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How to decarbonize the world, at scale – MIT News

The world in recent years has largely been moving on from debates about the need to curb carbon emissions and focusing more on action the development, implementation, and deployment of the technological, economic, and policy measures to spur the scale of reductions needed by mid-century. That was the message Robert Stoner, the interim director of the MIT Energy Initiative (MITEI), gave in his opening remarks at the 2023 MITEI Annual Research Conference.

Attendees at the two-day conference included faculty members, researchers, industry and financial leaders, government officials, and students, as well as more than 50 online participants from around the world.

We are at an extraordinary inflection point. We have this narrow window in time to mitigate the worst effects of climate change by transforming our entire energy system and economy, said Jonah Wagner, the chief strategist of the U.S. Department of Energys (DOE) Loan Programs Office, in one of the conferences keynote speeches.

Yet the solutions exist, he said. Most of the technologies that we need to deploy to stay close to the international target of 1.5 degrees Celsius warming are proven and ready to go, he said. We have over 80 percent of the technologies we will need through 2030, and at least half of the technologies we will need through 2050.

For example, Wagner pointed to the newly commissioned advanced nuclear power plant near Augusta, Georgia the first new nuclear reactor built in the United States in a generation, partly funded through DOE loans. It will be the largest source of clean power in America, he said. Though implementing all the needed technologies in the United States through mid-century will cost an estimated $10 trillion, or about $300 billion a year, most of that money will come from the private sector, he said.

As the United States faces what he describes as a tsunami of distributed energy production, one key example of the strategy thats needed going forward, he said, is encouraging the development of virtual power plants (VPPs). The U.S. power grid is growing, he said, and will add 200 gigawatts of peak demand by 2030. But rather than building new, large power plants to satisfy that need, much of the increase can be accommodated by VPPs, he said which are aggregations of distributed energy resources like rooftop solar with batteries, like electric vehicles (EVs) and chargers, like smart appliances, commercial and industrial loads on the grid that can be used together to help balance supply and demand just like a traditional power plant. For example, by shifting the time of demand for some applications where the timing is not critical, such as recharging EVs late at night instead of right after getting home from work when demand may be peaking, the need for extra peak power can be alleviated.

Such programs offer a broad range of benefits, including affordability, reliability and resilience, decarbonization, and emissions reductions. But implementing such systems on a wide scale requires some up-front help, he explained. Payment for consumers to enroll in programs that allow such time adjustments is the majority of the cost of establishing VPPs, he says, and that means most of the money spent on VPPs goes back into the pockets of American consumers. But to make that happen, there is a need for standardization of VPP operations so that we are not recreating the wheel every single time we deploy a pilot or an effort with a utility.

The conferences other keynote speaker, Anne White, the vice provost and associate vice president for research administration at MIT, cited devastating recent floods, wildfires, and many other extreme weather-related crises around the world that have been exacerbated by climate change. We saw in myriad ways that energy concerns and climate concerns are one and the same, she said. So, we must urgently develop and scale low-carbon and zero-carbon solutions to prevent future warming. And we must do this with a practical, systems-based approach that considers efficiency, affordability, equity, and sustainability for how the world will meet its energy needs.

White added that at MIT, we are mobilizing everything. People at MIT feel a strong sense of responsibility for dealing with these global issues, she said, and I think its because we believe we have tools that can really make a difference.

Among the specific promising technologies that have sprung from MITs labs, she pointed out, is the rapid development of fusion technology that led to MIT spinoff company Commonwealth Fusion Systems, which aims to build a demonstration unit of a practical fusion power reactor by the decades end. Thats an outcome of decades of research, she emphasized the kinds of early-stage risky work that only academic labs, with help from government grants, can carry out.

For example, she pointed to the more than 200 projects that MITEI has provided seed funds of $150,000 each for two years, totaling over $28 million to date. Such early support is a key part of producing the kind of transformative innovation we know we all need. In addition, MITs The Engine has also helped launch not only Commonwealth Fusion Systems, but also Form Energy, a company building a plant in West Virginia to manufacture advanced iron-air batteries for renewable energy storage, and many others.

Following that theme of supporting early innovation, the conference featured two panels that served to highlight the work of students and alumni and their energy-related startup companies. First, a startup showcase, moderated by Catarina Madeira, the director of MITs Startup Exchange, featured presentations about seven recent spinoff companies that are developing cutting-edge technologies that emerged from MIT research. These included:

Later in the conference, a student slam competition featured presentations by 11 students who described results of energy projects they had been working on this past summer. The projects were as diverse as analyzing opposition to wind farms in Maine, how best to allocate EV charging stations, optimizing bioenergy production, recycling the lithium from batteries, encouraging adoption of heat pumps, and conflict analysis about energy project siting. Attendees voted on the quality of the student presentations, and electrical engineering and computer science student Tori Hagenlocker was declared first-place winner for her talk on heat pump adoption.

Students were also featured in a first-time addition to the conference: a panel discussion among five current or recent students, giving their perspective on todays energy issues and priorities, and how they are working toward trying to make a difference. Andres Alvarez, a recent graduate in nuclear engineering, described his work with a startup focused on identifying and supporting early-stage ideas that have potential. Graduate student Dyanna Jaye of urban studies and planning spoke about her work helping to launch a group called the Sunrise Movement to try to drive climate change as a top priority for the country, and her work helping to develop the Green New Deal.

Peter Scott, a graduate student in mechanical engineering who is studying green hydrogen production, spoke of the need for a very drastic and rapid phaseout of current, existing fossil fuels and a halt on developing new sources. Amar Dayal, an MBA candidate at the MIT Sloan School of Management, talked about the interplay between technology and policy, and the crucial role that legislation like the Inflation Reduction Act can have in enabling new energy technology to make the climb to commercialization. And Shreyaa Raghavan, a doctoral student in the Institute of Data, Systems, and Society, talked about the importance of multidisciplinary approaches to climate issues, including the important role of computer science. She added that MIT does well on this compared to other institutions, and sustainability and decarbonization is a pillar in a lot of the different departments and programs that exist here.

Some recent recipients of MITEIs Seed Fund grants reported on their progress in a panel discussion moderated by MITEI Executive Director Martha Broad. Seed grant recipient Ariel Furst, a professor of chemical engineering, pointed out that access to electricity is very much concentrated in the global North and that, overall, one in 10 people worldwide lacks access to electricity and some 2.5 billion people rely on dirty fuels to heat their homes and cook their food, with impacts on both health and climate. The solution her project is developing involves using DNA molecules combined with catalysts to passively convert captured carbon dioxide into ethylene, a widely used chemical feedstock and fuel. Kerri Cahoy, a professor of aeronautics and astronautics, described her work on a system for monitoring methane emissions and power-line conditions by using satellite-based sensors. She and her team found that power lines often begin emitting detectable broadband radio frequencies long before they actually fail in a way that could spark fires.

Admir Masic, an associate professor of civil and environmental engineering, described work on mining the ocean for minerals such as magnesium hydroxide to be used for carbon capture. The process can turn carbon dioxide into solid material that is stable over geological times and potentially usable as a construction material. Kripa Varanasi, a professor of mechanical engineering, said that over the years MITEI seed funding helped some of his projects that went on to become startup companies, and some of them are thriving. He described ongoing work on a new kind of electrolyzer for green hydrogen production. He developed a system using bubble-attracting surfaces to increase the efficiency of bioreactors that generate hydrogen fuel.

A series of panel discussions over the two days covered a range of topics related to technologies and policies that could make a difference in combating climate change. On the technological side, one panel led by Randall Field, the executive director of MITEIs Future Energy Systems Center, looked at large, hard-to-decarbonize industrial processes. Antoine Allanore, a professor of metallurgy, described progress in developing innovative processes for producing iron and steel, among the worlds most used commodities, in a way that drastically reduces greenhouse gas emissions. Greg Wilson of JERA Americas described the potential for ammonia produced from renewable sources to substitute for natural gas in power plants, greatly reducing emissions. Yet-Ming Chiang, a professor in materials science and engineering, described ways to decarbonize cement production using a novel low-temperature process. And Guiyan Zang, a research scientist at MITEI, spoke of efforts to reduce the carbon footprint of producing ethylene, a major industrial chemical, by using an electrochemical process.

Another panel, led by Jacopo Buongiorno, professor of nuclear science and engineering, explored the brightening future for expansion of nuclear power, including new, small, modular reactors that are finally emerging into commercial demonstration. There is for the first time truly here in the U.S. in at least a decade-and-a-half, a lot of excitement, a lot of attention towards nuclear, Buongiorno said. Nuclear power currently produces 45 to 50 percent of the nations carbon-free electricity, the panelists said, and with the first new nuclear power plant in decades now in operation, the stage is set for significant growth.

Carbon capture and sequestration was the subject of a panel led by David Babson, the executive director of MITs Climate Grand Challenges program. MIT professors Betar Gallant and Kripa Varanasi and industry representatives Elisabeth Birkeland from Equinor and Luc Huyse from Chevron Technology Ventures described significant progress in various approaches to recovering carbon dioxide from power plant emissions, from the air, and from the ocean, and converting it into fuels, construction materials, or other valuable commodities.

Some panel discussions also addressed the financial and policy side of the climate issue. A panel on geopolitical implications of the energy transition was moderated by MITEI Deputy Director of Policy Christopher Knittel, who said energy has always been synonymous with geopolitics. He said that as concerns shift from where to find the oil and gas to where is the cobalt and nickel and other elements that will be needed, not only are we worried about where the deposits of natural resources are, but were going to be more and more worried about how governments are incentivizing the transition to developing this new mix of natural resources. Panelist Suzanne Berger, an Institute professor, said were now at a moment of unique openness and opportunity for creating a new American production system, one that is much more efficient and less carbon-producing.

One panel dealt with the investors perspective on the possibilities and pitfalls of emerging energy technologies. Moderator Jacqueline Pless, an assistant professor in MIT Sloan, said theres a lot of momentum now in this space. Its a really ripe time for investing, but the risks are real. Tons of investment is needed in some very big and uncertain technologies.

The role that large, established companies can play in leading a transition to cleaner energy was addressed by another panel. Moderator J.J. Laukatis, MITEIs director of member services, said that the scale of this transformation is massive, and it will also be very different from anything weve seen in the past. Were going to have to scale up complex new technologies and systems across the board, from hydrogen to EVs to the electrical grid, at rates we havent done before. And doing so will require a concerted effort that includes industry as well as government and academia.

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Learning to forget a weapon in the arsenal against harmful AI – EurekAlert

With the AI summit well underway, researchers are keen to raise the very real problem associated with the technology teaching it how to forget.

Society is now abuzz withmodern AIand its exceptional capabilities; we are constantly reminded its potential benefits, across so many areas, permeating practically all facets of our lives but also its dangers.

In an emerging field of research, scientists are highlighting an important weapon in our arsenal towards mitigating the risks of AI machine unlearning. They are helping to figure out new ways of making AI models known as Deep Neural Networks (DNNs) forget data which poses a risk to society.

The problem is re-training AI programmes to forget data is a very expensive and an arduous task. Modern DNNs such as those based on Large Language Models (like ChatGPT, Bard, etc.)require massive resources to be trained and take weeks or months to do so. They also requiretens of Gigawatt-hours of energyfor every training programme, some research estimating as much energy as to power thousands on households for one year.

Machine Unlearningis a burgeoning field of research that could remove troublesome data from DNNs quickly, cheaply and using less resources. The goal is to do so while continuing to ensure high accuracy. Computer Science experts at the University of Warwick, in collaboration with Google DeepMind, are at the forefront of this research.

Professor Peter Triantafillou, Department of Computer Science, University of Warwick, recently co-authored a publication Towards Unbounded Machine Unlearning. He said: DNNs are extremelycomplexstructures, comprised of up to trillions of parameters. Often, welack a solid understandingof exactly how and why they achieve their goals. Given their complexity, and the complexity and size of the datasets they are trained on,DNNs may beharmful to society.

DNNs may be harmful, for example, by being trained on data with biases thus propagating negative stereotypes. The data might reflect existing prejudices, stereotypes and faulty societal assumptions such as a bias that doctors are male, nurses female or even racial prejudices.

DNNs might also contain data with erroneous annotations for example, the incorrect labelling of items, such as labelling an image as being a deep fake or not.

Alarmingly, DNNs may be trained on data which violates the privacy of individuals. This poses a huge challenge to mega-tech companies, with significant legislation in place (for example GDPR) which aims to safeguard the right to be forgotten that is the right of any individual to request that their data be deleted from any dataset and AI programme.

Our recent research has derived a new machine unlearning algorithm that ensures DNNs can forget dodgy data, without compromising overall AI performance. The algorithm can be introduced to the DNN, causing it to specifically forget the data we need it to, without having to re-train it entirely from scratch again. Its the only work thatdifferentiated the needs, requirements, and metrics for successamong the three different types of data needed to be forgotten: biases, erroneous annotations and issues of privacy.

Machine unlearning is an exciting field of research that can be an important tool towards mitigating the risks of AI.

Read the full paper here: https://arxiv.org/abs/2302.09880

Notes to Editors

This research is to be presented intheThirty-Seventh Annual Conference on Neural Information Processing Systems(NeurIPS), in December 2023. It is a collaborative effort between Professor Peter, a PhD student at the Department of Computer Science at the University of Warwick (Meghdad Kurmanji) and researchers from Google DeepMind (Eleni Triantafillou and Jamie Hayes).

The team are also organizing the first ever competition on machine unlearning in NeurIPS 2023, https://unlearning-challenge.github.io/, hosted by Kaggle (with currently ca. 950 participating teams from across the world) to derive unlearning algorithms for a challenging task (unlearning faces from a face data set),https://www.kaggle.com/competitions/neurips-2023-machine-unlearning/leaderboard. At the same time, we are organizing a workshop on machine unlearning inNeurIPS 2023.

Media contact

University of Warwick press office contact:

Annie Slinn 07876876934

Communications Officer |Press & Media Relations | University of Warwick Email: annie.slinn@warwick.ac.uk

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Patriot League Announces 2023 Men’s and Women’s Cross Country … – Patriot League Official Athletic Site

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BETHLEHEM, Pa. Navy senior Sam Keeny and Boston University sophomore Vera Sjberg were selected as the 2023 Patriot League Mens and Womens Cross Country Scholar-Athletes of the Year when the League office announced honors on Friday.Keeny and Sjberg were also selected to the seven- and eight-member Academic All-Patriot League mens and womens squads.Four mens and womens cross-country student-athletes are multiple-time Academic All-Patriot League honorees. The group is led by American Universitys Row Sullivan, the philosophy and political science major who carries a 3.94 cumulative GPA, and they become just the eighth-ever three-time Academic All-Patriot League winner in mens cross country. Keeny, and Boston University graduate student Aksel Laudon (biomedical engineering/medical school, 4.00 GPA) were both repeat selections on the mens academic team.

Lehigh University senior Christina Yakaboski (economics, 3.95 GPA) was the lone repeat honoree on the womens academic team. To be eligible for the Scholar-Athlete of the Year award and Academic All-Patriot League Team, a student-athlete must have at least a 3.20 cumulative GPA and be a starter or key player in their sport. Freshmen or students in their first academic year at their school are not eligible for the honor.2023 Patriot League Mens Cross Country Scholar-Athlete of the YearSam Keeny, Navy, Sr., Annapolis, Md./South River

*Keeny earned First Team All-Patriot League honors for the third-consecutive season by finishing second at the Patriot League Championship with an 8K time of 24:46.3. *The mechanical engineering major carries a cumulative GPA of 3.26.*He earned Academic All-League honors for the second-consecutive year, while also making the Commandants List six times and appearing on the Superintendents List

2023 Patriot League Womens Cross Country Scholar-Athlete of the YearVera Sjberg, Boston University, So., Stockholm, Sweden/Rubeck*Sjberg earned First Team All-Patriot League honors at the 2023 League championship by finishing second with a 6K time of 21:45.1.*The Stockholm, Sweden native is an English major at Boston University with a 3.98 cumulative GPA. Eleven Student-Athletes Make Cross Country Academic Squad for the First TimeSjberg highlights a list of 11 first-time Academic All-Patriot League Cross Country selections, including seven on the womens team. Sjberg is joined by American graduate student Rachael Potter (elementary education, 3.97 GPA), Army West Point senior Claire Lewis (4.22 GPA), Bucknell junior Keely Misutka (chemistry, 3.96 GPA), Colgate junior Kara Shepard (anthropology, 3.98 GPA), Lehigh junior Maddie Hayes (biology, 4.0 GPA), and Navy senior Ellie Abraham (history, 3.82 GPA).

Boston University graduate student Robert Hannon (computer science, 3.65 GPA), Colgate junior Owen Holland (mathematical economics, 3.87 GPA), Holy Cross junior William Schimitsch (computer science, 4.00 GPA), and Navy senior Joe Reimann (computer science, 3.99 GPA) all earned first-time honors for the mens academic squad.

Four All-Patriot League Honorees Earn All-Academic Honors

In mens cross country, Keeny appeared on the Academic All-Patriot League team, while also earning first-team All-Patriot League honors for his performance at the 2023 Patriot League Cross Country Championships. Hannon made the Academic All-Patriot League team, while collecting second-team All-Patriot League honors.

Sjberg highlighted a trio of womens cross country runners who earned both Academic All-Patriot League and first-team All-Patriot League honors for their performance throughout the season. Lewis and Abraham also earned recognition on both lists. 2023 Mens Cross Country Academic All-Patriot League Team

Row Sullivan, American, Gr.

Robert Hannon, Boston University, Sr.

Aksel Laudon, Boston University, Gr.

Owen Holland, Colgate, Jr.

William Schimitsch, Holy Cross, Jr.

Sam Keeny, Navy, Sr.

Joe Reimann, Navy, Sr.

2023 Womens Cross Country Academic All-Patriot League Team*Rachael Potter, American, Gr.

Claire Lewis, Army West Point, Sr.

Vera Sjoberg, Boston University, So.

Keeley Misutka, Bucknell, Jr.

Kara Shepard, Colgate, Jr.

Maddie Hayes, Lehigh, Jr.

Christina Yakaboski, Lehigh, Sr.

Ellie Abraham, Navy, Sr.

*Additional student-athlete selected to the womens team due to a tie in the voting

Patriot League Mens and Womens Scholar-Athlete of the YearThe Patriot League Scholar-Athlete of the Year for each sport comprises the pool of nominees for the Patriot League Male and Female Scholar-Athlete of the Year honor, given out over the summer. One male and one female are selected for this honor.ABOUT THE PATRIOT LEAGUEThe Patriot League is in its fourth decade of academic and athletic achievement, continually demonstrating that student-athlete can excel at both academics and athletics without sacrificing high standards. The Patriot Leagues athletic success is achieved while its member institutions remain committed to its founding principle of admitting and graduating student-athletes that are academically representative of their class. Participation in athletics at Patriot League institutions is viewed as an important component of a well-rounded education.

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Using language to give robots a better grasp of an open-ended world – MIT News

Imagine youre visiting a friend abroad, and you look inside their fridge to see what would make for a great breakfast. Many of the items initially appear foreign to you, with each one encased in unfamiliar packaging and containers. Despite these visual distinctions, you begin to understand what each one is used for and pick them up as needed.

Inspired by humans' ability to handle unfamiliar objects, a group from MITs Computer Science and Artificial Intelligence Laboratory (CSAIL) designed Feature Fields for Robotic Manipulation (F3RM), a system that blends 2D images with foundation model features into 3D scenes to help robots identify and grasp nearby items. F3RM can interpret open-ended language prompts from humans, making the method helpful in real-world environments that contain thousands of objects, like warehouses and households.

F3RM offers robots the ability to interpret open-ended text prompts using natural language, helping the machines manipulate objects. As a result, the machines can understand less-specific requests from humans and still complete the desired task. For example, if a user asks the robot to pick up a tall mug, the robot can locate and grab the item that best fits that description.

Making robots that can actually generalize in the real world is incredibly hard, says Ge Yang, postdoc at the National Science Foundation AI Institute for Artificial Intelligence and Fundamental Interactions and MIT CSAIL. We really want to figure out how to do that, so with this project, we try to push for an aggressive level of generalization, from just three or four objects to anything we find in MITs Stata Center. We wanted to learn how to make robots as flexible as ourselves, since we can grasp and place objects even though we've never seen them before.

Learning whats where by looking

The method could assist robots with picking items in large fulfillment centers with inevitable clutter and unpredictability. In these warehouses, robots are often given a description of the inventory that they're required to identify. The robots must match the text provided to an object, regardless of variations in packaging, so that customers orders are shipped correctly.

For example, the fulfillment centers of major online retailers can contain millions of items, many of which a robot will have never encountered before. To operate at such a scale, robots need to understand the geometry and semantics of different items, with some being in tight spaces. With F3RMs advanced spatial and semantic perception abilities, a robot could become more effective at locating an object, placing it in a bin, and then sending it along for packaging. Ultimately, this would help factory workers ship customers orders more efficiently.

One thing that often surprises people with F3RM is that the same system also works on a room and building scale, and can be used to build simulation environments for robot learning and large maps, says Yang. But before we scale up this work further, we want to first make this system work really fast. This way, we can use this type of representation for more dynamic robotic control tasks, hopefully in real-time, so that robots that handle more dynamic tasks can use it for perception.

The MIT team notes that F3RMs ability to understand different scenes could make it useful in urban and household environments. For example, the approach could help personalized robots identify and pick up specific items. The system aids robots in grasping their surroundings both physically and perceptively.

Visual perception was defined by David Marr as the problem of knowing what is where by looking, says senior author Phillip Isola, MIT associate professor of electrical engineering and computer science and CSAIL principal investigator. Recent foundation models have gotten really good at knowing what they are looking at; they can recognize thousands of object categories and provide detailed text descriptions of images. At the same time, radiance fields have gotten really good at representing where stuff is in a scene. The combination of these two approaches can create a representation of what is where in 3D, and what our work shows is that this combination is especially useful for robotic tasks, which require manipulating objects in 3D.

Creating a digital twin

F3RM begins to understand its surroundings by taking pictures on a selfie stick. The mounted camera snaps 50 images at different poses, enabling it to build a neural radiance field (NeRF), a deep learning method that takes 2D images to construct a 3D scene. This collage of RGB photos creates a digital twin of its surroundings in the form of a 360-degree representation of whats nearby.

In addition to a highly detailed neural radiance field, F3RM also builds a feature field to augment geometry with semantic information. The system uses CLIP, a vision foundation model trained on hundreds of millions of images to efficiently learn visual concepts. By reconstructing the 2D CLIP features for the images taken by the selfie stick, F3RM effectively lifts the 2D features into a 3D representation.

Keeping things open-ended

After receiving a few demonstrations, the robot applies what it knows about geometry and semantics to grasp objects it has never encountered before. Once a user submits a text query, the robot searches through the space of possible grasps to identify those most likely to succeed in picking up the object requested by the user. Each potential option is scored based on its relevance to the prompt, similarity to the demonstrations the robot has been trained on, and if it causes any collisions. The highest-scored grasp is then chosen and executed.

To demonstrate the systems ability to interpret open-ended requests from humans, the researchers prompted the robot to pick up Baymax, a character from Disneys Big Hero 6. While F3RM had never been directly trained to pick up a toy of the cartoon superhero, the robot used its spatial awareness and vision-language features from the foundation models to decide which object to grasp and how to pick it up.

F3RM also enables users to specify which object they want the robot to handle at different levels of linguistic detail. For example, if there is a metal mug and a glass mug, the user can ask the robot for the glass mug. If the bot sees two glass mugs and one of them is filled with coffee and the other with juice, the user can ask for the glass mug with coffee. The foundation model features embedded within the feature field enable this level of open-ended understanding.

If I showed a person how to pick up a mug by the lip, they could easily transfer that knowledge to pick up objects with similar geometries such as bowls, measuring beakers, or even rolls of tape. For robots, achieving this level of adaptability has been quite challenging, says MIT PhD student, CSAIL affiliate, and co-lead author William Shen. F3RM combines geometric understanding with semantics from foundation models trained on internet-scale data to enable this level of aggressive generalization from just a small number of demonstrations.

Shen and Yang wrote the paper under the supervision of Isola, with MIT professor and CSAIL principal investigator Leslie Pack Kaelbling and undergraduate students Alan Yu and Jansen Wong as co-authors. The team was supported, in part, by Amazon.com Services, the National Science Foundation, the Air Force Office of Scientific Research, the Office of Naval Researchs Multidisciplinary University Initiative, the Army Research Office, the MIT-IBM Watson Lab, and the MIT Quest for Intelligence. Their work will be presented at the 2023 Conference on Robot Learning.

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NSF Award to Aid Interdisciplinary Researchers in Detecting Cancer – Lehigh University

To determine whether a patient has cancer, a bodily fluid sample is taken, exposed in a vial to several types of DNAwrapped carbon nanotubes, and the fluorescence of each nanotube is recorded. One of the goals of the project, DNA-Nanocarbon Hybrid Materials for Perception-Based, Analyte-Agnostic Sensing, is to have an automated disease detection system into which data about a bodily fluid can be inputted.

The NSF project focuses on how the system works and how it can be improved. Some of the fundamental questions researchers are looking to answer, according to Jagota, include, Is there a rationale for why the nanotubes hybridized with DNA can detect diseases? and What is the mechanism by which it detects?

One issue is that the modulation of fluorescence of the DNA-carbon nanotubes isnt very specific, meaning a shift in fluorescence can be triggered by one of many biomarkers, not just, in this case, by the ones that reveal a patient has cancer.

Most people, they'll say, Ah, that's useless. You can't use this for sensing, Jagota says.

With many other molecules present in blood, its essentially impossible for any single type of DNA-carbon nanotube to detect whether a cancer biomarker is present. To account for a mixture of molecules in the sample, many types of DNA-nanotubes are needed for collective analysis, he says.

You try to ask the question did they all shift in some way? Jagota says. Can I train this system? Can I expose it to different combinations of different concentrations of my analyte and look at the output, and from that output, can I train a machine-learning algorithm? Can I train a black box which says, You tell me how each one of these shifted and I'll tell you whether this molecule was present or not?

By identifying and using a number of sensors, researchers can be more confident theyre finding what is associated with a biomarker and not something else in the blood, Davison says. That could lead to figuring out how to detect other characteristics or disease states in people.

If there's nothing there at all, there's sort of a baseline fluorescence that will happen, Davison says. When one of these nanotubes has attached to some other molecule, then it can change how it fluoresces either by increasing the brightness or changing the frequency and those are the things that we're measuring.

Davison used the human nose as an analogy for the work theyre doing: Inside the nose, there are different receptors for scent, but its not as simple as one scent per sensor. A collection of sensors activating is what allows people to recognize a particular smell.

The researchers dont want to have to rely on one sensor in this project; they want a set of sensors detectingor not detectinga recognizable pattern.

A big worry for us is that we could have lots of conflicting compounds that are discoverable in blood that aren't what we're looking for, but similarly excite the sensors that we have, Davison says.

One of the broader questions the NSF project asks, according to Davison, is how can they identify the best set of sensors, which they expect need to be as diverse as possible.

Davisons area of the project is in machine learning. He says one of the challenges includes building reliable prediction systems with little data. Unlike big tech companies, such as Google or Meta, which have millions of data points, Davison says theyre more likely to have just a few hundred data points because their data corresponds to real patients.

How can we be as accurate as possible even though we have a small data set to work with? Davison asks.

He says they also have to figure out how to represent the data gathered. For instance, should measurements gathered from the fluorescence of the sample be represented directly with the wavelengths, in the differences or something else?

A separate National Institutes of Health award aims to make the process suitable for clinical practice. Memorial Sloan Kettering Cancer Center is the lead on the NIH project with researchers from Lehigh, the National Institute of Standards and Technology (NIST) and a collaborator from the University of Maryland contributing.

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Professor/Associate Professor/Assistant Professor, School of … – Times Higher Education

Department: School of Computer Science and Engineering

Position: Professor/Associate Professor/Assistant Professor

Description

School of Computer Science and Engineering (Former Faculty of Information Technology) is one of the first four faculties established in the newly founded Macau University of Science and Technology in 2000. It offers a comprehensive suite of degrees: Bachelor of Science, Master of Science, and Doctor of Philosophy degrees. It has strong research programs with support from Macau Science and Technology Development Fund, National Natural Science Foundation of China, and Ministry of Science and Technology of the Peoples Republic of China.

To cope with our developmental plan, applications are invited from those with excellent academic achievements in the following areas:

Qualifications

Remuneration Package

Remuneration and appointment rank offered will be commensurate with the successful applicants academic qualification, professional experience and current position. Medical benefits, annual leave, provident fund, allowance and bonus, on job training program or overseas study opportunities will be provided by the University.

Application Procedure

Qualified candidates should apply the position online at the Universitys careers site (https://www.must.edu.mo/en/careers) and upload an up-to-date curriculum vitae with expected salary, copies of ID/passport, certified copies of academic certificates, transcripts, testimonial of professional experience, publications and academic research outline etc.

To browse for more information about MUST, please visit https://www.must.edu.mo/en

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Math and Science grad student seminar series returns for fall – Brock University

Trees and bees are the focus of upcoming biological sciences research presentations that will kick off the Graduate Mathematics and Science Students (GRAMSS) Seminar Series for the 2023-24 academic year.

The series unites graduate students, post-doctoral fellows and faculty members from a wide range of disciplines within the Faculty of Mathematics and Science to foster a supportive and multidisciplinary environment for research exchange, said GRAMSS Communications Officer and Seminar Co-ordinator Ricardo Alva (BSc 19), a Master of Science student studying cell and molecular biology.

Over the past year, weve featured engaging talks in mathematics, physics, health sciences and computer sciences, along with a guest speaker from McMaster University, he said. With two upcoming talks in biological sciences, this series offers invaluable opportunities for learning, networking, socializing and honing oral presentation skills.

The GRAMSS Seminar Series is planned to take place bi-weekly on Thursdays from 1 to 2 p.m. and is open to all Brock graduate and undergraduate students as well as faculty and staff.Upcoming presentations include:

Information regarding additional presentations will be shared on GRAMSS Instagram and X pages.

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Math and Science grad student seminar series returns for fall - Brock University

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The Future of Fully Homomorphic Encryption – IEEE Spectrum

This sponsored article is brought to you by NYU Tandon School of Engineering.

In our digital age, where information flows seamlessly through the vast network of the internet, the importance of encrypted data cannot be overstated. As we share, communicate, and store an increasing amount of sensitive information online, the need to safeguard it from prying eyes and malicious actors becomes paramount. Encryption serves as the digital guardian, placing our data in a lockbox of algorithms that only those with the proper key can unlock.

Whether its personal messages, health data, financial transactions, or confidential business communications, encryption plays a pivotal role in maintaining privacy and ensuring the integrity of our digital interactions. Typically, data encryption protects data in transit: its locked in an encrypted container for transit over potentially unsecured networks, then unlocked at the other end, by the other party for analysis. But outsourcing to a third-party is inherently insecure.

NYU Tandon School of Engineering

Brandon Reagen, Assistant Professor of Computer Science and Engineering and Electrical and Computer Engineering at the NYU Tandon School of Engineering.

But what if encryption didnt just exist in transit and sit unprotected on either end of the transmission? What if it was possible to do all of your computer work from basic apps to complicated algorithms fully encrypted, from beginning to end.

That is the task being taken up by Brandon Reagen, Assistant Professor of Computer Science and Engineering and Electrical and Computer Engineering at the NYU Tandon School of Engineering. Reagen, who is also a member of the NYU Center for Cybersecurity, focuses his research on designing specialized hardware accelerators for applications including privacy preserving computation. And now, he is proving that the future of computing can be privacy-forward while making huge advances in information processing and hardware design.

In a world where cyber threats are ever-evolving and data breaches are a constant concern, encrypted data acts as a shield against unauthorized access, identity theft, and other cybercrimes. It provides individuals, businesses, and organizations with a secure foundation upon which they can build trust and confidence in the digital realm.

The goal of cybersecurity researchers is the protection of your data from all sorts of bad actors cybercriminals, data-hungry companies, and authoritarian governments. And Reagen believes encrypted computing could hold an answer. This sort of encryption can give you three major things: improved security, complete confidentiality and sometimes control over how your data is used, says Reagen. Its a totally new level of privacy.

My aim is to develop ways to run expensive applications, for example, massive neural networks, cost-effectively and efficiently, anywhere, from massive servers to smartphones Brandon Reagen, NYU Tandon

Fully homomorphic encryption (FHE), one type of privacy preserving computation, offers a solution to this challenge. FHE enables computation on encrypted data, or ciphertext, to keep data protected at all times. The benefits of FHE are significant, from enabling the use of untrusted networks to enhancing data privacy. FHE is an advanced cryptographic technique, widely considered the holy grail of encryption, that enables users to process encrypted data while the data or models remain encrypted, preserving data privacy throughout the data computation process, not just during transit.

While a number of FHE solutions have been developed, running FHE in software on standard processing hardware remains untenable for practical data security applications due to the massive processing overhead. Reagen and his colleagues have recently been working on a DARPA-funded project called The Data Protection in Virtual Environments (DPRIVE) program, that seeks to speed up FHE computation to more usable levels.

Specifically, the program seeks to develop novel approaches to data movement and management, parallel processing, custom functional units, compiler technology, and formal verification methods that ensure the design of the FHE implementation is effective and accurate, while also dramatically decreasing the performance penalty incurred by FHE computations. The target accelerator should reduce the computational run time overhead by many orders of magnitude compared to current software-based FHE computations on conventional CPUs, and accelerate FHE calculations to within one order of magnitude of current performance on unencrypted data.

While FHE has been shown to be possible, the hardware required for it to be practical is still rapidly being developed by researchers. Reagen and his team are designing it from the ground up, including new chips, datapaths, memory hierarchies, and software stacks to make it all work together.

The team was the first to show that the extreme levels of speedup needed to make HE feasible was possible. And by early next year, theyll begin manufacturing of their prototypes to further their field testing.

Reagen who earned a doctoral degree in computer science from Harvard in 2018 and undergraduate degrees in computer systems engineering and applied mathematics from the University of Massachusetts, Amherst, in 2012 focused on creating specialized hardware accelerators for applications like deep learning. These accelerators enhance specialized hardware that can be made orders of magnitude more efficient than general-purpose platforms like CPUs. Enabling accelerators requires changes to the entire compute stack, and to bring about this change, he has made several contributions to lowering the barrier of using accelerators as general architectural constructs, including benchmarking, simulation infrastructure, and System on a Chip (SoC) design.

My aim is to develop ways to run expensive applications, for example, massive neural networks, cost-effectively and efficiently, anywhere, from massive servers to smartphones, he says.

Before coming to NYU Tandon, Reagen was a former research scientist on Facebooks AI Infrastructure Research team, where he became deeply involved in studying privacy. This combination of a deep cutting-edge computer hardware background and a commitment to digital security made him a perfect fit for NYU Tandon and the NYU Center for Cybersecurity, which has been at the forefront of cybersecurity research since its inception.

A lot of the big problems that we have in the world right now revolve around data. Consider global health coming off of COVID: if we had better ways of computing global health data analytics and sharing information without exposing private data, we might have been able to respond to the crisis more effectively and sooner Brandon Reagen, NYU Tandon

For Reagen, this is an exciting moment in the history of privacy preserving computation, a field that will have huge implications for the future of data and computing.

Im an optimist I think this could have as big an impact as the Internet itself, says Reagen. And the reason is that, if you think about a lot of the big problems that we have in the world right now, a lot of them revolve around data. Consider global health. Were just coming off of COVID, and if we had better ways of computing global health data analytics and sharing information without exposing private data, we might have been able to respond to the crisis more effectively and sooner. If we had better ways of sharing data about climate change data from all over the world, without exposing what each individual country or state or city was actually emitting, you could imagine better ways of managing and fighting global climate change. These problems are, in large part, problems of data, and this kind of software can help us solve them.

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Encryption coming to law enforcement radio in Sioux Falls, Rapid City – KELOLAND.com

SIOUX FALLS, S.D. (KELO) Law enforcement radio communications will soon be encrypted in South Dakotas two biggest communities.

The Sioux Falls Police Department, Rapid City Police Department, Minnehaha County Sheriffs Office and Pennington County Sheriffs Office jointly announced Friday the public will not be able to listen to their radio communication beginning Monday, November 13. Authorities cited officer safety as well as consideration for victims and witnesses when announcing the change.

We have had numerous situations just recently where we are catching people engaged in criminal activity who are listening to our scanners either in their earbuds or in their phone app, Sioux Falls Police Chief Jon Thum said.

Thum, Rapid City Chief of Police Don Hedrick, Pennington County Sheriff Brian Mueller and Minnehaha County Sheriff Mike Milstead all shared their thoughts at a news conference Friday.

KELOLAND News will have much more from them Friday night at 10 Central time.

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Encryption coming to law enforcement radio in Sioux Falls, Rapid City - KELOLAND.com

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Dallas County says it prevented file encryption during ransomware attack – StateScoop

External cybersecurity professionals helped prevent any encryption of Dallas County government files or systems following a recent cyberattack, the city announced this week.

A ransomware attack the county detected Oct. 19 is the second to occur in North Texas in just five months after the Dallas city government was subject to a ransomware attack in May, disrupting several city services and leaking the personal information of more than 30,000 residents.

Once Dallas County detected the cyber incident, it retained cybersecurity professionals from the private sector to assist in efforts to contain the threat and investigate the attack, according to the latest cybersecurity notification update provided by the county.

Currently, our work with the cybersecurity firm is ongoing, the update read. While our goal is to be transparent and forthcoming with information relating to the incident, we do not want to make premature assumptions about the extent of impact or other details, which may evolve as the forensic investigation advances.

Though the county maintains that files were not encrypted, CBS News on Tuesday reported that the hacker group Play has claimed responsibility for the attack and that it stole thousands of files. The group stated that private documents of Dallas County departments will go up for sale on the dark web if an unspecified ransome is not paid by Friday, according to CBS News.

Lauren Trimble, chief of staff for Dallas County Judge Clay Lewis Jenkins, did not confirm claims from the ransomware group and did not provide more details, citing the ongoing investigation.

The county said the threat was contained thanks to an endpoint detection and response tool deployed across its network, forced password changes for all users, mandated multi-factor authentication for remote access and blocking traffic to malicious IP addresses.

Currently, there is no evidence of ongoing threat actor activity in our environment, update read. Given these measures and findings, it appears at this time that the incident has been successfully contained and that Dallas Countys systems are secure for use.

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