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1. SoK: On the Offensive Potential of AI

Author

Schröer, Saskia Laura, Apruzzese, Giovanni, Human, Soheil, Laskov, Pavel, Anderson, Hyrum S., Bernroider, Edward W. N., Fass, Aurore, Nassi, Ben, Rimmer, Vera, Roli, Fabio, Salam, Samer, Shen, Ashley, Sunyaev, Ali, Wadhwa-Brown, Tim, Wagner, Isabel, and Wang, Gang

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

Our society increasingly benefits from Artificial Intelligence (AI). Unfortunately, more and more evidence shows that AI is also used for offensive purposes. Prior works have revealed various examples of use cases in which the deployment of AI can lead to violation of security and privacy objectives. No extant work, however, has been able to draw a holistic picture of the offensive potential of AI. In this SoK paper we seek to lay the ground for a systematic analysis of the heterogeneous capabilities of offensive AI. In particular we (i) account for AI risks to both humans and systems while (ii) consolidating and distilling knowledge from academic literature, expert opinions, industrial venues, as well as laypeople -- all of which being valuable sources of information on offensive AI. To enable alignment of such diverse sources of knowledge, we devise a common set of criteria reflecting essential technological factors related to offensive AI. With the help of such criteria, we systematically analyze: 95 research papers; 38 InfoSec briefings (from, e.g., BlackHat); the responses of a user study (N=549) entailing individuals with diverse backgrounds and expertise; and the opinion of 12 experts. Our contributions not only reveal concerning ways (some of which overlooked by prior work) in which AI can be offensively used today, but also represent a foothold to address this threat in the years to come., Comment: Systematization of Knowledge (SoK) paper. Accepted to the 3rd IEEE Conference on Secure and Trustworthy Machine Learning (SaTML'25)

Published
2024

2. Elephant in the Room: Dissecting and Reflecting on the Evolution of Online Social Network Research

Author

Pajola, Luca, Schröer, Saskia Laura, Tricomi, Pier Paolo, Conti, Mauro, and Apruzzese, Giovanni

Subjects
Computer Science - Social and Information Networks
Abstract

Billions of individuals engage with Online Social Networks (OSN) daily. The owners of OSN try to meet the demands of their end-users while complying with business necessities. Such necessities may, however, lead to the adoption of restrictive data access policies that hinder research activities from "external" scientists -- who may, in turn, resort to other means (e.g., rely on static datasets) for their studies. Given the abundance of literature on OSN, we -- as academics -- should take a step back and reflect on what we have done so far, after having written thousands of papers on OSN. This is the first paper that provides a holistic outlook to the entire body of research that focused on OSN -- since the seminal work by Acquisti and Gross (2006). First, we search through over 1 million peer-reviewed publications, and derive 13,842 papers that focus on OSN: we organize the metadata of these works in the Minerva-OSN dataset, the first of its kind -- which we publicly release. Next, by analyzing Minerva-OSN, we provide factual evidence elucidating trends and aspects that deserve to be brought to light, such as the predominant focus on Twitter or the difficulty in obtaining OSN data. Finally, as a constructive step to guide future research, we carry out an expert survey (n=50) with established scientists in this field, and coalesce suggestions to improve the status quo such as an increased involvement of OSN owners. Our findings should inspire a reflection to "rescue" research on OSN. Doing so would improve the overall OSN ecosystem, benefiting both their owners and end-users and, hence, our society., Comment: Accepted at ICWSM 2025

Published
2024

3. LLM4PM: A case study on using Large Language Models for Process Modeling in Enterprise Organizations

Author

Ziche, Clara and Apruzzese, Giovanni

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

We investigate the potential of using Large Language Models (LLM) to support process model creation in organizational contexts. Specifically, we carry out a case study wherein we develop and test an LLM-based chatbot, PRODIGY (PROcess moDellIng Guidance for You), in a multinational company, the Hilti Group. We are particularly interested in understanding how LLM can aid (human) modellers in creating process flow diagrams. To this purpose, we first conduct a preliminary user study (n=10) with professional process modellers from Hilti, inquiring for various pain-points they encounter in their daily routines. Then, we use their responses to design and implement PRODIGY. Finally, we evaluate PRODIGY by letting our user study's participants use PRODIGY, and then ask for their opinion on the pros and cons of PRODIGY. We coalesce our results in actionable takeaways. Through our research, we showcase the first practical application of LLM for process modelling in the real world, shedding light on how industries can leverage LLM to enhance their Business Process Management activities., Comment: 10 pages, 6 figures

Published
2024
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4. Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiation

Author

Lange, Kevin, Fontana, Federico, Rossi, Francesco, Varile, Mattia, and Apruzzese, Giovanni

Subjects
Computer Science - Machine Learning
Abstract

Modern spacecraft are increasingly relying on machine learning (ML). However, physical equipment in space is subject to various natural hazards, such as radiation, which may inhibit the correct operation of computing devices. Despite plenty of evidence showing the damage that naturally-induced faults can cause to ML-related hardware, we observe that the effects of radiation on ML models for space applications are not well-studied. This is a problem: without understanding how ML models are affected by these natural phenomena, it is uncertain "where to start from" to develop radiation-tolerant ML software. As ML researchers, we attempt to tackle this dilemma. By partnering up with space-industry practitioners specialized in ML, we perform a reflective analysis of the state of the art. We provide factual evidence that prior work did not thoroughly examine the impact of natural hazards on ML models meant for spacecraft. Then, through a "negative result", we show that some existing open-source technologies can hardly be used by researchers to study the effects of radiation for some applications of ML in satellites. As a constructive step forward, we perform simple experiments showcasing how to leverage current frameworks to assess the robustness of practical ML models for cloud detection against radiation-induced faults. Our evaluation reveals that not all faults are as devastating as claimed by some prior work. By publicly releasing our resources, we provide a foothold -- usable by researchers without access to spacecraft -- for spearheading development of space-tolerant ML models.

Published
2024

5. 'Are Adversarial Phishing Webpages a Threat in Reality?' Understanding the Users' Perception of Adversarial Webpages

Author

Yuan, Ying, Hao, Qingying, Apruzzese, Giovanni, Conti, Mauro, and Wang, Gang

Subjects
Computer Science - Cryptography and Security
Abstract

Machine learning based phishing website detectors (ML-PWD) are a critical part of today's anti-phishing solutions in operation. Unfortunately, ML-PWD are prone to adversarial evasions, evidenced by both academic studies and analyses of real-world adversarial phishing webpages. However, existing works mostly focused on assessing adversarial phishing webpages against ML-PWD, while neglecting a crucial aspect: investigating whether they can deceive the actual target of phishing -- the end users. In this paper, we fill this gap by conducting two user studies (n=470) to examine how human users perceive adversarial phishing webpages, spanning both synthetically crafted ones (which we create by evading a state-of-the-art ML-PWD) as well as real adversarial webpages (taken from the wild Web) that bypassed a production-grade ML-PWD. Our findings confirm that adversarial phishing is a threat to both users and ML-PWD, since most adversarial phishing webpages have comparable effectiveness on users w.r.t. unperturbed ones. However, not all adversarial perturbations are equally effective. For example, those with added typos are significantly more noticeable to users, who tend to overlook perturbations of higher visual magnitude (such as replacing the background). We also show that users' self-reported frequency of visiting a brand's website has a statistically negative correlation with their phishing detection accuracy, which is likely caused by overconfidence. We release our resources.

Published
2024
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6. Longitudinal patterns and predictors of response to standard-of-care therapy in lupus nephritis: data from the Accelerating Medicines Partnership Lupus Network

Author

Izmirly, Peter M., Kim, Mimi Y., Carlucci, Philip M., Preisinger, Katherine, Cohen, Brooke Z., Deonaraine, Kristina, Zaminski, Devyn, Dall’Era, Maria, Kalunian, Kenneth, Fava, Andrea, Belmont, H. Michael, Wu, Ming, Putterman, Chaim, Anolik, Jennifer, Barnas, Jennifer L., Diamond, Betty, Davidson, Anne, Wofsy, David, Kamen, Diane, James, Judith A., Guthridge, Joel M., Apruzzese, William, Rao, Deepak A., Weisman, Michael H., Petri, Michelle, Buyon, Jill, and Furie, Richard

Published
2024
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7. Understanding the Process of Data Labeling in Cybersecurity

Author

Braun, Tobias, Pekaric, Irdin, and Apruzzese, Giovanni

Subjects
Computer Science - Cryptography and Security
Abstract

Many domains now leverage the benefits of Machine Learning (ML), which promises solutions that can autonomously learn to solve complex tasks by training over some data. Unfortunately, in cyberthreat detection, high-quality data is hard to come by. Moreover, for some specific applications of ML, such data must be labeled by human operators. Many works "assume" that labeling is tough/challenging/costly in cyberthreat detection, thereby proposing solutions to address such a hurdle. Yet, we found no work that specifically addresses the process of labeling 'from the viewpoint of ML security practitioners'. This is a problem: to this date, it is still mostly unknown how labeling is done in practice -- thereby preventing one from pinpointing "what is needed" in the real world. In this paper, we take the first step to build a bridge between academic research and security practice in the context of data labeling. First, we reach out to five subject matter experts and carry out open interviews to identify pain points in their labeling routines. Then, by using our findings as a scaffold, we conduct a user study with 13 practitioners from large security companies, and ask detailed questions on subjects such as active learning, costs of labeling, and revision of labels. Finally, we perform proof-of-concept experiments addressing labeling-related aspects in cyberthreat detection that are sometimes overlooked in research. Altogether, our contributions and recommendations serve as a stepping stone to future endeavors aimed at improving the quality and robustness of ML-driven security systems. We release our resources.

Published
2023
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8. On Two Orderings of Lattice Paths

Author

Apruzzese, PJ and Cong, Kevin

Subjects
Mathematics - Combinatorics, Mathematics - Number Theory, 06A07, 11J06, 05C70
Abstract

The \emph{Markov numbers} are positive integers appearing as solutions to the Diophantine equation $x^2 + y^2 + z^2 = 3xyz$. These numbers are very well-studied and have many combinatorial properties, as well as being the source of the long-standing unicity conjecture. In 2018, \c{C}anak\c{c}{\i} and Schiffler showed that the Markov number $m_{\frac{a}{b}}$ is the number of perfect matchings of a certain snake graph corresponding to the Christoffel path from $(0,0)$ to $(a,b)$. Based on this correspondence, Schiffler in 2023 introduced two orderings on lattice paths. For any path $\omega$, associate a snake graph $\mathcal{G}(\omega)$ and a continued fraction $g(\omega)$. The ordering $<_M$ is given by the number of perfect matchings on $\mathcal{G}(\omega)$, and the ordering $<_L$ is given by the Lagrange number of $g(\omega)$. In this work, we settle two conjectures of Schiffler. First, we show that the path $\omega(a,b) = RR\cdots R UU \cdots U$ is the unique maximum over all lattice paths from $(0,0)$ to $(a,b)$ with respect to both orderings $<_M$ and $<_L$. We then use this result to prove that $\sup L(\omega)$ over all lattice paths is exactly $1+\sqrt5$., Comment: 11 pages, 2 figures

Published
2023

9. Urine proteomic signatures of histological class, activity, chronicity, and treatment response in lupus nephritis.

Author

Fava, Andrea, Buyon, Jill, Magder, Laurence, Hodgin, Jeff, Rosenberg, Avi, Demeke, Dawit, Rao, Deepak, Arazi, Arnon, Celia, Alessandra, Putterman, Chaim, Anolik, Jennifer, Barnas, Jennifer, DallEra, Maria, Wofsy, David, Furie, Richard, Kamen, Diane, Kalunian, Kenneth, James, Judith, Guthridge, Joel, Atta, Mohamed, Monroy Trujillo, Jose, Fine, Derek, Clancy, Robert, Belmont, H, Izmirly, Peter, Apruzzese, William, Goldman, Daniel, Berthier, Celine, Hoover, Paul, Hacohen, Nir, Raychaudhuri, Soumya, Davidson, Anne, Diamond, Betty, and Petri, Michelle

Subjects
Autoimmunity, Diagnostics, Lupus, Nephrology, Proteomics, Humans, Lupus Nephritis, Proteomics, Proteinuria, Inflammation, Aggression
Abstract

Lupus nephritis (LN) is a pathologically heterogenous autoimmune disease linked to end-stage kidney disease and mortality. Better therapeutic strategies are needed as only 30%-40% of patients completely respond to treatment. Noninvasive biomarkers of intrarenal inflammation may guide more precise approaches. Because urine collects the byproducts of kidney inflammation, we studied the urine proteomic profiles of 225 patients with LN (573 samples) in the longitudinal Accelerating Medicines Partnership in RA/SLE cohort. Urinary biomarkers of monocyte/neutrophil degranulation (i.e., PR3, S100A8, azurocidin, catalase, cathepsins, MMP8), macrophage activation (i.e., CD163, CD206, galectin-1), wound healing/matrix degradation (i.e., nidogen-1, decorin), and IL-16 characterized the aggressive proliferative LN classes and significantly correlated with histological activity. A decline of these biomarkers after 3 months of treatment predicted the 1-year response more robustly than proteinuria, the standard of care (AUC: CD206 0.91, EGFR 0.9, CD163 0.89, proteinuria 0.8). Candidate biomarkers were validated and provide potentially treatable targets. We propose these biomarkers of intrarenal immunological activity as noninvasive tools to diagnose LN and guide treatment and as surrogate endpoints for clinical trials. These findings provide insights into the processes involved in LN activity. This data set is a public resource to generate and test hypotheses and validate biomarkers.

Published
2024

10. Attacking logo-based phishing website detectors with adversarial perturbations

Author

Lee, Jehyun, Xin, Zhe, See, Melanie Ng Pei, Sabharwal, Kanav, Apruzzese, Giovanni, and Divakaran, Dinil Mon

Subjects
Computer Science - Cryptography and Security
Abstract

Recent times have witnessed the rise of anti-phishing schemes powered by deep learning (DL). In particular, logo-based phishing detectors rely on DL models from Computer Vision to identify logos of well-known brands on webpages, to detect malicious webpages that imitate a given brand. For instance, Siamese networks have demonstrated notable performance for these tasks, enabling the corresponding anti-phishing solutions to detect even "zero-day" phishing webpages. In this work, we take the next step of studying the robustness of logo-based phishing detectors against adversarial ML attacks. We propose a novel attack exploiting generative adversarial perturbations to craft "adversarial logos" that evade phishing detectors. We evaluate our attacks through: (i) experiments on datasets containing real logos, to evaluate the robustness of state-of-the-art phishing detectors; and (ii) user studies to gauge whether our adversarial logos can deceive human eyes. The results show that our proposed attack is capable of crafting perturbed logos subtle enough to evade various DL models-achieving an evasion rate of up to 95%. Moreover, users are not able to spot significant differences between generated adversarial logos and original ones., Comment: To appear in ESORICS 2023

Published
2023

11. Deconstruction of rheumatoid arthritis synovium defines inflammatory subtypes.

Author

Zhang, Fan, Jonsson, Anna, Nathan, Aparna, Millard, Nghia, Curtis, Michelle, Xiao, Qian, Gutierrez-Arcelus, Maria, Apruzzese, William, Watts, Gerald, Weisenfeld, Dana, Nayar, Saba, Rangel-Moreno, Javier, Meednu, Nida, Marks, Kathryne, Mantel, Ian, Kang, Joyce, Rumker, Laurie, Mears, Joseph, Slowikowski, Kamil, Weinand, Kathryn, Orange, Dana, Geraldino-Pardilla, Laura, Deane, Kevin, Tabechian, Darren, Ceponis, Arnoldas, Firestein, Gary, Maybury, Mark, Sahbudin, Ilfita, Ben-Artzi, Ami, Mandelin, Arthur, Nerviani, Alessandra, Lewis, Myles, Rivellese, Felice, Pitzalis, Costantino, Hughes, Laura, Horowitz, Diane, DiCarlo, Edward, Gravallese, Ellen, Boyce, Brendan, Moreland, Larry, Goodman, Susan, Perlman, Harris, Holers, V, Liao, Katherine, Filer, Andrew, Bykerk, Vivian, Wei, Kevin, Rao, Deepak, Donlin, Laura, Anolik, Jennifer, Brenner, Michael, and Raychaudhuri, Soumya

Subjects
Humans, Arthritis, Rheumatoid, Cytokines, Inflammation, Synovial Membrane, T-Lymphocytes, B-Lymphocytes, Genetic Predisposition to Disease, Phenotype, Single-Cell Gene Expression Analysis
Abstract

Rheumatoid arthritis is a prototypical autoimmune disease that causes joint inflammation and destruction1. There is currently no cure for rheumatoid arthritis, and the effectiveness of treatments varies across patients, suggesting an undefined pathogenic diversity1,2. Here, to deconstruct the cell states and pathways that characterize this pathogenic heterogeneity, we profiled the full spectrum of cells in inflamed synovium from patients with rheumatoid arthritis. We used multi-modal single-cell RNA-sequencing and surface protein data coupled with histology of synovial tissue from 79 donors to build single-cell atlas of rheumatoid arthritis synovial tissue that includes more than 314,000 cells. We stratified tissues into six groups, referred to as cell-type abundance phenotypes (CTAPs), each characterized by selectively enriched cell states. These CTAPs demonstrate the diversity of synovial inflammation in rheumatoid arthritis, ranging from samples enriched for T and B cells to those largely lacking lymphocytes. Disease-relevant cell states, cytokines, risk genes, histology and serology metrics are associated with particular CTAPs. CTAPs are dynamic and can predict treatment response, highlighting the clinical utility of classifying rheumatoid arthritis synovial phenotypes. This comprehensive atlas and molecular, tissue-based stratification of rheumatoid arthritis synovial tissue reveal new insights into rheumatoid arthritis pathology and heterogeneity that could inform novel targeted treatments.

Published
2023

12. SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection

Author

Apruzzese, Giovanni, Laskov, Pavel, and Schneider, Johannes

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture
Abstract

Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For Network Intrusion Detection (NID), however, scientific advances in ML are still seen with skepticism by practitioners. This disconnection is due to the intrinsically limited scope of research papers, many of which primarily aim to demonstrate new methods ``outperforming'' prior work -- oftentimes overlooking the practical implications for deploying the proposed solutions in real systems. Unfortunately, the value of ML for NID depends on a plethora of factors, such as hardware, that are often neglected in scientific literature. This paper aims to reduce the practitioners' skepticism towards ML for NID by "changing" the evaluation methodology adopted in research. After elucidating which "factors" influence the operational deployment of ML in NID, we propose the notion of "pragmatic assessment", which enable practitioners to gauge the real value of ML methods for NID. Then, we show that the state-of-research hardly allows one to estimate the value of ML for NID. As a constructive step forward, we carry out a pragmatic assessment. We re-assess existing ML methods for NID, focusing on the classification of malicious network traffic, and consider: hundreds of configuration settings; diverse adversarial scenarios; and four hardware platforms. Our large and reproducible evaluations enable estimating the quality of ML for NID. We also validate our claims through a user-study with security practitioners.

Published
2023

13. A new indirect measurement method of the electron temperature for the Protosphera's pinch plasma

Author

Iannarelli, D., Napoli, F., Alladio, F., Apruzzese, G., Bombarda, F., Buratti, P., Delfini, J., De Ninno, A., Filippi, F., Fiorucci, D., Ingenito, A., Mannori, S., Micozzi, P., and Teofilatto, P.

Subjects
Physics - Plasma Physics
Abstract

This article presents a new method for estimating the electron temperature of the Protosphera's screw pinch. The temperature radial profile is obtained by a self-consistent modeling of a 1D MHD equilibrium along with a 0D power balance of the plasma column, given measurements and estimates of the axial pinch plasma current, of the plasma rotational frequency and, at the equatorial plane, of the electron density radial profile, of the edge poloidal magnetic field, of the edge electron temperature and of the neutrals pressure in the vacuum vessel. The plasma is considered in equilibrium with its neutral phase and in constant rotation. A MATLAB code has been developed with the aim of estimating the MHD radial equilibrium profiles, the thermodynamic plasma state and the neutrals profile. The numerical estimates are compared with available experimental data showing a good agreement., Comment: 4 pages, 6 figures, 1 table, research presented to the "6th ICFDT"

Published
2023
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14. 'Real Attackers Don't Compute Gradients': Bridging the Gap Between Adversarial ML Research and Practice

Author

Apruzzese, Giovanni, Anderson, Hyrum S., Dambra, Savino, Freeman, David, Pierazzi, Fabio, and Roundy, Kevin A.

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

Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of machine learning (ML) models, and numerous other papers propose defenses that can withstand most attacks. However, abundant real-world evidence suggests that actual attackers use simple tactics to subvert ML-driven systems, and as a result security practitioners have not prioritized adversarial ML defenses. Motivated by the apparent gap between researchers and practitioners, this position paper aims to bridge the two domains. We first present three real-world case studies from which we can glean practical insights unknown or neglected in research. Next we analyze all adversarial ML papers recently published in top security conferences, highlighting positive trends and blind spots. Finally, we state positions on precise and cost-driven threat modeling, collaboration between industry and academia, and reproducible research. We believe that our positions, if adopted, will increase the real-world impact of future endeavours in adversarial ML, bringing both researchers and practitioners closer to their shared goal of improving the security of ML systems.

Published
2022

15. Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors

Author

Apruzzese, Giovanni and Subrahmanian, V. S.

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

Although machine learning based algorithms have been extensively used for detecting phishing websites, there has been relatively little work on how adversaries may attack such "phishing detectors" (PDs for short). In this paper, we propose a set of Gray-Box attacks on PDs that an adversary may use which vary depending on the knowledge that he has about the PD. We show that these attacks severely degrade the effectiveness of several existing PDs. We then propose the concept of operation chains that iteratively map an original set of features to a new set of features and develop the "Protective Operation Chain" (POC for short) algorithm. POC leverages the combination of random feature selection and feature mappings in order to increase the attacker's uncertainty about the target PD. Using 3 existing publicly available datasets plus a fourth that we have created and will release upon the publication of this paper, we show that POC is more robust to these attacks than past competing work, while preserving predictive performance when no adversarial attacks are present. Moreover, POC is robust to attacks on 13 different classifiers, not just one. These results are shown to be statistically significant at the p < 0.001 level.

Published
2022
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16. Cybersecurity in the Smart Grid: Practitioners' Perspective

Author

Meyer, Jacqueline and Apruzzese, Giovanni

Subjects
Computer Science - Cryptography and Security, Computer Science - Computers and Society
Abstract

The Smart Grid (SG) is a cornerstone of modern society, providing the energy required to sustain billions of lives and thousands of industries. Unfortunately, as one of the most critical infrastructures of our World, the SG is an attractive target for attackers. The problem is aggravated by the increasing adoption of digitalisation, which further increases the SG's exposure to cyberthreats. Successful exploitation of such exposure leads to entire countries being paralysed, which is an unacceptable -- but ultimately inescapable -- risk. This paper aims to mitigate this risk by elucidating the perspective of real practitioners on the cybersecurity of the SG. We interviewed 18 entities, operating in diverse countries in Europe and covering all domains of the SG -- from energy generation, to its delivery. Our analysis highlights a stark contrast between (a)research and practice, but also between (b) public and private entities. For instance: some threats appear to be much less dangerous than what is claimed in related papers; some technological paradigms have dubious utility for practitioners, but are actively promoted by literature; finally, practitioners may either under- or over-estimate their own cybersecurity capabilities. We derive four takeaways that enable future endeavours to improve the overall cybersecurity in the SG. We conjecture that most of the problems are due to an improper communication between researchers, practitioners and regulatory bodies -- which, despite sharing a common goal, tend to neglect the viewpoint of the other `spheres'.

Published
2022

17. Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning

Author

Yuan, Ying, Apruzzese, Giovanni, and Conti, Mauro

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture
Abstract

Existing literature on adversarial Machine Learning (ML) focuses either on showing attacks that break every ML model, or defenses that withstand most attacks. Unfortunately, little consideration is given to the actual feasibility of the attack or the defense. Moreover, adversarial samples are often crafted in the "feature-space", making the corresponding evaluations of questionable value. Simply put, the current situation does not allow to estimate the actual threat posed by adversarial attacks, leading to a lack of secure ML systems. We aim to clarify such confusion in this paper. By considering the application of ML for Phishing Website Detection (PWD), we formalize the "evasion-space" in which an adversarial perturbation can be introduced to fool a ML-PWD -- demonstrating that even perturbations in the "feature-space" are useful. Then, we propose a realistic threat model describing evasion attacks against ML-PWD that are cheap to stage, and hence intrinsically more attractive for real phishers. After that, we perform the first statistically validated assessment of state-of-the-art ML-PWD against 12 evasion attacks. Our evaluation shows (i) the true efficacy of evasion attempts that are more likely to occur; and (ii) the impact of perturbations crafted in different evasion-spaces. Our realistic evasion attempts induce a statistically significant degradation (3-10% at p<0.05), and their cheap cost makes them a subtle threat. Notably, however, some ML-PWD are immune to our most realistic attacks (p=0.22). Finally, as an additional contribution of this journal publication, we are the first to consider the intriguing case wherein an attacker introduces perturbations in multiple evasion-spaces at the same time. These new results show that simultaneously applying perturbations in the problem- and feature-space can cause a drop in the detection rate from 0.95 to 0.

Published
2022
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18. Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2

Author

Tricomi, Pier Paolo, Facciolo, Lisa, Apruzzese, Giovanni, and Conti, Mauro

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

Did you know that over 70 million of Dota2 players have their in-game data freely accessible? What if such data is used in malicious ways? This paper is the first to investigate such a problem. Motivated by the widespread popularity of video games, we propose the first threat model for Attribute Inference Attacks (AIA) in the Dota2 context. We explain how (and why) attackers can exploit the abundant public data in the Dota2 ecosystem to infer private information about its players. Due to lack of concrete evidence on the efficacy of our AIA, we empirically prove and assess their impact in reality. By conducting an extensive survey on $\sim$500 Dota2 players spanning over 26k matches, we verify whether a correlation exists between a player's Dota2 activity and their real-life. Then, after finding such a link ($p$ < 0.01 and $\rho$ > 0.3), we ethically perform diverse AIA. We leverage the capabilities of machine learning to infer real-life attributes of the respondents of our survey by using their publicly available in-game data. Our results show that, by applyingdomain expertise, some AIA can reach up to 98% precision and over 90% accuracy. This paper hence raises the alarm on a subtle, but concrete threat that can potentially affect the entire competitive gaming landscape. We alerted the developers of Dota2.

Published
2022
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20. Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples

Author

Apruzzese, Giovanni, Vladimirov, Rodion, Tastemirova, Aliya, and Laskov, Pavel

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture
Abstract

Fifth Generation (5G) networks must support billions of heterogeneous devices while guaranteeing optimal Quality of Service (QoS). Such requirements are impossible to meet with human effort alone, and Machine Learning (ML) represents a core asset in 5G. ML, however, is known to be vulnerable to adversarial examples; moreover, as our paper will show, the 5G context is exposed to a yet another type of adversarial ML attacks that cannot be formalized with existing threat models. Proactive assessment of such risks is also challenging due to the lack of ML-powered 5G equipment available for adversarial ML research. To tackle these problems, we propose a novel adversarial ML threat model that is particularly suited to 5G scenarios, and is agnostic to the precise function solved by ML. In contrast to existing ML threat models, our attacks do not require any compromise of the target 5G system while still being viable due to the QoS guarantees and the open nature of 5G networks. Furthermore, we propose an original framework for realistic ML security assessments based on public data. We proactively evaluate our threat model on 6 applications of ML envisioned in 5G. Our attacks affect both the training and the inference stages, can degrade the performance of state-of-the-art ML systems, and have a lower entry barrier than previous attacks.

Published
2022
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21. The Role of Machine Learning in Cybersecurity

Author

Apruzzese, Giovanni, Laskov, Pavel, de Oca, Edgardo Montes, Mallouli, Wissam, Rapa, Luis Burdalo, Grammatopoulos, Athanasios Vasileios, and Di Franco, Fabio

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

Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such discrepancy has its root cause in the current state-of-the-art, which does not allow to identify the role of ML in cybersecurity. The full potential of ML will never be unleashed unless its pros and cons are understood by a broad audience. This paper is the first attempt to provide a holistic understanding of the role of ML in the entire cybersecurity domain -- to any potential reader with an interest in this topic. We highlight the advantages of ML with respect to human-driven detection methods, as well as the additional tasks that can be addressed by ML in cybersecurity. Moreover, we elucidate various intrinsic problems affecting real ML deployments in cybersecurity. Finally, we present how various stakeholders can contribute to future developments of ML in cybersecurity, which is essential for further progress in this field. Our contributions are complemented with two real case studies describing industrial applications of ML as defense against cyber-threats.

Published
2022
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22. DISTINCT PERIPHERAL IMMUNOPHENOTYPING UNDERLINES HISTOPATHOLOGICAL FEATURES IN PATIENTS WITH LUPUS NEPHRITIS IN A LARGE MULTI-CENTER COHORT

Author

Horisberger, A, Griffith, A, Keegan, J, Howard, K, Pulford, J, Murzin, E, Hancock, B, Ghosh, T, Sasaki, T, Fava, A, Arcelus, M Gutierrez, Eisenhaure, T, Guthridge, J, Arazi, A, Hoover, P, Dall'era, M, Wofsy, D, Kamen, DL, Kalunian, K, Furie, R, Belmont, HM, Izmirly, P, Clancy, R, Hildeman, D, Woodle, ES, Apruzzese, W, Mcmahon, M, Grossman, J, Barnas, J, Payan-Schober, F, Ishimori, M, Weisman, M, Kretzler, M, Berthier, C, Hodgin, J, Putterman, C, Hacohen, N, Brenner, M, Anolik, JH, Davidson, A, James, JA, Raychaudhuri, S, Petri, MA, Buyon, J, Diamond, B, Amp, TAMPRN, Zhang, F, Lederer, J, and Rao, D

Subjects
Kidneys, -Omics, Systemic lupus erythematosus, Clinical Sciences, Immunology, Public Health and Health Services, Arthritis & Rheumatology, Clinical sciences
Published
2023

23. SoK: The Impact of Unlabelled Data in Cyberthreat Detection

Author

Apruzzese, Giovanni, Laskov, Pavel, and Tastemirova, Aliya

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

Machine learning (ML) has become an important paradigm for cyberthreat detection (CTD) in the recent years. A substantial research effort has been invested in the development of specialized algorithms for CTD tasks. From the operational perspective, however, the progress of ML-based CTD is hindered by the difficulty in obtaining the large sets of labelled data to train ML detectors. A potential solution to this problem are semisupervised learning (SsL) methods, which combine small labelled datasets with large amounts of unlabelled data. This paper is aimed at systematization of existing work on SsL for CTD and, in particular, on understanding the utility of unlabelled data in such systems. To this end, we analyze the cost of labelling in various CTD tasks and develop a formal cost model for SsL in this context. Building on this foundation, we formalize a set of requirements for evaluation of SsL methods, which elucidates the contribution of unlabelled data. We review the state-of-the-art and observe that no previous work meets such requirements. To address this problem, we propose a framework for assessing the benefits of unlabelled data in SsL. We showcase an application of this framework by performing the first benchmark evaluation that highlights the tradeoffs of 9 existing SsL methods on 9 public datasets. Our findings verify that, in some cases, unlabelled data provides a small, but statistically significant, performance gain. This paper highlights that SsL in CTD has a lot of room for improvement, which should stimulate future research in this field.

Published
2022
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24. Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike

Author

Schneider, Johannes and Apruzzese, Giovanni

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

We propose to generate adversarial samples by modifying activations of upper layers encoding semantically meaningful concepts. The original sample is shifted towards a target sample, yielding an adversarial sample, by using the modified activations to reconstruct the original sample. A human might (and possibly should) notice differences between the original and the adversarial sample. Depending on the attacker-provided constraints, an adversarial sample can exhibit subtle differences or appear like a "forged" sample from another class. Our approach and goal are in stark contrast to common attacks involving perturbations of single pixels that are not recognizable by humans. Our approach is relevant in, e.g., multi-stage processing of inputs, where both humans and machines are involved in decision-making because invisible perturbations will not fool a human. Our evaluation focuses on deep neural networks. We also show the transferability of our adversarial examples among networks., Comment: Accepted at IEEE Symposium on Security and Privacy (S&P) Workshop on Deep Learning and Security, 2022

Published
2022

25. The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems

Author

Apruzzese, Giovanni, Pajola, Luca, and Conti, Mauro

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture
Abstract

Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning (ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where benign and malicious samples are clearly labelled. Such labels demand costly expert knowledge, resulting in a lack of real deployments, as well as on papers always relying on the same outdated data. The situation improved recently, as some efforts disclosed their labelled datasets. However, most past works used such datasets just as a 'yet another' testbed, overlooking the added potential provided by such availability. In contrast, we promote using such existing labelled data to cross-evaluate ML-NIDS. Such approach received only limited attention and, due to its complexity, requires a dedicated treatment. We hence propose the first cross-evaluation model. Our model highlights the broader range of realistic use-cases that can be assessed via cross-evaluations, allowing the discovery of still unknown qualities of state-of-the-art ML-NIDS. For instance, their detection surface can be extended--at no additional labelling cost. However, conducting such cross-evaluations is challenging. Hence, we propose the first framework, XeNIDS, for reliable cross-evaluations based on Network Flows. By using XeNIDS on six well-known datasets, we demonstrate the concealed potential, but also the risks, of cross-evaluations of ML-NIDS.

Published
2022
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26. High incidence of proliferative and membranous nephritis in SLE patients with low proteinuria in the Accelerating Medicines Partnership.

Author

Carlucci, Philip M, Li, Jessica, Fava, Andrea, Deonaraine, Kristina K, Wofsy, David, James, Judith A, Putterman, Chaim, Diamond, Betty, Davidson, Anne, Fine, Derek M, Monroy-Trujillo, Jose, Atta, Mohamed G, DeJager, Wade, Guthridge, Joel M, Haag, Kristin, Rao, Deepak A, Brenner, Michael B, Lederer, James A, Apruzzese, William, Belmont, H Michael, Izmirly, Peter M, Zaminski, Devyn, Wu, Ming, Connery, Sean, Payan-Schober, Fernanda, Furie, Richard, Dall’Era, Maria, Cho, Kerry, Kamen, Diane, Kalunian, Kenneth, Anolik, Jennifer, Barnas, Jennifer, Ishimori, Mariko, Weisman, Michael H, Goff, Jennifer, Dunn, Patrick J, Raychaudhuri, Soumya, Zhang, Fan, Korsunsky, Ilya, Nathan, Aparna, Mears, Joseph, Ishigaki, Kazuyoshi, Xiao, Qian, Millard, Nghia, Weinand, Kathryn, Sakaue, Saori, Utz, PJ, Mao, Rong, Robinson, Bill, Maecker, Holden, Macwana, Susan, Bridges, S Louis, Bykerk, Vivian, Donlin, Laura, Goodman, Susan, DiCarlo, Edward, Smith, Melanie, Lakhanpal, Amit, Sherman, Heather, Singaraju, Anvita, Shakib, Lorien, Ritchlin, Christopher, Boyce, Brendan, Tabechian, Darren, McDavid, Andrew, Rangel-Moreno, Javier, Meednu, Nida, Albrecht, Jen, Wei, Kevin, Helena Jonsson, A, Simmons, Daimon, Keras, Gregory, Keegan, Joshua, Watts, Gerald, Li Zhu, Yuhong, Chicoine, Adam, Jian Li, Zhihan, Gravallese, Ellen M, Howard, Kaitlyn, McGeachy, Mandy, Firestein, Gary S, Boyle, David L, Ceponis, Arnold, Gregersen, Peter K, Horowitz, Diane, Perlman, Harris, Dominguez, Salina, Cuda, Carla M, Mandolin, Arthur M, Thakrar, Anjali, Bathon, Joan M, Hughes, Laura, Michael Holers, V, Seifert, Jennifer, Deane, Kevin, Moreland, Larry W, Filer, Andrew, Raza, Karim, Sahbudin, Ilfita, and Pitzalis, Costantino

Subjects
Clinical Research, Kidney Disease, Lupus, Autoimmune Disease, Humans, Lupus Nephritis, Prospective Studies, Incidence, Proteinuria, Kidney Function Tests, Kidney, systemic lupus erythematosus, lupus nephritis, diagnosis, Accelerating Medicines Partnership (AMP) RA/SLE Network, Clinical Sciences, Immunology, Public Health and Health Services, Arthritis & Rheumatology
Abstract

ObjectiveDelayed detection of LN associates with worse outcomes. There are conflicting recommendations regarding a threshold level of proteinuria at which biopsy will likely yield actionable management. This study addressed the association of urine protein:creatinine ratios (UPCR) with clinical characteristics and investigated the incidence of proliferative and membranous histology in patients with a UPCR between 0.5 and 1.MethodsA total of 275 SLE patients (113 first biopsy, 162 repeat) were enrolled in the multicentre multi-ethnic/racial Accelerating Medicines Partnership across 15 US sites at the time of a clinically indicated renal biopsy. Patients were followed for 1 year.ResultsAt biopsy, 54 patients had UPCR

Published
2022

27. Hypercapnia during transcatheter aortic valve replacement under monitored anaesthesia care: a retrospective cohort study

Author

Patricia Apruzzese, Tzonghuei Chen, Shyamal Asher, Harry Owusu-Dapaah, Gustavo Gonzalez, and Andrew Maslow

Subjects
Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Background Acute intraoperative hypercapnia and respiratory acidosis, which can occur during monitored anaesthesia care (MAC), pose significant cardiopulmonary risks for patients with aortic stenosis undergoing transcatheter aortic valve replacement (TAVR). The goal of the present study is to assess the incidence, risk factors and impact of intraoperative hypercapnia during MAC for patients undergoing transfemoral TAVR.Methods Data was collected retrospectively from the electronic medical record of 201 consecutive patients with available intraoperative arterial blood gas (ABG) data who underwent percutaneous transfemoral TAVR with MAC using propofol and dexmedetomidine. ABGs (pH, arterial partial pressure of carbon dioxide (PaCO2) and arterial partial pressure of oxygen) were performed at the start of each case (baseline), immediately prior to valve deployment (ValveDepl), and on arrival to the postanaesthesia care unit. Data was analysed using Fisher’s exact test, unpaired Student’s t-test, Wilcoxon rank sum or univariate linear regression as appropriate based on PaCO2 and pH during ValveDepl (PaCO2-ValveDepl, pH-ValveDepl) and change in PaCO2 and pH from baseline to ValveDepl (PaCO2-%increase, pH-%decrease) to determine their association with preoperative demographic data, intraoperative anaesthetic and vasoactive medications and postoperative outcomes.Results PaCO2 increased by a mean of 28.4% and was higher than baseline in 91% of patients. Younger age, male sex, increased weight and increased propofol dose contributed to higher PaCO2-ValveDepl and greater PaCO2-%increase. Patients with PaCO2-ValveDepl>60 mm Hg, pH≤7.2 and greater pH-%decrease were more likely to receive vasoactive medications, but perioperative PaCO2 and pH were not associated with adverse postoperative outcomes.Conclusions Transient significant hypercapnia commonly occurs during transfemoral TAVR with deep sedation using propofol and dexmedetomidine. Although the incidence of postoperative outcomes does not appear to be affected by hypercapnia, the need for vasopressors and inotropes is increased. If deep sedation is required for TAVR, hypercapnia and the need for haemodynamic and ventilatory support should be anticipated.

Published
2024
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28. Urine Proteomics and Renal Single‐Cell Transcriptomics Implicate Interleukin‐16 in Lupus Nephritis

Author

Fava, Andrea, Rao, Deepak A, Mohan, Chandra, Zhang, Ting, Rosenberg, Avi, Fenaroli, Paride, Belmont, H Michael, Izmirly, Peter, Clancy, Robert, Trujillo, Jose Monroy, Fine, Derek, Arazi, Arnon, Berthier, Celine C, Davidson, Anne, James, Judith A, Diamond, Betty, Hacohen, Nir, Wofsy, David, Raychaudhuri, Soumya, Apruzzese, William, Network, the Accelerating Medicines Partnership in Rheumatoid Arthritis and Systemic Lupus Erythematosus, Buyon, Jill, and Petri, Michelle

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Kidney Disease, Clinical Research, Lupus, Autoimmune Disease, Biotechnology, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Renal and urogenital, Inflammatory and immune system, Biological Products, Biomarkers, Female, Humans, Interleukin-16, Kidney, Lupus Nephritis, Male, Proteomics, Single-Cell Analysis, Transcriptome, Accelerating Medicines Partnership in Rheumatoid Arthritis and Systemic Lupus Erythematosus Network, Immunology, Public Health and Health Services, Arthritis & Rheumatology, Clinical sciences
Abstract

ObjectiveCurrent lupus nephritis (LN) treatments are effective in only 30% of patients, emphasizing the need for novel therapeutic strategies. We undertook this study to develop mechanistic hypotheses and explore novel biomarkers by analyzing the longitudinal urinary proteomic profiles in LN patients undergoing treatment.MethodsWe quantified 1,000 urinary proteins in 30 patients with LN at the time of the diagnostic renal biopsy and after 3, 6, and 12 months. The proteins and molecular pathways detected in the urine proteome were then analyzed with respect to baseline clinical features and longitudinal trajectories. The intrarenal expression of candidate biomarkers was evaluated using single-cell transcriptomics of renal biopsy sections from LN patients.ResultsOur analysis revealed multiple biologic pathways, including chemotaxis, neutrophil activation, platelet degranulation, and extracellular matrix organization, which could be noninvasively quantified and monitored in the urine. We identified 237 urinary biomarkers associated with LN, as compared to controls without systemic lupus erythematosus. Interleukin-16 (IL-16), CD163, and transforming growth factor β mirrored intrarenal nephritis activity. Response to treatment was paralleled by a reduction in urinary IL-16, a CD4 ligand with proinflammatory and chemotactic properties. Single-cell RNA sequencing independently demonstrated that IL16 is the second most expressed cytokine by most infiltrating immune cells in LN kidneys. IL-16-producing cells were found at key sites of kidney injury.ConclusionUrine proteomics may profoundly change the diagnosis and management of LN by noninvasively monitoring active intrarenal biologic pathways. These findings implicate IL-16 in LN pathogenesis, designating it as a potentially treatable target and biomarker.

Published
2022

29. Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems

Author

Apruzzese, Giovanni, Andreolini, Mauro, Ferretti, Luca, Marchetti, Mirco, and Colajanni, Michele

Subjects
Computer Science - Cryptography and Security
Abstract

The incremental diffusion of machine learning algorithms in supporting cybersecurity is creating novel defensive opportunities but also new types of risks. Multiple researches have shown that machine learning methods are vulnerable to adversarial attacks that create tiny perturbations aimed at decreasing the effectiveness of detecting threats. We observe that existing literature assumes threat models that are inappropriate for realistic cybersecurity scenarios because they consider opponents with complete knowledge about the cyber detector or that can freely interact with the target systems. By focusing on Network Intrusion Detection Systems based on machine learning, we identify and model the real capabilities and circumstances required by attackers to carry out feasible and successful adversarial attacks. We then apply our model to several adversarial attacks proposed in literature and highlight the limits and merits that can result in actual adversarial attacks. The contributions of this paper can help hardening defensive systems by letting cyber defenders address the most critical and real issues, and can benefit researchers by allowing them to devise novel forms of adversarial attacks based on realistic threat models.

Published
2021
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30. On the Evaluation of Sequential Machine Learning for Network Intrusion Detection

Author

Corsini, Andrea, Yang, Shanchieh Jay, and Apruzzese, Giovanni

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

Recent advances in deep learning renewed the research interests in machine learning for Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to sequential learning models, due to their ability to extract the temporal characteristics of Network traffic Flows (NetFlows), and use them for NIDS tasks. However, the applications of these sequential models often consist of transferring and adapting methodologies directly from other fields, without an in-depth investigation on how to leverage the specific circumstances of cybersecurity scenarios; moreover, there is a lack of comprehensive studies on sequential models that rely on NetFlow data, which presents significant advantages over traditional full packet captures. We tackle this problem in this paper. We propose a detailed methodology to extract temporal sequences of NetFlows that denote patterns of malicious activities. Then, we apply this methodology to compare the efficacy of sequential learning models against traditional static learning models. In particular, we perform a fair comparison of a `sequential' Long Short-Term Memory (LSTM) against a `static' Feedforward Neural Networks (FNN) in distinct environments represented by two well-known datasets for NIDS: the CICIDS2017 and the CTU13. Our results highlight that LSTM achieves comparable performance to FNN in the CICIDS2017 with over 99.5\% F1-score; while obtaining superior performance in the CTU13, with 95.7\% F1-score against 91.5\%. This paper thus paves the way to future applications of sequential learning models for NIDS.

Published
2021
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31. 1002 Single cell transcriptomics in kidney tissue from African American patients enrolled in the accelerating medicines partnership (AMP) implicates tubular cells in the pathogenesis of APOL1 associated lupus nephritis

Author

Richard Furie, Brad Rovin, Michelle Petri, Judith A James, Kenneth Kalunian, Maria Dall’Era, Jill Buyon, Chaim Putterman, Peter Izmirly, Betty Diamond, David Wofsy, Ming Wu, Soumya Raychaudhuri, Philip M Carlucci, Qian Xiao, Nir Hacohen, Jennifer Anolik, H Michael Belmont, Robert Clancy, Kelly Ruggles, ANNE DAVIDSON, Deepak Rao, Celine C Berthier, Andrea Fava, Diane Kamen, Joel Guthridge, Arnon Arazi, Jose Monroy-Trujillo, Kristin Haag, William Apruzzese, Fernanda Payan-Schober, Kerry Cho, Joseph Mears, Wade DeJager, Paul Hoover, Kristina Deonaraine, Siddarth Gurajala, Derek Fine, Devyn Zaminski, Jasmine Shwetar, Katie Preisinger, and Sethu Madhavan

Subjects
Immunologic diseases. Allergy, RC581-607
Published
2024
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32. 1012 Deep peripheral blood immunophenotyping identifies a subgroup of lupus nephritis patients characterized by high type 1 interferon signaling, persistent activated immune cells and poor renal response to standard of care at 1 year

Author

Richard Furie, Judith A James, Maria Dall’Era, Michelle A Petri, Chaim Putterman, Peter Izmirly, Jill P Buyon, Diane L Kamen, Betty Diamond, David Wofsy, Soumya Raychaudhuri, Jennifer Grossman, Fan Zhang, Jun Inamo, Nir Hacohen, Alice Horisberger, Kenneth C Kalunian, Michael B Brenner, Mariko Ishimori, David Hildeman, Robert Clancy, ANNE DAVIDSON, Matthias Kretzler, Michael Weisman, Celine C Berthier, Andrea Fava, Takanori Sasaki, Jennifer L Barnas, Jennifer H Anolik, Paul J Hoover, Deepak A Rao, Joshua Keegan, James A Lederer, Joel Guthridge, Michael Belmont, Arnon Arazi, William Apruzzese, Fernanda Payan-Schober, Jeffrey Hodgin, Thomas M Eisenhaure, Steve Woodle, Maureen A McMahon, Ekaterina Murzin, Katie Preisinger, Alec Griffith, Kaitlyn Howard, Tusharkanti Ghosh, Rebecca Beuschel, John Pulford, Brandon Hancock, and Maria Gutierrez-Arcelus

Subjects
Immunologic diseases. Allergy, RC581-607
Published
2024
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33. 1009 In-depth analysis of myeloid cell subsets in lupus nephritis kidneys provides insights into disease mechanisms: lessons from the accelerating medicines partnership (AMP) in RA/SLE consortium

Author

Richard Furie, Joel M Guthridge, Judith A James, Maria Dall’Era, Michelle A Petri, Chaim Putterman, Jill P Buyon, Diane L Kamen, Betty Diamond, David Wofsy, Soumya Raychaudhuri, Thomas Tuschl, Jennifer Grossman, Peter M Izmirly, Qian Xiao, Nir Hacohen, Kenneth C Kalunian, Michael B Brenner, Mariko Ishimori, H Michael Belmont, Robert Clancy, ANNE DAVIDSON, Matthias Kretzler, Michael Weisman, Celine C Berthier, Andrea Fava, Jennifer L Barnas, Jennifer H Anolik, Paul J Hoover, Deepak A Rao, Arnon Arazi, William Apruzzese, Fernanda Payan-Schober, Joseph Mears, Saori Sakaue, James Lederer, Michael Peters, Tony Jones, Thomas M Eisenhaure, Siddarth Gurajala, Derek Fine, David A Hildeman, Steve Woodle, Maureen A McMahon, and Jeffery Hodgin

Subjects
Immunologic diseases. Allergy, RC581-607
Published
2024
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35. Safety of procuring research tissue during a clinically indicated kidney biopsy from patients with lupus: data from the Accelerating Medicines Partnership RA/SLE Network.

Author

Deonaraine, Kristina K, Carlucci, Philip M, Fava, Andrea, Li, Jessica, Wofsy, David, James, Judith A, Putterman, Chaim, Diamond, Betty, Davidson, Anne, Fine, Derek M, Monroy-Trujillo, Jose, Atta, Mohamed G, Haag, Kristin, Rao, Deepak A, Apruzzese, William, Belmont, H Michael, Izmirly, Peter M, Wu, Ming, Connery, Sean, Payan-Schober, Fernanda, Furie, Richard A, Berthier, Celine C, Dall'Era, Maria, Cho, Kerry, Kamen, Diane L, Kalunian, Kenneth, Anolik, Jennifer, Ishimori, Mariko, Weisman, Michael H, Accelerating Medicines Partnership RA/SLE network, Petri, Michelle A, and Buyon, Jill P

Subjects
Accelerating Medicines Partnership RA/SLE network, autoimmunity, lupus erythematosus, lupus nephritis, systemic
Abstract

ObjectivesIn lupus nephritis the pathological diagnosis from tissue retrieved during kidney biopsy drives treatment and management. Despite recent approval of new drugs, complete remission rates remain well under aspirational levels, necessitating identification of new therapeutic targets by greater dissection of the pathways to tissue inflammation and injury. This study assessed the safety of kidney biopsies in patients with SLE enrolled in the Accelerating Medicines Partnership, a consortium formed to molecularly deconstruct nephritis.Methods475 patients with SLE across 15 clinical sites in the USA consented to obtain tissue for research purposes during a clinically indicated kidney biopsy. Adverse events (AEs) were documented for 30 days following the procedure and were determined to be related or unrelated by all site investigators. Serious AEs were defined according to the National Institutes of Health reporting guidelines.Results34 patients (7.2%) experienced a procedure-related AE: 30 with haematoma, 2 with jets, 1 with pain and 1 with an arteriovenous fistula. Eighteen (3.8%) experienced a serious AE requiring hospitalisation; four patients (0.8%) required a blood transfusion related to the kidney biopsy. At one site where the number of cores retrieved during the biopsy was recorded, the mean was 3.4 for those who experienced a related AE (n=9) and 3.07 for those who did not experience any AE (n=140). All related AEs resolved.ConclusionsProcurement of research tissue should be considered feasible, accompanied by a complication risk likely no greater than that incurred for standard clinical purposes. In the quest for targeted treatments personalised based on molecular findings, enhanced diagnostics beyond histology will likely be required.

Published
2021

36. Hardening Random Forest Cyber Detectors Against Adversarial Attacks

Author

Apruzzese, Giovanni, Andreolini, Mauro, Colajanni, Michele, and Marchetti, Mirco

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

Machine learning algorithms are effective in several applications, but they are not as much successful when applied to intrusion detection in cyber security. Due to the high sensitivity to their training data, cyber detectors based on machine learning are vulnerable to targeted adversarial attacks that involve the perturbation of initial samples. Existing defenses assume unrealistic scenarios; their results are underwhelming in non-adversarial settings; or they can be applied only to machine learning algorithms that perform poorly for cyber security. We present an original methodology for countering adversarial perturbations targeting intrusion detection systems based on random forests. As a practical application, we integrate the proposed defense method in a cyber detector analyzing network traffic. The experimental results on millions of labelled network flows show that the new detector has a twofold value: it outperforms state-of-the-art detectors that are subject to adversarial attacks; it exhibits robust results both in adversarial and non-adversarial scenarios.

Published
2019
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38. Stability conditions for affine type A

Author

Apruzzese, P. J. and Igusa, Kiyoshi

Subjects
Mathematics - Representation Theory, 16G20
Abstract

We construct maximal green sequences of maximal length for any affine quiver of type $A$. We determine which sets of modules (equivalently $c$-vectors) can occur in such sequences and, among these, which are given by a linear stability condition (also called a central charge). There is always at least one such maximal set which is linear. The proofs use representation theory and three kinds of diagrams shown in Figure 1. Background material is reviewed with details presented in two separate papers [arxiv:1706.06986] and [arXiv:1706.06503]., Comment: 31 pages, 14 figures, presented by the second author at Idun Reiten's 75 birthday conference

Published
2018

39. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry

Author

Zhang, Fan, Wei, Kevin, Slowikowski, Kamil, Fonseka, Chamith Y, Rao, Deepak A, Kelly, Stephen, Goodman, Susan M, Tabechian, Darren, Hughes, Laura B, Salomon-Escoto, Karen, Watts, Gerald FM, Jonsson, A Helena, Rangel-Moreno, Javier, Meednu, Nida, Rozo, Cristina, Apruzzese, William, Eisenhaure, Thomas M, Lieb, David J, Boyle, David L, Mandelin, Arthur M, Boyce, Brendan F, DiCarlo, Edward, Gravallese, Ellen M, Gregersen, Peter K, Moreland, Larry, Firestein, Gary S, Hacohen, Nir, Nusbaum, Chad, Lederer, James A, Perlman, Harris, Pitzalis, Costantino, Filer, Andrew, Holers, V Michael, Bykerk, Vivian P, Donlin, Laura T, Anolik, Jennifer H, Brenner, Michael B, and Raychaudhuri, Soumya

Subjects
Clinical Research, Autoimmune Disease, Arthritis, Human Genome, Rheumatoid Arthritis, Genetics, Aetiology, 2.1 Biological and endogenous factors, Inflammatory and immune system, Good Health and Well Being, Arthritis, Rheumatoid, Autoimmunity, Biomarkers, Computational Biology, Cross-Sectional Studies, Cytokines, Fibroblasts, Flow Cytometry, Gene Expression, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Histocompatibility Antigens Class II, Humans, Leukocytes, Monocytes, Signal Transduction, Single-Cell Analysis, Synovial Membrane, T-Lymphocyte Subsets, Transcriptome, Workflow, Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Consortium, Immunology
Abstract

To define the cell populations that drive joint inflammation in rheumatoid arthritis (RA), we applied single-cell RNA sequencing (scRNA-seq), mass cytometry, bulk RNA sequencing (RNA-seq) and flow cytometry to T cells, B cells, monocytes, and fibroblasts from 51 samples of synovial tissue from patients with RA or osteoarthritis (OA). Utilizing an integrated strategy based on canonical correlation analysis of 5,265 scRNA-seq profiles, we identified 18 unique cell populations. Combining mass cytometry and transcriptomics revealed cell states expanded in RA synovia: THY1(CD90)+HLA-DRAhi sublining fibroblasts, IL1B+ pro-inflammatory monocytes, ITGAX+TBX21+ autoimmune-associated B cells and PDCD1+ peripheral helper T (TPH) cells and follicular helper T (TFH) cells. We defined distinct subsets of CD8+ T cells characterized by GZMK+, GZMB+, and GNLY+ phenotypes. We mapped inflammatory mediators to their source cell populations; for example, we attributed IL6 expression to THY1+HLA-DRAhi fibroblasts and IL1B production to pro-inflammatory monocytes. These populations are potentially key mediators of RA pathogenesis.

Published
2019

40. The immune cell landscape in kidneys of patients with lupus nephritis

Author

Arazi, Arnon, Rao, Deepak A, Berthier, Celine C, Davidson, Anne, Liu, Yanyan, Hoover, Paul J, Chicoine, Adam, Eisenhaure, Thomas M, Jonsson, A Helena, Li, Shuqiang, Lieb, David J, Zhang, Fan, Slowikowski, Kamil, Browne, Edward P, Noma, Akiko, Sutherby, Danielle, Steelman, Scott, Smilek, Dawn E, Tosta, Patti, Apruzzese, William, Massarotti, Elena, Dall’Era, Maria, Park, Meyeon, Kamen, Diane L, Furie, Richard A, Payan-Schober, Fernanda, Pendergraft, William F, McInnis, Elizabeth A, Buyon, Jill P, Petri, Michelle A, Putterman, Chaim, Kalunian, Kenneth C, Woodle, E Steve, Lederer, James A, Hildeman, David A, Nusbaum, Chad, Raychaudhuri, Soumya, Kretzler, Matthias, Anolik, Jennifer H, Brenner, Michael B, Wofsy, David, Hacohen, Nir, and Diamond, Betty

Subjects
Biomedical and Clinical Sciences, Immunology, Kidney Disease, Genetics, Autoimmune Disease, Clinical Research, Lupus, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, 4.1 Discovery and preclinical testing of markers and technologies, Inflammatory and immune system, Renal and urogenital, Biomarkers, Biopsy, Cluster Analysis, Computational Biology, Epithelial Cells, Flow Cytometry, Gene Expression Profiling, Gene Expression Regulation, Humans, Immunophenotyping, Interferons, Kidney, Leukocytes, Lupus Nephritis, Lymphocytes, Molecular Sequence Annotation, Myeloid Cells, Single-Cell Analysis, Transcriptome, Accelerating Medicines Partnership in SLE network, Biochemistry and cell biology
Abstract

Lupus nephritis is a potentially fatal autoimmune disease for which the current treatment is ineffective and often toxic. To develop mechanistic hypotheses of disease, we analyzed kidney samples from patients with lupus nephritis and from healthy control subjects using single-cell RNA sequencing. Our analysis revealed 21 subsets of leukocytes active in disease, including multiple populations of myeloid cells, T cells, natural killer cells and B cells that demonstrated both pro-inflammatory responses and inflammation-resolving responses. We found evidence of local activation of B cells correlated with an age-associated B-cell signature and evidence of progressive stages of monocyte differentiation within the kidney. A clear interferon response was observed in most cells. Two chemokine receptors, CXCR4 and CX3CR1, were broadly expressed, implying a potentially central role in cell trafficking. Gene expression of immune cells in urine and kidney was highly correlated, which would suggest that urine might serve as a surrogate for kidney biopsies.

Published
2019

41. Transesophageal echocardiographic imaging of the coronary sinus: a retrospective analysis of mid-esophageal views and a novel transgastric view

Author

Tzonghuei Chen, Geoffrey Hayward, Patricia Apruzzese, and Andrew Maslow

Subjects
Coronary sinus, Retrograde cardioplegia, Transesophageal echocardiography, Anesthesiology, RD78.3-87.3
Abstract

Abstract Background Transesophageal echocardiographic imaging plays an important role in assessing coronary sinus anatomy prior to placement of a retrograde cardioplegia cannula. The coronary sinus can be imaged in the long axis by advancing the TEE probe from the mid-esophageal 4-chamber view or using a modified mid-esophageal bicaval view, while a short axis view can be obtained in the mid-esophageal 2-chamber view. While use of a transgastric view is only briefly mentioned in the literature as an alternative to mid-esophageal views, the authors commonly include it in our comprehensive transesophageal echocardiographic exam of the coronary sinus. This study examines the various imaging strategies. We hypothesize that the transgastric view offers comparable coronary sinus imaging to the mid-esophageal views. Methods After approval by our institutional review board, the intraoperative transesophageal echocardiographic exams for 50 consecutive elective cardiac surgical patients with a comprehensive echocardiographic assessment of the coronary sinus were retrospectively reviewed and analyzed to evaluate imaging of the coronary sinus in the various views. For each view, we noted and recorded if the coronary sinus and coronary sinus cannula were visualized. Statistical analysis required pairwise comparisons between each of the 4 views. P values were calculated using McNemar’s Exact test. Results Both the coronary sinus and coronary sinus cannula were visualized a majority of the time for each view. There was no statistically significant difference between each view in its ability to visualize the coronary sinus, nor was there a statistically significant difference between each view in its ability to visualize the coronary sinus cannula. Conclusions Use of a transgastric window provides the echocardiographer with an effective alternate modality for imaging the coronary sinus when mid-esophageal views are limited.

Published
2022
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42. LO-006 Change in urinary biomarkers at three months predicts 1-year treatment response of lupus nephritis better than proteinuria

Author

Richard Furie, Michelle Petri, Judith A James, Kenneth Kalunian, Maria Dall’Era, Jill Buyon, Chaim Putterman, Peter Izmirly, Betty Diamond, David Wofsy, Soumya Raychaudhuri, Daniel Goldman, Nir Hacohen, Jennifer Anolik, H Michael Belmont, Laurence Magder, Robert Clancy, ANNE DAVIDSON, Deepak Rao, Andrea Fava, Diane Kamen, Joel Guthridge, Celine Berthier, Arnon Arazi, Jose Monroy-Trujillo, Mohamed G Atta, William Apruzzese, Jennifer Barnas, Paul Hoover, Jeffrey Hodgin, Derek Fine, Alessandra Ida Celia, Avi Rosenberg, and Dawit Demeke

Subjects
Immunologic diseases. Allergy, RC581-607
Published
2023
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43. LP-098 Proteomic analysis of histological lesions in lupus nephritis identifies an inflammatory signature of fibrous crescents

Author

Laurence S Magder, Michelle Petri, Jill Buyon, Peter Izmirly, Betty Diamond, Paride Fenaroli, Daniel W Goldman, ANNE DAVIDSON, Andrea Fava, Judith James, Michael Belmont, Jose Monroy-Trujillo, Mohamed G Atta, William Apruzzese, Jeffrey Hodgin, Derek Fine, Alessandra Ida Celia, Dwita Demeke, and Avi Rosenberg

Subjects
Immunologic diseases. Allergy, RC581-607
Published
2023
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44. Overview of the EUROfusion Tokamak Exploitation programme in support of ITER and DEMO

Author

E. Joffrin, M. Wischmeier, M. Baruzzo, A. Hakola, A. Kappatou, D. Keeling, B. Labit, E. Tsitrone, N. Vianello, the ASDEX Upgrade Team, JET Contributors, the MAST-U Team, the TCV Team, the WEST Team, the EUROfusion Tokamak Exploitation Team:, D. Abate, J. Adamek, M. Agostini, C. Albert, F.C.P. Albert Devasagayam, S. Aleiferis, E. Alessi, J. Alhage, S. Allan, J. Allcock, M. Alonzo, G. Anastasiou, E. Andersson Sunden, C. Angioni, Y. Anquetin, L. Appel, G.M. Apruzzese, M. Ariola, C. Arnas, J.F. Artaud, W. Arter, O. Asztalos, L. Aucone, M.H. Aumeunier, F. Auriemma, J. Ayllon, E. Aymerich, A. Baciero, F. Bagnato, L. Bähner, F. Bairaktaris, P. Balázs, L. Balbinot, I. Balboa, M. Balden, A. Balestri, M. Baquero Ruiz, T. Barberis, C. Barcellona, O. Bardsley, S. Benkadda, T. Bensadon, E. Bernard, M. Bernert, H. Betar, R. Bianchetti Morales, J. Bielecki, R. Bilato, P. Bilkova, W. Bin, G. Birkenmeier, R. Bisson, P. Blanchard, A. Bleasdale, V. Bobkov, A. Boboc, A. Bock, K. Bogar, P. Bohm, T. Bolzonella, F. Bombarda, N. Bonanomi, L. Boncagni, D. Bonfiglio, R. Bonifetto, M. Bonotto, D. Borodin, I. Borodkina, T.O.S.J. Bosman, C. Bourdelle, C. Bowman, S. Brezinsek, D. Brida, F. Brochard, R. Brunet, D. Brunetti, V. Bruno, R. Buchholz, J. Buermans, H. Bufferand, P. Buratti, A. Burckhart, J. Cai, R. Calado, J. Caloud, S. Cancelli, F. Cani, B. Cannas, M. Cappelli, S. Carcangiu, A. Cardinali, S. Carli, D. Carnevale, M. Carole, M. Carpita, D. Carralero, F. Caruggi, I.S. Carvalho, I. Casiraghi, A. Casolari, F.J. Casson, C. Castaldo, A. Cathey, F. Causa, J. Cavalier, M. Cavedon, J. Cazabonne, M. Cecconello, L. Ceelen, A. Celora, J. Cerovsky, C.D. Challis, R. Chandra, A. Chankin, B. Chapman, H. Chen, M. Chernyshova, A.G. Chiariello, P. Chmielewski, A. Chomiczewska, C. Cianfarani, G. Ciraolo, J. Citrin, F. Clairet, S. Coda, R. Coelho, J.W. Coenen, I.H. Coffey, C. Colandrea, L. Colas, S. Conroy, C. Contre, N.J. Conway, L. Cordaro, Y. Corre, D. Costa, S. Costea, D. Coster, X. Courtois, C. Cowley, T. Craciunescu, G. Croci, A.M. Croitoru, K. Crombe, D.J. Cruz Zabala, G. Cseh, T. Czarski, A. Da Ros, A. Dal Molin, M. Dalla Rosa, Y. Damizia, O. D’Arcangelo, P. David, M. De Angeli, E. De la Cal, E. De La Luna, G. De Tommasi, J. Decker, R. Dejarnac, D. Del Sarto, G. Derks, C. Desgranges, P. Devynck, S. Di Genova, L.E. di Grazia, A. Di Siena, M. Dicorato, M. Diez, M. Dimitrova, T. Dittmar, L. Dittrich, J.J. Domínguez Palacios Durán, P. Donnel, D. Douai, S. Dowson, S. Doyle, M. Dreval, P. Drews, L. Dubus, R. Dumont, D. Dunai, M. Dunne, A. Durif, F. Durodie, G. Durr Legoupil Nicoud, B. Duval, R. Dux, T. Eich, A. Ekedahl, S. Elmore, G. Ericsson, J. Eriksson, B. Eriksson, F. Eriksson, S. Ertmer, A. Escarguel, B. Esposito, T. Estrada, E. Fable, M. Faitsch, N. Fakhrayi Mofrad, A. Fanni, T. Farley, M. Farník, N. Fedorczak, F. Felici, X. Feng, J. Ferreira, D. Ferreira, N. Ferron, O. Fevrier, O. Ficker, A.R. Field, A. Figueiredo, N. Fil, D. Fiorucci, M. Firdaouss, R. Fischer, M. Fitzgerald, M. Flebbe, M. Fontana, J. Fontdecaba Climent, A. Frank, E. Fransson, L. Frassinetti, D. Frigione, S. Futatani, R. Futtersack, S. Gabriellini, D. Gadariya, D. Galassi, K. Galazka, J. Galdon, S. Galeani, D. Gallart, A. Gallo, C. Galperti, M. Gambrioli, S. Garavaglia, J. Garcia, M. Garcia Munoz, J. Gardarein, L. Garzotti, J. Gaspar, R. Gatto, P. Gaudio, M. Gelfusa, J. Gerardin, S.N. Gerasimov, R. Gerru Miguelanez, G. Gervasini, Z. Ghani, F.M. Ghezzi, G. Ghillardi, L. Giannone, S. Gibson, L. Gil, A. Gillgren, E. Giovannozzi, C. Giroud, G. Giruzzi, T. Gleiter, M. Gobbin, V. Goloborodko, A. González Ganzábal, T. Goodman, V. Gopakumar, G. Gorini, T. Görler, S. Gorno, G. Granucci, D. Greenhouse, G. Grenfell, M. Griener, W. Gromelski, M. Groth, O. Grover, M. Gruca, A. Gude, C. Guillemaut, R. Guirlet, J. Gunn, T. Gyergyek, L. Hagg, J. Hall, C.J. Ham, M. Hamed, T. Happel, G. Harrer, J. Harrison, D. Harting, N.C. Hawkes, P. Heinrich, S. Henderson, P. Hennequin, R. Henriques, S. Heuraux, J. Hidalgo Salaverri, J. Hillairet, J.C. Hillesheim, A. Hjalmarsson, A. Ho, J. Hobirk, E. Hodille, M. Hölzl, M. Hoppe, J. Horacek, N. Horsten, L. Horvath, M. Houry, K. Hromasova, J. Huang, Z. Huang, A. Huber, E. Huett, P. Huynh, A. Iantchenko, M. Imrisek, P. Innocente, C. Ionita Schrittwieser, H. Isliker, P. Ivanova, I. Ivanova Stanik, M. Jablczynska, S. Jachmich, A.S. Jacobsen, P. Jacquet, A. Jansen van Vuuren, A. Jardin, H. Järleblad, A. Järvinen, F. Jaulmes, T. Jensen, I. Jepu, S. Jessica, T. Johnson, A. Juven, J. Kalis, J. Karhunen, R. Karimov, A.N. Karpushov, S. Kasilov, Y. Kazakov, P.V. Kazantzidis, W. Kernbichler, HT. Kim, D.B. King, V.G. Kiptily, A. Kirjasuo, K.K. Kirov, A. Kirschner, A. Kit, T. Kiviniemi, F. Kjær, E. Klinkby, A. Knieps, U. Knoche, M. Kochan, F. Köchl, G. Kocsis, J.T.W. Koenders, L. Kogan, Y. Kolesnichenko, Y. Kominis, M. Komm, M. Kong, B. Kool, S.B. Korsholm, D. Kos, M. Koubiti, J. Kovacic, Y. Kovtun, E. Kowalska Strzeciwilk, K. Koziol, M. Kozulia, A. Krämer Flecken, A. Kreter, K. Krieger, U. Kruezi, O. Krutkin, O. Kudlacek, U. Kumar, H. Kumpulainen, M.H. Kushoro, R. Kwiatkowski, M. La Matina, M. Lacquaniti, L. Laguardia, P. Lainer, P. Lang, M. Larsen, E. Laszynska, K.D. Lawson, A. Lazaros, E. Lazzaro, M.Y.K. Lee, S. Leerink, M. Lehnen, M. Lennholm, E. Lerche, Y. Liang, A. Lier, J. Likonen, O. Linder, B. Lipschultz, A. Listopad, X. Litaudon, E. Litherland Smith, D. Liuzza, T. Loarer, P.J. Lomas, J. Lombardo, N. Lonigro, R. Lorenzini, C. Lowry, T. Luda di Cortemiglia, A. Ludvig Osipov, T. Lunt, V. Lutsenko, E. Macusova, R. Mäenpää, P. Maget, C.F. Maggi, J. Mailloux, S. Makarov, K. Malinowski, P. Manas, A. Mancini, D. Mancini, P. Mantica, M. Mantsinen, J. Manyer, M. Maraschek, G. Marceca, G. Marcer, C. Marchetto, S. Marchioni, A. Mariani, M. Marin, M. Markl, T. Markovic, D. Marocco, S. Marsden, L. Martellucci, P. Martin, C. Martin, F. Martinelli, L. Martinelli, J.R. Martin Solis, R. Martone, M. Maslov, R. Masocco, M. Mattei, G.F. Matthews, D. Matveev, E. Matveeva, M.L. Mayoral, D. Mazon, S. Mazzi, C. Mazzotta, G. McArdle, R. McDermott, K. McKay, A.G. Meigs, C. Meineri, A. Mele, V. Menkovski, S. Menmuir, A. Merle, H. Meyer, K. Mikszuta Michalik, D. Milanesio, F. Militello, A. Milocco, I.G. Miron, J. Mitchell, R. Mitteau, V. Mitterauer, J. Mlynar, V. Moiseenko, P. Molna, F. Mombelli, C. Monti, A. Montisci, J. Morales, P. Moreau, J.M. Moret, A. Moro, D. Moulton, P. Mulholland, M. Muraglia, A. Murari, A. Muraro, P. Muscente, D. Mykytchuk, F. Nabais, Y. Nakeva, F. Napoli, E. Nardon, M.F. Nave, R.D. Nem, A. Nielsen, S.K. Nielsen, M. Nocente, R. Nouailletas, S. Nowak, H. Nyström, R. Ochoukov, N. Offeddu, S. Olasz, C. Olde, F. Oliva, D. Oliveira, H.J.C. Oliver, P. Ollus, J. Ongena, F.P. Orsitto, N. Osborne, R. Otin, P. Oyola Dominguez, D.I. Palade, S. Palomba, O. Pan, N. Panadero, E. Panontin, A. Papadopoulos, P. Papagiannis, G. Papp, V.V. Parail, C. Pardanaud, J. Parisi, A. Parrott, K. Paschalidis, M. Passoni, F. Pastore, A. Patel, B. Patel, A. Pau, G. Pautasso, R. Pavlichenko, E. Pawelec, B. Pegourie, G. Pelka, E. Peluso, A. Perek, E. Perelli Cippo, C. Perez Von Thun, P. Petersson, G. Petravich, Y. Peysson, V. Piergotti, L. Pigatto, C. Piron, L. Piron, A. Pironti, F. Pisano, U. Plank, B. Ploeckl, V. Plyusnin, A. Podolnik, Y. Poels, G. Pokol, J. Poley, G. Por, M. Poradzinski, F. Porcelli, L. Porte, C. Possieri, A. Poulsen, I. Predebon, G. Pucella, M. Pueschel, P. Puglia, O. Putignano, T. Pütterich, V. Quadri, A. Quercia, M. Rabinski, L. Radovanovic, R. Ragona, H. Raj, M. Rasinski, J. Rasmussen, G. Ratta, S. Ratynskaia, R. Rayaprolu, M. Rebai, A. Redl, D. Rees, D. Refy, M. Reich, H. Reimerdes, B.C.G. Reman, O. Renders, C. Reux, D. Ricci, M. Richou, S. Rienacker, D. Rigamonti, F. Rigollet, F.G. Rimini, D. Ripamonti, N. Rispoli, N. Rivals, J.F. Rivero Rodriguez, C. Roach, G. Rocchi, S. Rode, P. Rodrigues, J. Romazanov, C.F. Romero Madrid, J. Rosato, R. Rossi, G. Rubino, J. Rueda Rueda, J. Ruiz Ruiz, P. Ryan, D. Ryan, S. Saarelma, R. Sabot, M. Salewski, A. Salmi, L. Sanchis, A. Sand, J. Santos, K. Särkimäki, M. Sassano, O. Sauter, G. Schettini, S. Schmuck, P. Schneider, N. Schoonheere, R. Schramm, R. Schrittwieser, C. Schuster, N. Schwarz, F. Sciortino, M. Scotto D’Abusco, S. Scully, A. Selce, L. Senni, M. Senstius, G. Sergienko, S.E. Sharapov, R. Sharma, A. Shaw, U. Sheikh, G. Sias, B. Sieglin, S.A. Silburn, C. Silva, A. Silva, D. Silvagni, B. Simmendefeldt Schmidt, L. Simons, J. Simpson, L. Singh, S. Sipilä, Y. Siusko, S. Smith, A. Snicker, E.R. Solano, V. Solokha, M. Sos, C. Sozzi, F. Spineanu, G. Spizzo, M. Spolaore, L. Spolladore, C. Srinivasan, A. Stagni, Z. Stancar, G. Stankunas, J. Stober, P. Strand, C.I. Stuart, F. Subba, G.Y. Sun, H.J. Sun, W. Suttrop, J. Svoboda, T. Szepesi, G. Szepesi, B. Tal, T. Tala, P. Tamain, G. Tardini, M. Tardocchi, D. Taylor, G. Telesca, A. Tenaglia, A. Terra, D. Terranova, D. Testa, C. Theiler, E. Tholerus, B. Thomas, E. Thoren, A. Thornton, A. Thrysoe, Q. TICHIT, W. Tierens, A. Titarenko, P. Tolias, E. Tomasina, M. Tomes, E. Tonello, A. Tookey, M. Toscano Jiménez, C. Tsironis, C. Tsui, A. Tykhyy, M. Ugoletti, M. Usoltseva, D.F. Valcarcel, A. Valentini, M. Valisa, M. Vallar, M. Valovic, SI. Valvis, M. van Berkel, D. Van Eester, S. Van Mulders, M. van Rossem, R. Vann, B. Vanovac, J. Varela Rodriguez, J. Varje, S. Vartanian, M. Vecsei, L. Velarde Gallardo, M. Veranda, T. Verdier, G. Verdoolaege, K. Verhaegh, L. Vermare, G. Verona Rinati, J. Vicente, E. Viezzer, L. Vignitchouk, F. Villone, B. Vincent, P. Vincenzi, M.O. Vlad, G. Vogel, I. Voitsekhovitch, I. Voldiner, P. Vondracek, N.M.T. VU, T. Vuoriheimo, C. Wade, E. Wang, T. Wauters, M. Weiland, H. Weisen, N. Wendler, D. Weston, A. Widdowson, S. Wiesen, M. Wiesenberger, T. Wijkamp, M. Willensdorfer, T. Wilson, A. Wojenski, C. Wuethrich, I. Wyss, L. Xiang, S. Xu, D. Yadykin, Y. Yakovenko, H. Yang, V. Yanovskiy, R. Yi, B. Zaar, G. Zadvitskiy, L. Zakharov, P. Zanca, D. Zarzoso, Y. Zayachuk, J. Zebrowski, M. Zerbini, P. Zestanakis, C. F. B. Zimmermann, M. Zlobinski, A. Zohar, V.K. Zotta, X. Zou, M. Zuin, M. Zurita, and I. Zychor

Subjects
JET, ASDEX Upgrade, MAST-U, TCV, WEST, Tokamak Exploitation Task Force, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Within the 9th European Framework programme, since 2021 EUROfusion is operating five tokamaks under the auspices of a single Task Force called ‘Tokamak Exploitation’. The goal is to benefit from the complementary capabilities of each machine in a coordinated way and help in developing a scientific output scalable to future largre machines. The programme of this Task Force ensures that ASDEX Upgrade, MAST-U, TCV, WEST and JET (since 2022) work together to achieve the objectives of Missions 1 and 2 of the EUROfusion Roadmap: i) demonstrate plasma scenarios that increase the success margin of ITER and satisfy the requirements of DEMO and, ii) demonstrate an integrated approach that can handle the large power leaving ITER and DEMO plasmas. The Tokamak Exploitation task force has therefore organized experiments on these two missions with the goal to strengthen the physics and operational basis for the ITER baseline scenario and for exploiting the recent plasma exhaust enhancements in all four devices (PEX: Plasma EXhaust) for exploring the solution for handling heat and particle exhaust in ITER and develop the conceptual solutions for DEMO. The ITER Baseline scenario has been developed in a similar way in ASDEX Upgrade, TCV and JET. Key risks for ITER such as disruptions and run-aways have been also investigated in TCV, ASDEX Upgrade and JET. Experiments have explored successfully different divertor configurations (standard, super-X, snowflakes) in MAST-U and TCV and studied tungsten melting in WEST and ASDEX Upgrade. The input from the smaller devices to JET has also been proven successful to set-up novel control schemes on disruption avoidance and detachment.

Published
2024
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45. Stability analysis of plasma waves driven by runaway electrons in tokamak hot plasmas

Author

C. Castaldo, L. Della Volpe, R. Fedele, W. Bin, P. Buratti, A. Cardinali, F. Napoli, M. Marinucci, G. Apruzzese, C. Cianfarani, E. Giovannozzi, and O. Tudisco

Subjects
runaway, waves, tokamak, instabilities, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

The local stability analysis of plasma waves driven by runaway electrons (REs) has been performed considering hot plasma Maxwellian background, with electron and ion temperatures of the order of 1 keV. It is shown that hot plasma waves, namely electron plasma waves (EPWs) and ion Bernstein waves (IBWs) can be driven unstable by RE at their coalescence frequency via Cherenkov resonance by RE with energy distribution peaked at about 8 MeV. A skew-normal distribution is used as a model of the RE energy distribution. The EPW and IBW couples of waves occur between any successive ion-cyclotron harmonics frequencies nf _ci , above the lower hybrid resonance. At their confluence, the perpendicular group velocity vanishes and significant RF emissions are expected. The frequency gap between two successive confluences is ∼ f _ci . Groups of RF line emissions, separated by almost constant frequency gap ∼ f _ci /2 are detected during various quiescent runaway plasma discharges in the FTU tokamak. The analysis of a specific discharge suggests that the frequencies of the line emissions observed and the frequencies occurring at the EPW-IBW confluences are in reasonable agreement. A possible explanation of the line emissions with ∼ f _ci /2 gap in terms of nonlinear mode coupling is proposed.

Published
2024
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46. Divertor Tokamak Test facility project: status of design and implementation

Author

Francesco Romanelli, on behalf of DTT Contributors, D. Abate, E. Acampora, D. Agguiaro, R. Agnello, P. Agostinetti, M. Agostini, A. Aimetta, R. Albanese, G. Alberti, M. Albino, E. Alessi, S. Almaviva, M. Alonzo, R. Ambrosino, P. Andreoli, M. Angelone, M. Angelucci, C. Angioni, A. Angrisani Armenio, P. Antonini, D. Aprile, G. Apruzzese, M. Aquilini, G. Aragone, P. Arena, M. Ariola, G. Artaserse, L. Aucone, A. Augieri, F. Auriemma, J. Ayllon Guerola, N. Badodi, B. Baiocchi, L. Balbinot, C. Baldacchini, A. Balestri, T. Barberis, G. Barone, L. Barucca, M. Baruzzo, S. Begozzi, V. Belardi, F. Belli, A. Belpane, F. Beone, S. Bertolami, S. Bianucci, S. Bifaretti, S. Bigioni, W. Bin, P. Boccali, B. Boeswirth, E. Bogazzi, R. Bojoi, S. Bollanti, T. Bolzonella, F. Bombarda, M. Bonan, N. Bonanomi, A. Bonaventura, L. Boncagni, M. Bonesso, D. Bonfiglio, R. Bonifetto, D. Bonomi, D. Borgogno, T. Borzone, S. Botti, E. Boz, F. Braghin, M. Brena, S. Brezinsek, M. Brombin, A. Bruschi, S. Buonocore, P. Buratti, D. Busi, G. Calabrò, M. Caldora, G. Calvo, G. Camera, G. Campana, S. Candela, V. Candela, F. Cani, L. Cantone, F. Capaldo, S. Cappello, M. Caponero, S. Carchella, A. Cardinali, D. Carnevale, L. Carraro, C. Carrelli, V. Casalegno, I. Casiraghi, C. Castaldo, A. Castaldo, G. Castro, A. Carpignano, F. Causa, R. Cavazzana, M. Cavedon, M. Cavenago, M. Cecchini, S. Ceccuzzi, G. Celentano, L. Celona, C. Centioli, G.V. Centomani, S. Cesaroni, A.G. Chiariello, R. Chomicz, C. Cianfarani, F. Cichocki, M. Cinque, A. Cioffi, M. Ciotti, M. Cipriani, S. Ciufo, V. Claps, G. Claps, V. Coccorese, D. Coccorese, A. Colangeli, T. Coltella, F. Consoli, F. Cordella, D. Corradini, O. Costa, F. Crea, A. Cremona, F. Crescenzi, F. Crisanti, G. Cristofari, G. Croci, A. Cucchiaro, D. D’Ambrosio, M. Dal Molin, M. Dalla Palma, F. Danè, C. Day, M. De Angeli, V. De Leo, R. De Luca, E. De Marchi, G. De Marzi, G. De Masi, E. De Nardi, C. De Piccoli, G. De Sano, M. De Santis, G. De Tommasi, A. Del Nevo, A. Delfino, A. Della Corte, P. Deodati, S. Desiderati, E. Di Ferdinando, M.G. Di Florio, G. Di Gironimo, L.E. Di Grazia, V. Di Marzo, F. Di Paolo, E. Di Pietro, M. Di Pietrantonio, M. Di Prinzio, A. Di Silvestre, A. Di Zenobio, R. Dima, A. Domenichelli, A. Doria, G. Dose, S. Dubbioso, S. Dulla, I. Duran, M. Eboli, M. Elitropi, E. Emanuelli, B. Esposito, P. Ettorre, C. Fabbri, F. Fabbri, M. Fadone, M.M. Faggiano, F. Falcioni, M.V. Falessi, F. Fanale, P. Fanelli, A. Fassina, M. Favaretto, G. Favero, M. Ferraris, F. Ferrazza, C. Ferretti, A. Ferro, N. Ferron, C. Fiamozzi Zignani, L. Figini, F. Filippi, M. Filippini, A. Fimiani, M. Fincato, F. Fiorenza, D. Fiorucci, D. Flammini, F. Flora, N. Fonnesu, P. Franz, L. Frassinetti, A. Frattolillo, R. Freda, R. Fresa, A. Frescura, P. Frosi, M. Fulici, M. Furno Palumbo, V. Fusco, P. Fusco, L. Gabellier, P. Gaetani, E. Gaio, E. Gajetti, A. Galatà, J. Galdon Quiroga, D.L. Galindo Huertas, S. Gammino, G. Gandolfo, S. Garavaglia, J. Garcia Lopez, M. Garcia Muñoz, P. Gaudio, M. Gelfusa, G. Gervasini, L. Giannini, M. Giarrusso, C. Gil, F. Giorgetti, E. Giovannozzi, G. Giruzzi, L. Giudicotti, M. Gobbin, G. Gorini, G. Granucci, D. Grasso, T. Grasso, S. Grazioso, H. Greuner, G. Griva, G. Grosso, S. Guerini, J.P. Gunn, V. Hauer, J. Hidalgo Salaverri, M. Hoppe, M. Houry, M. Hoelzl, A. Iaboni, M. Iafrati, A. Iaiunese, V. Imbriani, D. Indrigo, P. Innocente, F. Koechl, B. Končar, A. Kryzhanovskyy, L. Laguardia, D.A. Lampasi, C. Lanchi, F. Lanzotti, A. Lanzotti, M. Laquaniti, F. Leone, J. Li, M. Libè, F. Lisanti, D. Liuzza, F. Locati, R. Lombroni, R. Lorenzini, P. Lorusso, L. Lotto, J. Loureiro, F. Lucca, T. Luda Di Cortemiglia, P. Maccari, G. Maddaluno, S. Magagnino, G. Manca, A. Mancini, P. Mandalà, B. Mandolesi, F. Mandrile, G. Manduchi, S. Manfrin, M. Manganelli, P. Mantica, G. Marchiori, N. Marconato, G. Marelli, A. Mariani, A. Marin, R. Marinari, M. Marinelli, F. Marino, P. Marino, D. Marocco, R. Marsilio, E. Martelli, P. Martin, F. Martinelli, G. Martini, R. Martone, A. Marucci, D. Marzullo, V. Masala, D. Mascali, F. Mascari, A. Masi, N. Massanova, S. Mastrostefano, M. Mattei, G. Mauro, S. Mauro, C. Meineri, L. Melaragni, A. Mele, P. Meller, S. Meloni, I. Menicucci, G. Messina, L. Mezi, G. Miccichè, M. Micheletti, S. Migliori, D. Milanesio, F. Milazzo, R. Milazzo, P. Minelli, S. Minucci, F. Mirizzi, M. Missirlian, D. Monarca, C. Monti, M. Mori, A. Moriani, L. Morici, A. Moro, F. Moro, P. Mosetti, R. Mozzillo, A. Murari, A. Muraro, D. Murra, P. Muscente, S. Musumeci, L. Muzzi, G.F. Nallo, F. Napoli, E. Nardon, E. Naselli, R. Neu, M. Nocente, M. Notazio, S. Nowak, E. Ocello, A. Oliva, V. Orsetti, A. Orsini, F.P. Orsitto, M. Ortino, M. Ottavi, G. Paccagnella, D. Pacella, I. Pagani, N. Paganucci, A. Pagliaro, V. Palazzolo, M. Palermo, S. Palomba, F. Panza, D. Paoletti, M. Parisi, R. Pasqualotto, S. Passarello, M. Passoni, T. Patton, L. Pelliccia, A. Peloso, A. Pepato, E. Perelli, A. Perencin, S. Peruzzo, A. Pesenti, N. Pedroni, P. Petrolini, V. Piergotti, A. Pidatella, L. Pigatto, M. Pillon, T. Pinna, S. Pipolo, S. Piras, C. Piron, L. Piron, A. Pironti, M. Pistilli, D. Placido, A. Pizzuto, P. Platania, A. Polimadei, F. Pollastrone, G.M. Polli, N. Pomaro, F. Pompili, C. Ponti, F. Porcelli, V. Prandelli, A. Previti, A. Princiotta, G. Pucino, F. Quaglia, A. Quercia, F. Raffaelli, G. Ramogida, G. Ranieri, B. Raspante, D. Ravarotto, G.L. Ravera, A. Reale, P. Rebesan, M. Recchia, D. Regine, F. Renno, B. Riccardi, D. Ricci, D. Rigamonti, M. Ripani, N. Rispoli, S. Roccella, G. Rocchi, H. Roche, M. Romanato, F. Romanelli, G. Romanelli, R. Romaniello, A. Romano, M. Romano, R. Romano, R. Rossi, G. Rubinacci, G. Rubino, S. Rubino, J. Rueda Rueda, A. Rufoloni, C. Salvia, P. Salvini, M. Scarpari, A. Salvitti, L. Salvò, S. Sandri, F. Santoro, A. Satriano, L. Savoldi, C. Scardino, G. Schettini, S. Schmuck, J. Scionti, M. Scisciò, M. Scungio, K. Sedlak, L. Senni, G. Sias, A. Sibio, A. Simonetto, L. Singh, A. Sirignano, C. Sozzi, I. Spada, S. Spagnolo, L. Spinicci, G. Spizzo, M. Spolaore, C. Stefanini, H. Strobel, F. Subba, F. Taccogna, B. Taheri, C. Tantos, A. Tarallo, M. Tarantino, G. Tardini, M. Tardocchi, P. Tarfila, A. Tenaglia, C. Terlizzi, D. Terranova, D. Testa, E. Testa, R. Testoni, V. Toigo, G. Torrisi, A. Trotta, G. Trovato, E. Tsitrone, A. Tuccillo, O. Tudisco, M. Turcato, S. Turtù, A. Uccello, M. Ugoletti, O. Uras, M. Uras, M. Utili, V. Vaccaro, F. Valentini, L. Valletti, M. Valisa, D. Van Eester, D. Vanzan, E. Vassallo, G. Vecchi, M. Vellucci, I. Venneri, G. Ventura, M. Veranda, L. Verdini, C. Verona, G. Verona Rinati, F. Veronese, N. Vianello, F. Viganò, O. Villano, R. Villari, F. Villone, P. Vincenzi, V. Vitale, F. Vivio, G. Vlad, M. Wischmeier, H.S. Wu, I. Wyss, R. Zanino, B. Zaniol, F. Zanon, A. Zappatore, G. Zavarise, P. Zito, A. Zoppoli, M. Zucchetti, M. Zuin, and P. Zumbolo

Subjects
divertor, exhaust, plasma scenarios, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

An overview is presented of the progress since 2021 in the construction and scientific programme preparation of the Divertor Tokamak Test (DTT) facility. Licensing for building construction has been granted at the end of 2021. Licensing for Cat. A radiologic source has been also granted in 2022. The construction of the toroidal field magnet system is progressing. The prototype of the 170 GHz gyrotron has been produced and it is now under test on the FALCON facility. The design of the vacuum vessel, the poloidal field coils and the civil infrastructures has been completed. The shape of the first DTT divertor has been agreed with EUROfusion to test different plasma and exhaust scenarios: single null, double null, X-divertor and negative triangularity plasmas. A detailed research plan is being elaborated with the involvement of the EUROfusion laboratories.

Published
2024
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47. Experimental research on the TCV tokamak

Author

B.P. Duval, A. Abdolmaleki, M. Agostini, C.J. Ajay, S. Alberti, E. Alessi, G. Anastasiou, Y. Andrèbe, G.M. Apruzzese, F. Auriemma, J. Ayllon-Guerola, F. Bagnato, A. Baillod, F. Bairaktaris, L. Balbinot, A. Balestri, M. Baquero-Ruiz, C. Barcellona, M. Bernert, W. Bin, P. Blanchard, J. Boedo, T. Bolzonella, F. Bombarda, L. Boncagni, M. Bonotto, T.O.S.J. Bosman, D. Brida, D. Brunetti, J. Buchli, J. Buerman, P. Buratti, A. Burckhart, D. Busil, J. Caloud, Y. Camenen, A. Cardinali, S. Carli, D. Carnevale, F. Carpanese, M. Carpita, C. Castaldo, F. Causa, J. Cavalier, M. Cavedon, J.A. Cazabonne, J. Cerovsky, B. Chapman, M. Chernyshova, P. Chmielewski, A. Chomiczewska, G. Ciraolo, S. Coda, C. Colandrea, C. Contré, R. Coosemans, L. Cordaro, S. Costea, T. Craciunescu, K. Crombe, A. Dal Molin, O. D’Arcangelo, D. de Las Casas, J. Decker, J. Degrave, H. de Oliveira, G.L. Derks, L.E. di Grazia, C. Donner, M. Dreval, M.G. Dunne, G. Durr-Legoupil-Nicoud, B. Esposito, T. Ewalds, M. Faitsch, M. Farník, A. Fasoli, F. Felici, J. Ferreira, O. Février, O. Ficker, A. Frank, E. Fransson, L. Frassinetti, L. Fritz, I. Furno, D. Galassi, K. Gałązka, J. Galdon-Quiroga, S. Galeani, C. Galperti, S. Garavaglia, M. Garcia-Munoz, P. Gaudio, M. Gelfusa, J. Genoud, R. Gerrú Miguelanez, G. Ghillardi, M. Giacomin, L. Gil, A. Gillgren, C. Giroud, T. Golfinopoulos, T. Goodman, G. Gorini, S. Gorno, G. Grenfell, M. Griener, M. Gruca, T. Gyergyek, R. Hafner, M. Hamed, D. Hamm, W. Han, G. Harrer, J.R. Harrison, D. Hassabis, S. Henderson, P. Hennequin, J. Hidalgo-Salaverri, J-P. Hogge, M. Hoppe, J. Horacek, A. Huber, E. Huett, A. Iantchenko, P. Innocente, C. Ionita-Schrittwieser, I. Ivanova Stanik, M. Jablczynska, A. Jansen van Vuuren, A. Jardin, H. Järleblad, A.E. Järvinen, J. Kalis, R. Karimov, A.N. Karpushov, K. Kavukcuoglu, J. Kay, Y. Kazakov, J. Keeling, A. Kirjasuo, J.T.W. Koenders, P. Kohli, M. Komm, M. Kong, J. Kovacic, E. Kowalska-Strzeciwilk, O. Krutkin, O. Kudlacek, U. Kumar, R. Kwiatkowski, B. Labit, L. Laguardia, E. Laszynska, A. Lazaros, K. Lee, E. Lerche, B. Linehan, D. Liuzza, T. Lunt, E. Macusova, D. Mancini, P. Mantica, M. Maraschek, G. Marceca, S. Marchioni, A. Mariani, M. Marin, A. Marinoni, L. Martellucci, Y. Martin, P. Martin, L. Martinelli, F. Martinelli, J.R. Martin-Solis, S. Masillo, R. Masocco, V. Masson, A. Mathews, M. Mattei, D. Mazon, S. Mazzi, S.Y. Medvedev, C. Meineri, A. Mele, V. Menkovski, A. Merle, H. Meyer, K. Mikszuta-Michalik, I.G. Miron, P.A. Molina Cabrera, A. Moro, A. Murari, P. Muscente, D. Mykytchuk, F. Nabais, F. Napoli, R.D. Nem, M. Neunert, S.K. Nielsen, A. Nielsen, M. Nocente, S. Noury, S. Nowak, H. Nyström, N. Offeddu, S. Olasz, F. Oliva, D.S. Oliveira, F.P. Orsitto, N. Osborne, P. Oyola Dominguez, O. Pan, E. Panontin, A.D. Papadopoulos, P. Papagiannis, G. Papp, M. Passoni, F. Pastore, A. Pau, R.O. Pavlichenko, A.C. Pedersen, M. Pedrini, G. Pelka, E. Peluso, A. Perek, C. Perez Von Thun, F. Pesamosca, D. Pfau, V. Piergotti, L. Pigatto, C. Piron, L. Piron, A. Pironti, U. Plank, V. Plyusnin, Y.R.J. Poels, G.I. Pokol, J. Poley-Sanjuan, M. Poradzinski, L. Porte, C. Possieri, A. Poulsen, M.J. Pueschel, T. Pütterich, V. Quadri, M. Rabinski, R. Ragona, H. Raj, A. Redl, H. Reimerdes, C. Reux, D. Ricci, M. Riedmiller, S. Rienäcker, D. Rigamonti, N. Rispoli, J.F. Rivero-Rodriguez, C.F. Romero Madrid, J. Rueda Rueda, P.J. Ryan, M. Salewski, A. Salmi, M. Sassano, O. Sauter, N. Schoonheere, R.W. Schrittwieser, F. Sciortino, A. Selce, L. Senni, S. Sharapov, U.A. Sheikh, B. Sieglin, M. Silva, D. Silvagni, B. Simmendefeldt Schmidt, L. Simons, E.R. Solano, C. Sozzi, M. Spolaore, L. Spolladore, A. Stagni, P. Strand, G. Sun, W. Suttrop, J. Svoboda, B. Tal, T. Tala, P. Tamain, M. Tardocchi, A. Tema Biwole, A. Tenaglia, D. Terranova, D. Testa, C. Theiler, A. Thornton, A.S. Thrysoe, M. Tomes, E. Tonello, H. Torreblanca, B. Tracey, M. Tsimpoukelli, C. Tsironis, C.K. Tsui, M. Ugoletti, M. Vallar, M. van Berkel, S. van Mulders, M. van Rossem, C. Venturini, M. Veranda, T. Verdier, K. Verhaegh, L. Vermare, N. Vianello, E. Viezzer, F. Villone, B. Vincent, P. Vincenzi, I. Voitsekhovitch, L. Votta, N.M.T. Vu, Y. Wang, E. Wang, T. Wauters, M. Weiland, H. Weisen, N. Wendler, S. Wiesen, M. Wiesenberger, T. Wijkamp, C. Wüthrich, D. Yadykin, H. Yang, V. Yanovskiy, J. Zebrowski, P. Zestanakis, M. Zuin, and M. Zurita

Subjects
TCV, review, plasma, SPC, EPFL, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Tokamak à configuration variable (TCV), recently celebrating 30 years of near-continual operation, continues in its missions to advance outstanding key physics and operational scenario issues for ITER and the design of future power plants such as DEMO. The main machine heating systems and operational changes are first described. Then follow five sections: plasma scenarios. ITER Base-Line (IBL) discharges, triangularity studies together with X3 heating and N2 seeding. Edge localised mode suppression, with a high radiation region near the X-point is reported with N _2 injection with and without divertor baffles in a snowflake configuration. Negative triangularity (NT) discharges attained record, albeit transient, β _N ∼ 3 with lower turbulence, higher low-Z impurity transport, vertical stability and density limits and core transport better than the IBL. Positive triangularity L-Mode linear and saturated ohmic confinement confinement saturation, often-correlated with intrinsic toroidal rotation reversals, was probed for D, H and He working gases. H-mode confinement and pedestal studies were extended to low collisionality with electron cyclotron heating obtaining steady state electron iternal transport barrier with neutral beam heating (NBH), and NBH driven H-mode configurations with off-axis co-electron cyclotron current drive. Fast particle physics. The physics of disruptions, runaway electrons and fast ions (FIs) was developed using near-full current conversion at disruption with recombination thresholds characterised for impurity species (Ne, Ar, Kr). Different flushing gases (D2, H2) and pathways to trigger a benign disruption were explored. The 55 kV NBH II generated a rich Alfvénic spectrum modulating the FI fas ion loss detector signal. NT configurations showed less toroidal Alfvén excitation activity preferentially affecting higher FI pitch angles. Scrape-off layer and edge physics. gas puff imaging systems characterised turbulent plasma ejection for several advanced divertor configurations, including NT. Combined diagnostic array divertor state analysis in detachment conditions was compared to modelling revealing an importance for molecular processes. Divertor physics. Internal gas baffles diversified to include shorter/longer structures on the high and/or low field side to probe compressive efficiency. Divertor studies concentrated upon mitigating target power, facilitating detachment and increasing the radiated power fraction employing alternative divertor geometries, optimised X-point radiator regimes and long-legged configurations. Smaller-than-expected improvements with total flux expansion were better modelled when including parallel flows. Peak outer target heat flux reduction was achieved (>50%) for high flux-expansion geometries, maintaining core performance ( H _98 > 1). A reduction in target heat loads and facilitated detachment access at lower core densities is reported. Real-time control. TCV’s real-time control upgrades employed MIMO gas injector control of stable, robust, partial detachment and plasma β feedback control avoiding neoclassical tearing modes with plasma confinement changes. Machine-learning enhancements include trajectory tracking disruption proximity and avoidance as well as a first-of-its-kind reinforcement learning-based controller for the plasma equilibrium trained entirely on a free-boundary simulator. Finally, a short description of TCV’s immediate future plans will be given.

Published
2024
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48. De l’activisme à la négociation urbaine. Dynamiques d’engagement politique et environnemental à la Floating University de Berlin

Author

Antoine Apruzzese

Subjects
Activism, Urbanism, Temporary, Political, Utopia, Architecture, NA1-9428, Geography. Anthropology. Recreation
Abstract

The Floating University project, developed in 2018 by the Berlin-based collective Raumlabor, is a complex and hybrid architectural project, thought of as a laboratory of collective experimentation on the relationship between our urban societies and the environment. The project came after ten years of temporary urban interventions on the site of the former Tempelhof airport. Raumlabor’s projects aimed to set up the cultural conditions for the development of a local community that shares common social and environmental values and is capable of acting upon its own condition of inhabiting the city. The architectural and programmatic evolution of the site shows different dynamics of political engagement, both from the collective and the association that have taken over and kept the project afloat following Raumlabor.

Published
2023
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49. 1107 Immune cell heterogeneity in lupus nephritis kidneys and its relation to histopathological features: lessons from the accelerating medicines partnership (AMP) in SLE Consortium

Author

Richard Furie, Joel M Guthridge, Judith A James, Maria Dall’Era, Michelle A Petri, Chaim Putterman, Jill P Buyon, Diane L Kamen, Betty Diamond, David Wofsy, Soumya Raychaudhuri, Thomas Tuschl, Jennifer Grossman, Peter M Izmirly, Qian Xiao, Nir Hacohen, Kenneth C Kalunian, Michael B Brenner, Mariko Ishimori, H Michael Belmont, Robert Clancy, ANNE DAVIDSON, Matthias Kretzler, Michael Weisman, Celine C Berthier, Andrea Fava, Jennifer L Barnas, Jennifer H Anolik, Paul J Hoover, Deepak A Rao, Arnon Arazi, William Apruzzese, Fernanda Payan-Schober, Saori Sakaue, James Lederer, Michael Peters, Tony Jones, Jospeh Mears, Thomas M Eisenhaure, Siddarth Gurajala, Derek Fine, David A Hildeman, Steve Woodle, Maureen A McMahon, and Jeffery Hodgin

Subjects
Immunologic diseases. Allergy, RC581-607
Published
2022
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50. 1112 Change in urinary biomarkers at three months predicts 1-year treatment response of lupus nephritisbetter than proteinuria

Author

Judith A James, Maria Dall’Era, Jill Buyon, Peter Izmirly, Betty Diamond, Soumya Raychaudhuri, Nir Hacohen, Daniel W Goldman, H Michael Belmont, Laurence Magder, ANNE DAVIDSON, Deepak Rao, Andrea Fava, Arnon Arazi, Mohamed G Atta, William Apruzzese, Derek Fine, Joel Guthdridge, and Jose Monroy Trujillo

Subjects
Immunologic diseases. Allergy, RC581-607
Published
2022
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