Novel Method for Improving the Capacity of Optical MIMO System Using MGDM

In current local area networks, multimode fibers (MMFs), primarily graded index (GI) MMFs, are the main types of fibers employed for data communications. Due to their enormous bandwidth, it is considered that they are the main channel medium that can offer broadband multiservices using optical multiplexing techniques. Amongst these, mode group diversity multiplexing (MGDM) has been proposed as a way to integrate various services over an MMF network by exciting different groups of modes that can be used as independent and parallel communication channels. In this paper, we study optical multiple-input–multiple-output (O-MIMO) systems using MGDM techniques while also optimizing the launching conditions of light at the fiber inputs and the spot size, radial offset, angular offset, wavelength, and the radii of the segment areas of the detectors. We propose a new approach based on the optimization of launching and detection conditions in order to increase the capacity of an O-MIMO link using the MGDM technique. We propose a (3 $times$ 3) O-MIMO system, where our simulation results show significant improvement in GI MMFs' capacity compared with existing O-MIMO systems. Optical multiple-input-multiple-output multiplexing of parallel communication multichannels over a single multimode fiber network. Optical multiple-input-multiple-output multiplexing of parallel communication multichannels over a single multimode fiber network

A Functional Variant of the Dimethylarginine Dimethylaminohydrolase-2 Gene Is Associated with Insulin Sensitivity

Background: Asymmetric dimethylarginine (ADMA) is an endogenous inhibitor of endothelial nitric oxide synthase, which was associated with insulin resistance. Dimethylarginine dimethylaminohydrolase (DDAH) is the major determinant of plasma ADMA. Examining data from the DIAGRAM+ (Diabetes Genetics Replication And Meta-analysis), we identified a variant (rs9267551) in the DDAH2 gene nominally associated with type 2 diabetes (P =3610 25). Methodology/Principal Findings: initially, we assessed the functional impact of rs9267551 in human endothelial cells (HUVECs), observing that the G allele had a lower transcriptional activity resulting in reduced expression of DDAH2 and decreased NO production in primary HUVECs naturally carrying it. We then proceeded to investigate whether this variant is associated with insulin sensitivity in vivo. To this end, two cohorts of nondiabetic subjects of European ancestry were studied. In sample 1 (n = 958) insulin sensitivity was determined by the insulin sensitivity index (ISI), while in sample 2 (n = 527) it was measured with a euglycemic-hyperinsulinemic clamp. In sample 1, carriers of the GG genotype had lower ISI than carriers of the C allele (67633 vs.79644; P = 0.003 after adjusting for age, gender, and BMI). ADMA levels were higher in subjects carrying the GG genotype than in carriers of the C allele (0.6860.14 vs. 0.5760.14 mmol/l; P = 0.04). In sample 2, glucose disposal was lower in GG carriers as compared with C carriers (9.364.1 vs. 11.064.2 mg6Kg 21 free fat mass6min 21; P = 0.009)

Pilot Study of the Association of the DDAH2 −449G Polymorphism with Asymmetric Dimethylarginine and Hemodynamic Shock in Pediatric Sepsis

Genetic variability in the regulation of the nitric oxide (NO) pathway may influence hemodynamic changes in pediatric sepsis. We sought to determine whether functional polymorphisms in DDAH2, which metabolizes the NO synthase inhibitor asymmetric dimethylarginine (ADMA), are associated with susceptibility to sepsis, plasma ADMA, distinct hemodynamic states, and vasopressor requirements in pediatric septic shock.In a prospective study, blood and buccal swabs were obtained from 82 patients ≤ 18 years (29 with severe sepsis/septic shock plus 27 febrile and 26 healthy controls). Plasma ADMA was measured using tandem mass spectrometry. DDAH2 gene was partially sequenced to determine the -871 6g/7 g insertion/deletion and -449G/C single nucleotide polymorphisms. Shock type ("warm" versus "cold") was characterized by clinical assessment. The -871 7g allele was more common in septic (17%) then febrile (4%) and healthy (8%) patients, though this was not significant after controlling for sex and race (p = 0.96). ADMA did not differ between -871 6g/7 g genotypes. While genotype frequencies also did not vary between groups for the -449G/C SNP (p = 0.75), septic patients with at least one -449G allele had lower ADMA (median, IQR 0.36, 0.30-0.41 µmol/L) than patients with the -449CC genotype (0.55, 0.49-0.64 µmol/L, p = 0.008) and exhibited a higher incidence of "cold" shock (45% versus 0%, p = 0.01). However, after controlling for race, the association with shock type became non-significant (p = 0.32). Neither polymorphism was associated with inotrope score or vasoactive infusion duration.The -449G polymorphism in the DDAH2 gene was associated with both low plasma ADMA and an increased likelihood of presenting with "cold" shock in pediatric sepsis, but not with vasopressor requirement. Race, however, was an important confounder. These results support and justify the need for larger studies in racially homogenous populations to further examine whether genotypic differences in NO metabolism contribute to phenotypic variability in sepsis pathophysiology

Altimetry for the future: Building on 25 years of progress

In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the “Green” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion

Augmenter of liver regeneration

‘Augmenter of liver regeneration’ (ALR) (also known as hepatic stimulatory substance or hepatopoietin) was originally found to promote growth of hepatocytes in the regenerating or injured liver. ALR is expressed ubiquitously in all organs, and exclusively in hepatocytes in the liver. ALR, a survival factor for hepatocytes, exhibits significant homology with ERV1 (essential for respiration and viability) protein that is essential for the survival of the yeast, Saccharomyces cerevisiae. ALR comprises 198 to 205 amino acids (approximately 22 kDa), but is post-translationally modified to three high molecular weight species (approximately 38 to 42 kDa) found in hepatocytes. ALR is present in mitochondria, cytosol, endoplasmic reticulum, and nucleus. Mitochondrial ALR may be involved in oxidative phosphorylation, but also functions as sulfhydryl oxidase and cytochrome c reductase, and causes Fe/S maturation of proteins. ALR, secreted by hepatocytes, stimulates synthesis of TNF-α, IL-6, and nitric oxide in Kupffer cells via a G-protein coupled receptor. While the 22 kDa rat recombinant ALR does not stimulate DNA synthesis in hepatocytes, the short form (15 kDa) of human recombinant ALR was reported to be equipotent as or even stronger than TGF-α or HGF as a mitogen for hepatocytes. Altered serum ALR levels in certain pathological conditions suggest that it may be a diagnostic marker for liver injury/disease. Although ALR appears to have multiple functions, the knowledge of its role in various organs, including the liver, is extremely inadequate, and it is not known whether different ALR species have distinct functions. Future research should provide better understanding of the expression and functions of this enigmatic molecule

Eight weeks of episodic visual navigation inside a non-stationary environment using adaptive spherical views

This paper presents a long-term experiment where a mobile robot uses adaptive spherical views to localize itself and navigate inside a non-stationary office environment. The office contains seven members of staff and experiences a continuous change in its appearance over time due to their daily activities. The experiment runs as an episodic navigation task in the office over a period of eight weeks. The spherical views are stored in the nodes of a pose graph and they are updated in response to the changes in the environment. The updating mechanism is inspired by the concepts of long- and short-term memories. The experimental evaluation is done using three performance metrics which evaluate the quality of both the adaptive spherical views and the navigation over time

Online Monitoring of Object Detection Performance During Deployment

During deployment, an object detector is expected to operate at a similar performance level reported on its testing dataset. However, when deployed onboard mobile robots that operate under varying and complex environmental conditions, the detector’s performance can fluctuate and occasionally degrade severely without warning. Undetected, this can lead the robot to take unsafe and risky actions based on low-quality and unreliable object detections. We address this problem and introduce a cascaded neural network that monitors the performance of the object detector by predicting the quality of its mean average precision (mAP) on a sliding window of the input frames. The proposed cascaded network exploits the internal features from the deep neural network of the object detector. We evaluate our proposed approach using different combinations of autonomous driving datasets and object detectors.Quazi Marufur Rahman, Niko Sünderhauf and Feras Dayou

Run-time monitoring of machine learning for robotic perception: a survey of emerging trends

As deep learning continues to dominate all state-of-the-art computer vision tasks, it is increasingly becoming an essential building block for robotic perception. This raises important questions concerning the safety and reliability of learning-based perception systems. There is an established field that studies safety certification and convergence guarantees of complex software systems at design-time. However, the unknown future deployment environments of an autonomous system and the complexity of learning-based perception make the generalization of design-time verification to run-time problematic. In the face of this challenge, more attention is starting to focus on run-time monitoring of performance and reliability of perception systems with several trends emerging in the literature in the face of this challenge. This paper attempts to identify these trends and summarize the various approaches to the topic.Quazi Marufur Rahman, Peter Corke, Feras Dayou

Robot navigation in unseen spaces using an abstract map

Human navigation in built environments depends on symbolic spatial information which has unrealized potential to enhance robot navigation capabilities. Information sources, such as labels, signs, maps, planners, spoken directions, and navigational gestures communicate a wealth of spatial information to the navigators of built environments; a wealth of information that robots typically ignore. We present a robot navigation system that uses the same symbolic spatial information employed by humans to purposefully navigate in unseen built environments with a level of performance comparable to humans. The navigation system uses a novel data structure called the abstract map to imagine malleable spatial models for unseen spaces from spatial symbols. Sensorimotor perceptions from a robot are then employed to provide purposeful navigation to symbolic goal locations in the unseen environment. We show how a dynamic system can be used to create malleable spatial models for the abstract map, and provide an open-source implementation to encourage future work in the area of symbolic navigation. The symbolic navigation performance of humans and a robot is evaluated in a real-world built environment. This article concludes with a qualitative analysis of human navigation strategies, providing further insights into how the symbolic navigation capabilities of robots in unseen built environments can be improved in the future.Ben Talbot, Feras Dayoub, Peter Corke, and Gordon Wyet

Class anchor clustering: A loss for distance-based open set recognition

In open set recognition, deep neural networks encounter object classes that were unknown during training. Existing open set classifiers distinguish between known and unknown classes by measuring distance in a network’s logit space, assuming that known classes cluster closer to the training data than unknown classes. However, this approach is applied post-hoc to networks trained with crossentropy loss, which does not guarantee this clustering behaviour. To overcome this limitation, we introduce the Class Anchor Clustering (CAC) loss. CAC is a distance-based loss that explicitly trains known classes to form tight clusters around anchored class-dependent centres in the logit space. We show that training with CAC achieves state-of-the-art performance for distance-based open set classifiers on all six standard benchmark datasets, with a 15.2% AUROC increase on the challenging TinyImageNet, without sacrificing classification accuracy. We also show that our anchored class centres achieve higher open set performance than learnt class centres, particularly on object-based datasets and large numbers of training classes.Dimity Miller, Niko Sunderhauf, Michael Milford, Feras Dayou