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It has been previously demonstrated [Panov et al. Physics of Atomic Nuclei 84(2021)1037] that the TAIGA-HiSCORE Cherenkov array, originally built for cosmic ray physics and ultrahigh-energy gamma-ray astronomy studies using the extensive air shower method, can be used in conventional optical astronomy for wide-field searches for rare nanosecond optical transients of astrophysical origin. The FOV of the facility is on the scale of 1~ster, and it is capable of detecting very rare transients in the visible light range with fluxes greater than approximately 3000~quanta/m$^2$/10~ns (10~ns is the apparatus integration time) and pulse durations of 10\,ns. Among the potential sources of distant nanosecond optical transients are the evaporation of primary black holes, magnetic reconnection in the accretion disks of black holes, and signals from distant lasers of extraterrestrial civilizations. The paper describes the methods and results of the search for optical transients using the TAIGA-HiSCORE Cherenkov array from 2018 to 2022 (four winter seasons of data collection). No reliable astrophysical candidates for optical transients were found. We set an upper bound on the flux of the searched events as $\sim 1\times10^{-3}$\,events/ster/h., Comment: 20 figures, 46 pages, submitted to Astronomy Reports

This study examines the potential of using math-themed postage stamps in mathematics lessons as a tool to engage students and integrate the subject with history, art, and culture. Since the first mathematical stamps appeared in the early 20th century, featuring prominent scholars like Carl Friedrich Gauss and Isaac Newton, they serve not only as philatelic artifacts but also as historical carriers of knowledge. The paper presents several practical projects to interest students, such as creating their own math stamps, investigating the price trends of math-themed stamps, and developing a timeline of mathematical discoveries depicted in philatelic issues. The proposed projects develop students' mathematical skills in areas such as percentage calculations, general arithmetic, working with time intervals, and statistical analysis. Students can analyze shapes, symmetry, and patterns on stamps, study principles of proportion, and explore geometric figures. Using stamps broadens students' horizons, providing an opportunity to become familiar with renowned mathematicians from different eras, countries, and cultures. This also offers students a new perspective on the subject, presenting mathematical discoveries as part of the world's cultural heritage. Postage stamps dedicated to mathematics can become a powerful tool for visualizing theoretical knowledge, stimulating interest in mathematics, and encouraging independent research among students., Comment: This is an English translation of the article [Ivanova N., Marky i Matematyma, U sviti matematyky, 2 (Issue 1) (2024), 10-17 / in Ukrainian, DOI: https://doi.org/10.17721/1029-4171.2024/2.2]

The phenomenological description of the cosmological particle production in the framework of the induced gravity is investigated. It appears that the source terms with the particle number density in the creation law can be interpreted as the invisible part of the Universe. It is shown that there is a gauge that restores the General Relativity in which our model is equivalent to the $f(R)$-gravity for $f \propto R^{\frac{3}{2}}$, Comment: Invited paper. To be published in Academia Quantum

We study analysis of complex systems using a Quantitative Theory of Meaning developed as an extention of Shannon's Communication Theory. The approach consideres complexity not in terms of the manifestation of its effects which are manifestation of the dynamics of the system, but in terms of primary causes and taking into account the topology of the system. Here, the dynamics of the system are provided by reflexive communication between heterogenious agents that make up the system. Unlike Shannon's Communication Theory the Theory of Meaning imposes restrictions on the complex systems being analyzed. Non-linearity and specific dynamics of the system arise as a consequence of the topology of the system. This topology also suggests a method for analyzing complex systems, the logistic Continuous Wavelet Transform (CWT). The paper also lays the foundation for future research in various fields studying complex systems of interacting geterogeneous agents, which may form a new paradigm for better understanding the structure, mechanisms, and dynamics of complex systems., Comment: 37 pages, 8 figures

Luminous Red Novae (LRNe) are enigmatic transient events distinguished by a rapid rise in luminosity, a plateau in luminosity, and spectra which become redder with time. The best-observed system before, during, and after the outburst is V1309 Sco. We model a candidate V1309 Sco progenitor binary configuration (1.52+0.16Msun) using the Smoothed Particle Hydrodynamics (SPH) code StarSmasher with a modified energy equation that implements flux-limited emission-diffusion radiative transport in a Lagrangian case. We developed an imaging technique allowing us to capture the flux an observer would measure. In this novel method, the outgoing radiative flux of each SPH particle in the observer's direction is attenuated by other particles along the path to the observer. We investigated how the light curve is affected in various models: with and without dust formation; constant, Planck, or Rosseland mean opacities; different donor star sizes; different companion star masses and types; radiative heating included in our modified energy equation; and different SPH simulation resolutions. The resulting evolution in bolometric luminosity and spectrum peak temperature is in good agreement with V1309 Sco observations. Our simulations rule out V1309 Sco models that do not assume dust formation., Comment: 29 pages, 18 figures, accepted to Astrophysical Journal

We propose an unsupervised image segmentation method using features from pre-trained text-to-image diffusion models. Inspired by classic spectral clustering approaches, we construct adjacency matrices from self-attention layers between image patches and recursively partition using Normalised Cuts. A key insight is that self-attention probability distributions, which capture semantic relations between patches, can be interpreted as a transition matrix for random walks across the image. We leverage this by first using Random Walk Normalized Cuts directly on these self-attention activations to partition the image, minimizing transition probabilities between clusters while maximizing coherence within clusters. Applied recursively, this yields a hierarchical segmentation that reflects the rich semantics in the pre-trained attention layers, without any additional training. Next, we explore other ways to build the NCuts adjacency matrix from features, and how we can use the random walk interpretation of self-attention to capture long-range relationships. Finally, we propose an approach to automatically determine the NCut cost criterion, avoiding the need to tune this manually. We quantitatively analyse the effect incorporating different features, a constant versus dynamic NCut threshold, and incorporating multi-node paths when constructing the NCuts adjacency matrix. We show that our approach surpasses all existing methods for zero-shot unsupervised segmentation, achieving state-of-the-art results on COCO-Stuff-27 and Cityscapes.

Accurately detecting and classifying damage in analogue media such as paintings, photographs, textiles, mosaics, and frescoes is essential for cultural heritage preservation. While machine learning models excel in correcting degradation if the damage operator is known a priori, we show that they fail to robustly predict where the damage is even after supervised training; thus, reliable damage detection remains a challenge. Motivated by this, we introduce ARTeFACT, a dataset for damage detection in diverse types analogue media, with over 11,000 annotations covering 15 kinds of damage across various subjects, media, and historical provenance. Furthermore, we contribute human-verified text prompts describing the semantic contents of the images, and derive additional textual descriptions of the annotated damage. We evaluate CNN, Transformer, diffusion-based segmentation models, and foundation vision models in zero-shot, supervised, unsupervised and text-guided settings, revealing their limitations in generalising across media types. Our dataset is available at $\href{https://daniela997.github.io/ARTeFACT/}{https://daniela997.github.io/ARTeFACT/}$ as the first-of-its-kind benchmark for analogue media damage detection and restoration., Comment: Accepted for publication at WACV 2025

Data grows in value when joined and combined; likewise the power of voice grows in ensemble. With 15 UK choirs, we explore opportunities for bottom-up data governance of a jointly created Choral AI Dataset. Guided by a survey of chorister attitudes towards generative AI models trained using their data, we explore opportunities to create empowering governance structures that go beyond opt in and opt out. We test the development of novel mechanisms such as a Trusted Data Intermediary (TDI) to enable governance of the dataset amongst the choirs and AI developers. We hope our findings can contribute to growing efforts to advance collective data governance practices and shape a more creative, empowering future for arts communities in the generative AI ecosystem., Comment: Presented at the NeurIPS 2024 Workshop on Creativity & Generative AI

The era of large language models (LLM) raises questions not only about how to train models, but also about how to evaluate them. Despite numerous existing benchmarks, insufficient attention is often given to creating assessments that test LLMs in a valid and reliable manner. To address this challenge, we accommodate the Evidence-centered design (ECD) methodology and propose a comprehensive approach to benchmark development based on rigorous psychometric principles. In this paper, we have made the first attempt to illustrate this approach by creating a new benchmark in the field of pedagogy and education, highlighting the limitations of existing benchmark development approach and taking into account the development of LLMs. We conclude that a new approach to benchmarking is required to match the growing complexity of AI applications in the educational context. We construct a novel benchmark guided by the Bloom's taxonomy and rigorously designed by a consortium of education experts trained in test development. Thus the current benchmark provides an academically robust and practical assessment tool tailored for LLMs, rather than human participants. Tested empirically on the GPT model in the Russian language, it evaluates model performance across varied task complexities, revealing critical gaps in current LLM capabilities. Our results indicate that while generative AI tools hold significant promise for education - potentially supporting tasks such as personalized tutoring, real-time feedback, and multilingual learning - their reliability as autonomous teachers' assistants right now remain rather limited, particularly in tasks requiring deeper cognitive engagement., Comment: 36 pages, 2 figures

The Zirconium (Z = 40) isotopic chain has attracted interest for more than four decades. The abrupt lowering of the energy of the first $2^+$ state and the increase in the transition strength B(E2; $2_1^\rightarrow 0_1^+$ going from $^{98}$Zr to $^{100}$Zr has been the first example of "quantum phase transition" in nuclear shapes, which has few equivalents in the nuclear chart. Although a multitude of experiments have been performed to measure nuclear properties related to nuclear shapes and collectivity in the region, none of the measured lifetimes were obtained using the Recoil Distance Doppler Shift method in the $\gamma\gamma$-coincidence mode where a gate on the direct feeding transition of the state of interest allows a strict control of systematical errors. This work reports the results of lifetime measurements for the first yrast excited states in $^{98-104}$Zr carried out to extract reduced transition probabilities. The new lifetime values in $\gamma\gamma$-coincidence and $\gamma$-single mode are compared with the results of former experiments. Recent predictions of the Interacting Boson Model with Configuration Mixing, the Symmetry Conserving Configuration Mixing model based on the Hartree-Fock-Bogoliubov approach and the Monte Carlo Shell Model are presented and compared with the experimental data.

Ozone depletion events are recurring phenomena in both polar regions, characterized by significant interannual variability. In this study, the Environment and Climate Change Canada (ECCC) Global Deterministic Prediction System is used to investigate the medium-range predictability of ozone and weather throughout the anomalous polar ozone depletion events of 2020. The system includes ozone assimilation and makes use of a prognostic ozone field for the computation of heating rates. The ozone scheme uses simplified photochemical modules to represent the impact of both gas-phase and heterogeneous reactions throughout polar ozone depletion events. The study shows that during the Boreal and Austral spring seasons, the predictability of the total ozone column exceeds 10 days and is comparable to the predictability of large-scale weather variables. It also demonstrates that over both polar regions, the inclusion of ozone radiative coupling has a significant impact on the temperature and wind distributions throughout the stratosphere. Over Antarctica, the ozone coupled forecasts are systematically colder at all lead times, which helps eliminate a temperature bias present in the model using climatological ozone. The strength of the polar vortex also increases significantly throughout the lower stratosphere, in better agreement with zonal wind analyses. Over the Arctic the use of an ozone-interactive model also produces significant changes in the temperature and wind forecasts, but the impact on the quality of the weather forecasts is generally neutral. The study shows the overall benefits of using ozone coupled models in the highly perturbed springtime conditions of the polar regions., Comment: Submitted on 6 March 2024 (v1), last revised 27 August 2024 (this version, v2)

The NOvA collaboration reports cross-section measurements for $\nu_{\mu}$ charged-current interactions with low hadronic energy (maximum kinetic energy of 250 MeV for protons and 175 MeV for pions) in the NOvA Near Detector. The results are presented as a double-differential cross section as a function of the direct observables of the final-state muon kinematics. Results are also presented as a single-differential cross section as a function of the derived square of the four-momentum transfer, $Q^{2}$, and as a function of the derived neutrino energy. The data correspond to an accumulated 8.09$\times10^{20}$ protons-on-target (POT) in the neutrino mode of the NuMI beam, with a narrow band of neutrino energies peaked at 1.8 GeV. The analysis provides a sample of neutrino-nucleus interactions with an enhanced fraction of quasi-elastic and two-particle-two-hole (2p2h) interactions. This enhancement allows quantitative comparisons with various nuclear models. We find strong disagreement between data and theory-based models in various regions of the muon kinematic phase space, especially in the forward muon direction., Comment: 20 pages, 12 figures. The second version includes an additional citation and adds four previously missing authors

The paper focuses on the link between information, investors' expectations and market price movement. EUR/USD market is examined from communication-theoretical perspective on the dynamics of information and meaning. We build upon the quantitative theory of meaning as a complement to the quantitative theory of information. Different groups of investors entertain different criteria to process information, so that the same information can be supplied with different meanings. Meanings shape investors' expectations which are revealed in market asset price movement. This dynamics can be captured by non-linear evolutionary equation. We use a computationally efficient technique of logistic Continuous Wavelet Transformation (CWT) to analyze EUR/USD market. The results reveal the latent EUR/USD trend structure which coincides with the model predicted time series indicating that proposed model can adequately describe some patterns of investors' behavior. Proposed methodology can be used to better understand and forecast future market assets' price movement.

Double- and single-differential cross sections for inclusive charged-current neutrino-nucleus scattering are reported for the kinematic domain 0 to 2 GeV/c in three-momentum transfer and 0 to 2 GeV in available energy, at a mean muon-neutrino energy of 1.86 GeV. The measurements are based on an estimated 995,760 muon-neutrino CC interactions in the scintillator medium of the NOvA Near Detector. The subdomain populated by 2-particle-2-hole reactions is identified by the cross-section excess relative to predictions for neutrino-nucleus scattering that are constrained by a data control sample. Models for 2-particle-2- hole processes are rated by chi-square comparisons of the predicted-versus-measured muon-neutrino CC inclusive cross section over the full phase space and in the restricted subdomain. Shortfalls are observed in neutrino generator predictions obtained using the theory-based Val`encia and SuSAv2 2p2h models., Comment: 20 pages, 14 figures

Merging datasets across institutions is a lengthy and costly procedure, especially when it involves private information. Data hosts may therefore want to prospectively gauge which datasets are most beneficial to merge with, without revealing sensitive information. For causal estimation this is particularly challenging as the value of a merge will depend not only on the reduction in epistemic uncertainty but also the improvement in overlap. To address this challenge, we introduce the first cryptographically secure information-theoretic approach for quantifying the value of a merge in the context of heterogeneous treatment effect estimation. We do this by evaluating the Expected Information Gain (EIG) and utilising multi-party computation to ensure it can be securely computed without revealing any raw data. As we demonstrate, this can be used with differential privacy (DP) to ensure privacy requirements whilst preserving more accurate computation than naive DP alone. To the best of our knowledge, this work presents the first privacy-preserving method for dataset acquisition tailored to causal estimation. We demonstrate the effectiveness and reliability of our method on a range of simulated and realistic benchmarks. The code is available anonymously.

Humans activate muscles to shape the mechanical interaction with their environment, but can they harness this control mechanism to best sense the environment? We investigated how participants adapt their muscle activation to visual and haptic information when tracking a randomly moving target with a robotic interface. The results exhibit a differentiated effect of these sensory modalities, where participants' muscle cocontraction increases with the haptic noise and decreases with the visual noise, in apparent contradiction to previous results. These results can be explained, and reconciled with previous findings, when considering muscle spring like mechanics, where stiffness increases with cocontraction to regulate motion guidance. Increasing cocontraction to more closely follow the motion plan favors accurate visual over haptic information, while decreasing it avoids injecting visual noise and relies on accurate haptic information. We formulated this active sensing mechanism as the optimization of visuo-haptic information and effort. This OIE model can explain the adaptation of muscle activity to unimodal and multimodal sensory information when interacting with fixed or dynamic environments, or with another human, and can be used to optimize human-robot interaction.

Despite recent advances in large-scale text-to-image generative models, manipulating real images with these models remains a challenging problem. The main limitations of existing editing methods are that they either fail to perform with consistent quality on a wide range of image edits or require time-consuming hyperparameter tuning or fine-tuning of the diffusion model to preserve the image-specific appearance of the input image. We propose a novel approach that is built upon a modified diffusion sampling process via the guidance mechanism. In this work, we explore the self-guidance technique to preserve the overall structure of the input image and its local regions appearance that should not be edited. In particular, we explicitly introduce layout-preserving energy functions that are aimed to save local and global structures of the source image. Additionally, we propose a noise rescaling mechanism that allows to preserve noise distribution by balancing the norms of classifier-free guidance and our proposed guiders during generation. Such a guiding approach does not require fine-tuning the diffusion model and exact inversion process. As a result, the proposed method provides a fast and high-quality editing mechanism. In our experiments, we show through human evaluation and quantitative analysis that the proposed method allows to produce desired editing which is more preferable by humans and also achieves a better trade-off between editing quality and preservation of the original image. Our code is available at https://github.com/MACderRu/Guide-and-Rescale., Comment: Accepted to ECCV 2024. The project page is available at https://macderru.github.io/Guide-and-Rescale

Automatic image anomaly detection is important for quality inspection in the manufacturing industry. The usual unsupervised anomaly detection approach is to train a model for each object class using a dataset of normal samples. However, a more realistic problem is zero-/few-shot anomaly detection where zero or only a few normal samples are available. This makes the training of object-specific models challenging. Recently, large foundation vision-language models have shown strong zero-shot performance in various downstream tasks. While these models have learned complex relationships between vision and language, they are not specifically designed for the tasks of anomaly detection. In this paper, we propose the Few-shot/zero-shot Anomaly Detection Engine (FADE) which leverages the vision-language CLIP model and adjusts it for the purpose of industrial anomaly detection. Specifically, we improve language-guided anomaly segmentation 1) by adapting CLIP to extract multi-scale image patch embeddings that are better aligned with language and 2) by automatically generating an ensemble of text prompts related to industrial anomaly detection. 3) We use additional vision-based guidance from the query and reference images to further improve both zero-shot and few-shot anomaly detection. On the MVTec-AD (and VisA) dataset, FADE outperforms other state-of-the-art methods in anomaly segmentation with pixel-AUROC of 89.6% (91.5%) in zero-shot and 95.4% (97.5%) in 1-normal-shot. Code is available at https://github.com/BMVC-FADE/BMVC-FADE., Comment: 13 pages, 2 figures, Accepted for BMVC 2024

We study the Yang-Baxter algebras $A(K,X,r)$ associated to finite set-theoretic solutions $(X,r)$ of the braid relations. We introduce an equivalent set of quadratic relations $\Re\subseteq G$, where $G$ is the reduced Gr\"obner basis of $(\Re)$. We show that if $(X,r)$ is left-nondegenerate and idempotent then $\Re= G$ and the Yang-Baxter algebra is PBW. We use graphical methods to study the global dimension of PBW algebras in the $n$-generated case and apply this to Yang-Baxter algebras in the left-nondegenerate idempotent case. We study the $d$-Veronese subalgebras for a class of quadratic algebras and use this to show that for $(X,r)$ left-nondegenerate idempotent, the $d$-Veronese subalgebra $A(K,X,r)^{(d)}$ can be identified with $A(K,X,r^{(d)})$, where $(X,r^{(d)})$ are all left-nondegenerate idempotent solutions. We determined the Segre product in the left-nondegenerate idempotent setting. Our results apply to a previously studied class of `permutation idempotent' solutions, where we show that all their Yang-Baxter algebras for a given cardinality of $X$ are isomorphic and are isomorphic to their $d$-Veronese subalgebras. In the linearised setting, we construct the Koszul dual of the Yang-Baxter algebra and the Nichols-Woronowicz algebra in the idempotent case, showing that the latter is quadratic. We also construct noncommutative differentials on some of these quadratic algebras., Comment: 40 pages, 3 figures. arXiv admin note: text overlap with arXiv:2308.11427

Accurately detecting and classifying damage in analogue media such as paintings, photographs, textiles, mosaics, and frescoes is essential for cultural heritage preservation. While machine learning models excel in correcting global degradation if the damage operator is known a priori, we show that they fail to predict where the damage is even after supervised training; thus, reliable damage detection remains a challenge. We introduce DamBench, a dataset for damage detection in diverse analogue media, with over 11,000 annotations covering 15 damage types across various subjects and media. We evaluate CNN, Transformer, and text-guided diffusion segmentation models, revealing their limitations in generalising across media types.

As deep learning systems are scaled up to many billions of parameters, relating their internal structure to external behaviors becomes very challenging. Although daunting, this problem is not new: Neuroscientists and cognitive scientists have accumulated decades of experience analyzing a particularly complex system - the brain. In this work, we argue that interpreting both biological and artificial neural systems requires analyzing those systems at multiple levels of analysis, with different analytic tools for each level. We first lay out a joint grand challenge among scientists who study the brain and who study artificial neural networks: understanding how distributed neural mechanisms give rise to complex cognition and behavior. We then present a series of analytical tools that can be used to analyze biological and artificial neural systems, organizing those tools according to Marr's three levels of analysis: computation/behavior, algorithm/representation, and implementation. Overall, the multilevel interpretability framework provides a principled way to tackle neural system complexity; links structure, computation, and behavior; clarifies assumptions and research priorities at each level; and paves the way toward a unified effort for understanding intelligent systems, may they be biological or artificial.

Lanthanum-hydrogen system and its derivatives remain among the most promising for achieving room-temperature superconductivity. In this study, we examined the formation of ternary lanthanum-scandium superhydrides at pressures up to 220 GPa. The primary product of the LaSc alloy's reaction with hydrogen is newly discovered cubic (La,Sc)H$_{12}$, demonstrating clear superconducting transition in all six channels of the van der Pauw scheme below 244-248 K. In this compound with an unusually large unit cell volume, virtually no magnetoresistance was observed in fields up to 68 Tesla. Synthesized samples of (La,Sc)H$_{12}$ demonstrate pronounced superconducting diode and SQUID-like effects at a record high temperature of 226 K. Furthermore, our analysis revealed the formation of lower hexagonal polyhydrides (La,Sc)H$_{6-7}$, which could potentially account for the anomaly in electrical resistance observed near 274 K. This anomalous behavior may be of a superconducting nature, since low-temperature diffraction studies indicate the absence of corresponding structural phase transitions in La-Sc polyhydrides.

The lack of benchmark data on the real minerals, native ammonium carriers in Solar System gives rise to controversial opinions on extraterrestrial ammonium reservoirs. We herein report on discovery of the first mineral carrier of meteoritic ammonium and show its relevance to the compositional and spectral characteristics of cometary and asteroidal bodies. Chemically distant from previously inferred volatile organics or ammoniated phyllosilicates, it is an aqueous metal-ammonium sulfate related to a family of so-called Tutton salts. Nickeloan boussingaultite, (NH4)2(Mg,Ni)(SO4)2 6H2O, occurs in Orgueil, a primitive carbonaceous chondrite closely related to (162173) Ryugu and (101955) Bennu, the C-type asteroids. The available spectroscopic, chemical and mineralogical data signify that natural Tutton salts perfectly fit into the role of ammonium reservoir under conditions of cometary nuclei and carbonaceous asteroids.

The recently proposed Segment Anything Model (SAM) is a general tool for image segmentation, but it requires additional adaptation and careful fine-tuning for medical image segmentation, especially for small, irregularly-shaped, and boundary-ambiguous anatomical structures such as the knee cartilage that is of interest in this work. Repaired cartilage, after certain surgical procedures, exhibits imaging patterns unseen to pre-training, posing further challenges for using models like SAM with or without general-purpose fine-tuning. To address this, we propose a novel registration-based prompt engineering framework for medical image segmentation using SAM. This approach utilises established image registration algorithms to align the new image (to-be-segmented) and a small number of reference images, without requiring segmentation labels. The spatial transformations generated by registration align either the new image or pre-defined point-based prompts, before using them as input to SAM. This strategy, requiring as few as five reference images with defined point prompts, effectively prompts SAM for inference on new images, without needing any segmentation labels. Evaluation of MR images from patients who received cartilage stem cell therapy yielded Dice scores of 0.89, 0.87, 0.53, and 0.52 for segmenting femur, tibia, femoral- and tibial cartilages, respectively. This outperforms atlas-based label fusion and is comparable to supervised nnUNet, an upper-bound fair baseline in this application, both of which require full segmentation labels for reference samples. The codes are available at: https://github.com/chrissyinreallife/KneeSegmentWithSAM.git, Comment: Accepted to the 11th International Workshop on Biomedical Image Registration (WBIR 2024)

The analysis of photometrical observations of comet 29P/Schwassmann-Wachmann 1 is presented. The broadband observations were carried out for 15 nights from 2012 to 2019 at the Lisnyky observational station of Taras Shevchenko National University of Kyiv. Apparent magnitudes and dust productivity level $Af\rho$ in filter R were calculated. Middle and height dust activity of the comet is characterized by parameter $Af\rho$ which varied from 1246 to 17563 cm during all periods of observation. Based on the morphological analysis, four jet-like structures were detected in the coma on almost all dates. Using the geometrical model for the jet structure interpretation during all observation sets, we obtained following results: the nucleus rotation periodof 57\pm2 days, the rotational axis orientation, the locations of active regions for four jet-like structures within a narrow belt near the equator., Comment: 9 pages, 4 figures

We use a computationally efficient technique of Logistic Continuous Wavelet transform (CWT) to analyze patent data for Switzerland, Germany, USA, and Brszil for the period 1980-2000. We found that patent growth dynamics follows the dynamics of innovation system synergy in the framework of Triple Helix model of innovations where observed non-linear actors' interactions are provided by biased information exchange between heterogenious actors. Suggested approach reveals the latent trend structure in patent and innovation dynamics and may help policymakers identify the potential drivers of patent and innovation activity and form informed policy for boosting innovation development. The paper also privides a foundation for future research in differnt fields studying complex systems of interacting heterogenious agents., Comment: 45 pages, 22 figures

We present a latent diffusion model over 3D scenes, that can be trained using only 2D image data. To achieve this, we first design an autoencoder that maps multi-view images to 3D Gaussian splats, and simultaneously builds a compressed latent representation of these splats. Then, we train a multi-view diffusion model over the latent space to learn an efficient generative model. This pipeline does not require object masks nor depths, and is suitable for complex scenes with arbitrary camera positions. We conduct careful experiments on two large-scale datasets of complex real-world scenes -- MVImgNet and RealEstate10K. We show that our approach enables generating 3D scenes in as little as 0.2 seconds, either from scratch, from a single input view, or from sparse input views. It produces diverse and high-quality results while running an order of magnitude faster than non-latent diffusion models and earlier NeRF-based generative models

Pathology, the microscopic examination of diseased tissue, is critical for diagnosing various medical conditions, particularly cancers. Traditional methods are labor-intensive and prone to human error. Digital pathology, which converts glass slides into high-resolution digital images for analysis by computer algorithms, revolutionizes the field by enhancing diagnostic accuracy, consistency, and efficiency through automated image analysis and large-scale data processing. Foundational transformer pretraining is crucial for developing robust, generalizable models as it enables learning from vast amounts of unannotated data. This paper introduces the Hibou family of foundational vision transformers for pathology, leveraging the DINOv2 framework to pretrain two model variants, Hibou-B and Hibou-L, on a proprietary dataset of over 1 million whole slide images (WSIs) representing diverse tissue types and staining techniques. Our pretrained models demonstrate superior performance on both patch-level and slide-level benchmarks, surpassing existing state-of-the-art methods. Notably, Hibou-L achieves the highest average accuracy across multiple benchmark datasets. To support further research and application in the field, we have open-sourced the Hibou models, which can be accessed at https://github.com/HistAI/hibou.

We present a method to obtain rapid mass loss rates in binary systems, specifically at the onset of MT episodes. The method unifies atmospheric (underflow) and $L_1$ stream (overflow) mass rates in a single continuous procedure. The method uses averaged 3D properties of the binaries, such as effective binary potential and effective binary acceleration, to both evolve the donor and obtain properties of the matter at the $L_1$ plane. In the case of underflow, we obtain atmospheric stratification. Our method can be used for binaries with an extensive range of mass ratios, $0.01 \le q \le 100$, and can also be applied to hot donors. The considered examples show that the MT rates obtained with this revised formalism always differ from the optically thin and optically thick MT rates widely used during the computations of binary evolution., Comment: Accepted to ApJ. 13 pages, 6 figures

The ability to build and leverage world models is essential for a general-purpose AI agent. Testing such capabilities is hard, in part because the building blocks of world models are ill-defined. We present Elements of World Knowledge (EWOK), a framework for evaluating world modeling in language models by testing their ability to use knowledge of a concept to match a target text with a plausible/implausible context. EWOK targets specific concepts from multiple knowledge domains known to be vital for world modeling in humans. Domains range from social interactions (help/hinder) to spatial relations (left/right). Both, contexts and targets are minimal pairs. Objects, agents, and locations in the items can be flexibly filled in enabling easy generation of multiple controlled datasets. We then introduce EWOK-CORE-1.0, a dataset of 4,374 items covering 11 world knowledge domains. We evaluate 20 openweights large language models (1.3B--70B parameters) across a battery of evaluation paradigms along with a human norming study comprising 12,480 measurements. The overall performance of all tested models is worse than human performance, with results varying drastically across domains. These data highlight simple cases where even large models fail and present rich avenues for targeted research on LLM world modeling capabilities., Comment: 21 pages (11 main), 7 figures. Authors Anna Ivanova, Aalok Sathe, Benjamin Lipkin contributed equally

The intense research for efficient low-temperature thermoelectric materials motivates the exploration of innovative compounds and composite systems. This study examines the effects of integrating low-dimensional titanium trisulfide (TiS$_3$) into bulk tin-based halide perovskites (CsSnI$_3$) for use in thermoelectric applications. The addition of small amounts of two-dimensional titanium trisulfide (TiS$_3$) to bulk tin-based halide perovskites (CsSnI$_3$) significantly enhanced the structural stability of the composite material and suppressed oxidation processes. The CsSnI$_3$-TiS$_3$ composites demonstrated stabilization of temperature-dependent electrical properties (conductivity and Seebeck coefficient). This study provides valuable insights into the promising approach of using low-dimensional TiS3 as an additive to stabilize the thermoelectric performance of CsSnI$_3$., Comment: 11 pages, 4 figures

Pipelines of state-of-the-art spectrographs dedicated to planet detection provide, for each exposure, series of Cross-Correlation Functions (CCFs) built with a Binary Mask (BM), and the absolute radial velocity (RV) derived from Gaussian fit of a weighted average CCF$_{tot}$ of the CCFs. Here we tested the benefits of the application of the shift finding algorithm developed by Pierre Connes directly to the total CCF$_{tot}$, comparing the resulting RV shifts (DRVs) with the results of the Gaussian fits. In a second step, we investigated how the individual DRV profiles along the velocity grid can be used as an easy tool for detection of stellar line shape variations. We tested this new algorithm on 1151 archived spectra of the K2.5 V star HD 40307 obtained with ESO/ESPRESSO during a one-week campaign in 2018. Tests were performed based on the comparison of DRVs with RVs from Gaussian fits. DRV profiles along the velocity grid (DRV(i)) were scrutinized and compared with direct CCF$_{tot}$ ratios. The dispersion of residuals from a linear fit to RVs from 406 spectra recorded within one night, was found to be $\sigma$=1.03 and 0.83 ms$^{-1}$ for the Gaussian fit and the new algorithm respectively, a significant 20\% improvement in accuracy. The two full one-week series obtained during the campaign were also fitted with a 3-planet system Keplerian model. The residual divergence between data and best-fit model is significantly smaller for the new algorithm than for the Gaussian fit. This new algorithm is an easy tool allowing to obtain additional diagnostics on the RV measurements in series of exposures. It increases the accuracy of velocity variation determinations. Also, departures from constancy of the DRVi profiles, as well as varying differences between RVs from this new method and RVs from a Gaussian fit provide diagnostics of line shape variations due to stellar activity., Comment: 21 pages, 17 figures, to appear in Astronomy and Astrophysics

{\alpha}-RuCl$_3$, a narrow-band Mott insulator with large work function, offers intriguing potential as a quantum material or as a charge acceptor for electrical contacts in van der Waals devices. In this work, we perform a systematic study of the optical reflection contrast of {\alpha}-RuCl$_3$ nanoflakes on oxidized silicon wafers and estimate the accuracy of this imaging technique to assess the crystal thickness. Via spectroscopic micro-ellipsometry measurements, we characterize the wavelength-dependent complex refractive index of {\alpha}-RuCl$_3$ nanoflakes of varying thickness in the visible and near-infrared. Building on these results, we simulate the optical contrast of {\alpha}-RuCl$_3$ nanoflakes with thicknesses below 100 nm on SiO$_2$/Si substrates under different illumination conditions. We compare the simulated optical contrast with experimental values extracted from optical microscopy images and obtain good agreement. Finally, we show that optical contrast imaging allows us to retrieve the thickness of the RuCl$_3$ nanoflakes exfoliated on an oxidized silicon substrate with a mean deviation of -0.2 nm for thicknesses below 100 nm with a standard deviation of only 1 nm. Our results demonstrate that optical contrast can be used as a non-invasive, fast, and reliable technique to estimate the {\alpha}-RuCl$_3$ thickness.

The monograph summarizes and analyzes the current state of scientific research in the field of interactive artificial intelligence systems, text generation systems, diagnostics of the competitiveness of specialists, in the areas of correct color rendering in image formation, informatization of the work of graduate students, accessible technology for creating three-dimensional 3D models. The monograph will be useful both to specialists and employees of companies working in the IT field, as well as teachers, masters, students and graduate students of higher educational institutions, as well as anyone interested in issues related to information technology. The monograph was compiled based on the results of the 16-th international scientific and practical conference Information technologies and automation - 2023, which took place in October 2023 at Odessa National University of Technology., Comment: Monograph Scientific publication (issue). Published By Iowa State University Digital Press. ISBN 978-1-958291-07-8; 273 pages; Published May 1, 2024