Miracle-dz/UniFAR-FTUs huggingface dataset:
The Origin of Compressible Magnetic Turbulence in the Very Local Interstellar Medium

Voyager 1 observed compressible magnetic turbulence in the very local interstellar medium (VLISM). We show that inner heliosheath (IHS) fast- and slow-mode waves incident on the heliopause (HP) generate VLISM fast-mode waves only that propagate into the VLISM. We suggest that this is the origin of compressible turbulence in the VLISM. We show that fast- and slow-mode waves transmitted across a tangential discontinuity such as the HP are strongly refracted on crossing the HP and subsequently propagate at highly oblique angles to the VLISM magnetic field. Thus, fast-mode waves in the VLISM contribute primarily to the compressible and not the transverse components of the VLISM fluctuating magnetic field variance since and . If the fast- and slow-mode waves in the IHS exhibit a Kolmogorov-like power spectral density, as appears to be observed by Voyager 1, then the corresponding transmitted spectral density in the VLISM forms an amplified power law. Consequently, the HP “radiates” fast-mode fluctuations into the VLISM, and the heliosphere therefore mediates the character of turbulence in the VLISM. In particular, we predict the form of the VLISM magnetic turbulence power spectral density to be a superposition of the background pristine interstellar turbulence spectrum and the fast-mode spectrum generated by the interaction of fast- and slow-mode IHS waves with the HP, i.e., a power law with an enhanced feature or “bump” corresponding to the contribution by fast-mode turbulence radiated by the HP. We note the applicability of these results to the generation of compressible turbulence in the local interstellar medium surrounding the asterospheres of stars.
Note sur la conjecture de Greenberg

Nous etudions la conjecture de Greenberg sur les l-invariants d'Iwasawa des corps totalement reels, en termes de classes logarithmiques. Sous la conjecture de Leopoldt, et donc en particulier pour les corps abeliens, nous montrons que la conjecture de Greenberg postule tout simplement la capitulation des classes logarithmiques dans la Zl-tour cyclotomique. Dans le cas completement decompose, nous prouvons de meme que le critere suffisant introduit par Gras exprime tout simplement la trivialite du l-groupe des classes logarithmiques du corps de base considere. Enfin, dans le cas abelien, nous explicitons la structure des l-groupes de classes circulaires dans le contexte de la conjecture faible.
Second moment modelling for the numerical simulation of passive scalar dispersion of air pollutants in urban environments

Since the Industrial Revolution mankind has needed to deal with increasing air pollution problems as a result of manufacturing, mining, transportation, and power production. Air pollution concerns the interaction of gases and particles emitted into the atmosphere with the surrounding environment. This interaction can redirect pollutants toward sensitive areas, concentrate different species above acceptable levels, or even mitigate concentration levels by enhancing diffusion and dispersion. The present EU environmental legislation has been implemented to control these high pollutant concentrations and improve the air quality conditions in urban areas. The numerical simulation of dispersion has shown to be a useful tool for both the scientific description of pollution phenomena and for planning and decision making. The numerical simulation of pollution dispersion in urban environments by means of solution of the statistically steady Reynolds Averaged Navier Stokes (RANS) equations is known to be strongly dependent on turbulence models. If pollution dispersion is modelled, the turbulence models do not only have to be used for the Reynolds stresses, but also for the turbulent scalar fluxes. While the influence of several turbulence models for Reynolds stresses on pollution dispersion in urban environments has already been examined several times, the turbulent scalar fluxes are usually modelled by the simple gradient diffusion assumption. In the present work, the influence of more advanced models for the turbulent scalar fluxes on the dispersion of pollutants is examined. Two different wind tunnel experiments, a two-dimensional (2D) street canyon and a three-dimensional (3D) urban area model, were selected for the validation of the models' performance. In total, five anisotropic algebraic flux models and two second moment models were implemented in the commercial software FLUENT 6.3. All these models together with the simple gradient diffusion model (with different turbulent Schmidt numbers) are used and the results are compared with measurements using statistical performance measures to assess their predictive capability. All evaluated models showed good general agreement in comparison to the experiments. The anisotropic models provided better concentration predictions than the isotropic models in 2D simulations. However, these improvements were very small in 3D simulations and usually disappeared. In the end, modelling improvements based on the sensitivity analysis of model coefficients, numerical and experimental model limitations and other parameters and assumptions relevant for the simulation of passive scalar pollution dispersion are presented and discussed.
A note on estimation of the shape of density level sets of star-shaped distributions

Abstract Elliptically contoured distributions generalize the multivariate normal distributions in such a way that the density generators need not be exponential. However, as the name suggests, elliptically contoured distributions remain to be restricted in that the proportional density level sets ought to be ellipsoids. In star-shaped distributions, this restriction is relaxed and the density level sets are allowed to be boundaries of arbitrary proportional star-shaped sets. In this note, we propose a non parametric estimator of the shape of density level sets of star-shaped distributions, and prove its strong consistency with respect to the Hausdorff distance. We illustrate our estimator with simulated and real data.
Finite-time feedback control of an input-delay system with nonlinear saturating actuators

Finite-time feedback control of input-delay system with nonlinear saturating actuators is addressed in this paper. First, a state feedback controller is designed to make the closed-loop systems finite-time stable. Sufficient conditions which can guarantee finite-time stability of an input-delay system with saturating actuators are given in terms of the comparison function method. For the case where the states cannot be measured, an observer is presented to estimate the unavailable states and the observer-controller compensator strategy is proposed. Finally, an example is employed to illustrate the efficiency of the proposed method. It is not difficult to conclude from the analysis and the simulation results that by virtue of the proposed method the transient characteristics of the closed-loop system can be easily guaranteed to comply with the requirements of the practice.
Induced log-concavity of equivariant matroid invariants

Inspired by the notion of equivariant log-concavity, we introduce the concept of induced log-concavity for a sequence of representations of a finite group. For an equivariant matroid equipped with a symmetric group action or a finite general linear group action, we transform the problem of proving the induced log-concavity of matroid invariants to that of proving the Schur positivity of symmetric functions. We prove the induced log-concavity of the equivariant Kazhdan-Lusztig polynomials of $q$-niform matroids equipped with the action of a finite general linear group, as well as that of the equivariant Kazhdan-Lusztig polynomials of uniform matroids equipped with the action of a symmetric group. As a consequence of the former, we obtain the log-concavity of Kazhdan-Lusztig polynomials of $q$-niform matroids, thus providing further positive evidence for Elias, Proudfoot and Wakefield's log-concavity conjecture on the matroid Kazhdan-Lusztig polynomials. From the latter we obtain the log-concavity of Kazhdan-Lusztig polynomials of uniform matroids, which was recently proved by Xie and Zhang by using a computer algebra approach. We also establish the induced log-concavity of the equivariant characteristic polynomials and the equivariant inverse Kazhdan-Lusztig polynomials for $q$-niform matroids and uniform matroids.
On the Coalitional Rationality and the Inverse Problem for Shapley Value and the Semivalues

In cooperative game theory, a central problem is to allocate fairly the win of the grand coalition to the players who agreed to cooperate and form the grand coalition. Such allocations are obtained by means of values, having some fairness properties, expressed in most cases by groups of axioms. In an earlier work, we solved what we called the Inverse Problem for Semivalues, in which the main result was offering an explicit formula providing the set of all games with an a priori given Semivalue, associated with a given weight vector. However, in this set there is an infinite set of games for which the Semivalues are not coalitional rational, perhaps not efficient, so that these are not fair practical solutions of the above fundamental problem. Among the Semivalues, coalitional rational solutions for the Shapley Value and the Banzhaf Value have been given in two more recent works. In the present paper, based upon a general potential basis, relative to Semivalues, for a given game and a given Semivalue, we solve the connected problem: in the Inverse Set, find out a game with the same Semivalue, which is also coalitional rational. Several examples will illustrate the corresponding numerical technique.
Unraveling cosmological anisotropies within stochastic gravitational wave backgrounds

Identifying the anisotropies in a cosmologically sourced stochastic gravitational wave background (SGWB) would be of significance in shedding light on the nature of primordial inhomogeneities. For example, if SGWB carries isocurvature fluctuations, it would provide evidence for a multi-field inflationary origin of these inhomogeneities. However, this is challenging in practice due to finite detector sensitivity and also the presence of the astrophysical foregrounds that can compete with the cosmological signal. In this work, we explore the prospects for measuring cosmological SGWB anisotropies in the presence of an astrophysical counterpart and detector noise. To illustrate the main idea, we perform a Fisher analysis using a well-motivated cosmological SGWB template corresponding to a first order phase transition, and an astrophysical SGWB template corresponding to extra-galactic binary mergers, and compute the uncertainty with which various parameters characterizing the isotropic and anisotropic components can be extracted. We also discuss some subtleties and caveats involving shot noise in the astrophysical foreground. Overall, we show that upcoming experiments, e.g., LISA, Taiji, Einstein Telescope, Cosmic Explorer, and BBO, can all be effective in discovering plausible anisotropic cosmological SGWBs.
Chloride-induced corrosion of steel in concrete -- insights from bimodal neutron and X-ray microtomography combined with ex-situ microscopy

The steel-concrete interface (SCI) is known to play a major role in corrosion of steel in concrete, but a fundamental understanding is still lacking. One reason is that concrete's opacity complicates the study of internal processes. Here, we report on the application of bimodal X-ray and neutron microtomography as in-situ imaging techniques to elucidate the mechanism of steel corrosion in concrete. The study demonstrates that the segmentation of the specimen components of relevance - steel, cementitious matrix, aggregates, voids, corrosion products - obtained through bimodal X-ray and neutron imaging is more reliable than that based on the results of each of the two techniques separately. Further, we suggest the combination of tomographic in-situ imaging with ex-situ SEM analysis of targeted sections, selected on the basis of the segmented tomograms. These in-situ and ex-situ characterization techniques were applied to study localized corrosion in a very early stage, on reinforced concrete cores retrieved from a concrete bridge. A number of interesting observations were made. First, the acquired images revealed the formation of several corrosion sites close to each other. Second, the morphology of the corrosion pits was relatively shallow. Finally, only about half of the total 31 corrosion initiation spots were in close proximity to interfacial macroscopic air voids, and above 90 percent of the more than 160 interfacial macroscopic air voids were free from corrosion. The findings have implications for the mechanistic understanding of corrosion of steel in concrete and suggest that multimodal in-situ imaging is a valuable technique for further related studies.
Direct coupling of nonlinear integrated cavities for all-optical reservoir computing

We consider theoretically a network of directly coupled optical microcavities to implement a space-multiplexed optical neural network in an integrated nanophotonic circuit. Nonlinear photonic network integrations based on direct coupling ensures a highly dense integration, reducing the chip footprint by several orders of magnitude compared to other implementations. Different nonlinear effects inherent to such microcavities are studied when used for realizing an all-optical autonomous computing substrate, here based on the reservoir computing concept. We provide an in-depth analysis of the impact of basic microcavity parameters on computational metrics of the system, namely, the dimensionality and the consistency. Importantly, we find that differences between frequencies and bandwidths of supermodes formed by the direct coupling is the determining factor of the reservoir's dimensionality and its scalability. The network's dimensionality can be improved with frequency-shifting nonlinear effects such as the Kerr effect, while two-photon absorption has an opposite effect. Finally, we demonstrate in simulation that the proposed reservoir is capable of solving the Mackey-Glass prediction and the optical signal recovery tasks at GHz timescale.
Latent Gaussian dynamic factor modeling and forecasting for multivariate count time series

This work considers estimation and forecasting in a multivariate count time series model based on a copula-type transformation of a Gaussian dynamic factor model. The estimation is based on second-order properties of the count and underlying Gaussian models and applies to the case where the model dimension is larger than the sample length. In addition, novel cross-validation schemes are suggested for model selection. The forecasting is carried out through a particle-based sequential Monte Carlo, leveraging Kalman filtering techniques. A simulation study and an application are also considered.
Meta-Transformer: A Unified Framework for Multimodal Learning

Multimodal learning aims to build models that can process and relate information from multiple modalities. Despite years of development in this field, it still remains challenging to design a unified network for processing various modalities ($\textit{e.g.}$ natural language, 2D images, 3D point clouds, audio, video, time series, tabular data) due to the inherent gaps among them. In this work, we propose a framework, named Meta-Transformer, that leverages a $\textbf{frozen}$ encoder to perform multimodal perception without any paired multimodal training data. In Meta-Transformer, the raw input data from various modalities are mapped into a shared token space, allowing a subsequent encoder with frozen parameters to extract high-level semantic features of the input data. Composed of three main components: a unified data tokenizer, a modality-shared encoder, and task-specific heads for downstream tasks, Meta-Transformer is the first framework to perform unified learning across 12 modalities with unpaired data. Experiments on different benchmarks reveal that Meta-Transformer can handle a wide range of tasks including fundamental perception (text, image, point cloud, audio, video), practical application (X-Ray, infrared, hyperspectral, and IMU), and data mining (graph, tabular, and time-series). Meta-Transformer indicates a promising future for developing unified multimodal intelligence with transformers. Code will be available at https://github.com/invictus717/MetaTransformer
Neutrinos from GRB 221009A: producing ALPs and explaining LHAASO anomalous γ event

We propose a novel explanation for the 18 TeV gamma ray from GRB 221009A observed by LHAASO. High-energy neutrinos are converted into axion-like particles (ALPs) via their interaction with the cosmic neutrino background. Subsequently, ALPs are converted into high-energy photons in the magnetic field of our galaxy. We compute the fluxes of neutrinos, ALPs, and photons reaching Earth. IceCube's constraints on the neutrino flux from GRB 221009A translate into a severe upper bound on the photon flux. We find a range of parameters where all existing bounds are satisfied and the 18 TeV LHAASO photon can be explained. In the future, the specific correlation between the photon and neutrino flux reaching Earth from powerful neutrino sources with energies larger than 10 TeV such as GRBs or AGNs, can be used as a tool to differentiate our explanation from the alternatives suggested in the literature. We discuss how the interactions of our scenario can be embedded within electroweak gauge-invariant models, avoiding various cosmological and terrestrial bounds. We comment on the possibility of explaining the 251 TeV photon observed by the Carpet-2 detector, taking into account the bounds from the observation of high-energy neutrinos from TXS 0506+056.
ProvLight: Efficient Workflow Provenance Capture on the Edge-to-Cloud Continuum

Modern scientific workflows require hybrid infrastructures combining numerous decentralized resources on the IoT/Edge interconnected to Cloud/HPC systems (aka the Computing Continuum) to enable their optimized execution. Understanding and optimizing the performance of such complex Edge-to-Cloud workflows is challenging. Capturing the provenance of key performance indicators, with their related data and processes, may assist in understanding and optimizing workflow executions. However, the capture overhead can be prohibitive, particularly in resource-constrained devices, such as the ones on the IoT/Edge.To address this challenge, based on a performance analysis of existing systems, we propose ProvLight, a tool to enable efficient provenance capture on the IoT/Edge. We leverage simplified data models, data compression and grouping, and lightweight transmission protocols to reduce overheads. We further integrate ProvLight into the E2Clab framework to enable workflow provenance capture across the Edge-to-Cloud Continuum. This integration makes E2Clab a promising platform for the performance optimization of applications through reproducible experiments.We validate ProvLight at a large scale with synthetic workloads on 64 real-life IoT/Edge devices in the FIT IoT LAB testbed. Evaluations show that ProvLight outperforms state-of-the-art systems like ProvLake and DfAnalyzer in resource-constrained devices. ProvLight is 26—37x faster to capture and transmit provenance data; uses 5—7x less CPU; 2x less memory; transmits 2x less data; and consumes 2—2.5x less energy. ProvLight [1] and E2Clab [2] are available as open-source tools.
Integrating Pretrained ASR and LM to Perform Sequence Generation for Spoken Language Understanding

There has been an increased interest in the integration of pretrained speech recognition (ASR) and language models (LM) into the SLU framework. However, prior methods often struggle with a vocabulary mismatch between pretrained models, and LM cannot be directly utilized as they diverge from its NLU formulation. In this study, we propose a three-pass end-to-end (E2E) SLU system that effectively integrates ASR and LM subnetworks into the SLU formulation for sequence generation tasks. In the first pass, our architecture predicts ASR transcripts using the ASR subnetwork. This is followed by the LM subnetwork, which makes an initial SLU prediction. Finally, in the third pass, the deliberation subnetwork conditions on representations from the ASR and LM subnetworks to make the final prediction. Our proposed three-pass SLU system shows improved performance over cascaded and E2E SLU models on two benchmark SLU datasets, SLURP and SLUE, especially on acoustically challenging utterances.
FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated Learning

Federated Learning (FL) offers a collaborative training framework, allowing multiple clients to contribute to a shared model without compromising data privacy. Due to the heterogeneous nature of local datasets, updated client models may overfit and diverge from one another, commonly known as the problem of client drift. In this paper, we propose FedBug (Federated Learning with Bottom-Up Gradual Unfreezing), a novel FL framework designed to effectively mitigate client drift. FedBug adaptively leverages the client model parameters, distributed by the server at each global round, as the reference points for cross-client alignment. Specifically, on the client side, FedBug begins by freezing the entire model, then gradually unfreezes the layers, from the input layer to the output layer. This bottom-up approach allows models to train the newly thawed layers to project data into a latent space, wherein the separating hyperplanes remain consistent across all clients. We theoretically analyze FedBug in a novel over-parameterization FL setup, revealing its superior convergence rate compared to FedAvg. Through comprehensive experiments, spanning various datasets, training conditions, and network architectures, we validate the efficacy of FedBug. Our contributions encompass a novel FL framework, theoretical analysis, and empirical validation, demonstrating the wide potential and applicability of FedBug.
Comprehensive exploration of 
t
-channel simplified models of dark matter

We analyse six classes of t-channel dark matter simplified models in which the Standard Model field content is extended by a coloured mediator and a dark matter state. The two new states are enforced to be odd under a new parity, while all Standard Model fields are taken even so that dark matter stability is guaranteed. We study several possibilities for the spin of the new particles and the self-conjugate property of the dark matter, and we focus on model configurations in which the dark matter couples to the right-handed up quark for simplicity. We investigate how the parameter spaces of the six models can be constrained by current and future cosmological, astrophysical and collider searches, and we highlight the strong complementary between those probes. Our results demonstrate that scenarios featuring a complex (non self-conjugate) dark matter field are excluded by cosmology and astrophysics alone, the only possibility to avoid these bounds being to invoke very weak couplings and mechanisms such as conversion-driven freeze-out. For models with self-conjugate dark matter, mediator and dark matter masses are pushed deep into the TeV regime, with the lower limits on the mediator mass reaching 3 to 4 TeV and those on the dark matter mass 1 to 2 TeV. In large parts of the parameter space these strong bounds are driven by same-sign mediator pair production, a channel so far not considered in the experimental analyses embedding t-channel dark matter model interpretations.