Miracle-dz/UniFAR-FTUs huggingface dataset:
Aortic Arch Baroreceptor Stimulation in an Experimental Goat Model: A Novel Method to Lower Blood Pressure

The effect of aortic baroreceptor stimulation on blood pressure manipulation was assessed using the goat species Capra aegagrus hircus. The aim of this study was to manipulate blood pressure with future intention to treat high blood pressure in humans. The ages of the animals ranged from 6 months to 2 years. A standard anesthesia protocol was used. A lateral thoracotomy was performed to gain access to the aortic arch. Data was collected with the Vigileo system. Pre stimulation blood pressure was compared with maximum post stimulation blood pressure values. Results were analyzed with the Wilcoxon signed rank test. In the study 38 animals were enrolled. Baroreceptor stimulation was performed for each animal using 3 different electrodes each of which emits an electrical impulse. In the pilot phase of the study, the median baseline blood pressure prior to stimulation of the baroreceptors was 110.8 mmHg. After stimulation the median blood pressure decreased to 88 mmHg. The average decrease in blood pressure was 22.8 mmHg. This decrease of blood pressure after stimulation of the baroreceptors is statistically significant (p < 0.0001) and the proof of concept was shown. During the extended phase all three probes had a significant effect on blood pressure lowering (p < 0.0001). The study confirmed that aortic baroreceptor stimulation has an effect on blood pressure lowering. This is a novel field of blood pressure manipulation. The hemodynamic effects of long-term aortic baroreceptor stimulation are unknown. Further investigations need to be done to determine whether a similar effect can be induced in different species such as primates and humans.
Spatio-Temporal Dynamics of Benthic Macrofaunal Communities in Relation to the Recovery of Coastal Aquaculture Operations Following the 2011 Great East Japan Earthquake and Tsunami

The 2011 Great East Japan Earthquake and tsunami wiped out over 1200 shellfish and ascidian culture long-lines and ~120 salmon farm cages that comprised the entire aquaculture installations in Onagawa Bay, Japan, and severely altered the associated ecosystem. A year later, we launched a coordinated monitoring programme to measure the extent of the damage caused by the disaster and monitor the change in the state of the marine ecosystem. As part of this effort, we conducted multi-seasonal sampling to characterise spatio-temporal variation in benthic macrofaunal community and a range of environmental parameters across Onagawa Bay between March 2012 and January 2018. The 492 total macrofaunal species recorded included Polychaeta (38.8 %), Bivalvia (13.2 %), Amphipoda (10.8 %), Decapoda (9.6 %), Gastropoda (9.3 %) and Echinodermata (4.3 %). At the outermost reference site, macrofaunal abundance, biomass, and species diversity were all consistently high throughout the study period. Inside Onagawa Bay, macrofauna metrics increased steadily from the lowest values at the beginning of the study to the highest over time. During the same period, the spatial extent of aquaculture facilities for long-lines and fish cages recovered steadily to within 60.8 % and 74.8 % of the original state, respectively. The significant variables identified by multivariate analysis to explain spatio-temporal variability in benthic macrofaunal communities were: (1) proximity to the nearest aquaculture facilities; (2) wind fetch length (exposure); (3) sediment grain size; (4) the total area of aquaculture facilities. This study suggests that coastal aquaculture operations may strongly influence the occurrence and distribution of benthic macrofaunal communities and thereby influence the recovery of seafloor biota at ecosystem scales following a catastrophic natural disaster.
Optimal Multiplexed Erasure Codes for Streaming Messages with Different Decoding Delays

This paper considers multiplexing two sequences of messages with two different decoding delays over a packet erasure channel. In each time slot, the source constructs a packet based on the current and previous messages and transmits the packet, which may be erased when the packet travels from the source to the destination. The destination must perfectly recover every source message in the first sequence subject to a decoding delay Tv and every source message in the second sequence subject to a shorter decoding delay Tu ≤ Tv. We assume that the channel loss model introduces a burst erasure of a fixed length B on the discrete timeline. Under this channel loss assumption, the capacity region for the case where Tv ≤ Tu + B was previously solved. In this paper, we fully characterize the capacity region for the remaining case Tv > Tu + B.
A Fully Bayesian Infinite Generative Model for Dynamic Texture Segmentation

Generative dynamic texture models (GDTMs) are widely used for dynamic texture (DT) segmentation in the video sequences. GDTMs represent DTs as a set of linear dynamical systems (LDSs). A major limitation of these models concerns the automatic selection of a proper number of DTs. Dirichlet process mixture (DPM) models which have appeared recently as the cornerstone of the non-parametric Bayesian statistics, is an optimistic candidate toward resolving this issue. Under this motivation to resolve the aforementioned drawback, we propose a novel non-parametric fully Bayesian approach for DT segmentation, formulated on the basis of a joint DPM and GDTM construction. This interaction causes the algorithm to overcome the problem of automatic segmentation properly. We derive the Variational Bayesian Expectation-Maximization (VBEM) inference for the proposed model. Moreover, in the E-step of inference, we apply Rauch-Tung-Striebel smoother (RTSS) algorithm on Variational Bayesian LDSs. Ultimately, experiments on different video sequences are performed. Experiment results indicate that the proposed algorithm outperforms the previous methods in efficiency and accuracy noticeably.
Supervised Learning for Multi-Block Incomplete Data

In the supervised high dimensional settings with a large number of variables and a low number of individuals, one objective is to select the relevant variables and thus to reduce the dimension. That subspace selection is often managed with supervised tools. However, some data can be missing, compromising the validity of the sub-space selection. We propose a Partial Least Square (PLS) based method, called Multi-block Data-Driven sparse PLS mdd-sPLS, allowing jointly variable selection and subspace estimation while training and testing missing data imputation through a new algorithm called Koh-Lanta. This method was challenged through simulations against existing methods such as mean imputation, nipals, softImpute and imputeMFA. In the context of supervised analysis of high dimensional data, the proposed method shows the lowest prediction error of the response variables. So far this is the only method combining data imputation and response variable prediction. The superiority of the supervised multi-block mdd-sPLS method increases with the intra-block and inter-block correlations. The application to a real data-set from a rVSV-ZEBOV Ebola vaccine trial revealed interesting and biologically relevant results. The method is implemented in a R-package available on the CRAN and a Python-package available on pypi.
On the Performance and Optimization for MEC Networks using Uplink NOMA

In this paper, we investigate a non-orthogonal multiple access (NOMA) based mobile edge computing (MEC) network, in which two users may partially offload their respective tasks to a single MEC server through uplink NOMA. We propose a new offloading scheme that can operate in three different modes, namely the partial computation offloading, the complete local computation, and the complete offloading. We further derive a closed-form expression of the successful computation probability for the proposed scheme. As part of the proposed offloading scheme, we formulate a problem to maximize the successful computation probability by jointly optimizing the time for offloading, the power allocation of the two users and the offloading ratios which decide how many tasks should be offloaded to the MEC server. We obtain the optimal solutions in the closed forms. Simulation results show that our proposed scheme can achieve the highest successful computation probability than the existing schemes.
Kelly Cache Networks

We study networks of M/M/1 queues in which nodes act as caches that store objects. Exogenous requests for objects are routed towards nodes that store them; as a result, object traffic in the network is determined not only by demand but, crucially, by where objects are cached. We determine how to place objects in caches to attain a certain design objective, such as, e.g., minimizing network congestion or retrieval delays. We show that for a broad class of objectives, including minimizing both the expected network delay and the sum of network queue lengths, this optimization problem can be cast as an NP-hard submodular maximization problem. We show that so-called continuous greedy algorithm [1] attains a ratio arbitrarily close to 1–1/ e$\approx$ 0.63 using a deterministic estimation via a power series; this drastically reduces execution time over prior art, which resorts to sampling. Finally, we show that our results generalize, beyond M/M/1 queues, to networks of Mmlk and symmetric M/D/1 queues.
Reasoning About Foreign Function Interfaces Without Modelling the Foreign Language

Object-oriented programming has long been regarded as too inefficient for SIMD high-performance computing, despite the fact that many important HPC applications have an inherent object structure. On SIMD accelerators, including GPUs, this is mainly due to performance problems with memory allocation and memory access: There are a few libraries that support parallel memory allocation directly on accelerator devices, but all of them suffer from uncoalesed memory accesses. We discovered a broad class of object-oriented programs with many important real-world applications that can be implemented efficiently on massively parallel SIMD accelerators. We call this class Single-Method Multiple-Objects (SMMO), because parallelism is expressed by running a method on all objects of a type. To make fast GPU programming available to average programmers, we developed DynaSOAr, a CUDA framework for SMMO applications. DynaSOAr consists of (1) a fully-parallel, lock-free, dynamic memory allocator, (2) a data layout DSL and (3) an efficient, parallel do-all operation. DynaSOAr achieves performance superior to state-of-the-art GPU memory allocators by controlling both memory allocation and memory access. DynaSOAr improves the usage of allocated memory with a Structure of Arrays data layout and achieves low memory fragmentation through efficient management of free and allocated memory blocks with lock-free, hierarchical bitmaps. Contrary to other allocators, our design is heavily based on atomic operations, trading raw (de)allocation performance for better overall application performance. In our benchmarks, DynaSOAr achieves a speedup of application code of up to 3x over state-of-the-art allocators. Moreover, DynaSOAr manages heap memory more efficiently than other allocators, allowing programmers to run up to 2x larger problem sizes with the same amount of memory.
L'artisanat d'un métier d'art: l'expérience de l'authenticité et sa réalisation dans les lieux de rencontre entre artisan et amateur éclairé

Cette recherche a été conduite à la demande d'une ville de France qui souhaite engager des actions de promotion des artisans d'un métier d'art afin d'attirer les amateurs éclairés. Une recherche naturelle a permis de réaliser des interviews en profondeur auprès d'artisans et d'amateurs éclairés. Deux analyses de contenu ont été réalisées. La première, d'inspiration structuraliste, a permis de définir l'«expérience de l'authenticité» que partagent les artisans et leurs clients. La seconde, d'inspiration phéno-ménologique, a permis de comprendre comment cette expérience était - ou n'était pas - mise en exergue dans les deux lieux traditionnels de rencontre que sont l'atelier et les expositions publiques. Nous concluons l'article par une mise en perspective de notre définition d'expérience de l'authenticité à la lumière, d'une part des propositions fondamentales de Peirce et, d'autre part des principales propositions actuelles en marketing.
River Ice Segmentation With Deep Learning

This article deals with the problem of computing surface concentrations for two types of river ice from digital images acquired during freeze-up. It presents the results of attempting to solve this problem using several state-of-the-art semantic segmentation methods based on deep convolutional neural networks (CNNs). This task presents two main challenges—very limited availability of labeled training data and presence of noisy labels due to the great difficulty of visually distinguishing between the two types of ice, even for human experts. The results are used to analyze the extent to which some of the best deep learning methods currently in existence can handle these challenges. The code and data used in the experiments are made publicly available to facilitate further work in this domain.
Influence of image segmentation on pulse wave propagation models of the pulmonary circulation

Computational fluid dynamics (CFD) models are emerging as mathematical tools to assist in the diagnostic assessment of cardiovascular disease. Recent advances in medical imaging and computational algorithms for analyzing said images have made subject-specific modeling of the cardiovascular system a feasible task. This is particularly important in the case of pulmonary hypertension, as the pulmonary circulation transports oxygen to the lungs via a rapidly bifurcating network of highly compliant blood vessels. However, medical image analysis is subject to uncertainty, which can lead to variation in fluid model predictions. This study addresses this issue by developing a computational algorithm to determine pulmonary hemodynamics (blood pressure and flow) in a large network of arterial vessels and subsequently quantify the uncertainty of these predictions to geometric variation associated with image segmentation. We introduce uncertainty in the blood vessel network by generating multiple segmentations of a pulmonary tree from a single image of an excised mouse lung using randomly selected pre-segmentation parameters. We use a custom MATLAB algorithm to extract vessel radii, length, and connectivity for each pulmonary network generated. We quantify the uncertainty in geometric features of the network using density estimation techniques to specify probability densities for vessel radius and length. We sample from these distributions, compute model parameters, and propagate uncertainties to pressure and flow predictions using a one-dimensional (1D) fluid dynamics model. Results show that variation in network connectivity is a larger contributor to hemodynamic uncertainty in comparison to changes in vessel radius and length.
Mobil Sağlık Uygulamalarının Hastanelerde Kullanılabilirliği: Hastane Yöneticileri Üzerine Bir Araştırma

Amac: Mobil teknolojiler saglik hizmetlerinde ortaya cikan cogu problem ile basa cikmada bir arac olarak gorulmektedir. Bu calismanin amaci, mobil saglik uygulamalarinin hastanelerde kullanilabilirligini arastirmaktir. Yontem: Calismada nitel arastirma yontemi kullanilmistir. Arastirmanin evrenini Izmir ilinde bir kamu hastanesinde calisan 18 ust ve orta kademe yoneticisi olusturmaktadir. Calismada amacli ornekleme yontemlerinden kartopu ornekleme yontemi kullanilmistir. Arastirma, alaninda uzman 2 kisinin gorusu alinarak hazirlanan 5 demografik soru ve 15 ifade ile gerceklestirilmistir. Arastirma, derinlemesine, bireysel, yari yapilandirilmis yuz yuze mulakat teknigi kullanilarak 10 yonetici ile yapilmistir. Verilerin analizinde Walcott’un 3 asamadan olusan nitel veri analizi sureci benimsenmistir. Bulgular: Arastirma sonuclarina gore mobil saglik uygulamalarinin hastanelerde kullanilabilir bir arac oldugu saptanmistir. Mobil saglik uygulama kullaniminin bazi dezavantajlari oldugu da ortaya konmustur. Mobil saglik uygulama kullanimi icin teknik ve hukuki alt yapinin guclendirilmesi, hasta bilgilerinin bu uygulamalar uzerinden akisi onunde engel olusturabilecek her turlu unsurun elimine edilmesi ve tam bir guvenlik soz konusu olmadan hasta bilgilerinin bu uygulamalarda akisinin problem yaratacagi saptanmistir. Sonuc: Sonuc olarak, ulkemiz icin mobil iletisim teknolojilerinin sundugu firsatlardan yararlanilmasi icin mobil saglik uygulama kullanimina iliskin pilot bolgelerin secilmesi, elde edilen bulgular isiginda gerekli duzenlemelerin yapilmasi, bu duzenleme ve girisimlerin Turkiye’de yayginlastirilmasinin daha cagdas saglik hizmetleri icin oldukca gerekli oldugu dusunulmektedir.
New Drugs for Pediatric Asthma

Asthma is the most common chronic disease in children. As suggested by international guidelines, the main goals of asthma treatment are symptoms control and lung function preservation, through a stepwise and control-based approach. The first line therapy based on inhaled corticosteroids may fail to reach control in more than one third of patients, especially adolescents, and in these lung function and quality of life may progressively worsen. Treatment with omalizumab, the first anti-immunoglobulin E recombinant humanized monoclonal antibody, has been definitely approved in pediatric uncontrolled asthma. In this review, we discuss the mechanisms and potential roles of emerging therapies for pediatric severe asthma. Novel biologic drugs (i.e., dupilumab, mepolizumab, reslizumab, and benralizumab) seem to be promising in reducing annual exacerbation rates and steroid-use in glucocorticoid-dependent cases, but available data are few and limited to adolescents and adults. Evidences on the use of the muscarinic antagonist tiotropium as controller medication in pediatric settings are progressively growing, sustaining an application as asthma maintenance treatment in children aged >6 years and in preschool children with persistent asthmatic symptoms, but well powered trials are needed to confirm its safety and efficacy. New inhaled corticosteroids (i.e., ciclesonide and mometasone) are effective as once-daily controller therapy, but long-term studies in the different pediatric ages are needed to compare effectiveness and safety to usual treatments. At present, the role of macrolides in pediatric severe asthma is controversial and their administration is not recommended routinely, but may be considered in children with neutrophilic asthma for reducing daily oral steroids administration and improving lung function. Despite the availability of several novel therapeutic strategies for uncontrolled asthma, future trials targeted at specific pediatric age subgroups are needed to support evidences of safety and efficacy also in children.
Leaving All Proxy Server Logs to Paragraph Vector

Cyberattack techniques continue to evolve every day. Detecting unseen drive-by-download attacks or C&C traffic is a challenging task. Pattern-matching-based techniques and using malicious blacklists are not efficient anymore, because attackers easily change the traffic pattern or infrastructure to avoid detection. Therefore, many behaviorbased detection methods have been proposed, which use the immutable characteristic of the traffic. These previous methods, however, focus on the attack technique, and can only detect drive-by-download (DbD) attacks or C&C traffic which have the immutable characteristic. These traditional methods have to devise the feature vectors. This paper proposes a generic detection method, which is independent of attack methods and does not need devising feature vectors. Our method uses Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length texts and classifiers. Our method uses Paragraph Vector to capture the context in proxy server logs. We conducted cross-validation, timeline analysis and cross-dataset validation with multiple datasets. The experimental results show our method can detect unseen DbD attacks and C&C traffic in proxy server logs. The best F-measure achieved 0.97 in the timeline analysis and 0.96 on the other dataset.
Resting EEG Features and Their Application in Depressive Disorders

This research is aimed to analysis the resting EEG features in depression and the application in clinic. Sixteen patients with depression and sixteen healthy controls were involved in this study. Both features from the alpha and beta frequency bands were selected to analysis in this study. First the features' sensitivity to the group-difference and the correlation to the clinical HAMD scale score were analyzed, and then the classification method was used to further test the role of the resting EEG features in depression. The results showed that the difference between depression and healthy controls in the absolute power of beta band in the left prefrontal lobe was significant. And the alpha left-right asymmetry in the prefrontal cortex had a correlation with HAMD scale score. In addition, the classification based on the features showed that there was a relative higher accuracy rate to identify the depressions than to identify the healthy controls. Specifically, the classification based on alpha asymmetry was higher than that based on beta asymmetry, and the absolute power in beta band was higher than that in alpha band. Alpha asymmetry is a traditional sensitive resting EEG features for depression, this study provide new evidence to support the view. The findings here further suggest that absolute power in beta band would be important biomarker in depression.
D-MIMOO – Distributed MIMO for Office Wi-Fi Networks

We present D-MIMOO, a distributed MIMO Wi-Fi architecture that boosts average network throughput compared to state-of-the-art access points with co-located antennas (baseline configuration). Designing a distributed MIMO system invites us to redesign fundamental Wi-Fi concepts, such as carrier sensing multiple access (CSMA) which governs channel access among Wi-Fi access points. We also propose a novel way of using channel reciprocity and the network topology to select downlink MU-MIMO recipients. The proposed solutions are standards compliant, do not require modifications at the user equipment and hence will work with legacy 802.11ac devices. We compare the performance of the D-MIMOO architecture to a baseline dense enterprise Wi-Fi deployment, and observe 2.5× improvement in median and 104% increase in average downlink per-stream throughput.
Real Friendship and Virtual Friendship: Differences in Similarity of Contents/People and Proposal of Classification Models on SNS

When people anonymously use social network sites (SNSs) like Twitter, they may interact with not only real world friends but also strangers, who are not acquaintances in the real world. Therefore, both of real friendship (RF) and virtual friendship (VF) coexist in these SNSs. In this research, we investigated the differences in similarity of user pairs in Japan by their types of relationship, i.e. RF or VF. The primary results indicated that RF user pairs have more common follow users on SNSs than VF user pairs, and that contents posted by VF user pairs include more similar words than RF user pairs. It is implied that two users with RF have a similar interest in neighborhood users, and two users with VF are interested in similar topics. After that, we built the models to classify user pairs into RF or VF using the similarity measures. These models showed high performance to distinguish between RF and VF (their F-measures are larger than 0.80).