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Current version identification (VI) datasets often lack sufficient size and musical diversity to train robust neural networks (NNs). Additionally, their non-representative clique size distributions prevent realistic system evaluations. To address these challenges, we explore the untapped potential of the rich editorial metadata in the Discogs music database and create a large dataset of musical versions containing about 1,900,000 versions across 348,000 cliques. Utilizing a high-precision search algorithm, we map this dataset to official music uploads on YouTube, resulting in a dataset of approximately 493,000 versions across 98,000 cliques. This dataset offers over nine times the number of cliques and over four times the number of versions than existing datasets. We demonstrate the utility of our dataset by training a baseline NN without extensive model complexities or data augmentations, which achieves competitive results on the SHS100K and Da-TACOS datasets. Our dataset, along with the tools used for its creation, the extracted audio features, and a trained model, are all publicly available online.

A generalised extraction procedure for magnetic interactions using effective Hamiltonians applicable to systems with more than two sites featuring local spins $S_i \geq 1$ is presented. To this end, closed, non-recursive expressions pertaining to chains of arbitrary equal spins are derived with the graphical method of angular momentum. The method is illustrated by extracting magnetic couplings from ab initio calculations on a [CaMn$_3^{(\text{IV})}$O$_4$] cubane. An extension to non-sequential coupling schemes is shown to be conducive to expressing additional symmetries of certain spin Hamiltonians.

The operation of a small-size Cherenkov gamma-ray telescope TAIGA-IACT with camera on SiPMs OnSemi MicroFJ-60035 has been modelled by multiparticle Monte Carlo (MC) methods. The model implies that telescope camera is equipped with two specific types of filters of 290-590 nm (visible+NUV) and 220-320 nm (MUV+UVB)-bands, each covering half of the camera pixels in some uniform order. This allows one to measure the fraction of UV-radiation in total amount of Cherenkov radiation of an extensive air shower (EAS), that can be used for efficient gamma-hadron separation. The corresponding quality factor takes values up to 5.07 in the 10-100 TeV range depending on the distance to EAS axis and camera orientation., Comment: 22 pages, 5 figures, 10 tables, accepted to Research in Astronomy and Astrophysics

We give an overview of the science objectives and mission design of the Spectroscopic Time-Resolving Observatory for Broadband Energy X-rays (STROBE-X) observatory, which has been proposed as a NASA probe-class (~$1.5B) mission in response to the Astro2020 recommendation for an X-ray probe., Comment: 11 pages, 5 figures, accepted for publication in JATIS

A recent experiment showed that a Cherenkov radiation spectrum from thin radiators possessing frequency dispersion could transform into quasi-monochromatic spectral lines. Simulations based on the standard Geant4 toolkit could not correctly describe the experimental results because Geant4 uses the traditional Frank-Tamm theory developed for an ideal (infinitely thick) radiator. Because of that, our endeavors in this paper are to present a new way of utilizing Cherenkov radiation using new Gneat4 models, while the paper should be used as a helping text to understand the Cherenkov process in Geant4 and what one can expect out of new models. Now, one can analyze quasi-monochromatic Cherenkov spectral lines using Geant4, which is a significant milestone for investigating the possibilities of this technique., Comment: 11 pages, 6 figures, 1 table

The planted random subgraph detection conjecture of Abram et al. (TCC 2023) asserts the pseudorandomness of a pair of graphs $(H, G)$, where $G$ is an Erdos-Renyi random graph on $n$ vertices, and $H$ is a random induced subgraph of $G$ on $k$ vertices. Assuming the hardness of distinguishing these two distributions (with two leaked vertices), Abram et al. construct communication-efficient, computationally secure (1) 2-party private simultaneous messages (PSM) and (2) secret sharing for forbidden graph structures. We prove the low-degree hardness of detecting planted random subgraphs all the way up to $k\leq n^{1 - \Omega(1)}$. This improves over Abram et al.'s analysis for $k \leq n^{1/2 - \Omega(1)}$. The hardness extends to $r$-uniform hypergraphs for constant $r$. Our analysis is tight in the distinguisher's degree, its advantage, and in the number of leaked vertices. Extending the constructions of Abram et al, we apply the conjecture towards (1) communication-optimal multiparty PSM protocols for random functions and (2) bit secret sharing with share size $(1 + \epsilon)\log n$ for any $\epsilon > 0$ in which arbitrary minimal coalitions of up to $r$ parties can reconstruct and secrecy holds against all unqualified subsets of up to $\ell = o(\epsilon \log n)^{1/(r-1)}$ parties.

Recent constraints on neutron star mass and radius have advanced our understanding of the equation of state of cold dense matter. Some of them have been obtained by modeling the pulses of three millisecond X-ray pulsars observed by the Neutron Star Interior Composition Explorer (NICER). Here, we present a Bayesian parameter inference for a fourth pulsar, PSR J1231-1411, using the same technique with NICER and XMM-Newton data. When applying a broad mass-inclination prior from radio timing measurements and the emission region geometry model that can best explain the data, we find likely-converged results only when using a limited radius prior. If limiting the radius to be consistent with the previous observational constraints and equation of state analyses, we infer the radius to be $12.6 \pm 0.3$ km and the mass to be $1.04_{-0.03}^{+0.05}$ $M_\odot$, each reported as the posterior credible interval bounded by the $16\,\%$ and $84\,\%$ quantiles. If using an uninformative prior but limited between $10$ and $14$ km, we find otherwise similar results, but $R_{\mathrm{eq}} = 13.5_{-0.5}^{+0.3}$ km for the radius. In both cases, we find a non-antipodal hot region geometry where one emitting spot is at the equator or slightly above, surrounded by a large colder region, and where a non-circular hot region lies close to southern rotational pole. If using a wider radius prior, we only find solutions that fit the data significantly worse. We discuss the challenges in finding the better fitting solutions, possibly related to the weak interpulse feature in the pulse profile., Comment: 22 pages, 13 figures (2 of which are figure sets), 3 tables, accepted for publication in ApJ

This paper deals with the black-box optimization problem. In this setup, we do not have access to the gradient of the objective function, therefore, we need to estimate it somehow. We propose a new type of approximation JAGUAR, that memorizes information from previous iterations and requires $\mathcal{O}(1)$ oracle calls. We implement this approximation in the Frank-Wolfe and Gradient Descent algorithms and prove the convergence of these methods with different types of zero-order oracle. Our theoretical analysis covers scenarios of non-convex, convex and PL-condition cases. Also in this paper, we consider the stochastic minimization problem on the set $Q$ with noise in the zero-order oracle; this setup is quite unpopular in the literature, but we prove that the JAGUAR approximation is robust not only in deterministic minimization problems, but also in the stochastic case. We perform experiments to compare our gradient estimator with those already known in the literature and confirm the dominance of our methods., Comment: 29 pages, 5 algorithms, 3 figures, 1 table

Pulse profile modelling (PPM) is a technique for inferring mass, radius and hotspot properties of millisecond pulsars. PPM is now regularly used for analysis of rotation-powered millisecond pulsars (RMPs) with data from the Neutron Star Interior Composition ExploreR (NICER). Extending PPM to accreting millisecond pulsars (AMPs) is attractive, because they are a different source class featuring bright X-ray radiation from hotspots powered by accretion. In this paper, we present a modification of one of the PPM codes, X-PSI, so that it can be used for AMPs. In particular, we implement a model of an accretion disc and atmosphere model appropriate for the hotspots of AMPs, and improve the overall computational efficiency. We then test parameter recovery with synthetic NICER data in two scenarios with reasonable parameters for AMPs. We find in the first scenario, where the hotspot is large, that we are able to tightly and accurately constrain all parameters including mass and radius. In the second scenario, which is a high inclination system with a smaller hotspot, we find degeneracy between a subset of model parameters and a slight bias in the inferred mass and radius. This analysis of synthetic data lays the ground work for future analysis of AMPs with NICER data. Such an analysis could be complemented by future (joint) analysis of polarization data from the Imaging X-ray Polarimetry Explorer (IXPE)., Comment: 14 pages, 5 figures, 3 tables. Paper submitted to MNRAS

Despite the huge number of research into the three-body problem in physics and mathematics, the study of this problem still remains relevant both from the point of view of its broad application and taking into account its fundamental significance for the theory of dynamical systems. In addition, to solve the problem of quantum-to-classical transition, it is important to answer the question: is irreversibility fundamental to the description of the classical world? To answer this question, we considered a reference classical dynamical system, the general three-body problem, formulating it in conformal Euclidean space and rigorously proving its equivalence to the Newtonian three-body problem. It is shown that a curved configuration space with a local coordinate system reveals new hidden symmetries of the internal motion of a dynamical system, which makes it possible to reduce the problem to a 6th order system instead of the known 8th order. The most important consequence of this consideration is that the chronologizing parameter of the motion of a system of particles, which we call internal time, is in the general case irreversible, which is characteristic of the general three-body problem. An equation is derived that describes the evolution of the flow of geodesic trajectories, with the help of which the entropy of the system is constructed. New criteria for assessing the complexity of a low-dimensional dynamic system and the dimension of stochastic fractal structures arising in three-dimensional space are obtained. An effective mathematical algorithm has been developed for the numerical simulation of the general three-body problem, which is traditionally a difficult-to-solve system of stiff ordinary differential equations., Comment: 34 pages, 10 figures

Non-Hermitian systems are known to possess unique singularities in the scattering spectra such as exceptional points, bound states in the continuum, Diabolic points, and anapole states, which are usually considered to be independent. Here, we demonstrate the fundamental relationships between non-Hermitian singularities and observe them experimentally in the scattering spectra. We reveal that exceptional points appear in the anapole regime, and diabolic points are associated with superscattering. We confirm our findings with microwave experiments by measuring the scattering spectra of subwavelength Mie-resonant ceramic rings. Our study underpins the generic behavior of non-Hermitian singularities in the scattering spectra of subwavelength resonators, uncovering their novel applications in non-Hermitian nonlinear optics and topological photonics., Comment: 19 pages, 5 figures

We show that a slab of wire medium composed of thin parallel metallic wires can naturally support bound states in the continuum (BICs) formed in an unusual way. The revealed BICs appear due to the strong spatial dispersion making possible the propagation of longitudinal plasma-like waves and TEM polarized modes with a flat band. The symmetry-protected (at-$\Gamma$) BICs are formed due to the polarization mismatch between the longitudinal plasma-like waves and transversal plane waves in the surrounding space, while the accidental (off-$\Gamma$) BICs appear as a result of the Friedrich-Wintgen destructive interference between them. All revealed BICs can be well-described analytically without the use of the Bloch theorem within effective medium approximation when the wire medium behaves as homogeneous 1D anisotropic plasma with strong spatial dispersion., Comment: 6 pages, 4 figures, Supplemental Material

Multimodal models that jointly process audio and language hold great promise in audio understanding and are increasingly being adopted in the music domain. By allowing users to query via text and obtain information about a given audio input, these models have the potential to enable a variety of music understanding tasks via language-based interfaces. However, their evaluation poses considerable challenges, and it remains unclear how to effectively assess their ability to correctly interpret music-related inputs with current methods. Motivated by this, we introduce MuChoMusic, a benchmark for evaluating music understanding in multimodal language models focused on audio. MuChoMusic comprises 1,187 multiple-choice questions, all validated by human annotators, on 644 music tracks sourced from two publicly available music datasets, and covering a wide variety of genres. Questions in the benchmark are crafted to assess knowledge and reasoning abilities across several dimensions that cover fundamental musical concepts and their relation to cultural and functional contexts. Through the holistic analysis afforded by the benchmark, we evaluate five open-source models and identify several pitfalls, including an over-reliance on the language modality, pointing to a need for better multimodal integration. Data and code are open-sourced., Comment: Accepted at ISMIR 2024. Data: https://doi.org/10.5281/zenodo.12709974 Code: https://github.com/mulab-mir/muchomusic Supplementary material: https://mulab-mir.github.io/muchomusic

We report Bayesian inference of the mass, radius and hot X-ray emitting region properties - using data from the Neutron Star Interior Composition ExploreR (NICER) - for the brightest rotation-powered millisecond X-ray pulsar PSR J0437$\unicode{x2013}$4715. Our modeling is conditional on informative tight priors on mass, distance and binary inclination obtained from radio pulsar timing using the Parkes Pulsar Timing Array (PPTA) (Reardon et al. 2024), and we use NICER background models to constrain the non-source background, cross-checking with data from XMM-Newton. We assume two distinct hot emitting regions, and various parameterized hot region geometries that are defined in terms of overlapping circles; while simplified, these capture many of the possibilities suggested by detailed modeling of return current heating. For the preferred model identified by our analysis we infer a mass of $M = 1.418 \pm 0.037$ M$_\odot$ (largely informed by the PPTA mass prior) and an equatorial radius of $R = 11.36^{+0.95}_{-0.63}$ km, each reported as the posterior credible interval bounded by the 16% and 84% quantiles. This radius favors softer dense matter equations of state and is highly consistent with constraints derived from gravitational wave measurements of neutron star binary mergers. The hot regions are inferred to be non-antipodal, and hence inconsistent with a pure centered dipole magnetic field., Comment: 27 pages, 16 figures, + appendices. Accepted in ApJL. Data files and reproduction package available in Zenodo

While standard evaluation scores for generative models are mostly reference-based, a reference-dependent assessment of generative models could be generally difficult due to the unavailability of applicable reference datasets. Recently, the reference-free entropy scores, VENDI and RKE, have been proposed to evaluate the diversity of generated data. However, estimating these scores from data leads to significant computational costs for large-scale generative models. In this work, we leverage the random Fourier features framework to reduce the computational price and propose the Fourier-based Kernel Entropy Approximation (FKEA) method. We utilize FKEA's approximated eigenspectrum of the kernel matrix to efficiently estimate the mentioned entropy scores. Furthermore, we show the application of FKEA's proxy eigenvectors to reveal the method's identified modes in evaluating the diversity of produced samples. We provide a stochastic implementation of the FKEA assessment algorithm with a complexity $O(n)$ linearly growing with sample size $n$. We extensively evaluate FKEA's numerical performance in application to standard image, text, and video datasets. Our empirical results indicate the method's scalability and interpretability applied to large-scale generative models. The codebase is available at https://github.com/aziksh-ospanov/FKEA.

We present the first systematic X-ray pulse timing analysis of the six members of the so-called "Magnificent Seven" nearby thermally-emitting isolated neutron stars (XINS) with detected pulsations. Using the extensive collection of archival XMM-Newton, Chandra, and NICER observations spanning over two decades, we obtain the first firm measurement of the spin-down rate for RX J2143.0+0654, while for the rest we improve upon previously published spin ephemerides and extend them by up to an additional decade. Five of the XINS follow steady spin-down with no indication of major anomalies in their long-term timing behavior; the notable exception is RX J0720.4-3125, for which, in addition to confirming the previously identified glitch, we detect a second spin derivative. The high quality folded X-ray pulse profiles produced with the updated timing solutions exhibit diverse and complex morphologies, as well as striking energy dependence. These peculiarities cannot be readily explained by blackbody-like isotropic emission and simple hot spot configurations, hinting at the presence of complex multi-temperature surface heat distributions and highly anisotropic radiation patterns, such as may arise from a strongly magnetized atmospheric layer., Comment: 28 pages, 8 figures, 8 tables; accepted for publication in the Astrophysical Journal. The pulse timing solutions for the six neutron stars are included in the TeX source of this article in the form of Tempo-style parameter files

PSR J0740+6620 is the neutron star with the highest precisely determined mass, inferred from radio observations to be $2.08\pm0.07\,\rm M_\odot$. Measurements of its radius therefore hold promise to constrain the properties of the cold, catalyzed, high-density matter in neutron star cores. Previously, Miller et al. (2021) and Riley et al. (2021) reported measurements of the radius of PSR J0740+6620 based on Neutron Star Interior Composition Explorer (NICER) observations accumulated through 17 April 2020, and an exploratory analysis utilizing NICER background estimates and a data set accumulated through 28 December 2021 was presented in Salmi et al. (2022). Here we report an updated radius measurement, derived by fitting models of X-ray emission from the neutron star surface to NICER data accumulated through 21 April 2022, totaling $\sim1.1$ Ms additional exposure compared to the data set analyzed in Miller et al. (2021) and Riley et al. (2021), and to data from X-ray Multi-Mirror (XMM-Newton) observations. We find that the equatorial circumferential radius of PSR J0740+6620 is $12.92_{-1.13}^{+2.09}$ km (68% credibility), a fractional uncertainty $\sim83\%$ the width of that reported in Miller et al. (2021), in line with statistical expectations given the additional data. If we were to require the radius to be less than 16 km, as was done in Salmi et al. (2024), then our 68% credible region would become $R=12.76^{+1.49}_{-1.02}$ km, which is close to the headline result of Salmi et al. (2024). Our updated measurements, along with other laboratory and astrophysical constraints, imply a slightly softer equation of state than that inferred from our previous measurements., Comment: 18 pages, 8 figures, +appendices. Accepted in ApJ

We report an updated analysis of the radius, mass, and heated surface regions of the massive pulsar PSR J0740+6620 using Neutron Star Interior Composition Explorer (NICER) data from 2018 September 21 to 2022 April 21, a substantial increase in data set size compared to previous analyses. Using a tight mass prior from radio timing measurements and jointly modeling the new NICER data with XMM-Newton data, the inferred equatorial radius and gravitational mass are $12.49_{-0.88}^{+1.28}$ km and $2.073_{-0.069}^{+0.069}$ $M_\odot$ respectively, each reported as the posterior credible interval bounded by the $16\,\%$ and $84\,\%$ quantiles, with an estimated systematic error $\lesssim 0.1$ km. This result was obtained using the best computationally feasible sampler settings providing a strong radius lower limit but a slightly more uncertain radius upper limit. The inferred radius interval is also close to the $R=12.76_{-1.02}^{+1.49}$ km obtained by Dittmann et al., when they require the radius to be less than $16$ km as we do. The results continue to disfavor very soft equations of state for dense matter, with $R<11.15$ km for this high-mass pulsar excluded at the $95\,\%$ probability. The results do not depend significantly on the assumed cross-calibration uncertainty between NICER and XMM-Newton. Using simulated data that resemble the actual observations, we also show that our pipeline is capable of recovering parameters for the inferred models reported in this paper., Comment: 17 pages, 9 figures (2 of which are figure sets), 2 tables, published in ApJ

Pulse Profile Modeling (PPM), the technique used to infer mass, radius and geometric parameters for rotation-powered millisecond pulsars using data from the Neutron Star Interior Composition Explorer (NICER), relies on relativistic ray-tracing of thermal X-ray photons from hot spots on the neutron star surface to the observer. To verify our ray-tracing codes we have in the past conducted cross-tests for simple hot spot geometries, focusing primarily on the implementation of the space-time model. In this paper, we present verification for test problems that explore the more complex hot spot geometries that are now being employed in the NICER PPM analyses. We conclude that the accuracy of our computed waveforms is in general sufficiently high for analyses of current NICER data sets. We have however identified some extreme configurations where extra care may be needed., Comment: Submitted to ApJ

The sparse dictionary coding framework represents signals as a linear combination of a few predefined dictionary atoms. It has been employed for images, time series, graph signals and recently for 2-way (or 2D) spatio-temporal data employing jointly temporal and spatial dictionaries. Large and over-complete dictionaries enable high-quality models, but also pose scalability challenges which are exacerbated in multi-dictionary settings. Hence, an important problem that we address in this paper is: How to scale multi-dictionary coding for large dictionaries and datasets? We propose a multi-dictionary atom selection technique for low-rank sparse coding named LRMDS. To enable scalability to large dictionaries and datasets, it progressively selects groups of row-column atom pairs based on their alignment with the data and performs convex relaxation coding via the corresponding sub-dictionaries. We demonstrate both theoretically and experimentally that when the data has a low-rank encoding with a sparse subset of the atoms, LRMDS is able to select them with strong guarantees under mild assumptions. Furthermore, we demonstrate the scalability and quality of LRMDS in both synthetic and real-world datasets and for a range of coding dictionaries. It achieves 3X to 10X speed-up compared to baselines, while obtaining up to two orders of magnitude improvement in representation quality on some of the real world datasets given a fixed target number of atoms., Comment: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 25--29, 2024, Barcelona, Spain

Long traces and large event logs that originate from sensors and prediction models are becoming more common in our data-rich world. In such circumstances, conformance checking, a key task in process mining, can become computationally infeasible due to the exponential complexity of finding an optimal alignment. This paper introduces a novel sliding window approach to address these scalability challenges while preserving the interpretability of alignment-based methods. By breaking down traces into manageable subtraces and iteratively aligning each with the process model, our method significantly reduces the search space. The approach uses global information that captures structural properties of the trace and the process model to make informed alignment decisions, discarding unpromising alignments even if they are optimal for a local subtrace. This improves the overall accuracy of the results. Experimental evaluations demonstrate that the proposed method consistently finds optimal alignments in most cases and highlight its scalability. This is further supported by a theoretical complexity analysis, which shows the reduced growth of the search space compared to other common conformance checking methods. This work provides a valuable contribution towards efficient conformance checking for large-scale process mining applications.

To date, computational methods for modeling defects (vacancies, adsorbates, etc.) rely on periodic supercells in which the defect is far enough from its repeated image such that they can be assumed non-interacting. Defects in real solids, however, can be spaced microns or more apart, whereas affordable supercells for density functional theory calculations are no wider than a few nanometers. The relative proximity and periodic repetition of the defect's images may lead to unphysical artifacts, especially if the defect is charged and/or open-shell. Furthermore, to avoid divergence of the periodic electrostatics, a compensating background charge must be introduced if the defect is charged. Even if post-hoc corrections are used, this is a source of unquantifiable error and, in some cases, renders energies useless. In this communication, we introduce an embedding formalism such that a pristine, primitive unit cell may be used for the periodic mean field, after which atoms may be moved or charged within an embedded fragment. This fragment can then be treated with a post-Hartree Fock method to capture important electron correlations pertaining to the defect. By eliminating the need for compensating background charges and periodicity of the defect, we circumvent all associated unphysicalities and numerical issues. Furthermore, the primitive cell calculations drastically reduce computational expense compared to supercell approaches. This method is size-intensive with respect to energy differences and can be routinely applied even to multireference defects, localized excited states, etc. using a variety of fragment solvers. In examining with this approach bond-breaking in a fluorine-substituted graphane monolayer, a difficult testing ground for condensed-phase electronic structure methods, we observe key aspects of the dissociation pathway, specifically a covalent-to-ionic avoided crossing., Comment: This manuscript has not yet been submitted to a journal

Pulse profile modelling (PPM) is a comprehensive relativistic ray-tracing technique employed to determine the properties of neutron stars. In this study, we apply this technique to the Type I X-ray burster and accretion-powered millisecond pulsar XTE J1814-338, extracting its fundamental properties using PPM of its thermonuclear burst oscillations. Using data from its 2003 outburst, and a single uniform temperature hot spot model, we infer XTE J1814-338 to be located at a distance of $7.2^{+0.3}_{-0.4}$ kpc, with a mass of $1.21^{+0.05}_{-0.05}$ M$_\odot$ and an equatorial radius of $7.0^{+0.4}_{-0.4}$ km. Our results also offer insight into the time evolution of the hot spot but point to some potential shortcomings of the single uniform temperature hot spot model. We explore the implications of this result, including what we can learn about thermonuclear burst oscillation mechanisms and the importance of modelling the accretion contribution to the emission during the burst., Comment: Accepted for publication in MNRAS. The Zenodo link is public

Resonant states underlie a variety of metastructures that exhibit remarkable capabilities for effective control of acoustic waves at subwavelength scales. The development of metamaterials relies on the rigorous mode engineering providing the implementation of the desired properties. At the same time, the application of metamaterials is still limited as their building blocks are frequently characterized by complicated geometry and can't be tuned easily. In this work, we consider a simple system of coupled Helmholtz resonators and study their properties associated with the tuning of coupling strength and symmetry breaking. We numerically and experimentally demonstrate the excitation of quasi-bound state in the continuum in the resonators placed in a free space and in a rectangular cavity. It is also shown that tuning the intrinsic losses via introducing porous inserts can lead to spectral splitting or merging of quasi-\textit{bound states in the continuum} and occurrence of \textit{exceptional points}. The obtained results will open new opportunities for the development of simple and easy-tunable metastructures based on Helmholtz resonances.

We use a recently proposed scheme of matrix extension of dispersionless integrable systems for the Abelian case, in which it leads to linear equations, connected with the initial dispersionless system. In the examples considered, these equations can be interpreted in terms of Abelian gauge fields on the geometric background defined by the dispersionless system. They are also connected with the linearisation of initial systems. We construct solutions to these linear equations in terms of wave functions of the Lax pair for dispersionless system, which is represented in terms of some vector fields., Comment: 16 pages

Periodic optical structures, such as diffraction grating and numerous photonic crystals, are one of the staples of modern nanophotonics for the manipulation of electromagnetic radiation. The array of subwavelength dielectric rods is one of the simplest platforms, which, despite its simplicity exhibits extraordinary wave phenomena, such as diffraction anomalies and narrow reflective resonances. Despite the well-documented properties of infinite periodic systems, the behavior of these diffractive effects in systems incorporating a finite number of elements is studied to a far lesser extent. Here we study theoretically and numerically the evolution of collective spectral features in finite arrays of dielectric rods. We develop an analytical model of light scattering by a finite array of circular rods based on the coupled dipoles approximation and analyze the spectral features of finite arrays within the developed model. Finally, we validate the results of the analytical model using full-wave numerical simulations.

Large Language Models (LLMs) have shown impressive abilities in data annotation, opening the way for new approaches to solve classic NLP problems. In this paper, we show how to use LLMs to create NuNER, a compact language representation model specialized in the Named Entity Recognition (NER) task. NuNER can be fine-tuned to solve downstream NER problems in a data-efficient way, outperforming similar-sized foundation models in the few-shot regime and competing with much larger LLMs. We find that the size and entity-type diversity of the pre-training dataset are key to achieving good performance. We view NuNER as a member of the broader family of task-specific foundation models, recently unlocked by LLMs.

We present PECMAE, an interpretable model for music audio classification based on prototype learning. Our model is based on a previous method, APNet, which jointly learns an autoencoder and a prototypical network. Instead, we propose to decouple both training processes. This enables us to leverage existing self-supervised autoencoders pre-trained on much larger data (EnCodecMAE), providing representations with better generalization. APNet allows prototypes' reconstruction to waveforms for interpretability relying on the nearest training data samples. In contrast, we explore using a diffusion decoder that allows reconstruction without such dependency. We evaluate our method on datasets for music instrument classification (Medley-Solos-DB) and genre recognition (GTZAN and a larger in-house dataset), the latter being a more challenging task not addressed with prototypical networks before. We find that the prototype-based models preserve most of the performance achieved with the autoencoder embeddings, while the sonification of prototypes benefits understanding the behavior of the classifier.

As deep learning (DL) models are widely and effectively used in Machine Learning as a Service (MLaaS) platforms, there is a rapidly growing interest in DL watermarking techniques that can be used to confirm the ownership of a particular model. Unfortunately, these methods usually produce watermarks susceptible to model stealing attacks. In our research, we introduce a novel trigger set-based watermarking approach that demonstrates resilience against functionality stealing attacks, particularly those involving extraction and distillation. Our approach does not require additional model training and can be applied to any model architecture. The key idea of our method is to compute the trigger set, which is transferable between the source model and the set of proxy models with a high probability. In our experimental study, we show that if the probability of the set being transferable is reasonably high, it can be effectively used for ownership verification of the stolen model. We evaluate our method on multiple benchmarks and show that our approach outperforms current state-of-the-art watermarking techniques in all considered experimental setups.

Programmable linear optical interferometers are promising for classical and quantum applications. Their integrated design makes it possible to create more scalable and stable devices. To use them in practice, one has to reconstruct the whole device model taking the manufacturing errors into account. The inability to address individual interferometer elements complicates the reconstruction problem. A naive approach is to train the model via some complex optimization procedure. A faster optimization-free algorithm has been recently proposed [Opt. Express 31, 16729 (2023)]. However, it requires the full transfer matrix tomography while a more practical setup measures only the fields intensities at the interferometer output. In this paper, we propose the modification of the fast algorithm, which uses additional set of interferometer configurations in order to reconstruct the model in the case of intensity-only measurements. We show that it performs slightly worse than the original fast algorithm but it is more practical and still does not require intensive numerical optimization.

Microfluidic technology provides a solution to the challenge of continuous CaCO$_3$ particle synthesis. In this study, we utilized a 3D-printed microfluidic chip to synthesize CaCO$_3$ micro and nanoparticles in vaterite form. Our primary focus was on investigating a continuous one-phase synthesis method tailored for the crystallization of these particles. By employing a combination of confocal and scanning electron microscopy, along with Raman spectroscopy, we were able to thoroughly evaluate the synthesis efficiency. This evaluation included aspects such as particle size distribution, morphology, and polymorph composition. The results unveiled the existence of two distinct synthesis regimes within the 3D-printed microfluidic chips, featuring a channel cross-section of 2 mm$^2$. In the first regime, characterized by turbulence, particles with an average diameter of around 2 $\mu$m were produced, displaying a broad size distribution. Conversely, the second regime, marked by laminar flow, led to the synthesis of submicron particles (approximately 800-900 nm in diameter) and even nanosized particles (70-80 nm). This research significantly contributes valuable insights to both the understanding and optimization of microfluidic synthesis processes, particularly in achieving controlled production of submicron and nanoscale particles., Comment: 12 pages, 7 figures

Efficient trapping of light in nanostructures is essential for the development of optical devices that are based on the interaction between light and matter. In this work, we show theoretically and experimentally that one-dimensional arrays of subwavelength dielectric Mie-resonant particles can support collective resonances with increased $Q$-factors. We demonstrate that the increase of the $Q$-factor can be explained by interaction between the collective electric and magnetic dipole modes of the chain resulting in appearance of the inflection point at the band edge. The considered effect is studied experimentally in the chain of high-index ceramic cylinders in the microwave spectral range., Comment: 11 pages, 8 figures

Music Information Retrieval (MIR) research is increasingly leveraging representation learning to obtain more compact, powerful music audio representations for various downstream MIR tasks. However, current representation evaluation methods are fragmented due to discrepancies in audio and label preprocessing, downstream model and metric implementations, data availability, and computational resources, often leading to inconsistent and limited results. In this work, we introduce mir_ref, an MIR Representation Evaluation Framework focused on seamless, transparent, local-first experiment orchestration to support representation development. It features implementations of a variety of components such as MIR datasets, tasks, embedding models, and tools for result analysis and visualization, while facilitating the implementation of custom components. To demonstrate its utility, we use it to conduct an extensive evaluation of several embedding models across various tasks and datasets, including evaluating their robustness to various audio perturbations and the ease of extracting relevant information from them., Comment: Machine Learning for Audio Workshop, Neural Information Processing Systems (NeurIPS) 2023, New Orleans, LA