NMIMS, Navi Mumbai campus, as a part of its vision on holistic development of the students, recently hosted its third edition of the popular music festival, “Requiem 3.0.” on 12 to 14 January 2023 which was sponsored by TradeBook. The three-day event was a celebration of music and the power it holds to bring people together. Featuring a variety of music competitions, including Bollywood solo, Classical solo, Western solo, Underground rap, and Power of instruments which had more than 500 participants and 29 colleges across the country, also sub events like Rangmanch, Sportify, and Debatable saw a huge participation.
The festival was inaugurated with a grand banner drop and a Pooja for the smooth progress of the event and featured a star-studded night with well-known musicians of the industry, guest Artists Concert, and a DJ night across the three days. Star night had performances of Jannat and Crook fame Bollywood playback singer Nikhil D’Souza and upcoming artists Vaani Bhasin and Anish Chhabra. The event was hosted by GD Sayal who is an accomplished performer and a seasoned host.
“Music stimulates the right brain and inculcates lateral thinking. Through this fest we aim at providing a platform to showcase the incredible talent of our students and participants across India. The journey of Requiem has been truly remarkable and we are proud to see it reach this level of success,” Dr. Parthasarathi N. Mukherjee, Director, SVKM’s NMIMS, Navi Mumbai Campus.
On this occasion Dr. Suma Gundugola, faculty in charge Music committee, Navi Mumbai said “Music is the strongest form of communication. Requiem is a celebration of all those talented musicians who perform and create unforgettable experiences for their audiences. In years to come Requiem will become the largest Music festival in Mumbai “
The event ended on a high note with an after-hours party featuring DJ Dhyan with Garvit and Priyansh, where the audience danced and enjoyed themselves to the hilt. The Music Committee of SVKM’s NMIMS, Navi Mumbai campus, which was established in 2018 to encourage musical talent among students, has been organizing music fests regularly.
Study: Superconductivity switches on and off in “magic-angle” graphene
A quick electric pulse completely flips the material’s electronic properties, opening a route to ultrafast, brain-inspired, superconducting electronics
Written by Jennifer Chu, MIT News Office
With some careful twisting and stacking, MIT physicists have revealed a new and exotic property in “magic-angle” graphene: superconductivity that can be turned on and off with an electric pulse, much like a light switch.
The discovery could lead to ultrafast, energy-efficient superconducting transistors for neuromorphic devices — electronics designed to operate in a way similar to the rapid on/off firing of neurons in the human brain.
Magic-angle graphene refers to a very particular stacking of graphene — an atom-thin material made from carbon atoms that are linked in a hexagonal pattern resembling chicken wire. When one sheet of graphene is stacked atop a second sheet at a precise “magic” angle, the twisted structure creates a slightly offset “moiré” pattern, or superlattice, that is able to support a host of surprising electronic behaviors.
In 2018, Pablo Jarillo-Herrero and his group at MIT were the first to demonstrate magic-angle twisted bilayer graphene. They showed that the new bilayer structure could behave as an insulator, much like wood, when they applied a certain continuous electric field. When they upped the field, the insulator suddenly morphed into a superconductor, allowing electrons to flow, friction-free.
That discovery gave rise to “twistronics,” a field that explores how certain electronic properties emerge from the twisting and layering of two-dimensional materials. Researchers including Jarillo-Herrero have continued to reveal surprising properties in magic-angle graphene, including various ways to switch the material between different electronic states. So far, such “switches” have acted more like dimmers, in that researchers must continuously apply an electric or magnetic field to turn on superconductivity, and keep it on.
Now Jarillo-Herrero and his team have shown that superconductivity in magic-angle graphene can be switched on, and kept on, with just a short pulse rather than a continuous electric field. The key, they found was a combination of twisting and stacking.
In a paper appearing today in Nature Nanotechnology, the team reports that, by stacking magic-angle graphene between two offset layers of boron nitride — a two-dimensional insulating material — the unique alignment of the sandwich structure enabled the researchers to turn graphene’s superconductivity on and off with a short electric pulse.
“For the vast majority of materials, if you remove the electric field, zzzzip, the electric state is gone,” says Jarillo-Herrero, who is the Cecil and Ida Green Professor of Physics at MIT. “This is the first time that a superconducting material has been made that can be electrically switched on and off, abruptly. This could pave the way for a new generation of twisted, graphene-based superconducting electronics.”
His MIT co-authors are lead author Dahlia Klein, Li-Qiao Xia, and David MacNeill, along with Kenji Watanabe and Takashi Taniguchi of the National Institute for Materials Science in Japan.
Flipping the switch
In 2019, a team at Stanford University discovered that magic-angle graphene could be coerced into a ferromagnetic state. Ferromagnets are materials that retain their magnetic properties, even in the absence of an externally applied magnetic field.
The researchers found that magic-angle graphene could exhibit ferromagnetic properties in a way that could be tuned on and off. This happened when the graphene sheets were layered between two sheets of boron nitride such that the crystal structure of the graphene was aligned to one of the boron nitride layers. The arrangement resembled a cheese sandwich in which the top slice of bread and the cheese orientations are aligned, but the bottom slice of bread is rotated at a random angle with respect to the top slice. The result intrigued the MIT group.
“We were trying to get a stronger magnet by aligning both slices,” Jarillo-Herrero says. “Instead, we found something completely different.”
In their current study, the team fabricated a sandwich of carefully angled and stacked materials. The “cheese” of the sandwich consisted of magic-angle graphene — two graphene sheets, the top rotated slightly at the “magic” angle of 1.1 degrees with respect to the bottom sheet. Above this structure, they placed a layer of boron nitride, exactly aligned with the top graphene sheet. Finally, they placed a second layer of boron nitride below the entire structure and offset it by 30 degrees with respect to the top layer of boron nitride.
The team then measured the electrical resistance of the graphene layers as they applied a gate voltage. They found, as others have, that the twisted bilayer graphene switched electronic states, changing between insulating, conducting, and superconducting states at certain known voltages.
What the group did not expect was that each electronic state persisted rather than immediately disappearing once the voltage was removed — a property known as bistability. They found that, at a particular voltage, the graphene layers turned into a superconductor, and remained superconducting, even as the researchers removed this voltage.
This bistable effect suggests that superconductivity can be turned on and off with short electric pulses rather than a continuous electric field, similar to flicking a light switch. It isn’t clear what enables this switchable superconductivity, though the researchers suspect it has something to do with the special alignment of the twisted graphene to both boron nitride layers, which enables a ferroelectric-like response of the system. (Ferroelectric materials display bistability in their electric properties.)
“By paying attention to the stacking, you could add another tuning knob to the growing complexity of magic-angle, superconducting devices,” Klein says.
For now, the team sees the new superconducting switch as another tool researchers can consider as they develop materials for faster, smaller, more energy-efficient electronics.
“People are trying to build electronic devices that do calculations in a way that’s inspired by the brain,” Jarillo-Herrero says. “In the brain, we have neurons that, beyond a certain threshold, they fire. Similarly, we now have found a way for magic-angle graphene to switch superconductivity abruptly, beyond a certain threshold. This is a key property in realizing neuromorphic computing.”
This research was supported in part by the Air Force Office of Scientific Research, the Army Research Office, and the Gordon and Betty Moore Foundation.
NMIMS SBM Offers MBA (Part-Time) Program for Working Executives
The program focuses on providing a holistic education to enhance their employability
Start your professional learning journey now with the best-in-class MBA (Part-Time) program with the most sought-after prestigious NMIMS, School of Business Management, featured in the top 100 global B-schools as per Financial Times MiM 2022 ranking. A highly respected business school in India with a legacy of 41 years and a prestigious faculty announces admissions open for their MBA (Part-Time) program at their Mumbai campus.
This program offers an opportunity for working executives to acquire a high-end compact management qualification through rigorous and qualitative in-class learning and practical exposure to industry expertise. The program focuses on providing a holistic education to enhance their employability, exposing working executives to contemporary trends and practices in management. It also provides excellent academic resources coupled with industry best practices to boost the managerial competence of executives.
NMIMS SBM MBA (Part-Time) is accredited by Advance Collegiate Schools of Business (AACSB) for working executives who want to enhance their skills and advance their careers. NMIMS has world-class pedagogy to provide students with a comprehensive and engaging learning experience. NMIMS SBM has a team of experts with extensive experience in the business world. The program structure is upto 40% hybrid and 60% in the classroom, whereas Bloomberg Certification Program will help students use financial analysis tools more efficiently.
“The program is specifically meant for executives who have spent quality time in the industry and have adequate exposure to managerial roles and responsibilities. The two-year MBA (Part-Time) program will offer an opportunity for participants to hone their managerial skills and enable them to contribute better to their decision-making. It has been designed to empower students with a well-planned schedule that allows for a balance between study and work,” said Dr. Prashant Mishra, Dean School of Business Management.
Dr. Pradeep Pai, Program Chairperson, MBA (Part-Time), School of Business Management, said, “NMIMS SBM is proud to offer this innovative program to working professionals who wish to take the next step in their careers. We believe that this program will be a preferred executive education program for working professionals seeking to upgrade their qualifications by acquiring a widely acclaimed MBA degree.”
The MBA (Part-Time) program goes beyond traditional education by providing learners value-added workshops and industry connections. Our curriculum is regularly updated to ensure students receive the most current knowledge. The program helps them gain a practical and theoretical understanding of the industry and creates a network of professionals for them as they progress.
- 50% in Graduation from a recognized University in any discipline. (Distance/Part time/Full time)
- Minimum 3 years of work experience in an executive or supervisory capacity or self-employed after graduation & up to the date of written test/personal interview.
- The work experience should be full-time experience and should NOT include internships, projects, training periods, trainee (management, engineering), etc.
Written Test conducted for MBA (Part-Time) by NMIMS OR Candidates with GMAT score of 600 and above (GMAT score of last 5 years up to the closure of registrations will be considered) OR Candidates with a score of 200 and above in NMAT by GMAC examinations for 2020 admission AND Personal Interview
When should data scientists try a new technique?
A new measure can help scientists decide which estimation method to use when modeling a particular data problem
Written by Adam Zewe, MIT News Office
If a scientist wanted to forecast ocean currents to understand how pollution travels after an oil spill, she could use a common approach that looks at currents traveling between 10 and 200 kilometers. Or, she could choose a newer model that also includes shorter currents. This might be more accurate, but it could also require learning new software or running new computational experiments. How to know if it will be worth the time, cost, and effort to use the new method?
A new approach developed by MIT researchers could help data scientists answer this question, whether they are looking at statistics on ocean currents, violent crime, children’s reading ability, or any number of other types of datasets.
The team created a new measure, known as the “c-value,” that helps users choose between techniques based on the chance that a new method is more accurate for a specific dataset. This measure answers the question “is it likely that the new method is more accurate for this data than the common approach?”
Traditionally, statisticians compare methods by averaging a method’s accuracy across all possible datasets. But just because a new method is better for all datasets on average doesn’t mean it will actually provide a better estimate using one particular dataset. Averages are not application-specific.
So, researchers from MIT and elsewhere created the c-value, which is a dataset-specific tool. A high c-value means it is unlikely a new method will be less accurate than the original method on a specific data problem.
In their proof-of-concept paper, the researchers describe and evaluate the c-value using real-world data analysis problems: modeling ocean currents, estimating violent crime in neighborhoods, and approximating student reading ability at schools. They show how the c-value could help statisticians and data analysts achieve more accurate results by indicating when to use alternative estimation methods they otherwise might have ignored.
“What we are trying to do with this particular work is come up with something that is data specific. The classical notion of risk is really natural for someone developing a new method. That person wants their method to work well for all of their users on average. But a user of a method wants something that will work on their individual problem. We’ve shown that the c-value is a very practical proof-of-concept in that direction,” says senior author Tamara Broderick, an associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society.
She’s joined on the paper by Brian Trippe PhD ’22, a former graduate student in Broderick’s group who is now a postdoc at Columbia University; and Sameer Deshpande ’13, a former postdoc in Broderick’s group who is now an assistant professor at the University of Wisconsin at Madison. An accepted version of the paper is posted online in the Journal of the American Statistical Association.
The c-value is designed to help with data problems in which researchers seek to estimate an unknown parameter using a dataset, such as estimating average student reading ability from a dataset of assessment results and student survey responses. A researcher has two estimation methods and must decide which to use for this particular problem.
The better estimation method is the one that results in less “loss,” which means the estimate will be closer to the ground truth. Conder again the forecasting of ocean currents: Perhaps being off by a few meters per hour isn’t so bad, but being off by many kilometers per hour makes the estimate useless. The ground truth is unknown, though; the scientist is trying to estimate it. Therefore, one can never actually compute the loss of an estimate for their specific data. That’s what makes comparing estimates challenging. The c-value helps a scientist navigate this challenge.
The c-value equation uses a specific dataset to compute the estimate with each method, and then once more to compute the c-value between the methods. If the c-value is large, it is unlikely that the alternative method is going to be worse and yield less accurate estimates than the original method.
“In our case, we are assuming that you conservatively want to stay with the default estimator, and you only want to go to the new estimator if you feel very confident about it. With a high c-value, it’s likely that the new estimate is more accurate. If you get a low c-value, you can’t say anything conclusive. You might have actually done better, but you just don’t know,” Broderick explains.
Probing the theory
The researchers put that theory to the test by evaluating three real-world data analysis problems.
For one, they used the c-value to help determine which approach is best for modeling ocean currents, a problem Trippe has been tackling. Accurate models are important for predicting the dispersion of contaminants, like pollution from an oil spill. The team found that estimating ocean currents using multiple scales, one larger and one smaller, likely yields higher accuracy than using only larger scale measurements.
“Oceans researchers are studying this, and the c-value can provide some statistical ‘oomph’ to support modeling the smaller scale,” Broderick says.
In another example, the researchers sought to predict violent crime in census tracts in Philadelphia, an application Deshpande has been studying. Using the c-value, they found that one could get better estimates about violent crime rates by incorporating information about census-tract-level nonviolent crime into the analysis. They also used the c-value to show that additionally leveraging violent crime data from neighboring census tracts in the analysis isn’t likely to provide further accuracy improvements.
“That doesn’t mean there isn’t an improvement, that just means that we don’t feel confident saying that you will get it,” she says.
Now that they have proven the c-value in theory and shown how it could be used to tackle real-world data problems, the researchers want to expand the measure to more types of data and a wider set of model classes.
The ultimate goal is to create a measure that is general enough for many more data analysis problems, and while there is still a lot of work to do to realize that objective, Broderick says this is an important and exciting first step in the right direction.
This research was supported, in part, by an Advanced Research Projects Agency-Energy grant, a National Science Foundation CAREER Award, the Office of Naval Research, and the Wisconsin Alumni Research Foundation.
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