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  1. Article ; Online: SAĞLIK KURUMLARININ ETKİNLİKLERİNİN VERİ ZARFLAMA ANALİZİ İLE DEĞERLENDİRİLMESİ

    Faruk YILMAZ / İlhan Kerem ŞENEL

    Sosyal Güvence Dergisi, Vol 0, Iss 15, Pp 63-

    2019  Volume 88

    Abstract: Günümüzde, mülkiyetine bakılmaksızın tüm işletmeler kaçınılmaz bir rekabet ortamı içerisinde faaliyetlerini gerçekleştirmektedir. Bu durum işletmeleri, rekabet gücü elde etmelerini sağlayacak bazı önlemler almaya ve bu yolla maliyetlerini azaltmaya ... ...

    Abstract Günümüzde, mülkiyetine bakılmaksızın tüm işletmeler kaçınılmaz bir rekabet ortamı içerisinde faaliyetlerini gerçekleştirmektedir. Bu durum işletmeleri, rekabet gücü elde etmelerini sağlayacak bazı önlemler almaya ve bu yolla maliyetlerini azaltmaya yönlendirmiştir. Bu bağlamda sağlık hizmetleri sunumunda maliyetin önemli bir kısmı komplike vakaların ele alındığı eğitim ve araştırma statüsündeki hastanelerde gerçekleşmektedir. Bu nedenle çalışmada Ankara, İstanbul ve İzmir’de faaliyet gösteren eğitim ve araştırma statüsüne sahip Genel Eğitim Hastaneleri, Üniversite Hastaneleri ve Sağlık Bakanlığı-Üniversite Ortak Hastaneleri değerlendirilmiştir. Bu çalışmada ele alınan 45 hastanenin görece etkinliklerinin değerlendirilmesinde matematiksel programlama tabanlı Veri Zarflama Analizi (VZA) kullanılmıştır. Analizde girdi değişkeni olarak uzman hekim, hemşire, diğer sağlık personeli ve yatak sayısı; çıktı değişkeni olarak ayaktan muayene sayısı, taburcu olan hasta sayısı, yatak doluluk oranı ve ameliyat sayısı belirlenmiştir. Analiz sonucunda 13 hastanenin (,89) toplam etkin, 18 hastanenin () teknik etkin ve 14 hastanenin (,11) ise ölçek etkin olduğu saptanmıştır. Ayrıca analizde etkin olmayan hastanelerin atıl değerleri hesaplanmış ve etkinlik hedefleri oluşturulmuştur. Sonuç olarak eğitim ve araştırma statüsüne sahip bu hastanelerde kaynakların genellikle etkin biçimde kullanılamadığı sonucuna varılmıştır.
    Keywords etkinlik ; sağlık kurumları ; veri zarflama analizi ; Social Sciences ; H ; Social insurance. Social security. Pension ; HD7088-7252
    Language English
    Publishing date 2019-08-01T00:00:00Z
    Publisher Sosyal Güvenlik Uzmanları Derneği
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Does He Wink or Does He Nod? A Challenging Benchmark for Evaluating Word Understanding of Language Models

    Senel, Lutfi Kerem / Schütze, Hinrich

    2021  

    Abstract: Recent progress in pretraining language models on large corpora has resulted in large performance gains on many NLP tasks. These large models acquire linguistic knowledge during pretraining, which helps to improve performance on downstream tasks via fine- ...

    Abstract Recent progress in pretraining language models on large corpora has resulted in large performance gains on many NLP tasks. These large models acquire linguistic knowledge during pretraining, which helps to improve performance on downstream tasks via fine-tuning. To assess what kind of knowledge is acquired, language models are commonly probed by querying them with `fill in the blank' style cloze questions. Existing probing datasets mainly focus on knowledge about relations between words and entities. We introduce WDLMPro (Word Definition Language Model Probing) to evaluate word understanding directly using dictionary definitions of words. In our experiments, three popular pretrained language models struggle to match words and their definitions. This indicates that they understand many words poorly and that our new probing task is a difficult challenge that could help guide research on LMs in the future.

    Comment: 5 pages, to appear in EACL 2021
    Keywords Computer Science - Computation and Language
    Subject code 410
    Publishing date 2021-02-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: CoDA21

    Senel, Lütfi Kerem / Schick, Timo / Schütze, Hinrich

    Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment

    2022  

    Abstract: Pretrained language models (PLMs) have achieved superhuman performance on many benchmarks, creating a need for harder tasks. We introduce CoDA21 (Context Definition Alignment), a challenging benchmark that measures natural language understanding (NLU) ... ...

    Abstract Pretrained language models (PLMs) have achieved superhuman performance on many benchmarks, creating a need for harder tasks. We introduce CoDA21 (Context Definition Alignment), a challenging benchmark that measures natural language understanding (NLU) capabilities of PLMs: Given a definition and a context each for k words, but not the words themselves, the task is to align the k definitions with the k contexts. CoDA21 requires a deep understanding of contexts and definitions, including complex inference and world knowledge. We find that there is a large gap between human and PLM performance, suggesting that CoDA21 measures an aspect of NLU that is not sufficiently covered in existing benchmarks.

    Comment: To appear in ACL 2022, 5 pages, 2 figures
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Publishing date 2022-03-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article: Predicting the progress of COVID-19: The case for Turkey/ COVID-19’un ilerleme sürecinin tahmini: Türkiye örneği

    Özdinç, Mesut / Şenel, Kerem / Öztürkcan, Selcen / Akgül, Ahmet

    Turk. Klinikleri J. Med. Sci.

    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #689867
    Database COVID19

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  5. Article ; Online: Single Parameter Estimation Approach for Robust Estimation of SIR Model With Limited and Noisy Data: The Case for COVID-19.

    Senel, Kerem / Ozdinc, Mesut / Ozturkcan, Selcen

    Disaster medicine and public health preparedness

    2020  Volume 15, Issue 3, Page(s) e8–e22

    Abstract: Objective: The susceptible-infected-removed (SIR) model and its variants are widely used to predict the progress of coronavirus disease 2019 (COVID-19) worldwide, despite their rather simplistic nature. Nevertheless, robust estimation of the SIR model ... ...

    Abstract Objective: The susceptible-infected-removed (SIR) model and its variants are widely used to predict the progress of coronavirus disease 2019 (COVID-19) worldwide, despite their rather simplistic nature. Nevertheless, robust estimation of the SIR model presents a significant challenge, particularly with limited and possibly noisy data in the initial phase of the pandemic.
    Methods: The K-means algorithm is used to perform a cluster analysis of the top 10 countries with the highest number of COVID-19 cases, to observe if there are any significant differences among countries in terms of robustness.
    Results: As a result of model variation tests, the robustness of parameter estimates is found to be particularly problematic in developing countries. The incompatibility of parameter estimates with the observed characteristics of COVID-19 is another potential problem. Hence, a series of research questions are visited.
    Conclusions: We propose a Single Parameter Estimation (SPE) approach to circumvent these potential problems if the basic SIR is the model of choice, and we check the robustness of this new approach by model variation and structured permutation tests. Dissemination of quality predictions is critical for policy- and decision-makers in shedding light on the next phases of the pandemic.
    MeSH term(s) Algorithms ; COVID-19/epidemiology ; Epidemiologic Methods ; Humans ; Models, Statistical ; Pandemics ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-06-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2375268-3
    ISSN 1938-744X ; 1935-7893
    ISSN (online) 1938-744X
    ISSN 1935-7893
    DOI 10.1017/dmp.2020.220
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Book ; Online: Towards Language-Based Modulation of Assistive Robots through Multimodal Models

    Wicke, Philipp / Şenel, Lüfti Kerem / Zhang, Shengqiang / Figueredo, Luis / Naceri, Abdeldjallil / Haddadin, Sami / Schütze, Hinrich

    2023  

    Abstract: In the field of Geriatronics, enabling effective and transparent communication between humans and robots is crucial for enhancing the acceptance and performance of assistive robots. Our early-stage research project investigates the potential of language- ... ...

    Abstract In the field of Geriatronics, enabling effective and transparent communication between humans and robots is crucial for enhancing the acceptance and performance of assistive robots. Our early-stage research project investigates the potential of language-based modulation as a means to improve human-robot interaction. We propose to explore real-time modulation during task execution, leveraging language cues, visual references, and multimodal inputs. By developing transparent and interpretable methods, we aim to enable robots to adapt and respond to language commands, enhancing their usability and flexibility. Through the exchange of insights and knowledge at the workshop, we seek to gather valuable feedback to advance our research and contribute to the development of interactive robotic systems for Geriatronics and beyond.

    Comment: GERIATRONICS SUMMIT 2023
    Keywords Computer Science - Robotics
    Publishing date 2023-06-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: SPE approach for robust estimation of SIR model with limited and noisy data

    Şenel, Kerem / Senel, Kerem / Özdinç, Mesut / Ozdinc, Mesut / Öztürkcan, Didem Selcen / Ozturkcan, Didem Selcen

    the case for COVID-19

    2020  

    Abstract: The SIR model and its variants are widely used to predict the progress of COVID-19 worldwide, despite their rather simplistic nature. Nevertheless, robust estimation of the SIR model presents a significant challenge, particularly with limited and ... ...

    Abstract The SIR model and its variants are widely used to predict the progress of COVID-19 worldwide, despite their rather simplistic nature. Nevertheless, robust estimation of the SIR model presents a significant challenge, particularly with limited and possibly noisy data in the initial phase of the pandemic. K-means algorithm is used to perform a cluster analysis of the top ten countries with the highest number of COVID-19 cases, to observe if there are any significant differences among countries in terms of robustness. As a result of model variation tests, the robustness of parameter estimates is found to be particularly problematic in developing countries. The incompatibility of parameter estimates with the observed characteristics of COVID-19 is another potential problem. Hence, a series of research questions are visited. We propose a SPE (“Single Parameter Estimation”) approach to circumvent these potential problems if the basic SIR is the model of choice, and we check the robustness of this new approach by model variation and structured permutation tests. Dissemination of quality predictions is critical for policy and decision-makers in shedding light on the next phases of the pandemic.
    Keywords R Medicine (General) ; covid19
    Subject code 310
    Publishing date 2020-06-25
    Publisher Cambridge University Press
    Publishing country tr
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article: Instantaneous r for COVID-19 in turkey: Estimation by bayesian statistical inference/ Türkiye’de COVID-19 için anlık r hesaplaması: Bayesyen istatistiksel çıkarım ile tahmin

    Şenel, Kerem / Özdinç, Mesut / Öztürkcan, Selcen / Akgül, Ahmet

    Turk. Klinikleri J. Med. Sci.

    Abstract: The instantaneous R in Turkey is estimated by Bayesian statistical inference that utilizes a 68-days-long dataset from the beginning of the COVID-19 outbreak in Turkey for monitoring the progression of the pandemic. As it is also globally adapted, ... ...

    Abstract The instantaneous R in Turkey is estimated by Bayesian statistical inference that utilizes a 68-days-long dataset from the beginning of the COVID-19 outbreak in Turkey for monitoring the progression of the pandemic. As it is also globally adapted, enforced social distancing measures help to keep the instantaneous reproduction number below one. The low levels of instantaneous R are referred to as a basis for several countries to relax their country-wide restrictions, while hindsight involves a possible second wave of infections to follow in China, Germany, and South Korea. Thus, policy and decision-makers need to be vigilant regarding the pandemic's progress. It is not yet sure if it is possible to maintain the instantaneous reproduction number below one, especially at the lack of societal measures.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #661613
    Database COVID19

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  9. Book ; Online: Graph Neural Networks for Multiparallel Word Alignment

    Imani, Ayyoob / Şenel, Lütfi Kerem / Sabet, Masoud Jalili / Yvon, François / Schütze, Hinrich

    2022  

    Abstract: After a period of decrease, interest in word alignments is increasing again for their usefulness in domains such as typological research, cross-lingual annotation projection, and machine translation. Generally, alignment algorithms only use bitext and do ...

    Abstract After a period of decrease, interest in word alignments is increasing again for their usefulness in domains such as typological research, cross-lingual annotation projection, and machine translation. Generally, alignment algorithms only use bitext and do not make use of the fact that many parallel corpora are multiparallel. Here, we compute high-quality word alignments between multiple language pairs by considering all language pairs together. First, we create a multiparallel word alignment graph, joining all bilingual word alignment pairs in one graph. Next, we use graph neural networks (GNNs) to exploit the graph structure. Our GNN approach (i) utilizes information about the meaning, position, and language of the input words, (ii) incorporates information from multiple parallel sentences, (iii) adds and removes edges from the initial alignments, and (iv) yields a prediction model that can generalize beyond the training sentences. We show that community detection provides valuable information for multiparallel word alignment. Our method outperforms previous work on three word-alignment datasets and on a downstream task.
    Keywords Computer Science - Computation and Language
    Subject code 400
    Publishing date 2022-03-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: LoHoRavens

    Zhang, Shengqiang / Wicke, Philipp / Şenel, Lütfi Kerem / Figueredo, Luis / Naceri, Abdeldjallil / Haddadin, Sami / Plank, Barbara / Schütze, Hinrich

    A Long-Horizon Language-Conditioned Benchmark for Robotic Tabletop Manipulation

    2023  

    Abstract: The convergence of embodied agents and large language models (LLMs) has brought significant advancements to embodied instruction following. Particularly, the strong reasoning capabilities of LLMs make it possible for robots to perform long-horizon tasks ... ...

    Abstract The convergence of embodied agents and large language models (LLMs) has brought significant advancements to embodied instruction following. Particularly, the strong reasoning capabilities of LLMs make it possible for robots to perform long-horizon tasks without expensive annotated demonstrations. However, public benchmarks for testing the long-horizon reasoning capabilities of language-conditioned robots in various scenarios are still missing. To fill this gap, this work focuses on the tabletop manipulation task and releases a simulation benchmark, \textit{LoHoRavens}, which covers various long-horizon reasoning aspects spanning color, size, space, arithmetics and reference. Furthermore, there is a key modality bridging problem for long-horizon manipulation tasks with LLMs: how to incorporate the observation feedback during robot execution for the LLM's closed-loop planning, which is however less studied by prior work. We investigate two methods of bridging the modality gap: caption generation and learnable interface for incorporating explicit and implicit observation feedback to the LLM, respectively. These methods serve as the two baselines for our proposed benchmark. Experiments show that both methods struggle to solve some tasks, indicating long-horizon manipulation tasks are still challenging for current popular models. We expect the proposed public benchmark and baselines can help the community develop better models for long-horizon tabletop manipulation tasks.

    Comment: 6 pages, 4 figures. The video and code of LoHoRavens are available at https://shengqiang-zhang.github.io/lohoravens-webpage/
    Keywords Computer Science - Robotics ; Computer Science - Computation and Language ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2023-10-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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