Open-Source Face Recognition Frameworks: A Review of the Landscape

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The role of intravascular ultrasound guidance in the treatment of intramural hematoma probably caused by spontaneous coronary artery dissection in a young woman with acute anterior myocardial infarction

Spontaneous coronary artery dissection (SCAD) is known to be a rare but fatal cause of acute coronary syndromes. It is more frequent in young women, particularly in the peripartum period. Intravascular ultrasound (IVUS) has an important role in the diagnosis and management of SCAD. Intramural hematoma that occurs between adventitial and media layer of the vessel wall may occlude the true lumen. IVUS can identify intimal tears, the extension of intramural hematoma and show the adequate compression of intramural hematoma after percutaneous coronary intervention. We present a case of intramural hematoma caused by SCAD in a young woman presenting with acute anterior myocardial infarction, and the role of IVUS in the diagnosis and management of SCAD. (Cardiol J 2012; 19, 5: 532-535

P Wave Dispersion is Increased in Pulmonary Stenosis

Aim: The right atrium pressure load is increased in pulmonary stenosis (PS) that is a congenital anomaly and this changes the electrophysiological characteristics of the atria. However, there is not enough data on the issue of P wave dispersion (PWD) in PS. Methods: Forty- two patients diagnosed as having valvular PS with echocardiography and 33 completely healthy individuals as the control group were included in the study. P wave duration, p wave maximum (p max) and p minimum (p min) were calculated from resting electrocariography (ECG) obtained at the rate of 50 mm/sec. P wave dispersion was derived by subtracting p min from p max. The mean pressure gradient (MPG) at the pulmonary valve, structure of the valve and diameters of the right and left atria were measured with echocardiography. The data from two groups were compared with the Mann-Whitney U test and correlation analysis was performed with the Pearson correlation technique. Results: There wasn’t any statistically significance in the comparison of age, left atrial diameter and p min between two groups. While the MPG at the pulmonary valve was 43.11 ± 18.8 mmHg in PS patients, it was 8.4 ± 4.5 mmHg in the control group. While p max was 107.1 ± 11.5 in PS group, it was 98.2 ± 5.1 in control group (p=0.01), PWD was 40.4 ± 1.2 in PS group, and 27.2 ± 9.3 in the control group (p=0.01)Moreover, while the diameter of the right atrium in PS group was greater than that of the control group, (38.7 ± 3.9 vs 30.2 ± 2.5, p=0.02). We detected a correlation between PWD and pressure gradient in regression analysis. Conclusion: P wave dispersion and p max are increased in PS. While PWD was correlated with the pressure gradient that is the degree of narrowing, it was not correlated with the diameters of the right and left atria

Fake narratives, dominant discourses : the role and influence of algorithms on the online South African land reform debate

Abstract: Within this paper the authors explore the discourse surrounding algorithmic processes, by examining the way a search engine’s result influenced an online debate about land reform in South Africa. The article begins by reflecting on the rise of the internet contributing to issues around fake news. Then it continues to discuss the incident that serves as case study for this paper. In June 2018 Twitter users criticised the search engine Google for only displaying photos of white people when one type the words “squatter camps in South Africa” into the Google Image search bar. In the debate that followed, many of the online users accused Google of propagating a biased narrative to destabilise land reform. The paper’s purpose is to explain the role of algorithms to readers from the fields of humanities and social sciences without a technical background in computer science. Through exploring the “squatter camps in South Africa”‐case study, the authors intend to reveal to the reader the power online search engines have in shaping the public debate. Yet, through conducting their own search on different international search engines, but using the same key words, they prove that it is not in fact the algorithms that are biased, but rather the online data that is generated by internet users themselves

Türkiye'de ülke içinde yerinden edilme sorunu: tespitler ve çözüm önerileri = The problem of internal displacement in Turkey: assessment and policy proposals

Bu rapor, Doç. Dr. A. Tamer Aker (psikiyatr, Kocaeli Üniversitesi), Yrd. Doç. Dr. A. Betül Çelik (siyaset bilimci, Sabancı Üniversitesi), Dilek Kurban (hukuk doktoru, TESEV), Doç. Dr. Turgay Ünalan (nüfusbilimci, Hacettepe Üniversitesi) ve Yrd. Doç. Dr. H. Deniz Yükseker'den (sosyolog, Koç Üniversitesi) oluşan TESEV Ülke İçinde Yerinden Edilme Araştırma ve İzleme Grubu tarafından hazırlanmıştır. Grup, yerinden edilmeyi çatışma ortamının keskinleştirdiği devlet merkezli düşünüşün ve çeşitli ideolojik kamplaşmaların ötesinde, yurttaşlık haklarının yeniden tesisi ve toplumsal rehabilitasyon eksenlerinde ve insani boyutları bağlamında ele almaktadır

A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery

Accurate segmentation of river water in close-range Remote Sensing (RS) images is vital for efficient environmental monitoring and management. However, this task poses significant difficulties due to the dynamic nature of water, which exhibits varying colors and textures reflecting the sky and surrounding structures along the riverbanks. This study addresses these complexities by evaluating and comparing several well-known deep-learning (DL) techniques on four river scene datasets. To achieve this, we fine-tuned the recently introduced 'Segment Anything Model' (SAM) along with popular DL segmentation models such as U-Net, DeepLabV3+, LinkNet, PSPNet, and PAN, all using ResNet50 pre-trained on ImageNet as a backbone. Experimental results highlight the diverse performances of these models in river water segmentation. Notably, fine-tuned SAM demonstrates superior performance, followed by U-Net(ResNet50), despite their higher computational costs. In contrast, PSPNet(ResNet50), while less effective, proves to be the most efficient in terms of execution time. In addition to these findings, we introduce a novel river water segmentation dataset, LuFI-RiverSnap. v1 (Dataset link), characterized by a more diverse range of scenes and accurate masks compared to existing datasets. To facilitate reproducible research in remote sensing and computer vision, we release the implementations of the fine-tuned SAM model (Code link). The findings from this research, coupled with the presented dataset and the accuracy achieved by fine-tuned SAM segmentation, can support tracking river changes, understanding river water level trends, and exploring river ecosystem dynamics. These can also provide valuable insights for practitioners and researchers seeking models tailored to specific image characteristics with practical means in disaster risk reduction, such as rapid assessments of inundations during floods or automatic extractions of gauge data in watersheds