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  • richardmitnick 2:21 pm on April 16, 2019 Permalink | Reply
    Tags: , , , , Mathematics, Natural Sciences, The Brendan Iribe Center for Computer Science and Engineering, UMIACS-University of Maryland Institute for Advanced Computer Studies,   

    From University of Maryland CMNS: “University of Maryland Launches Center for Machine Learning” 

    U Maryland bloc

    From University of Maryland


    CMNS

    April 16, 2019

    Abby Robinson
    301-405-5845
    abbyr@umd.edu

    The University of Maryland recently launched a multidisciplinary center that uses powerful computing tools to address challenges in big data, computer vision, health care, financial transactions and more.

    The University of Maryland Center for Machine Learning will unify and enhance numerous activities in machine learning already underway on the Maryland campus.

    1
    University of Maryland computer science faculty member Thomas Goldstein (on left, with visiting graduate student) is a member of the new Center for Machine Learning. Goldstein’s research focuses on large-scale optimization and distributed algorithms for big data. Photo: John T. Consoli.

    Machine learning uses algorithms and statistical models so that computer systems can effectively perform a task without explicit instructions, relying instead on patterns and inference. At UMD, for example, computer vision experts are “training” computers to identify and match key facial characteristics by having machines analyze millions of images publicly available on social media.

    Researchers at UMD are exploring other applications such as groundbreaking work in cancer genomics; powerful algorithms to improve the selection process for organ transplants; and an innovative system that can quickly find, translate and summarize information from almost any language in the world.

    “We wanted to capitalize on the significant strengths we already have in machine learning, provide additional support, and embrace fresh opportunities arising from new facilities and partnerships,” said Mihai Pop, professor of computer science and director of the University of Maryland Institute for Advanced Computer Studies (UMIACS).

    The center officially launched with a workshop last month featuring talks and panel discussions from machine learning experts in auditory systems, biology and medicine, business, chemistry, natural language processing, and security.

    Initial funding for the center comes from the College of Computer, Mathematical, and Natural Sciences (CMNS) and UMIACS, which will provide technical and administrative support.

    An inaugural partner of the center, financial and technology leader Capital One, provided additional support, including endowing three faculty positions in machine learning and computer science. Those positions received matching funding from the state’s Maryland E-Nnovation Initiative.

    Capital One has also provided funding for research projects that align with the organization’s need to stay on the cutting edge in areas like fraud detection and enhancing the customer experience with more personalized, real-time features.

    “We are proud to be a part of the launch of the University of Maryland Center for Machine Learning, and are thrilled to extend our partnership with the university in this field,” said Dave Castillo, the company’s managing vice president at the Center for Machine Learning and Emerging Technology. “At Capital One, we believe forward-leaning technologies like machine learning can provide our customers greater protection, security, confidence and control of their finances. We look forward to advancing breakthrough work with the University of Maryland in years to come.”

    3
    University of Maryland computer science faculty members David Jacobs (left) and Furong Huang (right) are part of the new Center for Machine Learning. Jacobs is an expert in computer vision and is the center’s interim director; Huang is conducting research in neural networks. Photo: John T. Consoli.

    David Jacobs, a professor of computer science with an appointment in UMIACS, will serve as interim director of the new center.

    To jumpstart the center’s activities, Jacobs has recruited a core group of faculty members in computer science and UMIACS: John Dickerson, Soheil Feizi, Thomas Goldstein, Furong Huang and Aravind Srinivasan.

    Faculty members from mathematics, chemistry, biology, physics, linguistics, and data science are also heavily involved in machine learning applications, and Jacobs said he expects many of them to be active in the center through direct or affiliate appointments.

    “We want the center to be a focal point across the campus where faculty, students, and visiting scholars can come to learn about the latest technologies and theoretical applications based in machine learning,” he said.

    Key to the center’s success will be a robust computational infrastructure that is needed to perform complex computations involving massive amounts of data.

    This is where UMIACS plays an important role, Jacobs said, with the institute’s technical staff already supporting multiple machine learning activities in computer vision and computational linguistics.

    Plans call for CMNS, UMIACS and other organizations to invest substantially in new computing resources for the machine learning center, Jacobs added.

    4
    The Brendan Iribe Center for Computer Science and Engineering. Photo: John T. Consoli.

    The center will be located in the Brendan Iribe Center for Computer Science and Engineering, a new state-of-the-art facility at the entrance to campus that will be officially dedicated later this month. In addition to the very latest in computing resources, the Brendan Iribe Center promotes collaboration and connectivity through its open design and multiple meeting areas.

    The Brendan Iribe Center is directly adjacent to the university’s Discovery District, where researchers working in Capital One’s Tech Incubator and other tech startups can interact with UMD faculty members and students on topics related to machine learning.

    Amitabh Varshney, professor of computer science and dean of CMNS, said the center will be a valuable resource for the state of Maryland and the region—both for students seeking the latest knowledge and skills and for companies wanting professional development training for their employees.

    “We have new educational activities planned by the college that include professional master’s programs in machine learning and data science and analytics,” Varshney said. “We want to leverage our location near numerous federal agencies and private corporations that are interested in expanding their workforce capabilities in these areas.”

    See the full article here .

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    Please help promote STEM in your local schools.

    Stem Education Coalition

    U Maryland Campus

    About CMNS

    The thirst for new knowledge is a fundamental and defining characteristic of humankind. It is also at the heart of scientific endeavor and discovery. As we seek to understand our world, across a host of complexly interconnected phenomena and over scales of time and distance that were virtually inaccessible to us a generation ago, our discoveries shape that world. At the forefront of many of these discoveries is the College of Computer, Mathematical, and Natural Sciences (CMNS).

    CMNS is home to 12 major research institutes and centers and to 10 academic departments: astronomy, atmospheric and oceanic science, biology, cell biology and molecular genetics, chemistry and biochemistry, computer science, entomology, geology, mathematics, and physics.

    Our Faculty

    Our faculty are at the cutting edge over the full range of these disciplines. Our physicists fill in major gaps in our fundamental understanding of matter, participating in the recent Higgs boson discovery, and demonstrating the first-ever teleportation of information between atoms. Our astronomers probe the origin of the universe with one of the world’s premier radio observatories, and have just discovered water on the moon. Our computer scientists are developing the principles for guaranteed security and privacy in information systems.

    Our Research

    Driven by the pursuit of excellence, the University of Maryland has enjoyed a remarkable rise in accomplishment and reputation over the past two decades. By any measure, Maryland is now one of the nation’s preeminent public research universities and on a path to become one of the world’s best. To fulfill this promise, we must capitalize on our momentum, fully exploit our competitive advantages, and pursue ambitious goals with great discipline and entrepreneurial spirit. This promise is within reach. This strategic plan is our working agenda.

    The plan is comprehensive, bold, and action oriented. It sets forth a vision of the University as an institution unmatched in its capacity to attract talent, address the most important issues of our time, and produce the leaders of tomorrow. The plan will guide the investment of our human and material resources as we strengthen our undergraduate and graduate programs and expand research, outreach and partnerships, become a truly international center, and enhance our surrounding community.

    Our success will benefit Maryland in the near and long term, strengthen the State’s competitive capacity in a challenging and changing environment and enrich the economic, social and cultural life of the region. We will be a catalyst for progress, the State’s most valuable asset, and an indispensable contributor to the nation’s well-being. Achieving the goals of Transforming Maryland requires broad-based and sustained support from our extended community. We ask our stakeholders to join with us to make the University an institution of world-class quality with world-wide reach and unparalleled impact as it serves the people and the state of Maryland.

    Our researchers are also at the cusp of the new biology for the 21st century, with bioscience emerging as a key area in almost all CMNS disciplines. Entomologists are learning how climate change affects the behavior of insects, and earth science faculty are coupling physical and biosphere data to predict that change. Geochemists are discovering how our planet evolved to support life, and biologists and entomologists are discovering how evolutionary processes have operated in living organisms. Our biologists have learned how human generated sound affects aquatic organisms, and cell biologists and computer scientists use advanced genomics to study disease and host-pathogen interactions. Our mathematicians are modeling the spread of AIDS, while our astronomers are searching for habitable exoplanets.

    Our Education

    CMNS is also a national resource for educating and training the next generation of leaders. Many of our major programs are ranked among the top 10 of public research universities in the nation. CMNS offers every student a high-quality, innovative and cross-disciplinary educational experience that is also affordable. Strongly committed to making science and mathematics studies available to all, CMNS actively encourages and supports the recruitment and retention of women and minorities.

    Our Students

    Our students have the unique opportunity to work closely with first-class faculty in state-of-the-art labs both on and off campus, conducting real-world, high-impact research on some of the most exciting problems of modern science. 87% of our undergraduates conduct research and/or hold internships while earning their bachelor’s degree. CMNS degrees command respect around the world, and open doors to a wide variety of rewarding career options. Many students continue on to graduate school; others find challenging positions in high-tech industry or federal laboratories, and some join professions such as medicine, teaching, and law.

     
  • richardmitnick 2:24 pm on February 6, 2019 Permalink | Reply
    Tags: Active Learning Initiative funds nine projects, Biological and Environmental Engineering, , Ecology and Evolutionary Biology, Entomology, In all 70 faculty members will work on substantially changing the way they teach in more than 40 courses to over 4500 students. The work will be supported by 17 new teaching innovation postdoctoral fe, Information Science, Mathematics, Mechanical and Aerospace Engineering, Natural Resources, Psychology, The School of Integrative Plant Science   

    From Cornell Chronicle: “Active Learning Initiative funds nine projects” 

    Cornell Bloc

    From Cornell Chronicle

    February 6, 2019
    Daniel Aloi
    dea35@cornell.edu

    1
    Students work together in Introduction to Evolutionary Biology and Diversity, an Active Learning Initiative course. Cornell Brand Communications File Photo.

    Cornell’s Active Learning Initiative (ALI) will nearly double in scope and impact with a new round of funding for innovative projects to enhance undergraduate teaching and learning in nine departments.

    In the first universitywide ALI grant competition, about $5 million has been awarded in substantial new grants ranging from $195,000 to almost $1 million, spread over two to five years. The funded projects will affect courses at all levels, including sequences aimed at majors, survey courses for non-majors, and introductory, online and lab courses.

    In all, 70 faculty members will work on substantially changing the way they teach in more than 40 courses to over 4,500 students. The work will be supported by 17 new teaching innovation postdoctoral fellows across the projects.

    The initiative aims to improve teaching and learning in groups of courses by introducing active learning and other research-based pedagogies drawn from a variety of disciplines. Two previous grant cycles in 2014 and 2017 focused on projects within the College of Arts and Sciences.

    Undergraduate teaching departments across the university received a call for proposals last fall. The Departments of Mathematics and of Ecology and Evolutionary Biology won their second ALI grants, and large projects in information science and engineering are among those funded this cycle.

    “We received many excellent and thoughtful proposals,” said Vice Provost for Academic Innovation Julia Thom-Levy, who supervises the initiative with support from the Center for Teaching Innovation. “Over the three competitions, we have already or will work with more than 100 faculty in 16 departments and four colleges, putting Cornell at the cutting edge of innovation in undergraduate education. This is an extremely exciting development, and many people have worked hard to get us to this point.”

    The grants have so far supported projects in the natural sciences, social sciences, engineering, mathematics and the humanities. Projects are jointly funded by ALI and the respective colleges, with support for the initiative coming from the Office of the Provost and a donor.

    The departments and projects funded:

    Information Science will transform six core courses over the next three years. Faculty and postdocs will incorporate innovative techniques for activities in and out of the classroom, including live-coding collaborations and group data visualization projects. The project explores how to facilitate student learning and implement collaborative classwork and peer feedback with increasingly large class sizes. Impact: more than 1,500 students over three years.

    Mathematics will redesign two linear algebra courses providing foundational math knowledge for many fields, with a target of improving students’ conceptual understanding and ability to model real-life situations; and the department will continue to develop instructor training. Impact: more than 400 students a year. The department received a three-year ALI grant in 2017 to transform two introductory calculus courses and a proofs course, together serving more than 900 students a year.

    Biological and Environmental Engineering: Three existing courses and one new course will focus on developing problem-solving skills that span disciplines, allowing students to transfer skills and knowledge across courses and contexts, and identify and develop solutions to complex problems. Overall impact of the three-year grant: About 200 students will take these courses every year.

    Ecology and Evolutionary Biology faculty will take active learning a step further following a five-year ALI grant in 2014 that transformed two core introductory courses. A new, online active learning version of one, Evolutionary Biology and Diversity, will launch to run parallel to the classroom course during the academic year and on its own in the summer. Goals of the three-year project include reaching a broader, more diverse group of students without increasing an already large class size; and establishing a model for designing online courses and assessing their effectiveness in comparison to the in-person course that is already offered on campus.

    Entomology faculty will redesign three popular classes for non-majors with a three-year grant. Active learning modules will be incorporated to prompt students to practice thinking and communicating like scientists, and learn to critically evaluate and interpret scientific information. Impact: more than 300 students a year.

    Mechanical and Aerospace Engineering faculty have developed a plan to transform six courses and combine the best elements of project teams and coursework through case-based learning. The courses are taken simultaneously by nearly all MAE students as juniors, allowing for projects and assignments spanning multiple courses, focusing on different aspects of the same engineering challenge. Impact of the project, funded by a four-year grant: “a richer and more applied engineering experience” for more than 130 students a year.

    The School of Integrative Plant Science plans to further transform its core 10-course undergraduate curriculum with a five-year grant. SIPS revised the curriculum when it was established in 2015, and enrollment in the major has since more than doubled in size. The grant will support the work of 14 faculty members and four postdocs, developing in-class activities to improve student learning and targeting the laboratory components of the program by moving away from observational labs and toward experimental labs.

    Natural Resources: Faculty teaching in the multidisciplinary Environmental and Sustainability Sciences (ESS) major will redesign an online course on Climate Solutions and a Field Biology course, and develop new courses aimed at collaboratively solving complex environmental problems, such as improving water resource management and assessing environmental policy. Climate Solutions students on campus can engage in discussions with students from around the world taking a parallel MOOC version of the class. Natural resources faculty will lead these efforts over three years; the rapidly growing ESS major involves 75 faculty members from 22 departments across the Colleges of Agriculture and Life Sciences and Arts and Sciences.

    Psychology: Introduction to Psychology, one of the largest courses at Cornell with more than 800 students, will be transformed as part of an ALI-funded, three-year project to implement active learning strategies in several undergraduate courses. Faculty aim to introduce polling questions and student discussion in the large course and more inquiry-driven group work in smaller classes. The project will target learning outcomes established by the American Psychological Association.

    “The new projects build on impressive results from previous competitions within the College of Arts and Sciences,” said Peter Lepage, the Goldwin Smith Professor of Physics and director of ALI. “Research shows that student learning can be improved dramatically through active learning, and that is what we are finding at Cornell.”

    ALI, together with the Center for Teaching Innovation, works with departments throughout the grant period, helps train staff in active learning and helps departments design assessments to measure impacts.

    See the full article here .


    five-ways-keep-your-child-safe-school-shootings

    Please help promote STEM in your local schools.

    Stem Education Coalition

    Once called “the first American university” by educational historian Frederick Rudolph, Cornell University represents a distinctive mix of eminent scholarship and democratic ideals. Adding practical subjects to the classics and admitting qualified students regardless of nationality, race, social circumstance, gender, or religion was quite a departure when Cornell was founded in 1865.

    Today’s Cornell reflects this heritage of egalitarian excellence. It is home to the nation’s first colleges devoted to hotel administration, industrial and labor relations, and veterinary medicine. Both a private university and the land-grant institution of New York State, Cornell University is the most educationally diverse member of the Ivy League.

    On the Ithaca campus alone nearly 20,000 students representing every state and 120 countries choose from among 4,000 courses in 11 undergraduate, graduate, and professional schools. Many undergraduates participate in a wide range of interdisciplinary programs, play meaningful roles in original research, and study in Cornell programs in Washington, New York City, and the world over.

     
  • richardmitnick 2:57 pm on May 8, 2018 Permalink | Reply
    Tags: , , , , Mathematics, , ,   

    From Symmetry: “Leveling the playing field” 

    Symmetry Mag
    From Symmetry

    1
    Photo by Eleanor Starkman

    05/08/18
    Ali Sundermier

    [When I read this article, my first reaction was that this is all worthless. I have been running a series in this blog which highlights “Women in STEM” in all of the phases that the expression implies. The simple fact is that there is and continues to be and will continue to be gender bias in the physical sciences (and probably elsewhere, but this is my area of choice). This is certainly unfair to women, but it is also unfair to all of mankind. We are losing a lot of great and powerful minds and voices as we try to push the future of knowledge and quality of life for all. So I am doing the post. But in all fields men need to call on and respect women if things are to improve. I personally see no evidence of this. As long as women only get to talk to women there will be no progress.]

    Conferences for Undergraduate Women in Physics aims to encourage more women and gender minorities to pursue careers in physics and improve diversity in the field.

    Nicole Pfiester, an engineering grad student at Tufts University, says she has been interested in physics since she was a child. She says she loves learning how things work, and physics provides a foundation for doing just that.

    But when Pfiester began pursuing a degree in physics as an undergraduate at Purdue University in 2006, she had a hard time feeling like she belonged in the male-dominated field.

    “In a class of about 30 physics students,” she says, “I think two of us were women. I just always stood out. I was kind of shy back then and much more inclined to open up to other women than I was to men, especially in study groups. Not being around people I could relate to, while it didn’t make things impossible, definitely made things more difficult.”

    In 2008, two years into her undergraduate career, Pfiester attended a conference at the University of Michigan that was designed to address this very issue. The meeting was part of the Conferences for Undergraduate Women in Physics, or CUWiP, a collection of annual three-day regional conferences to give undergraduate women a sense of belonging and motivate them to continue in the field.

    Pfiester says it was amazing to see so many female physicists in the same room and to learn that they had all gone through similar experiences. It inspired her and the other students she was with to start their own Women in Physics chapter at Purdue. Since then, the school has hosted two separate CUWiP events, in 2011 and 2015.

    “Just seeing that there are other people like you doing what it is you want to do is really powerful,” Pfiester says. “It can really help you get through some difficult moments where it’s really easy, especially in college, to feel like you don’t belong. When you see other people experiencing the same struggles and, even more importantly, you see role models who look and talk like you, you realize that this is something you can do, too. I always left those conferences really energized and ready to get back into it.”

    CUWiP was founded in 2006 when two graduate students at the University of Southern California realized that only 21 percent of US undergraduates in physics were women, a percentage that dropped even further in physics with career progression. In the 12 years since then, the percentage of undergraduate physics degrees going to women in the US has not grown, but CUWiP has. What began as one conference with 27 attendees has developed into a string of conferences held at sites across the country, as well as in Canada and the UK, with more than 1500 attendees per year. Since the American Physical Society took the conference under its umbrella in 2012, the number of participants has continued to grow every year.

    The conferences are supported by the National Science Foundation, the Department of Energy and the host institutions. Most student transportation to the conferences is almost covered by the students’ home institutions, and APS provides extensive administrative support. In addition, local organizing committees contribute a significant volunteer effort.

    “We want to provide women, gender minorities and anyone who attends the conference access to information and resources that are going to help them continue in science careers,” says Pearl Sandick, a dark-matter physicist at the University of Utah and chair of the National Organizing Committee for CUWiP.

    Some of the goals of the conference, Sandick says, are to make sure people leave with a greater sense of community, identify themselves more as physicists, become more aware of gender issues in physics, and feel valued and respected in their field. They accomplish this through workshops and panels featuring accomplished female physicists in a broad range of professions.

    2
    Before the beginning of the shared video keynote talk, attendees at each CUWiP site cheer and wave on video. This gives a sense of the national scale of the conference and the huge number of people involved.
    Courtesy of Columbia University

    “Often students come to the conference and are very discouraged,” says past chair Daniela Bortoletto, a high-energy physicist at the University of Oxford who organizes CUWiP in the UK. “But then they meet these extremely accomplished scientists who tell the stories of their lives, and they learn that everybody struggles at different steps, everybody gets discouraged at some point, and there are ups and downs in everyone’s careers. I think it’s valuable to see that. The students walk out of the conference with a lot more confidence.”

    Through CUWiP, the organizers hope to equip students to make informed decisions about their education and expose them to the kinds of career opportunities that are open to them as physics majors, whether it means going to grad school or going into industry or science policy.

    “Not every student in physics is aware that physicists do all kinds of things,” says Kate Scholberg, a neutrino physicist at Duke and past chair. “Everybody who has been a physics undergrad gets the question, ‘What are you going to do with that?’ We want to show students there’s a lot more out there than grad school and help them expand their professional networks.”

    They also reach back to try to make conditions better for the next generations of physicists.

    At the 2018 conference, Hope Marks, now a senior at Utah State University majoring in physics, participated in a workshop in which she wrote a letter to her high school physics teacher, who she says really sparked her interest in the field.

    “I really liked the experiments we did and talking about some of the modern discoveries of physics,” she says. “I loved how physics allows us to explore the world from particles even smaller than atoms to literally the entire universe.”

    The workshop was meant to encourage high school science and math teachers to support women in their classes.

    One of the challenges to organizing the conferences, says Pat Burchat, an observational cosmologist at Stanford University and past chair, is to build experiences that are engaging and accessible for undergraduate women.

    “The tendency of organizers is naturally to think about the kinds of conferences they go to,” says Burchat says, “which usually consist of a bunch of research talks, often full of people sitting passively listening to someone talk. We want to make sure CUWiP consists of a lot of interactive sessions and workshops to keep the students engaged.”

    Candace Bryan, a physics major at the University of Utah who has wanted to be an astronomer since elementary school, says one of the most encouraging parts of the conference was learning about imposter syndrome, which occurs when someone believes that they have made it to where they are only by chance and don’t feel deserving of their achievements.

    “Science can be such an intimidating field,” she says. “It was the first time I’d ever heard that phrase, and it was really freeing to hear about it and know that so many others felt the same way. Every single person in that room raised their hand when they asked, ‘Who here has experienced imposter syndrome?’ That was really powerful. It helped me to try to move past that and improve awareness.”

    Women feeling imposter syndrome sometimes interpret their struggles as a sign that they don’t belong in physics, Bryan says.

    “It’s important to support women in physics and make sure they know there are other women out there who are struggling with the same things,” she says.

    “It was really inspirational for everyone to see how far they had come and receive encouragement to keep going. It was really nice to have that feeling after conference of ‘I can go to that class and kill it,’ or ‘I can take that test and not feel like I’m going to fail.’ And if you do fail, it’s OK, because everyone else has at some point. The important thing is to keep going.”

    See the full article here .

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    Symmetry is a joint Fermilab/SLAC publication.


     
  • richardmitnick 7:44 pm on April 24, 2018 Permalink | Reply
    Tags: , “Newton was the first physicist” says Sylvester James Gates a physicist at Brown University, Mathematics, Peter Woit - “When you go far enough back you really can’t tell who’s a physicist and who’s a mathematician”, , Riemannian geometry, , The relationship between physics and mathematics goes back to the beginning of both subjects   

    From Symmetry: “The coevolution of physics and math” 

    Symmetry Mag
    Symmetry

    04/24/18
    Evelyn Lamb

    1
    Artwork by Sandbox Studio, Chicago

    Breakthroughs in physics sometimes require an assist from the field of mathematics—and vice versa.

    In 1912, Albert Einstein, then a 33-year-old theoretical physicist at the Eidgenössische Technische Hochschule in Zürich, was in the midst of developing an extension to his theory of special relativity.

    With special relativity, he had codified the relationship between the dimensions of space and time. Now, seven years later, he was trying to incorporate into his theory the effects of gravity. This feat—a revolution in physics that would supplant Isaac Newton’s law of universal gravitation and result in Einstein’s theory of general relativity—would require some new ideas.

    Fortunately, Einstein’s friend and collaborator Marcel Grossmann swooped in like a waiter bearing an exotic, appetizing delight (at least in a mathematician’s overactive imagination): Riemannian geometry.

    This mathematical framework, developed in the mid-19th century by German mathematician Bernhard Riemann, was something of a revolution itself. It represented a shift in mathematical thinking from viewing mathematical shapes as subsets of the three-dimensional space they lived in to thinking about their properties intrinsically. For example, a sphere can be described as the set of points in 3-dimensional space that lie exactly 1 unit away from a central point. But it can also be described as a 2-dimensional object that has particular curvature properties at every single point. This alternative definition isn’t terribly important for understanding the sphere itself but ends up being very useful with more complicated manifolds or higher-dimensional spaces.

    By Einstein’s time, the theory was still new enough that it hadn’t completely permeated through mathematics, but it happened to be exactly what Einstein needed. Riemannian geometry gave him the foundation he needed to formulate the precise equations of general relativity. Einstein and Grossmann were able to publish their work later that year.

    “It’s hard to imagine how he would have come up with relativity without help from mathematicians,” says Peter Woit, a theoretical physicist in the Mathematics Department at Columbia University.

    The story of general relativity could go to mathematicians’ heads. Here mathematics seems to be a benevolent patron, blessing the benighted world of physics with just the right equations at the right time.

    But of course the interplay between mathematics and physics is much more complicated than that. They weren’t even separate disciplines for most of recorded history. Ancient Greek, Egyptian and Babylonian mathematics took as an assumption the fact that we live in a world in which distance, time and gravity behave in a certain way.

    “Newton was the first physicist,” says Sylvester James Gates, a physicist at Brown University. “In order to reach the pinnacle, he had to invent a new piece of mathematics; it’s called calculus.”

    Calculus made some classical geometry problems easier to solve, but its foremost purpose to Newton was to give him a way to analyze the motion and change he observed in physics. In that story, mathematics is perhaps more of a butler, hired to help keep the affairs in order, than a savior.

    Even after physics and mathematics began their separate evolutionary paths, the disciplines were closely linked. “When you go far enough back, you really can’t tell who’s a physicist and who’s a mathematician,” Woit says. (As a mathematician, I was a bit scandalized the first time I saw Emmy Noether’s name attached to physics! I knew her primarily through abstract algebra.)

    Throughout the history of the two fields, mathematics and physics have each contributed important ideas to the other. Mathematician Hermann Weyl’s work on mathematical objects called Lie groups provided an important basis for understanding symmetry in quantum mechanics. In his 1930 book The Principles of Quantum Mechanics, theoretical physicist Paul Dirac introduced the Dirac delta function to help describe the concept in particle physics of a pointlike particle—anything so small that it would be modeled by a point in an idealized situation. A picture of the Dirac delta function looks like a horizontal line lying along the bottom of the x axis of a graph, at x=0, except at the place where it intersects with the y axis, where it explodes into a line pointing up to infinity. Dirac declared that the integral of this function, the measure of the area underneath it, was equal to 1. Strictly speaking, no such function exists, but Dirac’s use of the Dirac delta eventually spurred mathematician Laurent Schwartz to develop the theory of distributions in a mathematically rigorous way. Today distributions are extraordinarily useful in the mathematical fields of ordinary and partial differential equations.

    Though modern researchers focus their work more and more tightly, the line between physics and mathematics is still a blurry one. A physicist has won the Fields Medal, one of the most prestigious accolades in mathematics. And a mathematician, Maxim Kontsevich, has won the new Breakthrough Prizes in both mathematics and physics. One can attend seminar talks about quantum field theory, black holes, and string theory in both math and physics departments. Since 2011, the annual String Math conference has brought mathematicians and physicists together to work on the intersection of their fields in string theory and quantum field theory.

    String theory is perhaps the best recent example of the interplay between mathematics and physics, for reasons that eventually bring us back to Einstein and the question of gravity.

    String theory is a theoretical framework in which those pointlike particles Dirac was describing become one-dimensional objects called strings. Part of the theoretical model for those strings corresponds to gravitons, theoretical particles that carry the force of gravity.

    Most humans will tell you that we perceive the universe as having three spatial dimensions and one dimension of time. But string theory naturally lives in 10 dimensions. In 1984, as the number of physicists working on string theory ballooned, a group of researchers including Edward Witten, the physicist who was later awarded a Fields Medal, discovered that the extra six dimensions of string theory needed to be part of a space known as a Calabi-Yau manifold.

    When mathematicians joined the fray to try to figure out what structures these manifolds could have, physicists were hoping for just a few candidates. Instead, they found boatloads of Calabi-Yaus. Mathematicians still have not finished classifying them. They haven’t even determined whether their classification has a finite number of pieces.

    As mathematicians and physicists studied these spaces, they discovered an interesting duality between Calabi-Yau manifolds. Two manifolds that seem completely different can end up describing the same physics. This idea, called mirror symmetry, has blossomed in mathematics, leading to entire new research avenues. The framework of string theory has almost become a playground for mathematicians, yielding countless new avenues of exploration.

    Mina Aganagic, a theoretical physicist at the University of California, Berkeley, believes string theory and related topics will continue to provide these connections between physics and math.

    “In some sense, we’ve explored a very small part of string theory and a very small number of its predictions,” she says. Mathematicians and their focus on detailed rigorous proofs bring one point of view to the field, and physicists, with their tendency to prioritize intuitive understanding, bring another. “That’s what makes the relationship so satisfying.”

    The relationship between physics and mathematics goes back to the beginning of both subjects; as the fields have advanced, this relationship has gotten more and more tangled, a complicated tapestry. There is seemingly no end to the places where a well-placed set of tools for making calculations could help physicists, or where a probing question from physics could inspire mathematicians to create entirely new mathematical objects or theories.

    See the full article here .

    Please help promote STEM in your local schools.

    STEM Icon

    Stem Education Coalition

    Symmetry is a joint Fermilab/SLAC publication.


     
  • richardmitnick 12:12 pm on January 25, 2018 Permalink | Reply
    Tags: , , , Mathematicians work to expand their new pictorial mathematical language into other areas, Mathematics, Picture-perfect approach to science   

    From Harvard Gazette: “Picture-perfect approach to science” 

    Harvard University
    Harvard University


    Harvard Gazette

    January 24, 2018
    Peter Reuell

    1
    Zhengwei Liu (left) and Arthur Jaffe are leading a new project to expand quon, their pictorial math language developed to help understand quantum information theory, into new fields from algebra to M-theory. Stephanie Mitchell/Harvard Staff Photographer.

    Mathematicians work to expand their new pictorial mathematical language into other areas.

    A picture is worth 1,000 words, the saying goes, but a group of Harvard-based scientists is hoping that it may also be worth the same number of equations.

    Pictorial laws appear to unify ideas from disparate, interdisciplinary fields of knowledge, linking them beautifully like elements of a da Vinci painting. The group is working to expand the pictorial mathematical language first outlined last year by Arthur Jaffe, the Landon T. Clay Professor of Mathematics and Theoretical Science, and postdoctoral fellow Zhengwei Liu.

    “There is one word you can take away from this: excitement,” Jaffe said. “And that’s because we’re not trying just to solve a problem here or there, but we are trying to develop a new way to think about mathematics, through developing and using different mathematical languages based on pictures in two, three, and more dimensions.”

    Last year they created a 3-D language called quon, which they used to understand concepts related to quantum information theory. Now, new research has offered tantalizing hints that quon could offer insights into a host of other areas in mathematics, from algebra to Fourier analysis, as well as in theoretical physics, from statistical physics to string theory. The researchers describe their vision of the project in a paper that appeared Jan. 2 in the journal Proceedings of the National Academy of Sciences.

    “There has been a great deal of evolution in this work over the past year, and we think this is the tip of the iceberg,” Jaffe said. “We’ve discovered that the ideas we used for quantum information are relevant to a much broader spectrum of subjects. We are very grateful to have received a grant from the Templeton Religion Trust that enabled us to assemble a team of researchers last summer to pursue this project further, including undergraduates, graduate students, and postdocs, as well as senior collaborators at other institutions.”

    The core team involves distinguished mathematicians such as Adrian Ocneanu, a visiting professor this year at Harvard, Vaughan Jones, and Alina Vdovina. As important are rising stars who have come to Harvard from around the world, including Jinsong Wu from the Harbin Institute of Technology and William Norledge, a recent graduate from the University of Newcastle. Also involved are students such as Alex Wozniakowski, one of the original members of the project and now a student at Nanyang Technological University in Singapore, visiting graduate students Kaifeng Bu from Zhejiang University in Hangzhou, China, Weichen Gu and Boqing Xue from the Chinese Academy of Sciences in Beijing, Harvard graduate student Sruthi Narayanan, and Chase Bendarz, an undergraduate at Northwestern University and Harvard.

    2
    An illustration of the project is pictured in Lyman Building at Harvard University. Stephanie Mitchell/Harvard Staff Photographer.

    While images have been used in mathematics since ancient times, Jaffe and colleagues believe that the team’s approach, which involves applying pictures to math generally and using images to explore the connections between math and subjects such as physics and cognitive science, may mark the emergence of a new field.

    Among the sort of problems the team has already been able to solve, Liu said, is a pictorial way to think about Fourier analysis.

    “We developed this, motivated by several ideas from Ocneanu,” he said. “Immediately, we used this to give new insights into quantum information. But we also found that we could prove an elaborate algebraic identity for formula 6j-symbols,” a standard tool in representation theory, in theoretical physics, and in chemistry.

    That identity had been found in an elementary case, but Harvard mathematician Shamil Shakirov conjectured that it was true in a general form. The group has now posted a proof on arXiv.org that is under review for publication later in the year. Another very general family of identities that the group has understood simply using the geometric Fourier transform is known as the Verlinde fusion formulas.

    “By looking at the mathematical analysis of pictures, we also found some really unexpected new inequalities. They generalize the famous uncertainty principles of [Werner] Heisenberg and of [Lucien] Hardy and become parts of a larger story,” Liu said. “So the mathematics of the picture languages themselves is quite interesting to understand. We then see their implications on other topics.”

    “I am very taken by this project, because before this, I was working on quantum information, but the only way I knew to do that was using linear algebra,” said Bu. “But working with Arthur and Zhengwei, we’ve been able to use this pictorial language to derive new ideas and geometric tools that we can use to develop new quantum protocols. They have already been useful, and we foresee that these ideas could have wide-ranging applications in the future.

    “It’s amazing, I think, that we can use a simple pictorial language to describe very complicated algebra equations,” Bu continued. “I think this is not only a new approach, but a new field for mathematics.”

    Ocneanu interjected, “Ultimately what higher-dimensional picture language does is to translate the structure of space into mathematics in a natural way.”

    Whereas traditional, linear algebra flattens 3-D concepts into a single line of equations, he said, the picture language allows scientists to use 3-D and higher-dimensional spaces to translate the world around them.

    “Space, or more generally space-time, is a kind of computational machine,” said Ocneanu. “We should really translate what space is doing into the kinds of things mathematicians use, so we can read the structure of space.”

    For Norledge, the new mathematical language is striking in the way it builds from a handful of relatively simple concepts into a complex theory.

    “My background is in representation theory; my thesis is in this area of math called geometric group theory,” he said. “So with a background of using pictures and geometric objects, it helps to apply mathematics in this way. We’re still trying to realize this, but if this all goes through and succeeds, you’ve got a very beautiful area of mathematics where you start with just a few axioms, and just from that beginning you can generalize this highly nontrivial theory with this beautiful structure.”

    “We hope that eventually one can implement the ideas we are studying in new theoretical-physics models, as well as in some practical terms,” Jaffe said. “To share in our excitement, take a look at our website.”

    See the full article here .

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    Harvard University campus
    Harvard is the oldest institution of higher education in the United States, established in 1636 by vote of the Great and General Court of the Massachusetts Bay Colony. It was named after the College’s first benefactor, the young minister John Harvard of Charlestown, who upon his death in 1638 left his library and half his estate to the institution. A statue of John Harvard stands today in front of University Hall in Harvard Yard, and is perhaps the University’s best known landmark.

    Harvard University has 12 degree-granting Schools in addition to the Radcliffe Institute for Advanced Study. The University has grown from nine students with a single master to an enrollment of more than 20,000 degree candidates including undergraduate, graduate, and professional students. There are more than 360,000 living alumni in the U.S. and over 190 other countries.

     
  • richardmitnick 2:51 pm on November 10, 2017 Permalink | Reply
    Tags: , Mathematics, , Taco-The Tensor Algebra Compiler   

    From MIT: “Faster big-data analysis” 

    MIT News
    MIT Widget

    MIT News

    October 30, 2017
    Larry Hardesty

    1
    A new MIT computer system speeds computations involving “sparse tensors,” multidimensional data arrays that consist mostly of zeroes. Image: Christine Daniloff, MIT

    System for performing “tensor algebra” offers 100-fold speedups over previous software packages.

    We live in the age of big data, but most of that data is “sparse.” Imagine, for instance, a massive table that mapped all of Amazon’s customers against all of its products, with a “1” for each product a given customer bought and a “0” otherwise. The table would be mostly zeroes.

    With sparse data, analytic algorithms end up doing a lot of addition and multiplication by zero, which is wasted computation. Programmers get around this by writing custom code to avoid zero entries, but that code is complex, and it generally applies only to a narrow range of problems.

    At the Association for Computing Machinery’s Conference on Systems, Programming, Languages and Applications: Software for Humanity (SPLASH), researchers from MIT, the French Alternative Energies and Atomic Energy Commission, and Adobe Research recently presented a new system that automatically produces code optimized for sparse data.

    That code offers a 100-fold speedup over existing, non-optimized software packages. And its performance is comparable to that of meticulously hand-optimized code for specific sparse-data operations, while requiring far less work on the programmer’s part.

    The system is called Taco, for tensor algebra compiler. In computer-science parlance, a data structure like the Amazon table is called a “matrix,” and a tensor is just a higher-dimensional analogue of a matrix. If that Amazon table also mapped customers and products against the customers’ product ratings on the Amazon site and the words used in their product reviews, the result would be a four-dimensional tensor.

    “Sparse representations have been there for more than 60 years,” says Saman Amarasinghe, an MIT professor of electrical engineering and computer science (EECS) and senior author on the new paper. “But nobody knew how to generate code for them automatically. People figured out a few very specific operations — sparse matrix-vector multiply, sparse matrix-vector multiply plus a vector, sparse matrix-matrix multiply, sparse matrix-matrix-matrix multiply. The biggest contribution we make is the ability to generate code for any tensor-algebra expression when the matrices are sparse.”

    Joining Amarasinghe on the paper are first author Fredrik Kjolstad, an MIT graduate student in EECS; Stephen Chou, also a graduate student in EECS; David Lugato of the French Alternative Energies and Atomic Energy Commission; and Shoaib Kamil of Adobe Research.

    Science paper:
    The Tensor Algebra Compiler

    Custom kernels

    In recent years, the mathematical manipulation of tensors — tensor algebra — has become crucial to not only big-data analysis but machine learning, too. And it’s been a staple of scientific research since Einstein’s time.

    Traditionally, to handle tensor algebra, mathematics software has decomposed tensor operations into their constituent parts. So, for instance, if a computation required two tensors to be multiplied and then added to a third, the software would run its standard tensor multiplication routine on the first two tensors, store the result, and then run its standard tensor addition routine.

    In the age of big data, however, this approach is too time-consuming. For efficient operation on massive data sets, Kjolstad explains, every sequence of tensor operations requires its own “kernel,” or computational template.

    “If you do it in one kernel, you can do it all at once, and you can make it go faster, instead of having to put the output in memory and then read it back in so that you can add it to something else,” Kjolstad says. “You can just do it in the same loop.”

    Computer science researchers have developed kernels for some of the tensor operations most common in machine learning and big-data analytics, such as those enumerated by Amarasinghe. But the number of possible kernels is infinite: The kernel for adding together three tensors, for instance, is different from the kernel for adding together four, and the kernel for adding three three-dimensional tensors is different from the kernel for adding three four-dimensional tensors.

    Many tensor operations involve multiplying an entry from one tensor with one from another. If either entry is zero, so is their product, and programs for manipulating large, sparse matrices can waste a huge amount of time adding and multiplying zeroes.

    Hand-optimized code for sparse tensors identifies zero entries and streamlines operations involving them — either carrying forward the nonzero entries in additions or omitting multiplications entirely. This makes tensor manipulations much faster, but it requires the programmer to do a lot more work.

    The code for multiplying two matrices — a simple type of tensor, with only two dimensions, like a table — might, for instance, take 12 lines if the matrix is full (meaning that none of the entries can be omitted). But if the matrix is sparse, the same operation can require 100 lines of code or more, to track omissions and elisions.

    Enter Taco

    Taco adds all that extra code automatically. The programmer simply specifies the size of a tensor, whether it’s full or sparse, and the location of the file from which it should import its values. For any given operation on two tensors, Taco builds a hierarchical map that indicates, first, which paired entries from both tensors are nonzero and, then, which entries from each tensor are paired with zeroes. All pairs of zeroes it simply discards.

    Taco also uses an efficient indexing scheme to store only the nonzero values of sparse tensors. With zero entries included, a publicly released tensor from Amazon, which maps customer ID numbers against purchases and descriptive terms culled from reviews, takes up 107 exabytes of data, or roughly 10 times the estimated storage capacity of all of Google’s servers. But using the Taco compression scheme, it takes up only 13 gigabytes — small enough to fit on a smartphone.

    “Many research groups over the last two decades have attempted to solve the compiler-optimization and code-generation problem for sparse-matrix computations but made little progress,” says Saday Sadayappan, a professor of computer science and engineering at Ohio State University, who was not involved in the research. “The recent developments from Fred and Saman represent a fundamental breakthrough on this long-standing open problem.”

    “Their compiler now enables application developers to specify very complex sparse matrix or tensor computations in a very easy and convenient high-level notation, from which the compiler automatically generates very efficient code,” he continues. “For several sparse computations, the generated code from the compiler has been shown to be comparable or better than painstakingly developed manual implementations. This has the potential to be a real game-changer. It is one of the most exciting advances in recent times in the area of compiler optimization.”

    See the full article here .

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  • richardmitnick 11:24 am on May 28, 2017 Permalink | Reply
    Tags: , , , , , , Mathematics, ,   

    From LLNL: Women in STEM-“Lab engages girls at San Joaquin STEM event” 


    Lawrence Livermore National Laboratory

    Carenda L Martin
    martin59@llnl.gov
    925-424-4715

    The Laboratory participated in an educational outreach event held last month titled, “Engaging Girls in STEM: Making a Connection for Action,” at the San Joaquin County Office of Education facility in Stockton.

    More than 300 young women in grades 6-12 attended the program, which is part of a statewide initiative to encourage young girls and women to pursue education and careers in science, technology, engineering and math (STEM) related fields. The event was hosted by the San Joaquin County Office of Education, State Department of Education and the California Commission on the Status of Women and Girls.

    1
    Girls donned 3D googles to take a 360-degree virtual reality tour of the Lab’s National Ignition and Additive Manufacturing facilities.
    No image credit.

    A panel of women working in STEM fields was featured along with an exhibitor fair, showcasing various STEM programs and professions, such as LLNL, Association of Women in Science, CSU Sacramento, San Joaquin Delta College, University of the Pacific, Stockton Astronomical Society and the World of Wonders (WOW) Museum. Occupational therapists, engineers, microbiologists, neuroscientists, physicians and computer scientists also showcased hands-on, industry-based activities.

    The Laboratory was well represented with a booth that featured 360 degree tours of the National Ignition and Additive Manufacturing facilities via 3D goggles, and a booth with giveaways and information about the San Joaquin Expanding Your Horizons conference for girls, which is now in its 25th year and led and organized by a committee of Lab volunteers.

    Also featured was the Laboratory’s popular Fun With Science program, presented by Nick Williams, featuring experiments involving states of matter, chemistry, electricity, air pressure, etc.

    Employee volunteers included Cary Gellner, Carrie Martin, Norma McTyer (retired), Jeene Villanueva along with Joanna Albala, LLNL’s education program manager, who facilitated the Lab’s involvement.

    3
    STEM Lab volunteers included (from left) Joanna Albala, Jeene Villanueva, Cary Gellner, Carrie Martin, Norma McTyer (retired) and Nick Williams.

    See the full article here .

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    Operated by Lawrence Livermore National Security, LLC, for the Department of Energy’s National Nuclear Security
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  • richardmitnick 8:43 pm on May 21, 2017 Permalink | Reply
    Tags: , , , , , Mathematics, NanoFab, ,   

    From NIST: “Nanocollaboration Leads to Big Things” 

    NIST

    May 12, 2017 [Nothing like being timely getting into social media.]

    Ben Stein
    benjamin.stein@nist.gov
    (301) 975-2763

    1
    Entrance to NIST’s Advanced Measurement Laboratory in Gaithersburg, Maryland. Credit: Photo Courtesy HDR Architecture, Inc./Steve Hall Copyright Hedrich Blessing

    Roche Sequencing Solutions engineer Juraj Topolancik was looking for a way to decode DNA from cancer patients in a matter of minutes.

    Rajesh Krishnamurthy, a researcher with the startup company 3i Diagnostics, needed help in fabricating a key component of a device that rapidly identifies infection-causing bacteria.

    Ranbir Singh, an engineer with GeneSiC Semiconductor Inc., in Dulles, Virginia, sought to construct and analyze a semiconductor chip that transmits voltages large enough to power electric cars and spacecraft.

    These researchers all credit the NanoFab, located at the Center for Nanoscale Science and Technology (CNST) on the Gaithersburg, Maryland campus of the National Institute of Standards and Technology (NIST). The NanoFab provides cutting-edge nanotechnology capabilities for NIST scientists that is also accessible to outside users, with supplying the state-of-art tools, know-how and dependability to realize their goals.


    Learn more about the CNST NanoFab, where scientists from government, academia and industry can use commercial, state-of-the-art tools at economical rates, and get help from dedicated, full-time technical support staff. Voices: David Baldwin (Great Ball of Light, Inc.), Elisa Williams (Scientific & Biomedical Microsystems), George Coles (Johns Hopkins Applied Physics Laboratory) and William Osborn (NIST).

    When Krishnamurthy, whose company is based in Germantown, Maryland, needed an infrared filter for the bacteria-identifying chip, proximity was but one factor in reaching out to the NanoFab.

    “Even more important was the level of expertise you have here,” he says. “The attention to detail and the trust we have in the staff is so important—we didn’t have to worry if they would do a good job, which gives us tremendous peace of mind,” Krishnamurthy notes.

    The NanoFab also aided his project in another, unexpected way. Krishnamurthy had initially thought that the design for his company’s device would require a costly, highly customized silicon chip. But in reviewing design plans with engineers at the NanoFab, “they came up with a very creative way” to use a more standard, less expensive silicon wafer that would achieve the same goals, he notes.

    “The impact in the short term is that we didn’t have to pay as much [to build and test] the device at the NanoFab, which matters quite a bit because we’re a start-up company,” says Krishnamurthy. “In the long run, this will be a huge factor in [enabling us to mass produce] the device, keeping our costs low because, thanks to the input from the NanoFab, the source material is not a custom material.”

    Singh came to the NanoFab with a different mission. His company is developing a gallium nitride semiconductor device durable enough to transmit hundreds to thousands of volts without deteriorating. He relies on the NanoFab’s metal deposition tools and high-resolution lithography instruments to finish building and assess the properties of the device.

    2
    Semiconductor device, fabricated with the help of the NanoFab, designed to transmit high voltages.
    Credit: GeneSiC Semiconductor Inc.

    “Not only is there a wide diversity of tools, but within each task there are multiple technologies,” Singh adds.

    For instance, he notes, technologies offered at the NanoFab for depositing exquisitely thin and highly uniform layers of metal—which Singh found crucial for making reliable electrical contacts—include both evaporation and sputtering, he says.

    The wide range of metals available for deposition at the NanoFab, uncommon at other nanotech facilities, was another draw.

    “We needed different metals compared to those commonly used on silicon wafers and the NanoFab provided those materials,” notes Singh.

    Topolancik, the Roche Sequencing Solutions engineer, needed high precision etching and deposition tools to fabricate a device that may ultimately improve cancer treatment. His company‘s plan to rapidly sequence DNA from cancer patients could quickly determine if potential anti-cancer drugs and those already in use are producing the genetic mutations necessary to fight cancer.

    “We want to know if the drug is working, and if not, to stop using it and change the treatment,” says Topolancik.

    In the standard method to sequence the double-stranded DNA molecule, a strand is peeled off and resynthesized, base by base, with each base—cytosine, adenine, guanine and thymine—tagged with a different fluorescent label.

    “It’s a very accurate but slow method,” says Topolancik.

    Instead of peeling apart the molecule, Topolancik is devising a method to read DNA directly, a much faster process. Borrowing a technique from the magnetic recording industry, he sandwiches the DNA between two electrodes separated by a gap just nanometers in width.

    3

    Illustration of experiment to directly identify the base pairs of a DNA strand (denoted by A, C, T, G in graph). Tunneling current flows through DNA placed between two closely spaced electrodes. Different bases allow different amounts of current to flow, revealing the components of the DNA molecule.
    Credit: J. Topolancik/Roche Sequencing Solutions

    According to quantum theory, if the gap is small enough, electrons will spontaneously “tunnel” from one electrode to the other. In Topolancik’s setup, the tunneling electrons must pass through the DNA in order to reach the other electrode.

    The strength of the tunneling current identifies the bases of the DNA trapped between the electrodes. It’s an extremely rapid process, but for the technique to work reliably, the electrodes and the gap between them must be fabricated with extraordinarily high precision.

    That’s where the NanoFab comes in. To deposit layers of different metals just nanometers in thickness on a wafer, Topolancik relies on the NanoFab’s ion beam deposition tool. And to etch a pattern in those ultrathin, supersmooth layers without disturbing them—a final step in fabricating the electrodes—requires the NanoFab’s ion etching instrument.

    “These are specialty tools that are not usually accessible in academic facilities, but here [at the NanoFab] you have full, 24/7 access to them,” says Topolancik. “And if a tool goes down, it gets fixed right away,” he adds. “People here care about you, they want you to succeed because that’s the mission of the NanoFab.” As a result, he notes, “I can get done here in two weeks what would take half a year any place else.”


    Take a 360-degree walking tour of the CNST NanoFab in this video!

    See the full article here.

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    NIST Campus, Gaitherberg, MD, USA

    NIST Mission, Vision, Core Competencies, and Core Values

    NIST’s mission

    To promote U.S. innovation and industrial competitiveness by advancing measurement science, standards, and technology in ways that enhance economic security and improve our quality of life.
    NIST’s vision

    NIST will be the world’s leader in creating critical measurement solutions and promoting equitable standards. Our efforts stimulate innovation, foster industrial competitiveness, and improve the quality of life.
    NIST’s core competencies

    Measurement science
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    NIST’s core values

    NIST is an organization with strong values, reflected both in our history and our current work. NIST leadership and staff will uphold these values to ensure a high performing environment that is safe and respectful of all.

    Perseverance: We take the long view, planning the future with scientific knowledge and imagination to ensure continued impact and relevance for our stakeholders.
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    Inclusivity: We work collaboratively to harness the diversity of people and ideas, both inside and outside of NIST, to attain the best solutions to multidisciplinary challenges.
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  • richardmitnick 7:46 am on May 17, 2017 Permalink | Reply
    Tags: Andrea Bertozzi, Mathematics, ,   

    From UCLA: Women in STEM-“UCLA innovator gets creative with applied mathematics” Andrea Bertozzi 

    UCLA bloc

    UCLA

    Andrea Bertozzi puts math to work solving real-world problems

    May 15, 2017
    Nico Correia

    1
    UCLA mathematician Andrea Bertozzi works on a wide range of problems, ranging from the prediction of crime to the deployment of robotic bees. UCLA

    While her grade school classmates were learning the alphabet and how to count to five, Andrea Bertozzi remembers studying negative numbers and modular arithmetic.

    Math often gets a bad rap as an uncreative left brain-oriented activity, but Bertozzi recalls that, as a child, she was fascinated with it because of its creative potential.

    “Teachers have trouble teaching it that way,” said Bertozzi, a professor of mathematics and director of applied mathematics at UCLA, and the inaugural holder of UCLA’s Betsy Wood Knapp Chair for Innovation and Creativity. “They’re not looking at it the right way.”

    As the director of applied mathematics at UCLA and a member of the UCLA Institute for Digital Research and Education’s Executive Committee, Bertozzi and her colleagues conceive of math as a creative medium that can be practically used to solve real-world problems. “Our department is not one that does routine applications,” she said. “We develop new math on the boundary with other fields.”

    One of Bertozzi’s most publicized projects is an ideal illustration of math in action. In a partnership with the Los Angeles Police Department, Bertozzi and UCLA anthropology professor Jeffrey Brantingham head a research team that developed a mathematical model to predicts where and when crime will most likely happen, based on historical crime data in targeted areas so that police officers can preemptively patrol these districts.

    The model they and their team developed based on an algorithm that “learns,” evolves and adapts to new crime data is based on earthquake science. It takes a triggering event such as a property crime or a burglary and treats it similarly to aftershocks following an earthquake that can be tracked by scientists to figure out where and when the next one will occur.

    Another of Bertozzi’s projects, the deployment of robotic bees, is being done in collaboration with Spring Berman, a robotics expert and an assistant professor of mechanical and aerospace engineering at Arizona State University.

    2
    This ground-based robotic bee was developed by undergraduates under Andrea Bertozzi’s direction to test algorithms needed to guide pollinating “bees” to designated plants.

    Since the late 1990s, the population of bees has plunged because of a combination of factors. Earlier this year, the rusty-patched bumblebee landed on the US Fish and Wildlife Service’s list of endangered species. Without bees to pollinate, humanity runs the risk of losing a wide swath of the world’s flora. One solution that scientists are looking into is the development of robotic bees.

    That’s where Bertozzi’s creative mathematical abilities come in.

    Bertozzi and Berman are studying algorithms that would send out a cloud of these robotic pollinators to certain plants. In the applied math lab at UCLA, undergraduates have created earthbound robotic bees to test path-planning algorithms for simple robots without GPS trackers. The group is planning to present the results of testbed simulation “flights” at a conference.

    Bertozzi isn’t exaggerating when she says she is working on a broad research agenda. Her interest in non-linear partial differential equations and applied mathematics has led to projects in everything from image-processing to cooperative robotics and high-dimensional data analysis.

    “It turns out that a lot of my recent projects have social components,” she said. “I have a lot of ideas; we work on those that I can pitch to the funding agencies.” She and her students have used a powerful computer resource at UCLA, the Hoffman2 Cluster, provided by the Institute of Digital Research and Education, to do their complex calculations.

    Although her research goals are all complex, Bertozzi has a concise philosophy on math.

    “You can think of math as a language that describes the real world,” said Bertozzi. “It’s about always reinventing and adding different structures to things.”

    See the full article here .

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    For nearly 100 years, UCLA has been a pioneer, persevering through impossibility, turning the futile into the attainable.

    We doubt the critics, reject the status quo and see opportunity in dissatisfaction. Our campus, faculty and students are driven by optimism. It is not naïve; it is essential. And it has fueled every accomplishment, allowing us to redefine what’s possible, time after time.

    This can-do perspective has brought us 12 Nobel Prizes, 12 Rhodes Scholarships, more NCAA titles than any university and more Olympic medals than most nations. Our faculty and alumni helped create the Internet and pioneered reverse osmosis. And more than 100 companies have been created based on technology developed at UCLA.

     
  • richardmitnick 8:58 am on March 4, 2017 Permalink | Reply
    Tags: , , Making math more Lego-like, Mathematics   

    From Harvard: “Making math more Lego-like” 

    Harvard University
    Harvard University

    March 2, 2017
    Peter Reuell

    1
    “[A] picture is worth 1,000 symbols,” quips Professor Arthur Jaffe (left). Jaffe and postdoctoral fellow Zhengwei Liu have developed a pictorial mathematical language that can convey pages of algebraic equations in a single 3-D drawing. Rose Lincoln/Harvard Staff Photographer

    Galileo called mathematics the “language with which God wrote the universe.” He described a picture-language, and now that language has a new dimension.

    The Harvard trio of Arthur Jaffe, the Landon T. Clay Professor of Mathematics and Theoretical Science, postdoctoral fellow Zhengwei Liu, and researcher Alex Wozniakowski has developed a 3-D picture-language for mathematics with potential as a tool across a range of topics, from pure math to physics.

    Though not the first pictorial language of mathematics, the new one, called quon, holds promise for being able to transmit not only complex concepts, but also vast amounts of detail in relatively simple images. The language is described in a February 2017 paper published in the Proceedings of the National Academy of Sciences.

    “It’s a big deal,” said Jacob Biamonte of the Quantum Complexity Science Initiative after reading the research. “The paper will set a new foundation for a vast topic.”

    “This paper is the result of work we’ve been doing for the past year and a half, and we regard this as the start of something new and exciting,” Jaffe said. “It seems to be the tip of an iceberg. We invented our language to solve a problem in quantum information, but we have already found that this language led us to the discovery of new mathematical results in other areas of mathematics. We expect that it will also have interesting applications in physics.”

    When it comes to the “language” of mathematics, humans start with the basics — by learning their numbers. As we get older, however, things become more complex.

    “We learn to use algebra, and we use letters to represent variables or other values that might be altered,” Liu said. “Now, when we look at research work, we see fewer numbers and more letters and formulas. One of our aims is to replace ‘symbol proof’ by ‘picture proof.’”

    The new language relies on images to convey the same information that is found in traditional algebraic equations — and in some cases, even more.

    “An image can contain information that is very hard to describe algebraically,” Liu said. “It is very easy to transmit meaning through an image, and easy for people to understand what they see in an image, so we visualize these concepts and instead of words or letters can communicate via pictures.”

    “So this pictorial language for mathematics can give you insights and a way of thinking that you don’t see in the usual, algebraic way of approaching mathematics,” Jaffe said. “For centuries there has been a great deal of interaction between mathematics and physics because people were thinking about the same things, but from different points of view. When we put the two subjects together, we found many new insights, and this new language can take that into another dimension.”

    In their most recent work, the researchers moved their language into a more literal realm, creating 3-D images that, when manipulated, can trigger mathematical insights.

    “Where before we had been working in two dimensions, we now see that it’s valuable to have a language that’s Lego-like, and in three dimensions,” Jaffe said. “By pushing these pictures around, or working with them like an object you can deform, the images can have different mathematical meanings, and in that way we can create equations.”

    Among their pictorial feats, Jaffe said, are the complex equations used to describe quantum teleportation. The researchers have pictures for the Pauli matrices, which are fundamental components of quantum information protocols. This shows that the standard protocols are topological, and also leads to discovery of new protocols.

    “It turns out one picture is worth 1,000 symbols,” Jaffe said.

    “We could describe this algebraically, and it might require an entire page of equations,” Liu added. “But we can do that in one picture, so it can capture a lot of information.”

    Having found a fit with quantum information, the researchers are now exploring how their language might also be useful in a number of other subjects in mathematics and physics.

    “We don’t want to make claims at this point,” Jaffe said, “but we believe and are thinking about quite a few other areas where this picture-language could be important.”

    See the full article here .

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    Harvard University campus

    Harvard is the oldest institution of higher education in the United States, established in 1636 by vote of the Great and General Court of the Massachusetts Bay Colony. It was named after the College’s first benefactor, the young minister John Harvard of Charlestown, who upon his death in 1638 left his library and half his estate to the institution. A statue of John Harvard stands today in front of University Hall in Harvard Yard, and is perhaps the University’s best known landmark.

    Harvard University has 12 degree-granting Schools in addition to the Radcliffe Institute for Advanced Study. The University has grown from nine students with a single master to an enrollment of more than 20,000 degree candidates including undergraduate, graduate, and professional students. There are more than 360,000 living alumni in the U.S. and over 190 other countries.

     
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