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5,621 results on '"Erdogan A"'

1. Generation and Maintenance of Patient-Derived Endometrial Cancer Organoids

Author

Divya Gowthaman, Arielle Katcher, Megan Gorman, Brian Yueh, Aaron Nizam, Charlie Chung, Erdogan Akyildiz, Marina Frimer, Gary L Goldberg, and Semir Beyaz

Subjects
Biology (General), QH301-705.5
Abstract

Endometrial cancer (EC) is the leading cause of gynecologic cancer morbidity and mortality in the U.S. Despite advancements in cancer research, EC death rates are increasing, particularly high-grade endometrial cancers. The development of three-dimensional (3D) patient-derived organoid (PDO) models for EC is crucial, as they provide a more accurate representation of the biological and genetic complexity of a patient’s tumor compared to traditional 2D cell lines. Here, we describe a protocol for cultivating PDO models from normal endometrium and EC across different EC subtypes. These EC PDO models can be expanded across multiple passages and facilitate the exploration of tumor behavior and drug responses, thereby advancing our understanding of the disease and potentially leading to more effective and individualized novel therapeutic strategies.

Published
2024
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2. Experiences of Dermatologists and Patients Regarding Psoriasis and Its Connection to Psoriatic Arthritis in Saudi Arabia

Author

Fatani MI, Al-homood I, Bedaiwi M, Al Natour S, Erdogan A, Alsharafi A, and Attar SM

Subjects
autoimmune disease, disease pathway, patient satisfaction, provider perspective, Dermatology, RL1-803
Abstract

Mohammad I Fatani,1 Ibrahim Al-homood,2 Mohamed Bedaiwi,3 Sahar Al Natour,4 Alper Erdogan,5 Aya Alsharafi,5 Suzan M Attar6 1Department of Dermatology, Heraa Hospital, Mecca, Makkah, Kingdom of Saudi Arabia; 2Department of Rheumatology, King Fahad Medical City (KFMC), Riyadh, Riyadh Province, Kingdom of Saudi Arabia; 3Department of Rheumatology, King Saud University, Riyadh, Riyadh Province, Kingdom of Saudi Arabia; 4Department of Dermatology, Imam Abdulrahman Bin Faisal University, Dammam, Eastern Province, Kingdom of Saudi Arabia; 5Eli Lilly and Company, Riyadh, Riyadh Province, Kingdom of Saudi Arabia; 6Department of Internal Medicine, King Abdulaziz University, Jeddah, Makkah, Kingdom of Saudi ArabiaCorrespondence: Mohammad I Fatani, Department of Dermatology, Heraa Hospital, Al Madinah Al Munawarah Road, Mecca, Makkah, 24227, Kingdom of Saudi Arabia, Email m_fatani@yahoo.comPurpose: Psoriasis is a chronic, inflammatory, immune-mediated skin disease that has significant impact on a patient’s quality of life, yet it remains challenging for dermatologists to successfully identify and manage. Without effective screening, diagnosis and treatments, psoriasis can potentially progress to psoriatic arthritis. A descriptive, observational cross-sectional study of Saudi Arabian dermatologists and patients with psoriasis was conducted to explore dermatologist and patient perspectives of psoriasis, including diagnosis, management, disease course and unmet needs.Patients and Methods: This study involved a quantitative questionnaire administered to 31 dermatologists and 90 patients with psoriasis at eight medical centers and was analyzed using descriptive statistics.Results: Dermatologists and patients perceived that psoriasis treatment was initiated promptly and that follow-up visits were sufficient. Their perspectives differed in the time to diagnosis and patient reaction, symptom severity, input into treatment goals and educational needs. The dermatologists’ concerns about underdiagnosed psoriasis (13%) were primarily related to patient awareness (87%), physician awareness (58%), and the absence of a regular screening program (52%). Only 31% of patients with psoriasis were highly satisfied with their psoriasis treatment, with 78% experiencing unpleasant symptoms of pain or swelling in joints indicative of psoriatic arthritis. However, only 56% of these patients reported these symptoms to their physicians. When dermatologists were made aware of this difference, referrals to a rheumatologist increased.Conclusion: The study highlights the importance of strengthening psoriasis management by enhancing dermatologist referral and screening practices, adopting a multidisciplinary approach to care, and improving education and resources for physicians and patients. These results can help to inform the improvement of psoriasis screening, diagnosis and treatment strategies and ensure that expectations meet treatment outcomes. Further research exploring the dermatologist and patient perspectives of the disease pathway from psoriasis to psoriatic arthritis and tailor-made treatment approaches is recommended.Keywords: autoimmune disease, disease pathway, patient satisfaction, provider perspective

Published
2024

3. The Turkish Nation as a Bridge: Imagining a Nation in Turkish Parliamentary Discourse

Author

Erdogan Aykaç

Subjects
bridge metaphor, imagined turkish communities, discursive strategies, turkish parliament, foreign policy, Social Sciences
Abstract

This article analyses Turkish parliamentary discourse about Turkish communities living outside of Turkey from 1988 to 2016. It focuses particularly on the usage of the bridge metaphor in discursive strategies towards these communities; concentrated mainly in former Ottoman territories and parts of Eurasia. The article argues that Turkish parliamentarians used the bridge metaphor to frame Turkish communities as part of both the Turkish nation and the nation where they lived, thereby constituting their liminal and in-between identity. Parliamentarians continuously (re-)imagine, (re-)construct, and (re-)produce the Turkish nation by using different discursive strategies that included uniqueness, sameness or difference. They used identity markers as ethnicity, language, geography, history, and religion to address these strategies. Metaphorically framing Turkish communities as a bridge provided them a dominant bridge role, namely that of friendship and peace. By transforming Turkish communities into a bridge of friendship and peace, through different dimensions, they believed that they would have a positive and crucial role for the country where they live and for Turkey. This bridge role provided opportunities as well as limits, illustrating the interplay between discourse and foreign policy developments.

Published
2023
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4. Program Evaluation Study for Measurement and Evaluation Course in Distance Education

Author

Muhammed Akinci and Erdogan Köse

Abstract

This study aims to evaluate the online measurement and evaluation course in teacher training programs during the COVID-19 process. In the study, we sought answers to two primary research questions: What are the opinions of teachers and school administrators regarding their measurement and evaluation competencies? Does the online "measurement and evaluation" course have the qualities of an effective program in the "antecedents, transactions, and outcomes" dimension? We structured the research into two phases within a multistage evaluation design framework. The findings show that there were problems and positive aspects in all dimensions of the program. For example, adapting teacher training programs developed before COVID-19 to distance education processes was challenging. In distance education, some practices contradict the modern teaching and assessment approach. Such problems were reflected in teachers' acquisition of measurement and evaluation competencies. The achievement test we applied to the observed groups also confirmed these findings. For this reason, responsible organizations should not ignore the fact that we cannot renounce distance education. During program development, they should reconsider how the teachers will acquire measurement and evaluation competencies and how we will measure and evaluate in distance education.

Published
2024

5. Double Media-Based Modulation Scheme for High-Rate Wireless Communication Systems

Author

Ozden, Burak Ahmet, Aydin, Erdogan, and Cogen, Fatih

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Current wireless communication technologies are insufficient in the face of ever-increasing demands. Therefore, novel and high-performance communication systems are needed. In this paper, a novel high data rate and high-performance index modulation scheme called double media-based modulation (DMBM) is proposed. The DMBM system doubles the number of mirror activation patterns (MAPs) and the number of transmitted symbols compared to the traditional MBM system during the same symbol period. In this way, the spectral efficiency of the DMBM is doubled and the error performance improves as the number of bits carried in the indices increases. Performance analysis of the DMBM scheme is evaluated for $M$-ary quadrature amplitude modulation ($M$-QAM) on Rayleigh fading channels. The error performance of the proposed DMBM system is compared with spatial modulation (SM), quadrature SM (QSM), MBM, and double SM (DSM) techniques. Also, the throughput, complexity, energy efficiency, spectral efficiency, and capacity analyses for the proposed DMBM system and SM, QSM, MBM, and DSM systems are presented. All analysis results show that the proposed DMBM system is superior to the compared systems., Comment: 7 pages, 7 figures, 2 tables

Published
2024

6. Full-spectrum pairwise coalescence of eigenstates in a class of non-Hermitian Hamiltonians

Author

Erdogan, Yusuf H. and Haque, Masudul

Subjects
Quantum Physics
Abstract

We consider non-Hermitian tight-binding one-dimensional Hamiltonians and show that imposing a certain symmetry causes all eigenvalues to pair up and the corresponding eigenstates to coalesce in pairs. This Pairwise Coalescence (PC) is an enhanced version of an exceptional point -- the complete spectrum pairs up, not just one pair of eigenstates. The symmetry is that of reflection excluding the central two sites, and allowing flipping of non-reciprocal hoppings (``generalized off-center reflection symmetry''). Two simple examples of PC exist in the literature -- our construction encompasses these examples and extends them to a vast class of Hamiltonians. We study several families of such Hamiltonians, extend to cases of full-spectrum higher-order coalescences, and show how the PC point corresponds to amplified non-orthogonality of the eigenstates and enhanced loss of norm in time evolution., Comment: 15 pages

Published
2024

7. Stochastic Communication Avoidance for Recommendation Systems

Author

Erdogan, Lutfi Eren, Kanakagiri, Vijay Anand Raghava, Keutzer, Kurt, and Dong, Zhen

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

One of the major bottlenecks for efficient deployment of neural network based recommendation systems is the memory footprint of their embedding tables. Although many neural network based recommendation systems could benefit from the faster on-chip memory access and increased computational power of hardware accelerators, the large embedding tables in these models often cannot fit on the constrained memory of accelerators. Despite the pervasiveness of these models, prior methods in memory optimization and parallelism fail to address the memory and communication costs of large embedding tables on accelerators. As a result, the majority of models are trained on CPUs, while current implementations of accelerators are hindered by issues such as bottlenecks in inter-device communication and main memory lookups. In this paper, we propose a theoretical framework that analyses the communication costs of arbitrary distributed systems that use lookup tables. We use this framework to propose algorithms that maximize throughput subject to memory, computation, and communication constraints. Furthermore, we demonstrate that our method achieves strong theoretical performance across dataset distributions and memory constraints, applicable to a wide range of use cases from mobile federated learning to warehouse-scale computation. We implement our framework and algorithms in PyTorch and achieve up to 6x increases in training throughput on GPU systems over baselines, on the Criteo Terabytes dataset.

Published
2024

8. Optimizing Cladding Elasticity to Enhance Sensitivity in Silicon Photonic Ultrasound Sensors

Author

Erdogan, R. Tufan, Filonenko, Georgy A., Picken, Stephen J., Steeneken, Peter G., and Westerveld, Wouter J.

Subjects
Physics - Optics, Physics - Applied Physics
Abstract

Ultrasound is widely used in medical imaging, and emerging photo-acoustic imaging is crucial for disease diagnosis. Currently, high-end photo-acoustic imaging systems rely on piezo-electric materials for detecting ultrasound waves, which come with sensitivity, noise, and bandwidth limitations. Advanced applications demand a large matrix of broadband, high-resolution, and scalable ultrasound sensors. Silicon photonic circuits have been introduced to meet these requirements by detecting ultrasound-induced deformation and stress in silicon waveguides. Although higher sensitivities could facilitate the exploration of new applications, the high stiffness of the waveguide materials constrains the intrinsic sensitivity of the silicon photonic circuits to ultrasound signals. Here, we explore the impact of the mechanical properties of a polymer cladding on the sensitivity of silicon photonic ultrasound sensors. Our model and experiments reveal that optimizing the polymer cladding's stiffness enhances the resonance wavelength sensitivity. Experimentally, we show a fourfold increase in the sensitivity compared to the sensors without a cladding polymer and, a twofold sensitivity increase compared to the sensors with a cladding polymer of saturated cross-linking density. Interestingly, comparing experiments with the optomechanical model suggests that the change in Young's Modulus alone cannot explain the sensitivity increase. In conclusion, polymer-coated silicon photonic ultrasound sensors exhibit potential for advanced photo-acoustic imaging applications. It offers the prospect of increasing the ultrasound detection sensitivity of silicon photonic ultrasound sensors while using CMOS-compatible processes. This paves the way to integrate the polymer-coated silicon photonic ultrasound sensors with electronics to utilize the sensors in advanced medical imaging applications.

Published
2024
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9. Efficient and Scalable Estimation of Tool Representations in Vector Space

Author

Moon, Suhong, Jha, Siddharth, Erdogan, Lutfi Eren, Kim, Sehoon, Lim, Woosang, Keutzer, Kurt, and Gholami, Amir

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Recent advancements in function calling and tool use have significantly enhanced the capabilities of large language models (LLMs) by enabling them to interact with external information sources and execute complex tasks. However, the limited context window of LLMs presents challenges when a large number of tools are available, necessitating efficient methods to manage prompt length and maintain accuracy. Existing approaches, such as fine-tuning LLMs or leveraging their reasoning capabilities, either require frequent retraining or incur significant latency overhead. A more efficient solution involves training smaller models to retrieve the most relevant tools for a given query, although this requires high quality, domain-specific data. To address those challenges, we present a novel framework for generating synthetic data for tool retrieval applications and an efficient data-driven tool retrieval strategy using small encoder models. Empowered by LLMs, we create ToolBank, a new tool retrieval dataset that reflects real human user usages. For tool retrieval methodologies, we propose novel approaches: (1) Tool2Vec: usage-driven tool embedding generation for tool retrieval, (2) ToolRefiner: a staged retrieval method that iteratively improves the quality of retrieved tools, and (3) MLC: framing tool retrieval as a multi-label classification problem. With these new methods, we achieve improvements of up to 27.28 in Recall@K on the ToolBench dataset and 30.5 in Recall@K on ToolBank. Additionally, we present further experimental results to rigorously validate our methods. Our code is available at \url{https://github.com/SqueezeAILab/Tool2Vec}

Published
2024

10. TinyAgent: Function Calling at the Edge

Author

Erdogan, Lutfi Eren, Lee, Nicholas, Jha, Siddharth, Kim, Sehoon, Tabrizi, Ryan, Moon, Suhong, Hooper, Coleman, Anumanchipalli, Gopala, Keutzer, Kurt, and Gholami, Amir

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Recent large language models (LLMs) have enabled the development of advanced agentic systems that can integrate various tools and APIs to fulfill user queries through function calling. However, the deployment of these LLMs on the edge has not been explored since they typically require cloud-based infrastructure due to their substantial model size and computational demands. To this end, we present TinyAgent, an end-to-end framework for training and deploying task-specific small language model agents capable of function calling for driving agentic systems at the edge. We first show how to enable accurate function calling for open-source models via the LLMCompiler framework. We then systematically curate a high-quality dataset for function calling, which we use to fine-tune two small language models, TinyAgent-1.1B and 7B. For efficient inference, we introduce a novel tool retrieval method to reduce the input prompt length and utilize quantization to further accelerate the inference speed. As a driving application, we demonstrate a local Siri-like system for Apple's MacBook that can execute user commands through text or voice input. Our results show that our models can achieve, and even surpass, the function-calling capabilities of larger models like GPT-4-Turbo, while being fully deployed at the edge. We open-source our dataset, models, and installable package and provide a demo video for our MacBook assistant agent., Comment: EMNLP 2024 Demo

Published
2024

11. The Effect of Dialogic Reading Practices on the Fluent Reading Skills of Primary School 2nd Grade Students

Author

Ergün Yurtbakan and Tolga Erdogan

Abstract

In this study, the effect of dialogic reading practices on the development of fluent reading skills of primary school 2nd grade students under the guidance of teachers and parents was examined. In the study the quasi-experimental design of the quantitative research approach was applied. A total of 30 students (first experimental group: 10, second experimental group: 10 and control group: 10) studying in the second grade of primary school in Macka district of Trabzon province were included in the study. The student reading fluency scale (by the teacher) which was prepared by the researchers was applied to the students as a pre-post-retention test In the application, which lasted for five weeks twice a week; dialogic reading was implemented with the experimental groups (1st experimental group: parents-teacher-students, 2nd experimental group: teacher-students), and traditional reading was implemented with the control group. Since the assumptions of the ANCOVA test could not be met after the application, the significant difference between the pre-posttest and post-retention test scores of each group was determined with the Wilcoxon Signed Rank Test. The significance between the pre-post test and retention test scores of the groups was determined by the Kruskal Wallis test. At the end of the study; it was determined that the dialogic reading practice performed in the presence of parents and teachers created a statistically significant difference in the automaticity and accuracy sub-dimension of fluent reading, but not in the prosody sub-dimension. The development of dialogic reading under the guidance of parents and teachers in the automaticity and accuracy sub-dimension of fluent reading continues in permanence. [This article includes an extended summary in Turkish.]

Published
2024

12. Evaluation of Cooperative Learning in Science Education: A Mixed-Meta Method Study

Author

Mehmet Diyaddin Yasar, Mustafa Erdogan, Veli Batdi, and Ülkü Cinkara

Abstract

The aim of this study is to reveal the effect of cooperative learning method in science education on academic achievement with mixed-meta method. For this purpose, various national and international databases were searched, and among 81 articles and 37 theses covering the period between 2001 and 2022, 46 articles and 31 theses that included pre-test-post-test data and included the statistical information required for experimental or quasi-experimental analysis were preferred for meta-analysis. The effect size of 77 studies included in the analysis was found to be g = 1.070 and it was seen that the cooperative learning method in science education had a positive and large effect on academic achievement. On the other hand, as a result of the meta-thematic analysis, it was determined that the cooperative learning method increased academic achievement due to the meaningful and permanent realisation of learning. In addition, thanks to the activities in which the student plays a central role, it is understood that it develops positive attitudes towards the science course depending on the results such as making the course enjoyable and fun, making it popular and interesting; it is effective in gaining 21st century skills such as creativity, communication and cooperation, productivity and entrepreneurship. It is thought that examining the effects of cooperative learning method in science education with mixed-meta method will contribute to the literature by providing comprehensive results on the subject.

Published
2024

13. Cosmological measurement of the gravitational constant $G$ using the CMB

Author

Lamine, Brahim, Ozdalkiran, Yacob, Mirouze, Louis, Erdogan, Furkan, Ilic, Stéphane, Tutusaus, Isaac, Kou, Raphael, and Blanchard, Alain

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Recent cosmological observations have provided numerous new observations with increasing precision that have led to the era of precision cosmology. The exquisite quality of these observations opens new possibilities towards measuring fundamental constants with good precision and at scales which are complementary to the laboratory ones. In particular, the cosmic microwave background (CMB) temperature and polarization spectra contain a wealth quantity of information, well beyond the basic cosmological parameters. In this paper, we update the precision on a cosmological determination of $G$ by using the latest Planck data release (PR4) in combination with the latest baryon acoustic oscillation (BAO) from the Dark Energy Spectroscopic Instrument (DESI) data release 1. We demonstrate a precision of $2.1\%$, corresponding to a $\sim25\%$ improvement compared to the literature. This measurement is compatible with laboratory ones within one standard deviation. Finally, we show that this cosmological measurement of $G$ is robust against several assumptions made on the cosmological model, in particular when considering a non-standard dark energy fluid or non-flat models., Comment: 8 pages, 10 figures

Published
2024

14. Characterizing Prompt Compression Methods for Long Context Inference

Author

Jha, Siddharth, Erdogan, Lutfi Eren, Kim, Sehoon, Keutzer, Kurt, and Gholami, Amir

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Long context inference presents challenges at the system level with increased compute and memory requirements, as well as from an accuracy perspective in being able to reason over long contexts. Recently, several methods have been proposed to compress the prompt to reduce the context length. However, there has been little work on comparing the different proposed methods across different tasks through a standardized analysis. This has led to conflicting results. To address this, here we perform a comprehensive characterization and evaluation of different prompt compression methods. In particular, we analyze extractive compression, summarization-based abstractive compression, and token pruning methods. Surprisingly, we find that extractive compression often outperforms all the other approaches, and enables up to 10x compression with minimal accuracy degradation. Interestingly, we also find that despite several recent claims, token pruning methods often lag behind extractive compression. We only found marginal improvements on summarization tasks., Comment: Es-FoMo @ ICML 2024

Published
2024

15. $L^p$-continuity of wave operators for higher order Schr\'odinger operators with threshold eigenvalues in high dimensions

Author

Erdogan, M. Burak, Green, William R., and LaMaster, Kevin

Subjects
Mathematics - Analysis of PDEs, Mathematical Physics
Abstract

We consider the higher order Schr\"odinger operator $H=(-\Delta)^m+V(x)$ in $n$ dimensions with real-valued potential $V$ when $n>4m$, $m\in \mathbb N$. We adapt our recent results for $m>1$ to show that when $H$ has a threshold eigenvalue the wave operators are bounded on $L^p(\mathbb R^n)$ for the natural range $1\leq p<\frac{n}{2m}$ in both even and odd dimensions. The approach used works without distinguishing even and odd cases, and matches the range of boundedness in the classical case when $m=1$. The proof applies in the classical $m=1$ case as well and simplifies the argument., Comment: 23 pages. arXiv admin note: text overlap with arXiv:2207.14264

Published
2024

16. Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

Author

Aamir, M., Acar, B., Adamov, G., Adams, T., Adloff, C., Afanasiev, S., Agrawal, C., Ahmad, A., Ahmed, H. A., Akbar, S., Akchurin, N., Akgul, B., Akgun, B., Akpinar, R. O., Aktas, E., AlKadhim, A., Alexakhin, V., Alimena, J., Alison, J., Alpana, A., Alshehri, W., Dominguez, P. Alvarez, Alyari, M., Amendola, C., Amir, R. B., Andersen, S. B., Andreev, Y., Antoszczuk, P. D., Aras, U., Ardila, L., Aspell, P., Avila, M., Awad, I., Aydilek, O., Azimi, Z., Pretel, A. Aznar, Bach, O. A., Bainbridge, R., Bakshi, A., Bam, B., Banerjee, S., Barney, D., Bayraktar, O., Beaudette, F., Beaujean, F., Becheva, E., Behera, P. K., Belloni, A., Bergauer, T., Besancon, M., Bylund, O. Bessidskaia, Bhatt, L., Bhowmil, D., Blekman, F., Blinov, P., Bloch, P., Bodek, A., Boger, a., Bonnemaison, A., Bouyjou, F., Brennan, L., Brondolin, E., Brusamolino, A., Bubanja, I., Perraguin, A. Buchot, Bunin, P., Misura, A. Burazin, Butler-nalin, A., Cakir, A., Callier, S., Campbell, S., Canderan, K., Cankocak, K., Cappati, A., Caregari, S., Carron, S., Carty, C., Cauchois, A., Ceard, L., Cerci, S., Chang, P. J., Chatterjee, R. M., Chatterjee, S., Chattopadhyay, P., Chatzistavrou, T., Chaudhary, M. S., Chauhan, A., Chen, J. A., Chen, J., Chen, Y., Cheng, K., Cheung, H., Chhikara, J., Chiron, A., Chiusi, M., Chokheli, D., Chudasama, R., Clement, E., Mendez, S. Coco, Coko, D., Coskun, K., Couderc, F., Crossman, B., Cui, Z., Cuisset, T., Cummings, G., Curtis, E. M., D'Alfonso, M., D-hler-ball, J., Dadazhanova, O., Damgov, J., Das, I., DasGupta, S., Dauncey, P., Mendes, A. David Tinoco, Davies, G., Davignon, O., DeLa, P. deBarbaroC., DeSilva, M., DeWit, A., Debbins, P., Defranchis, M. M., Delagnes, E., Devouge, P., Dewangan, C., DiGuglielmo, G., Diehl, L., Dilsiz, K., Dincer, G. G., Dittmann, J., Dragicevic, M., Du, D., Dubinchik, B., Dugad, S., Dulucq, F., Dumanoglu, I., Duran, B., Dutta, S., Dutta, V., Dychkant, A., Dünser, M., Edberg, T., Ehle, I. T., Berni, A. El, Elias, F., Eno, S. C., Erdogan, E. N., Erkmen, B., Ershov, Y., Ertorer, E. Y., Extier, S., Eychenne, L., Fedar, Y. E., Fedi, G., De Almeida, J. P. Figueiredo De De Sá Sousa, Alves, B. A. Fontana Santos Santos, Frahm, E., Francis, K., Freeman, J., French, T., Gaede, F., Gandhi, P. K., Ganjour, S., Garcia-Bellido, A., Gastaldi, F., Gazi, L., Gecse, Z., Gerwig, H., Gevin, O., Ghosh, S., Gill, K., Gleyzer, S., Godinovic, N., Goek, M., Goettlicher, P., Goff, R., Golunov, A., Gonultas, B., Martínez, J. D. González, Gorbounov, N., Gouskos, L., Gray, A., Gray, L., Grieco, C., Groenroos, S., Groner, D., Gruber, A., Grummer, A., Grönroos, S., Guilloux, F., Guler, Y., Gungordu, A. D., Guo, J., Guo, K., Guler, E. Gurpinar, Gutti, H. K., Guvenli, A. A., Gülmez, E., Hacisahinoglu, B., Halkin, Y., Machado, G. Hamilton Ilha, Hare, H. S., Hatakeyama, K., Heering, A. H., Hegde, V., Heintz, U., Hinton, N., Hinzmann, A., Hirschauer, J., Hitlin, D., Hos, İ., Hou, B., Hou, X., Howard, A., Howe, C., Hsieh, H., Hsu, T., Hua, H., Hummer, F., Imran, M., Incandela, J., Iren, E., Isildak, B., Jackson, P. S., Jackson, W. J., Jain, S., Jana, P., Jaroslavceva, J., Jena, S., Jige, A., Jordano, P. P., Joshi, U., Kaadze, K., Kafizov, A., Kalipoliti, L., Tharayil, A. Kallil, Kaluzinska, O., Kamble, S., Kaminskiy, A., Kanemura, M., Kanso, H., Kao, Y., Kapic, A., Kapsiak, C., Karjavine, V., Karmakar, S., Karneyeu, A., Kaya, M., Topaksu, A. Kayis, Kaynak, B., Kazhykarim, Y., Khan, F. A., Khudiakov, A., Kieseler, J., Kim, R. S., Klijnsma, T., Kloiber, E. G., Klute, M., Kocak, Z., Kodali, K. R., Koetz, K., Kolberg, T., Kolcu, O. B., Komaragiri, J. R., Komm, M., Kopsalis, I., Krause, H. A., Krawczyk, M. A., Vinayakam, T. R. Krishnaswamy, Kristiansen, K., Kristic, A., Krohn, M., Kronheim, B., Krüger, K., Kudtarkar, C., Kulis, S., Kumar, M., Kumar, N., Kumar, S., Verma, R. Kumar, Kunori, S., Kunts, A., Kuo, C., Kurenkov, A., Kuryatkov, V., Kyre, S., Ladenson, J., Lamichhane, K., Landsberg, G., Langford, J., Laudrain, A., Laughlin, R., Lawhorn, J., Dortz, O. Le, Lee, S. W., Lektauers, A., Lelas, D., Leon, M., Levchuk, L., Li, A. J., Li, J., Li, Y., Liang, Z., Liao, H., Lin, K., Lin, W., Lin, Z., Lincoln, D., Linssen, L., Litomin, A., Liu, G., Liu, Y., Lobanov, A., Lohezic, V., Loiseau, T., Lu, C., Lu, R., Lu, S. Y., Lukens, P., Mackenzie, M., Magnan, A., Magniette, F., Mahjoub, A., Mahon, D., Majumder, G., Makarenko, V., Malakhov, A., Malgeri, L., Mallios, S., Mandloi, C., Mankel, A., Mannelli, M., Mans, J., Mantilla, C., Martinez, G., Massa, C., Masterson, P., Matthewman, M., Matveev, V., Mayekar, S., Mazlov, I., Mehta, A., Mestvirishvili, A., Miao, Y., Milella, G., Mirza, I. R., Mitra, P., Moccia, S., Mohanty, G. B., Monti, F., Moortgat, F., Murthy, S., Music, J., Musienko, Y., Nabili, S., Nayak, S., Nelson, J. W., Nema, A., Neutelings, I., Niedziela, J., Nikitenko, A., Noonan, D., Noy, M., Nurdan, K., Obraztsov, S., Ochando, C., Ogul, H., Olsson, J., Onel, Y., Ozkorucuklu, S., Paganis, E., Palit, P., Pan, R., Pandey, S., Pantaleo, F., Papageorgakis, C., Paramesvaran, S., Paranjpe, M. M., Parolia, S., Parsons, A. G., Parygin, P., Paulini, M., Paus, C., Peñaló, K., Pedro, K., Pekic, V., Peltola, T., Peng, B., Perego, A., Perini, D., Petrilli, A., Pham, H., Pierre-Emile, T., Podem, S. K., Popov, V., Portales, L., Potok, O., Pradeep, P. B., Pramanik, R., Prosper, H., Prvan, M., Qasim, S. R., Qu, H., Quast, T., Trivino, A. Quiroga, Rabour, L., Raicevic, N., Rajpoot, H., Rao, M. A., Rapacz, K., Redjeb, W., Reinecke, M., Revering, M., Roberts, A., Rohlf, J., Rosado, P., Rose, A., Rothman, S., Rout, P. K., Rovere, M., Rumerio, P., Rusack, R., Rygaard, L., Ryjov, V., Sadivnycha, S., Sahin, M. Ö., Sakarya, U., Salerno, R., Saradhy, R., Saraf, M., Sarbandi, K., Sarkisla, M. A., Satyshev, I., Saud, N., Sauvan, J., Schindler, G., Schmidt, A., Schmidt, I., Schmitt, M. H., Sculac, A., Sculac, T., Sedelnikov, A., Seez, C., Sefkow, F., Selivanova, D., Selvaggi, M., Sergeychik, V., Sert, H., Shahid, M., Sharma, P., Sharma, R., Sharma, S., Shelake, M., Shenai, A., Shih, C. W., Shinde, R., Shmygol, D., Shukla, R., Sicking, E., Silva, P., Simsek, C., Simsek, E., Sirasva, B. K., Sirois, Y., Song, S., Song, Y., Soudais, G., Sriram, S., StJacques, R. R., StahlLeiton, A. G., Steen, A., Stein, J., Strait, J., Strobbe, N., Su, X., Sukhov, E., Suleiman, A., Cerci, D. Sunar, Suryadevara, P., Swain, K., Syal, C., Tali, B., Tanay, K., Tang, W., Tanvir, A., Tao, J., Tarabini, A., Tatli, T., Taylor, R., Taysi, Z. C., Teafoe, G., Tee, C. Z., Terrill, W., Thienpont, D., Thomas, R., Titov, M., Todd, C., Todd, E., Toms, M., Tosun, A., Troska, J., Tsai, L., Tsamalaidze, Z., Tsionou, D., Tsipolitis, G., Tsirigoti, M., Tu, R., Polat, S. N. Tural, Undleeb, S., Usai, E., Uslan, E., Ustinov, V., Vernazza, E., Viahin, O., Viazlo, O., Vichoudis, P., Vijay, A., Virdee, T., Voirin, E., Vojinovic, M., Voytishin, N., Vámi, T. Á., Wade, A., Walter, D., Wang, C., Wang, F., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Wanlin, E., Wayne, M., Wetzel, J., Whitbeck, A., Wickwire, R., Wilmot, D., Wilson, J., Wu, H., Xiao, M., Yang, J., Yazici, B., Ye, Y., Yetkin, T., Yi, R., Yohay, R., Yu, T., Yuan, C., Yuan, X., Yuksel, O., YushmanoV, I., Yusuff, I., Zabi, A., Zareckis, D., Zarubin, A., Zehetner, P., Zghiche, A., Zhang, C., Zhang, D., Zhang, H., Zhang, J., Zhang, Z., Zhao, X., Zhong, J., Zhou, Y., and Zorbilmez, Ç.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment, Physics - Data Analysis, Statistics and Probability
Abstract

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated., Comment: Prepared for submission to JINST

Published
2024

17. Lattice Lipschitz superposition operators on Banach function spaces

Author

Arnau, Roger, Calabuig, Jose M., Erdoğan, Ezgi, and Pérez, Enrique A. Sánchez

Subjects
Mathematics - Functional Analysis, 47J10, 46E30, 26A16
Abstract

We analyse and characterise the notion of lattice Lipschitz operator (a class of superposition operators, diagonal Lipschitz maps) when defined between Banach function spaces. After showing some general results, we restrict our attention to the case of those Lipschitz operators which are representable by pointwise composition with a strongly measurable function. Mimicking the classical definition and characterizations of (linear) multiplication operators between Banach function spaces, we show that under certain conditions the requirement for a diagonal Lipschitz operator to be well-defined between two such spaces $X(\mu)$ and $Y(\mu)$ is that it can be represented by a strongly measurable function which belongs to the Bochner space $\mathcal M(X,Y) \big(\mu, Lip_0(\mathbb R) \big). $ Here, $\mathcal M(X,Y) $ is the space of multiplication operators between $X(\mu)$ and $Y(\mu),$ and $Lip_0(\mathbb R)$ is the space of real-valued Lipschitz maps with real variable that are equal to $0$ in $0. $ This opens the door to a better understanding of these maps, as well as finding the relation of these operators to some normed tensor products and other classes of maps.

Published
2024

18. The Roles of Collective Teacher Efficacy and Commitment in the Relationship between Turkish Language Teachers' Job Satisfaction and School Principal Distributed Leadership

Author

Kürsat Kaya, Onur Erdogan, Yilmaz Yesil, and Ferudun Sezgin

Abstract

The purpose of the current study was to investigate the relationship between principal distributed leadership and teacher job satisfaction, with the mediating role of collective teacher efficacy. It also investigated the moderating role of teacher commitment in the link between collective teacher efficacy and teacher job satisfaction. Drawing data from a sample of 338 Turkish language teachers in secondary schools in Turkey, the current study employed a cross-sectional survey design. Moderated mediation structural equation modeling was utilized to analyze the data. Results provided a moderate direct association between distributed leadership and teacher job satisfaction and an indirect relationship mediated by collective teacher efficacy. Researchers also found the moderating role of teacher commitment in the relationship between collective teacher efficacy and teacher job satisfaction to be positive and significant. Results provide evidence from a non-Western developing context and Turkish language teachers, contributing to the global knowledge base by confirming the positive and direct link between distributed leadership and teacher job satisfaction while highlighting the significant mediating role of collective teacher efficacy between these two constructs and the moderating role of teacher commitment in reflecting this role in teacher job satisfaction. Implications for policymakers and practitioners are discussed.

Published
2024
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19. Evaluation of the Frequency, Risk Factors, and Outcomes of ROP in Infants With a BW >1500 Grs or GA ≥33 Wks in Turkey. (TR-ROP-2)

Author

Hacettepe University, Gazi University, Gaziosmanpasa Research and Education Hospital, Celal Bayar University, Selcuk University, Recep Tayyip Erdogan University, Karadeniz Technical University, Istinye University, Biruni University, T.C. ORDU ÜNİVERSİTESİ, Dr. Lutfi Kirdar Kartal Training and Research Hospital, Pamukkale University, Kanuni Sultan Suleyman Training and Research Hospital, Konya City Hospital, Kecioren Education and Training Hospital, Ataturk University, Meda Hospital, Eskisehir Osmangazi University, Necmettin Erbakan University, Kırıkkale University, Sisli Hamidiye Etfal Training and Research Hospital, Acibadem Atakent University Hospital, Balikesir Ataturk City Hospital, Kocaeli University, Acibadem Maslak Hospital, Erzurum City Hospital, Afyon Karahisar Hospital, Ankara Koru Hospital, University of Health Sciences Gulhane Hospital, Bursa Yüksek İhtisas Education and Research Hospital, Namik Kemal University, Bursa City Hospital, Kayseri City Hospital, Ankara Etlik City Hospital, Zonguldak Bulent Ecevit University, Gazi Yasargil Training and Research Hospital, Inonu University, Dr. Behcet Uz Children's Hospital, Ankara Training and Research Hospital, Tepecik Training and Research Hospital, Aydin Adnan Menderes University, Dicle University, Akdeniz University, Umraniye Education and Research Hospital, Ankara Bilkent City Hospital, Afyonkarahisar University of Health Sciences Health Application and Research Center, Harran University, Sanliurfa Mehmet Akif Inan Education and Research Hospital, Istanbul University - Cerrahpasa (IUC), Uludag University, şanlıurfa training and research Hospital, Dokuz Eylul University, Bursa Doruk Yıldırım Hospital, Malatya Turgut Ozal University, Abant Izzet Baysal University, Alanya Training and Research Hospital, İstanbul Training and Research Hospital, Baskent Univeristy Adana Hospital, Kahramanmaras Sutcu Imam University, Batman Training and Research Hospital, Etlik Zubeyde Hanım Women's Health Care, Training and Research Hospital, Acıbadem Atunizade Hospital, Adiyaman Training and Research Hospital, Zeynep Kamil Maternity and Pediatric Research and Training Hospital, Ondokuz Mayıs University, Trakya University, Trabzon Kanuni Education and Research Hospital, Seyhan Hospital, Esenyurt University Hospital, Başakşehir Çam & Sakura City Hospital, Sakarya University, Mardin Training and Research Hospital, Karabuk University, Giresun Training and Research Hospital, Koc University Hospital, Tekirdağ City Hospital, Medipol University Mega Hospital, Adana City Training and Research Hospital, Istanbul University, Marmara University Pendik Training and Research Hospital, Medikal Park Ankara Hospital, Medipol University Bahcelievler Hospital, Muğla Sıtkı Koçman University, Yuzuncu Yıl University, Sanko University, Antalya Training and Research Hospital, Firat University, Ankara Güven Hospital, Private Batman Hospital, İstanbul Bağcılar Training and Research Hospital, Istanbul Aydın University, Mersin City Hospital, and Sezin Unal, Sezin Unal, MD

Published
2024

20. Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking

Author

Acikgoz, Emre Can, Erdogan, Mete, and Yuret, Deniz

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Large Language Models (LLMs) are becoming crucial across various fields, emphasizing the urgency for high-quality models in underrepresented languages. This study explores the unique challenges faced by low-resource languages, such as data scarcity, model selection, evaluation, and computational limitations, with a special focus on Turkish. We conduct an in-depth analysis to evaluate the impact of training strategies, model choices, and data availability on the performance of LLMs designed for underrepresented languages. Our approach includes two methodologies: (i) adapting existing LLMs originally pretrained in English to understand Turkish, and (ii) developing a model from the ground up using Turkish pretraining data, both supplemented with supervised fine-tuning on a novel Turkish instruction-tuning dataset aimed at enhancing reasoning capabilities. The relative performance of these methods is evaluated through the creation of a new leaderboard for Turkish LLMs, featuring benchmarks that assess different reasoning and knowledge skills. Furthermore, we conducted experiments on data and model scaling, both during pretraining and fine-tuning, simultaneously emphasizing the capacity for knowledge transfer across languages and addressing the challenges of catastrophic forgetting encountered during fine-tuning on a different language. Our goal is to offer a detailed guide for advancing the LLM framework in low-resource linguistic contexts, thereby making natural language processing (NLP) benefits more globally accessible.

Published
2024

21. Numerical approximation of SDEs driven by fractional Brownian motion for all $H\in(0,1)$ using WIS integration

Author

Erdogan, Utku, Lord, Gabriel J., and Schieven, Roy B.

Subjects
Mathematics - Numerical Analysis, 65C30, 60H35, 60G22
Abstract

We examine the numerical approximation of a quasilinear stochastic differential equation (SDE) with multiplicative fractional Brownian motion. The stochastic integral is interpreted in the Wick-It\^o-Skorohod (WIS) sense that is well defined and centered for all $H\in(0,1)$. We give an introduction to the theory of WIS integration before we examine existence and uniqueness of a solution to the SDE. We then introduce our numerical method which is based on the theoretical results in \cite{Mishura2008article, Mishura2008} for $H\geq \frac{1}{2}$. We construct explicitly a translation operator required for the practical implementation of the method and are not aware of any other implementation of a numerical method for the WIS SDE. We then prove a strong convergence result that gives, in the non-autonomous case, an error of $O(\Delta t^H)$ and in the non-autonomous case $O(\Delta t^{\min(H,\zeta)})$, where $\zeta$ is a H\"older continuity parameter. We present some numerical experiments and conjecture that the theoretical results may not be optimal since we observe numerically a rate of $\min(H+\frac{1}{2},1)$ in the autonomous case. This work opens up the possibility to efficiently simulate SDEs for all $H$ values, including small values of $H$ when the stochastic integral is interpreted in the WIS sense.

Published
2024

22. Smoothing estimates for weakly nonlinear internal waves in a rotating ocean

Author

Erdogan, M. B. and Tzirakis, N.

Subjects
Mathematics - Analysis of PDEs, 35Q53
Abstract

In this paper we study the effect of rotation on nonlinear wave phenomena in weakly dispersive media modeled by the Korteweg-de Vries equation on the real line. It is well known that smoothing in the case of the KdV equation with periodic boundary conditions is a result of the presence of high frequency waves that weaken the nonlinearity through time averaging, \cite{bit}, \cite{et1}. High frequency interactions through normal form transformations can be thought of as adding a fast rotating term in certain toy PDE models that arrest the development of singularities, \cite{bit}. It is crucial for this phenomena that the zero Fourier mode can be removed due to the conservation of the mean. On the real line this mechanism breaks down as the resonance sets close to zero frequency are sizable and normal form transformations are not useful, \cite{imt}, and hence smoothing fails. The model we study is a perturbation of the KdV equation on a rotating frame of reference. A combination of a suitable normal form transformation along with the restricted norm method of Bourgain \cite{Bou2} implies smoothing on the real line., Comment: 17 pages

Published
2024

23. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Author

Gemini Team, Georgiev, Petko, Lei, Ving Ian, Burnell, Ryan, Bai, Libin, Gulati, Anmol, Tanzer, Garrett, Vincent, Damien, Pan, Zhufeng, Wang, Shibo, Mariooryad, Soroosh, Ding, Yifan, Geng, Xinyang, Alcober, Fred, Frostig, Roy, Omernick, Mark, Walker, Lexi, Paduraru, Cosmin, Sorokin, Christina, Tacchetti, Andrea, Gaffney, Colin, Daruki, Samira, Sercinoglu, Olcan, Gleicher, Zach, Love, Juliette, Voigtlaender, Paul, Jain, Rohan, Surita, Gabriela, Mohamed, Kareem, Blevins, Rory, Ahn, Junwhan, Zhu, Tao, Kawintiranon, Kornraphop, Firat, Orhan, Gu, Yiming, Zhang, Yujing, Rahtz, Matthew, Faruqui, Manaal, Clay, Natalie, Gilmer, Justin, Co-Reyes, JD, Penchev, Ivo, Zhu, Rui, Morioka, Nobuyuki, Hui, Kevin, Haridasan, Krishna, Campos, Victor, Mahdieh, Mahdis, Guo, Mandy, Hassan, Samer, Kilgour, Kevin, Vezer, Arpi, Cheng, Heng-Tze, de Liedekerke, Raoul, Goyal, Siddharth, Barham, Paul, Strouse, DJ, Noury, Seb, Adler, Jonas, Sundararajan, Mukund, Vikram, Sharad, Lepikhin, Dmitry, Paganini, Michela, Garcia, Xavier, Yang, Fan, Valter, Dasha, Trebacz, Maja, Vodrahalli, Kiran, Asawaroengchai, Chulayuth, Ring, Roman, Kalb, Norbert, Soares, Livio Baldini, Brahma, Siddhartha, Steiner, David, Yu, Tianhe, Mentzer, Fabian, He, Antoine, Gonzalez, Lucas, Xu, Bibo, Kaufman, Raphael Lopez, Shafey, Laurent El, Oh, Junhyuk, Hennigan, Tom, Driessche, George van den, Odoom, Seth, Lucic, Mario, Roelofs, Becca, Lall, Sid, Marathe, Amit, Chan, Betty, Ontanon, Santiago, He, Luheng, Teplyashin, Denis, Lai, Jonathan, Crone, Phil, Damoc, Bogdan, Ho, Lewis, Riedel, Sebastian, Lenc, Karel, Yeh, Chih-Kuan, Chowdhery, Aakanksha, Xu, Yang, Kazemi, Mehran, Amid, Ehsan, Petrushkina, Anastasia, Swersky, Kevin, Khodaei, Ali, Chen, Gowoon, Larkin, Chris, Pinto, Mario, Yan, Geng, Badia, Adria Puigdomenech, Patil, Piyush, Hansen, Steven, Orr, Dave, Arnold, Sebastien M. R., Grimstad, Jordan, Dai, Andrew, Douglas, Sholto, Sinha, Rishika, Yadav, Vikas, Chen, Xi, Gribovskaya, Elena, Austin, Jacob, Zhao, Jeffrey, Patel, Kaushal, Komarek, Paul, Austin, Sophia, Borgeaud, Sebastian, Friso, Linda, Goyal, Abhimanyu, Caine, Ben, Cao, Kris, Chung, Da-Woon, Lamm, Matthew, Barth-Maron, Gabe, Kagohara, Thais, Olszewska, Kate, Chen, Mia, Shivakumar, Kaushik, Agarwal, Rishabh, Godhia, Harshal, Rajwar, Ravi, Snaider, Javier, Dotiwalla, Xerxes, Liu, Yuan, Barua, Aditya, Ungureanu, Victor, Zhang, Yuan, Batsaikhan, Bat-Orgil, Wirth, Mateo, Qin, James, Danihelka, Ivo, Doshi, Tulsee, Chadwick, Martin, Chen, Jilin, Jain, Sanil, Le, Quoc, Kar, Arjun, Gurumurthy, Madhu, Li, Cheng, Sang, Ruoxin, Liu, Fangyu, Lamprou, Lampros, Munoz, Rich, Lintz, Nathan, Mehta, Harsh, Howard, Heidi, Reynolds, Malcolm, Aroyo, Lora, Wang, Quan, Blanco, Lorenzo, Cassirer, Albin, Griffith, Jordan, Das, Dipanjan, Lee, Stephan, Sygnowski, Jakub, Fisher, Zach, Besley, James, Powell, Richard, Ahmed, Zafarali, Paulus, Dominik, Reitter, David, Borsos, Zalan, Joshi, Rishabh, Pope, Aedan, Hand, Steven, Selo, Vittorio, Jain, Vihan, Sethi, Nikhil, Goel, Megha, Makino, Takaki, May, Rhys, Yang, Zhen, Schalkwyk, Johan, Butterfield, Christina, Hauth, Anja, Goldin, Alex, Hawkins, Will, Senter, Evan, Brin, Sergey, Woodman, Oliver, Ritter, Marvin, Noland, Eric, Giang, Minh, Bolina, Vijay, Lee, Lisa, Blyth, Tim, Mackinnon, Ian, Reid, Machel, Sarvana, Obaid, Silver, David, Chen, Alexander, Wang, Lily, Maggiore, Loren, Chang, Oscar, Attaluri, Nithya, Thornton, Gregory, Chiu, Chung-Cheng, Bunyan, Oskar, Levine, Nir, Chung, Timothy, Eltyshev, Evgenii, Si, Xiance, Lillicrap, Timothy, Brady, Demetra, Aggarwal, Vaibhav, Wu, Boxi, Xu, Yuanzhong, McIlroy, Ross, Badola, Kartikeya, Sandhu, Paramjit, Moreira, Erica, Stokowiec, Wojciech, Hemsley, Ross, Li, Dong, Tudor, Alex, Shyam, Pranav, Rahimtoroghi, Elahe, Haykal, Salem, Sprechmann, Pablo, Zhou, Xiang, Mincu, Diana, Li, Yujia, Addanki, Ravi, Krishna, Kalpesh, Wu, Xiao, Frechette, Alexandre, Eyal, Matan, Dafoe, Allan, Lacey, Dave, Whang, Jay, Avrahami, Thi, Zhang, Ye, Taropa, Emanuel, Lin, Hanzhao, Toyama, Daniel, Rutherford, Eliza, Sano, Motoki, Choe, HyunJeong, Tomala, Alex, Safranek-Shrader, Chalence, Kassner, Nora, Pajarskas, Mantas, Harvey, Matt, Sechrist, Sean, Fortunato, Meire, Lyu, Christina, Elsayed, Gamaleldin, Kuang, Chenkai, Lottes, James, Chu, Eric, Jia, Chao, Chen, Chih-Wei, Humphreys, Peter, Baumli, Kate, Tao, Connie, Samuel, Rajkumar, Santos, Cicero Nogueira dos, Andreassen, Anders, Rakićević, Nemanja, Grewe, Dominik, Kumar, Aviral, Winkler, Stephanie, Caton, Jonathan, Brock, Andrew, Dalmia, Sid, Sheahan, Hannah, Barr, Iain, Miao, Yingjie, Natsev, Paul, Devlin, Jacob, Behbahani, Feryal, Prost, Flavien, Sun, Yanhua, Myaskovsky, Artiom, Pillai, Thanumalayan Sankaranarayana, Hurt, Dan, Lazaridou, Angeliki, Xiong, Xi, Zheng, Ce, Pardo, Fabio, Li, Xiaowei, Horgan, Dan, Stanton, Joe, Ambar, Moran, Xia, Fei, Lince, Alejandro, Wang, Mingqiu, Mustafa, Basil, Webson, Albert, Lee, Hyo, Anil, Rohan, Wicke, Martin, Dozat, Timothy, Sinha, Abhishek, Piqueras, Enrique, Dabir, Elahe, Upadhyay, Shyam, Boral, Anudhyan, Hendricks, Lisa Anne, Fry, Corey, Djolonga, Josip, Su, Yi, Walker, Jake, Labanowski, Jane, Huang, Ronny, Misra, Vedant, Chen, Jeremy, Skerry-Ryan, RJ, Singh, Avi, Rijhwani, Shruti, Yu, Dian, Castro-Ros, Alex, Changpinyo, Beer, Datta, Romina, Bagri, Sumit, Hrafnkelsson, Arnar Mar, Maggioni, Marcello, Zheng, Daniel, Sulsky, Yury, Hou, Shaobo, Paine, Tom Le, Yang, Antoine, Riesa, Jason, Rogozinska, Dominika, Marcus, Dror, Badawy, Dalia El, Zhang, Qiao, Wang, Luyu, Miller, Helen, Greer, Jeremy, Sjos, Lars Lowe, Nova, Azade, Zen, Heiga, Chaabouni, Rahma, Rosca, Mihaela, Jiang, Jiepu, Chen, Charlie, Liu, Ruibo, Sainath, Tara, Krikun, Maxim, Polozov, Alex, Lespiau, Jean-Baptiste, Newlan, Josh, Cankara, Zeyncep, Kwak, Soo, Xu, Yunhan, Chen, Phil, Coenen, Andy, Meyer, Clemens, Tsihlas, Katerina, Ma, Ada, Gottweis, Juraj, Xing, Jinwei, Gu, Chenjie, Miao, Jin, Frank, Christian, Cankara, Zeynep, Ganapathy, Sanjay, Dasgupta, Ishita, Hughes-Fitt, Steph, Chen, Heng, Reid, David, Rong, Keran, Fan, Hongmin, van Amersfoort, Joost, Zhuang, Vincent, Cohen, Aaron, Gu, Shixiang Shane, Mohananey, Anhad, Ilic, Anastasija, Tobin, Taylor, Wieting, John, Bortsova, Anna, Thacker, Phoebe, Wang, Emma, Caveness, Emily, Chiu, Justin, Sezener, Eren, Kaskasoli, Alex, Baker, Steven, Millican, Katie, Elhawaty, Mohamed, Aisopos, Kostas, Lebsack, Carl, Byrd, Nathan, Dai, Hanjun, Jia, Wenhao, Wiethoff, Matthew, Davoodi, Elnaz, Weston, Albert, Yagati, Lakshman, Ahuja, Arun, Gao, Isabel, Pundak, Golan, Zhang, Susan, Azzam, Michael, Sim, Khe Chai, Caelles, Sergi, Keeling, James, Sharma, Abhanshu, Swing, Andy, Li, YaGuang, Liu, Chenxi, Bostock, Carrie Grimes, Bansal, Yamini, Nado, Zachary, Anand, Ankesh, Lipschultz, Josh, Karmarkar, Abhijit, Proleev, Lev, Ittycheriah, Abe, Yeganeh, Soheil Hassas, Polovets, George, Faust, Aleksandra, Sun, Jiao, Rrustemi, Alban, Li, Pen, Shivanna, Rakesh, Liu, Jeremiah, Welty, Chris, Lebron, Federico, Baddepudi, Anirudh, Krause, Sebastian, Parisotto, Emilio, Soricut, Radu, Xu, Zheng, Bloxwich, Dawn, Johnson, Melvin, Neyshabur, Behnam, Mao-Jones, Justin, Wang, Renshen, Ramasesh, Vinay, Abbas, Zaheer, Guez, Arthur, Segal, Constant, Nguyen, Duc Dung, Svensson, James, Hou, Le, York, Sarah, Milan, Kieran, Bridgers, Sophie, Gworek, Wiktor, Tagliasacchi, Marco, Lee-Thorp, James, Chang, Michael, Guseynov, Alexey, Hartman, Ale Jakse, Kwong, Michael, Zhao, Ruizhe, Kashem, Sheleem, Cole, Elizabeth, Miech, Antoine, Tanburn, Richard, Phuong, Mary, Pavetic, Filip, Cevey, Sebastien, Comanescu, Ramona, Ives, Richard, Yang, Sherry, Du, Cosmo, Li, Bo, Zhang, Zizhao, Iinuma, Mariko, Hu, Clara Huiyi, Roy, Aurko, Bijwadia, Shaan, Zhu, Zhenkai, Martins, Danilo, Saputro, Rachel, Gergely, Anita, Zheng, Steven, Jia, Dawei, Antonoglou, Ioannis, Sadovsky, Adam, Gu, Shane, Bi, Yingying, Andreev, Alek, Samangooei, Sina, Khan, Mina, Kocisky, Tomas, Filos, Angelos, Kumar, Chintu, Bishop, Colton, Yu, Adams, Hodkinson, Sarah, Mittal, Sid, Shah, Premal, Moufarek, Alexandre, Cheng, Yong, Bloniarz, Adam, Lee, Jaehoon, Pejman, Pedram, Michel, Paul, Spencer, Stephen, Feinberg, Vladimir, Xiong, Xuehan, Savinov, Nikolay, Smith, Charlotte, Shakeri, Siamak, Tran, Dustin, Chesus, Mary, Bohnet, Bernd, Tucker, George, von Glehn, Tamara, Muir, Carrie, Mao, Yiran, Kazawa, Hideto, Slone, Ambrose, Soparkar, Kedar, Shrivastava, Disha, Cobon-Kerr, James, Sharman, Michael, Pavagadhi, Jay, Araya, Carlos, Misiunas, Karolis, Ghelani, Nimesh, Laskin, Michael, Barker, David, Li, Qiujia, Briukhov, Anton, Houlsby, Neil, Glaese, Mia, Lakshminarayanan, Balaji, Schucher, Nathan, Tang, Yunhao, Collins, Eli, Lim, Hyeontaek, Feng, Fangxiaoyu, Recasens, Adria, Lai, Guangda, Magni, Alberto, De Cao, Nicola, Siddhant, Aditya, Ashwood, Zoe, Orbay, Jordi, Dehghani, Mostafa, Brennan, Jenny, He, Yifan, Xu, Kelvin, Gao, Yang, Saroufim, Carl, Molloy, James, Wu, Xinyi, Arnold, Seb, Chang, Solomon, Schrittwieser, Julian, Buchatskaya, Elena, Radpour, Soroush, Polacek, Martin, Giordano, Skye, Bapna, Ankur, Tokumine, Simon, Hellendoorn, Vincent, Sottiaux, Thibault, Cogan, Sarah, Severyn, Aliaksei, Saleh, Mohammad, Thakoor, Shantanu, Shefey, Laurent, Qiao, Siyuan, Gaba, Meenu, Chang, Shuo-yiin, Swanson, Craig, Zhang, Biao, Lee, Benjamin, Rubenstein, Paul Kishan, Song, Gan, Kwiatkowski, Tom, Koop, Anna, Kannan, Ajay, Kao, David, Schuh, Parker, Stjerngren, Axel, Ghiasi, Golnaz, Gibson, Gena, Vilnis, Luke, Yuan, Ye, Ferreira, Felipe Tiengo, Kamath, Aishwarya, Klimenko, Ted, Franko, Ken, Xiao, Kefan, Bhattacharya, Indro, Patel, Miteyan, Wang, Rui, Morris, Alex, Strudel, Robin, Sharma, Vivek, Choy, Peter, Hashemi, Sayed Hadi, Landon, Jessica, Finkelstein, Mara, Jhakra, Priya, Frye, Justin, Barnes, Megan, Mauger, Matthew, Daun, Dennis, Baatarsukh, Khuslen, Tung, Matthew, Farhan, Wael, Michalewski, Henryk, Viola, Fabio, Quitry, Felix de Chaumont, Lan, Charline Le, Hudson, Tom, Wang, Qingze, Fischer, Felix, Zheng, Ivy, White, Elspeth, Dragan, Anca, Alayrac, Jean-baptiste, Ni, Eric, Pritzel, Alexander, Iwanicki, Adam, Isard, Michael, Bulanova, Anna, Zilka, Lukas, Dyer, Ethan, Sachan, Devendra, Srinivasan, Srivatsan, Muckenhirn, Hannah, Cai, Honglong, Mandhane, Amol, Tariq, Mukarram, Rae, Jack W., Wang, Gary, Ayoub, Kareem, FitzGerald, Nicholas, Zhao, Yao, Han, Woohyun, Alberti, Chris, Garrette, Dan, Krishnakumar, Kashyap, Gimenez, Mai, Levskaya, Anselm, Sohn, Daniel, Matak, Josip, Iturrate, Inaki, Chang, Michael B., Xiang, Jackie, Cao, Yuan, Ranka, Nishant, Brown, Geoff, Hutter, Adrian, Mirrokni, Vahab, Chen, Nanxin, Yao, Kaisheng, Egyed, Zoltan, Galilee, Francois, Liechty, Tyler, Kallakuri, Praveen, Palmer, Evan, Ghemawat, Sanjay, Liu, Jasmine, Tao, David, Thornton, Chloe, Green, Tim, Jasarevic, Mimi, Lin, Sharon, Cotruta, Victor, Tan, Yi-Xuan, Fiedel, Noah, Yu, Hongkun, Chi, Ed, Neitz, Alexander, Heitkaemper, Jens, Sinha, Anu, Zhou, Denny, Sun, Yi, Kaed, Charbel, Hulse, Brice, Mishra, Swaroop, Georgaki, Maria, Kudugunta, Sneha, Farabet, Clement, Shafran, Izhak, Vlasic, Daniel, Tsitsulin, Anton, Ananthanarayanan, Rajagopal, Carin, Alen, Su, Guolong, Sun, Pei, V, Shashank, Carvajal, Gabriel, Broder, Josef, Comsa, Iulia, Repina, Alena, Wong, William, Chen, Warren Weilun, Hawkins, Peter, Filonov, Egor, Loher, Lucia, Hirnschall, Christoph, Wang, Weiyi, Ye, Jingchen, Burns, Andrea, Cate, Hardie, Wright, Diana Gage, Piccinini, Federico, Zhang, Lei, Lin, Chu-Cheng, Gog, Ionel, Kulizhskaya, Yana, Sreevatsa, Ashwin, Song, Shuang, Cobo, Luis C., Iyer, Anand, Tekur, Chetan, Garrido, Guillermo, Xiao, Zhuyun, Kemp, Rupert, Zheng, Huaixiu Steven, Li, Hui, Agarwal, Ananth, Ngani, Christel, Goshvadi, Kati, Santamaria-Fernandez, Rebeca, Fica, Wojciech, Chen, Xinyun, Gorgolewski, Chris, Sun, Sean, Garg, Roopal, Ye, Xinyu, Eslami, S. M. Ali, Hua, Nan, Simon, Jon, Joshi, Pratik, Kim, Yelin, Tenney, Ian, Potluri, Sahitya, Thiet, Lam Nguyen, Yuan, Quan, Luisier, Florian, Chronopoulou, Alexandra, Scellato, Salvatore, Srinivasan, Praveen, Chen, Minmin, Koverkathu, Vinod, Dalibard, Valentin, Xu, Yaming, Saeta, Brennan, Anderson, Keith, Sellam, Thibault, Fernando, Nick, Huot, Fantine, Jung, Junehyuk, Varadarajan, Mani, Quinn, Michael, Raul, Amit, Le, Maigo, Habalov, Ruslan, Clark, Jon, Jalan, Komal, Bullard, Kalesha, Singhal, Achintya, Luong, Thang, Wang, Boyu, Rajayogam, Sujeevan, Eisenschlos, Julian, Jia, Johnson, Finchelstein, Daniel, Yakubovich, Alex, Balle, Daniel, Fink, Michael, Agarwal, Sameer, Li, Jing, Dvijotham, Dj, Pal, Shalini, Kang, Kai, Konzelmann, Jaclyn, Beattie, Jennifer, Dousse, Olivier, Wu, Diane, Crocker, Remi, Elkind, Chen, Jonnalagadda, Siddhartha Reddy, Lee, Jong, Holtmann-Rice, Dan, Kallarackal, Krystal, Liu, Rosanne, Vnukov, Denis, Vats, Neera, Invernizzi, Luca, Jafari, Mohsen, Zhou, Huanjie, Taylor, Lilly, Prendki, Jennifer, Wu, Marcus, Eccles, Tom, Liu, Tianqi, Kopparapu, Kavya, Beaufays, Francoise, Angermueller, Christof, Marzoca, Andreea, Sarcar, Shourya, Dib, Hilal, Stanway, Jeff, Perbet, Frank, Trdin, Nejc, Sterneck, Rachel, Khorlin, Andrey, Li, Dinghua, Wu, Xihui, Goenka, Sonam, Madras, David, Goldshtein, Sasha, Gierke, Willi, Zhou, Tong, Liu, Yaxin, Liang, Yannie, White, Anais, Li, Yunjie, Singh, Shreya, Bahargam, Sanaz, Epstein, Mark, Basu, Sujoy, Lao, Li, Ozturel, Adnan, Crous, Carl, Zhai, Alex, Lu, Han, Tung, Zora, Gaur, Neeraj, Walton, Alanna, Dixon, Lucas, Zhang, Ming, Globerson, Amir, Uy, Grant, Bolt, Andrew, Wiles, Olivia, Nasr, Milad, Shumailov, Ilia, Selvi, Marco, Piccinno, Francesco, Aguilar, Ricardo, McCarthy, Sara, Khalman, Misha, Shukla, Mrinal, Galic, Vlado, Carpenter, John, Villela, Kevin, Zhang, Haibin, Richardson, Harry, Martens, James, Bosnjak, Matko, Belle, Shreyas Rammohan, Seibert, Jeff, Alnahlawi, Mahmoud, McWilliams, Brian, Singh, Sankalp, Louis, Annie, Ding, Wen, Popovici, Dan, Simicich, Lenin, Knight, Laura, Mehta, Pulkit, Gupta, Nishesh, Shi, Chongyang, Fatehi, Saaber, Mitrovic, Jovana, Grills, Alex, Pagadora, Joseph, Petrova, Dessie, Eisenbud, Danielle, Zhang, Zhishuai, Yates, Damion, Mittal, Bhavishya, Tripuraneni, Nilesh, Assael, Yannis, Brovelli, Thomas, Jain, Prateek, Velimirovic, Mihajlo, Akbulut, Canfer, Mu, Jiaqi, Macherey, Wolfgang, Kumar, Ravin, Xu, Jun, Qureshi, Haroon, Comanici, Gheorghe, Wiesner, Jeremy, Gong, Zhitao, Ruddock, Anton, Bauer, Matthias, Felt, Nick, GP, Anirudh, Arnab, Anurag, Zelle, Dustin, Rothfuss, Jonas, Rosgen, Bill, Shenoy, Ashish, Seybold, Bryan, Li, Xinjian, Mudigonda, Jayaram, Erdogan, Goker, Xia, Jiawei, Simsa, Jiri, Michi, Andrea, Yao, Yi, Yew, Christopher, Kan, Steven, Caswell, Isaac, Radebaugh, Carey, Elisseeff, Andre, Valenzuela, Pedro, McKinney, Kay, Paterson, Kim, Cui, Albert, Latorre-Chimoto, Eri, Kim, Solomon, Zeng, William, Durden, Ken, Ponnapalli, Priya, Sosea, Tiberiu, Choquette-Choo, Christopher A., Manyika, James, Robenek, Brona, Vashisht, Harsha, Pereira, Sebastien, Lam, Hoi, Velic, Marko, Owusu-Afriyie, Denese, Lee, Katherine, Bolukbasi, Tolga, Parrish, Alicia, Lu, Shawn, Park, Jane, Venkatraman, Balaji, Talbert, Alice, Rosique, Lambert, Cheng, Yuchung, Sozanschi, Andrei, Paszke, Adam, Kumar, Praveen, Austin, Jessica, Li, Lu, Salama, Khalid, Kim, Wooyeol, Dukkipati, Nandita, Baryshnikov, Anthony, Kaplanis, Christos, Sheng, XiangHai, Chervonyi, Yuri, Unlu, Caglar, Casas, Diego de Las, Askham, Harry, Tunyasuvunakool, Kathryn, Gimeno, Felix, Poder, Siim, Kwak, Chester, Miecnikowski, Matt, Dimitriev, Alek, Parisi, Aaron, Liu, Dangyi, Tsai, Tomy, Shevlane, Toby, Kouridi, Christina, Garmon, Drew, Goedeckemeyer, Adrian, Brown, Adam R., Vijayakumar, Anitha, Elqursh, Ali, Jazayeri, Sadegh, Huang, Jin, Carthy, Sara Mc, Hoover, Jay, Kim, Lucy, Kumar, Sandeep, Chen, Wei, Biles, Courtney, Bingham, Garrett, Rosen, Evan, Wang, Lisa, Tan, Qijun, Engel, David, Pongetti, Francesco, de Cesare, Dario, Hwang, Dongseong, Yu, Lily, Pullman, Jennifer, Narayanan, Srini, Levin, Kyle, Gopal, Siddharth, Li, Megan, Aharoni, Asaf, Trinh, Trieu, Lo, Jessica, Casagrande, Norman, Vij, Roopali, Matthey, Loic, Ramadhana, Bramandia, Matthews, Austin, Carey, CJ, Johnson, Matthew, Goranova, Kremena, Shah, Rohin, Ashraf, Shereen, Dasgupta, Kingshuk, Larsen, Rasmus, Wang, Yicheng, Vuyyuru, Manish Reddy, Jiang, Chong, Ijazi, Joana, Osawa, Kazuki, Smith, Celine, Boppana, Ramya Sree, Bilal, Taylan, Koizumi, Yuma, Xu, Ying, Altun, Yasemin, Shabat, Nir, Bariach, Ben, Korchemniy, Alex, Choo, Kiam, Ronneberger, Olaf, Iwuanyanwu, Chimezie, Zhao, Shubin, Soergel, David, Hsieh, Cho-Jui, Cai, Irene, Iqbal, Shariq, Sundermeyer, Martin, Chen, Zhe, Bursztein, Elie, Malaviya, Chaitanya, Biadsy, Fadi, Shroff, Prakash, Dhillon, Inderjit, Latkar, Tejasi, Dyer, Chris, Forbes, Hannah, Nicosia, Massimo, Nikolaev, Vitaly, Greene, Somer, Georgiev, Marin, Wang, Pidong, Martin, Nina, Sedghi, Hanie, Zhang, John, Banzal, Praseem, Fritz, Doug, Rao, Vikram, Wang, Xuezhi, Zhang, Jiageng, Patraucean, Viorica, Du, Dayou, Mordatch, Igor, Jurin, Ivan, Liu, Lewis, Dubey, Ayush, Mohan, Abhi, Nowakowski, Janek, Ion, Vlad-Doru, Wei, Nan, Tojo, Reiko, Raad, Maria Abi, Hudson, Drew A., Keshava, Vaishakh, Agrawal, Shubham, Ramirez, Kevin, Wu, Zhichun, Nguyen, Hoang, Liu, Ji, Sewak, Madhavi, Petrini, Bryce, Choi, DongHyun, Philips, Ivan, Wang, Ziyue, Bica, Ioana, Garg, Ankush, Wilkiewicz, Jarek, Agrawal, Priyanka, Guo, Danhao, Xue, Emily, Shaik, Naseer, Leach, Andrew, Khan, Sadh MNM, Wiesinger, Julia, Jerome, Sammy, Chakladar, Abhishek, Wang, Alek Wenjiao, Ornduff, Tina, Abu, Folake, Ghaffarkhah, Alireza, Wainwright, Marcus, Cortes, Mario, Liu, Frederick, Maynez, Joshua, Terzis, Andreas, Samangouei, Pouya, Mansour, Riham, Kępa, Tomasz, Aubet, François-Xavier, Algymr, Anton, Banica, Dan, Weisz, Agoston, Orban, Andras, Senges, Alexandre, Andrejczuk, Ewa, Geller, Mark, Santo, Niccolo Dal, Anklin, Valentin, Merey, Majd Al, Baeuml, Martin, Strohman, Trevor, Bai, Junwen, Petrov, Slav, Wu, Yonghui, Hassabis, Demis, Kavukcuoglu, Koray, Dean, Jeffrey, and Vinyals, Oriol

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

Published
2024

24. On the Development of the Concept of the Weak Cosmic Censorship Conjecture in General Relativity

Author

Erdogan, Ahmet Cem

Subjects
General Relativity and Quantum Cosmology, High Energy Physics - Theory
Abstract

In this thesis, we scrutinize the literature to trace the evolution of the concepts surrounding the weak cosmic censorship conjecture and the attempts to prove or disprove it. We discuss its development over the years, its conceptual transformations, and the various thought experiments constructed to validate or invalidate the weak cosmic censorship conjecture. We also discuss possible connections with blackhole thermodynamics, and end with an evaluation of the current status about the subject, and finally, speculations about the future., Comment: Master Thesis

Published
2024

32. The prevalence and topographic distribution of penile calcification in a large cohort: a retrospective cross-sectional study

Author

Baran, Caner, Culha, Mehmet Gokhan, Bayraktarli, Recep Yilmaz, Ozgur, Berat Cem, Bayraktar, Arif Bedirhan, Oktem, Cagri, Cek, Hakki Mete, Akgul, Burak, Alkan, Aykut, Aglamis, Erdogan, Ogras, Mehmet Sezai, Kacan, Turgay, Kaya, Coskun, Aydin, Mehmet Erhan, Ergun, Kasim Emre, Yoldas, Mehmet, Cakan, Murat, Durak, Huseyin Mert, Gonultas, Serkan, Bulut, Berk, Dogan, Kazim, Efiloglu, Ozgur, Akalin, Mustafa Kaan, Yildirim, Asif, Danacioglu, Yavuz Onur, Demirelli, Erhan, Balci, Mustafa Bahadir Can, Gezmis, Cem Tugrul, Tuncer, Murat, Can, Murat, Kirdag, Mustafa Koray, Aydin, Mustafa, Yazar, Selim, Calik, Gokhan, Guzelburc, Vahit, Guzel, Ahmet, Otunctemur, Alper, and Kadioglu, Ates

Published
2024
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34. Dyslipidemia in children with chronic kidney disease—findings from the Cardiovascular Comorbidity in Children with Chronic Kidney Disease (4C) study

Author

Mencarelli, Francesca, Azukaitis, Karolis, Kirchner, Marietta, Bayazit, Aysun, Duzova, Ali, Canpolat, Nur, Bulut, Ipek Kaplan, Obrycki, Lukasz, Ranchin, Bruno, Shroff, Rukshana, Caliskan, Salim, Candan, Cengiz, Yilmaz, Alev, Özcakar, Zeynep Birsin, Halpay, Harika, Kiyak, Aysel, Erdogan, Hakan, Gellermann, Jutta, Balat, Ayse, Melk, Anette, Schaefer, Franz, and Querfeld, Uwe

Published
2024
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37. An energy-based material model for the simulation of shape memory alloys under complex boundary value problems

Author

Erdogan, C., Bode, T., and Junker, P.

Subjects
Computer Science - Computational Engineering, Finance, and Science
Abstract

Shape memory alloys are remarkable 'smart' materials used in a broad spectrum of applications, ranging from aerospace to robotics, thanks to their unique thermomechanical coupling capabilities. Given the complex properties of shape memory alloys, which are largely influenced by thermal and mechanical loads, as well as their loading history, predicting their behavior can be challenging. Consequently, there exists a pronounced demand for an efficient material model to simulate the behavior of these alloys. This paper introduces a material model rooted in Hamilton's principle. The key advantages of the presented material model encompass a more accurate depiction of the internal variable evolution and heightened robustness. As such, the proposed material model signifies an advancement in the realistic and efficient simulation of shape memory alloys.

Published
2024

38. FuseFormer: A Transformer for Visual and Thermal Image Fusion

Author

Erdogan, Aytekin and Akagündüz, Erdem

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Due to the lack of a definitive ground truth for the image fusion problem, the loss functions are structured based on evaluation metrics, such as the structural similarity index measure (SSIM). However, in doing so, a bias is introduced toward the SSIM and, consequently, the input visual band image. The objective of this study is to propose a novel methodology for the image fusion problem that mitigates the limitations associated with using classical evaluation metrics as loss functions. Our approach integrates a transformer-based multi-scale fusion strategy that adeptly addresses local and global context information. This integration not only refines the individual components of the image fusion process but also significantly enhances the overall efficacy of the method. Our proposed method follows a two-stage training approach, where an auto-encoder is initially trained to extract deep features at multiple scales in the first stage. For the second stage, we integrate our fusion block and change the loss function as mentioned. The multi-scale features are fused using a combination of Convolutional Neural Networks (CNNs) and Transformers. The CNNs are utilized to capture local features, while the Transformer handles the integration of general context features. Through extensive experiments on various benchmark datasets, our proposed method, along with the novel loss function definition, demonstrates superior performance compared to other competitive fusion algorithms., Comment: 8 pages, 6 figures, 6 tables

Published
2024

39. Poisoning $\times$ Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks

Author

Erdogan, Ege, Geisler, Simon, and Günnemann, Stephan

Subjects
Computer Science - Machine Learning
Abstract

It is well-known that deep learning models are vulnerable to small input perturbations. Such perturbed instances are called adversarial examples. Adversarial examples are commonly crafted to fool a model either at training time (poisoning) or test time (evasion). In this work, we study the symbiosis of poisoning and evasion. We show that combining both threat models can substantially improve the devastating efficacy of adversarial attacks. Specifically, we study the robustness of Graph Neural Networks (GNNs) under structure perturbations and devise a memory-efficient adaptive end-to-end attack for the novel threat model using first-order optimization., Comment: NeurIPS 2023 New Frontiers in Graph Learning Workshop (NeurIPS GLFrontiers 2023)

Published
2023

40. Accelerated Real-Life (ARL) Testing and Characterization of Automotive LiDAR Sensors to facilitate the Development and Validation of Enhanced Sensor Models

Author

Kettelgerdes, Marcel, Hillmann, Tjorven, Hirmer, Thomas, Erdogan, Hüseyin, Wunderle, Bernhard, and Elger, Gordon

Subjects
Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

In the realm of automated driving simulation and sensor modeling, the need for highly accurate sensor models is paramount for ensuring the reliability and safety of advanced driving assistance systems (ADAS). Hence, numerous works focus on the development of high-fidelity models of ADAS sensors, such as camera, Radar as well as modern LiDAR systems to simulate the sensor behavior in different driving scenarios, even under varying environmental conditions, considering for example adverse weather effects. However, aging effects of sensors, leading to suboptimal system performance, are mostly overlooked by current simulation techniques. This paper introduces a cutting-edge Hardware-in-the-Loop (HiL) test bench designed for the automated, accelerated aging and characterization of Automotive LiDAR sensors. The primary objective of this research is to address the aging effects of LiDAR sensors over the product life cycle, specifically focusing on aspects such as laser beam profile deterioration, output power reduction and intrinsic parameter drift, which are mostly neglected in current sensor models. By that, this proceeding research is intended to path the way, not only towards identifying and modeling respective degradation effects, but also to suggest quantitative model validation metrics., Comment: 9th Symposium Driving Simulation 2023, Brunswick, Germany

Published
2023

41. Free-Space Optical (FSO) Satellite Networks Performance Analysis: Transmission Power, Latency, and Outage Probability

Author

Liang, Jintao, Chaudhry, Aizaz U., Erdogan, Eylem, Yanikomeroglu, Halim, Kurt, Gunes Karabulut, Hu, Peng, Ahmed, Khaled, and Martel, Stephane

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

In free-space optical satellite networks (FSOSNs), satellites can have different laser inter-satellite link (LISL) ranges for connectivity. Greater LISL ranges can reduce network latency of the path but can also result in an increase in transmission power for satellites on the path. Consequently, this tradeoff between satellite transmission power and network latency should be investigated, and in this work we examine it in FSOSNs drawing on the Starlink Phase 1 Version 3 and Kuiper Shell 2 constellations for different LISL ranges and different inter-continental connections. We use appropriate system models for calculating the average satellite transmission power and network latency. The results show that the mean network latency decreases and mean average satellite transmission power increases with an increase in LISL range. For the Toronto--Sydney inter-continental connection in an FSOSN with Starlink's Phase 1 Version 3 constellation, when the LISL range is approximately 2,900 km, the mean network latency and mean average satellite transmission power intersect are approximately 135 ms and 380 mW, respectively. For an FSOSN with the Kuiper Shell 2 constellation in this inter-continental connection, this LISL range is around 3,800 km, and the two parameters are approximately 120 ms and 700 mW, respectively. For the Toronto--Istanbul and Toronto--London inter-continental connections, the LISL ranges at the intersection are different and vary from 2,600 km to 3,400 km. Furthermore, we analyze outage probability performance of optical uplink/downlink due to atmosphere attenuation and turbulence., Comment: Accepted for publication in IEEE Open Journal of Vehicular Technology

Published
2023

42. Exploring Epistemic Aspects of Engineering for K-12 Science and Engineering Education

Author

Ezgi Yesilyurt, Hasan Deniz, and Erdogan Kaya

Abstract

Background: Since the advent of the Next Generation Science Standards (NGSS), there has been an increasing focus on engineering in K-12 education. As educators and researchers have gained a better understanding of the nature of engineering in the decade following the release of the NGSS, there are new opportunities for growth and complexity. Purpose: This study used an epistemological approach to seek insights from experts in the science and engineering communities about the prominent aspects of engineering for K-12 education. Design/Method: This mixed-methods study employed a three-round Delphi study and a focus group meeting to elicit experts' opinions on the epistemic aspects of engineering that could shed light on the nature of engineering knowledge and practices. Constructivist grounded theory methodology was used to identify preliminary themes concerning the epistemic aspects of engineering and then to develop main themes by combining relevant themes together. Results: The analysis process yielded 21 preliminary themes that reflect key ideas about the engineering knowledge base, engineering design activities, and values and norms of the engineering community. Additionally, the current study identified eight main themes that captured the broader patterns in the data. Conclusions: The outcome of this study, including the preliminary themes and main themes concerning the epistemic aspects of engineering, could serve as a conceptual tool for establishing and improving students' conceptions of engineering and as a guide for designing and implementing engineering design activities in K-12 education settings.

Published
2024
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46. Technostress in Medical Students during Pandemic-Prompted Distance Education: Adaptation of Technostress Scale Based on Person-Environment Misfit Theory

Author

Erdogan, Aliriza, Öztürk, Mustafa, Erdogan, Pinar, Zor, Ramazan Kürsad, Çinaroglu, Selim, Öztorun, Kenan, and Kayabas, Üner

Abstract

This paper investigates the phenomenon of technostress in medical students and its predictors during pandemic-prompted distance education. From a sample of 259 students in a school of medicine at a public university in Turkey, the data were collected using convenience sampling through an online questionnaire based on person-environment misfit theory and were analyzed by means of descriptive and inferential statistics. Descriptively, the participant students reported experiencing relatively mild level of technostress. As a result of the multiple regression analysis, regular attendance and participation, adequate learning environment, perceived negativity of distance education, perceived need for psychological support, and year at medical school were all found to be significant predictors of technostress in medical students. This is the first study evaluating technostress on medical students. Students who do not attend classes regularly, do not have an adequate learning environment, have higher degree of perceived negativity of distance education, have need for psychological support and are at higher grade levels experience more technostress. Finally, according to the person-environment misfit theory the major component of technostress in medical students originated from the tool-related component. Although the level of technostress was relatively low, technological tools generate pressure even on the digital native generation.

Published
2022

47. Investigation of Doctoral Theses in the Field of Physics Education in Turkiye in Terms of Theoretical Framework and Conceptual Framework

Author

Erdogan, Sükran

Abstract

The theoretical framework and conceptual framework help to determine the purpose of scientific research, facilitate the analysis of data, organize the thoughts of researchers, increase the reliability of research, and ensure that research is shared in a wide area. Although these two concepts seem to be basically the same, they have structural differences and the roles they assume are not same. Despite this, it is seen that many researchers use these two concepts when structuring their academic studies without fully distinguishing between them. In this research, 87 doctoral theses in the field of physics education in Turkiye from past to present were examined in terms of theoretical framework and conceptual framework. In the study, the case study, which is one of the qualitative research approaches, was used and the obtained data were analyzed with descriptive method. As a result of the findings obtained, it was seen that all of the theses had at least one theoretical framework or conceptual framework, and instead of these two titles, five different titles were used: theoretical framework in 29 of 87 theses, conceptual framework in 18, introduction in 18, literature review in 15 and general information in 7. When the distribution of theses in terms of theoretical framework and conceptual framework was analyzed, 64 of the 87 theses were evaluated under the titles of conceptual framework and 23 of them were evaluated under theoretical framework titles. In addition, when the distribution of approaches, theories, models, strategies and methods used in the theses was examined, it was concluded that they were mostly accumulated in the learning-teaching, conceptual understanding, cognitive dimension and affective dimension categories.

Published
2023

48. Effects of Using the Staged Self-Directed Learning Model at Distance English Learning

Author

Dogru, Erdogan and Özen, Rasit

Abstract

This research aimed to investigate the effects of giving learners self directed learning (SDL) instruction based on the Staged Self-Directed Learning Model (SSDLM) during distance English learning on their SDL readiness, achievement, attitudes toward English course and distance education. The study sample consisted of one hundred forty-six freshmen enrolled in two different departments at the Faculty of Education in Bolu Abant Izzet Baysal University in Türkiye in 2017/2018 Academic Year. All the participants took part in a twelve-week quasi-experimental study with pre-test and post-test. The English course program was common to both groups, but the SDL instruction program prepared according to the principles of SSDLM was unique to the experimental group only. The research data were collected using the Self-Directed Learning Readiness Scale, Attitude toward English Courses Scale, Attitude toward Distance Education Scale, and Achievement Test developed specifically for this English course. The obtained data were analyzed through the independent samples t-tests with bootstrap technique. The findings revealed no observable difference in SDL readiness scores between the groups. It may be because changing the way people learn requires longer time. However, remarkable differences in some other variables were observed in favor of the experimental group. These variables were attitude toward learning, importance of English and attitude toward distance education. These findings can be evaluated as the indication of some developments on the brink. All the findings were discussed in detail with this in mind and, suggestions were made accordingly.

Published
2023

49. Evaluation of the Training to Make Preservice Science Teachers Use Web 2.0 Tools during Teaching

Author

Erdogan, Ayberk and Yildirim, Nagihan

Abstract

This study examines the effect of a training program on pre-service teachers' ability to design materials using Web 2.0 tools by applying training to pre-service science teachers to use Web 2.0 tools. In the research, a holistic single case study method was used. Semi-structured interviews, "Teachers' Digital Teaching Material Development Self-Efficacy Scale" and document analysis were used as data collection tools. The sample of the research consists of 16 pre-service science teachers studying in the Science teaching program of the Faculty of Education of Recep Tayyip Erdogan University in the fall semester of the academic year 2021-2022. The "Teachers' Digital Teaching Material Development Self-Efficacy Scale" was applied to the pre-service teachers twice as a pre- and post-test. The training for the teaching of Web 2.0 tools was completed during a total of 25 40-minute lessons outside of the participants' class hours and in three weeks. At the end of the study, improved teaching Web 2.0 tools for education, pre-service teachers teaching material that has a positive impact on developing digital competencies, to increase their awareness of Web 2.0 tools for use in the learning process, it provides enough about to be able to use Web 2.0 tools to start to see and realize the importance of the concept of Web 2.0 tools in teaching that the use is concluded. In accordance with these results, in order to use the Web 2.0 tools for research, pre-service teachers, elective courses in undergraduate programs, etc. the proposals have been terminated.

Published
2023

50. The Effect of Out-of-School Learning Activities on Gifted Students' Affective and Behavioral Tendencies towards the Environment

Author

Ayaydin, Yakup, Sesen, Burçin Acar, Erdogan, Sezen Camci, Ün, Duygu, and Özçiris, Hakan

Abstract

This study investigates the effect of out-of-school activities on gifted students' affective and behavioral tendencies. Mixed method research with a single group pre and post-test experimental design was used in this study. Twenty-five gifted students from Turkey-Istanbul participated in this study. "Environmental Affect Scale" and "Environmental Behavior Scale" were used, and semi-structured interviews were conducted. Results showed that out-of-school activities positively affected the students' affective and behavioral tendencies toward the environment. It was also found that the awareness of students on environmental problems increased, and they began to produce their solutions to these problems.

Published
2023

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