AUV NILM Update

Work on the AUV NILM project has proceeded along two main fronts: (1) NILM System Development; (2) Diagnostics for Li-Ion Batterie

Recent and upcoming BCI progress: overview, analysis, and recommendations

Brain–computer interfaces (BCIs) are finally moving out of the laboratory and beginning to gain acceptance in real-world situations. As BCIs gain attention with broader groups of users, including persons with different disabilities and healthy users, numerous practical questions gain importance. What are the most practical ways to detect and analyze brain activity in field settings? Which devices and applications are most useful for different people? How can we make BCIs more natural and sensitive, and how can BCI technologies improve usability? What are some general trends and issues, such as combining different BCIs or assessing and comparing performance? This book chapter provides an overview of the different sections of this book, providing a summary of how authors address these and other questions. We also present some predictions and recommendations that ensue from our experience from discussing these and other issues with our authors and other researchers and developers within the BCI community. We conclude that, although some directions are hard to predict, the field is definitely growing and changing rapidly, and will continue doing so in the next several years

Context–aware Learning for Generative Models

This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters through expectation-maximization (EM) improves over the regular unsupervised case and can approach the performances of supervised learning, despite the absence of any explicit ground-truth data labeling. By direct application of the missing information principle (MIP), the algorithms' performances are proven to range between the conventional supervised and unsupervised MLE extremities proportionally to the information content of the contextual assistance provided. The acquired benefits regard higher estimation precision, smaller standard errors, faster convergence rates, and improved classification accuracy or regression fitness shown in various scenarios while also highlighting important properties and differences among the outlined situations. Applicability is showcased with three real-world unsupervised classification scenarios employing Gaussian mixture models. Importantly, we exemplify the natural extension of this methodology to any type of generative model by deriving an equivalent context-aware algorithm for variational autoencoders (VAs), thus broadening the spectrum of applicability to unsupervised deep learning with artificial neural networks. The latter is contrasted with a neural-symbolic algorithm exploiting side information

FYCO1 Frameshift Deletion in Wirehaired Pointing Griffon Dogs with Juvenile Cataract

Different breed-specific inherited cataracts have been described in dogs. In this study, we investigated an inbred family of Wirehaired Pointing Griffon dogs in which three offspring were affected by juvenile cataract. The pedigree suggested monogenic autosomal recessive inheritance of the trait. Whole-genome sequencing of an affected dog revealed 12 protein-changing variants that were not present in 566 control genomes, of which two were located in functional candidate genes, FYCO1 and CRYGB. Targeted genotyping of both variants in the investigated family excluded CRYGB and revealed perfect co-segregation of the FYCO1 variant with the juvenile cataract phenotype. This variant, FYCO1:c.2024delG, represents a 1 bp frameshift deletion predicted to truncate ~50 of the open reading frame p.(Ser675Thrfs*5). FYCO1 encodes the FYVE and coiled-coil domain autophagy adaptor 1, a known regulator of lens autophagy, which is required for the normal homeostasis in the eye. In humans, at least 37 pathogenic variants in FYCO1 have been shown to cause autosomal recessive cataract. Fcyo1−/− knockout mice also develop cataracts. Together with the current knowledge on FYCO1 variants and their functional impact in humans and mice, our data strongly suggest FYCO1:c.2024delG as a candidate causative variant for the observed juvenile cataract in Wirehaired Pointing Griffon dogs. To the best of our knowledge, this study represents the first report of a FYCO1-related cataract in domestic animals

Brain-Computer Interfaces, Virtual Reality, and Videogames

Major challenges must be tackled for brain-computer interfaces to mature into an established communications medium for VR applications, which will range from basic neuroscience studies to developing optimal peripherals and mental gamepads and more efficient brain-signal processing techniques

Is this settlement intersected by a ditch? A comparison between magnetic prospection data, ALS data, and archaeological and geological excavation results from the Early Bronze Age fortified hilltop settlement of Ratzersdorf, Lower Austria

In this case study we present preliminary results from a joint analysis of magnetometry data, remote sensing data, and excavation results generated in the course of research on the Early Bronze Age fortified hilltop settlement of Ratzersdorf/Am Dachsgraben in Lower Austria. In an effort to evaluate the interpretive potential of each data set we conclude that a combined analysis of all available data is essential for a comprehensive understanding of anthropogenic and natural features and formation processes. At the Ratzersdorf site specifically, the visibility of both anthropogenic and geological structures in the magnetometry data demonstrates the importance of the combination of complementary data for the verification or falsification of preliminary interpretive ideas

PCYT1A Missense Variant in Vizslas with Disproportionate Dwarfism

Disproportionate dwarfism phenotypes represent a heterogeneous subset of skeletal dysplasias and have been described in many species including humans and dogs. In this study, we investigated Vizsla dogs that were affected by disproportionate dwarfism that we propose to designate as skeletal dysplasia 3 (SD3). The most striking skeletal changes comprised a marked shortening and deformation of the humerus and femur. An extended pedigree with six affected dogs suggested autosomal recessive inheritance. Combined linkage and homozygosity mapping localized a potential genetic defect to a ~4 Mb interval on chromosome 33. We sequenced the genome of an affected dog, and comparison with 926 control genomes revealed a single, private protein-changing variant in the critical interval, PCYT1A:XM_038583131.1:c.673T>C, predicted to cause an exchange of a highly conserved amino acid, XP_038439059.1:p.(Y225H). We observed perfect co-segregation of the genotypes with the phenotype in the studied family. When genotyping additional Vizslas, we encountered a single dog with disproportionate dwarfism that did not carry the mutant PCYT1A allele, which we hypothesize was due to heterogeneity. In the remaining 130 dogs, we observed perfect genotype–phenotype association, and none of the unaffected dogs were homozygous for the mutant PCYT1A allele. PCYT1A loss-of-function variants cause spondylometaphyseal dysplasia with cone–rod dystrophy (SMD-CRD) in humans. The skeletal changes in Vizslas were comparable to human patients. So far, no ocular phenotype has been recognized in dwarf Vizslas. We propose the PCYT1A missense variant as a candidate causative variant for SD3. Our data facilitate genetic testing of Vizslas to prevent the unintentional breeding of further affected puppies

A swamp as an obstacle to approach – archaeological and geoelectrical investigations on the Early Bronze Age fortification of Ratzersdorf, Lower Austria

Interdisciplinary cooperation between geoelectrics and archaeology made it possible to identify a swamp as an integral part of a defense concept for the first time in Austria at the Early Bronze Age hilltop settlement of Ratzersdorf, Lower Austria. The marsh and a spring were included as natural topographic structures in the defense conception of the fortification

Subject-oriented training for motor imagery brain-computer interfaces

Successful operation of motor imagery (MI)-based brain-computer interfaces (BCI) requires mutual adaptation between the human subject and the BCI. Traditional training methods, as well as more recent ones based on co-adaptation, have mainly focused on the machine-learning aspects of BCI training. This work presents a novel co-adaptive training protocol shifting the focus on subject-related performances and the optimal accommodation of the interactions between the two learning agents of the BCI loop. Preliminary results with 8 able-bodied individuals demonstrate that the proposed method has been able to bring 3 naive users into control of a MI BCI within a few runs and to improve the BCI performances of 3 experienced BCI users by an average of 0.36 bits/sec