SSC CPO Online Test Series Preparation- A Complete Guide for SSC CPO Online preparation ans Study material.
Hope you all are preparing well! As the exam season is on, SSC CPO exam creates a buzz all around the increasing number of aspirants this year. The Staff Selection Commission (SSC) every year conducts SSC CPO examination for to fill vacancies in posts of Sub- Inspector (SI) in CAPF, Assistant Sub Inspector (ASI) and Sub- Inspector (SI) in Delhi Police under SSC CPO SI Recruitment drive. SSC CPO online test series preparation has to be systematic as per the latest exam pattern and syllabus of SSC CPO exam.
The first four sections will be of 2 hours' time duration and the Paper II will also be of 2 hours.
The SSC recruitment basically is for four posts. So, the preparation which will be involved for this exam will be SSC SI preparation (or) SSC CAPF preparation (or) SSC ASI preparation. We recommend you to prepare from standard books for SSC CPO preparation and get a good grasp of concepts asked in the SSC CPO exam.
We offer you some incredible tips for SSC SI preparation, SSC ASI preparation and SSC CAPF preparation.
Take up standard SSC CPO mock tests in the category of SSC SI mock tests/ SSC CPAF mock tests/ SSC ASI mock tests and practice questions from them. They will give you boosts in your SSC CPO preparation.
For reasoning part, make sure that you word hard on your logical thinking part.
The most frequently asked topics include Number series, Non-verbal, Classification (odd one out) test, Mathematical operations, cubes and cubical dice test, Coding-Decoding, Arrangement-Direction test, Syllogisms, Statements and conclusions etc.
Practice reasoning questions from previous year question papers. It will give you an idea of the question pattern over the few years.
For general awareness part, make sure that you go through different topics simultaneously on a daily basis.
Static GK is the most important one. Prepare static GK well.
Stay updated with the current affairs as well on a daily basis.
Read newspapers, magazines but also go through static GK portions from GK books and science books as it becomes 80 percent of the questions asked.
Go through questions from history, geography, economics, science, etc.
For quantitative aptitude part, make sure that you work on calculations- calculating with great speed and accuracy.
Focus on two basic things in this section- Hard work and Practice.
The most frequently asked topics include Ratio and Partnership, Average and Allegations, Time speed and distance, Algebra, Percentage, Data interpretation, Number theory, Trigonometry, Geometry, etc.
Take mock tests and practice. Take up various SSC CPO mock tests so that you reach the level of 100 percent performance.
For English language part, practice, reading and writing are the pillars of your good score.
The most frequently asked topics include Vocabulary, Spelling test, Sentence completion, Reading Comprehension Grammar (including Active passive and Direct and indirect Speech) and Idioms,.
Build up vocabulary as it will help you learn new words. Good vocabulary will enhance your writing skills as well.
Go for a standard book and prepare the grammar topics asked in the exam. Take a detailed look at topics like basic grammar, active and passive voice and direct and indirect speech.
Practice reading comprehension questions as well.
Make SSC CPO online test series preparation as the first step towards your goal. Your success is our commitment. Good luck!


is director of federal communications for the Natural Resources Defense Council (NRDC) and co-author of "In Deep Water: The Anatomy of a Disaster, the Fate of the Gulf and Ending Our Oil Addiction"(OR Books, 2010). He contributed this article to LiveScience's Expert Voices: Op-Ed & Insights.
Fifty years after his "I Have a Dream" speech, the immortal words of the Rev. Dr. Martin Luther King Jr. remind us of the enduring impact the civil rights movement has had not only on the lives of African-Americans, but on us all.
A central theme of his speech, and the movement it embodied, is that the goals of freedom, equality and justice cannot be secured for any of us so long as they are denied to some of us.
"We cannot walk alone," King said, because the common destiny of every American is "inextricably bound" to all the rest.
That fundamental belief helped advance the cause of African-Americans, but also of women, people with disabilities, gays, immigrants and others still striving for the basic rights that form part of the promise of American ideals.
The environmental movement and all who benefit from it also owe a debt to King.
When he stood on the steps of the Lincoln Memorial to deliver those resolute words on August 28, 1963, there were few national safeguards to protect our air, water, wildlife and lands. Our government was not organized to stop polluters from putting our health and resources at risk. The voices of those who stood up to industry were often squelched or ignored.
Environmentalists watched as King's movement moved the conscience of the nation and pressed Congress to enact the Civil Rights Act, the Voting Rights Act and other landmark legislation aimed at making racial equality the law of the land.
Taking a page out of that playbook, and inspired by the legislative progress King helped to achieve, early environmentalists began advocating for the protections we need to be enshrined in law.
The result was the Clean Air Act, the Clean Water Act, the Endangered Species Act, and most foundational of all, the National Environmental Policy Act. It makes environmental considerations mandatory for major actions undertaken or permitted by the federal government and guarantees that public environmental concerns will be heard.
There is, though, something more about the way environmental quality is bound up in the larger fight King spearheaded for justice, freedom and equality.
All too often, industrial pollution takes its heaviest toll from among those who live on what King called "islands of poverty," the low-income quarters of our cities, the bottomlands of our rural communities, the industrial zones where we've sacrificed environmental quality for corporate profits.
Were he alive today, it's not hard to guess what Dr. King would have to say about the appalling rates of asthma among low-income people living in the most polluted parts of cities like Houston, Los Angeles and Cleveland. It's pretty clear what his position would be on subsistence farmers worried about the impact that fracking might have on local water supplies, or the plight of millions of people struggling to cope with rising sea level, drought, wildfires and other front-line impacts of climate change. And we know he would have both grieved and condemned the death and destruction visited upon the people of New Orleans eight years ago, when Hurricane Katrina skipped unimpeded across coastal buffer lands ravaged by decades of oil and gas operations, to slam into the Crescent City.
"I have a dream," Dr. King said 50 years ago this week. "It is a dream deeply rooted in the American dream."
Part of this great leader's genius, part of his strength, part of what inspires us still, is his recognition that the American dream is not just for the few, it is the sum of all our dreams. In the decades since, that has guided one movement after another in our national quest to build that more perfect union of our forebear's own dreams. Countless decades from now, it will still be what matters most.
The views expressed are those of the author and do not necessarily reflect the views of the publisher. This article was originally published on LiveScience.


text:
Tag Archives: Miss Universe
Hooray For Miss USA
September 9, 2018 by Sarah Wengert
Photography by the Miss Universe organization
When 12-year-old Sarah Rose Summers asked her parents if she could compete in the National American Miss pageant, they gave her a major side eye.
"I was one of the shyest girls," says Summers, now 23. "I was the one who latched onto my mom's leg and bawled my eyes out when she tried to drop me at dance class. I wouldn't make eye contact with strangers or speak in the classroom."
Summers' parents said yes, thinking she'd forget about the whole thing, but her interest persisted. Little did her family know that this was merely the first chapter in a storybook journey that would end with Summers being crowned Miss USA in May 2018—becoming the first woman from Nebraska to win the crown. (Summers had previously won the National American Miss Junior Teen title in 2010; she was also the first Nebraskan to win that honor.)
"I was the first to win from Nebraska then and now," Summers says. "My goal after [National American Miss Junior Teen] was always to [represent] Nebraska on the Miss USA stage."
Regarding her history-making Miss USA win, Summers is proud to have made her mark and hopes it will inspire Nebraska girls to reach for their own dreams.
"I'm so honored to have the opportunity to encourage them, that even if their dreams don't seem attainable, they should chase them anyway—because here I am," Summers says.
Summers, whose Miss USA win precipitated an immediate move to New York City, was born and raised in Papillion, surrounded by a tight-knit extended family.
"My whole family lives within 30 minutes from our house—meaning my grandparents, cousins, second cousins…I was very fortunate to be raised so close to family," Summers says. "Being in Nebraska, you always have your feet firmly on the ground. You know your roots, you know your neighbors' names, and it's not that way everywhere. I'm grateful for growing up in Nebraska where you can form those relationships with everyone you see."
In what sounds vaguely like a scene from the Netflix series Unbreakable Kimmy Schmidt, Summers shares a story of her first time jogging through Central Park, greeting everyone she passed, just as she would back in Nebraska.
"I got a lot of crazy looks when I was smiling and waving at strangers in Central Park, but I got a few smiles back, so I'm just going to keep that up and bring that Nebraska joy to the city," she says.
In the moments before being named in the top 15, Summers says she had butterflies in her stomach, but pride in her performance and proximity to good friends put her at ease.
"I knew that I had 110 percent been myself and felt strong in my performance. I was hoping to hear my name, but, if not, I was content and knew where to walk offstage," Summers says. "I was really at peace and just soaking it up. Then I was called right after my friend, Miss New Jersey [Alexa Noone], so that was pretty amazing. I got to run down and hug her."
Summers then advanced to the top 10—still hopeful, but at peace with any outcome. She maintained this feeling through the top two, which came down to her and one of her best Miss USA friends, Caelynn Miller-Keyes of North Carolina.
"We connected on a lot of levels, like being goal-driven women and having a strong faith. It was like a children's book, the two of us being up there," Summers says. "It was a whirlwind of emotions. I remember it up to that point, but then when they said 'Nebraska' I had to watch it back to really understand it all. I don't remember crying. I remember falling to the ground and then Kára [McCullough], last year's Miss USA, leaned down and said, 'Can I crown you now?' and I said, 'Yes, of course, please do.'"
Summers' entire family traveled to Shreveport, Louisana, for the Miss USA pageant, including her brother, Scott, who later texted: "I've been to about 15 pageants for you but I was not mentally prepared for five eliminations in 90 minutes. That was stressful. It's usually a top 15, top 5, and then announcement of places. That was just brutal on your brother."
While Summers and her family will have to prepare for another nail-biter when she competes in the Miss Universe pageant in late 2018, right now she's focused on her newfound opportunity to impact others on a larger scale.
"I made a difference in Nebraska as Miss Nebraska USA and was already making a difference as Sarah Rose Summers, but this opportunity plunges me forward to be able to speak on topics I'm passionate about and to affect others and causes that I feel called to. To work with the Miss Universe Organization and have the opportunity to travel, meet people, and make connections is amazing," Summers says.
Project Sunshine is one partner organization she's excited to work with. They organize volunteers to comfort pediatric patients and their families in various medical settings—a cause near and dear to Summers personally and professionally.
"I connect deeply to [the Project Sunshine mission] because I was hospitalized at Children's in Omaha when I was younger with something called ITP," Summers says. "I was only there for a short time, thank goodness, but I remember how scary it was for my family and me."
The experience propelled Summers to volunteer at hospitals in Omaha and Fort Worth, where she attended Texas Christian University and discovered her calling to become a certified child life specialist. After completing her schooling and clinical rotations, she sat for her certification exam in April 2018.
Summers' advice for others who seek to reach their goals is to write them down, a practice she learned working part-time for Lululemon.
"Write your goals down, first and foremost. Then, if you're bold enough, share them with others so they can help hold you accountable, and help drive you to that goal, and maybe even form strategies around how to make it happen," Summers says.
While she earned her title as one of the world's most beautiful women, Summers defines beauty holistically. She hopes to set a realistic, attainable, healthy example for girls and women of all ages.
"The Miss Universe mantra—'confidently beautiful'—is about more than your figure, size, height, face structure, skin, or hair color. It's being confident in who you are, from your looks to your beliefs, and knowing that you're happy with who you are, proud of your accomplishments, and confident in your aspirations," Summers says. "Beauty is so much deeper than physical attributes."
To Summers, pageants are also about so much more than beauty.
"[The pageant] is an opportunity for like-minded, encouraging women to come together, get to know each other, share their stories, and become friends," Summers says. "It's just another outlet for us as women to empower each other."
Visit missuniverse.com/missusa to learn more.
This article was printed in the September/October 2018 edition of Omaha Magazine. To receive the magazine, click here to subscribe.
Posted in: Omaha Magazine, People
Topics: Miss Nebraska, Miss Universe, Miss USA, Nebraska, Omaha, Omaha Magazine, Sarah Rose Summers, Sarah Wengert

redpajama_set_name: RedPajamaCommonCrawl


text:
Tag Archives: Insider Trading
activism, Congress, corruption
JOIN MASSIVE Nationwide Campaign To Take On Nancy Pelosi and Insider Trading By Congress…With ONE Click!
Patty McMurray
Kevin Freeman, host of Economic War Room on BlazeTV, has launched a national campaign slamming Nancy Pelosi for protecting corrupt insider trading by members of Congress. More than 1,450,000 emails have already been sent to Congress!
In 60 seconds anyone in America can send an email to all 535 members of Congress and tell them to stop the DC greed! The corrupt ruling class has gotten away with this for too long. It's time to turn up the heat! TAKE ACTION HERE
Freeman describes the issue like this:
Nancy Pelosi has made a fortune worth more than $100 million – all while working on a government salary. HOW?! How are she and other politicians becoming multi-millionaires while supposedly working for "us" in Congress?
The corruption is deeply disturbing: Democrats and Republicans alike are pigging out on 'Pelosi Perks'.
Politicians in Washington are giving themselves big salaries, big pensions, and big benefits. And – as if that wasn't bad enough – they're making MILLIONS on Wall Street with insider trading. In fact, Nancy Pelosi and her husband made more than $30 million just from trading tech stocks. They bought their shares – and then she started blocking efforts to rein in Big Tech. And no wonder: by protecting them, she's hurting us while making a FORTUNE.
Our elected officials have lined their pockets at our expense for too long!
Economic War Room has just released its expose of this outrage, and we've launched a nationwide campaign so every American can take action to demand an end to Pelosi Perks.
You can watch Freeman's Economic War Room special on the issue featuring Tom Fitton of Judicial Watch, Jenny Beth Martin of Tea Party Patriots, and Justin Danhoff of the Free Enterprise Project here.
CongressInsider TradingNancy PelosiSTOCK ActTake Action
corruption, Economy, law, politics
14 Members of Congress Accused of Violating Federal Law in New Report
14 Dec 2021 Ryan DeLarme 1 Comment
By Abby Liebing,
A new report has found that 14 members of Congress have been violating the STOCK Act and handle their finances in a way "that could expose them to ethical problems."
Insider reported the following Republican lawmakers were among those in the "danger" zone of financial compliance: Rep. Pat Fallon of Texas; Rep. Dan Meuser of Pennsylvania; Rep. Blake Moore of Utah; Rep. Lance Gooden of Texas; Rep. Diana Harshbarger of Tennessee; Rep. Kevin Hern of Oklahoma; Sen. Tommy Tuberville of Alabama; and Rep. Chris Jacobs of New York.
The list continued with several Democrats who had violated the STOCK Act as well: Sen. Dianne Feinstein of California; Rep. Susie Lee of Nevada; Rep. Tom Malinowski of New Jersey; Rep. Sean Patrick Maloney of New York; Rep. Kim Schrier of Washington; and Rep. Tom Suozzi of New York.
So what is the STOCK Act? An acronym for "Stop Trading on Congressional Knowledge Act," the 2012 provision made it illegal for Congress members to participate in insider trading, Investopedia reported.
To many, it was surprising that this had to be formalized in law since many just assumed insider trading was illegal. But until 2012, "trading based on material nonpublic information — otherwise known as insider trading — was both legal and commonplace among members of Congress," Investopedia explained.
Nine years ago, the STOCK Act made it expressly illegal. The act was widely supported in Congress, by both Republicans and Democrats. In the House, it passed 417-2 and in the Senate, it passed with a 96-3 vote.
Now at least 14 Congress members have failed to properly disclose financial trades.
"These lawmakers have violated the Stop Trading on Congressional Knowledge Act of 2012 by failing to properly disclose financial trades, many to a significant degree," Insider reported.
"Some also employ senior staff members who've violated the STOCK Act or haven't themselves taken proactive steps to avoid potential conflicts of interest," the outlet found.
Perhaps the most prominent member of Congress listed among the 14 is Feinstein.
The senator violated the STOCK Act one time, which totaled about $15,000. She said that she paid the fine for not disclosing her trade, but did not provide any proof.
Schrier violated the act once, totaling at least $500,000. Schrier did not comment or confirm whether the fines had been paid.
Other members of Congress reportedly violated the STOCK Act over 100 times.
Fallon had 118 late disclosures, which totaled about $9,113,000.
"Upon learning of his STOCK Act violation, Congressman Fallon self-reported himself to the Ethics Committee and proactively paid his fines. The congressman has not been late on reporting stock transactions since his discovery of his first infringements," Austin Higginbotham, Fallon's communications director, said in a statement.
Suozzi violated the STOCK Act about 300 times, which resulted in at least $3,200,000. Suozzi did not comment on whether he had paid the fines necessary for these violations.
The infringements regarding the STOCK Act are nothing new, though.
In September, an outside ethics group complained that seven House lawmakers didn't disclose stock trades, NPR reported.
"The lack of accountability we've seen in regard to STOCK Act compliance is basically giving elected officials the green light to buy and sell stocks based on information gained from committee meetings without any transparency for their voters," Kedric Payne, general counsel and senior director of ethics at the Campaign Legal Center, wrote in a statement.
In July, Forbes also put the spotlight on three Republican lawmakers who did not report "up to $22 million in stock trades made earlier this year" — Tuberville, Moore and Fallon.
But while the 14 members of Congress identified by Insider were the ones with very clear trading violations, Insider also shone a light on other lawmakers who did not properly report their trading.
"Insider and several other news organizations have this year identified 47 members of Congress who've failed to properly report their financial trades as mandated by the Stop Trading on Congressional Knowledge Act of 2012, also known as the STOCK Act," the outlet reported.
In light of all these violations, many ethics groups and government watchdogs are pushing for fines and punishments for STOCK Act violations to be harsher.
"While lawmakers who violate the STOCK Act face a fine, the penalty is usually small — $200 is the standard amount — or waived by House or Senate ethics officials," Insider reported.
"Ethics watchdogs and even some members of Congress have called for stricter penalties or even a ban on federal lawmakers from trading individual stocks, although neither has come to pass."
CongressInsider TradingSTOCK Act


In 2009 I wrote the first edition of Scrum Product Ownership as a way of helping Product Owners understand their roles and responsibilities better. Before that, it was mostly an exercise in guessing and survival. In 2013, I updated the book in a second edition. In both books I took on the topic of Backlog Grooming.
As it turns out the term "grooming" is losing its luster in the community and terms like maintenance and refinement are replacing it. I believe the latest copy of the Scrum Guide uses the term refinement. So I will try to start using Backlog Refinement consistently throughout this article. That being said, I really, really like the implications of the term grooming.
The Product Backlog is an ordered list of everything that might be needed in the product and is the single source of requirements for any changes to be made to the product. The Product Owner is responsible for the Product Backlog, including its content, availability, and ordering.
A Product Backlog is never complete. The earliest development of it only lays out the initially known and best-understood requirements. The Product Backlog evolves as the product and the environment in which it will be used evolves. The Product Backlog is dynamic; it constantly changes to identify what the product needs to be appropriate, competitive, and useful. As long as a product exists, its Product Backlog also exists.
The Product Backlog lists all features, functions, requirements, enhancements, and fixes that constitute the changes to be made to the product in future releases. Product Backlog items have the attributes of a description, order, estimate and value.
Product Backlog refinement is the act of adding detail, estimates, and order to items in the Product Backlog. This is an ongoing process in which the Product Owner and the Development Team collaborate on the details of Product Backlog items. During Product Backlog refinement, items are reviewed and revised. The Scrum Team decides how and when refinement is done. Refinement usually consumes no more than 10% of the capacity of the Development Team. However, Product Backlog items can be updated at any time by the Product Owner or at the Product Owner's discretion.
Higher ordered Product Backlog items are usually clearer and more detailed than lower ordered ones. More precise estimates are made based on the greater clarity and increased detail; the lower the order, the less detail. Product Backlog items that will occupy the Development Team for the upcoming Sprint are refined so that any one item can reasonably be "Done" within the Sprint time-box. Product Backlog items that can be "Done" by the Development Team within one Sprint are deemed "Ready" for selection in a Sprint Planning. Product Backlog items usually acquire this degree of transparency through the above described refining activities.
Now that we've explored what a Product Backlog and Refinement is, by process definition, let me share some of my experiences around effective refinement.
1. Regularly Scheduled Refinement Meetings – I'm a big fan of creating a tempo of regularly scheduled Backlog Refinement meetings within your teams. I usually schedule 1-2 of them a week for an hour each. The entire team is invited and expected to attend. I want everyone to come to the meeting "backlog aware", that is, they've looked at likely refinement candidates before the meeting AND they have thoughts surrounding (size, ordering, design, dependencies, strategy, quality, risks, and optimal flow).
I also recommend that a team member take detailed notes that capture the valuable discussions and next-step decisions that are made. This is invaluable information and you don't want to lose it. I normally ask members to round-robin note keeping, which helps to keep everyone engaged in the meeting, and in the backlog.
2. Rigorous Prioritization – You must truly reinforce the notion of order or priority in your backlogs. I think of the Highlander movies and the phrase – "There can be only One" in this regard, so please don't overload priorities.
And I look for the team to consider and balance against all of these variables when setting priority. For example, it should never be the case that customer value always drives prioritization without consideration of technically sound solutions.
3. Examine Your Stories Frequently – I often encounter teams who only refine their stories once. In that context they write them, refine the wording, write acceptance tests, estimate them, and order them – all at the same time. I could see doing that for trivial or straightforward stories, but never for complex ones.
I much prefer an approach where the team "samples" the stories over several refinement meetings. Taking the story from concept or idea (Epic) and methodically breaking it down into refined and executable stories. I sometimes recommend to teams that—a "good story" should be refined a minimum of 3-4 times during its evolutionary lifetime. And this includes sufficient space in between refinement discussions so that the team has time to think about the story in relation to all of their other work and the project and product goals.
4. Egg Timer – I usually recommend that teams stay aware of their story refinement velocity, that is, how many stories do they discuss in a 1-hour meeting. I often see velocities of 1-2-3 stories, which to me implies over –discussion. I prefer the team have a goal of "advancing" stories in their refinement meetings and not necessarily complete them in one sitting.
The real point of a backlog refinement meeting is not to complete stories as quickly as possible, but to advance the understanding and clarity around the stories. As long as the team makes progress, and keeps chipping away at the stories, I'm happy with their progress. You might ask, what is a fair or rough velocity goal? I'm not sure there's a magic number, but refining a story every 5-6 minutes might be a reasonable goal—so perhaps 10-12 per 1-hour meeting.
5. The Estimates are NOT the most important thing – We're in the middle of a refinement meeting and leveraging Planning Poker as a means of collaborative estimation. In one case, 2 developers have been debating whether the story is 5 or 8 points in size for the last 30 minutes. Eventually, the Scrum Master has to move on and the team still hasn't agreed on the estimate. In another case, the testers on the team think a story is 13 points, but the developers strongly disagree. And the story ends up being estimated at 3 points. After this happens a few times, the testers disengage in the estimation and simply acquiesce to the developers on all estimates.
In both cases, the estimates (numbers) have been the focus point of the team. I strongly would argue that the estimates are much less valuable than the DISCUSSION that the process of Planning Poker estimate enables.
Who cares if it's a 5 vs. an 8? At the end of the day, pick a reasonable, relative value and move on. BUT, have rich, deep, collaborative discussions across the team about the story. Hear everyone's experience. Hear their concerns. Listen to what's said and unsaid. And as a team, come to a fair and balanced relative estimate for "all the work" to move the story to meet your Definition of Done. That's the value that the estimates drive.
6. Talk Less, Spike More – I've noticed a very common anti-pattern in many refinement sessions that teams explore each story far too deeply. They wax into conjecture and guessing about design challenges. They debate implementation approaches. They argue estimates on stories that are still quite immature and ill defined. In a word, they talk too much about the stories.
I'd much rather they define a spike, do some research, write some code, talk to a customer, and do whatever it takes to replace "talking" with "real learning and knowledge". And I don't think this happens in a roomful of people. It happens individually and more effectively in pairs looking at real prototypes and code.
I have a personal heuristic that up to 20% of the stories in a typical Product Backlog deserve to be spiked. What I more commonly see from most teams is 1-2% of their stories getting spiked. I think most software projects have more complexity than that, which is exactly what a user story - research spike is intended to mitigate.
7. Remember, Refinement is Planning Too! - Backlog refinement goes far beyond requirements definition. It's also part design, part planning, part strategy, and part risk mitigation within your sprints. This is one of the reasons I prefer that teams sample their stories multiple times and leave a little time between refinement sessions. It allows for "think time" across the team, which leads to better stories, better execution, and more consistent delivery of value.
Another important point is to look beyond the individual stories when you're refining your backlogs. Instead, ask your Product Owner about features and themes (collections) of stories that they want delivered together or that are related. Here you should be looking at dependencies and risks. But also look for efficiency in implementation. For example, coupling a set of four stories together in priority so that you can do some related refactoring and improve the service API that all four will leverage.
8. Reserve 10% of the Teams Time – Too many teams make the mistake of reserving too little time for backlog refinement. I remember early on in my Scrum journey hearing Ken Schwaber talk about "reserving" 10% of the teams' time for refinement. You see mention of that in the latest Scrum Guide (above) as well.
That would be 4 hours, per team member, per week. He made the point that it's that important and the team needs the time to do it well. Fast-forward to today, and most teams might allocate a 1-hour meeting per week to refinement and cancel that if the sprint is running into challenges. Teams need to make a serious commitment to their refinement activity. And remember, it's not just in meetings. I believe each team member should take some personal time each week to work on improving the backlog. That might be improving the acceptance criteria in a story, or asking targeted questions, or suggesting a story be spiked, by who and for how long, or writing a technical story to clean up some technical debt.
The point is refinement should be a continuous activity by all team members and not something relegated mostly to meetings or to the Product Owner.
9. Never Cancel Refinement, in fact, Do More! – As I alluded to in the last suggestion, it often seems like teams find excuses to defer or cancel backlog refinement whenever possible. But what I've found is that this is the last thing you should do if you're challenged in your sprints. Refinement goes way beyond requirements definition. It's also part design, part work planning and tasking, part strategy, and part risk mitigation within your sprints. Point being, if you're struggling, don't stop planning your work. It will only lead to deeper struggles and missed commitments.
Let me add another heuristic here. If you find your sprint planning meetings going on and on, then you've probably been under refining. Conversely, if you are performing sufficient backlog refinement within your team, your sprint planning meetings should be effective, focused, and quite short.
10. Develop & Apply Readiness Criteria – From my perspective, there are very few things worse for an agile team to do than to take an under-cooked story (under refined) into a sprint. Inevitably that story "blows up" into more than the team expected in size, scope, complexity, dependencies, or something else and it causes the sprint to fail.
Beyond the disappointment to the Product Owner and business impact, it causes the team to lose confidence and feel bad. Not only that, the work typically falls to the next sprint to complete, so there is a cascading effect.
I wrote about a method for avoiding this called Readiness Criteria here. I'd strongly recommend you apply this technique. Consider it story exit criteria from your backlog refinement sessions.
To keep the refinement meetings interesting, we'll examine stories through these various lenses—sometimes shifting the view in the same refinement meeting. One advantage is it forces the Product Owner and team to "connect the dots" between the now and the future, both from a functional and architectural perspective. It also motivates the team because they get glimpses of their future work.
They break the stories down too far, further than they really need to. Sometimes they even break them into tasks, which should be deferred to sprint planning.
In this related article, I refer to the notion of creating an anchor story for each sprint. This story would be large and complex, but it would be sized to fit the sprint—in other words, it would be an "executable" story. This story would more heavily influence the sprints theme or goal and other stories would build around and support it.
If you take this anchored view to your backlog, then finding these stories and building content around them would be an aspect of your refinement thinking.
There are five-core scrum activities defined in the Agile Atlas as central to your execution of Scrum.
All of them are important and have specific values they add to your agile execution and results as a team. I think of Backlog Refinement as being the "planning" focused ceremony. Not just planning for the sprint, but planning across a multitude of factors that a team needs to keep their "eyes on" to successfully deploy customer solutions. A huge part of it relates to what I call look-ahead.
I hope this article has helped to widen your thinking about Backlog Refinement—the what and the how of it. But at the end of the day, these are simply my thoughts and experiences. Bring yours to bear in your contexts and see what approaches will work for you.


The ice is thawing, the snow is melting, and the smell of spring is in the air. Freezing temperatures are creeping up ever so slowly toward tolerability. Now that the sun isn't going down so early and the weather outside isn't cold enough to make you wonder why you aren't living in the Caribbean, you probably want to get out and enjoy the season for the first time this year.
Regardless of how great the weather is or how much fun you're having, there's always the lingering worries in the back of your mind about your home. It's natural. Did I lock the back door? Did I leave the oven on? Are my pets OK? Is someone breaking in while I'm away?
Even though break ins are relatively rare, there have been plenty of cases in the news recently of thieves breaking into houses after discovering the family was away via their social media feed. It's the reality of the world we live in. Thankfully, Fluent Home Edmonton can be the key to having a stress-free spring break. Here's how you can free up some extra mental space and fill it up with fun instead of fuss!
The old "did I leave the oven on" cliche exists for a reason: we always worry about whether or not we've secured our homes when we leave them. It's how our minds work. Locking the back door, switching off appliances, turning off the lights, AC, and TV, it all swirls around in our heads and not knowing the answer can drive us crazy.
So instead of wracking your brain the whole weekend about whether or not you shut the garage door or took that frozen pizza out of the oven, you can know for sure.
Crime in Canada is relatively rare. We don't want you to think that just because you're away from home that you'll be the victim of a break-in. However, crime is a reality of the world we live in. There have been plenty of instances where families have come home from vacation to a house much emptier than they remember. Worrying about the safety of your home and belongings is one of the biggest stress factors on vacation. You can put an end to it with home automation and smart security.
We can help take the stress of your spring break away by giving you access to 24/7 security services. Our eyes will always be on your house while you're on vacation. If something goes wrong, the police will be notified immediately. You'll even be able to monitor the home yourself from the app. If there's any unusual movement, you'll be alerted immediately.
We doubt that your pets are going to throw a wild party while you're gone (if they do, that would be awesome!), but it's still a good idea to check in on them once in awhile. If you've got a neighbor to keep an eye on them, then that's great, but nothing compares to being able to spy on them 24/7. Chances are they'll be napping in that same spot right in the middle of the floor, sleeping on the windowsill, or waiting patiently by the door for you to come home, but it's still calming to know they're OK. You can even coordinate with your pet sitter via the app!
Heading out of town this spring to enjoy the great outdoors? That's a great idea! Edmonton and the surrounding area have some of the most beautiful natural parks in the world. If you want to enjoy them stress free, then smart home automation from Fluent Home can help ease your worries and let you enjoy the holiday.


Many community members visited Queen's Park on Tuesday to see the aftermath of the Arenex collapse for themselves.
Many community members, including members of the Shasta Trampoline Club, visited Queen's Park on Tuesday to see the aftermath of the Arenex collapse for themselves.
Many Royal City residents are devastated by the loss of a place that become like a second home.
Community members expressed sadness as news began to spread that the Arenex was destroyed after its roof collapsed Monday night.
Steve Quinlan's children were members of the Shasta Trampoline Club and his daughter continues to coach for the club.
Thirteen-year-old Carly Anderson has been involved in the non-competitive gymnastics program at the Arenex since she was about three years old.
New Westminster native John McMahon has been part of the ball hockey league at the Arenex since 1987.
Gurjeevan Singh, a 20-year-old Port Moody resident, practises and coaches shooting at the indoor shooting range at the Arenex every few days. He has "no idea" where he'll go now.
"There's really none that have this type of program. Here, it's built for competitive shooting. Any other range it's just recreational," he said.
Singh's father woke him up at 7 a.m. on Tuesday to tell him the building had collapsed, and he quickly ran downstairs to watch the news.
"My heart just sank," he said.
For the past 15 year, Scott Steinson has been playing badminton at the Arenex, a place that not only provides good-quality badminton but a welcoming atmosphere. A Coquitlam resident, Steinson detoured on his way to work so he could stop and see the damage.
Dean Gibson, the city's director of parks and recreation, said the city will be focusing its efforts on finding temporary accommodations so program can be up and running in two or three months. He said it could take two or three years to build a permanent structure.
Gibson said it will be "a considerable undertaking" to find alternative locations for the programs offered in the Arenex.


The library has purchased a 3D printer to focus on the use of this emerging technology and to offer an innovative and creative space for the community to gather.
It is the process of taking a three-dimensional digital model and making a physical object. Succeeding layers of filament are laid down in thin layers to build the object.
You may use a 3D modeling program to create your own file. Please be sure to bring the file in on a USB drive or a SD card.
Your 3D model file should be saved as a STL file otherwise it will not be printable.
If you are not familiar with 3D modeling software, you can browse and choose your designs from a number of different websites: One is called Thingiverse (www.thingiverse.com/) and another is available through the Tinkercad software (www.tinkercad.com).
Who and when can I print?
For projects that require a longer time to complete or potentially run through the night, the patron may leave and pick-up his/her project the next day. The projects will be placed in protective covering and placed on the Holds Pick-Up shelf categorized by the user's last name.
The Powers Memorial Library's 3D printer is available to the public to make three-dimensional objects in plastic using a design uploaded from a digital computer file or created using Tinkercad software.
d. Violates another's intellectual property rights. For example, the printer must not be used to reproduce material that is subject to copyright, patent or trademark protection.
e. Weapons and salacious objects are strictly prohibited.
II. Powers Memorial Library reserves the right to refuse any 3D printing request.
III. Powers Memorial Library is not responsible for any damage, loss or security of data arising from the use of its computers or networks, nor for the functionality or quality of content produced on the 3D printer.
If you would like to donate to the filament fund, you may make a donation to the Powers Memorial Library. All filament donations will be accepted at the circulation desk.
Please note again that only designated library employees will have hands-on access to the 3D printer.


text:
Unique global partnership to combat blindness caused by diabetes
The Fred Hollows Foundation has signed up to a 10-year partnership with the International Diabetes Federation to tackle a challenging complication of the disease that can lead to blindness.
"By 2035, diabetes could become the leading cause of blindness," says Brian Doolan, CEO of The Fred Hollows Foundation, "but Fred Hollows wasn't a man who walked away from a challenge."
With diabetes, fluctuating blood sugar levels can damage the retina, causing blurred or distorted vision and eventually blindness if left untreated – a condition called diabetic retinopathy.
The partnership was announced today at the prestigious Baker IDI Institute just days before the start of the World Congress on Diabetes in Melbourne.
"We are proud that Fred's Foundation is establishing this partnership with the IDF, and while we are daunted by the size of the task before us we are excited by the opportunity of working with The Federation to make sure people with diabetes do not suffer unnecessary blindness."
Mr Doolan describes the new partnership as a timely one given the sharp rise in diabetes globally.
"Urgent action is needed to help people avoid blindness from diabetic retinopathy through access to a sight-saving eye examination once a year.
"Unless we do the groundwork now, four out of five of those people won't have access to adequate eye care services."
The Prime Minister Tony Abbott has welcomed the alliance and says the Federal Government will play its part fighting the disease and its consequences.
"The government has plans to support the development of a new National Diabetes Strategy.
"This will inform how existing health resources can be better coordinated to address one of the most serious health challenges facing our country and our world," he said.
The new alliance will embed eye care as part of general health care for people living with diabetes and work towards new global guidelines on treatment.
The partnership will also collaborate on rolling out diabetes programs in a range of developing countries that are expected to include mass screenings.
It will invest in low cost treatment services to address diabetic retinopathy, and contribute towards building a skilled workforce to provide good quality care in all aspects of screening, treatment and management of the condition.
Some 1.3 million Australians have diabetes and, of these, 300,000 have diabetic retinopathy while more than half will have suffered some form of vision impairment.
International Diabetes Federation


text:
Corning Shares Jump On LCD Glass Deal With Samsung
Shares of Corning Inc. jumped Tuesday after it announced a new tie-up with a Samsung Electronics subsidiary that will boost the glass maker's earnings immediately and guarantees that it will supply Samsung with liquid crystal display glass through 2023.
CORNING, New York (AP) -- Shares of Corning Inc. jumped Tuesday after it announced a new tie-up with a Samsung Electronics subsidiary that will boost the glass maker's earnings immediately and guarantees that it will supply Samsung with liquid crystal display glass through 2023.
Corning, the maker of Gorilla Glass screens for smartphones and tablets, will acquire the South Korean company's 43 percent stake in Samsung Corning Precision Materials, an LCD glass joint venture in Korea, and will buy out other minority shareholders.
The joint venture makes glass substrates, the key material in high-end LCD televisions, monitors and mobile devices.
In exchange for giving up its stake in the venture, Samsung Display will receive $1.9 billion worth of Corning preferred shares. It will also invest another $400 million in preferred shares of Corning. If converted, the shares would give Samsung Display a 7.4 percent stake in Corning. They are not convertible for seven years.
The transactions are expected to close in the first quarter of 2014.
Corning said its board had also approved buying back $2 billion worth of its shares, which it expects will cover the dilutive effect of issuing preferred shares to Samsung Display.
Taking over the venture gives Corning immediate access to $1.2 billion in cash on the joint venture's balance sheet. The company believes it will add $2 billion in annual sales and 20 percent to earnings, excluding one-time items, in 2014 and 2015.
Part of the benefit comes from the share buyback as well as $100 million in expected cost savings in 2015.
Shares of the Corning, N.Y.-based company rose 25 percent to $19.24 in after-hours trading.
Corning also said Tuesday that it expects to earn 33 cents per share in the third quarter, excluding one-time items. That beats Wall Street's prediction by a penny. It also said revenue rose 5 percent to $2.1 billion, in line with analysts' expectations. The company will release full results for the July-September quarter on Oct. 30.
Apple Unveils New Macs, iPad Ahead Of Holidays
Center For Automotive Research Gets $1.5M Grant
Judge Mulls Restraining Order In Wind Turbine Suit


text:
A's beat: Doubront to start against Dodgers on Tuesday
Whole staff at Bay Area bagel shop quit over 'toxic management'
Video shows violent pit bull attack inside SF's Main Library
Nearly 4,000 Kaiser Patients May Have Received Incorrect Amount Of Covid...
This small Bay Area town is an unlikely hotbed of live music
Major clothing retailer in SF's Westfield Mall to close for good
Major Bay Area highway could be fully underwater as soon as 2040
The rise and fall of San Francisco's coolest clothing brand
Body of missing woman found in California Aqueduct
California man dies after caged golf cart sinks
The unluckiest man in the unluckiest industry
Sports // Oakland Athletics
Susan Slusser
Aug. 17, 2015 Updated: Aug. 17, 2015 9:54 p.m.
Oakland Athletics pitcher Felix Doubront throws against the Toronto Blue Jays during the fifth inning of a baseball game, Wednesday, Aug. 12, 2015, in Toronto. (Fred Thornhill/The Canadian Press via AP)Fred Thornhill/Associated Press
BALTIMORE — Felix Doubront will make his first start for the A's on Tuesday, and lucky him, his opposing number will be Dodgers star Clayton Kershaw.
At least Doubront got back to California at a reasonable time: He was sent ahead of the rest of the team Monday so he could get a good night's rest.
Everyone else? Well, anticipated arrival in Oakland after a night game Monday at Camden Yards: sometime after 2 a.m.
"It doesn't seem like it's the right thing to do as far as getting us out of here and then having to travel cross country and play again tomorrow," manager Bob Melvin said. "That's just my feeling."
Melvin said the team won't be expected to report to the Coliseum until 4:45 p.m. and the A's won't hit on the field but will do some hitting in the batting cage instead.
Doubront, acquired from Toronto in a cash deal of just over the waiver-claim fee of $20,000, is 28-24 with a 4.47 ERA in 77 big-league starts, as opposed to 1-0 with a 7.40 ERA in 32 relief appearances. He has made two relief appearances with Oakland, including 61/3 innings at Toronto last week.
"He gave up a couple of runs but for a number of reasons, he did well," Melvin said. "He did well for a stretch of that game and certainly not having to use anyone else in the bullpen was big at a time we'd had to use the bullpen quite a bit."
Doubront has faced the Dodgers twice and is 1-0 with an 11.05 ERA, allowing 12 hits, five walks and nine earned runs in 71/3 innings pitched.
Jesse Chavez will start Wednesday and then the A's will reassess the rotation.
Doolittle plans: Reliever Sean Doolittle (shoulder) struck out all three batters he faced in his rehab appearance on Sunday, but he'll have two more outings with Triple-A Nashville before returning to the A's, likely on the next road trip.
"Good again, that's the best he's felt, and each and every time he's said that," Melvin said.
Susan Slusser is a San Francisco Chronicle staff writer.
vs. Dodgers
7:05 p.m. CSNCA
Kershaw (10-6)
vs. Doubront (1-1)
12:35 p.m. CSNCA
Latos (4-9)
vs. Chavez (6-12)
Leading off
Back in the fold: Left-hander Brad Mills, designated for assignment on Saturday, cleared waivers and was assigned to Triple-A Nashville. Mills gave up seven hits and three runs in five innings in a spot start with the A's on Friday.
— Susan Slusser
Reach Susan on
Susan Slusser has worked at The San Francisco Chronicle since 1996. She is the Giants beat writer. Previously, she covered the A's full-time from 1999 to 2021.
Slusser's book about the A's, 100 Things A's Fans Need to Know and Do Before They Die, came out in 2014 and she and A's radio announcer Ken Korach released a new book, If These Walls Could Talk, Tales from the Oakland A's Dugout, Locker Room and Press Box, in 2019. She is also a correspondent for the MLB Network.


Source: Journal of Wildlife Management. 76(6): 1107.
In the initial Editor's Message of this volume, I stated my intent to involve more students in the publication process. A number of people commented on it being a good idea, but only a couple have followed up. One was Paul Krausman, President of The Wildlife Society. We matched graduate students from the University of Montana wildlife program with manuscripts where each student served as a reviewer. Students were the third reviewer on manuscripts within their area of interest and expertise. I found the experience to be entirely positive. Student reviews were often the first returned from the 3 reviewers. Many of the reviews were very insightful and provided useful information to the Associate Editor, the authors, and me. In fact, many of the reviews identified key points not raised by the other reviewers and these points were pretty much on target. As with all reviewers, they received a copy of my decision letter as well as copies of the other review comments. This allowed them to see how their review contributed to the fate of the manuscript. Hopefully, the experience gave them a good perspective of many aspects of the review process, a perspective that I hope will serve them well in their career. I invite any faculty who would like to explore similar experiences for their students to get in touch with me.
Block, Bill. 2012. Editor's message: Student involvement. Journal of Wildlife Management. 76(6): 1107.


Kings Cross station was built in a hurry. The Great Northern Railway had succeeded against strong opposition in gaining Parliamentary authority to build its main line from Doncaster to London and in 1850 reached a temporary terminus at Maiden Lane. This soon showed signs of weakness and its replacement was urgent.
Kings Cross station opened in 1852 to the design of Lewis Cubitt, whose uncle and cousin were both engaged as engineers by the railway. The design is alleged to be based on the Czar of Russia's riding stables. The station was built on the site of a fever hospital which is also said to have been where Queen Boudicca fought a battle. She is even said to be buried under platform 8.
The ribs of the twin glazed semi-circular roofs were made of timber, a late use of this material for the purpose, and these had to be replaced with iron, the eastern shed in 1866, the western in 1886. In 1972, a one-storey extension designed in-house by British Rail was constructed in front of the station. While the extension was intended to be temporary, it still stands. Many consider the extension unattractive, not the least because it obscures the Grade I-listed façade of the original station.
Prior to the construction of the extension, the station façade had already become hidden behind a small terrace of shops. This extension is scheduled to be demolished, revealing once again the full glory of the Lewis Cubitt architecture, when a new ticket hall and concourse area is built on the station's western side.


Owner Susanne Anderson live on the property and have a 24 hour security monitoring system to keep an eye on your furry family members at all times.
Full service dog grooming and limited day and overnight boarding.
A mountain destination dedicated to dogs and cats featuring quality food, treats, toys, accessories and a dog wash.


text:
\section{Introduction}\label{sec:introduction}}

\IEEEPARstart{I}{n} recent years, the incidence of cardiovascular diseases (CVDs) has practically exploded, which has become a significant threat to human life due to high mortality. Continuous monitoring of CVDs for patients well in advance has been proven an effective measure to save lives. Electrocardiogram (ECG) \mbox{\cite{van2004clinical}} is a common non-invasive measurement that reflects the physiological state of the heart, and it is one of the most important diagnostic tools in the current age. With the development of smart wearable devices in recent years, patients can acquire ECG devices ubiquitously for personal healthcare monitoring. Although ECG signals are convenient to collect, it remains challenging for medical professionals and cardiologists to analyze such multifarious data. Hence, automatic ECG classification modeling has become an important topic in the research community.

Time series is a common type of data format for representing ECG signals, a collection of values presenting signal strengths ordered by timestamp sequentially. ECG classification or cardiovascular diseases detection problem can be formulated as a time series classification problem. It is a problem of assigning one of a predefined class to a time series, e.g., classifying a signal of ECG motions as normal or an impending atrial fibrillation \cite{yao2020multi} or classifying signals from different patients with or without chronic obstructive pulmonary diseases \cite{alkukhun2014electrocardiographic} \cite{GOUDIS2015264}. Conventional time series classification works on a given fixed-length time series and assumes accessing the entire input time series at making a decision. Unfortunately, it cannot meet real-world requirements in many scenarios. For example, in the ICU because different patients are monitored at different times and duration, this leads to generating varied-length time series data. In time-sensitive applications, making a decision as early as possible is crucial for improving practicability. For instance, early diagnosis can provide patients with timely and effective treatment, which is vital as many heart diseases are fatal in a short time. As a result, early time series classification for ECG signals is an important research issue. In this study, we focus on solving the early time series classification problem, which aims to early classify a time series with confidence by seeing as few data as possible.

\begin{figure}
    \centering
    \includegraphics[width=\linewidth]{fig1.pdf}
    \caption{The  proposed snippet policy network contains four modules, which are snippet generator, backbone network, controlling agent and discriminator.}
    \label{fig:SPN}
\end{figure}

Early time series classification (eTSC) has two distinct goals: classifying a given time series early and accurately. However, the earlier the time series has to be classified, the fewer data points are provided for this task, which usually leads to lower accuracy. In contrast, the higher the classification accuracy is desired, the more measurements are needed, and the decision would be made at a later time. Apparently, these two goals are contradictory in nature. It is a highly challenging issue of early time series classification in determining the earliest point when an incoming time series can be correctly classified. Take patient care as an example; collecting more physical signals of the patients to be analyzed naturally leads to a higher accurate diagnosis, but may miss valuable time preparing an emergency plan for saving a life.

Most existing approaches in eTSC, such as \cite{wang2016earliness, mori2017reliable, xing2012early, xing2011extracting}, assume that all time series being classified have a defined start time. Consequently, these methods assume that characteristic patterns appear roughly at the same offset in all time series, and try to find the fixed fraction of each time series in the training pool. In such settings, the threshold of earliness depends on the average accuracy of classification, which implies that it is not designed to find the optimal solution. For example, monitoring sensors start their observations at arbitrary points in time of a time series for different patients, and a characteristic pattern would appear at arbitrary points in time of a time series. Intuitively, existing methods are expected to analyze a time series when the observed devices start simultaneously and the characteristic patterns appear simultaneously; while in CVDs monitoring applications, the ECG signals would be collected at an arbitrary time. In this case, it is sub-optimal to use the existing methods for solving ECG early classification problem.

In this paper, to address the above issues, we propose a snippet policy network (SPN), as shown in Figure \ref{fig:SPN}, which embodies a deep reinforcement neural network model  to learn multivariate information from varied-length ECG signals. The proposed framework consists of four modules: snippet generator, backbone network, controlling agent, and discriminator. To well model the ECG signals regarding the periodicity, we use the snippet generator to split the original time series into snippet series. Then, to capture inner-snippet spatial dependency and inter-snippet temporal dependency, the backbone network that combines convolutional and recurrent networks is built to learn hidden representations. To find an appropriate prediction time point, an agent controls the backbone network's whole process and informs the discriminator. Finally, the discriminator receives the controlling agent's notification and produces the predictive result according to the hidden representations from the backbone network.

The contributions of this paper are the following:
\begin{itemize}
    \item We address the problem of classifying cardiovascular diseases based on varied-length ECG signals as early as possible, which has not been well explored in the research community.
    \item To solve the problem of ECG early classification, we propose a novel deep reinforcement learning framework consisting of four modules, snippet generator, backbone network, controlling agent, and discriminator.
    \item The controlling agent solves the bi-objective optimization problem solution of the earliness and accuracy goals in conflict. It allows the model to classify CVDs accurately as well as to find the earlier prediction point in time.
    \item The proposed model is evaluated on a public CVDs classification dataset and has demonstrated its performance in classifying different cardiovascular diseases. Experimental results show that the proposed framework outperforms existing state-of-the-art approaches.
\end{itemize}

The rest of this paper is organized as follows: Section 2 briefly outlines the existing related works. Section 3 introduces the architecture of the proposed SPN framework, while the experimental evaluation results are described in Section 4. The conclusion of this paper is summarized in Section 5.

\section{Related Work}
The early time series classification problem has attracted researchers from data mining and machine learning communities in the past decade. According to the strategy of the existing eTSC approaches used, they can be broadly divided into two different branches, which are feature-based methods and series-based methods. Feature-based methods extract meaningful patterns and exploit these patterns to build the early classifier. On the other hand, series-based methods use the raw time series directly to learn a classification model.

\subsection{Feature-based Methods}
In 2011, Xing et al.\cite{xing2011extracting} developed the method, called Early Distinctive Shapelet Classification (EDSC), for extracting meaningful patterns in the eTSC problem. They proposed an interpretable feature called local shapelets, which are essentially sub-sequences of time series. Sub-sequences of time series are intuitive to present the physical meaning to end-users, and can effectively capture the local similarity among time series, so they have high interpretability. Ghalwash et al.\cite{ghalwash2012early} generalized the definition of local shapelets to a multivariate context and proposed a method for early classification of multivariate time series accordingly. The proposed method, Multivariate Shapelets Detection (MSD), extracts patterns from all dimensions of the time series, which is called multivariate shaplet. A multivariate shapelet consists of multiple segments, where each segment is extracted from exactly one dimension. To extract interpretable patterns from multivariate time series data, Ghalwash et al.\mbox{\cite{ghalwash2013extraction}} proposed an optimization-based method for building predictive models on multivariate time series and mining relevant temporal interpretable patterns for early classification (IPED). The IPED method extracts a full-dimensional shapelet for each class from the binary matrix by solving a convex-concave optimization problem. The imbalanced class dataset in classification is a classical problem in the data mining field, and it remains a challenge on early time series classification problems. In 2019, He et al.\cite{he2019ensemble} proposed an adaptive ensemble framework to learn an early classification model on imbalanced multivariate time series data. The proposed ensemble framework was designed based on combining the multiple under-sampling approaches, dynamical subspace generation method, cluster-based shapelet selection method, and associate-pattern mining approach to deal with the implicit issues of inter-class and intra-class imbalances. These methods build an eTSC model by extracting features, resulting in increased computing complexity. Moreover, they achieve poor performance when facing long time series data. Our proposed method adopts the snippets concept and adaptive neural architecture to solve these issues mentioned above, which can effectively and efficiently handle long-time series early classification problems.

\subsection{Series-based Methods}

Xing et al.\cite{xing2012early} published the first work to introduce the problem of eTSC from a series-based approach. The authors developed the ECTS model based on the 1-nearest neighbor (1-NN) approach and the concept of minimum prediction length (MPL). In this paper, time series with the same 1-NN are clustered at first. The optimal prefix length for each cluster is calculated by analyzing the stability of the 1-NN decision for increasing data points in time. Then, the 1-NN approach is adopted to search among the clusters and label the class for each time series. In 2013, Parrish et al.\cite{parrish2013classifying} presented a method, called RelClass, based on quadratic discriminant analysis (QDA). A reliability score is defined as the probability that the predicted class for the truncated and the whole time series will be the same. At each timestamp of a given time series, RelClass checks if the reliability is higher than a user-defined threshold. Mori et al.\cite{mori2017early} proposed an early classification framework, SR2-CF2, based on combining a set of probabilistic classifiers and a stopping rule, designed by minimizing the earliness cost and accuracy cost. The method is conceptually simple and does not require complex parameter settings. These pioneer works started the early time series classification trend, but they left some research issues to be further addressed, such as multivariate time series and varied-length time series early classification problems. Mori et al. \cite{mori2017reliable} trained classifiers at specific timestamps, i.e., at percentages of the full-time series length. It learns a safe timestamp as the fraction of the time series, which states the model's best prediction timing. Furthermore, a reliability threshold is learned using the difference between the two highest class probabilities. Only predictions passing this threshold after the safe timestamp are chosen. Schafer and Leser \mbox{\cite{schafer2019teaser}} pointed out that time series is the non-fixed length in many real-world scenarios. To address this challenge, they proposed an ensemble framework, called Two-tier Early and Accurate Series classifiER (TEASER), for solving eTSC. TEASER's decision for a prediction is treated as a classification problem, in which master classifiers continuously analyze the output of probabilistic, while slave classifiers decide if their results should be trusted or not.

In 2016, Wang et al.\cite{wang2016earliness} proposed the first deep learning-based early time series classification framework that leverages the information at different scales and captures the interpretable features at a very early stage. For handling the multivariate time series early classification problem, Huang et al.\cite{huang2018multivariate} devised a deep learning framework based on combining convolutional neural networks and long short-term memory with learning feature representation and relationship embedding in the extended sequences with time lags. Additionally, Martinez et al.\cite{martinez2018deep} addressed the early time series classification task with a novel approach based on reinforcement learning. The authors introduced an early classifier agent, an end-to-end reinforcement learning that can perform early classification efficiently. In order to improve the interpretability of the deep learning-based model for eTSC, Hsu et al.\mbox{\cite{hsu2019multivariate}} adopted an attention mechanism to identify the critical segments related to model performance, providing a base to facilitate a better understanding of the model. The above works, including our published related works, solve the early classification problem using fixed-length time series or sub-time series. Under real-world scenarios, the length of ECG signals varies length, and they are collected at different points in time. In this circumstance, the existing approaches cannot meet the real-world mission of early time series classification. In 2019, Hartvigsen et al.\cite{10.1145/3292500.3330974} proposed a reinforcement learning framework, called EARLIEST, for solving early classification problems and providing early halting point function. Russwurm et al.\cite{russwurm2019end} proposed a generic, end-to-end trainable framework for early time series classification (ETEeTSC). This framework embeds a learnable decision mechanism used in some existing models, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Many emerging approaches, including deep learning and reinforcement learning-based models, have been proposed to tackle different eTSC problems, but they still fall short when applying to real-world applications, e.g., CVDs early classification problems. As some critical issues remain to be satisfactorily addressed, such as long time series and varied-length time series, we propose in this paper a novel framework based on deep learning architecture for solving eTSC with the issues we mentioned above.

\begin{figure*}[h]
    \centering
    \includegraphics[width = \linewidth]{framework.pdf}
    \caption{Overview of the proposed SPN. The input time series $X$ is split into a snippet, by the snippet generator. A selected snippet $b_{t}$ is fed to the backbone network sequentially, generating the hidden states $H_{t}$. Hidden state $H_{t}$ is then passed to the controlling agent to decide whether to keep running or to output the result, according to the intermediate parameterized distribution $\pi_{t}$ . The controlling agent informs the snippet generator feeding the next snippet into the backbone network if keeping running. Otherwise, the hidden state is passed into the discriminator and then producing the final result $\hat{y}$.}
    \label{fig:framework}
\end{figure*}

\section{Methodology}
\subsection{Problem Formulation}
ECG early classification is a real-world application of the multi-varied early time series classification problem. A multi-varied time series, such as a multi-lead ECG signal, reflects a specific individual's physiological status.  In this problem, a model is mandated to predict the correct class from varied-length ECG records as early as possible. Given a set of labeled multivariate time series (ECG signals), $D = (X, y)$ containing $N$ time series instances, $X^{(i)},i=1,…,N$ and the corresponding labels, $y^{(i)},i=1,…,N$. For each sample $X^{(i)}=\{x_1^i,x_2^i,…,x_L^i\}$, where $x_l^i$ contains $M$ variables recorded at timestamp $l$ and $L$ indicates the length of a given time series. The aim of eTSC is to learn a model, $f_\theta$, with parameter $\theta$, which generates a label $\hat{y}$ from time series $X$ with $l < L$.

\subsection{Model Architecture}
The proposed model, named Snippet Policy Network (SPN), is constructed with three functional parts. First, to generate snippets of given ECG signals, second, to model ECG snippets for classification, and third, to select a halting snippet to make a task-dependently appropriate prediction. In detail, the snippet policy network is a deep neural network consisting of four sub-modules: (1) Snippet generator to split multivariate time series as sub-series, called snippets; (2) Backbone network to model multivariate snippets based on the output of snippet generator continuously, generating hidden representations; (3) Controlling Agent controls the whole process at each snippet and decides whether or not interrupting the backbone network and outputting the final result; (4) Discriminator outputs a final prediction result for a given snippet if the controlling agent decides to activate it. The overview of our proposed architecture is shown in Figure \ref{fig:framework}.

\subsection{Snippet Generator}
ECG signal is a special multivariate time series with periodic characteristics as it reflects physiology activities of the heart. To well analyze such kind of data, we propose the snippet generator, which splits the original time series into individual sub-series segments according to the periodicity. In detail for ECG data, the snippet can be generated as a heartbeat-based sub-time series, meaning that each snippet represents a single heartbeat. The input of the snippet generator is a time series sample $X^{(i)}$, and the output is the set of snippets $B^{(i)}$. $B^{(i)} = \{b_{1}^{i},b_{2}^{i},...,b_{T}^{i}\}$, where $b_{t}^{i}$ is a single snippet, $t$ means the index and $T$ is the number of snippet by $X^{(i)}$. By inheriting from $X^{(i)}$, each $b^{(i)} \in B^{(i)}$ shares the same label $y^{(i)}$. In this paper, our focus is not placed on heartbeat detection, but instead have adopted the existing method proposed in \mbox{\cite{christov2004real}}.  With reference to the QRS-detection method in \cite{christov2004real}, the heartbeats of each ECG signal are extracted and then sorted chronically.

\subsection{Backbone Network}

Backbone network aims to model the local spatial dependency of each snippet and the global temporal dependency across snippets to generate valuable hidden representations. A multi-layer convolutional network is adopted in the backbone network to learn the correlations between each variate of the input snippets and then capture the hidden spatial state. As the different scales of the ECG problem arise, the convolutional network's depth and design would be varied based on the specific target. It is worthy to notice that our proposed model was designed to be compatible with various convolutional neural networks. Moreover, to propagate the information across different snippets, a recurrent network, LSTM, is designed to leverage the hidden spatial state generated by the convolutional network and generate the spatial-temporal hidden state.

\subsubsection{Convolutional Neural Networks Block}
The CNN block is designed to learn inner-snippet spatial dependency and output the snippet spatial hidden state $S_t$. Mainly, this block consists of convolution layers and pooling layers, which are responsible for extracting effectively hidden states from the snippet of ECG signals automatically. The convolution layer can be regarded as a fuzzy filter that captures the local spatial dependency of each snippet of the input ECG signals and reduces noises. The pooling layer aims to reduce the feature maps' dimensions and summarize critical features generated by a convolution layer. The specific network structure and parameters are detailed in Section 4. In short, the input snippet $b_{t}$ from an ECG signal $X$ is feed into the CNN block and then converted into a low-dimensional hidden state $S_{t}$. The output of the CNN block can be represented as follows:

\begin{equation}
    S_{t} = CNN(b_{t})
\end{equation}

\subsubsection{Long short-term Memory Block}
Following the CNN block, a recurrent network extended with long-short term memory (LSTM)\mbox{\cite{hochreiter1997long}} cells propagates the snippet information, mapping spatial hidden states generated at each snippet to a hidden vector presentation that stands for temporal state information. The recurrent network learns to encode each hidden spatial state of a snippet to hidden temporal representations as a state vector. Hidden state vector $H_t$ is computed by combining the current snippet spatial hidden state $S_t$ and the previous hidden state $H_{(t-1)}$, hence the recurrent nature of the model. The LSTM block passes the information of $H_{(t-1)}$ and records the information of $S_t$. Therefore, $H_t$ is the spatial-temporal hidden vector state that comprehensively combines the inner-snippet spatial dependency and inter-snippet temporal dependency. The spatial-temporal hidden state $H_t$ is then passed on to the next module, called the controlling agent, to manipulate the output decision and the discriminator to output the prediction result. Specifically, the hidden state vector $H_t$ can be obtained as follows:

\begin{equation}
    H_{t} = LSTM (S_{t}, H_{t-1})
\end{equation}

\begin{flushleft}
where $S_t$ is the hidden spatial state produced by the convolutional neural networks block, $H_{t-1}$ denotes the cell's output and state at time $t-1$ inside the LSTM cell, respectively.
\end{flushleft}

\subsection{Controlling Agent}
The controlling agent is the key component that controls the workflow, deciding whether the backbone network keeps on working or activates the discriminator to generate a prediction result. To achieve this goal, we adopt reinforcement learning techniques\mbox{\cite{10.5555/3009657.3009806}}, solving a Partially-Observable Markov Decision Process (POMDP)\mbox{\cite{Spaan2012}}. Each snippet's spatial-temporal hidden state of an input ECG signal can be seen as an individual state; each state's action is chosen by using the well-learned policy, and a reward is obtained according to the quality of the selected action. The controlling agent is trained by gradient-based policy learning in which the objective is to optimize long-term rewards according to the performance of the discriminator. Critical components for a given reinforcement learning framework are detailed below.

\subsubsection{State}

In reinforcement learning, states are the representations of the task's current environment, which describes the current situation. In our case, the agent's state is the set of currently perceived snippets, $O_1,O_2,…,O_t$, essentially the outputs of the backbone network. Here, the spatial-temporal hidden state $H_t$ of $O_t$ that encodes spatial and temporal information is used as an observation. It is the core information that decides the selection of an action by the learned policy.

\subsubsection{Policy}
The policy is the strategy that the controlling agent employs to determine the next action based on the current state. A good way to reduce the number of states is by using a neural network-based policy\mbox{\cite{1416876}}, where the inputs are states and outputs are actions. Here, the policy selects an action by the current state $H_t$,  $a_t = \pi_{\theta}( H_t ) $. As introduced in the previous section, $H_t$ is the low-dimensional hidden state encoded inner and inter-dependency for representing an ECG-signal snippet. In this work, we adopt a fully-connected neural network to approximate this policy function. The policy here is a function for mapping the current state $H_t$ to the parameterized distribution of the set of actions.

\subsubsection{Action}
Action is what the controlling agent can do in each state. Actions control the whole process between the backbone network and discriminator as following situations arise: if $a_t=0$, the agent moves forward to the next observation, and keeps inputting the snippet into the backbone network, then acquires the spatial-temporal hidden state of the corresponding snippet. On the other hand, if $a_t=1$, the agent selects to interrupt the backbone network and then informs the discriminator to predict a label by feeding the hidden vector state $H_t$. Then the approximated probability $\pi_{t}$ fits a Bernoulli distribution, for sampling an action according to $P(p= 1) = \pi_{t}$. Once, based on the output from Bernoulli distribution, when the agent selects interrupting the backbone network or the observation series is finished $(t=T)$, $t$ is considered to be the interrupting time point $\tau$.

\subsubsection{Reward}
The reward is a measure to quantify the parameters of the current policy. To boost collaboration between the agent and the discriminator, it must observe the discriminator's returns and measure the discriminator's degree of success. Thus, when the discriminator gives a correct label, the current policy's reward is $r_t=1$; otherwise, the reward is called punishment, denoted as $r_t=-1$. In this paper, we consider long-term rewards in the proposed SPN. For example, if the controlling agent stops at a time point $(t=5)$, and it outputs a correct answer, the reward will be $5$. In this case, if the controlling agent produces an incorrect answer, the reward will be $-5$. Overall, the objective of this agent is to maximize the total reward.

\subsection{Discriminator}
The discriminator is the final module of our proposed framework, which aims to predict a label $\hat{y}$ of a given time series by mapping the hidden state $H_t$ into a lower-dimensional space using a fully-connected network. The resulting low dimensional vector is then normalized to label probabilities via the softmax function. The equation is shown below:

\begin{equation}
    \hat{y} = \mathop{\arg\max}_{i} P(Y=i | H_t)
\end{equation}

\section{Experimental Evaluation}
In this section, we use a public real-world 12-lead ECG dataset to conduct a series of experiments to evaluate the proposed model's performance.

\subsection{Experiment Setting}
\subsubsection{Environment}
The proposed model and the state-of-the-art methods are trained and tested on a server with a Xeon Gold CPU, 128GB memory, and a GPU card, Nvidia V100. This server runs an Ubuntu 18.04 system, and the models are implemented based on the PyTorch\cite{paszke2017automatic} 0.4.1.

\subsubsection{Data Source}
The ECG data used in this study was from the 1st China Physiological Signal Challenge \cite{liu2018open}. This dataset contains 6877 12-lead ECG records from 6 s to 60 s. A total of 8 types of arrhythmia (i.e., AF, I-AVB, LBBB, RBBB, PAC, PVC, STD, and STE) and normal sinus rhythms were to be classified in these records. These records were collected from 11 hospitals and sampled at 500 Hz. We run 10-fold cross-validation on this dataset for comparing the methods.

\subsubsection{Performance Metrics}
In this research, typical classification metrics, including accuracy, earliness, precision, recall, and F1-score, are used for comparing our proposed model with the state-of-the-art methods. In addition, the harmonic mean is conducted to comprehensively evaluate the performance of the competing methods in balancing accuracy and earliness.

\textbf{Accuracy:} It is a performance evaluation measure that is the proportion of correct predictions and total predictions, formally defined as:

\begin{equation}
Accuracy = \frac{1}{m}\sum_{i=1}^{m}{(y_{i}==\hat{y}_{i})}
\end{equation}

\begin{flushleft}
where $m$ is the number of total sample instances in the testing set; $y_{i} $ and $ \hat{y}_{i} $ are the ground truth class and the predicted class for the $i^{th}$ sample, respectively. The accuracy of a model is calculated as the percentage of correct predictions of the test samples. Moreover,
\end{flushleft}

\begin{equation}
Earliness = \frac{1}{m}\sum_{i=1}^{m}{ \frac{s}{L}}
\end{equation}

\begin{flushleft}
where $s$ is the time point at prediction for a single time series made by a given eTSC model and $L$ is the time series's length.
\end{flushleft}

As noted in the introduction, eTSC thus has two naturally contradictory optimization goals. Hence, eTSC can be evaluated in different directions, comparing accuracies by keeping earliness constant or comparing earliness by keeping accuracy constant. To evaluate two goals simultaneously, a popular choice that appeared in the previous research \mbox{\cite{schafer2019teaser}} is the harmonic mean of earliness and accuracy. Harmonic mean (HM) is a metric for measuring the bi-objective optimization problems. In our case, we measure earliness and accuracy comprehensively as defined below.

\begin{equation}
    HM = \frac{ 2 \times (1-Earliness) \times Accuracy}{(1-Earliness)+Accuracy}
\end{equation}

Moreover, the average precision, recall rate, and F1-score are adopted for measuring the multi-classification performances. The details are shown as follows:

\begin{equation}
    Precision = \frac{TP}{TP+FP}
\end{equation}

\begin{equation}
    Recall = \frac{TP}{TP+FN}
\end{equation}

\begin{equation}
    F1 = \frac{2 \times (Precision \times Recall) }{Precision + Recall}
\end{equation}

For a specific class in the multi-classification problems, TP (true positive) indicates the number of correctly classified samples, FN (false negative) is the number of samples that are misclassified into other classes, and FP (false positive) refers to the number of samples with the other class that misclassified in this class.

\subsubsection{Competing Methods}
We compare our model with the following state-of-the-art approaches. To experiment with these methods, we either use the authors' public codebase or follow their original paper to implement it to the best of our knowledge.

\begin{itemize}
    \item SR2-CF2 \cite{mori2017early}: This is a feature-based model for early time series classification, in which the features are generated according to a given distance function. Based on the gene algorithm, it outputs a confident classification time point.
    \item EARLIEST \cite{10.1145/3292500.3330974}:  This is a reinforcement learning-based approach. It outputs the classification result by a well-trained policy network.
    \item TEASER \cite{schafer2019teaser}: Based on a series of sub-classifiers for each time point, TEASER is proposed to deal with varied-length time series early classification problem.
    \item MDDNN \cite{huang2018multivariate}: This is a deep learning-based model that combines CNN and LSTM to solve the early time series classification problem.
    \item ETEeTSC \cite{russwurm2019end}: This is another deep learning-based early time series classification model. Based on a new loss function, this model can optimize accuracy and earliness simultaneously.
\end{itemize}

Note that we do not compare our methods with the classical works, such as ECTS \cite{xing2012early} and EDSC \cite{xing2011extracting}. The reason is that these methods appear to be out-of-date, and they were outperformed by the above models, SR2-CF2, TEASER, and ETEeTSC.

\subsubsection{Parameters Setting}
In the backbone convolutional neural networks of the proposed framework, 13 convolutional layers divided into five blocks are used for learning snippet spatial dependency. For each convolutional layer, a batch normalization layer (Batch-norm) \mbox{\cite{ioffe2015batch}} and a rectified linear unit (ReLU) function \mbox{\cite{nair2010rectified}} are adopted. The kernel size of 3, boundary padding of 1, and stride of 1 are set for all convolutional layers. Moreover, a pooling layer with kernel size 3 and stride size 3 controls the output size for each block. Therefore, the input length was maintained during convolutions and only adjusted by pooling layers for each block. A single LSTM layer with 256 cells is used for learning snippet temporal dependency in the long short-term memory block. In the training phase, we use the Adam optimizer \mbox{\cite{kingma2014adam}} training in each mini-batch and update the parameters. The learning rate is set as $10^{-3}$ and divided by five at every 20 epochs, eventually terminated at 100 epochs. All the training data is divided into mini-batches for network training, and the mini-batch size is set as 32.

\begin{table*}[!ht]

\caption{Performance Comparison on ICBEB dataset.}
\label{tab:icbeb}
\centering
\renewcommand{\arraystretch}{1.35}
\begin{tabular}{|c|c|c|c|c|c|c|} \hline \hline
& Accuracy & Earliness & Precision & Recall & F1-score & Harmonic Mean \\
\hline \hline

SR2-CF2 & 0.167 $\pm$ 0.009 & 0.228 $\pm$ 0.002 & 0.579 $\pm$ 0.110 & 0.160 $\pm$ 0.009 & 0.102 $\pm$ 0.011 & 0.274 $\pm$ 0.013 \\ \hline

EARLIEST & 0.283 $\pm$ 0.009 & \textbf{0.001 $\pm$ 0.001} & 0.176 $\pm$ 0.050 & 0.151 $\pm$ 0.011 & 0.115 $\pm$ 0.013 & 0.441 $\pm$ 0.010 \\ \hline

TEASER   & 0.456 $\pm$ 0.020 & 0.549 $\pm$ 0.009 & 0.409 $\pm$ 0.032 & 0.366 $\pm$ 0.015 & 0.368 $\pm$ 0.016 & 0.453 $\pm$ 0.012 \\ \hline

MDDNN    & 0.585 $\pm$ 0.015 & 0.455 $\pm$ 0.005 & 0.522 $\pm$ 0.016 & 0.511 $\pm$ 0.015 & 0.511 $\pm$ 0.015 & 0.564 $\pm$ 0.007 \\ \hline

ETEeTSC  & 0.735 $\pm$ 0.028 & 0.416 $\pm$ 0.037 & 0.704 $\pm$ 0.037 & 0.686 $\pm$ 0.033 & 0.687 $\pm$ 0.038 & 0.649 $\pm$ 0.022 \\ \hline

SPN & 0.796 $\pm$ 0.013 & 0.387 $\pm$ 0.011 & 0.769 $\pm$ 0.017 & 0.748 $\pm$ 0.016 & 0.751 $\pm$ 0.015 & 0.694 $\pm$ 0.008\\
\hline \hline

\end{tabular}%
\end{table*}

\subsection{Experiment Results}
\subsubsection{Performance Comparison} \label{sec:performance}
Table \mbox{\ref{tab:icbeb}} shows the CVDs classification performance of the proposed method and competing models for the 12-lead ECG dataset.  As shown in Table \mbox{\ref{tab:icbeb}}, our proposed model achieves the CVDs classification task's best performance because it can simultaneously learn the inner-spatial and inter-temporal dependencies. Considering the feature-based models, SR2-CF2 and TEASER, the results demonstrate that these approaches cannot solve ECG early classification satisfactorily. The reason is that these methods are designed and evaluated in the short length time-series datasets, in which they cannot acquire a good result while handling long time series. On the other hand, deep-learning-based methods, such as MDDNN and ETEeTSC, show acceptable accuracy for the classification task, but these methods' earliness metric does not look good. EARLIEST is the first reinforcement learning-based model for early time series classification. It shows good earliness performance, but the poor classification performance largely due to its premature decision because of its overly simplified architecture. Overall, our proposed method presents the best result. It is worthy to note that around $7\%$ classification performance improvement is gained by our proposed method compared to the state-of-the-art methods in terms of precision, recall, accuracy, and F1-score.

As discussed in the related work section, since SR2-CF2 is a feature-based model, the features for its classifier are extracted based on a given distance function. In general, it cannot work well on the long and varied length time series classification. The same problem also appears in the experiments of the EARLIEST and TEASER. These models solve the trade-off between accuracy and earliness in the early time classification problem, but the design of these approaches does not consider the long-length time series data. For processing long time series, the weight parameters between accuracy and earliness of these models are inappropriately set due to the penalty of time being much larger than the penalty of accuracy. For MDDNN and ETEeTSC, their performances are better than others as they exploit deep neural architecture, while ETEeTSC has a unique ability to output the prediction time point automatically. In contrast, SPN solves the ECG early classification problem with long-length and varied-length time-series properties well, and the result is remarkable, meeting all real-world application criteria.

\section{Conclusion}
This paper has addressed the problem of cardiovascular disease early classification based on varied-length multi-lead ECG. It is a critical real-world application, and this problem has not been well studied in the research community. For solving this problem, a novel deep reinforcement learning framework, Snippet Policy Network, was proposed consisting of four modules, snippet generator, backbone network, controlling agent, and discriminator. The backbone network is proposed to learn the inner-snippet spatial correlations and inter-snippet temporal correlations by combining convolutional and recurrent network architectures. A controlling agent with reinforcement architecture is proposed to solve the earliness and accuracy conflicting goals of bi-objective optimization. This agent allows the model to classify the CVDs accurately and search for the earlier prediction time point. The discriminator is proposed to make a classification result by mapping the features generated by the backbone network. Through a series of experiments, the results demonstrate that our proposed model outperforms the state-of-the-art methods at least $7\%$ in terms of precision, recall, accuracy, F1-score, and harmonic mean. Moreover, our model achieves high accuracy result of more than $80\%$ for multiple disease classifications, filling the gap between the research community and medical practices. All in all, Snippet Policy Network presents a valid solution to the problem of cardiovascular disease early classification based on varied-length multi-lead ECG.

In our future work, we aim to improve the agent mechanism and explore the Snippet Policy Network's data interpretability. We believe that data interpretability may help the medical professional better understand the principle of CVDs classification.

\ifCLASSOPTIONcaptionsoff
  \newpage
\fi

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
<?php
namespace Craft;

abstract class ElementType extends BaseEnum
{
	// Constants
	// =========================================================================

	const Asset       = 'Asset';
	const Category    = 'Category';
	const Entry       = 'Entry';
	const GlobalSet   = 'GlobalSet';
	const MatrixBlock = 'MatrixBlock';
	const Tag         = 'Tag';
	const User        = 'User';
}

redpajama_set_name: RedPajamaGithub