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Examples of non-scattering inhomogeneities
No full textInternational audienceWe consider the scattering of waves by a penetrable inclusion embedded in some reference medium. We exhibit examples of materials and geometries for which non-scattering frequencies exist, i.e. for which at some frequencies there are incident fields which produce null scattered fields outside of the inhomogeneity. We show in particular that certain domains with corners or even cusps can support non-scattering frequencies. We relate the latter, for some inclusions, to resonance frequencies for Dirichlet or Neumann cavities. We also find situations where incident non-scattering fields solve the Helmholtz equation in a neighbourhood of the inhomogeneity and not in the whole space. In relation with invisibility, we give examples of inclusions of anisotropic materials which are non-scattering for all real frequencies. We prove that corresponding material indices must have a special structure on the boundary
VeriFogOps: Automated Deployment Tool Selection and CI/CD Pipeline Generation for Verifying Fog Systems at Deployment Time
No full textInternational audienceFog Computing consists in decentralizing the Cloud by geographically distributing computation, storage, network resources, and related services. Among other benefits, it allows reducing bandwidth usage, limiting latency, or minimizing data transfers. However, Fog systems engineering remains challenging and quite often error-prone. Following best practices in software engineering, verification tasks can be performed before such systems are concretely deployed. Works already exist on verifying non-functional properties of Fog systems at different previous steps of the life cycle. This paper goes one step further and presents the VeriFogOps approach. This approach allows to automatically select deployment tools, based on expressed Quality of Service (QoS) requirements, and then generate relevant CI/CD pipelines supporting the deployment of Fog systems. We implemented and validated our approach via two realistic use cases, considering different QoS solutions and deployment tools. This work, developed in direct collaboration with our industrial partner Smile, goes towards the direction of a more comprehensive support for the entire life cycle of Fog systems, from design to actual deployment and execution
A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging
No full textInternational audienceBackground and objective: Magnetic resonance imaging (MRI) plays a critical role in prostate cancer diagnosis, but is limited by variability in interpretation and diagnostic accuracy. This systematic review evaluates the current state of deep learning (DL) models in enhancing the automatic detection, localization, and characterization of clinically significant prostate cancer (csPCa) on MRI.Methods: A systematic search was conducted across Medline/PubMed, Embase, Web of Science, and ScienceDirect for studies published between January 2020 and September 2023. Studies were included if these presented and validated fully automated DL models for csPCa detection on MRI, with pathology confirmation. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and the Checklist for Artificial Intelligence in Medical Imaging.Key findings and limitations: Twenty-five studies met the inclusion criteria, showing promising results in detecting and characterizing csPCa. However, significant heterogeneity in study designs, validation strategies, and datasets complicates direct comparisons. Only one-third of studies performed external validation, highlighting a critical gap in generalizability. The reliance on internal validation limits a broader application of these findings, and the lack of standardized methodologies hinders the integration of DL models into clinical practice.Conclusions and clinical implications: DL models demonstrate significant potential in improving prostate cancer diagnostics on MRI. However, challenges in validation, generalizability, and clinical implementation must be addressed. Future research should focus on standardizing methodologies, ensuring external validation and conducting prospective clinical trials to facilitate the adoption of artificial intelligence (AI) in routine clinical settings. These findings support the cautious integration of AI into clinical practice, with further studies needed to confirm their efficacy in diverse clinical environments.Patient summary: In this study, we reviewed how artificial intelligence (AI) models can help doctors better detect and understand aggressive prostate cancer using magnetic resonance imaging scans. We found that while these AI tools show promise, these tools need more testing and validation in different hospitals before these can be used widely in patient care
Generalized quantum asymptotic equipartition
No full textcomments are welcomeInternational audienceWe establish a generalized quantum asymptotic equipartition property (AEP) beyond the i.i.d. framework where the random samples are drawn from two sets of quantum states. In particular, under suitable assumptions on the sets, we prove that all operationally relevant divergences converge to the quantum relative entropy between the sets. More specifically, both the smoothed min- and max-relative entropy approach the regularized relative entropy between the sets. Notably, the asymptotic limit has explicit convergence guarantees and can be efficiently estimated through convex optimization programs, despite the regularization, provided that the sets have efficient descriptions. We give four applications of this result: (i) The generalized AEP directly implies a new generalized quantum Stein's lemma for conducting quantum hypothesis testing between two sets of quantum states. (ii) We introduce a quantum version of adversarial hypothesis testing where the tester plays against an adversary who possesses internal quantum memory and controls the quantum device and show that the optimal error exponent is precisely characterized by a new notion of quantum channel divergence, named the minimum output channel divergence. (iii) We derive a relative entropy accumulation theorem stating that the smoothed min-relative entropy between two sequential processes of quantum channels can be lower bounded by the sum of the regularized minimum output channel divergences. (iv) We apply our generalized AEP to quantum resource theories and provide improved and efficient bounds for entanglement distillation, magic state distillation, and the entanglement cost of quantum states and channels. At a technical level, we establish new additivity and chain rule properties for the measured relative entropy which we expect will have more applications
Ultrasound guided transcutaneous phrenic nerve stimulation in critically ill patients: a new method to evaluate diaphragmatic function
No full textInternational audienceBackground : Diaphragm dysfunction is common in intensive care unit and associated with weaning failure and mortality. Diagnosis gold standard is the transdiaphragmatic or tracheal pressure induced by magnetic phrenic nerve stimulation. However, the equipment is not commonly available and requires specific technical skills. We aimed to evaluate ultrasound guided transcutaneous phrenic nerve stimulation for daily bedside assessment of diaphragm function by targeted electrical phrenic nerve stimulation. Methods : In this randomized cross-over study we compared a new method of ultrasound guided transcutaneous electrical phrenic nerve stimulation (SONOTEPS method) using a peripheral nerve stimulator, with the magnetic phrenic nerve stimulation. Intensive care unit adult patients under mechanical ventilation with a Richmond-Agitation-Sedation-Scale score of -4 or -5 were included. Each patient received the two methods of stimulation, in a randomized order. The primary outcome was the tracheal pressure (Ptrach) induced by stimulation. Results : We analyzed 232 measures of Ptrach from 116 patients of whom 77 presented a diaphragm dysfunction (Ptrach < 11 cmH2O) and 50 a severe diaphragm dysfunction (Ptrach < 8 cmH2O). The Passing-Bablok regression showed no significant differences (intercept A of -0.03 [CI95:-0.83-0.52] and slope B of 0.98 [CI95:0.90-1.05]) between SONOTEPS method and magnetic stimulation which were positively correlated (R²=0.639). The mean bias was -1.08 (CI95 5.02, -7.18) cmH2O. The receiver operating curves showed an excellent performance for the diagnosis of diaphragm dysfunction and severe diaphragm dysfunction with respectively an area under curve of 0.90 (CI95 0.83-0.97) and 0.88 (CI95 0.82-0.95). This performance was not significantly affected by the body mass index or the presence of a neck catheter. Conclusions : The SONOTEPS method is a simple and accurate tool for bedside assessment of diaphragm function with ultrasound guided transcutaneous phrenic nerve stimulation in sedated patients with no or minimal spontaneous respiratory activity
Machine learning for NeuroImaging data analysis
No full textInternational audienceMachine learning has become an ubiquitous tool for neuroimaging data analysis over the last two decades. It has opened up the possibility of assessing relationships between brain characteristics -and most importantly, brain activity -with many different behavioural covariates, both in the field of cognitive neuroscience and for population studies. The use of machine learning requires some expertise, as there are some pitfalls to avoid, such as biased assessments due to some form of data leakage, or reliance on underpowered datasets leading to erroneous conclusions. The era of larger datasets is upon us, and the field of generative AI brings a new perspective to the field.</div
Strong Converse for Classical-Quantum Degraded Broadcast Channels
No full textWe consider the transmission of classical information through a degraded broadcast channel, whose outputs are two quantum systems, with the state of one being a degraded version of the other. Yard et al. proved that the capacity region of such a channel is contained in a region characterized by certain entropic quantities. We prove that this region satisfies the strong converse property, that is, the maximal probability of error incurred in transmitting information at rates lying outside this region converges to one exponentially in the number of uses of the channel. In establishing this result, we prove a second-order Fano-type inequality, which might be of independent interest. A powerful analytical tool which we employ in our proofs is the tensorization property of the quantum reverse hypercontractivity for the quantum depolarizing semigroup
ReDOSN a customizable Decentralised Social Network
No full textSocial Networks have become a major part of people life, used to communicate and cooperate between humans and even with connected objects. Current social networks are server based, either centralised or federated. This poses security, privacy and performance issues as the server administrators must be trusted and the server is a single point of failures. Decentralised social networks proposed to tackle those issues lack customization. We propose a model to create customizable social network, focusing on the decentralisation and its security. We show how it can be implemented using existing decentralized technologies, arguing that data availability is not a hard constraint
Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs
No full textInternational audienceIn education, the capability of generating human-like text of Large Language Models (LLMs) inspired work on how they can increase the efficiency of learning and teaching. We study the affordability of these models for educators and students by investigating how LLMs answer multiple-choice questions (MCQs) with respect to hardware constraints and refinement techniques. We explore this space by using generic pre-trained LLMs (the 7B, 13B, and 70B variants of LLaMA-2) to answer 162 undergraduate-level MCQs from a course on Programming Languages (PL)-the MCQ dataset is a contribution of this work, which we make publicly available. Specifically, we dissect how different factors, such as using readily-available material-(parts of) the course's textbook-for fine-tuning and quantisation (to decrease resource usage) can change the accuracy of the responses. The main takeaway is that smaller textbook-based finetuned models outperform generic larger ones (whose pre-training requires conspicuous resources), making the usage of LLMs for answering MCQs resource-and material-wise affordable
Designing a safe forward chaining tactic using productive proofs
No full textInternational audienceWe present a proof-theoretic treatment of forward chaining and saturation within a multisorted, first-order intuitionistic logic with equality. The notions of polarity and focused proofs are central to our approach since they provide a characterization of geometric implications as bipolar formulas as well as a natural setting to describe forward chaining and the concept of productive proofs. We identify conditions under which forward chaining with a given set of formulas is guaranteed to saturate in a finite number of steps. The motivation for this research stems, in part, from exploring avenues to automate the Abella theorem prover, which relies on relational specifications, and where theorems in typical proof developments are essentially bipolar formulas. We illustrate the potential benefits of automating forward chaining and saturation for Abella by presenting examples that compute congruence closure and assist in other equational and relational reasoning tasks