Miracle-dz/UniFAR-FTUs huggingface dataset:
Thrombolysis with rt-PA for an ischemic stroke in boy treated with Fontan operation

Abstract We first report the case of a 14-year-old boy, previously treated with a modified Fontan operation at the age of 2, who underwent an intra-arterial thrombolysis for an ischemic stroke with a favorable outcome. No controlled randomized trials on thrombolysis among childhood stroke have been conducted yet. This and other similar cases suggest that thrombolysis may also be safe and beneficial in pediatric patients and those cases may benefit from a prompt access to specialized stroke centers.
Relying on treeOPT Algorithm

Research in collaborative editing tends to have been undertaken in isolation rather than as part of a general information or application infrastructure. Our goal is to develop a universal information platform that can support collaboration in a range of application domains. Since not all user groups have the same conventions and not all tasks have the same requirements, this implies that it should be possible to customize the collaborative editor at the level of both communities and individual tasks. One of the keys to customization is to use a structured rather than linear representation of documents that can be applied to both textual and graphical editors. In this paper, we propose the treeOPT (tree OPerational Transformation) algorithm that, relying on a tree representation of documents, applies the operational transformation mechanism recursively over the different document levels. Applications using this algorithm achieve better efficiency, the possibility of working at different granularity levels and improvements in the semantic consistency.
The Electric Autonomous Diala-Ride Problem : An Optimization Framework for Routing , Scheduling , and Battery Management

Ride sharing is modifying urban mobility by offering reliable and convenient on-demand door-to-door services at any time. This new buiness model is of great interest in the area of city logistics and transportation engineering, as it creates an alternative between private cars (which are expensive, experience congestion, and are polluting) and public transport (which have fixed times and higher accessibility costs). Given the constant increase in demand, ride-sharing businesses are currently planning to expand their portfolio to include on-demand door-to-door transit by the use of electric Autonomous Vehicles (AVs) ([1],[2]). The Dial-a-Ride Problem (DARP) is a class of combinatorial optimization problems developed for the operational planning of on-demand door-to-door transportation systems. The DARP consists in defining minimum cost routes and schedules for a fleet of vehicles serving a set of customers with given pickup and dropoff locations. Typically, the objective function considers the operator’s interest, although some studies have also considered users’ criteria [3]. Common operational constraints include vehicles’ capacities, maximum vehicles-route duration, and maximum user ride times. In addition, service start times at pickup and dropoff locations are usually limited by time-window constraints. Finally, static and dynamic versions of the DARP have been widely studied in the literature. In the first case, demand is fully known in advance, whereas in the second case demand is revealed online. For further details on DARP models and algorithms, the reader is referred to [3]. The autonomous vehicles at the focus of this study are electric, thus the planning problem is supplemented with a battery management problem. That is, to ensure continuous and safe service, the batteries’ state of charge should be maintained between predefined bounds. In the vehicle routing literature, several recent studies have incorporated battery management aspects ([4],[5],[6],[7]). However, to the best of our knowledge, battery management has not been studied in the context of DARP. In this study, we introduce the Electric Autonomous Dial-a-Ride Problem (e-ADARP). The e-ADARP objective incorporates minimizing vehicles’ routing costs and users’ excess ride time. We focus on the static version of the problem and assume all demands need to be satisfied. The e-ADARP process flow is given in Figure 1. Dynamic cases will be considered in a future work.
The legendary Asch conformity study reconsidered

The Asch Experiments: The Classical Psychological Study of Conformity We behave under the influence of others, such as parents, teachers, supervisors, friends, and even unknown others. We tend to choose the merchandises our favorite stars or athletes recommend in TV commercials. Many people consult the comments on the Internet when they choose where to dine in a city, even though they were written by totally unknown others. We are a species of social animals. Therefore, it is no wonder we are under the influence of the society we belong. Researchers in social psychology named this influenced behavior “conformity,” and have experimentally studied a variety of factors affecting the conformity behavior in the laboratory. Among them, the most well-known is the Asch conformity experiment (Asch, 1956). Solomon Asch [1907-1996: Fig.1] was a Polish-born social psychologist in the US. Asch presented a series of figures as shown in Fig.2 and asked a participant to choose the line from the right side that appeared the same length as the line on the left. It was a simple task, so no one would make an erroneous choice under normal condition. However, Asch demonstrated that about 30% of participants made wrong choices when the other responders answered a wrong line unanimously. Asch had trained those responders beforehand to pretend they were participants and give wrong answers to apply social pressure to the genuine participants who were to answer under pressure. The Asch experiments have been replicated under various conditions in a variety of cultural background (see Bond & Smith, 1996). Now, it is regarded as a psychological fact that people tend to conform to the majority even in a simple perceptual task. Fig.1: Solomon E. Asch
b-carotene and a-tocopherol coencapsulated in nanostructured lipid carriers of murumuru ( Astrocaryum murumuru ) butter produced by phase inversion temperature method : characterisation , dynamic in vitro digestion and cell viability study

Hydrophobic bioactives can be more easily incorporated into food and have their bioavailability enhanced if nanostructured lipid carriers (NLC) are used as carriers. In the present study, betacarotene-loaded NLC were produced by low emulsification using murumuru butter and a mixture of Span 80 and Cremophor RH40 as surfactants. Their average diameter was 35nm and alpha-tocopherol was required to protect the encapsulated b-carotene. Besides the evaluation of their physicochemical stability, NLC were submitted to dynamic in vitro digestion and cell viability assays with Caco-2 and HEPG cells. The bioaccessibility of beta-carotene in the dynamic system was about 42%. Regarding cell viability, results indicated NLC were toxic to the cell cultures tested. Such high toxicity is probably related to the type of surfactant used and to the extremely reduced particle size, which may have led to an intense and fast permeation of the NLC through the cells. ARTICLE HISTORY Received 1 October 2018 Accepted 14 February 2019
Time-Modulated Hardware Trojans : Clock-Based and Interface-Based Examples

Hardware Trojans are an important threat to the security of integrated circuits. They assume a malicious manufacturer able to infect implementations with hard-to-detect circuit modifications that can compromise their security. Hardware Trojans are sometimes classified as digital (if they are triggered and send their payload as regular outputs on a communication interface) or physical (if they are triggered and send their payload via a physical side-channel such as an EM signal). Typical examples of digital hardware Trojans are cheat codes, which are triggered under some rare input conditions, and time bombs, which are triggered when a counter internal to the implementation reaches some value. In this paper, we investigate a class of physical hardware Trojans that can trigger malicious circuitry thanks to a standard communication interface (as a digital hardware Trojan), by exploiting a timing side-channel. We denote these physical hardware Trojans as Time-Modulated, since they exploit the rhythm at which computations are performed, and provide two exemplary instances of such Trojans. The first one is clock-based: it exploits a recent idea of hardware Trojan using a side-channel by Ender et al. at ASIACRYPT 2017, and can inject an exploitable fault that applies to any AES implementation. The second one is interface-based: it exploits the delays between multiple message blocks as proposed by Shield et al. at AISC 2015. We extend this work to describe denial-of-service and key recovery attacks against a Trojanresilient implementation designed following a recent proposal by Dziembowski et al. at CCS 2016. Despite the latter did only claim security against arbitrary digital hardware Trojans, our results show that limited additional (physical) capabilities allow an adversary to circumvent these formal security guarantees.
Mapping temporal extent of Chiang Mai floods using coupled 1-D and quasi 2-D floodplain inundation models

A coupling of a 1-D flood routing model and quasi 2-D floodplain inundation model is applied for mapping spacetime flood extent. The routing model is formulated based on a non-linear storage-discharge relationship which is converted from an observed and synthetic rating curve. To draw the rating curve, required parameters for each reaches are estimated from hydraulic properties, floodplain geometry and vegetation and building cover of compound channels. The shape of the floodplain is defined by using fitting exercise based on the reverse approach between past and simulated inundation flood extent, to solve the current problem of inadequate topographic input data for floodplain. Mapping of daily flood can be generated relying on flat water levels. The quasi 2-D raster model is tested and applied to generate more realistic water surface and is used to estimate flood extent. The model is applied to the floodplains of Chiang Mai, north of Thailand and used to estimate a time series of hourly flood maps. Extending from daily to hourly flood extent, mapping development provides more details of flood inundation extent and depth.
Taming Latency in Data Centers Via Active Congestion-Probing

In cloud environments, interactive applications deployed in data centers often generate swarms of short-lived data transfers (or flows) that face dramatic competition for the scarce switch buffer space from other short-lived as well as the long-lived flows. In the presence of bloated queues, such short-lived flows often experience multiple packet losses per round-trip time which often triggers the timeout-based loss recovery mechanism. A direct consequence of this is an inflated application response time that turns out to be orders of magnitude larger than what it should be. A data center aware TCP protocol (DCTCP) was designed as a new TCP specifically to address this issue, however, it does not consider its co-existence with other transport protocol (e.g., CuBIC and NewReno of Linux). In such situations, which are abundant in multi-tenant data centers, the legacy large initial congestion window sizes (e.g., 10 segments), induce multiple packet losses at the onset of a TCP flow, which forces timeout and even binary exponential backoff. In this paper, we propose a novel Hypervisor-based, application-transparent approach for active congestion probing to enable the hypervisor to infer on-path congestion before the TCP connection is fully established for new traffic to avoid such massive packet losses and timeout. The so-called ProBoSCIS mechanism does not require any changes to TCP, works with all versions of TCP and does not need any special network hardware features other than those that exist in today's data center commodity switches. We show its effectiveness via ns2 simulation and demonstrate its practical feasibility by implementing and deploying it in a small-scale data center test-bed. We show the significant reduction in application latency by adopting ProBoSCIS in a series of real experiments.
An Hα kinematic survey of the Herschel Reference Survey

Aims. We present new 2D high resolution Fabry–Perot spectroscopic observations of 152 star-forming galaxies that are part of the Herschel Reference Survey (HRS), which is a complete K-band selected, volume-limited sample of nearby galaxies that spans a wide range of stellar mass and morphological types. Methods. By using improved data reduction techniques, that provide adaptive binning based on Voronoi tessellation, and using large field-of-view observations, we derived high spectral resolution (R > 10 000) Hα datacubes from which we computed Hα maps and radial 2D velocity fields that are based on several of thousand independent measurements. A robust method based on such fields allowed us to accurately compute rotation curves and kinematical parameters, for which uncertainties are calculated using a method based on the power spectrum of the residual velocity fields. Results. We checked the consistency of the rotation curves by comparing our maximum rotational velocities to those derived from H I data, and by computing the i-band, NIR, stellar, and baryonic Tully-Fisher relations. We used this set of kinematical data combined with those available at other frequencies to study, for the first time, the relation between the dynamical and the total baryonic mass (stars, atomic and molecular gas, metals, and dust) and to derive the baryonic and dynamical main sequence on a representative sample of the local universe.
Neural mechanisms underlying the computation of socially inferred rewards

No one knows everything. Therefore, it is often not enough to rely solely on one’s own knowledge, nor to indiscriminately follow advice from others. The current work examines the neural systems that support the human ability to capitalize on imperfect social information to support decision-making. Participants completed an fMRI task where they could choose to stay with an option of known value or switch to a hidden option, while receiving advice from an advisor who had access to both options, no options, or only the option that was hidden from participants. First, we find that value-guided regions (including dorsal striatum, dMPFC) preferentially track the expected value of the hidden option when it is the only option the advisor can access. Second, the advisor’s knowledge state is represented in regions that support social reasoning (precuneus, vMPFC). Our results suggest that neural systems that support social cognition and value-based decision-making support computations that enable humans to harness social information to vicariously explore the value of latent options.
Multi-view Multi-task Learning for Improving Autonomous Mammogram Diagnosis

The number of women requiring screening and diagnostic mammography is increasing. The recent promise of machine learning on medical images have led to an inﬂux of studies using deep learning for autonomous mammogram diagnosis. We present a novel multi-view multi-task (MVMT) convolutional neural network (CNN) trained to predict the radiological assessments known to be associated with cancer, such as breast density, conspicuity, etc., in addition to cancer diagnosis. We show on full-ﬁeld mammograms that multi-task learning has three advantages: 1) learning reﬁned feature representations associated with cancer improves the classiﬁcation performance of the diagnosis task, 2) issuing radiological assessments provides an additional layer of model interpretability that a radiologist can use to debug and scrutinize the diagnoses provided by the CNN, and 3) improves the radiological workﬂow by providing automated annotation of radiological reports. Results obtained on a private dataset of over 7,000 patients show that our MVMT network attained an AUROC and AUPRC of 0 . 855 ± 0 . 021 and 0 . 646 ± 0 . 023, respectively, and improved on the performance of other state-of-the-art multi-view CNNs.
Effect of Graphitic Nanomaterials on Thermal, Mechanical and Morphological Properties of Polypropylene Nanocomposites

Graphitic nanomaterials such as GO, RGO and carbon nanotubes (CNT) have attracted great interest in recent years because of their various applications such as mechanical, thermal, optical, and electrical properties [1-6]. PP is one of the commercial importance polyolefins because of its cost-effectiveness as well as intrinsic properties of low density, high stiffness, good tensile strength and inertness toward acids, alkalis and solvents [7]. PP has also been used in a wide range of applications including packaging [8], textiles [9], automotive components [10,11], aerospace [12,13], home applications [14], construction [15,16] etc. as per say; therefore, the improvement in the properties of PP is further needed. Researchers around the world tried various nanofillers such as MMT [17], nTiO2 [18,19], nBaCO3 [20], CaCO3 [21], nano polystyrene [22], Tea Dust /GO [23], graphene [24,25] and CNT [26] to improve thermal and mechanical properties of PP matrix. Some of the researchers used functionalization of graphene using 4,4’-diphenylmethane diisocyanate (MDI) and stearic acid to improve thermal and mechanical properties of PP [24]. Ryu [27] have done the functionalization of GO by alkylamine, and studied the crystallization, mechanical and electrical properties of isotactic polypropylene nanocomposites. Polypropylene composites of alkylamine-modified GO were prepared through a melt blending technique using maleic anhydride-grafted PP as a compatibilizer. Significant improvements were observed in melt crystallization characteristics, mechanical and electrical properties upon loading alkyl amine-modified GO in PP due to strong interfacial interactions between the polymer and modified GO.
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling

A core problem of information retrieval (IR) is relevance matching, which is to rank documents by relevance to a user’s query. On the other hand, many NLP problems, such as question answering and paraphrase identification, can be considered variants of semantic matching, which is to measure the semantic distance between two pieces of short texts. While at a high level both relevance and semantic matching require modeling textual similarity, many existing techniques for one cannot be easily adapted to the other. To bridge this gap, we propose a novel model, HCAN (Hybrid Co-Attention Network), that comprises (1) a hybrid encoder module that includes ConvNet-based and LSTM-based encoders, (2) a relevance matching module that measures soft term matches with importance weighting at multiple granularities, and (3) a semantic matching module with co-attention mechanisms that capture context-aware semantic relatedness. Evaluations on multiple IR and NLP benchmarks demonstrate state-of-the-art effectiveness compared to approaches that do not exploit pretraining on external data. Extensive ablation studies suggest that relevance and semantic matching signals are complementary across many problem settings, regardless of the choice of underlying encoders.
Approaches in Educational Psychology Training and Practice

This article has two aims: to introduce participatory research approaches with children and young people, and to consider such approaches within educational psychology training, practice and research. A range of ways of conceptualising and approaching participatory research is explored. Models applied to researching with children and young people specifically are then explained. A critical analysis of participatory research methods is offered, outlining powerrelated criticisms, ethical considerations and practical issues. The focus then turns to educational psychology, looking at applications of such approaches in researching with children and young people, the group that educational psychologists (EPs) work with predominantly. It is proposed that participatory research methods are highly relevant to the profession, both in the training of EPs and for practising psychologists. This is set in the context of the Doctorate in Educational and Child Psychology at the University of East London. It is suggested that educational psychology research should take an increasingly participatory focus.
THE BONE-INDUCING AGENT IN SAOS-2 CELL EXTRACTS AND SECRETIONS

An extractable bone-inducing agent (BIA) is present in lysates of Saos-2 cultured human osteosarcoma cells. Recently it was observed that: (1) Saos-2 cells secrete BIA into their conditioned medium as well as retaining it intracellularly, and (2) Saos-2 cells express bone morphogenetic proteins (BMPs)-1, 2, 3, 4 and 6. Any or all of these BMPs (plus possible unknown factors) may explain Saos-2 cell bone induction. However, we also found that U20S human osteosarcoma cells (comparable to Saos-2) also express high levels of BMPs, but are non-osteoinductive. These data suggest that Saos-2 cells may express a unique combination of morphogenetic factors and/or previously unidentified osteoinductive molecule(s).
One-Class Classification for high-dimensional data by Faris Elghlan

This M.Sc. thesis report investigates the application of one-class classification techniques to complex highdimensional data. The aim of a one-class classifier is to separate target data from non-target data, but only a dataset containing target data is available for training. The issue with high-dimensional data is that it is difficult to perform density estimation due to the ‘curse of dimensionality’. Most conventional method for one-class classification rely on density estimation. This thesis focusses on the use of autoencoders and generative adversarial networks (GANs) for one-class classification problems involving image data. Autoencoders can learn encoding and decoding functions for samples from the target dataset. These encoding and decoding functions are, however, expected to not perform well for non-target samples, as they have never been seen during the training phase. This makes it possible to separate target and non-target data. For GANs, the discriminator is used to distinguish between target and non-target data. Autoencoders and GANs are evaluated extensively in this report. Their behavior, desired parameters and strengths and weaknesses are evaluated by performing experiments. The main findings are that GANs do not perform well for one-class classification tasks, because of mode collapse and insufficient sampling of the non-target data. Even for extremely simple datasets these issues were observed. Autoencoders are shown to perform much better and behave according to the theoretical expectations. Faris Elghlan Delft, August 2019