Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Prevalence of Blood Group of Saudi Patients in King Abdulaziz Medical City-Riyadh

Background: Blood groups have been the subject of research because of the importance of blood transfusion in surgery as well as treatment of anemia and other hemorrhagic conditions. Blood groups and immune hematological problems of blood transfusion constitute an extremely interesting, but complex study area and their investigations have much to offer to hematology and immunohematology fields. Aim: The aim of our study is to provide essential data about the distribution of ABO and Rhesus blood groups among Saudi populations and to determine the ABO and Rh phenotype among patients who received blood or blood products in King Abdulaziz Medical City-Riyadh. Methods: For this purpose, we retrospectively evaluated the results of ABO grouping and Rh grouping performed among some patients who received blood or blood products at King Abdulaziz Medical City Riyadh during the year 2013. Results: Participants in this study included four hundred patients (100 children; 28 youths, 88 adults and 184 olds). The gender distribution showed 232 males and 168 females. ABO phenotypes result showed that blood group A is 27%, group B 22%, group AB 3%, and group O is 48%. The percentages of positive and negative Rh grouping phenotypes were 96% and 4%, respectively. Conclusion: In conclusion, our study shows that the blood group O is highly frequent among Saudi population, while the blood group AB is the lowest. Rh-negative phenotypes were not as frequent as Rh-positive.
Parametric sensitivity analysis of microscrews

Parametric Sensitivity Analysis of Microscrews By Zachary F Witzgall The research and development of a popular, publicly available microscrew system required an analysis of fastener strength. This analysis was performed using a Finite Element Analysis (FEA) program. ANSYS was used to model and stress the fasteners through various loading schemes. A simplified model was constructed due to limitations in model size. The analysis required key parameters to be changed slightly. These parameters were socket depth and socket diameter. Changing these parameters had a substantial affect on the strength of the fastener. Torque and rotation to tension analyses were performed on the microscrew to determine the validity of the ANSYS computation results. This data also provided complimentary information on the mechanics of a threaded assembly.
Paediatric Percutaneous Bone Anchored Hearing Devices. Special consideration in children

Hearing amplification technology has been evolving since the 19th century. Currently in most audiology departments, the mainstay of hearing rehabilitation is performed with conventional air and bone conduction aids. These are costeffective, non-invasive hearing aids but are not without their drawbacks. This chapter explores the advantages and disadvantages of conventional hearing aids compared with the bone-anchored hearing aids. Although the bone-anchored hearing aids are a more expensive invasive option, there is increasing evidence that the benefits outweigh the disadvantages. Users report improved quality of life, health status and audiological rehabilitation.
A Boundary-aware Neural Model for Nested Named Entity Recognition

In natural language processing, it is common that many entities contain other entities inside them. Most existing works on named entity recognition (NER) only deal with flat entities but ignore nested ones. We propose a boundary-aware neural model for nested NER which leverages entity boundaries to predict entity categorical labels. Our model can locate entities precisely by detecting boundaries using sequence labeling models. Based on the detected boundaries, our model utilizes the boundary-relevant regions to predict entity categorical labels, which can decrease computation cost and relieve error propagation problem in layered sequence labeling model. We introduce multitask learning to capture the dependencies of entity boundaries and their categorical labels, which helps to improve the performance of identifying entities. We conduct our experiments on GENIA dataset and the experimental results demonstrate that our model outperforms other state-of-the-art methods.
Hyperbolic Graph Convolutional Neural Networks

Graph convolutional neural networks (GCNs) embed nodes in a graph into Euclidean space, which has been shown to incur a large distortion when embedding real-world graphs with scale-free or hierarchical structure. Hyperbolic geometry offers an exciting alternative, as it enables embeddings with much smaller distortion. However, extending GCNs to hyperbolic geometry presents several unique challenges because it is not clear how to define neural network operations, such as feature transformation and aggregation, in hyperbolic space. Furthermore, since input features are often Euclidean, it is unclear how to transform the features into hyperbolic embeddings with the right amount of curvature. Here we propose Hyperbolic Graph Convolutional Neural Network (HGCN), the first inductive hyperbolic GCN that leverages both the expressiveness of GCNs and hyperbolic geometry to learn inductive node representations for hierarchical and scale-free graphs. We derive GCNs operations in the hyperboloid model of hyperbolic space and map Euclidean input features to embeddings in hyperbolic spaces with different trainable curvature at each layer. Experiments demonstrate that HGCN learns embeddings that preserve hierarchical structure, and leads to improved performance when compared to Euclidean analogs, even with very low dimensional embeddings: compared to state-of-the-art GCNs, HGCN achieves an error reduction of up to 63.1% in ROC AUC for link prediction and of up to 47.5% in F1 score for node classification, also improving state-of-the art on the Pubmed dataset.
Learning to Copy for Automatic Post-Editing

Automatic post-editing (APE), which aims to correct errors in the output of machine translation systems in a post-processing step, is an important task in natural language processing. While recent work has achieved considerable performance gains by using neural networks, how to model the copying mechanism for APE remains a challenge. In this work, we propose a new method for modeling copying for APE. To better identify translation errors, our method learns the representations of source sentences and system outputs in an interactive way. These representations are used to explicitly indicate which words in the system outputs should be copied. Finally, CopyNet (Gu et.al., 2016) can be combined with our method to place the copied words in correct positions in post-edited translations. Experiments on the datasets of the WMT 2016-2017 APE shared tasks show that our approach outperforms all best published results.
Combinatorial Bandits with Relative Feedback

We consider combinatorial online learning with subset choices when only relative feedback information from subsets is available, instead of bandit or semi-bandit feedback which is absolute. Specifically, we study two regret minimisation problems over subsets of a finite ground set $[n]$, with subset-wise relative preference information feedback according to the Multinomial logit choice model. In the first setting, the learner can play subsets of size bounded by a maximum size and receives top-$m$ rank-ordered feedback, while in the second setting the learner can play subsets of a fixed size $k$ with a full subset ranking observed as feedback. For both settings, we devise instance-dependent and order-optimal regret algorithms with regret $O(\frac{n}{m} \ln T)$ and $O(\frac{n}{k} \ln T)$, respectively. We derive fundamental limits on the regret performance of online learning with subset-wise preferences, proving the tightness of our regret guarantees. Our results also show the value of eliciting more general top-$m$ rank-ordered feedback over single winner feedback ($m=1$). Our theoretical results are corroborated with empirical evaluations.
Mind and body in the discursive construction of M.E. : a struggle for authorship of an illness.

This thesis is an analysis of the discursive construction of a controversial medical condition (Myalgic Encephalomyelitis) and how it has been made sense of, in and through the discourse of medical scientists, general practitioners, and M E sufferers. Various kinds of text and talk are analysed from the perspective of discursive psychology, arguing that versions of reality are a product of participants' constructive and dialogical practices. (I include my own text as an example of this.) The analysis of scientific texts, illness narratives, diagnostic narratives, and M E self-help group discussions, show how explanations about the status of M E and its causes are embedded in accounts of the identity of sufferers, the nature and status of medical explanation, and the competencies of medical practitioners. There is a sense in which the controversial topic of this thesis provides an ideal forum for examining both lay and professional reasoning practices about illness, in a context where the 'objective' world of 'medical facts' threatens to disintegrate into merely subjective points of view. One of my concerns has been to show how the participants themselves orient to, and manage this 'reality disjuncture' by means of a variety of discursive devices. The main body of the thesis addresses this problem through issues such as: the significance of diagnostic labels, different models of medicine, and the relevance of mind and body in explanations for illness. Finally, there is an analysis of the narrative constructions of M E sufferers and GPs, to show how psychosocial 'evidence' is used to warrant the speaker's interpretation of illness as either a mental or physical phenomenon.
Limits of Private Learning with Access to Public Data

We consider learning problems where the training set consists of two types of examples: private and public. The goal is to design a learning algorithm that satisfies differential privacy only with respect to the private examples. This setting interpolates between private learning (where all examples are private) and classical learning (where all examples are public). We study the limits of learning in this setting in terms of private and public sample complexities. We show that any hypothesis class of VC-dimension $d$ can be agnostically learned up to an excess error of $\alpha$ using only (roughly) $d/\alpha$ public examples and $d/\alpha^2$ private labeled examples. This result holds even when the public examples are unlabeled. This gives a quadratic improvement over the standard $d/\alpha^2$ upper bound on the public sample complexity (where private examples can be ignored altogether if the public examples are labeled). Furthermore, we give a nearly matching lower bound, which we prove via a generic reduction from this setting to the one of private learning without public data.
Equifinality and Pathways to Environmental Concern: A Fuzzy-Set Analysis

Studying how people understand and develop concern for environmental problems is a key area of research within environmental sociology. Previous research shows that numerous social factors have measurable effects on environmental concern. However, results tend to be somewhat inconsistent across studies on this topic. One possible explanation for this is because these social factors are typically examined as independent from one another. However, these factors are interrelated in complex ways, as shown by research on the moderating effects of race and political ideology on education. Using qualitative comparative analysis (QCA), this study examines the complex ways in which previously identified social factors interact with one another to affect environmental concern. The findings suggest that aside from political ideology, all of the other factors should be understood in combination with others. The findings also suggest equifinality and asymmetry as there are multiple distinct pathways to environmental concern.
IMMUNOTHERAPY IN COMBINATION WITH BEHAVIORAL ENRICHMENT IN A CANINE MODEL OF AGING

OF DISSERTATION IMMUNOTHERAPY IN COMBINATION WITH BEHAVIORAL ENRICHMENT IN A CANINE MODEL OF AGING Alzheimer’s disease (AD) is characterized by cognitive decline and hallmark neuropathology, including β-amyloid (Aβ). Therapeutic strategies for AD are focusing on reducing Aβ. Canines develop Aβ neuropathology and cognitive decline with age similar to AD patients. In previous studies, immunization with Aβ1-42 (VAC) in aged canines decreased brain Aβ but did not improve cognition. Behavioral enrichment (ENR) improved cognition without reducing brain Aβ. We hypothesized that VAC combined with ENR would provide cognitive benefits and reduce Aβ neuropathology, as compared individual VAC and ENR treatments. Aged beagles were placed into groups: control, VAC with fibrillar Aβ1-42, ENR, and combination treatment (VAC+ENR) for 18 months. Learning and memory was evaluated throughout the study. Serum IgG antibody titers, cerebral spinal fluid (CSF) and brain Aβ were measured. Serum anti-Aβ1-42 IgG increased significantly in VAC animals. ENR but not VAC significantly increased CSF Aβ140. No cognitive improvements were observed in any group. VAC significantly reduced brain Aβ1-40 and 1-42, as well as reduced plaque load. An overall slowing of plaque accumulation was seen in the ENR group. VAC and ENR were able to modify pathology when used as separate treatments; however, the combination treatment did not succeed in further reducing Aβ or improving cognition. Previous AD clinical trials using immunotherapy yielded similar outcomes to our study showing reduced Aβ pathology but little to no cognitive improvements. In combination these results suggest that future studies should focus on prevention approaches both in the canine model and in human clinical trials.
In-orbit demonstration of artificial intelligence applied to hyperspectral and thermal sensing from space

cosine Remote Sensing is leading the first ever demonstration of on-board Artificial Intelligence (AI) applied to the combination of hyperspectral and thermal imaging. The sensing device is a miniaturized reflective optical instrument equipped with unprecedented processing capabilities. The European Space Agency (ESA) has contracted cosine Remote Sensing to highly integrate Thermal Infrared (TIR) technologies into a miniaturized Visible-Near-InfraRed (VNIR) hyperspectral imager and to fit the combined spectral channels in a volume of less than two litres. The imager is named HyperScout-2 as it will use the HyperScout-1 platform, that has flight heritage, as building block to further integrate spectral channels. HyperScout-2 is equipped with a hybrid processing platform composed of a CPU, GPU and VPU. The VPU is a state of art vision processing unit developed by Intel and is for the first time flew in space as part of HyperScout-2. HyperScout-2 will enable experimental programs to investigate the use of Artificial Intelligence for a variety of applications in the field of object detection and data inference. The first application that will be run on is cloud screening. HyperScout-2 will be used as in-orbit test-bed to benchmark the performance of such a miniaturized class of systems as well as to perform hands-on investigations to forecast the benefits of combining frequent coregistrated measurements in the VNIR and TIR from nanosatellites, with less frequent but very accurate measurements performed by institutional satellites such as the Copernicus fleet. This initiative is named PhiSat-1 and is part of the ESA EOP initiative to leverage small satellites to foster technology breakthrough developments. This contribution reports on a general description of HyperScout-2 as well as about the fast track program in which is implemented. We will also highlight the way the space asset will be exploited especially regarding the understanding of the potential of small systems deployed within small satellites constellation if integrated into ecosystems made of small and large systems. The PhiSat-1 is implemented as an enhancement of the FSSCat mission in a 6U cubesat based on Tyvak International platform integrating cosine Remote Sensing hyperspectral/thermal system. The cloud screening application is led by cosine Remote Sensing and supported by Sinergise, Ubotica and University of Pisa.
EMB: Efficient Multimedia Broadcast in Multi-Tier Mobile Networks

Multimedia broadcast and multicast services (MBMS) in mobile networks has been widely addressed, however an investigation of such a technology in emerging, multi-tier, scenarios is still lacking. Notably, user clustering and resource allocation are extremely challenging in multi-tier networks, and imperative to maximize system capacity and improve quality of user-experience (QoE) in MBMS. Thus, in this paper we propose a clustering and resource allocation approach, named EMB, which specifically addresses heterogeneous networks and accounts for the fact that multimedia content is adaptively encoded into scalable layers depending on the QoE requirements and channel conditions of the heterogeneous users. Importantly, we prove that our clustering algorithm yields Pareto efficient broadcasting areas, multimedia encoding parameters, and resource allocation, in a way that is also fair to the users. Furthermore, numerical results obtained under realistic conditions and using real-world video content, show that the proposed EMB results in lower churn count (i.e., higher number of served users), higher throughput, and increased QoE, while using fewer network resources.
Balancing Efficiency and Fairness in On-Demand Ridesourcing

We investigate the problem of assigning trip requests to available vehicles in on-demand ridesourcing. Much of the literature has focused on maximizing the total value of served requests, achieving efficiency on the passengers’ side. However, such solutions may result in some drivers being assigned to insufficient or undesired trips, therefore losing fairness from the drivers’ perspective. In this paper, we focus on both the system efficiency and the fairness among drivers and quantitatively analyze the trade-offs between these two objectives. In particular, we give an explicit answer to the question of whether there always exists an assignment that achieves any target efficiency and fairness. We also propose a simple reassignment algorithm that can achieve any selected trade-off. Finally, we demonstrate the effectiveness of the algorithms through extensive experiments on real-world datasets.
The effeciveness and determinants of effectiveness of antiretroviral therapy for adults in the Western Cape Province, South Africa

Antiretroviral therapy (ART) first became available in the public sector in the Western Cape Province in Khayelitsha in 2001. This thesis describes the effectiveness of ART in Khayelitsha and the Province, following adult patients for up to five years on ART, and examining temporal trends over seven years during which time the availability of ART in the Province increased dramatically. Associations are explored with a range of clinical outcomes, and regimen durability and tolerability are described, together with regimen effectiveness when ART is administered to patients co-infected with tuberculosis. The results chapters of the thesis are presented in the form of published or submitted papers. The first paper corrects for under-ascertainment of mortality through linkages with the death registry. After five years on ART, four out of five patients were still alive. Survival did not deteriorate in more recent years despite the large increase in patient numbers. Patients who remained virologically suppressed experienced on average continued CD4 count recovery throughout follow-up to five years. The second paper describes the tolerability of each commonly used first-line antiretroviral drug in two townships in the Western Cape. Treatment-limiting toxicities were frequent and continued throughout follow-up in patients on stavudine (21% by 3 years on ART). Symptomatic hyperlactataemia or lactic acidosis as well as lipodystrophy were strongly associated with women initiating ART with a high initial body mass. The third paper explores the effectiveness of ART when co-administered with tuberculosis treatment, identifying that co-infected patients initiating nevirapinebased ART may be at a higher risk of virological failure, but that concurrent tuberculosis treatment did not otherwise compromise ART outcomes. The fourth paper, based on a household survey, provides an in-depth description of the Khayelitsha population demonstrating comparability with many of the urban settings in which ART is provided in the region. The final paper demonstrates that outcomes have not been compromised by the wider availability of ART in the Western Cape Province. Un ive rsi ty of Ca p To wn The thesis concludes that the Khayelitsha and Provincial analyses provide considerable reassurance that the anticipated benefits of ART have not to date been eroded by health system weaknesses or contextual challenges. Un ive rsi ty of Ca pe To wn
Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition

In this paper, we study differentiable neural architecture search (NAS) methods for natural language processing. In particular, we improve differentiable architecture search by removing the softmax-local constraint. Also, we apply differentiable NAS to named entity recognition (NER). It is the first time that differentiable NAS methods are adopted in NLP tasks other than language modeling. On both the PTB language modeling and CoNLL-2003 English NER data, our method outperforms strong baselines. It achieves a new state-of-the-art on the NER task.
PRADO: Projection Attention Networks for Document Classification On-Device

Recently, there has been a great interest in the development of small and accurate neural networks that run entirely on devices such as mobile phones, smart watches and IoT. This enables user privacy, consistent user experience and low latency. Although a wide range of applications have been targeted from wake word detection to short text classification, yet there are no on-device networks for long text classification. We propose a novel projection attention neural network PRADO that combines trainable projections with attention and convolutions. We evaluate our approach on multiple large document text classification tasks. Our results show the effectiveness of the trainable projection model in finding semantically similar phrases and reaching high performance while maintaining compact size. Using this approach, we train tiny neural networks just 200 Kilobytes in size that improve over prior CNN and LSTM models and achieve near state of the art performance on multiple long document classification tasks. We also apply our model for transfer learning, show its robustness and ability to further improve the performance in limited data scenarios.