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The extraction of causal information from textual data is crucial in the industry for identifying and mitigating potential failures, enhancing process efficiency, prompting quality improvements, and addressing various operational challenges. This paper presents a study on the development of automated methods for causal information extraction from actual industrial documents in the semiconductor manufacturing industry. The study proposes two types of causal information extraction methods, single-stage sequence tagging (SST) and multi-stage sequence tagging (MST), and evaluates their performance using existing documents from a semiconductor manufacturing company, including presentation slides and FMEA (Failure Mode and Effects Analysis) documents. The study also investigates the effect of representation learning on downstream tasks. The presented case study showcases that the proposed MST methods for extracting causal information from industrial documents are suitable for practical applications, especially for semi structured documents such as FMEAs, with a 93\% F1 score. Additionally, MST achieves a 73\% F1 score on texts extracted from presentation slides. Finally, the study highlights the importance of choosing a language model that is more aligned with the domain and in-domain fine-tuning., Comment: 17 pages, 2 figures

Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions about aspects that AI systems must fulfill to be considered trustworthy. In this paper, we synthesize existing conceptualizations of trustworthy AI along six requirements: 1) human agency and oversight, 2) fairness and non-discrimination, 3) transparency and explainability, 4) robustness and accuracy, 5) privacy and security, and 6) accountability. For each one, we provide a definition, describe how it can be established and evaluated, and discuss requirement-specific research challenges. Finally, we conclude this analysis by identifying overarching research challenges across the requirements with respect to 1) interdisciplinary research, 2) conceptual clarity, 3) context-dependency, 4) dynamics in evolving systems, and 5) investigations in real-world contexts. Thus, this paper synthesizes and consolidates a wide-ranging and active discussion currently taking place in various academic sub-communities and public forums. It aims to serve as a reference for a broad audience and as a basis for future research directions., Comment: Accepted in Frontiers in Big Data and AI, Research Topic: Towards Fair AI for Trustworthy Artificial Intelligence

There are strong arguments that quantum mechanics may be nonlinear in its dynamics. A discovery of nonlinearity would hint at a novel understanding of the interplay between gravity and quantum field theory, for example. As such, experiments searching for potential nonlinear effects in the electromagnetic sector are important. Here we outline such an experiment, consisting of a stream of random bits (which were generated using Rigetti's Aspen-M-3 chip) as input to an RF signal generator coupled to a cryogenic detector. Projective measurements of the qubit state, which is originally prepared in an equal superposition, serve as the random binary output of a signal generator. Thereafter, spectral analysis of the RF detector would yield a detectable excess signal predicted to arise from such a nonlinear effect. A comparison between the projective measurements of the quantum bits vs the classical baseline showed no power excess. This sets a new limit on the electromagnetic nonlinearity parameter $|\epsilon| \lessapprox 1.15 \times 10^{-12}$, at a 90.0% confidence level. This is the most stringent limit on nonlinear quantum mechanics thus far and an improvement by nearly a factor of 50 over the previous experimental limit.

We introduce "book links" as a generalization of braids in open book decompositions; this new class of objects includes both braids and plats as special cases. We then prove a version of Markov's theorem in this general setting by extending the theory of open book foliations., Comment: 11 pages, 9 figures

This study examines the pitch content in traditional Ghanaian seperewa (Akan harp-lute) songs, utilizing a unique dataset from field recordings of the mid-twentieth century. We selected 71 songs and used Demucs to isolate vocals from instrumental tracks. We then retrieved the F0 content from these isolated tacks and applied Gaussian Mixture Models (GMM) to approximate musical scales. Comparative F0 analysis between vocals and seperewa revealed higher microtonal deviations from equal temperament in vocal tracks. We also note challenges in using MIR tools for musical scale approximation in non-Western music. Our research contributes to the quantitative study of pitch in traditional music of Sub-Saharan Africa.

A new age-structured diffusive model for the mathematical modelling of epidemics is suggested. The model can be considered as a generalization of two models suggested earlier for the same purposes. The Lie symmetry classification of the model is derived. It is shown that the model admits an infinite-dimensional Lie algebra of invariance. Using the Lie symmetries, exact solutions, in particular those of the travelling wave types and in terms of special functions, are constructed. An example of application of the correctly-specified exact solution for calculation of total numbers of infected individuals during an epidemic is presented.

Learning-based image compression methods have improved in recent years and started to outperform traditional codecs. However, neural-network approaches can unexpectedly introduce visual artifacts in some images. We therefore propose methods to separately detect three types of artifacts (texture and boundary degradation, color change, and text corruption), to localize the affected regions, and to quantify the artifact strength. We consider only those regions that exhibit distortion due solely to the neural compression but that a traditional codec recovers successfully at a comparable bitrate. We employed our methods to collect artifacts for the JPEG AI verification model with respect to HM-18.0, the H.265 reference software. We processed about 350,000 unique images from the Open Images dataset using different compression-quality parameters; the result is a dataset of 46,440 artifacts validated through crowd-sourced subjective assessment. Our proposed dataset and methods are valuable for testing neural-network-based image codecs, identifying bugs in these codecs, and enhancing their performance. We make source code of the methods and the dataset publicly available.

Squeezed states of light belong to the most prominent nonclassical resources. They have compelling applications in metrology, which has been demonstrated by their routine exploitation for improving the sensitivity of a gravitational-wave detector since 2010. Here, we report on the direct measurement of 15 dB squeezed vacuum states of light and their application to calibrate the quantum efficiency of photoelectric detection. The object of calibration is a customized InGaAs positive intrinsic negative (p-i-n) photodiode optimized for high external quantum efficiency. The calibration yields a value of 99.5% with a 0.5% (k = 2) uncertainty for a photon flux of the order 10^17/s at a wavelength of 1064 nm. The calibration neither requires any standard nor knowledge of the incident light power and thus represents a valuable application of squeezed states of light in quantum metrology.

Einstein's General Theory of Relativity predicts that accelerating mass distributions produce gravitational radiation, analogous to electromagnetic radiation from accelerating charges. These gravitational waves have not been directly detected to date, but are expected to open a new window to the Universe in the near future. Suitable telescopes are kilometre-scale laser interferometers measuring the distance between quasi free-falling mirrors. Recent advances in quantum metrology may now provide the required sensitivity boost. So-called squeezed light is able to quantum entangle the high-power laser fields in the interferometer arms, and could play a key role in the realization of gravitational wave astronomy., Comment: Review article about the development of squeezed light for gravitational wave detectors

Background: Ultra-high dose rate proton therapy shows promise in tissue sparing by enhancing therapeutic ratio through the FLASH effect. In radiotherapy, accurate in vivo dosimetry is crucial for quality assurance, but remains challenging for UHDR as existing systems lack spatial and temporal resolution to verify dose and dose rate in complex anatomical regions, especially for PBS proton therapy. Purpose: To develop and evaluate a novel 3D surface dosimetry method for UHDR PBS proton therapy using high-speed imaging of a scintillator array for real-time, high-resolution surface dose monitoring. The spatial, temporal, and dosimetric components are validated via imaging of a QA phantom and comparison against TPS predictions. Methods: A deformable multi-element scintillator array was designed with 7.5mm element pitch and 0.5mm inter-element gap. Scintillation linearity with dose was evaluated with variation in response to increasing imaging and irradiation angles. WED testing evaluated beam attenuation at two energy levels. Scintillation emission was imaged at 1kHz and mesh position was monitored via 2-camera stereovision. System setup was validated using a 3D QA phantom to assess spatial accuracy and guide setup correction. Stereovision properties of array elements guided angular correction and geometric transformation. Kernel-based residual spot fitting derived cumulative dose maps compared to TPS dose profile of 5x5cm UHDR PBS delivery using 3%/2mm gamma analysis. PBS and maximum dose rate maps were calculated. Results: Setup achieved average localization error of 0.62 mm, surpassing typical 1+ mm clinical threshold. Intensity correction based on angular information yielded cumulative spot dose uncertainty of ~1% (5.428mGy). Processed dose map compared to TPS via gamma analysis showed 99.9% passing rate at 3%/2mm. WED of the array measured 1.1mm, minimizing impact on dose distribution., Comment: 17 pages, 9 figures, submitted to Medical Physics

To safely and efficiently solve motion planning problems in multi-agent settings, most approaches attempt to solve a joint optimization that explicitly accounts for the responses triggered in other agents. This often results in solutions with an exponential computational complexity, making these methods intractable for complex scenarios with many agents. While sequential predict-and-plan approaches are more scalable, they tend to perform poorly in highly interactive environments. This paper proposes a method to improve the interactive capabilities of sequential predict-and-plan methods in multi-agent navigation problems by introducing predictability as an optimization objective. We interpret predictability through the use of general prediction models, by allowing agents to predict themselves and estimate how they align with these external predictions. We formally introduce this behavior through the free-energy of the system, which reduces under appropriate bounds to the Kullback-Leibler divergence between plan and prediction, and use this as a penalty for unpredictable trajectories.The proposed interpretation of predictability allows agents to more robustly leverage prediction models, and fosters a soft social convention that accelerates agreement on coordination strategies without the need of explicit high level control or communication. We show how this predictability-aware planning leads to lower-cost trajectories and reduces planning effort in a set of multi-robot problems, including autonomous driving experiments with human driver data, where we show that the benefits of considering predictability apply even when only the ego-agent uses this strategy., Comment: Videos and other additional materials can be found at https://romanchiva.github.io/PAProjectPage/

For a very long time, computational approaches to the design of new materials have relied on an iterative process of finding a candidate material and modeling its properties. AI has played a crucial role in this regard, helping to accelerate the discovery and optimization of crystal properties and structures through advanced computational methodologies and data-driven approaches. To address the problem of new materials design and fasten the process of new materials search, we have applied latest generative approaches to the problem of crystal structure design, trying to solve the inverse problem: by given properties generate a structure that satisfies them without utilizing supercomputer powers. In our work we propose two approaches: 1) conditional structure modification: optimization of the stability of an arbitrary atomic configuration, using the energy difference between the most energetically favorable structure and all its less stable polymorphs and 2) conditional structure generation. We used a representation for materials that includes the following information: lattice, atom coordinates, atom types, chemical features, space group and formation energy of the structure. The loss function was optimized to take into account the periodic boundary conditions of crystal structures. We have applied Diffusion models approach, Flow matching, usual Autoencoder (AE) and compared the results of the models and approaches. As a metric for the study, physical PyMatGen matcher was employed: we compare target structure with generated one using default tolerances. So far, our modifier and generator produce structures with needed properties with accuracy 41% and 82% respectively. To prove the offered methodology efficiency, inference have been carried out, resulting in several potentially new structures with formation energy below the AFLOW-derived convex hulls.

Charge density wave (CDW) orders in YNiC2 are studied by means of combined experimental and computational techniques. On the experimental side, single crystals grown by the floating-zone method were examined by means of X-ray diffraction, as well as transport and thermal techniques. Density functional theory (DFT) calculations founded on the experimentally determined parent and CDW-modified crystal structures provide details of electronic and phononic structures as well as electron-phonon coupling and resolve changes inflicted upon entering the different CDW phases. Thereby, contrasting effects of subsequently emerging CDW states characterized by incommensurate q_{1ic} and commensurate q_{2c} modulation vectors are revealed. The former state, on-setting below T_{1ic}~ 305 K, weakly modifies the electronic structure by opening an almost isotropic gap on a minor part of the Fermi surface (FS). The latter phase, which takes over below T_{2c}~ 272 K has a more pronounced impact on physical properties via a decomposition of larger parts of the FS. These dissimilar behaviors are directly reflected in the electronic transport anisotropy, which is significantly weakened in the q_{2c}-type CDW state. As revealed by our DFT studies, CDW phases are very close in energy and their origin is directly related to the anisotropy of electron-phonon coupling, which is linked to a specific orbital character of related FS sheets. Specific heat and thermal expansion studies reveal a nearly reversible first-order phase transition at around T_{2c}~ 272 K, where both CDW phases co-exist within a T-interval of about 10 K., Comment: Manuscript with 15 pages and 11 figures; Supplemental Material with 10 pages and 9 figures

Data scarcity and distribution shifts often hinder the ability of machine learning models to generalize when applied to proteins and other biological data. Self-supervised pre-training on large datasets is a common method to enhance generalization. However, striving to perform well on all possible proteins can limit model's capacity to excel on any specific one, even though practitioners are often most interested in accurate predictions for the individual protein they study. To address this limitation, we propose an orthogonal approach to achieve generalization. Building on the prevalence of self-supervised pre-training, we introduce a method for self-supervised fine-tuning at test time, allowing models to adapt to the test protein of interest on the fly and without requiring any additional data. We study our test-time training (TTT) method through the lens of perplexity minimization and show that it consistently enhances generalization across different models, their scales, and datasets. Notably, our method leads to new state-of-the-art results on the standard benchmark for protein fitness prediction, improves protein structure prediction for challenging targets, and enhances function prediction accuracy.

Using the folding procedure, we investigate the bound state of the $\Omega$+$\alpha$ system based on $\Omega$-$N$ ($^{5}S_{2}$) HAL QCD potential. Previous theoretical analyses have indicated the existence of a deeply bound ground state, which is attributed to the strong $\Omega$-nucleon interaction. By employing well-established parameterizations of nucleon density within the alpha particle, and the central HAL QCD $\Omega$-$N$ potential, we performed numerical calculations for the folding $\Omega$-$\alpha$ potential. Our results show that the $V_{\Omega\alpha}(r)$ potential can be accurately fitted using a Woods-Saxon function, with a phenomenological parameter $R = 1.1A^{1/3} \approx 1.74$ fm ($A=4$) in the asymptotic region where $2 < r < 3$ fm. We provide a thorough description of the corresponding numerical procedure. Our evaluation of the binding energy of the $\Omega$+$\alpha$ system within the cluster model is consistent with both previous and recent reported findings. To further validate the folding procedure, we also calculated the $\Xi$-$\alpha$ folding potential based on a simulation of the ESC08c $Y$-$N$ Nijmegen model. A comprehensive comparison between the $\Xi$-$\alpha$ folding and $\Xi$-$ \alpha$ phenomenological potentials is presented and discussed., Comment: 9 pages, 3 Figures

Chemical vapour deposition enables large-domain growth of ideal graphene, yet many applications of graphene require the controlled inclusion of specific defects. We present a one-step chemical vapour deposition procedure aimed at retaining the precursor topology when incorporated into the grown carbonaceous film. When azupyrene, the molecular analogue of the Stone-Wales defect in graphene, is used as a precursor, carbonaceous monolayers with a range of morphologies are produced as a function of the copper substrate growth temperature. The higher the substrate temperature during deposition, the closer the resulting monolayer is to ideal graphene. Analysis, with a set of complementary materials characterisation techniques, reveals morphological changes closely correlated with changes in the atomic adsorption heights, network topology, and concentration of 5-/7-membered carbon rings. The engineered defective carbon monolayers can be transferred to different substrates, potentially enabling applications in nanoelectronics, sensorics, and catalysis., Comment: manuscript and SI provided

We report the results of a spectroscopic survey of candidate T subdwarfs identified by the Backyard Worlds: Planet 9 program. Near-infrared spectra of 31 sources with red $J-W2$ colors and large $J$-band reduced proper motions show varying signatures of subsolar metallicity, including strong collision-induced H$_2$ absorption, obscured methane and water features, and weak K I absorption. These metallicity signatures are supported by spectral model fits and 3D velocities, indicating thick disk and halo population membership for several sources. We identify three new metal-poor T subdwarfs ([M/H] $\lesssim$ $-$0.5), CWISE J062316.19+071505.6, WISEA J152443.14$-$262001.8, and CWISE J211250.11-052925.2; and 19 new "mild" subdwarfs with modest metal deficiency ([M/H] $\lesssim$ $-$0.25). We also identify three metal-rich brown dwarfs with thick disk kinematics. We provide kinematic evidence that the extreme L subdwarf 2MASS J053253.46+824646.5 and the mild T subdwarf CWISE J113010.07+313944.7 may be part of the Thamnos population, while the T subdwarf CWISE J155349.96+693355.2 may be part of the Helmi stream. We define a metallicity classification system for T dwarfs that adds mild subdwarfs (d/sdT), subdwarfs (sdT), and extreme subdwarfs (esdT) to the existing dwarf sequence. We also define a metallicity spectral index that correlates with metallicities inferred from spectral model fits and iron abundances from stellar primaries of benchmark T dwarf companions. This expansion of the T dwarf classification system supports investigations of ancient, metal-poor brown dwarfs now being uncovered in deep imaging and spectroscopic surveys., Comment: 82 pages, 19 figures, accepted to ApJS

Dry synthesis is a highly versatile method for the fabrication of nanoporous metal films, since it enables easy and reproducible deposition of single or multi-layer(s) of nanostructured materials that can find intriguing applications in plasmonics, photochemistry and photocatalysis, to name a few. Here, we extend the use of this methodology to the preparation of copper nanoislands that represent an affordable and versatile example of disordered plasmonic substrate. We perform detailed characterizations of the system using several techniques such as spectroscopic ellipsometry, cathodoluminescence, electron energy loss spectroscopy, ultrafast pump-probe spectroscopy and second-harmonic generation with the aim to investigate the optical properties of these systems in an unprecedented systematic way. Our study represents the starting point for future applications of this new disordered plasmonic system ranging from sensing to photochemistry and photocatalysis.

Anomalous parity violation in four dimensions would be significant for phenomenology (baryogenesis, gravitational waves) and mathematical physics. Over the past decade, there has been a controversy in the literature as to whether free Weyl fermions give rise to (anomalous) parity violation in the trace of the energy momentum tensor; expressed by the Pontryagin densities $R\tilde R$ and $F\tilde F$ in the gravity and the gauge sector respectively. In Ref.$^1$, we have shown, using path integral methods, that the trace anomaly of a free Weyl fermion does not violate parity (i.e the absence of the Pontryagin density). In a subsequent work$^2$ we came to the stronger conclusion that for any theory compatible with dimensional regularisation, the Pontryagin-terms are equally absent. It is the \textit{finiteness} of the diffeomorphism, the Lorentz and the gauge anomalies that prevents anomalous parity violation., Comment: 15 pages, contribution to the 17th Marcel Grossmann Meeting

We present the conceptual design of an experiment to measure the electron's electric dipole moment (eEDM) using $^{138}$BaOH molecules in an optical lattice. The BaOH molecule is laser-coolable and highly sensitive to the eEDM, making it an attractive candidate for such a precision measurement, and capturing it in an optical lattice offers potentially very long coherence times. We study possibilities and limitations of this approach, identify the most crucial limiting factors and ways to overcome them. The proposed apparatus can reach a statistical error of $10^{-30}\,e\,$cm by measuring spin precession on a total number of $5 \times 10^9$ molecules over a span of 120 days.

This paper presents a novel approach for simulating plasma instabilities in tokamak plasmas using the piecewise field-aligned finite element method in combination with the particle-in-cell method. Our method traditionally aligns the computational grid but defines the basis functions in piecewise field-aligned coordinates to avoid grid deformation while naturally representing the field-aligned mode structures. This scheme is formulated and implemented numerically. It also applied to the unstructured triangular meshes in principle. We have conducted linear benchmark tests, which agree well with previous results and traditional schemes. Furthermore, multiple-$n$ simulations are also carried out as a proof of principle, demonstrating the efficiency of this scheme in nonlinear turbulence simulations within the framework of the finite element method., Comment: 27 pages, 10 figures, 20th edition of the European Fusion Theory Conference (EFTC 2023), 8th Asia-Pacific Conference on Plasma Physics(AAPPS-DPP2024)

Cloud formation and magnetic effects are both expected to significantly impact the structures and observable properties of hot Jupiter atmospheres. For some hot Jupiters, thermal ionization and condensation can coexist in a single atmosphere, and both processes are important. We present a grid of general circulation models across a wide range of irradiation temperatures with and without incorporating the effects of magnetism and cloud formation to investigate how these processes work in tandem. We find that clouds are present in the atmosphere at all modeled irradiation temperatures, while magnetic effects are negligible for planets with irradiation temperatures cooler than 2000 K. At and above this threshold, clouds and magnetic fields shape atmospheres together, with mutual feedback. Models that include magnetism, through their influence on the temperature structure, produce more longitudinally symmetric dayside cloud coverage and more equatorially concentrated clouds on the nightside and morning terminator. To indicate how these processes would affect observables, we generate bolometric thermal and reflected phase curves from these models. The combination of clouds and magnetic effects increases thermal phase curve amplitudes and decreases peak offsets more than either process does individually., Comment: 19 pages, 8 figures, submitted to ApJ

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: \textit{de novo} molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at \url{https://github.com/pluskal-lab/MassSpecGym}.

Instantons, semi-classical trajectories of quantum tunneling in imaginary time, have long been used to study thermodynamic and transport properties in a myriad of condensed matter and high energy systems. A recent experiment in superconducting circuits [Phys. Rev. Lett. 126, 197701, (2021)] provided first evidence for direct dynamical signatures of instantons (phase slips), manifested by order-unity inelastic decay probabilities for photons with which they interact, motivating the development of a scattering theory of instantons [Phys. Rev. Lett. 126, 137701, (2021)]. While this framework successfully predicted the measured inelastic decay rates of the photons for several experimental devices, it is valid only if the tunneling time of the instantons is much shorter than the relaxation time of the environment in which they are embedded, and requires a closed analytical expression for the instanton trajectory. Here, we amend these issues by incorporating numerical methods that lift some of the previously applied approximations. Our results agree with the experimental measurements, also for devices with shorter relaxation times, without fitting parameters. This framework should be useful in many other quantum field theoretical contexts., Comment: 15 pages, 4 figures. The first two authors contributed equally

We study the Higgs boson decay into dark matter (DM) in the framework of freeze-in at stronger coupling. Even though the Higgs-DM coupling is significant, up to order one, DM does not thermalize due to the Boltzmann suppression of its production at low temperatures. We find that this mechanism leads to observable Higgs decay into invisible final states with the branching fraction of 10% and below, while producing the correct DM relic abundance. This applies to the DM masses down to the MeV scale, which requires a careful treatment of the hadronic production modes. For DM masses below the muon threshold, the Boltzmann suppression is not operative and the freeze-in nature of the production mechanism is instead guaranteed by the smallness of the electron Yukawa coupling. As a result, MeV DM with a significant coupling to the Higgs boson remains non-thermal as long as the reheating temperature does not exceed $\mathcal{O}(100)\;$MeV. Our findings indicate that there are good prospects for observing light non-thermal DM via invisible Higgs decay at the LHC and FCC., Comment: 23 pages, 9 figures, formulas corrected

We consider extreme type-II superconductors modeled by the Ginzburg--Landau energy with a pinning term $a_\varepsilon(x)$, which we assume to be a bounded measurable function such that $b\leq a_\varepsilon(x)\leq 1$ for some constant $b>0$. A crucial feature of this type of superconductors is the occurrence of vortices, which appear above the so-called first critical field $H_{c_1}$. In this paper we estimate this value and characterize the behavior of the Meissner solution, the unique vortexless configuration that globally minimizes the energy below $H_{c_1}$. In addition, we show that beyond this value, for applied fields whose strength is slightly below the so-called superheating field $H_{sh}$, there exists a unique Meissner-type solution that locally minimizes the energy., Comment: 36 pages

Evanescently coupled waveguide arrays provide a tabletop platform to realize a variety of Hamiltonians, where physical waveguides correspond to the individual sites of a tight-binding lattice. Nontrivial spatial structure of the waveguide modes enriches this picture and uncovers further possibilities. Here, we demonstrate that the effective coupling between $p$-like modes of adjacent photonic waveguides changes its sign depending on their relative orientation vanishing for a proper alignment at a so-called magic angle. Using femtosecond laser-written waveguides, we demonstrate this experimentally for $p$-mode dimers and graphene-like photonic lattices exhibiting quasi-flat bands at this angle. We observe diffraction-free propagation of corner and bulk states providing a robust experimental evidence of a two-dimensional Aharonov-Bohm-like caging in an optically switchable system., Comment: 7 pages, 5 figures, 8 page-long Supplementary Materials

We report Magnetospheric Multiscale observations of oxygen ions (O+) during a coronal mass ejection in April 2023 when the solar wind was sub-Alfv\'enic and Alfv\'en wings formed. For the first time, O+ characteristics are studied at the contact region between the unshocked solar wind and the magnetosphere. The O+ ions show energies between 100s eV and ~30 keV. The possible sources are the ring current, the warm plasma cloak, and the ionosphere. The O+ ions exhibit bi-directional streaming along newly-formed closed field lines (CFLs), and dominantly anti-parallel on earlier-formed CFLs. Escaping O+ ions in the unshocked solar wind are observed. During the recovery phase, the O+ pitch-angle distribution associated with flux tubes shows dispersion, indicating potential loss to the solar wind. Our results show escaping as well as trapped O+ ions in the region where a magnetic cloud, an Alfv\'en wing, and magnetospheric field lines are mixed.

We investigate the mechanics of two ribbons bound at one end and pulled apart at the other ends. We observe the development of a boundary layer at their junction and provide simple analytical expressions for its geometry. Remarkably, the ratio of the bending stiffnesses fully prescribes its shape independently of the pulling force. Our results challenge the standard assumption of neglecting bending stiffness of thin shells at large tensile loading. In addition, we show how the rotational stiffness of the structure exhibits a non-linear but universal behavior. Our findings are relevant to a number of systems where slender structures such as rods and plates are coupled and provide a straightforward method for measuring stiffness of thin membranes.

With the arrival of the next generation of ultra-deep optical imaging surveys reaching $\mu_V$$\sim$30 mag/arcsec$^2$ (3$\sigma$; 10"$\times$10"), the removal of scattered light due to the point spread function (PSF) effect remains a critical step for the scientific exploitation of the low surface brightness information contained in these data. Because virtually all pixels in the ground-based images are affected by an unwanted screen of light with a brightness greater than $\mu_V$$\sim$29 mag/arcsec$^2$, the characterization of the extended PSF (R$>$5 arcmin) is mandatory. We describe the procedure used to construct the extended PSFs of the LIGHTS survey in the g- and r-band images taken with the Large Binocular Cameras (LBCs) of the Large Binocular Telescope (LBT). We produce PSFs with a radial extension of 7.5 arcmins. These are later extended to 30 arcmins following an empirically motivated power-law extrapolation of their behaviour in their outermost regions. As an example of the application of our methodology, we subtract the scattered light around the galaxy NGC3198. The result of this subtraction clearly shows the outermost parts of the galaxy's disc, which have been obscured by the influence of nearby bright stars. Following a reproducible science philosophy, we make all the PSF models and the scripts used to do the analysis of this paper publicly available., Comment: 13 pages, 14 figures, 1 tables

We investigate the best constant $J(n,d)$ such that Jackson's inequality \[ \inf_{\mathrm{deg}(g) \leq d} \|f - g\|_{\infty} \leq J(n,d) \, s(f), \] holds for all functions $f$ on the hypercube $\{0,1\}^n$, where $s(f)$ denotes the sensitivity of $f$. We show that the quantity $J(n, 0.499n)$ is bounded below by an absolute positive constant, independent of $n$. This complements Wagner's theorem, which establishes that $J(n,d)\leq 1 $. As a first application we show that reverse Bernstein inequality fails in the tail space $L^{1}_{\geq 0.499n}$ improving over previously known counterexamples in $L^{1}_{\geq C \log \log (n)}$. As a second application, we show that there exists a function $f : \{0,1\}^n \to [-1,1]$ whose sensitivity $s(f)$ remains constant, independent of $n$, while the approximate degree grows linearly with $n$. This result implies that the sensitivity theorem $s(f) \geq \Omega(\mathrm{deg}(f)^C)$ fails in the strongest sense for bounded real-valued functions even when $\mathrm{deg}(f)$ is relaxed to the approximate degree. We also show that in the regime $d = (1 - \delta)n$, the bound \[ J(n,d) \leq C \min\{\delta, \max\{\delta^2, n^{-2/3}\}\} \] holds. Moreover, when restricted to symmetric real-valued functions, we obtain $J_{\mathrm{symmetric}}(n,d) \leq C/d$ and the decay $1/d$ is sharp. Finally, we present results for a subspace approximation problem: we show that there exists a subspace $E$ of dimension $2^{n-1}$ such that $\inf_{g \in E} \|f - g\|_{\infty} \leq s(f)/n$ holds for all $f$.

By analyzing the spectral energy distributions (SEDs) of resolved stars in nearby galaxies, we can constrain their stellar properties and line-of-sight dust extinction. From the Scylla survey, we obtain ultraviolet to near-infrared photometry from Wide Field Camera 3 onboard the {\it Hubble Space Telescope} for more than 1.5 million stars in the SMC and LMC. We use the Bayesian Extinction and Stellar Tool (BEAST) to analyze the multi-band SEDs of these sources and characterize their initial masses, ages, metallicities, distances, and line-of-sight extinction properties (e.g.~$A_V$, $R_V$). We apply quality cuts and perform validation simulations to construct a catalog of over 550,000 stars with high-reliability SED fits, which we use to analyze the stellar content and extinction properties of the SMC and LMC. We detect stars with masses as low as 0.6 $M_{\odot}$. Observed stellar age distributions show a jump in stars around 6 Gyrs ago, which is in agreement with other star-formation histories. Extinctions ($A_V$) in both galaxies follow a log-normal distribution. We compare $A_V$ with ancillary gas and dust tracers like $HI$, $H_\alpha$, and far infrared (FIR) dust emission and find positive correlations on a field-by-field basis. We convert observed $A_V$ to predicted dust surface densities using the Draine et. al. (2014) model and find $A_V$-based dust surface densities are a factor of $\sim$2.5 lower than observed FIR-based dust surface densities, a correction factor similar to other studies., Comment: Submitted to ApJ, 31 pages

Grasping is a fundamental skill in robotics with diverse applications across medical, industrial, and domestic domains. However, current approaches for predicting valid grasps are often tailored to specific grippers, limiting their applicability when gripper designs change. To address this limitation, we explore the transfer of grasping strategies between various gripper designs, enabling the use of data from diverse sources. In this work, we present an approach based on equivariant diffusion that facilitates gripper-agnostic encoding of scenes containing graspable objects and gripper-aware decoding of grasp poses by integrating gripper geometry into the model. We also develop a dataset generation framework that produces cluttered scenes with variable-sized object heaps, improving the training of grasp synthesis methods. Experimental evaluation on diverse object datasets demonstrates the generalizability of our approach across gripper architectures, ranging from simple parallel-jaw grippers to humanoid hands, outperforming both single-gripper and multi-gripper state-of-the-art methods., Comment: 8 pages

Simultaneous space-time focusing (SSTF) is often claimed to reduce the longitudinal extent of the high-intensity region near the focus, in contradiction to the original work on this topic. Here we seek to address this confusion by using numerical and analytic methods to investigate the degree of localisation of the spatio-spectral and spatio-temporal amplitudes and intensities of a SSTF pulse. The analytic method yields, for bi-Gaussian input pulses, expressions for the on-axis bandwidth, pulse duration, and pulse-front tilt (PFT) of the SSTF pulse, and is found to be in excellent agreement with numerical calculations. To provide further insight, we propose a method for determining the transverse input profile of a non-SSTF pulse with equivalent spatial focusing. We find that the longitudinal variations of the peak axial intensities of the SSTF and spatially equivalent (SE) pulses are the same, apart from a constant factor, and hence that SSTF does not constrain the region of high intensity more than a non-SSTF pulse with equivalent focusing. We demonstrate that one possible cause of confusion in the literature arises from the fact that a simplistic method for calculating the pulse intensity exaggerates the degree of intensity localisation owing to its failure to account for the spatio-temporal and spatio-spectral couplings inherent to SSTF pulses.

The problem of detecting fake data inspires the following seemingly simple mathematical question. Sample a data point $X$ from the standard normal distribution in $\mathbb{R}^n$. An adversary observes $X$ and corrupts it by adding a vector $rt$, where they can choose any vector $t$ from a fixed set $T$ of the adversary's "tricks", and where $r>0$ is a fixed radius. The adversary's choice of $t=t(X)$ may depend on the true data $X$. The adversary wants to hide the corruption by making the fake data $X+rt$ statistically indistinguishable from the real data $X$. What is the largest radius $r=r(T)$ for which the adversary can create an undetectable fake? We show that for highly symmetric sets $T$, the detectability radius $r(T)$ is approximately twice the scaled Gaussian width of $T$. The upper bound actually holds for arbitrary sets $T$ and generalizes to arbitrary, non-Gaussian distributions of real data $X$. The lower bound may fail for not highly symmetric $T$, but we conjecture that this problem can be solved by considering the focused version of the Gaussian width of $T$, which focuses on the most important directions of $T$., Comment: 16 pages

The residualization procedure has been applied in many different fields to estimate models with multicollinearity. However, there exists a lack of understanding of this methodology and some authors discourage its use. This paper aims to contribute to a better understanding of the residualization procedure to promote an adequate application and interpretation of it among statistics and data sciences. We highlight its interesting potential application, not only to mitigate multicollinearity but also when the study is oriented to the analysis of the isolated effect of independent variables. The relation between the residualization methodology and the Frisch-Waugh-Lovell (FWL) theorem is also analyzed, concluding that, although both provide the same estimations, the interpretation of the estimated coefficients is different. These different interpretations justify the application of the residualization methodology regardless of the FWL theorem. A real data example is presented for a better illustration of the contribution of this paper., Comment: 10 pages, 2 tables, working paper

We propose a method to enhance the performance of Large Language Models (LLMs) by integrating quantum computing and quantum-inspired techniques. Specifically, our approach involves replacing the weight matrices in the Self-Attention and Multi-layer Perceptron layers with a combination of two variational quantum circuits and a quantum-inspired tensor network, such as a Matrix Product Operator (MPO). This substitution enables the reproduction of classical LLM functionality by decomposing weight matrices through the application of tensor network disentanglers and MPOs, leveraging well-established tensor network techniques. By incorporating more complex and deeper quantum circuits, along with increasing the bond dimensions of the MPOs, our method captures additional correlations within the quantum-enhanced LLM, leading to improved accuracy beyond classical models while maintaining low memory overhead., Comment: 4 pages, 2 figures

In this work, we introduce an equivariant deformation of the $B$ model on the sphere with a $U(1)$-action. We present the deformed supersymmetry transformations and corresponding Lagrangians and study observables in the supercharge cohomology. The inclusion of equivariance allows for the introduction of novel, position-dependent observables on the sphere, which have no counterparts in the conventional $B$ model. Two specific cases we explore in detail are position-dependent superpotentials and complex structure deformations. In both instances, the theory exhibits notable differences from the standard $B$ model, revealing intriguing new features., Comment: 38 pages, ref corrected

This report presents a comparative analysis of open-source vulnerability scanners for conversational large language models (LLMs). As LLMs become integral to various applications, they also present potential attack surfaces, exposed to security risks such as information leakage and jailbreak attacks. Our study evaluates prominent scanners - Garak, Giskard, PyRIT, and CyberSecEval - that adapt red-teaming practices to expose these vulnerabilities. We detail the distinctive features and practical use of these scanners, outline unifying principles of their design and perform quantitative evaluations to compare them. These evaluations uncover significant reliability issues in detecting successful attacks, highlighting a fundamental gap for future development. Additionally, we contribute a preliminary labelled dataset, which serves as an initial step to bridge this gap. Based on the above, we provide strategic recommendations to assist organizations choose the most suitable scanner for their red-teaming needs, accounting for customizability, test suite comprehensiveness, and industry-specific use cases., Comment: 15 pages, 11 figures

This paper examines the interdependence between green financial instruments, represented by green bonds and green stocks, and a set of major conventional assets, such as Treasury, investment-grade and high-yield corporate bonds, general stocks, crude oil, and gold. To that end, a novel wavelet-based network approach that allows for assessing the degree of interconnection between green financial products and traditional asset classes across different investment horizons is applied. The~empirical results show that green bonds are tightly linked to Treasury and investment-grade corporate bonds, while green stocks are strongly tied to general stocks, regardless of the specific time period and investment horizon considered. However, despite their common climate-friendly nature, there is no a remarkable association between green bonds and green stocks. This means that these green investments constitute basically two independent asset classes, with a distinct risk-return profile and aimed at a different type of investor. Furthermore, green financial products have a weak connection with high-yield corporate bonds and crude oil. These findings can have important implications for investors and policy makers in terms of investment decision, hedging strategies, and sustainability and energy policies., Comment: 24 pages, 4 figures

We present a large-scale evaluation of 30 cognitive biases in 20 state-of-the-art large language models (LLMs) under various decision-making scenarios. Our contributions include a novel general-purpose test framework for reliable and large-scale generation of tests for LLMs, a benchmark dataset with 30,000 tests for detecting cognitive biases in LLMs, and a comprehensive assessment of the biases found in the 20 evaluated LLMs. Our work confirms and broadens previous findings suggesting the presence of cognitive biases in LLMs by reporting evidence of all 30 tested biases in at least some of the 20 LLMs. We publish our framework code to encourage future research on biases in LLMs: https://github.com/simonmalberg/cognitive-biases-in-llms

EEG preprocessing varies widely between studies, but its impact on stimulus classification performance remains poorly understood. To address this gap, we analyzed seven experiments with 40 participants drawn from the public ERP CORE dataset. We systematically varied key preprocessing steps, such as filtering, referencing, baseline interval, detrending, and multiple artifact correction steps. Then we performed trial-wise binary classification (i.e., decoding) using neural networks (EEGNet), or time-resolved logistic regressions. Our findings demonstrate that preprocessing choices influenced decoding performance considerably. All artifact correction steps reduced decoding performance across all experiments and models, while higher high-pass filter cutoffs consistently enhanced decoding. For EEGNet, baseline correction further improved performance, and for time-resolved classifiers, linear detrending and lower low-pass filter cutoffs were beneficial. Other optimal preprocessing choices were specific for each experiment. The current results underline the importance of carefully selecting preprocessing steps for EEG-based decoding. If not corrected, artifacts facilitate decoding but compromise conclusive interpretation.

This paper describes the STCON system for the CHiME-8 Challenge Task 1 (DASR) aimed at distant automatic speech transcription and diarization with multiple recording devices. Our main attention was paid to carefully trained and tuned diarization pipeline and speaker counting. This allowed to significantly reduce diarization error rate (DER) and obtain more reliable segments for speech separation and recognition. To improve source separation, we designed a Guided Target speaker Extraction (G-TSE) model and used it in conjunction with the traditional Guided Source Separation (GSS) method. To train various parts of our pipeline, we investigated several data augmentation and generation techniques, which helped us to improve the overall system quality.

While deep learning has expanded the possibilities for highly expressive variational families, the practical benefits of these tools for variational inference (VI) are often limited by the minimization of the traditional Kullback-Leibler objective, which can yield suboptimal solutions. A major challenge in this context is \emph{mode collapse}: the phenomenon where a model concentrates on a few modes of the target distribution during training, despite being statistically capable of expressing them all. In this work, we carry a theoretical investigation of mode collapse for the gradient flow on Gaussian mixture models. We identify the key low-dimensional statistics characterizing the flow, and derive a closed set of low-dimensional equations governing their evolution. Leveraging this compact description, we show that mode collapse is present even in statistically favorable scenarios, and identify two key mechanisms driving it: mean alignment and vanishing weight. Our theoretical findings are consistent with the implementation of VI using normalizing flows, a class of popular generative models, thereby offering practical insights.

Scylla is a deep Hubble Space Telescope survey of the stellar populations, interstellar medium and star formation in the LMC and SMC. As a pure-parallel complement to the Ultraviolet Legacy Library of Young Stars as Essential Standards (ULLYSES) survey, Scylla obtained 342 orbits of ultraviolet (UV) through near-infrared (IR) imaging of the LMC and SMC with Wide Field Camera 3. In this paper, we describe the science objectives, observing strategy, data reduction procedure, and initial results from our photometric analysis of 96 observed fields. Although our observations were constrained by ULYSSES primary exposures, we imaged all fields in at least two filters (F475W and F814W), and 64% of fields in at least three and as many as seven WFC3 filters spanning the UV to IR. Overall, we reach average 50% completeness of $m_{\rm F225W}=26.0$, $m_{\rm F275W}=26.2$, $m_{\rm F336W}=26.9$, $m_{\rm F475W}=27.8$, $m_{\rm F814W}=25.5$, $m_{\rm F110W}=24.7$, and $m_{\rm F160W}=24.0$ Vega magnitudes in our photometric catalogs, which is faintward of the ancient main sequence turnoff in all filters. The primary science goals of Scylla include characterizing the structure and properties of dust in the MCs, as well as their spatially-resolved star formation and chemical enrichment histories. Our images and photometric catalogs, which represent the widest-area coverage of MCs with HST photometry to date, are available as a high-level science product at the Barbara A. Mikulski Archive for Space Telescopes., Comment: 25 pages, 16 figures, 8 tables. Accepted for publication in ApJS

We present a proposal for a future light dark matter search experiment at the Electron Stretcher Accelerator ELSA in Bonn: Lohengrin. It employs the fixed-target missing momentum based technique for searching for dark-sector particles. The Lohengrin experiment uses a high intensity electron beam that is shot onto a thin target to produce mainly SM bremsstrahlung and - in rare occasions - possibly new particles coupling feebly to the electron. A well motivated candidate for such a new particle is the dark photon, a new massive gauge boson arising from a new gauge interaction in a dark sector and mixing kinetically with the standard model photon. The Lohengrin experiment is estimated to reach sensitivity to couplings small enough to explain the relic abundance of dark matter in various models for dark photon masses between approximately 1 MeV and approximately 100 MeV.

PAC-Bayes learning is a comprehensive setting for (i) studying the generalisation ability of learning algorithms and (ii) deriving new learning algorithms by optimising a generalisation bound. However, optimising generalisation bounds might not always be viable for tractable or computational reasons, or both. For example, iteratively querying the empirical risk might prove computationally expensive. In response, we introduce a novel principled strategy for building an iterative learning algorithm via the optimisation of a sequence of surrogate training objectives, inherited from PAC-Bayes generalisation bounds. The key argument is to replace the empirical risk (seen as a function of hypotheses) in the generalisation bound by its projection onto a constructible low dimensional functional space: these projections can be queried much more efficiently than the initial risk. On top of providing that generic recipe for learning via surrogate PAC-Bayes bounds, we (i) contribute theoretical results establishing that iteratively optimising our surrogates implies the optimisation of the original generalisation bounds, (ii) instantiate this strategy to the framework of meta-learning, introducing a meta-objective offering a closed form expression for meta-gradient, (iii) illustrate our approach with numerical experiments inspired by an industrial biochemical problem.