responsible for her success. That could also be a reason for him to end his life. "No Limit" is dedicated to Audrey Mestre, who lost her life in similar circumstances.
Is 'No Limits' based on a true story?
"No Limits" is loosely based on the tragic death of Audrey Mestre, an up-and-coming freediver. The film maintains that it is a work of fiction and that not everything shown is based on reality. The death of Audrey Mestre was similar to that of Roxana, as the film shows. Audrey was a talented freediver who was trained by her husband, Francisco Ferreras, a popular figure in the diving world. It is said that on the day of the final Mestre dive, Ferreras was responsible for the air tank lift bag. Its dysfunction gave rise to a series of controversies blaming Ferreras for his death. Freediving safety standards were not maintained and the organization responsible for them was underfunded. There were two security divers and no doctors to attend to Mestre when she was brought ashore. She had a pulse and could have been saved if a medical team had been available.
The gross negligence that led to his death sparked massive outrage, although Mestre's family chose not to investigate the case. Francisco Ferreras maintained his innocence. We will never know what happened on October 12, 2002. It may be a strained relationship coupled with a lack of safety equipment that led to the loss of an extraordinary freediver. Supposedly, Audrey was lonely before her last dive. The video of Audrey's final dive that exists on the internet is chilling and heartbreaking. At the age of 28, Mestre left too soon.
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Visa is required for citizens of Suriname to visit Vietnam. Suriname nationals may have 2 options: Visit Vietnam embassy in Suriname or apply for a visa on arrival to Vietnam from Suriname online. Suriname citizens may apply for both tourist and business visa to Vietnam for the stay of 1 to 6 months from Suriname. Please check this link for visa fee for Suriname citizens.


You may wonder why I am writing this blog. What is the story behind it and why choose the name, I dream of Daisy? I thought I would explain the background in more detail.
I was born with one of the most life limiting genetic conditions among the Caucasian population and I grew up thinking I would never have children. Although it is physically possible because all my parts were there, the strain of carrying a pregnancy to term could put me in grave danger. Growing up, mortality and procreation was a topic generally not discussed at hospital appointments and was definitely never discussed at home. There are questions you just don't ask for fear of the answer and some things you are best not knowing. I remember at one point the life expectancy was around 30 years and I lived my life thinking that I would drop dead on or around then. Medical science is advancing but even more important, is your mental ability to fight for life, live your life and look after yourself. I am sure that together with the improved medical science it's possible you could beat the odds against you but complications associated with carrying a pregnancy to term and mostly the thought of leaving a young child without a mother, I always thought that it wouldn't happen to me. I had spent my childhood and young adulthood preparing myself for that life, a life without a child of my own to love because of my health.
My mum brought my brother and I up on her own and throughout my life she has been the one teaching me to stand up for what I wanted and encouraged me through the hard times. It is her get up and go, her belief in me and her positive attitude that has taken me to where I am today. I owe her for allowing me to believe I can lead a "normal" life. In my late teens mum gave me further hope that I could maybe have a baby one day by offering to be a surrogate. She would joke that by using her I could be assured of the baby being handed over.
In my early 20s I met the man who later was to become my husband and I did explain how a life with me would most likely result in a life without a child. In the beginning he explained that it was me he wanted to be with and if we didn't have children, it wasn't meant to be. He was in the armed forced and was posted in Germany and to enable me to join him we decided to get married. I was excited as I planned our wedding and was looking forward to us spending our life together. When the Army learned of my condition they would not allow me to join my husband once married due to fear that they could not cope with my often hectic, treatment regime. Looking back, I can partly agree with this decision but at the time the pressure of planning the wedding and the uncertainty of how my husband and I would be together caused too much of a strain on my health and I lost a lot of weight.
We did fight the armed forces and in the end my husband got posted locally and later left the Army, which meant we could finally be together. It was when we were together that his desire for a child grew and we had several discussions about it. After six months of talking and thinking and two young people I knew that had been taken away from the world in such a cruel way, I decided life was for living. I was going to take a risk and have a child, the one thing I never even allowed myself to dream of. I told my husband that yes, lets do it, lets have a child and at this point in time my life was on the up. I realised that I was happily married to my husband and life can throw you surprises; even those living without my condition don't know what tomorrow holds. I had a husband wanting to be a dad and I could have a child and be blessed with being a mum. I just knew that I would love my child for as long as possible and that together we were going to create good family values and that he would be supported by my family if that fateful day came……..where I had to leave them.


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Opinion: These five technology stocks give investors more reward for their risk
Earnings season for the technology sector is just about over. It's been a mixed bag.
Slower economic growth and higher interest rates put a damper on revenue. Some of the biggest names in tech have come up short, leading to rare selloffs. Meta
META,
and Alphabet
GOOG,
come to mind.
While high-flying tech companies may see their growth on hold, it will likely be a group of incumbent, steady enterprise-technology companies that will step up and deliver.
In a recent column, I pointed to the trendlines of deflationary technologies such as automation and artificial intelligence (AI), and premium-tier consumer products being a safer play. ServiceNow
and Apple
rose to the top of the list.
As companies look to cut costs, they will be turning to critical technology including infrastructure, security, data and AI to maintain and accelerate productivity while making sure they meet compliance and security needs across their IT landscape. Much of this will be derived from maximizing existing software, assets and plumbing that often comes from traditional IT and software vendors, many of whom have been categorized as legacy technology companies.
Cisco's
CSCO,
robust recent quarter, in which the company beat analysts' estimates and raised guidance, may serve as a reminder — and indicator — that enterprise-centric companies that have long taken a back seat to fast-growth big tech companies are where investors should be looking for a good balance of risk and reward.
Five software and infrastructure companies to watch
Juniper: Juniper
JNPR,
delivered a beat-and-raise quarter. The company is well-positioned for steady performance in the infrastructure space and has done well in the past few years to diversify from its telco roots to grow in areas like enterprise and cloud. The company has doubled down on its AI-driven architecture and cybersecurity business. I see Juniper, as I do Cisco, being in the right place at the right time. The company should be able to capitalize on critical infrastructure spending to shore up enterprise IT and security.
Hewlett Packard Enterprise: Hewlett Packard Enterprise
HPE,
has been in a multiyear transition from a big-iron infrastructure company to what CEO Antonio Neri described as an "everything as a service" company. Its GreenLake portfolio has been at the forefront of a trend to consumption-based private cloud connecting the enterprise data center to the public cloud. Valued relatively cheaply with a price-to-earnings (P/E) ratio of 5.5, the company is rapidly nearing $1 billion in annual recurring revenue (ARR), which is a big step in its transition to subscription. Still, the stock yields 3.08%. Growth has been harder to come by for HPE through the pandemic, but as we see the likes of IBM and Cisco doing well, it could be a positive sign for HPE when it reports earnings next week.
Oracle: With over 70% of its revenue being predictable, Oracle
ORCL,
is one of the most steady dividend-yielding software names on the market. The company has a robust core business that its customers depend on in all macroeconomic climates, which is why it is hard not to see Oracle as a safer tech play today. Complementing the steady core business is its rapidly growing cloud offering, which has collectively surpassed $10 billion in revenue annually and continues to grow on the strength of its cross-sell strategies, coupled with a strong performance from its NetSuite and Fusion offerings. Altogether, investors get a mix of stability, yield and growth
SAP: While SAP
SAP,
and Oracle may trade jabs on their quarterly earnings, they share common properties that make them interesting plays for investors. SAP has also leaned heavily into its cloud business, and while it isn't doing the infrastructure as a service like Oracle is, it has seen fast growth in migrating its customers to the cloud. With 80% of company revenue now falling under what it considers "more predictable revenue," the company saw cloud revenue grow 38% in the third quarter — or 25% at constant currencies — while its cloud backlog grew at the same clip. Currency headwinds have been even harder on the Germany-based software giant, but its highly predictable revenue, coupled with its cloud growth and almost 2% yield provides balance for investors.
Salesforce: Down more than 50% from its 12-month highs, there are some significant concerns about Salesforce
+3.04%.
Most notably, there's a likelihood of slower enterprise spending. However, as companies are laying off employees, the need for critical systems of record that can quickly be deployed in the cloud is an ample opportunity, and Salesforce could be in an excellent position to capitalize. Like ServiceNow, Salesforce offers automation, AI and other SaaS-based tools that companies can use for sales, marketing and service. And with its consumption-based usage model, companies can spin up their services more quickly to meet enterprise needs. It still trades at a high multiple, making it a riskier bet, but one that has great long-term potential. Salesforce will likely accelerate the second that the economy turns up.
Daniel Newman is the principal analyst at Futurum Research, which provides or has provided research, analysis, advising or consulting to Oracle, Cisco, Juniper and dozens of other technology companies. Neither he nor his firm holds any equity positions in companies cited. Follow him on Twitter @danielnewmanUV.
Source: https://www.marketwatch.com/story/these-five-technology-stocks-give-investors-more-reward-for-their-risk-as-big-tech-flames-out-11669131807?siteid=yhoof2&yptr=yahoo
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Victorino E. Góngora (Campeche, 1874-1947) fue ingeniero y diputado mexicano de 1916 y 1917.

Desde pequeño realizó sus estudios en Europa donde se gradúa de ingeniero en la Universidad de Gante en 1896

Se sumó a la Revolución, al unirse al carrancismo. Posteriormente, formó parte del Congreso Constituyente como diputado por el distrito XIV, Paso del Macho, de Veracruz.
Presenta junto con sus compañeros constituyentes Heriberto Jara Corona y Carlos L. Gracidas la iniciativa al artículo 123 de la Constitución Política de los Estados Unidos Mexicanos

Los diputados que formaron parte del Congreso, al terminar sus labores en él, escribieron un pensamiento sobre la Constitución, el diputado Góngora, marcó su ideal en la siguiente cita.

Referencias

Senadores de la XXVII Legislatura de México

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Toronto's Fashion District (also known as the Garment District) is a commercial and residential district downtown. It is located between the intersection of Bathurst Street to the west, Spadina Avenue to the east, Queen Street West to the north and King Street to the south.
What It's Like
Many former garment warehouses house clothing outlets while others have been refurbished into artists' studios and galleries, publishers, software and tech design offices and condos and lofts. The area is positively lined with restaurants and bars.
The area's are singles and couples, that live in mid- and high-rise condo buildings that overlook the lower buildings that house boutique shops.
The downtown lifestyle is in its prime here, with – shocking! – Toronto's most fashionable residents roaming the streets, storefronts, bars, restaurants, clubs and events of the neighbourhood.
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Your holiday time is precious – why not invest it in a once-in-a-lifetime experience? If you're looking to make your next break a real show stopper – the world's most precious natural places, UNESCO natural World Heritage sites, should be top of your list. Recognised by UNESCO for their outstanding universal value, World Heritage sites offer far more than just stunning views and culture. You're also likely to spot some of the globe's most precious wildlife, including many species that are listed as endangered, whilst seeing how people living in and around these protected landscapes experience the boon of these natural wonders.
For many business travellers, these Heritage sites may be just a short escape from UK or international business trip and they will provide an unforgettable experience. With many of them facing uncertain futures due to threats from industrial activity, there has never been a better time to tick them off your bucket list. Your visit can also make a difference – the visitor fees at these sites help fund projects to protect natural heritage, benefitting local people and incentivising them to want to protect the outstanding universal value for generations to come.
There are 3 incredible natural World Heritage sites right on our doorstep here in the British Isles – have you visited any of them? From Northern Ireland's Giant's Causeway, to the Jurassic (Dorset and East Devon) Coast and St Kilda in Scotland, each offers something unique. The Giant's Causeway is made up of some 40,000 uniform polygonal columns of Basalt, which have inspired legends of giants roaming Ireland throughout history and becoming the symbol of Northern Ireland.
If giants were to use the causeway to cross the ocean over to Scotland, they'd reach St Kilda in the Hebrides. Boasting some of the highest cliff faces in Europe and offering a home to many rare and endangered birds, this volcanic archipelago is a trip not to be missed. This breathtaking area of outstanding beauty harbours evidence of human dependence from over 2,000 years ago.
Lastly the Jurassic coast, which spans the Dorset and east Devon coastline. If exploring rocky arches and collecting fossils sounds like a day well spent to you, then the Jurassic coast is a must see. Spanning a magnificent 155km of the UK's shoreline, the chances are you've visited one of the many undeveloped beaches included in this World Heritage site already. For those that crave a peaceful and secluded walk, the Jurassic coastal path is an essential for your bucket list.
Crystal clear turquoise waters, glorious sunshine, coral reef stretching along 360 kms of coastline and over 500 species of fish to see during your ocean dip. Step away from your desk and the bustle of the city and experience the tranquillity of scuba diving in the world famous Blue Hole Natural Monument, one of the 7 Protected Areas that make up this UNESCO World Heritage site. At 125 meters in depth, the collapsed cave is home to many species of shark, including the iconic hammer head – but it's not only sharks you'll be likely to catch a glimpse of. Three species of turtle inhabit the waters of Belize, as well as the slow and elusive manatee. Whether this amazing place will survive for the next generation depends on whether the government of Belize follows through with protection commitments they made to UNESCO. The site has languished on the 'in danger' list since 2009. If you are in Central America, it's a natural heritage experience not to be missed nestled in the only English speaking country in the region.
If your business takes you to Africa, you can't get much further away from your ordinary life than this stunning, largely untouched Tanzanian game reserve. Home to almost half of Africa's lions, the largest population of wild dogs, as well as elephants and rhinos, in the dry season you're practically guaranteed to get a close up view of these precious species. Sadly, poaching has dramatically decreased the reserve's elephant and rhino numbers. Industrial scale threats to the heritage including oil, gas and mining concessions and poaching have resulted in UNESCO listing Selous as 'in danger' in 2014. The landscapes are not to be trifled with, with the flowing rivers, luscious woodlands and miles of photographic opportunities, it's earned its place amongst the world's most precious places.
Already in Asia? The Sumatran rainforests might sound like something from a Hollywood movie, but this World Heritage site offers unlimited real life adventure opportunities to soak up natural heritage. Spanning 2.5 million hectares, the canopy of vibrant greens is estimated to be made up of 10,000 species of plant and offers a home to an impressive repertoire of mammals, such as the orangutan. Despite its rainforest title, there are a diverse range of varying environments to explore, with the highest volcano in Indonesia falling within one of the three National Parks that makes up the heritage site, cave systems, rocky backdrops and the highest lake in Asia, all ready to be explored.
Many United States business travellers take advantage of being in Florida. Florida offers hours of burning sunshine and beautiful beaches, but this World Heritage site is a sanctuary of wilderness in an otherwise bustling state. Sitting right at the southern tip of Florida, it offers a walk on the wild side for those who like adventure, with the potential for dangerous encounters with crocodiles, alligators and the Florida panther. UNESCO noted that the site has seen a significant reduction of both marine and estuarine biodiversity. However it remains the most important breeding ground for wading birds in North America. The mangroves offer a peaceful sanctuary for any advanced or novice fisherman – in fact, the park has been named one of the best places in the world for fishing. Be sure to keep your eyes peeled for flashes of pink as the flamingos pass you by.
This stunning European World Heritage site of unique beauty can be found in Andalusia, Spain. If you're an avid bird watcher, this is the one for you. Six million migratory birds visit the largest wetland in the European Union during the winter months, including some that are rare and elusive. Tranquillity doesn't even begin to describe the 38 kms of pristine, construction-free beach that you could have at your disposal. If it's varied wilderness and solitude that you're looking for, look no further than Doñana.
With 238 World Heritage sites that are listed for their natural features by UNESCO in almost 100 countries, you won't ever be too far from a visit. The UNESCO World Heritage brand signals the most precious in protected areas around the world.
Is there a natural World Heritage site you're itching to discover on your next business trip?


•Acemoglu, D, and P Restrepo (2017), "Robots and Jobs: Evidence from US Labor Markets", NBER Working Paper No. 23285.
•Arntz, M, T Gregory and U Zierahn (2016), "The Risk of Automation for Jobs in OECD countries: A Comparative Analysis", OECD Social, Employment and Migration Working Paper No. 189.
•Atkinson, R (2013), "Stop saying that robots are destroying jobs — they are not", Technology Review, September.
•Autor, D (2015), "Why are there still so many jobs? The history and future of workplace automation", Journal of Economic Perspectives 29 (3): 3–30.
•Brynjolfsson E, L Hitt and H Kim, (2011) "Strength in Numbers: How does data-driven decision-making affect firm performance?", MIT Sloan School of Management, December.
•Bughin, J, E Hazan, S Ramaswamy, M Chui, T Allas, P Dahlström, N Henke and M Trench (2017), Artificial Intelligence: the next digital frontier, McKinsey Global Institute.
•Bughin, J (2016), "Reaping the benefits of big data in telecom", Journal of Big Data 3(1): 14.
•Bughin, J and N van Zeebroeck (2017), "The best response to digital disruption," Sloan Management Review, Summer.
•Crafts, N, and T C Mills (2017), "Trend TFP Growth in the United States: Forecasts versus Outcomes", CEPR Discussion Paper No. 12029.
•Frey, C, and M Osborne (2013), "The Future of Employment: How Susceptible are Jobs to Computerisation?" Oxford Martin School Working Paper, September.
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\section{Introduction}
\label{sec:introduction}

World Health Organization (WHO) recommends to managing the provides Particulate Matter (PM) level with providing their guideline as shown in Table~\ref{table:pm-guideline} because it can infiltrate into the deeper site of humans via respiratory organ \cite{who2006aqg}. For detail, the PM can trigger not only respiratory diseases \cite{faustini2011respiratory} but also cardiovascular disease \cite{du2016aircardio}, lung cancer \cite{hamra2014airlung}, or some other diseases.

For managing PM level with referring Table~\ref{table:pm-guideline}, the process for measuring the air condition should be preceded. In the Republic of Korea, the Air Korea, operated by Korea Environment Corporation, provides measured values of $SO_2$, $CO$, $O_3$, $NO_2$, $PM_{2.5}$ and $PM_{10}$ with unit of one hour. For informing the air pollution to the public, the higher spatial resolution may be more effective than lower. However, the resolution provided by them is relatively low because of the cost of maintaining the high-end PM measuring sensor.

Two types of sensors can be used for PM measurement, each based on Beta Attenuation Monitor (BAM) and Tapered Element Oscillating Microbalance (TEOM) methods \cite{badura2018sensor, bulot2019sensor}. The characteristic of the BAM-based sensor is higher precision of measurement, but it needs high maintenance costs as in the case of Air Korea. On the other hand, the TEOM-based sensor needs a lower cost than the BAM-based sensor. For the above reason, when using a BAM-based sensor, increasing the spatial resolution it is difficult but it can be eased via using a cost-effective TEOM-based sensor.

\begin{table}[h]
    \centering
    \small
    \caption{Air Quality Guideline (AQG) for Particulate Matter (PM) level management from World Health Organization (WHO) \cite{who2006aqg}. The unit of each value is $\mu$g/$m^3$. When the diameter of PM is $2.5\mu$g/$m^3$ it called $PM_{2.5}$, and when it between $2.5\mu$g/$m^3$ and $10\mu$g/$m^3$, called $PM_{10}$.}
    \begin{tabular}{lrr}
        \toprule
            \textbf{} & \textbf{Annual mean} & \textbf{24-hour mean} \\
        \midrule
            $PM_{2.5}$ & 10 & 25 \\
        \midrule
            $PM_{10}$ & 20 & 50 \\
        \bottomrule
    \end{tabular}
    \label{table:pm-guideline}
\end{table}

The two coefficients, one of them is Pearson's correlation coefficient and the other is a coefficient of determination for 1-hour averages, are used as proof for the cost-effectiveness of TEOM-based sensor. Those coefficients are already measured as 0.91 and 0.81 respectively \cite{badura2018sensor}.

The TEOM-based sensor can alternate the BAM-based sensor to monitor the PM level. Thus, We have installed TEOM-based sensors relatively densely in several regions as a trial. However, the limitation of the TEOM-based sensor is that it has a more probability malfunctioning in the measuring process than the BAM-based sensor because it more affected by the external environment; we call overall malfunctions as an anomaly. Thus, we propose the novel architecture for anomaly detection to maintain a TEOM-based sensor. Because, if an anomaly detecting solution is provided with the TEOM-based sensor, the efficiency of the PM level monitoring cost can be made more effective.

The organization of this paper is described as follows. In Section~\ref{sec:related_works}, we summarize previous studies that have efforted for anomaly detection. We present the proposed architecture and experimental results in Section~\ref{sec:proposed_architecture} and Section~\ref{sec:experiments} respectively, and conclude the whole content in final section. We only deal with anomaly detection task of functioning sensors in this paper. Thus, the task after anomaly detection, can be categorized to correcting the collected values or maintain the sensor via engineer, will be handled in future study.

\section{Related Works}
\label{sec:related_works}

Several previous studies have conducted the anomaly detection via classification approach with various methods \cite{himmelblau1992classicfication, tellenbach2011classification, eduardo2013svmclassification, jan2017svmclassification, lee2018cnnclassification}. However, there are some problem for using classification method such as difficulty of collecting diverse abnormal cases and labeling cost from collected data to specific category.

For detecting anomaly, the above problems can be eased by regression-based method such as One-Class SVM \cite{steinwart2005classification}, Auto-Encoder (AE) \cite{mayu2014autoencoder, chong2017autoencoder}, or Long-Short Term Memory (LSTM) \cite{malhortra2015lstmtime, chauhan2015ecglstm}. The idea of regression-based method is such simple. Also, the cost of data preparing to use regression-based machine learning or deep learning-based anomaly detection algorithm is not high. Because we need only the normal case (healthy) data for training, and it does not need the categorization process. Moreover, the labeling task is needed for measuring performance quantitatively, but it is quite simple in this case because each sample needs only checked whether normal or not.

Recently, there are several neural network-based anomaly detection models are published \cite{haowen2018vaekpi, thomas2017anogan, donahue2016bigan, samet2018ganomaly, wang2019advae, zhang2019lvead, klar2019conad}. The generative neural network which trained with variational bound can make the user desired data from the random noise \cite{haowen2018vaekpi, kingma2014vae}. However, generating data from noise as same as they do is not essential in anomaly detection. Moreover, because of using variational bound with distribution assumption, the above deep learning architectures may generate the blurred data.

The Generative Adversarial Network (GAN)-based anomaly detection model is published as named as AnoGAN \cite{thomas2017anogan}. The AnoGAN can generate more sharped data than the variational bound-based model. However, it needs to find the most close generated sample from noise with input data for determining abnormal or not. Because of the above procedure, the throughput of AnoGAN is lower than the typical AE-based architecture.

Some of the recent research, such as BiGAN \cite{donahue2016bigan} and GANomaly \cite{samet2018ganomaly}, eased the limitation of AnoGAN. These models directly generate the sample via input data and use them for anomaly detection. BiGAN \cite{donahue2016bigan} has the limitation of generating high-resolution samples because encoder and decoder share the parameters. However, GANomaly \cite{samet2018ganomaly} eases the above limitation via simply using separated parameters for encoder and decoder respectively.

One other research tried to generate data consistently with avoiding to generate the blurred sample \cite{klar2019conad}. For achieving their purpose, they apply the multiple hypothesis to the last layer of the generator. Their neural network generates multiple samples and the best sample that close to input data among those is selected. However, one of the unsatisfied things is that they still use the variational bound as same as Variational Auto-Encoder (VAE) \cite{kingma2014vae} for training. Thus it can be regarded that has still restricted for generating clear, not blurred, data.

The preprocessing is one of the additional considerations to construct an anomaly detection system for reducing the computational cost. For example, data dimension reduction via preprocessing can be highly reduce the computational cost \cite{serpico2003dimcomp, yan2006dimstream, thangavel2009dimtheory, balzanella2010dimtime, park2018fared}. However, our PM sensor collects the value at every one hour, so the preprocessing is not needed to reduce computational cost such as time.

\section{Proposed Architecture}
\label{sec:proposed_architecture}

In this section, we present the proposed architecture for anomaly detection in the PM sensor. We compare the property of two kinds of convolution method firstly. Then, we present the structure of the neural network that we propose. Finally, we describe the anomaly detection procedure.

\subsection{Reason for Using 1D Convolution}
\label{subsed:reason1d}

When using the image data as the input, the 2 dimensional (2D) convolutional layer can be used for constructing the neural network typically. However, we use the 1 dimensional (1D) convolutional layer because our data is 1D signal data.

The signal data can have the multiple channel. For example, $PM_{2.5}$ and $PM_{10}$ can represent first and second channel respectively. The 2D convolution can be used for processing the multi-channel signal data, but it is not efficient as shown in Figure~\ref{fig:conv1d2d}.

\begin{figure}[h]
    \begin{center}
		\includegraphics[width=0.8\linewidth]{figures/1dconv2dconv}
	\end{center}
	\vspace*{-5mm}
	\caption{The examples of generating the output via 1D and 2D convolution. In 2D convolution case, the zero-padding method is needed for aggregating time information with maintaining feature dimension.}
	\label{fig:conv1d2d}
\end{figure}

In order to minimize the information loss for each channel, our purpose is only aggregating the time information via a convolutional layer like a recurrent neural network \cite{mikolov2010recurrent}. In the case of 1D convolution, time information can be summarized while maintaining channel information in natural. However, if the neural network constructed with 2D convolution, the channel information of the input data will be reduced unintentionally. For avoiding this problem the zero-padding can be used as shown in Figure~\ref{fig:archtecture}, for maintaining the channel dimension,  but the last channel of the generated output probably has less information than the front channels. Thus, we adopt the 1D convolutional layer for constructing the neural network.

\subsection{Multiple Hypothesis-based Architecture}
\label{subsed:multiple_hypothesis}

We have already summarized related works in Section~\ref{sec:related_works}. UUsing the variational bound with distribution assumption causes generating blurred the sample. Also, the AnoGAN-like architecture has lower throughput because it needs a matching procedure for finding the most closer generated sample to input data. The helpful thing is multiple hypothesis that can produce more relevant results and can work more robustly \cite{blackman2004multiple, streller2004multiple, rupprecht2017multiple}.

We construct the neural network with reference to the concept of GANomaly as a backbone architecture because it can ease the several mentioned limitations of previous anomaly detection models. We also apply the multiple hypothesis method in the last layer of the generator. Because the multiple hypothesis can utilize to maintain the quality of the output consistently via selecting the best output. We name the procedure of selecting the best output as hypothesis pruning.

The multiple hypothesis generates several samples from the input data directly and generates one additional sample from the random noise. Each generated sample is used as a hypothesis respectively but the samples that are not in the best case will be used as a regularization term. The overall architecture of the neural network that we proposed is shown in Figure~\ref{fig:archtecture}.

\begin{figure*}[h]
    \begin{center}
		\begin{tabular}{cc}
    			\includegraphics[width=0.40\linewidth]{figures/autoencoder} &
    			\includegraphics[width=0.40\linewidth]{figures/adversarialnet}\\
		\end{tabular}
	\end{center}
	\vspace*{-5mm}
	\caption{The architecture of the Hypothesis Pruning GAN (HP-GAN). We use a single encoder, generator, and discriminator but the last layer of the generator is constructed with the multiple hypothesis that colored with cyan. Pruning is conducted after generating the branches by multiple hypothesis.}
	\label{fig:archtecture}
\end{figure*}

The loss functions for training above neural network are shown in Equation~\ref{eq:loss_enc} to Equation~\ref{eq:loss_total}. The symbol $E$, $G$, and $D$ are representing encoder, generator, and discriminator respectively. Also, $x$, $z$, and $f_{l}$ mean input data, encoded latent vector, and feature map of $l$-th layer. The three loss functions $\mathcal{L}_{enc}$, $\mathcal{L}_{gen}$, and $\mathcal{L}_{adv}$ are discripted for encoder, generator, and discriminator respectively; shown in Equation~\ref{eq:loss_enc}, \ref{eq:loss_gen}, and \ref{eq:loss_adv}. Equation~\ref{eq:loss_enc} and \ref{eq:loss_gen}, are constructed with Winner-Take-All (WTA) theory \cite{wolfgang2000wta}. The adversarial losses, from Equation~\ref{eq:loss_adv_noise} to \ref{eq:loss_adv_feature}, are aggregated in Equation~\ref{eq:loss_adv}.

\begin{equation}
    \label{eq:loss_enc}
    \mathcal{L}_{enc} = ||E(x)-E(G(E(x))_{best}||_{2} = ||z-\hat{z}_{best}||_{2}
\end{equation}

\begin{equation}
    \label{eq:loss_gen}
    \mathcal{L}_{gen} = |x-G(E(x))_{best}|_{1} = |x-\hat{x}_{best}|_{1}
\end{equation}

\begin{equation}
    \label{eq:loss_adv_noise}
    \mathcal{L}_{adv_{noise}} = ||D(x)-D(\hat{x}_{noise})||_{2}
\end{equation}

\begin{equation}
    \label{eq:loss_adv_best}
    \mathcal{L}_{adv_{best}} = ||D(x)-D(\hat{x}_{best})||_{2}
\end{equation}

\begin{equation}
    \label{eq:loss_adv_others}
    \mathcal{L}_{adv_{others}} = ||D(x)-D(\hat{x}_{others})||_{2} / \textit{number of others}
\end{equation}

\begin{equation}
    \label{eq:loss_adv_feature}
    \mathcal{L}_{adv_{feature}} = \sum_{l=0}^{L}||f_{l}(x)-f_{l}(\hat{x}_{others})||_{2}
\end{equation}

\begin{equation}
    \label{eq:loss_adv}
    \mathcal{L}_{adv} = \mathcal{L}_{adv_{noise}} + \mathcal{L}_{adv_{best}} + \mathcal{L}_{adv_{others}} + \mathcal{L}_{adv_{feature}}
\end{equation}

For optimizing the parameters at once, we summarize the three losses $\mathcal{L}_{enc}$, $\mathcal{L}_{gen}$, and $\mathcal{L}_{adv}$ with each weighting coefficient $w_{enc}$, $w_{gen}$, and $w_{adv}$. The weighting coefficients are set with values 1, 50, and 1 respectively. These coefficients are referenced from GANomaly \cite{samet2018ganomaly} and they can be modified by the hyperparameter tuning process. We conduct the hyperparameter tuning in Section~\ref{subsec:various_hyperparameter}, but finding the best weighting coefficient is not covered in this paper; we only finding the best kernel size, number of the convolutional block, and learning rate.

\begin{equation}
    \label{eq:loss_total}
    \mathcal{L} = w_{enc}\mathcal{L}_{enc} + w_{gen}\mathcal{L}_{gen} + w_{adv}\mathcal{L}_{adv}
\end{equation}

We train the HP-GAN via Algorithm~\ref{algo:training_algorithm}. We use the Xavier initializer \cite{glorot2010understanding} for initializing the neural network, and use Adam optimizer \cite{kingma2014adam} for optimizing the parameters.

\begin{algorithm}
	\caption{Training algorithm.}
	\hspace*{3.5mm}\textbf{Input:} Measured PM values $x$, and random noise $z_{noise}$\\
	\hspace*{3.5mm}\textbf{Output:} Generated PM values $\hat{X}$ with multiple hypothesis
	\begin{algorithmic}
		\STATE Initialize parameters of neural network by Xavier initializer \cite{glorot2010understanding}
		\WHILE{the loss has not converged}
            \STATE Get $\hat{X}$ by forward propagation
            \STATE Prune $\hat{X}$ as $\hat{x}_{best}$ or others
            \STATE Compute losses $\mathcal{L}_{enc}$, $\mathcal{L}_{gen}$, and $\mathcal{L}_{adv}$
            \STATE Summarizing losses as a total loss $\mathcal{L}$
            \STATE Update parameters by backward propagation with Adam optimizer \cite{kingma2014adam}
		\ENDWHILE
	\end{algorithmic}
	\label{algo:training_algorithm}
\end{algorithm}

\subsection{Anomaly Detection Method}
\label{subsed:anomaly_detection}

We describe the anomaly detection procedure in this section. The abnormality decision method is simple as shown in Algorithm~\ref{algo:anomaly_detection}. For using the above algorithm, input data must be measured via PM sensor firstly with containing two channels; $PM_{2.5}$ and $PM_{10}$. Then, the generation procedure is conducted via input the data to the neural network. We set the decision boundary $\theta$ using $\mu$ and $\sigma$ of the training data as shown in Equation~\ref{eq:decision}. The $\mu$ and $\sigma$ represent the mean and standard deviation of the mean square error between the input and the best hypothesis of the generated samples. For reference, if the user wants to change the sensitivity or specificity of the anomaly detection, $\theta$ can be adjusted.

\begin{equation}
    \label{eq:decision}
    \theta = \mu + (1.5 * \sigma)
\end{equation}

\begin{algorithm}
	\caption{Abnormality decision algorithm.}
	\hspace*{3.5mm}\textbf{Input:} Measured PM values $x$  \\
	\hspace*{3.5mm}\textbf{Output:} Selected best hypothesis $\hat{x}_{best}$ among the generated samples
	\begin{algorithmic}
	    \STATE $\theta \leftarrow$ set threshold based on training data
        \IF{$||\hat{x}-\hat{x}_{best}||_{2} \geqslant \theta$}
            \STATE $x$ is abnormal
        \ELSE
            \STATE $x$ is normal
        \ENDIF
	\end{algorithmic}
	\label{algo:anomaly_detection}
\end{algorithm}

\section{Experiments}
\label{sec:experiments}

In this section, we present our PM dataset and show experimental results for various neural network architectures. For assessing each model, we use Area Under the Receiver Operating characteristics Curve (AUROC) \cite{fawcett2006roc}, Area Under the Precision-Recall Curve (AUPRC) \cite{saito2015prc}, and Mean Square Error (MSE) as the performance indicators.

\subsection{Dataset}
\label{subsed:dataset}

We have collected the PM dataset from the 12 locations in the Daegu Metropolitan Jungang Library, Korea. For collecting data, we use the TEOM-based sensor as shown in Figure~\ref{fig:sensor} and the collected dataset is shown in Table~\ref{table:dataset-pm}.

\begin{figure}[h]
    \begin{center}
		\includegraphics[width=0.4\linewidth]{figures/sensor}
	\end{center}
	\vspace*{-5mm}
	\caption{TEOM-based PM sensor.}
	\label{fig:sensor}
\end{figure}

\begin{table}[h]
    \centering
    \small
    \caption{The collected $PM_{2.5}$ and $PM_{10}$ values via TEOM-based sensors. The each sample $x \in \rm I\!R^{24x2}$ represents two values ($PM_{2.5}$ and $PM_{10}$) for 24-hour.}
    \begin{tabular}{cccc}
        \toprule
            \textbf{} & \textbf{Normal} & \textbf{Abnormal} & \textbf{Total} \\
        \midrule
            Number of Sample & 249 & 73 & 322\\
        \bottomrule
    \end{tabular}
    \label{table:dataset-pm}
\end{table}

Each sample $x \in \rm I\!R^{24x2}$ contains the information of $PM_{2.5}$ and $PM_{10}$ for one day with 1-hour unit. The label of normal or abnormal is determined by meteorologists. We use 80\% of the normal for training, and the other normal samples and whole abnormal samples are used for assessing the performance.

\subsection{Experiment with Published Architecture}
\label{subsed:published_architecture}

First of all, we conduct experiments for confirming which architecture among the previous studies is effective for anomaly detection. We adopt five known architectures and reconstruct them using 1D convolutional layer \cite{kingma2014vae, samet2018ganomaly, wang2019advae, zhang2019lvead, klar2019conad}. Each network such as encoder or generator uses three convolutional block, and each convolutional block consists two convolutional layer with elu activation \cite{clevert2015elu}. The max-pooling is applied at the last of each convolutional block. We use two fully connected layers for the rear of the encoder and discriminator. Also, the generator includes two fully connected layers at the front of them.

We measure the performance and summarize them in Table~\ref{table:performance-quali-skdata}. We also present the best-generated samples selected from multiple hypothesis in Figure~\ref{fig:performance-quanti-skdata} for qualitively analysis.

\begin{table*}[h]
    \centering
    \small
    \caption{Measured performance of experiment with published architecture. The purpose of this experiment is confirming which style of the architecture can detect anomaly better.}
    \begin{tabular}{lccccccc}
        \toprule
            \textbf{Aarchitecture} & \textbf{$PM_{2.5}$} & \textbf{$PM_{10}$}
            & \textbf{AUROC} & \textbf{AUPRC}
            & \textbf{MSE} & \textbf{Tr-Time} & \textbf{Te-Time} \\
        \midrule
            VAE \cite{kingma2014vae} & O & O & 0.92151 & 0.95629 & 0.01170 & 00:16:43 & 0.41122 \\
                & O & - & 0.91699 & 0.95617 & 0.01424 & \textbf{00:14:26} & 0.30716 \\
                & - & O & 0.92630 & 0.95643 & 0.01772 & 00:17:14 & 0.36180 \\
        \midrule
            GANomaly \cite{samet2018ganomaly} & O & O & \textbf{0.93397} & \textbf{0.96485} & 0.01080 & 00:20:10 & 0.34627 \\
                & O & - & 0.92753 & 0.96209 & 0.00914 & 00:21:43 & 0.31621 \\
                & - & O & 0.92301 & 0.95882 & \textbf{0.01020} & 00:24:21 & \textbf{0.30096} \\
        \midrule
            adVAE \cite{wang2019advae} & O & O & 0.92849 & 0.95704 & 0.01716 & 00:23:42 & 0.40858 \\
                & O & - & 0.92096 & 0.94832 & 0.02646 & 00:19:32 & 0.31841 \\
                & - & O & 0.94233 & 0.95760 & 0.02264 & 00:19:28 & 0.32030 \\
        \midrule
            LVEAD \cite{zhang2019lvead} & O & O & 0.88384 & 0.91724 & 0.01944 & 11:16:55 & 1.03436 \\
                & O & - & 0.85370 & 0.87660 & 0.03764 & 10:37:55 & 1.03876 \\
                & - & O & 0.91575 & 0.94738 & 0.01838 & 11:57:14 & 1.01579 \\
        \midrule
            ConAD \cite{klar2019conad} & O & O & 0.92452 & 0.95638 & 0.02667 & 00:20:22 & 0.34142 \\
                & O & - & 0.92342 & 0.95606 & 0.01524 & 00:30:05 & 0.31843 \\
                & - & O & 0.91616 & 0.94154 & 0.01799 & 00:30:02 & 0.30392 \\
        \bottomrule
    \end{tabular}
    \label{table:performance-quali-skdata}
\end{table*}

Time consumption for training and test procedure of four architectures other than LVEAD are similar to each other but LVEAD needs much longer time. Thus, LVEAD is not efficient in the time consumption viewpoint.

The GANomaly shows the highest AUROC and AUPRC. Also, it has the lowest MSE among the above five architectures, so if who wants to use the known neural network without developing novel architecture GANomaly can be recommended.

\begin{figure}[h]
    \begin{center}
		\begin{tabular}{ccc}
    			\includegraphics[width=0.28\linewidth]{figures/pm-publicmodel-vae} &
    			\includegraphics[width=0.28\linewidth]{figures/pm-publicmodel-ganomaly} &
    			\includegraphics[width=0.28\linewidth]{figures/pm-publicmodel-advae} \\
    			VAE & GANomaly & adVAE \\
		    \midrule
    			\includegraphics[width=0.28\linewidth]{figures/pm-publicmodel-lvead} &
    			\includegraphics[width=0.28\linewidth]{figures/pm-publicmodel-conad} & \\
    			LVEAD & ConAD \\
		\end{tabular}
	\end{center}
	\vspace*{-5mm}
	\caption{The generated sample from the five architectures for qualitative analysis. The x-axis of each subfigure represents an hour of the day and the label of the y-axis is noted in each subfigure.}
	\label{fig:performance-quanti-skdata}
\end{figure}

For analyzing qualitative results, GANomaly generates the most similar sample to the input. The ConAD and VAE follow after GANomaly. The common method of VAE and ConAD is variational bound. The above method can be regarded as a reason for generating smoothed output. Thus, we conduct an ablation study to find and confirm the cause of performance degradation in the next section.

\subsection{Ablation Experiment}
\label{subsed:ablation_experiment}

We construct HP-GAN by referring to previous studies. HP-GAN, the novel unproven architecture, needs to confirm that it can work better than other architectures. We compose the experiments to verify the ability of HP-GAN with our dataset. The several kinds of architectures other than HP-GAN are constructed for ablation experiments. The ablation experiment is used to find the cause of the performance impediment \cite{sajid2015ablation, richard2019ablation}.

The six ablation architectures are constructed that contain the GANomaly and HP-GAN. Latent vector Matching (LM), Variational Bound (VB), and Multiple Hypothesis (MH) are used as the conditions for construct these architectures. The purpose of using LM is minimizing the Euclidean distance between latent vector $z$ (from $x$) and $\hat{z}$ (from $\hat{x}$). The VB is used for minimizing the Kullback–Leibler divergence between latent vector $z$ and normal distribution. The MH, last condition, is used to generate consistent output based on (WTA) theory.

The mini-batch size, number of the epoch, the dimension of the latent vector $z$, and the learning rate are used equally for all architectures. The above hyperparameters are set as 32, 1000, and 0.0001 respectively. The experimental result is summarized in Table~\ref{table:performance-ablation-skdata}.

\begin{table*}[h]
    \centering
    \small
    \caption{The performance of the six ablation (combination) architectures. We use three condition Latent vector Matching (LM), Variational Bound (VB), and Multiple Hypothesis (MH) for ablation study.}
    \begin{tabular}{lcccccccc}
        \toprule
            \textbf{Architecture} & \textbf{LM} & \textbf{VB} & \textbf{MH} & \textbf{$PM_{2.5}$}
            & \textbf{$PM_{10}$} & \textbf{AUROC} & \textbf{AUPRC} & \textbf{MSE} \\
        \midrule
            \multirow{3}{*}{\begin{tabular}[x]{@{}l@{}}Ablation-1\\(LM-GAN; GANomaly)\end{tabular}}
                & \multirow{3}{*}{O} & \multirow{3}{*}{-} & \multirow{3}{*}{-}
                & O & O & 0.93397 & 0.96485 & 0.01080 \\
                & & & & O & - & 0.92753 & 0.96209 & 0.00914 \\
                & & & & - & O & 0.92301 & 0.95882 & \textbf{0.01020} \\
        \midrule
            \multirow{3}{*}{\begin{tabular}[x]{@{}l@{}}Ablation-2\\(VB-GAN)\end{tabular}}
                & \multirow{3}{*}{-} & \multirow{3}{*}{O} & \multirow{3}{*}{-}
                & O & O  & 0.90548 & 0.93130 & 0.02148 \\
                & & & & O & - & 0.94192 & 0.96536 & 0.01924 \\
                & & & & - & O & 0.86767 & 0.91166 & 0.04300 \\
        \midrule
            \multirow{3}{*}{\begin{tabular}[x]{@{}l@{}}Ablation-3\\(LMVB-GAN)\end{tabular}}
                & \multirow{3}{*}{O} & \multirow{3}{*}{O} & \multirow{3}{*}{-}
                & O & O  & 0.89521 & 0.93441 & 0.02510 \\
                & & & & O & - & 0.86096 & 0.90572 & 0.04022 \\
                & & & & - & O & 0.89205 & 0.93045 & 0.02974 \\
        \midrule
            \multirow{3}{*}{\begin{tabular}[x]{@{}l@{}}Ablation-4\\(LMMH-GAN; Ours)\end{tabular}}
                & \multirow{3}{*}{O} & \multirow{3}{*}{-} & \multirow{3}{*}{O}
                & O & O  & 0.91699 & 0.95303 & 0.02550 \\
                & & & & O & - & 0.93219 & 0.96312 & 0.01534 \\
                & & & & - & O & 0.92616 & 0.95424 & 0.01230 \\
        \midrule
            \multirow{3}{*}{\begin{tabular}[x]{@{}l@{}}Ablation-5\\(VBMH-GAN)\end{tabular}}
                & \multirow{3}{*}{-} & \multirow{3}{*}{O} & \multirow{3}{*}{O}
                & O & O  & \textbf{0.94644} & \textbf{0.96462} & 0.03578 \\
                & & & & O & - & 0.93027 & 0.96153 & 0.01486 \\
                & & & & - & O & 0.90068 & 0.94807 & 0.01404 \\
        \midrule
            \multirow{3}{*}{\begin{tabular}[x]{@{}l@{}}Ablation-6\\(LMVBMH-GAN)\end{tabular}}
                & \multirow{3}{*}{O} & \multirow{3}{*}{O} & \multirow{3}{*}{O}
                & O & O  & 0.92603 & 0.94032 & 0.03019 \\
                & & & & O & - & 0.93123 & 0.96179 & 0.01531 \\
                & & & & - & O & 0.92712 & 0.95730 & 0.01922 \\
        \bottomrule
    \end{tabular}
    \label{table:performance-ablation-skdata}
\end{table*}

The Table~\ref{table:performance-ablation-skdata} quantitatively shows that VBMH-GAN has better AUROC, AUPRC than the other. However, as shown in Figure~\ref{fig:performance-ablation-skdata}, the qualitative result, the generated sample via LM-GAN (GANomaly) and LMMH-GAN (Ours; HP-GAN) show a better result than VBMH-GAN. Moreover, other VB-based architectures commonly show the blurred result.

\begin{figure}[h]
    \begin{center}
		\begin{tabular}{ccc}
    			\includegraphics[width=0.28\linewidth]{figures/pm-publicmodel-ganomaly} &
    			\includegraphics[width=0.28\linewidth]{figures/pm-ablation-vgan} &
    			\includegraphics[width=0.28\linewidth]{figures/pm-ablation-vzgan} \\
    			LM-GAN (GANomaly) & VB-GAN & LMVB-GAN \\
		    \midrule
    			\includegraphics[width=0.28\linewidth]{figures/pm-ablation-mhzgan} &
    			\includegraphics[width=0.28\linewidth]{figures/pm-ablation-mhvgan} &
    			\includegraphics[width=0.28\linewidth]{figures/pm-ablation-mhvzgan} \\
    			LMMH-GAN (Ours) & VBMH-GAN & LMVBMH-GAN \\
		\end{tabular}
	\end{center}
	\vspace*{-5mm}
	\caption{The generated sample from the six ablation architectures for qualitative analysis.}
	\label{fig:performance-ablation-skdata}
\end{figure}

In this experiment, we confirm that the quantitative result represents that the VBMH-GAN is the best architecture, but the qualitative result of them is not good. Thus, we need to conduct more experiment for verifying the above architectures using varied hyperparameters, because each model may need the specific hyperparameter for performing much better.

\subsection{Experiment with Various Hyperparameter}
\label{subsec:various_hyperparameter}

In this section, we assess four of neural networks with various hyperparameter set. For the experiment, we use the VAE as a baseline architecture because it is the first VB-based architecture. We also use GANomaly, based on LM, as one other baseline architecture. The other two architectures are our HP-GAN (LMMH-GAN) and VBMH-GAN.

We use the kernel size, number of convolutional blocks, and learning rate as the hyperparameter. Each convolutional block consists two convolutional layer with elu activation and one max pooling layer same as commented in Section~\ref{subsed:published_architecture}. We compose the 108 kinds of the hyperparameter set for experiment via combining three hyperparameters as shown in Table~\ref{table:hyperparameter_set}.

\begin{table}[h]
    \centering
    \small
    \caption{The hyperparameters for the experiment. We use the grid search method for hyperparameter tuning \cite{chicco2017ten}. The number of convolutional blocks is abbreviated as \# of conv-block.}
    \begin{center}
		\begin{tabular}{ll}
		    \toprule
		        \textbf{Hyperparameter} & \textbf{Values} \\
			\midrule
    			Kernel size & 3, 5, 7, 9, 11, and 13\\
                \# of conv-block & 2, 3, and 4  \\
                Learning rate & 5e-3, 1e-3, 5e-4, 1e-4, 5e-5, and 1e-5 \\
            \bottomrule
		\end{tabular}
	\end{center}
	\label{table:hyperparameter_set}
\end{table}

When the case of large kernel sizes such as 13, the time axis dimension of the feature vector can be smaller than the kernel of the last layer. However, the amount of the feature information and balance between feature vectors is not changed by the above situation, so we also use the large size kernels.

We present the measured performance for each hyperparameter with surface form in Figure~\ref{fig:hyperparameter_auroc_sk} and \ref{fig:hyperparameter_auprc_sk}. The height of the surface represents the anomaly detection ability; the higher height means higher performance. The flatten surface indicates that the neural network stably responds to hyperparameter changes.

When the number of the convolutional block is two or three, the surfaces for each architecture look like similar and relatively flatten then four-block cases. However, HP-GAN shows the most high and flatten surface in the four convolutional block case.

We also confirm that HP-GAN shows generally flatten surface in the AUPRC surfaces. Thus, we can conclude that HP-GAN may perform stably for anomaly detection than other architectures. However, we additionally conduct the experiment for confirming the average performance with the best hyperparameter of each model. The best hyperparameter set summarized in Table~\ref{table:best_hyperparameter}.

\begin{table}[h]
    \centering
    \small
    \caption{The best hyperparameter set selected from AUROC and AUPRC surface.}
    \begin{center}
		\begin{tabular}{lccc}
		    \toprule
		        \textbf{Architecture} & \textbf{Kernel size} & \textbf{\# of conv-block} & \textbf{Learning rate} \\
			\midrule
    			VAE & 7 & 3 (6 convolution) & 5e-4 \\
    			GANomaly & 9 & 4 (8 convolution) & 5e-4 \\
    			HP-GAN & 7 & 3 (6 convolution) & 1e-5 \\
    			VBMH-GAN & 7 & 3 (6 convolution) & 5e-5 \\
            \bottomrule
		\end{tabular}
	\end{center}
	\label{table:best_hyperparameter}
\end{table}

We repeat the experiment 30 times with the randomly shuffled dataset for Monte Carlo estimation \cite{kroese2014monte}. The performances are summarized with a mean $\pm$ standard deviation as shown in Table~\ref{table:monte_calro_sk}.

\begin{table}[h]
    \centering
    \small
    \caption{The measured AUROC, AUPRC, and MSE are provided with a mean $\pm$ standard deviation form. The experiment is conducted with the best hyperparameter set for each architecture.}
    \begin{center}
		\begin{tabular}{lccc}
		    \toprule
		        \textbf{Architecture} & \textbf{AUROC} & \textbf{AUPRC} & \textbf{MSE} \\
			\midrule
    			VAE & 0.918 $\pm$ 0.033 & 0.942 $\pm$ 0.028 & 0.025 $\pm$ 0.009 \\
                GANomaly & 0.911 $\pm$ 0.091 & 0.937 $\pm$ 0.062 & 0.027 $\pm$ 0.049 \\
                HP-GAN & \textbf{0.948 $\pm$ 0.010} & \textbf{0.967 $\pm$ 0.008} & \textbf{0.023 $\pm$ 0.016} \\
                VBMH-GAN & 0.944 $\pm$ 0.021 & 0.959 $\pm$ 0.020 & 0.033 $\pm$ 0.057 \\
            \bottomrule
		\end{tabular}
	\end{center}
	\label{table:monte_calro_sk}
\end{table}

In Table~\ref{table:monte_calro_sk}, the higher the mean represents better performance. On the other hand, the lower standard deviation means higher stability. The HP-GAN that we proposed in this paper shows the higher performance at every indicator. Moreover, HP-GAN has higher stability (lower standard deviation) at every indicator.

\section{Conclusion}
\label{sec:conclusion}

We experimentally show the cutting-edge performance at anomaly detection of our HP-GAN in the TEOM-based PM sensor. The HP-GAN is trained by latent vector matching with multiple hypothesis based on WTA theory. Our neural network, HP-GAN, can generate the output more clearly and consistently with avoiding blurring effect than other VB-based models when the input data is in the normal category. The mean of AUROC and AUPRC of HP-GAN are 0.037 0.080 higher than the second performance model VBMH-GAN. Also, the mean of MSE is best (lowest) among the whole architectures. Thus, we finally conclude our HP-GAN as a cutting-edge architecture for anomaly detection.

\clearpage

\begin{figure}[h]
    \begin{center}
		\begin{tabular}{ccc}
    			\includegraphics[width=0.30\linewidth]{figures/surface-roc-vae-lay0-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-roc-vae-lay1-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-roc-vae-lay2-sk} \\
			\midrule
    			\includegraphics[width=0.30\linewidth]{figures/surface-roc-ganomaly-lay0-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-roc-ganomaly-lay1-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-roc-ganomaly-lay2-sk} \\
			\midrule
    			\includegraphics[width=0.30\linewidth]{figures/surface-roc-mhzgan-lay0-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-roc-mhzgan-lay1-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-roc-mhzgan-lay2-sk} \\
			\midrule
    			\includegraphics[width=0.30\linewidth]{figures/surface-roc-mhvgan-lay0-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-roc-mhvgan-lay1-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-roc-mhvgan-lay2-sk} \\
		\end{tabular}
	\end{center}
	\vspace*{-5mm}
	\caption{The surface of AUROC with various hyperparameters. The AUROC of VAE, GANomaly, and HP-GAN (LMMH-GAN), and VBMH-GAN are shown in each row sequentially. Each column shows the result of two, three, and four convolutional blocks from the left to right.}
	\label{fig:hyperparameter_auroc_sk}
\end{figure}

\begin{figure}[h]
    \begin{center}
		\begin{tabular}{ccc}
    			\includegraphics[width=0.30\linewidth]{figures/surface-prc-vae-lay0-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-prc-vae-lay1-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-prc-vae-lay2-sk} \\
			\midrule
    			\includegraphics[width=0.30\linewidth]{figures/surface-prc-ganomaly-lay0-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-prc-ganomaly-lay1-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-prc-ganomaly-lay2-sk} \\
			\midrule
    			\includegraphics[width=0.30\linewidth]{figures/surface-prc-ganomaly-lay0-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-prc-ganomaly-lay1-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-prc-ganomaly-lay2-sk} \\
			\midrule
    			\includegraphics[width=0.30\linewidth]{figures/surface-prc-mhvgan-lay0-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-prc-mhvgan-lay1-sk} &
    			\includegraphics[width=0.30\linewidth]{figures/surface-prc-mhvgan-lay2-sk} \\
		\end{tabular}
	\end{center}
	\vspace*{-5mm}
	\caption{The surface of AUPRC with various hyperparameters. The order of the shown contents is the same as Figure~\ref{fig:hyperparameter_auroc_sk}.}
	\label{fig:hyperparameter_auprc_sk}
\end{figure}

\clearpage

\funding{This research received no external funding.}

\acknowledgments{Thanks to all the members of our team and Planet Co., Ltd. They have supported this research via not only data collection but also equipment for the experiment.}

\conflictsofinterest{The authors declare no conflict of interest.}

\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


text:
The last time a president admitted he secretly taped conversations, he had to resign
By Matt PearceStaff Writer
May 12, 2017 12:05 PM PT
You could say that it was with great interest Friday morning that Ken Hughes, a presidential historian with the University of Virginia's Miller Center, saw what the current president was saying on Twitter.
"James Comey better hope that there are no 'tapes' of our conversations before he starts leaking to the press!" Donald Trump tweeted, issuing an unusual public warning to the recently fired director of the FBI as more news stories are published about the pair's contentious relationship. Trump's tweet suggested, of course, that the president was privately recording his conversations.
"The first thing I asked is whether he realized that, if he does have tapes of his conversations with Comey, they're evidence in any investigation of whether his firing of Comey amount to obstruction of justice," said Hughes in an interview with the Los Angeles Times.
"If so," he added, "they are evidence related to a criminal investigation and therefore they can be subpoenaed, either by Congress, or by a prosecutor, or special prosecutor if one is appointed, and Trump has to turn them over, as the Supreme Court ruled way back in 1974."
Hughes would know. He is one of the nation's foremost experts on the voluminous secret recordings that President Nixon taped in the Oval Office, tapes that ultimately ended his presidency.
"Nixon found out the hard way that having tape recordings of your conversations can backfire," Hughes said.
Democrats in Congress, aware of the parallels — and raising questions over whether Trump fired the FBI director for investigating his administration's possible ties to Russia — have pounced immediately, demanding any such Trump tapes be turned over to Congress. During a press briefing Friday, White House spokesman Sean Spicer repeatedly declined to deny that Trump is secretly taping his conversations, telling reporters, "The president has nothing further to add on that."
The existence of Nixon's secret tapes were revealed during the investigation into the Watergate burglary of Democratic offices in 1972, which had been directed by Nixon's aides. A special prosecutor subpoenaed the tapes, and when Nixon objected, the U.S. Supreme Court ruled that the president couldn't claim "executive privilege" to keep the tapes secret.
(Front page on Oct. 20, 1973)
The tapes, once turned over, showed that Nixon had obstructed the investigation into the Watergate break-in. Nixon then resigned rather than face impeachment.
Today, the law is even less in favor of a president keeping such recordings secret.
"The law was very different back then," said Luke Nichter, an associate professor of history at Texas A&M University who has published two books about Nixon's tapes, and whose phone was ringing off the hook Friday. "The tapes were his personal record. Today, if the president sends a tweet, it's technically a public record."
That's due to an "avalanche" of new public-records laws that Congress passed to increase executive transparency after the scandals of the Nixon presidency, including the Presidential Records Act and an amendment to the Federal Records Act, Nichter said.
Then there was the cultural impact.
"When the Nixon tapes became public, what shocked people so much was the difference between what Nixon said in public and what Nixon said in private," said Nichter, who asked if Trump tapes would reveal the same thing — if there is a difference between the public and private Trump. "If he did do taping, we might finally learn the answer to that question."
matt.pearce@latimes.com
@mattdpearce
Sessions restores tough drug war policies that trigger mandatory minimum sentences
Trump's statements linking Russia investigation to Comey firing could lead to legal problems
Trump suggests he may have secretly taped White House meetings, expresses frustration at ongoing Russia questions
Matt Pearce
Matt Pearce is a reporter for the Los Angeles Times covering internet culture and podcasting.


A Coventry man is accused of putting his wife's life in danger by setting fire to his house.
A Coventry man is accused of putting his wife's life in danger by setting fire to his house.
Coventry Crown Court was told that Andrew Killpack's wife woke to discover waist-high flames outside the bedroom door.
She screamed for help and her husband raced up the stairs, eventually putting out the blaze with a fire extinguisher.
But investigators and police believe Killpack, aged 29, doused the landing in petrol and started the fire.
The couple had been married for about five years but the defendant's wife Natasha had told him the marriage was over and they were sleeping in separate bedrooms, the court heard.
Killpack, of Ashby Close, Binley, denies arson being reckless as to whether life was endangered.


text:
The times they are a changin'
Diversity is not about compliance or tolerance, sensitivity or charity. It sparks our innovation by valuing differences, by choosing to look at problems from a different angle. It looks for solutions that benefit everyone, equally. What could be more important to our industry? Our agencies and our work must reflect the diversity of the marketplaces we serve, from the talent in our commercials to the messages we send. It is imperative in today's world. But it must be genuine.
In India, DDB Mudra created one of the biggest street art collaborations for Puma Suede, a brand that has always been the voice of the street culture. Bringing together graffiti, rap and dance, they collaborated with eight rappers, seven street artists, and 32 dancers from four different cities with four different languages. The massive fusion project skyrocketed Desi Hip-Hop artist Divine from the slums of Mumbai to worldwide star.
In New Zealand, a simple casting decision added more to the story they told. Inspired by a true account of a former Lottery winner who pledged to share his winnings with his best mate, their film for NZ Lotteries connected with audiences across the country, casting a 'Pākehā bloke' (aka white Kiwi) alongside a younger, Indian man. The two-minute film broke the standard time block rules, reminding us that the right content is what captures attention.
Bill Bernbach and his teams broke all the rules of advertising. They grabbed people's attention with fresh images and a way of speaking that were pulled from the communities in which they lived. The talent in their ads was sometimes literally someone they passed on the street. The diversity of the agency and the work they produced stood out against the cookie cutter work of the day. They were tuned in to the changing society of their time and welcomed the responsibility they had as communicators to "uplift society".
We must never forget our role or the influence our work may have on the world.
Issue 75: All People Being Equal permalink Share


Make Hearing Conservation Part of Your Everyday Life.
With more than 30 years of providing innovative hearing safety solutions, Howard Leight understands that successful hearing conservation programs rely not only on great protectors, but on the people who use them.
At the forefront of hearing conservation efforts on and off the job, Howard Leight is committed to creating a new view of hearing protection and conservation that places people first.
Honeywell and Howard Leight are here to help build awareness about the risks, dangers and consequences of noise-induced hearing loss (NIHL). We're educators, engineers, audiologists and everyday people who care about Hearing Conservation. We're dedicated to promoting effective hearing safety practices on and off the job by collecting the latest and best information about Hearing Conservation and providing it to you in one web site.
This section of HowardLeight.com is a definitive source for regularly updated details about the latest advances, research and discoveries relating to NIHL. This section also provides an extensive library of existing materials and interactive resources for anyone interested in learning more about the ways NIHL can be measured, reduced and even eliminated as well as resources for your own hearing conservation program. To learn more select from the menu to the left or you can contact us here.


text:
Meet Our Team of Exceptional Veterinarians
The veterinarians of Saratoga Veterinary Hospital are a dedicated group of professionals who commit themselves to caring for the Wilton and greater Saratoga Springs pet communities. Learn more about our exceptional veterinarians on this page and be sure to make an appointment soon to experience their passion and expertise first-hand!
Contact our team of experts today at (518) 587-3832 to set up your next consultation visit with us.
Dr. Chris Brockett – Veterinarian/Owner
Dr. Chris was born and raised in Upstate NY in Dolgeville. He attended Cornell University for both his undergraduate and veterinary education. He, like most veterinarians, knew that he wanted to help animals from the very beginning and never had any other goal than to become a small animal private practice veterinarian. There was never any wavering from the strong bonds that he felt around almost any animal. That connection kept him on task throughout his education.
He and Dr. Leisa met as undergrads while both working as teaching assistants and in the same research department. Dr. Leisa always wanted to return to Saratoga, so upon graduating, Dr. Chris joined the staff at SVH. They were married that year and Dr. Leisa joined SVH one year later. They live in Wilton with their two children. Besides the children, the menagerie includes three dogs, two cats, a fish, a gecko, and a horse.
"Taking ownership of the practice and working hard to earn the trust of not only the pets and their owners, but also of the staff at SVH, has been the greatest professional challenge of my life and I wouldn't trade any of it. The chance to meet a young animal and have their owner place their care in your hands is a humbling experience. The opportunity to build that relationship, with owner and pet, over a journey that often stretches well beyond a decade is what I get the most joy from. Having a pet who bounds through the door with tail wagging, or who trusts me to reach into a carrier for them, is the reward that I have spent a lifetime working to achieve."
Dr. Leisa Brockett – Veterinarian/Owner
Dr. Leisa Brockett is a Saratoga Springs native. She began her career at Saratoga Veterinary Hospital as an animal caretaker in 1989, while still in high school. Dr. Leisa felt a deep connection with animals even as a small child and always knew she wanted to work with them as a career. Fascinated by the sciences in school, veterinary medicine seemed to satisfy the desire to combine her curiosity for biology with her love of, and desire to help, animals.
Dr. Leisa studied at Cornell University; completing a degree in Animal Science in 1992 and receiving her Doctor of Veterinary Medicine degree in 1996. During those years she met and married Dr. Chris, bonding over their love of animals, science and teaching. She also frequently returned home to spend time in her favorite place, Saratoga Springs.
Upon graduation, Dr. Leisa returned to Saratoga to join Dr. Chris at Saratoga Veterinary Hospital. Entering into practice with Dr. Sofarelli, her mentor and then owner, was an opportunity to come home to the practice that meant so much from the very beginnings of her dreams. Since then she has been blessed with two children, Megan and Alex, and many furry family members. Her fur family currently includes a horse, Beemer, a gecko, Wasabi, two cats, Cleo and C.C., and a motley crew of three dogs, Bailey, Annie and Sketch. All of them were acquired from owners that could no longer keep them.
Leisa and Chris purchased Saratoga Veterinary Hospital in 2001, completing the circle that began for her so many years ago. Since then, they have striven to continue the mission of serving the Saratoga community with warmth, compassion and medical excellence which Dr. Sofarelli laid in place. Whether she is helping clients to welcome a new puppy or kitten into their home, care for an old friend, or say goodbye to a beloved family member, Dr. Leisa is honored and humbled by the trust that clients place in her and works to honor that trust with warmth and kindness. She loves learning and researching to find the best solution for each family.
When she is not at work, Dr. Leisa enjoys reading, hiking, listening to and playing music and spending time with family and friends.
Dr. Jennifer Steeves – Veterinarian
Dr. Jennifer Steeves was born and raised in Nova Scotia, Canada. Growing up on a hobby farm she took care of animals both big and small and became intrigued with their inner workings and anatomy.
She studied Education and Science at Mount Allison University and Veterinary Medicine at Atlantic Veterinary College, University of Prince Edward Island, Canada, receiving her degree in 1998. Since graduation she has worked in multiple practices in New York and Vermont.
Dr. Steeves is passionate about learning; studying and obtaining certification in abdominal and cardiac ultrasound, stem cell therapy and companion animal laser therapy. Her patients include cats, dogs, pocket pets, birds and reptiles. She keeps an open mind and is always willing to try something new. In addition to her amazing skills, Dr. Steeves brings a great sense of humor to the practice, keeping us all relaxed and entertained.
She lives in Greenwich, NY with husband John, daughter Megan, two horses, Tally and Snowy, two cats, Quincey and Derby, and a flock of egg laying chickens. She enjoys working on her own hobby farm, gardening, cooking, and reading. She also enjoys taking the road less traveled, hiking and kayaking.
Dr. Kate Underhill – Veterinarian
Dr. Kate Underhill grew up in Charlton, NY, alongside various dogs, cats, rabbits, and horses. She aspired to become a veterinarian from a very early age, continued to hone this passion for animals throughout her life. She completed her undergraduate studies at Northeastern University in 2014, and in 2019 received her veterinary degree from the Cummings School of Veterinary Medicine at Tufts University. She then returned to the Capital District to start her career at Saratoga Veterinary Hospital.
Dr. Kate feels very fortunate to have made her childhood dream a reality, and loves that her job allows her to help animals as well as the people who care for them. She is passionate about fostering the human-animal bond to ensure pets are healthy, happy members of the family for as long as possible. She also appreciates that being a veterinarian gives her ample opportunities for continued learning and growth.
Dr. Kate currently resides in Ballston Lake with her husband, border collie mix named Missy, nova scotia duck tolling retriever named Eli, and cat named Oliver. Her pets are valued members of the family and she can't imagine life without them. Outside of the hospital you can usually find her hiking, cycling, reading, or spending time with family.
Awesome practice! Dr. Leisa was exceptional with my new puppy! She ensured that he was calm and happy during his vaccinations which was really comforting. She explained what we need to do as dog parents in the next months, answered all of my questions and made me feel at ease. She really cares about the animals and it shows!! Your practice is really miles apart from other places. Keep up the excellent work.
- The Parents of Sanji Winter
I love it here! The entire staff always makes you feel like family. I have been coming here for over 20 years and every visit has been amazing.
- C. Frieman


text:
'Torchwood' Star Joins 'Arrow'... as Count Vertigo?
by Scott Stoute
– on Aug 16, 2012
in TV News
While plans for a fifth season of BBC's Torchwood may be on the back-burner until further notice, the show's star, John Barrowman, is keeping busy on this side of the pond by taking up a new role on the CW's upcoming superhero drama, Arrow. The former Doctor Who alum has signed on for what will be a recurring role in the first season.
The series producers are remaining very tight lipped about Barrowman's character, only teasing that he will play a "well-dressed man who is as mysterious as he is wealthy … he is an acquaintance of the Queen family and a prominent figure in Starling City." It seems likely that whomever the enigmatic new character is, he almost certainly won't be on the side of justice. Already two names have jumped to the top of fans' thought-provoking list - let the speculation begin!
For those who aren't avid readers of the Green Arrow comics, the name Stanley Dover Sr might not be the first to leap out, but despite his limited appearances, the character played a somewhat pivotal role for the emerald archer. Dover Sr. was a seemingly kindly and wealthy philanthropist that took in the recently resurrected and amnesic Oliver Queen. In reality, Dover turned out to be demon-worshiper known to the public as the Star City Slayer (due to his penchant for killing children) who sought to claim Queen's soulless body to replace his own withering shell.
Another (and seemingly more prospective suspect) is a modern spin on classic Green Arrow villain, Count Vertigo. Count Werner Vertigo is the last descendant a royal family that ruled the small eastern European country of Vlatava. Vertigo possesses a device that destabilizes the inner ear of his victims and causes them to lose focus and balance to debilitating levels.
Stanley Dover Sr AKA The Star City Slayer and Count Vertigo
The possibility of Dover doesn't seem totally unreasonable, but to give this character the full effect it means you'd have to kill poor Ollie off in the show's first season, which seems highly improbable. Some even less likely possibilities that have been tossed around are versions of Hal Jordan (Green Lantern) and even Bruce Wayne. Despite Green Arrow's close relationship with both Green Lantern and Batman, don't hold your breath that they will even appear in the series, much less the first season.
Barrowman was also recently cast in Shonda Rhimes' ABC pilot Gilded Lilys, but the series failed to be pick up for the upcoming 2012-2013 TV season. Fans of Torchwood shouldn't be worried by Barrowman taking up other TV gigs – the actor has stated that despite his interest in resuming his beloved role of the dashing Captain Jack, the future of Torchwood rests in the hands of showrunner Russell T. Davies. Barrowman's new role on Arrow will also let the actor stretch his wings in a possibly darker role than the lighthearted portrayal of Captain Jack Harkness.
Arrow is stacking up its fair share of DC Comics characters: villains China White and Deadshot have already been cast, while the Arrow trailer dropped the hint that Queen was in the company of Deathstoke the Terminator during his lost years on a remote island. For comic fans, seeing this familiar names pop up is always a thrill. Who do you think Barrowman is playing?
Check back with us for more on Arrow and catch the premiere Wednesday, October 10 on the CW
Source: TV Guide
Tags: arrow
Jason Momoa to Guest Star on The Simpsons, Character Image Revealed


text:
Multimedia Home Gateway Shipments Set to Surge by 2015
EL SEGUNDO, CA - Global shipments of multimedia home gateways (MHGs) are expected to rise by a factor of more than 100 from 2011 to 2015 as pay-TV operators seek to unify the delivery of different forms of video content to all types of devices in homes, including media tablets and smartphones.
Global MHG shipments are set to climb to 9.6 million units in 2015 and 2016, up from just 90,000 in 2011, according to an IHS Screen Digest report entitled "Multimedia Home Gateways: The Future of the Digital Home," from the TV Technology Intelligence service at information and analytics provider IHS.
"A decade ago, set-top boxes (STBs) served as the bridge from analog to digital broadcasting," said Daniel Simmons, senior principal analyst at IHS and lead author of the report. "Today, MHGs are playing a similar role, acting as the bridge between broadcast and Internet protocol (IP) video distribution. With MHGs, cable and satellite operators can utilize the efficiency of broadcast television to provide advanced services and content to all kinds of IP-connectable devices, including today's increasingly popular mobile devices."
The Dual-network Conundrum
Pay-TV operators now use two separate delivery networks to deliver content to as many screens as possible. Television sets are accessed by STBs using managed delivery over broadcast networks owned by operators. Other devices—like notebook PCs, smartphones and media tablets—rely on content delivered over-the-top (OTT) of unmanaged third-party broadband connections.
Operators cannot effectively manage delivery over a third-party network. Because of this, they struggle to offer the same content, services and quality of services to IP-connectable devices as they can to television sets via their own STBs. This dual-network approach creates an inconsistent and frustrating experience for consumers and increases expenditures for operators as they are required to maintain an additional OTT delivery platform.
"MHGs allow operators to consolidate content delivery around their managed networks regardless of the viewing device," said Anna Maxbauer, senior analyst at IHS and one of the report's coauthors. "They can create a pay-TV experience that is identical and consistent across every screen in the home, where the majority of content is consumed. This improves the consumer experience and reduces OTT delivery costs,"
The increasing integration of Wi-Fi home networking within MHGs underscores the commitment of operators to support multiscreen video in the home. Wi-Fi penetration in MHGs is forecast to grow to 73 percent of units shipped by 2016, up from just 18 percent in 2012.
Moreover, Wi-Fi is only one of several new features being added to MHGs.
"The MHG is a server platform able to host a range of in-home services," said Stephen Froehlich, principal analyst for IHS, and another coauthor of the report. "Beyond Wi-Fi, some of the major services include gigabit broadband, transcoding, large applications, graphics processors, content storage and edge caching. The hardware and software requirements for each MHG are quite different from one operator to the next, making MHGs a profitable point-of-value creation for STB manufacturers and their suppliers for the foreseeable future."
Simmons added, "The capabilities of the MHG, and in particular its requirement to establish a ubiquitous whole home network, position pay-TV operators to offer a range on additional services that could elevate them above being simple purveyors of media access. Home automation and security, as well as smart energy and e-health services, all could be supported by the MHG and offer additional revenue opportunities."


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• Selenium is a wonderful antioxidant protecting cells from free radical damage.
• But it also helps cellular DNA repair which may help prevent carcinogenesis.
• Most importantly, selenium initiates apoptosis, which means that cancer cells may die before they have a chance to reproduce.
S. Lieberman, Ph. D and N. Bruning; The Real Vitamin & Mineral Book; Second Edition. Avery Publishing Group, New York, 1997; page 172.
LUNG -45%, COLO -RECTAL -58% PROSTATE -63%.
JAMA. Dec. 25,1996; 276 (24) : 1957-63.
Is Chlorella a Selenium Synergist?
Bernard Jensen, DC, Ph.D., a world investigator of nutritional and holistic therapies, has been a great advocate of Chlorella for years and helped popularize it in the US. This tiny green micro-algae is about the size of a human red blood cell. It was extensively researched in Japan and being a highly nutritious, condensed whole food, it was considered as "space food for the space programs in the US & USSR.
Chlorella is loaded with macro and micronutrients and we cannot possibly do justice to it in this brief description. It is a whole food and should be recommended in quantities of a few grams per day for maximum benefits.
The role of Chlorella in selenium, Chelate is primarily as a selenium synergist.
Perhaps the most fascinating property of chlorella is its growth factor (CGF) to which Japanese researchers attributed the benefits of enhanced tissue repair and regeneration (1,2).
body. Algin is a chelating agent which moves through the intestinal tract without being absorbed. It attaches to the lead and carries it out as it leaves the body" (4).
composition of the intestinal flora. The diet supplemented with 20% apple pectin significantly decreased the number and the incidence of AOM-induced colon tumors in rats.
1. Medical Research of Chlorella, by David Steenblock. M.S., D.O. & other review articles by Bernard Jensen, D.C., Ph.D. And others, from Health World publications; 1996 by Health & Happiness Publishing inc.
4. How to Get Well, by Paavo Airola, Ph.D. N.D.; Health Plus Publishers, 1974; page 170.


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[Risk No. 673] Nick was outside BNA smoking a cigarette when the wind blew his check-in papers out of his hand and right toward me. I said hello and asked where he was headed. "Boston." Oh, cool, I'm headed up to the Northeast soon. I shared that I saw him as someone who, once on board with something, remained committed to it. He smiled. Asked if I could pray a blessing over his day. Held hands and prayed Jesus would move mightily and that Nick would be moved mightily toward him.