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Class-Specific Variable Speed Limit for Traffic Flow Optimization on Road Networks
No full textInternational audienceThis paper presents a novel approach to traffic management in road networks, consisting in time-varying classspecific variable speed limit (VSL) restricted to a fraction of road users. In particular, we present a macroscopic approach where traffic dynamics is described by a multi-class Lighthill-Whitham-Richards (LWR) model, with two classes of users (controlled and uncontrolled vehicles). The model can be applied to a general road network and our goal is to optimize traffic performance by minimizing both the average travel time in the network and the total time spent in virtual buffers at the network entries to prevent spill-back scenarios. The optimization is performed by acting on different ratios of controlled vehicles, and we compare the performance of the proposed control strategy with a classical inflow control at the entries of the network. The numerical tests show that class-specific speed control outperforms inflow control, and highlight the importance of tailored traffic control strategies for road networks, offering insights into optimizing mobility, safety, and traffic efficiency
Abstract 6897: Development of immunomodulatory nanoparticles targeting HER2 for breast cancer therapy
No full textInternational audienceAbstract Nanoparticles (NPs) are emerging as a promising strategy in oncology, offering optimised drug delivery and the potential to modulate immune responses. In this study, we developed an innovative nanoparticle encapsulating docetaxel and functionalised with trastuzumab for the precise targeting of HER2-positive tumours (called ANC for Antibody Nanoparticle Conjufate). Pharmacokinetic analyses were performed using C57BL/6 mice, which were administered free docetaxel (1.6 mg/kg) and trastuzumab (135 ng/kg) via intraperitoneal injection (n = 29), or ANC (nanoparticles at the same docetaxel and trastuzumab doses as the free drugs, n = 40). Blood samples were collected at 0.5, 1, 2, 3, 6, 8, 12, and 24 hours post-injection, and the frozen plasma was analysed using by HPLC-UV. For biodistribution studies, DiR-labelled nanoparticles were administered to C57BL/6 mice, and imaging was performed on a daily basis using an IVIS Spectrum imager. Immunomodulation study was assessed by flow cytometry. The AUC0-∞ for the ANC (4250.79 ng.mL/h) was four times higher than for free docetaxel (1050.3 ng.mL-1.h-1). In addition, the half-life of the liposomal docetaxel (22.14 hours) was significantly three times longer than that of the free docetaxel (7.15 hours). Biodistribution studies in mouse models showed that the nanoparticle remained stable and did not reach the liver until 30 hours after injection. Immunomodulation studies showed that ANC had a different effect on the distribution of lymphocyte subpopulations compared to free drug, with a notable decrease in regulatory T cells (Tregs) in the ANC-treated group, suggesting a potential immunostimulatory effect. The potential immunomodulatory effects of these nanoparticles on anti-tumour immune responses are currently being investigated. These preliminary results demonstrate the dual benefit of targeted drug delivery and immune system engagement offored by these nanoparticles, which could pave the way for novel breast cancer treatments. Citation Format: Mathilde Dacos, Lea Plantureux, Erwan Diroff, Sarah Giacometti, Joseph Ciccolini, Raphaelle Fanciullino. Development of immunomodulatory nanoparticles targeting HER2 for breast cancer therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6897
<a href="https://mathematical-oncology.org/blog/mechanistic-learning-in-action-at-compo.html">Mechanistic learning in action at COMPO</a>
No full textEnergy-Aware Scheduling Strategies for Partially-Replicable Task Chains on Heterogeneous Processors
No full textThe arrival of heterogeneous (or hybrid) multicore architectures has brought new performance trade-offs for applications, and efficiency opportunities to systems. They have also increased the challenges related to thread scheduling, as tasks' execution times will vary depending if they are placed on big (performance) cores or little (efficient) ones. In this paper, we focus on the challenges heterogeneous multicore processors bring to partially-replicable task chains, such as the ones that implement digital communication standards in Software-Defined Radio (SDR). Our objective is to maximize the throughput of these task chains while also minimizing their power consumption. We model this problem as a pipelined workflow scheduling problem using pipelined and replicated parallelism on two types of resources whose objectives are to minimize the period and to use as many little cores as necessary. We propose two greedy heuristics (FERTAC and 2CATAC) and one optimal dynamic programming (HeRAD) solution to the problem. We evaluate our solutions and compare the quality of their schedules (in period and resource utilization) and their execution times using synthetic task chains. We also study an open source implementation of the DVB-S2 communication standard based on the StreamPU runtime. Leading processor vendors are covered with ARM, Apple, AMD, and Intel platforms. Both the achieved throughput and the energy consumption are evaluated. Our results demonstrate the benefits and drawbacks of the different proposed solutions. On average, FERTAC and 2CATAC achieve near-optimal solutions, with periods that are less than 10% worse than the optimal (HeRAD) using fewer than 2 extra cores. These three scheduling strategies now enable programmers and users of StreamPU to transparently make use of heterogeneous multicore processors and achieve a throughput that differs from its theoretical maximum by less than 6% on average. On the DVB-S2 receiver, it is also shown that the heterogeneous solutions outperform the best homogeneous ones in terms of energy efficiency by 8% on average
Integrating Environmental Regulations Into Autonomous Agricultural Robotics: A Case for Waterbody-Aware Fertilization
No full textInternational audienceThe operation of autonomous robots in the agricultural domain requires compliance with the regulatory aspects of the process. For instance, the improper spraying of chemicals near water bodies (e.g., pesticides or fertilizers) may cause significant environmental damage and, therefore, is strictly regulated at the legislation level. In this paper, we introduce a reasoning-enhanced framework to operate autonomous robots spreading chemicals near water bodies. Our framework leverages and extends semantic web vocabularies to integrate regulatory constraints and environmental conditions where the robot is operating. Then, it uses rules and reasoning to detect violations on real-time data generated by the autonomous robot. The inference of violations subsequently triggers actions controlling the robot behavior, but can also be transparently explained in our framework, thereby avoiding the robot to behave as a black-box to the supervising technician. Our approach has been implemented, and its feasibility showcased in a simulation environment
Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch
No full textInternational audienceWe consider the problem of computing differentially private approximate histograms and heavy hitters in a stream of elements. In the non-private setting, this is often done using the sketch of Misra and Gries [Science of Computer Programming, 1982]. Chan, Li, Shi, and Xu [PETS 2012] describe a differentially private version of the Misra-Gries sketch, but the amount of noise it adds can be large and scales linearly with the size of the sketch; the more accurate the sketch is, the more noise this approach has to add. We present a better mechanism for releasing a Misra-Gries sketch under (ε, δ)-differential privacy. It adds noise with magnitude independent of the size of the sketch; in fact, the maximum error coming from the noise is the same as the best known in the private non-streaming setting, up to a constant factor. Our mechanism is simple and likely to be practical. We also give a simple post-processing step of the Misra-Gries sketch that does not increase the worst-case error guarantee. It is sufficient to add noise to this new sketch with less than twice the magnitude of the non-streaming setting. This improves on the previous result for ε-differential privacy where the noise scales linearly to the size of the sketch. Finally, we consider a general setting where users can contribute multiple distinct elements. We present a new sketch with maximum error matching the Misra-Gries sketch. For many parameters in this setting our sketch can be released with less noise under (ε, δ)-differential privacy
Agent based modeling and simulation approach to identify and classify the key influential factors for satisfaction of public transport users
No full textInternational audienceThe issue of mobility from disadvantaged areas to places of interest for work, health care, education, or entertainment poses specific challenges that cannot be approached under the same prism as that of well-resourced areas. In such areas, commuters are often captive of available transportation modes. However, very few studies have focused on identifying the key factors that influence the satisfaction of these commuters. In this paper, we introduce an agent based modeling and simulation approach, to identify and classify these factors. We show, for the case study of a township in South Africa, that speed and quality of the infrastructure are crucial factors, while waiting time and accessibility are to be improved; safety and travel time being to be watched. Then recommendations are provided to improve the service according to these factors.</div
Better Gaussian Mechanism using Correlated Noise
No full textInternational audienceWe present a simple variant of the Gaussian mechanism for answering differentially private queries when the sensitivity space has a certain common structure. Our motivating problem is the fundamental task of answering d counting queries under the add/remove neighboring relation. The standard Gaussian mechanism solves this task by adding noise distributed as a Gaussian with variance scaled by d independently to each count. We show that adding a random variable distributed as a Gaussian with variance scaled by to all counts allows us to reduce the variance of the independent Gaussian noise samples to scale only with . The total noise added to each counting query follows a Gaussian distribution with standard deviation scaled by rather than . The central idea of our mechanism is simple and the technique is flexible. We show that applying our technique to another problem gives similar improvements over the standard Gaussian mechanism
Fermion Doubling in Quantum Cellular Automata
No full textA Quantum Cellular Automaton (QCA) is essentially an operator driving the evolution of particles on a lattice, through local unitaries. Because Δx=Δt=ε, QCAs constitute a privileged framework to cast the digital quantum simulation of relativistic quantum particles and their interactions with gauge fields, e.g., (3+1)D Quantum Electrodynamics (QED). But before they can be adopted, simulation schemes for high-energy physics need prove themselves against specific numerical issues, of which the most infamous is Fermion Doubling (FD). FD is well understood in particular in the discrete-space but continuous-time settings of real-time/Hamiltonian Lattice Gauge Theories (LGTs), as the appearance of spurious solutions for all Δx=ε≠0. We rigorously extend this analysis to the real-time discrete-space and discrete-time schemes that QCAs are. We demonstrate the existence of FD issues in QCAs. By applying a covering map on the Brillouin zone, we provide a flavoring-without-staggering way of fixing FD that does not break chiral symmetry. We explain how this method coexists with the Nielsen-Ninomiya no-go theorem, and illustrate this with a neutrino-like QCA
Causal decompositions of one-dimensional quantum cellular automata
No full textUnderstanding quantum theory's causal structure stands out as a major matter, since it radically departs from classical notions of causality. We present advances in the research program of causal decompositions, which investigates the existence of an equivalence between the causal and the compositional structures of unitary channels. Our results concern one-dimensional Quantum Cellular Automata (1D QCAs), i.e. unitary channels over a line of N quantum systems (with or without periodic boundary conditions) that feature a causality radius r: a given input cannot causally influence outputs at a distance more than r. We prove that, for N ≥ 4r +1, 1D QCAs all admit causal decompositions: a unitary channel is a 1D QCA if and only if it can be decomposed into a unitary routed circuit of nearest-neighbour interactions, in which its causal structure is compositionally obvious. This provides the first constructive form of 1D QCAs with causality radius one or more, fully elucidating their structure. In addition, we show that this decomposition can be taken to be translation-invariant for the case of translation-invariant QCAs. Our proof of these results makes use of innovative algebraic techniques, leveraging a new framework for capturing partitions into non-factor sub-C* algebras