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EAGER: Designing Infrastructure to Probe Distributed System Configurations: Temple University

Krishna Kant

[email protected]

Cyber systems depend on a number of parameters to configure the system properly. Incorrect setting of these parameters is known to be responsible for an overwhelming percentage of failures, poor service, and exploitation by hackers for cyberattacks. At the same time, diagnosing misconfigurations is a slow, largely manual process that routinely takes days or weeks because of poor understanding of the relationship between configuration parameters and system's response and interdependencies between the parameters. This project explores the capabilities required to automatically compose tests, run them, collect data, and analyze it to simplify the job of finding the root cause of the problem.<br/><br/>The project will build basic diagnosability capabilities in some commonly used services in the data center including domain name service, routing, and active directory along with suitable access control. The project will also explore how the diagnosis goals can be specified at a high-level and translated into a graph of basic tests connected via input-output relationships and further limited by access permissions, and probing locations. The diagnosis infrastructure will build and run the test, collect data and provide a systematic way of analyzing the data to isolate the problematic hardware/software components as much as possible so as to substantially accelerate the testing and root cause analysis. The infrastructure will also provide capabilities to store, rank, and reuse designed tests to make them more effective over time. <br/><br/>The infrastructure built under this project is expected to substantially reduce the cost, time, and effort for diagnosing the systems via automation of many aspects of the diagnosis. If successful, the approach can be applied to emerging cyber-physical systems where misconfiguration problems are likely to be even more critical in nature. <br/><br/>The project will enhance existing open-source software with basic diagnosability and develop additional software for building and running complex tests, introducing errors, and collecting/analyzing the results. The software and the data will be stored in a local archival system at Temple and will be preserved for at least three years beyond the award period. The data will be linked through the project webpage located at http://www.kkant.net/diagnosis<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

CNS Core: Small: Moving Machine Learning into the Next-Generation Cloud Flexibly, Agilely and Efficiently: University of North Carolina at Charlotte

Dazhao Cheng

[email protected]

Machine Learning (ML) is being used in new ways to develop intelligent software. Meanwhile, serverless computing is redefining how to use cloud computing platforms. With serverless computing, ML specialists only need to define a set of stateless functions that have access to a common data store. Current ML software systems are generally specialized for first-generation cloud computing systems that do not have the flexibility required for serverless infrastructures. This project aims to take advantage of serverless computing to deploy machine learning software. This simplifies the deployments, avoids the need for infrastructure maintenance, and includes built-in scalability and cost-control.<br/> <br/>Due to the ease of management and ability to rapidly scale, serverless computation has become the trend to build next-generation ML services and applications. This project proposes a unified serverless computing framework that aims to be flexible, agile and efficient for moving ML into the next-generation cloud to achieve better simplicity, manageability and productivity. In particular, to bridge the semantic gap between the serverful ML model and the serverless cloud platform, this project identifies three major tasks: fine-grained computation management, an efficient communication strategy and a cost-effective service model.<br/> <br/>This project aims for widespread serverless computing by removing server and operation system level details and simplifying the process of building and managing ML applications. It is a continuum along which developers and operations teams become more accustomed to increased automation and abstraction, and more comfortable breaking ML applications into simple, easy-to- manage microservices, application interfaces, and functions. The result is that developers are free to target the right tools for the right tasks and to build ML applications easily that span any number of different serverless services.<br/> <br/>This project emphasizes open-source software development. This will enhance the access to stream data analytic frameworks and broaden the project?s impact. Furthermore, the models and workloads/traces from this project may enable further research by others. The project repository[https://abclab-uncc.github.io/website/grants.html] (data, code, results, emulators, and simulators) will be maintained in the next 5 years.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

Evolutionary inference in the presence of gene tree discordance: Indiana University

Matthew Hahn

[email protected]

Large DNA sequencing projects have revealed that there is not just one way that species are related. Genes and pieces of the same genome can have different relationships. Sometimes these relationships differ because of ancient mating between species called hybridization. When hybridization occurs, important traits can be shared between unrelated species. This project will develop new methods to determine the history of traits shared between unrelated species. It also will develop methods to more accurately determine the relationships among species. The research will allow researchers to identify cases where key traits, such as insecticide resistance, arise once and are passed between species. The research combines mathematical modeling with new statistical approaches. These will be combined to produce open-source software for carrying out the analyses. The software will ensure that all scientists will be able to take advantage of the tools developed by the research, adding to the national infrastructure by enabling new biological discoveries. This work also will improve and accelerate biological research with multiple societal benefits, including helping to identify genes controlling important traits. This project will support the training of a postdoctoral researcher, graduate students, and undergraduates from groups that are underrepresented in science and technology careers. Workshops will be developed to provide training in the use of the software developed by this project.<br/><br/>Species trees make it possible to understand the evolutionary history of traits. Previous work has documented how gene trees that are discordant with the species tree lead to inaccurate inferences about the timing and direction of trait transitions, the number of trait transitions, how often such changes are thought to be driven by adaptive evolution, and even the species tree itself. This research goes beyond documenting the problems of gene tree discordance, providing solutions to these problems. The theoretical approach used will make it possible to incorporate the multiple causes of discordance?including introgression?into a single framework, allowing researchers to make statistically rigorous inferences about evolutionary processes in the presence of discordance. Integrating these results into a single software package enables these insights and methods to be applied widely, for both binary and continuous traits.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

RAPID: Making the Transition to Remote Science Teaching and Learning: Concord Consortium

Daniel Damelin

[email protected]

Across the nation, teachers have shifted to delivering instruction remotely due to COVID-19. This transition to remote instruction has presented challenges for secondary science teachers who previously engaged their students in hands-on learning through empirical tests and observations of real-world phenomena, but whose students might not now have the equipment or materials in their homes to enable hands-on investigations. In this project, Concord Consortium will develop and test a remote professional development program that is designed to support secondary science teachers in making the transition from face-to-face to remote instruction, while still providing their students with engaging opportunities to learn from empirical data. Through this professional development program, science teachers will gain practice in using an open-source tool that teaches data-based modeling in the context of complex systems. For example, this tool will enable students to use data related to the pandemic to develop models that can predict relevant outcomes. As students develop skills related to modeling, they will be better prepared for the STEM (science, technology, engineering, and mathematics) workforce of the future, which increasingly requires the ability to interpret and use large-scale data. Research will identify the features of professional development that support teachers in providing remote instruction that is aligned with the Next Generation Science Standards (NGSS) related to modeling and systems thinking. <br/><br/>Concord Consortium will provide remote professional development to ten secondary science teachers on modeling using complex systems via open-source software. They will collect surveys, interviews, and teacher-generated curricular materials to ascertain how the teachers develop pedagogical strategies for remote instruction, which are designed to support the development of their students' modeling skills and practices. Additionally, they will collect data from student work, including log files from the software, to determine how the students demonstrate modeling practices and knowledge of systems in the context of this remote instruction. The results from inductive descriptive analyses of these data will be submitted to empirical journals. Other dissemination materials will outline the design principles that support teachers in modifying NGSS-aligned curricular materials for remote delivery. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

CDS&E: Ab Initio Ultrafast Dynamics of Spin, Valley and Charge in Quantum Materials: University of California-Santa Cruz

Yuan Ping

[email protected]

This grant is being funded by the Condensed-Matter and Materials Theory program in the Division of Materials Research and by the Chemical Theory, Models, and Computational Methods program in the Division of Chemistry.<br/><br/><br/>Nontechnical Summary<br/><br/>The promise of quantum computers to perform calculations beyond the reach of any current or conceivable non-quantum computer has made them one of the nation's highest research priorities. This award supports computational research and education on the motion of electrons in quantum materials. Several recently-discovered materials exhibit the potential to store quantum information in individual electrons that may hold the key to the next generation of quantum computers and quantum communication. Realizing the full potential of these materials requires precise understanding of how long quantum information can be stored in electron spins and how it disappears eventually by interacting with the vibrations of atoms in the material.<br/><br/>The investigators will develop a computational methodology to simulate quantum electron motion on large supercomputers. They will use this technique to predict how electron spin changes over times ranging from femtoseconds to microseconds in several promising materials, such as lead halide perovskites, containing heavy atoms that couple spin to the movement of electrons. Electrons in transition-metal dichalcogenides, another alternative for storing quantum information, can be found in multiple so-called "valleys;" the investigators will also study how electron valley and electron spin couple. For each of these materials, they will simulate the interaction of these quantum states with extremely short laser pulses to interpret experimental measurements of spin and valley dynamics.<br/><br/>This award will also support the team's effort in increasing participation and representation of women in STEM disciplines, especially in the physical sciences. By integrating simulations into intuitive visualizations using augmented reality, they will make electron dynamics understandable to undergraduate and high school students. Finally, this project will strengthen the research infrastructure at UCSC, a Hispanic Serving Institution.<br/><br/><br/>Technical summary<br/><br/>The goal of this research project is to predict quantitatively quantum dynamics of electrons with spin, valley, or other internal degrees of freedom, entirely from first principles. The research team will develop a novel computational methodology and associated massively-parallel open-source software rapidly to evolve density matrices of quantum materials in a Lindbladian formulation, with ab initio treatment of electron-electron, electron-phonon, and electron-photon interactions. This will facilitate calculation of both coherent dynamics and dephasing of spin or valley polarization, along with their experimental signatures in ultrafast spectroscopy. Using this technique, they will investigate spin dynamics in systems with strong spin-orbit coupling and Rashba splitting such as lead halide perovskites and ferroelectric oxides, and valley dynamics in layered transition metal dichalcogenides. This fundamentally new predictive capability will facilitate quantitative analysis of ultrafast optical and free-electron laser measurements with linear and circular polarization, and accurate predictions of spin relaxation of quantum materials. This will be critical for the design and discovery of new material platforms for spintronics, valleytronics and quantum information.<br/><br/>The proposed work will arm the materials research community with first-principles quantum dynamics methods in open-source software. These will include a hierarchy of methods that keep track of different levels of coherence, with corresponding computational requirements ranging from a small computer cluster to future exascale supercomputers. It will thereby deliver a key computational technique necessary for predicting coherent and incoherent ultrafast dynamics in quantum materials, extending significantly beyond the capabilities of existing first-principles methods. The work funded in this project responds directly to one of NSF's 10 Big Ideas, the Quantum Leap, by facilitating quantitative simulation of spin relaxation and carrier dynamics critical for quantum information science. The educational activities associated with this project aim to increase participation and representation of women in STEM disciplines, especially in the physical sciences. It will expand the reach of materials simulations to K-12 education through the platform of augmented reality. This project will also strengthen the research infrastructure at UCSC, a Hispanic Serving Institution.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

CRII: III: Learning Dynamic Graph-based Precursors for Event Modeling: Stevens Institute of Technology

Yue Ning

[email protected]

From epidemic outbreaks to civil strife, societal events that involve large populations often deeply affect people?s lives and cause economic burden. Forecasting these events while providing context analysis helps social scientists and health practitioners to interpret and study human societies. Although many existing research efforts strive to forecast societal events, providing structured explanations for prediction is still limited given the underlying connections among entities, actions, and locations behind these events. This project presents a novel paradigm of identifying and organizing multiple types of precursors while predicting events. It identifies changing relations among entities as events evolve and studies the hidden geographical influence on events. Both entity relations and geographical connections are represented by dynamic graphs. Organizing event precursors in graphs greatly reduces the complexity of comprehending unstructured input data and delivers interpretable summarizations for event prediction. This work will involve educational activities such as development of course curriculum; training of graduate, undergraduate, and high-school students; encouraging participation of women and minority groups in academic research; and dissemination of outcomes such as software and datasets for the general public.<br/><br/>To achieve these goals, this project will integrate multiple data sources and analyze complex hierarchical features in modeling events. Although a variety of online data has been utilized to analyze and predict societal events, it also raises new challenges such as: (1) accounting for dynamic relationships within data sets; (2) preserving and learning complex knowledge structures with heterogeneous data sets; and (3) ensuring interpretable results for predictions and decision making. This project will address the challenges in the following ways: (i) it will integrate multi-source data by learning a unified multi-level semantic encoding; (ii) it will identify historical key semantics by paying attention to hierarchical text structures in a recurrent learning process; (iii) it will provide explanations for event prediction by incorporating local dynamic graph patterns and global influence graph patterns. The specific research aims will be complemented with an extensive set of evaluation plans including a retrospective evaluation on real-word event records and a user survey to evaluate graph visualizations of event precursors. The project results, including graph based empirical data, predictive evaluation tools, and open source software for analyzing events, will be shared with computer science research community and stakeholders in computational healthcare, and social science.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

CCRI: Planning: Enabling Quantum Computer Science and Engineering: Georgia Tech Research Corporation

Arijit Raychowdhury

[email protected]

Quantum computing promises to revolutionize the entire paradigm of computing with significant societal impact. Along with advances is research and development, it also requires the next-generation of quantum-trained workforce who will be able to take advantage of the technological changes. This project is a planning grant to determine the requirements for development of an academic hosted and online accessible trapped ion-based quantum computer testbed. A testbed of this nature will give computer scientists access to a functioning quantum computer to test new ideas in the area quantum algorithms. This planning grant would have four goals: 1) determine the desire of the broader quantum computer scientist community in having access to open quantum computer; 2) define the requirements for the minimum number of qubits to have an effective resource for researcher; 3) determine the at what level in the computing stack would researcher like access to the computer; 4) determine the appropriate balance between a computing resource the broad quantum community and the academic nature of the device as a tool to educational and workforce development. <br/> <br/>The approach in this planning grant is to enable the next generation of quantum computing platforms for computer engineering and computer science researchers to perform algorithm research. Georgia Tech Research Institute (GTRI) has demonstrated leadership in developing ion trap quantum system hardware over the last fifteen years. The project will leverage the expertise in this joint effort between GTRI and Georgia Institute of Technology and enable a ten-qubit quantum computing testbed with open source software infrastructure and capability of remote access. It is expected to directly impact the following research vectors: (1) Microarchitecture research to explore topics related to but not limited to physical model driven error correction, resiliency, noisy intermediate state quantum computing models etc. (2) Algorithm research to understand the fundamental advantages and limitations of quantum algorithms from space-time complexity on a practical system with its full software stack. (3) Applications research which will span over engineering as well as science disciplines ? with a focus on CISE related research.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

Phonon-Assisted Diffusion in Solids from First-Principles: Unraveling a New Mechanism for Fast Diffusion: University of Illinois at Chicago

Sara Kadkhodaei

[email protected]

Nontechnical Summary<br/><br/>Diffusion of molecules in liquids and gases is familiar: perfume, for example, can diffuse in air. Diffusion also takes place in high-temperature solids, and understanding how will be key to new applications. Many technologically-relevant materials, in particular those with high operating temperatures, experience different modes of collective atomic vibrations that are highly correlated. The effect of these correlated vibrations on diffusion is poorly understood, and existing theoretical models are extremely limited in incorporating them into the diffusion description. This project addresses this limitation by (a) devising a theoretical framework that describes the mass-transport behavior affected by the correlated collective atomic vibrations, and (b) implementing a computational tool to predict the diffusion coefficient without requiring experimental input.<br/><br/>The end product will be an open-access software toolkit with predictive capability for diffusivity, which will substantially accelerate the discovery and design of advanced materials for applications such as solid-state batteries and fuel cells. Educational activities include the development of a thorough tutorial on advanced topics in the kinetics of high-temperature materials, which will be distributed on the PI's website to provide a world-wide educational platform for students. Also, a graduate-level course about first-principles modeling for engineers will be developed by the PI.<br/><br/><br/>Technical Summary<br/><br/>There currently exist substantial gaps in fundamental understanding and theoretical modeling of diffusion phenomena in strongly anharmonic systems. Namely, (i) the effect of anharmonic vibrations on diffusion phenomena is not well understood, and (ii) existing computational models based on the harmonic approximation of zero-temperature energy fall short of predicting diffusivity in these systems. The goal of this project is to address these gaps by introducing a theoretical framework that combines stochastic sampling techniques and ab-initio calculations to identify the diffusion pathways on an effective energy surface.<br/><br/>The findings of this project will advance the fundamental understanding of anharmonic vibration effects on diffusion and will significantly expand the current limits of diffusion modeling capabilities. Additionally, it will provide a-priori predictive capability without requiring experimental input, which will substantially accelerate the optimization and design of new materials. The outcomes of this project will contribute to the advancement of two lines of materials research: (i) predicting diffusion properties for novel high-temperature solid phases and (ii) gaining new understanding of various diffusion-controlled processes (e.g., phase transformation, precipitate growth and coarsening, oxidation, and creep) by providing accurate diffusive mobility data for their simulation.<br/><br/>The university is a Hispanic-serving institution, and the PI will participate in two existing programs for recruiting minority students and women to engineering at UIC. She will train undergraduates and graduate students in research, and she will give introductory lectures at local high schools and develop a new graduate-level engineering course in first-principles modeling. The PI will release open-source software as well as data on diffusivity using standard repositories.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

CAREER: Using Fiction to Improve Real-World Information Systems: University of California-Berkeley

David Bamman

[email protected]

At a high level, this project aims to design computational methods to reason about the world of fiction, and, in turn, learn from fiction to inform the design of systems in the real world. While much work in artificial intelligence learns about the world from relatively short factual sources like news and Wikipedia, fiction offers a range of affordances for improving existing information systems and innovating new applications altogether. Unlike factual sources like news, fiction captures emotion, everyday action and commonsense, offering a vast source of information to bootstrap knowledge bases that can power question answering systems, conversational agents, and the next generation of artificial intelligence. This project will improve the performance of natural language understanding on fiction as a domain, and use it to explore two case studies: inferring the structure of everyday events in people's lives, including the relation between macro-level events (such as eating breakfast) and low-level micro-events (sitting down at the table, pouring another cup of coffee, putting the dishes in the sink); and learning the relationship between observed actions depicted in text and the broad-coverage mental attitude (such as joy, sadness, and surprise) of their agents. This project aims to draw in students and researchers in the social sciences and humanities, who have historically been underrepresented in computing. While the technical research carried out under this project directly speaks to how expertise in the social sciences and humanities can inform the computational design of information systems, the primary educational plan under this award will investigate one fundamental question: how to enable students outside STEM fields to learn and improve their skills in natural language processing, machine learning and data science. This work will engage researchers in the humanities and social sciences in technical research, teaching skills to students without technical backgrounds, and translating advances in computational methodology to advances in domain knowledge. <br/><br/>The fundamental work in this project aims to bridge the gap between computation and the humanities and social sciences by providing two case studies of how learning from a depicted world in fiction can improve systems that reason about the real world. This is a new frontier that can not only teach us about the limitations of current systems for textual entailment and sentiment analysis, but can also open up new areas of research at this intersection. This work will make progress on two tasks enabled by fiction: inferring the sequential and hierarchical order of commonplace actions, in which a single macro-event is comprised of several micro-events, and inferring the latent attitudes of people mentioned in text given observations of their actions. Both case studies draw on fiction as a source of knowledge, and require the development of computational models optimized to bridge the gap between fiction and reality. Concretely, this work will result in the publication of a new dataset of contemporary fiction, labeled for entities and coreference between them (which has the potential to yield a new state of the art for nested entity recognition and coreference resolution for this domain), a knowledge base of everyday actions extracted from fiction, open-source software for modeling hierarchical events and learning mental attitudes from observed actions, and publications at academic venues detailing the methodologies created under the scope of this project.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

CC* Compute: Interactive Data Analysis Platform: William Marsh Rice University

Klara Jelinkova

[email protected]

Rice University researchers engaged in groundbreaking data-intensive science and engineering increasingly depend on access to real-time data analysis facilities required for their research. These research activities include image processing, computer vision, and machine learning, spanning multiple fields, such as geological sciences, statistics, computer science, and physics. Each of these problems areas or use cases can be addressed by shared computational infrastructure leveraging GPU accelerators for interactive computing. The system provides a significant resource for enabling science but also for educating the next generation of computational scientists in the latest GPU-computing techniques through the outreach of the Center for Research Computing. <br/><br/>The resource includes nine compute nodes, each with 40 cores, 384GB RAM, 4TB NVMe storage, and 8 NVIDIA Quadro RTX 6000 GPUs. The systems are interconnected via high-performance networking and hosted on a Science DMZ integrating them with the Open Science Grid as well as commercial cloud allowing both increased utilization as part of national OSG efforts and the ability to utilize cloud resources for load bursting. The system leverages an open-source software stack designed to support containerization, enabling each researcher to utilize their own unique set of software and toolkits while sharing common hardware and a common cloud access platform. Moreover, the infrastructure is part of a larger technology ecosystem that leverages federated identity and access management as part of InCommon, advanced networking with science DMZ, and Information Security Office that supports not only university data and technology security but includes targeted outreach for research data and protocol security.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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