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This paper introduces a modular, non-deep learning method for filtering and refining sparse correspondences in image matching. Assuming that motion flow within the scene can be approximated by local homography transformations, matches are aggregated into overlapping clusters corresponding to virtual planes using an iterative RANSAC-based approach, with non-conforming correspondences discarded. Moreover, the underlying planar structural design provides an explicit map between local patches associated with the matches, enabling optional refinement of keypoint positions through cross-correlation template matching after patch reprojection. Finally, to enhance robustness and fault-tolerance against violations of the piece-wise planar approximation assumption, a further strategy is designed for minimizing relative patch distortion in the plane reprojection by introducing an intermediate homography that projects both patches into a common plane. The proposed method is extensively evaluated on standard datasets and image matching pipelines, and compared with state-of-the-art approaches. Unlike other current comparisons, the proposed benchmark also takes into account the more general, real, and practical cases where camera intrinsics are unavailable. Experimental results demonstrate that our proposed non-deep learning, geometry-based approach achieves performances that are either superior to or on par with recent state-of-the-art deep learning methods. Finally, this study suggests that there are still development potential in actual image matching solutions in the considered research direction, which could be in the future incorporated in novel deep image matching architectures., Comment: project page: https://github.com/fb82/MiHo

Context. Kinematically decoupled cores (KDCs) are often found in the centers of early-type galaxies. Aims. We aim to investigate the kinematics, structure, and stellar populations of the KDC residing in the early-type galaxy NGC 4494 to understand its formation. Methods. We used long-slit spectroscopic data obtained with the FORS2 instrument on the VLT to measure the stellar kinematics and stellar populations. We performed a spectroscopic decomposition to disentangle the properties of the KDC from those of the host galaxy and construct models of the observed rotation curve. Results. The rotation curve is characterized by two symmetric dips at |R|=6", where the rotation velocity drops to zero. Contrary to previous studies that explained the decoupled structure as a rapidly co-rotating disk, our analysis clearly shows that it is a counter-rotating component. A counter-rotating core is indeed needed to reproduce the observed dip in the velocity curve. The properties of the stellar populations of the decoupled core and the main galaxy are very similar: old stars (12-13 Gyr) with slightly super-solar metallicities (0<[Z/H]<0.15 dex) and alpha-enhanced (0 <[alpha/Fe]< 0.15 dex). Conclusions. Our results indicate that the counter-rotating component is a disk of about 1 kpc in diameter that is obscured by dust in the central 0.12 kpc. The properties of its stellar populations suggest that it formed from the same material as the main stellar body of the host galaxy. This could have happened via internal processes such as the precession of a pre-existing rotating core, or, alternatively, via gas accretion in retrograde orbits followed by star formation. In the latter scenario, the accretion event occurred almost simultaneously with the formation of the galaxy, using material that had the same composition as the gas from which the stars in the main body of the galaxy were formed., Comment: 10 pages, 8 figures, accepted for publication in Astronomy and Astrophysics

The goal of this project is to construct an estimator for the masses of supermassive black holes in active galactic nuclei (AGNs) based on the broad Halpha emission line. We make use of published reverberation mapping data. We remeasure all Halpha time lags from the original data as we find that often the reverberation measurements are improved by detrending the light curves. We produce mass estimators that require only the Halpha luminosity and the width of the Halpha emission line as characterized by either the FWHM or the line dispersion. It is possible, on the basis of a single spectrum covering the Halpha emission line, to estimate the mass of the central supermassive black hole in AGNs, taking into account all three parameters believed to affect mass measurement: luminosity, line width, and Eddington ratio. The typical formal accuracy in such estimates is of order 0.2-0.3 dex., Comment: 15 pages, 9 figures, submitted to A&A

Accurate multi-decadal wind power predictions are crucial for sustainable energy transitions but are challenged by the coarse spatial resolution of global climate models (GCMs). This study examines the impact of spatial resolution on wind power forecasts by analyzing historical wind speed outputs from ten CMIP6 GCMs in Germany, using ERA5 reanalysis as a reference. Results show that the choice of GCM is the primary influence on wind speed output, with higher resolution models partly, but not consistently, improving predictions. While high-resolution models better capture extreme wind speeds, they do not systematically improve the prediction of the whole wind speed distribution. The data set MPI-ESM1-2-HR (MPI-HR) was found to represent the wind speed distribution particularly faithfully, while the MIROC6 (JAP) data set showed substantial underestimation for the German region compared to ERA5. These findings underscore the complexity of wind speed modeling for power predictions and emphasize the need for careful GCM selection and appropriate downscaling and bias correction methods., Comment: Master's Thesis

UAV-based biodiversity conservation applications have exhibited many data acquisition advantages for researchers. UAV platforms with embedded data processing hardware can support conservation challenges through 3D habitat mapping, surveillance and monitoring solutions. High-quality real-time scene reconstruction as well as real-time UAV localization can optimize the exploration vs exploitation balance of single or collaborative mission. In this work, we explore the potential of two collaborative frameworks - Visual Simultaneous Localization and Mapping (V-SLAM) and Structure-from-Motion (SfM) for 3D mapping purposes and compare results with standard offline approaches., Comment: accepted at IMAV 2024

Deep learning has long been dominated by multi-layer perceptrons (MLPs), which have demonstrated superiority over other optimizable models in various domains. Recently, a new alternative to MLPs has emerged - Kolmogorov-Arnold Networks (KAN)- which are based on a fundamentally different mathematical framework. According to their authors, KANs address several major issues in MLPs, such as catastrophic forgetting in continual learning scenarios. However, this claim has only been supported by results from a regression task on a toy 1D dataset. In this paper, we extend the investigation by evaluating the performance of KANs in continual learning tasks within computer vision, specifically using the MNIST datasets. To this end, we conduct a structured analysis of the behavior of MLPs and two KAN-based models in a class-incremental learning scenario, ensuring that the architectures involved have the same number of trainable parameters. Our results demonstrate that an efficient version of KAN outperforms both traditional MLPs and the original KAN implementation. We further analyze the influence of hyperparameters in MLPs and KANs, as well as the impact of certain trainable parameters in KANs, such as bias and scale weights. Additionally, we provide a preliminary investigation of recent KAN-based convolutional networks and compare their performance with that of traditional convolutional neural networks. Our codes can be found at https://github.com/MrPio/KAN-Continual_Learning_tests.

A critical step in the digital surface models(DSM) generation is feature matching. Off-track (or multi-date) satellite stereo images, in particular, can challenge the performance of feature matching due to spectral distortions between images, long baseline, and wide intersection angles. Feature matching methods have evolved over the years from handcrafted methods (e.g., SIFT) to learning-based methods (e.g., SuperPoint and SuperGlue). In this paper, we compare the performance of different features, also known as feature extraction and matching methods, applied to satellite imagery. A wide range of stereo pairs(~500) covering two separate study sites are used. SIFT, as a widely used classic feature extraction and matching algorithm, is compared with seven deep-learning matching methods: SuperGlue, LightGlue, LoFTR, ASpanFormer, DKM, GIM-LightGlue, and GIM-DKM. Results demonstrate that traditional matching methods are still competitive in this age of deep learning, although for particular scenarios learning-based methods are very promising., Comment: ECCV2024 Workshop - TradiCV

Notch signaling is a ubiquitous and versatile intercellular signaling system that drives collective behaviors and pattern formation in biological tissues. During embryonic development, Notch is involved in generation of collective biochemical oscillations that form the vertebrate body segments, and its failure results in embryonic defects. Notch ligands of the Delta family are key components of this collective rhythm, but it is unclear how different Delta ligands with distinct properties contribute to relaying information among cells. Motivated by the zebrafish segmentation clock, in this work we propose a theory describing interactions between biochemical oscillators, where Notch receptor is bound by both oscillatory and nonoscillatory Delta ligands. Based on previous in vitro binding studies, we first consider Notch activation by Delta dimers. This hypothesis is consistent with experimental observations in conditions of perturbed Notch signaling. Then we test an alternative hypothesis where Delta monomers directly bind and activate Notch, and show that this second model can also describe the experimental observations. We show that these two hypotheses assign different roles for a non-oscillatory ligand, as a binding partner or as a baseline signal. Finally, we discuss experiments to distinguish between the two scenarios. Broadly, this work highlights how a multiplicity of ligands may be harnessed by a signaling system to generate versatile responses.

In the last twenty years, Structure from Motion (SfM) has been a constant research hotspot in the fields of photogrammetry, computer vision, robotics etc., whereas real-time performance is just a recent topic of growing interest. This work builds upon the original on-the-fly SfM (Zhan et al., 2024) and presents an updated version with three new advancements to get better 3D from what you capture: (i) real-time image matching is further boosted by employing the Hierarchical Navigable Small World (HNSW) graphs, thus more true positive overlapping image candidates are faster identified; (ii) a self-adaptive weighting strategy is proposed for robust hierarchical local bundle adjustment to improve the SfM results; (iii) multiple agents are included for supporting collaborative SfM and seamlessly merge multiple 3D reconstructions into a complete 3D scene when commonly registered images appear. Various comprehensive experiments demonstrate that the proposed SfM method (named on-the-fly SfMv2) can generate more complete and robust 3D reconstructions in a high time-efficient way. Code is available at http://yifeiyu225.github.io/on-the-flySfMv2.github.io/.

The WHT Enhanced Area Velocity Explorer (WEAVE) is a new, massively multiplexing spectrograph. This new instrument will be exploited to obtain high S/N spectra of $\sim$25000 galaxies at intermediate redshifts for the WEAVE Stellar Population Survey (WEAVE-StePS). We test machine learning methods for retrieving the key physical parameters of galaxies from WEAVE-StePS-like spectra using both photometric and spectroscopic information at various S/Ns and redshifts. We simulated $\sim$105000 galaxy spectra assuming SFH with an exponentially declining star formation rate, covering a wide range of ages, stellar metallicities, sSFRs, and dust extinctions. We then evaluated the ability of the random forest and KNN algorithms to correctly predict such parameters assuming no measurement errors. We checked how much the predictive ability deteriorates for different S/Ns and redshifts, finding that both algorithms still accurately estimate the ages and metallicities with low bias. The dispersion varies from 0.08-0.16 dex for ages and 0.11-0.25 dex for metallicity, depending on the redshift and S/N. For dust attenuation, we find a similarly low bias and dispersion. For the sSFR, we find a very good constraining power for star-forming galaxies, log sSFR$\gtrsim$ -11, where the bias is $\sim$ 0.01 dex and the dispersion is $\sim$ 0.10 dex. For more quiescent galaxies, with log sSFR$\lesssim$ -11, we find a higher bias, 0.61-0.86 dex, and a higher dispersion, $\sim$ 0.4 dex, for different S/Ns and redshifts. Generally, we find that the RF outperforms the KNN. Finally, the retrieved sSFR was used to successfully classify galaxies as part of the blue cloud, green valley, or red sequence. We demonstrate that machine learning algorithms can accurately estimate the physical parameters of simulated galaxies even at relatively low S/N=10 per angstrom spectra with available ancillary photometric information., Comment: 19 pages, 10 + 2 figures, 4 tables, accepted in A&A

Synchronization of mobile oscillators occurs in numerous contexts, including physical, chemical, biological and engineered systems. In vertebrate embryonic development, a segmental body structure is generated by a population of mobile oscillators. Cells in this population produce autonomous gene expression rhythms, and interact with their neighbors through local signaling. These cells form an extended tissue where frequency and cell mobility gradients coexist. Gene expression kinematic waves travel through this tissue and pattern the segment boundaries. It has been shown that oscillator mobility promotes global synchronization. However, in vertebrate segment formation, mobility may also introduce local fluctuations in kinematic waves and impair segment boundaries. Here we derive a general framework for mobile oscillators that relates local mobility fluctuations to synchronization dynamics and pattern robustness. We formulate a statistical description of mobile phase oscillators in terms of a probability density. We obtain and solve diffusion equations for the average phase and variance, revealing the relationship between local fluctuations and global synchronization in a homogeneous population of oscillators. Analysis of the probability density for large mobility identifies a mean-field transition, where locally coupled oscillators start behaving as if each oscillator was coupled with all the others. We extend the statistical description to inhomogeneous systems to address the gradients present in the vertebrate segmenting tissue. The theory relates pattern stability to mobility, coupling and pattern wavelength. The general approach of the statistical description may be applied to mobile oscillators in other contexts, as well as to other patterning systems where mobility is present., Comment: 19 pages, 9 figures, 1 table

The assessment of corporate sustainability performance is extremely relevant in facilitating the transition to a green and low-carbon intensity economy. However, companies located in different areas may be subject to different sustainability and environmental risks and policies. Henceforth, the main objective of this paper is to investigate the spatial and temporal pattern of the sustainability evaluations of European firms. We leverage on a large dataset containing information about companies' sustainability performances, measured by MSCI ESG ratings, and geographical coordinates of firms in Western Europe between 2013 and 2023. By means of a modified version of the Chavent et al. (2018) hierarchical algorithm, we conduct a spatial clustering analysis, combining sustainability and spatial information, and a spatiotemporal clustering analysis, which combines the time dynamics of multiple sustainability features and spatial dissimilarities, to detect groups of firms with homogeneous sustainability performance. We are able to build cross-national and cross-industry clusters with remarkable differences in terms of sustainability scores. Among other results, in the spatio-temporal analysis, we observe a high degree of geographical overlap among clusters, indicating that the temporal dynamics in sustainability assessment are relevant within a multidimensional approach. Our findings help to capture the diversity of ESG ratings across Western Europe and may assist practitioners and policymakers in evaluating companies facing different sustainability-linked risks in different areas.

The state of a finite-dimensional quantum system is described by a density matrix that can be decomposed into a real diagonal, a real off-diagonal and and an imaginary off-diagonal part. The latter plays a peculiar role. While it is intuitively clear that some of the imaginary coordinates cannot have the same extension as their real counterparts the precise relation is not obvious. We give a complete characterization of the constraints in terms of tight inequalities for real and imaginary Bloch-type coordinates. Our description entails a three-dimensional Bloch ball-type model for the state space. We uncover a surprising qualitative difference for the state-space boundaries in even and odd dimensions., Comment: 7 pages, 3 figures, comments welcomed

Methods. We utilized the combined five SRG/eROSITA All-Sky Survey data (eRASS:5) to perform X-ray imaging and spectral analyses of the Centaurus cluster in various directions to large radii. Surface brightness (SB) profiles out to $2R_{200}$ were constructed. We acquired gas temperature, metallicity, and normalization per area profiles out to $R_{200}$. We compared our results with previous Centaurus studies, cluster outskirts measurements, and simulations. Comprehensive sky background analysis was done across the FoV, in particular, to assess the variation of the eROSITA Bubble emission that partially contaminates the field. Results. The processed X-ray images show the known sloshing-induced structures in the core. The core ($r\leq11~\mathrm{kpc}$) is better described with a 2T model than a 1T model. Here, we measured lower T from the cooler component (~1.0 keV) and higher Z ($\sim\!1.6Z_\odot$), signifying an iron bias. In the intermediate radial range, we observed prominent SB and normalization per area excesses in the eastern sector (Cen 45 location), reaching out to $R_{500}$. Temperature enhancements near the location of Cen 45 imply that the gas is shock-heated due to the interaction with Cen 30, the significant excess behind Cen 45 center might be the tail/ram-pressure-stripped gas. We found good agreement between the outskirt temperatures with the profile from simulations and fit from Suzaku outskirts measurements. We detected significant SB emission to the sky background level out to $R_{200}$ with a $3.5\sigma$ and followed by $2.9\sigma$ at $1.1R_{200}$. The metallicity at $R_{500}-R_{200}$ is low but within the ranges of other outskirts studies. Conclusions. We present the first measurement of ICM morphology and properties of Centaurus cluster sampling the whole azimuth beyond $30'$, increasing the probed volume by a factor of almost 30., Comment: Submitted to the Astronomy & Astrophysics journal: 19 pages, 10 figures (main text), 2 figure (appendix)

The analysis of glioblastoma (GB) cell locomotion and its modeling inspired by Levy random walks is presented herein. We study such walks occurring on a two-dimensional plane where the walk is similar to the motion of a bird flying with a constant velocity, but with random changes of direction in time. The intelligence of the bird is signaled by the instantaneous changes of flying direction, which become invisible in the time series obtained by projecting the 2D walk either on the x axis or the y axis. We establish that the projected 1D time series share the statistical complexity of time series frequently used to monitor physiological processes, shedding light on the role of crucial events (CE-s) in pathophysiology. Such CE-s are signified by abrupt changes of flying direction which are invisible in the 1D physiological time series. We establish a connection between the complex scaling index \delta generated by the CE-s through \mu_{R} = 2 - \delta , where \mu_{R} is the inverse power law index of the probability density function of the time interval between consecutive failures of the process of interest. We argue that the identification of empirical indices along with their theoretical relations afford important measures to control cancer., Comment: 30 pages, 44 references, 13 figures

Fashion illustration is a crucial medium for designers to convey their creative vision and transform design concepts into tangible representations that showcase the interplay between clothing and the human body. In the context of fashion design, computer vision techniques have the potential to enhance and streamline the design process. Departing from prior research primarily focused on virtual try-on, this paper tackles the task of multimodal-conditioned fashion image editing. Our approach aims to generate human-centric fashion images guided by multimodal prompts, including text, human body poses, garment sketches, and fabric textures. To address this problem, we propose extending latent diffusion models to incorporate these multiple modalities and modifying the structure of the denoising network, taking multimodal prompts as input. To condition the proposed architecture on fabric textures, we employ textual inversion techniques and let diverse cross-attention layers of the denoising network attend to textual and texture information, thus incorporating different granularity conditioning details. Given the lack of datasets for the task, we extend two existing fashion datasets, Dress Code and VITON-HD, with multimodal annotations. Experimental evaluations demonstrate the effectiveness of our proposed approach in terms of realism and coherence concerning the provided multimodal inputs.

This study proposes a new approach to assessing urban accessibility using the competitive equilibrium method, an adaptation of the balancing cost method that incorporates competition among users into dual accessibility metrics. The need for this method arises from verifying the inability of the balancing cost method to measure the competitive dynamics of transport systems precisely. The method works by continuously updating available opportunities based on the number of competitors, capturing competition effects, and differentiating regions according to demand and supply. The application results in three cities in the interior of S\~ao Paulo demonstrate that competitive equilibrium is significantly more sensitive to employment supply variations and can measure potential competitors by area. In addition, the data used, and the computational tools developed are made available, allowing for the replication of the work and application in future studies., Comment: 13 pages, 7 figures, Preprint for journal publication

A stochastic inverse heat transfer problem is formulated to infer the transient heat flux, treated as an unknown Neumann boundary condition. Therefore, an Ensemble-based Simultaneous Input and State Filtering as a Data Assimilation technique is utilized for simultaneous temperature distribution prediction and heat flux estimation. This approach is incorporated with Radial Basis Functions not only to lessen the size of unknown inputs but also to mitigate the computational burden of this technique. The procedure applies to the specific case of a mold used in Continuous Casting machinery, and it is based on the sequential availability of temperature provided by thermocouples inside the mold. Our research represents a significant contribution to achieving probabilistic boundary condition estimation in real-time handling with noisy measurements and errors in the model. We additionally demonstrate the procedure's dependence on some hyperparameters that are not documented in the existing literature. Accurate real-time prediction of the heat flux is imperative for the smooth operation of Continuous Casting machinery at the boundary region where the Continuous Casting mold and the molten steel meet which is not also physically measurable. Thus, this paves the way for efficient real-time monitoring and control, which is critical for preventing caster shutdowns.

The Schmidt number represents the genuine entanglement dimension of a bipartite quantum state. We derive simple criteria for the Schmidt number of a density matrix in arbitrary local dimensions. They are based on the trace norm of correlations obtained from seminal families of quantum measurements, specifically symmetric informationally complete measurements and mutually unbiased bases. Our criteria are strictly stronger than both the well-known fidelity witness criterion and the computable cross-norms or realignment criterion., Comment: 7+3 pages

Large Language Models (LLMs) are increasingly bringing advances to Natural Language Processing. However, low-resource languages, those lacking extensive prominence in datasets for various NLP tasks, or where existing datasets are not as substantial, such as Portuguese, already obtain several benefits from LLMs, but not to the same extent. LLMs trained on multilingual datasets normally struggle to respond to prompts in Portuguese satisfactorily, presenting, for example, code switching in their responses. This work proposes a fine-tuned LLaMA 2-based model for Portuguese prompts named Bode in two versions: 7B and 13B. We evaluate the performance of this model in classification tasks using the zero-shot approach with in-context learning, and compare it with other LLMs. Our main contribution is to bring an LLM with satisfactory results in the Portuguese language, as well as to provide a model that is free for research or commercial purposes., Comment: 10 pages, 3 figures

Dwarf barred galaxies are the perfect candidates for hosting slowly-rotating bars. They are common in dense environments and they have a relatively shallow potential well, making them prone to heating by interactions. When an interaction induces bar formation, the bar should rotate slowly. They reside in massive and centrally-concentrated dark matter halos, which slow down the bar rotation through dynamical friction. While predictions suggest that slow bars should be common, measurements of bar pattern speed, using the Tremaine-Weinberg method, show that bars are mostly fast in the local Universe. We present a photometric and kinematic characterisation of bars hosted by two dwarf galaxies in the Virgo Cluster, NGC 4483 and NGC 4516. We derive the bar length and strength using the Next Generation Virgo Survey imaging and the circular velocity, bar pattern speed, and rotation rate using spectroscopy from the Multi Unit Spectroscopic Explorer. Including the previously studied galaxy IC 3167, we compare the bar properties of the three dwarf galaxies with those of their massive counterparts from literature. Bars in the dwarf galaxies are shorter and weaker, and rotate slightly slower with respect to those in massive galaxies. This could be due to a different bar formation mechanism and/or to a large dark matter fraction in the centre of dwarf galaxies. We show that it is possible to push the application of the Tremaine-Weinberg method to the galaxy low mass regime., Comment: Accepted for publication in MNRAS