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Customer demand, regulatory pressure, and engineering efficiency are the driving forces behind the industry-wide trend of moving from siloed engines and services that are optimized in isolation to highly integrated solutions. This is confirmed by the wide adoption of open formats, shared component libraries, and the meteoric success of integrated data lake experiences such as Microsoft Fabric. In this paper, we study the implications of this trend to Query Optimizer (QO) and discuss our experience of building Calcite and extending Cascades into QO components of Microsoft SQL Server, Fabric Data Warehouse (DW), and SCOPE. We weigh the pros and cons of a drastic change in direction: moving from bespoke QOs or library-sharing (\`a la Calcite) to rewriting the QO stack and fully embracing Query Optimizer as a Service (QOaaS). We report on some early successes and stumbles as we explore these ideas with prototypes compatible with Fabric DW and Spark. The benefits include centralized workload-level optimizations, multi-engine federation, and accelerated feature creation, but the challenges are equally daunting. We plan to engage CIDR audience in a debate on this exciting topic.

We explicitly construct nondegenerate braided $\mathbb{Z}_2$-crossed tensor categories of the form $\operatorname{Vect}_\Gamma\oplus\operatorname{Vect}_{\Gamma/2\Gamma}$. They are $\mathbb{Z}_2$-crossed extensions, in the sense of arXiv:0909.3140, of the braided tensor category $\operatorname{Vect}_\Gamma$ with $\mathbb{Z}_2$-action given by $-\mathrm{id}$ on the finite, abelian group $\Gamma$. Thus, we obtain generalisations of the Tambara-Yamagami categories, where now the abelian group $\Gamma$ may have even order and the nontrivial sector $\operatorname{Vect}_{\Gamma/2\Gamma}$ more than one simple object. The idea for this construction comes from a physically motivated approach in arXiv:2409.16357 to construct $\mathbb{Z}_2$-crossed extensions of $\operatorname{Vect}_\Gamma$ for any $\Gamma$ from an infinite Tambara-Yamagami category $\operatorname{Vect}_{\mathbb{R}^d}\oplus\operatorname{Vect}$, which itself is not fully rigorously defined, and then using condensation from $\operatorname{Vect}_{\mathbb{R}^d}$ to $\operatorname{Vect}_\Gamma$, which we prove commutes with crossed extensions. The $\mathbb{Z}_2$-equivariantisation of $\operatorname{Vect}_\Gamma\oplus\operatorname{Vect}_{\Gamma/2\Gamma}$ yields new modular tensor categories, which correspond to the orbifold of an arbitrary lattice vertex operator algebra under a lift of $-\mathrm{id}$, as discussed in arXiv:2409.16357., Comment: 47 pages, LaTeX; the construction in this paper was originally part of a longer paper arXiv:2409.16357, from which it is split off to improve readability

Let G be a locally compact group and let $\phi$ be a positive definite function on G with $\phi(e)=1$. This function defines a multiplication operator $M_\phi$ on the Fourier algebra $A(G)$ of $G$. The aim of this paper is to classify the ergodic properties of the operators $M_\phi$, focusing on several key factors, including the subgroup $H_\phi=\{x\in G\colon \phi(x)=1\}$, the spectrum of $M_\phi$, or how ``spread-out'' a power of $M_\phi$ can be. We show that the multiplication operator $M_\phi$ is uniformly mean ergodic if and only if $H_\phi$ is open and 1 is not an accumulation point of the spectrum of $M_\phi$. Equivalently, this happens when some power of $\phi$ is not far, in the multiplier norm, from a function supported on finitely many cosets of $H_\phi$. Additionally, we show that the powers of $M_\phi$ converge in norm if, and only if, the operator is uniformly mean ergodic and $H_\phi =\{x\in G\colon |\phi(x)|=1\}$., Comment: 21 pages

We study the twisted Kitaev quantum double model within the framework of Local Topological Order (LTO). We extend its definition to arbitrary 2D lattices, enabling an explicit characterization of the ground state space through invariant spaces of monomial representations. We reformulate the LTO conditions for including general lattices and prove that the twisted model satisfies all four LTO axioms on any 2D lattice. As a corollary, we show that its ground state space is a quantum error-correcting code., Comment: 29 pages

The electrorheological (ER) characterization of low-viscosity fluids is paramount for producing micro- and nanoscale products through electrohydrodynamic (EHD) techniques, such as EHD-jet printing, electrospray, and electrospinning. Key properties such as viscosity, surface tension, dielectric properties, electrical conductivity, and relaxation time significantly influence both the quality and properties of the final products and the efficiency of the industrial process. ER characterization is essential for studying the macroscopic effects of the interaction between these physicochemical properties under controlled flow kinematics. Researchers may face several technical challenges in performing rigorous ER characterization of low-viscosity fluids. This characterization is crucial for formulating inks compatible with the EHD process and for understanding fluid dynamics in EHD processes to ensure stable printing conditions and achieve high-resolution, accurate prints. This work highlights the inherent limitations of current ER cells and proposes methodologies to mitigate their impact on measurement accuracy. Furthermore, we explore the potential of microfluidic technologies to offer innovative solutions for the ER characterization of low-viscosity fluids.

any practical multiobjective optimization (MOO) problems include discrete decision variables and/or nonlinear model equations and exhibit disconnected or smooth but nonconvex Pareto surfaces. Scalarization methods, such as the weighted-sum and sandwich (SD) algorithms, are common approaches to solving MOO problems but may fail on nonconvex or discontinuous Pareto fronts. In the current work, motivated by the well-known normal boundary intersection (NBI) method and the SD algorithm, we present SDNBI, a new algorithm for bi-objective optimization (BOO) designed to address the theoretical and numerical challenges associated with the reliable solution of general nonconvex and discrete BOO problems. The main improvements in the algorithm are the effective exploration of the nonconvex regions of the Pareto front and, uniquely, the early identification of regions where no additional Pareto solutions exist. The performance of the SDNBI algorithm is assessed based on the accuracy of the approximation of the Pareto front constructed over the disconnected nonconvex objective domains. The new algorithm is compared with two MOO approaches, the modified NBI method and the SD algorithm, using published benchmark problems. The results indicate that the SDNBI algorithm outperforms the modified NBI and SD algorithms in solving convex, nonconvex-continuous, and combinatorial problems, both in terms of computational cost and of the overall quality of the Pareto-optimal set, suggesting that the SDNBI algorithm is a promising alternative for solving BOO problems.

This article discusses a p-adic version of the infinite potential well in quantum mechanics (QM). This model describes the confinement of a particle in a p-adic ball. We rigorously solve the Cauchy problem for the Schr\"odinger equation and determine the stationary solutions. The p-adic balls are fractal objects. By dividing a p-adic ball into a finite number of sub-balls and using the wavefunctions of the infinite potential well, we construct a continuous-time quantum walk (CTQW) on a fully connected graph, where each vertex corresponds to a sub-ball in the partition of the original ball. In this way, we establish a connection between p-adic QM and quantum computing., Comment: The main theorems were strengthened. A new section, Discussion, was added at the end of the paper. Several typos were corrected. arXiv admin note: text overlap with arXiv:2308.01283

We introduce the condensed fiber product of two $G$-crossed braided fusion categories, generalizing existing constructions in the literature. We show that this product is closely related to the cohomological construction known as zesting. Furthermore, the condensed fiber product defines a monoidal structure on the 2-category of $G$-crossed and braided extensions of braided categories with a fixed transparent symmetric pointed subcategory.

Unitary gates with high entangling capabilities are relevant for several quantum-enhanced technologies. For symmetric multiqubit systems, such as spin states or bosonic systems, the particle exchange symmetry restricts these gates and also the set of not-entangled states. In this work, we analyze the entangling power of unitary gates in these systems by reformulating it as an inner product between vectors with components given by SU$(2)$ invariants. For small number of qubits, this approach allows us to study analytically the entangling power including the detection of the unitary gate that maximizes it. We observe that extremal unitary gates exhibit entanglement distributions with high rotational symmetry, same that are linked to a convex combination of Husimi functions of certain states. Furthermore, we explore the connection between entangling power and the Schmidt numbers admissible in some quantum state subspaces. Thus, the geometrical approach presented here suggests new paths for studying entangling power linked to other concepts in quantum information theory., Comment: 16 pages, 4 figures

This article studies the dynamics of the mean-field approximation of continuous random networks. These networks are stochastic integrodifferential equations driven by Gaussian noise. The kernels in the integral operators are realizations of generalized Gaussian random variables. The equation controls the time evolution of a macroscopic state interpreted as neural activity, which depends on position and time. The position is an element of a measurable space. Such a network corresponds to a statistical field theory (STF) given by a momenta-generating functional. Discrete versions of the mentioned networks appeared in spin glasses and as models of artificial neural networks (NNs). Each of these discrete networks corresponds to a lattice SFT, where the action contains a finite number of neurons and two scalar fields for each neuron. Recently, it has been proposed that these networks can be used as models for deep learning. In this application, the number of neurons is astronomical; consequently, continuous models are required. In this article, we develop mathematically rigorous, continuous versions of the mean-field theory approximation and the double-copy system that allow us to derive a condition for the criticality of continuous stochastic networks via the largest Lyapunov exponent. We use two basic architectures; in the first one, the space of neurons is the real line, and then the neurons are organized in one layer; in the second one, the space of neurons is the p-adic line, and then the neurons are organized in an infinite, fractal, tree-like structure. Networks with both types of architectures exhibit a type edge of the chaos organization.

In this article, we develop tools for computing $G$-crossed extensions of braided tensor categories. Their equivariantisations appear as categories of modules of fixed-point subalgebras (or orbifolds) of vertex operator algebras and are often difficult to determine. As the first tool, we show how the seminal work of Etingof, Nikshych and Ostrik on the uniqueness of $G$-crossed extensions can be used to determine the category of modules of orbifold vertex operator algebras. As an application, we determine the modular tensor category of the orbifold of a lattice vertex operator algebra under a lift of $-\mathrm{id}$ for a lattice with odd-order discriminant form. In that case, the de-equivariantisation is of Tambara-Yamagami type. As the second tool, we describe how $G$-crossed extensions and condensations by commutative algebras commute in a suitable sense. This leads to an effective approach to compute new $G$-crossed extensions. As one application, we produce the coherence data that are then used in arXiv:2411.12251 to define a generalisation of the Tambara-Yamagami categories with more than one simple object in the twisted sector. This also yields the modular tensor category of the orbifold of an arbitrary lattice vertex operator algebra under a lift of $-\mathrm{id}$. Finally, we sketch how to categorically approach the general problem of lattice orbifolds under lifts of arbitrary lattice involutions., Comment: 54 pages, LaTeX; the construction of the generalised Tambara-Yamagami categories was split off and can now be found in arXiv:2411.12251 to improve readability

Developing dedicated neuromorphic computing platforms optimized for embedded or edge-computing applications requires time-consuming design, fabrication, and deployment of full-custom neuromorphic processors.bTo ensure that initial prototyping efforts, exploring the properties of different network architectures and parameter settings, lead to realistic results it is important to use simulation frameworks that match as best as possible the properties of the final hardware. This is particularly challenging for neuromorphic hardware platforms made using mixed-signal analog/digital circuits, due to the variability and noise sensitivity of their components. In this paper, we address this challenge by developing a software spiking neural network simulator explicitly designed to account for the properties of mixed-signal neuromorphic circuits, including device mismatch variability. The simulator, called ARCANA (A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures), is designed to reproduce the dynamics of mixed-signal synapse and neuron electronic circuits with autogradient differentiation for parameter optimization and GPU acceleration. We demonstrate the effectiveness of this approach by matching software simulation results with measurements made from an existing neuromorphic processor. We show how the results obtained provide a reliable estimate of the behavior of the spiking neural network trained in software, once deployed in hardware. This framework enables the development and innovation of new learning rules and processing architectures in neuromorphic embedded systems., Comment: 17 pages

In the digital era, the internet and social media have transformed communication but have also facilitated the spread of hate speech and disinformation, leading to radicalization, polarization, and toxicity. This is especially concerning for media outlets due to their significant role in shaping public discourse. This study examines the topics, sentiments, and hate prevalence in 337,807 response messages (website comments and tweets) to news from five Spanish media outlets (La Vanguardia, ABC, El Pa\'is, El Mundo, and 20 Minutos) in January 2021. These public reactions were originally labeled as distinct types of hate by experts following an original procedure, and they are now classified into three sentiment values (negative, neutral, or positive) and main topics. The BERTopic unsupervised framework was used to extract 81 topics, manually named with the help of Large Language Models (LLMs) and grouped into nine primary categories. Results show social issues (22.22%), expressions and slang (20.35%), and political issues (11.80%) as the most discussed. Content is mainly negative (62.7%) and neutral (28.57%), with low positivity (8.73%). Toxic narratives relate to conversation expressions, gender, feminism, and COVID-19. Despite low levels of hate speech (3.98%), the study confirms high toxicity in online responses to social and political topics., Comment: 24 pages

In this paper, the potential use of multi-block chemometric methods to provide improved unsupervised classification of compositionally complex materials through the integration of multi-modal spectrometric data sets (one XRF, two NIR, and two FT-Raman) was tested. We concluded that multi-block HPLS models are effective at combining multi-modal spectrometric data to provide a more comprehensive classification of compositionally complex samples, and VIP can reduce HPLS model complexity, while increasing its data interpretability., Comment: Manuscript (30 pages) and supporting information (30 pages). Submitted to journal

We introduce surfaces at infinity, a class of rational surfaces linked to curves with only one place at infinity. The cone of curves of these surfaces is finite polyhedral and minimally generated. We also introduce the $\delta$-semigroup of a surface at infinity and consider the set $\mathcal{S}$ of surfaces at infinity having the same $\delta$-semigroup. We study how the generators of the cone of curves of surfaces in $\mathcal{S}$ behave., Comment: Comments are welcome

Jamming transitions and the rheology of granular avalanches in fluids are investigated using experiments and numerical simulations. Simulations use the lattice-Boltzmann method coupled with the discrete element method, providing detailed stress and deformation data. Both simulations and experiments present a perfect match with each other in carefully conducted deposition experiments, validating the simulation method. We analyze transient rheological laws and jamming transitions using our recently introduced length-scale ratio $G$. $G$ serves as a unified metric for the pressure and shear rate capturing the dynamics of sheared fluid-granular systems. Two key transition points, $G_{Y}$ and $G_{0}$, categorize the material's state into solid-like, creeping, and fluid-like states. Yielding at $G_{Y}$ marks the transition from solid-like to creeping, while $G_{0}$ signifies the shift to the fluid-like state. The $\mu-G$ relationship converges towards the equilibrium $\mu_{eq}(G)$ after $G>G_0$ showing the critical point where the established rheological laws for steady states apply during transient conditions.

An equivalent version of the Borodin-Kostochka Conjecture, due to Cranston and Rabern, says that any graph with $\chi = \Delta = 9$ contains $K_3 \lor E_6$ as a subgraph. Here we prove several results in support of this conjecture, where vertex-criticality and forbidden substructure conditions get us either close or all the way to containing $K_3 \lor E_6$.

This work provides a complete rheological characterization of molybdenum disulfide (MoS2) inks in the presence of electric fields. Several concentrations of MoS2 are studied and dispersed in a viscoelastic fluid. The lubrication effects are present in the ink when the MoS2 concentration is higher than 0.10% w/w. The dielectric properties show the impossibility of a positive electrorheological effect for all MoS2-inks studied. The formation of vortices and electromigration of MoS2 particles occur under the influence of an external electric field. These two phenomena affect the rheological behavior of MoS2-inks under shear flow condition. Relatively to the extensional rheology experiments, the particle migration and the vortex formation promote anisotropy on the rheological properties of the inks which affects the relaxation time, the formation of beads-on-a-string and the uniaxial elongational flow condition is no longer valid. When the electric field strength is 1.5 kV/mm, the formation of Taylor's cone is observed and independent of MoS2 concentration.

The weighted bounded negativity conjecture considers a smooth projective surface $X$ and looks for a common lower bound on the quotients $C^2/(D\cdot C)^2$, where $C$ runs over the integral curves on $X$ and $D$ over the big and nef divisors on $X$ such that $D \cdot C >0$. We focus our study on rational surfaces $Z$. Setting $\pi: Z \rightarrow Z_0$ a composition of blowups giving rise to $Z$, where $Z_0$ is the projective plane or a Hirzebruch surface, we give a common lower bound on $C^2/(H^* \cdot C)^2$ whenever $H^*$ is the pull-back of a nef divisor $H$ on $Z_0$. In addition, we prove that, only in the case when a nef divisor $D$ on $Z$ approaches the boundary of the nef cone, the quotients $C^2/(D\cdot C)^2$ could tend to minus infinity., Comment: Comments are welcome

We present a catalog of stellar parameters (effective temperature $T_{\rm eff}$, surface gravity $\log g$, age, and metallicity [Fe/H]) and elemental-abundance ratios ([C/Fe], [Mg/Fe], and [$\alpha$/Fe]) for some five million stars (4.5 million dwarfs and 0.5 million giants stars) in the Milky Way, based on stellar colors from the Javalambre Photometric Local Universe Survey (J-PLUS) DR3 and \textit{Gaia} EDR3. These estimates are obtained through the construction of a large spectroscopic training set with parameters and abundances adjusted to uniform scales, and trained with a Kernel Principal Component Analysis. Owing to the seven narrow/medium-band filters employed by J-PLUS, we obtain precisions in the abundance estimates that are as good or better than derived from medium-resolution spectroscopy for stars covering a wide range of the parameter space: 0.10-0.20 dex for [Fe/H] and [C/Fe], and 0.05 dex for [Mg/Fe] and [$\alpha$/Fe]. Moreover, systematic errors due to the influence of molecular carbon bands on previous photometric-metallicity estimates (which only included two narrow/medium-band blue filters) have now been removed, resulting in photometric-metallicity estimates down to [Fe/H] $\sim -4.0$, with typical uncertainties of 0.25 dex and 0.40 dex for dwarfs and giants, respectively. This large photometric sample should prove useful for the exploration of the assembly and chemical-evolution history of our Galaxy., Comment: 31 pages, 20 figures, 3 tables, accepeted by ApJ

Computer vision technologies markedly enhance the automation capabilities of robotic-assisted minimally invasive surgery (RAMIS) through advanced tool tracking, detection, and localization. However, the limited availability of comprehensive surgical datasets for training represents a significant challenge in this field. This research introduces a novel method that employs 3D Gaussian Splatting to generate synthetic surgical datasets. We propose a method for extracting and combining 3D Gaussian representations of surgical instruments and background operating environments, transforming and combining them to generate high-fidelity synthetic surgical scenarios. We developed a data recording system capable of acquiring images alongside tool and camera poses in a surgical scene. Using this pose data, we synthetically replicate the scene, thereby enabling direct comparisons of the synthetic image quality (29.592 PSNR). As a further validation, we compared two YOLOv5 models trained on the synthetic and real data, respectively, and assessed their performance in an unseen real-world test dataset. Comparing the performances, we observe an improvement in neural network performance, with the synthetic-trained model outperforming the real-world trained model by 12%, testing both on real-world data., Comment: This paper has already been accepted by INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION (MICCAI 2024)

We present a state sum construction that assigns a scalar to a skeleton in a closed oriented three-dimensional manifold. The input datum is the pivotal bicategory $\mathbf{Mod}^{\mathrm{sph}}(\mathcal{A})$ of spherical module categories over a spherical fusion category $\mathcal{A}$. The interplay of algebraic structures in this pivotal bicategory with moves of skeleta ensures that our state sum is independent of the skeleton on the manifold. We show that the bicategorical invariant recovers the value of the standard Turaev-Viro invariant associated to $\mathcal{A}$, thereby proving the independence of the Turaev-Viro invariant under pivotal Morita equivalence without recurring to the Reshetikhin-Turaev construction. A key ingredient for the construction is the evaluation of graphs on the sphere with labels in $\mathbf{Mod}^{\mathrm{sph}}(\mathcal{A})$ that we develop in this article. A central tool are Nakayama-twisted traces on pivotal bimodule categories which we study beyond semisimplicity., Comment: 48 pages

Garth Dales asked whether a Banach algebra $\mA$ with an Arens regular closed ideal $\mJ$ and an Arens regular quotient $\mA/\mJ$ is necessarily Arens regular. We prove in this note that, for a class of Banach algebras including the standard algebras in harmonic analysis, Garth's conditions force the algebra to be even reflexive. We also give examples of Banach algebras with Garth's conditions, that are not Arens regular.

Accessing the terahertz (THz) spectral domain through surface-enhanced Raman spectroscopy (SERS) is challenging and opens up the study of low-frequency molecular and electronic excitations. Compared to direct THz probing of heterogenous ensembles, the extreme plasmonic confinement of visible light to deep sub-wavelength scales allows the study of hundreds or even single molecules. We show that self-assembled molecular monolayers of a set of simple aromatic thiols confined inside single-particle plasmonic nanocavities can be distinguished by their low-wavenumber spectral peaks below 200 cm-1, after removal of a bosonic inelastic contribution and an exponential background from the spectrum Developing environment-dependent density-functional-theory simulations of the metal-molecule configuration enables the assignment and classification of their THz vibrations as well as the identification of intermolecular coupling effects and of the influence of the gold surface configuration Furthermore, we show dramatically narrower THz SERS spectra from individual molecules at picocavities, which indicates the possibility to study intrinsic vibrational properties beyond inhomogeneous broadening further supporting the key role of local environment.

In this paper, combining the Rashevsky-Chow-Sussmann (orbit) theorem with the Ambrose-Singer theorem, we introduce the notion of controllable principal connections on principal $G$-bundles. Using this concept, under a mild assumption of compactness, we estimate the Gromov-Hausdorff distance between principal $G$-bundles and certain reductive homogeneous $G$-spaces. In addition, we prove that every reduction of the structure group $G$ to a closed connected subgroup gives rise to a sequence of Riemannian metrics on the total space for which the underlying sequence of metric spaces converges, in the Gromov-Housdorff topology, to a normal reductive homogeneous $G$-space. This last finding allows one to detect the presence of certain reductive homogeneous $G$-spaces in the Gromov-Housdorff closure of the moduli space of Riemannian metrics of the total space of the bundle through topological invariants provided by obstruction theory., Comment: 13 pages, Comments are welcome

We present the two exact solutions of the Einstein-Nonlinear electrodynamics equations that generalize the Kerr-Newman solution. We determined the generalized electromagnetic potentials using the alignment between the tetrad vectors of the metric and the eigenvectors of the electromagnetic field tensor. It turns out that there are only two possible nonlinear electromagnetic generalizations of the Kerr-Newman geometry, corresponding to different electromagnetic potentials. The new solutions possess horizons and satisfy physical energy conditions such that they can represent black holes with nonlinear electromagnetic charges, characterized by the parameters of mass, angular momentum, charge, and one nonlinear parameter; the nonlinear parameter resembles the effect of a cosmological constant, negative or positive, such that the solutions are asymptotically AdS or dS. The canonical form of the electromagnetic nonlinear energy-momentum tensor is analyzed in relation with the energy conditions; it is shown that the conformal symmetry is broken by the electromagnetic nonlinear matter; the corresponding nonlinear electromagnetic Lagrangian as a function of the coordinates is presented as well., Comment: 10 pages 2 Figures

We present the overview and first results from the North-PHASE Legacy Survey, which follows six young clusters for five years, using the 2 deg$^2$ FoV of the JAST80 telescope from the Javalambre Observatory (Spain). North-PHASE investigates stellar variability on timescales from days to years for thousands of young stars distributed over entire clusters. This allows us to find new YSO, characterise accretion and study inner disk evolution within the cluster context. Each region (Tr37, CepOB3, IC5070, IC348, NGC2264, and NGC1333) is observed in six filters (SDSS griz, u band, and J0660, which covers H$\alpha$), detecting cluster members as well as field variable stars. Tr37 is used to prove feasibility and optimise the variability analysis techniques. In Tr37, variability reveals 50 new YSO, most of them proper motion outliers. North-PHASE independently confirms the youth of astrometric members, efficiently distinguishes accreting and non-accreting stars, reveals the extent of the cluster populations along Tr37/IC1396 bright rims, and detects variability resulting from rotation, dips, and irregular bursts. The proper motion outliers unveil a more complex star formation history than inferred from Gaia alone, and variability highlights previously hidden proper motion deviations in the surrounding clouds. We also find that non-YSO variables identified by North-PHASE cover a different variability parameter space and include long-period variables, eclipsing binaries, RR Lyr, and $\delta$ Scuti stars. These early results also emphasize the power of variability to complete the picture of star formation where it is missed by astrometry., Comment: Accepted by MNRAS

We report the first detection of coherent elastic neutrino-nucleus scattering (CEvNS) on germanium, measured at the Spallation Neutron Source at Oak Ridge National Laboratory. The Ge-Mini detector of the COHERENT collaboration employs large-mass, low-noise, high-purity germanium spectrometers, enabling excellent energy resolution, and an analysis threshold of 1.5 keV electron-equivalent ionization energy. We observe a on-beam excess of 20.6$_{+7.1}^{-6.3}$ counts with a total exposure of 10.22 GWhkg and we reject the no-CEvNS hypothesis with 3.9 sigma significance. The result agrees with the predicted standard model of particle physics signal rate within 2 sigma., Comment: 7 pages, 5 figures

The PROSPECT experiment is designed to perform precise searches for antineutrino disappearance at short distances (7 - 9~m) from compact nuclear reactor cores. This Letter reports results from a new neutrino oscillation analysis performed using the complete data sample from the PROSPECT-I detector operated at the High Flux Isotope Reactor in 2018. The analysis uses a multi-period selection of inverse beta decay neutrino interactions with reduced backgrounds and enhanced statistical power to set limits on electron-flavor disappearance caused by mixing with sterile neutrinos with 0.2 - 20 eV$^2$ mass splittings. Inverse beta decay positron energy spectra from six different reactor-detector distance ranges are found to be statistically consistent with one another, as would be expected in the absence of sterile neutrino oscillations. The data excludes at 95% confidence level the existence of sterile neutrinos in regions above 3~eV$^2$ previously unexplored by terrestrial experiments, including all space below 10~eV$^2$ suggested by the recently strengthened Gallium Anomaly. The best-fit point of the Neutrino-4 reactor experiment's claimed observation of short-baseline oscillation is ruled out at more than five standard deviations., Comment: 6 pages, 4 figures

The prevalence of misinformation and disinformation poses a significant challenge in today's digital landscape. That is why several methods and tools are proposed to analyze and understand these phenomena from a scientific perspective. To assess how the mis/disinformation is being conceptualized and evaluated in the literature, this paper surveys the existing frameworks, models and simulations of mis/disinformation dynamics by performing a systematic literature review up to 2023. After applying the PRISMA methodology, 57 research papers are inspected to determine (1) the terminology and definitions of mis/disinformation, (2) the methods used to represent mis/disinformation, (3) the primary purpose beyond modeling and simulating mis/disinformation, (4) the context where the mis/disinformation is studied, and (5) the validation of the proposed methods for understanding mis/disinformation. The main findings reveal a consistent essence definition of misinformation and disinformation across studies, with intent as the key distinguishing factor. Research predominantly uses social frameworks, epidemiological models, and belief updating simulations. These studies aim to estimate the effectiveness of mis/disinformation, primarily in health and politics. The preferred validation strategy is to compare methods with real-world data and statistics. Finally, this paper identifies current trends and open challenges in the mis/disinformation research field, providing recommendations for future work agenda.

The PROSPECT-I detector has several features that enable measurement of the direction of a compact neutrino source. In this paper, a detailed report on the directional measurements made on electron antineutrinos emitted from the High Flux Isotope Reactor is presented. With an estimated true neutrino (reactor to detector) direction of $\phi = 40.8\unicode{xB0} \pm 0.7\unicode{xB0}$ and $\theta = 98.6\unicode{xB0} \pm 0.4\unicode{xB0}$, the PROSPECT-I detector is able to reconstruct an average neutrino direction of $\phi = 39.4\unicode{xB0} \pm 2.9\unicode{xB0}$ and $\theta = 97.6\unicode{xB0} \pm 1.6\unicode{xB0}$. This measurement is made with approximately 48000 Inverse Beta Decay signal events and is the most precise directional reconstruction of reactor antineutrinos to date.

The first goal of this article is to introduce a new type of p-adic reaction-diffusion cellular neural network with delay. We study the stability of these networks and provide numerical simulations of their responses. The second goal is to provide a quick review of the state of the art of p-adic cellular neural networks and their applications to image processing.

Simulation of assistive devices on pathological gait through musculoskeletal models offers the potential and advantages of estimating the effect of the device in several biomechanical variables and the device characteristics ahead of manufacturing. In this study, we introduce a novel musculoskeletal modelling approach to simulate the biomechanical impact of ankle foot orthoses (AFO) on gait in individuals with spinal cord injury (SCI). Leveraging data from the Swiss Paraplegic Center, we constructed anatomically and muscularly scaled models for SCI-AFO users, aiming to predict changes in gait kinematics and kinetics. The importance of this work lies in its potential to enhance rehabilitation strategies and improve quality of life by enabling the pre-manufacturing assessment of assistive devices. Despite the application of musculoskeletal models in simulating walking aids effects in other conditions, no predictive model currently exists for SCI gait. Evaluation through RMSE showed similar results compared with other pathologies, simulation errors ranged between 0.23 to 2.3 degrees in kinematics. Moreover, the model was able to capture ankle joint muscular asymmetries and predict symmetry improvements with AFO use. However, the simulation did not reveal all the AFO effects, indicating a need for more personalized model parameters and optimized muscle activation to fully replicate orthosis effects on SCI gait., Comment: 7 pages, 3 figures, 3 Tables. Submitted to the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. To be published in the proceedings of the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Zero-shot entity and relation classification models leverage available external information of unseen classes -- e.g., textual descriptions -- to annotate input text data. Thanks to the minimum data requirement, Zero-Shot Learning (ZSL) methods have high value in practice, especially in applications where labeled data is scarce. Even though recent research in ZSL has demonstrated significant results, our analysis reveals that those methods are sensitive to provided textual descriptions of entities (or relations). Even a minor modification of descriptions can lead to a change in the decision boundary between entity (or relation) classes. In this paper, we formally define the problem of identifying effective descriptions for zero shot inference. We propose a strategy for generating variations of an initial description, a heuristic for ranking them and an ensemble method capable of boosting the predictions of zero-shot models through description enhancement. Empirical results on four different entity and relation classification datasets show that our proposed method outperform existing approaches and achieve new SOTA results on these datasets under the ZSL settings. The source code of the proposed solutions and the evaluation framework are open-sourced.

Unlike traditional statistical methods, Conformal Prediction (CP) allows for the determination of valid and accurate confidence levels associated with individual predictions based only on exchangeability of the data. We here introduce a new feature selection method that takes advantage of the CP framework. Our proposal, named Conformal Recursive Feature Elimination (CRFE), identifies and recursively removes features that increase the non-conformity of a dataset. We also present an automatic stopping criterion for CRFE, as well as a new index to measure consistency between subsets of features. CRFE selections are compared to the classical Recursive Feature Elimination (RFE) method on several multiclass datasets by using multiple partitions of the data. The results show that CRFE clearly outperforms RFE in half of the datasets, while achieving similar performance in the rest. The automatic stopping criterion provides subsets of effective and non-redundant features without computing any classification performance.

In this paper, we present numerical and experimental results on helicity oscillations in a liquid-metal Rayleigh-B\'enard (RB) convection cell, with an aspect ratio of 0.5. We find that helicity oscillations occur during transitions of flow states that are characterised by significant changes in the Reynolds number. Moreover, we also observe helicity oscillations at flow conditions where the temporal gradient of the change in the Reynolds number is significantly smaller than that of the helicity. Notably, the helicity oscillations observed during the transient double-roll state exhibit characteristics remarkably similar to those associated with the Tayler Instability., Comment: 10 pages, 16 figures

The thermodynamics and transport properties of polymeric materials are essential for the design of reactors and for the development of polymer deconstruction processes. Existing property prediction tools such as correlations based on entropy scaling, kinetic gas theory, and free-volume model are inadequate for polymers. In this paper, we introduce a data-driven model for polyolefins based on data from molecular dynamics simulations that can accurately predict the transport properties of polyethylenes and their binary mixtures with higher n-alkanes across a range of temperatures, pressures, concentrations, and oligomer molecular weights.

In this paper, we present a detailed mathematical description of the error correction process for Kitaev's model for finite Abelian groups. The number of errors Kitaev's model can correct depends on the lattice and its topology. Although there is a theoretical maximum number of errors that can be corrected, we prove that correcting this number of errors, in general, is an NP-complete problem. Consequently, we introduce a polynomial-time correction algorithm that corrects a number of errors below the theoretical maximum., Comment: 32 pages

Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrollers, is constrained by limited resources. In this survey, we provide comprehensive design guidance tailored for these devices, detailing the meticulous design of lightweight models, compression methods, and hardware acceleration strategies. The principal goal of this work is to explore methods and concepts for getting around hardware constraints without compromising the model's accuracy. Additionally, we explore two notable paths for lightweight deep learning in the future: deployment techniques for TinyML and Large Language Models. Although these paths undoubtedly have potential, they also present significant challenges, encouraging research into unexplored areas., Comment: 40 pages

We construct all integral non-group-theoretical modular categories of dimension $p^2q^2$, where $p$ and $q$ are distinct prime numbers, establishing that a necessary and sufficient condition for their existence is that $p \mid q+1$, and their rank is $p^2 + \frac{q^2 - 1}{p}$., Comment: 9 pages