Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Profiles of the Balmer Line Hβ Emitted from an Afterglow Plasma Without and With a Superposed Magnetic Field up to 10 Tesla

Abstract The profile of the Hβ line emitted from an afterglow plasma in pure hydrogen gas and a mixture of hydrogen and helium has been measured in axial direction using a multichannel spectrum analyzer with additional temporal resolution. Measurements were performed without and with a superposed magnetic field (B ≦ 10.8 Tesla) in axial direction. With magnetic field we observed a rather strong dependence of the wavelength positions of the two lateral "Zeeman components" (intensity maximum due to combined Stark-Zeeman effects). This feature is important in radiative transfer calculations and for the spectroscopic determination of magnetic fields in plasmas. - Both with and without magnetic field we always observed a line structure superposed on the Hβ profile. The structure disappeared in an active electrical discharge and changed with gas composition. The line structure is interpreted as being due to electronic transitions of molecular hydrogen. It is very probable that the "satellite structures" of Balmer lines observed by other authors under quite different experimental conditions and interpreted as dynamical Stark effect splitting due to fluctuating electric fields have in most cases the same molecular origin. Our earlier published line profiles of Hβ for B = 0 [J. Ramette and H. W. Drawin, Z. Naturforsch. 31 a, 401 (1976) ] are revised in the present paper.
Temporal Difference Models: Model-Free Deep RL for Model-Based Control

Model-free reinforcement learning (RL) is a powerful, general tool for learning complex behaviors. However, its sample efficiency is often impractically large for solving challenging real-world problems, even with off-policy algorithms such as Q-learning. A limiting factor in classic model-free RL is that the learning signal consists only of scalar rewards, ignoring much of the rich information contained in state transition tuples. Model-based RL uses this information, by training a predictive model, but often does not achieve the same asymptotic performance as model-free RL due to model bias. We introduce temporal difference models (TDMs), a family of goal-conditioned value functions that can be trained with model-free learning and used for model-based control. TDMs combine the benefits of model-free and model-based RL: they leverage the rich information in state transitions to learn very efficiently, while still attaining asymptotic performance that exceeds that of direct model-based RL methods. Our experimental results show that, on a range of continuous control tasks, TDMs provide a substantial improvement in efficiency compared to state-of-the-art model-based and model-free methods.
Parametric Loop Division for 3D Localization in Wireless Sensor Networks

Localization in Wireless Sensor Networks (WSNs) has been an active topic for more than two decades. A variety of algorithms were proposed to improve the localization accuracy. However, they are either limited to two-dimensional (2D) space, or require specific sensor deployment for proper operations. In this paper, we proposed a three-dimensional (3D) localization scheme for WSNs based on the well-known parametric Loop division (PLD) algorithm. The proposed scheme localizes a sensor node in a region bounded by a network of anchor nodes. By iteratively shrinking that region towards its center point, the proposed scheme provides better localization accuracy as compared to existing schemes. Furthermore, it is cost-effective and independent of environmental irregularity. We provide an analytical framework for the proposed scheme and find its lower bound accuracy. Simulation results shows that the proposed algorithm provides an average localization accuracy of 0.89 m with a standard deviation of 1.2 m.
A Weekly Indicator of Surface Moisture Status from Satellite Data for Operational Monitoring of Crop Conditions

The triangle method has been applied to derive a weekly indicator of evaporative fraction on vegetated areas in a temperate region in Northern Italy. Daily MODIS Aqua Land Surface Temperature (MYD11A1) data has been combined with air temperature maps and 8-day composite MODIS NDVI (MOD13Q1/MYD13Q1) data to estimate the Evaporative Fraction (EF) at 1 km resolution, on a daily basis. Measurements at two eddy covariance towers located within the study area have been exploited to assess the reliability of satellite based EF estimations as well as the robustness of input data. Weekly syntheses of the daily EF indicator (EFw) were then derived at regional scale for the years 2010, 2011 and 2012 as a proxy of overall surface moisture condition. EFw showed a temporal behavior consistent with growing cycles and agro-practices of the main crops cultivated in the study area (rice, forages and corn). Comparison with official regional corn yield data showed that variations in EFw cumulated over summer are related with crop production shortages induced by water scarcity. These results suggest that weekly-averaged EF estimated from MODIS data is sensible to water stress conditions and can be used as an indicator of crops’ moisture conditions at agronomical district level. Advantages and disadvantages of the proposed approach to provide information useful to issue operational near real time bulletins on crop conditions at regional scale are discussed.
Self-Attentive Residual Decoder for Neural Machine Translation

Neural sequence-to-sequence networks with attention have achieved remarkable performance for machine translation. One of the reasons for their effectiveness is their ability to capture relevant source-side contextual information at each time-step prediction through an attention mechanism. However, the target-side context is solely based on the sequence model which, in practice, is prone to a recency bias and lacks the ability to capture effectively non-sequential dependencies among words. To address this limitation, we propose a target-side-attentive residual recurrent network for decoding, where attention over previous words contributes directly to the prediction of the next word. The residual learning facilitates the flow of information from the distant past and is able to emphasize any of the previously translated words, hence it gains access to a wider context. The proposed model outperforms a neural MT baseline as well as a memory and self-attention network on three language pairs. The analysis of the attention learned by the decoder confirms that it emphasizes a wider context, and that it captures syntactic-like structures.
FEXIPRO: Fast and Exact Inner Product Retrieval in Recommender Systems

Recommender systems have many successful applications in e-commerce and social media, including Amazon, Netflix, and Yelp. Matrix Factorization (MF) is one of the most popular recommendation approaches; the original user-product rating matrix R with millions of rows and columns is decomposed into a user matrix Q and an item matrix P, such that the product QT P approximates R. Each column q (p) of Q (P) holds the latent factors of the corresponding user (item), and qT p is a prediction of the rating to item p by user q. Recommender systems based on MF suggest to a user in q the items with the top-k scores in qT P. For this problem, we propose a Fast and EXact Inner PROduct retrieval (FEXIPRO) framework, based on sequential scan, which includes three elements. First, FEXIPRO applies an SVD transformation to P, after which the first several dimensions capture a large percentage of the inner products. This enables us to prune item vectors by only computing their partial inner products with q. Second, we construct an integer approximation version of P, which can be used to compute fast upper bounds for the inner products that can prune item vectors. Finally, we apply a lossless transformation to P, such that the resulting matrix has only positive values, allowing for the inner products to be monotonically increasing with dimensionality. Experiments on real data demonstrate that our framework outperforms alternative approaches typically by an order of magnitude.
Risk and surrogate benefit for pediatric Phase I trials in oncology: A systematic review with meta-analysis

Background Pediatric Phase I cancer trials are critical for establishing the safety and dosing of anti-cancer treatments in children. Their implementation, however, must contend with the rarity of many pediatric cancers and limits on allowable risk in minors. The aim of this study is to describe the risk and benefit for pediatric cancer Phase I trials. Methods and findings Our protocol was prospectively registered in PROSPERO (CRD42015015961). We systematically searched Embase and PubMed for solid and hematological malignancy Phase I pediatric trials published between 1 January 2004 and 1 March 2015. We included pediatric cancer Phase I studies, defined as “small sample size, non‑randomized, dose escalation studies that defined the recommended dose for subsequent study of a new drug in each schedule tested. ” We measured risk using grade 3, 4, and 5 (fatal) drug-related adverse events (AEs) and benefit using objective response rates. When possible, data were meta-analyzed. We identified 170 studies meeting our eligibility criteria, accounting for 4,604 patients. The pooled overall objective response rate was 10.29% (95% CI 8.33% to 12.25%), and was lower in solid tumors, 3.17% (95% CI 2.62% to 3.72%), compared with hematological malignancies, 27.90% (95% CI 20.53% to 35.27%); p < 0.001. The overall fatal (grade 5) AE rate was 2.09% (95% CI 1.45% to 2.72%). Across the 4,604 evaluated patients, there were 4,675 grade 3 and 4 drug-related AEs, with an average grade 3/4 AE rate per person equal to 1.32. Our study had the following limitations: trials included in our review were heterogeneous (to minimize heterogeneity, we separated types of therapy and cancer types), and we relied on published data only and encountered challenges with the quality of reporting. Conclusions Our meta-analysis suggests that, on the whole, AE and response rates in pediatric Phase I trials are similar to those in adult Phase I trials. Our findings provide an empirical basis for the refinement and review of pediatric Phase I trials, and for communication about their risk and benefit.
Closed-form solution to cooperative visual-inertial structure from motion

This paper considers the problem of visual-inertial sensor fusion in the cooperative case and it provides new theoretical contributions, which regard its observability and its resolvability in closed form. The case of two agents is investigated. Each agent is equipped with inertial sensors (accelerometer and gyroscope) and with a monocular camera. By using the monocular camera, each agent can observe the other agent. No additional camera observations (e.g., of external point features in the environment) are considered. All the inertial sensors are assumed to be affected by a bias. First, the entire observable state is analytically derived. This state includes the absolute scale, the relative velocity between the two agents, the three Euler angles that express the rotation between the two agent frames and all the accelerometer and gyroscope biases. Second, the paper provides the extension of the closed-form solution given in [19] (which holds for a single agent) to the aforementioned cooperative case. The impact of the presence of the bias on the performance of this closed-form solution is investigated. As in the case of a single agent, this performance is significantly sensitive to the presence of a bias on the gyroscope, while, the presence of a bias on the accelerometer is negligible. Finally, a simple and effective method to obtain the gyroscope bias is proposed. Extensive simulations clearly show that the proposed method is successful. It is amazing that, it is possible to automatically retrieve the absolute scale and simultaneously calibrate the gyroscopes not only without any prior knowledge (as in [13]), but also without external point features in the environment.
Learning to Translate with Source and Target Syntax

Statistical translation models that try to capture the recursive structure of language have been widely adopted over the last few years. These models make use of varying amounts of information from linguistic theory: some use none at all, some use information about the grammar of the target language, some use information about the grammar of the source language. But progress has been slower on translation models that are able to learn the relationship between the grammars of both the source and target language. We discuss the reasons why this has been a challenge, review existing attempts to meet this challenge, and show how some old and new ideas can be combined into a simple approach that uses both source and target syntax for significant improvements in translation accuracy.
Modeling Electrode Place Discrimination in Cochlear Implant Stimulation

Objective: By modeling the cochlear implant (CI) electrode-to-nerve interface and quantifying electrode discriminability in the model, we address the questions of how many individual channels can be distinguished by CI recipients and the extent to which performance might be improved by inserting electrodes deeper into the cochlea. Method: We adapt an artificial neural network to model electrode discrimination as well as a commonly used psychophysical measure (four-interval forced-choice) in CI stimulation and predict how well the locations of the stimulating electrodes can be inferred from simulated auditory nerve spiking patterns. Results: We show that a longer electrode leads to better electrode place discrimination in our model. For a simulated four-interval forced-choice procedure, correct classification rates significantly reduce with decreasing distance between the test electrodes and the reference electrodes, and higher correct classification rates may be achieved by the basal electrodes than apical electrodes. Conclusion: Our results suggest that enhanced electrode discriminability results from a longer CI electrode array, and the locations where the errors occur along the electrode array are not only affected by the distance between electrodes but also the twirling angle between electrodes. Significance: Our models and simulations provide theoretical insights into several important clinically relevant problems that will inform future designs of CI electrode arrays and stimulation strategies.
Effect of maternal intrahepatic cholestasis on fetal steroid metabolism.

Estriol, estriol sulfate, progesterone, and 17 neutral steroid sulfates, including estriol precursors and progesterone metabolites, were determined in 27 cord plasma samples collected after pregnancies complicated by intrahepatic cholestasis of the mother. The levels of these steroids were compared with those in the cord plasma of 42 healthy controls. In the cord plasma, the steroid profile after pregnancies complicated by maternal intrahepatic cholestasis differed greatly from that seen after uncomplicated pregnancy. Two main differences were found. In the disulfate fraction, the concentrations of two pregnanediol isomers, 5alpha-pregnane-3alpha,20alpha-diol and 5beta-pregnane-3alpha,20alpha-diol, were high after cholestasis. Other investigators have shown that, as a result of cholestasis, these pregnanediol sulfates circulate in greatly elevated amounts in the maternal plasma. Our results indicate that in cholestasis these steroids cross the placenta into the fetal compartment, where they circulate in elevated amounts as disulfates. Secondly, the concentrations of several steroid sulfates known to be synthesized by the fetus were significantly lower in the cholestasis group than in the healthy controls. This was especially true of 16alpha-hydroxydehydroepiandrosterone sulfate and 16alpha-hydroxypregnenolone sulfate. These results suggest that, in pregnancies complicated by maternal intrahepatic cholestasis, impairment of fetal steroid synthesis, and especially of 16alpha-hydroxylation, occurs in the fetal compartment.Thus, the changes in maternal steroid metabolism caused by cholestasis are reflected in the steroid profile of the fetoplacental circulation. Furthermore, maternal intrahepatic cholestasis may result in the production of some substance which crosses the placenta and affects fetal steroid metabolism.
Methods for Quantifying the Clinical Significance of Change During Intervention Program Participation

Assessing the practical or clinical significance (CS) of an intervention program’s outcomes is useful in determining its effectiveness. The CS approach gives information beyond traditional analyses by quantifying the proportions of people who meaningfully improve and deteriorate. We link latent transition analyses (LTA) to the CS literature and use a case study to contrast it with the long-standing Jacobson and Truax (JT) approach. Data came from 2,717 individuals convicted of a substance-related offense who participated in an indicated prevention program Prime For Life® (PFL). We selected outcomes describing drinking beliefs and behavior. Both CS approaches categorized a majority of participants as improved (i.e., transitioning from baseline subgroups with risky behaviors and cognitions into posttest subgroups showing lower risk). Results demonstrate how the JT approach allows the assessment of improvements on individual outcomes, while the LTA provides more nuanced information about risk groupings. Selecting a CS approach depends on research goals, availability of normative data, and data considerations. JT is an appropriate method when evaluating single outcomes. In contrast, LTA is better when a multivariate description is desired, advanced missing data handling methods are needed, or outcomes are not normally distributed. Although infrequently done, evaluating CS provides useful information about program effectiveness.
Fast Multiplexed Polymerase Chain Reaction for Conventional and Microfluidic Short Tandem Repeat Analysis

Abstract: The time required for short tandem repeat (STR) amplification is determined by the temperature ramp rates of the thermal cycler, the components of the reaction mix, and the properties of the reaction vessel. Multiplex amplifications in microfluidic biochip‐based and conventional tube‐based thermal cyclers have been demonstrated in 17.3 and 19 min, respectively. Optimized 28‐cycle amplification protocols generated alleles with signal strengths above calling thresholds, heterozygous peak height ratios of greater than 0.65, and incomplete nontemplate nucleotide addition and stutter of less than 15%. Full CODIS‐compatible profiles were generated using the Profiler Plus ID, COfiler and Identifiler primer sets. PCR performance over a wide range of DNA template levels from 0.006 to 4 ng was characterized by separation and detection on a microfluidic electrophoresis system, Genebench‐FX™. The fast multiplex PCR approach has the potential to reduce process time and cost for STR analysis and enables development of a fully integrated microfluidic forensic DNA analysis system.
Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters

The main objective of this study is to analyze the potential use of Sentinel-1 (S1) radar data for the estimation of soil characteristics (roughness and water content) and cereal vegetation parameters (leaf area index (LAI), and vegetation height (H)) in agricultural areas. Simultaneously to several radar acquisitions made between 2015 and 2017, using S1 sensors over the Kairouan Plain (Tunisia, North Africa), ground measurements of soil roughness, soil water content, LAI and H were recorded. The NDVI (normalized difference vegetation index) index computed from Landsat optical images revealed a strong correlation with in situ measurements of LAI. The sensitivity of the S1 measurements to variations in soil moisture, which has been reported in several scientific publications, is confirmed in this study. This sensitivity decreases with increasing vegetation cover growth (NDVI), and is stronger in the VV (vertical) polarization than in the VH cross-polarization. The results also reveal a similar increase in the dynamic range of radar signals observed in the VV and VH polarizations as a function of soil roughness. The sensitivity of S1 measurements to vegetation parameters (LAI and H) in the VV polarization is also determined, showing that the radar signal strength decreases when the vegetation parameters increase. No vegetation parameter sensitivity is observed in the VH polarization, probably as a consequence of volume scattering effects.
Recent developments with rhodanine as a scaffold for drug discovery

ABSTRACT Introduction: Rhodanines, as one of the 4-thiazolidinones subtypes, are recognized as privileged heterocycles in medicinal chemistry. The main achievements include the development of drug-like molecules with numerous biological activities as well as approved drugs. Among rhodanines, 5-ene-rhodanines are of special interest, and are often claimed as pan assay interference compounds due to Michael acceptor functionality. Areas covered: Herein, the synthetic protocols for rhodanines and their transformation are reviewed. Biological activity is briefly discussed as well as biotargets, mode of actions and optimization directions. Furthermore, the utilization of 5-ene-rhodanines in Michael additions are discussed while both pro and contra arguments have been outlined within medicinal chemistry application. Expert opinion: Rhodanines remain privileged heterocycles in drug discovery. They are accessible building blocks for optimization and transformation into related heterocycles, simplified analogues and fused heterocycles with a thiazolidine framework. Michael acceptor functionality, as well as the thesis about low selectivity towards biotargets of rhodanines, must be confirmed experimentally and it cannot be based on just the presence of conjugated α,β-unsaturated carbonyl. Moreover, the positive aspects of Michael acceptors must be considered as well as their multitarget properties. New criteria for target affinity must be found. In conclusion, rhodanines are generally not problematic per se.
Metabolic Cycles Are Linked to the Cardiovascular Diurnal Rhythm in Rats with Essential Hypertension

Background The loss of diurnal rhythm in blood pressure (BP) is an important predictor of end-organ damage in hypertensive and diabetic patients. Recent evidence has suggested that two major physiological circadian rhythms, the metabolic and cardiovascular rhythms, are subject to regulation by overlapping molecular pathways, indicating that dysregulation of metabolic cycles could desynchronize the normal diurnal rhythm of BP with the daily light/dark cycle. However, little is known about the impact of changes in metabolic cycles on BP diurnal rhythm. Methodology/Principal Findings To test the hypothesis that feeding-fasting cycles could affect the diurnal pattern of BP, we used spontaneously hypertensive rats (SHR) which develop essential hypertension with disrupted diurnal BP rhythms and examined whether abnormal BP rhythms in SHR were caused by alteration in the daily feeding rhythm. We found that SHR exhibit attenuated feeding rhythm which accompanies disrupted rhythms in metabolic gene expression not only in metabolic tissues but also in cardiovascular tissues. More importantly, the correction of abnormal feeding rhythms in SHR restored the daily BP rhythm and was accompanied by changes in the timing of expression of key circadian and metabolic genes in cardiovascular tissues. Conclusions/Significance These results indicate that the metabolic cycle is an important determinant of the cardiovascular diurnal rhythm and that disrupted BP rhythms in hypertensive patients can be normalized by manipulating feeding cycles.