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We generalise BMS algebras in three dimensions by the introduction of an arbitrary real parameter $\lambda$, recovering the standard algebras (BMS, extended BMS and Weyl-BMS) for $\lambda=-1$. We exhibit a realisation of the (centreless) Weyl $\lambda$-BMS algebra in terms of the symplectic structure on the space of solutions of the massless Klein-Gordon equation in $2+1$, using the eigenstates of the spacetime momentum operator. The quadratic Casimir of the Lorentz algebra plays an essential r\^ole in the construction. The Weyl $\lambda$-BMS algebra admits a three-parameter family of central extensions, resulting in the (centrally extended) Weyl-BMS algebra, which we reformulate in terms of operator product expansions. We construct the BRST complex of a putative Weyl-BMS string and show that the BRST cohomology is isomorphic to the chiral ring of a topologically twisted $N=2$ superconformal field theory. We also comment on the obstructions to obtaining a conformal BMS Lie algebra -- that is, one that includes in addition the special-conformal generators -- and the need to consider a W-algebra. We then construct the quantum version of this W-algebra in terms of operator product expansions. We show that this W-algebra does not admit a BRST complex., Comment: v2: 22 pages; references added

Geometric reconstruction and SLAM with endoscopic images have seen significant advancements in recent years. In most medical specialties, the endoscopes used are monocular, and the algorithms applied are typically extensions of those designed for external environments, resulting in 3D reconstructions up to an unknown scale factor. In this paper, we take advantage of the fact that standard endoscopes are equipped with near-light sources positioned at a small but non-zero baseline from the camera. By leveraging the inverse-square law of light decay, we enable, for the first time, monocular reconstructions with accurate metric scale. This paves the way to transform any endoscope into a metric device, which is essential for practical applications such as measuring polyps, stenosis, or the extent of tissue affected by disease., Comment: ICRA 2025

Recent advancements in music generation are raising multiple concerns about the implications of AI in creative music processes, current business models and impacts related to intellectual property management. A relevant discussion and related technical challenge is the potential replication and plagiarism of the training set in AI-generated music, which could lead to misuse of data and intellectual property rights violations. To tackle this issue, we present the Music Replication Assessment (MiRA) tool: a model-independent open evaluation method based on diverse audio music similarity metrics to assess data replication. We evaluate the ability of five metrics to identify exact replication by conducting a controlled replication experiment in different music genres using synthetic samples. Our results show that the proposed methodology can estimate exact data replication with a proportion higher than 10%. By introducing the MiRA tool, we intend to encourage the open evaluation of music-generative models by researchers, developers, and users concerning data replication, highlighting the importance of the ethical, social, legal, and economic consequences. Code and examples are available for reproducibility purposes., Comment: Accepted at ISMIR 2024

We introduce a surrogate-based black-box optimization method, termed Polynomial-model-based optimization (PMBO). The algorithm alternates polynomial approximation with Bayesian optimization steps, using Gaussian processes to model the error between the objective and its polynomial fit. We describe the algorithmic design of PMBO and compare the results of the performance of PMBO with several optimization methods for a set of analytic test functions. The results show that PMBO outperforms the classic Bayesian optimization and is robust with respect to the choice of its correlation function family and its hyper-parameter setting, which, on the contrary, need to be carefully tuned in classic Bayesian optimization. Remarkably, PMBO performs comparably with state-of-the-art evolutionary algorithms such as the Covariance Matrix Adaptation -- Evolution Strategy (CMA-ES). This finding suggests that PMBO emerges as the pivotal choice among surrogate-based optimization methods when addressing low-dimensional optimization problems. Hereby, the simple nature of polynomials opens the opportunity for interpretation and analysis of the inferred surrogate model, providing a macroscopic perspective on the landscape of the objective function., Comment: 16 pages, 6 figures

Ever since the initial experimental observation of correlated insulators and superconductivity in the flat Dirac bands of magic angle twisted bilayer graphene, a search for the microscopic description that explains its strong electronic interactions has begun. While the seemingly disagreeing electronic transport and scanning tunneling microscopy experiments suggest a dichotomy between local and extended electronic orbitals, definitive experimental evidence merging the two patterns together has been much sought after. Here, we report on the local photothermoelectric measurements in the flat electronic bands of twisted symmetric trilayer graphene (TSTG). We use a cryogenic scanning near-field optical microscope with an oscillating atomic force microscopy (AFM) tip irradiated by the infrared photons to create a nanoscopic hot spot in the planar samples, which generates a photocurrent that we probe globally. We observe a breakdown of the non-interacting Mott formalism at low temperatures (10K), signaling the importance of the electronic interactions. Our measurements reveal an overall negative offset of the Seebeck coefficient and significant peaks of the local photovoltage values at all positive integer fillings of the TSTG's moir\'e superlattice, further indicating a substantial deviation from the classical two-band semiconductor Seebeck response. We explain these observations using the interacting topological heavy-fermion model. In addition, our data reveal a spatial variation of the relative interaction strength dependent on the measured local twist angle (1.2{\deg} - 1.6{\deg}). Our findings provide experimental evidence of heavy fermion behaviour in the topological flat bands of moir\'e graphene and epitomize an avenue to apply local thermoelectric measurements to other strongly correlated materials in the disorder-free limit.

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.

This article presents a general framework for the transport of probability measures towards minimum divergence generative modeling and sampling using ordinary differential equations (ODEs) and Reproducing Kernel Hilbert Spaces (RKHSs), inspired by ideas from diffeomorphic matching and image registration. A theoretical analysis of the proposed method is presented, giving a priori error bounds in terms of the complexity of the model, the number of samples in the training set, and model misspecification. An extensive suite of numerical experiments further highlights the properties, strengths, and weaknesses of the method and extends its applicability to other tasks, such as conditional simulation and inference.

Some of the synthesis methods and physical properties of iron-oxide based magnetic nanoparticles such as Fe3-xO4 and CoxFe3-xO4 are reviewed because of their interest in health, environmental applications, and ultra-high-density magnetic recording. Unlike high crystalline quality nanoparticles larger than a few nanometers that show bulk-like magnetic and electronic properties, nanostructures with increasing structural defects yield a progressive worsening of their general performance due to frozen magnetic disorder and local breaking of their crystalline symmetry. Thus, it is shown that single-crystal, monophasic nanoparticles do not exhibit significant surface or finite-size effects, such as spin canting, reduced saturation magnetization, high closure magnetic fields, hysteresis-loop shift or dead magnetic layer features which are mostly associated with crystallographic defective systems. Besides, the key role of the nanoparticle coating, surface anisotropy, and inter-particle interactions are discussed. Finally, the results of some single particle techniques -- magnetic force microscopy, X-ray photoemission electron microscopy, and electron magnetic chiral dichroism -- that allow studying individual nanoparticles down to sub-nanometer resolution with element, valence and magnetic selectivity, are presented. All in all, the intimate, fundamental correlation of the nanostructure (crystalline, chemical, magnetic) to the physical properties of the nanoparticles is ascertained.

Inspired by geometrically frustrated magnetic systems, we present the optical response of three cases of hexagonal lattices of plasmonic nanoelements. All of them were designed using a metal-insulator-metal configuration to enhance absorption of light, with elements in close proximity to exploit near-field coupling, and with triangular symmetry to induce frustration of the dipolar polarization in the gaps between neighboring structures. Both simulations and experimental results demonstrate that these systems behave as perfect absorbers in the visible and/or the near infrared. Besides, the numerical study of the time evolution shows that they exhibit a relatively extended time response over which the system fluctuates between localized and collective modes. It is of particular interest the echoed excitation of surface lattice resonance modes, which are still present at long times because of the geometric frustration inherent to the triangular lattice. It is worth noting that the excitation of collective modes is also enhanced in other types of arrays where dipolar excitations of the nanoelements are hampered by the symmetry of the array. However, we would like to emphasize that the enhancement in triangular arrays can be significantly larger because of the inherent geometric incompatibility of dipolar excitations and three-fold symmetry axes.

We present a system consisting of two stacked chiral plasmonic nanoelements, so-called triskelia, that exhibits a high degree of circular dichroism. The optical modes arising from the interactions between the two elements are the main responsible for the dichroic signal. Their excitation in the absorption cross section is favored when the circular polarization of the light is opposite to the helicity of the system, so that an intense near-field distribution with 3D character is excited between the two triskelia, which in turn causes the dichroic response. Therefore, the stacking, in itself, provides a simple way to tune both the value of the circular dichroism, up to 60%, and its spectral distribution in the visible and near infrared range. We show how these interaction-driven modes can be controlled by finely tuning the distance and the relative twist angle between the triskelia, yielding maximum values of the dichroism at 20{\deg} and 100{\deg} for left- and right-handed circularly polarized light, respectively. Despite the three-fold symmetry of the elements, these two situations are not completely equivalent since the interplay between the handedness of the stack and the chirality of each single element breaks the symmetry between clockwise and anticlockwise rotation angles around 0{\deg}. This reveals the occurrence of clear helicity-dependent resonances. The proposed structure can be thus finely tuned to tailor the dichroic signal for applications at will, such as highly efficient helicity-sensitive surface spectroscopies or single-photon polarization detectors, among others.

Plasmonic lattice nanostructures are of technological interest because of their capacity to manipulate light below the diffraction limit. Here, we present a detailed study of dark and bright modes in the visible and near-infrared energy regime of an inverted plasmonic honeycomb lattice by a combination of Au+ focused ion beam lithography with nanometric resolution, optical and electron spectroscopy, and finite-difference time-domain simulations. The lattice consists of slits carved in a gold thin film, exhibiting hotspots and a set of bright and dark modes. We proposed that some of the dark modes detected by electron energy-loss spectroscopy are caused by antiferroelectric arrangements of the slit polarizations with two times the size of the hexagonal unit cell. The plasmonic resonances take place within the 0.5_2 eV energy range, indicating that they could be suitable for a synergistic coupling with excitons in two-dimensional transition metal dichalcogenides materials or for designing nanoscale sensing platforms based on near-field enhancement over a metallic surface.

We present an optimization-based framework to construct confidence intervals for functionals in constrained inverse problems, ensuring valid one-at-a-time frequentist coverage guarantees. Our approach builds upon the now-called strict bounds intervals, originally pioneered by Burrus (1965) and Rust and Burrus (1972), which offer ways to directly incorporate any side information about the parameters during inference without introducing external biases. This family of methods allows for uncertainty quantification in ill-posed inverse problems without needing to select a regularizing prior. By tying optimization-based intervals to an inversion of a constrained likelihood ratio test, we translate interval coverage guarantees into type I error control and characterize the resulting interval via solutions to optimization problems. Along the way, we refute the Burrus conjecture, which posited that, for possibly rank-deficient linear Gaussian models with positivity constraints, a correction based on the quantile of the chi-squared distribution with one degree of freedom suffices to shorten intervals while maintaining frequentist coverage guarantees. Our framework provides a novel approach to analyzing the conjecture, and we construct a counterexample employing a stochastic dominance argument, which we also use to disprove a general form of the conjecture. We illustrate our framework with several numerical examples and provide directions for extensions beyond the Rust-Burrus method for nonlinear, non-Gaussian settings with general constraints., Comment: 54 pages, V3: minor changes in related work and discussion

Ultrafast manipulation of vibrational coherence is an emergent route to control the structure of solids. However, this strategy can only induce long-range correlations and cannot modify atomic structure locally, which is required in many technologically-relevant phase transitions. Here, we demonstrate that ultrafast lasers can generate incoherent structural fluctuations which are more efficient for material control than coherent vibrations, extending optical control to a wider range of materials. We observe that local, non-equilibrium lattice distortions generated by a weak laser pulse reduce the energy barrier to switch between insulating and metallic states in vanadium dioxide by 6%. Seeding inhomogeneous structural-fluctuations presents an alternative, more energy efficient, route for controlling materials that may be applicable to all solids, including those used in data and energy storage devices.

We propose a new approach to 3D reconstruction from sequences of images acquired by monocular endoscopes. It is based on two key insights. First, endoluminal cavities are watertight, a property naturally enforced by modeling them in terms of a signed distance function. Second, the scene illumination is variable. It comes from the endoscope's light sources and decays with the inverse of the squared distance to the surface. To exploit these insights, we build on NeuS, a neural implicit surface reconstruction technique with an outstanding capability to learn appearance and a SDF surface model from multiple views, but currently limited to scenes with static illumination. To remove this limitation and exploit the relation between pixel brightness and depth, we modify the NeuS architecture to explicitly account for it and introduce a calibrated photometric model of the endoscope's camera and light source. Our method is the first one to produce watertight reconstructions of whole colon sections. We demonstrate excellent accuracy on phantom imagery. Remarkably, the watertight prior combined with illumination decline, allows to complete the reconstruction of unseen portions of the surface with acceptable accuracy, paving the way to automatic quality assessment of cancer screening explorations, measuring the global percentage of observed mucosa., Comment: 12 pages, 7 figures, 1 table, submitted to MICCAI 2023

The requirements for broadband photodetection are becoming exceedingly demanding in hyperspectral imaging. Whilst intrinsic photoconductor arrays based on mercury cadmium telluride represent the most sensitive and suitable technology, their optical spectrum imposes a narrow spectral range with a sharp absorption edge that cuts their operation to < 25 um. Here, we demonstrate a giant ultra-broadband photoconductivity in twisted double bilayer graphene heterostructures spanning a spectral range of 2 - 100 um with internal quantum efficiencies ~ 40 % at speeds of 100 kHz. The giant response originates from unique properties of twist-decoupled heterostructures including pristine, crystal field induced terahertz band gaps, parallel photoactive channels, and strong photoconductivity enhancements caused by interlayer screening of electronic interactions by respective layers acting as sub-atomic spaced proximity screening gates. Our work demonstrates a rare instance of an intrinsic infrared-terahertz photoconductor that is complementary metal-oxide-semiconductor compatible and array integratable, and introduces twist-decoupled graphene heterostructures as a viable route for engineering gapped graphene photodetectors with 3D scalability.

Single-view depth estimation can be remarkably effective if there is enough ground-truth depth data for supervised training. However, there are scenarios, especially in medicine in the case of endoscopies, where such data cannot be obtained. In such cases, multi-view self-supervision and synthetic-to-real transfer serve as alternative approaches, however, with a considerable performance reduction in comparison to supervised case. Instead, we propose a single-view self-supervised method that achieves a performance similar to the supervised case. In some medical devices, such as endoscopes, the camera and light sources are co-located at a small distance from the target surfaces. Thus, we can exploit that, for any given albedo and surface orientation, pixel brightness is inversely proportional to the square of the distance to the surface, providing a strong single-view self-supervisory signal. In our experiments, our self-supervised models deliver accuracies comparable to those of fully supervised ones, while being applicable without depth ground-truth data.

We construct a Lorentz invariant massive particle model in (2+1) space-time with an enlarged set of symmetries which includes Bondi-Metzner-Sachs (BMS) translations (supertranslations), using the non-linear realization framework. The Hamiltonian formalism for the resulting Lagrangian is constructed, and the infinite phase-space constraints and the set of gauge transformations are analysed. We also compute the massless limit of the theory in phase-space. After eliminating the gauge degrees of freedom, the physical reduced space is left only with the degrees of freedom of a standard Poincar\'e particle but with a residual set of symmetries that we prove to be BMS. A similar result for the massless limit, including in this case superrotations, is pointed out., Comment: 30 pages, version accepted for publication in JHEP

The explosion of content generated by artificial intelligence (AI) models has initiated a cultural shift in arts, music, and media, whereby roles are changing, values are shifting, and conventions are challenged. The vast, readily available dataset of the Internet has created an environment for AI models to be trained on any content on the Web. With AI models shared openly and used by many globally, how does this new paradigm shift challenge the status quo in artistic practices? What kind of changes will AI technology bring to music, arts, and new media?

We introduce a priori Sobolev-space error estimates for the solution of nonlinear, and possibly parametric, PDEs using Gaussian process and kernel based methods. The primary assumptions are: (1) a continuous embedding of the reproducing kernel Hilbert space of the kernel into a Sobolev space of sufficient regularity; and (2) the stability of the differential operator and the solution map of the PDE between corresponding Sobolev spaces. The proof is articulated around Sobolev norm error estimates for kernel interpolants and relies on the minimizing norm property of the solution. The error estimates demonstrate dimension-benign convergence rates if the solution space of the PDE is smooth enough. We illustrate these points with applications to high-dimensional nonlinear elliptic PDEs and parametric PDEs. Although some recent machine learning methods have been presented as breaking the curse of dimensionality in solving high-dimensional PDEs, our analysis suggests a more nuanced picture: there is a trade-off between the regularity of the solution and the presence of the curse of dimensionality. Therefore, our results are in line with the understanding that the curse is absent when the solution is regular enough.

In order to discern how reflective practice functions in an interactive context, this chapter offers an initial approach to understanding how pre-service teachers construct and represent their teacher identities, using a series of interactions designed to stimulate joint reflection in the framework of the proPIC project. The analysis focuses on the use of personal pronouns as a means of understanding the arguments on which the participating students construct their individual teacher identities. The authors first identify the personal pronouns that the students use to transmit their teacher identities and then provide examples of how these identities are developed by using the pronouns in the course of the reflective interaction. [For the complete volume, "Promoting Professionalism, Innovation and Transnational Collaboration: A New Approach to Foreign Language Teacher Education," see ED624249.]

Pre-service training of language teachers requires tools that foster the development of teaching competences, and the construction of their teacher identities. One crucial tool in that respect is the e-portfolio. Thus, in the framework of the proPIC Europa project, the e-portfolio is a dynamic artefact in which students collate learning evidence that helps to construct their teacher identity. This chapter presents the concept of the e-portfolio as it has been applied in the proPIC project, together with the associated learning objectives and constituent elements. It looks at the e-portfolio from the perspective of the common formative actions carried out across the participating Higher Education Institutions (HEIs) and the specific actions implemented in each context, which were based in the context of the overall study programme. The chapter also presents some of the outcomes, showcasing a number of e-portfolios that were constructed by the pre-service teachers participating in the project.

We present a general kernel-based framework for learning operators between Banach spaces along with a priori error analysis and comprehensive numerical comparisons with popular neural net (NN) approaches such as Deep Operator Net (DeepONet) [Lu et al.] and Fourier Neural Operator (FNO) [Li et al.]. We consider the setting where the input/output spaces of target operator $\mathcal{G}^\dagger\,:\, \mathcal{U}\to \mathcal{V}$ are reproducing kernel Hilbert spaces (RKHS), the data comes in the form of partial observations $\phi(u_i), \varphi(v_i)$ of input/output functions $v_i=\mathcal{G}^\dagger(u_i)$ ($i=1,\ldots,N$), and the measurement operators $\phi\,:\, \mathcal{U}\to \mathbb{R}^n$ and $\varphi\,:\, \mathcal{V} \to \mathbb{R}^m$ are linear. Writing $\psi\,:\, \mathbb{R}^n \to \mathcal{U}$ and $\chi\,:\, \mathbb{R}^m \to \mathcal{V}$ for the optimal recovery maps associated with $\phi$ and $\varphi$, we approximate $\mathcal{G}^\dagger$ with $\bar{\mathcal{G}}=\chi \circ \bar{f} \circ \phi$ where $\bar{f}$ is an optimal recovery approximation of $f^\dagger:=\varphi \circ \mathcal{G}^\dagger \circ \psi\,:\,\mathbb{R}^n \to \mathbb{R}^m$. We show that, even when using vanilla kernels (e.g., linear or Mat\'{e}rn), our approach is competitive in terms of cost-accuracy trade-off and either matches or beats the performance of NN methods on a majority of benchmarks. Additionally, our framework offers several advantages inherited from kernel methods: simplicity, interpretability, convergence guarantees, a priori error estimates, and Bayesian uncertainty quantification. As such, it can serve as a natural benchmark for operator learning., Comment: 35 pages, 10 figures

Second-order superlattices form when moir\'e superlattices of similar periodicities interfere with each other, leading to even larger superlattice periodicities. These crystalline structures have been engineered utilizing two-dimensional (2D) materials such as graphene and hexagonal boron nitride (hBN) under specific alignment conditions. Such specific alignment has shown to play a crucial role in facilitating correlation-driven topological phases featuring the quantized anomalous Hall effect. While signatures of second-order superlattices have been identified in magnetotransport experiments, any real-space visualization is lacking to date. In this work, we present \NT{electronic transport measurements and cryogenic nanoscale photovoltage (PV) measurements} that reveal a second-order superlattice in magic-angle twisted bilayer graphene closely aligned to hBN. This is evidenced by long-range periodic photovoltage modulations across the entire sample backed by the corresponding electronic transport features. Supported by theoretical calculations, our experimental data show that even minuscule strain and twist-angle variations on the order of 0.01$^\circ$ can lead to a drastic change of the second-order superlattice structure between local one-dimensional, square or triangular types. Our real-space observations therefore serve as a strong `magnifying glass' for strain and twist angle and can shed new light on the mechanisms responsible for the breaking of spatial symmetries in twisted bilayer graphene, and pave an avenue to engineer long-range superlattice structures in 2D materials using strain fields., Comment: 10 pages, 5 figures