Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing the Similarity between Artificial and Training Images in Generative Models: The PicusLabMed Contribution

Generative models represent one of the most innovative and interesting applications of artificial intelligence (AI), able to generate realistic synthetic data by learning the characteristics of the training samples. In medical imaging, they are widely used to generate high-resolution medical images belonging to different modalities, improving diagnosis and patient care. However, the surprising performance of generative models has raised concerns about the relationship between artificial and real instances in terms of similarity, which may introduce privacy and ethical issues. To this aim, the ImageCLEFmed GAN challenge has been organized, asking participants to evaluate the hypothesis that generative models produce images containing the fingerprints of the samples used during the training. In this paper, we describe the methodology implemented to take part in the competition, exploiting the ability of deep neural networks to provide a high-level representation of the input data.
UETCorn at MEDIQA-Sum 2023: Template-based Summarization for Clinical Note Generation from Doctor-Patient Conversation

Clinical note summarization is a challenging task that aims to extract the most relevant information from unstructured text documents and present it concisely and coherently. Large language models, such as OpenAI’s GPT-3.5, have limitations in terms of trustworthiness and cost-effectiveness for generating complete clinical notes from patient-doctor dialogues. In this paper, we propose a novel template-based approach of for clinical note summarization from dialogue. Our model is under the control of a specific template, which is constructed by our own team and relies on expert validation. The semantic-based partition module fills in the template with key facts extracted from the dialogue using a combination of rule-based and neural methods. The template-based summarization module fine-tunes state-of-the-art (SOTA) BART models and other strategies to generate fluent and informative summaries corresponding to parts in the template. We evaluated our approach on released datasets of MEDIQA-Sum 2023 Shared Tasks, which contain clinical notes from dialogue for various problems. Our approach achieves the best ROUGE-2 and ROUGE-L scores for full note summarization, outperforming several strong baselines.
Profiling Cryptocurrency Influencers using Few-shot Learning

This research provides a novel method for identifying cryptocurrency influencers on social media in a low-resource environment. The analysis focuses on English-language Twitter messages and divides influencers into impact categories ranging from minimal to massive. With a maximum of 10 English tweets per user, the dataset consists of 32 people per category. By comparing the suggested approach to two baseline models—Usercharacter Logistic Regression and t5-large (bi-encoders) using zero-shot and label tuning few-shot methods—the proposed system is evaluated using the Macro F1 measure. The findings show that the suggested approach operates effectively in low-resource environments and has the potential to be used to further in-depth studies of influencer profiling.
Elsevier at SimpleText: Passage Retrieval by Fine-tuning GPL on Scientific Documents

CLEF SimpleText Lab is centered around finding relevant passages from a large collection of scientific documents in response to a lay query, detecting and explaining difficult terminology within those passages, and finally simplifying the passages. The first task is similar to the ad-hoc retrieval task in which given a topic/query, the goal is to retrieve relevant passages, but in addition to the relevance, ranking models should assess documents based on their readability/complexity as well. This paper describes our approach towards building a ranking model to tackle the first task. To build the ranking model, we first evaluate performance of several models on a proprietary test collection constructed based on scientific documents across multiple science domains. Then, we fine-tune the best performing model on a large collection of unlabelled documents using the Generative Pseudo Labeling approach. The key contribution and findings of our approach is that a bi-encoder model, trained on the MS-Marco dataset, fine-tuned further on a large collection of unlabelled scientific passages achieves the highest performance on the proprietary dataset which is specifically designed for the scientific passage retrieval task. Finally, fine-tuning a model in the same fashion, but only using the Computer Science queries from the test collection has proven to be successful for SimpleText Task 1.
DSHacker at CheckThat!-2023: Check-Worthiness in Multigenre and Multilingual Content With GPT-3.5 Data Augmentation

This article showcases our approach to check-worthiness detection, a task within the CheckThat! Lab of the 2023 CLEF Conference. This task aimed to design a system capable of determining if a claim, provided in diverse data formats such as tweets, debate snippets, and speech transcriptions, necessitates fact-checking. Our method combined a unified framework for processing content in three languages - English, Spanish and Arabic. At the heart of our system is the XLM-RoBERTa, a pre-trained multilingual model. To enhance its performance, we applied data augmentation strategies using GPT-3.5 provided by OpenAI, which included generating paraphrases and translating text fragments to create a rich dataset. The system’s effectiveness is evidenced by its performance against baseline results in all languages, notably winning first place with an F1 score of 0.641 in the Spanish category. Additionally, our exploration sheds light on the attributes of the model’s performance in processing different languages, highlighting its exceptional performance in Spanish and indicating room for improvement in handling complex Arabic language structures.
UBO Team @ CLEF JOKER 2023 Track For Task 1, 2 and 3 - Applying AI Models In Regards To Pun Translation

Understanding and translating humorous wordplay poses challenges for both humans and computers due to the need for recognizing implicit cultural references, comprehending word formation processes, and interpreting multiple meanings. Therefore we will try to translate pun from English to French or Spanish within the JOKER track. This abstract will provide an overview of the subject and the different tasks involved. It will then delve into a description of each model employed, followed by an examination of the results obtained. Finally, a conclusion will be drawn based on the findings.
SEUPD@CLEF: Team Squid on LongEval-Retrieval

This is the report based on the work done for LongEval Task 1: Retrieval at CLEF 2023, by team Squid (whose participants are from the University of Padua). Information retrieval (IR) systems have played an increasingly important role in our society and people’s daily lives. Although they have become more and more powerful during the last decades, their temporal persistence is still causing drops in performance, thus failing to achieve good temporal generalisability. To investigate and improve the resolution of this issue, in this paper, we present and discuss the various solutions submitted to the first CLEF 2023 shared task (LongEval-Retrieval), which precisely requires the development of temporal information retrieval systems.
SEUPD@CLEF: RAFJAM on Longitudinal Evaluation of Model Performance

This paper is intended to be a report of the work we have done for the CLEF 2023 LongEval Lab, whose main goal is to evaluate and improve performances of IR models along time. We have implemented a basic retrieval system and then modified and extended it, focusing on different query expansion techniques, involving the use of synonyms and pseudo-relevance feedback. We will provide a description of our ideas, code and other development details, along with statistical analysis of the runs of our systems on different test collections
Hospital-wide education committees and high-quality residency training. Perspectives on medical education.

Introduction High-quality residency training is of utmost importance for residents to become competent medical specialists. Hospital-wide education committees have been adopted by several healthcare systems to govern postgraduate medical education and to support continuous quality improvement of residency training. To understand the functioning and potential of such committees, this study examined the mechanisms through which hospital-wide education committees strive to enable continuous quality improvement in residency training. Methods Focus group studies with a constructivist grounded theory approach were performed between April 2015 and August 2016. A purposeful sample of hospital-wide education committees led to seven focus groups. Results Hospital-wide education committees strived to enable continuous quality improvement of residency training by the following mechanisms: creating an organization-wide quality culture, an organization-wide quality structure and by collaborating with external stakeholders. However, the committees were ﬁrst and foremost eager to claim a strategic position within the organization they represent. All identiﬁed mechanisms were interdependent and ongoing. Discussion From a governance perspective, the position of hospital-wide education committees in the Netherlands is uniquely contributing to the call for institutional accountability for the quality of residency training. When implementing hospital-wide education committees, shared responsibility of the committees and the departments that actually provide residency training should be addressed. Although committees vary in the strategies they use to impact continuous quality improvement of residency training, they increasingly have the ability to undertake supporting actions and are working step by step to contribute to high-quality postgraduate medical education.
Clark Digital

Hosting or local families taking in displaced families is an important way to shelter persons displaced during war or by natural disaster. While field-level evidence of hosting is on the rise, academic and policy-related scholarship on hosting is scant. Based on an extensive literature review and supplemented by the author ’ s own work experience in the humanitarian sector, this paper identifies and summarizes ten aspects that shape the hosting environment and its associated support programs. These aspects provide insight to humanitarian actors that support hosting situations rather than allowing them to play out on their own. These aspects potentially serve (1) as programmatic criteria that humanitarian actors and aid agencies should consider when designing and supporting hosting programs and (2) are substantively rich areas that would expand the research agenda on displacement and humanitarian response and assistance. This paper has implications for both humanitarian practice and research, including how members of the humanitarian community conceptual hosting as a social relationship.
Advanced testing of SiC power MOSFET modules for electric motor drives

SiC power MOSFETs are an advancement in the power electronics technology today available on the market. Respect to Si-based power devices, they offer potential for higher operating temperatures, higher breakdown voltage with low on-state resistance, and lower switching losses permitting much higher switching frequencies. To date, the attempts to apply the SiC power devices to the field of AC drives are relented by the high price of such new devices and by the challenges related to electromagnetic compatibility and reliability. A new test rig is proposed, built for comprehensively testing SiC power MOSFET modules in real operating conditions, using off-the-shelf hardware. The presented results focus on the evaluation of on-resistance as a function of drain current and junction temperature, and on dv/dt evaluation as a function of external gate resistance. Different modules are tested, custom realized using SiC MOSFET dies by two leading manufacturers. Finally, a new methodology for on-line estimation of the devices junction temperature is presented.
A Systematic Review On CNN For Face Mask Detection: Current Trends And Future Directions

: The COVID-19 pandemic has necessitated the development of face mask detection systems to enforce safety measures and monitor compliance. This abstract provides an overview of various datasets and approaches used for face mask detection using Convolutional Neural Networks (CNN). The datasets described include NO-MASK, UMD Faces, Annotated facial landmarks in the wild, UMD Masks, FDNA, and more. Researchers have created large-scale datasets such as NO-MASK, UMD Masks, and UMD Faces, which provide annotated images for training deep networks. These datasets have played a vital role in advancing face mask detection algorithms. Additionally, datasets like COVID-19 screening on chest X-ray images have been used for anomaly detection and screening purposes. Numerous deep learning approaches have been proposed for face mask detection. Techniques such as neural networks with augmentation, vision-based frameworks, deep learning-based anomaly detection, and hybrid fusion frameworks have been utilized. These approaches aim to accurately identify and classify faces with or without masks.
Coastal landuse change detection using remote sensing technique: case study in Banten Bay, West Java Island, Indonesia

Various forms of coastal landuse covering study site (46,785.69 hectares) were observed to underwent changes as evidently detected between satellite images sensed in 1994 and 2001 at the Banten Bay. It was important to identify what these changes were. Therefore, appropriate change detection must be selected. The image preprocessing step involved removing errors from raster data. This was done by performing basic processes, such as, radiometric correction, geometric correction and image calibration. The image processing step comprised of supervised classification and change detection techniques. Red green method gave the best result for detecting coastal landuse change because the numbers of changed area closely resembled the total number of changed area reference. Every coastal landuse category increased in utility area except for natural area. Observed reduction in the area size of natural area was due to growth rate of population and increased activities along this area.
Bridging the Gap between the Body and the Machine: Embodied Learning with Interventional Brain Computer Interfaces?

This review aims to understand how the body and the brain interact with different brain computer interfaces (BCI) and to analyze the implications of these tools on embodied learning in the educational field. Through a theoretical approach a review of the literature is developed by studying the relationship between the body, the brain and BCI. To conduct this research, the keywords “embodied learning”, “cognition”, “digital learning”, “body”, “brain-computer interface” were used in Pubmed, Frontiers, Google Scholar and Researchgate. There are multiple concepts related to digitization and they can vary from owning digital tools such as computers, phones, virtual reality devices to even using interventional BCI. BCI are being reported safe and are capable of reversing physical and cognitive disabilities. The impact of these tools is variable according to their nature, the environmental factors linked to their use, and the condition of the brain and body while using them. With the massive development of technology nowadays many in-terrogations are coming into surface about the relationship between the human and the machine, and at what level the digital world will be able to interfere with our lives and integrate our bodies.
KEAP1/NRF2 Mutations in Stem Cells Define an Aggressive Subset of Head and Neck Cancer Patients Who Have a Poor Prognosis, Lung Metastasis, and Therapeutic Failure

Simple Summary Mutations in head and neck cancer result in abnormal tumor cell growth, increase the risk of distant metastasis, and lead to therapeutic failure. Progenitor cells, head and neck cancer stem cells, and bulk tumor cells may all harbor these mutations, and identifying the mutations in stem cells may facilitate stratifying patients who are likely to benefit from treatment. Patients with head and neck cancer whose tumor exhibits Keap1/Nrf2 mutations in their stem cells are significantly more likely to develop rapid lung metastasis and fail treatment. Our findings suggest a molecular genotyping of head and neck cancer stem cells, which may facilitate personalized treatment strategies and assist in identifying patients who would benefit from chemotherapy, radiotherapy, and targeted therapies. Abstract Mutations in Keap1/Nrf2 in head and neck cancer result in abnormal cell growth. Progenitor cells, bulk tumor cells, and head and neck cancer stem cells (HN-CSCs) may all harbor these mutations. Nevertheless, whether Keap1/Nrf2 mutations in HN-CSCs have an impact on clinical outcomes is unknown. Cancerous HN-CSCs and benign stem cells were obtained from freshly resected head and neck cancer patients (n = 50) via flow cytometry cell sorting and tested for Keap1/Nrf2 mutations. The existence of Keap1/Nrf2 mutations in HN-CSCs, as well as their correlations with tumor mutations, pathologic tumor stage, tumor histologic grades, lung metastasis, treatment outcomes, and the patient’s age and conditions, are assessed at the last follow-up visit. Thirteen tumors were found to have Keap1/Nrf2 mutations in their HN-CSCs. More than half of the lung metastases and disease progression occurred in HN-CSCs with mutations. Patients whose tumors carried Keap1/Nrf2 mutations in their HN-CSCs had significantly shorter progression-free survival, overall survival, and time of treatment failure than their non-HN-CSC counterparts. These associations were partly driven by HN-CSCs, in which Keap1/Nrf2 mutations were overrepresented in fast progressors and associated with an increased risk of disease progression. Our findings suggest that molecular genotyping of HN-CSCs may facilitate personalized treatment strategies and assist in identifying patients who are likely to benefit from chemotherapy.
Investment under Uncertainty and Policy Change Investment under Uncertainty and Policy Change

Existing real options literature provides relatively little insight into the impact of structural changes of the economic environment on the investment decision of the …rm. We propose a method to model the impact of a policy change on investment behavior in which, contrary to the earlier models based on Poisson processes, uncertainty concerning the moment of the change can be explicitly accounted for. Moreover, probabilities of the change depend on the state of the dynamic system, what ensures the consistency of the action of the policy maker. We model the policy change as an upward jump in the (net) investment cost, which is, for instance, caused by a reduction in the investment tax credit. The …rm has an incomplete information concerning the trigger value of the process for which the jump occurs. We derive the optimal investment rule maximizing the value of the …rm. It is shown that the impact of trigger value uncertainty is non-monotonic: the investment threshold decreases with the trigger value uncertainty for low levels of uncertainty, while the reverse is true for high uncertainty levels. Finally, we present policy implications for the authority that result from the …rm's value-maximizing behavior. ¤ This research was undertaken with support from the European Union's Phare ACE Programme 1997. The content of the publication is the sole responsibility of the authors and in no way represents the views of the Commission or its services. The authors would like to thank Kuno Huisman and Uli Hege for helpful comments and suggestions. All remaining errors are ours.
Environmental Factors Determining the Pre-Restoration Benthic Macroinvertebrate Assemblage In A Stream Used By Cattle

i Acknowledgements . ii List of Figures . iii List of Tables . iv Introduction . 1 Materials and Methods . 6 Study Area . 6 Field Sampling . 8 Laboratory Analysis . 9 Data Analysis . 11 Results . 12 Description of Benthic Macroinvertebrate Assemblage. 12 Relationships of Environmental Factors and Benthic Macroinvertebrate Assemblages. 24 Measurements Versus Visual Estimates of Substrate Composition for Explaining Macroinvertebrate Assemblages . 31 Discussion. 42 Benthic Macroinvertebrate Assemblage . 42 Measurements Versus Visual Estimates of Substrate Composition . 45 Literature Cited . 48
The importance of flow and turbulence characteristics for hydrokinetic energy development on the Tanana River at Nenana, Alaska

The site selection for the installation of hydrokinetic devices along a river reach is an issue of fundamental importance. While it is acknowledged that multiple factors such as accessibility, navigation, safety, and hydraulics, among many others, must be considered in the final decision, this article focuses on the influence of river morphology on turbulence flow parameters. Specifically, continuous high-resolution velocity measurements from a hydrokinetic resource assessment on the Tanana River near Nenana in the interior of Alaska are analysed to estimate, kinetic energy (KE), turbulent kinetic energy (TKE) as well as KE partition at particular locations. To accomplish these tasks, two different methods used to rotate the coordinate systems into the main flow direction are correlated and compared in order to extract the turbulent parameters from the main flow. The coordinate system methods include the streamline coordinate rotation and the extraction of statistical fluctuations from the average flow. The streamline coordinate rotation method is chosen to extract the TKE in temporal series. The calculated TKE was up to 30 per cent of the total flow KE in measurements located in pools and dissipated downstream, beyond the highly turbulent locations of bathymetric depressions and river bends. The overall KE increased over a bed free of major macro-obstacles and reduced depth, where the TKE fraction accounted for only 2 per cent of the total KE. The visualizations of the KE and TKE are compared with the river morphology, leading to identification of helical flow, flow separation, and turbulent flow structures along the river bend and in pools to help the decision-making process in hydrokinetic planning.