45,293 results on '"Ince AT"'
Search Results
2. Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description
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Kilickaya, Sertac, Ahishali, Mete, Celebioglu, Cansu, Sohrab, Fahad, Eren, Levent, Ince, Turker, Askar, Murat, and Gabbouj, Moncef
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Computer Science - Sound ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
The frequent breakdowns and malfunctions of industrial equipment have driven increasing interest in utilizing cost-effective and easy-to-deploy sensors, such as microphones, for effective condition monitoring of machinery. Microphones offer a low-cost alternative to widely used condition monitoring sensors with their high bandwidth and capability to detect subtle anomalies that other sensors might have less sensitivity. In this study, we investigate malfunctioning industrial machines to evaluate and compare anomaly detection performance across different machine types and fault conditions. Log-Mel spectrograms of machinery sound are used as input, and the performance is evaluated using the area under the curve (AUC) score for two different methods: baseline dense autoencoder (AE) and one-class deep Support Vector Data Description (deep SVDD) with different subspace dimensions. Our results over the MIMII sound dataset demonstrate that the deep SVDD method with a subspace dimension of 2 provides superior anomaly detection performance, achieving average AUC scores of 0.84, 0.80, and 0.69 for 6 dB, 0 dB, and -6 dB signal-to-noise ratios (SNRs), respectively, compared to 0.82, 0.72, and 0.64 for the baseline model. Moreover, deep SVDD requires 7.4 times fewer trainable parameters than the baseline dense AE, emphasizing its advantage in both effectiveness and computational efficiency., Comment: To be published in 2025 IEEE Symposium Series on Computational Intelligence
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- 2024
3. Blind Underwater Image Restoration using Co-Operational Regressor Networks
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Devecioglu, Ozer Can, Kiranyaz, Serkan, Ince, Turker, and Gabbouj, Moncef
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
The exploration of underwater environments is essential for applications such as biological research, archaeology, and infrastructure maintenanceHowever, underwater imaging is challenging due to the waters unique properties, including scattering, absorption, color distortion, and reduced visibility. To address such visual degradations, a variety of approaches have been proposed covering from basic signal processing methods to deep learning models; however, none of them has proven to be consistently successful. In this paper, we propose a novel machine learning model, Co-Operational Regressor Networks (CoRe-Nets), designed to achieve the best possible underwater image restoration. A CoRe-Net consists of two co-operating networks: the Apprentice Regressor (AR), responsible for image transformation, and the Master Regressor (MR), which evaluates the Peak Signal-to-Noise Ratio (PSNR) of the images generated by the AR and feeds it back to AR. CoRe-Nets are built on Self-Organized Operational Neural Networks (Self-ONNs), which offer a superior learning capability by modulating nonlinearity in kernel transformations. The effectiveness of the proposed model is demonstrated on the benchmark Large Scale Underwater Image (LSUI) dataset. Leveraging the joint learning capabilities of the two cooperating networks, the proposed model achieves the state-of-art restoration performance with significantly reduced computational complexity and often presents such results that can even surpass the visual quality of the ground truth with a 2-pass application. Our results and the optimized PyTorch implementation of the proposed approach are now publicly shared on GitHub., Comment: 11 pages
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- 2024
4. Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training
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Kesgin, H. Toprak, Yuce, M. Kaan, Dogan, Eren, Uzun, M. Egemen, Uz, Atahan, Ince, Elif, Erdem, Yusuf, Shbib, Osama, Zeer, Ahmed, and Amasyali, M. Fatih
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
In this study, we develop and assess new corpus selection and training methodologies to improve the effectiveness of Turkish language models. Specifically, we adapted Large Language Model generated datasets and translated English datasets into Turkish, integrating these resources into the training process. This approach led to substantial enhancements in model accuracy for both few-shot and zero-shot learning scenarios. Furthermore, the merging of these adapted models was found to markedly improve their performance. Human evaluative metrics, including task-specific performance assessments, further demonstrated that these adapted models possess a greater aptitude for comprehending the Turkish language and addressing logic-based queries. This research underscores the importance of refining corpus selection strategies to optimize the performance of multilingual models, particularly for under-resourced languages like Turkish., Comment: 2024 Innovations in Intelligent Systems and Applications Conference (ASYU)
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- 2024
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5. Cosmos-LLaVA: Chatting with the Visual Cosmos-LLaVA: G\'orselle Sohbet Etmek
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Zeer, Ahmed, Dogan, Eren, Erdem, Yusuf, Ince, Elif, Shbib, Osama, Uzun, M. Egemen, Uz, Atahan, Yuce, M. Kaan, Kesgin, H. Toprak, and Amasyali, M. Fatih
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Computer Science - Artificial Intelligence ,Computer Science - Computation and Language ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
In this study, a Turkish visual instruction model was developed and various model architectures and dataset combinations were analysed to improve the performance of this model. The Cosmos-LLaVA model, which is built by combining different large language models and image coders, is designed to overcome the deficiencies in the Turkish language. In the experiments, the effects of fine-tuning with various datasets on the model performance are analysed in detail. The results show that model architecture and dataset selection have a significant impact on performance. Bu \c{c}al{\i}\c{s}mada bir T\"urk\c{c}e g\"orsel talimat modeli geli\c{s}tirilerek bu modelin performans{\i}n{\i} art{\i}rmaya y\"onelik \c{c}e\c{s}itli model mimarileri ve veri k\"umesi kombinasyonlar{\i} derinlemesine incelenmi\c{s}tir. Farkl{\i} b\"uy\"uk dil modelleri ve g\"or\"unt\"u kodlay{\i}c{\i}lar{\i}n{\i}n bir araya getirilmesiyle olu\c{s}turulan Cosmos-LLaVA modeli, T\"urk\c{c}e dilindeki eksiklikleri gidermeye y\"onelik olarak tasarlanm{\i}\c{s}t{\i}r. Yap{\i}lan deneylerde, \c{c}e\c{s}itli veri k\"umeleri ile yap{\i}lan ince ayarlar{\i}n model performans{\i}n{\i} nas{\i}l etkiledi\u{g}i detayl{\i} olarak ele al{\i}nm{\i}\c{s}t{\i}r. Sonu\c{c}lar, model mimarisi ve veri k\"umesi se\c{c}iminin performans \"uzerinde \"onemli bir etkiye sahip oldu\u{g}unu g\"ostermektedir., Comment: in Turkish language, 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)
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- 2024
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6. Professional Learning for Leadership in Early Years: Comparing Sweden and England
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Amanda Ince, Liz Bullough, and Susanne Sahlin
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This paper critically reviews England's National Professional Qualification of Early Years Leadership (NPQEYL) and Sweden's National Principal Training Program (NPTP). The design and implementation of these programs are critiqued considering the process of professional learning, with view of offering recommendations for future policies on professionalisation through leadership professional learning. The study compares the professional development using a simple multiple case study design. Analysis of sources highlight common themes of leadership, curriculum, agency, and professionalism. These are discussed in relation to how such programs may contribute to the early years sector, leading to recommendations to inform future professional learning opportunities.
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- 2024
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7. Voices from the Field: Integrating E-Portfolios in Early Childhood Education
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Emine Ferda Bedel, Sema Ince, and Serenay Basalev Acar
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Digital technologies are increasingly integrated into early childhood education (ECE), prompting discussions regarding their potential benefits and drawbacks. Research has shown that technology designed for children's developmental needs can enhance learning and development, while also improving communication between parents, teachers, and children. However, issues like access, equity, ethics, and teacher workload require careful consideration. This qualitative study explores the expected outcomes of integrating e-portfolios into ECE through one-on-one interviews with key stakeholder groups. After gathering general opinions, the participants were asked to provide detailed feedback on how the application affects children, teachers, and parents. In addition to revealing a diverse range of voices regarding the implementation of e-portfolios, this study aims to utilize the information gathered from participants for informed decision-making, offering valuable insights for the improved e-portfolio design and implementation. The study involved 34 Turkish participants from five stakeholder categories: ECE and primary school teachers, faculty members from ECE and primary school education departments, and parents. Results revealed that e-portfolios were viewed as highly beneficial, providing permanent and comprehensive documentation, facilitating data transfer to subsequent schooling levels, enabling multidimensional assessment, promoting child-centred practices, improving communication, enhancing reflective thinking and digital skills, and allowing resource utilization. However, participants raised concerns about workload, documentation gaps, and ethical issues. While e-portfolios hold promise for integrating digital technologies into ECE, current teacher education may lack sufficient training in key areas like digital literacy, documentation, assessment, and ethics. Further education in these areas can help ensure effective implementation and positive outcomes.
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- 2024
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8. Risk-aware Trajectory Prediction by Incorporating Spatio-temporal Traffic Interaction Analysis
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Thuremella, Divya, Ince, Lewis, and Kunze, Lars
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Computer Science - Robotics ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition - Abstract
To operate in open-ended environments where humans interact in complex, diverse ways, autonomous robots must learn to predict their behaviour, especially when that behavior is potentially dangerous to other agents or to the robot. However, reducing the risk of accidents requires prior knowledge of where potential collisions may occur and how. Therefore, we propose to gain this information by analyzing locations and speeds that commonly correspond to high-risk interactions within the dataset, and use it within training to generate better predictions in high risk situations. Through these location-based and speed-based re-weighting techniques, we achieve improved overall performance, as measured by most-likely FDE and KDE, as well as improved performance on high-speed vehicles, and vehicles within high-risk locations. 2023 IEEE International Conference on Robotics and Automation (ICRA)
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- 2024
9. Detect Llama -- Finding Vulnerabilities in Smart Contracts using Large Language Models
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Ince, Peter, Luo, Xiapu, Yu, Jiangshan, Liu, Joseph K., and Du, Xiaoning
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Computer Science - Cryptography and Security - Abstract
In this paper, we test the hypothesis that although OpenAI's GPT-4 performs well generally, we can fine-tune open-source models to outperform GPT-4 in smart contract vulnerability detection. We fine-tune two models from Meta's Code Llama and a dataset of 17k prompts, Detect Llama - Foundation and Detect Llama - Instruct, and we also fine-tune OpenAI's GPT-3.5 Turbo model (GPT-3.5FT). We then evaluate these models, plus a random baseline, on a testset we develop against GPT-4, and GPT-4 Turbo's, detection of eight vulnerabilities from the dataset and the two top identified vulnerabilities - and their weighted F1 scores. We find that for binary classification (i.e., is this smart contract vulnerable?), our two best-performing models, GPT-3.5FT and Detect Llama - Foundation, achieve F1 scores of $0.776$ and $0.68$, outperforming both GPT-4 and GPT-4 Turbo, $0.66$ and $0.675$. For the evaluation against individual vulnerability identification, our top two models, GPT-3.5FT and Detect Llama - Foundation, both significantly outperformed GPT-4 and GPT-4 Turbo in both weighted F1 for all vulnerabilities ($0.61$ and $0.56$ respectively against GPT-4's $0.218$ and GPT-4 Turbo's $0.243$) and weighted F1 for the top two identified vulnerabilities ($0.719$ for GPT-3.5FT, $0.674$ for Detect Llama - Foundation against GPT-4's $0.363$ and GPT-4 Turbo's $0.429$).
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- 2024
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10. Machine learning-based prediction of Clostridium growth in pork meat using explainable artificial intelligence
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Ince, Volkan, Bader-El-Den, Mohamed, Alderton, Jack, Arabikhan, Farzad, Sari, Omer Faruk, and Sansom, Annette
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- 2025
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11. Investigation of oral health findings and genotype correlations in osteogenesis imperfecta
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Demir, Kübra, Güleç, Çağrı, Aslanger, Ayça, Öztürk, Ayşe Pınar, Özsait Selçuk, Bilge, Tuna İnce, Elif Bahar, and Toksoy, Güven
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- 2024
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12. Effects of the physico-mechanical properties on water tightness performance of ethylene propylene diene monomer-based tunnel segment gasket
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Altuncu, Ekrem, Ince Baysal, Nevcan, and Tuccar Kilic, Nilay
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- 2024
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13. Tivozanib Monotherapy in the Frontline Setting for Patients with Metastatic Renal Cell Carcinoma and Favorable Prognosis
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Frazer, Ricky, Arranz, José Ángel, Estévez, Sergio Vázquez, Parikh, Omi, Krabbe, Laura-Maria, Vasudev, Naveen S., Doehn, Christian, Marschner, Norbert, Waddell, Tom, Ince, Will, and Goebell, Peter J.
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- 2024
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14. Transcendent Moral Motivation and Virtue Development: A Meditation-Based Experiment Exploring Self-Transcending and Self-Enhancing Motives
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Williams, Emily G., Ratchford, Juliette L., Pawl, Timothy, Balkaya-Ince, Merve, and Schnitker, Sarah A.
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- 2024
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15. Using Positive Psychology to Uplift Minority Youth: The Importance of Sound Conceptualization and Contextualization
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Balkaya-Ince, Merve, Tahseen, Madiha, and Schnitker, Sarah A.
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- 2024
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16. Seismic performance assessment of structures in Malatya Province after 6 February 2023 Turkey earthquake sequences
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Özmen, Alper, İnce, Ozan, Taş, Ömer Faruk, Atar, Muhammed, Ozbulut, Osman E., and Sayın, Erkut
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- 2024
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17. Role of intravenous vitamin C on outcomes in hospitalized patients with moderate or severe COVID-19: a real life data of Turkish patients
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Uz, Burak, İnce, Özgür, Gümüş, Can, Gökosmanoğlu, Feyzi, Özgür, Emrah Gökay, and Bekiroğlu, Gülnaz Nural
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- 2024
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18. Exogenous progesterone rescue in patients with low mid-luteal serum progesterone levels undergoing true natural vitrified-warmed blastocyst transfer
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Erden, Murat, Mumusoglu, Sezcan, Ozbek, Irem Yarali, Ince, Onur, Esteves, Sandro C., Humaidan, Peter, and Yarali, Hakan
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- 2024
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19. Treatment approach in bilateral pulmonary hydatid cysts: analysis of 107 consecutive cases
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Aydin, Yener, Ulas, Ali Bilal, Kasali, Kamber, Dostbil, Aysenur, Ince, Ilker, Eroglu, Atilla, and Kocak, Hikmet
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- 2024
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20. Novel image pixel scrambling technique for efficient color image encryption in resource-constrained IoT devices
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İnce, Cemile, İnce, Kenan, and Hanbay, Davut
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- 2024
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21. The Effect of English Teachers' Perfectionism and Self-Efficacy Perceptions on Their Professional Motivation Levels
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Seçgin, Pelin Çelik, Ince, Murat, and Eker, Cevat
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The purpose of this study was to examine the effect of English language teachers' perfectionism and self-efficacy perceptions on their professional motivation levels. The research was a relational survey model and causal comparison research. The study consisted of 450 English teachers working in official primary and secondary schools affiliated to the Ministry of National Education (MoNE) in Zonguldak province and its districts in the 2021-2022 academic year. "Personal Information Form", "Multidimensional Perfectionism Scale", "Teacher Self-Efficacy Scale" and "Teacher Professional Motivation Scale" were used as data collection tools. Frequency, percentage, arithmetic mean, t-test for independent groups, one-way analysis of variance, correlation analysis and regression analysis were used to analyse the data. As a result of the study, it was found that English language teachers' perfectionism varied in favour of teachers with 0-5 years of professional seniority according to professional seniority; it did not vary according to gender and institution of employment. It was seen that self-efficacy perceptions changed in favour of female teachers according to gender, in favour of teachers with 0-5 years of professional seniority according to professional seniority, and in favour of teachers working in primary schools according to the institution of employment. It was seen that professional motivation levels changed in favour of teachers with 0-5 years of professional seniority according to professional seniority, in favour of teachers working in primary schools according to the institution of employment; it did not change according to gender. There was no significant relationship between English teachers' professional motivation levels and their perfectionism, but there was a significant relationship between English teachers' professional motivation levels and their self-efficacy perceptions. A positive, significant and low level relationship was found between perfectionism and self-efficacy perception general scores. The variables "order", "parental criticism", "parental expectations" and "effectiveness for classroom management" were found to be predictors of English teachers' professional motivation levels.
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- 2023
22. Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare
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Acikgoz, Emre Can, İnce, Osman Batur, Bench, Rayene, Boz, Arda Anıl, Kesen, İlker, Erdem, Aykut, and Erdem, Erkut
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language - Abstract
The integration of Large Language Models (LLMs) into healthcare promises to transform medical diagnostics, research, and patient care. Yet, the progression of medical LLMs faces obstacles such as complex training requirements, rigorous evaluation demands, and the dominance of proprietary models that restrict academic exploration. Transparent, comprehensive access to LLM resources is essential for advancing the field, fostering reproducibility, and encouraging innovation in healthcare AI. We present Hippocrates, an open-source LLM framework specifically developed for the medical domain. In stark contrast to previous efforts, it offers unrestricted access to its training datasets, codebase, checkpoints, and evaluation protocols. This open approach is designed to stimulate collaborative research, allowing the community to build upon, refine, and rigorously evaluate medical LLMs within a transparent ecosystem. Also, we introduce Hippo, a family of 7B models tailored for the medical domain, fine-tuned from Mistral and LLaMA2 through continual pre-training, instruction tuning, and reinforcement learning from human and AI feedback. Our models outperform existing open medical LLMs models by a large-margin, even surpassing models with 70B parameters. Through Hippocrates, we aspire to unlock the full potential of LLMs not just to advance medical knowledge and patient care but also to democratize the benefits of AI research in healthcare, making them available across the globe.
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- 2024
23. Sequential Compositional Generalization in Multimodal Models
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Yagcioglu, Semih, İnce, Osman Batur, Erdem, Aykut, Erdem, Erkut, Elliott, Desmond, and Yuret, Deniz
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Computer Science - Computation and Language - Abstract
The rise of large-scale multimodal models has paved the pathway for groundbreaking advances in generative modeling and reasoning, unlocking transformative applications in a variety of complex tasks. However, a pressing question that remains is their genuine capability for stronger forms of generalization, which has been largely underexplored in the multimodal setting. Our study aims to address this by examining sequential compositional generalization using \textsc{CompAct} (\underline{Comp}ositional \underline{Act}ivities)\footnote{Project Page: \url{http://cyberiada.github.io/CompAct}}, a carefully constructed, perceptually grounded dataset set within a rich backdrop of egocentric kitchen activity videos. Each instance in our dataset is represented with a combination of raw video footage, naturally occurring sound, and crowd-sourced step-by-step descriptions. More importantly, our setup ensures that the individual concepts are consistently distributed across training and evaluation sets, while their compositions are novel in the evaluation set. We conduct a comprehensive assessment of several unimodal and multimodal models. Our findings reveal that bi-modal and tri-modal models exhibit a clear edge over their text-only counterparts. This highlights the importance of multimodality while charting a trajectory for future research in this domain., Comment: Accepted to the main conference of NAACL (2024) as a long paper
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- 2024
24. HPV-YAP1 oncogenic alliance drives malignant transformation of fallopian tube epithelial cells
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He, Chunbo, Lv, Xiangmin, Liu, Jiyuan, Ruan, Jinpeng, Chen, Peichao, Huang, Cong, Angeletti, Peter C, Hua, Guohua, Moness, Madelyn Leigh, Shi, Davie, Dhar, Anjali, Yang, Siyi, Murphy, Savannah, Montoute, Isabelle, Chen, Xingcheng, Islam, Kazi Nazrul, George, Sophia, Ince, Tan A, Drapkin, Ronny, Guda, Chittibabu, Davis, John S, and Wang, Cheng
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- 2024
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25. Antikoagulation in der hausärztlichen Praxis
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Feickert, Sebastian and Ince, Hüseyin
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- 2024
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26. Metabolomics Approach to Explore Bioactive Natural Products Derived From Plant-Root-Associated Streptomyces
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Kum, Ekrem and İnce, Ebru
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- 2024
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27. Dispelling myths about the use of magnetic resonance imaging in the diagnosis of adnexal torsion
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Batur, Ali, Arslan, Volkan, Öztürk İnce, Elif, Kılınç, Aslı Nur, Erdemir, Ahmet Gürkan, and Kutsal Mergen, Esra
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- 2024
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28. Potential diagnostic and prognostic biomarkers of pediatric Burkitt lymphoma identified through miRNA expression profiling
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Küçük, Can, Esmeray Sönmez, Esra, Hatipoğlu, Tevfik, Yuan, Hongling, Hu, Xiaozhou, Ceylan, Arda, Siviş, Zuhal Önder, Demirağ, Bengü, Ataseven, Eda, İnce, Dilek, Altun, Zekiye, Aktaş, Safiye, Özsan, Nazan, Erdağ, Taner Kemal, Ayhan, Yavuz Selim, Demir Gündoğan, Begümhan, Çetingül, Nazan, Özer, Erdener, Kutluk, Tezer, and Olgun, Nur
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- 2024
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29. Graft Paste to Tackle Nasal Dorsal Irregularities: A Comparative, Prospective, Double Blinded Study
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Zuhour, Moath, Ismayilzade, Majid, and Ince, Bilsev
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- 2024
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30. Successful localisation of recurrent thyroid cancer using preoperative patent blue dye injection
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Evranos, B. O., Ince, N., Ataş, H., Polat, S. B., Ahsen, H., Imga, N. N., Dirikoc, A., Topaloglu, O., Tutuncu, T., Ersoy, R., and Cakir, B.
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- 2024
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31. Professional learning for leadership in early years: Comparing Sweden and England
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Ince, Amanda, Bullough, Liz, and Sahlin, Susanne
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- 2024
32. Exploring Sound vs Vibration for Robust Fault Detection on Rotating Machinery
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Kiranyaz, Serkan, Devecioglu, Ozer Can, Alhams, Amir, Sassi, Sadok, Ince, Turker, Avci, Onur, and Gabbouj, Moncef
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Computer Science - Sound ,Computer Science - Artificial Intelligence ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
Robust and real-time detection of faults on rotating machinery has become an ultimate objective for predictive maintenance in various industries. Vibration-based Deep Learning (DL) methodologies have become the de facto standard for bearing fault detection as they can produce state-of-the-art detection performances under certain conditions. Despite such particular focus on the vibration signal, the utilization of sound, on the other hand, has been neglected whilst only a few studies have been proposed during the last two decades, all of which were based on a conventional ML approach. One major reason is the lack of a benchmark dataset providing a large volume of both vibration and sound data over several working conditions for different machines and sensor locations. In this study, we address this need by presenting the new benchmark Qatar University Dual-Machine Bearing Fault Benchmark dataset (QU-DMBF), which encapsulates sound and vibration data from two different motors operating under 1080 working conditions overall. Then we draw the focus on the major limitations and drawbacks of vibration-based fault detection due to numerous installation and operational conditions. Finally, we propose the first DL approach for sound-based fault detection and perform comparative evaluations between the sound and vibration over the QU-DMBF dataset. A wide range of experimental results shows that the sound-based fault detection method is significantly more robust than its vibration-based counterpart, as it is entirely independent of the sensor location, cost-effective (requiring no sensor and sensor maintenance), and can achieve the same level of the best detection performance by its vibration-based counterpart. With this study, the QU-DMBF dataset, the optimized source codes in PyTorch, and comparative evaluations are now publicly shared., Comment: 8 pages
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- 2023
33. Search for diphoton resonances in the 66 to 110 GeV mass range using pp collisions at s = 13 TeV with the ATLAS detector
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Aad, G., Abbott, B., Abeling, K., Abicht, N. J., Abidi, S. H., Aboulhorma, A., Abramowicz, H., Abreu, H., Abulaiti, Y., Acharya, B. S., Adam Bourdarios, C., Adamczyk, L., Addepalli, S. V., Addison, M. J., Adelman, J., Adiguzel, A., Adye, T., Affolder, A. A., Afik, Y., Agaras, M. N., Agarwala, J., Aggarwal, A., Agheorghiesei, C., Ahmad, A., Ahmadov, F., Ahmed, W. S., Ahuja, S., Ai, X., Aielli, G., Aikot, A., Ait Tamlihat, M., Aitbenchikh, B., Aizenberg, I., Akbiyik, M., Åkesson, T. P. A., Akimov, A. V., Akiyama, D., Akolkar, N. N., Aktas, S., Al Khoury, K., Alberghi, G. L., Albert, J., Albicocco, P., Albouy, G. L., Alderweireldt, S., Alegria, Z. L., Aleksa, M., Aleksandrov, I. N., Alexa, C., Alexopoulos, T., Alfonsi, F., Algren, M., Alhroob, M., Ali, B., Ali, H. M. J., Ali, S., Alibocus, S. W., Aliev, M., Alimonti, G., Alkakhi, W., Allaire, C., Allbrooke, B. M. M., Allen, J. F., Allendes Flores, C. A., Allport, P. P., Aloisio, A., Alonso, F., Alpigiani, C., Alvarez Estevez, M., Alvarez Fernandez, A., Alves Cardoso, M., Alviggi, M. G., Aly, M., Amaral Coutinho, Y., Ambler, A., Amelung, C., Amerl, M., Ames, C. G., Amidei, D., Amor Dos Santos, S. P., Amos, K. R., Ananiev, V., Anastopoulos, C., Andeen, T., Anders, J. K., Andrean, S. Y., Andreazza, A., Angelidakis, S., Angerami, A., Anisenkov, A. V., Annovi, A., Antel, C., Anthony, M. T., Antipov, E., Antonelli, M., Anulli, F., Aoki, M., Aoki, T., Aparisi Pozo, J. A., Aparo, M. A., Aperio Bella, L., Appelt, C., Apyan, A., Aranzabal, N., Arbiol Val, S. J., Arcangeletti, C., Arce, A. T. H., Arena, E., Arguin, J-F., Argyropoulos, S., Arling, J.-H., Arnaez, O., Arnold, H., Artoni, G., Asada, H., Asai, K., Asai, S., Asbah, N. A., Assahsah, J., Assamagan, K., Astalos, R., Atashi, S., Atkin, R. J., Atkinson, M., Atmani, H., Atmasiddha, P. A., Augsten, K., Auricchio, S., Auriol, A. D., Austrup, V. 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C., Stoicea, G., Stolarski, M., Stonjek, S., Straessner, A., Strandberg, J., Strandberg, S., Stratmann, M., Strauss, M., Strebler, T., Strizenec, P., Ströhmer, R., Strom, D. M., Stroynowski, R., Strubig, A., Stucci, S. A., Stugu, B., Stupak, J., Styles, N. A., Su, D., Su, S., Su, W., Su, X., Sugizaki, K., Sulin, V. V., Sullivan, M. J., Sultan, D. M. S., Sultanaliyeva, L., Sultansoy, S., Sumida, T., Sun, S., Sun, S., Sunneborn Gudnadottir, O., Sur, N., Sutton, M. R., Suzuki, H., Svatos, M., Swiatlowski, M., Swirski, T., Sykora, I., Sykora, M., Sykora, T., Ta, D., Tackmann, K., Taffard, A., Tafirout, R., Tafoya Vargas, J. S., Takeva, E. P., Takubo, Y., Talby, M., Talyshev, A. A., Tam, K. C., Tamir, N. M., Tanaka, A., Tanaka, J., Tanaka, R., Tanasini, M., Tao, Z., Tapia Araya, S., Tapprogge, S., Tarek Abouelfadl Mohamed, A., Tarem, S., Tariq, K., Tarna, G., Tartarelli, G. F., Tas, P., Tasevsky, M., Tassi, E., Tate, A. C., Tateno, G., Tayalati, Y., Taylor, G. N., Taylor, W., Tee, A. 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K., Zakareishvili, T., Zakharchuk, N., Zambito, S., Zamora Saa, J. A., Zang, J., Zanzi, D., Zaplatilek, O., Zeitnitz, C., Zeng, H., Zeng, J. C., Zenger, Jr, D. T., Zenin, O., Ženiš, T., Zenz, S., Zerradi, S., Zerwas, D., Zhai, M., Zhang, B., Zhang, D. F., Zhang, J., Zhang, J., Zhang, K., Zhang, L., Zhang, P., Zhang, R., Zhang, S., Zhang, S., Zhang, T., Zhang, X., Zhang, X., Zhang, Y., Zhang, Y., Zhang, Y., Zhang, Z., Zhang, Z., Zhao, H., Zhao, T., Zhao, Y., Zhao, Z., Zhemchugov, A., Zheng, J., Zheng, K., Zheng, X., Zheng, Z., Zhong, D., Zhou, B., Zhou, H., Zhou, N., Zhou, Y., Zhou, Y., Zhu, C. G., Zhu, J., Zhu, Y., Zhu, Y., Zhuang, X., Zhukov, K., Zhulanov, V., Zimine, N. I., Zinsser, J., Ziolkowski, M., Živković, L., Zoccoli, A., Zoch, K., Zorbas, T. G., Zormpa, O., Zou, W., and Zwalinski, L.
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- 2025
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34. Process optimization and modeling of Pb(II) ions adsorption on a new and reusable biocomposite material using experimental design approach
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Ince, Muharrem, Kaplan Ince, Olcay, and Alp Kavlo, Hevidar
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- 2024
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35. Enhanced Anaerobic Mono-digestion and Co-digestion of Crop Residues by NaOH Alkali Pre-treatment: Digestion Performance and Microbial Community Dynamics
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Mansour, Mohamed, Ince, Orhan, Uzun, Omer, Ozbayram, E. Gozde, Miraloglu, H. Ibrahim, and Ince, Bahar
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- 2024
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36. Use of extracorporeal blood purification therapies in sepsis: the current paradigm, available evidence, and future perspectives
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Bottari, Gabriella, Ranieri, Vito Marco, Ince, Can, Pesenti, Antonio, Aucella, Filippo, Scandroglio, Anna Maria, Ronco, Claudio, and Vincent, Jean-Louis
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- 2024
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37. Microwave-assisted synthesis of antimony oxide nanoparticles for the determination of trace cadmium in mulberry leaf tea matrices by flame atomic absorption spectrophotometry
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İnce, Merve Nur, Serbest, Hakan, and Bakirdere, Sezgin
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- 2024
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38. Effects of different ventilatory settings on alveolar and pulmonary microvessel dimensions in pigs
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Damiani, Elisa, Casarotta, Erika, Di Bella, Caterina, Galosi, Margherita, Angorini, Alessio, Serino, Federica, Tambella, Adolfo Maria, Laus, Fulvio, Zuccari, Samuele, Salvucci Salice, Alessio, Domizi, Roberta, Carsetti, Andrea, Ince, Can, and Donati, Abele
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- 2024
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39. Reconstructive surgical treatment of peri-implantitis with use of a chitosan brush for decontamination- case series with 1-year follow-up
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İnce Kuka, Gizem and Gürsoy, Hare
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- 2024
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40. Effect of fluid and driving pressure on cyclical “on–off” flow of pulmonary microcirculation during mechanical ventilation
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Yuan, Siyi, Chen, Xiangyu, Mi, Liangyu, Chi, Yi, Huang, Haoping, Liu, Bo, Yue, Chaofu, Zhao, Zeming, Su, Longxiang, Long, Yun, Akin, Şakir, Ince, Can, and He, Huaiwu
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- 2024
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41. Analytical prediction of genetic contribution across multiple recurrent backcrossing generations
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Ajayi, Temitayo, LaCombe, Jason, Ince, Güven, and Yeats, Trevor
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- 2024
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42. The impact of dentofacial characteristics and malocclusions on dissatisfaction with dentofacial appearance
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İnce-Bingöl, Sinem and Kaya, Burçak
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- 2024
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43. Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone
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Fazli Besheli, Behrang, Sha, Zhiyi, Gavvala, Jay R., Karamursel, Sacit, Quach, Michael, Swamy, Chandra Prakash, Ayyoubi, Amir Hossein, Goldman, Alica M., Curry, Daniel J., Sheth, Sameer A., Darrow, David, Miller, Kai J., Francis, David J., Worrell, Gregory A., Henry, Thomas R., and Ince, Nuri F.
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- 2024
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44. Hemoadsorption improves kidney microcirculatory oxygenation and oxygen consumption, ameliorates tubular injury, and improves kidney function in a rat model of sepsis-induced AKI
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Ergin, Bülent, Kutucu, Deniz Erol, Kapucu, Aysegul, van Dam, Wijnie, Moretto, Lorenza, Heyman, Paul, and Ince, Can
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- 2024
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45. Lyme serology in non-endemic countries: Why do we request it and what do we find?
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Yigit, Metin, Kalayci, Furkan, Yuzdemir, Hasan Salih, Kilic, Enes Kaan, Ince, Yunus Emre, and Ozkaya-Parlakay, Aslinur
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- 2024
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46. Securing long-term immunity: The possible necessity of supplementary measles vaccination
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Kalayci, Furkan, Yigit, Metin, Gulhan, Belgin, Kanik Yuksek, Saliha, Kilic, Enes Kaan, Ince, Yunus Emre, Demircioglu Kalayci, Betul, Yuzdemir, Hasan Salih, and Ozkaya Parlakay, Aslinur
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- 2024
- Full Text
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47. Liquid crystalline elastomers as artificial muscles and flexible actuators for robotics/hybrid engineered machinery
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Ince, Joshua, Prasad, Krishnamurthy, Subhani, Karamat, Duffy, Alan, and Salim, Nisa
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- 2024
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48. GDEMM2024: Global Digital Elevation Merged Model 2024 for surface, bedrock, ice thickness, and land-type masks
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Ince, E. Sinem, Abrykosov, Oleh, and Förste, Christoph
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- 2024
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49. Correction: Characterising illness stages and recovery trajectories of eating disorders in young people via remote measurement technology (STORY): a multi-centre prospective cohort study protocol
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Kuehne, Carina, Phillips, Matthew D., Moody, Sarah, Bryson, Callum, Campbell, Iain C., Conde, Pauline, Cummins, Nicholas, Desrivières, Sylvane, Dineley, Judith, Dobson, Richard, Douglas, Daire, Folarin, Amos, Gallop, Lucy, Hemmings, Amelia, İnce, Başak, Mason, Luke, Rashid, Zulqarnain, Bromell, Alice, Sims, Christopher, Allen, Karina, Bailie, Chantal, Bains, Parveen, Basher, Mike, Battisti, Francesca, Baudinet, Julian, Bristow, Katherine, Dawson, Nicola, Dodd, Lizzie, Frater, Victoria, Freudenthal, Robert, Gripton, Beth, Kan, Carol, Khor, Joel W. T., Kotze, Nicus, Laverack, Stuart, Martin, Lee, Maxwell, Sarah, McDonald, Sarah, McKnight, Delysia, McKay, Ruairidh, Merrin, Jessica, Nash, Mel, Nicholls, Dasha, Palmer, Shirlie, Pearce, Samantha, Roberts, Catherine, Serpell, Lucy, Severs, Emilia, Simic, Mima, Staton, Amelia, Westaway, Sian, Sharpe, Helen, and Schmidt, Ulrike
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- 2024
- Full Text
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50. Evaluation of stabbing assault injuries in a tertiary emergency department: a retrospective observational study
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Kartal Yeter, Nil Deniz, Karaca, Mehmet Ali, Yeter, Ahmet Sefa, Öztürk İnce, Elif, and Erbil, Bülent
- Published
- 2024
- Full Text
- View/download PDF
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