Miracle-dz/UniFAR-FTUs huggingface dataset:
Krill and the unity of biology

This supplement features some of the printed papers from the Second International Symposium on Krill (Santa Cruz, Calif., August 1999). Krill have long been recognized as key prey species for fish, birds, and marine mammals and as a target for fisheries in both hemispheres. However, the study of krill can contribute in many other ar- eas of biology. Krill are an excellent model system for studies of growth and maturity, for connections between theory, experiment, and observation, and for tests of evolutionary theory in the field. Krill are also ideal model organisms for studying the interaction between environmental and organismal variability. Because they are widely distributed but only moderately speciose, the study of krill can also shed light on the relationship between abundance and range. The study of krill is an inherently interdisciplinary field, crossing boundaries and using a combination of oceanography, biochem - istry, physiology, evolution, and ecology to understand krill and their role in the ecosystem. Resume : Ce supplement contient les textes de certaines presentations faites au Second symposium international sur le krill (Second International Symposium on Krill) tenu a Santa Cruz, Calif. , en aout 1999. Depuis longtemps, on sait que les especes du krill sont des proies essentielles pour les poissons, les oiseaux et les mammiferes marins et une res - source convoitee par les peches commerciales dans les deux hemispheres. L'etude du krill peut aussi faire progresser plusieurs disciplines biologiques. C'est un systeme ideal pour analyser la croissance et la maturite, pour evaluer les rapports entre la theorie, l'experimentation et l'observation, ainsi que pour elaborer en nature des tests relatifs a la theorie de l'evolution. Les organismes qui le composent sont aussi d'excellents modeles pour mettre en lumiere les in- teractions entre la variabilite de l'environnement et celle des organismes. Parce que le krill a une repartition geogra- phique etendue et une richesse en especes moyenne, il permet l'etude des relations entre l'abondance et l'aire de distribution. Il s'agit essentiellement d'un domaine de recherche interdisciplinaire qui, a l'aide de l'oceanographie, de la biochimie, de la physiologie, de la biologie evolutive et de l'ecologie, cherche a comprendre la nature du krill et son role dans l'ecosysteme. (Traduit par la Redaction) Mangel and Nicol 5
A combination of catalase and trehalose as additives to conventional freezing medium results in improved cryoprotection of human hematopoietic cells with reference to in vitro migration and adhesion properties

BACKGROUND: Cryopreservation of hematopoietic cells from cord blood is an essential component in unrelated transplant settings. Cell damage during freezing is caused by multiple factors, of which membrane damage and oxygen free radical generation are two major factors. It was reported earlier that a combination of catalase and trehalose as additives in freezing medium affords better cryoprotection in terms of long‐term culture assays.
A proposal of adaptive probabilistic model of context applied to visual recognition

Within the increasing automation of tasks, it is necessary to obtain relevant information from images, to have clarity of the actions that must be performed. In this process, it is possible to detect objects individually, however this mode can generate a considerable error due to the enormous number of ways in which an object can be presented. It is therefore necessary to improve the level of accuracy, and a way to achieve this is taking into account the relationships between objects as well. Until now, context-oriented models generate relationships between objects without discriminating between input images. It is necessary that the model operates on each particular image, to reduce the error and obtain a conclusive result. In response to the above, this work proposes an adaptive probabilistic context-model. The model in question is a functional that processes the occurrence probability of the objects in each image, and generates the relations between the detectable objects using a Bayesian network in the form of a tree. This model is updated with the features of each image, requiring a minimization of the quadratic error through a numerical approximation, obtained by Newton-Raphson method. Comparisons were made between different heuristic proposals, as well as tests on different context-oriented databases, in order to validate the results. On the other hand, tests were performed using features extracted through histograms of orient gradients and Deep Learning. It was found that an improvement in the prediction of the order of 20% is feasible in the testing process.
Evaluating the DTLS Protocol from CoAP in Fog-to-Fog Communications

The evolution of the Internet of Things (IoT) and the vast amount of data that has been sent to the Cloud have pushed the horizon to Fog computing paradigm. Thus, Cloud processing is migrating to the edge of the network. As a consequence, Fog-to-Fog communications are becoming one of the main concerns regarding IoT security. Recent works have presented the CoAP protocol as a secure approach for Fog devices communications. CoAP's security is based on DTLS and has adjustments to support unreliability issues on UDP communications. However, the DTLS protocol was not designed to be used in Fog-to-Fog communications. Although some research efforts have worked on DTLS optimizations, none of them has analyzed its suitability in the Fog computing perspective, which involves time-critical applications and radio access networks (RANs). Thus, this paper evaluates the DTLS protocol from CoAP in Fog-to-Fog communications analyzing performance, overhead, and handshake issues when operating in RANs. Tests revealed that DTLS from CoAP is suitable for Fog-to-Fog communications using HSPA+ and LTE as radio access networks.
Deep Learning Based Camera Pose Estimation in Multi-view Environment

This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose.
Run for Fun: Shape Body and Knowledge through Playing

This study investigates kindergarten children’s attitudes towards physical activity play and their acquirement of health knowledge conveyed in the game. A digital platform, Run-Around, which was composed of animation and interactive physical running game, was designed to promote enjoyment in physical activity play while learn health concept naturally at the same time. 20 kindergarten children were invited to play with the system. The results showed that most children had learned the concept of healthy diet and body fat. However, some children could not connect the relation between running length and calorie consumption. Nevertheless, most children still had fun playing the gift sending task by running on the board to visit famous world attractions. These findings confirmed that Run-Around platform achieved the aim of serving as a pleasurable game that promotes physical activity play.
Sampling Rate of Heart Rate Variability Impacts the Ability to Detect Acidemia in Ovine Fetuses Near-Term

Background: To evaluate the impact of sampling rate on the predictive capability of continuous fetal heart rate (FHR) variability (fHRV) monitoring for detecting fetal acidemia during labor, we tested the performance of the root mean square of successive differences (RMSSD) in R–R intervals from the ECG when acquired with the sampling rate of 4 Hz currently available in FHR monitors, in comparison to the gold standard of 1000 Hz. Methods: Near-term ovine fetuses (N = 9) were chronically prepared with precordial electrodes for recording ECG, vascular catheters for blood sampling, and an umbilical cord occluder. For 1 min every 2.5 min, animals underwent mild partial umbilical cord occlusions (UCO) × 1 h, moderate partial UCO × 1 h, then complete UCO × 2 h, or until arterial pH reached <7.00. Arterial blood samples were drawn at baseline and every 20 min during the UCO series. RMSSD was calculated continuously in 5 min windows using an automated, standardized system (CIMVA.com). Results are presented as mean ± SEM with significance assumed for p < 0.05. Results: Repetitive UCO resulted in pH decreasing from 7.35 ± 0.01 to 7.00 ± 0.03. In all nine animals, RMSSD increased from 16.7 ± 1.0 ms at baseline to 44.4 ± 2.3 ms, 70 ± 15 min prior to reaching the pH nadir when sampled at 1000 Hz. When sampled at 4 Hz, RMSSD at baseline measured 36.1 ± 6.0 ms and showed no significant increase during the UCO series until the pH nadir was reached. Consequently, early detection of severe hypoxic-acidemia would have been missed in all fetuses. Conclusion: RMSSD as a measure of fHRV when calculated from FHR sampled at 1000 Hz allowed for the early detection of worsening hypoxic-acidemia in each fetus. However, when calculated at the low sampling rate of 4 Hz used clinically, RMSSD remained unchanged until terminally when the nadir pH was reached. For early detection of fetal acidemia during labor, more sensitive means of acquiring FHR are therefore recommended than currently deployed, e.g., trans-abdominal fetal ECG.
Speech Enhancement using Convolutional Neural Network with Skip Connections

Eliminating the negative effect of adverse environmental noise has been an intriguing but challenging research topic for speech enhancement and speech recognition. Numerous techniques using neural networks have achieved favorable denoising performance in recent years. This paper presents a denoising architecture of Convolutional Neural Network (CNN) and its derivates with skip connection(s) for speech denoising. The skip connection forces the CNN to learn the residual error between the noisy speech and the clean speech and the clean speech is finally estimated by subtracting the learned residual error from the noisy input. Experimental results demonstrate that the proposed CNN structure provides better denoising ability than Wiener filtering in noise reduction even when the model was tested using the data and noise set not included in the training set. It is also shown that the proposed model with single skip connection achieves considerably better performance under several real noise conditions even at lower Signal-to-Noise Ratio (SNR).
Bidirectional LSTM with Extended Input Context

Long short-term memory (LSTM) unit has been widely used in speech recognition tasks, both for acoustic model and language model. For offline speech recognition task, bidirectional LSTM (BLSTM) is the state-of-the-art acoustic model. In this paper, we propose the BLSTM with extended input context (BLSTM-E), which achieves higher speech recognition accuracy than the standard BLSTM. Time delay neural network (TDNN) or element-wise scale block-sum network (ESBN) is used to extend the input context of forward and backward LSTM. Our experiments show that the proposed ESBN-BLSTM-E can achieve 0.9% absolute reduction in word error rate (WER) trained on one 1000 hours Chinese conversational telephone speech (CTS) compared with the standard BLSTM. Meanwhile, compared with the standard BLSTM, ESBN-BLSTM-E reduces relative 22.1% model parameter size.
Two Pathways for Base Excision Repair in Mammalian Cells (*)

Abasic sites (apurinic/apyrimidinic, AP sites) are the most common DNA lesions generated by both spontaneous and induced base loss. In a previous study we have shown that circular plasmid molecules containing multiple AP sites are efficiently repaired by Chinese hamster extracts in an in vitro repair assay. An average patch size of 6.6 nucleotides for a single AP site was calculated. To define the exact repair patch, a circular DNA duplex with a single AP site was constructed. The repair synthesis carried out by hamster and human cell extracts was characterized by restriction endonuclease analysis of the area containing the lesion. The results indicate that, besides the repair events involving the incorporation of a single nucleotide at the lesion site, repair synthesis occurred also 3′ to the AP site and involved a repair patch of approximately 7 nucleotides. This alternative repair pathway was completely inhibited by the presence in the repair reaction of a polyclonal antibody raised against human proliferating cell nuclear antigen. These data give the first evidence that mammalian cell extracts repair natural AP sites by two distinct pathways: a single nucleotide gap filling reaction targeted at the AP site and a proliferating cell nuclear antigen-dependent pathway that removes a short oligonucleotide containing the abasic site and 3′-flanking nucleotides.
Data Augmentation using Conditional Generative Adversarial Networks for Robust Speech Recognition

For noise robust speech recognition, data mismatch between training and test is a significant challenge. To reduce this mismatch, traditional approach of data augmentation usually adds noise to original waveform directly. A recent work utilizes generative adversarial network (GAN) to generate data for speech recognition. In this work, we explore conditional generative adversarial network (cGAN) for data augmentation to further improve speech recognition in noisy environments. Two different conditions are explored, including the acoustic state for each speech frame and the original paired clean speech for each speech frame. Different from using basic GAN, these newly designed cGANs incorporate the specific conditions into data generation and provide true labels directly. The proposed cGAN-based data augmentation approach is evaluated on both Aurora4 and AMI-SDM, which have noise types such as additive noise, channel distortion and reverberation. Experimental results show that the cGAN-based method consistently outperforms GAN-based one under all noisy conditions, and a relative 6% to 10% WER reduction can be obtained upon an advanced acoustic model.
Feature Extraction and Selection for Real-Time Emotion Recognition in Video Games Players

Different physiological theories and experiments have studied the link between emotions and humans' information provided by biofeedback sensors. However, only few works have been proposed regarding the acquisition of physiological data in order to investigate the emotions of video game players. In this paper, we propose an overview of different features which can be extracted from a set of physiological data acquired from players during video game sessions. Moreover, we provide a method to select only the most important features to use in a generic supervised learning algorithm. With these features, researchers can develop a model able to predict, in real-time, the players' emotions. Thus, we have conducted a set of experiments, in which we have acquired a set of physiological information, and the self-assessed participants' emotional state. On these data, we have applied a feature selection algorithm providing an overview of the most interesting physiological signals and features that should be considered during the studies on emotions in video game research field.
Approximation of Pore Space with Ellipsoids: A Comparison of a Geometrical Method with a Statistical one

We work with tomographic images of pore space in soil. The images have large dimensions and so in order to speed-up biological simulations (as drainage or diffusion process in soil), we want to describe the pore space with a number of geometrical primitives significantly smaller than the number of voxels in pore space. In this paper, we use the curve skeleton of a volume to segment it into some regions. We describe the method to compute the curve skeleton and to segment it with a simple segment approximation. We approximate each obtained region with an ellipsoid. The set of final ellipsoids represents the geometry of pore space and will be used in future simulations. We compare this method which we call geometrical method with the one described in the paper [8], which we name statistical method (using k-means algorithm).
Combining Depth Maps through 3D Weighted Least Squares in Shape from Focus

In this paper, we attempt to combine depth maps obtained by applying a number of focus measure operators in such a manner that their contributions in the resultant depth map are governed by an additional statistical clue of the shape. To achieve this goal, we propose to apply 3D weighted least squares on the depth map volume by computing weights from the guidance volume. This guidance volume has been extracted from the focus measure volumes. Thus, by inducing additional structural information of the object contained in the guidance volume, we strive to improve the resultant depth map. The proposed method has been tested using image sequences of synthetic and real objects. Experimental results have demonstrated the effectiveness of the proposed method.
Outliers Detection in One Dimensional Meteorological Data Stream

Sensors networks are some of technologies mostly used to gather informations from the environment. Indeed they collect a lot of data and send them to based stations for treatment. For meteorological monitoring, used sensors usually provide one dimensional data like temperatures, precipitations, humidity, etc. These data are speedily generated in the way that they form what we frequently call stream data. Among these data, there are usually bad values called outliers that need to be removed from the data stream. Many algorithms used to detect these outliers are usually designed for data with a static distribution and they do not consider the dynamic aspect of this distribution. However, one of the main characteristic of meteorological data is the dynamic behavior of the data distribution. Moreover, the speed of the data stream imposes to the outliers detection algorithms to be very fast in the data processing, otherwise a significant number of data could be lost. Regarding most of algorithms studied in the literature, it could be argued that these algorithms are not suitable for outliers detection in meteorological data stream. One of the reason is because of their time complexity and also their weak ability to easily detect contextual outliers. This work proposes a new outliers detection algorithm for one dimensional numeric data stream based on two filters that offer a complexity of O(n) and detect contextual outliers well, with a good precision.
Status of the European Union Project FASTGRID

High voltage direct current super-grids are one attractive solution for the transmission of bulk power of renewable electricity over long distances. Their protection is still an issue and superconducting fault current limiters (SCFCL) offer attractive perspectives. However, the actual superconducting tapes are not yet properly designed for operation at high voltages (>100 kV): The electric field developed during the current limitation is still too low (approximately 50 V/m for 50 ms) and the limiter requires too long lengths of tape. The European project FASTGRID aims at improving the properties of the REBCO tapes to enhance significantly (by 2–3 times) the electric field limit and so the economical SCFCL attractiveness. We use advanced THEVA tapes. Substantial improvements are also planned on the stabilizer (shunt) layer. Several shunt ways are under investigations. Other improvement on the tape properties will be carried out, namely the increase of the normal zone propagation velocity by at least one order of magnitude, with the help of the innovative current flow diverter architecture. The optimized conductor will be used in a SCFCL module (≈1.5 kA–50 kV) tested at 65 K. We will monitor the temperature along the conductor using an attached optical fibre. FASTGRID will also develop innovative tapes based on sapphire substrate, which can tolerate very high electric fields, in the range of kilovolts per meter. Validated on the laboratory scale, this game-changing technology needs to be implemented at long lengths with an industrial process. We will provide an overview of the project and its first results.
A Study of Adherence to the AAO-HNS “Clinical Practice Guideline: Adult Sinusitis”

A retrospective study was conducted to determine if physicians in otolaryngology practice adhered to the clinical practice guideline for adult sinusitis that had been issued by the American Academy of Otolaryngology–Head and Neck Surgery (AAO–HNS) 3 years earlier. We analyzed data obtained from the charts of 90 adults who had presented to an otolaryngology outpatient department with a diagnosis of chronic rhinosinusitis (CRS), acute bacterial rhinosinusitis (ABRS), or acute viral rhinosinusitis (AVRS); there were 76 cases of CRS, 11 cases of ABRS, and 3 cases of AVRS. Our goal was to ascertain how closely the treating physician had adhered to the AAO-HNS recommendations with respect to diagnosis, treatment, and prevention of these diseases. The study group was made up of 10 otolaryngologists. We evaluated 7 clinical practice metrics for CRS, 7 metrics for ABRS, and 3 for AVRS. We found that individual physician adherence rates for cases of CRS ranged from 0 to 100%; average scores for the 7 metrics ranged from 4 to 88%. For cases of ABRS, adherence scores ranged from 0 to 100%; average scores for the 7 metrics ranged from 0 to 41 %. For AVRS, the rate of adherence for all 3 metrics was 0%. This study revealed wide variations in adherence to the AAO-HNS guideline, but overall adherence was generally poor. Adherence appeared to be worse for the acute types of rhinosinusitis than for chronic rhinosinusitis. In view of these findings, a worksheet was developed that clinicians could use to improve compliance with the guidelines.
A stock forecasting method based on combination of SDAE and BP

The neural network, as a non-linear system with large-scale parallel distributed processing, has been widely used in the field of prediction. This paper proposes a stock forecasting method based on the combination of Stacked Denoising AutoEncoders (SDAE) and Back Propagation Neural Network (BPNN). Using more than 4100 historical data of Sinopec Group from 2001/8/8 to 2018/8/16 for Seventeen years, then process the data and select six features of them for training and prediction. The experiment first uses SDAE for training in the pre-training stage to obtain the weights. Then the trained weights of SDAE are assigned to the Back Propagation Neural Network (BPNN) for fine-tuning to optimize the entire network structure. Then optimize the network parameters for further step and chose the best optimal hyperparameter combination. Finally, the prediction is performed on the test set, the Mean Absolute Error (MAE), Error Variance (EV), and Absolute Maximum Error (AME) between the predicted value and the actual value are used as evaluation performance evaluation criteria. The results show that this neural network model can achieve high precision, and provides an effective method for predicting the stock market artificial neural network with many influencing factors and unclear mechanism.
Positively playful: when videogames lead to player wellbeing

Videogames are an increasingly popular entertainment choice, yet we have a limited understanding of their potential wellbeing benefits. The current research used an online survey (N = 429) to investigate how gameplay choices and the psychological experience of gameplay impact on player wellbeing. Specifically, a hierarchical multiple regression was conducted to determine if, controlling for age and gender, current gameplay choices (amount of play, game genre, mode of play) and play experience (flow, psychological need satisfaction) predicted current wellbeing. Results indicated that age, social play, relatedness during gameplay and flow were positively associated with player wellbeing. Implications for our understanding of player wellbeing, as well as directions for future research are discussed.