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1. DIII-D research to provide solutions for ITER and fusion energy

Author

C.T. Holcomb, for the DIII-D Team:, J. Abbate, A. Abe, A. Abrams, P. Adebayo-Ige, S. Agabian, S. Ahmed, N. Aiba, N. Akcay, T. Akiyama, R. Albosta, P. Aleynikov, S. Allen, H. Anand, J. Anderson, Y. Andrew, M. Ashburn, A. Ashourvan, M. Austin, G. Avdeeva, D. Ayala, M. Ayub, E. Bagdy, S. Banerjee, K. Barada, L. Bardoczi, O. Bardsley, J. Barr, E. Bass, A. Battey, Z. Bayler, L. Baylor, T. Bechtel, M. Beidler, E. Belli, T. Benedett, Z. Bergstrom, M. Berkel, T. Bernard, N. Bertelli, R. Bielajew, G. Bodner, J. Boedo, R. Boivin, T. Bolzonella, P. Bonoli, A. Bortolon, S. Bose, M. Boyer, W. Boyes, L. Bradley, R. Brambila, A. Braun, D. Brennan, S. Bringuier, L. Brodsky, M. Brookman, J. Brooks, D. Brower, W. Brown, J. Buck, S. Buczek, D. Burgess, M. Burke, K. Burrell, J. Butt, R. Buttery, I. Bykov, P. Byrne, A. Cacheris, K. Callahan, J. Callen, D. Campbell, J. Candy, J. Canik, L. Cappelli, T. Carlstrom, R. Carr, W. Carrig, B. Carter, T. Carter, I. Carvalho, W. Cary, L. Casali, L. Ceelen, M. Cengher, M. Cha, R. Chaban, V. Chan, B. Chapman, I. Char, J. Chen, R. Chen, X. Chen, Y. Chen, J. Chiriboga, E. Cho, G. Choi, W. Choi, H. Choudhury, S. Chowdhury, C. Chrystal, Y. Chung, R. Churchill, R. Clark, M. Clement, J. Coburn, S. Coda, R. Coffee, C. Collins, J. Colmenares-Fernandez, W. Conlin, R. Coon, T. Cote, A. Creely, N. Crocker, C. Crowe, B. Crowley, T. Crowley, M. Curie, D. Curreli, A. Dal Molin, J. Damba, E. Dart, A. Dautt-Silva, K. Davda, A. De, N. de Boucaud, Y. de Jong, P. DE VRIES, A. de-Villeroche, G. DeGrandchamp, J. deGrassie, D. Demers, S. Denk, E. DeShazer, S. Di Genova, A. Diallo, A. Dimits, R. Ding, S. Ding, D. Donovan, X. Du, J. Dunsmore, A. Dupuy, J. Duran, A. Dvorak, F. Effenberg, N. Eidietis, D. Elder, D. Eldon, Y. Elsey, D. Ennis, K. Erickson, D. Ernst, M. Fajardo, H. Farre-Kaga, M. Fenstermacher, N. Ferraro, J. Ferron, A. Feyrer, P. Fimognari, R. Finden, D. Finkenthal, R. Fitzpatrick, S. Flanagan, B. Ford, W. Fox, S. Freiberger, L. Fu, K. Gage, V. Gajaraj, I. Garcia, F. Garcia, A. Garcia, M. Garcia Munoz, D. Garnier, A. Garofalo, A. Gattuso, B. Geiger, K. Gentle, Y. Ghai, K. Gill, F. Glass, P. Gohil, X. Gong, J. Gonzalez-Martin, Y. Gorelov, V. Graber, R. Granetz, C. Gray, C. Greenfield, B. Grierson, R. Groebner, W. Grosnickle, M. Groth, S. Gu, H. Guo, J. Guterl, W. Guttenfelder, R. Hager, S. Hahn, M. Halfmoon, J. Hall, V. Hall-Chen, F. Halpern, G. Hammett, X. Han, C. Hansen, E. Hansen, J. Hanson, M. Hanson, A. Harris, R. Harvey, S. Haskey, D. Hatch, W. Hayashi, A. Hayes, W. Heidbrink, J. Herfindal, J. Hicok, E. Hinson, T. Hisakado, C. Holcomb, C. Holland, L. Holland, E. Hollmann, A. Holm, I. Holmes, K. Holtrop, R. Hong, R. Hood, L. Horvath, S. Houshmandyar, N. Howard, E. Howell, W. Hu, Y. Hu, Q. Hu, Y. Huang, J. Huang, A. Huang, A. Hubbard, J. Hughes, D. Humphreys, J. Hurtado, A. Hyatt, K. Imada, V. Izzo, A. Jalalvand, S. Jardin, A. Jarvinen, Y. Jeon, H. Ji, X. Jian, L. Jian, Y. Jiang, C. Johnson, J. Johnson, M. Jones, S. Joung, P. Jouzdani, E. Jung, E. Kallenberg, R. Kalling, D. Kaplan, A. Kaptanoglu, D. Kellman, J. Kennedy, F. Khabanov, J. Kim, H. Kim, E. Kim, S. Kim, K. Kim, C. Kim, T. Kim, J. King, A. Kinsey, D. Kirk, D. Klasing, A. Kleiner, M. Knolker, M. Kochan, B. Koel, J. Koenders, M. Koepke, R. Kolasinski, E. Kolemen, E. Kostadinova, M. Kostuk, G. Kramer, R. Kube, N. Kumar, R. La Haye, F. Laggner, C. Lahban, H. Lan, R. Landry, R. Lantsov, L. Lao, C. Lasnier, C. Lau, R. Leccacorvi, J. Leddy, M. Lee, S. Lee, K. Lee, R. Lee, M. Lehnen, A. Leonard, E. Leppink, M. LeSher, J. Lestz, J. Leuer, N. Leuthold, G. Li, X. Li, Y. Li, L. Li, N. Li, Z. Li, D. Lin, Z. Lin, Y. Lin, E. Linsenmayer, J. Liu, D. Liu, C. Liu, Z. Liu, Y. Liu, A. Loarte-Prieto, S. Loch, L. LoDestro, N. Logan, J. Lohr, J. Lore, U. Losada Rodriguez, J. Loughran, M. Lowell, T. Luce, N. Luhmann, P. Lunia, R. Lunsford, L. Lupin-Jimenez, A. Lvovskiy, B. Lyons, X. Ma, J. MacDonald, T. Macwan, R. Maingi, M. Major, L. Malhotra, M. Margo, C. Marini, A. Marinoni, A. Maris, E. Martin, J. Mateja, R. Mattes, R. Maurizio, D. Mauzey, L. McAllister, G. McArdle, J. McClenaghan, K. McCollam, G. McKee, K. McLaughlin, A. McLean, V. Mehta, E. Meier, S. Meitner, J. Menard, O. Meneghini, G. Merlo, S. Messer, W. Meyer, C. Michael, D. Miller, M. Miller, J. Mitchell, E. Mitra, C. Moeller, M. Mohamed, S. Molesworth, K. Montes, S. Mordijck, S. Morosohk, A. Moser, D. Mueller, S. Munaretto, C. Murphy, C. Muscatello, R. Myers, A. Nagy, D. Nath, M. Navarro, R. Nazikian, T. Neiser, A. Nelson, P. Nesbet, F. Nespoli, P. Nguyen, D. Nguyen, R. Nguyen, J. Nichols, M. Nocente, L. Nuckols, R. Nygren, T. Odstrcil, M. Okabayashi, E. Olofsson, D. Orlov, D. Orozco, N. Osborne, T. Osborne, F. OShea, D. Pace, D. Packard, A. Pajares Martinez, C. Pakosta, C. Pan, M. Pandya, D. Panici, A. Pankin, Y. Park, J. Park, C. Parker, S. Parker, P. Parks, M. Parsons, S. Paruchuri, C. Paz-Soldan, T. Pederson, W. Peebles, B. Penaflor, E. Perez, L. Periasamy, R. Perillo, C. Petty, M. Pharr, D. Pierce, C. Pierren, S. Pierson, A. Pigarov, L. Pigatto, D. Piglowski, S. Pinches, R. Pinsker, R. Pitts, J. Pizzo, M. Podesta, Z. Popovic, M. Porkolab, Q. Pratt, G. Prechel, I. Pusztai, P. Puthan-Naduvakkate, J. Qian, X. Qin, O. Ra, T. Raines, K. Rakers, K. Rath, J. Rauch, C. Rea, R. Reed, A. Reiman, M. Reinke, R. Reksoatmodjo, Q. Ren, J. Ren, Y. Ren, M. Rensink, T. Rhodes, N. Richner, J. Ridzon, G. Riggs, J. Riquezes, P. Rodriguez Fernandez, T. Rognlien, G. Ronchi, L. Rondini, R. Rosati, A. Rosenthal, M. Ross, J. Rost, A. Rothstein, J. Roveto, J. Ruane, D. Rudakov, R. Rupani, G. Rutherford, S. Sabbagh, J. Sachdev, N. Sadeghi, A. Salmi, F. Salvador, B. Sammuli, C. Samuell, A. Sandorfi, C. Sang, D. Santa, J. Sarff, O. Sauter, H. Savelli, C. Schaefer, H. Schamis, J. Schellpfeffer, D. Schissel, L. Schmitz, O. Schmitz, P. Schroeder, K. Schultz, E. Schuster, F. Sciortino, F. Scotti, J. Scoville, A. Seltzman, J. Seo, J. Serrano, I. Sfiligoi, M. Shafer, R. Shapov, H. Shen, N. Shi, D. Shiraki, B. Short, R. Shousha, H. Si, C. Sierra, G. Sinclair, P. Sinha, G. Sips, C. Skinner, T. Slendebroek, J. Slief, R. Smirnov, S. Smith, D. Smith, G. Snoep, P. Snyder, W. Solomon, X. Song, A. Sontag, V. Soukhanovskii, D. Spong, J. Squire, G. Staebler, L. Stagner, T. Stange, P. Stangeby, E. Starling, S. Stewart, T. Stoltzfus-Dueck, S. Storment, E. Strait, D. Su, L. Sugiyama, P. Sun, Y. Sun, X. Sun, C. Sung, W. Suttrop, Y. Suzuki, R. Sweeney, B. Taczak, Y. Takemura, S. Tang, W. Tang, G. Tardini, D. Taussig, K. Teixeira, K. Thackston, D. Thomas, K. Thome, Y. Tinguely, M. Tobin, J. Tooker, A. Torrezan de Sousa, P. Traverso, G. Trevisan, E. Trier, D. Truong, C. Tsui, F. Turco, A. Turnbull, L. Turner, E. Unterberg, B. Van Compernolle, R. van Kampen, M. Van Zeeland, B. Victor, R. Vieira, E. Viezzer, S. Vincena, D. Vollmer, J. Wai, M. Walker, R. Waltz, W. Wampler, L. Wang, Y. Wang, H. Wang, Z. Wang, G. Wang, A. Wang, J. Watkins, M. Watkins, T. Watts, L. Webber, K. Weber, W. Wehner, X. Wei, D. Weisberg, A. Welander, A. Welsh, A. White, R. Wilcox, G. Wilkie, T. Wilks, M. Willensdorfer, H. Wilson, A. Wingen, M. Wu, D. Wu, S. Wukitch, J. Xia, R. Xie, Z. Xing, G. Xu, X. Xu, Z. Yan, X. Yang, L. Yang, S. Yang, J. Yang, M. Yoo, G. YU, J. Yu, A. Zalzali, A. Zamengo, V. Zamkovska, S. Zamperini, K. Zarrabi, E. Zeger, K. Zeller, L. Zeng, X. Zhang, J. Zhang, B. Zhang, B. Zhao, C. Zhao, Y. Zheng, Y. Zhu, J. Zhu, J. Ziegel, J. Zimmerman, and C. Zuniga

Subjects
DIII-D, tokamak, overview, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

The DIII-D tokamak has elucidated crucial physics and developed projectable solutions for ITER and fusion power plants in the key areas of core performance, boundary heat and particle transport, and integrated scenario operation, with closing the core-edge integration knowledge gap being the overarching mission. New experimental validation of high-fidelity, multi-channel, non-linear gyrokinetic turbulent transport models for ITER provides strong confidence it will achieve Q ⩾ 10 operation. Experiments identify options for easing H-mode access in hydrogen, and give new insight into the isotopic dependence of transport and confinement. Analysis of 2,1 islands in unoptimized low-torque IBS demonstration discharges suggests their onset time occurs randomly in the constant β phase, most often triggered by non-linear 3-wave coupling, thus identifying an NTM seeding mechanism to avoid. Pure deuterium SPI for disruption mitigation is shown to provide favorable slow cooling, but poor core assimilation, suggesting paths for improved SPI on ITER. At the boundary, measured neutral density and ionization source fluxes are strongly poloidally asymmetric, implying a 2D treatment is needed to model pedestal fuelling. Detailed measurements of pedestal and SOL quantities and impurity charge state radiation in detached divertors has validated edge fluid modelling and new self-consistent ‘pedestal-to-divertor’ integrated modeling that can be used to optimize reactors. New feedback adaptive ELM control minimizes confinement reduction, and RMP ELM suppression with sustained high core performance was obtained for the first time with the outer strike point in a W-coated, compact and unpumped small-angle slot divertor. Advances have been made in integrated operational scenarios for ITER and power plants. Wide pedestal intrinsically ELM-free QH-modes are produced with more reactor-relevant conditions, Low torque IBS with W-equivalent radiators can exhibit predator-prey oscillations in T _e and radiation which need control. High- β _P scenarios with q _min > 2, q _95 –7.9, β _N > 4, β _T –3.3% and H _98y2 > 1.5 are sustained with high density ( $\bar n$ = 7E19 m ^−3 , f _G –1) for 6 τ _E , improving confidence in steady-state tokamak reactors. Diverted NT plasmas achieve high core performance with a non-ELMing edge, offering a possible highly attractive core-edge integration solution for reactors.

Published
2024
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2. LLMPirate: LLMs for Black-box Hardware IP Piracy

Author

Gohil, Vasudev, DeLorenzo, Matthew, Nallam, Veera Vishwa Achuta Sai Venkat, See, Joey, and Rajendran, Jeyavijayan

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

The rapid advancement of large language models (LLMs) has enabled the ability to effectively analyze and generate code nearly instantaneously, resulting in their widespread adoption in software development. Following this advancement, researchers and companies have begun integrating LLMs across the hardware design and verification process. However, these highly potent LLMs can also induce new attack scenarios upon security vulnerabilities across the hardware development process. One such attack vector that has not been explored is intellectual property (IP) piracy. Given that this attack can manifest as rewriting hardware designs to evade piracy detection, it is essential to thoroughly evaluate LLM capabilities in performing this task and assess the mitigation abilities of current IP piracy detection tools. Therefore, in this work, we propose LLMPirate, the first LLM-based technique able to generate pirated variations of circuit designs that successfully evade detection across multiple state-of-the-art piracy detection tools. We devise three solutions to overcome challenges related to integration of LLMs for hardware circuit designs, scalability to large circuits, and effectiveness, resulting in an end-to-end automated, efficient, and practical formulation. We perform an extensive experimental evaluation of LLMPirate using eight LLMs of varying sizes and capabilities and assess their performance in pirating various circuit designs against four state-of-the-art, widely-used piracy detection tools. Our experiments demonstrate that LLMPirate is able to consistently evade detection on 100% of tested circuits across every detection tool. Additionally, we showcase the ramifications of LLMPirate using case studies on IBEX and MOR1KX processors and a GPS module, that we successfully pirate. We envision that our work motivates and fosters the development of better IP piracy detection tools., Comment: Accepted by NDSS Symposium 2025

Published
2024

3. osl-ephys: A Python toolbox for the analysis of electrophysiology data

Author

van Es, Mats W. J., Gohil, Chetan, Quinn, Andrew J., and Woolrich, Mark W.

Subjects
Quantitative Biology - Quantitative Methods, Quantitative Biology - Neurons and Cognition
Abstract

We describe OHBA Software Library for the analysis of electrophysiological data (osl-ephys). This toolbox builds on top of the widely used MNE-Python package and provides unique analysis tools for magneto-/electro-encephalography (M/EEG) sensor and source space analysis, which can be used modularly. In particular, it facilitates processing large amounts of data using batch parallel processing, with high standards for reproducibility through a config API and log keeping, and efficient quality assurance by producing HTML processing reports. It also provides new functionality for doing coregistration, source reconstruction and parcellation in volumetric space, allowing for an alternative pipeline that avoids the need for surface-based processing, e.g., through the use of Fieldtrip. Here, we introduce osl-ephys by presenting examples applied to a publicly available M/EEG data (the multimodal faces dataset). osl-ephys is open-source software distributed on the Apache License and available as a Python package through PyPi and GitHub.

Published
2024

4. VARS: Vision-based Assessment of Risk in Security Systems

Author

Gupta, Pranav, Gohil, Pratham, and S, Sridhar

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

The accurate prediction of danger levels in video content is critical for enhancing safety and security systems, particularly in environments where quick and reliable assessments are essential. In this study, we perform a comparative analysis of various machine learning and deep learning models to predict danger ratings in a custom dataset of 100 videos, each containing 50 frames, annotated with human-rated danger scores ranging from 0 to 10. The danger ratings are further classified into three categories: no alert (less than 7)and high alert (greater than equal to 7). Our evaluation covers classical machine learning models, such as Support Vector Machines, as well as Neural Networks, and transformer-based models. Model performance is assessed using standard metrics such as accuracy, F1-score, and mean absolute error (MAE), and the results are compared to identify the most robust approach. This research contributes to developing a more accurate and generalizable danger assessment framework for video-based risk detection.

Published
2024

5. The Sunk Carbon Fallacy: Rethinking Carbon Footprint Metrics for Effective Carbon-Aware Scheduling

Author

Bashir, Noman, Gohil, Varun, Belavadi, Anagha, Shahrad, Mohammad, Irwin, David, Olivetti, Elsa, and Delimitrou, Christina

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Computers and Society, Computer Science - Emerging Technologies, Computer Science - Performance
Abstract

The rapid increase in computing demand and its corresponding energy consumption have focused attention on computing's impact on the climate and sustainability. Prior work proposes metrics that quantify computing's carbon footprint across several lifecycle phases, including its supply chain, operation, and end-of-life. Industry uses these metrics to optimize the carbon footprint of manufacturing hardware and running computing applications. Unfortunately, prior work on optimizing datacenters' carbon footprint often succumbs to the \emph{sunk cost fallacy} by considering embodied carbon emissions (a sunk cost) when making operational decisions (i.e., job scheduling and placement), which leads to operational decisions that do not always reduce the total carbon footprint. In this paper, we evaluate carbon-aware job scheduling and placement on a given set of servers for a number of carbon accounting metrics. Our analysis reveals state-of-the-art carbon accounting metrics that include embodied carbon emissions when making operational decisions can actually increase the total carbon footprint of executing a set of jobs. We study the factors that affect the added carbon cost of such suboptimal decision-making. We then use a real-world case study from a datacenter to demonstrate how the sunk carbon fallacy manifests itself in practice. Finally, we discuss the implications of our findings in better guiding effective carbon-aware scheduling in on-premise and cloud datacenters.

Published
2024
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6. ViDAS: Vision-based Danger Assessment and Scoring

Author

Gupta, Pranav, Krishnan, Advith, Nanda, Naman, Eswar, Ananth, Agarwal, Deeksha, Gohil, Pratham, and Goel, Pratyush

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We present a novel dataset aimed at advancing danger analysis and assessment by addressing the challenge of quantifying danger in video content and identifying how human-like a Large Language Model (LLM) evaluator is for the same. This is achieved by compiling a collection of 100 YouTube videos featuring various events. Each video is annotated by human participants who provided danger ratings on a scale from 0 (no danger to humans) to 10 (life-threatening), with precise timestamps indicating moments of heightened danger. Additionally, we leverage LLMs to independently assess the danger levels in these videos using video summaries. We introduce Mean Squared Error (MSE) scores for multimodal meta-evaluation of the alignment between human and LLM danger assessments. Our dataset not only contributes a new resource for danger assessment in video content but also demonstrates the potential of LLMs in achieving human-like evaluations., Comment: Preprint

Published
2024

7. Sr4Os3O12- A Layered Osmate (V,VI) that is Magnetic close to Room Temperature

Author

Thakur, Gohil S., Hübner, Julia-Maria, Finzel, Kati, Doert, Thomas, and Ruck, Michael

Subjects
Condensed Matter - Materials Science, Condensed Matter - Strongly Correlated Electrons
Abstract

Black crystals of the mixed-valence osmate(V,VI) Sr4Os3O12 were grown via a gas phase reaction in an evacuated silica tube. The material is attracted to a permanent magnet at room temperature (295 K) but loses this property when heated or cooled. In this temperature interval, soft magnetic behavior with vanishing coercivity is observed, but the saturation magnetization is only 0.05 MuB per formula unit. Resistivity measurements show that Sr4Os3O12 is a semiconductor with a small band gap of about 0.26 eV at room temperature. X-ray diffraction on a single-crystal revealed a rhombohedral structure with the space group R-3m and lattice parameters ar = 554.64(2) pm and cr = 2700.1(1) pm at 300(1) K. The compound crystallizes in the La4Ti3O12 structure type and is isostructural to Sr4CoRe2O12. The structure is a rhombohedral stack of layered blocks, each of which can be considered as a section of three layers of the cubic perovskite structure cut perpendicular to one of its threefold axes. The Os-O-Os bond angle of about 175{/deg} favors superexchange that leads to antiferromagnetic coupling between the osmium atoms with 5d3 and 5d2 configurations. Sr4Os3O12 exhibits strong lattice dynamics. One aspect is the antiferro-rotative lattice modes of the corner-sharing [OsO6] octahedra, which freeze upon cooling. At 100 K, an ordered, yet twinned triclinic superstructure is formed. Strong spin-orbit coupling and other effects cause deformations of the [OsO6] octahedra that differ significantly at 300 K and 100 K, suggesting a change in the electronic configuration. Competing ground states could also explain the temperature dependent band gap and the magnetic fluctuations manifested in the magnetization peak at room temperature., Comment: Accepted for publication in ZAAC (9 pages and 9 figures)

Published
2024
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8. Analysis of Seawater Quality Parameter and Treatment with Hydrodynamic Cavitation Method

Author

Patil, Divya, Gohil, Pankaj P., and Desai, Hemangi

Subjects
Physics - Physics and Society, Physics - Atmospheric and Oceanic Physics
Abstract

The aim of the research was to compare the quality parameters of Seawater before and after hydrodynamic cavitation treatment. The Hydrodynamic Cavitation Method for water treatment gives the highest reduction in Turbidity (100%), the second highest reduction in TSS (83.86%), and the lowest reduction in Na+ (8.47%), according to the study and analysis of various quality parameters. When compared to CPCB Water Quality Criteria, treated water is suitable for outdoor bathing; it again satisfies the standards for classes SW-I, SW-II, SW-III, SW-IV and SW-V, i.e., treated sea water can be used for a variety of purposes, including bathing, contact water sports, commercial fishing, mariculture, ecologically sensitive zones, aesthetics, harbour, waters Navigation and Controlled water disposal. The SAR value of treated water, which is 1.72, indicates it is appropriate for all types of soil and crops. The treated water's WQI, which is 65, showed that it is of Fair quality and may be used for industrial and irrigation uses. Water quality can be improved by recycling the treated water for an additional 24 hours using the hydrodynamic cavitation method. Recycling of one time treated sea water will result in higher-quality water that can be used for a variety of purposes. The Hydrodynamic Cavitation method by using venturi - orifice is proven to be the most effective over the other methods. Because it does not require any chemical reagent, hence does not produce any hazardous chemical waste, and maintains an eco-friendly and economically sustainable, environment benign technique for the treatment of Seawater., Comment: 19 Pages, 14 Tables, 9 Figures

Published
2024

9. CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation

Author

DeLorenzo, Matthew, Gohil, Vasudev, and Rajendran, Jeyavijayan

Subjects
Computer Science - Computation and Language
Abstract

Large Language Models (LLMs) have proved effective and efficient in generating code, leading to their utilization within the hardware design process. Prior works evaluating LLMs' abilities for register transfer level code generation solely focus on functional correctness. However, the creativity associated with these LLMs, or the ability to generate novel and unique solutions, is a metric not as well understood, in part due to the challenge of quantifying this quality. To address this research gap, we present CreativeEval, a framework for evaluating the creativity of LLMs within the context of generating hardware designs. We quantify four creative sub-components, fluency, flexibility, originality, and elaboration, through various prompting and post-processing techniques. We then evaluate multiple popular LLMs (including GPT models, CodeLlama, and VeriGen) upon this creativity metric, with results indicating GPT-3.5 as the most creative model in generating hardware designs.

Published
2024

11. AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning

Author

Gohil, Vasudev, Patnaik, Satwik, Kalathil, Dileep, and Rajendran, Jeyavijayan

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security
Abstract

Machine learning has shown great promise in addressing several critical hardware security problems. In particular, researchers have developed novel graph neural network (GNN)-based techniques for detecting intellectual property (IP) piracy, detecting hardware Trojans (HTs), and reverse engineering circuits, to name a few. These techniques have demonstrated outstanding accuracy and have received much attention in the community. However, since these techniques are used for security applications, it is imperative to evaluate them thoroughly and ensure they are robust and do not compromise the security of integrated circuits. In this work, we propose AttackGNN, the first red-team attack on GNN-based techniques in hardware security. To this end, we devise a novel reinforcement learning (RL) agent that generates adversarial examples, i.e., circuits, against the GNN-based techniques. We overcome three challenges related to effectiveness, scalability, and generality to devise a potent RL agent. We target five GNN-based techniques for four crucial classes of problems in hardware security: IP piracy, detecting/localizing HTs, reverse engineering, and hardware obfuscation. Through our approach, we craft circuits that fool all GNNs considered in this work. For instance, to evade IP piracy detection, we generate adversarial pirated circuits that fool the GNN-based defense into classifying our crafted circuits as not pirated. For attacking HT localization GNN, our attack generates HT-infested circuits that fool the defense on all tested circuits. We obtain a similar 100% success rate against GNNs for all classes of problems., Comment: To appear in USENIX Security Symposium, 2024

Published
2024

12. Make Every Move Count: LLM-based High-Quality RTL Code Generation Using MCTS

Author

DeLorenzo, Matthew, Chowdhury, Animesh Basak, Gohil, Vasudev, Thakur, Shailja, Karri, Ramesh, Garg, Siddharth, and Rajendran, Jeyavijayan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Hardware Architecture
Abstract

Existing large language models (LLMs) for register transfer level code generation face challenges like compilation failures and suboptimal power, performance, and area (PPA) efficiency. This is due to the lack of PPA awareness in conventional transformer decoding algorithms. In response, we present an automated transformer decoding algorithm that integrates Monte Carlo tree-search for lookahead, guiding the transformer to produce compilable, functionally correct, and PPA-optimized code. Empirical evaluation with a fine-tuned language model on RTL codesets shows that our proposed technique consistently generates functionally correct code compared to prompting-only methods and effectively addresses the PPA-unawareness drawback of naive large language models. For the largest design generated by the state-of-the-art LLM (16-bit adder), our technique can achieve a 31.8% improvement in the area-delay product.

Published
2024

13. Using LLM such as ChatGPT for Designing and Implementing a RISC Processor: Execution,Challenges and Limitations

Author

Hossain, Shadeeb, Gohil, Aayush, and Wang, Yizhou

Subjects
Computer Science - Machine Learning, Computer Science - Hardware Architecture, Computer Science - Software Engineering
Abstract

This paper discusses the feasibility of using Large Language Models LLM for code generation with a particular application in designing an RISC. The paper also reviews the associated steps such as parsing, tokenization, encoding, attention mechanism, sampling the tokens and iterations during code generation. The generated code for the RISC components is verified through testbenches and hardware implementation on a FPGA board. Four metric parameters Correct output on the first iteration, Number of errors embedded in the code, Number of trials required to achieve the code and Failure to generate the code after three iterations, are used to compare the efficiency of using LLM in programming. In all the cases, the generated code had significant errors and human intervention was always required to fix the bugs. LLM can therefore be used to complement a programmer code design.

Published
2024

14. Stewardship Prompts to Improve Antibiotic Selection for Urinary Tract Infection

Author

Gohil, Shruti K, Septimus, Edward, Kleinman, Ken, Varma, Neha, Avery, Taliser R, Heim, Lauren, Rahm, Risa, Cooper, William S, Cooper, Mandelin, McLean, Laura E, Nickolay, Naoise G, Weinstein, Robert A, Burgess, L Hayley, Coady, Micaela H, Rosen, Edward, Sljivo, Selsebil, Sands, Kenneth E, Moody, Julia, Vigeant, Justin, Rashid, Syma, Gilbert, Rebecca F, Smith, Kim N, Carver, Brandon, Poland, Russell E, Hickok, Jason, Sturdevant, SG, Calderwood, Michael S, Weiland, Anastasiia, Kubiak, David W, Reddy, Sujan, Neuhauser, Melinda M, Srinivasan, Arjun, Jernigan, John A, Hayden, Mary K, Gowda, Abinav, Eibensteiner, Katyuska, Wolf, Robert, Perlin, Jonathan B, Platt, Richard, and Huang, Susan S

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Clinical Sciences, Infectious Diseases, Urologic Diseases, Patient Safety, Women's Health, Infection, Adult, Aged, Female, Humans, Male, Middle Aged, Anti-Bacterial Agents, Antimicrobial Stewardship, Drug Resistance, Multiple, Bacterial, Hospitals, Community, Length of Stay, Medical Order Entry Systems, Urinary Tract Infections, Aged, 80 and over, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

ImportanceUrinary tract infection (UTI) is the second most common infection leading to hospitalization and is often associated with gram-negative multidrug-resistant organisms (MDROs). Clinicians overuse extended-spectrum antibiotics although most patients are at low risk for MDRO infection. Safe strategies to limit overuse of empiric antibiotics are needed.ObjectiveTo evaluate whether computerized provider order entry (CPOE) prompts providing patient- and pathogen-specific MDRO risk estimates could reduce use of empiric extended-spectrum antibiotics for treatment of UTI.Design, setting, and participantsCluster-randomized trial in 59 US community hospitals comparing the effect of a CPOE stewardship bundle (education, feedback, and real-time and risk-based CPOE prompts; 29 hospitals) vs routine stewardship (n = 30 hospitals) on antibiotic selection during the first 3 hospital days (empiric period) in noncritically ill adults (≥18 years) hospitalized with UTI with an 18-month baseline (April 1, 2017-September 30, 2018) and 15-month intervention period (April 1, 2019-June 30, 2020).InterventionsCPOE prompts recommending empiric standard-spectrum antibiotics in patients ordered to receive extended-spectrum antibiotics who have low estimated absolute risk (

Published
2024

15. Stewardship Prompts to Improve Antibiotic Selection for Pneumonia

Author

Gohil, Shruti K, Septimus, Edward, Kleinman, Ken, Varma, Neha, Avery, Taliser R, Heim, Lauren, Rahm, Risa, Cooper, William S, Cooper, Mandelin, McLean, Laura E, Nickolay, Naoise G, Weinstein, Robert A, Burgess, L Hayley, Coady, Micaela H, Rosen, Edward, Sljivo, Selsebil, Sands, Kenneth E, Moody, Julia, Vigeant, Justin, Rashid, Syma, Gilbert, Rebecca F, Smith, Kim N, Carver, Brandon, Poland, Russell E, Hickok, Jason, Sturdevant, SG, Calderwood, Michael S, Weiland, Anastasiia, Kubiak, David W, Reddy, Sujan, Neuhauser, Melinda M, Srinivasan, Arjun, Jernigan, John A, Hayden, Mary K, Gowda, Abinav, Eibensteiner, Katyuska, Wolf, Robert, Perlin, Jonathan B, Platt, Richard, and Huang, Susan S

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Clinical Sciences, Pneumonia, Pneumonia & Influenza, Infectious Diseases, Patient Safety, Comparative Effectiveness Research, Lung, Clinical Research, Infection, Aged, Female, Humans, Male, Middle Aged, Anti-Bacterial Agents, Antimicrobial Stewardship, Drug Resistance, Multiple, Bacterial, Hospitalization, Medical Order Entry Systems, Pneumonia, Bacterial, United States, Aged, 80 and over, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

ImportancePneumonia is the most common infection requiring hospitalization and is a major reason for overuse of extended-spectrum antibiotics. Despite low risk of multidrug-resistant organism (MDRO) infection, clinical uncertainty often drives initial antibiotic selection. Strategies to limit empiric antibiotic overuse for patients with pneumonia are needed.ObjectiveTo evaluate whether computerized provider order entry (CPOE) prompts providing patient- and pathogen-specific MDRO infection risk estimates could reduce empiric extended-spectrum antibiotics for non-critically ill patients admitted with pneumonia.Design, setting, and participantsCluster-randomized trial in 59 US community hospitals comparing the effect of a CPOE stewardship bundle (education, feedback, and real-time MDRO risk-based CPOE prompts; n = 29 hospitals) vs routine stewardship (n = 30 hospitals) on antibiotic selection during the first 3 hospital days (empiric period) in non-critically ill adults (≥18 years) hospitalized with pneumonia. There was an 18-month baseline period from April 1, 2017, to September 30, 2018, and a 15-month intervention period from April 1, 2019, to June 30, 2020.InterventionCPOE prompts recommending standard-spectrum antibiotics in patients ordered to receive extended-spectrum antibiotics during the empiric period who have low estimated absolute risk (

Published
2024

19. MABFuzz: Multi-Armed Bandit Algorithms for Fuzzing Processors

Author

Gohil, Vasudev, Kande, Rahul, Chen, Chen, Sadeghi, Ahmad-Reza, and Rajendran, Jeyavijayan

Subjects
Computer Science - Cryptography and Security
Abstract

As the complexities of processors keep increasing, the task of effectively verifying their integrity and security becomes ever more daunting. The intricate web of instructions, microarchitectural features, and interdependencies woven into modern processors pose a formidable challenge for even the most diligent verification and security engineers. To tackle this growing concern, recently, researchers have developed fuzzing techniques explicitly tailored for hardware processors. However, a prevailing issue with these hardware fuzzers is their heavy reliance on static strategies to make decisions in their algorithms. To address this problem, we develop a novel dynamic and adaptive decision-making framework, MABFuzz, that uses multi-armed bandit (MAB) algorithms to fuzz processors. MABFuzz is agnostic to, and hence, applicable to, any existing hardware fuzzer. In the process of designing MABFuzz, we encounter challenges related to the compatibility of MAB algorithms with fuzzers and maximizing their efficacy for fuzzing. We overcome these challenges by modifying the fuzzing process and tailoring MAB algorithms to accommodate special requirements for hardware fuzzing. We integrate three widely used MAB algorithms in a state-of-the-art hardware fuzzer and evaluate them on three popular RISC-V-based processors. Experimental results demonstrate the ability of MABFuzz to cover a broader spectrum of processors' intricate landscapes and doing so with remarkable efficiency. In particular, MABFuzz achieves up to 308x speedup in detecting vulnerabilities and up to 5x speedup in achieving coverage compared to a state-of-the-art technique., Comment: To be published at Design, Automation and Test in Europe Conference, 2024

Published
2023

22. Global scaling of the heat transport in fusion plasmas

Author

Sara Moradi, Johan Anderson, Michele Romanelli, Hyun-Tae Kim, JET contributors, X. Litaudon, S. Abduallev, M. Abhangi, P. Abreu, M. Afzal, K. M. Aggarwal, T. Ahlgren, J. H. Ahn, L. Aho-Mantila, N. Aiba, M. Airila, R. Albanese, V. Aldred, D. Alegre, E. Alessi, P. Aleynikov, A. Alfier, A. Alkseev, M. Allinson, B. Alper, E. Alves, G. Ambrosino, R. Ambrosino, L. Amicucci, V. Amosov, E. Andersson Sundén, M. Angelone, M. Anghel, C. Angioni, L. Appel, C. Appelbee, P. Arena, M. Ariola, H. Arnichand, S. Arshad, A. Ash, N. Ashikawa, V. Aslanyan, O. Asunta, F. Auriemma, Y. Austin, L. Avotina, M. D. Axton, C. Ayres, M. Bacharis, A. Baciero, D. Baião, S. Bailey, A. Baker, I. Balboa, M. Balden, N. Balshaw, R. Bament, J. W. Banks, Y. F. Baranov, M. A. Barnard, D. Barnes, M. Barnes, R. Barnsley, A. Baron Wiechec, L. Barrera Orte, M. Baruzzo, V. Basiuk, M. Bassan, R. Bastow, A. Batista, P. Batistoni, R. Baughan, B. Bauvir, L. Baylor, B. Bazylev, J. Beal, P. S. Beaumont, M. Beckers, B. Beckett, A. Becoulet, N. Bekris, M. Beldishevski, K. Bell, F. Belli, M. Bellinger, É. Belonohy, N. Ben Ayed, N. A. Benterman, H. Bergsȧker, J. Bernardo, M. Bernert, M. Berry, L. Bertalot, C. Besliu, M. Beurskens, B. Bieg, J. Bielecki, T. Biewer, M. Bigi, P. Bìlkovà, F. Binda, A. Bisoffi, J. P. S. Bizarro, C. Björkas, J. Blackburn, K. Blackman, T. R. Blackman, P. Blanchard, P. Blatchford, V. Bobkov, A. Boboc, G. Bodnàr, O. Bogar, I. Bolshakova, T. Bolzonella, N. Bonanomi, F. Bonelli, J. Boom, J. Booth, D. Borba, D. Borodin, I. Borodkina, A. Botrugno, C. Bottereau, P. Boulting, C. Bourdelle, M. Bowden, C. Bower, C. Bowman, T. Boyce, C. Boyd, H. J. Boyer, J. M. A. Bradshaw, V. Braic, R. Bravanec, B. Breizman, S. Bremond, P. D. Brennan, S. Breton, A. Brett, S. Brezinsek, M. D. J. Bright, M. Brix, W. Broeckx, M. Brombin, A. Brosawski, D. P. D. Brown, M. Brown, E. Bruno, J. Bucalossi, J. Buch, J. Buchanan, M. A. Buckley, R. Budny, H. Bufferand, M. Bulman, N. Bulmer, P. Bunting, P. Buratti, A. Burckhart, A. Buscarino, A. Busse, N. K. Butler, I. Bykov, J. Byrne, P. Cahyna, G. Calabrò, I. Calvo, Y. Camenen, P. Camp, D. C. Campling, J. Cane, B. Cannas, A. J. Capel, P. J. Card, A. Cardinali, P. Carman, M. Carr, D. Carralero, L. Carraro, B. B. Carvalho, I. Carvalho, P. Carvalho, F. J. Casson, C. Castaldo, N. Catarino, J. Caumont, F. Causa, R. Cavazzana, K. Cave-Ayland, M. Cavinato, M. Cecconello, S. Ceccuzzi, E. Cecil, A. Cenedese, R. Cesario, C. D. Challis, M. Chandler, D. Chandra, C. S. Chang, A. Chankin, I. T. Chapman, S. C. Chapman, M. Chernyshova, G. Chitarin, G. Ciraolo, D. Ciric, J. Citrin, F. Clairet, E. Clark, M. Clark, R. Clarkson, D. Clatworthy, C. Clements, M. Cleverly, J. P. Coad, P. A. Coates, A. Cobalt, V. Coccorese, V. Cocilovo, S. Coda, R. Coelho, J. W. Coenen, I. Coffey, L. Colas, S. Collins, D. Conka, S. Conroy, N. Conway, D. Coombs, D. Cooper, S. R. Cooper, C. Corradino, Y. Corre, G. Corrigan, S. Cortes, D. Coster, A. S. Couchman, M. P. Cox, T. Craciunescu, S. Cramp, R. Craven, F. Crisanti, G. Croci, D. Croft, K. Crombé, R. Crowe, N. Cruz, G. Cseh, A. Cufar, A. Cullen, M. Curuia, A. Czarnecka, H. Dabirikhah, P. Dalgliesh, S. Dalley, J. Dankowski, D. Darrow, O. Davies, W. Davis, C. Day, I. E. Day, M. De Bock, A. de Castro, E. de la Cal, E. de la Luna, G. De Masi, J. L. de Pablos, G. De Temmerman, G. De Tommasi, P. de Vries, K. Deakin, J. Deane, F. Degli Agostini, R. Dejarnac, E. Delabie, N. den Harder, R. O. Dendy, J. Denis, P. Denner, S. Devaux, P. Devynck, F. Di Maio, A. Di Siena, C. Di Troia, P. Dinca, R. Dinca, B. Ding, T. Dittmar, H. Doerk, R. P. Doerner, T. Donné, S. E. Dorling, S. Dormido-Canto, S. Doswon, D. Douai, P. T. Doyle, A. Drenik, P. Drewelow, P. Drews, Ph. Duckworth, R. Dumont, P. Dumortier, D. Dunai, M. Dunne, I. Duran, F. Durodié, P. Dutta, B. P. Duval, R. Dux, K. Dylst, N. Dzysiuk, P. V. Edappala, J. Edmond, A. M. Edwards, J. Edwards, Th. Eich, A. Ekedahl, R. El-Jorf, C. G. Elsmore, M. Enachescu, G. Ericsson, F. Eriksson, J. Eriksson, L. G. Eriksson, B. Esposito, S. Esquembri, H. G. Esser, D. Esteve, B. Evans, G. E. Evans, G. Evison, G. D. Ewart, D. Fagan, M. Faitsch, D. Falie, A. Fanni, A. Fasoli, J. M. Faustin, N. Fawlk, L. Fazendeiro, N. Fedorczak, R. C. Felton, K. Fenton, A. Fernades, H. Fernandes, J. Ferreira, J. A. Fessey, O. Février, O. Ficker, A. Field, S. Fietz, A. Figueiredo, J. Figueiredo, A. Fil, P. Finburg, M. Firdaouss, U. Fischer, L. Fittill, M. Fitzgerald, D. Flammini, J. Flanagan, C. Fleming, K. Flinders, N. Fonnesu, J. M. Fontdecaba, A. Formisano, L. Forsythe, L. Fortuna, E. Fortuna-Zalesna, M. Fortune, S. Foster, T. Franke, T. Franklin, M. Frasca, L. Frassinetti, M. Freisinger, R. Fresa, D. Frigione, V. Fuchs, D. Fuller, S. Futatani, J. Fyvie, K. Gàl, D. Galassi, K. Galazka, J. Galdon-Quiroga, J. Gallagher, D. Gallart, R. Galvão, X. Gao, Y. Gao, J. Garcia, A. Garcia-Carrasco, M. Garca-Munoz, J.-L. Gardarein, L. Garzotti, P. Gaudio, E. Gauthier, D. F. Gear, S. J. Gee, B. Geiger, M. Gelfusa, S. Gerasimov, G. Gervasini, M. Gethins, Z. Ghani, M. Ghate, M. Gherendi, J. C. Giacalone, L. Giacomelli, C. S. Gibson, T. Giegerich, C. Gil, L. Gil, S. Gilligan, D. Gin, E. Giovannozzi, J. B. Girardo, C. Giroud, G. Giruzzi, S. Glöggler, J. Godwin, J. Goff, P. Gohil, V. Goloborod'ko, R. Gomes, B. Goncalves, M. Goniche, M. Goodliffe, A. Goodyear, G. Gorini, M. Gosk, R. Goulding, A. Goussarov, R. Gowland, B. Graham, M. E. Graham, J. P. Graves, N. Grazier, P. Grazier, N. R. Green, H. Greuner, B. Grierson, F. S. Griph, C. Grisolia, D. Grist, M. Groth, R. Grove, C. N. Grundy, J. Grzonka, D. Guard, C. Guérard, C. Guillemaut, R. Guirlet, C. Gurl, H. H. Utoh, L. J. Hackett, S. Hacquin, A. Hagar, R. Hager, A. Hakola, M. Halitovs, S. J. Hall, S. P. Hallworth Cook, C. Hamlyn-Harris, K. Hammond, C. Harrington, J. Harrison, D. Harting, F. Hasenbeck, Y. Hatano, D. R. Hatch, T. D. V. Haupt, J. Hawes, N. C. Hawkes, J. Hawkins, P. Hawkins, P. W. Haydon, N. Hayter, S. Hazel, P. J. L. Heesterman, K. Heinola, C. Hellesen, T. Hellsten, W. Helou, O. N. Hemming, T. C. Hender, M. Henderson, S. S. Henderson, R. Henriques, D. Hepple, G. Hermon, P. Hertout, C. Hidalgo, E. G. Highcock, M. Hill, J. Hillairet, J. Hillesheim, D. Hillis, K. Hizanidis, A. Hjalmarsson, J. Hobirk, E. Hodille, C. H. A. Hogben, G. M. D. Hogeweij, A. Hollingsworth, S. Hollis, D. A. Homfray, J. Horàcek, G. Hornung, A. R. Horton, L. D. Horton, L. Horvath, S. P. Hotchin, M. R. Hough, P. J. Howarth, A. Hubbard, A. Huber, V. Huber, T. M. Huddleston, M. Hughes, G. T. A. Huijsmans, C. L. Hunter, P. Huynh, A. M. Hynes, D. Iglesias, N. Imazawa, F. Imbeaux, M. Imrìŝek, M. Incelli, P. Innocente, M. Irishkin, I. Ivanova-Stanik, S. Jachmich, A. S. Jacobsen, P. Jacquet, J. Jansons, A. Jardin, A. Järvinen, F. Jaulmes, S. Jednoróq, I. Jenkins, C. Jeong, I. Jepu, E. Joffrin, R. Johnson, T. Johnson, Jane Johnston, L. Joita, G. Jones, T. T. C. Jones, K. K. Hoshino, A. Kallenbach, K. Kamiya, J. Kaniewski, A. Kantor, A. Kappatou, J. Karhunen, D. Karkinsky, I. Karnowska, M. Kaufman, G. Kaveney, Y. Kazakov, V. Kazantzidis, D. L. Keeling, T. Keenan, J. Keep, M. Kempenaars, C. Kennedy, D. Kenny, J. Kent, O. N. Kent, E. Khilkevich, H. T. Kim, H. S. Kim, A. Kinch, C. King, D. King, R. F. King, D. J. Kinna, V. Kiptily, A. Kirk, K. Kirov, A. Kirschner, G. Kizane, C. Klepper, A. Klix, P. Knight, S. J. Knipe, S. Knott, T. Kobuchi, F. Köchl, G. Kocsis, I. Kodeli, L. Kogan, D. Kogut, S. Koivuranta, Y. Kominis, M. Köppen, B. Kos, T. Koskela, H. R. Koslowski, M. Koubiti, M. Kovari, E. Kowalska-Strzeciwilk, A. Krasilnikov, V. Krasilnikov, N. Krawczyk, M. Kresina, K. Krieger, A. Krivska, U. Kruezi, I. Ksiazek, A. Kukushkin, A. Kundu, T. Kurki-Suonio, S. Kwak, R. Kwiatkowski, O. J. Kwon, L. Laguardia, A. Lahtinen, A. Laing, N. Lam, H. T. Lambertz, C. Lane, P. T. Lang, S. Lanthaler, J. Lapins, A. Lasa, J. R. Last, E. Laszynska, R. Lawless, A. Lawson, K. D. Lawson, A. Lazaros, E. Lazzaro, J. Leddy, S. Lee, X. Lefebvre, H. J. Leggate, J. Lehmann, M. Lehnen, D. Leichtle, P. Leichuer, F. Leipold, I. Lengar, M. Lennholm, E. Lerche, A. Lescinskis, S. Lesnoj, E. Letellier, M. Leyland, W. Leysen, L. Li, Y. Liang, J. Likonen, J. Linke, Ch. Linsmeier, B. Lipschultz, G. Liu, Y. Liu, V. P. Lo Schiavo, T. Loarer, A. Loarte, R. C. Lobel, B. Lomanowski, P. J. Lomas, J. Lönnroth, J. M. López, J. López-Razola, R. Lorenzini, U. Losada, J. J. Lovell, A. B. Loving, C. Lowry, T. Luce, R. M. A. Lucock, A. Lukin, C. Luna, M. Lungaroni, C. P. Lungu, M. Lungu, A. Lunniss, I. Lupelli, A. Lyssoivan, N. Macdonald, P. Macheta, K. Maczewa, B. Magesh, P. Maget, C. Maggi, H. Maier, J. Mailloux, T. Makkonen, R. Makwana, A. Malaquias, A. Malizia, P. Manas, A. Manning, M. E. Manso, P. Mantica, M. Mantsinen, A. Manzanares, Ph. Maquet, Y. Marandet, N. Marcenko, C. Marchetto, O. Marchuk, M. Marinelli, M. Marinucci, T. Markovic, D. Marocco, L. Marot, C. A. Marren, R. Marshal, A. Martin, Y. Martin, A. Martín de Aguilera, F. J. Martínez, J. R. Martín-Solís, Y. Martynova, S. Maruyama, A. Masiello, M. Maslov, S. Matejcik, M. Mattei, G. F. Matthews, F. Maviglia, M. Mayer, M. L. Mayoral, T. May-Smith, D. Mazon, C. Mazzotta, R. McAdams, P. J. McCarthy, K. G. McClements, O. McCormack, P. A. McCullen, D. McDonald, S. McIntosh, R. McKean, J. McKehon, R. C. Meadows, A. Meakins, F. Medina, M. Medland, S. Medley, S. Meigh, A. G. Meigs, G. Meisl, S. Meitner, L. Meneses, S. Menmuir, K. Mergia, I. R. Merrigan, Ph. Mertens, S. Meshchaninov, A. Messiaen, H. Meyer, S. Mianowski, R. Michling, D. Middleton-Gear, J. Miettunen, F. Militello, E. Militello-Asp, G. Miloshevsky, F. Mink, S. Minucci, Y. Miyoshi, J. Mlynàr, D. Molina, I. Monakhov, M. Moneti, R. Mooney, S. Moradi, S. Mordijck, L. Moreira, R. Moreno, F. Moro, A. W. Morris, J. Morris, L. Moser, S. Mosher, D. Moulton, A. Murari, A. Muraro, S. Murphy, N. N. Asakura, Y. S. Na, F. Nabais, R. Naish, T. Nakano, E. Nardon, V. Naulin, M. F. F. Nave, I. Nedzelski, G. Nemtsev, F. Nespoli, A. Neto, R. Neu, V. S. Neverov, M. Newman, K. J. Nicholls, T. Nicolas, A. H. Nielsen, P. Nielsen, E. Nilsson, D. Nishijima, C. Noble, M. Nocente, D. Nodwell, K. Nordlund, H. Nordman, R. Nouailletas, I. Nunes, M. Oberkofler, T. Odupitan, M. T. Ogawa, T. O'Gorman, M. Okabayashi, R. Olney, O. Omolayo, M. O'Mullane, J. Ongena, F. Orsitto, J. Orszagh, B. I. Oswuigwe, R. Otin, A. Owen, R. Paccagnella, N. Pace, D. Pacella, L. W. Packer, A. Page, E. Pajuste, S. Palazzo, S. Pamela, S. Panja, P. Papp, R. Paprok, V. Parail, M. Park, F. Parra Diaz, M. Parsons, R. Pasqualotto, A. Patel, S. Pathak, D. Paton, H. Patten, A. Pau, E. Pawelec, C. Paz Soldan, A. Peackoc, I. J. Pearson, S.-P. Pehkonen, E. Peluso, C. Penot, A. Pereira, R. Pereira, P. P. Pereira Puglia, C. Perez von Thun, S. Peruzzo, S. Peschanyi, M. Peterka, P. Petersson, G. Petravich, A. Petre, N. Petrella, V. Petrzilka, Y. Peysson, D. Pfefferlé, V. Philipps, M. Pillon, G. Pintsuk, P. Piovesan, A. Pires dos Reis, L. Piron, A. Pironti, F. Pisano, R. Pitts, F. Pizzo, V. Plyusnin, N. Pomaro, O. G. Pompilian, P. J. Pool, S. Popovichev, M. T. Porfiri, C. Porosnicu, M. Porton, G. Possnert, S. Potzel, T. Powell, J. Pozzi, V. Prajapati, R. Prakash, G. Prestopino, D. Price, M. Price, R. Price, P. Prior, R. Proudfoot, G. Pucella, P. Puglia, M. E. Puiatti, D. Pulley, K. Purahoo, Th. Pütterich, E. Rachlew, M. Rack, R. Ragona, M. S. J. Rainford, A. Rakha, G. Ramogida, S. Ranjan, C. J. Rapson, J. J. Rasmussen, K. Rathod, G. Rattà, S. Ratynskaia, G. Ravera, C. Rayner, M. Rebai, D. Reece, A. Reed, D. Réfy, B. Regan, J. Regana, M. Reich, N. Reid, F. Reimold, M. Reinhart, M. Reinke, D. Reiser, D. Rendell, C. Reux, S. D. A. Reyes Cortes, S. Reynolds, V. Riccardo, N. Richardson, K. Riddle, D. Rigamonti, F. G. Rimini, J. Risner, M. Riva, C. Roach, R. J. Robins, S. A. Robinson, T. Robinson, D. W. Robson, R. Roccella, R. Rodionov, P. Rodrigues, J. Rodriguez, V. Rohde, F. Romanelli, M. Romanelli, S. Romanelli, J. Romazanov, S. Rowe, M. Rubel, G. Rubinacci, G. Rubino, L. Ruchko, M. Ruiz, C. Ruset, J. Rzadkiewicz, S. Saarelma, R. Sabot, E. Safi, P. Sagar, G. Saibene, F. Saint-Laurent, M. Salewski, A. Salmi, R. Salmon, F. Salzedas, D. Samaddar, U. Samm, D. Sandiford, P. Santa, M. I. K. Santala, B. Santos, A. Santucci, F. Sartori, R. Sartori, O. Sauter, R. Scannell, T. Schlummer, K. Schmid, V. Schmidt, S. Schmuck, M. Schneider, K. Schöpf, D. Schwörer, S. D. Scott, G. Sergienko, M. Sertoli, A. Shabbir, S. E. Sharapov, A. Shaw, R. Shaw, H. Sheikh, A. Shepherd, A. Shevelev, A. Shumack, G. Sias, M. Sibbald, B. Sieglin, S. Silburn, A. Silva, C. Silva, P. A. Simmons, J. Simpson, J. Simpson-Hutchinson, A. Sinha, S. K. Sipilä, A. C. C. Sips, P. Sirén, A. Sirinelli, H. Sjöstrand, M. Skiba, R. Skilton, K. Slabkowska, B. Slade, N. Smith, P. G. Smith, R. Smith, T. J. Smith, M. Smithies, L. Snoj, S. Soare, E. R. Solano, A. Somers, C. Sommariva, P. Sonato, A. Sopplesa, J. Sousa, C. Sozzi, S. Spagnolo, T. Spelzini, F. Spineanu, G. Stables, I. Stamatelatos, M. F. Stamp, P. Staniec, G. Stankunas, C. Stan-Sion, M. J. Stead, E. Stefanikova, I. Stepanov, A. V. Stephen, M. Stephen, A. Stevens, B. D. Stevens, J. Strachan, P. Strand, H. R. Strauss, P. Ström, G. Stubbs, W. Studholme, F. Subba, H. P. Summers, J. Svensson, L. Swiderski, T. Szabolics, M. Szawlowski, G. Szepesi, T. T. Suzuki, B. Tàl, T. Tala, A. R. Talbot, S. Talebzadeh, C. Taliercio, P. Tamain, C. Tame, W. Tang, M. Tardocchi, L. Taroni, D. Taylor, K. A. Taylor, D. Tegnered, G. Telesca, N. Teplova, D. Terranova, D. Testa, E. Tholerus, J. Thomas, J. D. Thomas, P. Thomas, A. Thompson, C.-A. Thompson, V. K. Thompson, L. Thorne, A. Thornton, A. S. Thrysoe, P. A. Tigwell, N. Tipton, I. Tiseanu, H. Tojo, M. Tokitani, P. Tolias, M. Tomes, P. Tonner, M. Towndrow, P. Trimble, M. Tripsky, M. Tsalas, P. Tsavalas, D. Tskhakaya jun, I. Turner, M. M. Turner, M. Turnyanskiy, G. Tvalashvili, S. G. J. Tyrrell, A. Uccello, Z. Ul-Abidin, J. Uljanovs, D. Ulyatt, H. Urano, I. Uytdenhouwen, A. P. Vadgama, D. Valcarcel, M. Valentinuzzi, M. Valisa, P. Vallejos Olivares, M. Valovic, M. Van De Mortel, D. Van Eester, W. Van Renterghem, G. J. van Rooij, J. Varje, S. Varoutis, S. Vartanian, K. Vasava, T. Vasilopoulou, J. Vega, G. Verdoolaege, R. Verhoeven, C. Verona, G. Verona Rinati, E. Veshchev, N. Vianello, J. Vicente, E. Viezzer, S. Villari, F. Villone, P. Vincenzi, I. Vinyar, B. Viola, A. Vitins, Z. Vizvary, M. Vlad, I. Voitsekhovitch, P. Vondràcek, N. Vora, T. Vu, W. W. Pires de Sa, B. Wakeling, C. W. F. Waldon, N. Walkden, M. Walker, R. Walker, M. Walsh, E. Wang, N. Wang, S. Warder, R. J. Warren, J. Waterhouse, N. W. Watkins, C. Watts, T. Wauters, A. Weckmann, J. Weiland, H. Weisen, M. Weiszflog, C. Wellstood, A. T. West, M. R. Wheatley, S. Whetham, A. M. Whitehead, B. D. Whitehead, A. M. Widdowson, S. Wiesen, J. Wilkinson, J. Williams, M. Williams, A. R. Wilson, D. J. Wilson, H. R. Wilson, J. Wilson, M. Wischmeier, G. Withenshaw, A. Withycombe, D. M. Witts, D. Wood, R. Wood, C. Woodley, S. Wray, J. Wright, J. C. Wright, J. Wu, S. Wukitch, A. Wynn, T. Xu, D. Yadikin, W. Yanling, L. Yao, V. Yavorskij, M. G. Yoo, C. Young, D. Young, I. D. Young, R. Young, J. Zacks, R. Zagorski, F. S. Zaitsev, R. Zanino, A. Zarins, K. D. Zastrow, M. Zerbini, W. Zhang, Y. Zhou, E. Zilli, V. Zoita, S. Zoletnik, and I. Zychor

Subjects
Physics, QC1-999
Abstract

A global heat flux model based on a fractional derivative of plasma pressure is proposed for the heat transport in fusion plasmas. The degree of the fractional derivative of the heat flux, α, is defined through the power balance analysis of the steady state. The model was used to obtain the experimental values of α for a large database of the Joint European Torus (JET) carbon-wall as well as ITER like-wall plasmas. The fractional degrees of the electron heat flux are found to be α

Published
2020
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23. On the synthesis methodologies to prepare Pb$_9$Cu(PO$_4$)$_6$O -- phase, composition, magnetic analysis and absence of superconductivity

Author

Thakur, Gohil S., Schulze, Manuel, and Ruck, Michael

Subjects
Condensed Matter - Superconductivity, Condensed Matter - Materials Science
Abstract

We present the results of our various attempts to prepare the presumably room-temperature and ambient-pressure superconducting compound Pb$_9$Cu(PO$_4$)$_6$O (LK-99). We experimented with various starting materials, and used several synthesis techniques like, sealed quartz tubes and air-sintering pathways to prepare the reported phase. Repetition of the exact synthesis process followed by Lee et al failed to reproduce the superconducting phase yielding only a multiphase sample. None of our prepared samples exhibits Meissner effect or Levitation. Very importantly, no copper (or only a trace amount) could be detected in any of the designated LK-99 phase with PXRD pattern identical to the reported one. Additionally, some dark greyish flakes in some samples responded towards the magnet in a way imitating half-levitation. The magnetic measurement on those sample suggest it is diamagnetic in the measured temperature range of 2-325 K but show a weak soft ferromagnetic behavior at 2 K, the origin of which is still unknown to us., Comment: 16 pages, 8 figure and 1 table

Published
2023

24. PSOFuzz: Fuzzing Processors with Particle Swarm Optimization

Author

Chen, Chen, Gohil, Vasudev, Kande, Rahul, Sadeghi, Ahmad-Reza, and Rajendran, Jeyavijayan

Subjects
Computer Science - Cryptography and Security
Abstract

Hardware security vulnerabilities in computing systems compromise the security defenses of not only the hardware but also the software running on it. Recent research has shown that hardware fuzzing is a promising technique to efficiently detect such vulnerabilities in large-scale designs such as modern processors. However, the current fuzzing techniques do not adjust their strategies dynamically toward faster and higher design space exploration, resulting in slow vulnerability detection, evident through their low design coverage. To address this problem, we propose PSOFuzz, which uses particle swarm optimization (PSO) to schedule the mutation operators and to generate initial input programs dynamically with the objective of detecting vulnerabilities quickly. Unlike traditional PSO, which finds a single optimal solution, we use a modified PSO that dynamically computes the optimal solution for selecting mutation operators required to explore new design regions in hardware. We also address the challenge of inefficient initial seed generation by employing PSO-based seed generation. Including these optimizations, our final formulation outperforms fuzzers without PSO. Experiments show that PSOFuzz achieves up to 15.25$\times$ speedup for vulnerability detection and up to 2.22$\times$ speedup for coverage compared to the state-of-the-art simulation-based hardware fuzzer., Comment: To be published in the proceedings of the ICCAD, 2023

Published
2023

25. Central-line team effort: Recognizing insertion-site concerns in nursing homes

Author

Nguyen, Kristine P, Singh, Raveena D, Saavedra, Raheeb, Gohil, Shruti K, Billimek, John T, Tam, Steven P, Steinberg, Karl E, Porter, Lori, and Huang, Susan S

Subjects
Biomedical and Clinical Sciences, Health Sciences, Humans, Nursing Homes, Catheterization, Medical and Health Sciences, Epidemiology, Biomedical and clinical sciences, Health sciences
Published
2023

26. Decolonization in Nursing Homes to Prevent Infection and Hospitalization

Author

Miller, Loren G, McKinnell, James A, Singh, Raveena D, Gussin, Gabrielle M, Kleinman, Ken, Saavedra, Raheeb, Mendez, Job, Catuna, Tabitha D, Felix, James, Chang, Justin, Heim, Lauren, Franco, Ryan, Tjoa, Thomas, Stone, Nimalie D, Steinberg, Karl, Beecham, Nancy, Montgomery, Jocelyn, Walters, DeAnn, Park, Steven, Tam, Steven, Gohil, Shruti K, Robinson, Philip A, Estevez, Marlene, Lewis, Brian, Shimabukuro, Julie A, Tchakalian, Gregory, Miner, Aaron, Torres, Crystal, Evans, Kaye D, Bittencourt, Cassiana E, He, Jiayi, Lee, Eunjung, Nedelcu, Christine, Lu, Julia, Agrawal, Shalini, Sturdevant, S Gwynn, Peterson, Ellena, and Huang, Susan S

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, Clinical Trials and Supportive Activities, Clinical Research, Prevention, Health Services, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundNursing home residents are at high risk for infection, hospitalization, and colonization with multidrug-resistant organisms.MethodsWe performed a cluster-randomized trial of universal decolonization as compared with routine-care bathing in nursing homes. The trial included an 18-month baseline period and an 18-month intervention period. Decolonization entailed the use of chlorhexidine for all routine bathing and showering and administration of nasal povidone-iodine twice daily for the first 5 days after admission and then twice daily for 5 days every other week. The primary outcome was transfer to a hospital due to infection. The secondary outcome was transfer to a hospital for any reason. An intention-to-treat (as-assigned) difference-in-differences analysis was performed for each outcome with the use of generalized linear mixed models to compare the intervention period with the baseline period across trial groups.ResultsData were obtained from 28 nursing homes with a total of 28,956 residents. Among the transfers to a hospital in the routine-care group, 62.2% (the mean across facilities) were due to infection during the baseline period and 62.6% were due to infection during the intervention period (risk ratio, 1.00; 95% confidence interval [CI], 0.96 to 1.04). The corresponding values in the decolonization group were 62.9% and 52.2% (risk ratio, 0.83; 95% CI, 0.79 to 0.88), for a difference in risk ratio, as compared with routine care, of 16.6% (95% CI, 11.0 to 21.8; P

Published
2023

36. Assessing past versus present severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection: A survey of criteria for discontinuing precautions in asymptomatic patients testing positive on admission.

Author

Gohil, Shruti K, De St Maurice, Annabelle, Yokoe, Deborah S, Cohen, Stuart H, Torriani, Francesca J, Grein, Jonathan D, Robinson, Philip A, Mabalot, Shannon, Park, Jessica, Pedrani, Paula, Platt, Richard, and Huang, Susan S

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Lung, Vaccine Related, Infectious Diseases, Pneumonia, Prevention, Biodefense, Pneumonia & Influenza, Emerging Infectious Diseases, Infection, Good Health and Well Being, Medical and Health Sciences, Epidemiology, Biomedical and clinical sciences, Health sciences
Abstract

Infection prevention program leaders report frequent use of criteria to distinguish recently recovered coronavirus disease 2019 (COVID-19) cases from actively infectious cases when incidentally positive asymptomatic patients were identified on routine severe acute respiratory coronavirus virus 2 (SARS-CoV-2) polymerase chain reaction (PCR) testing. Guidance on appropriate interpretation of high-sensitivity molecular tests can prevent harm from unnecessary precautions that delay admission and impede medical care.

Published
2023

37. Impact of measurement and feedback on chlorhexidine gluconate bathing among intensive care unit patients: A multicenter study.

Author

Rhee, Yoona, Hayden, Mary, Schoeny, Michael, Baker, Arthur, Baker, Meghan, Gohil, Shruti, Rhee, Chanu, Talati, Naasha, Warren, David, Welbel, Sharon, Lolans, Karen, Bahadori, Bardia, Bell, Pamela, Bravo, Heilen, Dangana, Thelma, Fukuda, Christine, Habrock Bach, Tracey, Nelson, Alicia, Simms, Andrew, Tolomeo, Pam, Wolf, Robert, Yelin, Rachel, and Lin, Michael

Subjects
Adult, Humans, Feedback, Critical Care, Intensive Care Units, Chlorhexidine
Abstract

OBJECTIVE: To assess whether measurement and feedback of chlorhexidine gluconate (CHG) skin concentrations can improve CHG bathing practice across multiple intensive care units (ICUs). DESIGN: A before-and-after quality improvement study measuring patient CHG skin concentrations during 6 point-prevalence surveys (3 surveys each during baseline and intervention periods). SETTING: The study was conducted across 7 geographically diverse ICUs with routine CHG bathing. PARTICIPANTS: Adult patients in the medical ICU. METHODS: CHG skin concentrations were measured at the neck, axilla, and inguinal region using a semiquantitative colorimetric assay. Aggregate unit-level CHG skin concentration measurements from the baseline period and each intervention period survey were reported back to ICU leadership, which then used routine education and quality improvement activities to improve CHG bathing practice. We used multilevel linear models to assess the impact of intervention on CHG skin concentrations. RESULTS: We enrolled 681 (93%) of 736 eligible patients; 92% received a CHG bath prior to survey. At baseline, CHG skin concentrations were lowest on the neck, compared to axillary or inguinal regions (P < .001). CHG was not detected on 33% of necks, 19% of axillae, and 18% of inguinal regions (P < .001 for differences in body sites). During the intervention period, ICUs that used CHG-impregnated cloths had a 3-fold increase in patient CHG skin concentrations as compared to baseline (P < .001). CONCLUSIONS: Routine CHG bathing performance in the ICU varied across multiple hospitals. Measurement and feedback of CHG skin concentrations can be an important tool to improve CHG bathing practice.

Published
2023

38. Base Excision Repair: Mechanisms and Impact in Biology, Disease, and Medicine

Author

Gohil, Dhara, Sarker, Altaf H, and Roy, Rabindra

Subjects
Biochemistry and Cell Biology, Biological Sciences, Genetics, Aging, 2.1 Biological and endogenous factors, Aetiology, Humans, Animals, Mice, Mice, Knockout, Medicine, DNA Repair, Biology, 5′-Gap, APE1, NEIL1/2, OGG1, PARP, PNKP, POL β, RECQ1, XPF-ERCC1, XRCC1, base excision repair, nucleotide incision repair, replication-associated, transcription-associated, Other Chemical Sciences, Other Biological Sciences, Chemical Physics, Biochemistry and cell biology, Microbiology, Medicinal and biomolecular chemistry
Abstract

Base excision repair (BER) corrects forms of oxidative, deamination, alkylation, and abasic single-base damage that appear to have minimal effects on the helix. Since its discovery in 1974, the field has grown in several facets: mechanisms, biology and physiology, understanding deficiencies and human disease, and using BER genes as potential inhibitory targets to develop therapeutics. Within its segregation of short nucleotide (SN-) and long patch (LP-), there are currently six known global mechanisms, with emerging work in transcription- and replication-associated BER. Knockouts (KOs) of BER genes in mouse models showed that single glycosylase knockout had minimal phenotypic impact, but the effects were clearly seen in double knockouts. However, KOs of downstream enzymes showed critical impact on the health and survival of mice. BER gene deficiency contributes to cancer, inflammation, aging, and neurodegenerative disorders. Medicinal targets are being developed for single or combinatorial therapies, but only PARP and APE1 have yet to reach the clinical stage.

Published
2023

39. Coronavirus disease 2019 (COVID-19) infection prevention practices that exceed Centers for Disease Control and Prevention (CDC) guidance: Balancing extra caution against impediments to care.

Author

Gohil, Shruti K, Septimus, Edward, Sands, Kenneth E, Blanchard, Eunice Jackie, Moody, Julia, de St Maurice, Annabelle, Yokoe, Deborah, Kwon, Jennie, Grein, Jonathan, Cohen, Stuart, Uslan, Daniel, Vasudev, Milind, Mauricio, Amarah, Mabalot, Shannon, Coady, Micaela H, Sljivo, Selsebil, Smith, Kimberly, Carver, Brandon, Poland, Russell, Perlin, Jonathan, Platt, Richard, Huang, Susan S, and Centers for Disease Control and Prevention Epicenters Program

Subjects
Centers for Disease Control and Prevention Epicenters Program, Prevention, Infectious Diseases, Neurodegenerative, Clinical Research, Good Health and Well Being, Medical and Health Sciences, Epidemiology
Abstract

In a survey of infection prevention programs, leaders reported frequent clinical and infection prevention practice modifications to avoid coronavirus disease 2019 (COVID-19) exposure that exceeded national guidance. Future pandemic responses should emphasize balanced approaches to precautions, prioritize educational campaigns to manage safety concerns, and generate an evidence-base that can guide appropriate infection prevention practices.

Published
2023

40. Patient and Epidemiological Factors Associated With Influenza Testing in Hospitalized Adults With Acute Respiratory Illnesses, 2016-2017 to 2019-2020.

Author

Dalton, Alexandra F, Couture, Alexia, DeSilva, Malini B, Irving, Stephanie A, Gohil, Shruti, Rao, Suchitra, Fink, Rebecca V, Naleway, Allison L, Guo, Zijing, Sundaresan, Devi, Birch, Rebecca J, Ball, Sarah, Zheng, Kai, Ong, Toan C, Reed, Carrie, and Bozio, Catherine H

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Clinical Sciences, Emerging Infectious Diseases, Influenza, Infectious Diseases, Clinical Research, Health Services, Prevention, Lung, Rare Diseases, Pneumonia & Influenza, Infection, Good Health and Well Being, acute respiratory illness, clinical testing, hospitalization, influenza, Clinical sciences, Medical microbiology
Abstract

BackgroundData are limited on influenza testing among adults with acute respiratory illness (ARI)-associated hospitalizations. We identified factors associated with influenza testing in adult ARI-associated hospitalizations across the 2016-2017 through 2019-2020 influenza seasons.MethodsUsing data from 4 health systems in the United States, we identified hospitalizations that had an ARI discharge diagnosis or respiratory virus test. A hospitalization with influenza testing was based on testing performed within 14 days before through 72 hours after admission. We used random forest analysis to identify patient characteristics and influenza activity indicators that were most important in terms of their relationship to influenza testing.ResultsAcross 4 seasons, testing rates ranged from 14.8%-19.4% at 3 pooled sites and 60.1%-78.5% at a fourth site with different testing practices. Discharge diagnoses of pneumonia or infectious disease of noninfluenza etiology, presence of ARI signs/symptoms, hospital admission month, and influenza-like illness activity level were consistently among the variables with the greatest relative importance.ConclusionsSelect ARI diagnoses and indicators of influenza activity were the most important factors associated with influenza testing among ARI-associated hospitalizations. Improved understanding of which patients are tested may enhance influenza burden estimates and allow for more timely clinical management of influenza-associated hospitalizations.

Published
2023

41. Epidemiology and genomics of a slow outbreak of methicillin-resistant Staphyloccus aureus (MRSA) in a neonatal intensive care unit: Successful chronic decolonization of MRSA-positive healthcare personnel

Author

Quan, Kathleen A, Sater, Mohamad RA, Uy, Cherry, Clifton-Koeppel, Robin, Dickey, Linda L, Wilson, William, Patton, Pat, Chang, Wayne, Samuelson, Pamela, Lagoudas, Georgia K, Allen, Teri, Merchant, Lenny, Gannotta, Rick, Bittencourt, Cassiana E, Soto, JC, Evans, Kaye D, Blainey, Paul C, Murray, John, Shelton, Dawn, Lee, Helen S, Zahn, Matthew, Wolfe, Julia, Madey, Keith, Yim, Jennifer, Gohil, Shruti K, Grad, Yonatan H, and Huang, Susan S

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Infant Mortality, Vaccine Related, Infectious Diseases, Emerging Infectious Diseases, Human Genome, Rare Diseases, Genetics, Pediatric, Antimicrobial Resistance, Prevention, Perinatal Period - Conditions Originating in Perinatal Period, Infection, Good Health and Well Being, Infant, Newborn, Infant, Humans, Methicillin-Resistant Staphylococcus aureus, Methicillin Resistance, Intensive Care Units, Neonatal, Staphylococcal Infections, Disease Outbreaks, Genomics, Delivery of Health Care, Medical and Health Sciences, Epidemiology, Biomedical and clinical sciences, Health sciences
Abstract

ObjectiveTo describe the genomic analysis and epidemiologic response related to a slow and prolonged methicillin-resistant Staphylococcus aureus (MRSA) outbreak.DesignProspective observational study.SettingNeonatal intensive care unit (NICU).MethodsWe conducted an epidemiologic investigation of a NICU MRSA outbreak involving serial baby and staff screening to identify opportunities for decolonization. Whole-genome sequencing was performed on MRSA isolates.ResultsA NICU with excellent hand hygiene compliance and longstanding minimal healthcare-associated infections experienced an MRSA outbreak involving 15 babies and 6 healthcare personnel (HCP). In total, 12 cases occurred slowly over a 1-year period (mean, 30.7 days apart) followed by 3 additional cases 7 months later. Multiple progressive infection prevention interventions were implemented, including contact precautions and cohorting of MRSA-positive babies, hand hygiene observers, enhanced environmental cleaning, screening of babies and staff, and decolonization of carriers. Only decolonization of HCP found to be persistent carriers of MRSA was successful in stopping transmission and ending the outbreak. Genomic analyses identified bidirectional transmission between babies and HCP during the outbreak.ConclusionsIn comparison to fast outbreaks, outbreaks that are "slow and sustained" may be more common to units with strong existing infection prevention practices such that a series of breaches have to align to result in a case. We identified a slow outbreak that persisted among staff and babies and was only stopped by identifying and decolonizing persistent MRSA carriage among staff. A repeated decolonization regimen was successful in allowing previously persistent carriers to safely continue work duties.

Published
2023

42. Biomedical image analysis competitions: The state of current participation practice

Author

Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Godau, Patrick, Cheplygina, Veronika, Kozubek, Michal, Ali, Sharib, Gupta, Anubha, Kybic, Jan, Noble, Alison, de Solórzano, Carlos Ortiz, Pachade, Samiksha, Petitjean, Caroline, Sage, Daniel, Wei, Donglai, Wilden, Elizabeth, Alapatt, Deepak, Andrearczyk, Vincent, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bawa, Vivek Singh, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Choi, Jinwook, Commowick, Olivier, Daum, Marie, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Eichhorn, Hannah, Engelhardt, Sandy, Ganz, Melanie, Girard, Gabriel, Hansen, Lasse, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Kim, Hyunjeong, Landman, Bennett, Li, Hongwei Bran, Li, Jianning, Ma, Jun, Martel, Anne, Martín-Isla, Carlos, Menze, Bjoern, Nwoye, Chinedu Innocent, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Sudre, Carole, van Wijnen, Kimberlin, Vardazaryan, Armine, Vercauteren, Tom, Wagner, Martin, Wang, Chuanbo, Yap, Moi Hoon, Yu, Zeyun, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Bao, Rina, Choi, Chanyeol, Cohen, Andrew, Dzyubachyk, Oleh, Galdran, Adrian, Gan, Tianyuan, Guo, Tianqi, Gupta, Pradyumna, Haithami, Mahmood, Ho, Edward, Jang, Ikbeom, Li, Zhili, Luo, Zhengbo, Lux, Filip, Makrogiannis, Sokratis, Müller, Dominik, Oh, Young-tack, Pang, Subeen, Pape, Constantin, Polat, Gorkem, Reed, Charlotte Rosalie, Ryu, Kanghyun, Scherr, Tim, Thambawita, Vajira, Wang, Haoyu, Wang, Xinliang, Xu, Kele, Yeh, Hung, Yeo, Doyeob, Yuan, Yixuan, Zeng, Yan, Zhao, Xin, Abbing, Julian, Adam, Jannes, Adluru, Nagesh, Agethen, Niklas, Ahmed, Salman, Khalil, Yasmina Al, Alenyà, Mireia, Alhoniemi, Esa, An, Chengyang, Anwar, Talha, Arega, Tewodros Weldebirhan, Avisdris, Netanell, Aydogan, Dogu Baran, Bai, Yingbin, Calisto, Maria Baldeon, Basaran, Berke Doga, Beetz, Marcel, Bian, Cheng, Bian, Hao, Blansit, Kevin, Bloch, Louise, Bohnsack, Robert, Bosticardo, Sara, Breen, Jack, Brudfors, Mikael, Brüngel, Raphael, Cabezas, Mariano, Cacciola, Alberto, Chen, Zhiwei, Chen, Yucong, Chen, Daniel Tianming, Cho, Minjeong, Choi, Min-Kook, Xie, Chuantao Xie Chuantao, Cobzas, Dana, Cohen-Adad, Julien, Acero, Jorge Corral, Das, Sujit Kumar, de Oliveira, Marcela, Deng, Hanqiu, Dong, Guiming, Doorenbos, Lars, Efird, Cory, Escalera, Sergio, Fan, Di, Serj, Mehdi Fatan, Fenneteau, Alexandre, Fidon, Lucas, Filipiak, Patryk, Finzel, René, Freitas, Nuno R., Friedrich, Christoph M., Fulton, Mitchell, Gaida, Finn, Galati, Francesco, Galazis, Christoforos, Gan, Chang Hee, Gao, Zheyao, Gao, Shengbo, Gazda, Matej, Gerats, Beerend, Getty, Neil, Gibicar, Adam, Gifford, Ryan, Gohil, Sajan, Grammatikopoulou, Maria, Grzech, Daniel, Güley, Orhun, Günnemann, Timo, Guo, Chunxu, Guy, Sylvain, Ha, Heonjin, Han, Luyi, Han, Il Song, Hatamizadeh, Ali, He, Tian, Heo, Jimin, Hitziger, Sebastian, Hong, SeulGi, Hong, SeungBum, Huang, Rian, Huang, Ziyan, Huellebrand, Markus, Huschauer, Stephan, Hussain, Mustaffa, Inubushi, Tomoo, Polat, Ece Isik, Jafaritadi, Mojtaba, Jeong, SeongHun, Jian, Bailiang, Jiang, Yuanhong, Jiang, Zhifan, Jin, Yueming, Joshi, Smriti, Kadkhodamohammadi, Abdolrahim, Kamraoui, Reda Abdellah, Kang, Inha, Kang, Junghwa, Karimi, Davood, Khademi, April, Khan, Muhammad Irfan, Khan, Suleiman A., Khantwal, Rishab, Kim, Kwang-Ju, Kline, Timothy, Kondo, Satoshi, Kontio, Elina, Krenzer, Adrian, Kroviakov, Artem, Kuijf, Hugo, Kumar, Satyadwyoom, La Rosa, Francesco, Lad, Abhi, Lee, Doohee, Lee, Minho, Lena, Chiara, Li, Hao, Li, Ling, Li, Xingyu, Liao, Fuyuan, Liao, KuanLun, Oliveira, Arlindo Limede, Lin, Chaonan, Lin, Shan, Linardos, Akis, Linguraru, Marius George, Liu, Han, Liu, Tao, Liu, Di, Liu, Yanling, Lourenço-Silva, João, Lu, Jingpei, Lu, Jiangshan, Luengo, Imanol, Lund, Christina B., Luu, Huan Minh, Lv, Yi, Macar, Uzay, Maechler, Leon, L., Sina Mansour, Marshall, Kenji, Mazher, Moona, McKinley, Richard, Medela, Alfonso, Meissen, Felix, Meng, Mingyuan, Miller, Dylan, Mirjahanmardi, Seyed Hossein, Mishra, Arnab, Mitha, Samir, Mohy-ud-Din, Hassan, Mok, Tony Chi Wing, Murugesan, Gowtham Krishnan, Karthik, Enamundram Naga, Nalawade, Sahil, Nalepa, Jakub, Naser, Mohamed, Nateghi, Ramin, Naveed, Hammad, Nguyen, Quang-Minh, Quoc, Cuong Nguyen, Nichyporuk, Brennan, Oliveira, Bruno, Owen, David, Pal, Jimut Bahan, Pan, Junwen, Pan, Wentao, Pang, Winnie, Park, Bogyu, Pawar, Vivek, Pawar, Kamlesh, Peven, Michael, Philipp, Lena, Pieciak, Tomasz, Plotka, Szymon, Plutat, Marcel, Pourakpour, Fattaneh, Preložnik, Domen, Punithakumar, Kumaradevan, Qayyum, Abdul, Queirós, Sandro, Rahmim, Arman, Razavi, Salar, Ren, Jintao, Rezaei, Mina, Rico, Jonathan Adam, Rieu, ZunHyan, Rink, Markus, Roth, Johannes, Ruiz-Gonzalez, Yusely, Saeed, Numan, Saha, Anindo, Salem, Mostafa, Sanchez-Matilla, Ricardo, Schilling, Kurt, Shao, Wei, Shen, Zhiqiang, Shi, Ruize, Shi, Pengcheng, Sobotka, Daniel, Soulier, Théodore, Fadida, Bella Specktor, Stoyanov, Danail, Mun, Timothy Sum Hon, Sun, Xiaowu, Tao, Rong, Thaler, Franz, Théberge, Antoine, Thielke, Felix, Torres, Helena, Wahid, Kareem A., Wang, Jiacheng, Wang, YiFei, Wang, Wei, Wang, Xiong, Wen, Jianhui, Wen, Ning, Wodzinski, Marek, Wu, Ye, Xia, Fangfang, Xiang, Tianqi, Xiaofei, Chen, Xu, Lizhan, Xue, Tingting, Yang, Yuxuan, Yang, Lin, Yao, Kai, Yao, Huifeng, Yazdani, Amirsaeed, Yip, Michael, Yoo, Hwanseung, Yousefirizi, Fereshteh, Yu, Shunkai, Yu, Lei, Zamora, Jonathan, Zeineldin, Ramy Ashraf, Zeng, Dewen, Zhang, Jianpeng, Zhang, Bokai, Zhang, Jiapeng, Zhang, Fan, Zhang, Huahong, Zhao, Zhongchen, Zhao, Zixuan, Zhao, Jiachen, Zhao, Can, Zheng, Qingshuo, Zhi, Yuheng, Zhou, Ziqi, Zou, Baosheng, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, and Maier-Hein, Lena

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Published
2022

44. Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges

Author

Patnaik, Satwik, Gohil, Vasudev, Guo, Hao, Jeyavijayan, and Rajendran

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Reinforcement learning (RL) is a machine learning paradigm where an autonomous agent learns to make an optimal sequence of decisions by interacting with the underlying environment. The promise demonstrated by RL-guided workflows in unraveling electronic design automation problems has encouraged hardware security researchers to utilize autonomous RL agents in solving domain-specific problems. From the perspective of hardware security, such autonomous agents are appealing as they can generate optimal actions in an unknown adversarial environment. On the other hand, the continued globalization of the integrated circuit supply chain has forced chip fabrication to off-shore, untrustworthy entities, leading to increased concerns about the security of the hardware. Furthermore, the unknown adversarial environment and increasing design complexity make it challenging for defenders to detect subtle modifications made by attackers (a.k.a. hardware Trojans). In this brief, we outline the development of RL agents in detecting hardware Trojans, one of the most challenging hardware security problems. Additionally, we outline potential opportunities and enlist the challenges of applying RL to solve hardware security problems., Comment: To Appear in 2022 19th International SoC Conference (ISOCC 2022), October 2022

Published
2022

45. ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning

Author

Gohil, Vasudev, Guo, Hao, Patnaik, Satwik, Jeyavijayan, and Rajendran

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Stealthy hardware Trojans (HTs) inserted during the fabrication of integrated circuits can bypass the security of critical infrastructures. Although researchers have proposed many techniques to detect HTs, several limitations exist, including: (i) a low success rate, (ii) high algorithmic complexity, and (iii) a large number of test patterns. Furthermore, the most pertinent drawback of prior detection techniques stems from an incorrect evaluation methodology, i.e., they assume that an adversary inserts HTs randomly. Such inappropriate adversarial assumptions enable detection techniques to claim high HT detection accuracy, leading to a "false sense of security." Unfortunately, to the best of our knowledge, despite more than a decade of research on detecting HTs inserted during fabrication, there have been no concerted efforts to perform a systematic evaluation of HT detection techniques. In this paper, we play the role of a realistic adversary and question the efficacy of HT detection techniques by developing an automated, scalable, and practical attack framework, ATTRITION, using reinforcement learning (RL). ATTRITION evades eight detection techniques across two HT detection categories, showcasing its agnostic behavior. ATTRITION achieves average attack success rates of $47\times$ and $211\times$ compared to randomly inserted HTs against state-of-the-art HT detection techniques. We demonstrate ATTRITION's ability to evade detection techniques by evaluating designs ranging from the widely-used academic suites to larger designs such as the open-source MIPS and mor1kx processors to AES and a GPS module. Additionally, we showcase the impact of ATTRITION-generated HTs through two case studies (privilege escalation and kill switch) on the mor1kx processor. We envision that our work, along with our released HT benchmarks and models, fosters the development of better HT detection techniques., Comment: To Appear in 2022 ACM SIGSAC Conference on Computer and Communications Security (CCS), November 2022

Published
2022

46. DETERRENT: Detecting Trojans using Reinforcement Learning

Author

Gohil, Vasudev, Patnaik, Satwik, Guo, Hao, Kalathil, Dileep, Jeyavijayan, and Rajendran

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security
Abstract

Insertion of hardware Trojans (HTs) in integrated circuits is a pernicious threat. Since HTs are activated under rare trigger conditions, detecting them using random logic simulations is infeasible. In this work, we design a reinforcement learning (RL) agent that circumvents the exponential search space and returns a minimal set of patterns that is most likely to detect HTs. Experimental results on a variety of benchmarks demonstrate the efficacy and scalability of our RL agent, which obtains a significant reduction ($169\times$) in the number of test patterns required while maintaining or improving coverage ($95.75\%$) compared to the state-of-the-art techniques., Comment: Published in 2022 Design Automation Conference (DAC)

Published
2022
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49. Travelling Salesman Problem: Parallel Implementations & Analysis

Author

Gohil, Amey, Tayal, Manan, Sahu, Tezan, and Sawalpurkar, Vyankatesh

Subjects
Computer Science - Data Structures and Algorithms
Abstract

The Traveling Salesman Problem (often called TSP) is a classic algorithmic problem in the field of computer science and operations research. It is an NP-Hard problem focused on optimization. TSP has several applications even in its purest formulation, such as planning, logistics, and the manufacture of microchips; and can be slightly modified to appear as a sub-problem in many areas, such as DNA sequencing. In this paper, a study on parallelization of the Brute Force approach (under several paradigms) of the Travelling Salesman Problem is presented. Detailed timing studies for the serial and various parallel implementations of the Travelling Salesman Problem have also been illustrated.

Published
2022

50. 1668. The SAFER Lines Project: A Mobile-App Strategy for Prevention of Outpatient Central Line Associated Bloodstream Infection (CLABSI)

Author

Gohil, Shruti K, Bahadori, Bardia, Nourollahi, Shereen, Tjoa, Thomas T, Saito, Hiroki, Bethlahmy, Jessica, Alsharif, Mohamad, Chang, Justin, Nelson, Edward L, Van Etten, Richard A, Armendariz, Linda, Torres, Vincent, Masson, Sandra, Rasheed, Syma, Esteves, Marlene, Saavedra, Raheeb, Singh, Raveena D, and Huang, Susan S

Subjects
Prevention, Clinical Research, Cancer, Good Health and Well Being
Abstract

Abstract Background Outpatient peripherally inserted central catheters (PICC) use has grown without standardized protocols for their management. We assessed the impact of a mobile app strategy for outpatient CLABSI prevention using photo-monitoring, assessment, and response to lines with local inflammation/infection in a cohort of cancer clinic patients. Methods This prospective cohort study evaluated adults with PICCs at an academic cancer clinic at baseline (7/2015–12/2016) and after implementing the SAFER (Standardizing Assessment For Effective Response) Lines program (intervention 5/2017–11/2018). This included a mobile app enabling (1) clinic assessment of localized inflammation or infection defined as Central Line Insertion Site Assessment (CLISA) score 2 or 3, respectively (Table 1), (2) photo-documentation, and (3) score-based automated physician alerts for remote response. We assessed demographics, malignancy type, and line characteristics. Generalized linear mixed effects model assessed program impact on frequency of CLISA 2 or 3 lines, clustered by patient. Cox proportional hazards and Kaplan Meier models assessed days to line removal after CLISA 2 or 3 were identified. Results Among 4,894 assessments of 528 PICCs in 380 outpatients, there were 272 lines (199 patients) at baseline and 256 lines (181 patients) after SAFER program implementation. Mean age, gender, PICC dwell time, and history of prior PICC were similar at baseline and intervention. The proportion of inflamed (CLISA 2) and infected (CLISA 3) lines decreased 40% (from 26% to 16%, and 19% to 11%, respectively) during intervention compared to baseline. Lines with peeling dressings decreased 80% (from 46% to 9%). Mean days to removal of inflamed lines decreased 59% (from 19 to 8 days); removal of infected lines decreased 85% (from 11 to < 2 days). Intervention was associated with 46% lower risk of local inflammation/infection (OR 0.46, CI=0.26–0.83, p< 0.01, Table 2) and faster line removal when such lines were identified (HR 0.18, CI=0.12–0.27, p< 0.01). Conclusion The SAFER Lines mobile app and program decreased the frequency of locally inflamed or infected PICC insertion sites and increased the speed of removal when local inflammation/infection was found in cancer clinic patients. Disclosures Raheeb Saavedra, AS, Medline: Conducted studies in which hospitals and nursing homes received contributed antiseptic and/or environmental cleaning products|Stryker: Conducted clinical studies in which hospitals and nursing homes received contributed antiseptic products|Xttrium Laboratories: Conducted clinical studies in which hospitals and nursing homes received contributed antiseptic products Raveena D. Singh, MA, Medline: Conducted studies in which hospitals and nursing homes received contributed antiseptic and/or environmental cleaning products|Stryker: Conducted clinical studies in which hospitals and nursing homes received contributed antiseptic products|Xttrium Laboratories: Conducted clinical studies in which hospitals and nursing homes received contributed antiseptic products Susan S. Huang, MD, MPH, Medline: Conducted studies in which hospitals and nursing homes received contributed antiseptic and/or environmental cleaning products|Molnlyke: Conducted clinical studies in which hospitals received contributed antiseptic product|Stryker: Conducted clinical studies in which hospitals and nursing homes received contributed antiseptic products|Xttrium Laboratories: Conducted clinical studies in which hospitals and nursing homes received contributed antiseptic product.

Published
2022

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