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The University's monthly email newsletter strives to communicate to important external audiences the exciting University-related events, programs, awards, recognitions and other items of interest happening on campus and in our creative community.
All programs Animation (BFA) Art BFA (interdisciplinary) Art Education (BFA & MAT) Art Education (MAT) Book Arts & Printmaking (MFA) Creative Writing (BFA) Dance (BFA) Dance (MFA) Educational Program Design (Online MEd) Educational Technology (Online MEd) Expanded Drawing & Print Media (BFA in Fine Arts) Film & Animation (BFA) Film (BFA) Film Design (BFA) Fine Arts (BFA) Game Art (BFA) Graphic Design (BFA) Illustration (BFA) Interaction Design (BFA) Jazz Studies (MM) Museum Exhibition Planning & Design (MFA) Museum Studies (MA) Music - Composition (BM) Music - Instrumental Performance (BM) Music - Vocal Performance (BM) Music Business, Entrepreneurship & Technology (BS) Music Education (BM & MAT) Music Education (MAT) Music Education (MM) Painting (BFA in Fine Arts) Photography (BFA) Product Design (BFA) Product Design (MDes) Screenwriting (BFA) Sculpture (BFA in Fine Arts) Theater - Acting (BFA) Theater - Devised Performance (MFA & Certificate) Theater - Directing, Playwriting & Production (BFA) Theater - Musical Theater (BFA) Theater Design & Technology (BFA)
Reception for Wilmer Wilson IV: To lessen the demand of visibility
Friday, June 3, 5-7 pm
Join us for a closing reception for Wilmer Wilson IV: To lessen the demands of visibility, which is on view at the Rosenwald-Wolf Gallery.
The reception is free and open to the public. No registration is required.
Questlove, Aaron Dessner to receive honorary degrees at Commencement
When University of the Arts celebrates the accomplishments of the Class of 2022 during Commencement on May 19, it will also recognize two influential and multitalented artists with honorary degrees: Ahmir "Questlove" Thompson and Aaron Dessner.
Inaugural PhD in Creativity Fellow Announced
James Brandon Lewis, a critically acclaimed composer, saxophonist and writer, has been named the inaugural recipient of The Balvenie Fellowship in University of the Arts' PhD in Creativity program.
Through His Films, Alum Explores the History of Retro Gaming
As a true child of the 1980s, Zach Weddington BFA '99 (Film and Video Production) feels most at home in an arcade, immersed in the chittering of pinball machines and seemingly endless stacks of quarters and tokens.
University of the Arts Announces Inspiration Lab Artist-in-Residence Program
Beginning in summer 2022, University of the Arts will welcome 10 artists-in-residence from within the university community and around the world, as they accelerate and cultivate contributions to contemporary knowledge production.
UArts Awards Inaugural Round of Creative Research & Innovation Grants
University of the Arts has announced the recipients of the inaugural round of the Grants for Creative Research & Innovation program, providing funding to 11 innovative projects led by UArts faculty and staff.
Alum's Cinematography Draws Praise
After years of touring the world as the guitarist for the Philadelphia punk band R.A.M.B.O., Andrew Wheeler BFA '01 (Film) has traded dim, sweaty venues for the much brighter lights of Hollywood.
Lightbox Film Center Premieres New Cut of Wayne Wang's 'Life is Cheap… But Toilet Paper is Expensive'
On Friday, March 4, 2022, Lightbox Film Center at University of the Arts will present the U.S. premiere of a never-before-seen director's cut of Wayne Wang's 1989 film Life is Cheap… But Toilet Paper is Expensive.
Remembering William Daley: 1925-2022
University of the Arts was deeply saddened to learn of ceramic artist William Daley's passing on Jan. 16 at the age of 96. For nearly 40 years, Daley was an important and influential figure at UArts, where his studio practice intertwined with his life as an arts educator.
Two Alumni Included in 2022 Whitney Biennial
Two UArts alumni, Alex Da Corte BFA '04 (Printmaking) and Dave McKenzie BFA '00 (Printmaking), are among the 63 artists named to the Whitney Museum of American Art's 2022 Biennial. This year's exhibition, on view April 6 to Sept. 5, will be the 80th iteration of the series originally begun by the museum's founder, Gertrude Vanderbilt Whitney, in 1932.
Dance Alum Realizes Jasper Johns Costumes for Art Museum Performances
As the Philadelphia Museum of Art winds down its landmark retrospective Jasper Johns: Mind/Mirror, a series of Philadelphia Dance Company performances ran from Jan. 14 to 16, 2022, that underscored the artist's deep and abiding artistic connections to dance.
UArts Receives First It's On Us PA Grant
University of the Arts is one of 34 Pennsylvania institutions of higher education to receive an It's On Us PA grant, Gov. Tom Wolf announced last week.
Graphic Design Students Explore New Territory
This fall, Graphic Design students at UArts experienced a new evolution in the School of Design's curriculum. In Design for Interaction, a class co-taught by Graphic Design Program Director Angela Riechers and designer Ksenya Samarskaya, the students engaged with immersive media through a playful, experimental approach, a critical element of the school's new pedagogical strategy.
Alum Shapes User Experience for Comcast's First Smart TV
As televisions get smarter and more complicated, Martin Highley BFA '07 (Graphic Design) hopes Comcast's XClass television—the cable TV and technology company's first foray into consumer TVs—simplifies things. Highley, an associate creative director at Comcast, led the team that developed the project's user-experience platform.
Alum's New Book Seeks to Make Museums More Accessible to Children
Growing up in Philadelphia, Nhi Nguyen MA '19 (Museum Education) was surrounded by world-class museums and cultural attractions but never set foot in one until she was 16.


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County to demolish former jail
By Jessica Dyer / Journal Staff Writer
Published: Tuesday, January 12th, 2021 at 8:00pm
Updated: Tuesday, January 12th, 2021 at 11:37pm
The old county jail, known as the Regional Correctional Center when it held federal inmates, has been vacant for years. (Jim Thompson/Albuquerque Journal)
Bernalillo County is ready to raze the old Downtown jail.
The county commission on Tuesday night approved a $2.4 million contract with Guzman Construction Solutions to demolish the former lockup near Fourth Street and Roma NW. The county will use the property for a 149-space parking lot to accommodate county vehicles.
The teardown should begin in about a month and will involve a "controlled, non-explosive" process that will take about 10 months to complete, according to a county news release.
The building – called the Regional Correctional Center – had not been used as a jail for years. The county in 2003 moved its inmates to a West Side facility. A private company subsequently leased the Downtown property to house federal inmates, but that ended in 2011.
Rehabilitation of the building would have been costly; one analysis determined it would need about $36 million in renovations to restore it to a usable jail.
In the meantime, the county was spending about $60,000 annually on the building's utilities and insurance, according to prior Journal reporting.
"Following its use as a correctional center, the old jail had a good run at producing some revenues in other capacities," Bernalillo County Manager Julie Morgas Baca said in a statement. "But the revenue is not enough to sustain the building that has become dilapidated and in ill repair."
The county had considered demolition for some time, even including the cost in last fiscal year's budget.
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On Saturday, the Prime Minister, who is on a two-day visit to the state capital, attended the concluding session of 'Transforming Urban Landscape' convention.
Praising Yogi Adityanath, PM Modi said the chief minister has focussed on overall development.
Speaking at the launch of various development projects in Lucknow, Narendra Modi said, a total of 81 projects will aid government's smart city mission.
Modi thanked the farming community for giving up their land and aiding the land acquisition process for various development projects costing over Rs 60,000 crore.
Hailing the Uttar Pradesh government, he said,"The manner in which development works have been carried out in such a short span of time & old methods have been changed, was never seen before in Uttar Pradesh."
Praising Yogi Adityanath, he said the chief minister has focussed on overall development. Yogi Adityanath has turned intent into investment, said Modi.
Modi stated that that India is working tirelessly towards clean and green energy. Talking about the coal sector, he said coal was a subject of shame earlier, but now, it is yielded huge results and oversaw huge transformation.
Recognising the role of the industrial class, Modi said they play an important role in nation's overall development. Hitting out at the Congress, he said the 'Grand Old Party' used the industrialists to further their own cause. He went on to say "I don't fear standing with corporates as I have right intentions."
Around 80 leading industrialists, including Kumar Mangalam Birla, chairman of Aditya Birla group, Gautam Adani, chairman of Adani group, Subhash Chandra, chairman of Essel group and Sanjeev Puri, MD, ITC, besides top magnates of other major business houses participated in the event, which comes close on the heels of 'Investors Summit' in February this year.
He also laid the foundation stones for various projects worth 60,000 crore rupees in Lucknow.
These infrastructure projects comes on eve of third anniversary of three key government initiatives related to Urban Development - Pradhan Mantri Awas Yojana, PMAY (Urban), Atal Mission for Rejuvenation of Urban Transfornation, AMRUT and the Smart Cities Mission.
Narendra Modi calls Mamata Banerjee "sticker didi"


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#herecomestheflood
Dikes but also dunes are an important part of our fight against water. But these two alone will not protect us from floods. That is why the Delta Plan in the Netherlands and the Sigmaplan in Belgium are in effect. Both plans contain measures to make the river and sea environment more climate-proof. If they did not, the Geopark map would look very different in time.
Deltaplan
During the 1953 flood disaster, the need for a planned approach to flood prevention became apparent. Twenty days after the flood disaster, the Delta Commission was established and came up with a number of recommendations. From these recommendations, the most visible project in Zeeland was the Oosterschelde barrier. This barrier closed the Oosterschelde with sliding doors that could only be closed during storms and high water levels.
Sigmaplan
Awareness in Flanders for a systematic plan arose from a 1976 flooding of (among others) Ruisbroek. The Sigma Plan is deployed to make more than 260 kilometers of river land climate-proof until 2100. In addition, the Sigma Plan focuses on achieving European nature goals by, for example, giving room for the river in a designated nature reserve.
Hoping to raise awareness, but also change behavior, Rem van den Bosch has found a creative way to visualize the bleak reality. At numerous locations in the Geopark, a model indicates how high the water would rise without the measures that protect us from it. The models wear clothing that refers to the Zeeland costume, the part of Geopark Schelde Delta most vulnerable to the sea.
More information about this project can be found here.


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The Last O.G. (season 4)
An ex-con is shocked to see just how much the world has changed when he is released from prison for good behavior after a 15-year stint and returns to his newly gentrified Brooklyn neighborhood. (more…)
A comedy set in the offices of Heaven Inc. When God plans to destroy the Earth, two low-level angels must convince their boss to save humanity. They bet him they can pull off their most impossible miracle yet: help two humans fall in… (more…)
Final Space (season 3)
Final Space is a TV series starring Fred Armisen, Tom Kenny, and Olan Rogers. An astronaut named Gary and his planet-destroying sidekick called Mooncake embark on serialized journeys through space in order to unlock the mystery of… (more…)
Chad (season 1)
Chad is a TV series starring Nasim Pedrad, Jake Ryan, and Saba Homayoon. A 14-year-old Persian boy desperately tries to fit in, cope with his mother's dating life, and figure out his cultural identity. (more…)
Search Party (season 4)
Search Party is a TV series starring Alia Shawkat, John Reynolds, and John Early. Search Party is a dark comedy about four self-absorbed twenty-somethings who become entangled in an ominous mystery when a former college acquaintance… (more…)
According to the plot of a black detective comedy, a group of friends decides to find their former classmate, who disappeared under strange circumstances. Over time, the team of self-proclaimed detectives goes deeper and deeper into the mystery. Their investigation leads to consequences that test the company's friendship for strength. In addition to the detective line, the series explores issues of friendship and relationships, depression and the search for identity. (more…)
Fifteen years ago, petty drug dealer Tray was arrested and convicted. Now he goes free and does not recognize his native Brooklyn, occupied by white and Asian hipsters, as well as black gays. His ex-girlfriend and the love of his life over the past years married a white guy and headed a charity fund. She no longer wants to have anything in common with Trey, but he cannot let her go, because she is raising two of his children who were born shortly after Tray was in prison.
This is a story about how two lower angels are trying to prove to God that the Earth and the people living on this planet are useful. They definitely do not need to be destroyed, because there is something in the world that is worth treasuring and for which it is worth continuing to live. It's about love. In the Heaven Inc., however, they believe that this is not enough to allow people to continue to live. True, God gives a chance to his angels, offering those to do everything possible so that two completely different people will finally fall in love. This task is extremely difficult, but the main characters will try to implement a complex plan. (more…)
The bad-loafer Gary accidentally destroys several ships of the space fleet, for which he is sentenced to five years in prison on a prison ship. Dying from boredom and dreaming about cookies, Gary repairs satellites. And once the hero of the series "Final Space" gets a cute green creature, which becomes his friend. But this creature is hunted by a powerful Lord Commander. But Gary does not give up, and therefore gets involved in a dangerous. adventure.
The Detour (season 4)
This is a comedy series that tells how a married couple with two children decides to make a long trip from Syracuse to Florida by car. On the way, they will have to spend 17 hours, but the head of the family assures the spouse that they will save a lot of money this way. At the very beginning of the journey, all the characters of the series "The Detour" were full of optimism and anticipated an unforgettable journey, but in the end it turned into a real nightmare. (more…)


For Those Who Recently Adopted Sourcing, Start Thinking About Next Generation Sourcing Now!
SI has been about next generation sourcing since the day it began. No matter how good you think you have it now, it's not good enough. Why? Most of you are still on last decade's sourcing platforms which, especially if you never had anything like them before, is a great start (and maybe beyond your wildest dreams if you were in e-mail and spreadsheet world), but not good enough. What you are going to find out, as SI told the Procurement leaders seven years ago today in its post on Next Generation Sourcing, all good things come to an end.
Once the allocation has been optimized across the supply base in a way that minimizes unit costs, transportation costs, (interim) storage costs, etc., re-running the optimization won't lower costs further unless something changes — such as the identification of a new supplier, an alternate material (that is cheaper), additional demand (that increases the economy of scale), or a more powerful optimization model is provided.
Once contract management and monitoring is put in place and no invoices are paid that are not for delivered, defect-free products, at contracted rates, there is no little on-contract leakage to be stopped.
Once controls are put in place to stop off-contract purchases that should be on-contract (through integration of the e-Procurement system with the Contract Management system), there is no little off-contract leakage to be stopped.
Once a SIM with a powerful supplier portal and information / (compliance) documentation monitoring and alerting system is put in place, there is no additional time savings from information maintenance offloading.
Once a SPM which automatically collects organizational data and metrics is put in place, there is no additional time savings from automating supplier scorecard production.
Once a SRM with proper corrective action requests / corrective action monitoring and integration system is put in place, there is no additional time savings from quick-and-easy semi-automated resolutions.
Once an audit recovery system is put in place that not only 3-way matches invoices but identifies when rebate or discount targets are hit and automatically applies the discounts to current and future invoices, there is no more savings from high-priced audit recovery services.
Once integrated contract negotiation and e-Signature is implemented, there is no more process time savings from being able to track all updates by both parties and do sign-offs quickly.
At some point, your year-over-year returns will start to trail off … somewhere between the three and five year mark, depending on how much spend you are able to put through managed sourcing events every year and how much you are able to use the system to support it. So don't stand still. Start identifying your biggest weaknesses and looking for the next generation system to address them when the opportunity costs of not taking advantage of the opportunities you are missing gets too high.
So where do you start? Stay tuned.
This entry was posted in Procurement Innovation, Sourcing Innovation on February 5, 2018 by thedoctor.


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What is Performance Engineering?
Performance engineering is proactive, continuous, and end-to-end application performance testing and monitoring. It allows seamless collaboration between teams, tools, and processes through continuous feedback loops. Here, it's not just testers who are responsible for quality assurance but developers, performance engineers, product owners, and business analysts as well.
"Performance engineering is a cultural shift in the way organizations view their essential processes. Performance engineering embraces practices and capabilities that build quality and performance throughout an organization." - Joe Colantonio of Test Guild Podcast: Six Top Trends for Performance in 2020 and Beyond
By leveraging right-sized tools spanning developers to performance engineers, Performance Engineering enables shift-left performance testing and shift-right application performance monitoring. It's difficult to appreciate just how much of a departure performance engineering is from traditional performance testing without understanding classic performance testing is.
What is the Difference Between Performance Testing and Performance Engineering?
Classic performance testing is effectively a subset of performance engineering. It usually entails running a single round of load testing as part of the post-development quality assurance (QA) cycle. Performance testing involves checking the speed, reliability, scalability, stability, response time, and resource use of an application under the anticipated workload. Before we get into the differences between performance engineering and performance testing, we'll first take a look at performance testing in isolation and why it's, by itself, no longer sustainable.
First, the testing is viewed in isolation and treated as an afterthought that only begins at the end of functional testing.
Second, working in silos causes large communication gaps between project sub-teams and prevents the collaboration required to deliver a high quality product.
Third, by the time performance testing kicks in, the organization has already sunk substantial time, effort, and finances into the application's design, development, and promotion.
Fourth, performance testing is often treated as an afterthought and isn't included in the 'done' criteria preceding release. So at this juncture, the business needs the app in production urgently and expects no delays. In this context, QA's feedback happens too late to fix completely before the release. Inevitably, a high number of performance problems would unnecessarily make it to the production environment just so the release remained within schedule. Fixing a defect in production is much more expensive and disruptive than doing so early in development.
Fifth, traditional performance testing may have been perfect for the Waterfall model but is out of place in today's DevOps-centric world. DevOps reduces the failure rate of new releases by shrinking the time between when a change is committed to the system and when the change is placed in production. Continuous integration and continuous delivery (CI/CD) makes sure the software is always in a releasable state throughout its lifecycle. DevOps also focuses on realigning the organization to support end-to-end collaboration between stakeholders, functions and tools. To meet the rapid delivery demands of DevOps, software development needs a more evolved performance testing approach. That new approach is software performance engineering.
Now, let's delve into the key differences between performance engineering vs performance testing.
First, performance testing is a quality check of the application's load handling and responsiveness. It establishes how well the system will bear a production load and anticipates the issues that could come up during heavy load conditions. Performance engineering seeks to design the application from the start with performance metrics in mind and facilitate the discovery of issues early in development.
Second, performance testing is a QA process that usually takes place when a round of software development is complete. Performance engineering is a continuous process that is embedded in all phases of the software development cycle – from design, to development, and into the end-user experience.
Third, performance testing is conducted by the QA team while performance engineering involves RND and QA.
Performance Engineering Concepts
Through the following concepts, DevOps and performance engineering deliver consistent production performance results, allowing customers to deploy applications efficiently with more confidence and to roll out high performing, stable software that fulfil user expectations.
End-to-End Optimization
Performance engineering delivers end-to-end system optimization through a continuous testing and monitoring process. This shifts-left performance and load testing into the development process. That's unlike traditional performance testing where the testing took place after functional testing was stabilized and the code released.
Once the code is released, performance engineering tracks the app in production by tapping into application performance monitoring (APM) tools.
Cross-Functional Team of Performance Stakeholders
Performance engineering enables collaboration among project stakeholders – from business analysts to developers. Maintaining high performance levels that enhance the customer experience, keeping up with the pace of business, and managing end-to-end performance makes everyone, not just QA/Performance Engineers, a steward of product performance. Here's how.
Testing Center of Excellence
The testing Center of Excellence (CoE) serves as the trusted testing advisor and the custodian of best practices. The CoE supports different business units, different testing methods (such as DevOps and Agile), and has the flexibility to recommend performance testing and testing tools as needed. To build better testing models and improve test quality, the CoE acts as the single point of consolidating and reusing test data that has been generated and collected across multiple business units over time.
Performance Engineer
The performance engineer provides a holistic view of all code in development to ensure performance testing criteria is comprehensive, encompasses the bigger picture, and factors all distinct pieces of code in development. The performance engineer is the primary user of performance testing tools and has a high degree of expertise in scripting, designing, running, and analyzing test results. Performance engineering brings the performance engineer to the early phase of development where they can provide performance metrics and scenarios required for code to be considered ready for release. Early involvement means the performance engineer can ensure the solution satisfies the performance expectations set out at the beginning of development. They also confirm that the architecture and design is consistent throughout development.
Developers are experts at coding but often light in both functional and performance testing. They work in their integrated development environment (IDE) and tend to use their preferred tools with little inclination toward learning new ones. Performance engineering shifts-left performance testing which brings it into the software developer's area of responsibility. With input from the performance engineer, software developers can run performance testing while writing code. The developer doesn't release their code before it passes performance testing criteria.
DevTester
The devtester doesn't exist in classic performance testing since there are clear distinctions between software developers and performance engineers. With performance engineering, the devtester emerges as a stakeholder that connects performance engineering and developer teams. They bridge the gap by having solid coding and testing skills though not quite at the same level of expertise as the developer and the performance engineer. They can run tests quickly and have much greater flexibility than developers in using different tools as is needed.
Business Analysts and Application Engineers
By shifting-right the testing, performance engineering brings on board business analysts and application engineers. This guarantees that business and application performance requirements defining the quality of the user experience are incorporated into the performance criteria. These two roles monitor the app in production to ensure top-notch application performance at all times.
Get the Right Performance Engineering Partner
Performance engineering is transforming the software development landscape as well as the job descriptions of all who are engaged in it. And with a greater number of roles now involved, the need for tools and technology to streamline the process is greater than ever before. Performance engineering calls for end-to-end integration and collaboration from right-to-left and left-to-right along with real-time insights and analytics. Traditional performance testing vendors aren't adequately equipped to address this wave of chaotic change. Micro Focus though has the proven experience and technology solutions to convert the test chaos into engineered order.
Micro Focus performance engineering open architecture supports testing across any protocol and application type in any development environment. It permits stakeholders, from developers to business analysts, to use numerous vendor and open source tools so as to enable complete CI/CD integration at scale. Micro Focus tool integrations provide the ability to quickly eliminate the wait time of development and the testing that slows down application delivery. The integrations do this by enabling the rapid creation of realistic simulations of APIs, network conditions, and virtual services. Micro Focus performance engineering solutions build upon existing on-premises or cloud infrastructure and promote asset reuse to take advantage of existing investments. This helps scale quickly to meet the demands of performance testing of multiple applications across the enterprise.
Traditional performance testing did not begin until functional testing was complete and didn't start identifying defects and root causes until the end of performance testing. Micro Focus performance engineering entails continuous end-to-end measurement and analysis of defects to pinpoint root cause in real-time even before the performance test ends. Performance criteria are included in definition and requirements of 'done'. Micro Focus real-time analytics help the performance engineer provide feedback to the developer fast so as to initiate troubleshooting early in the development process. Synthetic monitoring and real user monitoring in production provide insights into performance issues that have escaped testing and must be fixed in the next release. Capture and analysis of end user sentiment from a performance perspective provides more specific feedback to developers so as to optimize the application for better performance.
Best practices for implementing Performance Engineering
Learn how to adopt a combined "shift left" and "shift right" performance engineering approach to build a highly productive software development organization.
Project-based performance testing to identify performance issues.
Collaborative performance testing platform for globally distributed teams.
Cloud-based solution to easily plan, run and scale performance tests.
Discover, design, and simulate services and APIs to remove dependencies and bottlenecks.
Easy-to-use performance testing solution for optimizing application performance.
What is Performance Testing?
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Micro Focus Introduces New Family of Software, Delivering Unified Performance Engineering at Massive Scale
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Q: Launch console application before user login I have a C# console application, which interacts with a SQL Server Database, and I want to configure it to launch at system startup and before user login. My application is an infinite loop that reads/writes data from/to database (after processing), and writes log messages to the console window.
I tried windows task scheduler. The application launches, but I had two problems:

*

*The app can't access the database (Error message: " Cannot open database "MyDataBase" requested by the login. The login failed." extracted from a log file )

*When I log in to windows, I can't retrieve the app console, and it doesn't figure in the task manager's process list.

I don't really know if this issue has a solution (or maybe a workaround) in windows, but I really need it because the server is in a distant location, and we suffer from power cut-offs in that area, so I have to check it periodically and launch my app each time the cut-off occurs.
Thanks for help.

A: I suspect the right thing to do here is to use two programs.  First, a windows service that starts on boot and restarts whenever it fails. This service likely will need to either connect with SQL server credentials (not Windows credentials) OR run under a service ID that has access to the database.  The second program would start on user login and would read/display any log messages from the windows service.  Essentially it would function like "tail" on the log file written by the service, though you could come up with a more sophisticated logger registration process so that it could receive messages directly from the service if you desire.

A: You probably want to refactor your app to become a Windows Service.  You'll have to live with the fact that you can't "access" the app itself directly.  Typically, you'd create a companion app that can manage the service.  Think of the services that you're familiar with on Windows... IIS, SQL Server, etc... at a high level, they all work this way.

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Search results for "Sarah Chalke"
An eager young rookie joins the ragtag small-town police force led by his dad as they bumble, squabble and snort their way through a big drug case. KissAnime Review: Paradise…
Rick is a mentally-unbalanced but scientifically-gifted old man who has recently reconnected with his family. He spends most of his time involving his young grandson Morty in dangerous, outlandish adventures…
Genre: Action & Adventure, Animation, Comedy, Sci-Fi & Fantasy
Prep & Landing: Naughty vs. Nice
Lanny and Wayne are at it again! With the Big 2-5 fast approaching, Wayne and Lanny must race to recover classified North Pole technology which has fallen into the hands…
Genre: Animation, Comedy, Family
Prep & Landing: Tiny's Big Adventure
Tiny's Big Adventure is a two-minute computer animated short that debuted on Disney.com. The short features Magee's assistant, Tiny, trying to make a pot of coffee and in the process,…
Prep & Landing Stocking Stuffer: Operation: Secret Santa
Wayne and Lanny, now partners, are called by Magee to meet with a secret contact – Mrs. Claus, who sends them on a new mission to retrieve a box from…
Prep & Landing
Wayne gets a new rookie partner, Lanny, after his previous partner got the promotion he wanted. Lanny has to remind Wayne of the Spirit of Christmas and the importance of…


Over the last 25 years, technology has played an more and more-essential function within the discipline of education. You should buy objects as much as Apple's limits of 1 desktop per academic 12 months, one Mac mini, one pocket book, two iPads, two shows and two items of software program with the reductions. You possibly can visit the Apple Store for Colleges here and the Apple Retailer for Universities right here You'll must register with Apple before you may make any purchases.
It is usually best to go online from your university network in case you can, but be ready for Apple to ask you for some form of official identification that proves you are a scholar, the father or mother or guardian of a student, or a teacher, or otherwise involved in the academic establishment.
Apple's share has been dropping steadily in the previous few years. Apple did not lead with that information and the pricing for the brand new 9.7 iPad flagship is only for schooling establishments, but make no mistake, it's a essential strategic move and one that might assist revive the company's tablet business.
Unleash your inventive potential with the Professional Apps Bundle for qualifying faculty students, academics, and training institutions. Observe that should you're pondering of buying a Mac, not for yourself or your little one however for the establishment itself (for college students to work on), then Apple has other stores for you to use.


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News In Image
South African man escapes jaws of whale
PORT ELIZABETH, South Africa (AFP) – Rainer Schimpf has narrowly survived after being caught in the jaws of a whale.
The 51-year-old marine conservationist was swept into the mouth of a large Bryde's whale off the coast of the southern South African town of Port Elizabeth while filming a sardine run last month.
"Looming up out of darkness below came a Bryde's whale shooting up into the ball of fish, gulping all in its path," he told AFP, adding that his legs were hanging from the mammal's mouth during the incident at the end of February.
Schimpf's wife Silke and a photographer watched on in horror from their boat, chartered to watch the famous sardine run which creates a marine feeding frenzy off Africa's southern coast.
"I felt some pressure around my waist and I immediately knew what had happened.
"(It) accidentally included me in its mouth together with its main meal – the sardines."
Bryde's whales can weigh up to 30 tonnes when mature and typically eat krill and fish. – AFP
Schimpf, an award-winning conservationist with 20-years experience, said the ordeal lasted in "matter of seconds".
"(Then) the whale realised its mistake and opened its mouth releasing me. I was washed out with what felt like tonnes of water from its mouth," he said.
Bryde's whales can weigh up to 30 tonnes when mature and typically eat krill and fish. When he finally reached the surface he took a breath and rejoined his fellow snorkellers who were unaware of the unfolding drama.
"We swam back to the vessel, climbed up and checked if I and the camera were OK – no broken bones, no cracked ribs – so all was good," he said.
"Pumped up with adrenaline and not wanting to miss the bait ball action, I went back into the sea, this time looking for sharks," he added with a smile. Schimpf has extensively documented Orcas killing dolphins and their hunting behaviour.
He is also an authority on sardine tracking and the predators that follow the sardine runs.
Schimpf remains undeterred following his narrow escape, telling AFP that "if I was to be reborn, I would like to come back as a whale".
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text:
Sotah's Seclusion
Disciple's Disciple
Scheming Woman
Bari V'shema
Sacred Waters - Daf 24
Nezirus of a Son - Daf 23
Repentance in the same Situation
The Gemora (Daf Yomi: Sotah 28b)states that the case where a sotah is deemed tamei is only when the doubt arisen in a private domain.
The Minchas Chinuch (mitzvah 365) poses the following question: The Rambam in Hilchos Sotah (1:3) rules that it is possible for a husband to warn his wife against secluding with two men at the same time. If she secludes herself with them, she will become forbidden until she drinks. Why is she forbidden? Since there are three people there (the woman and the two men), it should have a status of being a public domain, and she should be permitted!
Incidentally, it is evident from the Rambam that a woman can become a sotah even though she did not violate the laws of yichud (a woman being secluded with a man), for there is no yichud in a case of a woman and two men. This can also be proven from the halacha that a woman can become a sotah when she secludes herself with her father or brother, even thought here is no prohibition of yichud there as well.
Reb Avi Lebowitz brings another proof to this concept: The Gemora in Brochos (31b) states that Chanah threatened Hashem that if she doesn't have a child, she will behave in a manner where Elkanah will warn her and she will then seclude herself with that man. After drinking the water and emerging innocent, she would be blessed with children according to Rabbi Akiva, who says that if she was barren, she will be blessed with a child. How could a righteous woman like Chanah put herself in a situation where she would be violating a prohibition of yichud? Clearly, it must be possible to accomplish a seclusion that would render a woman a sotah even without a yichud prohibition! (There are commentators who discuss various scenarios where a woman can be rendered a sotah from secluding herself with one man even without violating the issur of yichud.)
Labels: daf yomi, Gemora Sotah, sotah, talmud, yichud
The Mishna states: Rabbi Yehoshua said: Who will remove the dust from your eyes, Rabban Yochanan ben Zakkai! For you used to say that another generation is destined to declare a loaf (of terumah) tahor even though it is a shlishi, since there is no verse in the Torah which states that it is tamei. But does not your student, Rabbi Akiva, cite a verse from the Torah that it is tamei, as it is said, whatever is in it, shall be tamei.
The Rambam in his introduction to his Yad HaChazakah, he writes that Rabban Yochanan ben Zakkai had five outstanding students. They were: Rabbi Eliezer the Great, Rabbi Yehoshua, Rabbi Yosi HaKohen, Rabbi Shimon ben Nesanel and Rabbi Elozar ben Arach. The Rambam continues by stating that Rabbi Akiva studied under Rabbi Eliezer the Great. It would seem that Rabbi Akiva was not a disciple of Rabban Yochanan ben Zakkai.
It is evident from the Gemora at the end of Tractate Makkos that Rabbi Akiva lived in the times after the destruction of the Beis HaMikdosh. The Maharsha in Yevamos (16a) speaks this out as well. The Gemora there records an incident: In the times of Rabbi Dosa ben Hurkenas, the co-wife of a daughter was permitted to the yavam. This ruling was extremely troubling to the Sages, because Rabbi Dosa was a great Torah scholar and his eyes had stopped seeing, so that he was unable to come to the Beis Medrash to study. A discussion took place as to who should go and notify him that they disagree with him. Rabbi Yehoshua said to them, "I will go." They asked, "And who will go after him?" Rabbi Elozar ben Azaryah agreed to go. They asked, "And who will go after him?" Rabbi Akiva agreed to go.
The Maharsha explains: Rabbi Yehoshua went first because he was the oldest, for he was a student of Rabban Yochanan ben Zakkai in the times when the Beis HaMikdash was in existence. Rabbi Elozar ben Azaryah was appointed to be the Nasi after the destruction of the Beis HaMikdash. Rabbi Akiva was in the times of Ben Kuziva in Beitar years afterwards, and that is why he went last. It emerges that Rabbi Akiva was clearly not a student of Rabban Yochanan ben Zakkai, but rather, a student of his student!
Rabbi Yaakov Marcus in his sefer Minchas Yaakov asks: Why did Rabbi Yehoshua refer to Rabbi Akiva as the disciple of Rabban Yochanan ben Zakkai, when he was in fact his disciple's disciple? Further on in the Mishna, Rabbi Yehoshua (ben Chananyah) refers to Rabbi Yehoshua (ben Hurkanos) as being the student of the student of Rabban Yochanan ben Zakkai! Why was he not as accurate in the first part of the Mishna? (It would seem that the Rambam in his elucidation to the Mishnayos addresses this point as well.)
Labels: Beis Hamikdosh, daf yomi, Gemora Sotah, talmud, Tannaim
The Gemora (Daf Yomi: Sotah 26a) cites a braisa: It is written: Then she shall be innocent and she shall bear seed. This teaches us that if she was barren, she will be blessed with a child; these are the words of Rabbi Akiva. Rabbi Yishmael asked him: If so, all the barren women will seclude themselves (and upon emerging innocent after drinking the bitter waters) and then be blessed with children, and since this one (a woman who remains loyal to her husband and avoids suspicion) did not seclude herself, she will lose out (and remain barren)! What then is the verse teaching us? It teaches us that if she used to have painful births, she will now give birth with ease; if she gave birth to females, she will now give birth to males; if she used to have short children, she will now have tall children; if she used to give birth to dark-skinned babies, she will now give birth to fair ones.
Tosfos asks: Can't we ask the same question on Rabbi Yishmael as he asked on Rabbi Akiva? A woman who was suffering severe childbirth pains would seclude herself, and after drinking the bitter waters, she will give birth "pain-free"!
Tosfos HaRosh answers that these blessings are not as great as that of a barren woman giving birth. A woman wound not go to such lengths (secluding herself with another man) just to receive these blessings.
The Haflaah in Panim Yafos answers that a woman who was used to painful childbirths will not seclude herself with another man, for she will be afraid that her husband will rather divorce her than have her degraded in Beis Din. A barren woman, however, is not concerned that her husband might divorce her, for after ten years without children, he will anyway divorce her. She therefore has nothing to lose by secluding herself.
The Minchas Kenaos answers by asking the following question: While it is understandable that the woman might employ such a scheme, but where will she find a man to go along with her? An ordinary man will not want to transgress the prohibition of secluding with a married woman. It is illogical to assume that she will seclude herself with a wicked man, for he will want to cohabit with her, and her intention is only to be blessed with children from her husband.
We are compelled to say like the Sifri states that if the husband never had children, he will also be blessed with children. Accordingly, she will find a barren man to seclude with, for he also wants to have a child.
This concern is only according to Rabbi Akiva, who maintains that the barren women will be blessed with children. Accordingly, it can be extended to a man as well. However, according to Rabbi Yishmael, who holds that the blessing is in regards to pain-free child birth, this is only applicable to the woman, and not the man.
Labels: birth, blessing, daf yomi, Gemora Sotah, Panim Yafos, sotah, talmud, tosfos
The Mishna had stated: If their husbands died before they had a chance to drink, Beis Shamai says that they collect their kesuvah and do not drink. Beis Hillel says: Either they drink, or do not take a kesuvah.
The Gemora (Daf Yomi: Sotah 25a - b)explains the argument: Beis Shamai maintains that a debt from a document which awaits collection is considered as if it has already been collected. [The widow wishes to collect from her husband's property for her kesuvah. Her claim to these properties is questionable, for perhaps she committed adultery and forfeits her kesuvah. Since it is regarded as if she is already in possession of the properties, the husband's heirs are trying to take the property away from her. The burden of proof rests on them. If they cannot provide proof, she collects her kesuvah.] Beis Hillel, however, holds that we do not regard the document as if it already collected (and therefore she is trying to take the properties from the husband's heirs; the burden of proof is on her).
Tosfos HaRosh in Kesuvos (81a) asks: Why doesn't the Gemora explain Beis Shamai's reasoning based upon the principle of "bari v'shema bari adif"? Since the woman's claim is a definite one, because she asserts that she did not defile herself, and the heirs' claims are only an uncertain one, for they do not know if she defiled herself or not, her claim should be the stronger one, and therefore – win out!
They answer that the principle would not apply in this case for the following reason: Her definite claim is a weak one, for she knows that the husband's heirs cannot counter her claim, for they have no way of knowing. Their claim, although it is an uncertain one, is a sound one, for there is a strong presumption of guilt based upon the fact that the husband warned her and she went against his warning by secluding herself with that man (raglayim l'davar). When the definite claim is a weak one, it cannot be superior than an uncertain strong claim.
Reb Dovid Parvarsky inquires as to the reason to the above qualification. Is it because the definite claim is a weak one, and that is why it cannot win over the uncertain claim? Accordingly, even if the doubtful claim is a weak one, the definite claim would still not be victorious! Or perhaps, it is because the uncertain claim is a powerful one? Accordingly, even if the definite claim would also be strong, it would not be able to overpower the uncertain claim!
Labels: bari v'shema, daf yomi, Gemora Sotah, Reb Dovid Pervarsky, sotah, tosfos
The Rambam rules that if the sotah says, "I defiled myself," the bitter waters are poured out because there is no sanctity in them.
Rav Shach in Avi Ezri asks: How can the Rambam say that there is no sanctity in the water, when he also rules that if the waters remain overnight, they will be disqualified? Evidently, the waters are sacred, for otherwise, it would not be invalidated on account of standing overnight in the ministering vessel!
He answers: When the sotah says that she has been defiled, there is no necessity for the waters any longer, it is therefore regarded as a kodoshim animal that died, which loses its sanctity.
The Mikdash Mordechai questions this from the fact that one is still obligated to treat the dead animal with sanctity! How can the Rambam rule that the waters should just be spilled out? It should be poured into the special drain designated for sacred liquids!
He answers that a sacrificial offering that died retains its sanctity because halachically the korban is still required to be offered, but this particular animal is not suitable to be brought any longer. However, when the woman says, "I have defiled myself," the waters accomplished its mission; although not in the most direct manner. The sotah admitted because she was frightened from the affects of the water. Since the waters brought about its intended result, it has fulfilled its assignment, and therefore, it does not retain any sanctity.
Labels: daf yomi, Gemora Sotah, mikdash mordechai, rambam, sotah, talmud
The Mishna states: A father can impose upon his son a nezirus vow, but a mother cannot impose a nezirus vow on her son.
Rashi comments that the son remains a nazir even after he becomes an adult.
Tosfos in Nazir (28b) disagrees and maintains that as soon as the son becomes an adult, he is no longer a nazir.
Tosfos Yom Tov asks on Rashi: If a father cannot impose nezirus on his adult son, why would the nezirus that he imposed upon him as a minor remain when he becomes an adult?
The Reshash explains that there is a clear distinction between the two cases. A father does not have the authority to impose nezirus upon his adult son. However, when the father imposed nezirus upon his minor son, the child became a nazir. Once he is a nazir, why should we say that the nezirus goes away when he becomes an adult? Since he was a nazir up until now, nothing changes and he remains a nazir.
Labels: daf yomi, Gemora Sotah, nazir, Rashi, reshash, tosfos, tosfos yom tov
It is written [Yirmiyah 22:10]: Cry intensely for one who leaves, because he will not return again and see the land of his birthplace. Rav Yehudah said: This is referring to one who departs this world without children.
Rav Huna said: The verse is referring to a person who committed a sin and repeated it. The Gemora states: Rav Huna is following his reasoning stated elsewhere that one who commits a sin and repeats it; it has become permitted to him.
The Gemora asks: Do you actually think that it is permitted? The Gemora answers: Rav Huna means that it becomes to him as if it was permitted.
The Gemora (Yoma 86b) explains that a true penitent is one who committed a sin in the past and then the opportunity for the same sins comes again a first time and a second time and he is saved from the sin on both occasions.
The Sefer Chasidim writes that a person should not put himself into a situation where he is tempted to sin, because he may not be able to withstand temptation.
The Tzlach questions the words of the Sefer Chasidim from the commentary of the Kli Yakar in Parshas Chukas, who writes regarding the phenomena of the Parah Adumah that the Parah Adumah was capable of rendering pure those that were impure and conversely, rendering impure those that were pure.
The Kli Yakar likens this idea to certain medicines that are beneficial for one who is ill but can prove fatal for one who is healthy. There is a parallel between remedying the body and remedying the soul. One who wishes to repent must be with the same woman that he sinned with the first time, at the same time of the year in which he had sinned, and at the same place where he sinned with her. Thus, the temptation to sin is particularly strong, as his Evil Inclination will entice him to respond exactly as he did before. By resisting the temptation, he demonstrates that he is a true penitent.
The Kli Yakar adds that this is what the Gemora (Brochos 34b) means when it states that in the place where penitents stand, the completely righteous do not stand, i.e. the completely righteous cannot stand in a place of temptation. Yet, according to the Sefer Chasidim, a righteous person is not permitted to endanger himself by entering into such a situation.
TRANSGRESSION COMMITTED ONLY ONCE
The Gemora states that if one commits a transgression and repeats it, it becomes like it is permitted to him.
Rav Shach was once giving rebuke and he questioned if there is any among us that have committed a sin and not repeated it. Woe is to us.
The Mabit in Beis Elokim (shaar hateshuva ch 11) writes that our sages have said if one commits a transgression three times, it becomes like it is permitted to him. Did he have a different version in the Gemora than us? Our Gemora states this to be correct if a person commits a sin even twice.
Labels: Gemora Brochos, Gemora Sotah, Gemora Yoma, kli yakar, mabit, medicine, parah adumah, Parshas Chukas, Rav Shach, repent, sefer chasidim, sinner, transgression


text:
\section{Introduction} \label{sec: introduction}
\label{sec:Introduction}
\IEEEPARstart{S}{emantic} segmentation is a task to assign each pixel in a scene image with a semantic category. One of the biggest challenges of this task is the numerous, time-prohibitive efforts entailed in the manual labeling of a large set of training images.

To alleviate, or even free researchers from high costs of laborious annotations, more attention has been paid to weakly supervised semantic segmentation, in which annotations can be performed in a much-eased manner: rather than associating all pixels from an image with a label, weaker supervisions on the training images such as bounding boxes~\cite{box,box2}, scribbles~\cite{scribble}, image labels~\cite{irn,psa,ssdd,cvpr17web,cvpr18web,triplet,sec,cbts,seam,Pathak215,dcsm,meff,lee_iccv2019,tmm-zhang2019decoupled}, and points~\cite{point} can be used as substitute annotations to train.

Compared with other annotations, the image-level label is cost-friendly and readily accessible to the public. There is a broad choice of large image corpora with image-level labels such as ImageNet~\cite{imgnet}, PASCAL VOC~\cite{voc}, and images retrieved by search engines with their keywords as labels \cite{webvideo-seg}.
However, image-level labels merely provide categorical cues without any shape/texture information of any objects. It is crucial to utilize such cues to automatically produce coherent regions of objects with their pixel-level categorical information.
To this end, Class Activation Maps (CAM)~\cite{cam} has been wildly used to explore the localization and shape cues from image-level labels~\cite{irn,psa,ssdd,cvpr17web,cvpr18web,triplet,sec,cbts,seam}.
However, there is no guarantee of the completeness of the object activation regions of CAM since they are trained only with the classification objective. Incomplete or wrong activation maps obtained by CAM may lead to sub-optimal performance.

To improve the quality of the object activation maps, many approaches are proposed to use the results from CAM as the seed activation regions and gradually refine their quality. Ahn~\textit{et al.}~\cite{irn} use low-level image features to enhance the coherence of the activation maps. Wang~\textit{et al.}~\cite{seam} expand the seed object regions by fusing the object activation maps computed on different spatial scales of the input image. Object regions that are not activated by CAM at one scale could be re-activated at a coarser level. However, this method does not explore the rich cross-image context within the same class in a training set.
CIAN~\cite{cian} addresses the cross-image context by computing an affinity map for each pixel of an image to another. However, the pixel of the query may not be the foreground and could end up retrieving the wrongly activated backgrounds.

Hu~\textit{et al.}~\cite{seenet} find the cross-image context leveraging two self-erasing networks with hiding and erasing strategy to explore more object regions. New regions will be activated by the classification objective if the old ones are masked. However, this method cannot automatically stop the erasing even after the entire foreground is masked, resulting in the undesirable background being activated.

Inspired by the previous works, we propose that the key to improve the quality of the object activation maps is 1) a method to locate more complete object regions and 2) a mechanism to suppress wrongly activated background regions. We hold an assumption that the object regions possess significantly less diversity compared with the background. We use it to address the two key points:
To point 1), we find that multiple training samples of the same class provide a diversity of object regions, see Figure~\ref{Figure:motivation}. One activated object region in an image is helpful to explore more similar regions in another image of the same class. In particular, we generate the region prototypes to capture the diversity by collecting regions with similar features across the same object category images; then, we locate the inactivated object regions in these images by comparing their feature maps to the region prototypes. Suppose one prototype is extracted from image A; this prototype will be compared to all regions in image A and the pairing image B, including all regions of the other prototypes. Similar regions are highlighted and assigned with large confidence values as new object regions.

To point 2), if an activated region belongs to the background, it is difficult to propagate its information, among others. Since there is more diversity among backgrounds, fewer compared regions during prototype voting resemble the activated background region, and its information propagation is minimized. Our region comparison serves as a voting mechanism to assign them with lower confidence scores.

\input{Figures/motivation}

With the region prototype mechanism on cross-images, our proposed network generates better object activation maps to train a segmentation model. We conduct extensive experiments on PASCAL VOC 2012~\cite{voc} and MS COCO \cite{coco14} to validate the effectiveness of our network. However, these datasets both have abundant training samples and do not cover a practical scenario in which fewer training samples are available. A good method should produce a high-quality pseudo groundtruth even when the training samples are scarce. Thus, we investigate the robustness of the proposed method on datasets of reduced training samples (\textit{e.g.} up to $1/16$ of the number of training samples in PASCAL). Our main contributions are summarized as follows:

\begin{itemize}
	\item  The proposed extraction of region prototypes and their equal treatments ensure the reactivation of rare object regions that are not activated by CAM while preventing them from being dominated by prevalent features.
	\item The proposed voting mechanism on prototypes propagates important object information while suppressing backgrounds simultaneously, bringing in more robustness to either abundant or limited training images.
    \item We are the first to evaluate weakly supervised segmentation methods under limited training samples and in different backbones. The proposed method exhibits promising results under both conditions.
    \item We improve the performances on PASCAL VOC 2012 and MS COCO dataset and achieve new state-of-the-art.
\end{itemize}

\input{Figures/figure_overall}

\input{Figures/prototype-extractor}
\section{Related Work}
\subsection{Semantic segmentation}
Semantic segmentation is a fundamental computer vision task that assigns each pixel in the image with a category. Currently, state-of-the-art methods handle image semantic segmentation as a dense prediction task and adopt fully convolutional networks to make predictions~\cite{chen2018deeplab,long2015fully}. To make pixel-level dense predictions, encoder-decoder structures~\cite{tip-encoder-decoder1,tip-encoder-decoder2,tmm-kang2018depth,tmm-shi2018hierarchical,tmm-zhang2019decoupled,lwd2020crnet,lwd2020guided,lwd2021few,liu2021few_transport,lwd2022long,hou2022distilling} are widely used to reconstruct high-resolution prediction maps. Typically, an encoder gradually downsamples the feature maps, acquiring a large field-of-view and capturing the semantic object information. Then, the decoder gradually recovers the fine-grained information.

The field-of-view information is important for semantic segmentation tasks. Dilation connections~\cite{dilation} are often used to increase the field-of-view and then fuse high-level and low-level features for better predictions. We also follow the encoder-decoder design in our network and opt to transfer the guidance information in the low-resolution maps and use decoders to recover details.

\subsection{Weakly supervised semantic segmentation.}
To alleviate the data deficiency problem in image segmentation task, the weak supervision have been explored, such as supervision with bounding boxes~\cite{box,box2}, scribbles~\cite{scribble}, and image labels~\cite{irn,psa,ssdd,cvpr17web,cvpr18web,triplet,sec,cbts,Pathak215,dcsm,meff,seam,benet}.
The weak supervision methods have the nature of less expensive to be annotated and easily acquired.

Previous state-of-the-art methods~\cite{irn,psa,erasing} mostly adopt CAMs~\cite{cam} to localize the objects. However, the CAMs can only locate the most discriminative object regions, which is insufficient to train a segmentation model.  Researchers have designed various ways to propagate the seed regions to the entire object to explore more object-relative regions.
Hou~\textit{et al.}~\cite{seenet} expand the seed regions by erasing the currently discovered regions with prohibit attention and re-train the erased image.
SPM~\cite{spm} expands the seed regions with a discrepancy and intersection loss. Ahn~\textit{et al.}~\cite{irn} propagate the seed regions to reach the boundary and utilize the inter-pixel relationship to refine the final pseudo ground mask.
CIAN~\cite{cian} retrieves the cross-image relation by calculating the dot product between every pixel from different images, which do not distinguish the foreground and background. In contrast to CIAN, we generate the region prototypes only from the confident object regions.
CONTA~\cite{conta} proposes a structural causal model to remove the confounding bias in image-level classification. Our RPNet also can suppress the wrong foreground by a voting mechanism with multiple prototypes.

Compared with previous methods, our method expands the seed region by leveraging the cross-image relations from a novel region prototype perspective to mine the inactive object regions and suppress the dislocated foreground.
SEAM\cite{seam} learns a network to enforce the classifier to recognize the common semantics from co-attentive objects. The cues of the foreground sometimes have to be refined by extra saliency information. In contrast, the proposed RPNet selects the confident object regions from different images to retrieve similar object parts while the background features can be suppressed simultaneously.
In particular, we further compare the pixel correlation module (PCM) proposed by SEAM~\cite{seam} to our RPNet, including:
1. The PCM in SEAM computes attention maps with simple pixel-to-pixel differences, which misses the neighboring semantic information. The proposed PR-Net addresses this problem by leveraging region-based prototypes to compute our attention maps. The difference in our attention maps comes from low-level image features vs. high-level semantic features.
2. The SEAM computes attention maps at the pixel level of its feature maps without considering whether this pixel belongs to the foreground or the background. As an example shown in Figure~\ref{Figure:different-methods}, if one background pixel on the feature maps is wrongly activated, other background pixels similar to this one will be re-activated as foreground through its correlation module, even though they were labeled as background before. Thus, in SEAM, background regions are more prone to be falsely activated. On the contrary, our RPNet carefully selects regions with high foreground confidence as prototypes before propagating their information to re-activate other unnoticed regions. The RPNet tries to guarantee that only correct (foreground) information is propagated.
3. SEAM's correlation module is confined within single images, while our RPNet uses cross-image to explore the diversity of the foreground features. More robustness and completeness of the object activation maps are expected in the outputs of the RPNet.

MCI\cite{mining-cross} learns a network to enforce the classifier to recognize the common semantics from co-attentive objects. The cues of background sometimes need to be refined by extra saliency information. Our RPNet selects the confident object regions from different images to simultaneously retrieve similar object parts while suppressing the background features.

IIC\cite{zhang2020interimage} and our proposed method both seek to re-activate object regions of the same object category in different images. However, this work maintains a memory bank, and the centers of prototypes are used, which could cause the prototypes to be dominated by some most popular object regions, e.g., memory centers could be severely biased to dog heads rather than dogs' whole bodies. This is perhaps less serious for detection tasks (which is the focus of \cite{zhang2020interimage}) but is very harmful to image segmentation.
\section{Proposed Method}
\subsection{Motivation}
\label{section:motivation}
The object activation maps of CAM are sub-optimal for fully supervised segmentation training because they are trained via a classification objective. We argue that such maps suffer from two types of flaws: 1)incomplete object regions (foreground) and 2) falsely activated cluttered regions (background). Generally, when the training set has abundant samples in each category, the trained model benefits from it and tends to be robust to backgrounds. However, at the same time, the activated object region tends to be more incomplete since some less distinctive parts tend to be suppressed by the classification objective, \textit{e.g.} the torso part of a mammal which is less distinctive compared with its head. This means flaw 1) is more likely to happen. When the training samples become scarce, flaw 2) happens more as some backgrounds could be discriminative with fewer data available to counter possible overfitting. See Figure \ref{Fig:decrease-data} and our observation in the experiment part in Sec. \ref{sec:decress_training} on truncated versions of \textit{PASCAL VOC 2012}. It is crucial for a method to be robust to both abundant or scarce training samples and address the two mentioned flaws. To our knowledge, we are the first to investigate the number of training samples and handle both flaws elegantly to produce high-quality object activation maps.~\cite{seam},\cite{cian} and~\cite{seenet} consider the flaw 1) only, while~\cite{conta} considers flaw 2) only.

\subsection{Method Pipeline}
As shown in Figure~\ref{Figure:overall}, given a training set of images with image-level labels, we train a model that automatically produces initial object regions. The model extracts object features similar to CAM~\cite{cam} but is from multiple spatial resolutions. Based on these features, we explore the diversity of the training images in each object class: at each iteration, we compute a group of cross-image region prototype vectors on a pair of sample images that contain a common object class. These prototypes are compared with the feature maps of the inputs and find out more inactive foreground regions. Two loss functions, namely classification loss, and self-supervised loss, are used to ensure the precision of the foreground activation and suppress the background regions during training. The trained model is used to predict maps of initial object regions on the input images with their image-level labels.

The proposed method can be integrated into the popular pipeline for weakly supervised segmentation training~\cite{irn,psa,ssdd,conta,seam}: After the initial object regions are computed, boundary refinement~\cite{irn} is applied on them to generate the pseudo groundtruth, on which the fully-supervised segmentation model can be trained.

\subsection{Revisiting Class Activation Maps}
The proposed method starts with the results of CAM~\cite{cam} and explores useful cross-image context based on them. CAM computes the object activation maps via a classification network backbone connected with a global average pooling (\textbf{GAP}) layer as follows:
\begin{equation}
    A(x,y) =g( \frac{\Theta _{}^{T} f_n(x,y) }  { \max (\Theta _{}^{T} f_n(x,y) )} ),
    \label{equation_cam}
\end{equation}
\begin{equation}
    CAM(x,y) = A(x,y) \cdot c,
\end{equation}
where $\textit{$f_n$} \in \mathbb{R}^{X \times Y \times D}$ denotes the feature maps extracted from the last convolution block. $(x,y)$ denote 2D coordinates, $\Theta$ is the classification weights and $g$ is the is the activation function, which is $ReLU$ in this paper. The $X, Y$ are the feature sizes while the $D$ is the channel dimension.
The spatially normalized object activation map is denoted as $A \in \mathbb{R}^{X \times Y \times C}$ and the per-class object activation map $CAM(x,y)$ is computed by masking $A$ with the image-level label vector $c$ containing total $C$ classes.

\input{Figures/visulize_background}
\subsection{Region Prototypical Network}
\label{sec: rpnet}
We propose the region prototype that captures distinctive features among sample images of the same object category: prototypes extracted from different samples are compared with their feature maps. We define this step as \textbf{region comparison} as shown in Figure \ref{Figure:motivation}. We hold a simple assumption: the diversity of the object regions is \textbf{far less} than the diversity of background.
e.g., we random sample some pictures from the VOC 2012, as shown in Figure~\ref{Figure:background}, the foreground contains `person, table, sofa.' However, the background includes `fresco, cartoon, grass, house, window, TV, food, cup, trademark...' In one training iteration, only the object class "person" is selected as the foreground, while large parts of cluttered background regions with more diversified features (compared with features of "person") are compared. Our assumption is based on the diversity comparison between "single object category" vs. all other objects and backgrounds instead of "all objects" vs. backgrounds.

Thus, if an extracted prototype is truly an object region, with the help of the diversity of the training images, this prototype's information will propagate well during training and re-activate similar regions from other samples. On the contrary, if the prototype is background wrongly activated by the classification objective, the information propagation is much slower than object regions: the diversity of background is significantly more, and these prototypes can hardly find their similar neighbors.
As the example shown in Figure~\ref{Figure:motivation}, only the bird's head is activated in Image B, while the head and body regions are activated in Image A. The body features in image A can be used to guide image B to find out its inactivated body regions and highlight them. Based on this finding, we build a region prototype network to extract cross-image object features as prototypes and take pairs of images within the same object category as inputs. This aims to have full utilization of the object diversity among the training set. We are not the first to use image pairs as inputs: \cite{cian} explores cross-image context by computing an affinity map for each pixel for image pairs.

\textbf{Generate regions prototype vectors.} To fully explore the object information, we compute the normalized activation feature maps $O  \in \mathbb{R}^{X \times Y \times D}$ on multiple feature maps of the image pair, \textit{e.g.} $\{f_n(x,y)|n=1...N\}$, where $N$ is the index of resnet blocks in a classification network backbone. We unify of the number of channels by concatenation of each $f_n$ with a 256 channel $1\times1$ convolution layer:
\begin{equation}
    O_n(x,y) = conv\ast g(\frac{f_n(x,y)}{\max f_n(x,y)}).
    \label{equation:object_activation}
\end{equation}

Unlike the prototypical network~\cite{prototipical} which spatially averages the entire feature maps as the prototypes and introduces noise, our RPNet carefully selects activation regions as the region prototype vectors with high confidence only. Specifically, given a set of object activation maps $\{O_n(x,y)\}$, we compute their confident object activation maps $O_n^{'} \in \mathbb{R}^{X \times Y \times 1}$ by performing a maximum operation over the spatial dimension (\textbf{$cmax$}):
\begin{equation}
    O_n^{'}(x,y) = cmax(O_n(x,y)).
\end{equation}

In equation \ref{equation:object_activation}, We do not use the image-level label $c$ as CAM does in equation \ref{equation_cam} to locate object regions, due to the inherent flaw of the classification objective previously discussed. Instead, all channels are given equal possibilities to be highlighted as the foreground to produce more complete object regions. We generate the region prototype vectors $\{p_n \in \mathbb{R}^{1 \times 1 \times D}\}$ as:

\input{Figures/decrease-data}
\input{Figures/figure-different-backbone}
\input{Tables/pseudo_mask}
\input{Tables/reduce_data}

\input{Tables/backbone}
\input{Tables/feature_comparison}
\input{Tables/hyper-parameter}

\input{Figures/foreground}

\input{Tables/gauss}
\input{Tables/two_way}

\input{Tables/compare_soa_combine}
\input{Tables/coco-soa}

\begin{equation}
p_n = \frac{\sum_{x,y}^{}f_n(x,y)M_n{(x,y)} }{ \sum_{x,y}^{} M_n{(x,y)}}.
\label{equation:prototype}
\end{equation}
where
\begin{equation}
M_n(x,y) = \begin{cases}
    $Z$, & \text{if $O_n^{'}(x,y) > \beta$},\\
    0, & \text{otherwise}.
  \end{cases}
\label{equation:masks}
\end{equation}
and $Z$ follows Bernoulli distribution $X\sim B(x,\beta)$ and is equal to $1$ with a probability $\beta$. For each $f_n$, we produce a binary confidence mask $M_n{} \in \mathbb{R}^{X \times Y \times 1}$ by thresholding its corresponding confident object activation map $O^{'}$ with $\beta$, as in equation \ref{equation:masks}. The feature $f_n$ is then masked by $M_n$ to produce a prototype vector $p_n$ by average pooling at the spatial dimension with normalization, see equation \ref{equation:prototype}. The parameter $\beta$ helps randomly discard some of the activated regions from $M_n$ during training, and the location of the corresponding $f_n$ will be masked. This is inspired by \cite{seenet} to introduce robustness and help find more object regions. The effectiveness of discarding the activated regions is validated with experiments in Sec.~\ref{section:ablative-study}. The complete flow of prototype generation is illustrated in Figure~\ref{Figure:protype-extractor}.

\textbf{Region comparison.}
\label{sec:generate map}
With the region prototype vectors, we try to filter the unconfident object-related regions in a non-parametric metric learning manner. At first, we calculate the similarity maps $s_n  \in \mathbb{R}^{X \times Y \times 1}$ between the feature maps of the last deep CNN block $f_N$ and the region prototype vectors $p_n$, where $n={1...2N}$ at each spatial position; then, we find the maximum similarity and assign every spatial position with a probability to object foreground depending on it. Collecting all the max similarity values as a set, we form the foreground probability maps $\hat{F}$. We follow Oreshkin~\textit{et al.}~\cite{prototipical} to use cosine as our similarity function, but we do not utilize the scale factor. The foreground probability maps $\hat{F} \in \mathbb{R}^{X \times Y \times 1}$ are generated with the following steps:

\begin{equation}
    s_n(x,y) = \textit{cos}(p_n,f_N(x,y)), n=1...2N,
\end{equation}

\begin{equation}
    \hat{F}(x,y) = max(s_n(x,y)),
\end{equation}
where $N$ is the number of feature maps extracted from different blocks of the CNN network. We also show the visualization examples of the foreground probability maps for PASCAL VOC 2012 dataset in Figure~\ref{Figure:foreground}.

We discuss how we address the two flaws mentioned in Sec. \ref{section:motivation}. For flaw 1), object regions that are not activated in one sample by the classification objective can be re-activated by other samples, through searching prototypes with high confidence value $O'$ and high similarity value $s$. Similar prototypes can be found with sufficient diversity of the training set and can re-activate via feature enhancement discussed in the following section. For flaw 2), if a prototype $p_i$ belonging to the background is wrongly extracted, although its confidence scores $O'_i$ is high, it is difficult to find regions that are highly similar to it on other training images due to the diversity assumption. Thus, its information propagation is limited.

\textbf{Feature enhancement by re-weighting the feature maps.}
After we obtain the foreground probability maps $\hat{F}$, we can locate the object more accurately and comprehensively. We extract the feature maps of the last block $f_N(x,y)$ by multiplying with its confidence map $\hat{F}$ (note that there are two $\hat{F}$ and each corresponds to one of the image pairs). As the naive $\hat{F}$ is sparse, we utilize a Gaussian Filter~\cite{gauss} to smooth $\hat{F}$. In particular, we re-weight the original feature representations in the following way:

\begin{equation}
    G(x,y) = \frac{1}{2 \pi \sigma ^{2}} e^{- \frac{x^{2} + y^{2}}{2 \sigma ^{2}}},
\label{2dgaussian}
\end{equation}

\begin{equation}
    \mathbf{f_N(x,y)^{'}} = G(\hat{F}_{(x,y)})\cdot \mathbf{f_N(x,y)},
\end{equation}
where $f_N(x,y)^{'} \in \mathbb{R}^{X \times Y \times D}$ denote the enhanced object activation maps.
With the enhanced object activation maps, we employ an average pooling followed by a $1 \times 1$ convolution layer as the final classifier to predict the classification.

\input{Figures/figure_differ_methods}
\input{Figures/quality_coco}
\input{Figures/quality-results}

\input{Tables/details}

\subsection{Training Process}
We choose a multi-label soft cross-entropy classification loss $\mathcal{L}_{c}$ to make sure that the activated regions on $f_N^{'}$ still possess discrimination power to the object categories indicated by the image-level labels.

To solve the problem of incomplete object regions motioned in section~\ref{sec: introduction} and constraint the $f_N^{'}$ from deviating too much from the original feature maps $f_N$, which may cause divergence, we add a self-supervised loss $\mathcal{L}_{s}$ to compare both maps.

\begin{equation}
 \mathcal{L}=\mathcal{L}_{c}+\lambda\mathcal{L}_{s},
 \label{equation:loss_sum}
\end{equation}

\vspace{-1em}
\begin{multline}
\mathcal{L}_{c}=- \sum_{i=1}^{N}( u[i] * log(\frac{1}{1+exp(-v[i])}) \\ +(1-u[i]) * log(exp(\frac{-v[i]}{1+exp(-v[i])}))),
\end{multline}

 \begin{equation}
 \mathcal{L}_{s}=\sum_{i=1}^{HW}{(\mathbf{f_N'}-\mathbf{f_N})^2}.
  \end{equation}

Here $v$ denotes the predicted probability of class $i$ while $u[i]$ denotes the image level groundtruth of $i_{th}$ class. $f_N$ and $f_N'$ denote the object activation maps and its enhanced ones. We balance the losses with $\lambda$.
\section{Experiment}
\subsection{Dataset and Evaluation Metric}
Following the experiment set-ups in previous works, we train and evaluate our RPNet on the PASCAL VOC 2012~\cite{voc} and MS COCO~\cite{coco14} dataset. The PASCAL VOC2012 training set is extended with images from~\cite{moredata} and finally gets 10582 training images, 1449 validation images, and 1456 testing images. MS-COCO~\cite{coco14} contains 81 classes, 80k training images, and 40k val images. We use image-level class labels only.
We adopt the standard mean Intersection-over-Union(mIoU) as the evaluation metric for all of our experiments.

\subsection{Implementation Details}
All the backbones are pretrained on ImageNet~\cite{imgnet}, and we replace the stride of the last layer from 2 to 1 to maintain the feature map size. The batch size is set to 16.
We apply the random crop, random scale, and random flip to the images for data augmentation during the training process. The learning rate is initially set to be 0.02 with a decrease rate of 0.9 at every iteration with polynomial decay~\cite{parsenet}. We use SGD as the optimizer and train the network for 5 epochs. We set the probability $\beta$ to 0.5 to discard the object activation regions as described in generate region prototypes, Sec. \ref{sec: rpnet} , and set the $\sigma$ to 3 as motioned in feature enhancement, equation~\ref{2dgaussian}, set the $\lambda$ in equation~\ref{equation:loss_sum} to be 10 and set the $\alpha$ to be 0.3 in equation~\ref{equation:masks}. We adopt the boundary refinement method proposed in~\cite{irn} and synthesize the pixel-level pseudo groundtruth to train a DeepLab-LargeFOV~\cite{deeplab} with Resnet-50 backbone as our semantic segmentation model.
Finally, we follow the previous works~\cite{seenet,sec,ssdd} to utilize denseCRF~\cite{crf} as post-processing to further refine our predictions.

\subsection{Ablative Analysis} \label{section:ablative-study}
In this section, if not specified, we employ Resnext-50 as the network backbone as default; all experiments are conducted on PASCAL VOC 2012 training set and evaluated with standard mIoU. We choose the ResNext-50 instead of ResNet50 as the backbone because choosing ResNext-50 over Resnet-50 is part of our contribution: each channel group from a grouped convolution layer could learn a unique representation of the data to form the fused result to contain more activation regions, which provides significant improvements.

\textbf{The Suitable Backbone.}
As is shown in Figure~\ref{Figure:different-backbone}, we compare backbones and argue that the grouped convolution achieves a significant positive influence on the weakly supervised segmentation task: each channel group could learn a unique representation of the data \cite{group_convolution} and the fused result is more robust.
We compare different backbone choices in Table~\ref{Table:backbone}. We find that group convolution plays more important roles compared with the number of parameters and network structures.

\textbf{Feature comparison.}
In Table~\ref{Table:feature-comparison}, We compare our model variants that utilize different level features from our backbone encoder to generate the foreground probability maps.
We experiment with the feature comparison with single block features and combining multiple block features. The best performance is achieved by combining block 2 and block 4. The possible explanation is that block2 corresponds to relatively low-level localization cues but lacks semantic cues while block4 is the opposite. This combination makes up for each other's shortcomings and gives play to their advantages, which leads to the final best performance. The block3 achieves the best performance with a single block is used; the possible explanation is that block3 contains both the low-level cues and the high-level cues consistent with our previous explanation.

\textbf{The hyper-parameter choices.}
We investigate the choices of $\beta$, the possibility to discard a patch of the region during prototype generation. As shown in Table~\ref{Table:hyper-parameter}, the best performance achieves when discarding the patches with the probability of 0.5.

\textbf{The effectiveness of Gaussian Filter.}
We utilize the Gaussian Filter to smooth the naive sparse $\hat{F}$. We validate the effectiveness of the Gaussian Filter in Table~\ref{Table:Gauss_filter}. The Gaussian filter can bring 0.5 mIoU improvement on the VOC 2012 training set.

\textbf{The effectiveness of cross-image relations.}
To validate the effectiveness of our region prototype and the effectiveness of cross-image relations, we compare our network to a baseline model that does not employ our region prototype mechanism.
As is shown in Table~\ref{Table:two-way}, when the region prototype vectors only from one image, our methods can bring 0.7 mIoU improvement over the baseline. While utilizing the cross-image relations with two images, our RPNet can bring 1.7 mIoU improvement. The results show that significant improvement when we use one or two images. Due to the GPU memory limitation, we report results of up to 4 images per iteration.

\subsection{Decrease the number of training samples}
\label{sec:decress_training}
We investigate the robustness of the proposed method on datasets of reduced training samples. We reduce the number of training samples of each object category from PASCAL VOC 2012 and re-train our RPNet, and the baseline \cite{irn}. As shown in Table \ref{Table:decrease data}, the performance baseline method \cite{irn} deteriorates significantly as the number of samples is reduced to $1/16$ to its original number, but our proposed method's quality drops slowly.
As shown in Figure~\ref{Fig:decrease-data}, decreasing the training image, the baseline method can not identify the object accurately, especially with more background regions that are wrongly activated. This is because the fewer images, the less diversity required from the extracted features for classification. This leads to the activation regions are not precisely the objects yet still discriminative.
Even with the decrease of the training images, our method still attempts to explore the discriminative regions and the cross-image similarities among these regions. Thus, more falsely activated backgrounds are suppressed. Even if the number of images of each class is decreased to one, the method still performs non-local feature comparison within one image, suppressing as much background as possible.
Since there is more diversity among backgrounds, fewer compared regions during prototype voting resemble the activated background region, and its information propagation is minimized. Our region comparison serves as a voting mechanism to assign them with lower confidence scores, which suppresses the inaccurate background.

\subsection{Analysis of Pseudo Labels}
The performance of our synthesized segmentation labels is measured with standard mIoU on PASCAL VOC 2012~\cite{voc} and MS COCO~\cite{coco14} training data. As shown in Table~\ref{psedo_mask}, with the same boundary refine method~\cite{irn}, the synthesized segmentation labels with enhanced object activation maps perform better than the base object activation maps and other state-of-the-art methods~\cite{conta} on both datasets. With the same baseline(IRN) and Boundary Refine method, our RPNet performance 9.7 mIoU is higher than the most current state-of-the-art method CONTA~\cite{conta} on the large dataset MS COCO.

\subsection{Comparison with State-of-the-art Results}
For performance comparison, as shown in Table~\ref{Table:comapre-soa-no-ad} and Table~\ref{Table:coco-soa}, we achieve state-of-the-art performances on both datasets. Our method does not require any additional training information and even outperforms some methods with it, as in \cite{dcsp,gain,dsrg,seenet,salins,cvpr18web}.

Our RPNet outperforms the results of all previous methods with all types of backbone, such as Resnet-50, Resnext-50, and Resnext-101.
We also verify our method with/without the denseCRF post-processing. It is observed that our method achieves the best performance regardless of denseCRF.

As shown in Table~\ref{Table:coco-soa}, we evaluate our methods on the MS COCO dataset, which contains more classes and training images; this means the images have more diversity. Our RPNet achieves new state-of-the-art performances; to be noticed, our methods achieve more than 5 mIoU improvement over the other state-of-the-art methods. As shown in Table~\ref{psedo_mask}, our RPNet can generate high-quality pseudos segmentation masks, which achieves 17.5 mIoU improvement over the baseline and 9.7 mIoU higher than the previous state-of-the-art methods.

We also show the qualitative examples on the PASCAL VOC 2012 in Figure~\ref{Fig:quality-results} and MS COCO in Figure~\ref{Fig:quality-results-coco}. It is particularly interesting to note that in the top left image of Figure~\ref{Fig:quality-results-coco}, the ground truth misses an object(the handphone), but our prediction is able to segment it accurately.
We also provide the detailed results on PASCAL VOC 2012 in Table~\ref{Table:details of the results}.
\section{Conclusion}
We propose a novel framework to generate accurate pixel-level segmentation labels from better object activation maps with image-level labels only. Similar object parts across images are identified via region feature comparison. Object confidence is propagated between regions to discover new object areas while background regions are suppressed. The performances on PASCAL VOC 2012 and MS COCO dataset validate our methods and achieve new state-of-the-art.
\section*{Acknowledgements}
This work is supported by the Delta-NTU Corporate Lab with funding support from Delta Electronics Inc. and the National Research Foundation (NRF) Singapore (SMA-RP10).
This work is also partly supported by the National Research Foundation Singapore under its AI Singapore Programme (Award Number: AISG-RP-2018-003) and the MOE Tier-1 research grants: RG28/18 (S), RG22/19 (S) and RG95/20, and the National Natural Science Foundation of China (No.61902077).

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