9,917 results on '"Kuijper A"'
Search Results
2. Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation
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Rheude, Tillmann, Wirtz, Andreas, Kuijper, Arjan, and Wesarg, Stefan
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Convolutional neural networks (CNNs) achieve prevailing results in segmentation tasks nowadays and represent the state-of-the-art for image-based analysis. However, the understanding of the accurate decision-making process of a CNN is rather unknown. The research area of explainable artificial intelligence (xAI) primarily revolves around understanding and interpreting this black-box behavior. One way of interpreting a CNN is the use of class activation maps (CAMs) that represent heatmaps to indicate the importance of image areas for the prediction of the CNN. For classification tasks, a variety of CAM algorithms exist. But for segmentation tasks, only one CAM algorithm for the interpretation of the output of a CNN exist. We propose a transfer between existing classification- and segmentation-based methods for more detailed, explainable, and consistent results which show salient pixels in semantic segmentation tasks. The resulting Seg-HiRes-Grad CAM is an extension of the segmentation-based Seg-Grad CAM with the transfer to the classification-based HiRes CAM. Our method improves the previously-mentioned existing segmentation-based method by adjusting it to recently published classification-based methods. Especially for medical image segmentation, this transfer solves existing explainability disadvantages., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:023
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- 2024
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3. End-to-End Probabilistic Geometry-Guided Regression for 6DoF Object Pose Estimation
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Pöllabauer, Thomas, Li, Jiayin, Knauthe, Volker, Berkei, Sarah, and Kuijper, Arjan
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Computer Science - Computer Vision and Pattern Recognition - Abstract
6D object pose estimation is the problem of identifying the position and orientation of an object relative to a chosen coordinate system, which is a core technology for modern XR applications. State-of-the-art 6D object pose estimators directly predict an object pose given an object observation. Due to the ill-posed nature of the pose estimation problem, where multiple different poses can correspond to a single observation, generating additional plausible estimates per observation can be valuable. To address this, we reformulate the state-of-the-art algorithm GDRNPP and introduce EPRO-GDR (End-to-End Probabilistic Geometry-Guided Regression). Instead of predicting a single pose per detection, we estimate a probability density distribution of the pose. Using the evaluation procedure defined by the BOP (Benchmark for 6D Object Pose Estimation) Challenge, we test our approach on four of its core datasets and demonstrate superior quantitative results for EPRO-GDR on LM-O, YCB-V, and ITODD. Our probabilistic solution shows that predicting a pose distribution instead of a single pose can improve state-of-the-art single-view pose estimation while providing the additional benefit of being able to sample multiple meaningful pose candidates.
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- 2024
4. Transparency Distortion Robustness for SOTA Image Segmentation Tasks
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Knauthe, Volker, Rak, Arne, Wirth, Tristan, Pöllabauer, Thomas, Metzler, Simon, Kuijper, Arjan, and Fellner, Dieter W.
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Semantic Image Segmentation facilitates a multitude of real-world applications ranging from autonomous driving over industrial process supervision to vision aids for human beings. These models are usually trained in a supervised fashion using example inputs. Distribution Shifts between these examples and the inputs in operation may cause erroneous segmentations. The robustness of semantic segmentation models against distribution shifts caused by differing camera or lighting setups, lens distortions, adversarial inputs and image corruptions has been topic of recent research. However, robustness against spatially varying radial distortion effects that can be caused by uneven glass structures (e.g. windows) or the chaotic refraction in heated air has not been addressed by the research community yet. We propose a method to synthetically augment existing datasets with spatially varying distortions. Our experiments show, that these distortion effects degrade the performance of state-of-the-art segmentation models. Pretraining and enlarged model capacities proof to be suitable strategies for mitigating performance degradation to some degree, while fine-tuning on distorted images only leads to marginal performance improvements.
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- 2024
5. Influence of Water Droplet Contamination for Transparency Segmentation
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Knauthe, Volker, Weitz, Paul, Pöllabauer, Thomas, Wirth, Tristan, Rak, Arne, Kuijper, Arjan, and Fellner, Dieter W.
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Computer vision techniques are on the rise for industrial applications, like process supervision and autonomous agents, e.g., in the healthcare domain and dangerous environments. While the general usability of these techniques is high, there are still challenging real-world use-cases. Especially transparent structures, which can appear in the form of glass doors, protective casings or everyday objects like glasses, pose a challenge for computer vision methods. This paper evaluates the combination of transparent objects in conjunction with (naturally occurring) contamination through environmental effects like hazing. We introduce a novel publicly available dataset containing 489 images incorporating three grades of water droplet contamination on transparent structures and examine the resulting influence on transparency handling. Our findings show, that contaminated transparent objects are easier to segment and that we are able to distinguish between different severity levels of contamination with a current state-of-the art machine-learning model. This in turn opens up the possibility to enhance computer vision systems regarding resilience against, e.g., datashifts through contaminated protection casings or implement an automated cleaning alert.
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- 2024
6. Fast Training Data Acquisition for Object Detection and Segmentation using Black Screen Luminance Keying
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Pöllabauer, Thomas, Knauthe, Volker, Boller, André, Kuijper, Arjan, and Fellner, Dieter
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Deep Neural Networks (DNNs) require large amounts of annotated training data for a good performance. Often this data is generated using manual labeling (error-prone and time-consuming) or rendering (requiring geometry and material information). Both approaches make it difficult or uneconomic to apply them to many small-scale applications. A fast and straightforward approach of acquiring the necessary training data would allow the adoption of deep learning to even the smallest of applications. Chroma keying is the process of replacing a color (usually blue or green) with another background. Instead of chroma keying, we propose luminance keying for fast and straightforward training image acquisition. We deploy a black screen with high light absorption (99.99\%) to record roughly 1-minute long videos of our target objects, circumventing typical problems of chroma keying, such as color bleeding or color overlap between background color and object color. Next we automatically mask our objects using simple brightness thresholding, saving the need for manual annotation. Finally, we automatically place the objects on random backgrounds and train a 2D object detector. We do extensive evaluation of the performance on the widely-used YCB-V object set and compare favourably to other conventional techniques such as rendering, without needing 3D meshes, materials or any other information of our target objects and in a fraction of the time needed for other approaches. Our work demonstrates highly accurate training data acquisition allowing to start training state-of-the-art networks within minutes., Comment: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2024
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- 2024
7. A Survey on Service Users' Perspectives about Information and Shared Decision-Making in Psychotropic Drug Prescriptions in People with Intellectual Disabilities
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Gerda de Kuijper, Josien Jonker, Rory Sheehan, and Angela Hassiotis
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Background: In people with intellectual disabilities and mental disorders and/or challenging behaviours, rates of psychotropic drug prescription are high. In clinical treatments and evaluations, all stakeholders should be involved in a process of shared decision-making (SDM). We aimed to investigate the perspectives of clients and their carers on clients' treatments with psychotropic drugs. Methods: We conducted a survey among adults with intellectual disabilities in a Dutch mental healthcare centre providing community, outpatient and inpatient care. Data were collected between January and June 2022. Questions focused on experiences with the provision of information, treatment involvement and SDM and participants' wishes in this regard. Findings: Respondents (57 clients and 21 carers) were largely satisfied with the overall care from their clinicians, and with how information on the pharmacological treatment was provided verbally, but written information was insufficient or not provided. Seventy per cent of clients and 60% of carers reported being involved in medication decision-making. However, over 75% of participants desired greater involvement in SDM and over 60% in medication reviews. Conclusions: Service users and representatives were satisfied about the treatment and verbal information on their psychotropic drug use. The provision of written information, the SDM process and ongoing evaluation of psychotropic medication use could be improved.
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- 2024
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8. Lightweight Conceptual Dictionary Learning for Text Classification Using Information Compression
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Wan, Li, Alpcan, Tansu, Kuijper, Margreta, and Viterbo, Emanuele
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Computer Science - Computation and Language ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Signal Processing - Abstract
We propose a novel, lightweight supervised dictionary learning framework for text classification based on data compression and representation. This two-phase algorithm initially employs the Lempel-Ziv-Welch (LZW) algorithm to construct a dictionary from text datasets, focusing on the conceptual significance of dictionary elements. Subsequently, dictionaries are refined considering label data, optimizing dictionary atoms to enhance discriminative power based on mutual information and class distribution. This process generates discriminative numerical representations, facilitating the training of simple classifiers such as SVMs and neural networks. We evaluate our algorithm's information-theoretic performance using information bottleneck principles and introduce the information plane area rank (IPAR) as a novel metric to quantify the information-theoretic performance. Tested on six benchmark text datasets, our algorithm competes closely with top models, especially in limited-vocabulary contexts, using significantly fewer parameters. \review{Our algorithm closely matches top-performing models, deviating by only ~2\% on limited-vocabulary datasets, using just 10\% of their parameters. However, it falls short on diverse-vocabulary datasets, likely due to the LZW algorithm's constraints with low-repetition data. This contrast highlights its efficiency and limitations across different dataset types., Comment: 12 pages, TKDE format
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- 2024
9. One-to-many Reconstruction of 3D Geometry of cultural Artifacts using a synthetically trained Generative Model
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Pöllabauer, Thomas, Kühn, Julius, Li, Jiayi, and Kuijper, Arjan
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Estimating the 3D shape of an object using a single image is a difficult problem. Modern approaches achieve good results for general objects, based on real photographs, but worse results on less expressive representations such as historic sketches. Our automated approach generates a variety of detailed 3D representation from a single sketch, depicting a medieval statue, and can be guided by multi-modal inputs, such as text prompts. It relies solely on synthetic data for training, making it adoptable even in cases of only small numbers of training examples. Our solution allows domain experts such as a curators to interactively reconstruct potential appearances of lost artifacts.
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- 2024
10. Extending 6D Object Pose Estimators for Stereo Vision
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Pöllabauer, Thomas, Emrich, Jan, Knauthe, Volker, and Kuijper, Arjan
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Estimating the 6D pose of objects accurately, quickly, and robustly remains a difficult task. However, recent methods for directly regressing poses from RGB images using dense features have achieved state-of-the-art results. Stereo vision, which provides an additional perspective on the object, can help reduce pose ambiguity and occlusion. Moreover, stereo can directly infer the distance of an object, while mono-vision requires internalized knowledge of the object's size. To extend the state-of-the-art in 6D object pose estimation to stereo, we created a BOP compatible stereo version of the YCB-V dataset. Our method outperforms state-of-the-art 6D pose estimation algorithms by utilizing stereo vision and can easily be adopted for other dense feature-based algorithms., Comment: 4th International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI)
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- 2024
11. MRSA-verspreiding is ook een probleem buiten zorginstellingen
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Vendrik, Karuna, Zwittink, Romy, Notermans, Daan, Tjon-A-Tsien, Aimée, der Kuil, Wieke Altorf-van, de Boer, Annemarie, Hendrickx, A. P. A., Dimmendaal, M., Kuijper, E. J., and der Linden, C. Schneeberger-van
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- 2024
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12. Evaluation of the performance of a CdZnTe-based soft $\gamma$-ray detector for CubeSat payloads
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de Kuijper, Kees, Diwan, Rishank, Pal, Partha Sarathi, Ritter, Andreas, Parkinson, Pablo M. Saz, Kong, Andy C. T., and Parker, Quentin A.
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Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
The low-energy $\gamma$-ray (0.1-30 MeV) sky has been relatively unexplored since the decommissioning of the COMPTEL instrument on the Compton Gamma-Ray Observatory (CGRO) satellite in 2000. However, the study of this part of the energy spectrum (the ``MeV gap") is crucial for addressing numerous unresolved questions in high-energy and multi-messenger astrophysics. Although several large MeV $\gamma$-ray missions like AMEGO and e-ASTROGAM are being proposed, they are predominantly in the developmental phase, with launches not anticipated until the next decade at the earliest. In recent times, there has been a surge in proposed CubeSat missions as cost-effective and rapidly implementable ``pathfinder" alternatives. A MeV CubeSat dedicated to $\gamma$-ray astronomy has the potential to serve as a demonstrator for future, larger-scale MeV payloads. This paper presents a $\gamma$-ray payload design featuring a CdZnTe crystal calorimeter module developed by IDEAS. We report the detailed results of simulations to assess the performance of this proposed payload and compare it with those of previous $\gamma$-ray instruments., Comment: Submitted in Experimental Astronomy(Springer), 25 pages, 7 figures
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- 2024
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13. An Empirical Study of Uncertainty Estimation Techniques for Detecting Drift in Data Streams
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Winter, Anton, Jourdan, Nicolas, Wirth, Tristan, Knauthe, Volker, and Kuijper, Arjan
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Computer Science - Machine Learning - Abstract
In safety-critical domains such as autonomous driving and medical diagnosis, the reliability of machine learning models is crucial. One significant challenge to reliability is concept drift, which can cause model deterioration over time. Traditionally, drift detectors rely on true labels, which are often scarce and costly. This study conducts a comprehensive empirical evaluation of using uncertainty values as substitutes for error rates in detecting drifts, aiming to alleviate the reliance on labeled post-deployment data. We examine five uncertainty estimation methods in conjunction with the ADWIN detector across seven real-world datasets. Our results reveal that while the SWAG method exhibits superior calibration, the overall accuracy in detecting drifts is not notably impacted by the choice of uncertainty estimation method, with even the most basic method demonstrating competitive performance. These findings offer valuable insights into the practical applicability of uncertainty-based drift detection in real-world, safety-critical applications., Comment: NeurIPS 2023: Workshop on Distribution Shifts
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- 2023
14. Generalization of Fitness Exercise Recognition from Doppler Measurements by Domain-adaption and Few-Shot Learning
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Fu, Biying, Damer, Naser, Kirchbuchner, Florian, and Kuijper, Arjan
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Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition - Abstract
In previous works, a mobile application was developed using an unmodified commercial off-the-shelf smartphone to recognize whole-body exercises. The working principle was based on the ultrasound Doppler sensing with the device built-in hardware. Applying such a lab-environment trained model on realistic application variations causes a significant drop in performance, and thus decimate its applicability. The reason of the reduced performance can be manifold. It could be induced by the user, environment, and device variations in realistic scenarios. Such scenarios are often more complex and diverse, which can be challenging to anticipate in the initial training data. To study and overcome this issue, this paper presents a database with controlled and uncontrolled subsets of fitness exercises. We propose two concepts to utilize small adaption data to successfully improve model generalization in an uncontrolled environment, increasing the recognition accuracy by two to six folds compared to the baseline for different users., Comment: accepted at International Conference on Pattern Recognition (ICPR) workshop 2021
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- 2023
15. Bias and Diversity in Synthetic-based Face Recognition
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Huber, Marco, Luu, Anh Thi, Boutros, Fadi, Kuijper, Arjan, and Damer, Naser
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Synthetic data is emerging as a substitute for authentic data to solve ethical and legal challenges in handling authentic face data. The current models can create real-looking face images of people who do not exist. However, it is a known and sensitive problem that face recognition systems are susceptible to bias, i.e. performance differences between different demographic and non-demographics attributes, which can lead to unfair decisions. In this work, we investigate how the diversity of synthetic face recognition datasets compares to authentic datasets, and how the distribution of the training data of the generative models affects the distribution of the synthetic data. To do this, we looked at the distribution of gender, ethnicity, age, and head position. Furthermore, we investigated the concrete bias of three recent synthetic-based face recognition models on the studied attributes in comparison to a baseline model trained on authentic data. Our results show that the generator generate a similar distribution as the used training data in terms of the different attributes. With regard to bias, it can be seen that the synthetic-based models share a similar bias behavior with the authentic-based models. However, with the uncovered lower intra-identity attribute consistency seems to be beneficial in reducing bias., Comment: Accepted for presentation at WACV2024
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- 2023
16. NeRF-FF: a plug-in method to mitigate defocus blur for runtime optimized neural radiance fields
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Wirth, Tristan, Rak, Arne, von Buelow, Max, Knauthe, Volker, Kuijper, Arjan, and Fellner, Dieter W.
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- 2024
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17. Mammal responses to global changes in human activity vary by trophic group and landscape
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Burton, A. Cole, Beirne, Christopher, Gaynor, Kaitlyn M., Sun, Catherine, Granados, Alys, Allen, Maximilian L., Alston, Jesse M., Alvarenga, Guilherme C., Calderón, Francisco Samuel Álvarez, Amir, Zachary, Anhalt-Depies, Christine, Appel, Cara, Arroyo-Arce, Stephanny, Balme, Guy, Bar-Massada, Avi, Barcelos, Daniele, Barr, Evan, Barthelmess, Erika L., Baruzzi, Carolina, Basak, Sayantani M., Beenaerts, Natalie, Belmaker, Jonathan, Belova, Olgirda, Bezarević, Branko, Bird, Tori, Bogan, Daniel A., Bogdanović, Neda, Boyce, Andy, Boyce, Mark, Brandt, LaRoy, Brodie, Jedediah F., Brooke, Jarred, Bubnicki, Jakub W., Cagnacci, Francesca, Carr, Benjamin Scott, Carvalho, João, Casaer, Jim, Černe, Rok, Chen, Ron, Chow, Emily, Churski, Marcin, Cincotta, Connor, Ćirović, Duško, Coates, T. D., Compton, Justin, Coon, Courtney, Cove, Michael V., Crupi, Anthony P., Farra, Simone Dal, Darracq, Andrea K., Davis, Miranda, Dawe, Kimberly, De Waele, Valerie, Descalzo, Esther, Diserens, Tom A., Drimaj, Jakub, Duľa, Martin, Ellis-Felege, Susan, Ellison, Caroline, Ertürk, Alper, Fantle-Lepczyk, Jean, Favreau, Jorie, Fennell, Mitch, Ferreras, Pablo, Ferretti, Francesco, Fiderer, Christian, Finnegan, Laura, Fisher, Jason T., Fisher-Reid, M. Caitlin, Flaherty, Elizabeth A., Fležar, Urša, Flousek, Jiří, Foca, Jennifer M., Ford, Adam, Franzetti, Barbara, Frey, Sandra, Fritts, Sarah, Frýbová, Šárka, Furnas, Brett, Gerber, Brian, Geyle, Hayley M., Giménez, Diego G., Giordano, Anthony J., Gomercic, Tomislav, Gompper, Matthew E., Gräbin, Diogo Maia, Gray, Morgan, Green, Austin, Hagen, Robert, Hagen, Robert (Bob), Hammerich, Steven, Hanekom, Catharine, Hansen, Christopher, Hasstedt, Steven, Hebblewhite, Mark, Heurich, Marco, Hofmeester, Tim R., Hubbard, Tru, Jachowski, David, Jansen, Patrick A., Jaspers, Kodi Jo, Jensen, Alex, Jordan, Mark, Kaizer, Mariane C., Kelly, Marcella J., Kohl, Michel T., Kramer-Schadt, Stephanie, Krofel, Miha, Krug, Andrea, Kuhn, Kellie M., Kuijper, Dries P. J., Kuprewicz, Erin K., Kusak, Josip, Kutal, Miroslav, Lafferty, Diana J. R., LaRose, Summer, Lashley, Marcus, Lathrop, Richard, Lee, Jr, Thomas E., Lepczyk, Christopher, Lesmeister, Damon B., Licoppe, Alain, Linnell, Marco, Loch, Jan, Long, Robert, Lonsinger, Robert C., Louvrier, Julie, Luskin, Matthew Scott, MacKay, Paula, Maher, Sean, Manet, Benoît, Mann, Gareth K. H., Marshall, Andrew J., Mason, David, McDonald, Zara, McKay, Tracy, McShea, William J., Mechler, Matt, Miaud, Claude, Millspaugh, Joshua J., Monteza-Moreno, Claudio M., Moreira-Arce, Dario, Mullen, Kayleigh, Nagy, Christopher, Naidoo, Robin, Namir, Itai, Nelson, Carrie, O’Neill, Brian, O’Mara, M. Teague, Oberosler, Valentina, Osorio, Christian, Ossi, Federico, Palencia, Pablo, Pearson, Kimberly, Pedrotti, Luca, Pekins, Charles E., Pendergast, Mary, Pinho, Fernando F., Plhal, Radim, Pocasangre-Orellana, Xochilt, Price, Melissa, Procko, Michael, Proctor, Mike D., Ramalho, Emiliano Esterci, Ranc, Nathan, Reljic, Slaven, Remine, Katie, Rentz, Michael, Revord, Ronald, Reyna-Hurtado, Rafael, Risch, Derek, Ritchie, Euan G., Romero, Andrea, Rota, Christopher, Rovero, Francesco, Rowe, Helen, Rutz, Christian, Salvatori, Marco, Sandow, Derek, Schalk, Christopher M., Scherger, Jenna, Schipper, Jan, Scognamillo, Daniel G., Şekercioğlu, Çağan H., Semenzato, Paola, Sevin, Jennifer, Shamon, Hila, Shier, Catherine, Silva-Rodríguez, Eduardo A., Sindicic, Magda, Smyth, Lucy K., Soyumert, Anil, Sprague, Tiffany, St. Clair, Colleen Cassady, Stenglein, Jennifer, Stephens, Philip A., Stępniak, Kinga Magdalena, Stevens, Michael, Stevenson, Cassondra, Ternyik, Bálint, Thomson, Ian, Torres, Rita T., Tremblay, Joan, Urrutia, Tomas, Vacher, Jean-Pierre, Visscher, Darcy, Webb, Stephen L., Weber, Julian, Weiss, Katherine C. B., Whipple, Laura S., Whittier, Christopher A., Whittington, Jesse, Wierzbowska, Izabela, Wikelski, Martin, Williamson, Jacque, Wilmers, Christopher C., Windle, Todd, Wittmer, Heiko U., Zharikov, Yuri, Zorn, Adam, and Kays, Roland
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- 2024
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18. Algebraic $K$-theory for squares categories
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Campbell, Jonathan, Kuijper, Josefien, Merling, Mona, and Zakharevich, Inna
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Mathematics - K-Theory and Homology ,Mathematics - Algebraic Geometry ,Mathematics - Algebraic Topology - Abstract
In this paper we introduce a new formalism for $K$-theory, called squares $K$-theory. This formalism allows us to simultaneously generalize the usual three-term relation $[B] = [A] + [C]$ for an exact sequence $A \hookrightarrow B \twoheadrightarrow C$ or for a subtractive sequence $A\hookrightarrow B \leftarrow C$, by defining $K_0$ of exact and subtractive categories to satisfy a four-term relation $[A]+[D]= [C] + [B]$ for a ``good'' square diagram with these corners. Examples that rely on this formalism are $K$-theory of smooth manifolds of a fixed dimension and $K$-theory of (smooth and) complete varieties. Another application we give of this theory is the construction of a derived motivic measure taking value in the $K$-theory of homotopy sheaves., Comment: 28 pages
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- 2023
19. An axiomatization of six-functor formalisms
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Kuijper, Josefien
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Mathematics - Algebraic Geometry ,Mathematics - K-Theory and Homology ,14F99 - Abstract
In this paper, we consider some variations on Mann's definition $\infty$-categorical definition of abstract six-functor formalisms. We consider Nagata six-functor formalisms, that have the additional requirement of having Grothendieck and Wirthm\"uller contexts. We also consider local six-functor formalisms, which in addition to this, take values in presentable stable $\infty$-categories, and have recollements. Using Nagata's compactification theorem, we show that Nagata six-functor formalism on varieties can be given by just specifying adjoint triples for open immersions and for proper morphisms, satisfying certain compatibilities. The existence of recollements is (almost) equivalent to a hypersheaf condition for a Grothendieck topology on the category of ``varieties and spans consisting of an open immersion and a proper map''. Using this characterisation, we show that the category of local six-functor formalisms embeds faithfully into the category of lax symmetric monoidal functors from the category of smooth and complete varieties to the category of presentable stable $\infty$-categories and adjoint triples. We characterise which lax symmetric monoidal functors on complete varieties, taking values in the category of presentable stable $\infty$-categories and adjoint triples, extend to local six-functor formalisms., Comment: v3: revisions throughout, improved readability. Added section 4.2, superseding arXiv:2309.11444
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- 2023
20. A symmetric monoidal Comparison Lemma
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Kuijper, Josefien
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Mathematics - Category Theory ,18F20 (primary) 14F06, 18F20 (secondary) - Abstract
In this note we study symmetric monoidal functors from a symmetric monoidal 1-category to a cartesian symmetric monoidal $\infty$-category, which are in addition hypersheaves for a certain topology. We prove a symmetric monoidal version of the Comparison Lemma, for lax as well as strong symmetric monoidal hypersheaves. For a strong symmetric monoidal functor between symmetric monoidal 1-categories with topologies generated by suitable cd-structures, we show that if the conditions of the Comparison Lemma are satisfied, then there is also an equivalence between categories of lax and strong symmetric monoidal hypersheaves respectively, taking values in a complete cartesian symmetric monoidal $\infty$-category. As an application of this result, we prove a lax symmetric monoidal version of our previous result about hypersheaves that encode compactly supported cohomology theories., Comment: v2: major revisions to fix mistake in previous version, main results unchanged or improved
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- 2023
21. Performance Evaluation of a silicon-based 6U Cubesat detector for soft $\gamma$-ray astronomy
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Diwan, Rishank, de Kuijper, Kees, Pal, Partha Sarathi, Ritter, Andreas, Parkinson, Pablo Saz, Kong, Andy C. T., and Parker, Quentin
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Astrophysics - Instrumentation and Methods for Astrophysics ,Astrophysics - High Energy Astrophysical Phenomena - Abstract
The observation of the low-energy $\gamma$-ray (0.1-30 MeV) sky has been significantly limited since the COMPTEL instrument was decommissioned aboard the Compton Gamma-ray Observer (CGRO) satellite in 2000. The exploration of $\gamma$-ray photons within this energy band, often referred to as the \say{MeV gap}, is crucial to address numerous unresolved mysteries in high-energy and multi-messenger astrophysics. Although several large MeV $\gamma$-ray missions have been proposed (e.g., e-ASTROGAM, AMEGO, COSI), most of these are in the planning phase, with launches not expected until the next decade, at the earliest. Recently, there has been a surge in proposed CubeSat missions as cost-effective and rapidly implementable \say{pathfinder} alternatives. A MeV CubeSat payload dedicated to $\gamma$-ray astronomy could serve as a valuable demonstrator for large-scale future MeV payloads. This paper proposes a $\gamma$-ray payload design with a Silicon-based tracker and a Ceasium-Iodide-based calorimeter. We report the results of a simulation study to assess the performance of this payload concept and compare the results with those of previous $\gamma$-ray instruments. As part of the performance assessment and comparison, we show that with our proposed payload design, a sensitivity better than IBIS can be achieved for energies between 0.1 and 10 MeV, and for energies up to around 1 MeV, the achieved sensitivity is comparable to COMPTEL, therefore opening up a window towards cost-effective observational astronomy with comparable performance to past missions., Comment: Submitted in Space: Science & Technology
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- 2023
22. IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models
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Boutros, Fadi, Grebe, Jonas Henry, Kuijper, Arjan, and Damer, Naser
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Computer Science - Computer Vision and Pattern Recognition - Abstract
The availability of large-scale authentic face databases has been crucial to the significant advances made in face recognition research over the past decade. However, legal and ethical concerns led to the recent retraction of many of these databases by their creators, raising questions about the continuity of future face recognition research without one of its key resources. Synthetic datasets have emerged as a promising alternative to privacy-sensitive authentic data for face recognition development. However, recent synthetic datasets that are used to train face recognition models suffer either from limitations in intra-class diversity or cross-class (identity) discrimination, leading to less optimal accuracies, far away from the accuracies achieved by models trained on authentic data. This paper targets this issue by proposing IDiff-Face, a novel approach based on conditional latent diffusion models for synthetic identity generation with realistic identity variations for face recognition training. Through extensive evaluations, our proposed synthetic-based face recognition approach pushed the limits of state-of-the-art performances, achieving, for example, 98.00% accuracy on the Labeled Faces in the Wild (LFW) benchmark, far ahead from the recent synthetic-based face recognition solutions with 95.40% and bridging the gap to authentic-based face recognition with 99.82% accuracy., Comment: Accepted at ICCV2023
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- 2023
23. Effects of Positive Behaviour Support Delivered by Direct Staff on Challenging Behaviours and Quality of Life of Adults with Intellectual Disabilities: A Multicentre Cluster-Controlled Trial
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Bruinsma, Eke, van den Hoofdakker, Barbara J., Hoekstra, Pieter J., de Kuijper, Gerda M., and de Bildt, Annelies A.
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Background: Effects of staff provided positive behaviour support (PBS) for individuals with intellectual disabilities are unclear. Method: Using a multicentre non-randomised cluster controlled design, 26 teams of residential group homes, including 245 staff members of 167 individuals with intellectual disabilities, were allocated to a PBS or control group. Conducting multilevel analyses (n = 123) we examined individuals' changes in irritability, other challenging behaviours and quality of life. Results: Compared to controls, irritability did not significantly decrease more in the intervention group, but lethargic behaviours did. Personal development and self-determination significantly increased. Irritability of individuals in the PBS group with higher levels of irritability or lower levels of intellectual disability significantly reduced more compared to controls. Conclusions: PBS was effective in reducing irritability of individuals with severe levels of irritability or intellectual disabilities. Moreover, PBS decreased lethargic behaviours and improved several domains of quality of life.
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- 2024
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24. ExFaceGAN: Exploring Identity Directions in GAN's Learned Latent Space for Synthetic Identity Generation
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Boutros, Fadi, Klemt, Marcel, Fang, Meiling, Kuijper, Arjan, and Damer, Naser
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Deep generative models have recently presented impressive results in generating realistic face images of random synthetic identities. To generate multiple samples of a certain synthetic identity, previous works proposed to disentangle the latent space of GANs by incorporating additional supervision or regularization, enabling the manipulation of certain attributes. Others proposed to disentangle specific factors in unconditional pretrained GANs latent spaces to control their output, which also requires supervision by attribute classifiers. Moreover, these attributes are entangled in GAN's latent space, making it difficult to manipulate them without affecting the identity information. We propose in this work a framework, ExFaceGAN, to disentangle identity information in pretrained GANs latent spaces, enabling the generation of multiple samples of any synthetic identity. Given a reference latent code of any synthetic image and latent space of pretrained GAN, our ExFaceGAN learns an identity directional boundary that disentangles the latent space into two sub-spaces, with latent codes of samples that are either identity similar or dissimilar to a reference image. By sampling from each side of the boundary, our ExFaceGAN can generate multiple samples of synthetic identity without the need for designing a dedicated architecture or supervision from attribute classifiers. We demonstrate the generalizability and effectiveness of ExFaceGAN by integrating it into learned latent spaces of three SOTA GAN approaches. As an example of the practical benefit of our ExFaceGAN, we empirically prove that data generated by ExFaceGAN can be successfully used to train face recognition models (\url{https://github.com/fdbtrs/ExFaceGAN})., Comment: Accepted at IJCB 2023
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- 2023
25. The impact of insect herbivory on biogeochemical cycling in broadleaved forests varies with temperature
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Hwang, Bernice C., Giardina, Christian P., Adu-Bredu, Stephen, Barrios-Garcia, M. Noelia, Calvo-Alvarado, Julio C., Dargie, Greta C., Diao, Haoyu, Duboscq-Carra, Virginia G., Hemp, Andreas, Hemp, Claudia, Huasco, Walter Huaraca, Ivanov, Aleksandr V., Johnson, Nels G., Kuijper, Dries P. J., Lewis, Simon L., Lobos-Catalán, Paulina, Malhi, Yadvinder, Marshall, Andrew R., Mumladze, Levan, Ngute, Alain Senghor K., Palma, Ana C., Petritan, Ion Catalin, Rordriguez-Cabal, Mariano A., Suspense, Ifo A., Zagidullina, Asiia, Andersson, Tommi, Galiano-Cabrera, Darcy F., Jiménez-Castillo, Mylthon, Churski, Marcin, Gage, Shelley A., Filippova, Nina, Francisco, Kainana S., Gaglianese-Woody, Morgan, Iankoshvili, Giorgi, Kaswamila, Mgeta Adidas, Lyatuu, Herman, Mampouya Wenina, Y. E., Materu, Brayan, Mbemba, M., Moritz, Ruslan, Orang, Karma, Plyusnin, Sergey, Puma Vilca, Beisit L., Rodríguez-Solís, Maria, Šamonil, Pavel, Stępniak, Kinga M., Walsh, Seana K., Xu, Han, and Metcalfe, Daniel B.
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- 2024
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26. Validation of the Charlson Comorbidity Index for the prediction of 30-day and 1-year mortality among patients who underwent hip fracture surgery
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de Haan, Eveline, van Oosten, Benthe, van Rijckevorsel, Veronique. A. J. I. M., Kuijper, T. Martijn, de Jong, Louis, and Roukema, Gert R.
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- 2024
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27. Sex differences in pain catastrophizing and its relation to the transition from acute pain to chronic pain
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Le, Linh H.L., Brown, Vanessa A.V., Mol, Sander, Azijli, Kaoutar, Kuijper, Martijn M., Becker, Leonie, and Koopman, Seppe S.H.A.
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- 2024
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28. Long-term beneficial effect of faecal microbiota transplantation on colonisation of multidrug-resistant bacteria and resistome abundance in patients with recurrent Clostridioides difficile infection
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Nooij, Sam, Vendrik, Karuna E. W., Zwittink, Romy D., Ducarmon, Quinten R., Keller, Josbert J., Kuijper, Ed J., and Terveer, Elisabeth M.
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- 2024
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29. Spatio-temporal interactions between the red fox and the wolf in two contrasting European landscapes
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Lazzeri, Lorenzo, Ferretti, F., Churski, M., Diserens, T. A., Oliveira, R., Schmidt, K., and Kuijper, D. P. J.
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- 2024
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30. De regierol versterken in het ziekenhuis
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Felder, Martijn, Kuijper, Syb, Wallenburg, Iris, and Bal, Roland
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- 2024
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31. Ubiquitous multi-occupant detection in smart environments
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Fährmann, Daniel, Boutros, Fadi, Kubon, Philipp, Kirchbuchner, Florian, Kuijper, Arjan, and Damer, Naser
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- 2024
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32. Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition
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Kolf, Jan Niklas, Rieber, Tim, Elliesen, Jurek, Boutros, Fadi, Kuijper, Arjan, and Damer, Naser
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Many of the commonly used datasets for face recognition development are collected from the internet without proper user consent. Due to the increasing focus on privacy in the social and legal frameworks, the use and distribution of these datasets are being restricted and strongly questioned. These databases, which have a realistically high variability of data per identity, have enabled the success of face recognition models. To build on this success and to align with privacy concerns, synthetic databases, consisting purely of synthetic persons, are increasingly being created and used in the development of face recognition solutions. In this work, we present a three-player generative adversarial network (GAN) framework, namely IDnet, that enables the integration of identity information into the generation process. The third player in our IDnet aims at forcing the generator to learn to generate identity-separable face images. We empirically proved that our IDnet synthetic images are of higher identity discrimination in comparison to the conventional two-player GAN, while maintaining a realistic intra-identity variation. We further studied the identity link between the authentic identities used to train the generator and the generated synthetic identities, showing very low similarities between these identities. We demonstrated the applicability of our IDnet data in training face recognition models by evaluating these models on a wide set of face recognition benchmarks. In comparison to the state-of-the-art works in synthetic-based face recognition, our solution achieved comparable results to a recent rendering-based approach and outperformed all existing GAN-based approaches. The training code and the synthetic face image dataset are publicly available ( https://github.com/fdbtrs/Synthetic-Face-Recognition )., Comment: Accepted at CVPR Workshops
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- 2023
33. Extension of Dictionary-Based Compression Algorithms for the Quantitative Visualization of Patterns from Log Files
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Cherepanov, Igor, Joewono, Jonathan Geraldi, Kuijper, Arjan, and Kohlhammer, Jörn
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Computer Science - Information Retrieval ,Computer Science - Data Structures and Algorithms ,Computer Science - Human-Computer Interaction - Abstract
Many services today massively and continuously produce log files of different and varying formats. These logs are important since they contain information about the application activities, which is necessary for improvements by analyzing the behavior and maintaining the security and stability of the system. It is a common practice to store log files in a compressed form to reduce the sheer size of these files. A compression algorithm identifies frequent patterns in a log file to remove redundant information. This work presents an approach to detect frequent patterns in textual data that can be simultaneously registered during the file compression process with low consumption of resources. The log file can be visualized with the possibility to explore the extracted patterns using metrics based on such properties as frequency, length and root prefixes of the acquired pattern. This allows an analyst to gain the relevant insights more efficiently reducing the need for manual labor-intensive inspection in the log data. The extension of the implemented dictionary-based compression algorithm has the advantage of recognizing patterns in log files of any format and eliminates the need to manually perform preparation for any preprocessing of log files., Comment: submitted to EuroVA 2023
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- 2023
34. The impact of insect herbivory on biogeochemical cycling in broadleaved forests varies with temperature
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Bernice C. Hwang, Christian P. Giardina, Stephen Adu-Bredu, M. Noelia Barrios-Garcia, Julio C. Calvo-Alvarado, Greta C. Dargie, Haoyu Diao, Virginia G. Duboscq-Carra, Andreas Hemp, Claudia Hemp, Walter Huaraca Huasco, Aleksandr V. Ivanov, Nels G. Johnson, Dries P. J. Kuijper, Simon L. Lewis, Paulina Lobos-Catalán, Yadvinder Malhi, Andrew R. Marshall, Levan Mumladze, Alain Senghor K. Ngute, Ana C. Palma, Ion Catalin Petritan, Mariano A. Rordriguez-Cabal, Ifo A. Suspense, Asiia Zagidullina, Tommi Andersson, Darcy F. Galiano-Cabrera, Mylthon Jiménez-Castillo, Marcin Churski, Shelley A. Gage, Nina Filippova, Kainana S. Francisco, Morgan Gaglianese-Woody, Giorgi Iankoshvili, Mgeta Adidas Kaswamila, Herman Lyatuu, Y. E. Mampouya Wenina, Brayan Materu, M. Mbemba, Ruslan Moritz, Karma Orang, Sergey Plyusnin, Beisit L. Puma Vilca, Maria Rodríguez-Solís, Pavel Šamonil, Kinga M. Stępniak, Seana K. Walsh, Han Xu, and Daniel B. Metcalfe
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Science - Abstract
Abstract Herbivorous insects alter biogeochemical cycling within forests, but the magnitude of these impacts, their global variation, and drivers of this variation remain poorly understood. To address this knowledge gap and help improve biogeochemical models, we established a global network of 74 plots within 40 mature, undisturbed broadleaved forests. We analyzed freshly senesced and green leaves for carbon, nitrogen, phosphorus and silica concentrations, foliar production and herbivory, and stand-level nutrient fluxes. We show more nutrient release by insect herbivores at non-outbreak levels in tropical forests than temperate and boreal forests, that these fluxes increase strongly with mean annual temperature, and that they exceed atmospheric deposition inputs in some localities. Thus, background levels of insect herbivory are sufficiently large to both alter ecosystem element cycling and influence terrestrial carbon cycling. Further, climate can affect interactions between natural populations of plants and herbivores with important consequences for global biogeochemical cycles across broadleaved forests.
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- 2024
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35. Validation of the Charlson Comorbidity Index for the prediction of 30-day and 1-year mortality among patients who underwent hip fracture surgery
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Eveline de Haan, Benthe van Oosten, Veronique. A. J. I. M. van Rijckevorsel, T. Martijn Kuijper, Louis de Jong, and Gert R. Roukema
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Surgery ,RD1-811 - Abstract
Abstract Introduction The aim of our study was to validate the original Charlson Comorbidity Index (1987) (CCI) and adjusted CCI (2011) as a prediction model for 30-day and 1-year mortality after hip fracture surgery. The secondary aim of this study was to verify each variable of the CCI as a factor associated with 30-day and 1-year mortality. Methods A prospective database of two-level II trauma teaching hospitals in the Netherlands was used. The original CCI from 1987 and the adjusted CCI were calculated based on medical history. To validate the original CCI and the adjusted CCI, the CCI was plotted against the observed 30-day and 1-year mortality, and the area under the curve (AUC) was calculated. Results A total of 3523 patients were included in this cohort study. The mean of the original CCI in this cohort was 5.1 (SD ± 2.0) and 4.6 (SD ± 1.9) for the adjusted CCI. The AUCs of the prediction models were 0.674 and 0.696 for 30-day mortality for the original and adjusted CCIs, respectively. The AUCs for 1-year mortality were 0.705 and 0.717 for the original and adjusted CCIs, respectively. Conclusions A higher original and adjusted CCI is associated with a higher mortality rate. The AUC was relatively low for 30-day and 1-year mortality for both the original and adjusted CCIs compared to other prediction models for hip fracture patients in our cohort. The CCI is not recommended for the prediction of 30-day and 1-year mortality in hip fracture patients.
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- 2024
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36. Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data
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Jakub W. Bubnicki, Ben Norton, Steven J. Baskauf, Tom Bruce, Francesca Cagnacci, Jim Casaer, Marcin Churski, Joris P. G. M. Cromsigt, Simone Dal Farra, Christian Fiderer, Tavis D. Forrester, Heidi Hendry, Marco Heurich, Tim R. Hofmeester, Patrick A. Jansen, Roland Kays, Dries P. J. Kuijper, Yorick Liefting, John D. C. Linnell, Matthew S. Luskin, Christopher Mann, Tanja Milotic, Peggy Newman, Jürgen Niedballa, Damiano Oldoni, Federico Ossi, Tim Robertson, Francesco Rovero, Marcus Rowcliffe, Lorenzo Seidenari, Izabela Stachowicz, Dan Stowell, Mathias W. Tobler, John Wieczorek, Fridolin Zimmermann, and Peter Desmet
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Biodiversity data ,camera traps ,data exchange ,data sharing ,information standards ,Technology ,Ecology ,QH540-549.5 - Abstract
Abstract Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap‐derived Big Data are becoming increasingly solvable with the help of scalable cyber‐infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media‐based and event‐based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in‐depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard.
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- 2024
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37. Unsupervised Face Recognition using Unlabeled Synthetic Data
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Boutros, Fadi, Klemt, Marcel, Fang, Meiling, Kuijper, Arjan, and Damer, Naser
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Over the past years, the main research innovations in face recognition focused on training deep neural networks on large-scale identity-labeled datasets using variations of multi-class classification losses. However, many of these datasets are retreated by their creators due to increased privacy and ethical concerns. Very recently, privacy-friendly synthetic data has been proposed as an alternative to privacy-sensitive authentic data to comply with privacy regulations and to ensure the continuity of face recognition research. In this paper, we propose an unsupervised face recognition model based on unlabeled synthetic data (USynthFace). Our proposed USynthFace learns to maximize the similarity between two augmented images of the same synthetic instance. We enable this by a large set of geometric and color transformations in addition to GAN-based augmentation that contributes to the USynthFace model training. We also conduct numerous empirical studies on different components of our USynthFace. With the proposed set of augmentation operations, we proved the effectiveness of our USynthFace in achieving relatively high recognition accuracies using unlabeled synthetic data., Comment: Accepted at Face and gesture 2023
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- 2022
38. Methotrexate treatment hampers induction of vaccine-specific CD4 T cell responses in patients with IMID
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Sander W Tas, Joep Killestein, Joost Raaphorst, Taco W Kuijpers, Alexandre E Voskuyl, Gertjan Wolbink, Theo Rispens, Anneke J van der Kooi, Anja Ten Brinke, Karina de Leeuw, Abraham Rutgers, Juan J Garcia-Vallejo, Frederike J Bemelman, YK Onno Teng, Phyllis I Spuls, Mark Löwenberg, Jelle de Wit, Diane van der Woude, Marcel W Bekkenk, Luuk Wieske, Esther Brusse, Laura Boekel, Filip Eftimov, Eileen W Stalman, Maurice Steenhuis, Sofie Keijzer, Olvi Cristianawati, Koos P J van Dam, Adriaan G Volkers, Annelie H Musters, Nicoline F Post, Angela L Bosma, Marc L Hilhorst, Yosta Vegting, Bo Broens, Barbara Horváth, Annabel M Ruiter, Matthias H Busch, Dirk Jan Hijnen, Niels J M Verstegen, Pieter A van Doorn, Jan JGM Verschuuren, Laura Y L Kummer, Ruth R Hagen, Christine Kreher, Lisan H Kuijper, Mariël C Duurland, Veronique A L Konijn, Carolien E van de Sandt, Laura Fernández Blanco, Amélie Bos, Charlotte Menage, Tineke Jorritsma, Jet van den Dijssel, Rivka de Jongh, Tom Ashhurst, Marit J van Gils, Mathieu Claireaux, Sija Marieke van Ham, Renée CF van Allaart, Adája E Baars, George Elias, Cécile ACM van Els, H Stephan Goedee, Geert RAM D’Haens, Papay BP Jallah, Elham S Mirfazeli, Jim BD Keijser, Lotte van Ouwerkerk, Pieter van Paassen, Agner R Parra Sanchez, W Ludo van der Pol, Corine RG Schreurs, R Bart Takkenberg, and Koos AH Zwinderman
- Subjects
Medicine - Abstract
Objectives Methotrexate (MTX) is one of the most commonly used medications to treat rheumatoid arthritis (RA). However, the effect of MTX treatment on cellular immune responses remains incompletely understood. This raises concerns about the vulnerability of these patients to emerging infections and following vaccination.Methods In the current study, we investigated the impact of MTX treatment in patients with immune-mediated inflammatory disease on B and CD4 T cell SARS-CoV-2 vaccination responses. Eighteen patients with RA and two patients with psoriatic arthritis on MTX monotherapy were included, as well as 10 patients with RA without immunosuppressive treatment, and 29 healthy controls. CD4 T and B cell responses were analysed 7 days and 3–6 months after two SARS-CoV-2 messenger RNA vaccinations. High-dimensional flow cytometry analysis was used to analyse fresh whole blood, an activation-induced marker assay to measure antigen-specific CD4 T cells, and spike probes to study antigen-specific B cells.Results Seven days following two SARS-CoV-2 vaccinations, total B and T cell counts were similar between MTX-treated patients and controls. In addition, spike-specific B cell frequencies were unaffected. Remarkably, the frequency of antigen-specific CD4 T cells was reduced in patients using MTX and correlated strongly with anti-RBD IgG antibodies. These results suggest that decreased CD4 T cell activity may result in slower vaccination antibody responses in MTX-treated patients.Conclusion Taken together, MTX treatment reduces vaccine-induced CD4 T cell activation, which correlates with lower antibody responses.Trial registration number NL8900.
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- 2024
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39. Reduced human disturbance increases diurnal activity in wolves, but not Eurasian lynx
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Adam F. Smith, Katharina Kasper, Lorenzo Lazzeri, Michael Schulte, Svitlana Kudrenko, Elise Say-Sallaz, Marcin Churski, Dmitry Shamovich, Serhii Obrizan, Serhii Domashevsky, Kateryna Korepanova, Andriy-Taras Bashta, Rostyslav Zhuravchak, Martin Gahbauer, Bartosz Pirga, Viktar Fenchuk, Josip Kusak, Francesco Ferretti, Dries P.J. Kuijper, Krzysztof Schmidt, and Marco Heurich
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Human disturbance ,Nocturnality ,Dynamic landscape of fear ,Camera traps ,Activity patterns ,Chronobiology ,Ecology ,QH540-549.5 - Abstract
Wildlife in the Anthropocene is increasingly spatially and temporally constrained by lethal and non-lethal human disturbance. For large carnivores with extensive space requirements, like wolves and Eurasian lynx, avoiding human disturbance in European landscapes is challenging when sufficient space with low disturbance is rarely available. Consequently, investigating behavioural adjustments to human presence is critical to understanding the capacity to adapt to human disturbance. We hypothesised that under low human disturbance conditions, large carnivores would adjust their temporal behaviours to make use of daytime, and when daytime human disturbance is high, they would opt for nocturnality. Using camera trap data from nine European study sites along a gradient in human disturbance, we analysed wolf and Eurasian lynx activity patterns. Our data spanned multiple years, 2014 – 2022, and we focused our analysis on September until April, when most large carnivore monitoring takes place. For wolves, our analysis revealed i) increased nocturnal behaviour, ii) decreased diurnal overlap with increasing human activity, and iii) a significant association between a higher probability of nocturnal activity and increasing human disturbance. For Eurasian lynx, we found iv) consistently nocturnal behaviours across all study sites, regardless of human disturbance, and v) no association between human disturbance and increased probability of being active during the night. Our results show that wolves can adjust to diurnal or cathemeral behaviours under low human disturbance, but shift to nocturnality when human disturbance increases. Eurasian lynx, however, consistently maintain their nocturnal behaviour, which we attribute to their principal hunting strategy of stalk and ambush. If human disturbance constrains large carnivore activity to nighttime, it could influence their interactions with prey, leading to cascading effects in the ecosystem. On the other hand, maintaining nocturnal behaviours in human-dominated landscapes may benefit large carnivore conservation, by decreasing negative interactions with humans thereby contributing to a landscape of coexistence.
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- 2024
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40. Bias and Diversity in Synthetic-based Face Recognition.
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Marco Huber, Anh Thi Luu, Fadi Boutros, Arjan Kuijper, and Naser Damer
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- 2024
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41. The ESCMID Study Group for Clostridioides difficile: History, Role, and Perspectives
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Coia, John E., Kuijper, Ed J., Fitzpatrick, Fidelma, Mastrantonio, Paola, editor, and Rupnik, Maja, editor
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- 2024
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42. Diagnostic Guidance for C. difficile Infections
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van Prehn, Joffrey, Crobach, Monique J. T., Baktash, Amoe, Duszenko, Nikolas, Kuijper, Ed J., Mastrantonio, Paola, editor, and Rupnik, Maja, editor
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- 2024
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43. Dietary butyrate ameliorates metabolic health associated with selective proliferation of gut Lachnospiraceae bacterium 28-4.
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Li, Zhuang, Zhou, Enchen, Liu, Cong, Wicks, Hope, Yildiz, Sena, Razack, Farhana, Ying, Zhixiong, Kooijman, Sander, Koonen, Debby, Heijink, Marieke, Kostidis, Sarantos, Giera, Martin, Sanders, Ingrid, Kuijper, Ed, Smits, Wiep, van Dijk, Ko, Rensen, Patrick, and Wang, Yanan
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Endocrinology ,Microbiology ,Obesity ,Humans ,Animals ,Mice ,Butyrates ,Obesity ,Metabolic Syndrome ,RNA ,Ribosomal ,16S ,Weight Gain ,Cell Proliferation - Abstract
Short-chain fatty acids, including butyrate, have multiple metabolic benefits in individuals who are lean but not in individuals with metabolic syndrome, with the underlying mechanisms still being unclear. We aimed to investigate the role of gut microbiota in the induction of metabolic benefits of dietary butyrate. We performed antibiotic-induced microbiota depletion of the gut and fecal microbiota transplantation (FMT) in APOE*3-Leiden.CETP mice, a well-established translational model for developing human-like metabolic syndrome, and revealed that dietary butyrate reduced appetite and ameliorated high-fat diet-induced (HFD-induced) weight gain dependent on the presence of gut microbiota. FMT from butyrate-treated lean donor mice, but not butyrate-treated obese donor mice, into gut microbiota-depleted recipient mice reduced food intake, attenuated HFD-induced weight gain, and improved insulin resistance. 16S rRNA and metagenomic sequencing on cecal bacterial DNA of recipient mice implied that these effects were accompanied by the selective proliferation of Lachnospiraceae bacterium 28-4 in the gut as induced by butyrate. Collectively, our findings reveal a crucial role of gut microbiota in the beneficial metabolic effects of dietary butyrate as strongly associated with the abundance of Lachnospiraceae bacterium 28-4.
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- 2023
44. Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition
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Huber, Marco, Terhörst, Philipp, Kirchbuchner, Florian, Damer, Naser, and Kuijper, Arjan
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Face Recognition (FR) is increasingly used in critical verification decisions and thus, there is a need for assessing the trustworthiness of such decisions. The confidence of a decision is often based on the overall performance of the model or on the image quality. We propose to propagate model uncertainties to scores and decisions in an effort to increase the transparency of verification decisions. This work presents two contributions. First, we propose an approach to estimate the uncertainty of face comparison scores. Second, we introduce a confidence measure of the system's decision to provide insights into the verification decision. The suitability of the comparison scores uncertainties and the verification decision confidences have been experimentally proven on three face recognition models on two datasets., Comment: Accepted at British Machine Vision Conference (BMVC) 2022
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- 2022
45. Fairness in Face Presentation Attack Detection
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Fang, Meiling, Yang, Wufei, Kuijper, Arjan, Struc, Vitomir, and Damer, Naser
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Face recognition (FR) algorithms have been proven to exhibit discriminatory behaviors against certain demographic and non-demographic groups, raising ethical and legal concerns regarding their deployment in real-world scenarios. Despite the growing number of fairness studies in FR, the fairness of face presentation attack detection (PAD) has been overlooked, mainly due to the lack of appropriately annotated data. To avoid and mitigate the potential negative impact of such behavior, it is essential to assess the fairness in face PAD and develop fair PAD models. To enable fairness analysis in face PAD, we present a Combined Attribute Annotated PAD Dataset (CAAD-PAD), offering seven human-annotated attribute labels. Then, we comprehensively analyze the fairness of PAD and its relation to the nature of the training data and the Operational Decision Threshold Assignment (ODTA) through a set of face PAD solutions. Additionally, we propose a novel metric, the Accuracy Balanced Fairness (ABF), that jointly represents both the PAD fairness and the absolute PAD performance. The experimental results pointed out that female and faces with occluding features (e.g. eyeglasses, beard, etc.) are relatively less protected than male and non-occlusion groups by all PAD solutions. To alleviate this observed unfairness, we propose a plug-and-play data augmentation method, FairSWAP, to disrupt the identity/semantic information and encourage models to mine the attack clues. The extensive experimental results indicate that FairSWAP leads to better-performing and fairer face PADs in 10 out of 12 investigated cases., Comment: Accepted at Pattern Recognition
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- 2022
46. What Works for Whom in School-Based Anti-bullying Interventions? An Individual Participant Data Meta-analysis
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Hensums, Maud, de Mooij, Brechtje, Kuijper, Steven C., Fekkes, Minne, and Overbeek, Geertjan
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- 2023
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47. Sex differences in pain catastrophizing and its relation to the transition from acute pain to chronic pain
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Linh H.L. Le, Vanessa A.V. Brown, Sander Mol, Kaoutar Azijli, Martijn M. Kuijper, Leonie Becker, and Seppe S.H.A. Koopman
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Pain chronification ,Pain catastrophizing ,Differences between sexes ,Anesthesiology ,RD78.3-87.3 - Abstract
Abstract Background and importance Differences exist between sexes in pain and pain-related outcomes, such as development of chronic pain. Previous studies suggested a higher risk for pain chronification in female patients. Furthermore, pain catastrophizing is an important risk factor for chronification of pain. However, it is unclear whether sex differences in catastrophic thinking could explain the sex differences in pain chronification. Objectives The aim of this study was to examine sex differences in pain catastrophizing. Additionally, we investigated pain catastrophizing as a potential mediator of sex differences in the transition of acute to chronic pain. Design, settings and participants Adults visiting one of the 15 participating emergency departments in the Netherlands with acute pain-related complaints. Subjects had to meet inclusion criteria and complete questionnaires about their health and pain. Outcomes measure and analysis The outcomes in this prospective cohort study were pain catastrophizing (short form pain catastrophizing) and pain chronification at 90 days (Numeric Rating Scale ≥ 1). Data was analysed using univariate and multivariable logistic regression models. Finally, stratified regression analyses were conducted to assess whether differences in pain catastrophizing accounted for observed differences in pain chronification between sexes. Main results In total 1,906 patients were included. Females catastrophized pain significantly more than males (p
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- 2024
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48. Risk Factors for 30-Days Mortality After Proximal Femoral Fracture Surgery, a Cohort Study
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de Haan E, Roukema GR, van Rijckevorsel VA, Kuijper TM, and de Jong L
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hip fracture ,mortality ,independent risk factors ,clinical outcomes ,Geriatrics ,RC952-954.6 - Abstract
Eveline de Haan,1,2 Gert R Roukema,1 Veronique AJIM van Rijckevorsel,1 Tjallingius M Kuijper,3 Louis de Jong1 On behalf of Dutch Hip Fracture RegistryCollaboration1Surgery Department, Maasstad Hospital, Rotterdam, Zuid-Holland, the Netherlands; 2Surgery Department, Franciscus Hospital, Rotterdam, Zuid-Holland, the Netherlands; 3Science Board, Maasstad Hospital, Rotterdam, Zuid-Holland, the NetherlandsCorrespondence: Eveline de Haan, Email e.eveline.de.haan@gmail.comPurpose: The primary objective of this study was to identify new risk factors and to confirm previously reported risk factors associated with 30-day mortality after hip fracture surgery.Patients and methods: A prospective hip fracture database was used to obtain data. In total, 3523 patients who underwent hip fracture surgery between 2011 and 2021 were included. Univariable and multivariable logistic regression was used to screen and identify candidate risk factors. Twenty-seven baseline factors and 16 peri-operative factors were included in the univariable analysis and 28 of those factors were included in multivariable analysis.Results: 8.6% of the patients who underwent hip fracture surgery died within 30 days after surgery. Prognostic factors associated with 30-day mortality after hip fracture surgery were as follows: age 90– 100 years (OR = 4.7, 95% CI: 1.07– 19.98, p = 0.041) and above 100 years (OR = 11.3, 95% CI: 1.28– 100.26, p = 0.029), male gender (OR = 2.6, 95% CI: 1.97– 3.33, p < 0.001), American Society of Anesthesiologists (ASA) 3 and ASA 4 (OR = 2.1, 95% CI: 1.44– 3.14, p < 0.001), medical history of dementia (OR = 1.7, 95% CI: 1.25– 2.36, p = 0.001), decreased albumin level (OR = 0.94, 95% CI: 0.92– 0.97, p < 0.001), decreased glomerular filtration rate (GFR) (OR = 0.98, 95% CI: 0.98– 0.99, p < 0.001), residential status of nursing home (OR = 2.1, 95% CI: 1.44– 2.87, p < 0.001), higher Katz Index of Independence in Activities of Daily Living (KATZ-ADL) score (OR = 1.1, 95% CI: 1.01– 1.16, p=0.018) and postoperative pneumonia (OR = 2.4, 95% CI: 1.72– 3.38, p < 0.001).Conclusion: A high mortality rate in patients after acute hip fracture surgery is known. Factors that are associated with an increased mortality are age above 90 years, male gender, ASA 3 and ASA 4, medical history of dementia, decreased albumin, decreased GFR, residential status of nursing home, higher KATZ-ADL score and postoperative pneumonia.Keywords: hip fracture, mortality, independent risk factors, clinical outcomes
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- 2024
49. Long-term beneficial effect of faecal microbiota transplantation on colonisation of multidrug-resistant bacteria and resistome abundance in patients with recurrent Clostridioides difficile infection
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Sam Nooij, Karuna E. W. Vendrik, Romy D. Zwittink, Quinten R. Ducarmon, Josbert J. Keller, Ed J. Kuijper, Elisabeth M. Terveer, and on behalf of the Netherlands Donor Feces Bank study group
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C. diff ,Antibiotic resistance ,MDRO ,Faecal microbiota transplantation ,Medicine ,Genetics ,QH426-470 - Abstract
Abstract Background Multidrug-resistant (MDR) bacteria are a growing global threat, especially in healthcare facilities. Faecal microbiota transplantation (FMT) is an effective prevention strategy for recurrences of Clostridioides difficile infections and can also be useful for other microbiota-related diseases. Methods We study the effect of FMT in patients with multiple recurrent C. difficile infections on colonisation with MDR bacteria and antibiotic resistance genes (ARG) on the short (3 weeks) and long term (1–3 years), combining culture methods and faecal metagenomics. Results Based on MDR culture (n = 87 patients), we notice a decrease of 11.5% in the colonisation rate of MDR bacteria after FMT (20/87 before FMT = 23%, 10/87 3 weeks after FMT). Metagenomic sequencing of patient stool samples (n = 63) shows a reduction in relative abundances of ARGs in faeces, while the number of different resistance genes in patients remained higher compared to stools of their corresponding healthy donors (n = 11). Furthermore, plasmid predictions in metagenomic data indicate that patients harboured increased levels of resistance plasmids, which appear unaffected by FMT. In the long term (n = 22 patients), the recipients’ resistomes are still donor-like, suggesting the effect of FMT may last for years. Conclusions Taken together, we hypothesise that FMT restores the gut microbiota to a composition that is closer to the composition of healthy donors, and potential pathogens are either lost or decreased to very low abundances. This process, however, does not end in the days following FMT. It may take months for the gut microbiome to re-establish a balanced state. Even though a reservoir of resistance genes remains, a notable part of which on plasmids, FMT decreases the total load of resistance genes.
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- 2024
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50. QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization
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Boutros, Fadi, Damer, Naser, and Kuijper, Arjan
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Deep learning-based face recognition models follow the common trend in deep neural networks by utilizing full-precision floating-point networks with high computational costs. Deploying such networks in use-cases constrained by computational requirements is often infeasible due to the large memory required by the full-precision model. Previous compact face recognition approaches proposed to design special compact architectures and train them from scratch using real training data, which may not be available in a real-world scenario due to privacy concerns. We present in this work the QuantFace solution based on low-bit precision format model quantization. QuantFace reduces the required computational cost of the existing face recognition models without the need for designing a particular architecture or accessing real training data. QuantFace introduces privacy-friendly synthetic face data to the quantization process to mitigate potential privacy concerns and issues related to the accessibility to real training data. Through extensive evaluation experiments on seven benchmarks and four network architectures, we demonstrate that QuantFace can successfully reduce the model size up to 5x while maintaining, to a large degree, the verification performance of the full-precision model without accessing real training datasets., Comment: Accepted ICPR 2022
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- 2022
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