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Inferring Communities of Interest in Collaborative Learning-based Recommender Systems
No full textInternational audienceCollaborative-learning-based recommender systems, such as those employing Federated Learning (FL) and Gossip Learning (GL), allow users to train models while keeping their history of liked items on their devices. While those methods were seen as promising for enhancing privacy, recent research has shown that collaborative learning can be vulnerable to various privacy attacks. In this paper, we propose a novel attack called Community Inference Attack (CIA), which enables an adversary to identify community members based on a set of target items. What sets CIA apart is its efficiency: it operates at a low computational cost by eliminating the need for training surrogate models. Instead, it uses a comparison-based approach, inferring sensitive information by comparing users' models rather than targeting any specific individual model. To evaluate the effectiveness of CIA, we conduct experiments on three real-world recommendation datasets using two recommendation models under both fedarated and gossip-like settings. The results demonstrate that CIA can be up to 10 times more accurate than random guessing. Additionally, we evaluate two mitigation strategies: Differentially Private Stochastic Gradient Descent (DP-SGD) and a Share less policy, which involves sharing fewer, less sensitive model parameters. Our findings suggest that the Share less strategy offers a better privacy-utility trade-off, especially in GL
, , un oscillateur harmonique quantique ?
No full textWe observe numerically the discrepency between the spectrum of operators obtained by the quantification of classical isochrone systems and the spectrum of the quantum harmonic oscillator . We are particularly interested in the operator with
Déterminer un graphe à partir de son graphe de reconfiguration
No full textGiven a graph and a natural number , the -recolouring graph is the graph whose vertices are the -colourings of and whose edges link pairs of colourings which differ at exactly one vertex of . Recently, Hogan et al. proved that can be determined from provided is large enough (quadratic in the number of vertices of ). We improve this bound by showing that colours suffice, and provide examples of families of graphs for which colours do not suffice. We then extend this result to -Kempe-recolouring graphs, whose vertices are again the -colourings of a graph and whose edges link pairs of colourings which differ by swapping the two colours in a connected component induced by selecting those two colours. We show that colours suffice to determine in this case. Finally, we investigate the case of independent set reconfiguration, proving that in only a few trivial cases is one guaranteed to be able to determine a graph
Isolation Forest Meets Link Prediction: A Novel Framework for Backbone Extraction
No full textInternational audienceComplex networks appear across various disciplines, including social sciences, biology, finance, and transportation. Their complexity often obscures critical structural and functional relationships, making analysis difficult. Backbone extraction simplifies networks by preserving key connections while reducing complexity. Traditional backbone extraction methods apply fixed thresholds on computed scores, making them highly sensitive to parameter choices. In a previous work we introduced similarity-based and embedding-based link prediction techniques for backbone extraction. However, these methods depend on predefined thresholds. They preserve either the top or bottom fraction of scores and discard the remaining fraction, which may contain structurally significant interactions. To overcome these limitations, this study presents a novel backbone extraction framework integrating link prediction with anomaly detection
Backbone extraction through statistical edge filtering: A comparative study
No full textInternational audienceThe backbone extraction process is pivotal in expediting analysis and enhancing visualization in network applications. This study systematically compares seven influential statistical hypothesis-testing backbone edge filtering methods (Disparity Filter (DF), Polya Urn Filter (PF), Marginal Likelihood Filter (MLF), Noise Corrected (NC), Enhanced Configuration Model Filter (ECM), Global Statistical Significance Filter (GloSS), and Locally Adaptive Network Sparsification Filter (LANS)) across diverse networks. A similarity analysis reveals that backbones extracted with the ECM and DF filters exhibit minimal overlap with backbones derived from their alternatives. Interestingly, ordering the other methods from GloSS to NC, PF, LANS, and MLF, we observe that each method’s output encapsulates the backbone of the previous one. Correlation analysis between edge features (weight, degree, betweenness) and the test significance level reveals that the DF and LANS filters favor high-weighted edges while ECM assigns them lower significance to edges with high degrees. Furthermore, the results suggest a limited influence of the edge betweenness on the filtering process. The backbones global properties analysis (edge fraction, node fraction, weight fraction, weight entropy, reachability, number of components, and transitivity) identifies three typical behavior types for each property. Notably, the LANS filter preserves all nodes and weight entropy. In contrast, DF, PF, ECM, and GloSS significantly reduce network size. The MLF, NC, and ECM filters preserve network connectivity and weight entropy. Distribution analysis highlights the PU filter’s ability to capture the original weight distribution. NC filter closely exhibits a similar capability. NC and MLF filters excel for degree distribution. These insights offer valuable guidance for selecting appropriate backbone extraction methods based on specific properties
A Martingale approach to continuous Portfolio Optimization under CVaR like constraints
No full textWe study a continuous-time portfolio optimization problem under an explicit constraint on the Deviation Conditional Value-at-Risk (DCVaR), defined as the difference between the CVaR and the expected terminal wealth. While the mean-CVaR framework has been widely explored, its time-inconsistency complicates the use of dynamic programming. We follow the martingale approach in a complete market setting, as in Gao et al. [4], and extend it by retaining an explicit DCVaR constraint in the problem formulation.The optimal terminal wealth is obtained by solving a convex constrained minimization problem. This leads to a tractable and interpretable characterization of the optimal strategy
Novel Electrical Characterization Method for Antiferroelectric like ZrO2 using a Positive Up Negative Down Approach: For more than a decade, ferroelectric and antiferroelectric ultra-thin films of fluorite-structured materials have drawn significant attention for a wide variety of applications requiring high integration density [1]. At high doping, HZO layers can become antiferroelectric (AFE) [2]. Antiferroelectric HZO holds significant promise for nanosupercapacitors, owing to its potential for high energy storage density (ESD) and high efficiency [3]. The FE properties of Metal/Insulator/Metal (MIM) capacitors will be presented, the insulator is constituted by different AFE monolayer thicknesses, from 5.5 to 18 nm, made by Atomic Layer Deposition (ALD). The new PUND-AFE technique extends the traditional Positive-Up Negative-Down (PUND) method to antiferroelectric materials by introducing resting voltage biases to isolate the films FE behavior. This approach effectively separates dielectric and leakage currents from the ferroelectric switching components, enabling accurate analysis of the antiferroelectric like behavior. To understand the PUND-AFE method, an electrical characterization of an AFE capacitor with ZrO2(10 nm) at 3.5 MV/cm is showed in the figure. First a standard DHM protocol: corresponding (a) current versus voltage as a function of cycle number and (b) P-E loops as a function of cycle number. Then the same with the PUND-AFE protocol. We then extract by PUND-AFE: (e) the remnant up and down polarizations as a function of cycle number and (f) the coercive fields as a function of cycle number. PUND-AFE protocol is then used over several ZrO2 AFE capacitors thicknesses between 5.5 nm and 18 nm at 3.5 MV/cm in function of the endurance, showing in g) the variation of the remnant polarization and in h) variation of the coercive fields. Notably, the method reveals a significant dependence of remnant polarization and coercive fields on the thickness of the ZrO2 layer, offering valuable insights for optimizing energy storage applications. [1] Ferroelectricity in hafnium oxide thin films, Appl Phys Lett. 99, 102903 (2011).[2] Ferroelectricity and Antiferroelectricity of Doped Thin HfO2‐Based Films Advanced Materials, 27(11), 1811-1831 (2015).[3] Comparative performance of fluorite-structured materials for nanosupercapacitor applications. APL Materials, 12(7).
No full textInternational audienceFor more than a decade, ferroelectric and antiferroelectric ultra-thin films of fluorite-structured materials have drawn significant attention for a wide variety of applications requiring high integration density [1]. At high doping, HZO layers can become antiferroelectric (AFE) [2]. Antiferroelectric HZO holds significant promise for nanosupercapacitors, owing to its potential for high energy storage density (ESD) and high efficiency [3]. The FE properties of Metal/Insulator/Metal (MIM) capacitors will be presented; the insulator is constituted by different AFE monolayer thicknesses, from 5.5 to 18 nm, made by Atomic Layer Deposition (ALD). The new PUND-AFE technique extends the traditional Positive-Up Negative-Down (PUND) method to antiferroelectric materials by introducing resting voltage biases to isolate the films FE behavior. This approach effectively separates dielectric and leakage currents from the ferroelectric switching components, enabling accurate analysis of the antiferroelectric like behavior. To understand the PUND-AFE method, an electrical characterization of an AFE capacitor with ZrO2(10 nm) at 3.5 MV/cm is showed in the figure. First a standard DHM protocol: corresponding (a) current versus voltage as a function of cycle number and (b) P-E loops as a function of cycle number. Then the same with the PUND-AFE protocol. We then extract by PUND-AFE: (e) the remnant up and down polarizations as a function of cycle number and (f) the coercive fields as a function of cycle number. PUND-AFE protocol is then used over several ZrO2 AFE capacitors thicknesses between 5.5 nm and 18 nm at 3.5 MV/cm in function of the endurance, showing in g) the variation of the remnant polarization and in h) variation of the coercive fields. Notably, the method reveals a significant dependence of remnant polarization and coercive fields on the thickness of the ZrO2 layer, offering valuable insights for optimizing energy storage applications.[1] Ferroelectricity in hafnium oxide thin films, Appl Phys Lett. 99, 102903 (2011).[2] Ferroelectricity and Antiferroelectricity of Doped Thin HfO2‐Based Films Advanced Materials, 27(11), 1811-1831 (2015).[3] Comparative performance of fluorite-structured materials for nanosupercapacitor applications. APL Materials, 12(7)
Torque Observation of WRSM With Model Uncertainties for EV Applications
No full textInternational audienceIn this article, we propose a torque observation method based on a linear parameter varying (LPV) approach for a wound rotor synchronous machine (WRSM) used in electric vehicles (EVs), specifically for the Renault ZOE. The novelty of our approach lies in its ability to handle a wide range of uncertainties and parameter variations, such as speed fluctuations and model uncertainties in both magnetic flux and resistance. This enables more accurate and robust torque estimation, which is crucial for the demanding performance requirements of EV applications. We present a comprehensive observation methodology, which includes a state and unknown input observability study, robust LPV observer design, and a convergence analysis. The effectiveness of this approach is demonstrated through both simulations and experimental tests conducted on the BEMEVE real-power test bench. To highlight its merits, the performance of the LPV observer is compared to different types of observers
Comparison of losses measurements of ferrites for design purposes
No full textInternational audienceFerrites are the most common used materials in power electronics magnetics components. The design phase of magnetic components deeply rely on the magnetic losses reliability that will have a critical impact on the final design. This study presents a comparative experimental analysis based on measurements conducted using three distinct test benches. Results are compared to the datasheet and between benches
