Miracle-dz/UniFAR-FTUs huggingface dataset:
Regression Analysis of Historical Blood Donors to Improve Clinic Scheduling

: The Canadian Blood Services (CBS) is responsible for the collection, storage and distribution of blood products throughout the country. Like all civilian hospitals and medical facilities, the Canadian Armed Forces (CAF) Health Services System relies on CBS to provide it with required blood products through the Canadian Armed Forces Blood Distribution System. Under normal circumstances, CBS collects all blood products through organized events including mobile and permanent clinics, where prospective donors attend via either pre-booked appointments or unscheduled walk-ins. Of those who make appointments, only a portion show-up for their appointment and of these only some yield a successful donation. As donation clinics are capacity-constrained by both the labour-force and infrastructure, CBS is motivated to maximize the utilisa-tion of existing resources through implementation of an overbooking policy. Leveraging historical data, a statistical analysis was conducted to identify factors inﬂuencing conversion rates to aid in developing an improved scheduling policy. The location of the centre, the day of the week as well as demographic groups were included as candidate independent variables in a regression model to forecast the proportion of pre-booked appointments that are attended and yield a collection.
Use of Language Models for Document Stream Segmentation

: Page stream segmentation into single documents is a very common task which is practiced in companies and administrations when processing their incoming mail. It is not a straightforward task because the limits of the documents are not always obvious, and it is not always easy to ﬁnd common features between the pages of the same document. In this paper, we seek to compare existing segmentation models and propose a new segmentation one based on GRUs (Gated Recurrent Unit) and an attention mechanism, named AGRU. This model uses the text content of the previous page and the current page to determine if both pages belong to the same document. So, due to its attention mechanism, this model is capable to recognize words that deﬁne the ﬁrst page of a document. Training and evaluation are carried out on two datasets: Tobacco-800 and READ-Corpus. The former is a public dataset on which our model reaches an F1 score equal to 90%, and the later is private for which our model reaches an F1 score equal to 96%.
Interdependent Multi-task Learning for Simultaneous Segmentation and Detection

: Lightweight, fast, and accurate deep-learning algorithms are essential for practical deployment in real-world use-cases. Semantic segmentation and object detection are the principal tasks of visual perception. A multi-task network signiﬁcantly reduces the number of parameters compared to two independent networks running simultaneously for each task. Generally, multi-task networks have shared encoders and multiple independent task-speciﬁc decoders. Instead, we modeled our network to exploit the features from both encoder and decoder. We propose the multi-task network that performs both segmentation and detection with only 37.9 million parameters and inference time of 74 milliseconds on a consumer-grade GPU. This network performs two tasks with much fewer parameters and in much less inference time compared to each single task network.
Exploring the Dependencies between Behavioral and Neuro-physiological Time-series Extracted from Conversations between Humans and Artificial Agents

: Whole-brain neuroimaging using functional Magnetic Resonance Imaging (fMRI) provides valuable data to localize brain activity in space and time. Here, we use a unique corpus including fMRI and behavior recorded when participants discussed with a human or a conversational robot. Temporal dynamic is crucial when studying conversation, yet identifying relationship between the participants’ behavior and their brain activity is technically challenging given the time resolution of fMRI. We propose here an approach developed to extract neurophysiological and behavioral time-series from the corpus and analyse their causal relationships. Pre-processing entails the construction of discrete neurophysiological time-series from functionally well deﬁned brain areas, as well as behavioral time-series describing higher-order behaviors extracted from synchronized raw audio, video and eyetracking recordings. The second step consists in applying machine learning models to predict brain activity on the basis of various aspects of behavior given knowledge about the functional role of the areas under scrutiny. Results demonstrate the speciﬁcity of the behaviors allowing the predictions of the activity in functional brain areas.
Femoral Artery Thrombosis in a Very Low Birth Weight Preterm Newborn

Abstract Tromboses are considered to be rare disorders in the pediatric population. However, they occur more frequently during the neonatal period. Potential risk factors for thrombosis onset include frequent use of peripheral, umbilical or central venous / arterial catheters, inflammation, disseminated intravascular coagulation, liver disorders, hypovolemia, asphyxia, congenital heart disease, polycythemia and dehydration. On the seventh day of life in male premature infant born in 29th / 30th gestation week, we noticed an increased level of inflammatory parameters. The patient’s right leg was edematous and cold with immeasurable pulse and pressure over the femoral artery, mottled skin and no spontaneous mobility. In the following days, edema becomes generalized and abdominal meteorism with bile vomiting appeared. Vascular surgeon established a diagnosis of the acute femoral artery stenosis (CW Doppler). We initiated continuous infusion of unfractionated heparin, but due to the development of hemorrhagic syndrome, next day we replaced it with low molecular weight heparin. The hemorrhagic disease was treated with tranexamic acid, fresh frozen plasma and concentrated platelets of the corresponding blood group. Signs of recirculation were registered with a palpable femoral pulse. CW Doppler confirmed complete recovery. In spite of the fact that antithrombotic therapy can cause numerous complications, it should be considered in vital indications.
Software Beamforming in Medical Ultrasound Imaging - a blessing and a curse

Medical ultrasound (US) imaging is a non-invasive imaging modality. Smaller and cheaper US systems make US imaging available to more people, leading to a democratization of medical US imaging. The improvements of general processing hardware allow the reconstruction of US images to be done in software. These implementations are known as software beamforming and provide access to the US data earlier in the processing chain. Adaptive beamforming exploits the early access to the full US data with algorithms adapting the processing to the data. Adaptive beamforming claims improved image quality. The improved image will potentially result in an improved diagnosis. Adaptive beamformers have seen enormous popularity in the research community with exponential growth in the number of papers published. However, the complexity of the algorithms makes them hard to re-implement, making a thorough comparison of the algorithms difficult. The UltraSound ToolBox (USTB https://www.USTB.no) is an open source processing framework facilitating the comparison of imaging techniques and the dissemination of research results. The USTB, including the implementation of several state-of-the-art adaptive beamformers, has partly been developed in this thesis and used to produce most of the results presented. The results show that some of the contrast improvements reported in the literature turn out to be from secondary effects of adaptive processing. More specifically, we show that many state-of-the-art algorithms alter the dynamic range. These dynamic range alterations are invalidating the conventional contrast metrics. Said differently; many adaptive algorithms are so flexible that they instead of improving the image quality are merely optimizing the metrics used to evaluate the image quality. We suggest a dynamic range test, compromising data, and code, to assess whether an algorithm alters the dynamic range. A thorough review of the contrast metrics used in US imaging shows there is no consensus on the metrics used in the research literature. Therefore, our introduction of the generalized contrast to noise ratio (GCNR) is essential since this is a contrast metric immune to dynamic range alterations. The GCNR is a remedy for the curse of the metric breaking abilities of software beamforming. Software beamforming also has its blessings. The flexible implementations made possible by software beamforming does lead to improved image quality. The improved resolution of the minimum variance adaptive beamformer does lead to enhanced visualization of the interventricular septum in the human heart. The ability to do beamforming in software allows the implementation of the full reconstruction chain from raw data to the final rendered images on an iPhone. As well as the results presented in the published papers, this thesis does a thorough review of the software beamforming processing chain as implemented in the USTB.
Análisis de estructuras temporales en euskera y creación de un corpus

espanolTesis titulada “Euskarazko denbora-egituren azterketa eta corpusaren sorrera / Analysis of Basque temporal constructions and the creation of a corpus”, defendida por Begona Altuna Diaz en la Universidad del Pais Vasco (UPV/EHU) y elaborada bajo la direccion de las doctoras Arantza Diaz de Ilarraza (Departamento de Lenguajes y Sistemas Informaticos) y Maria Jesus Aranzabe (Departamento de Lengua Vasca y Comunicacion). La defensa se celebro el 21 de noviembre de 2018 en la Facultad de Informatica (UPV/EHU) en San Sebastian ante el tribunal formado por Kepa Sarasola (Presidente, Universidad del Pais Vasco (UPV/EHU)), Itziar Aduriz (Secretaria, Universidad de Barcelona (UB)) y Ricardo Etxepare (Vocal, Centre National de Recherche Scientifique (CNRS)). La tesis obtuvo la calificacion de sobresaliente Cum Laude otorgada por unanimidad y mencion internacional. EnglishPh. D. thesis entitled “Euskarazko denbora-egituren azterketa eta corpusaren sorrera / Analysis of Basque temporal constructions and the creation of a corpus”, defended by Begona Altuna Diaz at the University of the Basque Country (UPV/EHU) under the supervision of Dr. Arantza Diaz de Ilarraza (Languages and Computer Systems Department) and Dr. Maria Jesus Aranzabe (Basque Language and Communication Department). The thesis defense was held on the 21st of November 2018 at the Computer Science Faculty (UPV/EHU) in San Sebastian and the members of the commission were Dr. Kepa Sarasola (President, University of Basque Country (UPV/EHU)), Dr. Itziar Aduriz (Secretary, Universidad de Barcelona (UB)) and Dr. Ricardo Etxepare (Vocal, Centre National de Recherche Scientifique (CNRS)). The thesis was awarded an excellent grade and Cum Laude honours and the international mention.
Sentiment Analysis from Sound Spectrograms via Soft BoVW and Temporal Structure Modelling

: Monitoring and analysis of human sentiments is currently one of the hottest research topics in the ﬁeld of human-computer interaction, having many applications. However, in order to become practical in daily life, sentiment recognition techniques should analyze data collected in an unobtrusive way. For this reason, analyzing audio signals of human speech (as opposed to say biometrics) is considered key to potential emotion recognition systems. In this work, we expand upon previous efforts to analyze speech signals using computer vision techniques on their spectrograms. In particular, we utilize ORB descriptors on keypoints distributed on a regular grid over the spectrogram to obtain an intermediate representation. Firstly, a technique similar to Bag-of-Visual-Words (BoVW) is used, where a visual vocabulary is created by clustering keypoint descriptors, but instead a soft candidacy score is used to construct the histogram descriptors of the signal. Furthermore, a technique which takes into account the temporal structure of the spectrograms is examined, allowing for effective model regularization. Both of these techniques are evaluated in several popular emotion recognition datasets, with results indicating an improvement over the simple BoVW method.
The challenge of individualistic mycelium

A marine propeller which has a boss having inner and outer cylinders coaxially disposed with each other in such a manner that at least one opposing surface of the inner and outer cylinders is obliquely formed in trough shape of wedge type and the other opposing surface thereof is obliquely formed correspondingly in crest shape of wedge type, and a resilient member disposed in the gap between the oblique opposing surfaces of the inner and outer cylinders so as to integrate both the cylinders.
A Highly Constrained Integer Linear Program with Fuzzy Boundary Conditions for Scheduling in Online Tutoring

: Scheduling personnel is an important aspect of many organizations and can rapidly become unsustainable to perform manually. It is crucial that the automated algorithm consistently generates a high quality schedule. This paper discusses a linear integer program with a set of constraints, developed in collaboration with a scheduling expert, required to generate reliable schedules for a live online tutoring platform. The focus in the algorithm is shifted away from the optimization of an objective function to adopting hard constraints that guarantee any solution will be sufﬁciently good. Furthermore, a graceful degradation protocol to implement fuzzy boundary conditions for the constraints ensure that appropriate compromises can be made in most cases to ﬁnd a solution. Lastly, a comparison of the automated algorithm to a domain expert shows a 5% higher topic coverage and 10% lower cost in favor of the former which has led to a rapid adoption of the automated scheduler by the organization.
Scientific and subversive: The two faces of the fourth era of political campaigning

This article sets out the case that democracies are now entering a fourth phase of “data-driven” political campaigning. Building on the existing campaigns literature, we identify several key shifts in practice that define the new phase, namely: (1) an organizational and strategic dependency on digital technology and “big data,” (2) a reliance on networked communication, (3) the individualized micro-targeting of campaign messages, and (4) the internationalization of the campaign sphere. Departing from prior studies, we also argue that the new phase is distinguished, by a bifurcation, into two variants—the scientific and the subversive. While sharing a common core, these two modes differ, in that the former retains a commitment to the normative goals of campaigning, that is, to mobilize and inform voters, while the latter explicitly rejects and subverts these aims, focusing instead on demobilization and the spread of misinformation. Both are presented as abstract or “ideal” types, although we do point out how features of each have appeared in recent election campaigns by mainstream and populist parties. We conclude by discussing the implications of these trends for the long-term future health of democracy.
A Method to Identify the Cause of Misrecognition for Offline Handwritten Japanese Character Recognition using Deep Learning

: In this research, we propose a method to identify the cause of misrecognition in offline handwritten character recognition using a convolutional neural network (CNN). In our method, the CNN learns not only character images augmented by applying an image processing method, but also those generated from character models with stroke structures. Using these character models, the proposed method can generate character images which lack one stroke. By learning the augmented character images lacking a stroke, the CNN can identify the presence of each stroke in the characters to be recognized. Subsequently, by adding dense layers to the final layer and learning the character images, obtaining the CNN for the offline handwritten character recognition becomes possible. The obtained CNN has nodes that can represent the presence of the strokes and can identify which strokes are the cause of misrecognition. The effectiveness of the proposed method is confirmed from character recognition experiments targeting 440 types of Japanese characters.
An Improvement of Genetic Algorithm based on Dynamic Operators Rates Controlled by the Population Performance

: This work presents a hybrid approach of genetic algorithm with dynamic operators rates that adapt to the phases of the evolutionary process. The operator’s rates are controlled by the amplitude variation and standard deviation of the objective function. Besides, a new stopping criterion is presented to be used in conjunction with the proposed algorithm. The developed approach is tested with six optimization benchmark functions from the literature. The results are compared to the genetic algorithm with constant rates in terms of the number of function evaluations, the number of iterations, execution time and optimum solution analysis.
FotonNet: A Hardware-efficient Object Detection System using 3D-depth Segmentation and 2D-deep Neural Network Classifier

: Object detection and classiﬁcation is one of the most crucial computer vision problems. Ever since the introduction of deep learning, we have witnessed a dramatic increase in the accuracy of this object detection problem. However, most of these improvements have occurred using conventional 2D image processing. Recently, low-cost 3D-image sensors, such as the Microsoft Kinect (Time-of-Flight) or the Apple FaceID (Structured-Light), can provide 3D-depth or point cloud data that can be added to a convolutional neural network, acting as an extra set of dimensions. We are proposing a hardware-based approach for Object Detection by moving region of interest identiﬁcation closer to sensor node in the hardware. Due to this approach, we do not need a large dataset with depth images to retrain the network. Our 2D + 3D system takes the 3D-data to determine the object region followed by any conventional 2D-DNN, such as AlexNet. In this method, our approach can readily dissociate the information collected from the Point Cloud and 2D-Image data and combine both operations later. Hence, our system can use any existing trained 2D network on a large image dataset and does not require a large 3D-depth dataset for new training. Experimental object detection results across 30 images show an accuracy of 0.67, whereas 0.54 and 0.51 for FasterRCNN and YOLO, respectively.
Hate Speech Detection using Word Embedding and Deep Learning in the Arabic Language Context

: Hate speech over online social networks is a worldwide problem that leads for diminishing the cohesion of civil societies. The rapid spread of social media websites is accompanied with an increasing number of social media users which showed a higher rate of hate speech, as well. The objective of this paper is to propose a smart deep learning approach for the automatic detection of cyber hate speech. Particularly, the detection of hate speech on Twitter on the Arabic region. Hence, a dataset is collected from Twitter that captures the hate expressions in different topics at the Arabic region. A set of features extracted from the dataset based on a word embedding mechanism. The word embeddings fed into a deep learning framework. The implemented deep learning approach is a hybrid of convolutional neural network (CNN) and long short-term memory (LSTM) network. The proposed approach achieved good results in classifying tweets as Hate or Normal regarding accuracy, precision, recall, and F1 measure.
Acute cardiotoxicity associated with alemtuzumab infusion for multiple sclerosis

Alemtuzumab infusion is rarely associated with serious cardiac toxicity. We report a case of acute troponin-negative chest pain with dynamic T-wave changes, immediately following first infusion of alemtuzumab in a patient with multiple sclerosis. The chest pain and ECG (electrocardiogram) changes improved with cessation of alemtuzumab and conservative management. The presumed cause was infusion-associated cytokine release, but the precise mechanism is unknown.
Intravenous immunoglobulin significantly reduces exposure of concomitantly administered anti‐C5 monoclonal antibody tesidolumab

Awareness of drug‐drug interactions is critical in organ transplant recipient management. However, biologic agents interfering with monoclonal antibodies is not widely considered. We report the effect of high‐dose intravenous immunoglobulin (IVIg) on safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) of the human anti‐C5 monoclonal antibody tesidolumab (LFG316) in end‐stage renal disease patients awaiting kidney transplant. In this single‐center, phase 1, open‐label, parallel‐group study, 8 patients were assigned to receive either single‐dose tesidolumab + IVIg or tesidolumab alone, with 56‐day follow‐up. Within‐group PK parameters were consistent. Mean tesidolumab exposure decreased 34%, clearance increased 63%, and half‐life decreased 41% comparing tesidolumab + IVIg to tesidolumab alone. IVIg influence on tesidolumab elimination was most evident in the first 3 weeks. Complete suppression of both total and alternative complement activities was maintained for 4 weeks in the tesidolumab alone group and for 2 weeks in the tesidolumab + IVIg group. Tesidolumab was well tolerated. IVIg infused before tesidolumab affected tesidolumab PK and PD, resulting in a shortened period of full complement activity inhibition. These findings suggest a clinically relevant impact of IVIg on monoclonal antibody clearance and indirectly hint at an IVIg mechanism of action in treating autoimmune diseases and allosensitization by accelerating pathogenic IgG antibody degradation.
Targeting spinal TRAF6 expression attenuates chronic visceral pain in adult rats with neonatal colonic inflammation

Background Irritable bowel syndrome is one of the most common gastrointestinal disorders. It is featured by abdominal pain in conjunction with altered bowel habits. However, the pathophysiology of the syndrome remains largely unknown. Tumor necrosis factor receptor-associated factor 6 (TRAF6) has been reported to be involved in neuropathic pain. The aim of this study was to investigate roles and mechanisms of TRAF6 in the chronic visceral hypersensitivity. Methods Visceral hypersensitivity was induced by neonatal colonic inflammation and was identified by colorectal distention. The protein level, RNA level, and cellular distribution of TRAF6 and its related molecules were detected with Western blot, quantitative polymerase chain reaction, and immunofluorescence. In vitro spinal cord slice recording technique was performed to determine the synaptic transmission activities. Results Neonatal colonic inflammation rats displayed visceral hypersensitivity at the age of six weeks. The expression of TRAF6 was obviously upregulated in spinal cord dorsal horn of neonatal colonic inflammation rats at the age of six weeks. Immunofluorescence study showed that TRAF6 was dominantly expressed in spinal astrocytes. Intrathecal injection of TRAF6 small interfering RNA (siRNA) significantly reduced the amplitude of spontaneous excitatory postsynaptic currents at the spinal dorsal horn level. Furthermore, knockdown of TRAF6 led to a significant downregulation of cystathionine β synthetase expression in the spinal dorsal horn of neonatal colonic inflammation rats. Importantly, intrathecal injection of TRAF6 siRNA remarkably alleviated visceral hypersensitivity of neonatal colonic inflammation rats. Conclusions Our results suggested that the upregulation of TRAF6 contributed to visceral pain hypersensitivity, which is likely mediated by regulating cystathionine β synthetase expression in the spinal dorsal horn. Our findings suggest that TRAF6 might act as a potential target for the treatment of chronic visceral pain in irritable bowel syndrome patients.