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Impact of wheat-legume mix intercrops on wheat epidemics by modelling
No full textInternational audienceHighlights: • Simulated intercropping decrease disease intensity and improve protectiveness while canopy indicators predict such effects. • Pea intercropped with wheat decreased disease intensity compared with faba bean. • Nitrogen fertilization increased disease intensity. • This study stressed the critical lack of experimental data on disease in intercropping.Abstract: Context : Intercropping is a promising strategy for integrated disease management and agroecological transition, although experimental and modelling studies are scarce.Objectives: This study aims to understand and quantify the impact of non-host species choice and nitrogen (N) fertilization on disease epidemics in the context of intercropping.Methods: We collected existing experimental data on LAI based on a literature survey of non-diseased wheat intercropped with different non-host legume species (pea and faba bean) and N fertilization treatments. Based on a foliar epidemic model for intercropping, we simulated epidemics directly on these experimental data of LAI. The model is parameterized for two wheat fungal diseases: Septoria tritici blotch, a rain-borne disease, and wheat leaf rust, an air-borne disease.Results: Our results indicate that intercropping can decrease disease intensity and improve protectiveness for both diseases. Effect depends however on species choice as pea intercropped with wheat leads to lower disease intensity and better intercropping protectiveness compared with faba bean, whereas N fertilization increased disease intensity. We also found that crop indicators describing wheat leaf area index (LAI) can predict disease intensity, whereas indicators describing companion LAI can better predict intercropping protectiveness.Conclusions: Intercropping can significantly reduce fungal epidemics on wheat, and intercropping management practices can be optimized for effective disease management in wheat-legume intercrops. The dilution effect is more related to disease intensity, while the barrier effect is more related to intercropping protectiveness.Implications: These findings pave the way for identifying field indicators to predict epidemics. However, this study also stressed the critical lack of experimental data on disease in intercropping
Algebraic hierarchical partitioning to improve H-matrix compression
No full textInternational audienceSolving large dense problems is a challenging task in many industrial applications such as computational electromagnetics. H-matrices may solve these problems efficiently while significantly reducing storage requirements. The compression rate and efficiency of H-matrices depend on the partitioning of the unknowns. This partitioning must be hierarchical and should respect geometric criteria in order to maximize compression. A fixed block size constraint is added to accommodate load balancing and performance concerns on HPC runtime systems. Numerous partitioning schemes exist, but few meet all requirements. Some geometric methods such as recursive coordinates bipartition, space-filling curves and cobblestone sorting provide acceptable results whereas algebraic graph partitioner generally do not. We propose a method to build a graph from the mesh to combine geometric and physical properties, suited to provide adapted partitions reliably. We review fitting geometric partitioning methods and compare them to our algebraic approach using relevant metrics such as compression rates before and after factorization and execution time of H-matrix assembly, factorization and solving step. We also study resulting partitions based on their volume, overlap and distance. Our contribution shows significant improvements in compression rates and execution times for complex 3D objects with multiple materials which are representative of industrial applications.</div
NURBSFit: Robust Fitting of NURBS Surfaces to Point Clouds
No full textInternational audienceNURBS surfaces are compact parametric representations widely used in Computer-Aided Design (CAD) modeling. Decomposing raw 3D data measurements into a set of such elements is a challenging problem that existing methods approach by learning from CAD databases to both segment synthetic data and fit parametric shapes on each segment. Unfortunately, these methods generalize poorly to raw data measurements, with low robustness to imperfect data and complex objects and low scalability. To address this issue, we propose NURBSFIT, an algorithm that fits NURBS surfaces to unorganized 3D point clouds, such as those generated by laser and photogrammetry acquisition systems. Starting with a fine configuration of planar patches that approximate the object geometry, our algorithm performs merging operations that progressively regroup pairs of adjacent patches into fewer, more expressive NURBS surfaces. This process is designed to be both robust and performant with a series of technical ingredients that include an energy that controls the global quality of a configuration of NURBS surfaces and an efficient ordering of the merging operations based on a cost-efficient quadric surface fitting analysis. We show the potential of our algorithm on both synthetic and real-world data and its efficiency against existing primitive fitting methods with results both simpler and geometrically more accurate. Our implementation is available at: https://github.com/lizOnly/nurbsfit
Corrections to “On the data complexity of consistent query answering over graph databases [Journal of Computer and System Sciences 88 (2017) 164–194]”
No full textInternational audienceApplications of graph databases are prone to inconsistency due to interoperability issues. This raises the need for studying query answering over inconsistent graph databases in a simple but general framework. We follow the approach of consistent query answering (CQA), and study its data complexity over graph databases for conjunctive regular-path queries (CRPQs) and conjunctive regular-path constraints (CRPCs). We deal with subset, superset and symmetric-difference repairs. Without restrictions, CQA is undecidable for the semantics of superset- and symmetric-difference repairs, and \Pi_2^P-complete for subset-repairs. However, we identify restrictions on CRPCs and databases that lead to decidability, and even tractability of CQA
Parametric Resonance: Bridging Optimal Control Theory and Dynamical System Stability
No full textThis work demonstrates that time-optimal control laws inherently align with the parametric resonance conditions governing dynamical stability. By bridging Pontryagin's maximum principle with Floquet theory, it establishes a unified mathematical framework linking control optimization to parametric instability. It provides new insights for controlling dynamical systems and may find applications from classical mechanics to quantum technologies.</div
From research to Deuxfleurs and back again: towards digital service infrastructure as commons
No full textInternational audienceWe introduce Deuxfleurs -- a hosting collective in France -- and summarize its relation to research; we then propose a preliminary analysis of Deuxfleurs' digital service infrastructure as a Common Pool Resource (CPR). Digital service infrastructure are well-studied in Computer Science from a technical point of view, but their governance is often a blind spot. Conversely, digital commons is an active field of research, but proposed results often ignore the materiality of the infrastructure. Combining the two aspects fits the definition of "undone" Computer Science and can open interesting research questions
Toward unprivileged, portable and generic network topology discovery
No full textInternational audienceWith the increase in size and complexity of supercomputers, it has become crucial to match applications and communication libraries to the underlying network topology. This matching may allow minimizing the time spent in waiting for high-latency communication and limiting contention on the network. While MPI implementations rely mostly on software such as hwloc to retrieve information about nodes topology, no tool currently gathers network topology information in a generic and portable fashion.In this paper, we propose an algorithm inspired by the Steiner Spanner problem that exploits end-to-end latency measurements to reconstruct a network topology. Our solution reconstructs the topology graph from a matrix of measured communication times. This is achieved by iteratively adding nodes to the graph while trying to match the shortest path length in the graph to the communication times. The total weight and the number of edges in the graph are also minimized
Scalable Simulation of Fermionic Encoding Performance on Noisy Quantum Computers
No full textA compelling application of quantum computers with thousands of qubits is quantum simulation. Simulating fermionic systems is both a problem with clear real-world applications and a computationally challenging task. In order to simulate a system of fermions on a quantum computer, one has to first map the fermionic Hamiltonian to a qubit Hamiltonian. The most popular such mapping is the Jordan-Wigner encoding, which suffers from inefficiencies caused by the high weight of some encoded operators. As a result, alternative local encodings have been proposed that solve this problem at the expense of a constant factor increase in the number of qubits required. Some such encodings possess local stabilizers, i.e., Pauli operators that act as the logical identity on the encoded fermionic modes. A natural error mitigation approach in these cases is to measure the stabilizers and discard any run where a measurement returns a -1 outcome. Using a high-performance stabilizer simulator, we classically simulate the performance of a local encoding known as the Derby-Klassen encoding and compare its performance with the Jordan-Wigner encoding and the ternary tree encoding. Our simulations use more complex error models and significantly larger system sizes (up to ) than in previous work. We find that the high sampling requirements of postselection methods with the Derby-Klassen encoding pose a limitation to its applicability in near-term devices and call for more encoding-specific circuit optimizations
Salience-SGG: Enhancing Unbiased Scene Graph Generation with Iterative Salience Estimation
No full textInternational audienceScene Graph Generation (SGG) suffers from a longtailed distribution, where a few predicate classes dominate while many others are underrepresented, leading to biased models that underperform on rare relations. Unbiased-SGG methods address this by implementing debiasing strategies, but often at the cost of spatial understanding-resulting in over-reliance on semantic priors. We introduce Salience-SGG, a novel framework featuring an Iterative Salience Decoder (ISD) that emphasizes triplets with salient spatial structures. To support this, we propose semantic-agnostic salience labels guiding ISD. Evaluations on Visual Genome, Open Images V6, and GQA-200 show that Salience-SGG achieves state-of-the-art performance and improves existing Unbiased-SGG methods in their spatial understanding as demonstrated by the Pairwise Localization Average Precision
Efficient Memory Usage For Edge FaaS Platforms
No full textInternational audienceFunction as a Service (FaaS) is a great fit for data and event processing in Edge environments. These environments are characterized by resource-constrained devices that require efficient memory usage optimizations. Existing optimization so- lutions for memory in high-end clusters such as data centers cannot be used in Edge environments because they either depend on unavailable hardware features such as RDMA or require resource-intensive analysis such as periodic memory scanning, compression, or decompression. In this paper, we introduce Extensible RUNtimes (ERUN), a lightweight mechanism for existing FaaS runtimes aimed at optimizing memory utilization by reducing the memory usage of idle sandboxes (i.e., those awaiting function execution). ERUN operates through two main actions: Shrink and Expand. The Shrink operation unloads libraries and reclaims memory from the sandboxes, while the Expand operation quickly reloads the libraries when a function is executed. The Expand operation leverages an in-memory store that maintains a single instance of discarded libraries on the node. We implement the ERUN mechanism, which can be applied to any runtime environment, in a Python runtime. We extensively evaluated our prototype in a 10-node Edge cluster using 10 popular FaaS functions. The results show that ERUN can reduce idle sandbox memory usage by up to 23.13× and improve the warm-start ratio by 1.38×, while incurring less than 2% overhead on function execution time and energy usage