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2025 EN
Benjamin Biaggi · Jan Draisma · Sarah Eggleston
We study the subrank of real order-three tensors and give an upper bound tothe subrank of a real tensor given its complex subrank. Using similar argumentsto those used by Bernardi-Blekherman-Ottaviani, we show that all subranksbetween the minimal typical subrank and the maximal typical subrank, whichequals the generic subrank, are also typical. We then study small tensorformats with more than one typical subrank. In particular, we construct a $3\times 3 \times 5$-tensor with subrank $2$ and show that the subrank of the $4\times 4 \times 4$-quaternion multiplication tensor is $2$. Finally, weconsider the tensor associated to componentwise complex multiplication in$\mathbb{C}^n$ and show that this tensor has real subrank $n$ - informally, nomore than $n$ real scalar multiplications can be carried out using a devicethat does $n$ complex scalar multiplications. We also prove a version of thisresult for other real division algebras.
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2025 EN
Matheus Rakes · Maíra Chagas Morais · Maria Eduarda Sperotto
+8 more
The present study investigates the compatibility of mycoinsecticides based onisolates IBCB66 and Simbi BB15 of Beauveria bassiana and Esalq-1296 ofCordyceps javanica, which are registered for the management of Dalbulus maidisin Brazil, with synthetic fungicides. Irrespective of the fungicide, a totalinhibition in the number of colony-forming units (CFUs), vegetative growth,conidiogenesis, and conidial viability of the three tested isolates wasobserved, with their incompatibility being indicated in the in vitro bioassays.However, the use of formulated mycoinsecticides mitigated the impact of thesexenobiotics on the number of CFUs, with the commercial mycoinsecticideFlyControl (B. bassiana isolate Simbi BB15) being the least sensitive to thefungicides propiconazole + difenoconazole, bixafem + prothioconazole +trifloxystrobin and trifloxystrobin + tebuconazole. Nevertheless, an increasein exposure time (from 1.5 to 3 hours) generally led to an increase in thetoxicity of fungicides towards entomopathogens. Physical-chemical compatibilityassessments indicated that physical incompatibilities were observed, dependingon the mycoinsecticide formulation. In addition, in vivo bioassays employing D.maidis adults demonstrated that, despite a synergistic effect on mortality incertain binary mixtures, no cadavers exposed to such mixtures exhibited fungalextrusion. Furthermore, analyses using UHPLC/MS/MS revealed alterations in thedegradation kinetics (k) of the active ingredient (a.i.) pyraclostrobin, withchanges greater than tenfold being observed in the different formulations ofthe fungicides that were tested. Consequently, given the diminished degradationkinetics of the active ingredients in maize plants, the implementation ofmycoinsecticides should precede, in isolation, the application of syntheticfungicides within the framework of phytosanitary management of maize crops.
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2025 EN
Gustavo Zottis Girotto · Maximilian Jaugstetter · Dongwoo Kim
+5 more
The conversion of CO2 into high-value chemicals through a photoreductionreaction in water is a promising route to reduce the dependence on fossilfuels. Ag nanoparticles can drive this reaction via localized surface plasmonresonance, but their low selectivity limits usage in industry. Enhancingselectivity toward hydrocarbons or alcohols requires addition of a co-catalystsuch as Cu. However, the stabilized surface state created by Ag-Cu interactionsis still poorly understood. In this work, soft x-ray Ambient-Pressure X-rayPhotoelectron Spectroscopy (AP-XPS) and Grazing-Incidence X-ray Scattering(AP-GIXS) were used to investigate the evolution of Ag-Cu nanoparticles underCO2RR-like conditions. AP-XPS revealed Ag and Cu surface and sub-surfacediffusion, while AP-GIXS tracked change of shape and size of nanoparticlesinduced by diffusion mechanics. Under 532 nm laser irradiation, furtheroxidation of Cu and Ag sub-surface diffusion were observed, providinginvaluable insights into the dynamic restructuring of the catalyst underreaction conditions.
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2025 EN
Carlo Alberto De Bernardi · Jacopo Somaglia
We study the relations between different notions of almost locally uniformlyrotund points that appear in literature. We show that every non-reflexiveBanach space admits an equivalent norm having a point in the corresponding unitsphere which is not almost locally uniformly rotund, and which is stronglyexposed by all its supporting functionals. This result is in contrast with acharacterization due to P. Bandyopadhyay, D. Huang, and B.-L. Lin from 2004. Wealso show that such a characterization remains true in reflexive Banach spaces.
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2025 EN
Sara Bernardi · Paolo Begnamino · Marco Pizzi
+1 more
In recent years, research and development in nanoscale science and technologyhave grown significantly, with electrical transport playing a key role. Anatural challenge for its description is to shed light on anomalous behavioursobserved in a variety of low-dimensional systems. We use a synergisticcombination of experimental and mathematical modelling to explore the transportproperties of the electrical discharge observed within a micro-gap based sensorimmersed in fluids with different insulating properties. Data from laboratoryexperiments are collected and used to inform and calibrate four mathematicalmodels that comprise partial differential equations describing different kindsof transport, including anomalous diffusion: the Gaussian Model with TimeDependent Diffusion Coefficient, the Porous Medium Equation, theKardar-Parisi-Zhang Equation and the Telegrapher Equation. Performance analysisof the models through data fitting reveals that the Gaussian Model with aTime-Dependent Diffusion Coefficient most effectively describes the observedphenomena. This model proves particularly valuable in characterizing thetransport properties of electrical discharges when the micro-electrodes areimmersed in a wide range of insulating as well as conductive fluids. Indeed, itcan suitably reproduce a range of behaviours spanning from clogging to bursts,allowing accurate and quite general fluid classification. Finally, we apply thedata-driven mathematical modeling approach to ethanol-water mixtures. Theresults show the model's potential for accurate prediction, making it apromising method for analyzing and classifying fluids with unknown insulatingproperties.
Resource
2025 EN
Aryan Shrivastava · Paula Akemi Aoyagui
Language models (LMs) are increasingly being integrated into a wide range ofapplications, yet the modern evaluation paradigm does not sufficiently reflecthow they are actually being used. Current evaluations rely on benchmarks thatoften lack direct applicability to the real-world contexts in which LMs arebeing deployed. To address this gap, we propose Dimensional and ContextualEvaluation (DICE), an approach that evaluates LMs on granular,context-dependent dimensions. In this position paper, we begin by examining theinsufficiency of existing LM benchmarks, highlighting their limitedapplicability to real-world use cases. Next, we propose a set of granularevaluation parameters that capture dimensions of LM behavior that are moremeaningful to stakeholders across a variety of application domains.Specifically, we introduce the concept of context-agnostic parameters - such asrobustness, coherence, and epistemic honesty - and context-specific parametersthat must be tailored to the specific contextual constraints and demands ofstakeholders choosing to deploy LMs into a particular setting. We then discusspotential approaches to operationalize this evaluation framework, finishingwith the opportunities and challenges DICE presents to the LM evaluationlandscape. Ultimately, this work serves as a practical and approachablestarting point for context-specific and stakeholder-relevant evaluation of LMs.
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2025 EN
Joris Sturm · Ivan Maliyov · Dominik Christiansen
+3 more
The combination of Maxwell and X-ray Bloch equations forms an appropriateframework to describe ultrafast time-resolved X-ray experiments on attosecondtime scale in crystalline solids. However, broadband experiments such as X-rayabsorption near edge spectroscopy or resonant inelastic X-ray scatteringrequire a detailed knowledge of the electronic structure and transition matrixelements. Here, we show how to fill this gap by combining the Maxwell-X-rayBloch formalism with first-principles calculations treating explicitly the corestates. The resulting X-ray absorption spectrum recovers key spectralsignatures which were missing in our previous work relying on a semi-empiricaltight-binding approach.
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2025 EN
Luca Baracco · Olga Bernardi · Corentin Fierobe
We focus on the outer length billiard dynamics, acting on the exterior of astrictly-convex planar domain. We first show that ellipses are totallyintegrable. We then provide an explicit representation of first order terms forthe formal Taylor expansion of the corresponding Mather's $\beta$-function.Finally, we provide explicit Lazutkin coordinates up to order 4.
Resource
2025 EN
S. A. K. Leeney · H. T. J. Bevins · E. de Lera Acedo
+35 more
Radiometers are crucial instruments in radio astronomy, forming the primarycomponent of nearly all radio telescopes. They measure the intensity ofelectromagnetic radiation, converting this radiation into electrical signals. Aradiometer's primary components are an antenna and a Low Noise Amplifier (LNA),which is the core of the ``receiver'' chain. Instrumental effects introduced bythe receiver are typically corrected or removed during calibration. However,impedance mismatches between the antenna and receiver can introduce unwantedsignal reflections and distortions. Traditional calibration methods, such asDicke switching, alternate the receiver input between the antenna and awell-characterised reference source to mitigate errors by comparison. Recentadvances in Machine Learning (ML) offer promising alternatives. Neuralnetworks, which are trained using known signal sources, provide a powerfulmeans to model and calibrate complex systems where traditional analyticalapproaches struggle. These methods are especially relevant for detecting thefaint sky-averaged 21-cm signal from atomic hydrogen at high redshifts. This isone of the main challenges in observational Cosmology today. Here, for thefirst time, we introduce and test a machine learning-based calibrationframework capable of achieving the precision required for radiometricexperiments aiming to detect the 21-cm line.
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2025 EN
Francesco Shankar · Mariangela Bernardi · Daniel Roberts
+17 more
The correlations between Supermassive Black Holes (SMBHs) and their hostgalaxies still defy our understanding from both the observational andtheoretical perspectives. Here we perform pairwise residual analysis on thelatest sample of local inactive galaxies with a uniform calibration of theirphotometric properties and with dynamically measured masses of their centralSMBHs. The residuals reveal that stellar velocity dispersion $\sigma$ and,possibly host dark matter halo mass $M_{\rm halo}$, appear as the galacticproperties most correlated with SMBH mass, with a secondary (weaker)correlation with spheroidal (bulge) mass $M_{\rm sph}$, as also corroborated byadditional Machine Learning tests. These findings may favour energetic/kineticfeedback from Active Galactic Nuclei (AGN) as the main driver in shaping SMBHscaling relations. Two state-of-the-art hydrodynamic simulations, inclusive ofkinetic AGN feedback, are able to broadly capture the mean trends observed inthe residuals, although they tend to either favour $M_{\rm sph}$ as the mostfundamental property, or generate too flat residuals. Increasing AGN feedbackkinetic output does not improve the comparison with the data. In the Appendixwe also show that the galaxies with dynamically measured SMBHs are biased highin $\sigma$ at fixed luminosity with respect to the full sample of localgalaxies, proving that this bias is not a byproduct of stellar massdiscrepancies. Overall, our results suggest that probing the SMBH-galaxyscaling relations in terms of total stellar mass alone may induce biases, andthat either current data sets are incomplete, and/or that more insightfulmodelling is required to fully reproduce observations.