text:
Advanced Data Analytics: Making Your Business Smarter
Today's analytics are going further, thanks to hardware and software designed to help organizations make better decisions.
From augmentation with machine learning to predictive modeling that forecasts the future, advanced analytics enable new use cases and data-driven decision-making.
Overview of Advanced Data Analytics
As organizations grow and develop their analytics strategy, analytics changes from examining historical data to understanding the present and future.
Advanced analytics techniques enable new solutions across a range of industries, from manufacturing to healthcare.
Some of the most exciting developments include augmenting analytics with Artificial Intelligence (AI) and forecasting future outcomes using predictive analytics.
Making fully informed, data-driven decisions in a modern organization depends on data analytics. Now, the competitive advantage belongs to organizations that can access and understand large data sets. This includes unstructured and previously "dark" forms of data and information from the intelligent edge.
Moving from traditional to advanced forms of analytics can help organizations get smarter about customers, processes, and products. Accurate business intelligence (BI) and predictive modeling make it easier to explore new approaches and strategies.
The Changing Landscape of Advanced Analytics
The most-exciting frontiers of analytics no longer depend on traditional sources of data or methods of analysis. Advanced analytics has expanded the data analytics field in two key ways:
Focus on the future: Older analytics typically looked to the past for a greater understanding of historical data. Advanced data analytics often take a forward-focused approach, solving problems before they emerge and predicting the likely impact of business decisions in advance.
Broader data approach: Advances in analytics make it possible to learn more from data sources that had previously been untapped. These include images, audio, video, and IoT sensor data, using hardware and software optimized to deliver accelerated analytics performance.
As analytics capabilities become more advanced, the number of devices and quantity of data involved can scale immensely.
Why Use Advanced Analytics?
For organizations seeking more data-driven decision-making capabilities, the promise of advanced analytics is immense: collect more information, which yields more insight and can even predict the future.
However, as many companies discover when they begin to expand their capabilities, an effective advanced analytics strategy cannot be built overnight. It requires a foundation in traditional analytics and data ingestion.
Developing an organizational analytics strategy starts with understanding the maturity level of your current analytics efforts. Once you assess your level of analytics maturity, you can break through the roadblocks to achieve the next level of insight from your information. For support, see Intel's guide, From Data to Insights.
Getting the most out of big data analytics requires an approach based on optimizing hardware and software for scalability, performance, and cost-effectiveness.
Advanced Data Analysis Methods
The most-advanced use cases in analytics make it possible to understand more information from more sources. As a result, insights and decisions are generated automatically in a short period of time.
As the number of connected devices at the intelligent edge grows, so too does the demand for understanding collected data in real time. With real-time analytics, information can be processed and modeled like never before, with insights emerging as soon as new information enters the data stream.
From improving fleet operations to ensuring patient safety, real-time analytics holds the promise of delivering insight when it can make the most difference: right away.
Using predictive analytics, businesses gain the ability to understand likely future outcomes—and optimize those outcomes to achieve their biggest goals.
Large-scale predictive analytics require dedicated infrastructure capable of handling a full range of analytics workloads. Flexibility is key: achieving success in predictive analytics requires a data infrastructure adaptable enough to enable platforms like SAP HANA or Oracle Analytics Cloud. It should also support streaming analytics solutions and open source analytics tools like Spark.
Some of the most-promising developments from data scientists have emerged from the convergence of analytics and AI. Neural networks and machine learning have revolutionized the ability of computers to make inferences and understand a wide range of unstructured data.
By augmenting analytics with AI, retailers can better understand how customers move through stores, and manufacturers can more easily identify defective products. Doctors can get a better understanding of medical imaging, while warehouses can enable smart inventory tracking.
With technology designed to accelerate the development and deployment of AI at the intelligent edge, including the Intel® Distribution of OpenVINO™ toolkit,1 Intel is committed to enabling AI analytics for the world's most innovative businesses.
Whether your organization is just beginning to explore advanced analytics or already has made significant investments in next-generation analytics technologies, Intel offers support. Our deep ecosystem of hardware, software, and partner technologies can help your data scientists achieve a higher level of analytics maturity and data-driven growth.
Intel® Technologies for Analytics
Intel® technology is changing the way modern organizations do analytics. With use cases that span many industries—and the globe—Intel works to continuously drive analytics forward while helping businesses optimize for performance and cost-effectiveness.
With a broad ecosystem of technologies and partners to help businesses create the solutions of tomorrow, Intel powers advanced analytics for enterprises worldwide. From the data center to the edge, Intel works at every point in the analytics ecosystem to deliver maximum value and performance.
Intel® Xeon® Scalable processors analyze massive amounts of data at fast speeds, whether at the edge, in the data center, or in the cloud.
Intel® Optane™ technology represents a new approach to memory and storage that helps overcome bottlenecks in how data is moved and stored.
Intel® FPGAs provide acceleration within the data center to improve response times.
Intel® Select Solutions are verified for optimal performance, eliminating guesswork and accelerating solution deployment.
常見問題集 (FAQ)
常見問題集
What Is Advanced Data Analytics?
Advanced data analytics can include a wide range of approaches, from analytics augmented with AI to real-time and predictive analytics that provide instant, forward-looking insights.
Why Is Advanced Analytics Important?
Advanced analytics offers the opportunity to understand more information at a greater level of depth than ever before. Advanced analytics can help to achieve a wide range of business goals, from streamlining operations to delighting customers.
What Is the Difference Between Analytics and Advanced Data Analytics?
While traditional analytics focused on understanding what had already happened, advanced analytics emphasizes the present and future. Advanced data analytics is also capable of analyzing more types of data at a larger scale than traditional analytics, including unstructured data.
What Are Advanced Data Analysis Methods?
Real-time analytics keep insights as current as the data stream, while predictive analytics help businesses create statistical models of likely future outcomes. Augmented analytics uses the power of machine learning and other types of AI to make analytics smarter.
What Is the Purpose of Analytics?
Analytics transforms raw data into insight, for use in business intelligence (BI) or other applications. Analytics can drive greater operational efficiencies and improved experience across the organization.
Learn more about Intel® technologies for analytics.
Find out how analytics can help organizations deliver reliable, actionable insights, and how to evolve your analytics strategy.
Get the most from analytics
AI Analytics
Learn more about how AI is revolutionizing analytics, providing unique insights for a broad range of industries and use cases.
Augment analytics with AI
Harness your data to gain a competitive advantage by making actionable predictions about the future.
See further with predictive analytics
注意事項與免責聲明
Intel® 技術可能需要搭配啟用的硬體、軟體或服務啟動。// 沒有產品或元件能提供絕對的安全性。// 您的成本和成果可能有所落差。// Intel 並不控制或審核第三方的資料。您應該參考其他來源以評估準確性。
Why Advanced Analytics?
Intel® Technologies
1Intel® compilers may or may not optimize to the same degree for non-Intel® microprocessors for optimizations that are not unique to Intel® microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel® microprocessors. Certain optimizations not specific to Intel® microarchitecture are reserved for Intel® microprocessors. Please refer to the applicable product user and reference guides for more information regarding the specific instruction sets covered by this notice.


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Valkyria Chronicles™ II
Experience the critically acclaimed tactical RPG franchise exclusively created for PSP!
www.classification.gov.au Violence
OVERVIEW Story Presentation Gameplay Multiplayer GAME FEATURES
• Featuring an enhanced BLiTZ Battle System with more weapon and character customisation, new military units and classes than ever before
• Battle in over 200 single player missions as well as a four player Ad Hoc co-operative mode
• Thirty distinct, beautifully rendered and action packed maps to explore in over ten stunning environments
Exclusive to PSP, Valkyria Chronicles II is the sequel to the acclaimed PlayStation 3 game. You play as Avan Hardins, a hot-headed 17-year-old enrolled at Lanseal Military Academy, anxious to uncover the details of his decorated brother's mysterious death. With Gallia's army in tatters following the Second European War, Avan and his fellow cadets find themselves hastily called up to the front line to combat the Gallian Revolutionary Army.
As your strategic prowess grows, your fledgling recruits prove their mettle in a series of bruising encounters through the streets and fields of Gallia. And as you gradually push back the rebel assault, so you discover the truth behind the demise of Avan's brother.
It's impossible to talk about Valkyria Chronicles II without mentioning its stunning art direction. Mixing evocative watercolour washes with the bold lines of Japanese anime, every aspect is a visual treat. Comic book dialogue blends into vibrantly animated cut-scenes, and the missions themselves are a riot of colourful detail that belie the dark story underneath.
Moving from the lavishly designed Academy, with its larger than life characters, to one of a variety of battlefields, you're first presented with a simplified overview of the objective, with your troops represented by markers on a map. On selecting a unit, you then swoop down into a third-person view, where you can appreciate the stylised setting and clever level design at close quarters.
Valkyria Chronicles II is as accessible as it is beautiful, with a number of simple tutorial missions getting you into the swing of things, while there's a comprehensive bank of information to check out if ever you're stuck for a winning strategy. Even though there's a grand story to be told and an even grander battle to be fought, you'll be guided at your own pace – and before you know it, you'll be itching to get out onto that battlefield.
Tactical genius
Before you flex your trigger finger in anger, it's a good idea to get to know Lanseal Military Academy, the seat of learning that doubles as your grand command post. It's here that you select the squad to accompany Avan on each mission, make essential upgrades to weapons and equipment, fine tune your tactics and find out about your recruits. As you collect battle experience from each sortie, you'll also be able to improve your squad's effectiveness with a visit to the fearsome drill sergeant. Meanwhile, tanks and armoured vehicles can be enhanced using the cash earned from completing each mission.
With your squad at peak battle readiness, it's time to launch yourself into turn-based strategy gaming with a difference. Instead of positioning your character and selecting from a range of menu options, as you would in a traditional role-playing game, Valkyria Chronicles II puts its lovingly crafted world at the centre of the action. Once you've decided on your squad, you're ready to deploy right into your troops' boots, in Gallia's farmsteads, market towns and cities.
Combat is a tense affair that switches seamlessly between the ground level skirmishes of Action Mode and the bigger picture in Command Mode. You take direct control of each unit, manoeuvring soldiers and vehicles into positions that give the best chance of gaining the upper hand. Selecting a unit in Command Mode uses a Command Point, while movement on the ground drains your Action Points meter, so the trick is to work your squad into a good position without leaving them high and dry. Once you've used up all of your Command Points in setting up clever covering fire angles, it's time to sit back and hope the enemy walks into your trap…
It's a melting pot of ideas presented into 200 bite-sized missions perfect for PSP, yet with so much upgrading, planning, manoeuvring, fighting and outsmarting to be done, taking Valkyria Chronicles II bite by bite won't be enough. It's one of the most intelligent and absorbing games around.
Given that Valkyria Chronicles II packs such a comprehensive campaign mode, you'd be forgiven for thinking it was strictly a single player pursuit. Yet Ad Hoc Mode lets you wirelessly hook up with other players in a range of competitive and cooperative scenarios, the ideal excuse for getting a group of friends together for a Valkyria Chronicles II session.
Should you throw down the gauntlet to another player, you'll be able to bring your battle hardened army to bear across a series of multiplayer maps. To save mismatches, you can introduce a handicap to even things out, although a spell toughening up in the single player missions is the best remedy.
While the versus match-ups provide the best opportunity for bragging rights, the cooperative mode allows you and up to three other players to team up against the enemy. You can play through missions from the main game, so as well as adding a huge amount of variety to play, you can call on help in the trickier scenarios. Both modes offer you the chance to take what you've learned from the single player mode and bamboozle your friends.
1 Player Types

redpajama_set_name: RedPajamaCommonCrawl


text:
About Walter
Walter Sendzik was born and raised in St. Catharines. In 2014, as a first time candidate for political office he was elected as Mayor of St. Catharines. Prior to this, Walter was the Chief Executive Officer of the Greater Niagara Chamber of Commerce. During his seven years at the Chamber, Walter led the evolution of the local chamber into a truly Niagara Chamber with more than 1,400 members. Under Walter's leadership, the GNCC established itself as the largest business organization in Niagara and the third largest in Ontario. It is also the only Chamber in Niagara to receive accreditation with distinction for operational excellence by the Canadian Chamber of Commerce. Prior to the Chamber, Walter founded Vines Publishing, company specializing in wine and travel magazines and wine books. He sold the company in 2005.
Walter has served on several local and provincial boards including the board of the LCBO, where he chaired the governance & compensation committee. He currently serves on the boards of the Niagara College Board of Trustees, Niagara Regional Housing and the Municipal Property Assessment Corporation (MPAC). In 2008 Walter was award the Bernie Gillespie Award by the Ontario Chamber of Commerce as top executive of the year. He is also a recipient of a Niagara Top 40 Under 40 Award. Walter has co-authored five books including Vines: The Buyer's Guide to Canadian Wine and The Insider's Guide to Niagara Wine Country. Walter holds a Masters from McGill University and an Honours Bachelor of Arts degree from Wilfrid Laurier University.
Walter was elected on a platform to address poverty in our community, improve local governance as well as transit and long-term budgeting, and has been working consistently on these priority areas with staff, community leaders at the City and Regional levels. As Mayor, Walter is focused on bringing strategic direction and focus to the City – at Council and in the corporation. At City Hall Mayor Sendzik is focused on inspiring a culture of excellent customer service and building a compassionate city.
m jAbout Walter 09.18.2018


My Wittenberg experience also introduced me to wonderful professors who changed my perspective and cared about me as a person. I remember gaining a new understanding of international relations thanks to Dr. Gerry Hudson in political science. I also took a class with Elizabeth Brinkman on women in literature, and to this day I seek out works by women authors.
From a scholar-athlete standpoint, I found Wittenberg to be a place that challenged me athletically just as it did intellectually. I learned in both the classroom and on the field of play to work together, to explore, and to challenge the status quo, asking what is possible at every step.
In my professional pursuits as a full-time lawyer and now as lieutenant governor, I regularly use the skills I gained through the liberal arts—critical thinking, problem solving, and research and analysis, to understand the issue at hand. I also seek to mentor others just as I was mentored at Wittenberg.
Sheila Simon, elected as lieutenant governor of Illinois in 2010, is the second-highest executive officer in Illinois. As the governor's point person, Simon leads efforts to increase college completion, inspire ethical government, improve services in rural areas, advocate for women, and keep Illinois waterways clean and safe, among other initiatives.
Simon received a BA from Wittenberg University in 1983. A stellar student-leader and All-American high jumper while at Wittenberg, she was recently honored by the Chicago Sky of the Women's National Basketball Association as one of nine members of the Sky's Honorary Title IX Team, created as part of the league's celebration of the 40-year anniversary of passage of the landmark Education Amendments Act. Simon received a JD from Georgetown University Law Center in 1987.


Changing monitoring state from "Offline" to "Error: Connection error, see Terminal tab"
Why do you have the GPX plugin installed? You certainly shouldn't need that for the Ender 3.
Also it doesn't appear to like AUTO port or baudrate, so instead of leaving those at AUTO select one of the options offered to you in the dropdown.


Thanks for choosing Chesapeake Roofing!
Leaving a review for us is easy!
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3. Click the "Write a Review" button and leave us a review!
Use the link below to get started and follow the instructions. If you're on your phone, scroll down for the Google Map App instructions.
2. Scroll down until you see the Rate and Review section. If you're on a desktop computer, click the "write a review" link, if you're on a mobile device select how many stars you would like to give Chesapeake Roofing and then write your review. Once you're done writing your review, click post to add your review.
1. Once the Google Maps App has downloaded. Open it on your phone and use the search bar to type in "Chesapeake Roofing". The app will show you our location.
2. Scroll down to the Rate and Review section. Select how many stars you would like to give Chesapeake Roofing and then write your review. Once you're done writing your review, click post to add your review.
Thanks again for showing your continued support for Chesapeake Roofing!


Customer Service Representatives are the link between the Fayette County Water System and the customer. They adjust bills, answer questions, and try to help with any problem that arises with the customer's water service. They must have an overall grasp of the entire operation in order to find the right avenue to a solution for a customer's problem. This section also is responsible for putting new customers on the system and removing those who move off.
Meter Readers are probably the most visible group within the System. Every meter is read each month and the consumer's bill is based on their readings. This group is now using a new radio reading system to gather readings where new radio read meters are installed.
Billing Operations works closely with the meter readers, loading and unloading customer data to and from the meter reading equipment. The group also processes the bills for mailing, payments received, bank drafts, and electronic payments.


text:
Q: alternate row value minus SQL Hi Guys I have a data set from my query that looks like this:
Date   |  Count   | Activity
10 Nov |    10    |    A
11 Nov |    11    |    A
10 Nov |    12    |    B
11 Nov |    13    |    B

I am trying to achieve this result. Basically the logic is the 2nd row will minus the 1st row, 4th row minus the 3rd row.
Date   |  Count   | Activity  | Diff
10 Nov |    10    |    A      |  0
11 Nov |    11    |    A      |  1
10 Nov |    12    |    B      |  0
11 Nov |    13    |    B      |  1

My current query looks like this:
select  DATE, count(distinct(ID)) as Count,
count(distinct(ID)) - LAG(count(distinct(ID)),1) over (order by count(distinct(ID))) as Eng_change
from (Select DATA.*,PRODUCT.MAPPING from DATA left join PRODUCT on DATA.Part_Number=PRODUCT.PRODUCT_NUMBER ) OVERALLFUNNEL
WHERE ACTIVITY_RANK>5
group by OVERALLFUNNEL.ACTIVITY,OVERALLFUNNEL.DATE
ORDER BY ACTIVITY_RANK ASC

Using lag will give minus always the previous row but that's not what I want.
Any help or function?
regards

A: If I understand your problem correctly, you want the difference within each activity.  If so:
select DATE, activity,
       count(distinct ID) as Count,
       (count(distinct ID) -
        LAG(count(distinct ID), 1) over (partition by activity
                                         order by count(distinct ID)
                                        )
      ) as Eng_change

This will give NULL for the first value.  If you want 0, use coalesce() or something similar.

redpajama_set_name: RedPajamaStackExchange


Hoa Lu, the capital of Dai Co Viet (an old name of Vietnam) under the Dinh and Le dynasties bears with it a legend. Being the capital of the first fundamental Government of Vietnam, Hoa Lu witnessed many important historical events: the union of Vietnam after "12 Lords Rebellion", the war with the Song dynasty, the defense against the ancient Cambodian, and the formation of Hanoi nowadays. With the total size of 13.87 square kilometers, Hoa Lu old capital is an attractive place that history lovers should not miss out.
Hoa Lu old capital has a variety of historical places for visitors to pay a trip.
First and foremost, attractions catching tourists ' eyes are temples, where people worship the Emperors and royal people that ruled here more than 1000 years ago: Dinh Tien Hoang Emperor temple, Le Dai Hanh Emperor temple, Phat Kim Princess temple (Phat Kim is a tragic fate daughter of DinhTien Hoang Emperor), and the newest one is Ly Thai To Emperor temple, which built in order to celebrate the 1000th anniversary of Hanoi. Besides, Hoa Lu has the four sacred temples of Hoa Lu (Hoa Lu Tu Tran) to worship the Gods of four directions: Quy Minh temple, Thien Ton temple, Cao Son temple and Nguyen temple. Moreover, Hoa Lu also has its own One Pillar Pagoda like the One-Pillar Pagoda in Hanoi, but not to be confused with the one in Hanoi, Hoa Lu One-Pillar Pagoda has a Pillar full of phrase by Buddha in front of it, which stands there more than 1000 years. Along with these temples and pagodas, plenty of monuments, tombs, shrines worship many other famous people and Gods are attractive places as well.
Secondly, Hoa Lu still keeps the remaining of ancients fortress built by Dinh dynasty more than 1000 years ago. The Imperial place lays under the ground for 3m and now become a very interesting place to visit. Tourists will have the chance to see the old items used by the royal family in ancient time. The fortress outside is built based on the natural advantages: the mountain provides good background, the deep valley is a basement, and the rivers supply water for people inside the fortress and would become traps to fight against enemy in any case. This fortress is a very solid one that can defense against even the strongest enemy for months. On the other hand, inside the fortress is marvelous scenery for photo-taking and sight-seeing. In addition, the ancient town of Hoa Lu can easily captures tourists by its unique aspects.
The last but not least element that impresses visitors to Hoa Lu is the Festival Truong Yen which is held in February and March (Lunar calendar) which includes a chain of events that regain the spirit of the ancients Emperors: the March of Water, the Ritual for Dinh and Le Emperors, the performance of the old times, the Battles of DinhTien Hoang when he was a child, Cheo and Tuong performances, and a beauty contest for Hoa Lu women. This Festival brings back the memories of the old times, and helps the young people to have chance to know more about the traditions of their ancestors.
Hoa Lu can be a convenient stop on the way from Hue to Hanoi or if you are already in Hanoi, you can easily book a day tour to Ninh Binh which is only 90km south of the capital city.


text:
This article was published on February 14, 2013
Second Life creator Linden Lab launches Versu, an AI-powered iPad take on 'Choose Your Own Adventure'
Martin Bryant
Martin Bryant is founder of Big Revolution, where he helps tech companies refine their proposition and positioning, and develops high-qualit (show all) Martin Bryant is founder of Big Revolution, where he helps tech companies refine their proposition and positioning, and develops high-quality, compelling content for them. He previously served in several roles at TNW, including Editor-in-Chief. He left the company in April 2016 for pastures new.
While it's best known for once-trendy virtual world Second Life, Linden Lab has a more diverse roster than just that. Witness, for example, Versu, a new 'interactive fiction' app that has just launched for the iPad.
At first glance, Versu appears like any other ebook reader app, with a virtual library of titles to select from and a store from which to buy more. However, dive in and you'll discover that the stories here offer a multi-layered, interactive experience that draws on the popular 'Choose Your Own Adventure' format while offering players much more choice in the direction they take each story.
The result of Linden Lab's acquisition of startup LittleTextPeople one year ago, Versu uses artificial intelligence to control how different characters with different personalities react to your actions.
Each 'episode' in Versu allows you to pick a character to play. You're then thrown into a story that unfolds in the form of a play script. As the story progresses, you have different objectives you must complete to move it forward. These are multiple choice, but there are also optional 'actions' you can also choose from at almost any point, which influence how events unfold.
Actions can also unlock hidden 'achievements' within each episode (e.g., 'Make others dislike one another') that aren't core to completing the story but add an extra degree of fun to proceedings as you discover them.
This is just the first stage of the Versu project. The free iPad app contains three episodes, plus an additional one that can be bought as an in-app purchase for $4.99. More episodes and an Android version are planned. More intriguingly, Linden plans to launch a tool in the future that will allow anyone to create their own interactive stories.
Meanwhile, Linden Lab is busy with other projects too. It claims that Second Life is still a thriving, with a virtual economy worth half a billion US dollars in 2012. Other apps it has recently launched include a sandbox PC game called Patterns, an iOS and Android app called Creatorverse, and Dio, a Web-based platform for creating interactive 'rooms' about any subject. The company also recently acquired iPad game Blocksworld, helping its goal to provide 'shared creative spaces' of many kinds.
➤ Versu
Image credit: Thinkstock
Published February 14, 2013 - 2:50 pm UTC


WordCrazy, by developer Dangerous Playground, is a fun Android word game in the same vein as Scrabble and Words With Friends. Word Crazy separates itself from the pack in a few innovative ways, and with the free price there's basically no reason not to give this Android game a try.
WordCrazy may seem like your standard crossword puzzle style game, and in many ways that is accurate, but this Android game really defines itself with some unique features that make WordCrazy especially exciting.
If you're into Scrabble or other word based board games then you probably already realize that the more players you can involve the better. WordCrazy gives you the change to play with up to four other people who can be random strangers, friends, or computer bots.
Don't have any friends playing WordCrazy yet? No problem! The bots make great opponents, and their skill level scales to your particular abilities as the game progresses. If you jump right into the game and start playing a bot, don't be surprised if you're beat the first round. Like any game with an adaptable bot system, it may take a few games to get the right level of competiveness in WordCrazy.
A replay feature allows you to view any move over again, giving you a chance to analyze your opponent's moves and gameplay styles. There are plenty of board styles to help keep things interesting, and push notifications help you keep track of the game without constantly keeping the app open in the background. You'll also see the standard word and letter multipliers, plus WordCrazy keeps track of your stats as well as your friends.
English may not be your first, or preferred, language. In that case WordCrazy for Android also offers up a variety of languages. You can play in English, Spanish, Portuguese, German, French, Italian, or Dutch. This impressive offering of languages really pushes WordCrazy into the category of great Android wordplay games.
WordCrazy requires Android version 2.1 and up. It's available for free and rated Everyone. A small expedite fee was paid by the developer to speed up the publication of this Android game review.


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You are here: Home » Regional news » Asia » Neda Urges Private Firms to go into Halal Food Production
Neda Urges Private Firms to go into Halal Food Production
THE National Economic
and Development Authority (Neda) urged the private sector to explore
prospects of going into halal food production, which is also in step
with the goals set in the updated Medium-Term Philippine Development
Plan (MTPDP).
a statement, Neda Acting Director General Augusto Santos explained that
halal products are food and non-food items that are sanctioned under
Islamic law as "ritually fit" to be sold or served to Muslims.
said that in the MTPDP, Mindanao is envisioned to be the center for
halal food production, with the intention of tapping the emerging
export markets for halal food products.
are encouraging the Philippine private sector to find out if there are
opportunities for Philippine businesses to go into the global halal
market," Santos said during the recent visit of new Pakistan Ambassador
to the Philippines Mohsin Razi in Neda.
said Pakistan is the second- largest Muslim country in the world, and
as such, one of the potential markets for the export of halal products.
his visit, Razi expressed his government's desire to be a full dialogue
partner with the Association of Southeast Asian Nations and reiterated
Pakistan's support for the Philippines' bid to become an observer in
the Organization of the Islamic Conference.
added that the 17-member delegation of the Bishops-Ulama Conference
(BUC) will visit Pakistan this week, upon the invitation of President
Asif Ali Zardari, to learn about the country's peace process and how
these could be done in Mindanao.
BUC, which was tapped by the Philippine government to spearhead a
civil-society initiative for peace and development in Mindanao, is
composed of Mindanao members of the Catholic Bishops' Conference of the
Philippines, Ulama League of the Philippines and the National Council
of Churches of the Philippines.
said the Philippines and Pakistan were among the first countries to
establish foreign relations in 1949. Both countries are also known in
the international community for their approaches toward achieving peace
and development through interfaith dialogue.
« Guidelines For Halal Medicines Published
Industry Minister optimistic exports to Egypt to grow 20 pct »


Microsoft unleashed a new trailer for Killer Instinct: Season 3, which focuses on another character addition.
Mira is getting ready to make an impressive "entrance" into the game's roster and she will be made available for Windows 10 and Xbox One owners.
You can watch the video with Mira below and let us know in the comment section below if you'll invest your money on Killer Instinct: Season 3.


Prune your plumeria plant in the spring, if desired, to control its size and shape or remove diseased, dead or broken branches. Minimize pruning stress by using only sharpened and sterilized pruning shears to make clean, angled cuts when pruning your plumeria.
Make sure that the first frost does not damage the plants. In summer, at very high temperatures, plumeria can be watered every other day. In the third year of life the plant can be up to 1 m in height. If necessary, Plumeria planted in a container larger. The three-year seedling can bloom in summer.


You may even kneel down now.
Indeed...a lot of good stuff was had from this Christmas for me.
Agree. Some missions were tough, but doable....WoWs WG did fine on my record. By the way, the Duke of York is pretty good if played correctly...so many po-po it.
Was a good holiday for myself also. Thanks WG.
Enjoyed the Vampire missions; everything else was Random or Ranked in the main. Thanks for thinking about all your player base WG.
Yeah, the campaigns are great. Gave me a jump in two of the lines I've been meaning/wanting to play, German DDs and French cruisers. I'm on mission 5 of the NY Raid and should have the Arctic Scharnhorst camo by tomorrow. The Arctic camouflages look amazing and I can't wait to unlock the Scharnhorst one. WG, all ships should get arctic camouflages, they're just too beautiful not to.
Per the wiki, the Arctic camouflages for DoY and Scharnhorst only provide a 5% reduction in the cost of post-battle service, but the normal Type 10 that comes on a premium Tier VII ship gives 10% reduction. Is there an error in the wiki or do you really lose 2100 credits per battle for switching to the Arctic camo?
I have both, they are exactly the same benefit wise. There is zero point in the new camo unless you just like looking like you are covered in snow..
Jockboot statement if I ever saw one.


In strategic benefit planning, ACS partners with you to develop and implement an annual renewal strategy for your group benefit plans. This includes a holistic assessment of your current plan offering, considering all the impacts it has on your firm. We can then compare your situation to the marketplace and customize a strategy that fits your priorities.
For example, we will review your current plan designs and employee contribution strategy. From this review we will work with you to determine what changes, if any, should be made to your plans. Perhaps plan deductibles or coinsurance levels need to be updated? Maybe the employee contribution strategy needs to be modified? Whatever needs to be done we will work with you to help you accomplish your goals.


I've got a fun Easter treat to share with you today -- one that's no-bake and so simple that you can make them with your little ones next Saturday afternoon ahead of your Sunday Easter celebrations! I shared these festive No-Bake Easter Egg Mini Cakes last year, and they were very popular with y'all...so I decided to spotlight them again this year. The cakes were inspired by a petite four recipe I saw at the Woman's Day website. My mini cakes come in two flavors: peanut butter and chocolate-hazelnut.
To up the chocolate factor on these tasty Easter treats, they're decorated with peanut butter-filled chocolate eggs and chocolate-hazelnut candy. I used items from Vosges Haut-Chocolat's Easter collection, but feel free to have fun and choose décor that speaks to you. For setting up a themed display for these Easter mini cakes, be sure to also pick up some Easter grass and miniature chocolate bunnies!
What makes these two-serving mini cakes no-bake? The secret is frozen pound cake that's cut with an egg-shaped cookie cutter. The peanut butter version is filled with a silky peanut butter buttercream, while the chocolate version is filled with a decadent chocolate-hazelnut buttercream. The filled mini cakes are then glazed with a simple ganache made from candy melts and whipping cream. I used pastel-flecked white glaze for the peanut butter cakes and milk chocolate and colorful confetti sprinkles for the chocolate- hazelnut cakes. These cakes are a blast to make and decorate. I don't know about you, but I'd much rather hunt for these tasty Easter eggs on Easter morning! Wait until you see how fun these easy treats are to create.
Step 1: Using a serrated knife, carefully remove the crust from the exterior of the thawed pound cake. Cut the pound cake in half horizontally, making two even layers. Use a 3-inch egg-shaped cookie cutter to cut three pieces of cake from each layer – you will have six pieces total. Freeze the scraps that remain in a plastic zip-top bag for another use.
Step 2: Make the buttercream fillings by combining the butter, powdered sugar, and vanilla in a small bowl. Divide this mixture into two equal portions. Add the peanut butter to half of the buttercream and stir well to combine. Add the chocolate-hazelnut spread to the remaining half of the buttercream and combine. Spread the peanut butter buttercream on two pound cake pieces, and spread the chocolate-hazelnut spread on one pound cake piece. Top with the three remaining pound cake pieces to make two-layer egg-shaped cakes.
Step 3: Place the three mini cakes on a wire rack over parchment paper. Heat the chocolate candy melts and whipping cream in the microwave for 1-2 minutes, until the candy melts have softened. Stir until the mixture combines into a pourable ganache. Pour the chocolate ganache over the chocolate-hazelnut mini cake. Add a generous amount of rainbow sprinkles to the top of the mini cake, adding additional Easter décor if desired.
Step 4: Heat the white candy melts and whipping cream in the microwave for 1-2 minutes, until the candy melts have softened. Stir until the mixture combines into a pourable ganache. Pour the white ganache over the two peanut butter mini cakes. Top each with a peanut butter chocolate egg. Let the cakes cool until the ganache hardens – approximately 1 hour. Serve the mini cakes on a festive Easter plate…or in a pile of green Easter grass surrounded by miniature chocolate Easter bunnies!
These fun little cakes are totally adorable, easy to make, and taste amazing They slice easily, and the buttercream fillings are smooth and decadent. I love that the décor options are totally up to you – for the peanut butter cakes, feel free to sub in your favorite color of white chocolate candy melts. For the chocolate hazelnut cakes, have fun picking out your favorite colorful sprinkles. If you need more than six servings, simply double the ingredients to feed a larger crowd.


NECAL brand products are approved in numerous applications in the market by Ford, General Motors, Fait Chrysler, Tesla, Audi, VW, Polaris, Harley Davidson and many more. Following is a sampling of products used in automotive interiors to attach appliques and aluminum or stainless-steel trim. In addition, they are also used in some "under the hood "exterior applications.
5130 | Using heat, pressure and dwell, the material provides a destructible bond. It is a good agent in bonding aluminum to plastic and will reduce "oil can" sound.


text:
Looking past the stat line: Noah Syndergaard
Noah Syndergaard has been hit hard at Las Vegas this season but his long-term outlook remains unchanged.
By Dan Weigel Aug 19, 2014, 11:00am EDT
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Jonathan Dyer-USA TODAY Sports
"Don't scout the stat line" is a phrase commonly heard in scouting circles, especially when it comes to scouting Minor League or amateur players. There are a multitude of factors that play into a players' stat line other than his true talent, including luck, age relative to his level, park factors, health, and transition to pro ball. For these reasons, it is essential to separate the numbers from the true talent, but when the numbers do not match the talent, it is often worth a second look to examine the causes of the poor numbers. Was the initial evaluation of the player inaccurate or can the poor performance be explained by other reasons? Have the tools that helped the player initially climb the lists regressed?
Using the Baseball Prospectus Midseason Top 50 Prospect list as our guide, there are four pitching prospects whose 2014 statistics do not match their high rankings. These prospects are, with their BP midseason ranking in parenthesis, Mets' righty Noah Syndergaard (9), Reds' righty Robert Stephenson (10), Diamondbacks' righty Archie Bradley (14), and Astros' righty Mark Appel (34).
This week, I'll be examining one of these players each day. In today's post, the first in this series, I will discuss Mets' righthander Noah Syndergaard.
Syndergaard's 2014 Statistics
118.1 IP, 4.72 ERA, 1.507 ERA, 138 Hits, 11 HR, 40 BB, 127 K
Prior to 2014, many Mets fans expected Syndergaard to be the 2014 version of Zack Wheeler by performing well in the Minor Leagues, earning a midseason promotion to New York, and establishing himself in the Mets' rotation for years to come. Although that expectation was not met this season, there is little cause for concern about the hard throwing righthander's long-term outlook.
New York Mets blog Amazin' Avenue
Though it seems like he has been around for quite a while, Syndergaard is still just 21 years old and already in Triple-A. The third-youngest player on an Opening Day PCL roster, many players his age are either still in college or finding their way in the low minors. Second, the big righty is throwing in one of the most hitter-friendly parks in one of the most hitter friendly leagues in all of Minor League baseball. This will certainly negatively affect his numbers, especially the hits and home runs allowed.
These things considered, there are many positives in Syndergaard's stat line this season. Most importantly, he is a hard thrower who has stayed healthy and logged 118.1 innings this season. He will get a few more starts before the end of the season and is on track for close to a full MLB starters' workload next season, assuming he makes the club. Syndergaard also has struck out more than a batter per inning, which suggests his stuff is fine, and has walked a reasonable 3.04 hitters per nine. His K/BB ratio, one of the statistics I most highly value, is a solid 3.18.
When we factor in the park effects, the most likely contributor to his .379 BABIP and 11 home runs (despite allowing just 78 fly balls – a 24.68% rate), his numbers look even better. Both the hit and home run totals are inflated and reflect neither his true talent nor his performance this season. If these numbers were to normalize, Syndergaard's ERA and WHIP would drop to the point where this discussion would no longer be necessary.
Instead of being disappointed, I am pleased with how Syndergaard has thrown this season. He got hit around in a hitter's park a bit, but he still showed signs of dominance by racking up the sixth most strikeouts in all of Triple-A baseball this season. The tools that landed him ninth on the BP Midseason Top 50 are intact, and though the young fireballer hasn't been able to make his MLB debut and solidify himself as planned, his future outlook remains unchanged.
The Mets may hold Syndergaard in the Minors for a few weeks to secure an extra year of team control, but expect Syndergaard to spend most of the 2015 season in New York. Once he gets there, initially expect lots of strikeouts, a few more walks, and average ratios. In the long run, however, Syndergaard could use his frontline stuff to become a frontline starter and join a stacked young pitching core including the aforementioned Zack Wheeler and the returning Matt Harvey.
All statistics courtesy of FanGraphs and MLB Farm. Prospect rankings courtesy of Baseball Prospectus
Dan Weigel, who has no idea how Syndergaard got the nickname "Thor", is a contributing writer for Minor League Ball and Beyond the Box Score. His tweets about prospects and stats are found at @DanWiggles38.


She should be dressed a lot more conservatively, lower hemline and so should the little princesses, bluntly put.
Her hemline is well below the knee, just as low as that of Queen Sofia. How much conservative do you need?
As for 9-5 Queen, Queen Maxima only has had one after-5 royal function these two weeks; attending the football game against Australia yesterday. So I guess that in your point of view, Queen Maxima is a 9-5 Queen as well.
simple ceremony done with much dignity - very good. They look like a very loving and close family - the girls were so well behaved although looked a bit awed by all the attention.
Mrs Plastic face got what she wanted. Careful what you wish for, they say. Enjoy it while you can, Letizia. You still don't know what you signed yourself up for.
Forgive my ignorance but is being sworn-in the same as being crowned? Will there be a coronation at a later date or is this the only ceremony that will take place? Thank you!
I think the Infantas look absolutely adorable no matter what they wear - I'll have to watch a video before I pass judgement on their behaviour though, but they're both still very young - I'm sure they'll learn as they get older.
No, this is it; because of economy and the trend of a 'streamlined' monarchy that eschews the genuine grandeur of a genuinely regal coronation. Of course, considering her past I think it's just as well really.
Any bets that she's going to fail at being there for her husband? The monarchy is in a fragile state and certainly, she should (better) make it a point to be there as much as she possibly can for her husband and kids and make sure she's there for all of them.
No, this is it; because of economy and the trend of a 'streamlined' monarchy that eschews the genuine grandeur of a genuinely regal coronation.
Already in the Spanish Constitution of 1978 a coronation was no longer a possibility. So it has nothing to do with streamlining the monarchy or with the present economic situation.
A Coronation automatically means a political role (crowning the Head of State) for members of the clergy. Since Spain knows a full separation of church and state, a coronation was not longer a possibility.
The only crowned monarch in Europe (and probably the world, I don't think the Japanese Emperor is crowned) is Queen Elizabeth II and as we know, the UK has no separation of church and state.
As for grandeur, if you have paid attention to the Dutch enthronement ceremony, you would know that the absence of a coronation doesn't automatically mean the absence of grandeur. In this respect the economic situation does play a role, had the Spanish economy been better, there might have been a bit more grandeur. At the same time, the form and shape of the passing of the throne in The Netherlands is very much different from that in Spain when it comes to location, the amount of time in which everything had to be arranged. But also in the guests invited.
In that respect, the Spanish ceremony resembles the Belgian ceremony of last July much more; no foreign royal guests, taking place within the House of Parliament and being arranged in a short(er) period of time.
It's true though, isn't it, that long before the Spanish Constitution of 1978, there was no crowning of Spanish monarchs? Few, if any, since medieval times, that is? I believe Charles wants a Coronation stripped of some of the pomp and ritual, when he is crowned. No State Coach, for example.
I thought Juan Carlos was the first not to be crowned, but according to Wikipedia the last formal crowning of a Spanish monarch was Eleanor of Navarra in 1479.
After that, Spanish monarchs have been proclaimed, at first in a religious ceremony but since the 18th century in the Cortes, the Spanish parliament.
So indeed...the crowning of Spanish monarchs is something from way, way back.
I love the history lessons I get on this forum.
The speaker of the lower house of Parliament, Jesus Posada, proclaimed Felipe VI king by declaring: "Long live Spain! Long live the king!"
"Long live the king!" has been used in countries like England and France. With the proclamation in Spain, this is the first time I have known that a country's name is announced before "Long live the king!" is uttered.
I doubt any in parliament have any decent knowledge of protocol, well maybe not even the royal staff know considering all their fails.


text:
Most played PC games
Spotify's premium subscribers 2015-2019
Number of World of Warcraft subscribers
Global all time unit sales of Call of Duty franchise games as of February 2019
Advertising spending in the U.S. 2015-2022
Video Game Industry - Statistics & Facts
Film Industry - Statistics & Facts
Media & Advertising›
Daily TV viewing time per person in the United Kingdom (UK) 2018, by month
Published by Statista Research Department, Sep 25, 2019
This statistic shows the average daily TV consumption time of individuals in the United Kingdom (UK) in 2018, broken down by month. Average weekly viewing time was generally higher in winter compared to the summer months, at 215 minutes per day per person in January compared to 177 minutes in August.
Average daily time spent watching TV per individual in the United Kingdom (UK) in 2018, by month (in minutes)
Time in minutes*
4 years and older
* Figures calculated based on information provided by the source.
Television industry in the United Kingdom (UK)
Leading TV channels in the United Kingdom (UK) as of Q3 2019, by quarterly reach
TV consumption in the UK: hours of daily viewing per individual 2010-2017, by age
Most-watched sports games on TV in the United Kingdom (UK) 2018
Most-watched TV programs in the United Kingdom (UK) 2018
Statistics on "Television industry in the United Kingdom (UK)"
TV industry revenue in the United Kingdom (UK) 2004-2018, by source
TV industry revenue in the United Kingdom (UK) 2004-2018, by sector
TV industry revenue: distribution of non-broadcast revenue in the UK 2017, by source
TV programming and broadcasting activities GVA in the United Kingdom (UK) 2008-2015
Export revenue of UK-produced TV programs 2015/16, by type and territory
Leading television companies in Europe 2018, ranked by revenues
Audiovisual industry market leaders in the UK 2018, by audiovisual turnover
Television networks ranked by revenues in the United Kingdom (UK) as of December 2018
TV platforms ranked by number of households in the United Kingdom (UK) 2018
Audience share of the main TV channels in the UK 2010-2018
Number of multichannel TV homes in the United Kingdom (UK) 2000-2019
Number of multichannel TV homes in the United Kingdom (UK) 2000-2019, by platform
Number of TV households in the UK 2009-2018, by technology
Age distribution of TV households in the United Kingdom (UK) 2018, by platform
TV reach in the United Kingdom (UK) as of Q3 2019, by population segment
Household spending per month on television services in the UK 2007-2018
TV consumption in the UK: daily viewing time per individual 2010-2017, by gender
Genre breakdown of TV viewing in the United Kingdom (UK) 2018
Time-shifted share of TV viewing in the UK 2017, by DTR ownership
Number of TV show episodes watched per binge watching session in the UK 2016
TV channels available in the United Kingdom (UK) 2019 by genre
Most-watched TV programs in the United Kingdom (UK) in 2018
Opinion on quality of TV programs in the United Kingdom (UK) 2005-2018
Opinion on reasons for TV program quality decline in the United Kingdom (UK) 2018
Television advertising revenue in the UK 2011-2020
TV net advertising revenue in the United Kingdom (UK) 2010-2017, by source
Advertising media ranked by profit ROI in the United Kingdom (UK) 2014-2017
Ad media ranked by short-term profit share for FMCG brands in the UK 2014-2017
Ad media ranked by short-term profit share for retail brands in the UK 2014-2017
Leading TV commercial sales houses in the UK 2017, by commercial impacts share
Most-viewed TV advertisers in the United Kingdom (UK) 2018
Broadcast TV commercial impacts in the United Kingdom (UK) 2009-2018
Pay-TV revenues in the United Kingdom (UK) 2011-2018
Pay-TV revenues in the United Kingdom (UK) 2012-2018, by platform
Online TV revenues in the United Kingdom (UK) 2006-2018
SVOD services take-up in the United Kingdom (UK) Q1 2014-Q3 2018
Leading online television services used in the United Kingdom (UK) H1 2018
Online TV viewing time in the UK in September 2018, by player
Frequency of online film and TV program downloading in the United Kingdom (UK) 2018
Most streamed online on-demand TV programs in the United Kingdom (UK) 2018
Weekly TV viewing time per person in the United Kingdom (UK) 2018
U.S. TV consumption: average viewing time in 2018, by employment status
Weekly TV watching among Millennials in the U.S. 2016, by type
Survey on usage of different types of fee-based TV-services in Finland 2016
Share of the population watching television in Denmark 2018, by device and methods
Live television viewing occasions in the United Kingdom (UK) 2012
Frequency of television news consumption among youth in the Netherlands 2018
Online TV services used weekly in the UK 2013, by viewer's age
TV habits worldwide 2017
Broadcaster share of total TV viewing in the United Kingdom (UK) 2010-2025
Total cumulative time spent watching TV in the U.S. in 2016, by device
Share of population watching TV content daily in Flanders 2018, by type and channel
TV preferences among internet users in the U.S. 2017
Italy: viewers habits in second screen use 2016, by frequency
Live TV viewing by device and age in the UK 2013
Companions for watching linear TV shows, movies or videos in the U.S. 2017
TV watching frequency among gamers in the U.S. 2016, by age
TV consumption habits worldwide 2017, by age group
Italy: firm producing refined petroleum products in Italy 2014-2016, by legal form
Industry revenue of "manufacture of refractory products" in Austria 2011-2023
Television industry in the United Kingdom (UK) Television in the U.S. Pay TV in the U.S. Online TV in the U.S. Subscription Video On Demand
BARB. (January 1, 2019). Average daily time spent watching TV per individual in the United Kingdom (UK) in 2018, by month (in minutes) [Graph]. In Statista. Retrieved January 21, 2020, from https://cdn1.statista.com/statistics/811393/daily-tv-viewing-time-by-month-united-kingdom-uk/
BARB. "Average daily time spent watching TV per individual in the United Kingdom (UK) in 2018, by month (in minutes)." Chart. January 1, 2019. Statista. Accessed January 21, 2020. https://cdn1.statista.com/statistics/811393/daily-tv-viewing-time-by-month-united-kingdom-uk/
BARB. (2019). Average daily time spent watching TV per individual in the United Kingdom (UK) in 2018, by month (in minutes). Statista. Statista Inc.. Accessed: January 21, 2020. https://cdn1.statista.com/statistics/811393/daily-tv-viewing-time-by-month-united-kingdom-uk/
BARB. "Average Daily Time Spent Watching Tv per Individual in The United Kingdom (Uk) in 2018, by Month (in Minutes)." Statista, Statista Inc., 1 Jan 2019, https://cdn1.statista.com/statistics/811393/daily-tv-viewing-time-by-month-united-kingdom-uk/
BARB, Average daily time spent watching TV per individual in the United Kingdom (UK) in 2018, by month (in minutes) Statista, https://cdn1.statista.com/statistics/811393/daily-tv-viewing-time-by-month-united-kingdom-uk/ (last visited January 21, 2020)


text:
\section*{Background}
Gene functional modules within the genomic data are often identified to find genes sharing the same functions, being involved in the same pathway or interacting with each other.
To find these functionally related gene sets, clustering methods are commonly used. K-means and AHC (Agglomerative Hierarchical Clustering) are the most frequently used clustering algorithms to cluster gene expression data. Recently, NMF and its variants have also been successfully adopted for clustering gene expression \cite{Wang2013} and gene-phenotype association data \cite{Hwang2012}. NMF has advantages over other methods because of its interpretability and good performance.

More recently, multi-view NMF methods have been proposed. Collective NMF (ColNMF), proposed by Singh, can get more consistent results by using the shared coefficient matrix but different basis matrices across different views \cite{Singh2008}. Zhang and Zhou proposed a multiple NMF framework to integrate multiple types of genomic data to identify microRNA-gene regulatory modules jointly \cite{Zhang2011}. Additionally, NMF-based methods integrated with some structure information were also proposed and have achieved better results. Pehkonen adopted NMF to analyze association data between gene and Gene Ontology (GO), in which the association matrix has been enriched according to the ``true path rule" \cite{Valentini2010} of gene ontology hierarchy \cite{Pehkonen2005}. Hwang and Kuang proposed a nonnegative matrix tri-factorization method to cluster phenotypes and genes simultaneously \cite{Hwang2012}. Focusing on predicting missing traits for plants, HPMF incorporates hierarchical phylogenetic information into matrix factorization \cite{Shan2012}.

In the context of gene clustering, one useful structural feature that has never been integrated with multi-view NMF is the hierarchical structure phenotype ontology which is potentially helpful for clustering genes. In this work, we assume that the clustering of genes with different representations in multiple views should be consistent, i.e. genes will be consistently clustered by the association with phenotypes at different levels (or granularity) of the hierarchy in a phenotype ontology, in which each level of the phenotype ontology provides a different view for clustering genes.

Based on this motivation, we propose a multi-view NMF-based method called CMNMF (consistent multiple non-negative matrix factorization) for mining functional gene modules, in which the hierarchical structure of phenotype is introduced as prior knowledge.
In detail, a consistency constraint on gene clusters and a hierarchical mapping constraint among the phenotypes in two consecutive levels in the ontology are introduced in the loss function. In the experiment, we apply CMNMF on gene-phenotype association data of mouse and human. CMNMF is compared with the baseline methods by measuring the performance of predicting KEGG pathways and protein-protein interaction networks. Furthermore, GO enrichment analysis equipped with DAVID tool \cite{Dennis2003} is performed to evaluate the biological significance of the gene clusters.

\begin{figure}[!t]
  \centering
  \begin{minipage}{.80\linewidth}
  \centering
    \includegraphics[width=\linewidth]{model2_11_11.pdf}
  \end{minipage}
  \caption{Illustration of the CMNMF framework. (a) The gene-phenotype\\  associations are divided into two matrices according to the level of phenotype \\ ontologies, and the two matrices share the same gene set. (b) consistent const-\\raint on factorized gene clusters. (c) Hierarchical mapping constraint on the \\phenotype ontologies at parent and child levels.}
  \label{fig:model}
\end{figure}

\section*{Materials and Method}
\definecolor{shadecolor}{rgb}{255,255,0}
\subsection*{\textbf{Data Preparation}}
Mouse gene-phenotype associations were downloaded from Mouse Genome Informatics (MGI)\cite{Blake2003} in Feb. 2016, including 15,524 associations between 5,971 phenotypes and 1,350 genes. More specifically, 3,414 phenotype ontologies were at level 7 and their parents, 2,557 phenotype ontologies were at level 6.
The phenotype ontology levels in our experiment were chosen by the ``most frequent level of annotation" criterion \cite{Espinosa2011}.
Two versions of mouse protein-protein interaction (PPI) network were obtained from the BIOGRID (Feb. 2016 and Sep. 2016) \cite{Chatr-Aryamontri2015} and two versions of 292 mouse KEGG pathways (Feb. 2016 and Sep. 2016) were extracted for evaluation \cite{Kanehisa2016}.

53,929 human gene-phenotype associations between 3,280 genes and 5,948 phenotypes were downloaded in Feb. 2016 from Human Phenotype Ontology (HPO) project \cite{Kohler2014}. 3,707 phenotype ontologies at level 8 and their parents, 2,241 phenotype ontologies at level 7, were applied in the following experiments. Two versions of human PPI  networks were obtained from the BIOGRID (Feb. 2016 and Sep. 2016) and two versions of 296 human KEGG pathways (Feb. 2016 and Sep. 2016) were extracted for evaluation. A table summary of the data used in this paper can be found in {\emph{Supporting Information}}.

\subsection*{\textbf{Problem Formulation}}
The notations used in the models are summarized in Table \ref{Tab:Notations}. Let $n$ be the number of genes, $m$ be the number of phenotypes, and the gene-phenotype associations are represented by a binary matrix $\bm{A}_{(n \times m)}$ with 1 for entries of known associations and 0 otherwise. The loss function of factorizing matrix $\bm{A}$ is used to derive gene clusters $\bm{G}_{(n \times k)}$ and phenotype clusters $\bm{P}_{(k \times m)}$ based on gene-phenotype associations, where $k$ is the number of clusters. The loss function of factorizing matrix $\bm{A}$ can be defined as:
\begin{equation}\label{equ:NMF}
\begin{split}
L(\bm{P},\bm{G}) =  ||\bm{A}-\bm{GP}||^{2}_{F}.
\end{split}
\end{equation}

However, the loss function mentioned above does not consider the hierarchical structure of phenotype ontologies. To address this problem, we design a framework by considering phenotypes at different levels in the separate hierarchical structure separately (Fig. \ref{fig:model}(a)). In this framework, we assume the gene clustering derived from both parent and child phenotype ontology levels should be identical (Fig. \ref{fig:model}(b)). In addition, a phenotype mapping constraint is also considered to reinforce the consistency between learned phenotype clusters at parent level and those at child level
(Fig. \ref{fig:model}(c), mapping relations are represented by dot lines). By optimizing the components mentioned above, we propose a CMNMF algorithm to learn the gene clusters from gene-phenotype associations with different levels of phenotype ontologies.

\begin{table}[t!]
 \caption{Summary of notations.}\label{Tab:Notations}
\begin{tabular}{|l|l|}
    \hline
    Notations & Explanations\\
    \hline\hline
    $\bm{A}$ & Genome-phenome association matrix\\
    $\bm{A}_1$ & Genome-phenome association matrix with phenotype\\
    & ontology at parent level\\
    $\bm{A}_2$ & Genome-phenome association matrix with phenotype\\
    & ontology at child level\\
    $\bm{G}$ & Gene cluster membership matrix\\
    $\bm{P}$ & Phenotype cluster membership matrix\\
    $\bm{G}_0$ & Annotated gene cluster membership matrix\\
    $\bm{P}_1$ & Phenotype cluster membership matrix at parent level\\
    $\bm{P}_2$ & Phenotype cluster membership matrix at child level\\
    $\bm{M}$ & Phenotype ontologies relationship matrix\\
    $n$ & Number of genes\\
    $m$ & Number of phenotypes\\
    $k$ & Number of latent clusters\\
    $m_1$ & Number of phenotypes at parent level\\
    $m_2$ & Number of phenotypes at child level\\
    \hline
  \end{tabular}
\end{table}

\subsection*{\textbf{Loss Functions for Penalizing Inconsistency}}
Motivated by the assumption mentioned above, the phenotype ontologies are divided into two sets according to the two adjacent levels. Two gene-phenotype association matrices $(\bm{A}_1)_{n\times m_1}$ and $(\bm{A}_{2})_{n\times m_2}$ are set up based on the original gene-phenotype association matrix $\bm{A}$ and the two sets of phenotype ontologies. We assume that the gene clusters $\bm{G}_{n\times k}$, derived by matrix factorization on $\bm{A}_{1}$ and $\bm{A}_{2}$, should be consistent, although the genes are annotated by phenotype ontologies at adjacent levels. $\bm{G}_{n\times k}$ can be derived by optimizing the following loss function:
\begin{equation}\label{equ:L_O}
{L_C} = \mathop {\min }\limits_{\bm{G},{\bm{P}_1},{\bm{P}_2}} \left\| \bm{A}_1 - \bm{G{P}}_1 \right\|_F^2 + \alpha \left\| {\bm{A}_2} - \bm{GP}_2 \right\|_F^2,
\end{equation}
where $\alpha$ is a hyper-parameter to balance the two matrix factorization problems. To reinforce the hierarchical mapping relationships between phenotypes at parent level and child level, the hierarchical mapping constraint on phenotype ontologies is added to the loss function,
\begin{equation}\label{equ:L_H}
\begin{split}
{L_H} &= \sum\limits_{ij} {{\bm{M}_{ij}}} ||\bm{P}_1^{(i)}-\bm{P}_2^{(j)}||^{2} \\

\end{split}
\end{equation}
$\bm{M}_{m_1\times m_2}$ denotes the hierarchical mapping relation matrix between phenotype ontologies at adjacent levels. $\bm{M}_{ij}$ is set to 1 if there is a parent-child association between phenotype $i$ and phenotype $j$, otherwise 0. We reinforce the hierarchical mapping constraint by maximizing the similarity between the phenotype ontologies with parent-child mapping relation in gene-phenotype network $\bm{A}_{1}$ and $\bm{A}_{2}$. By combining the two components, the loss function can be formulated as follows:
\begin{equation}\label{equ:R-CMNMF1}
\begin{split}
{L} =& \left\| \bm{A}_1 - \bm{G{P}}_1 \right\|_F^2 + \alpha \left\| {\bm{A}_2} - \bm{GP}_2 \right\|_F^2+\beta \sum\limits_{ij} {{\bm{M}_{ij}}} ||\bm{P}_1^{(i)}-\bm{P}_2^{(j)}||^{2}\\
\mathrm{s.t. }\quad
& \bm{G}\geq 0,\ \bm{P}_1\geq 0,\ \bm{P}_2\geq 0
\end{split}
\end{equation}
where $\beta>0$ is a hyper-parameter to balance the two components.

\subsection*{\textbf{The CMNMF Algorithm}}
To minimize the loss function in Equation (\ref{equ:R-CMNMF1}), an alternative iterative schema is adopted. It solves the problem with respect to one variable while fixing the other variables. In the original NMF \cite{DanielD.Lee2001}, the loss function in Equation (\ref{equ:R-CMNMF1}) is not convex on $\bm{G}$, $\bm{P}_1$, and $\bm{P}_2$ jointly, but it is convex on one variable with the other two fixed.
 In the following subsections, the steps of deriving $\bm{G}$, $\bm{P}_1$ and $\bm{P}_2$ are presented separately. The complete CMNMF algorithm is outlined in \textbf{Algorithm \ref{alg:CMNMF}}.

\subsubsection*{\textbf{Computation of $G$ in CMNMF}}
Loss function in Equation (\ref{equ:R-CMNMF1}) can be rewritten as:
\begin{equation}\label{equ:R-CMNMF11}
\begin{split}
{L} =& \left\| \bm{A}_1 - \bm{G{P}}_1 \right\|_F^2 + \alpha \left\| {\bm{A}_2} - \bm{GP}_2 \right\|_F^2+
\beta \big(tr(\bm{P}_1\bm{D}_1\bm{P}_1^{T})+tr(\bm{P}_2\bm{D}_2\bm{P}_2^{T})\\
&-2tr({\bm{P}_1}\bm{MP}_2^T)\big)\\
\end{split}
\end{equation}
where $(\bm{D}_1)_{m_1\times m_1}$ and $(\bm{D}_2)_{m_2\times m_2}$ are diagonal matrices with $(\bm{D}_1)_{ii} = \sum_{j}^{}\bm{M}_{ij}$ and $(\bm{D}_2)_{jj} = \sum_{i}^{}\bm{M}_{ij}$ respectively. When variables $\bm{P}_1$ and $\bm{P}_2$ are fixed, the partial derivative of Equation (\ref{equ:R-CMNMF11}) with respect to $\bm{G}$ is:
\begin{equation}\label{equ:G_gradient}\nonumber
\begin{split}
\frac{\partial{L}}{\partial{\bm{G}}}=
&-2(\bm{A}_1{\bm{P}_1^T} - \bm{G}{\bm{P}_1}{\bm{P}_1^T})-2\alpha(\bm{A}_2{\bm{P}_2^T} - \bm{G}{\bm{P}_2}{\bm{P}_2^T})
\end{split}
\end{equation}
and the multiplicative update rule is:
\begin{equation}\label{equ:updating_G}\nonumber
\bm{G}_{ij}\leftarrow \bm{G}_{ij}
\frac{(\bm{A}_1\bm{P}_1^T+\alpha \bm{A}_2\bm{P}_2^T)_{ij}}
{(\bm{GP}_1\bm{P}_1^T+\alpha \bm{GP}_2\bm{P}_2^T
)_{ij}}
\end{equation}

\subsubsection*{\textbf{Computation of $\bm{P}_1$ and $\bm{P}_2$ in CMNMF}}
When $\bm{G}$ is fixed, the partial derivatives of Equation (\ref{equ:R-CMNMF11}) with respect to $\bm{P}_1$ and $\bm{P}_2$ are:
\begin{equation}\label{equ:P1_gradient}\nonumber
\begin{split}
&\frac{\partial{L(\bm{P}_1)}}{\partial{\bm{P}_1}}=
-2(\bm{G}^T\bm{A}_1-{\bm{G}^T\bm{GP}_1})+2\beta(\bm{P}_1\bm{D}_1- \bm{P}_2\bm{M}^T)
\\
&\frac{\partial{L(\bm{P}_2)}}{\partial{\bm{P}_2}}=
-2\alpha(\bm{G}^T\bm{A}_2-{\bm{G}^T\bm{GP}_2})+2\beta(\bm{P}_2\bm{D}_2- \bm{P}_1\bm{M})
\end{split}
\end{equation}
and the multiplicative update rule is ($\bm{P}_2$ is fixed when we calculate $\bm{P}_1$, vice versa):
\begin{equation}\label{updating_P}\nonumber
\begin{split}
&(\bm{P}_1)_{ij}\leftarrow (\bm{P}_1)_{ij}
\frac{(\bm{G}^T\bm{A}_1+\beta \bm{P}_2\bm{M}^T)_{ij}}
{(\bm{G}^T\bm{GP}_1+\beta \bm{P}_1\bm{D}_1)_{ij}}\\
&(\bm{P}_2)_{ij}\leftarrow (\bm{P}_2)_{ij}
\frac{(\alpha \bm{G}^T\bm{A}_2+\beta \bm{P}_1\bm{M})_{ij}}
{(\alpha \bm{G}^T\bm{GP}_2+\beta \bm{P}_2\bm{D}_2)_{ij}}
\end{split}
\end{equation}
For the loss function of original NMF $L(\bm{P},\bm{G}) = ||\bm{A}-\bm{GP}||^{2}_{F}$, it is easy to check that if $\bm{G}$ and $\bm{P}$ are the solutions, then $\bm{GD}$, $\bm{D}^{-1}\bm{P}_1$ will also form a solution for any positive diagonal matrix $\bm{D}$. To eliminate this uncertainty, in practice it will be further required that the Euclidean length of each column vector in matrix $\bm{G}$ is 1 \cite{Xu2003,Cai2011}. The matrix $\bm{P}$ will be adjusted accordingly so that $\bm{GP}$ does not change. This can be achieved by:
\begin{equation}\label{}
\begin{split}
\bm{G}_{ik}\leftarrow \frac{\bm{G}_{ik}}{\sqrt{\sum_{i}^{}\bm{G}_{ik}^{2}}},\quad
\bm{P}_{kj}\leftarrow \bm{P}_{kj}\sqrt{\sum_{i}^{}\bm{G}_{ik}^{2}}
\end{split}
\end{equation}
This strategy has been adopted in CMNMF as well. After the multiplicative updating procedure converges, the
Euclidean length of each column vector in matrix $\bm{G}$ is set to 1 and the matrix $\bm{P}_1$ and $\bm{P}_2$ are adjusted with following rules:
\begin{equation}\label{}
\begin{split}
&\bm{G}_{ik}\leftarrow \frac{\bm{G}_{ik}}{\sqrt{\sum_{i}^{}\bm{G}_{ik}^{2}}}\\
&(\bm{P}_1)_{kj}\leftarrow (\bm{P}_1)_{kj}\sqrt{\sum_{i}^{}\bm{G}_{ik}^{2}},\quad
(\bm{P}_2)_{kj}\leftarrow (\bm{P}_2)_{kj}\sqrt{\sum_{i}^{}\bm{G}_{ik}^{2}}
\end{split}
\end{equation}

\begin{algorithm}[t]
\caption{\textbf{CMNMF}}\label{alg:CMNMF}
\renewcommand{\algorithmicrequire}{\textbf{Input:}}
\renewcommand{\algorithmicensure}{\textbf{Output:}}
\label{alg:pf}
\begin{algorithmic}[1]
\REQUIRE
$\bm{A}_1$: gene-phenotype association matrix at parent level\\
\ \ \ \ $\bm{A}_2$: gene-phenotype association matrix at child level\\
\ \ \ \ {$\alpha$, $\beta$}: hyper-parameters
\ENSURE \text{model parameters} ${\bm{G}}, {\bm{P}_1}, {\bm{P}_2}$
\STATE ${\bm{G}},{\bm{P}_1},{\bm{P}_2}$ $\leftarrow$ random values
\REPEAT
    \STATE Update $\bm{G}_{ij}\leftarrow \bm{G}_{ij}\frac{(\bm{A}_1\bm{P}_1^T+\alpha \bm{A}_2\bm{P}_2^T)_{ij}}{(\bm{G}\bm{P}_1\bm{P}_1^T+\alpha \bm{G}\bm{P}_2\bm{P}_2^T
    )_{ij}}    $
    \STATE Update $(\bm{P}_1)_{ij}\leftarrow (\bm{P}_1)_{ij}\frac{(\bm{G}^T\bm{A}_1+\beta \bm{P}_2\bm{M}^T)_{ij}}{(\bm{G}^T\bm{GP}_1+\beta \bm{P}_1\bm{D}_1)_{ij}}$,\\
     Update $(\bm{P}_2)_{ij}\leftarrow (\bm{P}_2)_{ij}\frac{(\alpha \bm{G}^T\bm{A}_2+\beta \bm{P}_1\bm{M})_{ij}}{(\alpha \bm{G}^T\bm{GP}_2+\beta \bm{P}_2\bm{D}_2)_{ij}}$

\UNTIL convergence
\STATE Normalize $\bm{G}_{ik}\leftarrow \frac{\bm{G}_{ik}}{\sqrt{\sum_{i}^{}\bm{G}_{ik}^{2}}}$
\STATE Normalize $(\bm{P}_1)_{kj}\leftarrow (\bm{P}_1)_{kj}\sqrt{\sum_{i}^{}\bm{G}_{ik}^{2}},\quad
(\bm{P}_2)_{kj}\leftarrow (\bm{P}_2)_{kj}\sqrt{\sum_{i}^{}\bm{G}_{ik}^{2}}$
\RETURN ${\bm{G}},{\bm{P}_1},{\bm{P}_2}$
\end{algorithmic}
\end{algorithm}

\subsubsection*{\textbf{Parameter Tuning}}
\begin{figure}[!h]
  \centering
  \begin{minipage}[b]{\linewidth}
   \includegraphics[width=\linewidth]{mouse_pathway_result.pdf}

  \end{minipage}
  \caption{$F_1$ scores under different $\alpha$ and $\beta$ combinations   }
  \label{fig:alpha_beta}
\end{figure}
We use old versions of PPI network (Feb. 2016) and KEGG pathways (Feb. 2016) as validation set to select the best parameters for each method. The selected  parameters have been used in each method to get the performance results by using new versions of PPI network (Sep. 2016) and KEGG pathways (Sep. 2016) as test set.

We perform our experiments on two biological datasets, mouse species dataset and human species dataset. In parameter tuning process, the validation experiment results for each method have been repeated 10 times independently and the average results are applied for parameter tuning. As the parameter tuning processes for mouse data and human data are similar, we show the details on mouse KEGG pathways data as an illustration. Parameter tuning processes on mouse PPI network and human data (human KEGG pathways and human PPI network) are described in {\emph{Supporting Information}}.

The hyper-parameters $\alpha$ and $\beta$ have been tuned by grid with $F_1$ measure. $\alpha$ balances the contributions of two factorization problems on different phenotype ontology levels. When $\alpha$ is close to 0, CMNMF becomes NMF. $\beta$ controls the hierarchical structure effects of phenotype ontologies. When $\beta$ is set to 0, CMNMF becomes ColNMF (Collective NMF) \cite{Singh2008}. The performance of CMNMF with different $\alpha$ and $\beta$ combinations while taking old versions of mouse KEGG pathways (Feb. 2016) as validation set is shown in Fig. \ref{fig:alpha_beta}. We search {$\alpha$ in \{0.001, 0.01, 0.1, 1, 10, 100, 1000\}} and {$\beta$ in \{0.001, 0.01, 0.1, 1, 10, 100, 1000\}}, the darker the color, the higher the $F_1$ score with the corresponding $\alpha$ and $\beta$ combinations. In this experiment, $\alpha=100$ and $\beta = 1000$ are chosen as the best parameters while mouse KEGG pathways data are used as validation set.
The detailed parameter tuning for baseline methods is described in {\emph{Supporting Information}}.

\subsection*{\textbf{Evaluation}}
There are two types of evaluation indices: external indices and internal indices \cite{Rendon2011}. Because internal indices require extracting additional node features for measuring the similarity between nodes, we choose external indices in the experiments. These external indices, including $F_1$ measure, Jaccard Index, Rand Index, Precision and Recall, are applied to show the consistency between the learned gene clusters and the known KEGG pathways or the gene pairs in the PPI network. The higher the value, the more consistent the learned gene clusters and the known gene sets are.
\section*{Results}
In this section, we first demonstrate the properties of CMNMF compared with NMF on a small MGI mouse gene-phenotype association matrix. Then CMNMF is compared with seven baseline methods by evaluating the consistency between identified gene modules and the new versions of KEGG pathways (Sep. 2016) or gene pairs in the PPI network (Sep. 2016). Moreover, Gene Ontology enrichment analysis is performed to evaluate the biological significance of discovered gene modules.

\subsection*{\textbf{CMNMF on a Small Mouse Gene-Phenotype Associations}}
\begin{figure}[!h]
  \centering
  \begin{minipage}[b]{.5\linewidth}
   \includegraphics[width=\linewidth]{v_a.pdf}
    \centerline{(a)}

  \end{minipage}
   \begin{minipage}[b]{.45\linewidth}
   \includegraphics[width=\linewidth]{v_b.pdf}
    \centerline{(b)}
  \end{minipage}
  \hfil
  \begin{minipage}{.3\linewidth}
   \includegraphics[width=\linewidth]{v_c.pdf
   \hfil
    \centerline{(c)}
  \end{minipage}
  \hfil
  \begin{minipage}{.3\linewidth}
   \includegraphics[width=\linewidth]{v_d.pdf
    \hfil
    \centerline{(d)}
  \end{minipage}
  \hfil
  \begin{minipage}{.3\linewidth}
   \includegraphics[width=\linewidth]{v_e.pdf
    \hfil
    \centerline{(e)}
  \end{minipage}
  \caption{Performance of NMF, CMNMF($\beta$=0) and CMNMF($\beta$=1) on a small \protect \\ gene-phenotype association matrix. (a) Original gene-phenotype association \protect \\ matrix A. (b) Matrix $\bm{A}$ is divided into $\bm{A}_1$ and $\bm{A}_2$ according to the level of  \protect \\phenotype ontology hierarchy. The hierarchical mapping among phenotype \protect \\ ontologies are
   represented by dot lines. (c) Clustering result of NMF.\protect \\ (d) Clustering result of
   CMNMF($\beta$=0). (e) Clustering result of CMNMF($\beta$=1).
   }
  \label{fig:sampled_result}
\end{figure}

To illustrate the effects of consistency constraint (the first two terms in Equation (\ref{equ:R-CMNMF1})) and structure mapping constraint (the last term in Equation (\ref{equ:R-CMNMF1})), we demonstrate the performance of NMF, CMNMF($\beta$=0) and CMNMF($\beta$=1) on a small gene-phenotype association matrix from MGI in Fig. \ref{fig:sampled_result}(a). The gene set in the experiment are selected from three mouse KEGG pathways. In detail, Mafa, Ins2, Abcc8 are from pathway MMU4930 (Type-II diabetes mellitus). Ikbkg, Nfkbia, Ctnnb1 are from MMU5215 (Prostate cancer). Tlr4, Vegfa, Tgfb2 are from MMU5205 (Proteoglycans in cancer). The hierarchical relationships between phenotype ontologies associated with selected genes are shown in Fig. \ref{fig:sampled_result}(b). An effective algorithm should assign the genes from a KEGG pathway into the same cluster.

Fig. \ref{fig:sampled_result}(c), \ref{fig:sampled_result}(d), and \ref{fig:sampled_result}(e) represent the clustering results with NMF, CMNMF($\beta$=0) and CMNMF($\beta$=1), respectively. Compared with Fig. \ref{fig:sampled_result}(c), the significant improvement can be observed by considering the multiple levels of the hierarchy in the phenotype ontology in Fig. \ref{fig:sampled_result}(d) and \ref{fig:sampled_result}(e). By reinforcing the relationship among the phenotypes in different levels, CMNMF($\beta$=1) assigns gene Ctnnb1 to the right cluster comparing with CMNMF($\beta$=0).  Note that the clustering result shown in Fig. \ref{fig:sampled_result}(e) agrees with gene members in the KEGG pathways.

\subsection*{\textbf{Comparison with Baseline Methods by Mining Gene Modules}}
In this section, we evaluate the gene clusters identified by CMNMF with KEGG pathways and PPI network. Seven clustering methods, agglomerative hierarchical clustering (AHC) \cite{Miyamoto1990}, agglomerative hierarchical clustering with pairwise constraints \cite{Miyamoto2010} (Constrained AHC), K-means, pairwise constrained K-means \cite{Basu2004} (Constrained K-means), NMF \cite{Lee1999}, HMF (Hierarchical Matrix Factorization) \cite{AliMashhoori2012} and ColNMF (Collective NMF) \cite{Singh2008}, are compared in the experiment.
Please notice AHC and K-means are unsupervised clustering methods with no parameters,
 in order to have a relatively fair comparison with other methods, we introduce additional pairwise constraints AHC \cite{Miyamoto2010} and pairwise constraints K-means \cite{Wagstaff2001}, the old versions of KEGG pathways (Feb. 2016) and PPI network (Feb. 2016) are used as pairwise constraint validation set to help get clustering results.
For CMNMF, HMF and ColNMF, the gene-phenotype association matrix  $\bm{A}$ is divided into two matrices $\bm{A}_1$ and $\bm{A}_2$ according to the levels of phenotype ontologies. For AHC, Constrained AHC, K-means, Constrained K-means, and NMF, the entire gene-phenotype association matrix is applied. Moreover, the associations to parent phenotype terms in the ontology have also been included, i.e. using the ``true path rule" to enrich the association matrix. For NMF, HMF, ColNMF and CMNMF, the gene clustering results are row-normalized by z-score and $G_{ij}$ is set as 0 if it is less than 3.
Six validation indices are reported in Table \ref{tab:measurements_mouse_pathway}-\ref{tab:measurements_human_ppi}.

        \begin{table}[!h]
        \centering
        \caption{Evaluation results on mouse KEGG pathways.}\label{tab:measurements_mouse_pathway}
        \begin{tabular}{c|cccccc}
        \hline
        &$F_1$ measure&Precision&Recall&Jaccard Index& Rand Index\\
        \hline
        AHC&0.0871&0.0624&0.1443&0.0455&0.7654\\
        Constrained AHC&0.0925&0.0554&\textbf{0.2803}&0.0485&0.5738\\
        K-means&0.0517&0.0760&0.0392&0.0265&\textbf{0.8886}\\
        Constrained K-means&0.0554&0.0750&0.0439&0.0285&0.8839\\
        NMF&0.1037&\textbf{0.1084}&0.0993&0.0547&0.8668\\
        HMF&0.0818&0.1080&0.0658&0.0426&0.8855\\
        ColNMF&0.0883&0.1074&0.0750&0.0462&0.8799\\
        CMNMF&\textbf{0.1181}&0.0898&0.1726&\textbf{0.0628}&0.8002\\

        \hline
        \end{tabular}
        \end{table}

        \begin{table}[!h]
        \centering
        \caption{Evaluation results on mouse PPI.}\label{tab:measurements_mouse_ppi}
        \begin{tabular}{c|cccccc}
        \hline
        &$F_1$ measure&Precision&Recall&Jaccard Index& Rand Index\\
        \hline
        AHC&0.0037&0.0019&0.1420&0.0019&0.8321\\
        Constrained AHC& 0.0080&0.0040&\textbf{0.6491}&{0.0040}&0.6421\\
        K-means&0.0041&0.0022&0.0426&0.0021&0.9544\\
        Constrained K-means&0.0107&0.0056&0.1197&0.0054&0.9509\\
        NMF&0.0126&0.0065&0.1988&0.0063&0.9305\\
        HMF&0.0137&0.0072&0.1298&0.0069&\textbf{0.9584}\\
        ColNMF&0.0120&0.0062&0.1420&0.0060&0.9478\\
        CMNMF&\textbf{0.0138}&\textbf{0.0072}&0.1521&\textbf{0.0070}&0.9518\\

        \hline
        \end{tabular}
        \end{table}
\begin{table}[!h]
        \centering
        \caption{Evaluation results on human KEGG pathways.}\label{tab:measurements_human_pathway}
        \begin{tabular}{c|cccccc}
        \hline
        &$F_1$ measure&Precision&Recall&Jaccard Index& Rand Index\\
        \hline
        AHC&0.0808&0.0998&0.0679&0.0421&\textbf{0.9044}\\
        Constrained AHC&0.0952&0.0570&\textbf{0.2892}&0.0500&0.6599\\
        K-means&0.0787&0.0829&0.0748&0.0409&0.8915\\
        Constrained K-means&0.0889&0.0813&0.0980&0.0465&0.8756\\
        NMF&0.0966&0.0891&0.1056&0.0508&0.8778\\
        HMF&0.0863&\textbf{0.1034}&0.0741&0.0451&0.9029\\
        ColNMF&0.0850&0.0869&0.0831&0.0444&0.8893\\
        CMNMF&\textbf{0.1046}&0.0727&0.1886&\textbf{0.0552}&0.8023\\

        \hline
        \end{tabular}
        \end{table}

        \begin{table}[!h]
        \centering
        \caption{Evaluation results on human PPI.}\label{tab:measurements_human_ppi}
        \begin{tabular}{c|cccccc}
        \hline
        &$F_1$ measure&Precision&Recall&Jaccard Index& Rand Index\\
        \hline
        AHC&0.0096&0.0055&0.0410&0.0048&0.9709\\
        Constrained AHC&0.0089&0.0045&\textbf{0.4403}&0.0045&0.6637\\
        K-means&0.0095&0.0051&0.0743&0.0048&0.9464\\
        Constrained K-means&0.0117&0.0068&0.0422&0.0059&\textbf{0.9754}\\
        NMF&0.0141&0.0074&0.1457&0.0071&0.9300\\
        HMF&0.0142&0.0077&0.0927&0.0072&0.9557\\
        ColNMF&0.0156&0.0083&0.1303&0.0079&0.9432\\
        CMNMF&\textbf{0.0166}&\textbf{0.0089}&0.1203&\textbf{0.0084}&0.9510\\

        \hline
        \end{tabular}
        \end{table}

\subsubsection*{{Validation by Known KEGG Pathways and Protein-Protein Interactions}}
New versions of KEGG pathways (Sep. 2016) and PPI network (Sep. 2016) are applied as known gene relationships to test the performance of gene clustering results. Table \ref{tab:measurements_mouse_pathway} and Table \ref{tab:measurements_mouse_ppi} show the evaluation results on mouse data with KEGG pathways and PPI network, respectively. The evaluation results on human data are reported in Table \ref{tab:measurements_human_pathway} and Table \ref{tab:measurements_human_ppi}. The best results across all the methods are bold. Comparing with the baseline methods, it is clear that CMNMF outperforms other methods on $F_1$ measure, Jaccard Index in all cases for both mouse and human data. It demonstrates the advantage of combining the consistency constraint with two levels of gene-phenotype association information and the structure constraint with parent-child phenotype ontology mapping information. In particular, comparing with the conventional NMF, the performance of CMNMF is improved with the additional knowledge from the consistency constraint. Moreover, the phenotype structure constraint in CMNMF reinforces the learning results following the mapping relation in the phenotype ontology, so CMNMF gets better performance comparing with ColNMF (CMNMF with $\beta=0$). However, AHC works better than other methods with index ``Recall''. We analyse the clustering results of AHC and find a few large-scale gene clusters (with more than four hundred genes), these large gene clusters would result in an increase in index ``Recall'' and a decrease in ``Precision''. The \emph{centroid} criterion is applied in AHC which tends to find the pair of clusters that leads to minimum increase in total inter-cluster Euclidean distances when merging the clusters. Therefore the compact clustering results identified by AHC will benefit the ``Recall" score. We also notice CMNMF does not achieve the best performance on ``Rand Index''. As we know, ``Rand Index'' takes true negative gene pairs into consideration, however, in most cases the experiment results are evaluated on what we have known, i.e. the true positive gene pairs. True negative gene pairs are dominant in the original data involved in the experiments (accounting for 95\%-99\% of all gene pairs), this would lead to a bias comparison between different methods. Overall, the CMNMF outperforms all current clustering methods and the improvement is obvious.

\subsubsection*{{Validation by Latest Protein-Protein Interactions}
CMNMF is also tested on the latest protein-protein interactions added between Feb. 2016 and Sep. 2016 from BIOGRID. The parameters $\alpha$ and $\beta$ are tuned by the old version PPI network (Feb. 2016) mentioned in the previous section with $F_1$ measure. The results are reported in Table \ref{tab:measurements_mouse_latest_ppi} and Table \ref{tab:measurements_human_latest_ppi} for mouse and human data, with the best $\alpha$ and $\beta$ respectively.
Comparing with baseline methods, CMNMF also outperforms them on $F_1$ measure, Jaccard Index and Precision.
\begin{table}[!h]
        \centering
        \caption{Evaluation results on latest mouse PPI.}\label{tab:measurements_mouse_latest_ppi}
        \begin{tabular}{c|cccccc}
        \hline
        &$F_1$ measure&Precision&Recall&Jaccard Index& Rand Index\\
        \hline
        AHC&0.0015&0.0008&0.1373&0.0008&0.8330\\
        Constrained AHC&{0.0034}&{0.0017}&\textbf{0.6569}&{0.0017}&0.6417\\
        K-means&0.0017&0.0009&0.0392&0.0009&0.9577\\
        Constrained K-means&0.0045&0.0023&0.1176&0.0022&0.9519\\
        NMF&0.0052&0.0026&0.1936&0.0026&0.9312\\
        HMF&0.0053&0.0027&0.1176&0.0027&\textbf{0.9593}\\
        ColNMF&0.0047&0.0024&0.1324&0.0024&0.9488\\
        CMNMF&\textbf{0.0055}&\textbf{0.0028}&0.1495&\textbf{0.0028}&0.9507\\
        \hline
        \end{tabular}
    \end{table}
    \begin{table}[!h]
        \centering
        \caption{Evaluation results on latest human PPI.}\label{tab:measurements_human_latest_ppi}
        \begin{tabular}{c|cccccc}
        \hline
        &$F_1$ measure&Precision&Recall&Jaccard Index& Rand Index\\
        \hline
        AHC&0.0043&0.0023&0.0388&0.0021&0.9727\\
        Constrained AHC&0.0039&0.0019&\textbf{0.4313}&0.0019&0.6639\\
        K-means&0.0040&0.0021&0.0759&0.0020&0.9431\\
        Constrained K-means&0.0041&0.0022&0.0309&0.0020&\textbf{0.9771}\\
        NMF&0.0057&0.0029&0.1309&0.0029&0.9313\\
        HMF&0.0056&0.0029&0.0791&0.0028&0.9572\\
        ColNMF&0.0063&0.0032&0.1160&0.0032&0.9446\\
        CMNMF&\textbf{0.0065}&\textbf{0.0034}&0.1033&\textbf{0.0033}&0.9524\\
        \hline
        \end{tabular}
    \end{table}
\subsection*{\textbf{Biological Analysis on Gene Clusters}}
We further study the functional roles of the identified human gene clusters with enrichment analysis against Gene Ontology (GO) using DAVID \cite{Dennis2003}. The enriched GO terms by selected gene clusters are reported in Table \ref{tab:GO_Enrichment}, the P-value and FDR adjusted P-value are also presented. It is clear that gene clusters found by CMNMF are biological functionally relevant.
\begin{table}[!h]
            \centering
            \caption{Enrichment anaylsis on gene clusters mined by CMNMF with Gene Ontology}\label{tab:GO_Enrichment}
            \begin{tabular}{cllll}
            \hline
            No &Gene Cluster& Most Related GO Terms &P-value&{FDR}\\
            \hline
            \hline
            \multirow{3}{*}{239}&
            \multirow{3}{11em}{BCL2, EGFR, FOXO1,\\TGFA, IKBKB,PDGFB, \\PDGFRA, CREB3L3, \emph{etc.}}&
             positive regulation of cell proliferation;&2.2E-06&3.0E-03\\
             &&wound healing;&1.3E-05&1.8E-02\\
             &&protein heterodimerization activity;&7.3E-05&7.4E-02\\
            \hline
            \multirow{3}{*}{173}&
            \multirow{3}{11em}{CLAM1, NOS1, ITPR1,\\ATP2A2, CYCS,GRIN2A, \\CACNA1C, COX6A1, \emph{etc.}}&
             regulation of cardiac muscle contraction &6.8E-07&9.3E-04
             \\&&by regulation of the release of sequestered calciumion;\\
             &&regulation of ryanodine-sensitive calcium-release&6.8E-07&9.3E-04\\&&channel activity;\\
             &&regulation of cardiac muscle contraction;&9.3E-07&1.3E-03\\
            \hline
            \multirow{3}{*}{143}&
            \multirow{3}{11em}{BRAF, SMAD3, NRAS,\\RAF1, SOS1, TGFB2,\\PTPN11, TGFBR1, \emph{etc.}}&
             intracellular;&2.3E-04&2.1E-01\\
             &&MAPK cascade;&2.9E-04&4.0E-01\\
             &&regulation of Rho protein signal transduction;&8.3E-04&1.1E-00\\
            \hline
            \multirow{3}{*}{140}&
            \multirow{3}{11em}{CDK4, ERCC1, G6PC,\\ GAD2,GHR, GPI1,\\ PDX1,MAFA, \emph{etc.}}&
             mitochondrial respiratory chain complex I assembly;&5.7E-33&7.6E-30\\
             &&mitochondrial inner membrane;&5.6E-16&6.0E-13\\
             &&mitochondrion;&1.8E-09&1.9E-06\\
            \hline
            \multirow{3}{*}{127}&
            \multirow{3}{11em}{GRIN1, HTT, LAMC3,\\DCTN1, SOD1, TBP,\\SLC25A4,NDUFA1, \emph{etc.}}&
             extracellular matrix organization;&1.5E-07&2.1E-04\\
             &&cell adhesion;&1.0E-05&1.4E-02\\
             &&basement membrane;&2.0E-05&1.9E-02\\
            \hline
            \multirow{3}{*}{2}&
            \multirow{3}{11em}{CYTB,ND5,ND2,\\COX1,ND1,COX3,\\ND6,ATP6, \emph{etc.}}&
             mitochondrial electron transport, NADH to ubiquinone;&2.1E-11&2.0E-08\\
             &&NADH dehydrogenase (ubiquinone) activity;&2.2E-11&1.5E-08\\
             &&mitochondrial respiratory chain complex I;&5.8E-09&4.3E-06\\
            \hline
            \end{tabular}
            \begin{tablenotes}
      \small
      \item The first column is the index of gene clusters identified by CMNMF. For each gene cluster, a part of genes are presented in the second column. Three most related GO terms, corresponding P-values and false discovery rate (FDR) are reported in the following columns.
    \end{tablenotes}
            \end{table}

By matching the corresponding gene cluster, the genes in No. 239 cluster have been found associated with prostate cancer.
McDonnell found that BCL2 expression was augmented following androgen ablation and was correlated with the progression of prostate cancer from androgen dependence to androgen independence \cite{McDonnell1992}.
Upregulation of epidermal growth factor receptor (EGFR) and subsequent increased in extracellular-regulated kinase (ERK) and Akt signaling were implicated in prostate cancer progression \cite{Gan2010}.
Zhang showed that the FOXO1 protein, a key downstream effector of the tumor suppressor PTEN, inhibited the transcriptional activity of Runx2 in prostate cancer cells \cite{ZhangHaijun2011}.
Liu examined the effects of TGFA and EGF on both cell proliferation and 3H-thymidine incorporation in an androgen-dependent human prostate cancer cell line, ALVA101, in serum-free medium \cite{Liu1993}.

By matching the corresponding gene cluster, the genes in No. 2 cluster have been found associated with Parkinson disease.
Tanaka analyzed amino acid variations in the cytochrome b (CYTB) molecule of 64 Japanese centenarians and found out the most striking feature of centenarian CYTB was the rareness of amino acid variations in contrast to the variety of amino acid replacements in patients with Parkinson's disease \cite{Tanaka2002}.
Heteroplasmic mutations in a specific region of ND5 largely segregated Parkinson's disease from controls and might be of major pathogenic importance in idiopathic Parkinson's disease \cite{Parker2005}.
Ksel found a heteroplasmic mtDNAG5460A missense mutation in the ND2 subunit gene of NADH dehydrogenase was three times more frequent in Parkinson cases (4/21) compared to controls (5/77). In Ksel's study, he provided a hint that the ND25460 mutation, in combination with other factors, could play a role in disease pathogenesis in a subset of Parkinson patients \cite{Ksel1996}.
Teismann used acetylsalicylic acid, a COX-1/COX-2 inhibitor, to study the possible role of the isoenzymes of cyclooxygenase COX-1 and COX-2 in the MPTP  mouse model of Parkinson's disease \cite{Teismann2001}.

\section*{Conclusions}
Our results indicate that the gene clusters identified by CMNMF are biologically relevant with the additional information from phenotype ontology hierarchical structure. The evaluation experiments on KEGG pathways and PPI network of mouse and human data show the advantage of CMNMF over other baseline methods. The experiment on the latest PPI dataset shows the possibility of identifying new protein-protein interactions. Furthermore, the gene modules identified by CMNMF are enriched with biologically relevant functions.

\renewcommand{\enotesize}{\normalsize}

\section*{Declarations}
\begin{backmatter}

\section*{Acknowledgements}
We would like to thank Rui Kuang, Wei Zhang for their help with this work.
This work is supported by the National Natural Science Foundation of China (No. 61300166 and No. 61105049), the Natural Science Foundation of Tianjin (No. 14JCQNJC00600), and the Science and Technology Planning Project of Tianjin (No. 13ZCZDGX01098).
\section*{Open Access}
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

\section*{Competing interests}
The authors declare that they have no competing interests.

\section*{Author's contributions}
MQX and YGZ originally design the model. YGZ worked on the method, experiment, analyses, and writing of the manuscript. YJX  contributed on method and analyses.  XF, YXH and JHL contributed on the experiment. ZCH contributed on the method. MQX and YLH contributed on writing of the manuscript. All authors read and approved the final manuscript.
\section*{Additional Files}
  \subsection*{Additional file 1 --- Supporting Information (SI)}
    This supplement file includes some additional detailed illustrative text and tables we have mentioned in our paper. We give clearly explanatory text in Supporting Information, please refer to it for more details.
\bibliographystyle{bmc-mathphys}

redpajama_set_name: RedPajamaArXiv


appleisdabest, 24 Sep 2015do u undestand this test is flawed? iphone cameras has always been da best, dont trust in ever... moreIphone? When i think phone camera, it never cross my thought to buy an iphone for its 8mp camera(12mp on the new model). Poor lowlight performance. Yes, I play around with it at the Apple shop. I asked the worker there why the camera so bad. They blame the store lighting; which is perfectly bright. Lame excuses.
appleisdabest, 24 Sep 2015do u undestand this test is flawed? iphone cameras has always been da best, dont trust in ever... moreI tested both my ip6 and s6 side by side.. nothing best coming from ip6. So what now? Want me to use your iphone next?
appleisdabest, 24 Sep 2015do u undestand this test is flawed? iphone cameras has always been da best, dont trust in ever... morewhile your comment is flawed. iphone 6 have serious colour issues especially in lowlight.
What? iPhone is not the best with its 2012 specs, which wipes all the android flagships??? Awww.
do u undestand this test is flawed? iphone cameras has always been da best, dont trust in everything u see in these websites, take an iphone n test urself, u will see that wat im talking about, iphone is always superior.
AnonD-265082, 24 Sep 2015No one has ever called it the best. Even Marques Brownlee said that the S6 is better and said ... moreLG G4 owner can't accept their phone camera is less powerful than S6 edge. Even Peter Lik is using Galaxy S6 edge+. He can sell the images its captured for millions of dollar. What can LG G4 camera do? Not worth buying if you a photographer.
when are people going to understand that DxO is just a bunch of numbers that can be read or..read and interpreted. I have used so many smartphones, but when i use a camera for it's sole hardware, then there are good ones and bad ones. Auto settings are just point and shoot - get an iphone or an S6. If you want to go further, start composing images and create an image which is truly impressive even at night, then just forget the current masses and go for an LG G4! there's maybe a Panasonic DMC-CM1 and some OnePlus One out there which have manual settings. It's like getting a pro DSLR and giving it only a good Auto Mode.
They sure took their time. Since then we have got two beastly phones launch and I am sure they will rank close if not better.
i choose G4 instead of G3 for my next upgrade.
or not. G2 is still a bit like beast.
AnonD-394030, 24 Sep 20152nd best? Dafaq? Its definitely better than S6, S6 Edge and edge plus and the Note 5. Can't be... moreNo one has ever called it the best. Even Marques Brownlee said that the S6 is better and said the S6 edge+ and Note5 are the best on the market. You a bit in denial.
LG was all talk at their event as usual. First the screen, now the camera.