Miracle-dz/UniFAR-FTUs huggingface dataset:
S-Parameter Model of Three Parallel Interconnect Lines Generating Negative Group-Delay Effect

This paper develops a negative group-delay (NGD) microwave circuit theory regarding topology consists of three parallel interconnect lines (3-PILs). The NGD topology under study is built using completely distributed microstrip lines. The 3-PIL NGD theory is established from the S-parameters which are determined from the general admittance matrix of the 3-PILs. The analytical expressions of reflection coefficient (S<sub>11</sub>), transmission coefficient (S<sub>21</sub>) and group-delay (<inline-formula> <tex-math notation="LaTeX">$\tau$ </tex-math></inline-formula>) which behave as periodical functions are presented. The frequency period proper to the 3-PIL topology is established. Then, S<sub>11</sub>, S<sub>21</sub> and <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> illustrating the possibility to generate bandpass NGD function are formulated in function of the PIL parameter physical lengths and attenuation loss. Then, the NGD characterization of the 3-PIL topology is presented. The relevance of the NGD theory is verified with simulations and experimentations around the NGD center frequency of about 2.3 GHz. To do this, a 3-PIL microstrip circuit is designed and fabricated as proof-of-concept. It is shown that the simulated and measured group-delays are well-correlated. As expected theoretically, the demonstrator with identical characteristic impedance enables to generate an NGD level of approximately −2 ns at 2.3 GHz. In the future, the NGD function can be potentially used for the microwave signal integrity improvement.
Appraising of the Clinical Practice Guidelines Quality in the Non-Pharmacological Management of Chemotherapy-Induced Febrile Neutropenia; A Review

Background: Febrile neutropenia is a common and serious chemotherapy side effect, is associated with increased mortality, morbidity, and treatment expenditures. Several CPGs (Clinical practice guidelines) have been released for managing chemotherapy-induced febrile neutropenia. The aim of this study is Appraisal of the clinical practice Guidelines quality in the management of chemotherapy-induced febrile neutropenia. Methods: A review study with a systematic search of the present CPGs for the management of chemotherapy-induced febrile neutropenia. After screening the CPGs based on eligibility criteria, three CPGs were selected and 5 independent reviewers appraised them for methodological quality by using the AGREE II Instrument. Results: Three CPGs were included; all of them were evidence-based guidelines. The clarity of presentation domain scored the highest and the applicability domain has the lowest score among all domains of AGREE instrument and the rest of domains scored as descending respectively; Scope and purpose, stakeholder involvement, editorial independence, rigor of development. In general, the intra-class correlation coefficient (ICC) scores of all domains were very good according to the Landis and Koch’s scale, except the Applicability domain scored as substantial. Conclusions: This study showed the quality of appraised CPGs. Three domains of these CPGs based on the AGREE instrument scored less than other domains and were in relatively unfavorable status: applicability, rigor of development, editorial independence. Given the importance of these domains in guideline implementation, it is necessary to take actions for reducing these defects.
The Influence of Stress and Anxiety on the Expectation, Perception and Memory of Dental Pain in Schoolchildren

The aim of this study was to investigate the association of stress and anxiety with the expectation, perception and memory of dental pain among schoolchildren. A follow-up study involving 46 children aged 9 to 12 years was conducted in a public school in the city of Petropolis (RJ), Brazil. Demographic characteristics, stress (children’s stress scale), and state and trait anxiety (state–trait anxiety inventory) were recorded before a dental procedure to restore the occlusal surface of a permanent first molar under local anaesthetic. Dental pain was assessed using the faces pain scale before (dental pain expectation), immediately after (dental pain perception) and six weeks after (memory of dental pain) the dental procedure. Dental pain expectation scores were significantly higher than dental pain perception, independent of the levels of stress, state anxiety and trait anxiety. Children with high scores of stress (OR 1.05 95%CI 1.02–1.09), state anxiety (OR 1.15 95%CI 1.05–1.27) and trait anxiety (OR 1.18 95%CI 1.07–1.30) were more likely to report greater scores of dental pain expectation. Children anticipated more dental pain than what was actually perceived after the dental restoration. Children with greater levels of stress and anxiety have a distorted evaluation of expected dental pain before the dental procedure.
Bioaccessibility of Marine Carotenoids

The benefit of carotenoids to human health is undeniable and consequently, their use for this purpose is growing rapidly. Additionally, the nutraceutical properties of carotenoids have attracted attention of the food industry, especially in a new market area, the ‘cosmeceuticals. ’ Marine organisms (microalgae, seaweeds, animals, etc.) are a rich source of carotenoids, with optimal properties for industrial production and biotechnological manipulation. Consequently, several papers have reviewed the analysis, characterization, extraction and determination methods, biological functions and industrial applications. But, now, the bioaccessibility and bioactivity of marine carotenoids has not been focused of any review, although important achievements have been published. The specific and diverse characteristic of the marine matrix determines the bioavailability of carotenoids, some of them unique in the nature. Considering the importance of the bioavailability not just from the health and nutritional point of view but also to the food and pharmaceutical industry, we consider that the present review responds to an actual demand.
Automatic Sleep Stage Classification Using Single-Channel EEG: Learning Sequential Features with Attention-Based Recurrent Neural Networks

We propose in this work a feature learning approach using deep bidirectional recurrent neural networks (RNNs) with attention mechanism for single-channel automatic sleep stage classification. We firstly decompose an EEG epoch into multiple small frames and subsequently transform them into a sequence of frame-wise feature vectors. Given the training sequences, the attention-based RNN is trained in a sequence-to-label fashion for sleep stage classification. Due to discriminative training, the network is expected to encode information of an input sequence into a high-level feature vector after the attention layer. We, therefore, treat the trained network as a feature extractor and extract these feature vectors for classification which is accomplished by a linear SVM classifier. We also propose a discriminative method to learn a filter bank with a DNN for preprocessing purpose. Filtering the frame-wise feature vectors with the learned filter bank beforehand leads to further improvement on the classification performance. The proposed approach demonstrates good performance on the Sleep-EDF dataset.
Effect of Ginkgo Biloba Extract on N-Methyl-D-Aspartic Acid Receptor Subunit 2B Expression in a Salicylate-Induced Ototoxicity Model

Objectives. Sodium salicylate (SS) is well known for its ototoxic properties that induce functional and morphological changes in the cochlea and brain. Ginkgo biloba extract (GBE) has been widely used for treatment of various neurodegenerative diseases; however, its effects on salicylate-induced ototoxicity remain unclear. Herein, we examined the effects of EGb 761 (EGb), a standard form of GBE, on the plasticity of the N-methyl-D-aspartate receptor subunit 2B (GluN2B) in the inferior colliculus (IC) following SS administration. Methods. Seven-week-old Sprague Dawley rats (n=24) were randomly allocated to control, SS, EGb, and EGb+SS groups. The SS group received a single intraperitoneal SS injection (350 mg/kg), the EGb group received EGb orally for 5 consecutive days (40 mg/kg), and the EGb+SS group received EGb for 5 consecutive days, followed by an SS injection. The auditory brainstem responses (ABRs) were assessed at baseline and 2 hours after SS administration. GluN2B expression was examined by Western blot and immunohistochemistry. Results. There were no significant differences in ABR threshold shifts among the groups. The expression of the GluN2B protein normalized by which of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was significantly lower in the EGb+SS group, as compared to the SS group (P=0.012). Weak and diffused GluN2B immunoreactivity was detected in the IC neural cells of the EGb+SS group, while those of the SS group exhibited strong and diffused GluN2B positivity. Conclusion. EGb may play a role in regulating the GluN2B expression in the IC of salicylate-induced ototoxicity model.
A closed-form estimator for meta-analysis and surrogate markers evaluation

ABSTRACT Estimating complex linear mixed models using an iterative full maximum likelihood estimator can be cumbersome in some cases. With small and unbalanced datasets, convergence problems are common. Also, for large datasets, iterative procedures can be computationally prohibitive. To overcome these computational issues, an unbiased two-stage closed-form estimator for the multivariate linear mixed model is proposed. It is rooted in pseudo-likelihood-based split-sample methodology and useful, for example, when evaluating normally distributed endpoints in a meta-analytic context. However, applications go well beyond this framework. Its statistical and computational performance is assessed via simulation. The method is applied to a study in schizophrenia.
Do We Need an Irritable Subtype of ADHD? Replication and Extension of a Promising Temperament Profile Approach to ADHD Subtyping

Attention deficit hyperactivity disorder (ADHD) is emblematic of unresolved heterogeneity in psychiatric disorders— the variation in biological, clinical, and psychological correlates that impedes progress on etiology. One approach to this problem is to characterize subgroups using measures rooted in biological or psychological theory, consistent with the National Institute of Mental Health’s research domain criteria initiative. Within ADHD, a promising application involves using emotion trait profiles that can address the role of irritability as a complicating feature for ADHD. Here, a new sample of 186 children with ADHD was evaluated using community detection analysis to determine if meaningful subprofiles existed and if they replicated those previously identified. The new sample and a prior sample were pooled for evaluation of (a) method dependence, (b) longitudinal assessment of the stability of classifications, and (c) clinical prediction 2 years later. Three temperament profiles were confirmed within the ADHD group: one with normative emotional functioning (“mild”), one with high surgency (“surgent”), and one with high negative affect (“irritable”). Profiles were similar across statistical clustering approaches. The irritable group had the highest external validity: It was moderately stable over time and it enhanced prospective prediction of clinical outcomes beyond standard baseline indicators. The irritable group was not reducible to ADHD + oppositional defiant disorder, ADHD + disruptive mood dysregulation disorder, or other patterns of comorbidity. Among the negative affect domains studied, trait proneness to anger uniquely contributed to clinical prediction. Results extend our understanding of chronic irritability in psychiatric disorders and provide prospects for a fresh approach to assessing ADHD heterogeneity focused on the distinction between ADHD with and without anger/irritability.
Complementary Chassis Systems for Ground Vehicles Safety

Redundancy of integrated systems has always been used in order to increase vehicle's safety. Some new technologies are however too expensive to be redundant. This paper proposes a new way to preserve the safety by enabling a kind of complementarity between different chassis systems. Optimization-based control allocation redistribution algorithms are used in order to find the best way to combine conflicting systems. Results have shown that one system can take over another one when a failure occurs making the control logic fault-tolerant and reconfigurable. This better suits future cars as additional chassis systems are intended to be more integrated.
Evaluating Resilience: Development and Validation of the Situated Subjective Resilience Questionnaire for Adults (SSRQA)

Abstract Although resilience varies depending on the specific type of adverse situation faced by the individual, to date resilience questionnaires do not consider its situational character. This study aims to develop and validate the Situated Subjective Resilience Questionnaire for Adults (SSRQA), which assesses resilience in five different adverse contexts. A total of 584 Spanish adults (including general population and clinical samples individuals) completed the SSRQA and other measures of resilience, optimism, and self-efficacy. Confirmatory factor analysis showed that the SSRQA structure fitted the situational model better (χ2/df = 1.90; CFI = .96; TLI = .95; RMSEA = .07) than the non-situational version (χ2/df = 4.99; CFI = .79; TLI = .76; RMSEA = .15). The SSRQA was shown to be reliable (α = .90) and to be significantly and positively correlated with other resilience measures (p < .001) and, to a lower degree, with optimism and self-efficacy (p < .001). Degree of exposure to each adverse situation was negatively correlated with resilience in the face of that situation (p < .05), supporting a vulnerability to stress model. The SSRQA has been demonstrated to be a reliable and valid situated measure for resilience towards different adverse contexts.
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation

It is widely believed that learning good representations is one of the main reasons for the success of deep neural networks. Although highly intuitive, there is a lack of theory and systematic approach quantitatively characterizing what representations do deep neural networks learn. In this work, we move a tiny step towards a theory and better understanding of the representations. Specifically, we study a simpler problem: How similar are the representations learned by two networks with identical architecture but trained from different initializations. We develop a rigorous theory based on the neuron activation subspace match model. The theory gives a complete characterization of the structure of neuron activation subspace matches, where the core concepts are maximum match and simple match which describe the overall and the finest similarity between sets of neurons in two networks respectively. We also propose efficient algorithms to find the maximum match and simple matches. Finally, we conduct extensive experiments using our algorithms. Experimental results suggest that, surprisingly, representations learned by the same convolutional layers of networks trained from different initializations are not as similar as prevalently expected, at least in terms of subspace match.
Insulin-Related Lipohypertrophy: Lipogenic Action or Tissue Trauma?

Lipohypertrophy has been suggested as an outcome of lipogenic action of insulin and/or injection-related tissue trauma. In a cross-sectional study, we evaluated the predictors of lipohypertrophy in 372 type 1 diabetes patients (mean age 17.1 years) receiving subcutaneous insulin with pen and/or syringes for ≥3 months. On examining injection sites with inspection and palpation technique, 62.1% patients demonstrated lipohypertrophy. Univariate analysis showed that gender, BMI, HbA1c, injection device, rotation, injection area, needle length, insulin regimen, and total daily dose of insulin were associated with lipohypertrophy (p < 0.05). Notably, the mean needle reuse was comparable in patients with or without lipohypertrophy (8.1 vs. 7.2, p = 0.534). In multivariate logistic regression, gender, HbA1c, TDD, injection devices, and needle length lost its significance. Further, injections over smaller area (≤8.5 × 5.5 cm) and non-rotation of sites were found to be strongest independent predictor of lipohypertrophy (p < 0.0005 for both) with increased odds of 23.2 (95% CI 9.1–59.2) and 6.3 (95% CI 3.4–11.9) times, respectively. Being underweight was also a significant independent predictor (odds ratio [OR] 13.0 [95% CI 2.2–75.2], p = 0.004). Compared to rapid plus long-acting analogs, regular insulin plus long-acting analogs and conventional premixed insulin users had 3.2 (95% CI 1.5–6.8, p = 0.003) and 4.6 (95% CI 1.4–15.7, p = 0.014) fold higher risk of lipohypertrophy (mean injection frequency 4.01 vs. 4.01 vs. 2.09, respectively). Sub-group analysis showed that lipohypertrophy was 79% less likely in patients with multiple daily injections (≥4) than twice-daily regimen (OR 0.21, p < 0.0005). Moreover, lipohypertrophy was reduced to half with bolus doses of rapid-acting insulin analogs than regular insulin (p = 0.003), even though mean injection frequency was comparable (4.01 vs. 3.93, p = 0.229). This difference was statistically insignificant for basal doses with NPH or long-acting analogs (p = 0.069). Therefore, injection area, rotation, BMI, and insulin regimen are the best predictors of lipohypertrophy and together could correctly identify lipohypertrophy status in 84.4% patients with excellent discrimination capability (AUC = 0.906, p < 0.0005). In conclusion, findings of our study suggest that delivering rapidly absorbed insulin analogs over large injection area along with greater split of total daily doses reduce insulin-induced lipogenesis and outplay tissue trauma added through frequent injections and needle reuse.
Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset

Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales. Fortunately, most music is also highly structured and can be represented as discrete note events played on musical instruments. Herein, we show that by using notes as an intermediate representation, we can train a suite of models capable of transcribing, composing, and synthesizing audio waveforms with coherent musical structure on timescales spanning six orders of magnitude (~0.1 ms to ~100 s), a process we call Wave2Midi2Wave. This large advance in the state of the art is enabled by our release of the new MAESTRO (MIDI and Audio Edited for Synchronous TRacks and Organization) dataset, composed of over 172 hours of virtuosic piano performances captured with fine alignment (~3 ms) between note labels and audio waveforms. The networks and the dataset together present a promising approach toward creating new expressive and interpretable neural models of music.
Learning Abstract Options

Building systems that autonomously create temporal abstractions from data is a key challenge in scaling learning and planning in reinforcement learning. One popular approach for addressing this challenge is the options framework (Sutton et al., 1999). However, only recently in (Bacon et al., 2017) was a policy gradient theorem derived for online learning of general purpose options in an end to end fashion. In this work, we extend previous work on this topic that only focuses on learning a two-level hierarchy including options and primitive actions to enable learning simultaneously at multiple resolutions in time. We achieve this by considering an arbitrarily deep hierarchy of options where high level temporally extended options are composed of lower level options with finer resolutions in time. We extend results from (Bacon et al., 2017) and derive policy gradient theorems for a deep hierarchy of options. Our proposed hierarchical option-critic architecture is capable of learning internal policies, termination conditions, and hierarchical compositions over options without the need for any intrinsic rewards or subgoals. Our empirical results in both discrete and continuous environments demonstrate the efficiency of our framework.
Spatially Resolved Recombination Analysis of CuInxGa1-xSe2 Absorbers With Alkali Postdeposition Treatments

In this contribution, we probe spatial variations in charge-carrier recombination in CuInxGa1-x Se2 (CIGS) absorbers grown on soda–lime glass (SLG) and alkali-free sapphire substrates with NaF and KF postdeposition treatments (PDTs). Temperature-and illumination-dependent device measurements are used to track interface recombination and recombination in the quasi-neutral region. The analysis of these data reveals that the benefit of alkali PDTs depends on the substrate: interface recombination is reduced in devices grown on sapphire substrates, whereas recombination in the quasi-neutral regions is reduced in devices grown on SLG substrates. Cathodoluminescence (CL) spectrum imaging is used to study the spatial distribution of recombination with respect to the grain structure. The grain-boundary CL contrast is similar in films with no PDT, NaF PDT, or KF PDT. A reduced grain-boundary contrast is observed with a NaF + KF PDT; however, suggesting a reduced recombination strength at the grain boundaries (GBs) for combined NaF + KF treatment. CL spectra indicate band tailing, consistent with the fluctuating potential model. Fluctuating potentials are believed to reduce open-circuit voltage, but their spatial distribution has not been studied. Here, CL spectrum imaging data are used to generate maps of the root-mean-square value of the potential energy fluctuations—γ. These maps reveal a bimodal γ distribution for all samples: γ is generally in the range ∼15–50 meV or ∼100–180 meV. The higher γ range is more significantly affected by the PDTs; after the PDTs, it is strongly correlated with GBs. The lower γ range is correlated with higher emission intensity regions, typically grain interiors, and increases in area fraction after the PDTs. These results demonstrate how spatially resolved luminescence and device characterization measurements can be used to monitor changes in recombination in CIGS films and photovoltaic devices. Such measurements can complement empirical device optimization and help improve device performance.