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Institution

University of Cagliari

EducationCagliari, Italy
About: University of Cagliari is a education organization based out in Cagliari, Italy. It is known for research contribution in the topics: Population & Dopamine. The organization has 11029 authors who have published 29046 publications receiving 771023 citations. The organization is also known as: Università degli Studi di Cagliari & Universita degli Studi di Cagliari.


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TL;DR: In this article, a back-gradient optimization algorithm is proposed to compute the gradient of interest through automatic gradient extraction, while also reversing the learning procedure to drastically reduce the attack complexity, which is able to target a wider class of learning algorithms, trained with gradient-based procedures.
Abstract: A number of online services nowadays rely upon machine learning to extract valuable information from data collected in the wild. This exposes learning algorithms to the threat of data poisoning, i.e., a coordinate attack in which a fraction of the training data is controlled by the attacker and manipulated to subvert the learning process. To date, these attacks have been devised only against a limited class of binary learning algorithms, due to the inherent complexity of the gradient-based procedure used to optimize the poisoning points (a.k.a. adversarial training examples). In this work, we rst extend the de nition of poisoning attacks to multiclass problems. We then propose a novel poisoning algorithm based on the idea of back-gradient optimization, i.e., to compute the gradient of interest through automatic di erentiation, while also reversing the learning procedure to drastically reduce the attack complexity. Compared to current poisoning strategies, our approach is able to target a wider class of learning algorithms, trained with gradient- based procedures, including neural networks and deep learning architectures. We empirically evaluate its e ectiveness on several application examples, including spam ltering, malware detection, and handwritten digit recognition. We nally show that, similarly to adversarial test examples, adversarial training examples can also be transferred across di erent learning algorithms.

138 citations

Journal ArticleDOI
C. Ahdida1, Raffaele Albanese2, A. Alexandrov, A. M. Anokhina3  +345 moreInstitutions (50)
TL;DR: In this article, heavy neutral leptons (HNLs) are used to explain the origin of neutrino masses, generate the observed matter-antimatter asymmetry in the Universe and provide a dark matter candidate.
Abstract: Heavy Neutral Leptons (HNLs) are hypothetical particles predicted by many extensions of the Standard Model. These particles can, among other things, explain the origin of neutrino masses, generate the observed matter-antimatter asymmetry in the Universe and provide a dark matter candidate.

138 citations

Journal ArticleDOI
TL;DR: The present review will focus on the current knowledge of the HERV Env expression, summarizing its role in human physiology and its possible pathogenic effects in various cancer and autoimmune disorders, and analyzes HERv Env possible exploitation for the development of innovative therapeutic strategies.
Abstract: Human endogenous retroviruses (HERVs) are relics of ancient infections accounting for about the 8% of our genome. Despite their persistence in human DNA led to the accumulation of mutations, HERVs are still contributing to the human transcriptome, and a growing number of findings suggests that their expression products may have a role in various diseases. Among HERV products, the envelope proteins (Env) are currently highly investigated for their pathogenic properties, which could likely be participating to several disorders with complex etiology, particularly in the contexts of autoimmunity and cancer. In fact, HERV Env proteins have been shown, on the one side, to trigger both innate and adaptive immunity, prompting inflammatory, cytotoxic and apoptotic reactions; and, on the other side, to prevent the immune response activation, presenting immunosuppressive properties and acting as immune downregulators. In addition, HERV Env proteins have been shown to induce abnormal cell-cell fusion, possibly contributing to tumor development and metastasizing processes. Remarkably, even highly defective HERV env genes and alternative env splicing variants can provide further mechanisms of pathogenesis. A well-known example is the HERV-K(HML2) env gene that, depending on the presence or the absence of a 292-bp deletion, can originate two proteins of different length (Np9 and Rec) proposed to have oncogenic properties. The understanding of their involvement in complex pathological disorders made HERV Env proteins potential targets for therapeutic interventions. Of note, a monoclonal antibody directed against a HERV-W Env is currently under clinical trial as therapeutic approach for multiple sclerosis, representing the first HERV-based treatment. The present review will focus on the current knowledge of the HERV Env expression, summarizing its role in human physiology and its possible pathogenic effects in various cancer and autoimmune disorders. It moreover analyzes HERV Env possible exploitation for the development of innovative therapeutic strategies.

138 citations

Journal ArticleDOI
TL;DR: A decentralized algorithm to estimate the eigenvalues of the Laplacian matrix that encodes the network topology of a multi-agent system that considers network topologies modeled by undirected graphs.

138 citations

Journal ArticleDOI
TL;DR: In this article, the relative sea-level rise scenarios for the year 2100 from four areas of the Italian peninsula are depicted, based on the Rahmstorf (2007) and IPCC-AR5 reports 2013 for the RCP-8.5 scenarios adjusted for the rates of vertical land movements.

137 citations


Authors

Showing all 11160 results

NameH-indexPapersCitations
Herbert W. Marsh15264689512
Michele Parrinello13363794674
Dafna D. Gladman129103675273
Peter J. Anderson12096663635
Alessandro Vespignani11841963824
C. Patrignani1171754110008
Hermine Katharina Wöhri11662955540
Francesco Muntoni11596352629
Giancarlo Comi10996154270
Giorgio Parisi10894160746
Luca Benini101145347862
Alessandro Cardini101128853804
Nicola Serra100104246640
Jurg Keller9938935628
Giulio Usai9751739392
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202374
2022230
20211,898
20201,903
20191,636
20181,600