Palestinian rights: A literary multiple-genre approach

The year 1948 marked a turning point in the Palestinian history and a shift in the process of art production. Nakba constituted drastic changes on the political, social and demographic contexts of the Palestinians. That was necessarily followed by a similar alteration in the forms of expression adopted to articulate the new status quo of the land and its people. The artistry of the Palestinians emerged as surmounting the imposed limitations of Israeli occupation and opened up new spaces for freedom of expression denied to Palestinians. The fragmented Palestinians functioned creatively as they articulated their diverse experiences of displacement and alienation through different modes of art producing a mosaic structure that dynamically served the Palestinian cause. This thesis attempts to study the variations of the Palestinian cause with special reference to the human rights issues expressed in different genres: novel, novella, poetry, theatrical performance, cartoons and cinema. The various genres examine different authors and different experiences of artistic expression. The voices are Palestinian as in the case of Ghassan Kanafani, Sharif Elmusa, Naji al-Ali, and Hany Abu Assad, and also non-Palestinian as in the case of Egyptian-Italian novelist, Randa Ghazy and the voice of the American Rachel Corrie resurrected in a play edited by Alan Rickman and Katharine Viner. These voices adopt different genres -- a novella in Arabic (Kanafani), poetry in English (Elmusa), cartoons (al-Ali), film (Abu Assad), a novel in Italian (Ghazy), and a play in English (Rickman and Viner). The variety of genres and languages complement each other in drawing a vivid portrait of dispossessed Palestinians, denial of their human rights, and the ways of creatively expressing the Palestinian predicament

Multimodal Imaging of Acute Central Retinal Artery Occlusion

The aim of this study was to describe fluorescein angiography (FA), ocular coherence tomography (OCT) and ocular coherence tomography angiography (OCTA) in the diagnosis of acute central retinal artery occlusion (CRAO). This is an observational case series study performed at Sohag Ophthalmic Investigation Center. Fifteen patients presented by a sudden marked unilateral diminution of vision were included. Corrected Distance Visual acuity (CDVA), color fundus photos, FA, OCT and OCTA, imaging obtained in the first week of presentation and imaging of the other normal eye as a control were assessed. Central macular thickness (CMT), parafoveal inner retinal layers thickness and parafoveal outer retinal thickness in diseased and contralateral normal eyes were compared. Fifteen patients (mean age 52.67 years, 11-74 years old) including 66.7% male entered the study. CDVA ranged from no perception of light to 0.05 (20/400). Fundus examination showed a cherry red spot in 10 cases (66.7 %) and retinal whitening in 9 cases (60%), arteriolar narrowing in 7 (46.67%), optic disc edema in 4 (26.67%), optic disc pallor in 5 (33.3%) and cattle trucking in 5 (33.3%). Fluorescein angiography showed delayed arteriovenous transit time > 23 seconds in 8 cases (53.33 %) and normal FA in 4 cases (26.67 %). OCT revealed increased hyperreflective of the inner retinal layers in comparison to hyporeflective inner retinal layers in all cases (100%) and significant increase in CMT in 10 cases (66.67%). The mean ± standard deviation (SD) of CMT (CRAO) was 306.5 ± 27.9 (P < 0.001), the parafoveal inner retinal thickness (CRAO) 345 ± 51.8 µm (P < 0.001) and the parafoveal outer retinal thickness (CRAO) 120.9 ± 13.6 µm (P < 0.001). OCTA was performed and clear images obtained in 11 cases (73.33%). Disruption of superficial and deep capillary plexus was found in all cases. We concluded that the OCT is the most confirmative imaging method in the diagnosis of acute CRAO even in the absence of fundus signs. OCTA confirms the diagnosis, but it cannot be performed in some cases. Epub: October 1, 2019

Considering Race a Problem of Transfer Learning

As biometric applications are fielded to serve large population groups, issues of performance differences between individual sub-groups are becoming increasingly important. In this paper we examine cases where we believe race is one such factor. We look in particular at two forms of problem; facial classification and image synthesis. We take the novel approach of considering race as a boundary for transfer learning in both the task (facial classification) and the domain (synthesis over distinct datasets). We demonstrate a series of techniques to improve transfer learning of facial classification; outperforming similar models trained in the target’s own domain. We conduct a study to evaluate the performance drop of Generative Adversarial Networks trained to conduct image synthesis, in this process, we produce a new annotation for the Celeb-A dataset by race. These networks are trained solely on one race and tested on another - demonstrating the subsets of the CelebA to be distinct domains for this tas

Development of an Automated Pain Facial Expression Detection System for Sheep (Ovis Aries).

The use of technology to optimize the production and management of each individual animal is becoming key to good farming. There is a need for the real-time systematic detection and control of disease in animals in order to limit the impact on animal welfare and food supply. Diseases such as footrot and mastitis cause significant pain in sheep, and so early detection is vital to ensuring effective treatment and preventing the spread across the flock. Facial expression scoring to assess pain in humans and non-humans is now well utilized, and the Sheep Pain Facial Expression Scale (SPFES) is a tool that can reliably detect pain in this species. The SPFES currently requires manual scoring, leaving it open to observer bias, and it is also time-consuming. The ability of a computer to automatically detect and direct a producer as to where assessment and treatment are needed would increase the chances of controlling the spread of disease. It would also aid in the prevention of resistance across the individual, farm, and landscape at both national and international levels. In this paper, we present our framework for an integrated novel system based on techniques originally applied for human facial expression recognition that could be implemented at the farm level. To the authors' knowledge, this is the first time that this technology has been applied to sheep to assess pain

Autism: A Neurodevelopmental Disorder and a Stratum for Comorbidities

Autism is a neurodevelopmental disorder which is more common in males than females. It is characterized by social communication disorders and restricted repetitive behaviors. There is wide heterogeneity in its etiology, clinical presentations, management and consequently prognosis. Although the etiology of autism remains unclear, the most currently proven theory is that it is a complex neurodevelopmental disorder that displays “brain network abnormalities”. fMRI studies have shown decreased brain connectivity or functional synchronization between frontal and more posterior cortical regions. Dynamic brain activity through high resolution electroencephalograghy (EEG) has revealed local overconnectivity and long-range underconnectivity. This disrupted connectivity pattern would involve connectivity between hemispheres (corpus callosum), together with axonal and synaptic connectivity within each hemisphere. Inconsistent morphometric changes involving both gray and white matter structure also exist. Clinically, autism is associated with multiple comorbidities (somatic, neurologic and psychiatric); some of which are attention deficit hyperactivity disorder, dyspraxia, and sensory processing disorders

FATURA: A Multi-Layout Invoice Image Dataset for Document Analysis and Understanding

Document analysis and understanding models often require extensive annotated data to be trained. However, various document-related tasks extend beyond mere text transcription, requiring both textual content and precise bounding-box annotations to identify different document elements. Collecting such data becomes particularly challenging, especially in the context of invoices, where privacy concerns add an additional layer of complexity. In this paper, we introduce FATURA, a pivotal resource for researchers in the field of document analysis and understanding. FATURA is a highly diverse dataset featuring multi-layout, annotated invoice document images. Comprising $10,000$ invoices with $50$ distinct layouts, it represents the largest openly accessible image dataset of invoice documents known to date. We also provide comprehensive benchmarks for various document analysis and understanding tasks and conduct experiments under diverse training and evaluation scenarios. The dataset is freely accessible at https://zenodo.org/record/8261508, empowering researchers to advance the field of document analysis and understanding