Modular domain-to-domain translation network

Domain-to-domain translation methods map images from a source domain to corresponding images from a target domain. The two domains contain images from the same classes, but these images look different. Recent approaches use generative adversarial networks in various configurations and architectures to perform the translation. By using GANs, they inevitably inherit their problems like training instability and mode collapse. We propose a novel approach to the problem that does not use a GAN. Instead, it relies on an hierarchical architecture that encapsulates information of the target domain by using individually trained networks. This hierarchical architecture is then trained as one unified deep network. Using this approach, we show that images from the original domain are translated to the target domain both for the case when there is a one-to-one correspondence between the images of the two domains and for the case that such correspondence information is absent. We visualize and evaluate the translation from one information domain to the other and discuss the proposed model's relation to the conditional generative adversarial networks. We further argue that deep learning can benefit from the proposed hierarchical architecture

Two Distinct, Geographically Overlapping Lineages of the Corallimorpharian Ricordea Florida (Cnidaria: Hexacorallia: Ricordeidae)

We examined the genetic variation of the corallimorpharian Ricordea florida; it is distributed throughout the Caribbean region and is heavily harvested for the marine aquarium trade. Eighty-four distinct individuals of R. florida were sequenced from four geographically distant Caribbean locations (Curaçao, Florida, Guadeloupe, and Puerto Rico). Analysis of the ribosomal nuclear region (ITS1, 5.8S, ITS2) uncovered two geographically partially overlapping genetic lineages in R. florida, probably representing two cryptic species. Lineage 1 was found in Florida and Puerto Rico, and Lineage 2 was found in Florida, Puerto Rico, Guadeloupe, and Curaçao. Because of the multi-allelic nature of the ITS region, four individuals from Lineage 1 and six from Lineage 2 were cloned to evaluate the levels of hidden intra-individual variability. Pairwise genetic comparisons indicated that the levels of intra-individual and intra-lineage variability (\u3c1%) were approximately an order of magnitude lower than the divergence (~9%) observed between the two lineages. The fishery regulations of the aquarium trade regard R. florida as one species. More refined regulations should take into account the presence of two genetic lineages, and they should be managed separately in order to preserve the long-term evolutionary potential of this corallimorpharian. The discovery of two distinct lineages in R. florida illustrates the importance of evaluating genetic variability in harvested species prior to the implementation of management policies

Emergency TeleOrthoPaedics m-health system for wireless communication links

For the first time, a complete wireless and mobile emergency TeleOrthoPaedics system with field trials and expert opinion is presented. The system enables doctors in a remote area to obtain a second opinion from doctors in the hospital using secured wireless telecommunication networks. Doctors can exchange securely medical images and video as well as other important data, and thus perform remote consultations, fast and accurately using a user friendly interface, via a reliable and secure telemedicine system of low cost. The quality of the transmitted compressed (JPEG2000) images was measured using different metrics and doctors opinions. The results have shown that all metrics were within acceptable limits. The performance of the system was evaluated successfully under different wireless communication links based on real data

Measuring the impact of blockchain on healthcare applications

Blockchain is a technology with potential for making ground breaking steps in addressing social, economic and healthcare challenges. The global information technology scene is being overcrowded with blockchain applications with special focus on the vast healthcare market [12]. The value of information related to healthcare creates a clear path for applying blockchain as a solution for some of the challenges in the healthcare sector, in particular with the goal of creating a fair and transparent way for sharing information and patient data. It is however a fact that while blockchain technology introduces additional complexity to the implementation healthcare software, the benefit the technology actually brings still remains unclear and difficult to evaluate. This vision paper demonstrates our research focus on providing a body of knowledge and tools to help evaluate this impact of blockchain on eHealth applications. In particular, we identify that such a research effort has to explicitly consider cost of addressing challenges inherent to the eHealth domain like integration of disparate software systems (hospitals, research institutions, government agencies, health insurance and pharmaceutical companies, etc.), the potential introduction of cryptocurrencies in healthcare systems, degree of patient service improvement, transparency and compliance to laws and regulations, and others. The more traditional influencing factors, like cost of development and running, licenses for using third-party software services, and the ones inherent to blockchain like cost of computation and energy will also have to be taken into consideration in the metrics model.</p

Filter-based approach for ornamentation detection and recognition in singing folk music

This is a Conference paper presented by the authors at the CAiP 2015; 16th International Conference on Computer Analysis of Images and Patterns, held in Malta from the 2 to 4 September, 2015.Ornamentations in music play a significant role for the emotion which a performer or a composer aims to create. The automated identification of ornamentations enhances the understanding of music, which can be used as a feature for tasks such as performer identification or mood classification. Existing methods rely on a pre-processing step that performs note segmentation. We propose an alternative method by adapting the existing two-dimensional COSFIRE filter approach to onedimension (1D) for the automatic identification of ornamentations in monophonic folk songs. We construct a set of 1D COSFIRE filters that are selective for the 12 notes of the Western music theory. The response of a 1D COSFIRE filter is computed as the geometric mean of the differences between the fundamental frequency values in a local neighbourhood and the preferred values at the corresponding positions. We apply the proposed 1D COSFIRE filters to the pitch tracks of a song at every position along the entire signal, which in turn give response values in the range [0,1]. The 1D COSFIRE filters that we propose are effective to recognize meaningful musical information which can be transformed into symbolic representations and used for further analysis. We demonstrate the effectiveness of the proposed methodology in a new data set that we introduce, which comprises five monophonic Cypriot folk tunes consisting of 428 ornamentations. The proposed method is effective for the detection and recognition of ornamentations in singing folk music.This research was funded from the Republic of Cyprus through the Cyprus research promotion foundation and also supported by the University of Cyprus by the research grant ANΘPΩΠIΣTIKEΣ / ANΘPΩ / 0311(BE) / 19.peer-reviewe

Intelligent Noninvasive Diagnosis of Aneuploidy:Raw Values and Highly Imbalanced Dataset

The objective of this paper is to introduce a noninvasive diagnosis procedure for aneuploidy and to minimize the social and financial cost of prenatal diagnosis tests that are performed for fetal aneuploidies in an early stage of pregnancy. We propose a method by using artificial neural networks trained with data from singleton pregnancy cases, while undergoing first trimester screening. Three different datasets' with a total of 122 362 euploid and 967 aneuploid cases were used in this study. The data for each case contained markers collected from the mother and the fetus. This study, unlike previous studies published by the authors for a similar problem differs in three basic principles: 1) the training of the artificial neural networks is done by using the markers' values in their raw form (unprocessed), 2) a balanced training dataset is created and used by selecting only a representative number of euploids for the training phase, and 3) emphasis is given to the financials and suggest hierarchy and necessity of the available tests. The proposed artificial neural networks models were optimized in the sense of reaching a minimum false positive rate and at the same time securing a 100% detection rate for Trisomy 21. These systems correctly identify other aneuploidies (Trisomies 13&18, Turner, and Triploid syndromes) at a detection rate greater than 80%. In conclusion, we demonstrate that artificial neural network systems can contribute in providing noninvasive, effective early screening for fetal aneuploidies with results that compare favorably to other existing methods

Proposal for an eHealth Based Ecosystem Serving National Healthcare

The European Union (EU)'s keen concern about citizens' health and well-being advancement has been expressed at all levels. It has been understood that at present, these can only be achieved through coordinated actions at the individual member states' level based on EU directives, as well as through promoting and funding R&D and expanding the use of eHealth technologies. Despite the diversities and particularities among member states, common values such as universal access to good quality healthcare, equity, and solidarity have been widely accepted across EU. That demanded the adoption of policies and follow directives, which streamlined actions to bridge healthcare gaps, and facilitate cross-border healthcare. This paper articulates a framework for deriving a national healthcare system, based on interoperable Electronic Health Record (EHR) with safeguarding healthcare quality, enabling quadruple helix (Public, Academia, Industry, NGOs) driven R&D and guided by a patient-centered approach. A methodology to develop an integrated EHR at National level is proposed as a prerequisite for eHealth and put into perspective. Recommendations are given for the steps needed, from the managerial, legal, technical, and financial concerns in developing an open access, patient-centered national healthcare system based on the context and constraints of a country. The example of a small country to apply the proposed methodology is demonstrated. Stakeholders, including citizens, healthcare professionals, academia, and the industry are mobilized, enabled, and incentivized for implementing the methodology. Experiences are aspired to be offered as lessons learned for other countries to adapt on their environment

Iterative Approximate Consensus in the presence of Byzantine Link Failures

This paper explores the problem of reaching approximate consensus in synchronous point-to-point networks, where each directed link of the underlying communication graph represents a communication channel between a pair of nodes. We adopt the transient Byzantine link failure model [15, 16], where an omniscient adversary controls a subset of the directed communication links, but the nodes are assumed to be fault-free. Recent work has addressed the problem of reaching approximate consen- sus in incomplete graphs with Byzantine nodes using a restricted class of iterative algorithms that maintain only a small amount of memory across iterations [22, 21, 23, 12]. However, to the best of our knowledge, we are the first to consider approximate consensus in the presence of Byzan- tine links. We extend our past work that provided exact characterization of graphs in which the iterative approximate consensus problem in the presence of Byzantine node failures is solvable [22, 21]. In particular, we prove a tight necessary and sufficient condition on the underlying com- munication graph for the existence of iterative approximate consensus algorithms under transient Byzantine link model. The condition answers (part of) the open problem stated in [16].Comment: arXiv admin note: text overlap with arXiv:1202.609

Modified triangular posterior osteosynthesis of unstable sacrum fracture

We report preliminary results for unstable sacral fractures treated with a modified posterior triangular osteosynthesis. Seven patients were admitted to our trauma center with an unstable sacral fracture. The average age was 31years (22-41). There were four vertical shear lesions of the pelvis and three transverse fracture of the upper sacrum. The vertical shear injuries were initially treated with an anterior external fixator inserted at the time of admission. Definitive surgery was performed at a mean time of 9days after trauma. The operation consisted in a posterior fixation combining a vertebropelvic distraction osteosynthesis with pedicle screws and a rod system, whereby the transverse fixation was obtained using a 6mm rod as a cross-link between the two main rods. Late displacement of the posterior pelvis or fracture was measured on X-ray films according to the criteria of Henderson. The patients were followed-up for a minimum time of 12months. Four patients who presented with a pre-operative perineal neurological impairment made a complete recovery. No iatrogenic nerve injury was reported. One case of deep infection was managed successfully with surgical debridement and local antibiotics. All patients complained of symptoms related to the prominence of the iliac screws. The metalwork was removed in all cases after healing of the fracture, at a mean time of 4.3months after surgery. No loss of reduction of fracture was seen at final radiological follow-up. The preliminary results are promising. The fixation is sufficiently stable to allow an immediate progressive weight-bearing, and safe nursing care in polytrauma cases. The only problem seems to be related to prominent heads of the distal screw

First Trimester Noninvasive Prenatal Diagnosis:A Computational Intelligence Approach

The objective of this study is to examine the potential value of using machine learning techniques such as artificial neural network (ANN) schemes for the noninvasive estimation, at 11-13 weeks of gestation, the risk for euploidy, trisomy 21 (T21), and other chromosomal aneuploidies (O.C.A.), from suitable sonographic, biochemical markers, and other relevant data. A database(1) consisted of 51,208 singleton pregnancy cases, while undergoing first trimester screening for aneuploidies has been used for the building, training, and verification of the proposed method. From all the data collected for each case from the mother and the fetus, the following 9 are considered by the collaborating obstetricians as the most relevant to the problem in question: maternal age, previous pregnancy with T21, fetal crown-rump length, serum free beta-hCG in multiples of the median (MoM), pregnancy-associated plasma protein-A in MoM, nuchal translucency thickness, nasal bone, tricuspid flow, and ductus venosus flow. The dataset was randomly divided into a training set that was used to guide the development of various ANN schemes, support vector machines, and k-nearest neighbor models. An evaluation set used to determine the performance of the developed systems. The evaluation set, totally unknown to the proposed system, contained 16,898 cases of euploidy fetuses, 129 cases of T21, and 76 cases of O.C.A. The best results were obtained by the ANN system, which identified correctly all T21 cases, i.e., 0% false negative rate (FNR) and 96.1% of euploidies, i.e., 3.9% false positive rate (FPR), meaning that no child would have been born with T21 if only that 3.9% of all pregnancies had been sent for invasive testing. The aim of this work is to produce a practical tool for the obstetrician which will ideally provide 0% FNR and to recommend the minimum possible number of cases for further testing such as invasive. In conclusion, it was demonstrated that ANN schemes can provide an effective early screening for fetal aneuploidies at a low FPR with results that compare favorably to those of existing systems