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1. MFTIQ: Multi-Flow Tracker with Independent Matching Quality Estimation

Author: Serych, Jonas, Neoral, Michal, and Matas, Jiri
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: In this work, we present MFTIQ, a novel dense long-term tracking model that advances the Multi-Flow Tracker (MFT) framework to address challenges in point-level visual tracking in video sequences. MFTIQ builds upon the flow-chaining concepts of MFT, integrating an Independent Quality (IQ) module that separates correspondence quality estimation from optical flow computations. This decoupling significantly enhances the accuracy and flexibility of the tracking process, allowing MFTIQ to maintain reliable trajectory predictions even in scenarios of prolonged occlusions and complex dynamics. Designed to be "plug-and-play", MFTIQ can be employed with any off-the-shelf optical flow method without the need for fine-tuning or architectural modifications. Experimental validations on the TAP-Vid Davis dataset show that MFTIQ with RoMa optical flow not only surpasses MFT but also performs comparably to state-of-the-art trackers while having substantially faster processing speed. Code and models available at https://github.com/serycjon/MFTIQ ., Comment: accepted to WACV 2025
Published: 2024

2. Air entrainment by turbulent plunging jets: effect of jet roughness revisited

Author: Redor, Ivan, Guyot, Gregory, Obligado, Martin, Matas, Jean-Philippe, and Cartellier, Alain
Subjects: Physics - Fluid Dynamics
Abstract: The amount of air entrained by vertical water jets impacting a large pool is revisited. To test available phenomenological models, new data on the jet deformation at impact and on the entrained air flow rate were collected both on a small-scale (height of fall H about 1 m, jet diameter D0 = 7.6 mm) and a large-scale (H up to 9 m, D0 up to 213 mm) facilities. Conditions for which jet break-up occurred were not considered. For short heights of fall (H less than a few D0), the jet deformation remains smaller than 0.1 jet diameter, and the entrained air flow rate happens to grow as Ui^3/2, where Ui is the jet velocity at impact. This scaling agrees with the air film model proposed by Sene, 1988. At larger fall heights, even though conditions leading to jet break-up were avoided, the jets exhibited complex topologies, including strong deformations and/or interface stripping and/or jet aeration. Further, the roughness model initiated by Henderson, McCarthy and Molloy, 1970 which stipulates that the entrained air flow rate corresponds to the air trapped within jet corrugations, was found valid for these conditions. More precisely, for corrugated jets, the effective roughness amounts to the maximum jet deformation (as measured from the 90% detection probability on the diameter pdf) or equivalently to about two times the total deformation of one side of the jet (where the total deformation of one side of the jet is experimentally evaluated as the standard deviation of the position of one jet edge). However, for jets experiencing strong stripping or aeration (the latter being identified by a threshold on the growth of the jet diameter with the falling distance), the effective roughness amounts to about 0.8 times the maximum jet deformation or equivalently to 1.1 times the total deformation of one side of the jet. Compared with corrugated jets, the effective roughness is thus diminished by half.
Published: 2024

3. Optimal network sizes for most robust Turing patterns

Author: Shaberi, Hazlam S. Ahmad, Kappassov, Aibek, Matas-Gil, Antonio, and Endres, Robert G.
Subjects: Quantitative Biology - Molecular Networks
Abstract: Many cellular patterns exhibit a reaction-diffusion component, suggesting that Turing instability may contribute to pattern formation. However, biological gene-regulatory pathways are more complex than simple Turing activator-inhibitor models and generally do not require fine-tuning of parameters as dictated by the Turing conditions. To address these issues, we employ random matrix theory to analyze the Jacobian matrices of larger networks with robust statistical properties. Our analysis reveals that Turing patterns are more likely to occur by chance than previously thought and that the most robust Turing networks have an optimal size, surprisingly consisting only of a handful of molecular species, thus significantly increasing their identifiability in biological systems. This optimal size emerges from a tradeoff between the highest stability in small networks and the greatest instability with diffusion in large networks. Furthermore, we find that with multiple immobile nodes, differential diffusion ceases to be important for Turing patterns. Our findings may inform future synthetic biology approaches and provide insights into bridging the gap to complex developmental pathways., Comment: 18 pages with SI in total, including 8 maintext and 5 SI figures
Published: 2024

4. The BRAVO Semantic Segmentation Challenge Results in UNCV2024

Author: Vu, Tuan-Hung, Valle, Eduardo, Bursuc, Andrei, Kerssies, Tommie, de Geus, Daan, Dubbelman, Gijs, Qian, Long, Zhu, Bingke, Chen, Yingying, Tang, Ming, Wang, Jinqiao, Vojíř, Tomáš, Šochman, Jan, Matas, Jiří, Smith, Michael, Ferrie, Frank, Basu, Shamik, Sakaridis, Christos, and Van Gool, Luc
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios. We define two categories of reliability: (1) semantic reliability, which reflects the model's accuracy and calibration when exposed to various perturbations; and (2) OOD reliability, which measures the model's ability to detect object classes that are unknown during training. The challenge attracted nearly 100 submissions from international teams representing notable research institutions. The results reveal interesting insights into the importance of large-scale pre-training and minimal architectural design in developing robust and reliable semantic segmentation models., Comment: ECCV 2024 proceeding paper of the BRAVO challenge 2024, see https://benchmarks.elsa-ai.eu/?ch=1&com=introduction Corrected numbers in Tables 1,3,4,5 and 10
Published: 2024

5. FungiTastic: A multi-modal dataset and benchmark for image categorization

Author: Picek, Lukas, Janouskova, Klara, Sulc, Milan, and Matas, Jiri
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We introduce a new, challenging benchmark and a dataset, FungiTastic, based on fungal records continuously collected over a twenty-year span. The dataset is labeled and curated by experts and consists of about 350k multimodal observations of 5k fine-grained categories (species). The fungi observations include photographs and additional data, e.g., meteorological and climatic data, satellite images, and body part segmentation masks. FungiTastic is one of the few benchmarks that include a test set with DNA-sequenced ground truth of unprecedented label reliability. The benchmark is designed to support (i) standard closed-set classification, (ii) open-set classification, (iii) multi-modal classification, (iv) few-shot learning, (v) domain shift, and many more. We provide baseline methods tailored for many use-cases, a multitude of ready-to-use pre-trained models on HuggingFace and a framework for model training. A comprehensive documentation describing the dataset features and the baselines are available at https://bohemianvra.github.io/FungiTastic/ and https://www.kaggle.com/datasets/picekl/fungitastic.
Published: 2024

6. WildFusion: Individual Animal Identification with Calibrated Similarity Fusion

Author: Cermak, Vojtěch, Picek, Lukas, Adam, Lukáš, Neumann, Lukáš, and Matas, Jiří
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We propose a new method - WildFusion - for individual identification of a broad range of animal species. The method fuses deep scores (e.g., MegaDescriptor or DINOv2) and local matching similarity (e.g., LoFTR and LightGlue) to identify individual animals. The global and local information fusion is facilitated by similarity score calibration. In a zero-shot setting, relying on local similarity score only, WildFusion achieved mean accuracy, measured on 17 datasets, of 76.2%. This is better than the state-of-the-art model, MegaDescriptor-L, whose training set included 15 of the 17 datasets. If a dataset-specific calibration is applied, mean accuracy increases by 2.3% percentage points. WildFusion, with both local and global similarity scores, outperforms the state-of-the-art significantly - mean accuracy reached 84.0%, an increase of 8.5 percentage points; the mean relative error drops by 35%. We make the code and pre-trained models publicly available5, enabling immediate use in ecology and conservation.
Published: 2024

7. Animal Identification with Independent Foreground and Background Modeling

Author: Picek, Lukas, Neumann, Lukas, and Matas, Jiri
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We propose a method that robustly exploits background and foreground in visual identification of individual animals. Experiments show that their automatic separation, made easy with methods like Segment Anything, together with independent foreground and background-related modeling, improves results. The two predictions are combined in a principled way, thanks to novel Per-Instance Temperature Scaling that helps the classifier to deal with appearance ambiguities in training and to produce calibrated outputs in the inference phase. For identity prediction from the background, we propose novel spatial and temporal models. On two problems, the relative error w.r.t. the baseline was reduced by 22.3% and 8.8%, respectively. For cases where objects appear in new locations, an example of background drift, accuracy doubles.
Published: 2024

8. EEPPR: Event-based Estimation of Periodic Phenomena Rate using Correlation in 3D

Author: Kolář, Jakub, Špetlík, Radim, and Matas, Jiří
Subjects: Computer Science - Computer Vision and Pattern Recognition, I.4.8
Abstract: We present a novel method for measuring the rate of periodic phenomena (e.g., rotation, flicker, and vibration), by an event camera, a device asynchronously reporting brightness changes at independently operating pixels with high temporal resolution. The approach assumes that for a periodic phenomenon, a highly similar set of events is generated within a spatio-temporal window at a time difference corresponding to its period. The sets of similar events are detected by a correlation in the spatio-temporal event stream space. The proposed method, EEPPR, is evaluated on a dataset of 12 sequences of periodic phenomena, i.e. flashing light and vibration, and periodic motion, e.g., rotation, ranging from 3.2 Hz to 2 kHz (equivalent to 192 - 120 000 RPM). EEPPR significantly outperforms published methods on this dataset, achieving a mean relative error of 0.1%, setting new state-of-the-art. The dataset and codes are publicly available on GitHub., Comment: 8 pages, 2 figues, 3 tables
Published: 2024

9. Predicting the Best of N Visual Trackers

Author: Alawode, Basit, Javed, Sajid, Mahmood, Arif, and Matas, Jiri
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: We observe that the performance of SOTA visual trackers surprisingly strongly varies across different video attributes and datasets. No single tracker remains the best performer across all tracking attributes and datasets. To bridge this gap, for a given video sequence, we predict the "Best of the N Trackers", called the BofN meta-tracker. At its core, a Tracking Performance Prediction Network (TP2N) selects a predicted best performing visual tracker for the given video sequence using only a few initial frames. We also introduce a frame-level BofN meta-tracker which keeps predicting best performer after regular temporal intervals. The TP2N is based on self-supervised learning architectures MocoV2, SwAv, BT, and DINO; experiments show that the DINO with ViT-S as a backbone performs the best. The video-level BofN meta-tracker outperforms, by a large margin, existing SOTA trackers on nine standard benchmarks - LaSOT, TrackingNet, GOT-10K, VOT2019, VOT2021, VOT2022, UAV123, OTB100, and WebUAV-3M. Further improvement is achieved by the frame-level BofN meta-tracker effectively handling variations in the tracking scenarios within long sequences. For instance, on GOT-10k, BofN meta-tracker average overlap is 88.7% and 91.1% with video and frame-level settings respectively. The best performing tracker, RTS, achieves 85.20% AO. On VOT2022, BofN expected average overlap is 67.88% and 70.98% with video and frame level settings, compared to the best performing ARTrack, 64.12%. This work also presents an extensive evaluation of competitive tracking methods on all commonly used benchmarks, following their protocols. The code, the trained models, and the results will soon be made publicly available on https://github.com/BasitAlawode/Best_of_N_Trackers.
Published: 2024

10. AI-Driven Skin Cancer Diagnosis: Grad-CAM and Expert Annotations for Enhanced Interpretability

Author: Matas, Iván, Serrano, Carmen, Silva, Francisca, Serrano, Amalia, Toledo-Pastrana, Tomás, and Acha, Begoña
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Information Retrieval, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: An AI tool has been developed to provide interpretable support for the diagnosis of BCC via teledermatology, thus speeding up referrals and optimizing resource utilization. The interpretability is provided in two ways: on the one hand, the main BCC dermoscopic patterns are found in the image to justify the BCC/Non BCC classification. Secondly, based on the common visual XAI Grad-CAM, a clinically inspired visual explanation is developed where the relevant features for diagnosis are located. Since there is no established ground truth for BCC dermoscopic features, a standard reference is inferred from the diagnosis of four dermatologists using an Expectation Maximization (EM) based algorithm. The results demonstrate significant improvements in classification accuracy and interpretability, positioning this approach as a valuable tool for early BCC detection and referral to dermatologists. The BCC/non-BCC classification achieved an accuracy rate of 90%. For Clinically-inspired XAI results, the detection of BCC patterns useful to clinicians reaches 99% accuracy. As for the Clinically-inspired Visual XAI results, the mean of the Grad-CAM normalized value within the manually segmented clinical features is 0.57, while outside this region it is 0.16. This indicates that the model struggles to accurately identify the regions of the BCC patterns. These results prove the ability of the AI tool to provide a useful explanation., Comment: 8 pages, 4 figures, 4 tables, under review
Published: 2024

11. Concordance in basal cell carcinoma diagnosis. Building a proper ground truth to train Artificial Intelligence tools

Author: Silva-Clavería, Francisca, Serrano, Carmen, Matas, Iván, Serrano, Amalia, Toledo-Pastrana, Tomás, Moreno-Ramírez, David, and Acha, Begoña
Subjects: Quantitative Biology - Quantitative Methods, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Information Retrieval, Statistics - Methodology
Abstract: Background: The existence of different basal cell carcinoma (BCC) clinical criteria cannot be objectively validated. An adequate ground-truth is needed to train an artificial intelligence (AI) tool that explains the BCC diagnosis by providing its dermoscopic features. Objectives: To determine the consensus among dermatologists on dermoscopic criteria of 204 BCC. To analyze the performance of an AI tool when the ground-truth is inferred. Methods: A single center, diagnostic and prospective study was conducted to analyze the agreement in dermoscopic criteria by four dermatologists and then derive a reference standard. 1434 dermoscopic images have been used, that were taken by a primary health physician, sent via teledermatology, and diagnosed by a dermatologist. They were randomly selected from the teledermatology platform (2019-2021). 204 of them were tested with an AI tool; the remainder trained it. The performance of the AI tool trained using the ground-truth of one dermatologist versus the ground-truth statistically inferred from the consensus of four dermatologists was analyzed using McNemar's test and Hamming distance. Results: Dermatologists achieve perfect agreement in the diagnosis of BCC (Fleiss-Kappa=0.9079), and a high correlation with the biopsy (PPV=0.9670). However, there is low agreement in detecting some dermoscopic criteria. Statistical differences were found in the performance of the AI tool trained using the ground-truth of one dermatologist versus the ground-truth statistically inferred from the consensus of four dermatologists. Conclusions: Care should be taken when training an AI tool to determine the BCC patterns present in a lesion. Ground-truth should be established from multiple dermatologists., Comment: Manuscript word count: 3000, Number of figures: 2, Number of tables: 3
Published: 2024

12. Breaking the Frame: Visual Place Recognition by Overlap Prediction

Author: Wei, Tong, Lindenberger, Philipp, Matas, Jiri, and Barath, Daniel
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Visual place recognition methods struggle with occlusions and partial visual overlaps. We propose a novel visual place recognition approach based on overlap prediction, called VOP, shifting from traditional reliance on global image similarities and local features to image overlap prediction. VOP proceeds co-visible image sections by obtaining patch-level embeddings using a Vision Transformer backbone and establishing patch-to-patch correspondences without requiring expensive feature detection and matching. Our approach uses a voting mechanism to assess overlap scores for potential database images. It provides a nuanced image retrieval metric in challenging scenarios. Experimental results show that VOP leads to more accurate relative pose estimation and localization results on the retrieved image pairs than state-of-the-art baselines on a number of large-scale, real-world indoor and outdoor benchmarks. The code is available at https://github.com/weitong8591/vop.git.
Published: 2024

13. PixOOD: Pixel-Level Out-of-Distribution Detection

Author: Vojíř, Tomáš, Šochman, Jan, and Matas, Jiří
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We propose a dense image prediction out-of-distribution detection algorithm, called PixOOD, which does not require training on samples of anomalous data and is not designed for a specific application which avoids traditional training biases. In order to model the complex intra-class variability of the in-distribution data at the pixel level, we propose an online data condensation algorithm which is more robust than standard K-means and is easily trainable through SGD. We evaluate PixOOD on a wide range of problems. It achieved state-of-the-art results on four out of seven datasets, while being competitive on the rest. The source code is available at https://github.com/vojirt/PixOOD., Comment: published at ECCV2024, table 1,2 improved results for the PixOOD variants thanks to fixing bug in normalization of input image
Published: 2024

14. MINCE II. Neutron capture elements

Author: François, P., Cescutti, G., Bonifacio, P., Caffau, E., Monaco, L., Steffen, M., Puschnig, J., Calura, F., Cristallo, S., Di Marcantonio, P., Dobrovolskas, V., Franchini, M., Gallagher, A. J., Hansen, C. J., Korn, A., Kuvinskas, A., Lallement, R., Lombardo, L., Lucertini, F., Magrini, L., Pinto, A. M. Matas, Matteucci, F., Mucciarelli, A., Sbordone, L., Spite, M., Spitoni, E., and Valentini, M.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: The MINCE (Measuring at Intermediate metallicity Neutron-Capture Elements) project aims to gather the abundances of neutron-capture elements but also of light elements and iron peak elements in a large sample of giant stars in this metallicity range. T The aim of this work is to study the chemical evolution of galactic sub-components recently identified (i.e. Gaia Sausage Enceladus (GSE), Sequoia). We used high signal-to-noise ratios, high-resolution spectra and standard 1D LTE spectrum synthesis to determine the detailed abundances. We could determine the abundances for up to 10 neutron-capture elements (Sr, Y, Zr, Ba, La, Ce, Pr, Nd, Sm and Eu) in 33 stars. The general trends of abundance ratios [n-capture element/Fe] versus [Fe/H] are in agreement with the results found in the literature. When our sample is divided in sub-groups depending on their kinematics, we found that the run of [Sr/Ba] vs [Ba/H] for the stars belonging to the GSE accretion event shows a tight anti-correlation. The results for the Sequoia stars, although based on a very limited sample, shows a [Sr/Ba] systematically higher than the [Sr/Ba] found in the GSE stars at a given [Ba/H] hinting at a different nucleosynthetic history. Stochastic chemical evolution models have been computed to understand the evolution of the GSE chemical composition of Sr and Ba. The first conclusions are that the GSE chemical evolution is similar to the evolution of a dwarf galaxy with galactic winds and inefficient star formation. Detailed abundances of neutron-capture elements have been measured in high-resolution, high signal-to-noise spectra of intermediate metal-poor stars, the metallicity range covered by the MINCE project. These abundances have been compared to detailed stochastic models of galactic chemical evolution., Comment: 18 pages, 15 figures
Published: 2024

15. Periorbital necrotizing fasciitis: clinical perspectives on nine cases

Author: Oliver-Gutierrez, David, van der Veen, R. L. P., Ros-Sánchez, Elena, Segura-Duch, Gloria, Alonso, Tirso, Herranz-Cabarcos, Alejandra, Matas, Jessica, Castro Seco, Roberto, Arcediano, Miguel Ángel, Zapata, Miguel Ángel, and Oliveres, Joan
Published: 2024
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16. BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects

Author: Hodan, Tomas, Sundermeyer, Martin, Labbe, Yann, Nguyen, Van Nguyen, Wang, Gu, Brachmann, Eric, Drost, Bertram, Lepetit, Vincent, Rother, Carsten, and Matas, Jiri
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract: We present the evaluation methodology, datasets and results of the BOP Challenge 2023, the fifth in a series of public competitions organized to capture the state of the art in model-based 6D object pose estimation from an RGB/RGB-D image and related tasks. Besides the three tasks from 2022 (model-based 2D detection, 2D segmentation, and 6D localization of objects seen during training), the 2023 challenge introduced new variants of these tasks focused on objects unseen during training. In the new tasks, methods were required to learn new objects during a short onboarding stage (max 5 minutes, 1 GPU) from provided 3D object models. The best 2023 method for 6D localization of unseen objects (GenFlow) notably reached the accuracy of the best 2020 method for seen objects (CosyPose), although being noticeably slower. The best 2023 method for seen objects (GPose) achieved a moderate accuracy improvement but a significant 43% run-time improvement compared to the best 2022 counterpart (GDRNPP). Since 2017, the accuracy of 6D localization of seen objects has improved by more than 50% (from 56.9 to 85.6 AR_C). The online evaluation system stays open and is available at: http://bop.felk.cvut.cz/., Comment: arXiv admin note: substantial text overlap with arXiv:2302.13075
Published: 2024

17. A Predictive Model for Catalytic Methane Pyrolysis

Author: Pototschnig, Ulrich, Matas, Martin, Scheiblehner, David, Neuschitzer, David, Obenaus-Emler, Robert, Antrekowitsch, Helmut, and Holec, David
Subjects: Condensed Matter - Materials Science, Physics - Chemical Physics
Abstract: Methane pyrolysis provides a scalable alternative to conventional hydrogen production methods, avoiding greenhouse gas emissions. However, high operating temperatures limit economic feasibility on an industrial scale. A major scientific goal is, therefore, to find a catalyst material that lowers operating temperatures, making methane pyrolysis economically viable. In this work, we derive a model that provides a qualitative comparison of possible catalyst materials. The model is based on calculations of adsorption energies using density functional theory. Thirty different elements were considered. Adsorption energies of intermediate molecules in the methane pyrolysis reaction correlate linearly with the adsorption energy of carbon. Moreover, the adsorption energy increases in magnitude with decreasing group number in the $d$-block of the periodic table. For a temperature range between 600 and 1200 K and a normalized partial pressure range for $\mathrm{H_2}$ between $10^{-1}$ and $10^{-5}$, a total of eighteen different materials were found to be optimal catalysts at least once. This indicates that catalyst selection and reactor operating conditions should be well-matched. The present work establishes the foundation for future large-scale studies of surfaces, alloy compositions, and material classes using machine learning algorithms.
Published: 2024
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18. EE3P: Event-based Estimation of Periodic Phenomena Properties

Author: Kolář, Jakub, Špetlík, Radim, and Matas, Jiří
Subjects: Computer Science - Computer Vision and Pattern Recognition, I.4.8
Abstract: We introduce a novel method for measuring properties of periodic phenomena with an event camera, a device asynchronously reporting brightness changes at independently operating pixels. The approach assumes that for fast periodic phenomena, in any spatial window where it occurs, a very similar set of events is generated at the time difference corresponding to the frequency of the motion. To estimate the frequency, we compute correlations of spatio-temporal windows in the event space. The period is calculated from the time differences between the peaks of the correlation responses. The method is contactless, eliminating the need for markers, and does not need distinguishable landmarks. We evaluate the proposed method on three instances of periodic phenomena: (i) light flashes, (ii) vibration, and (iii) rotational speed. In all experiments, our method achieves a relative error lower than 0.04%, which is within the error margin of ground truth measurements., Comment: 9 pages, 55 figures, accepted and presented at CVWW24, published in Proceedings of the 27th Computer Vision Winter Workshop, 2024
Published: 2024

19. Dense Matchers for Dense Tracking

Author: Jelínek, Tomáš, Šerých, Jonáš, and Matas, Jiří
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Optical flow is a useful input for various applications, including 3D reconstruction, pose estimation, tracking, and structure-from-motion. Despite its utility, the field of dense long-term tracking, especially over wide baselines, has not been extensively explored. This paper extends the concept of combining multiple optical flows over logarithmically spaced intervals as proposed by MFT. We demonstrate the compatibility of MFT with different optical flow networks, yielding results that surpass their individual performance. Moreover, we present a simple yet effective combination of these networks within the MFT framework. This approach proves to be competitive with more sophisticated, non-causal methods in terms of position prediction accuracy, highlighting the potential of MFT in enhancing long-term tracking applications.
Published: 2024

20. High-speed stars. II. An unbound star, young stars, bulge metal-poor stars, and Aurora candidates

Author: Bonifacio, P., Caffau, E., Monaco, L., Sbordone, L., Spite, M., Mucciarelli, A., François, P., Lombardo, L., and Pinto, A. D. M. Matas
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The data from the Gaia satellite led us to revise our conception of the Galaxy structure and history. Hitherto unknown components have been discovered and a deep re-thinking of what the Galactic halo is is in progress. We selected from the Gaia catalogue stars with extreme transverse velocities with respect to the Sun ($|V_T| > 500 $ and observed them with FORS2 at the ESO VLT, to classify them using both their chemical and dynamical properties. Two apparently young stars, identified in paper\,I, were observed with UVES. We derived abundances for Na, Mg, Ca, Ti, Mn, and Fe, analysing the spectra with while for Ba we used line profile fitting. We computed actions from parallaxes and kinematical data. The stars span the metallicity range $ Fe/H -0.5$ with $ Fe/H = -1.6$. Star GHS143 has a total speed of about 1440 which is almost three times faster than the local escape velocity of 522 strongly implying this star is unbound to the Galaxy. Remarkably, this star is not escaping from the Galaxy, but it is falling into it. Ten stars are apparently young with masses in excess of 1.3M. Their interpretation as evolved blue stragglers is doubtful. The existence of a young metal-poor population is possible. The two stars observed with UVES show no lithium, suggesting they are blue stragglers. We detected a metal-poor population, confined to the bulge, that we call SpiteF, and argue that it is the result of a recent accretion event. We detect 102 candidates of the Aurora population that should have formed prior to the formation of the disc. Our sample is non-homogeneous and mainly retrograde. The stars are metal poor, and 23 have Fe/H -2.0$. Our selection is efficient at finding very metal-poor stars, but it selects peculiar populations., Comment: Astronomy and Astrophysics - In press
Published: 2024
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21. A New Dataset and a Distractor-Aware Architecture for Transparent Object Tracking

Author: Lukezic, Alan, Trojer, Ziga, Matas, Jiri, and Kristan, Matej
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Performance of modern trackers degrades substantially on transparent objects compared to opaque objects. This is largely due to two distinct reasons. Transparent objects are unique in that their appearance is directly affected by the background. Furthermore, transparent object scenes often contain many visually similar objects (distractors), which often lead to tracking failure. However, development of modern tracking architectures requires large training sets, which do not exist in transparent object tracking. We present two contributions addressing the aforementioned issues. We propose the first transparent object tracking training dataset Trans2k that consists of over 2k sequences with 104,343 images overall, annotated by bounding boxes and segmentation masks. Standard trackers trained on this dataset consistently improve by up to 16%. Our second contribution is a new distractor-aware transparent object tracker (DiTra) that treats localization accuracy and target identification as separate tasks and implements them by a novel architecture. DiTra sets a new state-of-the-art in transparent object tracking and generalizes well to opaque objects., Comment: Under the review. arXiv admin note: substantial text overlap with arXiv:2210.03436
Published: 2024

22. Physiological and Molecular Responses of ‘Dusa’ Avocado Rootstock to Water Stress: Insights for Drought Adaptation

Author: Moreno-Ortega Guillermo, Zumaquero Adela, Matas Antonio, Nicholas A. Olivier, van den Berg Noëlani, Elena Palomo-Ríos, Martínez-Ferri Elsa, and Pliego Clara
Subjects: Persea americana, abiotic stress, water deprivation, microarray, Botany, QK1-989
Abstract: Avocado consumption is increasing year by year, and its cultivation has spread to many countries with low water availability, which threatens the sustainability and profitability of avocado orchards. However, to date, there is not much information on the behavior of commercial avocado rootstocks against drought. The aim of this research was to evaluate the physiological and molecular responses of ‘Dusa’ avocado rootstock to different levels of water stress. Plants were deficit irrigated until soil water content reached 50% (mild-WS) and 25% (severe-WS) of field capacity. Leaf water potential (Ψw), net CO2 assimilation rates (AN), transpiration rate (E), stomatal conductance (gs), and plant transpiration rates significantly decreased under both WS treatments, reaching significantly lower values in severe-WS plants. After rewatering, mild- and severe-WS plants showed a fast recovery in most physiological parameters measured. To analyze root response to different levels of drought stress, a cDNA avocado stress microarray was carried out. Plants showed a wide transcriptome response linked to the higher degree of water stress, and functional enrichment of differentially expressed genes (DEGs) revealed abundance of common sequences associated with water stress, as well as specific categories for mild-WS and severe-WS. DEGs previously linked to drought tolerance showed overexpression under both water stress levels, i.e., several transcription factors, genes related to abscisic acid (ABA) response, redox homeostasis, osmoprotection, and cell-wall organization. Taken altogether, physiological and molecular data highlight the good performance of ‘Dusa’ rootstock under low-water-availability conditions, although further water stress experiments must be carried out under field conditions.
Published: 2021
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23. PixOOD: Pixel-Level Out-of-Distribution Detection

Author: Vojíř, Tomáš, Šochman, Jan, Matas, Jiří, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor
Published: 2025
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24. Assessment of the Multiple Sclerosis Severity Score and the Age-Related Multiple Sclerosis Severity Score as health indicators in a population-based cohort

Author: Bau, Laura, Matas, Elisabet, Romero-Pinel, Lucía, León, Isabel, Muñoz-Vendrell, Albert, Arroyo-Pereiro, Pablo, Martínez-Yélamos, Antonio, and Martínez-Yélamos, Sergio
Published: 2024
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25. Impact of Tissue Damage and Hemodynamics on Restenosis Following Percutaneous Transluminal Angioplasty: A Patient-Specific Multiscale Model

Author: Corti, Anna, Marradi, Matilde, Çelikbudak Orhon, Cemre, Boccafoschi, Francesca, Büchler, Philippe, Rodriguez Matas, Jose F., and Chiastra, Claudio
Published: 2024
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26. Incoherent ${\rm J}/\psi$ production at large $|t|$ identifies the onset of saturation at the LHC

Author: Cepila, Jan, Contreras, Jesus Guillermo, Matas, Marek, and Ridzikova, Alexandra
Subjects: High Energy Physics - Phenomenology, High Energy Physics - Experiment
Abstract: We predict that the onset of gluon saturation can be uniquely identified using incoherent ${\rm J}/\psi$ production in Pb$\unicode{x2013}$Pb collisions at currently accessible energies of the LHC. The diffractive incoherent photo-production of a ${\rm J}/\psi$ vector meson off a hadron provides information on the partonic structure of the hadron. Within the Good-Walker approach it specifically measures the variance over possible target configurations of the hadronic colour field. For this process then, gluon saturation sets in when the cross section reaches a maximum, as a function of the centre-of-mass energy of the photon-hadron system ($W$), and then decreases. We benchmark the energy-dependent hot-spot model against data from HERA and the LHC and demonstrate a good description of the available data. We show that the study of the energy dependence of the incoherent production of ${\rm J}/\psi$ allows us to pinpoint the onset of saturation effects by selecting the region of Mandelstam-$t$ around 1 GeV$^2$ where the contribution of hot spots is dominant. We predict the onset of saturation in a Pb target to occur for $W$ around a few hundred GeV. This can be measured with current data in ultra-peripheral Pb$\unicode{x2013}$Pb collisions at the LHC., Comment: 18 pages, 3 figures
Published: 2023

27. Extreme local statistics in random graphs: maximum tree extension counts

Author: Araújo, Pedro, Griffiths, Simon, Šileikis, Matas, and Warnke, Lutz
Subjects: Mathematics - Probability, Mathematics - Combinatorics, 05C80, 60G70, 60C05
Abstract: We consider maximum rooted tree extension counts in random graphs, i.e., we consider M_n = \max_v X_v where X_v counts the number of copies of a given tree in G_{n,p} rooted at vertex v. We determine the asymptotics of M_n when the random graph is not too sparse, specifically when the edge probability p=p(n) satisfies p(1-p)n \gg \log n. The problem is more difficult in the sparser regime 1 \ll pn \ll \log n, where we determine the asymptotics of M_n for specific classes of trees. Interestingly, here our large deviation type optimization arguments reveal that the behavior of M_n changes as we vary p=p(n), due to different mechanisms that can make the maximum large., Comment: 38 pages; added Gumbel corollary
Published: 2023

28. Anthropometric and Demographic Features Affect the Interpretation of Cerebrospinal Fluid Biomarkers in Patients with Different Dementia Syndromes and Cognitively Healthy Adults

Author: de Oliveira, Fabricio Ferreira, Miraldo, Marjorie Câmara, de Castro-Neto, Eduardo Ferreira, de Almeida, Sandro Soares, de Andrade Matas, Sandro Luiz, Bertolucci, Paulo Henrique Ferreira, and da Graça Naffah-Mazzacoratti, Maria
Published: 2024
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29. DNA methylation governs the sensitivity of repeats to restriction by the HUSH-MORC2 corepressor

Author: Pandiloski, Ninoslav, Horváth, Vivien, Karlsson, Ofelia, Koutounidou, Symela, Dorazehi, Fereshteh, Christoforidou, Georgia, Matas-Fuentes, Jon, Gerdes, Patricia, Garza, Raquel, Jönsson, Marie E., Adami, Anita, Atacho, Diahann A. M., Johansson, Jenny G., Englund, Elisabet, Kokaia, Zaal, Jakobsson, Johan, and Douse, Christopher H.
Published: 2024
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30. Fetal endocrine axes mRNA expression levels are related to sex and intrauterine position

Author: Yael, Ariel, Fishman, Ruth, Matas, Devorah, Doniger, Tirza, Vortman, Yoni, and Koren, Lee
Published: 2024
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31. Gut microbiome and resistome characterization of pigs treated with commonly used post-weaning diarrhea treatments

Author: Guitart-Matas, Judith, Ballester, Maria, Fraile, Lorenzo, Darwich, Laila, Giler-Baquerizo, Noemí, Tarres, Joaquim, López-Soria, Sergio, Ramayo-Caldas, Yuliaxis, and Migura-Garcia, Lourdes
Published: 2024
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32. Relationship of thymic area with clinical-epidemiological variables and values of T-lymphocyte subpopulations in peripheral blood of children with recurrent infections

Author: Sotomayor Lugo, Francisco, Zúñiga Rosales, Yaíma, Pérez Martín, Oliver, Hernández Reyes, Evelyn, Antiguas Valdés, Evelyn M., Fundora Hernández, Hermes, Rodríguez Guitiérrez, Katia, Matas González, Yaima, Hernández, Imilla Casado, Villegas Valverde, Carlos Agustín, Torres Rives, Bárbara, Tam Rey, Lázara Minerva, González Díaz, Ihosvany, Hernández Rego, Yaquima, Pita, Ana María Simón, Macías Abraham, Consuelo, and Marcheco Teruel, Beatriz
Published: 2024
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33. Goodbye Hartmann trial: a prospective, international, multicenter, observational study on the current use of a surgical procedure developed a century ago

Author: Perrone, Gennaro, Giuffrida, Mario, Abu-Zidan, Fikri, Kruger, Vitor F., Livrini, Marco, Petracca, Gabriele Luciano, Rossi, Giorgio, Tarasconi, Antonio, Tian, Brian W. C. A., Bonati, Elena, Mentz, Ricardo, Mazzini, Federico N., Campana, Juan P., Gasser, Elisabeth, Kafka-Ritsch, Reinhold, Felsenreich, Daniel M., Dawoud, Christopher, Riss, Stefan, Gomes, Carlos Augusto, Gomes, Felipe Couto, Gonzaga, Ricardo Alessandro Teixeira, Canton, Cassio Alfred Brattig, Pereira, Bruno Monteiro, Fraga, Gustavo P., Zem, Leticia Gonçalves, Cordeiro-Fonseca, Vinicius, de Mesquita Tauil, Renato, Atanasov, Boyko, Belev, Nikolay, Kovachev, Nikola, Meléndez, L. Juan José, Dimova, Ana, Dimov, Stefan, Zelić, Zdravko, Augustin, Goran, Bogdanić, Branko, Morić, Trpimir, Chouillard, Elie, Bajul, Melinda, De Simone, Belinda, Panis, Yves, Esposito, Francesco, Notarnicola, Margherita, Lauka, Lelde, Fabbri, Anna, Hentati, Hassen, Fnaiech, Iskander, Aurélien, Venara, Bougard, Marie, Roulet, Maxime, Demetrashvili, Zaza, Pipia, Irakli, Merabishvili, Giorgi, Bouliaris, Konstantinos, Koukoulis, Georgios, Doudakmanis, Christos, Xenaki, Sofia, Chrysos, Emmanuel, Kokkinakis, Stamatios, Vassiliu, Panteleimon, Michalopoulos, Nikolaos, Margaris, Ioannis, Kechagias, Aristotelis, Avgerinos, Konstantinos, Katunin, Jevgeni, Lostoridis, Eftychios, Nagorni, Eleni-Aikaterini, Pujante, Antonio, Mulita, Francesk, Maroulis, Ioannis, Vailas, Michail, Marinis, Athanasios, Siannis, Ioannis, Bourbouteli, Eirini, Manatakis, Dimitrios K., Tasis, Nikolaos, Acheimastos, Vasileios, Maria, Sotiropoulou, Stylianos, Kapiris, Kuzeridis, Harilaos, Korkolis, Dimitrios, Fradelos, Evangelos, Kavalieratos, George, Petropoulou, Thalia, Polydorou, Andreas, Papacostantinou, Ioannis, Triantafyllou, Tania, Kimpizi, Despina, Theodorou, Dimitrios, Toutouzas, Konstantinos, Chamzin, Alexandros, Frountzas, Maximos, Schizas, Dimitrios, Karavokyros, Ioannis, Syllaios, Athanasios, Charalabopoulos, Alexandros, Boura, Maria, Baili, Efstratia, Ioannidis, Orestis, Loutzidou, Lydia, Anestiadou, Elissavet, Tsouknidas, Ioannis, Petrakis, Georgios, Polenta, Eleni, Bains, Lovenish, Gupta, Rahul, Singh, Sudhir K., Khanduri, Archana, Bala, Miklosh, Kedar, Asaf, Pisano, Marcello, Podda, Mauro, Pisanu, Adolfo, Martines, Gennaro, Trigiante, Giuseppe, Lantone, Giuliano, Agrusa, Antonino, Di Buono, Giuseppe, Buscemi, Salvatore, Veroux, Massimiliano, Gioco, Rossella, Veroux, Gastone, Oragano, Luigi, Zonta, Sandro, Lovisetto, Federico, Feo, Carlo V., Pesce, Antonio, Fabbri, Nicolò, Lantone, Giulio, Marino, Fabio, Perrone, Fabrizio, Vincenti, Leonardo, Papagni, Vincenzo, Picciariello, Arcangelo, Rossi, Stefano, Picardi, Biagio, Del Monte, Simone Rossi, Visconti, Diego, Osella, Giulia, Petruzzelli, Luca, Pignata, Giusto, Andreuccetti, Jacopo, D’Alessio, Rossella, Buonfantino, Massimo, Guaitoli, Eleonora, Spinelli, Stefano, Sampietro, Gianluca Matteo, Corbellini, Carlo, Lorusso, Leonardo, Frontali, Alice, Pezzoli, Isabella, Bonomi, Alessandro, Chierici, Andrea, Cotsoglou, Christian, Manca, Giuseppe, Delvecchio, Antonella, Musa, Nicola, Casati, Massimiliano, Letizia, Laface, Abate, Emmanuele, Ercolani, Giorgio, D’Acapito, Fabrizio, Solaini, Leonardo, Guercioni, Gianluca, Cicconi, Simone, Sasia, Diego, Borghi, Felice, Giraudo, Giorgio, Sena, Giuseppe, Castaldo, Pasquale, Cardamone, Eugenia, Portale, Giuseppe, Zuin, Matteo, Spolverato, Ylenia, Esposito, Marialusia, Isernia, Roberta Maria, Di Salvo, Maria, Manunza, Romina, Esposito, Giuseppe, Agus, Marcello, Asti, Emanuele Luigi Giuseppe, Bernardi, Daniele Tiziano, Tonucci, Tommaso Panici, Luppi, Davide, Casadei, Massimiliano, Bonilauri, Stefano, Pezzolla, Angela, Panebianco, Annunziata, Laforgia, Rita, De Luca, Maurizio, Zese, Monica, Parini, Dario, Jovine, Elio, De Sario, Giuseppina, Lombardi, Raffaele, Aprea, Giovanni, Palomba, Giuseppe, Capuano, Marianna, Argenio, Giulio, Orio, Gianluca, Armellino, Mariano Fortunato, Troian, Marina, Guerra, Martina, Nagliati, Carlo, Biloslavo, Alan, Germani, Paola, Aizza, Giada, Monsellato, Igor, Chahrour, Ali Chaouki, Anania, Gabriele, Bombardini, Cristina, Bagolini, Francesco, Sganga, Gabriele, Fransvea, Pietro, Bianchi, Valentina, Boati, Paolo, Ferrara, Francesco, Palmieri, Francesco, Cianci, Pasquale, Gattulli, Domenico, Restini, Enrico, Cillara, Nicola, Cannavera, Alessandro, Nita, Gabriela Elisa, Sarnari, Jlenia, Roscio, Francesco, Clerici, Federico, Scandroglio, Ildo, Berti, Stefano, Cadeo, Alessandro, Filippelli, Alice, Conti, Luigi, Grassi, Carmine, Cattaneo, Gaetano Maria, Pighin, Marina, Papis, Davide, Gambino, Giovanni, Bertino, Vanessa, Schifano, Domenico, Prando, Daniela, Fogato, Luisella, Cavallo, Fabio, Ansaloni, Luca, Picheo, Roberto, Pontarolo, Nicholas, Depalma, Norma, Spampinato, Marcello, D’Ugo, Stefano, Lepre, Luca, Capponi, Michela Giulii, Campa, Rossella Domenica, Sarro, Giuliano, Dinuzzi, Vincenza Paola, Olmi, Stefano, Uccelli, Matteo, Ferrari, Davide, Inama, Marco, Moretto, Gianluigi, Fontana, Michele, Favi, Francesco, Picariello, Erika, Rampini, Alessia, Barberis, Andrea, Azzinnaro, Antonio, Oliva, Alba, Totaro, Luigi, Benzoni, Ilaria, Ranieri, Valerio, Capolupo, Gabriella Teresa, Carannante, Filippo, Caricato, Marco, Ronconi, Maurizio, Casiraghi, Silvia, Casole, Giovanni, Pantalone, Desire, Alemanno, Giovanni, Scheiterle, Maximilian, Ceresoli, Marco, Cereda, Marco, Fumagalli, Chiara, Zanzi, Federico, Bolzon, Stefano, Guerra, Enrico, Lecchi, Francesca, Cellerino, Paola, Ardito, Antonella, Scaramuzzo, Rosa, Balla, Andrea, Lepiane, Pasquale, Tartaglia, Nicola, Ambrosi, Antonio, Pavone, Giovanna, Palini, Gian Marco, Veneroni, Simone, Garulli, Gianluca, Ricci, Claudio, Torre, Beatrice, Russo, Iris Shari, Rottoli, Matteo, Tanzanu, Marta, Belvedere, Angela, Milone, Marco, Manigrasso, Michele, De Palma, Giovanni Domenico, Piccoli, Micaela, Pattacini, Gianmaria Casoni, Magnone, Stefano, Bertoli, Paolo, Pisano, Michele, Massucco, Paolo, Palisi, Marco, Luzzi, Andrea-Pierre, Fleres, Francesco, Clarizia, Guglielmo, Spolini, Alessandro, Kobe, Yoshiro, Toma, Takayuki, Shimamura, Fumihiko, Parker, Robert, Ranketi, Sinkeet, Mitei, Mercy, Svagzdys, Saulius, Pauzas, Henrikas, Zilinskas, Justas, Poskus, Tomas, Kryzauskas, Marius, Jakubauskas, Matas, Zakaria, Andee Dzulkarnaen, Zakaria, Zaidi, Wong, Michael Pak-Kai, Jusoh, Asri Che, Zakaria, Muhammad Nazreen, Cruz, Daniel Rios, Elizalde, Aurea Barbara Rodriguez, Reynaud, Alejandro Bañon, Hernandez, Edgard Efren Lozada, Monroy, Jose maria Victor Palomo, Hinojosa-Ugarte, Diego, Quiodettis, Martha, Du Bois, María Esther, Latorraca, José, Major, Piotr, Pędziwiatr, Michał, Pisarska-Adamczyk, Magdalena, Walędziak, Maciej, Kwiatkowski, Andrzej, Czyżykowski, Łukasz, da Costa, Silvia Dantas, Pereira, Bela, Ferreira, Ana Rita Oliveira, Almeida, Filipe, Rocha, Ricardo, Carneiro, Carla, Perez, Diego Pita, Carvas, João, Rocha, Catarina, Ferreira, Cátia, Marques, Rita, Fernandes, Urânia, Leao, Pedro, Goulart, André, Pereira, Rita Gonçalves, Patrocínio, Sara Daniela Direito, de Mendonça, Nuno Gonçalo Gonçalves, Manso, Maria Isabel Cerqueira, Morais, Henrique Manuel Cardoso, Cardoso, Paulo Sebastião, Calu, Valentin, Miron, Adrian, Toma, Elena Adelina, Gachabayov, Mahir, Abdullaev, Abakar, Litvin, Andrey, Nechay, Taras, Tyagunov, Alexander, Yuldashev, Anvar, Bradley, Alison, Wilson, Michael, Panyko, Arpád, Látečková, Zuzana, Lacko, Vladimír, Lesko, Dusan, Soltes, Marek, Radonak, Jozef, Turrado-Rodriguez, Victor, Termes-Serra, Roser, Morales-Sevillano, Xavier, Lapolla, Pierfrancesco, Mingoli, Andrea, Brachini, Gioia, Degiuli, Maurizio, Sofia, Silvia, Reddavid, Rossella, de Manzoni Garberini, Andrea, Buffone, Angelica, del Pozo, Eduardo Perea, Aparicio-Sánchez, Daniel, Dos Barbeito, Sandra, Estaire-Gómez, Mercedes, Vitón-Herrero, Rebeca, de los Ángeles Gil Olarte-Marquez, Mª, Gil-Martínez, José, Alconchel, Felipe, Nicolás-López, Tatiana, Rahy-Martin, Aida Cristina, Pelloni, María, Bañolas-Suarez, Raquel, Mendoza-Moreno, Fernando, Nisa, Francisca García-Moreno, Díez-Alonso, Manuel, Rodas, María Elisa Valle, Agundez, María Carmona, Andrés, María Inmaculada Pérez, Moreira, Claudia Cristina Lopes, Perez, Aintzane Lizarazu, Ponce, Iñigo Augusto, González-Castillo, Ana María, Membrilla-Fernández, Estela, Salvans, Silvia, Serradilla-Martín, Mario, Pardo, Pablo Sancho, Rivera-Alonso, Daniel, Dziakova, Jana, Huguet, Jose Mugüerza, Valle, Naila Pagès, Ruiz, Enrique Colás, Valcárcel, Cristina Rey, Moreno, Cristina Ruiz, Salazar, Yeniffer Tatiana Moreno, García, Juan Jesús Rubio, Micó, Silvia Sevila, López, Joaquín Ruiz, Farré, Silvia Pérez, Gomez, Maite Santamaria, Petit, Nuria Mestres, Titos-García, Alberto, Aranda-Narváez, Jose Manuel, Romacho-López, Laura, Sánchez-Guillén, Luis, Aranaz-Ostariz, Veronica, Bosch-Ramírez, Marina, Martínez-Pérez, Aleix, Martínez-López, Elías, Sebastián-Tomás, Juan Carlos, Jimenez-Riera, Granada, Jimenez-Vega, Javier, Cuellar, Jose Aurelio Navas, Campos-Serra, Andrea, Muñoz-Campaña, Anna, Gràcia-Roman, Raquel, Alegre, Javier Martínez, Pinto, Francisca Lima, O’Sullivan, Sara Nuñez, Antona, Francisco Blanco, Jiménez, Beatriz Muñoz, López-Sánchez, Jaime, Carmona, Zahira Gómez, Fernández, Rocio Torres, Sierra, Isabel Blesa, de León, Laura Román García, Moreno, Verónica Polaino, Iglesias, Eva, Cumplido, Paola Lora, Bravo, Altea Arango, Simó, Ignacio Rey, Domínguez, Carlota López, Caamaño, Aloia Guerreiro, Lozano, Rafael Calleja, Martínez, Manuel Durán, Torres, Álvaro Naranjo, de Quiros, Javier Tomas Morales Bernaldo, Pellino, Gianluca, Cloquell, Miriam Moratal, Moller, Elsa García, Jalal-Eldin, Sami, Abdoun, Ahmed K., Hamid, Hytham K. S., Lohsiriwat, Varut, Mongkhonsupphawan, Aitsariya, Baraket, Oussama, Ayed, Karim, Abbassi, Imed, Ali, Ali Ben, Ammar, Houssem, Kchaou, Ali, Tlili, Ahmed, Zribi, Imen, Colak, Elif, Polat, Suleyman, Koylu, Zehra Alan, Guner, Ali, Usta, Mehmet Arif, Reis, Murat Emre, Mantoglu, Baris, Gonullu, Emre, Akin, Emrah, Altintoprak, Fatih, Bayhan, Zulfu, Firat, Necattin, Isik, Arda, Memis, Ufuk, Bayrak, Mehmet, Altıntaş, Yasemin, Kara, Yasin, Bozkurt, Mehmet Abdussamet, Kocataş, Ali, Das, Koray, Seker, Ahmet, Ozer, Nazmi, Atici, Semra Demirli, Tuncer, Korhan, Kaya, Tayfun, Ozkan, Zeynep, Ilhan, Onur, Agackiran, Ibrahim, Uzunoglu, Mustafa Yener, Demirbas, Eren, Altinel, Yuksel, Meric, Serhat, Hacım, Nadir Adnan, Uymaz, Derya Salim, Omarov, Nail, Balık, Emre, Tebala, Giovanni D., Khalil, Hany, Rana, Mridul, Khan, Mansoor, Florence, Charlotte, Swaminathan, Christie, Leo, Cosimo Alex, Liasis, Lampros, Watfah, Josef, Trostchansky, Ivan, Delgado, Edward, Pontillo, Marcelo, Latifi, Rifat, Coimbra, Raul, Edwards, Sara, Lopez, Ana, Velmahos, George, Dorken, Ander, Gebran, Anthony, Palmer, Amanda, Oury, Jeffrey, Bardes, James M., Seng, Sirivan Suon, Coffua, Lauren S., Ratnasekera, Asanthi, Egodage, Tanya, Echeverria-Rosario, Karla, Armento, Isabella, Napolitano, Lena M., Sangji, Naveen F., Hemmila, Mark, Quick, Jacob A., Austin, Tyler R., Hyman, Theodore S., Curtiss, William, McClure, Amanda, Cairl, Nicholas, Biffl, Walter L., Truong, Hung P., Schaffer, Kathryn, Reames, Summer, Banchini, Filippo, Capelli, Patrizio, Coccolini, Federico, Sartelli, Massimo, Bravi, Francesca, Vallicelli, Carlo, Agnoletti, Vanni, Baiocchi, Gian Luca, and Catena, Fausto
Published: 2024
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34. Extreme phenotype sampling and next generation sequencing to identify genetic variants associated with tacrolimus in African American kidney transplant recipients

Author: Mohamed, Moataz E., Guo, Bin, Wu, Baolin, Schladt, David P., Muthusamy, Amutha, Guan, Weihua, Abrahante, Juan E., Onyeaghala, Guillaume, Saqr, Abdelrahman, Pankratz, Nathan, Agarwal, Gaurav, Mannon, Roslyn B., Matas, Arthur J., Oetting, William S., Remmel, Rory P., Israni, Ajay K., Jacobson, Pamala A., and Dorr, Casey R.
Published: 2024
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35. Elevating Students’ Narrative Writing Ability Through Local Legend Picture Series

Author: Axel Syailendra, Sayit Abdul Karim, Gordan Matas, Ramli Ramli, and Hijril Ismail
Subjects: english narrative text, legendary picture series, students’ perceptions, writing skill, Special aspects of education, LC8-6691, Language and Literature
Abstract: This research aimed to detect students' ability to write narrative text using local legendary picture series and further explore their perceptions of using picture series in writing narratives. A descriptive quantitative research design was employed, and 30 students (10 males and 20 females) participated as respondents in the present study. Furthermore, the students were asked to write a narrative text based on a series of pictures from the local legend of Danau Toba. The findings indicated that picture series significantly enhanced students' narrative writing skills. The average scores revealed that the organization, vocabulary, and grammar were 3.19, 3.43, and 2.92 respectively, categorized into the "Good" level. Meanwhile, the average score for mechanical ability was 2.46, categorized as "Enough". Furthermore, the majority of students viewed positively to the use of picture series, with 46.67% strongly agreeing and 46.67% agreeing that picture series enhanced their ability to absorb and retain information. Thus, the picture series used are effective media for learning narrative writing skills. This study may serve pedagogical implication to the teaching of writing skills. Therefore, the researchers recommend that teachers integrate picture series in their teaching strategies and next researchers could explore the use of picture series in other text types and across various English language skills to provide broader insights and potentially more robust pedagogical strategies.
Published: 2024
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36. Single Image Test-Time Adaptation for Segmentation

Author: Janouskova, Klara, Shor, Tamir, Baskin, Chaim, and Matas, Jiri
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Test-Time Adaptation (TTA) methods improve the robustness of deep neural networks to domain shift on a variety of tasks such as image classification or segmentation. This work explores adapting segmentation models to a single unlabelled image with no other data available at test-time. In particular, this work focuses on adaptation by optimizing self-supervised losses at test-time. Multiple baselines based on different principles are evaluated under diverse conditions and a novel adversarial training is introduced for adaptation with mask refinement. Our additions to the baselines result in a 3.51 and 3.28 % increase over non-adapted baselines, without these improvements, the increase would be 1.7 and 2.16 % only., Comment: TMLR accepted paper
Published: 2023

37. Unraveling biochemical spatial patterns: machine learning approaches to the inverse problem of Turing patterns

Author: Matas-Gil, Antonio and Endres, Robert G.
Subjects: Physics - Biological Physics, Mathematics - Dynamical Systems, Nonlinear Sciences - Pattern Formation and Solitons, Quantitative Biology - Quantitative Methods
Abstract: The diffusion-driven Turing instability is a potential mechanism for spatial pattern formation in numerous biological and chemical systems. However, engineering these patterns and demonstrating that they are produced by this mechanism is challenging. To address this, we aim to solve the inverse problem in artificial and experimental Turing patterns. This task is challenging since high levels of noise corrupt the patterns and slight changes in initial conditions can lead to different patterns. We used both least squares to explore the problem and physics-informed neural networks to build a noise-robust method. We elucidate the functionality of our network in scenarios mimicking biological noise levels and showcase its application through a prototype involving an experimentally obtained chemical pattern. The findings reveal the significant promise of machine learning in steering the creation of synthetic patterns in bioengineering, thereby advancing our grasp of morphological intricacies within biological systems while acknowledging existing limitations.
Published: 2023

38. Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement

Author: Alawode, Basit, Dharejo, Fayaz Ali, Ummar, Mehnaz, Guo, Yuhang, Mahmood, Arif, Werghi, Naoufel, Khan, Fahad Shahbaz, Matas, Jiri, and Javed, Sajid
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: This paper presents a new dataset and general tracker enhancement method for Underwater Visual Object Tracking (UVOT). Despite its significance, underwater tracking has remained unexplored due to data inaccessibility. It poses distinct challenges; the underwater environment exhibits non-uniform lighting conditions, low visibility, lack of sharpness, low contrast, camouflage, and reflections from suspended particles. Performance of traditional tracking methods designed primarily for terrestrial or open-air scenarios drops in such conditions. We address the problem by proposing a novel underwater image enhancement algorithm designed specifically to boost tracking quality. The method has resulted in a significant performance improvement, of up to 5.0% AUC, of state-of-the-art (SOTA) visual trackers. To develop robust and accurate UVOT methods, large-scale datasets are required. To this end, we introduce a large-scale UVOT benchmark dataset consisting of 400 video segments and 275,000 manually annotated frames enabling underwater training and evaluation of deep trackers. The videos are labelled with several underwater-specific tracking attributes including watercolor variation, target distractors, camouflage, target relative size, and low visibility conditions. The UVOT400 dataset, tracking results, and the code are publicly available on: https://github.com/BasitAlawode/UWVOT400.
Published: 2023

39. A Joint Fermi-GBM and Swift-BAT Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run

Author: Fletcher, C., Wood, J., Hamburg, R., Veres, P., Hui, C. M., Bissaldi, E., Briggs, M. S., Burns, E., Cleveland, W. H., Giles, M. M., Goldstein, A., Hristov, B. A., Kocevski, D., Lesage, S., Mailyan, B., Malacaria, C., Poolakkil, S., von Kienlin, A., Wilson-Hodge, C. A., Team, The Fermi Gamma-ray Burst Monitor, Crnogorčević, M., DeLaunay, J., Tohuvavohu, A., Caputo, R., Cenko, S. B., Laha, S., Parsotan, T., Abbott, R., Abe, H., Acernese, F., Ackley, K., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Allocca, A., Altin, P. A., Amato, A., Anand, C., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andríc, T., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Asada, H., Asali, Y., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Austin, C., Babak, S., Badaracco, F., Bader, M. K. M., Badger, C., Bae, S., Bae, Y., Baer, A. M., Bagnasco, S., Bai, Y., Baird, J., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Balsamo, A., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Barayoga, J. C., Barbieri, C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Barton, M. A., Bartos, I., Basak, S., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Bazzan, M., Becher, B. R., Becsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belahcene, I., Benedetto, V., Beniwal, D., Benjamin, M. G., Bennett, T. F., Bentley, J. D., BenYaala, M., Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Bini, S., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. A., Blackburn, J. K., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. A., Bork, R., Boschi, V., Bose, N., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branch, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Caesar, M., Cagnoli, G., Cahillane, C., Bustillo, J. Calderòn, Callaghan, J. D., Callister, T. A., Calloni, E., Cameron, J., Camp, J. B., Canepa, M., Canevarolo, S., Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carney, M. F., Carpinelli, M., Carrillo, G., Carullo, G., Carver, T. L., Díaz, J. Casanueva, Casentini, C., Castaldi, G., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Subrahmanya, S. Chalathadka, Champion, E., Chan, C. H., Chan, C., Chan, C. L., Chan, K., Chan, M., Chandra, K., Chang, I. P., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, P., Chase, E. A., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, C., Chen, D., Chen, H. Y., Chen, J., Chen, K., Chen, X., Chen, Y. B., Chen, Y. R., Chen, Z., Cheng, H., Cheong, C. K., Cheung, H. Y., Chia, H. Y., Chiadini, F., Chiang, C. Y., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Choudhary, R. K., Choudhary, S., Christensen, N., Chu, Q., Chu, Y. K., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Cipriano, F., Clara, F., Clark, J. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. F., Cohen, D. E., Colleoni, M., Collette, C. G., Colombo, A., Colpi, M., Compton, C. M., Constancio Jr., M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Corley, K. R., Cornish, N. J., Corre, D., Corsi, A., Cortese, S., Costa, C. A., Cotesta, R., Cottingham, R., Coughlin, M. W., Coulon, J. P., Countryman, S. T., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Croquette, M., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cummings, R., Cunningham, L., Cuoco, E., lo, M. Cury, Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., Dana, A., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Datta, Sayak, Datta, Sayantani, Dattilo, V., Dave, I., Davier, M., Davis, D., Davis, M. C., Daw, E. J., Dean, R., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., De Lillo, N., Dell'Aquila, D., Del Pozzo, W., DeMarchi, L. M., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhurandhar, S., D'iaz, M. C., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giorgio, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Di Lieto, A., Di Michele, A., Ding, B., Di Pace, S., Di Palma, I., Di Renzo, F., Divakarla, A. K., Dmitriev, A., Doctor, Z., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Doravari, S., Drago, M., Driggers, J. C., Drori, Y., Ducoin, J. G., Dupej, P., Dupletsa, U., Durante, O., D'Urso, D., Duverne, P. A., Dwyer, S. E., Eassa, C., Easter, P. J., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eguchi, S., Eichholz, J., Eikenberry, S. S., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Engelby, E., Enomoto, Y., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etienne, Z., Etzel, T., Evans, M., Evans, T. M., Evstafyeva, T., Ewing, B. E., Fabrizi, F., Faedi, F., Fafone, V., Fair, H., Fairhurst, S., Fan, P. C., Farah, A. M., Farinon, S., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Floden, E., Fong, H. K., Font, J. A., Fornal, B., Forsyth, P. W. F., Franke, A., Frasca, S., Frasconi, F., Freed, J. P., Frei, Z., Freise, A., Freitas, O., Frey, R., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fujii, Y., Fujikawa, Y., Fujimoto, Y., Fulda, P., Fyffe, M., Gabbard, H. A., Gabella, W. E., Gadre, B. U., Gair, J. R., Gais, J., Galaudage, S., Gamba, R., Ganapathy, D., Ganguly, A., Gao, D., Gaonkar, S. G., Garaventa, B., Núñez, C. García, García-Quirós, C., Garufi, F., Gateley, B., Gayathri, V., Ge, G. G., Gemme, G., Gennai, A., George, J., Gerberding, O., Gergely, L., Gewecke, P., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Tathagata, Giacomazzo, B., Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giesler, M., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Gleckl, A. E., Godwin, P., Goetz, E., Goetz, R., Gohlke, N., Golomb, J., Goncharov, B., Gonzalez, G., Gosselin, M., Gouaty, R., Gould, D. W., Goyal, S., Grace, B., Grado, A., Graham, V., Granata, M., Granata, V., Grant, A., Gras, S., Grassia, P., Gray, C., Gray, R., Greco, G., Green, A. C., Green, R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimes, E., Grimm, S. J., Grote, H., Grunewald, S., Gruning, P., Gruson, A. S., Guerra, D., Guidi, G. M., Guimaraes, A. R., Guixe, G., Gulati, H. K., Gunny, A. M., Guo, H. K., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, I. M., Gupta, P., Gupta, S. K., Gustafson, R., Guzman, F., Ha, S., Hadiputrawan, I. P. W., Haegel, L., Haino, S., Halim, O., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O., Hansen, H., Hansen, T. J., Hanson, J., Harder, T., Haris, K., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Hasegawa, K., Haskell, B., Haster, C. J., Hathaway, J. S., Hattori, K., Haughian, K., Hayakawa, H., Hayama, K., Hayes, F. J., Healy, J., Heidmann, A., Heidt, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J., Hennig, M. H., Henshaw, C., Hernandez, A. G., Vivanco, F. Hernandez, Heurs, M., Hewitt, A. L., Higginbotham, S., Hild, S., Hill, P., Himemoto, Y., Hines, A. S., Hirata, N., Hirose, C., Ho, T. C., Hochheim, S., Hofman, D., Hohmann, J. N., Holcomb, D. G., Holland, N. A., Hollows, I. J., Holmes, Z. J., Holt, K., Holz, D. E., Hong, Q., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hoyland, D., Hreibi, A., Hsieh, B. H., Hsieh, H. F., Hsiung, C., Hsu, Y., Huang, H. Y., Huang, P., Huang, Y. C., Huang, Y. J., Huang, Yiting, Huang, Yiwen, Hübner, M. T., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huttner, S. H., Huxford, R., Huynh-Dinh, T., Ide, S., Idzkowski, B., Iess, A., Inayoshi, K., Inoue, Y., Iosif, P., Isi, M., Isleif, K., Ito, K., Itoh, Y., Iyer, B. R., JaberianHamedan, V., Jacqmin, T., Jacquet, P. E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., Jan, A. Z., Jani, K., Janquart, J., Janssens, K., Janthalur, N. N., Jaranowski, P., Jariwala, D., Jaume, R., Jenkins, A. C., Jenner, K., Jeon, C., Jia, W., Jiang, J., Jin, H. B., Johns, G. R., Johnston, R., Jones, A. W., Jones, D. I., Jones, P., Jones, R., Joshi, P., Ju, L., Jue, A., Jung, P., Jung, K., Junker, J., Juste, V., Kaihotsu, K., Kajita, T., Kakizaki, M., Kalaghatgi, C. V., Kalogera, V., Kamai, B., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kao, Y., Kapadia, S. J., Kapasi, D. P., Karathanasis, C., Karki, S., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, T., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaur, T., Kawabe, K., Kawaguchi, K., Kéfélian, F., Keitel, D., Key, J. S., Khadka, S., Khalili, F. Y., Khan, S., Khanam, T., Khazanov, E. A., Khetan, N., Khursheed, M., Kijbunchoo, N., Kim, A., Kim, C., Kim, J. C., Kim, J., Kim, K., Kim, W. S., Kim, Y. M., Kimball, C., Kimura, N., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Klimenko, S., Klinger, T., Knee, A. M., Knowles, T. D., Knust, N., Knyazev, E., Kobayashi, Y., Koch, P., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Kolitsidou, P., Kolstein, M., Komori, K., Kondrashov, V., Kong, A. K. H., Kontos, A., Koper, N., Korobko, M., Kovalam, M., Koyama, N., Kozak, D. B., Kozakai, C., Kringel, V., Krishnendu, N. V., Królak, A., Kuehn, G., Kuei, F., Kuijer, P., Kulkarni, S., Kumar, A., Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuromiya, Y., Kuroyanagi, S., Kwak, K., Lacaille, G., Lagabbe, P., Laghi, D., Lalande, E., Lalleman, M., Lam, T. L., Lamberts, A., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., LeBohec, S., Lecoeuche, Y. K., Lee, E., Lee, H. M., Lee, H. W., Lee, K., Lee, R., Legred, I. N., Lehmann, J., Lemaitre, A., Lenti, M., Leonardi, M., Leonova, E., Leroy, N., Letendre, N., Levesque, C., Levin, Y., Leviton, J. N., Leyde, K., Li, A. K. Y., Li, B., Li, J., Li, K. L., Li, P., Li, T. G. F., Li, X., Lin, C. Y., Lin, E. T., Lin, F. K., Lin, F. L., Lin, H. L., Lin, L. C. C., Linde, F., Linker, S. D., Linley, J. N., Littenberg, T. B., Liu, G. C., Liu, J., Liu, K., Liu, X., Llamas, F., Lo, R. K. L., Lo, T., London, L. T., Longo, A., Lopez, D., Portilla, M. Lopez, Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lott, T. P., Lough, J. D., Lousto, C. O., Lovelace, G., Lucaccioni, J. F., Lück, H., Lumaca, D., Lundgren, A. P., Luo, L. W., Lynam, J. E., Maarif, M., Macas, R., Machtinger, J. B., MacInnis, M., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Hernandez, I. Magaña, Magazzù, C., Magee, R. M., Maggiore, R., Magnozzi, M., Mahesh, S., Majorana, E., Maksimovic, I., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mansell, G. L., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Pina, D. Marín, Marion, F., Mark, Z., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, M., Martinez, V. A., Martinez, V., Martinovic, K., Martynov, D. V., Marx, E. J., Masalehdan, H., Mason, K., Massera, E., Masserot, A., Masso-Reid, M., Mastrogiovanni, S., Matas, A., Mateu-Lucena, M., Matichard, F., Matiushechkina, M., Mavalvala, N., McCann, J. J., McCarthy, R., McClelland, D. E., McClincy, P. K., McCormick, S., McCuller, L., McGhee, G. I., McGuire, S. C., McIsaac, C., McIver, J., McRae, T., McWilliams, S. T., Meacher, D., Mehmet, M., Mehta, A. K., Meijer, Q., Melatos, A., Melchor, D. A., Mendell, G., Menendez-Vazquez, A., Menoni, C. S., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Merritt, J. D., Merzougui, M., Meshkov, S., Messenger, C., Messick, C., Meyers, P. M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Mihaylov, D. P., Milano, L., Miller, A. L., Miller, A., Miller, B., Millhouse, M., Mills, J. C., Milotti, E., Minenkov, Y., Mio, N., Mir, Ll. M., Miravet-Tenés, M., Mishkin, A., Mishra, C., Mishra, T., Mistry, T., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Miyakawa, O., Miyo, K., Miyoki, S., Mo, Geoffrey, Modafferi, L. M., Moguel, E., Mogushi, K., Mohapatra, S. R. P., Mohite, S. R., Molina, I., Molina-Ruiz, M., Mondin, M., Montani, M., Moore, C. J., Moragues, J., Moraru, D., Morawski, F., More, A., Moreno, C., Moreno, G., Mori, Y., Morisaki, S., Morisue, N., Moriwaki, Y., Mours, B., Mow-Lowry, C. M., Mozzon, S., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, Soma, Mukherjee, Subroto, Mukherjee, Suvodip, Mukund, N., Mullavey, A., Munch, J., Muniz, E. A., Murray, P. G., Musenich, R., Muusse, S., Nadji, S. L., Nagano, K., Nagar, A., Nakamura, K., Nakano, H., Nakano, M., Nakayama, Y., Napolano, V., Nardecchia, I., Narola, H., Naticchioni, L., Nayak, B., Nayak, R. K., Neil, B. F., Neilson, J., Nelson, A., Nelson, T. J. N., Nery, M., Neubauer, P., Neunzert, A., Ng, K. Y., Ng, S. W. S., Nguyen, C., Nguyen, P., Nguyen, T., Quynh, L. Nguyen, Ni, J., Ni, W. T., Nichols, S. A., Nishimoto, T., Nishizawa, A., Nissanke, S., Nitoglia, E., Nocera, F., Norman, M., North, C., Nozaki, S., Nurbek, G., Nuttall, L. K., Obayashi, Y., Oberling, J., O'Brien, B. D., O'Dell, J., Oelker, E., Ogaki, W., Oganesyan, G., Oh, J. J., Oh, K., Oh, S. H., Ohashi, M., Ohashi, T., Ohkawa, M., Ohme, F., Ohta, H., Okada, M. A., Okutani, Y., Olivetto, C., Oohara, K., Oram, R., O'Reilly, B., Ormiston, R. G., Ormsby, N. D., O'Shaughnessy, R., O'Shea, E., Oshino, S., Ossokine, S., Osthelder, C., Otabe, S., Ottaway, D. J., Overmier, H., Pace, A. E., Pagano, G., Pagano, R., Page, M. A., Pagliaroli, G., Pai, A., Pai, S. A., Pal, S., Palamos, J. R., Palashov, O., Palomba, C., Pan, H., Pan, K. C., Panda, P. K., Pang, P. T. H., Pankow, C., Pannarale, F., Pant, B. C., Panther, F. H., Paoletti, F., Paoli, A., Paolone, A., Pappas, G., Parisi, A., Park, H., Park, J., Parker, W., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patel, M., Pathak, M., Patricelli, B., Patron, A. 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A., Prokhorov, L., Prosposito, P., Prudenzi, L., Puecher, A., Punturo, M., Puosi, F., Puppo, P., Purrer, M., Qi, H., Quartey, N., Quetschke, V., Quinonez, P. J., Quitzow-James, R., Raab, F. J., Raaijmakers, G., Radkins, H., Radulesco, N., Raffai, P., Rail, S. X., Raja, S., Rajan, C., Ramirez, K. E., Ramirez, T. D., Ramos-Buades, A., Rana, J., Rapagnani, P., Ray, A., Raymond, V., Raza, N., Razzano, M., Read, J., Rees, L. A., Regimbau, T., Rei, L., Reid, S., Reid, S. W., Reitze, D. H., Relton, P., Renzini, A., Rettegno, P., Revenu, B., Reza, A., Rezac, M., Ricci, F., Richards, D., Richardson, J. W., Richardson, L., Riemenschneider, G., Riles, K., Rinaldi, S., Rink, K., Robertson, N. A., Robie, R., Robinet, F., Rocchi, A., Rodriguez, S., Rolland, L., Rollins, J. G., Romanelli, M., Romano, R., Romel, C. L., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Rosa, L., Rose, C. A., Rosinska, D., Ross, M. P., Rowan, S., Rowlinson, S. 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Z., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Suzuki, Toshikazu, Swinkels, B. L., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takano, S., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tanaka, K., Tanaka, Taiki, Tanaka, Takahiro, Tanasijczuk, A. J., Tanioka, S., Tanner, D. B., Tao, D., Tao, L., Tapia, R. D., Martin, E. N. Tapia San, Taranto, C., Taruya, A., Tasson, J. D., Tenorio, R., Terhune, J. E. S., Terkowski, L., Thirugnanasambandam, M. P., Thomas, M., Thomas, P., Thompson, E. E., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Toivonen, A. M., Tolley, A. E., Tomaru, T., Tomura, T., Tonelli, M., Tornasi, Z., Torres-Forne, A., Torrie, C. I., Melo, I. Tosta e, Toyra, D., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsai, D., Tsang, K. W., Tsang, T., Tsao, J. S., Tse, M., Tso, R., Tsuchida, S., Tsukada, L., Tsuna, D., Tsutsui, T., Turbang, K., Turconi, M., Tuyenbayev, D., Ubhi, A. S., Uchiyama, T., Udall, R. P., Ueda, A., Uehara, T., Ueno, K., Ueshima, G., Unnikrishnan, C. S., Urban, A. L., Ushiba, T., Utina, A., Vajente, G., Vajpeyi, A., Valdes, G., Valentini, M., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van Haevermaet, H., van Heijningen, J. V., van Putten, M. H. P. M., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasuth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venneberg, J., Venugopalan, G., Verkindt, D., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Vicere, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Villa-Ortega, V., Vinet, J. Y., Virtuoso, A., Vitale, S., Vocca, H., von Reis, E. R. G., von Wrangel, J. S. A., Vorvick, C., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, J., Wang, J. Z., Wang, W. H., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watchi, J., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Wessels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wilken, D., Williams, D., Williams, M. J., Williamson, A. R., Willis, J. L., Willke, B., Wilson, D. J., Wipf, C. C., Wlodarczyk, T., Woan, G., Woehler, J., Wofford, J. K., Wong, D., Wong, I. C. F., Wright, M., Wu, C., Wu, D. S., Wu, H., Xiao, D. M. Wysocki L., Yamada, T., Yamamoto, 288 H., Yamamoto, K., Yamamoto, T., Yamashita, K., Yamazaki, R., Yang, F. W., Yang, K. Z., Yang, L., Yang, Y. C., Yang, Y., Yang, Yang, Yap, M. J., Yeeles, D. W., Yeh, S. W., Yelikar, A. B., Ying, M., Yokoyama, J., Yokozawa, T., Yoo, J., Yoshioka, T., Yu, Hang, Yu, Haocun, Yuzurihara, H., Zadrozny, A., Zanolin, M., Zeidler, S., Zelenova, T., Zendri, J. P., Zevin, M., Zhan, M., Zhang, H., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, G., Zhao, Y., Zhao, Yue, Zhou, R., Zhou, Z., Zhu, X. J., Zhu, Z. H., Zimmerman, A. B., Zucker, M. E., Zweizig, J., Collaboration, The LIGO Scientific, Collaboration, the Virgo, and Collaboration, the KAGRA
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present Fermi Gamma-ray Burst Monitor (Fermi-GBM) and Swift Burst Alert Telescope (Swift-BAT) searches for gamma-ray/X-ray counterparts to gravitational wave (GW) candidate events identified during the third observing run of the Advanced LIGO and Advanced Virgo detectors. Using Fermi-GBM on-board triggers and sub-threshold gamma-ray burst (GRB) candidates found in the Fermi-GBM ground analyses, the Targeted Search and the Untargeted Search, we investigate whether there are any coincident GRBs associated with the GWs. We also search the Swift-BAT rate data around the GW times to determine whether a GRB counterpart is present. No counterparts are found. Using both the Fermi-GBM Targeted Search and the Swift-BAT search, we calculate flux upper limits and present joint upper limits on the gamma-ray luminosity of each GW. Given these limits, we constrain theoretical models for the emission of gamma-rays from binary black hole mergers.
Published: 2023

40. Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data

Author: Purkrabek, Miroslav and Matas, Jiri
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Methods and datasets for human pose estimation focus predominantly on side- and front-view scenarios. We overcome the limitation by leveraging synthetic data and introduce RePoGen (RarE POses GENerator), an SMPL-based method for generating synthetic humans with comprehensive control over pose and view. Experiments on top-view datasets and a new dataset of real images with diverse poses show that adding the RePoGen data to the COCO dataset outperforms previous approaches to top- and bottom-view pose estimation without harming performance on common views. An ablation study shows that anatomical plausibility, a property prior research focused on, is not a prerequisite for effective performance. The introduced dataset and the corresponding code are available on https://mirapurkrabek.github.io/RePoGen-paper/ ., Comment: https://mirapurkrabek.github.io/RePoGen-paper/
Published: 2023
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41. Baseline serum neurofilament light chain levels differentiate aggressive from benign forms of relapsing–remitting multiple sclerosis: a 20-year follow-up cohort

Author: Arroyo Pereiro, Pablo, Muñoz-Vendrell, Albert, León Moreno, Isabel, Bau, Laura, Matas, Elisabet, Romero-Pinel, Lucía, Martínez Yélamos, Antonio, Martínez Yélamos, Sergio, and Andrés-Benito, Pol
Published: 2024
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42. On the necessity to include arterial pre-stress in patient-specific simulations of minimally invasive procedures

Author: Ramella, Anna, Lissoni, Vittorio, Bridio, Sara, Rodriguez Matas, Jose Felix, Trimarchi, Santi, Grossi, Benedetta, Stefanini, Giulio G., Migliavacca, Francesco, and Luraghi, Giulia
Published: 2024
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43. MFT: Long-Term Tracking of Every Pixel

Author: Neoral, Michal, Šerých, Jonáš, and Matas, Jiří
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We propose MFT -- Multi-Flow dense Tracker -- a novel method for dense, pixel-level, long-term tracking. The approach exploits optical flows estimated not only between consecutive frames, but also for pairs of frames at logarithmically spaced intervals. It selects the most reliable sequence of flows on the basis of estimates of its geometric accuracy and the probability of occlusion, both provided by a pre-trained CNN. We show that MFT achieves competitive performance on the TAP-Vid benchmark, outperforming baselines by a significant margin, and tracking densely orders of magnitude faster than the state-of-the-art point-tracking methods. The method is insensitive to medium-length occlusions and it is robustified by estimating flow with respect to the reference frame, which reduces drift., Comment: accepted to WACV 2024. Code at https://github.com/serycjon/MFT
Published: 2023

44. Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abe, H., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Alléné, C., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Ansoldi, S., Antelis, J. M., Antier, S., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Arogeti, M., Aronson, S. M., Asada, H., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Babak, S., Badaracco, F., Badger, C., Bae, S., Bae, Y., Bagnasco, S., Bai, Y., Baier, J. G., Baird, J., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Barton, M. A., Bartos, I., Basak, S., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belahcene, I., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., BenYaala, M., Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Bilicki, M., Billingsley, G., Bini, S., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Bork, R., Boschi, V., Bose, N., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branch, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Caneva, G., Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakalis, W., Subrahmanya, S. Chalathadka, Champion, E., Chan, C. -H., Chan, C., Chan, C. L., Chan, K., Chan, M., Chandra, K., Chang, I. P., Chang, W., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, C., Chen, D., Chen, H. Y., Chen, J., Chen, K., Chen, X., Chen, Y. -B., Chen, Y. -R., Chen, Y., Cheng, H., Chessa, P., Cheung, H. Y., Chia, H. Y., Chiadini, F., Chiang, C-Y., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Choudhary, R. K., Choudhary, S., Christensen, N., Chu, Q., Chu, Y-K., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Cohen, D. E., Colleoni, M., Collette, C. G., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Coschizza, A. C., Cotesta, R., Cottingham, R., Coughlin, M. W., Coulon, J. -P., Countryman, S. T., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Craig, K., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Croquette, M., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cummings, R., Cuoco, E., Curyło, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., Dana, A., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Datta, Sayak, Datta, Sayantani, Dattilo, V., Dave, I., Davier, M., Davis, D., Davis, M. C., Daw, E. J., Dax, M., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., De Lillo, N., Dell'Aquila, D., Del Pozzo, W., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhurandhar, S., Diab, R., Díaz, M. C., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giorgio, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Lieto, A., Di Michele, A., Di Pace, S., Di Palma, I., Di Renzo, F., Divakarla, A. K., Dmitriev, A., Doctor, Z., Doleva, P. P., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., Ducoin, J. -G., Dunn, L., Dupletsa, U., Durante, O., D'Urso, D., Duverne, P. -A., Dwyer, S. E., Eassa, C., Easter, P. J., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eguchi, S., Eichholz, J., Eikenberry, S. S., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Engelby, E., Enomoto, Y., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evans, T. M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fair, H., Fairhurst, S., Fan, P. C., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Floden, E., Fong, H. K., Font, J. A., Fornal, B., Forsyth, P. W. F., Franke, A., Frasca, S., Frasconi, F., Freed, J. P., Frei, Z., Freise, A., Freitas, O., Frey, R., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fujii, Y., Fujikawa, Y., Fujimoto, Y., Fulda, P., Fyffe, M., Gabbard, H. A., Gabella, W. E., Gadre, B. U., Gair, J. 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Subjects: General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects., Comment: 28 pages, 11 figures
Published: 2023

45. Tracking by 3D Model Estimation of Unknown Objects in Videos

Author: Rozumnyi, Denys, Matas, Jiri, Pollefeys, Marc, Ferrari, Vittorio, and Oswald, Martin R.
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract: Most model-free visual object tracking methods formulate the tracking task as object location estimation given by a 2D segmentation or a bounding box in each video frame. We argue that this representation is limited and instead propose to guide and improve 2D tracking with an explicit object representation, namely the textured 3D shape and 6DoF pose in each video frame. Our representation tackles a complex long-term dense correspondence problem between all 3D points on the object for all video frames, including frames where some points are invisible. To achieve that, the estimation is driven by re-rendering the input video frames as well as possible through differentiable rendering, which has not been used for tracking before. The proposed optimization minimizes a novel loss function to estimate the best 3D shape, texture, and 6DoF pose. We improve the state-of-the-art in 2D segmentation tracking on three different datasets with mostly rigid objects.
Published: 2023

46. Hybrid Threats in Lithuania: Awareness of the Concept and Recognition of Their Forms of Realization Among Young People

Author: Irmina Matonytė, Ieva Gajauskaitė, and Matas Jasinavičius
Subjects: national security, hybrid threat, focus group discussion, socialization, perception, young people, lithuania, Political science
Abstract: Russia’s annexation of Crimea in 2014 and its large-scale invasion of Ukraine in 2022 have brought the concept of hybrid warfare, hybrid threats (hereinafter, HT), etc., to the political agenda of Lithuania and the West. Nevertheless, academic research and expert knowledge on the topic of HT, their perception, and identification of their forms of realization are scarce. Such research is complicated because the phenomenon of HT does not have a clear definition and is not based on a pre-defined set of characteristics. Thus, it is impossible to solve the HT problems in one universally acceptable way. In this context, knowledge and perception of HT among young people becomes particularly relevant because of their receptiveness and sensitivity to new information (or disinformation) and their important role in national security. The article presents empirical research on the knowledge and perception of Lithuanian youth about HT. The research aims to reveal the diversity of knowledge and the maturity of the perception of hybrid threats among Lithuanian youth. The definition of hybrid threats used in the article is: Hybrid threats are the malicious actions of external (foreign) actors to undermine citizens’ trust in democratic institutions by military and non-military means. Five focus group discussions (hereinafter, FGDs) were conducted between September 2022 and March 2023 - three with young people (aged 18-26, conscript age in Lithuania) and two with high school students (aged 15-19). The FGDs comprised a total of 41 individuals (22 young men and 19 young females). Conclusions. The concept of a hybrid threat is difficult to understand and rarely heard among Lithuanian youth. The most common primary association of HT is with cyber-attacks and propaganda. The least common associations of HT are with law, public administration, and diplomacy. The sectors of economy, energy, education and science were highlighted during the FGDs. Young people often perceive domestic problems as HT and do not associate them with the intentional malicious activities of external actors. The research did not reveal that the experience of participating in social activities and NGOs strongly influences whether and how much young people know about HT.
Published: 2024

47. DNA methylation governs the sensitivity of repeats to restriction by the HUSH-MORC2 corepressor

Author: Ninoslav Pandiloski, Vivien Horváth, Ofelia Karlsson, Symela Koutounidou, Fereshteh Dorazehi, Georgia Christoforidou, Jon Matas-Fuentes, Patricia Gerdes, Raquel Garza, Marie E. Jönsson, Anita Adami, Diahann A. M. Atacho, Jenny G. Johansson, Elisabet Englund, Zaal Kokaia, Johan Jakobsson, and Christopher H. Douse
Subjects: Science
Abstract: Abstract The human silencing hub (HUSH) complex binds to transcripts of LINE-1 retrotransposons (L1s) and other genomic repeats, recruiting MORC2 and other effectors to remodel chromatin. How HUSH and MORC2 operate alongside DNA methylation, a central epigenetic regulator of repeat transcription, remains largely unknown. Here we interrogate this relationship in human neural progenitor cells (hNPCs), a somatic model of brain development that tolerates removal of DNA methyltransferase DNMT1. Upon loss of MORC2 or HUSH subunit TASOR in hNPCs, L1s remain silenced by robust promoter methylation. However, genome demethylation and activation of evolutionarily-young L1s attracts MORC2 binding, and simultaneous depletion of DNMT1 and MORC2 causes massive accumulation of L1 transcripts. We identify the same mechanistic hierarchy at pericentromeric α-satellites and clustered protocadherin genes, repetitive elements important for chromosome structure and neurodevelopment respectively. Our data delineate the epigenetic control of repeats in somatic cells, with implications for understanding the vital functions of HUSH-MORC2 in hypomethylated contexts throughout human development.
Published: 2024
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48. Fetal endocrine axes mRNA expression levels are related to sex and intrauterine position

Author: Ariel Yael, Ruth Fishman, Devorah Matas, Tirza Doniger, Yoni Vortman, and Lee Koren
Subjects: Cortisol, Glucocorticoids, Hair testing, HPA-axis, HPG-axis, Intrauterine position, Medicine, Physiology, QP1-981
Abstract: Abstract Background The hypothalamic–pituitary–adrenal (HPA) and -gonadal (HPG) axes are two major pathways that connect the neural and endocrine systems in vertebrates. Factors such as prenatal stress and maternal exposure to exogenous steroids have been shown to affect these pathways during fetal development. Another less studied factor is the transfer of hormones across fetuses in multifetal pregnancies. This form of transfer has been shown to influence the morphology, anatomy, physiology, and behavior of the offspring in litter-bearing mammals, an influence termed the intrauterine position (IUP) effect. In this study, we sought to delineate how the IUP effects HPA and HPG brain receptors, peptides, and enzymes (hereafter components) in utero and how these influences may differ between males and females. Methods We utilized the unconventional model of culled free-ranging nutria (Myocastor coypus), with its large natural variation. We collected brain tissues from nutria fetuses and quantified the expression of key HPA and HPG components in three brain regions: prefrontal cortex, hypothalamus, and striatum. Results We found an interaction between sex and IUP in the mineralocorticoid receptor (MR), gonadotropin-releasing hormone receptor (GNRHR), androgen receptor (AR), and estrogen receptor alpha (ESR1). IUP was significant in both gonadotropin-releasing hormone (GnRH) and its receptor GNRHR, but in different ways. In the hypothalamus, fetuses adjacent to same-sex neighbors had higher expression of GnRH than fetuses neighboring the opposite sex. Conversely, in the cortex, GNRHR exhibited the inverse pattern, and fetuses that were neighboring the opposite sex had higher expression levels than those neighboring the same sex. Regardless of IUP, in most components that showed significant sex differences, female fetuses had higher mRNA expression levels than male fetuses. We also found that HPA and HPG components were highly related in the early stages of gestation, and that there was an interaction between sex and developmental stage. In the early stages of pregnancy, female component expression levels were more correlated than males’, but in the last trimester of pregnancy, male components were more related to each other than female’s. Conclusions This study suggests that there are sexually different mechanisms to regulate the HPA and HPG axes during fetal development. Higher mRNA expression levels of endocrine axes components may be a mechanism to help females cope with prolonged androgen exposure over a long gestational period. Additionally, these findings suggest different coordination requirements of male and female endocrine axes during stages of fetal development.
Published: 2024
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49. FABP5 in keratinocyte carcinomas: expression, secretion, and its impact on tumor aggressiveness

Author: Matas-Nadal, Clara, Gatius, Sonia, Ribes-Santolaria, Marina, Guasch-Vallés, Marta, Gomez Arbones, Xavier, Casanova, Josep Manel, Aguayo-Ortiz, Rafael Sergio, and Garí, Eloi
Published: 2024
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50. Living Donation and Pregnancy-Related Complications: State of the Evidence and Call To Action for Improved Risk Assessment

Author: Rossi, Ana P., Katz-Greenberg, Goni, Coscia, Lisa, Brady, Carla W., Doligalski, Christina, Irani, Roxanna A., Matas, Arthur, Shah, Silvi, Lentine, Krista L., Afshar, Yalda, Bonn, Julie, Brady, Carla, Byrns, Jennifer, Casale, Jillian P., Constantinescu, Serban, Coscia, Lisa, DeFilippis, Ersilia, Doligalski, Christina, Feld, Lauren, George, Roshan, Goldberg, Aviva, Iltis, Ana, Irani, Roxanna, Jesudason, Shilpa, Katz-Greenberg, Goni, Kittleson, Michelle M., Lentine, Krista, Levine, Deborah, Licciardi, Fred, Matas, Arthur, Moritz, Michael, OʼNeill, Kathleen E., Rao, Swati, Rossi, Ana, Sarkar, Monika, Shah, Silvi, Shingina, Alexandra, and Uccellini, Kim
Published: 2024
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