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Search for lepton-number-violating decays
No full textInternational audienceA search is performed for lepton-number-violating decays, using data collected by the LHCb experiment in proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 5.4 fb. No significant signal is observed, and upper limits are set on the branching fractions, and , at the 95% confidence level
WiCaliper: simultaneous material and 3D size sensing for everyday objects using WiFi
No full textInternational audienceAlongside the ongoing standardization efforts for WiFi sensing, WiFi has emerged as a leading technology for Integrated Sensing and Communications (ISAC) with numerous sensing applications demonstrating its significant potentials. Material and size sensing, essential in quality control and digital twins, has drawn much interest. Yet, simultaneous material and 3D size sensing remains challenging, primarily due to the lack of suitable sensing models for objects at near-wavelength scales. This paper introduces WiCaliper, the first WiFi-based system addressing this problem. Its core innovation is DP-CSI, a novel sensing model that captures both diffraction and penetration effects to characterize the relationship between channel state information and the material, shape, and size of everyday 3D objects. To effectively solve for multiple object parameters, WiCaliper employs a two-step estimation process consisting of closed-form property function recovery and multi-view joint parameter optimization. Experimental evaluations show that it achieves 95% material classification accuracy and a 1.5 cm median error in 3D size sensing. This work advances ISAC theory by establishing principles for wavelength-scale 3D object sensing, paving the way for new sensing applications
A low-complexity equalizer design for OTFS modulation in doubly-dispersive channels
No full textInternational audienceOrthogonal time frequency space (OTFS) modulation has emerged as a promising technique for reliable communication over rapidly time-varying channels, thanks to its robustness against Doppler effects and delay spread. However, the design of efficient equalizers for OTFS systems remains a key challenge due to the high computational complexity involved. In this paper, we propose a low-complexity linear equalizer for OTFS modulation that exploits the inherent structure of the effective channel matrix. Specifically, the proposed method leverages the block-circulant property and the sparse nature of the OTFS channel matrix and applies a Choleskybased decomposition to significantly reduce computational cost. Simulation results confirm that the proposed equalizer achieves notable improvements in both bit error rate performance and computational efficiency, outperforming existing state-of-the-art techniques
Offline Contextual Bandit with Counterfactual Sample Identification
No full textRecsys '25, CONSEQUENCES: Causality, Counterfactuals & Sequential Decision-Making WorkshopIn production systems, contextual bandit approaches often rely on direct reward models that take both action and context as input. However, these models can suffer from confounding, making it difficult to isolate the effect of the action from that of the context. We present Counterfactual Sample Identification, a new approach that re-frames the problem: rather than predicting reward, it learns to recognize which action led to a successful (binary) outcome by com-paring it to a counterfactual action sampled from the logging policy under the same context. The method is theoretically grounded and consistently outperforms direct models in both synthetic experi- ments and real-world deployments
Non-Asymptotic Convergence of Discrete Diffusion Models: Masked and Random Walk dynamics
No full textDiffusion models for continuous state spaces based on Gaussian noising processes are now relatively well understood, as many works have focused on their theoretical analysis. In contrast, results for diffusion models on discrete state spaces remain limited and pose significant challenges, particularly due to their combinatorial structure and their more recent introduction in generative modelling. In this work, we establish new and sharp convergence guarantees for three popular discrete diffusion models (DDMs). Two of these models are designed for finite state spaces and are based respectively on the random walk and the masking process. The third DDM we consider is defined on the countably infinite space and uses a drifted random walk as its forward process. For each of these models, the backward process can be characterized by a discrete score function that can, in principle, be estimated. However, even with perfect access to these scores, simulating the exact backward process is infeasible, and one must rely on approximations. In this work, we study Euler-type approximations and establish convergence bounds in both Kullback-Leibler divergence and total variation distance for the resulting models, under minimal assumptions on the data distribution. In particular, we show that the computational complexity of each method scales linearly in the dimension, up to logarithmic factors. Furthermore, to the best of our knowledge, this study provides the first non-asymptotic convergence guarantees for these noising processes that do not rely on boundedness assumptions on the estimated score
Assessing Stimuli Detectability and Pleasantness for Auditory BCI
No full textInternational audienceBrain-Computer Interfaces (BCIs) enable device control by analyzing brain activity. In reactive auditory BCIs based on steady-state auditory evoked potentials, users are exposed to amplitude-modulated sine waves at given frequencies that encode information (i.e. the type of action expected), while their brain activity is analyzed to infer the intended action based on the frequency retrieved. However, listening to sine-wave may be perceived as unpleasant over time. This study compares the use of pure-tones with alternative sounds, including artificial stimuli (such as a Brownian noise) and natural sounds (such as cicada song and cat's purr) by measuring brain responses of 48 subjects to these different stimuli, all amplitude-modulated at 40 Hz. The Signal-to-Noise Ratio (SNR) (i.e. the ratio between the power spectrum of electroencephalographic signals in response to the target stimulus and that in response to a silence stimulus) is computed at 40 Hz for each type of stimulus. It reveals that the 40-Hz modulation frequency is clearly more identifiable when carried by a pure tone than when carried by the other sounds, with an SNR increase up to more than 5 dB. The cicada song stimulus is a promising alternative, still requiring improvement to achieve the level of detectability observed for pure tones. The experiment is conducted at two different sound levels to assess whether increasing the listening level increases the SNR, but the opposite trend is found. Questionnaires indicate that more than half of the participants find pure tones annoying and prefer other sounds, confirming that this study is worth pursuing
Modelling the yield stress of cement pastes and mortars containing heterogeneous and unconventional aggregates like raw crushed wind turbine blade
No full textInternational audienceAbstract Determining the yield stress of cementitious materials is crucial for casting and concrete mix design. Fresh concrete possesses yield stress, behaving as a solid with viscoelastic properties below this threshold. When the yield stress is exceeded, concrete flows with a steady-state behavior commonly described by the Bingham or Herschel-Bulkley models. As the construction industry increasingly consumes more and more scarce raw materials, there is a growing need to develop and explore alternative construction materials to replace traditional ones while valorizing waste. Raw Crushed Wind Turbine Blade (RCWTB) has demonstrated interesting results when included in cementitious mixtures. However, a full characterization of rheology including the yield stress of mixtures containing RCWTB is still missing and would be of great practical interest. In this paper, the yield stress of cementitious pastes and mortars containing RCWTB with two different water/cement ratios is measured. Results demonstrate higher yield stress for higher RCWTB inclusion, this is mainly due to the bridge effect of the Glass Fiber Reinforced Polymer (GFRP) contained in the RCWTB. Finally, a physical model is applied for RCWTB to predict GFRP fibers maximum packing fraction based on their geometry, elastic properties, and the rheology of the surrounding cement-based material. This model is then validated with experimental yield stress of cement pastes and mortars
Beyond Log-Concavity and Score Regularity: Improved Convergence Bounds for Score-Based Generative Models in W2 -distance
No full textInternational audienceScore-based Generative Models (SGMs) aim to sample from a target distribution by learning score functions using samples perturbed by Gaussian noise. Existing convergence bounds for SGMs in the W2-distance rely on stringent assumptions about the data distribution. In this work, we present a novel framework for analyzing W2-convergence in SGMs, significantly relaxing traditional assumptions such as log-concavity and score regularity. Leveraging the regularization properties of the Ornstein-Uhlenbeck (OU) process, we show that weak log-concavity of the data distribution evolves into log-concavity over time. This transition is rigorously quantified through a PDE-based analysis of the Hamilton-Jacobi-Bellman equation governing the log-density of the forward process. Moreover, we establish that the drift of the time-reversed OU process alternates between contractive and noncontractive regimes, reflecting the dynamics of concavity. Our approach circumvents the need for stringent regularity conditions on the score function and its estimators, relying instead on milder, more practical assumptions. We demonstrate the wide applicability of this framework through explicit computations on Gaussian mixture models, illustrating its versatility and potential for broader classes of data distributions
Measurement of the Higgs boson total decay width using the H WW e decay channel in proton-proton collisions at = 13 TeV
No full textInternational audienceThe Higgs boson (H) decay width is determined from the ratio of off- and on-shell production of H WW e using proton-proton collision data corresponding to an integrated luminosity of 138 fb collected at = 13 TeV by the CMS experiment at the LHC. The off-shell signal strength is measured as = 1.2. The Higgs boson total decay width is = 3.9 MeV, in agreement with the standard model prediction. The uncertainty in this result represents a factor of three improvement over the previous CMS result in this decay channel
Bubbling wormholes and matrix models
No full textInternational audienceThe thermofield double state entangles two copies of a CFT via a sum over energy eigenstates and is dual to the two-sided eternal black hole. We explore an analogous construction using sums over gauge group representations of half-BPS Wilson loops in multiple copies of super Yang-Mills. These sums act as delta function-like operators that correlate the eigenvalues of the corresponding half-BPS matrix models. We suggest that the holographic duals are ''bubbling wormhole'' geometries: multi-covers of AdS whose conformal boundary consists of multiple four-spheres intersecting on a common circle. We analyze the matrix model free energy, discuss its bulk interpretation, and study probe loops in these backgrounds