Miracle-dz/UniFAR-FTUs huggingface dataset:
Postnatal Development of the Subcellular Structures and Purinergic Signaling of Deiters’ Cells along the Tonotopic Axis of the Cochlea

Exploring the development of the hearing organ helps in the understanding of hearing and hearing impairments and it promotes the development of the regenerative approaches-based therapeutic efforts. The role of supporting cells in the development of the organ of Corti is much less elucidated than that of the cochlear sensory receptor cells. The use of our recently published method of single-cell electroporation loading of a fluorescent Ca2+ probe in the mouse hemicochlea preparation provided an appropriate means to investigate the Deiters’ cells at the subcellular level in two different cochlear turns (apical, middle). Deiters’ cell’s soma and process elongated, and the process became slimmer by maturation without tonotopic preference. The tonotopically heterogeneous spontaneous Ca2+ activity less frequently occurred by maturation and implied subcellular difference. The exogenous ATP- and UTP-evoked Ca2+ responses were maturation-dependent and showed P2Y receptor dominance in the apical turn. By monitoring the basic structural dimensions of this supporting cell type as well as its spontaneous and evoked purinergic Ca2+ signaling in the hemicochlea preparation in different stages in the critical postnatal P5-25 developmental period for the first time, we showed that the soma and the phalangeal process of the Deiters’ cells go through age- and tonotopy-dependent changes in the morphometric parameters and purinergic signaling.
Population statistics and population registration in Norway. Part 21

Summary The second part of this paper deals with the legal basis of medico-statistical data, and shows the type of report made by Medical Officers of Health and by private practitioners to the Norwegian Central Statistical Office. These reports deal with infectious diseases, with certain types of mental disease, and certain special diseases like tuberculosis. The system of classifying causes of death is also explained. Other sections deal with hospital and morbidity statistics and statistics obtained from recruiting authorities.
Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios

Physiological sensors can be used to detect changes in the emotional state of users with affective computing. This has lately been applied in the educational domain, aimed to better support learners during the learning process. For this purpose, we have developed the AICARP (Ambient Intelligence Context-aware Affective Recommender Platform) infrastructure, which detects changes in the emotional state of the user and provides personalized multisensorial support to help manage the emotional state by taking advantage of ambient intelligence features. We have developed a third version of this infrastructure, AICARP.V3, which addresses several problems detected in the data acquisition stage of the second version, (i.e., intrusion of the pulse sensor, poor resolution and low signal to noise ratio in the galvanic skin response sensor and slow response time of the temperature sensor) and extends the capabilities to integrate new actuators. This improved incorporates a new acquisition platform (shield) called PhyAS (Physiological Acquisition Shield), which reduces the number of control units to only one, and supports both gathering physiological signals with better precision and delivering multisensory feedback with more flexibility, by means of new actuators that can be added/discarded on top of just that single shield. The improvements in the quality of the acquired signals allow better recognition of the emotional states. Thereof, AICARP.V3 gives a more accurate personalized emotional support to the user, based on a rule-based approach that triggers multisensorial feedback, if necessary. This represents progress in solving an open problem: develop systems that perform as effectively as a human expert in a complex task such as the recognition of emotional states.
Characteristics of a Magnetic Field Sensor with a Concentrating-Conducting Magnetic Flux Structure

A magnetic field sensor with a new concentrating-conducting magnetic flux structure (CCMFS) is proposed in this paper, using a silicon-on insulator (SOI) Hall element fabricated by complementary metal oxide semiconductor (CMOS) technology as a magnetic sensitive unit. By fixing the CCMFS above the Hall element packaged on a printed circuit board (PCB) based on inner-connect wire bonding technology, a non-magnetized package can subsequently be obtained. To analyze the inner magnetic field vector distribution of the CCMFS, a simulation model was built based on a finite element software, where the CCMFS was processed using Ni-Fe alloys material by a low speed wire-cut electric discharge technology. The test results showed that the measurement of magnetic fields along a sensitive and a non-sensitive axis can be achieved when VDD = 5.0 V at room temperature, with magnetic sensitivities of 122 mV/T and 132 mV/T in a testing range from −30 mT to 30 mT, respectively. This study makes it possible to not only realize the detection of magnetic field, but also to significantly improve the sensitivity of the sensor along a non-sensitive axis.
Behavior and Quality of Life at 6 Years for Children With Hypoplastic Left Heart Syndrome

Behavior and QoL were assessed among an HLHS cohort; sociodemographic factors and indicators of course complexity predicted behavior and QoL outcomes. BrightcoveDefaultPlayer10.1542/6138649651001PEDS-VA_2019-1010 Video Abstract OBJECTIVES: We measured behavioral, quality of life (QoL), and functional status outcomes for 6-year-old children with hypoplastic left heart syndrome enrolled in the Single Ventricle Reconstruction Trial. We sought to compare these outcomes with those in the normative population and to analyze risk factors for worse outcomes within the single-ventricle group. METHODS: Parent-response instruments included the Vineland Adaptive Behavior Scales, Second Edition (Vineland-II) (primary outcome), Behavior Assessment System for Children 2, Pediatric Quality of Life Inventory 4.0, and other measures of QoL and functional status. We compared subjects with those in the normative sample using 1-sample Wilcoxon rank tests and assessed outcome predictors using multivariable regression. RESULTS: Of 325 eligible patients, 250 (77%) participated. Compared with population norms, participants had lower scores on the Vineland-II motor skills domain (90 ± 17 vs 100 ± 15; P < .001), with 11% scoring >2 SDs below the normative mean. On nearly all major domains, more study subjects (3.3%–19.7%) scored outside the normal range than anticipated for the general population. Independent risk factors for lower Vineland-II scores included perioperative extracorporeal membrane oxygenation, male sex, use of regional cerebral perfusion, catheterization after stage 2 operation, visual problems, seizure history, and more complications after 2 years (R2 = 0.32). Independent predictors of worse Behavior Assessment System for Children 2 (R2 = 0.07–0.20) and Pediatric Quality of Life Inventory 4.0 (R2 = 0.17–0.25) domain scores also included sociodemographic factors and measures of morbidity and/or greater course complexity. CONCLUSIONS: At 6 years, children with hypoplastic left heart syndrome had difficulty in areas of adaptive behavior, behavioral symptoms, QoL, and functional status. Principal risks for adverse outcomes include sociodemographic factors and measures of greater course complexity. However, models reveal less than one-third of outcome variance.
THE IMPACT OF CHILDHOOD FOOD ALLERGY ON QUALITY OF LIFE

Sicherer S, Noone S, Munoz-Furlong A. Ann Allergy Immunol. 2001;87:461–464 Food allergy affects 6% to 8% of children. Many patients and their families live in constant vigilance and fear, which can potentially impact their daily life activities. This study evaluates the impact of food allergy on quality of life. Two hundred fifty-three parents of children with food allergy who are members of the Food Allergy and Anaphylaxis Network (FAAN) participated in answering the Children’s Health Questionnaire (CHQ-PF50) self-administered questionnaire. The mean age of …
Exploring the Binding Mechanism and Dynamics of EndoMS/NucS to Mismatched dsDNA

The well-known mismatch repair (MMR) machinery, MutS/MutL, is absent in numerous Archaea and some Bacteria. Recent studies have shown that EndoMS/NucS has the ability to cleave double-stranded DNA (dsDNA) containing a mismatched base pair, which suggests a novel mismatch repair process. However, the recognition mechanism and the binding process of EndoMS/NucS in the MMR pathway remain unclear. In this study, we investigate the binding dynamics of EndoMS/NucS to mismatched dsDNA and its energy as a function of the angle between the two C-terminal domains of EndoMS/NucS, through molecular docking and extensive molecular dynamics (MD) simulations. It is found that there exists a half-open transition state corresponding to an energy barrier (at an activation angle of approximately 80∘) between the open state and the closed state, according to the energy curve. When the angle is larger than the activation angle, the C-terminal domains can move freely and tend to change to the open state (local energy minimum). Otherwise, the C-terminal domains will interact with the mismatched dsDNA directly and converge to the closed state at the global energy minimum. As such, this two-state system enables the exposed N-terminal domains of EndoMS/NucS to recognize mismatched dsDNA during the open state and then stabilize the binding of the C-terminal domains of EndoMS/NucS to the mismatched dsDNA during the closed state. We also investigate how the EndoMS/NucS recognizes and binds to mismatched dsDNA, as well as the effects of K+ ions. The results provide insights into the recognition and binding mechanisms of EndoMS/NucS to mismatched dsDNA in the MMR pathway.
Hierarchical Feature-Aware Tracking

In this paper, we propose a hierarchical feature-aware tracking framework for efficient visual tracking. Recent years, ensembled trackers which combine multiple component trackers have achieved impressive performance. In ensembled trackers, the decision of results is usually a post-event process, i.e., tracking result for each tracker is first obtained and then the suitable one is selected according to result ensemble. In this paper, we propose a pre-event method. We construct an expert pool with each expert being one set of features. For each frame, several experts are first selected in the pool according to their past performance and then they are used to predict the object. The selection rate of each expert in the pool is then updated and tracking result is obtained according to result ensemble. We propose a novel pre-known expert-adaptive selection strategy. Since the process is more efficient, more experts can be constructed by fusing more types of features which leads to more robustness. Moreover, with the novel expert selection strategy, overfitting caused by fixed experts for each frame can be mitigated. Experiments on several public available datasets demonstrate the superiority of the proposed method and its state-of-the-art performance among ensembled trackers.
End-to-End Speech Enhancement Based on Discrete Cosine Transform

Previous speech enhancement methods focus on estimating the short-time spectrum of speech signals due to its short-term stability. However, these methods often only estimate the clean magnitude spectrum and reuse the noisy phase when resynthesize speech signals, which is unlikely a valid short-time Fourier transform (STFT). Recently, DNN based speech enhancement methods mainly joint estimation of the magnitude and phase spectrum. These methods usually give better performance than magnitude spectrum estimation but need much larger computation and memory overhead. In this paper, we propose using the Discrete Cosine Transform (DCT) to reconstruct a valid short-time spectrum. Under the U-net structure, we enhance the real spectrogram and finally achieve perfect performance.
Secure Coded Caching with Colluding Users

In a secure coded caching system, a central server balances the traffic flow between peak and off-peak periods by distributing some public data to the users' caches in advance. Meanwhile, these data are securely protected against the possible colluding users, who might share their cache. We model the system as a flow network and study its capacity region via a network information-theoretic approach. Due to the difficulty of characterizing the capacity region straightforwardly, our approach would be two folded from the perspective of network information theory. On one hand, we identify the inner bound of capacity region by proposing a coded caching scheme to achieve a low load secure data transmission. On the other hand, we also establish outer bounds on the capacity region, which show that our proposed scheme is order optimal for a certain regime of parameters.
Signal-Based Properties: Taxonomy and Logic-based Characterization

The behavior of a cyber-physical system (CPS) is usually defined in terms of the input and output signals processed by sensors and actuators. Requirements specifications of CPSs are typically expressed using signal-based temporal properties. Expressing such requirements is challenging, because of (1) the many features that can be used to characterize a signal behavior; (2) the broad variation in expressiveness of the specification languages (i.e., temporal logics) used for defining signal-based temporal properties. Thus, system and software engineers need effective guidance on selecting appropriate signal behavior types and an adequate specification language, based on the type of requirements they have to define. In this paper, we present a taxonomy of the various types of signal-based properties and provide, for each type, a comprehensive and detailed description as well as a formalization in a temporal logic. Furthermore, we review the expressiveness of state-of-the-art signal-based temporal logics in terms of the property types identified in the taxonomy. Moreover, we report on the application of our taxonomy to classify the requirements specifications of an industrial case study in the aerospace domain, in order to assess the feasibility of using the property types included in our taxonomy and the completeness of the latter.
Controlling Utterance Length in NMT-based Word Segmentation with Attention

One of the basic tasks of computational language documentation (CLD) is to identify word boundaries in an unsegmented phonemic stream. While several unsupervised monolingual word segmentation algorithms exist in the literature, they are challenged in real-world CLD settings by the small amount of available data. A possible remedy is to take advantage of glosses or translation in a foreign, well- resourced, language, which often exist for such data. In this paper, we explore and compare ways to exploit neural machine translation models to perform unsupervised boundary detection with bilingual information, notably introducing a new loss function for jointly learning alignment and segmentation. We experiment with an actual under-resourced language, Mboshi, and show that these techniques can effectively control the output segmentation length.
The Full Kepler Phase Curve of the Eclipsing Hot White Dwarf Binary System KOI-964

We analyze the full Kepler phase curve of KOI-964, a binary system consisting of a hot white dwarf on an eclipsing orbit around an A-type host star. Using all 18 quarters of long-cadence photometry, we carry out a joint light-curve fit and obtain improved phase-curve amplitudes, occultation depths, orbital parameters, and transit ephemeris over the previous results of Carter et al. A periodogram of the residuals from the phase-curve fit reveals an additional stellar variability signal from the host star with a characteristic period of 0.620276 ± 0.000011 days and a full amplitude of 24 ± 2 ppm. We also present new Keck/HIRES radial velocity observations, which we use to measure the orbit and obtain a mass ratio of q = 0.106 ± 0.012. Combining this measurement with the results of a stellar isochrone analysis, we find that the masses of the host star and white dwarf companion are 2.23 ± 0.12 M⊙ and , respectively. The effective temperatures of the two components are K and 15,080 ± 400 K, respectively, and we determine the age of the system to be Gyr. We use the measured system properties to compute predicted phase-curve amplitudes and find that while the measured Doppler-boosting and mutual illumination components agree well with theory, the ellipsoidal distortion amplitude is significantly underestimated. We detail possible explanations for this discrepancy, including interactions between the dynamical tide of the host star and the tidal bulge and possible nonsynchronous rotation of the host star.
Experimental Statistics for Mirzakhani’s Theorem

Abstract In her seminal 2008 article, Maryam Mirzakhani showed that the ratio that two topological types of curves occur in is a rational number. In this article, we describe the process by which we obtained experimental evidence that separating and non-separating curves on the surface of genus two occur in the ratio . 1
Projected Newton method for noise constrained Tikhonov regularization

Tikhonov regularization is a popular approach to obtain a meaningful solution for ill-conditioned linear least squares problems. A relatively simple way of choosing a good regularization parameter is given by Morozov’s discrepancy principle. However, most approaches require the solution of the Tikhonov problem for many different values of the regularization parameter, which is computationally demanding for large scale problems. We propose a new and efficient algorithm which simultaneously solves the Tikhonov problem and finds the corresponding regularization parameter such that the discrepancy principle is satisfied. We achieve this by formulating the problem as a nonlinear system of equations and solving this system using a line search method. We obtain a good search direction by projecting the problem onto a low dimensional Krylov subspace and computing the Newton direction for the projected problem. This projected Newton direction, which is significantly less computationally expensive to calculate than the true Newton direction, is then combined with a backtracking line search to obtain a globally convergent algorithm, which we refer to as the projected Newton method. We prove convergence of the algorithm and illustrate the improved performance over current state-of-the-art solvers with some numerical experiments.
Phonon Transport Controlled by Ferromagnetic Resonance

The resonant coupling of phonons and magnons is important for the interconversion of phononic and spin degrees of freedom. We studied the phonon transmission in LiNbO3 manipulated by the dynamic magnetization in a Ni thin film. It was observed that the phonons could be absorbed strongly through resonant magnon-phonon coupling, which was realized by optimizing the interfacial coupling between Ni and LiNbO3. The line shapes of phonon transmission were further investigated considering the magnon-phonon interconversion in the elastically driven ferromagnetic resonance process. The results promote unique routes for phonon manipulation and detection in the presence of magnetization dynamics.
Learning to Answer Subjective, Specific Product-Related Queries using Customer Reviews by Adversarial Domain Adaptation

Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences. Thus they have the potential to automatically answer specific queries about products, and to address the problems of answer starvation and answer augmentation on associated consumer Q & A forums, by providing good answer alternatives. In this work, we explore several recently successful neural approaches to modeling sentence pairs, that could better learn the relationship between questions and ground truth answers, and thus help infer reviews that can best answer a question or augment a given answer. In particular, we hypothesize that our adversarial domain adaptation-based approach, due to its ability to additionally learn domain-invariant features from a large number of unlabeled, unpaired question-review samples, would perform better than our proposed baselines, at answering specific, subjective product-related queries using reviews. We validate this hypothesis using a small gold standard dataset of question-review pairs evaluated by human experts, significantly surpassing our chosen baselines. Moreover, our approach, using no labeled question-review sentence pair data for training, gives performance at par with another method utilizing labeled question-review samples for the same task.
End-to-End Speech Recognition: A review for the French Language

Recently, end-to-end ASR based either on sequence-to-sequence networks or on the CTC objective function gained a lot of interest from the community, achieving competitive results over traditional systems using robust but complex pipelines. One of the main features of end-to-end systems, in addition to the ability to free themselves from extra linguistic resources such as dictionaries or language models, is the capacity to model acoustic units such as characters, subwords or directly words; opening up the capacity to directly translate speech with different representations or levels of knowledge depending on the target language. In this paper we propose a review of the existing end-to-end ASR approaches for the French language. We compare results to conventional state-of-the-art ASR systems and discuss which units are more suited to model the French language.
Utilizing dependencies to obtain subsets of reachable sets

Reachability analysis, in general, is a fundamental method that supports formally-correct synthesis, robust model predictive control, set-based observers, fault detection, invariant computation, and conformance checking, to name but a few. In many of these applications, one requires to compute a reachable set starting within a previously computed reachable set. While it was previously required to re-compute the entire reachable set, we demonstrate that one can leverage the dependencies of states within the previously computed set. As a result, we almost instantly obtain an over-approximative subset of a previously computed reachable set by evaluating analytical maps. The advantages of our novel method are demonstrated for falsification of systems, optimization over reachable sets, and synthesizing safe maneuver automata. In all of these applications, the computation time is reduced significantly.
Weighted Edge Sampling for Static Graphs

Graph Sampling provides an efficient yet inexpensive solution for analyzing large graphs. While extracting small representative subgraphs from large graphs, the challenge is to capture the properties of the original graph. Several sampling algorithms have been proposed in previous studies, but they lack in extracting good samples. In this paper, we propose a new sampling method called Weighted Edge Sampling. In this method, we give equal weight to all the edges in the beginning. During the sampling process, we sample an edge with the probability proportional to its weight. When an edge is sampled, we increase the weight of its neighboring edges and this increases their probability to be sampled. Our method extracts the neighborhood of a sampled edge more efficiently than previous approaches. We evaluate the efficacy of our sampling approach empirically using several real-world data sets and compare it with some of the previous approaches. We find that our method produces samples that better match the original graphs. We also calculate the Root Mean Square Error and Kolmogorov Smirnov distance to compare the results quantitatively.