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This paper introduces and defines a novel concept in sustainable investing, termed crosswashing, and explore its impact on ESG (Environmental, Social, and Governance) ratings through quantitative analysis using a Multi-Criteria Decision Making (MCDM) model. The study emphasises that this specific form of greenwashing is not currently considered in existing ESG assessments, potentially leading to an inflated perception of corporate ethical practices. Unlike traditional greenwashing, crosswashing involves companies strategically investing in sustainable activities to boost Environmental, Social, and Governance (ESG) scores while preserving nonsustainable core operations. By unveiling the nuances of crosswashing, the research contributes to a more nuanced understanding of sustainable investing, offering insights for improved evaluation and regulation of corporate environmental and ethical responsibilities.

We show that Global Navigation Satellite Systems (GNSS) and gravimeters on Earth and in space can potentially offer the most accurate direct measurement of local density of near-Earth asteroid-mass Primordial Black Holes (PBHs) and Dark Matter (DM) clumps in the solar system by means of gravitational influence. Using semi-analytical methods and Monte Carlo simulation, this paper revisits the analysis of the trajectories of DM clumps in the solar system, including both captured objects and hyperbolic trajectories. A link is thus made between the frequency and distance of Earth overflights for a given mass flux, and a direct measure of dark matter clump density in the solar system. We then model the signature of a close flyby of a DM object on orbital data from GNSS satellites and gravity measurements from gravimeters. We thus obtain a first assessment of the single probe sensitivity. It paves the way for an exhaustive statistical analysis of 28 years of gravimeters and GNSS data to obtain observational constraints on the density of the PBHs and DM clumps within the solar system, for the mass range $[10^8-10^{17}]$ kg. In addition, our methodology offers a possibility of direct detection in cases where DM clumps are endowed with an additional long-range clump-matter fifth-force beyond gravity., Comment: 27 pages, 30 figures

With the increasing demand for dynamic behaviors in automotive use cases, Software Defined Vehicles (SDVs) have emerged as a promising solution by bringing dynamic onboard service management capabilities. While users may request a wide range of services during vehicle operation, background tasks such as cooperative Vehicle-to-Everything (V2X) services can activate on-the-fly in response to real-time road conditions. In this dynamic environment, the efficient allocation of onboard resources becomes a complex challenge, in order to meet mixed-criticality onboard Quality-of-Service (QoS) network requirements while ensuring an optimal user experience. Additionally, the ever-evolving real-time network connectivity and computational availability conditions further complicate the process. In this context, we present a dynamic resource-based onboard service orchestration algorithm that considers real-time in-vehicle and V2X network health, along with onboard resource constraints, to select degraded modes for onboard applications and maximize user experience. To enable dynamic orchestration, we introduce the concept of Automotive eXperience Integrity Level (AXIL) which expresses a runtime priority for non-safety-critical applications. This algorithm produces near-optimal solutions while significantly reducing execution time compared to straightforward methods as demonstrated by simulation results. With this approach, we aim to enable efficient onboard execution for a user experience-focused service orchestration., Comment: Preprint for submission at IEEE Transactions on Intelligent Transportation Systems

In this paper, using the Milnor-type theorem technique, we provide on each nilpotent five dimensional Lie group, some global existence result of a pair (g, c) consisting of a left-invariant Riemannian metric g and a positive constant c such that Ric(g) =cT, where Ric(g) is the Ricci curvature of g and T a given left-invariant symmetric (0, 2)-tensor field.

Techniques to achieve various forms of test coverage, such as branch coverage, typically do not iterate loops; in other words, they treat a loop as a conditional, executed zero or one time. Existing work by the author and collaborators produces test suites guaranteeing full branch coverage. More recent work has shown that by "unrolling" loops the approach can find significantly more bugs. The present discussion provides the theoretical basis and precise definition for this concept of unrolling. While initially motivated by the need to improve standard test coverage practices (which execute loop bodies only once), to better testing coverage, the framework presented here is applicable to any form of reasoning about loops.

A wireless sensor network often relies on a fusion center to process the data collected by each of its sensing nodes. Such an approach relies on the continuous transmission of raw data to the fusion center, which typically has a major impact on the sensors' battery life. To address this issue in the particular context of spatial filtering and signal fusion problems, we recently proposed the Distributed Adaptive Signal Fusion (DASF) algorithm, which distributively computes a spatial filter expressed as the solution of a smooth optimization problem involving the network-wide sensor signal statistics. In this work, we show that the DASF algorithm can be extended to compute the filters associated with a certain class of non-smooth optimization problems. This extension makes the addition of sparsity-inducing norms to the problem's cost function possible, allowing sensor selection to be performed in a distributed fashion, alongside the filtering task of interest, thereby further reducing the network's energy consumption. We provide a description of the algorithm, prove its convergence, and validate its performance and solution tracking capabilities with numerical experiments., Comment: This work has been submitted to the IEEE Transactions on Signal Processing for publication

We numerically investigate azimuthal modulation instability in an optical fiber supporting orbital angular momentum modes only, i.e. a vortex fiber, by means of the scalar multimode unidirectional pulse propagation equation. We demonstrate that the nonlinear stage of azimuthal modulation instability taking place in such a ring-core fiber, with anomalous rotation group-velocity dispersion, can be simply described by analytical breather solutions of the corresponding nonlinear Schr\"odinger equation. Azimuthal soliton dynamics and nonlinear compression are also unveiled as well as specific spatial rotating features as a function of the topological charge involved. Our results open a new route for studying transverse nonlinear waves in optical fibers and for manipulating orbital angular momentum states.

We summarize the Fermilab Accelerator Complex Evolution (ACE) Science Workshop, held on June 14-15, 2023. The workshop presented the strategy for the ACE program in two phases: ACE Main Injector Ramp and Target (MIRT) upgrade and ACE Booster Replacement (BR) upgrade. Four plenary sessions covered the primary experimental physics thrusts: Muon Collider, Neutrinos, Charged Lepton Flavor Violation, and Dark Sectors. Additional physics and technology ideas were presented from the community that could expand or augment the ACE science program. Given the physics framing, a parallel session at the workshop was dedicated to discussing priorities for accelerator R\&D. Finally, physics discussion sessions concluded the workshop where experts from the different experimental physics thrusts were brought together to begin understanding the synergies between the different physics drivers and technologies. In December of 2023, the P5 report was released setting the physics priorities for the field in the next decade and beyond, and identified ACE as an important component of the future US accelerator-based program. Given the presentations and discussions at the ACE Science Workshop and the findings of the P5 report, we lay out the topics for study to determine the physics priorities and design goals of the Fermilab ACE project in the near-term.

Neural network pruning serves as a critical technique for enhancing the efficiency of deep learning models. Unlike unstructured pruning, which only sets specific parameters to zero, structured pruning eliminates entire channels, thus yielding direct computational and storage benefits. However, the diverse patterns for coupling parameters, such as residual connections and group convolutions, the diverse deep learning frameworks, and the various time stages at which pruning can be performed make existing pruning methods less adaptable to different architectures, frameworks, and pruning criteria. To address this, we introduce Structurally Prune Anything (SPA), a versatile structured pruning framework that can prune neural networks with any architecture, from any framework, and at any stage of training. SPA leverages a standardized computational graph and ONNX representation to prune diverse neural network architectures without the need for manual intervention. SPA employs a group-level importance estimation method, which groups dependent computational operators, estimates their importance, and prunes unimportant coupled channels. This enables the transfer of various existing pruning criteria into a structured group style. As a result, SPA supports pruning at any time, either before training, after training with fine-tuning, or after training without fine-tuning. In the context of the latter, we introduce Optimal Brain SPA (OBSPA), an algorithm that achieves state-of-the-art pruning results needing neither fine-tuning nor calibration data. In extensive experiments, SPA shows competitive to state-of-the-art pruning performance across various architectures, from popular frameworks, at different pruning times.