Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy and tolerability of adjunctive lacosamide in pediatric patients with focal seizures

Objective To evaluate efficacy and tolerability of adjunctive lacosamide in children and adolescents with uncontrolled focal (partial-onset) seizures. Methods In this double-blind trial (SP0969; NCT01921205), patients (age ≥4–<17 years) with uncontrolled focal seizures were randomized (1:1) to adjunctive lacosamide/placebo. After a 6-week titration, patients who reached the target dose range for their weight (<30 kg: 8–12 mg/kg/d oral solution; ≥30–<50 kg: 6–8 mg/kg/d oral solution; ≥50 kg: 300–400 mg/d tablets) entered a 10-week maintenance period. The primary outcome was change in focal seizure frequency per 28 days from baseline to maintenance. Results Three hundred forty-three patients were randomized; 306 (lacosamide 152 of 171 [88.9%]; placebo 154 of 172 [89.5%]) completed treatment (titration and maintenance). Adverse events (AEs) were the most common reasons for discontinuation during treatment (lacosamide 4.1%; placebo 5.8%). From baseline to maintenance, percent reduction in focal seizure frequency per 28 days for lacosamide (n = 170) vs placebo (n = 168) was 31.7% (p = 0.0003). During maintenance, median percent reduction in focal seizure frequency per 28 days was 51.7% for lacosamide and 21.7% for placebo. Fifty percent responder rates (≥50% reduction) were 52.9% and 33.3% (odds ratio 2.17, p = 0.0006). During treatment, treatment-emergent AEs were reported by 67.8% lacosamide-treated patients (placebo 58.1%), most commonly (≥10%) somnolence (14.0%, placebo 5.2%) and dizziness (10.5%, placebo 3.5%). Conclusions Adjunctive lacosamide was efficacious in reducing seizure frequency and generally well tolerated in patients (age ≥4–<17 years) with focal seizures. ClinicalTrials.gov identifier: NCT01921205. Classification of evidence This trial provides Class I evidence that for children and adolescents with uncontrolled focal seizures, adjunctive lacosamide reduces seizure frequency.
Coner : A Collaborative Approach for Long-Tail Named Entity Recognition in Scienti c

Named Entity Recognition (NER) for rare long-tail entities as e.g., often found in domain-specific scientific publications is a challenging task, as typically the extensive training data and test data for finetuning NER algorithms is lacking. Recent approaches presented promising solutions relying on training NER algorithms in an iterative weaklysupervised fashion, thus limiting human interaction to only providing a small set of seed terms. Such approaches heavily rely on heuristics in order to cope with the limited training data size. As these heuristics are prone to failure, the overall achievable performance is limited. In this paper, we therefore introduce a collaborative approach which incrementally incorporates human feedback on the relevance of extracted entities into the training cycle of such iterative NER algorithms. This approach, called Coner, allows to still train new domain specific rare long-tail NER extractors with low costs, but with ever increasing performance while the algorithm is actively used in an application.
Daily Aircraft Routing and Scheduling

In this paper we consider the daily aircraft routing and scheduling problem DARSP. It consists of determining daily schedules which maximize the anticipated profits derived from the aircraft of a heterogeneous fleet. This fleet must cover a set of operational flight legs with known departure time windows, durations and profits according to the aircraft type. We present two models for this problem: a Set Partitioning type formulation and a time constrained multicommodity network flow formulation. We describe the network structure of the subproblem when a column generation technique is applied to solve the linear relaxation of the first model and when a Dantzig-Wolfe decomposition approach is used to solve the linear relaxation of the second model. The linear relaxation of the first model provides upper bounds. Integer solutions to the overall problem are derived through branch-and-bound. By exploiting the equivalence between the two formulations, we propose various optimal branching strategies compatible with the column generation technique. Finally we report computational results obtained on data provided by two different airlines. These results show that significant profit improvement can be generated by solving the DARSP using our approach and that this can be obtained in a reasonable amount of CPU time.
Ethnographic study using Normalization Process Theory to understand the implementation process of infection prevention and control guidelines in Ireland

Objective The aim of this study was to explore how infection prevention and control (IPC) guidelines are used and understood by healthcare professionals, patients and families. Design Ethnographic study with 59 hours of non-participant observation and 57 conversational interviews. Data analysis was underpinned by the Normalization Process Theory (NPT) as a theoretical framework. Setting Four hospitals in Ireland. Participants Healthcare professionals, patient and families. Results Five themes emerged through the analysis. Four themes provided evidence of the NPT elements (coherence, cognitive participation, collective action and reflexive monitoring). Our findings revealed the existence of a ‘dissonance between IPC guidelines and the reality of clinical practice’ (theme 1) and ‘Challenges to legitimatize guidelines’ recommendations in practice’ (theme 3). These elements contributed to ‘Symbolic implementation of IPC guidelines’ (theme 2), which was also determined by a ‘Lack of shared reflection upon IPC practices’ (theme 4) and a clinical context of ‘Workforce fragmentation, time pressure and lack of prioritization of IPC’ (theme 5). Conclusions Our analysis identified themes that provide a comprehensive understanding of elements needed for the successful or unsuccessful implementation of IPC guidelines. Our findings suggest that implementation of IPC guidelines is regularly operationalised through the reproduction of IPC symbols, rather than through adherence to performance of the evidence-based recommendations. Our findings also provide insights into changes to make IPC guidelines that align with clinical work.
An a posteriori error analysis based on non-intrusive spectral projections for systems of random conservation laws

We present an a posteriori error analysis for one-dimensional random hyperbolic systems of conservation laws. For the discretization of the random space we consider the Non-Intrusive Spectral Projection method, the spatio-temporal discretization uses the Runge--Kutta Discontinuous Galerkin method. We derive an a posteriori error estimator using smooth reconstructions of the numerical solution, which combined with the relative entropy stability framework yields computable error bounds for the space-stochastic discretization error. Moreover, we show that the estimator admits a splitting into a stochastic and deterministic part.
ground penetrating radar and electrical resistivity tomography for locating buried building foundations: a case study in the city centre of Thessaloniki, Greece

Ground penetrating radar (GPR) and electrical resistivity tomography (ERT) surveys have been carried out in the city centre of Thessaloniki (N. Greece), for investigating possible locations of buried building foundations. Geophysical survey has been chosen as a non-destructive investigation method since the area is currently used as a car parking and it is covered by asphalt. The geoelectrical sections derived from ERT data in combination with the GPR profiles provided a broad view of the subsurface. Regarding ERT, high resistivity values can be related to buried building remains, while lower resistivity values are more related to the surrounding geological materials. GPR surveying can also indicate man-made structures buried in the ground. Even though the two geophysical methods are affected in different ways by the subsurface conditions, the processed underground images from both techniques revealed great similarity. High resistivity anomalies and distinct GPR signals were observed in certain locations of the area under investigation, which are attributed to buried building foundations as well as the geological structure of the area.
Genotoxic action of Luna Experience-SC 400 fungicide on rat bone marrow

Abstract Background: Fungicides describe all chemicals used to control fungi that infect plants. Luna Experience SC-400 is a new line of fungicide that consist of Fluopyram and Tebuconazole. Objective: In this study, We investigated the genotoxicty and cytotoxicty of Luna Experience-SC 400 using comet assay, micronucleus test and polychromatic erythrocytes number in rat bone marrow. The present study is the first report indicating the effects of genotoxic and cytotoxic of Luna experience SC-400 on rat bone marrow cells. Material and Methods: We used three different doses (5mg/kg, 10mg/kg, 20mg/kg) of Luna Experience SC 400 at 48 h intervals during 30 days by gavage in rats.Genotoxicity was evaluated using comet assay and micronucleus test and cytotoxicity was measured the PCE/NCE rate in rat bone marrow. Results: Based on these experimental results, we report that Luna Experience-SC 400 fungicide presents genotoxic and cytotoxic potential on rat bone marrow. There is a significant difference between negative control group and all the doses of Luna Experience-SC 400 (p < 0.05) for comet assay and micronucleus. Even moderate and high doses of fungicides seem to have reached the values of almost positive control group for Genetic Damage Index (GDI) and Damaged Cell Percentage (DCP). In this study, we also investigated the PCE/NCE rate. Fungicide caused a decrease in the level of significant in the PCE/NCE ratio (p < 0.05). Conclusion: Our in vivo study suggests that the gavage exposure to Luna experience SC 400 used in the present investigation may be genotoxic and cytotoxic in rat bone marrow in view of these findings. Because this findings is first report represented in the pesticide biology, it is important to carry out more investigations using various cytogenetic tests under different experimental conditions to definitively resolve the the possible genotoxic and cytotoxic risk associated with new generation pesticides-fungicides.
DO ADOLESCENTS WITH T 1 DM DIFFER FROM THEIR PEERS IN HEALTH , EATING HABITS AND SOCIAL SUPPORT ?

Objective: The aim of this study was to analyse differences in health, eating habits and social support in adolescents with type 1 diabetes mellitus (T1DM) in comparison to peers with another long-term illness or without any medical condition. Methods: We used self-reported data from the cross-sectional Health Behaviour in School-aged Children study collected in 2014 among Slovak adolescents as well as data from adolescents with T1DM collected in outpatient settings (11 to 15 years old, N = 8,910, 50.3% of boys). Logistic regression models and general linear models were used to analyse differences between adolescents with T1DM and their peers with and without long-term illness in self-rated health, life satisfaction, health complaints, regular breakfast, sweets and soft drink consumption, and perceived support from family, teachers and classmates. Results: Adolescents with T1DM reported worse self-rated health and suffer from more health complaints, but they have lower chance of having breakfast irregularly in comparison to their peers with another long-term illness or without any medical condition. Moreover, compared with their peers, adolescents with T1DM perceived stronger support from teachers and classmates, but weaker support from their family. We did not confirm any differences in life satisfaction, sweets and soft drink consumption between adolescents with T1DM and their peers. Conclusions: Adolescents with T1DM reported more regular eating habits, no difference in life satisfaction and more social support outside the family in comparison to their peers. However, their worse self-rated health, more health complaints and weaker support from family should be considered in interventions targeting psychosocial adjustment of adolescents with T1DM.
Shock absorbing surfaces for collecting fruit citrus during the mechanical harvesting of citrus

Damage to fresh citrus caused by impact of fruits onto collecting surfaces has restricted the adoption of mechanical harvesting. Two different experiments were developed in this research work: citrus free dropping experiment and shock absorbing capacity experiment. In the citrus fruit free dropping experiment, mandarin, orange and lemon fruit damage was studied. Three reception surfaces were studied (concrete floor, elevated canvases provided with a frame and wheels and concrete floor covered with shock absorber canvases). Three dropping heights were studied. In the shock absorbing capacity experiment, an electronic sphere and a triaxial accelerometer were used to measure the shocking capacity of seven reception surfaces (ground, ground covered with shock absorber canvases, ground covered with weeds, ground covered with “mulch” and elevated canvases provided with a frame and wheels). Elevated canvases had the higher shock absorbing capacity compared to the other surfaces (260 m s -2 maximum acceleration compared with 1753 m s -2 to 2772 m s -2 ). Weeds, mulch and shock absorbing canvases showed significantly higher shock absorbing capacity than the field ground. Besides, shock absorber canvases covering concrete floor reduce impact and fruit damage (1866 m s -2 maximum acceleration compared to 2477 m s -2 ). Citrus damage susceptibility during harvest depend on the variety. Elevated canvases and shock absorber canvases could be used as reception systems in fresh market citrus mechanical harvesting.
A Robust Image Watermarking System Based on Deep Neural Networks

Digital image watermarking is the process of embedding and extracting watermark covertly on a carrier image. Incorporating deep learning networks with image watermarking has attracted increasing attention during recent years. However, existing deep learning-based watermarking systems cannot achieve robustness, blindness, and automated embedding and extraction simultaneously. In this paper, a fully automated image watermarking system based on deep neural networks is proposed to generalize the image watermarking processes. An unsupervised deep learning structure and a novel loss computation are proposed to achieve high capacity and high robustness without any prior knowledge of possible attacks. Furthermore, a challenging application of watermark extraction from camera-captured images is provided to validate the practicality as well as the robustness of the proposed system. Experimental results show the superiority performance of the proposed system as comparing against several currently available techniques.
Statistical properties of a tangentially driven active filament

Active polymers play a central role in many biological systems, from bacterial flagella to cellular cytoskeletons. Minimal models of semiflexible active filaments have been used to study a variety of interesting phenomena in active systems, such as defect dynamics in active nematics, clustering and laning in motility assays, and conformational properties of chromatin in eukaryotic cells. In this paper, we map a semiflexible polymer to an exactly solvable active Rouse chain, which enables us to analytically compute configurational and dynamical properties of active polymers with arbitrary rigidity. Upon mapping back to the semiflexible filament, we see that the center of mass diffusion coefficient grows linearly with an activity parameter that is renormalized by the polymer persistence length. These results closely agree with numerical data obtained from microscopic simulations.
First order phase transitions and the thermodynamic limit

We consider simple mean field continuum models for first order liquid–liquid demixing and solid–liquid phase transitions and show how the Maxwell construction at phase coexistence emerges on going from finite-size closed systems to the thermodynamic limit. The theories considered are the Cahn–Hilliard model of phase separation, which is also a model for the liquid-gas transition, and the phase field crystal model of the solid–liquid transition. Our results show that states comprising the Maxwell line depend strongly on the mean density with spatially localized structures playing a key role in the approach to the thermodynamic limit.
Note on Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler by Garbuno-Inigo, Hoffmann, Li and Stuart

An interacting system of Langevin dynamics driven particles has been proposed for sampling from a given posterior density by Garbuno-Inigo, Hoffmann, Li and Stuart in Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler (arXiv:1903:08866v2). The proposed formulation is primarily studied from a formal mean-field limit perspective, while the theoretical behaviour under a finite particle size is left as an open problem. In this note we demonstrate that the particle-based covariance interaction term requires a non-trivial correction. We also show that the corrected dynamics samples exactly from the desired posterior provided that the empirical covariance matrix of the particle system remains non-singular and the posterior log-density satisfies the standard Bakry-Emery criterion.
Review of Recent Trends in Measuring the Computing Systems Intelligence

Many difficult problems, from the philosophy of computation point of view, could require computing systems that have some kind of intelligence in order to be solved. Recently, we have seen a large number of artificial intelligent systems used in a number of scientific, technical and social domains. Usage of such an approach often has a focus on healthcare. These systems can provide solutions to a very large set of problems such as, but not limited to: elder patient care; medical diagnosis; medical decision support; out-of-hospital emergency care; drug classification among others. A recent key focus is that most of these developed intelligent systems are agent-based approaches, or in other words, they can be considered as agent-based intelligent systems (ABISs). ABISs are formally based on a set of interacting intelligent agents (IAs) in addition to the use of intelligent cooperative approaches namely forming intelligent cooperative multiagent systems (ICMASs). The main direction of study consists in the possibility to measure the artificial systems intelligence, frequently called machine intelligence quotient (MIQ). Recently, we performed some research related to the measuring of the machine intelligence. There is presented a comprehensive review of the scientific literature related to the measuring of the MIQ. We consider that the measuring of the machine intelligence is very actual and important, which could allow the differentiation of ABISs based on their intelligence, choosing of the agent-based systems able to solve the most intelligently specific problems. As the main conclusion of the performed study, we mention that cannot be given a unanimous definition of the ABISs intelligence. Even if the machine intelligence cannot be defined, it could be measured. We discuss this affirmation more in-depth in the paper. This is similar to the human intelligence that is not understood very well but can be measured using human intelligence tests.
When Did Life Likely Emerge on Earth in an RNA‐First Process?

The widespread presence of ribonucleic acid (RNA) catalysts and cofactors in Earth's biosphere today suggests that RNA was the first biopolymer to support Darwinian evolution. However, most "path-hypotheses" to generate building blocks for RNA require reduced nitrogen-containing compounds not made in useful amounts in the CO2-N2-H2O atmospheres of the Hadean. We review models for Earth's impact history that invoke a single ~10^23 kg impactor (Moneta) to account for measured amounts of platinum, gold, and other siderophilic ("iron-loving") elements on the Earth and Moon. If it were the last sterilizing impactor, Moneta would have reduced the atmosphere but not its mantle, opening a "window of opportunity" for RNA synthesis, a period when RNA precursors rained from the atmosphere to land holding oxidized minerals that stabilize advanced RNA precursors and RNA. Surprisingly, this combination of physics, geology, and chemistry suggests a time when RNA formation was most probable, ~120 +/- 100 million years after Moneta's impact, or ~4.36 +/- 0.1 billion years ago. Uncertainties in this time are driven by uncertainties in rates of productive atmosphere loss and amounts of sub-aerial land.
The Implicit Honesty Premium: Why Honest Advice Is More Persuasive than Highly Informed Advice

Recipients of advice expect it to be both highly informed and honest. Suspecting either one of these attributes reduces the use of the advice. Does the degree of advice use depend on the reason for suspecting its accuracy? Five experiments tested the effect of the type of suspicion on advice taking. We find that recipients of advice discount it more severely when they suspect intentional bias than when they suspect unintentional error, for example, due to the advisor's insufficient knowledge. The effect persisted when we controlled for, and disclosed, the actual accuracy of the advice; it persisted when participants' own evaluations of the quality of the advice, as well as their desire to receive it, were equally high under both types of suspicion. Finally, we find the effect of suspicion on advice use stems from the different attributions of uncertainty associated with each type of suspicion. The results suggest people place an implicit premium on advisors' honesty, and demonstrate the importance of establishing reputation for advisors' success. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Low-Resource Name Tagging Learned with Weakly Labeled Data

Name tagging in low-resource languages or domains suffers from inadequate training data. Existing work heavily relies on additional information, while leaving those noisy annotations unexplored that extensively exist on the web. In this paper, we propose a novel neural model for name tagging solely based on weakly labeled (WL) data, so that it can be applied in any low-resource settings. To take the best advantage of all WL sentences, we split them into high-quality and noisy portions for two modules, respectively: (1) a classification module focusing on the large portion of noisy data can efficiently and robustly pretrain the tag classifier by capturing textual context semantics; and (2) a costly sequence labeling module focusing on high-quality data utilizes Partial-CRFs with non-entity sampling to achieve global optimum. Two modules are combined via shared parameters. Extensive experiments involving five low-resource languages and fine-grained food domain demonstrate our superior performance (6% and 7.8% F1 gains on average) as well as efficiency.
uniblock: Scoring and Filtering Corpus with Unicode Block Information

The preprocessing pipelines in Natural Language Processing usually involve a step of removing sentences consisted of illegal characters. The definition of illegal characters and the specific removal strategy depend on the task, language, domain, etc, which often lead to tiresome and repetitive scripting of rules. In this paper, we introduce a simple statistical method, uniblock, to overcome this problem. For each sentence, uniblock generates a fixed-size feature vector using Unicode block information of the characters. A Gaussian mixture model is then estimated on some clean corpus using variational inference. The learned model can then be used to score sentences and filter corpus. We present experimental results on Sentiment Analysis, Language Modeling and Machine Translation, and show the simplicity and effectiveness of our method.